Project_3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UHs39h-Uf0T7"
   },
   "source": [
    "# Project 3: Computer Vision\n",
    "##### Satjyot Atwal\n",
    "##### Hung Quach\n",
    "##### Zining Yang"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_f6E8-YPi9TR"
   },
   "source": [
    "## Imports and Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "irSAs3eAi6Bb",
    "outputId": "62374c0a-cccd-4241-e21f-b8fe9c05bfb5"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: Line magic function `%tensorflow_version` not found.\n"
     ]
    }
   ],
   "source": [
    "# Use TensorFlow version 2\n",
    "%tensorflow_version 2.x \n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import random\n",
    "from google.colab import drive\n",
    "\n",
    "import tensorflow as tf\n",
    "from keras.datasets import cifar10\n",
    "from tensorflow.keras.models import Sequential, load_model\n",
    "from tensorflow.keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from tensorflow.keras.applications.vgg16 import VGG16\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "        \n",
    "from sklearn import metrics\n",
    "from sklearn import preprocessing\n",
    "\n",
    "# Google Drive is mounted to /content/gdrive/My Drive/\n",
    "save_path = \"/content/gdrive/My Drive/Saved_NN/\"\n",
    "\n",
    "\n",
    "def cm_visual(y_true, pred, outcome):\n",
    "    cm = metrics.confusion_matrix(y_true, pred)\n",
    "    print(cm)\n",
    "\n",
    "    plt.figure(figsize=(8, 8))\n",
    "    plot_confusion_matrix(cm, outcome)\n",
    "    plt.show()\n",
    "\n",
    "    print(metrics.classification_report(y_true, pred))\n",
    "\n",
    "\n",
    "# Plot a confusion matrix.\n",
    "# cm is the confusion matrix, names are the names of the classes.\n",
    "def plot_confusion_matrix(cm, names, title='Confusion matrix', cmap=plt.cm.Blues):\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(names))\n",
    "    plt.xticks(tick_marks, names, rotation=45)\n",
    "    plt.yticks(tick_marks, names)\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Xf565s6GSu3h"
   },
   "source": [
    "## Verify I am using Tensorflow Version 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "JJIo-w-gSYDb",
    "outputId": "349cf8ce-83c5-4340-a895-e79b38528fe0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: tensorflow\n",
      "Version: 2.1.0\n",
      "Summary: TensorFlow is an open source machine learning framework for everyone.\n",
      "Home-page: https://www.tensorflow.org/\n",
      "Author: Google Inc.\n",
      "Author-email: packages@tensorflow.org\n",
      "License: Apache 2.0\n",
      "Location: /usr/local/lib/python3.6/dist-packages\n",
      "Requires: numpy, termcolor, google-pasta, gast, keras-applications, tensorflow-estimator, grpcio, wrapt, astor, keras-preprocessing, opt-einsum, wheel, scipy, six, absl-py, protobuf, tensorboard\n",
      "Required-by: tensorflow-federated, fancyimpute\n"
     ]
    }
   ],
   "source": [
    "!pip show tensorflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Q8sVv9KSidON"
   },
   "source": [
    "## Verify Notebook is using GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "ZbMSVzLPictf",
    "outputId": "5f781ccb-b768-4da7-ff88-86c83fb16d79"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'tf' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-a32920475ac0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgpu_device_name\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;31mNameError\u001b[0m: name 'tf' is not defined"
     ]
    }
   ],
   "source": [
    "tf.test.gpu_device_name()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EEKUE3MuuRl4"
   },
   "source": [
    "## Connect My Google Drive to Google Colab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "ewPgII3vuQuT",
    "outputId": "9ead3d57-bfce-4397-d41b-b5a2c454e50c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "drive.mount('/content/gdrive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RyWtFWkRje_R"
   },
   "source": [
    "## Load CIFAR Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "eWLmwuIffsVV",
    "outputId": "bd0a3401-3b1a-43e8-97c1-790b6f198496"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'cifar10' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-4676c82266d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcifar10\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_data\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[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\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[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'cifar10' is not defined"
     ]
    }
   ],
   "source": [
    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
    "print(x_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "vRiOPvz6xJwL",
    "outputId": "a78b6ece-6d06-499b-eb40-26752f5c84a7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uint8\n",
      "uint8\n",
      "uint8\n",
      "int64\n"
     ]
    }
   ],
   "source": [
    "print(x_train.dtype)\n",
    "print(x_test.dtype)\n",
    "print(y_train.dtype)\n",
    "print(y_test.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "colab_type": "code",
    "id": "KgXAon3wJY78",
    "outputId": "3406f008-e28d-491c-8a64-2fe2cf5164ee"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fd69ac67400>"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LFmlFtmWwu/vzAO4KjJ9E9/O7EOK3AH2DTohEULALkQgKdiESQcEuRCIo2IVI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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x_test[2802])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "X-tkqXkIkrlj",
    "outputId": "775a9d71-f463-45bf-b664-772773d76b96"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[ 59,  62,  63],\n",
       "         [ 43,  46,  45],\n",
       "         [ 50,  48,  43],\n",
       "         ...,\n",
       "         [158, 132, 108],\n",
       "         [152, 125, 102],\n",
       "         [148, 124, 103]],\n",
       "\n",
       "        [[ 16,  20,  20],\n",
       "         [  0,   0,   0],\n",
       "         [ 18,   8,   0],\n",
       "         ...,\n",
       "         [123,  88,  55],\n",
       "         [119,  83,  50],\n",
       "         [122,  87,  57]],\n",
       "\n",
       "        [[ 25,  24,  21],\n",
       "         [ 16,   7,   0],\n",
       "         [ 49,  27,   8],\n",
       "         ...,\n",
       "         [118,  84,  50],\n",
       "         [120,  84,  50],\n",
       "         [109,  73,  42]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[208, 170,  96],\n",
       "         [201, 153,  34],\n",
       "         [198, 161,  26],\n",
       "         ...,\n",
       "         [160, 133,  70],\n",
       "         [ 56,  31,   7],\n",
       "         [ 53,  34,  20]],\n",
       "\n",
       "        [[180, 139,  96],\n",
       "         [173, 123,  42],\n",
       "         [186, 144,  30],\n",
       "         ...,\n",
       "         [184, 148,  94],\n",
       "         [ 97,  62,  34],\n",
       "         [ 83,  53,  34]],\n",
       "\n",
       "        [[177, 144, 116],\n",
       "         [168, 129,  94],\n",
       "         [179, 142,  87],\n",
       "         ...,\n",
       "         [216, 184, 140],\n",
       "         [151, 118,  84],\n",
       "         [123,  92,  72]]],\n",
       "\n",
       "\n",
       "       [[[154, 177, 187],\n",
       "         [126, 137, 136],\n",
       "         [105, 104,  95],\n",
       "         ...,\n",
       "         [ 91,  95,  71],\n",
       "         [ 87,  90,  71],\n",
       "         [ 79,  81,  70]],\n",
       "\n",
       "        [[140, 160, 169],\n",
       "         [145, 153, 154],\n",
       "         [125, 125, 118],\n",
       "         ...,\n",
       "         [ 96,  99,  78],\n",
       "         [ 77,  80,  62],\n",
       "         [ 71,  73,  61]],\n",
       "\n",
       "        [[140, 155, 164],\n",
       "         [139, 146, 149],\n",
       "         [115, 115, 112],\n",
       "         ...,\n",
       "         [ 79,  82,  64],\n",
       "         [ 68,  70,  55],\n",
       "         [ 67,  69,  55]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[175, 167, 166],\n",
       "         [156, 154, 160],\n",
       "         [154, 160, 170],\n",
       "         ...,\n",
       "         [ 42,  34,  36],\n",
       "         [ 61,  53,  57],\n",
       "         [ 93,  83,  91]],\n",
       "\n",
       "        [[165, 154, 128],\n",
       "         [156, 152, 130],\n",
       "         [159, 161, 142],\n",
       "         ...,\n",
       "         [103,  93,  96],\n",
       "         [123, 114, 120],\n",
       "         [131, 121, 131]],\n",
       "\n",
       "        [[163, 148, 120],\n",
       "         [158, 148, 122],\n",
       "         [163, 156, 133],\n",
       "         ...,\n",
       "         [143, 133, 139],\n",
       "         [143, 134, 142],\n",
       "         [143, 133, 144]]],\n",
       "\n",
       "\n",
       "       [[[255, 255, 255],\n",
       "         [253, 253, 253],\n",
       "         [253, 253, 253],\n",
       "         ...,\n",
       "         [253, 253, 253],\n",
       "         [253, 253, 253],\n",
       "         [253, 253, 253]],\n",
       "\n",
       "        [[255, 255, 255],\n",
       "         [255, 255, 255],\n",
       "         [255, 255, 255],\n",
       "         ...,\n",
       "         [255, 255, 255],\n",
       "         [255, 255, 255],\n",
       "         [255, 255, 255]],\n",
       "\n",
       "        [[255, 255, 255],\n",
       "         [254, 254, 254],\n",
       "         [254, 254, 254],\n",
       "         ...,\n",
       "         [254, 254, 254],\n",
       "         [254, 254, 254],\n",
       "         [254, 254, 254]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[113, 120, 112],\n",
       "         [111, 118, 111],\n",
       "         [105, 112, 106],\n",
       "         ...,\n",
       "         [ 72,  81,  80],\n",
       "         [ 72,  80,  79],\n",
       "         [ 72,  80,  79]],\n",
       "\n",
       "        [[111, 118, 110],\n",
       "         [104, 111, 104],\n",
       "         [ 99, 106,  98],\n",
       "         ...,\n",
       "         [ 68,  75,  73],\n",
       "         [ 70,  76,  75],\n",
       "         [ 78,  84,  82]],\n",
       "\n",
       "        [[106, 113, 105],\n",
       "         [ 99, 106,  98],\n",
       "         [ 95, 102,  94],\n",
       "         ...,\n",
       "         [ 78,  85,  83],\n",
       "         [ 79,  85,  83],\n",
       "         [ 80,  86,  84]]],\n",
       "\n",
       "\n",
       "       ...,\n",
       "\n",
       "\n",
       "       [[[ 35, 178, 235],\n",
       "         [ 40, 176, 239],\n",
       "         [ 42, 176, 241],\n",
       "         ...,\n",
       "         [ 99, 177, 219],\n",
       "         [ 79, 147, 197],\n",
       "         [ 89, 148, 189]],\n",
       "\n",
       "        [[ 57, 182, 234],\n",
       "         [ 44, 184, 250],\n",
       "         [ 50, 183, 240],\n",
       "         ...,\n",
       "         [156, 182, 200],\n",
       "         [141, 177, 206],\n",
       "         [116, 149, 175]],\n",
       "\n",
       "        [[ 98, 197, 237],\n",
       "         [ 64, 189, 252],\n",
       "         [ 69, 192, 245],\n",
       "         ...,\n",
       "         [188, 195, 206],\n",
       "         [119, 135, 147],\n",
       "         [ 61,  79,  90]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[ 73,  79,  77],\n",
       "         [ 53,  63,  68],\n",
       "         [ 54,  68,  80],\n",
       "         ...,\n",
       "         [ 17,  40,  64],\n",
       "         [ 21,  36,  51],\n",
       "         [ 33,  48,  49]],\n",
       "\n",
       "        [[ 61,  68,  75],\n",
       "         [ 55,  70,  86],\n",
       "         [ 57,  79, 103],\n",
       "         ...,\n",
       "         [ 24,  48,  72],\n",
       "         [ 17,  35,  53],\n",
       "         [  7,  23,  32]],\n",
       "\n",
       "        [[ 44,  56,  73],\n",
       "         [ 46,  66,  88],\n",
       "         [ 49,  77, 105],\n",
       "         ...,\n",
       "         [ 27,  52,  77],\n",
       "         [ 21,  43,  66],\n",
       "         [ 12,  31,  50]]],\n",
       "\n",
       "\n",
       "       [[[189, 211, 240],\n",
       "         [186, 208, 236],\n",
       "         [185, 207, 235],\n",
       "         ...,\n",
       "         [175, 195, 224],\n",
       "         [172, 194, 222],\n",
       "         [169, 194, 220]],\n",
       "\n",
       "        [[194, 210, 239],\n",
       "         [191, 207, 236],\n",
       "         [190, 206, 235],\n",
       "         ...,\n",
       "         [173, 192, 220],\n",
       "         [171, 191, 218],\n",
       "         [167, 190, 216]],\n",
       "\n",
       "        [[208, 219, 244],\n",
       "         [205, 216, 240],\n",
       "         [204, 215, 239],\n",
       "         ...,\n",
       "         [175, 191, 217],\n",
       "         [172, 190, 216],\n",
       "         [169, 191, 215]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[207, 199, 181],\n",
       "         [203, 195, 175],\n",
       "         [203, 196, 173],\n",
       "         ...,\n",
       "         [135, 132, 127],\n",
       "         [162, 158, 150],\n",
       "         [168, 163, 151]],\n",
       "\n",
       "        [[198, 190, 170],\n",
       "         [189, 181, 159],\n",
       "         [180, 172, 147],\n",
       "         ...,\n",
       "         [178, 171, 160],\n",
       "         [175, 169, 156],\n",
       "         [175, 169, 154]],\n",
       "\n",
       "        [[198, 189, 173],\n",
       "         [189, 181, 162],\n",
       "         [178, 170, 149],\n",
       "         ...,\n",
       "         [195, 184, 169],\n",
       "         [196, 189, 171],\n",
       "         [195, 190, 171]]],\n",
       "\n",
       "\n",
       "       [[[229, 229, 239],\n",
       "         [236, 237, 247],\n",
       "         [234, 236, 247],\n",
       "         ...,\n",
       "         [217, 219, 233],\n",
       "         [221, 223, 234],\n",
       "         [222, 223, 233]],\n",
       "\n",
       "        [[222, 221, 229],\n",
       "         [239, 239, 249],\n",
       "         [233, 234, 246],\n",
       "         ...,\n",
       "         [223, 223, 236],\n",
       "         [227, 228, 238],\n",
       "         [210, 211, 220]],\n",
       "\n",
       "        [[213, 206, 211],\n",
       "         [234, 232, 239],\n",
       "         [231, 233, 244],\n",
       "         ...,\n",
       "         [220, 220, 232],\n",
       "         [220, 219, 232],\n",
       "         [202, 203, 215]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[150, 143, 135],\n",
       "         [140, 135, 127],\n",
       "         [132, 127, 120],\n",
       "         ...,\n",
       "         [224, 222, 218],\n",
       "         [230, 228, 225],\n",
       "         [241, 241, 238]],\n",
       "\n",
       "        [[137, 132, 126],\n",
       "         [130, 127, 120],\n",
       "         [125, 121, 115],\n",
       "         ...,\n",
       "         [181, 180, 178],\n",
       "         [202, 201, 198],\n",
       "         [212, 211, 207]],\n",
       "\n",
       "        [[122, 119, 114],\n",
       "         [118, 116, 110],\n",
       "         [120, 116, 111],\n",
       "         ...,\n",
       "         [179, 177, 173],\n",
       "         [164, 164, 162],\n",
       "         [163, 163, 161]]]], dtype=uint8)"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 136
    },
    "colab_type": "code",
    "id": "85l_BDxqjodA",
    "outputId": "3106c55e-3860-4f54-f0b6-95fdbdc7441a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6],\n",
       "       [9],\n",
       "       [9],\n",
       "       ...,\n",
       "       [9],\n",
       "       [1],\n",
       "       [1]], dtype=uint8)"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "Gv5mJ0z5jrG3",
    "outputId": "d6ca637f-4059-4ed6-8786-ccc98676f036"
   },
   "outputs": [
    {
     "data": {
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       "         [126,  91,  36],\n",
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       "\n",
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       "         [159, 114,  45],\n",
       "         ...,\n",
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       "         [125,  91,  32],\n",
       "         [119,  88,  34]],\n",
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       "         [158, 111,  36],\n",
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       "         [130,  95,  34],\n",
       "         [120,  89,  33]],\n",
       "\n",
       "        ...,\n",
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       "         [ 31,  88, 137],\n",
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       "         [ 13,  64, 108],\n",
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       "\n",
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       "         ...,\n",
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       "\n",
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       "         ...,\n",
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       "\n",
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       "       [[[235, 235, 235],\n",
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       "         ...,\n",
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       "         ...,\n",
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       "         ...,\n",
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       "         ...,\n",
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       "\n",
       "\n",
       "       ...,\n",
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       "       [[[ 20,  15,  12],\n",
       "         [ 19,  14,  11],\n",
       "         [ 15,  14,  11],\n",
       "         ...,\n",
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       "\n",
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       "         [ 18,  17,  12],\n",
       "         ...,\n",
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       "         [ 12,  11,   9]],\n",
       "\n",
       "        [[ 21,  16,  13],\n",
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       "         [ 20,  18,  11],\n",
       "         ...,\n",
       "         [ 12,  11,   9],\n",
       "         [ 12,  11,   9],\n",
       "         [ 13,  12,  10]],\n",
       "\n",
       "        ...,\n",
       "\n",
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       "         [ 34,  26,  15],\n",
       "         ...,\n",
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       "\n",
       "        [[ 33,  25,  14],\n",
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       "         [ 34,  26,  15],\n",
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       "         [ 33,  25,  14],\n",
       "         ...,\n",
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       "\n",
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       "       [[[ 25,  40,  12],\n",
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       "         ...,\n",
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       "\n",
       "        [[ 12,  25,   6],\n",
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       "         ...,\n",
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       "         [104, 134,  86],\n",
       "         ...,\n",
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       "\n",
       "        [[103, 132,  83],\n",
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       "         [107, 135,  87],\n",
       "         ...,\n",
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       "\n",
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       "         [101, 128,  81],\n",
       "         ...,\n",
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       "         [ 95, 123,  81],\n",
       "         [ 92, 120,  80]]],\n",
       "\n",
       "\n",
       "       [[[ 73,  78,  75],\n",
       "         [ 98, 103, 113],\n",
       "         [ 99, 106, 114],\n",
       "         ...,\n",
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       "\n",
       "        [[ 69,  73,  70],\n",
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       "         [ 68,  75,  81],\n",
       "         ...,\n",
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       "\n",
       "        [[ 69,  73,  70],\n",
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       "         ...,\n",
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       "\n",
       "        ...,\n",
       "\n",
       "        [[123, 128,  96],\n",
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       "         [129, 128, 100],\n",
       "         ...,\n",
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       "         [ 62,  60,  55],\n",
       "         [ 27,  27,  28]],\n",
       "\n",
       "        [[115, 121,  91],\n",
       "         [123, 124,  95],\n",
       "         [129, 126,  99],\n",
       "         ...,\n",
       "         [115, 116,  94],\n",
       "         [ 66,  65,  59],\n",
       "         [ 27,  27,  27]],\n",
       "\n",
       "        [[116, 120,  90],\n",
       "         [121, 122,  94],\n",
       "         [129, 128, 101],\n",
       "         ...,\n",
       "         [116, 115,  94],\n",
       "         [ 68,  65,  58],\n",
       "         [ 27,  26,  26]]]], dtype=uint8)"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 136
    },
    "colab_type": "code",
    "id": "UdUJoD2KjsFn",
    "outputId": "f6224484-e851-4f1b-dd16-9eeff6fd7c1d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3],\n",
       "       [8],\n",
       "       [8],\n",
       "       ...,\n",
       "       [5],\n",
       "       [1],\n",
       "       [7]])"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FJ5h8oNgz-hu"
   },
   "source": [
    "## Datapreprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Nrfy8PVnzZwb"
   },
   "source": [
    "##### Upsampling the input images to 64 by 64 pixels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "1WNeOG1izaEY",
    "outputId": "0107e735-998a-416e-b5f2-f3444fa8ddb7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 64, 64, 3)\n",
      "(10000, 64, 64, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensorflow.python.framework.ops.EagerTensor"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train = tf.image.resize(x_train, (64, 64))\n",
    "x_test = tf.image.resize(x_test, (64, 64))\n",
    "print(x_train.shape)\n",
    "print(x_test.shape)\n",
    "type(x_train)\n",
    "type(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "xp1jMVnT1dSX",
    "outputId": "f8aaca4b-a0e7-4034-d414-cf836d43a966"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "x_train = x_train.numpy()\n",
    "x_test = x_test.numpy()\n",
    "print(type(x_train))\n",
    "print(type(x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oFCg1BPKxt7T"
   },
   "source": [
    "##### Convert to float32 for normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "t9PNuSTexl4S"
   },
   "outputs": [],
   "source": [
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "33F3KF9vxu0N"
   },
   "source": [
    "##### Normalize the data values to the range [0, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "JyDyqijjxmws",
    "outputId": "219428fd-61dd-4063-b42e-c6bfb5d9a781"
   },
   "outputs": [
    {
     "data": {
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       "         [0.21568628, 0.22745098, 0.22941177],\n",
       "         [0.18431373, 0.19607843, 0.19411765],\n",
       "         ...,\n",
       "         [0.5921569 , 0.48921567, 0.40098038],\n",
       "         [0.58431375, 0.4872549 , 0.40294117],\n",
       "         [0.5803922 , 0.4862745 , 0.40392157]],\n",
       "\n",
       "        [[0.18921569, 0.20196079, 0.20490196],\n",
       "         [0.17352942, 0.18529412, 0.1867647 ],\n",
       "         [0.14215687, 0.15196079, 0.1504902 ],\n",
       "         ...,\n",
       "         [0.5615196 , 0.4492647 , 0.3514706 ],\n",
       "         [0.55710787, 0.4497549 , 0.35637254],\n",
       "         [0.55490196, 0.45      , 0.35882354]],\n",
       "\n",
       "        [[0.10490196, 0.11960784, 0.12058824],\n",
       "         [0.08921569, 0.10098039, 0.10147059],\n",
       "         [0.05784314, 0.06372549, 0.0632353 ],\n",
       "         ...,\n",
       "         [0.5002451 , 0.36936274, 0.25245097],\n",
       "         [0.5026961 , 0.3747549 , 0.2632353 ],\n",
       "         [0.50392157, 0.37745097, 0.26862746]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[0.7029412 , 0.55      , 0.39607844],\n",
       "         [0.69558823, 0.53455883, 0.3509804 ],\n",
       "         [0.68088233, 0.5036765 , 0.26078433],\n",
       "         ...,\n",
       "         [0.41617647, 0.28504902, 0.17941177],\n",
       "         [0.38186276, 0.25906864, 0.17352942],\n",
       "         [0.3647059 , 0.24607843, 0.17058824]],\n",
       "\n",
       "        [[0.6970588 , 0.5598039 , 0.43529412],\n",
       "         [0.6887255 , 0.5448529 , 0.40588236],\n",
       "         [0.6720588 , 0.514951  , 0.34705883],\n",
       "         ...,\n",
       "         [0.5151961 , 0.3865196 , 0.27156863],\n",
       "         [0.46715686, 0.34387255, 0.25392157],\n",
       "         [0.44313726, 0.32254902, 0.24509804]],\n",
       "\n",
       "        [[0.69411767, 0.5647059 , 0.45490196],\n",
       "         [0.6852941 , 0.55      , 0.43333334],\n",
       "         [0.66764706, 0.5205882 , 0.39019608],\n",
       "         ...,\n",
       "         [0.5647059 , 0.4372549 , 0.31764707],\n",
       "         [0.50980395, 0.38627452, 0.29411766],\n",
       "         [0.48235294, 0.36078432, 0.28235295]]],\n",
       "\n",
       "\n",
       "       [[[0.6039216 , 0.69411767, 0.73333335],\n",
       "         [0.5764706 , 0.654902  , 0.68333334],\n",
       "         [0.52156866, 0.5764706 , 0.5833333 ],\n",
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       "\n",
       "        [[0.47843137, 0.46666667, 0.44705883],\n",
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       "         ...,\n",
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       "         [0.6392157 , 0.6392157 , 0.6313726 ]]]], dtype=float32)"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train /= 255\n",
    "x_test /= 255\n",
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "c2Pk1lc_Kdsv",
    "outputId": "74d520e4-6eb9-4599-9ecb-a08b735d02b7"
   },
   "outputs": [
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       "         [0.9404412 , 0.9492647 , 0.9671569 ],\n",
       "         [0.9411765 , 0.95098037, 0.96960783]],\n",
       "\n",
       "        [[0.654902  , 0.7745098 , 0.89117646],\n",
       "         [0.65637255, 0.7730392 , 0.8879902 ],\n",
       "         [0.65931374, 0.77009803, 0.88161767],\n",
       "         ...,\n",
       "         [0.95612746, 0.95906866, 0.975     ],\n",
       "         [0.95661765, 0.9615196 , 0.97794116],\n",
       "         [0.95686275, 0.9627451 , 0.9794118 ]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[0.09509804, 0.14019608, 0.16078432],\n",
       "         [0.09852941, 0.14362745, 0.16446078],\n",
       "         [0.10539216, 0.1504902 , 0.17181373],\n",
       "         ...,\n",
       "         [0.01715686, 0.02401961, 0.0127451 ],\n",
       "         [0.02009804, 0.02696078, 0.01470588],\n",
       "         [0.02156863, 0.02843137, 0.01568628]],\n",
       "\n",
       "        [[0.10490196, 0.15392157, 0.1764706 ],\n",
       "         [0.10735294, 0.15637255, 0.17965686],\n",
       "         [0.1122549 , 0.16127451, 0.1860294 ],\n",
       "         ...,\n",
       "         [0.01813726, 0.02303922, 0.01470588],\n",
       "         [0.02303922, 0.02794118, 0.02058824],\n",
       "         [0.0254902 , 0.03039216, 0.02352941]],\n",
       "\n",
       "        [[0.10980392, 0.16078432, 0.18431373],\n",
       "         [0.11176471, 0.1627451 , 0.1872549 ],\n",
       "         [0.11568628, 0.16666667, 0.19313726],\n",
       "         ...,\n",
       "         [0.01862745, 0.02254902, 0.01568628],\n",
       "         [0.0245098 , 0.02843137, 0.02352941],\n",
       "         [0.02745098, 0.03137255, 0.02745098]]],\n",
       "\n",
       "\n",
       "       ...,\n",
       "\n",
       "\n",
       "       [[[0.07843138, 0.05882353, 0.04705882],\n",
       "         [0.07745098, 0.05784314, 0.04607843],\n",
       "         [0.0754902 , 0.05588235, 0.04411765],\n",
       "         ...,\n",
       "         [0.04803922, 0.04411765, 0.03627451],\n",
       "         [0.05      , 0.04607843, 0.0382353 ],\n",
       "         [0.05098039, 0.04705882, 0.03921569]],\n",
       "\n",
       "        [[0.07941177, 0.05980392, 0.04803922],\n",
       "         [0.07843138, 0.05906863, 0.04730392],\n",
       "         [0.07647059, 0.05759804, 0.04583333],\n",
       "         ...,\n",
       "         [0.04632353, 0.04240196, 0.03455883],\n",
       "         [0.04877451, 0.04485294, 0.03700981],\n",
       "         [0.05      , 0.04607843, 0.0382353 ]],\n",
       "\n",
       "        [[0.08137255, 0.06176471, 0.05      ],\n",
       "         [0.08039216, 0.06151961, 0.0497549 ],\n",
       "         [0.07843138, 0.06102941, 0.04926471],\n",
       "         ...,\n",
       "         [0.04289216, 0.03897059, 0.03112745],\n",
       "         [0.04632353, 0.04240196, 0.03455883],\n",
       "         [0.04803922, 0.04411765, 0.03627451]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[0.12745099, 0.09607843, 0.05294118],\n",
       "         [0.12843138, 0.09705883, 0.05392157],\n",
       "         [0.13039216, 0.09901961, 0.05588235],\n",
       "         ...,\n",
       "         [0.10318627, 0.09093137, 0.20857844],\n",
       "         [0.0997549 , 0.08651961, 0.19240196],\n",
       "         [0.09803922, 0.08431373, 0.18431373]],\n",
       "\n",
       "        [[0.12352941, 0.09215686, 0.04901961],\n",
       "         [0.1245098 , 0.09313726, 0.05      ],\n",
       "         [0.1264706 , 0.09509804, 0.05196078],\n",
       "         ...,\n",
       "         [0.10171568, 0.08848039, 0.20416667],\n",
       "         [0.0992647 , 0.08308823, 0.19093138],\n",
       "         [0.09803922, 0.08039216, 0.18431373]],\n",
       "\n",
       "        [[0.12156863, 0.09019608, 0.04705882],\n",
       "         [0.12254902, 0.09117647, 0.04803922],\n",
       "         [0.1245098 , 0.09313726, 0.05      ],\n",
       "         ...,\n",
       "         [0.10098039, 0.0872549 , 0.20196079],\n",
       "         [0.09901961, 0.08137255, 0.19019608],\n",
       "         [0.09803922, 0.07843138, 0.18431373]]],\n",
       "\n",
       "\n",
       "       [[[0.09803922, 0.15686275, 0.04705882],\n",
       "         [0.0882353 , 0.15294118, 0.0382353 ],\n",
       "         [0.06862745, 0.14509805, 0.02058824],\n",
       "         ...,\n",
       "         [0.34411764, 0.41960785, 0.41960785],\n",
       "         [0.3107843 , 0.37254903, 0.38039216],\n",
       "         [0.29411766, 0.34901962, 0.36078432]],\n",
       "\n",
       "        [[0.08529412, 0.14215687, 0.04117647],\n",
       "         [0.07990196, 0.14215687, 0.03480392],\n",
       "         [0.06911765, 0.14215687, 0.02205882],\n",
       "         ...,\n",
       "         [0.39313725, 0.46691176, 0.47696078],\n",
       "         [0.34607843, 0.40661764, 0.425     ],\n",
       "         [0.32254902, 0.3764706 , 0.3990196 ]],\n",
       "\n",
       "        [[0.05980392, 0.1127451 , 0.02941176],\n",
       "         [0.0632353 , 0.12058824, 0.02794118],\n",
       "         [0.07009804, 0.13627452, 0.025     ],\n",
       "         ...,\n",
       "         [0.49117646, 0.5615196 , 0.59166664],\n",
       "         [0.41666666, 0.4747549 , 0.5142157 ],\n",
       "         [0.37941176, 0.43137255, 0.47549018]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[0.39607844, 0.5117647 , 0.32058823],\n",
       "         [0.39681372, 0.51029414, 0.32009804],\n",
       "         [0.39828432, 0.50735295, 0.31911764],\n",
       "         ...,\n",
       "         [0.37843138, 0.48897058, 0.32254902],\n",
       "         [0.36666667, 0.47867647, 0.31470588],\n",
       "         [0.36078432, 0.4735294 , 0.3107843 ]],\n",
       "\n",
       "        [[0.38039216, 0.5       , 0.3107843 ],\n",
       "         [0.38063726, 0.49754903, 0.3093137 ],\n",
       "         [0.38112745, 0.49264705, 0.30637255],\n",
       "         ...,\n",
       "         [0.37254903, 0.48259804, 0.31862745],\n",
       "         [0.3647059 , 0.4752451 , 0.31470588],\n",
       "         [0.36078432, 0.4715686 , 0.3127451 ]],\n",
       "\n",
       "        [[0.37254903, 0.49411765, 0.30588236],\n",
       "         [0.37254903, 0.49117646, 0.30392158],\n",
       "         [0.37254903, 0.4852941 , 0.3       ],\n",
       "         ...,\n",
       "         [0.36960784, 0.47941175, 0.31666666],\n",
       "         [0.36372548, 0.4735294 , 0.31470588],\n",
       "         [0.36078432, 0.47058824, 0.3137255 ]]],\n",
       "\n",
       "\n",
       "       [[[0.28627452, 0.30588236, 0.29411766],\n",
       "         [0.3107843 , 0.33039215, 0.33137256],\n",
       "         [0.35980392, 0.37941176, 0.40588236],\n",
       "         ...,\n",
       "         [0.59607846, 0.6490196 , 0.67156863],\n",
       "         [0.7294118 , 0.77843136, 0.8068628 ],\n",
       "         [0.79607844, 0.84313726, 0.8745098 ]],\n",
       "\n",
       "        [[0.28235295, 0.3009804 , 0.28921568],\n",
       "         [0.30441177, 0.32328433, 0.32377452],\n",
       "         [0.3485294 , 0.36789215, 0.39289215],\n",
       "         ...,\n",
       "         [0.52867645, 0.57965684, 0.5955882 ],\n",
       "         [0.6526961 , 0.7017157 , 0.7240196 ],\n",
       "         [0.7147059 , 0.7627451 , 0.7882353 ]],\n",
       "\n",
       "        [[0.27450982, 0.29117647, 0.27941176],\n",
       "         [0.29166666, 0.30906862, 0.30857843],\n",
       "         [0.3259804 , 0.34485295, 0.36691177],\n",
       "         ...,\n",
       "         [0.39387256, 0.44093138, 0.44362745],\n",
       "         [0.49926472, 0.5482843 , 0.55833334],\n",
       "         [0.55196077, 0.6019608 , 0.6156863 ]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[0.45196077, 0.4735294 , 0.35588235],\n",
       "         [0.45906863, 0.4762255 , 0.35980392],\n",
       "         [0.4732843 , 0.48161766, 0.36764705],\n",
       "         ...,\n",
       "         [0.22205882, 0.21740197, 0.19901961],\n",
       "         [0.14460784, 0.14240196, 0.13627452],\n",
       "         [0.10588235, 0.10490196, 0.10490196]],\n",
       "\n",
       "        [[0.45392156, 0.4715686 , 0.35392156],\n",
       "         [0.4595588 , 0.47377452, 0.35784313],\n",
       "         [0.47083333, 0.47818628, 0.36568627],\n",
       "         ...,\n",
       "         [0.225     , 0.21691176, 0.19705883],\n",
       "         [0.14558823, 0.14093137, 0.13431373],\n",
       "         [0.10588235, 0.10294118, 0.10294118]],\n",
       "\n",
       "        [[0.45490196, 0.47058824, 0.3529412 ],\n",
       "         [0.4598039 , 0.47254902, 0.35686275],\n",
       "         [0.46960783, 0.4764706 , 0.3647059 ],\n",
       "         ...,\n",
       "         [0.22647059, 0.21666667, 0.19607843],\n",
       "         [0.14607844, 0.14019608, 0.13333334],\n",
       "         [0.10588235, 0.10196079, 0.10196079]]]], dtype=float32)"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "e_baMC1Ry3ng"
   },
   "source": [
    "##### One-hot encode output column (image categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Wz18NWmsyir3"
   },
   "outputs": [],
   "source": [
    "num_classes = 10\n",
    "y_train = tf.keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = tf.keras.utils.to_categorical(y_test, num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "id": "0lxwcnFhybVg",
    "outputId": "6af05ee9-d6d4-4130-b860-edf787b8d5d5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 64, 64, 3)\n",
      "(10000, 64, 64, 3)\n",
      "(50000, 10)\n",
      "(10000, 10)\n",
      "(64, 64, 3)\n",
      "float32\n",
      "float32\n",
      "float32\n",
      "float32\n"
     ]
    }
   ],
   "source": [
    "input_shape = (x_train.shape[1], x_train.shape[2], x_train.shape[3])\n",
    "print(x_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)\n",
    "print(input_shape)\n",
    "\n",
    "print(x_train.dtype)\n",
    "print(x_test.dtype)\n",
    "print(y_train.dtype)\n",
    "print(y_test.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SxKj9YMQlaCp"
   },
   "source": [
    "## CNN without Transfer Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 731
    },
    "colab_type": "code",
    "id": "W4OK4s9illwl",
    "outputId": "9aeb7e1f-72cd-4d91-ff7b-8719e91dde5c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Run:  1\n",
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/1000\n",
      "50000/50000 - 17s - loss: 1.5731 - val_loss: 1.2638\n",
      "Epoch 2/1000\n",
      "50000/50000 - 9s - loss: 1.1498 - val_loss: 0.9909\n",
      "Epoch 3/1000\n",
      "50000/50000 - 9s - loss: 0.9441 - val_loss: 0.8908\n",
      "Epoch 4/1000\n",
      "50000/50000 - 8s - loss: 0.8082 - val_loss: 0.8297\n",
      "Epoch 5/1000\n",
      "50000/50000 - 9s - loss: 0.6971 - val_loss: 0.8046\n",
      "Epoch 6/1000\n",
      "50000/50000 - 8s - loss: 0.5806 - val_loss: 0.8392\n",
      "Epoch 7/1000\n",
      "50000/50000 - 8s - loss: 0.4980 - val_loss: 0.8181\n",
      "Epoch 8/1000\n",
      "50000/50000 - 8s - loss: 0.4215 - val_loss: 0.8919\n",
      "Epoch 00008: early stopping\n",
      "\n",
      "Run:  2\n",
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/1000\n",
      "50000/50000 - 9s - loss: 1.5125 - val_loss: 1.1696\n",
      "Epoch 2/1000\n",
      "50000/50000 - 8s - loss: 1.1039 - val_loss: 0.9661\n",
      "Epoch 3/1000\n",
      "50000/50000 - 8s - loss: 0.9116 - val_loss: 0.9078\n",
      "Epoch 4/1000\n",
      "50000/50000 - 8s - loss: 0.7718 - val_loss: 0.8732\n",
      "Epoch 5/1000\n",
      "50000/50000 - 8s - loss: 0.6596 - val_loss: 0.8452\n",
      "Epoch 6/1000\n",
      "50000/50000 - 9s - loss: 0.5622 - val_loss: 0.7994\n",
      "Epoch 7/1000\n",
      "50000/50000 - 8s - loss: 0.4659 - val_loss: 0.8324\n",
      "Epoch 8/1000\n",
      "50000/50000 - 8s - loss: 0.3846 - val_loss: 0.8970\n",
      "Epoch 9/1000\n",
      "50000/50000 - 8s - loss: 0.3270 - val_loss: 0.9401\n",
      "Epoch 00009: early stopping\n"
     ]
    }
   ],
   "source": [
    "# Save best model with best weights\n",
    "checkpointer = ModelCheckpoint(filepath=os.path.join(save_path, \"cnn_no_tl.hdf5\"), \n",
    "                               verbose=0, save_best_only=True)\n",
    "\n",
    "# Run model training two times to jump out of local optimum \n",
    "for i in range(2):\n",
    "    print('\\nRun: ', i + 1)\n",
    "    \n",
    "    cnn_no_tl = Sequential()\n",
    "    \n",
    "    cnn_no_tl.add(Conv2D(32, kernel_size=(3, 3), strides=(1, 1), activation='relu', input_shape=input_shape))\n",
    "    cnn_no_tl.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n",
    "    cnn_no_tl.add(Conv2D(32, kernel_size=(3, 3), strides=(1, 1), activation='relu'))\n",
    "    cnn_no_tl.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n",
    "    cnn_no_tl.add(Conv2D(64, kernel_size=(3, 3), strides=(1, 1), activation='relu'))\n",
    "    cnn_no_tl.add(Conv2D(64, kernel_size=(3, 3), strides=(1, 1), activation='relu'))\n",
    "    cnn_no_tl.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n",
    "    cnn_no_tl.add(Flatten())\n",
    "    cnn_no_tl.add(Dense(512, activation='relu'))\n",
    "    cnn_no_tl.add(Dropout(0.5))\n",
    "    cnn_no_tl.add(Dense(y_train.shape[1], activation='softmax'))\n",
    "\n",
    "    # Use categorical crossentropy loss since it is a classification problem\n",
    "    cnn_no_tl.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.001, decay=1e-6))\n",
    "\n",
    "    # Stop training when model stops improving\n",
    "    monitor = EarlyStopping(monitor='val_loss', min_delta=1e-3, patience=3, verbose=1, mode='auto')\n",
    "\n",
    "    cnn_no_tl.fit(x_train, y_train, validation_data=(x_test, y_test), \n",
    "                   callbacks=[monitor, checkpointer], verbose=2, epochs=1000, batch_size=128)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yFWHYNuw4kEN"
   },
   "source": [
    "## CNN with Transfer Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xgsjTuMW61vi"
   },
   "source": [
    "##### Load pre_trained VGG16 model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 833
    },
    "colab_type": "code",
    "id": "_1Uj1mdZ5mJZ",
    "outputId": "1b395851-bae1-46d4-85e8-a06097f233a4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "58892288/58889256 [==============================] - 2s 0us/step\n",
      "Model: \"vgg16\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 64, 64, 3)]       0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 64, 64, 64)        1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 64, 64, 64)        36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 32, 32, 64)        0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 32, 32, 128)       73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 32, 32, 128)       147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 16, 16, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 16, 16, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 16, 16, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 16, 16, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 8, 8, 256)         0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 8, 8, 512)         1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 8, 8, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 8, 8, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 4, 4, 512)         0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 4, 4, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 4, 4, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 4, 4, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 2, 2, 512)         0         \n",
      "=================================================================\n",
      "Total params: 14,714,688\n",
      "Trainable params: 14,714,688\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "vgg_model = VGG16(weights='imagenet', include_top=False, input_shape=input_shape)\n",
    "vgg_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TSz8BLvO67xa"
   },
   "source": [
    "##### Train my own CNN model on top of pre-trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 901
    },
    "colab_type": "code",
    "id": "LkhPzIep4jiI",
    "outputId": "acb1163f-e222-472e-f451-d9b47c5faed2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Run:  1\n",
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/1000\n",
      "50000/50000 - 16s - loss: 1.2586 - val_loss: 1.0095\n",
      "Epoch 2/1000\n",
      "50000/50000 - 14s - loss: 1.0188 - val_loss: 0.9420\n",
      "Epoch 3/1000\n",
      "50000/50000 - 14s - loss: 0.9504 - val_loss: 0.9116\n",
      "Epoch 4/1000\n",
      "50000/50000 - 14s - loss: 0.9059 - val_loss: 0.8832\n",
      "Epoch 5/1000\n",
      "50000/50000 - 14s - loss: 0.8678 - val_loss: 0.8684\n",
      "Epoch 6/1000\n",
      "50000/50000 - 14s - loss: 0.8389 - val_loss: 0.8518\n",
      "Epoch 7/1000\n",
      "50000/50000 - 13s - loss: 0.8124 - val_loss: 0.8709\n",
      "Epoch 8/1000\n",
      "50000/50000 - 13s - loss: 0.7827 - val_loss: 0.8548\n",
      "Epoch 9/1000\n",
      "50000/50000 - 13s - loss: 0.7645 - val_loss: 0.8634\n",
      "Epoch 00009: early stopping\n",
      "\n",
      "Run:  2\n",
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/1000\n",
      "50000/50000 - 14s - loss: 1.2565 - val_loss: 1.0023\n",
      "Epoch 2/1000\n",
      "50000/50000 - 13s - loss: 1.0173 - val_loss: 0.9446\n",
      "Epoch 3/1000\n",
      "50000/50000 - 13s - loss: 0.9518 - val_loss: 0.9227\n",
      "Epoch 4/1000\n",
      "50000/50000 - 13s - loss: 0.9033 - val_loss: 0.8896\n",
      "Epoch 5/1000\n",
      "50000/50000 - 13s - loss: 0.8702 - val_loss: 0.8820\n",
      "Epoch 6/1000\n",
      "50000/50000 - 13s - loss: 0.8350 - val_loss: 0.8775\n",
      "Epoch 7/1000\n",
      "50000/50000 - 14s - loss: 0.8081 - val_loss: 0.8452\n",
      "Epoch 8/1000\n",
      "50000/50000 - 14s - loss: 0.7834 - val_loss: 0.8318\n",
      "Epoch 9/1000\n",
      "50000/50000 - 13s - loss: 0.7595 - val_loss: 0.8579\n",
      "Epoch 10/1000\n",
      "50000/50000 - 14s - loss: 0.7390 - val_loss: 0.8281\n",
      "Epoch 11/1000\n",
      "50000/50000 - 13s - loss: 0.7187 - val_loss: 0.8314\n",
      "Epoch 12/1000\n",
      "50000/50000 - 13s - loss: 0.7018 - val_loss: 0.8440\n",
      "Epoch 13/1000\n",
      "50000/50000 - 13s - loss: 0.6861 - val_loss: 0.8320\n",
      "Epoch 00013: early stopping\n"
     ]
    }
   ],
   "source": [
    "# Save best model with best weights\n",
    "checkpointer = ModelCheckpoint(filepath=os.path.join(save_path, \"cnn_tl.hdf5\"), \n",
    "                               verbose=0, save_best_only=True)\n",
    "\n",
    "# Run model training two times to jump out of local optimum \n",
    "for i in range(2):\n",
    "    print('\\nRun: ', i + 1)\n",
    "    \n",
    "    # Apply layers of VGG16 model to cnn_tl\n",
    "    cnn_tl = Sequential()\n",
    "\n",
    "    for layer in vgg_model.layers:\n",
    "      cnn_tl.add(layer)\n",
    "\n",
    "    # Fix weights of the pre-trained model\n",
    "    for layer in cnn_tl.layers:\n",
    "      layer.trainable = False\n",
    "\n",
    "    cnn_tl.add(Flatten())\n",
    "    cnn_tl.add(Dense(512, activation='relu'))\n",
    "    cnn_tl.add(Dropout(0.5))\n",
    "    cnn_tl.add(Dense(y_train.shape[1], activation='softmax'))\n",
    "\n",
    "    # Use categorical crossentropy loss since it is a classification problem\n",
    "    cnn_tl.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.001, decay=1e-6))\n",
    "\n",
    "    # Stop training when model stops improving\n",
    "    monitor = EarlyStopping(monitor='val_loss', min_delta=1e-3, patience=3, verbose=1, mode='auto')\n",
    "\n",
    "    cnn_tl.fit(x_train, y_train, validation_data=(x_test, y_test), \n",
    "                   callbacks=[monitor, checkpointer], verbose=2, epochs=1000, batch_size=128)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5CcmErkE4MBq"
   },
   "source": [
    "## Load neural networks from each saved hdf5 file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CtC9KKfU4Exz"
   },
   "outputs": [],
   "source": [
    "saved_cnn_no_tl = load_model(os.path.join(save_path, \"cnn_no_tl.hdf5\"))\n",
    "\n",
    "saved_cnn_tl = load_model(os.path.join(save_path, \"cnn_tl.hdf5\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7kwGP_hz8Kly"
   },
   "source": [
    "##### No Transfer Learning CNN Model Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 493
    },
    "colab_type": "code",
    "id": "VIZudryS4E8V",
    "outputId": "8efdbc10-f546-4139-ed98-b46ce33347d2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_4 (Conv2D)            (None, 62, 62, 32)        896       \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 60, 60, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 30, 30, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 28, 28, 64)        18496     \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 26, 26, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 13, 13, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 10816)             0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 512)               5538304   \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                5130      \n",
      "=================================================================\n",
      "Total params: 5,609,002\n",
      "Trainable params: 5,609,002\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "saved_cnn_no_tl.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "S6mw3RKYyRgP"
   },
   "source": [
    "##### Transfer Learning CNN Model Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 901
    },
    "colab_type": "code",
    "id": "T2uQ9g-w8P_X",
    "outputId": "7bc1f5fd-bbb6-40ff-cd38-62bf38663802"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "block1_conv1 (Conv2D)        (None, 64, 64, 64)        1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 64, 64, 64)        36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 32, 32, 64)        0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 32, 32, 128)       73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 32, 32, 128)       147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 16, 16, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 16, 16, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 16, 16, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 16, 16, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 8, 8, 256)         0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 8, 8, 512)         1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 8, 8, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 8, 8, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 4, 4, 512)         0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 4, 4, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 4, 4, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 4, 4, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 2, 2, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 512)               1049088   \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 10)                5130      \n",
      "=================================================================\n",
      "Total params: 15,768,906\n",
      "Trainable params: 1,054,218\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "saved_cnn_tl.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "H_KKhGPnFA7S"
   },
   "source": [
    "## Model Evaluations¶\n",
    "#### Accuracy, Precision, Recall and F1 scores of each model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8WCVtSstHxgj"
   },
   "source": [
    "##### NO Transfer Learning CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "rOYcvMO7FBlC",
    "outputId": "6a51a179-e208-4321-ab80-6f73e94bf012"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NO Transfer Learning CNN Accuracy score: 0.7316\n",
      "NO Transfer Learning CNN Precision score: 0.7325610653934176\n",
      "NO Transfer Learning CNN Recall score: 0.7316\n",
      "NO Transfer Learning CNN F1 score: 0.7297457016889314\n"
     ]
    }
   ],
   "source": [
    "y_true = np.argmax(y_test, axis=1) \n",
    "\n",
    "cnn_no_tl_pred = saved_cnn_no_tl.predict(x_test)\n",
    "cnn_no_tl_pred = np.argmax(cnn_no_tl_pred, axis=1) # raw probabilities to choose class (highest probability)\n",
    "\n",
    "cnn_no_tl_acc = metrics.accuracy_score(y_true, cnn_no_tl_pred)\n",
    "print(\"NO Transfer Learning CNN Accuracy score: {}\".format(cnn_no_tl_acc))\n",
    "cnn_no_tl_prec = metrics.precision_score(y_true, cnn_no_tl_pred, average= \"weighted\")\n",
    "print(\"NO Transfer Learning CNN Precision score: {}\".format(cnn_no_tl_prec))\n",
    "cnn_no_tl_rec = metrics.recall_score(y_true, cnn_no_tl_pred, average= \"weighted\")\n",
    "print(\"NO Transfer Learning CNN Recall score: {}\".format(cnn_no_tl_rec))\n",
    "cnn_no_tl_score = metrics.f1_score(y_true, cnn_no_tl_pred, average= \"weighted\")\n",
    "print(\"NO Transfer Learning CNN F1 score: {}\".format(cnn_no_tl_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9o8RAAW8H5Gl"
   },
   "source": [
    "##### Transfer Learning CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "zs3QRLCjGgCI",
    "outputId": "887f92d3-c088-4e0f-df45-6cd7cce9e66a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transfer Learning CNN Accuracy score: 0.7142\n",
      "Transfer Learning CNN Precision score: 0.7129620524957372\n",
      "Transfer Learning CNN Recall score: 0.7142\n",
      "Transfer Learning CNN F1 score: 0.7119400101934159\n"
     ]
    }
   ],
   "source": [
    "cnn_tl_pred = saved_cnn_tl.predict(x_test)\n",
    "cnn_tl_pred = np.argmax(cnn_tl_pred, axis=1) # raw probabilities to choose class (highest probability)\n",
    "\n",
    "cnn_tl_acc = metrics.accuracy_score(y_true, cnn_tl_pred)\n",
    "print(\"Transfer Learning CNN Accuracy score: {}\".format(cnn_tl_acc))\n",
    "cnn_tl_prec = metrics.precision_score(y_true, cnn_tl_pred, average= \"weighted\")\n",
    "print(\"Transfer Learning CNN Precision score: {}\".format(cnn_tl_prec))\n",
    "cnn_tl_rec = metrics.recall_score(y_true, cnn_tl_pred, average= \"weighted\")\n",
    "print(\"Transfer Learning CNN Recall score: {}\".format(cnn_tl_rec))\n",
    "cnn_tl_score = metrics.f1_score(y_true, cnn_tl_pred, average= \"weighted\")\n",
    "print(\"Transfer Learning CNN F1 score: {}\".format(cnn_tl_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cw7uENZJFYl9"
   },
   "source": [
    "## Confusion Matrices, Plots, and Classification Reports\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mysfYLHEMs3C"
   },
   "outputs": [],
   "source": [
    "categories = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "chjv9azAIA4Q"
   },
   "source": [
    "##### NO Transfer Learning CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "gwIb57eCFaua",
    "outputId": "a276dc92-4877-4678-9e94-03ffe7393efd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[809  15  34  16  12   6  11   7  65  25]\n",
      " [ 16 877   0   9   5   3   7   6  28  49]\n",
      " [ 79   4 540  74  69  73 106  32  15   8]\n",
      " [ 26  13  59 553  45 182  72  26  15   9]\n",
      " [ 29   5  61  80 630  38  96  47  10   4]\n",
      " [ 17   4  31 176  44 655  24  31  14   4]\n",
      " [  3   4  28  43  18  24 868   4   6   2]\n",
      " [ 12   4  30  43  48  63  14 777   3   6]\n",
      " [ 62  40  10  16   4   6   4   3 836  19]\n",
      " [ 38  94   5  17   4   7   7  21  36 771]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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CdgWe3u5fCbyzbxFJkiTNw3zmxDywqnZM8iOAqrosyVp9jkuSJK0mKzG3uCHJ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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.74      0.81      0.77      1000\n",
      "           1       0.83      0.88      0.85      1000\n",
      "           2       0.68      0.54      0.60      1000\n",
      "           3       0.54      0.55      0.55      1000\n",
      "           4       0.72      0.63      0.67      1000\n",
      "           5       0.62      0.66      0.64      1000\n",
      "           6       0.72      0.87      0.79      1000\n",
      "           7       0.81      0.78      0.80      1000\n",
      "           8       0.81      0.84      0.82      1000\n",
      "           9       0.86      0.77      0.81      1000\n",
      "\n",
      "    accuracy                           0.73     10000\n",
      "   macro avg       0.73      0.73      0.73     10000\n",
      "weighted avg       0.73      0.73      0.73     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cm_visual(y_true, cnn_no_tl_pred, categories)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ae9G_tHXIBh3"
   },
   "source": [
    "##### Transfer Learning CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "a-K2CZo5Ib0B",
    "outputId": "386da4fa-8e78-4147-f812-15e108969d5c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[810  18  21   6  27   4  12  10  68  24]\n",
      " [ 22 809   5  10   7  10  17  13  35  72]\n",
      " [ 74  13 610  40  88  40 102  23   9   1]\n",
      " [ 26  23  51 493  69 147 105  38  17  31]\n",
      " [ 20   9  43  58 692  22  76  59  11  10]\n",
      " [ 10  11  50 145  58 596  53  53  12  12]\n",
      " [  7  13  35  39  56  42 792   1  12   3]\n",
      " [ 14  10  36  33  70  58  15 743   7  14]\n",
      " [ 67  36  10   7   9   5   8   6 827  25]\n",
      " [ 43  93   7  21   9  11   8  12  26 770]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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J0nAI4zOw10qMJEkaStO57UCAFwEPqKp3JNkOuE9V/WTWo5MkSatszngUYqZV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\ngg==\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.74      0.81      0.77      1000\n",
      "           1       0.78      0.81      0.80      1000\n",
      "           2       0.70      0.61      0.65      1000\n",
      "           3       0.58      0.49      0.53      1000\n",
      "           4       0.64      0.69      0.66      1000\n",
      "           5       0.64      0.60      0.62      1000\n",
      "           6       0.67      0.79      0.72      1000\n",
      "           7       0.78      0.74      0.76      1000\n",
      "           8       0.81      0.83      0.82      1000\n",
      "           9       0.80      0.77      0.78      1000\n",
      "\n",
      "    accuracy                           0.71     10000\n",
      "   macro avg       0.71      0.71      0.71     10000\n",
      "weighted avg       0.71      0.71      0.71     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cm_visual(y_true, cnn_tl_pred, categories)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RWLc82NWUbzd"
   },
   "source": [
    "## Example Image Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "VudUPd7ROz92",
    "outputId": "d0b2ed1f-d6e8-470d-f12a-3bff0d9b7719"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Image #3092\n",
      "True Category: 5 - dog\n",
      "NO Transfer Learning CNN Predicted Category: 5 - dog\n",
      "Transfer Learning CNN Predicted Category: 5 - dog\n"
     ]
    },
    {
     "data": {
      "image/png": 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91My+AOD9ZvbvAXwGwHs3aCsQCJwTNlmN/3MAr13z+Vex8N8DgcDfAmw/gu60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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Image #5600\n",
      "True Category: 6 - frog\n",
      "NO Transfer Learning CNN Predicted Category: 6 - frog\n",
      "Transfer Learning CNN Predicted Category: 6 - frog\n"
     ]
    },
    {
     "data": {
      "image/png": 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4nef9ee2vXbcXR9dpBCdLXJtERJ69Z2mzFR+/7IHAriAmeyCwI4jJHgjsCLbq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\n5oK0JaDDPRxms/um3XBt1SB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OlVLC4jwRQs0N1oWTFU+3CM4Szt5k45XXTiP0aJ9X5rWc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\nrD2wvahqWwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Image #5365\n",
      "True Category: 8 - ship\n",
      "NO Transfer Learning CNN Predicted Category: 8 - ship\n",
      "Transfer Learning CNN Predicted Category: 1 - automobile\n"
     ]
    },
    {
     "data": {
      "image/png": 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03g0zEAjcKY5T6y0BOOJO6tN/CcDbAfz09O9PAPhFAL+59FhIC8vT2JIEkRFR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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Image #2676\n",
      "True Category: 4 - deer\n",
      "NO Transfer Learning CNN Predicted Category: 6 - frog\n",
      "Transfer Learning CNN Predicted Category: 3 - cat\n"
     ]
    },
    {
     "data": {
      "image/png": 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vCcCxXV3P/xKAtwD4qfnnjwH4BQC/vq6tvk84vDEzYW5c96bMtWt5/foNb6I4\nuo3MbpMovMRRSvIz1nP5HTKz61K051ngQKLTpkTJHFHoVCUa5FWdqTilpLiPSUsmMU1E0VgmtBmL\nV3RiuDYk2G4UraZ652wWawSgpeXH1qq2BbWpkYI9iU2wzn2prgu3Dw9m4jhqcIE2o2VJPwEb3Q0n\n+Cxk9eT1WjYZuTWluCu+cjBRb3Iy/C3VAxySvj+afO+U4pM0h+SWCcN2EvW4UE6X+rpyC8HMynkF\n18sAPg7grwBcTenEgXkKwKtO01YgEDgfnOphTyl1KaUHANwP4I0Avvu0BzCzR8zskpldekEm5QKB\nwObwbVFvKaWrAD4B4PsBXLRcO+l+AE+v+M6jKaUHU0oP3il6b4FAYHO4qc9uZq8E0KSUrprZDoC3\nYTY59wkA7wTwQQAPAfjIzdrq+x5Hh7O3+1gy27hcbyeUGpe4Za0JpTd4vRQ/l8Um3LIKNrIfKr+F\nLHQ4PspWSjHw4ZU1DWsaKF1FlFqvdFhe7mhOQLO8XIaWMl6OSuRsqtW+sokvzqIdBdE9ScbDZff5\nbrgQXw6XVV+zWFecjHxznt9Y45ZC318cgstzB534/SDhiUoy83i+API9JwJC7fca+kvXrBLt+dEu\nhVsTJdgdSjYiHcuE0LTjG2HNcJ6GZ78PwGM2uyMKAB9OKX3MzL4E4INm9h8BfBbAB07RViAQOCec\nZjb+zwC8YcnnX8XMfw8EAn8HsNEIuj4lTOalbtpWhCfIzOTSRwAwYjOKTfVGKBj6WiVnxiIDTjRC\n9c6JllNt7o4omKOjbGJN+hfcfuUk71ft+T7WVFqoFjqsQKZg2JNRkQu2hR2FBp/dxt8qy9X0nQo+\nGJnMFZmOnVq+3C/VnVixXyXHYt+lU0EGNqeZRtRjcdltU9eIXB4+tpbqZtdINfapX61k7TWUGel0\n3sXcL9kVlYhLLrPNNRMOFiJJs7uYZKyquX+7TokuYuMDgS1BPOyBwJZgw4kwPaZzIYBOzKGSIpFc\nRBH8zHFFiSW1xP5z1JJJAkrNs/hkEo6lIuh1murtxESuyEzjyW0b+zbYvailDNCA9IwHIu/MQgsF\n9V8Jy8LppYl5Th3zs+CadLOcnQB8kowVbJqKEAeXq5I+MoPgvCYxs7nFhVwa7j9vXChrS5FlwsJU\nxIakkma9JYot0bWoZEw7SoRRaXC+f8Zd3k9ny3eofRUSqalsVEn9baWKa8PtV3JvDuZm/JrXd7zZ\nA4EtQTzsgcCWIB72QGBLsFmfPSW0c/+nF9WFinz2nR0v4DgcZTps1Oblo4n3hxuOuJIoJcZ0krPq\nrk58ht0B+fOqGz8iX5ZL97ZrpMYAABzjSURBVA72fCmhmiLj6uSHuBpk/7IWB2tylM+7JT/0Qrvn\n9tsjsctaOMaKxDHYCTYRZGAXWCPjEvrl+6nPvqrGE2TuYA1F59v32ziC0ZXikgi3drzaZx/Q+Axr\nuk57vmRzR/1tpGT4Id0GjYidHNF8zRGVWy5UcISmCAZS/olvA2YHUymiInVeL/0jguq4kQVhUWp7\n5ZZAIPCyQjzsgcCWYLNmPBLadmbGSwAQWO6tFtosOS2y/Hm7oJ3G4g/exOc9OXpvKlpkh1TRtBZX\nY4862bFgxVDMMq7OOvWRgl2fTb1GxQmIxmnoXBqp9soc464IZ3ACEFM8nQhxJEev+eZ7Z5JzRJ6Y\niGm1Gb8ylktpM066kYQcA0f50YZCrjtvMom+LPP4DMiML4beNWqouzfkmh2SW6ZFeRsKdZzSxlJo\n235ArtFiCODJIgf2LZjqI3IPOxFFmStuFEG9BQKBeNgDgS1BPOyBwJbgHGq9tfPF1eWKlT1goYGe\nQlbVZ2eN8AWPkXxDFkPsxAnjmlzaRkd0G4dXmpT/dbrxEhLLFFgrteTYZx/Tton4kIlCbtt9TyHt\n7easuorqhiUVO2DKy8SRdsIWHFbrd+OvKS3X0nyHE7nQDDtyxjUjzlFvLptPQkVH2S+vREB0d5TH\nZzggn73ycx0TmiOZ9P5EjUVGktSZo3kFvqcX9DW4/6VupN0ornuw63cc7efr2cq8RTGfdND6AG6f\n1YcNBAIvJ8TDHghsCc7BjJ+bp8L3lMQ5LGRGsZADZ5uJvb9K7xzwv2rO0hEahDPAVMeupKg8Y4EN\nMdVdaeBeNctIp0woNXZX3LLoyxdE9/QS0cVjMEpkqmr5J/claYP35bGHB+uvqVtWOvOf9NfU5WF3\nSF2egnX4aDmJmc2UlJjxOzvZjK8H2cUpCrlmdCkqiKgIme5JHhleN+PMNn9tve7hAv+Yj13nYw13\n/VjtNPncxJs4qRdQlKvf3/FmDwS2BPGwBwJbgo0nwvTdzIwvZOaVzZcF2WMyETsy52p4M8eVU1JX\ngLZ5iTGZAabovUqi8IZDmvUlgY1eTKeWZnZ7SaooyATV5BSeteaKqY0wBleuZs27VsQ3Sj45ckMG\no9WCII2wAgVF6LGoiGaqsACJRr9Vg3y84SCHgg1rHxZWlFz5VBwFFpQgsQk11Wsqu1RJBF1J2wp2\nr7RiLN8fC2WuqI96z9HxKgp5qyWJxUc2uk2uXwNiUHZ2/Vj1XXZDlOU5rrar5bXccVZuCQQCLyvE\nwx4IbAniYQ8EtgSbpd6Q0HezaDCNkisoHEsFEJlv64gWGkgjiXzbvtcYJmqfqyIt6IwTRSLCECwC\nmdbonXOKXSn9YFqnLtX/Y/3zvKg++/iIyk0JtbdPEXU7e6RRP/LH4ojFXsQ/XekmvhQqPMElmSTL\ny+gaViX7td7fZt+zL3X+hEprD8kfrn3024DWS9GDZ1YxkfB9knM2ygpMWqqbIhZvHHqV04OjHN04\npPtDXGoUNHiFpP4ZrVd0f+8M/XlyxOKg9PMsx0KY5e2g3uZlmz9rZh+br7/WzD5lZk+a2YfMbHCz\nNgKBwPnh2zHj3w3gCVr/JQC/klL6TgBXADx8OzsWCARuL05lxpvZ/QD+KYD/BOBf2yzE7S0Afmq+\ny2MAfgHAr69tKCWkubbaYhHNbA6VIl7B5nQiWqQtxa4kE0gpKT5gVbBJr+YnJTNIgggn4Uy7vJ8K\nFVSsFS/tVxxlJWYrm8JcfbQVs48FNypxeaYdVQHtmRpTjTguGes2wRKPDyd6+P34gwXNd5Z5J3O/\nE8UOF9ko48Hmf0nmf1V5I7KgdaUA2fZl7fkklYLZrJ9O/L1z40Z2my4/f9VvO7p+svzKO3M/9mq5\nh8nd4nsdAHrSg+eBG4m7wlRzJ2b8sSu2UF6L+7Byi8evAvh5ZMf3FQCupnRy9z8F4FWnbCsQCJwD\nbvqwm9mPAbicUvrMizmAmT1iZpfM7NLBpLv5FwKBwJngNGb8DwD4cTP7UcwqEd0B4NcAXDSzav52\nvx/A08u+nFJ6FMCjAHD/K0YLab6BQGAzOE199vcBeB8AmNmbAfyblNJPm9nvAHgngA8CeAjAR051\nxGPfRbS/jym52S7qd7Gfy1lp/rejJkNFErkcnVKRD2wLCojkXwrX1LM4gcu80tLL1JpQY/2aOQE+\ndnL9Ej+X5iZK8Q057Jjr4mkGH6/2OoHCwhau/yo4yfuJeAXRhVNk/zJpE0wnybxCT/6ngeqtqQY+\nC0JKH/uGhEqoRkB75MOYX7h2eLL8/JXrbtvl5791svzc5W+6bdM2+/MXhxdPlss9H+pa0bmV0P7n\nZb4lVDzThcjW8ozMM+mKNcXebiWo5j2YTdY9iZkP/4FbaCsQCJwxvq2gmpTSJwF8cr78VQBvvP1d\nCgQCZ4ENR9BlqNnXEZ2UNAqoZsqBo7uETmLRBRGlYLPYKOItqTtBZlQnNEZPJrMrQ6wiGqz90IkZ\nTzxXv6BURhFeK7P0gOFepmTuuOA16Pb3sx76zi5Flqk2GduOasZzj7j/mpXGUXhJo86y2d2RSEcN\nifyijLu+89sqasOcyIVq/pGog/SjIdN9fJBN9RvXvKn+zRcOTpb/5oUjt+2vyXR/9rnn3LaSaNzu\nLtL/S542G5JYRl3ovUlRhJwxqZQoR2aq6zg331X4xe2zcksgEHhZIR72QGBLsOHyT9nM6mSWGhRU\nZFq9sl8eqbWg5MUJLmsqjq7j/9x+YhGxJhprp5ULlVRt6TIA1GTOlUMRlKA2R2Tuq2k2pOPdccGX\nMdrZJZEHmo1X1sGbuxopSGNAM91a7ZXzRVSqmk1yFrnoIdFjPblvvQhxTFj7zflNbj826zu57hMy\n4w9uZFP9ypVrbr/naAb+G986cNuepqi5a1evuG37OySOQS5JLTfZiFJHBpolU1GSTJ/bayTSDpbX\nC0m+KhaYnUXEmz0Q2BLEwx4IbAniYQ8EtgQbp96O3e8FioR9eNHV9hFGXAZIRACIJlr02TM6yhRT\nv5xdw0Kyq0AiiiWJKA5EC5196pFQXgNqsxp62oyzvgrybdXvHxAVubOz47e5jEEW81gtUKERdOyL\nu8hGfTesidbizLnk5hyklBVlD3a9j2pjEciOhSc0go4y3TqJTptS/68d5PafP/A++99czX7515/x\nUXLPk3/fiYDocJRLSg1pzmEk1PIO8v0yEuqwJpGKcZHbH5eHbr92SsduJm4bTvz7oN4Cga1HPOyB\nwJZg49RbNzfpVCLO0ThrNhYUjVUINVES3WMSucaaa0yhVUKDlFTWqRANsIIqglakf14L9TYi7e/d\ngTfZBmT+F5U3wVnzmyPNSi1RRX2uaxFy4EQbomd0SJMTnhCXhxNLnDnu21hjxTvfy1WMld1Wp/v4\n+6Bz+oKy3zozns5tmvKYqvBJS5Fwk86b6j2t71T+4HsUyFaTCIVNfBReOsrmPqT0VF3QNeRB1RJP\n9LWu02s2j6ALMz4QCMTDHghsCeJhDwS2BBun3tq5X6r+mQ+9lCyylrPUsl+UWhGVJB/eoOGEtEwU\n12DH++UX7tg/WR5d2Hfb6p0cmlqSr6y/mMM6D+sFaZ997F6Gn9upEosQ+nNp1tS040y6foXfDAhB\ns+DQL1/WkNhFBUraRFRfWtOPkvsv2Y4um9BlNMrB3BjIvUOjOtjJ8yV3yjzLmEJ6v/XCC24bX4uh\n0JR37lCdgS776dMbIkxJy2nstef9fZXvzVHh52MqGrxGBrIvZve+rXl/x5s9ENgSxMMeCGwJNku9\nmZ1Er6kZ366hZ4qGzVj6ppjxbLr3Wo6IIsu4vFQ98FphF/bvOFnev3in2zbcJXOrcjyI24+13EdD\n0SKjaLuk5aLJLOYL00n7Pbk1muXFpahYd08l9p3hqxFpHF3HYh6mdOZqjXInCsJm/MKeRIlqH8k8\nZxeil0ZYH1816Bpug9wEjkIEgP07Mg169927btugoMg4uTv36XVpyFFtR0c+Qs9oILvWU8a7pKU/\nGJHbt+bptFbe08dlttekdMabPRDYEsTDHghsCTYeQdfaCjOeZ141VIvkgDk5otDkDjIzezkzNlUL\nMvVUeGI4yubc3p4Xhtil9YpMQi011ZPJphVSmXWoxLZmuWfuY69Vbel7vUQKdh2PFR1LzNuSq9Xq\njD51q6UOa4kPTnZZkD7rl28r1uj1dSq6RuDkF40em5KbN1VJax47utbVUJKXdrJLcvcrvBlPxXAx\nkhOtKCGlP8ym+42pT1RpuHSYRH421P/hUT5YOdCbOC+qduIx46HRkIx4swcCW4J42AOBLUE87IHA\nlmDzPvvcpejF9+kcBbM6gq4kn8w0G8x9Tegk/l0jf9UkdYuj6yr15ykyjn32UoU4iFppxb9kMUdx\nX10AGWcvlVKGuKY+JpkTaCYUYcg69DoeTrzC96N31NsqEQqJ1lKnnf10FySnAhh0XC2VxeNK5bY6\niWJrOi6lLduoXyx2IpWVUNA8yGhXBESpXPRALxoVK21pILXsc4scXdc0Mq9A3xvSHEA18RF0LG6y\nqLky+2QhY5Rw2vrsXwNwHbM5mjal9KCZ3Q3gQwBeA+BrAN6VUrqyqo1AIHC++HbM+B9OKT2QUnpw\nvv5eAI+nlF4H4PH5eiAQeIniVsz4dwB483z5McxqwL1n7TdSNmuVXevJhtVIKqcnx2awViZlO1gp\nHi7lRKa7mrctmYFciRTwdIerpFoqdZWP1apZRccupa5TRe04CkVM3xG5F6WYvuwC9eROVKq1vkIY\nAvCa/tx7k35U5NYUap7zWFErVeXP2YtorKkmy3r+abVrpPUIWrcr3TuVuJGkH9cLNdZR3aVGvZWa\njneB3CuhzSYkujJtPS03JVevpm2Dykdf1kX2PWpNojqmtNeY8ad9sycAf2JmnzGzR+af3ZtSema+\n/CyAe0/ZViAQOAec9s3+gymlp83sOwB83Mz+gjemlJLpbNkc8x+HRwBgf7detksgENgATvVmTyk9\nPf9/GcAfYFaq+Tkzuw8A5v8vr/juoymlB1NKD+6MVidOBAKBs8VN3+xmtgegSCldny//CID/AOCj\nAB4C8P75/4+c6ognPqWWEF7hr0LCT9mRE+E+55cv+OzLqTc1R7p+tf+X3DrPHSh9R+sqDMhU0ELo\nKPnb7nPxlSlzznSshpz5R/MDQo1xKWktt8zzIs5eW9Nfddm5Sd5vTUXhdZLnrsEFgUw3p7OuSeb5\nJCuSVksZb6aFuyT117gmX80hzpJ9R5MH/dS3MR7n9bIjn33qRS6GxBcOIcIW8wxErQHg97k57gXw\nB/MLVgH4bymlPzKzTwP4sJk9DODrAN51irYCgcA54aYPe0rpqwC+Z8nn3wTw1rPoVCAQuP3YsAad\noZybG1rChxUJlD5gc7ole6vXGQeirgqhtQout+xsTqXNVmeUOTOeTd1CRChoKiSpaoQ7lq4vF41Q\n85ab1Ii0IZWo6jniSqX4XRll6Qe1yddiYT/WxuvULVtuTi4wQ6tcBngXxVeYFhOc1tVdAY8BUZZa\nNjnRzdQVnvJKRL1NRHiCM/W8eIpkIw5zvxq5JxouW00nWrdixrfZjN9N3oyv0+x8tMwXI2LjA4Et\nQTzsgcCWIB72QGBLsFGf3QCUWK5Uwy5eUsqLfCH2dtQ76bwkittWOFpuTbaWi8zVelr90v0WnGpH\nr/l+uPpl4l85UULOAtRss45DUX37TBtxstxCGCX7yhLCyi26LENpw4Wpio/ObXBI8mJW1hpKjecL\nutVzKS4zTzMmSWi0dFSkXBfqR23+sWhIWHMqapeuy0ydSvs9DXEj53lYUllsKldey1C1lPnXN37u\noJqPVbdGcTLe7IHAliAe9kBgS7Dx8k8nlpSabGyaSdZRz0Y/mUpqsjRsTovgg9kK6m0h0o6WtfNs\n4ndLP166vmpr00i2Gbsr1C+TFo3ED1IvIch0Ahy816r2/IrMNsBHANakv9+q+dyy+SytMPXJ5q3u\nx26NRBvy8TrORlTBDtbRl/eXEyjl5qUfqwtIeVepEPO8TPkRMirXpEIfLDzayHlOVgmlira9NXmd\nMysBoGhm/dKS1W6flVsCgcDLCvGwBwJbgo2b8cdWhiZwFIn1zqUCKxmariTQQgVTNvHFnGPRiFUz\n85Bqr2rgsoa6SyTx/W1Z31tLK3E0oIhGGJduYnEM3wsXOaia72z+u1nwJaplJ0syBnxt1uWmuDHQ\n1tmN4vyTTly0lsdDhCfa5ab7VMbNmfsL0YbL9fTaVt1I0rGTBJS2oQg3jd6jq1MkTlDy/XAsjNBI\nUzpPrtDbmB8rDspLU9XCm623/fLIRSDe7IHA1iAe9kBgSxAPeyCwJdisz27IjkfS6LHsnxRCvRXM\nmRSrfXZe7zSSin7XeuezK0XH+vXq1xHFQxFMrWQ4tZQlpZFUzucT5y31K3xgZdccjei39U4nneYp\nVOiRqTcZ75U13ITOZKpM2CQUlFXGx2rEL++mNKad+uxMVzW0LPuxL6uy9D1Th3RdtN4a1Wa7cXDD\nbyOfnQUqAC96asSb6b3DUY/qbzc0BgdNni9ok9eeb8b5e9MXJDN0PPs/6VRcg/q6cksgEHhZIR72\nQGBLsHnq7ZjOksQJpp1MtpVME7mWJDGDt6k2G9NQlOiQCh+lVJCunUZLOeOL+qhJJj3xLlxGGoDL\n3tHvcZf7tJo24wjAQtyQhOVm/EJZZv6OyrWvot7UrXFrq6m9deWW2VRfjKAjM96Z9GrG81jJ+4v2\nTXxd4M3dlszn6UTKLbP+vjwynGjjjq3+REv7eesc3ZTdHOqHsmt02mNNkjlua4EEzYg3eyCwJYiH\nPRDYEsTDHghsCTbrsyecOIvrwgnVN2Q3hOu+aTgra2Y34og6f5j0t5OICxr58EpXlVQfjJO69BfT\nhZuu8csX0+OWl5IupL5wUda07C8hR0uuEpCY9WONGASLY9DX1lFjvfioJfGFjlLU+Q0WqJBQT5/p\nRte203DZ5fMDs0a43h0JkprOGeXlQq4L75ra1f13463zIJwl2ei25aKVlapXuJqC/t5s50XoCo2t\nJsSbPRDYEsTDHghsCTZMvaUTKiSJSei0wBfKHNMim02q4Ua2UquugDNjWXDAm8glmcilmMgs6mDe\nRl55JP01dVZmKdF19M2etefFRO4p7a3XSEE2VV3WmweP/qL5TNFeHJW4xgTXUlk9ZTH2TJtNvQ3b\nEq3ViYnM5vqUIhanot1OAWiwQlLKXIYg2bilmPEragLM+t8vXQa8WAQPTysRonwuOlZGbgiX7a69\nNDzaFSXMAKCcp1paqRRoxqne7GZ20cx+18z+wsyeMLPvN7O7zezjZvaV+f+7TtNWIBA4H5zWjP81\nAH+UUvpuzEpBPQHgvQAeTym9DsDj8/VAIPASxWmquN4J4IcA/HMASClNAUzN7B0A3jzf7TEAnwTw\nnrWNpZQlcLVMDZk2mkTgbFCe5pRZWSvzerkwRc4z3VQSSGSDSzfTLfXkXWQcdWmN6FylwhCsxybb\nnAwfn6aYt1PSrluo8FrncytXlHECfOKKulQs2+yCwtawB+oKcGJJS6Z7K9FpHZnkOoM9pes7oQi3\nceNdgTGNh7ITbNZWfD3V2uWISGUd6HgqOJLIrOd8KHV5xmTWN5I4xaXKhoPMDpVDuWblatahmJvx\nxWor/lRv9tcC+FsA/9XMPmtm/2VeuvnelNIz832exazaayAQeIniNA97BeB7Afx6SukNAA4gJnua\nkbZL329m9oiZXTKzS4eT1UXnAoHA2eI0D/tTAJ5KKX1qvv67mD38z5nZfQAw/3952ZdTSo+mlB5M\nKT24O1zD+AcCgTPFaeqzP2tm3zCz70opfRmzmuxfmv89BOD98/8fuenREoATn12zwTgjSagP+kli\n3fFCM+dc6V7fPGd91eTn1uLzckaZ+sPMgDmfXas/0bKWEGaxgwWNby5bzeyS2EzThsUlJEOQrqjT\nnVAxhXWikiuCGRd9di7r5Lc0RJUx3da1qwUntQ2X9cbUm5Q+4iy4yla/v1h/P8mcEQthqrBFR+vq\ns7P4Bui6aAbfmE5uaiJKUbFoCV2Xob8yiYUzugXVkuNvYRVOy7P/KwC/ZWYDAF8F8C8wswo+bGYP\nA/g6gHedsq1AIHAOONXDnlL6HIAHl2x66+3tTiAQOCtsNoKuT7DpzCRKC4kqXPpIqbcVNrPYlTwj\noGINA+LiRmTjD8XeL9lsFZOosxV686qBT6ZYIWICLvBOLDHWfGdzVFlKNiW1wuuAqDefmCGNsDu0\nxpVhyk7N+HWa8kzfeRpKtQFXl4Za1YbKM3B/q9rf0gNar2h8Vb++bYkq7L1uPEcDqsvGghiuxNPU\nj/eUXI1x59u/kcYny91BNvHrRm6QYT6XJnla+FhzMa2ZA4/Y+EBgSxAPeyCwJYiHPRDYEmw8681O\nfBfx2ZkWUd+NVpk+0eS4dT47U2C18419G17QUkQabQUtt9AGhVCqLKO5k1l5bKbo1Ffm8FnTUFfn\nY68pqbyift6sH7Z0v8U2Vr8reF8vYLlOmFKFPljYwk2m+BacAKfMP5S8LX+u2Ws9zWlo7b5VJcMV\nLgtQrkvHdeukVDcLWvJyqqWmAdFtrcxrHWcnrgvdjjd7ILAliIc9ENgS2KJpdoYHM/tbzAJw7gHw\n/MYOvBwvhT4A0Q9F9MPj2+3HP0gpvXLZho0+7CcHNbuUUloWpLNVfYh+RD822Y8w4wOBLUE87IHA\nluC8HvZHz+m4jJdCH4DohyL64XHb+nEuPnsgENg8wowPBLYEG33YzeztZvZlM3vSzDamRmtmv2Fm\nl83sC/TZxqWwzezVZvYJM/uSmX3RzN59Hn0xs5GZ/amZfX7ej1+cf/5aM/vU/Pp8aK5fcOYws3Ku\nb/ix8+qHmX3NzP7czD5nZpfmn53HPXJmsu0be9htJun6nwH8EwCvB/CTZvb6DR3+NwG8XT47Dyns\nFsDPpZReD+BNAH5mPgab7ssEwFtSSt8D4AEAbzezNwH4JQC/klL6TgBXADx8xv04xrsxkyc/xnn1\n44dTSg8Q1XUe98jZybanlDbyB+D7Afwxrb8PwPs2ePzXAPgCrX8ZwH3z5fsAfHlTfaE+fATA286z\nLwB2AfxfAN+HWfBGtex6neHx75/fwG8B8DHMguXPox9fA3CPfLbR6wLgTgD/D/O5tNvdj02a8a8C\n8A1af2r+2XnhXKWwzew1AN4A4FPn0Ze56fw5zIRCPw7grwBcTelEqWFT1+dXAfw8cmbUK86pHwnA\nn5jZZ8zskflnm74uZyrbHhN0WC+FfRYwswsAfg/Az6aUrp1HX1JKXUrpAczerG8E8N1nfUyFmf0Y\ngMsppc9s+thL8IMppe/FzM38GTP7Id64oetyS7LtN8MmH/anAbya1u+ff3ZeOJUU9u2GmdWYPei/\nlVL6/fPsCwCklK4C+ARm5vJFyyVyNnF9fgDAj5vZ1wB8EDNT/tfOoR9IKT09/38ZwB9g9gO46ety\nS7LtN8MmH/ZPA3jdfKZ1AOAnAHx0g8dXfBQzCWzgtFLYtwibJY5/AMATKaVfPq++mNkrzezifHkH\ns3mDJzB76N+5qX6klN6XUro/pfQazO6H/5lS+ulN98PM9sxs/3gZwI8A+AI2fF1SSs8C+IaZfdf8\no2PZ9tvTj7Oe+JCJhh8F8JeY+Yf/boPH/W0AzwBoMPv1fBgz3/BxAF8B8D8A3L2BfvwgZibYnwH4\n3PzvRzfdFwD/CMBn5/34AoB/P//8HwL4UwBPAvgdAMMNXqM3A/jYefRjfrzPz/++eHxvntM98gCA\nS/Nr898B3HW7+hERdIHAliAm6AKBLUE87IHAliAe9kBgSxAPeyCwJYiHPRDYEsTDHghsCeJhDwS2\nBPGwBwJbgv8P/zV7pkwHri8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Image #5307\n",
      "True Category: 1 - automobile\n",
      "NO Transfer Learning CNN Predicted Category: 1 - automobile\n",
      "Transfer Learning CNN Predicted Category: 1 - automobile\n"
     ]
    },
    {
     "data": {
      "image/png": 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UmT5OyIwX835PlHhFpOWWBFjoebZK0HVg1frs6byC6yUAnwLwNwB2Q1jEET4N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\nBqQqrkacwJrri/PW1jRjHf1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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    sample = random.randint(0, 9999)\n",
    "    image = x_test[sample]\n",
    "\n",
    "    plt.imshow(image)\n",
    "     \n",
    "    true_category_index = y_true[sample]\n",
    "    cnn_no_tl_pred_category_index = cnn_no_tl_pred[sample]\n",
    "    cnn_tl_pred_category_index = cnn_tl_pred[sample]\n",
    "    print(f\"\\nImage #{sample}\") \n",
    "    print(f\"True Category: {true_category_index} - {categories[true_category_index]}\")\n",
    "    print(f\"NO Transfer Learning CNN Predicted Category: {cnn_no_tl_pred_category_index} - {categories[cnn_no_tl_pred_category_index]}\")\n",
    "    print(f\"Transfer Learning CNN Predicted Category: {cnn_tl_pred_category_index} - {categories[cnn_tl_pred_category_index]}\")\n",
    "    \n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "machine_shape": "hm",
   "name": "Project 3 CSC 180",
   "provenance": [],
   "toc_visible": true
  },
  "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}