CustomInceptionV3BestModel.ipynb

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      "name": "CustomInceptionV3BestModel.ipynb",
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      "source": [
        "# CZ4042 Project - Custom Inception V3 model (best model)\n",
        "## By Team Project Car Formula, with members:\n",
        "### Tham Wei Jie Marcus, U1821649D\n",
        "### Padhi Abhinandan, U1823860D\n",
        "### Chan Beng Chung, U1821173A\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "66y7kxktQoix",
        "outputId": "074bdc2e-2689-4d21-9c29-1fde66e3baa9"
      },
      "source": [
        "# IGNORE ALL WARNINGS\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# STANDARD LIBRARIES\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# FILE MANIPULATION\n",
        "from scipy.io import loadmat\n",
        "import os\n",
        "import random\n",
        "import pickle\n",
        "\n",
        "# UTILITIES\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "# CNN\n",
        "import tensorflow as tf\n",
        "print(\"TensorFlow version: \", tf.__version__,'\\n')\n",
        "\n",
        "# Keras\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.models import Sequential\n",
        "\n",
        "\n",
        "# For our Custom Inception V3 model\n",
        "from tensorflow.keras.layers import AveragePooling2D\n",
        "from tensorflow.keras.layers import Dropout\n",
        "from tensorflow.keras.layers import Flatten\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import Input\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras import regularizers\n",
        "from tensorflow.keras.layers.experimental import preprocessing\n",
        "from tensorflow.python.keras import backend\n",
        "from tensorflow.python.keras.applications import imagenet_utils\n",
        "from tensorflow.python.keras.engine import training\n",
        "from tensorflow.python.keras.layers import VersionAwareLayers\n",
        "from tensorflow.python.keras.utils import data_utils\n",
        "from tensorflow.python.keras.utils import layer_utils\n",
        "from tensorflow.python.lib.io import file_io\n",
        "from tensorflow.python.util.tf_export import keras_export\n",
        "layers = VersionAwareLayers()\n",
        "\n",
        "## Check GPU being used\n",
        "gpu_info = !nvidia-smi\n",
        "gpu_info = '\\n'.join(gpu_info)\n",
        "if gpu_info.find('failed') >= 0:\n",
        "  print('Select the Runtime > \"Change runtime type\" menu to enable a GPU accelerator, ')\n",
        "  print('and then re-execute this cell.')\n",
        "else:\n",
        "  print(gpu_info)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow version:  2.3.0 \n",
            "\n",
            "Mon Nov 23 13:43:42 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 455.38       Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   35C    P0    26W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "|                               |                      |                 ERR! |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LuimyVE1Qq_6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "131d9907-d9f2-48d0-ec8b-384494b8e501"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "!unzip '/content/drive/MyDrive/Project Car Formula/5050.zip' -d /content/CZ4042_Project/"
      ],
      "execution_count": null,
      "outputs": [
        {
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tJ48ByQTQoi2"
      },
      "source": [
        "# Constants\n",
        "batch_size = 32\n",
        "img_height = 224\n",
        "img_width = 224\n",
        "IMAGE_SHAPE = (img_height, img_width, 3)\n",
        "CLASS_NUM = 196\n",
        "\n",
        "epochs=100\n",
        "\n",
        "AUTOTUNE = tf.data.experimental.AUTOTUNE # For performance"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pQ3ZxPQHQoi6",
        "outputId": "9925b884-5e4a-4803-fa16-6e1f8ee9f15a"
      },
      "source": [
        "working_dir = '/content/CZ4042_Project/CNNstanfordcars/working'\n",
        "train_dir = working_dir + '/data/train'\n",
        "train_ds = keras.preprocessing.image_dataset_from_directory(train_dir, validation_split = 0.2, subset = \"training\", seed = 123, image_size=(img_height, img_width), batch_size = batch_size)\n",
        "val_ds = keras.preprocessing.image_dataset_from_directory(train_dir, validation_split = 0.2, subset = \"validation\", seed = 123, image_size=(img_height, img_width), batch_size = batch_size)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 8144 files belonging to 196 classes.\n",
            "Using 6516 files for training.\n",
            "Found 8144 files belonging to 196 classes.\n",
            "Using 1628 files for validation.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m47hRFSYQoi9",
        "outputId": "0886db69-9501-4989-9742-c4ec1465b65f"
      },
      "source": [
        "class_names = train_ds.class_names\n",
        "num_classes = len(class_names)\n",
        "print(\"Number of classes: \", num_classes)\n",
        "print(\"Classes:\")\n",
        "print(class_names)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of classes:  196\n",
            "Classes:\n",
            "['0001', '0002', '0003', '0004', '0005', '0006', '0007', '0008', '0009', '0010', '0011', '0012', '0013', '0014', '0015', '0016', '0017', '0018', '0019', '0020', '0021', '0022', '0023', '0024', '0025', '0026', '0027', '0028', '0029', '0030', '0031', '0032', '0033', '0034', '0035', '0036', '0037', '0038', '0039', '0040', '0041', '0042', '0043', '0044', '0045', '0046', '0047', '0048', '0049', '0050', '0051', '0052', '0053', '0054', '0055', '0056', '0057', '0058', '0059', '0060', '0061', '0062', '0063', '0064', '0065', '0066', '0067', '0068', '0069', '0070', '0071', '0072', '0073', '0074', '0075', '0076', '0077', '0078', '0079', '0080', '0081', '0082', '0083', '0084', '0085', '0086', '0087', '0088', '0089', '0090', '0091', '0092', '0093', '0094', '0095', '0096', '0097', '0098', '0099', '0100', '0101', '0102', '0103', '0104', '0105', '0106', '0107', '0108', '0109', '0110', '0111', '0112', '0113', '0114', '0115', '0116', '0117', '0118', '0119', '0120', '0121', '0122', '0123', '0124', '0125', '0126', '0127', '0128', '0129', '0130', '0131', '0132', '0133', '0134', '0135', '0136', '0137', '0138', '0139', '0140', '0141', '0142', '0143', '0144', '0145', '0146', '0147', '0148', '0149', '0150', '0151', '0152', '0153', '0154', '0155', '0156', '0157', '0158', '0159', '0160', '0161', '0162', '0163', '0164', '0165', '0166', '0167', '0168', '0169', '0170', '0171', '0172', '0173', '0174', '0175', '0176', '0177', '0178', '0179', '0180', '0181', '0182', '0183', '0184', '0185', '0186', '0187', '0188', '0189', '0190', '0191', '0192', '0193', '0194', '0195', '0196']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yfRTFnZgQojE",
        "outputId": "b49617dd-b7ec-4400-976e-90dc79dc1e52"
      },
      "source": [
        "# Batch has size = 32, Image dimensions = (224,224), 3 channels (RGB)\n",
        "for image_batch, labels_batch in train_ds:\n",
        "    print(image_batch.shape)\n",
        "    print(labels_batch.shape)\n",
        "    break"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(32, 224, 224, 3)\n",
            "(32,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ktxMmnQWQojI"
      },
      "source": [
        "# Optimizing for performance\n",
        "train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE) # Cache, shuffle, and prefetch for train set\n",
        "val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE) # Same for validation set"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HyE8mKHPAnFC"
      },
      "source": [
        "# Our Custom Inception V3 model for TensorFlow / Keras.\n",
        "# Modifications include regularization, data augmentation layers, and additional denser layers on top\n",
        "# Reference paper: Rethinking the Inception Architecture for Computer Vision (http://arxiv.org/abs/1512.00567) (CVPR 2016)\n",
        "# Reference for code: TensorFlow InceptionV3 Python Source Code\n",
        "\n",
        "def Custom_InceptionV3(\n",
        "    weights='imagenet',\n",
        "    input_shape=None,\n",
        "    pooling=None,\n",
        "    classifier_activation='softmax'):\n",
        "\n",
        "    # constants\n",
        "    channel_axis = 3\n",
        "    if input_shape == None:\n",
        "        input_shape = (224, 224, 3)\n",
        "    \n",
        "    # image input\n",
        "    img_input = layers.Input(shape=input_shape)\n",
        "\n",
        "    # start of main model using conv2d_bn layers (conv2d and batch_normalization layers combined)\n",
        "    x = conv2d_bn(img_input, 32, 3, 3, strides=(2, 2), padding='valid')\n",
        "    x = conv2d_bn(x, 32, 3, 3, padding='valid')\n",
        "    x = conv2d_bn(x, 64, 3, 3)\n",
        "    x = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)\n",
        "\n",
        "    x = conv2d_bn(x, 80, 1, 1, padding='valid')\n",
        "    x = conv2d_bn(x, 192, 3, 3, padding='valid')\n",
        "    x = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)\n",
        "\n",
        "    # mixed 0: 35 x 35 x 256\n",
        "    branch1x1 = conv2d_bn(x, 64, 1, 1)\n",
        "\n",
        "    branch5x5 = conv2d_bn(x, 48, 1, 1)\n",
        "    branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)\n",
        "\n",
        "    branch3x3dbl = conv2d_bn(x, 64, 1, 1)\n",
        "    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)\n",
        "    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)\n",
        "\n",
        "    branch_pool = layers.AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)\n",
        "    branch_pool = conv2d_bn(branch_pool, 32, 1, 1)\n",
        "    x = layers.concatenate([branch1x1, branch5x5, branch3x3dbl, branch_pool],\n",
        "                            axis=channel_axis,\n",
        "                            name='mixed0')\n",
        "\n",
        "    # mixed 1: 35 x 35 x 288\n",
        "    branch1x1 = conv2d_bn(x, 64, 1, 1)\n",
        "\n",
        "    branch5x5 = conv2d_bn(x, 48, 1, 1)\n",
        "    branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)\n",
        "\n",
        "    branch3x3dbl = conv2d_bn(x, 64, 1, 1)\n",
        "    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)\n",
        "    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)\n",
        "\n",
        "    branch_pool = layers.AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)\n",
        "    branch_pool = conv2d_bn(branch_pool, 64, 1, 1)\n",
        "    x = layers.concatenate([branch1x1, branch5x5, branch3x3dbl, branch_pool],\n",
        "                            axis=channel_axis,\n",
        "                            name='mixed1')\n",
        "\n",
        "    # mixed 2: 35 x 35 x 288\n",
        "    branch1x1 = conv2d_bn(x, 64, 1, 1)\n",
        "\n",
        "    branch5x5 = conv2d_bn(x, 48, 1, 1)\n",
        "    branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)\n",
        "\n",
        "    branch3x3dbl = conv2d_bn(x, 64, 1, 1)\n",
        "    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)\n",
        "    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)\n",
        "\n",
        "    branch_pool = layers.AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)\n",
        "    branch_pool = conv2d_bn(branch_pool, 64, 1, 1)\n",
        "    x = layers.concatenate([branch1x1, branch5x5, branch3x3dbl, branch_pool],\n",
        "                            axis=channel_axis,\n",
        "                            name='mixed2')\n",
        "\n",
        "    # mixed 3: 17 x 17 x 768\n",
        "    branch3x3 = conv2d_bn(x, 384, 3, 3, strides=(2, 2), padding='valid')\n",
        "\n",
        "    branch3x3dbl = conv2d_bn(x, 64, 1, 1)\n",
        "    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)\n",
        "    branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3, strides=(2, 2), padding='valid')\n",
        "\n",
        "    branch_pool = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)\n",
        "    x = layers.concatenate([branch3x3, branch3x3dbl, branch_pool],\n",
        "                            axis=channel_axis,\n",
        "                            name='mixed3')\n",
        "\n",
        "    # mixed 4: 17 x 17 x 768\n",
        "    branch1x1 = conv2d_bn(x, 192, 1, 1)\n",
        "\n",
        "    branch7x7 = conv2d_bn(x, 128, 1, 1)\n",
        "    branch7x7 = conv2d_bn(branch7x7, 128, 1, 7)\n",
        "    branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)\n",
        "\n",
        "    branch7x7dbl = conv2d_bn(x, 128, 1, 1)\n",
        "    branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1)\n",
        "    branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 1, 7)\n",
        "    branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1)\n",
        "    branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)\n",
        "\n",
        "    branch_pool = layers.AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)\n",
        "    branch_pool = conv2d_bn(branch_pool, 192, 1, 1)\n",
        "    x = layers.concatenate([branch1x1, branch7x7, branch7x7dbl, branch_pool],\n",
        "                            axis=channel_axis,\n",
        "                            name='mixed4')\n",
        "\n",
        "    # mixed 5, 6: 17 x 17 x 768\n",
        "    for i in range(2):\n",
        "        branch1x1 = conv2d_bn(x, 192, 1, 1)\n",
        "\n",
        "        branch7x7 = conv2d_bn(x, 160, 1, 1)\n",
        "        branch7x7 = conv2d_bn(branch7x7, 160, 1, 7)\n",
        "        branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)\n",
        "\n",
        "        branch7x7dbl = conv2d_bn(x, 160, 1, 1)\n",
        "        branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1)\n",
        "        branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 1, 7)\n",
        "        branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1)\n",
        "        branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)\n",
        "\n",
        "        branch_pool = layers.AveragePooling2D((3, 3),\n",
        "                                                strides=(1, 1),\n",
        "                                                padding='same')(x)\n",
        "        branch_pool = conv2d_bn(branch_pool, 192, 1, 1)\n",
        "        x = layers.concatenate([branch1x1, branch7x7, branch7x7dbl, branch_pool],\n",
        "                                axis=channel_axis,\n",
        "                                name='mixed' + str(5 + i))\n",
        "\n",
        "    # mixed 7: 17 x 17 x 768\n",
        "    branch1x1 = conv2d_bn(x, 192, 1, 1)\n",
        "\n",
        "    branch7x7 = conv2d_bn(x, 192, 1, 1)\n",
        "    branch7x7 = conv2d_bn(branch7x7, 192, 1, 7)\n",
        "    branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)\n",
        "\n",
        "    branch7x7dbl = conv2d_bn(x, 192, 1, 1)\n",
        "    branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1)\n",
        "    branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)\n",
        "    branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1)\n",
        "    branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)\n",
        "\n",
        "    branch_pool = layers.AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)\n",
        "    branch_pool = conv2d_bn(branch_pool, 192, 1, 1)\n",
        "    x = layers.concatenate([branch1x1, branch7x7, branch7x7dbl, branch_pool],\n",
        "                            axis=channel_axis,\n",
        "                            name='mixed7')\n",
        "\n",
        "    # mixed 8: 8 x 8 x 1280\n",
        "    branch3x3 = conv2d_bn(x, 192, 1, 1)\n",
        "    branch3x3 = conv2d_bn(branch3x3, 320, 3, 3, strides=(2, 2), padding='valid')\n",
        "\n",
        "    branch7x7x3 = conv2d_bn(x, 192, 1, 1)\n",
        "    branch7x7x3 = conv2d_bn(branch7x7x3, 192, 1, 7)\n",
        "    branch7x7x3 = conv2d_bn(branch7x7x3, 192, 7, 1)\n",
        "    branch7x7x3 = conv2d_bn(branch7x7x3, 192, 3, 3, strides=(2, 2), padding='valid')\n",
        "\n",
        "    branch_pool = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)\n",
        "    x = layers.concatenate([branch3x3, branch7x7x3, branch_pool],\n",
        "                            axis=channel_axis,\n",
        "                            name='mixed8')\n",
        "\n",
        "    # mixed 9: 8 x 8 x 2048\n",
        "    for i in range(2):\n",
        "        branch1x1 = conv2d_bn(x, 320, 1, 1)\n",
        "\n",
        "        branch3x3 = conv2d_bn(x, 384, 1, 1)\n",
        "        branch3x3_1 = conv2d_bn(branch3x3, 384, 1, 3)\n",
        "        branch3x3_2 = conv2d_bn(branch3x3, 384, 3, 1)\n",
        "        branch3x3 = layers.concatenate([branch3x3_1, branch3x3_2],\n",
        "                                        axis=channel_axis,\n",
        "                                        name='mixed9_' + str(i))\n",
        "\n",
        "        branch3x3dbl = conv2d_bn(x, 448, 1, 1)\n",
        "        branch3x3dbl = conv2d_bn(branch3x3dbl, 384, 3, 3)\n",
        "        branch3x3dbl_1 = conv2d_bn(branch3x3dbl, 384, 1, 3)\n",
        "        branch3x3dbl_2 = conv2d_bn(branch3x3dbl, 384, 3, 1)\n",
        "        branch3x3dbl = layers.concatenate([branch3x3dbl_1, branch3x3dbl_2],\n",
        "                                            axis=channel_axis)\n",
        "\n",
        "        branch_pool = layers.AveragePooling2D((3, 3),\n",
        "                                                strides=(1, 1),\n",
        "                                                padding='same')(x)\n",
        "        branch_pool = conv2d_bn(branch_pool, 192, 1, 1)\n",
        "        x = layers.concatenate([branch1x1, branch3x3, branch3x3dbl, branch_pool],\n",
        "                                axis=channel_axis,\n",
        "                                name='mixed' + str(9 + i))\n",
        "\n",
        "    if pooling == 'avg':\n",
        "        x = layers.GlobalAveragePooling2D()(x)\n",
        "    elif pooling == 'max':\n",
        "        x = layers.GlobalMaxPooling2D()(x)\n",
        "\n",
        "    # finializing model\n",
        "    inputs = img_input\n",
        "    model = training.Model(inputs, x, name='inception_v3')\n",
        "\n",
        "    # Load imagenet weights\n",
        "    WEIGHTS_PATH_NO_TOP = (\n",
        "    'https://storage.googleapis.com/tensorflow/keras-applications/'\n",
        "    'inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5')\n",
        "    weights_path = data_utils.get_file(\n",
        "        'inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5',\n",
        "        WEIGHTS_PATH_NO_TOP,\n",
        "        cache_subdir='models',\n",
        "        file_hash='bcbd6486424b2319ff4ef7d526e38f63')\n",
        "    model.load_weights(weights_path)\n",
        "    \n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nV5_e8JYiHXu"
      },
      "source": [
        "# Utility function to apply Conv2D and BatchNormalization.\n",
        "def conv2d_bn(x, filters, num_row, num_col, padding='same', strides=(1, 1), name=None):\n",
        "    if name is not None:\n",
        "        bn_name = name + '_bn'\n",
        "        conv_name = name + '_conv'\n",
        "    else:\n",
        "        bn_name = None\n",
        "        conv_name = None\n",
        "    \n",
        "    bn_axis = 3 # Channels last\n",
        "    \n",
        "    x = layers.Conv2D(filters, (num_row, num_col),\n",
        "                      strides=strides,\n",
        "                      padding=padding,\n",
        "                      use_bias=False,\n",
        "                      kernel_regularizer=keras.regularizers.l2(1e-5),\n",
        "                      name=conv_name)(x)\n",
        "    x = layers.BatchNormalization(axis=bn_axis, scale=False, name=bn_name)(x)\n",
        "    x = layers.Activation('relu', name=name)(x)\n",
        "    \n",
        "    return x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jAHCYb6zEoUH",
        "outputId": "02c8e699-6224-4fdc-a890-a100758c09bc"
      },
      "source": [
        "# data augmentation (mix of random horizontal flips, rotations, and zooms) and rescaling to [0,1] range\n",
        "input_shape = (img_height, img_width, 3)\n",
        "img_input = keras.Input(shape=input_shape)\n",
        "x = preprocessing.RandomFlip(\"horizontal\")(img_input)\n",
        "x = preprocessing.RandomRotation(0.1)(x)\n",
        "x = preprocessing.RandomZoom(0.1)(x)\n",
        "x = preprocessing.Rescaling(1.0 / 255)(x)\n",
        "\n",
        "# Custom Inception V3 model\n",
        "x = Custom_InceptionV3(weights=\"imagenet\", input_shape=input_shape)(x)\n",
        "\n",
        "# final dense/FC layers\n",
        "x = Dropout(0.5)(x)\n",
        "x = Flatten(name=\"flatten\")(x)\n",
        "x = Dense(256, activation=\"relu\", kernel_regularizer=keras.regularizers.l2(1e-5))(x)\n",
        "x = Dropout(0.5)(x)\n",
        "x = Dense(num_classes, activation=None)(x)\n",
        "\n",
        "model = Model(inputs=img_input, outputs=x)\n",
        "optimizer = keras.optimizers.SGD(learning_rate=0.01, momentum=0.1)\n",
        "model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              optimizer=optimizer,\n",
        "              metrics=[\"accuracy\"])\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "87916544/87910968 [==============================] - 1s 0us/step\n",
            "Model: \"functional_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 224, 224, 3)]     0         \n",
            "_________________________________________________________________\n",
            "random_flip (RandomFlip)     (None, 224, 224, 3)       0         \n",
            "_________________________________________________________________\n",
            "random_rotation (RandomRotat (None, 224, 224, 3)       0         \n",
            "_________________________________________________________________\n",
            "random_zoom (RandomZoom)     (None, 224, 224, 3)       0         \n",
            "_________________________________________________________________\n",
            "rescaling (Rescaling)        (None, 224, 224, 3)       0         \n",
            "_________________________________________________________________\n",
            "inception_v3 (Functional)    (None, 5, 5, 2048)        21802784  \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 5, 5, 2048)        0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 51200)             0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 256)               13107456  \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 196)               50372     \n",
            "=================================================================\n",
            "Total params: 34,960,612\n",
            "Trainable params: 34,926,180\n",
            "Non-trainable params: 34,432\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QowdXK8Mr-R_"
      },
      "source": [
        "checkpoint_path = '/content/drive/MyDrive/CZ4042_Project/training/Custom_InceptionV3_BEST_2'\n",
        "\n",
        "# Create a callback that saves the model's weights\n",
        "model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,\n",
        "                                                 save_weights_only=True,\n",
        "                                                 monitor='val_accuracy',\n",
        "                                                 mode='max',\n",
        "                                                 save_best_only=True,\n",
        "                                                 verbose=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5cfSBQWIQojS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b8a96d40-9b52-4a67-d009-88f38a121834"
      },
      "source": [
        "history = model.fit(\n",
        "    train_ds,\n",
        "    validation_data=val_ds,\n",
        "    epochs=epochs,\n",
        "    verbose = 2,\n",
        "    callbacks=[model_checkpoint_callback]\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "204/204 - 39s - loss: 5.9014 - accuracy: 0.0052 - val_loss: 5.8626 - val_accuracy: 0.0049\n",
            "Epoch 2/100\n",
            "204/204 - 35s - loss: 5.8574 - accuracy: 0.0058 - val_loss: 5.8615 - val_accuracy: 0.0055\n",
            "Epoch 3/100\n",
            "204/204 - 35s - loss: 5.8547 - accuracy: 0.0084 - val_loss: 5.8537 - val_accuracy: 0.0104\n",
            "Epoch 4/100\n",
            "204/204 - 34s - loss: 5.8522 - accuracy: 0.0075 - val_loss: 5.8533 - val_accuracy: 0.0080\n",
            "Epoch 5/100\n",
            "204/204 - 34s - loss: 5.8526 - accuracy: 0.0081 - val_loss: 5.8714 - val_accuracy: 0.0049\n",
            "Epoch 6/100\n",
            "204/204 - 34s - loss: 5.8510 - accuracy: 0.0064 - val_loss: 5.8510 - val_accuracy: 0.0055\n",
            "Epoch 7/100\n",
            "204/204 - 34s - loss: 5.8496 - accuracy: 0.0069 - val_loss: 5.8490 - val_accuracy: 0.0037\n",
            "Epoch 8/100\n",
            "204/204 - 34s - loss: 5.8476 - accuracy: 0.0068 - val_loss: 5.8470 - val_accuracy: 0.0043\n",
            "Epoch 9/100\n",
            "204/204 - 33s - loss: 5.8442 - accuracy: 0.0077 - val_loss: 5.8737 - val_accuracy: 0.0055\n",
            "Epoch 10/100\n",
            "204/204 - 34s - loss: 5.8322 - accuracy: 0.0063 - val_loss: 5.8227 - val_accuracy: 0.0098\n",
            "Epoch 11/100\n",
            "204/204 - 35s - loss: 5.8171 - accuracy: 0.0080 - val_loss: 5.7953 - val_accuracy: 0.0154\n",
            "Epoch 12/100\n",
            "204/204 - 33s - loss: 5.7937 - accuracy: 0.0089 - val_loss: 5.7689 - val_accuracy: 0.0104\n",
            "Epoch 13/100\n",
            "204/204 - 34s - loss: 5.7679 - accuracy: 0.0080 - val_loss: 5.7553 - val_accuracy: 0.0111\n",
            "Epoch 14/100\n",
            "204/204 - 34s - loss: 5.7300 - accuracy: 0.0103 - val_loss: 5.6693 - val_accuracy: 0.0147\n",
            "Epoch 15/100\n",
            "204/204 - 34s - loss: 5.6796 - accuracy: 0.0193 - val_loss: 5.6072 - val_accuracy: 0.0252\n",
            "Epoch 16/100\n",
            "204/204 - 36s - loss: 5.6025 - accuracy: 0.0256 - val_loss: 5.5220 - val_accuracy: 0.0313\n",
            "Epoch 17/100\n",
            "204/204 - 33s - loss: 5.5465 - accuracy: 0.0255 - val_loss: 5.3985 - val_accuracy: 0.0301\n",
            "Epoch 18/100\n",
            "204/204 - 34s - loss: 5.4462 - accuracy: 0.0364 - val_loss: 5.2918 - val_accuracy: 0.0491\n",
            "Epoch 19/100\n",
            "204/204 - 35s - loss: 5.3520 - accuracy: 0.0422 - val_loss: 5.1540 - val_accuracy: 0.0657\n",
            "Epoch 20/100\n",
            "204/204 - 33s - loss: 5.2627 - accuracy: 0.0460 - val_loss: 5.1010 - val_accuracy: 0.0645\n",
            "Epoch 21/100\n",
            "204/204 - 35s - loss: 5.1444 - accuracy: 0.0586 - val_loss: 4.9342 - val_accuracy: 0.0805\n",
            "Epoch 22/100\n",
            "204/204 - 34s - loss: 5.0271 - accuracy: 0.0743 - val_loss: 4.7527 - val_accuracy: 0.1075\n",
            "Epoch 23/100\n",
            "204/204 - 34s - loss: 4.8509 - accuracy: 0.0790 - val_loss: 4.5816 - val_accuracy: 0.1271\n",
            "Epoch 24/100\n",
            "204/204 - 35s - loss: 4.6875 - accuracy: 0.1057 - val_loss: 4.3670 - val_accuracy: 0.1529\n",
            "Epoch 25/100\n",
            "204/204 - 34s - loss: 4.4996 - accuracy: 0.1180 - val_loss: 4.1544 - val_accuracy: 0.1794\n",
            "Epoch 26/100\n",
            "204/204 - 35s - loss: 4.3140 - accuracy: 0.1542 - val_loss: 3.9099 - val_accuracy: 0.2224\n",
            "Epoch 27/100\n",
            "204/204 - 34s - loss: 4.1143 - accuracy: 0.1834 - val_loss: 3.6535 - val_accuracy: 0.2733\n",
            "Epoch 28/100\n",
            "204/204 - 33s - loss: 3.8853 - accuracy: 0.2104 - val_loss: 3.6056 - val_accuracy: 0.2654\n",
            "Epoch 29/100\n",
            "204/204 - 34s - loss: 3.6873 - accuracy: 0.2468 - val_loss: 3.3803 - val_accuracy: 0.3133\n",
            "Epoch 30/100\n",
            "204/204 - 35s - loss: 3.5055 - accuracy: 0.2816 - val_loss: 3.1451 - val_accuracy: 0.3612\n",
            "Epoch 31/100\n",
            "204/204 - 35s - loss: 3.2654 - accuracy: 0.3272 - val_loss: 2.9198 - val_accuracy: 0.3943\n",
            "Epoch 32/100\n",
            "204/204 - 35s - loss: 3.0194 - accuracy: 0.3686 - val_loss: 2.6661 - val_accuracy: 0.4367\n",
            "Epoch 33/100\n",
            "204/204 - 35s - loss: 2.8168 - accuracy: 0.4124 - val_loss: 2.5628 - val_accuracy: 0.4914\n",
            "Epoch 34/100\n",
            "204/204 - 35s - loss: 2.5736 - accuracy: 0.4642 - val_loss: 2.3660 - val_accuracy: 0.5055\n",
            "Epoch 35/100\n",
            "204/204 - 36s - loss: 2.3882 - accuracy: 0.5020 - val_loss: 2.1458 - val_accuracy: 0.5651\n",
            "Epoch 36/100\n",
            "204/204 - 35s - loss: 2.2213 - accuracy: 0.5439 - val_loss: 2.0840 - val_accuracy: 0.5811\n",
            "Epoch 37/100\n",
            "204/204 - 35s - loss: 2.0515 - accuracy: 0.5858 - val_loss: 1.9750 - val_accuracy: 0.6173\n",
            "Epoch 38/100\n",
            "204/204 - 36s - loss: 1.8799 - accuracy: 0.6258 - val_loss: 1.8922 - val_accuracy: 0.6407\n",
            "Epoch 39/100\n",
            "204/204 - 34s - loss: 1.7222 - accuracy: 0.6642 - val_loss: 2.0539 - val_accuracy: 0.6167\n",
            "Epoch 40/100\n",
            "204/204 - 34s - loss: 1.6226 - accuracy: 0.6935 - val_loss: 2.0877 - val_accuracy: 0.6155\n",
            "Epoch 41/100\n",
            "204/204 - 35s - loss: 1.5095 - accuracy: 0.7207 - val_loss: 1.8485 - val_accuracy: 0.6652\n",
            "Epoch 42/100\n",
            "204/204 - 35s - loss: 1.3957 - accuracy: 0.7537 - val_loss: 1.8733 - val_accuracy: 0.6720\n",
            "Epoch 43/100\n",
            "204/204 - 35s - loss: 1.2768 - accuracy: 0.7948 - val_loss: 1.8112 - val_accuracy: 0.6923\n",
            "Epoch 44/100\n",
            "204/204 - 34s - loss: 1.2360 - accuracy: 0.8019 - val_loss: 1.8946 - val_accuracy: 0.6837\n",
            "Epoch 45/100\n",
            "204/204 - 35s - loss: 1.1190 - accuracy: 0.8355 - val_loss: 1.7563 - val_accuracy: 0.7144\n",
            "Epoch 46/100\n",
            "204/204 - 34s - loss: 1.0840 - accuracy: 0.8428 - val_loss: 1.7838 - val_accuracy: 0.7015\n",
            "Epoch 47/100\n",
            "204/204 - 36s - loss: 1.0315 - accuracy: 0.8646 - val_loss: 1.6823 - val_accuracy: 0.7316\n",
            "Epoch 48/100\n",
            "204/204 - 33s - loss: 0.9959 - accuracy: 0.8743 - val_loss: 1.8273 - val_accuracy: 0.7187\n",
            "Epoch 49/100\n",
            "204/204 - 33s - loss: 0.9669 - accuracy: 0.8774 - val_loss: 1.7139 - val_accuracy: 0.7310\n",
            "Epoch 50/100\n",
            "204/204 - 34s - loss: 0.9162 - accuracy: 0.8940 - val_loss: 1.7602 - val_accuracy: 0.7340\n",
            "Epoch 51/100\n",
            "204/204 - 35s - loss: 0.8793 - accuracy: 0.9061 - val_loss: 1.6860 - val_accuracy: 0.7604\n",
            "Epoch 52/100\n",
            "204/204 - 34s - loss: 0.8267 - accuracy: 0.9225 - val_loss: 1.6520 - val_accuracy: 0.7635\n",
            "Epoch 53/100\n",
            "204/204 - 34s - loss: 0.8185 - accuracy: 0.9246 - val_loss: 1.7546 - val_accuracy: 0.7537\n",
            "Epoch 54/100\n",
            "204/204 - 33s - loss: 0.8044 - accuracy: 0.9299 - val_loss: 1.6898 - val_accuracy: 0.7561\n",
            "Epoch 55/100\n",
            "204/204 - 35s - loss: 0.8014 - accuracy: 0.9342 - val_loss: 1.6703 - val_accuracy: 0.7666\n",
            "Epoch 56/100\n",
            "204/204 - 34s - loss: 0.7678 - accuracy: 0.9414 - val_loss: 1.7992 - val_accuracy: 0.7543\n",
            "Epoch 57/100\n",
            "204/204 - 36s - loss: 0.7521 - accuracy: 0.9471 - val_loss: 1.6667 - val_accuracy: 0.7709\n",
            "Epoch 58/100\n",
            "204/204 - 34s - loss: 0.7575 - accuracy: 0.9461 - val_loss: 1.8975 - val_accuracy: 0.7371\n",
            "Epoch 59/100\n",
            "204/204 - 34s - loss: 0.7363 - accuracy: 0.9504 - val_loss: 1.7413 - val_accuracy: 0.7697\n",
            "Epoch 60/100\n",
            "204/204 - 34s - loss: 0.7249 - accuracy: 0.9553 - val_loss: 1.7848 - val_accuracy: 0.7666\n",
            "Epoch 61/100\n",
            "204/204 - 34s - loss: 0.7102 - accuracy: 0.9622 - val_loss: 1.6705 - val_accuracy: 0.7684\n",
            "Epoch 62/100\n",
            "204/204 - 35s - loss: 0.6890 - accuracy: 0.9678 - val_loss: 1.7991 - val_accuracy: 0.7733\n",
            "Epoch 63/100\n",
            "204/204 - 34s - loss: 0.6880 - accuracy: 0.9638 - val_loss: 1.8452 - val_accuracy: 0.7531\n",
            "Epoch 64/100\n",
            "204/204 - 34s - loss: 0.6823 - accuracy: 0.9681 - val_loss: 1.7712 - val_accuracy: 0.7697\n",
            "Epoch 65/100\n",
            "204/204 - 36s - loss: 0.6801 - accuracy: 0.9701 - val_loss: 1.7036 - val_accuracy: 0.7746\n",
            "Epoch 66/100\n",
            "204/204 - 35s - loss: 0.6876 - accuracy: 0.9664 - val_loss: 1.7804 - val_accuracy: 0.7758\n",
            "Epoch 67/100\n",
            "204/204 - 34s - loss: 0.6737 - accuracy: 0.9721 - val_loss: 1.7967 - val_accuracy: 0.7727\n",
            "Epoch 68/100\n",
            "204/204 - 34s - loss: 0.6504 - accuracy: 0.9773 - val_loss: 1.8807 - val_accuracy: 0.7531\n",
            "Epoch 69/100\n",
            "204/204 - 34s - loss: 0.6506 - accuracy: 0.9761 - val_loss: 1.8096 - val_accuracy: 0.7746\n",
            "Epoch 70/100\n",
            "204/204 - 34s - loss: 0.6562 - accuracy: 0.9753 - val_loss: 1.7974 - val_accuracy: 0.7715\n",
            "Epoch 71/100\n",
            "204/204 - 34s - loss: 0.6632 - accuracy: 0.9770 - val_loss: 1.7343 - val_accuracy: 0.7746\n",
            "Epoch 72/100\n",
            "204/204 - 34s - loss: 0.6521 - accuracy: 0.9782 - val_loss: 1.7465 - val_accuracy: 0.7752\n",
            "Epoch 73/100\n",
            "204/204 - 34s - loss: 0.6450 - accuracy: 0.9802 - val_loss: 1.8157 - val_accuracy: 0.7727\n",
            "Epoch 74/100\n",
            "204/204 - 35s - loss: 0.6316 - accuracy: 0.9834 - val_loss: 1.7702 - val_accuracy: 0.7770\n",
            "Epoch 75/100\n",
            "204/204 - 36s - loss: 0.6367 - accuracy: 0.9785 - val_loss: 1.7353 - val_accuracy: 0.7801\n",
            "Epoch 76/100\n",
            "204/204 - 35s - loss: 0.6342 - accuracy: 0.9814 - val_loss: 1.7464 - val_accuracy: 0.7862\n",
            "Epoch 77/100\n",
            "204/204 - 34s - loss: 0.6327 - accuracy: 0.9834 - val_loss: 1.8057 - val_accuracy: 0.7715\n",
            "Epoch 78/100\n",
            "204/204 - 34s - loss: 0.6270 - accuracy: 0.9842 - val_loss: 1.7766 - val_accuracy: 0.7813\n",
            "Epoch 79/100\n",
            "204/204 - 36s - loss: 0.6243 - accuracy: 0.9856 - val_loss: 1.7524 - val_accuracy: 0.7918\n",
            "Epoch 80/100\n",
            "204/204 - 34s - loss: 0.6196 - accuracy: 0.9862 - val_loss: 1.7874 - val_accuracy: 0.7881\n",
            "Epoch 81/100\n",
            "204/204 - 34s - loss: 0.6205 - accuracy: 0.9863 - val_loss: 1.7903 - val_accuracy: 0.7850\n",
            "Epoch 82/100\n",
            "204/204 - 34s - loss: 0.6222 - accuracy: 0.9859 - val_loss: 1.7322 - val_accuracy: 0.7918\n",
            "Epoch 83/100\n",
            "204/204 - 34s - loss: 0.6170 - accuracy: 0.9874 - val_loss: 1.8403 - val_accuracy: 0.7912\n",
            "Epoch 84/100\n",
            "204/204 - 35s - loss: 0.6161 - accuracy: 0.9868 - val_loss: 1.7289 - val_accuracy: 0.7973\n",
            "Epoch 85/100\n",
            "204/204 - 34s - loss: 0.6197 - accuracy: 0.9868 - val_loss: 1.7681 - val_accuracy: 0.7942\n",
            "Epoch 86/100\n",
            "204/204 - 35s - loss: 0.6150 - accuracy: 0.9880 - val_loss: 1.8260 - val_accuracy: 0.8016\n",
            "Epoch 87/100\n",
            "204/204 - 34s - loss: 0.6234 - accuracy: 0.9848 - val_loss: 1.7896 - val_accuracy: 0.7936\n",
            "Epoch 88/100\n",
            "204/204 - 34s - loss: 0.6039 - accuracy: 0.9911 - val_loss: 1.7624 - val_accuracy: 0.8010\n",
            "Epoch 89/100\n",
            "204/204 - 34s - loss: 0.6052 - accuracy: 0.9917 - val_loss: 1.7986 - val_accuracy: 0.7936\n",
            "Epoch 90/100\n",
            "204/204 - 34s - loss: 0.6034 - accuracy: 0.9905 - val_loss: 1.8060 - val_accuracy: 0.7961\n",
            "Epoch 91/100\n",
            "204/204 - 34s - loss: 0.6044 - accuracy: 0.9908 - val_loss: 1.8505 - val_accuracy: 0.7948\n",
            "Epoch 92/100\n",
            "204/204 - 34s - loss: 0.6052 - accuracy: 0.9906 - val_loss: 1.8628 - val_accuracy: 0.7930\n",
            "Epoch 93/100\n",
            "204/204 - 34s - loss: 0.6134 - accuracy: 0.9879 - val_loss: 1.7945 - val_accuracy: 0.7973\n",
            "Epoch 94/100\n",
            "204/204 - 34s - loss: 0.6053 - accuracy: 0.9903 - val_loss: 1.9003 - val_accuracy: 0.7936\n",
            "Epoch 95/100\n",
            "204/204 - 34s - loss: 0.6035 - accuracy: 0.9919 - val_loss: 1.8186 - val_accuracy: 0.7930\n",
            "Epoch 96/100\n",
            "204/204 - 34s - loss: 0.6001 - accuracy: 0.9914 - val_loss: 1.7932 - val_accuracy: 0.7936\n",
            "Epoch 97/100\n",
            "204/204 - 35s - loss: 0.5999 - accuracy: 0.9913 - val_loss: 1.8012 - val_accuracy: 0.8022\n",
            "Epoch 98/100\n",
            "204/204 - 35s - loss: 0.5993 - accuracy: 0.9929 - val_loss: 1.7710 - val_accuracy: 0.8028\n",
            "Epoch 99/100\n",
            "204/204 - 34s - loss: 0.6013 - accuracy: 0.9909 - val_loss: 1.8245 - val_accuracy: 0.7942\n",
            "Epoch 100/100\n",
            "204/204 - 34s - loss: 0.6024 - accuracy: 0.9911 - val_loss: 1.8654 - val_accuracy: 0.7948\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TVaveN89Ob3U"
      },
      "source": [
        "# Extract Loss Values\n",
        "train_loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "# Extract Accuracy Values\n",
        "train_acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "\n",
        "instanced_variables = [train_loss, val_loss, train_acc, val_acc]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eBE8igWJdBBt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "683fdc91-459a-44fa-ee0a-1226b0db1959"
      },
      "source": [
        "#Save model\n",
        "model.save('/content/drive/MyDrive/CZ4042_Project/Custom_InceptionV3_BEST_2')\n",
        "\n",
        "#Save variables\n",
        "filename = '/content/drive/MyDrive/CZ4042_Project/Custom_InceptionV3_BEST_2_variables'\n",
        "outfile = open(filename,'wb')\n",
        "pickle.dump(instanced_variables, outfile)\n",
        "outfile.close() "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
            "INFO:tensorflow:Assets written to: /content/drive/MyDrive/CZ4042_Project/Custom_InceptionV3_BEST_2/assets\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OuODpoIFQojV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "e1c86b0e-1619-4c93-ee00-ba46ef702601"
      },
      "source": [
        "epochs_range = range(epochs)\n",
        "\n",
        "plt.plot(epochs_range, train_acc, label='Training Accuracy')\n",
        "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(loc='lower right')\n",
        "plt.savefig('/content/drive/MyDrive/CZ4042_Project/InceptionV3_BEST_2_acc.pdf')\n",
        "plt.savefig('/content/drive/MyDrive/CZ4042_Project/InceptionV3_BEST_2_acc.png')\n",
        "plt.show()\n",
        "plt.close()\n",
        "\n",
        "plt.plot(epochs_range, train_loss, label='Training Loss')\n",
        "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(loc='upper right')\n",
        "plt.savefig('/content/drive/MyDrive/CZ4042_Project/InceptionV3_BEST_2_loss.pdf')\n",
        "plt.savefig('/content/drive/MyDrive/CZ4042_Project/InceptionV3_BEST_2_loss.png')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DVvOCMitQojY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ecc5ae44-954e-4bf8-c7e0-51a97dc128c3"
      },
      "source": [
        "test_ds = keras.preprocessing.image_dataset_from_directory(working_dir + '/data/test', seed = 123, image_size=(img_height, img_width), batch_size = batch_size)\n",
        "test_ds = test_ds.cache().prefetch(buffer_size=AUTOTUNE)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 8041 files belonging to 196 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ObkIDXfBDFiA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55f5ec61-2052-42d4-b500-e905f7a0fdbf"
      },
      "source": [
        "model.load_weights(checkpoint_path)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7f9ae2fe8400>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f-eqy893Eiew"
      },
      "source": [
        "model.save_weights('/content/drive/MyDrive/CZ4042_Project/InceptionV3_main_6')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eu2319PgQojc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f123711-da91-4836-d5f7-6acd5d6a602b"
      },
      "source": [
        "test_loss, test_acc = model.evaluate(\n",
        "    x=test_ds, \n",
        "    y=None, \n",
        "    batch_size=batch_size)\n",
        "print(\"Test accuracy:\", test_acc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "252/252 [==============================] - 13s 53ms/step - loss: 1.7584 - accuracy: 0.8077\n",
            "Test accuracy: 0.8077353835105896\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xXB-wNm7Hnzr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd01496b-7414-4d81-d354-43c9cf6e9e42"
      },
      "source": [
        "val_loss_, val_acc_ = model.evaluate(\n",
        "    x=val_ds, \n",
        "    y=None, \n",
        "    batch_size=batch_size)\n",
        "print(\"Validation accuracy:\", val_acc_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "51/51 [==============================] - 2s 49ms/step - loss: 1.7710 - accuracy: 0.8028\n",
            "Validation accuracy: 0.8028255701065063\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1c7FmB-RQojf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "outputId": "862f0209-bbcb-43cd-c12e-83c207e07e26"
      },
      "source": [
        "# Test the model on an unseen image from the test set\n",
        "import cv2\n",
        "path_to_train_matfile = '/content/CZ4042_Project/CNNstanfordcars/devkit/cars_train_annos.mat'\n",
        "raw_train_data = loadmat(path_to_train_matfile)\n",
        "path_to_test_matfile = '/content/CZ4042_Project/CNNstanfordcars/devkit/cars_test_annos_withlabels.mat'\n",
        "raw_test_data = loadmat(path_to_test_matfile)\n",
        "train_annots_data = np.transpose(raw_train_data[\"annotations\"])\n",
        "test_annots_data = np.transpose(raw_test_data[\"annotations\"])\n",
        "\n",
        "random_index = random.randint(0, 8041)\n",
        "test_path = '/content/CZ4042_Project/CNNstanfordcars/cars_test/cars_test'\n",
        "relative_path = (test_annots_data[random_index,:])[0][5][0]\n",
        "random_car = cv2.imread(os.path.join(test_path, relative_path))\n",
        "car_rgb = cv2.cvtColor(random_car, cv2.COLOR_BGR2RGB)\n",
        "plt.imshow(car_rgb)\n",
        "path_to_labels_matfile = '/content/CZ4042_Project/CNNstanfordcars/devkit/cars_meta.mat'\n",
        "raw_labels_data = loadmat(path_to_labels_matfile)\n",
        "\n",
        "img = keras.preprocessing.image.load_img(\n",
        "    os.path.join(test_path, relative_path), target_size=(img_height, img_width)\n",
        ")\n",
        "img_array = keras.preprocessing.image.img_to_array(img)\n",
        "img_array = tf.expand_dims(img_array, 0) # Create a batch\n",
        "\n",
        "predictions = model.predict(img_array)\n",
        "score = predictions[0]\n",
        "\n",
        "print(\"Actual car:   \", raw_labels_data[\"class_names\"][0][test_annots_data[random_index][0][4][0][0] - 1][0])\n",
        "print(\"Predicted car: {}\"\n",
        "    .format(raw_labels_data[\"class_names\"][0][int(class_names[np.argmax(score)]) - 1][0]))\n",
        "print(\"Actual Class:   \", test_annots_data[random_index][0][4][0][0])\n",
        "print(\"Predicted Class:\", int(class_names[np.argmax(score)]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Actual car:    BMW X3 SUV 2012\n",
            "Predicted car: BMW X3 SUV 2012\n",
            "Actual Class:    37\n",
            "Predicted Class: 37\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dgqa_jZil_aS"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}