00-mnist-cnn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "94751161",
   "metadata": {},
   "source": [
    "# Baseline CNN\n",
    "\n",
    "Before working on the MIL, I'd like to produce a CNN which can train decently well on our three datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9a0aae4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Physical GPUs, 1 Logical GPUs\n",
      "2.4.1\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers\n",
    "import utils\n",
    "\n",
    "N_CLASSES = 10\n",
    "\n",
    "utils.gpu_fix()\n",
    "\n",
    "tf.random.set_seed(42)\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41525de1",
   "metadata": {},
   "source": [
    "Define the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e18b77b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def define_model(shape=(28, 28, 1)):\n",
    "  model = tf.keras.Sequential(\n",
    "    utils.baseline_layers(shape, N_CLASSES)\n",
    "  )\n",
    "  utils.compile(model, N_CLASSES)\n",
    "  return model\n",
    "\n",
    "print(define_model(shape=(32, 32, 3)).summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9d8d4c61-0fbb-4759-9b91-5002bfd8a852",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- Data -----\n",
      "Train : x=(50000, 32, 32, 3), y=(50000, 10)\n",
      "Test  : x=(10000, 32, 32, 3), y=(10000, 10)\n",
      "Splits: train=[5000 5000 5000 5000 5000 5000 5000 5000 5000 5000], test=[1000 1000 1000 1000 1000 1000 1000 1000 1000 1000]\n",
      "----------------\n",
      "Epoch 1/10\n",
      "782/782 [==============================] - 27s 28ms/step - loss: 1.8067 - precision: 0.5902 - recall: 0.0964 - auc: 0.7852 - f1: 0.3311 - tp: 3251.8416 - tn: 224044.5390 - fp: 1745.6220 - fn: 21835.9540 - val_loss: 1.2471 - val_precision: 0.7786 - val_recall: 0.3081 - val_auc: 0.9149 - val_f1: 0.5572 - val_tp: 3081.0000 - val_tn: 89124.0000 - val_fp: 876.0000 - val_fn: 6919.0000\n",
      "Epoch 2/10\n",
      "782/782 [==============================] - 19s 25ms/step - loss: 1.3041 - precision: 0.7196 - recall: 0.3289 - auc: 0.9032 - f1: 0.5321 - tp: 8414.5019 - tn: 222514.4866 - fp: 3275.6743 - fn: 16673.2937 - val_loss: 1.1105 - val_precision: 0.7757 - val_recall: 0.4234 - val_auc: 0.9310 - val_f1: 0.5957 - val_tp: 4234.0000 - val_tn: 88776.0000 - val_fp: 1224.0000 - val_fn: 5766.0000\n",
      "Epoch 3/10\n",
      "782/782 [==============================] - 19s 25ms/step - loss: 1.1756 - precision: 0.7417 - recall: 0.4052 - auc: 0.9216 - f1: 0.5810 - tp: 10284.8033 - tn: 222232.8531 - fp: 3557.3078 - fn: 14802.9923 - val_loss: 1.0060 - val_precision: 0.7868 - val_recall: 0.4831 - val_auc: 0.9432 - val_f1: 0.6440 - val_tp: 4831.0000 - val_tn: 88691.0000 - val_fp: 1309.0000 - val_fn: 5169.0000\n",
      "Epoch 4/10\n",
      "782/782 [==============================] - 19s 25ms/step - loss: 1.0943 - precision: 0.7641 - recall: 0.4549 - auc: 0.9319 - f1: 0.6148 - tp: 11518.4278 - tn: 222229.5645 - fp: 3560.5964 - fn: 13569.3678 - val_loss: 0.9478 - val_precision: 0.8112 - val_recall: 0.5120 - val_auc: 0.9500 - val_f1: 0.6634 - val_tp: 5120.0000 - val_tn: 88808.0000 - val_fp: 1192.0000 - val_fn: 4880.0000\n",
      "Epoch 5/10\n",
      "782/782 [==============================] - 19s 25ms/step - loss: 1.0296 - precision: 0.7713 - recall: 0.4923 - auc: 0.9398 - f1: 0.6356 - tp: 12304.7101 - tn: 222135.6743 - fp: 3654.4866 - fn: 12783.0856 - val_loss: 0.9319 - val_precision: 0.8124 - val_recall: 0.5222 - val_auc: 0.9512 - val_f1: 0.6757 - val_tp: 5222.0000 - val_tn: 88794.0000 - val_fp: 1206.0000 - val_fn: 4778.0000\n",
      "Epoch 6/10\n",
      "782/782 [==============================] - 20s 25ms/step - loss: 1.0056 - precision: 0.7754 - recall: 0.5028 - auc: 0.9424 - f1: 0.6449 - tp: 12705.4189 - tn: 222164.7254 - fp: 3625.4355 - fn: 12382.3768 - val_loss: 0.9158 - val_precision: 0.8034 - val_recall: 0.5457 - val_auc: 0.9523 - val_f1: 0.6785 - val_tp: 5457.0000 - val_tn: 88665.0000 - val_fp: 1335.0000 - val_fn: 4543.0000\n",
      "Epoch 7/10\n",
      "782/782 [==============================] - 20s 26ms/step - loss: 0.9566 - precision: 0.7848 - recall: 0.5331 - auc: 0.9478 - f1: 0.6614 - tp: 13331.9361 - tn: 222152.9080 - fp: 3637.2529 - fn: 11755.8595 - val_loss: 0.8753 - val_precision: 0.8345 - val_recall: 0.5460 - val_auc: 0.9576 - val_f1: 0.6901 - val_tp: 5460.0000 - val_tn: 88917.0000 - val_fp: 1083.0000 - val_fn: 4540.0000\n",
      "Epoch 8/10\n",
      "782/782 [==============================] - 19s 25ms/step - loss: 0.9419 - precision: 0.7896 - recall: 0.5408 - auc: 0.9494 - f1: 0.6635 - tp: 13635.0830 - tn: 222154.9106 - fp: 3635.2503 - fn: 11452.7126 - val_loss: 0.8330 - val_precision: 0.8386 - val_recall: 0.5816 - val_auc: 0.9613 - val_f1: 0.7147 - val_tp: 5816.0000 - val_tn: 88881.0000 - val_fp: 1119.0000 - val_fn: 4184.0000\n",
      "Epoch 9/10\n",
      "782/782 [==============================] - 20s 26ms/step - loss: 0.9007 - precision: 0.7949 - recall: 0.5617 - auc: 0.9535 - f1: 0.6850 - tp: 14071.2312 - tn: 222133.8442 - fp: 3656.3167 - fn: 11016.5645 - val_loss: 0.8416 - val_precision: 0.8399 - val_recall: 0.5706 - val_auc: 0.9606 - val_f1: 0.7125 - val_tp: 5706.0000 - val_tn: 88912.0000 - val_fp: 1088.0000 - val_fn: 4294.0000\n",
      "Epoch 10/10\n",
      "782/782 [==============================] - 20s 25ms/step - loss: 0.8842 - precision: 0.7983 - recall: 0.5675 - auc: 0.9554 - f1: 0.6817 - tp: 14300.2542 - tn: 222174.6935 - fp: 3615.4674 - fn: 10787.5415 - val_loss: 0.8122 - val_precision: 0.8371 - val_recall: 0.5970 - val_auc: 0.9629 - val_f1: 0.7182 - val_tp: 5970.0000 - val_tn: 88838.0000 - val_fp: 1162.0000 - val_fn: 4030.0000\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "history = utils.evaluate(define_model(shape=(32, 32, 3)), utils.load_cifar10())\n",
    "utils.plot_history(history, title='CIFAR-10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d7c7d218",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- Data -----\n",
      "Train : x=(60000, 28, 28, 1), y=(60000, 10)\n",
      "Test  : x=(10000, 28, 28, 1), y=(10000, 10)\n",
      "Splits: train=[6000 6000 6000 6000 6000 6000 6000 6000 6000 6000], test=[1000 1000 1000 1000 1000 1000 1000 1000 1000 1000]\n",
      "----------------\n",
      "Epoch 1/10\n",
      "938/938 [==============================] - 23s 23ms/step - loss: 0.7741 - precision: 0.8248 - recall: 0.6214 - auc: 0.9595 - f1: 0.7135 - tp: 20732.8009 - tn: 267115.6709 - fp: 3603.1022 - fn: 9347.0628 - val_loss: 0.3658 - val_precision: 0.9019 - val_recall: 0.8300 - val_auc: 0.9918 - val_f1: 0.8650 - val_tp: 8300.0000 - val_tn: 89097.0000 - val_fp: 903.0000 - val_fn: 1700.0000\n",
      "Epoch 2/10\n",
      "938/938 [==============================] - 20s 22ms/step - loss: 0.3786 - precision: 0.8945 - recall: 0.8321 - auc: 0.9907 - f1: 0.8606 - tp: 25067.0522 - tn: 267745.0937 - fp: 2973.6794 - fn: 5012.8115 - val_loss: 0.3319 - val_precision: 0.9019 - val_recall: 0.8504 - val_auc: 0.9929 - val_f1: 0.8721 - val_tp: 8504.0000 - val_tn: 89075.0000 - val_fp: 925.0000 - val_fn: 1496.0000\n",
      "Epoch 3/10\n",
      "938/938 [==============================] - 21s 22ms/step - loss: 0.3295 - precision: 0.9044 - recall: 0.8555 - auc: 0.9925 - f1: 0.8789 - tp: 25811.7146 - tn: 268060.8498 - fp: 2657.9233 - fn: 4268.1491 - val_loss: 0.2758 - val_precision: 0.9170 - val_recall: 0.8788 - val_auc: 0.9947 - val_f1: 0.8978 - val_tp: 8788.0000 - val_tn: 89205.0000 - val_fp: 795.0000 - val_fn: 1212.0000\n",
      "Epoch 4/10\n",
      "938/938 [==============================] - 20s 22ms/step - loss: 0.2912 - precision: 0.9143 - recall: 0.8741 - auc: 0.9941 - f1: 0.8930 - tp: 26291.0202 - tn: 268247.4153 - fp: 2471.3578 - fn: 3788.8435 - val_loss: 0.2584 - val_precision: 0.9192 - val_recall: 0.8907 - val_auc: 0.9947 - val_f1: 0.9037 - val_tp: 8907.0000 - val_tn: 89217.0000 - val_fp: 783.0000 - val_fn: 1093.0000\n",
      "Epoch 5/10\n",
      "938/938 [==============================] - 22s 23ms/step - loss: 0.2699 - precision: 0.9191 - recall: 0.8825 - auc: 0.9949 - f1: 0.8992 - tp: 26555.2918 - tn: 268383.8892 - fp: 2334.8839 - fn: 3524.5719 - val_loss: 0.2515 - val_precision: 0.9243 - val_recall: 0.8912 - val_auc: 0.9953 - val_f1: 0.9059 - val_tp: 8912.0000 - val_tn: 89270.0000 - val_fp: 730.0000 - val_fn: 1088.0000\n",
      "Epoch 6/10\n",
      "938/938 [==============================] - 21s 22ms/step - loss: 0.2612 - precision: 0.9223 - recall: 0.8887 - auc: 0.9952 - f1: 0.9046 - tp: 26759.4654 - tn: 268473.0756 - fp: 2245.6976 - fn: 3320.3983 - val_loss: 0.2449 - val_precision: 0.9239 - val_recall: 0.8962 - val_auc: 0.9954 - val_f1: 0.9089 - val_tp: 8962.0000 - val_tn: 89262.0000 - val_fp: 738.0000 - val_fn: 1038.0000\n",
      "Epoch 7/10\n",
      "938/938 [==============================] - 21s 23ms/step - loss: 0.2410 - precision: 0.9256 - recall: 0.8963 - auc: 0.9958 - f1: 0.9097 - tp: 26931.3482 - tn: 268537.4185 - fp: 2181.3546 - fn: 3148.5154 - val_loss: 0.2306 - val_precision: 0.9314 - val_recall: 0.9042 - val_auc: 0.9956 - val_f1: 0.9173 - val_tp: 9042.0000 - val_tn: 89334.0000 - val_fp: 666.0000 - val_fn: 958.0000\n",
      "Epoch 8/10\n",
      "938/938 [==============================] - 22s 23ms/step - loss: 0.2247 - precision: 0.9302 - recall: 0.9037 - auc: 0.9962 - f1: 0.9147 - tp: 27165.4356 - tn: 268663.0522 - fp: 2055.7210 - fn: 2914.4281 - val_loss: 0.2311 - val_precision: 0.9307 - val_recall: 0.9032 - val_auc: 0.9957 - val_f1: 0.9164 - val_tp: 9032.0000 - val_tn: 89327.0000 - val_fp: 673.0000 - val_fn: 968.0000\n",
      "Epoch 9/10\n",
      "938/938 [==============================] - 21s 22ms/step - loss: 0.2252 - precision: 0.9288 - recall: 0.9022 - auc: 0.9963 - f1: 0.9155 - tp: 27161.9670 - tn: 268646.8317 - fp: 2071.9414 - fn: 2917.8967 - val_loss: 0.2225 - val_precision: 0.9313 - val_recall: 0.9059 - val_auc: 0.9963 - val_f1: 0.9180 - val_tp: 9059.0000 - val_tn: 89332.0000 - val_fp: 668.0000 - val_fn: 941.0000\n",
      "Epoch 10/10\n",
      "938/938 [==============================] - 21s 23ms/step - loss: 0.2158 - precision: 0.9324 - recall: 0.9074 - auc: 0.9965 - f1: 0.9202 - tp: 27309.6486 - tn: 268746.5048 - fp: 1972.2684 - fn: 2770.2151 - val_loss: 0.2174 - val_precision: 0.9338 - val_recall: 0.9107 - val_auc: 0.9961 - val_f1: 0.9212 - val_tp: 9107.0000 - val_tn: 89354.0000 - val_fp: 646.0000 - val_fn: 893.0000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "history = utils.evaluate(define_model(), utils.load_fashion())\n",
    "utils.plot_history(history, title='Fashion')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "82648d49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- Data -----\n",
      "Train : x=(60000, 28, 28, 1), y=(60000, 10)\n",
      "Test  : x=(10000, 28, 28, 1), y=(10000, 10)\n",
      "Splits: train=[5923 6742 5958 6131 5842 5421 5918 6265 5851 5949], test=[ 980 1135 1032 1010  982  892  958 1028  974 1009]\n",
      "----------------\n",
      "Epoch 1/10\n",
      "938/938 [==============================] - 24s 24ms/step - loss: 0.4940 - precision: 0.9145 - recall: 0.7791 - auc: 0.9753 - f1: 0.8381 - tp: 25877.4430 - tn: 269194.3589 - fp: 1524.4143 - fn: 4202.4207 - val_loss: 0.0529 - val_precision: 0.9852 - val_recall: 0.9804 - val_auc: 0.9996 - val_f1: 0.9822 - val_tp: 9804.0000 - val_tn: 89853.0000 - val_fp: 147.0000 - val_fn: 196.0000\n",
      "Epoch 2/10\n",
      "938/938 [==============================] - 21s 22ms/step - loss: 0.0875 - precision: 0.9758 - recall: 0.9688 - auc: 0.9988 - f1: 0.9719 - tp: 29161.5708 - tn: 270014.7071 - fp: 704.0660 - fn: 918.2929 - val_loss: 0.0346 - val_precision: 0.9898 - val_recall: 0.9869 - val_auc: 0.9998 - val_f1: 0.9882 - val_tp: 9869.0000 - val_tn: 89898.0000 - val_fp: 102.0000 - val_fn: 131.0000\n",
      "Epoch 3/10\n",
      "938/938 [==============================] - 21s 22ms/step - loss: 0.0670 - precision: 0.9812 - recall: 0.9759 - auc: 0.9990 - f1: 0.9782 - tp: 29370.6124 - tn: 270164.5133 - fp: 554.2599 - fn: 709.2513 - val_loss: 0.0292 - val_precision: 0.9911 - val_recall: 0.9892 - val_auc: 0.9998 - val_f1: 0.9901 - val_tp: 9892.0000 - val_tn: 89911.0000 - val_fp: 89.0000 - val_fn: 108.0000\n",
      "Epoch 4/10\n",
      "938/938 [==============================] - 21s 23ms/step - loss: 0.0564 - precision: 0.9845 - recall: 0.9806 - auc: 0.9992 - f1: 0.9826 - tp: 29499.1448 - tn: 270257.6976 - fp: 461.0756 - fn: 580.7188 - val_loss: 0.0263 - val_precision: 0.9925 - val_recall: 0.9905 - val_auc: 0.9998 - val_f1: 0.9912 - val_tp: 9905.0000 - val_tn: 89925.0000 - val_fp: 75.0000 - val_fn: 95.0000\n",
      "Epoch 5/10\n",
      "938/938 [==============================] - 22s 23ms/step - loss: 0.0500 - precision: 0.9861 - recall: 0.9827 - auc: 0.9993 - f1: 0.9843 - tp: 29571.8136 - tn: 270306.7796 - fp: 411.9936 - fn: 508.0501 - val_loss: 0.0274 - val_precision: 0.9917 - val_recall: 0.9898 - val_auc: 0.9997 - val_f1: 0.9901 - val_tp: 9898.0000 - val_tn: 89917.0000 - val_fp: 83.0000 - val_fn: 102.0000\n",
      "Epoch 6/10\n",
      "938/938 [==============================] - 21s 23ms/step - loss: 0.0431 - precision: 0.9880 - recall: 0.9855 - auc: 0.9994 - f1: 0.9867 - tp: 29633.3014 - tn: 270347.5389 - fp: 371.2343 - fn: 446.5623 - val_loss: 0.0262 - val_precision: 0.9916 - val_recall: 0.9897 - val_auc: 0.9997 - val_f1: 0.9906 - val_tp: 9897.0000 - val_tn: 89916.0000 - val_fp: 84.0000 - val_fn: 103.0000\n",
      "Epoch 7/10\n",
      "938/938 [==============================] - 21s 23ms/step - loss: 0.0391 - precision: 0.9883 - recall: 0.9864 - auc: 0.9995 - f1: 0.9871 - tp: 29662.8605 - tn: 270363.2151 - fp: 355.5580 - fn: 417.0032 - val_loss: 0.0246 - val_precision: 0.9924 - val_recall: 0.9910 - val_auc: 0.9998 - val_f1: 0.9916 - val_tp: 9910.0000 - val_tn: 89924.0000 - val_fp: 76.0000 - val_fn: 90.0000\n",
      "Epoch 8/10\n",
      "938/938 [==============================] - 22s 24ms/step - loss: 0.0355 - precision: 0.9893 - recall: 0.9872 - auc: 0.9995 - f1: 0.9880 - tp: 29695.2183 - tn: 270395.3131 - fp: 323.4601 - fn: 384.6454 - val_loss: 0.0229 - val_precision: 0.9927 - val_recall: 0.9914 - val_auc: 0.9998 - val_f1: 0.9921 - val_tp: 9914.0000 - val_tn: 89927.0000 - val_fp: 73.0000 - val_fn: 86.0000\n",
      "Epoch 9/10\n",
      "938/938 [==============================] - 21s 22ms/step - loss: 0.0330 - precision: 0.9897 - recall: 0.9879 - auc: 0.9997 - f1: 0.9886 - tp: 29715.1693 - tn: 270408.2109 - fp: 310.5623 - fn: 364.6944 - val_loss: 0.0245 - val_precision: 0.9925 - val_recall: 0.9913 - val_auc: 0.9997 - val_f1: 0.9919 - val_tp: 9913.0000 - val_tn: 89925.0000 - val_fp: 75.0000 - val_fn: 87.0000\n",
      "Epoch 10/10\n",
      "938/938 [==============================] - 21s 22ms/step - loss: 0.0296 - precision: 0.9909 - recall: 0.9893 - auc: 0.9996 - f1: 0.9901 - tp: 29751.5240 - tn: 270438.2535 - fp: 280.5197 - fn: 328.3397 - val_loss: 0.0235 - val_precision: 0.9927 - val_recall: 0.9915 - val_auc: 0.9997 - val_f1: 0.9920 - val_tp: 9915.0000 - val_tn: 89927.0000 - val_fp: 73.0000 - val_fn: 85.0000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "history = utils.evaluate(define_model(), utils.load_mnist())\n",
    "utils.plot_history(history, title='MNIST')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}