Covid_19_Sequential.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Covid 19-Sequential.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNRQrCobgUv/Ye3mfcLZbtH",
      "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/Divyakathirvel26/Covid-19/blob/master/Covid_19_Sequential.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ip8jSOcYbjwx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "d6441035-1935-406a-cae1-2400c23c7ec6"
      },
      "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": "9_zY2RhKchpq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras import preprocessing\n",
        "from tensorflow.keras import models\n",
        "from tensorflow.keras.layers import Conv2D\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import Flatten\n",
        "from tensorflow.keras.preprocessing import image\n",
        "from tensorflow.keras.layers import MaxPooling2D\n",
        "from tensorflow.keras.models import Sequential\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ipxOr-20eP6f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "classifier = Sequential()\n",
        "\n",
        "classifier.add(Conv2D(32, (3, 3), input_shape=(224, 224, 3),\n",
        "               activation='relu'))\n",
        "\n",
        "classifier.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "classifier.add(Conv2D(32, (3, 3), activation='relu'))\n",
        "classifier.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "classifier.add(Flatten())\n",
        "\n",
        "classifier.add(Dense(units=128, activation='relu'))\n",
        "classifier.add(Dense(units=2, activation='sigmoid'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iUP_p2rKeUvg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cU6uP3dveY47",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a8930f57-357b-435b-9731-13971843b821"
      },
      "source": [
        "train_datagen=ImageDataGenerator(rescale=1. / 255, shear_range=0.2,\n",
        "        zoom_range=0.2, horizontal_flip=True)\n",
        "train_generator=train_datagen.flow_from_directory('drive/My Drive/covid-data/', # 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)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 519 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EA5gHihVeeQW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "35a10fc0-b803-40c0-8f20-766da5ae8207"
      },
      "source": [
        "print(train_generator.n)\n",
        "print(train_generator.batch_size)\n",
        "print(253//32)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "519\n",
            "32\n",
            "7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BeXIqEVVeloD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "classifier.compile(optimizer='Adam',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nC4ZznPmeoKC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 768
        },
        "outputId": "0b02f0c6-41c1-4c3e-d5dd-9f6815e72a21"
      },
      "source": [
        "step_size_train=train_generator.n//train_generator.batch_size\n",
        "r = classifier.fit_generator(generator=train_generator,\n",
        "                   steps_per_epoch=step_size_train,\n",
        "                   epochs=20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-21-ec3a3c56227f>:4: 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",
            "16/16 [==============================] - 100s 6s/step - loss: 0.9523 - accuracy: 0.5359\n",
            "Epoch 2/20\n",
            "16/16 [==============================] - 9s 543ms/step - loss: 0.6906 - accuracy: 0.5667\n",
            "Epoch 3/20\n",
            "16/16 [==============================] - 9s 549ms/step - loss: 0.6806 - accuracy: 0.5832\n",
            "Epoch 4/20\n",
            "16/16 [==============================] - 9s 547ms/step - loss: 0.6726 - accuracy: 0.5811\n",
            "Epoch 5/20\n",
            "16/16 [==============================] - 9s 572ms/step - loss: 0.6558 - accuracy: 0.6181\n",
            "Epoch 6/20\n",
            "16/16 [==============================] - 9s 575ms/step - loss: 0.6557 - accuracy: 0.6037\n",
            "Epoch 7/20\n",
            "16/16 [==============================] - 9s 557ms/step - loss: 0.6444 - accuracy: 0.6181\n",
            "Epoch 8/20\n",
            "16/16 [==============================] - 9s 558ms/step - loss: 0.6387 - accuracy: 0.6078\n",
            "Epoch 9/20\n",
            "16/16 [==============================] - 9s 562ms/step - loss: 0.6438 - accuracy: 0.6140\n",
            "Epoch 10/20\n",
            "16/16 [==============================] - 9s 561ms/step - loss: 0.6393 - accuracy: 0.6222\n",
            "Epoch 11/20\n",
            "16/16 [==============================] - 9s 546ms/step - loss: 0.6387 - accuracy: 0.6386\n",
            "Epoch 12/20\n",
            "16/16 [==============================] - 9s 584ms/step - loss: 0.6515 - accuracy: 0.6386\n",
            "Epoch 13/20\n",
            "16/16 [==============================] - 9s 561ms/step - loss: 0.6119 - accuracy: 0.6571\n",
            "Epoch 14/20\n",
            "16/16 [==============================] - 9s 555ms/step - loss: 0.5937 - accuracy: 0.6817\n",
            "Epoch 15/20\n",
            "16/16 [==============================] - 9s 558ms/step - loss: 0.6075 - accuracy: 0.6817\n",
            "Epoch 16/20\n",
            "16/16 [==============================] - 9s 552ms/step - loss: 0.6062 - accuracy: 0.6653\n",
            "Epoch 17/20\n",
            "16/16 [==============================] - 9s 559ms/step - loss: 0.6080 - accuracy: 0.6694\n",
            "Epoch 18/20\n",
            "16/16 [==============================] - 9s 551ms/step - loss: 0.6111 - accuracy: 0.6735\n",
            "Epoch 19/20\n",
            "16/16 [==============================] - 9s 556ms/step - loss: 0.5808 - accuracy: 0.6982\n",
            "Epoch 20/20\n",
            "16/16 [==============================] - 9s 548ms/step - loss: 0.6041 - accuracy: 0.6879\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YlppJ-TtetFK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "7aa79502-5356-462c-909d-629f57d438d8"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "print(r.history.keys())\n",
        "# Loss\n",
        "plt.plot(r.history['loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['loss', 'accuracy'])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N_yqSI6KgEej",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "ea89b6a4-273c-4a1a-b042-058d3cc4cc35"
      },
      "source": [
        "plt.plot(r.history['accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('acc')\n",
        "plt.xlabel('epoch')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "plCE4xGeg-rJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}