CS229_mobilenet_retrain.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of mobilenet_retrain.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "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/PhilipPfeffer/haptic_vest/blob/main/CS229_mobilenet_retrain.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cjM-Gs5mC1M_"
      },
      "source": [
        "# Retrained MobileNet\n",
        "MobileNet has been trained for many applications, including image segmentation, bounding box detection and ImageNet classification. In this notebook, we retrain the smallest version of MobileNet to classify whether an image in our dataset contains a 'person', a 'car' or 'neither."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eVMp0mRPCuRk"
      },
      "source": [
        "# Acknowledgements\n",
        "This was inspired by https://www.tensorflow.org/tutorials/images/transfer_learning (Accessed 29 Oct 2020)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZR2LqUJTDXXX"
      },
      "source": [
        "# Code"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pdHj6yDLDbUJ"
      },
      "source": [
        "## Imports\n",
        "General Setup; GPU setup is under Training."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iid8Rzd0DaMt"
      },
      "source": [
        "from numpy import loadtxt\n",
        "import numpy as np\n",
        "import os\n",
        "import shutil\n",
        "import math\n",
        "import zipfile\n",
        "import matplotlib.pyplot as plt\n",
        "import itertools\n",
        "\n",
        "import keras\n",
        "from keras import layers\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing import image_dataset_from_directory\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.optimizers import Optimizer\n",
        "from keras import backend as K\n",
        "\n",
        "import sklearn \n",
        "from sklearn import metrics\n",
        "import tensorflow_addons as tfa\n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gA0a-Hx7NF2Z"
      },
      "source": [
        "# Unzip Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TgJlst6BNK3d"
      },
      "source": [
        "dataset_name = 'dataset_binary'\n",
        "zip_file_path = '/content/' + dataset_name + '.zip'\n",
        "unzipped_dir = '/content'\n",
        "dataset_X = '/content/' + dataset_name\n",
        "with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:\n",
        "    zip_ref.extractall(unzipped_dir)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fmMhUtUaSz7-"
      },
      "source": [
        "# GPU Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VRE5Ysf4DwkT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a8544dd3-f8ec-4598-907e-a67dd7e7234a"
      },
      "source": [
        "device_name = tf.test.gpu_device_name()\n",
        "if device_name != '/device:GPU:0':\n",
        "  raise SystemError('GPU device not found')\n",
        "print('Found GPU at: {}'.format(device_name))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found GPU at: /device:GPU:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xzjz28s_WA1m"
      },
      "source": [
        "# Create Dataloader"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tz40P_RcEyPt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d360c3f9-828d-46c5-a49a-f6b58a274562"
      },
      "source": [
        "mobilenet_version1 = False\n",
        "\n",
        "BATCH_SIZE = 32\n",
        "ORIG_IMG_SIZE = (96, 96)\n",
        "ORIG_COLOR_MODE ='grayscale'\n",
        "IMG_SIZE = (128,128) if mobilenet_version1 else ORIG_IMG_SIZE\n",
        "COLOR_MODE = 'rgb'\n",
        "\n",
        "\n",
        "train_dataset = image_dataset_from_directory(dataset_X +'/train/', label_mode='categorical', shuffle=True, batch_size=BATCH_SIZE, image_size=IMG_SIZE, color_mode=COLOR_MODE)\n",
        "dev_dataset = image_dataset_from_directory(dataset_X +'/dev/', label_mode='categorical', shuffle=True, batch_size=BATCH_SIZE, image_size=IMG_SIZE, color_mode=COLOR_MODE)\n",
        "test_dataset = image_dataset_from_directory(dataset_X +'/test/', label_mode='categorical', shuffle=True, batch_size=BATCH_SIZE, image_size=IMG_SIZE, color_mode=COLOR_MODE)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 33915 files belonging to 2 classes.\n",
            "Found 5339 files belonging to 2 classes.\n",
            "Found 2819 files belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XGlQv6WbAibr"
      },
      "source": [
        "def show_example(train_dataset):\n",
        "  for image, _ in train_dataset.take(1):\n",
        "    plt.figure(figsize=(10, 10))\n",
        "    first_image = np.asarray(image[0]).squeeze()\n",
        "    print(first_image.shape)\n",
        "    # for i in range(9):\n",
        "    #   ax = plt.subplot(3, 3, i + 1)\n",
        "      # augmented_image = data_augmentation(tf.expand_dims(first_image, 0))\n",
        "      # plt.imshow(augmented_image[0] / 255)\n",
        "    # print(image)\n",
        "    plt.imshow(first_image / 255)\n",
        "    plt.axis('off')\n",
        "    \n",
        "    break \n",
        "\n",
        "# Run with this line:\n",
        "# show_example(train_dataset)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gMRNuXkQWNM4"
      },
      "source": [
        "AUTOTUNE = tf.data.experimental.AUTOTUNE\n",
        "\n",
        "train_dataset = train_dataset.prefetch(buffer_size=AUTOTUNE)\n",
        "dev_dataset = dev_dataset.prefetch(buffer_size=AUTOTUNE)\n",
        "test_dataset = test_dataset.prefetch(buffer_size=AUTOTUNE)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bSsXMIeDWq98"
      },
      "source": [
        "# MobileNet Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yOt4Y9_SWovP"
      },
      "source": [
        "preprocess_input = tf.keras.applications.mobilenet.preprocess_input"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ug0WhjiwWqMl"
      },
      "source": [
        "# Loading model from https://www.tensorflow.org/lite/guide/hosted_models#image_classification\n",
        "\n",
        "# Create the base model from the pre-trained model MobileNet\n",
        "IMG_SHAPE = IMG_SIZE + (3,)\n",
        "\n",
        "#Same syntax for MobileNetV2 and V1\n",
        "\n",
        "base_model = None\n",
        "if mobilenet_version1:\n",
        "  base_model = tf.keras.applications.MobileNet(input_shape=IMG_SHAPE,\n",
        "                                               alpha=0.25, # just add this\n",
        "                                               include_top=False,\n",
        "                                               weights='imagenet')\n",
        "else:\n",
        "  base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,\n",
        "                                               alpha=0.35,#0.25, # just add this\n",
        "                                               include_top=False,\n",
        "                                               weights='imagenet')"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xvIVDGZk5_YH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "60b7f5b2-6bcb-4e8b-d91d-f3b35af5cb3b"
      },
      "source": [
        "image_batch, label_batch = next(iter(train_dataset))\n",
        "feature_batch = base_model(image_batch)\n",
        "print(feature_batch.shape)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(32, 3, 3, 1280)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gzPOcYly6FoS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3611d694-74f7-4c1b-d550-88288288a6ee"
      },
      "source": [
        "base_model.trainable = False\n",
        "base_model.summary()\n",
        "global_average_layer = tf.keras.layers.GlobalAveragePooling2D()\n",
        "feature_batch_average = global_average_layer(feature_batch)\n",
        "print(feature_batch_average.shape)\n",
        "prediction_layer = tf.keras.layers.Dense(2, activation='softmax')\n",
        "prediction_batch = prediction_layer(feature_batch_average)\n",
        "print(prediction_batch.shape)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"mobilenetv2_0.35_96\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_3 (InputLayer)            [(None, 96, 96, 3)]  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "Conv1_pad (ZeroPadding2D)       (None, 97, 97, 3)    0           input_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "Conv1 (Conv2D)                  (None, 48, 48, 16)   432         Conv1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "bn_Conv1 (BatchNormalization)   (None, 48, 48, 16)   64          Conv1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "Conv1_relu (ReLU)               (None, 48, 48, 16)   0           bn_Conv1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise (Depthw (None, 48, 48, 16)   144         Conv1_relu[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise_BN (Bat (None, 48, 48, 16)   64          expanded_conv_depthwise[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise_relu (R (None, 48, 48, 16)   0           expanded_conv_depthwise_BN[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_project (Conv2D)  (None, 48, 48, 8)    128         expanded_conv_depthwise_relu[0][0\n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_project_BN (Batch (None, 48, 48, 8)    32          expanded_conv_project[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand (Conv2D)         (None, 48, 48, 48)   384         expanded_conv_project_BN[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand_BN (BatchNormali (None, 48, 48, 48)   192         block_1_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand_relu (ReLU)      (None, 48, 48, 48)   0           block_1_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_1_pad (ZeroPadding2D)     (None, 49, 49, 48)   0           block_1_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise (DepthwiseCon (None, 24, 24, 48)   432         block_1_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise_BN (BatchNorm (None, 24, 24, 48)   192         block_1_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise_relu (ReLU)   (None, 24, 24, 48)   0           block_1_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_1_project (Conv2D)        (None, 24, 24, 8)    384         block_1_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_1_project_BN (BatchNormal (None, 24, 24, 8)    32          block_1_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand (Conv2D)         (None, 24, 24, 48)   384         block_1_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand_BN (BatchNormali (None, 24, 24, 48)   192         block_2_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand_relu (ReLU)      (None, 24, 24, 48)   0           block_2_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise (DepthwiseCon (None, 24, 24, 48)   432         block_2_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise_BN (BatchNorm (None, 24, 24, 48)   192         block_2_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise_relu (ReLU)   (None, 24, 24, 48)   0           block_2_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_2_project (Conv2D)        (None, 24, 24, 8)    384         block_2_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_2_project_BN (BatchNormal (None, 24, 24, 8)    32          block_2_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_2_add (Add)               (None, 24, 24, 8)    0           block_1_project_BN[0][0]         \n",
            "                                                                 block_2_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand (Conv2D)         (None, 24, 24, 48)   384         block_2_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand_BN (BatchNormali (None, 24, 24, 48)   192         block_3_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand_relu (ReLU)      (None, 24, 24, 48)   0           block_3_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_3_pad (ZeroPadding2D)     (None, 25, 25, 48)   0           block_3_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise (DepthwiseCon (None, 12, 12, 48)   432         block_3_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise_BN (BatchNorm (None, 12, 12, 48)   192         block_3_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise_relu (ReLU)   (None, 12, 12, 48)   0           block_3_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_3_project (Conv2D)        (None, 12, 12, 16)   768         block_3_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_3_project_BN (BatchNormal (None, 12, 12, 16)   64          block_3_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand (Conv2D)         (None, 12, 12, 96)   1536        block_3_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand_BN (BatchNormali (None, 12, 12, 96)   384         block_4_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand_relu (ReLU)      (None, 12, 12, 96)   0           block_4_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise (DepthwiseCon (None, 12, 12, 96)   864         block_4_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise_BN (BatchNorm (None, 12, 12, 96)   384         block_4_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise_relu (ReLU)   (None, 12, 12, 96)   0           block_4_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_4_project (Conv2D)        (None, 12, 12, 16)   1536        block_4_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_4_project_BN (BatchNormal (None, 12, 12, 16)   64          block_4_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_4_add (Add)               (None, 12, 12, 16)   0           block_3_project_BN[0][0]         \n",
            "                                                                 block_4_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand (Conv2D)         (None, 12, 12, 96)   1536        block_4_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand_BN (BatchNormali (None, 12, 12, 96)   384         block_5_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand_relu (ReLU)      (None, 12, 12, 96)   0           block_5_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise (DepthwiseCon (None, 12, 12, 96)   864         block_5_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise_BN (BatchNorm (None, 12, 12, 96)   384         block_5_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise_relu (ReLU)   (None, 12, 12, 96)   0           block_5_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_5_project (Conv2D)        (None, 12, 12, 16)   1536        block_5_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_5_project_BN (BatchNormal (None, 12, 12, 16)   64          block_5_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_5_add (Add)               (None, 12, 12, 16)   0           block_4_add[0][0]                \n",
            "                                                                 block_5_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand (Conv2D)         (None, 12, 12, 96)   1536        block_5_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand_BN (BatchNormali (None, 12, 12, 96)   384         block_6_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand_relu (ReLU)      (None, 12, 12, 96)   0           block_6_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_6_pad (ZeroPadding2D)     (None, 13, 13, 96)   0           block_6_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise (DepthwiseCon (None, 6, 6, 96)     864         block_6_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise_BN (BatchNorm (None, 6, 6, 96)     384         block_6_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise_relu (ReLU)   (None, 6, 6, 96)     0           block_6_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_6_project (Conv2D)        (None, 6, 6, 24)     2304        block_6_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_6_project_BN (BatchNormal (None, 6, 6, 24)     96          block_6_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand (Conv2D)         (None, 6, 6, 144)    3456        block_6_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand_BN (BatchNormali (None, 6, 6, 144)    576         block_7_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand_relu (ReLU)      (None, 6, 6, 144)    0           block_7_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise (DepthwiseCon (None, 6, 6, 144)    1296        block_7_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise_BN (BatchNorm (None, 6, 6, 144)    576         block_7_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise_relu (ReLU)   (None, 6, 6, 144)    0           block_7_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_7_project (Conv2D)        (None, 6, 6, 24)     3456        block_7_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_7_project_BN (BatchNormal (None, 6, 6, 24)     96          block_7_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_add (Add)               (None, 6, 6, 24)     0           block_6_project_BN[0][0]         \n",
            "                                                                 block_7_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand (Conv2D)         (None, 6, 6, 144)    3456        block_7_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand_BN (BatchNormali (None, 6, 6, 144)    576         block_8_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand_relu (ReLU)      (None, 6, 6, 144)    0           block_8_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise (DepthwiseCon (None, 6, 6, 144)    1296        block_8_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise_BN (BatchNorm (None, 6, 6, 144)    576         block_8_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise_relu (ReLU)   (None, 6, 6, 144)    0           block_8_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_8_project (Conv2D)        (None, 6, 6, 24)     3456        block_8_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_8_project_BN (BatchNormal (None, 6, 6, 24)     96          block_8_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_8_add (Add)               (None, 6, 6, 24)     0           block_7_add[0][0]                \n",
            "                                                                 block_8_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand (Conv2D)         (None, 6, 6, 144)    3456        block_8_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand_BN (BatchNormali (None, 6, 6, 144)    576         block_9_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand_relu (ReLU)      (None, 6, 6, 144)    0           block_9_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise (DepthwiseCon (None, 6, 6, 144)    1296        block_9_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise_BN (BatchNorm (None, 6, 6, 144)    576         block_9_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise_relu (ReLU)   (None, 6, 6, 144)    0           block_9_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_9_project (Conv2D)        (None, 6, 6, 24)     3456        block_9_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_9_project_BN (BatchNormal (None, 6, 6, 24)     96          block_9_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_9_add (Add)               (None, 6, 6, 24)     0           block_8_add[0][0]                \n",
            "                                                                 block_9_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand (Conv2D)        (None, 6, 6, 144)    3456        block_9_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand_BN (BatchNormal (None, 6, 6, 144)    576         block_10_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand_relu (ReLU)     (None, 6, 6, 144)    0           block_10_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise (DepthwiseCo (None, 6, 6, 144)    1296        block_10_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise_BN (BatchNor (None, 6, 6, 144)    576         block_10_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise_relu (ReLU)  (None, 6, 6, 144)    0           block_10_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_10_project (Conv2D)       (None, 6, 6, 32)     4608        block_10_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_10_project_BN (BatchNorma (None, 6, 6, 32)     128         block_10_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand (Conv2D)        (None, 6, 6, 192)    6144        block_10_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand_BN (BatchNormal (None, 6, 6, 192)    768         block_11_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand_relu (ReLU)     (None, 6, 6, 192)    0           block_11_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise (DepthwiseCo (None, 6, 6, 192)    1728        block_11_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise_BN (BatchNor (None, 6, 6, 192)    768         block_11_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise_relu (ReLU)  (None, 6, 6, 192)    0           block_11_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_11_project (Conv2D)       (None, 6, 6, 32)     6144        block_11_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_11_project_BN (BatchNorma (None, 6, 6, 32)     128         block_11_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_add (Add)              (None, 6, 6, 32)     0           block_10_project_BN[0][0]        \n",
            "                                                                 block_11_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand (Conv2D)        (None, 6, 6, 192)    6144        block_11_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand_BN (BatchNormal (None, 6, 6, 192)    768         block_12_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand_relu (ReLU)     (None, 6, 6, 192)    0           block_12_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise (DepthwiseCo (None, 6, 6, 192)    1728        block_12_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise_BN (BatchNor (None, 6, 6, 192)    768         block_12_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise_relu (ReLU)  (None, 6, 6, 192)    0           block_12_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_12_project (Conv2D)       (None, 6, 6, 32)     6144        block_12_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_12_project_BN (BatchNorma (None, 6, 6, 32)     128         block_12_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_12_add (Add)              (None, 6, 6, 32)     0           block_11_add[0][0]               \n",
            "                                                                 block_12_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand (Conv2D)        (None, 6, 6, 192)    6144        block_12_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand_BN (BatchNormal (None, 6, 6, 192)    768         block_13_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand_relu (ReLU)     (None, 6, 6, 192)    0           block_13_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_13_pad (ZeroPadding2D)    (None, 7, 7, 192)    0           block_13_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise (DepthwiseCo (None, 3, 3, 192)    1728        block_13_pad[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise_BN (BatchNor (None, 3, 3, 192)    768         block_13_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise_relu (ReLU)  (None, 3, 3, 192)    0           block_13_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_13_project (Conv2D)       (None, 3, 3, 56)     10752       block_13_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_13_project_BN (BatchNorma (None, 3, 3, 56)     224         block_13_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand (Conv2D)        (None, 3, 3, 336)    18816       block_13_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand_BN (BatchNormal (None, 3, 3, 336)    1344        block_14_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand_relu (ReLU)     (None, 3, 3, 336)    0           block_14_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise (DepthwiseCo (None, 3, 3, 336)    3024        block_14_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise_BN (BatchNor (None, 3, 3, 336)    1344        block_14_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise_relu (ReLU)  (None, 3, 3, 336)    0           block_14_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_14_project (Conv2D)       (None, 3, 3, 56)     18816       block_14_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_14_project_BN (BatchNorma (None, 3, 3, 56)     224         block_14_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_14_add (Add)              (None, 3, 3, 56)     0           block_13_project_BN[0][0]        \n",
            "                                                                 block_14_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand (Conv2D)        (None, 3, 3, 336)    18816       block_14_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand_BN (BatchNormal (None, 3, 3, 336)    1344        block_15_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand_relu (ReLU)     (None, 3, 3, 336)    0           block_15_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise (DepthwiseCo (None, 3, 3, 336)    3024        block_15_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise_BN (BatchNor (None, 3, 3, 336)    1344        block_15_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise_relu (ReLU)  (None, 3, 3, 336)    0           block_15_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_15_project (Conv2D)       (None, 3, 3, 56)     18816       block_15_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_15_project_BN (BatchNorma (None, 3, 3, 56)     224         block_15_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_15_add (Add)              (None, 3, 3, 56)     0           block_14_add[0][0]               \n",
            "                                                                 block_15_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand (Conv2D)        (None, 3, 3, 336)    18816       block_15_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand_BN (BatchNormal (None, 3, 3, 336)    1344        block_16_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand_relu (ReLU)     (None, 3, 3, 336)    0           block_16_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise (DepthwiseCo (None, 3, 3, 336)    3024        block_16_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise_BN (BatchNor (None, 3, 3, 336)    1344        block_16_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise_relu (ReLU)  (None, 3, 3, 336)    0           block_16_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_16_project (Conv2D)       (None, 3, 3, 112)    37632       block_16_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_16_project_BN (BatchNorma (None, 3, 3, 112)    448         block_16_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "Conv_1 (Conv2D)                 (None, 3, 3, 1280)   143360      block_16_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "Conv_1_bn (BatchNormalization)  (None, 3, 3, 1280)   5120        Conv_1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "out_relu (ReLU)                 (None, 3, 3, 1280)   0           Conv_1_bn[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 410,208\n",
            "Trainable params: 0\n",
            "Non-trainable params: 410,208\n",
            "__________________________________________________________________________________________________\n",
            "(32, 1280)\n",
            "(32, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x79cxMdqWXVz"
      },
      "source": [
        "## Data Augmentation\n",
        "Add data augmentation to all images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5yp3jXrNga_4"
      },
      "source": [
        "use_data_augmentation = True"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FJNMVeyAWekg"
      },
      "source": [
        "data_augmentation = None\n",
        "if use_data_augmentation:\n",
        "  data_augmentation = tf.keras.Sequential([\n",
        "    tf.keras.layers.experimental.preprocessing.RandomFlip('horizontal'),\n",
        "    tf.keras.layers.experimental.preprocessing.RandomRotation(0.1),\n",
        "  ])"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9pQzpq2r6oeV"
      },
      "source": [
        "inputs = tf.keras.Input(shape=IMG_SHAPE)\n",
        "\n",
        "x = None\n",
        "if use_data_augmentation:\n",
        "  x = data_augmentation(inputs)\n",
        "  x = preprocess_input(x)\n",
        "else:\n",
        "  x = preprocess_input(inputs)\n",
        "\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",
        "model = tf.keras.Model(inputs, outputs)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Y6semJ_oLRZ"
      },
      "source": [
        "def f1_score(num_classes):\n",
        "  return tfa.metrics.F1Score(num_classes=num_classes, average=\"weighted\")"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FYESsRISo_bJ"
      },
      "source": [
        "num_classes = 2"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QzO2-KVsT7V2"
      },
      "source": [
        "## Standard Optimizer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pdrda6DI6qOv"
      },
      "source": [
        "# base_learning_rate = 0.0005\n",
        "# Compile model with accuracy and weighted F1 Score\n",
        "# model.compile(loss='categorical_crossentropy', optimizer=tf.keras.optimizers.Adam(lr=base_learning_rate), metrics=['accuracy',  f1_score(num_classes)])"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VO1i8mf_CTnx"
      },
      "source": [
        "## Custom Optimizer\n",
        "Implements model with power of two weights."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ZurY0rHCfHP"
      },
      "source": [
        "def closestpow2(num):\n",
        "  return K.cast(K.sign(num)*K.pow(2.0, K.round(K.log(K.abs(num))/K.log(2.0))), dtype='float32')\n",
        "\n",
        "class Pow2Update(Optimizer):\n",
        "  def __init__(self, learning_rate=0.001, name=\"Pow2Update\", **kwargs):\n",
        "    super(Pow2Update, self).__init__(name, **kwargs)\n",
        "    # self.lr = lr\n",
        "    self._set_hyper(\"learning_rate\", kwargs.get(\"lr\", learning_rate))\n",
        "\n",
        "  @tf.function\n",
        "  def _resource_apply_dense(self, grad, var):\n",
        "    var_device, var_dtype = var.device, var.dtype.base_dtype\n",
        "    lr_t = self._decayed_lr(var_dtype)\n",
        "\n",
        "    var.assign(closestpow2(var - lr_t*grad))\n",
        "  \n",
        "  def _resource_apply_sparse(self, grad, var):\n",
        "        raise NotImplementedError\n",
        "\n",
        "  def get_config(self):\n",
        "      base_config = super().get_config()\n",
        "      return {\n",
        "          **base_config,\n",
        "          \"learning_rate\": self._serialize_hyperparameter(\"learning_rate\"),\n",
        "          # \"pow2\": closestpow2(-0.51),\n",
        "      }\n"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XX7R-1yCPzzE",
        "outputId": "3e3d0445-d7c9-40a0-f884-ac416c827069"
      },
      "source": [
        "po = Pow2Update()\n",
        "\n",
        "po.get_config()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'learning_rate': 0.001, 'name': 'Pow2Update'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YdIWpaGgC_U7"
      },
      "source": [
        "model.compile(loss='categorical_crossentropy', optimizer=Pow2Update(learning_rate=0.05), metrics=['accuracy', f1_score(num_classes)])"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uYhIw-po6sNJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5b3f8458-81e6-4793-98fa-ae16e4f12739"
      },
      "source": [
        "model.summary()\n",
        "# len(model.trainable_variables)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_4 (InputLayer)         [(None, 96, 96, 3)]       0         \n",
            "_________________________________________________________________\n",
            "sequential_1 (Sequential)    (None, 96, 96, 3)         0         \n",
            "_________________________________________________________________\n",
            "tf_op_layer_RealDiv_1 (Tenso [(None, 96, 96, 3)]       0         \n",
            "_________________________________________________________________\n",
            "tf_op_layer_Sub_1 (TensorFlo [(None, 96, 96, 3)]       0         \n",
            "_________________________________________________________________\n",
            "mobilenetv2_0.35_96 (Functio (None, 3, 3, 1280)        410208    \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d_1 ( (None, 1280)              0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 1280)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 2)                 2562      \n",
            "=================================================================\n",
            "Total params: 412,770\n",
            "Trainable params: 2,562\n",
            "Non-trainable params: 410,208\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5u2_j4_36wIK"
      },
      "source": [
        "# Training (Stage 1: Frozen)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "os1atSoV6xBe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "15555964-5e90-49ad-fa82-8d55d269beca"
      },
      "source": [
        "initial_epochs = 20\n",
        "\n",
        "loss0, accuracy0, f1_score0 = model.evaluate(dev_dataset)\n",
        "print(\"initial loss: {:.2f}\".format(loss0))\n",
        "print(\"initial accuracy: {:.2f}\".format(accuracy0))\n",
        "print(\"initial f1_score: {:.2f}\".format(f1_score0))"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "167/167 [==============================] - 3s 18ms/step - loss: 0.9913 - accuracy: 0.4746 - f1_score: 0.4317\n",
            "initial loss: 0.99\n",
            "initial accuracy: 0.47\n",
            "initial f1_score: 0.43\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L9xWWRpkh-q-"
      },
      "source": [
        "# Configure eary stopping\n",
        "early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=3)"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qFEyDXXu62Co",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b93854c9-1b86-4a75-eaba-87ae65f39483"
      },
      "source": [
        "history = model.fit(train_dataset,\n",
        "                epochs=initial_epochs,\n",
        "                validation_data=dev_dataset)\n",
        "                # callbacks=[early_stopping])"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "1060/1060 [==============================] - 24s 22ms/step - loss: 0.1183 - accuracy: 0.9558 - f1_score: 0.9557 - val_loss: 0.0650 - val_accuracy: 0.9792 - val_f1_score: 0.9792\n",
            "Epoch 2/20\n",
            "1060/1060 [==============================] - 25s 24ms/step - loss: 0.1074 - accuracy: 0.9604 - f1_score: 0.9604 - val_loss: 0.0612 - val_accuracy: 0.9798 - val_f1_score: 0.9798\n",
            "Epoch 3/20\n",
            "1060/1060 [==============================] - 25s 23ms/step - loss: 0.1001 - accuracy: 0.9641 - f1_score: 0.9641 - val_loss: 0.0551 - val_accuracy: 0.9824 - val_f1_score: 0.9824\n",
            "Epoch 4/20\n",
            "1060/1060 [==============================] - 25s 24ms/step - loss: 0.1002 - accuracy: 0.9627 - f1_score: 0.9627 - val_loss: 0.0566 - val_accuracy: 0.9811 - val_f1_score: 0.9811\n",
            "Epoch 5/20\n",
            "1060/1060 [==============================] - 26s 25ms/step - loss: 0.0970 - accuracy: 0.9652 - f1_score: 0.9651 - val_loss: 0.0565 - val_accuracy: 0.9822 - val_f1_score: 0.9822\n",
            "Epoch 6/20\n",
            "1060/1060 [==============================] - 28s 26ms/step - loss: 0.0969 - accuracy: 0.9640 - f1_score: 0.9640 - val_loss: 0.0628 - val_accuracy: 0.9801 - val_f1_score: 0.9801\n",
            "Epoch 7/20\n",
            "1060/1060 [==============================] - 27s 26ms/step - loss: 0.0976 - accuracy: 0.9646 - f1_score: 0.9645 - val_loss: 0.0616 - val_accuracy: 0.9796 - val_f1_score: 0.9796\n",
            "Epoch 8/20\n",
            "1060/1060 [==============================] - 27s 25ms/step - loss: 0.1019 - accuracy: 0.9621 - f1_score: 0.9621 - val_loss: 0.0578 - val_accuracy: 0.9816 - val_f1_score: 0.9816\n",
            "Epoch 9/20\n",
            "1060/1060 [==============================] - 27s 25ms/step - loss: 0.0974 - accuracy: 0.9646 - f1_score: 0.9646 - val_loss: 0.0597 - val_accuracy: 0.9807 - val_f1_score: 0.9807\n",
            "Epoch 10/20\n",
            "1060/1060 [==============================] - 26s 25ms/step - loss: 0.0991 - accuracy: 0.9639 - f1_score: 0.9639 - val_loss: 0.0563 - val_accuracy: 0.9803 - val_f1_score: 0.9803\n",
            "Epoch 11/20\n",
            "1060/1060 [==============================] - 26s 25ms/step - loss: 0.0983 - accuracy: 0.9633 - f1_score: 0.9632 - val_loss: 0.0529 - val_accuracy: 0.9828 - val_f1_score: 0.9828\n",
            "Epoch 12/20\n",
            "1060/1060 [==============================] - 27s 25ms/step - loss: 0.1001 - accuracy: 0.9633 - f1_score: 0.9633 - val_loss: 0.0536 - val_accuracy: 0.9824 - val_f1_score: 0.9824\n",
            "Epoch 13/20\n",
            "1060/1060 [==============================] - 27s 25ms/step - loss: 0.0951 - accuracy: 0.9653 - f1_score: 0.9652 - val_loss: 0.0540 - val_accuracy: 0.9815 - val_f1_score: 0.9815\n",
            "Epoch 14/20\n",
            "1060/1060 [==============================] - 27s 25ms/step - loss: 0.0957 - accuracy: 0.9651 - f1_score: 0.9651 - val_loss: 0.0506 - val_accuracy: 0.9828 - val_f1_score: 0.9828\n",
            "Epoch 15/20\n",
            "1060/1060 [==============================] - 26s 25ms/step - loss: 0.0955 - accuracy: 0.9649 - f1_score: 0.9649 - val_loss: 0.0562 - val_accuracy: 0.9822 - val_f1_score: 0.9822\n",
            "Epoch 16/20\n",
            "1060/1060 [==============================] - 26s 25ms/step - loss: 0.0956 - accuracy: 0.9650 - f1_score: 0.9650 - val_loss: 0.0507 - val_accuracy: 0.9835 - val_f1_score: 0.9835\n",
            "Epoch 17/20\n",
            "1060/1060 [==============================] - 26s 25ms/step - loss: 0.0977 - accuracy: 0.9649 - f1_score: 0.9649 - val_loss: 0.0593 - val_accuracy: 0.9809 - val_f1_score: 0.9809\n",
            "Epoch 18/20\n",
            "1060/1060 [==============================] - 26s 24ms/step - loss: 0.0976 - accuracy: 0.9646 - f1_score: 0.9645 - val_loss: 0.0594 - val_accuracy: 0.9807 - val_f1_score: 0.9807\n",
            "Epoch 19/20\n",
            "1060/1060 [==============================] - 26s 24ms/step - loss: 0.1006 - accuracy: 0.9631 - f1_score: 0.9631 - val_loss: 0.0533 - val_accuracy: 0.9820 - val_f1_score: 0.9820\n",
            "Epoch 20/20\n",
            "1060/1060 [==============================] - 26s 24ms/step - loss: 0.0962 - accuracy: 0.9645 - f1_score: 0.9645 - val_loss: 0.0551 - val_accuracy: 0.9816 - val_f1_score: 0.9816\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wx2hUUeykNWR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ffcbfaa1-4187-4d7c-b4a4-1fd6de6195d5"
      },
      "source": [
        "model.save('/content/binary_mobile_model_stage1')\n",
        "\n",
        "save_to_drive=True\n",
        "if save_to_drive:\n",
        "  model.save('/content/drive/My Drive/Haptic Vest/binary_mobile_model_stage1')"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: /content/binary_mobile_model_stage1/assets\n",
            "INFO:tensorflow:Assets written to: /content/drive/My Drive/Haptic Vest/binary_mobile_model_stage1/assets\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IBQujW8n63_m"
      },
      "source": [
        "# Learning Plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a84Nsi-d65VL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "outputId": "241a5911-6ef4-4113-deb4-d5f09920f87e"
      },
      "source": [
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "\n",
        "f1_score_hist = history.history['f1_score']\n",
        "val_f1_score = history.history['val_f1_score']\n",
        "\n",
        "plt.figure(figsize=(12, 12))\n",
        "plt.subplot(3, 1, 1)\n",
        "plt.plot(acc, label='Training Accuracy')\n",
        "plt.plot(val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([min(plt.ylim()),1.0])\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(3, 1, 2)\n",
        "plt.plot(loss, label='Training Loss')\n",
        "plt.plot(val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.ylabel('Cross Entropy')\n",
        "plt.ylim([0.2,0.])\n",
        "plt.title('Training and Validation Loss')\n",
        "\n",
        "plt.subplot(3, 1, 3)\n",
        "plt.plot(f1_score_hist, label='Training Weighted F1 Score')\n",
        "plt.plot(val_f1_score, label='Validation Weighted F1 Score')\n",
        "plt.legend(loc='lower right')\n",
        "plt.ylabel('Weighted F1 Score')\n",
        "plt.ylim([0.9,1.0])\n",
        "plt.title('Training and Validation Weighted F1 Score')\n",
        "plt.xlabel('epoch')\n",
        "plt.show()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IV5FbFvW69_V"
      },
      "source": [
        "From GOOGLE\n",
        "Note: If you are wondering why the validation metrics are clearly better than the training metrics, the main factor is because layers like tf.keras.layers.BatchNormalization and tf.keras.layers.Dropout affect accuracy during training. They are turned off when calculating validation loss.\n",
        "\n",
        "To a lesser extent, it is also because training metrics report the average for an epoch, while validation metrics are evaluated after the epoch, so validation metrics see a model that has trained slightly longer."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z4vG-d57ZTXK"
      },
      "source": [
        "At this point, only the last two layers have rounded weights"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VpqwIaWQLNhy"
      },
      "source": [
        "# Training (Stage 2: Unfrozen)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kqc4XQrm6_7x"
      },
      "source": [
        "base_model.trainable = True"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PUTdfEipLTE8"
      },
      "source": [
        "# # Let's take a look to see how many layers are in the base model\n",
        "# print(\"Number of layers in the base model: \", len(base_model.layers))\n",
        "\n",
        "# # Fine-tune from this layer onwards\n",
        "# fine_tune_at = 100\n",
        "\n",
        "# # Freeze all the layers before the `fine_tune_at` layer\n",
        "# for layer in base_model.layers[:fine_tune_at]:\n",
        "#   layer.trainable =  False"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zKLJLvvZaEax"
      },
      "source": [
        "loaded_model = keras.models.load_model('/content/binary_mobile_model_stage1', custom_objects={'Pow2Update':Pow2Update(), 'F1Score': tfa.metrics.F1Score})"
      ],
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EmMh1UHkbJ7d"
      },
      "source": [
        "model.set_weights(loaded_model.get_weights())"
      ],
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SgmESZNMbE_d",
        "outputId": "7b7fb5ab-6692-4b99-c056-fddc7b97662c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model.get_weights()[-10][0]"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.0033118022"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Zh591LNLYWv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ae5bc324-48f5-4995-af68-da832f5f7c58"
      },
      "source": [
        "model.compile(loss='categorical_crossentropy', \n",
        "              optimizer=Pow2Update(learning_rate=0.01),\n",
        "              metrics=['accuracy', f1_score(num_classes)])\n",
        "# model.compile(loss='categorical_crossentropy', optimizer=tf.keras.optimizers.Adam(lr=base_learning_rate), metrics=['accuracy',  f1_score(num_classes)])\n",
        "\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_4 (InputLayer)         [(None, 96, 96, 3)]       0         \n",
            "_________________________________________________________________\n",
            "sequential_1 (Sequential)    (None, 96, 96, 3)         0         \n",
            "_________________________________________________________________\n",
            "tf_op_layer_RealDiv_1 (Tenso [(None, 96, 96, 3)]       0         \n",
            "_________________________________________________________________\n",
            "tf_op_layer_Sub_1 (TensorFlo [(None, 96, 96, 3)]       0         \n",
            "_________________________________________________________________\n",
            "mobilenetv2_0.35_96 (Functio (None, 3, 3, 1280)        410208    \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d_1 ( (None, 1280)              0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 1280)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 2)                 2562      \n",
            "=================================================================\n",
            "Total params: 412,770\n",
            "Trainable params: 398,690\n",
            "Non-trainable params: 14,080\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "il0A-Q8dLbGH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed1a79bb-5ed0-48e5-a7f4-44b0fc1a4c96"
      },
      "source": [
        "len(model.trainable_variables)"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "158"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "952qmYxROoOw"
      },
      "source": [
        "### Continue training the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xk_UpGR5LlFM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "outputId": "c37a3f84-4994-44c8-c28c-f0ffa4c9fc82"
      },
      "source": [
        "fine_tune_epochs = 10\n",
        "total_epochs =  initial_epochs + fine_tune_epochs\n",
        "\n",
        "history_fine = model.fit(train_dataset,\n",
        "                         epochs=total_epochs,\n",
        "                         initial_epoch=history.epoch[-1],\n",
        "                         validation_data=dev_dataset)\n",
        "                        #  callbacks=[early_stopping])"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 20/30\n",
            " 178/1060 [====>.........................] - ETA: 38s - loss: 8.5226 - accuracy: 0.5342 - f1_score: 0.5341"
          ],
          "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-107-46a49b01877c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m                          \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtotal_epochs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m                          \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\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----> 7\u001b[0;31m                          validation_data=dev_dataset)\n\u001b[0m\u001b[1;32m      8\u001b[0m                         \u001b[0;31m#  callbacks=[early_stopping])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\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   1101\u001b[0m               \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtmp_logs\u001b[0m  \u001b[0;31m# No error, now safe to assign to logs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1102\u001b[0m               \u001b[0mend_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep_increment\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1103\u001b[0;31m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mend_step\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\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   1104\u001b[0m         \u001b[0mepoch_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/callbacks.py\u001b[0m in \u001b[0;36mon_train_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m    438\u001b[0m     \"\"\"\n\u001b[1;32m    439\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_call_train_batch_hooks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mModeKeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'end'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlogs\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    441\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    442\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mon_test_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\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",
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            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/callbacks.py\u001b[0m in \u001b[0;36mon_train_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m    959\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    960\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mon_train_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\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--> 961\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_update_progbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\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    962\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    963\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mon_test_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\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/keras/callbacks.py\u001b[0m in \u001b[0;36m_batch_update_progbar\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m   1014\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1015\u001b[0m       \u001b[0;31m# Only block async when verbose = 1.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1016\u001b[0;31m       \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numpy_or_python_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogs\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   1017\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinalize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1018\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/utils/tf_utils.py\u001b[0m in \u001b[0;36mto_numpy_or_python_type\u001b[0;34m(tensors)\u001b[0m\n\u001b[1;32m    535\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m  \u001b[0;31m# Don't turn ragged or sparse tensors to NumPy.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    536\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 537\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mnest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_to_single_numpy_or_python_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensors\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    538\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    539\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/nest.py\u001b[0m in \u001b[0;36mmap_structure\u001b[0;34m(func, *structure, **kwargs)\u001b[0m\n\u001b[1;32m    633\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    634\u001b[0m   return pack_sequence_as(\n\u001b[0;32m--> 635\u001b[0;31m       \u001b[0mstructure\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mentries\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    636\u001b[0m       expand_composites=expand_composites)\n\u001b[1;32m    637\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/nest.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    633\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    634\u001b[0m   return pack_sequence_as(\n\u001b[0;32m--> 635\u001b[0;31m       \u001b[0mstructure\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mentries\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    636\u001b[0m       expand_composites=expand_composites)\n\u001b[1;32m    637\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/utils/tf_utils.py\u001b[0m in \u001b[0;36m_to_single_numpy_or_python_type\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m    531\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_to_single_numpy_or_python_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\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    532\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\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--> 533\u001b[0;31m       \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    534\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    535\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m  \u001b[0;31m# Don't turn ragged or sparse tensors to NumPy.\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/framework/ops.py\u001b[0m in \u001b[0;36mnumpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1061\u001b[0m     \"\"\"\n\u001b[1;32m   1062\u001b[0m     \u001b[0;31m# TODO(slebedev): Consider avoiding a copy for non-CPU or remote tensors.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1063\u001b[0;31m     \u001b[0mmaybe_arr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numpy\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[0m\u001b[1;32m   1064\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_arr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1065\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m_numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1027\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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   1028\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1029\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numpy_internal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1030\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;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1031\u001b[0m       \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\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;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KcU6PiVjm-ep",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ccfa1710-eb0a-421f-c7d2-4f51389730a0"
      },
      "source": [
        "model.save('/content/three_model_stage2_v2_aug')\n",
        "\n",
        "save_to_drive=True\n",
        "if save_to_drive:\n",
        "  model.save('/content/drive/My Drive/Haptic Vest/three_model_stage2_v2_aug')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: /content/three_model_stage2_v2_aug/assets\n",
            "INFO:tensorflow:Assets written to: /content/drive/My Drive/Haptic Vest/three_model_stage2_v2_aug/assets\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ywzj-BgHOtDN"
      },
      "source": [
        "acc += history_fine.history['accuracy']\n",
        "val_acc += history_fine.history['val_accuracy']\n",
        "\n",
        "loss += history_fine.history['loss']\n",
        "val_loss += history_fine.history['val_loss']\n",
        "\n",
        "f1_score_hist += history_fine.history['f1_score']\n",
        "val_f1_score += history_fine.history['val_f1_score']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fA7R1ZBTO_oL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "outputId": "113a6746-afbb-46dd-9aa6-d948877f0067"
      },
      "source": [
        "end_stage1 = 10\n",
        "end_stage2 = 14\n",
        "plt.figure(figsize=(12, 12))\n",
        "plt.subplot(3, 1, 1)\n",
        "plt.plot(acc, label='Training Accuracy')\n",
        "plt.plot(val_acc, label='Validation Accuracy')\n",
        "plt.ylim([0.7, 1])\n",
        "plt.plot([end_stage1-1,end_stage1-1],\n",
        "          plt.ylim(), label='Start Fine Tuning')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(3, 1, 2)\n",
        "plt.plot(loss, label='Training Loss')\n",
        "plt.plot(val_loss, label='Validation Loss')\n",
        "plt.ylim([0, 1.0])\n",
        "plt.plot([end_stage1-1,end_stage1-1],\n",
        "         plt.ylim(), label='Start Fine Tuning')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Training and Validation Loss')\n",
        "\n",
        "plt.subplot(3, 1, 3)\n",
        "plt.plot(f1_score_hist, label='Training Weighted F1 Score')\n",
        "plt.plot(val_f1_score, label='Validation Weighted F1 Score')\n",
        "plt.legend(loc='lower right')\n",
        "plt.ylabel('Weighted F1 Score')\n",
        "plt.ylim([0.5,1.0])\n",
        "plt.plot([end_stage1-1,end_stage1-1],\n",
        "         plt.ylim(), label='Start Fine Tuning')\n",
        "plt.title('Training and Validation Weighted F1 Score')\n",
        "plt.xlabel('epoch')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mRmJm7W9hzaJ"
      },
      "source": [
        "# Round base weights"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wsFJE-Avhwil"
      },
      "source": [
        "loaded_model = keras.models.load_model('/content/binary_mobile_model_stage1', custom_objects={'Pow2Update':Pow2Update(), 'F1Score': tfa.metrics.F1Score})"
      ],
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KiBhmPrbh2KO"
      },
      "source": [
        "def closestpow2fixedptnumpy(num):\n",
        "  return np.sign(num)*np.array(np.power(2, np.round(np.log(np.absolute(num))/np.log(2))), dtype=float)\n",
        "\n",
        "def round2(weights):\n",
        "  return np.round_(weights,2)\n",
        "\n",
        "def modify_weights(weights, transform):\n",
        "  new_weights = []\n",
        "  for layer_num, layer_weights in enumerate(weights):\n",
        "    if layer_num < len(weights):\n",
        "      new_weights.append(transform(layer_weights))\n",
        "      # print(f\"=============== Layer {layer_num} ===============\\n\")\n",
        "      # print(layer_weights)\n",
        "      # print(new_weights[layer_num])\n",
        "      # print('\\n')\n",
        "  return new_weights\n",
        "\n",
        "transform = round2\n",
        "old_weights = loaded_model.get_weights() # returs a numpy list of weights\n",
        "new_weights = modify_weights(old_weights, transform)"
      ],
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zpQbLfysiWNU"
      },
      "source": [
        "loaded_model.set_weights(new_weights)"
      ],
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fP88Of6lidpt",
        "outputId": "a1290190-3368-4030-b462-0f35e6beaad3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "loss, accuracy, f1_score = loaded_model.evaluate(train_dataset)\n",
        "print('Train accuracy :', accuracy)\n",
        "print('Train F1 Score :', f1_score)\n",
        "\n",
        "loss, accuracy, f1_score = loaded_model.evaluate(dev_dataset)\n",
        "print('Dev accuracy :', accuracy)\n",
        "print('Dev F1 Score :', f1_score)"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1060/1060 [==============================] - 21s 19ms/step - loss: 0.0699 - accuracy: 0.9761 - f1_score: 0.9763\n",
            "Train accuracy : 0.9761462211608887\n",
            "Train F1 Score : 0.9762771129608154\n",
            "167/167 [==============================] - 3s 20ms/step - loss: 0.0663 - accuracy: 0.9779 - f1_score: 0.9779\n",
            "Dev accuracy : 0.9778984785079956\n",
            "Dev F1 Score : 0.9778919816017151\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AZUVHqLsntMv"
      },
      "source": [
        "# Load Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C3y85n3Gnu7F"
      },
      "source": [
        "# load_model_path='/content/three_model_stage1'\n",
        "# load_model_path='/content/three_model_stage2'\n",
        "# three_model_stage1_aug\n",
        "load_model_path = '/content/drive/My Drive/Haptic Vest/binary_mobile_model_stage1'\n",
        "loaded_model = keras.models.load_model(load_model_path, custom_objects={'Pow2Update': Pow2Update(), 'F1Score': tfa.metrics.F1Score})"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sk5bUxgJPB9Y"
      },
      "source": [
        "# Evaluation and prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qTcAfN5AyFgL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50c92914-5e2d-4ed3-9068-82e754d369f1"
      },
      "source": [
        "loss, accuracy, f1_score = loaded_model.evaluate(train_dataset)\n",
        "print('Train accuracy :', accuracy)\n",
        "print('Train F1 Score :', f1_score)\n",
        "\n",
        "loss, accuracy, f1_score = loaded_model.evaluate(dev_dataset)\n",
        "print('Dev accuracy :', accuracy)\n",
        "print('Dev F1 Score :', f1_score)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1060/1060 [==============================] - 20s 19ms/step - loss: 0.0560 - accuracy: 0.9811 - f1_score: 0.9812\n",
            "Train accuracy : 0.9811292886734009\n",
            "Train F1 Score : 0.9812101721763611\n",
            "167/167 [==============================] - 3s 18ms/step - loss: 0.0551 - accuracy: 0.9816 - f1_score: 0.9816\n",
            "Dev accuracy : 0.9816445112228394\n",
            "Dev F1 Score : 0.9816418886184692\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3y8S4TUSw3Ov",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "289d7729-a29f-4a3b-d458-e5c093f8f3d0"
      },
      "source": [
        "loss, accuracy, f1_score = loaded_model.evaluate(test_dataset)\n",
        "print('Test accuracy :', accuracy)\n",
        "print('Test F1 Score :', f1_score)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "89/89 [==============================] - 2s 18ms/step - loss: 0.0454 - accuracy: 0.9855 - f1_score: 0.9855\n",
            "Test accuracy : 0.9854558110237122\n",
            "Test F1 Score : 0.9854537844657898\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NVS4CH1BPFZO"
      },
      "source": [
        "# # Define classes\n",
        "# classes = ['neither', 'person', None , 'car']\n",
        "\n",
        "# #Retrieve a batch of images from the test set\n",
        "# image_batch, label_batch = test_dataset.as_numpy_iterator().next()\n",
        "# predictions = model.predict_on_batch(image_batch).flatten()\n",
        "\n",
        "# # Apply a sigmoid since our model returns logits\n",
        "# predictions = tf.nn.sigmoid(predictions)\n",
        "# predictions = tf.where(predictions < 0.5, 0, 1)\n",
        "\n",
        "# # print('Predictions:\\n', predictions.numpy())\n",
        "# # print('Labels:\\n', label_batch)\n",
        "\n",
        "# plt.figure(figsize=(10, 10))\n",
        "# for i in range(9):\n",
        "#   ax = plt.subplot(3, 3, i + 1)\n",
        "#   plt.imshow(image_batch[i].astype(\"uint8\"))\n",
        "#   plt.title(classes[predictions[i]])\n",
        "#   plt.axis(\"off\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e23YFVezFuw5"
      },
      "source": [
        "# Incorrect Predictions: Confusion Matrix & Highest Losses"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lB-S3pABj_1E",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "44c22f95-933e-4cdd-ac6f-cb68fc1951bf"
      },
      "source": [
        "def to_one_hot(class_id_pre):\n",
        "  class_idx_test = class_id_pre if class_id_pre < 2 else 2\n",
        "  one_hot = [0,0,0]\n",
        "  one_hot[class_idx_test] = 1\n",
        "  return one_hot\n",
        "\n",
        "to_one_hot(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 0, 1]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dHrZD-YtURkC"
      },
      "source": [
        "incorrect_predictions = []"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "86UuDal4rcky",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5960d156-b20e-4ad6-8fbd-61d479b4263b"
      },
      "source": [
        "confusion_matrix = np.zeros((3,3), dtype=int)\n",
        "\n",
        "# for entry in os.scandir(dataset_X):\n",
        "class_ids = [0,1,3]\n",
        "counter = 0\n",
        "bad_counter = 0\n",
        "for class_id in class_ids:\n",
        "  base_test_path = '/content/dataset_three/dev/' + str(class_id) + '/'\n",
        "  for entry in os.scandir(base_test_path):\n",
        "    try:\n",
        "      test_img = keras.preprocessing.image.load_img(entry.path, target_size=IMG_SIZE)\n",
        "      img_array = keras.preprocessing.image.img_to_array(test_img)\n",
        "      img_array = tf.expand_dims(img_array, 0)  # Create batch axis\n",
        "      predictions = loaded_model.predict(img_array)\n",
        "      score = predictions[0]\n",
        "      class_pred = class_ids[np.argmax(score)]\n",
        "      # print(f\"Actual | Prediction: {class_id} | {class_pred}\")\n",
        "      class_id_idx = class_id if class_id < 2 else 2\n",
        "      class_pred_idx = class_pred if class_pred < 2 else 2\n",
        "      if class_id != class_pred:\n",
        "        # print(f\"WRONG: {os.path.basename(entry)}.\\nActual/Pred: {class_id} | {class_pred}\")\n",
        "        loss = tf.keras.losses.categorical_crossentropy(to_one_hot(class_id), score)\n",
        "        incorrect_predictions.append((float(loss), entry.path, class_id, class_pred))\n",
        "      confusion_matrix[class_id_idx][class_pred_idx] += 1\n",
        "    except:\n",
        "      print(f'BAD FILE: {entry.path}')\n",
        "      bad_counter +=1\n",
        "      if bad_counter == len(class_ids)+1:  # in case there's a problem, this should break the loop (but ignore the .DS_Store files)\n",
        "        assert False\n",
        "\n",
        "    if counter % 1000 == 0:\n",
        "      print(f\"{counter} images processed\")\n",
        "    counter += 1\n",
        "\n",
        "\n",
        "print(confusion_matrix)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 images processed\n",
            "1000 images processed\n",
            "BAD FILE: /content/dataset_three/dev/0/.DS_Store\n",
            "2000 images processed\n",
            "3000 images processed\n",
            "4000 images processed\n",
            "5000 images processed\n",
            "BAD FILE: /content/dataset_three/dev/1/.DS_Store\n",
            "6000 images processed\n",
            "7000 images processed\n",
            "BAD FILE: /content/dataset_three/dev/3/.DS_Store\n",
            "8000 images processed\n",
            "9000 images processed\n",
            "[[3651  191   44]\n",
            " [ 949 1706   15]\n",
            " [  53    1 2615]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-r7a4gPXGXeE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dfa10c27-781b-4d1a-b03b-f4f38a08d8f4"
      },
      "source": [
        "saved_confusion = confusion_matrix\n",
        "print(saved_confusion)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[3651  191   44]\n",
            " [ 949 1706   15]\n",
            " [  53    1 2615]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WKkGJcL0OHTq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "outputId": "c9b8123d-9903-49ca-d366-873be8131f06"
      },
      "source": [
        "classes = ['Neither', 'Person', 'Car']\n",
        "cm = saved_confusion\n",
        "plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n",
        "plt.title('Confusion Matrix')\n",
        "plt.colorbar()\n",
        "tick_marks = np.arange(len(classes))\n",
        "plt.xticks(tick_marks, classes, rotation=45)\n",
        "plt.yticks(tick_marks, classes)\n",
        "\n",
        "thresh = cm.max() / 2.\n",
        "for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "    plt.text(j, i, cm[i, j],\n",
        "              horizontalalignment=\"center\",\n",
        "              color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.ylabel('True label')\n",
        "plt.xlabel('Predicted label')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, -8.234018715767732, 'Predicted label')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5QwN+uH8zFpgDDAe+A341s9Vhk+lA4/C6MTANIKxfRNSkUFRewj4lKq+77v8GLkh4fxFwv6TxIYYPgXOK7fMc8Iiki4iaEUrzKNAM+CJ80sxl3aeRcy6HbUCb7LyymgsAwlf6dpJqAS8DrdMQYlIZS7JmVj3h9WygWsL7ecCJJezzBPBEeP0x63fhOqDY/s3C67XA1WFJ9H5YnHM5LN1dtMzsV0nvAfsAtSQVhNrqtsCMsNkMYDtgeugCuhXRDbDC8kKJ+5TIn/hyzsVbGrpwhW6jtcLrLYBDgW+A91j3TbkX8Gp4PTi8J6x/16JeAoOBkyRVCT0TWgCfl3XuWHTSd865EiltNdmGwEBJ+USVy+fN7HVJE4HnJN0CfAk8FrZ/DHhK0lRgAXASgJlNkPQ8Ud/71cD5ZfUsAE+yzrmYS0eSNbPxwO4llH9PCb0DzOx34PhSjtUP6JfquT3JOudiK10PI2STJ1nnXLzF/LHZZDzJOufiK31tslnjSdY5F2ueZJ1zLoM8yTrnXAbFfXqZZDzJOudiKxcG5U7Gk6xzLtY8yTrnXAZ5knXOuQzyNlnnnMsU7yfrnHOZIyDHc6wnWedcnHnvAuecy6gcz7GeZJ1zMSbI8xtfzjmXGcKTrHPOZZQ3FzjnXAb5jS/nnMsQVYA22dye18E5V8GpaJCYspakR5G2k/SepImSJki6OJTfIGmGpLFhOSJhn6skTZU0SdJhCeVdQ9lUSX2Tndtrss65WEtTa8Fq4HIz+0JSDWCMpOFh3V1m9q/1z6k2RDPU7gw0At6W1DKsvp9oSvHpwChJg81sYmkn9iTrnIu1NM1WOwuYFV4vlvQN0LiMXboBz5nZCuCHMDV44ay2U8Mst0h6LmxbapL15gLnXHwpqskmWzbokFIzounBPwtFF0gaL2mApNqhrDEwLWG36aGstPJSeZJ1zsVWYT/ZZAtQT9LohKVPiceTqgMvApeY2W/Ag8AOQDuimu6/030N3lzgnIu1FJsL5plZxyTHqUSUYJ8xs5cAzGx2wvpHgNfD2xnAdgm7bxvKKKO8RF6Tdc7FWjqaCxRl6seAb8zszoTyhgmbHQN8HV4PBk6SVEVSc6AF8DkwCmghqbmkykQ3xwaXdW6vyQItt2/Mo/+7OdthxM6/Pvgu2yHE0vVdWmU7hFjKSG/W9I0n+yfgVOArSWND2dVAd0ntAAN+BM4GMLMJkp4nuqG1GjjfzNYASLoAeAvIBwaY2YSyTuxJ1jkXW+kaT9bMPqLkz4GhZezTD+hXQvnQsvYrzpOscy7GlPNPfHmSdc7Fmo9d4JxzmbIR/WDjxpOscy62ojbZ3M6ynmSdc7HmbbLOOZdBXpN1zrlM8TZZ55zLHPmU4M45l1k5nmM9yTrn4i3fb3w551xmKH1jF2SNJ1nnXKzleEW29CQr6T9EI9OUyMwuykhEzjmXoCLXZEeXWxTOOVcCAXkVNcma2cDE95KqmdmyzIfknHPr5HpzQdKZESTtI2ki8G14v5ukBzIemXPOKeonm2yJs1Smn7kbOAyYD2Bm44D9MhmUc84VSvdsteUtpd4FZjat2KfFmsyE45xz61ToNtkE0yR1AizM9ngx8E1mw3LOuUiuj8KVSnPBOcD5QGNgJtH85OdnMijnnIPUmgriXtFNmmTNbJ6Z9TCzBmZW38xOMbP55RGcc87lSUmXZCRtJ+k9SRMlTZB0cSivI2m4pCnhZ+1QLkn3Spoqabyk9gnH6hW2nyKpV9L4Uwhue0mvSZoraY6kVyVtn/SqnHMuDZTCkoLVwOVm1gbYGzhfUhugL/COmbUA3gnvAQ4HWoSlD/AgREkZuB7YC9gTuL4wMZcmleaC/wLPAw2BRsAg4NnUrss55zaeiAaISbYkY2azzOyL8Hox0X2lxkA3oPCZgIHA0eF1N+BJi4wEaklqSNTTariZLTCzhcBwoGtZ504lyVYzs6fMbHVYngaqprCfc85tmtT7ydaTNDph6VP6IdUM2B34DGhgZrPCql+ABuF1Y2Bawm7TQ1lp5aUqa+yCOuHlG5L6As8RjWVwIjC0rIM651y6pHhja56ZdUx+LFUHXgQuMbPfErummplJKnW8lo1VVheuMURJtTCKsxPWGXBVuoNxzrni0vVEV+iC+iLwjJm9FIpnS2poZrNCc8CcUD4D2C5h921D2QzggGLl75d13lKbC8ysuZltH34WX/zG10YaNLA/PY/qxKlH7sPzTzy43rrnBtxH51Z1+HVB1Hlj8aJfufr8U+n1533pc9whfD95YjZCzpghd1/NvSd34tHz/lxU9srtlzLggqMZcMHRPHD6QQy44OiidZ8+/xD9e3fh4T5d+X7MiKLy35f8xsu3XsTDZx/OI2cfwYxvvizX6ygvZ/c+gyaNtqZDu7ZFZbfcdAPbN23MXh3asVeHdrz5RsX6khk9jJB8SXqcKFM/BnxjZncmrBoMFPYQ6AW8mlDeM/Qy2BtYFJoV3gK6SKodbnh1CWWlSumJL0ltgTYktMWa2ZOp7OvW+X7yRF4b9CQPD3qbgkqVuaL38XQ68DC2bbo9s2dN5/OP36NBo22Ltn+y/5202Kktt97/FD99N5k7b/ob9wx8JYtXkF67HHIMHY7qwet39i0qO7rvXUWv33n0dqpUqwHAvJ+nMvHDofR+8HWWzJ/Dc9ecTp+H3yQvP5+3H+7H9h06c8zV97Jm1UpWrfi93K+lPJza6zTOOe8Cep/Rc73yCy++lEsvuyJLUWVemp74+hNwKvCVpLGh7GrgduB5SWcCPwEnhHVDgSOAqcAy4HQAM1sg6WZgVNjuJjNbUGb8ySKTdD3wn7AcCNwB/CXlS3NFfvpuMm127UDVLapRUFBAuz068cGw1wH4z23XcN6VN6731ejH7ybRfu9omIimO7Tklxk/s2DenBKPnYuatN2DqjW2KnGdmfHtiDdps/+RAEwZ+Q5t9juCgkqVqbXNttRu1IRZk8fz+9LFTPt6NLt2OQ6A/EqVqVq9ZrldQ3nat/N+1KlTJ/mGFYiUnn6yZvaRmcnMdjWzdmEZambzzexgM2thZocUJszQq+B8M9vBzHYxs9EJxxpgZjuG5fFk506ld8FxwMHAL2Z2OrAbUPJfhitT85Y7MW7MSBYtXMDvy5cx8sPhzPllBiPeHkr9rRuyY+u2622/Y+u2fDDsNQAmjh/D7JnTmPvLzGyEXu6mTRjNlrXqUqdxMwAWz59NjXoNi9bXqLsNi+fPZtEv06m2VR2G3HUVAy48hqH3XMvK3zevETn7P3Afe+y+K2f3PoOFCxdmO5y0q/BPfAHLzWwtsFpSTaKG4e2S7PMHktZIGivpa0mDJFXb0GPkumY7tKJH74u47My/ckXv49mx9S6sWrmCpx66kzMvvvoP25/S52KWLF7E6d3248WnHqHFTruSl5+fhcjL3zcfDGGnUIsty9q1q/ll6kTaH9GdM/7zMpWqbsHIQY+UQ4TxcNbZ5zJx0nd8NmYs2zRsSN8rL892SGm3OQx1OFpSLeARoh4HXwCfbsS5locqeltgJdGYCElJqlDzkB11/Kk89tJ73PfMEGpsVYvmO7Zm1vSfOb1bZ44/aDfm/jKTM489gPlzZ7Nl9Zpcfdv9PP7qh1x7x4P8unAejbZrmu1LyLi1a1Yz6ZPh7LTfEUVlNeo2YPG8WUXvF8//hRp1G1Cj7jbUqNeARq13A6D1nw5j9tSKdYOwLA0aNCA/P5+8vDzOOPMsRo/+PNshpV2Fr8ma2Xlm9quZ9QcOBXqFZoNNMQLYUdKWkgZI+lzSl5K6AUg6TdJgSe8C70hqKOnDhJpw57Bdd0lfhbJ/FB5c0hJJ/SSNkzRSUoOSwyh/C+fPBWD2zOl8OOx1uh7Tndc+ncygd8cx6N1x1N+mEY+99D516zdg8W+LWLVyJQCvDXqS3Tp2YssK2t6Y6McvP6Xuts2pWW+borId9zqIiR8OZfWqlfz6y3QWzPiJhi13pXqd+tSs35D507+P9h33KXWb7JCt0MvdrFnrPnhefeVl2uzctoytc4+U/GmvuE8ZXtbDCO3LWlf4iNqGCjXTw4E3gWuAd83sjFBb/lzS22HT9sCu4W7e5cBbZtZPUj5QTVIj4B9AB2AhMEzS0Wb2CrAlMNLMrpF0B3AWcEuxOPoQPZO83h39TLv2wl4s+nUBBQWVuPT6O6hRs/Tm7Z++m0S/vucjRPMWrenb795yi7M8vPqPy/j5q1Es/20h9/fcn317XMhuhx3HxA+H0Gb/o9bbtn7TFuy07+E8es6R5OXn0+W864qaTg49+1pe++eVrFm9ilrbbMeRl9yajcvJuJ6ndGfEB+8zb948dmi2Lf933Y18+MH7jB83Fkk0bdaM/zzwULbDTLu4NwckI7OSH3CQ9F4Z+5mZHbRBJ5LWAF+FtyOAy4FPiLqFrQ7ldYieDd4L2L+wxixpP2AA8DTwipmNDbXev5pZz7DNmcDOZnaZpBVA1fAEx4nAoWbWu7TYWrfd3R596d0NuZzNwpCpFacnQzpd36VVtkOIpT/t1ZExY0anNSNuvWNbO/Gfg5Jud9+xbcak8sRXNpQ1keKBaT7XcjNrl1gQOgj/1cwmFSvfC1iaEMuHIdEeCTwh6U5gURnnWmXrPj3WkGJ/YOdcvIjcr8mmcuMrk94CLgzJFkm7l7SRpKbAbDN7BHiUqCnhc2B/SfVCE0J34IPyCds5V14K8pIvcZbtGt7NRBM1jpeUB/wAHFXCdgcAV0paBSwBeoZnjfsC7xF94A0xs1dL2Nc5l6Oi3gO5XZMttyRrZtVLKFvO+gPPFJY/ATyR8H4g68Z8TNzuWUoY2zbxXGb2AvDCRobtnMuymHceSCqVx2ol6RRJ14X3TSTtmfnQnHNuM+gnCzwA7EPU5gmwGLg/YxE551xQOCX4po5dkE2pNBfsZWbtJX0JYGYLJVXOcFzOOQdAfrxzaFKpJNlV4e69AUiqD6zNaFTOOUd00yvuNdVkUmkuuBd4GdhaUj/gI6BiPlLjnIudXG+TTVqTNbNnJI0hGu5QwNFm9k3GI3POOXK/d0HSJCupCdHI4K8llpnZz5kMzDnnCqcEz2WptMkOYd2EilWB5sAkYOcMxuWcc5DiHF5xlkpzwS6J78PoXOdlLCLnnEsgcjvLbvATX2b2RRjAxTnnMqpwttpclsoTX5clLFdI+i+weUw05ZzLunRMCQ4QJgiYI+nrhLIbJM0IEwKMlXREwrqrJE2VNEnSYQnlXUPZ1DB+SplSqcnWSHi9mqiN9sXULss55zZemm98PQHcBzxZrPwuM/vXeueV2gAnEd17agS8LallWH0/0Swx04FRkgabWalzHpWZZMNDCDXMrOJO6u6ci6809oMN41I3S3HzbsBzZrYC+EHSVKBwzJapZvY9gKTnwralJtlSmwskFZjZGuBPKQblnHNpl+LYBfUkjU5Y+mzAKS6QND40J9QOZY2BaQnbTA9lpZWXqqya7OdEg2OPlTQYGMT6sxW8lPIlOOfcRtiAG1/zNnL6mQeJxrW28PPfwBkbcZxSpdImWxWYDxzEuv6yBniSdc5lXCYfmzWz2evOo0eA18PbGcB2CZtuG8ooo7xEZSXZrSVdBnzNuuRaFFuZkTvnXBoIkZ/BLCupoZkVzqt+DFG+AxgM/DfMJ9gIaEH07V5AC0nNiZLrScDJZZ2jrCSbD1SHEnsCe5J1zmVeGp/4kvQs0VRW9SRNB64HDpDUjiin/UiYqcXMJkh6nuiG1mrg/HCPCkkXEM1PmA8MMLMJZZ23rCQ7y8xu2pSLcs65TZWuoQ7NrHsJxY+VsX0/oF8J5UOBoamet6wkm+PPWTjncl00JXi2o9g0ZSXZg8stCuecK0WFHYXLzBaUZyDOOVecSG1mgTgrtynBnXNugymagiaXeZJ1zsVabqdYT7LOuRgrnBI8l3mSdc7FWo7f9/Ik65yLM3mbrHPOZYr3LnDOuQzzmqxzzmWK/MZXhVCtSj67N6uV7TBip+P2tZNvtBlqf92wbIcQSz/M/C3tx/TmAuecyzBvLnDOuQzK7RTrSdY5F3M5XpH1JOuciy9BRmdGKA+eZJ1zMSaU4w0GnmSdc7GW4xVZT7LOufiKunDldpb1JOuciy9BXu/WAYsAABPkSURBVI53lM3x8J1zFZ1S+C+l40gDJM2R9HVCWR1JwyVNCT9rh3JJulfSVEnjJbVP2KdX2H6KpF7JzutJ1jkXW9F4ssmXFD0BdC1W1hd4x8xaAO+E9wCHAy3C0gd4EKKkTDSV+F7AnsD1hYm5NJ5knXOxlq6arJl9CBSfu7AbMDC8HggcnVD+pEVGArUkNQQOA4ab2QIzWwgM54+Jez3eJuuci7UUexfUkzQ64f3DZvZwCvs1MLNZ4fUvQIPwujEwLWG76aGstPJSeZJ1zsXWBjyMMM/MOm7KuczMJNmmHKMk3lzgnIuxVBoLNqmL1+zQDED4OSeUzwC2S9hu21BWWnmpPMk65+JLUXNBsmUTDAYKewj0Al5NKO8ZehnsDSwKzQpvAV0k1Q43vLqEslJ5c4FzLtbS9SiCpGeBA4jab6cT9RK4HXhe0pnAT8AJYfOhwBHAVGAZcDqAmS2QdDMwKmx3k5kVv5m2Hk+yzrnYSueU4GbWvZRVB5ewrQHnl3KcAcCAVM/rSdY5F2s+doFzzmWQj8LlnHMZ5DVZ55zLoBzPsZ5knXPxJXwiReecy5xN7webdZ5knXOxluM51pOscy7mcjzLepJ1zsWYT6ToNlGbls2pXr0G+fn5FBQUMOLTUdx0w/8x5LXB5OXlUb/+1jz06OM0bNQo26Fmxdm9z+CNoa9Tf+utGTP26+Q75LhttqrCbcfvQr3qlTGD50dN5+lPfgagxz7b0X3vJqxda3wwaS7/fnMKW21Ribt77MYujWvy8hcz6ffat0XHeqJ3R+rXqMKKVWsA6P34FyxYujIr17WxCgftzmWeZGNg6LB3qVevXtH7Sy67kutuuBmAB+67l9v63cS99/fPVnhZdWqv0zjnvAvofUbPbIdSLlavNe4YOolvZi6mWuV8Xrhgbz6dOp+61Stz0E5bc8y9n7BqjVFny8oArFy9lv8Mn0qLBtXZsUH1Pxzvb89/xYQZv5X3ZaRXjidZH4UrhmrWrFn0etmypTnfhWVT7Nt5P+rUqZPtMMrNvMUr+WbmYgCWrVzD93OWsnXNKpy013Y8+sEPrFoTDXdaWCNdvmoNX/z0KytWr81azJmW4aEOM85rslkmRLcjD0MSZ/Tuwxm9+wBww3XX8OwzT1Gz5lYMHfZulqN02dCoVlV2alSD8dMWcWXXlnRoVpuLu7Rgxeo1/HPoZL5OoYba7687s3atMWzCHPq/9305RJ1+uV7HyKmarKRtJD0n6TtJYyQNldQy23FtiuHvjeDjz8bw0uChPNz/AT4a8SEAN9zUj0nf/cyJ3U/moQfvy3KUrrxVq5zPPT3acduQSSxdsYb8/Dy2qlaJkx78jH+9MZk7u++W9Bh/e/4rjr73U055eBQdmtXiL7s3LIfI0yzz48lmXM4kWUXfmV8G3jezHcysA3AV6+bkKXNfSbG81kaNo+mBtt56a/7c7WjGjPp8vfUnntSDV19+KRuhuSwpyBN3n7wbr4+dxdsTooH6f1n0O8MnzAbgq+m/sdaM2ltWKvM4c35bAUTNDkPG/cIu226V2cAzJNebC2KZeEpxILDKzIruAJnZOOBLSe9I+kLSV5K6AUhqJmmSpCeBr1l/yohYWLp0KYsXLy56/e7bw2mzc1umTplStM3rr71Ky1atsxWiy4Kbj92Z7+cuZeDHPxWVvTtxDntuH7VNN61bjUr5eSxcuqrUY+TniVrVoiRckCf2b12fqbOXZDbwDIgeq83tmmwutcm2BcaUUP47cIyZ/SapHjBS0uCwrgXQK0zpGztzZs+m+wnHArB69WpOOKk7hx7WlZNPPI4pkyeRl5dHkyZNuee+B7Mcafb0PKU7Iz54n3nz5rFDs235v+tu5LQzzsx2WBnTvmkturVvxKRZi3npgr0BuHvYVF4aM4Nbjt2ZVy/uxKrVa7n6hXXd2YZf2ZnqVQqolC8ObrM1Zz0+hpkLf+eR0ztQkCfy88Sn381n0Kjp2bqsTRLzHJqUogHA40/SRUBzM7u0WHkl4C5gP2At0ApoDlQF3jOz5qUcrw/QB2C7Jk06fDPlx8wFn6Pyc72DYoa0v25YtkOIpR8ev4Dlsyan9R9N293a26A3RyTdrk2j6mM2dbbaTMml5oIJQIcSynsA9YEOZtYOmE2UYAGWlnYwM3vYzDqaWcd69eqnPVjnXHrkSUmXOMulJPsuUCXUQAGQtCvQFJhjZqskHRjeO+cqCKWwxFnOJNkwsdkxwCGhC9cE4DaiWSU7SvoK6Al8W8ZhnHO5Jk1ZVtKP4eb4WEmjQ1kdScMlTQk/a4dySbpX0lRJ4yW139jwc+nGF2Y2k3VT9ibap5Rd2mYwHOdchkU5NK111QPNbF7C+77AO2Z2u6S+4f3fgcOJbpy3APYCHgw/N1jO1GSdc5uhzD+M0A0YGF4PBI5OKH/SIiOBWpI26mkOT7LOuVhLMcnWkzQ6YelTwqEMGBaeFi1c38DMZoXXv7Du4abGwLSEfaeHsg2WU80FzrnNTcpPdM1LoQvXvmY2Q9LWwHBJ692/MTOTlPY+rV6Tdc7FWrqaC8xsRvg5h+gR/T2B2YXNAOHnnLD5DNZ/SnTbULbBPMk652IrlY4FqeRYSVtKqlH4GuhC9Lj9YKBX2KwX8Gp4PRjoGXoZ7A0sSmhW2CDeXOCci7U0jafcAHg5HKsA+K+ZvSlpFPC8pDOBn1jXe2kocAQwFVgGnL6xJ/Yk65yLtXTkWDP7HvjD+JBmNh84uIRyA87f9DN7knXOxVzcn+hKxpOscy6+cmAow2Q8yTrnYi63s6wnWedcbPmU4M45l2HeXOCccxkU9zm8kvEk65yLt9zOsZ5knXPxJXmbrHPOZZQ3FzjnXCbldo71JOuci7ccz7GeZJ1z8eZduJxzLkNE/Kf8TsbHk3XOuQzymqxzLtZyvCLrSdY5F2/ehcs55zLEH0ZwzrlM8yTrnHOZ480FzjmXQX7jyznnMijHc6wnWedcvKVpSvCs8STrnIstkfvNBYqmF9+8SZoL/JTtOIJ6wLxsBxFD/nspWZx+L03NrH46DyjpTaJrTGaemXVN57nTxZNszEgabWYdsx1H3PjvpWT+e4k/H7vAOecyyJOsc85lkCfZ+Hk42wHElP9eSua/l5jzNlnnnMsgr8k651wGeZJ1zrkM8iSbYxQef1GuPwbj0sb/LcSbJ9kcIkm2rhG9XVaDcbGQ+G9CUotsx+P+yJNsDkn4YzoVeFZSjc29FrO5X3/Cv4mTgYGStshySK4YH7sgx0g6ELgI+IuZLZZUAKzOclhZUawW1xOoBPxsZsOzG1n5knQccDZwupktl5RnZmuzHZeLeE025kqoqVUBGgK9AcxstaTN8v9jQoI9ErgK2A04SdIVWQ0sw0r4N7EG6AwcCOAJNl42yz/OXFGsptZaUnMzexPoCewg6WKI/qg210Qr6SygO3CwmV0EDAJaSbo8u5FlRrF/EzUkVTOzl4l+BxdLOja7EbrivLkgxhL+mK4EDo1e6ivgfuABoI+kLczs9s2l9lLs5h9APnAy8DLwIvBRKO8l6SIzu7e8Y8ykhH8TVwAdgcaSLjOz/0laAVwnqYqZPZvVQF0RT7IxJ+kg4EAz6yLpfmAHM/tO0gygMnCypNpmtjC7kWZesVpcc2C2mfWXtBh4QtJ0M/tM0sdE7dQTshlvOknqQPSBMh7oBRwOdAHeA16QdJaZvSKpMlGN9nVgSbEPJJcF/lhtzBSvqUnaB+gE1AL2ILrhtVJSezP7InxdXJateLNB0iXAUcBaYATwH6Br+NnNzD4pocabsyR1BW4mur6Pia51MHACsBfwPnALcKqZDZFU3cyWZClcV4wn2RgpVlPLN7M1kloRNQ+sAY4xs2WSzgNOAo40s8VZDLncSeoEPAjsT5Rg2gJNzOzi0EZ9CbATsKIiJFlJ+wOPAieb2ahQJqAJ8DjRh+4SSZ8CS8L75VkL2P2BNxfEhKTtgPnAMkkXEd28WQRcC7xAdPf4EklG1AZ50uaQYEuokdYEppjZr8BbkmYDN0ra3czukfSkmf2enWgzogNwn5mNklRgZqvNzMJsHtOBYyXlAxOBmzzBxs9meUc6ThSpBTwNnBq6I50CDAe2A14DHiKqtawFqgHHmVmFaW8sTbGa/f6SGgCjgNrhgQzMbCzwK1HtFWBRVoJNs4RuWs2Bwild1iRsshoYR/Thezlwp5nFZQoll8CbC7KssOO4pAOAa4BfgMFmNiisH0g0x9ExoS12s+toHmr2ZxD9Dn6QdAJRb4tVwGfA34HDK2KSCTc+rwb+bmZjQlc9haaki4APiR7AWJDVQF2pvCabRZJqE7WtAUwhurmxLbCPpOoAZtYLWAa8kpUgsyCxs3348OkN7BcSbEtgLnAXUZJtDZxYERNs8BlRt7QTJXUws7UhwXYn6mUw3xNsvHlNNosk7QvsR/QE135mtpukvYHbgOeAZwrvEktqaGazshdt+SjWRNAZmE1Ui11LVCk4FJgEPGxm728ONXtJjYEzgYOB0cBy4DiiZqOvsxmbS85rslkgqa2kI83sI6A9cBpwK4CZjQT+j+iPqHdCjbbCJ1hYr7P9KcA/iW50TSOq8b8CHEY0fXujsH2FTrAAZjaD6HdxDbCY6PfxF0+wucFrsuUs3Ak+mKhTeT7QlKjPZyXgTWBEaHs9hGggmJ7hTvpmI9Tm7wIuCQ8XVDazlWHdcUBfoi5Nk7MZp3Op8JpsOTOzNWY2jCjB3g60MLOrgd+AvwK7SjqbqBfBSZtDgi3WBiugbnh7bnhEdKWkqpIOBc4hGm3KE6zLCV6TLSclPMlVDfgLcAjwDlEb7DVAM+AgoieXvspCqOWqWBvsrsBqM5sYOuH/FZhJ1D1pZejCtdrM5mcxZOc2iCfZclAskfQiGq5whZkNTOiONNzMnpdUB8g3s7lZDDnjSvjQuQQ4BlhA1HRyLrAL0fP5i4GbC5sMnMsl/sRXOZJ0DtCDqE1xhKQFwBuAAceFr8ZPZTPGclRA1AULSX8CuprZ/pKuJhq2cJqk6YCIxkmtQfREnHM5xWuyGSSpCVE/xqWS6gL3Et3MOp6o1naUma2SVAk4Evhsc+hFENpWzwDGEnVJmkQ06MmORL0tjgy/l0PM7O3NcRAcV3F4ks2Q0H54NVF3m/5hEI+7gS2ArYnuji9XNC7oB4WDf1R0YUSpm4CniH4P2xIl2hOBFUCX8Gz+6UQ3uQ73zvYul3lzQebMJXrOvj1wuqT7gFnAjUC9kGBPIGo+eDF7YZaf0N48lOim3muhpn8H8DXR48RXAldIqkc0Xmp3T7Au13mSTTNF0zLnmdkkSc8QDVhyONDHzP4hqSkwRNI0oq/HPc3shyyGXG7MbIGkPwN3SPrAzH4Oo4q1NLNHJK0CGhDd+PqrmU3JasDOpYE3F6RRaHedC8wjqrGuAR4mGppwR2CWmT0kqS3RB9w8M5uerXizRdLhRO3TbxE9uXWqmS3NblTOZYYn2TQLoya9DVxM1AWpNtFgyiuJOtm/DTxRwcY83WDhibZhwDZmNkfRXGXLw7oKM6uBc55kMyDcPb+XaIrqBkQPF5wE7EnULvsnM6sQ455uilCj/RfRHGZzsh2Pc5ngSTZDwuDbdwF7h7bI2kRtjdXM7MesBhcjkroB1xPNvGpeg3UVjSfZDAo1tXuAffxR0NLJJ/5zFZj3LsggM3tD0RTNbxcOuJztmOLIE6yryLwmWw68pubc5suTrHPOZZCPJ+uccxnkSdY55zLIk6xzzmWQJ1nnnMsgT7KuRJLWSBor6WtJg8J0ORt7rCfCBIhIelRSmzK2PUBSp404x49h9K6Uyotts0E9PyTdEIaodC4pT7KuNMvNrJ2ZtSUad+GcxJWSNqqPtZn1NrOJZWxyALDBSda5uPIk61IxAtgx1DJHSBoMTJSUL+mfkkZJGh9m2UWR+yRNkvQ20eDchHXvS+oYXneV9IWkcZLekdSMKJlfGmrRnSXVl/RiOMeoMFUNkupKGiZpgqRHiaapKZOkVySNCfv0KbburlD+jqT6oWwHSW+GfUZIap2OX6bbvPgTX65MocZ6OPBmKGoPtDWzH0KiWmRme0iqAnwsaRiwO9AKaEM0QM5EYECx49YHHgH2C8eqE8Z46A8sMbN/he3+C9xlZh+FQb7fAnYiGu/gIzO7KYwTcWYKl3NGOMcWwChJL4bHnbcERpvZpZKuC8e+gGiYynPMbIqkvYAHiAb7cS5lnmRdabaQNDa8HgE8RvQ1/vOEQca7ALsWtrcCWwEtgP2AZ81sDTBT0rslHH9v4MPCY5UxA8IhQBupqKJaU1L1cI5jw75DJC1M4ZouknRMeL1diHU+sBb4Xyh/GngpnKMTMCjh3FVSOIdz6/Ek60qz3MzaJRaEZJM4uLaAC83srWLbHZHGOPKIRjJbb/zdhMSXEkkHECXsfcxsmaT3gaqlbG7hvL8W/x04t6G8TdZtireAcxXNtouklpK2BD4ETgxttg2JpvQubiSwn6TmYd86oXwx0fTfhYYBFxa+kVSY9D4kmnGicLSz2kli3QpYGBJsa6KadKE8oLA2fjJRM8RvwA+Sjg/nkKTdkpzDuT/wJOs2xaNE7a1fSPoaeIjo29HLwJSw7kng0+I7mtlcoA/RV/NxrPu6/hpwTOGNL6Ip1DuGG2sTWdfL4UaiJD2BqNng5ySxvgkUSPoGuJ0oyRdaCuwZruEgotl0IZrk8swQ3wSgWwq/E+fW4wPEOOdcBnlN1jnnMsiTrHPOZZAnWeecyyBPss45l0GeZJ1zLoM8yTrnXAZ5knXOuQz6fyRwLfLDIP6eAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OGxRb7Q_oJ-c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b95065f5-070a-4495-a94c-45270e38a1d9"
      },
      "source": [
        "def get_k_worst_preds(k, preds):\n",
        "  return sorted(preds, key=lambda elem: float(elem[0]), reverse=True)[:k]\n",
        "\n",
        "print(get_k_worst_preds(9, incorrect_predictions))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[(16.11809539794922, '/content/dataset_three/dev/1/000000171539.jpg', 1, 3), (14.389531135559082, '/content/dataset_three/dev/3/cartrain_06775.jpg', 3, 0), (11.551213264465332, '/content/dataset_three/dev/3/cartest_03047.jpg', 3, 0), (11.188151359558105, '/content/dataset_three/dev/3/cartrain_05325.jpg', 3, 0), (10.29440975189209, '/content/dataset_three/dev/0/000000174771.jpg', 0, 3), (10.183858871459961, '/content/dataset_three/dev/1/000000480747.jpg', 1, 0), (9.994457244873047, '/content/dataset_three/dev/1/000000229449.jpg', 1, 3), (9.644373893737793, '/content/dataset_three/dev/0/000000255279.jpg', 0, 3), (9.32481575012207, '/content/dataset_three/dev/1/000000343954.jpg', 1, 0)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vD9DL9WYxGtO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1fad0cac-64bc-4f48-cd01-94604104b29c"
      },
      "source": [
        "def non_car_sorter(elem):\n",
        "  if elem[2] != 3 and elem[3] != 3:\n",
        "    return float(elem[0])\n",
        "  return 0\n",
        "\n",
        "def get_k_worst_non_car_preds(k, preds):\n",
        "  return sorted(preds, key=non_car_sorter, reverse=True)[:k]\n",
        "\n",
        "print(get_k_worst_non_car_preds(9, incorrect_predictions))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[(10.183858871459961, '/content/dataset_three/dev/1/000000480747.jpg', 1, 0), (9.32481575012207, '/content/dataset_three/dev/1/000000343954.jpg', 1, 0), (8.620025634765625, '/content/dataset_three/dev/1/000000464366.jpg', 1, 0), (8.537582397460938, '/content/dataset_three/dev/1/000000174042.jpg', 1, 0), (8.488677978515625, '/content/dataset_three/dev/1/000000129129.jpg', 1, 0), (8.263171195983887, '/content/dataset_three/dev/1/000000065562.jpg', 1, 0), (7.777741432189941, '/content/dataset_three/dev/1/000000493610.jpg', 1, 0), (7.62580680847168, '/content/dataset_three/dev/1/000000318231.jpg', 1, 0), (7.581358432769775, '/content/dataset_three/dev/1/000000259703.jpg', 1, 0)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F3XFC2IEbZ7R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 862
        },
        "outputId": "0cf9f6ee-d336-43f3-f3ff-7e6c0a4892a5"
      },
      "source": [
        "# for i in range(9):\n",
        "#   plt.imshow(image_batch[i].astype(\"uint8\"))\n",
        "#   plt.title(classes[predictions[i]])\n",
        "\n",
        "plt.figure(figsize=(15, 15))\n",
        "num_incorrect_display = 9\n",
        "display_idx = 0\n",
        "loss_display_function = get_k_worst_non_car_preds\n",
        "# loss_display_function = get_k_worst_preds\n",
        "for loss, incorrect_path, incorrect_actual, incorrect_pred in loss_display_function(num_incorrect_display, incorrect_predictions):\n",
        "  display_idx += 1\n",
        "  loss_rounded = np.round_(float(loss),2)\n",
        "  incorrect_img = keras.preprocessing.image.load_img(incorrect_path, target_size=IMG_SIZE)\n",
        "  incorrect_img_array = keras.preprocessing.image.img_to_array(incorrect_img)\n",
        "  ax = plt.subplot(3, 3, display_idx)\n",
        "  # plt.figure()\n",
        "  plt.axis(\"off\")\n",
        "  plt.title(f\"Loss: {loss_rounded} | Actual: {incorrect_actual} | Predicted: {incorrect_pred}\") #\\n{incorrect_path}\n",
        "  plt.imshow(incorrect_img_array.astype(\"uint8\"))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NW4lWrnOQiXo"
      },
      "source": [
        "# Microcontroller (Arduino)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bHZkrpQvPJu3"
      },
      "source": [
        "## Convert the model to tflite"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hIBJszYe7AaI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5f687c3e-8d01-44dd-c5b5-9d400f0108f3"
      },
      "source": [
        "# NORMAL UNQUANTISED\n",
        "# Convert\n",
        "converter = tf.lite.TFLiteConverter.from_keras_model(loaded_model)\n",
        "tflite_model = converter.convert()\n",
        "\n",
        "# Save\n",
        "with open('mobilenetv2.tflite', 'wb') as f:\n",
        "  f.write(tflite_model)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: /tmp/tmp9jfkb6ki/assets\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: /tmp/tmp9jfkb6ki/assets\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E2zpQYpC8-JH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "outputId": "547bd9bc-317e-4010-9085-c464bb5a3fcb"
      },
      "source": [
        "plt.figure(figsize=(10, 10))\n",
        "for images, labels in train_dataset.take(1):\n",
        "  print(images[0].numpy().shape)\n",
        "  for i in range(32):\n",
        "    ax = plt.subplot(6, 6, i + 1)\n",
        "    plt.imshow(images[i].numpy().astype(\"uint8\"))\n",
        "    plt.axis(\"off\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(128, 128, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 32 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sjsIOguBHs9X"
      },
      "source": [
        "NOTE: If you run this, you have to rerun the cell that creates the dataloader and NOT run the prefetching cell."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9aiWq4BMFtr_"
      },
      "source": [
        "# !pip install tf-nightly"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O49BohHiPMSt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c7998022-f81d-4c62-bbf8-8379aa268387"
      },
      "source": [
        "# UINT8 Quantised\n",
        "def representative_data_gen():\n",
        "  for images, labels in train_dataset.take(1):\n",
        "    for i in range(32):\n",
        "      yield [tf.expand_dims(images[i], 0)]\n",
        "\n",
        "converter = tf.lite.TFLiteConverter.from_keras_model(loaded_model)\n",
        "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
        "converter.representative_dataset = representative_data_gen\n",
        "# Ensure that if any ops can't be quantized, the converter throws an error\n",
        "converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]\n",
        "# Set the input and output tensors to uint8 (APIs added in r2.3)\n",
        "converter.inference_input_type = tf.uint8\n",
        "converter.inference_output_type = tf.uint8\n",
        "\n",
        "tflite_model_quant = converter.convert()\n",
        "\n",
        "# Save\n",
        "with open('mobilenetv2_quant.tflite', 'wb') as f:\n",
        "  f.write(tflite_model_quant)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:absl:Found untraced functions such as _defun_call, _defun_call while saving (showing 2 of 2). These functions will not be directly callable after loading.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: /tmp/tmp6znx_r36/assets\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: /tmp/tmp6znx_r36/assets\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mY-y1odN3EKt"
      },
      "source": [
        "## Convert tflite to C++"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vlBjXL4x3lvR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "256376d2-c0fd-4872-e147-dc0dff9a5660"
      },
      "source": [
        "# Install xxd if it is not available\n",
        "!apt-get update && apt-get -qq install xxd\n",
        "# Convert to a C source file, i.e, a TensorFlow Lite for Microcontrollers model\n",
        "# !xxd -i {MODEL_TFLITE} > {MODEL_TFLITE_MICRO}\n",
        "!xxd -i base_model.tflite > base_model.cc\n",
        "# Update variable names\n",
        "# REPLACE_TEXT = MODEL_TFLITE.replace('/', '_').replace('.', '_')\n",
        "# !sed -i 's/'{REPLACE_TEXT}'/g_model/g' {MODEL_TFLITE_MICRO}"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Hit:1 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ InRelease\n",
            "Ign:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease\n",
            "Hit:3 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic InRelease\n",
            "Hit:4 http://archive.ubuntu.com/ubuntu bionic InRelease\n",
            "Hit:5 http://security.ubuntu.com/ubuntu bionic-security InRelease\n",
            "Ign:6 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  InRelease\n",
            "Hit:7 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Release\n",
            "Hit:8 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  Release\n",
            "Hit:9 http://archive.ubuntu.com/ubuntu bionic-updates InRelease\n",
            "Hit:10 http://archive.ubuntu.com/ubuntu bionic-backports InRelease\n",
            "Hit:11 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease\n",
            "Reading package lists... Done\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Ju9EtmX3niz"
      },
      "source": [
        "# Print the C source file\n",
        "!cat {MODEL_TFLITE_MICRO}"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}