Currency_Transfer_Learning.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Currency-Transfer-Learning.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "xCssZqjloaZ7",
        "2go9OoXuors0",
        "QEg24MCEoxb0"
      ],
      "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/r-prateek/Indian-Currency-Notes-Classifier/blob/main/Currency_Transfer_Learning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lsqMSJT8oS2m"
      },
      "source": [
        "# **Import Dataset from Kaggle**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JEnYupUDv5z6"
      },
      "source": [
        "! pip install --upgrade --force-reinstall --no-deps kaggle\n",
        "from google.colab import files\n",
        "files.upload()\n",
        "! mkdir ~/.kaggle\n",
        "! cp kaggle.json ~/.kaggle/\n",
        "! chmod 600 ~/.kaggle/kaggle.json\n",
        "! kaggle datasets list\n",
        "! kaggle datasets download -d vishalmane109/indian-currency-note-images-dataset-2020\n",
        "! unzip indian-currency-note-images-dataset-2020.zip -d dataset"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xCssZqjloaZ7"
      },
      "source": [
        "#**Import required libraries**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "79MLUac4oiTD"
      },
      "source": [
        "\n",
        "Libraries used:\n",
        "```\n",
        "tensorflow\n",
        "matplotlib\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iOpN0W9sxjRf"
      },
      "source": [
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2go9OoXuors0"
      },
      "source": [
        "# **Data Augmentation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JDDxnwPBxpze"
      },
      "source": [
        "train_path = \"/content/dataset/Indian currency dataset v1/training\"\n",
        "test_path = \"/content/dataset/Indian currency dataset v1/validation\""
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DoakYqZtx09F"
      },
      "source": [
        "datagen = tf.keras.preprocessing.image.ImageDataGenerator(\n",
        "    rescale = 1./255,\n",
        "    rotation_range = 5,\n",
        "    #zoom_range = 0.1,\n",
        "    horizontal_flip = True,\n",
        "    vertical_flip = True,\n",
        "    #brightness_range = (3,8)\n",
        ")"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eqxTXPoVx3Rs",
        "outputId": "509b00cd-aa5e-48a5-d9ea-2afaf89002ad"
      },
      "source": [
        "train_data = datagen.flow_from_directory(\n",
        "    train_path,\n",
        "    target_size = (224, 224),\n",
        "    color_mode = 'rgb',\n",
        "    batch_size = 32,\n",
        "    shuffle = True,\n",
        "    seed = 865\n",
        ")"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 3566 images belonging to 8 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WPwld_bLx5kz",
        "outputId": "395e36c1-4a5b-4c24-ab7a-9550a8a7a2b1"
      },
      "source": [
        "test_data = datagen.flow_from_directory(\n",
        "    test_path,\n",
        "    target_size = (224, 224),\n",
        "    color_mode = 'rgb',\n",
        "    batch_size = 32,\n",
        "    shuffle = True,\n",
        "    seed = 865\n",
        ")"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 345 images belonging to 8 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QEg24MCEoxb0"
      },
      "source": [
        "# **Using pretrained model Resnet50V2**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N4illNgIo24S"
      },
      "source": [
        "* Using model trained on *imagenet* dataset\n",
        "* Resizing input shape to 50 x 50"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F5Fz52tIx77J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "304964a1-cc1d-4bb5-d70e-57d571e3c923"
      },
      "source": [
        "ptm = tf.keras.applications.MobileNetV2( \n",
        "    include_top = False,\n",
        "    weights = 'imagenet',\n",
        "    input_shape = (224, 224, 3)\n",
        ")"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5\n",
            "9412608/9406464 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V3rycfmayZll",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b3405979-47f3-4765-e0c5-7073c5eae76f"
      },
      "source": [
        "ptm.trainable = False \n",
        "ptm.summary()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"mobilenetv2_1.00_224\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "Conv1 (Conv2D)                  (None, 112, 112, 32) 864         input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "bn_Conv1 (BatchNormalization)   (None, 112, 112, 32) 128         Conv1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "Conv1_relu (ReLU)               (None, 112, 112, 32) 0           bn_Conv1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise (Depthw (None, 112, 112, 32) 288         Conv1_relu[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise_BN (Bat (None, 112, 112, 32) 128         expanded_conv_depthwise[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise_relu (R (None, 112, 112, 32) 0           expanded_conv_depthwise_BN[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_project (Conv2D)  (None, 112, 112, 16) 512         expanded_conv_depthwise_relu[0][0\n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_project_BN (Batch (None, 112, 112, 16) 64          expanded_conv_project[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand (Conv2D)         (None, 112, 112, 96) 1536        expanded_conv_project_BN[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand_BN (BatchNormali (None, 112, 112, 96) 384         block_1_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand_relu (ReLU)      (None, 112, 112, 96) 0           block_1_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_1_pad (ZeroPadding2D)     (None, 113, 113, 96) 0           block_1_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise (DepthwiseCon (None, 56, 56, 96)   864         block_1_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise_BN (BatchNorm (None, 56, 56, 96)   384         block_1_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise_relu (ReLU)   (None, 56, 56, 96)   0           block_1_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_1_project (Conv2D)        (None, 56, 56, 24)   2304        block_1_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_1_project_BN (BatchNormal (None, 56, 56, 24)   96          block_1_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand (Conv2D)         (None, 56, 56, 144)  3456        block_1_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand_BN (BatchNormali (None, 56, 56, 144)  576         block_2_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand_relu (ReLU)      (None, 56, 56, 144)  0           block_2_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise (DepthwiseCon (None, 56, 56, 144)  1296        block_2_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise_BN (BatchNorm (None, 56, 56, 144)  576         block_2_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise_relu (ReLU)   (None, 56, 56, 144)  0           block_2_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_2_project (Conv2D)        (None, 56, 56, 24)   3456        block_2_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_2_project_BN (BatchNormal (None, 56, 56, 24)   96          block_2_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_2_add (Add)               (None, 56, 56, 24)   0           block_1_project_BN[0][0]         \n",
            "                                                                 block_2_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand (Conv2D)         (None, 56, 56, 144)  3456        block_2_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand_BN (BatchNormali (None, 56, 56, 144)  576         block_3_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand_relu (ReLU)      (None, 56, 56, 144)  0           block_3_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_3_pad (ZeroPadding2D)     (None, 57, 57, 144)  0           block_3_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise (DepthwiseCon (None, 28, 28, 144)  1296        block_3_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise_BN (BatchNorm (None, 28, 28, 144)  576         block_3_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise_relu (ReLU)   (None, 28, 28, 144)  0           block_3_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_3_project (Conv2D)        (None, 28, 28, 32)   4608        block_3_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_3_project_BN (BatchNormal (None, 28, 28, 32)   128         block_3_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand (Conv2D)         (None, 28, 28, 192)  6144        block_3_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_4_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_4_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise (DepthwiseCon (None, 28, 28, 192)  1728        block_4_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise_BN (BatchNorm (None, 28, 28, 192)  768         block_4_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise_relu (ReLU)   (None, 28, 28, 192)  0           block_4_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_4_project (Conv2D)        (None, 28, 28, 32)   6144        block_4_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_4_project_BN (BatchNormal (None, 28, 28, 32)   128         block_4_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_4_add (Add)               (None, 28, 28, 32)   0           block_3_project_BN[0][0]         \n",
            "                                                                 block_4_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand (Conv2D)         (None, 28, 28, 192)  6144        block_4_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_5_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_5_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise (DepthwiseCon (None, 28, 28, 192)  1728        block_5_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise_BN (BatchNorm (None, 28, 28, 192)  768         block_5_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise_relu (ReLU)   (None, 28, 28, 192)  0           block_5_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_5_project (Conv2D)        (None, 28, 28, 32)   6144        block_5_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_5_project_BN (BatchNormal (None, 28, 28, 32)   128         block_5_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_5_add (Add)               (None, 28, 28, 32)   0           block_4_add[0][0]                \n",
            "                                                                 block_5_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand (Conv2D)         (None, 28, 28, 192)  6144        block_5_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_6_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_6_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_6_pad (ZeroPadding2D)     (None, 29, 29, 192)  0           block_6_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise (DepthwiseCon (None, 14, 14, 192)  1728        block_6_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise_BN (BatchNorm (None, 14, 14, 192)  768         block_6_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise_relu (ReLU)   (None, 14, 14, 192)  0           block_6_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_6_project (Conv2D)        (None, 14, 14, 64)   12288       block_6_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_6_project_BN (BatchNormal (None, 14, 14, 64)   256         block_6_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand (Conv2D)         (None, 14, 14, 384)  24576       block_6_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_7_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_7_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_7_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_7_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_7_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_7_project (Conv2D)        (None, 14, 14, 64)   24576       block_7_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_7_project_BN (BatchNormal (None, 14, 14, 64)   256         block_7_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_add (Add)               (None, 14, 14, 64)   0           block_6_project_BN[0][0]         \n",
            "                                                                 block_7_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand (Conv2D)         (None, 14, 14, 384)  24576       block_7_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_8_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_8_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_8_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_8_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_8_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_8_project (Conv2D)        (None, 14, 14, 64)   24576       block_8_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_8_project_BN (BatchNormal (None, 14, 14, 64)   256         block_8_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_8_add (Add)               (None, 14, 14, 64)   0           block_7_add[0][0]                \n",
            "                                                                 block_8_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand (Conv2D)         (None, 14, 14, 384)  24576       block_8_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_9_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_9_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_9_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_9_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_9_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_9_project (Conv2D)        (None, 14, 14, 64)   24576       block_9_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_9_project_BN (BatchNormal (None, 14, 14, 64)   256         block_9_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_9_add (Add)               (None, 14, 14, 64)   0           block_8_add[0][0]                \n",
            "                                                                 block_9_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand (Conv2D)        (None, 14, 14, 384)  24576       block_9_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand_BN (BatchNormal (None, 14, 14, 384)  1536        block_10_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand_relu (ReLU)     (None, 14, 14, 384)  0           block_10_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise (DepthwiseCo (None, 14, 14, 384)  3456        block_10_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise_BN (BatchNor (None, 14, 14, 384)  1536        block_10_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise_relu (ReLU)  (None, 14, 14, 384)  0           block_10_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_10_project (Conv2D)       (None, 14, 14, 96)   36864       block_10_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_10_project_BN (BatchNorma (None, 14, 14, 96)   384         block_10_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand (Conv2D)        (None, 14, 14, 576)  55296       block_10_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_11_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_11_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise (DepthwiseCo (None, 14, 14, 576)  5184        block_11_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise_BN (BatchNor (None, 14, 14, 576)  2304        block_11_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise_relu (ReLU)  (None, 14, 14, 576)  0           block_11_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_11_project (Conv2D)       (None, 14, 14, 96)   55296       block_11_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_11_project_BN (BatchNorma (None, 14, 14, 96)   384         block_11_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_add (Add)              (None, 14, 14, 96)   0           block_10_project_BN[0][0]        \n",
            "                                                                 block_11_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand (Conv2D)        (None, 14, 14, 576)  55296       block_11_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_12_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_12_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise (DepthwiseCo (None, 14, 14, 576)  5184        block_12_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise_BN (BatchNor (None, 14, 14, 576)  2304        block_12_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise_relu (ReLU)  (None, 14, 14, 576)  0           block_12_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_12_project (Conv2D)       (None, 14, 14, 96)   55296       block_12_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_12_project_BN (BatchNorma (None, 14, 14, 96)   384         block_12_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_12_add (Add)              (None, 14, 14, 96)   0           block_11_add[0][0]               \n",
            "                                                                 block_12_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand (Conv2D)        (None, 14, 14, 576)  55296       block_12_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_13_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_13_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_13_pad (ZeroPadding2D)    (None, 15, 15, 576)  0           block_13_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise (DepthwiseCo (None, 7, 7, 576)    5184        block_13_pad[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise_BN (BatchNor (None, 7, 7, 576)    2304        block_13_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise_relu (ReLU)  (None, 7, 7, 576)    0           block_13_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_13_project (Conv2D)       (None, 7, 7, 160)    92160       block_13_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_13_project_BN (BatchNorma (None, 7, 7, 160)    640         block_13_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand (Conv2D)        (None, 7, 7, 960)    153600      block_13_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_14_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_14_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_14_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_14_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_14_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_14_project (Conv2D)       (None, 7, 7, 160)    153600      block_14_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_14_project_BN (BatchNorma (None, 7, 7, 160)    640         block_14_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_14_add (Add)              (None, 7, 7, 160)    0           block_13_project_BN[0][0]        \n",
            "                                                                 block_14_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand (Conv2D)        (None, 7, 7, 960)    153600      block_14_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_15_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_15_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_15_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_15_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_15_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_15_project (Conv2D)       (None, 7, 7, 160)    153600      block_15_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_15_project_BN (BatchNorma (None, 7, 7, 160)    640         block_15_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_15_add (Add)              (None, 7, 7, 160)    0           block_14_add[0][0]               \n",
            "                                                                 block_15_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand (Conv2D)        (None, 7, 7, 960)    153600      block_15_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_16_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_16_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_16_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_16_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_16_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_16_project (Conv2D)       (None, 7, 7, 320)    307200      block_16_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_16_project_BN (BatchNorma (None, 7, 7, 320)    1280        block_16_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "Conv_1 (Conv2D)                 (None, 7, 7, 1280)   409600      block_16_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "Conv_1_bn (BatchNormalization)  (None, 7, 7, 1280)   5120        Conv_1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "out_relu (ReLU)                 (None, 7, 7, 1280)   0           Conv_1_bn[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 2,257,984\n",
            "Trainable params: 0\n",
            "Non-trainable params: 2,257,984\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uJqTZqlqpGhD"
      },
      "source": [
        "# **Creating model required**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xok_y6VNpJq9"
      },
      "source": [
        "Features of the model:\n",
        "\n",
        "* Model has pretrained model for feature extraction\n",
        "* Uses GlobalMaxPooling2D\n",
        "* Artificial Deep Neural Network uses 3 Dense hidden layers with activations ReLu, and an output layer with activation 'Softmax'\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xfNZnzXQygjC"
      },
      "source": [
        "model = tf.keras.Sequential()"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZV3pdXkhykha"
      },
      "source": [
        "model.add(ptm)\n",
        "model.add(tf.keras.layers.GlobalAveragePooling2D())\n",
        "model.add(tf.keras.layers.Dense(1024, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(8, activation='softmax'))"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FY6QQWJXzsIb",
        "outputId": "b6b0a7ef-fa09-4bfa-c316-3e8ef5508fc3"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "mobilenetv2_1.00_224 (Functi (None, 7, 7, 1280)        2257984   \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d (Gl (None, 1280)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1024)              1311744   \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 8)                 8200      \n",
            "=================================================================\n",
            "Total params: 3,577,928\n",
            "Trainable params: 1,319,944\n",
            "Non-trainable params: 2,257,984\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ib_hSaBGpv6U"
      },
      "source": [
        "> Using *Adam* as the optimizer and loss fucnction as categorical CrossEntropy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r5LrY1WOzvDD"
      },
      "source": [
        "model.compile(optimizer='adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2r2AVuOhp5lH"
      },
      "source": [
        "> Training the model with validation data, for 40 epochs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iKUaRREez5H_",
        "outputId": "43b5cf71-0876-4666-af1e-12a711e4a2ff"
      },
      "source": [
        "history = model.fit(train_data, validation_data=test_data, epochs = 25)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/25\n",
            "112/112 [==============================] - ETA: 0s - loss: 1.4778 - accuracy: 0.5088"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:788: UserWarning: Corrupt EXIF data.  Expecting to read 4 bytes but only got 0. \n",
            "  warnings.warn(str(msg))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r112/112 [==============================] - 295s 2s/step - loss: 1.4744 - accuracy: 0.5098 - val_loss: 0.6837 - val_accuracy: 0.7420\n",
            "Epoch 2/25\n",
            "112/112 [==============================] - 260s 2s/step - loss: 0.6165 - accuracy: 0.7807 - val_loss: 0.6346 - val_accuracy: 0.8000\n",
            "Epoch 3/25\n",
            "112/112 [==============================] - 260s 2s/step - loss: 0.4899 - accuracy: 0.8340 - val_loss: 0.4916 - val_accuracy: 0.8406\n",
            "Epoch 4/25\n",
            "112/112 [==============================] - 259s 2s/step - loss: 0.3631 - accuracy: 0.8792 - val_loss: 0.4871 - val_accuracy: 0.8551\n",
            "Epoch 5/25\n",
            "112/112 [==============================] - 259s 2s/step - loss: 0.2889 - accuracy: 0.9026 - val_loss: 0.4019 - val_accuracy: 0.8580\n",
            "Epoch 6/25\n",
            "112/112 [==============================] - 260s 2s/step - loss: 0.2524 - accuracy: 0.9188 - val_loss: 0.5154 - val_accuracy: 0.8058\n",
            "Epoch 7/25\n",
            "112/112 [==============================] - 260s 2s/step - loss: 0.2580 - accuracy: 0.9075 - val_loss: 0.4681 - val_accuracy: 0.8348\n",
            "Epoch 8/25\n",
            "112/112 [==============================] - 260s 2s/step - loss: 0.2308 - accuracy: 0.9258 - val_loss: 0.3320 - val_accuracy: 0.8870\n",
            "Epoch 9/25\n",
            "112/112 [==============================] - 260s 2s/step - loss: 0.1662 - accuracy: 0.9481 - val_loss: 0.3451 - val_accuracy: 0.8957\n",
            "Epoch 10/25\n",
            "112/112 [==============================] - 261s 2s/step - loss: 0.1399 - accuracy: 0.9499 - val_loss: 0.3869 - val_accuracy: 0.8928\n",
            "Epoch 11/25\n",
            "112/112 [==============================] - 261s 2s/step - loss: 0.1257 - accuracy: 0.9594 - val_loss: 0.3209 - val_accuracy: 0.8899\n",
            "Epoch 12/25\n",
            "112/112 [==============================] - 263s 2s/step - loss: 0.1116 - accuracy: 0.9687 - val_loss: 0.3590 - val_accuracy: 0.8783\n",
            "Epoch 13/25\n",
            "112/112 [==============================] - 262s 2s/step - loss: 0.1142 - accuracy: 0.9668 - val_loss: 0.3721 - val_accuracy: 0.8986\n",
            "Epoch 14/25\n",
            "112/112 [==============================] - 260s 2s/step - loss: 0.0899 - accuracy: 0.9755 - val_loss: 0.3119 - val_accuracy: 0.9072\n",
            "Epoch 15/25\n",
            "112/112 [==============================] - 259s 2s/step - loss: 0.1236 - accuracy: 0.9615 - val_loss: 0.4019 - val_accuracy: 0.9072\n",
            "Epoch 16/25\n",
            "112/112 [==============================] - 261s 2s/step - loss: 0.0932 - accuracy: 0.9687 - val_loss: 0.3746 - val_accuracy: 0.8928\n",
            "Epoch 17/25\n",
            "112/112 [==============================] - 260s 2s/step - loss: 0.1207 - accuracy: 0.9517 - val_loss: 0.4595 - val_accuracy: 0.8725\n",
            "Epoch 18/25\n",
            "112/112 [==============================] - 260s 2s/step - loss: 0.0872 - accuracy: 0.9658 - val_loss: 0.3412 - val_accuracy: 0.9043\n",
            "Epoch 19/25\n",
            "112/112 [==============================] - 259s 2s/step - loss: 0.0698 - accuracy: 0.9748 - val_loss: 0.3117 - val_accuracy: 0.9043\n",
            "Epoch 20/25\n",
            "112/112 [==============================] - 259s 2s/step - loss: 0.0876 - accuracy: 0.9681 - val_loss: 0.2871 - val_accuracy: 0.9101\n",
            "Epoch 21/25\n",
            "112/112 [==============================] - 259s 2s/step - loss: 0.0891 - accuracy: 0.9656 - val_loss: 0.2915 - val_accuracy: 0.9217\n",
            "Epoch 22/25\n",
            "112/112 [==============================] - 259s 2s/step - loss: 0.0710 - accuracy: 0.9748 - val_loss: 0.3240 - val_accuracy: 0.9043\n",
            "Epoch 23/25\n",
            "112/112 [==============================] - 260s 2s/step - loss: 0.0664 - accuracy: 0.9781 - val_loss: 0.3841 - val_accuracy: 0.8783\n",
            "Epoch 24/25\n",
            "112/112 [==============================] - 260s 2s/step - loss: 0.0736 - accuracy: 0.9736 - val_loss: 0.3549 - val_accuracy: 0.9043\n",
            "Epoch 25/25\n",
            "112/112 [==============================] - 260s 2s/step - loss: 0.0615 - accuracy: 0.9842 - val_loss: 0.3571 - val_accuracy: 0.9159\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UU0yYXp3p-rI"
      },
      "source": [
        "# **Plotting Graphs**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LcToQPQvqAsa"
      },
      "source": [
        "> Using ```matplotlib.pyplot```\n",
        "* Plotting graph of Accuracy vs Validation Accuracy for each epoch.\n",
        "* Plotting graph of Loss vs Validation Loss for each epoch"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vsDicMbskw8F",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "9c6e77aa-4b8d-47b3-9796-f3bf1af45be5"
      },
      "source": [
        "plt.plot(history.history['accuracy'], label = 'Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label = 'Validation Accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend(loc = \"lower right\")"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f5ed13ab650>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "xUJPavyFlOwU",
        "outputId": "25334d78-6593-4722-f595-8c8c8cf44ced"
      },
      "source": [
        "plt.plot(history.history['loss'], label = 'Loss')\n",
        "plt.plot(history.history['val_loss'], label = 'Validation Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend(loc = \"upper right\")"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f5e7c48e7d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}