DenseNet_transfer_learning_Normalization.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RuthKassahun/Melanoma_Classification/blob/main/DenseNet_transfer_learning_Normalization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow\n",
        "from tensorflow import keras\n",
        "from keras import Sequential\n",
        "from keras.layers import Dense,Flatten\n",
        "#from keras.applications.vgg16 import VGG16\n",
        "from tensorflow.keras.applications.resnet50 import ResNet50"
      ],
      "metadata": {
        "id": "8dAgedWkp8Pi"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ChjnmP4LVv30",
        "outputId": "8d6b5123-0939-48ac-cb92-73bed5758bdc"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "conv_base = ResNet50(\n",
        "    weights='imagenet',\n",
        "    include_top = False,\n",
        "    input_shape=(150,150,3)\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xGeyBIoxp_ji",
        "outputId": "d26ad9d5-0d97-49b4-a3df-7cc729734350"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94765736/94765736 [==============================] - 7s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "conv_base.trainable = True\n",
        "\n",
        "set_trainable = False\n",
        "\n",
        "for layer in conv_base.layers:\n",
        "  if layer.name == 'block5_conv1':\n",
        "    set_trainable = True\n",
        "  if set_trainable:\n",
        "    layer.trainable = True\n",
        "  else:\n",
        "    layer.trainable = False\n",
        "\n",
        "for layer in conv_base.layers:\n",
        "  print(layer.name,layer.trainable)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rsRlwtCUqB81",
        "outputId": "1b8b67bd-ac00-46cf-b64b-762427e10f03"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input_1 False\n",
            "conv1_pad False\n",
            "conv1_conv False\n",
            "conv1_bn False\n",
            "conv1_relu False\n",
            "pool1_pad False\n",
            "pool1_pool False\n",
            "conv2_block1_1_conv False\n",
            "conv2_block1_1_bn False\n",
            "conv2_block1_1_relu False\n",
            "conv2_block1_2_conv False\n",
            "conv2_block1_2_bn False\n",
            "conv2_block1_2_relu False\n",
            "conv2_block1_0_conv False\n",
            "conv2_block1_3_conv False\n",
            "conv2_block1_0_bn False\n",
            "conv2_block1_3_bn False\n",
            "conv2_block1_add False\n",
            "conv2_block1_out False\n",
            "conv2_block2_1_conv False\n",
            "conv2_block2_1_bn False\n",
            "conv2_block2_1_relu False\n",
            "conv2_block2_2_conv False\n",
            "conv2_block2_2_bn False\n",
            "conv2_block2_2_relu False\n",
            "conv2_block2_3_conv False\n",
            "conv2_block2_3_bn False\n",
            "conv2_block2_add False\n",
            "conv2_block2_out False\n",
            "conv2_block3_1_conv False\n",
            "conv2_block3_1_bn False\n",
            "conv2_block3_1_relu False\n",
            "conv2_block3_2_conv False\n",
            "conv2_block3_2_bn False\n",
            "conv2_block3_2_relu False\n",
            "conv2_block3_3_conv False\n",
            "conv2_block3_3_bn False\n",
            "conv2_block3_add False\n",
            "conv2_block3_out False\n",
            "conv3_block1_1_conv False\n",
            "conv3_block1_1_bn False\n",
            "conv3_block1_1_relu False\n",
            "conv3_block1_2_conv False\n",
            "conv3_block1_2_bn False\n",
            "conv3_block1_2_relu False\n",
            "conv3_block1_0_conv False\n",
            "conv3_block1_3_conv False\n",
            "conv3_block1_0_bn False\n",
            "conv3_block1_3_bn False\n",
            "conv3_block1_add False\n",
            "conv3_block1_out False\n",
            "conv3_block2_1_conv False\n",
            "conv3_block2_1_bn False\n",
            "conv3_block2_1_relu False\n",
            "conv3_block2_2_conv False\n",
            "conv3_block2_2_bn False\n",
            "conv3_block2_2_relu False\n",
            "conv3_block2_3_conv False\n",
            "conv3_block2_3_bn False\n",
            "conv3_block2_add False\n",
            "conv3_block2_out False\n",
            "conv3_block3_1_conv False\n",
            "conv3_block3_1_bn False\n",
            "conv3_block3_1_relu False\n",
            "conv3_block3_2_conv False\n",
            "conv3_block3_2_bn False\n",
            "conv3_block3_2_relu False\n",
            "conv3_block3_3_conv False\n",
            "conv3_block3_3_bn False\n",
            "conv3_block3_add False\n",
            "conv3_block3_out False\n",
            "conv3_block4_1_conv False\n",
            "conv3_block4_1_bn False\n",
            "conv3_block4_1_relu False\n",
            "conv3_block4_2_conv False\n",
            "conv3_block4_2_bn False\n",
            "conv3_block4_2_relu False\n",
            "conv3_block4_3_conv False\n",
            "conv3_block4_3_bn False\n",
            "conv3_block4_add False\n",
            "conv3_block4_out False\n",
            "conv4_block1_1_conv False\n",
            "conv4_block1_1_bn False\n",
            "conv4_block1_1_relu False\n",
            "conv4_block1_2_conv False\n",
            "conv4_block1_2_bn False\n",
            "conv4_block1_2_relu False\n",
            "conv4_block1_0_conv False\n",
            "conv4_block1_3_conv False\n",
            "conv4_block1_0_bn False\n",
            "conv4_block1_3_bn False\n",
            "conv4_block1_add False\n",
            "conv4_block1_out False\n",
            "conv4_block2_1_conv False\n",
            "conv4_block2_1_bn False\n",
            "conv4_block2_1_relu False\n",
            "conv4_block2_2_conv False\n",
            "conv4_block2_2_bn False\n",
            "conv4_block2_2_relu False\n",
            "conv4_block2_3_conv False\n",
            "conv4_block2_3_bn False\n",
            "conv4_block2_add False\n",
            "conv4_block2_out False\n",
            "conv4_block3_1_conv False\n",
            "conv4_block3_1_bn False\n",
            "conv4_block3_1_relu False\n",
            "conv4_block3_2_conv False\n",
            "conv4_block3_2_bn False\n",
            "conv4_block3_2_relu False\n",
            "conv4_block3_3_conv False\n",
            "conv4_block3_3_bn False\n",
            "conv4_block3_add False\n",
            "conv4_block3_out False\n",
            "conv4_block4_1_conv False\n",
            "conv4_block4_1_bn False\n",
            "conv4_block4_1_relu False\n",
            "conv4_block4_2_conv False\n",
            "conv4_block4_2_bn False\n",
            "conv4_block4_2_relu False\n",
            "conv4_block4_3_conv False\n",
            "conv4_block4_3_bn False\n",
            "conv4_block4_add False\n",
            "conv4_block4_out False\n",
            "conv4_block5_1_conv False\n",
            "conv4_block5_1_bn False\n",
            "conv4_block5_1_relu False\n",
            "conv4_block5_2_conv False\n",
            "conv4_block5_2_bn False\n",
            "conv4_block5_2_relu False\n",
            "conv4_block5_3_conv False\n",
            "conv4_block5_3_bn False\n",
            "conv4_block5_add False\n",
            "conv4_block5_out False\n",
            "conv4_block6_1_conv False\n",
            "conv4_block6_1_bn False\n",
            "conv4_block6_1_relu False\n",
            "conv4_block6_2_conv False\n",
            "conv4_block6_2_bn False\n",
            "conv4_block6_2_relu False\n",
            "conv4_block6_3_conv False\n",
            "conv4_block6_3_bn False\n",
            "conv4_block6_add False\n",
            "conv4_block6_out False\n",
            "conv5_block1_1_conv False\n",
            "conv5_block1_1_bn False\n",
            "conv5_block1_1_relu False\n",
            "conv5_block1_2_conv False\n",
            "conv5_block1_2_bn False\n",
            "conv5_block1_2_relu False\n",
            "conv5_block1_0_conv False\n",
            "conv5_block1_3_conv False\n",
            "conv5_block1_0_bn False\n",
            "conv5_block1_3_bn False\n",
            "conv5_block1_add False\n",
            "conv5_block1_out False\n",
            "conv5_block2_1_conv False\n",
            "conv5_block2_1_bn False\n",
            "conv5_block2_1_relu False\n",
            "conv5_block2_2_conv False\n",
            "conv5_block2_2_bn False\n",
            "conv5_block2_2_relu False\n",
            "conv5_block2_3_conv False\n",
            "conv5_block2_3_bn False\n",
            "conv5_block2_add False\n",
            "conv5_block2_out False\n",
            "conv5_block3_1_conv False\n",
            "conv5_block3_1_bn False\n",
            "conv5_block3_1_relu False\n",
            "conv5_block3_2_conv False\n",
            "conv5_block3_2_bn False\n",
            "conv5_block3_2_relu False\n",
            "conv5_block3_3_conv False\n",
            "conv5_block3_3_bn False\n",
            "conv5_block3_add False\n",
            "conv5_block3_out False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "conv_base.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qMNnxAe8qxaj",
        "outputId": "9a5100dd-d221-41a0-80b1-06f51e2a10b9"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"resnet50\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)           [(None, 150, 150, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " conv1_pad (ZeroPadding2D)      (None, 156, 156, 3)  0           ['input_1[0][0]']                \n",
            "                                                                                                  \n",
            " conv1_conv (Conv2D)            (None, 75, 75, 64)   9472        ['conv1_pad[0][0]']              \n",
            "                                                                                                  \n",
            " conv1_bn (BatchNormalization)  (None, 75, 75, 64)   256         ['conv1_conv[0][0]']             \n",
            "                                                                                                  \n",
            " conv1_relu (Activation)        (None, 75, 75, 64)   0           ['conv1_bn[0][0]']               \n",
            "                                                                                                  \n",
            " pool1_pad (ZeroPadding2D)      (None, 77, 77, 64)   0           ['conv1_relu[0][0]']             \n",
            "                                                                                                  \n",
            " pool1_pool (MaxPooling2D)      (None, 38, 38, 64)   0           ['pool1_pad[0][0]']              \n",
            "                                                                                                  \n",
            " conv2_block1_1_conv (Conv2D)   (None, 38, 38, 64)   4160        ['pool1_pool[0][0]']             \n",
            "                                                                                                  \n",
            " conv2_block1_1_bn (BatchNormal  (None, 38, 38, 64)  256         ['conv2_block1_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block1_1_relu (Activatio  (None, 38, 38, 64)  0           ['conv2_block1_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_2_conv (Conv2D)   (None, 38, 38, 64)   36928       ['conv2_block1_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block1_2_bn (BatchNormal  (None, 38, 38, 64)  256         ['conv2_block1_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block1_2_relu (Activatio  (None, 38, 38, 64)  0           ['conv2_block1_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_0_conv (Conv2D)   (None, 38, 38, 256)  16640       ['pool1_pool[0][0]']             \n",
            "                                                                                                  \n",
            " conv2_block1_3_conv (Conv2D)   (None, 38, 38, 256)  16640       ['conv2_block1_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block1_0_bn (BatchNormal  (None, 38, 38, 256)  1024       ['conv2_block1_0_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block1_3_bn (BatchNormal  (None, 38, 38, 256)  1024       ['conv2_block1_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block1_add (Add)         (None, 38, 38, 256)  0           ['conv2_block1_0_bn[0][0]',      \n",
            "                                                                  'conv2_block1_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv2_block1_out (Activation)  (None, 38, 38, 256)  0           ['conv2_block1_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv2_block2_1_conv (Conv2D)   (None, 38, 38, 64)   16448       ['conv2_block1_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv2_block2_1_bn (BatchNormal  (None, 38, 38, 64)  256         ['conv2_block2_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block2_1_relu (Activatio  (None, 38, 38, 64)  0           ['conv2_block2_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block2_2_conv (Conv2D)   (None, 38, 38, 64)   36928       ['conv2_block2_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block2_2_bn (BatchNormal  (None, 38, 38, 64)  256         ['conv2_block2_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block2_2_relu (Activatio  (None, 38, 38, 64)  0           ['conv2_block2_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block2_3_conv (Conv2D)   (None, 38, 38, 256)  16640       ['conv2_block2_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block2_3_bn (BatchNormal  (None, 38, 38, 256)  1024       ['conv2_block2_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block2_add (Add)         (None, 38, 38, 256)  0           ['conv2_block1_out[0][0]',       \n",
            "                                                                  'conv2_block2_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv2_block2_out (Activation)  (None, 38, 38, 256)  0           ['conv2_block2_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv2_block3_1_conv (Conv2D)   (None, 38, 38, 64)   16448       ['conv2_block2_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv2_block3_1_bn (BatchNormal  (None, 38, 38, 64)  256         ['conv2_block3_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block3_1_relu (Activatio  (None, 38, 38, 64)  0           ['conv2_block3_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block3_2_conv (Conv2D)   (None, 38, 38, 64)   36928       ['conv2_block3_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block3_2_bn (BatchNormal  (None, 38, 38, 64)  256         ['conv2_block3_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block3_2_relu (Activatio  (None, 38, 38, 64)  0           ['conv2_block3_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block3_3_conv (Conv2D)   (None, 38, 38, 256)  16640       ['conv2_block3_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block3_3_bn (BatchNormal  (None, 38, 38, 256)  1024       ['conv2_block3_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block3_add (Add)         (None, 38, 38, 256)  0           ['conv2_block2_out[0][0]',       \n",
            "                                                                  'conv2_block3_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv2_block3_out (Activation)  (None, 38, 38, 256)  0           ['conv2_block3_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block1_1_conv (Conv2D)   (None, 19, 19, 128)  32896       ['conv2_block3_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block1_1_bn (BatchNormal  (None, 19, 19, 128)  512        ['conv3_block1_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block1_1_relu (Activatio  (None, 19, 19, 128)  0          ['conv3_block1_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_2_conv (Conv2D)   (None, 19, 19, 128)  147584      ['conv3_block1_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block1_2_bn (BatchNormal  (None, 19, 19, 128)  512        ['conv3_block1_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block1_2_relu (Activatio  (None, 19, 19, 128)  0          ['conv3_block1_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_0_conv (Conv2D)   (None, 19, 19, 512)  131584      ['conv2_block3_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block1_3_conv (Conv2D)   (None, 19, 19, 512)  66048       ['conv3_block1_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block1_0_bn (BatchNormal  (None, 19, 19, 512)  2048       ['conv3_block1_0_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block1_3_bn (BatchNormal  (None, 19, 19, 512)  2048       ['conv3_block1_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block1_add (Add)         (None, 19, 19, 512)  0           ['conv3_block1_0_bn[0][0]',      \n",
            "                                                                  'conv3_block1_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv3_block1_out (Activation)  (None, 19, 19, 512)  0           ['conv3_block1_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block2_1_conv (Conv2D)   (None, 19, 19, 128)  65664       ['conv3_block1_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block2_1_bn (BatchNormal  (None, 19, 19, 128)  512        ['conv3_block2_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block2_1_relu (Activatio  (None, 19, 19, 128)  0          ['conv3_block2_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block2_2_conv (Conv2D)   (None, 19, 19, 128)  147584      ['conv3_block2_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block2_2_bn (BatchNormal  (None, 19, 19, 128)  512        ['conv3_block2_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block2_2_relu (Activatio  (None, 19, 19, 128)  0          ['conv3_block2_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block2_3_conv (Conv2D)   (None, 19, 19, 512)  66048       ['conv3_block2_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block2_3_bn (BatchNormal  (None, 19, 19, 512)  2048       ['conv3_block2_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block2_add (Add)         (None, 19, 19, 512)  0           ['conv3_block1_out[0][0]',       \n",
            "                                                                  'conv3_block2_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv3_block2_out (Activation)  (None, 19, 19, 512)  0           ['conv3_block2_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block3_1_conv (Conv2D)   (None, 19, 19, 128)  65664       ['conv3_block2_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block3_1_bn (BatchNormal  (None, 19, 19, 128)  512        ['conv3_block3_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block3_1_relu (Activatio  (None, 19, 19, 128)  0          ['conv3_block3_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block3_2_conv (Conv2D)   (None, 19, 19, 128)  147584      ['conv3_block3_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block3_2_bn (BatchNormal  (None, 19, 19, 128)  512        ['conv3_block3_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block3_2_relu (Activatio  (None, 19, 19, 128)  0          ['conv3_block3_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block3_3_conv (Conv2D)   (None, 19, 19, 512)  66048       ['conv3_block3_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block3_3_bn (BatchNormal  (None, 19, 19, 512)  2048       ['conv3_block3_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block3_add (Add)         (None, 19, 19, 512)  0           ['conv3_block2_out[0][0]',       \n",
            "                                                                  'conv3_block3_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv3_block3_out (Activation)  (None, 19, 19, 512)  0           ['conv3_block3_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block4_1_conv (Conv2D)   (None, 19, 19, 128)  65664       ['conv3_block3_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block4_1_bn (BatchNormal  (None, 19, 19, 128)  512        ['conv3_block4_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block4_1_relu (Activatio  (None, 19, 19, 128)  0          ['conv3_block4_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block4_2_conv (Conv2D)   (None, 19, 19, 128)  147584      ['conv3_block4_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block4_2_bn (BatchNormal  (None, 19, 19, 128)  512        ['conv3_block4_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block4_2_relu (Activatio  (None, 19, 19, 128)  0          ['conv3_block4_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block4_3_conv (Conv2D)   (None, 19, 19, 512)  66048       ['conv3_block4_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block4_3_bn (BatchNormal  (None, 19, 19, 512)  2048       ['conv3_block4_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block4_add (Add)         (None, 19, 19, 512)  0           ['conv3_block3_out[0][0]',       \n",
            "                                                                  'conv3_block4_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv3_block4_out (Activation)  (None, 19, 19, 512)  0           ['conv3_block4_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block1_1_conv (Conv2D)   (None, 10, 10, 256)  131328      ['conv3_block4_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block1_1_bn (BatchNormal  (None, 10, 10, 256)  1024       ['conv4_block1_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block1_1_relu (Activatio  (None, 10, 10, 256)  0          ['conv4_block1_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_2_conv (Conv2D)   (None, 10, 10, 256)  590080      ['conv4_block1_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block1_2_bn (BatchNormal  (None, 10, 10, 256)  1024       ['conv4_block1_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block1_2_relu (Activatio  (None, 10, 10, 256)  0          ['conv4_block1_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_0_conv (Conv2D)   (None, 10, 10, 1024  525312      ['conv3_block4_out[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block1_3_conv (Conv2D)   (None, 10, 10, 1024  263168      ['conv4_block1_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block1_0_bn (BatchNormal  (None, 10, 10, 1024  4096       ['conv4_block1_0_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block1_3_bn (BatchNormal  (None, 10, 10, 1024  4096       ['conv4_block1_3_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block1_add (Add)         (None, 10, 10, 1024  0           ['conv4_block1_0_bn[0][0]',      \n",
            "                                )                                 'conv4_block1_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv4_block1_out (Activation)  (None, 10, 10, 1024  0           ['conv4_block1_add[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block2_1_conv (Conv2D)   (None, 10, 10, 256)  262400      ['conv4_block1_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block2_1_bn (BatchNormal  (None, 10, 10, 256)  1024       ['conv4_block2_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block2_1_relu (Activatio  (None, 10, 10, 256)  0          ['conv4_block2_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block2_2_conv (Conv2D)   (None, 10, 10, 256)  590080      ['conv4_block2_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block2_2_bn (BatchNormal  (None, 10, 10, 256)  1024       ['conv4_block2_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block2_2_relu (Activatio  (None, 10, 10, 256)  0          ['conv4_block2_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block2_3_conv (Conv2D)   (None, 10, 10, 1024  263168      ['conv4_block2_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block2_3_bn (BatchNormal  (None, 10, 10, 1024  4096       ['conv4_block2_3_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block2_add (Add)         (None, 10, 10, 1024  0           ['conv4_block1_out[0][0]',       \n",
            "                                )                                 'conv4_block2_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv4_block2_out (Activation)  (None, 10, 10, 1024  0           ['conv4_block2_add[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block3_1_conv (Conv2D)   (None, 10, 10, 256)  262400      ['conv4_block2_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block3_1_bn (BatchNormal  (None, 10, 10, 256)  1024       ['conv4_block3_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block3_1_relu (Activatio  (None, 10, 10, 256)  0          ['conv4_block3_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block3_2_conv (Conv2D)   (None, 10, 10, 256)  590080      ['conv4_block3_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block3_2_bn (BatchNormal  (None, 10, 10, 256)  1024       ['conv4_block3_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block3_2_relu (Activatio  (None, 10, 10, 256)  0          ['conv4_block3_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block3_3_conv (Conv2D)   (None, 10, 10, 1024  263168      ['conv4_block3_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block3_3_bn (BatchNormal  (None, 10, 10, 1024  4096       ['conv4_block3_3_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block3_add (Add)         (None, 10, 10, 1024  0           ['conv4_block2_out[0][0]',       \n",
            "                                )                                 'conv4_block3_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv4_block3_out (Activation)  (None, 10, 10, 1024  0           ['conv4_block3_add[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block4_1_conv (Conv2D)   (None, 10, 10, 256)  262400      ['conv4_block3_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block4_1_bn (BatchNormal  (None, 10, 10, 256)  1024       ['conv4_block4_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block4_1_relu (Activatio  (None, 10, 10, 256)  0          ['conv4_block4_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block4_2_conv (Conv2D)   (None, 10, 10, 256)  590080      ['conv4_block4_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block4_2_bn (BatchNormal  (None, 10, 10, 256)  1024       ['conv4_block4_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block4_2_relu (Activatio  (None, 10, 10, 256)  0          ['conv4_block4_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block4_3_conv (Conv2D)   (None, 10, 10, 1024  263168      ['conv4_block4_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block4_3_bn (BatchNormal  (None, 10, 10, 1024  4096       ['conv4_block4_3_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block4_add (Add)         (None, 10, 10, 1024  0           ['conv4_block3_out[0][0]',       \n",
            "                                )                                 'conv4_block4_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv4_block4_out (Activation)  (None, 10, 10, 1024  0           ['conv4_block4_add[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block5_1_conv (Conv2D)   (None, 10, 10, 256)  262400      ['conv4_block4_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block5_1_bn (BatchNormal  (None, 10, 10, 256)  1024       ['conv4_block5_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block5_1_relu (Activatio  (None, 10, 10, 256)  0          ['conv4_block5_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block5_2_conv (Conv2D)   (None, 10, 10, 256)  590080      ['conv4_block5_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block5_2_bn (BatchNormal  (None, 10, 10, 256)  1024       ['conv4_block5_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block5_2_relu (Activatio  (None, 10, 10, 256)  0          ['conv4_block5_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block5_3_conv (Conv2D)   (None, 10, 10, 1024  263168      ['conv4_block5_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block5_3_bn (BatchNormal  (None, 10, 10, 1024  4096       ['conv4_block5_3_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block5_add (Add)         (None, 10, 10, 1024  0           ['conv4_block4_out[0][0]',       \n",
            "                                )                                 'conv4_block5_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv4_block5_out (Activation)  (None, 10, 10, 1024  0           ['conv4_block5_add[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block6_1_conv (Conv2D)   (None, 10, 10, 256)  262400      ['conv4_block5_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block6_1_bn (BatchNormal  (None, 10, 10, 256)  1024       ['conv4_block6_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block6_1_relu (Activatio  (None, 10, 10, 256)  0          ['conv4_block6_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block6_2_conv (Conv2D)   (None, 10, 10, 256)  590080      ['conv4_block6_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block6_2_bn (BatchNormal  (None, 10, 10, 256)  1024       ['conv4_block6_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block6_2_relu (Activatio  (None, 10, 10, 256)  0          ['conv4_block6_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block6_3_conv (Conv2D)   (None, 10, 10, 1024  263168      ['conv4_block6_2_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block6_3_bn (BatchNormal  (None, 10, 10, 1024  4096       ['conv4_block6_3_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " conv4_block6_add (Add)         (None, 10, 10, 1024  0           ['conv4_block5_out[0][0]',       \n",
            "                                )                                 'conv4_block6_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv4_block6_out (Activation)  (None, 10, 10, 1024  0           ['conv4_block6_add[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv5_block1_1_conv (Conv2D)   (None, 5, 5, 512)    524800      ['conv4_block6_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block1_1_bn (BatchNormal  (None, 5, 5, 512)   2048        ['conv5_block1_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block1_1_relu (Activatio  (None, 5, 5, 512)   0           ['conv5_block1_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block1_2_conv (Conv2D)   (None, 5, 5, 512)    2359808     ['conv5_block1_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block1_2_bn (BatchNormal  (None, 5, 5, 512)   2048        ['conv5_block1_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block1_2_relu (Activatio  (None, 5, 5, 512)   0           ['conv5_block1_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block1_0_conv (Conv2D)   (None, 5, 5, 2048)   2099200     ['conv4_block6_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block1_3_conv (Conv2D)   (None, 5, 5, 2048)   1050624     ['conv5_block1_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block1_0_bn (BatchNormal  (None, 5, 5, 2048)  8192        ['conv5_block1_0_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block1_3_bn (BatchNormal  (None, 5, 5, 2048)  8192        ['conv5_block1_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block1_add (Add)         (None, 5, 5, 2048)   0           ['conv5_block1_0_bn[0][0]',      \n",
            "                                                                  'conv5_block1_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv5_block1_out (Activation)  (None, 5, 5, 2048)   0           ['conv5_block1_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block2_1_conv (Conv2D)   (None, 5, 5, 512)    1049088     ['conv5_block1_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block2_1_bn (BatchNormal  (None, 5, 5, 512)   2048        ['conv5_block2_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block2_1_relu (Activatio  (None, 5, 5, 512)   0           ['conv5_block2_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block2_2_conv (Conv2D)   (None, 5, 5, 512)    2359808     ['conv5_block2_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block2_2_bn (BatchNormal  (None, 5, 5, 512)   2048        ['conv5_block2_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block2_2_relu (Activatio  (None, 5, 5, 512)   0           ['conv5_block2_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block2_3_conv (Conv2D)   (None, 5, 5, 2048)   1050624     ['conv5_block2_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block2_3_bn (BatchNormal  (None, 5, 5, 2048)  8192        ['conv5_block2_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block2_add (Add)         (None, 5, 5, 2048)   0           ['conv5_block1_out[0][0]',       \n",
            "                                                                  'conv5_block2_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv5_block2_out (Activation)  (None, 5, 5, 2048)   0           ['conv5_block2_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block3_1_conv (Conv2D)   (None, 5, 5, 512)    1049088     ['conv5_block2_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block3_1_bn (BatchNormal  (None, 5, 5, 512)   2048        ['conv5_block3_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block3_1_relu (Activatio  (None, 5, 5, 512)   0           ['conv5_block3_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block3_2_conv (Conv2D)   (None, 5, 5, 512)    2359808     ['conv5_block3_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block3_2_bn (BatchNormal  (None, 5, 5, 512)   2048        ['conv5_block3_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block3_2_relu (Activatio  (None, 5, 5, 512)   0           ['conv5_block3_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block3_3_conv (Conv2D)   (None, 5, 5, 2048)   1050624     ['conv5_block3_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block3_3_bn (BatchNormal  (None, 5, 5, 2048)  8192        ['conv5_block3_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block3_add (Add)         (None, 5, 5, 2048)   0           ['conv5_block2_out[0][0]',       \n",
            "                                                                  'conv5_block3_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv5_block3_out (Activation)  (None, 5, 5, 2048)   0           ['conv5_block3_add[0][0]']       \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 23,587,712\n",
            "Trainable params: 0\n",
            "Non-trainable params: 23,587,712\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(conv_base)\n",
        "model.add(Flatten())\n",
        "model.add(Dense(256,activation='relu'))\n",
        "model.add(Dense(1,activation='sigmoid'))"
      ],
      "metadata": {
        "id": "MuCh9EqrsMPK"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# generators\n",
        "train_ds = keras.utils.image_dataset_from_directory(\n",
        "    directory = '/content/drive/MyDrive/MultiClass_Skin_lesion/train/',\n",
        "    labels='inferred',\n",
        "    label_mode = 'int',\n",
        "    batch_size=32,\n",
        "    image_size=(150,150)\n",
        ")\n",
        "\n",
        "validation_ds = keras.utils.image_dataset_from_directory(\n",
        "    directory = '/content/drive/MyDrive/MultiClass_Skin_lesion/val/',\n",
        "    labels='inferred',\n",
        "    label_mode = 'int',\n",
        "    batch_size=32,\n",
        "    image_size=(150,150)\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "js3QmFghq1AC",
        "outputId": "d6cb2f09-76dc-4159-b819-c4faf0da95ca"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 5082 files belonging to 3 classes.\n",
            "Found 1270 files belonging to 3 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalize\n",
        "def process(image,label):\n",
        "    image = tensorflow.cast(image/255. ,tensorflow.float32)\n",
        "    return image,label\n",
        "\n",
        "train_ds = train_ds.map(process)\n",
        "validation_ds = validation_ds.map(process)"
      ],
      "metadata": {
        "id": "DjbuBEQkr3DE"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow-addons"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FtEE8sj1XJNw",
        "outputId": "859f6ba7-9fd6-44a3-ec34-c31d421fe753"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting tensorflow-addons\n",
            "  Downloading tensorflow_addons-0.19.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m50.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from tensorflow-addons) (21.3)\n",
            "Requirement already satisfied: typeguard>=2.7 in /usr/local/lib/python3.8/dist-packages (from tensorflow-addons) (2.7.1)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging->tensorflow-addons) (3.0.9)\n",
            "Installing collected packages: tensorflow-addons\n",
            "Successfully installed tensorflow-addons-0.19.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow_addons as tfa"
      ],
      "metadata": {
        "id": "9HnlI2uQYYve"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    optimizer=keras.optimizers.RMSprop(lr=1e-5),\n",
        "    loss='binary_crossentropy',\n",
        "    metrics=['accuracy']\n",
        "  )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nxUjDM6Nr5Qp",
        "outputId": "553cec6c-b2b1-48b0-b6d2-b628f0d76030"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/keras/optimizers/optimizer_v2/rmsprop.py:135: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(RMSprop, self).__init__(name, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(train_ds,epochs=10,validation_data=validation_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tiat79J_sG6W",
        "outputId": "27be7761-09ef-4cb0-e671-38ad80218c83"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "159/159 [==============================] - 2625s 16s/step - loss: 0.6278 - accuracy: 0.5305 - val_loss: 0.6068 - val_accuracy: 0.5339\n",
            "Epoch 2/10\n",
            "159/159 [==============================] - 59s 357ms/step - loss: 0.6130 - accuracy: 0.5370 - val_loss: 0.5963 - val_accuracy: 0.5339\n",
            "Epoch 3/10\n",
            "159/159 [==============================] - 57s 346ms/step - loss: 0.6039 - accuracy: 0.5354 - val_loss: 0.5877 - val_accuracy: 0.5339\n",
            "Epoch 4/10\n",
            "159/159 [==============================] - 57s 346ms/step - loss: 0.5974 - accuracy: 0.5403 - val_loss: 0.6044 - val_accuracy: 0.6094\n",
            "Epoch 5/10\n",
            "159/159 [==============================] - 66s 404ms/step - loss: 0.5897 - accuracy: 0.5403 - val_loss: 0.5749 - val_accuracy: 0.5339\n",
            "Epoch 6/10\n",
            "159/159 [==============================] - 57s 344ms/step - loss: 0.5810 - accuracy: 0.5439 - val_loss: 0.5691 - val_accuracy: 0.5339\n",
            "Epoch 7/10\n",
            "159/159 [==============================] - 57s 346ms/step - loss: 0.5784 - accuracy: 0.5496 - val_loss: 0.6018 - val_accuracy: 0.6630\n",
            "Epoch 8/10\n",
            "159/159 [==============================] - 63s 368ms/step - loss: 0.5713 - accuracy: 0.5557 - val_loss: 0.5591 - val_accuracy: 0.5346\n",
            "Epoch 9/10\n",
            "159/159 [==============================] - 57s 347ms/step - loss: 0.5672 - accuracy: 0.5527 - val_loss: 0.5561 - val_accuracy: 0.5472\n",
            "Epoch 10/10\n",
            "159/159 [==============================] - 56s 342ms/step - loss: 0.5606 - accuracy: 0.5618 - val_loss: 0.5635 - val_accuracy: 0.6299\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(history.history['accuracy'],color='red',label='train')\n",
        "plt.plot(history.history['val_accuracy'],color='blue',label='validation')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "M8c6eVF4sVTT",
        "outputId": "f49a33f3-6092-4139-db0e-b4d8ef34b7a7"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'],color='red',label='train')\n",
        "plt.plot(history.history['val_loss'],color='blue',label='validation')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "Ap3-7R0swWTo",
        "outputId": "0d39e42d-29cd-421d-c2c3-e349e48402fc"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction_vgg = model.predict(validation_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xjaBZM7xiKO7",
        "outputId": "84e34bef-921b-44e7-8653-fd60331b5a83"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "40/40 [==============================] - 12s 230ms/step\n"
          ]
        }
      ]
    }
  ]
}