Module_06.ipynb

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  "metadata": {
    "colab": {
      "name": "Copy of Module-06.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tylercraigc4/machine-learning/blob/master/Module_06.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mA0HPVmIBT4C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a617345-93bf-49f3-b120-26a8bdb3ef04"
      },
      "source": [
        "# Note: After you run this cell, the training and test data will be available in\n",
        "# the file browser. (Click the folder icon on the left to view it)\n",
        "#\n",
        "# If you don't see the data after the cell completes, click the refresh button\n",
        "# in the file browser (folder icon with circular arrow)\n",
        "\n",
        "# First, let's download and unzip the data\n",
        "!echo \"Downloading files...\"\n",
        "!wget -q https://github.com/byui-cse/cse450-course/raw/master/data/roadsigns/training1.zip\n",
        "!wget -q https://github.com/byui-cse/cse450-course/raw/master/data/roadsigns/training2.zip\n",
        "!wget -q https://github.com/byui-cse/cse450-course/raw/master/data/roadsigns/test.zip\n",
        "\n",
        "!echo \"Unzipping files...\"\n",
        "!unzip -q /content/training1.zip\n",
        "!unzip -q /content/training2.zip\n",
        "!unzip -q /content/test.zip\n",
        "\n",
        "# Combine the two traning directories\n",
        "!echo \"Mergining training data...\"\n",
        "!mkdir /content/training\n",
        "!mv /content/training1/* /content/training\n",
        "!mv /content/training2/* /content/training\n",
        "\n",
        "# Cleanup\n",
        "!echo \"Cleaning up...\"\n",
        "!rmdir /content/training1\n",
        "!rmdir /content/training2\n",
        "!rm training1.zip\n",
        "!rm training2.zip\n",
        "!rm test.zip\n",
        "\n",
        "!echo \"Data ready.\"\n",
        "\n",
        "!rm -rf ./logs/\n"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading files...\n",
            "Unzipping files...\n",
            "replace test/04354.jpg? [y]es, [n]o, [A]ll, [N]one, [r]ename: N\n",
            "Mergining training data...\n",
            "mkdir: cannot create directory ‘/content/training’: File exists\n",
            "mv: cannot move '/content/training1/00000' to '/content/training/00000': Directory not empty\n",
            "mv: cannot move '/content/training1/00001' to '/content/training/00001': Directory not empty\n",
            "mv: cannot move '/content/training1/00002' to '/content/training/00002': Directory not empty\n",
            "mv: cannot move '/content/training1/00003' to '/content/training/00003': Directory not empty\n",
            "mv: cannot move '/content/training1/00004' to '/content/training/00004': Directory not empty\n",
            "mv: cannot move '/content/training1/00005' to '/content/training/00005': Directory not empty\n",
            "mv: cannot move '/content/training1/00006' to '/content/training/00006': Directory not empty\n",
            "mv: cannot move '/content/training1/00007' to '/content/training/00007': Directory not empty\n",
            "mv: cannot move '/content/training1/00008' to '/content/training/00008': Directory not empty\n",
            "mv: cannot move '/content/training1/00009' to '/content/training/00009': Directory not empty\n",
            "mv: cannot move '/content/training1/00010' to '/content/training/00010': Directory not empty\n",
            "mv: cannot move '/content/training1/00011' to '/content/training/00011': Directory not empty\n",
            "mv: cannot move '/content/training1/00012' to '/content/training/00012': Directory not empty\n",
            "mv: cannot move '/content/training1/00013' to '/content/training/00013': Directory not empty\n",
            "mv: cannot move '/content/training1/00014' to '/content/training/00014': Directory not empty\n",
            "mv: cannot move '/content/training1/00015' to '/content/training/00015': Directory not empty\n",
            "mv: cannot move '/content/training1/00016' to '/content/training/00016': Directory not empty\n",
            "mv: cannot move '/content/training1/00017' to '/content/training/00017': Directory not empty\n",
            "mv: cannot move '/content/training1/00018' to '/content/training/00018': Directory not empty\n",
            "mv: cannot move '/content/training1/00019' to '/content/training/00019': Directory not empty\n",
            "mv: cannot move '/content/training1/00020' to '/content/training/00020': Directory not empty\n",
            "mv: cannot move '/content/training2/00021' to '/content/training/00021': Directory not empty\n",
            "mv: cannot move '/content/training2/00022' to '/content/training/00022': Directory not empty\n",
            "mv: cannot move '/content/training2/00023' to '/content/training/00023': Directory not empty\n",
            "mv: cannot move '/content/training2/00024' to '/content/training/00024': Directory not empty\n",
            "mv: cannot move '/content/training2/00025' to '/content/training/00025': Directory not empty\n",
            "mv: cannot move '/content/training2/00026' to '/content/training/00026': Directory not empty\n",
            "mv: cannot move '/content/training2/00027' to '/content/training/00027': Directory not empty\n",
            "mv: cannot move '/content/training2/00028' to '/content/training/00028': Directory not empty\n",
            "mv: cannot move '/content/training2/00029' to '/content/training/00029': Directory not empty\n",
            "mv: cannot move '/content/training2/00030' to '/content/training/00030': Directory not empty\n",
            "mv: cannot move '/content/training2/00031' to '/content/training/00031': Directory not empty\n",
            "mv: cannot move '/content/training2/00032' to '/content/training/00032': Directory not empty\n",
            "mv: cannot move '/content/training2/00033' to '/content/training/00033': Directory not empty\n",
            "mv: cannot move '/content/training2/00034' to '/content/training/00034': Directory not empty\n",
            "mv: cannot move '/content/training2/00035' to '/content/training/00035': Directory not empty\n",
            "mv: cannot move '/content/training2/00036' to '/content/training/00036': Directory not empty\n",
            "mv: cannot move '/content/training2/00037' to '/content/training/00037': Directory not empty\n",
            "mv: cannot move '/content/training2/00038' to '/content/training/00038': Directory not empty\n",
            "mv: cannot move '/content/training2/00039' to '/content/training/00039': Directory not empty\n",
            "mv: cannot move '/content/training2/00040' to '/content/training/00040': Directory not empty\n",
            "mv: cannot move '/content/training2/00041' to '/content/training/00041': Directory not empty\n",
            "mv: cannot move '/content/training2/00042' to '/content/training/00042': Directory not empty\n",
            "Cleaning up...\n",
            "rmdir: failed to remove '/content/training1': Directory not empty\n",
            "rmdir: failed to remove '/content/training2': Directory not empty\n",
            "Data ready.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VXpjAGsOVwmd",
        "outputId": "fb338f3f-5528-43e4-889f-cf26a29b6642"
      },
      "source": [
        "!mkdir /content/gdrive"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mkdir: cannot create directory ‘/content/gdrive’: File exists\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s4xknFlTD6Cs",
        "outputId": "4a0bedbc-b9c8-4ba2-f212-72d821050632"
      },
      "source": [
        "!ls /content/"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "gdrive\tsample_data  saved_model  test\ttraining  training1  training2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "StiU5QcPPxqQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b425d550-ffae-40ed-92eb-9eeb881b3b06"
      },
      "source": [
        "# Import libraries\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from google.colab import files, drive\n",
        "import datetime\n",
        "%load_ext tensorboard"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The tensorboard extension is already loaded. To reload it, use:\n",
            "  %reload_ext tensorboard\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XpQHm3bkUxrf",
        "outputId": "eae1eb98-879d-49c1-ca8f-6990368df56f"
      },
      "source": [
        "drive.mount(\"/content/gdrive\")"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "YvWGbOadDhQ2",
        "outputId": "ff118699-aaf1-42c6-b68a-d334ff398e51"
      },
      "source": [
        "test_classes = pd.read_csv(\"/content/test/test_classes.csv\")\r\n",
        "test_classes.head()"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Filename</th>\n",
              "      <th>ClassId</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>00000.jpg</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>00001.jpg</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>00002.jpg</td>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>00003.jpg</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>00004.jpg</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Filename  ClassId\n",
              "0  00000.jpg       16\n",
              "1  00001.jpg        1\n",
              "2  00002.jpg       38\n",
              "3  00003.jpg       33\n",
              "4  00004.jpg       11"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ALLMN63FPyEQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca9d144c-fb92-49ef-8233-b91eb22c2350"
      },
      "source": [
        "# Create an image training dataset\n",
        "from tensorflow.keras.preprocessing import image_dataset_from_directory\n",
        "\n",
        "# We're using keras' image_dataset_from_directory method to load our image data.\n",
        "# See (https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image_dataset_from_directory) for details\n",
        "#\n",
        "# A couple of things to note:\n",
        "# 1. We're specifying a number for the seed, so we'll always get the same shuffle and split of our images.\n",
        "# 2. Class names are inferred automatically from the image subdirectory names.\n",
        "# 3. We're splitting the training data into 80% training, 20% validation. \n",
        "\n",
        "\n",
        "\n",
        "training_dir = '/content/training/'\n",
        "IMG_SIZE = (100, 100)\n",
        "BATCH_SIZE = 32\n",
        "\n",
        "# Split up the training data images into training and validations sets\n",
        "training_data = image_dataset_from_directory(training_dir, validation_split=.2, subset='training', seed=42, image_size=IMG_SIZE, batch_size=BATCH_SIZE, shuffle=True)\n",
        "validation_data = image_dataset_from_directory(training_dir, validation_split=.2, subset='validation', seed=42, image_size=IMG_SIZE, batch_size=BATCH_SIZE, shuffle=True)"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 39209 files belonging to 43 classes.\n",
            "Using 31368 files for training.\n",
            "Found 39209 files belonging to 43 classes.\n",
            "Using 7841 files for validation.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RnGBwGVZPyyh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "outputId": "5f1b7d10-f662-457b-9651-3e613719d43b"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# View first 9 images and their class labels\n",
        "plt.figure(figsize=(10, 10))\n",
        "for images, labels in training_data.take(1):\n",
        "  for i in range(9):\n",
        "    ax = plt.subplot(3, 3, i + 1)\n",
        "    plt.imshow(images[i].numpy().astype(\"uint8\"))\n",
        "    plt.title(training_data.class_names[labels[i]])\n",
        "    plt.axis(\"off\")\n"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DF6X7xXTZmMx"
      },
      "source": [
        "# configure the dataset for performance\n",
        "\n",
        "AUTOTUNE = tf.data.experimental.AUTOTUNE\n",
        "\n",
        "train_dataset = training_data.prefetch(buffer_size=AUTOTUNE)\n",
        "validation_dataset = validation_data.prefetch(buffer_size=AUTOTUNE)\n"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qGAMvcOG9SGO"
      },
      "source": [
        "# rescaling the data to fit into our network\n",
        "\n",
        "preprocess_input = tf.keras.applications.mobilenet_v2.preprocess_input\n",
        "\n",
        "rescale = tf.keras.Sequential([tf.keras.layers.experimental.preprocessing.Rescaling(1.0/255)])\n"
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CVSfaqgKPzE2"
      },
      "source": [
        "IMG_SHAPE = IMG_SIZE + (3,)\n",
        "\n",
        "# base model based on the MobileNetV2 network\n",
        "\n",
        "base_model = tf.keras.applications.MobileNetV2(weights=\"imagenet\", input_shape=IMG_SHAPE, include_top=False) \n"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tRtH9piHThM9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d26d74c-0a71-4c31-bf93-bdca81bf3e32"
      },
      "source": [
        "base_model.summary()"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"mobilenetv2_1.00_224\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_3 (InputLayer)            [(None, 100, 100, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "Conv1_pad (ZeroPadding2D)       (None, 101, 101, 3)  0           input_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "Conv1 (Conv2D)                  (None, 50, 50, 32)   864         Conv1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "bn_Conv1 (BatchNormalization)   (None, 50, 50, 32)   128         Conv1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "Conv1_relu (ReLU)               (None, 50, 50, 32)   0           bn_Conv1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise (Depthw (None, 50, 50, 32)   288         Conv1_relu[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise_BN (Bat (None, 50, 50, 32)   128         expanded_conv_depthwise[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise_relu (R (None, 50, 50, 32)   0           expanded_conv_depthwise_BN[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_project (Conv2D)  (None, 50, 50, 16)   512         expanded_conv_depthwise_relu[0][0\n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_project_BN (Batch (None, 50, 50, 16)   64          expanded_conv_project[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand (Conv2D)         (None, 50, 50, 96)   1536        expanded_conv_project_BN[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand_BN (BatchNormali (None, 50, 50, 96)   384         block_1_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand_relu (ReLU)      (None, 50, 50, 96)   0           block_1_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_1_pad (ZeroPadding2D)     (None, 51, 51, 96)   0           block_1_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise (DepthwiseCon (None, 25, 25, 96)   864         block_1_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise_BN (BatchNorm (None, 25, 25, 96)   384         block_1_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise_relu (ReLU)   (None, 25, 25, 96)   0           block_1_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_1_project (Conv2D)        (None, 25, 25, 24)   2304        block_1_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_1_project_BN (BatchNormal (None, 25, 25, 24)   96          block_1_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand (Conv2D)         (None, 25, 25, 144)  3456        block_1_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand_BN (BatchNormali (None, 25, 25, 144)  576         block_2_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand_relu (ReLU)      (None, 25, 25, 144)  0           block_2_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise (DepthwiseCon (None, 25, 25, 144)  1296        block_2_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise_BN (BatchNorm (None, 25, 25, 144)  576         block_2_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise_relu (ReLU)   (None, 25, 25, 144)  0           block_2_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_2_project (Conv2D)        (None, 25, 25, 24)   3456        block_2_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_2_project_BN (BatchNormal (None, 25, 25, 24)   96          block_2_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_2_add (Add)               (None, 25, 25, 24)   0           block_1_project_BN[0][0]         \n",
            "                                                                 block_2_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand (Conv2D)         (None, 25, 25, 144)  3456        block_2_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand_BN (BatchNormali (None, 25, 25, 144)  576         block_3_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand_relu (ReLU)      (None, 25, 25, 144)  0           block_3_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_3_pad (ZeroPadding2D)     (None, 27, 27, 144)  0           block_3_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise (DepthwiseCon (None, 13, 13, 144)  1296        block_3_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise_BN (BatchNorm (None, 13, 13, 144)  576         block_3_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise_relu (ReLU)   (None, 13, 13, 144)  0           block_3_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_3_project (Conv2D)        (None, 13, 13, 32)   4608        block_3_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_3_project_BN (BatchNormal (None, 13, 13, 32)   128         block_3_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand (Conv2D)         (None, 13, 13, 192)  6144        block_3_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand_BN (BatchNormali (None, 13, 13, 192)  768         block_4_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand_relu (ReLU)      (None, 13, 13, 192)  0           block_4_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise (DepthwiseCon (None, 13, 13, 192)  1728        block_4_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise_BN (BatchNorm (None, 13, 13, 192)  768         block_4_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise_relu (ReLU)   (None, 13, 13, 192)  0           block_4_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_4_project (Conv2D)        (None, 13, 13, 32)   6144        block_4_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_4_project_BN (BatchNormal (None, 13, 13, 32)   128         block_4_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_4_add (Add)               (None, 13, 13, 32)   0           block_3_project_BN[0][0]         \n",
            "                                                                 block_4_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand (Conv2D)         (None, 13, 13, 192)  6144        block_4_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand_BN (BatchNormali (None, 13, 13, 192)  768         block_5_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand_relu (ReLU)      (None, 13, 13, 192)  0           block_5_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise (DepthwiseCon (None, 13, 13, 192)  1728        block_5_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise_BN (BatchNorm (None, 13, 13, 192)  768         block_5_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise_relu (ReLU)   (None, 13, 13, 192)  0           block_5_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_5_project (Conv2D)        (None, 13, 13, 32)   6144        block_5_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_5_project_BN (BatchNormal (None, 13, 13, 32)   128         block_5_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_5_add (Add)               (None, 13, 13, 32)   0           block_4_add[0][0]                \n",
            "                                                                 block_5_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand (Conv2D)         (None, 13, 13, 192)  6144        block_5_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand_BN (BatchNormali (None, 13, 13, 192)  768         block_6_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand_relu (ReLU)      (None, 13, 13, 192)  0           block_6_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_6_pad (ZeroPadding2D)     (None, 15, 15, 192)  0           block_6_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise (DepthwiseCon (None, 7, 7, 192)    1728        block_6_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise_BN (BatchNorm (None, 7, 7, 192)    768         block_6_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise_relu (ReLU)   (None, 7, 7, 192)    0           block_6_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_6_project (Conv2D)        (None, 7, 7, 64)     12288       block_6_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_6_project_BN (BatchNormal (None, 7, 7, 64)     256         block_6_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand (Conv2D)         (None, 7, 7, 384)    24576       block_6_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand_BN (BatchNormali (None, 7, 7, 384)    1536        block_7_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand_relu (ReLU)      (None, 7, 7, 384)    0           block_7_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise (DepthwiseCon (None, 7, 7, 384)    3456        block_7_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise_BN (BatchNorm (None, 7, 7, 384)    1536        block_7_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise_relu (ReLU)   (None, 7, 7, 384)    0           block_7_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_7_project (Conv2D)        (None, 7, 7, 64)     24576       block_7_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_7_project_BN (BatchNormal (None, 7, 7, 64)     256         block_7_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_add (Add)               (None, 7, 7, 64)     0           block_6_project_BN[0][0]         \n",
            "                                                                 block_7_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand (Conv2D)         (None, 7, 7, 384)    24576       block_7_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand_BN (BatchNormali (None, 7, 7, 384)    1536        block_8_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand_relu (ReLU)      (None, 7, 7, 384)    0           block_8_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise (DepthwiseCon (None, 7, 7, 384)    3456        block_8_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise_BN (BatchNorm (None, 7, 7, 384)    1536        block_8_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise_relu (ReLU)   (None, 7, 7, 384)    0           block_8_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_8_project (Conv2D)        (None, 7, 7, 64)     24576       block_8_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_8_project_BN (BatchNormal (None, 7, 7, 64)     256         block_8_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_8_add (Add)               (None, 7, 7, 64)     0           block_7_add[0][0]                \n",
            "                                                                 block_8_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand (Conv2D)         (None, 7, 7, 384)    24576       block_8_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand_BN (BatchNormali (None, 7, 7, 384)    1536        block_9_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand_relu (ReLU)      (None, 7, 7, 384)    0           block_9_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise (DepthwiseCon (None, 7, 7, 384)    3456        block_9_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise_BN (BatchNorm (None, 7, 7, 384)    1536        block_9_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise_relu (ReLU)   (None, 7, 7, 384)    0           block_9_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_9_project (Conv2D)        (None, 7, 7, 64)     24576       block_9_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_9_project_BN (BatchNormal (None, 7, 7, 64)     256         block_9_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_9_add (Add)               (None, 7, 7, 64)     0           block_8_add[0][0]                \n",
            "                                                                 block_9_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand (Conv2D)        (None, 7, 7, 384)    24576       block_9_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand_BN (BatchNormal (None, 7, 7, 384)    1536        block_10_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand_relu (ReLU)     (None, 7, 7, 384)    0           block_10_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise (DepthwiseCo (None, 7, 7, 384)    3456        block_10_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise_BN (BatchNor (None, 7, 7, 384)    1536        block_10_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise_relu (ReLU)  (None, 7, 7, 384)    0           block_10_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_10_project (Conv2D)       (None, 7, 7, 96)     36864       block_10_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_10_project_BN (BatchNorma (None, 7, 7, 96)     384         block_10_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand (Conv2D)        (None, 7, 7, 576)    55296       block_10_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand_BN (BatchNormal (None, 7, 7, 576)    2304        block_11_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand_relu (ReLU)     (None, 7, 7, 576)    0           block_11_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise (DepthwiseCo (None, 7, 7, 576)    5184        block_11_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise_BN (BatchNor (None, 7, 7, 576)    2304        block_11_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise_relu (ReLU)  (None, 7, 7, 576)    0           block_11_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_11_project (Conv2D)       (None, 7, 7, 96)     55296       block_11_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_11_project_BN (BatchNorma (None, 7, 7, 96)     384         block_11_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_add (Add)              (None, 7, 7, 96)     0           block_10_project_BN[0][0]        \n",
            "                                                                 block_11_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand (Conv2D)        (None, 7, 7, 576)    55296       block_11_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand_BN (BatchNormal (None, 7, 7, 576)    2304        block_12_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand_relu (ReLU)     (None, 7, 7, 576)    0           block_12_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise (DepthwiseCo (None, 7, 7, 576)    5184        block_12_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise_BN (BatchNor (None, 7, 7, 576)    2304        block_12_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise_relu (ReLU)  (None, 7, 7, 576)    0           block_12_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_12_project (Conv2D)       (None, 7, 7, 96)     55296       block_12_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_12_project_BN (BatchNorma (None, 7, 7, 96)     384         block_12_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_12_add (Add)              (None, 7, 7, 96)     0           block_11_add[0][0]               \n",
            "                                                                 block_12_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand (Conv2D)        (None, 7, 7, 576)    55296       block_12_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand_BN (BatchNormal (None, 7, 7, 576)    2304        block_13_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand_relu (ReLU)     (None, 7, 7, 576)    0           block_13_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_13_pad (ZeroPadding2D)    (None, 9, 9, 576)    0           block_13_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise (DepthwiseCo (None, 4, 4, 576)    5184        block_13_pad[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise_BN (BatchNor (None, 4, 4, 576)    2304        block_13_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise_relu (ReLU)  (None, 4, 4, 576)    0           block_13_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_13_project (Conv2D)       (None, 4, 4, 160)    92160       block_13_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_13_project_BN (BatchNorma (None, 4, 4, 160)    640         block_13_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand (Conv2D)        (None, 4, 4, 960)    153600      block_13_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand_BN (BatchNormal (None, 4, 4, 960)    3840        block_14_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand_relu (ReLU)     (None, 4, 4, 960)    0           block_14_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise (DepthwiseCo (None, 4, 4, 960)    8640        block_14_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise_BN (BatchNor (None, 4, 4, 960)    3840        block_14_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise_relu (ReLU)  (None, 4, 4, 960)    0           block_14_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_14_project (Conv2D)       (None, 4, 4, 160)    153600      block_14_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_14_project_BN (BatchNorma (None, 4, 4, 160)    640         block_14_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_14_add (Add)              (None, 4, 4, 160)    0           block_13_project_BN[0][0]        \n",
            "                                                                 block_14_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand (Conv2D)        (None, 4, 4, 960)    153600      block_14_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand_BN (BatchNormal (None, 4, 4, 960)    3840        block_15_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand_relu (ReLU)     (None, 4, 4, 960)    0           block_15_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise (DepthwiseCo (None, 4, 4, 960)    8640        block_15_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise_BN (BatchNor (None, 4, 4, 960)    3840        block_15_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise_relu (ReLU)  (None, 4, 4, 960)    0           block_15_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_15_project (Conv2D)       (None, 4, 4, 160)    153600      block_15_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_15_project_BN (BatchNorma (None, 4, 4, 160)    640         block_15_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_15_add (Add)              (None, 4, 4, 160)    0           block_14_add[0][0]               \n",
            "                                                                 block_15_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand (Conv2D)        (None, 4, 4, 960)    153600      block_15_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand_BN (BatchNormal (None, 4, 4, 960)    3840        block_16_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand_relu (ReLU)     (None, 4, 4, 960)    0           block_16_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise (DepthwiseCo (None, 4, 4, 960)    8640        block_16_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise_BN (BatchNor (None, 4, 4, 960)    3840        block_16_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise_relu (ReLU)  (None, 4, 4, 960)    0           block_16_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_16_project (Conv2D)       (None, 4, 4, 320)    307200      block_16_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_16_project_BN (BatchNorma (None, 4, 4, 320)    1280        block_16_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "Conv_1 (Conv2D)                 (None, 4, 4, 1280)   409600      block_16_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "Conv_1_bn (BatchNormalization)  (None, 4, 4, 1280)   5120        Conv_1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "out_relu (ReLU)                 (None, 4, 4, 1280)   0           Conv_1_bn[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 2,257,984\n",
            "Trainable params: 2,223,872\n",
            "Non-trainable params: 34,112\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VAcwJ_xcTSf4"
      },
      "source": [
        "# freeze the base model\n",
        "base_model.trainable = False\n",
        "\n",
        "# add a classification head\n",
        "global_average_layer = tf.keras.layers.GlobalAveragePooling2D() # used to convert the features to a single 1280-element vector per image\n",
        "\n",
        "# add a prediction layer\n",
        "prediction_layer = tf.keras.layers.Dense(43, activation=\"softmax\")\n"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pW16-dsZfUgF"
      },
      "source": [
        "# build the model by chaining together everything\n",
        "\n",
        "inputs = tf.keras.Input(shape=(100, 100, 3))\n",
        "x = preprocess_input(inputs)\n",
        "x = base_model(x, training=False)\n",
        "x = global_average_layer(x)\n",
        "x = tf.keras.layers.Dropout(0.2)(x)\n",
        "outputs = prediction_layer(x)\n",
        "\n",
        "\n",
        "model = tf.keras.Model(inputs, outputs)"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WRMtXKf4fTnO"
      },
      "source": [
        "# compile the model to be trained\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(lr=0.0001),\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=[\"accuracy\"])\n",
        "\n",
        "log_dir = \"logs/fit/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
        "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZJXbEWEShIYQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "81fd8f5f-6e53-4aab-fdde-907484b9bc55"
      },
      "source": [
        "# get the initial loss and accuracy\n",
        "inital_epochs = 50\n",
        "loss0, accuracy0 = model.evaluate(validation_dataset)\n",
        "\n",
        "print(\"initial loss: {:.2f}\".format(loss0))\n",
        "print(\"initial accuracy: {:.2f}\".format(accuracy0))"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "246/246 [==============================] - 5s 18ms/step - loss: 3.1091 - accuracy: 0.7170\n",
            "initial loss: 3.11\n",
            "initial accuracy: 0.72\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yjZ8sm2ziCCO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "be1b7b96-4154-4c36-d490-f11a579a2d36"
      },
      "source": [
        "# train the model\n",
        "history = model.fit(train_dataset, epochs=inital_epochs, validation_data=validation_dataset, callbacks=[tensorboard_callback])"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/25\n",
            "981/981 [==============================] - 23s 23ms/step - loss: 3.1071 - accuracy: 0.7199 - val_loss: 3.1064 - val_accuracy: 0.7197\n",
            "Epoch 2/25\n",
            "981/981 [==============================] - 23s 23ms/step - loss: 3.1030 - accuracy: 0.7234 - val_loss: 3.1039 - val_accuracy: 0.7226\n",
            "Epoch 3/25\n",
            "981/981 [==============================] - 23s 23ms/step - loss: 3.1009 - accuracy: 0.7251 - val_loss: 3.1017 - val_accuracy: 0.7247\n",
            "Epoch 4/25\n",
            "981/981 [==============================] - 23s 24ms/step - loss: 3.0981 - accuracy: 0.7272 - val_loss: 3.0993 - val_accuracy: 0.7259\n",
            "Epoch 5/25\n",
            "981/981 [==============================] - 23s 24ms/step - loss: 3.0966 - accuracy: 0.7298 - val_loss: 3.0971 - val_accuracy: 0.7272\n",
            "Epoch 6/25\n",
            "981/981 [==============================] - 23s 23ms/step - loss: 3.0945 - accuracy: 0.7310 - val_loss: 3.0958 - val_accuracy: 0.7294\n",
            "Epoch 7/25\n",
            "981/981 [==============================] - 25s 26ms/step - loss: 3.0925 - accuracy: 0.7322 - val_loss: 3.0943 - val_accuracy: 0.7294\n",
            "Epoch 8/25\n",
            "981/981 [==============================] - 26s 27ms/step - loss: 3.0902 - accuracy: 0.7339 - val_loss: 3.0916 - val_accuracy: 0.7326\n",
            "Epoch 9/25\n",
            "981/981 [==============================] - 26s 26ms/step - loss: 3.0885 - accuracy: 0.7358 - val_loss: 3.0907 - val_accuracy: 0.7327\n",
            "Epoch 10/25\n",
            "981/981 [==============================] - 26s 27ms/step - loss: 3.0876 - accuracy: 0.7360 - val_loss: 3.0887 - val_accuracy: 0.7359\n",
            "Epoch 11/25\n",
            "981/981 [==============================] - 26s 27ms/step - loss: 3.0868 - accuracy: 0.7358 - val_loss: 3.0875 - val_accuracy: 0.7360\n",
            "Epoch 12/25\n",
            "981/981 [==============================] - 26s 27ms/step - loss: 3.0842 - accuracy: 0.7396 - val_loss: 3.0861 - val_accuracy: 0.7375\n",
            "Epoch 13/25\n",
            "981/981 [==============================] - 26s 27ms/step - loss: 3.0838 - accuracy: 0.7388 - val_loss: 3.0849 - val_accuracy: 0.7396\n",
            "Epoch 14/25\n",
            "981/981 [==============================] - 26s 27ms/step - loss: 3.0814 - accuracy: 0.7405 - val_loss: 3.0839 - val_accuracy: 0.7398\n",
            "Epoch 15/25\n",
            "981/981 [==============================] - 26s 26ms/step - loss: 3.0806 - accuracy: 0.7408 - val_loss: 3.0825 - val_accuracy: 0.7402\n",
            "Epoch 16/25\n",
            "981/981 [==============================] - 26s 27ms/step - loss: 3.0806 - accuracy: 0.7422 - val_loss: 3.0813 - val_accuracy: 0.7414\n",
            "Epoch 17/25\n",
            "981/981 [==============================] - 26s 27ms/step - loss: 3.0794 - accuracy: 0.7427 - val_loss: 3.0813 - val_accuracy: 0.7414\n",
            "Epoch 18/25\n",
            "981/981 [==============================] - 26s 26ms/step - loss: 3.0776 - accuracy: 0.7443 - val_loss: 3.0802 - val_accuracy: 0.7421\n",
            "Epoch 19/25\n",
            "981/981 [==============================] - 26s 27ms/step - loss: 3.0769 - accuracy: 0.7444 - val_loss: 3.0795 - val_accuracy: 0.7423\n",
            "Epoch 20/25\n",
            "981/981 [==============================] - 26s 26ms/step - loss: 3.0754 - accuracy: 0.7461 - val_loss: 3.0778 - val_accuracy: 0.7430\n",
            "Epoch 21/25\n",
            "981/981 [==============================] - 26s 27ms/step - loss: 3.0741 - accuracy: 0.7460 - val_loss: 3.0770 - val_accuracy: 0.7442\n",
            "Epoch 22/25\n",
            "981/981 [==============================] - 26s 27ms/step - loss: 3.0744 - accuracy: 0.7466 - val_loss: 3.0762 - val_accuracy: 0.7457\n",
            "Epoch 23/25\n",
            "981/981 [==============================] - 26s 27ms/step - loss: 3.0731 - accuracy: 0.7477 - val_loss: 3.0755 - val_accuracy: 0.7461\n",
            "Epoch 24/25\n",
            "981/981 [==============================] - 26s 27ms/step - loss: 3.0728 - accuracy: 0.7484 - val_loss: 3.0750 - val_accuracy: 0.7458\n",
            "Epoch 25/25\n",
            "981/981 [==============================] - 26s 27ms/step - loss: 3.0710 - accuracy: 0.7489 - val_loss: 3.0739 - val_accuracy: 0.7461\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wdz5B0AyscJ_"
      },
      "source": [
        "# save the model\n",
        "!mkdir -p saved_model\n",
        "model.save(\"saved_model/tyler_model.h5\")\n"
      ],
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bftd175UOVzR"
      },
      "source": [
        "# load the model\n",
        "fine_tuned_model = tf.keras.models.load_model('saved_model/tyler_model.h5')"
      ],
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bxAQsebRouRo",
        "outputId": "1b233037-9b40-4166-fec0-4d06b1cc604a"
      },
      "source": [
        "# Fine Tuning\n",
        "\n",
        "# un-freeze the top layers of the model\n",
        "base_model.trainable = True\n",
        "\n",
        "# lets see how many layers are in the base model\n",
        "print(\"Number of layers in the base model: \", len(base_model.layers))\n"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of layers in the base model:  155\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iK4trZ1rp4Gl"
      },
      "source": [
        "# lets fine tune from the 100th layer onwards\n",
        "\n",
        "fine_tune_at = 100\n",
        "\n",
        "# Freeze all the layers before the \"fine_tune_at\" layer\n",
        "\n",
        "for layer in base_model.layers[:fine_tune_at]:\n",
        "  layer.trainable=False\n"
      ],
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PxhjZsmOqfX-"
      },
      "source": [
        "# recompile the model setting the learning rate very low so we don't overfit\n",
        "\n",
        "fine_tuned_model.compile(optimizer=tf.keras.optimizers.Adam(lr=0.0001/10),\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=[\"accuracy\"])"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R7QxG4hLq26h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 647
        },
        "outputId": "d0f80766-aa01-472a-ad9b-5111096fd763"
      },
      "source": [
        "# continue training the model with fine tuning \n",
        "\n",
        "fine_tuning_epochs = 25\n",
        "\n",
        "history_fine = fine_tuned_model.fit(train_dataset, epochs=fine_tuning_epochs, validation_data=validation_dataset, callbacks=[tensorboard_callback])\n"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/25\n",
            "981/981 [==============================] - 23s 23ms/step - loss: 3.0704 - accuracy: 0.7501 - val_loss: 3.0726 - val_accuracy: 0.7474\n",
            "Epoch 2/25\n",
            "981/981 [==============================] - 24s 25ms/step - loss: 3.0699 - accuracy: 0.7503 - val_loss: 3.0724 - val_accuracy: 0.7474\n",
            "Epoch 3/25\n",
            "981/981 [==============================] - 24s 25ms/step - loss: 3.0690 - accuracy: 0.7518 - val_loss: 3.0724 - val_accuracy: 0.7475\n",
            "Epoch 4/25\n",
            "981/981 [==============================] - 25s 25ms/step - loss: 3.0708 - accuracy: 0.7496 - val_loss: 3.0722 - val_accuracy: 0.7475\n",
            "Epoch 5/25\n",
            "981/981 [==============================] - 24s 24ms/step - loss: 3.0700 - accuracy: 0.7497 - val_loss: 3.0721 - val_accuracy: 0.7484\n",
            "Epoch 6/25\n",
            "981/981 [==============================] - 24s 25ms/step - loss: 3.0688 - accuracy: 0.7508 - val_loss: 3.0720 - val_accuracy: 0.7479\n",
            "Epoch 7/25\n",
            "981/981 [==============================] - 25s 25ms/step - loss: 3.0686 - accuracy: 0.7513 - val_loss: 3.0719 - val_accuracy: 0.7488\n",
            "Epoch 8/25\n",
            "981/981 [==============================] - 24s 25ms/step - loss: 3.0690 - accuracy: 0.7505 - val_loss: 3.0719 - val_accuracy: 0.7482\n",
            "Epoch 9/25\n",
            "110/981 [==>...........................] - ETA: 18s - loss: 3.0680 - accuracy: 0.7554"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-90-e1f62a6a7e15>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mfine_tuning_epochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m25\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mhistory_fine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfine_tuned_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_dataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfine_tuning_epochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_dataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtensorboard_callback\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    106\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_method_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    109\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m     \u001b[0;31m# Running inside `run_distribute_coordinator` already.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1096\u001b[0m                 batch_size=batch_size):\n\u001b[1;32m   1097\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1098\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\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   1099\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1100\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    778\u001b[0m       \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    779\u001b[0m         \u001b[0mcompiler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"nonXla\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 780\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\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    781\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    782\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    805\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    806\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 807\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    808\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    809\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2827\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2828\u001b[0m       \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2829\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2830\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2831\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[0;34m(self, args, kwargs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1846\u001b[0m                            resource_variable_ops.BaseResourceVariable))],\n\u001b[1;32m   1847\u001b[0m         \u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1848\u001b[0;31m         cancellation_manager=cancellation_manager)\n\u001b[0m\u001b[1;32m   1849\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1850\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1922\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1923\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1924\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1925\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1926\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    548\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    549\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    551\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    552\u001b[0m           outputs = execute.execute_with_cancellation(\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CZcwIrZDrrRZ"
      },
      "source": [
        "# save the model\n",
        "!mkdir -p saved_model\n",
        "fine_tuned_model.save(\"saved_model/tyler_model.h5\")\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Odi9z1ODN0FQ"
      },
      "source": [
        "files.download(\"saved_model/tyler_model.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wL-yf_aqg-eO"
      },
      "source": [
        "!pip install coremltools"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yKAIaY-lf8p4"
      },
      "source": [
        "# save as a coreML file to import into Apple's MLKit framework\n",
        "\n",
        "import coremltools as ct\n",
        "core_ml_model = ct.convert('saved_model/tyler_model.h5')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pIgxaKwai1sa"
      },
      "source": [
        "core_ml_model.save(\"saved_model/tyler_apple_model.mlmodel\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sWDshDS5hQD1"
      },
      "source": [
        "!ls saved_model/"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WUuTfomugye4"
      },
      "source": [
        "files.download('saved_model/tyler_apple_model.mlmodel')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZRkJjAZXo05a"
      },
      "source": [
        "%tensorboard --logdir logs/fit"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}