Assignment_8_Autoencoders.ipynb

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      "name": "Assignment 8: Autoencoders.ipynb",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/surajsrivathsa/ovgu_deeplearning/blob/master/Assignment_8_Autoencoders.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "id": "FU_9dD0WiFPt",
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      "source": [
        "Deep Learning programming task\n",
        "\n",
        "**Assignment 8:** Autoencoders\n",
        "\n",
        "**Team members:**\n",
        "1. Sanjeeth Busnur Indushekar: 224133 : sanjeeth.busnur@st.ovgu.de\n",
        "2. Aditya Dey : 230580 : aditya.dey@st.ovgu.de\n",
        "3. Suraj Shashidhar: 230052 : suraj.shashidhar@st.ovgu.de"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "16GTsDxJd8Dt",
        "colab_type": "text"
      },
      "source": [
        "**Experiments**\n",
        "\n",
        "1. **Simple MLP Based Autoencoders**\n",
        "\n",
        "a) Activations: Sigmoid, ReLU, Tanh\n",
        "\n",
        "b) Loss Functions used: Mean squared error\n",
        "2. **Convolutional Autoencoders**\n",
        "\n",
        "a) Use any number of filters and layers, While decoding last layer must have same filters as input image. Conv2DTranspose is the layer at decoder.\n",
        "\n",
        "b) With Upsampling at decoder.\n",
        "\n",
        "c) With FC layer at encoder.\n",
        "\n",
        "3. **Model interpretation (Used MNSIT for interpretability)**\n",
        "\n",
        "a) Display filters\n",
        "\n",
        "b) Changing the encoder states manually and output the image. Analyze it.\n",
        "\n",
        "\n",
        "4. **Unsupervised Pretraining**\n",
        "\n",
        "a) Train autoencoder – freeze encoder – train classifier on top (as described above).\n",
        "\n",
        "b) Train autoencoder – train classifier on top of encoder. Do not freeze the encoder, i.e. the encoder is “fine-tuned” on the labeled subset of data as well.\n",
        "\n",
        "c) Train a classifier directly on the labeled subset; no pretraining. For fairness, it should have the same architecture as the encoder + classifier above.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Adh3WsE1dmkG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf\n",
        "from sklearn import preprocessing\n",
        "from google.colab import files\n",
        "from google.colab import drive\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import copy\n",
        "import numpy as np\n",
        "import random"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "59zPOCe1KwKa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "069aed26-fbd3-446b-ee40-2d88613795af"
      },
      "source": [
        "from tensorflow import keras\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.preprocessing import image\n",
        "from tensorflow.keras import datasets, layers, models\n",
        "from tensorflow.keras import initializers\n",
        "from datasets import MNISTDataset"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
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          "base_uri": "https://localhost:8080/",
          "height": 110
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      "source": [
        "files.upload()"
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      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "      Upload widget is only available when the cell has been executed in the\n",
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          "text": [
            "Saving datasets.py to datasets.py\n"
          ],
          "name": "stdout"
        },
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          "data": {
            "text/plain": [
              "{'datasets.py': b'import numpy as np\\n\\n\\nclass MNISTDataset:\\n    \"\"\"\\'Bare minimum\\' class to wrap MNIST numpy arrays into a dataset.\"\"\"\\n    def __init__(self, train_imgs, train_lbs, test_imgs, test_lbls, batch_size,\\n                 to01=True, shuffle=True, seed=None):\\n        \"\"\"\\n        Use seed optionally to always get the same shuffling (-> reproducible\\n        results).\\n        \"\"\"\\n        self.batch_size = batch_size\\n        self.train_data = train_imgs\\n        self.train_labels = train_lbs.astype(np.int32)\\n        self.test_data = test_imgs\\n        self.test_labels = test_lbls.astype(np.int32)\\n\\n        if to01:\\n            # int in [0, 255] -> float in [0, 1]\\n            self.train_data = self.train_data.astype(np.float32) / 255\\n            self.test_data = self.test_data.astype(np.float32) / 255\\n\\n        self.size = self.train_data.shape[0]\\n\\n        if seed:\\n            np.random.seed(seed)\\n        if shuffle:\\n            self.shuffle_train()\\n        self.shuffle = shuffle\\n        self.current_pos = 0\\n\\n    def next_batch(self):\\n        \"\"\"Either gets the next batch, or optionally shuffles and starts a\\n        new epoch.\"\"\"\\n        end_pos = self.current_pos + self.batch_size\\n        if end_pos < self.size:\\n            batch = (self.train_data[self.current_pos:end_pos],\\n                     self.train_labels[self.current_pos:end_pos])\\n            self.current_pos += self.batch_size\\n        else:\\n            # we return what\\'s left (-> possibly smaller batch!) and prepare\\n            # the start of a new epoch\\n            batch = (self.train_data[self.current_pos:self.size],\\n                     self.train_labels[self.current_pos:self.size])\\n            if self.shuffle:\\n                self.shuffle_train()\\n            self.current_pos = 0\\n            # print(\"Starting new epoch...\")\\n        return batch\\n\\n    def shuffle_train(self):\\n        shuffled_inds = np.arange(self.train_data.shape[0])\\n        np.random.shuffle(shuffled_inds)\\n        self.train_data = self.train_data[shuffled_inds]\\n        self.train_labels = self.train_labels[shuffled_inds]\\n'}"
            ]
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            "tags": []
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        }
      },
      "source": [
        "  print(os.getcwd())\n",
        "  print(tf.__version__)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n",
            "2.2.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F420Gu-7f3x8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "676Zm3kMgNJP",
        "colab_type": "code",
        "outputId": "b8db43cf-14e0-4151-bad3-61511d66860e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "source": [
        "\n",
        "path = '.'\n",
        " \n",
        "files = os.listdir(path)\n",
        "for name in files:\n",
        "    print(name)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            ".config\n",
            "datasets.py\n",
            "conv_model_with_fc_noise.zip\n",
            "__pycache__\n",
            "sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "boxULgyQB4I-",
        "colab_type": "text"
      },
      "source": [
        "# **Data Preprocessing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G3ihYv6aB7ld",
        "colab_type": "code",
        "outputId": "1d659d4c-c711-4aa5-fe44-9760cf21800f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        }
      },
      "source": [
        "mnist = tf.keras.datasets.mnist\n",
        "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\n",
        "plt.imshow(train_images[4], cmap=\"Greys_r\")\n",
        "print(train_labels[1:10])\n",
        "\n",
        "#Normalizing images\n",
        "train_images = train_images / 255.0\n",
        "\n",
        "test_images = test_images / 255.0"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "[0 4 1 9 2 1 3 1 4]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M8B2q_L-B7oM",
        "colab_type": "code",
        "outputId": "f0304473-b6a2-4449-d2bc-76037b0668ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "flattened_data = MNISTDataset(train_images.reshape([-1, 784]), train_labels, \n",
        "                    test_images.reshape([-1, 784]), test_labels,\n",
        "                    batch_size=128)\n",
        "print(train_images.reshape([-1, 784]).shape)\n",
        "train_images_flattened = train_images.reshape([-1, 784])\n",
        "test_images_flattened = test_images.reshape([-1, 784])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 784)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y2Xm-LxiB7vA",
        "colab_type": "code",
        "outputId": "425e0fd9-f113-48c2-c844-af25a653b780",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "print(train_images.shape)\n",
        "print(train_labels.shape)\n",
        "print(test_images.shape)\n",
        "print(test_labels.shape)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 28, 28)\n",
            "(60000,)\n",
            "(10000, 28, 28)\n",
            "(10000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2mEAF-k1Kjhf",
        "colab_type": "text"
      },
      "source": [
        "# **Simple MLP Autoencoders**\n",
        "\n",
        "1) Using Mean squared error/MAE with relu, tanh and sigmoid\n",
        "\n",
        "**Summary:**\n",
        "\n",
        "1) Most of the models created did not have any sort of regularization, denoising to prevent network to copying the input to output rather than learning. Even with this models that had sigmoid or tanh in hidden layers did not learn anything (even copying). Instead they cheated by trying to decrease the loss by simply outputing white pixel cluster near centers and block at edges. model_tanh, model_sigmoids output can be seen here as an example.\n",
        "\n",
        "2) Model with relu at hidden and sigmoid at output gave relatively better performance than others, as it learnt to predict/or atleast copy input to output\n",
        "\n",
        "3) Model with relu as hidden and linear at the output gave blurred versions of the images and they were not as clear as with sigmoid at output. This could be because linear at output could have negative values or any other values that don't scale/fit well when converted to pixel values, whilst sigmoids output is between 0/1 makes it better for output layer in this case.\n",
        "\n",
        "4) Same with Model with sigmoid as hidden and linear as out. The images were blurred but also some wrong predictions. For example it was matching anything with stright stroke to 7(1->7, 7-> 7), similary 3 to 8"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SVybwuXZEqgP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create_mlp_model(input_shape=(28, 28), layer_dims=[256,128,64,32,64,128,256,784],output_activation=\"sigmoid\",hidden_activation=\"relu\"):\n",
        "  model = keras.Sequential([keras.layers.Flatten(input_shape=input_shape)])\n",
        "  #Loop till penultimate layer that is the penultimate decoder\n",
        "  for i in range (len(layer_dims)-1):\n",
        "    model.add(tf.keras.layers.Dense(units=layer_dims[i],activation=hidden_activation))\n",
        "  \n",
        "  #Add the output at end\n",
        "  model.add(tf.keras.layers.Dense(units=layer_dims[-1],activation=output_activation)) \n",
        "  return model;\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gf3JLqQbJwGb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_relu = create_mlp_model(input_shape=(28, 28), layer_dims=[256,128,64,32,64,128,256,784],output_activation=\"sigmoid\",hidden_activation=\"relu\")\n",
        "model_sigmoid = create_mlp_model(input_shape=(28, 28), layer_dims=[256,128,64,32,64,128,256,784],output_activation=\"sigmoid\",hidden_activation=\"sigmoid\")\n",
        "model_tanh = create_mlp_model(input_shape=(28, 28), layer_dims=[256,128,64,32,64,128,256,784],output_activation=\"sigmoid\",hidden_activation=\"tanh\")\n",
        "model_linear = create_mlp_model(input_shape=(28, 28), layer_dims=[256,128,64,32,64,128,256,784],output_activation=\"sigmoid\",hidden_activation=\"linear\")\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "51eKS9-ZVgoD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_linear2 = create_mlp_model(input_shape=(28, 28), layer_dims=[256,128,64,32,64,128,256,784],output_activation=\"linear\",hidden_activation=\"sigmoid\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qOZPQd0BX4H6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_linear3 = create_mlp_model(input_shape=(28, 28), layer_dims=[256,128,64,32,64,128,256,784],output_activation=\"linear\",hidden_activation=\"relu\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2qGY3EHUZchI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_relu2 = create_mlp_model(input_shape=(28, 28), layer_dims=[256,128,64,32,64,128,256,784],output_activation=\"sigmoid\",hidden_activation=\"relu\")\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q4S3gne4KqDZ",
        "colab_type": "code",
        "outputId": "49874ecb-cb92-406b-a78d-8d24bd465fe0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        }
      },
      "source": [
        "#Printing model summary to initial check\n",
        "model_relu.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_16\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten_1 (Flatten)          (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense_75 (Dense)             (None, 256)               200960    \n",
            "_________________________________________________________________\n",
            "dense_76 (Dense)             (None, 128)               32896     \n",
            "_________________________________________________________________\n",
            "dense_77 (Dense)             (None, 64)                8256      \n",
            "_________________________________________________________________\n",
            "dense_78 (Dense)             (None, 32)                2080      \n",
            "_________________________________________________________________\n",
            "dense_79 (Dense)             (None, 64)                2112      \n",
            "_________________________________________________________________\n",
            "dense_80 (Dense)             (None, 128)               8320      \n",
            "_________________________________________________________________\n",
            "dense_81 (Dense)             (None, 256)               33024     \n",
            "_________________________________________________________________\n",
            "dense_82 (Dense)             (None, 784)               201488    \n",
            "=================================================================\n",
            "Total params: 489,136\n",
            "Trainable params: 489,136\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b_GpJP8gL0xo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def compile_mlp_model(model,loss=\"MAE\", metrics=[\"accuracy\"], optimizer = \"adam\"):\n",
        "  if(loss == \"MAE\"):\n",
        "    model.compile(optimizer=optimizer, loss=tf.keras.losses.MAE, metrics=metrics)\n",
        "  elif (loss == \"MSQE\"):\n",
        "    model.compile(optimizer=optimizer, loss=tf.keras.losses.mean_squared_error, metrics=metrics)\n",
        "  else:\n",
        "    model.compile(optimizer=optimizer, loss=tf.keras.losses.binary_crossentropy, metrics=metrics)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GbrJEJa5JyCM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "compile_mlp_model(model_linear, loss = \"MSQE\", metrics=[\"accuracy\", tf.keras.metrics.FalseNegatives(), tf.keras.metrics.FalsePositives()])\n",
        "compile_mlp_model(model_tanh, loss = \"MSQE\", metrics=[\"accuracy\", tf.keras.metrics.FalseNegatives(), tf.keras.metrics.FalsePositives()])\n",
        "compile_mlp_model(model_sigmoid, loss = \"MSQE\", metrics=[\"accuracy\", tf.keras.metrics.FalseNegatives(), tf.keras.metrics.FalsePositives()])\n",
        "compile_mlp_model(model_relu, loss = \"MSQE\", metrics=[\"accuracy\", tf.keras.metrics.FalseNegatives(), tf.keras.metrics.FalsePositives()])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IaAWx6sqVpG0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "compile_mlp_model(model_linear2, loss = \"MSQE\", metrics=[\"accuracy\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UDt5I8fAX1mY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "compile_mlp_model(model_linear2, loss = \"MSQE\", metrics=[\"accuracy\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tbQ6p7N3YCl8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "compile_mlp_model(model_linear3, loss = \"MSQE\", metrics=[\"accuracy\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "esrdEM85ZaX0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "compile_mlp_model(model_sigmoid, loss = \"MSQE\", metrics=[\"accuracy\", tf.keras.metrics.FalseNegatives(), tf.keras.metrics.FalsePositives()])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HFBHzKdpZq_r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "compile_mlp_model(model_relu2, metrics=[\"accuracy\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lg2H4wmzWG-s",
        "colab_type": "code",
        "outputId": "6f602839-1d11-4d8c-ad69-613ce3403b45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        }
      },
      "source": [
        "model_linear2.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_21\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten_6 (Flatten)          (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense_115 (Dense)            (None, 256)               200960    \n",
            "_________________________________________________________________\n",
            "dense_116 (Dense)            (None, 128)               32896     \n",
            "_________________________________________________________________\n",
            "dense_117 (Dense)            (None, 64)                8256      \n",
            "_________________________________________________________________\n",
            "dense_118 (Dense)            (None, 32)                2080      \n",
            "_________________________________________________________________\n",
            "dense_119 (Dense)            (None, 64)                2112      \n",
            "_________________________________________________________________\n",
            "dense_120 (Dense)            (None, 128)               8320      \n",
            "_________________________________________________________________\n",
            "dense_121 (Dense)            (None, 256)               33024     \n",
            "_________________________________________________________________\n",
            "dense_122 (Dense)            (None, 784)               201488    \n",
            "=================================================================\n",
            "Total params: 489,136\n",
            "Trainable params: 489,136\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f8Hi7u0jZxtX",
        "colab_type": "code",
        "outputId": "585aa195-8cf9-48a7-e2d1-66237b6f0652",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        }
      },
      "source": [
        "model_relu2.fit(train_images, train_images_flattened,  epochs=10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 33.2768 - accuracy: 0.0148\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 33.2867 - accuracy: 0.0140\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 33.2887 - accuracy: 0.0139\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 33.2864 - accuracy: 0.0145\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 33.2914 - accuracy: 0.0120\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 33.2915 - accuracy: 0.0121\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 33.2915 - accuracy: 0.0121\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 33.2915 - accuracy: 0.0121\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 33.2915 - accuracy: 0.0121\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 33.2915 - accuracy: 0.0121\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fb4910201d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dwAx0lHIYFRI",
        "colab_type": "code",
        "outputId": "f83c7375-48a1-491d-e49d-9cde41c64a32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        }
      },
      "source": [
        "model_linear3.fit(train_images, train_images_flattened,  epochs=10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 1702.9707 - accuracy: 0.0105\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 1137.7051 - accuracy: 0.0115\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 1016.9025 - accuracy: 0.0133\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 952.8574 - accuracy: 0.0125\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 909.0300 - accuracy: 0.0131\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 879.6067 - accuracy: 0.0127\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 855.2213 - accuracy: 0.0121\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 837.8210 - accuracy: 0.0124\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 821.3605 - accuracy: 0.0127\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 800.4500 - accuracy: 0.0133\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fb48dd7eeb8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YpEDi7lPVudK",
        "colab_type": "code",
        "outputId": "c6ab142a-f41b-4342-a3d4-6e5aff91bc66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        }
      },
      "source": [
        "model_linear2.fit(train_images, train_images_flattened,  epochs=10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 4604.4775 - accuracy: 0.0137\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 4374.1289 - accuracy: 0.0146\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 4350.4541 - accuracy: 0.0127\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 4157.1177 - accuracy: 0.0116\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 3947.8176 - accuracy: 0.0113\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 3832.3359 - accuracy: 0.0114\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 3754.3936 - accuracy: 0.0116\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 3697.3875 - accuracy: 0.0123\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 3639.0488 - accuracy: 0.0146\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 14s 8ms/step - loss: 3519.6956 - accuracy: 0.0191\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fb48c4e9710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z7KSEZTGL4sy",
        "colab_type": "code",
        "outputId": "92ab4b56-ea56-4252-9320-307040ea8c87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        }
      },
      "source": [
        "model_linear.fit(train_images, train_images_flattened,  epochs=10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 7221.9443 - accuracy: 0.0000e+00 - false_negatives: 794109.0000 - false_positives: 13219973.0000\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 7221.9478 - accuracy: 0.0000e+00 - false_negatives: 794109.0000 - false_positives: 13219973.0000\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 7221.9409 - accuracy: 0.0000e+00 - false_negatives: 794109.0000 - false_positives: 13219971.0000\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 7221.9443 - accuracy: 0.0000e+00 - false_negatives: 794112.0000 - false_positives: 13219956.0000\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 7222.0732 - accuracy: 0.0000e+00 - false_negatives: 813141.0000 - false_positives: 13184653.0000\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 7222.2314 - accuracy: 0.0000e+00 - false_negatives: 836264.0000 - false_positives: 13142108.0000\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 7222.2305 - accuracy: 0.0000e+00 - false_negatives: 836264.0000 - false_positives: 13142108.0000\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 7222.2324 - accuracy: 0.0000e+00 - false_negatives: 836264.0000 - false_positives: 13142108.0000\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 7222.2236 - accuracy: 0.0000e+00 - false_negatives: 836264.0000 - false_positives: 13142108.0000\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 7222.2314 - accuracy: 0.0000e+00 - false_negatives: 836264.0000 - false_positives: 13142108.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fb48857fa20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MP-yGli8KcLY",
        "colab_type": "code",
        "outputId": "a6e32f2c-8e8d-4b70-8874-1b17456b6688",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        }
      },
      "source": [
        "model_relu.fit(train_images, train_images_flattened,  epochs=10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 12s 7ms/step - loss: 0.0309 - accuracy: 0.0103 - false_negatives_3: 3736640.0000 - false_positives_3: 453538.0000\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 12s 7ms/step - loss: 0.0173 - accuracy: 0.0105 - false_negatives_3: 3030544.0000 - false_positives_3: 180712.0000\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 12s 7ms/step - loss: 0.0143 - accuracy: 0.0100 - false_negatives_3: 2931204.0000 - false_positives_3: 128835.0000\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 12s 7ms/step - loss: 0.0126 - accuracy: 0.0114 - false_negatives_3: 2878755.0000 - false_positives_3: 101436.0000\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 13s 7ms/step - loss: 0.0114 - accuracy: 0.0123 - false_negatives_3: 2848355.0000 - false_positives_3: 83874.0000\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 12s 7ms/step - loss: 0.0106 - accuracy: 0.0131 - false_negatives_3: 2830101.0000 - false_positives_3: 73312.0000\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 12s 7ms/step - loss: 0.0101 - accuracy: 0.0142 - false_negatives_3: 2817747.0000 - false_positives_3: 65751.0000\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 0.0096 - accuracy: 0.0147 - false_negatives_3: 2808691.0000 - false_positives_3: 59579.0000\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 12s 7ms/step - loss: 0.0093 - accuracy: 0.0143 - false_negatives_3: 2800667.0000 - false_positives_3: 55233.0000\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 12s 7ms/step - loss: 0.0090 - accuracy: 0.0144 - false_negatives_3: 2796450.0000 - false_positives_3: 52191.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f980daf9c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UxR8Q6QgKcxn",
        "colab_type": "code",
        "outputId": "10ed7826-dea6-4bc1-df32-eae028213c0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        }
      },
      "source": [
        "model_sigmoid.fit(train_images, train_images_flattened,  epochs=10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 7217.2334 - accuracy: 0.0026 - false_negatives_2: 18343.0000 - false_positives_2: 22896954.0000\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 7217.0522 - accuracy: 0.0032 - false_negatives_2: 10986.0000 - false_positives_2: 23037136.0000\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 7217.0483 - accuracy: 0.0032 - false_negatives_2: 10748.0000 - false_positives_2: 23054412.0000\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 7217.0522 - accuracy: 0.0031 - false_negatives_2: 11118.0000 - false_positives_2: 22959520.0000\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 7217.0483 - accuracy: 0.0029 - false_negatives_2: 11061.0000 - false_positives_2: 22926828.0000\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 7217.0527 - accuracy: 0.0026 - false_negatives_2: 11475.0000 - false_positives_2: 22845726.0000\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 12s 7ms/step - loss: 7217.0444 - accuracy: 0.0027 - false_negatives_2: 11349.0000 - false_positives_2: 22850472.0000\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 7217.0469 - accuracy: 0.0032 - false_negatives_2: 11467.0000 - false_positives_2: 22823620.0000\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 7217.0405 - accuracy: 0.0038 - false_negatives_2: 11108.0000 - false_positives_2: 22887316.0000\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 12s 7ms/step - loss: 7217.0513 - accuracy: 0.0059 - false_negatives_2: 11170.0000 - false_positives_2: 22852600.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fb4907aba58>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZETlfuK_KdJk",
        "colab_type": "code",
        "outputId": "b5e245c7-e700-4e9f-9154-c5aea540947c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        }
      },
      "source": [
        "model_tanh.fit(train_images, train_images_flattened,  epochs=10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 7217.2412 - accuracy: 9.3333e-04 - false_negatives_1: 19685.0000 - false_positives_1: 22893664.0000\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 7217.0596 - accuracy: 0.0019 - false_negatives_1: 10933.0000 - false_positives_1: 23016404.0000\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 7217.0474 - accuracy: 0.0032 - false_negatives_1: 10644.0000 - false_positives_1: 23035794.0000\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 7217.0308 - accuracy: 0.0021 - false_negatives_1: 10113.0000 - false_positives_1: 22167686.0000\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 7217.0137 - accuracy: 0.0018 - false_negatives_1: 9544.0000 - false_positives_1: 21443900.0000\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 7216.9995 - accuracy: 0.0049 - false_negatives_1: 9934.0000 - false_positives_1: 20394544.0000\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 7216.9702 - accuracy: 0.0051 - false_negatives_1: 9246.0000 - false_positives_1: 19508072.0000\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 12s 7ms/step - loss: 7216.9644 - accuracy: 0.0045 - false_negatives_1: 9064.0000 - false_positives_1: 19135574.0000\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 7216.9570 - accuracy: 0.0052 - false_negatives_1: 9121.0000 - false_positives_1: 19018890.0000\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 7216.9658 - accuracy: 0.0057 - false_negatives_1: 9960.0000 - false_positives_1: 19213688.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fb49054c9e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NbCJCuurNb2T",
        "colab_type": "code",
        "outputId": "95409c84-c3f5-461d-efd3-fec53624c230",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "def evaluate_and_output_model(model, test_labels, test_images, reshape_matrix = [-1, 784], image_matrix=[10000,28,28], images_to_be_compared=5):\n",
        "  class_names = ['0', '1', '2', '3', '4','5', '6', '7', '8', '9']\n",
        "  print(test_images.shape)\n",
        "  #test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
        "  print(test_images.shape)\n",
        "  #print('\\n  Relu Test accuracy:', str(test_acc) + \" test loss: \" + str(test_loss) + \"\\n\")\n",
        "  test_output_model = model.predict(test_images)\n",
        "  model_output_images = test_output_model.reshape(image_matrix)\n",
        "  print(model_output_images.shape)\n",
        "  plt.figure(figsize=(10,10))\n",
        "  j = -1\n",
        "  for i in range(25):\n",
        "      plt.subplot(5,5,i+1)\n",
        "      plt.xticks([])\n",
        "      plt.yticks([])\n",
        "      plt.grid(False)\n",
        "      j = j+1\n",
        "      if(i%2 == 0):\n",
        "        plt.imshow(test_images[i+10], cmap=plt.cm.binary)\n",
        "      elif(j%2 == 1):\n",
        "        \n",
        "        plt.imshow(model_output_images[j-1+10], cmap=\"Greys_r\")\n",
        "      \n",
        "      #plt.xlabel(class_names[test_images[i]])\n",
        "  plt.show()\n",
        "\n",
        "\n",
        "\"\"\"\n",
        "test_loss, test_acc = model.evaluate(test_images.reshape([-1, 784]),  test_labels, verbose=2)\n",
        "test_output_model = model.predict(test_images.reshape([-1, 784]),)\n",
        "model_output_images = test_output_model.reshape([10000,28,28])\n",
        "plt.imshow(model2_output_images[7], cmap=\"Greys_r\")\n",
        "\"\"\""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\ntest_loss, test_acc = model.evaluate(test_images.reshape([-1, 784]),  test_labels, verbose=2)\\ntest_output_model = model.predict(test_images.reshape([-1, 784]),)\\nmodel_output_images = test_output_model.reshape([10000,28,28])\\nplt.imshow(model2_output_images[7], cmap=\"Greys_r\")\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sIuqNvAhaZ0a",
        "colab_type": "code",
        "outputId": "7b319fce-06ac-4d94-aaf0-9b395b627046",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 631
        }
      },
      "source": [
        "evaluate_and_output_model(model_relu2, test_labels, test_images)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10000, 28, 28)\n",
            "(10000, 28, 28)\n",
            "(10000, 28, 28)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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j0hszPQAAQAWKHgAAoEJkl7dyta3OT1S23EXdunXrrONvv/1WsvtNzmFMt3spLCwMdF79+vUl8x0fHWMz+tq1ayfZXN7y88gjj1jH5nLUlVdeGegal156qeQ5c+Z4nvfRRx8Fuh6CY1wGw0wPAABQgaIHAACoENnlrTBEcWpNA/eLBk033XRT1vrxpz/9SfLXX3/ted7FF1+cje7AwNjMrG7dulX6c/fSszkuJkyYYLV98803kletWhVe5xzHmT9/vuS2bdtabeecc45k8+XCyDwN45KZHgAAoAJFDwAAUIGiBwAAqBDZe3rCeOJj2Fv4ovjG2Kj48ssvJS9dutTzPPOt6plg3odwyy23SD548KB1nrlNvWPHjhntU75hbEaf+ZTxBQsWSD7rrLOs8/75z39Kdj+N3BxLYSsrK5PsfiJz3bp1Jc+bN0/yhRde6HkeGJdBMdMDAABUoOgBAAAqRHZ5y4/XNF6mn0gZlem5qDt8+HDWPuvQoUPW8Z///GfJflvnR48eLblly5bhd0wpxmY0NGjQQPKIESM8zzOfiP7EE09YbcXFxZJ37dol+cUXXwyji57MpfLhw4dLdi9D//GPf5TcoUOHjPYp7hiXRzDTAwAAVKDoAQAAKkRmecs9zeY3LeZ1R3g2X7gGW40aR/4qFRUVWW2lpaWSX3nlFavtBz/4Qcqf9dlnn0leuHCh1TZp0qRA17j88stT/lytGJs6DBw40PPYfGnwvn37PK9h7gZz/z1p2rRppb8zZcoU6/jRRx+VfODAAcnr16+3zrvhhhsk33PPPVZbp06dPPuYLxiX6WGmBwAAqEDRAwAAVKDoAQAAKkTmnh6/9cgobnuDrVatWpJXr15ttbVv316y+y3r5j0+F110keSNGzda55n3EZjXN+8hcBz7rcy7d++W7N6W3rx580r+V6AyjE0dKioqrOOPPvpIcs+ePSU3bNjQ8xp+bV5mzZplHQ8dOlTyVVddJdl9T8+rr74quVo1+7/fX3rppZT7ETeMy/Qw0wMAAFSg6AEAACpEZnkrm9LZ2ne0NhxRWFhoHZtPer3zzjuttpUrV1aazeUyx3GcVq1aSe7Tp4/kSy+91DrP3GZrfkfuFy02atTIs//IHcZmdr3wwguSr732WqvNfDTEokWLJLtf/Bk2cyntjTfekNylSxfrPPOJ62+99ZbV9vLLL0vu379/2F1UJ5/GJTM9AABABYoeAACgQiyXt7ymzPym1oL8/GiiMj0XNxdccIHk8847z2orKSmp9Hfcy1vuqe3/MHeYOI7jHDx4sNLzhgwZctR+ouoYm/Fi7oo0l7Mcxx5LgwcPlmwuOTmO4/To0SNDvXOc+vXrS37yySetNnMZbO/evVab+YRmlrcYlyZmegAAgAoUPQAAQAWKHgAAoEIs7+nxWif0e+ts0DXJdLfmIZiaNWtax2eccUaVrrdt27ZA51X1cxAMYzNezEc+7Nixw2ozn55u/vmab1zPpnXr1lnH3333nee5p512Wqa7EyuMyyOY6QEAACpQ9AAAABViubxl8puCS2ebnd90XFSm53DE4sWLc90FeGBsxsvYsWOtY/Olna+//rrkyy67zDrPfEL6pEmTJJ966qlp9cN8Aekjjzwi+eOPP7bOS3cbtXbaxyUzPQAAQAWKHgAAoEIsl7e8ptPCmO6M4nQcbFu3bpX81FNPeZ535plnSm7QoEFG+4R/Y2zGl3uMPPfcc5LN3VDuJzc/9thjkhcuXCi5WrX0/pv60KFDKf/OD3/4Q+t48uTJaX12vmJcHsFMDwAAUIGiBwAAqEDRAwAAVIjlPT2ZXJOM4hMkYTO3ru7Zs8fzvAsvvFByjRqx/KseO4zN/FGvXj3JW7Zskfz4449b5y1atEjy+vXrJbuf8FxVP/rRj6zjfv36SR4zZozV1rhx41A/O+4Yl0cw0wMAAFSg6AEAACrk1Zx/GFNrcZie0668vNyzrW7dupKvvvrqbHQHATA288eoUaM8j3fu3Cl537591nnz5s2TbD7Fee3atdZ55pOcu3btKrlFixbWebVr106h16iMxnHJTA8AAFCBogcAAKhA0QMAAFSI/T09cVtPRNU9++yznm0dO3aUXL169Wx0Bx4Ym/qccMIJlWbHcZx777230t8ZMGBARvsEm/ZxyUwPAABQgaIHAACoEPvlLehTXFws2T1V27lz52x3BwAQE8z0AAAAFSh6AACACixvIXbCeGEeAEAfZnoAAIAKFD0AAEAFih4AAKACRQ8AAFCBogcAAKhA0QMAAFRIpLL9N5FIlDuOU5a57qASLZPJZJOwL8p3mTN8n/mD7zK/hP598l3mjOd3mVLRAwAAEFcsbwEAABUoegAAgAoUPQAAQAWKHgAAoAJFDwAAUIGiBwAAqEDRAwAAVKDoAQAAKlD0AAAAFSh6AACAChQ9AABABYoeAACgAkUPAABQgaIHAACoQNEDAABUoOgBAAAqUPQAAAAVaqRyckFBQbKoqChDXUFlSktLnYqKikTY100kEsmwr4lAKpLJZJOwL8rYzD7GZt4JfWwyLnOjpKTE87tMqegpKipy1q5dG06vEEi3bt1y3QWEqywTF2VsZh9jM++EPjYZl7mRSCQ8v0uWtwAAgAoUPQAAQAWKHgAAoAJFDwAAUIGiBwAAqEDRAwAAVKDoAQAAKqT0nB4AMM2cOdM6vuGGGyS/+eabknv06JG1PgGAF2Z6AACAChQ9AABABZa3AKTtgQceyHUXACAwZnoAAIAKFD0AAEAFih4AAKAC9/QASMmnn35aaXYcx7nvvvsks00diB5zXP7617+22nr16pXt7mQdMz0AAEAFih4AAKCCyuWtsrIy6/jBBx+UvHbtWslz5syxzuvQoUNmO4aUJZNJz7ZEIpHFnuhRXFzs2VZYWJjFniDKGJvR8d5770lev3695EaNGuWiOznFTA8AAFCBogcAAKiQ18tbH330keTZs2dLXrBggXXenj17Kv39/v37W8fLly+XbO5aadmypXXeaaedlnpnlXBPeZvT3H7T4WF8ltfnIjV+y1vs2Iovxmb++O6776zjiRMnSq5Vq5bkJk2apHX9m2++WXK3bt0kX3TRRWldL5uY6QEAACpQ9AAAABUoegAAgAqxv6fHXLvcuHGj1XbOOedI3rlzZ8rX3r59u3V85plnSt67d69k930Mb7zxhuRq1agr/Zhr+35r+WHfU4Dg3E9dfvvttyU3b97canMfI74Ym/H1zDPPWMfbtm2TvGHDBsnp3tNTs2ZNyYsWLZLMPT0AAAARQdEDAABUiOXyVnl5ueTf/va3kqdPnx7o9xs2bGgdm0tV7q1+XueZPvzwQ+vYvAbLWzb3NLk5NZ7NafKgU/dwnPvvv9+zbciQIRn97Lfeekuye5nNZC65XXzxxZLZQh8cYzN/bNq0yToeNWqU5BNPPLHK1z/11FMlL1u2rMrXyyb+RQYAACpQ9AAAABUoegAAgAqxvKfn1ltvlTxv3jzP88zHbc+aNUtyq1atrPOmTp0q2bw3wI+51e+5556z2mrUiOUfa1YEvTfA7/4Cv8fjZ/LR+VqZ213dzjjjjFA/y7yHx3EcZ+jQoZL97ukxmfcgvfnmm1Yb9/h4Y2zGW2lpqeSHHnrIajP/zQzbJ598Innr1q1WW4sWLTL2uelipgcAAKhA0QMAAFSI7DqMue3bvS3WXE4yt4S7327+yCOPSH711VclX3fdddZ57i3nQXTp0kUyU+b+crXd1Q9bYaPJvT3ea0nrvvvus47NMWguif3yl7+0znMvn2nH2MwfixcvllyvXj2rbfjw4aF+lvlv8LfffivZvRTO8hYAAECOUPQAAAAVIru89eCDD0peunSp53lt27aVPGnSJKutV69ekr/++usq98l8CuXvfve7Kl8PwTHlnb/MJafi4mLP855++mnJP//5zz3PM3eU+V0P4WBs5oZ7p5T5RoL/+7//s9rSfbHof7hvATGXt6699lrJPXv2rNLnZAMzPQAAQAWKHgAAoAJFDwAAUCEy9/QcOnTIOr7nnnsC/Z651njJJZd4nteoUSPJV199tdX22muvSf7rX//qeY3Ro0dLbtmyZaD+IZwnsYa9tZY3OUeH31vcg97HE9QzzzwT6vXijrEZL+b28EcffdRqM//cxo8fH+rnNmvWzDpu3Lix5Li9gYCZHgAAoAJFDwAAUCEy81Lmk5Udx34p6M6dOz1/r06dOpJr165ttU2YMEGy+WRW91NevZbS3C9TvOqqqzz7gfR4Ta9n+kmxTJsHV1hY6Nnm9zJSP+YYNLeVu8ccS1C5w9iMnn379kmeNm2a1fbTn/5Usnk7RxiOO+446/gnP/lJqNfPJmZ6AACAChQ9AABAhcgsb1WvXt06XrFiheTly5dbbebd4p06dZJsPp3Zbf/+/ZKnTp1qtZlPa65fv77kxx9/3DqvQYMGntfHEe7pb7/paq+dGtl8ESL8XX/99daxudvqhhtusNrcL/j04v69/7j44otT7N1/M5fOmjdvbrVpXy5jbMabeQvHmWeeabW98847kidOnGi1nXLKKZIHDRokuaCgIK1+nH766ZLNtxOMGzcu0O+7x6X79pZMYqYHAACoQNEDAABUoOgBAAAqROaeHreGDRtKHjFiRJWv9+yzz0r2e2v70KFDJZtvVUdwfvcJsB01ftzr7+a28rfffttqmzlzpuSg9/eY/LbH+zGftGz26b777kvrevmKsRlv5iNaXn75ZavN/HfN/Vb0BQsWSL733nslH3PMMZ6f5fdk7N27d0s2H1tRVFRknde5c2fJ5v107vsE3Y+bySRmegAAgAoUPQAAQIXILm+FYdeuXZL9prlbtGghec6cORntE8KVzpbbo7XBnzk1bS4HO469Fd1cZnJPZ5tPYTb16NEjUB/M5SzHcZwbb7xRsrkcF8YWeKSHsZlZ7qWpYcOGeZ5rPr35yy+/lOx+0ffnn38u+d133/W83uzZsyXv2bNH8gcffGCdZ47FmjVrel4vm5jpAQAAKlD0AAAAFfJ6eWvgwIGS169f73ne5MmTJdeqVSujfYLNayrbb8o7yM+Phmnz9Jm7MNwvHH3ggQckm0tYXstZbm+99Zbn8eLFiwNd780335Ts3nmG4Bib+alu3bqebeaLRVu3bu15nvmWhI8//liy+0WnUVnSMjHTAwAAVKDoAQAAKlD0AAAAFfLqnp4tW7ZYxxs2bKj0PPNeH8dxnMsvvzxTXcJReK3f+70NOui9AulumUVw7qcum1vEzbexm9mPewu8F/Op0I5jb2HnPp5wMDYRxKWXXirZfJNCVDHTAwAAVKDoAQAAKsR+eWv79u2S+/bta7Xt27dPst9Tl6tXr56h3iEVflPj6Wx/9ZsmZ9o8M8ylJfPlo2Z2HHs8fvrpp57XM5/kbC5pmdvmkXmMTZgGDBgg+dFHH81hT1LHTA8AAFCBogcAAKgQ++Wt9957T3JpaanneaNHj5ZsTq0jt7ymudN9mquJafLoGjJkiGS/nV3mE5/NJzK7d2gFfVEpgmNsIghzl7R7B/X3vve9bHfnqJjpAQAAKlD0AAAAFSh6AACACrG8p+edd96RfNlll3meV7t2bcnmFjtERybvFeDJrtHl3sL+H+Z9O27mlvXCwsLQ+wQbYxNe+vfvL3nkyJGSzUfIOA739AAAAOQMRQ8AAFAhFstbBw4csI6nTJkieffu3Z6/d/zxx0uuV69e+B1DxoQx5c20eTz4PbkZ0cPYhPli0Y4dO0p2v7z7H//4R7a6FBgzPQAAQAWKHgAAoAJFDwAAUCEW9/T8/ve/t45ffvnlSs874YQTrOOXXnpJcrt27cLvGELFOj8QTYxNmGrUOFI6jB07VvJf//rXXHQnJcz0AAAAFSh6AACACrFY3qpevbp1bG6Xu/766yWPGTPGOq9Zs2aZ7RgAAIpNmDCh0hxVzPQAAAAVKHoAAIAKsVjeuuaaa3yPAQAAjoaZHgAAoAJFDwAAUIGiBwAAqEDRAwAAVKDoAQAAKlD0AAAAFRLJZDL4yYlEueM4ZZnrDirRMplMNgn7onyXOcP3mT/4LvNL6N8n32XOeH6XKRU9AAAAccXyFgAAUIGiBwAAqEDRAwAAVIThG7MAABRdSURBVKDoAQAAKlD0AAAAFSh6AACAChQ9AABABYoeAACgAkUPAABQgaIHAACoQNEDAABUoOgBAAAqUPQAAAAVKHoAAIAKFD0AAEAFih4AAKACRQ8AAFChRionJxKJZKY6Am/JZDIR9jULCgqSRUVFYV8WR1FSUlKRTCabhH1dxmZuMDbzRybGJuMyZzy/y5SKHuSPoqIiZ+3atbnuhjqJRKIs131AtDE2c4OxmVc8v0uWtwAAgAoUPQAAQAWKHgAAoAJFDwAAUIGiBwAAqEDRAwAAVKDoAQAAKlD0AAAAFSh6AACAChQ9AABABYoeAACgAkUPAABQgaIHAACoQNEDAABUoOgBAAAq1Mh1BwAAuVdaWmodt2rVKtTr/+IXv5BcWFgouX379tZ5Q4cOlZxIJELtA8BMDwAAUIGiBwAAqKByeSuZTHq2MZ0K5A5jMzrC/vN+7LHHAp1XUVEhefz48ZKrVeO/0XMln8Ylf4sAAIAKFD0AAECFWCxvuafWzOk0v2m3MD7L63MBMDbzSaNGjazj2267TfKDDz4oec+ePRntxzXXXCP5ggsukNyiRYuMfm4+YVx6Y6YHAACoQNEDAABUoOgBAAAqxOKeHjdzDdFvzTDstUt4Ky8vl9ykSZPAv3fgwAHJS5culbxkyRLrvGXLlkn2+/692latWmWd9+Mf/zhwHxEcYzO+GjRoYB1PmzZN8nnnnSe5Z8+e1nm1atWSPGjQIMmvvfaadd6//vWvlPu0YsUKyePGjUv59/FvjMsjmOkBAAAqUPQAAAAVYrG85beEkc3puKBThBrdddddkmfOnGm1mUtf5hKW4zjOAw88IHnz5s2SCwoKrPOuvPJKzzbTnXfeKdn8jszlMcdheSssjM38dejQIckzZszwPO+ss86S/NRTT0nevn27dd6cOXMk33///ZIPHjzoee1Zs2ZJHjZsmNV23HHHef6edoxLb8z0AAAAFSh6AACAChQ9AABAhVjc0xN0DTLo9uVsPqI7n40cOVLyscceK9m9tfQvf/mL5F27dlltXbp0kXzddddJHjt2bFp9Mr8/8z4E8x4Cx3GcESNGVNoHpIaxmT/Me3gcx3GmT58uefny5ZJbtmxpnTdv3rxKr3fSSSdZx+Z4XLt2rWT31naTeZ/fV199ZbVxT483xqU3ZnoAAIAKFD0AAECFyC5v5WpbnZ+obLnLFb+nJJtPVnb/Od1yyy2Sx4wZY7WF/eZk87PM/m3cuNE6z9w6z/JWahib+cm9xfyOO+6o9Dz3GHYvYwUxceJEyWvWrLHavLawu///Z/z48Sl/bj5jXAbDTA8AAFCBogcAAKgQ2eWtMERxai3O3EtEbdu2lWxOeQ8ePNg6z+8JymGrW7eu5Dp16kh2T8dWVFRkrU/4b4zN6CkuLvZs6969u+Rf/OIXVf6svn37Su7Vq5fVtnLlykp/x/3/PwifhnHJTA8AAFCBogcAAKhA0QMAAFSI7D09YTzxMewtfFF8Y2w2/epXv/I9jpp27dpJLikpyWFP8gtjM3+sW7dO8uzZs622Y445RrK5fb1Zs2ah9uGKK66wjr3u6YE/xmUwzPQAAAAVKHoAAIAKkV3e8uM1jZfpJ1JGZXoOwZhbYRcuXJjDnujB2IyXhx9+WPKnn35qtZ1zzjmSzS3miB/G5RHM9AAAABUoegAAgAqRWd5yT7P5TYt53RGezReuIV6iOM0aF4zN/LFnzx7r+Pnnn/c8d8SIEZnuDqqAcZkeZnoAAIAKFD0AAEAFih4AAKBCZO7p8VuP5H4MpGPNmjWS3WvXP/rRj7LdndhibOaPw4cPW8c7duzIUU9QVYzL9DDTAwAAVKDoAQAAKkRmeSub0tnad7Q2RM+HH34o2f19tW/fPtvdQQCMTXhp06ZNrrugVj6NS2Z6AACAChQ9AABAhVgub3lNmflNrQX5+dFEZXoOqevatat13KVLlxz1JL8xNvPHpk2bct0F5+STT5Y8cuTIHPYk3hiXRzDTAwAAVKDoAQAAKlD0AAAAFWJ5T4/XOqHfW2eDrkmmuzUP0VBeXi65oqJC8pgxY3LRHXUYm/njD3/4g+SxY8dKLioqCvVzNmzY4Nn2k5/8RHLDhg1D/VxNGJdHMNMDAABUoOgBAAAqxHJ5y+Q3BZfONju/6bioTM/BW0lJieSysjLJBQUFueiOaozNePvnP/8puXv37pKvv/5667xJkyZJXrFihWS/l5k+8sgjkj/44AOrrXXr1pJvvvnmFHqMILSPS2Z6AACAChQ9AABAhVgub3lNp6X71EhTFKfjENyoUaMk811mH2Mz2o4//njr2Bwvjz/+uOfvmbsip0yZYrXNmjVL8u7duyUfPHgwrT5OmDBBcqtWrdK6BmyMyyOY6QEAACpQ9AAAABUoegAAgAqxvKcnk2uSUXyCJIL7/PPPJTdt2lSy+URZZA5jM9qqVbP/O7dPnz6S/e7pMR06dMg6Nre2p+Puu++2jsePH1+l6+G/MS6PYKYHAACoQNEDAABUiOXylpcwptbiMD2HIzZt2mQdm9/f4MGDs90deGBsRtPIkSMl//CHP7Tavv/972fsc++66y7JN954o9XmXoJD5mgcl/ztAgAAKlD0AAAAFSh6AACACrG/pydu64kI17PPPmsdm9snx4wZk+3uwMDYjD7z/plmzZpZbe+++67kv//975KnTZtmnde5c2fJffv2lXzZZZd5fm6NGkf+6eHvSXZp//NmpgcAAKhA0QMAAFSI/fIW9DHf+Dx//nyrzXwKc0FBQdb6BMRdw4YNreOuXbtWmi+55JKs9QkIGzM9AABABYoeAACgAstbiJ2tW7dKLisrs9r69esnuUWLFlnrEwAg+pjpAQAAKlD0AAAAFSh6AACACtzTg1hzP1104cKFOeoJACDqmOkBAAAqUPQAAAAVEuYLGo96ciJR7jhO2VFPRJhaJpPJJmFflO8yZ/g+8wffZX4J/fvku8wZz+8ypaIHAAAgrljeAgAAKlD0AAAAFSh6AACAChQ9AABABYoeAACgAkUPAABQgaIHAACoQNEDAABUoOgBAAAqUPQAAAAVKHoAAIAKFD0AAEAFih4AAKACRQ8AAFCBogcAAKhA0QMAAFSg6AEAACrUSOXkgoKCZFFRUYa6gsqUlpY6FRUVibCvm0gkkmFfE4FUJJPJJmFflLGZfYzNvBP62GRc5kZJSYnnd5lS0VNUVOSsXbs2nF4hkG7duuW6CwhXWSYuytjMPsZm3gl9bDIucyORSHh+lyxvAQAAFSh6AACAChQ9AABABYoeAACgAkUPAABQgaIHAACoQNEDAABUoOgBAAAqUPQAAAAVKHoAAIAKFD0AAEAFih4AAKACRQ8AAFCBogcAAKhQI9cdABAv8+fPl/w///M/aV2jdevWlV7j/PPPt85r165dWtcHgMow0wMAAFSg6AEAACpEZnmrvLzcOn7iiSckL1u2zGpbvXq15EQiITmZTFrnebUNHjzYOm/48OGebYg293duMr9/pM+95PTqq69W+Zp///vfJU+cOFHyzp07rfNmzpxZ5c9CbjA2M6uwsNA6Pv744yXfcsstVtuwYcNC/eySkhLJr7zyiud5c+fOlbxt2zbJTZs2tc577bXXJHfo0CGMLnpipgcAAKhA0QMAAFSIzPLWZZddZh3/6U9/kuyeCjWP/aZJvdrcy2Xm9Fz79u0lt23b1qfHSEfQJchMfJbX5+K/rVq1SvLKlSuttoMHD0o+7bTTrLaWLVtWer1bb73VOt6wYYNkc/fW7NmzrfO6dOkiecSIEUfpNaqCsRkvTz31lHVs3ppx+eWXW21XXXVVlT7L/X0dOnRIsvn/B37M79V9O8v7778vmeUtAACAEFD0AAAAFSh6AACACpG5p6eiosI6NtcQmzRpYrWZ6/yDBg2SPG/ePM/rl5WVeX7W/v37Jc+aNUuyud0OmWF+z35r+WHfUwB/HTt2lPyzn/3Matu7d69k95hr1qxZoOub1zCZ9wo4juN88cUXga6H8DE2o613797WcXFxseR77rnHavPbVh6E3/1eccNMDwAAUIGiBwAAqBCZ5S33EyTN6bgxY8ZYbebylmns2LHW8aZNmySPGzdO8htvvOHZD7apZ5Z7WtScNs3mNHnQqXutGjduLPnJJ5/M2ufWrFnTOm7QoEHWPls7xma89enTR/Lpp59utZlPQza5l5k3btwo+Ywzzki5DwMHDrSOt2zZUul53bt3t45btGiR8meli5keAACgAkUPAABQgaIHAACoEJl7esyt55UdB3HgwAHr+KKLLpJs3t/jXifu2rWr5GuvvTblz0VwQe8N8Lu/wO/x+Jl8dD7St2/fPuvY3F5rGj9+vHU8atSojPUJNsZm/jj22GOt4zZt2gT6Pfe9QEEsX75c8meffeZ5nvl6iRdffNFqM98Qn2nM9AAAABUoegAAgAqRWd5K1x133CHZvbV28+bNkv3ezO7eLo9w5Wq7qx+2wmbeW2+9Jbl///5Wm9cTmd1bWZFZjE1U1bp16yR/9dVXnufVrVtXcjaXs9yY6QEAACpQ9AAAABViubxVUlIiefLkyZKD7ha48sorrfN69eoVdhcRMqa8o+mbb76xjs2X9E6cOFHywYMHPa9x0kknSe7UqVOIvUM2MDb1efzxxyXPmDEj0O80bdo0U91JCTM9AABABYoeAACgAkUPAABQIZb39LRr105y+/btJZtviHUc77XmpUuXWscvv/yyZPON7u43rg8ePDj1ziKUJ7GGvbWWNzmnr6ysTHKPHj2sNr8nsnrZvn275AEDBlhtd911l+RLLrkk5WvDH2MTQbifqH7NNddI9tumPmXKFMljx44Nv2NpYKYHAACoQNEDAABUiOXylvlkxw0bNkg2n87sOI4zf/58yaWlpZI///xz6zxzOvVXv/pVpT93HHsZrF+/fin2GpXxml7P9JNimTZP3+HDhyWns5zlxxynjuM4l156qeR77rlH8h/+8AfrPLa6h4+xqdvXX38t2f3v3f79+yv9nZo1a1rHAwcOlHzCCSeE2Lv0MdMDAABUoOgBAAAqxHJ5y4u5NOU4jjNu3DjJW7dulezevfXss89KNl9S6mbuJGF5y5vfk7H9zg1jJwkyr3bt2pKDLivddttt1nGDBg0qPc/9dNfXX39d8gcffCD5/PPPt85btmyZ5K5duwbqk0aMTfgxl7TOPvtsyW+//bZ1ntffmwcffNA67tKlS4i9CwczPQAAQAWKHgAAoAJFDwAAUCGv7ulxKygoqDS71xnNp0vecMMNkhcuXGidt3r1asm///3vJUflSZNR4XefANtR46+wsFDy+++/H+q1zcdROI59j8DTTz8tedu2bdZ55tPSV65cKfmUU04JtX9xx9iEyf2kZfNeVfM+Hr/7uEaNGiU5Dv8WMtMDAABUoOgBAAAq5PXyVjo2bdok2T3dax6bLz1F7qSz5fZobcidnj17Wsfdu3eXbL7Y8Pnnn7fOMx9JYT5xneWt3GFsRtMXX3whec6cOVbb3/72N8lBl0LPOOOMEHuXecz0AAAAFSh6AACACixvOY4za9YsySUlJZLdd6wvWLBAcu/evTPfMQW8prL9pryD/PxomDaPh+rVq0t+7rnnJJu7tRzHfsr68OHDJb/yyivWea1btw67i3mLsZmfXnrpJclTpkxJ6xoffvih5O9973tV7lM2MdMDAABUoOgBAAAqUPQAAAAV1NzTU15eLtl8W7rjOM4DDzwg2VxPbtKkiXXej3/84wz1Ti+v9Xu/t0EHvVcg3S2ziL5zzz3XOjbv6SktLZW8efNm6zzu6QmOsZk/zG3qv/3tbwP9TocOHSRfccUVVtvJJ58suVq1eM2dxKu3AAAAaaLoAQAAKuT18pb5dGVzi6t7ytucTm3btq3kjRs3ZrB3cPObGk9n+6vfNDnT5vFjjuc77rgjhz3Rh7EZL7t27bKOR48eLfmdd97x/L2aNWtKvvnmmyVfcsklIfYut5jpAQAAKlD0AAAAFfJqeWvkyJHW8bJlyyQfOHBAsnv6dNCgQZIXLlyYod6hMl7T3Ok+zdXENHk87N+/X7L5RHTHsZ/CXFxcLHn79u2e12vYsKHkxo0bh9FFlRib8WLu0HryySetthdeeCHQNf73f/9Xcj4taZmY6QEAACpQ9AAAABUoegAAgAqRvafHvAfHfa+Oybxvx+9JoS1atJB8//33W+eZ9/QguzJ5rwBPds0tc4u5+7s1n4JuvrF5zZo1aX2W+aRlczt7jx490roeGJtxM3bsWMlLliwJ9DsFBQXW8YQJE0LtUxQx0wMAAFSg6AEAACpEdnnLfCmouW3VcYJPjV500UWS586dK9k9pYfoCWPKm2nzzPj2228l79ixw2qbPHmy5AULFkj+7rvvqvy5TZs2lTx9+nSrbdiwYZLr169f5c+CN8ZmNHz55ZfW8bp161K+xogRI6zjVq1aValPccBMDwAAUIGiBwAAqEDRAwAAVIjsPT3l5eWS3dsl69atK7ldu3aSzbfCOo79ZnVEH+v88bBv3z7JTz/9tNW2bds2yUHv4znppJMkX3fddVZb9erVJV9//fUp9RPhYWxGj/te108++STQ77Vs2VKyuc1dC2Z6AACAChQ9AABAhcgub/Xu3Vvy5s2brbZzzz1X8i233JK1PgGw32J+4403Wm3uYwCZYT6mwXHsxziYj5Vwu/feeyW3adMm/I5FHDM9AABABYoeAACgQmSXt8wnRbqfGgkAAI7YuHFjrrsQC8z0AAAAFSh6AACAChQ9AABABYoeAACgAkUPAABQgaIHAACokHC/zNP35ESi3HGcssx1B5VomUwmm4R9Ub7LnOH7zB98l/kl9O+T7zJnPL/LlIoeAACAuGJ5CwAAqEDRAwAAVKDoAQAAKlD0AAAAFSh6AACAChQ9AABABYoeAACgAkUPAABQgaIHAACo8P8C+pEYO+1m/gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8c0cHqSdU4PY",
        "colab_type": "code",
        "outputId": "a44b7bfe-bd13-4cbd-d0fb-422f8dff93bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 631
        }
      },
      "source": [
        "evaluate_and_output_model(model_linear3, test_labels, test_images)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10000, 28, 28)\n",
            "(10000, 28, 28)\n",
            "(10000, 28, 28)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gNdbqiARU458",
        "colab_type": "code",
        "outputId": "d9aecf02-3e8d-485a-97a1-040c5ebf435b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 631
        }
      },
      "source": [
        "evaluate_and_output_model(model_relu, test_labels, test_images)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10000, 28, 28)\n",
            "(10000, 28, 28)\n",
            "(10000, 28, 28)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NyjvoGRCU5ak",
        "colab_type": "code",
        "outputId": "7439e687-6a6d-403d-c48f-979d6b28a0ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 631
        }
      },
      "source": [
        "evaluate_and_output_model(model_tanh, test_labels, test_images)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10000, 28, 28)\n",
            "(10000, 28, 28)\n",
            "(10000, 28, 28)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7u8J9_HuU5wB",
        "colab_type": "code",
        "outputId": "ae2deb90-5254-400f-e7d6-b05069988baa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 631
        }
      },
      "source": [
        "evaluate_and_output_model(model_sigmoid, test_labels, test_images)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10000, 28, 28)\n",
            "(10000, 28, 28)\n",
            "(10000, 28, 28)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ONbw-cXnXLCv",
        "colab_type": "code",
        "outputId": "eb6d0721-fb87-4304-d374-09c406b64174",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 631
        }
      },
      "source": [
        "evaluate_and_output_model(model_linear2, test_labels, test_images)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10000, 28, 28)\n",
            "(10000, 28, 28)\n",
            "(10000, 28, 28)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gzv_neieLjc_",
        "colab_type": "code",
        "outputId": "f9812833-4fc8-4795-d3d0-ca4230a183dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "model_relu.save(filepath=\"/content/model_relu\")\n",
        "model_tanh.save(filepath=\"/content/model_tanh\")\n",
        "model_sigmoid.save(filepath=\"/content/model_sigmoid\")\n",
        "model_linear.save(filepath=\"/content/model_linear\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: /content/model_relu/assets\n",
            "INFO:tensorflow:Assets written to: /content/model_tanh/assets\n",
            "INFO:tensorflow:Assets written to: /content/model_sigmoid/assets\n",
            "INFO:tensorflow:Assets written to: /content/model_linear/assets\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kKbAVI3bMpoh",
        "colab_type": "code",
        "outputId": "404f75a8-a7a0-4e11-f43a-3b70467aa73f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        }
      },
      "source": [
        "!zip -r /content/model_relu.zip /content/model_relu\n",
        "!zip -r /content/model_tanh.zip /content/model_tanh\n",
        "!zip -r /content/model_linear.zip /content/model_linear\n",
        "!zip -r /content/model_sigmoid.zip /content/model_sigmoid"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  adding: content/model_relu/ (stored 0%)\n",
            "  adding: content/model_relu/assets/ (stored 0%)\n",
            "  adding: content/model_relu/variables/ (stored 0%)\n",
            "  adding: content/model_relu/variables/variables.index (deflated 69%)\n",
            "  adding: content/model_relu/variables/variables.data-00000-of-00001 (deflated 32%)\n",
            "  adding: content/model_relu/saved_model.pb (deflated 89%)\n",
            "  adding: content/model_tanh/ (stored 0%)\n",
            "  adding: content/model_tanh/assets/ (stored 0%)\n",
            "  adding: content/model_tanh/variables/ (stored 0%)\n",
            "  adding: content/model_tanh/variables/variables.index (deflated 69%)\n",
            "  adding: content/model_tanh/variables/variables.data-00000-of-00001 (deflated 21%)\n",
            "  adding: content/model_tanh/saved_model.pb (deflated 90%)\n",
            "  adding: content/model_linear/ (stored 0%)\n",
            "  adding: content/model_linear/assets/ (stored 0%)\n",
            "  adding: content/model_linear/variables/ (stored 0%)\n",
            "  adding: content/model_linear/variables/variables.index (deflated 70%)\n",
            "  adding: content/model_linear/variables/variables.data-00000-of-00001 (deflated 41%)\n",
            "  adding: content/model_linear/saved_model.pb (deflated 90%)\n",
            "  adding: content/model_sigmoid/ (stored 0%)\n",
            "  adding: content/model_sigmoid/assets/ (stored 0%)\n",
            "  adding: content/model_sigmoid/variables/ (stored 0%)\n",
            "  adding: content/model_sigmoid/variables/variables.index (deflated 69%)\n",
            "  adding: content/model_sigmoid/variables/variables.data-00000-of-00001 (deflated 13%)\n",
            "  adding: content/model_sigmoid/saved_model.pb (deflated 90%)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lRZAdgYNOsoY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model2 = keras.Sequential([\n",
        "    keras.layers.Dense(256, activation=\"relu\"),\n",
        "    keras.layers.Dense(128, activation=\"relu\"),\n",
        "    keras.layers.Dense(64, activation=\"relu\"),\n",
        "    keras.layers.Dense(256, activation=\"sigmoid\"),\n",
        "    keras.layers.Dense(784)\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dhWkUnzTOsuZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model2.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.MAE, #tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z5COKICMOsrg",
        "colab_type": "code",
        "outputId": "fc116021-d491-4be9-fdad-aa32eb4375be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        }
      },
      "source": [
        "model2.fit(train_images_flattened, train_images_flattened,  epochs=10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 32.2754 - accuracy: 0.0126\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 31.8131 - accuracy: 0.0150\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 31.7963 - accuracy: 0.0149\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 31.7965 - accuracy: 0.0149\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 31.7962 - accuracy: 0.0148\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 31.7962 - accuracy: 0.0150\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 31.7963 - accuracy: 0.0149\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 31.7964 - accuracy: 0.0152\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 31.7963 - accuracy: 0.0150\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 31.7963 - accuracy: 0.0149\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fb48dfdc0f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E8wgsUuZQnZ7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_loss, test_acc = model_linear.evaluate(test_images,  test_labels, verbose=2)\n",
        "\n",
        "print('\\n  Relu Test accuracy:', str(test_acc) + \" test loss: \" + str(test_loss) + \"\\n\")\n",
        "\n",
        "test_loss, test_acc = model_relu.evaluate(test_images,  test_images, verbose=2)\n",
        "\n",
        "print('\\n Sigmoid Test accuracy:', str(test_acc) + \" test loss: \" + str(test_loss))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sD_lROHbWRHO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_output_model = model.predict(test_images.reshape([-1, 784]),)\n",
        "test_output_model2 = model2.predict(test_images.reshape([-1, 784]),)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d4n2z7uNWn4c",
        "colab_type": "code",
        "outputId": "2b17fdf5-7a08-431c-ba27-f6739586d8a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test_output_model2.shape\n",
        "#(10000, 28, 28)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 784)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UTk6EgnTWzen",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model2_output_images = test_output_model2.reshape([10000,28,28])\n",
        "model_output_images = test_output_model.reshape([10000,28,28])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HjWd7kfOXI6V",
        "colab_type": "code",
        "outputId": "33799b7e-92ce-4dc2-8039-66cf33ea752d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "plt.imshow(model2_output_images[7], cmap=\"Greys_r\")\n",
        "#plt.imshow(test_images.reshape([-1, 784])[7], cmap=\"Greys_r\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fb49644b400>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i7Rqa-IBEHbv",
        "colab_type": "code",
        "outputId": "ec40f344-f972-488c-ac5b-02d77b5852bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "plt.imshow(model2_output_images[12], cmap=\"Greys_r\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fb496008080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WepSO_1DXrEc",
        "colab_type": "code",
        "outputId": "c69b8daf-180f-4d66-f56c-91f598fd57d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "plt.imshow(model_output_images[7], cmap=\"Greys_r\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fb496489cc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n6yKNn5eEJ4y",
        "colab_type": "code",
        "outputId": "39416bd0-b74a-4214-e8e9-4d20d1568477",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "plt.imshow(model_output_images[0], cmap=\"Greys_r\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fb49619a240>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QN5FAc1SD8SN",
        "colab_type": "code",
        "outputId": "97649673-8ecd-4aaf-a6a4-901231e9de89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "plt.imshow(test_images[0], cmap=\"Greys_r\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fb49623b6a0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X0UzHjUHcvc0",
        "colab_type": "text"
      },
      "source": [
        "# **Convolutional Autoencoders**\n",
        "\n",
        "**1) Normal Convolution and deconvolution:** Without batchnorm, this gives crap results as network gets the data easy. With batch norm we are forcing network to learn or atleast properly copy input to output, So we saw a bit better results with batchnorm and de convolution. Also the numbers 3,9 which had problems in upsampling or FC seems to be looking fine in this setup. Hence we decided to keep batchnorm for rest of the models as default\n",
        "\n",
        "**2) Upscaling:** We can see some minute changes when we observer numbers 3 and 9. The 3 while upscaling has some extra black pixels at the tip of the number making 3 look like 8. Similary the white pixels in middle of number 9 seems to have vanished\n",
        "\n",
        "**3) Fully connected at the middle:** We can see some minute changes when we observer numbers 9. The white pixels in middle of number 9 seems to have vanished. This may be due to compression/FC layer packing all info densely and maybe this white area was redundant info to classify number as 9.\n",
        "\n",
        "\n",
        "**Errors:**  Since we did not model encoder/decoder seperately and stacked them together instead modelled encoder-decoder as part of same model, we faced some issues in decoder part as decoder output was 32 - 32 when it should be 28 - 28. We used valid padding in only first decoder layer and finally got the output dimension to 32*32. In future better way to code would be to stack separate encoder and decoder rather than putting them in same model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nkLItd7he5Vu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(train_images_raw, train_labels_raw), (test_images_raw, test_labels_raw) = mnist.load_data()\n",
        "\n",
        "train_data = tf.data.Dataset.from_tensor_slices(( train_images_raw.astype(np.float32)/255.0, train_images_raw.astype(np.float32)/255.0 ))\n",
        "\n",
        "train_data = train_data.shuffle(buffer_size = train_images_raw.shape[0]).batch(32)\n",
        "\n",
        "#test_data = tf.data.Dataset.from_tensor_slices(( test_images_raw.astype(np.float32)/255.0, test_labels_raw.astype(np.int32) ))\n",
        "test_data = tf.data.Dataset.from_tensor_slices(( test_images_raw.astype(np.float32)/255.0,  test_images_raw.astype(np.float32)/255.0 ))\n",
        "test_data = test_data.batch(32)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WkoWCC0de5bw",
        "colab_type": "code",
        "outputId": "d4c7012e-44c0-4f17-e241-b6eb4081a0c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "input_shape = (train_images_raw.shape[1], train_images_raw.shape[2], 1)\n",
        "print(input_shape)\n",
        "print(train_images_raw.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(28, 28, 1)\n",
            "(60000, 28, 28)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "08SCSKX7oUBx",
        "colab_type": "code",
        "outputId": "8983f10c-aaf8-4036-c393-6c929a189076",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        }
      },
      "source": [
        "\"\"\"\n",
        "def create_model():\n",
        "  model = models.Sequential()\n",
        "  model.add(layers.Conv2D(filters = 32, kernel_size=(3, 3),  padding = \"same\", activation=tf.nn.relu,name = \"conv_enc1\", input_shape = input_shape))\n",
        "  model.add(layers.MaxPool2D(pool_size=(2,2)))\n",
        "  model.add(layers.Conv2D(filters = 64, kernel_size=(3, 3), padding = \"same\", activation = tf.nn.relu, name = \"conv_enc2\"))\n",
        "  model.add(layers.MaxPool2D(pool_size=(2,2)))\n",
        "  model.add(layers.Conv2D(filters = 128, kernel_size=(3, 3),   padding = \"same\", activation = tf.nn.relu, name = \"conv_enc3\"))\n",
        "  model.add(layers.MaxPool2D(pool_size=(2,2)))\n",
        "  model.add(layers.Conv2D(filters = 64, kernel_size=(1, 1),  padding = \"same\", activation = tf.nn.relu, name = \"conv_compression_dim_reduce\"))\n",
        "  model.add(layers.Conv2DTranspose(filters = 32, kernel_size=(3, 3), strides=(2,2),  activation = tf.nn.relu, name = \"conv_dec1\"))\n",
        "  model.add(layers.BatchNormalization())\n",
        "  model.add(layers.Conv2DTranspose(filters = 16, kernel_size=(3, 3), strides=(2,2),  padding = \"same\", activation = tf.nn.relu, name = \"conv_dec2\"))\n",
        "  model.add(layers.BatchNormalization())\n",
        "  model.add(layers.Conv2DTranspose(filters = 1, kernel_size=(3, 3), strides=(2,2), padding = \"same\", activation = tf.nn.sigmoid, name = \"conv_dec3\"))\n",
        "  print(model.summary())\n",
        "  return model;\n",
        "\n",
        "conv_model1 = create_model()\n",
        "\"\"\""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_9\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv_enc1 (Conv2D)           (None, 28, 28, 32)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_18 (MaxPooling (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv_enc2 (Conv2D)           (None, 14, 14, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_19 (MaxPooling (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv_enc3 (Conv2D)           (None, 7, 7, 128)         73856     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_20 (MaxPooling (None, 3, 3, 128)         0         \n",
            "_________________________________________________________________\n",
            "conv_compression_dim_reduce  (None, 3, 3, 64)          8256      \n",
            "_________________________________________________________________\n",
            "conv_dec1 (Conv2DTranspose)  (None, 7, 7, 32)          18464     \n",
            "_________________________________________________________________\n",
            "batch_normalization_14 (Batc (None, 7, 7, 32)          128       \n",
            "_________________________________________________________________\n",
            "conv_dec2 (Conv2DTranspose)  (None, 14, 14, 16)        4624      \n",
            "_________________________________________________________________\n",
            "batch_normalization_15 (Batc (None, 14, 14, 16)        64        \n",
            "_________________________________________________________________\n",
            "conv_dec3 (Conv2DTranspose)  (None, 28, 28, 1)         145       \n",
            "=================================================================\n",
            "Total params: 124,353\n",
            "Trainable params: 124,257\n",
            "Non-trainable params: 96\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bdjv6lSRf52C",
        "colab_type": "code",
        "outputId": "d5182620-67f0-412b-d6cc-93fbe7387ac8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 846
        }
      },
      "source": [
        "def create_conv_autoencoder_model(encoder_layer_dims=[32,64,128], compression_layer_dim=64, decoder_layer_dims=[32, 16, 1], input_shape=(28,28,1), ks=3,\n",
        "                 compression_flag=True ,batch_norm_flag=True, dense_latent_rep_flag=False, upsampling_flag = False):\n",
        "  model = models.Sequential()\n",
        "  \n",
        "  #Build encoder part, add batch norm after encoder if batch norm flag is set to true\n",
        "  conv_enc_base_name = \"conv_enc_\"\n",
        "  conv_cmprsn_base_name = \"conv_compress_dims\"\n",
        "  conv_dec_base_name = \"conv_dec_\"\n",
        "  for i in range (len(encoder_layer_dims)):\n",
        "    #For first layer we need to specify shape\n",
        "    if(i == 0):\n",
        "      model.add(layers.Conv2D(filters = encoder_layer_dims[i], kernel_size=(ks, ks),  padding = \"same\", \n",
        "                              activation=tf.nn.relu, name = conv_enc_base_name + str(i), input_shape = input_shape))\n",
        "      if(batch_norm_flag):\n",
        "        model.add(layers.BatchNormalization()) \n",
        "\n",
        "      model.add(layers.MaxPool2D(pool_size=(2,2)))\n",
        "    else:\n",
        "      model.add(layers.Conv2D(filters = encoder_layer_dims[i], kernel_size=(ks, ks),  padding = \"same\", \n",
        "                              activation=tf.nn.relu, name = conv_enc_base_name + str(i)))\n",
        "      \n",
        "      if(batch_norm_flag):\n",
        "        model.add(layers.BatchNormalization()) \n",
        "\n",
        "      model.add(layers.MaxPool2D(pool_size=(2,2)))\n",
        "  \n",
        "  print(\"Encoder has been built, checking for compression flag\")\n",
        "\n",
        "  if(compression_flag):\n",
        "    model.add(layers.Conv2D(filters = compression_layer_dim, kernel_size=(1, 1),  padding = \"same\", activation = tf.nn.relu, name = conv_cmprsn_base_name))\n",
        "\n",
        "  # With one-d dense representation is hardcoded for this one casem without this we can pick dynamically\n",
        "  if(dense_latent_rep_flag):\n",
        "    model.add(layers.Flatten())\n",
        "    model.add(layers.Dense(256,activation=tf.nn.relu))\n",
        "    model.add(layers.Dense(64,activation=tf.nn.leaky_relu))\n",
        "    model.add(layers.Dense(252,activation=tf.nn.relu))\n",
        "    model.add(layers.Reshape((3, 3, 28), input_shape=(252,)))\n",
        "\n",
        "  print(\"FC layer added, printing model summary\")\n",
        "\n",
        "  #print(model.summary())\n",
        "  #Build Decoder , add batch norm before adding conv transpose, Use sigmoid at final layer and relu for others\n",
        "  if(upsampling_flag == False):\n",
        "    for i in range (len(decoder_layer_dims)):\n",
        "      if(i == 0):\n",
        "        if(batch_norm_flag):\n",
        "          model.add(layers.BatchNormalization()) \n",
        "\n",
        "        model.add(layers.Conv2DTranspose(filters = decoder_layer_dims[i], kernel_size=(ks, ks),  strides=(2,2), padding = \"valid\", \n",
        "                                  activation=tf.nn.relu, name = conv_dec_base_name + str(i)))\n",
        "        \n",
        "      elif(i < len(decoder_layer_dims)-1 and i > 0):\n",
        "\n",
        "        if(batch_norm_flag):\n",
        "          model.add(layers.BatchNormalization()) \n",
        "\n",
        "        model.add(layers.Conv2DTranspose(filters = decoder_layer_dims[i], kernel_size=(ks, ks),  strides=(2,2), padding = \"same\", \n",
        "                                  activation=tf.nn.relu, name = conv_dec_base_name + str(i)))\n",
        "      else:\n",
        "        if(batch_norm_flag):\n",
        "          model.add(layers.BatchNormalization())  \n",
        "\n",
        "        model.add(layers.Conv2DTranspose(filters = decoder_layer_dims[i], kernel_size=(ks, ks), strides=(2,2), padding = \"same\", \n",
        "                                  activation=tf.nn.sigmoid, name = conv_dec_base_name + str(i)))\n",
        "        \n",
        "  elif(upsampling_flag == True):\n",
        "      print(\"Upscaling with convolution choosen\")\n",
        "      for i in range (len(decoder_layer_dims)):\n",
        "        if(i == 0):\n",
        "          model.add(layers.UpSampling2D(size=(3, 3)))\n",
        "          if(batch_norm_flag):\n",
        "            model.add(layers.BatchNormalization()) \n",
        "\n",
        "          model.add(layers.Conv2D(filters = decoder_layer_dims[i], kernel_size=(ks, ks), padding = \"valid\", activation=tf.nn.relu, name = conv_dec_base_name + str(i)))\n",
        "          model.add(layers.UpSampling2D())\n",
        "\n",
        "        elif(i < len(decoder_layer_dims)-1 and i > 0):\n",
        "\n",
        "          if(batch_norm_flag):\n",
        "            model.add(layers.BatchNormalization()) \n",
        "\n",
        "          model.add(layers.Conv2D(filters = decoder_layer_dims[i], kernel_size=(ks, ks), padding = \"same\", activation=tf.nn.relu, name = conv_dec_base_name + str(i)))\n",
        "          model.add(layers.UpSampling2D())\n",
        "        else:\n",
        "          if(batch_norm_flag):\n",
        "            model.add(layers.BatchNormalization())  \n",
        "\n",
        "          model.add(layers.Conv2D(filters = decoder_layer_dims[i], kernel_size=(ks, ks),  padding = \"same\", activation=tf.nn.sigmoid, name = conv_dec_base_name + str(i)))\n",
        "        \n",
        "  print(\"Model has been built, printing summary\")\n",
        "  \n",
        "  print(model.summary())\n",
        "  return model;\n",
        "\n",
        "conv_model_with_upsampling = create_conv_autoencoder_model(upsampling_flag=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Encoder has been built, checking for compression flag\n",
            "FC layer added, printing model summary\n",
            "Upscaling with convolution choosen\n",
            "Model has been built, printing summary\n",
            "Model: \"sequential_26\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv_enc_0 (Conv2D)          (None, 28, 28, 32)        320       \n",
            "_________________________________________________________________\n",
            "batch_normalization_93 (Batc (None, 28, 28, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_69 (MaxPooling (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv_enc_1 (Conv2D)          (None, 14, 14, 64)        18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_94 (Batc (None, 14, 14, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_70 (MaxPooling (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv_enc_2 (Conv2D)          (None, 7, 7, 128)         73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_95 (Batc (None, 7, 7, 128)         512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_71 (MaxPooling (None, 3, 3, 128)         0         \n",
            "_________________________________________________________________\n",
            "conv_compress_dims (Conv2D)  (None, 3, 3, 64)          8256      \n",
            "_________________________________________________________________\n",
            "up_sampling2d_18 (UpSampling (None, 9, 9, 64)          0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_96 (Batc (None, 9, 9, 64)          256       \n",
            "_________________________________________________________________\n",
            "conv_dec_0 (Conv2D)          (None, 7, 7, 32)          18464     \n",
            "_________________________________________________________________\n",
            "up_sampling2d_19 (UpSampling (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_97 (Batc (None, 14, 14, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv_dec_1 (Conv2D)          (None, 14, 14, 16)        4624      \n",
            "_________________________________________________________________\n",
            "up_sampling2d_20 (UpSampling (None, 28, 28, 16)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_98 (Batc (None, 28, 28, 16)        64        \n",
            "_________________________________________________________________\n",
            "conv_dec_2 (Conv2D)          (None, 28, 28, 1)         145       \n",
            "=================================================================\n",
            "Total params: 125,505\n",
            "Trainable params: 124,833\n",
            "Non-trainable params: 672\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AME6JZrM_JjT",
        "colab_type": "code",
        "outputId": "ad90535f-edb3-4891-e97b-58ecb10b1eef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 765
        }
      },
      "source": [
        "conv_model_with_upsampling.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_26\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv_enc_0 (Conv2D)          (None, 28, 28, 32)        320       \n",
            "_________________________________________________________________\n",
            "batch_normalization_93 (Batc (None, 28, 28, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_69 (MaxPooling (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv_enc_1 (Conv2D)          (None, 14, 14, 64)        18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_94 (Batc (None, 14, 14, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_70 (MaxPooling (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv_enc_2 (Conv2D)          (None, 7, 7, 128)         73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_95 (Batc (None, 7, 7, 128)         512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_71 (MaxPooling (None, 3, 3, 128)         0         \n",
            "_________________________________________________________________\n",
            "conv_compress_dims (Conv2D)  (None, 3, 3, 64)          8256      \n",
            "_________________________________________________________________\n",
            "up_sampling2d_18 (UpSampling (None, 9, 9, 64)          0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_96 (Batc (None, 9, 9, 64)          256       \n",
            "_________________________________________________________________\n",
            "conv_dec_0 (Conv2D)          (None, 7, 7, 32)          18464     \n",
            "_________________________________________________________________\n",
            "up_sampling2d_19 (UpSampling (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_97 (Batc (None, 14, 14, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv_dec_1 (Conv2D)          (None, 14, 14, 16)        4624      \n",
            "_________________________________________________________________\n",
            "up_sampling2d_20 (UpSampling (None, 28, 28, 16)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_98 (Batc (None, 28, 28, 16)        64        \n",
            "_________________________________________________________________\n",
            "conv_dec_2 (Conv2D)          (None, 28, 28, 1)         145       \n",
            "=================================================================\n",
            "Total params: 125,505\n",
            "Trainable params: 124,833\n",
            "Non-trainable params: 672\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JIB-U9526FNc",
        "colab_type": "code",
        "outputId": "aeec537d-5117-4abc-96fd-fb871bea11e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 830
        }
      },
      "source": [
        "conv_model_with_fc = create_conv_autoencoder_model(dense_latent_rep_flag=True)\n",
        "conv_model_with_fc.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_18\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv_enc_0 (Conv2D)          (None, 28, 28, 32)        320       \n",
            "_________________________________________________________________\n",
            "batch_normalization_45 (Batc (None, 28, 28, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_45 (MaxPooling (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv_enc_1 (Conv2D)          (None, 14, 14, 64)        18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_46 (Batc (None, 14, 14, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_46 (MaxPooling (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv_enc_2 (Conv2D)          (None, 7, 7, 128)         73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_47 (Batc (None, 7, 7, 128)         512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_47 (MaxPooling (None, 3, 3, 128)         0         \n",
            "_________________________________________________________________\n",
            "conv_compress_dims (Conv2D)  (None, 3, 3, 64)          8256      \n",
            "_________________________________________________________________\n",
            "flatten_5 (Flatten)          (None, 576)               0         \n",
            "_________________________________________________________________\n",
            "dense_13 (Dense)             (None, 256)               147712    \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             (None, 64)                16448     \n",
            "_________________________________________________________________\n",
            "dense_15 (Dense)             (None, 252)               16380     \n",
            "_________________________________________________________________\n",
            "reshape_1 (Reshape)          (None, 3, 3, 28)          0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_48 (Batc (None, 3, 3, 28)          112       \n",
            "_________________________________________________________________\n",
            "conv_dec_0 (Conv2DTranspose) (None, 7, 7, 32)          8096      \n",
            "_________________________________________________________________\n",
            "batch_normalization_49 (Batc (None, 7, 7, 32)          128       \n",
            "_________________________________________________________________\n",
            "conv_dec_1 (Conv2DTranspose) (None, 14, 14, 16)        4624      \n",
            "_________________________________________________________________\n",
            "batch_normalization_50 (Batc (None, 14, 14, 16)        64        \n",
            "_________________________________________________________________\n",
            "conv_dec_2 (Conv2DTranspose) (None, 28, 28, 1)         145       \n",
            "=================================================================\n",
            "Total params: 295,533\n",
            "Trainable params: 294,933\n",
            "Non-trainable params: 600\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CodhK5n6wy72",
        "colab_type": "code",
        "outputId": "f20a5f34-9e48-42cf-ff36-192e33848af0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 667
        }
      },
      "source": [
        "conv_model_with_batchnorm = create_conv_autoencoder_model()\n",
        "conv_model_with_batchnorm.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_11\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv_enc_0 (Conv2D)          (None, 28, 28, 32)        320       \n",
            "_________________________________________________________________\n",
            "batch_normalization_22 (Batc (None, 28, 28, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_24 (MaxPooling (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv_enc_1 (Conv2D)          (None, 14, 14, 64)        18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_23 (Batc (None, 14, 14, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_25 (MaxPooling (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv_enc_2 (Conv2D)          (None, 7, 7, 128)         73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_24 (Batc (None, 7, 7, 128)         512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_26 (MaxPooling (None, 3, 3, 128)         0         \n",
            "_________________________________________________________________\n",
            "conv_compress_dims (Conv2D)  (None, 3, 3, 64)          8256      \n",
            "_________________________________________________________________\n",
            "batch_normalization_25 (Batc (None, 3, 3, 64)          256       \n",
            "_________________________________________________________________\n",
            "conv_dec_0 (Conv2DTranspose) (None, 7, 7, 32)          18464     \n",
            "_________________________________________________________________\n",
            "batch_normalization_26 (Batc (None, 7, 7, 32)          128       \n",
            "_________________________________________________________________\n",
            "conv_dec_1 (Conv2DTranspose) (None, 14, 14, 16)        4624      \n",
            "_________________________________________________________________\n",
            "batch_normalization_27 (Batc (None, 14, 14, 16)        64        \n",
            "_________________________________________________________________\n",
            "conv_dec_2 (Conv2DTranspose) (None, 28, 28, 1)         145       \n",
            "=================================================================\n",
            "Total params: 125,505\n",
            "Trainable params: 124,833\n",
            "Non-trainable params: 672\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tRtj3HG3sQm2",
        "colab_type": "code",
        "outputId": "a8892b2f-a23d-49e1-eb20-c796d002f27e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        }
      },
      "source": [
        "conv_model_without_batchnorm = create_conv_autoencoder_model(batch_norm_flag=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Encoder has been built, checking for compression flag\n",
            "Model has been built, printing summary\n",
            "Model: \"sequential_12\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv_enc_0 (Conv2D)          (None, 28, 28, 32)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_27 (MaxPooling (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv_enc_1 (Conv2D)          (None, 14, 14, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_28 (MaxPooling (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv_enc_2 (Conv2D)          (None, 7, 7, 128)         73856     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_29 (MaxPooling (None, 3, 3, 128)         0         \n",
            "_________________________________________________________________\n",
            "conv_compress_dims (Conv2D)  (None, 3, 3, 64)          8256      \n",
            "_________________________________________________________________\n",
            "conv_dec_0 (Conv2DTranspose) (None, 7, 7, 32)          18464     \n",
            "_________________________________________________________________\n",
            "conv_dec_1 (Conv2DTranspose) (None, 14, 14, 16)        4624      \n",
            "_________________________________________________________________\n",
            "conv_dec_2 (Conv2DTranspose) (None, 28, 28, 1)         145       \n",
            "=================================================================\n",
            "Total params: 124,161\n",
            "Trainable params: 124,161\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qukIFCUmvDKn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "conv_model_with_batchnorm.compile(optimizer=\"adam\", loss=tf.keras.losses.MeanAbsoluteError(), metrics=[\"accuracy\"])\n",
        "conv_model_without_batchnorm.compile(optimizer=\"adam\", loss=tf.keras.losses.MeanAbsoluteError(), metrics=[\"accuracy\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QRIeyttX3JWF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "conv_model_with_fc.compile(optimizer=\"adam\", loss=tf.keras.losses.MeanAbsoluteError(), metrics=[\"accuracy\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L7er_SXx_Mll",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "conv_model_with_upsampling.compile(optimizer=\"adam\", loss=tf.keras.losses.MeanAbsoluteError(), metrics=[\"accuracy\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nCEyoAeJyShw",
        "colab_type": "code",
        "outputId": "412ad824-e0ea-4fff-f018-fe5eda8466fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "x = tf.expand_dims(train_images_raw.astype(np.float32)/255.0 , -1)\n",
        "y = tf.expand_dims(train_images_raw.astype(np.float32)/255.0, -1)\n",
        "print(x.shape)\n",
        "print(x.dtype)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 28, 28, 1)\n",
            "<dtype: 'float32'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yclHvm5QsjhD",
        "colab_type": "code",
        "outputId": "2fc73b57-aa4c-4e48-f770-e47cb9e363be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        }
      },
      "source": [
        "conv_model_with_batchnorm.fit(x=x, y=y, epochs = 7)\n",
        "conv_model_without_batchnorm.fit(x=x, y=y, epochs = 7)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/7\n",
            "1875/1875 [==============================] - 14s 8ms/step - loss: 0.0497 - accuracy: 0.8055\n",
            "Epoch 2/7\n",
            "1875/1875 [==============================] - 14s 7ms/step - loss: 0.0179 - accuracy: 0.8153\n",
            "Epoch 3/7\n",
            "1875/1875 [==============================] - 14s 7ms/step - loss: 0.0157 - accuracy: 0.8154\n",
            "Epoch 4/7\n",
            "1875/1875 [==============================] - 14s 7ms/step - loss: 0.0147 - accuracy: 0.8154\n",
            "Epoch 5/7\n",
            "1875/1875 [==============================] - 14s 7ms/step - loss: 0.0139 - accuracy: 0.8154\n",
            "Epoch 6/7\n",
            "1875/1875 [==============================] - 14s 7ms/step - loss: 0.0133 - accuracy: 0.8154\n",
            "Epoch 7/7\n",
            "1875/1875 [==============================] - 13s 7ms/step - loss: 0.0128 - accuracy: 0.8154\n",
            "Epoch 1/7\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.1338 - accuracy: 0.8074\n",
            "Epoch 2/7\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.1307 - accuracy: 0.8088\n",
            "Epoch 3/7\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.1307 - accuracy: 0.8088\n",
            "Epoch 4/7\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.1307 - accuracy: 0.8088\n",
            "Epoch 5/7\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 0.1307 - accuracy: 0.8088\n",
            "Epoch 6/7\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.1307 - accuracy: 0.8088\n",
            "Epoch 7/7\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.1307 - accuracy: 0.8088\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f9148298ac8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RhOKAvca3T2E",
        "colab_type": "code",
        "outputId": "9d90b735-6de9-42fe-9461-d17728d133dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        }
      },
      "source": [
        "conv_model_with_fc.fit(x=x, y=y, epochs=7)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/7\n",
            "1875/1875 [==============================] - 15s 8ms/step - loss: 0.0787 - accuracy: 0.7871\n",
            "Epoch 2/7\n",
            "1875/1875 [==============================] - 14s 8ms/step - loss: 0.0303 - accuracy: 0.8138\n",
            "Epoch 3/7\n",
            "1875/1875 [==============================] - 15s 8ms/step - loss: 0.0266 - accuracy: 0.8144\n",
            "Epoch 4/7\n",
            "1875/1875 [==============================] - 15s 8ms/step - loss: 0.0247 - accuracy: 0.8147\n",
            "Epoch 5/7\n",
            "1875/1875 [==============================] - 15s 8ms/step - loss: 0.0234 - accuracy: 0.8148\n",
            "Epoch 6/7\n",
            "1875/1875 [==============================] - 15s 8ms/step - loss: 0.0224 - accuracy: 0.8149\n",
            "Epoch 7/7\n",
            "1875/1875 [==============================] - 15s 8ms/step - loss: 0.0217 - accuracy: 0.8150\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f9142a93d68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mvMdGQaN_RPj",
        "colab_type": "code",
        "outputId": "b9dbee69-74f0-4701-c877-95d23395f7c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        }
      },
      "source": [
        "conv_model_with_upsampling.fit(x=x, y=y, epochs=7)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/7\n",
            "1875/1875 [==============================] - 16s 9ms/step - loss: 0.0476 - accuracy: 0.8051\n",
            "Epoch 2/7\n",
            "1875/1875 [==============================] - 16s 8ms/step - loss: 0.0249 - accuracy: 0.8146\n",
            "Epoch 3/7\n",
            "1875/1875 [==============================] - 16s 9ms/step - loss: 0.0218 - accuracy: 0.8150\n",
            "Epoch 4/7\n",
            "1875/1875 [==============================] - 16s 9ms/step - loss: 0.0203 - accuracy: 0.8151\n",
            "Epoch 5/7\n",
            "1875/1875 [==============================] - 16s 9ms/step - loss: 0.0192 - accuracy: 0.8152\n",
            "Epoch 6/7\n",
            "1875/1875 [==============================] - 16s 9ms/step - loss: 0.0184 - accuracy: 0.8153\n",
            "Epoch 7/7\n",
            "1875/1875 [==============================] - 16s 9ms/step - loss: 0.0178 - accuracy: 0.8153\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f9147dafd30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5m24wEYi2hWC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def evaluate_and_output_model(model, test_images, images_to_be_compared=5):\n",
        "  class_names = ['0', '1', '2', '3', '4','5', '6', '7', '8', '9']\n",
        "  print(test_images.shape)\n",
        "  #test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
        "  print(test_images.shape)\n",
        "  #print('\\n  Relu Test accuracy:', str(test_acc) + \" test loss: \" + str(test_loss) + \"\\n\")\n",
        "  model_output_images = model.predict(test_images)\n",
        "  print(model_output_images.shape)\n",
        "  plt.figure(figsize=(10,10))\n",
        "  j = -1\n",
        "  for i in range(25):\n",
        "      plt.subplot(5,5,i+1)\n",
        "      plt.xticks([])\n",
        "      plt.yticks([])\n",
        "      plt.grid(False)\n",
        "      j = j+1\n",
        "      if(i%2 == 0):\n",
        "        plt.imshow(test_images[i+10][:,:,0], cmap=plt.cm.binary)\n",
        "      elif(j%2 == 1):\n",
        "        \n",
        "        plt.imshow(model_output_images[j-1+10][:,:,0], cmap=plt.cm.binary)\n",
        "      \n",
        "      #plt.xlabel(class_names[test_images[i]])\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7jypdb0Ntaeo",
        "colab_type": "code",
        "outputId": "04d317d6-15db-4ade-b78f-71fe08fcc600",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        }
      },
      "source": [
        "evaluate_and_output_model(conv_model_without_batchnorm, tf.expand_dims(test_images_raw.astype(np.float32)/255.0, -1))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10000, 28, 28, 1)\n",
            "(10000, 28, 28, 1)\n",
            "(10000, 28, 28, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bAS5I5ZYtgTB",
        "colab_type": "code",
        "outputId": "60cddc2c-fbc3-4c59-872c-85cc9edc06cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        }
      },
      "source": [
        "evaluate_and_output_model(conv_model_with_batchnorm, tf.expand_dims(test_images_raw.astype(np.float32)/255.0, -1))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10000, 28, 28, 1)\n",
            "(10000, 28, 28, 1)\n",
            "(10000, 28, 28, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u_qWaRqO30x_",
        "colab_type": "code",
        "outputId": "072a2d91-6f98-48b9-cad4-4cba82312e38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        }
      },
      "source": [
        "evaluate_and_output_model(conv_model_with_fc, tf.expand_dims(test_images_raw.astype(np.float32)/255.0, -1))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10000, 28, 28, 1)\n",
            "(10000, 28, 28, 1)\n",
            "(10000, 28, 28, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Pz09eQ5KEJz0AAMALdHoAAIAXUnZ4C37Qu2HrjMzWuHFjyQxfpxd3V3U93KU/w6ecckrC2gSEi78yAADAC3R6AACAFxjeApBw559/vmR9YGWLFi2S0RxEoHz58tb1f/7zH8m//PKL5G7duiWsTUC4eNIDAAC8QKcHAAB4gU4PAADwAnN6ACSc3nmZXZjTS5EiRazrSy65JEktASLHkx4AAOAFOj0AAMALWZEcOJaVlbXJGJMbv+agAHXz8/OrxvpFuZdJw/3MHNzLzBLz+8m9TJrAexlRpwcAACBdMbwFAAC8QKcHAAB4gU4PAADwAp0eAADgBTo9AADAC3R6AACAF+j0AAAAL9DpAQAAXqDTAwAAvECnBwAAeIFODwAA8AKdHgAA4AU6PQAAwAt0egAAgBfo9AAAAC/Q6QEAAF6g0wMAALxQNJLKVapUyc/Ozo5TU1CQnJwck5eXlxXr1+VeJseiRYvy8vPzq8b6dbmficdnM7PE47PJvUyOUPcyok5Pdna2WbhwYWxahbA0b948Lq/LvUyOrKys3Hi8Lvcz8fhsZpZ4fDa5l8kR6l4yvAUAALxApwcAAHiBTg8AAPACnR4AAOAFOj0AAMALdHoAAIAX6PQAAAAv0OkBAABeiGhzwnSTn58vOTf3f3sVVa1qb9RYpkyZhLUJAAAkB096AACAF+j0AAAAL2T08Fbfvn0lT5gwQXKFChWsehs3bpRcpEiR+DcMABJk586dklevXm2VjR07NvDnevfuLfnEE0+MfcNQKPq+GmPMa6+9Jnnp0qVWWcmSJSV36NBBcuvWra16WVkxPz835fCkBwAAeIFODwAA8AKdHgAA4IWMmtOjl6gbY8xbb70l+dChQ5K3bdtm1duwYYPkmjVrxql1AP508OBByXpuQtGi9lcS20lER/9O//Wvf0keNmxY2K/x7LPPSr7sssskjx492qpXqVKlaJqIMO3du1fy3XffLfmNN96w6q1fv17yvn37Al9vxIgRks877zyrbOrUqZJLly4deWPTAE96AACAF+j0AAAAL2TU8JZrz549Bf73ww6z+3qZ+hgv3Xz00UeS9fJLY4yZMWOG5N9++01ys2bNrHqXXnqp5EGDBsW4hfg7eoj5hx9+kHzttdda9ZYsWSL5wIEDkkuUKGHV0/fdfRSPYHoI45FHHonqNfR9+c9//iN53rx5Vr2XXnpJ8rnnnhvVeyHYmDFjJOshx/379wf+jLv1ip7eoe/r+++/b9XT36fz58+XXK1atQhanNp40gMAALxApwcAAHgho4a39CO8gq7/VKpUqZDXiB+9wsAYY7p06SL5q6++kuyuxKtdu7bkhg0bSnZ3mL3vvvsk161bV/IVV1wRZYsRyh9//GFdd+3aVfJnn30m2d09VtPDzXpVlzHG3HPPPZKLFSsm+cwzzwx8DRhTr169Av+7u+PugAEDJB933HFWmV4B9MADD0h2P8MXXXSR5IEDB0rWK42MYRpBuNyVVy+++KJk/b1YvHhxq55eRXf99dcHvv7PP/8sWU8pMMaYlStXStY7N8+ZM8eqd+SRRwa+fqrjmwIAAHiBTg8AAPACnR4AAOCFjJrT445Xu8tf/+Qu9dNjqEE/g+jl5eVJ7tixo1X23XffSdZzcJ577jmrXqtWrSQffvjhkt05PZ07d5Y8ffp0yXpHWbdML9Ns0KCBVc+HU4cjtXHjRsnnnHOOVbZ8+XLJetz/8ccft+oFzbFy5zPoOT5r166V7C7D1vewbdu2kvW/FZ/MmjWrwP+ut3Qw5q+7Kwdp2rSpZD1vyxhjNm/eLPnBBx+U/Msvv1j1JkyYIFnPz4JNf76MMSYnJ0ey/jxUr17dqqdPVi9btmxY7+XOydPffz/++KNk/Zkyxpjvv/9ecrp9xnjSAwAAvECnBwAAeCGjhrfcZc5BOzLrA9yMMWbmzJmSe/XqFfN2+U4feKiHs4yxh0D00Ii7HDOIXspujL2Drx6qnDt3rlUvaHhl165d1jXbGfx16wc9vPHTTz9ZZW3atJGsd3t1d4gNEmpZs36M/o9//MMq08Ndut7XX39t1XOHBDLV22+/LVkP0eotHSJx6qmnSn799detMr2tgN7FV+/ibIz9/ax3cXYPmfWdO/SnPzu6rFOnTla9cIe0tJIlS1rX559/vuSXX35Zsh7CNMaYfv36Sdb3OR2mA/CkBwAAeIFODwAA8AKdHgAA4IWMGkx15/QEzSNw6+mtt1F4U6dOta71cuXKlStbZcuWLZMc7jyeUOrXry9ZL+Hs2bNn4M9cfPHFkt0xbvx16fHChQslu78vvVQ63Hk84dJzP9xlsnqp+7Zt2yTr7RKM8WdOT/v27SXrU9Gjmffhcpcvjxw5UrLekmLr1q1WvVdeeUWy3lrCXUbvO/fftv6M6e1W3Dmrejl7uJ+93bt3W9f686vn8rnHwyxevFiynovZqFGjsN43mXjSAwAAvECnBwAAeCGjhrfcpbUHDhwI6+f043AUnt6t0xj70ah7knMsHrcHqVWrVlj1ypUrJzkdllwm2rXXXmtd66GkFi1aWGUVKlSIWzv059v9N6a3odDbDDRs2DBu7UlljRs3lqyHt0J5/vnnrWs9HOVuERDkyiuvlPz0008H1nO3OkAwvY3Dzp07Jevhe2OMmTJliuTTTjvNKtNTOPRu97Nnz7bqBW3z4v4t1UPeekuSMWPGWPXKlClT4OslE096AACAF+j0AAAAL2TU8JYr3BnslSpVinNL/OKu9tHuvvvuhLXjv//9r2T3YD3tkksuSURz0ope4ahXZ7hl7kGi8bRkyRLJmzZtCqw3fPhwyb4ebNm8efMC/7s7LKg/FwMGDLDK9DDmRx99FLvGGWNeeOEFye6KH32IbbodZhkL7t+tDh06SH7vvfckV61a1aqn/93379/fKtP32T1wOxp6GEzvyKyH34wxZtSoUZLDnW4QbzzpAQAAXqDTAwAAvECnBwAAeCGj5vS4Y6HuEvY/ucuSzzvvvLi1yRd6Z0+9q6dLn6oeD3oewr333itZL2k2xl6mfvzxx8e1TelI/7527NhhlenPj7sFQazppbJ67pW7hFbvWtu3b9+4tikd6F3GJ06cKPmss86y6m3YsEGyu7u2/izFWm5urmR3R2a9RHv8+PGSL7roosB6mcSdh6aXmOvv2bVr11r1Jk2aJPmZZ56xyvTnRW8hEvQ3MhL6u2LGjBlW2ZtvvilZfx/rbIwxhx2WuOcvPOkBAABeoNMDAAC8kFHDW+HupuvWO/bYY+PRHG+FuxN2LLjLLz/44APJoZbO9+7dW3LdunVj37A0pz8joZZ9x+LxuOYeBqwfl//666+BP3fFFVdI1geT+qp8+fKSe/ToEVhP74g+efJkq2z69OmSt2zZIvmtt96KRRMD6SGcq666SrI7DK2XSjdp0iSubUom/VnUOxwfc8wxVr2hQ4dKvummm6wyvVWBHnJytyJYv369ZD2s7X4u3ekCf3K/D/TS9mHDhknWS++NMeb111+XHM9d3Y3hSQ8AAPAEnR4AAOCFjH4OrGepayVKlLCu9QGFiI4eUsjOzrbKcnJyJL/77rtWWdOmTSN+r99++02yXrFgjDGDBg0K6zV69eoV8fv6RK+EdD8vevjyyy+/tMrOPvvsiN9LPzp3h1j0QZf6fd02PfHEExG/L2ydOnUKvNbfpb///nvga+jVYO40gmrVqhX4M4MHD7auJ0yYIHnXrl2SFy9ebNW74447JD/66KNW2YknnhjYxkylV0C5v2v9udQr+Nxhqu3bt0vWuyu7O2PrfwP6MNNp06ZZ9d544w3JGzdulPzJJ59Y9U466STJ7g7wsd5VnSc9AADAC3R6AACAF+j0AAAAL2TUnB53DFkvfdNL59zld7Feduuj4sWLS3bHa/WWAO4p63qOT7du3SQvXbrUqqfHkPXr6zkExthjz9u2bZPsLkuvXbt2Af8X+JOeo6WXkxpj3ye99N8YeyfgM844Q7I+8d4Y+9TnBQsWSF63bp1VT39utZo1a1rXeok2opOXl2dd//TTT5Lbtm0rOdSS4miWG48ePdq6vuyyyyT369dPsjunRy97dnf0ffvttyNuRyYJ9TdOl7m7cIc7v1Wf8H7UUUdJduf0jRs3TvJjjz0m2f07oOcF6VPljTHm/ffflxyLnZt50gMAALxApwcAAHgho4a3XO6y1j+5w1l6GCRoWSXCV6tWLetaL0MePny4VTZv3rwCsx4uM8aYevXqSdbDJldeeaVVTy+z1cOd7kGLlSpVCmw/bO3atbOuX3nlFcndu3e3yvTjbH1Qort9hB4+00NVp5xyilVP7xirh8Quv/xyq164u7HDpnfnveWWW6wyvTXE1KlTJbsHf8aaHkr79NNPJetlzcbYO65/8cUXVtk777wj2ZcDpfUUgGeffdYq01MC9MG97m7d8fwc3XnnnZLdrUv0UOXHH39slen73KBBg0K3gyc9AADAC3R6AACAFzJqeMudsR608sOtl5ubK5nhrdjr3Lmz5PPPP98qW7RoUYE/4w5vuY+2/6RXmBgTfBCeOwyD6OkVGu7quTVr1kjWw8ilS5e26unVVvper1271qqn7/u+ffsk6xVkiJ4eEtHDWcbYn6WuXbtK1kNOxhjTpk2bOLXOmHLlyknWw6rG2MNg+nBMY+wdmn0Z3tJ/7x588EGrTO9sre9f+/btrXruqsh4cYex9fCWO/1E7/J8//33F/q9edIDAAC8QKcHAAB4gU4PAADwQkbN6XHn8OzevbvAevv377eu9bLNFi1axL5hEO6Jua1bty7U6+k5JKEU9n1QMHfuld6dNRrufDt90rNWo0aNQr0P/j+95YO7G7beNVffF3f7gUT5/vvvretQO+mfcMIJ8W5OyqlSpUqB2Rj7c6TnP+mtCIwx5tZbb5Uci92PNf13N5Ids6tXrx7TdvCkBwAAeIFODwAA8EJGDW+tXr3aug5avuw+tmOZevqaMWNGspuAGJo4caJ1feDAAcl6+bI+8BCx0bdvX+taD0F8+OGHkq+++mqrnt4hfdCgQZKPOeaYqNqhDyB9/vnnJa9YscKq5w6F+k7/XVu2bJlVduqpp0rWw4T/+te/rHpLliyRPGLECMnh/o1074neBkEPl3777beBr+H+fW7SpElY7x0unvQAAAAv0OkBAABeyKjhrcGDB1vXegdXrXbt2tb1ZZddFrc2IfZWrVolecqUKYH1Tj/9dMl6B2CkFr0K5+mnnw6sp3fVdlcBovDcz8jrr78uWa+GcndufumllyRPmjRJcrSrf9zVteFo2bKldf3AAw9E9d6ZomTJktb1559/LlkPY06fPt2qp++lPij6yCOPtOrpqSP6YG/33umVYnpXaHcYTB9A3KpVK6vs+OOPN7HEkx4AAOAFOj0AAMALdHoAAIAX0n5Ozy+//CJ51qxZgfX0uOM777xjlbH8Nb3opavbt28PrHfRRRdJ1mPGSC1bt26VrE/9Nsa+bw899FDC2gRjypYtK/nXX3+V/PLLL1v19K6+ixcvluzu8FxY7sncHTp0kNynTx+rrHLlyjF973Sn58C9+OKLkt3T2PXSdj13y72XQdsFZGVlBV7rOWN169YNfN8hQ4ZYZWXKlCnwvaLFkx4AAOAFOj0AAMALafnMXz9a07tLVqpUyaqnH4vNnz9fcs2aNePYOsTbpk2bAstKly4t+aabbkpEc1BIehjS3UVd78J8xBFHJKxNCHbNNdcEXq9fv16yO1Q5fvx4yXoX54ULF1r19E7OJ598suQ6depY9fSUBUTH3b4lJydH8rZt2yTraSTG2EvT9aHP7tL2ChUqSG7QoIHkIkWKWPXcYbF44kkPAADwAp0eAADgBTo9AADAC2k5p0eP/3Xp0qXAjMw1c+bMwDK9Zbk7bozUoefu/N///V9gvXbt2knmfqa+6tWrF5iNMWbkyJEF/kzHjh3j2iZER8/H0XOr0h1PegAAgBfo9AAAAC+k5fAW/KZPBnaXOjZr1izRzUEU9LYTequJffv2WfWGDx+esDYByHw86QEAAF6g0wMAALzA8BbSTtBhd0gfJUuWlLx06VLJ7m7b7i68AFAYPOkBAABeoNMDAAC8QKcHAAB4gTk9AB6egTcAACAASURBVJKqTJkyBWYAiDWe9AAAAC/Q6QEAAF7IimT5b1ZW1iZjTG78moMC1M3Pz68a6xflXiYN9zNzcC8zS8zvJ/cyaQLvZUSdHgAAgHTF8BYAAPACnR4AAOAFOj0AAMALdHoAAIAX6PQAAAAv0OkBAABeoNMDAAC8QKcHAAB4gU4PAADwAp0eAADgBTo9AADAC3R6AACAF+j0AAAAL9DpAQAAXqDTAwAAvECnBwAAeIFODwAA8ELRSCpXqVIlPzs7O05NQUFycnJMXl5eVqxfl3uZHIsWLcrLz8+vGuvX5X4mHp/NzBKPzyb3MjlC3cuIOj3Z2dlm4cKFsWkVwtK8efO4vC73MjmysrJy4/G63M/Ey/TP5qFDhyTn5+dLPuwwe4BAl2VlZRWY00E8Ppupci99E+peRtTpAQD4R3d03M5MunVu4Dfm9AAAAC/Q6QEAAF6g0wMAALzAnB4AgDVx2Rhjxo0bJ3nt2rWSb7/9dqtehQoVJDO/B6mOJz0AAMALdHoAAIAXGN4CELXHH3/cur7jjjskf/7555LbtGmTsDYhfHqPnR9//NEqe+SRRySXLFlS8i233GLVY0gL6YQnPQAAwAt0egAAgBcY3gIQtSeffDLZTUAh7Nq1S3KPHj2ssm3btkl+8MEHJVeuXDn+DQPihCc9AADAC3R6AACAF+j0AAAALzCnB0BEVq9eXWA2xphRo0ZJZpl66nv77bclu0vWGzRoIPnGG2+UzBL19KY/l//617+ssnbt2iW6OQnHkx4AAOAFOj0AAMALXg5v5ebmWtdjxoyRvHDhQslPP/20Va9JkybxbRjCog9GfP/9962yFStWSD7hhBMkN2vWzKpXunRpyTyuj8z06dMDy2rVqpXAliBSegdmY+zhjYMHD1plekirSJEi8W0Y4uqbb76RvHjxYsmVKlVKRnOSiic9AADAC3R6AACAFzJ6eOunn36S/NRTT0meOHGiVW/79u0F/vx5551nXc+ZM0eyXrVSt25dq54eVkFs7Ny5U3L79u0l68e2xhhz4MAByUWL/u+ftx7OMsaYGjVqSL700kslDxw40Krn/hxCD2+xYiu1uUNYmzdvluzutHzVVVclpE2IPT0FwBj7e6148eKSq1atGtXr33PPPZKbN28uuVu3blG9XiLxpAcAAHiBTg8AAPACnR4AAOCFtJ/To8culy5dapWdc845ktevXx/xa69du9a6Pv300yXv2LFDsjuP4dNPP5V82GH0K8Ol76X+HRpjzMUXXyxZn/7sLsHV9u/fL9mdt6Wvhw0bJnnmzJlWPd2OChUqBL5XJnN3XV6wYIHk2rVrW2XuNVKL3tLBGGPWrVsnuWHDhlZZqVKlEtIml553pL8/2VoifK+++qp1vWbNGslLliyRHO2cnmLFikmeOnWqZOb0AAAApAg6PQAAwAtpOby1adMmyf/+978l62GKUNxhCj1U5S71C6qnuQf16ddgeCs0/ShbL5GdPXu2VW/v3r0F/rz7+w36feul7C49ROYO5WzcuFGyr8NbTzzxRGBZ9+7d4/reX3zxhWT33mh6yO2SSy6RzBJ62yuvvGJd68+FO5wV6+Ek/b34xx9/SL777rutevqe16xZU/JDDz1k1dNbgzD0ZVu2bJl1fc0110jWv9NoHXPMMZLd7+pUx19kAADgBTo9AADAC3R6AACAF9JyTs99990nefz48YH19Hbbo0ePllyvXj2r3pAhQyTruQGh6KV+r7/+ulWmjz+Abd++fdb1RRddJHnevHmS3Tk4eq6OXi4Zak5P0Dwgl3696667ziqrX79+WK+RyfRyV1fr1q1j+l56Pocxxlx22WWSQ83p0fQcpM8//9wq832Ojzv/Qn/O3OXLhZ0n88knn1jXt912m2R9rMyWLVusekHbkLjfzfreNmjQoFBtzQQ5OTmSn3nmGatM/82MtZUrV0petWqVVVanTp24vW+0eNIDAAC8QKcHAAB4IWXHYfQjTndZrB5O0sMZ7unmzz//vOT33ntP8q233mrVc5ech+Okk06S7Psj87+jh7T0LtnGGDN//nzJeum4O0RYtmxZySVKlJDsbiOg3yvUMnXtzDPPlDxy5EirrEiRImG9BmLDXR4fNKQ1atQo61p/BvWQ2O23327Vc4fPfKA/Vxs2bLDK9Pdny5YtY/pe7pCKXkZ9+OGHS3a/P/Vn+IMPPpDsDoPNmTNHsh4689WMGTMk6+9LY+ztQGJB/w3W2464Q+EMbwEAACQJnR4AAOCFlB3eGjNmjORZs2YF1mvUqJHkQYMGWWXt2rWTrHcAjZbehfK5554r9Ov54rHHHpPsHiSqH4fr1SLusJLeLbZatWqS3dVgv//+e1ht0itVpk+fLpmVd4mnh5z0vXBNmzZN8qWXXhpYT68oC/V6vtCfK3e1o/78dejQodDvtXv3bsmLFy8OfC899HXllVda9fR39VlnnSX5p59+suq98847kn0c3nJXSukTCe666y6rLNqDRf/kTgHRw1u33HKL5LZt2xbqfRKBJz0AAMALdHoAAIAX6PQAAAAvpMwEhv3791vXjz76aFg/p8caL7/88sB6lSpVknzTTTdZZe+//77kzz77LPA1evfuLblu3bphtc9H7jwbPacn1Cn2oeYe6OXn+l66SzO//PLLAl+7TJky1vVrr70muXz58oFtQvyFOsU93Hk84Xr11Vdj+nrpxt2lXH/mwt3iIZTc3NzA99K/7xtvvLHANrjefPNNye6WJMcdd1zU7UxXenn4hAkTrDI9Z6p///4xfd8aNWpY15UrV5acbvMgedIDAAC8QKcHAAB4IWWeS7nDGfpQ0PXr1wf+nF7KrHfqNcaYAQMGSNY7s7q7vAYNpbmHKfbr1y+wHfgf9zG5fuzq0o+29RCU3orAGHvXVr2Dsjsc6R4w+Sf30fgpp5wS2CbYatWqFVgW6jDSUPRnUC8rdz9zPg5BxdMZZ5xhXc+dO1fy2rVro3pN/fnWW42438d33HGH5HAPM92+fXtgWY8ePcJtYsbQW3IMHTrUKrvgggsk6ykAsaB30DbG/g5ONzzpAQAAXqDTAwAAvJAyw1vuDrz6sas+WM4Ye7b4iSeeKNkdEtF27twpeciQIVaZ3gG0XLlykl9++WWrHqt8wuM+1n7yySclv/HGG1aZHma69tprJbuPUzX9ON0dStOPzYsVKyZ58uTJgfUQmrvbrV5tpYcsjPnrAZ9B3J/70yWXXBJh6/5KD53Vrl3bKvN9uOzCCy+0rt966y3Jo0ePtsouvvhiyaFW6Ojf98cffyzZHaps0KBBZI01xtx5552BbdBTIHyhv1tPP/10q+yrr76SPHDgQKvs6KOPltylSxfJVapUiaodLVq0kKxPJ7jhhhvC+nn3c+lOb4knnvQAAAAv0OkBAABeoNMDAAC8kDJzelwVKlSQHIuliTNnzpQc6tT2yy67TLI+VR3hc+dnXXPNNQXmaG3YsEGyHvM3xh4b1svcfRz/jxV3/F3P1ViwYIFV9vjjj0sOd36PFmp5fCh6p2XdplGjRkX1epnK3bVez9dauHChVTZ16lTJeq6VPkndGPt0723btkk+99xzrXru98Kf9C7DxhgzYsQIyXqH9VatWln19N8IX+gtWvQp88bYf9fcU9EnTpwoeeTIkZJLliwZ+F567qQ7B1LfZ71tRXZ2tlWvWbNmkvV8OneeoDsPNJ540gMAALxApwcAAHghZYe3YmHLli2SQz3mrlOnjuSnn346rm1CdDZv3iy5fv36kvfs2WPVO+KIIyTrwypZoh47+tG0Hg42xl6KroeZ3MfZehdmTQ9JhqKHs4yxhzn1cFwslsBnEj08Yox9MOXDDz9slV1//fWS9fdi9erVrXpffPGFZH3YsLsceuPGjZL1zsJXXXWVVW/JkiWS9fCLOy3B98+0OzR1xRVXBNbVuzfr4Un3oG99j77++uvA13vqqack612zv/vuO6ue/izqLUSSiSc9AADAC3R6AACAFzJ6eKtTp06SFy9eHFjvgQcekFy8ePG4tgnhcQ8p1asAdu3aJdl9xDt79mzJeqgLsaNXYbgHjurdt/UQVtBwlksPlbjXM2bMCOv19KGz7soz2IYPHx5Ypu+lXtl16NAhq56+1t+fekjaGGPGjRsnWQ9P5ubmWvX0Ki/dhooVKwa2FeErXbp0YJneCT/UDtr6lIQVK1ZIdg86TZUhLY0nPQAAwAt0egAAgBfo9AAAAC9k1JyeX3/91brWSx81PdfHGGN69eoVryYhSq+//rp1rU9y1s4//3zrWp/+i/hzd13WS8T1aew6h+IugQ/inuCt54gwjyd8etm33gnZGGNatmwp+Z577pH8008/Bb6eXgI9adIkq0zvln7gwAHJegsKY4zp27evZL6bU9+VV14pOR12yeZJDwAA8AKdHgAA4IW0H95au3at5LPPPtsq07t+htp1OeggPCSWfjTep0+fwHp6GeRzzz1nlfm+S2uy6aElffiozsbYn8egoUtj7J2c9ZCWXjaP+Lj44osld+7cWfKgQYOsenroUg9b6UMpjbGXs+uDKQcPHmzV69q1q2Q9JIbU0bFjR8kTJkxIYksix78oAADgBTo9AADAC2k/vPXNN99IzsnJCazXu3dvyfrROlLHmDFjJOfl5QXW08OYVatWjWubEB/du3eXHGpll97xWe/I7K7QCvegUkRHDzM9+uijVtmqVask6x3RDx48aNXTu6ffcMMNkvVwljFMN0g3epW0u4L6qKOOSnRz/hZPegAAgBfo9AAAAC/Q6QEAAF5Iyzk9X331leSrr746sF6JEiUk6yV2SB07duyQPHTo0MB6+l5OnDgxrm1C/LlL2P+k5+249JL1WrVqxbxNCI+7LcSUKVMk6+1AJk+ebNUbMGCAZL3lAHN40s95550nuWfPnpL1FjLGMKcHAAAgaej0AAAAL6TF8NauXbusa72Dp7vrp1axYkXJZcuWjX3DEDG9Y6sxxjRt2lSy3kHbfYSuD7asUqVKnFqHZAi1czNSn/6s3njjjZL79etn1WMYK3Pog0WPP/54ye4Bsb/88kuimhQ2nvQAAAAv0OkBAABeoNMDAAC8kBZzesaNG2ddv/POOwXWq169unX99ttvS27cuHHsG4aIuXM2cnNzC6x3xBFHWNfDhg2TzEnqQGrSn03m8GSuokX/13Xo27ev5M8++ywZzYkIT3oAAIAX6PQAAAAvpMXwlvuYVC+Xu+222yT36dPHqlejRo34Ngxh0actz5071yrT91Y/Mn311VcD6wEAUoPeaVvnVMWTHgAA4AU6PQAAwAtpMbx18803h7xGajvssP/1rceOHWuVbdy4UXLLli0llypVKv4NAwB4hSc9AADAC3R6AACAF+j0AAAAL6TFnB6kN71Lq7szNjtlAwAShSc9AADAC3R6AACAF7Ly8/PDr5yVtckYU/AJkYiXuvn5+VVj/aLcy6ThfmYO7mVmifn95F4mTeC9jKjTAwAAkK4Y3gIAAF6g0wMAALxApwcAAHiBTg8AAPACnR4AAOAFOj0AAMALdHoAAIAX6PQAAAAv0OkBAABeoNMDAAC8QKcHAAB4gU4PAADwAp0eAADgBTo9AADAC3R6AACAF+j0AAAAL9DpAQAAXigaSeUqVarkZ2dnx6kpKEhOTo7Jy8vLivXrci+TY9GiRXn5+flVY/263M/E47OZWeLx2eReJkeoexlRpyc7O9ssXLgwNq1CWJo3bx6X1+VeJkdWVlZuPF6X+5l4fDYzSzw+m9zL5Ah1LxneAgAAXqDTAwAAvECnBwAAeIFODwAA8AKdHgAA4AU6PQAAwAt0egAAgBfo9AAAAC/Q6QEAAF6g0wMAALxApwcAAHghorO3UtGhQ4cCyw47jD4dACDz5efnSz5w4IBVtnXrVsl5eXmS3b+RNWrUkFyuXLnAeuksc/5PAAAAQqDTAwAAvECnBwAAeCEt5/Ts3btX8rhx4yR/++23Vr0777xTcoMGDSQXLWr/b2dlZcW6iQCQVnJycqzrevXqxfT1r732Wsm1atWSfOyxx1r1LrvsMsl8N4dPz29dvny5VdanTx/J3333neQ//vjDqqd/3/rvZJkyZax6ev5Q9erVJeu/ucYYc9111xX42snEkx4AAOAFOj0AAMALaTm8NW3aNMlPP/205BNOOMGqV61aNcn6cdz+/fsDX7tYsWKSU+VxHAAkWqy//1566aWw6ukl1f3795ecScum46FIkSKS69SpY5U1btxYsjsNRAv6O7lt27bAn9m+fbvkvn37WmX6Xg4aNCjwNRKJf0UAAMALdHoAAIAX0mJ4Sz9yM8aY2bNnS9Y7SD7zzDNWvSpVqoT1evrRnZ4BX7ly5cgbC+Avdu/ebV0vWrRIsl5NcsYZZ1j1qlatKlkPVzPUEXuVKlWyrv/5z39KHjNmjGQ9nBEPN998s+TOnTtLdodsEKx8+fLW9ahRoyQvXLhQ8s8//2zVc1dzRcr92/rggw9Kvv766yUH/W1OBL45AACAF+j0AAAAL9DpAQAAXkiLOT07duywrleuXClZ7/gY7jihuxRz7dq1kvUOz0888YRVTy8JhG3Tpk2S9TyMv7Nr1y7Js2bNkvzaa69Z9fQ8Lj1u7N7LoLKPPvrIqnfaaaeF3UYE03PgjDFmz549kl988UXJen6IMcbs3LlTsj4RumTJklY9fQ9bt24tecaMGVY9dz4KIufOAxk6dKjk888/X3Lbtm2tesWLF5fcpUsXye+//75Vb/PmzRG3ae7cuZJvuOGGiH8e/1+FChUkd+3aVfLo0aOtevr7s3Tp0gVmY+y/yb///nvg++rvgxEjRkh+/PHHw2l2XPCkBwAAeIFODwAA8ELKDm/px2x33XWXVaaXLurhrWh98MEHkt955x3J+nGcMcaULVu20O+VqR5++GHJ7qNLPfSlh7CMMebJJ5+UrA/Jc4cq//GPfwSWacOHD5esh0b08JgxDG/9Hf35c3dj/eGHHyTfc889Vtn//d//SdYHA+shLGPsAyePO+44ybVr17bqvffee5K/+uoryb169bLqvf7665LZST029I687nehdtZZZ0meMmWKZD1twBh793w9dUD/O3Hp4ZcrrrjCKjv88MMDfw42PQy9ePFiye4Sdf29OGnSJMmlSpWy6umh69tvv73A93E9//zzkvUSemMS+5nlSQ8AAPACnR4AAOAFOj0AAMALKTunR487Tp482Sq78847JbtjjdH4+OOPJW/YsEGyu6U2bD179pRcpkwZye7SUv373bJli1V20kknSb711lslu6f1hkvfMz0Pwd1+oEePHgW2wSf79u2zrvVcrOnTp0tet26dVU8vNy9WrJhVVr16dcn6frpz70qUKBFWG7du3Sr5mGOOkfzf//7Xqvfhhx9K1nNMED49h8cYY4YNGyZ5zpw5kuvWrWvVGz9+fIGvd+SRR1rX+vOoj0Jwl7Zrep6fXv5sDHN6IqG3H3jjjTcku3Np9D064ogjAl/vxhtvlKy3FXj33XcDf0bfvzVr1lhl7ly+eOJJDwAA8AKdHgAA4IWUGd5yh5L0EJZLn8Ibi/fS13pnV5a+2kLtkqx3VnZ/b/fee6/kPn36WGWxPjlZv5du39KlS616eum8T8NbekirXbt2Vpk++VyfYu7uktysWTPJjz32mFXWvHnzAl8jWnoI4+ijj5a8YMECq55+3K7vNZ/h8LlLzB966KEC67mfYXcYKxwDBw6UPH/+fKssaAm7+/3Tv3//iN/XF0uWLLGuR44cKfngwYOSTzzxRKteuN+FRYv+r+vw3HPPSa5fv75VTy9h1+/r7pCvp0rEG096AACAF+j0AAAAL6TM8FZubq51/emnn0o+/fTTrbJwDxYN4j7y1jvCLlu2THIsVoZlEneIqFGjRpL1I299oJ0xhb9fkdAH4+n75w5p5uXlJaxNqUSvTvztt9+sMr0Cr169epKfffZZq16bNm3i1Lq/0kNkof4d6UfniI5esedq1aqV5GuvvbbQ73X22WdLdodZ582bV+DPuN8/sOnPwAUXXGCV6SFDvXLSHVqMZkhar7xyV3zp7xj9HewOvyUST3oAAIAX6PQAAAAv0OkBAABeSOqcHj3GN2DAAKtMj0HGemmiO79j8+bNkvU8EJa72u6///6Q16mmcePGkvVybN/of+96Cbg+ud4YY44//njJ559/vuTixYvHsXXh27RpU2CZnoug/3/5DIf2/fffS37qqaesMr1VgV6+XqNGjZi2wd2tO2hOD0LTO9+vWrUqsJ7+e6rn8UWrSJEikvWOzsbY91YvX3d3g08knvQAAAAv0OkBAABeSOrw1h9//CFZP5ozxn4Upg84NKbwj6/d5a16eW7lypULfB+kH70UdtKkSUlsSXLpz0j58uUl611xjbF3WU3FYSG9W+yXX35plelhu1Rse6rS2xGsXr3aKjvnnHMk6yXmSE39+vULLNOf+1GjRsWtDR07drSu9dC4/nvvblGTSDzpAQAAXqDTAwAAvJAyw1suvRrDfZTdokULyVWrVi3wZ4wxplixYpL1ENm7775r1Rs3bpxkPRP90ksvteo1adJEMo/Q0wv366/05yMd5OTkBJbpXdW518G2b99uXb/xxhuBdXv06BHv5qAQ3Hu5cuXKwLrXX3+95Hh+PsqVK2dd692f9d/7AwcOxK0Nf4cnPQAAwAt0egAAgBfo9AAAAC8kdU5PxYoVJa9YscIqe/jhhyXruTTGGDN58mTJ+hTX7777zqqn5yzs2rVLsrtb5ZYtWwr8Gd0GY4x5/vnnJevTvJGa9AnC7vYDp5xySqKbg0LSn2FXw4YNE9iS9OXOpVi3bl2SWoLC0n+3jAk9T+bmm2+Od3OMMcbs37/futbzbPVcopNPPjkh7SkIT3oAAIAX6PQAAAAvJHV4SzviiCOs6yeffFKyu4OyvtbDFnv27LHq6SV8+lGguyPlhx9+KFkvsTv33HOtevowUqS+H3/8UbK7TPPYY49NdHNQSPqQQndp7FVXXZXo5iDBGMK0bd26NbDM3b5l9+7d8W6OMcaYDRs2WNf6b7Xendn925pIPOkBAABeoNMDAAC8kDLDW6HoXZILuv6THpoyxphmzZoVWE/v6GyMMaeeeqpkvbLrtNNOs+qx02v6clcL6MMrkbr08PXGjRsl65WfxhhTs2bNhLXJF8uWLUt2E0z9+vUl9+zZM4ktST3u6i39WXFXqw4ZMkTytGnTYtoOfTj4bbfdZpXpYbVKlSpJTub3L096AACAF+j0AAAAL9DpAQAAXkiLOT2xVr58eev6ggsukDxx4kTJ+gR3pIdNmzZJzsvLk9ynT59kNAeFpJep6/vZvn17q567RBeF9+KLL0ru27ev5Ozs7Ji+z5IlSwLLzjzzTMkVKlSI6fumO3fbBv0Z0PNsjDHmvffek/zLL79IPuqoo6x64c5b1XOGvvzyS8kff/yxVU/Pv+3atatkvXw90fimAAAAXqDTAwAAvODl8JZLD2PpA0f1o3Wkh0WLFknOzc2VXKVKlWQ0B4U0c+ZMyTt37pR8ySWXJKM5XtG767Zq1Uqyuyx50KBBkufOnSs51GGm+vBm96DoBg0aSL7nnnsiaLFfjjnmGOu6aNH//Tl3/3Zt27ZN8qWXXir5pZdesuo1atRIsh4u2759u1Xvsccek/zMM89Idnd+Llu2rGT97yaZ27/wpAcAAHiBTg8AAPACw1vGmP3790vWh7gtX77cqte2bduEtQnRueaaaySzg3b6cVed3HnnnZL14/YLL7wwYW3KJO5O1vrz8vLLLwf+nF4VOXjwYKts9OjRkvUwyt69e6Nq44ABAyTXq1cvqtfwweGHH25d65VY+rBlY+zVVno40T11QA8t6h3Q3d2fd+3aVeBru6uy7rrrLsnucFyy8KQHAAB4gU4PAADwAp0eAADgBeb0GGOaNGkiWS/1+/nnn616zOlJfXoculq1apL1jrJIXe6cHj1fQM9ZKF26dMLalEncnavPOOMMyaHm9Gh6DqQx9tL2aDzyyCPWdf/+/Qv1er5w72WvXr0k620EXPozpudgGWPM119/HXE7SpUqJXnkyJFWWSreS570AAAAL9DpAQAAXmB4yxhTu3ZtyXrJnbtkXT9qZzl0ali2bJl1re+LPuAO6eGHH36wrvVOsN26dUt0czJez549Jbds2dIqO+644+L2vg8//LBkvS2BMRweGy39e1y9erVVNn78eMnRnDTg/r2rU6eO5Lffflty48aNI37tRONfFwAA8AKdHgAA4AU6PQAAwAvM6TH2Utg2bdpI7t27t1WPeTypR5/CbYw976pPnz6Jbg6ioO/Z/fffb5Xp5dEXX3yxZD6LsaHnz9SoUcMq08uX9fYdQ4cOteo1a9ZM8tlnny356quvDnxffSI49zI2ihQpIvmpp56yyh599FHJ+jtz0aJFVr2VK1dKbt26teTu3btb9fRxFel2/3jSAwAAvECnBwAAeIHhLWNMmTJlJM+ZM0cySydTkz7x+YUXXrDK9C7MVapUSVibEL3NmzdLnj9/fmC9dHuMnm4qVKhgXZ988skF5ssvvzxhbUJs6L9xetgx1BBkpuKvOgAA8AKdHgAA4AWGtxx6BjxS06pVqyTn5uZaZR06dJCsdw1F6vr4448luweO6lU+eof0s846y6rH0BeAcPCkBwAAeIFODwAA8AKdHgAA4AXm9CCtuXM5Jk2alKSWIBJ6F+Zzzz1X8rJlywJ/pmLFipKZwwMgGjzpAQAAXqDTAwAAvJClHzP/beWsrE3GmNy/rYhYqpufn1811i/KvUwa7mfm4F5mlpjfT+5l0gTey4g6PQAAAOmK4S0AAOAFOj0AAMALdHoAAIAX6PQAAAAv0OkBAABeoNMDAAC8QKcHAAB4gU4PAADwAp0eAADgBTo9AADAC3R6AACAF+j0g6uehAAAEAxJREFUAAAAL9DpAQAAXqDTAwAAvECnBwAAeIFODwAA8AKdHgAA4IWikVSuUqVKfnZ2dpyagoLk5OSYvLy8rFi/LvcyORYtWpSXn59fNdavy/1MPD6bmSUen03uZXKEupcRdXqys7PNwoULY9MqhKV58+ZxeV3uZXJkZWXlxuN1uZ+Jx2czs8Tjs8m9TI5Q95LhLQAA4AU6PQAAwAt0egAAgBfo9AAAAC/Q6QEAAF6g0wMAALxApwcAAHiBTg8AAPACnR4AAOAFOj0AAMALER1DAQDITAcPHrSu9+7dK3n9+vWSixa1/2xUr15dcvHixePUOiA2eNIDAAC8QKcHAAB4gU4PAADwAnN6kFSHDh2SvHv3bskHDhwIrFe6dGnJJUuWjGPrUJAXXnhB8vXXXx/VazRo0KDA17jwwguteo0bN47q9VEw/RkzxpjHH39c8vPPP2+Vbdy4UbKe35Ofn2/V0/N42rdvL3n69OlWvVKlSkXRYiC2eNIDAAC8QKcHAAB4IWWGtzZt2mRdT548WfLs2bOtsk8++URyVlaWZPexa1BZ165drXpXXXVVYBki9/vvv1vX48ePl/zEE09YZXl5eZL37dsn2b2Xml4yW79+favs5ZdfltyyZcswW4xQ3CGn9957r9Cv+fPPP0seOHCgZL002hh7+AXRWbVqleSTTz7ZKtu8eXNYr6G/S1166Gvu3LmS3aHJpUuXStZD1IhOrVq1rOuKFStKvvfee62yK664IqbvvWjRIsnvvvtuYL2xY8dKXrNmjeRq1apZ9d5//33JTZo0iUUTA/GkBwAAeIFODwAA8ELKDG9dffXV1vV///tfye6jVX0d6rFrUJk7XKYfzx177LGSGzVqFKLF0LZs2SL5uOOOs8rcIYvC2r9/v+Qff/zRKjvnnHMk639DrVu3jmkbMt1HH30ked68eVaZHs444YQTrLK6desW+Hr33Xefdb1kyRLJevXWU089ZdU76aSTJPfo0eNvWo2CfPDBB5LdoWc9VFyhQgWrTK/E6tevn+RvvvnGqvfoo49K1sNlv/32m1VvxYoVkt1/N4jclClTrGs9NaNXr15Wmb5/0XCnG+jvYP19EIr+e+xOZ/n2228lM7wFAAAQA3R6AACAF+j0AAAAL6TMnB69dNkYewyxatWqVpke5+/SpYtkvTTalZubG/heO3fulDx69GjJerkdQvvjjz8k79mzxyoLNe+qfPnykjt27Cj56KOPturppeirV6+W7I416zkLd911l2R3mTU7OYd2/PHHS7744outsh07dkh2P3M1atQI6/X1a2h6roAxxmzdujWs10OwevXqSW7YsKFV1rNnT8k33nijVRa0g/Kpp55qXes5GHp7A3euh146z5yewnPvg94BW8+zMib0svJwhNoOJt3wpAcAAHiBTg8AAPBCygxvuTtI6sdxffr0scr08JbWt29f63rZsmWSb7jhBsmffvppYDtYph4dPayhd2U1xpgFCxZIbtasmVXWokULyaF2ab3gggsk68e67nCIfuyql6nrQxHx9ypXriz5lVdeSdj7FitWzLrWw5+IzmmnnSZ54cKFVplesh7tkMUPP/wgOdTyZQ4cja8zzjhDsv5eNcbeDVlzh5n1rtnRbPPRqVMn6/rXX38tsF6rVq2s6zp16kT8XtHiSQ8AAPACnR4AAOAFOj0AAMALKTOnRy89L+g6HLt27bKuu3XrJlnP73HHrvXJw7fcckvE7wv7d9q2bVurrE2bNmG9hl4WuW7dOqtM38sDBw5I1nMSjDGme/fukocPHy75sMPo3yeTe/yBXl6r9e/f37q+5ppr4tYmX+jPpjtnKhruvJ2BAwdKPnTokGT3lPWzzjqr0O+N8JQpU8a6drcqCOLOBQrHnDlzJLtHj2h6a4O33nrLKtMnxMcbfwkAAIAX6PQAAAAvpMzwVrQeeughye7S2uXLl0sOdTK7u1wesaV/3+7Onvv27ZM8cuRIyUOGDLHq6cfmWrly5azrZ599VjLL1JPriy++kHzeeedZZUE7MrtLWZE47mfz4MGDkjds2CDZPQVb78auP3MPPPCAVS+dd/FFsO+//16yuxu/prckSeRwlosnPQAAwAt0egAAgBfScnhr0aJFkvUj1FCHoumyf/zjH1a9du3axbqJCOA+4taHv44YMUJy0HCW+xoVKlSwykL9HGJPD08aYx/Sq1f1hNqp98gjj5R84oknxrB1+Dt6CMtdUaPv34oVKyTr1ZMuvYN2uCuGkH70AdD6ezuUatWqxas5EeFJDwAA8AKdHgAA4AU6PQAAwAtpOadH7/R57LHHStYnxBoTvERy1qxZ1vU777wjWZ/o7p643rVr18gbi5AqVaokWZ+0+/PPP1v13Plaf3J3ANUnSt90002SL7vsMqve4YcfHnljYYwxJjc3V7K723aoHVmDrF27VnLHjh2tsocffljy5ZdfHvFrIzS93Nid67h+/fqIX0/P97n77rutMj0PpGbNmhG/NpLH3VH95ptvlhxqmfrgwYMl9+3bN/YNiwJPegAAgBfo9AAAAC+k5fCW3tlxyZIlkvXuzMYY88ILL0jOycmRvHHjRqueHjq5//77C/zvxtjDYB06dIiw1ShIqVKlJH/33XeSx48fb9XTS6HXrFkj2V0yrYc4b7jhBsnuQbLTpk2T3Llz50ib7TU9hBHNcFYo+nNqjDFXXnml5EcffVTyiy++aNVjqXt09PDW5s2bo3oNfZjv7t27Jc+fP9+qd/TRR0vOzs6WvHDhQque/n5H8uidtt2/dzt37izwZ9wDbTt16iS5evXqMWxd9HjSAwAAvECnBwAAeCEth7eC6KEpY+zhjVWrVkl2V2/NnDlTsj6k1KVXkjC8FXt6qEuvDjDGmP79+0v+4YcfJOsVIcYYM336dMnr1q2TrB/VGmMPm+jVQ6zq+nslSpSQHO6w0j//+U/rWu/cq7m7u3744YeS9fDnhRdeaNWbPXu25JNPPjmsNsGYli1bSnaHH/Ru6SeddJJkd6flM888U/Ibb7whec6cOVY9/RlctmyZ5Hr16ln19He1/reG+NP3qH379pIXLFhg1QtaGT1mzBjrWv+7SRU86QEAAF6g0wMAALxApwcAAHgho+b0uKpUqVJgdscZ9fyRO+64Q/KkSZOsep988onkcePGSU6VnSYzWdGi//un2rRpU8mPP/64VW/QoEGSW7RoIXn16tVWPb3UXc9dYE7P36tVq5bkb7/9Nqav7S5X1nME9DYDetsCY+zd0ufNmydZL5PGX+nd7X/99VerTC9F1zmUHj16SNbL140x5tJLL5U8d+5cyZs2bbLqtWvXTvJXX30lOWgeCaLn7rSs56rqeTxBO+IbY8w111wjOR3+FvKkBwAAeIFODwAA8EJGD29FQy+ldB+n6mv9WBipQx9gWrx4ccnuvdTL4/XPILnatm1rXbdq1UqyPthQL402xl7mrHdcZ3grfHoIORbcoco333xTsl4OracNGGNvTfDee+9JPvfcc2PaPl9t3bpV8tNPP22Vffnll5JDDSfqstatW8ewdfHHkx4AAOAFOj0AAMALDG8ZY0aPHi150aJFkt0Z6xMnTpR86qmnxr9h+FvuPdKHzOohD9dxxx0nuUKFCrFvGGKiSJEikl9//XXJerWWMfYu61dddZXkd99916rXoEGDWDcRYdJDIvrwZr3K0hh7x3W9Mig3N9eqp4evEb63335b8uDBg6N6jR9//FHyUUcdVeg2JRJPegAAgBfo9AAAAC/Q6QEAAF7wZk6P3vVTn5ZujDFPPvmkZD3uXLVqVaveaaedFqfWIRL79++XfNNNN1llEyZMKLCeey/1kmd2ek0/7vJlPacnJydH8vLly616zOlJDXp5vHu6+9KlSyVv2bJFsp5vaYwxbdq0iVPrMo9epv7vf/87rJ9p0qSJ5Ouuu84qq1+/vuRwd+tOFenVWgAAgCjR6QEAAF7I6OEtvbuyXuLqPvLWy54bNWokWT9mRWK5S9H1AZOdO3eWrJe3uj9XrVo1ye7BmPoAWqQH/Xl+6KGHktgSREN/Nj/77DPJn376qVXv4MGDkvUwmP7vCE0PCxpjTO/evSXrQ1xdxYoVk3zPPfdIvvzyy2PYuuTiSQ8AAPACnR4AAOCFjBre6tmzp3U9e/Zsybt27ZLsrtbp0qWL5EmTJsWpdfg7a9eulfzggw9aZVOnTpX8+++/S3YPNbz22msljxo1SjK7t6aunTt3SnZX6OhdmKdPny5Z/1tx6R22K1euHIsmIkwHDhyQvGDBAqvsiSeekPzWW29J3rt3r1VPD2k1bNhQ8gknnBCzdmYivULrlVdescr0Ya+h3HjjjZIzaUhL40kPAADwAp0eAADgBTo9AADACyk7p0fPwXHn6mh63o67zFnP3alTp45kPbZsjD2nB7Gn74uej2OMfSq63ilbj08bYy9XLVeunOT333/fqte8eXPJ7LScXHqJufvZ1Lug6xOb58+fH9V76Z2W9XJ2du2Nnr5n27ZtkzxlyhSr3tixYyWvXLlS8u7duwNfT3N39NX3bMaMGZLLly8fTrO91bdvX8mvvfZaWD/jbt0xYMCAmLYpFfGkBwAAeIFODwAA8ELKDm/poQ69bNUY+zFpqCGMbt26SdaPYNmNN770QZ/GGDN48GDJTz/9tFWmlyvrx9x6Z1BjjDn55JMl6yGtsmXLFq6xiJgealy3bp1V9sADD0ieOHGi5EOHDhX6ffUO28OGDbPKrrjiCsl6+BPhC7WL77x58yTrz2wkgg5zfvnll61655xzjuQiRYpE9V4+cIcPv//++4hfo0ePHtZ1vXr1CtWmdMCTHgAA4AU6PQAAwAt0egAAgBdSdk7Ppk2bJLtLHfXRA40bN5asT4U1xj5ZHYmjlyAbY8xTTz0l2V2yrunt50eMGGGV3XLLLZJZip5c+h5OmzbNKluzZo3kcOfxHHnkkZJvvfVWq0zP6bjtttsiaif+nr5H7rER+iR0PU/P/fzpz62ed6XnVBpjbyXAvKvCc+e66u0CQqlbt65kvczdFzzpAQAAXqDTAwAAvJCyw1unnnqq5OXLl1tl5557ruR77703YW1CeI466ijrWi+L/Pzzz60yfXLyY489Jlk/Jkdq0aeY33nnnVaZe43UpreJOP/8860yfZK9HtJyd1BmWXly6G0ajLG3cdDbSrhGjhwpWZ9i7wue9AAAAC/Q6QEAAF5I2eEtPSTi7hqJ1KZX1xlj78LMyisgNbmfzeLFiyepJYjG0qVLk92EtMCTHgAA4AU6PQAAwAt0egAAgBdSdk4P0hfzdgAAqYgnPQAAwAt0egAAgBey3MM8Q1bOytpkjMmNX3NQgLr5+flVY/2i3Muk4X5mDu5lZon5/eReJk3gvYyo0wMAAJCuGN4CAABeoNMDAAC8QKcHAAB4gU4PAADwAp0eAADgBTo9AADAC3R6AACAF+j0AAAAL9DpAQAAXvh/ZOZDjGYng8wAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2807lXHY_yQq",
        "colab_type": "code",
        "outputId": "f7bcfeab-f981-46b0-ce36-8aee19614955",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        }
      },
      "source": [
        "evaluate_and_output_model(conv_model_with_upsampling, tf.expand_dims(test_images_raw.astype(np.float32)/255.0, -1))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10000, 28, 28, 1)\n",
            "(10000, 28, 28, 1)\n",
            "(10000, 28, 28, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N4KNKpIfALTD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir \"/content/conv_model_without_batchnorm\"\n",
        "!mkdir \"/content/conv_model_with_batchnorm\"\n",
        "!mkdir \"/content/conv_model_with_upsampling\"\n",
        "!mkdir \"/content/conv_model_with_fc\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8SXspJn0AfIO",
        "colab_type": "code",
        "outputId": "38f639f8-229c-4841-9f62-7e44d7075511",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "conv_model_without_batchnorm.save(\"/content/conv_model_without_batchnorm\")\n",
        "conv_model_with_batchnorm.save(\"/content/conv_model_with_batchnorm\")\n",
        "conv_model_with_upsampling.save(\"/content/conv_model_with_upsampling\")\n",
        "conv_model_with_fc.save(\"/content/conv_model_with_fc\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:1817: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "INFO:tensorflow:Assets written to: /content/conv_model_without_batchnorm/assets\n",
            "INFO:tensorflow:Assets written to: /content/conv_model_with_batchnorm/assets\n",
            "INFO:tensorflow:Assets written to: /content/conv_model_with_upsampling/assets\n",
            "INFO:tensorflow:Assets written to: /content/conv_model_with_fc/assets\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h99FxlEZAv5v",
        "colab_type": "code",
        "outputId": "a8a0d7aa-1b58-4801-cf86-75ac83deb563",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "source": [
        "!zip -r /content/conv_model_without_batchnorm.zip /content/conv_model_without_batchnorm\n",
        "!zip -r /content/conv_model_with_batchnorm.zip /content/conv_model_with_batchnorm\n",
        "!zip -r /content/conv_model_with_upsampling.zip /content/conv_model_with_upsampling\n",
        "!zip -r /content/conv_model_with_fc.zip /content/conv_model_with_fc"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "updating: content/conv_model_without_batchnorm/ (stored 0%)\n",
            "updating: content/conv_model_without_batchnorm/assets/ (stored 0%)\n",
            "updating: content/conv_model_without_batchnorm/saved_model.pb (deflated 90%)\n",
            "updating: content/conv_model_without_batchnorm/variables/ (stored 0%)\n",
            "updating: content/conv_model_without_batchnorm/variables/variables.index (deflated 70%)\n",
            "updating: content/conv_model_without_batchnorm/variables/variables.data-00001-of-00002 (deflated 19%)\n",
            "updating: content/conv_model_without_batchnorm/variables/variables.data-00000-of-00002 (deflated 82%)\n",
            "updating: content/conv_model_with_batchnorm/ (stored 0%)\n",
            "updating: content/conv_model_with_batchnorm/assets/ (stored 0%)\n",
            "updating: content/conv_model_with_batchnorm/saved_model.pb (deflated 91%)\n",
            "updating: content/conv_model_with_batchnorm/variables/ (stored 0%)\n",
            "updating: content/conv_model_with_batchnorm/variables/variables.index (deflated 75%)\n",
            "updating: content/conv_model_with_batchnorm/variables/variables.data-00001-of-00002 (deflated 8%)\n",
            "updating: content/conv_model_with_batchnorm/variables/variables.data-00000-of-00002 (deflated 83%)\n",
            "updating: content/conv_model_with_upsampling/ (stored 0%)\n",
            "updating: content/conv_model_with_upsampling/assets/ (stored 0%)\n",
            "updating: content/conv_model_with_upsampling/saved_model.pb (deflated 91%)\n",
            "updating: content/conv_model_with_upsampling/variables/ (stored 0%)\n",
            "updating: content/conv_model_with_upsampling/variables/variables.index (deflated 75%)\n",
            "updating: content/conv_model_with_upsampling/variables/variables.data-00001-of-00002 (deflated 8%)\n",
            "updating: content/conv_model_with_upsampling/variables/variables.data-00000-of-00002 (deflated 82%)\n",
            "updating: content/conv_model_with_fc/ (stored 0%)\n",
            "updating: content/conv_model_with_fc/assets/ (stored 0%)\n",
            "updating: content/conv_model_with_fc/saved_model.pb (deflated 91%)\n",
            "updating: content/conv_model_with_fc/variables/ (stored 0%)\n",
            "updating: content/conv_model_with_fc/variables/variables.index (deflated 74%)\n",
            "updating: content/conv_model_with_fc/variables/variables.data-00001-of-00002 (deflated 14%)\n",
            "updating: content/conv_model_with_fc/variables/variables.data-00000-of-00002 (deflated 83%)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o_bKN8gHB4LW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import files\n",
        "files.download(\"/content/file.zip\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nB-5_RuN9HWy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ePmn3D369IjX",
        "colab_type": "text"
      },
      "source": [
        "# **Learnings of Autoencoder**\n",
        "\n",
        "1) **Visualize filters of the last decoder. Reshape it into a 28,28 image and visualize some of them.(Used simple MLP based autoencoder)**\n",
        "\n",
        "As the MLP model was not trained with batch norm, we see that network has mostly learnt to copy rather than learning really. Its giving weird pixel weights which are incoherent. However if we pick only argmax pixel(winner takes all) column wise then some patterns seem to emerge. Especially at decoder side we can see some thick straight line strokes signifying numbers such as 7, 1,  9. Similary we can see pattern signifying 8, 5. But it is pretty clear that this network is a bit weak learner, hence some sort of additional noise, batch nor or dropout should be provided.\n",
        "\n",
        "2) **Manipulate encoder output, Use manipulated encoder output as input for decoder. Check the results. (Used convolutional autoecoder with FC)**\n",
        "\n",
        "Since batch norm was used we see this network as a bit better learner than others. We tried two things. \n",
        "\n",
        "a) Blocking some encoding  present in particular value range and displaying imge iteratively for some steps. We can see some changes in stroke thickness of number 7, if some encoding whose values between 50-30 not present\n",
        "\n",
        "b) Blocking all encoding not present in particular value range and displaying iteratively. We see that again pixels between 30-40 is somewhat singlehandedly producing the thick straight stroke, eventhough it couldn't complete the number 7. hence encodings in range of values 30-40 seems important.\n",
        "\n",
        "\n",
        "**Credits:** \n",
        "adding gaussian noise - https://stackoverflow.com/questions/43382045/keras-realtime-augmentation-adding-noise-and-contrast\n",
        "\n",
        "Image augemntation library: https://github.com/aleju/imgaug/tree/master/imgaug\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wTmuth8k5NOs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(train_images_raw, train_labels_raw), (test_images_raw, test_labels_raw) = mnist.load_data()\n",
        "\n",
        "train_data = tf.data.Dataset.from_tensor_slices(( train_images_raw.astype(np.float32)/255.0, train_images_raw.astype(np.float32)/255.0 ))\n",
        "\n",
        "train_data = train_data.shuffle(buffer_size = train_images_raw.shape[0]).batch(32)\n",
        "\n",
        "#test_data = tf.data.Dataset.from_tensor_slices(( test_images_raw.astype(np.float32)/255.0, test_labels_raw.astype(np.int32) ))\n",
        "test_data = tf.data.Dataset.from_tensor_slices(( test_images_raw.astype(np.float32)/255.0,  test_images_raw.astype(np.float32)/255.0 ))\n",
        "test_data = test_data.batch(32)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Q5_oZq2B_2t",
        "colab_type": "code",
        "outputId": "488e27c5-b9d2-46ea-e7ad-ed678e415b92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        }
      },
      "source": [
        "!unzip /content/model_relu.zip -d /content/model_relu\n",
        "!unzip /content/conv_model_with_fc.zip -d /content/conv_model_with_fc"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  /content/model_relu.zip\n",
            "   creating: /content/model_relu/variables/\n",
            "  inflating: /content/model_relu/variables/variables.data-00000-of-00001  \n",
            "   creating: /content/model_relu/__MACOSX/\n",
            "   creating: /content/model_relu/__MACOSX/variables/\n",
            "  inflating: /content/model_relu/__MACOSX/variables/._variables.data-00000-of-00001  \n",
            "  inflating: /content/model_relu/variables/variables.index  \n",
            "  inflating: /content/model_relu/__MACOSX/variables/._variables.index  \n",
            "  inflating: /content/model_relu/__MACOSX/._variables  \n",
            "  inflating: /content/model_relu/saved_model.pb  \n",
            "  inflating: /content/model_relu/__MACOSX/._saved_model.pb  \n",
            "   creating: /content/model_relu/assets/\n",
            "  inflating: /content/model_relu/__MACOSX/._assets  \n",
            "Archive:  /content/conv_model_with_fc.zip\n",
            "   creating: /content/conv_model_with_fc/variables/\n",
            "  inflating: /content/conv_model_with_fc/variables/variables.index  \n",
            "   creating: /content/conv_model_with_fc/__MACOSX/\n",
            "   creating: /content/conv_model_with_fc/__MACOSX/variables/\n",
            "  inflating: /content/conv_model_with_fc/__MACOSX/variables/._variables.index  \n",
            "  inflating: /content/conv_model_with_fc/variables/variables.data-00000-of-00002  \n",
            "  inflating: /content/conv_model_with_fc/__MACOSX/variables/._variables.data-00000-of-00002  \n",
            "  inflating: /content/conv_model_with_fc/variables/variables.data-00001-of-00002  \n",
            "  inflating: /content/conv_model_with_fc/__MACOSX/variables/._variables.data-00001-of-00002  \n",
            "  inflating: /content/conv_model_with_fc/__MACOSX/._variables  \n",
            "  inflating: /content/conv_model_with_fc/saved_model.pb  \n",
            "  inflating: /content/conv_model_with_fc/__MACOSX/._saved_model.pb  \n",
            "   creating: /content/conv_model_with_fc/assets/\n",
            "  inflating: /content/conv_model_with_fc/__MACOSX/._assets  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jfTiMUb4D_yd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%rm -rf /content/model_relu\n",
        "%rm -rf /content/conv_model_with_fc"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S4V_L8VC9jWZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mlp_model = tf.keras.models.load_model('/content/model_relu')\n",
        "conv_with_fc_model = tf.keras.models.load_model('/content/conv_model_with_fc')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rtRomUJ99je2",
        "colab_type": "code",
        "outputId": "2349960b-c383-4433-cb48-85e08a9b77c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        }
      },
      "source": [
        "mlp_model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_16\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten_1 (Flatten)          (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense_75 (Dense)             (None, 256)               200960    \n",
            "_________________________________________________________________\n",
            "dense_76 (Dense)             (None, 128)               32896     \n",
            "_________________________________________________________________\n",
            "dense_77 (Dense)             (None, 64)                8256      \n",
            "_________________________________________________________________\n",
            "dense_78 (Dense)             (None, 32)                2080      \n",
            "_________________________________________________________________\n",
            "dense_79 (Dense)             (None, 64)                2112      \n",
            "_________________________________________________________________\n",
            "dense_80 (Dense)             (None, 128)               8320      \n",
            "_________________________________________________________________\n",
            "dense_81 (Dense)             (None, 256)               33024     \n",
            "_________________________________________________________________\n",
            "dense_82 (Dense)             (None, 784)               201488    \n",
            "=================================================================\n",
            "Total params: 489,136\n",
            "Trainable params: 489,136\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zy6JBbui9jc0",
        "colab_type": "code",
        "outputId": "100669e3-e670-4ea1-f6fe-c272cfe2b583",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "for layer in mlp_model.layers: \n",
        "  print(layer.get_config())\n",
        "\n",
        "weights = mlp_model.get_weights()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'name': 'flatten_1', 'trainable': True, 'batch_input_shape': (None, 28, 28), 'dtype': 'float32', 'data_format': 'channels_last'}\n",
            "{'name': 'dense_75', 'trainable': True, 'dtype': 'float32', 'units': 256, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
            "{'name': 'dense_76', 'trainable': True, 'dtype': 'float32', 'units': 128, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
            "{'name': 'dense_77', 'trainable': True, 'dtype': 'float32', 'units': 64, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
            "{'name': 'dense_78', 'trainable': True, 'dtype': 'float32', 'units': 32, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
            "{'name': 'dense_79', 'trainable': True, 'dtype': 'float32', 'units': 64, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
            "{'name': 'dense_80', 'trainable': True, 'dtype': 'float32', 'units': 128, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
            "{'name': 'dense_81', 'trainable': True, 'dtype': 'float32', 'units': 256, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
            "{'name': 'dense_82', 'trainable': True, 'dtype': 'float32', 'units': 784, 'activation': 'sigmoid', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6XtVVmoYypoz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "print(first_layer_encoder_weights.shape)\n",
        "print(last_layer_decoder_weights.shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xr-nIL1jIlYv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "encoder_img = np.reshape(first_layer_encoder_weights[:,0], newshape=(28, 28))\n",
        "decoder_img = np.reshape(last_layer_decoder_weights[0,:], newshape=(28, 28))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XbdtMhZyNKK8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def maskMatrix(inp_tensor):\n",
        "  max_indices = tf.TensorArray(tf.dtypes.int64, 0, True)\n",
        "  #values, indices = tf.math.top_k(inp_tensor, k=2, sorted=True)\n",
        "  for x, i in enumerate(tf.math.argmax(inp_tensor, axis= 1).numpy()):\n",
        "    max_indices = max_indices.write(x, [x,i])\n",
        "  values = tf.ones(inp_tensor.shape[0], tf.dtypes.int64)\n",
        "  return tf.sparse.to_dense(tf.sparse.SparseTensor(max_indices.stack(), values, inp_tensor.shape))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ExNaK-2mMK_5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "encoder_img_tnsr = tf.convert_to_tensor(encoder_img, dtype=tf.dtypes.float32)\n",
        "#arg_max_encoder = tf.math.top_k(encoder_img_tnsr, k=2, sorted=True, name=None)\n",
        "arg_max_mask_encoder = maskMatrix(encoder_img_tnsr).numpy()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r6rIbZ3YPKzZ",
        "colab_type": "code",
        "outputId": "b39a0689-8703-41c5-b4b3-692bad7fae75",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "np.shape(arg_max_mask_encoder)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QjUXNL-dPTi3",
        "colab_type": "code",
        "outputId": "afd7db09-3287-4254-bb3b-6f067b3ba335",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "plt.imshow(arg_max_mask_encoder, cmap=plt.cm.binary)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f761efe4668>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l8EUWP9uKvAH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "decoder_img"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aSTZp2X5HWRW",
        "colab_type": "code",
        "outputId": "c4151289-7e8f-44fe-fd2b-7217db28885c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "plt.imshow(np.reshape(first_layer_encoder_weights[:,2], newshape=(28, 28)), cmap=plt.cm.binary)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f761ef48978>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OOyLsuYjHWLk",
        "colab_type": "code",
        "outputId": "3f609bca-6b2d-4983-b1c8-d87239ab88f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "plt.imshow(np.reshape(last_layer_decoder_weights[2,:], newshape=(28, 28)), cmap=plt.cm.binary)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f56346b9dd8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Hwha3EitfuK",
        "colab_type": "text"
      },
      "source": [
        "**Looks like the encoders are not learning anything, too much clutter and crappy output, cannot see any pattern. We see the same pattern in decoder too down below. Maybe we need to mask and get the top pixel in every row/column and visualize**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DztcdpN3qDw2",
        "colab_type": "code",
        "outputId": "a5bfa739-c7d9-4f74-9260-50a8fff57b98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 686
        }
      },
      "source": [
        "def output_model_encoders(weights, encoder_layer_num=0, filter_shape = (28, 28), images_to_be_compared=5):\n",
        "  #Get the encoder output for each layer\n",
        "  \n",
        "  plt.figure(figsize=(12,12))\n",
        "\n",
        "  for i in range(25):\n",
        "      plt.subplot(5,5,i+1)\n",
        "      plt.xticks([])\n",
        "      plt.yticks([])\n",
        "      plt.grid(False)\n",
        "      filter_image = np.reshape(weights[encoder_layer_num][:,i], newshape=filter_shape)\n",
        "      plt.imshow(filter_image, cmap=plt.cm.binary)      \n",
        "      #plt.xlabel(class_names[test_images[i]])\n",
        "  plt.show()\n",
        "\n",
        "output_model_encoders(weights) \n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w4cEW0hctxfM",
        "colab_type": "code",
        "outputId": "df16b0d3-9f19-4fca-8615-3f6114c9fa77",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 686
        }
      },
      "source": [
        "def output_model_decoders(weights, decoder_layer_num=14, filter_shape = (28, 28), images_to_be_compared=5):\n",
        "  #Get the decoder output for each layer\n",
        "  \n",
        "  plt.figure(figsize=(12,12))\n",
        "\n",
        "  for i in range(25):\n",
        "      plt.subplot(5,5,i+1)\n",
        "      plt.xticks([])\n",
        "      plt.yticks([])\n",
        "      plt.grid(False)\n",
        "      filter_image = np.reshape(weights[decoder_layer_num][i,:], newshape=filter_shape)\n",
        "      plt.imshow(filter_image, cmap=plt.cm.binary)      \n",
        "      #plt.xlabel(class_names[test_images[i]])\n",
        "  plt.show()\n",
        "\n",
        "output_model_decoders(weights) "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Yk9f4htUYY4wxxuQ1vmE1xhhjjDF5zWY5rDNmzIgdd9wxm+kB0X2iH0YHtW/fvpLZ+8bOxAMPPFDyl19+KZneBTtU6a/RTaHnFJHe956dhdynmC4gfTE6rHSjBg4cKPn++++XTB+0bt26krnvd5kyZbJf8/XmikaNGsXgwYOzuV27dvI4Owfpv/B9GjZsmGT2+Q4dOlQyO+W43zv9Gfas0kdjD2zyHPgX9LfoMfF9//333yXT97r00kslz5s3TzLHJXsj6bNx73I6ejw3S4sOHTpIBx/fS74OdiXTOf3jjz8ks4tyt912kzx69GjJnGPoaNJF5hzVu3dvyfRVIyJGjBghmb4lnVU6a9zH+9VXX5XMeZBrA+g/s6eVr5nHPOn4sV8xV6xatUr6KLkfe/ny5SVzP/fkPBiR9tD5PtIxf+WVVyTTUR81apRkesH0gOnVs5szIu2Zc40I+3cPPvjgEp8zr128tnBsc/949k9/9dVXknkMi/NyS4PmzZtLry67yfle0DNP+q8R6Y5dno9cR8L3gXPClVdeKZnrUjh26Jcme6v/RZMmTSTzPuGZZ56RPHv2bMlce3P88cdL5jWZYy95vY9In58HHXSQZHYCJ49xSWtt/AmrMcYYY4zJa3zDaowxxhhj8hrfsBpjjDHGmLxmsxzWVq1aiS9FL4j7pdPfZP8Z+zfHjBkjmXtY01ejm0L3sHbt2pK55y/dk+J6WNmZluwCLO5vsu+sffv2kum4sjOUfZr0jugVsc+vXLlykpO+zWOPPRalQUFBgTib48ePl8fpr/CY0TPkMaSjd+2110rm3sv0d+gt0t+hL0qHj85RRHqs9erVSzL3euZe5KeeeqpkHrOXXnpJMj1GupdbbbWVZHb50aFlnyk9ylyxYMEC8XfpwdOvnDVrlmR68j169JDMOYndz+xUpJ9Mz49OarKXOiKiYcOGkovrKaUTRx+Z45FuYbLjOCL93tOf/uuvvyTTGee8Ry+f82yyE5jnbq5YunRpPPHEE9lMB4/rGeiM0xGnf3nXXXeVmDkHsAf2559/lrzNNttI5rWHrnVx44Rjj+5k165dJbNXnN20dAPpbvLa88gjj0imi7nXXntJphv93+p2Xr16tXSb0i+mX8nOas7/n332mWS+LxwbPE708LmGgl3OvCfg3M/1CMX9N/ZF85rJebB///6SOXbY4cs5i9c3dqly/PN6lHS62YObxJ+wGmOMMcaYvMY3rMYYY4wxJq/xDasxxhhjjMlrCtiJWRJly5bNVKtWLZu5zzb38eae1uwDpCdBj4eZ/WR0TbhnL7vC6F2wJ644h5UdnfTB2KfHv0mnlP1ov/76q2S6JXRYmbln8BlnnCE5uc/xypUr459//tFCtRxQpUqVTNJ7o8vEcdCvXz/J9ADZ98teV/YVfvTRR5LZacpxx/edrhiPcXGOTYcOHSQPHz5cMr1FOj177rln6ncmOfzwwyVfd911krn/PPt8+RrodlasWFHyZ599NjmTyegJlQOaNWuWSfqFbdu2lccrVaokuXLlypLpKl544YWSeVzYG7r11ltLpiNOT5/OLPfMpmfP/dkj0uOLvY3sWWTHIudVdiYmHdOI9J7zO+ywg2T60HQh6Som+5znzp0bq1evzvmc0q5du0yyT5l9vcm9yCPS4+iII46Q3KhRI8l8zZz32XdNb56OIM9nOrbsjS2ue5IeIHvL+RrYO96yZUvJyb7jiIiioqISfz89dt4r8DVxzunSpYvk3XbbrVTmlLp162Z69uyZzXRr2dHLHmRes+l8c30Ae7rp2T/44IOS6clzPQHPN3ri7OONSK95qFevnmSOf17zeI09++yzJdP55ry4dOlSyRdddJFk+tHMyfVM++67b0yZMqXYOcWfsBpjjDHGmLzGN6zGGGOMMSav8Q2rMcYYY4zJazarh7Vp06Zx++23ZzM7DOnl0bU688wzJbPvj/2YO++8s+T3339fMjsX6RHSD6W7yP1x77vvviD0KemzrFq1SvLMmTMln3POOZL5mtm5RneKDhw7TNlnxj3ik24inahckclkYv369dlMH6Zp06aSH3roIcncV/v777+XPGHCBMl0gOgm05fhzyfHdES6c44dq/QWI9JjjZ5u/fr1JbO779BDD5XM3kieG+wH5muml8X9sdnVSeeW51auqFSpkjjJ7ANkPyw7ekeOHCmZTusvv/wi+c0335RM749ePo8ze1jpEm+//faSued8RLpnlZ29nOfoxPE5kLPOOksy57XkuRmRdl7Hjh0rmQ7dlltumf26pH2//5OsX79exji7aNljzO5MdjP/9ttvktll++ijj0rmPM794D///HPJ7Oqkt8j1GcWdb/vvv79kupd8TnQv2Z9bp04dyfS1eYyuueaaEr+fvd48BuxRLy2qVq0q5wh7WOl7nnfeeZLpzbPvdsWKFZJ53Hn94tz63HPPSaazyvUK9J3ZIxuRHu90+dkTTAeVHjvH/4gRI0r8ft53cF5dvHix5HPPPbfE5/Pv8CesxhhjjDEmr/ENqzHGGGOMyWt8w2qMMcYYY/KazXJY//7773jvvfeymX2UdFrYgcget1dffVUy3UF2idGDGDRokOQ5c+ZIZvclnVp6TPQ0ItJ7VtM/o1NKP4wuIV0T7nPfuHFjyfQtkx2IEemuPB6jpFfELrZcsXbtWnEk2clGX4x+Jt83jqvXXntNMrtw6YvSzaKTyr2d6RRyL+irr746yLBhwyTTe6Ln1KBBA8l8zezjpQdMf5xOH91n9gezd5LHpLRYtGiR7NtOP3jw4MGSeVx4/nGssG+Qe8Dz/OL5QzeYY5UOJ717uloRaQeN45m9rJwnOZ4LCwslcw94+mT0eNlZSkeWjmty/LPjOFesWLFCuhq32WYbeZyOKP1NeoE8pg888IDk5F70EekeVnqFdKXpNvPaQ/+U+8VHpJ3QZLdoRHos1qpVS/Ktt94qmXMQvV92iPMY8/EffvhBMj18HuPSYtGiRTK/sY92wIABkrn+gPcFPM6cI9jtvO+++0qmO8xe5WbNmknm+85M5zUifZ9CT5auP38Hr2kHHHCAZPa/8xrLDnzOy7wXZJ928nxgv34Sf8JqjDHGGGPyGt+wGmOMMcaYvMY3rMYYY4wxJq/ZLIe1WrVq4jbQNaC/yf4/OjT0OY855hjJZ5xxhuRu3bpJZj8g9wWnp3fQQQdJXr58uWTuAx4R0blzZ8l01NhnSR+FriJ7WDe1rzA7SHfffXfJ9NnYA5ncL5pdabmiXr168t4lveeI9PtEZyjZ81jcz7Ork17imDFjJLMzjntwc1xyj2zCcRiR7kycOHGiZO4PT+eNY/XGG2+UTIeVzindTjpDPBfbt28vma5maVGnTh15bXSntttuO8k8n+jqsvuR/ZaHHXaYZHaW0j2kD8o5ie7hK6+8Irm4zkTOW3xOdAvpzNEBp/s/f/58yU888YRk9k7yfKSzRw83uXc6HeFcUbNmTTmWfM4//fSTZPZn098/9thjJbMbmnMMO1B5PtITppdM15e5OIecfZ7s1ty4caNkvs/vvvuuZHZ8c6zTg+Tf4+M8Zjz36FGybztX1KtXT547XVrOtR9++KHkGTNmSOb5yT5aZt4TcE7r3r27ZDronGO4RoMd3RHp+5Qnn3xSMscSr6nslmV3OV9j1apVJZ988smSef2j7zxp0iTJybHDbvsk/oTVGGOMMcbkNb5hNcYYY4wxeY1vWI0xxhhjTF6zWQ7r6tWrxe8488wz5XF2Gg4dOlQy+8roFl588cWS6ZTSl6KTyd64vn37Su7Xr5/kyZMnS+Z+6xFp14/9Y3wOdJH4nOiH0aulv0LviD2t9HPox3zwwQfZr0vLNytbtmzUrFkzm+ndsueUbhSPaUFBgWT6ouzR4/7v9MXobrETke8xu2/ZaRoRcdVVV0nm+z5w4EDJdI/ZKXz55ZdL/uqrryQnu0sj0r127GllNyA9K+4fX1rMmTMn+vTpk808R+lrsT+wRYsWkvnz7M+kd9epUyfJdLHoRnIss+ORe3bTF42I+PjjjyXT5b/pppskc16lg83+aHbZPvzww5Lp9V555ZWSb7/9dsk8X5L7fvPczBXsYeW44PvCuZ3zMr3B3r17S6bXzmsZHVX6mcl5NyJi6dKlkjmHsIc5Iv2+0avdYYcdJPP6yZ509k3TS+S45DGl209Xmp4kj0FpkclkxPvclEvLNRSjRo2SzHsAzkGc63mceQ3nteDtt9+WzLmdDjv7biPS9w1875NzbHGP83xhNzN7Wjn+P/30U8lcm0Ofmscs2Vu+YMGC+Hf4E1ZjjDHGGJPX+IbVGGOMMcbkNb5hNcYYY4wxec1mOaxLliyJRx99NJvZiUiX8IorrpDMbi76mHRw6OjMmjVLctKlikh7SnRmq1WrJpm+G93KiHSnJ/2vZ555RvK1114rmW4JPZ9KlSpJZpfd008/LZnOGP3MO++8U/Jll12W/Zr9prli4cKFso81vUD6Ml26dJHM7+f7Tkc16csW9/voIB155JGS2enIfcTpILFTNSI9dtmDyrHKnjv63OypY1cgO4s5Tug9ca907q3OfcdLyz+rV6+eHLt69erJ49dcc41kOqdz5syRzD5M+sicE3hceRyvv/56yXTO2XtMv+x/033MDlz6yHTq2NPK8f7iiy+WmDmv0iejh8u+z+RYod+dK1auXCn7kz/++OPyOB3vjh07Sn722Wclc17n62AfNueQ5HUwIuLggw+WzGsRx+ELL7wgmd28ERGvv/66ZM47fA1cU0JPls+JXmGHDh0kDx8+XDI92woVKpSYS6t3lWzYsCH++OOPbE76kRHpayjXfXCscI4ZPHiwZDrnnJt5LaDvya5ndmaz+529yBHpLvLTTz+9xOfInm/2sPKay3sn+tV0ynk947xeq1YtyckO4ZLW2vgTVmOMMcYYk9f4htUYY4wxxuQ1vmE1xhhjjDF5TUEmk/lff3OjRo0ySX/io48+kscffPBByePGjZNMz4/7OdN55Z6ydEPoi7Fbj/2B7GCkK0EvKiLdh0mXgz9D527lypWS6Qayx5F9Z3Tg6Nmyk5Q+zkknnSSPTZ06NefFiVtttVXmoosuymZ6v/QAt9pqK8nz5s2TTB+Nfbn0CPn72KlIF4ydcc8//7xkus7MEWmHrk6dOpLZbUnvkB4T93pm3y47FNkNyL3T6e3ScaLX9dprr03OZDI7RY4pKCiQCYguFvfx3mknfUrsCxw5cqRk9g/SOS9fvrxk+sw8nzhHsc+QcxL7diPSThrHN+eI4447TjL38eZYql69umTu4805ivMmPeCFCxdKTnYGH3XUUTFt2rSczymtWrXKJM+Rl19+WR7/888/JbMzlK+J3bOnnXaaZPqgfJ95bdpzzz0l8z3m+gFed9nFG5Get9gVSy+W/Z28PvP6R4+WnjudVo6Dd955R/I999wjmedWs2bNSmVOqVixYiZ5Hm+33XbyOI8915GULatLezgWuA6Fx51zNx11Xt/Wr18vmY7rUUcdJZnXp4h0L+r7778vmeORvdx33323ZF4Pkr22Eemxdcstt0jm2KCzSq9+iy3+57PTQYMGxdy5c4udU/wJqzHGGGOMyWt8w2qMMcYYY/Ia37AaY4wxxpi8xjesxhhjjDEmr9msjQMKCgpESOYCJBbecsERC+G58IMLFDp37iyZYnGVKlUkUxw+/vjjJbPcljJ2cTIzF2lwUQY3BjjwwAMlc0ED5efPP/9c8ujRoyVzgRDlfZY7s+R79uzZ2a9LKuT9T7JmzZqYOXNmNrOMnUXoJ5xwgmQuKmEJ/oQJEyTzmLMk+ZBDDpHcv39/ydtuu63ksWPHSqZg/thjjwXhogu+b1wkwoVofF+5aGrIkCGSk4vaItJjl8XnM2bMkMzNFxYsWCCZ51quKFOmjCwS4uIWLkjg+TFs2DDJXODQrFkzyVw0xUWVXJzGBRAs9edGIfPnz5fMRZUR6fOQ5zDPBy4Y4vnAQngueNh9990l8/zhe73ffvtJXrduneTke8T5JldUqFBBysi5EKZRo0aSn3zyScn333+/5KOPPloyjykXrYwfP14y5wwupOGGLsnFrxHp+YIbD0SkC+u58PkHMToAACAASURBVIWLhbgpBscFr0X8fm4ExAWOXETFc42b1nDxX2lRvnz5aNiwoeQkHBs89n///bfk5LUsIr3xBu8zeP3hot7vv/9e8t577y2Z10cu4uL1KSK9EQCvUdwghZvjcOHypq4v3GiGmy0kNyyKSC8m5+YLyU0puIAyiT9hNcYYY4wxeY1vWI0xxhhjTF7jG1ZjjDHGGJPXbNbGAVtssUWmQoUK2Uyvp3379pJZgD1p0iTJdErppNKJpQtJT4hFyCzdv/nmmyVXrFhR8tKlS4MkX29E2k9hcT89WhbCb6rQfcCAAZJfeeUVyX/99ZdkFvLSKfv222+zX48dOzaWLl2a85LvwsLCTNIbpe9C34wl3GXKlJFcr149yS+99JLk5557rsTfRx/nlFNOkUy/rHHjxpI/+eQTycW5WRwn9LmSLnFE2mVmGTVdzSVLlkgeOnRoib+f5yY3Nrjqqqsk0/P9/PPPS6Xku0aNGpmkw8XCao5/+p9z586VzNf51FNPlfg4vXoW/997772SDz/8cMnLly+X/NBDD0nm849I+44rVqyQzHOYr4HO3bRp0yTTRWTx+KxZsyRz8xM+P55/ybHy0EMPxfz583M+p9SuXTuTPPacUzgu6NDRE+QxoDc/YsQIyXSV6YdyExweszZt2khu0aKFZHr1Eekydm6qwSJ/ergcy0VFRZI57ugh8lrDcUIvkmXwzK1atSqVOaVp06aZ5HWU6wM4Z3As8D6F5/TDDz8smcedY4+O+JtvvimZLjEdWo6tL774IgjnmSeeeEIy1zDQxSe8F+L1iBsYcc0Er6GcZ7lZSnKOWrhwYaxdu9YbBxhjjDHGmP/38A2rMcYYY4zJa3zDaowxxhhj8prNclhbt26dSfYe1qhRQx6nU0ov78svv5RM/5KOKX0zdjTSZezZs2eJ388OVTqxdB8j0q4fO9Xq1KkjmX1oU6dOlcxONTqw9IrYF3jBBRdIpu/GPrXk89tnn31iypQpOffNWrZsmUn6x+wxpZv18ccfS6ZrxQ7FvfbaS/Lq1asls9+XfcBnn322ZI5bOoH0Rem8RkTwPKKbzPeRr4mdh3Qlkx2UEeljxmPAcXnWWWdJZl8oj1m7du1KxTerW7duJulo0f+iZ04XkY4pX9cee+wh+ZhjjpHMHuQDDjhAMvtv77jjDsn0Semzcc6JSPtgu+22m2S6gvR62TvMOYV+JV8zOxp//fVXyd26dZNMTzd5PvTo0SOmT5+e8zmlRYsWmeR7wTmFnt+7774rme8zx9GmPGA6suwHbtKkiWR23/I9pX/KeTsiPU/xOffu3Vsy+z3pRp544omSR44cKblLly6SeW2hs8o+UK4Z4bn89NNPl5rDmuxb5vWGvjKf98CBAyX//PPPkjkn0ZF99dVXJdMlnjJlimTOObw2/Pbbb5Lp0Eakry+8F2LnfJ8+fSRzbPA1dOrUSTK7Ynmvxy7Vn376STLXfJx//vnZr0uaU/wJqzHGGGOMyWt8w2qMMcYYY/Ia37AaY4wxxpi8puzmfPOKFSvis88+y+ZddtlFHqdnR1eKriD3Nk7+7oiI7t27S6bjyv1pf/nlF8n0ktiHxn3F6cRGRFx44YWSuZ8zu2TpsE2cOFEy93++5557JLPPjL4K3Sp2lp566qmSt9lmm3/7u3NFlSpVxMnk+0wn7q233pJM/5J+GDvn6KvRH+W+3T/88INk9v0ed9xxks844wzJ7NGLSDtrdHb4nLk/O8c297umu8xxd9BBB0lmVyw9q8MOO0wy36PSokGDBnIe0xdO7gkekXbCX3/9dcn0Ofk4z3H2JtMD5D7hyfMpIqJv374l/j56+BFph409qXzvOP45VugW8vvp7NGJY68jjyF7IZNjbcGCBVEaLFiwQNxCXntq1qwpmeOkWrVqkuk6d+7cWTLHCZ0/rjXgtYXnU9myeqnltY3jPCKiXbt2kulLc04pX7685Jdfflkye1bp9vMY8VrHNSu83vLv0/fmmpFcUaFChdh2222zmT3G77zzjmQ64fXr15fMzly6ubwPYYdv3bp1JSe70SPSY4tzNV1jdrUX99/4Gp9//nnJnBO4LoTXB/audu3aVTK7annvRaeV91XlypXLfk1nP4k/YTXGGGOMMXmNb1iNMcYYY0xe4xtWY4wxxhiT12xWD2uFChUyyT1iuR863UR2d7G/cp999pHMrroOHTpIpstBv2yLLfT+m24XvUL23NHVikjvU3/bbbdJZjce/+Z+++0nmX2BF198cYm/j8eYr4EOLI9J0it6/fXXY+nSpTnvTKxTp06mR48e2Uxfiz2UX3/9teQjjjhC8nXXXSeZ+3Kzx5IeIjtG6aclu4Uj0r4O+wivvPLKIG+//bZk+l90ize15zzHMrv86PnSnZw8ebJk+mV0q3kMZs6cWSqdiW3bts0k3w/6wXSfuOc6e5EffPBByexYTLpSEREnnHCCZPpk9L6HDx8u+c4775TM94VdlRHpOYLPiWOJx4QONucodiTS52RH46677iqZx4TnQ9LxXr58eWzYsCHnc0rlypUzrVq1ymY643T52b/LtQVcC/D+++9LpvfIDnF6ivTu6JTTdea1je9JRNp15mt+4IEHJCe7LCPSnu+8efMkc6wPGjRIMsc6v5/nGq/vdLObNm1aKnNKixYtMrfffns2c+6jw837ljfeeEPykiVLJNNZvfbaayVPmjRJMq/xd911l2T6pXwf+D4W57ByjmCvL9cXsQeYzio7fHlvxnmV1xv+PL17evrJ+5ZZs2bFqlWr3MNqjDHGGGP+38M3rMYYY4wxJq/xDasxxhhjjMlrNquHtWrVquLR0Nc655xzJHPP3bZt20rmz1911VWSuf85vSC6Xtwzm7+frgr3s6X3FBHS5xaR9sHq1atX4nOgs3rKKadIZtce9/Slm8XOuJtuukkyO0mTva/FObq5YNmyZfHMM89kMztH6eDtv//+kunk8ec35ex98sknkumT0gNO+rbFPU6/jV2hEWnvlns302uiw0PHdd9995V87733Sqa7yS5Z7g/PccP94ulmlxaLFi2S10bXb/z48ZLZb8k5hd485wx6fltvvbVkOuTsJGV35R9//CF5zZo1ktmfG5GeMzp27Cj55JNPlrxo0SLJdNjYM0zXn92x9CXZIco+bP7+5N7s3Kc9VzRs2FBcQc7LnCc5B7Dbcu7cuZLZJ9u0aVPJPH94vvD3n3baaZLZO1mrVi3JnNOK+510GS+//HLJXAvA/eC5FoDdmzy36PHOmDFDMsc+10+sWrUq/htUrFgxdthhh2xmjzbPJ963sAObx429rHyfNtVRSheYcwrnZl4fizuuvF7wnOb5wveOfjTvW9g9yx5XdssecMABknk95Dyf7L79+eef49/hT1iNMcYYY0xe4xtWY4wxxhiT1/iG1RhjjDHG5DWb5bBWr15d9jP+66+/5HF6O/369ZNMT4/ddj/++KNkekPspqSrxa699u3bS6Z7yD3j6WVERMyZM0cyXSQ6pPRk2aXH/Z2PPvpoyewHZD8mO+LozPKYJh28G2+8MUqDhg0birvHvl0eZ+6RzZ7KoqIiyfx9dLno4NHV4t7O7BNkhxz9MjpLEen92/kcSXJf9Ii0f8b3kR4u+z/5GjlO6cyuX79e8qbOjVxRtmxZ2Qd+p520ppGu4NKlSyWzY5F7yvP8YAcv5yT6bFdccYXkZL9jRLqTkf4zf19EiN8dke6FpDM9ZMgQyXRS2alIT549kJxH+V6zc/HII4+UnHT9OY5yxYIFC+L666/P5kceeUQepxfPfe5nzpwpmXMOuzHZn03PkL2rPIab6tLl+go+/4iIF154QTLHMj1Y9p7yWsR5kv4xx8Wzzz4rmR4i11/wXKInXFosW7ZMPFSe8+y0/vTTTyWzw5Tv/ZlnnimZvdw8zt98841kOpocG7xO81pSnMPK58zxy9fM+4waNWpI5tgYN26cZM4JXDuwqd5WdqMne1p5rUviT1iNMcYYY0xe4xtWY4wxxhiT1/iG1RhjjDHG5DUFmUzmf/3N5cuXzyT9P/qZdEbZDcn9Zq+55hrJdGLYFcZ9udmhyD256Yqxm5JuCl2viHSf2KOPPiqZvY21a9eWzB5Ivub7779fMh1X9rLSE6bPcsghh0hO9gnOnTs3Vq9enfN9v8uVK5dJ+lXTp0+Xx9lZOmbMGMn0Lekp0itm/x978NjVyT24k3uUR6R9HrpYfP4R6f2g7777bsl03Oh/cz93dvedffbZkrlvN7+f5ybdZ/48Pd499tijVPb9bteuXeb111/P5i+++EIep9fOPsFzzz1XcufOnSW/+OKLkumfcSzQ3eJx4lijj8Z+QY7ViLRfye5W+ox07NiRyO5leqXcm5xeMJ07upEjRoyQnDwfdtlll/jqq69yPqfUqlUrw/M6CedlzrNcK0BvmJ3crVu3lswOa87bnKPmz58vmZ4xO1KLc1jpIq9YsULyHXfcIZlzyMMPPyx54sSJqb+RhF2eu+22m2QeI85xvD7zWvTNN9+UypzSsGHDTNKRZBfyxo0bJXNO+P777yVXq1ZNcps2bSRPmDBBMud23ofwPon985dccolkzlFJ3/Nf0K2nQ8o1DnS4uWaCXdAcr7zvYe8rvXuuQ2G3czK/99578ccffxQ7p/gTVmOMMcYYk9f4htUYY4wxxuQ1vmE1xhhjjDF5zWb1sFasWFEcMvZlsd+SfWLsnps2bZrk/v37S6Y7RZ+Unh59Tjqt7Nqjx0EvIyLtwFWuXFkyHTX6MTfccINkdsvSeaNTTL+GHhL3Jaen27x58+zX69ati9KgUaNGsu83O+K6d+8umZ2hPKb8efo13POaPak9evSQTAeVXiKdWjpFdJgi0h2G3H/6iSeekExXeVM+WrKDsrjfx3Np9OjRkumDs9OxOH+7NPjnn3/izz//zGZ24LJbee3atZIbNmwomeOfPansAKabSPeRe2xzz/nGjRtLpi966aWXBrn11lsl8xzmnu104uiDcW/zTc2zm+pjZscpu3GTx7i05pTatWuLv88uWnqAHTp0kMw+XDqqfB3sjeS1rmLFipLpSXK9Btc20L2mox6R9l7ZpUmPNtk9GpHuKW/ZsqXkd955R/J3330nmdea4cOHS+b6CvaR8nrNPtJcUVhYKNfVXr16yePsKKXfzPuKpGMfkT6/Vq9eLZnvEzu2Bw8eLJlzBH1QrgHp0qVLEPY/08Olj8yucs5zvFfi9Wb27NmSufaA3j39aHb6JscOx2ESf8JqjDHGGGPyGt+wGmOMMcaYvMY3rMYYY4wxJq/ZrB7WTp06ZZJuwvjx4+Vx+mb777+/5A8++EAy3Y/CwkLJdLfY+0Zvgy4i/TJ6HewrvPfee4PQRaSnw8xuV7oa9MM25WL169dPMvvWPvroI8nffvut5ORe6DfddFPMnTs3552JW265ZSbpkHXs2FEeZycpuyybNGki+emnn5bMvZWXLFkimS7ywIEDJY8aNUoy96fnOH7llVckc9xGpH0v9rLSQeX7yr9B5y65f3tExEUXXSSZ+1Gz/5deLp/P0KFDJX/88cel0pnYunXrTLJDc968efI4O3LpBnIPa/pq9KHooPJ8Of/88yXz/KX79fnnn0um+8WO1Ii0187xSbeQ/ZfsouWe8xwL7JalI87ORM7bvXv3llymTJns13vvvXdMmTIl53NKYWFhJjmm2RtMB4+vgd4w5wheBzlHJF9zRMSzzz4r+bnnnpNMB50uNccpPcrifgf/Bq9fPDfYnTlz5kzJHFf06NnHS7ea13/O4+ym3WmnnUplTqlevXom6WzyveVaF/asHnnkkZLpvNIp5fWH6wXYEcx1LuzX3XHHHSXTy+e6l4iIggI9BWfNmlXi4+y8p7PNdSG8/nCs8RiwV/iwww6T3KhRI8nJ+5Rjjz02vvvuO/ewGmOMMcaY//fwDasxxhhjjMlrfMNqjDHGGGPyms3qYV25cmVMmjQpm9nPRw+CrhS9CvpndHbosNLForvFbsvHHntMcqVKlSRzP9sqVaoEocfD7lbu4c4+Mj4n7vFLL4jPmdCZYxcePd+kk8c9y3PFunXrZL90ulT0M9mrSvr06SOZva3sXaVHyF47vu/0Ftkx99JLL0neeeedU8+RXZj0oNiDx322uU86HVN279Hho3/NY8JeSo5jdg1+/PHHURosWLAgbrrppmxmP2yDBg0ks1uSr+Ott96SzE5E9s3Sm2ffZtmyOkVefvnlks844wzJ9FM59iLSfdV0trk3OY/B3LlzJbNvmo41nXH2Gm+99daSx40bJ/m3336TnPT058+fH6VBxYoVY4cddshm7sdOTz7ZAx2RXr/AtQc1atSQTKebcwavTfQW6U3SQ6TjnjwH/gWPO8ciz+mmTZtK5rnE18jrJ+dBer/c055zWv369SXzeltaNGjQQPqU2bn79ttvS2YPMt1gurg8p7/88kvJdFTpS3OdCu+b6KDvsssukovrPqaHy79Jp5TnA3uEudbmrLPOksx5mPMie8nZhc7+6+ScVlK3sz9hNcYYY4wxeY1vWI0xxhhjTF7jG1ZjjDHGGJPXbJbDum7dOnGW6Ar2799fcvXq1SVz/2Q6sPQ32X135plnSub+tkcddZRk7mdLr4K+J7swi/sdp59+uuRu3bpJZjcdvV36Z3SX6Jawk43ff9JJJ0mm/5LsjDv11FOjNGjUqJH4xvRX2K157LHHSh4wYIDkr776SjK7OumP8u9x32+603Sv2E94yimnSKbfE5F2Idmvy/5P7hPOnjp2w9L7pZPEfbrpFrJrk74auzafeeaZKA2aNm0aDz/8cDZzjBYVFUlmbyp7UjmW6NGxm5mOOvsF6TN3795dMv1Susf0BiMiRowYIZl+JDt26WvyGNGb5770nDN+/PFHycuXL5dMv4yOXnLs0KvMFRUrVpR5Y/To0fI4zz92Z7L3mMeQ8y7nbc7DSUc/In3to6dM73Hs2LGSOW9HpN9H7jnPPtDDDz9c8pQpUyRzDQM9dXZj8trHLmeei1yLwL7Q999/P0qDVatWyXxIF5fd6uw9pptL+Ps+/PBDyXRYed/Cc4ZjkR3AXL9ANzkiPZ54TaSvTOd00KBBkjl+OU9y7QznCF7vDjzwQMlci3PJJZdkv6bvncSfsBpjjDHGmLzGN6zGGGOMMSav8Q2rMcYYY4zJazZLQKpZs6bseUzvjR4Qu+bYE3fjjTdKpqtYu3ZtyR988IFkehDsM+Mevs2bN5fMLrHiuifr1KkjmT2L3N+Z/Zv0iNgJd/PNN0umn8ke1wMOOEAyOxy5h3by+9kdmCsWLFggLh/9GXrA3Af8nnvukfzTTz9JZh8v/S86MJUrV5bMY0gXmz2UHBeDBw8Owtd02WWXSaYfPXXqVMnDhg2TzO5a9jzSw6XndN5550mmN8VziR5jaTF9+vRo2bJlNvO9atOmjWTuiU1/7P7775fMPbPpFtM5pe/M/dGTvm1E2qXkHEMXMiLtg/F30L+kU83xftttt0lmBynnWTp1dMo5r3JtQPJ84D7ouaJixYqx7bbbZjPdZa4l4DFgXy/9TJ5P7OO97777JB900EGS2ZXJ/epnzpwpmR58cf3bdJnZDcv3lX3R7Kt+7733JP/++++S+V5yHk6epxHpHljOUVWrVo3/BrVq1ZJ7E84Z7CXm2Fm2bJlkrjvhOhXeZ0ybNk0y72P493gPwN7xE044QTLvQSLS7x2dUvaoNmzYUDKd1BdffFEyu8f5GujM0rem78xrfvIYsts6iT9hNcYYY4wxeY1vWI0xxhhjTF7jG1ZjjDHGGJPXFHDP4xK/uaBgSUT8sslvNPlK00wmU2fT3/b/DY+T/1/gsWL+N3icmP8tHivmf8O/HSebdcNqjDHGGGNMaWMlwBhjjDHG5DW+YTXGGGOMMXmNb1iNMcYYY0xe4xtWY4wxxhiT1/iG1RhjjDHG5DW+YTXGGGOMMXmNb1iNMcYYY0xe4xtWY4wxxhiT1/iG1RhjjDHG5DW+YTXGGGOMMXmNb1iNMcYYY0xe4xtWY4wxxhiT1/iG1RhjjDHG5DW+YTXGGGOMMXmNb1iNMcYYY0xe4xtWY4wxxhiT1/iG1RhjjDHG5DW+YTXGGGOMMXmNb1iNMcYYY0xe4xtWY4wxxhiT1/iG1RhjjDHG5DVlN+ebK1eunKlWrdr//HBZ/fEqVapIXrlypeR//vlHcq1atSQvXrxY8oYNGyTXq1evxO9v3Lix5EWLFkmuWbOm5FWrVkkuV65ckEwmI7ly5cqSi4qKSvwbCxculFy7dm3Jv/32W4m/v0yZMpK33HJLyUuXLpW8bt06yXXr1s1+vXjx4igqKiqIHFOhQoVM1apVs7l+/fry+E8//SS5UaNGkvk+8BgSjgt+f/IYRKTfM/58QYEeovXr10vmMY5Ij5ONGzeW+Df5nP74448Sv5/jgn9vxYoVkrfaaivJ5cuXTz3nJDyXZ8yYsTSTydQp8Yf+AxQWFmaS5+0PP/wgj9eoUUMy55TCwkLJFStWlPz3339LXrt2rWTOMVtssUWJeVNjjecr58TinuOff/5ZYubf5FjZZpttSvx5/j3Oi9WrV5dcp46+7Zxj/vrrr+zXq1atinXr1uV8TqlatWom+V6vXr1aHudxX758uWS+Jo4rjgvOAZyT+B7w/OHjPB95fifny38xb948yc2bN099T5I1a9ZIrlSpkuRZs2ZJ5mtMXtsj0uPm999/L/FxzjENGjSQPH369FKZUypXrpxJjmm+rl9//VVys2bNJHPu5vvAc5r3NRyLnJs5Z/F94M8vWLBAMq8FEelzmPdG/Bu8D+F727JlS8mcl7fddlvJPKa8RrZu3Vryzz//LDl5jDZu3BgbN24sdk7ZrBvWatWqxUknnZTNPPA777yz5C+//FJycqKLiDjuuOMkDx06VPKyZcsk9+/fX/IDDzwg+Y477pB81113Se7Zs6fkb775RjJPsIj0ge/UqZPkd955R3KvXr0k33TTTZJPPfVUyZdeeqlkHkPeoO67776Sn3rqKclz5syRfP7552e/vuyyy6I0qFq1ahx00EHZfMUVV8jjxxxzjGQeo4YNG0q+5ZZbJHOC4LgYPHiw5PPOO08y37OLL75YcoUKFSTzfyrmz58fhBc8XlDHjx9f4nMaPXq05HfffVdyu3btJPMYfPTRR5JvuOEGyU2bNpXMG15OYG3btv0lSoHGjRvL+5EcNxERhx12mOTJkydLPuGEEyRvt912kt9//33JPD84sfMiz/PvwgsvlDxo0CDJvDjutttuQTjZv/TSS5JfeOEFyZdcconkUaNGSX7++edL/H0tWrSQfO+990o++OCDJZ9zzjmSH3vsMcmvvfZa9usPP/wwSoPCwsK46qqrsnn69OnyOG/43n77bclnnXWW5MMPP1wyL8i8GeP/YPI94ONjxoyRzGvThAkTJO++++5BknN3RMTIkSMl8yZk5syZktu3by957733lsyb7K5du0pu06aN5Ntvv10yb0L4wcOAAQMkt2zZslTmlOrVq8cpp5ySzQcccIA8zvPpmWeekcwbTJ7zvEbzf444Z3z66aeSOWfxf4b4P1PXX3+95A4dOgTp1q2b5AcffFAy/2cieXwi0tfM5557TnL37t0ljx07VvJFF10kmddMXp969+4t+ZNPPsl+zeOZxEqAMcYYY4zJazbrE9bVq1fHtGnTsvnQQw+Vx/l/CnPnzpXM/2vlpz477rijZP5zHT8R5adE/D+n4cOHS+7Xr59k/l9v8tPjf8FPeF588UXJ/CfHM888UzI/UeWnzvy/5g8++EDynnvuKblv376Sjz32WMn8v7fkJ3PF/bNTLihTpoz8XyL/74r/zMr/o+Qn4fwkIPkJT0TEjz/+KHmPPfaQzHH4xRdf/Jtn/n/hp+r8tJSfokVE3HnnnZL5aQPHNv+vnp+wcBxQNenRo4dkftLHTxapYVBz4DEsLWbNmhV77bVXNvMc5jnPf1rlp4OPP/64ZP6LB89xftLFf0E55JBDJPPTjj59+kjmvybwU7CItIrETwP5aTv/FYafCiePX0T6POcnXfz0j//k+e2330rmp5HJ1zR16tQoDRYvXhz33XdfNvPTR/4LAd/nffbZRzI/UTrjjDMkU1fjJ//8RPXAAw+UzE/V+Cn1uHHjJFP7iIi4/PLLJQ8ZMkTyPffcI5lzEH9+++23l9ykSRPJ1CZ4reK8l/zEOyI9h/DcKi02bNggGgv/VYafgPI48V8Y+C/J1JKS4zIiYpdddpE8bNgwyfyXY36iyPuirbfeWnJxitwTTzwhuVWrVpJ5TeW/wvDTdP6TPc8XznP8VJqf1vM1XnnllZKTcw7/VhJ/wmqMMcYYY/Ia37AaY4wxxpi8xjesxhhjjDEmr9ksh7VChQriV7BGg97ETjvtJJkeD/001m5wNR6dVVY50MtLrjyLSHt8dCm5ejci7bDyOZ122mmSv//+e8kDBw6UfO2110p+9dVXJdNleuSRRyRztThdEzqs1113XfbrTdVD/adYv369/K3vvvtOHuf7OHv2bMl0nzguPv74Y8kcd3QCjz76aMlcMUm/h6th6S3y+USkPSa+r1z1T/eS8DXSK2TjA18jvWBWH9GTYjMCV57nikqVKkXbtm2zuXPnzvL4iBEjJB9xxBGS6erSGX344Ycl33333ZLZEPHee+9JnjhxomTOOW+++abkG2+8UXJxzSOseaOTSjeQK3rptHLOeeuttyTzGNAT5lihG8mKm+K83FxTqVIlcTDZMMMaKTpy9DM5L0+aNEkyPWA6qpz3eW3hPM55mg4rHcCI9GtiMwLnKTbI8FrFOeXkk0+WfM0110hmVREbIXbYYQfJbHfh3y8t6tevL44l7zvo8vLY83Hep9x2222SOWfxGn3zzTdL5vvI841uMa+X9EMjO1fOGQAAIABJREFUIl5++WXJ9I35N/jecc0FxwrHN71gzqusYOPPc2x//fXX2a/p+CfxJ6zGGGOMMSav8Q2rMcYYY4zJa3zDaowxxhhj8prNclg3btwoO/p89dVX8ji3/GOPKrsn2aXHXWa4mw+7wtjtxa48ekb77befZHqEdHoi0luWff7555LZYUi/ix4tO0TZ8cYuW75GulH0hNkhl+x8o3OcK1auXClOGHvu6A7Tp+Ex4XvAHtUpU6ZIpmNEJ4YdieyJpc9GP5TjMCLi9NNPl0zvlZ4UxzbHBZ0k9u9yrPM1sdeVPhkdQO4WVlpUqVIldt1112ymr0WfjF4dM7cl5fnA7+d7+8svuhkPxxZ3D2L/LX8+2Vv9L+gzs0uZO8HRVaSrT6+XvY8cW/x5OrXnnnuuZLr9Xbp0yX7NbSxzRYUKFcTDptdLZ45OOXfT4049PD84x/Dawd3u6Khy5yE6q1dffXWJzyci7R3S56arzF3VOE9xPQTfd3r3nCP4GtlBTA+SfaR0SXPFwoULxRvl8+bczO50zoX0+7nT1CuvvCKZ5wv74Llmg2OV14qzzz5bMo9zxKa3dOc1ld2u3E2S/e+8hnOepT9Nr57XeM67SY+/OJ/7X/gTVmOMMcYYk9f4htUYY4wxxuQ1vmE1xhhjjDF5TQGdsZKoXr16Juk2XHDBBfI4uyLpsIwaNUoyXSm6g+yK5F7K7H0766yzJFepUkXy0KFDJdPT+Oyzz4Kwh5V+BX1M9ov9/fffkrmP8HbbbSeZDiv/HjsY6b+wl/KGG27Ifn3ooYfG1KlTc16iWL169cwee+yRzcmezYh0Dx19Gjp2fB+Tvzsi3TPJY3bHHXdIpvPHcUTPkV4yO+4i0vtLJz2/iLSXyOfInsg5c+ZIpgvNcUdPl+7mQw89JJn9o3SSjjrqqMmZTEYF6RxQpUqVTNK143GpWLGiZPrJ9NM4R7A7kntuz5w5U3LZsqr102/j2GUf7urVqyUnz79/B3seOQ927NhRMrtfmTm+6V9yDqJPxrHIeTw5diZOnBh//fVXzueUevXqZXr37p3NRx11lDzO4873iceU53SzZs0kn3feeZLZw0pnkM4e+3ePP/54yU899ZRk9sZGpM+FunXrSqYHyw5gHpN169ZJ5mteunSpZLrP7OMlCxYsKPH57bjjjqUyp7Rt2zaTXAOw9957y+NcS0P394UXXpDcvXt3yXRSeZw4948ePbrEn6cvzevfo48+KpkObUR6PRGd6aKiIsk8P+hk33vvvZK5HojfT4+Xc1bt2rUlc21Csoe1pDnFn7AaY4wxxpi8xjesxhhjjDEmr/ENqzHGGGOMyWs2q4e1QoUK4pUm+xMj0j2s3If7xx9/lMw9do888kjJ9NXY+cf9ntlvxj3j6RnRXeQevhERr7/+uuQTTjhBMn2XTfVGssfxwQcflExfjO4J/c2tttpKMl2pZOccHaNcUbFiRXFzp06dKo/zuPPxffbZRzJ9sVtvvVUyfTR2xtH75TGnn0Nvmb4oO1Ij0n423WL299JjouvM/d47deokmZ2KPGZ0Lx944AHJPCbMpQXHCt9rennsUPzrr78kb6qzlL2o9M2Y6ajS72QvcuPGjSUXN1bolPI5sY/6gAMOkLzllltK5rxFvzPZXxqRdu6ef/55yXfeeadkzrvJXkqeG7liiy22EJ/5pZdeksfZIcpeVa6XoFfIeZqvmf4ne105LjgnEZ7v77zzTup76DbzXGAfJ3tVucaE89qm3EvOGVwj0q9fP8mcg/bff//4b7B+/Xrp9fzuu+/kcfaach0JM6+p7D6vUaOGZHaast+da2U4Vnh93JSnHxHx3nvvST7jjDMks7eYbn/yeEWk1zTwPuf++++X3KpVK8lcL8R5NOmjR2gXOv3aJP6E1RhjjDHG5DW+YTXGGGOMMXmNb1iNMcYYY0xes1kOa/369WPAgAHZTFeDLiJdQvpp3P+cPa50sdgx2qJFC8nLly+XPHnyZMnskaN3wf13I9J7vtesWVPymjVrJNMvo1fL76f3Q+g60Wfhc2ZfZ3I/6I8//rjEv/Wfolq1auJD/fDDD/I4nyM72+glzpgxQzLdZXrBY8aMkcxeSvYZ0rfZVG9rcd7evHnzJNNl7Nu3r2R6g/Qaecw2bNggma/51FNPlUxn6e2335bM10AfvbRo3LixuOr05ugasoOQvap0EdnRS5d4c3tf6Ury/JsyZYpkOukRaWe6devWkjl+DzroIMl0D+mms1ubcxjdRnrB7ACmD518T3hu5opGjRrJmge6ynwNfN+uuuoqyb///rvka6+9VjKvPVybwGPOvl0+znH61ltvSeZ7HJHuuuT1lOsZOAdxXHFO2H333SXTi2e357HHHiu5Vq1aktlBPnbsWMns284Vc+bMEe+Uf5e9q0l/MiIi2QsdEXHIIYdI5hzAXm7ep3DO4Vx+1113Seb5y7HLtTgREePHj5fMeeeee+6RzHm0Z8+ekulHsxOfcxSPKV8TnW92PyfXgHAcJfEnrMYYY4wxJq/xDasxxhhjjMlrfMNqjDHGGGPyms1yWFetWiWOFr2bwsJCyXRK2VVZrlw5yfQg6AGddNJJkn/55RfJ9DboMdEr7Nq1q+TiHBs+B/adsSuPfhrdDe7RS7fw6aeflsx9j9mHxn6zpLMaoZ4R90nPFWXKlJFuus6dO8vjX375pWR2vtFh4Z7UgwYNkrx27VrJ7BukR8xePu4nT7eLHajnnHNOkCpVqkhmR+HgwYMls7eRPZF0lzmO6GPTuXvxxRclc+9yvgfsrSwtfvjhB3HEeE6yB5n+JTtG2XvKc5qvkx2K/HmOTXrgnOPon9EFi/i/82gSnsOcJ/k490bnPMpOT85Z9HTpZ9LJ4/mUPCb0Y3PFwoULpX+ZXZp05nj+8VpAL53eIfc+p6vMawmvPewAp89JT57+aER6XuNYateunWR2AtNh7datm+Tq1atLnjBhgmS6k5MmTZK84447SqYLSv+8tGjcuLE4zuyDp++/ZMkSyewB5ZoL9rLyms2+eT5O35nnK9c78Djy+hcR0b9/f8mc5ziv0sMdOXKk5ORapYi01857MTrgHN98DTxGBx98cPZrrgNI4k9YjTHGGGNMXuMbVmOMMcYYk9f4htUYY4wxxuQ1myU1rl27VtwD9onRg6ALRc+Czstvv/0mee7cuZJXrlwpmW4V+wK5JzbdCHpPhx56aBD6J+wApe/IfYO5b3337t0lH3jggZK5j/hPP/0kmb4LPSO6Ism91OnO5YqCggLxZemAct96eoH0Oeng0cXlOGLX7axZsySzc5QdqPTJ2OXJPbcj0p4hu/vY90n3efjw4ZIPP/xwyRwH7Cvda6+9JD/33HOSuT92r169JHPvaXpWuaJGjRpx5JFHZjPPN7qGdBMvuugiyRzjdIHp2bOPlm4x/esrrrhCMt1hen3cNzwivVc4jzU7eOlMc5979l+/8cYbkrkHPB05HmPuM07PPul8L1q0KEoDrp/gPMz3jecTxw17XNkJzs7wd999VzKdWDp7/P5hw4ZJvvLKKyUX1wHOOWTdunWSX3rpJcmcA3r06CGZ10eud+A44nqLPn36SGYPLMc6HdvSgtcfPq+lS5dK5roTrqHYaqutJO+5556Seb6w45euMNcz1K9fXzK72dmZyrEaEbFs2TLJvAbS0+X4XLx4seSddtpJMo8RO/M5L7Irlj3J7LMfN25c9mteb5P4E1ZjjDHGGJPX+IbVGGOMMcbkNb5hNcYYY4wxec1mOax///23dLXRo6P306pVK8kVKlSQTDeRrhU9IXZdvvnmm5LZr0bnlV4f90amMxuhbkVEen9n9jDS9WBXLDtF6dEOHDhQcrKfLCLd90c3i8fk0UcfzX7NXr9csXDhQumKo3NHl5jOKHvzeIwKCgok01XmuHvyyScl01ml90jHj12eHGfF/Tc+R3q6dF75Gug50nPiMaGDytdEB5YeFv240mLNmjWyH31RUZE8znNyyy23lNy7d2/JfF3777+/ZLpb7Fz8+uuvJXOs0AMcOnSoZO6vXhwc75z32P1M35n+JT3bZs2aSeY+9nTE6MTRh2NnY9Kzp2eZKzKZjMwj7E6mj0lvfdq0aZLfeustyX/99Zdkeo9cj3HLLbdI5jzPfmA6fF988YVknt8REXfffbfkxx9/XDKvt1zf0L59e8lfffWVZLqW7BBmVyavXVyLwA7jCy+8MP4brFmzRhxK+s077LCDZI4N+pw8TnRW6Q5zLPFa0LRpU8lcx8I55LXXXpPM8z0ivXaFY4c9qTxvr7nmGslce8NjwLHUsWPHEp8PvVyuqbj88suzX3MtUxJ/wmqMMcYYY/Ia37AaY4wxxpi8xjesxhhjjDEmrymgt1ISrVu3ziSdyFdeeUUep1fHx9l5eN5550m+7777JNPdoJfH/W3pGSX3no5I75/OHrnnn38+CH0yeq/sKEz2nkakHVg6qLvttptk+m30Y+jw0fGjb5l0bsePHx9//PGHCjU5oG3btplkRyD9Su7b/dhjj0lOOo0R6U45urj0wYYMGSKZzh4dprp160pu06aN5OSYj0h370akHR+6zeyKpbPKTkW+Jo6r6667TnKDBg0k03vk3ucvvPCCZDqszz///ORMJqNlfDmgYcOGmeT4OOaYY+RxulA8x9nJyz5LevPsVPznn38k8zhw/3ae/3SxONaLc4NvvvlmydwDnuObvhlfE50v7nXOXmN23bZs2VIyO0LpX99zzz3Zr0888cSYMWNGzueUypUrZ7bddtts5jGiu09vnu8bndNnn31W8s8//yyZfddcW8A5jfP4U089JZndnMW5wKNGjZLMuZ8uJLsv2QnM6zM7v+mD0wumu0lXmm4lO4onTpxYKnNKq1atMg899FA2J3ueI9LzP+87eBzYz06/k+sN2BHMuZ7uL2HfLd93rh2KSJ/jN910k2S6/C1atJDMayyPCfuueV/D+xSuBejWrVuJz7dr167Zr2fNmhWrVq0qdk7xJ6zGGGOMMSav8Q2rMcYYY4zJa3zDaowxxhhj8prN6mGdP3++9HmNHDlSHmcnIR2bESNGSKYHxP2f6SHRDaEfxt447m9LV4w9dsX1lNJ34R6622+/vWTuXX766adLpo/Cfe3pNnFf46TrEZE+RuwcTbqN5cqVi9Jg+fLl4vby79L7o9/SqVMnyf3795d8yimnSOYx4vvOvZjpKrOjlL4Oe/lef/31IAMGDJDMcbApT5ddl+zX3bBhg2T2gdJLZMdxsucuIu2CsjOxOJ87F1SqVEnOIXp4P/zwg2R2JrLzkB2i9OB79uwpmf4Zz79ff/1VMh10evUXX3yxZO7JHZEeK5yH2IlIF58+Jh3WDz74QDLPJ3aMcg95zqv008qXL5/9mh2TuaJNmzbS/cjXvPfee0tu3ry5ZDpzPCY85uwDpiNLR3D8+PGS2Us5ZswYyXSn2U0dkX4f6D6yr5p/k/Ne9+7dJXPe5WvivMjXwDUpnIcPO+ywEp9vrli2bFk888wz2Txp0iR5nHMjO0XpdHOscO7mWhh2ntKH5lzNscD3gfcMnGMi0j7y0UcfLfmNN96QzHmtZs2aknkvxGt4jRo1JH/++eeSOTbYBc2O/OT1ifOj/N5/+4gxxhhjjDF5gG9YjTHGGGNMXuMbVmOMMcYYk9dslsO65ZZbxr777pvN7PtjXxk9Pvqg9C2TbtS//l4S+p7cN3zy5MmS2a9Jt5EO7R133BHk3nvvlUx/5IYbbpBMV4OuyG233SaZPY88ZvSO6DqxT5A5eQzou+aKFStWSHdjcW5wkqVLl0pmJyl9TnbI0b9kR1yvXr0kJ7tpIyIuuOACyYWFhZLHjh0rmb5QRMSNN94omZ2E3Aec/jf3k7///vsls++XfbvsWOS5eeKJJ0o+4YQTJJ922mnx3+DPP/+UTli6tRUrVpRcu3ZtyXSzOBbYUzx48GDJPL/Zh8kuSXr0HFv0q4tzPNlzSt+Le7Jfdtllkk8++WTJPOfp1bLb9vbbb5d80kknSaYXzN7H5Njm384Vy5cvF3ec46J+/fqSuXZgm222kcxe1FNPPVVyssczIu1jcj0E5326zvSQDz/8cMn0PyPSazL+/PNPyewc5nvx3HPPSaYHz2sZr698zrye8txiZ/k777wT/w14n8L+WHY39+3bVzJfx6xZsyTz+nL99ddL5lodrl/gXM61P1dffbVkdpqyo7u459SqVSvJ7AWuVauWZI7nHj16SOacxcc53nkNveWWWyTzXiw5L3P+SuJPWI0xxhhjTF7jG1ZjjDHGGJPX+IbVGGOMMcbkNZvlsK5du1a8GvaJsX9st912k9y6dWvJ7D9jhyL3QmavK/vR2D3GHjvu596kSRPJxXXhcc/21atXS6aTSr+MrsY+++wjmR4Rjxn3Pl+0aJFk9jwuWbJEctLr5X7BuSKTyYi3ys5Eer90p9gTSeeOHW78+fPPP18yOxTpv919992S6YZx73fu9RyR9p85luiTsf+TDh73s6ZjSi+RvZGbcvp4LtHh47mRKxo0aCDzxpAhQ+Rx+s90pejqco6ZN2+eZPYN0t1iJyk9vvXr10tmLyw9/OL2DWdnJ/ehHzZsmOR27dpJ5ntJF59rBegFs1+TXbf04ejlJv1Aeoy5omrVqtK1Ss+Q3mAmk5E8c+ZMyXTIec5znm/WrJlkvu90mekQ0hn89NNPY1PQx+Y5zuvZoEGDJNM95jzIzHHEccpz4eyzz5ZMr57jrrSoWLGiHH92UPMay/UFyQ7XiHQnNc8H9rryPoROZnJ9R0R6DQXPx2SXekR6rU5ExCuvvCKZY4eeLdfK8DXxmsc5geOXHbtz5syRzLU4XKORdMTZnZ3En7AaY4wxxpi8xjesxhhjjDEmr/ENqzHGGGOMyWsK6PqURIMGDTJJ14GezznnnCOZfWOffPKJZPqh9Me4vzN9NXqAdB/YmUiflA5QcZ2C9Hq4jzz9MuZvvvlGMp08+iz0K+lqtWnTRjJ7KM844wzJyX6/xx9/PBYuXJjzzb9r1qyZSXY17r777vI4e+T4mul/0ZU+8MADJdP54+9bvny5ZHrG7FXlPsmjR4+WXFxPHPfx3n///SXTG2TP47vvviuZezcvXLhQ8j333COZx4z+Nl8T/XG62F26dJmcyWRUkM4BjRo1yiT3eed7QZ+LfbfscuZ7xZ+n882xwK7K33//XfKdd94pmb51z549JdM7jEj3S7Ozl4425yl6sZvyldm//OWXX0rm+cNeWDpzSee7Z8+eMX369JzPKWXLls0knUi6yvQp2bPKbtkXX3xRMp07+pp0ALk/PcfVEUccIZm+KK9tPOYR6XOaveNVqlSRzHE1cOBAyWeeeaZkupvDhw+XzL5qdjezL5T3A+xt7dWrV6nMKVWrVs0kPW52LbNT9LvvvpPM437kkUdK5lzJ6wvfS/rNPI7sCObzoVP7/fffB0nOoRHp84P3Xg8//LDkDh06SKarz+sJ195wDmL3LH3onXfeWXLyPmXSpElRVFRU7JziT1iNMcYYY0xe4xtWY4wxxhiT1/iG1RhjjDHG5DW+YTXGGGOMMXnNZm0cQCh5v/DCC5JnzJghmdL49OnTJbO8mWXQlLqrV68umWW3XAQ2ZswYyVyIQjE4IuKDDz6QTPmYCya4wIELjoYOHSqZReNJ+TgiLTNzkUidOnUks6C6efPm2a+5ICVXlClTRgrauSCBpcHcAIILXbiBBBee8fdfdNFFkjluGjVqJPmQQw6RzHHDBRscxxHpImWOfY5lZpZPc5EVN5woLCyUzAUVXJzEMnkuSCyujLo0KCgokPOex5rvPRc0sHSfGzg0btxY8l133SWZ58sjjzwimYusuICDCzU5llq2bBnk559/lswFPNzMgH+ThexcBMgC+YMOOkgyF6rxfOMcw5L95KIULnDMFfXr15cNQTjvcvEb54BNbTDBRY/HHXecZL5nXFRy1FFHSW7fvr1kXpv4OBe9RKTHMhffcLOPBx98UPItt9wimWORi3u4YJdl8hxHXBzIzAXDpUVBQYEs3uQcwbmU9zFckMSF0wcccIBkXk+4MJvn1+DBgyVzAS4XfXFDJb7PEemFzHzO3AiG1xveGy1evFjyscceK7lv376SueCdC6U5FrmxTnLjg+QGIcSfsBpjjDHGmLzGN6zGGGOMMSav8Q2rMcYYY4zJazbLYa1Vq1b06dMnm+l3zZs3TzLdwblz50quXbu2ZLoel112meQBAwZIPvrooyXTgd1jjz0k01fj99PVikiXI9Nxo2NKd/C3336T/Oyzz0pmoXTTpk0lszR8woQJks8777wSn++KFSuyX9MPzBVly5aVY023ie/bt99+K5mOaosWLSTT86Wzx40FWIj90ksvSR4yZIjkDz/8UDKdVY6riHTRP526adOmSd53330lV65cWfLMmTMlJ/29iHR5PIvPGzRoIJnHlOduca5labBkyRJxxugSLlmyRPKhhx4qmYXxdAm5gQPHIn01bkbC8nWeb9yMhM4tn19EesMUjiduMkEXkL+TXv1hhx0mmedD165dJXNeoMvPkvuk1//3339HaVCrVi3x6Ojcbb/99pL5+OWXXy6Z5x/XAnCO4cYBbdu2lfzqq69K5oY89EM3VcQekfahuR6C146CAu1a//XXXyX/888/krnxAMcBfz89fbqWdJ85junx5ormzZvLGgH6yGXL6m3PAw88IJkbJtBxHTVqlGR68XSLd9llF8m8B+AmMLwH4Njg+xCRnv+ff/55yVw3wvsSeu18zocffrhkzoscmzfccINkblLBtTtJP5vObhJ/wmqMMcYYY/Ia37AaY4wxxpi8xjesxhhjjDEmr9ksh3XlypXi5rHrji4HvSJ2ItLzmT17tj45uCb33XdfiT8/duzYf/fUIyJi3LhxkukZ0meLSHs5fE70YPk4/RP2b9IrWrt2rWS6hfTZunTpIvmII46QvOuuu2a/njhxYpQGGzduFFeQThB9F74GOqvdunWTzHHHHko6snSp+B4kj1FERN26dSWzV48dqxFpP/upp56SfPrpp0vmuKJHRX+NvXh8jXzO9Nk4TuklsmeSzz9XNGrUSBxkenU8rpxj2DfIHuQTTzxRMs9H/n76zfSp6MGzD5R9g+xHjEjPi3Tz2WlIX4xzRLNmzSRzXuzYsaNkdkv37NmzxLzXXntJTvZzsi84VyxZsiQeffTRbP7qq6/kca4tYA8kPUZ2kNJhpfPNHkuOO6416N69u2SOW55/nAMj0vPmfvvtJ5m+N7uZ+Tj7Q3k95Dih98iedXY581z7b/Wwzp8/X9Zy0OHm+cM1EHS4d955Z8mcK7nGoV27dpI5VunJb9iwQTLXH3z99deSOddHpLvIOVbouHJNBN1+dtfSZ+b3s6eV11yu8aADXqFChezXvHYl8SesxhhjjDEmr/ENqzHGGGOMyWt8w2qMMcYYY/KaAvpOJdGkSZNMss+OXV30N+lNLF26VPITTzwhmR4RvTv6mXRkZs2aJZmeEl1HuiLFOTfco529jvSMfvzxxxJ/59tvvy2ZfZjcL3rq1KmSub80+wLpGSWP+ZdffhlFRUX/XhD5D9GwYcPMaaedls30Id944w3J7CBlR+K2224rmf28dKkuvPBCydWrV5fM/sCioqISM7s2uQ9zRLpjkD4YOxHZa7fNNttI5thn9x8doD///FMyHbsLLrhAMnth6cz26tVrciaTUTEqB9SoUSOTdCSXL18uj9MfZr8g9/Gmx8d9u+mTcSyyE5X7qffr108ynVf6p/Q/I9IOKf1l/o633npLcv/+/SXTI6Xzxn7NNWvWlPg4YWdi8ud//PHHWLVqVc7nlKZNm2aSXiLXG9AtprdIb3j06NGSOYdssYV+lsNrGddj8D2iV/zLL79I5pzH8zli0/uzswOc46hixYqSOYewz7dSpUqSuX6C59pVV10luX379pLZxdmkSZNSmVOqVKmS2WGHHbKZ/mWySz4ifZ/A94Y/z+sTxwLvK3r16lXi93NNBruNmzdvLpld1BER9erVk7xo0SLJl1xyiWQ63ex2pVfPsXfWWWdJputfs2ZNyZzHe/ToITnpfO+xxx4xefLkYucUf8JqjDHGGGPyGt+wGmOMMcaYvMY3rMYYY4wxJq/ZrB7WDRs2SB8kPTl6Eccff7xkOjJ0NegNsY+TngR9NfqjdBXZXUmnh/1pEem+Me4pTV+SPhldKvpr7MLjPsR0q9jfSe+Xbklyb3N6I7lizZo18cMPP2Qzjxn7/ehS0SOkH8Z9j7lPN48hvV7u904/jT193Pv54IMPDsI+TnpH7L2j50SH6MUXX5TMflH2utLpGzJkiGSOS/Y+sj+0tKhSpYq83/Qv6WzTBbzxxhsl072i78nu5XPPPVcy35cjjzxSMr08evn0+vg+RaRdfDqi9NyvvPJKyfTV2BtJX5ku4++//y6Z7z29X3q+yX3I6fDnisWLF8ue6+ymZe/k9OnTJXOOGDNmjOQVK1ZIXrJkiWSe8+edd55k9kzSmaX/+fnnn0t++eWXg3CNB/spOW7o8X755ZeSeT3kOOFrYhcn32u6nOwTrVatWvy3SM6XnFv5XvIaTseUcymvH1zTwNfNrnU+H45FXm8453F+iEiveeA1l73AnDPoS/P6cNJJJ0nmuhTeC3JtDdeB8N4tOdZ4X5jEn7AaY4wxxpi8xjesxhhjjDEmr/ENqzHGGGOMyWs2y2FdtWqVOGb0dOiEsl+M7gW7H2+66SbJ9DfpsJJkR2xExPDhwyVzf9vZs2dLnjdvXup3PvbYY5LZl8dOUHpEfM10YgndqQ8//FAyXZKuXbuW+PuSHjFdzVxRWFgo+0rTK6RTeu2110pmLyTdaPoz3K993bp1kun98hiz25PudevWrSWzpzUiYp999pHMTsIJEyYpSrBlAAAgAElEQVRIpvPG3kZ6tPx+9rq+8847kpOeYUTaraR3RdeSbmeu2Lhxo/Qj0wllbyqPI3tQ6XLRT6PLRZ+TPbB0rXg+8riOHTtW8t133x2E3azXXXedZHroHO/JjuOItB/NferZBU3fmXPSiBEjJF988cWSe/funf2ana65YosttpB+zIkTJ8rjDz30kGS+Ro4Dvm9HHXWUZPZGtmnTRnKTJk0k00v+6KOPJPMYck6klx+Rfh94jiffh4i068w1HZuak/gc6W7yes1e82T3aXG/v7TIZDKxfv36bOY1PPlYRMS7774rmf5zct1ORNrV5ZzAscn3mh48O045lnr27CmZjmtE+t6G7n2FChUkDxs2TDI79DlPcjzzXmnkyJGS6U/zGs159/+w955hVlRrt/ZDlJxzFBGQpCgYEDFnRVRUFEEwIJhF3QYwB0TdYFbALaiYUFCMGDFHgoAYAAMSJUjOaX0/zvX2W+Outtlc31691znXuH/1cLXda1XNmlVU3zVm0gvmuS+J77AaY4wxxpicxhesxhhjjDEmp/EFqzHGGGOMyWl2ymGtWLGiuA30huiQ1qlTRzI9iLZt20rm2sj0hna0HjvdEPbO0bNgLyx7YCMiHn74Yclc853OG3sc6WOy65WeLl1GuiedOnWSvN9++0mmH5fslKPTlC2KFCki25IdbHR88vv/k7C3jv7l3XffLZn9gXPmzJHcp08fyey1pO/GHkuOy4iIdu10iWx28XGs0qulf8ZxxPWt6W7us88+kg844ADJmUxGMj3d4cOHx3+Dv/76SxwuenDsz6QPxl5jdknSxaWvxs7Rhg0bSuZY4X7gHEivj/shIu250h9LdhhHpN3BZB9pRPozsOe4R48ektm52Lx5c8nsFOazCMle5B059P8pypUrJ72f3Ib0/jjH8NkDdnyzp5jzLp1V/v/s92W/NjtL6e3T8csPzv08t9BL5JzEeXHcuHGSL774YsmVKlWSzLHN9epr1Kghmc5sYdGgQYN47LHH8vJvv/0mr7PTms+dsBf8xRdflPzTTz9J5vHx0EMPSaYjPnPmTMnDhg2TzOcP9t57b8n5dR/Tlee1FD1c9qTynMp5i8+/JJ9RiUg74N9//71kPlvAeTR5PuQ4TuI7rMYYY4wxJqfxBasxxhhjjMlpfMFqjDHGGGNymp12WE888cS8TI+Ifid75OiUcs1YrnVPr45rzNevX18y+7vYRUZPsGzZspLp6EREbNiwQTJ9FHq67EDkWuhcU5p+JLvw2APJDrbx48dLTu6fiIj+/fvnfc3+uWyxaNEi8eoqV64sr9MzZJdtt27dJO+7776S6Q127NhRMrsh2VfItaTp47DzlP3A7du3D8L9QCfunnvukUxXmd4S1zanT0bnjccWPSc6SPTB6Q0XFq1atYrPP/88Lx9zzDHyOl1B0rt3b8lffPGFZPbLsvOUfhuPkdNPP13y7bffLpmuF513+qkRaV+MPjM7RP/5z39K5nhmpyi9Un5mzkHsq+bYoAt55JFH5n1N3zZblC1bVjzt4447Tl6nFzhw4EDJ9Oi533lMcx7++uuvJXNc8tmEadOmSb7mmmskn3vuuZLZDR2RnofY/8k559VXX5X84IMPSmaPOfc7n0Fh1ya3OX1zepQlS5aM/wbsdj7iiCPkdR6zyfknIuLqq6+WzGcgODc3bdpUMq8B+IwEXWH6zzzn77///pLZCx6Rnqfo+vMcyi7xUaNGSe7evbtkPh/Evmx2Q/Mzc5uzpzW5DenbJvEdVmOMMcYYk9P4gtUYY4wxxuQ0vmA1xhhjjDE5zU45rAsXLoybb745L7Mjkd4cfU+6W3RO6bwefvjhkul60D2h88rORXZPst+Qa8pHpN0lvgeuuUtXcMyYMZJ//PFHyexx7dKli2R2xPH76THRO0r2svK9ZYty5cqJE7ajbkz2mnLtZ/oyr7/+umSOM/6+sWPHSn7ppZckszePbjOd2HvvvTcIvSS6jnSh6dHSTeb/z17IpJscseNuT35meors7SssNm/eLI4YHVD2VXJf0XeiC8ztROeV3jzdqt12200yxx77BNnhm5/Dyj7kihUrSqajzXkwue52fv8/O0h3NBY5NrhmPf3L8uXL531NTzJbFC1aVLY1XWV6tvQr2YM6ePBgydwnPFfw+Qdmeobc5uwA5/vheSYi7R2yY5vdsjNmzJC8YsUKyXT92aM6cuRIyfQS+X64zXjscb34wmKXXXaR45afg73D7Mjm8wbc19wPvI7g2OEcwWdr2JFNR52dqdyPEelrqXfffVcyu8yT/ewR6fNP8hiPiDjzzDMl85kHjn92o7PblvNo8jOdd9558Xf4DqsxxhhjjMlpfMFqjDHGGGNyGl+wGmOMMcaYnGanHNbSpUtL3xy9iR9++EEy3UR2e40ePVoy17imZ0Svgi4I3UL6Y3R26IdyPdyItGs0ZcoUyexR5Rq6XFOa6z/T16RbwjWs+fvo6LGL77/Rr7lx48aYNWtWXuZa5vR+K1SoIJm9rHRa6Kweeuihkuk5dujQQXK5cuUk0z9lJ90LL7wg+dZbbw1C74hjv2vXrpK5njV7Gekl0iNkz+SOHLratWtLZvdsy5YtJX/22WdRGMyfP1/6Hzl+6YOxu7loUf03N/00dphedtllkrmeOjtR77zzTsmcc7juNf1QjqWItKdL55pzBvunf/75Z8nsgua8xnmTv59e/MKFCyXTh0t+Rvp32WLVqlXi6x922GHyOjtEJ0yYIJnHA3tZN2/eLJnHJ59voK/J3mX2LLPjm924zBFpr5WuMTuLeS7gvMjvp6vPZ0z4GT/++GPJ7F3t2bOnZD47UFhs2LAhpk+fnpd3dN3A8wHnBL7O3lWes9lBSged24XPxXCO4xyY39zM51OqVasmmedcHuOc/3md884770jmtd/ll18uuXr16pLZEztkyBDJyTmN4y6J77AaY4wxxpicxhesxhhjjDEmp/EFqzHGGGOMyWl2ymEtVqyY+Bzs5vr1118lcz1legv0INirSs+CTirX1KWnl1x7OiLdG8ve12eeeSYIXUT6k3Rc2VW39957S2ZvJH0Xvmf6ZnQd6avReU36oYW1tnPp0qWlB7FJkybyOte9p2/G/c7ORbpd/Mzcz3fddZdk9gXz++nMsoeP65BHROy6666S6Zyyr5LrU9MTZIcwe+3ozLETkT2Ubdq0kZxcDz4i7TgVFrVr15Z1r7mmOjsSud3YkUvoo5HOnTtLpj/Nn8+xRh+OHY75uYlTp06VzDmGvcNbtmyRTNe/X79+kp988skCv59zCHtdeXxyDfukh8/XskWRIkWiRIkSefnNN9+U17/99lvJkyZNkjxz5kzJr7zyimT2pPJ44zz+008/Sb7hhhskH3300ZLpGfN5CXaIR6SfyWBncP369SVzHNBL5DabPXu2ZJ7P+f2NGzeWzHMZnwVo27ZtgT8/WyxatCjuuOOOvJz8OiLtZ9JxnTx5smS6vny2hu4wryv4++mXduvWTTI7uvl8Aq+TItLXUn/99ZdkPifywAMPSOZ1Bntb2c3M46Nhw4aSDz74YMmDBg2SzHkxeV1Z0HM3vsNqjDHGGGNyGl+wGmOMMcaYnMYXrMYYY4wxJqfZKYe1XLlyso4u/a+rrrpKMh0buoP0+uiK0N1gPyHXV092f0ak3Sx2NLIzkWsOR6RdxKuvvloyt8Gjjz4quW7dupLpGZ122mmSuY44fTJ2MFaqVEkyPdUff/wx7+sd+X7/KVauXCnuID3E3XffXTJ9mmTXb0TE6tWrJbN787jjjpPM9d/79u0rmb2X9PDYe8mfTzc7Iu2wcSyxx47eFNenpnPK/l32sLJzkZ2N7PrjuuHsK+W4zBZFihSJ0qVL52UeT+wwZHcz+2Tp2bEnlQ46O3Xbt28vmb4Zu6Zr1aolee7cuZLXrl0bhOto0yXkZ6hcubJkerycY9jhy59PR7xPnz6SOcdwLfTkuuDvv/9+FAZbtmwRb5vHJD10OuE8Huipz5s3TzL7sumwcg6gl8wuaH4/3x89xoi098rz3dChQyXTn2anMHtWZ8yYIZnzJscpO8bZD8oO5WRvbmFStWpV+ez8XK1bt5ZMp5V9tdxON910k2Qej3z+gD4zvXfOaTvqEefrEenxTI+Wz/+wh5jO6pVXXimZ24THQ3JOiEifD9nZy2u5iRMn5n3NruwkvsNqjDHGGGNyGl+wGmOMMcaYnMYXrMYYY4wxJqfZKYe1aNGi4pvR73r66acl03ugZ0enlX1l7CRlXyHdLbpY9Em5Jjz9OL7/iIiOHTtKZgcnu2fpbnCNavpg9GPY50k3hc4YnTx6RMn+wfx8umyw6667yrZkLyS3EV1neoCEfbnPPfecZPb/0cljB+OIESMkc1y9+OKLkrlmdn4/o0ePHpK7du0qmWuw8/uZr7jiCskcd1xLunv37pJ5bNCRpTdcWBQpUkT8Knp59ODYJ5js5ozYsb/JLmiOlQMOOEBysiM2v///008/lUy3kv5pRMTy5cslsyeVfhi9dPpinFM4fhcvXiyZbuJZZ50luV27dpLpPye3OZ8DyBYbN26UruEduf+cQzjeuQ05D/PZAO5XzjnsBOezCpzj+P/z3BeRfoaDvyPp/UVErF+/XjK7MOmcct/R7ezdu7dkPs/Bcw+7aQvq08wmJUuWjHr16uVlbjf6mTfffLPk5P8bkZ4T+DzCqlWrJHPssD+XTivnGHr87OilZx+RHr+dOnWSTC/+kksukbyj54HY08rzEbvW6WRzTuPYTjq0nM+S+A6rMcYYY4zJaXzBaowxxhhjchpfsBpjjDHGmJymCF26Ar+5SJGlEfFH9t6OyTINM5lM9Wz/Eo+T/yfwWDH/Dh4n5t/FY8X8O/ztONmpC1ZjjDHGGGMKGysBxhhjjDEmp/EFqzHGGGOMyWl8wWqMMcYYY3IaX7AaY4wxxpicxhesxhhjjDEmp/EFqzHGGGOMyWl8wWqMMcYYY3IaX7AaY4wxxpicxhesxhhjjDEmp/EFqzHGGGOMyWl8wWqMMcYYY3IaX7AaY4wxxpicxhesxhhjjDEmp/EFqzHGGGOMyWl8wWqMMcYYY3IaX7AaY4wxxpicxhesxhhjjDEmp/EFqzHGGGOMyWl8wWqMMcYYY3IaX7AaY4wxxpicxhesxhhjjDEmpym+M99ctWrVTIMGDfLymjVr5PXSpUtL3r59u+RNmzZJ/uuvvyTvtttukv/4448C//+KFStK/vPPPyXXqlVLctGien1eqVIlycWKFQvC91i5cuUC39PChQtTPyNJ8eK6yevXry95zpw5kuvVqyd5yZIlBf686tWrS167dm3e10uXLo01a9YUKfAN/gcoU6ZMJrltN2zYIK9v3bpVct26dSUXKaJvkeOKcJtv3LhRco0aNfj+JC9btqzA1/l+Oc4iIho3biw5ud3z+5nr1q2TvGLFCsn8zPx5/H1btmyRvMsuu0heuXKl5FWrVknmNl++fPmyTCajgykLlClTJpM8jhcvXszXJXO8J+ejiPS+rFChgmSODW5XzgEcuyVKlJC8evVqyRxr27ZtC8Lv4Ty5aNEiybVr15bM8V6nTp0C/3/CeZOfkWOD8+T69evl682bN2d9TilevHgmue0bNWokr//++++SOaeULVtWMs9dHFfchjw+qlWrVuDrPNfweF66dKlkjvOIiHLlyknmWNvRZ+C4ymQyknkuW758uWTu97lz50rmNuX5dv78+ZLXrl37X5lTuK94DuVcuaNjnGOLn5P7IXm8RKT3K8/ZnMsJj9eI9Ly3o7HAOYifkZnjk9uIcwav5fg65+HkZ96wYcPfzik7dcHaoEGD+OSTT/LyBx98IK/vvffeknlS/uWXXyQ//fTTkl944QXJffv2lfzrr79KPuGEEyTfe++9ki+55BLJ5cuXl3zyySdL5gEYEfHcc89JPvXUUyXzM912222SOUlUrVpV8iOPPCK5Z8+eku+55x7Jjz32WIE/76KLLpL8xRdf5H3dv3//KAwqVaoUF1xwQV6eMWOGvM6J8fbbb5fMyb1Vq1YF/r5bbrlF8o8//ij58ssvl9ymTRvJHId8nf9o4T6JiBgzZozkL7/8UjKPjW+//Vbyyy+/LHnPPfeU/Nlnn0l+9dVXJS9YsEAyL2j5/e+++65kTkCjRo3Sfy1miYoVK0avXr3y8oMPPiivc1/wJPvwww9L5r48/PDDJfN4/fzzzyVzjvjhhx8k8+KR25FzDifqiIhLL71UMi887r77bsk33HCDZM4xzHfddZdkXjR36tRJ8vfffy/5nXfekdy5c2fJkydPzvv6008/jcKgRIkSseuuu+blZ555Rl7v0aOH5EGDBkk+4IADJL///vuSeVHDY5wXAOeee65kXvQwcxxzHm/btm2QAw88UPJ7770necKECZJr1qwpmRc2vEg47bTTJI8ePVoyxwnH9v777y/5uuuuk/yPf/xD8ueff15oc0py/yTPRRHpc27Dhg0l8x+A3O48vq699lrJvABNHi8REYcccojkPn36SOYNEV5D8HiNiHjiiScKfA+8Frviiisk81qOx0fr1q0l80bam2++KZlj6e2335b8888/S05+5uQ1C7ESYIwxxhhjcpoivHoviOLFi2eSdyB4lc5/GYwbN04yb6Xzzzi8Cn/yyScl8y7SiBEjJPNuxhlnnCG5ZMmSkvmvAP6ZJiL9J8fx48dLvvHGGyU//vjjBb7OOzovvfSSZN7B4b9qBw8eLJl3gPn7kndAhgwZEvPmzcv6n++qVKmSOfroo/My717wTyS8q8w764SvH3PMMZKff/55yVdeeaVk3s2cOXOmZP7rj9oG78xHpMc+x27Tpk0lX3XVVZKbNGkimf9qr1KlSup3JuGfefkvYt5hnTRpkuRzzjlH8iWXXDI5k8m0K/CX/gdo0aJFJrm/+C933n3nWLnwwgslDxkyRDLnqK+//lrylClTJI8dO1bycccdJ3nixImSud849ninOyJi6tSpkqmc8G5h8+bNJfOObPv27SVTc+jXr5/ks846SzL/lMvPyDt5ybvOf/zxR2zcuDHrc0qzZs0yybmVd+J5fHCbUN2aPXu25IEDB0rmX2W4jbp06SKZd1T5V5lZs2ZJ5j7kOItI393nnfQBAwZI3m+//SRzHuOfmqkt8FqAmlLyr6sR6Tv3vIvNPyP36tWr0OaUZ599Ni8PHz5cXm/WrJlkKja//fabZP5VhsfjmWeeKfmtt96SvNdee0nm3U7eDeVfNHinPL9rtgceeKDAnzF9+nTJ/Iz8mRz/nDMOPvhgyZyzvvrqK8m89uK1VvIcftZZZ8UPP/yQ75ziO6zGGGOMMSan8QWrMcYYY4zJaXzBaowxxhhjcpqdaglo1qyZeJ98motuIZ+m49N6rHCie3XxxRdLpg/Gp/rp4bGWg35ahw4dJOf3RO+wYcMkDx06VPI333wjmU9q7qj+4aOPPpJMd5HNB3RRjj32WMlJdydCXav8WhCyQcOGDcUb+vDDD+X1E088UfLHH38smU/9cxvTW+Q4o69Gr/D444+XzH3GJ4Dp3NKNjkg7dfQSWStCF7p79+6S6STRD3vllVck86njI488UjKdOx4bp5xyimS609li48aN0iIxcuRIef2NN96QzEoYPt1Kv4z+13fffSeZ24H7iS4yn+jlWKAPfccddwRJ+t0R6bogjgXW8PA90wdjA8Whhx4quV071Qi5zelob968WXKysSK/2q5ssGDBAmk5YdMImxfYDkHHlMcDz0V8gp5PinMO4s8bNWpUga/z+OTxG5E+n3GccD+zXYUOLM81/Aw8v9IX32OPPSTz+Qn64nw+I9kGkk1WrFgh8yN/L9ty6MnzOuS+++6TzGciuB04R3Aub9GihWTWd7Llhs/F0D+NiHj00UclH3HEEZJ5jmObEOdZfoaTTjpJMp9L4bzMGkU+Z8LPlGw5yO9Zov/Bd1iNMcYYY0xO4wtWY4wxxhiT0/iC1RhjjDHG5DQ75bAuXbpUHE72qrL7jv1/9IS44gSdUq6iQreE7iNXrLj++uslc6UsrsRBrygivYIDVwuZNm2aZH5mduXxMyVXb4lI90gy8+fT4aMPk3Qr6cJlC46Tjh07yut0TtmvW6pUKclcNeqf//yn5N69e0vmsnLs22VXLf1Njkv2BdNHi0j3h9LTpXPHPlD6aVyilP8/vUQ6SlwJiC4nf39hOaukYsWKsmId3V8ef+x2pNtL34y9xuxA5YpG9AA5p/H45fHPPlB6ghHp3keu3sU5gy4/OxDpZHNfHnXUUZLp2dJ348976KGHJHfr1i3v6xdffDEKg0qVKslxyqVZ2W3JVZfYvcleVR5fP/30k2QeL9xmnHd31J3LeZyrm0Wkxw5/J5eDpcfLbkz2ol922WWS99lnH8ns3qSLyZWumLnCU2FRrlw5mRfo0vJ8QX+Yn5Nz6+uvvy6ZfbZciYrXHezIZtc7u9r5TMadd94ZhNdGfM9cipWONr1euvuco3iOpVfPHmJ6wjwek8cDV3VM4jusxhhjjDEmp/EFqzHGGGOMyWl8wWqMMcYYY3KanXJYy5cvL/7UvvvuK6+zI3TRokWSDzvsMMn0IMaMGSOZfYVt2rSRzK4w9ryyI3HPPfeUTDeE/WcRaQ+I68q///77kpctWyaZvhi78fiZucY8/TGuW8xONq4BfOqpp+Z9zT7FbFG+fHnZ1+yIu/nmmyXTbea69+yy3JGnSFfrwAMPlMx1jumk0m2mk8TO1Yj0fqtQoYJkfiZ6gsWL66FIp4geInsh2e3HjmR+P70qOnuFxZw5c+K8887Ly23btpXX2TdLN5FzCl0ufj/nEM4B3C8cW5yz2GdI35odjRHpbX/11VdLHjFihGSOJXYect5lj+rAgQMlP/zww5LpwXOe5Jr0ybHJ5wiyRa1ateS4pNvPcwV7H7ds2SKZ67ezt5WeMLuZuY/oDc+aNUsyxwV/f9euXYNwvuZY4rmEz0Nw/faDDjqowNebNGki+d1335U8f/58yXRg6enOnj07/husWrUq3n777bxMF5juL3s/6bDecMMNkvlMBrc757Dk/BYRMWjQIMnsNKVHz8xe8Ih01zgdaV6nsK+dnbv0mV9++WXJ9Hh5fuJzKfwM++23n+SkA17QdYrvsBpjjDHGmJzGF6zGGGOMMSan8QWrMcYYY4zJaXbKYY2IKFasWN7XFStWlNfo3XXu3FkyOxPpgnD9WbqHrVq1ksxORHoX7Gykh8HOR/ahRaS7XG+77TbJ7O+79957JdNPY6/jscceK5luCPvM2KdGF4T7IOlisU80WyxcuFC6TtnRxt469udOmjRJMvv92KvKtdDZjcnONzpHdLPYQcftlp9DNGXKFMnJbtGI9LjhmvZ0MfkZuG44Pcbdd99dMr3HTz75RDJ7+PLztwuD+vXrx/3335+X6ZBec801kuln8hjmvuGccMghh0jmvmWfLv02ent0G4cNGyaZDntExN577y2Za5PTwaYfyR7ip556SjL9Mfa4/utf/yrw+zt16iSZDl6y17KwPMVMJiMeatJRjEh7tzym2bvKLmj2/XKbsG+Wzjq9RM7DdFjZBU2HMCLtW7Nrln41z688ljj2d9ttN8k8P7LLky4zn0Vg93NBfZrZpHbt2nHLLbfkZfrOvC7gtue+45zD7UR/dPDgwZJ5LmDfPI9/zsWcQ/LbrvQ+2QfNjvnhw4dL5jl4jz32kMxtwPHL50p4fqK3T68+2flLFzqJ77AaY4wxxpicxhesxhhjjDEmp/EFqzHGGGOMyWl2ymGtXLmy9HqyB5V9mnRcuWY7u+zo2dG7oMdH33PevHmSy5YtK5ndfHR06INGpB1W+o/s62NPKtd3po9C15B+C3sfN27cKJn9anQjk92F77zzThQGNWrUkM/9xBNPyOt0cNjRRueV25guFn0YdpIm1z6PiNiwYYNkOoWLFy8u8Pfnt0b2I488IpndmOySpZt50kknSeY42FHXHz1FupX0w+n8sb+0sFi1alWMHz8+L7Ofkh77jvr/2HfLsZF02yLSnbrsbuYc8cILL0imb3r22WdL5n6IiHjrrbckc9/RuaOfzH5rfmaOJX7/119/LZke8NNPPy2Za9gntyF7bLPFtm3bxOHkfl2xYoVkHtP00tl7Sl+U3jwdVDrn9Enpe3Ic8/mOm266KcjQoUMlcx674oorJHPccB6l381nNjjHcFxw3uUctaP+0cJi69atMofTAc9kMpLZtZ506iPS53D+//Tk+dwJz/n0vnnuoG99zjnnSOYzHvl9D98zu2f5bM2AAQMks7+a13LsjuU5l9eCzZs3l0zHPDmv81yVxHdYjTHGGGNMTuMLVmOMMcYYk9P4gtUYY4wxxuQ0O+Wwbtq0Sdaupys1YcIEyRMnTpTMjlI6LuwY5frP/H3ly5eXzD40uiOlSpWSzDXi//zzzyBNmzaV/NBDD0mmw0aPlr2O7dq1k0yHlb4kO9no1LEzka5k0hNetmxZFAZlypQRnym5TnBEen34vn37Sr7gggskf/XVV5LZwzpjxgzJjz/+uGT2VtLf5H5/9NFHJXOt9UaNGgWhC0nfjO/xm2++kUyfm54je/bY63r33XdLrlq1qmR2D/L30SkqLEqWLCnHCLc91ztnRx+7JrndeDzMnTtXMnuUR40aJZnbkf4yndgd9RFGRNSpU0cyOzy5hvvHH38suXHjxgX+jo8++kgy3f8SJUoU+P3sKKXXmzwH8DmDbLFlyxYZo/Rw2SPJjm5uA36mli1bSqbnzt5KPivArs3nnntOcrK7NiKiSZMmkjl/RKQ9XJ5LuB+TLnhEuhOY55rLL79cMrcZn5/gvMpjkWOfXi/PA9miSJEism3Yscs5hefkWbNmSeY5vXLlypL5rAzPD0uWLJHMawg+J/Pee+9J5mAfN0gAACAASURBVH7mdUtE+pjdtm2b5NatW0vmNuC1GR1xjsXHHntMMq+T6KxyLNDbT/4+niuT+A6rMcYYY4zJaXzBaowxxhhjchpfsBpjjDHGmJxmpxzWP//8M+655568TGeFrgg9ITqUdLfYZ3b77bdLpmfHvjT2yBUrVkwyPQx2CLKrLyLdl5lc8zYi3bdHP/Loo4+WTI+WfWgffvih5IULF0pmDyQ72w466CDJdKcKg/nz58t2o2PHfsBddtlF8siRIyWzL5A+DPcj10rnWuv0y+hCc5vR3Vq1alUQ+mHsSGQ3H8fmvvvuK3nq1KmS6dnSQaV7yXHFvl76ZPSC+f3ZYvv27bL/2MFH95Z9g3RKOVbo5dFNfPbZZyVzTqMbTBesfv36kr/44gvJdevWDUKHlf2YXKee3bScp3j88DNzrLBfk8cb4Tz+xhtv5H1dUGfif5K1a9fKtuV7uvbaayXzGKUjmuynjkhvMzp47Lpl3zadQXZ/8tzG5zf4/RERhx56qGS6xrfeeqtkzik//fST5BdffFEyPUUe83T/e/fuLZlzFLcxveHCYtu2beLf0lsvWlTv09Hn5L7iOZ3bgfuB/bR77LGHZB6PBxxwgGQ+l3LZZZdJpi8dkXZI2Z3cr18/ydx3vDbjMxI8B9Ldp3fLOY3zZPI6MkKvEbh/kvgOqzHGGGOMyWl8wWqMMcYYY3IaX7AaY4wxxpicZqcc1m3btsmazXQ16NAMHjxYMrspL7roIsn0vbjGLzsX2WXJDkZ6SXRYub5ufs4N3Q26GAsWLJBcunRpyXQ9Pv/8c8nsHL3yyislc83p4sV1l9Gf4drqSW+JLme2qF+/vqyPnHTeItL7mWudT58+XTLHCb1e+pr0c0455RTJxx57rGR22HEc0BnKby1nOmnsaeS64HR82JlIH5uOG7tp+fPoZXFt9JtvvlkyOxcLi6VLl0rH5Y033iiv06ujc0ofmZ2IXLebYyPZKRqRHis33HCDZB7P7I195513JHNN7oi0G/jEE09IppNKl5/dspyDzj33XMn0p9n7SH+Z8+yBBx4o+fzzz8/7ml3Z2WLx4sXiSB533HHyOrcB+3XPPvtsyfQ5+/TpI5nnLvqjdFY7duwomc8+0L2mK02nLyLikEMOkTxmzBjJPXv2TP0/SdjdTNh3yXmZ7iXnFH7G7du3S2a/aWF58XPnzhXvk/3tv//+u+TkNU1E2n/m8cqxwU5RHk/Tpk2TzOsa+tjsYb7zzjsls3M4IuLll1+WzGdb+Ds5b/IYr1KliuTTTjtNMq+d2MfM8c3zGcfSkCFD8r7+7LPP4u/wHVZjjDHGGJPT+ILVGGOMMcbkNL5gNcYYY4wxOc1OOayrV6+WddXbt28vr9NT2G233SRzXW6uRVyzZk3JdEu4fi3dr7322ksyPaaDDz64wJ9HlyQi7czR26FvQkeOviN7C9kpSi+X24zbhL2SFStWlHzhhRfmfV1YazkvW7ZMulDZXfn8889L5trM9G24djI75k4++WTJP/zwg2Q6ffSIH3744SgIvp+333479T0zZ86UzM7g008/XTLHEffjJ598UuDP39Fa5jy2eGwcc8wxktnvW1gUK1ZMOvgGDBggr9Nn5him800HlXMQ/agTTzxRMntUDzvsMMk8Pjm2melWRkR06dJFMvdN9+7dJbOXkeORbj6dbI6liRMnSmaHMHsm2RGcXFudTn22aNSoUTzzzDN5ecqUKanXk9D951rpdErbtm0rmS41e5A5p9Cr5z5bu3atZO6DXr16BaH7z7HKOSGTyUhmjyvHCbtmeS7juYefcezYsZLZw04Ht7AoX768HLecM+il08fkXMhrAHar8/zDrnX24bI7mo4qe5g5FpcsWRKkWrVqkpcuXSqZXa68TmH/+2uvvSa5cePGkn/88UfJ7Frn+Yi/P/ncQoQ+yzNnzpz4O3yH1RhjjDHG5DS+YDXGGGOMMTmNL1iNMcYYY0xOs1MCUs2aNaNv3755mQ4rOwfZYcqOUHY/cg34Bx54QDLdRa4BT6eGXWJ0Q+im8PdHpNecZgfaoEGDJNNh+/rrryXTteI2/PLLLyXTs92Rh9q/f3/JSYcuv66/bLB69Wrp3ON2p9dHn4t+2caNGyWzR5LuNPcR9wm3EdeKpm921llnSea6zBFpD5fvid2u7OtdvHixZHo8dJfZzckeVrrM7F2lS92sWbP4b1CiRAnpDGQf7Y46S9kVSSecjuuoUaMk01+mS/zLL79Ipt/G3lV68OvWrQvCde85nuiDseMz2YMakR4r7CjlOt/snuWcRLeR/lrSoWVnZbbYsGGD+Mx06NhNS4ePc1/Lli0ls/uW5zI64dxH7GXluGnQoIFkdp5y3s/vZ7z00kuS6ZzydR7Tp556qmR6ha1atZLMbdymTRvJr7/+umR2RfN8zc7wbLFhwwbpPqUjTi++evXqkjn/33///ZK53Xk88fzFZzY417OzlM8X7L777pLZdxuRHs+zZs2SzB5iznvsQufxwHmRPa70apPPOkWktzk785PnI57LkvgOqzHGGGOMyWl8wWqMMcYYY3IaX7AaY4wxxpicZqcc1jVr1sTHH3+cl+lm0Hugm0Efjf2AdBXpBSXXko5Iu4XsP+NayvTJ6PHddNNNQbjG7qeffip5R34kfRf2pRUrVkwyndgDDjhAMn1Q+jdcEzvZQ5mfT5cNatasKV4e/Rn6X9yGyZ7HiHRHHH1RulV0o+kIsveOLvPIkSMls0eP69NHRHTt2lUy/emvvvpKMr2kn3/+WTK7/Zo2bSqZncWVKlWSzDXen3zySckXXXSRZO4jbqNsUaxYMekBTfbxRaQ9dvqbPMY5J3G70bnk56bvOXToUMl0ZOlfFy2q9wDofkWk+zXpvY4fP14yPxPHN11BzrvsmaTfzN/PscMO4WTHIl20bFGtWjXpPv3rr7/k9TFjxkimx8t17NnFSY+Xnd3sXubPZ7cz53k+e8Dji15jRMTAgQMlswO4RYsWkjnW9t9/f8nJc3dExK+//iqZ5yK6n+wknjFjhmT2k27dujX+G5QpU0Y6Z3nM831zzmFfLeccnr/4nAk7sumcjhgxQjJ7kNnRTR+b57+ItG/MDvrkcwIRae+W+45jiedgXge9+OKLktkdy+OHDmzyHPzoo4/G3+E7rMYYY4wxJqfxBasxxhhjjMlpfMFqjDHGGGNymp1yWEuUKCHuHNecpqfD9WS7desmmc4MPQh6f/x59DaefvppyewCo6fENbm5hnd+P5O9rHQxxo0bJ5n+I7+fHYdcw5d+C3009krS8z3ooIPyvi4sh7VChQrSuUtfhn5Mco3wiLTjR8/wyCOPlFy/fn3JDz30kGS60exppctMH5W+6aZNm4Jw3W52DrKjkC4kfe4mTZpI5rHBfOaZZ0q+4oorJNNh5c/nuCksypYtK544xwY/F9dTp6fHY/6VV16RTO+dfYTsrtxRfyb7P+mOcSxGpLtcOd7pI3PfXX755ZI5Xtmjyt5jfub99ttPMvuoR48eLTk5j9KLzBbLly8Xl5dz2XnnnSd5R+4/u20/+ugjyTw+uZ/pf/L76ZfSs6efyr7giPS5o2fPnpL5vAN7UU866STJ3I/sfmUvK7tn6V4mneKIiGXLlknmsVhYrF69WpxLHg/cN/fee6/k5HMfEemxxHM2j2du13333Vcy5xj6oJlMJgqC+yUi3XnPzzxgwADJfD6I81TVqlUl85xHR5vzND8DHW1exyQ9Y763JL7DaowxxhhjchpfsBpjjDHGmJzGF6zGGGOMMSanKbIjXyJJs2bNMsOHD8/L5cuXl9e55i7XxKVjw47T4sVVqWXHIb0Kulv0muh3sj+N/WbsiY2IeOSRRySzA/H444+XzHWI6aByzd2SJUtKZmcb3RKuQb/PPvtI5prCb775Zt7XH3/8caxcuVLlkSxQpkyZTLL/kmunH3LIIZLZYchxwP3GtZrZ8cZtfumll0pmNyed1gkTJkhm/yDX+I5Id2HSM2LHL90/elDs/rv++usl16hRQzLXCT/ttNMkly1bVnLnzp0l0/GrX7/+5Ewm0y6yTOvWrTOvvvpqXmYnLt1CHrNDhgyRzLHCLsnkGuMR6Z7VY489VjJdsKSbHZGeg+i4suMxIt0L2b59+wL/H3q9nEPo+nPO4Brz/H72Z7NX8uKLL5acfJbgueeeiz///DPrc0r16tUzp5xySl6mW0wvkU4251l+Zo5/9rSWKFFCMrchu3J5vH322WeS2YOcX7cz5yHOaxyLdCH5zAY/I8f+woULJbNP9OWXX5bMDmR24/L3z5kzp1DmlJYtW2aSjv8XX3whr//www+SOZeyd/i2226TzE7Su+66SzL3E5+DoYPO51q6dOkimefD33//PQj9ZHq59KF5bcXnNAYPHiy5b9++ktnbSqebY4Fd6/xMyd7W7777LtasWZPvnOI7rMYYY4wxJqfxBasxxhhjjMlpfMFqjDHGGGNyGl+wGmOMMcaYnGanFg5YsmSJyOyU+1n4evLJJ0tmWTIfLKGIO3fuXMksq2UpOIuLKZG///77kilj8+GHiPTDMxTduXgBC3T5UAYXP2D5MuVpPmDEh0goR7NkPLkN+fBQtihdunS0atUqL7OYnA/S8KEQPnTCh+feeecdydxHLBHnw398cI4P/x1++OGSKcFzHEakH0C4+uqrJa9YsUIyS8D5UBWLzevVqyeZDw5w4QI+4Mhji6XefICysJg1a5YcU3zggeOb+54PSfHByUcffVQyHzThQh0cq3x4oWLFipK5+Mjdd98tmXNQRPqBIRbAt23bVjLHFudZPhA0f/58yePHj5fMscIHRflQB4vQk7+Px3K22LRpU8yZMycvc17kgg3Jhzgi0uP/+++/L/B1jju+nnwALCL9UAnnDJ4XRo4cKZnHd0TE3nvvLZn7lQ9/8mEfPojJxUYWLVokmQX5s2bNkszPyGOBiyfwQbLCmmM2bdokDwFxIQ4+IHThhRdK5uIi/Fx8QDb50GhERK1atSTznM3zD88FU6ZMkcw5hNdVEel5i8dHnTp1JPNhUj6UyAejOc/yoV0+dMgH4PngMxe1SC5wlN8DiP+D77AaY4wxxpicxhesxhhjjDEmp/EFqzHGGGOMyWl2ymHduHGjFNBOnTpVXqfPRM+HBdh0o+i00uWaPHmyZLqGdMGOO+44yfT+WOhL/ywi7Rpdd911kp955hnJN998s+QRI0ZIZkkw/Ur6aaVKlZLMhQLoHdH7TRatb926NQqD+vXri99MZ45+DJ0hjosrrrhC8tlnny2Zn/mSSy6RzHFJz5HjYvr06ZKLFNEOYxY/R0QMGzZMMn1oOm39+vWTTG+HnhNdym+++UYyvSx6Vzw2evfuLZk+WmGxxx57iGPJ4+nrr7+WzIU8WGpPx5yOOF3Bxo0bS6aTzrHVoUMHyXSNuZAIF82IiKhevbrk/fffX/K3334rmYtMcJ7k8cPFRVhy379/f8mTJk0q8P/nswHJOYmvZYvixYtH5cqV8zId7D59+kimc8dnCbj4AovVuaAOny3gPuG5jfM4xxUXwLjvvvuCFC2q95O4wAq9Rc5rLP6nN3jZZZdJptPKOYavc3EFOrSccwqLlStXxhtvvJGXuYgKF5XgnELmzZsnmb40HVU+k0HWr18veUfPwfC5lLFjx6Z+JscCFzPgvLdx40bJHCucg7gN6YjT2+UzGbxO4e9Ljn8+V5DEd1iNMcYYY0xO4wtWY4wxxhiT0/iC1RhjjDHG5DQ75bBu3bpV+iRvv/12eZ2+GL0hOqU///yzZPpf7LI84ogjJLMLjL7pVVddJZmODR3b/HpY2d366aefSn755Zcl00lt0aKF5E8++UQynb0HHnhA8ptvvimZPa50/NiNm/Sa/vzzzygMVq5cKR25Rx55pLzetGlTyfXr15fMbcTPyHE1c+ZMyfSEb7nlFsk1a9aUTJ9z06ZNkjmO6ONEpB03dg7TI2R/J/czfW260Nu3by8wFytWTDI/Q69evSTT+y0slixZEg8//HBepjfH7UR/mHMGPT26vOyupHNOn4ydpHSV6dAOHz5cMrs0I9LONPcF3fwJEyZIpj/ZqVMnyexUpONKB4/HA11J9jcntwmPlWxRu3Zt8eYmTpworzPz3PTuu+9KbteuneQ99thDMrtvFyxYIJnPP9DLHzdunOTffvtNMnsy6Q1HpPcTe1Q5DsqWLSuZrmbdunUls9+THj3HYfI4jUjP0xzrbdq0kUyvMluUKlUqmjRpkpfpK3Ns3HbbbZI5ptnB+/jjj0umk8r+WvqjvE7idqaPzdc5x0VE3HPPPZJ53UInlc9E8PxEt5/XWuzU5RzG6xh6wuwIvv/++/O+Xrx4cfwdvsNqjDHGGGNyGl+wGmOMMcaYnMYXrMYYY4wxJqfZKYe1adOm4tHQHdxtt90ks7+SHgT7ytg7l+xSi0j3kSU9lYi0V0SXa5dddpFMN5LOTUTErrvuKrl79+6S6RHRuWM/Jp27gw8+WDI7S7kWOv0y9g3y9yfdLvqB2WLFihXisNID/OOPPyQn1xGOSK97/Pbbb0vmODrjjDMks3eVazMnfZmItGfMfmE6Tnw9Iu2b7chbmjZtmuRu3bpJpnc7atQoySeeeKJkrgfPLln64EOGDJHM9eYLi3Xr1olfRQ99wIABkrmeOn0nzkF0xOmv0RXm2GA/JntH6dXTJ+Ua2hERf/31l2R66fQrOf4OPPBAyfSb6UdyXmWX85VXXimZvhnnuORYpnudLbZu3SrdpZznOE/Sz+S5is8vbNmyRTLXi2ef7p133imZ68lzHPJ45DjM7/mCO+64QzK9Qj4DwmOFvaz06vmeOI+uWrVK8r/+9S/JdEP5LALnvMKiYsWKckzxGQVeV3BO4djh8VOjRg3JrVq1kkxP/r333pNMl3jOnDmSeT5iD2zDhg2DrFy5UjKdVXY3n3feeZK5TdhJz7FEl5+dvuxK5/mL58ekJ8zzdxLfYTXGGGOMMTmNL1iNMcYYY0xO4wtWY4wxxhiT0+yUw1qyZEnxJ9566y15nV7emDFjJNO9YNcd+/7oMnDtYq6py/Wf6e3RJaETS780It2n16hRI8l0Rdg1y2681q1bS6Z7mHQ/I9K9jvTb6H+yc7FMmTJ5X9PFyRZ16tQR727w4MHyOtclTq5NHpH2evk6fS96v3Rg6evQvaLDxG3IdZpff/31IDNmzJBM7+/000+XTKeHvXR04OjY0Y2m18guQa5fT2+S3bLslc0WDRs2FDeO3ZF0E3kMc6ywF5neH93DrVu3SuYa2/x97Nvkfp07d65krikfEVGuXLkC3xN7HOnJcq3xSpUqSaa3O2nSJMlr1qyRzDlp6NChkp944gnJyc9I3y9bFC9eXNxBzt30djn39+vXTzLdZT5vwfXMee6h77lu3TrJ9BqvueYayTxvsCc5v/dE75XdrexFZQcxu5kbNGggmR3jHIf0fum00veuUqVK/DfYtGmTPDfB64pFixZJ3rBhg2TuK84JnP/ZT8vjkbDzmtcAfDaHLjF7yCPS+/aKK66Q3Lx5c8l83ofjj45prVq1JPP5IF7L8dqPP5/zYrLbltdMSXyH1RhjjDHG5DS+YDXGGGOMMTmNL1iNMcYYY0xOs1MO68KFC2U9Z7oW9CbY7UWHlQ4Mu77atm0rmX2B9Cbouw0aNEgyvUK6Y+xYjEivS8/uOnbbcR1grvFO94mdjNddd51kbiO6JOytZEdo0oVkV2i2WLFihfT1ct1hOqPVqlWTTBf6u+++k8yfR5+tZ8+ekul60efkevL0a7hOObd5RMRFF10kmWOfHiK9KPph7K7l2s88Nnjssf+T3X5ca5ruZWGxYMEC8QM5p3CN6mOOOUYyXV6OnSOPPFIy9wPHwmeffSa5S5cukrnmPH23n376STKd14j0Mc2OT+7rd999VzLX/SZcx5vH/eWXXy6Znb/04ulvJo9Hdipni+XLl8ezzz6bl+kBEzqrnHN4buBnnDBhgmSu587jle+H44Y9x/Q96QlHpPs8+YwFzx3sjmXXMs+/dPnZS87ebnrCzM2aNZPMOa6w2LZtm4z5o48+Wl7n8cRjlNchPMbZ587jma4x+3F/++03yXRi+X7poM+ePTsInwvhdQs7cjmeOa/x/EOPlj2tPB6uv/56yXTCeTz27ds37+uC5hTfYTXGGGOMMTmNL1iNMcYYY0xO4wtWY4wxxhiT0xRhd2lB7Lnnnpmkb7HvvvvK63Sh6G+yu4ueBXtVuQYwXRJ2VdLJYR8a1+SmK0LPIyLtHnF7sbOTfgvfMx08+mXshWSHKLvt2L+ZdEcjdA3hGTNmxLp167TcLws0bdo089hjj+Vl9tS1adNGMn1MOqj0YdjPy21OJ4lrNXfr1k3yXXfdJZk9d9xH9N8i0u4l9/OJJ54omT2R9NM4dukQ0TlNOkARaYeW/aa77rqrZPrdxYoVm5zJZLT0NwuUKFEiU7Vq1b99vWnTppJXrFghmf1+9Ls4hyxYsEAy18Dm+uzs0+TYePvttyXTR6WzHpE+RukOsme1RIkSktm3yfFJt5GfmXMG1yGnU07nLnlsr1u3LrZt25b1OWWvvfbKJOdidobSTab39+OPP0pmFy3n6VdffVUyxxXPPfTou3fvLpnnDT4v8eWXXwbh+u/J9dbz+384Ntn1ynMHXed77rlHMj1efgY+b8E57uKLL5Z82mmnFcqcUrNmzUxyLXue53k8sOOa1wk8x9esWVNy+/btJXMssA+XndvsgeX5bvfdd5fM65yI9PmiQ4cOknltw95wdv6ye5zzbPJZpogdX8fwfMT+6uT5slOnTjF9+vR85xTfYTXGGGOMMTmNL1iNMcYYY0xO4wtWY4wxxhiT0+xUD2uxYsWiYsWKefmRRx6R19nvx/7LYcOGSea63/x5dBPZn8Y15rk++9577y2ZPhodIfYPRkTcf//9krkOMP2vCy+8UDJdK3aIHnXUUZLZG8nP2KNHD8n77LNP6j0nSXY20kvJFsuWLYvhw4fnZTp5dHLYe0dHNfmzItJrYJN27VSTYsch15fn+5kyZYpkOk/cZxHpHke6z/Rm2TVHR2jcuHGS6fzsueeekv/880/J33//veQDDjhAMv019pkWFjVq1JBjiu4gfTB2LXNf09/84osvJHPNbW73mTNnSv7hhx8ksxtzr732kkxvj/stIr1GOx3Vp556SnLRonpfYf369ZLpfP/jH/+QnHROI9KfsUKFCpJ5vLBbNunYcX9ki02bNskYZefo0KFDJbNbmd4uz0X0PXfUacp5l8dT5cqVJdM7XrdunWT6oBHprkvuR2YeCzyfsZuZ8xzHOj1geu933HGHZHZB81mFwqJixYpx7LHH5mV66XRSn3/+ecnc7nXr1pXMz805hS4vx8LgwYMl9+/fPwqCXjzfX0S6B5y+M8cnn5ngdQafkeA8yec46OXyORKe7+hTJ68VOd8l8R1WY4wxxhiT0/iC1RhjjDHG5DS+YDXGGGOMMTnNTjmsM2bMkPWC6e21bNlSMtfUZWfh559/Lvmll16SnOxSi0i7HOyOpCfEnli6LA899JBkdpdFpNcWP+644yTT7zr33HMl01ejn0FfjX1/XFecjlzHjh0lH3rooZKT/Wp8L9miVq1aMja4TvHUqVMlL168WDIdVvbY1ahRQzLdZn5/vXr1JNN3o29Dt5rOEsdERHpc3HrrrZLp3dLvevrppyXTaaNny2OBrjU/4zPPPCOZvlp+HcSFwbJly8SZ5L6gvzx9+nTJHAuTJ0+W/Pjjj0vmut90XLl+Ovty2QtLT5+dvq+88koQ+sJ0DTmeSfPmzSVzXW66id99951kOnacw0466aQCvz/pSnLcZovly5eLV8rxzGOS24TOKJ1u9rKyZ5IO7DnnnCOZzwfQKaf33qJFC8nsoo6I6Ny5c4E/k/MmuzjpT995552S6fHS2+Xv5/mSXbT8TDx/FxabN28WB5M9wnx25YQTTpDMscM5h8/GcE7gOZ7HH3tcOTYPO+wwyXRsuZ0j0s9I0OHmNuC1GGGPMOcxevN0wHkOZ/c0+6mTfvTmzZv/9n35DqsxxhhjjMlpfMFqjDHGGGNyGl+wGmOMMcaYnGanHNayZcvKurlcP/aDDz6QTK+OHYvsJ6MXMXbsWMnsCqOHwQ5Tuii33HKL5EaNGklmT2tEeg13ulPsRKPXQz+MXXhvvfWWZHaW0l+ZOHGiZPZOlilTRnLyM7FPNFusXbtW/GR2sDVs2FAy+/2aNGkiedu2bZJfe+01yewcpedLr5AuL90veo309Ng5F5HeD/TN2IM3ZswYyfyMdPLoYtaqVUty3759JXNtdPpqXF87v26/wqB69erSXfzggw/K63R933zzTcnsfuR24RxC34zeO8dm1apVJdPjo3PL///kk08Own3LscB1vS+44ALJdOLoh3Ftc84ZnBePOOIIyeyapX+ddMJLliwZhcGqVatkvt+4caO8zi5MOqV0+7lePL1CdopzHLKnkj+fHamcs/h8B+f5iIgvv/xSMju/6Ziyt5xdnFyD/qabbpLMcUYXmh4j/W26/pwDC4uVK1fKPEEfk8808DqEcwz9fnaIjhgxQjJ7vnm88ZjZvn27ZM4pq1evLvD3R0Q8/PDDktltTn+Z45fXTuybb926tWQ+P8QO/XfeeUcyu505dr/++uu8r9euXRt/h++wGmOMMcaYnMYXrMYYY4wxJqfxBasxxhhjjMlpimQymX/7m+vWrZvp06dPXqZXl1y3PiLdaUi3g2sf03HdtGmT5EWLFkmmL0oftHz58pJPOeUUyXQ66WlEpD1d+mdcY7patWqS6d0uXLhQMjvh6P3w93Ob03dhb+vBBx+c9/Xhhx8eU6dOVUEuC7Rt2zaTdFLY60gPuHfv3pK51vLvv/8umf7ajnwbdiDSJWiylwAAIABJREFUXR44cKBkdn/SMerVq1cQ+l/snuV61ElvMyLtMXKcsA+Uxy23AccRPUf2Svbr109ynTp1JmcyGT1As0DTpk0zSd+Pvap0C+mEL1u2TDLHDp1W+spdunSRPGHCBMn0l/nz+X44Njj2I9JrmdM/o3PKdcI559DjZc8r+zXpLnIbsueVHcPJdcTnzJkTGzZsyPqc0rp160zSA6Wnzl5Hen8VKlSQPGfOHMn8eZwD6Kg++eSTkukqs+e4R48ekjmv5wfPn3QteWzwZ9J1Zkcxu2QJvUT63jw/03llH2mHDh0KbU5JvneeD0aOHCmZffB8HoCd2vTa582bJ5nd6vSlOffymmHLli2Sn3vuOcl0lSPSnbd8nojvgdc+3Fd87uP666+XzK5yer7Jvv6ItO/MrtXkOWDSpEmxevXqfOcU32E1xhhjjDE5jS9YjTHGGGNMTuMLVmOMMcYYk9PsVA/rihUrxDO96qqr5HWuXUx3ip4RoXPKbr06depIXrp0qeTjjz9e8j777COZXhM9JTqxERF77bWX5BtuuEEy/ROuY8/uOrogXBP+008/lTx16lTJ7D+jh7R+/XrJSRelsHrxNm3aJN7p/vvvL6+zI7RcuXKS6cfQWeW63/z5dFLp4HEfcpyyr5DdoKNGjQpSvXp1ybNnz5ZMT5A+N8cJfe6KFStK5n6nm3n44YdLpnfFblz2AxcWc+bMkc9C/4v+GF1D9ldy/LMbcsOGDZLZAUx3i2u89+/fXzI7FTm26ItGpH1j+mcc7+xQ5LrhdPdvvPFGyTxerrvuOsl05OjwnXfeeZKTncNbt26NwmDTpk3SD0uvnfMk+6jZ7UjHnF4hz2V0xrmf2X3LZxfY502nlXNWRPr8Su+PXjuPcTqt9OzptN5zzz2SuY35LEDyeZaI9Dbk8xaFxeLFi8U153h//fXXJfMZBT5PwDmC1x0nnHBCgd/fsWNHyfvuu6/kmTNnSub5hq4w93tE2qWnU8p5i9dKfDbn2WeflUwHlv//G2+8IZk+M8cSHdukB8xzZxLfYTXGGGOMMTmNL1iNMcYYY0xO4wtWY4wxxhiT0+yUw1quXLk46KCD8jJ7V9nlRQeVTg69Pfpp9CjYP0iPKPne8vv+0aNHS+b6zvl5fPRP6PnQRSTscaSHyw43ruNNn4U+Jh1b9liee+65eV9zfd9sUbx4cXEq2X/LbZDsio1Ie3908GbMmCF5+vTpkukgJbdBRMR9990nmU4TxxX3MTvsIiK2bdsmme4lXUt2HHLt8gceeEAy1xUfP368ZHqNHTp0kMx9cMwxx0imi8ljM1uULFlS3PGePXvK69xu7Cj98ccfJbNvk/4X+2/pP5922mmSuQ453UT2b06bNk0yj8eItFvPfUkndb/99pNMX5pzFP9/djOz13XFihWSOQ/yMycd3GHDhkVh8Ndff4nfzGcBuBY6j1E6fpz7OTdyHNJBpSdM7/3+++8v8Pd37dpV8ocffhjk3XfflVyvXj3JTZo0KfB30Gmlb8yxz3HAbUiXedKkSZLHjRsnuaA14bNJ5cqVZfvyfYwdO1byo48+KpnXIey/feihhyTz+Dj11FMlH3HEEZLZn8v3xx5l+qN0biPS3ix7hunus6OXzjfn2W+//VYyPV4ej/Rs6el37txZcsuWLfO+psOfxHdYjTHGGGNMTuMLVmOMMcYYk9P4gtUYY4wxxuQ0RdgvV+A3FymyNCL+2OE3mlylYSaTqb7jb/v/h8fJ/xN4rJh/B48T8+/isWL+Hf52nOzUBasxxhhjjDGFjZUAY4wxxhiT0/iC1RhjjDHG5DS+YDXGGGOMMTmNL1iNMcYYY0xO4wtWY4wxxhiT0/iC1RhjjDHG5DS+YDXGGGOMMTmNL1iNMcYYY0xO4wtWY4wxxhiT0/iC1RhjjDHG5DS+YDXGGGOMMTmNL1iNMcYYY0xO4wtWY4wxxhiT0/iC1RhjjDHG5DS+YDXGGGOMMTmNL1iNMcYYY0xO4wtWY4wxxhiT0/iC1RhjjDHG5DS+YDXGGGOMMTmNL1iNMcYYY0xO4wtWY4wxxhiT0xTfmW+uWrVqpkGDBnl53bp18vqff/4pOfm9ERGrVq3izyvw/9+yZYvk9evXSy5btqzk0qVLSy5VqpTk+fPnS95tt90kL1u2LAjfw4YNGyTvuuuukufNmye5fv36qZ9Z0Pc3bNhQ8uLFiyXzM69du1ZyvXr1/vb9Ll68OFatWlWkwDf0H6BkyZKZ5Lbne9q2bZvkJUuWSOZ++fXXXyVXr15dcrFixSQvXbpUMvcBv5/7oGLFipI3bdokefPmzUH4nooX10OL+2nlypWpn5GkZs2akjl2+R45LvkZd9llF8kc67vvvrvkadOmLctkMvqhskCJEiUyJUuWzMsc/wsXLpTMscQ5hfuKc0byd+X3/dxOnMO2bt0quWhR/Tc/3y/HRUR6Tlm+fLnkKlWqSOYxv2DBAskbN26UXKNGjQJ/fq1atSRzrHKb8TMWKfK/U8iSJUsKZU6pWrVqJnkc8z39/PPPkitXriyZ82ijRo0kc5tyv3EfzJ07V3Ljxo0lcxtyXNSpU0cyz10REXPmzJHMc833338vmeNmzZo1Bf7/ZcqUkcw5hvM0fz7nIP4+vv+tW7cWypxSpUqVTHKe4FzL65YSJUpI5r7gnLGjfZk8PiIiFi1aJJnbbcWKFQW+H4515n8HXgtxGxDOITyf8P/nOZxjjccXt1nynL1mzZrYuHFjvnPKTl2wNmjQID755JO8/OWXX8rr9957r+THHntM8vjx4yX36NGjwP+fB9B3330n+YADDpC85557SuZJuH///pKff/55ySNGjAjCwfbDDz9I/te//iX56quvljxkyBDJ27dvl9yvXz/Jw4YNk/zAAw9I3m+//SR/+umnku+77z7JP/74Y97Xl1xySRQGpUqVkn3D/cqLjAcffFDyCy+8IPm0006T3KdPH8kVKlSQ/MQTT0jmNixfvrzkK6+8UnKnTp0k//LLL5J5soqI6Nu3r2RO7p9//rnkt956SzLHxVVXXSX5H//4h+QTTjhB8vTp0yVXqlRJMk+oHLdvvPGG5OrVq/8RhUDJkiWjVatWefnxxx+X12+++WbJgwcPlvzmm29K5r6aNm2a5Lp160re0UUBj0f+Y4gngttuu03yRRddFITz2osvvii5a9eukjnPcR6bOXOm5CuuuELyc889J/m6666TzLE6ZcoUybywSX5m/q5sUb9+/Xj//ffzcrly5eT1Aw88UPLpp58umXPQ/fffL5nblMcz9wHn0jFjxkjmuYrj4pZbbpGcPAb+hwsvvFAy5zWe3zgnfPDBB5IfeeQRyfvss4/ka665RvLq1asln3XWWQX+vgkTJkg+//zzJS9ZsqRQ5pR69erJ/Dpu3Dh5/auvvpLMGxotW7aUzH+03nrrrQVm/gPwzjvvlMzt9tJLL0nmxRyPv/z+ccOLWJ5PmjVrJnny5Mmpn5Hksssuk8xz5rfffiuZY+vJJ5+UfNNNN0nmNnv00Ufzvub+SmIlwBhjjDHG5DQ7dYe1SJEicruat775r8xLL71UMu8Gvv3225L5Z1P+S+i9996TzH958M7X5ZdfLpl3P5s0aSKZ/6KMiGjTpo3kc889V/LHH38smXf7eEfnmGOOkcw7acm7CBHpf+lwG73++uuS+WeZatWq5X3Nf/lli9q1a8eAAQPyciaTkdf5rz/++Yyvd+vWTfJrr70mmX+m7dWrl+RTTz1VMu/gnnzyyZL550XuA27jiPSfTHjnnO+BdzP4ZyseKx06dJDMOzL8sxXf49ixYyUPGjRIcn53jQuDUqVKyb/+eefq+uuvl8w7locccohk/hlzr732ksy/CvHOHO9K8a9EvMPKO7K8e3rmmWcG4ZzBOYH7+uyzz5bMv7LwbsfEiRMlP/TQQ5Kp2FAZaNu2rWT+lemPP/73Rhn1hmyxZs2a+Oijj/Iy/2TJuZ3H7GeffSaZd2ip4PCu2uGHHy75mWeekXzBBRdI5p1/Hn/cpj179gxy0kknSaauwjvj/AsjzxXvvvtugd/PO1v8K03yLlhEepzOnj1bMu8Q8652tli9enW88847eZn7nnMjtSBqRzfeeKPku+66SzL/esWxcOKJJ0rm3M6/dl188cWS+Ven/P6qwTuWtWvXlsxrsTPOOEPyyJEjJfNaaNKkSZJ57cfxzb/i8C8MPMcm5yTujyS+w2qMMcYYY3IaX7AaY4wxxpicxhesxhhjjDEmp9kpqXHLli1SPUVPh+4Unxyjz8Wn8/ikMx2YpDsVkW4ZoAuZ9Fgi0tUmdFvon0ZE7LvvvpLpHo4aNUoyvR5uAz6N/cUXX0im68jX+VQyPSb6NUnXMT9HNxts3LhR2gmSHm1E2tmjk0fX6vfff5fMp6jpb/7222+S6aPNmDFDMv1NeotHHnmkZPpnEeknSzm26FayGovbpGPHjpLp3bLRglUodKe5jb/++mvJfFK9sKhVq5Y0INBzYx3K7bffLpnePB1S+pj01/jELX1PNom0a9dOMj16/nyO1Yj0vqCbyJobPn3Nfc0ngOmnEc67bOk46KCDJNOFPPjgg/O+pguaLf744w/xl3mM8ylktj+0bt1aMh1ueoF0k8855xzJbIthNV7v3r0l0+On98g2mYj0PPTUU09JZi0kq7hGjx4tmU+r8+fR1eT5onnz5pKHDx8u+ZtvvpF82GGHSS4sh7VUqVLyXnnOpSPK8wdrFdluRGec1x185oLP3rD1hv8/vXy23PB4jEhfh9BT53UDv5/7mplji9uEcxprFtlkwnN8cs7ie0/iO6zGGGOMMSan8QWrMcYYY4zJaXzBaowxxhhjcpoidGsKonr16pkuXbrkZfb30Z9kFxdXFNp///0ls6/s6KOPlvzqq69K3mOPPSTPmjVLMv1RumLHHnus5BtuuCEIOz7/+usvyfR6kn5XRHr5PK7yxKUo2THKrtqnn35aMvvT2KXXuXPnvK+ff/75WLx4cdaXUaxRo0Ym6aTQyTn++OMl06fkOKJ/QxeZHiL3ETtR2XvH1cG4DCM7H/NbMYy/k/uV24BeInsjOY7of3PpSXYgJvd7RNo1pOvJlXyOOuqoyZlMRoXNLNC4ceNM0iek58bxz95ijh2uIMTVfDh2XnnlFcn0p+ik0xflWOB+pNuY3++gE02HlK4iV6lhdzOXXuVKV/x5fBahadOmkjnPJ///q666Kn755ZeszynVqlXLJFegozPHHmPOi3RGuQz43nvvLfnhhx+WzHMN9wFdZvZUcuUsOoLc5xHp7leudsfnKdhFS4+e8PkI9oPy99Ht5GpG9Lc5hzVv3rxQ5pT69etnkk4y+9a5eh0577zzJHNf8XmCU045RTKfT+Dcz/MNr0Po7bP7meeWiIgPP/xQMpdK5WdmjzCvjehUs4P3iCOOkExnm54wfx/P6XfccUfe16ecckp8//33+c4pvsNqjDHGGGNyGl+wGmOMMcaYnMYXrMYYY4wxJqfZKYe1Zs2amWTHGD0idtPRm2MfGdcqXrZsmWT2b9JF+eWXXySz24teIb09rlnPHJHuOWW3K9eQ5/rMdGDpwHH70wXp37+/ZK6lzjWC6WtOmTJFXps9e3bWfbO6detmks4WHT06dNyvU6dOlcz15LnuNjsY6fHSp2HvHbfZwIEDJbMXk/5NRNqlpJNGr/DZZ5+VzLHcq1cvyXQj2eXHn3/ooYdKPvfccyVzXLHzcfz48YXim5UrVy7Tpk2bvMy16fv06SOZXY88/r777jvJ3Pf0+hYuXCj5888/l8xORXr33G7skqaPHZGeF7muNuHxwH5q+mu77767ZB5f7Cilu//ll19KZv91cs669tpr49dff836nFK+fPlMslOXHjCfb5g2bZpk+pinn366ZHrr7OakZ/jRRx8V+PsOPPBAycWLa+U5HcP8HFbuF857n3zyieTBgwdL5rzJbmZ2fnMs03uke8meVTq0dFr/+OOPQplTKlSokEn2p7NnmD4xe7Y5J/D4pFu8o+cXHn/8ccn0Q+lbc86hl3/22WcHYbcyu8a57+l8s6+ajjaf46DvfPnll0tmnzWPN57/ktden3zySaxcudIOqzHGGGOM+b8PX7AaY4wxxpicxhesxhhjjDEmpym+42/5X2rVqiXdqnRGJ06cKJk9bex6ZC8r+znpCrLDlN167L6jc8PuSrqO7LWLiKhZs6ZkOm58D+wnY/8Ye1aLFtV/M9x9990Fvkeubc416un4JTsW6WJmiwoVKkiHLr1C7ge60Oyh47rh9Hzpd7KrdujQoZK5j7iO+FdffSWZfb5c2zki7ezMnTtXcseOHSWzY5guM71FutD0nviZ6GXRy6VryV7KwqJChQriwtFNpCtFd5fjnZ4fe1b5OZnpEtJJr1OnjmQ6slwzm/NDRHp8v/zyy5LHjBkjmc8G0EeuV6+eZG6ze++9V/Jbb70lmQ43fTO6ksleSPY3ZosiRYqIr0wP+LjjjpN8zDHHSD7hhBMkcxsneyAjIi644ALJo0ePlsxnEegJd+3aVTLPjfQc+XxHfv+Nn4kdv5zn1q1bJ3nAgAGS2QVN2GvODuPWrVtL5pzGZws472eLhg0bStc238eO/GI+S8M5gN48r2PYiU0XmccTu5x5Lrnnnnsk00+NSD9bw45dfua1a9dKZldzhQoVJN95552SOQ/yeLz99tsLfJ3XQa1atcr7mv5sEt9hNcYYY4wxOY0vWI0xxhhjTE7jC1ZjjDHGGJPT7JTD+ssvv0RyPWc6rOyio49Gl4p9guznpEeR9Bwi0q4iu/Ho8dGFpFNL5zUi7YIMHz5cco8ePSSzQ42ZLiPdDnbBcpvQd+Eav3Rck2tiT5o0KQqDTCYj6ynTxapWrZrk6tWrS+Z+5meil9i8eXPJ7Nr89ttvJT/11FOS2Sf44osvSqZDxA65iIjSpUtLpgdFrzDZExiR9goHDRokedddd5XMtc/pq9GJHTt2rGQ6etxm9I6zxfr168WnbdmypbzO7UTHlL3EZ555puSKFStKZjcl+zv5ua+99lrJ7I6mP8o5buPGjUG4Tj1dQDpv9GBbtGghmf4yj59//vOfkunE0lV8/fXXJSfn/IgQP7CwKFeunOx7eon8TFz3nl2ydJ15PHH99k2bNknmueTggw+WTN+Uv798+fKSeS6NSI9terc8f7EHlecm9qzy3MQOZHbXst+X7j/3CTvMC4vt27fH6tWr8/KJJ54or/MZhe+//14yvfQRI0ZIbtdOq2R5TmdmHy39Z547zjvvPMn0xPncSkTESSedVGBmF/r7778vmc4px2fyfB6RPiefddZZkunxsruW57ekf51fH/7/4DusxhhjjDEmp/EFqzHGGGOMyWl8wWqMMcYYY3KanXJYa9euLWuQv/HGG/I6uyJvvfVWyexJZccpfcwNGzZIps/J7j16e1zflt2T7N6jSxaR9hW5pjTfI3si2bmWdGsi0j4mu/HYfcdtynWKb775ZslJt4rdoNmiWLFi0rlHH6ZBgwaSOW7Ye0dPkLCDjtuYnYfcZ/vss49kOkT0dfLr62U3JntUa9WqJZmOK98zPUF6inQ96eztyKXmetolSpSI/wZ16tSJ2267LS/TH2ZPMbdzcj6KSDum9NPo+nId8OR7iUjvezq07ACml//7778HoVtPv5Fe+1577SWZcxLXvefxxnmQvjSdPTqtJ598suSkc84Oy2xRtGjRKFOmTF7mWuj0O9mdzM/Mfmw6p1x/np3inKd5fHOfcVzeddddkumoR6T9af5Oni+5xjzXl6frz/5QzqNHHXWU5B0de4cccohkOrD0u7PFsmXL5DkFdo6ecsopkn/++WfJ9P0JnXB687/88otkbheOzdq1a0tmv26bNm0kc/6ISLvyfE6D89AZZ5whmZ4sx+OcOXMks6uZnflff/215LffflsyrwGS/fPsaE3iO6zGGGOMMSan8QWrMcYYY4zJaXzBaowxxhhjcpoi9D4LolGjRplkX9fxxx8vr7O7rn379pLpwNC/pM9Jp4Y+Gbvz6NTceOONkm+55RbJ7Edr2rRpEPpfSY8qIqJz586S6Ueyn4++JDsP2WXH/j96t3TouOZvcv/ecccdMWfOHBWJs0DNmjUzSa9n/vz58jo7DOmf0aeku8VttmLFCsnsE2QPH8fdyJEjJbMjjh11dJAi0p+R3iw9JK5hz3W42ctIz5ddm/TDuT48Hbu6detKpvPUoEGDyZlMRgsHs0CLFi0y9AmT0NPjnMB1t3m80Illxx/7OM8//3zJ7M/l93M78vhcv359EPas0s2nJ0vfi147PVI6e+yy5e9jBy/He9euXSUn+5xnzJgRa9euzfqc0rx580yyd/uBBx6Q1zlnsNuS3Zc8xkeNGiWZjiyd8xo1akjm8U7/k148n7/gHBaRHgfsWeV+pAfP/mjOMfRseRzSY2SnMedZ9sJyG9euXbtQ5pTGjRtnkp/9ueeek9fpeNMxZb87txu3C/tpOQdxbj7nnHMkc07iMxx8LmXgwIFBeG11//33S+azNey453tmp/7MmTMl83zXpUsXyZxzeP5q0qSJ5GQf9rBhw2LhwoX5zim+w2qMMcYYY3IaX7AaY4wxxpicxhesxhhjjDEmp9kph7VOnTqZZM8gXSquCV+nTh3J7K+kG8L1brnGe/fu3SWzP5DOKt0R/n66jMkusL/7GXRUBwwYIHnixIkF/v902tipSM+WvY505uhqsY8t2YV3++23F4rD2rRp00zSPaQvw95J+mf0Z9hrl+x4jYjo06ePZHYucpuzc5GOLH/ft99+K5lec0S6u2/58uWS2d33zTffSOa4IuwL5VimO8m1nengsVePa7E/++yzheKbValSJZPcH/TiW7RoIZn7nn2y9Ph69+4tmd3R+++/v2T239Ipp6v40UcfSWbXZH6dvcOGDZPMeY6dhfR22btKF5/+Gr15+sr0O9mfyZ7KpMt46623xu+//571OaVFixaZpGfKZwc+/vhjyZzL6bzyM9IpfeyxxyRzv3Le/+KLLySz65O9lffdd5/k/Lx4uo3czy+88IJkrtdOt7Js2bKSeaxwDmOXJntdea774IMPJPN5j/LlyxfKnFK+fPlMcsxzO3HuZVcze045J9App+/MTlKOreuvv14y3eALLrhAMscKz/kR6XNgtWrVJPNah+c0jk96vuxx5c/n8cfzGZ81GD16tORkz3G7du1i0qRJdliNMcYYY8z/ffiC1RhjjDHG5DS+YDXGGGOMMTnNTi0EXapUKfFi2FHYoUMH/eHoB2RHaLLPLyLdVcl+zmRXV0S6h5XeEf20Ro0aSeZ673R+ItL9lez0ZH/Z5MmTJdMDopfE7lh6wBdddFHqPSWhe/LVV19JTvqjxYoVK/Bn/acoXbq0uIeXXHKJvM7eu5deekkyHTy6UvTFuI3o33Bcvf/++5LZJ0gf59hjj5VMJzci7V7ye/gz27VTlWvGjBmS6S02btxYMnvvuE3pLdJR5bFBt5NudbYoXry4eKnTp0+X13v06CGZ7hXXaO/YsaPk7777TvJtt90mefz48ZKvu+46yfSZ6bDuyKmlpx+RHr/ct5wn6WRznft69epJpqvIscaxc+CBB0rmuuJ77LGH5GRfNjtfs0WRIkXE3aXT3alTJ8njxo2TzOORHiGfDaB3yPXe+XwGv5/dzpyjeF5hL3N+33PSSSdJfvnllyVzHmNvOd8zXU763jxfzp07VzLdazqrnDcLi+rVq8tn4THOHlWOf56POJZ4XfLaa68V+P3lypWTvG7dur955/8H7veWLVtK5vEekR4LvI7gOZH98GeccYbkn376SfLnn38umf3vfA5l3rx5kjlPLly4UHLy+OA4S+I7rMYYY4wxJqfxBasxxhhjjMlpfMFqjDHGGGNymp1yWJcsWSJre3MtY7og9Bq4PjpdBTqk7PLq1q2b5BEjRkhm/xm7wujc0Kug8xORXjN6+PDhBf4Mun/s++P6zew3o/fDjjh24bEjka8n1wkvLN9sy5YtsWTJkrxMB47rZrN3kh2H9DM3b94smY4Pe1fp99Bj5Hrz9FG5Djg7ViPSn5FjdbfddpPMHkn25P3222+Seawke+si0n2/7HXk2KenxfXir7jiiigMypQpIy46x++VV14pma4gO33ZK805iQ4s1z+nQ8s1r7meO10wrluePA7+Bzqm9JuHDh0qmQ42PVvuS3r27LemYzd79mzJs2bNksxeyKQLyU7YbDF79mw5LrlNhgwZIpndyrfeeqtkjpurrrpKMrch5wh+/5QpUyTz3HfEEUdIpiPIZx0i0s+IVKlSRTL7qtn1fOmll0rmMx5t27aVvN9++0nm2OWxwjnsyy+/lHz22WdLprudLYoWLSrOMnuH6doOHjxYMs8v9C3Zec3rFD67w/3Ec/yvv/5a4M/j8UvfOiJ9ncFrLXq2559/vmR666tWrZLMscTjgduI5xu+P3bdJp8F4vZL4jusxhhjjDEmp/EFqzHGGGOMyWl8wWqMMcYYY3KanXJYGzZsKA4n/Uv2h91yyy2S2d3FNXm53jMdGDo1Tz/9tGQ6P3RH6HWws/GJJ54IcvPNN0umP3LttddK5trjixcvlrzvvvtK5jrEXPeY3Xl8z4ceeqhkfsb58+fnfU1PJVvMmzcv+vXrl5fZeZh8TxHpjlCuic1eSrrK7Op88803Jd94442S6a/R2aO/xm5bvh4Rceqpp0pu3ry5ZDqi9BbpxLEDkY4quzS5Ddj/yX3A3j364IXFqlWrpHOWbhPdq61bt0qmd3fhhRdK5nZeunSpZHp/nHM4Fm+44QbJdH25Zjb97Ij0Gu4XX3yxZM4Z7HHkPMh9zTnio48+ksw+T25j/jw65EmvN9nJmk3q1q0r7jnfI714zikLFiyQzJ5j9rh+8MEHktn29ySWAAAgAElEQVRRyuOF7jXPZdzm9IJ5/Eak5/67775bMud+PlPC98hxx3mS44TeL8+37CNNPt8SEdGzZ8/4b1CsWDHxxDlW6FfyeQG6uDxf0INnnzx/HscmtxPP+fRD+TzEqFGjgrCPmmOD3iyP223btknmdc7MmTMlt2/fXjKvBZs1ayb5hBNOkEyH+6yzzsr7evTo0fF3+A6rMcYYY4zJaXzBaowxxhhjchpfsBpjjDHGmJxmpxzW7du3S8clvbiKFStKHjt2rGSuLzto0CDJNWvWlEwfk14Euy3p/NDZ7NChg+TkevcR6fWfI9J9f/yM9GbpwCX7xSLSniw7PundDhs2TDIdOvabcZ3xpDtVunTpKAzq168vvYivvvqqvM51wNl9ybWeu3fvLpkOEXv02LPK9eTp+7BLkx10xYoVk0zfNCK9n/v06SOZHhOdVLqXXBecxxJhN2zSCYqIeO+99yTT0eN684XFtm3bZNuwj5LeHr14Hg/c7jvqt6UTSz+ULiR96O3bt0ume0wHNuL/PAuQhOOVPa10Utn5yd/BeZWeLV3EXr16SWYvJL375DmgaNHCueexaNGiGDhwYF7+8f9r78zDrZz3/v8pKg272pWU5qLIUYkj00Eccx2iiCRDmRIhnco8pJSpIiFUZMqQeUpmJzJLSNJEpUkTjdbvj+c667nfr3vbnX09z9rW87ver7/2+1q7vdda9/f+3ndrv77v78yZ8jgdUT7nF154QTL3Vmf3M98TOn3s4uQxoLPOOYxrF4raX56/o1+/fpKPOOIIyZxX2a/LHlS60XQpOQfR06UXTI+Rva2lxerVq2V+45oHvs7169dL7ty5s+R3331X8nHHHSd51KhRkulnco5ijzLPV/bt0i3mfVBE2ott166d5K5du0rmNfmMM86QTI/2wgsvlHzeeedJ5jzK95jnJ53x5PWTXdpJ/AmrMcYYY4zJa3zDaowxxhhj8hrfsBpjjDHGmLymRA7rwoULpXeUXV90P+h/tm7dWjJdKfZh0sugk0N/ivvj8vfRM2JPXVF7xHfq1EkyndFBgwZJpvtBr4c9jXQ/2H3HDjl6RZMmTZLMvb2T3Xxr166N0uDnn3+WvezHjh0rj7NHjg7pSy+9JJl7VrOD9Nlnn5VM94vOabIjNiLdEcdxddhhhxWbIyK6dOkiedmyZZKT70dEel9v+tTcw56OG7sz6cDSV6PvRtf5uuuuiz+Dhg0bynvDY0/3lnPCN998I5k+Gl2rc889VzL9UR4Xur7sTKVLuTX/OSLdK8znxPHJ31m3bl3J9H457/Ln33TTTZKnT58umb3DX375peRy5cplv+a+67miWrVq0iXJaw+9Xp5/w4YNk0xPnm4zvUeuTeBahFatWknmHESHdvbs2ZLpPUZEvPPOO5LZIcyfya5MHld685yX2YHMOYLrN/gec40E3wN2lOeKChUqRLNmzbJ5l112kcc5Fnjs6Iwmf1ZE+jjUrl1bMo8DM/1OjmXOOb/99ptk3tdEpPuoCe9LOC/dd999kjnHJPv3I9LrRrjW4KijjpLM9Ui8Xk6ePDn7ddKRJ/6E1RhjjDHG5DW+YTXGGGOMMXmNb1iNMcYYY0xe4xtWY4wxxhiT15Ro0VXjxo1Fzu3Tp488zgVEl156qeTCwkLJySLooqDYzsJcSuNctHX00UdLfvHFFyWzHJpicUTE3LlzJVPYZlE5Nz/gYpktW7ZIZvkyF7+wkJ4LNrgggjL/uHHjsl+zWDpXZDIZWfzC18DFbZTKeZy5wKh9+/aSH374YcmU4PnzuZCNUv2YMWMkUxjnQp2IiD322EPybbfdJrlt27aSJ06cKJkLHjp06CD5gAMOkMxi6NNOO00yF2Fx8SDPFZaSv/XWW1Ea/Prrr7JgZrfddpPHWTDNRVY8X7iQcsaMGZK58QDnkDp16kjmAqZFixZJ/uCDDyRzPuACp4j0ArjnnntOMs8Pzjn8/scff7zY38mSei7UPOWUUyR/9NFHkrkIK7lgo7QWcq5ZsyamTp2azbz2cNFSpUqVJHNhCzd44PnCjQS42JaLVph5jLmRARfJ7LrrrkG42JTnwpIlSyS/9tprkrlhCjeM4LnAzUZ4beKiLS4KS24WU9TzKy2qVasm71WPHj3kcS5c5hxx4oknSuaCPpbgf/bZZ5K5cLN3796SeRx5T8BFYFwQyI1HivqZ3JyAm5EMHjxYMq9XzLy+cKE0N3HiQmZunMNNK5KLjLngMYk/YTXGGGOMMXmNb1iNMcYYY0xe4xtWY4wxxhiT15TIYd28ebN4kBdeeKE8PnToUMks0aZDWaFCBcm33367ZG4UsO+++0qmY/PQQw9J3nHHHSXTm7j55psl052MSBe4b+010O2gH0P/kr4LnVm6UCxBpu/GUuKk6/jzzz9HabDNNttEjRo1snn48OHyOMvd16xZU+zP4/M+9dRTJZ9xxhmSWcJfr149ybvvvrvkZBF6RNo75nv+xBNPpJ4jHTQWjXMccRzQg6JHRQeWJd533XWXZPpv3KyBz5fnVmmxePFiKaGmW0gX8eOPP5ZMd5DHmm7icccdJ5mbSnDOoEfPDSCS4zwivWFDw4YNg1SvXl3yRRddJJlzxuLFiyXToeY8u3DhQsl0uDm+t99+e8ksEb/gggskP/DAA9mvR40aFaVBJpMR95DzJj1dbqDCzUZ4Du+3336Su3fvLvmxxx6TTCcwuaFORNqL5/qOgw46SPIVV1wRhEX7nAe5KQbXZNC9pH/NNR7cYIIeJK+XdCs5LuntlhZz584Vb5XPg2sS6O4+//zzkjkn0bPn+cTrWUFBgWS6wFz/cOihh0ru27evZG7QEBHx/vvvS95nn30k063n+iJuVkL/mWsouFaHZf88f3i+8D4quckE79OS+BNWY4wxxhiT1/iG1RhjjDHG5DW+YTXGGGOMMXlNiRzWOXPmRNeuXbOZ/hL7/OgGLliwQDKdV/YJ0vViz9tRRx0lmb1wdLPos9WtW1cyXcWIiI0bN0qmK8jn9Pvvv0umn0Yn9YsvvpBMh479anwNfH50WJNuFj2UXFGmTJnYdtv/HlqZTEYeZ7flt99+K5mdpHRaXnrpJcn0GNmhSA/yzjvvlMxePnrI7LalMxuR7lVl5y+9qQYNGkimP83eO7rN7BZ86qmnJNMZat26tWQ6sHSYSosKFSpE48aNs5kdfOz5vOqqqyTPnDlT8t/+9jfJdBnp+XEs0Q/r1auX5DfeeEPytGnTJNMDpNsVkfbLtuYrsxeYvuaTTz4pmeOVc0bSGY5IO+Fcm0BXMjlv06/NFZUrV5b3je8z54jjjz9eMntY6Rkmr2sR6XHHawPPVx4j+pxJRy8i7fEn+7L/DTuH2T3L7mTO71vzZHkucOzTzeT1nO/JnnvuKXnAgAHxZ1CtWjXpAeVY4Hj+6quvUv8+yfz58yWzl5j3NbwW8D6G30/HPNk3HJHuiua1pajvoX9M55tji93OdFjZq8q1M5wHuGaCHu9PP/0kOdmlXlxfvD9hNcYYY4wxeY1vWI0xxhhjTF7jG1ZjjDHGGJPXlMhhbdGihbgOdFSS3mJE2p1iNx174tiRyD2tuVcy3S16EXQh582bJ5m+HLsyI9IuBrvouCc7O9a4hztdQ+5dzt5HOnrsdeRr+uGHHyQnX2NRPl0uKFu2rDhlhYWF8jh9GL6HSZ8lImLy5MnFPk4Xmp4jfR7utU6nkF2b7CukqxWRdkoPO+wwySNGjJA8cOBAyW+//bZk7s3MbkweS3qNdPzoZdGh3VoXbq4oLCyUjsz7779fHmdPKruW6WLVrFlTMp1uen10CXlcNmzYIDnZQRoRcc0110iuWrWqZLqTERGdO3cu9mfSk6UzV79+fclTpkyRzLFIN3HIkCGSe/bsKXnZsmWSP/zwQ8nJDkc+19KC7jHnAO5dzuN85plnSua8TE+dDnmy3zoi4umnn5Z8/vnnS+Zahi+//FJyUZ2lf/nLXyTTA+Rz4Jxx+OGHS+bY57Wpdu3akuk2syv62WeflbxkyRLJvDbx3MgVK1eulOPJTmrep3Cs8H3jWp1DDjlE8oQJEySz85Qdv3fffbfkF154QXKrVq0ksyeW9xgR6Xurvffeu9jnyHmR64+4Vodjj9/P58xr7NKlSyVzfVFyjuK5msSfsBpjjDHGmLzGN6zGGGOMMSav8Q2rMcYYY4zJa0rksGYyGdkT+a233pLHuQf2/vvvL5k9q+zHpCNDL4L7QdPbYK9qo0aNJLPvk25LUXvEsz9z9erVkumjsVPxxhtvlMzXQNeDP5/7R3OvdPpp/PlJz4g+Xq5Yu3atdOLyPaRvRpeZvgudFjp9dLPoJPG40o3m3tDstqUzWNQ4YdcfHTs6cL1795a81157Sb7hhhtSvyPJ7rvvLvnll18u9nF6VOz35bn57rvvFvv7/7f46aefpLOSbhPHBjt96V/SD+M5Tu+vRo0akrl/O300HsdatWpJHjZsmOSizjnOW3Sk2afJ18h97em9P/roo5LptbMzkQ4tOxnZE1mUQ5drCgoKxAeuUKGCPM5rBbsw+f30MydNmiSZTjffQ85B7M+mI8vu6R9//FEyncOinjPH7tZ6Uem0co56/vnnJbNLlteWf/zjH5I5jrjGpLTmEFKvXj3pveUaiNdff10y50peg+m80t3l+gJ27vJ6RyeV5xfHFsfmBx98EIT+MDPXyvD6wmN9wQUXSN53330ln3TSSZJ5PrHX+KyzzpJM7z45/tmdncSfsBpjjDHGmLzGN6zGGGOMMSav8Q2rMcYYY4zJa0rksP7www/iKtAdZCfhe++9J7lv376S6SrS1+RexvTR6ODQ06NTQ8+P+47T3YqIWLhwoWT6LvRV6A3Rlxw8eLDkLVu2SO7WrZtkern0M7///nvJ9NuSbiL7FXNF2bJlpf+V7jAzPb/ff/9dMntU2ZdL5zTpz0akOw/pSh966KHFPk4fdYcddgiyaNEiyfSe2LPKHlR6hHyN7EjcaaedJHNvZ77H3DecncXc77q0qFOnTur4JilbVv9PzbFBB3Vr+6PTX+Ox5O9jjyrnPHae8rjyfI5Iu33ser799tsl0xk96qijJHNs0F+jV8t5ke4iM3spb7nllj/8XblixYoVMrdVrlxZHmdXLD245NqLiLT3e+edd0pmJzh9TR539gPT+eNaBnqVPCYR6bE3ffp0ybw+0nlldyZdSM5z7NZs27atZJ57fA3z58+XXJyLmEtWr14t6ybYUc2OUZ6P7L9NOvYREd27d5fMjl1e4+fMmSOZ9y304DlncE5q3rx5EL4GXvMOPPBAyeyv5hzy+eefS37zzTcljx8/XjK93quvvloy5yySnDc5ZyfxJ6zGGGOMMSav8Q2rMcYYY4zJa3zDaowxxhhj8poSOazly5cX14Bdd3RimNk3yP2WuY94kyZNJHO/5WnTpkm+7bbbJM+ePVsyXa6xY8dKvu6664Jwj2m6SXSp6KPRm+3QoYNk9p2xZ5I+Gz2kyy+/XDJ90GRfGl2aXNGoUSPppqPfVVhYKJk+DN9z+p98T1999VXJ7Hyj38PfR8dpn332kcxORnrHEWlHju81vSN2FPM5sruW78no0aMlcy9zupT9+/eXPHDgQMnsAiwtKlasGK1bt85mdhY2btxYcnIf+4j0seH78umnn0qmZ8/zhT2wHLsvvvii5JYtW0oePny4ZO4THpHuUeRYWLt2rWR20XK800ejI8fORTrf9DPZt3nPPfdIPuOMM7Jfc9znioKCgmjfvn02sxeVndv04Hjt4HG+8MILJbObmc4r52G+p+zzpSPIaxe9yIj0vPf1119LZp9vtWrVJC9YsEAyx1lynUFEuh+U5yI7w+nx0mem119arFmzRjrib775ZnmcTirfN87VdHd5fnI9wIgRIyR/9dVXktl5zU7eTZs2Sebcz+tVRPoc5vjnvMhjy67xIUOGSL7qqqskc57jmgleb9gzzrE0bty47NfLly+PP8KfsBpjjDHGmLzGN6zGGGOMMSav8Q2rMcYYY4zJa8rQmSyOpk2bZpI+1KpVq+RxdnWxN5UeELsk99hjD8l0teh7sr/smWeekUwn9dZbb5XMXtg+ffoEodtH14P+GLtOuacu4b709Gu22WYbydy3mL4me1iT3XqnnHJKzJw5M+cbgTdt2jST7B2kT8NuWvo35513nuQuXbpIZmdox44dJdP5Y6/ek08+KXnAgAGSeQzozCYdvn9DZ43HgWONPjc7EJO+XkREr169JNPtpCPUuXPnYp8P33P66JUqVfo4k8noD80BBQUFmeR5zz6/ZOdnRPqc577b9OjoDh5xxBGSr7jiCsmcD+lq0V+jf8b93YvqoqTvxfHNOYbuIfujOT55fpx77rmS6eGzM5hz3qhRoyQnOxVHjhwZCxcuzPmcUqVKlUybNm2yma+J5wd7KOls77LLLpLZYcouTF5L6KizD7tevXqSjz/+eMmHHHKI5KK6ndnN2qlTJ8m83vE189/T16ZLPWPGDMmcszguOOfw+fDcKCgoKJU5pV69epnkNYTHgtdU9sVvrRecvd283rA/l64wfdDDDjtMcp06dSSzb5fObES6H55eLrvEeZ9StWpVyRwbvIZzDuH15Mwzz5TMeZVe7iWXXJL9+tRTT/3D+xR/wmqMMcYYY/Ia37AaY4wxxpi8xjesxhhjjDEmrylRD2v16tXFF6RjSo+C+zGzB/WRRx6RTK+HPars52LfGd0O9tQdeeSRkumCFeXztmjRQjJdJ3o63EOXXXV8nJ4Q9zo/4IADJHPfde4hT2cu6adxT/JcsXbtWukQpJ9FJ4fOEHvoevfuLZndm+w0vPjiiyWvXLlScsOGDSWzqzPZXRuR9irph0akjwvdSHYODxo0SDI7h+nIcaxyf3e6lXQ/2cXJLsCiumVLg7Jly0qXMd0qjhW6UeymfOONNyTTS+ee8eecc45k+qXcj50uJH1TevLsjY1IO3HsTeUe7cx8jvQx6c1znmUPMt8T9lBynk12jib7E3PJhg0bYtasWdnM8f7+++9LPuaYYySzc5uwX5fXAh4zdtPuuuuukh999FHJHId8fg8//HDqObGblV2zvL6xW5Pds9OnT5dMd5njgp4w12vwWkavkWtaSotq1aqJZ33HHXfI41yjMGXKFMn05Dn31qxZUzI7RemP8n2nQ8v3id3rHJu8fkVEzJw5U/Lnn38umZ24ZcvqZ5X0bjln8F6M643YdcueZHr2HP9Tp07Nfs3rexJ/wmqMMcYYY/Ia37AaY4wxxpi8xjesxhhjjDEmrymRw7p27Vrp52KH6H777SeZHh89IP577qe+8847S2bPHDsP6UKys/TUU0+VTMenqM7E7777TjL9MfZh0h1MuhkRafeKr4l7j7NHkr+ffWZt27aV/Ouvv2a/5vufK6pWrSrdcqtXr5bHP/74Y8nca5nODl1odh7S/6R/w55V+qb0IseMGSOZfhqPYUTaXeS46devn+SktxkRMWHCBMnsjqWHNXLkSMl0pX/++WfJfM8/++wzyexhLS3KlCkj590111wjj++9996S6XjTMT377LMl831L7jEeETF//nzJt912m2TOYXSB6XfyfKQfGpF2DZcuXSqZDhxfw7x58yRz/DKzg5H+MztG6XTT02/VqlX2a87BuaJRo0biybH3kd4hnVEeV84xdPTo2NEXpXdIZ4+dpVzfwf3g6S1GpPuiuQaB8+puu+0mmV4indRkr21EureVPjj7R3mu0iM+4YQT4s9gm222kV5Rro1hxyh7TzlX8r6GYyvpy0ZE7LjjjsVmrouhg37RRRdJrlSpkuTWrVsH4boLnsN0uLlW4LnnnpPM+xZ68Zdddplk3nvRd968ebNk3tckXXiO6yT+hNUYY4wxxuQ1vmE1xhhjjDF5jW9YjTHGGGNMXlMih3X58uVx//33ZzO9H/ZTskOUe2KzO4z9f3Sn6GAuXLhQMvcJf/rppyVz//YDDzxQMt2SiLRvxg42dihyT3n2tvLxG2+8UTJ7JRs0aCCZ3XfsdKMv82c4rAUFBeIN0ROkR0ifk04QO9wI9+mm+8z+XnYs8j2jD0cfjS5YUd+T9Pwi0vtH05dmD+pHH30kmWOzQ4cOkulS08+mQ0f38KGHHoo/g82bN8eSJUuymQ5ocr6JSHci0tOjq9W+fXvJ7E2lI8vz8/TTT5dMJ5U/b88995T84YcfBqG3zk5c7k1eo0YNyck+0oh0X2fLli0l04ljLzIdOvYg051MevZ0MXPFggULZL9x/l46e3TG27VrJ5k9qieeeKJkvufsmeT+8Mm96yPSHeX05rmegnNWRPo4sOeU5wLnQc737HFlD2v16tUl9+rVSzI7w9k1Syf2z/LiFy5cGP37989mrgfgGgiOpe23314y50q6vPSXOSdwLJ188smSuX6BcA6rV69e6nvYNc5rLp3U8ePHS+a9F48171O4doDXN84xXDNBkutMirq+/ht/wmqMMcYYY/Ia37AaY4wxxpi8xjesxhhjjDEmrymRw1q/fn3pm0w6RRERX3zxhWT6ZezmoodHx4aeD/fxpjdBB2fBggWS2XHKnlb6cxG6b3ZExIwZMyTTg6VfRh+NPhkdOXaI8uezb429rHRsk6+ptHyz9evXy7GmMzR37lzJ9GXon7Ezrlu3bpLfe+89yexA5b7KdJroEHIc0lHivswR6ePM58Auv/PPP18yex/p0bKr86CDDpLMLk12Ig4ZMkRy0m2OCOktLE2233776N27dzbTS582bZrkrfnH7BylOzh69GjJ7DTlOcJO3lGjRknmceHYpjsWkXbkOFboMw8ePFgyjz3n3QceeEByp06dJNPpTjrEERHXX3+9ZPqdye/nOMoVu+yyi7jsgwYNksfp8XL9A88PeoF0vOmg8ufR/+S8zk5T9vWeccYZkjmvR6TXeHANBq9vXbp0KTZzDQnXfHCO4r+nG33TTTdJ5pzGeZzvaa4oKCiQ48v1Aj179pS8YsUKycOHD5e8atUqyexh5djjfc306dMlX3DBBZKTHaQR6f7bKVOmSOY9SUT6PKTvXLdu3WKfI9eNcO0Nzw/+PF5jubaGPjPvU5JrNIrqOf83/oTVGGOMMcbkNb5hNcYYY4wxeY1vWI0xxhhjTF5TIod11qxZskc8nRj2sNHfohty+eWXS2YHKfdDZ1cdu8HY30U3ix2md999t2T22kWk92yna0hvli4HnVe6UPTD2G3H18g9e+kJsU8z6edwP99csXLlSvFC2Sd47733Sub+8MlOtoi0W/XEE09I5jF45513JLMTld4ve/C4lzs76+g8FfVv+JzZD8r93NkZ/Oyzz0rmubW1Hlfu880uW/bksbd10qRJURpkMplYv359Nj/44IPyOPst2WF41llnSabPTM/92muvlcw5hWODncHs++T7TteLXdQRac+VPY88FpyD6GBz/NJNpJPKDtJ169ZJ5lijr5Z08OgY54oVK1aIB8e+W3qFXC/RpEkTyXRO6a1zr3O+5zfccINkdpDTk9xa1zPXMkSk5xBeL+nNJ13wiPQ8yu9nN2bZsvr5Fcchz03OafQSS6v3m7AHnF48rx/sLGXHLq+pPD+aNm0qmc4r53auU+EcQr+aY4PXy4j0fQcdVnruvF5xDQNfQ2FhoWReA3k9qVixomT2vNL9f+655/7wdyfxJ6zGGGOMMSav8Q2rMcYYY4zJa3zDaowxxhhj8poy7DUs9pvLlFkaEfO2+o0mX2mUyWS23/q3/c/wOPn/Ao8V85/gcWL+UzxWzH/CH46TEt2wGmOMMcYYU9pYCTDGGGOMMXmNb1iNMcYYY0xe4xtWY4wxxhiT1/iG1RhjjDHG5DW+YTXGGGOMMXmNb1iNMcYYY0xe4xtWY4wxxhiT1/iG1RhjjDHG5DW+YTXGGGOMMXmNb1iNMcYYY0xe4xtWY4wxxhiT1/iG1RhjjDHG5DW+YTXGGGOMMXmNb1iNMcYYY0xe4xtWY4wxxhiT1/iG1RhjjDHG5DW+YTXGGGOMMXmNb1iNMcYYY0xe4xtWY4wxxhiT1/iG1RhjjDHG5DW+YTXGGGOMMXnNtiX55po1a2YaNGiQzevWrZPHV69eLXnDhg2S69evL/mnn36SvMMOO0heuHCh5B133FFyhQoVJG/evPmPnnpERGzatEnykiVLJDdu3Dj1bxYsWCA5k8kU+xzKly8vuWbNmpK//fZbydWqVZNcp04dyfPnz5fM11i7dm3J226rh/TXX3/Nfr1ixYpYu3ZtmcgxlStXzhQWFmbzli1b5PE1a9ZI3n777SVXqlRJMsfZ+vXrJRcUFBT7/VWrVpX822+/Ffs4xy3HTbly5YLwuC9atKjY38HXSHicmVetWiWZz5njqGxZ/b/p0qVLi30+P/7447JMJqMHJgcUFBRkkucIzyfOKTyfeH7yfNhuu+0k8/xYuXKlZM45u+66q+Qff/xRMucsHqeNGzcGWbFiheQyZfSUbNiwoWSONz7nxYsXS+Z78Pvvv0v+5ZdfJHNscCxz3l2+fLn8rHXr1uV8TqlSpYqME47f5HUpIn3+cY7ge8pxxnO+Ro0akjnH8D3mMa1evXqxz69ixYpBqlSpIpnXHr4H/J28niWPW0RE5cqVJZf0PeEcM2/ePMnbbLON5FWrVpXKnFKjRo0M7zWS8NhxjuB9CY893weez7Vq1ZLM84tzN89Xjg2O7bVr18bW4Hjna0penyPScwKvB5w3+Rq2Ni/XrVu32H+fvCZv2LAhNm/eXOScUqIb1gYNGsTUqVOz+f3335fHX331Vcm82Ro6dKjkq666SvIll1wiecCAAZKvvvpqyTvvvLPkrZ3APGg333yz5HHjxgXp27evZE5MnBSYTznlFMkHHXSQ5I4dO0q+7LLLJF9wwQWS+RovuugiyTy5Pv300+zXfL25orCwMD0PWSwAACAASURBVHr37p3NvEF9++23JZ999tmSW7duLfmTTz6RPGPGDMkHH3yw5OnTp0v++9//Lvmrr76SfOSRR0qePXu2ZN6k8AIekf7PGMf6IYccInnPPfeUzIsRJ0Ee9+eff14yz7X+/ftL5gXxrrvukty2bVvJAwYM0KtPjqhZs2ZcccUV2dy0aVN5nHPKaaedJrlfv36Szz//fMm77babZE7UkyZNksw555lnnpE8cOBAyZdeeqlk3hT88MMPQR5//HHJvLDfcccdkjnennjiCcnDhw+XzDkj+Z/WiIjJkydL5tjgWL7++uslP/TQQ9mv77zzzigNatasGZdffvkf/t7bbrtN8pAhQyRz3uUFlOOMH2acdNJJkj/66CPJnOP4H6/jjjtO8g033CCZ/zGKiDjwwAMl8z+lY8aMkcwbr3vvvVdy8rhFRLRr104yb5ymTJkime8Jz71zzjlHMm/Sn3/++VKZU+rXrx/PPvtsNvM+4MMPP5TcvHlzydddd53krl27SuZ/ECdOnCi5V69ekvkfg+Rzi4jo06eP5Jtuukkyr9u874pIXz94veG9U6dOnSTzesLrAe8rXnrpJcndu3eXzHu5QYMGSX7hhRckz5w5M/s1r89JrAQYY4wxxpi8pkSfsG7atEk+peSnC7fccotk/mmJnxLxf4BffPGFZH76mPwfdkT6T7v8n0vyU76IiIsvvlgyP6G98sorg/DTBv5v7IEHHpA8a9YsyRdeeGGx/75nz56St/a/1Pfee08y3wN+FJ/8EyX/F5Yr1q5dGx988EE283+Q/JSar5GfKFGj4Cem/LPra6+9Jvn444+XzPeMn4rx00+OC/4pLCL96cP9998vmefC7bffLnnZsmWS+YnQW2+9JZnnxn777SeZn9rxEyJ+AsQ/O5UWy5Yti7Fjx2YzP0XiJ6R8/IADDpDMv3DwT0/8dJN/vuP7yD91NWrUSDL/SsTj/MgjjwThJ6T8891zzz0n+ZVXXpF83nnnSeb5s++++0rmnxA5Jx199NGS33zzTcn8i0jyT8f89CpXVKlSRV4X/1qW/IQmIn1O81Mp/omTn2qdeeaZxX7/WWedJZmfXvbo0UMyP8H9y1/+Ipl/1YlI/+l6//33l8x5kGOPz3HEiBGS+ck5x+GwYcMk85N7/jVj9913lzx37tz4M5g3b548N/6Vk0oZlTX+FYWfDvKazb8Q8p6B59Oxxx4rmXMQ/zrGsUElISI9njjekn/Fikgfe/7Vkn8B4Kf5nHepKVFn4V8AjznmGMn/+Mc/sl/z9SfxJ6zGGGOMMSav8Q2rMcYYY4zJa3zDaowxxhhj8poyJfEamzVrlrnxxhuz+d1335XH6eFxhT0dmgcffFAyXcUTTjhBMj0Krhim20Wn9fXXXy82n3vuuUHorxx66KGSuTKTq/O4EpOvkavr6B4+/PDDkrmalP4mfbekm3L44YfH559/nnPprGHDhpmkh8KKDK5IpJPDVc30CpN+bETaTabLVa9ePcn00T7//HPJo0aNkkznjxUgERETJkyQzGoSNh0cdthhxf6Or7/+WjK9W74HJ598smS6nnRq6enSqa1WrdrHmUxmr8gx1atXzyR9qDlz5sjj1157rWS6Ws2aNZO8zz77SKYrxbHHFcF/+9vfJHOO4/lIL5++KFfjRqRbMOgG0n/ke8JVyKwfok9GJ5zj+69//atkuv1ccd++ffvs1+PHj4/FixfnfE4pKCjItGnTJpvp6XLVMo8TvUU6ddOmTZPMKqJ33nlH8lFHHSWZ3jxXhrPJhG4zrxsRabeYazIGDx4smdcOvid8vGXLlpJbtWolmWtS6JN/9tlnknmu8fsPOeSQUplTdt1110xybQnnkPvuu08y/U5eL7ZWYdakSRPJvObzHuv777+XzOsR1/JwLi9qTqFDzesN723ogHOOoRPO6km2BrDdaGsNE1wLkGztuPTSS2P27NlFzin+hNUYY4wxxuQ1vmE1xhhjjDF5jW9YjTHGGGNMXlOiHtZFixaJN0OHlB4DPTl6eocffrjkzp07S2Y3GL+fHYv0iJK+bVHPh/1p3FkkIu1i0K9kxyi9Izpz9Hr22kuVnrvvvlty0heLSHe2bc3zTXpF9FRyRWFhoRzLl19+WR6nv0W/jF203L6WuxFxlxv2AbKTce+995ZMR4j9grfeeqtkOn9F/RvuoJTsmYtIHzf22tH3Zq8ed/Oiw8SuWO7sQ0eJPa2lReXKlcU7pWtLX4zOadJrjEi7xB06dJDM7ugTTzxRMn1ouozc4Yg9pNz1hq5lRNpZpZdOP4zH+vTTT5fMnkn6yOyKpmfLvkyODbqMyY7SonzuXNCoUSOZG7nLEnfrGj16tGRuoUtHnDsBsfObaxW4c9VOO+0kmWsJ6JxzHLIfOCLinnvukcxznG4x+0Q5bjjOeH1lXyjXFtAX5/fzcV6fS4tMJiNrO3i9oNvLY8v3hf4md8qin8njxF7lp556SjKvy3SNeb7TZY5IX2+4RTQdbI4NOtRF+MeS6cTyGsz3jNc7evfJ85XX6yT+hNUYY4wxxuQ1vmE1xhhjjDF5jW9YjTHGGGNMXlOiHtYaNWpkkj2k3G+W3ZL/+te/JNPFoP/58ccfS2aXHvvK2HdGX4z7gPPnsSuzqH2/2dVKt4nu04svviiZe1Kzj4/7itOJ43Ok/8bvX7FiheSkA7t48eLYsGFDzjsT69evn0l6Nrvssos8TieOPXn0BtldSz+HHiO9R7rV7Ors0qWLZPb00XE68sgjg7DX8bHHHpNMH43OEZ06Om4cd+wjbdiwoWQ6dnx+3Ov5oosukvzuu++WSmdihQoVMsneQ3pw3OOd3covvPCC5OHDh0tmJ++CBQskc790djDS06ebyDnnmmuukUyHPSLi6aeflsxeVjqmjRs3lsx5cs2aNZLpL/P84nhnnyYdMnYAd+vWLfv13XffHT/99FPO55SaNWtmkusD6Nhx/3bulc5xQ4fuoIMOkkwPmJ49vUR6hVu7rlatWlXye++9l/oeuo70cNkZzLmfXZxHHHGEZL6HvD7z3KILTT983Lhxklu0aCF56NChpTKn1KhRI5O8F6FvSd+Y6wt4rOlj0v+ky8v3pWPHjpLpfXOO4hoLusnNmzcPMn78eMnsFd5ajyp/B9cL8T6I9yW8D2JHMNdIfPrpp5KTc9zEiRNjyZIl7mE1xhhjjDH/9/ANqzHGGGOMyWt8w2qMMcYYY/KaEjmsu+66aybpStARZdcjHZyt7eNNl4OdpvR+ypUrJ3nWrFmS2YdWoUIFyezrLMo3o4fLvk16RHfeeadkuoZ03Pia2A1LN4X7hPP5cJ/w5F7oK1asiE2bNuXcN6tSpUomuS81nT2+B3RMOQ7Yp8teVvow7KWkI8i+Xnbr0tfZ2h7eEWmH9LzzzpNMZ46ebaVKlSRzP2m+Bv57+mj8fewe/O677yRz7/MxY8aUim/Wpk2bzNSpU7P5jTfekMfZjdy9e3fJ9Cs5J3EsjBw5UjI7dvnzOAfdfPPNkgsLCyVzPl24cGEQeq9Lly6V3LVrV8l05uigPvPMM5Lpa9Kb5/lFv5nOeLLTMiKioKAg+/WwYcNi/vz5OZ9TKleunEnOpXxNP/74o2S+h9y/nV79lVdeKZldmHT02NfLOYy9quzqpE/KeT0i4uSTT5ZMb5CZ/jfnLe4fT8+dPekcF5w36VJyjclXX30leeLEiaUyp1SqVCmTPL70N8866yzJtWvXlrzjjjtKfvvttyXT9+Qc9fnnn0tmhza71znWuP6BDvsPP/wQhN4te715zaQHz+sX73s4R9H5Znc6x3+DBg0kc21Ack3H0KFDY968eXZYjTHGGGPM/z18w2qMMcYYY/Ia37AaY4wxxpi8pkQOa0FBQWbPPffMZvZl7rfffpL32GMPyfQm6F2w35L7irNjkfvX0qFlVx5fKx1WehsRab+FDl3Lli0l0/+ij0Jn7ptvvpFMX/Ouu+6S/OCDD0qmA0svKbm3+oQJE2Lx4sU5981q1KiRSTpaI0aMkMfZAcreOjpCAwcOlEyvl12a7DxlBxydIbpddFT//ve/S6azFJHu+K1Vq5bknj17SuZrpCfIvk/61XwOdF7p0NIvo9NK3+zZZ58tFd+sefPmmaRX2rZtW3mce7DzHE+O74h0FzTfR7q/HFvHH3+85P79+0vmfMDnk+wojYjo0aNHEDpt9M3o4dLF57zJY8t5l4433cgxY8ZIPvHEEyXTX0uuPTj44IPj008/zfmc0qJFi0zyebI3mC4xvXQex6RjHxExb948yRx39AbpoE6ZMkUyHVpeN/76179K5nUjImLs2LGS6aWzd5XPuSh/OsmqVasks9+annvSXY5IjzvOsxxHLVu2LJU5hWNl8eLF8jjPv759+xab2YvcuXNnyfSj6QLzmk9f9NFHH5XcqVMnyTyO9FEj0vMWnW7eR/A1fPnll5J5/dhazzgdcK4DocPNtQrJPvpjjjkmvvjiCzusxhhjjDHm/x6+YTXGGGOMMXmNb1iNMcYYY0xes+3Wv+W/2WGHHcQdYv8fu7jobrCLix2m9M24BzBdqn/+85+S6bQuW7ZMMjtSBw0aJHno0KFB2G13yCGHSKabwa5X+pD01fgauF8zn/PPP/8s+YwzzpBMX+3999/Pfk0XJ1dst9120tHHXjq6w/TN6HdxD2327dJZnThxomQ6f9w7mi4291LfbrvtJLNXNiLdlUm/bGuu+K677iqZ/jYdJHpTPLfobfE10JkrqgeyNChTpoycM+xmbtSokWR68jyf6EZxDuCxZpflzjvvLLlp06aS6Wqxl5X9nJzDItLdzO3atZPMY8WxwZ5VzkmcE+gS3nvvvZI3bNgg+aijjpLMHsvkvM990XPFqlWrxLumw/rRRx9JpuPNOYD91TzneT6xu5PeO/t7eS3jtY7H5Prrrw/Sp08fyTfddJPk++67TzK9ePaJ7rTTTpI/+eQTyTxX+B7Ss+e59PXXX0vme1paLFmyRO4FeA1+5JFHJNN/vvbaayUn/cqI9LHlXEuPnfctvEazl3zAgAGSuX6BfmpE+r6A45c927Nnz5bcokULyZwT6tevL5nzJNdQ8Ho1ZMgQyXTGk4/z/UziT1iNMcYYY0xe4xtWY4wxxhiT1/iG1RhjjDHG5DUlclgrV64sDllyn/qItMfTpEkTyewno3dRsWJFyfR66HrQjbzlllsk0zVk79y0adMk02uKSPuWSSc0Iu1L0r+k+0R3kA4svSG6KPRh+Bro4SZ7LdmrlyvWrFkj+y8nu3sj/qtnLcluu+0mmb4ZvUCOC7rL7Oakh3zVVVdJ5n7x9erVKzbTe4xIe4kzZsyQTJ/rvffek8zeOvpqHJv01bi3M3vz2NNHZ5WecGmxadMmOQd4jpYrV05yx44dJdOdYmcunVf2FPP30TXkHvF0F/fZZx/JfB9r1KgRhOc8O3DZ7cz+ao4/zrv8/l69ehX7nNkpOm7cOMnsGKVjVxpUrVpVju3kyZPlcXq+7JWks7f//vtLZqco90K/9NJLJfM95++nY85jSo+R6ykiIqpXry6Zjik7ftnJzXly3333lczO4qeeekoyO4c5tvkec20Au6lLi9q1a8t8+c4778jj7CHmXMxrOK/JXJ/A3tY333xTMscG5256+rznYG8sXeGI9PWDx47dr7xvYIfu2WefLZnzLrtje/fuLZn3GrzesG8+2SH88ssvxx/hT1iNMcYYY0xe4xtWY4wxxhiT1/iG1RhjjDHG5DUlclg3b94sHVnsvqOXwz1v6XKwD43uAvsJ69SpI/mSSy6RzP2a6Z/RRaFXyD18I9K9jvRbTj/9dMnc95tddfRn6HfSZzvllFMk33HHHZLZkchOxhEjRmS/5v7xuaJ+/friYLKfl74n92Ono8O91bkffJs2bSSzx5JdtuzGZK8lnVd22NHpi4g44YQTJNOJoxvJ48TnTA+RLjQdpNatW0um28lxxf7fP8s3q1KlinimfN50xulOJft+IyJ69uwpecGCBZLpq22zzTaS6S6yI5jvE91iuo5FuYn0H1977TXJ7DBkryrnwWOPPVYyHTs6qcl91iMixo8fL3nNmjXF/vvGjRtnv2bvdK5Yv369+P/sYWWnKPthK1WqJJnzMM/Hyy+/XDJ7KXlt++yzzyTzfOS/f+uttyTz+Uek5yn61jw3unTpIplzCl1/eo28fnKNCbs4OQ/PnDlTcmmNDbJixQrZ655rFriWhvcR9J25HmHq1KmSFy5cKJnXfL6v7OzmWpv+/ftLTr6WiPT1KeK/1gIkYX80O3hHjRol+fbbb5d89dVXSx42bJhkXt/KlCkjmedncs6ISK/F6dq1a/Zrdlsn8SesxhhjjDEmr/ENqzHGGGOMyWt8w2qMMcYYY/KaEjmsZcqUka5U7jlNb4H7x7Lri/ul0zVMdohGpF3HZNdnRNoVefLJJyXfcMMNktkj17lz5yAffvihZO7fTL+E/iX3HaYvw17IF154QTK79uisshuPHXBJn42dlrli+fLl0oXKzjXu1UxHlY4R//1ee+0lme4zuy/ZZUuXl/2FHBccZ+zejUh3EtOLZXclnVK6m/x5tWrVkszu2uT+7hHpbk16uPx+upqlxebNm2WM0hGn30lXauDAgZJHjx4t+brrris2c2zxfaCrVb58ecnsK2Qf4d133x3kwQcflMxzNulzRaQ9XfZKsteV3v0VV1whmb41fU72Dvfr109ycl6lr5crypQpI/MXryXsrqR/2b59e8mTJk2SzDmHXjzHITvAd9xxR8m89rCHco899pDM9R0REbNmzZK8yy67SKaPzXmLriN97hNPPFEyxyq7nenZsvOYcwq7bkuLqlWrSscs+915n0Knu0OHDpI5J7BHletK7rnnnmK/n046/VA65bxeFtVTSr+YnjwdbjqrXIvDeZH3KYcffrhkvofs+OV7xHu9W2+9Nfs157Mk/oTVGGOMMcbkNb5hNcYYY4wxeY1vWI0xxhhjTF5TIof1u+++Ezfu4IMPlsfZHUlviF10dGAOPfRQyd26dZOc3G82Yus+6HPPPSeZHsXQoUMlc6/liLQHxE7CKlWqSOZr2Lhxo2T2odHXoKdLF4V7BtOFZNfs4MGDs1/PmzcvSoNKlSqJf8yuyzPPPFMyHT52GB5//PGSjznmGMns2eO4adKkiWR6kuxIZaci98h+4IEHgnAss4+XfhgdIL7GVq1aSaZXyMz94elSszM52X0akd5HvLT4+eefpROQc0afPn0k0yGll01fjL4ofWQ6r3Ri6UI+8cQTkjkHcuyyOzMi3dtIx5rnC38nXUb2pnKOOOCAAyRzrNCJpa85duxYyezfLA22bNkSq1evzmb2pv7www+S6V+y+5K+J/ty6S3SQV22bJlkXifY3cljzPmC4y4iorCwUDK9Wq4BYWf3NddcI5leO48ju57Zh8lzka/p6KOPlkyvt7RYv359fPPNN9nMfnVeszn/85qcHHcRaQeWfbj/+te/JHNdCscK53q+r1ynwvkhIu3psi/6yCOPlMx7sXXr1knm2KOPzDmpdu3akvme0JFln3byvonzTRJ/wmqMMcYYY/Ia37AaY4wxxpi8xjesxhhjjDEmrymRw9qsWTPpoOR+sHQruJdxspszIt3jyi4xOrEdO3aUnMlkJNPNos9GV4tOTq9evYLQBWFHWqNGjSSfeuqpktkXSG+W3tEtt9wimX1mdFZbtGghmR5R0p8p6vXlgvLly4uHQweIPahb63Tje8rjyu9nF+bcuXMl01namvtFV4wdihFpj5beER019kDuvffekuk5cu9xjl2ei3Snf/rpJ8l0Obl/Pfd+zhVVqlQRP+rFF1+Ux9lzyj5BuoB0TtllST+Z/Zd0vTjHsM+Wfhyfb1HeOJ3q66+/XjJ9Z7r1kydPlsyxM23aNMl0+TkW2W1bvXp1yfTuk93Q7PbMFex25jnZt29fyfTO6eTNnz9fMj1D9u/SM+Scwr5tdt126dJF8pw5cyTzOhGRdpPbtWsnmV4gr7/0Cunec17ma6pWrZpkXgvZ4/r4449L5rxdWmzYsEFeC33+X375RfJ3330nmeOdcwjnds7lvBbwPmjTpk2S6YPSIee6mKL6bUeOHCl5zz33LPY58FhxbHFe5JxCp5vrQnjfctNNNxX775NrMtavXx9/hD9hNcYYY4wxeY1vWI0xxhhjTF7jG1ZjjDHGGJPXlMhhnTNnjrg27CCkx0C3g3vA06WiL8ZuSHYq0s3iXsZ0T2644QbJ9JTo7ESkvVk6rMm+0YiIb7/9VjK78/ic2evKjredd95ZMvcApnPH57N58+bs13x/c8Uvv/wSTz/9dDbTjaJnyx5W+mX05AoKCiSzf5fjsGXLlpJr1qwpmc4Rjwn3Gb/vvvuC8FygS1mhQgXJPK50TqtWrSqZ3ZrsneSx/fHHHyVzP3l2c9IBLC1WrFgRjzzySDazn4/Hnq4VPTrOAXRz2XfLPa3pRi5atEgyPUAeZ/rRRTme9JF57Pga6b0PGTJE8mmnnSaZawPo6LGDkf4knXJ6usl5nederigoKJCO6euuu04ep1fIuf7YY4+VfPXVV0vmOGD/NY9j586dJTds2FDy2rVrJfNayGvTP//5zyDck75fv36SOS7oKV566aWS6bzyNbGHdcCAAZLLltXPt3i9vO222yTTFS0tqlWrJucAr5n0/9nRy/eF1xM6qN27d5dMHzrZCRsRcf7550ums8nzka7xq6++GoTn7Msvvyx52LBhknk94JoJdt5zPF922WWSuQ6E8yjnzc8//1xyciwV1TP7b/wJqzHGGGOMyWt8w2qMMcYYY/Ia37AaY4wxxpi8pkQO66ZNm2Lx4sXZzH5Auhn33nuv5KlTp0qmC5h02SLS+3DXqVNH8vfffy+ZPat0JekN0pVg/2ZExHvvvSeZXab0IbknNf0Y+jR8z+iwzp49WzIdWnY2ci/zpKfLvsVcsXHjxpSHmoQ9j9wXnH4L3ak33nhDMo/7HXfcIZmdc4MHD5bM95i9lnRw2YMZkfac6BXyd4waNUoyHTv24HFfbjpKHKf0rJYvXy6Z3i6doxEjRkRpUL169ejQoUM2c85gtzL7/NhDvLX+TfphdFyTPdMR6S5M7lHPsUff7OSTTw5Ct5Dju0aNGpI5J3De47G++OKLJe+3336Sk+93RPo9WLFihWTOk8nuW/b75ooqVaqIH8z3iPuP8z0oX768ZM6bZ555pmTO6/Q7+Z7Qc6cjy3FEZ7B9+/ZB6A7Tdeae9+ze/Mtf/iKZXZp169aVTM+wadOmxT5nzjn0t+lNlhabN2+W+S7pPkeke04vuOACyTwf6eLSR+Z6AnZF33rrrZLZu/rrr78W+zj9UV47ItLnMNcocI0F73V47Nj9mrzvi0jPIZznOC/zmluxYkXJybU/XDeQxJ+wGmOMMcaYvMY3rMYYY4wxJq/xDasxxhhjjMlrfMNqjDHGGGPymhItumratKksxmB5M8uXKfKy8DZZah+RFs8pjW+//faSWaxcpUoVyVzM89VXX0mm7Myy3Ih0yTALcsuVKyf5yiuvlExZmQI1Fy18+umnJfr+Tz75RPJDDz0kOVmozUVsuWLLli3yvCnvr1u3TvJnn30mmYuyKleuLJnl8lzIxkVZ/fv3l8zidEr3w4cPl8wFSixij0i/JpZqc9HGY489JpkLXcaMGSN55cqVkrnI45VXXpE8cuRIyVwMyE04uDCttChXrpws/uACoRtvvFEyNwrgsec5zRLv999/X/LXX38tmXMG5xQu6jrssMMkc5HXqaeeGoQLyT766CPJXBzJc54LOW+++WbJXKDHxTnnnnuuZC5QatasmeTGjRtLTi6OXbZsWZQGy5cvlwWnXOTBzRSSG5dEpOcUbq7w7rvvSuaiEh4zbuzBccJxxVJ/Lh6cMmVKEC7EJNyUhmONm8hwHuXCTm7IwrHNhZ7cAIPzLO8PuGlOrigsLJQNQmbOnCmP83q0++67S+acwe/nWGrevLlkLsI644wzJHNxOUv/e/bsKZnXbd5TFPWcZsyYUey/adCggWQuRKtXr55kjhW+Z7Vq1ZLMaybnLB6T5EIybr6UxJ+wGmOMMcaYvMY3rMYYY4wxJq/xDasxxhhjjMlrSuSwVqpUSbwZOi10ZlhWy+JhltxvzTu67LLLJF9//fWSu3fvLpkl/3S96AHS9YpI+yz8HSygZuk8v58eLf0UOqh09ipUqCD5m2++kZws9Y7QzRLoteSK+vXri1PGwmtuIMESbvpkJ5xwgmQ6r/Rn6GuyILt3796S6Y/Rb2OJOMdVRLpsmvnyyy+XTI9pl112kUzH7eyzz5ZMn5vnCh/nezxnzhzJdD9Li4ULF8Y///nPbOY5ztJulqNvzT3kJhT0QblxB13iK664QvJ5550nmX4nN5DgZicREd26dZPMEnwWuNOZZrE2xy/fM74mzln0lzlHNWnSRHLS4583b16UFsl5hOckvXXOkzzuvBbQTeZGBJw7//Wvf0mm08prIf89ny+vfRHpeYvHnRtQcFMLfv9ee+0l+aWXXpJMF3PcuHGSuWEKXWmOM77G0mLOnDkyRrkZCa+xXCfCtTacK3nOb9y4UfKHH34ouV+/fpI5RzVs2FAy19Lw2sFrQ0REjx49JHOO4XPk76Dbz/ODGxHwvoUbpiQd4oj0JhMc28nzObnZEfEnrMYYY4wxJq/xDasxxhhjjMlrfMNqjDHGGGPymhI5rEuXLo277rorm7/44gt5nE4MO/rYZTdw4EDJW+s8ff755yWzK+zqq6+WfP/990umi3X66adLplsSke5FPe644yTTjaILQveDPY/ly5eX3KJFC8nssn377bcl003kazjttNOyX7PvLVeUL19e3ms6q5s2bZJcsWJFyR07dpRMj5hOEl0rdqDSFyX0Q/nzF/S9XQAAFDFJREFU6PTx+Uake1PZa0fvlo5b/fr1JW/ZskUy/bT77rtPMj1EepDsnmUnIj3g0qKwsDA6d+6czXRUDz30UMk//vijZJ4/yZ7oiLTPSVfxjjvukEyfjY4suyzphrFvk93UEel5jb+T44sdiPQlX3/9dcn0w2rXri35999/l9y1a1fJdMh22203ycl5mt5irqhYsaL0ct96663yOF1jXpvoMtPX/P777yW3atVKMt9jOuf0HCdMmCCZx2TBggWSef5HpM9RrrHgXM9u53333VfyrFmzJLMvlGOX1ypea3777TfJ7IEdNmxY/BnssMMO0bdv32yeNGmSPP7ll19KZpc671s4d9L3Z1/ttGnTJNNj5/WNfnOyOz0i3ZnN+aKo55xcFxCR9tY5ljie6aCy857XYHq769evl8y1PfSEly9fnv2a/fxJ/AmrMcYYY4zJa3zDaowxxhhj8hrfsBpjjDHGmLymDDsyi6NZs2aZoUOHZjM7DOnBsYeVe1bTzWA/Gvc2PvDAAyXTdeB+69zLmG4W/VN6HhFpl+maa66RzC7YTp06SaYvwx5HekJHHHGEZHpAL774omT2obG3df/9989+PXny5Fi6dKluVJ4DateunUl6icku2Ij0caZHyHFBj5h+Jn0Yusp8Dzku6XatWbNG8ty5cyXTwY2IaNmypeSzzjpLMh0fPgf63xyb7Dyk18i9ovnv2fXHLkH6zTVr1vw4k8locWMOaNWqVSbp6rEbmQ4onfCjjz5aMrsf6bXTw6PLRR+N+4DvtNNOkundJ12siLQnGJH2J+nyc59tntPsZUz6ehHpzsNnn31WMp0+Ps4OYXrCSQ94/PjxsXjx4pzPKc2bN8+MHDkymznXcx6sUqWKZHq4vHYk56uI9PnGzlM6qhy39Bjph06cOFEyx1lEeiyxt5xuMt1Ker30u6+99lrJ9LPXrVsnuV27dpKXLl0qmX45+7N79OhRKnNK8+bNM6NHj85m9tHydfJ9Zu8x52Z2jtLvZEcv74tuu+02ybw+Pfnkk8X+PK7NiYh46qmnJHP+/+ijjyTz2NCBpSdPx5W+NPun6TfzXu6kk06SPHz48OzXr776aqxYsaLIOcWfsBpjjDHGmLzGN6zGGGOMMSav8Q2rMcYYY4zJa0rUw7pixQrptKS3R7+SXgO9BXaWcq/ipLMUke5opNtIx4auCfsP6fDQA4lIu1Hs80s6ohFpV5FdsXRDKlWqJJmdoOzS4x7x9NtefvllyUlfhm5arvj999+lh+2bb76Rx88991zJ9MHYvcl+P3qB7DSkP8b3iMe5evXqkgcNGiSZDt/XX38dZPDgwZI59ujw0JFjbyT3gqb3SCeO+0uzH5PPJ9lpGRFxzDHHxJ/BypUrxdmiP5zsEY5I91nSWWUHL89H9gey35ZzCvtw+b5yD3h2KC5cuDAI97mnU0fP/ZZbbpH897//XTL9s+Q6g4i0u0jo7XKtALtsk+fTK6+8UuzP/t9iy5Yt4lTyfGMvMY87+3fpsHIc8Pzg+ctrG8cpnT/2aTdt2lTyzz//HIQuI31rzmPdu3eXzPk+6QkW9fgee+whmf2jvHbRg6dTy/egtNhuu+3kd9MB5ZoEXqPZ985OUfrG7PClv8n1Cuxa5/WLnaWcD9hLHpF2Vnlvxjxv3jzJ9N4ffvhhyVwnwnsxnk/8eW3atJGcdIwjdC3CBx98EH+EP2E1xhhjjDF5jW9YjTHGGGNMXuMbVmOMMcYYk9eUyGFt0KCBOJF0UJ955hnJ7CykA8M929lPxv2Z6W6xB46djdyTm57gW2+9JfmSSy4JMmXKFMns69tnn30k082g80Z/7cgjj5Q8f/58yXfddZfkb7/9VjI7R+mmJHsr2beYK8qUKSP7M9N34d7NdFY5ri677DLJdFzYZUtn6bjjjpNMB+jNN9+UPHv2bMl0r+lKFwWdUnqy7JLt1auX5DFjxhSbuVc0uzz5mjgO2XXLXld6wLkik8lIry2dcTrddBX5Pr/22muSuSc25xCeLzx/2TV90UUXSab3R4e1QYMGQXbffXfJ7OzlOc5569Zbb5XM94zzLPs0zznnHMkDBgyQzLHK70/+Pjq+uWLlypXx+OOPZzM7trn+4ffff5fM40gHld2b7OakP0onnH3m7Mrl86ETSM8+In2t4Lw2atQoyeyT5rhiXyhdTHaM0wfnPNu1a1fJvFaxU7y04FqbOnXqyOP0Penajh07VjJ7wzt06CCZvjSvF5xTeD1iR/Ann3wi+YknnpBMxzwi7TvTO2dHLv3kG2+8UTLXI9Fz59obrksZP368ZL5H7DJPesI8V5L4E1ZjjDHGGJPX+IbVGGOMMcbkNb5hNcYYY4wxeU2JHNa5c+eKa8fOQu4Zz164Zs2aSaaTQ2+C/YT00zZu3CiZfWqvv/66ZHblseeV3XsRaf+LHk/Pnj0l16xZU/Jee+nWyfQn6cC98847kukxcQ9t9hF+9tlnkpOdcPSsckX16tXj2GOPzWY6efR+2RvJvdHZH8jOQjpA7Ci98sorJbPTlD8/ubd9RMTixYtja9DTveGGGyTzNW2t17GwsFAy+y7pENGr5V7ldC8//vjjYn8/HdtcsWrVKuk9ZOcg971nnx/9NJ6f9OLpDvJ1c2zxnGGf5n777SeZvaz0sSPSY+Xiiy+WTAe1f//+knks2c1MV5HnA31n+tB0Wlu1aiU56bOVVrfzb7/9Ji7vmWeeKY/Ta7/wwgsl87jSz2Q35umnny6ZjjmvFewgZXfuNttsI5m+KOfAiHTH96JFiyRzbHGO4Ljh9ZmuMtdn8FrCY927d2/J7GUdNmxY/Blsu+22ch3edlu9zbn00kslc37nfQn9zfvvv18yzz++L/Tw6ci+++67kjlnNWrUSHJR3c78N1zPw+sJjz37rNk3z/OHXejsKufYoUNbv359ycn7JN73JfEnrMYYY4wxJq/xDasxxhhjjMlrfMNqjDHGGGPymhI5rBs3bpR9ePv16yeP04WiK8ieOPak0g9jZyL703r06CGZbgg7VNnryv5COkJFfQ892Zdfflnyr7/+KpluE/vO2L/HDrfWrVtLpv/G76ePk/R46fLkirVr18bbb7+dzexV4zig37XjjjtK5nGrUaOGZPbasWOUHXDs3WMnHf3Ok08+WTJ9oYiICy64QDK7+tq3by+Z+1lz72b21NFxY4ci+0TpTnN/67Zt20qm71Za1K5dW5xMOt30ydidzP5M+pbsl+X7QL+TY4mePvcNv/nmmyXz/C/KTaTzVqtWrWJ/J/ssTznlFMncU57nC32yCRMmSOYcxfeEfZunnnpq9mvug54rypYtK943+6d5TtO3pCf42GOPSebr4PnIDmv2AfM9Yrc052Xma6+9Ngjndq6P4HHlfvF0Wps2bSqZvjXXGtBjp5vJc6VLly6S2ZPOrtxcUalSJblu0m9mRyl9T44FOuacY3g943WWvaqce/n76I9y7BXlBrMDn2treE1s2LChZK4P4rzFNRMci+w55jzJeZq+dPJejtfGJP6E1RhjjDHG5DW+YTXGGGOMMXmNb1iNMcYYY0xeUyKpsW7dujFo0KBsZqco3Sx6Q+xYZNcdfTN6FvQA6WKxl47uCD09Pt+77747CP1LdskefPDBktlHRneDnhG79pJ+WES6E/Grr76STNeE70ny3y9btixKg40bN8b8+fOz+bLLLpPH+TyuuOIKydyXmI4dO+F43JP7EkekO1HpwHIc0qHl3u877bRTEHq49LuSvbRFPWf63nyP2KN3wgknSKY3xQ5kelyktMYGqVatmjia7KdlXybdYM4RdGDZY0xnnO4X/WWez/RJ6S7TZ+PYjog4+uijJR900EGSr7rqKsncM57jlR2Kffr0kcze1pkzZ0rmXuU83y655BLJST9z2rRpURpkMhnxuun5Jp35iIhvvvlGMvt62bvKrmb2W3M9Rbdu3STTK+Q44Pl7+OGHS+YxKuo50n3kHMJ5iT3kRx11lGS+Br5n7AM+55xzJHPOY9cmPeHSYsmSJdKDS9+fTiq7lOmUsr+W6wvoAnfs2FEyHVSORXYKP/TQQ5I5B3GtTkT6NfKax3UavJfiHJPsPI5IXwOnT58umV4vxzvvo+hDH3/88dmvx4wZE3+EP2E1xhhjjDF5jW9YjTHGGGNMXuMbVmOMMcYYk9eUyGGtWbOm+FOPPPKIPP7SSy9JPvLIIyUfeOCBkpOeY0TE1KlTJdP1mDFjhmR2krIT9bTTTpNMT4+eEt3JiPQe8HQBx40bV+zP5F7le++9t2T2NtKrpZvCLlruyct+tGSP5T/+8Y8oDWrVqiV7b48ePVoer1KlimTuS0yfkx4jHTt2a9JdXr58uWQ6S/x53Cec47qovZzpANHFfPLJJyVzXLEHj54UxzZ7IPnvb7rpJslbtmyRTIeouP2bc8ny5cvF2eL4pUfHLkmeP+wgnT17tmQ6r3Qh2Q9I35MOLbsn6autXr06CJ/TIYccIpmONT3Zq6++WjJ7G+mMs9+6bt26kidNmiQ5ee5GpM/XcuXKZb+mx5gratSoIb2kPOd5XLmvPedVrh3gtYVrE/gec47h7+M8zXFEX5TefVFs3rxZ8rp16ySzR5VeLf1u9knTpWY3LNdj0MWeOHFisY/TzcwVlSpVkuss3xeO/3bt2knmNZcON4/lPffcI5lrLHj+8NrATt6NGzdK5pzD+SMiYuDAgZJ5PrAvmp7obrvtJpme74UXXiiZXalcM8HrHddY8PqUnOcfffTR+CP8CasxxhhjjMlrfMNqjDHGGGPyGt+wGmOMMcaYvKZEDuuqVavi+eefz+ZOnTrJ43Rm6AGx++vNN9+UzL2L6ZPRs2MnKvsJ2XXHbjH2m9FtjIi48cYbJbNPrEGDBpKnTJki+euvv5bMPk76Zdddd53k5PsdEXHHHXdIpl9D7zfZPfufeFL/G/z000/SJfnqq6/K43fddZdkds3SnaK7tXLlSsn00TgO2K9LR4/OKrs6eUzpvEakvSSObcJzZYcddpDMfcLpbtKLOuaYY4rNdCnpFLGzsbQoLCyU95teXnJP8Ii0S8ju5mRPdES6+5nHhf4a95Cnh8djz95kns9FeeP0XPmahg8fLpmePHshk556RES9evUk16pVSzJ7V/l86IfSp06+J/TvcgW7ndkputdee0lO9nBGpJ3TihUrSmZnOK81SW83It3Hu2jRIsnVq1eXzGsPHcCRI0cG4fv+3HPPSb7++usl09+mC8l//8orr0hu1KiRZO4HT3eac9SaNWsk89wsLTZv3hxLlizJZs4hS5culczzh/cVdEh5PnKtDO976DNz7PK+h+8z1xvw+hYRsXbtWsm8z1ixYoVkXkO5fof3IbzXYx88X1OTJk0kb+18HD9+fPZrnhtJ/AmrMcYYY4zJa3zDaowxxhhj8hrfsBpjjDHGmLymTCaT+Y+/uUKFCplk7yf/Lbu46HexK7Jly5aS6ZDSNWQPHD0KdlPSV2PPHL2hX375JQj7++issoeQHWncZ/itt96STI+WnaHsB+Tz4R7UdEeSrtbChQtjw4YNZSLHNGjQIJN0a+lf8jjRx6Q3zL2d+Z4NHTpU8oQJEyTTCaIvyq7cp556SjIdJT7/iLR3SO+VmXuTt2nTRjLHAY87nVm6n/Sm7rzzTsl0jtjD16ZNm48zmYyKgTmgWrVqmX322Seb6XPRp2Rm9+OLL74ouUKFCpL5Pr/xxhuS6YLx9/H8P+GEEyTTh6M7FpH2y9jNXKlSJcl8T+jRsxeYfnSfPn0kc99vuot8jfSfk3NMnz59YtasWTmfU1q2bJlJHmt2etI73GmnnSRzHPDawY7uDz/8UDI7xe+//37J9ODZi0nvMelYFvV4RPr62a1bt2KfI48b1yw8+OCDqd+RhF2y3OOe8zTXItCL5BzXt2/fUplTWrdunUnOA/TI6aFzXQi71jk30r+ku0v/mQ5tcl1JRMRxxx0nme4yvfqijiNfA+9DOE/Rg+f14oUXXpDMdSV0vnlvxh7YVatWSebYTs5Rb775ZqxcubLIOcWfsBpjjDHGmLzGN6zGGGOMMSav8Q2rMcYYY4zJa0rUw9qoUSNxJdhvyc5DQheRfWXs0mP/JnvdHn/8ccncL/3888+XTN/z7bffltyjR4/Uc+a+3PRN6GcmuwIj0nvM0+2gg0f/ZfDgwZLpX9Ld4r7kSX9mxIgRURpsu+22sh86fTP6LPSAJ0+eLJneL48BvUP2qNIvHTt2rGQ+v+nTp0umT7phw4Yg7Axmr912220nmftNJ3voIiKmTp0qefTo0ZLp1XKvZ45L7p/Nc43jsLQoW7asuHF0oYYMGSKZ/bJ0cdn5y2NLL29r/bVJvzYi3XVJb5BzFJ3yiLSjSieNni17VOna77777pJr1qwpmfuC07fk2KHvxjkp+fzYZ5or1qxZI2sS2HfL7kaes3S8eW3h+UL3mOstOE5++OEHyfRNn3nmGcmcQ2bNmhWEHdtcA8KxxTnipJNOksy+a/aotmjRQjLH0aeffiqZ5wbHArtoS4uVK1fKOgTOIeygvuaaayQ3b95cMrvN6biyy5nnc9WqVSXzesQe5gMPPFAyr0ePPvpoEN578TXS1Wf3MjvyeSz57/n9bdu2lcz++XfeeUcy79WS18cyZf5YifcnrMYYY4wxJq/xDasxxhhjjMlrfMNqjDHGGGPymhL1sJYpU2ZpRMzL3dMxOaZRJpPZfuvf9j/D4+T/CzxWzH+Cx4n5T/FYMf8JfzhOSnTDaowxxhhjTGljJcAYY4wxxuQ1vmE1xhhjjDF5jW9YjTHGGGNMXuMbVmOMMcYYk9f4htUYY4wxxuQ1vmE1xhhjjDF5jW9YjTHGGGNMXuMbVmOMMcYYk9f4htUYY4wxxuQ1/w/cTNuDXPJF8AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x864 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MdxO-jiSwIWj",
        "colab_type": "text"
      },
      "source": [
        "**Get the best brightest pixel at every column, instead of a single pixel for entire matrix(winner takes all at column level).** \n",
        "\n",
        "**Encoder side**: Some pattern seems to emerge, especially ther 6th image looks a lot like number 8. But it seems like this is also not completely clear. Our encoder might have learnt nothing useful(You are free to imagine whatever shape you can think of from the filters!)\n",
        "\n",
        "**Decoder side**: It seems there are some patterns in decoder , especially the last two images which seems like firing for numbers 3 and 8. Also the 8th image represents a straight line seen for numbers 1 and 7. This straight line pattern wasn't found for examined images in encoder side"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "clkqnc-Suher",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def maskMatrix(inp_tensor):\n",
        "  max_indices = tf.TensorArray(tf.dtypes.int64, 0, True)\n",
        "  #values, indices = tf.math.top_k(inp_tensor, k=2, sorted=True)\n",
        "  for x, i in enumerate(tf.math.argmax(inp_tensor, axis= 1).numpy()):\n",
        "    max_indices = max_indices.write(x, [x,i])\n",
        "  values = tf.ones(inp_tensor.shape[0], tf.dtypes.int64)\n",
        "  return tf.sparse.to_dense(tf.sparse.SparseTensor(max_indices.stack(), values, inp_tensor.shape))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Tkd78u4uhxg",
        "colab_type": "code",
        "outputId": "3e48d2e1-e934-4430-83a8-7c943989eb5c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 686
        }
      },
      "source": [
        "def output_model_encoders_with_argmax(weights, encoder_layer_num=0, filter_shape = (28, 28), images_to_be_compared=5):\n",
        "  #Get the encoder output for each layer\n",
        "  \n",
        "  plt.figure(figsize=(12,12))\n",
        "\n",
        "  for i in range(36):\n",
        "      plt.subplot(6,6,i+1)\n",
        "      plt.xticks([])\n",
        "      plt.yticks([])\n",
        "      plt.grid(False)\n",
        "      \n",
        "      numpy_filter_image = np.reshape(weights[encoder_layer_num][:,i], newshape=filter_shape)\n",
        "\n",
        "      #Convert numpy to tensor for the argmax function, convert it back to numpy after recieving output from function\n",
        "      encoder_img_tnsr = tf.convert_to_tensor(numpy_filter_image, dtype=tf.dtypes.float32)\n",
        "      arg_max_mask_encoder = maskMatrix(encoder_img_tnsr).numpy()\n",
        "      plt.imshow(arg_max_mask_encoder, cmap=plt.cm.binary)     \n",
        "\n",
        "\n",
        "      #plt.xlabel(class_names[test_images[i]])\n",
        "  plt.show()\n",
        "\n",
        "output_model_encoders_with_argmax(weights) "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 36 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6wkBilsquhup",
        "colab_type": "code",
        "outputId": "4fc510bc-122e-470a-9d40-3a04a4e582a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 686
        }
      },
      "source": [
        "def output_model_decoders_with_argmax(weights, decoder_layer_num=14, filter_shape = (28, 28), images_to_be_compared=5):\n",
        "  #Get the decoder output for each layer\n",
        "  \n",
        "  plt.figure(figsize=(12,12))\n",
        "\n",
        "  for i in range(36):\n",
        "      plt.subplot(6,6,i+1)\n",
        "      plt.xticks([])\n",
        "      plt.yticks([])\n",
        "      plt.grid(False)\n",
        "      \n",
        "      numpy_filter_image = np.reshape(weights[decoder_layer_num][i,:], newshape=filter_shape)\n",
        "\n",
        "      #Convert numpy to tensor for the argmax function, convert it back to numpy after recieving output from function\n",
        "      decoder_img_tnsr = tf.convert_to_tensor(numpy_filter_image, dtype=tf.dtypes.float32)\n",
        "      arg_max_mask_decoder = maskMatrix(decoder_img_tnsr).numpy()\n",
        "      plt.imshow(arg_max_mask_decoder, cmap=plt.cm.binary)     \n",
        "\n",
        "\n",
        "      #plt.xlabel(class_names[test_images[i]])\n",
        "  plt.show()\n",
        "\n",
        "output_model_decoders_with_argmax(weights) "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 36 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s2OHWWhVzg_7",
        "colab_type": "text"
      },
      "source": [
        "**Moving on to convolutional encoders**\n",
        "\n",
        "We pick the conv model with FC layer in between, this removes any confusion regardig which dimension should we change as FC has only one dimension for all the records in the batch(1, 64). We cannot split the last part of decoder model, so create a new model and transfer the weights of the decoder part only.\n",
        "Next time we can follow the coding guidelines mentioned in https://stackoverflow.com/questions/48603328/how-do-i-split-an-convolutional-autoencoder/48612403#48612403 to build a splittable model from scratch\n",
        "\n",
        "\n",
        "**Steps:**\n",
        "\n",
        "1) Split the Input-Encoder-Decoder-Output model to Input-Encoder and Decoder-Output model.\n",
        "\n",
        "2) Get the output of encoder for test images and pick a single image of dimension (1,64) from 10000 encodings.\n",
        "\n",
        "3) Change some values of the activations\n",
        "\n",
        "4) Pipe this as input  for decoder-Output network model\n",
        "\n",
        "5) Analyze the output.\n",
        "\n",
        "Steps 3,4,5 can be doe in a loop or a separate function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "21TMJgipuhrv",
        "colab_type": "code",
        "outputId": "0981ffd8-9072-4712-ee4f-f425e5fa0622",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        }
      },
      "source": [
        "for layer in conv_with_fc_model.layers: \n",
        "  print(layer.get_config())\n",
        "\n",
        "conv_with_fc_weights = conv_with_fc_model.get_weights()\n",
        "len(conv_with_fc_weights)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'name': 'conv_enc_0', 'trainable': True, 'batch_input_shape': (None, 28, 28, 1), 'dtype': 'float32', 'filters': 32, 'kernel_size': (3, 3), 'strides': (1, 1), 'padding': 'same', 'data_format': 'channels_last', 'dilation_rate': (1, 1), 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
            "{'name': 'batch_normalization_45', 'trainable': True, 'dtype': 'float32', 'axis': ListWrapper([3]), 'momentum': 0.99, 'epsilon': 0.001, 'center': True, 'scale': True, 'beta_initializer': {'class_name': 'Zeros', 'config': {}}, 'gamma_initializer': {'class_name': 'Ones', 'config': {}}, 'moving_mean_initializer': {'class_name': 'Zeros', 'config': {}}, 'moving_variance_initializer': {'class_name': 'Ones', 'config': {}}, 'beta_regularizer': None, 'gamma_regularizer': None, 'beta_constraint': None, 'gamma_constraint': None}\n",
            "{'name': 'max_pooling2d_45', 'trainable': True, 'dtype': 'float32', 'pool_size': (2, 2), 'padding': 'valid', 'strides': (2, 2), 'data_format': 'channels_last'}\n",
            "{'name': 'conv_enc_1', 'trainable': True, 'dtype': 'float32', 'filters': 64, 'kernel_size': (3, 3), 'strides': (1, 1), 'padding': 'same', 'data_format': 'channels_last', 'dilation_rate': (1, 1), 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
            "{'name': 'batch_normalization_46', 'trainable': True, 'dtype': 'float32', 'axis': ListWrapper([3]), 'momentum': 0.99, 'epsilon': 0.001, 'center': True, 'scale': True, 'beta_initializer': {'class_name': 'Zeros', 'config': {}}, 'gamma_initializer': {'class_name': 'Ones', 'config': {}}, 'moving_mean_initializer': {'class_name': 'Zeros', 'config': {}}, 'moving_variance_initializer': {'class_name': 'Ones', 'config': {}}, 'beta_regularizer': None, 'gamma_regularizer': None, 'beta_constraint': None, 'gamma_constraint': None}\n",
            "{'name': 'max_pooling2d_46', 'trainable': True, 'dtype': 'float32', 'pool_size': (2, 2), 'padding': 'valid', 'strides': (2, 2), 'data_format': 'channels_last'}\n",
            "{'name': 'conv_enc_2', 'trainable': True, 'dtype': 'float32', 'filters': 128, 'kernel_size': (3, 3), 'strides': (1, 1), 'padding': 'same', 'data_format': 'channels_last', 'dilation_rate': (1, 1), 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
            "{'name': 'batch_normalization_47', 'trainable': True, 'dtype': 'float32', 'axis': ListWrapper([3]), 'momentum': 0.99, 'epsilon': 0.001, 'center': True, 'scale': True, 'beta_initializer': {'class_name': 'Zeros', 'config': {}}, 'gamma_initializer': {'class_name': 'Ones', 'config': {}}, 'moving_mean_initializer': {'class_name': 'Zeros', 'config': {}}, 'moving_variance_initializer': {'class_name': 'Ones', 'config': {}}, 'beta_regularizer': None, 'gamma_regularizer': None, 'beta_constraint': None, 'gamma_constraint': None}\n",
            "{'name': 'max_pooling2d_47', 'trainable': True, 'dtype': 'float32', 'pool_size': (2, 2), 'padding': 'valid', 'strides': (2, 2), 'data_format': 'channels_last'}\n",
            "{'name': 'conv_compress_dims', 'trainable': True, 'dtype': 'float32', 'filters': 64, 'kernel_size': (1, 1), 'strides': (1, 1), 'padding': 'same', 'data_format': 'channels_last', 'dilation_rate': (1, 1), 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
            "{'name': 'flatten_5', 'trainable': True, 'dtype': 'float32', 'data_format': 'channels_last'}\n",
            "{'name': 'dense_13', 'trainable': True, 'dtype': 'float32', 'units': 256, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
            "{'name': 'dense_14', 'trainable': True, 'dtype': 'float32', 'units': 64, 'activation': 'leaky_relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
            "{'name': 'dense_15', 'trainable': True, 'dtype': 'float32', 'units': 252, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
            "{'name': 'reshape_1', 'trainable': True, 'batch_input_shape': (None, 252), 'dtype': 'float32', 'target_shape': (3, 3, 28)}\n",
            "{'name': 'batch_normalization_48', 'trainable': True, 'dtype': 'float32', 'axis': ListWrapper([3]), 'momentum': 0.99, 'epsilon': 0.001, 'center': True, 'scale': True, 'beta_initializer': {'class_name': 'Zeros', 'config': {}}, 'gamma_initializer': {'class_name': 'Ones', 'config': {}}, 'moving_mean_initializer': {'class_name': 'Zeros', 'config': {}}, 'moving_variance_initializer': {'class_name': 'Ones', 'config': {}}, 'beta_regularizer': None, 'gamma_regularizer': None, 'beta_constraint': None, 'gamma_constraint': None}\n",
            "{'name': 'conv_dec_0', 'trainable': True, 'dtype': 'float32', 'filters': 32, 'kernel_size': (3, 3), 'strides': (2, 2), 'padding': 'valid', 'data_format': 'channels_last', 'dilation_rate': (1, 1), 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'output_padding': None}\n",
            "{'name': 'batch_normalization_49', 'trainable': True, 'dtype': 'float32', 'axis': ListWrapper([3]), 'momentum': 0.99, 'epsilon': 0.001, 'center': True, 'scale': True, 'beta_initializer': {'class_name': 'Zeros', 'config': {}}, 'gamma_initializer': {'class_name': 'Ones', 'config': {}}, 'moving_mean_initializer': {'class_name': 'Zeros', 'config': {}}, 'moving_variance_initializer': {'class_name': 'Ones', 'config': {}}, 'beta_regularizer': None, 'gamma_regularizer': None, 'beta_constraint': None, 'gamma_constraint': None}\n",
            "{'name': 'conv_dec_1', 'trainable': True, 'dtype': 'float32', 'filters': 16, 'kernel_size': (3, 3), 'strides': (2, 2), 'padding': 'same', 'data_format': 'channels_last', 'dilation_rate': (1, 1), 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'output_padding': None}\n",
            "{'name': 'batch_normalization_50', 'trainable': True, 'dtype': 'float32', 'axis': ListWrapper([3]), 'momentum': 0.99, 'epsilon': 0.001, 'center': True, 'scale': True, 'beta_initializer': {'class_name': 'Zeros', 'config': {}}, 'gamma_initializer': {'class_name': 'Ones', 'config': {}}, 'moving_mean_initializer': {'class_name': 'Zeros', 'config': {}}, 'moving_variance_initializer': {'class_name': 'Ones', 'config': {}}, 'beta_regularizer': None, 'gamma_regularizer': None, 'beta_constraint': None, 'gamma_constraint': None}\n",
            "{'name': 'conv_dec_2', 'trainable': True, 'dtype': 'float32', 'filters': 1, 'kernel_size': (3, 3), 'strides': (2, 2), 'padding': 'same', 'data_format': 'channels_last', 'dilation_rate': (1, 1), 'activation': 'sigmoid', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'output_padding': None}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "44"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5pC4AdqBuho3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def evaluate_and_output_model(model, test_images, images_to_be_compared=5):\n",
        "  class_names = ['0', '1', '2', '3', '4','5', '6', '7', '8', '9']\n",
        "  print(test_images.shape)\n",
        "  #test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
        "  print(test_images.shape)\n",
        "  #print('\\n  Relu Test accuracy:', str(test_acc) + \" test loss: \" + str(test_loss) + \"\\n\")\n",
        "  model_output_images = model.predict(test_images)\n",
        "  print(model_output_images.shape)\n",
        "  plt.figure(figsize=(10,10))\n",
        "  j = -1\n",
        "  for i in range(25):\n",
        "      plt.subplot(5,5,i+1)\n",
        "      plt.xticks([])\n",
        "      plt.yticks([])\n",
        "      plt.grid(False)\n",
        "      j = j+1\n",
        "      if(i%2 == 0):\n",
        "        plt.imshow(test_images[i+10][:,:,0], cmap=plt.cm.binary)\n",
        "      elif(j%2 == 1):\n",
        "        \n",
        "        plt.imshow(model_output_images[j-1+10][:,:,0], cmap=plt.cm.binary)\n",
        "      \n",
        "      #plt.xlabel(class_names[test_images[i]])\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jIYDojGvuhm5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "conv_with_fc_model_output_images = conv_with_fc_model.predict(tf.expand_dims(test_images_raw.astype(np.float32)/255.0, -1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3pSJEKz68oPu",
        "colab_type": "code",
        "outputId": "b288af20-c09b-4760-f12d-5988b1796ca0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 905
        }
      },
      "source": [
        "conv_with_fc_model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_18\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv_enc_0 (Conv2D)          (None, 28, 28, 32)        320       \n",
            "_________________________________________________________________\n",
            "batch_normalization_45 (Batc (None, 28, 28, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_45 (MaxPooling (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv_enc_1 (Conv2D)          (None, 14, 14, 64)        18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_46 (Batc (None, 14, 14, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_46 (MaxPooling (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv_enc_2 (Conv2D)          (None, 7, 7, 128)         73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_47 (Batc (None, 7, 7, 128)         512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_47 (MaxPooling (None, 3, 3, 128)         0         \n",
            "_________________________________________________________________\n",
            "conv_compress_dims (Conv2D)  (None, 3, 3, 64)          8256      \n",
            "_________________________________________________________________\n",
            "flatten_5 (Flatten)          (None, 576)               0         \n",
            "_________________________________________________________________\n",
            "dense_13 (Dense)             (None, 256)               147712    \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             (None, 64)                16448     \n",
            "_________________________________________________________________\n",
            "dense_15 (Dense)             (None, 252)               16380     \n",
            "_________________________________________________________________\n",
            "reshape_1 (Reshape)          (None, 3, 3, 28)          0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_48 (Batc (None, 3, 3, 28)          112       \n",
            "_________________________________________________________________\n",
            "conv_dec_0 (Conv2DTranspose) (None, 7, 7, 32)          8096      \n",
            "_________________________________________________________________\n",
            "batch_normalization_49 (Batc (None, 7, 7, 32)          128       \n",
            "_________________________________________________________________\n",
            "conv_dec_1 (Conv2DTranspose) (None, 14, 14, 16)        4624      \n",
            "_________________________________________________________________\n",
            "batch_normalization_50 (Batc (None, 14, 14, 16)        64        \n",
            "_________________________________________________________________\n",
            "conv_dec_2 (Conv2DTranspose) (None, 28, 28, 1)         145       \n",
            "=================================================================\n",
            "Total params: 295,533\n",
            "Trainable params: 294,933\n",
            "Non-trainable params: 600\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tssy8QSkuhjd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "layer_name = 'dense_14'\n",
        "encoder_model = keras.Model(inputs=conv_with_fc_model.input, outputs=conv_with_fc_model.get_layer(layer_name).output)\n",
        "intermediate_encoder_output = encoder_model(tf.expand_dims(test_images_raw.astype(np.float32)/255.0, -1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1VMQ2T5-9JBT",
        "colab_type": "code",
        "outputId": "bf3d245b-3900-4968-f76b-814291ed28cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "intermediate_encoder_output.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([10000, 64])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BcFwuetfD8ZX",
        "colab_type": "code",
        "outputId": "a944316f-193f-4451-db87-4c48b5d59d56",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        }
      },
      "source": [
        "#  decoder_layer_dims=[32, 16, 1]\n",
        "#decoder_model.add(layers.Dense(64,input_shape = (None,64)))\n",
        "#model = keras.Sequential([keras.layers.Flatten(input_shape=input_shape)])\n",
        "\n",
        "decoder_model = models.Sequential()\n",
        "decoder_model.add(tf.keras.layers.InputLayer(input_shape=(64,)))\n",
        "decoder_model.add(layers.Dense(252,activation=tf.nn.relu))\n",
        "decoder_model.add(layers.Reshape((3, 3, 28), input_shape=(252,)))\n",
        "decoder_model.add(layers.BatchNormalization()) \n",
        "decoder_model.add(layers.Conv2DTranspose(filters = 32, kernel_size=(3, 3),  strides=(2,2), padding = \"valid\", activation=tf.nn.relu))\n",
        "decoder_model.add(layers.BatchNormalization()) \n",
        "decoder_model.add(layers.Conv2DTranspose(filters = 16, kernel_size=(3, 3),  strides=(2,2), padding = \"same\",  activation=tf.nn.relu))\n",
        "decoder_model.add(layers.BatchNormalization())  \n",
        "decoder_model.add(layers.Conv2DTranspose(filters = 1, kernel_size=(3, 3), strides=(2,2), padding = \"same\",  activation=tf.nn.sigmoid))\n",
        "\n",
        "decoder_model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_7 (Dense)              (None, 252)               16380     \n",
            "_________________________________________________________________\n",
            "reshape_3 (Reshape)          (None, 3, 3, 28)          0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_9 (Batch (None, 3, 3, 28)          112       \n",
            "_________________________________________________________________\n",
            "conv2d_transpose_9 (Conv2DTr (None, 7, 7, 32)          8096      \n",
            "_________________________________________________________________\n",
            "batch_normalization_10 (Batc (None, 7, 7, 32)          128       \n",
            "_________________________________________________________________\n",
            "conv2d_transpose_10 (Conv2DT (None, 14, 14, 16)        4624      \n",
            "_________________________________________________________________\n",
            "batch_normalization_11 (Batc (None, 14, 14, 16)        64        \n",
            "_________________________________________________________________\n",
            "conv2d_transpose_11 (Conv2DT (None, 28, 28, 1)         145       \n",
            "=================================================================\n",
            "Total params: 29,549\n",
            "Trainable params: 29,397\n",
            "Non-trainable params: 152\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FM5EItpTKJoV",
        "colab_type": "code",
        "outputId": "422c90e7-df3e-46d1-cfff-459f2f26d522",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 798
        }
      },
      "source": [
        "#tf.shape(conv_with_fc_weights[33])\n",
        "for i in range(len(conv_with_fc_weights)):\n",
        "  print(\"index: \" + str(i) + \" | \" + str(tf.shape(conv_with_fc_weights[i])))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "index: 0 | tf.Tensor([ 3  3  1 32], shape=(4,), dtype=int32)\n",
            "index: 1 | tf.Tensor([32], shape=(1,), dtype=int32)\n",
            "index: 2 | tf.Tensor([32], shape=(1,), dtype=int32)\n",
            "index: 3 | tf.Tensor([32], shape=(1,), dtype=int32)\n",
            "index: 4 | tf.Tensor([32], shape=(1,), dtype=int32)\n",
            "index: 5 | tf.Tensor([32], shape=(1,), dtype=int32)\n",
            "index: 6 | tf.Tensor([ 3  3 32 64], shape=(4,), dtype=int32)\n",
            "index: 7 | tf.Tensor([64], shape=(1,), dtype=int32)\n",
            "index: 8 | tf.Tensor([64], shape=(1,), dtype=int32)\n",
            "index: 9 | tf.Tensor([64], shape=(1,), dtype=int32)\n",
            "index: 10 | tf.Tensor([64], shape=(1,), dtype=int32)\n",
            "index: 11 | tf.Tensor([64], shape=(1,), dtype=int32)\n",
            "index: 12 | tf.Tensor([  3   3  64 128], shape=(4,), dtype=int32)\n",
            "index: 13 | tf.Tensor([128], shape=(1,), dtype=int32)\n",
            "index: 14 | tf.Tensor([128], shape=(1,), dtype=int32)\n",
            "index: 15 | tf.Tensor([128], shape=(1,), dtype=int32)\n",
            "index: 16 | tf.Tensor([128], shape=(1,), dtype=int32)\n",
            "index: 17 | tf.Tensor([128], shape=(1,), dtype=int32)\n",
            "index: 18 | tf.Tensor([  1   1 128  64], shape=(4,), dtype=int32)\n",
            "index: 19 | tf.Tensor([64], shape=(1,), dtype=int32)\n",
            "index: 20 | tf.Tensor([576 256], shape=(2,), dtype=int32)\n",
            "index: 21 | tf.Tensor([256], shape=(1,), dtype=int32)\n",
            "index: 22 | tf.Tensor([256  64], shape=(2,), dtype=int32)\n",
            "index: 23 | tf.Tensor([64], shape=(1,), dtype=int32)\n",
            "index: 24 | tf.Tensor([ 64 252], shape=(2,), dtype=int32)\n",
            "index: 25 | tf.Tensor([252], shape=(1,), dtype=int32)\n",
            "index: 26 | tf.Tensor([28], shape=(1,), dtype=int32)\n",
            "index: 27 | tf.Tensor([28], shape=(1,), dtype=int32)\n",
            "index: 28 | tf.Tensor([28], shape=(1,), dtype=int32)\n",
            "index: 29 | tf.Tensor([28], shape=(1,), dtype=int32)\n",
            "index: 30 | tf.Tensor([ 3  3 32 28], shape=(4,), dtype=int32)\n",
            "index: 31 | tf.Tensor([32], shape=(1,), dtype=int32)\n",
            "index: 32 | tf.Tensor([32], shape=(1,), dtype=int32)\n",
            "index: 33 | tf.Tensor([32], shape=(1,), dtype=int32)\n",
            "index: 34 | tf.Tensor([32], shape=(1,), dtype=int32)\n",
            "index: 35 | tf.Tensor([32], shape=(1,), dtype=int32)\n",
            "index: 36 | tf.Tensor([ 3  3 16 32], shape=(4,), dtype=int32)\n",
            "index: 37 | tf.Tensor([16], shape=(1,), dtype=int32)\n",
            "index: 38 | tf.Tensor([16], shape=(1,), dtype=int32)\n",
            "index: 39 | tf.Tensor([16], shape=(1,), dtype=int32)\n",
            "index: 40 | tf.Tensor([16], shape=(1,), dtype=int32)\n",
            "index: 41 | tf.Tensor([16], shape=(1,), dtype=int32)\n",
            "index: 42 | tf.Tensor([ 3  3  1 16], shape=(4,), dtype=int32)\n",
            "index: 43 | tf.Tensor([1], shape=(1,), dtype=int32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nz8CAc2-SjwD",
        "colab_type": "text"
      },
      "source": [
        "Set the weights of the decoder network to last half of the original network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gl3KSE6XLuwO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "decoder_model.set_weights(conv_with_fc_weights[24:])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cq7ttZ95AJAJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\"\"\"\n",
        "in_layer_name = 'dense_14'\n",
        "out_layer_name = 'conv_dec_2'\n",
        "decoder_model = keras.Model(inputs=conv_with_fc_model.get_layer(in_layer_name).output, outputs=conv_with_fc_model.get_layer(out_layer_name).output)\n",
        "final_decoder_output = decoder_model(intermediate_encoder_output)\n",
        "\"\"\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x5n8xX1AAI5u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def evaluate_decoder_output(model, test_images, images_to_be_compared=5):\n",
        "  print(test_images.shape)\n",
        "  #test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
        "  #print('\\n  Relu Test accuracy:', str(test_acc) + \" test loss: \" + str(test_loss) + \"\\n\")\n",
        "  model_output_images = model.predict(test_images)\n",
        "  print(model_output_images.shape)\n",
        "  plt.figure(figsize=(10,10))\n",
        "  for i in range(25):\n",
        "      plt.subplot(5,5,i+1)\n",
        "      plt.xticks([])\n",
        "      plt.yticks([])\n",
        "      plt.grid(False)\n",
        "      plt.imshow(model_output_images[i][:,:,0], cmap=plt.cm.binary)\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IL_9bcDdRvrm",
        "colab_type": "code",
        "outputId": "95038fa1-c15a-4929-92a0-cd49e0d1bfb9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        }
      },
      "source": [
        "evaluate_decoder_output(decoder_model,intermediate_encoder_output)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10000, 64)\n",
            "(10000, 28, 28, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cIBlUc4LSvi9",
        "colab_type": "code",
        "outputId": "a7995c5f-406d-4f29-c311-08ada5a9c1d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "intermediate_encoder_output[0,:]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(64,), dtype=float32, numpy=\n",
              "array([13.453639  , 31.630007  , 18.185455  , 35.50594   , -4.5379257 ,\n",
              "       -5.4144473 , 26.704481  , -7.1359696 , 32.901005  , 12.701061  ,\n",
              "       -7.1228943 ,  5.684326  , 32.59292   , 60.896988  ,  8.365037  ,\n",
              "        6.3924403 , 13.184331  , -7.024774  , 17.711899  , -0.44040972,\n",
              "       42.78759   , 14.10446   , 10.690807  , 45.10075   , 11.143951  ,\n",
              "       15.829571  , 36.0637    ,  5.4546623 , 58.81245   , 36.271484  ,\n",
              "       18.38586   , 37.012257  , 28.428572  , 15.633062  ,  6.103594  ,\n",
              "       21.474867  , 41.364563  , -2.7767713 , -0.59911764,  0.79074156,\n",
              "        5.509033  , 10.828833  , 38.360165  , 38.817566  , -0.9899268 ,\n",
              "       32.919807  ,  4.8763657 , 14.283403  , 14.318804  , 54.393093  ,\n",
              "       32.04976   , 30.06166   , 25.769444  , -1.8114014 , 21.5779    ,\n",
              "       35.061848  , -5.9616585 , 27.752203  , 25.972221  , 54.787918  ,\n",
              "       -0.6382202 , 22.784504  , 29.357204  , 20.013067  ], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ljyQe5-awrTQ",
        "colab_type": "text"
      },
      "source": [
        "**Just inverted all the encoding values by multiplying with -1. As ecpected we see that the pixels around the edges are getting activated and not the pixels in between as numbers are mostly present in middle of the picture**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Rw2jZq7Svm5",
        "colab_type": "code",
        "outputId": "be775467-47fa-4bb0-e7c1-00c74e5f32aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "changed_encoder_input = tf.expand_dims(tf.multiply(intermediate_encoder_output[0,:] , -1), 0)\n",
        "changed_encoder_input.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([1, 64])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ce_jgg5LXUrk",
        "colab_type": "code",
        "outputId": "42daf6af-a4d5-42a3-cef2-ba0fa446367b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        }
      },
      "source": [
        "ic = decoder_model.predict(changed_encoder_input)\n",
        "plt.figure(figsize=(4,4))\n",
        "plt.imshow(ic[0][:,:,0], cmap=plt.cm.binary)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cuHgokwwbDLR",
        "colab_type": "code",
        "outputId": "361cc37c-d67e-44e4-fa2a-90c4e5056b79",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "changed_encoder_input = tf.multiply(changed_encoder_input,-1)\n",
        "\n",
        "changed_encoder_input"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(1, 64), dtype=float32, numpy=\n",
              "array([[13.453639  , 31.630007  , 18.185455  , 35.50594   , -4.5379257 ,\n",
              "        -5.4144473 , 26.704481  , -7.1359696 , 32.901005  , 12.701061  ,\n",
              "        -7.1228943 ,  5.684326  , 32.59292   , 60.896988  ,  8.365037  ,\n",
              "         6.3924403 , 13.184331  , -7.024774  , 17.711899  , -0.44040972,\n",
              "        42.78759   , 14.10446   , 10.690807  , 45.10075   , 11.143951  ,\n",
              "        15.829571  , 36.0637    ,  5.4546623 , 58.81245   , 36.271484  ,\n",
              "        18.38586   , 37.012257  , 28.428572  , 15.633062  ,  6.103594  ,\n",
              "        21.474867  , 41.364563  , -2.7767713 , -0.59911764,  0.79074156,\n",
              "         5.509033  , 10.828833  , 38.360165  , 38.817566  , -0.9899268 ,\n",
              "        32.919807  ,  4.8763657 , 14.283403  , 14.318804  , 54.393093  ,\n",
              "        32.04976   , 30.06166   , 25.769444  , -1.8114014 , 21.5779    ,\n",
              "        35.061848  , -5.9616585 , 27.752203  , 25.972221  , 54.787918  ,\n",
              "        -0.6382202 , 22.784504  , 29.357204  , 20.013067  ]],\n",
              "      dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Umtlq44pcgHa",
        "colab_type": "code",
        "outputId": "038698b2-ed09-4269-ff7c-7763f1500716",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "lower_tensor = tf.greater(changed_encoder_input,y=40)\n",
        "upper_tensor = tf.less(changed_encoder_input, y=100)\n",
        "in_range = tf.logical_not(tf.logical_and(lower_tensor, upper_tensor))\n",
        "in_range\n",
        "new_encoder_state = tf.where(in_range, x=changed_encoder_input, y=0)\n",
        "new_encoder_state"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(1, 64), dtype=float32, numpy=\n",
              "array([[13.453639  , 31.630007  , 18.185455  , 35.50594   , -4.5379257 ,\n",
              "        -5.4144473 , 26.704481  , -7.1359696 , 32.901005  , 12.701061  ,\n",
              "        -7.1228943 ,  5.684326  , 32.59292   ,  0.        ,  8.365037  ,\n",
              "         6.3924403 , 13.184331  , -7.024774  , 17.711899  , -0.44040972,\n",
              "         0.        , 14.10446   , 10.690807  ,  0.        , 11.143951  ,\n",
              "        15.829571  , 36.0637    ,  5.4546623 ,  0.        , 36.271484  ,\n",
              "        18.38586   , 37.012257  , 28.428572  , 15.633062  ,  6.103594  ,\n",
              "        21.474867  ,  0.        , -2.7767713 , -0.59911764,  0.79074156,\n",
              "         5.509033  , 10.828833  , 38.360165  , 38.817566  , -0.9899268 ,\n",
              "        32.919807  ,  4.8763657 , 14.283403  , 14.318804  ,  0.        ,\n",
              "        32.04976   , 30.06166   , 25.769444  , -1.8114014 , 21.5779    ,\n",
              "        35.061848  , -5.9616585 , 27.752203  , 25.972221  ,  0.        ,\n",
              "        -0.6382202 , 22.784504  , 29.357204  , 20.013067  ]],\n",
              "      dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U2dDIcf0c-_U",
        "colab_type": "code",
        "outputId": "e0bfe11d-b3a6-4e3e-b829-3dcdc27d8289",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        }
      },
      "source": [
        "ic1 = decoder_model.predict(new_encoder_state)\n",
        "plt.figure(figsize=(4,4))\n",
        "plt.imshow(ic1[0][:,:,0], cmap=plt.cm.binary)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Xm5L3/Q3WTHOng1rGu14GWGb8bxr53JW7/HmlGhH2LkmT+n0/MdvWFNy9K7s9JOltNd9S1N3frqCb3R5qcD9/00zLeA+0zLia4Llr5PLnjQj7x5JuMbMfmNlwST+RtKEBfXyPmY3ITpzIzEZImqvmW4p6g6S27H6bpHcb2Mt3NMsy3nnLjKvBz13Dlz9397p/SbpffWfk/0fSPzeih5y+bpL0afb1WaN7k/Sm+l7W/a/6zm0skfR3kjZL2i3pPyWNbaLe/l3SDkmd6gtWa4N6u1N9L9E7JXVkX/c3+rlL9FWX5423ywJBcIIOCIKwA0EQdiAIwg4EQdiBIAg7EARhB4L4P8ZogRukoO/fAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J581vqOJkweW",
        "colab_type": "text"
      },
      "source": [
        "**inside range images:** Check out the stroke thickness changes of 7 which is particularly evident for images present at subplot 2,5,6,7. Especially image at 6 has some part cutoff. Hence encoding whoise value between 30-35 seems important \n",
        "\n",
        "**outside range images:** We again see that just with encoding values between 30-40, that is image at subplot position 3 seems to have figured out the straight line stroke which is necessary for numbers 9,1,7"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CPHqh01IevQE",
        "colab_type": "code",
        "outputId": "06eaf3fc-f19f-4bf1-bd1a-7081ea5dfa62",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 986
        }
      },
      "source": [
        "encoding = tf.expand_dims(intermediate_encoder_output[0,:] , 0)\n",
        "\n",
        "def evaluate_decoder_output(decoder_model=decoder_model, encoding=encoding, manipulation_range=[-30,60], images_to_be_compared=5):\n",
        "  print(encoding.shape)\n",
        "  steps = round((-1 * manipulation_range[0] + manipulation_range[1])/25)\n",
        "  num_itr = round((-1 * manipulation_range[0] + manipulation_range[1])/steps)\n",
        "\n",
        "  upper_limit = manipulation_range[1]\n",
        "  lower_limit = manipulation_range[1]-steps\n",
        "  plt.figure(figsize=(10,10))\n",
        "\n",
        "  for i in range(num_itr):\n",
        "      \n",
        "    print(\"Upper limit and lower limit encoding state: \" + str(upper_limit) + \" : \" + str(lower_limit))\n",
        "    lower_tensor = tf.greater(encoding,y=lower_limit)\n",
        "    upper_tensor = tf.less(encoding, y=upper_limit)\n",
        "    in_range = tf.logical_not(tf.logical_and(lower_tensor, upper_tensor))\n",
        "    not_in_range = tf.logical_and(lower_tensor, upper_tensor)\n",
        "\n",
        "    new_encoder_state_inside_range = tf.where(in_range, x=encoding, y=0)\n",
        "    new_encoder_state_outside_range = tf.where(not_in_range, x=encoding, y=0)\n",
        "    \n",
        "    model_output_images_inside_range = decoder_model.predict(new_encoder_state_inside_range)\n",
        "    model_output_images_outside_range = decoder_model.predict(new_encoder_state_outside_range)\n",
        "  \n",
        "    plt.subplot(5,5,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(model_output_images_inside_range[0][:,:,0], cmap=plt.cm.binary)\n",
        "    \n",
        "    upper_limit = lower_limit\n",
        "    lower_limit = lower_limit - steps\n",
        "\n",
        "  plt.show()\n",
        "\n",
        "  return;\n",
        "\n",
        "\"\"\"\n",
        "    plt.subplot(5,5,i+2)\n",
        "    \n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(model_output_images_outside_range[0][:,:,0], cmap=\"Greys_r\")\n",
        "\"\"\"\n",
        "\n",
        "  \n",
        "evaluate_decoder_output()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 64)\n",
            "Upper limit and lower limit encoding state: 60 : 56\n",
            "Upper limit and lower limit encoding state: 56 : 52\n",
            "Upper limit and lower limit encoding state: 52 : 48\n",
            "Upper limit and lower limit encoding state: 48 : 44\n",
            "Upper limit and lower limit encoding state: 44 : 40\n",
            "Upper limit and lower limit encoding state: 40 : 36\n",
            "Upper limit and lower limit encoding state: 36 : 32\n",
            "Upper limit and lower limit encoding state: 32 : 28\n",
            "Upper limit and lower limit encoding state: 28 : 24\n",
            "Upper limit and lower limit encoding state: 24 : 20\n",
            "Upper limit and lower limit encoding state: 20 : 16\n",
            "Upper limit and lower limit encoding state: 16 : 12\n",
            "Upper limit and lower limit encoding state: 12 : 8\n",
            "Upper limit and lower limit encoding state: 8 : 4\n",
            "Upper limit and lower limit encoding state: 4 : 0\n",
            "Upper limit and lower limit encoding state: 0 : -4\n",
            "Upper limit and lower limit encoding state: -4 : -8\n",
            "Upper limit and lower limit encoding state: -8 : -12\n",
            "Upper limit and lower limit encoding state: -12 : -16\n",
            "Upper limit and lower limit encoding state: -16 : -20\n",
            "Upper limit and lower limit encoding state: -20 : -24\n",
            "Upper limit and lower limit encoding state: -24 : -28\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 22 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MI-lwwXesyCz",
        "colab_type": "code",
        "outputId": "985e5f70-7cf5-44d8-af30-877c9e026e99",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        }
      },
      "source": [
        "encoding = tf.expand_dims(intermediate_encoder_output[0,:] , 0)\n",
        "\n",
        "def evaluate_decoder_output(decoder_model=decoder_model, encoding=encoding, manipulation_range=[-30,60], images_to_be_compared=5):\n",
        "  print(encoding.shape)\n",
        "  steps = round((-1 * manipulation_range[0] + manipulation_range[1])/10)\n",
        "  num_itr = round((-1 * manipulation_range[0] + manipulation_range[1])/steps)\n",
        "\n",
        "  upper_limit = manipulation_range[1]\n",
        "  lower_limit = manipulation_range[1]-steps\n",
        "  plt.figure(figsize=(10,10))\n",
        "\n",
        "  for i in range(num_itr):\n",
        "      \n",
        "    print(\"Upper limit and lower limit encoding state: \" + str(upper_limit) + \" : \" + str(lower_limit))\n",
        "    lower_tensor = tf.greater(encoding,y=lower_limit)\n",
        "    upper_tensor = tf.less(encoding, y=upper_limit)\n",
        "    in_range = tf.logical_not(tf.logical_and(lower_tensor, upper_tensor))\n",
        "    not_in_range = tf.logical_and(lower_tensor, upper_tensor)\n",
        "\n",
        "    new_encoder_state_inside_range = tf.where(in_range, x=encoding, y=0)\n",
        "    new_encoder_state_outside_range = tf.where(not_in_range, x=encoding, y=0)\n",
        "    \n",
        "    model_output_images_inside_range = decoder_model.predict(new_encoder_state_inside_range)\n",
        "    model_output_images_outside_range = decoder_model.predict(new_encoder_state_outside_range)\n",
        "  \n",
        "    plt.subplot(5,5,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(model_output_images_outside_range[0][:,:,0], cmap=plt.cm.binary)\n",
        "    \n",
        "    upper_limit = lower_limit\n",
        "    lower_limit = lower_limit - steps\n",
        "\n",
        "  plt.show()\n",
        "\n",
        "  return;\n",
        "\n",
        "\"\"\"\n",
        "    plt.subplot(5,5,i+2)\n",
        "    \n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(model_output_images_outside_range[0][:,:,0], cmap=\"Greys_r\")\n",
        "\"\"\"\n",
        "\n",
        "  \n",
        "evaluate_decoder_output()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 64)\n",
            "Upper limit and lower limit encoding state: 60 : 51\n",
            "Upper limit and lower limit encoding state: 51 : 42\n",
            "Upper limit and lower limit encoding state: 42 : 33\n",
            "Upper limit and lower limit encoding state: 33 : 24\n",
            "Upper limit and lower limit encoding state: 24 : 15\n",
            "Upper limit and lower limit encoding state: 15 : 6\n",
            "Upper limit and lower limit encoding state: 6 : -3\n",
            "Upper limit and lower limit encoding state: -3 : -12\n",
            "Upper limit and lower limit encoding state: -12 : -21\n",
            "Upper limit and lower limit encoding state: -21 : -30\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_WHw0ZEW9yNn",
        "colab_type": "text"
      },
      "source": [
        "# **Unsupervised Pretraining**\n",
        "\n",
        "1) Train autoencoder – freeze encoder – train classifier on top (as described above).\n",
        "\n",
        "2) Train autoencoder – train classifier on top of encoder. Do not freeze the encoder, i.e. the encoder is “fine-tuned” on the labeled subset of data as well.\n",
        "\n",
        "3) Train a classifier directly on the labeled subset; no pretraining. For fairness, it should have the same architecture as the encoder + classifier above.\n",
        "\n",
        "**Credits:**\n",
        "Logistic regression using Keras - https://medium.com/hackernoon/logistic-regression-with-tensorflow-and-keras-83d2487aed89\n",
        "\n",
        "**Summary:**\n",
        "\n",
        "**Option 1:** Provided the worst accuracy where freezed encoder output was used to train a neural network classifier. Accuracy around 68% and loss did not drop well.\n",
        "\n",
        "**Option 2:** When encoder was stacked on top of classifier and trained with it on dataset, we saw validation accuracy go up till 98-99% which was greater compared to simpler classifier. hence encoder was finetuned on the data. Also the loss decreased and saturated to much lower value than it did for freezed encoder Option 1.\n",
        "\n",
        "**Option 3:** Simple neural network classifier gave 96% validation accuracy\n",
        "\n",
        "**Error: **ResourceExhaustedError: OOM when allocating tensor with shape[60000,32,28,28] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [Op:Conv2D]\n",
        "\n",
        "The dataset may be too huge to fit/predict. do batchwise prediction manually and concatenate the results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wDHqnwo2K3XT",
        "colab_type": "text"
      },
      "source": [
        "**Option 3:** **Training a Logistic regression classifier**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wvLwPEm0KlSv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mnist = tf.keras.datasets.mnist\n",
        "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\n",
        "train_images, test_images = train_images / 255.0, test_images / 255.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wM0dFlGeK8Y5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "logistic_regression_model = tf.keras.models.Sequential([\n",
        "tf.keras.layers.Flatten(),\n",
        "tf.keras.layers.Dense(32, activation=tf.nn.relu),\n",
        "tf.keras.layers.Dense(16, activation=tf.nn.relu),\n",
        "tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BVH4D_l0K8eb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "logistic_regression_model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hNnkislDK8bw",
        "colab_type": "code",
        "outputId": "65228fb4-585f-4682-fa29-7116e1d370f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "logistic_regression_model.fit(train_images, train_labels, epochs=7, validation_data=(test_images, test_labels))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/7\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3829 - accuracy: 0.8886 - val_loss: 0.2128 - val_accuracy: 0.9384\n",
            "Epoch 2/7\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1897 - accuracy: 0.9443 - val_loss: 0.1628 - val_accuracy: 0.9515\n",
            "Epoch 3/7\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1505 - accuracy: 0.9556 - val_loss: 0.1464 - val_accuracy: 0.9561\n",
            "Epoch 4/7\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1255 - accuracy: 0.9623 - val_loss: 0.1413 - val_accuracy: 0.9552\n",
            "Epoch 5/7\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1097 - accuracy: 0.9670 - val_loss: 0.1225 - val_accuracy: 0.9629\n",
            "Epoch 6/7\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0968 - accuracy: 0.9710 - val_loss: 0.1297 - val_accuracy: 0.9618\n",
            "Epoch 7/7\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0868 - accuracy: 0.9731 - val_loss: 0.1326 - val_accuracy: 0.9626\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f022015f9b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zQ2KygPSjP3x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "56a5db19-1dc9-4a87-db4d-b9ca2c5e0855"
      },
      "source": [
        "logitstic_regresssion_model_output = logistic_regression_model(test_images)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Layer flatten is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
            "\n",
            "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
            "\n",
            "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VPsRIXnWMZJN",
        "colab_type": "text"
      },
      "source": [
        "**Option 1: Stacking freezed encoder output as input to new logistic regression model. Checking the metrics**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "APUXmtjuMG-t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "stacked_logistic_regression_model = tf.keras.models.Sequential([\n",
        "tf.keras.layers.Flatten(),\n",
        "tf.keras.layers.Dense(32, activation=tf.nn.relu),\n",
        "tf.keras.layers.Dense(16, activation=tf.nn.relu),\n",
        "tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZdM27IWLMHQ7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "stacked_logistic_regression_model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VuCQfL-NM-ba",
        "colab_type": "code",
        "outputId": "c8085a66-8d9b-4b00-f2b2-3d09e21aa5ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        }
      },
      "source": [
        "!unzip /content/conv_model_with_fc_noise.zip -d /content/conv_model_with_fc_noise\n",
        "conv_with_fc_model_noise = tf.keras.models.load_model('/content/conv_model_with_fc_noise')"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  /content/conv_model_with_fc_noise.zip\n",
            "replace /content/conv_model_with_fc_noise/variables/variables.index? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            "  inflating: /content/conv_model_with_fc_noise/variables/variables.index  \n",
            "replace /content/conv_model_with_fc_noise/__MACOSX/variables/._variables.index? [y]es, [n]o, [A]ll, [N]one, [r]ename: A\n",
            "  inflating: /content/conv_model_with_fc_noise/__MACOSX/variables/._variables.index  \n",
            "  inflating: /content/conv_model_with_fc_noise/variables/variables.data-00000-of-00002  \n",
            "  inflating: /content/conv_model_with_fc_noise/__MACOSX/variables/._variables.data-00000-of-00002  \n",
            "  inflating: /content/conv_model_with_fc_noise/variables/variables.data-00001-of-00002  \n",
            "  inflating: /content/conv_model_with_fc_noise/__MACOSX/variables/._variables.data-00001-of-00002  \n",
            "  inflating: /content/conv_model_with_fc_noise/__MACOSX/._variables  \n",
            "  inflating: /content/conv_model_with_fc_noise/saved_model.pb  \n",
            "  inflating: /content/conv_model_with_fc_noise/__MACOSX/._saved_model.pb  \n",
            "  inflating: /content/conv_model_with_fc_noise/__MACOSX/._assets  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lu5RBc2-M-hO",
        "colab_type": "code",
        "outputId": "ce54095c-df1d-4914-cd88-a7e20e88d88d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        }
      },
      "source": [
        "for layer in conv_with_fc_model_noise.layers: \n",
        "  print(layer.get_config())\n",
        "\n",
        "conv_with_fc_noise_weights = conv_with_fc_model_noise.get_weights()\n",
        "len(conv_with_fc_noise_weights)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'name': 'conv_enc_0', 'trainable': True, 'batch_input_shape': (None, 28, 28, 1), 'dtype': 'float32', 'filters': 32, 'kernel_size': (3, 3), 'strides': (1, 1), 'padding': 'same', 'data_format': 'channels_last', 'dilation_rate': (1, 1), 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
            "{'name': 'batch_normalization_6', 'trainable': True, 'dtype': 'float32', 'axis': ListWrapper([3]), 'momentum': 0.99, 'epsilon': 0.001, 'center': True, 'scale': True, 'beta_initializer': {'class_name': 'Zeros', 'config': {}}, 'gamma_initializer': {'class_name': 'Ones', 'config': {}}, 'moving_mean_initializer': {'class_name': 'Zeros', 'config': {}}, 'moving_variance_initializer': {'class_name': 'Ones', 'config': {}}, 'beta_regularizer': None, 'gamma_regularizer': None, 'beta_constraint': None, 'gamma_constraint': None}\n",
            "{'name': 'max_pooling2d_3', 'trainable': True, 'dtype': 'float32', 'pool_size': (2, 2), 'padding': 'valid', 'strides': (2, 2), 'data_format': 'channels_last'}\n",
            "{'name': 'conv_enc_1', 'trainable': True, 'dtype': 'float32', 'filters': 64, 'kernel_size': (3, 3), 'strides': (1, 1), 'padding': 'same', 'data_format': 'channels_last', 'dilation_rate': (1, 1), 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
            "{'name': 'batch_normalization_7', 'trainable': True, 'dtype': 'float32', 'axis': ListWrapper([3]), 'momentum': 0.99, 'epsilon': 0.001, 'center': True, 'scale': True, 'beta_initializer': {'class_name': 'Zeros', 'config': {}}, 'gamma_initializer': {'class_name': 'Ones', 'config': {}}, 'moving_mean_initializer': {'class_name': 'Zeros', 'config': {}}, 'moving_variance_initializer': {'class_name': 'Ones', 'config': {}}, 'beta_regularizer': None, 'gamma_regularizer': None, 'beta_constraint': None, 'gamma_constraint': None}\n",
            "{'name': 'max_pooling2d_4', 'trainable': True, 'dtype': 'float32', 'pool_size': (2, 2), 'padding': 'valid', 'strides': (2, 2), 'data_format': 'channels_last'}\n",
            "{'name': 'conv_enc_2', 'trainable': True, 'dtype': 'float32', 'filters': 128, 'kernel_size': (3, 3), 'strides': (1, 1), 'padding': 'same', 'data_format': 'channels_last', 'dilation_rate': (1, 1), 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
            "{'name': 'batch_normalization_8', 'trainable': True, 'dtype': 'float32', 'axis': ListWrapper([3]), 'momentum': 0.99, 'epsilon': 0.001, 'center': True, 'scale': True, 'beta_initializer': {'class_name': 'Zeros', 'config': {}}, 'gamma_initializer': {'class_name': 'Ones', 'config': {}}, 'moving_mean_initializer': {'class_name': 'Zeros', 'config': {}}, 'moving_variance_initializer': {'class_name': 'Ones', 'config': {}}, 'beta_regularizer': None, 'gamma_regularizer': None, 'beta_constraint': None, 'gamma_constraint': None}\n",
            "{'name': 'max_pooling2d_5', 'trainable': True, 'dtype': 'float32', 'pool_size': (2, 2), 'padding': 'valid', 'strides': (2, 2), 'data_format': 'channels_last'}\n",
            "{'name': 'conv_compress_dims', 'trainable': True, 'dtype': 'float32', 'filters': 64, 'kernel_size': (1, 1), 'strides': (1, 1), 'padding': 'same', 'data_format': 'channels_last', 'dilation_rate': (1, 1), 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
            "{'name': 'flatten_1', 'trainable': True, 'dtype': 'float32', 'data_format': 'channels_last'}\n",
            "{'name': 'dense_3', 'trainable': True, 'dtype': 'float32', 'units': 256, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
            "{'name': 'dense_4', 'trainable': True, 'dtype': 'float32', 'units': 64, 'activation': 'leaky_relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
            "{'name': 'dense_5', 'trainable': True, 'dtype': 'float32', 'units': 252, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
            "{'name': 'reshape_1', 'trainable': True, 'batch_input_shape': (None, 252), 'dtype': 'float32', 'target_shape': (3, 3, 28)}\n",
            "{'name': 'batch_normalization_9', 'trainable': True, 'dtype': 'float32', 'axis': ListWrapper([3]), 'momentum': 0.99, 'epsilon': 0.001, 'center': True, 'scale': True, 'beta_initializer': {'class_name': 'Zeros', 'config': {}}, 'gamma_initializer': {'class_name': 'Ones', 'config': {}}, 'moving_mean_initializer': {'class_name': 'Zeros', 'config': {}}, 'moving_variance_initializer': {'class_name': 'Ones', 'config': {}}, 'beta_regularizer': None, 'gamma_regularizer': None, 'beta_constraint': None, 'gamma_constraint': None}\n",
            "{'name': 'conv_dec_0', 'trainable': True, 'dtype': 'float32', 'filters': 32, 'kernel_size': (3, 3), 'strides': (2, 2), 'padding': 'valid', 'data_format': 'channels_last', 'dilation_rate': (1, 1), 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'output_padding': None}\n",
            "{'name': 'batch_normalization_10', 'trainable': True, 'dtype': 'float32', 'axis': ListWrapper([3]), 'momentum': 0.99, 'epsilon': 0.001, 'center': True, 'scale': True, 'beta_initializer': {'class_name': 'Zeros', 'config': {}}, 'gamma_initializer': {'class_name': 'Ones', 'config': {}}, 'moving_mean_initializer': {'class_name': 'Zeros', 'config': {}}, 'moving_variance_initializer': {'class_name': 'Ones', 'config': {}}, 'beta_regularizer': None, 'gamma_regularizer': None, 'beta_constraint': None, 'gamma_constraint': None}\n",
            "{'name': 'conv_dec_1', 'trainable': True, 'dtype': 'float32', 'filters': 16, 'kernel_size': (3, 3), 'strides': (2, 2), 'padding': 'same', 'data_format': 'channels_last', 'dilation_rate': (1, 1), 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'output_padding': None}\n",
            "{'name': 'batch_normalization_11', 'trainable': True, 'dtype': 'float32', 'axis': ListWrapper([3]), 'momentum': 0.99, 'epsilon': 0.001, 'center': True, 'scale': True, 'beta_initializer': {'class_name': 'Zeros', 'config': {}}, 'gamma_initializer': {'class_name': 'Ones', 'config': {}}, 'moving_mean_initializer': {'class_name': 'Zeros', 'config': {}}, 'moving_variance_initializer': {'class_name': 'Ones', 'config': {}}, 'beta_regularizer': None, 'gamma_regularizer': None, 'beta_constraint': None, 'gamma_constraint': None}\n",
            "{'name': 'conv_dec_2', 'trainable': True, 'dtype': 'float32', 'filters': 1, 'kernel_size': (3, 3), 'strides': (2, 2), 'padding': 'same', 'data_format': 'channels_last', 'dilation_rate': (1, 1), 'activation': 'sigmoid', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'output_padding': None}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "44"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F3Ul3GwvM-mS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "conv_with_fc_model_noise_output_images = conv_with_fc_model_noise.predict(tf.expand_dims(test_images.astype(np.float32), -1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8A64DQnZTY-t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9109107f-ec8c-453d-ce40-454428870c1d"
      },
      "source": [
        "tf.expand_dims(test_images.astype(np.float32), -1).shape"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([10000, 28, 28, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xIfQ-g6GM-eT",
        "colab_type": "code",
        "outputId": "cef5657a-fd80-43bd-d3a3-3ac3dd6e1e00",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "layer_name = 'dense_4'\n",
        "encoder_model = keras.Model(inputs=conv_with_fc_model_noise.input, outputs=conv_with_fc_model_noise.get_layer(layer_name).output)\n",
        "intermediate_encoder_test_output = encoder_model(tf.expand_dims(test_images.astype(np.float32), -1))\n",
        "intermediate_encoder_test_output.shape"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([10000, 64])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YRtooTsoUCh8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 656
        },
        "outputId": "cf551193-d8fc-4695-9092-c75228cd903b"
      },
      "source": [
        "encoder_model.summary()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 28, 28, 1)]       0         \n",
            "_________________________________________________________________\n",
            "conv_enc_0 (Conv2D)          (None, 28, 28, 32)        320       \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (Batch (None, 28, 28, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv_enc_1 (Conv2D)          (None, 14, 14, 64)        18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_7 (Batch (None, 14, 14, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv_enc_2 (Conv2D)          (None, 7, 7, 128)         73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_8 (Batch (None, 7, 7, 128)         512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2 (None, 3, 3, 128)         0         \n",
            "_________________________________________________________________\n",
            "conv_compress_dims (Conv2D)  (None, 3, 3, 64)          8256      \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 576)               0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 256)               147712    \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 64)                16448     \n",
            "=================================================================\n",
            "Total params: 265,984\n",
            "Trainable params: 265,536\n",
            "Non-trainable params: 448\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8IWE6XEDU84G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "all_train_images = tf.expand_dims(train_images.astype(np.float32), -1)\n",
        "train_images_1 = all_train_images[0:10000,:,:,:]\n",
        "train_images_2 = all_train_images[10000:20000,:,:,:]\n",
        "train_images_3 = all_train_images[20000:30000,:,:,:]\n",
        "train_images_4 = all_train_images[30000:40000,:,:,:]\n",
        "train_images_5 = all_train_images[40000:50000,:,:,:]\n",
        "train_images_6 = all_train_images[50000:60000,:,:,:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-xsS542AVer2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "intermediate_encoder_train_output_1 = encoder_model(train_images_1)\n",
        "intermediate_encoder_train_output_2 = encoder_model(train_images_2)\n",
        "intermediate_encoder_train_output_3 = encoder_model(train_images_3)\n",
        "intermediate_encoder_train_output_4 = encoder_model(train_images_4)\n",
        "intermediate_encoder_train_output_5 = encoder_model(train_images_5)\n",
        "intermediate_encoder_train_output_6 = encoder_model(train_images_6)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rm8p_otSVent",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "intermediate_encoder_train_output = tf.concat(values=[intermediate_encoder_train_output_1, intermediate_encoder_train_output_2, \n",
        "                                                      intermediate_encoder_train_output_3, intermediate_encoder_train_output_4, intermediate_encoder_train_output_5,\n",
        "                                                      intermediate_encoder_train_output_6], axis = 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nqTjVHzCT1Kj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "ebe937bc-eebd-49ed-e404-cc309c05e1c1"
      },
      "source": [
        "#intermediate_encoder_train_output = encoder_model()\n",
        "print(intermediate_encoder_test_output.shape)\n",
        "print(intermediate_encoder_train_output.shape)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10000, 64)\n",
            "(60000, 64)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6SLakBqxTPag",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1830efca-4889-4826-cfda-fae0b81838fb"
      },
      "source": [
        "tf.expand_dims(train_images.astype(np.float32), -1).shape"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([60000, 28, 28, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vj5tLPXbM1A0",
        "colab_type": "code",
        "outputId": "853b61c7-eb7a-4028-e11a-c921922fd468",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 745
        }
      },
      "source": [
        "stacked_logistic_regression_model.fit(intermediate_encoder_train_output, train_labels, epochs=20, validation_data=(intermediate_encoder_test_output, test_labels))"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.4402 - accuracy: 0.4576 - val_loss: 1.3489 - val_accuracy: 0.5003\n",
            "Epoch 2/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.4060 - accuracy: 0.4769 - val_loss: 1.3264 - val_accuracy: 0.5282\n",
            "Epoch 3/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.3592 - accuracy: 0.5081 - val_loss: 1.2455 - val_accuracy: 0.5602\n",
            "Epoch 4/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.3059 - accuracy: 0.5416 - val_loss: 1.2705 - val_accuracy: 0.5425\n",
            "Epoch 5/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.2265 - accuracy: 0.5799 - val_loss: 1.3537 - val_accuracy: 0.5027\n",
            "Epoch 6/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.1492 - accuracy: 0.6105 - val_loss: 1.0433 - val_accuracy: 0.6453\n",
            "Epoch 7/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.1045 - accuracy: 0.6256 - val_loss: 1.1280 - val_accuracy: 0.6170\n",
            "Epoch 8/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.0841 - accuracy: 0.6302 - val_loss: 0.9920 - val_accuracy: 0.6623\n",
            "Epoch 9/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.0571 - accuracy: 0.6413 - val_loss: 0.9988 - val_accuracy: 0.6421\n",
            "Epoch 10/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.0440 - accuracy: 0.6445 - val_loss: 0.9829 - val_accuracy: 0.6646\n",
            "Epoch 11/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.0309 - accuracy: 0.6482 - val_loss: 0.9645 - val_accuracy: 0.6717\n",
            "Epoch 12/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.0203 - accuracy: 0.6544 - val_loss: 1.0605 - val_accuracy: 0.6235\n",
            "Epoch 13/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.0228 - accuracy: 0.6507 - val_loss: 1.0369 - val_accuracy: 0.6356\n",
            "Epoch 14/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 1.0022 - accuracy: 0.6572 - val_loss: 0.9349 - val_accuracy: 0.6863\n",
            "Epoch 15/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.9969 - accuracy: 0.6611 - val_loss: 1.3347 - val_accuracy: 0.5606\n",
            "Epoch 16/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.9851 - accuracy: 0.6639 - val_loss: 0.9343 - val_accuracy: 0.6830\n",
            "Epoch 17/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.9823 - accuracy: 0.6642 - val_loss: 0.9137 - val_accuracy: 0.6937\n",
            "Epoch 18/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.9766 - accuracy: 0.6666 - val_loss: 0.9623 - val_accuracy: 0.6714\n",
            "Epoch 19/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.9666 - accuracy: 0.6704 - val_loss: 0.9380 - val_accuracy: 0.6859\n",
            "Epoch 20/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.9620 - accuracy: 0.6718 - val_loss: 0.9195 - val_accuracy: 0.6808\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f01c02bb7f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1k3nCGnWM1HE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_output = stacked_logistic_regression_model.predict(intermediate_encoder_test_output)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rZ6wD8KjU0HW",
        "colab_type": "code",
        "outputId": "10fcf111-c3ca-4a05-b367-6ec12fb17186",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "tf.math.argmax(test_output[:], axis=0)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=int64, numpy=array([9060, 2706, 5761, 9006, 7886, 7622, 8499, 7098, 7735, 4505])>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VLE0NQW7We2B",
        "colab_type": "code",
        "outputId": "39ef0b3a-f7c0-46e5-9d7f-28edcb78471d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "test_output[0,:]"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([8.3514471e-03, 2.4995202e-04, 3.2485509e-03, 6.3309461e-02,\n",
              "       2.6432124e-05, 5.7151191e-02, 2.4524304e-06, 8.5127425e-01,\n",
              "       4.1699256e-03, 1.2216434e-02], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vwW6d8w6M1ET",
        "colab_type": "code",
        "outputId": "b6fce29a-8784-47d2-b4a2-ca362d831302",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        }
      },
      "source": [
        "def display_model_output_with_actual_test_labels(model_output, actual_labels):\n",
        "  images to be compared = 10\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.10646329, 0.2668148 , 0.0400741 , ..., 0.08671366, 0.05830615,\n",
              "        0.07980853],\n",
              "       [0.11828209, 0.25999004, 0.0400731 , ..., 0.08917951, 0.05847553,\n",
              "        0.08011828],\n",
              "       [0.09121314, 0.2740859 , 0.04089353, ..., 0.08289477, 0.05898764,\n",
              "        0.07978667],\n",
              "       ...,\n",
              "       [0.0871958 , 0.26466927, 0.04268398, ..., 0.08071279, 0.05920096,\n",
              "        0.0845837 ],\n",
              "       [0.0939716 , 0.2625935 , 0.04204065, ..., 0.08204732, 0.05899849,\n",
              "        0.0825301 ],\n",
              "       [0.10860542, 0.24563655, 0.04175898, ..., 0.08154271, 0.05780326,\n",
              "        0.08091093]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vf8XL4a5omg-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "stacked_logistic_regression_model2 = tf.keras.models.Sequential([\n",
        "encoder_model,\n",
        "tf.keras.layers.Dense(32, activation=tf.nn.relu),\n",
        "tf.keras.layers.Dense(16, activation=tf.nn.relu),\n",
        "tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "])\n",
        "stacked_logistic_regression_model2.compile(optimizer='adam', loss='sparse_categorical_crossentropy',metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ThFRkiJNomeV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "5acdd057-27e9-4277-be2d-3b9f310a2809"
      },
      "source": [
        "stacked_logistic_regression_model2.fit(train_images, train_labels, epochs=7, validation_data=(test_images, test_labels))"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/7\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.5237 - accuracy: 0.8862 - val_loss: 0.0865 - val_accuracy: 0.9733\n",
            "Epoch 2/7\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0655 - accuracy: 0.9804 - val_loss: 0.0794 - val_accuracy: 0.9782\n",
            "Epoch 3/7\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0481 - accuracy: 0.9861 - val_loss: 0.0513 - val_accuracy: 0.9856\n",
            "Epoch 4/7\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0384 - accuracy: 0.9887 - val_loss: 0.0482 - val_accuracy: 0.9856\n",
            "Epoch 5/7\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0339 - accuracy: 0.9901 - val_loss: 0.0817 - val_accuracy: 0.9788\n",
            "Epoch 6/7\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0277 - accuracy: 0.9918 - val_loss: 0.0410 - val_accuracy: 0.9885\n",
            "Epoch 7/7\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0242 - accuracy: 0.9930 - val_loss: 0.0473 - val_accuracy: 0.9887\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7effa45dd9b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ATIJjMsJuosQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "option_2_output = stacked_logistic_regression_model2.predict(test_images)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EjIi7Wapombq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def compare_models(test_labels=test_labels,option_1_output = test_output, option_2_output=option_2_output, option_3_output = logistic_regression_model):\n",
        "\n",
        "  option_1_preds = tf.argmax(option_1_output, axis=1,output_type=tf.int32)\n",
        "  option_1_acc = tf.reduce_mean(tf.cast(tf.equal(option_1_preds, test_labels),tf.float32))\n",
        "  print(\"Accuracy of Option 1 set: \" + str(option_1_acc.numpy()))\n",
        "\n",
        "  option_2_preds = tf.argmax(option_2_output, axis=1,output_type=tf.int32)\n",
        "  option_2_acc = tf.reduce_mean(tf.cast(tf.equal(option_2_preds, test_labels),tf.float32))\n",
        "  print(\"Accuracy of Option 2 set: \" + str(option_2_acc.numpy()))\n",
        "  \n",
        "  option_3_preds = tf.argmax(option_3_output, axis=1,output_type=tf.int32)\n",
        "  option_3_acc = tf.reduce_mean(tf.cast(tf.equal(option_3_preds, test_labels),tf.float32))\n",
        "  print(\"Accuracy of Option 3 set: \" + str(option_3_acc.numpy()))\n",
        "\n",
        "  return ;"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RYcH0LIoomYw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "d59a72d3-1f0a-4c13-a64a-1ddb1ce6f3c5"
      },
      "source": [
        "compare_models(test_labels, option_1_output = test_output, option_3_output = logitstic_regresssion_model_output)"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of Option 1 set: 0.6808\n",
            "Accuracy of Option 2 set: 0.9887\n",
            "Accuracy of Option 3 set: 0.9626\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wB7sDJfmukcL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VId2QuKc7Hwi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3SOUr4e0XMsQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(train_images_raw, train_labels_raw), (test_images_raw, test_labels_raw) = mnist.load_data()\n",
        "\n",
        "train_data = tf.data.Dataset.from_tensor_slices(( train_images_raw.astype(np.float32)/255.0, train_images_raw.astype(np.float32)/255.0 ))\n",
        "\n",
        "train_data = train_data.shuffle(buffer_size = train_images_raw.shape[0]).batch(32)\n",
        "\n",
        "#test_data = tf.data.Dataset.from_tensor_slices(( test_images_raw.astype(np.float32)/255.0, test_labels_raw.astype(np.int32) ))\n",
        "test_data = tf.data.Dataset.from_tensor_slices(( test_images_raw.astype(np.float32)/255.0,  test_images_raw.astype(np.float32)/255.0 ))\n",
        "test_data = test_data.batch(32)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pfk-xmlVcKiq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "def add_noise(img):\n",
        "    '''Add random noise to an image'''\n",
        "    VARIABILITY = 25\n",
        "    deviation = VARIABILITY*random.random()\n",
        "    noise = np.random.normal(0, deviation, img.shape)\n",
        "    img += noise\n",
        "    np.clip(img, 0., 255.)\n",
        "    return img"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LPZHgGHAWQWm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "datagen = ImageDataGenerator(rotation_range = 15, horizontal_flip = True, width_shift_range = 0.1, height_shift_range = 0.1, zoom_range = 0.2, shear_range = 15, preprocessing_function=add_noise )\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0GKbY-mvWQds",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create_conv_autoencoder_model(encoder_layer_dims=[32,64,128], compression_layer_dim=64, decoder_layer_dims=[32, 16, 1], input_shape=(28,28,1), ks=3,\n",
        "                 compression_flag=True ,batch_norm_flag=True, dense_latent_rep_flag=False, upsampling_flag = False):\n",
        "  model = models.Sequential()\n",
        "  \n",
        "  #Build encoder part, add batch norm after encoder if batch norm flag is set to true\n",
        "  conv_enc_base_name = \"conv_enc_\"\n",
        "  conv_cmprsn_base_name = \"conv_compress_dims\"\n",
        "  conv_dec_base_name = \"conv_dec_\"\n",
        "  for i in range (len(encoder_layer_dims)):\n",
        "    #For first layer we need to specify shape\n",
        "    if(i == 0):\n",
        "      model.add(layers.Conv2D(filters = encoder_layer_dims[i], kernel_size=(ks, ks),  padding = \"same\", \n",
        "                              activation=tf.nn.relu, name = conv_enc_base_name + str(i), input_shape = input_shape))\n",
        "      if(batch_norm_flag):\n",
        "        model.add(layers.BatchNormalization()) \n",
        "\n",
        "      model.add(layers.MaxPool2D(pool_size=(2,2)))\n",
        "    else:\n",
        "      model.add(layers.Conv2D(filters = encoder_layer_dims[i], kernel_size=(ks, ks),  padding = \"same\", \n",
        "                              activation=tf.nn.relu, name = conv_enc_base_name + str(i)))\n",
        "      \n",
        "      if(batch_norm_flag):\n",
        "        model.add(layers.BatchNormalization()) \n",
        "\n",
        "      model.add(layers.MaxPool2D(pool_size=(2,2)))\n",
        "  \n",
        "  print(\"Encoder has been built, checking for compression flag\")\n",
        "\n",
        "  if(compression_flag):\n",
        "    model.add(layers.Conv2D(filters = compression_layer_dim, kernel_size=(1, 1),  padding = \"same\", activation = tf.nn.relu, name = conv_cmprsn_base_name))\n",
        "\n",
        "  # With one-d dense representation is hardcoded for this one casem without this we can pick dynamically\n",
        "  if(dense_latent_rep_flag):\n",
        "    model.add(layers.Flatten())\n",
        "    model.add(layers.Dense(256,activation=tf.nn.relu))\n",
        "    model.add(layers.Dense(64,activation=tf.nn.leaky_relu))\n",
        "    model.add(layers.Dense(252,activation=tf.nn.relu))\n",
        "    model.add(layers.Reshape((3, 3, 28), input_shape=(252,)))\n",
        "\n",
        "  print(\"FC layer added, printing model summary\")\n",
        "\n",
        "  #print(model.summary())\n",
        "  #Build Decoder , add batch norm before adding conv transpose, Use sigmoid at final layer and relu for others\n",
        "  if(upsampling_flag == False):\n",
        "    for i in range (len(decoder_layer_dims)):\n",
        "      if(i == 0):\n",
        "        if(batch_norm_flag):\n",
        "          model.add(layers.BatchNormalization()) \n",
        "\n",
        "        model.add(layers.Conv2DTranspose(filters = decoder_layer_dims[i], kernel_size=(ks, ks),  strides=(2,2), padding = \"valid\", \n",
        "                                  activation=tf.nn.relu, name = conv_dec_base_name + str(i)))\n",
        "        \n",
        "      elif(i < len(decoder_layer_dims)-1 and i > 0):\n",
        "\n",
        "        if(batch_norm_flag):\n",
        "          model.add(layers.BatchNormalization()) \n",
        "\n",
        "        model.add(layers.Conv2DTranspose(filters = decoder_layer_dims[i], kernel_size=(ks, ks),  strides=(2,2), padding = \"same\", \n",
        "                                  activation=tf.nn.relu, name = conv_dec_base_name + str(i)))\n",
        "      else:\n",
        "        if(batch_norm_flag):\n",
        "          model.add(layers.BatchNormalization())  \n",
        "\n",
        "        model.add(layers.Conv2DTranspose(filters = decoder_layer_dims[i], kernel_size=(ks, ks), strides=(2,2), padding = \"same\", \n",
        "                                  activation=tf.nn.sigmoid, name = conv_dec_base_name + str(i)))\n",
        "        \n",
        "  elif(upsampling_flag == True):\n",
        "      print(\"Upscaling with convolution choosen\")\n",
        "      for i in range (len(decoder_layer_dims)):\n",
        "        if(i == 0):\n",
        "          model.add(layers.UpSampling2D(size=(3, 3)))\n",
        "          if(batch_norm_flag):\n",
        "            model.add(layers.BatchNormalization()) \n",
        "\n",
        "          model.add(layers.Conv2D(filters = decoder_layer_dims[i], kernel_size=(ks, ks), padding = \"valid\", activation=tf.nn.relu, name = conv_dec_base_name + str(i)))\n",
        "          model.add(layers.UpSampling2D())\n",
        "\n",
        "        elif(i < len(decoder_layer_dims)-1 and i > 0):\n",
        "\n",
        "          if(batch_norm_flag):\n",
        "            model.add(layers.BatchNormalization()) \n",
        "\n",
        "          model.add(layers.Conv2D(filters = decoder_layer_dims[i], kernel_size=(ks, ks), padding = \"same\", activation=tf.nn.relu, name = conv_dec_base_name + str(i)))\n",
        "          model.add(layers.UpSampling2D())\n",
        "        else:\n",
        "          if(batch_norm_flag):\n",
        "            model.add(layers.BatchNormalization())  \n",
        "\n",
        "          model.add(layers.Conv2D(filters = decoder_layer_dims[i], kernel_size=(ks, ks),  padding = \"same\", activation=tf.nn.sigmoid, name = conv_dec_base_name + str(i)))\n",
        "        \n",
        "  print(\"Model has been built, printing summary\")\n",
        "  \n",
        "  print(model.summary())\n",
        "  return model;"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mT7N9-PaWQkG",
        "colab_type": "code",
        "outputId": "0c536753-1ddb-42cb-e84d-f54b24259f0d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "conv_model_with_fc = create_conv_autoencoder_model(dense_latent_rep_flag=True)\n",
        "conv_model_with_fc.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Encoder has been built, checking for compression flag\n",
            "FC layer added, printing model summary\n",
            "Model has been built, printing summary\n",
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv_enc_0 (Conv2D)          (None, 28, 28, 32)        320       \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (Batch (None, 28, 28, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv_enc_1 (Conv2D)          (None, 14, 14, 64)        18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_7 (Batch (None, 14, 14, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv_enc_2 (Conv2D)          (None, 7, 7, 128)         73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_8 (Batch (None, 7, 7, 128)         512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2 (None, 3, 3, 128)         0         \n",
            "_________________________________________________________________\n",
            "conv_compress_dims (Conv2D)  (None, 3, 3, 64)          8256      \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 576)               0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 256)               147712    \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 64)                16448     \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 252)               16380     \n",
            "_________________________________________________________________\n",
            "reshape_1 (Reshape)          (None, 3, 3, 28)          0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_9 (Batch (None, 3, 3, 28)          112       \n",
            "_________________________________________________________________\n",
            "conv_dec_0 (Conv2DTranspose) (None, 7, 7, 32)          8096      \n",
            "_________________________________________________________________\n",
            "batch_normalization_10 (Batc (None, 7, 7, 32)          128       \n",
            "_________________________________________________________________\n",
            "conv_dec_1 (Conv2DTranspose) (None, 14, 14, 16)        4624      \n",
            "_________________________________________________________________\n",
            "batch_normalization_11 (Batc (None, 14, 14, 16)        64        \n",
            "_________________________________________________________________\n",
            "conv_dec_2 (Conv2DTranspose) (None, 28, 28, 1)         145       \n",
            "=================================================================\n",
            "Total params: 295,533\n",
            "Trainable params: 294,933\n",
            "Non-trainable params: 600\n",
            "_________________________________________________________________\n",
            "None\n",
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv_enc_0 (Conv2D)          (None, 28, 28, 32)        320       \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (Batch (None, 28, 28, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv_enc_1 (Conv2D)          (None, 14, 14, 64)        18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_7 (Batch (None, 14, 14, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv_enc_2 (Conv2D)          (None, 7, 7, 128)         73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_8 (Batch (None, 7, 7, 128)         512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2 (None, 3, 3, 128)         0         \n",
            "_________________________________________________________________\n",
            "conv_compress_dims (Conv2D)  (None, 3, 3, 64)          8256      \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 576)               0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 256)               147712    \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 64)                16448     \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 252)               16380     \n",
            "_________________________________________________________________\n",
            "reshape_1 (Reshape)          (None, 3, 3, 28)          0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_9 (Batch (None, 3, 3, 28)          112       \n",
            "_________________________________________________________________\n",
            "conv_dec_0 (Conv2DTranspose) (None, 7, 7, 32)          8096      \n",
            "_________________________________________________________________\n",
            "batch_normalization_10 (Batc (None, 7, 7, 32)          128       \n",
            "_________________________________________________________________\n",
            "conv_dec_1 (Conv2DTranspose) (None, 14, 14, 16)        4624      \n",
            "_________________________________________________________________\n",
            "batch_normalization_11 (Batc (None, 14, 14, 16)        64        \n",
            "_________________________________________________________________\n",
            "conv_dec_2 (Conv2DTranspose) (None, 28, 28, 1)         145       \n",
            "=================================================================\n",
            "Total params: 295,533\n",
            "Trainable params: 294,933\n",
            "Non-trainable params: 600\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KvaaXpzCWQqm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "conv_model_with_fc.compile(optimizer=\"adam\", loss=tf.keras.losses.MeanAbsoluteError(), metrics=[\"accuracy\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KQauJldCWQou",
        "colab_type": "code",
        "outputId": "a04e2a6e-1813-4fc9-da14-3b639297b723",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "x = tf.expand_dims(train_images_raw.astype(np.float32)/255.0 , -1)\n",
        "y = tf.expand_dims(train_images_raw.astype(np.float32)/255.0, -1)\n",
        "print(x.shape)\n",
        "print(x.dtype)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 28, 28, 1)\n",
            "<dtype: 'float32'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "csboTMzyWQiY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#conv_model_with_fc.fit(x=x, y=y, epochs=7)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ExSKuaJzbNL1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_data = tf.expand_dims(test_images_raw.astype(np.float32)/255.0 , -1)\n",
        "#steps_per_epoch =  x.shape[0]/128,"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LIvg7Tc_aK38",
        "colab_type": "code",
        "outputId": "49db6919-b1ea-4a67-dd22-4c0576a34a20",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        }
      },
      "source": [
        "conv_model_with_fc.fit(datagen.flow(x, y, batch_size=128,shuffle=True), batch_size=128,  epochs = 10 ,validation_data =(test_data, test_data))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "469/469 [==============================] - 35s 75ms/step - loss: 0.1471 - accuracy: 0.7639 - val_loss: 0.1431 - val_accuracy: 0.7629\n",
            "Epoch 2/10\n",
            "469/469 [==============================] - 35s 75ms/step - loss: 0.1309 - accuracy: 0.7934 - val_loss: 0.1276 - val_accuracy: 0.8070\n",
            "Epoch 3/10\n",
            "469/469 [==============================] - 35s 74ms/step - loss: 0.1261 - accuracy: 0.7997 - val_loss: 0.1325 - val_accuracy: 0.7802\n",
            "Epoch 4/10\n",
            "469/469 [==============================] - 34s 73ms/step - loss: 0.1253 - accuracy: 0.8004 - val_loss: 0.1247 - val_accuracy: 0.8045\n",
            "Epoch 5/10\n",
            "469/469 [==============================] - 35s 74ms/step - loss: 0.1248 - accuracy: 0.8002 - val_loss: 0.1194 - val_accuracy: 0.8013\n",
            "Epoch 6/10\n",
            "469/469 [==============================] - 35s 74ms/step - loss: 0.1246 - accuracy: 0.8003 - val_loss: 0.1175 - val_accuracy: 0.7950\n",
            "Epoch 7/10\n",
            "469/469 [==============================] - 35s 75ms/step - loss: 0.1244 - accuracy: 0.8003 - val_loss: 0.1159 - val_accuracy: 0.8034\n",
            "Epoch 8/10\n",
            "469/469 [==============================] - 35s 74ms/step - loss: 0.1243 - accuracy: 0.8003 - val_loss: 0.1167 - val_accuracy: 0.8021\n",
            "Epoch 9/10\n",
            "469/469 [==============================] - 35s 74ms/step - loss: 0.1239 - accuracy: 0.8004 - val_loss: 0.1110 - val_accuracy: 0.7946\n",
            "Epoch 10/10\n",
            "469/469 [==============================] - 36s 76ms/step - loss: 0.1239 - accuracy: 0.8005 - val_loss: 0.1134 - val_accuracy: 0.7874\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fd3c77d2b00>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s4O6LctxkNqH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "final_output = conv_model_with_fc.predict(test_data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kE08RTgBka8H",
        "colab_type": "code",
        "outputId": "903830d1-e0f7-4ffb-a0ca-ea68a172090c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "final_output.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 28, 28, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vODPesnDbG_5",
        "colab_type": "code",
        "outputId": "3161fb1f-bb43-4162-fb23-2f6dc7ff42cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        }
      },
      "source": [
        "conv_model_with_fc.save(\"/content/conv_model_with_fc_noise\")\n",
        "!zip -r /content/conv_model_with_fc_noise.zip /content/conv_model_with_fc_noise"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:1817: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "INFO:tensorflow:Assets written to: /content/conv_model_with_fc_noise/assets\n",
            "  adding: content/conv_model_with_fc_noise/ (stored 0%)\n",
            "  adding: content/conv_model_with_fc_noise/saved_model.pb (deflated 91%)\n",
            "  adding: content/conv_model_with_fc_noise/assets/ (stored 0%)\n",
            "  adding: content/conv_model_with_fc_noise/variables/ (stored 0%)\n",
            "  adding: content/conv_model_with_fc_noise/variables/variables.index (deflated 74%)\n",
            "  adding: content/conv_model_with_fc_noise/variables/variables.data-00000-of-00002 (deflated 83%)\n",
            "  adding: content/conv_model_with_fc_noise/variables/variables.data-00001-of-00002 (deflated 14%)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nZXQkF09WQbR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#model_config1.fit(datagen.flow(x_train, y_train, batch_size), steps_per_epoch = 3*x_train.shape[0]/batch_size, \n",
        "#epochs = 10 ,validation_data =(x_test, y_test))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K_U1z85ziqj5",
        "colab_type": "code",
        "outputId": "31a3b5b6-53ae-4ce9-ba8b-009f30fc0a70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        }
      },
      "source": [
        "print(train_images_raw.shape)\n",
        "layer_name = 'dense_4'\n",
        "encoder_model_fc_noise = keras.Model(inputs=conv_model_with_fc.input, outputs=conv_model_with_fc.get_layer(layer_name).output)\n",
        "#intermediate_encoder_fc_noise_output = encoder_model_fc_noise(tf.expand_dims(train_images_raw.astype(np.float32)/255.0, -1))\n",
        "intermediate_encoder_fc_noise_output = encoder_model_fc_noise(train_images_raw.astype(np.float32))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 28, 28)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "ResourceExhaustedError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31m_FallbackException\u001b[0m                        Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_nn_ops.py\u001b[0m in \u001b[0;36mconv2d\u001b[0;34m(input, filter, strides, padding, use_cudnn_on_gpu, explicit_paddings, data_format, dilations, name)\u001b[0m\n\u001b[1;32m    925\u001b[0m         \u001b[0;34m\"explicit_paddings\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexplicit_paddings\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"data_format\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_format\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 926\u001b[0;31m         \"dilations\", dilations)\n\u001b[0m\u001b[1;32m    927\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0m_result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31m_FallbackException\u001b[0m: Expecting int64_t value for attr strides, got numpy.int32",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mResourceExhaustedError\u001b[0m                    Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-35-706c2fa6ba36>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mencoder_model_fc_noise\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconv_model_with_fc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconv_model_with_fc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_layer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m#intermediate_encoder_fc_noise_output = encoder_model_fc_noise(tf.expand_dims(train_images_raw.astype(np.float32)/255.0, -1))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mintermediate_encoder_fc_noise_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mencoder_model_fc_noise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_images_raw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    966\u001b[0m           with base_layer_utils.autocast_context_manager(\n\u001b[1;32m    967\u001b[0m               self._compute_dtype):\n\u001b[0;32m--> 968\u001b[0;31m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcast_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    969\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle_activity_regularization\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    970\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_mask_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_masks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, inputs, training, mask)\u001b[0m\n\u001b[1;32m    717\u001b[0m     return self._run_internal_graph(\n\u001b[1;32m    718\u001b[0m         \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 719\u001b[0;31m         convert_kwargs_to_constants=base_layer_utils.call_context().saving)\n\u001b[0m\u001b[1;32m    720\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    721\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mcompute_output_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py\u001b[0m in \u001b[0;36m_run_internal_graph\u001b[0;34m(self, inputs, training, mask, convert_kwargs_to_constants)\u001b[0m\n\u001b[1;32m    886\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    887\u001b[0m           \u001b[0;31m# Compute outputs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 888\u001b[0;31m           \u001b[0moutput_tensors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcomputed_tensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    889\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    890\u001b[0m           \u001b[0;31m# Update tensor_dict.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    966\u001b[0m           with base_layer_utils.autocast_context_manager(\n\u001b[1;32m    967\u001b[0m               self._compute_dtype):\n\u001b[0;32m--> 968\u001b[0;31m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcast_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    969\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle_activity_regularization\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    970\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_mask_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_masks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/convolutional.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m    205\u001b[0m       \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_causal_padding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 207\u001b[0;31m     \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convolution_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    208\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    209\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_bias\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_ops.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inp, filter)\u001b[0m\n\u001b[1;32m   1104\u001b[0m           call_from_convolution=False)\n\u001b[1;32m   1105\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1106\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1108\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_ops.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inp, filter)\u001b[0m\n\u001b[1;32m    636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    637\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=redefined-builtin\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 638\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    639\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    640\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_ops.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inp, filter)\u001b[0m\n\u001b[1;32m    235\u001b[0m         \u001b[0mpadding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpadding\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m         \u001b[0mdata_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_format\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 237\u001b[0;31m         name=self.name)\n\u001b[0m\u001b[1;32m    238\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_ops.py\u001b[0m in \u001b[0;36mconv2d\u001b[0;34m(input, filter, strides, padding, use_cudnn_on_gpu, data_format, dilations, name, filters)\u001b[0m\n\u001b[1;32m   2012\u001b[0m                            \u001b[0mdata_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata_format\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2013\u001b[0m                            \u001b[0mdilations\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdilations\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2014\u001b[0;31m                            name=name)\n\u001b[0m\u001b[1;32m   2015\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2016\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_nn_ops.py\u001b[0m in \u001b[0;36mconv2d\u001b[0;34m(input, filter, strides, padding, use_cudnn_on_gpu, explicit_paddings, data_format, dilations, name)\u001b[0m\n\u001b[1;32m    931\u001b[0m             \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstrides\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstrides\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_cudnn_on_gpu\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_cudnn_on_gpu\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    932\u001b[0m             \u001b[0mpadding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpadding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexplicit_paddings\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexplicit_paddings\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 933\u001b[0;31m             data_format=data_format, dilations=dilations, name=name, ctx=_ctx)\n\u001b[0m\u001b[1;32m    934\u001b[0m       \u001b[0;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_SymbolicException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    935\u001b[0m         \u001b[0;32mpass\u001b[0m  \u001b[0;31m# Add nodes to the TensorFlow graph.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_nn_ops.py\u001b[0m in \u001b[0;36mconv2d_eager_fallback\u001b[0;34m(input, filter, strides, padding, use_cudnn_on_gpu, explicit_paddings, data_format, dilations, name, ctx)\u001b[0m\n\u001b[1;32m   1020\u001b[0m   explicit_paddings, \"data_format\", data_format, \"dilations\", dilations)\n\u001b[1;32m   1021\u001b[0m   _result = _execute.execute(b\"Conv2D\", 1, inputs=_inputs_flat, attrs=_attrs,\n\u001b[0;32m-> 1022\u001b[0;31m                              ctx=ctx, name=name)\n\u001b[0m\u001b[1;32m   1023\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0m_execute\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmust_record_gradient\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1024\u001b[0m     _execute.record_gradient(\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mResourceExhaustedError\u001b[0m: OOM when allocating tensor with shape[60000,32,28,28] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [Op:Conv2D]"
          ]
        }
      ]
    }
  ]
}