PCOS_detection_from_DL_.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "zRuyPLpYLgXW"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import os \n",
        "import math\n",
        "import shutil\n",
        "import glob\n",
        "import zipfile"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip /content/PCOS.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Ny1AxzcNXdH",
        "outputId": "4cb5a283-0b5d-419e-fc3f-969dbc5af13e"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/PCOS.zip\n",
            "   creating: PCOS/infected/\n",
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            "  inflating: PCOS/notinfected/img_0_9808.jpg  \n",
            "  inflating: PCOS/notinfected/img_0_9809.jpg  \n",
            "  inflating: PCOS/notinfected/img_0_9812.jpg  \n",
            "  inflating: PCOS/notinfected/img_0_9813.jpg  \n",
            "  inflating: PCOS/notinfected/img_0_9821.jpg  \n",
            "  inflating: PCOS/notinfected/img_0_9823.jpg  \n",
            "  inflating: PCOS/notinfected/img_0_9832.jpg  \n",
            "  inflating: PCOS/notinfected/img_0_9840.jpg  \n",
            "  inflating: PCOS/notinfected/img_0_9851.jpg  \n",
            "  inflating: PCOS/notinfected/img_0_9854.jpg  \n",
            "  inflating: PCOS/notinfected/img_0_9866.jpg  \n",
            "  inflating: PCOS/notinfected/img_0_9899.jpg  \n",
            "  inflating: PCOS/notinfected/img_0_99.jpg  \n",
            "  inflating: PCOS/notinfected/img_0_9904.jpg  \n",
            "  inflating: PCOS/notinfected/img_0_9906.jpg  \n",
            "  inflating: PCOS/notinfected/img_0_991.jpg  \n",
            "  inflating: PCOS/notinfected/img_0_9914.jpg  \n",
            "  inflating: PCOS/notinfected/img_0_9931.jpg  \n",
            "  inflating: PCOS/notinfected/img_0_9948.jpg  \n",
            "  inflating: PCOS/notinfected/img_0_995.jpg  \n",
            "  inflating: PCOS/notinfected/img_0_9952.jpg  \n",
            "  inflating: PCOS/notinfected/img_0_9967.jpg  \n",
            "  inflating: PCOS/notinfected/img_0_9973.jpg  \n",
            "  inflating: PCOS/notinfected/img_0_9989.jpg  \n",
            "  inflating: PCOS/notinfected/img1.jpeg  \n",
            "  inflating: PCOS/notinfected/img2.png  \n",
            "  inflating: PCOS/notinfected/img3.jpg  \n",
            "  inflating: PCOS/notinfected/img4.jpg  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ROOT_DIR = '/content/PCOS'\n",
        "number_of_images = {}\n",
        "\n",
        "for dir in os.listdir(ROOT_DIR):\n",
        "   number_of_images[dir] = len(os.listdir(os.path.join(ROOT_DIR,dir)))\n",
        "   print(\"\",dir,\"\" ,number_of_images[dir])\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wYc-leP0M4MG",
        "outputId": "dada8462-2bbd-4b3e-8c01-fd3f67c1b099"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " notinfected  1143\n",
            " infected  781\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.applications.mobilenet import preprocess_input"
      ],
      "metadata": {
        "id": "a-UPNvRTY2nO"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocessingImage1(path):\n",
        "  image_data = ImageDataGenerator(zoom_range=0.2,shear_range=0.2,preprocessing_function= preprocess_input,horizontal_flip=True)\n",
        "  image = image_data.flow_from_directory(directory=path,target_size=(224,224),batch_size=32,class_mode='binary')\n",
        "  return image"
      ],
      "metadata": {
        "id": "L8WbPdg-ZxXY"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocessionfImage2(path):\n",
        "  \"\"\"\n",
        "  Input :path\n",
        "  Output : preprocessed Image\n",
        "  \"\"\"\n",
        "  image_data  = ImageDataGenerator(preprocessing_function= preprocess_input )\n",
        "  image = image_data.flow_from_directory(directory=path,target_size=(224,224),batch_size=32,class_mode='binary')\n",
        "\n",
        "  return image\n",
        "\n",
        "  \n"
      ],
      "metadata": {
        "id": "rVKJ3cNGYgSg"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will split the data such that \n",
        "70% for training \n",
        "15 % for vailadation\n",
        "15 % for testing \n",
        "\n"
      ],
      "metadata": {
        "id": "iM_YwejxL8Zb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def datafolder(path,split):\n",
        "  if not os.path.exists(\"./\"+path):\n",
        "    os.mkdir(\"./\"+path)\n",
        "\n",
        "    for dir in os.listdir(ROOT_DIR):\n",
        "      os.makedirs(\"./\"+path+\"/\"+dir)\n",
        "      for img in np.random.choice(a=os.listdir(os.path.join(ROOT_DIR,dir)),\n",
        "                                  size=(math.floor(split * number_of_images[dir])-5),replace=False):\n",
        "      \n",
        "          O = os.path.join(ROOT_DIR,dir,img)\n",
        "          D = os.path.join(\"./\"+path,dir)\n",
        "          shutil.copy(O,D)\n",
        "          os.remove(O)\n",
        "\n",
        "  else:\n",
        "     print(\"Folder already exist\")\n"
      ],
      "metadata": {
        "id": "Nu5XvB4_L6Ny"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "datafolder(\"train\",0.7)"
      ],
      "metadata": {
        "id": "IyxvwhXIMd89"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "datafolder(\"val\",0.15)"
      ],
      "metadata": {
        "id": "6R3O6yA_WscN"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "datafolder(\"test\",0.15)"
      ],
      "metadata": {
        "id": "eNMN493KTj2c"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "path ='/content/train'\n",
        "train_data = preprocessingImage1(path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ChRc2o_3ln5W",
        "outputId": "81caa9c0-d558-4b0b-834f-10689e6a001b"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 1336 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "path ='/content/test'\n",
        "test_data = preprocessionfImage2(path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fDnhat-yloHN",
        "outputId": "d985dda5-7d94-4d44-8cbe-a9541e6d898e"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 278 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "path = '/content/val'\n",
        "val_data = preprocessionfImage2(path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OTekmMcrloQO",
        "outputId": "eb46ef08-6bfe-4cae-cdbb-fb21648a69e8"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 278 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "Model Block\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "9VvIIvjVXAdM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.layers import Flatten,Dense\n",
        "from keras.models import Model,load_model\n",
        "from keras.applications.mobilenet import MobileNet\n",
        "import keras"
      ],
      "metadata": {
        "id": "gtieU5RwTug6"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base_model = MobileNet(input_shape=(224,224,3),include_top=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cXfQPhssYJI_",
        "outputId": "f50c2c3d-5863-4bc5-fb2c-1404a5c2e1ec"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet/mobilenet_1_0_224_tf_no_top.h5\n",
            "17225924/17225924 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for layer in base_model.layers:\n",
        "  layer.trainable = False"
      ],
      "metadata": {
        "id": "n4xSHkbWgasP"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x= Flatten()(base_model.output)\n",
        "x= Dense(units=1,activation='sigmoid')(x)"
      ],
      "metadata": {
        "id": "0PGi3lH7khwm"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(base_model.input,x)"
      ],
      "metadata": {
        "id": "lpUl1qUukl0B"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='rmsprop',loss=keras.losses.binary_crossentropy,metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "nduxdpjilNdu"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.callbacks import ModelCheckpoint,EarlyStopping\n",
        "\n",
        "mc = ModelCheckpoint(filepath=\"bestmodel.h5\",monitor='val_accuracy',verbose=1,save_best_only=True)\n",
        "\n",
        "#Early check points\n",
        "es = EarlyStopping(monitor=\"val_accuracy\",min_delta=0.01,patience=5,verbose=1)\n",
        "\n",
        "cb = [mc,es]"
      ],
      "metadata": {
        "id": "_LtrSXwG0Syw"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hist = model.fit_generator(train_data,\n",
        "                           steps_per_epoch=10,\n",
        "                           epochs=30,\n",
        "                           validation_data=val_data,\n",
        "                           validation_steps=16,\n",
        "                          callbacks=cb)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BBvyOzQ8lPuW",
        "outputId": "aad0edb2-8703-49fa-af74-ff337ae09c24"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "10/10 [==============================] - ETA: 0s - loss: 1.5717 - accuracy: 0.8625"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 16 batches). You may need to use the repeat() function when building your dataset.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Epoch 1: val_accuracy improved from -inf to 1.00000, saving model to bestmodel.h5\n",
            "10/10 [==============================] - 30s 3s/step - loss: 1.5717 - accuracy: 0.8625 - val_loss: 4.7749e-04 - val_accuracy: 1.0000\n",
            "Epoch 2/30\n",
            "10/10 [==============================] - ETA: 0s - loss: 0.0019 - accuracy: 1.0000    "
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/10 [==============================] - 17s 2s/step - loss: 0.0019 - accuracy: 1.0000\n",
            "Epoch 3/30\n",
            "10/10 [==============================] - ETA: 0s - loss: 1.7808e-07 - accuracy: 1.0000"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/10 [==============================] - 16s 2s/step - loss: 1.7808e-07 - accuracy: 1.0000\n",
            "Epoch 4/30\n",
            "10/10 [==============================] - ETA: 0s - loss: 5.1658e-05 - accuracy: 1.0000"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/10 [==============================] - 15s 1s/step - loss: 5.1658e-05 - accuracy: 1.0000\n",
            "Epoch 5/30\n",
            "10/10 [==============================] - ETA: 0s - loss: 2.6552e-04 - accuracy: 1.0000"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/10 [==============================] - 16s 2s/step - loss: 2.6552e-04 - accuracy: 1.0000\n",
            "Epoch 6/30\n",
            "10/10 [==============================] - ETA: 0s - loss: 1.2110e-08 - accuracy: 1.0000"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/10 [==============================] - 15s 1s/step - loss: 1.2110e-08 - accuracy: 1.0000\n",
            "Epoch 7/30\n",
            "10/10 [==============================] - ETA: 0s - loss: 3.6037e-07 - accuracy: 1.0000"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/10 [==============================] - 15s 1s/step - loss: 3.6037e-07 - accuracy: 1.0000\n",
            "Epoch 8/30\n",
            "10/10 [==============================] - ETA: 0s - loss: 2.0642e-08 - accuracy: 1.0000"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/10 [==============================] - 16s 2s/step - loss: 2.0642e-08 - accuracy: 1.0000\n",
            "Epoch 9/30\n",
            "10/10 [==============================] - ETA: 0s - loss: 2.1441e-07 - accuracy: 1.0000"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/10 [==============================] - 16s 2s/step - loss: 2.1441e-07 - accuracy: 1.0000\n",
            "Epoch 10/30\n",
            "10/10 [==============================] - ETA: 0s - loss: 1.1665e-08 - accuracy: 1.0000"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/10 [==============================] - 16s 2s/step - loss: 1.1665e-08 - accuracy: 1.0000\n",
            "Epoch 11/30\n",
            "10/10 [==============================] - ETA: 0s - loss: 2.4792e-07 - accuracy: 1.0000"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/10 [==============================] - 16s 2s/step - loss: 2.4792e-07 - accuracy: 1.0000\n",
            "Epoch 12/30\n",
            "10/10 [==============================] - ETA: 0s - loss: 7.3932e-07 - accuracy: 1.0000"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/10 [==============================] - 15s 2s/step - loss: 7.3932e-07 - accuracy: 1.0000\n",
            "Epoch 13/30\n",
            "10/10 [==============================] - ETA: 0s - loss: 2.8616e-07 - accuracy: 1.0000"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/10 [==============================] - 15s 1s/step - loss: 2.8616e-07 - accuracy: 1.0000\n",
            "Epoch 14/30\n",
            "10/10 [==============================] - ETA: 0s - loss: 6.1263e-08 - accuracy: 1.0000"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/10 [==============================] - 16s 2s/step - loss: 6.1263e-08 - accuracy: 1.0000\n",
            "Epoch 15/30\n",
            "10/10 [==============================] - ETA: 0s - loss: 1.5090e-06 - accuracy: 1.0000"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/10 [==============================] - 15s 1s/step - loss: 1.5090e-06 - accuracy: 1.0000\n",
            "Epoch 16/30\n",
            "10/10 [==============================] - ETA: 0s - loss: 5.9374e-09 - accuracy: 1.0000"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/10 [==============================] - 15s 2s/step - loss: 5.9374e-09 - accuracy: 1.0000\n",
            "Epoch 17/30\n",
            "10/10 [==============================] - ETA: 0s - loss: 3.9584e-07 - accuracy: 1.0000"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/10 [==============================] - 16s 1s/step - loss: 3.9584e-07 - accuracy: 1.0000\n",
            "Epoch 18/30\n",
            "10/10 [==============================] - ETA: 0s - loss: 3.8728e-07 - accuracy: 1.0000"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/10 [==============================] - 16s 2s/step - loss: 3.8728e-07 - accuracy: 1.0000\n",
            "Epoch 19/30\n",
            "10/10 [==============================] - ETA: 0s - loss: 2.4703e-08 - accuracy: 1.0000"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/10 [==============================] - 15s 1s/step - loss: 2.4703e-08 - accuracy: 1.0000\n",
            "Epoch 20/30\n",
            "10/10 [==============================] - ETA: 0s - loss: 5.8225e-08 - accuracy: 1.0000"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/10 [==============================] - 15s 1s/step - loss: 5.8225e-08 - accuracy: 1.0000\n",
            "Epoch 21/30\n",
            "10/10 [==============================] - ETA: 0s - loss: 8.7165e-09 - accuracy: 1.0000"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/10 [==============================] - 15s 1s/step - loss: 8.7165e-09 - accuracy: 1.0000\n",
            "Epoch 22/30\n",
            "10/10 [==============================] - ETA: 0s - loss: 1.5480e-08 - accuracy: 1.0000"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/10 [==============================] - 16s 2s/step - loss: 1.5480e-08 - accuracy: 1.0000\n",
            "Epoch 23/30\n",
            "10/10 [==============================] - ETA: 0s - loss: 0.0098 - accuracy: 0.9937    "
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/10 [==============================] - 16s 2s/step - loss: 0.0098 - accuracy: 0.9937\n",
            "Epoch 24/30\n",
            "10/10 [==============================] - ETA: 0s - loss: 1.1101 - accuracy: 0.9531"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/10 [==============================] - 15s 1s/step - loss: 1.1101 - accuracy: 0.9531\n",
            "Epoch 25/30\n",
            "10/10 [==============================] - ETA: 0s - loss: 1.5775e-08 - accuracy: 1.0000"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/10 [==============================] - 16s 2s/step - loss: 1.5775e-08 - accuracy: 1.0000\n",
            "Epoch 26/30\n",
            "10/10 [==============================] - ETA: 0s - loss: 1.3545e-09 - accuracy: 1.0000"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/10 [==============================] - 15s 1s/step - loss: 1.3545e-09 - accuracy: 1.0000\n",
            "Epoch 27/30\n",
            "10/10 [==============================] - ETA: 0s - loss: 4.0813e-11 - accuracy: 1.0000"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/10 [==============================] - 15s 1s/step - loss: 4.0813e-11 - accuracy: 1.0000\n",
            "Epoch 28/30\n",
            "10/10 [==============================] - ETA: 0s - loss: 1.6309e-10 - accuracy: 1.0000"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/10 [==============================] - 16s 1s/step - loss: 1.6309e-10 - accuracy: 1.0000\n",
            "Epoch 29/30\n",
            "10/10 [==============================] - ETA: 0s - loss: 1.2753e-10 - accuracy: 1.0000"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/10 [==============================] - 15s 1s/step - loss: 1.2753e-10 - accuracy: 1.0000\n",
            "Epoch 30/30\n",
            "10/10 [==============================] - ETA: 0s - loss: 1.0610e-10 - accuracy: 1.0000"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,accuracy\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r10/10 [==============================] - 16s 2s/step - loss: 1.0610e-10 - accuracy: 1.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = load_model(\"/content/bestmodel.h5\")"
      ],
      "metadata": {
        "id": "fMmkALBI0-zx"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "acc = model.evaluate_generator(test_data)[1]\n",
        "print(f\"our model accuracy is  {acc * 100} %\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hdz7cGM95MpX",
        "outputId": "1583831f-a24f-409c-f681-398aaaec55bb"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: UserWarning: `Model.evaluate_generator` is deprecated and will be removed in a future version. Please use `Model.evaluate`, which supports generators.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "our model accuracy is  100.0 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " Our model is perform well on the datasets.\n",
        "\n",
        "The accuracy is 100.0 %"
      ],
      "metadata": {
        "id": "3_d7ptOZ5la4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "h = hist.history\n",
        "h.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rxuQJqnC5dTM",
        "outputId": "8a187c4a-153f-491b-e80a-16a1475d7766"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.class_indices"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KpvQvuyt-_2Y",
        "outputId": "6f6ebfd1-006b-4c7b-f8e2-541416caeff0"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'infected': 0, 'notinfected': 1}"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data.class_indices"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fbbk8h8xBooZ",
        "outputId": "2742b439-dae5-40db-c9e5-3b0e1759eaf9"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'infected': 0, 'notinfected': 1}"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val_data.class_indices"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mOfB52X6BsSJ",
        "outputId": "b609cb71-e99f-426b-d906-ab693a474e55"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'infected': 0, 'notinfected': 1}"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#now get some random images and predict the class\n",
        "from keras.preprocessing import image\n",
        "import tensorflow as tf\n",
        "\n",
        "\n",
        "def predictimage(path):\n",
        "       \n",
        "      img = tf.keras.utils.load_img(path,target_size=(224,224))\n",
        "      i = tf.keras.utils.img_to_array(img)/255\n",
        "      input_arr= np.array([i])\n",
        "      input_arr.shape\n",
        "\n",
        "      pred =model.predict(input_arr)\n",
        "      if pred == 1:\n",
        "          print(\"Not Affected\")\n",
        "      else :\n",
        "         print(\"Affected\")\n",
        "      #display image\n",
        "      plt.imshow(input_arr[0],vmin=0, vmax=255)\n",
        "      plt.title(\"input Image\")\n",
        "      plt.show()"
      ],
      "metadata": {
        "id": "N0Pz3dlG54fT"
      },
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#It is the infected image sample\n",
        "predictimage(\"/content/PCOS/infected/img_0_113.jpg\") "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "FpjOjR_v6J_y",
        "outputId": "de830c00-7f83-479c-e18c-20595b3502a9"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 74ms/step\n",
            "Affected\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#It is the not affected sample images\n",
        "predictimage(\"/content/PCOS/notinfected/img_0_6858.jpg\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "EhKQCC0X6c0A",
        "outputId": "0db6c1c3-5bd9-4614-f29c-bd41504ec64a"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 72ms/step\n",
            "Not Affected\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#not infected image\n",
        "\n",
        "predictimage(\"/content/PCOS/notinfected/img_0_1772.jpg\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "GXSefkU_AASP",
        "outputId": "31201e38-ef7f-411e-e16a-823d889e1334"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 70ms/step\n",
            "Not Affected\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictimage(\"/content/PCOS/infected/img_0_9985.jpg\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "v3qL2JydAnOQ",
        "outputId": "9bfdf7c4-074a-4cce-ce5a-9e93aec6a5c6"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 72ms/step\n",
            "Affected\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yHp9D2uNBLwP"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}