Kvasir_dataset.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Kvasir_dataset.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/YogyaGamage/Kvasir-dataset/blob/master/Kvasir_dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pjfkBTEBmsCb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Code to read csv file into Colaboratory:\n",
        "!pip install -U -q PyDrive\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "# Authenticate and create the PyDrive client.\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lICsQs5h6uSS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "'https://drive.google.com/open?id=1ld2mCzpeymCjm7-QzPGBxwol0R4uhIeZ'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3x8La2ZZ60kt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6ed1033e-f7ce-4fd1-d924-663fa77895f8"
      },
      "source": [
        "link='https://drive.google.com/open?id=1ld2mCzpeymCjm7-QzPGBxwol0R4uhIeZ'\n",
        "fluff, id = link.split('=')\n",
        "print (id) # Verify that you have everything after '='"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1ld2mCzpeymCjm7-QzPGBxwol0R4uhIeZ\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z3gBYAlE9Vc2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "outputId": "373b1f1f-1c41-4d1d-800b-736f9324a1a0"
      },
      "source": [
        "from google.colab import drive\n",
        "\n",
        "# This will prompt for authorization.\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "!ls \"/content/drive/My Drive\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n",
            "'170018D_170046K_170174B_Critical Reading.gdoc'\n",
            " 170018D#170046K#170174B.gdoc\n",
            " 170174B.gdoc\n",
            " 20170205201797782109600019181.pdf\n",
            "'CIMA Certificate Paper C2 - Fundamentals Of Financial Accounting - Study Text.pdf'\n",
            "'Colab Notebooks'\n",
            " datathon.gdoc\n",
            " Document2.rtf\n",
            " files.zip\n",
            "'files.zip (Unzipped Files)'\n",
            "'How to get started with Drive.pdf'\n",
            "'InsTrucTions + KEY.txt'\n",
            "'InsTrucTions + KEY.txt.gdoc'\n",
            " IntM_draw.gsheet\n",
            " Module2.txt\n",
            "'MoraXtreme - Initial Round - Sample Answers.rar'\n",
            " train.zip\n",
            "'U 8 girls draw.gsheet'\n",
            " validation.zip\n",
            "'Vietnam Tour 7Days.pdf'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HSBi-Qrg_rjp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "outputId": "e2b67adf-9325-4fb1-88aa-97bd727f7fa6"
      },
      "source": [
        "!apt-get install -y -qq software-properties-common python-software-properties module-init-tools\n",
        "!add-apt-repository -y ppa:alessandro-strada/ppa 2>&1 > /dev/null\n",
        "!apt-get update -qq 2>&1 > /dev/null\n",
        "!apt-get -y install -qq google-drive-ocamlfuse fuse\n",
        "from google.colab import auth\n",
        "auth.authenticate_user()\n",
        "from oauth2client.client import GoogleCredentials\n",
        "creds = GoogleCredentials.get_application_default()\n",
        "import getpass\n",
        "!google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret} < /dev/null 2>&1 | grep URL\n",
        "vcode = getpass.getpass()\n",
        "!echo {vcode} | google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret}\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "E: Package 'python-software-properties' has no installation candidate\n",
            "Selecting previously unselected package google-drive-ocamlfuse.\n",
            "(Reading database ... 132681 files and directories currently installed.)\n",
            "Preparing to unpack .../google-drive-ocamlfuse_0.7.13-0ubuntu1~ubuntu18.04.1_amd64.deb ...\n",
            "Unpacking google-drive-ocamlfuse (0.7.13-0ubuntu1~ubuntu18.04.1) ...\n",
            "Setting up google-drive-ocamlfuse (0.7.13-0ubuntu1~ubuntu18.04.1) ...\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
            "WARNING:tensorflow:\n",
            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
            "For more information, please see:\n",
            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
            "  * https://github.com/tensorflow/addons\n",
            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
            "If you depend on functionality not listed there, please file an issue.\n",
            "\n",
            "Please, open the following URL in a web browser: https://accounts.google.com/o/oauth2/auth?client_id=32555940559.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive&response_type=code&access_type=offline&approval_prompt=force\n",
            "··········\n",
            "Please, open the following URL in a web browser: https://accounts.google.com/o/oauth2/auth?client_id=32555940559.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive&response_type=code&access_type=offline&approval_prompt=force\n",
            "Please enter the verification code: Access token retrieved correctly.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VofQWuqgAT0U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "0e10da21-ecac-43ae-eb04-8bdba4a7d516"
      },
      "source": [
        "!mkdir drive\n",
        "!google-drive-ocamlfuse drive\n",
        "!ls drive/\"Colab Notebooks\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mkdir: cannot create directory ‘drive’: File exists\n",
            "fuse: mountpoint is not empty\n",
            "fuse: if you are sure this is safe, use the 'nonempty' mount option\n",
            "ls: cannot access 'drive/Colab Notebooks': No such file or directory\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oGWRI4RX-2By",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!unzip '/content/drive/My Drive/train.zip'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HrqpwHM7CmVP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!unzip '/content/drive/My Drive/validation.zip'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Npt6BRc3DA-p",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "517c9104-5a80-463d-ed23-60d0f8250339"
      },
      "source": [
        "import keras\n",
        "from keras.utils import to_categorical\n",
        "\n",
        "from keras.models import Model\n",
        "from keras.layers import Dense, GlobalAveragePooling2D, Dropout\n",
        "from keras import optimizers\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "\n",
        "train_dir='/content/train'\n",
        "validation_dir='/content/validation'\n",
        "train_dyed_lifted_polyps_dir=os.path.join(train_dir, 'dyed-lifted-polyps')\n",
        "train_dyed_resection_margins_dir=os.path.join(train_dir, 'dyed-resection-margins')\n",
        "train_esophagitis_dir=os.path.join(train_dir, 'esophagitis')\n",
        "train_normal_cecum_dir=os.path.join(train_dir, 'normal-cecum')\n",
        "train_normal_pylorus_dir=os.path.join(train_dir, 'pylorus')\n",
        "train_normal_z_line_dir=os.path.join(train_dir, 'normal-z-line')\n",
        "train_polyps_dir=os.path.join(train_dir, 'polyps')\n",
        "train_ulcerative_colitis_dir=os.path.join(train_dir, 'ulcerative-colitis')\n",
        "\n",
        "validation_dyed_lifted_polyps_dir =os.path.join(validation_dir, 'dyed-lifted-polyps')\n",
        "validation_dyed_resection_margins_dir =os.path.join(validation_dir, 'dyed-resection-margins')\n",
        "validation_esophagitis_dir =os.path.join(validation_dir, 'esophagitis')\n",
        "validation_normal_cecum_dir =os.path.join(validation_dir, 'normal-cecum')\n",
        "validation_pylorus_dir =os.path.join(validation_dir, 'pylorus')\n",
        "validation_normal_z_line_dir =os.path.join(validation_dir, 'normal-z-line')\n",
        "validation_polyps_dir =os.path.join(validation_dir, 'polyps')\n",
        "validation_ulcerative_colitis_dir =os.path.join(validation_dir, 'ulcerative-colitis')\n",
        "\n",
        "\n",
        "IMG_HEIGHT = 224\n",
        "IMG_WIDTH = 224\n",
        "EPOCHS = 100\n",
        "BS = 64\n",
        "train_image_generator = keras.preprocessing.image.ImageDataGenerator(rescale=1./255) \n",
        "validation_image_generator = keras.preprocessing.image.ImageDataGenerator(rescale=1./255) \n",
        "\n",
        "train_data_gen = train_image_generator.flow_from_directory(batch_size=BS,\n",
        "                                                           directory=train_dir,\n",
        "                                                           shuffle=False,\n",
        "                                                           target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
        "                                                           class_mode='categorical',\n",
        "\t\t\t\t\t\t\t\t\t\t\t\t\t\t   )\n",
        "val_data_gen = validation_image_generator.flow_from_directory(batch_size=BS,\n",
        "                                                              directory=validation_dir,\n",
        "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t  shuffle=False,\n",
        "                                                              target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
        "                                                              class_mode='categorical')\n",
        " \n",
        "\n",
        "\n",
        "vgg16_model=keras.applications.vgg16.VGG16()\n",
        "\n",
        "\n",
        "\n",
        "model=keras.models.Sequential()\n",
        "for layer in vgg16_model.layers:\n",
        "    model.add(layer)\n",
        "\n",
        "model.layers.pop()\n",
        "for layer in model.layers:\n",
        "    layer.trainable=False\n",
        "\n",
        "\n",
        "model.add(keras.layers.Dense(512,activation='relu'))\n",
        "model.add(keras.layers.Dense(8,activation='softmax'))\n",
        "\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "                      optimizer='RMSprop',\n",
        "                      metrics=['accuracy'])\n",
        "history = model.fit_generator(\n",
        "    train_data_gen,\n",
        "    steps_per_epoch=5700/BS,\n",
        "    epochs=EPOCHS,\n",
        "    validation_data=val_data_gen,\n",
        "    validation_steps=2300/BS\n",
        ")\n",
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs_range = range(EPOCHS)\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
        "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(epochs_range, loss, label='Training Loss')\n",
        "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.show()\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 5700 images belonging to 8 classes.\n",
            "Found 2300 images belonging to 8 classes.\n",
            "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5\n",
            "553467904/553467096 [==============================] - 36s 0us/step\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:203: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3576: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "Epoch 1/100\n",
            "90/89 [==============================] - 172s 2s/step - loss: 2.0892 - acc: 0.0837 - val_loss: 2.0783 - val_acc: 0.0878\n",
            "Epoch 2/100\n",
            "90/89 [==============================] - 147s 2s/step - loss: 2.0829 - acc: 0.1226 - val_loss: 2.0760 - val_acc: 0.1609\n",
            "Epoch 3/100\n",
            "90/89 [==============================] - 149s 2s/step - loss: 2.0788 - acc: 0.1195 - val_loss: 2.0707 - val_acc: 0.1435\n",
            "Epoch 4/100\n",
            "90/89 [==============================] - 148s 2s/step - loss: 2.0772 - acc: 0.1553 - val_loss: 2.0672 - val_acc: 0.1870\n",
            "Epoch 5/100\n",
            "90/89 [==============================] - 149s 2s/step - loss: 2.0719 - acc: 0.1571 - val_loss: 2.0604 - val_acc: 0.1491\n",
            "Epoch 6/100\n",
            "90/89 [==============================] - 145s 2s/step - loss: 2.0630 - acc: 0.1625 - val_loss: 2.0550 - val_acc: 0.1313\n",
            "Epoch 7/100\n",
            "90/89 [==============================] - 153s 2s/step - loss: 2.0566 - acc: 0.1574 - val_loss: 2.0494 - val_acc: 0.1517\n",
            "Epoch 8/100\n",
            "90/89 [==============================] - 149s 2s/step - loss: 2.0487 - acc: 0.1872 - val_loss: 2.0370 - val_acc: 0.2478\n",
            "Epoch 9/100\n",
            "90/89 [==============================] - 144s 2s/step - loss: 2.0353 - acc: 0.2175 - val_loss: 2.0231 - val_acc: 0.2809\n",
            "Epoch 10/100\n",
            "90/89 [==============================] - 150s 2s/step - loss: 2.0265 - acc: 0.2402 - val_loss: 2.0128 - val_acc: 0.2457\n",
            "Epoch 11/100\n",
            "90/89 [==============================] - 148s 2s/step - loss: 2.0078 - acc: 0.2466 - val_loss: 1.9973 - val_acc: 0.2852\n",
            "Epoch 12/100\n",
            "90/89 [==============================] - 145s 2s/step - loss: 1.9975 - acc: 0.2536 - val_loss: 1.9810 - val_acc: 0.2822\n",
            "Epoch 13/100\n",
            "90/89 [==============================] - 151s 2s/step - loss: 1.9796 - acc: 0.2778 - val_loss: 1.9677 - val_acc: 0.2465\n",
            "Epoch 14/100\n",
            "90/89 [==============================] - 146s 2s/step - loss: 1.9617 - acc: 0.2780 - val_loss: 1.9446 - val_acc: 0.2865\n",
            "Epoch 15/100\n",
            "90/89 [==============================] - 147s 2s/step - loss: 1.9377 - acc: 0.2918 - val_loss: 1.9364 - val_acc: 0.2370\n",
            "Epoch 16/100\n",
            "90/89 [==============================] - 145s 2s/step - loss: 1.9221 - acc: 0.2813 - val_loss: 1.9068 - val_acc: 0.2891\n",
            "Epoch 17/100\n",
            "90/89 [==============================] - 148s 2s/step - loss: 1.9049 - acc: 0.2947 - val_loss: 1.8875 - val_acc: 0.2874\n",
            "Epoch 18/100\n",
            "90/89 [==============================] - 148s 2s/step - loss: 1.8795 - acc: 0.2949 - val_loss: 1.8674 - val_acc: 0.3052\n",
            "Epoch 19/100\n",
            "90/89 [==============================] - 148s 2s/step - loss: 1.8558 - acc: 0.3178 - val_loss: 1.8651 - val_acc: 0.2396\n",
            "Epoch 20/100\n",
            "90/89 [==============================] - 145s 2s/step - loss: 1.8495 - acc: 0.2991 - val_loss: 1.8298 - val_acc: 0.2848\n",
            "Epoch 21/100\n",
            "90/89 [==============================] - 152s 2s/step - loss: 1.8274 - acc: 0.3008 - val_loss: 1.8137 - val_acc: 0.2835\n",
            "Epoch 22/100\n",
            "90/89 [==============================] - 146s 2s/step - loss: 1.8052 - acc: 0.3209 - val_loss: 1.7961 - val_acc: 0.2852\n",
            "Epoch 23/100\n",
            "90/89 [==============================] - 147s 2s/step - loss: 1.7807 - acc: 0.3100 - val_loss: 1.7892 - val_acc: 0.2687\n",
            "Epoch 24/100\n",
            "90/89 [==============================] - 145s 2s/step - loss: 1.7766 - acc: 0.3109 - val_loss: 1.7571 - val_acc: 0.3083\n",
            "Epoch 25/100\n",
            "90/89 [==============================] - 146s 2s/step - loss: 1.7472 - acc: 0.3145 - val_loss: 1.7586 - val_acc: 0.3048\n",
            "Epoch 26/100\n",
            "90/89 [==============================] - 149s 2s/step - loss: 1.7324 - acc: 0.3241 - val_loss: 1.7303 - val_acc: 0.3130\n",
            "Epoch 27/100\n",
            "90/89 [==============================] - 150s 2s/step - loss: 1.7226 - acc: 0.3278 - val_loss: 1.7257 - val_acc: 0.2861\n",
            "Epoch 28/100\n",
            "90/89 [==============================] - 146s 2s/step - loss: 1.7061 - acc: 0.3274 - val_loss: 1.7115 - val_acc: 0.2887\n",
            "Epoch 29/100\n",
            "90/89 [==============================] - 151s 2s/step - loss: 1.6973 - acc: 0.3404 - val_loss: 1.6881 - val_acc: 0.3061\n",
            "Epoch 30/100\n",
            "90/89 [==============================] - 145s 2s/step - loss: 1.6711 - acc: 0.3362 - val_loss: 1.6686 - val_acc: 0.3148\n",
            "Epoch 31/100\n",
            "90/89 [==============================] - 142s 2s/step - loss: 1.6669 - acc: 0.3317 - val_loss: 1.6614 - val_acc: 0.3161\n",
            "Epoch 32/100\n",
            "90/89 [==============================] - 147s 2s/step - loss: 1.6579 - acc: 0.3370 - val_loss: 1.6452 - val_acc: 0.3517\n",
            "Epoch 33/100\n",
            "90/89 [==============================] - 147s 2s/step - loss: 1.6413 - acc: 0.3484 - val_loss: 1.6339 - val_acc: 0.3287\n",
            "Epoch 34/100\n",
            "90/89 [==============================] - 143s 2s/step - loss: 1.6241 - acc: 0.3432 - val_loss: 1.6502 - val_acc: 0.2987\n",
            "Epoch 35/100\n",
            "90/89 [==============================] - 145s 2s/step - loss: 1.6159 - acc: 0.3408 - val_loss: 1.6204 - val_acc: 0.3174\n",
            "Epoch 36/100\n",
            "90/89 [==============================] - 146s 2s/step - loss: 1.6086 - acc: 0.3547 - val_loss: 1.6139 - val_acc: 0.3017\n",
            "Epoch 37/100\n",
            "90/89 [==============================] - 143s 2s/step - loss: 1.5985 - acc: 0.3435 - val_loss: 1.5964 - val_acc: 0.3300\n",
            "Epoch 38/100\n",
            "90/89 [==============================] - 149s 2s/step - loss: 1.5851 - acc: 0.3439 - val_loss: 1.5868 - val_acc: 0.3683\n",
            "Epoch 39/100\n",
            "90/89 [==============================] - 142s 2s/step - loss: 1.5710 - acc: 0.3557 - val_loss: 1.5727 - val_acc: 0.3387\n",
            "Epoch 40/100\n",
            "90/89 [==============================] - 148s 2s/step - loss: 1.5739 - acc: 0.3583 - val_loss: 1.5632 - val_acc: 0.3748\n",
            "Epoch 41/100\n",
            "90/89 [==============================] - 146s 2s/step - loss: 1.5604 - acc: 0.3590 - val_loss: 1.5481 - val_acc: 0.3687\n",
            "Epoch 42/100\n",
            "90/89 [==============================] - 144s 2s/step - loss: 1.5514 - acc: 0.3538 - val_loss: 1.5456 - val_acc: 0.3465\n",
            "Epoch 43/100\n",
            "90/89 [==============================] - 147s 2s/step - loss: 1.5410 - acc: 0.3609 - val_loss: 1.5379 - val_acc: 0.3457\n",
            "Epoch 44/100\n",
            "90/89 [==============================] - 146s 2s/step - loss: 1.5324 - acc: 0.3531 - val_loss: 1.5233 - val_acc: 0.3735\n",
            "Epoch 45/100\n",
            "90/89 [==============================] - 146s 2s/step - loss: 1.5267 - acc: 0.3651 - val_loss: 1.5162 - val_acc: 0.3674\n",
            "Epoch 46/100\n",
            "90/89 [==============================] - 147s 2s/step - loss: 1.5186 - acc: 0.3612 - val_loss: 1.5155 - val_acc: 0.3596\n",
            "Epoch 47/100\n",
            "90/89 [==============================] - 148s 2s/step - loss: 1.5160 - acc: 0.3632 - val_loss: 1.5054 - val_acc: 0.3743\n",
            "Epoch 48/100\n",
            "90/89 [==============================] - 147s 2s/step - loss: 1.5083 - acc: 0.3708 - val_loss: 1.4912 - val_acc: 0.3704\n",
            "Epoch 49/100\n",
            "90/89 [==============================] - 146s 2s/step - loss: 1.4899 - acc: 0.3673 - val_loss: 1.4951 - val_acc: 0.3587\n",
            "Epoch 50/100\n",
            "90/89 [==============================] - 150s 2s/step - loss: 1.4906 - acc: 0.3671 - val_loss: 1.4830 - val_acc: 0.3817\n",
            "Epoch 51/100\n",
            "90/89 [==============================] - 145s 2s/step - loss: 1.4836 - acc: 0.3684 - val_loss: 1.4875 - val_acc: 0.3826\n",
            "Epoch 52/100\n",
            "90/89 [==============================] - 148s 2s/step - loss: 1.4797 - acc: 0.3654 - val_loss: 1.4699 - val_acc: 0.3691\n",
            "Epoch 53/100\n",
            "90/89 [==============================] - 148s 2s/step - loss: 1.4687 - acc: 0.3639 - val_loss: 1.4882 - val_acc: 0.3852\n",
            "Epoch 54/100\n",
            "90/89 [==============================] - 145s 2s/step - loss: 1.4721 - acc: 0.3637 - val_loss: 1.4732 - val_acc: 0.3878\n",
            "Epoch 55/100\n",
            "90/89 [==============================] - 148s 2s/step - loss: 1.4601 - acc: 0.3668 - val_loss: 1.4779 - val_acc: 0.3939\n",
            "Epoch 56/100\n",
            "90/89 [==============================] - 146s 2s/step - loss: 1.4562 - acc: 0.3623 - val_loss: 1.4446 - val_acc: 0.3961\n",
            "Epoch 57/100\n",
            "90/89 [==============================] - 147s 2s/step - loss: 1.4495 - acc: 0.3835 - val_loss: 1.4512 - val_acc: 0.3648\n",
            "Epoch 58/100\n",
            "90/89 [==============================] - 147s 2s/step - loss: 1.4441 - acc: 0.3783 - val_loss: 1.4381 - val_acc: 0.3852\n",
            "Epoch 59/100\n",
            "90/89 [==============================] - 150s 2s/step - loss: 1.4313 - acc: 0.3753 - val_loss: 1.4293 - val_acc: 0.4122\n",
            "Epoch 60/100\n",
            "90/89 [==============================] - 152s 2s/step - loss: 1.4289 - acc: 0.3816 - val_loss: 1.4385 - val_acc: 0.3743\n",
            "Epoch 61/100\n",
            "90/89 [==============================] - 144s 2s/step - loss: 1.4298 - acc: 0.3869 - val_loss: 1.4135 - val_acc: 0.4052\n",
            "Epoch 62/100\n",
            "90/89 [==============================] - 142s 2s/step - loss: 1.4242 - acc: 0.3762 - val_loss: 1.4217 - val_acc: 0.3874\n",
            "Epoch 63/100\n",
            "90/89 [==============================] - 152s 2s/step - loss: 1.4154 - acc: 0.3877 - val_loss: 1.4417 - val_acc: 0.4009\n",
            "Epoch 64/100\n",
            "90/89 [==============================] - 146s 2s/step - loss: 1.4182 - acc: 0.3800 - val_loss: 1.4039 - val_acc: 0.3878\n",
            "Epoch 65/100\n",
            "90/89 [==============================] - 146s 2s/step - loss: 1.4160 - acc: 0.3936 - val_loss: 1.4009 - val_acc: 0.3843\n",
            "Epoch 66/100\n",
            "90/89 [==============================] - 147s 2s/step - loss: 1.4089 - acc: 0.3862 - val_loss: 1.3986 - val_acc: 0.3752\n",
            "Epoch 67/100\n",
            "90/89 [==============================] - 148s 2s/step - loss: 1.4064 - acc: 0.3762 - val_loss: 1.3922 - val_acc: 0.4139\n",
            "Epoch 68/100\n",
            "90/89 [==============================] - 146s 2s/step - loss: 1.4051 - acc: 0.3967 - val_loss: 1.3923 - val_acc: 0.4030\n",
            "Epoch 69/100\n",
            "90/89 [==============================] - 151s 2s/step - loss: 1.3843 - acc: 0.3979 - val_loss: 1.4310 - val_acc: 0.3339\n",
            "Epoch 70/100\n",
            "90/89 [==============================] - 146s 2s/step - loss: 1.3900 - acc: 0.3800 - val_loss: 1.4042 - val_acc: 0.4087\n",
            "Epoch 71/100\n",
            "90/89 [==============================] - 149s 2s/step - loss: 1.3960 - acc: 0.3816 - val_loss: 1.3820 - val_acc: 0.3900\n",
            "Epoch 72/100\n",
            "90/89 [==============================] - 145s 2s/step - loss: 1.3869 - acc: 0.3868 - val_loss: 1.3756 - val_acc: 0.3804\n",
            "Epoch 73/100\n",
            "90/89 [==============================] - 148s 2s/step - loss: 1.3811 - acc: 0.3755 - val_loss: 1.3745 - val_acc: 0.3748\n",
            "Epoch 74/100\n",
            "90/89 [==============================] - 150s 2s/step - loss: 1.3851 - acc: 0.3847 - val_loss: 1.3758 - val_acc: 0.4022\n",
            "Epoch 75/100\n",
            "90/89 [==============================] - 147s 2s/step - loss: 1.3743 - acc: 0.3788 - val_loss: 1.3919 - val_acc: 0.4039\n",
            "Epoch 76/100\n",
            "90/89 [==============================] - 136s 2s/step - loss: 1.3733 - acc: 0.4010 - val_loss: 1.3518 - val_acc: 0.4261\n",
            "Epoch 77/100\n",
            "90/89 [==============================] - 149s 2s/step - loss: 1.3737 - acc: 0.4144 - val_loss: 1.3600 - val_acc: 0.4200\n",
            "Epoch 78/100\n",
            "90/89 [==============================] - 149s 2s/step - loss: 1.3720 - acc: 0.3948 - val_loss: 1.3687 - val_acc: 0.4226\n",
            "Epoch 79/100\n",
            "90/89 [==============================] - 146s 2s/step - loss: 1.3687 - acc: 0.4007 - val_loss: 1.3462 - val_acc: 0.4370\n",
            "Epoch 80/100\n",
            "90/89 [==============================] - 153s 2s/step - loss: 1.3558 - acc: 0.3804 - val_loss: 1.3586 - val_acc: 0.4265\n",
            "Epoch 81/100\n",
            "90/89 [==============================] - 147s 2s/step - loss: 1.3578 - acc: 0.4179 - val_loss: 1.3574 - val_acc: 0.3722\n",
            "Epoch 82/100\n",
            "90/89 [==============================] - 144s 2s/step - loss: 1.3573 - acc: 0.4123 - val_loss: 1.3513 - val_acc: 0.3961\n",
            "Epoch 83/100\n",
            "90/89 [==============================] - 151s 2s/step - loss: 1.3554 - acc: 0.3814 - val_loss: 1.3460 - val_acc: 0.3826\n",
            "Epoch 84/100\n",
            "90/89 [==============================] - 152s 2s/step - loss: 1.3521 - acc: 0.4116 - val_loss: 1.3364 - val_acc: 0.3991\n",
            "Epoch 85/100\n",
            "90/89 [==============================] - 146s 2s/step - loss: 1.3524 - acc: 0.3993 - val_loss: 1.3420 - val_acc: 0.4109\n",
            "Epoch 86/100\n",
            "90/89 [==============================] - 145s 2s/step - loss: 1.3479 - acc: 0.4078 - val_loss: 1.3541 - val_acc: 0.4013\n",
            "Epoch 87/100\n",
            "90/89 [==============================] - 145s 2s/step - loss: 1.3451 - acc: 0.4022 - val_loss: 1.3287 - val_acc: 0.4074\n",
            "Epoch 88/100\n",
            "90/89 [==============================] - 148s 2s/step - loss: 1.3397 - acc: 0.4149 - val_loss: 1.3409 - val_acc: 0.3626\n",
            "Epoch 89/100\n",
            "90/89 [==============================] - 144s 2s/step - loss: 1.3431 - acc: 0.3878 - val_loss: 1.3231 - val_acc: 0.4304\n",
            "Epoch 90/100\n",
            "90/89 [==============================] - 148s 2s/step - loss: 1.3410 - acc: 0.4094 - val_loss: 1.3192 - val_acc: 0.4283\n",
            "Epoch 91/100\n",
            "90/89 [==============================] - 147s 2s/step - loss: 1.3330 - acc: 0.4215 - val_loss: 1.3315 - val_acc: 0.4078\n",
            "Epoch 92/100\n",
            "90/89 [==============================] - 145s 2s/step - loss: 1.3329 - acc: 0.4057 - val_loss: 1.3194 - val_acc: 0.4465\n",
            "Epoch 93/100\n",
            "90/89 [==============================] - 146s 2s/step - loss: 1.3281 - acc: 0.3922 - val_loss: 1.3089 - val_acc: 0.4513\n",
            "Epoch 94/100\n",
            "90/89 [==============================] - 143s 2s/step - loss: 1.3205 - acc: 0.4132 - val_loss: 1.3194 - val_acc: 0.4474\n",
            "Epoch 95/100\n",
            "90/89 [==============================] - 148s 2s/step - loss: 1.3283 - acc: 0.4069 - val_loss: 1.3115 - val_acc: 0.4487\n",
            "Epoch 96/100\n",
            "90/89 [==============================] - 147s 2s/step - loss: 1.3209 - acc: 0.4285 - val_loss: 1.3025 - val_acc: 0.4357\n",
            "Epoch 97/100\n",
            "90/89 [==============================] - 148s 2s/step - loss: 1.3144 - acc: 0.4137 - val_loss: 1.3139 - val_acc: 0.3961\n",
            "Epoch 98/100\n",
            "90/89 [==============================] - 152s 2s/step - loss: 1.3218 - acc: 0.4114 - val_loss: 1.3007 - val_acc: 0.4404\n",
            "Epoch 99/100\n",
            "90/89 [==============================] - 147s 2s/step - loss: 1.3217 - acc: 0.4130 - val_loss: 1.2855 - val_acc: 0.4713\n",
            "Epoch 100/100\n",
            "90/89 [==============================] - 148s 2s/step - loss: 1.3136 - acc: 0.4165 - val_loss: 1.2982 - val_acc: 0.4487\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-c0b7bc19cff7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     76\u001b[0m     \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2300\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mBS\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m )\n\u001b[0;32m---> 78\u001b[0;31m \u001b[0macc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     79\u001b[0m \u001b[0mval_acc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'val_accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'accuracy'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wjGKcY5Wc1C3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "outputId": "095e2401-7c1c-4655-9113-0a0a8e0db405"
      },
      "source": [
        "import keras\n",
        "from keras.utils import to_categorical\n",
        "from tensorflow.python.keras.applications import ResNet50\n",
        "from tensorflow.python.keras.models import Sequential\n",
        "from keras.models import Model\n",
        "from keras.layers import Dense, GlobalAveragePooling2D, Dropout\n",
        "from keras import optimizers\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "\n",
        "train_dir='/content/train'\n",
        "validation_dir='/content/validation'\n",
        "train_dyed_lifted_polyps_dir=os.path.join(train_dir, 'dyed-lifted-polyps')\n",
        "train_dyed_resection_margins_dir=os.path.join(train_dir, 'dyed-resection-margins')\n",
        "train_esophagitis_dir=os.path.join(train_dir, 'esophagitis')\n",
        "train_normal_cecum_dir=os.path.join(train_dir, 'normal-cecum')\n",
        "train_normal_pylorus_dir=os.path.join(train_dir, 'pylorus')\n",
        "train_normal_z_line_dir=os.path.join(train_dir, 'normal-z-line')\n",
        "train_polyps_dir=os.path.join(train_dir, 'polyps')\n",
        "train_ulcerative_colitis_dir=os.path.join(train_dir, 'ulcerative-colitis')\n",
        "\n",
        "validation_dyed_lifted_polyps_dir =os.path.join(validation_dir, 'dyed-lifted-polyps')\n",
        "validation_dyed_resection_margins_dir =os.path.join(validation_dir, 'dyed-resection-margins')\n",
        "validation_esophagitis_dir =os.path.join(validation_dir, 'esophagitis')\n",
        "validation_normal_cecum_dir =os.path.join(validation_dir, 'normal-cecum')\n",
        "validation_pylorus_dir =os.path.join(validation_dir, 'pylorus')\n",
        "validation_normal_z_line_dir =os.path.join(validation_dir, 'normal-z-line')\n",
        "validation_polyps_dir =os.path.join(validation_dir, 'polyps')\n",
        "validation_ulcerative_colitis_dir =os.path.join(validation_dir, 'ulcerative-colitis')\n",
        "\n",
        "\n",
        "IMG_HEIGHT = 224\n",
        "IMG_WIDTH = 224\n",
        "EPOCHS = 50\n",
        "BS = 64\n",
        "train_image_generator = keras.preprocessing.image.ImageDataGenerator(rescale=1./255) \n",
        "validation_image_generator = keras.preprocessing.image.ImageDataGenerator(rescale=1./255) \n",
        "\n",
        "train_data_gen = train_image_generator.flow_from_directory(batch_size=BS,\n",
        "                                                           directory=train_dir,\n",
        "                                                           shuffle=False,\n",
        "                                                           target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
        "                                                           class_mode='categorical',\n",
        "\t\t\t\t\t\t\t\t\t\t\t\t\t\t   )\n",
        "val_data_gen = validation_image_generator.flow_from_directory(batch_size=BS,\n",
        "                                                              directory=validation_dir,\n",
        "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t  shuffle=False,\n",
        "                                                              target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
        "                                                              class_mode='categorical')\n",
        " \n",
        "\n",
        "\n",
        "conv_base = ResNet50(\n",
        "    include_top=False,\n",
        "    weights='imagenet')\n",
        "\n",
        "for layer in conv_base.layers:\n",
        "    layer.trainable = False\n",
        "#model.add(ResNet50(include_top = False,  weights = 'imagenet'))\n",
        "\n",
        "\n",
        "x = conv_base.output\n",
        "\n",
        "x = MaxPooling2D((2,2), strides=(2,2), padding='same')(x)\n",
        "x = Flatten(name='flatten')(x)\n",
        "x = Dropout(0.2)(x)\n",
        "x = Dense(512, activation='relu')(x)\n",
        "predictions = Dense(101, activation='softmax', name='predictions')(x)x = MaxPooling2D((2,2), strides=(2,2), padding='same')(x)\n",
        "x = Flatten(name='flatten')(x)\n",
        "x = Dropout(0.2)(x)\n",
        "x = Dense(512, activation='relu')(x)\n",
        "predictions = Dense(101, activation='softmax', name='predictions')(x)\n",
        "\n",
        "x = keras.layers.GlobalAveragePooling2D()(x)\n",
        "x = keras.layers.Dense(128, activation='relu')(x) \n",
        "predictions = keras.layers.Dense(8, activation='softmax')(x)\n",
        "model = Model(conv_base.input, predictions)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "                      optimizer='RMSprop',\n",
        "                      metrics=['accuracy'])\n",
        "history = model.fit_generator(\n",
        "    train_data_gen,\n",
        "    steps_per_epoch=5700/BS,\n",
        "    epochs=EPOCHS,\n",
        "    validation_data=val_data_gen,\n",
        "    validation_steps=2300/BS\n",
        ")\n",
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs_range = range(EPOCHS)\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
        "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(epochs_range, loss, label='Training Loss')\n",
        "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.show()\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 5700 images belonging to 8 classes.\n",
            "Found 2300 images belonging to 8 classes.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-9-28a79c9b819b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconv_base\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGlobalAveragePooling2D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDense\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'relu'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDense\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'softmax'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, **kwargs)\u001b[0m\n\u001b[1;32m    435\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    436\u001b[0m             \u001b[0;31m# Handle mask propagation.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 437\u001b[0;31m             \u001b[0mprevious_mask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_collect_previous_mask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    438\u001b[0m             \u001b[0muser_kwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_all_none\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprevious_mask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m_collect_previous_mask\u001b[0;34m(input_tensors)\u001b[0m\n\u001b[1;32m   1303\u001b[0m             \u001b[0minbound_layer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_keras_history\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1304\u001b[0m             \u001b[0mnode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minbound_layer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_inbound_nodes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnode_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1305\u001b[0;31m             \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput_masks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtensor_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1306\u001b[0m             \u001b[0mmasks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1307\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'Node' object has no attribute 'output_masks'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KBE4UsDgb6nw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "2ef6cf1c-6091-40b4-c87e-21f0d36935f9"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zWtnRAbFaOWC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "outputId": "7f41aa19-8dd3-48b6-9d0f-b259eeb09035"
      },
      "source": [
        "import keras\n",
        "from keras import backend as K\n",
        "from keras.layers.core import Dense, Activation\n",
        "from keras.optimizers import Adam\n",
        "from keras.metrics import categorical_crossentropy\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.preprocessing import image\n",
        "from keras.models import Model\n",
        "from keras.applications import imagenet_utils\n",
        "from keras.layers import Dense,GlobalAveragePooling2D\n",
        "from keras.applications import MobileNet\n",
        "from keras.applications.mobilenet import preprocess_input\n",
        "import numpy as np\n",
        "from IPython.display import Image\n",
        "from keras.optimizers import Adam\n",
        "import os\n",
        "train_dir='/content/train'\n",
        "validation_dir='/content/validation'\n",
        "train_dyed_lifted_polyps_dir=os.path.join(train_dir, 'dyed-lifted-polyps')\n",
        "train_dyed_resection_margins_dir=os.path.join(train_dir, 'dyed-resection-margins')\n",
        "train_esophagitis_dir=os.path.join(train_dir, 'esophagitis')\n",
        "train_normal_cecum_dir=os.path.join(train_dir, 'normal-cecum')\n",
        "train_normal_pylorus_dir=os.path.join(train_dir, 'pylorus')\n",
        "train_normal_z_line_dir=os.path.join(train_dir, 'normal-z-line')\n",
        "train_polyps_dir=os.path.join(train_dir, 'polyps')\n",
        "train_ulcerative_colitis_dir=os.path.join(train_dir, 'ulcerative-colitis')\n",
        "\n",
        "validation_dyed_lifted_polyps_dir =os.path.join(validation_dir, 'dyed-lifted-polyps')\n",
        "validation_dyed_resection_margins_dir =os.path.join(validation_dir, 'dyed-resection-margins')\n",
        "validation_esophagitis_dir =os.path.join(validation_dir, 'esophagitis')\n",
        "validation_normal_cecum_dir =os.path.join(validation_dir, 'normal-cecum')\n",
        "validation_pylorus_dir =os.path.join(validation_dir, 'pylorus')\n",
        "validation_normal_z_line_dir =os.path.join(validation_dir, 'normal-z-line')\n",
        "validation_polyps_dir =os.path.join(validation_dir, 'polyps')\n",
        "validation_ulcerative_colitis_dir =os.path.join(validation_dir, 'ulcerative-colitis')\n",
        "\n",
        "mobile = keras.applications.mobilenet.MobileNet()\n",
        "\n",
        "base_model=MobileNet(weights='imagenet',include_top=False) \n",
        "\n",
        "x=base_model.output\n",
        "x=GlobalAveragePooling2D()(x)\n",
        "\n",
        "x=Dense(1024,activation='relu')(x)  \n",
        "x=Dense(512,activation='relu')(x) \n",
        "preds=Dense(8,activation='softmax')(x) \n",
        "model=Model(inputs=base_model.input,outputs=preds)\n",
        "for layer in base_model.layers[:20]:\n",
        "    layer.trainable=False\n",
        "for layer in base_model.layers[20:]:\n",
        "    layer.trainable=True\n",
        "\n",
        "\n",
        "train_datagen=ImageDataGenerator(preprocessing_function=preprocess_input) #included in our dependencies\n",
        "\n",
        "train_generator=train_datagen.flow_from_directory(directory=train_dir,\n",
        "                                                 target_size=(128,128),\n",
        "                                                 color_mode='rgb',\n",
        "                                                 batch_size=64,\n",
        "                                                 class_mode='categorical',\n",
        "                                                 shuffle=True)\n",
        "\n",
        "test_image_gen = train_datagen.flow_from_directory(directory=validation_dir,\n",
        "                                               target_size=(128,128),\n",
        "                                               batch_size=64,class_mode='categorical')\n",
        "\n",
        "model.compile(optimizer='Adam',loss='categorical_crossentropy',metrics=['accuracy'])\n",
        "\n",
        "\n",
        "step_size_train=train_generator.n//train_generator.batch_size\n",
        "model.fit_generator(generator=train_generator,\n",
        "                   steps_per_epoch=step_size_train,\n",
        "                   epochs=10,validation_data=test_image_gen,\n",
        "    validation_steps=46)\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras_applications/mobilenet.py:207: UserWarning: `input_shape` is undefined or non-square, or `rows` is not in [128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n",
            "  warnings.warn('`input_shape` is undefined or non-square, '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Found 5700 images belonging to 8 classes.\n",
            "Found 2300 images belonging to 8 classes.\n",
            "Epoch 1/10\n",
            "114/114 [==============================] - 132s 1s/step - loss: 0.5577 - acc: 0.7956 - val_loss: 0.9423 - val_acc: 0.7452\n",
            "Epoch 2/10\n",
            "114/114 [==============================] - 109s 953ms/step - loss: 0.3121 - acc: 0.8844 - val_loss: 0.6264 - val_acc: 0.8152\n",
            "Epoch 3/10\n",
            "114/114 [==============================] - 106s 934ms/step - loss: 0.2546 - acc: 0.9084 - val_loss: 0.4113 - val_acc: 0.8587\n",
            "Epoch 4/10\n",
            "114/114 [==============================] - 106s 932ms/step - loss: 0.1946 - acc: 0.9300 - val_loss: 0.6538 - val_acc: 0.8222\n",
            "Epoch 5/10\n",
            "114/114 [==============================] - 106s 932ms/step - loss: 0.1635 - acc: 0.9409 - val_loss: 0.5377 - val_acc: 0.8413\n",
            "Epoch 6/10\n",
            "114/114 [==============================] - 106s 929ms/step - loss: 0.1424 - acc: 0.9511 - val_loss: 0.4471 - val_acc: 0.8530\n",
            "Epoch 7/10\n",
            "114/114 [==============================] - 106s 929ms/step - loss: 0.1225 - acc: 0.9596 - val_loss: 0.6867 - val_acc: 0.8200\n",
            "Epoch 8/10\n",
            "114/114 [==============================] - 106s 928ms/step - loss: 0.1163 - acc: 0.9625 - val_loss: 1.6049 - val_acc: 0.7139\n",
            "Epoch 9/10\n",
            "114/114 [==============================] - 106s 929ms/step - loss: 0.1179 - acc: 0.9635 - val_loss: 2.1785 - val_acc: 0.6139\n",
            "Epoch 10/10\n",
            "114/114 [==============================] - 106s 933ms/step - loss: 0.0642 - acc: 0.9804 - val_loss: 1.2285 - val_acc: 0.7478\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f47eae1e518>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c_ad_kxqgbzd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}