diff --git a/Indian Venomous Snakes Classification using DL/Model/snake-classification.ipynb b/Indian Venomous Snakes Classification using DL/Model/snake-classification.ipynb new file mode 100644 index 000000000..ab6c75e7b --- /dev/null +++ b/Indian Venomous Snakes Classification using DL/Model/snake-classification.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":3013107,"sourceType":"datasetVersion","datasetId":1834272}],"dockerImageVersionId":30716,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# importing modules and libraries\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport keras\nfrom keras.layers import Input, Input, Conv2D, MaxPooling2D, Flatten, ELU, Dense, BatchNormalization, Activation\nfrom keras.callbacks import ModelCheckpoint\nfrom keras.models import Sequential\nfrom keras.optimizers import Nadam\n\ntrain = keras.utils.image_dataset_from_directory(\n \"/kaggle/input/snake-dataset-india/Snake Images/train\",\n image_size=(224, 224),\n batch_size=20\n)\n\nnormalization_layer = keras.layers.Rescaling(1./255) # normalizing images from 0 to 255 to 0 to 1\nnormalized_dataset = train.map(lambda x, y: (normalization_layer(x), y)) # normalized images\n\nimages_list = [] # image container\nlabels_list = [] # label container\n\nfor images, labels in normalized_dataset: # taking out labels from folder name\n for image, label in zip(images, labels):\n image_array = image.numpy()\n images_list.append(image_array)\n labels_list.append(label.numpy())\n\nimage = np.asarray(images_list) # turning images to array\n\nlabel = np.asarray(labels_list) # labels as arrays","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-06-01T03:48:23.723263Z","iopub.execute_input":"2024-06-01T03:48:23.723561Z","iopub.status.idle":"2024-06-01T03:48:52.580485Z","shell.execute_reply.started":"2024-06-01T03:48:23.723524Z","shell.execute_reply":"2024-06-01T03:48:52.579529Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stderr","text":"2024-06-01 03:48:27.593257: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n2024-06-01 03:48:27.593375: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2024-06-01 03:48:27.846297: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n","output_type":"stream"},{"name":"stdout","text":"Found 1775 files belonging to 2 classes.\n","output_type":"stream"}]},{"cell_type":"code","source":"plt.imshow(image[150])\nplt.axis('off')","metadata":{"execution":{"iopub.status.busy":"2024-06-01T03:48:52.582311Z","iopub.execute_input":"2024-06-01T03:48:52.582640Z","iopub.status.idle":"2024-06-01T03:48:52.803786Z","shell.execute_reply.started":"2024-06-01T03:48:52.582612Z","shell.execute_reply":"2024-06-01T03:48:52.802772Z"},"trusted":true},"execution_count":2,"outputs":[{"execution_count":2,"output_type":"execute_result","data":{"text/plain":"(-0.5, 223.5, 223.5, -0.5)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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KkMUMqgkGAyw8JgtB6HccIwDLjY84sDNQyDiMWW1NhtcTgcZIKyEGQC4JxFiuJtMgwDrDHIvBnxCJuHg862EGRmrOsKBmCsMJJxHPHDd9/jv/vv/zv89ne/xf/lf/gf4KwF54Rffv4b5ssZl/MJQZnC3fEOg/c4TgP84OGHAcu6IqYMItsQQ9GgCpxkrdgxcs4IIYgdxgA5R3XRzchpBXKEGyYYMnAGuDse4I0BrwExROQ1gMkiMoGD+OV7a5HJIDGBMgOG9Tc2TaHuSPFcajdb+bs3DN46cHubvWxg8VgLdVOLV5BodbihHfStJ7yt7aAluL0Pes551ze+fVY/hltjbMfVP6c3QLef3fKQKfui9HXvmcYYZE4Av4SvQOKaWgQ6eUkBI6/733pmtX15TRJuidAt76z2757B9cbg9rr+vfKeDT6q8FjjCNCuad/ewrT2PmuZ9p4QcEtKL3PSzs1ev/b20t7Y+3tuMZvS13+4pjD6AckkEDO8cTo4MRpXw64xyJyrTQHrCmMN2BEsezhfRE5R+Uj/M2QAAzhj4ao9AaqayA+xMARHBs6KBmFfuF2JqmythWWGB9cAk2IAL/gxEYErweIKHeHK60MAMEMENgaUM0DqUsoZJiWw3jsag2EY8cMP3+O3v/0tfvOb3yDHiOfnJ+SUEMKqhmUgrCvSMCBbgrUjnLMYvENMAZnV4G5JvaEE0y/Ev3xHmZASYIxqbaxBNRCNi8EAZxBngBPGwcMS4TyOIBDWlMFQjYBZPInIquspqa1l81QpWCBRK41kMO8baV/boF9q7SEqEmxLBAmvQxT9+/ak8D1pu7yzMIW+L3sBSeWat7ZeimzvvyVhvyZJ91LvFRPhl98rj7/u84spJPTT+iWm11/7JUJ7i6Hsvatcvye913OPQvw2ZtYy3D0NZG8sb5Xi97S5dhy3GMOeBvOl/uxpNq/185Zm9ZaxAV/BFO4PR8QQ4SABZgCwIlWiXSY/xIg1Rawx4rwuYAKsdxinESNPWBdxo8wxISMgZYYnA2MtDn7EYMSmEOcFudFErLG4Px4xjCNGPwjEEzWaN4trqRsHcaU0BOcdjDWYDgc47zGMo9gA1lWYAUT6ZRIRuKjW1ODppFoBGQNbVDICiBk5bYDK/cMDhsHjcHfAH/7we/z2tz/Ce4vP5xP++tef8OnTR1wuZ9wdDuLyGBZcLoScAu4OIwx5OEsgwyCTwYgABG7zgxU33BCQDUAMOCMxHsYWfD9txB0ZhsR7KKcFUeMT3j1+B3/vkdeI0/MJMSSElBEzYIlgPYH8oEF4uqmy9MeySGBKqoRbMMCZxA7SbLw9QrZHlMtne66V5bOrwDxs0LHypy8Sn71DUCTpvf7ueRwVxpRSuoqILs9u7ylSXyFKvetqr+63ROs1ItIb6AsxaqGo/p4yUe07S3Cn0FG+6qNVGFDOwvWctc9smeOteb963ytMeU+C/dK+abUeuSarsCJ2S2NMXae9Pn1R6tbN9RpjKv1uDeqtQNHvq1Zb+bVEvZ+fW5/1+/4lA/1yezNTSJouQgilui2mJH9DpX6iLYJZXT0ZQMoCBYBIfe11cnJGNgmIEvRGqhnknBDmtWoc1gvmbp2DdRY5Z8zzDAYQokQcZ2aEFCVYLl+nLoAxMMoQikeSzhI4JrBCXpwZrC6zWXxJm4jsrIZmjfpsUnGgOYy//PIzlnXGhw+/4Hw64ee//Q3PT0/IOeHh8QFhXbEuM3KOCIGxLLMY4b0V+0aSmI664bGlbUg5iUG6vjapkKSbCND4AmEeMa5IiOAl4P5wB28kHsKo0T2kBHCCcx4+Q913UV12WZmjKetbIQZR2amJlq3wRLMZ99qtQ99CFb0P+CYhb2P9GpW/v76Vmvp3tH3Yg5j6A9i/oxDCa8eLfXV/j5Df+vvWGG7BGQQCSIImS19Jjc/13jLX6DSnG3P3NRrRXp/7OeuJ3x4M84LRdfcyo9mX++/sv3tVat75eI+Q7zHxvn/9e/aY2x7ze9Glneu+NMZbz3lLe3uaC8V3DYCcmlwxauglsxGGEpNQJjilpMwhY1mW2unMQATBQzQFOK5Y6DrPkkIiJzy4BwzeYzwcJPAtaZ6hnLCGIME2BKwxwHAWzSFlhW2CQFwAlnXBZZ5rhCkxwDEBKcn4jAGnXKOc2cjkF02EmcVITQSnXkqFIZT5+NOf/gyJF4gS9Xw5V7jr/ft3WOYLnp8Yl/MJa464XM7iPeQtYgqIKWpQWkLOsjw5ZyzrChODMiPBT3PONa5DHJcMrLMgZqQMpHVBiIywZrx/fIfBD7DGwmp8RQgLUmYM5DCwMAV1ZKtzkNUrrMSNoLB6ZojB95ogtRtyj2i2G7wPJiv39Hjrdljkp2Vat4hML6G37+0PWX8omfma6eNl5Gk/tlbqvyKwN4jHHgHs56r/u+83cB1jcRWdDABMyNRBJ7zNR71eGYYwBEaNWaMvE79b7bXremm9Z3D9+/p1uma4ItoTqQJ7o4+3JOavYXT9ut/aB3v37Enzt4j7rfnpn7EnNLzGEP7hmsKHn3+uwV1RI3rXsAJEIokPXgh/UpWfGX4cxWNGU0OklGBSBDJLuoqUoS43YGTEZQWMAZO4ZjrvMPoD3r17p8n1JqxhxdPzMzgG5JRhnQU4IyRhEKbx9EhFA9B/r+uKdRVvHqN2C86SsmPz680a1CaHQ9J0ZCT1cmCVWJmAGBMyZxyOR5H2rQXBgjnjeV4QQ8S6rIB36gorqu00jUgpaNqJok9JQi/nLA6HSdxPja2albh/AimJdiQeJAJ/GRDIlU0m/ufEYlRHEk1omWdYY7FGYaJuGEBrBHJCiAlriFhDBKykGSnR4cYQJASw/Fe34FV8SkuQWsLYS0TtT2vQbZ+xt6ELo0iFw+Ol2tzf0x/gvl/9IWq/L8nthkGguj031b6VPQdsifCYX8Zq9ATqNe1gb2zlOW855C/vvYaArLUbrwfEqy3rJO+892tazwDb+W61w5ap7Y2zl67beySXWXGhzZDsONs1PTP/klT+pbH0gsUtaK+9tr23xN3secL1YyytHUf/rrZvt/r9tQz97bmP1hVWJeyCsaaUQGrwLZlDE7K4fxqqXjXjNGINK5Z1hc+S3XS0DjlEMEfNeanBPWBkEjdIYyQT4DhNmKYJdvAVKipSjbFW0lZkkc6zkVQVpAYna7YAKGtMDbarEw6xQRSX1WJj2HwaANXERSZRKd0YQqJcBWhDpAn3JNC6RiynhGTEaFwW3mq0dE5io2DO6lnDIoCXVBKAnE0UKdDAkAThsbqWMgNsUKX4nAuxFlsBGwNnGTkFrOuClCSLq/WSNZRhsEZGSGIPsiXnEYu2UFiWbmsUZqDTsm1kvFSZ2++uNmkHxewdhPL9NaMoAMj1IX0N437t+XutPK9NjNi6qLbX7R3OW8zvNU2h7+veZ186+P3c95dv9+8z0qs+F8Ptzv17BOaWFN7P+5629qWx32rb/bq/6vjxYr1em/MvEcpbkvze93tM67X7vzTe1zSJ8uzX9tSv1YjenuaCCGQkeVoGAGMQOcOPIw53RwzHA8gZcArI0SIli8cfvsPheMT9/R1O5xOen0/ivgrgaD3W5wvC+QKXIOmtQ8AcIgInwFkcRo+79+9w9/iAw+GAoBlL53VBlEgrDOMIXhghRuQYYIzBw+Mj/DDAWYu7u7vqgXQ6nWBpywBJACBZdjEcxH0z5aS5jVJlIJ62zLAlDfM0TepCGuGcw+AHjNYhJokxIJXSc4xYkqSYuFzEfjCMo+gGKSGlgDkkpPNF0njo2lmN4TCQVCHejnDW1VxS4pIaQdYCtMF2YQ2YhgGjdxjMhJQZMTJimHFaZzANgLE43k0YDgesIeHf//hXIATQfIHxYnswRiCpZDYGBBbPHwkI34IBi797kfNyIU6qsRCJ9oGcq8fXrR+57FoKbtMTywa/TpTWGnR74kRE9f69dBbtPe1hOxwOmKapBi6GEF5Ide1z2qjrtu0xhF5K7AlnDw30gWytRtZLq8xcIaI6We14cU28SmRzgYRbibU1Apd04X0r17ZuvbfSlPSMpV3D0pc2gr18dusZ9TeVvuCqr7c00L012nN42NtLrYG5HXMZx57xuQrQzZz27sj9XLym+fb3vaZlfI2GUNqbmcIwCTF0gweMuggaght8NQDDGhBnGMdwBvB+gNfkciFG+GFFiiKtj34ExQyTMmiNyImQskTkGibAWxjvQM4iImNJAcu6Yg6r5O7RyXfOISYHa9Wd1Eh6icEPGLzH4/2D5FcnsYt464RoU0lvLdLvYTrAGCOH30jSu/Ywt/n1vfc4HA4YhgFJPTqMNdU1N0fNM5TKgsnixBhBJIR9UBfZENTjKXOF5VJKKqV6DH5QzcHAWoE0RKMgFEgpq8pfxkIkbr7Wi4tp8oTTeUYMCTAM57QWBBuQTfAazBdKPnaomysU2FJmkBODjEJWtJ9aut3AewQY7QFrrmvnuk2bce2d0mLI+z97B7k9zOWztl/ld3+4iidLjML4++/7Q9q29jDuaUVtf4Drug5XtoGegHftpd2lMPGXTOFqfdr7+RqOIrwk5v367j2z78dbNLRC3Pb2St+HlolfSeggGGv0XOQX7ylEuPRpD1Lqmc8tDee1OWjfV55ZPOhyzjVVyN672nG+5tLdPr/txy136a/Rvkp7M1Pwo/jTu8ELZMMiqRtrYZwFKVNAjiCysOoW6r3HMAxYhxVOi19YktoBFDJMzEhRpBtjjMacGcBZkHeAM1hzQgorLvMF8yqFepwVTxrnHVwSQsskUso4jpiGEdM44vFBmEIMAYuf4YxFygL3TOMoGyQzjgdhCheFktBoB4UotExhmqY6N5JtNSPnqNL/9rN5B1Et3gJcFwLZ3pPEDrGuOm+MwZciL7baGYwpMNO2kVIF2wvREaZHkmAKl3kFOICINZvtgMgWZBKGcUSOESlHhBRhiGGseF6xQlIgjWAgI2lOitF3R0V9TUosBImLltEQvMIQyt/OuRdaQ4Hw9ghnzxjKe/eIeNteYyTee4QQlBnn3XeWg7xH8G49twgZpX+xE0J6xtET5vJZK3VevfcV6fCF1J35BXPp52rv9540+xYtoV+Ddszts25J9/08y3stmBPKntybm1Y6B3AV+1Keuxfg2F/7Wl/aZ7VaQv95P77e021vDnoG2c/Vq8LYV7S3w0feYM0Zy/mEwzTBOgeTM2KOOJ8XuCT+/+e4amU1wH38iNN8wXk+4+l0wtPzE1JKGJ2He2/AmhzveT6Ds6TaPh7vYKcBcxaI5G9PnzQpXKpFaMa7AwquT85iOh7wm8FX2GJwrubwCcuKuAY8ff6MDx8+4Oeff8ZhmuCHAZzEDpFSggGJh9Mw4Ol0Eo+feZZqbCEA4wgiwufPn2GNweV8xnQ4SEpvo45/RpLpZTJABpxxOE5HHI8HOCcpwg0DnBjeC159f7yrG+H50zOWZcFzZrEdZN2MOSPGBSrDwXtJyTGNE1KSSGajuaKst9V+sSwLiAxADt6LKyrbEQDhMl+wBEZKwPfvHzHPF5xPz1iWC3IwcMcDYgyYcxSNkABWF11nDNgkGIjdp9+otySWtioZQ7zR9iTS8vPSICe2mVJ5rZeu2j60Fc/a79vn9QykldAul4vsgWWpSQnLu8r9RVPNCn2W95fqVz3jki1yTejLs9p7a+xAA4u17++NjmWsbbU2mM521hIgfsmMW4JojDh6tPPTMoMWJmkZXXlGeWapvNY+tw8s64lgu5fK2Nr5qPToap2LhrxvS2nXuN0Pe3PTEtF2L5br23XY2/PtGrXwTvueFq5q90Y/H3sCTT+mvee07f9QTSEDktMoBgw8ygKQHOw1RCQiwEA8kgwB1uB8uUjsAGecLxdc5lm8cEjdOYkkS2oW46NBhiVICmqWUpxrCphL2mhr4Z2Dd1qeMTNCDIKhosH0qPxoci9sgVA1vJ9ZEu5ZK6YqZoF9csa6LFjmGcuyXJdoLHmQTFMushAXaM6nGBFDRIqiJUiivgFeI5ZJd25Z2GqAxsvDBmgsCAxSLBtlI8TOiEMql41TCINK9zkmQI3fYGgBJO1ricuAVGlLyQrjilK0J6UIYtLnW/UIU+0ALw/F3kbspcN6qMu6dFJaufeWpEm0QRulrkMPAbQQQ2n9O9r5bZlD+06B+qjum3Zse2O+RRxuSb+3DuvenLbja4nUa1rQLkN4RXu4upZu92+PSLWft95VLXG/NW/9M9r7egLdz8drfWyve22P9nu1H0/b+r3WP2vvur3vyzW9prAHD95qL9a7m5/+XV/T3swUFnU1jTHA54icCUuOWFLEsi4gjmAirClKniM4/PLhFxARprsDUpb4gePxCDd4DNMohWGMFK6JOSGuK1ZLMDlg5iSeSDnjkiXl8ygOomJ/KJj9k6YiTgmHw0HSVb8bJaeS08I8nOGNxWEY8XB3ByJJ0X0YpMaysw7rGnB6esJPf/sbTvMFl2VBTCK1HY9HEFHF+pm3OgcxRlwuF90EojmllHA6ndUgPWIYRozjgJS8ZHuNAfO8wJigh1sWLqgBWfIbCUFe16iE0NQzHUJsPJWg+L4VRmiMzk1CWFeIRO0AJewZAMgAxkqJUTLITBhHB/CE+fkZOSaczs+YBg8zeNDgYZ1kphWawYCm1L6l+rcpooFNQisV7HLOtR52aywu0ncrfbZGOmtl0C0UUBMWNoeql7hKK+/rjddEVCNhrbUIIVw995a7ZG8L6KNpy3W9sbh/Vv95L0kXGLOM4RbcU8dpa2WSjUjJP0SIog2+IxDYNLj0zjj7Nd6DMPq5bj9r17B8Vvpd44a6Md96Z7+eYhe5tkO1EvqegADgSmvZk+j7v9v5aAWF/nvgOrp9rwZ3+/z+369Bhm2fbjGbv7d9BVMI4CyBXSEnsBqGM7MYHnVDWZL0Esa5SuSWRaqtcZFASFJSk0Y9G2dBOUp0ciCAMoKWP5A67VbSQHsLGIsMSbyXYkSYV0Cja4vaPs8zcpT4g2maqjRvjIGzDsM4YBxGHI/HugDn8xmXy4zPnz7jvM5YwirGXutwp9cRGYVhTIVrwCXdRkJMUX2mBXt31mHUxH3eiV0jZ9FwMiQqPMZUNYeswYCsVdBE4ldDWj20RtxvVVPy3sNo+m3Z3GJfyFFrZstsSR0EYrCxEKqQgZwAyshZvvPeIjqLmBOWZYZBhjMA4yCGfVcWpfRv363wNSm9V7FbCbiFCtr760Fj8WYyvH/g99qe1NQTgPK+Fg7pmcweUe8Zg/e+QpyFWd2SVPv+ULNHez/3Foa4pUn0z0DPEHS/APK7nENSNZK68RVtsB/za375/fd7EnubjbVc18NkPXNvx1feIddDxiMXomyb63l42a9+TC1jvSVl9+NpHQLa1mqdwLWtoH9X+/neuu4x/FvaU/+MW1rUW9rb4xRiBNRpPuYktQZYPdlJD5SRwjHGWRiFVnKWBHDFKF1aypqsDSz5c4y4uHKKQGBk4yoTIbvZD4QoMhJzTcFNEBiocPB5XpCdMAUh7FvdXuccDtMB4yhModwTYxQ8//kJc1gRYsTx7g7WEI6Hg/ZacyqRSO6yEGKXiCFiCav0U6V3Ke05YvADnBOvoZxaYi3pQ6Aun4UhcJP5UXIskQSnGbEXSNAcIxPDOa8agtZ/zlJutBjQy5pBU2ZAmZE4JyZ5bxam6ryFdQY5Auu6wBLg3Qb5kNbHLpCcjP91abHfxHtSdCEMPQbfElWiBnZTpnrtmXTdnz0N5pbE2PatXF80nbJvbrkstgfRGCPZdhXbbwn6a609yL3H0S2m0H/fE8G9fpaqeiQqXjv4LfakMAu8JDJ7BLr/vTdnLfNv5/pW4GLP8PvxbWtHKp+okKKa8y1toYd99vrf/t3v3VZDa5lwe01/HlomscdQr8fzUni6xaz6Pvc2tb1r3trenvuICu6t2UXL5FsD58VP3zoLGIJR11Wv2sI5zNWThbOU0zyfTnBMMJmrV1IiwEwDaHAIVt4BEiw854xxmuCt1AgY/YAYVpyMgbcOx8MBh3ECgfDxl1+wWovoI6wVV84SzSwRyAccj3cYpwnzPCOuC0Ci6k2HA6z3SDnj4eEed3f3uD/eSxoPkNY5KNKm/G2NlXrQ7OEGB6MS7zSNuL+/h3cyvqoFFBVIGUFlCjEhrAGX07lo+RiHCd5ZDL7EKVgYSHZWsNhniEm8j66IrOSLUhMcSOGjBIGLuCT/YLEVOevgrcXqDWIgxBiwkqTzWFOCK9qJdoyUrbQYck8MWwJWgsG8F++1KKGnsibFZtMIDS0x3Q6fEgWgjvUWod+TstrP91Tz/ll7UlwLe7SHlYjq+IrxufWkKte0z2t98dv398xmT1pt3933TZ3QXo6N6+xdE/zm/3vj35vHPabbX3tLS+yf0UOMPSPq1/LawE1XGmu7huV5r/V/j9j2n7XMqxdU9gSdW4JK21oBqO/LnhbWMpXy7n6e2u/K57eEotfam5lCKftoSTQCFImCjObTEc8bshbOeTgnkcvORiRIOu2kVdtK1k/AwLEae4kwGYBGYQriwCPSTVmUYRgwOI/JDxiswDHI4p46DRMOh4N4/2gqDWJgWVcwr1g15xLLYGSSC6NiSCZVJeJrWBFjwjRNGAaPko63bIoMhVxkZgS6cbLQfvBSrtQ5jOMAb31d0Jw3oiqr1B6cXDWJGIv7GzAOSlSbyGxqjL3GGFUEylg25m3UA4VBgLHgWlpTFAeZCwCcQWCpEW3KQZO5L6lC5EcYDzLXwD5LLzdp2YA9ka54flOroHX16yXKWyo3cPvg3ZKoegJ2S4J67eD0knormfdS+ka0bmcB7T1Zyt97Emf7jr2+X71DRYEe3iuCBPp3yT+2994gmHuSbf99Ow+vXddesydI9Nf2BG77d9njqP/v39Xvny/tlVv933vGXtt7fs9IvvSuL7V2P98SWPYY9lva2yuvOamENqhxkhjIJOUgLZFIsVpWUnzsR0zDgJgzjBUDdNAoYaQs0rDzMNbj7nAAEeEABgYHeIsZQoiWZQE72Sx3xyOOhyPe3T9INtOY8PnhQTxpUsb7d+9xmCb867/+K05Pz/jwt5/xxz/+EafTCfPlgsPxiIf7e6ScsKYIs8w1CO/+8RGHO9EexL5wqVJfikmC4YahkfTFAGwIGMcBBMXEBynr6bxI9dapDUCzzBYD8ZU0p+nHU0hS9Ec9gJgBHAEio3WdxY2VhiZhnGptqR4ocU8tjETqL4iWwJC5T8pAtnQhCWQcvDdwTvpcGENIAqsZ1T4MJNeSM3KNJmV9QXSLpN8Sx2JQHqcJzCzxEQ0mX2C8QlhKdGwLL7yYO+wfqFsE7ZbUWPp869CW63oPp3ZsRHQV+dvDHYUJtuMt72mJxi1iVIWA5vpdbFvRIcIe8XupKTQfy5+aL6xvZay9331LjIojRu+O+drc78E87fU9kWv/bQwhZ3EmYdWE2j23x5zLO/bSl9wi2C1j7iGodoy9YFM+25vLvu2Nu5f8b733S3PWX/9ae3uRnZLeYRg13XRGVj9kAiGnpOKnhtKD1LVUCJ43QtCcl6pnvEYM1sFbr9HEhMAZyRkkC1AUqCWFCE4SNTz6AXeHA96/e6cJ3xiHacLz0zN+/vln/OXPfwYR4fvvv0dYVlwWgYVKDqb7+zs8Pj5iPEgpT6mzMKjXyABmxv3DPT5/+ozn5+fqe76uYnQuByJqHYe6WNjgkeJdY9QgHJaEkgPJWSefaw54sIxPss2mLdLaj3CuHDYLcPGOkLcNw1A3TCwZa7FBOrIpGXK0pT/bxmp/hMAPxsE5A6i24JzDdDgALDmpQoygZYWhGc46FQBEWyRjquS518rGbIl+IWStB1BLKPeksSIVcuUK++/8knT62oEpP30sw5fGVq4Re9Z8BQ2U6wqxaAlq6/3SMojXCNVbDrlojJq6Pr+EJqi7v+zi7XmEPk6h7UtPGHsNqu/3lyTylhn3EMqX2vboTV/o33Nrncu/vyRR90bu1q5wq6/93L2m/XyNltAKBF/b/uGagi/QzTQhzAtSDlUVJZBI7plAGYiQJTLeSV0CBqwzFaIhBgLP8MbBa9I7YwxMilgNgzVzaDHiIkn6Cm+FKd3f3Um1NjDGYUBOGX/96Sf88uGDXA8ADOQYYZzFQCPGUaChh8dHMQY6C+usjMt7HI4HAITpMNXJe3p6qsFrJaiolfRa6YFo854pkE5JXZGSuJ4aMpImggXHZ4aWA40VqxSm4OuiG2rTSIuHRWFQAAEhIMQESkIEiAygUBR1XiWgYqQBiFilfNEAJHaENZGgwTiNyDGDk9SpJkQstIK9vKMhH/rcfSLQflYk/9a4XFoPIfTS83adLi7eHqnZE6pbxLXXAF47tL30Vgh7ERYK0yt7o2d6rSRb3v2aR0s/jj1327av5eNKQPScynWoaybXNwSVCuBIL57ZvqfH/99CqF6TfG9pdm3r771e1+sx7z3za9r1XDYecB3Eteeu3Pd5b27esnf39kH/nK8Z4z9cUzgej/DO43iYcE4ZHIU4anVGkXSy1AyzVlwbrfcga+AGg8mPmO6OOB6P4Mx4TgwHgiOLwTmACGsMSEFSLSzzWSTUJO9wIIzWYXQekx/rJuBB3E7v7u9xPp9xPl/w888/4+7uDu/fv8cf/sN/A+99DVzLzAq5kORQygkmW7F36GBKwrvz+VyDly6XC0IImKbpxYYBtgUTpsHqbZSrV1POUUpeyl31R9Rf9dQwpAxRbDXCXOSQrutaXWGLVkJkkLT8aQ6SaNA6hxhKaD3EISBJjQUo4Qc3moIhDIcBOSbEsIKQ4b3Bw8Md4hqxLgEpZuS8IiXBqY31gFHXY7MR6k0GFQ0DGkBY9seyilcXFeZprXwWwhUz6DexsrJNLSexqrwGG30NfNTf10MPLRzQEvQeP++Jfgv3tMSkwIClbnhhJG0yOREGXjKe9n2vEeJUJPnm+2JjsqrFVibYrp2ej/K+PeLXan9tP3qvJNp5TjvHe+tzq91mfkVAMCDaz3t0i/ns9eE1TbL93Xs39eNt72uFm7fCQbfaHu259Yyv1UJK+yqbgveSyG02psIfxDohKo1sm5BgbNbD4zA5j4MbMBiHjFztEmBxLQWgsEyQWsUhAinBKv20IBgGTIbkplYftLhGECDeR4cDcs4Ii+QOOh6P+O777zEMA86nE5ZlkYptzUKllKREJ2tR7VhqMBS8HQARosI74ziCjMEwDtumEBFdcPokpWqIWmkuiytqTBI3RgbGblKblTBuELgao2WT6UJTRuaEzAkCCmWAsjKZLHUZGomPSGAdNhBbAlUkucJQzCyaCwBHQAAL44JAdYN3om1lxhI1zQNLCU+fs5pBqGo8YIZ4xUqiOxjWDKmq6UAr4gFS96Iw4yTutaUJQVSXxWpEz+rAqxItF6N63j67+uklV25o3TU8cX1mevijXM+VMZVuXP0bovkJky+lS2V/cpZU8mjOh9Gxeefko8xIlK6ZAdMGw+pvZYUvCRsXSZnrNd3AUGCjHqJp76ljL0rD9qeufxfgVkXz8u7XJf492KYwWODa++it0jUzXy1E/47yu/VEek3a/hJz6uNVWsawp/ntaam33vOasNJ/3zOHPWb8azWlt2dJHSUw53h3xOnzk/Y4g5hghQbIDsqamYYyRhh4YzEME47DAXfDASCDyIwFRgrvcMLpcgYzY55nrDEgxAjEAAPA6yI4GJjEQIhIlwUgIUinT5/BnPHdu/dIa8DkBzyfTnh89w6/+e2P+Oc//AHj4PHnP/1Z4xqCHFZdtHVdEUPA3fEOXiW4IvGVOAwyYkDMIeN4d4fRCcMpsBKA+iwxKEsgmhA2Uigp4XR60gylHoPxGtvBYEPqvqv1EMyGXRojpCFxQmKjPwssGNYCOa+IcYHJShkSwToLQw6JGGwMsnMQQ1xGyrEay4kMDAwsZSQO4LhCymEY+HGEIQtig/kSELMUGjIhwbiINWcgE4odCQxYIzCUJQO2esCVITFLrQzOGadlrsQgxKg5q4xoFsbCkAYGOt7sSobq9+L9xUBTarKMrzAKSY5WpG2BGksWTaNG8sJ4i7Spm1o1xoxSq7oQPyKuhJUIV0Z2ZHW6cJtnVX1uLvCNPN8agrMG4zBuTCGKIGKNMIqccq3xAS4pPlAz76LUzShsgyHvqXyz1RBMFRRQiBUaxli1x+vxglG9BeMqaeltiUcCNA5GGulctbaGXtreMygX54OieRUIbo9w78N4OvaG4e9pIqU/vRbR9q3c0wdQlnvb371m22qPLVxYtMAy1j1ta2+uWu2078ctpKJnCP+HM4VCMJ21kkQUIttaxfqZqW58S7J5Bu/h/FDLQHJmxLhiDQGX80WiktUIy5BymWshtCQeLpMVfN3AAGvEer7g04ePko00Z8zLjHVZcb6c8enTZ4QYcHc8wmvqij//+U9gBv72009YFaoYFLN/enrC6XTC+XTGcZpqRKr3/irVQW8crF5ARNU3vxifl7jUgDQxPJeaygwpsXltoDKmOTQMlKA2ESozjNmMylaD+KIGEgpUlRqJlrbDr0XMyajWoFXk5JpyPQDOWJcZMaYX0q+1RookjSOIItZFNLllFWJegoaKf6u3DDKiMZT9KHCPHF6jo2NdXzZSl9voPJhCTEVQFriLREsQgVC0D1VM6hyWtqfKt5+37UtSaKvy12saA2w54NdruaXrKFqoPq2BozbCUfZVSlEZmhq5lZcIYeDKuFpJ9JZUfQseezF+GeRG1Hbm51bQXSvl35rXFoID9usilHe8ph201+4RQBEmisa46TZ7MMremvdMqmem5br+s7139JJ8/92X9mR/7y0m1q7LXh//HoYAfA18VCuXaaHvZoGEMOJKNba0SQAtDBBCkIRz64IYhSmQEee5NQSR3GOUHDLGYjAOSb1ycogI84IzS0nKpDDP+XLBp4+fcDqfkJnx+PAAglSLmxfJdPrxl1/qoWWduHmecT6f8fz8hLAu8E7qJBwOEvFc0t6WRbjyMOo8S8qmT0nqQhdJ1PnrkoOd/IOKRbC4d3IlHmXeX6b+zTkhxs3tzxAh6T3yI+kw0EQjg6TQTQsRkMIqJf0GUYEjpE/ilSWG75yBZQ5IWepZp7glQYRCCymL8TpzgS8U4mGo1AsYcIWchNmLhrONWQ9nGQyKH70+huuxr7NZIKoy1C81rn27vv5a7W4Z6Eup8eo9HaNo+1KEWGOLR9K2d1KSHFbFDlX7TwTKEmCY8wYl3oISWiKwZ6i+MQmKetKV1gBsfd7m4nru9qTodu72oIz22p4JvMYQXmtX61Dn6OU1e+PYm78e0nohFDTjufV3+8y/p91aw1sCQD+vf097e5bUmACnWT1TRgyS2mEYHcZxRKQVzAyv7orGGFyWBbwuWJAxjFJw5/n5Gcu64HQ6w1gh0gVCWJYF87IghlADwpy1oASAGeenZ1yeBf8s9QP8NGFdFsR5QVxWxJTw1z//Bf54wPR0J5XUQBhVCzgcDjifTlJe1Fo83N/jeDgghgBDBtM0vajUFWPEu3fvcH9/j8fHRzAzLpdLXZByfXFJnC8zYgwYxwGSW0ggi3fv3slzrQFzQgjxSlU2VgrqODfA2s0oKcTZV2aUUpFCowQUOgtnBzBD3VAFpjFJCCtDmDWIAGs10E0cAzIyQljgrBe3XIWrwAbkHAw55APDksU6i21h1XXKVbLNIGapMcEZHKW2BOeEQlMVHQKRQII19blCPMKHBHZLIEmUaBwyaxlvxcmdQiAEiHZayVcGkFVzKtCPPFuqxKm2oZ5tObcEv2Uu1wdLrlMioWlDUpbMtcZYGRMIZAlARkwi1HAW5lfyZ8k6CtgjUGOq0dwhFE10S8oH4i2jL+hKM2ohhDb47zW31r4VTUFvqLaLKjkDSNxoClpnJJe9ZITByQxSdaLgV7yrmPkqxXjPzPbqZ5R2rR3cGt/rhuKWmLafF7i4f28bybx3X6tl9JpQQRjatOt9jEk73+04289vaWv9nBQGdgs6+5r29noKOWv9gaSHUg4TlwOlUEDBGyUrqiTQ42WRtNJq1BV4SCegmaR+kO0iMrOk5db3VuPkukrFtlK0BCxZRJcF2crzrTEYte5B6wZZJH9DhGVeQIAk0DNbamxAFuZwOOB4PEq1NcU969xwl+OGUPHEdQ3InEBgOD8UUyGS5icqNSCKBlbe12/ucuhzzldjIFty38tvC2zwS8mhRACVpGMFk2cGIwNMsMahFPARwZ5Uc1FIxDX1imNCKm6qMcF7DWczQGyw6goPpCiZ1J0G0xFpDSOGoU0mzRAXWnCWeA21VUlKFapSbRUNuXXhfHnYrw8GNxIk7Xy2XSf3bvSyHt5dYlWewWLraNxOr64lQMI6iuYjYyz2Adn7cuFmE7nuw62DvzfmryEMtZ+9xNnZGa41iWtPsxfYk7ayPnsS7JegpP6d/T1b//X9rzy79KW/t482fzHOG/e+BnP11+xpd3tr85b1+hpI6u9hDG9mCogZyUSslxlg1mIn2A6ntSoV0pYaIUeknLHEALtYWOck8hZbCuXCREAEr+UtAfHmSBqAAyUOl2UB0KjIREjLAjIEP42YSCSR5/MZYZ6xpIjxMAFqQyhQV2EOpYLaNI64nM4AJEivjTYt+WweHh7w+PhYyzO2Elpbcq/kgsrMWGNEPCfNv2RxvB8V+gk16Ew0A6/PLSX8whU+DaAGfTFrZlTTRPpCPF5K6myGENkYY2XbTmEL461I4wwkZQB+8iiQk0A/0BoN8jzvPHggTNOIZVmROWBZAzIIdhglutkarCEhmwzyVkEfxjxfYA1woAHOW3ir8RAKJW0xKYDkkiIQZRDUxmAIzFKtDkTCBFMWrx7gKoVxO2ctUeiDjHoC235XWvmuBiphO4g9Vl7eXRh3ERikL8IQdItrn7Lug7a2ryR73PoqWkvf9gj+HnH7EmMo+hHremdqYiTKZ/wybqKfzz145lrT2tapt7+Uv8t1PZ7fCkTtZ9fjzABnoBEM9hhOK3j2xt7+s17D6ef1ah6b/rXf78E8X3r2XrsFV/Wtfe9/NU0hx4glJoSLpKX21qpLqXRkGCRb5xoCQkqYlxl+GGAArEngoRwDpukg+Ko1lXmklEBGoBtrLVJONctocgRDTqDrOcFYi2GaxEU0J8xrAIFgDcCDg3EGo1HNxarWEiM+P32GMaZK/CklnM9nFHiojrOxHzBzlcpbo6JzDvf393WjPz8/V83i/uFBYCS1ZYR1RbGBnc4XMEsBm5SE8A3jhGmacDhMePp8QooL5suikJBTkV/ddZPUSbi7uxPNyGi0s5WEhc45HPxBA9YkpXc1WqaC7RtQLliyqP3e+kqxJOW4aBHi+lqKERmMgxeJOWfEsIIBDAcp3WmJcFlmeGsw+IO4lao3iRB4KQrkrEUMKwCATQbD6BAVXyrGa9IAP4KUeQWAYl/QQaWUULxOyAgTI9PYJlBsF+VQqSs1EbY/t2cWrReEmkuKSJ+UGQakTHxL/JdSqXHQFr6BCiCmIfj6Dpa+S1lUeZ61WzCiwKJC7IpdqpWge0PnHuHpic41odjTprj7vOpMlXH0z01qy7rSZoGqwVNzllpNuggye7UMWom8/Pu1YL5rYpwr030LbNQ/r63B3b6zn9c9ab2X0PfWqRVI94SK9nl7Y+370zO7LzGvr2lvz5IaRFJNMcKXgi51okqUrcUaQ4WKhuL9klPNs2OdFHeRwKUFUD91C32GNRVSsdaK1Fl83VfBbsk7AIyEjMAJxITMimtaAzsOgrVbUw3ScQ0Vvx8GSWkRgtaIKB4yuF7QduMS0RV8U6T1vsj8oAQjZdEkpNqDSJohiJdJybRatB0JSNNMsZn1OjmoVp8fYhQDb0qYpgzAAJwU102C9bYHjsQLLLHAfVBjJhk1MLbQQOvcyJrKO4sUzyz4vSFxm002IhoSHDxGxJThbEY2kg6DWVyOvSI9su7bWwzQ4CKyisx6MTFAjI1UCagkp70lBKxGciH60j9TibFIwcVgrcxQuYAwAg12wia9F0M2gHo/9DbJNs5XBBIohK5kOrUK/bT5njQpYdUQNrhJnk2aU6pohFS1BxnLSyl9TyrdkyZfbVuHmhFff09AdSZpOnRli2h9fuoTCiNqHlcYQg8R9wSvlebbse4FCrYMjXFNAFtC3T5z7/7XiH3/71va5J4E32ofvWa1B/ncmo/+mW1rYbfXtIivbW9mCh9/+SDQRgh4ONzBWYvDNGEaJ0k09/AAMgZrDDAxgpwFrEiL1jsYlqXzo1Q7M8aIJ0tKIE2jDCKpD2BMTb99vL8TKCpGhMszQo7I64yg9Z1XSEGZtM4q2RkcDweM3mIYRtC6SuCYsQIVTROOGkma1RsqxageUrezWhaDUUnx3Nob2s1b/PKNFeZ3oM3ttEg+loB5npFixOWXj3geThiGEeu8SinPpHYDlnoURARnPcAEYzKGYVKGtCXOsxoMV43SxoAnScU9Xy5Y5gUlGtg4W1NYM4DT6QTvB4zjhHEYkBLjfLoAOQJZmFbRFHKStBzzuiKEjI8ff8ZhHDF4hxhWOCvwz8E7DNbUmtGF4HEu6S5EA6yqDIlILzl7lNAmaPAdFTYgByVn3U8qratRt4UcTJHyWWEBMLbYBKoMRBcZINToa7leCKPVOh3i7AAIM0jViFjqEF8fXpW2GymupClJaUsAWCTxzAnEW30OecZ+9HBrb2q1h14CvaVFtJ3chILCsvU+UgX1ypNKOSpIIcYssCVKvAZtz30xHy+l2/KuL2kBBaZttYZ+rsFl/W9L0S18U9ot20Dfl7a1sNhrkN0eMb+lybV/9+/s05ns3dM++7WUG29tb0+dzQBpNk+J0jQYx6l6S5ApePa2qRjym4x8QPp3HbgOxKoRcxxHCbyyFmSFmVjnELPEJORSkazsRBZCEbMYsMWgZwAcut5vE1qMu+Xvq8yl4CupplXtikZQvJZKUro6P7ogIUXt66byF7fCkhtJCA0jhFUJSwI4qFtikRyvM2y2ePnmmgowp1oBzxiB7wwRYK1kXQ0BcQ3IJUjMasoLhf3EtsuK2hQioYcpZ2TKcMYBhuAdSWU2J7UakDLWsCIYgNT7J2XCss4wGAC28AUaoVKLW2pXZyINKSvWB/0p/cpZmQGjBKkBqIFgPaQBFIPgJmF/Lc7ewihC1HVdiQBjZB1xTezL2gvcWILqrt9ZNJoyto1QqZybFSYr6BVd/97r/2sE7S3SYtGoiKi+9+p5qka19L58sV2nBJJJnQZw5fbcrkFPDN9CjHtJeI/QcqPhtVoY6mf7DOgWAX+tP+29/Rj+ERL6XnsNJurhrjdril9ob2YKjgwSMbJmdCMG7o7HmnCtQCGxRK4aQtb+FeNnK51U7NEQBjtgGAfc3d9J3WAnJTdJNYbLXDSDJjeMsZWQxBRxWWZRxWthGegh3eoYlLiDqG5xRUsoeZvKRLeG5rIQ5V5msTMcDoermIVCtBcl9IL/X6dHvrubpBToOIKIsCwW5/OMGDNyDvBNOUo5UEBKJaOoVSIlGHRlcLlUv5NSnKMfQMxI1mKZZ4RlwXq+QDshwYesBDlr+ogM0UJICF+x3WViEGWFQUTzKwFzhyha2Pn5hBUZzBHjOCCDcZ4XICew95ju7uDUiBqTQFnOEAwTMhGYqcJrUOlfNBqFBQxB6kvrRmSu0bNFKiqEN+cNr+8lpgI7tYS/JW5cIK7qEdVoE8Yo7LWlJ++hjZpfizcX461fr0NAKadqLdGFQhtx/XIcL/8ue+wWzNE8oX7WOouIDaVoCkoszUuiyapVZa2pUZ6ZlYGWYlR9n8pctZL2HgHrmcdezqlr2IYbGOzao6yXrntG/oIxbVtjt73U/LY+f1nguH7O11xb6FEraJb5KLbP17SQr21fFdFcgtCmccI0DHh4eMC6rjifz3h6fgYAXC4XrOopU5hATc4GiUUoqZwF4wcOxwnH4xHv378XqMlYnC9nmYw1YL1csJzOyGuAcR5WpVoDAw4JiBkmMYbRwzsPygyOCRG0ee3EVPu6zFJH4elJ0nUQNEkYb7mQQghXm7kt5O6cQ0oJ3ovX0OFwKGZNLGHBrHUaUswIIVai8fjunUJYI6ZpwrKsMNbXdAHeWnDONflem5673SA9QWKWugcxRpm3GGHJ4Pz5WUuFBkkZPo4YD2KkX9YVsxqL7x4epSgSIHEiV14xxdMLQtCdxThOMM5jDAFR80EVZhdDwKePZ8wKszwcj7AQabJ6STlfbcrCENrDLIS06i0aSFd0GFKYx5JVjQB6aACiVKXFQtCNKWmpVdKvKSy2vb0HtQgD1meVTUKo0rUYkX31r394eMSyzLL/14DWtbRtJW1JYfDbNeX9ppH8ypj3jZ1vkXRbIsKqrpSAx71rC6xV8jW1RLhte/AQqSFeH1afuXdP/1k/vl47aL/rA0e5IeY9U3yNAL8Y1xfodPuc6yDF/bncg3fa719re9pUv94tbPjafV/b3h7RrAyBrYV3khhvmqbqgjfPUnJzWdeaJ4eZq0dC6WyMEUYXquCszjn4wWOcJskdYwyWZZYFT5IBNIe45ZApOcYALeSuhlDjMFhXjZlZM6MWH/OCA6/KeS+XSzVIs9F0FUGymq7rigIdGGslWC9trqfFpgBIbENm8ciISoSL1LURkc2mIbEQFt4HsQlkcb31xkhdBdUASh9uHaYar0BSNztzRliDzAcI67pUhjMMA47HA4ZxwrzMuMwz4hrApMVzVEuQIj/pxQEscBMRwTkLN4hNYpokIHAaR/hxxDxfkHWdASBx8WMq3jsE0qAvbo2EKoEy82bgZPXfL4nvqMAemyG3zEVhAtfEvZ2xIuG/9Bjp89jUOxpGdf17gwuL08E4DkgpKhFtYDBse/82MW8IrP5PvtshvDeI3mtE54pQdFBEfw+DUbDfrwYiaPtV9n/fl93bOgaz9/3t8QOt9lN+v6ZF/RqIZY8B32IGr/27fPY1msKt9+7ZWP5ehgB8BVMw6u5nHNWMpMfjEcu8YFkWPD0/I6aE03IpzoxVkuZG/QFQA81awmOM2BGo4uXCEDgkkLoDjtbBwCCHoJuXMVkHNxAGIxW9hGFNqparvzsICZq+eVmwLgtCCDg9P8M7D+8douZmmueLagpa39lYTfMgEFPJ7FqSmBGAsAZxj10WLJcZ67qKVD6NmCZxEfXO4f7uToy504S7u3sAwA8//CB9JHHbXJYZ//5v/w5mScPR1mgoWkEhRiWGgoyBeXpC1DQhyBlOieIwDrg7HvHbH3+D490dUt6Y16rMq0BsORX8FmrwlzUSTyphFkZjEg7HIw4HydT67t073D88AET49OkjfvrLn8VbKkbMizAmdozBCiOxflCYKGFLYqcEiaD2CSX+KrHXet0wlSnsEZ1tP20/fclP4Bp6Kkyhd3+sfyuVLwVrbr2nhRF7qGHv4G6aQGEi0Cy6t4njBp+aq2e3EMYe/lz7UOAI4aJVY6jeVYAwYTUM3mIubV/2oIu9PpT7bmkH/fyUdWrntT0HG2N7KYm3hLNnnH1fb7WeePdMxzS0qn1W//trGVF7f9v/9rN+Hm8JHr+mvZkpFHfIoFCEeA9lMDGMs0jrgpDiJm0AVUonFKYiaSskPcBWsSuGgGWecXp61oUnyYyZWVJ2Dx5DHiuhnzVgTaTWUv7SYxwGWOdqwruUE5IumlUNx6qvPOcsvhMs6a4H58EESe5nM5A0URsZeGs1mRuBjdZfdmJgNyw5maSmQQRlwMLgMArTfHz3rno1gaGeK7GmrxgHqfxmjdR/NvE6qE89+SXOgzfCibIpSGAWUyU7RgwrEhiWCNYTxsMAclKrIeUIhgbZOQdA0zWrCm40m51zTiR6MkoMM2JOMGCJNJZ0qgIbTSMOhxFEBssyYvAea5ZqcjEnGPXll6hqK75FTBKopli+wCllbEocCyzQwMbmDYe5fN9j0vKm8l9pBfpQwKrCRgW+Kozj2gmhHMTifbQsy1VR9733b8RbHtzmm6o/UE8rltXf0xZ6P/p+3O3423v1j8J1rm05zOqC3LzyBhTxqsTNVff7orR+i7jtaVS3Cau6InTX3tofe4T+LXvpNWJ7S2N4jRl8CY57rU/9fPQC0lvG9Fp7M1NY5hkhRlwuF/z444/V/TSzVGXLpxNiCeSBeo1kBhdDqTEADAbnJfcPBFbKOYu7ZBIibZ2DtZIkx1grRCdNkmvFGpwvF3x+fqppF47HY5X6Sn6gtk5sshscMwyD5GbyUp/hYqwc+pjgDloxjQWOsuoOYozFYD0ybRXDnPM4jpOW0GRkZZhpDSAGvHF4PN7j3fv3+O0//RMAcUV8Pj1LESEqmgFJWVAdy+l0kg3urBbckTlKzADnqoGxHuyiolcYLQshXdcFOUUc7w4w3mC8m8AOCByxpBWJM6yTineA4PMSKsBwWkhHvMrEayiEKBHSKcLAIpOktDBG0oBPhxHTYYI1BssyYZoGxHVRV2LJPZU4wwweREAAtDaGSKOCEDFAgvmLhmBgrRrbC1MgAOp6W0qXtq2Xaq9yzrTYPCrJF6IMUwWZq+9QNIQsDK4h+nLvVmbUOXcV2V6ueWkDsoDaUCTgqqtBnYUx5Vor4poY9ZrC3vhvaQo17saYur+uIC7dW1zsKDtaTtuHF4SSi6ZxW0No+99+3j6nJWzt3OxpFDkzjPkyAez3RhnH1xLPt8JI/fe3NKG9PfwlRtILBntj+6+iKTjnavTx5XIRYyuLITUVAuEHWC9JrWJKUgS+ZFfVn2EYJNKVM6BqfShEtXj8QOsqe4/p7ghmFkMoiY90qRddIot79bJNVVF+wryASDB78bvnmoALrBqJupoG5xGWtW7KYlguyfWsajmsBCMWW8K6yrN0nAVvBwtB/fjxI3IIoGVBihHO+Upocs54enrCsiw4n8XI7v3GMIZhqLEShRiFEBAUYpkvs2TdVO+gAgaknLGGBXxSg/kaEaPMweHoYYzDMI4AFEZKQpByzhpcJcZRy6bGNRQYzhgDGCDGsFUc0/lfxkVSnISAqJXoXIgwxiLYBMskm4+U8CoprrCMlVoU6i1bK/wVYsWMF0SvJSatSm+MBlqy1MdmvMyAWVoLJ5V/M18nnmvV9bamRt+XVtXfAiE1qewV9NEXlS9P2pgCsEXV9zDNXrsFqZW9s2kO8j+yjWaE1wllS9TLv7fBv7z7SwSyXav++8JkWy2ihf5IeKjGMVxrGrcgrF8jTbdMvvzeI8y9pniLefTE+2vhpd4Q387J39u+iinYxpVTCuKUCF3FuZ2T4KecYWKUQvU7mCuI6iEvC1oGUzx8fByEgWgyNluMekogW2LZ5jMqjCenjGi0Xi4RopHiOilGGOdRjZaqRheDsrNWkgI1+HGPYQLXB78sUolH2DaLELwNFmDkmGqgTdLU4aUO9On5uaYPz2mrY+zUmEkAYkq17kCMUeYxs6a/Fny+layYs5YDlTWKkTWaGrCmxE2YSoiMJXBSwlHkZtqMqiXOo0Rsg2XNQgz1oHnv4byHDRExazAakdgykvxIqU4j7qWq+Yg0v8F2pT51lWKVAbbwxK0D3q+XaFX6g+0A31LBW9in1w5aQlCuk3TpL/Hu1yS/7QSUw9x7K20Mou/HLYJ36109bHPzPgJ6qn5rDLtMVW64/f2OVNuO5dol+6W20zLGMmdV09np9601+LUwS7+2t75r22uawq3vX+vX33v/l9qbmcKDGhKfT6fqMjmvK6wT6do6C0ceR5XsywL2TZK0acI7Vct1ZDDGYJ7Frc/oc0MIuLu7qymtRz/gu8d3AgWpOygJBiCaikpk0WhJzZTBLLEJMUSkGCXFMxrpEpq611pMw1i9d9oo5hrjoG6iMcYXqm1hLNZarOuK56cnKUKkgW7OWCRELLOUBQUEHrpcLjhdzkgh1gOVUgJSxuTF1fPu7k7zHyU4ff5yuQiTSRkcokJbFnf39+oNIxG3P/30Ew6HI7wfMPhRD64Ev+XMmJSRWOtrAZ9ljWBINttpOsJ5hzvvEGNCVNtRyvLu8+UMEDAOkxjZpwnHmGCMw/l8EQZCBvMakJnAsBidA9wI66T6m1G2aYjhtJiQNxaZAZvFe6nUWEjMFXrca/3BrYxcq8TxDhTR3lvWvD9YZa3F8eA6I2qMsaZQl+18zUyv98r2zKvrcc3wWi2hFUb2YIN+LHvS+S3i1H/eM8j+ub1Bvnngi3f3glRvGAdwpYUNamO71Y9W+5JrCsNnqBf81bv7PfIacf5S2xMa9uJheiHiWki7Hs+vgXlaRrcHw/292sLb4xTUkFsIqki3K3KWRxSJvXR2Lzd6UV2ZGYlL+ubtQLSDLsR9XdctxzxEYyk+9+Wwle+maQIR4aT1EmaFcwpExArzXGXWLJOZxeAsEcbXudBbAtFvznbMgBKeBCyXWXLq51wZmnXF9VNLd2q6hMs84zJfxLuJqB6M8nepBtduBiJJRW29BRwjG6mI55zFYTpgGD2WZYNCKhGT3BESpMYJZBLs+QzvvaQFIcJ1hk4hVaQwg7EGVrcNa46rGMWNl0HIKcOPI6bEAFmcL5L9iUGImYGUgHUFZ4YxDqMV+4mBQnIQ20nx8hLbM6PtEYNKVqQr6bBAS0UzK3abAou0wMitQ9lqfu2+Lb976KRNnNhLnldxHtgw8D3N44rIcfvefbtJyxxu/f6SVL733NckztcYaR1zc5bLXu2DrsrcbHNyu/raHkO77ivtKTdX9/fz8GsJcvus6zX7OmL8mqT/Fim/155uMZxf276KKQzjKG6o6tIZQqipsMdprAekBGsty/JiM7BKeTFvhBkqAbaL5J0DiLAsS40JKOkl3mkQmLW2StzGmMqwfv75ZynYM89bPAGjEvgi7RO2dNNgiWtY0qZVLMtyhWn2UkFrbyjfZc1tM18uWNcFp/MJ9/f3mKYJP/zmN6K654xlniWjbAxYlqVi9CWeoWV0xYbSpx92zklKbDKIdq1MYTpMEl2cNwZYGYPmSkoFxlFvlxKhLePcjhkrMQVY8ljRVu+XOYMDqp0pq5fVOB3ALEVy6OOT9FnTXOSYEOOCNDCs97C+wERSepOz1N4mAKUOsWFGAbP0481TCJtxuEiMKFpbgaiwTyRvEYVWmHlJtK8lv7IeRQhq16eFTKvQ0BmegU7y7rSI7dy8JOy3DL63pOGWobTXtWO9RVB6hrj3XADiUt7c1zLZW5pJe82eZN/CcvttY6i32mvr/WIMr1y3p4Hd+nf/eU+4f42WcKvPtyLEf017M1P46aefrtQ/5z2ORgJ1QATvPKx6B5W8QMVNr6jcxTuImZGbcpKrBr4xc61GdTgehUhBs02SYPBhlfrOx8MR0ziq77wQyGkYEVPE06fPOF/OuFwueHx8h0FhpuLz760FQeCJOrEs9oYYE4JGNLdMYRzHasQuAUvFtnG5XKpdoMqiLMZrBxIIS6GypP77l8tFS5JGEBlM44SHh3sMw4DD4SgV3OYZl/MFyzyDGTifT7hc5qo1xBjxcH+PYfSwkM1nrcH5JCVGmUXDORwOyFltECQpt8dxxFFjEYx1NX13YRhkTHWDBdTgF1PNceWdBZHDMA64zDOWNcCrh8swjmogFlsBMyFrvQYCSU0GEPyY4RIDRozZVAywUbQFu6kGQgCNATS9AmEL8ZfpbgjGjtTZH9727/LTVgXrvWTKIW6JV0vAyn1tWpO2GFJroBS+VSTmrl/NuW5tU71EXd75mmdO++5eOGvH1F/ffrYnqfdjvib2qJ5e7Vy2c7D3zJaoFSGrzGH1zLrJFEofbttM+vG+pfX9bAl7a2jupfxb0v4t7ay860sMub+nFfj2rv217c1MYZ7njSAoUbLOXmG7rISnSNAFqinSelmw3G1GZoF2Ut6Mq8MwSMoK0mpUXFRrId7yI77wYAM2WuAkRMzLjGVesC6rRDU7B6cMwZVNBoDIaiyFLiJDs4CKNtH7nZcxFKZQYK3W7kCq9aSUQZTBVoLBDEkgHAO1+lxOWzEZiZ/Q0o1q/DVG0muAxbh/Pp8xL7MwKGrcKMlUCc1oWuuYAopiY4xkVJUxbh5VwugcMqCeYkY9Y3StCJAApqLhZYkdADWJDTcmmBUSBABrHdzAGKeDGsclnTezuJjGzAiqrViTJUEei42FtJaBGLuLVsDbT0N0epV+72C1EvQe4dyTWnvcvH3GlyCYW+2aoJfrX0IJbV8LU9jr561x3iJIrYbyNX3tPy/v2n0G1+7ujuU1aGiPWfTP+BqI5HVG8vVtbx/tjavt31u1jtf6ufeOXuPoGd7fwxjezBRCCCrFHmotgTUGLOsqUu+ygPW6wklL/qDiKVS5Pou0V+SsUV0i0Qzu7u5ONAUl1MwsdRiMQVhXfPjlF3x2Tmorq1cS6XdPnz5jbdxcyzNr/QcU6AiI5fmNRFi0hOJqKERVbAxlLAWqSinh8+fPdQ6mSfz1U0pIeq93HodpwuO7Rzw9P+HDL79IDQkjkcGFCV3OZwRNbZFjksI157MYrU+nGhdhNYBuGAb1RBKmJAE8m/dGydxZtJicGdMo63d3d4fpcIQxFpd5rl4dYkhOWMNSmY33Uv85r2vVUMZxBHPG+XIRY7ATIzQjwJgAP04YD0f8y796LGvA6XTBeZ6xrgHkgQyDy2WGJZl/O3o4MBxQE7KVQi62bHAlpBKtcQ1T6OI1awy1SWypRvQRVaIvn7UV7t5KmHqi2UqoPbMq+6r8sxD6nuDLz6ZdyH3bKHvPpq8lAnsSb/v7FuF9jQFePQvC1Hv3khZ23dM+2p8CobYSeT/O6z5K6pI9KK3XrHpG347ppost9gnvHiTYBzeW1sPPrYbbz2//7r113dP8+jn8r8IUvPfww1AhEyKSaFV9eYjhavKKelWk0t4oTGXx1RZQXFXLgMs9pHABM8NrUFpMCZePHzcVzogfeivhF4m4EIBC6Od5xugH8QpyDsYw2Gx2hTLBZXMCqMFw5TnW2uo6K3WY16pVlKIpFRpbl9rPaRgVPlkwjhOGQYizuIzmK22sj71IMVboy6nmU/oVQ8C6LLDWwHsL552UvvSuagjMAsuVSGbvJQEeM1emLDmsWjuKJLYuyd1KDWyoe21Kmp/JShLD8ryQIiglkLGYjkcYH5HJCgNmQs4EZMYak6QTIULyEsleit8AWWtblxJp8pNzBhvVklqiLAu3MQRtjeBavyuM4TXNonzewwR7xL4/kLcO/Nabl9BJeW+pPSHveXkOe23nlvazx7T2ntWOtx/jnmTajnnPtsKspZH4y3j7Hpyzd01LT/rrWBEENAx2bx72GGjP/PbmstzbEvb+mpYp9MS978ctyKlfh1tzcksY+XsZQdvezBSGYcDQBFMREUwM1bojRJLhvDyyFKMpEcat6xSgaS+s5F+/u7/fGE2TiVSkvm1ii7F1XVd8+vQJ8zxLcBjkmlmzn7YLyCwG5DWLv/58ucASgbSYjzUMqDtqkXZKwfrSX+99DbArTGFd18qEWqZARmCiRFIAp1R3AyApLUhSIhwOBwzDgLu7OwDy2bKu4JxhlVEW+0VhMtUTSRlm6XNQ7x/vHZilxoV1BsPgsa6S3K9sphKbIIZRBlPGdJjqM+b5IlilsSKlclMcyCnh1/lOOWENKzxJam2SyxFThkni2TQe7mDcgAyLeY0IieESiTE/BoSY4IwYoR2RBFFpoiNJz4x64Blo7BwvGUK7v4qm0DP6/pC2B3/vsBZi19oH9lrPFNr3XHsKlZ9rSRIomsD2Pes4Ww2jJ+S3IIXXYJp2nvYITNvvW4yhHzOAKxj2pRq3z5j2JOS2b6/ZIfRqFQbqFrjZ3/bZPSPrCfKedtT3txUcWnsrcJ1zqX9OO8e3GNPeu/t+tAzh/xSmUCXjRu2GDrwQr8y5Rry2RrviWUNE1QhNDWe3RnL/MBRKIlSMnICr56zriufn5xor0Rpd51kyqxYpGND0HOuqRuoV67pgdAOcEfzekkG2FnfHOwAMpwnwBjV4k0r+5d3n87nmuynjfnx8xDzPkqYiJSQEyb/kgMQeYQ04P5/w9PSEFBOOh6NkFlWoyaqm893795qG+aEa5Uv0cEpJGPMwXG1mAHVeh8GrV5ZEBBNtHlLTNAmRtQZrWPHhwwesa4C1Dv/y3/23snZKhIgIh+MBISasa8A8X2Ctw93Dg2gu1uB0viDGoLEDEvT2w29+A7IOKQH/+d//iL/98gF+OGCcDnj3/nsAAkWFJYFIohMksD2LfQgAa9Q0YVPRWf+T9lKKuiIoUKaVtUiPSmcG0PQOxZ5EV4eqJ5iFyRejZxGG9q4Hvlxr95rgAJtxlAHkWngJdjOqbnRh/7B/iQj0UmfLBMs42s/bcrPlmvZdt5hNP/msg7wFifRj6GGxPkK8Jb49U5QLNhHhqjvdmr6mUd2Kedm7vv/8lrdarwneeveepnKLgZZrWyiqhT97Rv5r2puZQhl8IYgA4IYBIJWiSLHtBlPrOX4rfQsT2aCkch0gkbY19XbKV5u2pBXouXDxDOq5fY2LUNuCGHxNTb2RuD2cUMFDpR9C9aToF7mmeQCq0TbnXJHuGJOkiWg8JwpMNHgvBu8SsZvFO2kYtlKlpc/FqF1jHay9Glvpq7VOUmAbK77+OQuxVSZe8xxpMsB5FXzfOV+THTJrDYxS4EXeBInUVoO5VdhP56qVUIy1kFxJKz58/Ig//+nPMH7Ew8MjhuFQpfzCgAikGXQF+ktGgsm15IDui6IhKGk0G+EvtqFWO9gIkvQdjJpXqT0q+1LntZRa5q4nbnvBVa+5BF7vVQJp4rnt3ypV4qXkyJqD661S4GsSbvvsW/BGPy/tWPPO2d5r5ZvXJO3yzPZ366X0mkaz9bV+0vz7JdPoadGL/u4wi/7z9r23xnPrmXuM+bX2JU1hj9H19/w9jOHtqbOLIbLAISQGZKPlMwfFeVeFV6pNoJvYGsTlrNYXFmbBgGLt4voZQ0CKCeuy1HsLht8WuGHmSjTv7u5grcWnT5+q73+ZIGcMBrVd3N/f12C7ZVlwuVyqiHGZZ619HDfpI1NlKvLOjHmZkbN4Vt0/3GOc7vD999+L0fl8xs8//4xxHPHu3RZ9fXp+RowRx8NBEvMRIceIsKyYLxcMg2D7p9OpxoH87ne/q4n/WimgMAVrLcZhRKQA52S+l2VFiItK+BL38N137zVCPOH5+YRPnz4BRPAu4m8//1XhH5GIGZLPaDucUgkNDfRhrIHNJf0GIcWMeV6wrhF//svf8D//f/5n/K//8X8DjMWPP/4Iax2m4xHOCaRFKcNAq47FjHVlSYIHB+MdjDUwRmo4Jy4GYxIvJtBWm4EKfCAsolTRK5AdQ7UPorpvQfslDHvG3zoptMy4QJ2t9Ny6Ub5OwCXyFsW7Sut5gFET1LUMpLC/nqD3WsBrrVzbGyjbMewZZdtI7DI/ewJfaZVB7xDw9v62tc8qmnGZy6Kh9cFt5T7pp/Zfkwu231XhrpuL0lqBZq9fvYEcuDYa72lNe9J+f+2tKOsv7519uK183mstv7Z9VT0FEJBRpEn1466ulrJ5BV6wcNgWUXLiqyvnOMAP8lOSzBlrNKhKDnOOCetlllQayyIFfsx1wrsyASU53zRNuL+/h7W2xg1kzkhRPFiGQZPoWQfnPcgQ5mXGvC64rAvYyoKEHCpTKItkjQU5SVPBJEnjOEakHEGJYS1h8A7jNMCcDNgQjBaiGacR0+GA6TCpNCiaEFgDuRYxEi+XBfY7A2+dMLTLjPPljOejeGHFEEQuJtS4B5RgMCOlShGhtg6x78j8a7K+GEAELItg+UACkWgVyzJjGEcpE5pJi+QkWVFLcMaDjEUISSR5IxXOjLEYhhHGSDr0GDJOz2f85c9/wcePzzidVhhncDqd8enzLxhGCzc5WCupRzhLMSZkYA4BDAtjBlgLgESwKPbkWkeBNbtn0QLAADdQo6bDIFsyo+qh2XAN+bcyCClC3xBJoMJ5BbrcO3x7av4+RNF+Vu6Rv4th3ZCkFG8rsfX3lOf1hPU17aT87u15e8R1794WKmvds2++EwAx78JHPRPdI2rtmG7BdL2kXcq56iux17XXxrrX9gj8WyT8/n1761We/9qzWgbYC4H9PPWa5a01/hpG8RWaghw7yntuZISk9XGLRxFZs2X1zLzFOAwDxmmSspAabyC56pXTpYTMQJgFw5+XWes2uyvDdTHsPGsZ0BZiceqempXwskoc3nkMitUDjHlZMK8LlrCCrRQREggsIuUAq9HClgyMI4CFiCZixMRgFiJp1Qg7DA5kDZgA610dq0QYjwirEHZnrRB0TgiZEZZVvIfU4L0wI6wrLqczTs/PGIZB10DmsLjoQucrBTHKghnLwgBJ0RqiAoOIMTxzVqP4ipKmGpSxxhVuEOlcCkdkpFUD1ciCjAUgKbShVdms1sUYjINzA6x1mJeI83nGX//2M54+n7HMCdYnzPOM56dP+OGH7+AcwTpNCZ4SMkvd5iUwAAdngcEZGAOp8a1xEqIHmC0tO0sNacGWsgopSvIJIGOvUlxwCycp4SrEpyR0NFbugTIFo0yjQmndYe8JVn3elUReDupG5Mu/5Tuz/WBLTFgYw1tggVuaQ8+89hhcf0973S2s+iYsVPnuNUHbs9m0v1spvJ3HW5rXNYH+Oqn4S1pWT9teYyS35vSt/Wiv7//dM4aeSfTvbLWSNo3Pr2lv9z4qmLlqAiX5GwNaqF4kS+cdvHdSBU0Th8U1iCukdXh4fKyS87qukjJjEa8bTgkhZkRmLBep35BjEp93L7UQyiQ465CJ4Y24rYZ5QZgXsEuIa0CKcbNH5CwunWpUvmj958vlUmtA1GOrBICwuaFZKxHAg/eISYjc+kuonjyZGDFHXJZZXHMBfPf993h8fMRvf/tbHKcJhgzmeamSW4oJOUfEVbKiikssKrxVXGvPJ6kpPY6j1pqwNadSSUwnRn2I/z5nPL67xzSNOB5HLOuCp6fPeD49gZkx+AHOe9zdHVFy+U/jpMzSwIKQs0EaijHSIrNBSozLZa7Me5wmOD/g7u4Bd/cPGKcD/u3f/ojLecZPf/krmCMe7gcsSeoVz/MCP3g8Pj7i/nDEcpnx/PET4prUmL4iJwvkBKcb/DAKlc/iAiXrYq+rtFWtVSX/GqfQOCrEmEApIRKwWUpYJHPDVUgo5YUJgDMWsFpCVIWcYvtq2563UftTpP9rQrN9BoXEiGwVvGQbviRK/XtaQtIajUt7KbxtMMaVe/iN71psfw++6ZkVM8Pkfcm178NLjeol7NP+7lOIbL+3Oa1a4c5a7Gl2bduDcF5qJS9dW/fG0a9TaT0Md+vZewxj7+8WMurX8++BkN7MFPrNVeIKwJs7FoPBbBX7JaAYU71qCsXH3m3Vxa4wPx2gYMJyIL1zGFTCNyQScYoJJcbHKYOKIWK+zOI2yQxLEnmdU1Ips+IIAlFlTXWtknvRcIQKJSBLdC6pIbDkfrL6uXVSMAYkOf9TZpAGl4GoegoVbysx0q/ViytpZbJQ4hvKYQHh7niUSO3MOF/O2kcnTMhYLUnGoKRjqXqzlLg0JNlStSSPjlW0i5QTHDtYZzAOkxinNe1HTglG050PXvoNIuTANZmgbEQgxQRrGYYsnJPsq0DB4gWyc94hsqQhKQ4JfvCw3sOAEc4eyAFgEQRyzggxipuqTTKnStpbiKD1RiJAIqDNxtQBDW1riAQDMBopTQWrN1WFqHuj/C77vOx9ERb2IZB9Sfbl+WmZQyX+9fv2X9cE59Yhv6VBfIkg7PX7ljS+R+huQk780pjffv8aVNVes8cI96T2q7lsvrrV51uQ1S0G9RbC2hLwVgvZY95/T9sTANr3fYnpfU17M1Noelc7kSGF2UvxHQZgs+RJkCpqQrmNGkBJsXaBdvKWPjtncKlhECVHkIF69RwmHI+S9nmeZ6S4YjlfKkR0GCeAgeVywc/LqjaHAcdxwjgMCOuKnIVJOGNgISkWSrI17xyMcximsUqCgQgBBkELsVsrdZ8Ph6NIo9bh+XTWTKcZa5Y+Z50TIsIwjhg0u2mMEWFd8enzZ8lptMyqKeQaCT6NYy0R+s+/+x1++O47XC4X/Nt//jfEGHF3d1eJ07ouwkzSFkHpBwsQI7ODHyQ47XR+UlsCYxzFTTdGMRo7a/HDD99jHCc8PZ1EK3k+4f7+EX7wOE4jcpa1nS8nrMuKsGiUM0kqDbEtGDgzYPAH5Cx5jrwbMHiBATMnzbA7YhxGTMOI42HE6B2wrDgbxrIYnNYFKWXMaRGGZAzWlCojLOczsbIETo2gAoFeSCtNYqMRDGhxJMG7BcPX+wosxZA6DcUAmIuGYAGj6TlAGvWuUB2UMe8QGzQGYqAlNNfMoTArVCbyddh3efaXiFevPey9Y0/j6V1D27arwXBhuPvvec3tsyfgRUspv3stou9HyaO7p730/e6l+FsQTnvPHkNpifMeY2n/fYvptu/t/277+9r4e63y72UMby/HuSw1z1HKqUr+wOYrCyJxtzR2U8GJAFO0CAjEksS4vC4LwhoQlxVRC8uANVhIoQujNYKTVjYLYa0eUKYUnzGkcIAQBad9kQEa3YwMb8WuAEg6Z0LxslD8Uo2TqdhF1H7CkApmKScMgxD7h4cHfPj4EYtmirXOwXoH5x04SyBdyWkUNfPq56cntaFkjNNU31/VQL2OIAkGcYDANFoDIus11Uczq2sjSR4q8diRw3w6PeMyn1U7CdXQfn9/wDCMOB7vwMw1sM0YKU5kjVY24C2rao6SDE+0A8BYp1rQCGM8LpcFy5rwt59+wcdfPiOFhGURJwGyhHHweHx8gCEghAXsLRwBx+MEogznLMI6I6rH1RIiyARclgWDVwZCqiUkjbTu9z0XxVVgJSaJmdlKTsq0FaZRXJKLezIRbVh4Fk2n2B24MAXaMrEWZlwO6B6BaHH4ouLLvyU1g+R2KsbwVBnua9LtHpFof75GY9hnKEVDAJh7goz6u/yU5xAVNlg0OHrxvLZP2zO3gL1N6pX7ehfYTZtq/96Yc3lOG8ezp9H18M0eQb01d3uMpIdugGuPo7cS6degrVuMBehKrf7X1BRWTR0h8I7grB4bQygT473XcpWoXkMMqbrFOWNZVwSVQMIapGJYiMhKLEuumhqpR6SEUIhdDFuiOstcYaUilRlQTYdtjGTVzKq9lGRzMZoriYa0n1WKNGZL+EYlilYT9hmBtA7HIz4+PUldCLBIqU5iBZKVqGt9Qc2Mej6dQLQl/BNGKoSsxCqkGIUoKYTjNbJ7GAbRODr3PC5M1Fo4Z2CdQUoByxqwrsW9NgEQAnZ3d4dhGDGNU4V6CnGrxj00UZopXf1tyIAc4P0A7wcYspjnFWs445cPH/H58xNizAhrwLoEHO4GDH7Aw/09jAFSWJHTACJgmjzAI4whnE4SlJfXgDVFIBjMIYCMg/eiCQihkJq8thAoQL3bgNa7KKcsxLwQG0AhJ9EWWqZgGkJQ1gFEdS8AQCZCMd/tSa+34KSXkqv0sTAWFKgVwtDLc/q0Dm27DaNsUvYept1fuyetXvevyRbbeHP1TEFHB0KrKbxkBNfQTNsnvZ+4PvfaRXbzyNqgN1JGIu8yptQIaef6JWF/DRbqGeseNNOevX4f9FHM+1rky771e+c1Zr7Xx390eztTCAFVvWWJEC2+zCX9QvVWYLEJqPeqeniI1Pn86bPWDV7V+yjhMAh0ss4zpmHA4Hz1sLlcLlccn5mvmFCpeFWCxwBgvlyk06R+67z5lhs2WJYZa5QgOHIWFpLHiZBr1KsdhpqYjUFYQwRogVVPIOsH+GHEMAY4L9437fpw8W6aZ5zPZ2FkOeMwTRiPB9w9SHW0+3iPsIjB/fIs8QkfPnyoG6zYIEqW1MvlUtNqLMsiRlgC7nGvbrsG58uCZZ6R8pbeIsYEa5MYzIcRfhjw/HRCjAnjeKhrGKIYsOMaNJFeBCtDMc6qNuTx7t07OH9ASBb//sd/x5/+/Cf8r//xf8PT02c8PT/hdFqwrhn//LtH/NMP3+Nf/vmf4LxBWi54jgtGP+D+cIAlwjB4DQa0iImxpoyYA/xlRYaDcRm+EOeU4JTIO7WFFDKxeRuplKnS63XqBpHUa31oYyQxI5fAOtEKa1lIY4oTa7U5CM/ghpiJVG2MhbVCwFQOKduwIVZZpfCS+ljVW2yFnFqIoE9fUK7Zi7Nov9/7e8/vfu966Z94x4mAQrDkG/uH4G3qWS1aWvm9PQwtc4EiBdWLkeyVxlcEnJIAsKQUT0nWS1qBiDYGxizR9AYvpfXXJO/CPNvP9r7v4Zp2rsrPaykmemZd7t3TCNs1eU1T2YPISqDlLUjrazSIt0c0o8u1YbeEbdVVVGEbcSsVyZKLUqkrWLxq5mWu6aO9dSDeIl2L2l6I4YabK0F2rm6+MhkS85A3g2jzjCL5lsUQY6bUDzacJfYii1gpftYMkIGxEsgFY8RNNiacz5cqPVprMfgBRjHvdRUvrMQMY60YkrVYT4xRtBprpfaEs1JvwTqAJX8Rk2QGPV/OAIuGsoZVnh0kI+2yCnyWctpSVRupeJaSHGBwcQiwumkKnMHiFksW1jgNDlJpmK5zC8l8ydidMwAZjHlq4jwMEmc8n8/4+eMH/Pmnv+Lp+Qnn+SIxHmB4R3j/7gHv3z3i4e6IlIK44S4rHBgYBzhrABKmvoYIWIsUEhJnrCFhcQlDiGCrEjWr+VwFDybR0sBc4uvKaUGRJulKT1BCpZJtyWjERWNrD51qimgYT5GkmzdVItpKx/qA5me/iWT85QP7GjT0pft76GFPGm6JXOV+jeSP+ukesaozi+uxFs2CX8wb0fb9BldtzKN0V76n5j07/XmDsPzaHN1iIrfgu/7a67nbg8j2tYWv6WN/z977fy0T6NvbvY+qVuDhtKBODQZzriZpIwAhZ8ScsS6L2B4GJyp5ykhrwBoCLqez+qkzDuMEIiCBETnD5ARHRgjk+YykHPZRYwy84vTFeyepB06Jezidz3ViCgRVvIfGGHFZFoQUETmDkoPJYvTWPGzVuGn8WLdgVO+hz8/PsNZqpTKP+3unLqYRp9MZMYh0fnc4Cj7eEPK7uzuM44jD8SBR3VZqQmfOMMsCGEJICb98/LgV7VEYIeWEk2oKVbWumhuwLKW8pwORxTBMKJlGpbiO5Bj68OETDocAfgAOhztJea1eOUtYIc5fYmA21mL0HhkGAwPjdKgwW+SM5XLBf/nrB/ynf/vP+E//6T9hmRfkJEzfe2DwHv/tf/N7/OH3v8Nv3r/D6fkT5jnidD7B5ogweIyHI5wfcXd/jzUx6OmEmCXZ4GlZwbBgdjiOgLcGg8aSbHU8FG60DlC9oZCOAv8VMsfVH0vKwYINLBURXlxfCxRY9sKWgkUP+Avj8jUU0+Lnsge339vfG9wgEc229Hj34N/Cwst722t6YnPL/tBr37eIzFY3Qyap7LvGH+w64ljX49oGcI3zo/l+g7uu6zeX6GSZp35sDUykp2CDzPaJbfuzl2Rvr+1h+OX63jB/y4h+S5PrtYQv9aHXTPq2BwX+2vYVcQqSquFwPIrEa4x4DGFLP8Ga5jpFLVQTJIvqhEM1QpGiwwQ9FMxV63A+IxOwpgi2FpkAM3hwjABDpGsNmsoxIhRpOYnKH6IYfcmaGsxW7A0EwA+S/ntEhk0Jc1zBBKwxYE5yb+KMcZwwDiOGIsUxENYFMUSkIPaQ+TKjSC7TOArub12VSclQTTHx+PiAGKKkyABqcj1rpHSmJMv7LP0kwhLWmuZimibRKIYBQ07IhGoYJWsQg8BS43SQA54lZbWzDo/vxJgcgiQRTCnhcDji/v4R799/X6GSJcxYlhXzPGM6iKfX/eMDQKpBsGgKRSPKzPjw+YSfPz7h//3//Y/4tz/9Cb98/ozRiQcUGeDdwwPePd7hxx/e4+E4Ii8XWE4YDGM1DI4B59OTBPmNVgIalxXDOIFPM2JIOF1mSQnBUlaT9dqse048kUgLDin0UP3bFZYxBkxcvdyy2mA4Ak61rGpHUWaw/VCFEIGKZCvc036Kuhfqv5U+tVIv0GZCLYS6uGVfM4WekPetZw6vwUjt97eI0DVTy8IQKwGkK4K83ZNxVXOahe2+fIeMLWcVYxpGWRmNeiMaFg82Zmr6IDfVv2mbYynUWuw821j7OeiZ4R6c9qX5bJnCHoPumU3bl/L33rrs/d1/1o6pfX8/169BT29tb2YKbpDC7tNhEsiECGERiS5r4jUwA6atJSDG1lKhTHVBxR61sleBosjAeUmNHXMGsRRnN9ZuC2KMRKpaybNU/POLV07Q1BbeCfGfDod6ZHNSDyFn4TGAUkJARjaEmLMQ4pQQs0RAS8CSRrhyxhICwrqCYymtmWFVYrAksQ7eiZE9K4Ew1tTymslHjZiWYLN1XcEAjuuC8/mM56dnTMeDBqcJY4wpYlIs33gLlz0GsMRLGAPrHObLjLgGeDeIBBwhdRWcxcPDO4AZa1iwrgHLssL7EeMo7rWXZUHMK9YogXfPp2epjaCuwFy9kLTYzrC5qZ7mv+HD54/441/+hF8+fcTpMsPeT3BGXJEfHu7www/f4fH+DofRg+MKkyMsMiwxmKMawhMcZzjn4bRmhzEODAlktCZhsQGjVvRjUsYEBmmgFAyJJxYkM6qQIPXkoY24cHtoUgKIYHKGJShjKYwAwgyVxnOBPKozQ96TR7c/FdFgvvq0ObwNwUc58PtG5b22pw3c+vdrUujrz948geT+9h2bvUB+5/qZTPb27qs+tI8hbp6WN6ZSNQ6hATpBiuK18yWvkjQXpQ/7jPQ1orkn6e8R3NeI+R4U9xok9Vrr37Wn5b3GSP5ehgB8BVO4f3iQ/P/HO1hD4MxY50WI8RqQ41aqs3gLJU1el6MmCzMGSwqC5ROJt45zEsxkDOzgteD9imVeQaxxWkakwUwAINIJrIE3Uo+gRBnzRaTYx8dHUckN1fKQbAlsSLQPJykdLMS9MRFA7GCMGKLXnBCWC9wqCfWIAY5JIq7XFUgSuflwd49pHJHXiJlXnNJJUi8Y4HA4YpomvH94hPdiSD0/nxCyGHDPZ42q1lTcIQSY6ODA8MZLDAiAJQbAECZj4KcRfhrx448/wvsB1ln8+Y9/xocPH5AyMAwjvnv/PaaDpNF+//4RmROWZQaRwzIvMOSwLgmfPj4jcUbMYveAIYzHA4x3gDFIEP/+ECPCmsAgDKqxpZzxp5/+gj/+9Dd8PD1hyQHwouH44wF/+Ocf8d//67/g9//0W/z4w3fwBqA0Iy1npLDA5ghyDn4awDliXWZEkkM9He/w/gfCcY3IEVAOgDUmEAUA6qWUJIrbGQs/iE2Kk3g2sDFASpW4F8JirOwhLuI7UY2dkfQUrLEKjGzku1QgOhJjZtGcGIWIQb9nYUQwkLxOm3cRILWzRerWGuV5Mw5SSnDOXEEmhXhdQyovidRrn/e/e+K1RyxVMdaaIgXzv04/0Ur4wjzKe0qGKjk0jJKUMFfgriwHIDZEqJbANemiMnfWtWPRpgTKAoCiOZSJEkFTMvlu0E4ZU2l7qR/24jD6PFE97FSe2ccKtAwBuHZBfm0t2s/bPvfr00Z277m77mlHv7a9XVPwXnHwDUuLKelPBLOBySqdZa1ZHFNNNyFqMiHkKFvFQF3cqEoPxlpR9wGElEAMDMWyTyTun1IRvhJ74ywsSaCb81I4xlhN3tem8gZJpHIyiCzRsolz1VSsFVjCSH62Go8BvdeRpPWwxqkUk2r1s5DkJk4ZEQlsgFFVcKu5mFJMu8nXiiEdKslmWWGQlflkAmAIbvAA5KDePz5uNSMMiWdY0vQj1sH7AcM4VGkVkAA852T+YkyS4oOLLUXjHJy4yKacsATRAkNMmC8rGIQ1Zt3QwNPphPM8AySRy34cwJoh9/137/HD99/jh++/xzR4WE7ICZKKIiUYiAuhs0UzXBFIDOHOe9zfP0jiPVgs84z5dNE6z6ixAshZgQNIypOUKjFGsQ8ZAhJQAptQ/4/qmVSZQysVXyNA6tGk1zVftk8shAs658wynopLFfCJijtmqUFtkKm4Be9Ln33bk4Jfk0ivv2thkA2JKVNRoS16SaxevqcjTNAz3Y3hReZOKs9rmeo+pFXnVCe89LG9pp+iW9BY+bu1B9xirj1Bfi3wrmfI11Dc9fP3+lc+e42g94x/TwAo7/5721eV4yxRxJK+OmJZF4QQEUNQf+/NlzyrVB1DFMVYd+CahWjaaYAFQIYk7W1TZxUQnN8AcDSIlKXGZeZNwioZWUEEN2QMxehFVBOu1TKZxsCw4NBzWKVeAxGcG6R8JXvxMgrqlshSSpM1Gvo4CmzmyAEpIZMEhI3DiBSiEnaodrQV7TDOwnkvEd8N5uidQzIGixJWq3mkmAByFgbqi28IsAbT3VEM4M7iux++h3MeQcfxdD5jMFnxcxJ3WT9iXUKNRSCILaZUrjufL1hzBIhxfHeA8wPc4HE+z1jiiqfTs1SOixGn0yzPsOJkQMbiw+fPeDqdYf2ASYvnPH/4BD+O+P0ffo8//OH3+MOPv4GLF3BihEhYWdxdrSFJZe4cnpcVS8oIiIDzGMYJ94/3cG7AYTzil59/wV/++Cc8f/wsAkaBtJJmebUE7wfEvAr0pwwhkbLYbEBw1V5QgA6w0OpcKI0xcj1I7+mIP1M1JbSHs0ige5K37AkDQsa1R5jsyRizGq4NnNsk0J4h7BGW/p2vwQ7bM3JlSMLE7AumoGak+vleH8q/277UeAaVRFqiuEdQWy2o73P7vvZdLa7/2vy0mkL5rqRBb/vaflZd1htX31tZWvfeWa4pz2x/+jH2qbRvEfI9bQVApWn9d/8IewLwFUzhfJHc/ItWJEs5Y1bcO+UEo5BQSgKzSKWzCORcoQ7jHGyKSMTIRSWERCxnMGKICGsQt8lcNq64g7IeMGau0n+IEX/92891g1lrtYKbSDpl8kXiDWKPiAJfMQGwBnGegWVG5IyUGWsMYJJYBe9HIeDGYRpHjG6AYRb8OkRkMrisAUwW03HCD3dHzGFBSAIHxZDw/HSqMJpzYluxxiBRhEkJ7LyY34lwmA6w3mHw6hPOUgb1/v4ef/jDH2riwel4RIwRf/vwC9YU4QaPeVnB8wn+579hXmdM44D1ckFSW4gf1A1W3UmNAzwgCeG8AXPE5bziclm0Pw6ZJSVIzAwmCaADGaQMnM8rzqcV8ynCOYks/+4Hj+++/x739+9xHO8w+QkuRTAyvHFgP8DkhHNYkGLCMs9Y1og1Mc4pwg4HDEYdGu7u8E+//T0YwM8/yziXEPHpdIJDgoWFgUVmKdYktRhsTVeRcpLstsYgxVxtRESSwJANIUPGElOGuNKLrSiFJMWO0HoFSYqNlljKAaQXP7Jvt2R4zAaiJBEkjlD26Sge1tXQ3EuW5XdLoNrWE8uXefplE1XNRGGgaj9Rxw8oEzAkbr2bJqPQDpfnyL/byOuiXRVDL3U0iaiMr3U1LdBUmbPtWqpwVUsIy3tfXt8+s2UAt+Cytjm3kb8+7qO9tif0e3BQaa0nVm+n2IO1ynd7a9/3f+8dtwSIv6e9PXhtXTW4KFUXsKDeOinn6lGUmcApIQeJhDUQF0/vPNzgkSNJMjSSKmjCGHQSNBGeeDEZlYzdi8HnXJK8ZSyLGLONMVqK0tZNiMJBIQdfoqVLMFLBCTVvE6Gm2oaTzWc1oZ8kzHNaJ4Jk4xuHFAKiFp533uP+4QE+DFjjio8fPlapnFjEzSKNWGNq3p1sU4UxBi9zVOon5yxGcymSI7mQ5kWyjcaUcFEtY5hGRF7ARgzUl3lGigGX55MwhWXB3cOdZK7VcqdGySqMGKZDCJpGRLK8SrZXgwxS5MTC+0GgtZg1ajkhLBHGSK6l4/GIh8d3uDveYxomDMYrxi5iujcW2TksSSDEUv4xZXkemwir+8sYg8Nxkj4PA2AIKTHmdcVggYFK2gnSzChagCdvaUAoA4ZJE+sBZE2lKcV5MvMW0Ab9m1MGHMTQyYWAyv96iGAj1nuMoWgc6lGjWVyE6Bg410EkNyT9PWbw2t/Xl29EdfMMAkqqDSZTySxpBFp5JHO+OntARkoSUV68qMqEFgJ9hRLRlnqi9KNcu43v+vovtcLo2mf3dLAl3rewd+B2Su+9Z/UFisr1X4Kq9rSFPdhnDz56K4Hvx/r3tjczhdPzqRbJKTmPaqfAWNYgtoOSfI40ApYMDodDLa5DcUViRqAM6z3s4OFHyciZcq4FdOzxiMF53N/d1RKcMcYazdvWUxjHEff39zgejxi1WIx0TFNAhACeL+KJYkTitc7CjyPWKHETKUvwmzEGiUpiO4AgkuMcIwIIx3GCsxaDdTg/nbGcLxj9AGcInEQzQmLEZRE7hbE4jCOMN0DMGjDmtG50rLUmrLU4Ho4CZTkHZ+Qdh+MB7x8e8ft//uca0eydR7QrRuvwu3/6J/zmh9+A7ABnDEbvcfr0GfPlguBnWEtS6GcaMQwD3r1/Bz8MkgodCZkjLusJz/mE53jWinMSKW7doPEnA5wf8P67H8Sov6w4TBOsecb56QSwMP7f/cu/4L/559/hX/7Dv+K7uyNG7/Dp578izhes8wnWE4bhgPfHIwKAOTGSzTApY6WIyBFPTx+xpoRPnz8hqAvwb378DjkuWC4XxLiIOdcYJEgsS8ypRthzThqAp/YHs6UpYWaBc0iZAhe7gUj8XKkaNcSc67XochMB28FtpcAiFRZJ9KVkeF3x60s+7nuRyH265Ot3tASoIVQvaF7x2unf+5LQ7sFjV09i0TqK7tRf0//9jyRkLaRc09t035fWQlZ78NBr/d2T8Pc0uF5DuMXs9qCl16DD0kqeo9YJ4f8UTSGppd6mbTNaYwCt+ZtMlJxIhFqbmCHEwjirEbwWlp2o6SQeGdaYOkhmhjWm5vsx9hrfK66t5SCVovZtVHVNT0AAWO4BiZG66KfjNNaSoFkrkzEZkCUh0IQK6YAltQJpAjrkUmMBcESIhkCcwTFKDQiF1MSRomCsqJk3y48l8f13VipulcRsJRePNQZZg+LCsmI+X7BcpPZEYQ5hDfDOYRoPeHz/nbjiWos/xShFezTyWwzitkrKEkOh5WqyxDoI3BU0Z5DEmhAllDKdksZcPGcsaV4gBlJINX7DkNqVigRJBtZ4ZDvAWA8yEsNgrBFjM4SBohC+JAWBUgwINOPjh5+lMJJzOBxGECecny7ImZCIERJgibCECMeAVckxEyExw4I1hbb8lMDY5rhf7XHS61optmiV/bV9K/e0BGEvm2XZ568xg1/T9iANasfbSfBF4pb+yN+3YJGeIexBLZVo5dvzdMsu8fdi4XvSeU9s2/ft3d9L8j2hvaUl9ES/0Kr92g/7fW/bl7Slvg97kNRbNK7X2lcwBalLkF0CrAVZqsQbCrskJFDKmvLCgS3DGSlLaZ0XDxgWo1su8IyRlNZFPPGiU1cu2HouSN6eoWoTRXNoa0K32QIljfYI57161sihPxwPtTJbTAmERTO6Ar4JeiIIpBVWSdoHTqAs9QEcAYO1YOfEEyauOH/+rB49mjqchAEgS1IFYk3FwAyrRNPbzcBVrrdkagK2+TLj+ekJP//1b4hBtJoPHz9IsNnljHfffY/Hhwf867/+K4ZhgCOD0+fPeP78JMkJrcU4Ddt8skSPgnSj54Tz5YLLRQLYotpyQgiQ9IKy1uyywHmUK2MjZsmRNAcEt4ITi1F5jeJtBAvvD5oXJyFTlOylRogsUQJCQgZLneIUEdMKnyNCBP761z/h3eM7HL7/DY7HEYSE588JkUWSXyT8Gqd5xmgk0t2qtBqRYXUEbGSuQU0cLm90sh4u1TCIugPGrPEP22HsDX3yTL4iBv3hbQlge+1bE999qfXESbquyQBfEJKSg6nOyNX3PRFsBbdqVO6YA4pWhWZysU9U/9GQR9Gc9pIVtm0fbntppO+1i/77Vit57R1fgv9eW+PXNKqeKb0Gk31te3ucwuEo3sYh4hIjQOImSbRlRuWmTkHNT08SG5CQgZywaoAZK+wvWVAFCpqXpeLMJc3D5XSuRP/h4aEeoKIptNgugAovGY0o9t7DWIOjPYjLprM43B0BiFslckYOEX4cxTXVOdUYFPPPGZ4ANgZsEwwzKCekdcHd6HE/eqyzeCmt57N6uRjcjZMSeiktmpO45xIRViqZMSEkjBk5ZSznubrHJnX3PT8/Ia0B/+l/+V/quP/3f/s3pJQwHSZYYzF4j7vDAdM0AWAMo4f1EqeRkLGGAOdt9ZVfw4r544LLfMKyLvjlw88IUQv+wADESHkW/BsGHCKQGet8Ua1ghWEhuo6AnALWywV/+i9/RJoX3DuP9PvfI333Hd6PE6ZphKEHnOYnLGHGOc3IlEHWgWkBQJimAWSdGvjVpZgYOa24XJ6xrhekFGC9A5KkUQlLQICBzYyDHzD5AZN1yDnhEgIQPdgL063prxuDqWxC1Ky6YGFqBZpMEJdRkHgekdkOXE/8C5Fv4Qnbw6wNIyh/f4kotL/bd92CczZpvvGIyQCpIbgQsi1J3cYUbhGUnlH0rV5PqlF9gc7/oxjBXuuJ5R6UVIj8njTfz2/7efv3LaPxa1rFl/rdPqN/Zi943NTU/gHt7WkutBTlGiNCSjU7Z/HDd96LJKUBQLlx1WNocZScNJEbV9dC5BLkljW3f97q96aMGELNwFoS8BFRxRAPh0M9jGWx53kWab6t66xSs/MO4zBIVTUWo3ZO4vFuyMA5yXRZCABngkm5JkvLMYFYGNwwjvDWwbJoE/O6aNwDYRwGMbznjLhKTQPOm1fFBnMVLs9SSyESktkitmOIAGZ8+GXLnPrz3/4GZsa79+8x31+wznNNBrcRByAjq98+ww9OCxxJQsB5mfH09IRlnXG5zBtMonhyjhHJRmSXkJLgxWGRQjhZE94ZYlhDoJyRYsDnT5/giPDTT3/Fd9MBd87jcfhOyrMOI5YcEZjBWaK5yQDWeTgY0ODBZBCZYazsGYmJyQhhQYgrUo6wVu0IScq3Mmdc1lUJu4VVF+c1JdiUYFKC1bxIsqBqR2iNot15qgcPuWZerUbjSv9eSn/lvp4Z9Ne113/puvL3HhzStpefEQrsVS0kL6TLl+/8NcSl3sdN3MEbx9J//49srzE54Dof0635fU1C7+/90jh6iX/PXvBae6vd4L8afHQcJolLyAvm0wlrCFgPB4yHCXcP9zhMkxjWWIrOL/Oi2LFqCikih6ako0IIOSWElBBCwPP5pJguUAiUgWgi4zjWyN82/qBASTlnfNbKZufzuSbvKxx9miY47+G8E1uIvjMGqeeMlGEdMDkPNxgYJ7mdckpYU0RWyCsliR8YncHdJJXE8iBV4T5nhhlGuMHj/bt3mOcZHz9+xOcPv2BeFoyjuLiCJF20VHTTIDQwlsulBuUVhlbu+fzpMwAJyPv88ZPYa0hqLoAIf/ov/45xGpFSwtPzZ6xJ5jOzELa7xzscjkcYZ3BeLvj5w8/48OEDwrpiOkzwTuojxCQMOsYMjglhmbGGiLAu+PxxUimZYTjBG+DoraS6Xlf87S9/xfx0go8Zbo1Yns4wKeHx4Q7fv38HpgHGMSwnMBIyJfz42+9grMXnyxnzsgLmglXX9v5uQk4Z5/Mz5ssFOTH84LEsK54vFxysMLkla6oMYnCMyDlJLqt5xsIJD/4dpAoz18yyzAnZbAwgK/EvwkWMEZkyrPGbrcpc+4O3hL2VpPuoU2AzCt7yfW8FhN7esKcZ9AnmgGtisEmTsreKFN8+v+0/0UvtpcAxBaot7+iJ6dVYUrpiQOWePUL2NQS1H1/bir2xXLNHsFsNwDapc9rsyf072j61sHQ/nt5ltX33LWjwSwLB9dpsa74HW/Zz9F+NKSBzxcSLQbFELhdcX1woxXVzmMYa8u7YIOcESs2BUk2zFG9JabMPGDWMGiOG2OJRVDbnPM9arJ4rtFTqIYub6lKhkqvJ0r9TTLVgT24XNJe8RuU6ybq6LivSGkRLUJ93tmpHMVEkVjWwSQ4kp4ZcHZ+KpcYYwEg/avrxZoOqT2/Vvo01OE4CaznnJUI7JTw+PiIz13Kf1hg8PX3G5eIQYqxuus5L6m9rDYZpVAO7wRomTNOkmV4d7u/vxeXWWMzzAqIIcK7Zb2OUg561doU1Bg/HI+b7FY/3J5yXFfMaEJNoDE9Pn/HTTz+BQ4TnhMeHe3z+4T3IizE/ewY5qdfs3ADjHI4AiKzEDKibqrcWuRqvWWFGVBhwmA5SYjUB5K3GHghcEplBrBHsOYGNxDLIXgCKxlb3BkkkfoGXcs41V5VoFq/7hN+S/Pc+byGIPQbxNZj07f4AaLyqijbaX7fNwW38/e+R5Fti/PKd9OK6X/uOPQ8t4GVBpP79t/5+7V19u7Ue7b+/Vhu4RfD3mMUtweDXtrfXU8hC+AiSbyZbTVcdAi6zqPvee5jjUYj0OKrayrCRa9qLMgiR2gBOUp85R8mzj2EQ7xgnxH70Aw6HA8ZxxLIsCCHg6emplpEshG2aJrUlmJqIz9rOECYvlwC5ICVAU0xiCC79SAkgRs7SnxiCpFlYRaPwMIBzyDZpkB2ALBXTwFL7eHBeo7uhMBoDOibRFArsJmMsBYiKFsFKkKzaUSTFiJVylTHiNz/+iJwlodt0mOC9x+dPoj3My4wlrCCS+AVjSLPbHjAeJ4mkzgmXx/tql3l8fC99zWJ8pNWAILWVBz8grhGAxJ+QMorv3r0DR8LTxxkfn55BxLgsAZwjPn/6hP8SIj79/AvOHz7g4eEOv/nxBzx+/w7HhyPuvn/AaEeM41Hckq3Bnfcwxkm22xCAEOGdFYyJCeMwIsaE5/MZxjvYweNwfwdvLLBGGGfB1kqmWwICs6wrJ4ScwNnAMleYrKRUlwyp2+GqNRWgEj9tUttrTGBPgmslUeA6r86eZ1JLGPcI6Z4Ue5sRXf8bOqZtnXvG87Lve+Mu1/djvr5gH2Zpr+1/v5Uh3IJ5+rkrP3tEv1+HPU3sS+/+RzKCvffcWn9jzBejpvu+fm17e/Ca4s6jFopJOeEUFkTOCIskjks5YTxMksJgUqbADJgERDXYYTt8WXPvZGPgrMPxcMAwCvzCJGrZaT3BaQBbgVPu7++xamR1tRk4p6Umh+0wQqRdEGqsA61ijwghqJvnjHVeYI2t5SZhASausQTrsoKjVnAbvKqfhMt5xoUv4uHCxa4huaDmZcYaVmTOmA4HTIcJ9w8PYJYUGoUBSB4mjcB1TqtZMYxzRaQFc0aMYmMgY/D+u/c1CWBR8T99/IgQI57PJ6k1rcWIyYn313mRtBbeGq134PH47lHSeFiv0FzEfClamHhBsdXEECoADM7BOsb3795hMB5xzXh8vsfz+YKfP3zCsqxYTjNOp2eEywxeVkyHET9//AWPP3yHu8c7fH/6EdNxwvHhiBQDQMDD4x2sF5vP+3fvkDMjhAhrPZwfsC5Ba2dAHE2Nwz/9/vcYrMPl6RlZy7qmnBGgSQ4hNqtlDUgMDMaKAdZIhbotSZ15edjKf0WD7DSFPphpj3je0izaA97+u1zTPveWprDHPFrCuHk2KWyEDedv+1kS3LXP2IuYfWsrGvlrkvceoe779Wvaa4yshX/24KI+Ed5eX/aYXHvPa2P4Wu2v/771XivXt/vjH6EhlPZmppCTSK9O4Z3EDnOOyEnTILS51XULVumH8rYjDYGyCoCZQJoTxlkrdolhgPUOa1jlgK9rjUU4HA6VMRRi6JyrC1oWfpomLTiTKgTQZkQsrqxB7QkpiaRviKQ+QWIwiZE3RTF4A4CBqfUcAEJMETkm+GJYJKMRspL3KaUIgDGOg0ToHg6SmVRjGbKm1Sj5ibzxMEYDgEgMpjFF8fpilvgPEg8pGAMTwlZcSBnd5XyBGxyst6BBC94TY14XUFjhFNYiAN55MAMxZsSYakrvEAIsSfQx54wS0sslfxMzDuMAZOD77x5hB4dhGrCGCGsM4rJK2qGcMC8zUpbAtGiAS1iBwWO8jDjNM5blAkbGD+t7HI4HPDzeS2pwT4hRmL53HjnJnvJDwpAli/7ju3cYtI/zSUqZxizpz8ucJc5ioyCCdQkgK8kr2sNUiKJCLBXKQx16/X/b/h545ZbE+xrc1L/zNfhlu65c8GpvdpnSXp97gn5FZP9Oov6PansQXMu8yzW93efWs95CcHvG3K/p1xDtW/e+Bd76R7Q3MwXDmq5iGJGNeBMNOYCSAUdJSua0iEyIAafLCaMfQSTF2pMGOJWqupYBgrhmemsxDgO+/80PiBqhun4Wyf75+bku3jBoXhz1OGq5ZWsIe/fuHUIMWNalErmwhrpZ5llKgV4uF7kvZymekzUq0xJgodCSEiPnBRqaDrAgmAyEHJAy4zANVy5qa1gRLlJZjqwQr2macLi/k9rK64qff/kF83rBp6cnFBr07uE9Bi9RxDGlWrVNSlMGPD4+4v7+HnHdCsacz2ecmTH6QVJ2hAAYceccBslCep4vOJ9PUujHWkzTiPu7O3g7gDPjw4dPWJYF8+WCz58+I8Wk7q2a5T83yf5CQHIOx+OIwzTh7niP53nBaZ7hvMfT0zNG52ASQAmgKFrQ56dnnMIK9+kDPs0XkKaxni8nMGf8+ON7fP/Dd/j973+HH37zA8ZpRM4AO1njYRhhHSPBwPoBY8r4/X/4D/DW1qyvz5czLqcTOEutC84ZKTBCfMYwDCAyGCGQmjEOJVV1PWRU2OXWaOev+klDAHrf/vJZG3m8By3dkkxbDeaWptDDUuV9m6Qv/ZZhbRBRHSquCfstRtCPqXy+x0gMb3aM9v5eq+rH8ve0fj72tJQ2zmLP6+iWe/BrWk0/J32f+jl6iyCw14fCwNrv+3HsaaO/tr3dJXWQ7JjWkKTLVmustRaTsxi1xCRpKufzZcY0BolDWENlCt5YWCLYUnwbspmttzgejzhdzliCVCUbhxHm3btaxrIQeKLN3XSaJsnDFEItVVmYRogB8zwjxS1DKTNjXSUttCHC4AfQMFSNg4xkGs3qu1gOlwiRBCIDQxbOUnXBff/uXd3sS1wRUkDKqwRCGQs/DPDjCOu88BgTsYQVp8sFHz99AiDG+3E8AiSJ2yR9gxzulCVymyEpzAt0ZowRTSMl3B2PAByyIbjRw3hJQJhyxDIvWJQRsmL4IUTkqPER84x1WbEuK8K6ikHXe7UhbTEV0GAxMGNwDoABWPJGWWvxu9/+KBXX7h6Q14QcIk6fT1iWBU+nZ4lQN+JSmpKk554vJ+QUwTnifDrj+fMT7u7/iMNhwm//6Xc43t3j/iHj/v4R1lvMa4R1BHKE4/FOAh8NYYkBT+czLucziBmHcRTffAj8lnLGNE3w3gFZKs5r5qQq4RaWcM0k5FfLLm5JzGV/obluL/d9ua4nnO19fesJcPv5Xn+E4G9qAlHxRELz2etaSs8QXoMqNgIlE7XHAMo7bn3+FoL2GgPZI+Atw+xhv3ZtehvPrfe1fd+b+1vja8d+a7/cGu+e9lG+22Nmr13/lvZmpjAOo54OQvr/EfdfT45sW3on+NvKBYCISHHEFVUsNjm0po0Z25o2///jvMy0zdM0e8zYzS5575GZIQC42Goe1t4OD09EZJy6VWw/FiciAYfD5RLf+ta34kyIHrIUTJtGVC2NtUx+Zh5Hno5HZt8JLl2dQoh01omAXOF9K6Vo2gajNbt+J2JuMZYZ0B2H/V7opNby8PCwNLXty+u73Y465e3+/h7vPe/fv0fsdRKm0uyXvgm4DNeQQqrDWVemuZWmOC4QmIwGpPyIoJ42BmcsjWuxWvP+w8clW3k8PZFGSGkoXHwjUtZNizYWFaW7eZpmTucznz5/QiE1lZubO2FeNY5QBuDknAlRsguJmBuGcSAlmYGQ0kQIQQr7WqGdwXTSwZ1IxEkc4ziOxCgQmfeBefaQhVE1jxPzPDFPk2RUKUmWVB4clS/FSrIICzZWFEeJQvFri2P1ITJ9OzMPE/M489Off+LpeGQME7p1UivJEHzgdBoYz2dC8JyOJ341mj85jdbQ73b85//Hf+bu7j0hZA77O5xxUHpJ0FYotkqE8SYvTuF8Ook0t7VU8Cf4QEyZvfd0TfOlQ8hcfqvVww4rT7D+x5cP37qbtr53LVLcQhgvQQvXDPA1Y3XNMF3dDix9FhcH8WXE/lJE+5JTeBahrovbPDdC1zKCtcHbrvtbI931+lv562v78FKm9s/53utO+S9fttH/+ry8BPPVY/7vkincHG5IOeFjwIaZkDTWIKJjWWig+JmpSDFAxjiRwRjOAyQxtdqIMwgh0rhGlEGNJaXE50+fuH+453g60e16rBPl06pxdDqdFg8/Fuex3+8XFo04jJl5npZoJRUc/ML7Fk17UVXtUUWDKKZMVVoNSeTAO+sWWuxwHpiniafjE41z5H5H6xqy1hxHyVDmScT+jG353e/+ijl4TsPAw/HMw+lM2/d475mmka7fcxMzfvonYgooFWn3e/qbGyY/S8bhPb/73fdYY/HTTN/3IuA3z6SUccnSdi27/Y7u9kAmE8cT2mrQmeF0YpxG/DyKUywSFjnB7CONthjjeHd3wzS1tG7Alof09nBL1/fs+l5YWSUbc9pglcaPMj95GCbarmPfNuy7nUBjcUClhDGK//v//D9irEPbhmGaGceZv/37f+DXX3/ll19+IseIQdEayfxskdgYnyb+6//6f9C0HU3X8Y9/9Wdu7m55/933YA3KWn759EDXNty8+8Dv/9rT9DvGsyjDTsO56FTBH373e0iJ08MjOUvzHdHIiFFryZUccD5DRqagLTi7qInGGJceku0DuI5Gr0X9Sl24/VvHAUKoqNtZd+zXh742Oq4hyvV3XRNG+6KTl0uXs0TIl/4D+Z7nmk2vOZr6HdteB3g+Ae3aZ9dGq/6uBf5lEt3GKV1zpuv3ru3Xte1c4/lvDeg1I7t+b9sFvc4+0jM7c/mOev3W6790ftav1XOz/f71HIhr9xfw7F6qn32ro3izUzBGSwGWqrO+KsFlKUTnzEWzvbCCFmFepRbmhxBVyw1QdI9SzsLYKRBRExtykZquRdQa4deeiJo1rC+IsFZ8aTSSXawXRnbjUpR2zi1ZgBSTxWjqqoNkBWppbLOIxsWYCEokKKxNmJwYppFUOrK1sdKU1nWkScE4MkwTMUVCeQineaKxjrbtMMaSCULpbVtc1+CjyIhkBU3Re6qy2z6EZdiQTprG2UXbKeZUYK8IMTOV85mSTC2jaFLJoBeQvEdkv1NKRH9ppuvalraR+gb6Mvi+al2FIOJ5fhqxVpMbjdOaaDJGSyOg0oYPH9/R7w8cbt9xOk+czgNPJ1Fj/Xz/ieg9OWWcLoZPKfwcCTFx//kRY88Y61DKcns6o9se0zbYtuXpeCSmHdo6DreSZfl5ZB5HHj79ClnE+77//nvmabo4hdpZfnliyOV+qrPCc2mkefbfyiC89lDLJl+GHbYP6LUIevv3S1nDtSj7y/36stpcPyfn4jqU9Vrj1doYLt+nFM9mL2/25VpEvd7PColuz9HW8H0Nalu/v80GrkEo15zea+d6u1/XjPW29rK93luY6ZrRfu3eunZuv3Zs/+LwkQ+BOXiGaWIYZTpX0ArlpM6gyKhCY9SNdA53TYsCfNNQRhcLVINA00IJRKCeFJkHgS9ijIzjRMoZay3TJAXjeZ5FUsPaRUr76ekJeK5OeT6fRRrb2qKB1CyMJaUuaX7bdoVx45mmCaWKjpOTkZaHwwFrZFZAzJmcFdM0ETKcyzAaHxPjeVjAVNH/hz3gU+Q8Tdw/PjIHT1sgHB88f/j979k3jncf7grDKLG72dPv98ScRUNJK2zb4hoZjDOcz4zjyBy8HHPO2NahjIztjEFmKeQcSSny+f7zcl66tqdpGm5ubsmZIl3B4jSriWzbFqMN+8NeMrmmgcyScSktTnr2k8A+5xORGR9bur4npoTSia5zGNPw7XffcPf+A7//w7/Bh8Q0ew6HAz/88CMfPrzn6f5BCv/TjPeBaZyZp4CfJn795dNSY7l/OtLvd3w6nbj78J67jx+4/ac7bm5vuLm94Zvvv+Pm8O+wGsbzmT/9w9/TWEfXOP7wu9/x688/8/f/7b8RQsQov8impCRwWCqBR60txVgYX2WoDDVd57mxrPfSNrUHCgxqvhDDqw/r1thc62Jer/saLPDVBz4jPRhL30VasoW1U6jLS0ZmGxmv15d9LMHXK8dxDYp6DQ7ZrrfOKK4ZvWvbWHcxr1+7tlyDhLbfsb4mNZp/yem8BE19DT7bwozb++Sas10TG64d01uW3zR5zQfPeRoZx4EpBmgszgpNsynT1RJ5KSRW3Zhdv5N+hVSm5SahV4YYhYrZNstdZKzBJVeGvOTlwtWicu1JqDfHOI7PTpwqXiYnweKtsYuYniky1bUgXbWUjCk3ec7M3tNYjVYOpYX66WPCupb+AK5ppSN6mplDIibPXCJM1zjRhkoR/4sUUo/jWZRTKbRQJSM6M+IUv/nuW+Ywy742YuBd40hktJG6wFyksM/jyDgMS0Stig6QD57hfMIHzzyNSDCfJArPAtm1jbC2bg4HYpCZCMlLxjPPnnmaGMdJGhC1WTKppjj02rkOSFQdZZ8zAR9G8hjwcaKqkTrX0DSOmAQue3i8p+v2dG3LX/3xj/Rdj9WGz58+MZzOPNw/cD6deXx45HweJCOLEWUM2jpiTJzOI3/3d//Et5MnKs2vnz7hYyCTuTmIjEfbOpq2ZTifuXt3x/5woAXu7++lYx6RPDdaRPe899jyEDknTYciylgepnVH8GogzTUDdE3Lv65zzRCunck1I/dShLk1Mi8Zl2cg/xf7vN7Guij9skNYv1+Pd33sSj0fx7n+3ms8+0W5d8UkvLZcM5gvRd7r/XvLufuaodw6+q2hXp+HtWT21oivs4qXMsPt79euw/a+Wb/2ly5vdwpjcQoFDvExYI0SHXutBeIoipQhBGbvF4nldreTjuGURC4iR3Lh+YcYwIjkA0r6FVLjGJ7GJSIQ1ojAGtWQVzXVqTTOQfWSqhjFInRnhcYqTsEun/U+lId45RQKhGCSSFCjtIxsDAHjHL115DYyTxOPPhBiwseMDxFnNa2xog81e+bjEyGVblpk/OfsZ8mgXCtSzNbw8btvmP3MNE8YJ+wt2zgoxdKUs0xRm0eGSQrGfd+jikRGdWTD6UyIMmXNWlH6WUQJtaJtHLu+42a/Z54F+hlnkfEYzjKbYZ5mrJXmRKMv8iHVKVhry3kXWZKYPEpJj0RMijSBNpZ2t8c5TdNaYvSM45n7+898801D23b87ne/Y7/b44zl080tx+ORH91PPLh74hw4tmcZi5kLDOmsBCPnkR8/HQmA7Rre//peonFr+D59j+ta+l2PdY67d3d894c/8u7jB55++gllDLP3OKWJ2izGMIQgY2LLPZxT7WlRqDKZri6CkDyf/7t9yF+KfteRZV1vvc5LDuHa9tbL1mjJcjH69V9q83mlniupXqDgrxd8r0Wha6dQYbZrEe3XDOVrEe1rBn0NPW1rIq9lIdeO7ZqDXm9nvS/V4cFz2mv9vYXEXsokXnPC22tybdneO+v1f6uj+E1OIRZmDkajtS21gCIeFiPZSMdojhKlGuuwzvHx7h2xYPKP4YEYpKlonCemaSKpomejpTGrbVqsOZO5GKOqlFpPYNU6Gsfx2YOVUlpueGM0/a5n1+/48OFDuWiKh4eHAkmVyWwlC8k503YdCcU4eRJPgCKGICyotkOZTEZhnTgtUqJpd2itCDkTycScOY0D2hpc18o8gpzwxyNN13K4vaXd9zhradqGcRqx0ygzHgBtLVaphYmUU5bIu5PaQ7/fYY1djn+cJupIehmL2WOtkWi+TKl79vCrzU2EKmNHHfv9nr7v2e33tHV2RYqokNGGUk8SQTutM93OLeSdYRoFChsTKUemeWT0AWNbrLvn4eGBtttJoXucOT0eURn6xvHH33/PN+/f8/vvv+f3v37meDzxpx9/kBrEeWCepW8jzIEf//QD9w+fOQ9nvvn2G/6j/o/c3N7g2oZxPDGcTvz05z/xP88zrVH4QWRKKPfT7GeGcZQ6Wb5Mw6vv12bFzHPGUD1j1x667YNbf2oRWTKwi8qvUs8Hx7/2sFfjUnHrus3XPiefvVzySrzIVCXXOkD3OTZen6H1sVzbp+0xKyUQZG2+XEMq63W2EtxrQ3+tn2NbNP5a38ZLBn27XMsatsd33eF+GdF/KS743LG85Ai2+/JSJnFtf69lS9vtrp3yvwp8hFJi5DAko2WSmVnd5CCCeTUiqk1qStO6hlA4/TUdrzx8HyOuNBuhlAxKcRZj7eJlr1XSK1bbtu2KeeTLA6NA6TI3+jkWm3Ne6gjAkj1UeQzrLHNGoK1hrF9I7HoolL5CZgSlUJUiqcSIFI8kc42N9Choq0k5YecJ17Z0ux7XNJIVaYWJFlPYMFKI39wMhbVlrSU1qQzPqdi3GDmtMsYorNEF9inQj1J4Py9Z0OxnohfJ6eVhEi9RjJacjwoBiOGKl3nKMcjIzpxQ5HLsQhTQnsLQEacQcyAE6dcISUigdhgIPhEmz3QeSwAgBW5TjHOICescwzShtGEqmlZ1Il0MgfGcOB9PnHc7zueBp+ORh4cHYryws87nM/efPjM8PRXlXMnEyDCXjMrYCxOGcr/IvXLRRCLnEmqzwJxrI7p+8JZrtoqG6/36EpyxNZzb5bVI8prRkW2sXquHwWW/9Go2xHZbawN1zQC/tI/y8nUIZ+t4rmVI2yyqbnd7brYZwmuZwGtOc/sd2/26tt42CFj/vpYxbrOkl47lJQd1bd1r5+Fahrfd5lvOBfwGp9Af9mhrcY2TaJjM5Odl7rEuLCKrNcpadCPaR1YbOtfgs4IgEgkxJebg8cGLVHNKZQymQCdt29H34+IULsW/uERLtXntrkhUPzw88Pj4yDSNMoPZaGI0dKWYXOsI8+z59ddfGYYBrQ37/Z79/sDhcCgGXTGPI9M4cT6dUUrRdx27IHOcUxAjPHsvRsXZ0kAXmecJ0zjIhj7taNqWw+0NWGmGy0Zxc3PD+48fsSVimoYok96UWuolSimikslrVfjPlmymaRq6toXM0pX99PhI7Bu6tqG5OdB3LbtdzzDsGIYzwU9M45kUAiQxfDKzQrIIskAlSiW00qisihLtzDSVcasxME2jMJqmCaPFIchwHLn2Ps7iHKyVoT7WoHQmE0hx5v5hwHsZqRmmgB9mbg439P2O77/7nsZZWtfKrO6upe06fvz5FyJwnibJnNoghfgMKinC7Lm/v6f9sSMr+Ju//ituDgfe371jGEb+y3/53zh+vmc4PrHb7TApoXPm/uGBrmt59+520c+KMUKmBAglUg0C/S1mduMI1vTKbeS4jrirU1gbj7Us9XqU7PrhXtMcrxmv9UP/fJE9XmeI62xDa0VKL8NTW8P4UuT8LDIFSHEJal5i4Fz7/Etwzfrv+mxsl22m8Bbjd21/XnLQr2WC6wBgXQCv98X6moYyZOvasb90rq793h7f9h6p33mNBv2W5c1OQdUBNX0HpdFLT6PQIHNeZgsrkKcnZcEscxYxudpdKnuOQprH6tB6bYxo+ihFJksvQGZRFq3ZwDr6Ahan0batNE/5yzAeZ93ChpCJbHGhsF4ugF7SezHGlA5eGfZitMxlkH0I+Fm6fkPwwroyphxzgQqcEQPe90sPQciRkMJzw4AIAk7zTFgNJNLlhojlRgveS8bVlaE9Si9KnjEEcopU6CgXLFk6eKX5jPUNS8D7Sfo0VJUmV0RD2SYL/dfPFxhOIeywSnEN3qOcBqoMg2RG1lgZrdk4mqbF2oY5ZkKUAUQ+JGIQ+AlkBnSMM35WnI6PaG2LlHjEVPivb7m7u+U0Dbiz4zQMUvBVEkT4aeanH34s2Wfk3/3bv+FwuGHfd/zv/9v/j3/8278ljhNE6YtRMUCKMjDIPuePhxBKfissGoVaxrJ+9fm4EnHX7cKXyqT1vW20fG3ZbutaRP36kkuS83YY4ZphWr937TjKu8s6L8Eu2218zYhfc1DXYKmXDP21Y1tv57V9fc3JvAVm2/57u1/bTOol57nexrVzd22724zqrcvb4aNiAF3TYBonw90VpVgcl/R+AZiTUPlUhhTqvAF1yWqV8ON1wVqlniAPYkoJZ51Ez1aMenUK8HzMYXUKzrmFrlojbufsZQRlkbaosNHynQUCs1WVNF+8qsyark5BDKP3s8AxMUC2wqpajHHpbjWaVmvarqPreqYwkcM6WkwytCdJkXgpCJcfavQRRajuIvEMSonURJUbJ+dSC637cZlRkbnMB8gpE5WcQ2tAu+KMgWiqxLcixoRXMnJTIk0wWvZZHEJhStk61ESyBFXPabmuTSPqpn4QYcBhPBOjIqU6/lLgrpwDIcBwekIb6XhW2pYoS9G2DTe3B26GM9rIGEmtJYDQGfw088vPv4iTLY1mfdfzzTcf+V/HiX/8x3/CZeic4+PtjZwf8pJ1rh/AWFRysy4mVEn2BF8awa2Rqr+/FlG+BA+8ZIRfit63n9nu3xd/l5tr6xiuZRuvbXt7zOtt5FxgtiuO4TWI7CVo5aXlGky0PaZtNvXS/n/NeX3tta9F4S9lQC9BTfXnGiS2vT++5hC2GeC/OHx0HM+0OcpwlBRF0Kw0qplaQEMtxiqEgEWTVeR8PIEq8WwsEazWIo3QOIZxKDetKnBSpHXNMjN5GIZl9nKlStYO29pf0Pc9t7e3tG2Lc+Ik2q7BWRGr894vadaHDx/ImZUTuVBc53nGKNjvWlKS5q3bm9slA8oxkKInRo+fFeSI0UJn1SrjSjNZIhcYy2BSnYsrhvV4PIpsSBYJBqNF8K3+e54mmZ3gRYXVWIvVukAmxZGUNL1vWgzgGi0KpX5mOJ+IYSbHhDWGw36HMZI1OdvQNC1d16NxpAj3/qlIW4jTXRoABdXClAl2zhm0bnDWsNv1uMZhdCNT+IInJTDO0Hc7XNOitCFEzzQPHI8PGNtgjePd3UecMlgs0zARfGAYj8hNYDCuRVtL1+1QVmP7hil5ulMrIn4xk2Pm58+fGfzEOYz88suv/NOf/8x//B//I34ceX9zy8Pne3764Ufuuh1qv0Pd3JSYWR6clDPTNInSaqE5J1IpxJYHaF1HgC8etvXv7QP5klNYd/Je675dL/W+rNv+7VnCsqFnwc72GFL6MsLcGpX1ay9H7pfj39IzK7yy/fza2b3VcK0XpdRCFHjpnF87x+t92RaLrzng9fJaFL8+lq3TWF9P+FKSo667FjZcn9+6Xn39Wgaw3t4a3voXdwo+BJTWjPNIVELDinmbPpXo1gcZc6kkEp1GYdag1aJNXyPqihMK4lSGvKDIDurTWA9qDRXVE7uWCKgRv+CzejkplbFRO55dmdfsXEM9T1V+OoaAtopGy6QzayzWaHEIKWGtJlqDswYtwyGkqUspGZpTpsalzDKdrh6JKdF+nfCmlCqaQYbGimqndAnPF4OBwFlWG3wsWHXtaC7jOI1WaHM5/3K8ukT5GuWcUE21wWgZktM4h8KRVJbisnVYG6hzjnWZPWCMwjUOrZRE+qWJrdKEnWtBGVTtYLdmydqUNsVBW5EPNw5rHI0zWGVxWRONIkcQnrzczMaW69w1EDRJw82NaGs55ZiGmfE0EuaZeRoJOcg99qT4p3/8Rzrn+De//z3BB1onBX2tyr2XMmSWzKuOeF1uhMxynwjTLF+Kzy9E8NcM+9q4vBT1vRbpvxZlfy2K/3JZG+y6ne371w3mNRhs65gWGOaLrTw/pvW23xKd/5blpSyt/r3s4yuO4SXI6aXPvvb+9t64tk/b17afvbZcy1KvZXt/yfJmpzBOEz4E6U9o3CJjYWotIMvDFqeZcRgZz2dMgqQNIY9COS0yx9WgGWuxThrEcsGyVZaDa0pfAupisNu2XTKC8/m8ZArrmkCFllLKixJn1U6SE6f5+PEjXYV2StPWMAyLvk3bdridzITWWmM05blS7HcdjTUYpFCbC0XTGkvT9kUN1eKzGHQ/z2QSGlFkFYxeagfWGA4fPojhtJbH+yfGaRalT6XFKJdifeMcMUjmEL1HK9FpUo0DlQWbDzPDeCbFQIpaJpdhyNkukhpkjXO1WG2JOtMUUoDRusx+AKNZILiua9AKYRIlcQqH/R5rHW0jBV4U+DALXKUzxonI4F2+pes72l6ovmRNYywqQvYBVamtrROJENvQ9Qesa2j3B4Z5Ro+G79y30oUdDL/8+Wd++Kcf8OOEHyfafUuOiel85n/5f/2/+fFPf2LnHHH2/Js//jV5mrBaitLkBKTlfhnHEQ00BcasQYQxz6NapWrD2/UoMOdLg9a1Bqba3VyDmG1GcW2bddlq5byFjlqXCumURByon79AnnV5LVtZO77131snde219flYR7nP9vGV7//aci06X2+3/n3NIayvwTXHUD9zrSnxWkS+XrbR+ZqMsHUK1zKKrRN5yTGt92G9bLOlty5vdgpVSfTZDV2KjEqphb6Xcy41hVzoCJl5mpe5uhfubNlxKBmEdDhrJZz5mBKoCCXTMkaktfu+Z7/fF8ciMt25ROs1k6jcc6G/piUVWzNFaj1BbgRp+adQLLVRqGL0U4QpS8RdsxFcputbwizNTwKJiREXRpIUclPO5TgEl7+5ORQno2mKwX3/7h3WGIzSTMcRryZ0MdDWGnIUtoxKmTh75nFgHM5SAHdWrglCoQ0hLkOCatRWqajONhglxVitYlFDjZfCr5LCskhk1HOuV9RWivMpRk8Z6eeYZXwpZJEMz0JHVcajjMY4R9f1dP2OyXuCj8RB1FmZIil6SBlrHNoqrNUYqzBO4RpLNopsNcpZYkgwax6c1HJiyMSYabQml0z06fHI50/3/PrLJ+72e755/55PP/xI9gFDIidx5CgxjDknqRHlSNs0qKzIiZJZZUwRTzSVLlyN/pXovxq9+rdavb70GISL9MX2s9vl2YO8DuZz3jxAm8/JkS3Z+/rjW0dUNwd1f8WDvJTprGGxr0Wp8hR9aQSvRfWLYc9fGuSFGnxlu+vla+fzi+96Yblm4Jfv3kTpa3v4Ujfz1yL/a5lk3f41J3INQrwGb623+a/iFAQOkA7gxREotRSvyp5ApugcyehNciYWPF9VpoxiEcLLlL91kkiw0DNTzrA4EGEBta0MnO/aTrjoMeILDp65QAI5q9Kr8CUeKhfx+d/VOFAYR/W1VOofPgNOis1aGzAiREeKkBM+FN6+yhJll1GPIUWijwKNKEVfGvDECYiswrvbW3FSxfGo4uyM0hglIndigATz99MsHHtjiKGTS1j0iORHWES17iyDZgK5SWSdyRESkagrpJYhRxSy7znFhYK5zMp2FqUgJI9G+hKyVqQsektZnmR8CsQsiqNKK5TR3DQNrmlouxYzjEx65nweydGTgyfHWpdyJTsBYxDml1E4bclGk5SSKXiAray2lCXwLywotGYYRo7HE48Pj3z34QPfffct89MJP44QPdFL/ScTl/sjxEDKEVvqYqQa3Aj7qgYqFyNVTm8NgOSuFkOf0gqbUYvzSDHJOU/xC279OpLLWcyd5sqDLF8q3fBfyRIub6/WW6L51TrLiorLY1z6ibg8P6J+Wo/++VKN+fq7XjLcz7e/ev/aqitHeLExV3fh2bb/kuUlQ/zSutssaeuY1nTRl7Srrhnwa/WJrx3jS45hvc23LG92Cof9zZKDKruiVsbM5Cd5uDOYBK3r2L/byZjKmMjjjI/SOOX2MuhEtU4GrAdPvxchtXGaijKnYzydwUcsmq4Vfv6+30uROUacsdB25eALHzcGUDIQSE4IC1TTd90S6YsipidGKfJ2bUPwUjA9n57QwaLLSFCBeTq0thijCjwj/QpKg20M4xhJPpGOkaQSLT0fvv2IjxHOUVRUc8I5TbdruLu9Yzid0QrpVyhF88fP9xyfjnS2kQh/mpimkRgs89gzDSeG4ch4Phbo7nKj+ZKVOWOx2mDRxHHCTxPn85G97bGmQWeNipBm0Z3KOeNsQpEgB06nEyklbNOgnaLNDcpqcobzOJXAQOOLQ3h8OJamQanFAGSVizMx7G8udSZFxmqwOpN1IhNkLCsK5xTaKrSFrGSi3TAZsjYS1eeAjPecCGlG2cz+0BJN5HweafYi+IfKzD7xw8+/8off/x6lDd2h43Do+HC45eHTJ07HJz4//IrS4FpHigGfAvPTA8ZY2qajLc19MUiTZpm2UR83coiEeS6MK4HqcnHeSilUBqMUMWVyCCQtxpyYUEkyv2eaP+V59YXLXh2UVlqeo2oHUoacxDHI0371eVWqUJSrppFa/aytar68XGVpYiqfU8+7qQWevWwsl6g+xrRCAGrQlQvjUMbYrqEWWDWdFR00GfVrZcBTSmXfpS6nqsNaEcGqk1Cln2RdvH2ttrPOlLYw2FbocBv5r2GadQZwLUO4FqXXfy+IA9cdzxpiW0NT9Wd7rFuDv+6ZqNv7V8kU0CvIqHxJiBL5BB+kMSpDqy3KiQSDRpEIKGNknkLOS7ZhymzdlBNt0y7pbtu2OGOZzgMpy/trGQIxoGFJo4zWpMQSgQlzRG7YlC74Yk3ZtdZLE0mlYS5T14DgvUApSdZRhbJaexfGcSgU27SwOEKU/fHBo51F2QI1kUsTXVge9q6VcZDez+SUGc5nuYg+iNDgNLHre1KKixKsUpTO4rQ80xmZBZ2S6EhlbaQm0IjKqUIi6xgiKcjnjDJFY6p275aboEBuMUCsFGNruHQ9l+/LGbEJQtdESa+FLzMeXJLta1vPtZUaEhDTZVuNa0hJkVvQSZyCbRq5T4zGR6Ew+yw9CVkbQiwFfTLWSid72zU0YeY0jEumpLQMEbq/v+fx4WEZ59o1De/e3xH9RIwzPMpxaGOFRRczk/e4rHEWYhLorzb1JZ0lM0SC61iL0YpSUykZZq4Z9KpAvcqkr0EFi9FgZSTK66rUoBZYZQnKxWi/+qirze9leSHSZIWx1zVzlTZ5SYhuA5coIUakBeq6brjEcapLIZ8LBL3sy+q1V4+03J/PjuVK1Hwtkt+uv438r627fu9rWcV6vWvbfslp1XVealx86bu299e2Ke4ty5udQkixQB/y0IMihBkfpNM1TLPARv2Ormtp+14cBaAbC0XOWRkrndGuJYWZFOH2cAsKrBK5A60NJ/VEzpEQKrOnirGlInFcB49c9JCqEc05UjP76gCqoB4g8tchLBPdqq4SwDx7oXMauLm5wVlX5KSFgfT48CAFaIQ5ZIyRiWUhSC8ElwfDFlG5eZo5nU8cj0estXzzzUeenp6YpwmVJPzJKfPw+Mg0TrjGLayYaZ7KcCNP1mAai5ktKJGUCKU47toO60QN1molsNbsxWEnUVx1zqFNUz6XRNEShOVDXmZZ1Elu1cmK4yk3shJozjrZB6XUQuXN2eEaS9t1UqxvGuYpkMZZaMZth7OWfncLTSS5iZAFD7BNR0wZnxLTeWQOkVCm12UUrm1RSujDfd9zd3fLzc2BKQbS8SgFfaVxnWMcRv7xH/6R7z++49A3fPvuhv1hx+//8B05TyQmfvxVjkVGpCpSDgzTmRgNjVV4L7LZGInKSQK11eAlhEAo1OzqGGRU6/PCpTIaHS+vrw1AnRqnVOl4zxeWXQ3AjF5l5Sk9Q2ky+TKvZLVkXjL7Ly/biLnWw15rultnEbFArYEkz4p+vt2t1MwX+3zFUV5zQuv3L8f7csG8/r0dVvPSflz77vWxrkX3tlH7etkWprfnt277pZrDVtbnJeez3Ev6eUf9+ti33/m15c1O4eF0vMgpt600lWktBn5lNG3jSMAwC3SRYiIbjW4bGmdIKhNSRBdZa9c05CJCNo8j03kQeGcSAzUOM6b0GVTj3TQyalEpxTSNi9G/ubkhxsjj432BRljokduLmHN+NqAnBJHbMMZgG4NpHR8+fiOicE4MzTSNhCC1j0SmQWOM4+M33y7naQoeP3v+z//z71BaHvbT6Yz3grP7OTBPnof7R47HI4+fH7k93HB7e8t3339PSpndrmeeZ5lt/PRAjJFfP3+WjMk5bt7dFTnwQ3kgwPuIM4a+7wWuiIG+24kYoLXsdwe0NkzVgflAjFLfialbGgPfvXuHUop+v1/O2el0FqddmgRd4xCl1Ii1mq6/SJpbK7IfSpvS7yDHbW2LVg6wxCysNds3EMXhaNcIWSBGjI2YrAhJ5Exc09B0ndSzgkCSKsPuTx3dKJTemCLjOICWbCaEwMP9PT/88AP71uHsA//n3/8f3H/+hePpkaxloFJCo0yLUQ1tJ0Z2DgrrIxm99Jz4MqFMIBTzBS/+pWjYWivQjLrohKWUSpZ9yWJrnWK95CRKsdvl2fe8+JzXLarLhgWrQzrCi+tQqwJz+VFKEH9JgCQbEkVV6WIX6Ej+rXXNygESWpUeiGcy41J/Wkh89bvKr1zoayGuufkFGdtIesu5rOkZstILHvCac3jJMF4m0F0i8nU/x3Zb15hE8GW3+bp+9IyE8EI2sv5ZR/7b41oHHtvtVOHQ9efXcNLXljc7hWGahFJXhPCcKsqmRmOxpFI60taSC6Qyeb9w+7U22KwJFatNqTBwGlFVDYHoxaDGUATbYsbPM8MwLMZbMokLgyhEKRCa0nkcY+Tz51pEZZG/qNpJ6wtQDWH9O8WIUkLZdG3DYb8XQTsU4zDifZVUVrAyEIf9YZHq+PX+M+dh4NOvvwp330mmEEuUIc1pM8MwcjqeOMaEUZrbww3vP7yXBjPnlrnKIYpu08PD50XBtHUi4Hfz7k4udIbT8Vya4Bp8SuSkSh9Bw36/k4wKlmxgnEWobn2DAfR9L9lT3y9d5NMsDYJtodta65imiYwICLYgvQBlDoO1EhiklAvbxmIah8KiBFQjK41uDDoEGWajLUrJXaStRecMQZy0axxt69BaoLkUO7m2XYMrDKwQEiFVirBg++fzwP3DAz58zzjCz7+eGc6PTNMgFkprstJQ4DbrEjkmkUQPCaVCmWQHqvbQrLJOY8QZ5QrlbM7lglMbi1HqmVFIm47qa0sVF7wYdVVgzMvyekaw2bake8t78t2XMbU5qwX+UjKYm1ojqEHY8+1WgyQQW2bFRlTPI10yknGtd604gwqb5ZhWBlEcUW2qg4vUd0kuyyGtnN/q8NbGcm0862vX4KVrLMWvZSovbfsa9LM11C85qi3E9VKN4iWndy2z2273teXt2kdOMoOkBS9MyKhDZQTLxlgUitbawtSANEPIElXXucjZF9XMkHBOodCEOYgzmCIGvYi/1R6CGOPS1dz3Uqi2tpeic5YRkWYp9NXUTm6QtfRFvVhVsngcx+Vknk6nhRLrnAyk0drI3IFxZJpEafT9+/dFIC7SOlci5waQFFXlgqlGYQ0l8pKFaK05HY/89NNPkKWJbhiPWGO5u7vjb/7dv6ctx/349MjD4yPaaT59+pX/9nf/jXa/Y3d3I93EztH3tdBeinUZmXEMxCi9Ec413N6+K8cw8fT0yOwFZtsd9iKxvYqS2rZFKbV0gIsBE7pqbe6LRiRJjLX0bc88+wJ3idMU2E5GaoJBG0fTlHnYKFCGrDVRWzyZmCMqlnqTM7Q7hQmBxHkR1dMarBGxOlfk1Xe7lraV5sKYxca8u72jbR3OKKZZhvtAJubA58d7cp7IKrC/25GzIyeDnyD4TExG6lM+4v2ANWCdTPBzRlN1sowTp6WTQZXA4hK8Cu03g0A/hVlWH99FLluJ7Hwt3KtVoPOsmKlWBeVipJcom1cSha89z2plYLe4du2l2US9W3hivR1hXuXC1rocw3adZ8V1nuPgayilrnstkv7SkRa24ZVli61/sc+b710b9bWczlZc7pqzWB/T9pxdcwzb/arvb6P6t9Qj1q9v/15v4y3L2/sU2uaSUunLXGV5DmqqyVKIFkPtyChiXmnw1BSwnoyUUYJiYJQuk9LEKdTodH1gFeqpF6dtGilIF0Mdgi/7KanuusBcl7URfHaxQATulDiVcZogZ8bzQIoS8e67HaID5BcRQMkykshPZOkQ3lkr0bLKpWFPIirvA0+PR1mn32GSaPVY6zgcDnR9j9aa2c/CxuoEm8+wyH7UYTxz8EuXrjRXC4MrFLVPir5S8KE89/VmzEs9QH4uN1J1YLk07lVNqnreVKH6SjOc9GOUq0MMqaivXuiuxkiQsIYnMqo0smV8lOlvRmeMMlgt6qooMcg5i/Bf8DOKjGl7clIYq2gaW6Q3wBq57/b7HY1zpDgTl7nZ0j2vjUI4NgZrG1KyzLMmpoAPkdmXkaMofBB2VtWesloK67WguTYeSgn+n3O+9KYgAQkiQlvt7nIPC8SyNiByEdbR6TVDwOpzdXt1eRbJ5+fvq/pdqEs0X9YtT0BZvyA24omkK1+x3DfSoyGr1yg+l3tNkKoFeJJ7Zb1v9ad+W8ly6/HULv8lo1GXgnc9ri/gpxr9XQXgvoRn1su1CPylDGHrkN5iZOu2trMl3mqg11nOtSh/+/rXtvsvnin0h/3CTtDIjZNSNT4Zg4LSQKSrkFzXooLGj6KmSopCe9MXMbYcZUauQdGUImLXimyyc47TcF4KXuIMMuM4sd+LYe93O+Zp4un4tOgjyUWtU9XEKVS9pOoQ6oXaRidVwymGxOP9IzFGxvMgEtt9z8f3H8hZWEMxiPEbTmfmeWYYB5q2pWladoe90GznmRASZDGy8+gZT7/y/Xff09905EPk7u4d1jr2hwO73U6+c57o5ol+19P2HdoJFGWdRVtDTInj+UTygZwSd/2O4D2P9/c02mJKAT74wPF4Woa81BvV6qrIqlc0QynCp5SIWbSbuq6j73ZkMqfTaREV7Hc7XNPgjFtuxGn05BiZQyAU1VXnWpSqGGd9SEuPQ8qMPhCCzEZutMJqhTMWkzSzF+htGkfIkZxazM0t2YI2mbYTfauaETam5f279zirOT7dFyG+QaDF1qGUrKtVoml2hFCZaxPjNHE+z5gi9T7NHpKQB7RSNFdqCNVp1mCpdt7XTvhqCGvglLMMYFo+py/Za4X+a60CvnQ89R7dwlRrht2XSzViQKWG1mknShVbXg3jxWnnLEApqmS+hSkoG9LkrAh+HdGKw8kZshZbcDVyLdlUzsW96guvSKvnUbOsuTLO5X5F6xVDV6CuNax2DdK5ZpC3xn77cw26qZ9bF53Xr6+/s94X22zkrcZ5vR/b4vEX5+mN23zL8pua16B4v6IfE4tByjGSECOTY1oatBJZmnqck4JzibatMaXDtnYdK7QytE0HWTFPwlMPSaQtchZdpPP5TM6ZaRp5enrCe89uvxMaZUnjpRi6p97g6+JejWAvVE+1FKIPhwMgVMOAsHMq2ylnylxiKRDmlPBzYBjOTNPM6elpuSimF8nuHAVPD7OncU3B9x3TODGczzSuoe96+kZqHsN54O/+9m8FUhoGhnFkGKXuME+zdBVr6fR+enpi9p7Hp0dSCOSYeNCaMMs0s2/ff+T2cMO7uw/UudSpUHbfvXsnk8emkdP5VArbBzH+fS/nGxa2Utu2KKOX2Q0+BOI0cf/5nrZt2fUHyCt4sLCYULrIoiu0zqQcSEGYVro8KDFnghc6qdGQkiKGWrwUc2AMmNLDoHQmxFmyixTo+ob9oWe/3xGTJmNprBVV15hkyM8cOU9njA1EP+JsRtmq7QRKB1KaiWlCETFG0/aG4AU6lGhVJgUSIaqEmqYFJsnFyKeaJeS0sH8kgK6GLC9R7SW7WMMMfIEFSWZd7uTl/bwUWyvyU2uuiQumTt5G2SzPxPpH5WLIU9HUVemSQaia0V+Kyqk4+8s+rr6zHK88ryzbkYJC7ce4SGxQaM6sDW9xos+ynvJaLsGEKlmvUnX7cl5kv75kIq2N6jVs/rcu2wxuu721E1gL260d0hbOurY/v9WJ/Estb3YKS4RCiSxSucmTFOeyEkggZJGCjlqjna2YEplEqheTVcFsQTNK80pO0gyjIikn0eshk/KlSFdhAaWEGrluNBFDb7nc+Cyfu1AsV6J8WmbzVpjKh0DyM7kM/6kpbc4UKWuBinzwTCWKnSZpYhKFVic8/BoNJVFOVVXdNSRGpEPbWctutxOV0GHgpx9+xJQsIATpbRjHkRB86T8QIzSO0tR2Pp+lgzYlic5nmdV8d7gtGk5tSV/TIh3edh1ohY+hyIBHUSNValF1RYn8dXVkhQj5rPFomicyiHPXIuZXr2eMAV1qTAVpLEKIxcGSFjptjFW9VpMixCDd0nLuotAuy9xtlHRVx5RIOdC0jq5r6boW7yFmvdxXKcnMhpgy3s9I4B/K9DZTURikOhbI2YMCYzLWKoGaCu4j9055qLM0XBotM7LlmcglAKoQqdzWqca5FdlYP/zqMjtDjOEFYlqMS/2fuvxRzcPye4Oa1NUvr68NzdYpPH9f7LOo/S5QUK5IjowuXc+oFsOcqQOJZH9KprHao+eOQ9UbYoGtUrEBy/Euq1WHcIGmapapqq9ZTm5m7RCuwS4vFWDX5/yldV9zIC9BVFt4evv6W5zSb3UI/1xHt15+k1Oo8gtKAyljYiYrTVJmubVCSZ9Tks5epRWj90QvRiu7hmgtOlGkHCRykK7QYlw0UrdIiRCrnHPhQhcjsZ5UJdDiBattGtmOUnop7K2ls2vhWWst84h3u2VG8+l8Zk4RinAZShqcQBQ17+8fZNj96bzUNvquY7/f8+2339DtdmhjeHh8EAql0XR9jzGGaZ7xReeoXEK6ruOXp1/44c9/5vH4hDaa3/3ud1LM1IZPv/6CD57vvv2W/X6PVopxGKQ73Fr6vqOxluHxgeAsTkujnZ89s/eiwZQSj49HQph5//G9YORWVFC1UYszW3dSVgchBT85F4fDgS5GYgzL+Z0nj7VbXnVCZvOBNoCK+OALTThhrZO5DbNM3sspk8LEpFRhEgn7LOYiG+4cTlkySWjBMeJj5N3HO7I2fPvDZx6PA6ez53w8kXNiGM40jcCGk5+wUyJOI7kx5OBo20CMGbKHPKGYMFbqGG0Dg474HMqQozKcs0b23pOMwSKUZ5S6qOzWIqwqiqJ5ZXYLZJKz1IBqE2i+eINnz1wuljKn/Ow+v7z//Pc203gJZ1+/v47IU06lobRE7EavMoVLobgGVVo/N/jPjeO1gjSXgvByPFV77HI0GTBcMpRn52RtaFeHXB3adt2rn7sC86z3/zWDva33XFtvnSmsv/eaYuq1LOElx/bS9/1LL292CjFGcnEIpkRQGiXUQk25eOUE1FQ6hCJ2V04AJZUr0Iy00pdoSotDMVYG48ScZGxnGUd5iewNbSsibc7JUPuK49aTJ8VHTcWyt1lCPbnr/gVfisSx8ua1IiMSFKlIeGSVmXMmeM84jRhtRB21sJX6rpc5BUYzjIM89EbkrZUW8b6maTgcDjTOlchf5CKmceJUum/n9zOmdOcOBTK7eXeDLtLaIYSlo7qxjrZxeGukoc60JePKpRhemuNKhnU8nsgItNK2La4RrSaB5aZnOOiip5SkQa4yuTKN9GqkTJiliF51pLQRh6OMlu5pasNXqLE0lF53pRKaTFaZnISaGoLQZmNK0gvStmijl4f+oh2U6LqW3S4UmrKQAuZZxO1CqK9fRhDJHwqSQJ8xZVLyKBXRpkqcFzgpztKcOfuFUWadTIaTFZ8/oBVqhIuxr0VZnZGMGha4qT4nK/MEbCSeM6A2cE1d80oEubz3xmd6wdLh4nTW2/4NUepLhdn67y1HfxvFX4Nh1uuv/16v9xx1+/Lorxn6a4b4WqZwzVlcW3f7/rYGsd7m9hy95BSuZTr/vZa3dzSX1FtCP42B4vlLUXcFH5AzAUTKASUKllkYGjXyCSEIvljoaylnQkrs2wbXtYRxxMe41BFyzmUgfcPNzWFpZJvmqUA5UzmRYIwSbry5nPS106iFvqZpluinznD2ReCtwigRme6lS7dqyNI7cT4P3BwOsj+HA/ui4Nr3PdoYzuOADbEMJDLkkuh2bce+l8zEGCuifj4wTRPn4wlttIz79BK5nR6fMNbw7fffkBEZjuA9mSwzmZuGpm3EwVnNvt0xDzMpJIZhxFlXZCyE9/7p8yeM0bjGsD/sMcaisITSkd11QnO9sLnCIjWy2+8ly7KGaZJ+i/NxROui1+NM2bYrejaVv55J2VNwQhQarTJWJZQuziVKpD37wDBKNpALZu8ahyjf6ZWTSux2B0KApm2K3ElkHEdSKiqwvVtRkYV1pLKBpGXeR0rEOKN1xNpEJKOUJ4YRP4/M88Q8touulzCYSie9UhdRx1UwQV7NyJWTLrY1pcUJ1MBkYe9BOS9bTr1AK9XwVYHJyvevoMlfsqwx7jr7YzF+m3W3xr5+/loEu8bx4TKH4Ros88wR8qWR3e7rl7DPyxTRrxn21yLvbcZwzRls37+ml3TNqb2ltvFWiOna8pdkFG8fsjPNIpalFEGJXLNJIvpllIifaa1RRoFWJGD2EuVrpbEWVLYUuFKMFEimoC44cC4PyjCcl96EasQF6um5ubkF5KYbx5FhGDidjnIytDTOtU1L3++WAvmWi2+s6DMJPj7z9Pi4zEq2XYtpWzHYdVJaKRbbSj2MMkOh62TGg3PuwjxJCaMNUSdSzBBDyT5kBsLNXoraCvCzGMu2bfn48eMCUUyzMGKUFdZRiEXVE3EsKIrUs0S9KgkNt3UNaRbYbZ4mFIq+7yQ78Q33//gZZRRNdty+e0fTdDw9nphnv9RpUkqkcs5qBmWdlahda3Q2hcY5MwwnUYW1hqbdo5QW+E7LfVBltS8BhMJZW2i0CmNKhB0SWSVMBusEukiAsTLbQSJm6ZdQ5VwapdCq3jcCOfm5OAU/Ax3GanZ9z2HXka3B5IzOmRQiKckUPaMSziDKo9kzzWd8ECmU0U/Eks24zi1zxEFu4lzOVSw9LpWOrSjnsYjfkS6ZQeWh59Lmq5QqxdO8BFf1GVnMvhJ8XZKHvODv69i4FqNfyxYWkKYapKJKrNEkLSq9Io1+5bMvRMCv4fEvQTpvgWHWn3kJpnlulClw1uuZwGvf9zVDfO29a47hWr3iNUf0WlZwzRG/tm9/KcT0ZqeQYyoiVxpK0TiLpVv4yBJBXRpw6u1p6utKl6irQAqqxM/qgrWmLJDFVDpvUZKOi9ERGWbnrBSEC7zk/fzMeQTtsWXE5rL/+TkTAGS7IrEh2j1ieBWqcTJEJwlPfZqm5bhsjTx1xZgvaXEIAV2cQkyV5y7c/oxQBp0VmZA6wSwGmQvcti3W1QFBl1GjTSPd1XKm5AGWonjGGiPnMkQU4pwruyvrS/G9QlyuKUatxJ6i/CqDj6oDCGXCHoWhFULp6lVlTnaRxa6zqiUblAYzpWqXt67az/XsF+ivZA/1ftEVIpGkURsRyHONwSQhLlhnSn9BBlKRyl5R9Uo3vMicJxaRwEKfVIAt0+BQWZrNojiEnBLkVPocZI6CZC1e6lZWZmKkcj3X909lGy0S1jkvOHk95goRLT88hxgoT4i6fETORVbPXlsgnu26fBnRq83vZ2am7urqc/KVxZOsHc/quVn/Xi9fM651nde2Ac9VQV9yPHVb6+2/ZlyvOYSXMo/te1+Dib623muf+dq6r9VFfsvy3yVTkFGSkjZH7xc5gKA11kaMsyQt0NBieJVwj1srEhBG66U4m2IS+WQFcRUtDOOZYTjz9PgACg6HG9quLbDRDVprfAwcj0eGceDpeMR7MehNkd2oeLaujXZylojF2KLKQJ/Ve65p0DEuDJJpnqULuHQC66Llsut7dNuS9wdyzpyHMzEEqSt0HaYUHk/DidkHJj/T73c0TcPt7S1Ga6ZppLEOpTVTnOj6nu+//562b5mmif/63/4Pjqcj53HkP/3n/0TXdRzPT4vT+/j+HTFGjk9PRO8JMdHojtY2tK5Dt4qohSXVtw37Xc/d+3cYa1FGF7rrQIyK01mOs/ZteO/FKNSo3jmhJirNNE0Mo8BsT8dH/DwzTSM7uxOtoywFaGs1sTr+XAUN3ZIFxSy1gTl55jrNjYi20LaWnWkLJFkJAYacA0laXdDKoXUj2lGPjzw8fGYYRmL02JKlOCtEhTh5DApnDEY3RKSDOs5CPDAo+qYhO8v5NDKHRPCej999xOiGh4czKquSOUWUmpdMJRadp2UCYYEYc5AajDgFBHrStXHsQpKwqz6HBdqs7BxAlabEOtzqUsxXIgNSlqoEobhkCxfm1NqRlF6JupXiBC5QWPnYQgWqH/uy23j9e73e2ojVUbvX6KD187W2V43YhQb+HAJ6KVPYgmhrB7R2Btcgm2vv1+VrDm8NDW1hqrWTq3/Xukp97dp+raG05frwsmP416o3vL15re9LpKLwKJIKJOKSKlcKWb3RloMl42PA5Ewu+H1ONaKT7ZlioCunXmlF04jR3O13OCsFvnmeoRiaaRb5i77vaNtmNatZiaSya57dlHV/1lrjorZql6luMUZ8DASed+XWbVSHZo3h5vaWeZqYpxmVMkZXjfS8FMhDvMBhSglDZYrSu9D3vUT0MaGNxtluUYJ1Rbk1K4glUxnHsYiqafZdB7l0+pYRnToJ1bLKZccYoRTh5bzJyMnvvvuO0/nM/eMD57MwqPq+XzKT6hxs6d+w1j5rMIpRHGssY1Wtu8zErhmUUWbR9qmZh7FmkUr2JSuREa++sJtkBkNjLFXbSiCURAhVsz+TosIYkb04n2ZOx1OBiiRzMqV+hbuINOYkwUqGlTaSHE+dqCbHEokpoFRit9/TuI5hCEV+/JIl4X2pYVzGelbhuwobXYyMKgH4JYtenEKdT759uK9FtBsjde1zz7OI1bZeMKjVUCVAFdZRfUa2hmv9vS/VEK4Zy5ccxzZKf4uBux5Jl8NV17OalxzDteaza8e0zjx+ixG+lqmsGw/f+tn679e++y+Fi7bL24fsHA4lg09MKCl4UqImpZYBHcpoVKV/lgswe49RUR7AcoAV468Xx2hDV4rHxlq6IsxWm8pSzosRC2FG4A/Fzc3NcqKlgS3T911pTjLPWszrw1q/e57n5eFsi1T0MI2cp2mZ7JazsHwqpJNSwrYtd7d3/PzTT5ynE6QkBd+mwUcZzyksmFzgNTnuaZoYzgPH4xO3N7e0TUPfdrimpWvbpcAtKqMa10rmME4jx9NR2E7WcHdzI70S3kvEGyIqy/mvcFr0AaNEXfZ8PpOiUHH/8Ps/8Hh8Aq14fHpiHEc+fvggjWkFPoop4WK8NK8VJUwffCmMe9HtUWphgVUDJ2QETSjn2nsv2ZtWUm/KSJfyPHE8n4kxAElkK3A4VwvD4hBSzISQmGfpZicrjI5ok3l8PPL09CgMIWR0qNUCwRkl0hQKyDGXiXN1xkZenH2thyilmOdEjAqlAofDnr674eH+zJSnQp+9dMFXKLGSFaoTq2H7YnRV7ctZdZOXQGQdtGxx6C8iZX67sfjaUp+JdYS6NpprI7ven+f04y+ZRuv1toZ3XXzevnYNcvotx/JW6KW+vu5t+hoM9pcuNUOsf/+Wz60d7X+P5e2CeEoQSIEyc9HcSSWCWak/BmHTLEYR6Vwt01no3UVDaV5FpsYadvvdwsAYBpEnME52MaXE8XhcnEKNuKR5qWO3213SUaeJQfSJpmlajHmVu4Dneui1uxmEd763lqbvsc4yjZP0WBTWTyoZyX6347FtmcYR69xywQV/TjjXgFaYIhAYo0T8wQvE4koUnlLkeDry+PBA10n38B/+8Adm75n8zA8//0BMkW7fCeSW5NwoyuCgRoFJTOdSGB1Hkg+QsmRbSgQAf/nlZ56OT/R7mZU8TdOyjXo9nJOJdaZIcNdehVAa+aRTWWoau10nWUGN6tVKalhdMovKTquZWYyRYTwTo6jnWuuAjGvE4VUHLPUizzx5xlF0jMgKaxqMSRgtvQjez+x2HdCgtGMaJCtq+4b9ruVw6MlREaaIyqGwgFRpBlS4RuizkPEhg9KEotmEQjLHKAwsmSbnMMXxZEoHfGEerVSeV8b8kj2/BFdcM2YVfq3x/9eghN+6rI3/tYi+vlaPY+083rIvL0Eyle68/v61o1gkcDZMpbcZxFolub4/2+28lAFdg5p+2348dzzX4La3Zkb197V9+dda3uwUUkpCr1ugn7QYyKwUKUWUKnLJUfRslDHPq2CI8delpyEEjQpqUYh0jXT8hvL5mC7QR4UiYjFQUCCZAvForWXOgynNddkD/kKrTM/1YdY3e5XI0KUI6qzgwzHJ4BBjjIjKQWXcCharn+srrWEJa4tCZmE/VWZS3Z6un0mSsUzDSCbRti1379/jg2ecJ/70QyLlyK7v8T6UoqqwcKwpoyp1ZlLT0mdBls7bNV59PJ44DwPnaSz037gcc40KFwdR5ibUmzjUfoUYRdfKWtpO+ixE+uAiObxAFWoFFZQg4gLJCbvFLdIp0LRVuPAiSxBCEOc4TcRYFEKzgRxBh8VJt02D0i1aNYQpQM444+jajl23Q2VNjuXCZTHURpuFulzHDFTBQWuls74W2usgI5RIdCyGbZ0VpIui6TMjsvx3Wa5h3FsDWqGnWgMuL5bf9X/b95eXfnOvwrK/G+hn6wR+C/RRr+N6u9vvhi+b3NaR8VYp9fXv/gJ5e3UfrzmdreN+6djeuqzhsnV94a37+H/F8mancHx6KOzCzDyMEjlPBVu1MsReaRlWP44Tw3gWXruxRQNHjEnTCadce0XKkUzCGjEyXddKpBtkYErKEFc3otQNFG3XLq8dTyeJ4pyh2/W0XSvwSYF7Kq214r+ipXS5SCGEpcfBGMPh9oa+P9DudjRtw8k5Hu8fiDaUKWEQgufp6ZEQAsaYZWZDSsLJ11qz2+2KwVYM48BcomQpSPdL81os3PrPnz4xDB13d7f83/7Df8DHwDhP/N0//B3oln/7N/+W4TwwDgPTKJlCu9tBlKL9eBqXWc9WCcZfZbBzzvz6y8+chwGfIk3b0O/3iwLr8Sh0XltkN5xz7A4HEfkbBo7n0+JY9/s9bdtwe3NTHDwMg4wRrb0dslyuWa3VrDvKrbN0fVfqB+AaS4ySYVZJjsrekml4Rds/W3JURA1zEe+7vbnF2g6jG/w0k6LM6rg93PLx/XucsuhsZJIaVTuIkpUidQcujWeuaYqU+onj+ShzmpN8yDiZLHeZ010cZwii8LqGTEqwX89IzUy3EfraWDwzqqXgm5MIR1zUSrlMeqvHUL/nn+kYtrDPtuax3rc1xr6Fudbbq7W7a5nI9ru3OP9vgZCUum5E16+ti78vOYV6Ld7qiL62zrbG8luX7fl+a5b2ly6/jZKaM6zYRXIhI8HnMqBGFUGshCuNWQGwRfpZ9G4CMSn8PDN7jy+qpiFIo5o20o/w8ZuPMkBcF/XJVKNUU8ZHyokZx5HZe46nE23fEWKZdTxODKdheQiremqMccGBa12hOoVlZGdxfuuHg7bFWXm/yl1M40iKkaZxS2TdaNE9ClHGNQYfGYZRipRKyXEOAylFGddp7DJFrW2kQH4+nyVSTZE//OEPaCNc+xxkngGFcpkKN14bTdPKsCKtFJ1raExtWstLEb4OSVo/qPU81JpJPTcVdqvyDVVqoVo46S3IhJyWQjiwZAsxhqV4HUJgDn4R3DuUiXF1QI2gkGIEvJfP5QT73R5rWqzpmMaZFDNaW8CQkyIliCERfKRr9+x3e576E8F7jJZu777p6NuexhlxLADI+M3gA9McitiedMs7KwKNIcg5cU2DUoHgI2OBz6qxNCvl1LJZOUclY0wxFvhI3qznu2aW9bPX8HqlpLmtNj3WZR3Rbo1ENapqlT5cM8Y1Ml9nAuttrfej7ttrdYT1+uvMYJv5rDOC9Tbqd9dM9VrW9NLksJxlEiIqsn17u43XHE39ji2UdW29lzKeazBT/c5qh9YZ2LXl2vX9WtbyW+opb1neDh8VWCIXCCnnDLkU7XK83DxGMFrnnGCxKWEbRyEoMnsPiESzL70IzlpCDDydjtzd3rHb7bBOhr6Pc4n2vcxJEJmInexTEhaSjwF/fMK1DdM8M5yemCfPPM7LyatOYT10p2Lc1SmwRI/Cdlk7BaulJ0ApKeYen44CBWmDc82Cv9vSdBUGgXqGcWQo8t993xOCZxwH5snhrOVmv5fehds7nDFYZzk+SSEYrfjrv/5rici1NBDO84yiJYbAOJylY1lLVpCj1Hm6rqdrRIW2RurG2EXcTmYcPC8ErjualdbEYXgGvWXkWGt0LXUYyZqGYWAcx2ewWCh1pZpFHE8nvv3uW/q+5927d/jgeXh8KFGZ3GNVuG+ePVppDocbui7RtYHjUUaaRp9IUYtTiNKnME8z9s5wOOzZ73rmSRROW9ewa1v23Q5jFVMOJZpWTDHgfWQYTzJ+1Rq6/QGtoWo1pZxpuxaKMx+nkZji0qyonS300yruVgxrLkXylBZGUmU4rR1DvTcXY74yGLpkkWmBjC4sIsWXYx+vGdn1ssXK636sDV/9nN06Oy6R9rXsYCm0K/WF4a/rrUUo63tr2LE6hfWxbD+7fr++F2NGqwTEJWN4KZp+Df76UqfpOnV0vc76vG6XtUGvDkGeQ/OCxPllm+tru92HbRazdXbXMs7fuvwGmYsyvEZrdrteuptbKb7O08w8T8hQFVH/bNqWcZ7IiqVYHHOC0uU8l4H02mjavqMp6qLez3y+n9DGEGNimGam1ZwEY8TQ1ci863vhy88Tj4+PKAVPT49oFFbZpbehRssyV1gMZNXAr9GwVlqE284n1DxhrMXPQlvVShgkXSOF8jo6UCHYeJXKCDldGpu0iMiZ4vRyFjlt78vYyBjxTYvVIgyYFISQOR1PS2OfLjWSYRp4fHzkdDwhJB75vG4V2jqR69YXuu84jjLAqDxsTSv6S0WMQcTyCvuqymQvD1S+cKqtFd6/NK4VY3nOTNOAUsK/rzf7+XxemhB9ETKsENYcJBiY55kff/xJsrcUynVQHJ/OnIeBx4cj9/f30kQWNNEnpilyc3vHfrdD4/j06z2//PKJGBTO9nz37TtSSPzwpz8TfKCxhsPtLfuuxaBIIaASxEnopqiMygajRMJdKSCXYUkJQpF/bxpDKyUKfAigpF9hDDMhle51lEwKXBuNYpx0cRI1mxP2mH1W69kasPrQVwOQc15kLRRxKVhL7UJ6C1Tmwv7L8u9rvQbrZZsd1NfW67+Ef28dUA22rkFI2yh6+31rA2yMuUym26xzbZHvlzNzyXpeLiZf297bIaovm+LqsnXQa+e4vpZr57kOCC6Bm3kWGFzLmF7a35eu4z9n+U2F5trA5qzg4ak061SRNkhoLY7DWkOTpfGpFu5SkmxB6Krp8vBoaTazzjAOfjHEceVl60lJ6XmEYbQmlItSWUbjOMqgliK9UdP1tSeuF2Dh4iN4ckpJ5kTERINIKMiEMVXkOuSh1o0qmLEUH8m5aOkEqYNohVa2FGVbbLRL1zUgxltXwTguhdmiC5VqZjXPoGAcRsZBagrW6BKBZqKtVF8quCrnKecCL5WIUF348bk06K2Lo/UcrVP45YE1pbPXe7KXLEopOUZrLjd5zbxi2S6KCye/dJiHEJZMSNfXtUTiwUv0fj5LM91wnkgJvJeB8MY0kAx+jpyOAyiLMY627Tg+nTmdzmV2heXmsKdrWqwMaihwTiqd1+W+U4VxVSxpipGYKUVthzZqGTtqrF2YRDGVUasxYktTY7mKS4ew4rnRq5+9Bj1cM8bPjEJebbte46RRemU8Vz/UWsSV7b8GcWzf3+7P+v21gdpG/Nv3X9r2ej+ubX+7j+vXn32eapi/zJKuHdt2W68d+0vn69p+r7d77Ti2P9t9uOaoX3LKX5yD/yucgtBP9TIcvnEOmoS3rmjYlAJNKdSJMexQRpOdWeYDxBBQuVAhywHk8kgZYwtlcZCTo2W+ry4FzJTFmcyVJ1+YT9WY1e2HEDDKLP0B25O0Nn61+GwKU6rOSggLnCRzgaXYxxJ59600m5licKvUQo6lMFgZVc7RllnKwzBINuLnsj2BFYzWAk8Vmm+MAknElBhOJ3EK88hwHjifzyJ7rQodVOmiq6OWnpFKk4ohklR5YCtrqGlFgTYXbnj5TJ2yBlL7meZ5yago+PjpdCrXRcLTKsHdlSl54zjivUh2N127UHWrREnNpp6ejthGxo8abXG40qAGZM1wFghpHDzWtjSuxegOkuXx/sSvvzzx04/39P2Ort9xe9swjp/59Okzf/2H33O73/OH331P5yyttahc2HIhyDXVFwps5xoiUrM6jWdCzPiYaFqpybRZJn0lRZkhIjRUHyPRB1rraJuigKrq+QTUZV7CtYcdXnYIV5+/NTSkFSabS2F2+5mSKeSaMZTlWpS+LsBul3X/xDUcfB0Nr2Gv9T5fO87t+9v920Iyrxm5nFn0o1jqNy87v7fswzWj/do2tkv9/Pqz23O43n7NFtbrbLex3u5r3/vWfXxt+U2T10wtJsVAVCVl1VV7v/CKS2Spq+SE1qjaa8Al6tFFD4hcpBVK9OWDR2lF3/cY67CuXW7OaZ4X6nZMMlwmBF+MaJV2Nih1szQLVZG3dXpWsfKatsEqUraGEDLkeMlikPkEMQROp5NkFjnTdz3aObRWqHJ+jHMyfSsLn2WaJmwSBlbTNPjGSw9Dlg7cvuuw2uC0EYplvUEKNLPb7aR5TGWGMrTe1EwhX6ib5IIHa03btFgtTnfp4C46Ta5xOEURmyvsknJuKhur1llq4S0Ev+gjGWMw+kIrrl3ha2rr+oavzC+llETiqWYxGR8S4zgRU6JrW+gUZM3NzR3ee969+0Db7mibHfPkeTqd+fOff+Lh4UjwiVMcmOeEsZ+IIXGzP/Dh/Qfe3d2y6zsaY3BGFFlzFgpvKoN+RHpC45yBJFPGRPsoXTLZks0aa+m6vnxOMoS56EAZpbDRLAVlVaCo2rSwjqZhlRmvsOKXIvVnD3m+mPfl9WsPalmvBlqvbXe9fy/h71+L7l97fw2rbA3+GiNf9yS8ts1r+yrbu7y/Pp8vOZn1um/JSK5lCC858JcyrdeO59q6Xzu/L23vL3UI8JucgimTXVm48E2ZYNU0DhBmTj3MXKIlVbjfAmlEcpaRjdpo6VAllQEsAR+Ex6+Uout7XNPStJ1EPPkyIzgUaYRL74IYo8qcaUu/QyzFzlrkWVPO1swaOT57gZpSmSetBQfWWvoUYowFvpFI3RqLNRZs0bNRCtO4hWU1zTPDMJbGp0L3LEwlBbjSvey0DKyfCrzifVjYMLt+hzIKH/3SPSzTxTKpFPIjkZyKJDjSgNc1UniuFE9dHEnTSFOdCNOVIvMK1lr6A4oxz7lMSEtxOYeXCDPhfVjqPdcMUO0zUVotcGCNDWJxCiklbg43GOMw2nF7c4sPkbu79+x3N/T9DX//d//A09OJH374meEkfQvjMKJGT0bT9x03hwMf3r/n9uZA17W4MvNZKVBanEKIrPpcNNY15JCpg15ylmbLi1OQQKFOkss5M48TXouMiUETbWF0VYgISpQuT0Rlbm0f2gt19VJEXi8SAKnn65XXlrVfMDiXXoa3LVvntOzDJmJdX+Ot0b9mZOtxXnMMi7jgqsi7/t71ui85r+Us5/rel3DX9rPr329xRq/tV0pp6Vt5zZmts4DXHMV6/65BSdeyy2vn5S9xDm92Cm2BUERRVLphkxUGTRV3q4YsI9HgcTgT/YTJnlgULEHm7qpW5janlGlKkVMauSZ8mQccExjbLVFs26bFAIUgU7vCNBGCx3tP13W0bUu3a4U6G8SIxxCxUeAA1wgrqkpLp8LwaBpbOlYtcxRmTa2NOGOYxrGomgZyku2qDNM4LNi9sw5tjcBezjBOE6fTiS73NDVDUlJzcVaUS3f9DmcsTmtUTMzA5MUgx5x4Oj6hlOI8nJnGkRACXdtIraBAQkabhbFjjKHf9ez7HZDJZzieT2QPOkUaqzHaLmqvKZXi9+w5pdNSENdA17YiM6JUyRIqY8UUhzAznAfmyS9CiEopIQ00Ik8+jULjbFyDVhGjE11zkEa7BOfjyFlN3N6+J5f6wd3dRwSZt5zPM8fjJ/7rf/1bfv3lEz/9+Ctds+Nmd8un+TN+nnm8/8zHD3/Dv/2bv+L2doezmtPpkd45cA7dutKxbRnGkexTqXHFMntboM1UomwhJMjIz1zqFs415biBLBkgSKCTUiRravODBDsrA0W+0G7XS4VQ61LhyeX3CuK5BklAMearz5cXJbiq23z2rXl5/5pxXG977ejXUhT1/TWD6hrcst7OFhJZHHCMy/duM823LPV6yPaez0LewlmXz7xcS9g6kfXxrY9pq6f2Wjay/nubodTlGj14C9ltP/fS33/p8pvGcWaSpNqo5aZKBQ7QNTQqapAaWIqnojpDnbKWQZxIBq3SZShJkXhMSYbdKB0IIQIareU9VQbz5EKNXA8SV7VgXeAdjJZxnlqhCqNEDJvUMapGv9zwcndV6eWqe6TIWCtUU10KuhIJJmLwqJyK7IR0x9qmwVgjukXzJE1d9gJbpdIFW/F/vYr6coUuYpRolcxwHkBTZjWL3LPWWhQ2lVqcQvCVp11uutLsROmgklGLdbgRCxumXkepxYjiKoqFtuucjDbVWpXOYfneGCMpGhS6TL8TB69VOQ9WZLqddSilaVyLIhJ1xhpLiKJnNCPQoZ8COYP3kbbpSn1JnO7pNPH58wMPD0dSBJVl352xaEWZmdBxc7OXqXsqE2dP1BBlMs1S49EagZOkG225/jGlcm1KfaxcY60Fxls6mROLllNmBTcVyClnjcwfXhWLWRmiFZ17qaltM4CvwDJvgS2W54y1U7g8K7UXZ72lLfTyUpZQv+/aPn7NqK+dx1sM2TVc/fm/nx3gi8fyW5aXHMN62+tjeMnJvOQA1uuu17/2ud96vq5t/7cub9c+ygqlqjQ1ckMVYzDGSYyF1rRF9kFm6zaoHFBOobMiZUWYRbRORjUKi0IbXdglinmayRnGcSZEBWrAuSAReInCUoaYcvkRpotrW2zjRFaiGm9laFTCREvy4UIr7RtQipwjYwqEaSbGBnJinoW9NM6iqeOspmsb5q4h+pkYpB4gcgzSnPbp11+EkYPC9T3WOfr9TjKeUWQl2hDo2w4/z5JtKE0OieQDQQnrZRgGxmnifD4Ti9ka/UgGYpkXnFIShk0pNjsjsFeYw2LghmkgkRjngTl5lKtGPOOHgPWWNgYx1EqjIiSfmMeJUKasdU2LMxpn66QxQ1vGWyqti2CgpXGdNDDOAuvVYnbbdFjnULcWhcbYBh9KZth0BB84nwc00sn8dH9aag1//OtvaNqOn378zA8//MQ//MOf+Md//AE/B97ffKAxLSoo3h1u0AZ2h5bvv/vAx493kDwpBkIWeEcRyBxAlXGhOqFNgdwyxCiigX72+BDR1tJ0PeMs4zqN0WgrzDijBQY1VmjCKUdiVoTo8fOEQSS+cmlac9ZCzKSwgN4Xh7CKBGOMl6h+Y4zX0fM1dk+FJTYWocYCl8wDyiyKLONBy/MrHNYv4YgLRPilAaw8+/X+rAX06v7XLOAt9YeXnN3aQX15vKuAsBYbeW60t9+x3fbX3t++VjOmdabwVnhq/do6i7q2bGG1dU3q2nINUvrnLr+BfRRLYc6W2QKZ4IVxIzBQJGmN9Vaglyy6+hqH7SolVeQCag3gctMjGvdBDlymmEn0Ok5DKT4LlCM3Qo3OJP20rgywL6wnoR6WCE94r4WlZKS4u9sVZVcZGRqKnhCoIuscmUMkxNItSRlIY50IuGVp5KqRtnMtSoeFLlt1jkKMz26eWtiuDKOgRMCuPqCn4SyfK81iCYTNpKRIr7RMGxO5aiuyDaubQb4/8PT4yEkbYgxL01kM8r0+xGXmtIzF1AXWkxRYWFvyQI/jCEr6S2JMjOOAK2qwWhtcA/t9X5zZvNzszon8NUgPR+aSGmutilNraJqO3bzDh5n7hweZxqYVKSligOATx6czP/30C9Y2NG5H2/QlwMjc3t3Sdg2373q6rsP7CaMSqETbGYEQ88w0n8jZ0VgHOaDJaKOKRpbUDnK+dPla67BZoRJoY9FasqWURVIk+CKnXQQKvVKM01iyP5lhriQqKVkWPI/Jv4QT1q9frufFIWwj+Bef08XxLC/IPbKyPYuRhudZzMqAbQ3atYj1GvxSX3/JyK/3oRrELUzy2mfXn5N1yjS+Vcqwha7Wv7f7uP739rtfqxGs9+dr12R7Pl/Sg1q/X23jumHvtcxne87+u2QKAqWIYa7MFlKSZq0gqbPKwtPXUcnow5Ku17GXMQkXHSCEy4HnwgipRUlpYgmkFJmnCXTtZq4NVnUEY6FbKkXXtgJZlAYYjRhTuVXk5JsCLbWtaO3nlDH6sXyXGNgYUxGASwtTJkuIWSAEgaaMqvMTRDVUB1MatlZdjMvFl32ojkxqM4moAtM4LU5iKBBRKtBPVuqS4msW6MaUoniOEg7m1TWKIZZaSV7gJZnUpgp85Jcby1qRmhYNqhUdTgv9d5pKt3jwyxS6Xbk+1jphIPWKaR6fGTDryjhO6pB6RY15KsxWf0LwzH7m/uEJhegbpQhBZWLMDMPM/edHPn78nrbpcbaBkMkxsd8f2B92vP+wxznLPE+0TmGMomkswc/kEJj9GbJDq46cpMfClDGUEr2LY1DFiBtjsVmhszgFKcgrgfcK66he3xwTQYksulEaZwxKW1SBSkt6UsP01fN0iQS3jqAai61T2BqgrbGuv6tTqEZf3uAZLLU1K9vMo+7fS7WA7X5/aS9ehzteO6b172sOaE2jlaDt9W0DG0fCF9vcvrbd1mvvXXMMLx37tePZfm573rdO5SWZjO16/9zlzU7BWSMPUsGmQeHKyEKn6lzfDDkSA8zIIHSMJkXBop215E6VwTGiZRNyYJpEAO18PC8MGxmckwV+KZnFZC2mfGd1/I2VCVYkwZFVynRdSyYRU2CaBHJx1mK0w7mWaRSW0/k8MPtIVkYmhSWJxq2zuOViULqQvYwIDSKVoK3MO2icY3/YLxdDuq8jIQk8kRUi21EwenImx4ZCYpTi8QJfeJLKaFsKyEVAUFuNtoppmgllP0iJeZ5oisyFn0ZhXBXmkHRuO3TWKKexygrsZ8Wot66lbxppTEsRg/RpdJ3Mf346nTmeZU52BqlDaF2KsYr93i6F+Fpr6bqWrpdi/+w98zxxfBqJCVJWGG0xtmG3u6XrWu7u3okMxjjy8cN3hckU+fmne2LKdN2Bvr/h9vYjHz58T2Mbzo9nnDN0O0e/24sMhVacTkceHke+/+4dvWmwzsm9mD3jeE/QhjB3ONuK5IdtS/+BknGxpaek9la02pFQoC1k6XIO5fzHubK9RE8phszxdIYsmRat1LxUiKicF0ntdQ2hsry2RnDL1FkvL0Wl6yiyblsrqf0tjiFllPky0lSFWly38xJktZa5qKrC1YFso/z6e9vpu/6O7f6/ZMzWsNS2F0L29TIvZX2OtuftmkF/qQj9mtNdf/6167E9H/VY1r9fcghrscRr3/FaVvIvsfyGQrNE6FJEUyUSyUsLf9KKRJY5zjkRgjT4YDSqsegykvMSMNV0r+gWl4w3poQKAaPzAiEJPRCIkAsHPGfJQmp7vV9x/He7HolJLwwMZ93SazGOAyEEhrMMlpEiukSORmssjrRIYl8i/zqcp0IDFWpoGle+Dew4CgRVIC9TRjbqorVUewJM3cNcM4dYlC8F5tILm0stjCWFNJHNU7pkUU0mF8gs1/NUHA4UeLlMuJO6zWVCmRReJTNQWX6MFedQ77vMRe5clTpQjKngxWXjSJdw7Q6XhiqBCcWpZ1AG0ROsM6GlHjNPnnkO5CRCdDkpHh+PzD7y7Tc7jHLsemlyU8rgmoa+adl17ZJa+xCY/YwPEykFgZ9iJiWPjPGMhKQFDqI2lUElPiywTzkfQOlI12Ql0/EqTFqbDk259sSMRhF8XI4ruURSikBE1+ckVzipPtBi0C4PvPw8Nyj1fthGpOsnsxr08q9cHiYuLKbas8A16EGx3CuvRbvXDPsWH6/vrfe3Hkd9T/5ecluoxIic5NlmbYhZvQd1up8uQ58uX7uGuvhi+RocdG39r0FCb93OtdfW5+1a5rJ2ttcYSC85qt96nC8tb5/RXKZmpSRzBVRmmUmrjcYqMYohS9/BPM08nc9EBbuUMM5hrCuO5bLztREqZ6klBO+ZxlHglpwJMS1GUphGCqhYm0hUhxB4enoiZ1E6/fjxQ6H7OeZJ6J03NzdSX8iZH3/5gfP5zPl8xjkn3P2yP03fo1OiyZm27bDWkTMi8Db5AkVV2Qhh2dwcbpeLN7TS1euDp2lbDoc9xjlSSvzy889gLNmmRaeGFSWvzmAwBSYTA1xZUDKUaBiGojQbRSK8T/Rtiy69I4BIRyhb4BFNCGmpp+hKs3W2yJpfosCUcynoS6dy20pB/nAj+k01iolFhE4MqECGSgmM1i4KtDLT+OnxSEpgm5YuGxRWehOiYp4ix+NJhPPGuTSPZf70Tz8zDCNdc4dSjvfvvxW4jcS7d++42R+4PeyZ/Jk5eKbjSEwDOc94P2J0IIckQ3VywiCU0BhnidwBlVtUlk5lpcRYOSvzKWKUgrNShqw0ocByqUBGcm5KH0gZPTr7GT87vGuI5X7yISzF5yqrLddVDKDIhVyjnFbjKE7qYgy/NBDinL+Ed+pgI1mxZgtF1mTznfXerwZnHdFu54/U7315kdqc1pKFPHcEK/YTFTZOy3NdneD6XAgbsDo6g1J5CdTkuGXbF2HF647tmqF/2/G8vFyDcV6D1rbOdA0hrq+fWl2jtaJu3cY10cGXHPg/Z3l7pqBKkVcXxguKeRxJKCSAFABfGY1S0vClTd28lqg/eUgiGBeCX2bfCm1RjEptKKsRszCAmmcTm5YIq+C8wvQQmYU5zZyeTguNNIXy4FEvYEaqbqrIaFi0sTRtJ7CRa1A5oVOWB1npEndLPGWUUGl9GYyjM3Bzs6iONlZokkrJTGRn3TL8JvgyPCgErGuoYnJVUCwbOY+aywDzaZRGtrZtiLMnxyT7oCEXyqgtLBeRgpbRlEppgTLQSwSWcy5Nex7vZRvWWdquEZE+j9RXgFD6N5RS9P0O65xkV8MoSq+IU7BFZUFrma8Nol6aC1utbTqUtuwPdzRNjzGWeYqcjo+Mw8+cTmdmH8hZYW0jjtg0OCe9AF3f829u7vjll19IMXH37o7WWdCQciAmT2KmaQxNs8MYMRTWaXQuxfhyLud5wmgHaLzvUMZIhlulHIw0VkYfUKaRupRUYskpYbTCaotqnMzFaFqe1APzNDEOo2QeRpXk9zKRLQE6ZxQGpezS7yH3Oc8Mv/dhMQg1O1/mXyu1ZKsVsjNGlYh6PReapWiwNnypHAewNFuqzBIYrNevn6mv1Z+tBHQ1XJfvlswkpVAyI1gcQt1+BqUlsCx7d8nclBAAcq7qX0Jllx0RgoC1EnSIfpocU8rpiwylHsM1A/3cCV8/3muSHVs11S0jbCsNsm5YW/dk1HW3yq/rc3pNJeBryxaOXG/7rcubnUKhaiC8/QKXpFEudc7LFdYSIghbx9hSbNPkJAyjVDqQRf8nlWKvWQyj4hKhGKPp2laG2BiJVENpnltnoIoyhayc+GEYcNbirCGFVLp55WaUtFMcgrHiELS1SyZjrYWU0DovkV29eWtfQeX7z9MkQ05SQuXK0ZeLXKW3lVakMjFNfqSIjXuetmutyaX5SWWkgOk9kx9lu4oyizmLhAhAliY0aw1xlgzOe4/RboGdLjCdKu+LcCEKbCPGtWv7At3F0mRYePvlwXRNS9M0KCURfYiJnANGK5STcytOQb5H2j4UGoNzLda1HA63ONuhlObTp0eOTyd+/fUz5/NQGuMaun4HWHEOSIZzc9Px8eN3y7Ck/X4nR5ITmYRUshLWNfQ7V2AfyboMGlVUazMS7Qfv0VpUazUCQ1aJFbFXMjnQpiKeV2+anIrWlAQg2WVy2zBPQ2lmzCvYLZfzGUqULoX2auSrIyjcBrlWi/FIK2hEjGk1dvKAF9kSZZGIXHotKKy8ej/JH88f4ZxEo6lqlOk11LQy/Ov6wfL4XzGi62j2YgwlG085CxFBKTLp+Y4olvOw2K56vGoNqzw/puXcVUkdwaqLDUqUmb//LHjlWqS93s42E4Png3vW23mpRrJ+bbsv6wyhOoT1dfgtcNZ2vd+aObzZKcRYZLGbZkkB291BpLPnMgc5Z7QzNG1Lf7jB7fYkpcjGMk4TcRw5ns7C088XbLs2r83zhNaKrmvo+x1t23J7e1NgHGGqTNPM0xNMkzQ9VfmGnA19vyPGIBLalHygPMRd1y+ev+16rGuW7GaBafBC91QGq0TOmgTjeUBj6Nu+aCB55vPEfJ44mSP7tudwuOH9u3dkZUDnZVbEOI7MwRO8dGnnLLn8MAzCbskXfSJbMqYUI36YZPpcimANustYFEpLjQKtiCmy2+1omobj7IlTYpgmtE4YbehagzKO1jYoIw/OFALeT0zzgE8zu/2O/e1OhAedZhylmH46nwnFgfngpedAC7wi9ReN0obGtWjtsLEQDHJE64BSQhY47A3GOhq3x9qGlODzpyc+fbrnhz//KOKGpeZwd3eHsz1/+P1fL53nf/VXf+Tf/ft/Tybw9PSIM2pp8Gt2llZr+m7Hftew2znifMZZze3hhs5ZrFZ8+ulnkp9QOqKt9LKEMnvCR6FSo0QDKpW6hh9Gogko41A50xgRIDRa03WNOGNtyNFzaizTeEYbRdaZmMUZhDiTs8KgiApUVEuRViA6+8zY1ugSLpFkrZetjULVqFr/e/17wfUpDZ4VhlkZs8UY8YXv+MLoVSp11Q9bG6m1Aa3ZTy541ZZb/xJ8c21Zw1nr/ZKMb17+nQtrzLlmcTBvLQpfq4us11lH9mu9qvq8XpPnuHZcdb/XGda1/dlCdGsnst2Xa9/3l8JGdXm7U0gl/lGRMHtUztweblDakDL4mMgxSoFP8kSBZ1DIeFxpNJOCKDjXyKSxxtEUvNr7Gecczln2exkL6azQ+3LtMk6xFPoUWWlRa4XSFJWIUaKWXPT/K/gzjaPcaBRtfIRDr41ALdM8UXFwZ2Rugyo3tp+mBbZJSpOLwxCZ6ECYPdGHJWuhfEdKiTyzRN5wSZfnaSbHhEWVhryEbWxxCiL2pxI0hbWkUmk6QvT7VSkQV9lsmQdtMM5hTYPWFl26iq1z6AK3KK2XAT5VmkQ6rjUmu0LJDQs7xnuhzRojzYjCDOtQZJx17PcHkbwOnmEYgdUkNRTT5GGKeK/JaGJIPNw/MQ4TWhn6TuZT+CD9HlqLc2j7joeHz3SddCp/8+17jMn88MOfaJ2l6xr2rTSvOZso6ufl4dMY7UTOQ2uRmcbSdTvabodrG+lByQkfA03fFSHDwmLLWWCnVKdPlJwu5zJkKoBRBXZ0hCD3rDaQifggPRuTn3DSFUHSpijWRimEJ1UaOEusu+Dml46GtdHRRUJmXRgWAkaVAtcl05H3U5L8pCoO1B6Q2mRada94IaqtyzqyrU7rWsS9GCZdX7tkIfL+8+1eMqHnr63Xe/7vS6aUKrMvS41Cl2xrvZ/Pt/s22ujX6ibXtvG1da9lXNegupec1bXvqMFC/dw153ltf966vH3ITioCbOPE4/0DCvjuu99JxO8aQs6keWb2MzpmXIJUIItQRiYGL1Q+oy23t3fclElZfp5lrvNwZrfb0XUt+/1eoJoclwazWjQWgyRNdPt+R86ZECJW2wt9dRwZ5hOxMBOeHp8EocySzNqS9TjncE0jekhlXrPrLI2zhX7oOT0eaZzDlkYma0E1iWmQcZx+nImt0GFL2sI0jiJG5z2uaaQYr2uaHTkfT/h5LsVv+cyu70SG23tIGas1N+U8hOjRyMVvjROjngs1MGWU1di2YX97Q+M6jHbkLIqp3X4PKpFiwLUtWWeyTrS7jm4nnceuKYN6lESWOWdmPzMMEw+PD8SU+ebb79nt9jjXMI0jbdPy/Xe/X8Zxnk9/kvuk2LIYM7/+cs80B0KA83lmmjzn84i1jsPhHe/ff6BpWh6fjjRNQ9vu+P7737E77BiHI33fcHO753/4d/+Gfuf4X/4//09+9/13fPj2r/jDH7/HWcM4PDGcnxiGE/Mkg2+MdmjlUFkTZ41WLe/eH+h2O7Sz/Pzzj8ISS4H93Q37/Z4pJLyP5HEGojiIGMXgGnHWScE0RhQy4rPrHOSOftcUSChwnmVq3TzNNNrSGEdjpEjqgy73YRKF1mLAc2lYrCy2JQpOEKIEKc66krEpYpJAqSrgGoX0R2TpcYnBkxIlCxOj0FhhbNXaXSj1PfVKxLstiK6dwrX1LzWO6jjquhX+qU5mbfTzxhlUh1Lfr7UJcXLeV2hJXlNKP9uvbSf1ev9eisTXBn97zPW4t+utt7PNmta/11lfhYeuFYjXHe6vwUbX9uWaU/jnZg5vrykYg0HhXAv6iJ89909HYvCcTucluvQxE8eJKQram7M4FNEBkqHqrrB3KOltLX7mvE7NYjF8cgMoVWDxwAABAABJREFUxTJz2OhStEI/S1GtMWCsGJdGpCAeHu6ZJql97A977m5vBbJIiePxiWkuozStEY661mSfOD4+SQ/B7BnOZ+g6cA2hGGyDqJwmYB5HnpTgm1FnYk7M0yQdzVkib53kggfv8dMECNvq0O9onGRMKUm3bJhmFIrGOqE05gwhysNdurQVCh8zgaL2mjPGOenubfcY7Tg+Dcwh8uPPP9P3HcZqXNeCzWQdmcNMOkXUT3Bzc8O7d+/o+x6tNHfv7mQs50pOxDlH24njGs8DAKfTsIzjbFyLD4FPnx5o2x3WNHz48C3390/87d/9E9a0GN1gTcbZhq7bU9lRoHGu4bA/8M03Hznc7Pn55z+hDRxPD5zOD/hw5sOHW/pdAyoyzwMxaSY/MvmJ2c+M00wMkZ9/+kyjLRbNNMh87xDAx4yxGdf1ODLKaIEWjWE6nplmcVqHw00Z9KSWLBfkYfU+oE3GzGJwrVMcDr00PYbIOJ7xfpb73bbgOlLTkZJ0j3ufiLEObJI6zPL8VkTz2VIMyIrGumC4S0W5/rNSUSWCXtSKYckKUnEGUqC9DnfU39egqfV6dXvy/jZivRj/smvL8vwr1RevPzesz2m4tc5Qd6XWSV6LruF5hF23vY60r0Xr17ZVo/1r7KNr/77mMOo2tz0J22PfssTeWqt4aX/esrx98lqmdASLwFnKmXMp/p2ejlgnFMiYhb6Zi+pkQhp/quE3anOQKS/Ydb1AS8NKlrhJbgvKzIYyxGSVTkIpqhpR8Oz7XuiXZB4fHqT5THuU0vT9DqVH5tlzPMvs5Fgoq9ZanLXM0yCMkvMgTJ15xhlhIkXvC9vJko1Q/FKMFxVVA0nl0rxWopGUSBqskgghFMaRUYq+6+n7nl3f8/n+s/RlhDIytKpo5izjPxUI0z7XJ4GY5fsSosnTth1t22O043SeCdPM8XRCaWgQOClhUUHjiwgeOWKMjA5t2h6lNPvdnnkODOO0PJjGCpvIOYdG5jCPoxi/eQ6LvtHD/ROHG03fG/b7A+ezZzhP7PcNrrFYm3BOhudQ6Mi6zLre7Xbc3NxwuNnT9dKEeDo9Mg4nQpg53OxoW0smShE+wjyPQgn1opabfOSBJxrtsBiSz9gsmUtISbIw58qMC7sQJ6SJcMbPE4obnBVdKHGKVY0ql+5uhffFsBtF17cyGyJ6Zj8xlxGxJiusMss9XYMYwegD0m9T2HAvwAlXoQVqJLk2sM+xcdbvqRU3YxUl55wXmmr93BaOWBvP7fIcl6/PZYWN1hnIdeP03AlcItyXot110ffaPmyj/9eglNfO8bXv2zqGa47kJSP8UpZybZ7EernmFLYsppeO82v79NLyZqdw//RI13aYG4tpHCZFHh+fmKaR0+nEx48fadoWVTjb8zThkwxVCTEtKp39XthEdZrZNE3LkJ1d18mtExMGLRCPa8jjIIaVojnUCPMil4Kt0Yam7WibBmsd+92OnHrizQ3n84gxR0KKRT+nlYc8SsRX5RvatiWlTJwFNqhF8Kxl5jQZYq1FNI53t3fS0FSw9xAip/OZWUXp7C5MFa11qZ007A8HhvNZZgY74fOrxmK7hmbXM/30I8M4kBBl1rZrRUokzlKkVoBWtEOH0koMYc4EMtFYrNMok8hqQuM5DY+EFHG9JuaJaZ4xTjH5SQr/YV6YM8ZY9vtb+t0NznW8e/dBbo9s6fs9fb/DGTl/bduSAsRw5tOnB3a7HXfvbhiGmU+fT/x//8v/zu9+/0e++fgtf/VXH+h2B27v7mhci7MyL8HahqZt8V6aEw83PR8+vuMPf/0H3r0XTSNtFPf3n/jl0w8onck58t33H8Xwzkf+/KdP5JzQwDwJXOeUJib45TTS2YbGOvbdDlTiPI14FTHeiTPQwo6aZ19mSHjZn5hw1tA2DUyFJRfDIt1BSpJBpkTXdmit2e8PgJKpfSEwzeIYHJag3aXHI0WpSyD3vnN20ZJ6XkCsD7N6JlEtjkSCoTU+XQ0A1HpOKhTQVahO0RmLmkUmZhXKX4NG1lFxzeLXBu0LA5tzRUNLJL+4Imovwnpf18t121WqJBtntU6WBD4Oq+1crwusM4L1ubvGttpua/v5Ld30mnF+DU56yZHV9a45w2vZy2sOYbv/b13e7BSmWSJG77tlvOJ60VrGSsYUS7enJScleKW60OaaUlw2+qJ6motAm9Z6JSetUGW+4TKiMiWyzktklRPCwrCSHjtnaVxTqK3SIXu4uQGlOQ9nFEoMgI9FAVPE+3KidNWCcjLdTPZfWERjumQxxsiIyqbgsinFJVcPJFQRpUuh9B9kSmHc0LqG1AS6rhN115wZ5gntxNH66OX8WSMd4FamgqlkyvwBUTodhwFVZMGTluK3V2W283nAKBHZ82HEGsNh3zMOJ6GjarcM59FKS8FUS61lmibmyWOs9BhY09C4nsZJ70AMGT9HyJ7hPDKcR8Zx5nDzjn53yzQ/4qPi/uHM/jDQ9RNKW5qmZb/by32iYNd3GOsKDViTgbZt2O07us6RVcIHYV8N44nZD3LPmJKhJY9PgeF8JMWEtRo/Sabg2k7uDZLMXtYQEehHJQWBxekKNVrqSD54cpImM9X3y1S6lFLphWGRjoi5iBoGSFY0wYzTZRa1jI/V2oKKQkfNeRklm9a1g6VxCy6FWVmqrd7+CHv2OU5PoW7mQkSQtKCso9YRaP2OQmHlSwNzWffty8VYXbCvS45f97Pm+198uhjBL1+vdQOlMhe7J415sp+XVa9FxNcM+kuR80sO4Zpj2f59Dbq5Fvlv6xbX9uFaxrL+fW2df27t4KXlzU7hPJzJOQtbqHF0jRPWTRCevCs/IXiBWozGpUTMmTlc5hXs+r7g/ZY6Pa0OJamD3K2pTWPiEIIPIjldR2qiBMJKGT9PpVks05Y5w9YK80RryzfffEvf7/n111/JWfH4+CQGNifaplsiuOE0EpyjuW1pu5Zd29I2LdM4SkNXlKiqaxuarqPd9agyEMhag0qO7CxhOhN8xs8eoxLKiAyCM4auaajkjNM4Ms0zn58emHLEkxjmkZSjZGSNRVmDwQgXX8EcAj56eNJFwlyhmxasYUqK6D2P85EwT+QUuO0b+nd3fP/dO/7hHx6YpieU3pFiIMdE0zZYo5HWCsXxdKbrzjgXyclgdEvbQNvssMYyj5Ewjyg1cf/5nvMwcjpN/O4PPXfvvuXhaWYOil8+n+n2Z5puQGth/Xx4/47T8UiMgZubnUicF+kMYzRt13H77kC/awhxZPKBh8dPnIcz8zxyOOxpWsfNoQEyMc48PT0SvadtW2LpBdk1rfRPWI1pNNppohZZ8VwyQR2DTAvMIqNS1WklYGlpu47GCStKpvylIjVSelAIpJhJIZBsQpPBKYxxNG2Pcx0hZLyXgCglgRN1yTaUUkV0sBpzYUx9aYCuPYm1xvY86n6pMFmdhvxR50eEAkHB1hpfi1K33/ESri13kfxflSbPyz6vjWFevf48kr44ubysu844cv5yH9d7uy2+vmTsX6ofXDv+bea0/cz2HGyX7bV5qcaw/o7Xzvc6O3yrI38tm9gub3YKt7e3hS7quNkfaJwjTDNnd1oGh8zzzHkYpNDbdzRK9JCsr/CRGHVKBBajdDSTq2qnXSKocRzJOTKHiacngalylmJz182iVgpFehqaZlpS24V6ZwwfPnzg5kb2/fHpicenR6FuakN30y100fr9+/2eXdfRNQ0hRQiGrDWNa8psZMXoJ/7hz/8kctQ5lxnVZpG0NqlIcyipcRht0cqSooIsEXhKIz5ExmG6pNraYBuDbVqsa6SXIicscPv+A80k2HnIgYRoGTV9T7PrISumaeRxfCLlGUVit79lt+toW1sKnNKX0HbiPA83h8Kq0sSQCSExDjPzlIlRMY6+iPDJg1nnNseUOJ8GzqeBP/35R1JuSMlxuHnPH//4N/xP/+l/4nA4cHNzy3fffc80jtx//sTx+Mg0jTinaftWonolBz/6kcenSIhnHo+/EGPg/vOvKA1tYyWbiZ77+xN+PjNNA33XYfZ7bm9uFqdgpB2WSEAZgzJSOxEdIwUxoleS5iGKfHmMkbu7O6nv7Pd47xdhwfogmtLPIBThtHSqZxR5lqy2bRru7u6KdIpGpwxJLRRkEIKBPMmXIum6U/ia8ajLNQno+sA/Y6WUqH0x/lvoYf2Hel483RaV12quL8Ed1/bvmnG7sI9eL4heM3YvGjZFCRSvM4PW27xWj7gmurf9zBaSeukcXPtdt1/VC7YZQD33L8FG63P56nl44TP/ajWFvu+XzuPK7jFZ4Zv5mS5O8H6ZQ4yWTs6kYsHf1ye3sAVKOiit/qZMH8v4JIXp83jkfD4zTRNaG1KBfWphOhYp4zpDOZY+iJylwNs0Lc41S+EvPSQM0p/Qtu1yU1CcgnNOePslkqv1AdPIFDHh7nvOp9Ny4rUV41PnHkt3rykPpyYlVjUM0XKJQZgqYlhkzoE1ogIrxkwkm3PR9O/6nYgLzpYwniT5VyKj7ZqGTkkfgtYZjHxv2zZFEiAuP0oLzOZcy36/o21arDHMkxSNc1bEkIhRiajhHJlK3eTx8Umms4WInyPn88Cvvz7w668PfPjwyLsP33N7+47f//4PKKVompbdbi8NX30niqpGFRzd0rSWZfLZOJfGuTOzPwulc66wkUMhkf44DsQgg4raolJ7OByIXoYQhdnLvWYumG9MSdRnk17onsBCUa7/FkpsS9e2i8DixVCX66tWKp1L8TgSPNLFr2XuNiDsoymQc1zmj5vSd1I739dR8TXIoT7krxm89e/ls5e6rcArXKLVnDMV+K9KqnW5FkWvf1/bv/XyxXvr/WBbZ9hmCdfVTH9LlLterp3TrXG/xlq6tm9f2/YWfrt2DbeDiNbrvpTdbPe3/v1bDP1vXf/NTuH9+/fCyZ9lNrKawU8Tc8FyU9F5CSFgtGG32wmSmzNZFc2fKIZWxUhMRVTO6UW6uGsbxnESPHk4MfuJYTwRgjSrdF2Hc5Liy/jHhDGWnGAcJ56ejmV8p9x8IYjmvjRZ7RmnkdP5LJCFteLoakt54Q7P88z90xMxeo6PT5JFNBbdNujGEc8RMLSHHYf9gbZraVwjDKTJw+whJ7Sy5JSYQ+TXXz9jzAN93xOTdAiP3pOBu9sPNG1D13ZYJc1IKUaS0kTEoCkUbb9DNw1NDASFOMIcCaWQv7vb0bQaw3usBqMVd7ue4D1///d/y+zHwvwRobvdboc1ogOkkAzp3V1L8ArvEw/3J/w8cTrJPINxmHh4eBDBPq3puj1KddwevmMcMn/68yf+h3/f0Pd3fP/9X/HzLz9zPJ0JKeHahu+++xZrZLzm3fsDaAhpWBrfzsfP5KJlE+Yd2ihuDgIFGSXyKCkloh8BkUK/u72lbVsO/Q5vPd5aHueZDMIusoZsNGPwYpuUwmpxTE3XLoquu530uhwOh2UeR4pRFG1VGcVZjHmG5Z4xkt6RAT/NogJrFYfdXupHCYanE+P5zKkQIhIs6rk5l8mBwePKw1jl1VWpC+QMiQvtel3cXOvs1If/AuOsMoX6oxIpXQqrtdZRDcZa/G5tUF6LpLfrX+oc5fXMc/D/yrI2kNeglvX3X13yy/h7Xdbd41tBufV3vtWAbo31SxleDVxzFt0xqPNI7BcOfusMtlnhSw6sXqNrDuQa9PW15e3zFJwjAHOemaeZoDx+nJj9ZWiLVmqBmJxzzCU6Yn3wMUHVDClzgkXbR5XinnSD1mloSmmaRh7Cm5ubRZl0LnMLJMjXy2fm2XM6nWT0pJfCbFVCDSHIw1xmFSwXRqkyPCVyPB6JSbIOXzjhrmnIRkt9pOCx2hq0kw5i4yyZvGQBIidQC34sksoxyEMYc5JtG01nZDC8KUwrlTPzOKKNQRlDLpIgIYtpyEpJ4V5JIT0UJ9PqjHOGm5sdRiET2ozG+5lhPKOUKpmDLV3JrWQEYSYqcE6TG0uKktlY06CUIUWYRs84zsQIjbHFMffYpAh7h1KWeQpMc0Abze3tOx6fHhfZkqZxvHt/xzgcScmTEaPrw1wyD0+IE8KoyaQk9aBd35Tsz5eoNmP1pYCp5ElZYJ6cLhz8pmR8WmtS/YRW6CUbtMv8hHr964MXg5AlGtcsMzZ0gT3rg6ZQZJVRlWZMyRxKP4dSMvgpTDPzaPBpkqApRRk2lWQOudbCshPtLlPi9vXDnclJ/n2NabRdnkWpzzCiy6O4GKEkZybnt1JOr7OTVmuz4FHLDq33Y5M2bPb7pde+mi18kY1sIavncFBdZ/u9W2O+Fb9b78s1mOilc1OZW1tn+5Khvrb9a05rmz18bXlrxvVmp1Cnp6Ukg+pzjPhpJnoxWktabC1t1y5OQfTnLztVpaBzlnm/QveUuQWXZh4x8DElrLWlANjw/v3HpRvwfDozKb8M/AFKliC8+QpxjOOEc47dbrcwqKQzVJxCPaZ5nhmnic+fP5Oy0EorPNHuJMKfY1yizq7toMBG2poy4SuUnouVU4ACx0TO4TKwJmkwzgnkYAzaWtquQ+VM8BFtRKBvmmaBWEJYIAAhZZXGv+DJHg4q0TSGfXcjhfcMeZ45nxPn84ndbl+gNEdbYJ1Pv94zjR5NwrlM20jWII1kHVo5UtSMo2ccAwqLcx27fs+uvyFjMEbO1TRHhmGi73s+fvyGT59+FbqwMfR9x82h4enxV4bxiRAnQvKM00CMQuEMcSy1pUxODVop+n3HcD5zHEdAHpDGGVKS7uIcE4GAn/1iMEOsjlwcLVoVlo3Acq4tSqzO4dpWmvW0JpTgJgbZXt/3tI0pjCPEcWTp9taoIhMidZ2U8xIcxRCkT0Ypdr10u09u5FxGmsoku4CNQeAkrVFNI2QLk4uIoSyLZHR5rQpFXjNy66U6rfWSc372ypJplKLwSwZja1y3NYfnMNbqu0qwVbOeCzTylaif60bxa8u1qPta9L3+jvraNaN97RxccwTX9uHavq0lsOv31b+v7Wv93Hafr8FS195/CYZ6y/L25rVUBowoxThN0tlbaJd9J9O26g2bU+bh4YFQ2UdRJoJVUS2ynJQUE1HHWmvEk4tGi4jqKcC1jr7v6bqW9+/fk3NmGmfOp0FUL4MIybVtV1I0kWao751Op+WiGGtK81bGRokQa0Hx8elJCuXnM03X0LRSLHdNg20bwjThg0yTy2QIM/l4ZBhH+qZdhtyk+rDlhNWGxgk0k1Li06dPkjo2jvM8yrFMEyF4hnGgcd8IHbaMs0w5SYbjPcfTo2DjKS21CR8Dcc4EFQlhxpgG00pvQ85x6SBvm56m6WmbjpvDO9q2x5oOZ3u8Unz65Qnv7wkhY3VD0/b8/nd/jVKWtunp+wPWNBhjpbHscMCYlpQ1rl3Nlg4J6xzfffMdT6cHjIOmc7jW0jjFMJ/5fP8L/b5BWMUJpRPWZXY7VwbZBIIfUARuDz2zyqJBlEXZs3Ud0ySifcPpDKpAlkYGOc3BS/dyToiEu0I7CUCarqXrZZ6zZHfS5zIOgziDIjxXB/FYbdh1nTRfFplrlVZcdaWxTuZtxBCLsqyEAlpr9n3PPIxM44htGqk9xMgUPGrWOC3NknZRGb1MTasGNKW86AnVgjRl+xWWqMva8Gwx7y+1gfIF3lkZlfXn1nBFNSpruYZrkep2PkD9e73OS8saPlrDVWshuu228srp1G2sC+PXCrjVHqz3fV2kX0Nm22Ncf+7avl/LBuo2uq67OOMNhFWb2K7VFuo2tjLarzmE3+pU18tvmLwm1EHBVQuGlQW6qfIQVTY7hMDkZzCiqBmiyBGHggvD5QaXG1MeBPJFirYp8wbaXqK5tpUoN8YEeCnWLsqN9tmNU6GlGqHXk9q0Mi7SOEvOckzTNDFNE+fzeSkuyjHZZfJaKq1AWYFtHVJ7ltd9DLhg5DVjLlO9sjzMwpZqyRmOx6N0BXcdPosRiSEQExCkMcmYi1iZOEgRrZPMKZb+htqYdBlEEpNMQruwG1Qpuitc0y4U3bbpi7KpxdoWY6SY/HQ88/R4wpiWXb/n249/RBXpCWflmLuupd/19LuenAwpKxKKyc/EFKWI3Dhub284HPYcTz05i2hgygofJiY/4oLMO2gaQ86KnDXZGQKJOSTmeRTabApL9qCKoV4KpNVAZsFqswO7vC1YvUoyq1uVTnddAwNTO8tFynyeJwkuCoxjbEONtbXSaJWFXlmurdb6Ev0W7F7rUsROKyi1QFRN04jqa5CsQ6RPRKxRpws1NZWgYnmMUzEo+bmBWUM5Ly31c8uiLqDOM2O9iu7Xxqk+S9fw660xumyP5d579uXqsu56l3Pmq9u/ZuiuLdeyp5oFvIT3r8/FdhvrfVkb/Ncgn+3+PHNcSq2ezS+P9SW461oW8ZpzfQ2Ge+vyZqfw7t075mmWoTEhMKGKaJvBOVeidF+maI2chjPdfoc2lqQMfp6Y5wlnrLBfrF4E4fw8YbQWrfxirN+/f482GmP1Mt94OEv38+l0WvR2zueBlKDv9xijkYE+Mjoz+Fgi2MD5fMY6S9O2fIs4iOPpyDiOTNNEyhlrLXd3dxzubtjte87jyDBOPJ5PmEbw57/63d9Ig1POzINoIzmkaG6V4dE8MZwHHqYJpTKuMRxu9uK4jERzrm1onlrGeeTheBQDrxXn84mUAo25wFqNFSG15QFVBUaxhkO3Y0wzkYQIZiq0kv6GrKS5ytqWD+8PDOeR4BW73V1h82gOu/dYfcPn3vPnP33mv/yv/xVFw93de/76r/4DWltubm755ZdfAbi9k9nKzmnOw0RMCmVaur2j3Ts+fvueb757z4dv33H4U09zr/nxpz/hnMI5GMYjxmW6nWW/73n3/pZpGPDzzFlnnsLI6fjA08MDSin2vRjnXevoWnGs9/ePEj3nLDIgpUZka21HK2JOnMaBNEhW13St9EVoRcziyB+eHkWnaBgWWnTfdezajl3TCTSVZB52ziJwIQq0hpzswlyapml5kJ11NEqLcKLRoDW7/R60wicRyPPTJIFTcVi6NGLFGAkqyL1VmjiXyLDMqlhHiDW4qrMM1kZdVnpmFmAB0YqxX9Vl6ufeIiQHzymqzxe1BFWLQV6++VJvUIpnwdp6qRDzmh67jqzrdutyTd56G43Xz1TW11Zy5CVD/+zIVg7m+rHz7PX1NiuCsmi3ldG8W5LANjPb0ldfgsS2+3btmP5V4KOu61Ao5mmSJp6CF1ace56mBd5YUsyKqccgYmEx0rrC6a/TmorYmlIKWxyGNVYUQ7VGG7VsdxyH4nhEPE+kGQ4opXh6eipyASJwptRMDMMyya1t29JpqoUznqHtWrquWzqMgWXQTyxzeXNOz054iNJOb1UdJqSXITyKEtVqMFb2vT4MSuVl3KZSsNt1NJ3Dtq4UMjVaQZgn5jhIlJozs1JlOI/HtaLqOvm56CBJBKuVnAujpQ6RilRDTKCywbgGY4R5cv9ZOptPp5H97g5nG3733R85Pc38+OMnzqeJxjXs+h2qZH3CuS94P5GUAtYJ/NG0e3xsQcHNzY6mNZyHJ87DE6fzIzEdcU7TtZpMoOsdTWuxVlMH5aCh7zv8PEtTGZIJpCCQTU4J0/UCHzUNFBzcVKnxepYLtTinzDhLYRetaHVPVnAeBzH2VuoNqSiZGi1S6LU+Za1lHEeC90tdxDYNfYGhrLWiszR7fJB+aa0UTSu6UJXVlBMidEhP1/dyPsvckRwjWUvWHWMkagks6rOzGAb1XFLhpSi1GpUlSq5NEC8sC0TB89WuRbfXvm+7Hxf4pf6UzE5Vx1NrDs++7dn+b//e7u9Lxwx1iNGX6qHbbW8//1Ik/tJ+vHYOXspW6t/VEVZ1hjWEtN2/a8vX6gl1nWeZ4Cv7+9Lym/oUAMbBLRhndQi1+BxCEKoduQiFieGMIZBWQzqqBHAKAi3klFFGNIX2ux1d29F3beH8K06nM+M4cDwdRaKiGGbnHH2/43Q68csvv4rsdtvx/v1HtDoxDuPiFO7u7ioZSJqwUqLrO3b7Pbu+Z/ZV/+b/z9t/NcmybVl62LeUi4hIscURV9QthWIbqrqqUSRAGkA+wPhIg+EnAH+VNL4RJBvoAhpduq44eosUEeFiKT7MtTw8Y0fus09Vg34sT+YO4dqnGHPMMefSRS3U26wUtnFQbrhpmogmkkt2VGwzkAt1UBg0zhmZ+VA6ViFLJqMgpch2u0FbzY2+LRQ1w5vvvmUYjhwPB4wS6mde9JVmtrsNu90WNYioXkgRlWU2rdEN1jY40zLNAtfFUMaH4kRZNiV++O49d3cPfPvtD/zqV3/E61ef8a/+5E8IQXF3t+ebb76jazt2u2tylhGWbdeCSqASOQViyrTthqbtuLq5kSY/BTe3W1yjuX94y8PjO+7373h/P+KsZrdt0Cqx2ba0rcVYJaM0UwQSm01HDDOHxxZjtBAYolCRUwyihWUt274rI1SdRKAKstaM88RYhq/ElDgOA6rARaowx46HI96KnHXVs8op0bedsIvyqYny7v17DvsDh/2etuvYXl3Rb7fLNL55Fn2vyYeFptqUCXWVfRSD9Mloa9gMPZAZjkeiL7M2GopgZFzGvkZbINWSBdX6xDkUcr6sncIaZxd465QV1EUpodrmIiHxHD59qVlu/ftD47diKZUA6KRqmmssWf79vHF+Ckt9iI+vs5uslEwkvHCezvf3fBvrrGGdBZxv/zlHc378H4vOK3x+KUs6h6Y+VhNYH9el63Ipm7n0neeWT9c+miaZUTBIgVcBTdvKHGNjuM/CGHKNGCdjDU3fSTTtZkJoiaHj+vqa1jV0nRRQozdstxsaJ70EXdMWKY0GFEWfxjOOE9MoqbrWwvMFmbhk7YSiDKtJ0mFc0/rqlV3RoZfUPS1zadumkRT/eASkQ3qeZpSfpWnNycMeCp6/3+/FcCgNPqFSolVGMOecORwOeD9L5qA02tQJXIFpGgrcYUA1GGvZ7XZlnrTh/dvvkSg5ir6OAqJMl7u+uqJrHUaLdlCMkdFPEDJRQdt0aO1EtXT0zKNnHDwqB8IM+8eRYZj49tu3HA4DDw8HcmoZj4n/9F/9JV98/nP+4s8tWv3PpJiKkeu4utrx1de/JcYJYxRt5+h7Kdga12BdJgWh2X77/e9AZR7299w9vGX2A8EPeJ+JQXF9tSmT+yLeB+ZZ2Dq56l8Bt7c3SyOas7aML81Cb1WKFze3kvlYJwXuUr+aJ6HeZi2ZpXEyYtU6S9aaFALjPBEnz6yEHGGtpXWNMJGcK3TbmkXU+25EaU0XIkIEUss9r7Uu9Y0sRrxG1lnuQ++90GC1oe1l5Ok8z4yHAT9NxJgwKi7woS6ZmQJSoSSLYT/BKjnnJzBJfdDPoY2cpaZQB+roAulwbnRKKL82aNVArvsWLhnverxPcXtZa91fyEtQlHPdfP2uIqVTYbfWGNdzi+tMaqWeFpDrfjknciU5Js6N+fo418dVz+MaXjo3wpcM9CWHeA7rrbdbr1XNCnxhqJ1fw7UD/lgfSP3seVbwY87kx7Ke8+WTncI4joyTKD/mnJdU3tQHpNxwlWbq2gbXyMjLkGXWQbSGttAibZnfbJRmu9mIYSxZhtA6JUr0QeoUlYt+wtpOBwzS41Bnwq5P5sm7ywOgtcZQxM1yFpilNCvVAT1imKWTeLnoWW6qkAR/lnp3RKVUhpsAWWA0HzzOWLLOBcMVGCrGgJbJweRUp8jJLIGcT6Jr1hjRKs/Sy+GsFUkHrclJCqJKgcvCsqmOkqzwcyT4RAyJeY7kCH4aeXw8cjyM3N/ti/ifZho9x+PENHqsbXn16jM2mx3TNBJiYGN7trsNTeOYJoNzhqZ1dF2LbWQfZj+IBHdOvH//AyknDuOBECasVeQIlCH2KbfIoBTRIqrnXq6DFF6dc1ztdsQiH07KRCUT26JNInjYNBjXlAJ3Kj/CNlNWdP3XbLOaIeacSaXhL+UTJVCmmimZ61wgPm2kN0EV8oSsoxhaI1Bn4xq8j0T1NMqsxxRCKN3ucm+7pijDzl5gpMI4UhJBQDF8Sy2hPviy4ou8+fXfaww6JRGZzFnqLyuG9JkRu5x1rH+fv7c2RE8NISz1i0oIKE7h5LzUAi/lfBmfX+/f2nCv92GdEeTCZFzv36XzdL7/l7Z5HrFfOi+Xvnf+mfX5Oa9hPNn3lYF/ri7wMYP+4TV4HmL61OWTncJ3333HPM0c9ntUicZvX7yQDlGluHv/HqD0KDRSTO060azv+4WS2jZtwXClmcpZxxeffUbOifdv3/Hw8Mg4jGx6kQrwwS8yFyklrHVLJuB95OHhgZylEK4K1rzfH5hnj9aOtt1IVD1Op4iwaVBaMRyO+Gnm/v5u6TZ0VnB+66ywp5RiHicZaJ8TVpdBKdPMfByI3uNtK52vKI4HmajWNg2q60hNwzzLUJ2UIqbM+h2HA9N4ZCqNZSiBSdqmoXfN6XxZR+McV1dbHvd7hsORzfWOpuLiakTFiIrC3ffjLJH7DIcHz3icGAfP+/ePDMOMnxNX1zf83i+/IEaRUf77v/s1r1+/5vMvvqBte6Zp4t27d9zeXvOLX/6Mv/27K7IKMgXtastut+HxuOc4PPL92zdLzec3vwvSrNY5ttuen33xkuNg8PMoiqZ5xodMDOXBTKmwxCLjcWLbb7i9ueXzL7/EoDkeR7ydUXrCh4iaPCBMIlcgNx+EqCBDmlKZ367ouk7UZ61ZYKama1AxoTOYxmKdwbUGbUGZTNs3NJ3DOM3VzbV0RSuFtQ7XiCRKTBGnG9HI2mzoHvfMs2ecRkgZP81SP0uReQ40XYtr24WyvQ5EpmEQgbwCs2ZFkYDOGG9o6gNenFqNdLUWiZa1M6gR9jk8obJaofhq+VysjX4rb3Ee8Va4ty7VYa2d01OevUIUTfNyrur4zFOA9mGfxdqQnhvLWnS+ZPyq5E4KEZ8u1zmqYV1DP0/lSy7/rI14XZ5zJPW98w7z9TVZZ3SVOLPOfup759s6h6nqdVjvwzlkdimzO1/Hx5ZPdgqHw0GGkHhfhtpLQc8nqRfMc5W7SGQnJzx4L93LWtQm59kzDgNaafqu5ebqSiaCGcM8Rx73e969fcf+8ZFt35dpaHYZUi6dtCLmJpLDMmVLaTDayNyGlDgej+SsaNsOrUW2wM8zKWXm2dM4V7IKkScYjsficCzddVsE/doywzdyrLMMlMK2MkcYY8A5ktL0jUwjE13/iRzlYUwpE7x0+VprePnyxRIpHYej1Dv8WFmWDKUA3ljR+m9dQ2sF9joeBsZhZBymgjfLw14L5A/v72XutetICaJXPN4NDMeJYfBo1bHbbHG3HdvtFbe3rzgcjoCoo17fhMXhtq0oxW5vrrn+7DM+/9kXuEYzjAfGaWT2I3ePdyLOl6bSIeyYZjECwQ9FVjrSNBprHNb0NG0V35NaQgyeefICoxiFdZa2FfkJsiqd4KnM/VaEBLMPRWdKla5jR7/pSQqyBh8jqUAUMru3DCVSgAZSMbAkYo6E5NERlMrE5AnJ4kuvg2sbmrYTdlPjyEotMiVGSzf+ujYhg51Y9Lh88OhgShe11DHatqXpWpoCeUnCGdHITBGVJBuJOhBLF/Wiw7WiczdN8wF0cQ5nnOoKNctZUVlVLnMdTnWAjxmPc6N5/rlLf0uGfQ6FsOzvx6L6S5j5JWMnTKr8xKCer+NSxH3JwD+XIZx/fu2wLhnhS/0E58d7vm/nBn39mfPzfelYL52786zuU5dPdgr7vWjXpxhR1mIKVhbLXOM6LCemVFgcSJcnYBrH7L0M5HncC3Rzc8PN1VVhCmkRXLt/4O2bt7x//57dZkPbtVxd7xb8ti0RV9/3ReIiM5eJatY6YhS+/DhONK6j77ds+i0xRt7fvRPGSPDkzaZgrDDO89Lg1rYt5oXMsN30G0Y/E9PIOIwCMRiD6USvXxmLbgCT2PQb6U/IMA0TKWQoUs3eeywW5wyvX78UPaR5Yjg+EucJP0/LyMvBB0CR2o5tv1kyqRQjj48PDGXucwKKXrjQT7Xi7t17+m7D5rMtIUEKgcf7gcNhZBoiL199znaz4+b2FV3fs93tiFEXiu+A9wLXtF2LDzINTpzCK7742ZcYnfnqqyPH4cAwHHj/8I5Eot1Io1+/adFHj/eRYRwIAWyINK1FOUffG4R9mUnZS0d8KCKFMbPpNzSNbF9jSEFkIHyIhJghK2JWjLMX425AG41Tjs12UwYQwX44QpaBTBW3FpCkdtmWfSASAR9Bq4RSkZA6QrTMYRbYrmlwXSuSGG0rpIksfTCNk5kgzlh0lkKn0iJTkWIkeqFFmxDRIdDqHmUMbc60XcfsPXp/IAM+BFTKRB2xWaFMJgZDirbUA8yTSPQcS75kWD4wFhmqlrXWGpVS5f5wCUJaL+totEbzF51QgY8qPJRyelKvuIRzn+Phzxm39bKO4gUmyxej7Pr3cxDS+vguOZ3z75z/vmSk19nUeeb1XDH70rV7zpCvr8P6tUvL/+ZO4fHxcdGUNyii0qSclr4BY6QLuTqJmBNNLxS9piuF0ZwZx3GRqO77DW3b8sMP3/Nw/8DDwwPzLEVa0U9qSvQqNL+27RaZalNmNoDww7e7Hc7NhBCk0G1E1dRau6TBldMs2j8dbeuYphHvPZvNhqZpsEZwbJmlPC/ccmMtJmX2948ifjeMNFaGspudwRb9+E3XY5UpjWjCZLFOCphZgY+BYRxkoE3V7ClMbj9P4njnGac1m3I+gw8cD4NE79stPpTipNEoawg58//9q/+Jtun5w9/3fPnZz7m5ecmXX/6Cd+/u+X56x3Z7xc31LdvtFqW1wB0krDVcX1/x8tULPv/ic37xi59zd/eOmDwPb3/gm3/4W95+/w3v37/h/uFdGX05YSw4a9juOm5ud+x2W5zLDKNm9ke8H4lpommvcY0I8FEw5r5pCT4wtRNvwlumOKGVIYbE8XAsswoSPnqUkQZGVYqX37/5jrZxtF3D9c0Nrmm42u2EbmwMx2kkBMGYc4qkaMhRHgyjNcoqaWhTksnNZepfiBFjDsw+MM+R3eYKZyQL0UWXKkSB9GYv10mrOljHlnnO4tzl4S5F3BKhaySo6Duh3qYYOTZyL0lBOmGVNAPmKLOlbdOgbV6G86zltddGoRrIS9PH1gai1hgkkv3QgJwvl4z1WqJh/V1Zr2Rc1clopchlBkVGaidSQ6kOTkggFUpZQ1Hn0fb58ayhGqM09iMsqXOjeA4frT93/t3zba8N7SVYa/35nJ9OhKuvnXeFr7ezPt7n3nsOZvrnOIBLyyc7hRgjFBbDesmFU940DWYp+AXRDiq9BqY049QDM8bQ9T2ucWilOBwOAk+FgDbS8VvpfdUhLBIaT7x4cVJWHIAqHHOlpOhKweyk21dOZuWR29LZaoswXn09xSiwlxLJjBgCGskCchKxuuAD4/GIajtMK9BB1lJTqOsUKXC9yEWDWmYIywhKgTQWOWal0HosTJtZZgX7mazdQmWzxtJ3G9QsMxjQGt04dErc3x8xZma7ec9nr35Bv9lx+/IV3mfe/vAgwoPFmWaettV3XVN+Wra7DdN8ZJoOPD7c8eY7x/v3b7l/uGMcj6QcUCrTNKYwsyzOaVyjcY0hBI0xCkpDXYoBcun0LgNmGmMJNqKVwbkH/BwkCk+JcRpJQQqVxsjcC2scKcoDNk4jKQVSjsVgaxrdLsXiynaphWxtYhEopFyfMt1Pn2AVmWqXin6Rgmzo2o1M5qtNVEZDVMuDbkuQoc0Jz89R8g9Zv8GadGL9QFEAENjLlfskgmQatRiehNoskhkisId+Xqzu0m95Mqrm0ykXePrs5OVX5inUcR7hwsfZLE9ZPGfd1gph/eW6vpPRT+n5foCPOan1Z8TAIkKRfFijeG69z0E25w7vU5bzzOw86zj/3HPvn6/j0vGcO41L6zl3Zpe28bHlk52CtXYx7IvhU4Kr2qJpn3Lm+x++Z5xGwjFydXuDc3ZJIavS6Waz4fPPPqNrO1JOvH37lsNeIJwXt7eSJVgrNNWr7UIrFZ2gQErgfSQnSsQvsFLXbQC5sOMwsd8fuLu/Z/bz0sAmNYl2UUjt+w2vXr1aCneHw4HDIIPuKce77TdLl/Tj/T3zNDEcjqTdFXkTmbYj2TkobCXnLFrX7KZlnI7EGHj79h0pRULwIoBXRlFWOYQYpR5y9/ZOIp+c2TQ9ZDAotv2W29tXDOMojJucafoNATjsYX/cc3f/j/zJH/8Fr17/DMOWrv0t333zjpyLHATSXKeVouuFOry72tBve1zvuLm9Ypr3fPXNP/B4eMs33/4Tv/7NPzCOR5SObLc9211Pu9lirMY4ReNAE2isIjpNY2txMzFPA4qIs0q6oW3DdntNCplp9OwfB1IUOZRxnhjHCbIUdz//7AsJDFzDfn/geDgy+okQZ3ycaDcdSWVc3xCiF3G9eSb4GZ8rHTIWzNnQuEYoqNaiVTFuKZQ9zYx+Zg6ReQq0bQ9KuuMXBpKGFEXp1BetJI2WsZ+IfEXORaHVSJd1zWBIVQBbDLY1lrZpyMZKZ3bMkqFOs9AXg5d6lNYY/TRavhT11od/MQQXOteeRuHiKNLZe5cM0poKeimSf/pzes1Yw4Je5dqpLP8+CWNmnharLxvD8+Ovnw0hkLVesr9zCOrS8dfP1CbD0/58yPA6P+fn52etzVQ/L31FYTnm86zkg2uVP3SOzzn8er7O9+X8+C4d+6cuP0k6u8CxEl2XVm1folhj7aIHX+WjZVCKJ3m/qEhWqeCmaZaI2trayWxE/K72P1SaarlY3gdCSOSkJNpOGWtlitU0zSWbkBM2e89xGDgcDszzLEyT0pEaQr0BJP2vgnUxRqZpQmmJbpqmQVtD6xwehUoZiyYrQ3YNfdvSNcI8IlM6oCXKFd0hX2ocAzlH0JV+SOlVMKAyTSOd1ZvjQM6wtwdyRoTvTCoGrQMMISSGcZaO65xpN9d0bcfnX36BfXfP/uHI8ThyPI60bU/XCURXh873m7ZISosRJmeOwyPv33/PN19tubt7w/5wzzjs8UExe0NME0onus7RdpamMTirQWdCmBnGRIjCsNI60/eN6B0ViMB7zzROcp1txhqLT7EMStIC9TnpSZineYnMZTBPyRKNQltNv+1FZr0Um1NOZbb0tFCmpQAuFNf1na4ci+pubSyMSS/DcrIgPURkfKYvcJEymtIqJw+cYEOk4mhrNhBX3fmm1nxWtMlcqbBZ5GH6rockch3Jy1zvOJ2k433wct3Mh9z1S3DHsw9/ZnFcP8U4rI3V2hmcO6KnzuFpJ7T0QZQKP/Ucr2c0PF33uRG+mAWdG7384XFdhM9W79Xgtn7uks7TJaz/OYdzni1UmG2N/5+v59zxnW//0jVeO8SPFdf/JY7hJzmFnCWaqaJi3gvfOsa4COJZY6WNX1GcwiwGoDQpVW16UzqeNdUpNJBFf6bve9GAUQplTvomvhRic6II4ynaVkTVpmlePL9SGj97xmFchO42ul9OpHQ5gzG61BcaDofDsr9SVFYiyaEUjXXSNxATTmsoRea+6ejbTrpR80lJVi5aJMREngQSySRsUyErjbbFWKiMaxu6vpfBRGX+g1K6sG6kl8OV8Y7zHBmOQntMZK6ypm96vvjyS1LW3L175Hgc2e8HXt68out62lLMh0zftSIr7TQcBCI7HB959+4H2tZwd/+Ww/6ecTqiQ2b2ABHr1Emiwmmk9JJK7WBmnkUiW+lM1zeEIFH2cBwIeKZJC2ssg9YGRSIEuYbGSL1oUjN5nsWYGkPTNacGKgPaKjab7sSqKayceRa59LmQHUIhPMQyEhUlmZECCUicwxipiemkiTEUgT0REEwZYpaelNprkHMu2YIqgoWqCNvlpf8l5nK9imS3yhXWUNJcRWGsZDBa07etPITGEtRExDMVBxJKs5PSGh3tk4i0PuDnkhgfRI7n2ULBkZ4ah9pU9jzUsDbia2XWy0bm7L1S25d9O63vZPA+NIqXMoLnDHbO+Qkcev799TGcG/k13fbciJ5H9peykEvw0Pp6nBeE130Iz+3jB2fzwrleX/fzz66zvR9zZM8tP62mgOCijZNW/sf3d0zTLNLAxkjR7+pKHhpd4YCRuWjHg1BA9znzm9/8hs9fv+b29oVIUGT49uuvGY7HovnfLGwYkBsqxTKkJkiEWZvYcq7jIvdLUTl4gQV2ux0Am21fIgPK/OdM1zXUprL9fr9QX/uuE3x9I6yiHBK2DPO56XfQy4O/226FQmmMSDkfjwgZQvZz9rNkCSqjTblBFGA0IQnuHWJYIsrN7oqm3aCVQ2WFRnNz/UImpCXNm7fveP/VD7x7fycF0RAZY8OXv3D8m3/zf+RnP3+D1j13Dwf+w1//Hf/V/+kzrm9u+IM/+H2OwwHRcRKV2Rgz43DPcThyf/+W93ff8P0Pv2GaB2LwKB24ut5ye3tN28mM54eHd8QUGIYD4ySUzskPktEZS1eG+LRutwgRkkrTn09En0k+Mw4yevNwHNHG0W+MFIuPR4ZhwscAfiJmYZhlMsppHI42dVAE8ZJK5OSJUyAmUZi9ud6dCnm6RvAir75pOhojdF/rrETtpa8hZ8kCtdIY5TBOKKhz8DArjJ2WoTz9BnSxZyGEJXuOsdQCiiCesY0Y5pSlYY/SvGUMyhapDYCUmRPM6cTvTyGKOm6MoE/Dq867mc/hgicGIK+MIQpWhdGazefSx3DJgK5luNe9D/VnXSA+ff9k6MUdqmW6m8ymTuS8ngF9mX1T93PNv6/QzgcaR7Bka+v9uQTPrGGjus56fOsC8CUHs3Yk9TtwWU780rU5d1znhn29nvqZ9TU4P471+tfn5tzB/2/mFHLOnAce8ypTqFFN13WLYuU4TwtlrB6sNPXMPD48cL3bkVIs0hh2gaK898QQaZyIxYlInUIpjcpP8c1asKr7UA17jX66rit01mb5XioGeX1R601fm9u6RiAs8omholKmKbUU5xyta6RzOSdyqTnUKVmJSNXPR2WyEr0iZWSYe0iRlCWD8mVAT9+2OGfot1tyBJXANR1GW3JUDKPn3bt77h4O8p2Qub8/sL0Z+PIPf0bMmhcvXhNi4uHxUUaObja8fPmC/C4wzROzn9AJlM74MBDCgPcD4Ml5Ei0qEtYqrNU0rWWz6UkpcDhqYtFVUjkTcyhG8dR9K9pWDdKtqnCuLfWYSE6KEDJ+lil0FGlugKbtmH3AWkuIojt1HI9ieLQmkUSnByq5pdyTtXM3obU49CrvUHV9qry6s5I5iOGVQm6MkbjSphJpEkNWJROKARUku7ROrr21VmQ5agd8DYqr8S2OIFcsvjwvWitUmXMufRZFFPKsI7rejyFEKcCXBq4KpdbPrJdzI/hBSUEtp+1phH6BkvpcdH5pex86hRWz6aKzOjFozp3CjxmxZ2GYC+fg0nfP3zvPfp5b/6XzcCmz+pjx/Vh29bHv/Vgm8WPruOR8fmz5yZkCxQAHpcpwkkhOq9GCO5lb3PU97+7uhDVTZhMYbXi8f2COM2/evGG323F1JZh417Uyh6E4htZJExVa0fdbkSVumpLi18IQpbZQ6w2hqFeGIpbXc3V1JayiXLDtoomUc145ETk05xxXV1ds+06kmkMk+FmYRlnYK13bShNSJyJqKsNhEPntaRhh0ZgRo6CVEmw6RQ7DkSY1tCTmMFPlL7LWKG1omh3WWjZX18RZImuMI2MIMfP2/SO//u23DHMUFQwM33x3R7Ib/tP//L+k7a/4/rt3fPWb33L/sKffbLjabXEaQhp5927i/d33iJJrZpwP+DCBHplDZt4/lK5rzctXt1iX0SbhGkVKemH3xJgEM8/SRR2KpbamoXFdgQINWlm0aglBAoEUNeMQMdqj0PTdFm2ECdS2LSlnDuOR4zQwzSO/++YrgdW2G4wV7H/0k9A7C0WVnMlFq8lay9V290QiuRb9lNJoaxdYc/QTIUd89CShBdG1IklujSbkBEmE9UIpNrtCh7bWkghyL1KTPykIO6NK97vQmquMtBSdNcbZU52hGPdQIZCSJYQoheysZGZHU4b/WHt6XGvwUx3xB1HxhULzesnLZ5++vt6v+u9z7HotPFclqU/bX0XJlJOT1+vXKFVf+JDaeZ71PIepr/dNwcLsOt/ftRNdb2ftfJfhX/Ak+1mv45JO0nq/npz7M4d63uW8hv6eg6guZQkfg7guwVrn2denOoZPdgqAtOcH4dlHWyUA7JPWbRk0IwdjrMFGK/hwSX+7viMUZclxHHl4uBcdJK3ZbbeSLcSIKlizVmYlH2yQziVK6lcvrMzVFXTmFLWcDIKiMmlPqaho4VdqanUeIhEOfp6Zj6PUQkKqQgD4cSJbUbWsc3v9OC0OxhhLVhqRmBZjo7UmI0qyKSe01Ut/gipQ0nGccM1EY0FjGKbAcBy5uz+isoZoubs/chgC3WaHNg5lHDFrHg/ijKxr2Gx3ONcQfTjRYp0hJi8CdXHAGEXTGKyV6Ng1/cKuymU0qjaZeR64v4uEMC3nk4X2K1CYTGST62+1RWVD8iJlET0428po+2iYJk9KHq1maRDcbnHuNPzGp0A/9Li9w0fP3cMDm+DJJNq+hZzx0QsvXQklNKdMCoEG2Y+mEV0trbXoWaVE61wxgoo5CWQ3+RGfAlPwxCLfnTPkVva5IBwkJKtYF37VEuErtDUYpLFSayE6zF66tP1iuHW5z6QzutaeQpVQjnEpdstQH13GcmaRll8ZrrWhqL/PsevlvY85hmq85ZI+MTrr6LK+toasnjNaLHTg6pjEJzwVfpMNnozuh0b1uczhUlSulJLAID8tVJ+v8znDvHYIlzKfSwb+Un/Cj2VX62M7h6nOncH651MhoEuOar2Nn0K1/UnwUYV2pmkiF+aILoJttnQdm9UJrZLQFKPhnKNrO2Y1L9IY+/2Bq+0WrWQgSSjGNfqwsInUghdqjJFbTY67wEdRmo+otcAVXhhCAIVQY1nf8BS9I3moTs1rhjBP+HlmGI6kENGlYJhRBSdPJOukc1shEFqoU9sSSrM0HAEiSZGlT8FYszgEkGltKWcm7xkniaa7xjH5wONh4Pg4kKPC6o6H/cDoI1fNBtd0YsyBcfRkFMZIdmQL9rzcniqTkifEmRAm6bzVisYoUAZtuzKnYh2BZXyY8EEgpTUWKrhqcQraCjRjLAqDykoK5F7om8Y1KJ1JWnEMnnkKGOtxri1UYom+s4Y2CAvLNQ49aw6PBzIJY7V0MGslfRJYMlqw9xKoGGMgu5MQnpLCrwIa50hZOodzkABi9h4fA1OY8EWRVmsrsihtKmKG1TCkZfypDhpjxRCh5PoZZUpTpcVoC6pMyounh98aCZqcsYQcCDks925V+IQPlTIrzFkx77XhOcevnz6wP/I8L6yf0/P9sSi4GvZLEe65U1i+m+VZffpZtTxz6x29dAyXDOGlz6iz99ZG/jxTWDuO9VyDS0b6EuRzvp+XjPaPOYa1htW5AT+P7n/sfFx679y5fwxWu7T8ZKcQY+SwPzBby6btaAqsUyOC+llhJBmsdSJnHCPDMLDZbNhsNstgk2kccQVa+sUvfiEPUwjcvXsvowlXByPwRX1oNCll/OyZk1AeYxLH4JxEY8fjcZGw6Pp2kcsQw6afCH7tdju6roOcuR9FjuPh/R0k6cB12oCqTXgCWdT9sNqQVBIKZJaMapiG0ksgUW7TtOz6HU3XyECZFFBasdluGMaZ4zDy7u4RZ2c+f91zGGfe3z3y7//nv8bPkRfXnzHNgc3mhtuXX9Bvdmx21xzGo2j+BGni2V1d8eLlC5zVfPvd16Qw8/7Ndwzjka5zXF19Rts7ttuWptVA5OH4lnEYOBxKdoOicbbIW3u8t4XKKdGw1HA8kjVQmhMtw3Fm1pGMJsyRmDJGyXCfec74KeNnkdFWGNq2oy8Njvf7B0Cz3V3x6vVntH3L4/GROcy8f3hPNtIg2W06NAaTzckppCg6Q0rx+PiAMyKJvUhNZ4n298PIFBM+Fel0ozHKMs8yo+M4ScbVuJHGNDhjFiltbS0xJSY/E6a5SFwYGnOClJxrsMYJwwgItsxb1rrQljPTOC2jVZUS6qou/SxaadJVwE8TQ7k/U85Ltrt2FhVKWhuZJ9HgGWxz4YGW7+ZTRWFtGOszXLfxHCZ+Ca9+4qxSnTNSXz9NjFt//jzSf26956/p0hhrV3DhGupZP/Pn+1c/Ux3x+qeez3PJCngqV3Eu+bHe9/r6+fbX7KHnJEMuZR5rZ3Upg1m/V/dtndF9arbw6fBRkXImn2h1qpNodGk3h2U8Ytu2bDYbXPCFXjczz15YRUaExPws4w5r8a4rk9NUlTsuqXOqB6pUof8pMloqsUqiqVBmQIMM66mReO1m9uE0Es8YhVJizCgPXZXPlnSU5ffi4BCaamVWZWqkqLDOSBNVsiSlF0puVhKRWWfFGWwlCnZNQ/SS1TjXMvsin41CG8ftq9d4D+NRtH+GcWLTe7Rt6FuHtrbMgzjBZClEmsZxdbXDOUvOkfuH9+ToGaejdBy7jq53WFsNiBQ8rdE4Z2kat0B7xmq0djjraLsOU6CRELzMT865NGE5JHMTWrBWBqWMsLAKdVhmC9QublUm4JkFbkk58/jwiI+emDzWGBm01HcCrZBrzC6/k0SkImWxGkVZf0BqO2Ww0FzqTdM0E5RkfdZYlNKQJaMIURcDIj/GWoxxuFbk4Z0TqfecEt5HMFkMvSlMEFiK4AJVUeg39UGWWpivUX/ONG2zZK31gXVtI89SCMScIcocDxUDOhhcSpgzTn1+guPXYvfpt3xIVFjra/U7i+Mof6++sXRB19/rz5YcQK5J0RGDch7UUydSj12+lVAoycRWhIFzx3Mpur3kFHLNRvSHRvc8Yq7LJUjsY4yltZG95BwvOYT1cml79RjPYbunx/VhJnL+neeykvNtn//9seXTM4WYUUk488EHkpLRhyEl5uNASgnnLC9fvWaz2XL74pbNdov3nv3xyN3dHcf9kU3X46xEy/N4x35/4PHhEWctN9c3pbFIM86lCKwNtnEnj1+GliidiEl4/iFFxnnE+xlQdJsWZcAoQ/QFd84RZWSAeuMsMQbGcZBC8jBweHwUbNrJg9q2Ha5pCd4zF3jClkKhNloyA6uwjaF3HTYEtNPMCXxMKD+is7BH2isZdn99e710x077Mv7TdRiTgBnXdFzfvuBP/+Iv+eGzH7i9+Zr/4X/4dwxjRDnLbnfNZrMDlfFhZpwOjONejNro2fU9v/jyZ/zDX/97xuMjX3/9TzircTbz4uWWtrXE6Bmngbu7Oza+wTqNtqVRTxuOhwMpZdrG0XU9m25D2/bknLi/f+BxGhmPB6wVZdS23ZSxlIHHhyNKaZxt0AUu8SEQgojkJRLGGhH6K0XbKjD3j//0T6QcsFbz4uUNm37DZ69e4/2MD15YX2iZpxyEvRRnaeKT6yvNZBiDdg7bdWVGRuTh/p55njmOI7ZtMI3cf8poklY048AcA0pVZyTjMxvbFGqwDFUKs8ieTONIKs2cTaGdxpxQKULUopGUpKO6WtmqmzRMUp9RWnN1c4XRRno3jBHoNGWy0egUIAWh5UYvxWtYaKkVDtFKU2wtKhVHoJBOa6AGGycnIa8t3qumCimjdV4kXchSvCUJcy7HuBSnlzTESA3P6FM0nFgxecpvcYK1A1p6RrT8IYHN6rOXjNy5oT2PeI054ebnhm8d/a/XV3+f1xrgZGjXmcKailrf+xjkdOkY1hDc2jHU988d0nPF7UvbOc+yfipktF4+2SlsWpGrji7gZ79E4sEHxnGUk16aiULpZA5BJmwNx4HhODAeB4zSzM7hS/ep1lp0Z2bP9z/8sFzAWFNmo6X4jAjpOe3oWoGHVIxklVEGbGOIWU66j8LWQKmlO9k2VgbE9A3OWlI07LYbxkEzTSOPDw9Mw0hbdPNzgrbf0PYZU6JpqZdkGc3pNNlkYo4M0yAcdCNSDq0y2N4xh4lhGtDOgNUoVzquS4OUiEg6jG5pbGCcMofDyN/9zd+js2a72/En/+pf8eaHdzw+7KGwhhqnpJms1RyPET9NfP3rf+L4cMOL2x0qzmx7Q9tmjAloFaQLm4g2oE1GGyXnNSs6I2M/w+yXgr1RAu9prTAapCE7YUhYBV3TorQlRZgmMZTaWowBoxOZINLjqcxL8JPMH+g6bl9c07QNwzSShsg0jczzWOjKGddIZN66FqtFJ6hKc7SmIWuxWLE0M9aBT23X0fQ9zonhJwQSiqQMWVtMI02CTVek0VNmnGda19I2HcY1ONvQNp1E9UGym6qrZLQWCHGVYc5lDocxFhsjWgfm4AuslQqrSIyoqIaWKLh0Y+cEISbmIDWObDW6bWjzFp8SkSJPUupac/CFpu0W+W5dI+VUszAt0+ay5FiKylKyi3GKMSEDnuLTB31lbPRiVMRwq2Vd8lrVvjvVD4phXTKAtBj8Yq4wSp5HVbaVYlq+e6nAeyliP4+oK+GkvreGsc9fPzeUnxJJr43/et/W+7D++5JjuJRZXMqEgCdO49I5WX9//Z3z7Vza9qcsn+wUqrFMyS0DxkFEzKZ5lsae0hATilJqHW4+DaP8jJPMPfDiWECaTiKKEAOPj4/L9q6vr6V4bYyMwvQCFVlnMc5ClOKg0icIJ8Qy+i5HSWklZBLYyJkFIrHGko3IDOSYSEEc16QnfDNLn4Vx2LbFaEXXNtQ2/Rik4xajhMpIZPKT1BYaGS6kXYvLDeM8knRGudLhaoRMmTOkrIpQm0FrhzEtIRw5PB75p3/4J169fMXN9Qt++Xu/oml6/ubxbyjERYxVOKdpG41RmZw8b777jjAd0ekFZE/fWUTtOZNzQHoQhK+vyzmTHhJQ2ZJixs+hNAYqaQaSO4oFC0pJVKuVzHzIyjLOEhjMs6dRBq3WN3Yi+iKSGD2uuWKz7djuROpa7pGZcRKHEKNMZBuHkRQtTdvITASlSEmE5lrbUA2srdGb0iKg2MpAG2sbjLPElMk6SW+ASVjlaLqWrhfa7FwHGXU91jbYtsUWdd1Ussv6OGmlpDu5jM3MyCyLkGTAkQ/CONI6luEy5WotoP2JpikPdDlHJNFbCh4fI1krVGNxusNOo8B1tYAbIz6I4wkhgBYSByAGPoEyCqMtSSvIoo8lO6ALNFaj2lC+9xQyqovY/dWrgvx9aDhXUf4HX1m/voKa9GLQ0lLw5sxor6Plc4dw7hTWxh+ewjTnDW/r9z5lOY++n/veU7jsci3kUuH3OcjopziWNRT13Od/yjF/slO4urpaTnLf96sGm0At6CilOB6PHI9H3rx5AwjD582bN8vrOUsn5DRNy/SqesGGYeDx8ZF5nvmzP/sz0fTfbnl4eGCapuVzXdcxlyHtddiItZZxFBnsitk+iRKMdLFWeIos84C7tmXse775+mtpqkOx2V2x2W6LvHfHy5e3eC9Ca/d374hZ9JdiGSrzuN9LN3cvjXtN29JfveA4HKHw1iXCdIURFYlBJBXkYRVm1q9//Wt++P4dX331hj//13/GX/z5n/Nv/uJ/z8P9I7/7ze9IKXLY77m+3tL3LTe3VwzDAR9G/vEf/he2u57D8XO6TvP5F1f8/Gcvmf3A3d3E4+NbUgrc3r7AWcerl1spzsdIiplxmLi/e1iKdtFHxjAwHUYO1kAWlhUJGifGMyQYhgMxepSCfiNCg0Zr5hgkQyhwiTaavu+4utrSbzqOw8D7u3cMw0AIM9fX14Tg8bPMAp9nuNJXS1QaQsBqw253hYqZHBJTmDEp0W3KtdpuUFbgFacNfg6kmMq9K0y069sruk3PFALeHxiPA9vtFdvdls1mJ5mmDwyHgRCiaHA5B1ZkUVQW1Vtx7IkUA4HMMeaFs9+6VqapARTFVlKNhtUSXT88PBBzZg7CbEo5s9ntcKal326lqxkYD4cl85hKQ6hGicBfI9MBqyy7UMILQyzLbId1P8EaZrkEqVwyvueG7DxiruwopRTankfiUPXIAJw7TRyLKRWJeSMDk9r2g8i3/l0hnPqs18WXsabTNC3QWu1RCSEs9qkuawjnyYS6MxhofU7OX4dTP8P6PKy/cwnzr8SWKsZZt3mJUryuY6zXte6Kfo46e34OP+bMLi0/iX1UV7yu6C8CY+UkTdOJ014vTnUaTdHVr/hcvUHrxdNaF90ftbA+1jdk/XftXvbeo7WM5Kvfs9Yyz6INtAz9RsTq5mnmaI5s+g22cutLf0VTeObyd4MyRiZtZaGLxhAEFivjErMSWEkpaDe9TCzrOqGXThM+y0D5GBO2cdjGorUUgHOOGOvkoU219d7J/g1Dcapv+d3vfsef/elf0HUdv/zlL7h/eGA4HpjnCWMU49gUHB6axrHbNmw2DmczzkBKnhRnUvJ4L1HnNLUoOpwV2eucpEZU8WOUFMijj8TCk/fFiaZakE8J7zwpC3W1aWTmRd+3yw1uC/G/cULRNNagyITgOR4P4sALRp9iKkNrFFrlco6Ev1/uvoXFErwnh0zyRVBPKzYbaXDrNxtCYU8ZI5lhTq3QU2MkMyzwStu2xJTY7XalnqJLVEohGEgR25Q+HG2knpFylWJXgqfbAneW3pqMSJpIBgyVcRDLMeUkIzCzQrSSoBAWJLM11i6zpa07Ka3mzELjjsVZaW1o5akHKBlZwAdTKNRPM4Dnou5zPLsuz8Ep55HpkwJxEq0p+W79/odQy3nBVa2MeYVNnouM1xGy1noRG3wuun7u5xym+THDeSnafs7gnp/P9Wee601Yf+58vZdgtOcgrPVrl67Tjy2f7BSmaVp2vHaMntPics4cj8fltb7vUUqxLRpBIQS+/PJLjDEcDgeGQYbN7Pd7aTIq0tZaa/q+L1opLNuqc27H8SR0Vx1C/T3P87LeYXUCp0l6D/aPj/z8Zz9Hd0Kn1UXj//b2FqUUm82GOQoWnlRm9BP+zi8R33EaAWEatW1DYy2vv/wc1zR0bc+btw/sDw+839+V2RAtn199yWa3LaKCnkygbTeAIviI1oamkc7f4D1d53j79gf+ahz4y7/8z3n96jX/xX/xn/O3f/s3/Po3v+bx8Z5h2BPCSAgzroFf/uJLbm52fP7FS/aP75nmI4fDHd4PzNOBeZLztb+HvN3QWM12c4VWhseHA0YJ3x4QLZ5hOqXfVfW2dJvnJEV+XQxX153ky1PKjNOEUmCNpuvbQg7QpBzY7x84Hg+Co0+zdH2j6NuWnB2xcWggBE+YpY8EhOaLyjw8PBLnQJw8U/S0XcfN9Q27qyv67Yb7/UGMuTZsNhv6TkyTn2d8mKk0oavrK/rNhrbbEEoH8jSNkEtBNiU0UnBvy5CkYZqk8W2esMbSuKZAXJrhOJCVQVuNawVi1VHLRDZgGgZykC7nhDTFJaWlsbNtFiio67ul49k1jUBiTUPOk8wrH4ZF71prw3a7KwQMzRxCQfsSpmkEQl3REesc8hqp10BL1vVh1HkJg6/LJSMlG6rU8dM61w1ta2mZnE8Zi63zJUrQURel1DJPZD2veR0oioDh06j/kgFdO4N1prDe1nMR+zlEVbdTv1dfW//7fB1ruus6sL6UnXzM4J8Xlc+3cwmS+inLJzuFWhRep24gBrttW45HmTk8z/Oyg8fjEWstu91uNUYzLCnQZrNZHMc8z4zjuDiG0zAcuxQod7vdckOP47g4KufcAkPV9dQbcLPZYEuG4YuS5jxPtE1T4KOGtml5fHzEF7ntMXimEGmLwJtrHTK4J9FteplWdnWFtbpMLrsuKaxnf9xz/7jncDjSbXq2V1doLYN/MkYkoTuDV5K5fPXNd/Rdz3az40//9M/4xS9+xd///T9yPAzMk+ef/vEfSTHxl//m3/Du3Rt+99VvRMHUK1wjUzmNkRqDs3qpgViTUdoTvRZBP2XBSAZw3A9Enxk3HqMNw2FknubSiCapfLuSdMhl9vU8zcQokX3b92hroFBs5XppaSIkYuq4zALvZUSRdYoFOgPIq/S59huUrCzFWCibcp/FokPlZw9RHsi27dhsNlxdXdEWme0UpTHtOHvaRuY31DnKbSsF5Gny6OO4QFqhSF6P07Tcz84Yckpst1saY2msZR4H0BBSQBmhVcYyryGmiFVJaMumPOSZ4mwVKUiAo2a/GElVGvK01kJE0LpIawvk4lpRDt5dXzEcpU/i8XGP9562a2nmkWEcRWoeK3WF0hTn6lS4om5cn4f6vKyx6GqYz6P3teFbG5hLRc2TcRLBu5MBrDLyVWNIags551VGUVz/ymifO6P1ttf7Xbd9DrGsP/d0/z7k/sNTllJ97WO8/o/VBtb7fAlhOT+m8335mFG/5HDOv7fOpC5978eWT3YK5xdqjcNVjCwloReu07KqJ1QN/Lpdv0JE1bjXyL/ruuUE1s8BJRJNS+1gnkUuAU7S3ssFLTdhzTBqmjmNo2D6MdI0raSdScaE1nkKo/eMMVCqnGXiXH1QW9rGsbu+KowURdd3ZX7DWOYK+wJXCCslo6VWG4XCZ21HjtLd+/bte652Aa0cn3/+OdfXt+z3B77/7g3T+J43b95we/uC169fs9ltRPI5+QI9iXFCmUILLGqf1qJ10fZHk6IMmXG5QG9zwM9HchRGyjzINTOlEc+Un8a1dOWcx9IxHqMRuMc6KaA7EZqTcYhZmOr5NMjHWQNK8NSYRJtqnny5d1zpNC+NQjGW7vBAyqk0xRU4AuRahSDAjTHYto5VPQ1NqtDiPI5lCNSJcWONLZ3Jnnw44pqGfrvBGoXSGT17gQ+dZCs559JXIywfIY6V8ZjlJyZhwFXnIPCbwIuoUvhVpjRXRZFbKeNB19PoKKyclDOq8PqNMyha+s2GlEQmJea0DPiZvWfyc8HSS5NYyep0Ud9dP+CXDMwl4/scFHH+9weRqZK683rd9S1Zb62j8YFhJ1fV1jNY6Rmn9KQxSz1v9J/s3zPHU9ezrG/1+rnR/RhUdGn9l2Cjc2P+Kcb7ueuy/v75+fopjmC9fLJTqGqj605EOEFJ0zR9MGWoZgVw0iGa5xmlRL3UOUfXdYQQyuSxuNQnamRRb4C1yFmFiI7HI03TMI4jwzAsJ3wYBqbiOGrkVwXujvvDMhf38fGR4GUOcwzltcOe4zwzBqkJ9JuefteTjVA0N7stfdeyvb5CIbzrmCv9EF68fMF2d8VhmDDW4ZoNh+PIYZiYfWK3u+LFi5cYZ4h55K//9u/Ybbe8evGKP/mTf8V2u+Mv//L/wL//X/5XDvsjP7z5npcvb8u4TBlyc/viCms0t7fXTPMozBwttYH37+7oC+02zBC8IgZL2/ToVjONE9M0cTwO+PkoBm8lI+J9JKgEWUm/gbZoVSUftOgcmVJszEithUyqY1hzBlXGUGoFiKzEOI5E78khkGNYUv7GbqT3ZR4Zx5H9YU8ux2N3Zqn5yNzvSNbQtS2btsP1rcz/TpEwSpPXfr8XqHCaCT4yuol8fV1n1jMOE8dx5OtvvmGz2/LL3/+VaDA1os1ljcEahTMNYuXECM9JqM4xR1GHnQLjPJCyMLpSTNhoCSkwB4GXbOOWpj+TBJ5Tk2DtWWVs44oCbBSpixSlqC2nn43rsE2LyaIjNgwj/UZgx8fjgYRIczRNU2oRtkB7MlrUlLreeTFzEbdcLevoGnhSmK7fOc8c1kZtGZdLWo3szeR8chTy2ULeq1IpZT21FreWsD7f77p/aXVcgIhVnkExH8PRz1+r+1/XeQ6frXsULkFMl5xrPa7nahVrKO0867i0XIKMPlaT+OfARnX5ZKdQYR6tdeFNP80UmqYhJZEigFOVvxr42m28PonrVLEa73oh1nhf/fc8z4ujWd/gVY+pZhtriErS+JJSlrTdB88wDLx797bQY+cnxe4Kd8zeY2YjTCdnwRiaRkTwUpHLFlxUMG9rLa9eX6GU5XF/ZA6RyQeOo4x5nKaA1o7rq8xwHDnsjxwPA34KzGNA64bXrz/jP/vP/jO+/uprGeU5DuwPe4bxgFKZzabD2luaxvLy5Q33d3cM4yDwTBVtIxOdwftEjICy6CLH0LSanDU+ZJyrejwytzknwdVzKt3Ks8e6GXIWvaDZL9ecMnc31qgnygjJcsEWSmsMIkExTTO1s7VCK8LTL5BBjHItJo9SGWXXvbksxUStFE1RwLVdi7FWpCOiMHimaWKe/TLUKYZA65rFiXkfGI8D33//PZvjTuC9YhBkeFR9uICcpGEtBkIMjPMoo13TSYuowlOiYXXKHpTW2BL9a6PR0aCTQVuDJqOjdI3nQuteCsO6jP5ULM9W13dM00zbSd2mOtmU5JpXGXDx2xrRVn1qeOpz9NxyKVq9BFU8B1fIH+XvD6L2+lsyh1NEnpdadM6lZpWf6hSdb3+NQjwXYZ9H9pegnvMs55JxvRTJn5+z88+f78clCuxzGcf58hxEdum1/5jLJzuFm5ubJxdlzRbqum6hW1XqaHUUNWOobKE1+6ga+uqVt9vtYtCrEznHEOu2qyOp2cd+vwdY/p1WDimWIlsIscBPE/M083B3R/CitbTZbFCFyTTGgApqob0ejwO5a8jO0fciQ+B9YB4HQvDCZCrTw37vV7/P7uqGd3ePvHn3nt999TWPj0f2h4HjOKG14+WLwPv397x5+57DYWQe7wnhW/7xH3/LH/7hH/Lf/rf/LV/97is2m553797y/v1b3r17g1KJ2xdXNO6Wvm/57LNX/Pa3v+Hd27eLoZpLB7ArIzdDVGjdoE2HdRbnOoyVxjPrhD7aNFbmOnjPw14w6xBTAYNUmUomU9Sks9sQs0htRJVQWq6HDx6UFpmR0u06T7NkdscBa6XvxBpDpLCLsgjCVec8jaNEnRQ+O6oYd7l2xsl5vrq6wrSOkBKPj4+M88Q0zxwrzTbIDG+y9MK0TogA0zjy+PDIb379Gza7LV3f0/YCQW07t9ybqcCcx/1eein8WO4hzxzmMuFNjrtxwqqTLEJ+VFZk3aKsxpS+B03GNA6jIJuIsSKGmEKSXgSoHE6Bvgpjz1mHD5HN1RWb45FU7vMMTH4WxxDFsSxGOZ9EHyukuKZRwoeF1HPY5px6usjZnK2nLooTnLN+ds87c2sdRRWPX9cfz2io50Z5nc08icAVS4PeJUN6KaI+f+28qH4pIzh3JJdeq8sahlrv83p967/r8Zzv3/o763275BD+JdnBevlkp7CuB0ylG7kO/ah4rlkJiDnnuL29XRzFOAo8UCehDcOwwDv1hq2/ay0gZxm1WHsPlDpRYK+uriRaLN+p7IoYoziw1UkaR5HAnkehy3rvJQItUatzrkReidHPZGNom5YXty/oupaXL2+ZpxE/zYyDQQHbtMGXonYIM5t+Q99vubm95cXL1zwehgJlTVjXsLty3L5o6fsNh+PEv/9f/4Yfvv+BfrOh63oJTZNoCf32N7/D2oY/+ZP/HX/1V/8OP8/8wz/8PY+PDzSN4+Zmy3bb8+LlFe/veo6Dw7VCi4zTzP4oHeafvX6NayyusQsGvd32tH3Py1evSjtRJpYaiBgaybZSlGllKCN6PSERY5YmwKYTKZFSdK2aQ7rg6CkhxeQUmCbPPMuPLh3SzlqMTmXWskJraRJvnGW322C0lbGuUfYtp0TwUc6RpUAkET9KUfndu3dLtGybFmsdyUimQFb0XU/XdVxtr4kxo5Xl5z/7Bf12w+tXr7ja7ui6thTLhcr6eP/INA7sHx8IRW6iUke7vsM2luDDwpLrNh1N60r0X2oOKZCwJJ3BaRRGuttzJHtpfMtZGhIlaFbkIqaXlCL5QLRFWNI5kUq5ucE6J0q9SZrjZj8zzpbOC3GgRuE5szw3wBMjc+4c6nJuIM8dwqXlSeS+Wq+IK57j+6v9KP6rMtpySjJ+9KzgWw30GpNfF4Y/wKiebO95J7he/3kGcm60n1vvJbjmOcP+XDaz/mz997q3ZH0Ozvd/7TDXr/9LMohPdgp1IzWKr/S2asTXWGT9adv2CdMh57zUDuZ5Xoz5us+hrsdau2CHdbven+CLdW9CvWmrsF0tPmdY4KRpHEXZ1BgofPQa2egCT1Xd/OrUtlupH3RtS/BzaVaKy/AcuQilcca6xUG6xomhjDLroWlaGgxN25MSPDzs+f77N7x9+5btZoNRwnlPQdLmd+/ei1H/7DO6riWTefPmB1LyYlSdDLoX9lMxrKby3sMqkxJ+vTZaYI8ok9/aMmpUiruJ43CgWua266X4PM0oLfo5MWeBiQBVxOKca2QGQVCikZ1ESr3CUDFEkjrJPgtMdDrngpCIg5XxmtIc1jWt1DG0JsWV6mVx4FqpYuyENjzNk9A0lUAvfbmPEhQZEYUt40KrdPt2s+XFi5d0G8k4miJ6VxejdamRiCNNWYrJ2orkuGscugjoNV0j42kbh7ZV6iWhskzeiwXjTyqTC7xT4SGBNKUAq6gDHNJiRmPRS5JOZAle2r4nZxi6owyWmgM+Skf07D3OgiuZAmSB6dJTFdV1lP0xY1bfv1TkvPS+/LAEGxU2WgL6mhos6zgz2rB0O587pvV+V3jp6Wc+ZCxdWp5zDGs4+jxjOj9P6+M/X/el988dwKWM4MecxPl2PpbJ/EuXT3YKlfEzjuMSvZ+f4LXuezX89cAqO6gWiu/v79lut1xdXXF9fQ3A/f39kg10XYdSamEiVSinDvWp8tg1O6h1hfUNowu8VCGppsx+Fr0VYWoYbRbKaspSR0hakbWh7zqM0YyDMFn6rqdrW/quE2Nexj/mEo0dj0e++/47DsPIN99+x/54ZLPZsrt5hdaO7354y1dffc3f/d3f88MPb4kx4Zw0V2kFQYkR+N3vfsfnn33GF198weeff46fJ7759lv6UmjeHx6Y5wPj+MDd/XumeaDZWJIKZO3JJqKUwXYy8CiETEge72eUvsY4TdM7huPAHGce9wdSEsMdcyYpRcgyfjSWtD6jsG2Da1tc10lBFGmYSlmAJmMEhhrHqejuZJk0pqQPwxhhGuUC6VirGMeJWKBC6xxXuy1aW3LK7Pd7dHHQu822XHuWc32cRiYvNGPXCrbflEAhIllXlVQex5HgBVJqu44/+qM/ott0fPGzL4g5MY0TD4/3NI1lu90wDEPJhmUqnnG2MIoyyYnCqtGGvpfZ37ULzQePRi3NbD5GzDRBwchDlvGfMSeil76JnEHrMoAliHx5jJHBHUkp0/V5mf622W5onCMEz3gcOXJkHAf8NBPnSN/3QuG27UIvhlNQtW4QvWSA1gXbS12/6wJvfZ5P8IgCvTZ4dd2nQF6+U5WH9ULfXUfIa6jrHKKpduZj0fCliH+d8ayzgvU66+fqcs5IWncTr+Hs8/U9By+dO4rnIK31ttdw/bnTOSf9rPf/X+IgPtkpVON6vnPrHV43nlSYqb5eawrGmKU1vd4A62JMvWin92sB+tRwU4289AZIwS3nJJFZFOqeRMhm0dOpDW5N0xBmT06KrNIT2CqLtSIpSd+tlUjW+1lwUAWQnzxg0lchmck0TTw+PBATC/xydXXD1dUNGUP4+nuOx5GH+z0KKfCSFJh1mpk5Hg5M11fkFLi+2jJPhhQ9InVtCGEmJfBxFPZRDmRElVNr2W+tzMKf934uA4FOGL2fPeNYYL0yES4L5lbSeTHqMj+YZfZAHagkTrREwSXiVWIFxJhkiXgNlH2y5fjUov+jtSGmQUTllNBM61ClrIT105Z7ZLPZYKwlBClYSzAgEFGVL1lnnCTJSl3JemISlpm1DmMsL29vaDc9282G4zgwxcA8TYsshHTFB4w1ZYymkTnR5TxpLU7BGF1qN2LMUyxU2lQUfZPUXHStoyWpHiglXc4CfSiZ7a2qHHiGVMZ5ahkjGorWl7HSId50HSEm9DRJM19O6GkU8UdrcMoWyquwfHIuxXokqpbhsie0R6kyQ/qCUTvVBOrn14anGt9ESnWedC2wls+rkj0sUFl5lvWSHz1ZLhnQ+vsSPHLuAD7mLNafP1//pWVt/OHkGNb7ce686ufW2zzfh48tl87/Gr5bb/PcsfzHyBZ+UvPaOuquO1cN5NqY1+hCdG3CAuHUPoTqRNZRSz34+l15yB3WtnSdTE6b57lkIGMpGkeOx2HJLioufjwe5KEtcwckoutESbNpmaA0SAkX3FhL2zTy4GjhbmQFzpqi3DnTOGGOCL4tAnqbTS/Kq0WLaTgeeffuHcM4k5Oiazqurjbsrl7hQ2aeAsNh4vHhyKbvcM6SEkXewZC1XPj94YFx2DHPO16+uGaaHLMfUNphjWL2IylHYvJM80BEpDMgYzRlypcllyE00zSSc6GJKkMMiWGY2D/uGYaBw+FAnZilFjpIucFylhqAMXTF8NYaUk5VSbSQAbQW+YaSPQjfhKLqaU83bIF6rHOEmBi9F/jIWmHPlBu83i9N07C9kvnV+8cHJi8NiCFJb0DXdaKQ2nbSqAaoBG0j0M40SHPeOA5stzv6fsNnn31G23f0241kmXliHCaM8aScmAvVt+9atNFYp4gpE5MiiiI6xorhzyB01SCKsKaOA1UZmEGJqqkUQwVL11oTshemV5ZARHpK0tI742cvsF2FUMkil5IN7aYnxIgZBtGwCrFMQ0TmUZgGqxTaObmvKcSBjHzOQFGLXEX0pxkdcOo6rgy7upyMax1oIw5BKQWxsoxqFFvnn2QhFZQMkpTRWZHzU7dwydBeirCfYOk/AeJa/3udHZ2/fr4v6+New9qXPnMOS51/f52hXHJI69fWyEv97hLAXtj+Oaz0z1k+2SlUMbucM20Rrqq0sTWHue/75UAkkp84HA5LcfrVq1dordntpD2/Oo6chadc6wLSs+DYbjeCyTcidnU4HLi7u1s+W6P1mk3kLLBDNTwvXtzStI6+78g5M84jMYp+kQxQ0aBh9JNMTBtHdHEUSiH0zug5+pGchM7SNA3TOHJ7cyOyBKVJymgRlktJ8/rzn9N2O7r+GuO2HIeJeUq8fv0F/9V/+YLf/eY3HI8HUkgoq2lci24d1skFH45H7t+/w2nQrUWh2XQyMW2OCR9mDsNA0zk6LYOL0KA3fZG/zgzHg8wCnmVOhNYGsmYaPY8Ph0V8cD3y0RVBO61FXjnGiC4zKK6KTpBzToqvRKIINojmj5ww6XTOdV6EQC01qgRVonUjszKslel8BaLyPhCUZH1omYHs2qZw/rUUtlMiK2lmVMagCp25aVug6EklaFxD17YcjSWZWKSmpf7inEErkb/YbXds+g1aKaZ5EnXbXBVMZ0wWOCpFUUAdxxGF5qg1fbdBK12aNiWwsVb0ikR6QR7WpmnLsJ5y7NbICNeibloHBkmXt4yxDUpgozyJZpO1llTCe+0MrmvodxvJOnwgzbJ/x+OBRhlSG2mq+JpesblKE1lWVdKacm3kRwzLKTMQg5MLDJTODB4U1y9/13UvdkmhlMCGaAVR6kNZXYjY656sIJk1tXwdNcNTCOVSZvHcv+trl76zhs9qdnsetCqlFrblel3nGcO5cb5UDD436OvlPJu55EQuOcKPZT6fsvykmkLdYG30WOOO5yfyfK7s+Y5Xb1c7k9dZxvqGcO6p4J5SLGymGCPb7bbQYtsCH1XhMcG5rRWNGufcAmHlmBa+e31YKlww+xmDLfWFwlEvsgvkJDBWlIL0OE1kIJXIGCSjymisbYoMw5asWuZZHvjd9ortZxv2D3vI8Pj4sBREde0mLg9hShGjskhZKIW1WoyZlbnH4ywT04w1mAyQUUg2k2ImRZk3YK3BWSfzgxE67fEwMI4TIXi0XkeE9abKhYoaijMUA++sLfMoErqIn0kkWq6tVlh3ygrqdV5DEOtGIescTTyp7Up9Ii1T2lCqnGPZv3mWBjJgGdSj3UlqI5Xrnk2SfS1G2BhdHJHkQhIYQA6B3e4K6xzzPPF4VBynYzkYGY6TURglxedTEBSEZqrkWGKKBcaU/guSIirxjTkjTWwWFE72RVuCsUJhLSyklHIJLiSzSllcbogRW8+FKsVcLQOfmq5DZQhzYGZCpbxk5UbrokJqZaxOeX5kKU4gSxS/jqbXdQD5u+Z85ZvL87w2WidoKS//58n7yzqes1kro7o2oOcR/gf2pHz3OUO//vvc0F4yyjV7WAtynh/7kz6o1bqecwj1mM4ziEvb/tT9vHwK/2UOAX6CU3j79u1ysrbb7SJcV2cnVEO/3W6XB+fu7g6A169fL86iFqorHKW1XnSTqvetOH+MgaYR7aQKM9WOVYnILH/wB3+wFKvfvn3L4+Oj1DKQLtBXr16x3Wywxsr0t+MRhXTF3r54ITMbtObt27dLl3MRLJCO3CxMmt12w2bT8fu/+n1sgZHu7u54/+4d1lUlTct4nNBm5ue/SihlaNuemCzWBtq24+bmll/+4hf0Xc93333H//P/8X9nLCk/KhR56WtevnjJ69cv+OG7r6UmgER1Vmte3L4kJI9ttegzGc3D3b0YuizFUFSmaSz9ZsvN9Q1kYTe9efOOw/GBt+/fLcY/RqFbGqMXpdPheGScBg77PTF6QvS8fHmLquNHjZMRpIVNVmnExhhubm4AubGPx+OSLVY2WoWgtNbc3t4WyO+43NT1PqsMtMPhsECU79+9oes7NttNYQ6VbKE8oN4HksoYLdpa282Wx7sHKJmMtoYUI998/XVh/ij+9b/+c16/esV2t+X7H77n3fs3OGNJKB4f72VaXNdgS3ObMXbpos9Z0TYt19fXxCiy6KdoUxNzlNdKrcSWPovGNRhl8LNnOAzMQYgbNYDpmhYdPD4FxiQOzGizGucp3f9926NvMylEHt7dMxyOHA9HGWCUEl3T0nW9TAys+6VPzpbCUFob4vNi6uWu3KdGSy/rTEtd6twArjuUn8PAa0/Mk9fK52vguG4yBXGQiuehmHPnULd53ptQl+oQKky6PifrLKXu51pU8JJTWO//Gq66tH/rz1+Cti6t+6fUSD5l+WSnUKP1Kj5njDmpkQ7D8l6FkmqtIee8sJBqDaHueK0BAItG0dOb5TRmrxqFSjnNudA/C6upGp/ap5CSNARVvNnqk0yGNXUC2okxVZ2StVaai5QWrZucMTrSth1dt1ki3JTh6uqazWbDNI6M08T9w3uM29C2Dcf9gDVHmvbI/uB59/6B3/72dzw+7FFK07Qdn332OX/0x/+JNKe9f8vtzY7GOm6ub2jbTuQOliJTlfc4sr1pUQqsVYQwMc+JEGdyLgOHrCXrLJo80TNNQ8kgQOmMsZq2axa4J5WoWSmWhrbUySjSydkCL1XnLxBCLlBdbVBcX7d1lFevb3UI1trlOq2ZZGst/OoMKuOsijFKIV0vDsBUHneFMWukq0WiwzqLKRTcCoMIzVRk0LXSGCv7T2ZpkFNLVnDKPCGTG7tIPDvniqZUGW25QCinh7lpWrQOGC20UslS1NNiflqzV1gymXWN7vQTUVmcm9FGskulMBg0mqZxxNBIU10SIsA0T1KIz42I9SmB1wTeqzBR/V37G6pBr5BRhZZYruuHhV1FhQdr8XkNvayXen6eGC91Gl607mT+Mcz9Y6+t9/fH3n/u2M5fOzfMl977sf2p7z+H/69hqHPneWnfztf5/zensNlslu7l2gdwf3+/aBBVimh1CpWSmpKwgeCkqFojhtp9XI37drtd4KN19rFumAul+7hGktbaJdI8HA6EEHjx4gUhCoe976VxyZQO0RhjkczWpZs0LNlO1V5S2sis3+IUgg/0mw2bzbakuHL7v3j5gsY1fPfttxyHkR9+eMPLVz+jaRQPD3syDm03fPf9Hd9+94a/+Zu/5fb2BdPs+fN//a/5/Mtr/vVf/Dn/0//4b/nbv/lrbq53tG3PyxevcEYtRipHwbenaUDtIy/CFcpIUXl/ODJO40KvbRqhuMrIz8TsJ/I+UUcxKp1wzrDZdEu/QIwn6ZAqfaGNlvkLs8hexBgYxqOI2gWPa0USu2maRdm2Tts7HA7LDVthoXrNlFJLENG2LV3XPZnJUWsDeZUhTNO0DO+xRZ9IKWErGa1JSi1d61oLO6rrOpx1aC0T9owWts3jLBTWnEG7hrYR55ijjAUN3mOUYg4lUCjaTt7P5NwtTCdQaCWPj9SyVsauQIFtmeQmAoxpEferYo2hyGc8wckpU91KsBILZJV0XCJkoxXZ2gW3M6X3oWkb+Y6PpEkK5uM00rQNmbSI7+UUa8lnZUhk6xVWWkNE1TGsoZN6fctayp7zBBqq615HyM9BIAoWme9LBd/niqc1eHwOuvkxCOnpep53AOs6x/l3LkFd6+P/EJq7TFu9dF4+dtzr717qfP7nLp/sFF6+fLk4BThNPLqUGoIcXGUaHQ4HjDFL96cxZpHNzjkvcFTXdTw8PCyFaa1P6/DeczweF8ihnthaLD0cDktkaYzBNaLOCjBPE1OhX1aNGwVFEM8vhkyMoqPbbmk3G7TRxCBSB9vtjn7TczgORadn5uc//zlffPE5x+OR9/f33N8/cvvy56AMf/+P/0Tfv+X2xQNfff0D3795x7v7B97dP/D1N9+iteaXv/g5f/xHf8zd+3f89je/5vd++Us+++yVMK3GI+OwF70frcqA+4mYJt6/e4t2Gp8mHh7uORwOi3FN5KV+kKNMPns/vMcoGcqy6TcoDV1/YoE5ZxfDYIw4CqNE/qLrGmKIaKMI0ZNGYX9t2Akk4WT0pXWO4ygwyDCOcuOWErTVMsQoJelEHqZRDLvWIo+NJqSIdY62l+Yy0Q9ShPL5m9sbur7n+vaq3JF5kXrISjGX62jt6UE+DkcZAVs61BVwf3fHNAz0/Za+63j18iXzNPPDDz/w29/9lnEWwsF0HJj9tAzo8dEX+Q1Fs2lkiJFLHPZHgX7iowzGMXaJ+lOQ5sUa5HjtMUbjbMAayzwKTfawPywKuqMZC204MSdPyIlsNCkFUopstrsCsZZsIiRCDNIAaOR8ahTHtCf4wHEcaNqWzs90fUcG5uAFSqvzpleL4vnI+ZJxfGJoq0ErWUZ1Aku9KF1qOjsZf6NlXvslY3cO49TXci5spguZ6iXY6DkYZl3HrJ9ZWHYluFkLdF7KjtcZTv3MenDQuXOs56Rur76+ZuqtbWzd3jlF9bnz889dPn1GcxlwU6mf652o6X+N3Ot7FVqA06CcWrypGYMxht1ut7x3OByWk7c+cRXiqZh0PcnrZrraZV2Nf81ofAjs9/sTjKWkU1dktMNywQFcLX42DdpYog74Zl4mcFUHJdx+adiqGG1CWvtDTDw+PDKOER80P/zwhvd3D1jXyKCf/Z7vvv+OpnH8J//JH3F9fcXPfvYlr16/4vrqihgC01SUX5cKR8VbxTnopPDJM08z0zwVQbZcCue6jIOUPoXZz2jkdVd47jllQhRKZNs0C7dcBC4FQqiyDiiWjt+UIzGAD56sFU0jkW+IkvXN3pfJZEjxT5cuX7I0bNVmKAoDZX2jrymLlA7t8gBoY4SN1vf4II14iyqr1qcBQPmEO+fSKb3tNyXClmwhp7TqldDSnzBm7u/uCUlmIocyEc44RVKqdlesfso+RqkZpJhoMmglEtlkEd8LMYrESoHAfNOILlJp4KrqvDkpVFYE58m5qM+mSFQZlfVi+Co9VLKTYpRWrCFtDMYlmeQWFb5Ihc/ey7jOErjl1fcERspQDWZNI1bHuWQQK5bSsqhKa2Wh6K6X56CQ9d+qruAjy3MZw3MQzvlnLr12CWpZO7+PvX/pZzmWs9/nmc96veeZxCWoaL3uS07tueM8386nLD9J+yjnvAywCSEsTuD6+hqlpGv5xYsXy+s5Sz2haRqur6+5urpavO2LFy8WaKcK6t3f3xdZ5+PiNdfSFmsZ4ApV7Pd7tJZJbfM8L9//8mc/48uf/0wKgseB3/3udwsWLCl04uFwWIwDnCQ6QJFkrDKUGQpKa0LMzLPnOBx5/+4d33zzLUop9gehft7c3DLNnvD+nv3+iA9Hvvr6LW/fPxBS5s///M84HA68f/eO//DX/4Hf/u43fPHFa168uOW/+W/+b7RW4aeR3/3mHxmHA9M4sOktxoB1FF0YRfCB5CPH6cA0jMQ5sA+PWOeERtoknA3SkV0YS5UueffwnlgmvIUgXbKvXr4qjV5uwXSHYRCBO63o+43UfFwjzneeOY4DLkVs3/G43xNC4O3bt4Ux5ha4RxUm2sPj4xJJNUU23TWNNHVpTUhJJpvd3TFW2HEcCSmhrcW1Ld1mw6vXr3i4v+fuTorXKIWpLLISJYUQeP9ejpME7guBnDSiu9Q2LeSMn2Ye7u7JQIiRd2/f4oMn5UDbNzTOSo2m3DdNKxBXmEX1dRhkAmCMCYViu80YZZeRtH4OYoyDx5eAxllHchlnEjmeYNYwFxVYiqrw7IkqSRe1kYE7xhhSDKSoUdpitAKrIJUouPgrpTWmsSQSaUocp4H0kMBI70brah2o9FpIFaNkNWpZx1L/g8KukxG0MYt7kOdS5FSsLZPEzNMu3/MIuj5na8O4RPGrCHht+OpzuQ4W14YbIKyyg+ei5uegq/rZ8+xkvb31flf7V434OS32HCpbG/RLWca54b4kbV7fq+djfd7Wy3k95lMdwXr5yYJ4FaLJOS8MpFpPqBevMojWP/U79aLXmkAtMNb6Qo36u076CmpXdE3Ba1ZSP1tZLX3f0/f9wnBS6kQba5qG7Xa7pGYSmQlLwpXmqJplWCOnJKZEjgFKAU6IknlpAIox8bjf8+79HY+PBybvaRoplKcsBW4dEnnKMlBeG37vV78nKf3xwF/9u/+ReZ74p1//A19+/hlffv6a6TAxDUfu3r8DkgjMrSLSxlaeuxVZhSwdq8Y4MRyuoes35JjxIaJnOaa260tRMzEOIwHRy4kpiBEqGQUhL58Lwcs+lAfeGA0ql6a5iMIKPFSuyzAMS+1oneWZct3rfVNhxXrt1yl6zfbWhqEqhbZti7OOaZqWn5iSYOnLgyn0yYVXUzqMD4cDjbN0Ttg6XGVUmXMQvMcXee8UowwGcm1RmS1KnrpSgt3SvxHCiSShlZJaxhIMnZ6ZKhaYcxb9pXrcxoIWiMkYQzZlRGehU6dU+j9UFikISmd4ZcVoQ51wlmMm6wzJipCe1thGnkmUwkdPGhPNUSBDvdVLL0rKmVoqz+VaoRXmQtS7/K0VOn84zD7nLOSIs8zgfD3PrRd1IpZ88N5qOY+URefqZMDPi9gfqyusDXb97vl26vvnBh0uM5g+lgU8tzznJJ773KXt/Njn/6NnCmvtorqBOvGqbdslOqqOYf2zxsLWNK7qQOp7NfqvGGTNTNZy2jHGD5xCNfZVNTWlVDR/RNyuaZulvqCU4v1R5ijUbtndbicqqfVGQSJHXYxLLf5lWYE4iJzZ7w8YY3l43DNN0n2bhCVP1/don0h4tkqURX/5y18uGONXv/sN33//Lb/+9T+iCFztWubDA+Nhz93dOzabju2mEwYUZS5A72hcszguhUhPGCtibU3bsdnsOB4OMv84Q+Mcm6LCmmLC+wAF4qlGNatETJmUOHWspxoFKemD0Loof4ozsbkRxk4UCK/Wgeq10MagC224ssdqVFWhxOoU6v1UnUI1tvW+qwVpYy3jMDCOwnoLRZJCF0hMaYk0a9kzFbhqv38Uaua1Y9N1tNYSEax7GOeFxJBTxjgZ0ak0qMLgqj0BxkomtW6yVKjSse2wtrKoxEFprZ8ALbbMej53CjLyFFSFcIpTSEpYTyqXOo9WJ6qk0dIDoTNJR3LMMnXNyGhT75xkSlrhQ2BK8xLYSQHcYLPofmX11BAqpDO9Pi+XDOHlmsJlKGe9jvMMYVkuvH9puQStkMUprOmbH8sKLq3z0nYv1QDW3cT19XPK7nkmsHYKP3Z8P2U5N/jntZQ1bPVTMoaflCksD0I5sL7vaduWzWbD3d2dPGQrSey6Y5XdU/9d6wS1OFzfv7+/F25113F7eytQSZF1Tikt8E91ULlE5FUw78WLFzjnuLu7Q2nN4+MjV1dXOOf41a9+tQj6vXvzlhAC282G7XbL7e2t7Iv3HI+DpO4+FhaOoW0sMWVy4ZznlNHGcDgeCTHw+PBILhlFrXn12448eNIwMo4DwzjyT7/+B66udlxdXfPHf/KHvPrshn/8h7/jcX/Pw+M7/OGRFD2vXt/w6uULXr645e7dG6ZxYPSBzrXSRb1pBZIojVV4g2kcu+2Oz15/zvfpe0IQrNhax3Z7JZE+ohPUHqSeIzi3jBP1ZZqdL5lZdT7WGpTKJCQyDtETU5AhOFYt/cwJ6RA/OVB5bfKzUIM3IhynlS6T6mKpKZQbvNQd5iB6P8YamXPQNLRdR1Yw+5n9wz3D4SAwJgJzqElmKcjDAZRms5wyOWamcWRuWprSGGiUZtv3ZKDvS9QfI5vSHWwbwzgdpbBOFFXYpqEOxAnTjM6KvulwTthLMWVhteUqCS1zrp2ztKnlOBzRShFiQAcpWDvlFmg1zKJ7JGq5kdmYUt8oMySKmGPbONrGoY1dWEwxaLJOlHKQnNSUMUZzOB6YxoyfZEhQJtMNLY1zJGulMVIX4byMXD0N8r+TS1vbscq2ykt9t9Sgklo6lauh+qkGsBrZc0bPurh67mxqPeWS46mf+zGjeMmQX+q1OHcKa4LNeU3jHD57bg7Fc8slR3MJknrOAZ8X5T91+WSnUHekNpidR311B9YaHfUz1ajXyL3u8CJrXSK1tRLqqUh9qsrXyL72KdRspWKflcFUi8FTKUZaY+g22+XiWWuJpbB9ntHkku5HhEEiPcAyEL4WPnPOkonEAFPVrBdDJphshTNmZj9zPBzwIfHV11/x4vaWGAO7qy3GwFdfOZlLEEMR6FP0256r6y3X1zuO+3tikCKl0CFbrGukyK0MIj+tyQVQFomEErEqhbXNUsSHjHMNrgk415RrBNYaYhR4CEWJfitNMVMb5yqUUrn0NboHFlbZ+trDKdqqWWV1RotBK0HG+pGthebqlGtgkcrvUHthckaV9dSJXSFEyNLDoCnXVBu8NszThNWGbAwdMj/bOSOjQZNANRkpsIvwXhUZlCJ9SGWOdAhoJV3iXduilJapdEvBVs6V1gaLQhmNmWRW9brvIBdF0aZpsDqTYpZOaB1KuipwnYgxnkrdlGuh4DTjWZdrJpQCXCM9Q9ZZQjDoIFmL0JT98v24ZJ3FF6hSVFenZ379+6lxKb0V1VAtfQoffm9tQ87/luyED+6D9ecuGd26P0qpMkP9qTH8l0bkz0FM678/FpV/bH8/Zbs/ZTl3gj8lUzpfPtkp1NQ+lpT9fOPrekH9uxrvKkkRQlikhmtz0jRN7Pf75f1q2KWRTeTnq+G5vpZmsRcvXrDf7xkL9bFCFrW20LYt9w/3HN8Kzp1T4mp3teDTt7e3MuGrRCRVJrlOZwtAXBp7iuzwPEsGkQIhJYyVITA+B5TRWFOE8QKkLAXh2U+8e/uG3/72Gx72R/79f/gbPvvsNb/3q5/zf/k//5e8fv2C9+9/YNM5UJntrqOzhtuba17c3HBzfcX+4T1aZ7RJbDcb+n5TDHcgJVUgn0QIE1pbHh/3WOu4vr5ZJCm0dcWIpzI3uqXrN2LklVBSJQtqFkE5q0WcLUSPsRqjDE0r+1nVYr33TMNM27bc3t6KJlIIPD4+Ls7fe49zjuvr66U58ZtvvlkoxvV+qg/CEwmMQlaoneZKKVQSQw0lAFEKSrbpQxT2WukJ6NqOrunY9htiCDzu9zLvWGuMseyurnj14iXGObKC7l3LOI8cx+MiM2HqrOmchdo8e46HQWZt9D3b7a7syyPAAn0qVVhgWqNVGS+ZxWkphPWl7Ul+myRpU04QfWCyE2aSekDWAiPFWBh2WmNznYwn11UKzVL0ddqSszDvuk2/nMvi8zkOR3JqockYJVMD1UJkKHVB9bSDdx3tXjL4YthPzmS9nBdI6/rOo97nvnfJsK3hHKM1Tp8CkvX3P7b+upwXhT/myNbbrf++9Nm1HXwOnvqpS13HJadz7qwu7dunLp/sFGKBTaQjsxTxkOlPOSOiZrm0iJehJhVPDiGWsYweawLJys3lfcT7wDTNC1XRmESIkXGcirEoOj6JMg+3wWiLLj9G2yIX7JZ1WSsCclppHh/37B/3Sw3BWrt0uI7jyDAO+DATokAIc5hJWpNKIbIm0D4EpnkqBS1o+41Eo+VBXCSftTiFWoT1KREBZQzXV1ckEt988y3ffPM1r1694PWrW/rWsetbdBzLwB3FNI3c34sxcNaQ+66Iy8E4z4zTzOQDMWVQhhgCwzDz5s17rq6v6LuOrm0B0VCqUW517BXiUGRyTBhl6BrpQakYrU9eovIkBqNrOxTCVgkxE1Mg+oTte/q2Xcap5ij1ipwSWWucMTTW0hVigqYEGUWq2hgjQ+uVorEWZ0SQ0CipC4Q5EItU+aZrFzaQyFAXyMFYrNK0bbeIyznrimM0GK2kczsDSXM8HNDKsNuOmBhB6dIoKOMxSZSYWwx1SokwBcI0k3yECFZZDMLsmoZZsjNnJWovwnZZQULjXAu5KgPYAtnY0tAmUT6opaCvo5YfpBhMhugj8zhJYblxUizXusxygJI7kLUCa9CNxXUtlVqcgwxDyj4iHAaZjR0Br0A5h0Jj9dNMYQ2lPI3uVbmP6l1RjFap5zwxykoR86morUqWt16nyoWBdGZMl6N7JlKXps0TTX09A6J+dr2s92tdh1j/Pt/G+r1zo7s+R8u5WR/XP9M4X8pQ1ss50+j8c/+cjAN+glNYdN6LU6hFRpmOlRfPKQU3u4in1dm+U54JPuJtUY7MihgS3kdmL4JzqjA7dBThs6pVH4scgEAh0nGs689SsJbO0WmaS6FPjN5hX7piyyjQ6+vrQksV5dchePIYoRSPfQygnBwnuQiQyYD0yXvIYmw2vXRJa63puq5kE6EYAekuiDkVHXyFtpbbFzccDwd+ePOGb7/7Fq0SP//yNV1j6RvLfJCHlpwZh5FpHCS6d4bWilOo2Po0T8zek7NCKZHJnqbAu3f3bDZbyQa6TjqRh4FQJq/5wr+XB7g6BZFOaF2z9HAE78tQmlxo65qm6RAFzIz3ItecgqiPdk3DVXEK8zQxF9JANgZbHENbmGYqS+F8GkcocB4FQrDGiBE3GqOU0DYXXSzNru9OFEh/0tHRRmZNdF0RLPSexjka53CFjppDJGeIJIbjgNaG43HAOrn3UkjLDwkx7EXyIhen4KdA8qLCapRBY0g54iePxuAspQBMCaAE9nFGsqS2DBsS51gCrGI0K/RhlMYEhfYylyFHKfznEAnjTA5lQJSxZb5CjcAVda42RqGswXUCTxqjSVMgBxmtqsiQEjGFE1xntbDRCla4GLsK1SnQS69C3eLJGQjEpRcYSVhXxUCtOhw04gCWGogqBrkW2FcGdtnOBYdQlzUD8rz2UI3zc6yi89cuGfJLNYlLRd7zfT53MOvXf6zO8dx76xrCJef2Y9//lOXTx3GW/7QqjU9J2vRrMZMsvzdFtbRpGpmopQKNa5msR+tpYRi1bSfjE4vUdih1B8G/LdraBUc1Bffsug7XSDGzSgjUBjWgFJIn5tmLXIE2UAzQ/f09gqmX7yiIWSQbQmnqkZMp6Tg5kUjovOoezKIwKpLS0jksFMEG76VBDm3IaKaoeHg88LDfY11D02354mc/593bH3jc3/PVV78jzAN//Ac/o7UCMbx98wY/TfRdS06BnAOvX72ga1u2u36pwzw+7hnmiZRhd31D2/eMo3SOv3n7HcMw0w3zIg192O/FsIFMu9IaYxzWSkQd5hMvWuvCIkm1CnyqVyhlyo8lpREyYuiRoUJGa5S19G1L9B6fEjpn6awuMJ5SRWYhBClqF/jIlA5nrTWttRir6duWUDIHgQEz43AElVE64xojRscYqa9oQ98KnNAtDkEzPD6SQlG5VVLkfXx85HAcZOCREbbUZrsBIKXAfJyLgqzQREOMjI8yEEgnhVWOxrRYbRcHIni+YX84Apl58qBlv7SrVNR2gStiOM0hMapQTp3IjzQ0cv/NGj/p5RmMPkhmlxW4LLlM0TQiS/buCYQia66sKYOOCq1aK5iFku1nUQGuek65qNNa504Yf7nvxXudNJKUWkpQJVMof6eV08jyv6xOGUVGEhudM0V9CR0BpcnEJ1kCPGX4XKK255xxxj55r/7+sUj9x6LqS1DMp8JLlwrAH6uLPAeTXVqeW896Xf+S5ZOdQlYVv0QaakqmsBa1EyXIk+RFAMgyPKVyuEW/X9gKEuVb2q7DhEAIq8HjmQWCqQNgcoFCKqc8ZxkAk8uJmudZOkTziet+anwRaWMpdutFU6aeyHrDZp4O8K7pqKLcoGXfYow448oDZUmpFMSNRG5jKYhO00xCoJDagau0ksanYaBtG4xKJD/j/cw8T1irSXEmlalxqsznRQmMJ8cvekabzZbd9Q1dJ6Jrd/fviTEzTTNGy/SueZ6Xh9JZS84aZYWimoklOSmzE8o8YHnITmJvMkKxNjidCozGSJQfvCfW4UnFQNWbM5UpYpJpUgqxp4YkuVZgTF4K2IpT8doacyIlzFPJFCrPvqTRWkPKYE66Q20jWcL4+Fj2J1FyJNE/Uh7voxjtpZYhXcOx6B4lJYZS4DfJliv8Uxsdq6GU0a5W7o+UJGu1Dm0yrXNlnsKpcawKEaaUhAKbT7U6U3pSVAbiyriUh7AaawVF2LBG5gWCoXSIK6QAbTTZmKKjlQv9WjIxlU4jH401kv0rLQHCspwK3dURPC05sziP098s9105sPqSkBfWvkPlcmVWW7wAFcGHcE3KH0boH6tHPLf8lMj90rrX+3mpAP1j3z3/93MZzceyiOfW91OWn8Y+ggXfrDdR7S5GsTCNKtuo4tdNI0NuUhbRsVjGZzZNS9t2WGdkmtk4LNuqQ8+9n5ZC8zTJgJxxPA0h32w2y0WIMTD76UnPhAznUYQgOvahSCRUKMVa6WOoxqkavZSj0AsNeCU0yVooTTERg/RLbPsNu93uJFbmWjKa8c0dMSb2jwdM0xMjfP31N3g/orVmGCbG0XN9fcU8Hng8PhCTJxPQuhigwjyxRtO1LdPsC0vLkzJs+i2fvf6S159/TkyKd+/ecjgciDHxcL9nPGpyGcfZNK50EXcFtlCorCVaDTPTFBiGowxzMYarqyuUMmjtSFkRY+Z4nPC+zOdGnH/btsSYOB4H9vuDwH1VzC0KLJdzYBylRyKlWAr+jhi7QkIIJUM7zeio17YaXtnGgfuH9/R9h7Vd4d0nprEwhZTG2YRuivhi09CUInLMqRhLgcP2h4P4EONQRhhbdVCTUqdeBLRErzFLxqC0pus76TC2wkpLKeEatxAdDsNA9lIgtwWma6yjbzuudzsJPpJko5VsITWwAuMYmZOhlSY20qyYotSEUoHAKqZvtIydRSkSaZESCVlUXmtgZLRM3IsxMKdYOGsnI6xKbUdrTWj98vzUZdFKKoV3oR7nkp0UB1UdVrEVmKL/o9SSLdRihSp2RNLXTOUm1yLuOlNYswRPgcapdrAez7vOIH4smv5UJtClz6y/f6lvY/2dNaz0pCbzI47hxwrw6+/9S4vL6+WTncIwjszzxFgUK7VWRQjNYhu3RHL7/Z6maZjneZHEaNsWYy1d32PcIxSMeC4dt7EUBld+jlSG29ThPrVTWT5fDQ5Lw1T9exmkU6KW2tUsk96kLX9/2AtmHsUpOGdLtlMuVCluh3wSrLIlkuy7DoXCGst2s6FrO3bbDWTY9j397pasDIcx4+z3THPEEcj5NFtaKXCN6AbF6LFWs9tuaBuLVYlXr24Jsyd4GRxfQqslQuyaDrSm6bfST6CF3WO05eEXD7x794bjcU8MMmjFuVY+5yyNFbgtxVxSdi0MppiY5oBSImnd9TJBSxuZnJayZvaBeQ74uUS4uoqdabROSyOhzOQuQ2i0wE7WOunxKFG19E9Iyi+SKWX4TBklutSMtOZ0/6uFwGCtxQe5D4ZhEBA/K1KTSD5Aivjdlk3XQXFggeqsxHgaLWqqumDzzjkyGR9mgV2MErw+ijFt2ha0Znd1TVuEIWMpqle6dJXDQMlxtV1H2/Yy3W3T03d92YcgWVtK2EJYgKcGpPZ1qFayrRxPmk5amdJpXjucNaiIKgqoKhbnIadNovQ6OjQFKHDbEq2n05wCCcLUwiCTZ4KapixZvMzxptyc1cafQTbrCJ9Kc5ZVZQorKcsNrnJeZNHP+xQuGfp15rAeCbyeFf/c8lMcwsdqEOcZwfl3n/vec+t47vvPObfz9/+l0BH8lMlr81QoeRPowrwoLJEqHRFiJJXmNT97plkMtojLaVrdLNlECAGUZASV353TaepTLBe3aifVm7XKaFfIKpa6BrC6KcLyuz40dRxnKjOXpSeiRiMGpSIlC1/YJiAFxpwSpuvKOMcyU6LrS3OSo2vLqEs2XL/4DKUdv/36DcY0BJ8xRbmzbRtiAJU9bWuwTjP7idZqmtZhjEJjuLm+IvgZP8+i2VODrOIYRN7b0jYdRjsUhu1mh1Kaz15/zmG/F5pvCLTO4Xop0DtbunJTJqdIrRekBDFmQqjpuFr+VsqUtF9eq8QAaw06UYbKZHLWDMMoBcrCOMvFItVCoFw/mVdQJ6HJ9/1yjYWSaqlsruoRBbLKwkoqjmMu0uJ+LgX3XNg6IRL9jE6JHIJcV61LT1cundwUkb0GY93iFEIUiE9bI/UHZaDIZ7uy3c1mU+SzpfM9lS57U6QxrLEkW3Semoa+69h0HV3b0TiHR4xwZa9JRP4UEpHeBC2ChE6ufU6nEbgq13OlF6eQAaLUFVjJ5ywGsETsoicljKycSx0gFyZSEYgUx22LLLcu959a7sHqAHIx7jVwWRuzc5tYKlPyjwJ/nYz8qcvhHJN/AhetYLMFwsqnTvzzesPT/bkcmf9LDOml6H7tTJ5jLl0qDn9qpvAxZ/NTIbNLyyc7hff3d8zzzPF45Fpd03WKbdfh2pa2axmGgRgjx8NBcHalGSfBf501ZeCJISVRAD0ej4XVkQulU9Ls7XYjfQrWocmE1vHixS2bzRZjxPAMw4FxPJbi7uPiAMSwVAgiLhGZwEciHSxSGNKsNQxHDgcIwZOScN6NcZgkD4+wITI+zhg0Fs3uSrqgP//scw6HR+Z54uH+vTRC2ZYXrzRt39P3MmwHBX/wB7/PH/zhH/Lf/ff/Pb/77a/5t//2/8X33/yGFGf+57/6KzadY9M6vv/hO1orzqJrtxglsJQpA4KqGmvXdlTGyfF4JCtDv70hpsx2c8V2c8U4TqQoHbUiqy0F2Fz6GqbRMwwyjtMai207NlmMnFLgk9QGlBJNpSrZgA/4lKXgmRMpzFzf3NB1vThyL3Ms2qah63tc4+jajpvbG4bjkancQ1XFVhkRzKuDm6p8MsDk5yXjvHu4J6XIi9sbmtbRNJbDIBP7rFK4pqNpOhor1MR5EumNcRjYbXvBq7Vmc9WLYd/u0ErTOJmxrUrWkMm0fYtpLNpoEqLJdRzHpW5wdXVFiCLpfTgcl54cqWklhuFITCIM2FgpeMusbM/+8XFR9h3nYZX1lsi4wLCJIuKnSiEZVdhuhQkYWZ4ba0QVNxc9qpgSrXWLKmzVOcopoWIAJyNMrXOYrIQK6uPS1VznTStU0YAy0t9cmGPVwcQsziBXh0AtKFMyRLXUIkEcYa03pTorpBh5rWVWeg0DUqm/pRjJq74VyvtGa3SuNUO1qlU+Lyh3vnxKUfaSMV9//1KdY/2d84Lzp+zTpe2s96eud72tf24d5dLy6TWFGkaroimTytCOwq+uxd6UM05JYdSXKG3yE3MQqGgYB6Z5xPv5tBNlzm3jXLkxy8HBKoVlMf4nuetQBNWejtATox+L3o7sdgie4A3eStqdsyMEV05uLZAaafbSBhtLx3UKBO9JsVluXq0E3pIiXx2ILnUS72eyGhiKwerahi+++IKf//zni0RH3/VstluC18ze44wi2CqLLJz++vA0jRNsOQb8PDONI7ZpMVbwcqlxRB4eHkg5MQzTApkpxKjkIp5HMX5a58Uo2xIxGnPK+JYiZHnClTFLVCPkAIMQbzXGieaPdY5pnASzz0jfQCPvieFPZKVQ2ogxMvJbHE4s0uR1qp1cD5mREPAhgNLCSNpsaBpH00jR1s8yQc1qgyv1kBRTKX4HPHVWt5E5zq3MznauKUpqWvD8gs1nMloJC07XedjWEEu2opRGGSN8f2R+cu3KNiZhdCoGCrmXlEBe9XrWgrz8RKlTKGEuFYYqKSdRqAWMoWD/C1JfWEqisWSM3Bs5KuYQ5LlECBgG88QoVZJD03XL2E8VM8RYSAcSry/sqBiENmooBIsPI++qzPqkXlAIJ5RIvpo3iepPGcLpt7y7FJpLxkEq0+l0fmITcvlMpcLW7ONjxvcSbHOJ9vrccsnYfoqxX2d/58b9kvDfc/WQT3Fgayd06bg+1TF9+jyFvpPIKRc2RvDCDVcyAEb0b2QH+r7n5vqG5rBn9jOPRatmGAYeHh6IIQgvvmlpm4ZdGbJztd0tUVTwHhQ0Toa8SGYh3cfjOC4Y4tXV1eI46qCZrmvLxK6ROr1qHAfGacQHz2bTl2lhbimcaq1wzrDdboCGnC37xwNDkkEtm7aDJKTD6AN3797RNI7WORpnEF0ceLi/x6cHvvrqtxyPe168uOFP//RP+YM//EP+6q/+iru7t4zjxG67g9xhdVjE1Kq+zabvmaeRaZq4vb0F4P7+kYf7ex73BzZXN2y2mle7HcqIxMdvfvvbkkJ7jsMgsEApgFUtf2MszlisNlhtudrtQNWpYnIT1nP78PBQb6kn/SCuCTQhkmJAG03fSdNa27YM40zKCmUsru1ou1P3+ru7++WmbctY06ZpCDGjvCeVGQOZTCh89WGaSzSkaLse5yw3L17Sd5IpjMOIyorj41GmsmmD1Ua+HyKzl8L4w/0dXd/z5c++pN9tubq6lnkUs2f/eMQos2RjMZepZEZjrGV7s8XNExjN8ShzjxfNJi3DgeZ5ZhhHGtvgrGM4yLzp6tzbppGO2wx+lIY9MtJ/QC4QrET7oWS48zyS24zNBcIpNQWjDc5Ybm6vIUut4eHhwDTPDNOIaxxN17HppEjvY+A4jiJDXnpmbl69kMY+bQjDSJqDFLBjXhR+QUmjnE3YaDBNW0L4XKQ1iky2gqwUacE3xSnopXciQypNa0oXOFZeI0ttoUb7lVZKlvdzyiyFEfGW8iutFFHPGEvwIUZfDXD997peUT93zhpav38ejVfDe4L69JP31q+t659rOZ21Q6oB7XkRfX2c55Tb8+M9dywVSj8v3H/K8ukzmrtWegecJYaAygjdMqVlqIpzwinvu34Rqpvmif2bbzgMB+7v7/GTiLD1my2bXnDWvu9om5btdlMiYhmNqY3o/cQknaTjOJViWBkUjkJr0e5p24btdle0kWQ0pDFFwCx4hkFmOKcUMEaVXgq3rCvlVCIvg9EtWrWEWSIoZyW6322kcJlS5PHhgbaVcY7dy1tylsa3N2++43GYeHi4w1j4vV/9HFTi7ds3/A//7/8P0zQQ5pHb646+a3h9u6VtO7Z9R/uzX9C1bsHUAY77Q7nwAFILmKepYNqpGBPLPE0M48DhuCclT86RxmkZbpMSTeuouj5aixG0jTTxGauXGo7LCTS4tlluJONkxCVK/m7aBqUarDFsFtmSSlOV+Q2VWWaMYZpmpumwSFhsNt0CfeQMMUlxVzSvRGE0JaHV1vpDla0+7Pek2JBiI9sqcx400nBnlUWjaJwjBo/PMAxHMpmhCOc1fkYlhS/QVN/2AqEZs/D+s5YGxJBFuM80DuMDOmWMO81q7roBpWTIjy1U1ScPZxR6a/KS/chEOWHwVD2qCo1oo5daQMxxgQtVoVSHGIpDOU07NBpi8szzyPG4p029OPENuOL8U4h47UEptLN0fYcuhjbNM9FAUKCN9LFoYwQ6zYkUIOpMMnJea31BKxk+hFLkivkgz1LNa6phV3JjlBnMBVJSp54F+eaHVM7FQF+InOvyYwXZ8wgc+MAgP+cU1us83/6PNcOtX794TGfrrtn9c8ewXsc5RHVew3jO2f1HzxR0lR9wBj9OhdLpCy1NPmOMxToR4xIoR3DwqnM0DEe5obSjbRua8uOcxTlL0xT4iFwkElgKzCEGxnEg57x0Otd0VRcef993i/AayGQxU9g+uWQ3c5nbLHTKBtl5VxqVVMFoDUaLTHVwQQx/27Hppbg8TRPjMJKTFBlfcUsCYvDc39/x/vHA7CecM3z22RUxeN69f8df/4e/JqWAswbyS8hb9MsbnG1om57r3tI1thQ4Bd8djkeUUuKUVXFgsXDok8BzRguEMJdGNW2yOEtjJd0u1MQFflBqmZOglCIhzWQpy/wEnQ22sRK55ywF1BJtGGswrmQcxtC23YqGLI5BIDxXGEVGJpAFoaeKYN9phkLtf6jFTTgpcHofntARQXE4Hgu8FJZinbWGFJEGNVt6HKxl0tJd631AW790gjfzjEowTzPjNNE6YWRJNgQKjS+NXCFGIjLq0jhLTmCdI5sy66FtxbiVBjqtTlFffdhjCKQgD3LMpSitSwYgT+8SLYoUREank/yFQDFSJM8pSXe2aI+c+iqiwJzGSNBGSugs8Oukpatcay3T8rqOXCiu2YhxT6rKVtSfCjkVVmDKZFUKvSyPXq0MS8hfHBhUlOhUGKbUG0RGSpxCWq1GTkOBgtSqiLxanmPYnBvtc0dxjut/zOiev34pS6jvXVoubWNtsC8Z6Ev7f75fn1onOHdc6++dF7afWz5dEK/g9323EcVI72VgS4wEHwQCKsWtWDDuvpeo2jrRG9rstmz7DW2BikhSSDoU/X+rZb0aRTY1zRL4pxoea+1SlKyDeZRSS2dzzpn7+/sikDYvVFmthek0TRPv3r2l6zqa5gsxuI0jZaGkzrOHOGO1pbUt7spxtd1we3vD1W63FGytNdzf3ZFi4IvPXhJTYr+/5/vvv+HuMPCr3/8VGcscFH/91/+BH96855tvfxDDqg3jceTFzY7Pbm+56nqMdvzy51/SNxY/CyxCgm+//RZrDX/0x3/Eq1e3XF3veP/wSCZwd/eWXUq03XbpAxnGhmF4xPuZFAVm6TuJzHXBiSvja57HIlct86uPw7EYLMN2t6VUDyWCLUYaLTMH2lZmHh+H4YnKba0VVGkEUhmEU8a5SvYocFWdnDd7L45cCVYeU0KVaC6mRCqCdyHMPD6+x1lF4zS3Ny8W6CfOHj8FGi3OaLvZCp05w831NdpZpmnmh7dveX//QJyDdCJHcNqe6KnWkC0c5pE5eO4LkSGlTNf1tK7h9sVLYgj4aRZ6awg0rhH4Jabl3lTIWM39w6M0y2XQVrPdbjHGLI2XMZ/gjIrpd/WaGS0y10GjyYxHgVfv7u5wVgKXtmnR12VUbkr4eWR/d89sGzb9Bk1m03aEnPA5yUxpIJMIZILKxBJjKVX2p3ZaG7vMctZKkWMi+tIXYMXJRCuaX7kWFwTjkebLlLEJlM6Yyj8qNcCckmQR1aDFtOhGqVozKLCRVDvK71wy56ruzQkyWUfda7imfmY9SngNJz3HTFquy8poP5eZPJfNLFDXWVby3LbXGcMl+GitRL3edl3PJbG+n1J8/kmFZrk+eSk0p1SHtKgl3dSFtpaRyNzHwlcvGHLXd7SulaYn7wk+F+XIIIXKcsDRB9AU7SGJ9iuD6clJIpemLCdZSU4icjdLVFgvVNM0S9rY9x1d17K72p2kkH2Vc6hdoSUK1SIs1rhm2aZWiq5t8VNDCKpM7/IixUDCOc122xOTJo+JcRo5HqWx6+b6li+++IK+0Ww6gYp0oTo+PuwZNOQ4E0uhMfiSDRhDUzqqUxJM/3h8xDZtmcK1cDxKxCaR2ilDkKqPNqqIsRlmP5NiZBiPDKXmgwLlwNiTfMhy0xmFTnqZ25zz6SZdz7mu/SV1n6o8+tLpXujEYgRP+GtVTq2Yqq3jIJVaGuFEtyotxliVNo4aoS/TzYxknm3bYptC2bQiLudDkP4AJcX2vszVcM6BBp/rjOlaA5UMzZa5CqLVBNHEIlK4amDK0Pe9POxUfSRdNKVKobTMZyZlYdys+gGqYwCKJHh6At0ZK3IUMcZlbrZzoqVUGzK995IJEAmzJ+nSYFbOpTzLWQr/1mCSJTXCRDKFpZVTxfJXReGUSUpovmRNVlJLS4mlOS3VzCCfjPjqJrr49xoD16XgfnHJ+fRbqWX/1vfix4zyuajf2lCei8vV359STP6U5RJEdb7utRN4Dha7BGVd2s76OH6sZ+N8+XT4qBYtvWfyoi0UYwBtSLrMFgCyTqXd34gOUZhFW8iIztFuu5NiatczDaNADtOMTzIhq1JKx2EgkTHB4loR2eu1NAxlEsaWMZFWpIevrnbyYITAw8M9wzDIGMbSS/DixQ2bzYYYIy9e3NL3HbcvbtjvH3l4eGCaZV9O6W8CNFpb+rZZWE6xMJpurq5orGQ2wzAwjAMPD/fCiulbdrstc8jMUeogs/dsNh1/+qf/iv/rf/1fMx4f8fPI4e4tjetoXMc//MPfEqaBTe+42m3ZbXtp4c+UqW4C33g/lcLigNKWhCKkhhiFkWW0RjsHKlHlK6qBaZpTdHm/f2QePA+PD4zTyDiO2KY2RZ0eTplBDNaZYkzSsl/1Bl53sgPLpLX1LI11kU3gvn65PuMoNOWHh4eFMFC71ev6tVbsdh2zH5ino+D1KaKQ2oLTjk2ZvqetYrvZYqxhd70jkTlMI5MX+RFnLX3b8+Lqli+++Jzrqxusc8xhxg+jUJShCM5J1Np2ncyzKAJ+MZii+3VSESbDzc3N0n8Q5kj0AY0cd0KuiZ9nQgoYZ9ls3XJuFgOJiNXJs6eKk2sWFdd6nq21OKUKs0gzFVKBDoocI34cCQq8KgZdgzJGaKgqYbuWbEVhVsWMyZRMTy5/jomUCg22CtdFBAfCFeeAyIxk6aBXsDTFLXBQqTOcZDpYjPwCs8VTB/Ylw/ccBCR/XjaaJ+jxw4LsjxnNS9DR2qB/avR9KUO4tJ318lzt4TlHcun76/vpf5NMQSE3RggBP47EEHDaLIU+Cs0ua4VuW2xref/+kWke0Smyazuaqyt5UI0RKMNqnNWorgx8yWCsNO3UG9AaQ+daXOOWCGv2c9HEN9xc37DZbri+vhaHVQapmMJE6VxD27XsNpvlJF5td/R9y7bf4MvnK5QVpomsDVFpGi1CdVnLBLE5eo6Hx3Ljy0PounYZQK+MYcyZpAzbricfZ+K85/HukYf3j2w2W15c3/CzL7/k3//Vd7x78wN3b74ne8+2aYEG46QYiG2J2qJcQ9Sa+/0jlYOOyjLjwBhSmpnGR8aoSiH+IAYqJ5rG0jjHzdV1kQ03BBn4ACkyj0fiPMkEMWPZNB3bzZUIyWm7aFvZMhfAWotXMzOacRhFNsEXVdQyj0LBMkzJ6NMc7gqZKOBwPEKW+QRkYbMcD/tFuwdyKTo76oCbtm3IOeGsQhHIwTIMEyqDUw19JzO6m65DKYUPHm0tjerwIaKMZnd1xZUqEEZpdks5sz8cpMfj6qow6ZBsC4UrBe6qwaRylsJ1TOQUSy1nYv+wp9IkX9y+wJSMwmhDclVGXoQfY4rLrOscMvM0o6PU3/w0L6NSZz+VYm+ibVo2fU8ISZrzUCgf0ONEnU+ds0Iri7UZFTM5gQ8wpSA/KpGNzMaQgUJSWEZBMg5rwRZ6blKFpBELxbUMbVIARgManUqTnEnoJCJ5OiHPRkZqYwZ0WvU2lPpW1TSTGokQFFJKCyU3VeZRyUCKeS7JgbynS8ZT3nrStPackTyPvmuW97Fo+rwe8Nxnfsy4n9clzo37mi1Ul0vKq5ec1KX1rdf7U5afpH0k8g+VvpYwjS2yyJowz2XouILGoRXM88g0jWgNnROctysPbYwRqzXRGLQrTSdFhrn2P9SCYW0CirHMdEhFH8bIXOa+k5+chZtuSmGt6u43rqFxzcJxbpzo7NsyhlCUX4tssomQA5hImRghMtgxkHzk8fAIZBpraRvJYEz9aRwuZiJaxPLyLANThpl5mHlx84K+laEvw+HI3dv3vHv7jm3X8nh7A2i0adDWgbYkZcBYksrsxwFKAV7gJGH5QCLGiXmOpY4wFd0kUMgs4L7vhRWDqKamqMkxyDWLga5pybkhJ5HQsNaK4ciZGHPpfjW0rlkE2oY4EMMJNkrlJ4N0EqckstlWCtKbzUZgECh1HoHclJBimOdJvqv0/4+3/+ySJEvz/LDfVabcPURmVlZVq+kZENizh4fk98Ah+MUX3Bc8u1gAC0zrLpEqIlyYuIovnnvNPbwiq7OWu7SemKzMcO1mj/wLAhmKKJ2yFlW6QrLG6Fzgo5YxzDKHNoZBaVyRU8m5YvKFg+Gjxyhom0Y4EtqIR4dP+NkzLQsZhevalbQr4f0MlXS2LO1zYpnnUgnnsuAVhVrK67/Z3chFq3Wxu9SFlBdRPpB9JpbFcy4ijRoJCKnI0ccUWWbx0tbKoLJAuKs2l8oZryJ68SIkqArXAYXWVrpEMiEmfEgsMeBVAmtQUQqLrIrbGqr4gpizuU5OhBRJIUAWZJ1ZkUdlepNErValkgRySQBIR2CN3D7rsmiWM7gk/4sALrKpJYgJBPk84uHivlfhqKQEdX74FzuJy+OlgHw9srnuCC5vc/kYLx3Xo6nrUdFLSekfLcqv39fPjbM+t8D+JSOwL+8UUhJ/W9dgOqmqrVJY2aahciwYZkjRE8OMXyZiWNhuNmz7lpvtprh8ZZY5o1qH0ZBTQ67Y3KIlc3OzxbmG29u79Q1++PBhHUd0bUc/DPRtJ9j0xaOzwmnDdtgI+xPFzU4cv5riCha853g4ME9WRPjGkRQSp8ORECLDoNGNtHwxBzE0mbOQ2ILn/aePQGboOu7vbnFtg7Zizdi0HXPILCHxdBw57U+M+xMqCsP0zf1rjDK8++4d/9v/8p/561/+RAoj8ziyf3rin//5N9zebtncvsKYTFKRZCw+THz37ke0Ftcz5xy9a9hsN6V7iMzzJ6Z5YfETRlmstvSNGNVvh81azX94/w6twbnqd93w1euvRCVUm6LkKqOhOULKgdY2NI1jt9mxuIXZzsynGYr6aYVUnklc5lnLbK1wIkRnSLaDh8OBcTyxzPOKTrPWYppm/Y4/fHjPbrfj/v4eraUoOJ2eMFqx2WyYRy8GPDGSEWJcXXCHnLCNpbGGOEk3knOWYsEaluiJOTItMzmLub3pmsKs1YWwpTAF4kqmSE0LWEGXQDrPM9M88fj0KBczsm8aloHtdnfWQ3KuCOotxNIlrLayKRF8Wci7huwEFLHMCz4GltljtCMGSdIibJdYfBRuSJJKu2+64mPiZKybEvMyMnnP6D3JlcXttJROIePKQD6H0hEaOE0T0+nE4/5JdntZuBSudD6tUSLBrqpInioJAVkqy6pERpBarZD1RK4GcysLulLPtFKrVEet/lXt6mqALaMoMisq66UFce0YLl0iL5E3n0sMl6MmeJncdh1cP1epX9/nshN4ab/wucd86XeXCefyOquv4Zrp/LnX/rnjy5NCWbYZq7FKRkmqSEqrFZaaV12dmCLGahoEbmqLLLFaHy8LJG+V6pUKZC5Y7LZp6Lqe3W63Ilsu7UBrO18r1Wmc5MJPaWU21y7jkuE8TdK9GK3FeKbASqsuD0qtKpXqQlqiAiuS9KmrfHcl1AUnEswhQgyipe8XTwqRoe+It/DVmzdYY3j37kdOp6OQysicxokPnz5x/+oGbTRv3txLdWmFPZuDYpwnrNVYoxk2A23X0g+9MF+DR1ylA0olrFUCHzYAieAFDjxNE4fjXlRXu0Y+f0f5jNyFPlFcdwJKqVXzaXXSK4qaFZNfiWhVhqAS1q5PwqrWeTk/v9Ssqbep/3a5wK5sdu89pm1wTjSFvA7ERfSUaoeQySvZrr4+pYqxzzITYiB4MZsBpKIvMNKKmBLZ6Sw+ByUI+mUuS/RZKuvCT6iaPNKhZVnYw+pLoJWS7q9UtDX4WOckeKVI9a/q+x5KYJuXRZbBSsABXATJnKULyCngvSzhVYI2txhtijnRpYCeiBuiBUGUih91UoWpHBPJJKJShBhYguwOBd6ai6FTpnGyTD6LqrN2CSojMhjUIL5GDy77rxLVQQlvpnYD56bg4p4/EzSvg+p14LsOxtdJ4Poxa5Ct9/2S43ML4evXd90JfA4h9LmE8NKC/HpX8Y/GV1+6V/jipGC04PfbpkG3Utn7cRKNkigz9pzlYkoxEH2gaxpyEYyzzpQvv2KdM0bLDDTkIPhlrZimRAgLd7d3bLdbXr96xePjIzEE4QbkRNt1q1pmZUCPp5HNZlhJTs5aYlGuFB+HmfE0cjgeWJYZBcXPucE1biXiGS3Ep7ZrxBsBceqScYpA3WJJCmLhGTg8nYodIISkCRGmcWKZF2JK3N/dstvd8Pvf/Y4YI3/645+Kl3DEOMNpFFmMzbYn5sivf/MNrnO41qGsJS0wjhNNa2m0pRk6+s3A9m7H6XQkTYKtVCqidKJpxfrSOEUmME5HHp4+cjoeeXz8WIhvQppTStzyqi/2UuCfl2Sa2glUdysbQhmbSeCtVohVWhxYYao/tUasJz/rAvpSq+aSCVrl1yusWFzdFrpWeC67mxv8EpgOE8YUobgSaEQkTioN52SeP/uFpcqrlJ2CkO1kfyGvXUQT5yDjUJsj1b9hLGKP0yjKvTJOETSY0uJNEGLgeBQxwuo1wVVVqJTCWBnrVfBGLNpbN7c3RepDnPtM6XCtcwUaalZfCrG59ZxOYxnD5DXgJJ+JXpJ71hnrDKZtSAqmMMv4iEzScj+Rj1GoLPuzaVkY/Uz0QlQ9zRNdblZf8/X7zKALhFRXpnL9juv/cq7K2OfgVJfTJUFIvlPUuPWPKttLqOnPjX5eWl6/NNqpifqaY/KPRjWXncBLx+X7uF6Av5TUrgP8S0vxy+vypaRYf64X7F96fDmj2YlRjEYShFJykmVrSCFwDL5UdovMElXm9mYHSsg1KQZOx/0Ko0tV16TMaGV5BHFZyEE8ecMiNprLPBMWj9EKssEq2WFMFJz8NHE8HVGIO1vOmWkcxewdCRAhBNFbCgmn7Zo4nHU4I74IKWVB5rQNfd9hihJmzGkd06wXvILtbsvQD+wfn5jnhePxxO7mDa7pRbLaWlrr+PWvvqVpev7N//Df869/+AP/8T/+R5Z5LoGhE3VUrZi85/Fw5N3DI7q1bG93bG/uMI3FdUYWx43BdS3KGnwKhByIBJpGg3Joo+kag7WKGGZO48I0Hnl62ssS3hraxpXdjoxEfvjxBzG57zqBZcIqd1FP5ip9UcclyyK+FfXfQgi8ffu2yIc0a6dxeTE4d/YcqBDVzWaDUmqFqa6GTVqvr0U8ImR5LpBUTSh+FloZwiwL3NN44vb2DucabOsYJ2GxowV+aZuzOY6Qt6SrCzGg/cKw3Uqisxq9GHz0LMETQmZeYCmyKULKEmkHpYTHcX9/vyYwYQVrirgSkmgkwNRkWbutmFJZdFe0k3QEIQQeHh8ZC4vfuUZ8IwrCSTgMhrYVcqDoPQlSzC+euARikJ2YaxyubWl2A4mMnqXAySR06fpJWTSdtCEpSEah24akFTlETstELH4SunBKxFtbOA0qWXJVtlVZiNkqyPgoxnO3XT6RNfApyCqL+VGBuMtE6/Mz9LoYvvQxqMfnRjOXhcc/Gs9cP9aXHv/otVx3OJ+r8n+uo6m/v04cl/d5CW31S97LL+oUZLYqPMQabKvuSe0U6kWjEKloVRQMa6VnCyabXJZKSfx065tKKZaFdiQGzzJJQpBAbEBJKxzKAmweR+Z5kaq8KC9Wj95lnplGWQbLqEckAqwRKKtbDdQ1bdOS0vl3lfGLUrIvUWCSoGBiWaDJ2EVw6tVgPhWkhS06Q41zdF1LP2x5dX/HX/5seHp8kAWi0bimkc/WCDnsNE087Q/c3u1IgGs7skooHYsInBaClQIfAiGFdVTnqKJ1ChAuSU6QY/FUjoHGNmuFn1LCh8Dy+MTSiRdFhYFWbSn5TuR7naZp/QleBN18cXarwbzuEC7ns7Viq783xT6xBv7ramsdW5WqrY60lFJrV+KXBVe5FKq4AJZAUcmS0zLJcrdejHABYpCRklxgF0tM9fxiEvy8oIXqZ1LHI3JfOR/7AoW11pLDmTx1edFX18AQpTsKUZjec5F1yWRO40mSx7JwOo3MixDkci7dT/GNXmWtC5s/qvP8PKbCI8pRrhejcY2lbRqSyixZ3k+9TmV3VSDD5TGV1ZhW/CWSUvgY0VrgvCFFTBKZbXJZMqNQOoMpjPTScVE8SVCqiGe+fIgygiRQUBfxIP0kSL60QP25oFu/q8vH+CUz9p87rmf3n9s5fK4ruH5fnxv3/Fzi+Nzz/rdPCkbUOKfJS7eAYtP3KGUp2gCFVBULQiYXmQvN7Gd8WBjHka5tVzZjDnL7ZV7WJZ3OguwJ08QxRKLPLH7Bh8hQugClFFORLA5BjMe7pqVvWzrreDw+skwLYfGMh1Op2lj3EH3b4xq7IqEAbrc7MtA2LYqMDzPKidRATOWCU9D1Ld5rWTqWi+/+/lWRYW4JSZOz5ma3o3EtxjjGMeCMZuganFWk6GWxZizd0AnxbZnwk+cwTWD+QFaiN9W2Pa7t2GwHtM1onfH+xOI9h2MgpkXkLrSSJIZmngQZpBC708Y2bLc9qZjNKyUL0nn2pJQ57EfaVoTtNpuNBLacn42MYmGej4XBLAEzCvqr6B+9fv16DfKhWHPW0c9+vy++Fn2RtJBAWkdM0zTRNA2bzWYlMo3juAYF8e6WUc/xdOTdaU/X9GI0H8FoizMO1wgEues6DqeDQByTYP6n0ySmUM6w7bYYJ4zvxrYYbQlh4TQVB0AjY++kKEipZSVW9l3PJbFLaUXfteviOCweraQLapoG19gLO9GpBPnEUndaWZb7OWf2+yd8CEXOI6PQtK2g60REURBOwyDGTjmVyjkmrD6T4JQpyUOLtpU2IviotAJDgcam1V9bZ3BFkSAkkau+0ZplmvDzwuHhEUUiqCwJwnuBA2tD1gHVNGAsptEFYSQyIYWYLzIhyqxjpHXLkHNdtRSo90VHkc+ktjoOuQ6OlzvGGgwvk/FLAnSfq87r813e7pccPzdCgucV/Jc+x/X4CM4L8JfGZ5fPU1/TL02AX5wUlsWvPAWdISlZCFbkgVL6vNwr5h0hRJQWooo2lqbtxDoQ6RJSxVYbUZDUWlN842UJmJDRUYhFmuE8q6xCY0pJoDNW/GdDwcpbbWitqLDWqvT8SVHM1ssHnzNWW2JORW55hBBoy6w4pLSeqPMyF7azWU3Gu74r6pUN0xzxQWxEpZtJKCIkYSDnFBiGjhB9WYhq4hKZ5ll0841h2GxBaU6nGaVbrFVY16BUAhIp6SLx4RENoFgWncIOn2dJiJvNzbpsN6WCI3Px2Ym3dEpSBdcgbK0Vt7wiS1ETQ03C1fjIWsvt7e1aIfd9X86VZV0S1wtNtK/K7JvKUlcrmqe66l0+b32Oc+eh1s9cxkUjRhka29EUHSJdxy9lJ5FSEuRYDEzLhMORcKROkEiNawQ+rETGfBxHHvZPEkitpukaKXbSWSJbgo0qHWuptpMsu3UhDkqgOndLdbTkC1pLG1MSQhaNKJtXHkoq48oKALi/u6NtxQvEGbcqkEqnFmga+R4rIkgpJQY6hXtjjF67K7QqXYJU5mvntELBRa3VNo5Bb9Yx1f7xaeUnJ4o9aUyEImHhjCGjyWVUJByCGvzluVJKhfRWDsVKFoWLaji/XNnXAH/dgV7Ozy9v/7mAez2vB36SeF5KGpfqqi+hfl56Dniuonr5u+tO5/q+l+/rpftfd0cvPff1TuNLji+34xzncpIFDIqkFctS+ADFOtEYi7ZWZBeAZZGZYgKBsjZNMQiJ5Cwwwqw0xjaiO68NRsnSaspSiS+rfPIVozBK++qcmJF0XUf0nhQET91YR+56+r4T9JO1BO+Zl2WVIRanN2Q0pKXinOoOQ0HnZ1RZLMeyxIsxCu6+8CJijGx3O5xxWG0JccSHyHgSiKvMeiM5LTw9fCTGhdubDeN8xIeINoqQIqdpZNhscE3L6zdfYVzL4TShraFtLa5xgHxuIWl8gNO4ABFFRCMXqQ+B02kkLJ7b21eiGtu0gIGsiF46szksJVhQErgsl6dpwhjDq1ev1gAtPsxxfb81KTRNw125XU0c8zyLnlFBi9X71H/3fikiemkdU83zzOkkctN15yCdgVm7FaDg7hfxIU6JaZolKdx22MbRDWKgA+rZwnxeFpawcJpGWqrMu8h2NJ3YmZIV+8OBw/HAx48fsI2g5m71rTxzGSPJOZhXdFvOc0nG4TxOLHpBpiLuamdWUHRtYWyLZ4IS1VktzODjQbob0T+Sz+Lrb76W5GUdm0G0x56exKwnBC/nRh3hSCQQuYgsgn51lDNNY5nhFx8ErYr0uqoIABnzGPGm6IbKDhfUUlKQdEkKZccGCYKiNbJTiFGv8GZxsqhdgIgBKp4viAU1VkbS65jtp1U98AzqfLkIvoZAP7MRvQqG9X71dvV4Sd76pUBf7/tzyePyuA7c11ahl2zj6/3J5xLTSwnzMmFdJ8OfW4S/dHxxUnjYH0UaoGtFrC1nxmlZSWRN12PbFrd0pJwkoBmPdZbN3Y62kQXkWAJWDBmjQVkZ/Vgj5jJhkcCuTSPz8CQa/4tfCL4sIY0W9rPStE2DQoni5TiScma3k+e73d1ye3tD07Q4Z3l4eOD9h3fiyZCFUWut+EzXKvh4OnIMnilF2lMnVWcSe8mYRG/m9uaWb77+GmccRmken56EyDUHQBNi4ru/f1cISpo3b76m7zvev/s74/GRvrd0nUMHBTqL01rf8dVXX/Hb3/6G//F//H/y97/9lb/8+c+8f/eA1pmuNQyblq5zpKSJ0UJuUSUp5OSZp8CnT3u6pmG73dK2AwrFNC1Y41DKkEtX51yD0U7IfM1ziYp6MtaEsN1uV3e0Cg9umoambRmGYV1Qf/r0idPpxMePH9f5++V+QS6KtHpYXJ7IT09P63L7zZs37HY7vv322/WCnybxwpBid0vbOk6HEY1mt9ux296wGbbSERTegHQI4uHsUzEPUrJXqPpNPgSW5Etg9+V9ax4fHogpoIxoC9mCYFJarR1e0zRMk+xqpnEqibIVjkwJdmK6pHg67FcL2m3bsN1uxDwIGa2I9wX46IUsaA2vX7/iZnfDb37zrSQbFE3TklNmf3gkxoVxPKysa7UWZ5q2kdFo8J5xmpinifEgJkjaGLEbNZbYyV4mRhndRH3ezWgr8upNjDR9J0nHWXL5DGIJ5ppSCacs/I0S4E1JJrogzerty0lG7SOUlnR2vTiu+6nrYF9fb/2zBsvLZFADdw2StWu9RP/UvdrliKo+z2XCuK7Mr4P49c/PjYSu7/+SBMXlY/xcV3H5ej7nt3B5jf1X7xQWL9LGGUVloKdaaVfGaVnYzYvoHVltyErjmg5b2KTohUwQtIGSlbWxwry1zpJCIpLKfFxGU5XUtsT5GauxSiCQayVXKgctypJDPzD0g1yo1uDcsTiRFcboUjVYCoMzntm5MUWWYvQjEFSZwbZOXnfbtmKVmGRcssyLzKyNI6XMNJ7EQF0btEpolTmcDsS40LbiHJZI52tDwd39HW/efMWb12/44bsf2O+PeD9BTlgLd35L2g0yakgKEN8FjUgux6iZ58Bm2NL3A1pbYohM00zX6rIXysRiM2oKb+SyJa/w0+v2+HJuKfsVVdAucd0R1Ln5pUrl5d6mVs718erFVy/Oyo+o32OVQa/8CHmYiDayTI+heBVbu44ul1m8wcdxFBRRWZ7qZ/5lUuWGGFhYhN1cRqM5p0LEC8xePCpQHa6pzyEJRRfjbIFy+hVtJWPFapxSpMCR86tCZQUCW7oDMplzkqrez7EkIKULT6LsCFKOZTcxsywTPiwyQqXKY7NCaOU7K3uEnIjRiwJtNhgcBuH1rFU65XqAggCS8Y4pi3tiLu8lk5SMUDUKlVUZQxaZ7XI9UnYipnxWwonM0o0oOfdy2c3w+Tj6LMjXv79UsctT/7RSvwysl5yFlwLuJarppeD7cyOglzqLzwXyn+surh/jpc/jpT+v73P9Gv+rJ4WIZomZ07QIugLYDQPN0HOz3cm8N0XGxYM2KOu4ffWGfjNw9+aeaZo4jiMPj3uRWggRnav2fSQXmuNxnJmnme2wEVe2IpN9Mkb4CmWuK0bpgipSSjgUX715s+rsWNvgXItCEYJAW/28lOdzmFTISiEyFxRNIuOsY7fp2FgjNP9cRg3FynKz2bLb7mi7jrD4FUHiffEzWI7EGOhaWwKDY5r2LPOJOUSs0bz96p79aY8eR7TNhDBzPO75v/7bf8tvfvNb/tf/9L/x7//9v+ff/bv/mdvdDnLisH/kt7/7Nb/61dfc3+0KI7nDFTvNcdkLNDD33N9+zav7O/b7Rw6Hkffv3vHVV1/RdT3jccQvwpLt+4jWRkYabctutxM5ijK6AVbVUu89nz59WuGn8yyuZg9Pj+uJXn0q3pTvwVq7dhmn02lNGBLkw8oNsNby+PiIc47tdruOi+ZiJrTb7WQmHhq2esNpPHA6HSDrdWR2PJ5YZpHirhd21/d0Q8f2dsu4jLx7eE9WiWmZ+fjwCasdrWmw2kLWTKcRpRW73ZZxOqJHxdPjI7ok7L7v1n1L9MJHeHgQ73JnmnJxZvzsV+i1sQ5jDbvtFl3GnH3f07QttljWjvOEcQZjLbvdjsNBHvdvfzvyg9FM05HNZsPNbseH9x85Ho/sn56KzSosy4g1lvbuHmMtbWfJWfwtQvQolXCNoc/dqmdkalehzUreq2xw17h1B2HKa37z9i3j8cjjxweiMiRrefv2DTpm0rwwTwtL8ebOMZKWhE0R4xztppdxGqzjrLUQSCKZU4uR68B2WcHDucK/RnddEibr3qaOH2vyuO4IrhfS1wH2eocBPOs4rruClzqESxbz5e1+rjv43E7kpWQGz+G2l/f/b54U2r4XnwMli6rKU4kJQhKafIxBWJZotHHFUSswzgunUUzOT5NgvRtjV8x1VhqURltXfiKueMjWSlEE0pr1A6jVbM5nlMzt7S1t2xY7QU1OinleWJVCUxJvXrioPOVDPB6P0lo3mtRYcmPQZWlmnC3qmA3DsMFZYf7OBS4bynxcugeIUWP6csIhQnU+QTtsxN5KW+7vRSLjME80rWO72/Dr3/yaV6/u+X//z/+e7/7+PeNxQkUh093evGI73NI1W1JURCXaMs70tK2DBMY0ONtwd/uazTAU85pE0+xFRydk5mnGaMswCHmtzt/rYne32+EK07aecLIL8Kt3hchVVOvEC6hm+d39/X1Zbpu1qrskqsV4XkBrLd4Mt7e3NGXsVRfM4zjinHAqzvj+mcNpz/6wF4hyVsQ5El0iusiySMVunVsvKmMtJgljO+YgelZRLDF1FoMZXWbddWH+6tUrhnlgmkfxNijw2brDImViIe5prWldSwgyqppOoxQXu11BTDnazhRegexpMoXomSMheFKOq31qSqII670nhsSHD+84Hvfysz8KDyEGyNJp50x5vECMmmURX27Z5+RVM6wumKXJS5I4iioBLwS3Ws3LKEgUYa2z6Fwl3NNaMKQgSrAV7eVDAC2yFS616x5D+BHPn080034KP708LncNnxvPXC+D658vBdvrx3gGROGn1fbnqvL6by89T73/SyOl613Az42cXnqu+pif60Reel1fenxxUuh7gQrGIKMfciLmKrgVCWV04L2c3MY2+BjJi4fTyOF4ZL8/cCrGJ+2mhYpNVhqlLdY2GNdiYsaV5WUVnFsrtAu8e/1Q64V8d3fHMAw0TcOyBKZx4Xg8Fg9iOeEaJ/sLqVbOH2AIgZAiujHQWHIrSUFbQ7/ZriOZrusIPrB/lEWfn+UC1Uqkwa2RjqnrWsgiNXA47PE+cPf6HmUcWVtev7mnPXWcvv+erm9RRvO73/2Otun4D/+f/8C7H9+zTJ44yyL7n3//K17dvWY73DDPR0KGxhoa1zP0GzSazZD56s1bttuOxpkihpo59PtS4U/FEGnHdnuzBv/TeFqT7s2NaEXVBfA4jozjuC5uK/NZpLili6qL5JoYa4DXWq+V+yUzuSZksUKVwFoX27vdjuriNo7j+r2Lmc/E8bTn6fDI0+GRm+EWrTTzaaFrIrGRAGuMsK3rRaF14aU0DmKGhGD4Y2aJiMgeMot3jWMYBtquIcTA4+ODOAmWpFDfV06yc+n6jhRFi+vp6YnT6cRxvy/sbtbX33TNakBUZcZjjoQkZlBxrkgkKy6AVpOWSPAL797/iLWWru1Wb2O3Jr1afGRyFvKojOLyuj+KhaOTyncGipwUCdm5aFhlPkASjYw1ny95xYq1RUeBj4cYSeU8USGK2nAh6vngyRosmSaKxWui+C3IBbwmhphkvFrPqetAd9k1wMvQzkuF1OuAeRkvXkoKnxs/vYTyuf7dS49f//658dH17T+XOH4uQX7uNVz+939JQoBfkBS2uxvpBOYFrw05itbPEgIPj08yEkrSGlory6bHp4N4AByPqxidUhZrxEQeJQqL1oiS6WazwRlL6Ac2fSdY/lLl+xDoh0HmlEoVUk+kKwzaruvOJ4sW+rwPM9NyKm2wXWfYKsuX0TSNzD9zZrOR6maOnuAjIS0ivaxdEfMb6IdBks3pyA/ffQcRVNI0pilkuJbdqw22GKEs88zxdKLtGmxj2G47staErPi6v2c3Drz/8CO321dsN7e8vrljnjx///s7dpuBf/tvvuYvf/krb16/5n/6n/5fJUif+F//038kxYDKDr+15L4lxZm2bXj95o6cPUpF3r7dst3eYJuGP//pD0zzzPZmx/3dPV9//RZrZf+BFme0zWZDX3yuXYwY16CNwwfpDNs+0w8Dw7ApVb4gZw6HA6fTCe/9msgrSumHH37gdDpxOBwuNIziClPs+h5nHdvtjq7r2G22TMuML+OZ4ziR9CNPT0+M08iHj++ka5hn0iLjD50NzkBGY127diiL9yzRk41Uxq5pIClMjqgkqrE6awFOKMXu5obWOZrW0XVbtFZsh3aFzE77I9EtwstAieT4rTjFiWy4KudqxLiMbRqRVu86MoolJI6zMOJj0QcLwTNOo/AXQhDWtTFsNw19d0cMng8fPnI4HHj//j1fv/mKzWbD0A/SIaRM3wnqKmdxDoxpLvsF0TtKyZcR6khMYGwj+CGlCKnY6BaiKQpUElRQHc+mQjC12vD61SvitJB84PGwJ8wz03Fk2/UiyWJEDTYiO6tcBPFyYvULl3FvErBIQS+uQS4/l6v4nJRDvX2+uH1NGpcWrtcVe32MS25DPS4fty6w6+1fSiQv/ff13y8f8yWxvc+Nda4D/0uyHpev7/I9Xz9m/Wy+NEF8ucmO0WVpJbO/rDW2aLvkov9exwGrwBiyjE6+ip4JYkhdnAS5LKhyYQKLpLUsiskibVwXeUBRRzy/cQn0eoWO+hAwIeAL4SjGsLKHxf22fsDyU13kUEY4FUVADJWxWslrsbZIbGSWeWI8nTjs9zjd4LTDuh6rRYq7aVpcY8/PqxVt15CyeCDk4kOsjKhm9q2onb56dU9jLXNa8POCu73l7vaWjx8+stvt+NWvfsW7d+8YTyOn40SMgbbtmaZA03iOx5mUwHshRimduW17MpndzS1d17PMC7ubG7bbLf0gQSXFxGa7o2nLaKwAAnSBFIKinUZQMhpo2laWjuVzbNuzo12Fs646RcuyEt7OHIXS5uezu5rWmlY3qyOcVoVwlPO6VA0pErMsZZUyGOsQiWUFZfyoEM6LLG01MVdIMIgKuSaUKhkFKstIQ6FAi8SEMtUoJhdCohXhxOAJi0FlyE1DRQcoJavkpVhzKiVCd9Y5GbGpgioqWlnTPBKiL9IPxcMj+jJ69RASWjuMbTBGEbVwIi6Z/xLUSkDThedgTLE+rYFVOAimiL6qgg0QfUdViGMVLJLWhXMlosLz5WxOuaD9WnzMhJSZw6mYbi20TYPLok4bC+S3qqGKSGEdExWuwrOu5KdV+kvV+eXxUmL43JL5HwXDl+57/Xqu/+068H9ucfzS8vkfPcflv7+0t/i5TqjuTl56vf/Vdwq+KCZ672kLRvtm2AiSx3usOa6Qu6ZpaHshEoWUiEYcyrz3qMKiDCES5oW4eFQIhG6maxxd04hCatuyzDP7w4GHx0f2e3Flq/r2c5Fc7vquGKwHHvdPaCVt/lKUQRe/UDpmOSmjaOagFOMkFec8i0haJUZpIxXW0Pe0XUfXOOZZxPTe/fiBp4cn3v3wA/c3r7DDLTfbG0mQCqypyB2Di8IvePP2jSStXKpr51hiwhr45u0r7m5f8+bNN6gUCfPEtrXshpab3cDv/+m3fPPtt+y2W/74hz/ypz/9ib/85W+C/Angl8zH/on37/9O0zp+fP+B3a5ju+l4/fqOrtvw+jVMp5H7+yPffvPNWs1/eP8RHwJff/0t/dCz2WzKCQSgaPseyNhGhAf3+/3KYO4LqigU29C64E8p8fHjxzUZPD4+siwLp9Np7dT6YZCuzAfmZUHkRSxGWxa3CPw3RmH+qkCaFbZp2DQNw2YjHYePTOMoJMegsK5FGyfqtlqLhk8UfokxaoWhhjkIUVAJl8AW7wutpKJNc2AMnmk8Fg0vGI9HTofD6h0clgbvg4go5sTiPT/8+B7nHMPQS8fRdRhnxR4zHnks2lOH44FQipXNpi8OgtLZGqOpBEWlRPlUG1WEC6WyVVoLwbJ0ZRVZZ7SR928zuQFr8opEMqZKj1hSVijdCMEypqJXJJWkVnUrUFVY44rGA6nqu6YBL54qIUZRU10WJiecntM8FcJgxOZcbJQvUE1AJaepMkLStTgr7OSfC7if2wlc3q4Wp5fV8eV4qR6X4nKfSwDXncZ1wL98zsv9wHU1f/kcl93NSx3HSwvl69HXS13MSyOp/5Ljy5PCMhODuGy1doszls1mKCxiMWuRC0vmxTklMbAxmqi1zC2VIsdw1lvXGkyhxSfBdDtjyMYwLzPzPMmfy8Jclpy17fPeQ66kK1GalApJpKVX3H35zGOq4mTnjsZYi2tajHXFH1pm3ujyHnLVMRKWbC7IOaM1Q99zs7vhdnfL/f09ZOFTxBSIPqE1xBRIOTJsthhj+O6H79dKdwkSsLquxRhNjJ6PH9+x3x95+/YV200HObIZOrrWcTxIIvrrn//CNE20TbuKzwlnQIT1lsUzTQqtMg8Pe7TJxCAmPCmBtS3DsGG326G1I4TImzdvpf00iuPhIGJtpSNcTYSMIWXpxHThd8QQ+PDh/apUe3d39wzOZ4xYpSqlnnk0bzYbMtANfTFxYf3+Tycl1WZx6Fq8J/mF3d0tXddxs9utbm8fP3xknoSIVx35lFYYZARy7iYt2ogUuS6mzlqLyqnVVRRR3mMMslA/PD0VCfcqpijJr55/wYd1Rl/Pp2oxmguzeZ5nUpaO53A4MC+zqNqmCGS02V5UdvUClr+P44jYW+Z1Gd80DaY4lHnvRVAyyt7IaEPXdqLdZSxaCbGs7mcAARYog7Eti48sPsrerwYX1GqXiZIls66z/9JVVEKiVrLMdyESGk8CfBQklcpXM/6U1/3EdVcgiYE1UVxXxtdjkpeWutfH5zqDlwL6JUT1pcd5aRR0/VquX+dLQfxzx8+9j5duc723+Edd0Oe6qJ87fkFSWNZW/mbYCKu370VCezH4Yq2IXwqDUWSoKReRVkJ9T8JkEZldo6HY+OUsy6kYHTEZovclKSzP2LFKqQvcuiy7chlXrNhlc5n95Yyr46uUMh4vc1vX4FqDKpIdIcoP4TIpyLKQMobQ5fk3w4ab3Q33d3fc393JIjV4ljiTgsc6IzuWHBk2PdYZxj8e8V7m84uXnczt/Wu0Bu8nPn74kWla+Obr1zIiS4HN0NM1juPTE+9+/IG//vUvLIsvSaFbg0XbdeUzDEwzQOLhUQxpMoFlicSo0MbR91tevfoK60QE8M3rNzJznice/APzMmONcCBcI6MKpfVKPMs5r77K3333XWHWhoK2OXss1GWoUursTKY12+1W0Cs5s3+SfVTOeTWcz6pi5jPeL8zBc3N/R991fPPNN8LIHkeRUo8w5Rlfvn9T+DJitSlyEaaQwUQDqMIMzTqucs4JTLkkBe89Hz58YJ4mttuh8GHO0MfqbVAJWTVw191WKL4c8zwX4mPieDxIRT2NMsYpUvTaiOJrlv0xSgnBb1kWgZyiMFr8SJqmYR6n8j344tKW8V6kzBvboIovhgRxyuJZLs2mFECu6bGzR5sApdtJ67DwnJ7OSUGv13RNRkoJLDzHSGzFtS/E4jVRABwxZywyKlSF0PdS0Lr++/U8/XLBfB2YL//78nbXyeeloPi5TuFzS+PL31/rC9UEc32b687npeOl4P7Sc750v+v7v/T5/dxn8NLxxUnBWUMuCqYhLPhlYpmmgrAJxOjFcS0KAikmQe5oa3D9gDGapmshifGI00UnJUaSD0Am+IVZK3IKxMWv2krDIBfmfr9fq6QKefTes3jP6XgswbHl5uamfGmZp6e9kMvKyZyK/aG1lq7rZUdiJCnElGjbFlxCOejaDmeddEjTwjzP5CBw2td392z6HmvsmixPpyNZL2AijdYok8kqFRSqJqvEcdzz8PBI125oux6jMsf9A+++/x6nHEZZ3r654eHhiafHD/zqf/i3vHl1i0qR5BeiXxj6gbZxnE5HMRBKkZiEnft0OHF/u0WrgdPJFz6DoR9uUQpC0PigCFEzDLdyQhkromeniSUkQhQHvSUEmOFwOjKeTozTSNXtmeaJeZ45FhBBTdhN03B7e7sijZ6engBWsThdpSsaJ57W4ygL1mp3WarKelSxvFqUpLqPSOUnyxiDLEFN9I8kEZDEGtU6VwyLWPcmWlu0MjhlaGwjEivGrICHY99DTiKh0ndsN1t2O3EDDIVkN88zs1+KNtd5dDBN0xpYQxSdpq7vcI2Tkb1Sz8hvYsojBYiiESJliKQoj9J3FmNsYUvLWKwqxy6zRyn5TFvboHWRJUmZ4CPH40n2LhjaboezjrbpSEkRYyZoQ0xA0c8isRZw1liUlX3GchxFJmYc5fdo2iIh0zSNnAc+cJhOa5fZ+EUS9EXXWY8QwotQ2Mvg9qUVbk3W9ba1MLlMIvXv1yOW6wXs58Y5Lwnz1Z/63V9LTdTXdTnCqsflTvSljuT69V/f76XP7Pp2P3e/nzu+3E+hcetIqArPLfMMyLIsekkIqVTbPnhMsphscV2SC9BayAmNonUWlRIqJZZJRlN+mfBeGMqpMmONoe06lNarcqT8u4x/UBUX7ctFJ7eXpJDQx9Na1VWoXigt87KI9kzOQqpJWZ5PO4Npz1/KMgmhbp4XchTUhLWWHBO+CL3Ns0hK2y5jC+NWxsLi9JxzwjgxTF/8Qt9vsEY8fKNfGI8HwjKhG5Gy0CoR/czQNWyHnqFr6buWvm1oWoFFno7HoskfnynR3t7uBHWSIWeNwtK0so+JUbqUafK4RhboyyKJNYTCbi2dgWgZLYzTyDiNLCHISDBLUAp+eUavryO7Ctu81KSpldXlRXJ5shpjSpDIBd+ef3Lh+RAYC8qpyk1fXtTrn0UyIZYdQEpZtPojqzmQWNZrnDZYbTFKQAhKS2IbhqF0hVqsX4sek1JqlekIITBPEyEllD7LMYhy7/MA03XirxxjlRivc+j6HiQmR4nQ5T2UwFK+j7U6N/K5+RQQPaZU3APDmQtSZv7ei/SKqi1AAReIlwmsfUFmRYSJ7ASr1WbW5yAagigVoMHahlyqYdkNJfwSSIWdHcr5Uou7bMy6YE6FA6SUWt0N9dX3+NJS96Xjpdn89Xjn5373j57ncp9w+XN9bl+Pti6f8/KxXnr8zyWm673G597L517/lyzar48vTgr3d3eMxxPJe1IMTGPi8fGTuColuVirv6/3MvLR1mIbR78ZaEzL0LdQEsum78XzGdg/PnI6ntg/jUTvccbitJB9mq6V2XNK7I8HKCQm18pMXXYAi7SqTUM39Nzc3QIieawfn4p8bz5rGMVIiImHp0ch5FjLOItKZ9d0DENLtxHd/mWc2D884BcvHgIRQX5Yy+Fpzz4fGPeiy3M4Hbh7M4jImrOYFMAgLl4qs9lthBPgNLe3OzbDFqsghpnp9IQh0NqOoTVYlchh4n634Zs3r/j9b3/F73/3a777+68Y58A4zfztb39hu7+RzwdBO3m/8Jvf/obbu9cFieNwrmXYdBij2T898fQ0ktIHttsN1moWP65Vk3EOXWSqp2lkHI+8//Ch7A3EpMcYu1aMr169Wkd3x5Kkvv766/VCEebz2ce5jv4qdLIypJumKRWoQllhpy7e45qGvkAZT8cjf/7zn+WijElgsCHg2kbOJSVyK7pAnn0KxJgYTyeUAd1otjdbur4jBhktmlzF4yDMC0YJAe/Xv/41OSVOp4MkiWLCFGPk6fGpwGyPPO33oODVm7croXKaZ6BIVuiACppX9/eI8qlZk9k0j5xF9iif17JqTjWuOQMXgOADu+0OZy2nk2PSEynW+b3cV2sZh1JIceO4oLUkf79UjawjIck1EIsCceXxgHAWdFYiTqnUeedwFZzE5EgCekgJZTSfxo/oqAgx4dwk8h5yp+IcVxReY1yTgW0cYpL38ijn8r9feh3XQf1zlfd18Pxc9XwdRCuMunKkahFTf9bP7QIOev0ctbC5fs2fW3R/LnG9dJt/1C380oXzLxofBWuwVhfscsDPk8xaEb0hZQwxZbAGhcM2Dts0NM6uUNNYRlAxeIHsaV0geaKXZEqVmFUuC8GzPeNllq4Zum3b9Y1vNhucc3z48IG6R4gxrhVrzuHC2at2AmklZqlSTsk4KBfZbpHIqFWTMw05FT+HqCAriKoo2AjL2Borc9oC1T0cD1hn6fqO3c0Nr+eFm5sdfdvT2I7ONVhjsEZhNTij6BrDpm+xOqNSIPiZxip2m56QDiwerFVkoiiHkuj6lm9+9Q3/9Pvf8/XXX/PHP/wrD8sTKQbuX93S9x2NcxjT0nVbrHVkEg9P+xL4+rVDWPzE4j3TPBchOdnVpJzJMciM/gK5UhFGh8OB4/G4nqRCHvTPTl75fBeeDnuWSaxRZb5b9LMuKjFrLa0CV6r0mnhEhVPIXtvdjZyTQYJbTIlpnpn9xBIWWBLojJrBOCNuYEHMYaRfEGbVcb9HA85onDGQE4+Pj1I0nE6l0pclcFV5bdoGbQybYWC73XJzc8Puab/i8SXZRmKqYo4CNEhJwVwW3qZZrzNhjetVXqJxYvNatb2WZSnFibigyfmvVhRpZY6LxJTGGidJoWhyhSCaT6kwnisZrvIR1kBis3zmZZ/SlqRtaheBJuaMMiKuOGzEnOnjp0/y7zmukNQQAkuBDFc/FcXzwFh9wK/5AS9JYl8G88u9wEvIpXpcjm6u2cuX9/3cTL4WTfW/a1y53iNcj70uP9Pr8dL1c1w+xuXP9eu/Tn6fG4v9N98pWF2gbcbg1UIuxBujFFar4reswSuUET0e17aismiFsCZ6+EJpj8FjKDE1Bhk9VSneCk/SdfF21ua//KAuxdsq0sUYU5KCtOj6YmZ3+WXWD1MudKkC6hKytsmnwoaexpGu7Whdg3OWkKJYiCaN6O+MMit3tXrQ55l3ihxPRzF22e7Y7kQLaLPZ0NqGxojnQ+VCGA1WQ9dYNn2DIUHy+GXEqMzQO/ajwliwToNOxOwJOeGaLb/+za/59te/5s3r1/yH//gfeHp84rDfM04zu5sd3379FqUtTdtjnCYEz/E0gtZsrUNnI0Jxp8DiF+YC6VUlKYQCUeyse2atWuUwYoy8e/du3flc+jbXP4P3jKOoqVqtcVZkt3MB0+d01qexRrgD1loyMBU9JNXIuMcay939Pcs0MY8T0yyL2HmexJM5LCQ8qEw2mXZo0VZLp5ClUzBK9g9i35pxBXqpgMN+j9aa0TUrkkfGIqbM+RvxuO57hpIYhs3mwso0FZKaGCHp4hutqvOkOntc12W7nK9lcV1kxmOM+MUzz35dbIsBT4sqXIilLKFDEHl3skCrjRZkVU5Zzt0l1PBCeZGybwvhHExyJoUG7QrR0wmru0p8ZEREEGWxjaXLvZRFWngekVw4D4oQgyCbgLYI/tV5VirJvwoavqR19NJ4p35eIYT1NvWcq7+/DLCXQfX6serzXD/2Sx1FfZw1Zlwkk5eq+8vnvQ7s18nj8r1e3vfytXzuz+vX/xLD+0uPL/dTOB0Iy4LTCt04SIa2BDKrNSRZAFbdx4xADq2zGAUpeJYpMR6P5JRorPAAaMXpzDrDbreRhV+pTlJKHMejzLwX0dFXZWFdcf+kSNM4djc7wU0vC3/5219Lpal48+Yr0ftvRXZDL55u1eu3xLQQUhRSVt/y1ddf0w4W1yjelwpRRfF1tkrTGktnW7btFu8zKUHT9qKcaTXOiWLkMs6c5pFpmgnHo8hlDFu6vpdF3ywsT2MNQ9txf3OLnyaOITCfjnTO8Pb1PZrIeHjkL//6v/Px/fdM44GuEax5UgMRRciZH79/z/Zmyz//y+8JKfDX7/7O//6f/0+OhyPRe/76t7/TNI7/x//9/8avf/0rtje3vNneM2x3/Evzb1BKTpx3737keNzz/v2PLH7G+wlrxTgm+Mg8jUzTiL25xWqpZjebDVXS4nA48Le//U0CpzHc3soo73Q6sd9LBX1zc0OIgb6TRb4riT0mkVFZgi+jviCy0lbkIUxJELqcIykk0Ri62XFAdkRjkdhOOa7uadpqxL0pcTqeBOY8iVaPK45tZNgfDjIeDZ5Xd3fClm9FrqQaEYlrWpHOMIai5MPhcFjRR+s1M46cppFpntBWYa3wKGrwutyzLIsIDNbRkkBaF7SS55FEN7N/OhC8R2vD0A/c7G4lmEbxlwhBEpDs7yi6VrLnSnGS5DEHXOOKuY8Q8hKKmDIxSNIhZiZOtJ3IzbRNQ4WN1kX/8XgCo9eRo7KGYbcRPaUMw26D05bDpwdyzKhcjH6sQ1tDQmGexaqXRyHXf78Mepd/Xs74Lx/nckl8+ZgvVdrhIjFewlUvg2ztxurvrpnR9TVe/nn53y/tOurrvO4gLpPkdUdQb3fdoVy/38+NyT53fHFSmKdphZnWsY9WUtDXBaHKZZOFkBaNkbYwJ4G85SyL2Wr27eo4qswdnbO1Eb6o7M+6OVWQDGQhWBVTM0W7pSzZ5IuV+d+yzCuGvOrQiCiZXbsQHfQ6h65zb+ekUgveSzeCotAy0aYKrmVSEnKRMRrtNGhPLoiYGNMZ258rBd9irGZeJlBi7lP9nGMI5Ch6N85ahraDlJjHkdNpYjzuIQfhNiRD1IkpRPIiNo8pBTbbDdM0c9gfeNrvmUbBjR+OB4zWjNPEOE0cTye6Y0MXm1UqugbV0yi38X7G+5nNVpKew51HSDmvs+b6vdSL5fIiqTuE+rt64hsj34U1AreU6rSOSfKzbsEYASlYJ+PI83hJrd+tsRZVnNlylmWpMRptFcrW5CB/yhhmhqyIKpBsJW7JUavAlJKofq4jg0zO5/egVIFZk1fW9ul0erYArxdjLBWtMc26l6loHGGB+3J7qKPPVLpNqN1yLHIxEesuKtJ0EbTKAjlGub9ARCPWRNpOJD1iDNhk12WyUopUZcEpG+8s12tO4nle9wvaGBRaQCK6LoolFpjkJEEYg0GuC1uq/5ACKmdiEE8So6wQm8tnninBi5/yFK6Pl5ay1xOE63HQ9Vjourq/fs7L77g+z/U46/J216/vOrh/7r1cH5ev6frxPrdP+TkS3i9NCPALksL+6UFMdtp+rS5iwawn2XYJu7mwmrWRKkApGRWFcpL5SZZP0evye8GikzNt1xALKUiZ82hiWQT5c3d3t1ZYj4+PAhEtmbpq7SzLgrWWZZGFt0BSxbil73r6blhHGkLxL7ITZV4a1/YZGteQ2sjStsQlkEMkLB7VaEyjxcMZ4WsYZzGNYfR75uhZgizyclYsIQovA0k8jW3wWsh2bdOKWQ+aZZzIOTONI1+9esNXr96wLIHH/Z6/ffc9c4wYlXn15oakNZtp4uFw4PFwZJqPhLSwvdnw8eNHfvjhBz49PhB9pO8GPnx8JKXIEgKHw5G/f/cdnx4+0vUNv/rV1yI2dzzw6dMDp9OR02lkmkfmeaQfOtq2YbPtCbHYdua0egRUhVWQgFrlskGWkRWpU0/4vuwuQoqQ8rpTUJzHVJR5tCkKuH0/0PUd/WZgmiZOp7FYRGa6rmMq4nlaKzF22g4yznOakAPGafqhZZxFZ2hZnog+MMYsux0nVX60BpXPbfvNze16IRojjnLzPMtYqwTJGAKfPn5cC5JQArlzVUjPlsIn0XXtKk3uvV4/o2VZqCVWzsIxiFHsYQOxPK8gfIQ5L7BaGTmF9fPWxuCsK7asgQ8fP6GUwWjL61e6kA4TQuovXBLE8IoUUUkgyboULDllQRaGgLGWpmux2pB0KtadAggxRpOVLI2N1vRNy83tLTpnfkxCcJ1jYmhFcbbtO1KJe0kudBnxXQWw62r9MjDXgqGOhevvmmLBe50wLgPozwXKy8erz1H/vN5xAOueMue8jrMrUu26m7m870uJ4zoJXY5/aqK73lNcjpCu9yL/JccvkrkwSlyU6gwyFhax2FsKimFeZjlJrJC3dNZQKo6YhFxkkiLnJMvqRXTYFRlFI2Oi4NdgLeqdLVrLKKJ+KVV9syp01g/HWsu3335blp77dQYs4nkNXdfLXLcwTWtFJUgWj//e0/WWpjOEZREORohnAhMQvWcf9uRsQBlCFMhuu+nK6CMWSKGMPdrUiVNX2wn72DhSN0DMq82iNU4+yigWpEYbtpsNj35PjoHj8Yl22DAMG169vhftqdORpCGSGLYDw6anbRv6oWfYDDSuwROKTv8Waw3/9Pvfk1Pi7999zw8//p0YPP/0+9/QtE2BHVexttIJaI2PEZcSm7al6Tq6XubHdc79MD8U+Opcgtv55D4cDiiliseFwEG3mw0xF7ZyjKUaPV/UXZGqsM5h26Yg0AZc4+i7noxAiGtin4uPd0042mjuX93jWotxmtN8xFjNsO2wJ4sdDcMwskwzyzivz3t7d0eOgaXryq7D0g+DLFfLc02TLRecvD9Xdh0hhNVSVBmzotq6vmNjN6Qs34PssWQUdTod131AJYS1ZRHbNh1NI/4N4u4mg6qhF85O1w4rwVDrUsQ0bSHhWUF3RZHikHPRrqJ9qAJRBekAtPw9ueIpXpSOVencoirXSk6SqJ0V4IHWq4he9WJo2pbWObbDBts4kg/l84nEZWHxC9a5taNLUIQyVbm+CpP6ovK9nK3X41pc7jJh1KSrlHoGcrgeIdXjc9X1ZTA+g1U+H2xfqu7rf193JZ/rBi5fw+dGRS+97uvdwfVI6ZccX26yU3D8MoCUqj9G6RDI5yVVvKgIVZaRUr5oRW1FHJXZZfRBTOSBZGR57ZcF14hdpCuuTza6VQn1snqoy7llWYofs3gCNI0j58TxcJSTIYldX991K8JB5Yy1BmvMOjM+jkfa0dC0pjiT5SJtYbDalPY7CuxQSVKICXxswYJPnphjsSEUJFLj8lrxWuuw2uGaiIpi+LOOX7Koh14aCcmIKjLPE/12w7DpuNltRI1SZ6YwM3mp4qWid7RdQ9vJUjxnGXf1Q0ffdXz77bd8/PiRf/3Dv/K//Kf/xOHwxKfHj9zd3fH69Ss2m058o2OqwJTC1paK0DknJMRSHcUYV52jaRJYb62SQObqTePYbLbl+3FoY1ExEGJNzuXEraOiVkT5hs0G17Y0fSvL2XJxClJLAlPKqRQF8pl1XYd1lt3NTngYTvYJyij57pOcx13Xib3w7NcLvzL0ndYrdLpWnVopuk54LdM0r+MtbQymjG/EuyNjXEPTJDabTeE89Mx+lgAKq2LwPE1oLZU9sCKOTBVWLDab6zgKVje6tukK4qgsr1ek0fPlv/goK0yGVEyjTA245JUfoI0lOrt6pUjHklbxwFSvYaBRUizU8VEd++SSFLqmpR8GQbItuRj7hDIGLgjALKJ5okcm+KlcupNno7wSX+qoElTd0MvfMxKTLvYRl/yYdZle+Nrn+HiGAV8e14H2cpz9DLWUWcfeP7fE/UdjoOs/r1/LS+OievvPdQQ/l0i+5PjipIC1BBSj9xAiKotwnHOOxhqcFvKRsJuFxblpNyhtOJ32xSVKc3d/L05hp6O8oZS52dySc2KJniVGRr8Qp5nttuHX3/5K2nIf+Ntf/srpeGD/+Ejf92w3A33Xczyd+PDhAzF62Rc4ka7eDD1dwVLvmgaXIvF4WNUms1LEeSbME84h3rhJ4azGKMvt7obGOZrGYZS8v8PhJJyKh0fZJVjL+0/fy8jsnWG76WnaBts1lGzBtuloXUsTJIDE7JmPEzkmDJp+N/DbzT/x6eGjBFar2S8n/pc//GdJQMuM2bXowUKrSNpLdRlHDJ6uyfyb/8uv+Jf/7htev+748cdAzie6jWKJnoenPTlretPz+s1bptkzzYHoDWGxPH6YII4YRg6PU4GFBvnB8/jpgbaz+GWibR23u4FNtxEPAq/YPx15+PTI8XTEOcvd3V29btEm0fWWt1/fczodOZ4e+NMf/4jSmr7vhGHsxJrSNZZh6Lm/f81ms+Xt198CipgTf/7zX9gfntifDlSDmmWU1/rp8Z1U5VuLbUXE7zg+sTwueB9WDf/3WsZRWil6u8V2LY6meCor/va3vwvHZpp48/oVQ9/z7sNH2tJhJhnCMy4TyyJ7rs2wQynNdnsLpc4Ni+zM/Axm19E3tziz4MPC6WnPNJ/KYjnQuBaD5vbmjrbrkH1CYFkmjuOJaV4IaUYbzWbX4xfPMs08PHyicQ19N9C3HcZqlnFkmiT4Zi2L9m9+94au6+m6gcZ25Azz7Ak6kNOECrIAl0Wy8AVyFAnu09MTTSvn/+3dlpwT8zLi/YJWms40qJQJp7nYwyq+vv9WgpBW/P2H73h6euRvDx+I3pNjpFkOxE7zyhnyNOOnmb7psEoLEkwnQTm24gQ3hzJKNgZlZUSVlCYbSUqz96QkSKg2KxKamIpvQ86EwtAnKxRFTryIIdakkivXg0yOGaJIqkv+USVJqVVy/xx9WW9Tg3FNxnURfbkcvvzv6/3G5b/V+14mossx1KWb3PWIq3Y1l53Rf7NFM1qtMrhFePd84WvxkJUPzlBZk7o2g0kJ0hQwSvDtgnMWXLnGkhQyslDCqM0gGjhalcWWxldFxmkSSGfb0ve98BuK3pHo3ZRZt9UYJV60gpCKhEX2AjnnAp8LaA1925Iownr1dWcAvdp6Ql7nqMZZjDNFHjkXw5GZxkqVo4o8sMXQGhkZZR+JBa/il0VGRY0vy22DMgpttVTGGXyOZJ3BKmxry0WRiSkUcmrEGGgbw2ZoaZ3G+4mUFpSKDIOQ+1JMkhSKQY41DmG5GrQy5KSIPjOPnlkJozZlj7FgDPRDg7OWECJd61bbTJ0U0+O0nrDW2BVhUwlRqUBYc5afVExllFa4oMlZAqH4CsvYsVbUsfhiL0vg8HRgvz9wnI4Yp7FOupbqmeyclc7AlMVmqUxl0Sp+32hFU2QZtLJ4u8hitYwfJn/C+4CPAhSoY8eU5XuPIRZ9rlTOM4H5G6VWFzuFwsbqEdLiTIM1AmfNEeaizLv4WZbC1pWO0uCMI6UAquL2qxxGLtVwcUtL8jnGqFc4t0IRvBcyWoxYp1FWYTsJ+K6xQBKyG3EdWfnoQCscjixZXM5vm0vHJiO/jHRYOQdxddO5yHCXl1a6C6tt+VwyMUHMGYpETsqJJUWWKIZWMSVyzBi08EUKaoms0K7EmFw/VRl7iYObkmtXKRLys76EXHt7uV5REj8qq1sXtrgqS6tSTxe8ZLnLRdehlCpMFr0mh9oVrYH8qhB/aal9GfS/ZKTzS3//Uidx3SF8aWL48qRQMOSJXJAIoEprWdtMSQDCNhXbr/IBlypCo1ZTk5wgBaH91xmj9wGlLa5VaGtJwOhncpTxVCzjFaVkFLDb7tjtdigt6p6VQKbKnFQX2WCjNNYowryIhab3cqEbg24sXesY7m7ICo7jyDwt+DkwTbL82+3sunxCa2zj2N7s1oy9+Jk8TYzjxGRMsSaNNE1D33X0jRjJLMdZ8N05czqe1qVXUxBRMWfQim7TnzM8gDc0oUc1mqSkWpMPPtK1FmN7Rj+iCXz68AN+OmJU4u2bOzZdx36YSQF2u1uBWTqH1ZbGtjizYFRD9HDcT/gwywghLQyblu2u4+u339L1jpBOK2ro1f0rkk88vTuQEmhtub3d0LSOzWZgmk4si6jcmklzOOyJyYNKdH2x+1SiJmutousaeV1W5uEpHfH+e+bZczpN/OVPfxPtJacZtgNt13J7d4u1hs2mxzlJrIJyipxOIykm5mkRzSBjscqxG27Z7UrVO088Pj4wF7a2TLBUcT8Tie2qvhpiFMBEMchRSuNc8Qo2lk1BtFnryEkABbvdjqHf4UzHFCb8HDk8HVj8SIwL1jpIGWdsIdBlpnEmE9EamtahLMxhJKeIj4LiSSTRclKZEBbGlCAhnw+SUIahx7Qa3UogS/giiihuiQojRUE0ZJ2xuRGymdEY12Bdy3a3AyIQWfyJSsKTvaJAzXUJukkVYlEqla3KWNfQdj3DzY5pGkXEMHnsMjHOI3iJE1ZZLIrkF7LOUggVzwpV4opSiqyKEKGSJTlaEbUma/EpSWhJErlU90rOS21kzJdTVfA1JVEgO5TLMMe5M6iVuNV2HWsBK8iiJpJrJNBlUvi5HcLngvQ/Gvm8hHhakZgv7CJ+6fHFSaG22CJNoxHLGoAscMYonrfJy0UZfGCaJpSxwi9Ag1YswaOVLApjMU6pwsE5Z2FOWtk7KG04PD3JGiPK/M5awzAMontUPiBrLDc3N8JbKGgYciLlAmXUBqu0tN7e4xcvkgNaEEeuaYqOksK5SPSZZPK6EKxSDdWwHpDZb5ldGmOKqBk0RubAdcFlrcUv/tkXmWBdjhtj1qpUlrSCpqlf6rwssqDXurTDgdPxBFow1dY5ukZkQZZx5K9/+iP7pxE/TdxsBhrjaG1Liort9pbWaDpn2HYNQ9sQWodSkGJgmTOxuHQ9Pn3CmNe8+eqet2+/wTWGP//l/yAn4Y282r3BKEPjWrpuYBhkZ9A0VrwwlpmUVOlKFOM4SdmX4f7+VZnXQt8PNEUGXGtxbJvGJ+kakxZuxHxGPG36DcNmYLPZoDQYUyGxkhTqZ1iTuIgfNiiV1s+7aVqcM1gr39t4OjHPS1ErlfPM+1BIYqoo9J73ZZXjIiJ+4lNQ3c+0NijsmjxBxgHSYVQ2vaDWnGtWUcf9fo9SR8ZpxBhoWkH4xHyWv1Ziclz2TY2MQ4qUdk5SAVdb1aZt0FbGhyGcJVpk99DI+VWq6ZyT7PhKuZ3Lrm273ZCJ5BR4fJLzWwLPxQJYRbS2KJXKNVz2aVqx3WxwjSGrwPFw4OgsqgA7xmnGZo1VCmWkag9kVE6olDE5SWdQLXPVuRtQSsiyqhIfM0TlVxhthaOLdlQV+NNkndbEr9TzYP7S7P+aF7AG3YtuoYTA9bheUNefzz3PdYX/cwnhckx1HfSvNcBeeowvTRJf7rym60lU5mwoVHF5SoXQQ4IUIt7HNYBqW/Hm0maFKEbh54SQ1/9RZAtc20Ah5MzF05myGJYlXLOS26qGSt8PayehjSUnGb0Y54QQJ/TosvSK65tfzUvKSlkr6TAuF3wyhhDPgMqIvkwKVQBuWRZ0ktfqa1VZEktdSpUuezW7X8p+Y5UnKCZC9bFDjKu+TCqBbl4EMZNyLiQkGcn5ZeH9jz8SvWDq+6bBojFJXPM2Q7fKaPSto2sts7NyiqcoASiL3/Zhf+D1mzu6pmO72aEtTOPCPIvMw/7tid51ZR7d0nWdLHkL0UwpGdlIN6VZFo8xcqHstjtAloht05aRk8g5xBA5jSeWJeAXQYfFUDwwjF6fR7wBKmzw+ay2ahTVH9H1qQCF6hjXklIUiGbRjJLAKKY8MQoSp2kaGfMleSxFQfk48bGIQapW8XEuaCAlCKAKlwzBr9eI1hqTZXRny34jhMAcPDHloi+lyTQoC6l4IlQZbYncZYZckoJIx5wZ/k0jiLZsMsnPpbBZIOuy2G7XwChxIpdxH1QsiS4WrRBIsdo8Pl9gpiSdsypFn4TKtL6urmuxTpPyrVyvMeJPJ3KWpKuUQWspxnKZMssbVUVaJZ9n1M8CmnznWoscSYiRWJNGviSd6TVey93V+b9VvRifV+s/txh+tvSt+UDl64d4dt+XJDXq778kEXzuca8X1Ne7g+tO5JegkL5c+8g4FLJAU0qWPNYYcpRwXqsV8agtJ+HhgLEO5TqUlWXRUqQsIqz7ApkuKayrhjEtbdcVJc4nqfpTIsUASQxgDvsDp+OJcTzRtmIco6y0lJIsAiF4+q7HGYNFlByVdZgsM/+2HzDWkRI8fHqQoAgrU7d+WfM88/T0tOruVAJdJbrd3t6upu7zacTPC+F0hroexxMVTVQN0GubGQsSI6bE4ufCZJ2ficfFFNd/g6ruisgHKCXG6EFklH84/g1jZDGudYOOGYdILLQqEccDLnnuNx1f3d1gc+bpIEqySmuIggabxpHWtnz91bc8fNpzOh34P/7zH5nmIz4szMfA2zdf8d//7l/YbG4EhFamu8FHjHH03YZh0xauibBprYWvvvpmRZGFtfpumOeF02nidBzxPgIC7ey7gbdvZYe0vdvKLscpHp8eil9ALIQwXTouz9PTnsPhwDRNUjDEyDTNPDw8lIvknqX4ip9O4hD34f07rJXRS+scbeO4vRUQhMAmhey12+3K9+MYxxJsVS0iAoqE1lUNtfBgtMgw7G5uWOYjPswFApx5eHhgGhd8iLRtg7UKHyYCgmTzMZQAKPwDMtjG0DaWvhuIXpKGGhSNa2iblmQjIdcOIZKTiPGJmZKoFmstKrBSUSdSWdBWhN0yz7hGdj03N8WjPSwFMSioL60CWgdccb6zGrTTovVlLCm3uEaCt0HxKQRyTCzBi0ih0mAl6ActirbkhEMCe1IyolaIqCVKhPqEzGhxZDwQx0mkd6JnmovEu9KCiixw+XNQlcSQKy/iagdQj8u9wTVDWBXmXZb/d95J8NPAff24lwG98kteGgm9dL/LpfJLSaX+26V43/Xj/KPjF3QKukBRpaJflzFlnpe1BIVsQOlYqvIISct3rs+kJLJavZbXtrC0fsuyFFMO9QzyWntbgc+V2SBVeVEWvolz11KTU0hNuaBEX8WUxVmd5asinZAXqTBRgsNuXLNm+UvCSw1k1Y+4sqQvv7Bc4Ke1UliCL+zSaT2Rn2mi5CxoleIrcTwe1yQTS7IF1oTkmkba5Hw+kUTrhrK4yxglGko6iXQ3KaCSJ8wniJ7GajZ9S1j6ItcsJ91pihgli/ftZsvdzR37wwOfHh447MVTYfEzx+PEtF1WHsiy+BVwYI10D+uFSS4CbjKXtbYBckHwCDzYViEsLi8qXdjlYvXZdmIopIwCXWHRqXh4y9joUiZiGDY0TctutyvM+P3aXdZzWrSLimaVNThnRUOrfJfiA1EYo+XPvu/lnNMav6Ryil5UauXalu9YGMZta1Fa4LQLVbhOgvWyRHxB7ckFLHpgPnpCDhIMcyYqcXzLKROMwyZ3DlaZtVjTusBws3TBLstGrzLNheAJrqJn6n3Kvk7m6Wrd3ymtaFxLKiS1Jc+QA2FZyrWrSsEiPufFW0uuccoSvXYwriGpGh8SnsASA0ZpomKdHISc0TnLnoMsBSln0pqMZYxI6BgjXcqzjlujUWXB/zI/4aXjJez/Zfeh1PlzyUgCvUwI9fV97vH+UUdwWelfP+bnks1L/ITr5/4lx5fvFHQ1uS8nICXLa0EXqKylWtFRZHS9L9pECatKQLYW6bUSSlnEXFy+9JiKa9hUZsfTJJVCTKsSq0JmvK1za6Ds+66YzVsOp6NIShQtmXE6CZ49Q+McyllcJzhqYwzW2TK0ykxzdeoS0a7NZrtW5iEEjsfjuoCsIyU4swhr8KqBZihjCucc+8OBaZ44HA6yA0iihra2luWkm6aJGIUE1RWkUNt1pJzWkdUwyJI1RvGEsFaWYG3b4Yx45zpjZVdiND4JSTCmAF4zHx5Jy0hvNa92GxotVddqXhMXUrS8ur/j7Vdf8c3X3/Knf/dHvvv+e54ej0VbKDCNnmUK+CVyPIw8PDwyDAN939J3olYbC/s5BpHKllm8jFmWZWa/3xO8LG3bZqCOBayV8ZNStugJtdze7mjbjqQTSWVB0JTOdJ6XAvGc15n/zc0dd7f3NE1H07ScTiOn0yjn1IVT2na7ZegHTn3PdrOh7ztev36Fn6ey8+hRqizyyKuek+TxRPACe5QRlVyI5gK/X4O21g0oK/IkRX7COUkoyxzLvL9WgbKnm5cZHwPGarIxKALTPJFiojFiyOSNJ4UEWdFYvY4ytNZYLE3T4dx51u6D7C+6rkepHlWX/c4IqigDQRfGdp3fG2zXS9GXI8esIE+MaSTniErie26IIidixFSxFnyr6U7TSNfmvezEYiQugeM8YY0hqkxUch66HDFZQdaoHFFJHlerc1KwRvZCqXRxUiRQSHqyNE6xGjD9/PL1pdl+PV5a4Gp9qfJGQTMVtNP1qOmF43OJ43O3uf636+7lpUTxX3p8cVJQOYvoXYgEQCVD2zqqnosSRxdMEmZulXhQRto8bYulYwoluQjCQqX6ZZb5ewzkFDFG0zhHWyQAwhIYuk6yfqqV7Vmb/XQ6MU1yEaXScUzThEIzNwsgJ6F2DsFaCmqkXsh3d6/W5KSLJG7d6F+SYWoV2nVdGT+kZz9OC6RSBb/KP6iy1LqkyUuFaoWQt7IvZ+ZlZpqm1eJS6bMscNu2bLdb0WQqRKScMzFF+qaFphGyYCEGtkZhG43pHafTBH7k/fd/5TTOjIcHnElsB0fb3xXzoszx8ATZ8M3X/8RvfvUt9/d3kGGZPfOUsEZGdV999Q2bzQ3fffcDf/vb33n37kfevHnNdrcVBrrJKEyp6CzOtTJi0I5xFP/t03EChPm9LBVOmco8XtG2/cqatc7R9S3dpmfyM+M8ruQpIZhp2tYV7aUqjdHTtv2aBLpOyI3OnUEKIURcI25r1ig2m4E3b17z/d//zng68unTp9VdzFpDzon9fr/uLqbJk0ImZ4U4nClM36wXqnOyfDbWPNPxijHQdT1t69htW/HQjkkQRwqUtkSVUGEWwl2MzLFcY2WvVncW0zRJ4jULqU/FlEeRVFq7FRAoc4yBlD0pO3IOKJ2xTtH3rSACM6Q5r51QShkfAuRYXpcEeWcdbdsIRyAllE6gEjktxCCSHsqIqU4lpzojHV8MgbgETuOB8XhEabnWXSEkKq0JGrKWorEIy5bEpko3olcSoy37G1UKzuqBnVMusHiQLkhXECV1wV6/p5+bub802sm56kxl2SvATxLBS8H5pW7hej9w/W/XO4T6uJd2Apcx6vJ1fEnyuT6+HJIKZeMuWOeYITdOjNKNRhWqvcpgQ5mHZ+EcaGPKmEl8WytlXqCM5csu/y67AxEoc1VJNSVyCDTOEJUiLOEsxqUqe7Oc8FH0fZZi0zlOMylm2mYWnZ1SbmQliGVK5TFsNhKYi2ucL7Pua6/h+sU0TXNmRl9U/esSnouKoiQeCZASOGwJXCLmZtDa0LRixSh6/fLYl0SWOqqyzqKiXnWFUko0TkT7tMr4gmO3BZ2jnGZWiRQi+8ePTLNnmU5oZWgbQ980pCSL38ZpcjK8/eo1d7c39F0rST4kgo8Mbc92u+Fmd0vbdnz68R0fPz7w8ePD6pA3TRPOSWAXxq2glExBZs2zaFl5HwtapyyUi5hbRfFUxVFBmEkH0fc9icS0TGulVOXKwRZDH7N+tq5xTKMgxqr8Qd1nVCc0a6p3gWGz2XBzc8O7H75f2dp93xXVUfkup2kilYAQfBYp+OJmCYrUJrQ+j/ysFQJlhAsVThGAbJuOrtsKDNpHxP5ZZtbWGmI2hCRdUU5BZurl/Zky0vTeE5ZA1FHsYduAtoasL5KCKozkXJOEBHGtpbORhFddCMviuBRBEvwCSgn6T8AZunSDkRAESKJWHkUdY8ruLRVkotGatmkJ2jLHER8jx3GkaY+0sWHoBFVo6tK5JAPZJ0gE0mVMVCcVwufQ63hvDfYlaVfHOijjLAUVBZdzKinj59E+8EJATYUTdTEufBYrL/YR1//+0n7hped9aWRUv5PLnxpbrp/rOpH810cfodDF3jD4QFaR2Ep2lxFGPUGDzOTbFmWsdApdSwZCjsyrj/MiHAIjhCIKZLV+wtYYGucY+o5lPDGnCDFhtaLfigsWyH19jCxLFIlsII1ywqUsvINpngW+5opMsxJ0Q5hn2rala1tev34jC+zTkU8PD8yzMK7rbL+Oh5ZloWlESkMQLIn7+/uzjrsvDk0FYeS9Z5xGvC9s64LO6TqRZa4y3uIXINwGXxnXWdQ3lVKYxq23O6Mozl1M13alS1gkKYQFbQWrbRs4qcAUPB9+/BuzjxxPC5vdjqbp2GybUhEmbrYtsW/4l9//jtubLfM0rou60+nE1998zb/88z/z9dtv8dPEn/70Z3784QceHh5wzjGNE8EH+qEtInqCFnrz5ivmecL7hR9+eF9OZFEL1caKH3GmzPGlor+9vV9nxE3brIvSZVl4eHjg3bt3xBh48+a1BPXGFSiwput6jLakmHh4eLzQwJEk8/S0J0YZVzonC+W2cfR9y83NriiXhpJkpIMAEak7Ho+rZtZmuBHZ6JCYxplpWtDKlffgyghRvluBfoqKbN93fPXVW/p+oO+2PD0eOJ0m5mWSkQxniYVUk0KGodvQtR273Y4UM8GHFTadwvkzbFRL1jAtMykFUo5oLRP7YehwjcVahdYZZbIkIzRkRTZKiG3VYjdFFElE77JcP9Y5mjYQo1TuSidS9mSfiy+GoW2E0CcKyxKw2q7DRpGzWYLn8bAnqkTf99A4mqxwShONkl2fUiQtibLM7NC6VOh131hn/Bcx8RJ1JCOwC3joZezML1fQPze/X7uDZyiml+93WdlfPk793fVzfu41XP6uTjFqQXhWHn4u2He9a/jS48vHR3WOZy2pBq10xv1W3K5UuBR/AVmu5ZzPHUKBCfoYyKqwg63F5EyOjUhH5+oBrZknyzyJB3JOGWclSKsyf3Zti80CPW37jThBHQ6400lcn3wou+0CmMvCWlZKyainfJh1ri/vqUgG5zNX4Vzp65WbUBfMVZMJhGgk8L7ze6VUsy53UrcAAQAASURBVNba1Uxk1WbJZ8Kd0WatZuuJMy/C/rWqOT9miutS0hS3Mm2MfHZBJBL8NNIg/AajFJqMMUCOaCWLXWs11irIRSogRTZDB0rTNJYYPYf9k2gAOSez/7Zlu9lhjWNOI0+Pe5YloJQmhMh4mnifPtAPLX3f0rQS+IZhc+Z9TCLb0Hed4P2L5STUPUxDU2Cu9eTWWpBFT0+PPDw88OnTR56KSurNzQ1Nc17s6/I5kou3xTKvftyr/HTw6+tpC6y3cfYnHBP5063jlPNij/NIyjakJF1R1fZRqowvC/pIG6lcrbUo3QGOYRB1VqXUOlKts+IYxW97WiZijuXaE8kJ5xwVmFHHmcYYAWWALKKDQLJDiFIZU/g7ZQxTF8mpsM5jDFLtZ0O15kxltPsc3cJaGWutZJxTHjMX4EeJGKLllWWuX1nGWmkwRW7EyvIhkuWazyv9TEAjZXm5Prc8LBUxBFAFupQqOIW83lISAhVtVLoErkABmedJ4iLeXQfkZ0CCEvcuG416/r1U2df7f2ms/ZLjMrlc/lzvRX5JQoBfsmg2pgj7NiwxFlXU8wkjOvixoIcoTmQWlGGOsnyOqcgHpCg2idqhsxaPXa2xRlybcopCVgoenTyH/SPjONLaDu0ahq7D2gZtLNraQn3X3Ny/wjrHp8dHnvZPmMZxOh6JMUo7rYSRPc+zEH02m/I63bo49mW2XWeGAmUUKYcasNfqvBMVy2EYzknhNBJCXCvceZ5BnUlFSstQc3WaKskSEBa2PmuXaC3+ByRW3sQ8y94hU5aJxqKLM5mPkWmexJjodESFGWcsXdOgVaKxSlA+SpE7R9dZrNOEOBGCdFt3d1usbXBOMU8nQUxZQ98PWCNaOzc3txhjCSHx8eMDkGialhASfjnx7v07+qFlGDru7+9Qt5rtdsc8T0yTGA81jcNuXRkryQK6ntR1yb7ZDGIEU7qDeZ55ePfI9+9+4Lsfv+P9+/cYa3j16k5UatumfH5FIjpmcpRzyXvZNXi/oLWSIiPLGGUz9Ax9j9ZSlR+Px/L99hiji5CfxVhLXlEo8j3tdruytJUObp7PdpmSxGVv0bQGpVLZIbUYI8qxShmWObIsvhAjhdcwTicenx4Z55FuEF5GV4AHTXGBi0EqRlsMdZKW4iPlJGZBlM6kuPS5xq7vUYKGILdkpzNhdIMIMObi0XAOLHX0eRlfdCGpac3KlTDGFkMjRI4lVbatls9MG3Q2Ag5pGmzjyEYRFSw5CechJyGyqZoUhIAGlOTCGbFYdpm6oJSekcqQncTqeFf+Tdwf6zhN/UTT6HqhfFkMvCR/fblTeFbw1Wf8mWTw0h7h8n7/CI300mt+lvT4PNz1c8cvGh8pY3CmI3uxfxRyVpUW8IQQC+xSGJXGiFTFMi11iolrHTppfPLSSRjRWtGlGmXYCEbcGDQJ72diCKQYyDqVln/CNRmtA8sh0PQ925tb+r6naVtJWkp0V9q2XUlHwQcJ+oUh3LZtGUtYSUIFVWStZbPdrm1aVf9smmaVNj6dTuuXsN/v1y/xeDiyzLPoypdFs2tl2bzfixifSFPHdeRUYaaoVCSVT/JeLnyTq/80UOCdZ96DUlp2IdEXYp5UssHPqBTwKuEaR2cct/d3JDQ+qVVe5NPhAe8TwSd2u1dY1zKeDsSkiVkx9B2vX93x62/f8vbNa+5ub2WZiSoBXbDs280GHzyn8ch4klHR8XjisD/w8cMnDocT8+zlvEhwOBwZBmiaTH8xRhNZCididqW6XpZFiH5kttsN3+hvpJtJgRAiHz685927HzidTlhrefXqDUO/oW27snAWSGPbCvFMgkQZaZQ9jV9mQliYppFlqR4Qmqn4SKNkPv74+Fh2TQ3H45GUYDPsihrshmkUfaRlWco+JtAGh7WattMUfTceHx8JPnE4jBwOI8u8YJ1lnkcenz7x4eMHpnnim+4bxIt6S2MblNIF1RXxS0AX607dmHXpKgo0GmMNXedoO4exIouxPzyUnYGBrAk+oJXD2YwxCZWFhOecKYmgSOaTyVm6isrwjtETol+DpTYBo+S9LpMnxiy6YxcltdKKru+5u78XRWElGJ4leLluyXR+EXKmlTCldJ1GJCH0FaVZlc6qwpezHPm+SqDkeYCuvA1RnhVdo5/O4593F8/m/CWWURIRF93TZTK5fM7PyXY/f87nHcl1YL+8Xe1k6++rj8Pl8116TFwnkZ87vnzRnPNKWKvaIOuyIws/IEaxUtRWnLC0EZhqSkmE7oxo5OtsMH4uC+jzwjiXRZZRjkaJWJ2fBY1UZ4epeEOjNFonpnlBFU17YK2gTanMUaxVG0yFSanX2yitSZmztn/pfKTSlPHPsizPxkY5Z5ZlWQluNWkAq6Wi09WbOBf1S3h6epJ9yurXq0sistiwYI34I8tFJyJxbUEnte0ZbZJSFGFBU2WSYyH6+YLBFwRHKkivlDPWakkMfUdWGleQMuK5HIpMAut44jQthAg+QtNYtkPP/d0N281QIMFFNqJYY8roy5VFZiXlSNXpfeB4FHSYjHHkIlzmQNNErJWrShdeAuXcWpa5BHBdCpCw7nnEyMez+KVAXyWYP+2fijBfg1LVR9mV8dh5KW2MK6e1LCdjCKslpjDXAxVmmpLoH6UUieFMJMxZ5EqMcQx9LoHUscxxReXI+eNRKpOzoe3cer6P48gyew6HSRbNQdj5IQiDfhxlxwBnIyOtjCw510V5FI+S4mZmyvWZFUKiQ63dk1Ky15jnqZDXRBRQKVNgnLJMN9kI6qdYjlYJeRlbyqKcFK+CVxEITCJ7Qc4F/JGxppUlPeeFp3BPBkK6I2a5Jh73J4iBrGAJslQXLkIiqQvOdAGjpJiIWdwKJVjCs3kO+dm46yKUnceAFR1zEdnPiaA8nuJid8FPHnNNDPx0ZPNzieB6/FP/+6WkcHlcdzCXz1cf9/K+v2TJDL/EZKe0/K44bAnTNhGTXFwhJ5G9XhaazYZ2tyFlRQqRZDLaWmzjGHY7EhmvgbJQQwmRZTodaRQ4DVZlQloEJUOicQZSIEdF9CKJnFFEFHky8PTI6D3GWkIqbXvKNK1U2jprFAeWOdMPrhjCO8Z54TSO5BjQStG6Fl+0aiqs9ePHj/R9vy6aazKs1fzxeASk4s9kVJnXZ2Smm6KvoEAgknNAAqOI6fmgUIvMjKEkRiMnytuv38i/l1l3CAvez0BD01g+fvzEYb8XxnBK6Jww1tLabREYU+TGQdtA4zC9cBJU+fwkawuPw21ahq5BacPxOJJCIoXE7uaezm34737/a+52PSwjh2Vm3D/RNS0hCvpFAqFCIUms7Vo22zuUbnj/QUZ68zwxlCQZcyJ4CAbmOUFOaJMZx8cyFtHc3Nxwf/+KGEWGYl5GjLNsugGlc9kPzLSdYxylQlVaJBYkwYmNZ/DiOCcoGsN2ty2ckCMfPr6T0dbphC5InBA8Gemw6khnKmOo6tUcQmS/35NiZui3VCRUvRibIuqWsyyn7aKxrmcwLdYaPn36RAgJhT1bnGZJXjKCkqTuXBULdKQgO7vNZkPwgcV6bBld5XjG4xstwSX4wDhFfBwxJuPDxDyPaG0x2mHNAliWRbpxow2NzlijMKp08oj4XoqR43HE6IxWWRBZZExRD0gpkqJm0RltgkiIK03rDFWfdJpnGfndvaYZWm7f3BNi4Omw5/uP/zt5kVGHa1ra2Aq8tW1RuSmxWXg3yTYkFEspklSWnVwdK0k0FM20nDLKXs3da/IoaKRMrcQzAmQVRKVSVXZDIMMrL6GMgVFnVnLdH1x2CrUwuE4Aa2eln/tKv3TUyv+6C6lFZb1NvV39t8qz+rnHfun4ZZDULCeGVmWbX99gTqvAnZhvFCRRVmdzlIK6sc4W4bqilJovPIzL0iuQpBIrjObKjCXL81atItE31Csy5jTJ/D6V5V5WsN3tRBSuafBLoG0WcpL3kNdqLmBkcwhaKiK/+NXesyKAzktGdVGlP5/XKS0orZhiYT6P+OBXFIrMOGURX6sPXT6ftQopX2jXteu+Yl48+UIWImVhQM/zxDiNpBhKHaUwtija+kDSmmzEC0OlxFIqvdkvGONIiJqloEl6XOtQaKwTa1JFwmh5z0PbYEj4eSQlCF6MjaYJgfF6X4JDom033Nzs2G13pJz48OE9x9OJEAPbzVZO7JQQMTUj+kKLJ5RAHWPAOVOgv+G8XLxY8sqEWcZLQy9KqV3XopSma3sZ9RmDX/zKeo6xKVBCGXkejwceHh4YxyPRe5rGMWwGchadJKmw1YrqyflcQVtbIcUy9pqm+YwWKyPRapUZY6Aa4JDPoIyUMs7K6NToavGpRQX25paQwrpEDwVZpQtPQCu9dg45iXx7zrnM98vnUzrGZYkoFYipeI4X5c/VUSzVuyiRUgeW+SwKmJIUJCHE4i1VK1tduglzrsCTQFNDiGhlngW0XPYVqsCyldHoZGiWhbZrmKeq1TSjMjilCd6gs0hySGFfHidWQEhBZ9VxzvoZ17+flRhyVhcBuuwrLvcNqQr9ZUFWlbFUXaRfRMNy+co5uU4qLkY39e/yXM/RQy91AC9V9i8F8+vF9WXSuD5+6ZIZfiH6iCwWnBpVoJ15zVZSscUVUZDJIpZlJEhZJ228tRbKoisWc4xlWbDIskgUHT2ub4rWUSrsSjn5ZebsiFmJOiKKJSaO+z3TshBSRhlZCgr0c6BphAgVfGKZvTxuWfCGMiKyZUkp4yZhk9ZOoe4PaiVYUViX/1Y/o9rS1YSw3z9hnMU5y83NDpcM1omIV0rCADXW0LSOGAq2WgkPYrPZsNlsyMA4z0V7Jsh5mGQMUM3irSkciAIHVEYTlMyNs20IJeFOfil7mZm2BaUMTd8VtE9PW3gJgj6K+JDK96zYdA5yZBmP+JAIy8R2OwCJdAos81zUQCPDMHB//4q7u1c87Z/44cf3+GJO/6tvW5wVtIyzMhKpM3jvF8ZJkkLbOvq+K0J1xcO3tPlKCapFKmkrREpzvohV6cRyhmMRhJvnWaQyYiOJcV54fHzk/ft3HI8HrNZsNj1d31K1e2U0U7SNQsSYtJLjRBivo23akryeOBwOtO0GWwAJ3osESM6y8K2ziZwpFaQqAAYpkhYvHuObYUPTN2SdV5TbvMx0TV/kOFwhbiWST7JfyGVcWCRFMBlrNEtI+DAT44hSebW3FVKhiPflXOE7IiQXQ8bHmRBM4ZYcySmIrWjRTSLXkV+znpciy1V2Dz6SlCQ/6Xy1BNckCcuYuniWJLgZNvhiXjSOI6RMZxuWvEBMdFqY7loiIzkm0TYq/1thouUDzllkM6qW07PEVGZAFdxRiWziWSE8LMJPg7OqmQRZUqeaCMu9Lwll9fbXC+DL/cDlEvulmPslSaH+9z8aVf1X3ylIu7owjhOKjHMNNzcbKoMwFlOVzWaDVorD4Vhm/QpnHTEE9vsDj/s9PgRhhSYZH3Uamq7lqzdfMT49MB8PxJgx2nL/6hXLLEtsWc5accFCGKRLyqRlIc/CWlYFXaDLrFiXJWjbNsQQ8EuHXxbIxeEpaGLQa7VQfRAEqSJV1dPTkyylCz+hchYuv5i6e4iLLMZ9Ud5clgXHeY4KYLMTiYcQIMMwDLx69YrjcWRZliK8JxVY8ILnfnj4xDRNeO9xhWw2zxNKiYz36zdvIGfGk9iPLjFynCa56AvT2RhNyIqsLNqBaTuscfTbRqreMrMOIZJVpO2FRaxIRYgtQOnOWucw255f//pb9ocDp9ORT48PMCdcY7m7v+Xt11+tS/w//OFPvP36K17d3/HmzRuC93z68OEM+8zVgD2t8M+maViWwLt37zFG1GuHYcC2Dts4/AfPNI2idWOq2ZNUz9Y2ctGmzKePD8zzzOk00nUCdZVqdFrZyW3b8vVXb2i7lmHo8bNYbhqt6fuB7XbLZjOTYsJax2634+7uXubwlIodGMeJ25tXBY0VinfzsXg+iL9GJrIsc5n1y3I6J0WMmdN4QivN3f0d/a7HOstpPgkbevElgUam4yijIvTaKcRCEiNDVhHbGu5fvwIiKXs+fvqBGD3Wadq2p3EdRncopDtsu46u3dCZHcFHjoeTdCApcDwcUVo0nGLMkCOuJKeh3dAVm9tp9KQEMWYO8UiKGT8vRJMwOq0w8P3Do4BViPSbHoPi9//0T/zdOpHK94HAQu7rrjIRbcA1kjD7psVozf50Em0vm2tOo6zyLvYG4gqxBvaSEORcOS+Va5ewilWWRHJJelyXueVxFKCNhbV7PQfhuoOsS+BaLFZ+Qb3dNYS0BvBLJNMzrbSr8dDl41wnh8/9988dv3h85L3H2fNFK8skXz4lqXCzKobZOZdvqCCUYhQ/gxBKYC5QstJqtW1DsI5gDBRRq7bpSFFOeoy07kqpIkRVlmj67GtA+RDrl1HJZ5UU5pwjBk9OVX44lYuisCFjWk8EVd5HTQjOudUjt+ogheJVXD+fqiEkzNXKbaiMW1X+T60zZF0WiMNmWLkHU9F9yjk9223My7wSueosU8zhtaClojzv4hdp4dXZnUpE0sSIRGkwTqGtQ1uHK1pQdTwich8BrS3W6LIviCiVqFgOGSlpNttepBKsZlxGMgnvW2E93+xAUfwCDrx9+xVN+fx8Gcs1hSNwMR1aZRpETlsIfFrL+EZZLT9RlwtMlDtNqkKJBmNA50SKkmSmeSoMahl/1AQ+z/MKEhBZjAHXnDvAujfLWYQY6xJX9Jj6Esw1KWbmORR2r1/PxXrxppTXTtk5hw8Xdo3q/N0oXQKCruPDHtdaJi+s/AroIGXm01wYvRajjIx95MuTsWjMqKjOntjKcjg2hCCdqbMiTkcqrnRlNGutiEEqBJCRSeuYVUQCxTtBKdbnd04g5SLeqIlRnn8yM6GCRJIwD4x1oLQUTjHgkwR66xy7zZbNMHBoW6aDmCSlEAlZeA6pE7KauZiR5/K4OeWil1d3BjU7lv8qAIgCGUKp56Ogy2v4Ei10GZyvY+G6nlbnqv4ysF/CeY0xFyipn4+xl8dL/IOXRkuX93sJ2vrfZKfw9PREDB4/TxjTl2xchLvmReag1gokb/GcplFMSVDkYodHQRCYImFRf0PK4la2iEqorSMaXee2gqjZDE2BMp5YysJtuLnBlOftZW1NVOCahq7vePv2DbvtDdvNFqM1YfEc90+EEHCuKxeBJaVQWt6EL+24mK4P3N3dnWUTiuTFeWQW1qqgbVusVqRgC8xWlpa2EWbrssyFpCSQOqUU2+1WZu+7HWRVltliRH84HtDTicULzLO2pk3jsKXr2G7f0LQtTdcLgzol1DyhvWdXmOauKYtKY0jGrVo+68nuNCHLiCGFxDIv7I9PWC3BQwTXoHUUe8SEjzNZSdvf9Tfcv7rBNKqgZkZ+//vf8uvf/JaPHz8yjuLC1g89t7e3PDw88PDxE3/845/4l3/5Z2H8pjMprG1lP7DdDUyTCAkeDk/EFGmHjmG3YdhuOByOpBRoGkPbtUV22pbxXsM8LUyTJAO/FBe1Reb+x+OB/f6J/X7PzW7LMPRYKyich4dPPD4+4hdxQRtPJ56engpfxLDb3axFxzKLf8h4ElnuZVkKa1l2N9aK6urrV6/Zbnt2uy0Pj0vhxUhxIBDkDda6gnrzjKdR7DRny/F4LInYFq5P5tPDJzQaaxzbYYsr901R+EDTNGGzjPSazuKcKRLirYAgsirLZVl0y5JWxoTOWhG3VNIVoikJ07DdDjJOVNC44tHuGrKVkZxKZdGeFcnL3ktlWaQpNFbLHqHKZRMRp0aT6azjdtgSbu/4/jgRFy8mW0ksO2+7LUFbFjNDkCRVUkMpWnSZzqkVnFoHO5XkJ74w52CZyn7jssq+VtJ9aR8QUyqxTWE4M4lfqvrrvL8+7qU8DrycUK5/V//+EwnvdRz5vEu4TgKXncY/On7ZTqFk2gqHu5zvklnJMjknWXAFWaShTBGikvmsAmhaVBYtE6sMKktFmdcvSdq44CPBCxRQN5Jtx3HkcBqJMWG7Dl1YzgUDIJIQztF0LSnlIgEgQmaH44FQcdZRXKKUUSs782zOcs7ybdteLA79+uVebvprkLBKka0Vb4dyWtaTblkW8Q4ucDtXxhBN05aTVq1aSyD7Favcs4qldhdaCWLLNZa2azkcTyIVHgX+q52ld3YV3bPlPqHshJS2q6uXeG8XdmxW63w2pEBaIjpLMnfGlnIeUvCkrCFrnGnQxtD1TanodYHQKuZ5JMZQ3NIG+r5nv9/z8PjI05PM4JumIeW8kqOksBRrylSMbrquJ0QhFoaCwspZYK91RFh5GxWCWclV1lqiEyRMjImpQD1PJ7FEzaWirK29wEw1qqCHQpG1bp3M4us5KEtfRwoidy6ubxQSmlpHhrvdls12Qz+cpS+AskCnXNSUzlkcyITANqKDlvOgLLW1Fkc2WUpLBdq4hsY1GC0uf9VxLhI4jSdQLdq0NE1LhTpPk6C5qpm9KyRBrYoDe/l/dWlcYa81FhQL8hVyS4FlaqWK3pBZlQdSOAMoRCfNiKWo1jRaBDRjkARmtJbxUNsRllD2BiJZM88zVhm8a2XEpHVhbqeqqr2+vlyiFeqnCqeXSSFTPBz4+Sr98vdVoy2v8VD95H71qIG6xpVrZdNrOOrPHS+Ngq7/fOm2v/T4RX4Kl/OtKmdRt/c163rvRflca1IS6B5KiTG3VgXXbrDayEwuZ9H9B/FOTmKpmbOI5smiTiry7SDchXEcefj0icUHbl69ojWisxJyBqUZtltMgSLGKBdGmMUS8OnpiRRE8VEMz+U9OStiZyGEwhQ964mI0ql8YeM4Uq0z6xdaRy/OObSzkuyCpRr9ZKTtn+eZxXvmZWG729K2Dbe3N+V1isa81sInSFG6CWME9nfZvhotyqE5lrFE2/D43Y8FGSLIL22ssF9Lt6PLku/x4yd0hqwtIUhHEjknOKdtkRcoUgkh0BiRKzFGkrcgSDwpKzAGVRavXSdqpa6xNK1Da/m8Yorsdju22x193/PD99/z8OkTT09PPD3tsVbUTbuuoR+69SI6nTyukTGK1orFex4PAletFpn13LyULl+d2EqSqUnWGNlLjeOJw/FQOrIyHimj0aXsgax1oqircklEM40T4qK8tpF5Xtht78hZcTweV1b8NM+CKiqdZd8P7LY7ut5hrYxpgKL7v17FciGXIOa9J54iGFmWWuNWnkLSSZSHEUZ727WSsJRU6bKsXUhBoLDagGsMTdOV4kIxz4Hgw4oAck17ITciEM2KZMo5rkk2pSRktBIHRAAvriMUq86cldY1qKxkAR4p41RTuEFRlvHaMIeFGCLTOGK0Zjts2PQDM5MYVoVADol5HLFofNOLK53WAl4woG0VpKT8eTFuuSAV/CQp5LOiwEvV9iVc9Pnc/twpkH5a8V8mkstOod7/ehx1+XpfOl4aS30uIXzJ4/3c8cVJ4fXr14ynE49RZHQFdtgSo2ilPO2fxE8Y0Naiiz7L9RvrCiNYZchRiCeNBquVsBO9JywzTSEXzXPg6UkctBrXrfpH3gdOp1EW38Zi25Zxmom5oJ6K3q4YlUeOT0f8srDMnvvbW9pWdgQ5lbm0j0UDpraN5hl5rR7H43GtKOtuofIVmqZB5USOkeV0LItmmavK40wi8xEjXZHBHvoBXzwU5nkhxbiKC+YEbddgoikL5bZAH8cC87V8/PCB9+8/8Ne//h2tNTe3t5hGkpMFsjHYthOkToSoRF0m5sy8BHxYmOajzFmB13f3q3THMk0spMJYDWiRhiTXokAbqZTTjA+JZTmWZGoZxwMPj5p5HnHO8Nvf/qY41DWcThPTvJASPD3ty96gY7PZFPRXLAEo8NX2K371q1/J+M0v/PEvf8C1lqYX3+Uq2HY8yrJbihQR1atFy5s3r1gWz2F/EoTTxR4IxPA+xoDRgu/fbDbcbAVBdDzuqbIPd7d30tUkVr5CJdvVQFD3JanIZA+DsKpXUlnpyGrVKLISqSxIZYeVoqidmsagjSaphLGyo9jd3IhHSMqQBP2z3WxxxomkRIhMairBrkp1B0JIz7oUWWzLrF1r8WROWexhWxNJORKinL8xBtrGyr7GasZpJifh36zdcNmPdG1X3N+qH7bCL5GMjE3neRK9M2cKfF2KKhmpRhrrMLbh7du3hMUTFs+HH9+xf3zi/YcPTMOMsw192+GMIOQEHcYZbGDL5ELJppAKM6WquT6X66ixqcarWuRd7xNyzuty12Cr7NIqfX9JJnspiAPPksxLiaE+/+W46PLxrruWy0T1SzuPzx1fnBRWJVSEq1CPuoDLsGZcwVFbgS0qyaj1pLFl6aoyZdkKWgnjoI4mZBGkVkSF6LxElnkppcDZ1AYoi91Y2K2J0+lY3TgK7FUCcowyszZGlypF44s2S05CvhF8+nlGWKUvLj/gSxz69byRJLpQsfgzn9EMZ1SDUqzYdq012deLd1lnn3BRsdTnLBXxOI6gFZ1WBcYZmKexyFaIkqwxejUukv1OSXqFXS4LP0FcnE6jvK4Mu82WxrrnM8wYSBm8ogSj6o+lUCqTEbnklIPMpZVh8TPjKEgo5xyvX92Lo5mxa3V+uZuZJiE1zXNXPAgsbStonVWqRCnxdG7EIU2UZav1aq1YZRlaFViNNQU66sr3L+ePL4z1qhsUYxS5h6KhVOU7BOlV5NxLFyKFsnw+AjQ4s91tYcnL6VwvZLjU5clcwA0zhZmcEEtM6cAr7FRbTSjEylwyt9ZFFReFLmMaowwxlLl58RivGj+pEO1EqjyV111EFXNaUTfi6jYTnD9X/+WClNdVvi8vsO7gp7LIPY83VVnkGqUBcwHBzet1qjNoWyGw9aJiVVLVWjH0PalpSSFyfBLr3XlcMHqS8V4NqDkVvb/y97pAVrm8/rpXyFQY6YVmHxToxEuL2poQngshlu9NSRFbpyKfC8AvBexrqOlLieNZTHnheKkTuO5w/kuPL04K0zQxTpOwOheRvR7HEWv1OjrJwHa7pRsGus0Guz+wLJ5pFuKO1WZ1BTMIxjdlUCkhHh1ZvFzbFo1IMHgfsaah6xTH40kuSmP46s3XoGVUlJTieDoyjhOL97z7+AG03G7YDDRNy+3dLbYQdrq2XauLxYu+0jKPkAXXbV2DNc06Tqo8Ba1Fg6hWElUbP5QEILsLTy58gDr26ZwtiqsBIxmPpmgjLd4zTTOn02kdP1Sd+jq7rs5rNdg8Pj0BEHdxrQStVjROlD53uw2u7/j49MTj4Ynjn0XXSaO4290w9AP3r14hEuaeT5+eWOaZsHha17Lpe7wPTPPC6XQiR3GyStGt+vZoizYJ6yAl0V1SWpK70pnDQTytUXB7e8PvfveapmmJZflqnXhDdF1P07SMo8z3yZlXr17x5s1r/umffguI3tC//usfmKaJ3f2GprG4RrgfKcvyeCpKulKdyuP2fSO6R40Y7Wy3NxwOR6Zx4vHJFd8HI2i0XJVCHcMwCLQ3Ru7ublgWz2k5cTgesWZms9muhcH79+8BJXBeLc+tlaPuFKochhQWMpZRsBY1KcE0jYDYmIYg5lNd2zHsBqyzPOwfUGJRyOl4ZDYzbdkjdG2PocB5w4wxhrZp6boWn3RBf4XiPHeiWpjO8yJkztaglOd4PJLTCRAymdFGmP82E4LBFwnumBTzfMIvM9N4KoHoXAz6RXzRU840TqSzq55RKKJ5KkWiTuhs0BgiZ4+VEAIqU0yPLI11AmmNke/2f+c0jnx8eiSkRN8JLNYag0m6BJMzvLwmAlSRBlcUlnJZRK9BVT1LBNdjo8sAq7VeuQ2xBLCXgnz9U7reMqIu1/V1oK8jpnpcBvxLIMulWObnEEUvJZ1fenxxUjgejyUZWLKVikx8ikUl0RbC2M1N0Ze/yKh1iJdzgYblCieTqrMutcRfucwmjUWhiQ34JUCWpZwySjDoqpJh6pI0Psuc1ZS87zq6rme72cqJUPD9OWWMFlZuXTTVFr5KWIdwJrzURDAMwwpzrUiT+sUBJK3IMZJ1vZ8SQl9pXVNKxJzLhTlzOh3xPqzL0ZyrZkzBM+eIsZbb25vVn0HIXJlpGmXJuBlkkWzE4tRasSmcppHD6cTjfg8pY7XhZrtdERkVfrgZNhhlmPOE0VJl2+qTYS2udVij2Q7iXayVIutCzXaC8lmWQNNUrweRTYjRC5HLShCvu5Wb3Q1h8RzKWDAEIartdju++eZr4bpozadPD6sO0Pt3Qn5znSErJwYy68jGXMy8Y0GSpXVnMM8eo21Rf3Ur9LfOwY0VYbja8dZFca36pdtwxJDwya+queKkVir7AoeU/YZb59iyH5DkGoKhTWolekkXGgl+gaxpmkzXdRij6PriE24FAks+iy/GmOibjqHbQFZYXVRSS5fZdi2bzYYlLiSdC1lRGNzyvkUdoC7wjbZlwS4ucjfDDW3TFqhpQ0qG0JRO3xlSFBJj8DJyWrsFkLFTEpMrpeUaTrUpUGeJ/ZAiJgl7Gg1Ka6wVIEAIMyKPkWUU1Xdsdltc15Jj5jSOJShHbm52qCxy+E5pci67zxoIVO0FLvail1uG/NNKXT6j50G+dvkioidHNVrKFzuF6/HRT3cRL1f3L83/X7rfP9oj/P+SDOrxi5IC60wtoRAhOKMV2lCqvyJUljNL1QOBtaUUNENZYiFy2zkl9NqiltVNqbC0yuRGM5mJKqJWPXxRiqxKc3iRaWXBWLqXxtEPPX0vyJeqKnnYjwQfVqEvVJnplvtrozHGrsvmy0XmMAy0bUvf9yuZ7Jktp9HkGFH2DEk1tpJbFD4EQnGIm+aZuhCV55C5sSQbCf6zn+n6nq9vb3h8fFwlIFJKTJN4CG+HgdYJgieSxEnLyDz/dDqyP+xROdNYR2XqQp35NmyHHRoDsSR3bVBWmK6yM2npWsfdza6YlciCLZHL+C2wLIm2c5A1OYmy6Lx4hmFXgrbBe1n07m52IijYfmKcRmBis3nLbnfDt99+S9vKWLDCWY/HIx8+fCST2d1vwAiEsUpROGfxXkZF3i8SqMiFhzCXEUvH3Z2gyJytxEPOQUKBNVKNz/NM61xRCZVlbm5bTkFg1pI05OwWNJokBfm8XIFUC1InBPGQPh1PeK9J2ZylSkqXeTrNaG1BFTl2p2lbkZ5XRhX/BCAr8fseJzbtQNwmnHG4IjteJdbbtmWTNthgOXlx8Vu8Z5krJ6OlbSuLvYOsmGeBwS6LZ16KXLrtUU4c7WKUQKuNInhLTha/mIJAy6Qs13uVvPExoIOQ7bJcmMKTSWWMU7waTGFF12JsCtO6w8s5s9lsaNqWYSt/LvPMqVixppzY7LaoFMFnlDLkkty11ug1+uRnVX9NCp+b+9fjJbjnT5MCP7HjvDyuA/svTQovPc71v10+9/9fk0JKCWcN/dDjvaBWvPeEIoHQFHnoEALjPHMYR0JM5yq8zBSjD6AlDSQfyEFcoYzKaKswRgpQaWcVJIt1Ld0gHIOqmNl2HcZZdGMJOaNjoOnlQt/mjGsdbd/SdQPGWKlIfRAM/tN+JeF1XUPftTS2nkJALnIb5eLtum7V+K8eyW3bArIUqmqpKSUoi8/TNK1BSzoFSKkVpFHBkS+zSE70fc+wGQhBicxDkfGO8ezD3LbCkD2ejjSNk9HYMNB3MoOnLAnnUTwLsApnDcPQrfNap604iKnMw6dPkMQ3982rt0z9yKk/0ncDzhh8FNZ623b0Q0/XNvSFrV5n5ChFr03Z0xiRHkkQfKbvW3SZy3vveffuR6oIYNu0DP3AZrMp3VDi1avXvH79hjdvvvr/8vZfW3Jt2ZUgOLc8wsxcAFeEoMhq5ugaNfqxx+h+6P//g3roh64qJoNkXAW4Mjtiy36Ya28zeOCSl8zINCbyIgB3uIlz1l5rrinwT3/6R7y+vuDTp1/k4OLi21hmOWsLKFulGaF6WRsF63QX/rE7b7GfCeM4I2fexDkVelJJeNIwegbHJ9qah3XHx8cH3J1OGEZPTD4mxPBTz9poFNHz24oQEozOsofIMGaAE8fX5ocU0wbvLQoGlBLkIA0IYuuAWiWu0kMZcb4NEVXX3pzY5lUk1tnWWKzjBPiWd8KJyVoLXwZUXbHEtcMY7RBsTc0ge4m24zDWwAPwg4OxCilvKCI+894J3ZTTr7UafrBobCaK/ACIJmFPEblACj5djLU1yDExQKdkGLFosuaqN0iZ4ssUSd32fkABeB0eJkAp7NvOf3+rOGwrnNW0+4CjjYwldZpmfuiMLpahKpONxGnmyl83xfXXmEH9Hi8FVFSxYlT1JWzzNSrrLR31qpn40ur69utv4az3dfhre4aviev+s4//UMiOdRbOOyjN5W1KxM0rmo9QwSYq0W1doYyRZST4QZTKEJ1aASh6IOVCJoMmW4BLHYVtj6iF3opGYIxFLLBTThiEf64MRXDKGtJBlUICSNUc6T1fRavAGzt1JgKq+CFKCLgCyIsuFaVZBFdJTTOEZBpeeXU5bPCDfBDZcLEdkwSlZ9QkQeJVbhKrodTO1yKBPFSkfslMASTBTqnOPY8xdVvw+UCGTBG9QRKleAwbzG4wOAOtR4yezBSjDaZhhFEK277BKQcFg9FPUIXeO1Y7udTJNW+GdYBGTDQS5J1WoIzGMDgWTjDEPqWmXDXw4uefU8ZlWaC1hVYGCsRFvWdxqrUyxEdYZU9Pz/j8+RODlcQy/HQ3w3sHmCwEgiI7FiEdFIdmq87kMSbQreuGGGjstiwXQJalLWqSex8WklrIklGFaupxHDFNI+2y1d4PROcp2PJ+EJ57prkg6T/XG1KKSc4FOgM5X+3mc/ffqQIxKmgLFCSoQu8ggLBLEnW/VjTOK2NB3tkgQaFPmqqNu/Lv3paF24lUG1m2qiqaHf6+Le85IQEpBzKpSoY2Mz/7IuE5yFDiNaWNhiqyIyy0n6kVtIhXhIWUEhqrbohxQamabL1a5WmXhjGjyI4nxoBaC7RRhDK9k/eQhXSPO6ostG3R1NlUEcXevAEd3i0tl1mIMV904BUNkbguiOXtvPl3rs34r2P6rUjfHii3bqpfe7yHnG7/nX9rqvnalPA/5VA43R+6GZ3L9NOPMXSP9T1GbDFji79gDzwYDqcjjHXQSrIJouwFtIGBhooZOUYqnxUQvRjfWYNPz2eUUmCsx+9/9zscj0dsIWAXn/nj/T38NJIFYA2Mdzje38F5R+hKAdDA6zPTqz5/fhJXRcBZg8FZOGMxWAerDEbnCR1EujQ22mIV2MWKr3xOnAS2bQGEDTJ537H4Evk6OZlELMuKnCXO0xrBcQfshs6gMSfy4FPEvu4SHRn6UmmcJjjv8fb2hsuyYN83fDh+xOF4xLfff4fz6yuW8yJ0yx3LdoEfLGra8fF0gB885pkJX0ppaDVg3wLOb2cMZoKzA+4OH2ExwmAAhHfOouQA5ZGyQt4TLsuTFBegKOoKfnfyODxMOGDGn//1Z4ScENMG50dM0wBrPd7ezvj0y58xDjOsG6BA/ch8PGI+HmGtxcPjB6Rc8I//7U/43//3/y+eX57x93//d9DGw7oR3377O4zTgCW8IdcdJQcUDRijaE2hPYypiJGQ3h4WLMsZ57P4BqUIo40c+BXrvrCQeAfjNKy3qKrCVnaZ83zAPB/x4fFRJrdn2qw4hQ8fHzEMEwY/4l//9UcuVQePGDNCitRHGMkPUAIvCHyitaPCOibqAwYLNxpMRwM/Vqz7i+xFCpSkBW4xwFmLaRxx93CHeyis57UvlKdphNUGOUr6oWhhQorItfLnaMcsZq2gTEWuETEXrMsO+h7NON4dMA4T5pNHSRlvL694fX3Bvm/45ptHQjaSTV1LFR2IRtWabqdVIUegiqhx2TeST7yTQ4gmmFWRjadqYq606B5QCnSpsNJ4qFKwXihu1EbBj/x3hsljuayIIeL1/IZhsDicBuiigKp7lKeUUf6/m+5ay8/L9UqMKIkTkdbX5XMzx2sPJc0QlBYmJi1C2tTMr/nSs0ipJn4kstIYal88F3ncFv73i+/308avPW73Gu+X17/18dsVzaWipoRU2YWRXUAGiFIDzpcLFbUpCYvHSFSkQUzEf1sebKkVVRk0OhkhmyriqyTj4iTYsJKFXsQ4ke9NjF4hxQQ/sqM7PdzDDQOgFcLlTG1ATagosNbg/u5ENoMRrnapCNsunQlQEi/0FFOnb1aF3r3HFFBQoGXhbJoTLCrWdesXQtoTUhDdQQg9A6GUAhW5A3Ge74txlerhWml8Fyhua2yDZmUR9h0//vgj1nXpF4kxZFG9lsrwmn1HRcVxPsJbC6MUvNWwCshxF4jFwVuL6ioG76GrQZUENKUUnPUwlhNLo/ZVVGijUGvGHjd0NoetsOWqcEVV3HVkHqbeO3g/iscUl6XWEU4idVJ1pbiVRXxTE4/TiA/6Ed99961c5Pz8jWVlKaUiCgWV2HbE1YaaFtTOZuRUobVFikWuGwMtFtHKHAiFOAvrPKevwQttWiOEgE+fPuH1mUU6SdYA9Q/8vGqpON2d4PwABQutC4wpmA8HeEd4sVFpp8kLDDaRwlvpJNsICNwnZWzr0qEY45mjsVxWvn/a4TgPGPyAwVKwNk8z5nGGgsLldZHikzrlVEGj0zMLi/q2rgBW1AosC8kFd3ecEp110qhFhLAhhAX7vuL5RTyHGhe/AsDV+6hlUecsXkqosvNjAqISLUYv2AKXaNBvrEABhRnPQ1f4i58ZywPGacIgDq+AxqqZVqcMMBaHXKS+lCJanCzmdyAzCRB7bbCZVKRU46s1VtCMVv9ui3SjqUrnWfAl9HT7vN9DSW1SvJ0k+Pq+ZBP92tL43/uzX9tf/Bpb6WuP326IlzNHw1wQIhdB08iwEGcttn3vEAYqOtyitEFEyzwuKCqhVIOq2+kto26tyImQRFUsGLnTPclm8M1uwpDHn2KEExHa3emEqiVJLGXkHBFykANKwx8PpPmNc7dBfn16ptd+SAyUubWvqBWQ1LJSM+MqhV5mncU4DDcLza1fEHGLiIEMlSjMJMZKEprQVmPMI9kWxiBn3mRNDJWElqrl71ErQkp4fn4GtBYOPjsVZ5koFkJADBHWEXt3lnRbqzVUrYj7xtBBpenNZAyscUDhobBcVuLM8wQ/8GfGuKPYAidB6ikTHuRCscCqqw8LO5JrRwQQ6/eeKWRaokOtGCleD4Vb6+aKmCLWdcE8TZjnCR8+PGJdN7y9vdJV1CgqWbM0JoUXewyEM2oh1o9aUR1QsoLRllkCUN0wEVBwyhNOscTgadNBO5DBWlze3nDeA4LoJ4aBz9+JLUNMESEmHA4HDH7CugY5FIB5Is02xYw8FmgNHI+S9zA5hLgiBINpnMT+pfaGaRfSArNIDGCEDVUV0pDh/YB5PqBYWnbP44RpmMiqu+wIVfFAEFiyr1VV65groliwp5RxeVtgLbOyB8+iy1jSKFPXjhh3nN8SIUFZejOgid5LtXJi4MGgIYbVLEZo+iUxB6zttRZqGRSI6QszzVkL5n/TG4qwmJZ6M/Gzc07g2UwmUlb9MGDwV25AAZQcBr32EwET2Mt8tdT1GeNdIb2dAEi2INTdCCrvaay/Rg+9JcW0xy1k9B4O+tpC+fb7+vN+BzH9ZyGk33wopPNKjxnnoBNvfJPBgBBjMbkBugIb2ptaMU8ztLEo2LhUzgkZCkoVhJQYAiNFH6iAVnDO0I3Re4Sw4+1yQQ4B1tgbfYHCdr4gFwrVwr5BK4WiuDxatpU+Xk4WvFW8ZMR+OKy0t04h0QdIiTmYFDk/OEx2BBS719eXV8I8JSNl+tDEdFU5W09ve8Z/NtfLhuGKeyeutrxJltHNHI+TSCQWajS8ukZvchFLFsY0z2SLKAr7fvrxR6QYMYwj0hYxeI/70wlbWLiMW3cs64KffvoZp9M9xnHG8bigFIWUgNPxEVo77PEC4zSUHsWKY8cPP/2503qPxwPDdyy4/6kZRrPY/fzTJ+x7QNgDLpcVdMjUSOkJ1pyRkxI1rcUgluQKCQrcV9Cm3ACKqW3eG/zxj7+D1grrcsaPP/6IP/3pT6gl4vHDA775/iMVqglQVgNFo2aFGDNi4gLXWovT6Q45KuQEpvcZg8N8pFWFMdSFpOZzZHgrVFqthHXF8nbm9BUzTf/8gG++/ShJeysulwve3i44Hu8wDA4xZHg3wBhqI4y2CPsFWpMddTjMMIZRm+e3M56fn/D44R5Wc3HsjAacwsdHj6agtoND1QolA9Y5TMMB3g5w2iGAIkFjPIZhAirghxEp0RrGlwyVDWpR2LYF27bCWIVxGvHHP/6BpIsQ8Kd/+mfqGM4rtssP+Fn9jJ+PnhDrYOCthjvOCDHAGYNpHEk3TgUp8fqNkiRIV1UFpcXrTCxkmkFpBe+/kjOZQVVBKSvXDCeYh4cHzNOMz09PhFdDxuPjIw6HI47HeymEGvseEWPGOAQAGZcz8xfKkDA5DxgLY5ml0OioLTa2W2yjtaRSlIU2C7QcBmEktp2NVj3Ip4gtTVPC/VoBboW+abluD49baAj4chq5/f73h8LX9BO3FhrXfch/jon0mw+FHCKUtVDawnDdztEsM+zCCDvCasPoOlniQCYE1PakC0pVyJVLO+Y4X62K27LJ9A+Rm6lSE6JSqNqIiyMX1EkYN2+vL+QFKqaeaSuB3DJS7nuQnmnDeln4nLWBF+56KFfFrwGXcexUbgLDC6eFCkDtqitHB4G0rLXQtX3oBkBTNYtSWpRf7JZq59m3C5TWz6pfmO1CoB8SYbJxGJDlvdzWFajA4DxSs/e2DluA3FAJYY/Ytw3WDihZAeByGbBQAIzla9WGS+G4BwTh1Rur4b3FARMgCuGiFVRRMKYCNXM/caZwsJRKbAZKYLiKWpjq1XzrtVbwjjkbVOSK9YCi/fYwOkzjCKDi8+fPWJYzluWMy3LGMHmkeEf76AqGvYiSN6fKn1dZRGPMKKmK9UOBVqA9gsSqllKAnX5KDb5s10rYyQCL+w6Iv5TWCtM4YRhH7HvsBAa6jPKmNJJhXktFljQwBYg9doMP5DoTeEcrTr3skB3M7GmfUCo1BjVj8CPZTtajlqZGrmDDqfr70Zh+ptFoURFLmxUUpvGA0+GAj48fse8B27phnp6wqR05ZsRARwBVNoyjx+AOsEMzGbz6lhnNBq4o/tya22K7omoueVvGs9JK8PerSZ188gA0rs+u2U+0iUPs5QuLb0uL4y0h+oVhwOF4RC0RKEHCuBTDkKpChqaTqiJm1Iq8bhOK1LamFJdga3km8ooqYNoNquQ0ad/Tl9PXx20xvj0obov5rQL6/R7ha1PFbZG//XVrq3H79f+9j99+KCwbjPcwgtkqraAi3QtjrTBQ8MaiuIKYgJIyMXvFop1igqpVpgJGVo7eY3AWcd+RIgNTOAYmDMpDWYfTPHO5mwu2y4WK6GmCQTPd4zJqXRcM8wTrHabDzCqUC7LoCF5fXhFDQtwDLucLNBQ+fvgIezLwbsI5JfHbT91A7DrGXX/lwiIfUoAfBgzDgPlAmqp3DjWRdrssF4S4Yw8bkuDsw0TYqC3na2Wyl9aNZVC7TqFBWWR9Uf9hxQUUAEIIeH59wzwfMR8mDNrRlsFZBpyngvWyImw7oYVlR9oL9i2Rp+5nWMss4+mQMM0DxnnEZXvDHla8vL2SdugtDqcDBnkDlFgpG6tRcsR2ecPL8xveXt8wH44CRXhCJznBaH97yQK1YJpHhq/IFISSqHAeLabZwVmLEAJ++fQTXl6esO0LzudnWKdwd3eQBSdNELXW2NcoE5ciCyhVPH16kQV+xr4GaGUxjRPu7x/YyVvb92AUQXKnFfcdy/mM56dnhHWDsw3icjge7zBNEy7nFc7uMNpiXWh+t28BeiK0ty4LCKfR28hoLcwm9JwNQGFfA7Jlkts0nDCNBxzmEwCFnAqez09Y9wWH+Q7WGIx+xL5FhI33kjVOWFXMHmiur8ZaDKpCJY097PDaQXuFv/393+Dx8RH/5W//Dq+vb3h9fcPb8xmX8wXntwv29RXrZUGJBbVM+PAw4zCOGKcJzeojpQw4h6IrVC00ukutOFbAsMBqUOXddoDtK1JkUpqWfYRSdGZlrjd3Cu2wVFDinMzp4CruzBinGfdV4eH+ASVH5LSgpohaMnKIiKZAlQptHWG4WjrsZQ1Zik3XAzmSeuFXbQVxhZ40aV6cFuTQ7jkOwBdF+j29tBXultHya5BPe7xnLLXf39JY29fd/jtXP62/nDje/4x/6/Ef2CnIQkgokVoZGCjuI0vLSr0xTEsZry8vhHi2ACujW4oRLY3tcJjwcHeHsK1Y1xXbviKngLBV3J1OmMcRD3d3FNXsO95eXmGMxjgMUiA1jHNIOWOPAcM4MfrS0hZ626488BiJ9ccQZA/i4AePYfDwg6dlQqEj67pesIflulRD7TYTRd7clDO8ohvpOM/sZCD5vYrQUS4UqOXC7AQ7WOjCTOlG5Wt2z8YY7PvWBU2dfTSOqKh4fn7uBnwPj48YhwGPD4+8OFPGNI7QQp0d/cjOsWYc5iNOp3uEwEWs1k7ES7NMHh75RHjq7v6ARQ4F6wwUaD2writCDNi2RUJNKu5OM1XOhzvEwO2tMQ7eORyOJ2jFRLJagBASLpeFC1sAs5mQUQjZVcJGSmcokGK87yu2fYPRwOE44Xv9LRSAbV3w+dMnTPOMaZ458ZTKIu0MnPOYRVT4Lz/8GYNn9nQtQNPsEYYc8fHDN5jnA5PN1kWM6Jj6Nk0z/vn8T3j69BmKdlb4+OED1mVFrQrbuiOnAqMtlnVDjAnrsgNVQyuLTe3Qmp8dhZBKQnXYxR8OXDCHfUfOBd5ZUPRHG+YWJbqvkYtgyyKRVEJa2UzM44QUE/Ztx7ZsiIHRotYSptPGwFnAGYvj4YB5mvEP/+v/inmaoGrF5e2CTz/9ghIznHF4uH/A4Afspx2DzxjHa+610TS627Yd57cLnKMZnXJAUgWp0gSRmK3u4slB8qu1HAoQyCgn5pYoaNQMGGdhrMZhntAIB5zoPJSi19HT0xOZi4p7tXmacTwcJb+6QCHi9ekz1mXB29Mzakl0EVaGgVw38EtzLDAirMuoMp2Qkiol94bFU/vP1trw0CvcsX2t0N4eCF/r3L8G7byno7bHrS6h/V0r/Ldfe2vN/R6Wuv33f8vjt7OPaiWMkzInhRvWEEobc2TUkYNh3VZ66ISA0Q8ww4gsh0I1Gt7Ta36XQHtjtBwoCVZrTOOAw+EIpw0WbbBdlt4N05nUcZyPEeflAjeQRQLNLi2EXTKY6cbaYkO9ZfFqvGzqIAx0JPYf9h2lJmKjSkk3L5hpLl+c/lxW2p4fQYFP6xCaAO1qndvZB6b5odhe7KMs8JtfSlMcN/+lRmd7fHykLbO1VMqGCD/OZDRp3ZkvIQQGwN/f4/XtgrBHQFs4R1FfU+2OdcA4DRgn39PBnGWgSy0Ve9ihosL57YxmZzB6Bz0MGKcZ0xiREj937z3m6SAWCdQdrOvW4SVy3tltQZV+yNQCMLpXIcQNMewwVmGeRnjvECUp7fXlBUppjOMkNzsQ9gCjJ2hvMPgJMWScXy/AUfVg+wZzcOFsMQwMWMpVoJxEGq5WGm5gXOf5ckEJtOWIIWJdt34opMhQl5zytemQX0UW3twtaEmWuyZ5DX4ggSAwQtYYeiWVXJFjBVRBKUDYE/Y1YJo9cWxVsG87aqmY/IiSKHC7nGkX8vb2JqaDzLgwSnUr6g8fPuC73/0ezjq8/Pgj1suKl2dOU0ZrjMMEZy3SOML7DOd4bbKLB7x1CCoi7DucHXifVQBVWF6SDQ4lcJniwWCsgTLUEVQArnholVFzgBIqEONTqZzfd0bSUrdy1d80PY/W9HaaHh4xzzPmeRIxXZF7vOC56UBURXaEg4uI91ArE9zatFubNh9o4Na1kEph7gVVdTorVKX30depS19lAbU94Rd19WYquF0wf02X0H5/eyi0P//ChuNXGE1/9UlBG6GjKSoTjbUc2StZQ0VV/qoV2moMekQUiMQmhVoyYtiwrQt0BQwylvWCyzrDCtvod7/7HoCkb3mDkiOeX57w9vKKZVnw9vZCbrYnve8wzxjnCVMpmA4T1n3DHgM+f36mxblWGLyHdwPcnabUP0QycbQWywIyh6AZzuOSR8kNl45i2cEdgdYa2nkYZzHaGblWnNcF//TPf0KKEZe3Myahg27bAmU0/vC3f0DzoX96fkbKEXUvfQGdS6YYSor/7UkPoH/I4zAI5qrIrqq0SV4vC7Ztx2hHjNOIwzhh8PxsAIjQbUAMFaru8MPEKcmP0KgoMYrmIkHVRPqqBj48PmBZFqzLgvXt0q4qePF9GuwIq63g9QaDHcSN1GMaZh5+iSEpGjxEOqZcM4yumCd6BNVKq4wcd7xtC1C5Y/jmwyOWZcX5csZypn5kWxY45/D4+IgomRb0AAL2NWLzAUZZ/Nf/23+V7ok5Gvu24fnpGbVWvL2+YZpnlErxk5EFakqJ2xat8fd/+/f4cPeIFCPu7+7h7Igf/uVH5Fzw6dMnQjR+xOA8Rj/i8cSDZhgn5kiLF5W1hvYZilqUt7c3pBCRY8I3j8xyPh7v8PZ2wevzC35cPwFQMNph3c4sZpkHQskF3jKzYp5YxKsw4FgQaA6oFID1ggZHLmJR88P/9d9gtMHT02f88tMveH15g5OGJNuEsG/Yd9qaGwMMmxEqsevGjXsM8ClBa5rjpZzFxI8EEiiGNOWaoIyCiU4OBYbr5FS71qEJzXKIUtBpxU3TN9ZepgQOsNaRBBECnp6fkHLCvM54SA/wg8U4WizbhmXdsKw7NIDB0Ocqqwpddc9mVlTKCrtaCYR1FftVudahjLy3dFtWuXRjvdqoTLji+Lf/vb1/gS+hnts/uxWntULfolxLKV8Y4L3/3gYp3f47wJemfu3xP0SnUI0GjGbMpOWv2jrixj2uVZamGvqG7cXOQZY8WsGgmY3RvyRJB2mM7t46CiwU2056574zhetWKFIhmJuigrnuQBJnUW0N/EgWhdFGEqTEtCuTn3YduXhRG4FzsirIyEgJXEBWXhhclsn/r8TWu1a8nc+IkR1bshZOK5Sa4QcKi4yojpdtkQ9SCS4uylIITa9TYK/qx07x1UaKqkIKEUUnscymRoEOoVoiUMmc0MZAQUtgj3TEJkFDI5uEFCMMAGc0FCpSDKglQaFi9A4lOZTo+mdsYDA48uS9G0h9hQTAOLLNuBB1CLvkITeaaiMdKIUsAi8nugPqQ6QrjIHLRkBCaqKkxl0X1Y2BZo0VtbntdFgFKqWPv/sD1vWCdVux76u4h/o+7RWZUBQUvPM9B0GDHkj39/fsjrcN0zQzDCaE7mM0jpPQONk1G+342o3B4Ph7Yyysa0r4ij1sOL+9Uai4bfDO8TUp+oClGLmHg4YxfO8a8UJDieW7pOm1XJL+vrDzVaqK4rh08gLvow3PT0/QWuNyYXZHzpk6I/E2VMKsqaIpSBlQEqlLGJZwH9U1nGba9A0I519yUSoK0/FQgEJhpzJViBSVvmNVMarzhshhZWpvrwsKHR1oXwfUrnYOMQCqQBvavLQMeKMUrDK0zq+NIAtZyEvBhZU92XUJ3rtp1XTPrZO/ft/7fcGvQT/ta742Adx+/1/U2q8sqN/nKvza17Xff43W+tfXKVgDOAs9OOjRQzuHXAtQJDKvykl7uxkHL7JWcK3RMOMAawyOpwNtC1A6lo5aJPPVkre/B7y+nnF+O2Pfd3bKmn7xzaog5ATnPYZpQsrs/M/nN0zzhPkwYpai3ILSnbW4XBYpugo5OzmRqTbVGoh7RVSSWQyIPa5cNCXTBjhSP5ByQbgsdPNcVyRt4AzT0/w4smv0TvYQscNJpMGxyEFRaMMAHqG+igiwlNKFgO0iWGWhGPar5fZgHWE36SyUUrDeAygINWA9L1i3HTFkOEdRnVYKwzjieH8kYyYG1JSga8E8jiyQSiIPpSsZxAfqeKDtQckB06DhLWEqrQmVhI3aie4kK6I8oGKvGX5wGIdJbjZapiTRaTAikr5N2SUER1YVKjDPB4zDAGc0TocZTiIyl2XBuq7wxuHudId/+If/BZ8+/Yyn58+YRybnjdMswS7cvbRbitYtGtu2QANw2sBpg7jvuLydGVBvHC6XFdu64/y2QCuLu5PB6XjCMAxQygiVlHsLZ68QJzO1NZZlwfPTE/Z16/i/UgppCEhRfqUIMqiotWFRJbtvGgbM8wzvPU7HgwjVdobLGGCavBAU5FAQplfKEeGy45//lapurS02yQtvrDPrDIzTMEkhZS5acyFZoyBBi01MrVfmXMyZv1JEMcLjVxrIiV34DqhkoQx3C6Y6GO1k4hamHhRUASBaDeuo+C+loKaKWhOMVZwGJnoyXS5nQA6dfV9RqkWpBuu2Yd13bHGHVTTEyzkjG3PdGdRKtwKtYBXjVrXSQrX+S5ZPK6gNmrm1wn5/KPxakf+1Ig1cp4r3y+X2NW15rZT6wv7mdpn8a0yk/+zjt+8URg/tHcw0wE5c9KpagGRQkgJSlK6hsOuojadvYD0XXt452JkBKQ+nE0bPbmcP1A2gFDhnoKxliEdOCCmiqAplNUbHpTC0wh4Dovy99Q5jmLGsC0Iib3+eZ9wdGRgDAEXEMEZgoxiTfBBiWifFrBQlFE3VvY4MFLTm3iALO2LdNlTF6cE620NRBgAGEuRjGYYOORjHaZJAmCS50LonauVSUBIvkBgjYmaBdNbBSgYEo0KZZVuEPmmUBiSJK4aAl+dnKOG++4EFUxuDsCdE4XZba5FiwjAM8N7hMA5UX+8bfyXmM0zDwAQ8mZdTyjDWwRkLZ2iAGHIGJBaz3fDNT4cFhLoGpYWxUSlSUxqI0cpiPfcsiXEk176WisvljFoVDocjtm1HShSLee+EvUW3Xq0UBkfDttPhiOPhgGmcME8TYpzhxe3UjyNC5OHb1OqEBQ1tFKwnZdZY1JShcsWmRaFbIRnHCt988y2OxyPu7h4kVY0HtjG0e2g3NZljZOo1zPfWcXfbVpTCr922DblEjKPtcAVyAWqWPZHDPI40b/QO3lvEWBAj6dpQBcPoe/GKaSfD0gAonHy3fUPLfGhCSuMMtNWEepyGKwY6jyI4tKTeoGKPzMduiuTS54WMomgGqHSF0txD8XMVqFBVcUO1sEZqgrLcKRS6CUCMKYxoY6gbSr3w89qgTul0d+xOtEpLeA80qtFQssMoBQgpYQ0BtVYM1vddRywZNSnYlKgDEbbRlxsC1SdW9KlF0IKb2nt7gNymtX1td9A6/rZfvKWc3zKXbh/vD5qvLaj/WodBe/z2ScEZKGegnIX2kl9bM6oGEjInBgl9V1VBV4gXvYYDtQVemBEtN7hn1UqWsRKI4XbLXmrpN5IfBljvoLRCTBEVvJlMcsioCGKbTFYRBUemjV61RQa2qEsmkPEUbm+2QFcCdzFknGwQ7lM02SiVvk/GeSgj/vlywblaoCshJiML6I6PegeVABrG2S4GzDkDcgCgspgkxKtuA4B3VOqW7iF1FcVoEdCUlLHmtRe6UlrCm5M4Ti7xmw9MEXjCC3SSYmBkaMpw1jMUaSAMAgAhxM7AsJrjPI38RKAki7pW/Ju+g4ci//6246Ip3TWdroXQ1xKRCmNQnRjPTdMsOc4jjFY9/auUfGWTGEs3Wz8wLElYZs6R3eWHEcsWsIeIEPd+c/N6YBFoi2E6nlq5XvjcnfNoBoFcchIua869RrQcrdsLIcAPXj7H3C0vrOP00Ow71nVBCMwzHwaGOyGSzlorC6UxTDN0kpVhjEZKZJjR7iPDeCv8f4MCUj+F8CNQa2SglTGoSryLxKeJe2IqvJuNAydc/vv8rEovnBUyMaACqnLHrJU8Z2qIcqGNhTIGtWYe4FqgLq05TUChqka7pIUK84Tae5YQIvck1nKRPU2DFGC+7zTX4z5DGY5NtWSkQkhJg/ArhDiSSwEy/x5QqFry5er1Gu4PdVUst0dt1wyuxf5WOwB8HUrCzb/zfsJ4/9+vUVVvv//94695MPz2nYI3KE5+WQM4A6MMilEwqkDXDKUqSroW2WmaoDFhdq57kBhDS2fnrDitNiwYGIcB4zRimkfsYRcl6KFfpJMf+pu9bztiilQah8oL35AbPc4zpsHDyM1K7LTlAzgcjwcEgTYqqgS1ixeLeOSUYuAq/VRSqQgxIeaMVCpSqciFEMs4zxhGZizcHQ9QMQApYhXMeDrMtGTOqfO8Q0yYmrXCMHQLZzVcGQpaCujgB4wCG9RKaw9SKw1OxyO7GcWCnVLCvgUwbkIKZ4M0rMU08jCFUjQfUwpGvu7t5Rn/+s9/wvm8oJSKaTxIetkEJx5Ww2GQn0fHXFohkF3EAu56N5QzmTzthiFUyIt332la98sv507JnaYRWnFHMAwGzhVY6yUIiDuMlBLOl1cslwveXl5glME0TZimA+7u7nF3vMM0TtBK4enzZ7y8PON8fkFMG6zzuLt/lKS9hKfnF7KrbnpD7wld1nGAAjozjZeuxsPpThqAa7fP10rxHC2p556z8fz8jPP5DdYZXClQFY+Pj3h4uEcIZNqcz2es2wUhxH6djpNFrQ45s3NWqIgpoF4SzG4A6fzXdcH5/IYQd0x16gFQrtLKPJcEaMA6A/H6Q0g7CgqM416wICMWAJqmjVrxdY/DKE6/CSG90svIaO4SFYVqyihop+FHQma6VMQE1J3eY6VkIClko5GLQ0qR4jdFNhhyRdiZAZ4RoHRGqUQdYkrIJWJZIpbljFySiNZGOM/cjyaUDSkCmoFW2hmknBH2HSoXBDncR4llbd142HcoSwPO9lC4Tgutq/81hXGDlIEvD4f2vXj3d7eTwu2hcfv37XtvmyfgqnH4GoPpdup4z0z6zzx+u3hN7HajYu6yQoGBSMO14kUi/yeGiNQmKIWhd6q5W1XzZuN6R8tK31gLBVrwQlEu3zoCpblNq8LjTwJVAejh30rG95Z8FWOkF5JQBJsH/jzNsIbFFYUHTPW1e7tkJdiojIos5PS6aaH3pFeShhljEtphFbZI7ovtNpdyVOZNUFMhawcZydDzKO4RyKT3GsmI1hIoRKYXD6KUOeEoY2D9wPcFsrSDgrIZw8Tw9JQyA81VIfvHWTBUTNTYqEgh4Jeff8Qvv3zC0+fPFBkqg9EPYoom1Fk4jCNdT9vnpHQVpSkv8CBsoFJyF93Q6uB68StFe4ZSQqdkAgrzPPT30hi68dIwjp+BHxxD6fdFKIoF27ZDKQ3nBupX5hHDOEBrIES6hIaU8Pp65vWmGQqTc8Hb+Q1QwDAOfbFaqpErGBinAaoO2LeVdg65dD0Jl8jtJpZWHO05bX2PQm0H80Ks5dQ8CvzDf4vFIISAWs+SAXGR69SjIgs8QguPZYH8O4S7QtiFkUNWDhQkUdD0azvKe9x8iWqld1nz99qjQswasej+Z9bwuk2VtMucK2ImQaNUhVz6pSoHAzOXtVNQGdBVCdGE07fRCkBBTmT+VU3gqWUZxGb7AppCKtOEqRlacwdQcsHlohGdR6l0CLBW2EVVGjUUVK1kEqI/lcoF1dJaJlfH96hCwoBIqtCoQJ/odUcs5KLt/1VKXf9cpmR0lEH+9FcWwV+jqN5OF187FG7hpDZJt+9rf/b+3/9rPH7zoRCdQjEAVIappKM5GUOLJmzUPixAw0DwU60xWodYKkJJMFXDihq1FiAp4tSogLEeBQoxFQBkbFhjRe5+Vext+97pm427P3gWq8bvTznhsmx9ZD+dToQinIezjNJUVWFZFpzPZxqbDR52mJC1RZYsiFwq7bq3gD0mKBHipMpDR+8BZQvIKcJbi7ReUFPsh8FgJZ+gACpVIBbUUJAQUE1GTQV7JAffGscDUzEwRUELn95hDxF7SAgp06rYOthp4gUBim+UMTCq4vR4j3ma8Pb83JPt5mmENRbni8AkxsKoin254L/9tx/w8vKCz0+fMQ4SvnJ3Qko7Uo5IOWAaJzx+fMA8M9407Ak2aOz7IG6aijGntXbxHS94odflAmMEp9UGtQLbRsGXgsE0ssNeljfc3d1hHEd888032PeAy+WCeZ6Rc8b58oYQElLecVlWlArMhyPc4HG6O8IPHjlnvJ2fEVLElhJ++OUTlFK47AHDQBX0L59+hvce34zfthxAwcSJw9/dn+CtQ5Y4UDrhEvpwThqV9gsFSkUx70uym8okPby94LKcMR+YzeD9R3g/izirTRsVnz8/Yd937PsvkvB3gLWEslIOSDngsrxJkp8Rnyfmgq/r1lPm8oGiMGNF7X++fKHFKSVjWV+7z8+WNmnAOA1qbTA4wNaKmgyV6SljC4WXdK3wGUAGi7CqgKtQHtAOQCIkpAuvYwUepBUFKa5QpfCAqkxho++RgFIqYdkKQloxTZNAbQp7CNj2Dct6hnMex3BHHys/olSNhIq9JkTJZTHeoqwVa9iRQL+pUxRii5Ipp/LAVNbAVMsJox0MgiWRTUmBrpImoLuuKrnz6pf21u3xfiK41SjdLotb/WqTQPvz9m/d7qfa371fcn/toHk/KfwPoaSakYuaqhRCTsi1IARSSUtKiPvexVvN0aSkjISMCGGwvBtttMA12lqoChjnEVJGCAu2dUMqZDfIq+oFp9lAKKUwjiNhC2EbNFpZ8ylqHyBHUWKMgx9gnIP1Hul8xnlZMIwTrHQ/KWeEkLoFcYEm5xxc3latUaAwjsw6iJLlnGXcy0KJa1islvZEt4stZ2xLAhTHwpglZcoW4vjOYXAOap4xiyVD6d1sQfOUKbVgC4GvTdBkP4zwzQfJcWGvSsEwsPskZHY1CKu10DN/22CUxvF4ZG6vMRKcFIHAA74ZF1qrUYpGhe3FGgBeXl66gGsYBomRvB2D5cKvgIKhVbe4vrasZisBQqSjNgMxSFeYcJgP9MoqCs/PL/1nzocZp7sjzE6aZ8e+K7rIqAmhlEycLVOcFM6KcfBwcpPmWpBKwjDSpqPREnNJ8jw5LaRYe3H+8ccf8eOPP+J3v/tO0vo8FqNlckp9qXw+n5l5Lst1awmTppRwuVykOa3CyiqIkRMMbTuMZEbzeTXVu9YKy0JngJyzPG+ZaGuFAU0hY05IJclBIP5CUKi5QhfmCSjFUPoUC6+txJzrUik2HMfEbrwIlbqQHsouj4SJnLMIyyz9p7KY50VOLvQ74+ejFd0COB2xaIbApTgPToNamVeBymzylAqsWeGHWZiR125bNydlgVVLyhR/aoPB+b5TU4oMpHYgGK1hlBZfNdlvgocEES/6vVXFqb/R8H8Nsnmfq3BrWXH7uMJRXya8ta9//3Xvp4j2Wn7tQPiPPn77oeBYuHljsGNPhVBJScwgQMm0vpDiTC44rvkE5fqG9FNYukklb2IMZN3skYvWJItmBfT9wG2CUcOk+4dTJVi+vfliRBZzAjP0VMeFtaaiMcQoJnOQJaFAVDlLQSHcBVMxjCOgNDLIo7fOod4wD24Y0bwJQuycbADyHnI3QPfW63tplUGV5+WsE6YILQtSSqIaVV1IWOSQ20KAliCgQXYVzaEVhSOuMVoW58IMU9LhFNWphs45jOPUi0zJXHTnnIQ1dV0MK6HTeiEONPEdIyZD737a48vlmZj8Od/ft7ZXaZ9nw1+11j36tFYjDK6KlCp++unHfpC8vr7ifD7DSlKb9xYtLlGhjfn6ysMH35NxHJGKLNy97e9xShEomcl2ziIlJ0WpwQKN983CG2PCy8sL/vznP+N0YnCQdQe0dLTb92BdV1E+k5ffcr/nee4BS4TkrlbQZHKVbjOdcxZ6qe4HxLKs3VuJhAl5s+Xn5tooje/M11qBUQyQoa1DK+JJqNJiZVNo5FdE2c/UP94vQIW+Eae1LjjFRrEsyJk7Lobd8xeLMqQRAdpSnve4FYdR2qHUygOmlICoMpR21LD4ds2Tss6DgYSNlDPhxBCwh9CLvda6W3Dw/eB9hSKbptuhAKrf2arWTl+t+LJLb4+vFen2mm4fX+v2b4t+Oyzef8/tHuGvcRDcPn7zocCbMSMFYuu1FCBGiIIFSIwRNG2c1uKDWNG795QS1B4ApRBCkEWfZBjXilQKlnXFtq4s7GDnTusI3e1qzc3SxTrXbSCqfO3heGQhtOSil1JoiJcS3TtLgXNeOvuWoUDMnqPqjmXdEBI7Kjd4zI7KzsPdHVKpOK8rhpH+LNZ75gc4h6CAFCziRpz38voGpy2sMTjMM6EbZZDiSr600GG1fD8Vrkc0Ww3jeNHSrjsBSqwDAEIr5wsu64rj6Q52uLKZ9hA4KVUWiJQTSgVjMZWG8wOGWcOZAY8fHqVTcxjHGVqEWi0kRYv47vPTE/YQcFkWzPNBlsIDtm3r3X5bstJuwXR1JgDZP5CyOQptuH39siyYpgl3d3cwxiDGiB9//BGHwwH39/fYNobO/+H3v8eybDifF/zTP/1j1ycorXBZzvjdH/+A4/GIjx8fAdCK/O6e/+bj4yPzLYS2PE1UH3NZWVALKb/7tuHt+QmqAt88fqCCeJ6wbWJhIYQF5xxyYlzkslzw6dMv+Jd/+RP+/u//DkopHA4zQjgi54hx4te31/X582fqPY7Hnv19OBzw8PDQu8Snp8/Y942qYmM6k4kJenQVcNZi8INoQeRQjpH0Ydl/tN1HTqR9HqYZxjlo6xBlXxJCgjEOWjORsDUcOWZxgm0sM9qWF2HRcd+2Q4HBWqqphG/+j81CIsMqCSxftRz+lrtD2R8O1kEbLSp7Zn47N8D7EdOkxCW2Yl12xETzSKsVBjV0iGWaJtSUUe4jFijkELHujL+NKYruRGOeZihn4VB7eJZWGrlWTj219gNdqfb/XYtyLtd8t/ePVqS9939xKDT66fvifrtTuNUktMft198K6f6aUwLwH2Ef3TypUjJqLlC19l88fblkZpiLQTXiluicdMXUHqiosG5bP21bkdOQhbIcCFC0Im6do9K6L3qUHBQNttlD6MKSYRqZ0ew8x1+xzmYyHBXP7ND58611aEZ0ITDKcA88vLQUS+uvKtWqSseTW6fGAHmLbC1K5mEUYsT5fIZTpORaY4XDTyWtlgW6FcbINM2iHRiEYmmE8qd6x8nCQghj27b+/g0DvyeLojtohZoFztOaB4TEUlrj4OQiN0bj7v4eZNQYFMkwbqLD5sxaQdgjCrMq50K7Czcx5CfGdyE7tkNIXakqN23b8zR4SSn1BXTUF7UycbSu0xiDu7s7MfObe5F/e3vDtq04n8/9EGyjeHealZ/fnivk79Z1pU26ouFeigk5RmzLBaoCD6eTuHkquuACsMZzun13Iw7DgLu7e/jBQRvCh0opSZZTHWZq+7DbgtAmpAbH7fuOZbmglNwV1048oCr5GfJQQtmmsO8qduK9YY1llrIQaJTSNIAcBljnsQcuyFGDQHlsYEiiIB1ba8lEVhJkZHgt51KgVKJvVSacYhXpskbEfCkJwzBlmSAA1GYlLkVWGrJSFCAki+sLvN7v1jhUKFirwMss9vrUOn0t5IdhGFAOB9SYEc2OsgU2s3Wh9sda+CHLJFZEdwGoRpcRsgtuiux18GrQDevHe3ZS++97qOg9RPT+627//vqaviz07/cH70N9/hqP334oyLIw5wYVFZg2hkFk4pWdulUaTltoR4O2aRgEpgkIifuIt8uZL0KWwEbGtsbfb9CBEzm/lq81mt1we/nNEmLbNuwh9EnBeo9hnJCXBbUGLIK1llLwdr7AWjpZlgr4YWDRFAbHtrLjHcaRi7dh6J2V0lrMAOWDkG7ZWKpkk7VIiXj8vu34/PQMqzS8UBgV2G1ppWENdQQU2024v7/vWHrznNnDDigl1MsJIUY8PT3hcqGFg1J0sDwej0CtCNuKbQlAKTgMHt6xW2xMrBAiMGhAICZjLb759lvIVYaXl1fEnCXwx8CLRiGlhOfXF+g3LqlzLpjHGacj6HArHX8rwsMw4HA40OW11g6LtBuqHaa1VnGI3YXSOeFyuVwVyp6LY+9JRfzw4QNKYajO73//B9Ra8fLyQgfP8xktzKRNKFx6N92BQUypm/OFEPHy8oJxpvbh6emJxng54/z6AlUrvv/mWwyeB9gwEtIz2suAzAOoQTV3d3f44x//IFi67pRn6xxqTYix9EOhNTrtwdc34O7ujtTb8xnLckGttWdADMOAcz2LbQiu8IYiJNqmrFLY4SrNibAD4yrAaI15OmKcJvhhwCrFsrncam3F6rvCKItkstiPEEZCgdh7jAiJdhOEg0jGMd7AiVtu2+GQLs1QnlpVL+ANPmIjUZEMtTxaXyM4rzVVydTDYKFaaHYYk+gT2pIYDL3SUHDKwADYlhUv+xP2GLBudBT23mPIo+xYeDhozSV901+0miNjAp+TzAYNOmrv/y0j6D3uf6td+KKm3hwMt/YZt4/bwv8F9I7rzuJr+Qy3P+9rENS/9fjNh0LYJK1sD6iJo5U3BlZpWCsdRa1w0HDawILhJkVXYFRyeo8wPsvF6vsLaN3g2/mNzqopSV7ulfMORbvt5k/U3qRlWRETVaoNL1z3DTAa2hls+449RBahgV41DCipCIEjJQ3juFQmeQ4w1tGK21qUUnF+ecUeI453D90OWAseXkpl9q/hjUie+YRt3RjQUggRjQMZQFprTDHQfVFzWU4raHrMnM/nvjDbJLP44eER1jnYGPHTTz9hXVe8vr7heLrDYZpxOp6wbys+//wTLuc35BTxN7//Hko5oBZsO2/+NeyoRmPICa+Xs7zHlP0rpcn3BnA4HshYMZZTWBMrZSpot32H1hZH2QM0KCbnjGEY8PDwgIeHBww3Ooy2hF6WBUogxG+//RbjOPZD4Pn5mepegZiUUti2jTGjzuHtfIazNEj7wx9+j1oL/vEf/xEpJazbhtPpDtM04+npCf/yL/+Cn37+GYfDjMPhgNPpKDROQlE5s/g6b2HHQSxWqCCvsi/zNxkWXetinBARApRiQf/+++9wOh3x+z/8Dtu2YlkWvLw8SU2pIpYjHDBNE06nE/Z9B72ILv0Am4RR1t7DcRwwjrSUbhDsLlBIK1Repti7u1MvRstKFbA1toc7abVBQayjjYUzDlEXZFSywKqGgcboR+5fRkJOuTAVLaWEYALm6YhpGqB3BeSKXW+wmu/d4/0DxoFWKJfLKp+95iQxAM4OYus9oFZBn3MRQWhEvSzidbb2Az6EiFoDcqJ2ZRgMWgRoLTTkW/edCEat3WDzOE4YrMM6XvD68oqSE0JOGMBVW8wZISWYGDBmQrm5qk4pt4pRoDwgpMjeFPvmX9b2Ym2ZfKsveG9L8WvMoFtK6m2xf1/Qf43Oevt3/72P/0DyWmDCU2ZmggLZNE0ApbWGrvTKsZpMoIIqTBOg2UDrykOhHQSlUnhWqlBNS0ZRxLG1LL2ysAGU4Om3p3czkevsYYGTWlfculdtdM+3XdYNRcRrrYMstQKSQwxZhDaNABQTutZlhfUjPY2EoaOVFrsLGf+kmBtrYR2xZwOIM6m/fg/kgtFUynrrEHKDhXZ5iQpb459XeS/FZqNdfFQBD3DWIiqNGGm2lkSBmnWGArnpIaauJSm19kM05cRuV1O9q7SBH4f+3oSc+uvig1kRjXDQuv5J9k7jOOJwOHSfnjYJtM+i+VC1JTLpwoSSnp+fqVKttOHWWneWDkDmSfUAoDHPM47HYxfNKUBS0jTWlXDS29ub2GJcb9acsyjVFW5x3vbZDM5h8ANqznIwfgkVKfm8uZylwvh4OmKcRpziEf/6r/+CGOlLRUO8pkXgezXPc3+vWiGJMXbNQjuExnHs903OCftOa/VGhrjFnoeh9iI6DLQtKbVKNKYsndtiV/ZaxlgYnaA19UNGMfPEWS86HYNiBD+P5LfVShttZx1yckgCxWjNFLx2nY+SUEfvroqiOU0M0hg5NxCSyhV7iEDOyCkgxgSk2t8bADdFVqNWDWdboW1aIjGnLNyhWaHXeufFKoSNC7NceCBwX1mRahZRaoYu13+vTQJF/SUE1KEesfL5Gq7/a1PB+8f7Dr81u+3++Nr33R4wX5si/qexj/aXM+MrHU3jFABbAVPpa2Il+s4bhnJYpaAtRFzFC3x2FrEksnU8rXD3GLBFmqdtkQE4w+DhxxGQgJZ13RD2XTzXRyhzg08D5O07h6MwdawbkHPB+XzGttH7ZBzZTZ9O96j1GSUXvL2dBZaZO4MpRibDWesxHRieU6FwWTdAB+RcYYzDhw8fGWSPCmMdSi1YN2ZJDyOdSYdxxv3DIw7jhNF73J0eEGPAuqzYt52xoeJ8WfxIn/4U+w1fK033gA0//PlHORwoprPW4+PHb3G6O7FDTzfF1hgYSYiLXCWQPQJgnCc4Eb0tKxW1b+fXXlAeHz9yAXp3dy0+lZYId/d33VBOy8GwrisnnZbOJQfVt99+iw8fPsB7j23b+mTQ/r4dJG138PDwgJwzPn/+jLe3N5RScH9/D4C5EJfLhc6eqUCpN2htkDP9m77//ns4bzGMAy3MLxdaVAtMc5BkvGVZkBNzEKZ5gh+85F5PGIQGqxWf1/F4AGq92mBrzb1UjFCKz6OUIvski3GYhU6ZsSxvnA7CRtgGIJsJXxqYtYOowWj7vuPnn3+Whf/YbTQaY+lyWTFNB/FggugaAoDIoqxpIe3cgJQh1wNlzNQQQTpbEhqmYeLit2h4mzCNE7wf4d2AJnZrhWldNlRV4YyDMwOcGQBP5b1ur69WvL1dkHOFUtyjsLFQdE5VEulpHIZh6ocC7bPYPSbJbPeD7yzB2hhHOQJFI/ssuz4K6aolVb62PY3kOkApDEJ4+Oa7b/H6+kpLCw2kWlANhaitBkEpzMMIgAfRHiOsNvD2Sq1urLGObUnNfr9DuO32WydPhha+IF+0n9Wag3Z9tHuk0VLbIfk1S402Qbaff/s1/5nHbzfEixnGGYzachJQCkiZviIArEwMRtH3CGhpZaovSq21gj2qTpustQpbgt2o9eTTOz9I1i1H31QKak7QMWIL+/WEBP8t5zyGkR2z8x4xBjEZ4xveIB9jrwlqe4wwzsGVDOvajblBG/rNOOfgvIfSBodDRFUa0+GE+XCAHwaolMQ/hddHqczztUphF9//wzzjdDhicDRbi5Ge+su6oOTCzlYRWuIFgC8CviEwwMvLCy9M+eBbN0a8Gjhfztj3TX7mASUPMo1QkNWelxsnYrOduqolErNAlwJlmmLWcDnYbjQA0zxz8ews9ssGSGFqWpFhGPph1m6GW2FO+9U6e2NMZxVN0wStqZNYBapoC+h2KDjrAGWQ04aUKmrlQvbDhw/cjziDt7c3aMPpwjmH0/HY2VztcBqGQVLRrr71RXYbqFTQBpmi0ESYzlJLkRNQs3gl0eG1FN07ca017u7u0BS5+74ixB30j6r9Rg9i1MZ9gO3vW6OrspEZOxMLQN81tR0NwMyPto9oFOC209HaAFoj5yimgxnG8KCvMnUabeEsbc+ncYb3A1ClEEviGKqC1RZFkaHFJDtCxtY6TOOMdaXu4ny+9ELmnO/vCQV9VRx3E5xLQFVCXGRD00wq+05N2GrUEBgo0TNIT93/r9bWXWuQiyJIAQKsUIyPJ0JrMSWsy0KhW71a1ocYoeRQIMmFda8K1NGXy1VIIvpKdGkHwe0+4deK8m2Xz7rRmobU/43bXdPXvve26L9nMb3/2e8X07/l8R84FAqcAWbDDF6jFbIKVCjWCmcM7S3KlY1knaX1hCK3nocCrTGUvaYxxcYcsqYH1Hs3CO0LgCauV1ICtMayrv1iGcZRshMGnO7uOqf9fK7YwxvfRKWgjYM2VgyzeGHtIcA6jyEX2izUgst6gRXPfucHWTJ75KrghhHT8dTtoxECVLpyj2sFFdNO4/xM5s7xdML96Q7eOqR1w74H8cQ5y9hPmGY6iAhMAdM49WKapQA8Pz8jJmYRE2vmcnkLe1+Y1sKF7OgdWRQ5otaCCi2pdA7TfJDXr2Ccg5Fg9ArqKrQx1KRoQiS56VEUMB8JkQzDgF/iL4h7xL7vANAPKVpQpH5BNtbP7YVJq2l2Q8tCDPl4PEIphfv7e1HnXtXRrZg7yw5zWXZcLitqZWf17bffolTCB0/Pz/JzKoaB6uv7+zuEEPH8/IRhGDCMIwaJOQ0x0gIh83k2F9plYdhPLSzk3tHALiXSKrlwdiLog3BX+Hh8fCQtshY8v3xGfA1wzvbDsneb8n44oVV3NpR05/f397KzYmPjvcfhQIbR6+srAB4KTedwu2sbhwlaR4SckQU6DDuhqZRY2BW0CLo0zMFhmg5wbsC+8RBJOaKhF9Y4ZJ1RxRS3FvoWGKsxeo8YAsIe8Pr6KlYfER8+fIR3Xg5IHrCEECus8Szyio1SCAzxmaYRw+AxDiN1NTJdwDYlvgFkWV3lIG6LX200UBUPvRhRU8ZoHYxiPgYjckl7Tykh10rzylqx7RsnqsP1yGmq7wrC2y0pTskS//ZQaN18+307oG8L9G2xb7+/NYS8bRJuJ4Ev6vA7eKodCG1SeD9B3D5+687hNx8KR+sxKAtfFMaqYGFhJx4OzmjkSI735XymmC0n5MGz6xwc09pEucvm/yr31lYOD2OQSsFlXeRiJAY6DCNqVVIoG8baRDgG1iqMo8Y00aoipYxhSFJortTRlDKWhcwTYyyO4oXflr+1KqGDAtZe8cwSI2jaNgnObQhlFKpBnaWN9CCir5wy9pBQYu50vYyCy2VFSUWiDz0U+D2H0xHeDbTRUArjOHPhaS3Ol5U0PtJJgBixbaF3fimTFbLLHsI7A+cpZEOxXDAaBecH2l57h1KBVCrGiWltHz48diuBUgsuy4ItBIqScga05r+nZNfiHB4eH1BiRtl4KF4uF3z69KmL1/Z9x+fPn3E6nbAsC/785z9TmyJMotuLWSmFl5eX3vF+//332Pcdf/7zn3vAkpOJaNsClmXD5bKJ2pXOqMu6Yt0W5MqDbZ45RVln8eOPP/ab7ng64Xg84vPzE15eXvAvf/5X/N1/+Tt8/PgB33x8xFYrLpcX/Pjjj0gh4o/f/x6Pjw80ahR1+b4l0Yi8IoQohTb37ITX11csywXPz09YtxVVYKjbQtAmAWttnxraBFhKwbIs+OWXT7hcFhrAOYeHh7lDD4QxLQ6HI06nkxwKQ39frbXiTaQRArOgX1/PbBJKQQ4FKRQ0N9hpGDGPM8ZhggEtM9bLBhgW5YfTI8KwwyhLh9zSlth0Pp3HA6w2MBadIdjsNZwzInArzPMGxJMsoeQ2HYWurRiGCYfD/IWIsZSCdQ0dNhMVFLi8b8IzCsx4oBXkQudlqw3RC83m6O7xHmEPeLucSV2PHpgPqKXip59/xjQMmDxzX1B5bTNYiszA1iw1odwtXf+2cN8W4lbs2+9vC/V75tF7ltHXDoH3k8D7/cPX6K9/9UnBQcNUsDPW9Hl3jpxm7xxiAZSMV/S6EdELZ+z+weacURQvpFIbB58jILTi4SKSeBrv8uL3XhaPgsEDPBmhpOOxjQXA6ENjOHUYzUVxEZplK0LGWEwjfYUajKJaZ2sB0w6FSlsKOoNy2lHC21dKdZFdFQgmAYB8aBXoGomkkxwkRTKQXe/+jDb9vVFVTELkMGsPLgUzsi6d514bZ7py0a7wrpNQmjnk/c+EwlsqUs5iOKhxPMxwe5B40IpcMsIWBRJDhyb4c/izhnFEtQxgaXj4KjuKKPqMVvza0veWbdYu3DbxNZ2C1roL39r39yWwVti2qybCe9tH6ZQSYaeSJYKUUGGtFefzAiUaDy8wV4cSRCzZJpMi0M62bYh7wLqumCdqMZJc01z6XgOOWJwDWphUCJx0tn3ri+y2NGe3TPz4Vo3fHq0INpZWkh0IdRrtfWs3ve7W4l4chNvNb4yFrSC8KpkQtVAdnWLqvzTM9R4SI0gjYjIupPm/x2Fg47N5qEqb9kYuUVBwxgJuQFXzVVnc8XELpSqUwE9C8e+uwe1apureXBfXcn22rJN9T8IcvLocUMjHnYJqnbXUvmZaWcCdmJaaMB/IrNskwx2S1ibfBKMUnGacqqq47iqk0N5SgVvmQkHphfl22fu1ovyeKvq17v/2a7/W4X/t4GiPXyv+f/VJwaaCmiLWPSGYhcWz3sOM5PBncKemIYKzZrMAYQ9IkMq+b6R8VlooaKNRbzb4GzbkGCUEpSLXSuGYBMp3jDHbKytBkzkRc0JB7f4+4zR3C4DL5YIKFoJhHOE8tRPtw25B5OM0wVkF1i2NnGlZPM1HDOOE4XCAEqy2gN4xL68UT729veL+SNvu490Jl5c3vH5+wtMvn1BThlM8QLtfkCz+9m3Duq54WxcYZ3E6nTrf/+plM0qXwv1Bt4AYRzjvMM8TNSMtJjQl1JLQ/OlTzmQPqQ0xZewxwk+TsH+OmA8zDscDXs8XbPuO88uLdHkex9MRWhss6wqzW2hj8XC6g4FBdQlPT094eX3F6+urLMsU1nXti9g2IbQdU+uMc874wx+oQG5CNQCdl9+mM+fIwa+14u31X/h5GYPHxw8YBi9LOtJL133HMHgcDhNMIhnh5fkZzjtZSPu+/zgej/juu+86W+nz58/Ytw2Xy1l2MYrXjSiWg/hMrQtN6JbLRkx9mvHtt9/1a31dVyzrgm1dAUW7eE4FRoof34/T6dThkz41y6HQYDXmM/AgaruHdlg2++73uwcAXJ7XiiCeSyklHI93ACo+fviA0+lOCj274bDv2I2H+GLDGYvT4SSCTVFGK4XNrVjXBTknjJOHtRrOG4lG1ZgO/DyUAVKk2M2NzIgopbKxyRU5XXcbPNQ8nDtiPowCpzVFb9tJORgj/koC05IuTqPO5ABlFIAKZAb/1IqeGWEdmz8/eNw/PtIpFugqfLOucNYCfsClLkgh4jjNzIBx7RrLcoCK8FTea6UVdG1qZMJkLMxfdurtUSTJEfhLOurt47ccGrf/xn8WLnr/+O02F46QTUzMQU06Yh8GGK0kGES6fmtgYKigVFwSl3Zy54IcUz+5m922dZ6nvNaw1QNGA9ai5IpYUvflL3IyG+cEwaX5Hhr19YaiBoiQrtEo+4fTfE8YzUmKImX8UBSsKIDYZCR3ueRmkMWwDqUN05pKRUkF+7ZjW3eEPSL6CKeJsyqtacKXImoqmI68+KdxIm00MVmqmZaVlKABlJAQc0FSlOjXSvpvN+YqEDqwlknNoiojF2BGTvSuSdLq11JQtFidy8G4x4gMBhyt2wGuHbrmyjjRili6lSXpuqx8H0rFcZxpj6yo97hc3lCRpSNUUIpPmGpej+PpwOdRq7ij1r5fIH1x77uIcZz6uE3GGRlirbsGNKypuDuRjrpJVCtqhXdM+HOWaXQppm50lnPGusjUYDkpHg4HKHXDcNo2LOuKYRxg1IiUIpalIsieg0vRjBgZm+oNCyOpsOrKKhFyCsBuNecEJf4+xig06KNlM7RJui2ZWVwKlMqirXAIYe8QKoN2jLwfPIxIZWbBGseJ940yiHZEtAGH4YBagcHNGNyEwU/yPHm/5BIRowKqkf0YadWk6ipEraA1kBMV/9bS2K5kLZGgzH7gxMWJVDXqbsfjqTrOqqAJ/4bBdysR5x2cNQhxR/OUUmJLAbnnOQS3JDN5ozO9bmUBIDnR+KKrN8aIL5KF8RZ3D/fQZ2Yy5JpJSS8WiJWmllqjYsThcOxW4iWmTlevkhwHqSq9BtdGmZenlhsZ5bbIX5/Xr+0O/q3Hr1FP/6dSUqdhZKe07yK1r9hGJlzlaRQoiH5DxhgYZ4QKVxl/Vxm7lxtX3rQsBuYuKGtQtYFTCto6QGnUmhBLgbOSJFaJVxrnkUoFKj+gKoWvJZDXKlZoWsHKxXRNA2tnlWB8FaIw5uts0w0qGGgD2meXRH2G0cKAqBU1cwLaFi6QcxvLDTnbUFyQR3Lu4CYKkeZxQi4MFknibZ9LRRU6bI2J7pulIAeyIlhjuRTTlbsMozWceCYpI4lwSZZXFYhibFZKAUxGlgs3Jop29hRho8VlXTBhEutkQiBdf+Dou5NSwnK+IKeEkjIeT3cwHtC1Yg8rLhcu9Tmm01ZBqUphmDO4qyeEQK3Ccrmg0X69HzAMI0p5RhIbktOpyKHgZKF+wjiOwrQZ6PdfFe5Od7SRlrxpBWD0A8ZhvB74KcFLt5sE1oop4Xh3wgjgEA5Y1ouE3fCAX5YFj3cnTBKBGiTkflmWG9qglteg4byFd+KzdWPWeHsopBzlUNByjV3x8Gazcit4ah5HZBoxcIo+SCexA6G6tx0KOWds69YPmdN8pJ0DCrKNyC7i4I+E/tqh4CbU2vQbCSVHxJqhqngguQHOXYkCRrNfSykg7CuGwSBnNmUNrrOeX5vEOE8rdAissXZkjSuFul0HXjK+WTBj20vmJPYbzXriWkiV1Ahml+TefSpp4to90xqQthy2g4OtHg8fHwENxLRjXVaUnBGLQ6qVjZdmtKfxHjlE5ERlNicnf4XxCt8DDjFsToSJLBPS1a9IKzHsk9d5S1Fuj1to6BYeut0TvC/8/9mp4GuP33woPH74wDS0weNyWejhoxVSLeTSM82F7B7JSh28jI0CgYQYuqRdtRioWlFSZK6xN9DOoXqNnApCBqA0xSU5MwFMi4+PnPwKClWz+DVMehhHurfmjG3bvxi5AYGSasW+cdx+T+0qNSNJjGQBVZfaWkBr+HVFVQpriFi3HSEkVACHw4xvv/0IgwKj2OForXH/8ICPjx9gjcGH+ztJSUDn0e+BHRGhNAdjXd9FxJRQc+nhQjUX+hnJuDmNE47TjGEaUDVFaDVnhErbZSZgNivugUEzAKyhWC1n2nFfLoyDXBYa29GnhoW3xXqGEPHp02ehCw44jDOmYYCutLloFEQtHVxbEhpDTNm5Ey4XI5AI8fF5PnTL7M+fP3VcmRx026EjpdDhqI8fHlGEAeM9PbVawMwwjPjm228wzzPuH+/w6dMnbNuGj998RMoJv3z+jHHbGBbkHdZtxc8//4wtbKi14HfffytNRhEW3ASnTV+ct/1GIygMw9iZP7lkgUiTPH+qvGPakXNE2Pde5PpNj0Jdy8DDAWjZ3nzfrKN1CgVwtJH2noLIlAY5dIi1c4/zJh5JHiEeendsrEYLQ9pDxNPnz3QG8B7zYeSuKRvkklBKRIgBShnkGlDVCNssz3WCHRRgCopKKDUJqWKH2WhpM0ZPCqdiBWf+tVCsQQpqrS2wh++DcwbD4HA4TMycGBzyvwZRhm8IAcLyajtLMIxHRaxrQEZB1leFcU35an0tPyMJwaUCsLXAWIMPHz92QWaKPyHsAefLhTGhFUwSqmQmKekmm2NtSpE7BxhA177bw81hALSnXPv0VFSGLgXOX+m2t+rl97uC9rj93++ho/eHyPuDpP3dX33RzK5eEx5yFlWjQz5VscOuUP1wqAAPCPD0zLUQ869XybiqMualjKI0TKliC21QFPnAwPWktdp0nn47+fubdTMmtoOD6tl403mpvpRsI31TTvObv/wQus+J7A4YERhRKrDvEdtOOig0Mdd5noEcgEK7jCZMmvwAbxlvWHJGEhFUT8Aq/ElaVen2I6G2G4ZD63gajKVkUmiK8tzey8IJhmHo/FxMu/DEOprfYwDNt61hvzmVjmFfec/XmzhFcsu1MrQmyBW15F4EuUy9Fjelmvmd7kvDZmxnLA+rnDkxta65omJZLqI1CHJdedR61XBkxedKdlQSyIVajMPhgPkwd5y91so0tmT6+9U53qX2fYdSXAbXltInz9Eo000ZW1c+DF4Ee3NnENFqOvfdScP6daiIqXYR560JXlc0y3uoJLTeyKGgtXAF1NVfqXX1t/bxjEUNklveaK+B07CxfF71ar8dI1mARSi9nOwIjZRSkEpElSjPigybZdLKbUdV21DO5XWWYCGjYRPt2dEN77jvUfL7hq6VZm9RrpMRGyMaCCoFtLCiXBr1s2VE814ECkpOyChIbfmswGKO2269NXwVuRYUALZajPPc87+t84gxkcVXyc9ImYaa276RWaV0p6R2OLrdl4IX9nJUWzG5/hmuf/IX9fVr7KPb339tj/BrU8O/973/3uM3Hwo/vzz1TjobBWUc7DjAjh5mpJVBRcWWIxIqUkmw4vUSSqLz6L7BKEBZi8ENCDEgpoK0BuhYoJXFoB2cMcw/KISBkpi5NXM24JpE1DQOOWc4S0/3Umipu5zP4m+E7jzqnMPlIjzlwki/Zm/bdg70bb+KU2AMrPeA1jgvC3Kt2ELCuu2IKWM+HGAdje1qVEBJsNrAao2aM8ZhgBOjssvlguenJ/z8yy/Y1hXOioWCVthTQMgZeHqCdRbOOszHA4zm6F1zoVRfsPd2oOY9IiQWhbjvCMuKPQQ476GtGIMVRRpwptW5zmSvVDDvIoaIkrde+OKekYaMHDOqo2OmhoG3HuMwYZ4OGIcBOey4u7sXy22Jewyij6jA29sF3rNAQiC7cRrpv+MMQiQUebo79Y7phx9+wOVywbau+Pa77zDPI6nNhVBkTBHbGpAk8OZNNB/jSMO8+TALbMWsi3bjNzGilWSzKIpnKzTeb775hiykXOjTZQycEYadGPMBXIQ3DUSDMZo+o/kXjWK6tm0Ltn1FKbEftlkKILMJKmIExua95D2UTlhW+gXVWtC84yoKXl5feH3vsbvKkvXFg6H5Kz29PnP5q01PZQs1INeIWIL8ijCOYsVSgVh35JSxhDNSpJisHYheEuu0MdCuwo8aw+yAvWLbeUhoA+gedqP6AdjuwSrNDBqMGcmEypk+UsNgoHRBrQnbviDEDblEmKJQNOHRWqUDRmaDYipUKSiJqYhXYzwtUKi5uh9kxurul0tnJ1pjcP/4Ac9irf/502cx9DTAzAP706dPOIwTJj9idFyoyxHPgi+wdBG7baKG9UZeB0DxwEfbWeLrkM9tsX//Ne+//v3Xtq/5t/YNv+Xxmw+Ft+XSnorsURR0zTAoiLWgyEvNN1nNUbq7PUpUHmi72xxFczbIIM2soqCEhGISstIokYsfwhCGIhN1jbZrL7mZp2WBl1AhYpjQ82sBpqRdrZkjiAHWDle0N4zKyYpSIdgfIS3rqACG1pJwxlbJZSZ4pZLx8voCpyq80bg/3SFa24swjeIY0BMj4TbnPR4fHtBYbudl5W5BgaO9JK8poMMXOfFGqqUy20IssnMlXKZyQU2FI7TKgDIw0JzGahVPHE4n2vEmstogKwlZkQ7HaAtUhZJqFyodDkdM4yT2C6TSwlgcDg7H4/WzoUBJqMW5QSpiSijYcRMlvb693uQvOEzTiHEcUEruQqY97CLOAaz2V8aYNlBeVNCb8PHbslY8a7RSOF8uDAIahg5rkS6aRNmsYB31AjFEeX8ySjGwgzDelMK+792nqBU2ejuRQmm0AZzqIjKtNfzgMKcJOfM92fYNQV4PB23F9x0FAG1PjFXw3jL/oCpRQNNKe9v27jzqHSeWZgHPmFBadm/bgkaZbBRvP3hY7zBigh8dCjLWsEJnFtktbAhpR8j87yYuvEppuJ3ix8EPiGVH1aSmK1OhrCx/ZdHcpp4SiPVTfc5DodmQ03ocgIJMWBEh7nh+ecLb2zNeXp76dcRuW/QIna4ty2M7wOSMsotrgVbSTJIubq2Ve1r1z7Htn8gmItoxTBPmlHC8uwNSZr51YmNaY4YqBboC3swwSqGq3Kc/KbmtPPYJpU0Jqu8PjBwQ17+7hbX/vY7/lqb6tUnhf7pO4SwXiNaaeaVGw1QLW3goVNEUpG4gVXlYlIo9sUBUsNA2TNmoBK1EgVgzSozIxkHBoMREznBjDFSeso3K2F5gOxRKzsS4leqHQgs0b97yDc6xljh+NleefKObac38WNQqdg8WVm58ZQyqwDFGWRgryViVmOfLywsOg4ceBxzmGcFaxBixLQtyLjDOAUoh5swiMlp8+PCBXUYpyNAIMRLTHjwOxwPmkZ5CMfDPs4SpkFkToRXo6qjkxilVzI4KoDNUqbBKUw2dMvZtlW5LLlKloI0FQFGTEgaHURaqajKZCiMTDzMPhcPhwDxpbQCT2Tn7gUE+UpSbluDl5aUX0maHMU1zF67l59xts8lAmbtTaHNJDSGIwhg4HO6/oBE7rVCPR7kTxUU0qJ4yprTG+XzGPE/0M5IbeRUm0TiOMJbX877zminSZJRSaJsioiXgylLigddERArey4Jd6U6dJoWYSnkWvQD9dj08jRFuBAERVPD5GqvgB0P2WaGVRowBy6JwPp/l2k4YvEeMM5wb0CxF6GSbEGMWzJ6meFpbOLH+ZjYIYaVlWyRPoiBEORTSij1uWLY3gWtBvyTnMc2HvtOrmjQ43Q4FTQZSmxSiXJPNvqVWQDnJINfXZWutGbkoxBhwWc6IcRddS+5MnVoZjAWliVBrSJMxwKSMWOR+VSSeKDT2IbF7KLCwC4zaDk9i2TwUSqm4u9sQ9x1pl7CiGpHqBqc1nDE4jAOKVqR7Q0M1nyW5p2pfhvfyDDKmVGdQ3WL/tx5Ibel8+/i3ivqvHQZf+/3X/vevPX7zoaBHL4weeWFaoRiFrIGkZMmJgr1wkQxFZ9VaCqKEjWjjhESqGZsJxS5WyTuagRITUlXIkjI2DPTwacEorejIq+TSWJgaQJOPX+Xe7ylgtx8C/3ftY38F06M4Cek+GmrcLE89+dFuGBEiF+BbiFiWRMOtaYQSHN17z6wIpVFyYmKc5r87TDO8d/j4/Xeo0pVsOUOLRfnheMTDwyO0UkjCQGovQlWhmaaMrJjvUBUXbc44WgRIlKcfBszjhHUDQqlAqnIDa7pdDiPG0x0Pr30nPzwzLyOnghgSYqDg7zgdMU4z5nEilFVY7OZphvMOiLeWDCyGNLFLnU3DpTO9qZozaNMkNP1AU+jO89yFb21SyCl1WigtK2hvorRCTBGfPn+C1hrzYUIuBcNIUVfOdOHVhlBcjBEKwPF47Mlrl8sFURqJPAw8HNteSILkm2itQZT8cwjpQfcGpF2LxlgJ3EmwgQwivS4oRVL3oKCMQow7ct5RqhPqrcY4TAA0LucNuSSsK1XAQJWvjwhhx/39o1B7RWQXM7Z9RS7MGhhEpDmMA6zjf/ewYw0rlnAGREyaCzvzPS2IZUfC3qfKVCNyDciIsGJdHktAQgYMG6OKgpgjTM1QWaHW3HcQWpGRN4783sGP2LYdWm9yQHK3cXk+4+XlCRAxmB8sSo0oVfVyxdKmoVSB9w5VK9jQxHK0Y71uNNov2b0p2Vlo1qCYImJpgVEe3374gPPrK87Pr/jhT/+MsAckAPM+Igwel2WR/eAg0OL7Lh4d4rp9vD8IrlB16bWqEWXe7xfei9naf28Pi39Lx/Brf/Zrj998KBxORz5BUcOyA6HwrECyWvsBwFO6FipAK1iEtGp4olBIKxs8o8UHSRFHzZXydxh0RWUxpGe2om70NbqON6/qy8zbN/m6YNY3Xaws6tqORFSsAAB/s0QTfnHJGdpYVFRK+C1teXMhXGGM+NNbJwyLgm3bYMWob56plK05d01Cm5hqhRhzNdttTW8dRb8oFIG1DNPbvPMoufQRuF9kqtKMUCzCrbHww0DLC2uF9ljF9RVdGe4kpQpQfF/qVVTX1JyNAWVk2aagkCI1CUVYUimmns1LKiKBcO88oroWYcjIHCU6UmvdtQqtG7/GeFLglEU9227vUrlgbkwQL4prpTVW8UnSlvTDNm00hTRw40EjN1ITIpXc7BLYWJRSxI6B11Cz96bxHzvABkfxprvegLcMI10ViD/XngENAClSu1Cr7p22EkM3UqbZ4QfP+4GiJw9jGCpVShXiQ7sf2bJmsZMppcB61ytjc3FtrLdt21BqEsNELU6qFLuVKhQceV7GsLHKJcEqT8cV0dhAAzXxYAhh77oiQj2N/CEUamfl8PdCBLECpbViKEy8Pkm0ySrLeyw+QyWjVnb7tZMirovfzgIq1C9ocy2IWt0c3pWTGC3DLe6ORxgoIJGhlEQvl5vJnhglGmOhC00k6aQANEjxpn/74sHneLX34QHyl8yh2+L+a1PCrx0I/9bjrz4p/C//9R/6GP92PlPe7y2UNUgK2CMjFLd9pZ+I9ciRLJoq2LzRBjnQ4ySEgJIylBQOpTWKNlhDwL4HFFFFG9Wylhl0wy70y6nFWtOZINwZUBxlFPqCsxmRbduG8/mCUiqc8MNj5FheUTGOA7xvfi3Mfii5YoTqB5vV7BSWdUPYA/w4wh6PGJxFWlfUnPGv//pn3N/f4ftvv8OHh0fUUvB//Z//J7addgnWGhQAnz4/oWUM1FxoSS2FNoQggR9M32rMmE3omZDDJKYMZSBusQOGYepqYCh0X33nB5SbDmMYmQ1xOhyh6wXbsiIL1Gc1Q+xVJUtEy2RXM6GsFIOwLYivhz12ts8uNudaaxwOR6SUhAbMg2dZVoYEvT3j8fER8zzj4f4BtUKW1AolA7+8fEISKOpwPNJrBwY5r7isS4em5uMBWrq/N/kcYYCPHz9KfKdHiBEvz890D5XuP6aEdVsxTgNddr1DcDtx5AqEGPD29grIQdC8mZQI4XIu3Y20yM6MhGPegFQqk+VD6IC7Nt7AGufzBUCF8wZNyJVS6A1NU5TfaYt2P2ttBN5i7sa6kk5L8jQn9m3fEPYNUArT8dBJIOu29oN22RbsYeOBYA2mwwitq+zTeCAYa2AE+jjMB+RCGjdhVY3LcgZ1EQ6pEJ5cL+fO2Lq7uxcorVlYaIwTU9umaeZBVSv2XRoiSTmc54MsZQktlZIRQsbg6YEGUExYygaAmcopJigl8KfsHDXEsdlUGOWAwpAobXQ/oHIQQ0/P++X3v/sdZj/CW4tf/vwDVAVSCEi1YgsRJVV4R7deJTR1bWmvQ7sevqYvKabNMO9LymiDldr1cuuddOt62v6sfe0tk/JW59D3rV+Bmb78uX+lQwGab/6679jCjpgStNMwCnxTUnMT5AfsvEfOW7cYbvBLzBklFpwLPUaM1vCGy1zjPdacEAMtGcQipU8o285iT98bD2+YacsXDqzL0h0urdGSk8AOs5Qqi2cG61irMM+H/m/T7bNiHCeMg4N3BjGx4ClrMY8T5sMBh+MJ0Br7uiFsG1IIGPwAlIocc/uoGSZPDgQFdFUosJoJcFpou5+en1iIU8bHDx8YYxgT6W+FbCGtNU6nE8ZhREwUf0Vx82win+ZkGkvG4HhYb4mU15ZTnQtTthrRdgsBqVQY94plWbBcLkghAgrwAw3gpvkgViFKaLZWBIFVfhHuWNdNjOEiXl/fhFfv8Ic//AHWOgAtlSp3muo0HnCYTzgcZkzTQSCZiGFISDHj558+ASDZ4I9//BtM04zLsuHT52d8+vwZT8+fMQ4jPj8/YQ8btn2XEKbGUOGhGaRrdl58rhQQQ4TSdM+EYuf5888/IYaAFCIe7k4w2uDl5bVDWHfiwtuaj3ZT14qe2ldRey71tm1cDu+LiMB0d8Vt/kspRexhwyiHjgKV/Mu+IyWIfYTpQr4k+dxKQ7jus/zbpKa2ZLldwnoajZJMJr4vzaadezQucGOInBJqBlSAAuM4ndhJGGNRCq/TnBJQyQqk8Z1BLQ5Fa1LLhbXVFMq1AM2UchicLMQhTY3AwQpYVxa4eT5gHD1KyVi3RQz9WjgXU+ZK3oQMwNa8RXUqKLopV8Ksty27VnQxUNJAkHGXoWUjKsY1mEaPen+P+4d7ABXPn59AWj2QKoBccV42sp0MmWsskdektPbrloL8tQJ/C3G3r3lvg337vbeP9xNCOxz+ex//IZ1CLhlb2LuR1FCHjr/LSp0XntYUSinVoSMlBlc5c6FcY8Tg6CqpxQTLDgNwuVDFrK7jVb25eG5HRSPwQClckrZDI4QNRnx9mjfOuu7dQqBZBLRgmJxy990ZhwHj6DB4B7Mza6BqjcF7zNOEwzwjpoxlOSOFSCsMYkBygfGJ7/uOKp0JKpWdvUsdRi7gUsKriNhKzvj+u+8wOId9WWGUEvM17mNGsesmy8RhDwGXyxlqD4hJoySGhORaRFOiEMR5sjOO6q32gsEoMWfo8wXrumIXUzgqZek+ejxIpykaCC1QXNf3C7slRtI7933H8/Nzp1f+zd+Yvnhd1xX7nvuCdhwnjOOMcTxgGEbx2lfwbsRmdry8vIjIbcbpdC+eUJ8RE/2W3t5eqfQNO5x3MM5cdR3qanlcpUOzzgnsSK2KH2hFHeKOvGc8PT0RyqgKD/fMO369XLAswNsrg39aolwrTq24psTjVuF6Y7bs8LfzG0OAxHfpGqyj+PnsO4y2sLYCVcsUELoQjpoI/ru50NJaKXQY09oW4hK7ZqEHusjrLblKc2YwjANiirIHlBCXRE1PLgnGZBir4T2xf+c8IbuaqQ9JpIPmzPvGGg3lLIrWqAIz8vphLgp/NjEs52jTUQVyLvJ6CI0RMhvHCfM8ImeZlstGCFHU2lysRwCNTGBg9DUhr0ijV286agCy0wKJFYqHWqkZCtQIaVAc6pzF6TjjJHnZr8+vkLOA03IpKHuAjxEu2S80I+3xnir6/nF7cLwv7rd7z3+LWvq1ieD9PuL93/+Wx28+FF5eXnC+XPDy9toNzWKKwM4bL4nXvzK6wzr7vqPkig93HzEYC6c1nn/+Cfu6QoWIDx8ecRrvuqHe5fyG87ZiTRG1euickVayTmqtOMxzx3ab62QPRqkVXor9PE/d3iKlq3VA+17VMU+wcN9+CAC8sfR62gNqyQghIawbNmOZSmUM7g4HaKV79jFqxTRMMKCgK4aAFBPWuuIgKV+Hee75ApflwmLgHA32SsVBEsDCtKPmgvPrG33zFeGtu9MJ0zzjvC5YN2Y0E0dTmI5HaMvDdV1XvG0bu1lj4KcJqyhqtWEwe8sO4AnBDkmrisPxAOs8nDUoKWI5v+Hxwwe4kaZ7YQ8IITK3OaeuMwDoftqyEFoH1IWHcug2jx6jLbOHK00H1zWIl5PH4AumKePDI9XJj4+P+Pbb38E7jx9++AWfPz9JLnPA6e4O//fjoXsPPT0/QReD04MXA0BOic1WgQeUwS7X8OVyofdNcy3VjJK9O51wEny53ryGUgpeXl4INw3jF6+Lzp5X+EgpUl0JORHiCWGXmFZSe4ltG2jlkBLw+rqi5IKYMoxJwmgCSJ4A1nVDjMT9tYTKLMvyxRJznkecTvfwbsDxwCyJbQ90j3UD7o73mKcDLSQsP6M9rNj2leyrSnPCwzwzEwUKnz5/QlgTUshIJkFZmlAOzuE4H1hgZb/Xpu9SM0qJ19eoGXSEuvVExXVldnXPjZZ4zxAarTygQpMCbUiTbpTcbQsIe+E17Ujhts7BDwNqZpNmrWUjozVSDDf3nAKMRs2Ju0FdsNeEX37+M5z1sNrh+++/wzzNiHvCcl5xXnbcnQYoyWZhc7jCmC9rEtmMX04H7Rq5VplrOlvr8N/nKvxakb9dRr8nzrxXRv9nHr/5UGj86ltHx8bYKSV3fLA90ZaYVb94zlQ9V3DqgGoJbHwhWwjIMvLl22W0LALHcexcdxrdXTfxDZah57kFKk/9ltva39C+BVI92KT52WiBsxSots4p9cV0EE//dV1grIcdhm4bHmKSxaAWXyLAyWIaoObAe8+pSV0dGxulrkqOrDEGVhsMznWO/75fbTrkxWLbd4oBYxBjMMPAHDlwUrnQBVXUttY5hELrjltlepWFnHUGNhrxUmInhyqMMglNYg4390e1pE4zXdcVze64F8IbGmdjil07pyvu2vaBtXJxbW3rJD0Gn/Hw8IH7hocHWGMFpxd/qZTJrJrZ0VWQiikIrnyWV+FSrbRzV7KMN46q7BCCuGvy+iLzCrSkdp7spEZPlYmn6RTahNzx3WbcWK7LRE4FDinzfcglw2sP5zzGcSbAKKwZBVJeSQNt7xMEj04CozT1dwPwrgp4KFIwx3HE4GfxLvIome6kzQ4bVTLDnYN1RnYenCq8dSg1QhuNQey4SykoiTYNqiqGzQAi0LR0GlCJ97O8fiqoixyWbYFcJW62SrRpEB2C6hYVzTI+iyVOrei00i62jNf7NtcKU6X7v1aE64Jb3S6rSWeHVlCV1NZaC5T8qjnh/PYmoj8Ha8huO55OCCFj3XaBtBWgDV0aUuwklRaO02jzcruiW+DXVhGqLMmvItzbx21B/1p3/x5uev937+Gq99/z7z1+u07hcqHH/M1Nvm0bGid3nK62AnEPKDnjfD4DVSGeIrStgC69u89yepuWT5ATLtuGjArjHVIt0OJ8aGWJ+vj42E/k0Fw1ZYfhjcHDwz3pkc5i21ZcXl+xbhthHRGO0JudXcS6h37TtYPGOy6xU0i4nC8Ie8Aeo1g7VKx7gB9G3D08QBuKeZZVksGcFc2AwuFwFPVq6l45Ly/PKCVDWekOrMH9/T09jUrpUvqjeAKtUniXdcX5hz9j3TdsEoYTYsS6b4yhtAYfBw/rLACFkBPWfcPDNx8xzTNOpyNCzUigf71WACyT8oxSOM4zNCpi2OE94Y1tZTALc3S/gYKH0UDJCfvG3IRt3/D582fc3d11WEQphbu7OxyPR4zjiPP5fKXzCnOrMYlSKkziK8AWAsZRYxyd5Ax7/MM//FcMAmHVqrCsGy7LilQK/OjxN3/7R3z33Xf4u7/7O7ydX/Hy+tKv17aspSUzGS7ntzdAkTE0jhPtzl9fMc08PD98+EAYJVBTMQwDTtNBGDu5FyIa4zXr5yo7KtdZaM3PqUF9xhywrOfeLDnncDwe8OHxYw8l2jbmLb++XtAovVoXcPBQ8vPXL7DoWlsy3donnXma8PDwiHm8g9EOpWqgiPZG8hLCtmOexaLDM6vAGnppFaFzNngzhIAcM3IsqFnBaAcNAw16mw2Oav1QyNYzowVSRZL3RylCglo3WJWQ5ra166vAWn89GKV4BTn4CZ8RCmbSHfdXIcSeS+4K4PwA3BbD1o1KE9iCv1KmfkMZBaccUDNUpV9ZSRGfn35BjLTf+C9//w9wzuO73/0O+57phFAkrc1YpLwixwit0XdWbYfRYF8u2FXfcXGCohJaK6CYcm1ccN0pKJlYv1b833slfW3x/D9lUqitw9bqRvwhFE1jha1igJIQtogQLyiB9ManXz5h9B6j99DWwk+EUapzWBM/lKINjqc76Bigc+rZyk7p7tXurIVRCio3G2jFBCgrU8JAHnapYqNrLZQ2ojBUXIw0r6VcsG8bu0lrcJLC5oxF3Omx/vZCR00o1fOK317eoMyCy7Li7uERwzSiFi5xuXS2cMbi+MBYyfPljGW5YF0X/PLLL9DCplqXRQ41kGWlFfYYocCwccge5u50BwXg+flJuqwIYzWcdhj1iCysl/P6Bhtt942yg8MwD7DeIgmdLsaMkjJqrkDmJGa0RpmPKCCHewsRWvPGabuJmLlL2t8IZ729EbbK5Zoe1vQGqhfdsUNZADuiu7s7yZIgE6OU2oVjTtGiWytNgz8opMw90qfnF7ydzzwMS8Z3f/w9/p+Tx8dvPuB0PGGaJ+xxh/ce3337LYzRuDscMQ4eTp5Lo2EytD1J514xDL6n3BkAEbQ5D1vApWpstzeqXPeDH/vva2VinwJtqt+P9rSQLkiR5oOcQCaMwxGPj6Tq7sIi2vYdOXMv4bxDKQEVPMSbQR5v/trFdUyysyjFiOMs9xZKNxt59El5Wy+IKeKyLDjd3eF4lKxxzYmjM6y86wvw9bJi2zcYRYsTBXRr8sE5GE0dTc3ci62SuV0ht5tQZJEzAI1SgQIaZzLekn+Gwq5bGw0DI4ep5kQjec2oYBxoBrREiRpHi5C745F7zFKxb3vf7xSgC1pJ081kexmFkncoXWEN0YcYE87nBW+vF1yWHYeJuRPH4z0eH5mAuK4bci1Y9gs0EgwyXLUwNdOiRitUZUQTRzaXkoPWoKKqCi37jqqAnKlb0obPuCpa0fa4TxFb1pw7pRvAFwfC7aHQ/u5ruoa/+qFQ2opSxm+tuBBj52VhtERa5oIUE5a3pZ/c57c3lGEEpoLBcXSu3kNZhz0XuFKhjMYwTkhKoURA5Uy2gGCNRutuaAaxxdXErKDBsZc2FAopkmUjssf+9e1Krbl5BgVo76GtwTyOcNZxYR0TlsuKdd2kA/D83iKOoAC2fadtgjWEqbLYNJsZ2gLTOAKl4FKBbV1F8fzMRZofZN8isBUMlLMIkdkRh8ORvHRHKioptkUOnwzjyJ7yxmOPgd172JCygbEOUJX7Be+gLM0B2WmK06pkIqBUVGsaER3KGOTSLiotPQA7nJQzzpczLssFl+VCXyXRGDRYpU0KDUJqUEuttU8K5PiLfXdo9EbFLlb2PM61f3vEuu94fnnBj7/8gpgixnnEx+++xbe//5YuqtaKgRyzJR4f7gmDiLhIgdkJStEwrzG0chaxoliuGK2hrKXYEhopZOyFeHdbsjvnYKyhXUltNENagxTzl54z7aZtUwW5+rZbhgMtcW6D1hbGOISdk7hzDiGsyCXyYNAskCw0gDF8DUqjs528+Dpxz8Z79tZrad+ZFfHzLz+Tibdv8OMoRA8eat4NcJbXezMM3LcdRpkeYmO16XodBYgdBUWPIZI5aKwVim5Fym3X0BTAis2aIUxL3UGFyhmustK060XZ67VUU6XraCEXzkjOwjB4zNOIJEluu8A5VOzzkI45EQ6tRTRNnEas0r2hKfJ6384XvDyf6dvlJnz8MOF4ukcuQEw/IcaAdd/hdIU1QKrUZ8V83QF1VhEqFAr1WAKxF7BRqEqyWlqDK+rqJtlWhrRctOuqVvmfX2ob8O7P2uOWifQ/5FB4fn4WjL/2eMa4B14AMo4brfEWuIxaLis+Pn6AsxZh53LNOYe/+7u/g3cO67oKG2VHKnzjMiqgWvYrg1G83NhKkY1TK5AF78+ZwpWYIpZ1QfolQwu23Ub9mjIxccOfb7XBmhKq1jgcDkK7nIFSsS4Lnp+fsV4WhHXDYWYn9fD4AOfJHLpsCyoU/DgwgOV8xqtAJOM4YV0XrMsFnz99EobQBdrQ4fHy9kbaYeeci+eSjH0v51dc5ELetw37uqGiwDqH4/HIRZZMa8CVkRViwDgM0N5jnieh/UZclgu00igFDPWJEaoQO1YKMCJce/jwAcd8j/vHDwwrkWLmrMPgPe7v7tASxbyn387hcGB62ekk46/Cp0+/iL1FxuPjAw6HA+7u+PfDMDB31xpoqzA6j/l0wCgHQNgCQtyQ0o6YI6AUnp5f8E///Cf8//6P/wNFadw93OP//f/5f8F6C22Af/7TP+P8+or/65/+jJqowv7bv/kbaK3wen7Dul1wvmSgrZ9UxbqRrpllKjNadxGdKmThtBuvlILz+czgnWXFPNEm++PHj6K1odFcrbXvPh4fH/tNWGvtgqfGzmJ8pIFWFt7x9ylmOEf8fBgothuGAVBAjBpAkoPPAoqiQbLq+Dy1ouLbGC6FY8xYwhtSKti3gF1U2k257wcLbaqopC+kkDuL+XCA0grTPGLIDo3do7VGiLsE1yjEFJAy4L1FlOWtNmTqvZ7PsN5hkDzrWml8VyudUb0buQOzDi2i8yokbUwusXJB7Xh8Y8AVIUXYtn8bBkyHAw7zjOfXN4HiVhht4QfGy3Kykh1Pg25FTNd2GdbSXuf+/gNKtjB6xDwfJOYU+O677/Ddd9+jlILPn3/BDz/8GeOgmaNRM+IwAAByHjE4h2ngod/zIxRgDYPCctsZ4dYy7y9/VZ6B/XpsNuA1F0FuvjwYgC+ZSe8prX/1nUILD2lYbTsUarnNKrimZXnHguKk+/KOwp/5cIAXZWiMZLE0pkKUiE6lKBBrXWeVpbNC7fTPVvTJeCidj65kV9EerAeyxGo3pTYwugLilMhlMRdG67oiJVL+hmHANLEAQolaEQB0M9q6qjBz4VJYyfNJsqROMYLABCSbmVCYVuzEUor9gw0hIEL1KMdt3WAtaYhGui9jLaombNaWmR2PrbLkjxFhD5IyRvHdLkXBid+RaZ+j4yLayOeWI2GAlBIGP2ASTn4SJkdbILdffqCKu9GGqTzNgptWOO96BKnz7sr6amtS8WuqbE2RcsW+b6iodBjdCF+4cYQxCuM0Yhjp/jqNA/bNiSJVCSPI9/1OTDvFXOsCaI1Ucsfwq0yiClb8sSjOa0vl9pk0/JaHK4tLY8asku3civhXeeVtQuWtiqZ4bySH9t8q7CJrDbQxGAaPGHe+l0IFJrWUwjKKv7+kNPJ6ylAqMM84Zewh0vcqR4mlNJjVCOcslDiP8nNQyIU01ZQTFCqsMximARkZaq9ISQOq8t8uBUVR6RtLwiB7gVwLyRb9Vm1QF0QwWgC0+MmCWrVcA3yzajscmoJZKLOlFKjK5qqRQiAwW9PNpERTvZQSlJNls7zz7fNs+0Tgqiyv9dqxT9OMdKxQsJ1NlFLCMIyiUWFDnHJCTBpa1e62G2KENUyp85bNbFWKP0pfIzeL1lBFaPz/Vp1mSb1CRvJkea985cvrdVq9/S/w71Nkbx+/fadQay8kLaLxUitqzrS2nmbogfDA8XAAcsHheOyF8zAfcDwecXc6SQZr6tTFLCKUNWxk0ThL6IM0pX6j5vKXh0KzwM6FucOlsrNu6lN/g6+2AJP9BvsehgHOe5yXhSKlywXeGAze4XQ6YJoPOJ5OiIELZ3ZmquOvWhg/IQS8Xi6wbVwNoWO+LSzl4f6+M2JKbQ6i1BCkkrGIbbOqwLqs2NYF8zRynHYWfvAYphGxpO4GaoyGsWRCqVqZMXw+47KuwgojZrmvATUXuGnu43JLPWs0Wa0pyktCJmiW121X0LD5nDNvRmfB+EWxttD00OGQW1BqwjBMmKYJ9/d3/eauoGV0krSvZp/RimXMETkXvL29YN9XKFW5gG6ZwM7CDw6nuxNyTvjFO0x+wCS6FIA48x5WlELYjuO4wbbRVlxDdfO0JMp715ggpaIMGTAG0ziKy6ZmslatWORaeXt7kzQ4j2+++ebaLMl10m5GKuHp/aO0QYypHyhXLy+eHn7g0vpwOCAl7hRKL5hgqFMtV3hFmhX2Ihkp7aRqhtQ1OFUm8GGks60WhXMBMziAiqr4vXu44LJRkGYGg8P9DH/wWBZO/JcFWMPKAChdUVRFrhl2cFccXKxpKlhsCS9xhzT4EcZwD1gyUf9axXm5AlASciWMqpZWppSCs048vSy0tmjJc8ZqxLhj2y6SpR2v70+bqjvds2DfI7RRcK4x04B9D9Da4uH+AdN4xONjxjBM0EZhXZdOnGihSrWKFTcKQoxST2y33/Hi2KyVhrNsRJsVvwagacR6U9tvmEKyBwIUtYVoy3OZCkoFbvJm3tfp/97Hbz4UTscDmpTbOYZ4o0rIRS7YA/Nh52kUnYAhpKGp6B0HdieX5QytNC6Xs6SOZaiixD43QVk6opYslLZwhYma5N5o8h+0+C4hJfHWZ6dTQEtspUc0+4AKLgq14M+1srtoXPtt3xByZCH0HpPYEltrEEPAunHH8Pb2Bus9puMBfhy6SrX5J2WQBtdwdq00nCMt8ZuP3yCmiHVbuQwTH5rWNQ/jgJIKLutCmqECsuDed48PGA/kYa+XjXS+lDDNM4bBw2rT7SQu5zOWdcPgPZQ2qGDQSZXdDTMEyNWulZYBxlgorTqra5OFoYLw9IFOPfXei/XAhte3683rPZPBSMMcYC2N3qzVoJEbF5q18rMOKQPVo1oHP84Ch3AHUmsGkDFNHh8/PmI4TPDO4J//+U84ng50kJ1HWPMRad94o0H1TAGt0aGBbd8AreFE/KcUs6fb4q41F31wr7TbQAXmaYKWvda+h78QFjU47fHxke6xomX4cspoVEvexttOdtLb26uETik4b6+wnlYwVvVdSaOnEmLhtLKtob++lpLHAzsKK4oUVT9wx0Cc/5orwITB0jvVUjOW/YxcAwoinKeNOaN1NfzokZFRNzkIULAnJt75aYSVnOW7+zsYY+H9yENXJoVa20QrfJWWtyDbSuDm/dLUX9Raaasi8JJRmi4IxvdGpjVYy3rhhI+KYXQShERPrwaBAQCpoNfPpxR6na3rDmMyrGEEqTVM9UspYtt2ZnRIcNLpdMLj4yNKDqiVGdJtLEm5QMWIc7lg8GxQnLMwkIyYdhC0N6J9ABUdVlbtgESjsJZ+KLQ/7ehHQ1K+AiH9Zx+/+VAYBTNrNKuG8/NJtIxXxjw6EYxYWTxq5XuaEi2Qid/HGPqhcCvcqALH0Cpa/JPkZmaKmAKE/mpLRqmaNhuVFzrzmoEWDdnpYKp1bs2Olz9HZ+lOS4aRAn6rPk05Yt93rNuKZV3gWzGvVxWm1lp+NvokYU2DW3hAfvzwAcvKnN8YQi9IUCxg3ntkXbAsjAtVxvSF8eEwYxhGaHNdHNdc4AwT3SBur2HbEPfQu18tjIaWUa20EutzMSGsRSwRsmDHhLxSSoghIoggS/XPnjciYyYLYr5+bvM8ky0yeLRMXuYqUCmrZVkG8dfJKSJLXq3WZIdpDTKjxGHTOov5MMEOHkorfPr0M0LcEHPEt998g2me8PD4wNSwlHF+feWEpCE3EScSVQ1cvbrnOue4jM2ZJam060aBfYY8X1wh09jCjdSV/teW59M0fRHWdOtBU/vNziKQIt9b2kNzsoUaAIFVoYiDt+/pSJRMBE1BzufBQgcoTl8pIQhNkgV9gJd9XlUCL+aMIoez0rpTIaOE2kDTZBD6hl1mJSOh8lAooCmhMQ1+vLoE8BB18nyvedNXqKtVFWFvyCTTDAGV3A8t+5imdwI1QtIEpbnRhpkfzI1nIJE3vAadY/ZGU5k3tljODbKFLMBrV9PT/4nNYw65w4X7tmEbNyq1pxHH4xH7viDFHS0FqUKRNZnIFgIgDZREdeartxGU7BLe1W/VMaObC+f2cfNnX4OJvvzSf9sm49cev/lQMM3sCZWdjDG4Ox1I9QoR3hoAVbIDaOhmdTP6Mti3FW9t/1AKcsy9QI/TRJxePFFaF1pzkQUiPz3rB5lUTF9AQQPGSY6tM8glww0DxmnEdDjQEKsUhHUDCgUqMYUualOF+DyjBwv84DCOA6Zxoi0v6L3//PyM5+dnfP70CeNhxt3jA4ufjKfWGUzzjBwiaqbvzeBJiTxOVMd+/823+Pz0Ga/PL/3mDSHCFI743/zuWwDAsi7cv0wTPn77Ed47wl77TuvmdWXmbynwoijtAjgloiNtcDqdAKUQU0EOZIcYYY4ooRKGEBB++KFPAfrm2mmOkEksroFrKMi2rogpYI8LmkX08XjAOHk8PDx00d/5fIbaCs5ndm9U/XLplqy5FgEJ+akCHaQUQZdMHhAxBuxhw48//8BAlMOM/8f/9r/hMM/wzmOLCTEEPD89IeUI4yyWdUEIgUtJCS0ykcr70+EIBTqVaqUQ9sCkPIFc1nXB5VywratABc2Nl3oZa60oktU1rU6EnU3P08SHLZyn3bwta+J8fhPhmEEpB4ElSvfIWtcFu0wVXfUqOQ4xZlijuwsuoTvaReSUAEeraOcU/EjIZVlXhLjj9fIm+ylCWoDkeaQEJCDmALtZbGHBNE/wzgOqYllWvJ1f2cyhYN13jAPgh4HhUCgYp0lgDS7wr3svaZ6M6vcMcxSSTIWAgmNpVYCzBkbQCE703Cm2xLpSMmuLYo0xVmGeB4x1ENuUCdN4ELEj4enW0O6SmcH8CgWj0bOaL+f1iwODsLPFZbkg5YRpmjHPM/7w+z/g89PPeDu/otaMKtBZzEwGrGLv7lwzUaRWotVDJQvoCnQGlpYVg1KqGdSi9VG3j9spoT06aQVfHgD/nhDua4/ffCg0dWspGUlG76bg09ItK0BYDnS2zObKX2+0zRgCKaJK9A5aYd+VdMReFpXNIbQQ86wtG6X2LkL3F3jt1l2xMNXADf5q1JWkE6y09lWVFy/ArllLYJDtCt+2kJKlKW4mC1xtndvYmlJkR6/pFbOnhJQkTL5UeGOZG6E1swACQ9zpNBrbp9WXlVCq71Ts4HE4HXk4lYyQIrKIipLwwlGv2KLV5FtPY4bLGdM4okLBmIy4R2Sl+rLaGIOsE2oqlP4bg1IKhibCkkX6e/fFNqo2Jsv1z8XCwZquwq5CHMiFTB3udPiSc0nYtgUpUhFbM83/iIHzPT+dTtDiRLknkhJCIN98XVf88MOPuDudcJxnZjZUYcYVB+N0N2csYCBPi+dkCtkADSp7S2ZhtkqKbmLAU1a5wx+Mv3T9MGj0WxopkqrsXLNoCP2AiHLwFyELdJpqSTcLVV5Hxmh5TzOs1d1BAFBSVC38MMIY7i24XJYycVM8SiEXnr5CCTqS2hjijigkijZFNwZLEct0KEKtpZDaWVERLNlZ27aJVXftmHpMtEEnHGPgrbBtwM+y5V9/WcRqz7Bo070ChOIuHS7QHVzb/66QfUNOcv8RZ9HN+htkOzWDwL5LaKltopYmS4uU+pYXr1X7bFIvpK0+GVmKN3QAUvtaSFUp1+iALD5myBnWGKHC5o6aNHJAX3QLnNQy2IHmswu5r6Xm8Uhsf8F38T/Q/f8PoaTu+3UpRhuEAYN3qNWiOEe8MxfJRQ6IIXT7X2AiJBEj9nWDAjCOE4rKQFYIKcA5j9PoaZUsitxaCkzl4WG07guzhtO24q0Ui/WgaCXhpkFGWlocF5SbC5CMCGgIm8bCedcpgPu6SRh8gA7EYq2x4vppOIFME8Zx6DzucZphrTBGth25ZJzfzkjDQLZPrbBQ2C4LLucLLm9nbMuCEIPcoByFx3mCUpqHmvcw3uHu4R7OO6zLBWq9TmFJ/G/IyuDyySiD4zzDWIdcKk7HEypAdWjgZOI97Ruc8+xAc8G+bqKGBkaBzY7HYzcVyzdQWStqIUak3Lp5FqXz+Y2B8Qp9p9IK3rJcCDsKmyKEQJ8iYeRYLdRjKNw90E772+++w7oHnJYLfvr8CfV8Rs4Zl/OCdd2ACjzc3+Pv/+Zv4QwpzA+PH3graWCcdpoCSqANtPDna8U0jj1HwRiDFOi9VBKJE0ZpkgUyi962bzgI++rh4aHnMP/0009Y1xUvLy98byUzuR2YjXbdPuNWeHJO/ZpMqfT3bd1W7iUqlbs5ceFJl9QJ3o8ohapqHswRLUIVlQfClfdfse0rUo6w0WLZVpT6/2/vzZYjSbLswKOrLe4OICIya+lqDod8G5n//5uZEeluVnVlZiwA3G3TdR7OVXVEsklmcbrmgQITQUVmBgpwN1dTvffcs9TeWRtryCIqBTGHPgPT2qKiiPMsxYfOOkQ5mNveRKppAEBLEa01sr+bJt4hVv3dgdCS6EpJvepHraiQQ0zpPo/UniSHpDVyisTsg+Q7aKDWDG2AwTiBt9ARh1xSp0jfN1DFeYGSfAo0KNBKEXP0Pc8IU9AKtbY9b20+AqXEKLQAmVqIcBxCd2ZmyRGDZF1bOKsBTbJBg5yYeNiYblQstARo/qHkH5Uc2PcD9r91KPzPQEZvr9+evKaoMD32HVco7G7n7EBuqjWWVWubJXjfGQDthWqte4KVt04WZEaITNUKIbAaTOTek42gAc3fX3JG1lkGSsIDjxFVYChtTA+vAcDNMyVK3EshplcIzGrVMEm2fafzCT563shCoU+IEcY6TKeZ7INpwnQ5wwi7KVehYVYJLjl2erEYi8fHB5xPZ/zu46duVfzn//JnfP32Fc9fv2LPEQXANM9wAzHQmNklZQB7PBCXjL/89FexaaBl9nbssN6hoiIdB479wOvLC9J+CN4LCvmsxTSOwsyqfdO3DTaQxQ4Ac5u51NZik5UVQ0SQoTPTsQzWdcWyLHh5fgF0xYePl6445XA6YF13OMfKuBkS5lxwOp2htcHpPCJm2hvkGJFTwnW9cZ1BYxhmjMOEaTpBW4+qNC4xwvkB58sTbsuC27JgEohtHGcI51HSyoAjHd3h8xAL8VQzrDzM3SgNZFUV52DQfH5yV5Du+w5rDWbJ63DOUVsjQ/2XlxfsOxXwLUb0PmtiNQncfYwAdAyfVbDuBYWxBiZyo0iJ9tXWSjb2NPH+SZZDSr+g5A0pBihFskKIqSvvVaXi/AiHdAvsbI0xmE8n5hkbjXXbkGPGLt5e7P45Z0iyoZVScD6dYSSStW1ixxFRCxCOBC3eSgZW8PI3e0cf8hZJjBMfI8FolMR2piTzKYGLnXMYhwElc9bETlmh6NwLjChdUC4JqdCeIkXSXms1iKGIbbiG1gXG1K6rAahjyIXaBw67GdT1VqFf630mzO4tMVGwFNmfeL9iZhJjlkItpowjJNrkGAOtCX/Tvp0BWzmVzqhEJpzVgrU4cmhkAJkPqO+pp2+v/1438HfpFNqQsDSWTc5I2vQg7MH77yov3QbSTRmrNZQ10NAdcmoeIGyjlRidEU/mgIj0xvZ+7gpRbjQ9BU4BCkZu9t1nJKckdNfcB9A1U8zG7q21c6xsIX/WEDvUY2wi134cMVsHWA5otSH+XHAfWAcZdmmjMQ1nPJwveHx8hFNkU708P+P6esW+7YgoUJZwj/MOznumieUiLIaMmDO+Pj/LkL72/ARjDKq1POxSwrHt2BcGtRvvRDXOBxzpri/RMk/o7oyaA2evdTe465+3btzv1HOjGx5O98qd0Jum1z83y9BhhcY8KVmIA+KjBCihwg4Y/IBQgJjrHYKAkvwHSbSrbXOQSMn5jGUldRglw1uLYRgZ3pQJtdRakVNh6JB1KBVIKiGlFvt4TwaDsFAUANs2KmO7ihZS6VJjwu6nQUalMMKzaR+cc31tts6g2bTzv90HnnxQaeU8DFwDRmDMttbpHGDENn3CPM1SgWc46xF05CaYBKZLki8AdIihZ6O39ymbXQslAnhI5XyHt2TnEev3LMIvYuHO0AMs54KARL1MLVAw5BFx8EiGTB8sN3ir9PlIuycsFoWeWbmpcm6S0DKNobiG2oFRSxUCg6bOR+AoHgpiIVF5KJSsQBsSHraM+bxDOTzgCrIuHVZrn9vbw0zJPnHXlqR+T7QxMu/oAA9H6AI9xpxgU4J3hLfxpnNSuG/yePvVfghojVHV/X6+nR3c19L9z//f2EfeO5Dj66BRUWLAi5jN5ZRwEdO3y+nUsdciVMZcKcF3hvhxzhm36xU5k0LKxKKCZb0i5oxcC3F1bTBY172PoCpKiTiO3G9KjAc36aqpVqykQRZUOq6m3D/4mA7EnZkL1pA+qQxgikKStKnTiVYbsVY8v74AoPfPn/7xT/j46QPmhzO248DXlxeM80Tn1COwg7peMUmV+7//43/Aw/mMTw9P+Pr5F9xeF3z95XOHxQoKTNWY5glONoVnGUA757FsK9ZtxS9ff4HWCqfTdFdxeskFkJlCCAGvL3ytfhpgvccwjoRCmjpUWl7bfH7a3EAYI/u+M5Be/P2XZcG6rLhdr4ii+G1xlAzsqYAqIkjyVH87DtS89/I9Tclahc2UkFPBOIxQjwYVBvu64tgPODsiHqGH0t9uC759e5ZwJ2AYJlzGEf/5//g/xaFSIS9X5BCwLyu+ffmCl/0b/vxf/pVZASXjw8cPOF/O5LRXQKkkuhRqYGIIWG6LzGWAwbG79ZaD4waXdmdexxnaly9fmEB4vaJWVp7n87k/tG3mdLlc4P2AcXxjWDfPwmZDL0aenh47dXQcPecNOUOB1hcfPjxhHGec5ot0bQHzfJFDbsNxrD27GkpJME6F0hVF3UWHjY5cBO5otWMblhMfL4hHAER5PbgR8ArzOMEYdj7hiEAho6vx88dxhPO0m2Dhdsffv99Mc/997TAEdM8daQy+NnBnaBBncDmnDqNQYMoiRWnOFpJ4kO1bBmCglcc4nGGthzW+r0Nr2M3Uctd4NBEidSKW80EhCMQYCd05hxTvdN9xGmG8YSxpzsiJNOZiDNEIY5ArB/QxsNidpwktNKlND3gwoJ0kdBUAD9ZcMw8EQ0iOVN77wdEOg3/LbfV/9vrNh0KU7sBqGtMp3B8a8th5Qv/aE7wNbhstrG0qzUmyhatDUf7dBtBWsPzmXmo0XT2lk/quheIwyIiYykrkZAZqeXNyy+89DqAUWGP7B2Ss4YaYC51DpXVtnjI5R2FZET9F5RDvkMo9RFbTdDdUUh2y+swpIewHDmGkEDawDErB90yCbVsRU8Yg+ocGh6k2FGxVcCa8QcaC3CuBQwY/UGwFJX5M+Y3iXO5FZcvc/nvbNLzMAe6zA2K87e8Zct/u44WbmvVogTPDMPYKuVsby2ZQyibumAHrRnMx5zyyY7X5cHlAHOifDzC28+X5FW4Y4IaBjpgxo8QE45mnq61HjRn7dmDfDqzbjm/PL8g5EWKrgBWYson4rKFoL0um9LqsACQTW+YbyjV19ICXWpGFSaWU/u7hax1XGz63RLZ2AJzP5151HkdAS6Nrmy+r+ComeZyB1eqRkkbAAa247quwn0IIuF5vOA4qsMdxxNPTI55fCgJ2FFgoobNqRzZXKrVrFJp4KsQAXSx0uXcOp/mE2keZPDCMtkIT1fQ2Uyy6cqK/WU5ix200Bi+K30HCcSLnSY1j37QRKUW5dwoVrt/DqiuNGsHMduo2yGisIk5t3Qc/g/YzE/2oJkdPMwBKZRl6e1wuFwx+gjEeMSTsRxBDQ1b5pTQ0gZ1xVx3LwZZz7oNqrTW04fMyjiMcHDJyJ9cc+watFGdwOfc56CE2J5B9cPQjlDIwGnLH70YX7fnsDgHInUrPxtX0PbVd/xY09Fa78Ja6+luu33wobCsHTtYYnH7FvtjASX1F7awILXTUCvRDQyny1mMIzNIVutr5coHWGms8JJeZ6kVnmYXcmDAQM7ci+HEXCIkYa/ADrKMrKBKQSu5QEoDu/19igrO2p4oZY/DtK1O3BufwMJ0wSxxllMH3vm9YlgVWcHrmUQcmnUEJpCZhIYLVZ8Hj12XBclsIz4geIexJPtH7YrjdbogpYzzNDETxDtCcwdCUj9hjCLGzubRhJsM00lPmdDrBSaWbZRCZusKWv6nUe1ZEq+wBEEv/1UA554L5RH+oDx8/vrH3IGwwCB/cOY+Hh4femt83gSQYfEEICcAKpS2c93DDCGc9DQ3dgCRY7fPzC0KI+OWXzzg/PODxg8NxROQCLK9XjHPBMFFhnGLB87cXPD+/4vXlhs+/fEapFU8fPkCBm1WIFMJVOYimYcKWV+Rc8Hq9UvimNKwocbXWmOdJIBQm+oWD2SBNKd86LGO40b+N6mxxsR8+fBAL7hHLsvR7fT8UImph+qAfvEBHFVofSDl0E0iynw5GlP7yBcdx4MPTR8zTCZfLGSlFLApQoTaIG8omFDD61lgRKyoyr1YZZresDWcd5ukkaWwZ6eCw19v7pu2MAapCLhXxiDj2gHhEKK9hlME8TJimGcM0IEoKYBFmFaEWgVgjoTdjNYCxvw6aNKIjDN4NLDxbrnF5Y7Pj7iK84zgwTgO8n6CyhkoZ0RY4x+71hx9+wDydoZTDtu24XRcxI1Q9zyGXJNqFjJZE12jDPBSaCWGDAendpJ0GDH3FjuPA7WpwGIsUAnIM0gEoogO5IMkaGoeR/m7W9aF9m28pPqCoMqvsnYISV9kGU/13IKJ/S8z2t8BKvx0+Ev8eKNXjDUc/whmHyY/wfoBxVvj9ZGHs4rt/uy1IMSPsEQ+XC4ZJ43S+dHhhmqkWPKuH+6BG5g4qi2lgVRjHEyAb2nEEpJowDK47ChYoZDBwQxWKu45tQ46J9FatcD6fOtOkloJ4HCgxImwbUozIymCAwWAcTqczYkocbF4X7HvE7//hjxiGAf/b//Yf8dPPv+D1+to3QGK26FbEuSQc+0p2hRyYrfIfxxFGDrJcCm7CqAkx4PPnX7hpOo/ThSZltWQ6OiqFsKyIx4FjWdn2lorHywOU0oBpLrbMic6VG9nDOMH5AQ+XC1KM2LcN0zD2w3Z0HvOHUUSIBSFFGGtxvpzx8MgA9iNF7CniKAnDaYbRGufT1NPMGrxQqxIv/oivX78SjlpXeC+2I8phGDgUbJ4zpQLGeZz9CCiDlDOMsXDWIx4RpmqoXPHlp1+gzDcorRG2Hdu24ac//1mEesAf//hH2k787kdMM+NW/UAnXG0dBneC1SMGo2DqhrhmTJ6eSU+nR3jvYaomHOoH/HSakHLC7UrVe+usWgfUyBStQ2r/bozB7XaTrwW3200w+9IH1a3iXZYFuSSBaEVtXBgalFSF9wlFk9Xe5hcxMk9ZZ4Yu7duO/VhhHTUJ1rF7nuyIQaJcY4xAjKzINegllCqqFhZOpSmgBjuCkitSDEBVsMZjHEY8Xs4Y7ITjCFjXlRW5H4CqEUNCyZX6lXAADae3wHQaMV1GHPsgOc8FKQbcXq94fHzC4Ad8fPqA2+2KdV1ps685k9RGwe0WKSRxCxB9haK2aRg9pmlAvC5i0Ldjmk749MMn/O73P2IYTri+rFjE+VhrR1qqMlCK8wKK7hSUuXtuaaOhjDCMVKG9R7zPV7wi3DjNI4wzgKaoL5eMGEDtUEmkg6eM5XYgxgqjPJAY8est3SFa1ogCY0JzYVBREiFmi1zloXSnib+liL8VVbbC7Du7lX/vTsGKlwkq27zGZDDaANaTRmktxonYmxMTtSTZBW1ABEWu/DhNfTNt8ANhlTvsAEBwNsFfe7qSog0BiLNBqFq5FqBo2NbhCzspxggUikOIJ1cxj8uIsSCDwpKS2I42GpsbBkBpGEPGQYgtOMXj8vSIb88vUrwXwXIbXi8WGiUjCl+/UfFqZWtgnYP1NKJLkqnbKvN9P2CELkuBFBCOnd2TMVDSnkIBOWVEFeGnM5RWiNIdAVTlQtP0b55nDBIc09pYJzOFWgpamHxKtMvgoWAwaG4qxjJpKov5oJWEu3GYhG3CgR4qudc5V7p1rqyijj2SJ9rgB5NhIod1XLR3KMYPA3TKwg6j86eGAgpwrAxiyuKbf2wb1mWFF/iLAroRn3741HOpjTE9J9xoRywcFqgaJRZoz3yKaZz6xm2t+D3NU4+yBBqscDdj7K3+G+i0PbT7zkNrXdeuBTmOo1N8W7cdQuhOuhwVCeTXHT6zDEsha0hUvonivhRpepdS6hCDVgbaAJAcEmsYNIQGkQlY1phEVbFSpyW97jBtkdwNFMI688S0OC/utkyOMx1WKrn0OZYyogVy1N44b/n+Usa+HlwnKfUsZS9CPELBRogRBim5rjjXaAN8KUSskWAo3f3EKGzTmKaR+5Ef8Pq8IidCPeOYO/uJ97LK/qNhxJqFGheuy2ZhnQVOhVIwitYguiooo2Cq7omK0KpTfWvhWs25IB4R3gXse8Ax0H/LKkme1N9X9vycCV01Zbc8KN/Ngt6uwXYgAP/fYjl/86Fwmk9oEZspcOLfpP1ehndtGNlYQI2N8Va0E2OEcw4//vhjb9HeMhEGaa+v16ssBLANBu62A3IKxhCArMWJUCGVAu0MZj11BWNMFIvVXOnc6hzgHC0Rbrfup6IqH4h937FvGwbrMEwDnCJdMCWagFlrcTqd8Ic//BF/+etf+wM8DAN+/PGRg0xj6DRaOZANKSEJm0ZbcuX9NPWDNGZCScMwsCsQvPjx8RGPTzSSu15fMI8TxmHAZi02PyAfAfE4GGTz8EE2VnQudSmFvjyfPuF8FiPCSKW5cw6TzACO4+AhITOF5m+k5fU2quWyrlyaiir0wXlmYERSf/ft6KyObdux73dzthgLxsHC2QHWcrgYjrtSdxxnABxKN/M05yxGP2KaZizLKv5YBTElitn2AykEWOvw+PiED4+P+PHHH1gZn0Y8P7/gttzgnOFZVTikO/ZIhXfkZjf4CZfzBR8ePyLliNfrK3SituFPf/oPeHz4gIeHJ2zLyqwQyUQG0AeRy7J0ZtY4jgDQZwDLsvQB7+vra19HztGWmXbeO7RW8N4i54RtWwEwp2TwJ9qQKBYKSgEpR+TA57HlfbcBqDGacJTTqIr065RoaV9z7UPjJoLLOSMeSaBM2agUN3xKPQu8HXAaz/j49BFhjgILBmosQurvPxeJqIwB2oEeToVOusYYPDw+opaKzXH4miMraZSVB1xKYulOK/5aAWMctM5APWCswTyfaaniHDdpkPRAqugBYxycY2cDADFEPD8/4+XlGdfrq3Q3HsyzOBDjgacPF4zTCOsU9mMFBXYM4DmdzxRNxhY/XJFzQEqB8O048CWgMR8VQqLY1ygD4zwUDJbb1imqR4jC1tQSdUvKvVF3Ez8On2uXoUC9Fe3yejuvaXtos+dp/96uX+c5/7eu3w4fyWIkvZPOjd1rqNKuGYo4HZSIV8S/6EH8+Hu2rNhj0+zLiIkZLZOdMEBoiS2cYqmMQiAeycPpYJKW4s2qRsGUBJ1tZzMVqfgwMNeVv+NA2HYqD1Mm40Q6G2vYvrVhIM3quOasNawYAKHlHogh9EHsW1dWI8HwqlboAjHpKxSnaeYjK+Ebt/Q1ay0eHx+RS4G2BpeHC86nMwbn6cmkqP9w1kLNMzQUjocLtqtGOgLpgFLJKAUoQ9jIWUvOeqJidbnd7kpTUXRXYTC1w80Yg3meGdLyptrw3kubzdbXykBbViwH9a1SUUbcPs+YRsIhl8sF48hNPmemdNGemME1zYjv+npDiBEPD/dBtnMOuhgY79illAGTH5FTwnmc8fTwgIeHCwaBxOIRuc7eDM6bujinAitDe2ubx5VFzoUwZ6AwTxkGHjnnMY0zvnz+jHVZuxitHaAtTKgVPa1baO6nXjo1ACJSu6fTNSV4zmQPFTGGPI4IpQqMZsfhnIez6NU0q0RuHtYYscLWGAeuwxYsFGJALKzgY0pSQc+sjI3FBqYKhhz7pub9AKN5IEXL9zM4enm9GcfDaAuYjGIqY2yVRhbriKoKv+SZS5nJd5fLBc5ZXC4XpEAPqGMPiNuGZVu6uJL7BI0Jd4EJGSVLjYEWC3xkMVeMB4xuuS4MzNq2HbV8Ra0am6TOKdkbck6yucfeYSRRaxO+phmhkc/XGHaWUGIzIkadKSdUGQyy67p7ZaECVlsmPSre92meME6zBHRZ2ppDiZU+kxC9o72OEj+whoRw+Fy7NuzXG/5bnVFbn+36u3gfedcUg3IjKi18S87EPiNb9ZiS+OoLT9w7/Pjxkwwd6SnDE6sdDAq3244Yyf1lHiuNrLR8MCWRY34IptuEYof4/1StiKUnBx0NKko3zhsGUmGjJm103zbcXq8okQ+IEfXk4EhT9BLYrjUVrdQYSi6AtGStMqTHTcA080P2ks3ATudGlXEFmUm14ny5UGBWqW/IhYySNnweJQXLj0P/ebQF5gzEakMDvNOZAespQVdg16L5EIU1ux+NcR5p7VB4aKcQ8e3rt15NQAgBADukEEJX615OD6zIBdpqxm/d86cxwGIGZJEWGZCRZGDgncLjwxMZLnJAtHAY+hJd4dwArZ0EFG2IMePb8wv2jUEp83QSJhA5+Gbw5NdTzsqhXE64nM84zScZGibs606tglgb1Mwqcl02hJBwmSagMtTGOXZsSeYgxxHoSWQNPn76hIcHOfxqhdEGr6+v/R62IT01KvchoJYBsTEW0zTh6ekJOWd8+fq5Y+XjeKdvxhTEL4qVOzMeLIymKrrkAjVpOfSMHOIsAJy3UJq24sPgME50qC01y6ZLWietVEZMT4RUnHVAAYKKSOJFppXG6Cc4SYdLkX5KNHh0pIuWSuNHYUZZU3mQSNqasRrQFTEfyCVhXXcRYRGmMfqExx+e2OkcEX+9/RXLuuL19oIPHz7g8fGR0FIp2Le9CyYZwdm2LNXhrxg49zPG4nwa4YcTnPG43Ra8PK/IqWLbGirR3JF5j6GKdCgBOlQUCf0h6mH77MFaDaO5YcZIanWQojCXJixrLB8N5zkod9oiq4SiC85nhfP5jMvDmcQQKNyuV4aASRFqRUfTGYfS/b3VaOkGo6v74dAOhbcHRPuev9uhcBw7WuSkpkKNQ9+csO+l00tDCD1g5XQ+o2aD6+1GzNd7TNPHLv9f162Hjmstg2TgLo83vDH5DY21ncINatmOA9AKytFSQxkth05FbUI5azDogaewVEi1FHhNfvnlfMYkNLWSM7bbgm3dcNsW1Ap6Gwm+/fnLZ3x7ecaff/pXfP32FbVUnE4ztDG43a6wmlGIy+srnLWYhwlQfIAenx57JbKnwOzjbeMBMI4YxwEtTKdkmvg9fXiENwZhmimuShnDPMFqjXg6QVXwMEmsUlMpMAowRovTK+E9QCHljG3buj4hxijiLGbYvr6+dgjJSbUSM2csFcAog9Qqm7GCgp1Mr3L3nQPUddk6hOX9IJXyicN1Y/B6fcW27di2HcPA2dLL8wuOI2Bbd/HMH9ADkYzF09NHst1SxPPLK769POPl+QVaKfzu40c44zCPE8LBDvLl5RWnywkP5zP2yCAnayyGcYRzBZ8+fsSHp0c8PTxgGsWKWXMjMsbg8fIIP3ikVLDcqNf49vUb1nXtRIrmINpgoWlidkRTd//Lv/xL7yKJe/P+pRiQSsayrNASDlTlwCklw3uF8/ki87iKfadJm/ceRnIAlHYoNeI4qtiV1242x/kUBV01Veanj0a62RFnUUYDCl7YX3rSpMjWClXv1iN2YFdirUMtFa8vr1L5Z8JLkqXRiCHaKMRUO4271EJl+eAwjB6n0xnWOSy3FTlmpJAwjCxe5vPcqb2v1xuSwFI5ZUJChmrrddkQDupHmBmi4N0IP44w1qEWgxAiXl5W7FtCzlWgN49pGmEdu51SjRzEB6432pQMU+uUT9K1kd1GnQo91WptFFEDA2AaJ6FkD1hG5mSkXBBDJHNtD1Cl4tPTR/zh97/Hf/5P/wn79YptWfCXv/4rvGFuh3dc660zIMW9JcXpji60zb7NpBpU1GZa7b+/vf4uiuYYA3KpKKKQ7SdQ4fAxiS9JirHznZsvTjj2rhydpgm1VjJjMjHR1u40VscdH6PHSXoDtdxD4pm3G1ICtIK2FtoyE6C1WuTxiwWGNr3a1mBGgNOEScZpwujHbqURJGLxOA4Zqno4paENsO8bCipCzsIXpwFXrcB67IhVkTYao3jAsIJQGnCe3ky5ZEQU5FoRj8SOqhRRh7MCzjJvKRcelKTnZcRagTr1heI9w9TLwW5E27thn5U/jdgWdPVkuVcU7V6/TQNrAqOGDDXrj3aAKGFZaaUxmIF6BjFGa0Z5bYPxfkALQ9GSppfSm8xoqYBWCfdhEP3QiQfNDdc5OtIeQnF9eXnB519+gdEaD/OJJnZoIjlanZ/OJzjnEEuGKUXWV0FRTGaDpfbFiuFhTvdwnKZ43rYN67Lg9fUVuxj9NT1Cg9JKKZ2O2mCiFn/Zflb701lalmQZyPKz4vvke629K1OIiMhIe4AxqTuK1krWklIAdAXN34w0TwJJqtx1Q1QiawzD1C3dq4ilGjTinUZSSdaDwBZQ3z2bTT+SBH7UunaTTL4HBes0YhbVeCaxwDkrKnYPrS1QFecJKcuGz4rcq0GgL4f1tiMcB0KI/TU6y9lEjEmIJtxj6AhAxbsxDrkCpSQc+4FtC8i5YhxmeN/M8iR0qyikjA7hVVRY34SdBjEq5BIkgMfAmF+rtJmgSBo6ab9eVOVect1LKgACCghLT/OMh8cnVJnPcp/gLafDwFuTwkIXiK6C/683+ra22kHw9ut/9vrNh8L19do3W++deHkwNa3xy/vkX7Dv0zxDa4PPnz9DqVs/FJwoQyE/b5CBKxWzDDrvcY/e0zmzNA0EU7WMtfCZwhNlDJOfLANl9nAAIou3MisYne/D5MYFzjFiEOGQdRKfV2gzUcGErVIrxolZAfM84fl2xbJt+PL8DefLA+bzuTtjLssNYTtQUsEo9EvtDJQ1VCpKS6iVgQMP033bevSglcVfSsbtxhSpkhjxF9LB+1ALHFUvOA66kvrhDMwUFPmJ1ZIR8V87ZNqB6IU+qpXu979VG02x3HOFU+y0tjZ3aLGC0zDCO4/LfO5tbfOFucMghRYUmdRjHjj1DSXzbsf9/O2Fm6b3+Id/+COsdcL3p7J0HGnJvCwLPn/+Bf/8z/+Ef/0vf8E4ePz+0yeESHimQYT7tqLUJxIDNG3QldGoKDiOgCNsDG3SFdtOOnIMYqCXEr5+/YpSCr69PiMcBzs6YTj98MMPvVtoB+vT01Nfr62j/f3vf0+FtKedeOsa6PK6oxQIW4jitJaTADho5dAM0ppZ3r47aI1eGNFeXOAaVYRNl5BSQAw7WkLb5TLKPGeCMRopkIVUK6DAZ3Ue/ZvPr8ihxQ3bGotpmkFb8KN3mkZr4u8R1EJYg/PDBD9YGGcQEjsFSGYGQ5zESiWDRUatcIZ5DOMkqmmlkdI3ejnlAudoiTINA2KMuL0uXfx6Op0wK43LhTCkUkae/SqFSkSttBJhtO7c2T1MBzQoRWPfaQVvHdGDeT5RDwSFEL5KkVLElRbSPVk5FGsX9Cmt4YcBj09PLEBSxpefPyPsB4pSUNZimGcM24zt2HGkSDqr0SxqrRXFNk05T577i5L79zZqGLjbqbwVBr89EBqU+XeBj2i52wY8IvJRClAaRU7MIqdd22DeZhg3SGhZbt2hk6yToQ/OKCa5q2xbdcI3rsFZNj8MSAdgrSV8pO+Zq3cPlzeqP02inQYk+Jp/l8QqIqVExXSBhMxwSKgURCDG1CUfDtI1TTvB0VWPdG5Ff/3aGFHTKsDIzEiTKdXSsJqPfRS75eYdk0LAvm5YXq+yKFqUJSikAgeQ4zTK7MZLYPqAnAjdKC3MBqnuK9C93ZvTLCvUu0rTyLC9QQRtcEolbOxViLOWqmJ9P1z4M9EHd7l5TjV6nQzilOLgfhxHkHJJzr1RWkJMBng3yAKnhfTz81fkUvD8/Ix1XZBThDVaAmRE4ZparKU4s96usFbjdDlzadSEGHbs+wZVIu8NFLZlQQwBQTqBLEleRwhYtvW+eTlCBHfqselr9+2Ar20aP/zwQ/+e8/nc/65VvI1J0g4DHpbcxGJ82215IT+ArCQF6kuEN6pUSzJj1ZtSRhLbiJbOZgxjPqHoMZYLqeJKaXEBdvL5UHSmFFPJ2v1oWHyzqu6W8gD/myHTyA0OVZPXb4Kl0FMswlOgvTkqdUcaFKpWQ+g8hgTlNZTlDKo4zsmcQL5KrDe4NziBF4n5V6HEZkWDPnpnWczzCVppgaWYqpZzQspALrTKoGMshYIK9wq8BQY9PDzJxgrx6BImodKoqkpCW6KtDqhroEqdPyfsAZtdu+bpl8+fEbeVhpsd8YiIOcuh4lEjizL6KbEjawVekfX4VpPQBLOtc72HG/3tHcNvz1OIshFaEVqYuz6gDXV1EQ6+JRvi7fS7iX1ut1uvsIzR37XgpNVxs3bOoqW1Ga0Ydq3eYGmaEn1jTZeBN+WyEUZNS2qqtUpOLhdie9haq50qjb+0UhiM6wExALqNhB88xmmEPzb4FKVbapWu0M+06fxu5+8sKr4+UmerUv10b4dXzhlBqmtV2Y6mmJi3/HrlZjQ6cDjB/OdcGOZiHbFS66kA995jva2IoeVJc+isiPdgGsaeM6Dlc0PlDKFh5E2pnksRyh+v8gbPHIcBxRX+/0Wj0KpBZlGk3jmW2pStJCNoLW28fethX6ANKZnjSBih5CqD3wOvr1Q535Yb1uWGWjLGwWEaB3pYoXY/HKAi5YDb7RW5RMynUeC3iCNsWNcrws774rTB7XrFsROu0ErDOIuv377SnrpkeOcxiUNqc4Rt7Xqj/7Uuq+kPWjXfHtzT6dTvY2O3taCVIgSBbSMLqVbqcBpbj4c1oYVluYHZC3f/oHYotAKs6V0AEh2gNLSxks0MgXnF599K9+Esnz2lkGuUGT7nX7pws1Kt2pTziPRnklZZPDFWVBkFaNprpJSxih3McdyLHtOgXMMZXCm1wzRaFO/8LQrOemLtMNAyrPaewkdrPbSyKFWhJBYfxxGRE92KWXQy89p7B+cNashQoiiHKhTD6aZ5aoesxjTO8H7AETNdk0PsppHNOhsaJGPkjHyEntnuPIuHwQ8Ie4BRhoaY1yv++tNf4YxCigHascANMSLmiGZv0+Z5TWvDm6SgVe3xuO1ZBO7ZNsaYHm7U9tK/Va/w28VrbxY5KrdfZy0syExCH3CJmZ21WJYFAHC5XLom4fX1FVprnM/nXgE1//m3IoxhoIdP7fSwO1beFj+kSk85I+47lKHQZJwmHhBVidEWIxlzBVSp+PblK1KMcMYy81VpsgAAPMxnccEskpFr4YdmA64YizkOOD8+Yjt2dhoAvLf4+PEDrDKIgWwYgPbdWeYFkKQsPwh26n2n6wLoXk9P5wfoClC2RTPBHz5+REYL8yAE83q7IqHgEKpdqcIAKQz80BUwTmMaR1pyxNgXj7YczGulu6Np29C01mQC5dQ59a0DbAux5Cx500snIOSSABQyX7yFzgrOaVjLZKxSClQpGEYrYjmP6/WKI1Q8PtJQ8XSakFJAKYx6jClgPzb80z/9E7nwIqp7fDjjP/6HP+FyPuF3v/8E7zxKiXCWzqPx2PAvX37GEQ4caYM2Btd1wdfnr1jXBYP1GJzDaZxwvX7Dtm2Si6tQNyUEiITHp0dM40RmU2K4z7ZtmKYJ8zx3f6P2IDZa71u8t83QGjW2dWU5FVTw0G220XSaVRh86FnDj48PLLS8xZ//fOB63XEEbmjeG0jSu6heOcPhJl9EdAVs+4EkgVXMFwCgeCDlSoGVHQa4cYSfSodfUo6Ix4by7TPuHb+EVCky4/Z9w5AHDNljWC1S4cb8cr3iOCL2ECXHBDLHULDeQYsegj+TVt+1smgbhwmDHzEOE2IUMV9MvXK3op7nWs9SULYBrYK1VDlPEyNiB4kTJny44zh2rNsN0+gxncZ+aL9eV8RIBtoPP/we1nk8Pn3CX//6Ez5/+YzbcoNSCtNE2qtW3MB3SURUctCd5dCgBY/nPf32jJ+/fMFPP/+MHz59IBw5DojHgZfrFaUUTOOIwfseDLUfB2yhHxpQuZeVJAdpEfGvvT/XUuz9GkL6W67fHsdp7lbDpWSUrKG9l/ZUw4uKkFGCdwgCAJ1ApeVm+yviDLSEM9kULfF0FhOsQO6+SXjzZsV8r/nr5ISYImANrWkx9s0ry2aXc+4OhPu+0TnUe8DQ9CsEepUEFzvG34c2UAKJcJhpjMHkHFKRdljVPoxzzonHjutCniLgsVKqO1UqpWAzHSwbDRGVHOTBe8zjiDTPKCVj8A6neeZAv2Qsx3ZvK6WdbAlbu2rW01mqLr6uItqM1jUZozu8V/L3i6Z3UEnuK/AdNAK0+MaI49juCt8398dadhEtfJ6wDr/YoldxtxQ4yRkYw4FvyhEqNz45w1iCBL7klDDqEc7xAJnnEVqGxCVFMZVj1OIRNtyWG15fn6G0wW1bsCxX7McOPVZYQ7YMZ+p8ba1CtoZGby0bexg8bhIe1SwFhmHoyvsGszW4B/ieptpjGNU9r2LbtnvAimqiwaELQBvkw8G2g/M8SI/DIooNuNIAMl97Y40xZU42zFoFouBmizYglc5CzgnkQvaSMRba0QJGp4S4EsYINz4f7G7uIqgYI/aw09BOFRzxQEq522AcR2Qxpi2HzIri01oqHQhqlVRFyIFAncg48MCw1lHMmFusJ32yGj2aTgMBoavjae9hZKNsmSlQbRPlQZdSkvVKNXQji6DeqZ9K03q9DbF5oKbe6XtHDU2SwhS1QcnMUqig60LrzP0wIOWMdd9xXRaM3hEFqXR4DTFAK9qW1B7TSkt02dD4Vb5X0XcFfL17HL0dNv/dDoVR1MkMsa7INmGaBvKZBw/nDS0awnGHiq4vgqkXnM5nnM8XPD5dOr00xgPHsQlrgUyg4zjkpN7E2oAcXqVYbZLxWRCiZN8mUsq2feeh4CymExPMKpTg1RVYecrWXLCsK3JMxDRHA22ZxlVLYX5rSv2gortqxLYu4kdSoa3FME/iWSKmAfJANqzee4/QFJ45wYCvTRvTnCCEocWYznVdcRpHVD9gcJ4eUdYRJvAOHz884YgBewo4UmSg/XzCLBqJI0Zs+450RFhtYLWFP537BpS+fsO6bEg100ZCD4LLAuuxCnyx9fcArXCEgD0QZ3cyWG1zm5gCSomIYRWWS5XDPGOaHVwiNDQODtZp2XAJJ4W4AxE4AjUDxD4rUgb2o6K8Gea1wJVhsEjJI8QK6xSULqg1Isad84YUUVKmt1VOsE7DGnZanz//jIKC27bgCITFzpPHMBg8PV1QS8QwGKwLTfJyKfjxdx8xjAMeP3wQLnxkdbmsXZXfDsk2mO4W1Yqb208//UThYsn44YdPAj3RAO/Dhw/dmjnnjGmaSdcUkoD3HkqTfceUN49pHvDx4wc4b5BSkBWkkDNhV+uaW+kMLXAQNxiuZNYmWhxZ+XuW24page0IUIZmh/PlxFlZyViOG277gl9++Rm0CJ/7YcTQnIgQDoxlREgDphvDmdZ1w+cv3xBTxvnyhHkccDpdUArh1uX1lSZ3pWCWpMRhmBBCxLruyHPtcKhSzPVukBnzJahojilB7ztCSFBGOlrtOzuoFTdN8V0Kg8JiCqjIUDKoVxrdvifn3O1xrPXwjpBrCAnHHggljyMezo+YhgnODbD2imXdsAVCZeu6yobMGZLVBr//4x9wu96gtcGyLliWgvNpok7BW+xHQAykiZ9PM06yH3YaqtZQArdVKYh7QYm7a++vNQp/6/WbDwW+wSqLi6E4pZSOM2sZZGqlUN9oCWJK2NZNhkQKl4eHTj1t1VUTD1ljcSAQYz2kMikc8FljvnsdMUaGv8iDUeUTqMKP52NATLTkgpCLdNnEGpsqsIUEca4hniMCTVkZBjfFq1YKRwx0aNWcYTCMQ/XqujFv3g66s9ynlnGrtQYKOqR2yIa8LRusMjJI5Yc+T7N4zgs2mO9Q0+VyYWLcOJK+dwSyn8ABWGNwNaYRW/67aZaVwKD+s98Or6yBERFPV+861wddKUUUBRQlA7C2/hSrYmNUPyRKTojpkGzp3LUpuZB/3jziOWNxUlHr7zDex8cHTPPIEBYxjdu2BTHsSOHoG0xFgtIK0zQw/+I0IteKkAIaDKxUQUwBIezY9lXw4gPaCMYPcD4RIL5QBm6e8fREG5NjPzDPc4dU3/odNfZHVyxLl9vyFlgFT10EuG071nUBrRccpmnuHXFKQQSfG4AMY9Gtro1pgqUsc52Klj/MUCh6Ujk/iJi0maZpugkLEaJBM8CdINAyk0tz6NScF1ivMU4Deh9fKiwMqvJwAzsZbRS01TDOSDVfcT5fME0zxnHioZsMovfiKkDoiME6uW/i+74jWSvrwnb7mEbfJdMKnZd/Ol8k5pIzspwbTTqKjkKWp6IrAzorpyCGA0vl+4kxQiuLaBJeX14RQsIYaBPunO1EA6W0hP3ccyIAdvoNkoWgDNaSTvv48CSw2IC//OVfsK20QBkFGYh5Ry5FzCMdTkrdNVflHkGAhmC8eXa/M+VU970NfRf67dffdCi0F4Byx7PaJvx2g21QyTCMUCrgerv2F/oo1Lx+wikxwpJBWFMpBskbaIlR+s1NaJz6t0lhHHY2vxAu2/KmFcyliECfm31tVb5qw0DFjZ70ISigC8ka9KUN3TtrzmRayVBIyQHR6ZiNNVBB6EkgsE2CPCAfbjsYYwhinrbCao1NUtQA9JyD3i7KjMQag2EacLpcMIwDroIP96NI8f9LhpDq90+j4Y6SCPbmvgJSbcj7TjnDJHH9lM2IAS0tErWg6NpNzNoG2AafvJWFLI8E0SfQhjzEiCMcOM3n7slkxDrauQEteF1xEeDyeBbIDFi3BcuyYNtWDpjjQTYGKkpNcN7jdDljGB1KecDnr19QkIQQQRQxxYAj7Ni2Bfux4ggHBrFU1gWMjSwJKSc45zBPE9LjI8ZxxLZtGMeph+60r/ZwaqWgjME0ToRkLC3NSynYt0M0DrPYfTC9jYe0k7ja5qlEaHTfN0AVEV0RZm0soZTbveYabjGr4zBCGQPnc8fdQyCle/AjWlToMYS7TXRtTKEEpUHbZhRqIZyG8xbTPEj3w/hMpQFtFfzg5LDSKJUWJ8M4oFaF8+kEP1IjkWJGUmKVDoXcIDs5tAhDZvFVot35+czZirdeGDiqU3ibiGw+jegxoqmghoAiJIVmV2OEEl5k1tIIKSmyQs+lIsUMrR10jLherwhHRErcQ1rGhrVkXvJgojV9ljlN20tIbNEdAnPO43J5xDwnzNOEL19/xrpesW67zIscktD6123D6dS8wIp81kXGqCxc7msO3xUj7ZBsz7iIk9rm/e97KHD6r1Gr6qE2AFWQx84YylIrsvi3e0/zuSME7MuCdAQsuWB9WOEHLhZtHCwUtPUoFXi9LqSguQHHtkMDGJxDjRkxZsyXC1CB15crrjdSBrW1pOIVhoIYGKRAX5+igBzlgzuI1xkI88FaOEM2gyoZp5F8+pIySmfTaHg34vHhiW6dWiOMGSFF7DuZA8YCWhFaW5YrK5yY+mDRWQsnDpXPX75y/hG+hx9KERuKIyDoHa+vr/TY9078WtBzoJtplrcWjw+PVDd7hwoNox1SKAxCMhbT+QJrSAs8Xc6wg8ceApx3mGZRcNcK6x1mMfYahIHkBo9w0C4aitXO6XzGtm+IW0KUxDxacUjehQyguWk0L3qJDxWfeqiCiiyU0R0GDsVXqXLIpHLeQQstsc1N6OlEo0CYgiNurBRzQYgbrNEwWuH19o1q49njw4ePmM9nHJH6ihQTjCK9eltuSMeOdOzYtx2lZEy/+z2MorfS//V//z9Y1xXGePz44+/w8cMnGPkcb7crixBkBHEG5fPXSM98FsdxlCwKpuaFEGHMinGccbk84OHhCaUofPv2Ijj8IcK2hGW94Qg7SklIZcB6WCzbFTnRMaAJ1VAr16vSqNDIBX0zNVrBKotqKxwcpmECIAeXBqAyrAe96a1CTCv2NWNNLxR8WoVUNjiv8LsfP2CaJvzw8SPWdRG4sfZhalsHyjoYZeCqxgfHMJl5PtPDyg3QlfCm+/ARIeyIB+99g5TJ2JmRcqbSWtPzq+WlUEtEB94mzPPe4vTw2LUV6/6CmCNyzXRFgBJLChZU3rNQMspBlYxjW7Hvh8BrHihiG54A70cUkIL/dHmkY0GpQAD2uAFYcOw7UgwwSqEYLYpwajeGkZYw7GI0jPUYT1RM79tKKnrKuK4b2ZzGINSA5Qh4vt5QU4Y1BikWGFVgUQTqBq3OAUDdZy4KEI9b2py0Iu1v6Rd+O/vIOCh1H/Y0xWdrudqgo1kVG4GTnGWAfJEq7xAueJUhMKDECIxhJs6w6mwDXqN0T11qE/cYYv8yIM8bgmmjoCcwoWHSpRKOYd0DbZvvj+6Zxd475KSRKhAzv7MZhDnnyb9DczLVSEXmApJjW3KzOmb7WIRzrEDKblFK5ih00qSIj8Hk3nsMfuhW1u3TbapieVtCwbUiKDLddCunjArxePcDnLHw1sIPVGmjFLG/toSFJIOYm8qdvTCOI/w48O+FQWbEh7zBaEc4OlURAle0oV4RCmqFQBFCGuDwuUFYRTo/VnwpZSiV0DJxjbHd98WLB1YuCdtOqNAPHtooDKNHzQPhh5IZzqQV1oVQ0r5v4tHVbNtLp0JWIUI0k8WYAgDwfQj1kf5WK/bt6HAgs3JL5/g3A7QiG5p3HoN33aI6xYgUHXLk98eYepgKwKGuEais5IKEJEy8iHVZkEpERZFQmQIgEwsvRWydW+dcGzEFXXwYQ+8ctaaXjnFN2MWBawE1JEp0BiVmxHhgOWK/x7ShVj3IaRKoMiJyvcvHSmS29nztlBO0cqKPuYO8reu3lsljVjNaM6XYc66HgVobEhJED9SeCdBZoLkgN2YibfdDD8Ci95oSarzMFjPt7Hn3Id13Ri0tiApQljoGxIhaD3YflSJMZ60EVyWJ3uQ+UWuhLb8zqAndNjunhHDs4tekaOhZIWQc1VXxpRTclgXTIIUaqJ/aY4QWSnHKBVZYhWiICFSHxdqe3wqF39oV/Jt7/W/9xnk+dfysuzSCQ9QW/NFFZ0bDmgkxM/byD3/4A1JihX27Xsl0sDR+s97j9XplStm24+HyAD8NdEsFYZIqNMwWPhFixH7s2I4DOgTZNCuQMrN7U4aBhbbiDF/5/60C/ahpAoRv7r1nupvjnCNsOw+6wHa6KmoOmlvowwOdTIdjx/V24zxgWcjAsvTfyTZ3lgLN4CxQK758/txnDs5aeOdwuVwIBVmHyzjCO4dx8n2wq4yBTNngncWgFZKYCVYAt2VBzAnZUMcwzUyNG/2A8/kiQUIBXjyY5jcsmVR4GE8TbRWmaYIfh66aJPso9cOp1IplXYj7ZnZjzg+YJqq3aXfOn5tSlgftLnxqrKth8CgZ8D526OLjx493lbC8v8vlghADtm3Fl69fEATScc7i6cMT8vmEkhKOdYGSQfbPP/0rN0OBErZ9x+vrSzf804aFyjiOqOJI2lx753mC9yOs3MvjCOLRtGHdVuxhQwgHvnz9wiIjMSgop4x1WfDjDz/ifLrger12L6nhRqZME3w6N/SIVhTO4IzSXcC4rjeheS4wjpqJYXAkTZSIZeHP5hBa7Eys6xtPShmoATG8dAjBeeoryDxjoXMcAUekR5XSrGpzTtiOFT/98hOM5T1uQTCn+YR5mjEOI67qyizvPRCGsRkpyVxFkX0UYsQwzHCCAoyDKOfFLHEYPDANqGXuYVy1ggrl+QRrDEomRddow/lGKVCGWQ70iwJa9KmRTmxZVybLCcHCTvRoW6zGcezYdzoOo5IdVnJASYfMBDSqEeZdBnRqxZDF4+MD50Io2JYFL99unGQqwBoGe1lvse6kC9+2HWGJ2LYdD4+PGMcJX6WA3rcNtRScTidczif89a9/xT//8z/jH//xH0mdNRqhFCz7zkz7WnGkBKUNqtKw6KPAXhi0+UiR2cOvD4W/i05hmk8cDG8bK/zKU5fYfuobDQ3ydgBVFLBgMIcwctwRoUuBdqTbtehIBQXrUsfF2lXevJnr7YYWqWm8x+XsscrQMqWMwbheVZQk3PmQgFTgrSM7AWAQ+jDifJ6Bwu4mty8F2HHAbB3bdGPwy5cvVAw7C6+oWKRlBAU2ZCAwUnH19Il/q3gFOFT+8uUL7bW9x8ePH4VFQWFVTRnWiX+TZY6BMpobfi3QxWDbVmZMSyUdUpa/rzAj4TMvw+UmplJK92yEqljFRIFjAHR20jAQOlq3tQcdObEUb/kAe6C4qM03GkTSlLIvL6/Y9432G30wzarpJp8dO0qDnCpCuA+yDzGye355hrbsEkj5TNjDzopPDN+MHObfrlesyw2f//qvmMYBw0AVvRZaa8N7T+cTtSEjoyJJc/Vo+SBk/Ghs2yoceQ6VjTH4x3/8E54+PGEaJ2wHmUf7vpNxhruSv/kcsTI1nTJJ+3LpHhRjZluQEvnuE/7hH/4ohxaN2Th2SrCe1FIr+c3WkkDQICalKaycxQpbG48cpWjabgAUTqcT1633nZYZY8CyLTJk32i5PRM+dZbRosYyN6SLzDQ1CV+/fsWxH1BAzzMIIUBJZ2yd6/GVxNbbcDchxgPaDUL1nRCOHWEn03Dbd1yvLwhhx7Lc8PT4RI2Qd33ut+97n2k2fyzGdnKwfBxCHNh3qHGANTPmaRTsXQbywuvn/mXpNGA10rLciRZGS4dTkHLAy+tXhLBh+OaxrwuLHZW6Nb0fPFIpWPeDrzEnqFpgNTAODrpW1BSR4gENYBocDiSUqmC0xuPDA/7whz/0eYICkGpGKJLPXDWOnFCjQqoFo6WfmRFyzH0kKHPVUntoT62AVn8nSqofR1SloELo+oAiw563/jgtYLyZ3RGX95IFS6gBpdx5xNb05tJGvpwqG06VQ6G9pXVdO1Pn0zxhGCccIfRNrEFCAKGBnBj0Xov4q8hm+nB5wDhNmKZRvHL2LsXPIMbuB93pgt9eX3A6nzCpiY6pMqR1kgo2yqY6zzOMMiJAqqKEHuSBih0uenh8xMePHzEMQ2cU5RgFAmnDay7MToMtBS/XK15eX3G5XKCtwS7VVVVgrJ8z/SBqammtFTdnMQpU+j7HaO+jhyV5j+eXZ2w7HXFPp1NPa1NKMfNa3218KyB5COS13263Dn+c55mixqqR84FlWbGuDGIx2oPSvLsgMsrg+bbc4Ce21R8/fuy5vtZaWJHzGU0lezgO3K43/PzzT3i4XHC5nOTzv882Uk6YpgnDQM+uZb3hOMjMAmj0SEM2jX2nH5ExFpfLGeMw4ocfPmGWjbWWu36jZXFX2QArmoeUpnmfUnI4syPdN25oxlixOI+djDFNE5blinVbcYSNK78OsCNV/cYaGGdo56GBnB2OQNKCNQbTaYZzA1ANtrojHbyPtaJrJtoGngvpmfu+Y1tXLOuNG2BnjRmczmdYy0E5apUMa9pj3LZr08qRRVgKUjrYgStRafdha2N7Eb5LKcJZpt75wcl8JOMIB/Z9pbXIsVOJPAy00ZcwHADYA+d1MUScTmeMIzBIB1xKY5Qd3LRl6O29hzUaKY4sUGKUnIKKnA3V19VgWdY37DsyqFIuyDngdnvBurwyFlhxamS0grUDxtHjdDohyrrQKKglQYMuz0Y7USFnlBzp1Osdag7Ikkl9Pp9RAfzy+TNCODiQB2edSiuoWhBLRk08LKwaAC3wGahdbAzIX38Jr+XvcyiUqqC1xTBOKLJZxhjEd+getUKxmQTvhAgozhGUpprXWgtVmoimUbZIm6wD4SZ+rwdKRgpcOI1B1IaNp9MZ8/kE6xyrjNsV4zR3xs2+7div5AprrXG5nIU2WTHPJwwjN+tl2/DL518EfyYr6DyfME8c4JYQsF+vmDB3/yCAnc3gHCGjgZ5ByKIiVhohBVTFrmVwNOV7uFzorf/4hNM8cwicE+pEzUOp7JSsZ+50FvsA5tQahJzYGg8DdNYolS6kVjqDxiiBsDliiCjaoJrKjiJn7IHtfUwRw6g7fbKxZ/adSW61Vgzj+B3j6zgkk2JnAHoMEV++fO3d3bruQunjsPNAoMlcaD+TStNxOMFoC619H7gbYzqfvFmmf/78mQyPFLuqPIRAj/59xbavCGGXjZowlHeEUQropbVtKx4fP0A5kguOQOquc2InntrQn2Z7h7BNlKI+5OX1Geu2wDqLEA8oBTxJelgzaTTW4Hc//ghrDV5eXjBIol2bWRkxXSQbjZ9bzUUGqBbTOECjQKuKm3fQqsDoAmUpdiy1QktH2z6vZq0xjiPO54t0rVasygfEI6JkGjmeRM/y/PpMq5DbDSmTGt0s6KmtEHzcajhLu3PObAhLHEfAy/OLBM9L5Cc0aq4UZUrnYqyFcRYpZmQA3l9YvOSMdb0hhB1QGevtitvtim1jB6Z1RS4RuSTsx8ZZRClQonx+S5luHP02UwtphZYCrebMvOucEY4d2Rh4a2DmGaN3OA4eLjlF5kMA8jsyas0AJ5VQuqDUhOvt4DA5BDw+PmIcPM7zjBRX7DVRQZ8S9u2GkgO0KjhNTBdsjrC1ZDhVMVo6DJQYECOp5R+envDphx9w21ekW0ZSFbUkpFRRjUKpGltJMCXDJAWvNIoxwnYiU9JLkUM9UEEpEmHbBih/l0OhcII+DGM/FDrMIxROAKgqo4rPutGaiWDt5JIXDaGh1Tc6BzJT7r/PCHWsm6tlTuG7HkIG3eM4AtK+NwaEFWpraapeYQExY7WQapkzdCXEFQTX5s9lrCcH5mwlnSMjxcoG1jZQADI45D1oltFNhagVK2h+n+6pbLVWpBBRTOn4nzGG3jLqjdK7AqqWThNV8mcT2hXZKHiJViImZG1hVEaC8L9TpONkIdbbbDBK+xn9n0uvFqmyvesvGuzQaINJrKoZONMS6eQJ0xIaXhiSkhKHknTDdTjNFwAGtdwdHukeGplH4O5pZrlwuKqNDCxDBaLcJ5lhzfMsHct98EYShDDPcoKu5t5aV4j3k0FTfZMGKgPi1Bx/K263K1qMqha3z2EcGCPZ7JuVIgVUiWeUJNBRNc7Bfs1CV5TPuSoIlVd3dkjTtijcIYH2mnMp0Bm0z5ZZUHsOuD40tLJ9ADnPpLsOw9DJAs15M4ndPNe1qOsHJ9h+JiQmdHM+riSUlJSRYkZ1laFW6m69rYWerU27TxpKsagxRqOUVlgccrgWbOuCbVtkpkNVe5Ush1or54xl57BZaViBhyloo0FdygkoGlnuxeA86pDvB0cIKEYLrErzxCJrs93fnJqzaLNiyXzPLQ+70ksrZQo2S1EoOSLIUFsZJZqIQ/Y9DtI58+DnCVC53ZQwBkDR9/VhHO1vXDjEzkbQjsow4lzF0hxK9kXuj+rN/tkp+W+v+l/9w//w+u2GeJnt9+k0y6GQhFsciNXVNhXnItdKIYp6VJZ81wykLCZxWgPy4ZVKWlVVrAastUi42zjnnDHNM8TIH1D0WrpcLkx9euNJb7RGMAeQK6xWcNrCW4/tYADG9XpDCBFuJGU2xEj9AbiAY04IOWG0/LAenh5xvlwYUZn4sOSYuiCgyiYbJbkrJobnlJzhrMXguejIgwfWZcG+bX2B0wKhGaSRH17kcCkCORjH4fN8uQh8U/vmnoUNVEtFSMQtUQeYRFZUkoMvl4KClomgECOjDRsjDCBW24I9nBgXbtuGlBkK01S4TWfw5z//pcNVaKlrMsjMKeHWUsq8w9PTBzwIFTPFinXl71WKiVTDyIF/ENolB360MYB8NtCEfEIMeLpccD6f4f70R9HLFNSu7mxhTXvPSKZXD/MAvKdIrqm6gebrA4RwYN9pI70sC5o9yePTI6ZpwqdPn5gP3UwTNe0wjv3Auqzd6dRaixgSUswYHBPexqGt03b40suf4sMDOXNeUAtzyWuli6hOCqUqhLD34XjLQm/sHiOe/tN8QkmEdk/zCVaymNeF3VWMEdNpxDgR/qBYUePnn3/C7XaDygLXbDuMIlmjsW6K2DmQg0+1bjO1VC2+0twPKg7XKULLCvj65YoQA15flWSGRFwuJ/hBBubGdWPJGA/cDs5GNAw+ffgBzg8Y/SheURX7xhlbVYpQ7TjAWdMPwHBs0Erh08cPGCQyl4I5spByTDIjYQcSU0RRFUYV0WlUMbRM0KZA6YxaE/awosSCkoFluaGCViGs2qnpiCkjHgEoGRoKVgGqZuTAZ9SJlT0Mi+fT+Yyigdu2ivlegC0Ouhqm1EvGRa4VuhaYSkuXNh9Rfaflpd58CQnw3/dQcH7ANE94evpAsUcMWBvrQ7yHcmJ2cuMWo9aeLdxEXO3VFaGt1SjnaOG/p5iw1Q15Yv7uum9iaZwxny6AYrwlh9liwleljReKn59mDN7jNM+CFxvhQR+4Xm9QmsHzn/wnnE5nyUqlI2FF5YxAaKKqAslYnuLOYQs729UQhEete8UY5aBUkDxlodc13/UQAiv5nHqVN46jVDAWWVEV2TDSXAqWZWUlpm2nRrYOIecMJ37ux3GgpIxj3aBKRZnYqXHRb6IEbr5HBtY7tNjBt46K4zjAFXf30BH1eZGqfBgYJBMj30stdxW3FoocrTu4HlrWrXdM/RpHRkGicsPuPjIyCM0l44gHqYNKIcQAYzSGkXON/VgRU8R+bNCPF1aA5txdTpt/ZBMXlVL7oaC17QVGSkoqVHGKRTMDpCDodJ7FsI2Q1bZvuN1uHDIrhSqBUjkzf3ffd8J15R5CZdrmKAKm9vW2p6+VTLcgQ/GmYFVv2n52BSyEcmldOtfgutEtwLkAZyO0PANHCCi59OhOpVVnjnnvqSupNL2zDhis79RkI1Tn15dXoYhr1JwJB4PkBGel8i4kSDTVfgyE05zSOM00vRynSSCUKq8/wfsRRnMDPZ0mGG2QM1n2qMC2Mj8kxgxnPbRVzGQvTIZD1SgVuN2eqYuaRjq0ynorGSg5ifMtu0ClCOON44BaC5yzSJFdlLUGuSjkGMCoYOE0NndfcfLNOaG5LDEkqPYOnh5LWg59oJZMJX8svXMtsqk3UVpjVFEHdAKsBqzBsiyc4clCINLSHbrujEvpJBvxowjyoKp0pqJTUDJ7/Hc9FJie5THNJ4TjbuyVZSDaFIn7vpNG6oTfLkI3hTZv4OCj1Arkgqr4xqsMrmOIspglBU14/WQC3a0ElmXlptbxcMBZviOjxWZDMPGGlzZ/H6UVcin48OkjN6p5Eqw6I5UsgjPTh3R86Eh7K5XwU0yJDIBqOvMkF9LaeG9sp51aw5OeXPWAQ+i7WmlplbkRZJCLr7S8z1JkQ7N4eAi9quaHXL87+XNiHu+yrJg75quRQsS+kjFWwWwI6xy0NR0yemuta62DVRDvHd0PjfZZWpH6D8MIrQ4cezPDy52Dbkyht32HUYyoOu+zjxSLDMK/94OfpgnaarmX/J1QtVtG7MdKh1aZV1mrYazQPI+jT9WUIpxVKl0xOVT30ikUaH3fvFumBDUiPCCmiZW91o6dXaDiHBDbb2UEXuO9uWeBNJVtg6fukaItsKa1+E1DQd47u6L762dx0fj/kI+7FwQCXQRhhMWUYE3pB0+MsRciTQDYfHTmeeIzK2xBrb+3c3fic7auK+NrtUbNNKDs5n3yfFjpZEMiTHeEAF2plB/HEcNAMSQVv5KdUe55BFrR3lorjRAZYlMK2WMdolbMh88pIULBaAsFg1I5B3KeJIt2oNLe4T7cJjRIzYwRODjn3G1K2v4GBdT49v7LZiodAzUeGVkpmKK7TxZhHCIU0DwYGgTM7qqgRZfWksneLCTXlEIoSWtxd9YaCdR7bPsu3FOBPdV3APz9uRWYvcHYbV9oZ4CS/1W/sVX4zYfC12/PKLXicnnEtm2kkW0HauFCVGBFs28rJemO1TotE5qPu8BOud3IcseupQ0OEnYyeg8IXx1aQVXdcxj2nbS1dV1RShGtwSipauI14jycu/u+ECYiHv7y+ooQAj5+/IjHp0dczg+43tjWZlEMO3v3DRr8AO88tFRd4SDkZLSB0RUqHPfNT5KjJqGbnk4neOeRc8bT4yP2fejVpjYa59NZGCYaa7ihVhFoaW5Q60KYaRxoA6yUQpAoyxBiN8TLAA61YTMralWU+pdE7DtSuCdT654W1eC8tlm0ew1hA7WDad93hBixbmvXowzed+O9fd9xlAMlJ1SlAIgCdfA0HTQaw+BhNNk6IUQcIeA4js6WIntMsgYMcVPmSHv4SGZULhnLegPQMiz4sBIaosXFvfrXXWQWtgVQBs4mHHsUxa8RCwrdOwS6UvJBa1YUw0DbiWVb8fMvP2HfmXnw9PCEjx8+clOpYOqWvP7T6dTnMvM8Y54mTMMkFOx7WltMCSny0A4CtzWDxVI1jRPZenWIkT5Rd1uN1M3XFFBXib0cmXAmLKG2O1BsRnfXEEkBVgrww4DxNHUq7el8wb5veP72jGPdUXPGsRCGcc7h8fSAOlUmuVnOZVpIjN8WfvbTgHGcwUzlA68vL3h+/oZtO2AMrfOdpWnhMPAQe/n8Fah0E/DeojrLXARN3zHnqZYupeD5+QXbdnCzRcUpF6QYiFyIcKyW3CNW12VhAuPg0UwBh2FA2I/7zK627o731zvP/eMo7BotUKuWmYJY+Vt1h1yNRioVRUgdxxEQQ+rKbM4DKmpmh1ErkBXgtIIeqFmazye4icrtFAJ0AZAraiKKUBWL5QIglSqeXxXZEg6DPNe1FBTViqo7qeff9VA4joD94IO8bRuOfWPIuWIIRrMFBvBmg9Si6hXr6fJGXIEmvCu01RVoqXmZUPBExlIu96F2/dVp1zYzJzGTCgrhCDKkbIOiFu5CumDj6IcQJOFKBpKBXwaknVljAFH/QgHdf6jWHo9XFWEjpyy8ae6MquPsbWjWhqKtSmlVWXPDVIrKaGquSadrYeDt42xJd7XIwSr6DG0yD9V6txdX8r3dOls6BQ6F5WDIBVllYX7oPoCEUp3t1Q6Nxs//dfZwO6g5sGsKcAtnDSvM0tKfpOJplcyvJmKl3EkFd3qnQS4GOt/fk5HOtMGIZme73TQQPPferEOrZK51b8O7IlsrmHo3COThUNEsnBlmY6W6bcZnqUNDTZNQZR7WNuH22QKQQbLuXjhvkd63A3/I50v8/d4RFP6FUJQ5qNYyaGzD3WbkmGICE9EMWmpdKz7aOqBHF6v6nAvDsuRqVbT3DiVnCt4S4bFsY99Mm3mlkfdkZGBaau2Ke63IPio1YbltWJYV27qjFpDd5BpFXOIs5X7n3CIvR1jtJFfFyrCZtW4jPlDZLa/BtMAqeTbewHic2wTqGLYNRhM5uGeo39fh28PXOitrJVOMUgGjLLRi54e21oTuTSV3s7MWV1pj4PT9fhHDabU+CLGmBB0jnOfB3O6xMQZFIk2Lsiiq0C2hFGRBDGq5D8P1m26AcFN9w676bfs88DccCsu2wTqH1yv96Y99Q80ZbvCYpxn7ekOMR4dNGNLNV1PBD71lC5D9cee7k6VEa4RmrkYVqIIfOLiKMXISLw8R21wvG+uA8/nM6rhWXF9eEXNGEB/yhsd5P+Dh4aFX6tu6YxiYvbtcb+JDEpGOA8E5qEpbi8F58X8nvFRRYcUpEQCOGGk+Ng7IgQdYg5taC9zCMN567jcnWQqtJA6yZjhrMA0D5mnE5XRi+EipKBw94lh3xjeGQO+pEPqMoPnHGy1B7prio9bmFlQJ5BFLgpI7BEJF6r1ja5sWTf4YJdiGv0agkcH7frgd+87PzLtOwz32TbD9pmlpLLPvV2lKjR0VyO+23Fh1vusqaA/tOjS2LgviscMqjRhIhx2c6wN7aw2sYuUJqB7qAnFZVVm8aqQQGYah/57j2PmZhIBlXeXfuX6nccQ4cVCLSrfMmu+GhUq/PWSMsPPkMKj3L4YTNQ8fbkLWTTTiS5zLlFJQ22dqNUyh5gRawXoPN3h6JkWKpzKHVVBZwYrqeRxHCqxyRIgBt9uNPHcNaE+NBgsZh2EoGIcRWmk8PTzSRj5njM7TEt46zPPM51N9tw1BAbKB0yPtdqNS+fnbM12A1xVeRKvDMKJmZhqkRBv8EI5uhJdzxjyd8OHpI5R2UNBIB2HJGBIUVHcrdfIccS55h53aYV9LIfRXWUjN4vulOyOoMY0a0486mHEcBHI1PKiKgqoGGtQoQdX+ceZSEVLqnkwNwB/GEZMf4SQbo+P/xNMJWysga8Cf5y7MbPtHjBth58yOUWkjkaCi2q80fiPMpqDrGyZSIaGnVNX3qn/XQyHGJNGIgXzufZPWjwZmdx8XibBsm5IColScHObeKzNI1eeHASVnrMvK6EEtoi+lUASzJ+wgMXOo4h3EYfLD+YwPTx9w7FxUv3z+jJh4ajf7a+895tOMiznjer0Sjz0O7OsKZwxur7QXyMKJz0PGOAy94nsbaFFBgRsqW/KYIkVKzqIEsjOsNlAVZB+AHRHEh6nmIjnRtN3IMSIcO5bbFbmkTqV11guuWTvVMeWCr1+fJXMiwg0UBo5nWi4bqA4ZWFFww1ah2XIe0iqcti3167APAAAEOklEQVS/fW8FrKJvt1vvFABS/9Z16SIdxlO2mET0h89ag8vlTBWsUkjx6Cr3GE9yoFtYR9jvbSxpSvT+caPrZ0Zq1uv6Xlw0Uc4RDsSgoOXBABiJquXnUeRmkQufXKUcrJWsDFU6BPO2aWn/rd3vZVmxbdTBaM3ErY+fPmGeJijFQKNamPMbAyvUfd9gLcPuc0oIiBgsDd4aLg+BESBFkbcWzgDKWOQScRwVR8mIpfSuqh00ELy6e5ApDXLsa38P7XsZ5UoLk2VdEMT9c5wHjBNhVy+QijH3dD3nmPlgBHZQLeQGVGG3AifFiLgRDow5Y4kRxjr4IeF6vWHfDzx/e+5zp1me21qFDr5vsFb1z5XPSiULCuxET6dHeD/ip7/8jCRGi+M4gslrVf6ZGH/D/pVAJ1opZFlHx0GoyMn7fEuwyCl1Zt7bORjFeAYlVdRM80sNwqZF0T8qSHzvtu9kVtYKYxycoYnfPM5wxqEmcV/TtJxX8rqKVqhB98+4uTIwxyMAqcBlrg2tCw9qAFlXWff3bqA9042QUnsBVn9Vhv23r9+uU8hVKr374aAGysXvVq0t+Fr4+4wNEFy/tcmsRpurJgQmaK/dWksFtLH8ObinfvXBU4XQ13iqDsOAeaL6smN5KRNzcw4WLVzEYxwH2VCPLpppcwwOC1Ov9N5qMe7QEaEIeo0AFQUt87lBaE2jAaAv9u+Mqtqm0DqALFbgISBmer3EIHCI3P9mJxIiMWT6TSXYGGGtQ9GKzIo+HFd98KuNgsrkPN8bV9yx5lZZCNTWBpjdclxrDteFZZRyltSpIoM6gUpkPjCOY/eXaaKj3vLLoNK8GWy+PXhTSjDZdA0Hsfe7RfrbvIuUEi2zCw3JzBvL4ixQGnngBbXqzhZrOQ336y34LpuErPNmSRGOQ7yeaKNNrjzjXmtlIMwd54/txd4P2/LGmE/+rhauAw0FyGZPDyIgp9BhsHa9tem+HwjN+vz7723D4wYbUcPD9XkcB9zA19uMD9vAvWk2tDaY56kbVFraslJ0J9+/i/I9yHwopoQjJZhSAaWxbQf2nRYWkNlhO3xqFdFZDFDafbcGAeapxCHCWItpGjGNJ/ys3kCxztJ+u9nDaGohWiicahtR378ystZ3mPHNum9rr4DGgG9fp9IGtmrkyj7dKAsNMtkAhYoEBjqWHr1LGNcBiiJAqqodYg6Aqt99ZqUIjTq9IT7cFzuLspRRFAfrRXI0Gtvo+81e1m8jJryF6d8Ol/4Hl6p/i1PS+/V+vV/v1/v1v/T124Gm9+v9er/er/frf/nr/VB4v96v9+v9er/69X4ovF/v1/v1fr1f/Xo/FN6v9+v9er/er369Hwrv1/v1fr1f71e/3g+F9+v9er/er/erX++Hwvv1fr1f79f71a/3Q+H9er/er/fr/erX+6Hwfr1f79f79X716/8FDcPu6EDvOtMAAAAASUVORK5CYII="},"metadata":{}}]},{"cell_type":"code","source":"plt.imshow(image[250])\nplt.axis('off')","metadata":{"execution":{"iopub.status.busy":"2024-06-01T03:48:52.804807Z","iopub.execute_input":"2024-06-01T03:48:52.805178Z","iopub.status.idle":"2024-06-01T03:48:52.988851Z","shell.execute_reply.started":"2024-06-01T03:48:52.805144Z","shell.execute_reply":"2024-06-01T03:48:52.987991Z"},"trusted":true},"execution_count":3,"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"(-0.5, 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"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\n\n# Split the data\nx_train, x_temp, y_train, y_temp = train_test_split(image, label, test_size=0.3, random_state=42)\nx_test, x_val, y_test, y_val = train_test_split(x_temp, y_temp, test_size=0.5, random_state=42)\n\n# Print shapes\nprint(\"x_train:\", x_train.shape)\nprint(\"y_train:\", y_train.shape)\nprint(\"x_test:\", x_test.shape)\nprint(\"y_test:\", y_test.shape)\nprint(\"x_val:\", x_val.shape)\nprint(\"y_val:\", y_val.shape)","metadata":{"execution":{"iopub.status.busy":"2024-06-01T03:48:52.990257Z","iopub.execute_input":"2024-06-01T03:48:52.990978Z","iopub.status.idle":"2024-06-01T03:48:53.981708Z","shell.execute_reply.started":"2024-06-01T03:48:52.990937Z","shell.execute_reply":"2024-06-01T03:48:53.980613Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"x_train: (1242, 224, 224, 3)\ny_train: (1242,)\nx_test: (266, 224, 224, 3)\ny_test: (266,)\nx_val: (267, 224, 224, 3)\ny_val: (267,)\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### VGG16","metadata":{}},{"cell_type":"code","source":"from keras.applications import VGG16\nfrom keras.layers import Dense, Flatten, Dropout, GlobalAveragePooling2D\nfrom keras.models import Model\nfrom keras.callbacks import ReduceLROnPlateau\n\n# Load the VGG16 model and create the feature extractor\nvgg16_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\nfeature_extractor = Model(inputs=vgg16_model.input, outputs=vgg16_model.get_layer('block5_pool').output)\n\n# Freeze the convolutional base layers\nfor layer in feature_extractor.layers:\n layer.trainable = False\n\n# Add custom dense layers on top\nx = feature_extractor.output\nx = GlobalAveragePooling2D()(x)\nx = Dense(1024, activation='relu')(x)\noutput = Dense(1, activation='sigmoid')(x)\n\n# Create the final model\nmodel = Model(inputs=feature_extractor.input, outputs=output)\n\nmodel_checkpoint = ModelCheckpoint('model.keras', monitor='val_accuracy', save_best_only=True, verbose=1, mode='max')\nreduce_lr = ReduceLROnPlateau(mode='min', monitor='val_loss', factor=0.1, patience=5, min_lr=0.00000001, verbose=1)\n\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2024-05-31T14:59:54.086911Z","iopub.execute_input":"2024-05-31T14:59:54.087312Z","iopub.status.idle":"2024-05-31T14:59:55.013846Z","shell.execute_reply.started":"2024-05-31T14:59:54.087268Z","shell.execute_reply":"2024-05-31T14:59:55.012977Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n\u001b[1m58889256/58889256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"functional_3\"\u001b[0m\n","text/html":"
Model: \"functional_3\"\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m3\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_conv1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m1,792\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_conv2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m36,928\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_conv1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,856\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_conv2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m147,584\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m295,168\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m590,080\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv3 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m590,080\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m1,180,160\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv3 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv3 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ global_average_pooling2d │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n│ (\u001b[38;5;33mGlobalAveragePooling2D\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m525,312\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m1,025\u001b[0m │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n","text/html":"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃ Layer (type)                     Output Shape                  Param # ┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ input_layer (InputLayer)        │ (None, 224, 224, 3)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_conv1 (Conv2D)           │ (None, 224, 224, 64)   │         1,792 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_conv2 (Conv2D)           │ (None, 224, 224, 64)   │        36,928 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_pool (MaxPooling2D)      │ (None, 112, 112, 64)   │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_conv1 (Conv2D)           │ (None, 112, 112, 128)  │        73,856 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_conv2 (Conv2D)           │ (None, 112, 112, 128)  │       147,584 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_pool (MaxPooling2D)      │ (None, 56, 56, 128)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv1 (Conv2D)           │ (None, 56, 56, 256)    │       295,168 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv2 (Conv2D)           │ (None, 56, 56, 256)    │       590,080 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv3 (Conv2D)           │ (None, 56, 56, 256)    │       590,080 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_pool (MaxPooling2D)      │ (None, 28, 28, 256)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv1 (Conv2D)           │ (None, 28, 28, 512)    │     1,180,160 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv2 (Conv2D)           │ (None, 28, 28, 512)    │     2,359,808 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv3 (Conv2D)           │ (None, 28, 28, 512)    │     2,359,808 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_pool (MaxPooling2D)      │ (None, 14, 14, 512)    │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv1 (Conv2D)           │ (None, 14, 14, 512)    │     2,359,808 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv2 (Conv2D)           │ (None, 14, 14, 512)    │     2,359,808 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv3 (Conv2D)           │ (None, 14, 14, 512)    │     2,359,808 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_pool (MaxPooling2D)      │ (None, 7, 7, 512)      │             0 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ global_average_pooling2d        │ (None, 512)            │             0 │\n│ (GlobalAveragePooling2D)        │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense (Dense)                   │ (None, 1024)           │       525,312 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_1 (Dense)                 │ (None, 1)              │         1,025 │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m15,241,025\u001b[0m (58.14 MB)\n","text/html":"
 Total params: 15,241,025 (58.14 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m526,337\u001b[0m (2.01 MB)\n","text/html":"
 Trainable params: 526,337 (2.01 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m14,714,688\u001b[0m (56.13 MB)\n","text/html":"
 Non-trainable params: 14,714,688 (56.13 MB)\n
\n"},"metadata":{}}]},{"cell_type":"code","source":"model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\nhistory = model.fit(x_train, y_train, validation_data=(x_val, y_val), epochs=20, batch_size=32,callbacks=[reduce_lr,model_checkpoint])","metadata":{"execution":{"iopub.status.busy":"2024-05-31T14:59:55.015090Z","iopub.execute_input":"2024-05-31T14:59:55.015429Z","iopub.status.idle":"2024-05-31T15:03:21.314555Z","shell.execute_reply.started":"2024-05-31T14:59:55.015400Z","shell.execute_reply":"2024-05-31T15:03:21.313739Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"Epoch 1/20\n\u001b[1m 1/39\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m20:02\u001b[0m 32s/step - accuracy: 0.5625 - loss: 0.7029","output_type":"stream"},{"name":"stderr","text":"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nI0000 00:00:1717167628.260585 116 device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\nW0000 00:00:1717167628.281086 116 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 821ms/step - accuracy: 0.6369 - loss: 0.6573","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1717167660.776432 113 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\nEpoch 1: val_accuracy improved from -inf to 0.68165, saving model to model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 1s/step - accuracy: 0.6374 - loss: 0.6566 - val_accuracy: 0.6816 - val_loss: 0.5876 - learning_rate: 0.0010\nEpoch 2/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - accuracy: 0.7018 - loss: 0.5608\nEpoch 2: val_accuracy improved from 0.68165 to 0.70787, saving model to model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 172ms/step - accuracy: 0.7021 - loss: 0.5607 - val_accuracy: 0.7079 - val_loss: 0.5623 - learning_rate: 0.0010\nEpoch 3/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - accuracy: 0.7475 - loss: 0.5189\nEpoch 3: val_accuracy did not improve from 0.70787\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 162ms/step - accuracy: 0.7473 - loss: 0.5190 - val_accuracy: 0.6929 - val_loss: 0.5601 - learning_rate: 0.0010\nEpoch 4/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - accuracy: 0.7149 - loss: 0.5077\nEpoch 4: val_accuracy did not improve from 0.70787\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 160ms/step - accuracy: 0.7153 - loss: 0.5075 - val_accuracy: 0.6742 - val_loss: 0.6068 - learning_rate: 0.0010\nEpoch 5/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - accuracy: 0.7354 - loss: 0.4977\nEpoch 5: val_accuracy improved from 0.70787 to 0.71161, saving model to model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 166ms/step - accuracy: 0.7355 - loss: 0.4976 - val_accuracy: 0.7116 - val_loss: 0.5592 - learning_rate: 0.0010\nEpoch 6/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - accuracy: 0.7636 - loss: 0.4696\nEpoch 6: val_accuracy did not improve from 0.71161\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 159ms/step - accuracy: 0.7637 - loss: 0.4696 - val_accuracy: 0.6517 - val_loss: 0.6401 - learning_rate: 0.0010\nEpoch 7/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - accuracy: 0.7626 - loss: 0.4833\nEpoch 7: val_accuracy improved from 0.71161 to 0.71910, saving model to model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 166ms/step - accuracy: 0.7631 - loss: 0.4826 - val_accuracy: 0.7191 - val_loss: 0.5405 - learning_rate: 0.0010\nEpoch 8/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - accuracy: 0.8056 - loss: 0.4200\nEpoch 8: val_accuracy did not improve from 0.71910\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 160ms/step - accuracy: 0.8051 - loss: 0.4206 - val_accuracy: 0.7041 - val_loss: 0.5340 - learning_rate: 0.0010\nEpoch 9/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - accuracy: 0.8198 - loss: 0.4164\nEpoch 9: val_accuracy improved from 0.71910 to 0.72285, saving model to model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 168ms/step - accuracy: 0.8198 - loss: 0.4162 - val_accuracy: 0.7228 - val_loss: 0.5302 - learning_rate: 0.0010\nEpoch 10/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - accuracy: 0.8250 - loss: 0.3955\nEpoch 10: val_accuracy improved from 0.72285 to 0.72659, saving model to model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 169ms/step - accuracy: 0.8249 - loss: 0.3955 - val_accuracy: 0.7266 - val_loss: 0.5391 - learning_rate: 0.0010\nEpoch 11/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - accuracy: 0.8370 - loss: 0.3519\nEpoch 11: val_accuracy did not improve from 0.72659\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 164ms/step - accuracy: 0.8367 - loss: 0.3526 - val_accuracy: 0.7041 - val_loss: 0.5436 - learning_rate: 0.0010\nEpoch 12/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - accuracy: 0.8546 - loss: 0.3581\nEpoch 12: val_accuracy did not improve from 0.72659\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 164ms/step - accuracy: 0.8543 - loss: 0.3583 - val_accuracy: 0.7266 - val_loss: 0.5081 - learning_rate: 0.0010\nEpoch 13/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - accuracy: 0.8749 - loss: 0.3294\nEpoch 13: val_accuracy improved from 0.72659 to 0.73034, saving model to model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 171ms/step - accuracy: 0.8740 - loss: 0.3304 - val_accuracy: 0.7303 - val_loss: 0.5368 - learning_rate: 0.0010\nEpoch 14/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - accuracy: 0.8341 - loss: 0.3643\nEpoch 14: val_accuracy did not improve from 0.73034\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 162ms/step - accuracy: 0.8337 - loss: 0.3648 - val_accuracy: 0.7191 - val_loss: 0.5291 - learning_rate: 0.0010\nEpoch 15/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - accuracy: 0.8147 - loss: 0.3662\nEpoch 15: val_accuracy did not improve from 0.73034\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 162ms/step - accuracy: 0.8149 - loss: 0.3662 - val_accuracy: 0.7191 - val_loss: 0.5261 - learning_rate: 0.0010\nEpoch 16/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - accuracy: 0.8589 - loss: 0.3442\nEpoch 16: val_accuracy did not improve from 0.73034\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 162ms/step - accuracy: 0.8591 - loss: 0.3438 - val_accuracy: 0.7191 - val_loss: 0.5454 - learning_rate: 0.0010\nEpoch 17/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - accuracy: 0.8511 - loss: 0.3312\nEpoch 17: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n\nEpoch 17: val_accuracy did not improve from 0.73034\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 163ms/step - accuracy: 0.8509 - loss: 0.3314 - val_accuracy: 0.7004 - val_loss: 0.5766 - learning_rate: 0.0010\nEpoch 18/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - accuracy: 0.8891 - loss: 0.2893\nEpoch 18: val_accuracy improved from 0.73034 to 0.74532, saving model to model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 169ms/step - accuracy: 0.8892 - loss: 0.2893 - val_accuracy: 0.7453 - val_loss: 0.5084 - learning_rate: 1.0000e-04\nEpoch 19/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - accuracy: 0.8993 - loss: 0.2707\nEpoch 19: val_accuracy improved from 0.74532 to 0.75281, saving model to model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 170ms/step - accuracy: 0.8992 - loss: 0.2709 - val_accuracy: 0.7528 - val_loss: 0.5027 - learning_rate: 1.0000e-04\nEpoch 20/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - accuracy: 0.9047 - loss: 0.2747\nEpoch 20: val_accuracy did not improve from 0.75281\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 163ms/step - accuracy: 0.9048 - loss: 0.2746 - val_accuracy: 0.7491 - val_loss: 0.5074 - learning_rate: 1.0000e-04\n","output_type":"stream"}]},{"cell_type":"code","source":"plt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\nplt.xlabel(\"Epoch\")\nplt.ylabel(\"Loss\")\nplt.title(\"Loss per epoch\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-31T15:03:21.315896Z","iopub.execute_input":"2024-05-31T15:03:21.316187Z","iopub.status.idle":"2024-05-31T15:03:21.577075Z","shell.execute_reply.started":"2024-05-31T15:03:21.316160Z","shell.execute_reply":"2024-05-31T15:03:21.576165Z"},"trusted":true},"execution_count":7,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"plt.plot(history.history['accuracy'])\nplt.plot(history.history['val_accuracy'])\nplt.xlabel(\"Epoch\")\nplt.ylabel(\"Accuracy\")\nplt.title(\"Accuracy per epoch\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-31T15:03:21.578255Z","iopub.execute_input":"2024-05-31T15:03:21.578568Z","iopub.status.idle":"2024-05-31T15:03:21.812243Z","shell.execute_reply.started":"2024-05-31T15:03:21.578543Z","shell.execute_reply":"2024-05-31T15:03:21.811429Z"},"trusted":true},"execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"### ResNet50","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.applications import ResNet50\n\n# Load the ResNet50 model and create the feature extractor\nresnet50_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\nfeature_extractor = Model(inputs=resnet50_model.input, outputs=resnet50_model.get_layer('conv5_block3_out').output)\n\n# Freeze the convolutional base layers\nfor layer in feature_extractor.layers:\n layer.trainable = False\n\n# Add custom dense layers on top\nx = feature_extractor.output\nx = GlobalAveragePooling2D()(x)\nx = Dense(1024, activation='relu')(x)\n\noutput = Dense(1, activation='sigmoid')(x)\n\n# Create the final model\nmodel = Model(inputs=feature_extractor.input, outputs=output)\n\nmodel_checkpoint = ModelCheckpoint('att_model.keras', monitor='val_accuracy', save_best_only=True, verbose=1, mode='max')\nreduce_lr = ReduceLROnPlateau(mode='min', monitor='val_loss', factor=0.1, patience=5, min_lr=0.00000001, verbose=1)\n\n# Print the model summary\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2024-05-31T15:03:21.813492Z","iopub.execute_input":"2024-05-31T15:03:21.813769Z","iopub.status.idle":"2024-05-31T15:03:25.034950Z","shell.execute_reply.started":"2024-05-31T15:03:21.813744Z","shell.execute_reply":"2024-05-31T15:03:25.034086Z"},"trusted":true},"execution_count":9,"outputs":[{"name":"stdout","text":"Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n\u001b[1m94765736/94765736\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 0us/step\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"functional_7\"\u001b[0m\n","text/html":"
Model: \"functional_7\"\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0m┃\n┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n│ input_layer_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ - │\n│ (\u001b[38;5;33mInputLayer\u001b[0m) │ \u001b[38;5;34m3\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv1_pad │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m230\u001b[0m, \u001b[38;5;34m230\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ input_layer_1[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mZeroPadding2D\u001b[0m) │ \u001b[38;5;34m3\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv1_conv (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m9,472\u001b[0m │ conv1_pad[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv1_conv[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv1_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool1_pad │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m114\u001b[0m, \u001b[38;5;34m114\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv1_relu[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mZeroPadding2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool1_pool │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ pool1_pad[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m4,160\u001b[0m │ pool1_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv2_block1_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m36,928\u001b[0m │ conv2_block1_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv2_block1_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_0_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m16,640\u001b[0m │ pool1_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m16,640\u001b[0m │ conv2_block1_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv2_block1_0_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv2_block1_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_0_b… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ conv2_block1_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m16,448\u001b[0m │ conv2_block1_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv2_block2_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block2_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m36,928\u001b[0m │ conv2_block2_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv2_block2_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block2_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m16,640\u001b[0m │ conv2_block2_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv2_block2_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_out… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ conv2_block2_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block2_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m16,448\u001b[0m │ conv2_block2_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv2_block3_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block3_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m36,928\u001b[0m │ conv2_block3_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ conv2_block3_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block3_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m16,640\u001b[0m │ conv2_block3_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv2_block3_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block2_out… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ conv2_block3_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv2_block3_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m32,896\u001b[0m │ conv2_block3_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block1_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m147,584\u001b[0m │ conv3_block1_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block1_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_0_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m131,584\u001b[0m │ conv2_block3_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m66,048\u001b[0m │ conv3_block1_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m2,048\u001b[0m │ conv3_block1_0_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m2,048\u001b[0m │ conv3_block1_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_0_b… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ conv3_block1_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m65,664\u001b[0m │ conv3_block1_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block2_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block2_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m147,584\u001b[0m │ conv3_block2_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block2_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block2_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m66,048\u001b[0m │ conv3_block2_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m2,048\u001b[0m │ conv3_block2_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_out… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ conv3_block2_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block2_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m65,664\u001b[0m │ conv3_block2_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block3_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block3_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m147,584\u001b[0m │ conv3_block3_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block3_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block3_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m66,048\u001b[0m │ conv3_block3_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m2,048\u001b[0m │ conv3_block3_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block2_out… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ conv3_block3_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block3_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m65,664\u001b[0m │ conv3_block3_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block4_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block4_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m147,584\u001b[0m │ conv3_block4_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m512\u001b[0m │ conv3_block4_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block4_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m66,048\u001b[0m │ conv3_block4_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m2,048\u001b[0m │ conv3_block4_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block3_out… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ conv3_block4_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv3_block4_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m512\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m131,328\u001b[0m │ conv3_block4_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block1_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block1_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block1_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_0_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m525,312\u001b[0m │ conv3_block4_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block1_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block1_0_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block1_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_0_b… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ conv4_block1_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block1_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block2_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block2_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block2_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block2_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block2_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block2_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block2_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_out… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ conv4_block2_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block2_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block2_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block3_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block3_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block3_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block3_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block3_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block3_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block3_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block2_out… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ conv4_block3_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block3_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block3_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block4_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block4_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block4_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block4_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block4_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block4_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block4_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block3_out… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ conv4_block4_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block4_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block4_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block5_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block5_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block5_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block5_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block5_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block5_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block5_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block4_out… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ conv4_block5_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block5_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block5_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block6_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block6_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block6_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block6_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block6_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block6_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block6_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block5_out… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ conv4_block6_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv4_block6_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1024\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m524,800\u001b[0m │ conv4_block6_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv5_block1_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block1_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │ conv5_block1_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv5_block1_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block1_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_0_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m2,099,200\u001b[0m │ conv4_block6_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m1,050,624\u001b[0m │ conv5_block1_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m8,192\u001b[0m │ conv5_block1_0_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m8,192\u001b[0m │ conv5_block1_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv5_block1_0_b… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ conv5_block1_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv5_block1_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m1,049,088\u001b[0m │ conv5_block1_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv5_block2_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block2_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │ conv5_block2_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv5_block2_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block2_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m1,050,624\u001b[0m │ conv5_block2_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m8,192\u001b[0m │ conv5_block2_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv5_block1_out… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ conv5_block2_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv5_block2_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m1,049,088\u001b[0m │ conv5_block2_out… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv5_block3_1_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block3_1_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │ conv5_block3_1_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv5_block3_2_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block3_2_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m1,050,624\u001b[0m │ conv5_block3_2_r… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m8,192\u001b[0m │ conv5_block3_3_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_add │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv5_block2_out… │\n│ (\u001b[38;5;33mAdd\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ conv5_block3_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ conv5_block3_add… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ global_average_poo… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block3_out… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m2,098,176\u001b[0m │ global_average_p… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense_3 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m1,025\u001b[0m │ dense_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n","text/html":"
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n┃ Layer (type)         Output Shape          Param #  Connected to      ┃\n┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n│ input_layer_1       │ (None, 224, 224,  │          0 │ -                 │\n│ (InputLayer)        │ 3)                │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv1_pad           │ (None, 230, 230,  │          0 │ input_layer_1[0]… │\n│ (ZeroPadding2D)     │ 3)                │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv1_conv (Conv2D) │ (None, 112, 112,  │      9,472 │ conv1_pad[0][0]   │\n│                     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv1_bn            │ (None, 112, 112,  │        256 │ conv1_conv[0][0]  │\n│ (BatchNormalizatio…64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv1_relu          │ (None, 112, 112,  │          0 │ conv1_bn[0][0]    │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool1_pad           │ (None, 114, 114,  │          0 │ conv1_relu[0][0]  │\n│ (ZeroPadding2D)     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ pool1_pool          │ (None, 56, 56,    │          0 │ pool1_pad[0][0]   │\n│ (MaxPooling2D)      │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_1_conv │ (None, 56, 56,    │      4,160 │ pool1_pool[0][0]  │\n│ (Conv2D)            │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_1_bn   │ (None, 56, 56,    │        256 │ conv2_block1_1_c… │\n│ (BatchNormalizatio…64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_1_relu │ (None, 56, 56,    │          0 │ conv2_block1_1_b… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_2_conv │ (None, 56, 56,    │     36,928 │ conv2_block1_1_r… │\n│ (Conv2D)            │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_2_bn   │ (None, 56, 56,    │        256 │ conv2_block1_2_c… │\n│ (BatchNormalizatio…64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_2_relu │ (None, 56, 56,    │          0 │ conv2_block1_2_b… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_0_conv │ (None, 56, 56,    │     16,640 │ pool1_pool[0][0]  │\n│ (Conv2D)            │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_3_conv │ (None, 56, 56,    │     16,640 │ conv2_block1_2_r… │\n│ (Conv2D)            │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_0_bn   │ (None, 56, 56,    │      1,024 │ conv2_block1_0_c… │\n│ (BatchNormalizatio…256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_3_bn   │ (None, 56, 56,    │      1,024 │ conv2_block1_3_c… │\n│ (BatchNormalizatio…256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_add    │ (None, 56, 56,    │          0 │ conv2_block1_0_b… │\n│ (Add)               │ 256)              │            │ conv2_block1_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block1_out    │ (None, 56, 56,    │          0 │ conv2_block1_add… │\n│ (Activation)        │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_1_conv │ (None, 56, 56,    │     16,448 │ conv2_block1_out… │\n│ (Conv2D)            │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_1_bn   │ (None, 56, 56,    │        256 │ conv2_block2_1_c… │\n│ (BatchNormalizatio…64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_1_relu │ (None, 56, 56,    │          0 │ conv2_block2_1_b… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_2_conv │ (None, 56, 56,    │     36,928 │ conv2_block2_1_r… │\n│ (Conv2D)            │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_2_bn   │ (None, 56, 56,    │        256 │ conv2_block2_2_c… │\n│ (BatchNormalizatio…64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_2_relu │ (None, 56, 56,    │          0 │ conv2_block2_2_b… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_3_conv │ (None, 56, 56,    │     16,640 │ conv2_block2_2_r… │\n│ (Conv2D)            │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_3_bn   │ (None, 56, 56,    │      1,024 │ conv2_block2_3_c… │\n│ (BatchNormalizatio…256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_add    │ (None, 56, 56,    │          0 │ conv2_block1_out… │\n│ (Add)               │ 256)              │            │ conv2_block2_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block2_out    │ (None, 56, 56,    │          0 │ conv2_block2_add… │\n│ (Activation)        │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_1_conv │ (None, 56, 56,    │     16,448 │ conv2_block2_out… │\n│ (Conv2D)            │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_1_bn   │ (None, 56, 56,    │        256 │ conv2_block3_1_c… │\n│ (BatchNormalizatio…64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_1_relu │ (None, 56, 56,    │          0 │ conv2_block3_1_b… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_2_conv │ (None, 56, 56,    │     36,928 │ conv2_block3_1_r… │\n│ (Conv2D)            │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_2_bn   │ (None, 56, 56,    │        256 │ conv2_block3_2_c… │\n│ (BatchNormalizatio…64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_2_relu │ (None, 56, 56,    │          0 │ conv2_block3_2_b… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_3_conv │ (None, 56, 56,    │     16,640 │ conv2_block3_2_r… │\n│ (Conv2D)            │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_3_bn   │ (None, 56, 56,    │      1,024 │ conv2_block3_3_c… │\n│ (BatchNormalizatio…256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_add    │ (None, 56, 56,    │          0 │ conv2_block2_out… │\n│ (Add)               │ 256)              │            │ conv2_block3_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2_block3_out    │ (None, 56, 56,    │          0 │ conv2_block3_add… │\n│ (Activation)        │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_1_conv │ (None, 28, 28,    │     32,896 │ conv2_block3_out… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_1_bn   │ (None, 28, 28,    │        512 │ conv3_block1_1_c… │\n│ (BatchNormalizatio…128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_1_relu │ (None, 28, 28,    │          0 │ conv3_block1_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_2_conv │ (None, 28, 28,    │    147,584 │ conv3_block1_1_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_2_bn   │ (None, 28, 28,    │        512 │ conv3_block1_2_c… │\n│ (BatchNormalizatio…128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_2_relu │ (None, 28, 28,    │          0 │ conv3_block1_2_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_0_conv │ (None, 28, 28,    │    131,584 │ conv2_block3_out… │\n│ (Conv2D)            │ 512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_3_conv │ (None, 28, 28,    │     66,048 │ conv3_block1_2_r… │\n│ (Conv2D)            │ 512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_0_bn   │ (None, 28, 28,    │      2,048 │ conv3_block1_0_c… │\n│ (BatchNormalizatio…512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_3_bn   │ (None, 28, 28,    │      2,048 │ conv3_block1_3_c… │\n│ (BatchNormalizatio…512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_add    │ (None, 28, 28,    │          0 │ conv3_block1_0_b… │\n│ (Add)               │ 512)              │            │ conv3_block1_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block1_out    │ (None, 28, 28,    │          0 │ conv3_block1_add… │\n│ (Activation)        │ 512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_1_conv │ (None, 28, 28,    │     65,664 │ conv3_block1_out… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_1_bn   │ (None, 28, 28,    │        512 │ conv3_block2_1_c… │\n│ (BatchNormalizatio…128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_1_relu │ (None, 28, 28,    │          0 │ conv3_block2_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_2_conv │ (None, 28, 28,    │    147,584 │ conv3_block2_1_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_2_bn   │ (None, 28, 28,    │        512 │ conv3_block2_2_c… │\n│ (BatchNormalizatio…128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_2_relu │ (None, 28, 28,    │          0 │ conv3_block2_2_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_3_conv │ (None, 28, 28,    │     66,048 │ conv3_block2_2_r… │\n│ (Conv2D)            │ 512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_3_bn   │ (None, 28, 28,    │      2,048 │ conv3_block2_3_c… │\n│ (BatchNormalizatio…512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_add    │ (None, 28, 28,    │          0 │ conv3_block1_out… │\n│ (Add)               │ 512)              │            │ conv3_block2_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block2_out    │ (None, 28, 28,    │          0 │ conv3_block2_add… │\n│ (Activation)        │ 512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_1_conv │ (None, 28, 28,    │     65,664 │ conv3_block2_out… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_1_bn   │ (None, 28, 28,    │        512 │ conv3_block3_1_c… │\n│ (BatchNormalizatio…128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_1_relu │ (None, 28, 28,    │          0 │ conv3_block3_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_2_conv │ (None, 28, 28,    │    147,584 │ conv3_block3_1_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_2_bn   │ (None, 28, 28,    │        512 │ conv3_block3_2_c… │\n│ (BatchNormalizatio…128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_2_relu │ (None, 28, 28,    │          0 │ conv3_block3_2_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_3_conv │ (None, 28, 28,    │     66,048 │ conv3_block3_2_r… │\n│ (Conv2D)            │ 512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_3_bn   │ (None, 28, 28,    │      2,048 │ conv3_block3_3_c… │\n│ (BatchNormalizatio…512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_add    │ (None, 28, 28,    │          0 │ conv3_block2_out… │\n│ (Add)               │ 512)              │            │ conv3_block3_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block3_out    │ (None, 28, 28,    │          0 │ conv3_block3_add… │\n│ (Activation)        │ 512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_1_conv │ (None, 28, 28,    │     65,664 │ conv3_block3_out… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_1_bn   │ (None, 28, 28,    │        512 │ conv3_block4_1_c… │\n│ (BatchNormalizatio…128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_1_relu │ (None, 28, 28,    │          0 │ conv3_block4_1_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_2_conv │ (None, 28, 28,    │    147,584 │ conv3_block4_1_r… │\n│ (Conv2D)            │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_2_bn   │ (None, 28, 28,    │        512 │ conv3_block4_2_c… │\n│ (BatchNormalizatio…128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_2_relu │ (None, 28, 28,    │          0 │ conv3_block4_2_b… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_3_conv │ (None, 28, 28,    │     66,048 │ conv3_block4_2_r… │\n│ (Conv2D)            │ 512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_3_bn   │ (None, 28, 28,    │      2,048 │ conv3_block4_3_c… │\n│ (BatchNormalizatio…512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_add    │ (None, 28, 28,    │          0 │ conv3_block3_out… │\n│ (Add)               │ 512)              │            │ conv3_block4_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv3_block4_out    │ (None, 28, 28,    │          0 │ conv3_block4_add… │\n│ (Activation)        │ 512)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_1_conv │ (None, 14, 14,    │    131,328 │ conv3_block4_out… │\n│ (Conv2D)            │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_1_bn   │ (None, 14, 14,    │      1,024 │ conv4_block1_1_c… │\n│ (BatchNormalizatio…256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_1_relu │ (None, 14, 14,    │          0 │ conv4_block1_1_b… │\n│ (Activation)        │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_2_conv │ (None, 14, 14,    │    590,080 │ conv4_block1_1_r… │\n│ (Conv2D)            │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_2_bn   │ (None, 14, 14,    │      1,024 │ conv4_block1_2_c… │\n│ (BatchNormalizatio…256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_2_relu │ (None, 14, 14,    │          0 │ conv4_block1_2_b… │\n│ (Activation)        │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_0_conv │ (None, 14, 14,    │    525,312 │ conv3_block4_out… │\n│ (Conv2D)            │ 1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_3_conv │ (None, 14, 14,    │    263,168 │ conv4_block1_2_r… │\n│ (Conv2D)            │ 1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_0_bn   │ (None, 14, 14,    │      4,096 │ conv4_block1_0_c… │\n│ (BatchNormalizatio…1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_3_bn   │ (None, 14, 14,    │      4,096 │ conv4_block1_3_c… │\n│ (BatchNormalizatio…1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_add    │ (None, 14, 14,    │          0 │ conv4_block1_0_b… │\n│ (Add)               │ 1024)             │            │ conv4_block1_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block1_out    │ (None, 14, 14,    │          0 │ conv4_block1_add… │\n│ (Activation)        │ 1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_1_conv │ (None, 14, 14,    │    262,400 │ conv4_block1_out… │\n│ (Conv2D)            │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_1_bn   │ (None, 14, 14,    │      1,024 │ conv4_block2_1_c… │\n│ (BatchNormalizatio…256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_1_relu │ (None, 14, 14,    │          0 │ conv4_block2_1_b… │\n│ (Activation)        │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_2_conv │ (None, 14, 14,    │    590,080 │ conv4_block2_1_r… │\n│ (Conv2D)            │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_2_bn   │ (None, 14, 14,    │      1,024 │ conv4_block2_2_c… │\n│ (BatchNormalizatio…256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_2_relu │ (None, 14, 14,    │          0 │ conv4_block2_2_b… │\n│ (Activation)        │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_3_conv │ (None, 14, 14,    │    263,168 │ conv4_block2_2_r… │\n│ (Conv2D)            │ 1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_3_bn   │ (None, 14, 14,    │      4,096 │ conv4_block2_3_c… │\n│ (BatchNormalizatio…1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_add    │ (None, 14, 14,    │          0 │ conv4_block1_out… │\n│ (Add)               │ 1024)             │            │ conv4_block2_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block2_out    │ (None, 14, 14,    │          0 │ conv4_block2_add… │\n│ (Activation)        │ 1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_1_conv │ (None, 14, 14,    │    262,400 │ conv4_block2_out… │\n│ (Conv2D)            │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_1_bn   │ (None, 14, 14,    │      1,024 │ conv4_block3_1_c… │\n│ (BatchNormalizatio…256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_1_relu │ (None, 14, 14,    │          0 │ conv4_block3_1_b… │\n│ (Activation)        │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_2_conv │ (None, 14, 14,    │    590,080 │ conv4_block3_1_r… │\n│ (Conv2D)            │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_2_bn   │ (None, 14, 14,    │      1,024 │ conv4_block3_2_c… │\n│ (BatchNormalizatio…256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_2_relu │ (None, 14, 14,    │          0 │ conv4_block3_2_b… │\n│ (Activation)        │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_3_conv │ (None, 14, 14,    │    263,168 │ conv4_block3_2_r… │\n│ (Conv2D)            │ 1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_3_bn   │ (None, 14, 14,    │      4,096 │ conv4_block3_3_c… │\n│ (BatchNormalizatio…1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_add    │ (None, 14, 14,    │          0 │ conv4_block2_out… │\n│ (Add)               │ 1024)             │            │ conv4_block3_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block3_out    │ (None, 14, 14,    │          0 │ conv4_block3_add… │\n│ (Activation)        │ 1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_1_conv │ (None, 14, 14,    │    262,400 │ conv4_block3_out… │\n│ (Conv2D)            │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_1_bn   │ (None, 14, 14,    │      1,024 │ conv4_block4_1_c… │\n│ (BatchNormalizatio…256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_1_relu │ (None, 14, 14,    │          0 │ conv4_block4_1_b… │\n│ (Activation)        │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_2_conv │ (None, 14, 14,    │    590,080 │ conv4_block4_1_r… │\n│ (Conv2D)            │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_2_bn   │ (None, 14, 14,    │      1,024 │ conv4_block4_2_c… │\n│ (BatchNormalizatio…256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_2_relu │ (None, 14, 14,    │          0 │ conv4_block4_2_b… │\n│ (Activation)        │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_3_conv │ (None, 14, 14,    │    263,168 │ conv4_block4_2_r… │\n│ (Conv2D)            │ 1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_3_bn   │ (None, 14, 14,    │      4,096 │ conv4_block4_3_c… │\n│ (BatchNormalizatio…1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_add    │ (None, 14, 14,    │          0 │ conv4_block3_out… │\n│ (Add)               │ 1024)             │            │ conv4_block4_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block4_out    │ (None, 14, 14,    │          0 │ conv4_block4_add… │\n│ (Activation)        │ 1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_1_conv │ (None, 14, 14,    │    262,400 │ conv4_block4_out… │\n│ (Conv2D)            │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_1_bn   │ (None, 14, 14,    │      1,024 │ conv4_block5_1_c… │\n│ (BatchNormalizatio…256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_1_relu │ (None, 14, 14,    │          0 │ conv4_block5_1_b… │\n│ (Activation)        │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_2_conv │ (None, 14, 14,    │    590,080 │ conv4_block5_1_r… │\n│ (Conv2D)            │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_2_bn   │ (None, 14, 14,    │      1,024 │ conv4_block5_2_c… │\n│ (BatchNormalizatio…256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_2_relu │ (None, 14, 14,    │          0 │ conv4_block5_2_b… │\n│ (Activation)        │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_3_conv │ (None, 14, 14,    │    263,168 │ conv4_block5_2_r… │\n│ (Conv2D)            │ 1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_3_bn   │ (None, 14, 14,    │      4,096 │ conv4_block5_3_c… │\n│ (BatchNormalizatio…1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_add    │ (None, 14, 14,    │          0 │ conv4_block4_out… │\n│ (Add)               │ 1024)             │            │ conv4_block5_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block5_out    │ (None, 14, 14,    │          0 │ conv4_block5_add… │\n│ (Activation)        │ 1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_1_conv │ (None, 14, 14,    │    262,400 │ conv4_block5_out… │\n│ (Conv2D)            │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_1_bn   │ (None, 14, 14,    │      1,024 │ conv4_block6_1_c… │\n│ (BatchNormalizatio…256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_1_relu │ (None, 14, 14,    │          0 │ conv4_block6_1_b… │\n│ (Activation)        │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_2_conv │ (None, 14, 14,    │    590,080 │ conv4_block6_1_r… │\n│ (Conv2D)            │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_2_bn   │ (None, 14, 14,    │      1,024 │ conv4_block6_2_c… │\n│ (BatchNormalizatio…256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_2_relu │ (None, 14, 14,    │          0 │ conv4_block6_2_b… │\n│ (Activation)        │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_3_conv │ (None, 14, 14,    │    263,168 │ conv4_block6_2_r… │\n│ (Conv2D)            │ 1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_3_bn   │ (None, 14, 14,    │      4,096 │ conv4_block6_3_c… │\n│ (BatchNormalizatio…1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_add    │ (None, 14, 14,    │          0 │ conv4_block5_out… │\n│ (Add)               │ 1024)             │            │ conv4_block6_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv4_block6_out    │ (None, 14, 14,    │          0 │ conv4_block6_add… │\n│ (Activation)        │ 1024)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_1_conv │ (None, 7, 7, 512) │    524,800 │ conv4_block6_out… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_1_bn   │ (None, 7, 7, 512) │      2,048 │ conv5_block1_1_c… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_1_relu │ (None, 7, 7, 512) │          0 │ conv5_block1_1_b… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_2_conv │ (None, 7, 7, 512) │  2,359,808 │ conv5_block1_1_r… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_2_bn   │ (None, 7, 7, 512) │      2,048 │ conv5_block1_2_c… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_2_relu │ (None, 7, 7, 512) │          0 │ conv5_block1_2_b… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_0_conv │ (None, 7, 7,      │  2,099,200 │ conv4_block6_out… │\n│ (Conv2D)            │ 2048)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_3_conv │ (None, 7, 7,      │  1,050,624 │ conv5_block1_2_r… │\n│ (Conv2D)            │ 2048)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_0_bn   │ (None, 7, 7,      │      8,192 │ conv5_block1_0_c… │\n│ (BatchNormalizatio…2048)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_3_bn   │ (None, 7, 7,      │      8,192 │ conv5_block1_3_c… │\n│ (BatchNormalizatio…2048)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_add    │ (None, 7, 7,      │          0 │ conv5_block1_0_b… │\n│ (Add)               │ 2048)             │            │ conv5_block1_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block1_out    │ (None, 7, 7,      │          0 │ conv5_block1_add… │\n│ (Activation)        │ 2048)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_1_conv │ (None, 7, 7, 512) │  1,049,088 │ conv5_block1_out… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_1_bn   │ (None, 7, 7, 512) │      2,048 │ conv5_block2_1_c… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_1_relu │ (None, 7, 7, 512) │          0 │ conv5_block2_1_b… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_2_conv │ (None, 7, 7, 512) │  2,359,808 │ conv5_block2_1_r… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_2_bn   │ (None, 7, 7, 512) │      2,048 │ conv5_block2_2_c… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_2_relu │ (None, 7, 7, 512) │          0 │ conv5_block2_2_b… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_3_conv │ (None, 7, 7,      │  1,050,624 │ conv5_block2_2_r… │\n│ (Conv2D)            │ 2048)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_3_bn   │ (None, 7, 7,      │      8,192 │ conv5_block2_3_c… │\n│ (BatchNormalizatio…2048)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_add    │ (None, 7, 7,      │          0 │ conv5_block1_out… │\n│ (Add)               │ 2048)             │            │ conv5_block2_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block2_out    │ (None, 7, 7,      │          0 │ conv5_block2_add… │\n│ (Activation)        │ 2048)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_1_conv │ (None, 7, 7, 512) │  1,049,088 │ conv5_block2_out… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_1_bn   │ (None, 7, 7, 512) │      2,048 │ conv5_block3_1_c… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_1_relu │ (None, 7, 7, 512) │          0 │ conv5_block3_1_b… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_2_conv │ (None, 7, 7, 512) │  2,359,808 │ conv5_block3_1_r… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_2_bn   │ (None, 7, 7, 512) │      2,048 │ conv5_block3_2_c… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_2_relu │ (None, 7, 7, 512) │          0 │ conv5_block3_2_b… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_3_conv │ (None, 7, 7,      │  1,050,624 │ conv5_block3_2_r… │\n│ (Conv2D)            │ 2048)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_3_bn   │ (None, 7, 7,      │      8,192 │ conv5_block3_3_c… │\n│ (BatchNormalizatio…2048)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_add    │ (None, 7, 7,      │          0 │ conv5_block2_out… │\n│ (Add)               │ 2048)             │            │ conv5_block3_3_b… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv5_block3_out    │ (None, 7, 7,      │          0 │ conv5_block3_add… │\n│ (Activation)        │ 2048)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ global_average_poo… │ (None, 2048)      │          0 │ conv5_block3_out… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense_2 (Dense)     │ (None, 1024)      │  2,098,176 │ global_average_p… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense_3 (Dense)     │ (None, 1)         │      1,025 │ dense_2[0][0]     │\n└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m25,686,913\u001b[0m (97.99 MB)\n","text/html":"
 Total params: 25,686,913 (97.99 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2,099,201\u001b[0m (8.01 MB)\n","text/html":"
 Trainable params: 2,099,201 (8.01 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m23,587,712\u001b[0m (89.98 MB)\n","text/html":"
 Non-trainable params: 23,587,712 (89.98 MB)\n
\n"},"metadata":{}}]},{"cell_type":"code","source":"model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\nhistory = model.fit(x_train, y_train, validation_data=(x_val, y_val), epochs=20, batch_size=32,callbacks=[reduce_lr,model_checkpoint])","metadata":{"execution":{"iopub.status.busy":"2024-05-31T15:03:25.039570Z","iopub.execute_input":"2024-05-31T15:03:25.039917Z","iopub.status.idle":"2024-05-31T15:05:23.216811Z","shell.execute_reply.started":"2024-05-31T15:03:25.039884Z","shell.execute_reply":"2024-05-31T15:05:23.216005Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"Epoch 1/20\n\u001b[1m 2/39\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m3s\u001b[0m 84ms/step - accuracy: 0.2969 - loss: 1.3385 ","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1717167822.287872 114 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 285ms/step - accuracy: 0.4984 - loss: 0.9968","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1717167833.141442 114 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\nW0000 00:00:1717167837.516748 114 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\nEpoch 1: val_accuracy improved from -inf to 0.55431, saving model to att_model.keras\n","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1717167843.300021 114 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 570ms/step - accuracy: 0.4989 - loss: 0.9928 - val_accuracy: 0.5543 - val_loss: 0.6827 - learning_rate: 0.0010\nEpoch 2/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - accuracy: 0.5674 - loss: 0.7116\nEpoch 2: val_accuracy improved from 0.55431 to 0.60674, saving model to att_model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 122ms/step - accuracy: 0.5676 - loss: 0.7112 - val_accuracy: 0.6067 - val_loss: 0.6756 - learning_rate: 0.0010\nEpoch 3/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - accuracy: 0.6332 - loss: 0.6500\nEpoch 3: val_accuracy did not improve from 0.60674\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 102ms/step - accuracy: 0.6326 - loss: 0.6503 - val_accuracy: 0.6067 - val_loss: 0.6660 - learning_rate: 0.0010\nEpoch 4/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - accuracy: 0.6249 - loss: 0.6468\nEpoch 4: val_accuracy improved from 0.60674 to 0.61423, saving model to att_model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 122ms/step - accuracy: 0.6244 - loss: 0.6471 - val_accuracy: 0.6142 - val_loss: 0.6447 - learning_rate: 0.0010\nEpoch 5/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - accuracy: 0.5391 - loss: 0.6998\nEpoch 5: val_accuracy improved from 0.61423 to 0.62172, saving model to att_model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 123ms/step - accuracy: 0.5403 - loss: 0.6993 - val_accuracy: 0.6217 - val_loss: 0.6419 - learning_rate: 0.0010\nEpoch 6/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - accuracy: 0.6281 - loss: 0.6457\nEpoch 6: val_accuracy did not improve from 0.62172\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 101ms/step - accuracy: 0.6280 - loss: 0.6457 - val_accuracy: 0.6030 - val_loss: 0.6526 - learning_rate: 0.0010\nEpoch 7/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - accuracy: 0.6455 - loss: 0.6239\nEpoch 7: val_accuracy improved from 0.62172 to 0.63670, saving model to att_model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 121ms/step - accuracy: 0.6449 - loss: 0.6244 - val_accuracy: 0.6367 - val_loss: 0.6462 - learning_rate: 0.0010\nEpoch 8/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - accuracy: 0.6358 - loss: 0.6468\nEpoch 8: val_accuracy improved from 0.63670 to 0.64045, saving model to att_model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 124ms/step - accuracy: 0.6348 - loss: 0.6472 - val_accuracy: 0.6404 - val_loss: 0.6420 - learning_rate: 0.0010\nEpoch 9/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - accuracy: 0.6159 - loss: 0.6372\nEpoch 9: val_accuracy did not improve from 0.64045\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 100ms/step - accuracy: 0.6162 - loss: 0.6372 - val_accuracy: 0.6142 - val_loss: 0.6377 - learning_rate: 0.0010\nEpoch 10/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - accuracy: 0.6319 - loss: 0.6707\nEpoch 10: val_accuracy did not improve from 0.64045\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 99ms/step - accuracy: 0.6315 - loss: 0.6706 - val_accuracy: 0.4569 - val_loss: 0.7456 - learning_rate: 0.0010\nEpoch 11/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - accuracy: 0.5874 - loss: 0.6716\nEpoch 11: val_accuracy improved from 0.64045 to 0.65169, saving model to att_model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 119ms/step - accuracy: 0.5885 - loss: 0.6708 - val_accuracy: 0.6517 - val_loss: 0.6405 - learning_rate: 0.0010\nEpoch 12/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - accuracy: 0.6445 - loss: 0.6209\nEpoch 12: val_accuracy did not improve from 0.65169\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 99ms/step - accuracy: 0.6444 - loss: 0.6209 - val_accuracy: 0.5431 - val_loss: 0.6815 - learning_rate: 0.0010\nEpoch 13/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - accuracy: 0.6357 - loss: 0.6304\nEpoch 13: val_accuracy did not improve from 0.65169\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 99ms/step - accuracy: 0.6347 - loss: 0.6312 - val_accuracy: 0.5431 - val_loss: 0.6864 - learning_rate: 0.0010\nEpoch 14/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - accuracy: 0.6112 - loss: 0.6528\nEpoch 14: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n\nEpoch 14: val_accuracy did not improve from 0.65169\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 99ms/step - accuracy: 0.6117 - loss: 0.6523 - val_accuracy: 0.6330 - val_loss: 0.6382 - learning_rate: 0.0010\nEpoch 15/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - accuracy: 0.6623 - loss: 0.6092\nEpoch 15: val_accuracy did not improve from 0.65169\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 99ms/step - accuracy: 0.6623 - loss: 0.6092 - val_accuracy: 0.6479 - val_loss: 0.6358 - learning_rate: 1.0000e-04\nEpoch 16/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - accuracy: 0.6520 - loss: 0.6192\nEpoch 16: val_accuracy did not improve from 0.65169\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 99ms/step - accuracy: 0.6520 - loss: 0.6190 - val_accuracy: 0.6442 - val_loss: 0.6372 - learning_rate: 1.0000e-04\nEpoch 17/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - accuracy: 0.6819 - loss: 0.6028\nEpoch 17: val_accuracy did not improve from 0.65169\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 99ms/step - accuracy: 0.6814 - loss: 0.6030 - val_accuracy: 0.6330 - val_loss: 0.6406 - learning_rate: 1.0000e-04\nEpoch 18/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - accuracy: 0.6850 - loss: 0.6050\nEpoch 18: val_accuracy did not improve from 0.65169\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 99ms/step - accuracy: 0.6844 - loss: 0.6051 - val_accuracy: 0.6517 - val_loss: 0.6362 - learning_rate: 1.0000e-04\nEpoch 19/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - accuracy: 0.6797 - loss: 0.6141\nEpoch 19: val_accuracy did not improve from 0.65169\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 99ms/step - accuracy: 0.6794 - loss: 0.6139 - val_accuracy: 0.6105 - val_loss: 0.6376 - learning_rate: 1.0000e-04\nEpoch 20/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - accuracy: 0.6558 - loss: 0.5952\nEpoch 20: val_accuracy did not improve from 0.65169\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 100ms/step - accuracy: 0.6559 - loss: 0.5955 - val_accuracy: 0.6517 - val_loss: 0.6350 - learning_rate: 1.0000e-04\n","output_type":"stream"}]},{"cell_type":"code","source":"plt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\nplt.xlabel(\"Epoch\")\nplt.ylabel(\"Loss\")\nplt.title(\"Loss per epoch\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-31T15:05:23.218228Z","iopub.execute_input":"2024-05-31T15:05:23.218588Z","iopub.status.idle":"2024-05-31T15:05:23.486482Z","shell.execute_reply.started":"2024-05-31T15:05:23.218556Z","shell.execute_reply":"2024-05-31T15:05:23.485583Z"},"trusted":true},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"plt.plot(history.history['accuracy'])\nplt.plot(history.history['val_accuracy'])\nplt.xlabel(\"Epoch\")\nplt.ylabel(\"Accuracy\")\nplt.title(\"Accuracy per epoch\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-31T15:05:23.487549Z","iopub.execute_input":"2024-05-31T15:05:23.487801Z","iopub.status.idle":"2024-05-31T15:05:23.757828Z","shell.execute_reply.started":"2024-05-31T15:05:23.487779Z","shell.execute_reply":"2024-05-31T15:05:23.756682Z"},"trusted":true},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"### InceptionV3","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.applications import InceptionV3\n\n# Load the InceptionV3 model and create the feature extractor\ninception_model = InceptionV3(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\nfeature_extractor = Model(inputs=inception_model.input, outputs=inception_model.get_layer('mixed10').output)\n\n# Freeze the convolutional base layers\nfor layer in feature_extractor.layers:\n layer.trainable = False\n\n# Add custom dense layers on top\nx = feature_extractor.output\nx = GlobalAveragePooling2D()(x)\nx = Dense(1024, activation='relu')(x)\n\noutput = Dense(1, activation='sigmoid')(x)\n\n# Create the final model\nmodel = Model(inputs=feature_extractor.input, outputs=output)\n\nmodel_checkpoint = ModelCheckpoint('att_model.keras', monitor='val_accuracy', save_best_only=True, verbose=1, mode='max')\nreduce_lr = ReduceLROnPlateau(mode='min', monitor='val_loss', factor=0.1, patience=5, min_lr=0.00000001, verbose=1)\n\n# Print the model summary\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2024-05-31T15:05:23.758947Z","iopub.execute_input":"2024-05-31T15:05:23.759229Z","iopub.status.idle":"2024-05-31T15:05:26.668434Z","shell.execute_reply.started":"2024-05-31T15:05:23.759203Z","shell.execute_reply":"2024-05-31T15:05:26.667539Z"},"trusted":true},"execution_count":13,"outputs":[{"name":"stdout","text":"Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n\u001b[1m87910968/87910968\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"functional_11\"\u001b[0m\n","text/html":"
Model: \"functional_11\"\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0m┃\n┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n│ input_layer_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ - │\n│ (\u001b[38;5;33mInputLayer\u001b[0m) │ \u001b[38;5;34m3\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m111\u001b[0m, │ \u001b[38;5;34m864\u001b[0m │ input_layer_2[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m111\u001b[0m, │ \u001b[38;5;34m96\u001b[0m │ conv2d[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m111\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ activation[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m, │ \u001b[38;5;34m96\u001b[0m │ conv2d_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m, │ \u001b[38;5;34m18,432\u001b[0m │ activation_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m54\u001b[0m, \u001b[38;5;34m54\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m54\u001b[0m, \u001b[38;5;34m54\u001b[0m, │ \u001b[38;5;34m5,120\u001b[0m │ max_pooling2d[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m80\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m54\u001b[0m, \u001b[38;5;34m54\u001b[0m, │ \u001b[38;5;34m240\u001b[0m │ conv2d_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m80\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m54\u001b[0m, \u001b[38;5;34m54\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m80\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m, \u001b[38;5;34m52\u001b[0m, │ \u001b[38;5;34m138,240\u001b[0m │ activation_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m, \u001b[38;5;34m52\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m, \u001b[38;5;34m52\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_8 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m12,288\u001b[0m │ max_pooling2d_1[\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_8 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_6 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ max_pooling2d_1[\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_9 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m55,296\u001b[0m │ activation_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m144\u001b[0m │ conv2d_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ conv2d_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_6 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_9 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ max_pooling2d_1[\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m12,288\u001b[0m │ max_pooling2d_1[\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_7 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m76,800\u001b[0m │ activation_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_10 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m82,944\u001b[0m │ activation_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_11 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m6,144\u001b[0m │ average_pooling2… │\n│ │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ conv2d_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m96\u001b[0m │ conv2d_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_7 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_10 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_11 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed0 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ activation_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ │ │ activation_10[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ activation_11[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_15 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m16,384\u001b[0m │ mixed0[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_15 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_13 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m12,288\u001b[0m │ mixed0[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_16 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m55,296\u001b[0m │ activation_15[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m144\u001b[0m │ conv2d_13[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ conv2d_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_13 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_16 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ mixed0[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m256\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_12 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m16,384\u001b[0m │ mixed0[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_14 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m76,800\u001b[0m │ activation_13[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_17 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m82,944\u001b[0m │ activation_16[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_18 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m16,384\u001b[0m │ average_pooling2… │\n│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_14[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ conv2d_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_18[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_12 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_14 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_17 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_18 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_12[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ activation_14[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ activation_17[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ activation_18[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_22 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m18,432\u001b[0m │ mixed1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_22[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_22 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_20 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m13,824\u001b[0m │ mixed1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_23 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m55,296\u001b[0m │ activation_22[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m144\u001b[0m │ conv2d_20[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ conv2d_23[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_20 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_23 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ mixed1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_19 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m18,432\u001b[0m │ mixed1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_21 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m76,800\u001b[0m │ activation_20[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_24 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m82,944\u001b[0m │ activation_23[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_25 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m18,432\u001b[0m │ average_pooling2… │\n│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_19[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_21[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ conv2d_24[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_25[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_19 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_21 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_24 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_25 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_19[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ activation_21[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ activation_24[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ activation_25[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_27 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m18,432\u001b[0m │ mixed2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ conv2d_27[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_27 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_28 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m55,296\u001b[0m │ activation_27[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ conv2d_28[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_28 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_26 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m995,328\u001b[0m │ mixed2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m384\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_29 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m82,944\u001b[0m │ activation_28[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_26[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m384\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ conv2d_29[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_26 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m384\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_29 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m96\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ mixed2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_26[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m768\u001b[0m) │ │ activation_29[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ max_pooling2d_2[\u001b[38;5;34m…\u001b[0m │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_34 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m98,304\u001b[0m │ mixed3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m384\u001b[0m │ conv2d_34[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_34 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_35 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m114,688\u001b[0m │ activation_34[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m384\u001b[0m │ conv2d_35[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_35 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_31 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m98,304\u001b[0m │ mixed3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_36 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m114,688\u001b[0m │ activation_35[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m384\u001b[0m │ conv2d_31[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m384\u001b[0m │ conv2d_36[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_31 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_36 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_32 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m114,688\u001b[0m │ activation_31[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_37 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m114,688\u001b[0m │ activation_36[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m384\u001b[0m │ conv2d_32[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m384\u001b[0m │ conv2d_37[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_32 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_37 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m128\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ mixed3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m768\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_30 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ mixed3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_33 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m172,032\u001b[0m │ activation_32[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_38 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m172,032\u001b[0m │ activation_37[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_39 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ average_pooling2… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_30[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_33[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_38[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_39[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_30 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_33 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_38 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_39 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_30[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m768\u001b[0m) │ │ activation_33[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ activation_38[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ activation_39[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_44 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m122,880\u001b[0m │ mixed4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_44[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_44 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_45 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m179,200\u001b[0m │ activation_44[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_45[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_45 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_41 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m122,880\u001b[0m │ mixed4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_46 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m179,200\u001b[0m │ activation_45[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_41[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_46[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_41 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_46 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_42 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m179,200\u001b[0m │ activation_41[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_47 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m179,200\u001b[0m │ activation_46[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_42[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_47[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_42 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_47 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ mixed4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m768\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_40 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ mixed4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_43 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m215,040\u001b[0m │ activation_42[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_48 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m215,040\u001b[0m │ activation_47[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_49 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ average_pooling2… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_40[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_43[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_48[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_49[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_40 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_43 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_48 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_49 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_40[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m768\u001b[0m) │ │ activation_43[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ activation_48[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ activation_49[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_54 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m122,880\u001b[0m │ mixed5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_54[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_54 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_55 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m179,200\u001b[0m │ activation_54[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_55[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_55 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_51 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m122,880\u001b[0m │ mixed5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_56 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m179,200\u001b[0m │ activation_55[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_51[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_56[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_51 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_56 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_52 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m179,200\u001b[0m │ activation_51[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_57 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m179,200\u001b[0m │ activation_56[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_52[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m480\u001b[0m │ conv2d_57[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_52 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_57 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ mixed5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m768\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_50 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ mixed5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_53 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m215,040\u001b[0m │ activation_52[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_58 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m215,040\u001b[0m │ activation_57[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_59 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ average_pooling2… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_50[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_53[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_58[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_59[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_50 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_53 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_58 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_59 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed6 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_50[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m768\u001b[0m) │ │ activation_53[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ activation_58[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ activation_59[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_64 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ mixed6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_64[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_64 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_65 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m258,048\u001b[0m │ activation_64[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_65[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_65 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_61 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ mixed6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_66 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m258,048\u001b[0m │ activation_65[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_61[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_66[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_61 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_66 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_62 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m258,048\u001b[0m │ activation_61[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_67 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m258,048\u001b[0m │ activation_66[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_62[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_67[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_62 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_67 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_6 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ mixed6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m768\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_60 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ mixed6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_63 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m258,048\u001b[0m │ activation_62[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_68 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m258,048\u001b[0m │ activation_67[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_69 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ average_pooling2… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_60[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_63[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_68[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_69[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_60 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_63 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_68 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_69 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed7 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_60[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m768\u001b[0m) │ │ activation_63[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ activation_68[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ activation_69[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_72 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ mixed7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_72[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_72 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_73 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m258,048\u001b[0m │ activation_72[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_73[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_73 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_70 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m147,456\u001b[0m │ mixed7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_74 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m258,048\u001b[0m │ activation_73[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_70[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ conv2d_74[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_70 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_74 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_71 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m320\u001b[0m) │ \u001b[38;5;34m552,960\u001b[0m │ activation_70[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_75 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m331,776\u001b[0m │ activation_74[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m320\u001b[0m) │ \u001b[38;5;34m960\u001b[0m │ conv2d_71[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │ conv2d_75[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_71 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m320\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_75 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m768\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ mixed7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed8 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_71[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m1280\u001b[0m) │ │ activation_75[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ max_pooling2d_3[\u001b[38;5;34m…\u001b[0m │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_80 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m448\u001b[0m) │ \u001b[38;5;34m573,440\u001b[0m │ mixed8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m448\u001b[0m) │ \u001b[38;5;34m1,344\u001b[0m │ conv2d_80[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_80 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m448\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_77 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m491,520\u001b[0m │ mixed8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_81 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,548,288\u001b[0m │ activation_80[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_77[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_81[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_77 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_81 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_78 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m442,368\u001b[0m │ activation_77[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_79 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m442,368\u001b[0m │ activation_77[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_82 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m442,368\u001b[0m │ activation_81[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_83 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m442,368\u001b[0m │ activation_81[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_7 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ mixed8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m1280\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_76 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m320\u001b[0m) │ \u001b[38;5;34m409,600\u001b[0m │ mixed8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_78[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_79[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_82[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_83[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_84 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m245,760\u001b[0m │ average_pooling2… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m320\u001b[0m) │ \u001b[38;5;34m960\u001b[0m │ conv2d_76[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_78 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_79 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_82 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_83 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │ conv2d_84[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_76 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m320\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed9_0 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m768\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_78[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ activation_79[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ concatenate │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m768\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_82[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ activation_83[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_84 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed9 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_76[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ mixed9_0[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n│ │ │ │ concatenate[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n│ │ │ │ activation_84[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_89 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m448\u001b[0m) │ \u001b[38;5;34m917,504\u001b[0m │ mixed9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m448\u001b[0m) │ \u001b[38;5;34m1,344\u001b[0m │ conv2d_89[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_89 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m448\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_86 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m786,432\u001b[0m │ mixed9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_90 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,548,288\u001b[0m │ activation_89[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_86[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_90[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_86 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_90 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_87 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m442,368\u001b[0m │ activation_86[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_88 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m442,368\u001b[0m │ activation_86[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_91 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m442,368\u001b[0m │ activation_90[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_92 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m442,368\u001b[0m │ activation_90[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_8 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ mixed9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mAveragePooling2D\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_85 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m320\u001b[0m) │ \u001b[38;5;34m655,360\u001b[0m │ mixed9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_87[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_88[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_91[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,152\u001b[0m │ conv2d_92[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_93 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m393,216\u001b[0m │ average_pooling2… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m320\u001b[0m) │ \u001b[38;5;34m960\u001b[0m │ conv2d_85[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_87 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_88 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_91 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_92 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m576\u001b[0m │ conv2d_93[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_85 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m320\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed9_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m768\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_87[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ activation_88[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ concatenate_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m768\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_91[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ activation_92[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_93 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed10 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ activation_85[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m) │ \u001b[38;5;34m2048\u001b[0m) │ │ mixed9_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n│ │ │ │ concatenate_1[\u001b[38;5;34m0\u001b[0m]… │\n│ │ │ │ activation_93[\u001b[38;5;34m0\u001b[0m]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ global_average_poo… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ mixed10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense_4 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m2,098,176\u001b[0m │ global_average_p… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense_5 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m1,025\u001b[0m │ dense_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n","text/html":"
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n┃ Layer (type)         Output Shape          Param #  Connected to      ┃\n┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n│ input_layer_2       │ (None, 224, 224,  │          0 │ -                 │\n│ (InputLayer)        │ 3)                │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d (Conv2D)     │ (None, 111, 111,  │        864 │ input_layer_2[0]… │\n│                     │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalization │ (None, 111, 111,  │         96 │ conv2d[0][0]      │\n│ (BatchNormalizatio…32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation          │ (None, 111, 111,  │          0 │ batch_normalizat… │\n│ (Activation)        │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_1 (Conv2D)   │ (None, 109, 109,  │      9,216 │ activation[0][0]  │\n│                     │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 109, 109,  │         96 │ conv2d_1[0][0]    │\n│ (BatchNormalizatio…32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_1        │ (None, 109, 109,  │          0 │ batch_normalizat… │\n│ (Activation)        │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_2 (Conv2D)   │ (None, 109, 109,  │     18,432 │ activation_1[0][ │\n│                     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 109, 109,  │        192 │ conv2d_2[0][0]    │\n│ (BatchNormalizatio…64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_2        │ (None, 109, 109,  │          0 │ batch_normalizat… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d       │ (None, 54, 54,    │          0 │ activation_2[0][ │\n│ (MaxPooling2D)      │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_3 (Conv2D)   │ (None, 54, 54,    │      5,120 │ max_pooling2d[0]… │\n│                     │ 80)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 54, 54,    │        240 │ conv2d_3[0][0]    │\n│ (BatchNormalizatio…80)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_3        │ (None, 54, 54,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 80)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_4 (Conv2D)   │ (None, 52, 52,    │    138,240 │ activation_3[0][ │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 52, 52,    │        576 │ conv2d_4[0][0]    │\n│ (BatchNormalizatio…192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_4        │ (None, 52, 52,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_1     │ (None, 25, 25,    │          0 │ activation_4[0][ │\n│ (MaxPooling2D)      │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_8 (Conv2D)   │ (None, 25, 25,    │     12,288 │ max_pooling2d_1[ │\n│                     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        192 │ conv2d_8[0][0]    │\n│ (BatchNormalizatio…64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_8        │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_6 (Conv2D)   │ (None, 25, 25,    │      9,216 │ max_pooling2d_1[ │\n│                     │ 48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_9 (Conv2D)   │ (None, 25, 25,    │     55,296 │ activation_8[0][ │\n│                     │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        144 │ conv2d_6[0][0]    │\n│ (BatchNormalizatio…48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        288 │ conv2d_9[0][0]    │\n│ (BatchNormalizatio…96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_6        │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_9        │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d   │ (None, 25, 25,    │          0 │ max_pooling2d_1[ │\n│ (AveragePooling2D)  │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_5 (Conv2D)   │ (None, 25, 25,    │     12,288 │ max_pooling2d_1[ │\n│                     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_7 (Conv2D)   │ (None, 25, 25,    │     76,800 │ activation_6[0][ │\n│                     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_10 (Conv2D)  │ (None, 25, 25,    │     82,944 │ activation_9[0][ │\n│                     │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_11 (Conv2D)  │ (None, 25, 25,    │      6,144 │ average_pooling2… │\n│                     │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        192 │ conv2d_5[0][0]    │\n│ (BatchNormalizatio…64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        192 │ conv2d_7[0][0]    │\n│ (BatchNormalizatio…64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        288 │ conv2d_10[0][0]   │\n│ (BatchNormalizatio…96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │         96 │ conv2d_11[0][0]   │\n│ (BatchNormalizatio…32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_5        │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_7        │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_10       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_11       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed0              │ (None, 25, 25,    │          0 │ activation_5[0][ │\n│ (Concatenate)       │ 256)              │            │ activation_7[0][ │\n│                     │                   │            │ activation_10[0]… │\n│                     │                   │            │ activation_11[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_15 (Conv2D)  │ (None, 25, 25,    │     16,384 │ mixed0[0][0]      │\n│                     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        192 │ conv2d_15[0][0]   │\n│ (BatchNormalizatio…64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_15       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_13 (Conv2D)  │ (None, 25, 25,    │     12,288 │ mixed0[0][0]      │\n│                     │ 48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_16 (Conv2D)  │ (None, 25, 25,    │     55,296 │ activation_15[0]… │\n│                     │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        144 │ conv2d_13[0][0]   │\n│ (BatchNormalizatio…48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        288 │ conv2d_16[0][0]   │\n│ (BatchNormalizatio…96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_13       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_16       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_1 │ (None, 25, 25,    │          0 │ mixed0[0][0]      │\n│ (AveragePooling2D)  │ 256)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_12 (Conv2D)  │ (None, 25, 25,    │     16,384 │ mixed0[0][0]      │\n│                     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_14 (Conv2D)  │ (None, 25, 25,    │     76,800 │ activation_13[0]… │\n│                     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_17 (Conv2D)  │ (None, 25, 25,    │     82,944 │ activation_16[0]… │\n│                     │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_18 (Conv2D)  │ (None, 25, 25,    │     16,384 │ average_pooling2… │\n│                     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        192 │ conv2d_12[0][0]   │\n│ (BatchNormalizatio…64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        192 │ conv2d_14[0][0]   │\n│ (BatchNormalizatio…64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        288 │ conv2d_17[0][0]   │\n│ (BatchNormalizatio…96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        192 │ conv2d_18[0][0]   │\n│ (BatchNormalizatio…64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_12       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_14       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_17       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_18       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed1              │ (None, 25, 25,    │          0 │ activation_12[0]… │\n│ (Concatenate)       │ 288)              │            │ activation_14[0]… │\n│                     │                   │            │ activation_17[0]… │\n│                     │                   │            │ activation_18[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_22 (Conv2D)  │ (None, 25, 25,    │     18,432 │ mixed1[0][0]      │\n│                     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        192 │ conv2d_22[0][0]   │\n│ (BatchNormalizatio…64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_22       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_20 (Conv2D)  │ (None, 25, 25,    │     13,824 │ mixed1[0][0]      │\n│                     │ 48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_23 (Conv2D)  │ (None, 25, 25,    │     55,296 │ activation_22[0]… │\n│                     │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        144 │ conv2d_20[0][0]   │\n│ (BatchNormalizatio…48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        288 │ conv2d_23[0][0]   │\n│ (BatchNormalizatio…96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_20       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_23       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_2 │ (None, 25, 25,    │          0 │ mixed1[0][0]      │\n│ (AveragePooling2D)  │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_19 (Conv2D)  │ (None, 25, 25,    │     18,432 │ mixed1[0][0]      │\n│                     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_21 (Conv2D)  │ (None, 25, 25,    │     76,800 │ activation_20[0]… │\n│                     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_24 (Conv2D)  │ (None, 25, 25,    │     82,944 │ activation_23[0]… │\n│                     │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_25 (Conv2D)  │ (None, 25, 25,    │     18,432 │ average_pooling2… │\n│                     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        192 │ conv2d_19[0][0]   │\n│ (BatchNormalizatio…64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        192 │ conv2d_21[0][0]   │\n│ (BatchNormalizatio…64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        288 │ conv2d_24[0][0]   │\n│ (BatchNormalizatio…96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        192 │ conv2d_25[0][0]   │\n│ (BatchNormalizatio…64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_19       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_21       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_24       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_25       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed2              │ (None, 25, 25,    │          0 │ activation_19[0]… │\n│ (Concatenate)       │ 288)              │            │ activation_21[0]… │\n│                     │                   │            │ activation_24[0]… │\n│                     │                   │            │ activation_25[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_27 (Conv2D)  │ (None, 25, 25,    │     18,432 │ mixed2[0][0]      │\n│                     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        192 │ conv2d_27[0][0]   │\n│ (BatchNormalizatio…64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_27       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_28 (Conv2D)  │ (None, 25, 25,    │     55,296 │ activation_27[0]… │\n│                     │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 25, 25,    │        288 │ conv2d_28[0][0]   │\n│ (BatchNormalizatio…96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_28       │ (None, 25, 25,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_26 (Conv2D)  │ (None, 12, 12,    │    995,328 │ mixed2[0][0]      │\n│                     │ 384)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_29 (Conv2D)  │ (None, 12, 12,    │     82,944 │ activation_28[0]… │\n│                     │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │      1,152 │ conv2d_26[0][0]   │\n│ (BatchNormalizatio…384)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        288 │ conv2d_29[0][0]   │\n│ (BatchNormalizatio…96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_26       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 384)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_29       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 96)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_2     │ (None, 12, 12,    │          0 │ mixed2[0][0]      │\n│ (MaxPooling2D)      │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed3              │ (None, 12, 12,    │          0 │ activation_26[0]… │\n│ (Concatenate)       │ 768)              │            │ activation_29[0]… │\n│                     │                   │            │ max_pooling2d_2[ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_34 (Conv2D)  │ (None, 12, 12,    │     98,304 │ mixed3[0][0]      │\n│                     │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        384 │ conv2d_34[0][0]   │\n│ (BatchNormalizatio…128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_34       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_35 (Conv2D)  │ (None, 12, 12,    │    114,688 │ activation_34[0]… │\n│                     │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        384 │ conv2d_35[0][0]   │\n│ (BatchNormalizatio…128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_35       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_31 (Conv2D)  │ (None, 12, 12,    │     98,304 │ mixed3[0][0]      │\n│                     │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_36 (Conv2D)  │ (None, 12, 12,    │    114,688 │ activation_35[0]… │\n│                     │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        384 │ conv2d_31[0][0]   │\n│ (BatchNormalizatio…128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        384 │ conv2d_36[0][0]   │\n│ (BatchNormalizatio…128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_31       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_36       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_32 (Conv2D)  │ (None, 12, 12,    │    114,688 │ activation_31[0]… │\n│                     │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_37 (Conv2D)  │ (None, 12, 12,    │    114,688 │ activation_36[0]… │\n│                     │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        384 │ conv2d_32[0][0]   │\n│ (BatchNormalizatio…128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        384 │ conv2d_37[0][0]   │\n│ (BatchNormalizatio…128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_32       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_37       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 128)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_3 │ (None, 12, 12,    │          0 │ mixed3[0][0]      │\n│ (AveragePooling2D)  │ 768)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_30 (Conv2D)  │ (None, 12, 12,    │    147,456 │ mixed3[0][0]      │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_33 (Conv2D)  │ (None, 12, 12,    │    172,032 │ activation_32[0]… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_38 (Conv2D)  │ (None, 12, 12,    │    172,032 │ activation_37[0]… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_39 (Conv2D)  │ (None, 12, 12,    │    147,456 │ average_pooling2… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_30[0][0]   │\n│ (BatchNormalizatio…192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_33[0][0]   │\n│ (BatchNormalizatio…192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_38[0][0]   │\n│ (BatchNormalizatio…192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_39[0][0]   │\n│ (BatchNormalizatio…192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_30       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_33       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_38       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_39       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed4              │ (None, 12, 12,    │          0 │ activation_30[0]… │\n│ (Concatenate)       │ 768)              │            │ activation_33[0]… │\n│                     │                   │            │ activation_38[0]… │\n│                     │                   │            │ activation_39[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_44 (Conv2D)  │ (None, 12, 12,    │    122,880 │ mixed4[0][0]      │\n│                     │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        480 │ conv2d_44[0][0]   │\n│ (BatchNormalizatio…160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_44       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_45 (Conv2D)  │ (None, 12, 12,    │    179,200 │ activation_44[0]… │\n│                     │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        480 │ conv2d_45[0][0]   │\n│ (BatchNormalizatio…160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_45       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_41 (Conv2D)  │ (None, 12, 12,    │    122,880 │ mixed4[0][0]      │\n│                     │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_46 (Conv2D)  │ (None, 12, 12,    │    179,200 │ activation_45[0]… │\n│                     │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        480 │ conv2d_41[0][0]   │\n│ (BatchNormalizatio…160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        480 │ conv2d_46[0][0]   │\n│ (BatchNormalizatio…160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_41       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_46       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_42 (Conv2D)  │ (None, 12, 12,    │    179,200 │ activation_41[0]… │\n│                     │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_47 (Conv2D)  │ (None, 12, 12,    │    179,200 │ activation_46[0]… │\n│                     │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        480 │ conv2d_42[0][0]   │\n│ (BatchNormalizatio…160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        480 │ conv2d_47[0][0]   │\n│ (BatchNormalizatio…160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_42       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_47       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_4 │ (None, 12, 12,    │          0 │ mixed4[0][0]      │\n│ (AveragePooling2D)  │ 768)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_40 (Conv2D)  │ (None, 12, 12,    │    147,456 │ mixed4[0][0]      │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_43 (Conv2D)  │ (None, 12, 12,    │    215,040 │ activation_42[0]… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_48 (Conv2D)  │ (None, 12, 12,    │    215,040 │ activation_47[0]… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_49 (Conv2D)  │ (None, 12, 12,    │    147,456 │ average_pooling2… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_40[0][0]   │\n│ (BatchNormalizatio…192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_43[0][0]   │\n│ (BatchNormalizatio…192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_48[0][0]   │\n│ (BatchNormalizatio…192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_49[0][0]   │\n│ (BatchNormalizatio…192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_40       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_43       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_48       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_49       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed5              │ (None, 12, 12,    │          0 │ activation_40[0]… │\n│ (Concatenate)       │ 768)              │            │ activation_43[0]… │\n│                     │                   │            │ activation_48[0]… │\n│                     │                   │            │ activation_49[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_54 (Conv2D)  │ (None, 12, 12,    │    122,880 │ mixed5[0][0]      │\n│                     │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        480 │ conv2d_54[0][0]   │\n│ (BatchNormalizatio…160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_54       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_55 (Conv2D)  │ (None, 12, 12,    │    179,200 │ activation_54[0]… │\n│                     │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        480 │ conv2d_55[0][0]   │\n│ (BatchNormalizatio…160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_55       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_51 (Conv2D)  │ (None, 12, 12,    │    122,880 │ mixed5[0][0]      │\n│                     │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_56 (Conv2D)  │ (None, 12, 12,    │    179,200 │ activation_55[0]… │\n│                     │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        480 │ conv2d_51[0][0]   │\n│ (BatchNormalizatio…160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        480 │ conv2d_56[0][0]   │\n│ (BatchNormalizatio…160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_51       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_56       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_52 (Conv2D)  │ (None, 12, 12,    │    179,200 │ activation_51[0]… │\n│                     │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_57 (Conv2D)  │ (None, 12, 12,    │    179,200 │ activation_56[0]… │\n│                     │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        480 │ conv2d_52[0][0]   │\n│ (BatchNormalizatio…160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        480 │ conv2d_57[0][0]   │\n│ (BatchNormalizatio…160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_52       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_57       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_5 │ (None, 12, 12,    │          0 │ mixed5[0][0]      │\n│ (AveragePooling2D)  │ 768)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_50 (Conv2D)  │ (None, 12, 12,    │    147,456 │ mixed5[0][0]      │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_53 (Conv2D)  │ (None, 12, 12,    │    215,040 │ activation_52[0]… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_58 (Conv2D)  │ (None, 12, 12,    │    215,040 │ activation_57[0]… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_59 (Conv2D)  │ (None, 12, 12,    │    147,456 │ average_pooling2… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_50[0][0]   │\n│ (BatchNormalizatio…192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_53[0][0]   │\n│ (BatchNormalizatio…192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_58[0][0]   │\n│ (BatchNormalizatio…192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_59[0][0]   │\n│ (BatchNormalizatio…192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_50       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_53       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_58       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_59       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed6              │ (None, 12, 12,    │          0 │ activation_50[0]… │\n│ (Concatenate)       │ 768)              │            │ activation_53[0]… │\n│                     │                   │            │ activation_58[0]… │\n│                     │                   │            │ activation_59[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_64 (Conv2D)  │ (None, 12, 12,    │    147,456 │ mixed6[0][0]      │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_64[0][0]   │\n│ (BatchNormalizatio…192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_64       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_65 (Conv2D)  │ (None, 12, 12,    │    258,048 │ activation_64[0]… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_65[0][0]   │\n│ (BatchNormalizatio…192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_65       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_61 (Conv2D)  │ (None, 12, 12,    │    147,456 │ mixed6[0][0]      │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_66 (Conv2D)  │ (None, 12, 12,    │    258,048 │ activation_65[0]… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_61[0][0]   │\n│ (BatchNormalizatio…192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_66[0][0]   │\n│ (BatchNormalizatio…192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_61       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_66       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_62 (Conv2D)  │ (None, 12, 12,    │    258,048 │ activation_61[0]… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_67 (Conv2D)  │ (None, 12, 12,    │    258,048 │ activation_66[0]… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_62[0][0]   │\n│ (BatchNormalizatio…192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_67[0][0]   │\n│ (BatchNormalizatio…192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_62       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_67       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_6 │ (None, 12, 12,    │          0 │ mixed6[0][0]      │\n│ (AveragePooling2D)  │ 768)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_60 (Conv2D)  │ (None, 12, 12,    │    147,456 │ mixed6[0][0]      │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_63 (Conv2D)  │ (None, 12, 12,    │    258,048 │ activation_62[0]… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_68 (Conv2D)  │ (None, 12, 12,    │    258,048 │ activation_67[0]… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_69 (Conv2D)  │ (None, 12, 12,    │    147,456 │ average_pooling2… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_60[0][0]   │\n│ (BatchNormalizatio…192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_63[0][0]   │\n│ (BatchNormalizatio…192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_68[0][0]   │\n│ (BatchNormalizatio…192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_69[0][0]   │\n│ (BatchNormalizatio…192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_60       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_63       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_68       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_69       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed7              │ (None, 12, 12,    │          0 │ activation_60[0]… │\n│ (Concatenate)       │ 768)              │            │ activation_63[0]… │\n│                     │                   │            │ activation_68[0]… │\n│                     │                   │            │ activation_69[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_72 (Conv2D)  │ (None, 12, 12,    │    147,456 │ mixed7[0][0]      │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_72[0][0]   │\n│ (BatchNormalizatio…192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_72       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_73 (Conv2D)  │ (None, 12, 12,    │    258,048 │ activation_72[0]… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_73[0][0]   │\n│ (BatchNormalizatio…192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_73       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_70 (Conv2D)  │ (None, 12, 12,    │    147,456 │ mixed7[0][0]      │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_74 (Conv2D)  │ (None, 12, 12,    │    258,048 │ activation_73[0]… │\n│                     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_70[0][0]   │\n│ (BatchNormalizatio…192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 12, 12,    │        576 │ conv2d_74[0][0]   │\n│ (BatchNormalizatio…192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_70       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_74       │ (None, 12, 12,    │          0 │ batch_normalizat… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_71 (Conv2D)  │ (None, 5, 5, 320) │    552,960 │ activation_70[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_75 (Conv2D)  │ (None, 5, 5, 192) │    331,776 │ activation_74[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 320) │        960 │ conv2d_71[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 192) │        576 │ conv2d_75[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_71       │ (None, 5, 5, 320) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_75       │ (None, 5, 5, 192) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_3     │ (None, 5, 5, 768) │          0 │ mixed7[0][0]      │\n│ (MaxPooling2D)      │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed8              │ (None, 5, 5,      │          0 │ activation_71[0]… │\n│ (Concatenate)       │ 1280)             │            │ activation_75[0]… │\n│                     │                   │            │ max_pooling2d_3[ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_80 (Conv2D)  │ (None, 5, 5, 448) │    573,440 │ mixed8[0][0]      │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 448) │      1,344 │ conv2d_80[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_80       │ (None, 5, 5, 448) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_77 (Conv2D)  │ (None, 5, 5, 384) │    491,520 │ mixed8[0][0]      │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_81 (Conv2D)  │ (None, 5, 5, 384) │  1,548,288 │ activation_80[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 384) │      1,152 │ conv2d_77[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 384) │      1,152 │ conv2d_81[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_77       │ (None, 5, 5, 384) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_81       │ (None, 5, 5, 384) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_78 (Conv2D)  │ (None, 5, 5, 384) │    442,368 │ activation_77[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_79 (Conv2D)  │ (None, 5, 5, 384) │    442,368 │ activation_77[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_82 (Conv2D)  │ (None, 5, 5, 384) │    442,368 │ activation_81[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_83 (Conv2D)  │ (None, 5, 5, 384) │    442,368 │ activation_81[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_7 │ (None, 5, 5,      │          0 │ mixed8[0][0]      │\n│ (AveragePooling2D)  │ 1280)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_76 (Conv2D)  │ (None, 5, 5, 320) │    409,600 │ mixed8[0][0]      │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 384) │      1,152 │ conv2d_78[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 384) │      1,152 │ conv2d_79[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 384) │      1,152 │ conv2d_82[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 384) │      1,152 │ conv2d_83[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_84 (Conv2D)  │ (None, 5, 5, 192) │    245,760 │ average_pooling2… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 320) │        960 │ conv2d_76[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_78       │ (None, 5, 5, 384) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_79       │ (None, 5, 5, 384) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_82       │ (None, 5, 5, 384) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_83       │ (None, 5, 5, 384) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 192) │        576 │ conv2d_84[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_76       │ (None, 5, 5, 320) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed9_0            │ (None, 5, 5, 768) │          0 │ activation_78[0]… │\n│ (Concatenate)       │                   │            │ activation_79[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ concatenate         │ (None, 5, 5, 768) │          0 │ activation_82[0]… │\n│ (Concatenate)       │                   │            │ activation_83[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_84       │ (None, 5, 5, 192) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed9              │ (None, 5, 5,      │          0 │ activation_76[0]… │\n│ (Concatenate)       │ 2048)             │            │ mixed9_0[0][0],   │\n│                     │                   │            │ concatenate[0][0… │\n│                     │                   │            │ activation_84[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_89 (Conv2D)  │ (None, 5, 5, 448) │    917,504 │ mixed9[0][0]      │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 448) │      1,344 │ conv2d_89[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_89       │ (None, 5, 5, 448) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_86 (Conv2D)  │ (None, 5, 5, 384) │    786,432 │ mixed9[0][0]      │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_90 (Conv2D)  │ (None, 5, 5, 384) │  1,548,288 │ activation_89[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 384) │      1,152 │ conv2d_86[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 384) │      1,152 │ conv2d_90[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_86       │ (None, 5, 5, 384) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_90       │ (None, 5, 5, 384) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_87 (Conv2D)  │ (None, 5, 5, 384) │    442,368 │ activation_86[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_88 (Conv2D)  │ (None, 5, 5, 384) │    442,368 │ activation_86[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_91 (Conv2D)  │ (None, 5, 5, 384) │    442,368 │ activation_90[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_92 (Conv2D)  │ (None, 5, 5, 384) │    442,368 │ activation_90[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ average_pooling2d_8 │ (None, 5, 5,      │          0 │ mixed9[0][0]      │\n│ (AveragePooling2D)  │ 2048)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_85 (Conv2D)  │ (None, 5, 5, 320) │    655,360 │ mixed9[0][0]      │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 384) │      1,152 │ conv2d_87[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 384) │      1,152 │ conv2d_88[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 384) │      1,152 │ conv2d_91[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 384) │      1,152 │ conv2d_92[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_93 (Conv2D)  │ (None, 5, 5, 192) │    393,216 │ average_pooling2… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 320) │        960 │ conv2d_85[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_87       │ (None, 5, 5, 384) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_88       │ (None, 5, 5, 384) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_91       │ (None, 5, 5, 384) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_92       │ (None, 5, 5, 384) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ batch_normalizatio… │ (None, 5, 5, 192) │        576 │ conv2d_93[0][0]   │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_85       │ (None, 5, 5, 320) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed9_1            │ (None, 5, 5, 768) │          0 │ activation_87[0]… │\n│ (Concatenate)       │                   │            │ activation_88[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ concatenate_1       │ (None, 5, 5, 768) │          0 │ activation_91[0]… │\n│ (Concatenate)       │                   │            │ activation_92[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ activation_93       │ (None, 5, 5, 192) │          0 │ batch_normalizat… │\n│ (Activation)        │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ mixed10             │ (None, 5, 5,      │          0 │ activation_85[0]… │\n│ (Concatenate)       │ 2048)             │            │ mixed9_1[0][0],   │\n│                     │                   │            │ concatenate_1[0]… │\n│                     │                   │            │ activation_93[0]… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ global_average_poo… │ (None, 2048)      │          0 │ mixed10[0][0]     │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense_4 (Dense)     │ (None, 1024)      │  2,098,176 │ global_average_p… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense_5 (Dense)     │ (None, 1)         │      1,025 │ dense_4[0][0]     │\n└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m23,901,985\u001b[0m (91.18 MB)\n","text/html":"
 Total params: 23,901,985 (91.18 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2,099,201\u001b[0m (8.01 MB)\n","text/html":"
 Trainable params: 2,099,201 (8.01 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m21,802,784\u001b[0m (83.17 MB)\n","text/html":"
 Non-trainable params: 21,802,784 (83.17 MB)\n
\n"},"metadata":{}}]},{"cell_type":"code","source":"model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\nhistory = model.fit(x_train, y_train, validation_data=(x_val, y_val), epochs=20, batch_size=32,callbacks=[reduce_lr,model_checkpoint])","metadata":{"execution":{"iopub.status.busy":"2024-05-31T15:05:26.669637Z","iopub.execute_input":"2024-05-31T15:05:26.669916Z","iopub.status.idle":"2024-05-31T15:07:26.502680Z","shell.execute_reply.started":"2024-05-31T15:05:26.669891Z","shell.execute_reply":"2024-05-31T15:07:26.501868Z"},"trusted":true},"execution_count":14,"outputs":[{"name":"stdout","text":"Epoch 1/20\n\u001b[1m 2/39\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m3s\u001b[0m 99ms/step - accuracy: 0.5625 - loss: 1.3575 ","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1717167950.564707 116 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 436ms/step - accuracy: 0.6269 - loss: 1.2354","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1717167967.134152 114 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\nW0000 00:00:1717167973.737185 116 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\nEpoch 1: val_accuracy improved from -inf to 0.79775, saving model to att_model.keras\n","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1717167984.504000 114 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 918ms/step - accuracy: 0.6283 - loss: 1.2262 - val_accuracy: 0.7978 - val_loss: 0.4201 - learning_rate: 0.0010\nEpoch 2/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - accuracy: 0.8420 - loss: 0.3539\nEpoch 2: val_accuracy improved from 0.79775 to 0.83521, saving model to att_model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 108ms/step - accuracy: 0.8420 - loss: 0.3540 - val_accuracy: 0.8352 - val_loss: 0.3754 - learning_rate: 0.0010\nEpoch 3/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - accuracy: 0.8703 - loss: 0.2996\nEpoch 3: val_accuracy did not improve from 0.83521\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 79ms/step - accuracy: 0.8702 - loss: 0.2999 - val_accuracy: 0.7678 - val_loss: 0.4558 - learning_rate: 0.0010\nEpoch 4/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - accuracy: 0.8936 - loss: 0.2608\nEpoch 4: val_accuracy did not improve from 0.83521\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 78ms/step - accuracy: 0.8935 - loss: 0.2612 - val_accuracy: 0.8315 - val_loss: 0.3650 - learning_rate: 0.0010\nEpoch 5/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - accuracy: 0.9157 - loss: 0.2081\nEpoch 5: val_accuracy did not improve from 0.83521\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 78ms/step - accuracy: 0.9156 - loss: 0.2082 - val_accuracy: 0.8352 - val_loss: 0.3303 - learning_rate: 0.0010\nEpoch 6/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - accuracy: 0.9398 - loss: 0.1849\nEpoch 6: val_accuracy did not improve from 0.83521\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 78ms/step - accuracy: 0.9396 - loss: 0.1850 - val_accuracy: 0.8202 - val_loss: 0.3380 - learning_rate: 0.0010\nEpoch 7/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - accuracy: 0.9344 - loss: 0.1536\nEpoch 7: val_accuracy did not improve from 0.83521\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 78ms/step - accuracy: 0.9347 - loss: 0.1534 - val_accuracy: 0.8202 - val_loss: 0.3744 - learning_rate: 0.0010\nEpoch 8/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - accuracy: 0.9576 - loss: 0.1190\nEpoch 8: val_accuracy improved from 0.83521 to 0.84644, saving model to att_model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 102ms/step - accuracy: 0.9575 - loss: 0.1189 - val_accuracy: 0.8464 - val_loss: 0.3560 - learning_rate: 0.0010\nEpoch 9/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - accuracy: 0.9600 - loss: 0.1070\nEpoch 9: val_accuracy did not improve from 0.84644\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 77ms/step - accuracy: 0.9601 - loss: 0.1069 - val_accuracy: 0.8352 - val_loss: 0.3633 - learning_rate: 0.0010\nEpoch 10/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - accuracy: 0.9737 - loss: 0.0822\nEpoch 10: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n\nEpoch 10: val_accuracy did not improve from 0.84644\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 78ms/step - accuracy: 0.9738 - loss: 0.0822 - val_accuracy: 0.8390 - val_loss: 0.3615 - learning_rate: 0.0010\nEpoch 11/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - accuracy: 0.9931 - loss: 0.0525\nEpoch 11: val_accuracy improved from 0.84644 to 0.86891, saving model to att_model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 102ms/step - accuracy: 0.9931 - loss: 0.0524 - val_accuracy: 0.8689 - val_loss: 0.3523 - learning_rate: 1.0000e-04\nEpoch 12/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - accuracy: 0.9983 - loss: 0.0409\nEpoch 12: val_accuracy did not improve from 0.86891\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 77ms/step - accuracy: 0.9983 - loss: 0.0410 - val_accuracy: 0.8577 - val_loss: 0.3589 - learning_rate: 1.0000e-04\nEpoch 13/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - accuracy: 0.9954 - loss: 0.0441\nEpoch 13: val_accuracy did not improve from 0.86891\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 77ms/step - accuracy: 0.9955 - loss: 0.0440 - val_accuracy: 0.8539 - val_loss: 0.3630 - learning_rate: 1.0000e-04\nEpoch 14/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - accuracy: 0.9977 - loss: 0.0361\nEpoch 14: val_accuracy did not improve from 0.86891\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 77ms/step - accuracy: 0.9976 - loss: 0.0362 - val_accuracy: 0.8577 - val_loss: 0.3673 - learning_rate: 1.0000e-04\nEpoch 15/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - accuracy: 0.9988 - loss: 0.0355\nEpoch 15: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n\nEpoch 15: val_accuracy did not improve from 0.86891\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 77ms/step - accuracy: 0.9987 - loss: 0.0356 - val_accuracy: 0.8539 - val_loss: 0.3661 - learning_rate: 1.0000e-04\nEpoch 16/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - accuracy: 0.9972 - loss: 0.0364\nEpoch 16: val_accuracy did not improve from 0.86891\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 77ms/step - accuracy: 0.9973 - loss: 0.0364 - val_accuracy: 0.8539 - val_loss: 0.3658 - learning_rate: 1.0000e-05\nEpoch 17/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - accuracy: 0.9993 - loss: 0.0346\nEpoch 17: val_accuracy did not improve from 0.86891\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 78ms/step - accuracy: 0.9993 - loss: 0.0347 - val_accuracy: 0.8539 - val_loss: 0.3663 - learning_rate: 1.0000e-05\nEpoch 18/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - accuracy: 0.9995 - loss: 0.0344\nEpoch 18: val_accuracy did not improve from 0.86891\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 77ms/step - accuracy: 0.9995 - loss: 0.0344 - val_accuracy: 0.8539 - val_loss: 0.3662 - learning_rate: 1.0000e-05\nEpoch 19/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - accuracy: 0.9997 - loss: 0.0335\nEpoch 19: val_accuracy did not improve from 0.86891\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 78ms/step - accuracy: 0.9997 - loss: 0.0335 - val_accuracy: 0.8539 - val_loss: 0.3662 - learning_rate: 1.0000e-05\nEpoch 20/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - accuracy: 0.9999 - loss: 0.0350\nEpoch 20: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.\n\nEpoch 20: val_accuracy did not improve from 0.86891\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 78ms/step - accuracy: 0.9999 - loss: 0.0350 - val_accuracy: 0.8539 - val_loss: 0.3662 - learning_rate: 1.0000e-05\n","output_type":"stream"}]},{"cell_type":"code","source":"plt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\nplt.xlabel(\"Epoch\")\nplt.ylabel(\"Loss\")\nplt.title(\"Loss per epoch\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-31T15:07:26.507541Z","iopub.execute_input":"2024-05-31T15:07:26.507825Z","iopub.status.idle":"2024-05-31T15:07:26.721460Z","shell.execute_reply.started":"2024-05-31T15:07:26.507799Z","shell.execute_reply":"2024-05-31T15:07:26.720568Z"},"trusted":true},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"plt.plot(history.history['accuracy'])\nplt.plot(history.history['val_accuracy'])\nplt.xlabel(\"Epoch\")\nplt.ylabel(\"Accuracy\")\nplt.title(\"Accuracy per epoch\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-31T15:07:26.722560Z","iopub.execute_input":"2024-05-31T15:07:26.722829Z","iopub.status.idle":"2024-05-31T15:07:26.955953Z","shell.execute_reply.started":"2024-05-31T15:07:26.722804Z","shell.execute_reply":"2024-05-31T15:07:26.955067Z"},"trusted":true},"execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"### EfficientNetB7","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.applications import EfficientNetB7\n\n# Load the EfficientNetB7 model and create the feature extractor\nefficientnet_model = EfficientNetB7(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\nfeature_extractor = Model(inputs=efficientnet_model.input, outputs=efficientnet_model.get_layer('top_activation').output)\n\n# Freeze the convolutional base layers\nfor layer in feature_extractor.layers:\n layer.trainable = False\n\n# Add custom layers on top\nx = feature_extractor.output\nx = GlobalAveragePooling2D()(x)\nx = Dense(1024, activation='relu')(x)\n\noutput = Dense(1, activation='sigmoid')(x)\n\n# Create the final model\nmodel = Model(inputs=feature_extractor.input, outputs=output)\n\nmodel_checkpoint = ModelCheckpoint('att_model.keras', monitor='val_accuracy', save_best_only=True, verbose=1, mode='max')\nreduce_lr = ReduceLROnPlateau(mode='min', monitor='val_loss', factor=0.1, patience=5, min_lr=0.00000001, verbose=1)\n\n# Print the model summary\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2024-05-31T15:07:26.957408Z","iopub.execute_input":"2024-05-31T15:07:26.958172Z","iopub.status.idle":"2024-05-31T15:07:34.590078Z","shell.execute_reply.started":"2024-05-31T15:07:26.958132Z","shell.execute_reply":"2024-05-31T15:07:34.589163Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":"Downloading data from https://storage.googleapis.com/keras-applications/efficientnetb7_notop.h5\n\u001b[1m258076736/258076736\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 0us/step\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"functional_15\"\u001b[0m\n","text/html":"
Model: \"functional_15\"\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0m┃\n┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n│ input_layer_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ - │\n│ (\u001b[38;5;33mInputLayer\u001b[0m) │ \u001b[38;5;34m3\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ rescaling_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ input_layer_3[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mRescaling\u001b[0m) │ \u001b[38;5;34m3\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, │ \u001b[38;5;34m7\u001b[0m │ rescaling_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mNormalization\u001b[0m) │ \u001b[38;5;34m3\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ rescaling_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ normalization[\u001b[38;5;34m0\u001b[0m]… │\n│ (\u001b[38;5;33mRescaling\u001b[0m) │ \u001b[38;5;34m3\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ stem_conv_pad │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m225\u001b[0m, \u001b[38;5;34m225\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ rescaling_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mZeroPadding2D\u001b[0m) │ \u001b[38;5;34m3\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ stem_conv (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m1,728\u001b[0m │ stem_conv_pad[\u001b[38;5;34m0\u001b[0m]… │\n│ │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ stem_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ stem_conv[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ stem_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ stem_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1a_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m576\u001b[0m │ stem_activation[\u001b[38;5;34m…\u001b[0m │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1a_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m256\u001b[0m │ block1a_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1a_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block1a_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1a_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block1a_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1a_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block1a_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1a_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m1,040\u001b[0m │ block1a_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1a_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m1,088\u001b[0m │ block1a_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1a_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block1a_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m64\u001b[0m) │ │ block1a_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1a_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m2,048\u001b[0m │ block1a_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1a_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m128\u001b[0m │ block1a_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1b_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ block1a_project_… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1b_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m128\u001b[0m │ block1b_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1b_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block1b_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1b_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block1b_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1b_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block1b_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1b_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m8\u001b[0m) │ \u001b[38;5;34m264\u001b[0m │ block1b_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1b_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m288\u001b[0m │ block1b_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1b_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block1b_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ block1b_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1b_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ block1b_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1b_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m128\u001b[0m │ block1b_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1b_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block1b_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1b_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block1b_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m32\u001b[0m) │ │ block1a_project_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1c_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ block1b_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1c_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m128\u001b[0m │ block1c_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1c_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block1c_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1c_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block1c_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1c_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block1c_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1c_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m8\u001b[0m) │ \u001b[38;5;34m264\u001b[0m │ block1c_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1c_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m288\u001b[0m │ block1c_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1c_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block1c_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ block1c_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1c_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ block1c_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1c_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m128\u001b[0m │ block1c_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1c_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block1c_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1c_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block1c_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m32\u001b[0m) │ │ block1b_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1d_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m288\u001b[0m │ block1c_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1d_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m128\u001b[0m │ block1d_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1d_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block1d_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1d_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block1d_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1d_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block1d_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1d_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m8\u001b[0m) │ \u001b[38;5;34m264\u001b[0m │ block1d_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1d_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m288\u001b[0m │ block1d_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1d_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block1d_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ block1d_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1d_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m1,024\u001b[0m │ block1d_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1d_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m128\u001b[0m │ block1d_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1d_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block1d_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m32\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1d_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block1d_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m32\u001b[0m) │ │ block1c_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2a_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m6,144\u001b[0m │ block1d_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2a_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m768\u001b[0m │ block2a_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2a_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2a_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2a_dwconv_pad │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m113\u001b[0m, \u001b[38;5;34m113\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2a_expand_a… │\n│ (\u001b[38;5;33mZeroPadding2D\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2a_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,728\u001b[0m │ block2a_dwconv_p… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2a_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m768\u001b[0m │ block2a_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2a_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2a_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2a_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block2a_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2a_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block2a_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2a_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m8\u001b[0m) │ \u001b[38;5;34m1,544\u001b[0m │ block2a_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2a_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m192\u001b[0m) │ \u001b[38;5;34m1,728\u001b[0m │ block2a_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2a_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2a_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m192\u001b[0m) │ │ block2a_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2a_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ block2a_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2a_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ block2a_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2b_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m13,824\u001b[0m │ block2a_project_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2b_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,152\u001b[0m │ block2b_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2b_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2b_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2b_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m2,592\u001b[0m │ block2b_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2b_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,152\u001b[0m │ block2b_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2b_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2b_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2b_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m288\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block2b_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2b_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m288\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block2b_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2b_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m12\u001b[0m) │ \u001b[38;5;34m3,468\u001b[0m │ block2b_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2b_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m288\u001b[0m) │ \u001b[38;5;34m3,744\u001b[0m │ block2b_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2b_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2b_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ block2b_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2b_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m13,824\u001b[0m │ block2b_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2b_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ block2b_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2b_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2b_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2b_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2b_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m48\u001b[0m) │ │ block2a_project_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2c_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m13,824\u001b[0m │ block2b_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2c_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,152\u001b[0m │ block2c_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2c_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2c_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2c_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m2,592\u001b[0m │ block2c_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2c_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,152\u001b[0m │ block2c_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2c_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2c_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2c_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m288\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block2c_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2c_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m288\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block2c_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2c_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m12\u001b[0m) │ \u001b[38;5;34m3,468\u001b[0m │ block2c_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2c_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m288\u001b[0m) │ \u001b[38;5;34m3,744\u001b[0m │ block2c_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2c_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2c_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ block2c_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2c_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m13,824\u001b[0m │ block2c_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2c_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ block2c_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2c_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2c_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2c_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2c_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m48\u001b[0m) │ │ block2b_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2d_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m13,824\u001b[0m │ block2c_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2d_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,152\u001b[0m │ block2d_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2d_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2d_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2d_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m2,592\u001b[0m │ block2d_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2d_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,152\u001b[0m │ block2d_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2d_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2d_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2d_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m288\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block2d_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2d_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m288\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block2d_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2d_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m12\u001b[0m) │ \u001b[38;5;34m3,468\u001b[0m │ block2d_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2d_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m288\u001b[0m) │ \u001b[38;5;34m3,744\u001b[0m │ block2d_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2d_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2d_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ block2d_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2d_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m13,824\u001b[0m │ block2d_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2d_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ block2d_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2d_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2d_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2d_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2d_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m48\u001b[0m) │ │ block2c_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2e_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m13,824\u001b[0m │ block2d_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2e_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,152\u001b[0m │ block2e_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2e_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2e_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2e_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m2,592\u001b[0m │ block2e_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2e_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,152\u001b[0m │ block2e_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2e_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2e_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2e_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m288\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block2e_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2e_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m288\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block2e_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2e_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m12\u001b[0m) │ \u001b[38;5;34m3,468\u001b[0m │ block2e_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2e_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m288\u001b[0m) │ \u001b[38;5;34m3,744\u001b[0m │ block2e_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2e_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2e_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ block2e_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2e_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m13,824\u001b[0m │ block2e_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2e_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ block2e_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2e_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2e_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2e_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2e_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m48\u001b[0m) │ │ block2d_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2f_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m13,824\u001b[0m │ block2e_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2f_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,152\u001b[0m │ block2f_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2f_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2f_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2f_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m2,592\u001b[0m │ block2f_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2f_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,152\u001b[0m │ block2f_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2f_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2f_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2f_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m288\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block2f_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2f_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m288\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block2f_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2f_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m12\u001b[0m) │ \u001b[38;5;34m3,468\u001b[0m │ block2f_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2f_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m288\u001b[0m) │ \u001b[38;5;34m3,744\u001b[0m │ block2f_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2f_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2f_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ block2f_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2f_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m13,824\u001b[0m │ block2f_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2f_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ block2f_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2f_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2f_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2f_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2f_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m48\u001b[0m) │ │ block2e_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2g_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m13,824\u001b[0m │ block2f_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2g_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,152\u001b[0m │ block2g_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2g_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2g_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2g_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m2,592\u001b[0m │ block2g_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2g_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,152\u001b[0m │ block2g_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2g_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2g_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2g_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m288\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block2g_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2g_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m288\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block2g_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2g_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m12\u001b[0m) │ \u001b[38;5;34m3,468\u001b[0m │ block2g_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2g_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m288\u001b[0m) │ \u001b[38;5;34m3,744\u001b[0m │ block2g_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2g_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2g_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ block2g_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2g_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m13,824\u001b[0m │ block2g_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2g_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m192\u001b[0m │ block2g_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2g_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2g_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m48\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2g_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block2g_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m48\u001b[0m) │ │ block2f_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3a_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m13,824\u001b[0m │ block2g_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3a_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m1,152\u001b[0m │ block3a_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3a_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3a_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3a_dwconv_pad │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m59\u001b[0m, \u001b[38;5;34m59\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3a_expand_a… │\n│ (\u001b[38;5;33mZeroPadding2D\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3a_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m7,200\u001b[0m │ block3a_dwconv_p… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3a_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m1,152\u001b[0m │ block3a_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3a_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3a_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3a_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m288\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block3a_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3a_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m288\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block3a_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3a_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m12\u001b[0m) │ \u001b[38;5;34m3,468\u001b[0m │ block3a_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3a_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m288\u001b[0m) │ \u001b[38;5;34m3,744\u001b[0m │ block3a_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3a_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3a_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m288\u001b[0m) │ │ block3a_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3a_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m23,040\u001b[0m │ block3a_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m80\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3a_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m320\u001b[0m │ block3a_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m80\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3b_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m38,400\u001b[0m │ block3a_project_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3b_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m1,920\u001b[0m │ block3b_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3b_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3b_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3b_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m12,000\u001b[0m │ block3b_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3b_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m1,920\u001b[0m │ block3b_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3b_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3b_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3b_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m480\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block3b_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3b_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m480\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block3b_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3b_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m20\u001b[0m) │ \u001b[38;5;34m9,620\u001b[0m │ block3b_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3b_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m480\u001b[0m) │ \u001b[38;5;34m10,080\u001b[0m │ block3b_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3b_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3b_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ block3b_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3b_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m38,400\u001b[0m │ block3b_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m80\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3b_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m320\u001b[0m │ block3b_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m80\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3b_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3b_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m80\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3b_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3b_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m80\u001b[0m) │ │ block3a_project_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3c_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m38,400\u001b[0m │ block3b_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3c_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m1,920\u001b[0m │ block3c_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3c_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3c_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3c_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m12,000\u001b[0m │ block3c_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3c_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m1,920\u001b[0m │ block3c_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3c_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3c_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3c_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m480\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block3c_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3c_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m480\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block3c_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3c_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m20\u001b[0m) │ \u001b[38;5;34m9,620\u001b[0m │ block3c_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3c_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m480\u001b[0m) │ \u001b[38;5;34m10,080\u001b[0m │ block3c_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3c_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3c_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ block3c_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3c_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m38,400\u001b[0m │ block3c_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m80\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3c_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m320\u001b[0m │ block3c_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m80\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3c_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3c_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m80\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3c_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3c_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m80\u001b[0m) │ │ block3b_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3d_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m38,400\u001b[0m │ block3c_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3d_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m1,920\u001b[0m │ block3d_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3d_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3d_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3d_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m12,000\u001b[0m │ block3d_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3d_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m1,920\u001b[0m │ block3d_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3d_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3d_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3d_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m480\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block3d_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3d_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m480\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block3d_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3d_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m20\u001b[0m) │ \u001b[38;5;34m9,620\u001b[0m │ block3d_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3d_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m480\u001b[0m) │ \u001b[38;5;34m10,080\u001b[0m │ block3d_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3d_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3d_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ block3d_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3d_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m38,400\u001b[0m │ block3d_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m80\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3d_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m320\u001b[0m │ block3d_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m80\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3d_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3d_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m80\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3d_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3d_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m80\u001b[0m) │ │ block3c_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3e_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m38,400\u001b[0m │ block3d_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3e_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m1,920\u001b[0m │ block3e_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3e_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3e_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3e_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m12,000\u001b[0m │ block3e_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3e_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m1,920\u001b[0m │ block3e_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3e_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3e_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3e_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m480\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block3e_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3e_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m480\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block3e_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3e_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m20\u001b[0m) │ \u001b[38;5;34m9,620\u001b[0m │ block3e_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3e_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m480\u001b[0m) │ \u001b[38;5;34m10,080\u001b[0m │ block3e_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3e_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3e_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ block3e_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3e_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m38,400\u001b[0m │ block3e_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m80\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3e_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m320\u001b[0m │ block3e_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m80\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3e_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3e_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m80\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3e_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3e_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m80\u001b[0m) │ │ block3d_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3f_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m38,400\u001b[0m │ block3e_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3f_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m1,920\u001b[0m │ block3f_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3f_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3f_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3f_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m12,000\u001b[0m │ block3f_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3f_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m1,920\u001b[0m │ block3f_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3f_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3f_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3f_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m480\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block3f_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3f_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m480\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block3f_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3f_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m20\u001b[0m) │ \u001b[38;5;34m9,620\u001b[0m │ block3f_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3f_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m480\u001b[0m) │ \u001b[38;5;34m10,080\u001b[0m │ block3f_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3f_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3f_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ block3f_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3f_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m38,400\u001b[0m │ block3f_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m80\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3f_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m320\u001b[0m │ block3f_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m80\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3f_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3f_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m80\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3f_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3f_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m80\u001b[0m) │ │ block3e_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3g_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m38,400\u001b[0m │ block3f_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3g_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m1,920\u001b[0m │ block3g_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3g_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3g_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3g_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m12,000\u001b[0m │ block3g_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3g_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m1,920\u001b[0m │ block3g_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3g_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3g_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3g_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m480\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block3g_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3g_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m480\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block3g_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3g_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m20\u001b[0m) │ \u001b[38;5;34m9,620\u001b[0m │ block3g_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3g_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m480\u001b[0m) │ \u001b[38;5;34m10,080\u001b[0m │ block3g_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3g_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3g_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ block3g_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3g_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m38,400\u001b[0m │ block3g_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m80\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3g_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m320\u001b[0m │ block3g_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m80\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3g_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3g_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m80\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3g_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block3g_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m80\u001b[0m) │ │ block3f_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4a_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m38,400\u001b[0m │ block3g_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4a_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m1,920\u001b[0m │ block4a_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4a_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4a_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4a_dwconv_pad │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m29\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4a_expand_a… │\n│ (\u001b[38;5;33mZeroPadding2D\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4a_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m4,320\u001b[0m │ block4a_dwconv_p… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4a_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m1,920\u001b[0m │ block4a_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4a_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4a_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4a_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m480\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block4a_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4a_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m480\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block4a_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4a_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m20\u001b[0m) │ \u001b[38;5;34m9,620\u001b[0m │ block4a_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4a_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m480\u001b[0m) │ \u001b[38;5;34m10,080\u001b[0m │ block4a_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4a_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4a_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m480\u001b[0m) │ │ block4a_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4a_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m76,800\u001b[0m │ block4a_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4a_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m640\u001b[0m │ block4a_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4b_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m153,600\u001b[0m │ block4a_project_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4b_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,840\u001b[0m │ block4b_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4b_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4b_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4b_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m8,640\u001b[0m │ block4b_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4b_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,840\u001b[0m │ block4b_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4b_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4b_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4b_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block4b_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4b_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block4b_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4b_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m40\u001b[0m) │ \u001b[38;5;34m38,440\u001b[0m │ block4b_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4b_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m39,360\u001b[0m │ block4b_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4b_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4b_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ block4b_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4b_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m153,600\u001b[0m │ block4b_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4b_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m640\u001b[0m │ block4b_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4b_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4b_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4b_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4b_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ block4a_project_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4c_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m153,600\u001b[0m │ block4b_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4c_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,840\u001b[0m │ block4c_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4c_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4c_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4c_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m8,640\u001b[0m │ block4c_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4c_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,840\u001b[0m │ block4c_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4c_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4c_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4c_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block4c_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4c_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block4c_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4c_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m40\u001b[0m) │ \u001b[38;5;34m38,440\u001b[0m │ block4c_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4c_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m39,360\u001b[0m │ block4c_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4c_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4c_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ block4c_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4c_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m153,600\u001b[0m │ block4c_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4c_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m640\u001b[0m │ block4c_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4c_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4c_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4c_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4c_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ block4b_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4d_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m153,600\u001b[0m │ block4c_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4d_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,840\u001b[0m │ block4d_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4d_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4d_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4d_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m8,640\u001b[0m │ block4d_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4d_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,840\u001b[0m │ block4d_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4d_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4d_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4d_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block4d_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4d_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block4d_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4d_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m40\u001b[0m) │ \u001b[38;5;34m38,440\u001b[0m │ block4d_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4d_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m39,360\u001b[0m │ block4d_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4d_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4d_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ block4d_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4d_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m153,600\u001b[0m │ block4d_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4d_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m640\u001b[0m │ block4d_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4d_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4d_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4d_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4d_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ block4c_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4e_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m153,600\u001b[0m │ block4d_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4e_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,840\u001b[0m │ block4e_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4e_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4e_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4e_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m8,640\u001b[0m │ block4e_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4e_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,840\u001b[0m │ block4e_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4e_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4e_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4e_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block4e_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4e_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block4e_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4e_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m40\u001b[0m) │ \u001b[38;5;34m38,440\u001b[0m │ block4e_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4e_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m39,360\u001b[0m │ block4e_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4e_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4e_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ block4e_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4e_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m153,600\u001b[0m │ block4e_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4e_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m640\u001b[0m │ block4e_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4e_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4e_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4e_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4e_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ block4d_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4f_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m153,600\u001b[0m │ block4e_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4f_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,840\u001b[0m │ block4f_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4f_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4f_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4f_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m8,640\u001b[0m │ block4f_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4f_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,840\u001b[0m │ block4f_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4f_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4f_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4f_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block4f_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4f_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block4f_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4f_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m40\u001b[0m) │ \u001b[38;5;34m38,440\u001b[0m │ block4f_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4f_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m39,360\u001b[0m │ block4f_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4f_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4f_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ block4f_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4f_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m153,600\u001b[0m │ block4f_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4f_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m640\u001b[0m │ block4f_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4f_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4f_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4f_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4f_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ block4e_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4g_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m153,600\u001b[0m │ block4f_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4g_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,840\u001b[0m │ block4g_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4g_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4g_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4g_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m8,640\u001b[0m │ block4g_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4g_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,840\u001b[0m │ block4g_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4g_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4g_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4g_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block4g_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4g_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block4g_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4g_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m40\u001b[0m) │ \u001b[38;5;34m38,440\u001b[0m │ block4g_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4g_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m39,360\u001b[0m │ block4g_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4g_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4g_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ block4g_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4g_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m153,600\u001b[0m │ block4g_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4g_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m640\u001b[0m │ block4g_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4g_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4g_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4g_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4g_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ block4f_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4h_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m153,600\u001b[0m │ block4g_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4h_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,840\u001b[0m │ block4h_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4h_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4h_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4h_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m8,640\u001b[0m │ block4h_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4h_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,840\u001b[0m │ block4h_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4h_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4h_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4h_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block4h_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4h_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block4h_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4h_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m40\u001b[0m) │ \u001b[38;5;34m38,440\u001b[0m │ block4h_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4h_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m39,360\u001b[0m │ block4h_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4h_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4h_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ block4h_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4h_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m153,600\u001b[0m │ block4h_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4h_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m640\u001b[0m │ block4h_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4h_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4h_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4h_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4h_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ block4g_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4i_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m153,600\u001b[0m │ block4h_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4i_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,840\u001b[0m │ block4i_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4i_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4i_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4i_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m8,640\u001b[0m │ block4i_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4i_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,840\u001b[0m │ block4i_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4i_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4i_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4i_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block4i_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4i_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block4i_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4i_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m40\u001b[0m) │ \u001b[38;5;34m38,440\u001b[0m │ block4i_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4i_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m39,360\u001b[0m │ block4i_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4i_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4i_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ block4i_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4i_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m153,600\u001b[0m │ block4i_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4i_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m640\u001b[0m │ block4i_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4i_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4i_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4i_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4i_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ block4h_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4j_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m153,600\u001b[0m │ block4i_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4j_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,840\u001b[0m │ block4j_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4j_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4j_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4j_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m8,640\u001b[0m │ block4j_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4j_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,840\u001b[0m │ block4j_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4j_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4j_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4j_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block4j_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4j_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block4j_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4j_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m40\u001b[0m) │ \u001b[38;5;34m38,440\u001b[0m │ block4j_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4j_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m39,360\u001b[0m │ block4j_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4j_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4j_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ block4j_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4j_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m153,600\u001b[0m │ block4j_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4j_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m640\u001b[0m │ block4j_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4j_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4j_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m160\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4j_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block4j_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m160\u001b[0m) │ │ block4i_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5a_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m153,600\u001b[0m │ block4j_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5a_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,840\u001b[0m │ block5a_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5a_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5a_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5a_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m24,000\u001b[0m │ block5a_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5a_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m3,840\u001b[0m │ block5a_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5a_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5a_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5a_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block5a_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5a_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block5a_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5a_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m40\u001b[0m) │ \u001b[38;5;34m38,440\u001b[0m │ block5a_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5a_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m960\u001b[0m) │ \u001b[38;5;34m39,360\u001b[0m │ block5a_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5a_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5a_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m960\u001b[0m) │ │ block5a_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5a_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m215,040\u001b[0m │ block5a_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5a_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m896\u001b[0m │ block5a_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5b_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m301,056\u001b[0m │ block5a_project_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5b_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m5,376\u001b[0m │ block5b_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5b_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5b_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5b_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m33,600\u001b[0m │ block5b_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5b_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m5,376\u001b[0m │ block5b_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5b_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5b_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5b_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1344\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block5b_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5b_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5b_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5b_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m56\u001b[0m) │ \u001b[38;5;34m75,320\u001b[0m │ block5b_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5b_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m76,608\u001b[0m │ block5b_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5b_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5b_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ block5b_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5b_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m301,056\u001b[0m │ block5b_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5b_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m896\u001b[0m │ block5b_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5b_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5b_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5b_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5b_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m224\u001b[0m) │ │ block5a_project_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5c_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m301,056\u001b[0m │ block5b_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5c_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m5,376\u001b[0m │ block5c_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5c_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5c_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5c_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m33,600\u001b[0m │ block5c_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5c_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m5,376\u001b[0m │ block5c_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5c_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5c_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5c_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1344\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block5c_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5c_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5c_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5c_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m56\u001b[0m) │ \u001b[38;5;34m75,320\u001b[0m │ block5c_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5c_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m76,608\u001b[0m │ block5c_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5c_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5c_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ block5c_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5c_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m301,056\u001b[0m │ block5c_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5c_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m896\u001b[0m │ block5c_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5c_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5c_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5c_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5c_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m224\u001b[0m) │ │ block5b_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5d_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m301,056\u001b[0m │ block5c_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5d_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m5,376\u001b[0m │ block5d_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5d_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5d_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5d_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m33,600\u001b[0m │ block5d_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5d_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m5,376\u001b[0m │ block5d_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5d_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5d_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5d_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1344\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block5d_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5d_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5d_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5d_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m56\u001b[0m) │ \u001b[38;5;34m75,320\u001b[0m │ block5d_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5d_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m76,608\u001b[0m │ block5d_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5d_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5d_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ block5d_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5d_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m301,056\u001b[0m │ block5d_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5d_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m896\u001b[0m │ block5d_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5d_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5d_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5d_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5d_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m224\u001b[0m) │ │ block5c_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5e_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m301,056\u001b[0m │ block5d_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5e_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m5,376\u001b[0m │ block5e_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5e_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5e_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5e_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m33,600\u001b[0m │ block5e_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5e_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m5,376\u001b[0m │ block5e_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5e_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5e_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5e_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1344\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block5e_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5e_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5e_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5e_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m56\u001b[0m) │ \u001b[38;5;34m75,320\u001b[0m │ block5e_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5e_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m76,608\u001b[0m │ block5e_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5e_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5e_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ block5e_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5e_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m301,056\u001b[0m │ block5e_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5e_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m896\u001b[0m │ block5e_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5e_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5e_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5e_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5e_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m224\u001b[0m) │ │ block5d_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5f_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m301,056\u001b[0m │ block5e_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5f_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m5,376\u001b[0m │ block5f_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5f_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5f_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5f_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m33,600\u001b[0m │ block5f_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5f_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m5,376\u001b[0m │ block5f_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5f_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5f_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5f_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1344\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block5f_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5f_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5f_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5f_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m56\u001b[0m) │ \u001b[38;5;34m75,320\u001b[0m │ block5f_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5f_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m76,608\u001b[0m │ block5f_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5f_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5f_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ block5f_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5f_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m301,056\u001b[0m │ block5f_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5f_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m896\u001b[0m │ block5f_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5f_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5f_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5f_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5f_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m224\u001b[0m) │ │ block5e_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5g_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m301,056\u001b[0m │ block5f_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5g_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m5,376\u001b[0m │ block5g_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5g_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5g_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5g_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m33,600\u001b[0m │ block5g_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5g_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m5,376\u001b[0m │ block5g_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5g_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5g_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5g_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1344\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block5g_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5g_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5g_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5g_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m56\u001b[0m) │ \u001b[38;5;34m75,320\u001b[0m │ block5g_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5g_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m76,608\u001b[0m │ block5g_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5g_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5g_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ block5g_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5g_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m301,056\u001b[0m │ block5g_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5g_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m896\u001b[0m │ block5g_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5g_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5g_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5g_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5g_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m224\u001b[0m) │ │ block5f_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5h_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m301,056\u001b[0m │ block5g_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5h_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m5,376\u001b[0m │ block5h_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5h_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5h_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5h_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m33,600\u001b[0m │ block5h_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5h_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m5,376\u001b[0m │ block5h_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5h_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5h_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5h_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1344\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block5h_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5h_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5h_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5h_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m56\u001b[0m) │ \u001b[38;5;34m75,320\u001b[0m │ block5h_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5h_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m76,608\u001b[0m │ block5h_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5h_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5h_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ block5h_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5h_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m301,056\u001b[0m │ block5h_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5h_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m896\u001b[0m │ block5h_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5h_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5h_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5h_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5h_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m224\u001b[0m) │ │ block5g_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5i_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m301,056\u001b[0m │ block5h_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5i_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m5,376\u001b[0m │ block5i_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5i_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5i_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5i_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m33,600\u001b[0m │ block5i_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5i_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m5,376\u001b[0m │ block5i_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5i_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5i_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5i_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1344\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block5i_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5i_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5i_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5i_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m56\u001b[0m) │ \u001b[38;5;34m75,320\u001b[0m │ block5i_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5i_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m76,608\u001b[0m │ block5i_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5i_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5i_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ block5i_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5i_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m301,056\u001b[0m │ block5i_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5i_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m896\u001b[0m │ block5i_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5i_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5i_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5i_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5i_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m224\u001b[0m) │ │ block5h_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5j_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m301,056\u001b[0m │ block5i_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5j_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m5,376\u001b[0m │ block5j_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5j_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5j_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5j_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m33,600\u001b[0m │ block5j_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5j_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m5,376\u001b[0m │ block5j_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5j_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5j_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5j_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1344\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block5j_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5j_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5j_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5j_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m56\u001b[0m) │ \u001b[38;5;34m75,320\u001b[0m │ block5j_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5j_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m76,608\u001b[0m │ block5j_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5j_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5j_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ block5j_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5j_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m301,056\u001b[0m │ block5j_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5j_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m896\u001b[0m │ block5j_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5j_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5j_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ \u001b[38;5;34m224\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5j_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block5j_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ \u001b[38;5;34m224\u001b[0m) │ │ block5i_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6a_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m301,056\u001b[0m │ block5j_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6a_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m5,376\u001b[0m │ block6a_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6a_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6a_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6a_dwconv_pad │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m17\u001b[0m, \u001b[38;5;34m17\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6a_expand_a… │\n│ (\u001b[38;5;33mZeroPadding2D\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6a_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m33,600\u001b[0m │ block6a_dwconv_p… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6a_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m5,376\u001b[0m │ block6a_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6a_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6a_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6a_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1344\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6a_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6a_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6a_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6a_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m56\u001b[0m) │ \u001b[38;5;34m75,320\u001b[0m │ block6a_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6a_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m76,608\u001b[0m │ block6a_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6a_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6a_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m1344\u001b[0m) │ │ block6a_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6a_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m516,096\u001b[0m │ block6a_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6a_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,536\u001b[0m │ block6a_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6b_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m884,736\u001b[0m │ block6a_project_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6b_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ block6b_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6b_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6b_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6b_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m57,600\u001b[0m │ block6b_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6b_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ block6b_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6b_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6b_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6b_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2304\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6b_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6b_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6b_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6b_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m96\u001b[0m) │ \u001b[38;5;34m221,280\u001b[0m │ block6b_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6b_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m223,488\u001b[0m │ block6b_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6b_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6b_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ block6b_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6b_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m884,736\u001b[0m │ block6b_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6b_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,536\u001b[0m │ block6b_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6b_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6b_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6b_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6b_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ │ │ block6a_project_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6c_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m884,736\u001b[0m │ block6b_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6c_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ block6c_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6c_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6c_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6c_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m57,600\u001b[0m │ block6c_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6c_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ block6c_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6c_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6c_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6c_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2304\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6c_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6c_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6c_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6c_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m96\u001b[0m) │ \u001b[38;5;34m221,280\u001b[0m │ block6c_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6c_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m223,488\u001b[0m │ block6c_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6c_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6c_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ block6c_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6c_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m884,736\u001b[0m │ block6c_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6c_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,536\u001b[0m │ block6c_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6c_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6c_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6c_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6c_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ │ │ block6b_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6d_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m884,736\u001b[0m │ block6c_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6d_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ block6d_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6d_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6d_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6d_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m57,600\u001b[0m │ block6d_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6d_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ block6d_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6d_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6d_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6d_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2304\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6d_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6d_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6d_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6d_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m96\u001b[0m) │ \u001b[38;5;34m221,280\u001b[0m │ block6d_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6d_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m223,488\u001b[0m │ block6d_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6d_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6d_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ block6d_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6d_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m884,736\u001b[0m │ block6d_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6d_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,536\u001b[0m │ block6d_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6d_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6d_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6d_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6d_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ │ │ block6c_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6e_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m884,736\u001b[0m │ block6d_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6e_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ block6e_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6e_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6e_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6e_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m57,600\u001b[0m │ block6e_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6e_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ block6e_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6e_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6e_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6e_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2304\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6e_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6e_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6e_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6e_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m96\u001b[0m) │ \u001b[38;5;34m221,280\u001b[0m │ block6e_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6e_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m223,488\u001b[0m │ block6e_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6e_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6e_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ block6e_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6e_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m884,736\u001b[0m │ block6e_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6e_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,536\u001b[0m │ block6e_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6e_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6e_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6e_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6e_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ │ │ block6d_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6f_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m884,736\u001b[0m │ block6e_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6f_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ block6f_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6f_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6f_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6f_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m57,600\u001b[0m │ block6f_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6f_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ block6f_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6f_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6f_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6f_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2304\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6f_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6f_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6f_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6f_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m96\u001b[0m) │ \u001b[38;5;34m221,280\u001b[0m │ block6f_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6f_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m223,488\u001b[0m │ block6f_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6f_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6f_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ block6f_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6f_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m884,736\u001b[0m │ block6f_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6f_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,536\u001b[0m │ block6f_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6f_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6f_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6f_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6f_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ │ │ block6e_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6g_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m884,736\u001b[0m │ block6f_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6g_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ block6g_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6g_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6g_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6g_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m57,600\u001b[0m │ block6g_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6g_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ block6g_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6g_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6g_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6g_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2304\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6g_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6g_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6g_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6g_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m96\u001b[0m) │ \u001b[38;5;34m221,280\u001b[0m │ block6g_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6g_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m223,488\u001b[0m │ block6g_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6g_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6g_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ block6g_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6g_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m884,736\u001b[0m │ block6g_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6g_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,536\u001b[0m │ block6g_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6g_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6g_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6g_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6g_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ │ │ block6f_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6h_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m884,736\u001b[0m │ block6g_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6h_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ block6h_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6h_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6h_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6h_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m57,600\u001b[0m │ block6h_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6h_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ block6h_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6h_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6h_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6h_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2304\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6h_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6h_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6h_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6h_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m96\u001b[0m) │ \u001b[38;5;34m221,280\u001b[0m │ block6h_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6h_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m223,488\u001b[0m │ block6h_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6h_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6h_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ block6h_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6h_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m884,736\u001b[0m │ block6h_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6h_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,536\u001b[0m │ block6h_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6h_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6h_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6h_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6h_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ │ │ block6g_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6i_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m884,736\u001b[0m │ block6h_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6i_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ block6i_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6i_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6i_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6i_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m57,600\u001b[0m │ block6i_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6i_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ block6i_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6i_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6i_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6i_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2304\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6i_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6i_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6i_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6i_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m96\u001b[0m) │ \u001b[38;5;34m221,280\u001b[0m │ block6i_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6i_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m223,488\u001b[0m │ block6i_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6i_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6i_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ block6i_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6i_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m884,736\u001b[0m │ block6i_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6i_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,536\u001b[0m │ block6i_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6i_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6i_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6i_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6i_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ │ │ block6h_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6j_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m884,736\u001b[0m │ block6i_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6j_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ block6j_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6j_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6j_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6j_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m57,600\u001b[0m │ block6j_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6j_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ block6j_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6j_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6j_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6j_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2304\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6j_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6j_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6j_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6j_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m96\u001b[0m) │ \u001b[38;5;34m221,280\u001b[0m │ block6j_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6j_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m223,488\u001b[0m │ block6j_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6j_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6j_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ block6j_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6j_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m884,736\u001b[0m │ block6j_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6j_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,536\u001b[0m │ block6j_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6j_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6j_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6j_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6j_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ │ │ block6i_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6k_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m884,736\u001b[0m │ block6j_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6k_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ block6k_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6k_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6k_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6k_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m57,600\u001b[0m │ block6k_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6k_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ block6k_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6k_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6k_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6k_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2304\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6k_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6k_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6k_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6k_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m96\u001b[0m) │ \u001b[38;5;34m221,280\u001b[0m │ block6k_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6k_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m223,488\u001b[0m │ block6k_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6k_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6k_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ block6k_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6k_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m884,736\u001b[0m │ block6k_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6k_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,536\u001b[0m │ block6k_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6k_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6k_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6k_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6k_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ │ │ block6j_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6l_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m884,736\u001b[0m │ block6k_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6l_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ block6l_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6l_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6l_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6l_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m57,600\u001b[0m │ block6l_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6l_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ block6l_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6l_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6l_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6l_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2304\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6l_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6l_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6l_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6l_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m96\u001b[0m) │ \u001b[38;5;34m221,280\u001b[0m │ block6l_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6l_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m223,488\u001b[0m │ block6l_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6l_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6l_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ block6l_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6l_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m884,736\u001b[0m │ block6l_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6l_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,536\u001b[0m │ block6l_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6l_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6l_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6l_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6l_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ │ │ block6k_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6m_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m884,736\u001b[0m │ block6l_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6m_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ block6m_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6m_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6m_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6m_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m57,600\u001b[0m │ block6m_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6m_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ block6m_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6m_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6m_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6m_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2304\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6m_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6m_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6m_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6m_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m96\u001b[0m) │ \u001b[38;5;34m221,280\u001b[0m │ block6m_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6m_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m223,488\u001b[0m │ block6m_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6m_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block6m_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ block6m_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6m_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m884,736\u001b[0m │ block6m_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6m_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m1,536\u001b[0m │ block6m_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6m_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6m_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6m_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m384\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block6m_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ │ │ block6l_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7a_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m884,736\u001b[0m │ block6m_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7a_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ block7a_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7a_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block7a_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7a_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m20,736\u001b[0m │ block7a_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7a_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m9,216\u001b[0m │ block7a_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7a_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block7a_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7a_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2304\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block7a_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7a_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block7a_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7a_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m96\u001b[0m) │ \u001b[38;5;34m221,280\u001b[0m │ block7a_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7a_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m223,488\u001b[0m │ block7a_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7a_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block7a_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m2304\u001b[0m) │ │ block7a_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7a_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m640\u001b[0m) │ \u001b[38;5;34m1,474,560\u001b[0m │ block7a_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7a_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m640\u001b[0m) │ \u001b[38;5;34m2,560\u001b[0m │ block7a_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7b_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m2,457,600\u001b[0m │ block7a_project_… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m3840\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7b_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m15,360\u001b[0m │ block7b_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m3840\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7b_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block7b_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m3840\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7b_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m34,560\u001b[0m │ block7b_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m3840\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7b_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m15,360\u001b[0m │ block7b_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m3840\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7b_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block7b_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m3840\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7b_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3840\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block7b_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7b_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block7b_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ \u001b[38;5;34m3840\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7b_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m614,560\u001b[0m │ block7b_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7b_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m618,240\u001b[0m │ block7b_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m3840\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7b_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block7b_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m3840\u001b[0m) │ │ block7b_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7b_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m640\u001b[0m) │ \u001b[38;5;34m2,457,600\u001b[0m │ block7b_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7b_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m640\u001b[0m) │ \u001b[38;5;34m2,560\u001b[0m │ block7b_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7b_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m640\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block7b_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7b_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m640\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block7b_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ │ │ block7a_project_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7c_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m2,457,600\u001b[0m │ block7b_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m3840\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7c_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m15,360\u001b[0m │ block7c_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m3840\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7c_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block7c_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m3840\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7c_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m34,560\u001b[0m │ block7c_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m3840\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7c_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m15,360\u001b[0m │ block7c_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m3840\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7c_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block7c_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m3840\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7c_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3840\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block7c_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7c_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block7c_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ \u001b[38;5;34m3840\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7c_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m614,560\u001b[0m │ block7c_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7c_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m618,240\u001b[0m │ block7c_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m3840\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7c_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block7c_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m3840\u001b[0m) │ │ block7c_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7c_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m640\u001b[0m) │ \u001b[38;5;34m2,457,600\u001b[0m │ block7c_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7c_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m640\u001b[0m) │ \u001b[38;5;34m2,560\u001b[0m │ block7c_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7c_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m640\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block7c_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7c_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m640\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block7c_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ │ │ block7b_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7d_expand_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m2,457,600\u001b[0m │ block7c_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m3840\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7d_expand_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m15,360\u001b[0m │ block7d_expand_c… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m3840\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7d_expand_act… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block7d_expand_b… │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m3840\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7d_dwconv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m34,560\u001b[0m │ block7d_expand_a… │\n│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m3840\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7d_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m15,360\u001b[0m │ block7d_dwconv[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m3840\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7d_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block7d_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m3840\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7d_se_squeeze │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3840\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block7d_activati… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7d_se_reshape │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block7d_se_squee… │\n│ (\u001b[38;5;33mReshape\u001b[0m) │ \u001b[38;5;34m3840\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7d_se_reduce │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m160\u001b[0m) │ \u001b[38;5;34m614,560\u001b[0m │ block7d_se_resha… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7d_se_expand │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, │ \u001b[38;5;34m618,240\u001b[0m │ block7d_se_reduc… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m3840\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7d_se_excite │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ block7d_activati… │\n│ (\u001b[38;5;33mMultiply\u001b[0m) │ \u001b[38;5;34m3840\u001b[0m) │ │ block7d_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7d_project_co… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m640\u001b[0m) │ \u001b[38;5;34m2,457,600\u001b[0m │ block7d_se_excit… │\n│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7d_project_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m640\u001b[0m) │ \u001b[38;5;34m2,560\u001b[0m │ block7d_project_… │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7d_drop │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m640\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block7d_project_… │\n│ (\u001b[38;5;33mDropout\u001b[0m) │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7d_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m640\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ block7d_drop[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n│ │ │ │ block7c_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ top_conv (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m1,638,400\u001b[0m │ block7d_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ │ \u001b[38;5;34m2560\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ top_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m10,240\u001b[0m │ top_conv[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2560\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ top_activation │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ top_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m2560\u001b[0m) │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ global_average_poo… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2560\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ top_activation[\u001b[38;5;34m0\u001b[0m… │\n│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │ │ │ │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense_6 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m2,622,464\u001b[0m │ global_average_p… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense_7 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m1,025\u001b[0m │ dense_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n","text/html":"
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n┃ Layer (type)         Output Shape          Param #  Connected to      ┃\n┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n│ input_layer_3       │ (None, 224, 224,  │          0 │ -                 │\n│ (InputLayer)        │ 3)                │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ rescaling_1         │ (None, 224, 224,  │          0 │ input_layer_3[0]… │\n│ (Rescaling)         │ 3)                │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ normalization       │ (None, 224, 224,  │          7 │ rescaling_1[0][0] │\n│ (Normalization)     │ 3)                │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ rescaling_2         │ (None, 224, 224,  │          0 │ normalization[0]… │\n│ (Rescaling)         │ 3)                │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ stem_conv_pad       │ (None, 225, 225,  │          0 │ rescaling_2[0][0] │\n│ (ZeroPadding2D)     │ 3)                │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ stem_conv (Conv2D)  │ (None, 112, 112,  │      1,728 │ stem_conv_pad[0]… │\n│                     │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ stem_bn             │ (None, 112, 112,  │        256 │ stem_conv[0][0]   │\n│ (BatchNormalizatio…64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ stem_activation     │ (None, 112, 112,  │          0 │ stem_bn[0][0]     │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1a_dwconv      │ (None, 112, 112,  │        576 │ stem_activation[ │\n│ (DepthwiseConv2D)   │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1a_bn          │ (None, 112, 112,  │        256 │ block1a_dwconv[0… │\n│ (BatchNormalizatio…64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1a_activation  │ (None, 112, 112,  │          0 │ block1a_bn[0][0]  │\n│ (Activation)        │ 64)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1a_se_squeeze  │ (None, 64)        │          0 │ block1a_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1a_se_reshape  │ (None, 1, 1, 64)  │          0 │ block1a_se_squee… │\n│ (Reshape)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1a_se_reduce   │ (None, 1, 1, 16)  │      1,040 │ block1a_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1a_se_expand   │ (None, 1, 1, 64)  │      1,088 │ block1a_se_reduc… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1a_se_excite   │ (None, 112, 112,  │          0 │ block1a_activati… │\n│ (Multiply)          │ 64)               │            │ block1a_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1a_project_co… │ (None, 112, 112,  │      2,048 │ block1a_se_excit… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1a_project_bn  │ (None, 112, 112,  │        128 │ block1a_project_… │\n│ (BatchNormalizatio…32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1b_dwconv      │ (None, 112, 112,  │        288 │ block1a_project_… │\n│ (DepthwiseConv2D)   │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1b_bn          │ (None, 112, 112,  │        128 │ block1b_dwconv[0… │\n│ (BatchNormalizatio…32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1b_activation  │ (None, 112, 112,  │          0 │ block1b_bn[0][0]  │\n│ (Activation)        │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1b_se_squeeze  │ (None, 32)        │          0 │ block1b_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1b_se_reshape  │ (None, 1, 1, 32)  │          0 │ block1b_se_squee… │\n│ (Reshape)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1b_se_reduce   │ (None, 1, 1, 8)   │        264 │ block1b_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1b_se_expand   │ (None, 1, 1, 32)  │        288 │ block1b_se_reduc… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1b_se_excite   │ (None, 112, 112,  │          0 │ block1b_activati… │\n│ (Multiply)          │ 32)               │            │ block1b_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1b_project_co… │ (None, 112, 112,  │      1,024 │ block1b_se_excit… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1b_project_bn  │ (None, 112, 112,  │        128 │ block1b_project_… │\n│ (BatchNormalizatio…32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1b_drop        │ (None, 112, 112,  │          0 │ block1b_project_… │\n│ (Dropout)           │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1b_add (Add)   │ (None, 112, 112,  │          0 │ block1b_drop[0][ │\n│                     │ 32)               │            │ block1a_project_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1c_dwconv      │ (None, 112, 112,  │        288 │ block1b_add[0][0] │\n│ (DepthwiseConv2D)   │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1c_bn          │ (None, 112, 112,  │        128 │ block1c_dwconv[0… │\n│ (BatchNormalizatio…32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1c_activation  │ (None, 112, 112,  │          0 │ block1c_bn[0][0]  │\n│ (Activation)        │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1c_se_squeeze  │ (None, 32)        │          0 │ block1c_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1c_se_reshape  │ (None, 1, 1, 32)  │          0 │ block1c_se_squee… │\n│ (Reshape)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1c_se_reduce   │ (None, 1, 1, 8)   │        264 │ block1c_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1c_se_expand   │ (None, 1, 1, 32)  │        288 │ block1c_se_reduc… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1c_se_excite   │ (None, 112, 112,  │          0 │ block1c_activati… │\n│ (Multiply)          │ 32)               │            │ block1c_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1c_project_co… │ (None, 112, 112,  │      1,024 │ block1c_se_excit… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1c_project_bn  │ (None, 112, 112,  │        128 │ block1c_project_… │\n│ (BatchNormalizatio…32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1c_drop        │ (None, 112, 112,  │          0 │ block1c_project_… │\n│ (Dropout)           │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1c_add (Add)   │ (None, 112, 112,  │          0 │ block1c_drop[0][ │\n│                     │ 32)               │            │ block1b_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1d_dwconv      │ (None, 112, 112,  │        288 │ block1c_add[0][0] │\n│ (DepthwiseConv2D)   │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1d_bn          │ (None, 112, 112,  │        128 │ block1d_dwconv[0… │\n│ (BatchNormalizatio…32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1d_activation  │ (None, 112, 112,  │          0 │ block1d_bn[0][0]  │\n│ (Activation)        │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1d_se_squeeze  │ (None, 32)        │          0 │ block1d_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1d_se_reshape  │ (None, 1, 1, 32)  │          0 │ block1d_se_squee… │\n│ (Reshape)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1d_se_reduce   │ (None, 1, 1, 8)   │        264 │ block1d_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1d_se_expand   │ (None, 1, 1, 32)  │        288 │ block1d_se_reduc… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1d_se_excite   │ (None, 112, 112,  │          0 │ block1d_activati… │\n│ (Multiply)          │ 32)               │            │ block1d_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1d_project_co… │ (None, 112, 112,  │      1,024 │ block1d_se_excit… │\n│ (Conv2D)            │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1d_project_bn  │ (None, 112, 112,  │        128 │ block1d_project_… │\n│ (BatchNormalizatio…32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1d_drop        │ (None, 112, 112,  │          0 │ block1d_project_… │\n│ (Dropout)           │ 32)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block1d_add (Add)   │ (None, 112, 112,  │          0 │ block1d_drop[0][ │\n│                     │ 32)               │            │ block1c_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2a_expand_conv │ (None, 112, 112,  │      6,144 │ block1d_add[0][0] │\n│ (Conv2D)            │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2a_expand_bn   │ (None, 112, 112,  │        768 │ block2a_expand_c… │\n│ (BatchNormalizatio…192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2a_expand_act… │ (None, 112, 112,  │          0 │ block2a_expand_b… │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2a_dwconv_pad  │ (None, 113, 113,  │          0 │ block2a_expand_a… │\n│ (ZeroPadding2D)     │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2a_dwconv      │ (None, 56, 56,    │      1,728 │ block2a_dwconv_p… │\n│ (DepthwiseConv2D)   │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2a_bn          │ (None, 56, 56,    │        768 │ block2a_dwconv[0… │\n│ (BatchNormalizatio…192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2a_activation  │ (None, 56, 56,    │          0 │ block2a_bn[0][0]  │\n│ (Activation)        │ 192)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2a_se_squeeze  │ (None, 192)       │          0 │ block2a_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2a_se_reshape  │ (None, 1, 1, 192) │          0 │ block2a_se_squee… │\n│ (Reshape)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2a_se_reduce   │ (None, 1, 1, 8)   │      1,544 │ block2a_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2a_se_expand   │ (None, 1, 1, 192) │      1,728 │ block2a_se_reduc… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2a_se_excite   │ (None, 56, 56,    │          0 │ block2a_activati… │\n│ (Multiply)          │ 192)              │            │ block2a_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2a_project_co… │ (None, 56, 56,    │      9,216 │ block2a_se_excit… │\n│ (Conv2D)            │ 48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2a_project_bn  │ (None, 56, 56,    │        192 │ block2a_project_… │\n│ (BatchNormalizatio…48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2b_expand_conv │ (None, 56, 56,    │     13,824 │ block2a_project_… │\n│ (Conv2D)            │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2b_expand_bn   │ (None, 56, 56,    │      1,152 │ block2b_expand_c… │\n│ (BatchNormalizatio…288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2b_expand_act… │ (None, 56, 56,    │          0 │ block2b_expand_b… │\n│ (Activation)        │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2b_dwconv      │ (None, 56, 56,    │      2,592 │ block2b_expand_a… │\n│ (DepthwiseConv2D)   │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2b_bn          │ (None, 56, 56,    │      1,152 │ block2b_dwconv[0… │\n│ (BatchNormalizatio…288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2b_activation  │ (None, 56, 56,    │          0 │ block2b_bn[0][0]  │\n│ (Activation)        │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2b_se_squeeze  │ (None, 288)       │          0 │ block2b_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2b_se_reshape  │ (None, 1, 1, 288) │          0 │ block2b_se_squee… │\n│ (Reshape)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2b_se_reduce   │ (None, 1, 1, 12)  │      3,468 │ block2b_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2b_se_expand   │ (None, 1, 1, 288) │      3,744 │ block2b_se_reduc… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2b_se_excite   │ (None, 56, 56,    │          0 │ block2b_activati… │\n│ (Multiply)          │ 288)              │            │ block2b_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2b_project_co… │ (None, 56, 56,    │     13,824 │ block2b_se_excit… │\n│ (Conv2D)            │ 48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2b_project_bn  │ (None, 56, 56,    │        192 │ block2b_project_… │\n│ (BatchNormalizatio…48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2b_drop        │ (None, 56, 56,    │          0 │ block2b_project_… │\n│ (Dropout)           │ 48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2b_add (Add)   │ (None, 56, 56,    │          0 │ block2b_drop[0][ │\n│                     │ 48)               │            │ block2a_project_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2c_expand_conv │ (None, 56, 56,    │     13,824 │ block2b_add[0][0] │\n│ (Conv2D)            │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2c_expand_bn   │ (None, 56, 56,    │      1,152 │ block2c_expand_c… │\n│ (BatchNormalizatio…288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2c_expand_act… │ (None, 56, 56,    │          0 │ block2c_expand_b… │\n│ (Activation)        │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2c_dwconv      │ (None, 56, 56,    │      2,592 │ block2c_expand_a… │\n│ (DepthwiseConv2D)   │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2c_bn          │ (None, 56, 56,    │      1,152 │ block2c_dwconv[0… │\n│ (BatchNormalizatio…288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2c_activation  │ (None, 56, 56,    │          0 │ block2c_bn[0][0]  │\n│ (Activation)        │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2c_se_squeeze  │ (None, 288)       │          0 │ block2c_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2c_se_reshape  │ (None, 1, 1, 288) │          0 │ block2c_se_squee… │\n│ (Reshape)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2c_se_reduce   │ (None, 1, 1, 12)  │      3,468 │ block2c_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2c_se_expand   │ (None, 1, 1, 288) │      3,744 │ block2c_se_reduc… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2c_se_excite   │ (None, 56, 56,    │          0 │ block2c_activati… │\n│ (Multiply)          │ 288)              │            │ block2c_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2c_project_co… │ (None, 56, 56,    │     13,824 │ block2c_se_excit… │\n│ (Conv2D)            │ 48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2c_project_bn  │ (None, 56, 56,    │        192 │ block2c_project_… │\n│ (BatchNormalizatio…48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2c_drop        │ (None, 56, 56,    │          0 │ block2c_project_… │\n│ (Dropout)           │ 48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2c_add (Add)   │ (None, 56, 56,    │          0 │ block2c_drop[0][ │\n│                     │ 48)               │            │ block2b_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2d_expand_conv │ (None, 56, 56,    │     13,824 │ block2c_add[0][0] │\n│ (Conv2D)            │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2d_expand_bn   │ (None, 56, 56,    │      1,152 │ block2d_expand_c… │\n│ (BatchNormalizatio…288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2d_expand_act… │ (None, 56, 56,    │          0 │ block2d_expand_b… │\n│ (Activation)        │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2d_dwconv      │ (None, 56, 56,    │      2,592 │ block2d_expand_a… │\n│ (DepthwiseConv2D)   │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2d_bn          │ (None, 56, 56,    │      1,152 │ block2d_dwconv[0… │\n│ (BatchNormalizatio…288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2d_activation  │ (None, 56, 56,    │          0 │ block2d_bn[0][0]  │\n│ (Activation)        │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2d_se_squeeze  │ (None, 288)       │          0 │ block2d_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2d_se_reshape  │ (None, 1, 1, 288) │          0 │ block2d_se_squee… │\n│ (Reshape)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2d_se_reduce   │ (None, 1, 1, 12)  │      3,468 │ block2d_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2d_se_expand   │ (None, 1, 1, 288) │      3,744 │ block2d_se_reduc… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2d_se_excite   │ (None, 56, 56,    │          0 │ block2d_activati… │\n│ (Multiply)          │ 288)              │            │ block2d_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2d_project_co… │ (None, 56, 56,    │     13,824 │ block2d_se_excit… │\n│ (Conv2D)            │ 48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2d_project_bn  │ (None, 56, 56,    │        192 │ block2d_project_… │\n│ (BatchNormalizatio…48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2d_drop        │ (None, 56, 56,    │          0 │ block2d_project_… │\n│ (Dropout)           │ 48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2d_add (Add)   │ (None, 56, 56,    │          0 │ block2d_drop[0][ │\n│                     │ 48)               │            │ block2c_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2e_expand_conv │ (None, 56, 56,    │     13,824 │ block2d_add[0][0] │\n│ (Conv2D)            │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2e_expand_bn   │ (None, 56, 56,    │      1,152 │ block2e_expand_c… │\n│ (BatchNormalizatio…288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2e_expand_act… │ (None, 56, 56,    │          0 │ block2e_expand_b… │\n│ (Activation)        │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2e_dwconv      │ (None, 56, 56,    │      2,592 │ block2e_expand_a… │\n│ (DepthwiseConv2D)   │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2e_bn          │ (None, 56, 56,    │      1,152 │ block2e_dwconv[0… │\n│ (BatchNormalizatio…288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2e_activation  │ (None, 56, 56,    │          0 │ block2e_bn[0][0]  │\n│ (Activation)        │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2e_se_squeeze  │ (None, 288)       │          0 │ block2e_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2e_se_reshape  │ (None, 1, 1, 288) │          0 │ block2e_se_squee… │\n│ (Reshape)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2e_se_reduce   │ (None, 1, 1, 12)  │      3,468 │ block2e_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2e_se_expand   │ (None, 1, 1, 288) │      3,744 │ block2e_se_reduc… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2e_se_excite   │ (None, 56, 56,    │          0 │ block2e_activati… │\n│ (Multiply)          │ 288)              │            │ block2e_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2e_project_co… │ (None, 56, 56,    │     13,824 │ block2e_se_excit… │\n│ (Conv2D)            │ 48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2e_project_bn  │ (None, 56, 56,    │        192 │ block2e_project_… │\n│ (BatchNormalizatio…48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2e_drop        │ (None, 56, 56,    │          0 │ block2e_project_… │\n│ (Dropout)           │ 48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2e_add (Add)   │ (None, 56, 56,    │          0 │ block2e_drop[0][ │\n│                     │ 48)               │            │ block2d_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2f_expand_conv │ (None, 56, 56,    │     13,824 │ block2e_add[0][0] │\n│ (Conv2D)            │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2f_expand_bn   │ (None, 56, 56,    │      1,152 │ block2f_expand_c… │\n│ (BatchNormalizatio…288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2f_expand_act… │ (None, 56, 56,    │          0 │ block2f_expand_b… │\n│ (Activation)        │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2f_dwconv      │ (None, 56, 56,    │      2,592 │ block2f_expand_a… │\n│ (DepthwiseConv2D)   │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2f_bn          │ (None, 56, 56,    │      1,152 │ block2f_dwconv[0… │\n│ (BatchNormalizatio…288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2f_activation  │ (None, 56, 56,    │          0 │ block2f_bn[0][0]  │\n│ (Activation)        │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2f_se_squeeze  │ (None, 288)       │          0 │ block2f_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2f_se_reshape  │ (None, 1, 1, 288) │          0 │ block2f_se_squee… │\n│ (Reshape)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2f_se_reduce   │ (None, 1, 1, 12)  │      3,468 │ block2f_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2f_se_expand   │ (None, 1, 1, 288) │      3,744 │ block2f_se_reduc… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2f_se_excite   │ (None, 56, 56,    │          0 │ block2f_activati… │\n│ (Multiply)          │ 288)              │            │ block2f_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2f_project_co… │ (None, 56, 56,    │     13,824 │ block2f_se_excit… │\n│ (Conv2D)            │ 48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2f_project_bn  │ (None, 56, 56,    │        192 │ block2f_project_… │\n│ (BatchNormalizatio…48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2f_drop        │ (None, 56, 56,    │          0 │ block2f_project_… │\n│ (Dropout)           │ 48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2f_add (Add)   │ (None, 56, 56,    │          0 │ block2f_drop[0][ │\n│                     │ 48)               │            │ block2e_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2g_expand_conv │ (None, 56, 56,    │     13,824 │ block2f_add[0][0] │\n│ (Conv2D)            │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2g_expand_bn   │ (None, 56, 56,    │      1,152 │ block2g_expand_c… │\n│ (BatchNormalizatio…288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2g_expand_act… │ (None, 56, 56,    │          0 │ block2g_expand_b… │\n│ (Activation)        │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2g_dwconv      │ (None, 56, 56,    │      2,592 │ block2g_expand_a… │\n│ (DepthwiseConv2D)   │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2g_bn          │ (None, 56, 56,    │      1,152 │ block2g_dwconv[0… │\n│ (BatchNormalizatio…288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2g_activation  │ (None, 56, 56,    │          0 │ block2g_bn[0][0]  │\n│ (Activation)        │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2g_se_squeeze  │ (None, 288)       │          0 │ block2g_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2g_se_reshape  │ (None, 1, 1, 288) │          0 │ block2g_se_squee… │\n│ (Reshape)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2g_se_reduce   │ (None, 1, 1, 12)  │      3,468 │ block2g_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2g_se_expand   │ (None, 1, 1, 288) │      3,744 │ block2g_se_reduc… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2g_se_excite   │ (None, 56, 56,    │          0 │ block2g_activati… │\n│ (Multiply)          │ 288)              │            │ block2g_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2g_project_co… │ (None, 56, 56,    │     13,824 │ block2g_se_excit… │\n│ (Conv2D)            │ 48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2g_project_bn  │ (None, 56, 56,    │        192 │ block2g_project_… │\n│ (BatchNormalizatio…48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2g_drop        │ (None, 56, 56,    │          0 │ block2g_project_… │\n│ (Dropout)           │ 48)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block2g_add (Add)   │ (None, 56, 56,    │          0 │ block2g_drop[0][ │\n│                     │ 48)               │            │ block2f_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3a_expand_conv │ (None, 56, 56,    │     13,824 │ block2g_add[0][0] │\n│ (Conv2D)            │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3a_expand_bn   │ (None, 56, 56,    │      1,152 │ block3a_expand_c… │\n│ (BatchNormalizatio…288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3a_expand_act… │ (None, 56, 56,    │          0 │ block3a_expand_b… │\n│ (Activation)        │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3a_dwconv_pad  │ (None, 59, 59,    │          0 │ block3a_expand_a… │\n│ (ZeroPadding2D)     │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3a_dwconv      │ (None, 28, 28,    │      7,200 │ block3a_dwconv_p… │\n│ (DepthwiseConv2D)   │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3a_bn          │ (None, 28, 28,    │      1,152 │ block3a_dwconv[0… │\n│ (BatchNormalizatio…288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3a_activation  │ (None, 28, 28,    │          0 │ block3a_bn[0][0]  │\n│ (Activation)        │ 288)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3a_se_squeeze  │ (None, 288)       │          0 │ block3a_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3a_se_reshape  │ (None, 1, 1, 288) │          0 │ block3a_se_squee… │\n│ (Reshape)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3a_se_reduce   │ (None, 1, 1, 12)  │      3,468 │ block3a_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3a_se_expand   │ (None, 1, 1, 288) │      3,744 │ block3a_se_reduc… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3a_se_excite   │ (None, 28, 28,    │          0 │ block3a_activati… │\n│ (Multiply)          │ 288)              │            │ block3a_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3a_project_co… │ (None, 28, 28,    │     23,040 │ block3a_se_excit… │\n│ (Conv2D)            │ 80)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3a_project_bn  │ (None, 28, 28,    │        320 │ block3a_project_… │\n│ (BatchNormalizatio…80)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3b_expand_conv │ (None, 28, 28,    │     38,400 │ block3a_project_… │\n│ (Conv2D)            │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3b_expand_bn   │ (None, 28, 28,    │      1,920 │ block3b_expand_c… │\n│ (BatchNormalizatio…480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3b_expand_act… │ (None, 28, 28,    │          0 │ block3b_expand_b… │\n│ (Activation)        │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3b_dwconv      │ (None, 28, 28,    │     12,000 │ block3b_expand_a… │\n│ (DepthwiseConv2D)   │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3b_bn          │ (None, 28, 28,    │      1,920 │ block3b_dwconv[0… │\n│ (BatchNormalizatio…480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3b_activation  │ (None, 28, 28,    │          0 │ block3b_bn[0][0]  │\n│ (Activation)        │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3b_se_squeeze  │ (None, 480)       │          0 │ block3b_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3b_se_reshape  │ (None, 1, 1, 480) │          0 │ block3b_se_squee… │\n│ (Reshape)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3b_se_reduce   │ (None, 1, 1, 20)  │      9,620 │ block3b_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3b_se_expand   │ (None, 1, 1, 480) │     10,080 │ block3b_se_reduc… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3b_se_excite   │ (None, 28, 28,    │          0 │ block3b_activati… │\n│ (Multiply)          │ 480)              │            │ block3b_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3b_project_co… │ (None, 28, 28,    │     38,400 │ block3b_se_excit… │\n│ (Conv2D)            │ 80)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3b_project_bn  │ (None, 28, 28,    │        320 │ block3b_project_… │\n│ (BatchNormalizatio…80)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3b_drop        │ (None, 28, 28,    │          0 │ block3b_project_… │\n│ (Dropout)           │ 80)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3b_add (Add)   │ (None, 28, 28,    │          0 │ block3b_drop[0][ │\n│                     │ 80)               │            │ block3a_project_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3c_expand_conv │ (None, 28, 28,    │     38,400 │ block3b_add[0][0] │\n│ (Conv2D)            │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3c_expand_bn   │ (None, 28, 28,    │      1,920 │ block3c_expand_c… │\n│ (BatchNormalizatio…480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3c_expand_act… │ (None, 28, 28,    │          0 │ block3c_expand_b… │\n│ (Activation)        │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3c_dwconv      │ (None, 28, 28,    │     12,000 │ block3c_expand_a… │\n│ (DepthwiseConv2D)   │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3c_bn          │ (None, 28, 28,    │      1,920 │ block3c_dwconv[0… │\n│ (BatchNormalizatio…480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3c_activation  │ (None, 28, 28,    │          0 │ block3c_bn[0][0]  │\n│ (Activation)        │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3c_se_squeeze  │ (None, 480)       │          0 │ block3c_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3c_se_reshape  │ (None, 1, 1, 480) │          0 │ block3c_se_squee… │\n│ (Reshape)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3c_se_reduce   │ (None, 1, 1, 20)  │      9,620 │ block3c_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3c_se_expand   │ (None, 1, 1, 480) │     10,080 │ block3c_se_reduc… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3c_se_excite   │ (None, 28, 28,    │          0 │ block3c_activati… │\n│ (Multiply)          │ 480)              │            │ block3c_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3c_project_co… │ (None, 28, 28,    │     38,400 │ block3c_se_excit… │\n│ (Conv2D)            │ 80)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3c_project_bn  │ (None, 28, 28,    │        320 │ block3c_project_… │\n│ (BatchNormalizatio…80)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3c_drop        │ (None, 28, 28,    │          0 │ block3c_project_… │\n│ (Dropout)           │ 80)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3c_add (Add)   │ (None, 28, 28,    │          0 │ block3c_drop[0][ │\n│                     │ 80)               │            │ block3b_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3d_expand_conv │ (None, 28, 28,    │     38,400 │ block3c_add[0][0] │\n│ (Conv2D)            │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3d_expand_bn   │ (None, 28, 28,    │      1,920 │ block3d_expand_c… │\n│ (BatchNormalizatio…480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3d_expand_act… │ (None, 28, 28,    │          0 │ block3d_expand_b… │\n│ (Activation)        │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3d_dwconv      │ (None, 28, 28,    │     12,000 │ block3d_expand_a… │\n│ (DepthwiseConv2D)   │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3d_bn          │ (None, 28, 28,    │      1,920 │ block3d_dwconv[0… │\n│ (BatchNormalizatio…480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3d_activation  │ (None, 28, 28,    │          0 │ block3d_bn[0][0]  │\n│ (Activation)        │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3d_se_squeeze  │ (None, 480)       │          0 │ block3d_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3d_se_reshape  │ (None, 1, 1, 480) │          0 │ block3d_se_squee… │\n│ (Reshape)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3d_se_reduce   │ (None, 1, 1, 20)  │      9,620 │ block3d_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3d_se_expand   │ (None, 1, 1, 480) │     10,080 │ block3d_se_reduc… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3d_se_excite   │ (None, 28, 28,    │          0 │ block3d_activati… │\n│ (Multiply)          │ 480)              │            │ block3d_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3d_project_co… │ (None, 28, 28,    │     38,400 │ block3d_se_excit… │\n│ (Conv2D)            │ 80)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3d_project_bn  │ (None, 28, 28,    │        320 │ block3d_project_… │\n│ (BatchNormalizatio…80)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3d_drop        │ (None, 28, 28,    │          0 │ block3d_project_… │\n│ (Dropout)           │ 80)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3d_add (Add)   │ (None, 28, 28,    │          0 │ block3d_drop[0][ │\n│                     │ 80)               │            │ block3c_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3e_expand_conv │ (None, 28, 28,    │     38,400 │ block3d_add[0][0] │\n│ (Conv2D)            │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3e_expand_bn   │ (None, 28, 28,    │      1,920 │ block3e_expand_c… │\n│ (BatchNormalizatio…480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3e_expand_act… │ (None, 28, 28,    │          0 │ block3e_expand_b… │\n│ (Activation)        │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3e_dwconv      │ (None, 28, 28,    │     12,000 │ block3e_expand_a… │\n│ (DepthwiseConv2D)   │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3e_bn          │ (None, 28, 28,    │      1,920 │ block3e_dwconv[0… │\n│ (BatchNormalizatio…480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3e_activation  │ (None, 28, 28,    │          0 │ block3e_bn[0][0]  │\n│ (Activation)        │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3e_se_squeeze  │ (None, 480)       │          0 │ block3e_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3e_se_reshape  │ (None, 1, 1, 480) │          0 │ block3e_se_squee… │\n│ (Reshape)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3e_se_reduce   │ (None, 1, 1, 20)  │      9,620 │ block3e_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3e_se_expand   │ (None, 1, 1, 480) │     10,080 │ block3e_se_reduc… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3e_se_excite   │ (None, 28, 28,    │          0 │ block3e_activati… │\n│ (Multiply)          │ 480)              │            │ block3e_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3e_project_co… │ (None, 28, 28,    │     38,400 │ block3e_se_excit… │\n│ (Conv2D)            │ 80)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3e_project_bn  │ (None, 28, 28,    │        320 │ block3e_project_… │\n│ (BatchNormalizatio…80)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3e_drop        │ (None, 28, 28,    │          0 │ block3e_project_… │\n│ (Dropout)           │ 80)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3e_add (Add)   │ (None, 28, 28,    │          0 │ block3e_drop[0][ │\n│                     │ 80)               │            │ block3d_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3f_expand_conv │ (None, 28, 28,    │     38,400 │ block3e_add[0][0] │\n│ (Conv2D)            │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3f_expand_bn   │ (None, 28, 28,    │      1,920 │ block3f_expand_c… │\n│ (BatchNormalizatio…480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3f_expand_act… │ (None, 28, 28,    │          0 │ block3f_expand_b… │\n│ (Activation)        │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3f_dwconv      │ (None, 28, 28,    │     12,000 │ block3f_expand_a… │\n│ (DepthwiseConv2D)   │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3f_bn          │ (None, 28, 28,    │      1,920 │ block3f_dwconv[0… │\n│ (BatchNormalizatio…480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3f_activation  │ (None, 28, 28,    │          0 │ block3f_bn[0][0]  │\n│ (Activation)        │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3f_se_squeeze  │ (None, 480)       │          0 │ block3f_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3f_se_reshape  │ (None, 1, 1, 480) │          0 │ block3f_se_squee… │\n│ (Reshape)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3f_se_reduce   │ (None, 1, 1, 20)  │      9,620 │ block3f_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3f_se_expand   │ (None, 1, 1, 480) │     10,080 │ block3f_se_reduc… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3f_se_excite   │ (None, 28, 28,    │          0 │ block3f_activati… │\n│ (Multiply)          │ 480)              │            │ block3f_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3f_project_co… │ (None, 28, 28,    │     38,400 │ block3f_se_excit… │\n│ (Conv2D)            │ 80)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3f_project_bn  │ (None, 28, 28,    │        320 │ block3f_project_… │\n│ (BatchNormalizatio…80)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3f_drop        │ (None, 28, 28,    │          0 │ block3f_project_… │\n│ (Dropout)           │ 80)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3f_add (Add)   │ (None, 28, 28,    │          0 │ block3f_drop[0][ │\n│                     │ 80)               │            │ block3e_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3g_expand_conv │ (None, 28, 28,    │     38,400 │ block3f_add[0][0] │\n│ (Conv2D)            │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3g_expand_bn   │ (None, 28, 28,    │      1,920 │ block3g_expand_c… │\n│ (BatchNormalizatio…480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3g_expand_act… │ (None, 28, 28,    │          0 │ block3g_expand_b… │\n│ (Activation)        │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3g_dwconv      │ (None, 28, 28,    │     12,000 │ block3g_expand_a… │\n│ (DepthwiseConv2D)   │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3g_bn          │ (None, 28, 28,    │      1,920 │ block3g_dwconv[0… │\n│ (BatchNormalizatio…480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3g_activation  │ (None, 28, 28,    │          0 │ block3g_bn[0][0]  │\n│ (Activation)        │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3g_se_squeeze  │ (None, 480)       │          0 │ block3g_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3g_se_reshape  │ (None, 1, 1, 480) │          0 │ block3g_se_squee… │\n│ (Reshape)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3g_se_reduce   │ (None, 1, 1, 20)  │      9,620 │ block3g_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3g_se_expand   │ (None, 1, 1, 480) │     10,080 │ block3g_se_reduc… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3g_se_excite   │ (None, 28, 28,    │          0 │ block3g_activati… │\n│ (Multiply)          │ 480)              │            │ block3g_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3g_project_co… │ (None, 28, 28,    │     38,400 │ block3g_se_excit… │\n│ (Conv2D)            │ 80)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3g_project_bn  │ (None, 28, 28,    │        320 │ block3g_project_… │\n│ (BatchNormalizatio…80)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3g_drop        │ (None, 28, 28,    │          0 │ block3g_project_… │\n│ (Dropout)           │ 80)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block3g_add (Add)   │ (None, 28, 28,    │          0 │ block3g_drop[0][ │\n│                     │ 80)               │            │ block3f_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4a_expand_conv │ (None, 28, 28,    │     38,400 │ block3g_add[0][0] │\n│ (Conv2D)            │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4a_expand_bn   │ (None, 28, 28,    │      1,920 │ block4a_expand_c… │\n│ (BatchNormalizatio…480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4a_expand_act… │ (None, 28, 28,    │          0 │ block4a_expand_b… │\n│ (Activation)        │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4a_dwconv_pad  │ (None, 29, 29,    │          0 │ block4a_expand_a… │\n│ (ZeroPadding2D)     │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4a_dwconv      │ (None, 14, 14,    │      4,320 │ block4a_dwconv_p… │\n│ (DepthwiseConv2D)   │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4a_bn          │ (None, 14, 14,    │      1,920 │ block4a_dwconv[0… │\n│ (BatchNormalizatio…480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4a_activation  │ (None, 14, 14,    │          0 │ block4a_bn[0][0]  │\n│ (Activation)        │ 480)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4a_se_squeeze  │ (None, 480)       │          0 │ block4a_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4a_se_reshape  │ (None, 1, 1, 480) │          0 │ block4a_se_squee… │\n│ (Reshape)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4a_se_reduce   │ (None, 1, 1, 20)  │      9,620 │ block4a_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4a_se_expand   │ (None, 1, 1, 480) │     10,080 │ block4a_se_reduc… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4a_se_excite   │ (None, 14, 14,    │          0 │ block4a_activati… │\n│ (Multiply)          │ 480)              │            │ block4a_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4a_project_co… │ (None, 14, 14,    │     76,800 │ block4a_se_excit… │\n│ (Conv2D)            │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4a_project_bn  │ (None, 14, 14,    │        640 │ block4a_project_… │\n│ (BatchNormalizatio…160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4b_expand_conv │ (None, 14, 14,    │    153,600 │ block4a_project_… │\n│ (Conv2D)            │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4b_expand_bn   │ (None, 14, 14,    │      3,840 │ block4b_expand_c… │\n│ (BatchNormalizatio…960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4b_expand_act… │ (None, 14, 14,    │          0 │ block4b_expand_b… │\n│ (Activation)        │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4b_dwconv      │ (None, 14, 14,    │      8,640 │ block4b_expand_a… │\n│ (DepthwiseConv2D)   │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4b_bn          │ (None, 14, 14,    │      3,840 │ block4b_dwconv[0… │\n│ (BatchNormalizatio…960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4b_activation  │ (None, 14, 14,    │          0 │ block4b_bn[0][0]  │\n│ (Activation)        │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4b_se_squeeze  │ (None, 960)       │          0 │ block4b_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4b_se_reshape  │ (None, 1, 1, 960) │          0 │ block4b_se_squee… │\n│ (Reshape)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4b_se_reduce   │ (None, 1, 1, 40)  │     38,440 │ block4b_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4b_se_expand   │ (None, 1, 1, 960) │     39,360 │ block4b_se_reduc… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4b_se_excite   │ (None, 14, 14,    │          0 │ block4b_activati… │\n│ (Multiply)          │ 960)              │            │ block4b_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4b_project_co… │ (None, 14, 14,    │    153,600 │ block4b_se_excit… │\n│ (Conv2D)            │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4b_project_bn  │ (None, 14, 14,    │        640 │ block4b_project_… │\n│ (BatchNormalizatio…160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4b_drop        │ (None, 14, 14,    │          0 │ block4b_project_… │\n│ (Dropout)           │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4b_add (Add)   │ (None, 14, 14,    │          0 │ block4b_drop[0][ │\n│                     │ 160)              │            │ block4a_project_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4c_expand_conv │ (None, 14, 14,    │    153,600 │ block4b_add[0][0] │\n│ (Conv2D)            │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4c_expand_bn   │ (None, 14, 14,    │      3,840 │ block4c_expand_c… │\n│ (BatchNormalizatio…960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4c_expand_act… │ (None, 14, 14,    │          0 │ block4c_expand_b… │\n│ (Activation)        │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4c_dwconv      │ (None, 14, 14,    │      8,640 │ block4c_expand_a… │\n│ (DepthwiseConv2D)   │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4c_bn          │ (None, 14, 14,    │      3,840 │ block4c_dwconv[0… │\n│ (BatchNormalizatio…960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4c_activation  │ (None, 14, 14,    │          0 │ block4c_bn[0][0]  │\n│ (Activation)        │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4c_se_squeeze  │ (None, 960)       │          0 │ block4c_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4c_se_reshape  │ (None, 1, 1, 960) │          0 │ block4c_se_squee… │\n│ (Reshape)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4c_se_reduce   │ (None, 1, 1, 40)  │     38,440 │ block4c_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4c_se_expand   │ (None, 1, 1, 960) │     39,360 │ block4c_se_reduc… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4c_se_excite   │ (None, 14, 14,    │          0 │ block4c_activati… │\n│ (Multiply)          │ 960)              │            │ block4c_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4c_project_co… │ (None, 14, 14,    │    153,600 │ block4c_se_excit… │\n│ (Conv2D)            │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4c_project_bn  │ (None, 14, 14,    │        640 │ block4c_project_… │\n│ (BatchNormalizatio…160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4c_drop        │ (None, 14, 14,    │          0 │ block4c_project_… │\n│ (Dropout)           │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4c_add (Add)   │ (None, 14, 14,    │          0 │ block4c_drop[0][ │\n│                     │ 160)              │            │ block4b_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4d_expand_conv │ (None, 14, 14,    │    153,600 │ block4c_add[0][0] │\n│ (Conv2D)            │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4d_expand_bn   │ (None, 14, 14,    │      3,840 │ block4d_expand_c… │\n│ (BatchNormalizatio…960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4d_expand_act… │ (None, 14, 14,    │          0 │ block4d_expand_b… │\n│ (Activation)        │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4d_dwconv      │ (None, 14, 14,    │      8,640 │ block4d_expand_a… │\n│ (DepthwiseConv2D)   │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4d_bn          │ (None, 14, 14,    │      3,840 │ block4d_dwconv[0… │\n│ (BatchNormalizatio…960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4d_activation  │ (None, 14, 14,    │          0 │ block4d_bn[0][0]  │\n│ (Activation)        │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4d_se_squeeze  │ (None, 960)       │          0 │ block4d_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4d_se_reshape  │ (None, 1, 1, 960) │          0 │ block4d_se_squee… │\n│ (Reshape)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4d_se_reduce   │ (None, 1, 1, 40)  │     38,440 │ block4d_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4d_se_expand   │ (None, 1, 1, 960) │     39,360 │ block4d_se_reduc… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4d_se_excite   │ (None, 14, 14,    │          0 │ block4d_activati… │\n│ (Multiply)          │ 960)              │            │ block4d_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4d_project_co… │ (None, 14, 14,    │    153,600 │ block4d_se_excit… │\n│ (Conv2D)            │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4d_project_bn  │ (None, 14, 14,    │        640 │ block4d_project_… │\n│ (BatchNormalizatio…160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4d_drop        │ (None, 14, 14,    │          0 │ block4d_project_… │\n│ (Dropout)           │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4d_add (Add)   │ (None, 14, 14,    │          0 │ block4d_drop[0][ │\n│                     │ 160)              │            │ block4c_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4e_expand_conv │ (None, 14, 14,    │    153,600 │ block4d_add[0][0] │\n│ (Conv2D)            │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4e_expand_bn   │ (None, 14, 14,    │      3,840 │ block4e_expand_c… │\n│ (BatchNormalizatio…960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4e_expand_act… │ (None, 14, 14,    │          0 │ block4e_expand_b… │\n│ (Activation)        │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4e_dwconv      │ (None, 14, 14,    │      8,640 │ block4e_expand_a… │\n│ (DepthwiseConv2D)   │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4e_bn          │ (None, 14, 14,    │      3,840 │ block4e_dwconv[0… │\n│ (BatchNormalizatio…960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4e_activation  │ (None, 14, 14,    │          0 │ block4e_bn[0][0]  │\n│ (Activation)        │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4e_se_squeeze  │ (None, 960)       │          0 │ block4e_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4e_se_reshape  │ (None, 1, 1, 960) │          0 │ block4e_se_squee… │\n│ (Reshape)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4e_se_reduce   │ (None, 1, 1, 40)  │     38,440 │ block4e_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4e_se_expand   │ (None, 1, 1, 960) │     39,360 │ block4e_se_reduc… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4e_se_excite   │ (None, 14, 14,    │          0 │ block4e_activati… │\n│ (Multiply)          │ 960)              │            │ block4e_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4e_project_co… │ (None, 14, 14,    │    153,600 │ block4e_se_excit… │\n│ (Conv2D)            │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4e_project_bn  │ (None, 14, 14,    │        640 │ block4e_project_… │\n│ (BatchNormalizatio…160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4e_drop        │ (None, 14, 14,    │          0 │ block4e_project_… │\n│ (Dropout)           │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4e_add (Add)   │ (None, 14, 14,    │          0 │ block4e_drop[0][ │\n│                     │ 160)              │            │ block4d_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4f_expand_conv │ (None, 14, 14,    │    153,600 │ block4e_add[0][0] │\n│ (Conv2D)            │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4f_expand_bn   │ (None, 14, 14,    │      3,840 │ block4f_expand_c… │\n│ (BatchNormalizatio…960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4f_expand_act… │ (None, 14, 14,    │          0 │ block4f_expand_b… │\n│ (Activation)        │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4f_dwconv      │ (None, 14, 14,    │      8,640 │ block4f_expand_a… │\n│ (DepthwiseConv2D)   │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4f_bn          │ (None, 14, 14,    │      3,840 │ block4f_dwconv[0… │\n│ (BatchNormalizatio…960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4f_activation  │ (None, 14, 14,    │          0 │ block4f_bn[0][0]  │\n│ (Activation)        │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4f_se_squeeze  │ (None, 960)       │          0 │ block4f_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4f_se_reshape  │ (None, 1, 1, 960) │          0 │ block4f_se_squee… │\n│ (Reshape)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4f_se_reduce   │ (None, 1, 1, 40)  │     38,440 │ block4f_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4f_se_expand   │ (None, 1, 1, 960) │     39,360 │ block4f_se_reduc… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4f_se_excite   │ (None, 14, 14,    │          0 │ block4f_activati… │\n│ (Multiply)          │ 960)              │            │ block4f_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4f_project_co… │ (None, 14, 14,    │    153,600 │ block4f_se_excit… │\n│ (Conv2D)            │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4f_project_bn  │ (None, 14, 14,    │        640 │ block4f_project_… │\n│ (BatchNormalizatio…160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4f_drop        │ (None, 14, 14,    │          0 │ block4f_project_… │\n│ (Dropout)           │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4f_add (Add)   │ (None, 14, 14,    │          0 │ block4f_drop[0][ │\n│                     │ 160)              │            │ block4e_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4g_expand_conv │ (None, 14, 14,    │    153,600 │ block4f_add[0][0] │\n│ (Conv2D)            │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4g_expand_bn   │ (None, 14, 14,    │      3,840 │ block4g_expand_c… │\n│ (BatchNormalizatio…960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4g_expand_act… │ (None, 14, 14,    │          0 │ block4g_expand_b… │\n│ (Activation)        │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4g_dwconv      │ (None, 14, 14,    │      8,640 │ block4g_expand_a… │\n│ (DepthwiseConv2D)   │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4g_bn          │ (None, 14, 14,    │      3,840 │ block4g_dwconv[0… │\n│ (BatchNormalizatio…960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4g_activation  │ (None, 14, 14,    │          0 │ block4g_bn[0][0]  │\n│ (Activation)        │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4g_se_squeeze  │ (None, 960)       │          0 │ block4g_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4g_se_reshape  │ (None, 1, 1, 960) │          0 │ block4g_se_squee… │\n│ (Reshape)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4g_se_reduce   │ (None, 1, 1, 40)  │     38,440 │ block4g_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4g_se_expand   │ (None, 1, 1, 960) │     39,360 │ block4g_se_reduc… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4g_se_excite   │ (None, 14, 14,    │          0 │ block4g_activati… │\n│ (Multiply)          │ 960)              │            │ block4g_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4g_project_co… │ (None, 14, 14,    │    153,600 │ block4g_se_excit… │\n│ (Conv2D)            │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4g_project_bn  │ (None, 14, 14,    │        640 │ block4g_project_… │\n│ (BatchNormalizatio…160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4g_drop        │ (None, 14, 14,    │          0 │ block4g_project_… │\n│ (Dropout)           │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4g_add (Add)   │ (None, 14, 14,    │          0 │ block4g_drop[0][ │\n│                     │ 160)              │            │ block4f_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4h_expand_conv │ (None, 14, 14,    │    153,600 │ block4g_add[0][0] │\n│ (Conv2D)            │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4h_expand_bn   │ (None, 14, 14,    │      3,840 │ block4h_expand_c… │\n│ (BatchNormalizatio…960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4h_expand_act… │ (None, 14, 14,    │          0 │ block4h_expand_b… │\n│ (Activation)        │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4h_dwconv      │ (None, 14, 14,    │      8,640 │ block4h_expand_a… │\n│ (DepthwiseConv2D)   │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4h_bn          │ (None, 14, 14,    │      3,840 │ block4h_dwconv[0… │\n│ (BatchNormalizatio…960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4h_activation  │ (None, 14, 14,    │          0 │ block4h_bn[0][0]  │\n│ (Activation)        │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4h_se_squeeze  │ (None, 960)       │          0 │ block4h_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4h_se_reshape  │ (None, 1, 1, 960) │          0 │ block4h_se_squee… │\n│ (Reshape)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4h_se_reduce   │ (None, 1, 1, 40)  │     38,440 │ block4h_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4h_se_expand   │ (None, 1, 1, 960) │     39,360 │ block4h_se_reduc… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4h_se_excite   │ (None, 14, 14,    │          0 │ block4h_activati… │\n│ (Multiply)          │ 960)              │            │ block4h_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4h_project_co… │ (None, 14, 14,    │    153,600 │ block4h_se_excit… │\n│ (Conv2D)            │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4h_project_bn  │ (None, 14, 14,    │        640 │ block4h_project_… │\n│ (BatchNormalizatio…160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4h_drop        │ (None, 14, 14,    │          0 │ block4h_project_… │\n│ (Dropout)           │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4h_add (Add)   │ (None, 14, 14,    │          0 │ block4h_drop[0][ │\n│                     │ 160)              │            │ block4g_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4i_expand_conv │ (None, 14, 14,    │    153,600 │ block4h_add[0][0] │\n│ (Conv2D)            │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4i_expand_bn   │ (None, 14, 14,    │      3,840 │ block4i_expand_c… │\n│ (BatchNormalizatio…960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4i_expand_act… │ (None, 14, 14,    │          0 │ block4i_expand_b… │\n│ (Activation)        │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4i_dwconv      │ (None, 14, 14,    │      8,640 │ block4i_expand_a… │\n│ (DepthwiseConv2D)   │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4i_bn          │ (None, 14, 14,    │      3,840 │ block4i_dwconv[0… │\n│ (BatchNormalizatio…960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4i_activation  │ (None, 14, 14,    │          0 │ block4i_bn[0][0]  │\n│ (Activation)        │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4i_se_squeeze  │ (None, 960)       │          0 │ block4i_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4i_se_reshape  │ (None, 1, 1, 960) │          0 │ block4i_se_squee… │\n│ (Reshape)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4i_se_reduce   │ (None, 1, 1, 40)  │     38,440 │ block4i_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4i_se_expand   │ (None, 1, 1, 960) │     39,360 │ block4i_se_reduc… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4i_se_excite   │ (None, 14, 14,    │          0 │ block4i_activati… │\n│ (Multiply)          │ 960)              │            │ block4i_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4i_project_co… │ (None, 14, 14,    │    153,600 │ block4i_se_excit… │\n│ (Conv2D)            │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4i_project_bn  │ (None, 14, 14,    │        640 │ block4i_project_… │\n│ (BatchNormalizatio…160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4i_drop        │ (None, 14, 14,    │          0 │ block4i_project_… │\n│ (Dropout)           │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4i_add (Add)   │ (None, 14, 14,    │          0 │ block4i_drop[0][ │\n│                     │ 160)              │            │ block4h_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4j_expand_conv │ (None, 14, 14,    │    153,600 │ block4i_add[0][0] │\n│ (Conv2D)            │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4j_expand_bn   │ (None, 14, 14,    │      3,840 │ block4j_expand_c… │\n│ (BatchNormalizatio…960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4j_expand_act… │ (None, 14, 14,    │          0 │ block4j_expand_b… │\n│ (Activation)        │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4j_dwconv      │ (None, 14, 14,    │      8,640 │ block4j_expand_a… │\n│ (DepthwiseConv2D)   │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4j_bn          │ (None, 14, 14,    │      3,840 │ block4j_dwconv[0… │\n│ (BatchNormalizatio…960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4j_activation  │ (None, 14, 14,    │          0 │ block4j_bn[0][0]  │\n│ (Activation)        │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4j_se_squeeze  │ (None, 960)       │          0 │ block4j_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4j_se_reshape  │ (None, 1, 1, 960) │          0 │ block4j_se_squee… │\n│ (Reshape)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4j_se_reduce   │ (None, 1, 1, 40)  │     38,440 │ block4j_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4j_se_expand   │ (None, 1, 1, 960) │     39,360 │ block4j_se_reduc… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4j_se_excite   │ (None, 14, 14,    │          0 │ block4j_activati… │\n│ (Multiply)          │ 960)              │            │ block4j_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4j_project_co… │ (None, 14, 14,    │    153,600 │ block4j_se_excit… │\n│ (Conv2D)            │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4j_project_bn  │ (None, 14, 14,    │        640 │ block4j_project_… │\n│ (BatchNormalizatio…160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4j_drop        │ (None, 14, 14,    │          0 │ block4j_project_… │\n│ (Dropout)           │ 160)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block4j_add (Add)   │ (None, 14, 14,    │          0 │ block4j_drop[0][ │\n│                     │ 160)              │            │ block4i_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5a_expand_conv │ (None, 14, 14,    │    153,600 │ block4j_add[0][0] │\n│ (Conv2D)            │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5a_expand_bn   │ (None, 14, 14,    │      3,840 │ block5a_expand_c… │\n│ (BatchNormalizatio…960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5a_expand_act… │ (None, 14, 14,    │          0 │ block5a_expand_b… │\n│ (Activation)        │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5a_dwconv      │ (None, 14, 14,    │     24,000 │ block5a_expand_a… │\n│ (DepthwiseConv2D)   │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5a_bn          │ (None, 14, 14,    │      3,840 │ block5a_dwconv[0… │\n│ (BatchNormalizatio…960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5a_activation  │ (None, 14, 14,    │          0 │ block5a_bn[0][0]  │\n│ (Activation)        │ 960)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5a_se_squeeze  │ (None, 960)       │          0 │ block5a_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5a_se_reshape  │ (None, 1, 1, 960) │          0 │ block5a_se_squee… │\n│ (Reshape)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5a_se_reduce   │ (None, 1, 1, 40)  │     38,440 │ block5a_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5a_se_expand   │ (None, 1, 1, 960) │     39,360 │ block5a_se_reduc… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5a_se_excite   │ (None, 14, 14,    │          0 │ block5a_activati… │\n│ (Multiply)          │ 960)              │            │ block5a_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5a_project_co… │ (None, 14, 14,    │    215,040 │ block5a_se_excit… │\n│ (Conv2D)            │ 224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5a_project_bn  │ (None, 14, 14,    │        896 │ block5a_project_… │\n│ (BatchNormalizatio…224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5b_expand_conv │ (None, 14, 14,    │    301,056 │ block5a_project_… │\n│ (Conv2D)            │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5b_expand_bn   │ (None, 14, 14,    │      5,376 │ block5b_expand_c… │\n│ (BatchNormalizatio…1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5b_expand_act… │ (None, 14, 14,    │          0 │ block5b_expand_b… │\n│ (Activation)        │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5b_dwconv      │ (None, 14, 14,    │     33,600 │ block5b_expand_a… │\n│ (DepthwiseConv2D)   │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5b_bn          │ (None, 14, 14,    │      5,376 │ block5b_dwconv[0… │\n│ (BatchNormalizatio…1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5b_activation  │ (None, 14, 14,    │          0 │ block5b_bn[0][0]  │\n│ (Activation)        │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5b_se_squeeze  │ (None, 1344)      │          0 │ block5b_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5b_se_reshape  │ (None, 1, 1,      │          0 │ block5b_se_squee… │\n│ (Reshape)           │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5b_se_reduce   │ (None, 1, 1, 56)  │     75,320 │ block5b_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5b_se_expand   │ (None, 1, 1,      │     76,608 │ block5b_se_reduc… │\n│ (Conv2D)            │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5b_se_excite   │ (None, 14, 14,    │          0 │ block5b_activati… │\n│ (Multiply)          │ 1344)             │            │ block5b_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5b_project_co… │ (None, 14, 14,    │    301,056 │ block5b_se_excit… │\n│ (Conv2D)            │ 224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5b_project_bn  │ (None, 14, 14,    │        896 │ block5b_project_… │\n│ (BatchNormalizatio…224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5b_drop        │ (None, 14, 14,    │          0 │ block5b_project_… │\n│ (Dropout)           │ 224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5b_add (Add)   │ (None, 14, 14,    │          0 │ block5b_drop[0][ │\n│                     │ 224)              │            │ block5a_project_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5c_expand_conv │ (None, 14, 14,    │    301,056 │ block5b_add[0][0] │\n│ (Conv2D)            │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5c_expand_bn   │ (None, 14, 14,    │      5,376 │ block5c_expand_c… │\n│ (BatchNormalizatio…1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5c_expand_act… │ (None, 14, 14,    │          0 │ block5c_expand_b… │\n│ (Activation)        │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5c_dwconv      │ (None, 14, 14,    │     33,600 │ block5c_expand_a… │\n│ (DepthwiseConv2D)   │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5c_bn          │ (None, 14, 14,    │      5,376 │ block5c_dwconv[0… │\n│ (BatchNormalizatio…1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5c_activation  │ (None, 14, 14,    │          0 │ block5c_bn[0][0]  │\n│ (Activation)        │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5c_se_squeeze  │ (None, 1344)      │          0 │ block5c_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5c_se_reshape  │ (None, 1, 1,      │          0 │ block5c_se_squee… │\n│ (Reshape)           │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5c_se_reduce   │ (None, 1, 1, 56)  │     75,320 │ block5c_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5c_se_expand   │ (None, 1, 1,      │     76,608 │ block5c_se_reduc… │\n│ (Conv2D)            │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5c_se_excite   │ (None, 14, 14,    │          0 │ block5c_activati… │\n│ (Multiply)          │ 1344)             │            │ block5c_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5c_project_co… │ (None, 14, 14,    │    301,056 │ block5c_se_excit… │\n│ (Conv2D)            │ 224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5c_project_bn  │ (None, 14, 14,    │        896 │ block5c_project_… │\n│ (BatchNormalizatio…224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5c_drop        │ (None, 14, 14,    │          0 │ block5c_project_… │\n│ (Dropout)           │ 224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5c_add (Add)   │ (None, 14, 14,    │          0 │ block5c_drop[0][ │\n│                     │ 224)              │            │ block5b_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5d_expand_conv │ (None, 14, 14,    │    301,056 │ block5c_add[0][0] │\n│ (Conv2D)            │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5d_expand_bn   │ (None, 14, 14,    │      5,376 │ block5d_expand_c… │\n│ (BatchNormalizatio…1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5d_expand_act… │ (None, 14, 14,    │          0 │ block5d_expand_b… │\n│ (Activation)        │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5d_dwconv      │ (None, 14, 14,    │     33,600 │ block5d_expand_a… │\n│ (DepthwiseConv2D)   │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5d_bn          │ (None, 14, 14,    │      5,376 │ block5d_dwconv[0… │\n│ (BatchNormalizatio…1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5d_activation  │ (None, 14, 14,    │          0 │ block5d_bn[0][0]  │\n│ (Activation)        │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5d_se_squeeze  │ (None, 1344)      │          0 │ block5d_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5d_se_reshape  │ (None, 1, 1,      │          0 │ block5d_se_squee… │\n│ (Reshape)           │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5d_se_reduce   │ (None, 1, 1, 56)  │     75,320 │ block5d_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5d_se_expand   │ (None, 1, 1,      │     76,608 │ block5d_se_reduc… │\n│ (Conv2D)            │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5d_se_excite   │ (None, 14, 14,    │          0 │ block5d_activati… │\n│ (Multiply)          │ 1344)             │            │ block5d_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5d_project_co… │ (None, 14, 14,    │    301,056 │ block5d_se_excit… │\n│ (Conv2D)            │ 224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5d_project_bn  │ (None, 14, 14,    │        896 │ block5d_project_… │\n│ (BatchNormalizatio…224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5d_drop        │ (None, 14, 14,    │          0 │ block5d_project_… │\n│ (Dropout)           │ 224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5d_add (Add)   │ (None, 14, 14,    │          0 │ block5d_drop[0][ │\n│                     │ 224)              │            │ block5c_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5e_expand_conv │ (None, 14, 14,    │    301,056 │ block5d_add[0][0] │\n│ (Conv2D)            │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5e_expand_bn   │ (None, 14, 14,    │      5,376 │ block5e_expand_c… │\n│ (BatchNormalizatio…1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5e_expand_act… │ (None, 14, 14,    │          0 │ block5e_expand_b… │\n│ (Activation)        │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5e_dwconv      │ (None, 14, 14,    │     33,600 │ block5e_expand_a… │\n│ (DepthwiseConv2D)   │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5e_bn          │ (None, 14, 14,    │      5,376 │ block5e_dwconv[0… │\n│ (BatchNormalizatio…1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5e_activation  │ (None, 14, 14,    │          0 │ block5e_bn[0][0]  │\n│ (Activation)        │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5e_se_squeeze  │ (None, 1344)      │          0 │ block5e_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5e_se_reshape  │ (None, 1, 1,      │          0 │ block5e_se_squee… │\n│ (Reshape)           │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5e_se_reduce   │ (None, 1, 1, 56)  │     75,320 │ block5e_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5e_se_expand   │ (None, 1, 1,      │     76,608 │ block5e_se_reduc… │\n│ (Conv2D)            │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5e_se_excite   │ (None, 14, 14,    │          0 │ block5e_activati… │\n│ (Multiply)          │ 1344)             │            │ block5e_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5e_project_co… │ (None, 14, 14,    │    301,056 │ block5e_se_excit… │\n│ (Conv2D)            │ 224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5e_project_bn  │ (None, 14, 14,    │        896 │ block5e_project_… │\n│ (BatchNormalizatio…224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5e_drop        │ (None, 14, 14,    │          0 │ block5e_project_… │\n│ (Dropout)           │ 224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5e_add (Add)   │ (None, 14, 14,    │          0 │ block5e_drop[0][ │\n│                     │ 224)              │            │ block5d_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5f_expand_conv │ (None, 14, 14,    │    301,056 │ block5e_add[0][0] │\n│ (Conv2D)            │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5f_expand_bn   │ (None, 14, 14,    │      5,376 │ block5f_expand_c… │\n│ (BatchNormalizatio…1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5f_expand_act… │ (None, 14, 14,    │          0 │ block5f_expand_b… │\n│ (Activation)        │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5f_dwconv      │ (None, 14, 14,    │     33,600 │ block5f_expand_a… │\n│ (DepthwiseConv2D)   │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5f_bn          │ (None, 14, 14,    │      5,376 │ block5f_dwconv[0… │\n│ (BatchNormalizatio…1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5f_activation  │ (None, 14, 14,    │          0 │ block5f_bn[0][0]  │\n│ (Activation)        │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5f_se_squeeze  │ (None, 1344)      │          0 │ block5f_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5f_se_reshape  │ (None, 1, 1,      │          0 │ block5f_se_squee… │\n│ (Reshape)           │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5f_se_reduce   │ (None, 1, 1, 56)  │     75,320 │ block5f_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5f_se_expand   │ (None, 1, 1,      │     76,608 │ block5f_se_reduc… │\n│ (Conv2D)            │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5f_se_excite   │ (None, 14, 14,    │          0 │ block5f_activati… │\n│ (Multiply)          │ 1344)             │            │ block5f_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5f_project_co… │ (None, 14, 14,    │    301,056 │ block5f_se_excit… │\n│ (Conv2D)            │ 224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5f_project_bn  │ (None, 14, 14,    │        896 │ block5f_project_… │\n│ (BatchNormalizatio…224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5f_drop        │ (None, 14, 14,    │          0 │ block5f_project_… │\n│ (Dropout)           │ 224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5f_add (Add)   │ (None, 14, 14,    │          0 │ block5f_drop[0][ │\n│                     │ 224)              │            │ block5e_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5g_expand_conv │ (None, 14, 14,    │    301,056 │ block5f_add[0][0] │\n│ (Conv2D)            │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5g_expand_bn   │ (None, 14, 14,    │      5,376 │ block5g_expand_c… │\n│ (BatchNormalizatio…1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5g_expand_act… │ (None, 14, 14,    │          0 │ block5g_expand_b… │\n│ (Activation)        │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5g_dwconv      │ (None, 14, 14,    │     33,600 │ block5g_expand_a… │\n│ (DepthwiseConv2D)   │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5g_bn          │ (None, 14, 14,    │      5,376 │ block5g_dwconv[0… │\n│ (BatchNormalizatio…1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5g_activation  │ (None, 14, 14,    │          0 │ block5g_bn[0][0]  │\n│ (Activation)        │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5g_se_squeeze  │ (None, 1344)      │          0 │ block5g_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5g_se_reshape  │ (None, 1, 1,      │          0 │ block5g_se_squee… │\n│ (Reshape)           │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5g_se_reduce   │ (None, 1, 1, 56)  │     75,320 │ block5g_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5g_se_expand   │ (None, 1, 1,      │     76,608 │ block5g_se_reduc… │\n│ (Conv2D)            │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5g_se_excite   │ (None, 14, 14,    │          0 │ block5g_activati… │\n│ (Multiply)          │ 1344)             │            │ block5g_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5g_project_co… │ (None, 14, 14,    │    301,056 │ block5g_se_excit… │\n│ (Conv2D)            │ 224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5g_project_bn  │ (None, 14, 14,    │        896 │ block5g_project_… │\n│ (BatchNormalizatio…224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5g_drop        │ (None, 14, 14,    │          0 │ block5g_project_… │\n│ (Dropout)           │ 224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5g_add (Add)   │ (None, 14, 14,    │          0 │ block5g_drop[0][ │\n│                     │ 224)              │            │ block5f_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5h_expand_conv │ (None, 14, 14,    │    301,056 │ block5g_add[0][0] │\n│ (Conv2D)            │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5h_expand_bn   │ (None, 14, 14,    │      5,376 │ block5h_expand_c… │\n│ (BatchNormalizatio…1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5h_expand_act… │ (None, 14, 14,    │          0 │ block5h_expand_b… │\n│ (Activation)        │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5h_dwconv      │ (None, 14, 14,    │     33,600 │ block5h_expand_a… │\n│ (DepthwiseConv2D)   │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5h_bn          │ (None, 14, 14,    │      5,376 │ block5h_dwconv[0… │\n│ (BatchNormalizatio…1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5h_activation  │ (None, 14, 14,    │          0 │ block5h_bn[0][0]  │\n│ (Activation)        │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5h_se_squeeze  │ (None, 1344)      │          0 │ block5h_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5h_se_reshape  │ (None, 1, 1,      │          0 │ block5h_se_squee… │\n│ (Reshape)           │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5h_se_reduce   │ (None, 1, 1, 56)  │     75,320 │ block5h_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5h_se_expand   │ (None, 1, 1,      │     76,608 │ block5h_se_reduc… │\n│ (Conv2D)            │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5h_se_excite   │ (None, 14, 14,    │          0 │ block5h_activati… │\n│ (Multiply)          │ 1344)             │            │ block5h_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5h_project_co… │ (None, 14, 14,    │    301,056 │ block5h_se_excit… │\n│ (Conv2D)            │ 224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5h_project_bn  │ (None, 14, 14,    │        896 │ block5h_project_… │\n│ (BatchNormalizatio…224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5h_drop        │ (None, 14, 14,    │          0 │ block5h_project_… │\n│ (Dropout)           │ 224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5h_add (Add)   │ (None, 14, 14,    │          0 │ block5h_drop[0][ │\n│                     │ 224)              │            │ block5g_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5i_expand_conv │ (None, 14, 14,    │    301,056 │ block5h_add[0][0] │\n│ (Conv2D)            │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5i_expand_bn   │ (None, 14, 14,    │      5,376 │ block5i_expand_c… │\n│ (BatchNormalizatio…1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5i_expand_act… │ (None, 14, 14,    │          0 │ block5i_expand_b… │\n│ (Activation)        │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5i_dwconv      │ (None, 14, 14,    │     33,600 │ block5i_expand_a… │\n│ (DepthwiseConv2D)   │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5i_bn          │ (None, 14, 14,    │      5,376 │ block5i_dwconv[0… │\n│ (BatchNormalizatio…1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5i_activation  │ (None, 14, 14,    │          0 │ block5i_bn[0][0]  │\n│ (Activation)        │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5i_se_squeeze  │ (None, 1344)      │          0 │ block5i_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5i_se_reshape  │ (None, 1, 1,      │          0 │ block5i_se_squee… │\n│ (Reshape)           │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5i_se_reduce   │ (None, 1, 1, 56)  │     75,320 │ block5i_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5i_se_expand   │ (None, 1, 1,      │     76,608 │ block5i_se_reduc… │\n│ (Conv2D)            │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5i_se_excite   │ (None, 14, 14,    │          0 │ block5i_activati… │\n│ (Multiply)          │ 1344)             │            │ block5i_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5i_project_co… │ (None, 14, 14,    │    301,056 │ block5i_se_excit… │\n│ (Conv2D)            │ 224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5i_project_bn  │ (None, 14, 14,    │        896 │ block5i_project_… │\n│ (BatchNormalizatio…224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5i_drop        │ (None, 14, 14,    │          0 │ block5i_project_… │\n│ (Dropout)           │ 224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5i_add (Add)   │ (None, 14, 14,    │          0 │ block5i_drop[0][ │\n│                     │ 224)              │            │ block5h_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5j_expand_conv │ (None, 14, 14,    │    301,056 │ block5i_add[0][0] │\n│ (Conv2D)            │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5j_expand_bn   │ (None, 14, 14,    │      5,376 │ block5j_expand_c… │\n│ (BatchNormalizatio…1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5j_expand_act… │ (None, 14, 14,    │          0 │ block5j_expand_b… │\n│ (Activation)        │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5j_dwconv      │ (None, 14, 14,    │     33,600 │ block5j_expand_a… │\n│ (DepthwiseConv2D)   │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5j_bn          │ (None, 14, 14,    │      5,376 │ block5j_dwconv[0… │\n│ (BatchNormalizatio…1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5j_activation  │ (None, 14, 14,    │          0 │ block5j_bn[0][0]  │\n│ (Activation)        │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5j_se_squeeze  │ (None, 1344)      │          0 │ block5j_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5j_se_reshape  │ (None, 1, 1,      │          0 │ block5j_se_squee… │\n│ (Reshape)           │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5j_se_reduce   │ (None, 1, 1, 56)  │     75,320 │ block5j_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5j_se_expand   │ (None, 1, 1,      │     76,608 │ block5j_se_reduc… │\n│ (Conv2D)            │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5j_se_excite   │ (None, 14, 14,    │          0 │ block5j_activati… │\n│ (Multiply)          │ 1344)             │            │ block5j_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5j_project_co… │ (None, 14, 14,    │    301,056 │ block5j_se_excit… │\n│ (Conv2D)            │ 224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5j_project_bn  │ (None, 14, 14,    │        896 │ block5j_project_… │\n│ (BatchNormalizatio…224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5j_drop        │ (None, 14, 14,    │          0 │ block5j_project_… │\n│ (Dropout)           │ 224)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block5j_add (Add)   │ (None, 14, 14,    │          0 │ block5j_drop[0][ │\n│                     │ 224)              │            │ block5i_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6a_expand_conv │ (None, 14, 14,    │    301,056 │ block5j_add[0][0] │\n│ (Conv2D)            │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6a_expand_bn   │ (None, 14, 14,    │      5,376 │ block6a_expand_c… │\n│ (BatchNormalizatio…1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6a_expand_act… │ (None, 14, 14,    │          0 │ block6a_expand_b… │\n│ (Activation)        │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6a_dwconv_pad  │ (None, 17, 17,    │          0 │ block6a_expand_a… │\n│ (ZeroPadding2D)     │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6a_dwconv      │ (None, 7, 7,      │     33,600 │ block6a_dwconv_p… │\n│ (DepthwiseConv2D)   │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6a_bn          │ (None, 7, 7,      │      5,376 │ block6a_dwconv[0… │\n│ (BatchNormalizatio…1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6a_activation  │ (None, 7, 7,      │          0 │ block6a_bn[0][0]  │\n│ (Activation)        │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6a_se_squeeze  │ (None, 1344)      │          0 │ block6a_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6a_se_reshape  │ (None, 1, 1,      │          0 │ block6a_se_squee… │\n│ (Reshape)           │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6a_se_reduce   │ (None, 1, 1, 56)  │     75,320 │ block6a_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6a_se_expand   │ (None, 1, 1,      │     76,608 │ block6a_se_reduc… │\n│ (Conv2D)            │ 1344)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6a_se_excite   │ (None, 7, 7,      │          0 │ block6a_activati… │\n│ (Multiply)          │ 1344)             │            │ block6a_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6a_project_co… │ (None, 7, 7, 384) │    516,096 │ block6a_se_excit… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6a_project_bn  │ (None, 7, 7, 384) │      1,536 │ block6a_project_… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6b_expand_conv │ (None, 7, 7,      │    884,736 │ block6a_project_… │\n│ (Conv2D)            │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6b_expand_bn   │ (None, 7, 7,      │      9,216 │ block6b_expand_c… │\n│ (BatchNormalizatio…2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6b_expand_act… │ (None, 7, 7,      │          0 │ block6b_expand_b… │\n│ (Activation)        │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6b_dwconv      │ (None, 7, 7,      │     57,600 │ block6b_expand_a… │\n│ (DepthwiseConv2D)   │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6b_bn          │ (None, 7, 7,      │      9,216 │ block6b_dwconv[0… │\n│ (BatchNormalizatio…2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6b_activation  │ (None, 7, 7,      │          0 │ block6b_bn[0][0]  │\n│ (Activation)        │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6b_se_squeeze  │ (None, 2304)      │          0 │ block6b_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6b_se_reshape  │ (None, 1, 1,      │          0 │ block6b_se_squee… │\n│ (Reshape)           │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6b_se_reduce   │ (None, 1, 1, 96)  │    221,280 │ block6b_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6b_se_expand   │ (None, 1, 1,      │    223,488 │ block6b_se_reduc… │\n│ (Conv2D)            │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6b_se_excite   │ (None, 7, 7,      │          0 │ block6b_activati… │\n│ (Multiply)          │ 2304)             │            │ block6b_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6b_project_co… │ (None, 7, 7, 384) │    884,736 │ block6b_se_excit… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6b_project_bn  │ (None, 7, 7, 384) │      1,536 │ block6b_project_… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6b_drop        │ (None, 7, 7, 384) │          0 │ block6b_project_… │\n│ (Dropout)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6b_add (Add)   │ (None, 7, 7, 384) │          0 │ block6b_drop[0][ │\n│                     │                   │            │ block6a_project_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6c_expand_conv │ (None, 7, 7,      │    884,736 │ block6b_add[0][0] │\n│ (Conv2D)            │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6c_expand_bn   │ (None, 7, 7,      │      9,216 │ block6c_expand_c… │\n│ (BatchNormalizatio…2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6c_expand_act… │ (None, 7, 7,      │          0 │ block6c_expand_b… │\n│ (Activation)        │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6c_dwconv      │ (None, 7, 7,      │     57,600 │ block6c_expand_a… │\n│ (DepthwiseConv2D)   │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6c_bn          │ (None, 7, 7,      │      9,216 │ block6c_dwconv[0… │\n│ (BatchNormalizatio…2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6c_activation  │ (None, 7, 7,      │          0 │ block6c_bn[0][0]  │\n│ (Activation)        │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6c_se_squeeze  │ (None, 2304)      │          0 │ block6c_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6c_se_reshape  │ (None, 1, 1,      │          0 │ block6c_se_squee… │\n│ (Reshape)           │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6c_se_reduce   │ (None, 1, 1, 96)  │    221,280 │ block6c_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6c_se_expand   │ (None, 1, 1,      │    223,488 │ block6c_se_reduc… │\n│ (Conv2D)            │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6c_se_excite   │ (None, 7, 7,      │          0 │ block6c_activati… │\n│ (Multiply)          │ 2304)             │            │ block6c_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6c_project_co… │ (None, 7, 7, 384) │    884,736 │ block6c_se_excit… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6c_project_bn  │ (None, 7, 7, 384) │      1,536 │ block6c_project_… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6c_drop        │ (None, 7, 7, 384) │          0 │ block6c_project_… │\n│ (Dropout)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6c_add (Add)   │ (None, 7, 7, 384) │          0 │ block6c_drop[0][ │\n│                     │                   │            │ block6b_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6d_expand_conv │ (None, 7, 7,      │    884,736 │ block6c_add[0][0] │\n│ (Conv2D)            │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6d_expand_bn   │ (None, 7, 7,      │      9,216 │ block6d_expand_c… │\n│ (BatchNormalizatio…2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6d_expand_act… │ (None, 7, 7,      │          0 │ block6d_expand_b… │\n│ (Activation)        │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6d_dwconv      │ (None, 7, 7,      │     57,600 │ block6d_expand_a… │\n│ (DepthwiseConv2D)   │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6d_bn          │ (None, 7, 7,      │      9,216 │ block6d_dwconv[0… │\n│ (BatchNormalizatio…2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6d_activation  │ (None, 7, 7,      │          0 │ block6d_bn[0][0]  │\n│ (Activation)        │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6d_se_squeeze  │ (None, 2304)      │          0 │ block6d_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6d_se_reshape  │ (None, 1, 1,      │          0 │ block6d_se_squee… │\n│ (Reshape)           │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6d_se_reduce   │ (None, 1, 1, 96)  │    221,280 │ block6d_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6d_se_expand   │ (None, 1, 1,      │    223,488 │ block6d_se_reduc… │\n│ (Conv2D)            │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6d_se_excite   │ (None, 7, 7,      │          0 │ block6d_activati… │\n│ (Multiply)          │ 2304)             │            │ block6d_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6d_project_co… │ (None, 7, 7, 384) │    884,736 │ block6d_se_excit… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6d_project_bn  │ (None, 7, 7, 384) │      1,536 │ block6d_project_… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6d_drop        │ (None, 7, 7, 384) │          0 │ block6d_project_… │\n│ (Dropout)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6d_add (Add)   │ (None, 7, 7, 384) │          0 │ block6d_drop[0][ │\n│                     │                   │            │ block6c_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6e_expand_conv │ (None, 7, 7,      │    884,736 │ block6d_add[0][0] │\n│ (Conv2D)            │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6e_expand_bn   │ (None, 7, 7,      │      9,216 │ block6e_expand_c… │\n│ (BatchNormalizatio…2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6e_expand_act… │ (None, 7, 7,      │          0 │ block6e_expand_b… │\n│ (Activation)        │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6e_dwconv      │ (None, 7, 7,      │     57,600 │ block6e_expand_a… │\n│ (DepthwiseConv2D)   │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6e_bn          │ (None, 7, 7,      │      9,216 │ block6e_dwconv[0… │\n│ (BatchNormalizatio…2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6e_activation  │ (None, 7, 7,      │          0 │ block6e_bn[0][0]  │\n│ (Activation)        │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6e_se_squeeze  │ (None, 2304)      │          0 │ block6e_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6e_se_reshape  │ (None, 1, 1,      │          0 │ block6e_se_squee… │\n│ (Reshape)           │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6e_se_reduce   │ (None, 1, 1, 96)  │    221,280 │ block6e_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6e_se_expand   │ (None, 1, 1,      │    223,488 │ block6e_se_reduc… │\n│ (Conv2D)            │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6e_se_excite   │ (None, 7, 7,      │          0 │ block6e_activati… │\n│ (Multiply)          │ 2304)             │            │ block6e_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6e_project_co… │ (None, 7, 7, 384) │    884,736 │ block6e_se_excit… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6e_project_bn  │ (None, 7, 7, 384) │      1,536 │ block6e_project_… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6e_drop        │ (None, 7, 7, 384) │          0 │ block6e_project_… │\n│ (Dropout)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6e_add (Add)   │ (None, 7, 7, 384) │          0 │ block6e_drop[0][ │\n│                     │                   │            │ block6d_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6f_expand_conv │ (None, 7, 7,      │    884,736 │ block6e_add[0][0] │\n│ (Conv2D)            │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6f_expand_bn   │ (None, 7, 7,      │      9,216 │ block6f_expand_c… │\n│ (BatchNormalizatio…2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6f_expand_act… │ (None, 7, 7,      │          0 │ block6f_expand_b… │\n│ (Activation)        │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6f_dwconv      │ (None, 7, 7,      │     57,600 │ block6f_expand_a… │\n│ (DepthwiseConv2D)   │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6f_bn          │ (None, 7, 7,      │      9,216 │ block6f_dwconv[0… │\n│ (BatchNormalizatio…2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6f_activation  │ (None, 7, 7,      │          0 │ block6f_bn[0][0]  │\n│ (Activation)        │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6f_se_squeeze  │ (None, 2304)      │          0 │ block6f_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6f_se_reshape  │ (None, 1, 1,      │          0 │ block6f_se_squee… │\n│ (Reshape)           │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6f_se_reduce   │ (None, 1, 1, 96)  │    221,280 │ block6f_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6f_se_expand   │ (None, 1, 1,      │    223,488 │ block6f_se_reduc… │\n│ (Conv2D)            │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6f_se_excite   │ (None, 7, 7,      │          0 │ block6f_activati… │\n│ (Multiply)          │ 2304)             │            │ block6f_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6f_project_co… │ (None, 7, 7, 384) │    884,736 │ block6f_se_excit… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6f_project_bn  │ (None, 7, 7, 384) │      1,536 │ block6f_project_… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6f_drop        │ (None, 7, 7, 384) │          0 │ block6f_project_… │\n│ (Dropout)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6f_add (Add)   │ (None, 7, 7, 384) │          0 │ block6f_drop[0][ │\n│                     │                   │            │ block6e_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6g_expand_conv │ (None, 7, 7,      │    884,736 │ block6f_add[0][0] │\n│ (Conv2D)            │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6g_expand_bn   │ (None, 7, 7,      │      9,216 │ block6g_expand_c… │\n│ (BatchNormalizatio…2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6g_expand_act… │ (None, 7, 7,      │          0 │ block6g_expand_b… │\n│ (Activation)        │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6g_dwconv      │ (None, 7, 7,      │     57,600 │ block6g_expand_a… │\n│ (DepthwiseConv2D)   │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6g_bn          │ (None, 7, 7,      │      9,216 │ block6g_dwconv[0… │\n│ (BatchNormalizatio…2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6g_activation  │ (None, 7, 7,      │          0 │ block6g_bn[0][0]  │\n│ (Activation)        │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6g_se_squeeze  │ (None, 2304)      │          0 │ block6g_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6g_se_reshape  │ (None, 1, 1,      │          0 │ block6g_se_squee… │\n│ (Reshape)           │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6g_se_reduce   │ (None, 1, 1, 96)  │    221,280 │ block6g_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6g_se_expand   │ (None, 1, 1,      │    223,488 │ block6g_se_reduc… │\n│ (Conv2D)            │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6g_se_excite   │ (None, 7, 7,      │          0 │ block6g_activati… │\n│ (Multiply)          │ 2304)             │            │ block6g_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6g_project_co… │ (None, 7, 7, 384) │    884,736 │ block6g_se_excit… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6g_project_bn  │ (None, 7, 7, 384) │      1,536 │ block6g_project_… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6g_drop        │ (None, 7, 7, 384) │          0 │ block6g_project_… │\n│ (Dropout)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6g_add (Add)   │ (None, 7, 7, 384) │          0 │ block6g_drop[0][ │\n│                     │                   │            │ block6f_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6h_expand_conv │ (None, 7, 7,      │    884,736 │ block6g_add[0][0] │\n│ (Conv2D)            │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6h_expand_bn   │ (None, 7, 7,      │      9,216 │ block6h_expand_c… │\n│ (BatchNormalizatio…2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6h_expand_act… │ (None, 7, 7,      │          0 │ block6h_expand_b… │\n│ (Activation)        │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6h_dwconv      │ (None, 7, 7,      │     57,600 │ block6h_expand_a… │\n│ (DepthwiseConv2D)   │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6h_bn          │ (None, 7, 7,      │      9,216 │ block6h_dwconv[0… │\n│ (BatchNormalizatio…2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6h_activation  │ (None, 7, 7,      │          0 │ block6h_bn[0][0]  │\n│ (Activation)        │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6h_se_squeeze  │ (None, 2304)      │          0 │ block6h_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6h_se_reshape  │ (None, 1, 1,      │          0 │ block6h_se_squee… │\n│ (Reshape)           │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6h_se_reduce   │ (None, 1, 1, 96)  │    221,280 │ block6h_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6h_se_expand   │ (None, 1, 1,      │    223,488 │ block6h_se_reduc… │\n│ (Conv2D)            │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6h_se_excite   │ (None, 7, 7,      │          0 │ block6h_activati… │\n│ (Multiply)          │ 2304)             │            │ block6h_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6h_project_co… │ (None, 7, 7, 384) │    884,736 │ block6h_se_excit… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6h_project_bn  │ (None, 7, 7, 384) │      1,536 │ block6h_project_… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6h_drop        │ (None, 7, 7, 384) │          0 │ block6h_project_… │\n│ (Dropout)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6h_add (Add)   │ (None, 7, 7, 384) │          0 │ block6h_drop[0][ │\n│                     │                   │            │ block6g_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6i_expand_conv │ (None, 7, 7,      │    884,736 │ block6h_add[0][0] │\n│ (Conv2D)            │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6i_expand_bn   │ (None, 7, 7,      │      9,216 │ block6i_expand_c… │\n│ (BatchNormalizatio…2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6i_expand_act… │ (None, 7, 7,      │          0 │ block6i_expand_b… │\n│ (Activation)        │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6i_dwconv      │ (None, 7, 7,      │     57,600 │ block6i_expand_a… │\n│ (DepthwiseConv2D)   │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6i_bn          │ (None, 7, 7,      │      9,216 │ block6i_dwconv[0… │\n│ (BatchNormalizatio…2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6i_activation  │ (None, 7, 7,      │          0 │ block6i_bn[0][0]  │\n│ (Activation)        │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6i_se_squeeze  │ (None, 2304)      │          0 │ block6i_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6i_se_reshape  │ (None, 1, 1,      │          0 │ block6i_se_squee… │\n│ (Reshape)           │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6i_se_reduce   │ (None, 1, 1, 96)  │    221,280 │ block6i_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6i_se_expand   │ (None, 1, 1,      │    223,488 │ block6i_se_reduc… │\n│ (Conv2D)            │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6i_se_excite   │ (None, 7, 7,      │          0 │ block6i_activati… │\n│ (Multiply)          │ 2304)             │            │ block6i_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6i_project_co… │ (None, 7, 7, 384) │    884,736 │ block6i_se_excit… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6i_project_bn  │ (None, 7, 7, 384) │      1,536 │ block6i_project_… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6i_drop        │ (None, 7, 7, 384) │          0 │ block6i_project_… │\n│ (Dropout)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6i_add (Add)   │ (None, 7, 7, 384) │          0 │ block6i_drop[0][ │\n│                     │                   │            │ block6h_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6j_expand_conv │ (None, 7, 7,      │    884,736 │ block6i_add[0][0] │\n│ (Conv2D)            │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6j_expand_bn   │ (None, 7, 7,      │      9,216 │ block6j_expand_c… │\n│ (BatchNormalizatio…2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6j_expand_act… │ (None, 7, 7,      │          0 │ block6j_expand_b… │\n│ (Activation)        │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6j_dwconv      │ (None, 7, 7,      │     57,600 │ block6j_expand_a… │\n│ (DepthwiseConv2D)   │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6j_bn          │ (None, 7, 7,      │      9,216 │ block6j_dwconv[0… │\n│ (BatchNormalizatio…2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6j_activation  │ (None, 7, 7,      │          0 │ block6j_bn[0][0]  │\n│ (Activation)        │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6j_se_squeeze  │ (None, 2304)      │          0 │ block6j_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6j_se_reshape  │ (None, 1, 1,      │          0 │ block6j_se_squee… │\n│ (Reshape)           │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6j_se_reduce   │ (None, 1, 1, 96)  │    221,280 │ block6j_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6j_se_expand   │ (None, 1, 1,      │    223,488 │ block6j_se_reduc… │\n│ (Conv2D)            │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6j_se_excite   │ (None, 7, 7,      │          0 │ block6j_activati… │\n│ (Multiply)          │ 2304)             │            │ block6j_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6j_project_co… │ (None, 7, 7, 384) │    884,736 │ block6j_se_excit… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6j_project_bn  │ (None, 7, 7, 384) │      1,536 │ block6j_project_… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6j_drop        │ (None, 7, 7, 384) │          0 │ block6j_project_… │\n│ (Dropout)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6j_add (Add)   │ (None, 7, 7, 384) │          0 │ block6j_drop[0][ │\n│                     │                   │            │ block6i_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6k_expand_conv │ (None, 7, 7,      │    884,736 │ block6j_add[0][0] │\n│ (Conv2D)            │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6k_expand_bn   │ (None, 7, 7,      │      9,216 │ block6k_expand_c… │\n│ (BatchNormalizatio…2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6k_expand_act… │ (None, 7, 7,      │          0 │ block6k_expand_b… │\n│ (Activation)        │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6k_dwconv      │ (None, 7, 7,      │     57,600 │ block6k_expand_a… │\n│ (DepthwiseConv2D)   │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6k_bn          │ (None, 7, 7,      │      9,216 │ block6k_dwconv[0… │\n│ (BatchNormalizatio…2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6k_activation  │ (None, 7, 7,      │          0 │ block6k_bn[0][0]  │\n│ (Activation)        │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6k_se_squeeze  │ (None, 2304)      │          0 │ block6k_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6k_se_reshape  │ (None, 1, 1,      │          0 │ block6k_se_squee… │\n│ (Reshape)           │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6k_se_reduce   │ (None, 1, 1, 96)  │    221,280 │ block6k_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6k_se_expand   │ (None, 1, 1,      │    223,488 │ block6k_se_reduc… │\n│ (Conv2D)            │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6k_se_excite   │ (None, 7, 7,      │          0 │ block6k_activati… │\n│ (Multiply)          │ 2304)             │            │ block6k_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6k_project_co… │ (None, 7, 7, 384) │    884,736 │ block6k_se_excit… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6k_project_bn  │ (None, 7, 7, 384) │      1,536 │ block6k_project_… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6k_drop        │ (None, 7, 7, 384) │          0 │ block6k_project_… │\n│ (Dropout)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6k_add (Add)   │ (None, 7, 7, 384) │          0 │ block6k_drop[0][ │\n│                     │                   │            │ block6j_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6l_expand_conv │ (None, 7, 7,      │    884,736 │ block6k_add[0][0] │\n│ (Conv2D)            │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6l_expand_bn   │ (None, 7, 7,      │      9,216 │ block6l_expand_c… │\n│ (BatchNormalizatio…2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6l_expand_act… │ (None, 7, 7,      │          0 │ block6l_expand_b… │\n│ (Activation)        │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6l_dwconv      │ (None, 7, 7,      │     57,600 │ block6l_expand_a… │\n│ (DepthwiseConv2D)   │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6l_bn          │ (None, 7, 7,      │      9,216 │ block6l_dwconv[0… │\n│ (BatchNormalizatio…2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6l_activation  │ (None, 7, 7,      │          0 │ block6l_bn[0][0]  │\n│ (Activation)        │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6l_se_squeeze  │ (None, 2304)      │          0 │ block6l_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6l_se_reshape  │ (None, 1, 1,      │          0 │ block6l_se_squee… │\n│ (Reshape)           │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6l_se_reduce   │ (None, 1, 1, 96)  │    221,280 │ block6l_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6l_se_expand   │ (None, 1, 1,      │    223,488 │ block6l_se_reduc… │\n│ (Conv2D)            │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6l_se_excite   │ (None, 7, 7,      │          0 │ block6l_activati… │\n│ (Multiply)          │ 2304)             │            │ block6l_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6l_project_co… │ (None, 7, 7, 384) │    884,736 │ block6l_se_excit… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6l_project_bn  │ (None, 7, 7, 384) │      1,536 │ block6l_project_… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6l_drop        │ (None, 7, 7, 384) │          0 │ block6l_project_… │\n│ (Dropout)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6l_add (Add)   │ (None, 7, 7, 384) │          0 │ block6l_drop[0][ │\n│                     │                   │            │ block6k_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6m_expand_conv │ (None, 7, 7,      │    884,736 │ block6l_add[0][0] │\n│ (Conv2D)            │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6m_expand_bn   │ (None, 7, 7,      │      9,216 │ block6m_expand_c… │\n│ (BatchNormalizatio…2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6m_expand_act… │ (None, 7, 7,      │          0 │ block6m_expand_b… │\n│ (Activation)        │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6m_dwconv      │ (None, 7, 7,      │     57,600 │ block6m_expand_a… │\n│ (DepthwiseConv2D)   │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6m_bn          │ (None, 7, 7,      │      9,216 │ block6m_dwconv[0… │\n│ (BatchNormalizatio…2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6m_activation  │ (None, 7, 7,      │          0 │ block6m_bn[0][0]  │\n│ (Activation)        │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6m_se_squeeze  │ (None, 2304)      │          0 │ block6m_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6m_se_reshape  │ (None, 1, 1,      │          0 │ block6m_se_squee… │\n│ (Reshape)           │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6m_se_reduce   │ (None, 1, 1, 96)  │    221,280 │ block6m_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6m_se_expand   │ (None, 1, 1,      │    223,488 │ block6m_se_reduc… │\n│ (Conv2D)            │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6m_se_excite   │ (None, 7, 7,      │          0 │ block6m_activati… │\n│ (Multiply)          │ 2304)             │            │ block6m_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6m_project_co… │ (None, 7, 7, 384) │    884,736 │ block6m_se_excit… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6m_project_bn  │ (None, 7, 7, 384) │      1,536 │ block6m_project_… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6m_drop        │ (None, 7, 7, 384) │          0 │ block6m_project_… │\n│ (Dropout)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block6m_add (Add)   │ (None, 7, 7, 384) │          0 │ block6m_drop[0][ │\n│                     │                   │            │ block6l_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7a_expand_conv │ (None, 7, 7,      │    884,736 │ block6m_add[0][0] │\n│ (Conv2D)            │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7a_expand_bn   │ (None, 7, 7,      │      9,216 │ block7a_expand_c… │\n│ (BatchNormalizatio…2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7a_expand_act… │ (None, 7, 7,      │          0 │ block7a_expand_b… │\n│ (Activation)        │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7a_dwconv      │ (None, 7, 7,      │     20,736 │ block7a_expand_a… │\n│ (DepthwiseConv2D)   │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7a_bn          │ (None, 7, 7,      │      9,216 │ block7a_dwconv[0… │\n│ (BatchNormalizatio…2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7a_activation  │ (None, 7, 7,      │          0 │ block7a_bn[0][0]  │\n│ (Activation)        │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7a_se_squeeze  │ (None, 2304)      │          0 │ block7a_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7a_se_reshape  │ (None, 1, 1,      │          0 │ block7a_se_squee… │\n│ (Reshape)           │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7a_se_reduce   │ (None, 1, 1, 96)  │    221,280 │ block7a_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7a_se_expand   │ (None, 1, 1,      │    223,488 │ block7a_se_reduc… │\n│ (Conv2D)            │ 2304)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7a_se_excite   │ (None, 7, 7,      │          0 │ block7a_activati… │\n│ (Multiply)          │ 2304)             │            │ block7a_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7a_project_co… │ (None, 7, 7, 640) │  1,474,560 │ block7a_se_excit… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7a_project_bn  │ (None, 7, 7, 640) │      2,560 │ block7a_project_… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7b_expand_conv │ (None, 7, 7,      │  2,457,600 │ block7a_project_… │\n│ (Conv2D)            │ 3840)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7b_expand_bn   │ (None, 7, 7,      │     15,360 │ block7b_expand_c… │\n│ (BatchNormalizatio…3840)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7b_expand_act… │ (None, 7, 7,      │          0 │ block7b_expand_b… │\n│ (Activation)        │ 3840)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7b_dwconv      │ (None, 7, 7,      │     34,560 │ block7b_expand_a… │\n│ (DepthwiseConv2D)   │ 3840)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7b_bn          │ (None, 7, 7,      │     15,360 │ block7b_dwconv[0… │\n│ (BatchNormalizatio…3840)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7b_activation  │ (None, 7, 7,      │          0 │ block7b_bn[0][0]  │\n│ (Activation)        │ 3840)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7b_se_squeeze  │ (None, 3840)      │          0 │ block7b_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7b_se_reshape  │ (None, 1, 1,      │          0 │ block7b_se_squee… │\n│ (Reshape)           │ 3840)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7b_se_reduce   │ (None, 1, 1, 160) │    614,560 │ block7b_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7b_se_expand   │ (None, 1, 1,      │    618,240 │ block7b_se_reduc… │\n│ (Conv2D)            │ 3840)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7b_se_excite   │ (None, 7, 7,      │          0 │ block7b_activati… │\n│ (Multiply)          │ 3840)             │            │ block7b_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7b_project_co… │ (None, 7, 7, 640) │  2,457,600 │ block7b_se_excit… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7b_project_bn  │ (None, 7, 7, 640) │      2,560 │ block7b_project_… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7b_drop        │ (None, 7, 7, 640) │          0 │ block7b_project_… │\n│ (Dropout)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7b_add (Add)   │ (None, 7, 7, 640) │          0 │ block7b_drop[0][ │\n│                     │                   │            │ block7a_project_… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7c_expand_conv │ (None, 7, 7,      │  2,457,600 │ block7b_add[0][0] │\n│ (Conv2D)            │ 3840)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7c_expand_bn   │ (None, 7, 7,      │     15,360 │ block7c_expand_c… │\n│ (BatchNormalizatio…3840)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7c_expand_act… │ (None, 7, 7,      │          0 │ block7c_expand_b… │\n│ (Activation)        │ 3840)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7c_dwconv      │ (None, 7, 7,      │     34,560 │ block7c_expand_a… │\n│ (DepthwiseConv2D)   │ 3840)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7c_bn          │ (None, 7, 7,      │     15,360 │ block7c_dwconv[0… │\n│ (BatchNormalizatio…3840)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7c_activation  │ (None, 7, 7,      │          0 │ block7c_bn[0][0]  │\n│ (Activation)        │ 3840)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7c_se_squeeze  │ (None, 3840)      │          0 │ block7c_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7c_se_reshape  │ (None, 1, 1,      │          0 │ block7c_se_squee… │\n│ (Reshape)           │ 3840)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7c_se_reduce   │ (None, 1, 1, 160) │    614,560 │ block7c_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7c_se_expand   │ (None, 1, 1,      │    618,240 │ block7c_se_reduc… │\n│ (Conv2D)            │ 3840)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7c_se_excite   │ (None, 7, 7,      │          0 │ block7c_activati… │\n│ (Multiply)          │ 3840)             │            │ block7c_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7c_project_co… │ (None, 7, 7, 640) │  2,457,600 │ block7c_se_excit… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7c_project_bn  │ (None, 7, 7, 640) │      2,560 │ block7c_project_… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7c_drop        │ (None, 7, 7, 640) │          0 │ block7c_project_… │\n│ (Dropout)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7c_add (Add)   │ (None, 7, 7, 640) │          0 │ block7c_drop[0][ │\n│                     │                   │            │ block7b_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7d_expand_conv │ (None, 7, 7,      │  2,457,600 │ block7c_add[0][0] │\n│ (Conv2D)            │ 3840)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7d_expand_bn   │ (None, 7, 7,      │     15,360 │ block7d_expand_c… │\n│ (BatchNormalizatio…3840)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7d_expand_act… │ (None, 7, 7,      │          0 │ block7d_expand_b… │\n│ (Activation)        │ 3840)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7d_dwconv      │ (None, 7, 7,      │     34,560 │ block7d_expand_a… │\n│ (DepthwiseConv2D)   │ 3840)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7d_bn          │ (None, 7, 7,      │     15,360 │ block7d_dwconv[0… │\n│ (BatchNormalizatio…3840)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7d_activation  │ (None, 7, 7,      │          0 │ block7d_bn[0][0]  │\n│ (Activation)        │ 3840)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7d_se_squeeze  │ (None, 3840)      │          0 │ block7d_activati… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7d_se_reshape  │ (None, 1, 1,      │          0 │ block7d_se_squee… │\n│ (Reshape)           │ 3840)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7d_se_reduce   │ (None, 1, 1, 160) │    614,560 │ block7d_se_resha… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7d_se_expand   │ (None, 1, 1,      │    618,240 │ block7d_se_reduc… │\n│ (Conv2D)            │ 3840)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7d_se_excite   │ (None, 7, 7,      │          0 │ block7d_activati… │\n│ (Multiply)          │ 3840)             │            │ block7d_se_expan… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7d_project_co… │ (None, 7, 7, 640) │  2,457,600 │ block7d_se_excit… │\n│ (Conv2D)            │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7d_project_bn  │ (None, 7, 7, 640) │      2,560 │ block7d_project_… │\n│ (BatchNormalizatio… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7d_drop        │ (None, 7, 7, 640) │          0 │ block7d_project_… │\n│ (Dropout)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ block7d_add (Add)   │ (None, 7, 7, 640) │          0 │ block7d_drop[0][ │\n│                     │                   │            │ block7c_add[0][0] │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ top_conv (Conv2D)   │ (None, 7, 7,      │  1,638,400 │ block7d_add[0][0] │\n│                     │ 2560)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ top_bn              │ (None, 7, 7,      │     10,240 │ top_conv[0][0]    │\n│ (BatchNormalizatio…2560)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ top_activation      │ (None, 7, 7,      │          0 │ top_bn[0][0]      │\n│ (Activation)        │ 2560)             │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ global_average_poo… │ (None, 2560)      │          0 │ top_activation[0… │\n│ (GlobalAveragePool… │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense_6 (Dense)     │ (None, 1024)      │  2,622,464 │ global_average_p… │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dense_7 (Dense)     │ (None, 1)         │      1,025 │ dense_6[0][0]     │\n└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m66,721,176\u001b[0m (254.52 MB)\n","text/html":"
 Total params: 66,721,176 (254.52 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2,623,489\u001b[0m (10.01 MB)\n","text/html":"
 Trainable params: 2,623,489 (10.01 MB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m64,097,687\u001b[0m (244.51 MB)\n","text/html":"
 Non-trainable params: 64,097,687 (244.51 MB)\n
\n"},"metadata":{}}]},{"cell_type":"code","source":"model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\nhistory = model.fit(x_train, y_train, validation_data=(x_val, y_val), epochs=20, batch_size=32,callbacks=[reduce_lr,model_checkpoint])","metadata":{"execution":{"iopub.status.busy":"2024-05-31T15:07:34.591437Z","iopub.execute_input":"2024-05-31T15:07:34.592078Z","iopub.status.idle":"2024-05-31T15:16:11.478957Z","shell.execute_reply.started":"2024-05-31T15:07:34.592042Z","shell.execute_reply":"2024-05-31T15:16:11.477970Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stdout","text":"Epoch 1/20\n","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1717168190.932245 116 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3s/step - accuracy: 0.5471 - loss: 0.9047 ","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1717168296.043126 115 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\nW0000 00:00:1717168312.304892 113 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\nEpoch 1: val_accuracy improved from -inf to 0.60674, saving model to att_model.keras\n","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1717168328.356782 113 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m274s\u001b[0m 4s/step - accuracy: 0.5472 - loss: 0.9016 - val_accuracy: 0.6067 - val_loss: 0.6833 - learning_rate: 0.0010\nEpoch 2/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 254ms/step - accuracy: 0.5767 - loss: 0.6893\nEpoch 2: val_accuracy did not improve from 0.60674\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 310ms/step - accuracy: 0.5767 - loss: 0.6892 - val_accuracy: 0.6067 - val_loss: 0.6723 - learning_rate: 0.0010\nEpoch 3/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 252ms/step - accuracy: 0.5779 - loss: 0.6938\nEpoch 3: val_accuracy did not improve from 0.60674\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 307ms/step - accuracy: 0.5777 - loss: 0.6938 - val_accuracy: 0.6067 - val_loss: 0.6704 - learning_rate: 0.0010\nEpoch 4/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 247ms/step - accuracy: 0.6005 - loss: 0.6769\nEpoch 4: val_accuracy did not improve from 0.60674\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 301ms/step - accuracy: 0.6005 - loss: 0.6770 - val_accuracy: 0.6067 - val_loss: 0.6739 - learning_rate: 0.0010\nEpoch 5/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 243ms/step - accuracy: 0.6265 - loss: 0.6587\nEpoch 5: val_accuracy did not improve from 0.60674\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 296ms/step - accuracy: 0.6259 - loss: 0.6590 - val_accuracy: 0.6067 - val_loss: 0.6771 - learning_rate: 0.0010\nEpoch 6/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - accuracy: 0.5997 - loss: 0.6767\nEpoch 6: val_accuracy did not improve from 0.60674\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 293ms/step - accuracy: 0.5997 - loss: 0.6767 - val_accuracy: 0.6067 - val_loss: 0.6710 - learning_rate: 0.0010\nEpoch 7/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 246ms/step - accuracy: 0.6031 - loss: 0.6741\nEpoch 7: val_accuracy did not improve from 0.60674\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 301ms/step - accuracy: 0.6030 - loss: 0.6741 - val_accuracy: 0.6067 - val_loss: 0.6708 - learning_rate: 0.0010\nEpoch 8/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 253ms/step - accuracy: 0.6108 - loss: 0.6717\nEpoch 8: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n\nEpoch 8: val_accuracy did not improve from 0.60674\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 309ms/step - accuracy: 0.6105 - loss: 0.6719 - val_accuracy: 0.6067 - val_loss: 0.6712 - learning_rate: 0.0010\nEpoch 9/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 252ms/step - accuracy: 0.5922 - loss: 0.6789\nEpoch 9: val_accuracy did not improve from 0.60674\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 306ms/step - accuracy: 0.5924 - loss: 0.6788 - val_accuracy: 0.6067 - val_loss: 0.6702 - learning_rate: 1.0000e-04\nEpoch 10/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 247ms/step - accuracy: 0.6181 - loss: 0.6645\nEpoch 10: val_accuracy did not improve from 0.60674\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 301ms/step - accuracy: 0.6177 - loss: 0.6648 - val_accuracy: 0.6067 - val_loss: 0.6701 - learning_rate: 1.0000e-04\nEpoch 11/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 244ms/step - accuracy: 0.6038 - loss: 0.6718\nEpoch 11: val_accuracy did not improve from 0.60674\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 297ms/step - accuracy: 0.6037 - loss: 0.6719 - val_accuracy: 0.6067 - val_loss: 0.6704 - learning_rate: 1.0000e-04\nEpoch 12/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.5848 - loss: 0.6790\nEpoch 12: val_accuracy did not improve from 0.60674\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 293ms/step - accuracy: 0.5851 - loss: 0.6788 - val_accuracy: 0.6067 - val_loss: 0.6701 - learning_rate: 1.0000e-04\nEpoch 13/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 245ms/step - accuracy: 0.5904 - loss: 0.6766\nEpoch 13: val_accuracy did not improve from 0.60674\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 300ms/step - accuracy: 0.5907 - loss: 0.6765 - val_accuracy: 0.6067 - val_loss: 0.6701 - learning_rate: 1.0000e-04\nEpoch 14/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 253ms/step - accuracy: 0.6081 - loss: 0.6696\nEpoch 14: val_accuracy did not improve from 0.60674\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 309ms/step - accuracy: 0.6079 - loss: 0.6697 - val_accuracy: 0.6067 - val_loss: 0.6701 - learning_rate: 1.0000e-04\nEpoch 15/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 252ms/step - accuracy: 0.6138 - loss: 0.6671\nEpoch 15: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n\nEpoch 15: val_accuracy did not improve from 0.60674\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 307ms/step - accuracy: 0.6134 - loss: 0.6672 - val_accuracy: 0.6067 - val_loss: 0.6701 - learning_rate: 1.0000e-04\nEpoch 16/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 246ms/step - accuracy: 0.5988 - loss: 0.6734\nEpoch 16: val_accuracy did not improve from 0.60674\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 300ms/step - accuracy: 0.5989 - loss: 0.6734 - val_accuracy: 0.6067 - val_loss: 0.6702 - learning_rate: 1.0000e-05\nEpoch 17/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 243ms/step - accuracy: 0.5966 - loss: 0.6746\nEpoch 17: val_accuracy did not improve from 0.60674\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 297ms/step - accuracy: 0.5967 - loss: 0.6746 - val_accuracy: 0.6067 - val_loss: 0.6702 - learning_rate: 1.0000e-05\nEpoch 18/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.6089 - loss: 0.6695\nEpoch 18: val_accuracy did not improve from 0.60674\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 296ms/step - accuracy: 0.6087 - loss: 0.6696 - val_accuracy: 0.6067 - val_loss: 0.6702 - learning_rate: 1.0000e-05\nEpoch 19/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 243ms/step - accuracy: 0.6026 - loss: 0.6713\nEpoch 19: val_accuracy did not improve from 0.60674\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 296ms/step - accuracy: 0.6026 - loss: 0.6713 - val_accuracy: 0.6067 - val_loss: 0.6702 - learning_rate: 1.0000e-05\nEpoch 20/20\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 245ms/step - accuracy: 0.5968 - loss: 0.6749\nEpoch 20: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.\n\nEpoch 20: val_accuracy did not improve from 0.60674\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 299ms/step - accuracy: 0.5969 - loss: 0.6748 - val_accuracy: 0.6067 - val_loss: 0.6701 - learning_rate: 1.0000e-05\n","output_type":"stream"}]},{"cell_type":"code","source":"plt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\nplt.xlabel(\"Epoch\")\nplt.ylabel(\"Loss\")\nplt.title(\"Loss per epoch\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-31T15:16:11.487503Z","iopub.execute_input":"2024-05-31T15:16:11.487786Z","iopub.status.idle":"2024-05-31T15:16:11.768445Z","shell.execute_reply.started":"2024-05-31T15:16:11.487761Z","shell.execute_reply":"2024-05-31T15:16:11.767576Z"},"trusted":true},"execution_count":19,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"plt.plot(history.history['accuracy'])\nplt.plot(history.history['val_accuracy'])\nplt.xlabel(\"Epoch\")\nplt.ylabel(\"Accuracy\")\nplt.title(\"Accuracy per epoch\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-31T15:16:11.769904Z","iopub.execute_input":"2024-05-31T15:16:11.770366Z","iopub.status.idle":"2024-05-31T15:16:12.041330Z","shell.execute_reply.started":"2024-05-31T15:16:11.770331Z","shell.execute_reply":"2024-05-31T15:16:12.040482Z"},"trusted":true},"execution_count":20,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"### CNN","metadata":{}},{"cell_type":"code","source":"from keras.models import Sequential\nfrom keras.layers import GlobalAveragePooling2D, InputLayer\nfrom keras.callbacks import ReduceLROnPlateau\nfrom keras.regularizers import l1_l2\n\nmodel = Sequential()\n\nmodel.add(InputLayer(shape=(224, 224, 3)))\n\nmodel.add(Conv2D(64, 3, activation='relu', padding='same'))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=2))\n\nmodel.add(Conv2D(64, 3, activation='relu', padding='same'))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=2))\n\nmodel.add(Conv2D(64, 3, activation='relu', padding='same'))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=2))\n\nmodel.add(Conv2D(64, 3, activation='relu', padding='same'))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=2))\n\nmodel.add(GlobalAveragePooling2D())\n\nmodel.add(Dense(1024, activation='relu'))\n\nmodel.add(Dense(1, activation='sigmoid'))\n\nmodel.summary()\n\nmodel_checkpoint = ModelCheckpoint('model.keras', monitor='val_accuracy', save_best_only=True, verbose=1, mode='max')","metadata":{"execution":{"iopub.status.busy":"2024-05-31T20:24:45.192627Z","iopub.execute_input":"2024-05-31T20:24:45.193042Z","iopub.status.idle":"2024-05-31T20:24:46.746236Z","shell.execute_reply.started":"2024-05-31T20:24:45.193008Z","shell.execute_reply":"2024-05-31T20:24:46.745184Z"},"trusted":true},"execution_count":110,"outputs":[{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"sequential_10\"\u001b[0m\n","text/html":"
Model: \"sequential_10\"\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ conv2d_125 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m1,792\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_113 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_113 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_126 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m36,928\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_114 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_114 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_127 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m36,928\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_115 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_115 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_128 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m36,928\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_116 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_116 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ global_average_pooling2d_19 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n│ (\u001b[38;5;33mGlobalAveragePooling2D\u001b[0m) │ │ │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_56 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m66,560\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_57 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m1,025\u001b[0m │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n","text/html":"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃ Layer (type)                     Output Shape                  Param # ┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ conv2d_125 (Conv2D)             │ (None, 224, 224, 64)   │         1,792 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_113         │ (None, 224, 224, 64)   │           256 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_113               │ (None, 112, 112, 64)   │             0 │\n│ (MaxPooling2D)                  │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_126 (Conv2D)             │ (None, 112, 112, 64)   │        36,928 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_114         │ (None, 112, 112, 64)   │           256 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_114               │ (None, 56, 56, 64)     │             0 │\n│ (MaxPooling2D)                  │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_127 (Conv2D)             │ (None, 56, 56, 64)     │        36,928 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_115         │ (None, 56, 56, 64)     │           256 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_115               │ (None, 28, 28, 64)     │             0 │\n│ (MaxPooling2D)                  │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ conv2d_128 (Conv2D)             │ (None, 28, 28, 64)     │        36,928 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ batch_normalization_116         │ (None, 28, 28, 64)     │           256 │\n│ (BatchNormalization)            │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ max_pooling2d_116               │ (None, 14, 14, 64)     │             0 │\n│ (MaxPooling2D)                  │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ global_average_pooling2d_19     │ (None, 64)             │             0 │\n│ (GlobalAveragePooling2D)        │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_56 (Dense)                │ (None, 1024)           │        66,560 │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_57 (Dense)                │ (None, 1)              │         1,025 │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m181,185\u001b[0m (707.75 KB)\n","text/html":"
 Total params: 181,185 (707.75 KB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m180,673\u001b[0m (705.75 KB)\n","text/html":"
 Trainable params: 180,673 (705.75 KB)\n
\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m512\u001b[0m (2.00 KB)\n","text/html":"
 Non-trainable params: 512 (2.00 KB)\n
\n"},"metadata":{}}]},{"cell_type":"code","source":"model.compile(optimizer=Nadam(learning_rate=0.00005), loss='binary_crossentropy', metrics=['accuracy'])\nhistory = model.fit(x_train, y_train, validation_data=(x_val, y_val), epochs=50, batch_size=32,callbacks=[model_checkpoint])","metadata":{"execution":{"iopub.status.busy":"2024-05-31T20:24:46.747847Z","iopub.execute_input":"2024-05-31T20:24:46.748161Z","iopub.status.idle":"2024-05-31T20:27:35.001876Z","shell.execute_reply.started":"2024-05-31T20:24:46.748135Z","shell.execute_reply":"2024-05-31T20:27:35.000724Z"},"trusted":true},"execution_count":111,"outputs":[{"name":"stdout","text":"Epoch 1/50\n\u001b[1m 2/39\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m3s\u001b[0m 98ms/step - accuracy: 0.5469 - loss: 0.6898","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1717187096.253107 112 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step - accuracy: 0.6032 - loss: 0.6745","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1717187102.395177 110 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\nEpoch 1: val_accuracy improved from -inf to 0.40075, saving model to model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 213ms/step - accuracy: 0.6033 - loss: 0.6743 - val_accuracy: 0.4007 - val_loss: 0.6970\nEpoch 2/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - accuracy: 0.6250 - loss: 0.6540\nEpoch 2: val_accuracy did not improve from 0.40075\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 78ms/step - accuracy: 0.6248 - loss: 0.6541 - val_accuracy: 0.4007 - val_loss: 0.7040\nEpoch 3/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.6137 - loss: 0.6569\nEpoch 3: val_accuracy did not improve from 0.40075\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 78ms/step - accuracy: 0.6140 - loss: 0.6567 - val_accuracy: 0.4007 - val_loss: 0.7127\nEpoch 4/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.6730 - loss: 0.6162\nEpoch 4: val_accuracy did not improve from 0.40075\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 78ms/step - accuracy: 0.6720 - loss: 0.6167 - val_accuracy: 0.4007 - val_loss: 0.7261\nEpoch 5/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - accuracy: 0.6531 - loss: 0.6263\nEpoch 5: val_accuracy did not improve from 0.40075\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 78ms/step - accuracy: 0.6529 - loss: 0.6264 - val_accuracy: 0.4007 - val_loss: 0.7281\nEpoch 6/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - accuracy: 0.6639 - loss: 0.6220\nEpoch 6: val_accuracy did not improve from 0.40075\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 78ms/step - accuracy: 0.6638 - loss: 0.6221 - val_accuracy: 0.4007 - val_loss: 0.7207\nEpoch 7/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - accuracy: 0.6740 - loss: 0.6155\nEpoch 7: val_accuracy improved from 0.40075 to 0.40449, saving model to model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 83ms/step - accuracy: 0.6738 - loss: 0.6157 - val_accuracy: 0.4045 - val_loss: 0.7371\nEpoch 8/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - accuracy: 0.6553 - loss: 0.6187\nEpoch 8: val_accuracy did not improve from 0.40449\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 78ms/step - accuracy: 0.6554 - loss: 0.6186 - val_accuracy: 0.4045 - val_loss: 0.7376\nEpoch 9/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - accuracy: 0.7185 - loss: 0.5825\nEpoch 9: val_accuracy improved from 0.40449 to 0.48315, saving model to model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 81ms/step - accuracy: 0.7174 - loss: 0.5831 - val_accuracy: 0.4831 - val_loss: 0.7032\nEpoch 10/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - accuracy: 0.6908 - loss: 0.6093\nEpoch 10: val_accuracy did not improve from 0.48315\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 79ms/step - accuracy: 0.6905 - loss: 0.6092 - val_accuracy: 0.4532 - val_loss: 0.7085\nEpoch 11/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - accuracy: 0.6815 - loss: 0.6055\nEpoch 11: val_accuracy did not improve from 0.48315\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 78ms/step - accuracy: 0.6815 - loss: 0.6054 - val_accuracy: 0.4831 - val_loss: 0.6989\nEpoch 12/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.6778 - loss: 0.5843\nEpoch 12: val_accuracy improved from 0.48315 to 0.50187, saving model to model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 80ms/step - accuracy: 0.6776 - loss: 0.5846 - val_accuracy: 0.5019 - val_loss: 0.7026\nEpoch 13/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - accuracy: 0.6812 - loss: 0.5989\nEpoch 13: val_accuracy improved from 0.50187 to 0.60300, saving model to model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 80ms/step - accuracy: 0.6813 - loss: 0.5989 - val_accuracy: 0.6030 - val_loss: 0.6643\nEpoch 14/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.6999 - loss: 0.5820\nEpoch 14: val_accuracy improved from 0.60300 to 0.61798, saving model to model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 79ms/step - accuracy: 0.6997 - loss: 0.5820 - val_accuracy: 0.6180 - val_loss: 0.6610\nEpoch 15/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.6961 - loss: 0.5746\nEpoch 15: val_accuracy did not improve from 0.61798\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 77ms/step - accuracy: 0.6959 - loss: 0.5748 - val_accuracy: 0.5843 - val_loss: 0.6552\nEpoch 16/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.7089 - loss: 0.5679\nEpoch 16: val_accuracy did not improve from 0.61798\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 77ms/step - accuracy: 0.7087 - loss: 0.5681 - val_accuracy: 0.6180 - val_loss: 0.6442\nEpoch 17/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.6904 - loss: 0.5762\nEpoch 17: val_accuracy improved from 0.61798 to 0.62172, saving model to model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 80ms/step - accuracy: 0.6907 - loss: 0.5760 - val_accuracy: 0.6217 - val_loss: 0.6416\nEpoch 18/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.7087 - loss: 0.5698\nEpoch 18: val_accuracy improved from 0.62172 to 0.63670, saving model to model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 79ms/step - accuracy: 0.7086 - loss: 0.5697 - val_accuracy: 0.6367 - val_loss: 0.6397\nEpoch 19/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.7269 - loss: 0.5434\nEpoch 19: val_accuracy improved from 0.63670 to 0.65543, saving model to model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 79ms/step - accuracy: 0.7268 - loss: 0.5437 - val_accuracy: 0.6554 - val_loss: 0.6452\nEpoch 20/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.7229 - loss: 0.5532\nEpoch 20: val_accuracy did not improve from 0.65543\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 77ms/step - accuracy: 0.7227 - loss: 0.5533 - val_accuracy: 0.6404 - val_loss: 0.6354\nEpoch 21/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.7433 - loss: 0.5194\nEpoch 21: val_accuracy did not improve from 0.65543\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 77ms/step - accuracy: 0.7429 - loss: 0.5200 - val_accuracy: 0.6479 - val_loss: 0.6368\nEpoch 22/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.7486 - loss: 0.5172\nEpoch 22: val_accuracy did not improve from 0.65543\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 77ms/step - accuracy: 0.7480 - loss: 0.5179 - val_accuracy: 0.6442 - val_loss: 0.6414\nEpoch 23/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.7388 - loss: 0.5346\nEpoch 23: val_accuracy did not improve from 0.65543\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 77ms/step - accuracy: 0.7386 - loss: 0.5349 - val_accuracy: 0.6367 - val_loss: 0.6291\nEpoch 24/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - accuracy: 0.7519 - loss: 0.5235\nEpoch 24: val_accuracy did not improve from 0.65543\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 77ms/step - accuracy: 0.7517 - loss: 0.5235 - val_accuracy: 0.6517 - val_loss: 0.6421\nEpoch 25/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.7347 - loss: 0.5332\nEpoch 25: val_accuracy improved from 0.65543 to 0.68539, saving model to model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 79ms/step - accuracy: 0.7350 - loss: 0.5330 - val_accuracy: 0.6854 - val_loss: 0.6292\nEpoch 26/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.7832 - loss: 0.4971\nEpoch 26: val_accuracy did not improve from 0.68539\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 77ms/step - accuracy: 0.7825 - loss: 0.4977 - val_accuracy: 0.6292 - val_loss: 0.6469\nEpoch 27/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.7620 - loss: 0.5127\nEpoch 27: val_accuracy did not improve from 0.68539\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 77ms/step - accuracy: 0.7618 - loss: 0.5129 - val_accuracy: 0.6554 - val_loss: 0.6228\nEpoch 28/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.7537 - loss: 0.4998\nEpoch 28: val_accuracy did not improve from 0.68539\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 77ms/step - accuracy: 0.7536 - loss: 0.5001 - val_accuracy: 0.6667 - val_loss: 0.6237\nEpoch 29/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.7734 - loss: 0.4916\nEpoch 29: val_accuracy did not improve from 0.68539\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 77ms/step - accuracy: 0.7735 - loss: 0.4917 - val_accuracy: 0.6442 - val_loss: 0.6362\nEpoch 30/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.7700 - loss: 0.4915\nEpoch 30: val_accuracy did not improve from 0.68539\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 78ms/step - accuracy: 0.7698 - loss: 0.4917 - val_accuracy: 0.6704 - val_loss: 0.6237\nEpoch 31/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.7982 - loss: 0.4688\nEpoch 31: val_accuracy did not improve from 0.68539\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 78ms/step - accuracy: 0.7980 - loss: 0.4691 - val_accuracy: 0.6404 - val_loss: 0.6490\nEpoch 32/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.7724 - loss: 0.4896\nEpoch 32: val_accuracy improved from 0.68539 to 0.70037, saving model to model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 79ms/step - accuracy: 0.7725 - loss: 0.4894 - val_accuracy: 0.7004 - val_loss: 0.6223\nEpoch 33/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.7903 - loss: 0.4697\nEpoch 33: val_accuracy did not improve from 0.70037\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 77ms/step - accuracy: 0.7901 - loss: 0.4698 - val_accuracy: 0.6479 - val_loss: 0.6449\nEpoch 34/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.8029 - loss: 0.4541\nEpoch 34: val_accuracy did not improve from 0.70037\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 77ms/step - accuracy: 0.8026 - loss: 0.4544 - val_accuracy: 0.6554 - val_loss: 0.6463\nEpoch 35/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.8107 - loss: 0.4451\nEpoch 35: val_accuracy did not improve from 0.70037\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 77ms/step - accuracy: 0.8101 - loss: 0.4456 - val_accuracy: 0.6966 - val_loss: 0.6329\nEpoch 36/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - accuracy: 0.8061 - loss: 0.4501\nEpoch 36: val_accuracy did not improve from 0.70037\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 78ms/step - accuracy: 0.8061 - loss: 0.4503 - val_accuracy: 0.6742 - val_loss: 0.6353\nEpoch 37/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.8116 - loss: 0.4316\nEpoch 37: val_accuracy did not improve from 0.70037\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 78ms/step - accuracy: 0.8113 - loss: 0.4320 - val_accuracy: 0.6592 - val_loss: 0.6504\nEpoch 38/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.8121 - loss: 0.4434\nEpoch 38: val_accuracy did not improve from 0.70037\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 78ms/step - accuracy: 0.8120 - loss: 0.4433 - val_accuracy: 0.6667 - val_loss: 0.6405\nEpoch 39/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.8135 - loss: 0.4345\nEpoch 39: val_accuracy did not improve from 0.70037\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 77ms/step - accuracy: 0.8137 - loss: 0.4344 - val_accuracy: 0.6929 - val_loss: 0.6223\nEpoch 40/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.8434 - loss: 0.4017\nEpoch 40: val_accuracy improved from 0.70037 to 0.70412, saving model to model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 79ms/step - accuracy: 0.8428 - loss: 0.4023 - val_accuracy: 0.7041 - val_loss: 0.6341\nEpoch 41/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.8151 - loss: 0.4145\nEpoch 41: val_accuracy improved from 0.70412 to 0.71536, saving model to model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 79ms/step - accuracy: 0.8147 - loss: 0.4148 - val_accuracy: 0.7154 - val_loss: 0.6351\nEpoch 42/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.8309 - loss: 0.4183\nEpoch 42: val_accuracy did not improve from 0.71536\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 77ms/step - accuracy: 0.8306 - loss: 0.4184 - val_accuracy: 0.7041 - val_loss: 0.6123\nEpoch 43/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.8343 - loss: 0.3974\nEpoch 43: val_accuracy did not improve from 0.71536\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 77ms/step - accuracy: 0.8340 - loss: 0.3975 - val_accuracy: 0.6929 - val_loss: 0.6247\nEpoch 44/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.8524 - loss: 0.3754\nEpoch 44: val_accuracy improved from 0.71536 to 0.72285, saving model to model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 79ms/step - accuracy: 0.8521 - loss: 0.3758 - val_accuracy: 0.7228 - val_loss: 0.6328\nEpoch 45/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.8382 - loss: 0.3977\nEpoch 45: val_accuracy did not improve from 0.72285\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 77ms/step - accuracy: 0.8381 - loss: 0.3978 - val_accuracy: 0.6966 - val_loss: 0.6321\nEpoch 46/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.8599 - loss: 0.3687\nEpoch 46: val_accuracy improved from 0.72285 to 0.74157, saving model to model.keras\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 79ms/step - accuracy: 0.8597 - loss: 0.3689 - val_accuracy: 0.7416 - val_loss: 0.6047\nEpoch 47/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.8528 - loss: 0.3700\nEpoch 47: val_accuracy did not improve from 0.74157\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 77ms/step - accuracy: 0.8527 - loss: 0.3700 - val_accuracy: 0.7041 - val_loss: 0.5950\nEpoch 48/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.8541 - loss: 0.3600\nEpoch 48: val_accuracy did not improve from 0.74157\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 77ms/step - accuracy: 0.8541 - loss: 0.3603 - val_accuracy: 0.6966 - val_loss: 0.5990\nEpoch 49/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.8711 - loss: 0.3421\nEpoch 49: val_accuracy did not improve from 0.74157\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 77ms/step - accuracy: 0.8708 - loss: 0.3426 - val_accuracy: 0.7303 - val_loss: 0.6404\nEpoch 50/50\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.8555 - loss: 0.3591\nEpoch 50: val_accuracy did not improve from 0.74157\n\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 77ms/step - accuracy: 0.8558 - loss: 0.3590 - val_accuracy: 0.7228 - val_loss: 0.6350\n","output_type":"stream"}]},{"cell_type":"code","source":"# loading best model and testing\n\nfrom sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n\nmodel1 = keras.models.load_model('model.keras', custom_objects={\"ELU\": keras.layers.ELU,\"PReLU\": keras.layers.PReLU}) # loading saved model\n\ny_pred = model1.predict(x_test) # predicting\n\ny_pred = y_pred = (y_pred > 0.5).astype(int)\n\n# Accuracy\naccuracy = accuracy_score(y_test, y_pred)\nprint(\"Accuracy:\", accuracy)\n\n# Precision\nprecision = precision_score(y_test, y_pred)\nprint(\"Precision:\", precision)\n\n# Recall\nrecall = recall_score(y_test, y_pred)\nprint(\"Recall:\", recall)\n\n# F1 Score\nf1 = f1_score(y_test, y_pred)\nprint(\"F1 Score:\", f1)\n\n# Confusion Matrix\nconf_matrix = confusion_matrix(y_test, y_pred)\nprint(\"Confusion Matrix:\")\nprint(conf_matrix)","metadata":{"execution":{"iopub.status.busy":"2024-05-31T20:27:35.004110Z","iopub.execute_input":"2024-05-31T20:27:35.004462Z","iopub.status.idle":"2024-05-31T20:27:37.192866Z","shell.execute_reply.started":"2024-05-31T20:27:35.004431Z","shell.execute_reply":"2024-05-31T20:27:37.190841Z"},"trusted":true},"execution_count":112,"outputs":[{"name":"stdout","text":"\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 89ms/step\nAccuracy: 0.7443609022556391\nPrecision: 0.75\nRecall: 0.8108108108108109\nF1 Score: 0.7792207792207791\nConfusion Matrix:\n[[ 78 40]\n [ 28 120]]\n","output_type":"stream"}]},{"cell_type":"code","source":"model1.evaluate(x_test,y_test)","metadata":{"execution":{"iopub.status.busy":"2024-05-31T20:27:37.195852Z","iopub.execute_input":"2024-05-31T20:27:37.196287Z","iopub.status.idle":"2024-05-31T20:27:39.203999Z","shell.execute_reply.started":"2024-05-31T20:27:37.196247Z","shell.execute_reply":"2024-05-31T20:27:39.202827Z"},"trusted":true},"execution_count":113,"outputs":[{"name":"stdout","text":"\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 83ms/step - accuracy: 0.7319 - loss: 0.6041\n","output_type":"stream"},{"execution_count":113,"output_type":"execute_result","data":{"text/plain":"[0.576769232749939, 0.7443609237670898]"},"metadata":{}}]},{"cell_type":"code","source":"plt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\nplt.xlabel(\"Epoch\")\nplt.ylabel(\"Loss\")\nplt.title(\"Loss per epoch\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-31T20:27:39.205470Z","iopub.execute_input":"2024-05-31T20:27:39.205934Z","iopub.status.idle":"2024-05-31T20:27:39.481666Z","shell.execute_reply.started":"2024-05-31T20:27:39.205897Z","shell.execute_reply":"2024-05-31T20:27:39.480461Z"},"trusted":true},"execution_count":114,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"plt.plot(history.history['accuracy'])\nplt.plot(history.history['val_accuracy'])\nplt.xlabel(\"Epoch\")\nplt.ylabel(\"Accuracy\")\nplt.title(\"Accuracy per epoch\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-31T20:27:39.482998Z","iopub.execute_input":"2024-05-31T20:27:39.483327Z","iopub.status.idle":"2024-05-31T20:27:39.785445Z","shell.execute_reply.started":"2024-05-31T20:27:39.483300Z","shell.execute_reply":"2024-05-31T20:27:39.784360Z"},"trusted":true},"execution_count":115,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]}]} \ No newline at end of file diff --git a/Indian Venomous Snakes Classification using DL/README.md b/Indian Venomous Snakes Classification using DL/README.md index d9c61b2ad..bf4a2fa89 100644 --- a/Indian Venomous Snakes Classification using DL/README.md +++ b/Indian Venomous Snakes Classification using DL/README.md @@ -48,50 +48,50 @@ models used: - #### **Venomous**

- - + +

- #### **Non Venomous**

- - + +

- #### **CNN**

- - + +

- #### **InceptionV3**

- - + +

- #### **VGG16**

- - + +

- #### **EfficientNetB7**

- - + +

- #### **RESNET50**

- - + +

### 📈 **Performance of the Models based on the Accuracy Scores**