Corona_inceptionV3.ipynb

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  "metadata": {
    "colab": {
      "name": "Corona_inceptionV3.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "nprBwR040PZQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3a1fd606-5dab-4221-b627-1da3658561e5"
      },
      "source": [
        "!unzip '/content/drive/My Drive/Covid19Pred/Dataset_kaggle.zip'\n",
        "\n",
        "!pip install split_folders\n",
        "\n",
        "import split_folders\n",
        "split_folders.ratio('/content/Dataset_kaggle', output=\"output\", seed=1337, ratio=(.8, .2))\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D\n",
        "#from tensorflow.keras.applications.vgg16 import VGG16\n",
        "from tensorflow.keras.preprocessing import image\n",
        "from tensorflow.keras.applications.vgg16 import preprocess_input\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.applications import InceptionV3\n",
        "#from tensorflow.keras.applications.ResNet152 import preprocess_input\n",
        "\n",
        "image_size = [224,224]\n",
        "data_path = '/content/output'\n",
        "\n",
        "inc = InceptionV3(input_shape= image_size+[3],weights='imagenet',include_top=False)\n",
        "\n",
        "x = inc.output\n",
        "x = GlobalAveragePooling2D()(x)\n",
        "\n",
        "x = Dense(1024,activation='relu')(x)\n",
        "x = Dense(1024,activation='relu')(x)\n",
        "x = Dense(512, activation='relu')(x)\n",
        "\n",
        "preds = Dense(2,activation='softmax')(x)\n",
        "\n",
        "model = Model(inputs = inc.input,outputs=preds)\n",
        "\n",
        "for layer in inc.layers:\n",
        "    layer.trainable = False\n",
        "\n",
        "train_datagen=ImageDataGenerator(preprocessing_function=preprocess_input) #included in our dependencies\n",
        "test_datagen=ImageDataGenerator(preprocessing_function=preprocess_input)\n",
        "train_generator=train_datagen.flow_from_directory('/content/output/train', # this is where you specify the path to the main data folder\n",
        "                                                 target_size=(224,224),\n",
        "                                                 color_mode='rgb',\n",
        "                                                 batch_size=32,\n",
        "                                                 class_mode='categorical',\n",
        "                                                 shuffle=True)\n",
        "test_generator=test_datagen.flow_from_directory('/content/output/val', # this is where you specify the path to the main data folder\n",
        "                                                 target_size=(224,224),\n",
        "                                                 color_mode='rgb',\n",
        "                                                 batch_size=32,\n",
        "                                                 shuffle=False)\n",
        "\n",
        "model.compile(optimizer='Adam',\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "\n",
        "print(train_generator.n)\n",
        "print(train_generator.batch_size)\n",
        "print(746//32)\n",
        "\n",
        "step_size_train=train_generator.n//train_generator.batch_size\n",
        "r = model.fit_generator(generator=train_generator,\n",
        "                    validation_data=test_generator,  \n",
        "                   steps_per_epoch=step_size_train,\n",
        "                   epochs=30)\n",
        "\n",
        "\n",
        "acc=model.evaluate_generator(test_generator)\n",
        "print(acc[1])"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  /content/drive/My Drive/Covid19Pred/Dataset_kaggle.zip\n",
            "replace Dataset_kaggle/COVID/Covid (1).png? [y]es, [n]o, [A]ll, [N]one, [r]ename: N\n",
            "Requirement already satisfied: split_folders in /usr/local/lib/python3.6/dist-packages (0.3.1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Copying files: 2481 files [00:00, 2810.89 files/s]\n",
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Found 1984 images belonging to 2 classes.\n",
            "Found 497 images belonging to 2 classes.\n",
            "1984\n",
            "32\n",
            "23\n",
            "WARNING:tensorflow:From <ipython-input-1-b598b3875ead>:70: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use Model.fit, which supports generators.\n",
            "Epoch 1/30\n",
            "62/62 [==============================] - 16s 261ms/step - loss: 3.8217 - accuracy: 0.6915 - val_loss: 0.5588 - val_accuracy: 0.7565\n",
            "Epoch 2/30\n",
            "62/62 [==============================] - 14s 224ms/step - loss: 0.4146 - accuracy: 0.8155 - val_loss: 0.4808 - val_accuracy: 0.7767\n",
            "Epoch 3/30\n",
            "62/62 [==============================] - 14s 223ms/step - loss: 0.3821 - accuracy: 0.8503 - val_loss: 0.3630 - val_accuracy: 0.8390\n",
            "Epoch 4/30\n",
            "62/62 [==============================] - 14s 224ms/step - loss: 0.3129 - accuracy: 0.8649 - val_loss: 0.3830 - val_accuracy: 0.8491\n",
            "Epoch 5/30\n",
            "62/62 [==============================] - 14s 224ms/step - loss: 0.3140 - accuracy: 0.8659 - val_loss: 0.4061 - val_accuracy: 0.8390\n",
            "Epoch 6/30\n",
            "62/62 [==============================] - 14s 225ms/step - loss: 0.2483 - accuracy: 0.8896 - val_loss: 0.2727 - val_accuracy: 0.8793\n",
            "Epoch 7/30\n",
            "62/62 [==============================] - 14s 223ms/step - loss: 0.2896 - accuracy: 0.8740 - val_loss: 0.5493 - val_accuracy: 0.7887\n",
            "Epoch 8/30\n",
            "62/62 [==============================] - 14s 222ms/step - loss: 0.2380 - accuracy: 0.8957 - val_loss: 0.2333 - val_accuracy: 0.8994\n",
            "Epoch 9/30\n",
            "62/62 [==============================] - 14s 228ms/step - loss: 0.2667 - accuracy: 0.8942 - val_loss: 0.2632 - val_accuracy: 0.8853\n",
            "Epoch 10/30\n",
            "62/62 [==============================] - 14s 231ms/step - loss: 0.2735 - accuracy: 0.8810 - val_loss: 0.5526 - val_accuracy: 0.7586\n",
            "Epoch 11/30\n",
            "62/62 [==============================] - 15s 234ms/step - loss: 0.2336 - accuracy: 0.8997 - val_loss: 0.2909 - val_accuracy: 0.8873\n",
            "Epoch 12/30\n",
            "62/62 [==============================] - 14s 233ms/step - loss: 0.1975 - accuracy: 0.9214 - val_loss: 0.2437 - val_accuracy: 0.9014\n",
            "Epoch 13/30\n",
            "62/62 [==============================] - 14s 230ms/step - loss: 0.1984 - accuracy: 0.9214 - val_loss: 0.4141 - val_accuracy: 0.8692\n",
            "Epoch 14/30\n",
            "62/62 [==============================] - 14s 226ms/step - loss: 0.2617 - accuracy: 0.8962 - val_loss: 0.3713 - val_accuracy: 0.8531\n",
            "Epoch 15/30\n",
            "62/62 [==============================] - 14s 228ms/step - loss: 0.2147 - accuracy: 0.9143 - val_loss: 0.3345 - val_accuracy: 0.8652\n",
            "Epoch 16/30\n",
            "62/62 [==============================] - 14s 227ms/step - loss: 0.2086 - accuracy: 0.9148 - val_loss: 0.3578 - val_accuracy: 0.8773\n",
            "Epoch 17/30\n",
            "62/62 [==============================] - 14s 227ms/step - loss: 0.1533 - accuracy: 0.9430 - val_loss: 0.2785 - val_accuracy: 0.8994\n",
            "Epoch 18/30\n",
            "62/62 [==============================] - 14s 225ms/step - loss: 0.1197 - accuracy: 0.9526 - val_loss: 0.4529 - val_accuracy: 0.8873\n",
            "Epoch 19/30\n",
            "62/62 [==============================] - 14s 229ms/step - loss: 0.1279 - accuracy: 0.9466 - val_loss: 0.3972 - val_accuracy: 0.8692\n",
            "Epoch 20/30\n",
            "62/62 [==============================] - 14s 226ms/step - loss: 0.1333 - accuracy: 0.9471 - val_loss: 0.3390 - val_accuracy: 0.8753\n",
            "Epoch 21/30\n",
            "62/62 [==============================] - 14s 223ms/step - loss: 0.1300 - accuracy: 0.9466 - val_loss: 0.2134 - val_accuracy: 0.9155\n",
            "Epoch 22/30\n",
            "62/62 [==============================] - 14s 226ms/step - loss: 0.1550 - accuracy: 0.9350 - val_loss: 0.2582 - val_accuracy: 0.8994\n",
            "Epoch 23/30\n",
            "62/62 [==============================] - 14s 225ms/step - loss: 0.1152 - accuracy: 0.9551 - val_loss: 0.2522 - val_accuracy: 0.9095\n",
            "Epoch 24/30\n",
            "62/62 [==============================] - 14s 223ms/step - loss: 0.0790 - accuracy: 0.9662 - val_loss: 0.2320 - val_accuracy: 0.9155\n",
            "Epoch 25/30\n",
            "62/62 [==============================] - 14s 221ms/step - loss: 0.1238 - accuracy: 0.9511 - val_loss: 0.2686 - val_accuracy: 0.9235\n",
            "Epoch 26/30\n",
            "62/62 [==============================] - 14s 222ms/step - loss: 0.1325 - accuracy: 0.9511 - val_loss: 0.2178 - val_accuracy: 0.9215\n",
            "Epoch 27/30\n",
            "62/62 [==============================] - 14s 225ms/step - loss: 0.1145 - accuracy: 0.9521 - val_loss: 0.2566 - val_accuracy: 0.9074\n",
            "Epoch 28/30\n",
            "62/62 [==============================] - 14s 225ms/step - loss: 0.0941 - accuracy: 0.9637 - val_loss: 0.2712 - val_accuracy: 0.8913\n",
            "Epoch 29/30\n",
            "62/62 [==============================] - 14s 223ms/step - loss: 0.0805 - accuracy: 0.9743 - val_loss: 0.5054 - val_accuracy: 0.8612\n",
            "Epoch 30/30\n",
            "62/62 [==============================] - 14s 224ms/step - loss: 0.0926 - accuracy: 0.9632 - val_loss: 0.2715 - val_accuracy: 0.9115\n",
            "WARNING:tensorflow:From <ipython-input-1-b598b3875ead>:73: Model.evaluate_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use Model.evaluate, which supports generators.\n",
            "0.9114688038825989\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f2ZanERC1OfF",
        "colab_type": "code",
        "colab": {}
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      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
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}