From 83df56e1cf8acf492f89a94c16009f50de0ff033 Mon Sep 17 00:00:00 2001 From: jcollopy-tulane Date: Mon, 29 Apr 2024 12:21:44 -0500 Subject: [PATCH] Minor changes --- nlp/cli.py | 6 ++- notebooks/Experiment-CNN-1.ipynb | 73 +++++++++++++++++-------------- notebooks/Experiments-BERT.ipynb | 8 ---- notebooks/accuracy_plot.png | Bin 0 -> 52962 bytes 4 files changed, 46 insertions(+), 41 deletions(-) create mode 100644 notebooks/accuracy_plot.png diff --git a/nlp/cli.py b/nlp/cli.py index b73774f..e2a5380 100644 --- a/nlp/cli.py +++ b/nlp/cli.py @@ -190,7 +190,11 @@ def train_cnn(): pickle.dump(model, open(cnn_path, 'wb')) - +@main.command('train_cnn') +def train_bert(): + ''' + Get BERT + ''' if __name__ == "__main__": diff --git a/notebooks/Experiment-CNN-1.ipynb b/notebooks/Experiment-CNN-1.ipynb index 6c284b1..a1d0b72 100644 --- a/notebooks/Experiment-CNN-1.ipynb +++ b/notebooks/Experiment-CNN-1.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 13, + "execution_count": 2, "id": "70f2a5ec-67f6-4d3c-b912-717e401fc70e", "metadata": {}, "outputs": [], @@ -13,6 +13,7 @@ "import matplotlib.pyplot as plt\n", "import random\n", "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras import layers\n", "from tensorflow.keras.layers import Embedding, Flatten, Dense, Conv1D, MaxPooling1D, GlobalMaxPooling1D, Dropout\n", "from tensorflow.keras.optimizers.legacy import Adam\n", "from tensorflow.keras.preprocessing.text import Tokenizer\n", @@ -28,7 +29,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 3, "id": "7acbbe88-4550-41d6-b0bb-ede002bc4e0b", "metadata": {}, "outputs": [], @@ -40,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 4, "id": "1eac01e8-3c62-469b-96ea-d4babd8f9348", "metadata": {}, "outputs": [ @@ -77,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "id": "7f61ab0b-4594-456c-9b22-440cf9c5fabe", "metadata": {}, "outputs": [ @@ -85,7 +86,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "Training model with configuration: filter = 16, kernel = 4, num_1 = 40, lr = 0.01, dropout_rate = 0.5\n", + "Training model with configuration: filter = 16, kernel = 4, num_1 = 40, lr = 0.01, dropout_rate = 0.5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-04-29 12:08:14.275941: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Validation Accuracy: 0.5892\n", "Training model with configuration: filter = 16, kernel = 4, num_1 = 40, lr = 0.01, dropout_rate = 0.6\n", "Validation Accuracy: 0.5975\n", @@ -323,13 +337,13 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 6, "id": "7cf60181-88fc-48dd-be73-a19222236939", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -344,13 +358,18 @@ "ax.plot(best_history.history['val_accuracy'], 'b' ,label='Validation Accuracy')\n", "ax.set_xlabel(r'Epoch', fontsize=20)\n", "ax.set_ylabel(r'Accuracy', fontsize=20)\n", + "ax.set_title('Training and Validation Accuracy', fontsize=24)\n", "ax.legend()\n", - "ax.tick_params(labelsize=20)" + "ax.tick_params(labelsize=20)\n", + "\n", + "fig.savefig('/Users/jackiecollopy/Downloads/project-reddit/notebooks/accuracy_plot.png') \n", + "\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 7, "id": "8cfa7100-4fed-4538-b8ce-213a5efb4d1f", "metadata": {}, "outputs": [ @@ -359,9 +378,9 @@ "output_type": "stream", "text": [ "72/72 [==============================] - 0s 2ms/step\n", - "F1 Score: 0.692\n", - "Precision: 0.644\n", - "Recall: 0.748\n" + "F1 Score: 0.7\n", + "Precision: 0.641\n", + "Recall: 0.771\n" ] } ], @@ -381,17 +400,17 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 8, "id": "e23d7249-14e4-46d6-82df-c67265e2e925", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 25, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, @@ -406,7 +425,7 @@ }, { "data": { - "image/png": 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FYij79ctPeC1rWJPGpH2hV3b0bfG4AU9t+aKr3t7TU1+WJeqj/WfrztXHfsbHDvzsFD2bi7HalfOr17T/SActeyfd36ECLabNkv0333wjh8Ph2sbvuKSkJJWWlp6wT3Z2tuLj411HSkpKa4RqKvWN4dpXlqiuCUdd58JCHFowKV9nta/S7WvGnbSqH9nvS0WGN+nVnX1aKVrAe/WN4fqyLEEpCRVe9YuOsOvJX/9DtfZw3f3ClXI4PX8uOc4MTllcz8c/rSOAF+i1+ZiExeL+h2cYRrNzx917772qqKhwHUVFRa0RoqmEhzrUo2O5vvn+1rrjib5rwlHd9vw4VdSdfA+CiRf8W5s+767y2qjWChfwWnioQ907HtU3J1mwdyIxVruWTPuHmhwhumvNz2RvavO1zTgNxver8U/3MAI42bfZT2zHjh0VGhrarIovKytrVu0fZ7VaZbVyX6s//W7EFv3zi+4qqWynhOg6Tf9JgWKsdv390z4KtTj1h19sUN8uR3Tn2qsUYjGUGFMrSaqos6rpPyqblA4VurDrYc184eq2+irACd155fv612fdVHq0nTq0q1PGsI8VY7Xr1e3HRqDioupli69Wp9gaSVK3jkclSd9WR+vb6mhFR9i15PpXFRnepPtfulLtrI1qZ22UJJXXRAb0PK7ZtPaud2eSNkv2ERERSk9PV35+vn7+85+7zufn52vChAltFZbpJMXVKPvn+WofXa/ymijt/Kqzpq2cpJKKWHWJr9TwPgckSetu+X9u/aavGq+Cg2e5Xk+4YK/KqmL0/n6mVnBmSYqr1vz/2XjsZ7w2UruKknTj8p+rtOLYFqKX9zmgeZPedbXPnrxRkrTsnXQte+ci9Us+ogEpZZKkv/7uBbdrj1s0RSUnuXMFOJO06Ra369at069//Ws988wzGjJkiJYtW6bly5dr9+7d6tat2yn7s8UtzIAtbhHMWnOL25/n36jwmBOvOfJEY41d60etDMgtbtt04unaa6/Vt99+q4cfflglJSVKS0vTa6+95lGiBwDAGwzjt6EZM2ZoxowZbR0GAABBq82TPQAArYFn4wMAEOTMPIzPPSMAAAQ5KnsAgCmYubIn2QMATMHMyZ5hfAAAghyVPQDAFMxc2ZPsAQCmYMi32+cC+VmWJHsAgCmYubJnzh4AgCBHZQ8AMAUzV/YkewCAKZg52TOMDwBAkKOyBwCYgpkre5I9AMAUDMMiw4eE7UvftsYwPgAAQY7KHgBgCuxnDwBAkDPznD3D+AAABDkqewCAKZh5gR7JHgBgCmYexifZAwBMwcyVPXP2AAAEOSp7AIApGD4O4wdyZU+yBwCYgiHJMHzrH6gYxgcAIMhR2QMATMEpiyw8QQ8AgODFanwAABC0qOwBAKbgNCyy8FAdAACCl2H4uBo/gJfjM4wPAECQI9kDAEzh+AI9Xw5vdO/eXRaLpdnxm9/85vt4DM2bN0/JycmKiorS8OHDtXv3brdrNDQ0aObMmerYsaNiYmI0fvx4FRcXe/3dSfYAAFNo7WS/detWlZSUuI78/HxJ0jXXXCNJWrhwoRYtWqQlS5Zo69atstlsGjVqlKqqqlzXyMzM1Pr167V27Vpt3rxZ1dXVGjt2rBwOh1exkOwBAKZwfNc7Xw5vdOrUSTabzXW8+uqr6tWrl4YNGybDMJSTk6O5c+dq0qRJSktLU15enmpra7VmzRpJUkVFhVasWKHHH39cI0eO1MCBA7V69Wrt3LlTGzdu9CoWkj0AAF6orKx0OxoaGk7Zx263a/Xq1brppptksVhUWFio0tJSjR492tXGarVq2LBh2rJliySpoKBAjY2Nbm2Sk5OVlpbmauMpkj0AwBSOr8b35ZCklJQUxcfHu47s7OxTfvYrr7yio0eP6oYbbpAklZaWSpKSkpLc2iUlJbneKy0tVUREhDp06HDSNp7i1jsAgCkcS9i+PEHv2P8WFRUpLi7Odd5qtZ6y74oVKzRmzBglJye7nbdY3OMxDKPZueZxnLrNj1HZAwDghbi4OLfjVMn+4MGD2rhxo6ZPn+46Z7PZJKlZhV5WVuaq9m02m+x2u8rLy0/axlMkewCAKbT2avzjVq5cqc6dO+vqq692nevRo4dsNptrhb50bF5/06ZNGjp0qCQpPT1d4eHhbm1KSkq0a9cuVxtPMYwPADAFQ77tSX86fZ1Op1auXKlp06YpLOyHlGuxWJSZmamsrCylpqYqNTVVWVlZio6O1pQpUyRJ8fHxysjI0KxZs5SYmKiEhATNnj1bAwYM0MiRI72Kg2QPAEAL2bhxow4dOqSbbrqp2Xtz5sxRXV2dZsyYofLycg0ePFgbNmxQbGysq83ixYsVFhamyZMnq66uTiNGjFBubq5CQ0O9isNiGIH7tN/KykrFx8er/81ZCo2IbOtwgBYRag/Yv6LAKTns9fo0b64qKircFr350/Fc0fO5/1Vo9OnnCkdtvfZfn9WisbYUKnsAgDm0xTj+GYJkDwAwBx8W2R3vH6hYjQ8AQJCjsgcAmIKZ97Mn2QMATMGXe+WP9w9UDOMDABDkqOwBAOZgWHxbZBfAlT3JHgBgCmaes2cYHwCAIEdlDwAwBx6qAwBAcDPzanyPkv2TTz7p8QXvuOOO0w4GAAD4n0fJfvHixR5dzGKxkOwBAGeuAB6K94VHyb6wsLCl4wAAoEWZeRj/tFfj2+12ffbZZ2pqavJnPAAAtAzDD0eA8jrZ19bWKiMjQ9HR0erfv78OHTok6dhc/WOPPeb3AAEAgG+8Tvb33nuvPvnkE7377ruKjIx0nR85cqTWrVvn1+AAAPAfix+OwOT1rXevvPKK1q1bp0suuUQWyw9f/Nxzz9WXX37p1+AAAPAbE99n73Vlf+TIEXXu3LnZ+ZqaGrfkDwAAzgxeJ/uLLrpI//jHP1yvjyf45cuXa8iQIf6LDAAAfzLxAj2vh/Gzs7P1s5/9THv27FFTU5OeeOIJ7d69W++//742bdrUEjECAOA7E+9653VlP3ToUL333nuqra1Vr169tGHDBiUlJen9999Xenp6S8QIAAB8cFrPxh8wYIDy8vL8HQsAAC3GzFvcnlaydzgcWr9+vfbu3SuLxaJ+/fppwoQJCgtjXx0AwBnKxKvxvc7Ou3bt0oQJE1RaWqo+ffpIkj7//HN16tRJf/vb3zRgwAC/BwkAAE6f13P206dPV//+/VVcXKyPP/5YH3/8sYqKinTeeefplltuaYkYAQDw3fEFer4cAcrryv6TTz7Rtm3b1KFDB9e5Dh06aP78+brooov8GhwAAP5iMY4dvvQPVF5X9n369NHXX3/d7HxZWZl69+7tl6AAAPA7E99n71Gyr6ysdB1ZWVm644479NJLL6m4uFjFxcV66aWXlJmZqQULFrR0vAAAwEseDeO3b9/e7VG4hmFo8uTJrnPG9/cjjBs3Tg6HowXCBADARyZ+qI5Hyf6dd95p6TgAAGhZ3Hr33w0bNqyl4wAAAC3ktJ+CU1tbq0OHDslut7udP++883wOCgAAv6Oy99yRI0d044036vXXXz/h+8zZAwDOSCZO9l7fepeZmany8nJ98MEHioqK0htvvKG8vDylpqbqb3/7W0vECAAAfOB1Zf/222/rr3/9qy666CKFhISoW7duGjVqlOLi4pSdna2rr766JeIEAMA3Jl6N73VlX1NTo86dO0uSEhISdOTIEUnHdsL7+OOP/RsdAAB+cvwJer4cgeq0nqD32WefSZIuuOACPfvss/rqq6/0zDPPqEuXLn4PEAAA+MbrYfzMzEyVlJRIkh588EFdeeWVev755xUREaHc3Fx/xwcAgH+YeIGe18l+6tSprv8eOHCgDhw4oH//+9/q2rWrOnbs6NfgAACA7077PvvjoqOjdeGFF/ojFgAAWoxFPu5657dIWp9Hyf6uu+7y+IKLFi067WAAAID/eZTst2/f7tHF/nOznNbUafmHCrOEt8lnAy3tzcM72joEoMVUVjnVIa+VPszEt96xEQ4AwBxMvEDP61vvAABAYPF5gR4AAAHBxJU9yR4AYAq+PgXPVE/QAwAAgYXKHgBgDiYexj+tyn7VqlW69NJLlZycrIMHD0qScnJy9Ne//tWvwQEA4DeGHw4vffXVV/rVr36lxMRERUdH64ILLlBBQcEPIRmG5s2bp+TkZEVFRWn48OHavXu32zUaGho0c+ZMdezYUTExMRo/fryKi4u9isPrZL906VLddddduuqqq3T06FE5HA5JUvv27ZWTk+Pt5QAACErl5eW69NJLFR4ertdff1179uzR448/rvbt27vaLFy4UIsWLdKSJUu0detW2Ww2jRo1SlVVVa42mZmZWr9+vdauXavNmzerurpaY8eOdeVfT3id7J966iktX75cc+fOVWhoqOv8oEGDtHPnTm8vBwBAq2jtLW4XLFiglJQUrVy5UhdffLG6d++uESNGqFevXpKOVfU5OTmaO3euJk2apLS0NOXl5am2tlZr1qyRJFVUVGjFihV6/PHHNXLkSA0cOFCrV6/Wzp07tXHjRo9j8TrZFxYWauDAgc3OW61W1dTUeHs5AABax/En6PlySKqsrHQ7GhoaTvhxf/vb3zRo0CBdc8016ty5swYOHKjly5e73i8sLFRpaalGjx7tOme1WjVs2DBt2bJFklRQUKDGxka3NsnJyUpLS3O18YTXyb5Hjx7asWNHs/Ovv/66zj33XG8vBwBA6/DTnH1KSori4+NdR3Z29gk/bv/+/Vq6dKlSU1P15ptv6rbbbtMdd9yh5557TpJUWloqSUpKSnLrl5SU5HqvtLRUERER6tChw0nbeMLr1fh33323fvOb36i+vl6GYeijjz7SCy+8oOzsbP3pT3/y9nIAAASUoqIixcXFuV5brdYTtnM6nRo0aJCysrIkHdsWfvfu3Vq6dKmuv/56V7sf7ytjGMYp95rxpM1/8jrZ33jjjWpqatKcOXNUW1urKVOm6KyzztITTzyh6667ztvLAQDQKvz1UJ24uDi3ZH8yXbp0aTbi3a9fP7388suSJJvNJulY9d6lSxdXm7KyMle1b7PZZLfbVV5e7lbdl5WVaejQoR7Hflq33t188806ePCgysrKVFpaqqKiImVkZJzOpQAAaB2tfOvdpZdeqs8++8zt3Oeff65u3bpJOjYtbrPZlJ+f73rfbrdr06ZNrkSenp6u8PBwtzYlJSXatWuXV8nep4fqdOzY0ZfuAAAErd/97ncaOnSosrKyNHnyZH300UdatmyZli1bJunY8H1mZqaysrKUmpqq1NRUZWVlKTo6WlOmTJEkxcfHKyMjQ7NmzVJiYqISEhI0e/ZsDRgwQCNHjvQ4Fq+TfY8ePf7rPMH+/fu9vSQAAC3Px2F8byv7iy66SOvXr9e9996rhx9+WD169FBOTo6mTp3qajNnzhzV1dVpxowZKi8v1+DBg7VhwwbFxsa62ixevFhhYWGaPHmy6urqNGLECOXm5rrd/n4qFsMwvAr/iSeecHvd2Nio7du364033tDdd9+t3//+995czieVlZWKj4/XcE1QmCW81T4XaE1vHt7R1iEALaayyqkO5+xXRUWFR/Pgp/UZ3+eKnvdlKTQy8rSv46iv1/5H/7dFY20pXlf2d9555wnP/9///Z+2bdvmc0AAAMC//Lbr3ZgxY1wrDAEAOOO0wbPxzxR+2/XupZdeUkJCgr8uBwCAX5l5P3uvk/3AgQPdFugZhqHS0lIdOXJETz/9tF+DAwAAvvM62U+cONHtdUhIiDp16qThw4erb9++/ooLAAD4iVfJvqmpSd27d9eVV17pevIPAAABwdd59wAexvdqgV5YWJhuv/32k+7wAwDAmaq1t7g9k3i9Gn/w4MHavn17S8QCAABagNdz9jNmzNCsWbNUXFys9PR0xcTEuL1/3nnn+S04AAD8KoCrc194nOxvuukm5eTk6Nprr5Uk3XHHHa73LBaLa7s9h8Ph/ygBAPCViefsPU72eXl5euyxx1RYWNiS8QAAAD/zONkff4T+8a35AAAIJDxUx0P/bbc7AADOaAzje+acc845ZcL/7rvvfAoIAAD4l1fJ/qGHHlJ8fHxLxQIAQIthGN9D1113nTp37txSsQAA0HJMPIzv8UN1mK8HACAweb0aHwCAgGTiyt7jZO90OlsyDgAAWhRz9gAABDsTV/Zeb4QDAAACC5U9AMAcTFzZk+wBAKZg5jl7hvEBAAhyVPYAAHNgGB8AgODGMD4AAAhaVPYAAHNgGB8AgCBn4mTPMD4AAEGOyh4AYAqW7w9f+gcqkj0AwBxMPIxPsgcAmAK33gEAgKBFZQ8AMAeG8QEAMIEATti+YBgfAIAgR2UPADAFMy/QI9kDAMzBxHP2DOMDABDkqOwBAKbAMD4AAMGOYXwAABCsqOwBAKbAMD4AAMHOxMP4JHsAgDmYONkzZw8AQJCjsgcAmAJz9gAABDuG8QEAQLAi2QMATMFiGD4f3pg3b54sFovbYbPZXO8bhqF58+YpOTlZUVFRGj58uHbv3u12jYaGBs2cOVMdO3ZUTEyMxo8fr+LiYq+/O8keAGAOhh8OL/Xv318lJSWuY+fOna73Fi5cqEWLFmnJkiXaunWrbDabRo0apaqqKlebzMxMrV+/XmvXrtXmzZtVXV2tsWPHyuFweBUHc/YAAHihsrLS7bXVapXVaj1h27CwMLdq/jjDMJSTk6O5c+dq0qRJkqS8vDwlJSVpzZo1uvXWW1VRUaEVK1Zo1apVGjlypCRp9erVSklJ0caNG3XllVd6HDOVPQDAFI6vxvflkKSUlBTFx8e7juzs7JN+5hdffKHk5GT16NFD1113nfbv3y9JKiwsVGlpqUaPHu1qa7VaNWzYMG3ZskWSVFBQoMbGRrc2ycnJSktLc7XxFJU9AMAc/LQav6ioSHFxca7TJ6vqBw8erOeee07nnHOOvv76az366KMaOnSodu/erdLSUklSUlKSW5+kpCQdPHhQklRaWqqIiAh16NChWZvj/T1FsgcAwAtxcXFuyf5kxowZ4/rvAQMGaMiQIerVq5fy8vJ0ySWXSJIsFotbH8Mwmp37MU/a/BjD+AAAU/DXMP7piomJ0YABA/TFF1+45vF/XKGXlZW5qn2bzSa73a7y8vKTtvEUyR4AYA5tsBr/PzU0NGjv3r3q0qWLevToIZvNpvz8fNf7drtdmzZt0tChQyVJ6enpCg8Pd2tTUlKiXbt2udp4imF8AIAptPbjcmfPnq1x48apa9euKisr06OPPqrKykpNmzZNFotFmZmZysrKUmpqqlJTU5WVlaXo6GhNmTJFkhQfH6+MjAzNmjVLiYmJSkhI0OzZszVgwADX6nxPkewBAGgBxcXF+uUvf6lvvvlGnTp10iWXXKIPPvhA3bp1kyTNmTNHdXV1mjFjhsrLyzV48GBt2LBBsbGxrmssXrxYYWFhmjx5surq6jRixAjl5uYqNDTUq1gshuHlI4HOIJWVlYqPj9dwTVCYJbytwwFaxJuHd7R1CECLqaxyqsM5+1VRUeHRorfT+ozvc0X65PkKjYg87es47PUqeHFui8baUqjsAQCmEcg71/mCBXoAAAQ5KnsAgDkYxrHDl/4BimQPADCF1l6NfyZhGB8AgCBHZQ8AMAc/PRs/EJHsAQCmYHEeO3zpH6gYxgcAIMhR2Zvc2Ou/0dXXf6ukFLsk6eBnkXp+cZK2vXPsgRGXjjmqq379rVLPq1N8gkO3jzpH+3dHufrHtm/Sr2eX6sJh1eqUbFfld2Ha8ka88hbaVFvl3ROegJZSWx2ivIVdtOX1eB39Nky9+tfp9keK1eeCOjU1SrkLumjr23EqORihmDinBl5WpYz/PaxEW5PrGnf/orc+fb+d23WHjS/X/z5zsLW/Dk4Xw/gwqyMl4fpzVhcdPnBsP+ZR13yneSsP6Dejz9HBzyMVGe3Unq0x+ter7fW7PxY365+Q1KjEpCYtf7iLDn0eqc5n23XHY8VKTGrUo7d0b+VvA5zY4lkpOvBZpOY8dVAJSY16++UE/f7a3lr+7r8VFePQvp3RmpL5tXqeW6fqilA98+BZevCGnlryxudu1xkz9Rtdf/cPu5RZIwN4XNeEWI3fRv75z39q3LhxSk5OlsVi0SuvvNKW4ZjSh/nx2vp2nL7ab9VX+63KXdBF9TUh6pteI0l66+UEPb/Ypu3/jD1h/4OfRemRm7vrw/x4lRy06pP3YpW7oIsGj6pUSGgA/81A0Gios2jza+01/b4SDbikRmf1sOvXs0tlS7Hr1ecSFRPn1GPrvtSw8UeV0rtB/dJrNePRYn3xabTKit0fw22NMpTQucl1xMSR7APK8fvsfTkCVJsm+5qaGp1//vlasmRJW4aB74WEGBo2oVzWaKf2bos57evExDlUWx0ip8Pix+iA0+NwWOR0WBRhdU/M1iindn/U7oR9aipDZbEYiol3uJ1/5y8ddE3/NN08vI+WPZSs2mqWPSEwtOkw/pgxYzRmzBiP2zc0NKihocH1urKysiXCMp3ufeuU8/d9irA6VVcTooczuuvQF6e3WURshyZNyfxar61K9HOUwOmJbudUv/QarcmxqWvqAbXv1KR3X+mgf38crbN6NDRrb6+36M9Zybri5+WKif3hF4QrJn0nW4pdCZ2bdODfkfpzdhft3xOlx9Z92ZpfBz4w8zB+QM3ZZ2dn66GHHmrrMIJO8ZdWzRh1jmLiHPrJ1RWa/cQh3T2pt9cJP7qdQ488V6hDn0dq9SJbC0ULeG/OUwe16K6umnJhmkJCDfUeUKsrfl6ufTuj3do1NUpZt3eX4ZR+m+2+RuWqqd+5/rt733qd1bNBv/1ZH33xaZRSz6trle8BH5l4gV5AjUHde++9qqiocB1FRUVtHVJQaGoM0eEDVn3xabRWZndR4Z4oTZx+xKtrRMU4NH/NftXXhuihjO5yNDGEjzNHcne7/viXffrrvk+1ettuPfXaF2pqtMjW9YfKvqlRmn9rd5UWRSh77ZduVf2J9B5Qp7Bwp74qtLZ0+IDPAqqyt1qtslr5i9UawiM8/xU2ut2xRN9ot+jBG3qosSGgfoeEiURGOxUZ7VTV0VAVbIrT9PsOS/oh0X9VaNXCl/YpLsFxiisdu021qTFEiUmNLR02/IRhfJjWjb8v0da3Y3XkcISi2jk0fMJRnTe0WvdN7Snp2H30nc5qdP2DltKrXpJUXham8iPhiopxKOuF/bJGObVwZndFt3Mout2xfygrvg2T00mFj7a37d1YGYaU0qtBXxVG6E+PnKWze9Vr9LXfytEkPXJzD+3bGaWHn9svp8Oi78qO/dMY296h8AhDhw9E6O2/dNDFIyoVl+DQoc+tWvbQWeqdVqtzL6pp428Hj7HrHcyqfacm3f3UISV0blJtVagK90bqvqk99fH3t9pdMrpSs3N+mC7532cOSZJWPZ6k1Y/blHpenfql10qSct//t9u1r7+4n74ujmilbwKcXE1lqFZmd9E3JeGKbe/QpVcd1Y2/L1FYuFRaFKEPNsRLkmaM6uvWb+FL+3T+0GqFhRvasTlWr6zopPqaEHVMbtTgEZWaelepQnl2FAJAmyb76upq7du3z/W6sLBQO3bsUEJCgrp27dqGkZnH4lkp//X9/BcTlP9iwknf//T9droy+Xx/hwX41bDxRzVs/NETvmdLsevNwzv+a//OZzXqj3/Z91/b4MzHMH4b2bZtm6644grX67vuukuSNG3aNOXm5rZRVACAoGTi1fhtmuyHDx8uI4DnQAAACATM2QMATIFhfAAAgp3TOHb40j9AkewBAOZg4jl7nn4CAECQo7IHAJiCRT7O2fstktZHsgcAmIOJn6DHMD4AAEGOyh4AYArcegcAQLBjNT4AAAhWVPYAAFOwGIYsPiyy86VvWyPZAwDMwfn94Uv/AMUwPgAAQY7KHgBgCgzjAwAQ7Ey8Gp9kDwAwB56gBwAAghWVPQDAFHiCHgAAwY5hfAAAEKyo7AEApmBxHjt86R+oSPYAAHNgGB8AAAQrKnsAgDnwUB0AAIKbmR+XyzA+AABBjsoeAGAOJl6gR7IHAJiDId/2pA/cXM8wPgDAHI7P2ftynK7s7GxZLBZlZma6zhmGoXnz5ik5OVlRUVEaPny4du/e7davoaFBM2fOVMeOHRUTE6Px48eruLjY688n2QMA0IK2bt2qZcuW6bzzznM7v3DhQi1atEhLlizR1q1bZbPZNGrUKFVVVbnaZGZmav369Vq7dq02b96s6upqjR07Vg6Hw6sYSPYAAHMw9MO8/Wkd3n9kdXW1pk6dquXLl6tDhw4/hGIYysnJ0dy5czVp0iSlpaUpLy9PtbW1WrNmjSSpoqJCK1as0OOPP66RI0dq4MCBWr16tXbu3KmNGzd6FQfJHgBgDj4l+h8W91VWVrodDQ0NJ/3I3/zmN7r66qs1cuRIt/OFhYUqLS3V6NGjXeesVquGDRumLVu2SJIKCgrU2Njo1iY5OVlpaWmuNp4i2QMA4IWUlBTFx8e7juzs7BO2W7t2rT7++OMTvl9aWipJSkpKcjuflJTkeq+0tFQRERFuIwI/buMpVuMDAMzBKcniY39JRUVFiouLc522Wq3NmhYVFenOO+/Uhg0bFBkZedJLWizuARmG0ezcj3nS5seo7AEApuCv1fhxcXFux4mSfUFBgcrKypSenq6wsDCFhYVp06ZNevLJJxUWFuaq6H9coZeVlbnes9lsstvtKi8vP2kbT5HsAQDwsxEjRmjnzp3asWOH6xg0aJCmTp2qHTt2qGfPnrLZbMrPz3f1sdvt2rRpk4YOHSpJSk9PV3h4uFubkpIS7dq1y9XGUwzjAwDMoRWfoBcbG6u0tDS3czExMUpMTHSdz8zMVFZWllJTU5WamqqsrCxFR0drypQpkqT4+HhlZGRo1qxZSkxMVEJCgmbPnq0BAwY0W/B3KiR7AIA5nGGPy50zZ47q6uo0Y8YMlZeXa/DgwdqwYYNiY2NdbRYvXqywsDBNnjxZdXV1GjFihHJzcxUaGurVZ1kMI3Af9ltZWan4+HgN1wSFWcLbOhygRbx5eEdbhwC0mMoqpzqcs18VFRVui978+hnf54oR585WWGjz+XVPNTka9NaeP7ZorC2Fyh4AYA5nWGXfmkj2AABz8NOtd4GIZA8AMAVfN7PxpW9b49Y7AACCHJU9AMAcmLMHACDIOQ3J4kPCdgZusmcYHwCAIEdlDwAwB4bxAQAIdj4mewVusmcYHwCAIEdlDwAwB4bxAQAIck5DPg3FsxofAACcqajsAQDmYDiPHb70D1AkewCAOTBnDwBAkGPOHgAABCsqewCAOTCMDwBAkDPkY7L3WyStjmF8AACCHJU9AMAcGMYHACDIOZ2SfLhX3hm499kzjA8AQJCjsgcAmAPD+AAABDkTJ3uG8QEACHJU9gAAczDx43JJ9gAAUzAMpwwfdq7zpW9bI9kDAMzBMHyrzpmzBwAAZyoqewCAORg+ztkHcGVPsgcAmIPTKVl8mHcP4Dl7hvEBAAhyVPYAAHNgGB8AgOBmOJ0yfBjGD+Rb7xjGBwAgyFHZAwDMgWF8AACCnNOQLOZM9gzjAwAQ5KjsAQDmYBiSfLnPPnAre5I9AMAUDKchw4dhfINkDwDAGc5wyrfKnlvvAADAGYrKHgBgCgzjAwAQ7Ew8jB/Qyf74b1lNavTpOQnAmayyKnD/gQFOpbL62M93a1TNvuaKJjX6L5hWFtDJvqqqSpK0Wa+1cSRAy+lwTltHALS8qqoqxcfHt8i1IyIiZLPZtLnU91xhs9kUERHhh6hal8UI4EkIp9Opw4cPKzY2VhaLpa3DMYXKykqlpKSoqKhIcXFxbR0O4Ff8fLc+wzBUVVWl5ORkhYS03Jrx+vp62e12n68TERGhyMhIP0TUugK6sg8JCdHZZ5/d1mGYUlxcHP8YImjx8926Wqqi/0+RkZEBmaT9hVvvAAAIciR7AACCHMkeXrFarXrwwQdltVrbOhTA7/j5RrAK6AV6AADg1KjsAQAIciR7AACCHMkeAIAgR7IHACDIkezhsaefflo9evRQZGSk0tPT9a9//autQwL84p///KfGjRun5ORkWSwWvfLKK20dEuBXJHt4ZN26dcrMzNTcuXO1fft2XXbZZRozZowOHTrU1qEBPqupqdH555+vJUuWtHUoQIvg1jt4ZPDgwbrwwgu1dOlS17l+/fpp4sSJys7ObsPIAP+yWCxav369Jk6c2NahAH5DZY9TstvtKigo0OjRo93Ojx49Wlu2bGmjqAAAniLZ45S++eYbORwOJSUluZ1PSkpSaWlpG0UFAPAUyR4e+/E2woZhsLUwAAQAkj1OqWPHjgoNDW1WxZeVlTWr9gEAZx6SPU4pIiJC6enpys/Pdzufn5+voUOHtlFUAABPhbV1AAgMd911l379619r0KBBGjJkiJYtW6ZDhw7ptttua+vQAJ9VV1dr3759rteFhYXasWOHEhIS1LVr1zaMDPAPbr2Dx55++mktXLhQJSUlSktL0+LFi3X55Ze3dViAz959911dccUVzc5PmzZNubm5rR8Q4GckewAAghxz9gAABDmSPQAAQY5kDwBAkCPZAwAQ5Ej2AAAEOZI9AABBjmQPAECQI9kDABDkSPaAj+bNm6cLLrjA9fqGG27QxIkTWz2OAwcOyGKxaMeOHSdt0717d+Xk5Hh8zdzcXLVv397n2CwWi1555RWfrwPg9JDsEZRuuOEGWSwWWSwWhYeHq2fPnpo9e7Zqampa/LOfeOIJjx+x6kmCBgBfsREOgtbPfvYzrVy5Uo2NjfrXv/6l6dOnq6amRkuXLm3WtrGxUeHh4X753Pj4eL9cBwD8hcoeQctqtcpmsyklJUVTpkzR1KlTXUPJx4fe//znP6tnz56yWq0yDEMVFRW65ZZb1LlzZ8XFxemnP/2pPvnkE7frPvbYY0pKSlJsbKwyMjJUX1/v9v6Ph/GdTqcWLFig3r17y2q1qmvXrpo/f74kqUePHpKkgQMHymKxaPjw4a5+K1euVL9+/RQZGam+ffvq6aefdvucjz76SAMHDlRkZKQGDRqk7du3e/1ntGjRIg0YMEAxMTFKSUnRjBkzVF1d3azdK6+8onPOOUeRkZEaNWqUioqK3N7/+9//rvT0dEVGRqpnz5566KGH1NTU5HU8AFoGyR6mERUVpcbGRtfrffv26cUXX9TLL7/sGka/+uqrVVpaqtdee00FBQW68MILNWLECH333XeSpBdffFEPPvig5s+fr23btqlLly7NkvCP3XvvvVqwYIHuv/9+7dmzR2vWrFFSUpKkYwlbkjZu3KiSkhL95S9/kSQtX75cc+fO1fz587V3715lZWXp/vvvV15eniSppqZGY8eOVZ8+fVRQUKB58+Zp9uzZXv+ZhISE6Mknn9SuXbuUl5ent99+W3PmzHFrU1tbq/nz5ysvL0/vvfeeKisrdd1117nef/PNN/WrX/1Kd9xxh/bs2aNnn31Wubm5rl9oAJwBDCAITZs2zZgwYYLr9YcffmgkJiYakydPNgzDMB588EEjPDzcKCsrc7V56623jLi4OKO+vt7tWr169TKeffZZwzAMY8iQIcZtt93m9v7gwYON888//4SfXVlZaVitVmP58uUnjLOwsNCQZGzfvt3tfEpKirFmzRq3c4888ogxZMgQwzAM49lnnzUSEhKMmpoa1/tLly494bX+U7du3YzFixef9P0XX3zRSExMdL1euXKlIcn44IMPXOf27t1rSDI+/PBDwzAM47LLLjOysrLcrrNq1SqjS5curteSjPXr15/0cwG0LObsEbReffVVtWvXTk1NTWpsbNSECRP01FNPud7v1q2bOnXq5HpdUFCg6upqJSYmul2nrq5OX375pSRp7969uu2229zeHzJkiN55550TxrB37141NDRoxIgRHsd95MgRFRUVKSMjQzfffLPrfFNTk2s9wN69e3X++ecrOjraLQ5vvfPOO8rKytKePXtUWVmppqYm1dfXq6amRjExMZKksLAwDRo0yNWnb9++at++vfbu3auLL75YBQUF2rp1q1sl73A4VF9fr9raWrcYAbQNkj2C1hVXXKGlS5cqPDxcycnJzRbgHU9mxzmdTnXp0kXvvvtus2ud7u1nUVFRXvdxOp2Sjg3lDx482O290NBQSZJhGKcVz386ePCgrrrqKt1222165JFHlJCQoM2bNysjI8NtukM6duvcjx0/53Q69dBDD2nSpEnN2kRGRvocJwDfkewRtGJiYtS7d2+P21944YUqLS1VWFiYunfvfsI2/fr10wcffKDrr7/ede6DDz446TVTU1MVFRWlt956S9OnT2/2fkREhKRjlfBxSUlJOuuss7R//35NnTr1hNc999xztWrVKtXV1bl+ofhvcZzItm3b1NTUpMcff1whIceW77z44ovN2jU1NWnbtm26+OKLJUmfffaZjh49qr59+0o69uf22WefefVnDaB1keyB740cOVJDhgzRxIkTtWDBAvXp00eHDx/Wa6+9pokTJ2rQoEG68847NW3aNA0aNEg/+clP9Pzzz2v37t3q2bPnCa8ZGRmpe+65R3PmzFFERIQuvfRSHTlyRLt371ZGRoY6d+6sqKgovfHGGzr77LMVGRmp+Ph4zZs3T3fccYfi4uI0ZswYNTQ0aNu2bSovL9ddd92lKVOmaO7cucrIyNB9992nAwcO6I9//KNX37dXr15qamrSU089pXHjxum9997TM88806xdeHi4Zs6cqSeffFLh4eH67W9/q0suucSV/B944AGNHTtWKSkpuuaaaxQSEqJPP/1UO3fu1KOPPur9/xEA/I7V+MD3LBaLXnvtNV1++eW66aabdM455+i6667TgQMHXKvnr732Wj3wwAO65557lJ6eroMHD+r222//r9e9//77NWvWLD3wwAPq16+frr32WpWVlUk6Nh/+5JNP6tlnn1VycrImTJggSZo+fbr+9Kc/KTc3VwMGDNCwYcOUm5vrulWvXbt2+vvf/649e/Zo4MCBmjt3rhYsWODV973gggu0aNEiLViwQGlpaXr++eeVnZ3drF10dLTuueceTZkyRUOGDFFUVJTWrl3rev/KK6/Uq6++qvz8fF100UW65JJLtGjRInXr1s2reAC0HIvhj8k/AABwxqKyBwAgyJHsAQAIciR7AACCHMkeAIAgR7IHACDIkewBAAhyJHsAAIIcyR4AgCBHsgcAIMiR7AEACHIkewAAgtz/B3/BsCVCCtp5AAAAAElFTkSuQmCC", + "image/png": 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", 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" ] @@ -425,29 +444,19 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 9, "id": "9612ac53-cc11-4ee6-acff-d33cabf98e73", "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-04-28 23:30:38.827629: I tensorflow/core/common_runtime/executor.cc:1197] [/job:localhost/replica:0/task:0/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: indices[2,63] = 10780 is not in [0, 10686)\n", - "\t [[{{node sequential_119/embedding_119/embedding_lookup}}]]\n" - ] - }, - { - "ename": "InvalidArgumentError", - "evalue": "Graph execution error:\n\nDetected at node 'sequential_119/embedding_119/embedding_lookup' defined at (most recent call last):\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/runpy.py\", line 197, in _run_module_as_main\n return _run_code(code, main_globals, None,\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/runpy.py\", line 87, in _run_code\n exec(code, run_globals)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/ipykernel_launcher.py\", line 17, in \n app.launch_new_instance()\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/traitlets/config/application.py\", line 992, in launch_instance\n app.start()\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/ipykernel/kernelapp.py\", line 701, in start\n self.io_loop.start()\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/tornado/platform/asyncio.py\", line 195, in start\n self.asyncio_loop.run_forever()\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/asyncio/base_events.py\", line 601, in run_forever\n self._run_once()\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/asyncio/base_events.py\", line 1905, in _run_once\n handle._run()\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/asyncio/events.py\", line 80, in _run\n self._context.run(self._callback, *self._args)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 534, in dispatch_queue\n await self.process_one()\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 523, in process_one\n await dispatch(*args)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 429, in dispatch_shell\n await result\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 767, in execute_request\n reply_content = await reply_content\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/ipykernel/ipkernel.py\", line 429, in do_execute\n res = shell.run_cell(\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/ipykernel/zmqshell.py\", line 549, in run_cell\n return super().run_cell(*args, **kwargs)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3024, in run_cell\n result = self._run_cell(\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3079, in _run_cell\n result = runner(coro)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/IPython/core/async_helpers.py\", line 129, in _pseudo_sync_runner\n coro.send(None)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3284, in run_cell_async\n has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3466, in run_ast_nodes\n if await self.run_code(code, result, async_=asy):\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3526, in run_code\n exec(code_obj, self.user_global_ns, self.user_ns)\n File \"/var/folders/hs/br_4rpdj68nc3sfdpgv0xgn80000gn/T/ipykernel_77669/3444278365.py\", line 1, in \n predictions = best_model_cnn.predict(X_val)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/utils/traceback_utils.py\", line 65, in error_handler\n return fn(*args, **kwargs)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/engine/training.py\", line 2382, in predict\n tmp_batch_outputs = self.predict_function(iterator)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/engine/training.py\", line 2169, in predict_function\n return step_function(self, iterator)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/engine/training.py\", line 2155, in step_function\n outputs = model.distribute_strategy.run(run_step, args=(data,))\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/engine/training.py\", line 2143, in run_step\n outputs = model.predict_step(data)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/engine/training.py\", line 2111, in predict_step\n return self(x, training=False)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/utils/traceback_utils.py\", line 65, in error_handler\n return fn(*args, **kwargs)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/engine/training.py\", line 558, in __call__\n return super().__call__(*args, **kwargs)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/utils/traceback_utils.py\", line 65, in error_handler\n return fn(*args, **kwargs)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/engine/base_layer.py\", line 1145, in __call__\n outputs = call_fn(inputs, *args, **kwargs)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/utils/traceback_utils.py\", line 96, in error_handler\n return fn(*args, **kwargs)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/engine/sequential.py\", line 412, in call\n return super().call(inputs, training=training, mask=mask)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/engine/functional.py\", line 512, in call\n return self._run_internal_graph(inputs, training=training, mask=mask)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/engine/functional.py\", line 669, in _run_internal_graph\n outputs = node.layer(*args, **kwargs)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/utils/traceback_utils.py\", line 65, in error_handler\n return fn(*args, **kwargs)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/engine/base_layer.py\", line 1145, in __call__\n outputs = call_fn(inputs, *args, **kwargs)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/utils/traceback_utils.py\", line 96, in error_handler\n return fn(*args, **kwargs)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/layers/core/embedding.py\", line 272, in call\n out = tf.nn.embedding_lookup(self.embeddings, inputs)\nNode: 'sequential_119/embedding_119/embedding_lookup'\nindices[2,63] = 10780 is not in [0, 10686)\n\t [[{{node sequential_119/embedding_119/embedding_lookup}}]] [Op:__inference_predict_function_338434]", + "ename": "NameError", + "evalue": "name 'X_test' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[27], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m predictions \u001b[38;5;241m=\u001b[39m \u001b[43mbest_model_cnn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m predictions \u001b[38;5;241m=\u001b[39m (predictions \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0.5\u001b[39m)\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mint\u001b[39m) \n\u001b[1;32m 4\u001b[0m f1 \u001b[38;5;241m=\u001b[39m f1_score(y_test, predictions)\n", - "File \u001b[0;32m/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/utils/traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 67\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m 68\u001b[0m \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[0;32m---> 70\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 71\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n", - "File \u001b[0;32m/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/tensorflow/python/eager/execute.py:52\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 51\u001b[0m ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 52\u001b[0m tensors \u001b[38;5;241m=\u001b[39m pywrap_tfe\u001b[38;5;241m.\u001b[39mTFE_Py_Execute(ctx\u001b[38;5;241m.\u001b[39m_handle, device_name, op_name,\n\u001b[1;32m 53\u001b[0m inputs, attrs, num_outputs)\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "\u001b[0;31mInvalidArgumentError\u001b[0m: Graph execution error:\n\nDetected at node 'sequential_119/embedding_119/embedding_lookup' defined at (most recent call last):\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/runpy.py\", line 197, in _run_module_as_main\n return _run_code(code, main_globals, None,\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/runpy.py\", line 87, in _run_code\n exec(code, run_globals)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/ipykernel_launcher.py\", line 17, in \n app.launch_new_instance()\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/traitlets/config/application.py\", line 992, in launch_instance\n app.start()\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/ipykernel/kernelapp.py\", line 701, in start\n self.io_loop.start()\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/tornado/platform/asyncio.py\", line 195, in start\n self.asyncio_loop.run_forever()\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/asyncio/base_events.py\", line 601, in run_forever\n self._run_once()\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/asyncio/base_events.py\", line 1905, in _run_once\n handle._run()\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/asyncio/events.py\", line 80, in _run\n self._context.run(self._callback, *self._args)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 534, in dispatch_queue\n await self.process_one()\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 523, in process_one\n await dispatch(*args)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 429, in dispatch_shell\n await result\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 767, in execute_request\n reply_content = await reply_content\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/ipykernel/ipkernel.py\", line 429, in do_execute\n res = shell.run_cell(\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/ipykernel/zmqshell.py\", line 549, in run_cell\n return super().run_cell(*args, **kwargs)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3024, in run_cell\n result = self._run_cell(\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3079, in _run_cell\n result = runner(coro)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/IPython/core/async_helpers.py\", line 129, in _pseudo_sync_runner\n coro.send(None)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3284, in run_cell_async\n has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3466, in run_ast_nodes\n if await self.run_code(code, result, async_=asy):\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3526, in run_code\n exec(code_obj, self.user_global_ns, self.user_ns)\n File \"/var/folders/hs/br_4rpdj68nc3sfdpgv0xgn80000gn/T/ipykernel_77669/3444278365.py\", line 1, in \n predictions = best_model_cnn.predict(X_val)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/utils/traceback_utils.py\", line 65, in error_handler\n return fn(*args, **kwargs)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/engine/training.py\", line 2382, in predict\n tmp_batch_outputs = self.predict_function(iterator)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/engine/training.py\", line 2169, in predict_function\n return step_function(self, iterator)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/engine/training.py\", line 2155, in step_function\n outputs = model.distribute_strategy.run(run_step, args=(data,))\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/engine/training.py\", line 2143, in run_step\n outputs = model.predict_step(data)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/engine/training.py\", line 2111, in predict_step\n return self(x, training=False)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/utils/traceback_utils.py\", line 65, in error_handler\n return fn(*args, **kwargs)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/engine/training.py\", line 558, in __call__\n return super().__call__(*args, **kwargs)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/utils/traceback_utils.py\", line 65, in error_handler\n return fn(*args, **kwargs)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/engine/base_layer.py\", line 1145, in __call__\n outputs = call_fn(inputs, *args, **kwargs)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/utils/traceback_utils.py\", line 96, in error_handler\n return fn(*args, **kwargs)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/engine/sequential.py\", line 412, in call\n return super().call(inputs, training=training, mask=mask)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/engine/functional.py\", line 512, in call\n return self._run_internal_graph(inputs, training=training, mask=mask)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/engine/functional.py\", line 669, in _run_internal_graph\n outputs = node.layer(*args, **kwargs)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/utils/traceback_utils.py\", line 65, in error_handler\n return fn(*args, **kwargs)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/engine/base_layer.py\", line 1145, in __call__\n outputs = call_fn(inputs, *args, **kwargs)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/utils/traceback_utils.py\", line 96, in error_handler\n return fn(*args, **kwargs)\n File \"/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/keras/layers/core/embedding.py\", line 272, in call\n out = tf.nn.embedding_lookup(self.embeddings, inputs)\nNode: 'sequential_119/embedding_119/embedding_lookup'\nindices[2,63] = 10780 is not in [0, 10686)\n\t [[{{node sequential_119/embedding_119/embedding_lookup}}]] [Op:__inference_predict_function_338434]" + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m predictions \u001b[38;5;241m=\u001b[39m best_model_cnn\u001b[38;5;241m.\u001b[39mpredict(\u001b[43mX_test\u001b[49m)\n\u001b[1;32m 2\u001b[0m predictions \u001b[38;5;241m=\u001b[39m (predictions \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0.5\u001b[39m)\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mint\u001b[39m) \n\u001b[1;32m 4\u001b[0m f1 \u001b[38;5;241m=\u001b[39m f1_score(y_test, predictions)\n", + "\u001b[0;31mNameError\u001b[0m: name 'X_test' is not defined" ] } ], diff --git a/notebooks/Experiments-BERT.ipynb b/notebooks/Experiments-BERT.ipynb index 74f3ae9..e42126c 100644 --- a/notebooks/Experiments-BERT.ipynb +++ b/notebooks/Experiments-BERT.ipynb @@ -119,14 +119,6 @@ ")).batch(30).prefetch(1)\n" ] }, - { - "cell_type": "markdown", - "id": "bdbd73a3-094c-4a8a-a45b-8ce8aecdcd12", - "metadata": {}, - "source": [ - "### Without Custom Classifier" - ] - }, { "cell_type": "code", "execution_count": 15, diff --git a/notebooks/accuracy_plot.png b/notebooks/accuracy_plot.png new file mode 100644 index 0000000000000000000000000000000000000000..1d9b27055c0be8e0378d7c958a9169eb4b9f48ed GIT binary patch literal 52962 zcmeFZWmJ`0{4Kmq3nwNeT&B{jV1Y+FZUM#L2V60T)?$Nm0j+LSZ%{|1NqZ_tK0)sZgaJ zKB(>-*5B&nyiudQaM;b?PiejDkXrP_&J(9xkF7l@`E2pEUk4Y(Jp1q3f77poRxLfb 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