Corona_resnet152_part2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "asIzGLg9sBlZ",
    "outputId": "61cf6dff-db96-4451-de70-d23f3e33a4ff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  /content/drive/My Drive/Covid19Pred/Dataset_kaggle.zip\n",
      "   creating: Dataset_kaggle/\n",
      "   creating: Dataset_kaggle/COVID/\n",
      " extracting: Dataset_kaggle/COVID/Covid (1).png  \n",
      "  inflating: Dataset_kaggle/COVID/Covid (10).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (102).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (103).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (106).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (107).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (11).png  \n",
      " extracting: Dataset_kaggle/COVID/Covid (110).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (111).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (112).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (113).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (45).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (46).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (47).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (49).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (5).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (51).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (52).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (53).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (54).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (55).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (56).png  \n",
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      "  inflating: Dataset_kaggle/COVID/Covid (569).png  \n",
      " extracting: Dataset_kaggle/COVID/Covid (57).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (58).png  \n",
      "  inflating: Dataset_kaggle/COVID/Covid (580).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (59).png  \n",
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      "  inflating: Dataset_kaggle/COVID/Covid (598).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (6).png  \n",
      " extracting: Dataset_kaggle/COVID/Covid (60).png  \n",
      "  inflating: Dataset_kaggle/COVID/Covid (600).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (61).png  \n",
      "  inflating: Dataset_kaggle/COVID/Covid (610).png  \n",
      "  inflating: Dataset_kaggle/COVID/Covid (611).png  \n",
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      "  inflating: Dataset_kaggle/COVID/Covid (618).png  \n",
      "  inflating: Dataset_kaggle/COVID/Covid (619).png  \n",
      " extracting: Dataset_kaggle/COVID/Covid (62).png  \n",
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      "  inflating: Dataset_kaggle/COVID/Covid (621).png  \n",
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      "  inflating: Dataset_kaggle/COVID/Covid (628).png  \n",
      "  inflating: Dataset_kaggle/COVID/Covid (629).png  \n",
      " extracting: Dataset_kaggle/COVID/Covid (63).png  \n",
      "  inflating: Dataset_kaggle/COVID/Covid (630).png  \n",
      "  inflating: Dataset_kaggle/COVID/Covid (631).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (69).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (7).png  \n",
      " extracting: Dataset_kaggle/COVID/Covid (70).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (71).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (72).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (73).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (74).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (75).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (76).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (77).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (78).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (79).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (81).png  \n",
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      "  inflating: Dataset_kaggle/COVID/Covid (838).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (84).png  \n",
      "  inflating: Dataset_kaggle/COVID/Covid (840).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (85).png  \n",
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      "  inflating: Dataset_kaggle/COVID/Covid (858).png  \n",
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      " extracting: Dataset_kaggle/COVID/Covid (86).png  \n",
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      "  inflating: Dataset_kaggle/COVID/Covid (868).png  \n",
      "  inflating: Dataset_kaggle/COVID/Covid (869).png  \n",
      " extracting: Dataset_kaggle/COVID/Covid (87).png  \n",
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      "  inflating: Dataset_kaggle/COVID/Covid (878).png  \n",
      "  inflating: Dataset_kaggle/COVID/Covid (879).png  \n",
      " extracting: Dataset_kaggle/COVID/Covid (88).png  \n",
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      "Collecting split_folders\n",
      "  Downloading https://files.pythonhosted.org/packages/20/67/29dda743e6d23ac1ea3d16704d8bbb48d65faf3f1b1eaf53153b3da56c56/split_folders-0.3.1-py3-none-any.whl\n",
      "Installing collected packages: split-folders\n",
      "Successfully installed split-folders-0.3.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Copying files: 2481 files [00:00, 3511.24 files/s]\n",
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet152_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "234700800/234698864 [==============================] - 3s 0us/step\n",
      "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-1-8f0d36e44639>: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 1/20\n",
      "62/62 [==============================] - 17s 280ms/step - loss: 0.5534 - accuracy: 0.8095 - val_loss: 0.2555 - val_accuracy: 0.8833\n",
      "Epoch 2/20\n",
      "62/62 [==============================] - 15s 239ms/step - loss: 0.2359 - accuracy: 0.9027 - val_loss: 0.2233 - val_accuracy: 0.9054\n",
      "Epoch 3/20\n",
      "62/62 [==============================] - 15s 240ms/step - loss: 0.1782 - accuracy: 0.9284 - val_loss: 0.4083 - val_accuracy: 0.8652\n",
      "Epoch 4/20\n",
      "62/62 [==============================] - 15s 238ms/step - loss: 0.1562 - accuracy: 0.9380 - val_loss: 0.2073 - val_accuracy: 0.9195\n",
      "Epoch 5/20\n",
      "62/62 [==============================] - 15s 238ms/step - loss: 0.1154 - accuracy: 0.9556 - val_loss: 0.2843 - val_accuracy: 0.8994\n",
      "Epoch 6/20\n",
      "62/62 [==============================] - 15s 237ms/step - loss: 0.0750 - accuracy: 0.9718 - val_loss: 0.1709 - val_accuracy: 0.9416\n",
      "Epoch 7/20\n",
      "62/62 [==============================] - 15s 238ms/step - loss: 0.0659 - accuracy: 0.9743 - val_loss: 0.1649 - val_accuracy: 0.9457\n",
      "Epoch 8/20\n",
      "62/62 [==============================] - 15s 239ms/step - loss: 0.0516 - accuracy: 0.9783 - val_loss: 0.0964 - val_accuracy: 0.9618\n",
      "Epoch 9/20\n",
      "62/62 [==============================] - 15s 237ms/step - loss: 0.0654 - accuracy: 0.9738 - val_loss: 0.1600 - val_accuracy: 0.9437\n",
      "Epoch 10/20\n",
      "62/62 [==============================] - 15s 237ms/step - loss: 0.1137 - accuracy: 0.9602 - val_loss: 0.1502 - val_accuracy: 0.9497\n",
      "Epoch 11/20\n",
      "62/62 [==============================] - 15s 238ms/step - loss: 0.0484 - accuracy: 0.9803 - val_loss: 0.1390 - val_accuracy: 0.9497\n",
      "Epoch 12/20\n",
      "62/62 [==============================] - 15s 239ms/step - loss: 0.0235 - accuracy: 0.9929 - val_loss: 0.1214 - val_accuracy: 0.9598\n",
      "Epoch 13/20\n",
      "62/62 [==============================] - 15s 239ms/step - loss: 0.0389 - accuracy: 0.9869 - val_loss: 0.0894 - val_accuracy: 0.9658\n",
      "Epoch 14/20\n",
      "62/62 [==============================] - 15s 240ms/step - loss: 0.0211 - accuracy: 0.9929 - val_loss: 0.2548 - val_accuracy: 0.9416\n",
      "Epoch 15/20\n",
      "62/62 [==============================] - 15s 238ms/step - loss: 0.0363 - accuracy: 0.9854 - val_loss: 0.1116 - val_accuracy: 0.9618\n",
      "Epoch 16/20\n",
      "62/62 [==============================] - 15s 237ms/step - loss: 0.0167 - accuracy: 0.9940 - val_loss: 0.1620 - val_accuracy: 0.9598\n",
      "Epoch 17/20\n",
      "62/62 [==============================] - 15s 238ms/step - loss: 0.0320 - accuracy: 0.9904 - val_loss: 0.2389 - val_accuracy: 0.9215\n",
      "Epoch 18/20\n",
      "62/62 [==============================] - 15s 238ms/step - loss: 0.0483 - accuracy: 0.9793 - val_loss: 0.2688 - val_accuracy: 0.9235\n",
      "Epoch 19/20\n",
      "62/62 [==============================] - 15s 238ms/step - loss: 0.0203 - accuracy: 0.9940 - val_loss: 0.1141 - val_accuracy: 0.9658\n",
      "Epoch 20/20\n",
      "62/62 [==============================] - 15s 238ms/step - loss: 0.0041 - accuracy: 0.9990 - val_loss: 0.3922 - val_accuracy: 0.9296\n",
      "WARNING:tensorflow:From <ipython-input-1-8f0d36e44639>:72: 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",
      "0.9295774698257446\n"
     ]
    }
   ],
   "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 ResNet152\n",
    "#from tensorflow.keras.applications.ResNet152 import preprocess_input\n",
    "\n",
    "image_size = [224,224]\n",
    "data_path = '/content/output'\n",
    "\n",
    "resnet = ResNet152(input_shape= image_size+[3],weights='imagenet',include_top=False)\n",
    "\n",
    "x = resnet.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 = resnet.input,outputs=preds)\n",
    "\n",
    "for layer in resnet.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=20)\n",
    "\n",
    "acc=model.evaluate_generator(test_generator)\n",
    "print(acc[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "_3O1q6zVtJ8K",
    "outputId": "2c730f56-8bb7-46a4-9690-29bc091c6471"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9295774698257446\n"
     ]
    }
   ],
   "source": [
    "\n",
    "acc=model.evaluate_generator(test_generator)\n",
    "print(acc[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3d-0Dd6tv0sn"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Corona_resnet152_part2.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}