MURA_DenseNet_v1_7.ipynb

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  "metadata": {
    "colab": {
      "name": "MURA-DenseNet-v1.7.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VPz1RlDzUQL0"
      },
      "source": [
        "# MURA-Densenet-v1.6-Humerus\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zL4-xYNJQNMb"
      },
      "source": [
        "!pip install focal-loss\n",
        "!pip install keras-radam"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mkjRfywGQWRn"
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import cv2\n",
        "import random\n",
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "import keras.backend as K\n",
        "import matplotlib.pyplot as plt\n",
        "import keras\n",
        "import keras.backend as K\n",
        "from keras.models import Model\n",
        "from keras.layers import Input, Dense, Conv2D, Conv3D, DepthwiseConv2D, SeparableConv2D, Conv3DTranspose\n",
        "from keras.layers import Flatten, MaxPool2D, AvgPool2D, GlobalAvgPool2D, UpSampling2D, BatchNormalization\n",
        "from keras.layers import Concatenate, Add, Dropout, ReLU, Lambda, Activation, LeakyReLU, PReLU\n",
        "from keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint, Callback\n",
        "from keras.preprocessing.image import ImageDataGenerator, load_img#, image\n",
        "from keras import regularizers\n",
        "from IPython.display import SVG\n",
        "from keras.utils.vis_utils import model_to_dot\n",
        "from keras.applications.densenet import DenseNet169, DenseNet121, preprocess_input\n",
        "from time import time\n",
        "\n",
        "\n",
        "from focal_loss import BinaryFocalLoss\n",
        "from keras_radam import RAdam\n",
        "\n",
        "import datetime"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SE2tMLbKQZsU",
        "outputId": "0c77440d-2100-4e2b-9f38-eda4288d6679"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "txkV3l09Qdij"
      },
      "source": [
        "!unzip '/content/drive/My Drive/Dataset/MURA-v1.1.zip' "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-sa-ji_TQmmV"
      },
      "source": [
        "def listdir_nohidden(path):\n",
        "    '''\n",
        "    Utility function to find the list of files in a directory excluding the hidden files.\n",
        "    Args:\n",
        "        path: contains the path of the directory containing the images\n",
        "\n",
        "    '''\n",
        "    for f in os.listdir(path):\n",
        "        if not f.startswith('.'):\n",
        "            yield f"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4iU6lPsjQ4Qy"
      },
      "source": [
        "def create_images_metadata_csv(category,study_types):\n",
        "    '''\n",
        "    This function creates a csv file containing the path of images, label.\n",
        "    Args:\n",
        "        category: train or valid, depending on which csv is needed\n",
        "        study_types: the type of the body part in MURA Dataset, eg:  XR_SHOULDER\n",
        "    '''\n",
        "    image_data = {}\n",
        "    study_label = {'positive': 1, 'negative': 0}\n",
        "    #study_types = ['XR_ELBOW','XR_FINGER','XR_FOREARM','XR_HAND','XR_HUMERUS','XR_SHOULDER','XR_WRIST']\n",
        "    #study_types = ['XR_ELBOW']\n",
        "    i = 0\n",
        "    image_data[category] = pd.DataFrame(columns=['Path','Count', 'Label'])\n",
        "    for study_type in study_types: # Iterate throught every study types\n",
        "        DATA_DIR = 'MURA-v1.1/%s/%s/' % (category, study_type)\n",
        "        patients = list(os.walk(DATA_DIR))[0][1]  # list of patient folder names\n",
        "        for patient in tqdm(patients):  # for each patient folder\n",
        "            for study in os.listdir(DATA_DIR + patient):  # for each study in that patient folder\n",
        "                if(study != '.DS_Store'):\n",
        "                    label = study_label[study.split('_')[1]]  # get label 0 or 1\n",
        "                    path = DATA_DIR + patient + '/' + study + '/'  # path to this study\n",
        "                    for j in range(len(list(listdir_nohidden(path)))):\n",
        "                        image_path = path + 'image%s.png' % (j + 1)\n",
        "                        image_data[category].loc[i] = [image_path,1, label]  # add new row\n",
        "                        i += 1\n",
        "    image_data[category].to_csv(category+\"_image_data.csv\",index = None, header=False)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x0KgZTeDQ7ib"
      },
      "source": [
        "def getImageLabels(dataframe):\n",
        "  '''\n",
        "  This function is to get the labels of the images from the dataframe\n",
        "  Args:\n",
        "      dataframe: pandas dataframe containing the labels of the images\n",
        "\n",
        "  '''\n",
        "  labels = []\n",
        "  for i, data in tqdm(dataframe.iterrows()):\n",
        "#        img = cv2.imread(data['Path'])\n",
        "#         #random rotation\n",
        "#         angle = random.randint(-30,30)\n",
        "#         M = cv2.getRotationMatrix2D((img_width/2,img_height/2),angle,1)\n",
        "#         img = cv2.warpAffine(img,M,(img_width,img_height))\n",
        "      #resize\n",
        "#        img = cv2.resize(img,(img_width,img_height))    \n",
        "#        img = img[...,::-1].astype(np.float32)\n",
        "#        images.append(img)\n",
        "      labels.append(data['Label'])\n",
        "#    images = np.asarray(images).astype('float32') \n",
        "  #normalization\n",
        "#    mean = np.mean(images[:, :, :])\n",
        "#    std = np.std(images[:, :, :])\n",
        "#    images[:, :, :] = (images[:, :, :] - mean) / std\n",
        "  labels = np.asarray(labels)\n",
        "  return labels"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZLbE7a2NQ-rO"
      },
      "source": [
        "def getImage(dataframe, size):\n",
        "    '''\n",
        "    Function to process the images\n",
        "    Args:\n",
        "        dataframe: contains the path to the images in the directory\n",
        "        size: contains the value to which the shape of the image will resized\n",
        "    '''\n",
        "    Images = []\n",
        "    for i, data in tqdm(dataframe.iterrows()):\n",
        "      try:\n",
        "        image = cv2.imread(data['Path'])#cv2.IMREAD_GRAYSCALE)\n",
        "        image = cv2.resize(image,(size,size))\n",
        "        #image = randome_rotation_flip(image,size)\n",
        "        Images.append(image)\n",
        "\n",
        "      except Exception as e:\n",
        "        print(str(e))\n",
        "\n",
        "    Images = np.asarray(Images).astype('float32')\n",
        "\n",
        "    mean = np.mean(Images)\t\t\t#normalization\n",
        "    std = np.std(Images)\n",
        "    Images = (Images - mean) / std\n",
        "    \n",
        "    #if K.image_data_format() == \"channels_first\":\n",
        "    #  Images = np.expand_dims(Images,axis=3)\t\t   #Extended dimension 1\n",
        "    #if K.image_data_format() == \"channels_last\":\n",
        "    #  Images = np.expand_dims(Images,axis=3)             #Extended dimension 3(usebackend tensorflow:aixs=3; theano:axixs=1) \n",
        "    return Images\n"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5DfIyvgFREaz"
      },
      "source": [
        "def build_model():\n",
        "    base_model = DenseNet169(input_shape=(None, None,3),\n",
        "                             weights='imagenet',\n",
        "                             include_top=False,\n",
        "                             pooling='avg')\n",
        "\n",
        "\n",
        "    x = base_model.output\n",
        "\n",
        "    predictions = Dense(n_classes,activation='sigmoid')(x)\n",
        "    model = Model(inputs=base_model.input, outputs=predictions)\n",
        "    return model"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XSGVIWqDUCfr"
      },
      "source": [
        "### Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S-rZwni8Rizb",
        "outputId": "a1e05882-a53f-4bf2-eb69-d190cf8fdaa9"
      },
      "source": [
        "###################\n",
        "# Data processing #\n",
        "###################\n",
        "\n",
        "img_width, img_height = 224, 224\n",
        "#Keras ImageDataGenerator to load, transform the images of the dataset\n",
        "\n",
        "IMG_DATA_DIR = 'MURA-v1.1/'\n",
        "train_data_dir = IMG_DATA_DIR + 'train/XR_HUMERUS'\n",
        "valid_data_dir = IMG_DATA_DIR + 'valid/XR_HUMERUS'\n",
        "\n",
        "train_datagen = ImageDataGenerator(\n",
        "    rotation_range=30,\n",
        "    horizontal_flip=True\n",
        ")\n",
        "\n",
        "test_datagen = ImageDataGenerator(\n",
        "    rotation_range=30,\n",
        "    horizontal_flip=True\n",
        "\n",
        ")\n",
        "study_types = ['XR_HUMERUS']\n",
        "\n",
        "#TRAIN DATA\n",
        "create_images_metadata_csv('train',study_types)\n",
        "#VALID DATA\n",
        "create_images_metadata_csv('valid',study_types)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 587/587 [00:05<00:00, 115.03it/s]\n",
            "100%|██████████| 132/132 [00:01<00:00, 123.36it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LJztmkXwRn6Z"
      },
      "source": [
        "valid_image_df = pd.read_csv('valid_image_data.csv', names=['Path','Count', 'Label'])\n",
        "train_image_df = pd.read_csv('train_image_data.csv', names=['Path', 'Count','Label'])\n",
        "\n",
        "dd={}\n",
        "\n",
        "dd['train'] = train_image_df\n",
        "dd['valid'] = valid_image_df"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aujYyGwQRr7H",
        "outputId": "9397d95d-a891-4447-a383-43ad164eeeb2"
      },
      "source": [
        "train_image_labels = getImageLabels(train_image_df)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1272it [00:00, 7862.11it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9z7Onjv-Ruoa",
        "outputId": "6430efea-6d1b-4fc4-9e61-ed0a6a93ca14"
      },
      "source": [
        "train_images = getImage(train_image_df, size = 224)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1272it [00:05, 246.48it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-vjDo-M65Bvp",
        "outputId": "e0c9ff63-1aac-405d-d7e0-0e80b6ffeb39"
      },
      "source": [
        "train_images.shape\n"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1272, 224, 224, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_TxiT79kRyGc"
      },
      "source": [
        "train_datagen.fit(train_images,augment=True)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VCSwarbzR0rB"
      },
      "source": [
        "train_generator = train_datagen.flow(\n",
        "    x=train_images,\n",
        "    y=train_image_labels,\n",
        "    batch_size = 32\n",
        ")"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_L6c6SP2R624",
        "outputId": "d0303e05-19fd-4e7b-a2d6-5d50e7890f9e"
      },
      "source": [
        "valid_image_labels = getImageLabels(valid_image_df)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "288it [00:00, 7443.72it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OLvYk_lhR_Dv",
        "outputId": "b545e68c-0cd9-40a5-97b8-07b6f65c8298"
      },
      "source": [
        "valid_images = getImage(valid_image_df, size = 224)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "288it [00:01, 194.16it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sM_-sFxfSDK9"
      },
      "source": [
        "test_datagen.fit(valid_images,augment=True)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yTdsSZXaSF-l"
      },
      "source": [
        "validation_generator = test_datagen.flow(\n",
        "    x=valid_images,\n",
        "    y=valid_image_labels,\n",
        "    batch_size = 1\n",
        ")"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rc2WUD9ATuot"
      },
      "source": [
        "### DenseNet Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h1rXUaJnSIQ8",
        "outputId": "baaeac0c-f07d-4cb7-b95e-38635fec020d"
      },
      "source": [
        "###################\n",
        "# Construct model #\n",
        "###################\n",
        "\n",
        "\n",
        "\n",
        "#model parameters for training\n",
        "#K.set_learning_phase(1)\n",
        "nb_train_samples = len(train_images)\n",
        "nb_validation_samples = len(valid_images)\n",
        "epochs = 12\n",
        "batch_size = 32\n",
        "steps_per_epoch = nb_train_samples//batch_size\n",
        "print(steps_per_epoch)\n",
        "n_classes = 1"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "39\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fkqM6sfnSL_x"
      },
      "source": [
        "#Building a model\n",
        "input_shape = (224, 224, 1)\n",
        "#model = densenet(input_shape, n_classes)\n",
        "model = build_model()\n",
        "# Build optimizer\n",
        "#opt = keras.optimizers.Adam(lr=learning_rate, beta_1=0.9, beta_2=0.999, epsilon=0.1)\n",
        "#opt = RAdam(total_steps=5000, warmup_proportion=0.1, min_lr=1e-4,name='RAdam')\n",
        "\n",
        "\n",
        "model.compile(loss=BinaryFocalLoss(gamma = 2), optimizer='adam', metrics=['acc', 'mse'])\n",
        "#model.compile(loss=BinaryFocalLoss(gamma = 2), optimizer=opt, metrics=['acc', 'mse'])"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YFinSZrRSd4v"
      },
      "source": [
        "#callbacks for early stopping incase of reduced learning rate, loss unimprovement\n",
        "early_stop = EarlyStopping(monitor='val_loss', patience=8, verbose=1, min_delta=1e-4)\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=1, verbose=1, min_lr=0.0001)\n",
        "callbacks_list = [early_stop, reduce_lr]"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J2VTC2AISlZC",
        "outputId": "062c9b30-dfa1-4f3c-bb30-ac9d49ce8c6e"
      },
      "source": [
        "#train the module\n",
        "model_history = model.fit(\n",
        "    train_generator,\n",
        "    epochs=epochs,\n",
        "    workers=0,\n",
        "    use_multiprocessing=False,  \n",
        "    steps_per_epoch = nb_train_samples//batch_size,\n",
        "    validation_data=validation_generator,\n",
        "    validation_steps=nb_validation_samples //batch_size,\n",
        "    callbacks=callbacks_list\n",
        "    \n",
        ")"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/12\n",
            "39/39 [==============================] - 69s 1s/step - loss: 0.3685 - acc: 0.5718 - mse: 0.2666 - val_loss: 9.8715 - val_acc: 0.3333 - val_mse: 0.6666\n",
            "Epoch 2/12\n",
            "39/39 [==============================] - 48s 1s/step - loss: 0.1657 - acc: 0.6290 - mse: 0.2226 - val_loss: 10.7454 - val_acc: 0.3333 - val_mse: 0.6667\n",
            "\n",
            "Epoch 00002: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "Epoch 3/12\n",
            "39/39 [==============================] - 48s 1s/step - loss: 0.1357 - acc: 0.7669 - mse: 0.1839 - val_loss: 1.1606 - val_acc: 0.3333 - val_mse: 0.5013\n",
            "Epoch 4/12\n",
            "39/39 [==============================] - 48s 1s/step - loss: 0.1205 - acc: 0.8116 - mse: 0.1821 - val_loss: 0.0655 - val_acc: 0.8889 - val_mse: 0.1272\n",
            "Epoch 5/12\n",
            "39/39 [==============================] - 48s 1s/step - loss: 0.1139 - acc: 0.8146 - mse: 0.1730 - val_loss: 0.1083 - val_acc: 0.8889 - val_mse: 0.1644\n",
            "\n",
            "Epoch 00005: ReduceLROnPlateau reducing learning rate to 0.0001.\n",
            "Epoch 6/12\n",
            "39/39 [==============================] - 48s 1s/step - loss: 0.1081 - acc: 0.8108 - mse: 0.1647 - val_loss: 0.1155 - val_acc: 0.6667 - val_mse: 0.1611\n",
            "Epoch 7/12\n",
            "39/39 [==============================] - 48s 1s/step - loss: 0.1006 - acc: 0.8407 - mse: 0.1522 - val_loss: 0.0627 - val_acc: 0.8889 - val_mse: 0.1190\n",
            "Epoch 8/12\n",
            "39/39 [==============================] - 48s 1s/step - loss: 0.1072 - acc: 0.8217 - mse: 0.1605 - val_loss: 0.1729 - val_acc: 0.7778 - val_mse: 0.1878\n",
            "Epoch 9/12\n",
            "39/39 [==============================] - 48s 1s/step - loss: 0.1044 - acc: 0.8227 - mse: 0.1531 - val_loss: 0.0668 - val_acc: 0.7778 - val_mse: 0.1229\n",
            "Epoch 10/12\n",
            "39/39 [==============================] - 48s 1s/step - loss: 0.1023 - acc: 0.8280 - mse: 0.1499 - val_loss: 0.0354 - val_acc: 1.0000 - val_mse: 0.0861\n",
            "Epoch 11/12\n",
            "39/39 [==============================] - 48s 1s/step - loss: 0.0894 - acc: 0.8606 - mse: 0.1367 - val_loss: 0.1055 - val_acc: 0.7778 - val_mse: 0.1779\n",
            "Epoch 12/12\n",
            "39/39 [==============================] - 48s 1s/step - loss: 0.0912 - acc: 0.8479 - mse: 0.1413 - val_loss: 0.1637 - val_acc: 0.8889 - val_mse: 0.1726\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Mf2ajBiPu0N"
      },
      "source": [
        "model.save(\"MURA-DenseNet-v1.7-Humerus.h5\")"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SbXYRSxySn2w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "092fc1b8-4e14-4e70-bb0c-3e3b29cfe803"
      },
      "source": [
        "ev = model.evaluate(validation_generator, steps=nb_validation_samples,  workers=0, use_multiprocessing=False)\n",
        "ev[1]"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "288/288 [==============================] - 17s 59ms/step - loss: 0.1002 - acc: 0.8438 - mse: 0.1389\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.84375"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5t1WEOfJTqiE"
      },
      "source": [
        "### Performance Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "3-jQN-wwQsmX",
        "outputId": "ada2742c-8df3-43c7-a5c6-53164d07952c"
      },
      "source": [
        "#Now we have trained our model, we can see the metrics during the training proccess\n",
        "plt.figure(0)\n",
        "plt.plot(model_history.history['acc'],'r')\n",
        "plt.plot(model_history.history['val_acc'],'g')\n",
        "plt.xticks(np.arange(0, 12, 1))\n",
        "plt.rcParams['figure.figsize'] = (8, 6)\n",
        "plt.xlabel(\"Num of Epochs\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.title(\"Training Accuracy vs Validation Accuracy\")\n",
        "plt.legend(['train','validation'])\n",
        " \n",
        "plt.figure(1)\n",
        "plt.plot(model_history.history['loss'],'r')\n",
        "plt.plot(model_history.history['val_loss'],'g')\n",
        "plt.xticks(np.arange(0, 12, 1))\n",
        "plt.rcParams['figure.figsize'] = (8, 6)\n",
        "plt.xlabel(\"Num of Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.title(\"Training Loss vs Validation Loss\")\n",
        "plt.legend(['train','validation'])\n",
        "\n",
        "plt.figure(2)\n",
        "plt.plot(model_history.history['mse'],'r')\n",
        "plt.plot(model_history.history['val_mse'],'g')\n",
        "plt.xticks(np.arange(0, 12, 1))\n",
        "plt.rcParams['figure.figsize'] = (8, 6)\n",
        "plt.xlabel(\"Num of Epochs\")\n",
        "plt.ylabel(\"MSE\")\n",
        "plt.title(\"Training MSE vs Validation MSE\")\n",
        "plt.legend(['train','validation'])\n",
        " \n",
        "plt.show()\n"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ECcQeXQsRz4g"
      },
      "source": [
        "#pred = model.predict_generator(validation_generator, steps=1, batch_size=1, use_multiprocessing=False, max_queue_size=25, verbose=1)\n",
        "validation_generator.reset()\n",
        "#pred = model.predict_generator(validation_generator,steps=nb_validation_samples)\n",
        "pred_batch = model.predict_on_batch(valid_images)"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cEvuq_CfR_ti"
      },
      "source": [
        "predictions = []\n",
        "for p in pred_batch:\n",
        "    if(p > 0.5):\n",
        "        predictions+=[1]\n",
        "    else:\n",
        "        predictions+=[0]"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O8meoRTbSD_S"
      },
      "source": [
        "error = np.sum(np.not_equal(predictions, valid_image_labels)) / valid_image_labels.shape[0]  \n",
        "pred = predictions\n"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JASSNiwATgQR"
      },
      "source": [
        "### Confusion Matric"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 841
        },
        "id": "F95xLw23SNwZ",
        "outputId": "2b3f1f7f-dfa0-4295-b08a-787f1c3923a0"
      },
      "source": [
        "print('Confusion Matrix')\n",
        "from sklearn.metrics import confusion_matrix, classification_report, cohen_kappa_score\n",
        "import seaborn as sn\n",
        "cm = confusion_matrix( pred ,valid_image_labels)\n",
        "plt.figure(figsize = (30,20))\n",
        "sn.set(font_scale=1.4) #for label size\n",
        "sn.heatmap(cm, annot=True, annot_kws={\"size\": 20},cmap=\"YlGnBu\") # font size\n",
        "plt.show()"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2160x1440 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RqhrvxQiS-SE"
      },
      "source": [
        "### Kappa score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HRJV6FhcSQnx",
        "outputId": "e1d0dd50-35ee-4655-a20e-bd5ac8f76d4c"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report, cohen_kappa_score\n",
        "cohen_kappa_score(valid_image_labels, pred)"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6800618238021638"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q2vCGxRT_FIk"
      },
      "source": [
        "### Classification report"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zf5Q9YhvSaYO",
        "outputId": "0f72385b-455d-45ae-aa59-9d9366cc654f"
      },
      "source": [
        "print()\n",
        "print('Classification Report')\n",
        "print(classification_report(valid_image_labels, pred, target_names=[\"0\",\"1\"]))"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.86      0.85       148\n",
            "           1       0.85      0.82      0.83       140\n",
            "\n",
            "    accuracy                           0.84       288\n",
            "   macro avg       0.84      0.84      0.84       288\n",
            "weighted avg       0.84      0.84      0.84       288\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xG2ozPVsTCBz"
      },
      "source": [
        "### ROC score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nLx_MV7pS2sd"
      },
      "source": [
        "from sklearn.metrics import roc_curve\n",
        "fpr_keras, tpr_keras, thresholds_keras = roc_curve(valid_image_labels, pred_batch)"
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "crergcMnTOIH"
      },
      "source": [
        "from sklearn.metrics import auc\n",
        "auc_keras = auc(fpr_keras, tpr_keras)"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 831
        },
        "id": "_D71aDlDTUMj",
        "outputId": "121ee987-74ab-47f1-b4fa-f72d63d413e4"
      },
      "source": [
        "plt.figure(1)\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "plt.plot(fpr_keras, tpr_keras, label='Keras (area = {:.3f})'.format(auc_keras))\n",
        "plt.xlabel('False positive rate')\n",
        "plt.ylabel('True positive rate')\n",
        "plt.title('ROC curve')\n",
        "plt.legend(loc='best')\n",
        "plt.show()\n",
        "\n",
        "plt.figure(2)\n",
        "plt.xlim(0.0, 0.2)\n",
        "plt.ylim(0.65, 0.9)\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "plt.plot(fpr_keras, tpr_keras, label='Keras (area = {:.3f})'.format(auc_keras))\n",
        "plt.xlabel('False positive rate')\n",
        "plt.ylabel('True positive rate')\n",
        "plt.title('ROC curve (zoomed in at top left)')\n",
        "plt.legend(loc='best')\n",
        "plt.show()"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}