DCNN_Classifier.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Anonymous1846/ALL-Detection-Using-CNN/blob/master/DCNN_Classifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bz8z3s6bmZ-L"
      },
      "source": [
        "# **Problem Statement**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A7XLGtoAl0zO",
        "outputId": "3cba0579-35d1-448a-c36b-0bf9ace9f780"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.12.0\n"
          ]
        }
      ],
      "source": [
        "'''\n",
        "Problems Statement : Building a set of multiclass classifiers for luekemia prediction using the CNN and pretrained networks.\n",
        "                     The dataset as of now is obtained from the ALL-IDB1 and ALL-IDB2 and the ASH website.Blood cancer is a \n",
        "                     type of cancer that affects blood cells and is caused by DNA mutations. It can be difficult to identify \n",
        "                     and is often detected when blood cells begin to behave abnormally. Machine learning algorithms and manual \n",
        "                     image processing techniques have been used to extract key information about cancer cells. The proposed \n",
        "                     system uses advanced Convolutional Neural Network algorithms and a user-friendly interface to predict the \n",
        "                     subtype of cancer,and transfer learning and fine tuning methods are used to improve the performance of the \n",
        "                     CNN models.This system can aid in early cancer detection and therapy.\n",
        "''' \n",
        "\n",
        "import keras          #to check version, some APIs may not work in newer versions!\n",
        "print(keras.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q_JFZFFZFzII"
      },
      "source": [
        "# **Loading The Dataset From Google Drive.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fm-5FTeSxHP4",
        "outputId": "786e83db-2d21-40e9-bdb0-7ec26471c895"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "'''\n",
        "This is an optional block of code which can be executed if the \n",
        "google drive needs to be mounted on different account\n",
        "'''\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive',force_remount=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1hHkfosbhCO-"
      },
      "source": [
        "#**The Path To Training, Validation and Testing Datasets**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZNvpvCN5TJqy",
        "outputId": "45b535a6-0def-4fd7-99f3-0a3f05c0537d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The Training Images Path   :  /content/drive/MyDrive/DCNN Datasets_1/Training\n",
            "The Validation Images Path :  /content/drive/MyDrive/DCNN Datasets_1/Validation\n",
            "The Testing Images Path    :  /content/drive/MyDrive/DCNN Datasets_1/Testing\n"
          ]
        }
      ],
      "source": [
        "import os \n",
        "'''\n",
        "The Images required for the training, validation, and testing is found in the /DCNN Datasets directory !\n",
        "\n",
        "DCNN Datasets\n",
        "|\n",
        "|\n",
        "|\n",
        " ---- Training(80 %)\n",
        "      |\n",
        "      |\n",
        "      ----Normal \n",
        "      |\n",
        "      ----ALL\n",
        "      |\n",
        "      ----AML\n",
        "      |\n",
        "      ----CLL\n",
        "      |\n",
        "      ----CML\n",
        "|\n",
        "|\n",
        "---- Validation (10 %)\n",
        "      |\n",
        "      |\n",
        "      ----Normal \n",
        "      |\n",
        "      ----ALL\n",
        "      |\n",
        "      ----AML\n",
        "      |\n",
        "      ----CLL\n",
        "      |\n",
        "      ----CML\n",
        "|\n",
        "|\n",
        "---- Testing (10 %)\n",
        "      |\n",
        "      |\n",
        "      ----Normal \n",
        "      |\n",
        "      ----ALL\n",
        "      |\n",
        "      ----AML\n",
        "      |\n",
        "      ----CLL\n",
        "      |\n",
        "      ----CML\n",
        "\n",
        "      The images/dataset is divided into 80% training, 10% testing and 10% validation\n",
        "'''\n",
        "\n",
        "training_data_path    = \"/content/drive/MyDrive/DCNN Datasets_1/Training\"\n",
        "validation_data_path  = \"/content/drive/MyDrive/DCNN Datasets_1/Validation\"\n",
        "testing_data_path     = \"/content/drive/MyDrive/DCNN Datasets_1/Testing\"\n",
        "\n",
        "print(f'The Training Images Path   :  {training_data_path}')\n",
        "print(f'The Validation Images Path :  {validation_data_path}')\n",
        "print(f'The Testing Images Path    :  {testing_data_path}')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6crVs2N2O42e"
      },
      "outputs": [],
      "source": [
        "CATEGORIES = 5 #indicating normal, ALL, AML, CLL, CML."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2sEG6O7g-YaR"
      },
      "source": [
        "# **Current Model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4hO_YMP1-X-E"
      },
      "outputs": [],
      "source": [
        "architecture = 'ResNet'            #the current model to be trained/tested"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9yT7JaUnVPpU"
      },
      "source": [
        "# **Image Preprocessing Module for Custom and Pre-Trained Models**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qPeiTL-oVTay"
      },
      "outputs": [],
      "source": [
        "from keras.applications.inception_v3 import preprocess_input as inception_preprocess\n",
        "from keras.applications.efficientnet import preprocess_input as effnet_preprocess\n",
        "from keras.applications.vgg16 import preprocess_input as vgg_preprocess\n",
        "from keras.applications.resnet import preprocess_input as resnet_preprocess\n",
        "\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "def image_preprocess(architecture='Custom'):\n",
        "\n",
        "  \"\"\"\n",
        "  Preprocess an image for use with a CNN model.\n",
        "\n",
        "  Parameters:\n",
        "  - architetcure (str): Path to the image file\n",
        "\n",
        "  Returns:\n",
        "  - Preprocessed Images for training and validation \n",
        "\n",
        "  Raises:\n",
        "  - ValueError: If the image file cannot be read or if the image size is not equal to `target_size`.\n",
        "  \"\"\"\n",
        "  print(f'Architcture chosen : {architecture}')\n",
        "  if architecture == 'ResNet':\n",
        "    training_images_preprocessed=ImageDataGenerator(preprocessing_function=resnet_preprocess)\n",
        "    validate_images_preprocessed = ImageDataGenerator(preprocessing_function=resnet_preprocess)\n",
        "\n",
        "  elif architecture == 'VGG':\n",
        "    training_images_preprocessed = ImageDataGenerator(preprocessing_function=vgg_preprocess)\n",
        "    validate_images_preprocessed = ImageDataGenerator(preprocessing_function=vgg_preprocess)\n",
        "\n",
        "  elif architecture == 'InceptionV3':\n",
        "    training_images_preprocessed=ImageDataGenerator(preprocessing_function=inception_preprocess)\n",
        "    validate_images_preprocessed = ImageDataGenerator(preprocessing_function=inception_preprocess)\n",
        "\n",
        "  elif architecture == 'EffNet':\n",
        "    training_images_preprocessed=ImageDataGenerator(preprocessing_function=effnet_preprocess)\n",
        "    validate_images_preprocessed = ImageDataGenerator(preprocessing_function=effnet_preprocess)\n",
        "\n",
        "  else:\n",
        "    training_images_preprocessed = ImageDataGenerator(rescale=1./255,\n",
        "                                                        rotation_range=40,\n",
        "                                                        width_shift_range=0.2,\n",
        "                                                        height_shift_range=0.2,\n",
        "                                                        shear_range=0.2,\n",
        "                                                        zoom_range=0.2,\n",
        "                                                        horizontal_flip=True,\n",
        "                                                        vertical_flip=True,\n",
        "                                                        fill_mode='nearest')\n",
        "    validate_images_preprocessed = ImageDataGenerator(rescale=1./255)\n",
        "  return training_images_preprocessed,validate_images_preprocessed"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MfDyTPqwV8RF"
      },
      "source": [
        "# **Model Dictionary for Storing Image Diemensions**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3DCGcbxQ09wm"
      },
      "outputs": [],
      "source": [
        "'''\n",
        "For the purpose of setting the image diemensions for training the models we have assigned\n",
        "key-value pairs for model:(width,height) so that the appropriate model gets appropriate\n",
        "diemensions.\n",
        "'''\n",
        "\n",
        "model_dict = {'AlexNet':(227,227),\n",
        "              'Custom':(300,300),\n",
        "              'ResNet':(224,224),\n",
        "              'VGG':(224,224),\n",
        "              'InceptionV3':(299,299)}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GKsU5eX2cGIb",
        "outputId": "7448cf7f-e990-47c4-8bbd-b410405a2a9f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Architcture chosen : ResNet\n",
            "Architcture chosen : ResNet\n"
          ]
        }
      ],
      "source": [
        "training_images_preprocessed,validate_images_preprocessed = image_preprocess(architecture)[0],image_preprocess(architecture)[1]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xGPm_71JXPeC"
      },
      "source": [
        "# **Applying the Image Augmentation to Datasets !**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G9G6VPiSXVsR",
        "outputId": "7303cfca-a827-4b88-ce75-093d1f443cf9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 1126 images belonging to 5 classes.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/keras/preprocessing/image.py:202: UserWarning: Using \".tiff\" files with multiple bands will cause distortion. Please verify your output.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Image Shape : (224, 224)\n",
            "Found 143 images belonging to 5 classes.\n",
            "Image Shape : (224, 224)\n",
            "Found 135 images belonging to 5 classes.\n",
            "Image Shape : (224, 224)\n",
            "{'ALL': 0, 'AML': 1, 'CLL': 2, 'CML': 3, 'Normal': 4}\n"
          ]
        }
      ],
      "source": [
        "def apply_augmentation(data_directory,processing_mode,flag):\n",
        "  '''\n",
        "    The function augments the data, according to the processing mode.\n",
        "    The processing mode has defined earlier seperately for training and validation set !\n",
        "\n",
        "    params: training/validation directory,\n",
        "            the processing mode(different for training and validation dataset), \n",
        "            flag for model !\n",
        "\n",
        "    return: augmented data \n",
        "  '''\n",
        "  data_gen = processing_mode.flow_from_directory(\n",
        "  data_directory,                               \n",
        "  target_size = model_dict[flag],               \n",
        "  batch_size = 128,                             \n",
        "  class_mode='categorical')\n",
        "  print(f'Image Shape : {model_dict[flag]}')\n",
        "  return data_gen\n",
        "\n",
        "\n",
        "training_images_datagen = apply_augmentation(training_data_path,            \n",
        "                                             training_images_preprocessed,  \n",
        "                                             architecture)                  \n",
        "\n",
        "valid_images_datagen = apply_augmentation(validation_data_path,             \n",
        "                                          validate_images_preprocessed,\n",
        "                                          architecture)\n",
        "\n",
        "test_images_datagen = apply_augmentation(testing_data_path,                 \n",
        "                                         validate_images_preprocessed,\n",
        "                                         architecture)\n",
        "\n",
        "print(training_images_datagen.class_indices)          #class indices may give idea of the indices of corresponding cells\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lbC-FfxiDtCO"
      },
      "source": [
        "#**Using the mean pixel we have calculated the number of features !**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wtkmIsQNDssN",
        "outputId": "1fdf0cae-2867-4c2a-8808-6b30e3aa7b30"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The diemensions of the cancer cell: (784, 1000, 3)\n",
            "The mean pixels values for the cancer cells: 784000\n"
          ]
        }
      ],
      "source": [
        "from cv2 import imread\n",
        "\n",
        "sample_image = imread('/content/drive/MyDrive/DCNN Datasets/Training/CLL/61676-CLL cells with cytoplasmic vacuoles.Jpeg')\n",
        "print(\"The diemensions of the cancer cell: {}\".format(sample_image.shape))\n",
        "feature_matrix = np.zeros((sample_image.shape[0], sample_image.shape[1])) # for cancer cell\n",
        "\n",
        "\n",
        "def show_features(feature_matrix,img):\n",
        "  '''\n",
        "    The function will calculate the mean pixels values which are essential in understanding the number of possible \n",
        "    values/ features in the image !\n",
        "\n",
        "    params: fetaure matrix, image\n",
        "\n",
        "    return: None\n",
        "  '''\n",
        "  for i in range(1,img.shape[0]):\n",
        "    for j in range(1,img.shape[1]):\n",
        "        feature_matrix[i][j] = ((int(img[i,j,0]) + int(img[i,j,1]) + int(img[i,j,2]))/3) # mean for three channels !\n",
        "\n",
        "  features = np.reshape(feature_matrix, (img.shape[0]*img.shape[1])) \n",
        "  print(\"The mean pixels values for the cancer cells: {}\".format(features.shape[0]))\n",
        "\n",
        "show_features(feature_matrix,sample_image) # for cancer cell \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "psRQ77sbYuqN"
      },
      "source": [
        "# **Representation of Images.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "W3oV8CEwY6p8",
        "outputId": "b7d78716-2582-4f26-8151-f6713bcaeacc"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt \n",
        "def plot(image):\n",
        "  '''\n",
        "    The function is used to plot the augmented figures of the images, for visual demonstartion !\n",
        "\n",
        "    params: image object\n",
        "    return: None\n",
        "  '''\n",
        "  fig,axes=plt.subplots(1,5,figsize=(20,20))    # 20 x 20 images \n",
        "  axes=axes.flatten()\n",
        "  for imag,axis in zip(image,axes):              #iterate through the images matplotlib and cv2\n",
        "    axis.imshow(imag)\n",
        "  plt.tight_layout()\n",
        "  plt.show()\n",
        "\n",
        "training_images_datagen.class_indices\n",
        "sample_images=[training_images_datagen[0][0][0] for i in range(5)]\n",
        "plot(sample_images)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mSoEHjz8dZW7"
      },
      "outputs": [],
      "source": [
        "from keras.layers import MaxPooling2D,Conv2D,Dropout,Dense,Flatten,BatchNormalization,MaxPool2D,AveragePooling2D,GlobalAveragePooling2D\n",
        "from keras.models import Sequential"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ExfBhm4YE0f"
      },
      "source": [
        "# **Multiclass Test Classifier !**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "66QFUFr1YNlW",
        "outputId": "a5fea5d9-d467-4a1e-d49d-bf0fe62b0d27"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_5 (Conv2D)           (None, 298, 298, 32)      896       \n",
            "                                                                 \n",
            " max_pooling2d_5 (MaxPooling  (None, 149, 149, 32)     0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_6 (Conv2D)           (None, 147, 147, 32)      9248      \n",
            "                                                                 \n",
            " max_pooling2d_6 (MaxPooling  (None, 73, 73, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_7 (Conv2D)           (None, 71, 71, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_7 (MaxPooling  (None, 35, 35, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_8 (Conv2D)           (None, 33, 33, 64)        36928     \n",
            "                                                                 \n",
            " max_pooling2d_8 (MaxPooling  (None, 16, 16, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_9 (Conv2D)           (None, 14, 14, 64)        36928     \n",
            "                                                                 \n",
            " max_pooling2d_9 (MaxPooling  (None, 7, 7, 64)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 3136)              0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 2048)              6424576   \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 512)               1049088   \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 5)                 2565      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 7,578,725\n",
            "Trainable params: 7,578,725\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "'''\n",
        "The CNN model is used to predict whether the given data image is of ALL,AML,CLL,CML or Normal type,\n",
        "it contains 5 convolution and corresponding number of maxpooling layers.\n",
        "RelU function is added to each layer so that it can introduce non-linearity and \n",
        "avoid the negative values !\n",
        "'''\n",
        "\n",
        "custom_model  =  Sequential([Conv2D(32, (3,3), activation='relu', input_shape=(300, 300, 3)), \n",
        "                          MaxPooling2D(2, 2),\n",
        "                          Conv2D(32, (3,3), activation='relu'),\n",
        "                          MaxPooling2D(2,2),                                    \n",
        "                          Conv2D(64, (3,3), activation='relu'),\n",
        "                          MaxPooling2D(2,2),                                   \n",
        "                          Conv2D(64, (3,3), activation='relu'), \n",
        "                          MaxPooling2D(2,2),                                   \n",
        "                          Conv2D(64, (3,3), activation='relu'), \n",
        "                          MaxPooling2D(2,2),                                   \n",
        "                          Flatten(),                                                         \n",
        "                          Dense(2048, activation='relu'),\n",
        "                          Dense(512, activation='relu'),\n",
        "                          Dense(CATEGORIES, activation='softmax')])       #softmax gives prediction values for cancer cells\n",
        "\n",
        "custom_model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sD7ModzzAYbr"
      },
      "source": [
        "# **Multiclass Classifier Based On AlexNet**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s6K8U_fjAYGC",
        "outputId": "c3161ecf-128a-45cc-d5d1-f900be43853a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_5 (Conv2D)           (None, 55, 55, 96)        34944     \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 55, 55, 96)       384       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " max_pooling2d_5 (MaxPooling  (None, 27, 27, 96)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_6 (Conv2D)           (None, 27, 27, 256)       614656    \n",
            "                                                                 \n",
            " max_pooling2d_6 (MaxPooling  (None, 13, 13, 256)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_7 (Conv2D)           (None, 13, 13, 384)       885120    \n",
            "                                                                 \n",
            " conv2d_8 (Conv2D)           (None, 13, 13, 384)       1327488   \n",
            "                                                                 \n",
            " conv2d_9 (Conv2D)           (None, 13, 13, 256)       884992    \n",
            "                                                                 \n",
            " max_pooling2d_7 (MaxPooling  (None, 6, 6, 256)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten_2 (Flatten)         (None, 9216)              0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 4096)              37752832  \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 4096)              0         \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 4096)              16781312  \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 4096)              0         \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 5)                 20485     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 58,302,213\n",
            "Trainable params: 58,302,021\n",
            "Non-trainable params: 192\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "'''\n",
        "Implementation of the AlexNet architecture, which is an common architecture of the CNN model\n",
        "The AlexNet Alrchitecture has been modified since, the original one is capable of classifiying\n",
        "upto 1000 tensors, and requires more computational power !\n",
        "'''\n",
        "\n",
        "alexnet_model =Sequential([\n",
        "                    Conv2D(96, (11, 11), strides=(4,4), activation='relu', input_shape=(227, 227, 3)),\n",
        "                    BatchNormalization(),\n",
        "                    MaxPooling2D((3, 3),strides=(2,2)),\n",
        "                    Conv2D(256, (5, 5),padding='same', activation='relu'),                    \n",
        "                    MaxPooling2D((3, 3),strides=(2, 2)),\n",
        "                    Conv2D(384, (3, 3), padding='same',activation='relu'),\n",
        "                    Conv2D(384, (3, 3), padding='same',activation='relu'),\n",
        "                    Conv2D(256, (3, 3), padding='same',activation='relu'),\n",
        "                    MaxPooling2D((3, 3),strides=(2, 2)),\n",
        "                    Flatten(),\n",
        "                    Dense(4096, activation='relu'),                 \n",
        "                    Dropout(0.5),\n",
        "                    Dense(4096, activation='relu'),                \n",
        "                    Dropout(0.5),\n",
        "                    Dense(CATEGORIES, activation='softmax')])\n",
        "\n",
        "alexnet_model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DhKRDOYXxBoE"
      },
      "source": [
        "# **Multiclass Classifier Based On VGG 16**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fnUdpd8uw-da",
        "outputId": "8544b48d-d8a1-49a5-c865-d8f5ee277b2c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "58889256/58889256 [==============================] - 2s 0us/step\n",
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " vgg16 (Functional)          (None, 7, 7, 512)         14714688  \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 25088)             0         \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 25088)             0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 256)               6422784   \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 5)                 1285      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 21,138,757\n",
            "Trainable params: 13,503,493\n",
            "Non-trainable params: 7,635,264\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "'''\n",
        "  VGG-16 is a convolutional neural network model trained on the ImageNet dataset. \n",
        "  The model is characterized by its deep architecture, which consists of 16 layers, \n",
        "  including 13 convolutional layers and 3 fully connected layers. The convolutional layers \n",
        "  are organized into 5 blocks, with each block containing multiple convolutional layers\n",
        "  and a max pooling layer. The model takes an image as input and classifies it into one \n",
        "  of 1000 object categories.The VGG-16 model is widely used as a base model for image \n",
        "  classification and object detection tasks.\n",
        "'''\n",
        "\n",
        "from keras.applications.vgg16 import VGG16  \n",
        "\n",
        "# referencing the base model with imagenet weights and removing the top layers for fine-tuning\n",
        "vgg16_base =  VGG16(weights='imagenet',\n",
        "              include_top = False,\n",
        "              input_shape = (224, 224, 3))\n",
        "\n",
        "trainable = False\n",
        "\n",
        "for layer in vgg16_base.layers:\n",
        "    if layer.name == 'block5_conv1':\n",
        "        trainable = True\n",
        "    if trainable:\n",
        "        layer.trainable = True\n",
        "    else:\n",
        "        layer.trainable = False\n",
        "\n",
        "#fine-tuning the vgg16 model to classify the 5 types of cells\n",
        "vgg16_model = Sequential([vgg16_base,\n",
        "                          Flatten(),\n",
        "                          Dropout(0.5),\n",
        "                          Dense(256,activation='relu'),\n",
        "                          Dense(CATEGORIES,activation='softmax')])\n",
        "\n",
        "vgg16_model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9DgfCoeE4fa9"
      },
      "source": [
        "# **Multiclass Classifier Based On ResNet50**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cT64MEEA4e3H",
        "outputId": "cd9e1c8b-8af7-4fb2-c849-c16a89621845"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94765736/94765736 [==============================] - 3s 0us/step\n",
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " resnet50 (Functional)       (None, 7, 7, 2048)        23587712  \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 100352)            0         \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 100352)            0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 256)               25690368  \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 5)                 1285      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 49,279,365\n",
            "Trainable params: 40,141,829\n",
            "Non-trainable params: 9,137,536\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "'''\n",
        "  ResNet50 is a deep convolutional neural network that was trained on more than a million images from the ImageNet database. \n",
        "  The network is 50 layers deep and can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals.\n",
        "  It was developed by researchers at Microsoft Research in 2015, and has since become a popular model for image classification tasks \n",
        "  due to its high accuracy and efficient use of computational resources. The key innovations in ResNet50 are the use of residual \n",
        "  connections and batch normalization, which make it possible to train very deep networks without the problems of vanishing gradients\n",
        "'''\n",
        "\n",
        "from keras.applications.resnet import ResNet50   \n",
        "\n",
        "resnet50_base = ResNet50(weights=\"imagenet\", \n",
        "    include_top=False,\n",
        "    input_shape=(224,224,3)\n",
        ")\n",
        "\n",
        "for layer in resnet50_base.layers[:145]:\n",
        "   layer.trainable = False\n",
        "for layer in resnet50_base.layers[145:]:\n",
        "   layer.trainable = True\n",
        "\n",
        "resnet50_model = Sequential(\n",
        "                          [resnet50_base,                         \n",
        "                          Flatten(),\n",
        "                          Dropout(0.5),\n",
        "                          Dense(256,activation='relu'),\n",
        "                          Dropout(0.5),\n",
        "                          Dense(CATEGORIES, activation='softmax')\n",
        "                          ])\n",
        "\n",
        "resnet50_model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "78oleG1J4wdj"
      },
      "source": [
        "# **Multiclass Classifier Based On InceptionV3**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IOmUqsfG430J",
        "outputId": "663e9b00-31ab-4156-84d3-fe9464012821"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " inception_v3 (Functional)   (None, 8, 8, 2048)        21802784  \n",
            "                                                                 \n",
            " global_average_pooling2d (G  (None, 2048)             0         \n",
            " lobalAveragePooling2D)                                          \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 2048)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 5)                 10245     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 21,813,029\n",
            "Trainable params: 14,814,533\n",
            "Non-trainable params: 6,998,496\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "'''\n",
        "  InceptionV3 is a deep convolutional neural network that was trained for image classification on the ImageNet dataset. \n",
        "  The network is made up of a series of modules, called Inception modules, which are designed to identify and extract a wide variety of features from \n",
        "  an image at multiple scales. The key innovation in InceptionV3 is the use of depthwise separable convolutions, which significantly\n",
        "  reduce the number of parameters in the model, making it more efficient to run on resource-constrained devices. \n",
        "  It also includes a module called the \"auxiliary classifier\" which gives more informations about image during backpropagation, \n",
        "  which enhance its performance. InceptionV3 is widely used in image recognition, and related tasks like object detection and \n",
        "  semantic segmentation, Due to its high accuracy, low computation cost and scalable nature\n",
        "'''\n",
        "\n",
        "from keras.applications import InceptionV3 \n",
        "\n",
        "inceptionv3_base = InceptionV3(weights='imagenet', \n",
        "                                include_top=False, \n",
        "                                input_shape=(299, 299,3))\n",
        "# inceptionv3_base.trainable = False\n",
        "for layer in inceptionv3_base.layers[:200]:\n",
        "    layer.trainable = False\n",
        "for layer in inceptionv3_base.layers[200:]:\n",
        "    layer.trainable = True\n",
        "\n",
        "inceptionv3_model = Sequential()\n",
        "inceptionv3_model.add(inceptionv3_base)\n",
        "inceptionv3_model.add(GlobalAveragePooling2D())\n",
        "inceptionv3_model.add(Dropout(0.5))\n",
        "inceptionv3_model.add(Dense(CATEGORIES,activation='softmax'))\n",
        "inceptionv3_model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EVvlM0Zt59cN"
      },
      "source": [
        "# **Compile the model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w5dFUoZ46AMX",
        "outputId": "40948616-6ed6-426f-a1a2-90bd90c4cc10"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:absl:`lr` is deprecated, please use `learning_rate` instead, or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.RMSprop.\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.optimizers import RMSprop,Adam  \n",
        "\n",
        "                            \n",
        "def compile_model(model):\n",
        "  '''\n",
        "    Compiling the model using the loss function  crossentropy and RMSProp !\n",
        "    \n",
        "    params : model object \n",
        "    return : None\n",
        "  '''\n",
        "\n",
        "  model.compile(loss='categorical_crossentropy',\n",
        "                optimizer=RMSprop(lr=1e-5), #optionally use the Adam with learning rate 0.001 - 1e-5\n",
        "                metrics=['accuracy'])\n",
        "compile_model(custom_model)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "utjDFI0vhN-j"
      },
      "source": [
        "# **The Path To Model H5 Files**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lQQikA-3D72T"
      },
      "outputs": [],
      "source": [
        "'''\n",
        "The below callback list is used to moniter the validation accuracy, and the save the best version of the\n",
        "CNN model. The paths has been specified for later use !\n",
        "\n",
        "The Classifier model and the history file are saved in the DCNN Classifier Models directory, in the format:\n",
        "\n",
        "Model_Name_Directory\n",
        "                | \n",
        "                |\n",
        "                 ---- Model_File.h5\n",
        "                 ---- Model_History.pickle\n",
        "                 -----Model_Test_Accuracy.pickle\n",
        "'''\n",
        "\n",
        "custom_model_path          =          '/content/drive/MyDrive/DCNN Classifier Models /Test Classifier/Test Classifier_Model.h5'\n",
        "alexnet_model_path         =          '/content/drive/MyDrive/DCNN Classifier Models /AlexNet Classifier/AlexNet_Classifier_Model.h5'\n",
        "resnet50_model_path        =          '/content/drive/MyDrive/DCNN Classifier Models /ResNet Classifier/ResNet_Classifier_Model.h5'\n",
        "vgg16_model_path           =          '/content/drive/MyDrive/DCNN Classifier Models /VGGNet Classifier/VGGNet_Model.h5'\n",
        "inceptionv3_model_path     =          '/content/drive/MyDrive/DCNN Classifier Models /GoogleNet Classifier/GoogleNet_Model.h5'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5i-9ZzE5qICk"
      },
      "source": [
        "# **Validation and CallBack Lists**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1wug3BUMhWoF"
      },
      "source": [
        "# Early Stopping To Prevent Overfitting Of The Models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PgWlesAC4SI7"
      },
      "outputs": [],
      "source": [
        "'''\n",
        "Early stopping is a mechanism, which allows to moniter a value, specially the val_loss, and stop the \n",
        "training, at a particular patience level, to avoid overfitting !\n",
        "'''\n",
        "\n",
        "from keras.callbacks import ModelCheckpoint,EarlyStopping \n",
        "early_call_back = EarlyStopping(monitor=\"val_loss\",patience=8,verbose=1,mode=\"auto\",restore_best_weights=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xj2IHe2phdkI"
      },
      "source": [
        "# Model Check Points To Save The Best Weights"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kR2Rgy-AQvml"
      },
      "outputs": [],
      "source": [
        "'''\n",
        "Specifying the model checkpoint by monitering the value accuracy, with maximum value adjustments and adding\n",
        "that to callback list !\n",
        "'''\n",
        "\n",
        "callback_checkpoint = ModelCheckpoint(custom_model_path,monitor='val_accuracy',verbose=1,save_best_only=True,mode='max')\n",
        "callback_checkpoint_list = [callback_checkpoint,early_call_back]    #can remove the early stopping if needed !"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UovyYQST6aey"
      },
      "source": [
        "# **Training the datset based on given model !**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IASzW7LI6e7N",
        "outputId": "db13dc7c-b344-4675-f849-e1d8716c3bf7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/80\n",
            "\n",
            "Epoch 1: val_accuracy improved from -inf to 0.69930, saving model to /content/drive/MyDrive/DCNN Classifier Models /Test Classifier/Test Classifier_Model.h5\n",
            "3/3 - 172s - loss: 4.3893 - accuracy: 0.5000 - val_loss: 1.2604 - val_accuracy: 0.6993 - 172s/epoch - 57s/step\n",
            "Epoch 2/80\n",
            "\n",
            "Epoch 2: val_accuracy did not improve from 0.69930\n",
            "3/3 - 59s - loss: 1.8449 - accuracy: 0.7109 - val_loss: 1.2054 - val_accuracy: 0.6993 - 59s/epoch - 20s/step\n",
            "Epoch 3/80\n",
            "\n",
            "Epoch 3: val_accuracy did not improve from 0.69930\n",
            "3/3 - 45s - loss: 1.0579 - accuracy: 0.7057 - val_loss: 1.1427 - val_accuracy: 0.6993 - 45s/epoch - 15s/step\n",
            "Epoch 4/80\n",
            "\n",
            "Epoch 4: val_accuracy did not improve from 0.69930\n",
            "3/3 - 28s - loss: 0.8934 - accuracy: 0.7291 - val_loss: 0.8868 - val_accuracy: 0.6993 - 28s/epoch - 9s/step\n",
            "Epoch 5/80\n",
            "\n",
            "Epoch 5: val_accuracy did not improve from 0.69930\n",
            "3/3 - 19s - loss: 0.8509 - accuracy: 0.7458 - val_loss: 0.9648 - val_accuracy: 0.6993 - 19s/epoch - 6s/step\n",
            "Epoch 6/80\n",
            "\n",
            "Epoch 6: val_accuracy did not improve from 0.69930\n",
            "3/3 - 16s - loss: 0.8756 - accuracy: 0.7266 - val_loss: 0.9157 - val_accuracy: 0.6993 - 16s/epoch - 5s/step\n",
            "Epoch 7/80\n",
            "\n",
            "Epoch 7: val_accuracy did not improve from 0.69930\n",
            "3/3 - 14s - loss: 0.7600 - accuracy: 0.7346 - val_loss: 0.7809 - val_accuracy: 0.6993 - 14s/epoch - 5s/step\n",
            "Epoch 8/80\n",
            "\n",
            "Epoch 8: val_accuracy did not improve from 0.69930\n",
            "3/3 - 13s - loss: 0.6852 - accuracy: 0.7318 - val_loss: 0.8277 - val_accuracy: 0.6993 - 13s/epoch - 4s/step\n",
            "Epoch 9/80\n",
            "\n",
            "Epoch 9: val_accuracy did not improve from 0.69930\n",
            "3/3 - 15s - loss: 0.8657 - accuracy: 0.7266 - val_loss: 0.7268 - val_accuracy: 0.6993 - 15s/epoch - 5s/step\n",
            "Epoch 10/80\n",
            "\n",
            "Epoch 10: val_accuracy did not improve from 0.69930\n",
            "3/3 - 14s - loss: 0.5885 - accuracy: 0.7344 - val_loss: 0.6021 - val_accuracy: 0.6993 - 14s/epoch - 5s/step\n",
            "Epoch 11/80\n",
            "\n",
            "Epoch 11: val_accuracy did not improve from 0.69930\n",
            "3/3 - 13s - loss: 0.5861 - accuracy: 0.7370 - val_loss: 0.9568 - val_accuracy: 0.6993 - 13s/epoch - 4s/step\n",
            "Epoch 12/80\n",
            "\n",
            "Epoch 12: val_accuracy did not improve from 0.69930\n",
            "3/3 - 13s - loss: 0.8157 - accuracy: 0.6983 - val_loss: 0.5409 - val_accuracy: 0.6993 - 13s/epoch - 4s/step\n",
            "Epoch 13/80\n",
            "\n",
            "Epoch 13: val_accuracy did not improve from 0.69930\n",
            "3/3 - 15s - loss: 0.5244 - accuracy: 0.7318 - val_loss: 0.5273 - val_accuracy: 0.6993 - 15s/epoch - 5s/step\n",
            "Epoch 14/80\n",
            "\n",
            "Epoch 14: val_accuracy did not improve from 0.69930\n",
            "3/3 - 13s - loss: 0.4500 - accuracy: 0.7474 - val_loss: 0.5098 - val_accuracy: 0.6993 - 13s/epoch - 4s/step\n",
            "Epoch 15/80\n",
            "\n",
            "Epoch 15: val_accuracy improved from 0.69930 to 0.81818, saving model to /content/drive/MyDrive/DCNN Classifier Models /Test Classifier/Test Classifier_Model.h5\n",
            "3/3 - 15s - loss: 0.4819 - accuracy: 0.7370 - val_loss: 0.5141 - val_accuracy: 0.8182 - 15s/epoch - 5s/step\n",
            "Epoch 16/80\n",
            "\n",
            "Epoch 16: val_accuracy did not improve from 0.81818\n",
            "3/3 - 13s - loss: 0.5398 - accuracy: 0.8156 - val_loss: 0.5149 - val_accuracy: 0.8112 - 13s/epoch - 4s/step\n",
            "Epoch 17/80\n",
            "\n",
            "Epoch 17: val_accuracy improved from 0.81818 to 0.83916, saving model to /content/drive/MyDrive/DCNN Classifier Models /Test Classifier/Test Classifier_Model.h5\n",
            "3/3 - 14s - loss: 0.4530 - accuracy: 0.8021 - val_loss: 0.4729 - val_accuracy: 0.8392 - 14s/epoch - 5s/step\n",
            "Epoch 18/80\n",
            "\n",
            "Epoch 18: val_accuracy did not improve from 0.83916\n",
            "3/3 - 13s - loss: 0.6157 - accuracy: 0.7656 - val_loss: 1.0150 - val_accuracy: 0.6993 - 13s/epoch - 4s/step\n",
            "Epoch 19/80\n",
            "\n",
            "Epoch 19: val_accuracy did not improve from 0.83916\n",
            "3/3 - 14s - loss: 0.6164 - accuracy: 0.7570 - val_loss: 0.4647 - val_accuracy: 0.8112 - 14s/epoch - 5s/step\n",
            "Epoch 20/80\n",
            "\n",
            "Epoch 20: val_accuracy did not improve from 0.83916\n",
            "3/3 - 12s - loss: 0.4384 - accuracy: 0.7961 - val_loss: 0.4528 - val_accuracy: 0.8112 - 12s/epoch - 4s/step\n",
            "Epoch 21/80\n",
            "\n",
            "Epoch 21: val_accuracy did not improve from 0.83916\n",
            "3/3 - 13s - loss: 0.4530 - accuracy: 0.8101 - val_loss: 0.4363 - val_accuracy: 0.8182 - 13s/epoch - 4s/step\n",
            "Epoch 22/80\n",
            "\n",
            "Epoch 22: val_accuracy did not improve from 0.83916\n",
            "3/3 - 13s - loss: 0.4094 - accuracy: 0.8229 - val_loss: 0.4417 - val_accuracy: 0.8112 - 13s/epoch - 4s/step\n",
            "Epoch 23/80\n",
            "\n",
            "Epoch 23: val_accuracy did not improve from 0.83916\n",
            "3/3 - 13s - loss: 0.4571 - accuracy: 0.8047 - val_loss: 0.4347 - val_accuracy: 0.8252 - 13s/epoch - 4s/step\n",
            "Epoch 24/80\n",
            "\n",
            "Epoch 24: val_accuracy did not improve from 0.83916\n",
            "3/3 - 14s - loss: 0.4507 - accuracy: 0.7812 - val_loss: 0.4692 - val_accuracy: 0.7692 - 14s/epoch - 5s/step\n",
            "Epoch 25/80\n",
            "\n",
            "Epoch 25: val_accuracy improved from 0.83916 to 0.86014, saving model to /content/drive/MyDrive/DCNN Classifier Models /Test Classifier/Test Classifier_Model.h5\n",
            "3/3 - 13s - loss: 0.4738 - accuracy: 0.7933 - val_loss: 0.5117 - val_accuracy: 0.8601 - 13s/epoch - 4s/step\n",
            "Epoch 26/80\n",
            "\n",
            "Epoch 26: val_accuracy did not improve from 0.86014\n",
            "3/3 - 12s - loss: 0.4729 - accuracy: 0.8240 - val_loss: 0.4090 - val_accuracy: 0.8182 - 12s/epoch - 4s/step\n",
            "Epoch 27/80\n",
            "\n",
            "Epoch 27: val_accuracy did not improve from 0.86014\n",
            "3/3 - 13s - loss: 0.3691 - accuracy: 0.8184 - val_loss: 0.5443 - val_accuracy: 0.6993 - 13s/epoch - 4s/step\n",
            "Epoch 28/80\n",
            "\n",
            "Epoch 28: val_accuracy improved from 0.86014 to 0.88112, saving model to /content/drive/MyDrive/DCNN Classifier Models /Test Classifier/Test Classifier_Model.h5\n",
            "3/3 - 14s - loss: 1.2002 - accuracy: 0.5140 - val_loss: 0.5647 - val_accuracy: 0.8811 - 14s/epoch - 5s/step\n",
            "Epoch 29/80\n",
            "\n",
            "Epoch 29: val_accuracy did not improve from 0.88112\n",
            "3/3 - 13s - loss: 0.5205 - accuracy: 0.8125 - val_loss: 0.4183 - val_accuracy: 0.8811 - 13s/epoch - 4s/step\n",
            "Epoch 30/80\n",
            "\n",
            "Epoch 30: val_accuracy did not improve from 0.88112\n",
            "3/3 - 13s - loss: 0.4280 - accuracy: 0.7812 - val_loss: 0.3989 - val_accuracy: 0.8811 - 13s/epoch - 4s/step\n",
            "Epoch 31/80\n",
            "\n",
            "Epoch 31: val_accuracy did not improve from 0.88112\n",
            "3/3 - 13s - loss: 0.4246 - accuracy: 0.8017 - val_loss: 0.4099 - val_accuracy: 0.8322 - 13s/epoch - 4s/step\n",
            "Epoch 32/80\n",
            "\n",
            "Epoch 32: val_accuracy did not improve from 0.88112\n",
            "3/3 - 14s - loss: 0.4025 - accuracy: 0.8307 - val_loss: 0.3942 - val_accuracy: 0.8322 - 14s/epoch - 5s/step\n",
            "Epoch 33/80\n",
            "\n",
            "Epoch 33: val_accuracy did not improve from 0.88112\n",
            "3/3 - 11s - loss: 0.4100 - accuracy: 0.7793 - val_loss: 0.3862 - val_accuracy: 0.8252 - 11s/epoch - 4s/step\n",
            "Epoch 34/80\n",
            "\n",
            "Epoch 34: val_accuracy did not improve from 0.88112\n",
            "3/3 - 13s - loss: 0.3634 - accuracy: 0.8385 - val_loss: 0.3807 - val_accuracy: 0.8252 - 13s/epoch - 4s/step\n",
            "Epoch 35/80\n",
            "\n",
            "Epoch 35: val_accuracy did not improve from 0.88112\n",
            "3/3 - 14s - loss: 0.4197 - accuracy: 0.7839 - val_loss: 0.3771 - val_accuracy: 0.8182 - 14s/epoch - 5s/step\n",
            "Epoch 36/80\n",
            "\n",
            "Epoch 36: val_accuracy did not improve from 0.88112\n",
            "3/3 - 13s - loss: 0.3236 - accuracy: 0.8184 - val_loss: 0.3633 - val_accuracy: 0.8601 - 13s/epoch - 4s/step\n",
            "Epoch 37/80\n",
            "\n",
            "Epoch 37: val_accuracy did not improve from 0.88112\n",
            "3/3 - 14s - loss: 0.2891 - accuracy: 0.8385 - val_loss: 0.4769 - val_accuracy: 0.7133 - 14s/epoch - 5s/step\n",
            "Epoch 38/80\n",
            "\n",
            "Epoch 38: val_accuracy did not improve from 0.88112\n",
            "3/3 - 14s - loss: 0.3547 - accuracy: 0.8047 - val_loss: 0.4393 - val_accuracy: 0.7413 - 14s/epoch - 5s/step\n",
            "Epoch 39/80\n",
            "\n",
            "Epoch 39: val_accuracy did not improve from 0.88112\n",
            "3/3 - 13s - loss: 0.3953 - accuracy: 0.7891 - val_loss: 0.3710 - val_accuracy: 0.8042 - 13s/epoch - 4s/step\n",
            "Epoch 40/80\n",
            "\n",
            "Epoch 40: val_accuracy did not improve from 0.88112\n",
            "3/3 - 13s - loss: 0.3152 - accuracy: 0.8333 - val_loss: 0.7103 - val_accuracy: 0.6993 - 13s/epoch - 4s/step\n",
            "Epoch 41/80\n",
            "\n",
            "Epoch 41: val_accuracy did not improve from 0.88112\n",
            "3/3 - 13s - loss: 0.4764 - accuracy: 0.7865 - val_loss: 0.4601 - val_accuracy: 0.8392 - 13s/epoch - 4s/step\n",
            "Epoch 42/80\n",
            "\n",
            "Epoch 42: val_accuracy did not improve from 0.88112\n",
            "3/3 - 12s - loss: 0.3965 - accuracy: 0.8268 - val_loss: 0.4011 - val_accuracy: 0.7762 - 12s/epoch - 4s/step\n",
            "Epoch 43/80\n",
            "\n",
            "Epoch 43: val_accuracy did not improve from 0.88112\n",
            "3/3 - 15s - loss: 0.3433 - accuracy: 0.7995 - val_loss: 0.4235 - val_accuracy: 0.8252 - 15s/epoch - 5s/step\n",
            "Epoch 44/80\n",
            "\n",
            "Epoch 44: val_accuracy did not improve from 0.88112\n",
            "Restoring model weights from the end of the best epoch: 36.\n",
            "3/3 - 15s - loss: 0.3668 - accuracy: 0.7943 - val_loss: 0.3975 - val_accuracy: 0.8462 - 15s/epoch - 5s/step\n",
            "Epoch 44: early stopping\n"
          ]
        }
      ],
      "source": [
        "def train_the_model(model):\n",
        "  '''\n",
        "    The function takes in model as an arguement and trains it according to given epochs and batch size !\n",
        "    epochs:depending on the early stopping\n",
        "\n",
        "    Params: model object\n",
        "\n",
        "    return: trained model\n",
        "  '''\n",
        "  the_final_train = model.fit(\n",
        "                      training_images_datagen,\n",
        "                      callbacks       = callback_checkpoint_list,\n",
        "                      validation_data = valid_images_datagen,\n",
        "                      steps_per_epoch = 3,          #the steps per epoch is set to 2, but cannot be set to more than 3(because of error !) \n",
        "                      epochs          = 80,         #change according to best fitting accuracy !\n",
        "                      verbose         = 2,\n",
        "                    )\n",
        "  return the_final_train\n",
        "\n",
        "\n",
        "trained_model=train_the_model(custom_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SnfCsHlkHZzZ"
      },
      "source": [
        "# **Saving History Pickle File !**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hL7W5be4hky8"
      },
      "source": [
        "# Path To Store The Respective Model Training And Validation Losses"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uevOvCWNHZex"
      },
      "outputs": [],
      "source": [
        "custom_model_history      = '/content/drive/MyDrive/DCNN Classifier Models /Test Classifier/Test Classifier_Model.pickle'\n",
        "alexnet_model_history     = '/content/drive/MyDrive/DCNN Classifier Models /AlexNet Classifier/AlexNet_Classifier_Model.pickle'\n",
        "resnet50_model_history    = '/content/drive/MyDrive/DCNN Classifier Models /ResNet Classifier/ResNet_Classifier_Model.pickle'\n",
        "vgg16_model_history       = '/content/drive/MyDrive/DCNN Classifier Models /VGGNet Classifier/VGGNet_Model.pickle'\n",
        "inceptionv3_model_history   = '/content/drive/MyDrive/DCNN Classifier Models /GoogleNet Classifier/GoogleNet_Model.pickle'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "umQvEJ8hNqxs"
      },
      "source": [
        "# **Saving the Accuracy and Loss To A Pickle File**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aGvDV7DyW4Ls",
        "outputId": "ea667eca-c2b9-4524-92ce-00ac1fe89512"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "History Saved to /content/drive/MyDrive/DCNN Classifier Models /Test Classifier/Test Classifier_Model.pickle\n"
          ]
        }
      ],
      "source": [
        "import pickle \n",
        "def save_file(pickle_dir,model_history):\n",
        "  '''\n",
        "    The model cannot path cannot be directly used to retrieve the history thats\n",
        "    why we are saving the respective model history to a pickle file \n",
        "\n",
        "    params : model_history, path of pickle file \n",
        "    return : None\n",
        "\n",
        "    \n",
        "  '''\n",
        "  with open(pickle_dir,'wb') as binary_pickle:\n",
        "    binary_pickle.write(pickle.dumps(model_history))          #dumping to pickle before writing to pickle file !\n",
        "    print(f'History Saved to {pickle_dir}')\n",
        "\n",
        "save_file(custom_model_history,trained_model.history)  #the path of the model history pickle and the current model history !"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AKqlawEa-J0E"
      },
      "source": [
        "# **Accuracy and Loss Graph !**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "_8dEYa0P-JQY",
        "outputId": "4e919c66-3ea7-467f-de9d-5129b59fecec"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training Accuracy : 79.43 %\n",
            "Validation Accuracy : 84.62 %\n"
          ]
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_metrics(acc_path):\n",
        "  '''\n",
        "    The accuracy and the losses at each iteration/epoch is evaluated in the graph below\n",
        "    matplotlib is used for plotting.\n",
        "    \n",
        "    params: model history path (.pickle file)\n",
        "    return: None\n",
        "  '''\n",
        "  with open(acc_path,'rb') as model_history: #opening the pickle file which contains the dictionary !\n",
        "    model=model_history.read()\n",
        "  model=pickle.loads(model)\n",
        "  plt.plot(model['accuracy'])\n",
        "  plt.plot(model['val_accuracy'])\n",
        "  plt.title('model_accuracy')\n",
        "  plt.ylabel('accuracy')\n",
        "  plt.xlabel('epoch')\n",
        "  plt.legend(['train','test'],loc='lower left')\n",
        "  plt.show()\n",
        "\n",
        "  plt.plot(model['loss'])\n",
        "  plt.plot(model['val_loss'])\n",
        "  plt.title('model_loss')\n",
        "  plt.ylabel('loss')\n",
        "  plt.xlabel('epoch')\n",
        "  plt.legend(['train','val loss'],loc='lower left')\n",
        "  plt.show()\n",
        "  train_acc = model['accuracy'][-1]*100\n",
        "  val_acc   = model['val_accuracy'][-1]*100\n",
        "  print(f'Training Accuracy : {round(train_acc,2)} %')\n",
        "  print(f'Validation Accuracy : {round(val_acc,2)} %')\n",
        "\n",
        "plot_metrics(custom_model_history)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wmpf-FdxgmUt",
        "outputId": "0e8d1d1e-94d5-46e2-afbd-634866f1474b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "135 Test Images Found !\n"
          ]
        }
      ],
      "source": [
        "aml = testing_data_path+'/AML'\n",
        "all = testing_data_path+'/ALL'\n",
        "normal = testing_data_path+'/Normal'\n",
        "cll = testing_data_path+'/CLL'\n",
        "cml = testing_data_path+'/CML'\n",
        "test_aml = [ os.path.join(aml,i) for i in os.listdir(aml)]\n",
        "test_all = [ os.path.join(all,i) for i in os.listdir(all)]\n",
        "test_cml = [ os.path.join(cml,i) for i in os.listdir(cml)]\n",
        "test_cll = [ os.path.join(cll,i) for i in os.listdir(cll)]\n",
        "test_normal = [ os.path.join(normal,i) for i in os.listdir(normal)]\n",
        "tests = test_aml+test_all+test_cml+test_cll+test_normal\n",
        "print(f'{len(tests)} Test Images Found !')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "29RIUA9QhvRW"
      },
      "source": [
        "# **Getting The Prediction Values From The Models**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6jaER7Xn5u0F"
      },
      "outputs": [],
      "source": [
        "import keras.utils as image\n",
        "import tensorflow as tf\n",
        "\n",
        "def get_preds(model,image_path):\n",
        "  '''\n",
        "    The function to get prediction value, which basically returns the probablity values of \n",
        "    corresponding cancer types !\n",
        "    \n",
        "    params: model, image_location path\n",
        "    return: \n",
        "  '''\n",
        "  size = (224,224)                                                  # default set to 224 x 224 for VGG and ResNet50\n",
        "  if model == 'AlexNet':\n",
        "    model,size = alexnet_model_path,model_dict['AlexNet']\n",
        "  elif model == 'Custom':\n",
        "    model,size = custom_model_path,model_dict['Custom']\n",
        "  elif model == 'ResNet':\n",
        "    model = resnet50_model_path\n",
        "  elif model == 'InceptionV3':\n",
        "    model,size = inceptionv3_model_path,model_dict['InceptionV3']\n",
        "  elif model == 'VGG':\n",
        "    model = vgg16_model_path\n",
        "  else:\n",
        "    raise Exception('Invalid Model Arguments !')                      # exception thrown for invalid path !\n",
        "\n",
        "  model = keras.models.load_model(model)                              # loading the model.h5 from the drive !\n",
        "  img   = tf.keras.utils.load_img(image_path, target_size=size)       \n",
        "  img   = image.img_to_array(img)\n",
        "  img   = np.expand_dims(img, axis=0)                                 # expanding along one axis \n",
        "  result_vector = model.predict(img).round(1)\n",
        "  return result_vector"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DX4eLxWFN4CD"
      },
      "source": [
        "# Obtaining the Prediced values and Actual Values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ikNBk-S6iFL",
        "outputId": "95a76e5b-24c2-43ec-d59b-d14d61c4e759"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 1s 842ms/step\n",
            "1/1 [==============================] - 1s 1s/step\n",
            "1/1 [==============================] - 1s 833ms/step\n",
            "1/1 [==============================] - 1s 1s/step\n",
            "1/1 [==============================] - 1s 1s/step\n",
            "1/1 [==============================] - 1s 851ms/step\n",
            "1/1 [==============================] - 1s 1s/step\n",
            "1/1 [==============================] - 1s 1s/step\n",
            "1/1 [==============================] - 1s 845ms/step\n",
            "1/1 [==============================] - 1s 936ms/step\n",
            "1/1 [==============================] - 1s 885ms/step\n",
            "1/1 [==============================] - 1s 1s/step\n",
            "1/1 [==============================] - 1s 857ms/step\n",
            "1/1 [==============================] - 1s 840ms/step\n",
            "1/1 [==============================] - 1s 906ms/step\n",
            "1/1 [==============================] - 1s 840ms/step\n",
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            "1/1 [==============================] - 1s 838ms/step\n"
          ]
        }
      ],
      "source": [
        "y_preds = [get_preds('ResNet',x) for x in tests]\n",
        "y_true = test_images_datagen.classes\n",
        "class_labels = list(test_images_datagen.class_indices.keys())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JrKhi4iX5npK",
        "outputId": "4002c795-7b6c-4587-df0e-dd169459b5ea"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['ALL', 'AML', 'CLL', 'CML', 'Normal']\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 2 2 2 2 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4]\n"
          ]
        }
      ],
      "source": [
        "print(class_labels)\n",
        "print(y_true)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YCL0jqqgI0xJ"
      },
      "source": [
        "# **F-Score and Precision**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "1xJFQ1yaI6du",
        "outputId": "e60d77f4-0204-455b-85e9-07b26963fef1"
      },
      "outputs": [
        {
          "ename": "TypeError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-22-12679881649b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;31m# Convert y_preds to class labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0my_pred_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mclass_labels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_preds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-22-12679881649b>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;31m# Convert y_preds to class labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0my_pred_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mclass_labels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_preds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: only integer scalar arrays can be converted to a scalar index"
          ]
        }
      ],
      "source": [
        "# from sklearn.metrics import f1_score,precision_score,recall_score\n",
        "# y_pred_classes = np.argmax(y_preds, axis=1)\n",
        "# y_test_classes = np.argmax(y_true, axis=0)\n",
        "\n",
        "# f1 = f1_score(y_test_classes, y_pred_classes, average='weighted')\n",
        "# precision = precision_score(y_test_classes, y_pred_classes, average='weighted')\n",
        "# recall = recall_score(y_test_classes, y_pred_classes, average='weighted')\n",
        "from sklearn.metrics import precision_score, recall_score\n",
        "\n",
        "# Convert y_preds to class labels\n",
        "y_pred_labels = [class_labels[i] for i in y_preds]\n",
        "\n",
        "# Calculate precision and recall for each class\n",
        "for label in class_labels:\n",
        "    y_true_label = [1 if x == label else 0 for x in y_true]\n",
        "    y_pred_label = [1 if x == label else 0 for x in y_pred_labels]\n",
        "    precision = precision_score(y_true_label, y_pred_label)\n",
        "    recall = recall_score(y_true_label, y_pred_label)\n",
        "    print(f'Precision for {label}: {precision:.2f}')\n",
        "    print(f'Recall for {label}: {recall:.2f}')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "a6HDBbed85jj",
        "outputId": "3487c445-d016-4a23-c6ce-7d5e1f1452b3"
      },
      "outputs": [
        {
          "ename": "ValueError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-25-4b9b987ad3bc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;31m# Compute micro-average ROC curve and ROC area\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0mfpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"micro\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"micro\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mroc_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_preds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m \u001b[0mroc_auc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"micro\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mauc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"micro\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"micro\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_ranking.py\u001b[0m in \u001b[0;36mroc_curve\u001b[0;34m(y_true, y_score, pos_label, sample_weight, drop_intermediate)\u001b[0m\n\u001b[1;32m    960\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    961\u001b[0m     \"\"\"\n\u001b[0;32m--> 962\u001b[0;31m     fps, tps, thresholds = _binary_clf_curve(\n\u001b[0m\u001b[1;32m    963\u001b[0m         \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_score\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpos_label\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m     )\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_ranking.py\u001b[0m in \u001b[0;36m_binary_clf_curve\u001b[0;34m(y_true, y_score, pos_label, sample_weight)\u001b[0m\n\u001b[1;32m    729\u001b[0m     \u001b[0my_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype_of_target\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    730\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0my_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"binary\"\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0my_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"multiclass\"\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mpos_label\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 731\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"{0} format is not supported\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    732\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    733\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_score\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: multiclass format is not supported"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import roc_curve, auc\n",
        "from scipy import interp\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Assuming y_test and y_preds are the true labels and predicted labels, respectively\n",
        "n_classes = len(np.unique(y_true))\n",
        "\n",
        "# Compute ROC curve and ROC area for each class\n",
        "fpr = dict()\n",
        "tpr = dict()\n",
        "roc_auc = dict()\n",
        "for i in range(n_classes):\n",
        "    y_true_i = np.zeros_like(y_true)\n",
        "    y_true_i[y_true == i] = 1\n",
        "    fpr[i], tpr[i], _ = roc_curve(y_true_i, np.array(y_preds)[:, i])\n",
        "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
        "\n",
        "# Compute micro-average ROC curve and ROC area\n",
        "fpr[\"micro\"], tpr[\"micro\"], _ = roc_curve(np.array(y_true).ravel(),np.array(y_preds).ravel(),)\n",
        "roc_auc[\"micro\"] = auc(fpr[\"micro\"], tpr[\"micro\"])\n",
        "\n",
        "# Plot ROC curves for each class and micro-average\n",
        "plt.figure()\n",
        "lw = 2\n",
        "plt.plot(fpr[\"micro\"], tpr[\"micro\"],\n",
        "         label='micro-average ROC curve (area = {0:0.2f})'\n",
        "               ''.format(roc_auc[\"micro\"]),\n",
        "         color='deeppink', linestyle=':', linewidth=4)\n",
        "\n",
        "colors = ['aqua', 'darkorange', 'cornflowerblue', 'magenta', 'yellowgreen', 'cyan', 'red', 'blue', 'green', 'black']\n",
        "for i, color in zip(range(n_classes), colors):\n",
        "    plt.plot(fpr[i], tpr[i], color=color, lw=lw,\n",
        "             label='ROC curve of class {0} (area = {1:0.2f})'\n",
        "             ''.format(i, roc_auc[i]))\n",
        "\n",
        "plt.plot([0, 1], [0, 1], 'k--', lw=lw)\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver operating characteristic for multiclass CNN classifier')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uVEWz5QJkzsC",
        "outputId": "99737cdc-d81a-4c94-d015-0650ef29f575"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 2 2 2 2 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4]\n"
          ]
        }
      ],
      "source": [
        "print(y_true)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b7vqjo6MDutE"
      },
      "source": [
        "# **AUC ROC Metrics**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pNc-_YeeDuBt",
        "outputId": "44090dbc-5b8d-488c-a94c-c1d76a9dfea5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "\n",
        "def get_roc_auc(model):\n",
        "  '''\n",
        "  ROC AUC is generally used for binary classification parameter analysis, but for the \n",
        "  case of multiclass, it is used as a one vs many/one vs rest approach !\n",
        "\n",
        "  params: model st\n",
        "  '''\n",
        "\n",
        "  # auc = roc_auc_score(y_true, y_preds, multi_class='ovr')  #using the on vs rest approach !\n",
        "  y_pred_binary = np.round(y_preds)\n",
        "\n",
        "  # Calculate the AUC ROC score for each class\n",
        "  auroc = []\n",
        "  for i in y_preds:\n",
        "      try:\n",
        "          auroc.append(roc_auc_score(y_true, y_preds))\n",
        "      except ValueError:\n",
        "          pass\n",
        "  print(auroc)\n",
        "get_roc_auc(resnet50_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O7Munn0l7sNJ"
      },
      "source": [
        "# **Plot The ROC AUC Curve**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KrUcdLoh7gYi"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.metrics import roc_curve\n",
        "\n",
        "\n",
        "# generate some example data\n",
        "y_scores = y_preds\n",
        "\n",
        "# calculate AUC\n",
        "auc = roc_auc_score(y_true, y_scores,multi_class='ovr')\n",
        "\n",
        "# calculate ROC curve\n",
        "fpr, tpr, thresholds = roc_curve(y_true, y_scores)\n",
        "\n",
        "# plot ROC curve\n",
        "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % auc)\n",
        "plt.plot([0, 1], [0, 1], 'k--')  # random predictions curve\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.0])\n",
        "plt.xlabel('False Positive Rate or (1 - Specifity)')\n",
        "plt.ylabel('True Positive Rate or (Sensitivity)')\n",
        "plt.title('Receiver Operating Characteristic')\n",
        "plt.legend(loc=\"lower right\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SsPLPfYqXlJ1"
      },
      "source": [
        "#**Testing for Cancer vs Normal**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r9hAShN3yULC",
        "outputId": "9769e864-0ddc-4e2b-e53b-9f4f1e66369b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Prediction Based on the Custom Classifier !\n",
            "1/1 [==============================] - 7s 7s/step\n",
            "AML\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "def check_for_cancer(model,image_path):      \n",
        "  '''\n",
        "    The final module of the system which basically predicts whether given cell is of CML,CLL,ALL or AML,\n",
        "    based on the image and the prediction model file !\n",
        "\n",
        "    params: path to the model, the path to the image !\n",
        "    return: Cancer Prediction (str) \n",
        " \n",
        "   \n",
        "  '''           \n",
        "  result_vector = get_preds(model,image_path).tolist()  \n",
        "  c_array = {0:'ALL', 1:'AML', 2:'CLL', 3:'CML', 4:'Normal'}\n",
        "  index = result_vector.index(max(result_vector))                \n",
        "  return c_array[index]\n",
        "\n",
        "\n",
        "model  = 'Custom'\n",
        "print(f'Prediction Based on the {model} Classifier !')\n",
        "print(check_for_cancer(model,'/content/drive/MyDrive/DCNN Datasets_1/Training/ALL/Im023_1.tif'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "caCnVM8FOBh7"
      },
      "source": [
        "# Path to store the Test Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TitqEKz9nhWV"
      },
      "outputs": [],
      "source": [
        "# The path where the testing accuracies are stored \n",
        "custom_model_acc_path       = '/content/drive/MyDrive/DCNN Classifier Models /Test Classifier/Test Classifier_Model.pickle'\n",
        "alexnet_model_acc_path      = '/content/drive/MyDrive/DCNN Classifier Models /AlexNet Classifier/AlexNet_Classifier_Model_Acc.pickle'\n",
        "resnet_model_acc_path       = '/content/drive/MyDrive/DCNN Classifier Models /ResNet Classifier/ResNet_Classifier_Model_Acc.pickle'\n",
        "vgg16_model_acc_path        = '/content/drive/MyDrive/DCNN Classifier Models /VGGNet Classifier/VGGNet_Model_Acc.pickle'\n",
        "inceptionv3_model_acc_path  = '/content/drive/MyDrive/DCNN Classifier Models /GoogleNet Classifier/GoogleNet_Model_acc.pickle'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kgto-0NzLjgb"
      },
      "source": [
        "# **Save Testing Accuracy To Pickle File**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-WufWJ5SRDWR"
      },
      "outputs": [],
      "source": [
        "import pickle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "id": "QoLOpSkUzKd0",
        "outputId": "79528dff-692a-4cb3-d6a5-f0a370ddc1f9"
      },
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-11-59d746bdebb9>\u001b[0m in \u001b[0;36m<cell line: 16>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'Accuracy Saved to {pickle_file}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mget_accuracy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresnet50_model_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtest_images_datagen\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mresnet_model_acc_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'test_images_datagen' is not defined"
          ]
        }
      ],
      "source": [
        "def get_accuracy(model_path:str,test_datagen,pickle_file:str)->None:\n",
        "  '''\n",
        "    The optional block of code only run if the accuracy is to be calculated !\n",
        "    \n",
        "    params: model_path, test_generator, pickle_file path\n",
        "    return: None\n",
        "    \n",
        "'''\n",
        "\n",
        "  model = keras.models.load_model(model_path)\n",
        "  loss,accuracy = model.evaluate(test_datagen,batch_size = 128)\n",
        "  with open(pickle_file,'wb') as binary_pickle:\n",
        "    binary_pickle.write(pickle.dumps((loss,accuracy)))     #dumping to pickle before writing to pickle file !\n",
        "    print(f'Accuracy Saved to {pickle_file}')\n",
        "\n",
        "get_accuracy(resnet50_model_path,test_images_datagen,resnet_model_acc_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ggbfLA4e0n9k"
      },
      "source": [
        "# **Retrieve Accuracy From Pickle File**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dX0-qeet_NAe"
      },
      "outputs": [],
      "source": [
        "def show_accuracy(model:str,acc_path:str)->None:\n",
        "  '''\n",
        "    The model accuracy is originally stored in the drive folder for the sake of simplicity, we refer the \n",
        "    pickle file path and fetch accuarcy info of the respective model and print their accuracy\n",
        "\n",
        "    params: accuracy_path\n",
        "    return: None\n",
        "  '''\n",
        "\n",
        "  with open(acc_path,'rb') as pickle_file:\n",
        "    bin = pickle.loads(pickle_file.read()) \n",
        "    \n",
        "    acc = round((bin[1]),3)*100\n",
        "    print('Accuracy for {} is {:.3f}%'.format(model,acc))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8XQ9p6myJLso",
        "outputId": "1d94255c-eabd-4f47-cdb0-115b17cd5e87"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy for AlexNet is 88.100%\n",
            "Accuracy for ResNet is 93.300%\n",
            "Accuracy for InceptionV3 is 94.100%\n",
            "Accuracy for VGG is 91.100%\n",
            "Accuracy for Custom is 92.600%\n"
          ]
        }
      ],
      "source": [
        "show_accuracy('AlexNet',alexnet_model_acc_path )\n",
        "# show_accuracy('Custom',custom_model_acc_path )\n",
        "show_accuracy('ResNet',resnet_model_acc_path)\n",
        "show_accuracy('InceptionV3',inceptionv3_model_acc_path)\n",
        "show_accuracy('VGG',vgg16_model_acc_path)\n",
        "show_accuracy('Custom',custom_model_acc_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "58Muav-dkT1l"
      },
      "source": [
        "# **Installing Dependencies For Flask REST-API**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vqt2IIv9kaOd"
      },
      "source": [
        "!pip install flask-ngrok"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "yUK8d8GgPuOp",
        "outputId": "17acfe46-daf5-40fb-9da5-af32564834d5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "https://mxjnqb46xdf-496ff2e9c6d22116-5000-colab.googleusercontent.com/\n",
            "https://mxjnqb46xdf-496ff2e9c6d22116-5000-colab.googleusercontent.com/\n"
          ]
        }
      ],
      "source": [
        "from google.colab.output import eval_js\n",
        "print(eval_js(\"google.colab.kernel.proxyPort(5000)\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R5UfG7m0kimh",
        "outputId": "aeeb0552-c895-41c2-c608-2df8fe919df3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2023-06-06 13:36:17.272957: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
            "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "2023-06-06 13:36:20.500608: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            " * Serving Flask app 'app'\n",
            " * Debug mode: on\n",
            "\u001b[31m\u001b[1mWARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.\u001b[0m\n",
            " * Running on http://127.0.0.1:5000\n",
            "\u001b[33mPress CTRL+C to quit\u001b[0m\n",
            " * Restarting with stat\n",
            "2023-06-06 13:36:26.587409: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            " * Debugger is active!\n",
            " * Debugger PIN: 571-961-695\n",
            "127.0.0.1 - - [06/Jun/2023 13:36:51] \"GET / HTTP/1.1\" 200 -\n",
            "127.0.0.1 - - [06/Jun/2023 13:36:52] \"GET /static/js/script.js HTTP/1.1\" 200 -\n",
            "127.0.0.1 - - [06/Jun/2023 13:36:52] \"GET /static/css/styles.css HTTP/1.1\" 200 -\n",
            "127.0.0.1 - - [06/Jun/2023 13:36:55] \"\u001b[33mGET /favicon.ico HTTP/1.1\u001b[0m\" 404 -\n",
            "127.0.0.1 - - [06/Jun/2023 13:37:15] \"GET /static/css/styles.css HTTP/1.1\" 200 -\n",
            "127.0.0.1 - - [06/Jun/2023 13:37:16] \"GET / HTTP/1.1\" 200 -\n",
            "2023-06-06 13:37:24.625206: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:47] Overriding orig_value setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "127.0.0.1 - - [06/Jun/2023 13:37:25] \"POST /predict HTTP/1.1\" 200 -\n",
            " * Detected change in '/content/drive/MyDrive/REST-API-For-DCNN-Models-master/flask-api/app.py', reloading\n",
            " * Restarting with stat\n",
            "2023-06-06 13:38:21.792292: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            " * Debugger is active!\n",
            " * Debugger PIN: 571-961-695\n",
            "127.0.0.1 - - [06/Jun/2023 13:38:42] \"GET / HTTP/1.1\" 200 -\n",
            "127.0.0.1 - - [06/Jun/2023 13:38:43] \"GET /static/js/script.js HTTP/1.1\" 200 -\n",
            "127.0.0.1 - - [06/Jun/2023 13:38:43] \"GET /static/css/styles.css HTTP/1.1\" 200 -\n",
            "127.0.0.1 - - [06/Jun/2023 13:38:44] \"\u001b[33mGET /favicon.ico HTTP/1.1\u001b[0m\" 404 -\n",
            "2023-06-06 13:39:04.330558: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:47] Overriding orig_value setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "127.0.0.1 - - [06/Jun/2023 13:39:04] \"POST /predict HTTP/1.1\" 200 -\n",
            "127.0.0.1 - - [06/Jun/2023 13:39:33] \"GET / HTTP/1.1\" 200 -\n",
            "127.0.0.1 - - [06/Jun/2023 13:39:35] \"GET /static/css/styles.css HTTP/1.1\" 200 -\n",
            "127.0.0.1 - - [06/Jun/2023 13:39:35] \"GET /static/js/script.js HTTP/1.1\" 200 -\n",
            "127.0.0.1 - - [06/Jun/2023 13:39:38] \"\u001b[33mGET /favicon.ico HTTP/1.1\u001b[0m\" 404 -\n",
            "127.0.0.1 - - [06/Jun/2023 13:39:57] \"POST /predict HTTP/1.1\" 200 -\n",
            " * Detected change in '/content/drive/MyDrive/REST-API-For-DCNN-Models-master/flask-api/app.py', reloading\n",
            " * Restarting with stat\n",
            "2023-06-06 13:40:30.892692: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            " * Debugger is active!\n",
            " * Debugger PIN: 571-961-695\n",
            "127.0.0.1 - - [06/Jun/2023 13:40:32] \"GET / HTTP/1.1\" 200 -\n",
            "127.0.0.1 - - [06/Jun/2023 13:40:33] \"GET /static/css/styles.css HTTP/1.1\" 200 -\n",
            "127.0.0.1 - - [06/Jun/2023 13:40:33] \"GET /static/js/script.js HTTP/1.1\" 200 -\n",
            "127.0.0.1 - - [06/Jun/2023 13:40:34] \"\u001b[33mGET /favicon.ico HTTP/1.1\u001b[0m\" 404 -\n",
            "2023-06-06 13:41:01.447147: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:47] Overriding orig_value setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "127.0.0.1 - - [06/Jun/2023 13:41:01] \"POST /predict HTTP/1.1\" 200 -\n",
            " * Detected change in '/content/drive/MyDrive/REST-API-For-DCNN-Models-master/flask-api/app.py', reloading\n",
            " * Restarting with stat\n",
            "2023-06-06 13:41:11.612792: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            " * Debugger is active!\n",
            " * Debugger PIN: 571-961-695\n",
            "127.0.0.1 - - [06/Jun/2023 13:42:31] \"GET / HTTP/1.1\" 200 -\n",
            "127.0.0.1 - - [06/Jun/2023 13:42:32] \"GET /static/js/script.js HTTP/1.1\" 200 -\n",
            "127.0.0.1 - - [06/Jun/2023 13:42:32] \"GET /static/css/styles.css HTTP/1.1\" 200 -\n",
            "127.0.0.1 - - [06/Jun/2023 13:42:33] \"\u001b[33mGET /favicon.ico HTTP/1.1\u001b[0m\" 404 -\n"
          ]
        }
      ],
      "source": [
        "!python3 /content/drive/MyDrive/REST-API-For-DCNN-Models-master/flask-api/app.py"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iKC4Jub3TdrJ"
      },
      "source": [
        "# **Drug Discovery Module for Classified Cancer Images**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0n40RRnax7nz"
      },
      "outputs": [],
      "source": [
        "!pip install sparqlwrapper"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "jyvweHnUodMc",
        "outputId": "5332e296-81c6-4bd7-8cae-fc1ce0494b90"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import json\n",
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Load the JSON data\n",
        "with open('/content/drive/MyDrive/Luekemia.json') as f:\n",
        "    data = json.load(f)\n",
        "\n",
        "# Create an empty directed graph\n",
        "graph = nx.DiGraph()\n",
        "\n",
        "# Add nodes for leukemia types, drug combinations, and drug targets\n",
        "for leukemia_type, info in data.items():\n",
        "    graph.add_node(leukemia_type, type='Leukemia')\n",
        "    \n",
        "    for combo in info['Drug combinations']:\n",
        "      graph.add_node(combo['Name'], type='Drug combination')\n",
        "    \n",
        "    for target in combo['Targets']:\n",
        "        graph.add_node(target, type='Target')\n",
        "        \n",
        "        # Add edges for drug combinations to targets\n",
        "        graph.add_edge(combo['Name'], target, relation='targets')\n",
        "        \n",
        "        # Add edges for targets to leukemia types\n",
        "        graph.add_edge(target, leukemia_type, relation='treats')\n",
        "        \n",
        "  # Add edges for leukemia types to drug combinations\n",
        "    for combo in info['Drug combinations']:\n",
        "        graph.add_edge(leukemia_type, combo['Name'], relation='has drug combination')\n",
        "  # Draw the graph\n",
        "pos = nx.spring_layout(graph, seed=42)\n",
        "nx.draw(graph, pos, with_labels=True, font_size=10, font_weight='bold', node_size=1000, node_color='lightblue', alpha=0.8, arrowsize=20)\n",
        "edge_labels = nx.get_edge_attributes(graph, 'relation')\n",
        "nx.draw_networkx_edge_labels(graph, pos, edge_labels=edge_labels, font_size=8, font_weight='bold')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QZhnWfeBNV5f",
        "outputId": "17a079bc-9957-4497-9116-9cc93a29d0b3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "7+3:\n",
            "\tTargets: Topoisomerase II inhibitor, Nucleoside analogue\n",
            "\tDosages: \n",
            "CPX-351:\n",
            "\tTargets: Topoisomerase II inhibitor, Nucleoside analogue\n",
            "\tDosages: \n"
          ]
        }
      ],
      "source": [
        "leukemia_type = 'Acute myeloid leukemia'\n",
        "drug_combinations = [node for node in graph.successors(leukemia_type) if graph.nodes[node]['type'] == 'Drug combination']\n",
        "for drug in drug_combinations:\n",
        "    targets = []\n",
        "    dosages = []\n",
        "    for info in data[leukemia_type]['Drug combinations']:\n",
        "        if info['Name'] == drug:\n",
        "            targets = info['Targets']\n",
        "            #dosages = info['Dosages']\n",
        "            break\n",
        "    print(f\"{drug}:\")\n",
        "    print(f\"\\tTargets: {', '.join(targets)}\")\n",
        "    print(f\"\\tDosages: {', '.join(dosages)}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "-xPLDTf0d4Ao",
        "outputId": "84a16ddb-f1b6-46eb-993c-9496c452d7a1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "7+3:\n",
            "\tTargets: Topoisomerase II inhibitor, Nucleoside analogue\n",
            "CPX-351:\n",
            "\tTargets: Topoisomerase II inhibitor, Nucleoside analogue\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "leukemia_type = 'Acute myeloid leukemia'\n",
        "\n",
        "drug_combinations = [node for node in graph.successors(leukemia_type) if graph.nodes[node]['type'] == 'Drug combination']\n",
        "\n",
        "# Create a new directed graph\n",
        "relation_graph = nx.DiGraph()\n",
        "\n",
        "for drug in drug_combinations:\n",
        "    targets = []\n",
        "    dosages = []\n",
        "    for info in data[leukemia_type]['Drug combinations']:\n",
        "        if info['Name'] == drug:\n",
        "            targets = info['Targets']\n",
        "            # dosages = info['Dosages']\n",
        "            break\n",
        "\n",
        "    print(f\"{drug}:\")\n",
        "    print(f\"\\tTargets: {', '.join(targets)}\")\n",
        "    # print(f\"\\tDosages: {', '.join(dosages)}\")\n",
        "\n",
        "    # Add nodes and edges to the relation graph\n",
        "    relation_graph.add_node(drug, label='Drug')\n",
        "    for target in targets:\n",
        "        relation_graph.add_node(target, label='Target')\n",
        "        relation_graph.add_edge(drug, target)\n",
        "\n",
        "# Draw the relation graph\n",
        "pos = nx.spring_layout(relation_graph)\n",
        "labels = nx.get_node_attributes(relation_graph, 'label')\n",
        "nx.draw_networkx(relation_graph, pos, with_labels=True, node_color='lightblue', node_size=500)\n",
        "nx.draw_networkx_labels(relation_graph, pos, labels=labels)\n",
        "plt.show()\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}