Reference_Notebook_Milestone_2_Deep_Learning.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Reference_Notebook_Milestone_2_Deep_Learning.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/shajohn4/Portfolio/blob/main/Reference_Notebook_Milestone_2_Deep_Learning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vGsTRO4XBWKn"
      },
      "source": [
        "#<b>Milestone 2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HDM89XHyCxrA"
      },
      "source": [
        "###<b> Mounting the Drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JQi_degJC3dm",
        "outputId": "ebca37c2-3386-491e-f3ef-cb67b7801782"
      },
      "source": [
        "#Mounting the drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UC8-yLUUCcWh"
      },
      "source": [
        "###<b>Loading libraries</b>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qprkPYi_AmMT"
      },
      "source": [
        "#Importing libraries required to load the data\n",
        "\n",
        "import zipfile\n",
        "import os\n",
        "from PIL import Image\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, BatchNormalization, Dropout, Flatten, LeakyReLU, GlobalAvgPool2D\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras import optimizers\n",
        "\n",
        "#to ignore warnings\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# Remove the limit from the number of displayed columns and rows. It helps to see the entire dataframe while printing it\n",
        "pd.set_option(\"display.max_columns\", None)\n",
        "pd.set_option(\"display.max_rows\", 200)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nCqJk2XpCnJi"
      },
      "source": [
        "### <b>Let us load the data</b>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_syvBdMlDTsr"
      },
      "source": [
        "**Note:** \n",
        "- You must download the dataset from the link provided on Olympus and upload the same on your Google drive before executing the code in the next cell.\n",
        "- In case of any error, please make sure that the path of the file is correct as the path may be different for you."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ufMU62DICjLV"
      },
      "source": [
        "#Storing the path of the data file from the Google drive\n",
        "path = '/content/drive/MyDrive/CNN Project Malaria Detection/cell_images.zip'\n",
        "\n",
        "\n",
        "\n",
        "#The data is provided as a zip file so we need to extract the files from the zip file\n",
        "with zipfile.ZipFile(path, 'r') as zip_ref:\n",
        "    zip_ref.extractall()"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7-HLt-uoF0dF"
      },
      "source": [
        "The files have been extracted to the local session of Google Colab. The extracted folder would have the following structure:\n",
        "\n",
        "![Folder_Structure.PNG](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IW3fDq7gF4Hw"
      },
      "source": [
        "The extracted folder has different folders for train and test data which further contains the different sizes of images for parasitized and uninfected cells within the respective folder name. \n",
        "\n",
        "The size of all images must be the same and should be converted to 4D arrays so that they can be used as an input for the convolutional neural network. Also, we need to create the labels for both types of images to be able to train and test the model. \n",
        "\n",
        "Let's do the same for the training data first and then we will use the same code for the test data as well."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SjK02uF1DXdW"
      },
      "source": [
        "#Storing the path of the extracted \"train\" folder \n",
        "train_dir = '/content/cell_images/train'\n",
        "\n",
        "#Size of image so that each image has the same size\n",
        "SIZE = 64\n",
        "\n",
        "#Empty list to store the training images after they are converted to NumPy arrays\n",
        "train_images = []\n",
        "\n",
        "#Empty list to store the training labels (0 - uninfected, 1 - parasitized)\n",
        "train_labels = []"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2JNY4tSvDXgn"
      },
      "source": [
        "#We will run the same code for \"parasitized\" as well as \"uninfected\" folders within the \"train\" folder\n",
        "for folder_name in ['/parasitized/', '/uninfected/']:\n",
        "    \n",
        "    #Path of the folder\n",
        "    images_path = os.listdir(train_dir + folder_name)\n",
        "\n",
        "    for i, image_name in enumerate(images_path):\n",
        "        try:\n",
        "            #Opening each image using the path of that image\n",
        "            image = Image.open(train_dir + folder_name + image_name)\n",
        "\n",
        "            #Resizing each image to (224,224)\n",
        "            image = image.resize((SIZE, SIZE))\n",
        "\n",
        "            #Converting images to arrays and appending that array to the empty list defined above\n",
        "            train_images.append(np.array(image))\n",
        "\n",
        "            #Creating labels for parasitized and uninfected images\n",
        "            if folder_name=='/parasitized/':\n",
        "                train_labels.append(1)\n",
        "            else:\n",
        "                train_labels.append(0)\n",
        "        except Exception:\n",
        "            pass       \n",
        "\n",
        "#Converting lists to arrays\n",
        "train_images = np.array(train_images)\n",
        "train_labels = np.array(train_labels)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DfwXIsK_DXiv"
      },
      "source": [
        "#Storing the path of the extracted \"test\" folder \n",
        "test_dir = '/content/cell_images/test'\n",
        "\n",
        "#Size of image so that each image has the same size (it must be same as the train image size)\n",
        "SIZE = 64\n",
        "\n",
        "#Empty list to store the testing images after they are converted to NumPy arrays\n",
        "test_images = []\n",
        "\n",
        "#Empty list to store the testing labels (0 - uninfected, 1 - parasitized)\n",
        "test_labels = []"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "My6pm3AFDXk9"
      },
      "source": [
        "#We will run the same code for \"parasitized\" as well as \"uninfected\" folders within the \"test\" folder\n",
        "for folder_name in ['/parasitized/', '/uninfected/']:\n",
        "    \n",
        "    #Path of the folder\n",
        "    images_path = os.listdir(test_dir + folder_name)\n",
        "\n",
        "    for i, image_name in enumerate(images_path):\n",
        "        try:\n",
        "            #Opening each image using the path of that image\n",
        "            image = Image.open(test_dir + folder_name + image_name)\n",
        "            \n",
        "            #Resizing each image to (128,128)\n",
        "            image = image.resize((SIZE, SIZE))\n",
        "            \n",
        "            #Converting images to arrays and appending that array to the empty list defined above\n",
        "            test_images.append(np.array(image))\n",
        "            \n",
        "            #Creating labels for parasitized and uninfected images\n",
        "            if folder_name=='/parasitized/':\n",
        "                test_labels.append(1)\n",
        "            else:\n",
        "                test_labels.append(0)\n",
        "        except Exception:\n",
        "            pass       \n",
        "\n",
        "#Converting lists to arrays\n",
        "test_images = np.array(test_images)\n",
        "test_labels = np.array(test_labels)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7U71xb2XJe0t"
      },
      "source": [
        "###<b>Normalize the images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HqzvvNS9IvsP"
      },
      "source": [
        "# try to normalize the train and test images by dividing it by 255 and convert them to float32 using astype function\n",
        "train_images = (train_images/255).astype('float32')\n",
        "test_images = (test_images/255).astype('float32')"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9txGUBdSNT7J"
      },
      "source": [
        "As we have done our preprocessing required and performed some EDA to gain some insights in our Milestone-1 so now we will try to build our model and try evaluating its performance."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vtayck2-Pm3G"
      },
      "source": [
        "###<B>One Hot Encoding on the train and test labels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xDWl6dVtOMom"
      },
      "source": [
        "# Encoding Train Labels\n",
        "train_labels=to_categorical(train_labels,2)\n",
        "# Similarly let us try to encode test labels\n",
        "test_labels=to_categorical(test_labels,2)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G4_ZzqDp8T5w"
      },
      "source": [
        "### **Base Model**\n",
        "\n",
        "**Note:** The Base Model has been fully built and evaluated with all outputs shown to give an idea about the process of the creation and evaluation of the performance of a CNN architecture. A similar process can be followed in iterating to build better-performing CNN architectures."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qTMSr0Xw6x3b"
      },
      "source": [
        "####<b> Importing the required libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X8eWxdyDp_ZC"
      },
      "source": [
        "#Clearing backend\n",
        "from tensorflow.keras import backend\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D,MaxPooling2D,Dense,Flatten,Dropout  \n",
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "from random import shuffle\n",
        "\n",
        "backend.clear_session()\n",
        "#Fixing the seed for random number generators so that we can ensure we receive the same output everytime\n",
        "np.random.seed(42)\n",
        "import random\n",
        "random.seed(42)\n",
        "tf.random.set_seed(42)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fmwb4h0h64Km"
      },
      "source": [
        "####<b> Building the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IFrp3wznOMrR",
        "outputId": "24eb3426-4000-429a-93b4-e296d9bcb247"
      },
      "source": [
        "\n",
        "#creating sequential model\n",
        "model=Sequential()\n",
        "model.add(Conv2D(filters=32,kernel_size=2,padding=\"same\",activation=\"relu\",input_shape=(64,64,3)))\n",
        "model.add(MaxPooling2D(pool_size=2))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Conv2D(filters=32,kernel_size=2,padding=\"same\",activation=\"relu\"))\n",
        "model.add(MaxPooling2D(pool_size=2))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Conv2D(filters=32,kernel_size=2,padding=\"same\",activation=\"relu\"))\n",
        "model.add(MaxPooling2D(pool_size=2))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(512,activation=\"relu\"))\n",
        "model.add(Dropout(0.4))\n",
        "model.add(Dense(2,activation=\"softmax\"))#2 represent output layer neurons \n",
        "model.summary()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 64, 64, 32)        416       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 32, 32, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 32, 32, 32)        0         \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 32, 32, 32)        4128      \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 16, 16, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 16, 16, 32)        0         \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 16, 16, 32)        4128      \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 8, 8, 32)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 8, 8, 32)          0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 2048)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 512)               1049088   \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 512)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 2)                 1026      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,058,786\n",
            "Trainable params: 1,058,786\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Sh0OGP268Qm"
      },
      "source": [
        "####<b> Compiling the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fGGRMByKOMyG"
      },
      "source": [
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Dt8lFvz6_K6"
      },
      "source": [
        "<b> Using Callbacks "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FPwUYX3KOM34"
      },
      "source": [
        "callbacks = [EarlyStopping(monitor='val_loss', patience=2),\n",
        "             ModelCheckpoint('.mdl_wts.hdf5', monitor='val_loss', save_best_only=True)]"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "91af114l7DCt"
      },
      "source": [
        "<b> Fit and train our Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wbuu1DoSOM7N",
        "outputId": "120385f3-0f8b-4ef9-9211-71069ff40dbe"
      },
      "source": [
        "#Fit the model with min batch size as 32 can tune batch size to some factor of 2^power ] \n",
        "history=model.fit(train_images,train_labels,batch_size=32,callbacks=callbacks,validation_split=0.2,epochs=20,verbose=1)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "624/624 [==============================] - 87s 138ms/step - loss: 0.4388 - accuracy: 0.7873 - val_loss: 0.2132 - val_accuracy: 0.9387\n",
            "Epoch 2/20\n",
            "624/624 [==============================] - 84s 135ms/step - loss: 0.1708 - accuracy: 0.9401 - val_loss: 0.1981 - val_accuracy: 0.9643\n",
            "Epoch 3/20\n",
            "624/624 [==============================] - 84s 135ms/step - loss: 0.1567 - accuracy: 0.9469 - val_loss: 0.1788 - val_accuracy: 0.9651\n",
            "Epoch 4/20\n",
            "624/624 [==============================] - 84s 135ms/step - loss: 0.1480 - accuracy: 0.9502 - val_loss: 0.1827 - val_accuracy: 0.9625\n",
            "Epoch 5/20\n",
            "624/624 [==============================] - 86s 138ms/step - loss: 0.1397 - accuracy: 0.9535 - val_loss: 0.1393 - val_accuracy: 0.9730\n",
            "Epoch 6/20\n",
            "624/624 [==============================] - 85s 136ms/step - loss: 0.1323 - accuracy: 0.9550 - val_loss: 0.1453 - val_accuracy: 0.9669\n",
            "Epoch 7/20\n",
            "624/624 [==============================] - 85s 136ms/step - loss: 0.1268 - accuracy: 0.9561 - val_loss: 0.1479 - val_accuracy: 0.9704\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vn7bDXku7Ib8"
      },
      "source": [
        "####<b> Evaluating the model on test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kITviS2OOM9K",
        "outputId": "78b3f48a-4939-4dd6-cd8b-5428b7e7b7d2"
      },
      "source": [
        "accuracy = model.evaluate(test_images, test_labels, verbose=1)\n",
        "print('\\n', 'Test_Accuracy:-', accuracy[1])"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "82/82 [==============================] - 3s 35ms/step - loss: 0.1511 - accuracy: 0.9504\n",
            "\n",
            " Test_Accuracy:- 0.9503846168518066\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sSoNNG_T7PkT"
      },
      "source": [
        "<b> Plotting the confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "id": "ptD_M1p6OM--",
        "outputId": "a4f576d9-16ba-4c2c-c7b5-40f0cf663765"
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "pred = model.predict(test_images)\n",
        "pred = np.argmax(pred,axis = 1) \n",
        "y_true = np.argmax(test_labels,axis = 1)\n",
        "\n",
        "#Printing the classification report\n",
        "print(classification_report(y_true,pred))\n",
        "\n",
        "#Plotting the heatmap using confusion matrix\n",
        "cm = confusion_matrix(y_true,pred)\n",
        "plt.figure(figsize=(8,5))\n",
        "sns.heatmap(cm, annot=True,  fmt='.0f', xticklabels=['Uninfected', 'Parasitized'], yticklabels=['Uninfected', 'Parasitized'])\n",
        "plt.ylabel('Actual')\n",
        "plt.xlabel('Predicted')\n",
        "plt.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.97      0.95      1300\n",
            "           1       0.97      0.93      0.95      1300\n",
            "\n",
            "    accuracy                           0.95      2600\n",
            "   macro avg       0.95      0.95      0.95      2600\n",
            "weighted avg       0.95      0.95      0.95      2600\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sptxiNTMzy5EDq5mZWY7cFWxmZpajRmuxej1WMzOzHLnFamZmNdVlJoiQdBaw0J7viDi8kBqZmVmX0pUmiJi0yGphZmZdVnNXabFGxEWLsiJmZtY1dZmu4BaSvgAcC6wN9GhJj4gtC6yXmZl1EV1xVPClwJNAf+BE4HngoQLrZGZmXUhE5VtnVk5gXSEixgJzIuLuiNgfcGvVzMxyEc2qeOvMynncZk56nSlpB+BlYPniqmRmZl1Jlxm8VOIkScsCRwNnAcsARxZaKzMz6zK63OCliLgh7b4LbFFsdczMrKvp7PdMK1XOqOALaGOiiHSv1czM7HPpil3BN5Ts9wB2I7vPamZm9rl1xa7gv5UeSxoH3FtYjczMrEvpcl3BbRgAfDHvirS2Yr+tiy7CrHAfPnVNratg1ul1ua5gSe+z4D3WV8hmYjIzM/vcumJXcK9FUREzM+uaGq3F2uHMS5JuLyfNzMzM2l+PtQewJLCipOWAlj8plgH6LoK6mZlZF9BgY5fa7Qo+GDgC+BIwmU8D63vA2QXXy8zMuohG6wpubz3WM4AzJI2MiLMWYZ3MzKwLabTBS+WsbtMsqXfLgaTlJA0vsE5mZtaFNFexdWblBNaDIuKdloOIeBs4qLgqmZlZVxKo4q0cknpLGi/pKUlPSvqmpOUlTZT0THpdLuWVpDMlTZM0VdKG1X6ecgJrk6T5n0JSE9C92gLNzMxKNUflW5nOAG6OiLWA9YEngeOA2yNiAHB7OgbYjmwCpAHAMODcaj9POYH1ZuAKSUMkDQHGATdVW6CZmVmpZlTx1pG03OnmwFiAiJidel93AS5K2S4Cdk37uwAXR+Z+oLekPtV8nnKmNDyWLHofko6nAv9VTWFmZmatldu1W6H+wOvABZLWJ3u65cfAShExM+V5BVgp7fcFppec/1JKm0mFOmyxRkQz8ADwPLAxsCVZc9rMzOxzq2bwkqRhkiaVbMNaXbYbsCFwbkR8DZjFp92+AEREUMBjtO1NEPEV4PtpewO4IlXEi52bmVluqmmxRsRoYHQ7WV4CXoqIB9LxeLLA+qqkPhExM3X1vpbenwGsUnL+yimtYu21WJ8ia53uGBHfSs+yzqumEDMzs4Up4nGbiHgFmC5pzZQ0BHgCmAAMTWlDgevS/gRg3zQ6eBPg3ZIu44q0d491d2Av4E5JNwOXQzEd4WZm1nUV+FzqSOBSSd2BZ4H9yBqUV0o6AHgB2DPl/TuwPTAN+DDlrUp7My9dC1wraSmy0VJHAF+UdC5wTUTcWm2hZmZmLQoavERETAEGtvHWkDbyBjAij3LLGbw0KyIui4idyPqcH8HrsZqZWU6aVfnWmZXzuM18adaljm4Ym5mZla2c51LrSUWB1czMLG+NtmxcOTMvmZmZWZncYjUzs5rq7KvVVMqB1czMaqpZvsdqZmaWm0a7x+rAamZmNeWuYDMzsxx19udSK+XAamZmNeXnWM3MzHLke6xmZmY5clewmZlZjjx4yczMLEfuCjYzM8uRu4LNzMxy5K5gMzOzHDmwmpmZ5SjcFWxmZpYft1jNzMxy5MBqZmaWo0Z73GaxWlfAzMyskbjFamZmNeXnWM3MzHLke6xmZmY5cmA1MzPLUaMNXnJgNTOzmvI91jJIOqq99yPi9CLKNTOz+uOu4PL0Sq9rAl8HJqTjnYAHCyrTzMzqkLuCyxARJwJIugfYMCLeT8cnADcWUaaZmdWn5gYLrUXfY10JmF1yPDulmZmZAe4KrtTFwIOSrknHuwIXFVymmZnVkcZqrxYcWCPiN5JuAjZLSftFxCNFlmlmZvXFLdbKLQm8FxEXSPqCpP4R8dwiKNfMzOqAH7epgKRRwECy0cEXAIsDfwUGFVmumZnVj0YbvFT06ja7ATsDswAi4mU+fRTHzMyMqGLrzIoOrLMjYv7PQdJSBZdnZmZ1prmKrVySmiQ9IumGdNxf0gOSpkm6QlL3lL5EOp6W3u9X7ecpOrBeKenPQG9JBwG3AWMKLtPMzOpIM1HxVoEfA0+WHP8O+ENErAG8DRyQ0g8A3k7pf0j5qlJoYI2I04DxwN/I7rP+T0ScWWSZZmZmAJJWBnYgNegkCdiSLC5B9vjnrml/Fz59HHQ8MCTlr1jRg5d+CVwYERNL0oZFxOgiyzUzs/pR4D3TPwLH8OnYnhWAdyJibjp+Ceib9vsC0wEiYq6kd1P+NyottOiu4JHAzZK2KEk7pOAyzcysjlRzj1XSMEmTSrZhpdeUtCPwWkRMXnSfJFP0c6wzyJrXV0kaHxGnAg32xJKZmX0e1Txuk3o+2+v9HATsLGl7oAewDHAG2ZifbqnVujJZnCK9rgK8JKkbsCzwZsUVo/gWKxHxIvBtYG1JVwE9iy7TzMzqRxGP20TEzyJi5YjoB+wF3BERPwTuBL6bsg0Frkv7E9Ix6f070lMtFSs6sE4CiIiPI2I/4C6ge8FlmplZHSnycZs2HAscJWka2T3UsSl9LLBCSj8KOK7aAoqeK/igVsfnAOcUWaaZmdWXKHjKh4i4i6xhR0Q8C2zcRp6Pgf/Oo7xCAqukKyNiT0n/oo1We0SsV0S5ZmZWfzwJf3l+nF53LOj6ZmbWIDxXcBkiYmbaHR4RL5RuwPAiyrSODR+xH/c/dBP/fPAmxl7wR5ZYIrvd/ctRRzP5kdt4cPItHHzo0A6uYla8X57+F76913B2O+TT21y/HzOOnQ46ht0P/Tk//tUfee+DWQC889777H/sb9l4twP5zZ/aXu555AmnL3At61w8V3Bltm4jbbuCy7Q29OmzEoccOpTBm+3KNzfejqamxdjjuzvxw733oG/fPgzccGs23ug7/G38DbWuqhm7bL0Z5550zAJp3/zaulxz3v9y9bm/ZdW+/8WYK64HoHv3xTlsnz34yYHfb/Nat933ED179ii8zla9gqc0XOQKCaySDk33V9eUNLVkew6YWkSZ1rGmbt3o2bMHTU1N9OzZk1dmvsoBB/6QU04+i5ZR5W+8XtVjW2a5GvjVtVi214Jrdmy60Vfp1tQEwPprrcGrb7wFwJI9erDhumvSvfvin7nOhx99zMVX38zBe+1SfKWtaot4VHDhimqxXgbsRPZc0E4l20YRsXdBZVo7Zs58lbPOHMNjT/6Df//nn7z33vvccce99O//ZXbfYwfuuudaxl99Pqut3q/WVTXr0DW33s23vr5+h/nOung8Q3ffjh49/JRfZxZV/NeZFRVYIyKeB0YA75dsSFq+oDKtHb17L8MOO2zFeusOZs01NmXJJZdkz+/tQvcluvPxx58wePNduejCyznn3JNrXVWzdo0edx1NTU3suMWm7eZ76j8v8NLM1xgyaOAiqplVyy3W8lyWXieTTRIxuWSbtLCTSud+nD3nvYKq1jUN3mIQLzw/nTffeIu5c+dy/YRb+MYmG/Lyy69w/YRbALh+wq2ss85aNa6p2cJdO/Ee7n5wCicfcygdLTzy6JPP8Pgzz/GdoUey79G/5vkZr7DfMb9ZRDW1SjRai7WQx20iYsf02r/C8+bP/bjs0qt37p9cnZk+/WUGbrwBPXv24KOPPubbgzflkUf+xfvvfcBmm2/CC5eM51ubfYP/THuu1lU1a9O9k6ZywVU3csEpx9OzxxId5v/ejlvxvR23AmDGq69z2Kjfc8EpxxddTatCZ2+BVqroZeMGAVMiYpakvYENgT+m+YNtEZo86VGuu/Zm7rlvAnPnzmPqo49z4fmX07PnEvxl7B8Yftj+zPpgFiNH/KzWVTXjmJPP4aGpT/LOex8wZO/DGbHP7oy54npmz5nLsOOz9afXW2sN/mfkfgB8Z+iRfPDhR8yZO5c7/m8yo39zLKuv2re9IqwTaa5uSt5OS1XOMVzexaWpwPrAesCFZIvN7hkR3+7oXLdYrRG8PnVcratglovuq21c2Mpk+6y6e8W/7y954epOu1Ja0c+xzk2rA+wCnJ3mCu7VwTlmZtaFNNoEEUWvx/q+pJ8BewObS1oM+OzDZmZm1mV19gkfKlV0i/V7wCfAARHxCtmisqcWXKaZmdURjwquQAqmp5ccvwhcXGSZZmZWXxptVHChLVZJm0h6SNIHkmZLmifp3SLLNDOz+tJocwUXfY/1bGAv4CpgILAv8JWCyzQzszrS2bt2K1X0PVYiYhrQFBHzIuICYNuiyzQzs/rRaFMaFt1i/VBSd2CKpFOAmSyCYG5mZvWjyPkUaqHoILdPKuMwYBawCrBHwWWamVkd8T3WMklqAn4bET8EPgZOLKosMzOrX529a7dShQXWiJgnaVVJ3SNidlHlmJlZfWu0wUtF32N9FrhP0gSyrmAAIuL0hZ9iZmZdSWfv2q1U0YH1P2lbDM8RbGZmbWi0wUtFz7zk+6pmZtYu32OtgKQvAMcA6wA9WtIjYssiyzUzs/rRaPdYi37c5lLgKaA/2ajg54GHCi7TzMzqSKM9blN0YF0hIsYCcyLi7ojYH3Br1czMGlbRg5fmpNeZknYAXgaWL7hMMzOrIx68VJmTJC0LHA2cBSwDHFlwmWZmVkc6e9dupQoJrJJ6AIcAawB9gbERsUURZZmZWX1rtMFLRbVYLyLrBv4HsB2wNvDjgsoyM7M61uyu4LKsHRFfBZA0FniwoHLMzKzONVZYLS6wtgxaIiLmSiqoGDMzq3e+x1qe9SW9l/YF9EzHAiIilimoXDMzqzMOrGWIiKYirmtmZo2n0R63KXqCCDMzs3YVMfOSpFUk3SnpCUmPS/pxSl9e0kRJz6TX5VK6JJ0paZqkqZI2rPbzOLCamVlNRRX/lWEucHRErA1sAoyQtDZwHHB7RAwAbk/HkD3BMiBtw4Bzq/08DqxmZlZTEVHxVsY1Z0bEw2n/feBJsnkVdiF7JJT0umva3wW4ODL3A70l9anm8xQ985KZmVm7ih68JKkf8DXgAWCliJiZ3noFWCnt9wWml5z2UkqbSYXcYjUzs5qqpsUqaZikSSXbsLauLWlp4G/AERHxXqtygwIeo3WL1czMaqqaFmtEjAZGt5dH0uJkQfXSiLg6Jb8qqU9EzExdva+l9BnAKiWnr5zSKuYWq5mZ1VQRg5eUzUw0FngyIk4veWsCMDTtDwWuK0nfN40O3gR4t6TLuCJusZqZWU0VNFfwIGAf4F+SpqS0nwMnA1dKOgB4Adgzvfd3YHtgGvAhsF+1BTuwmplZw4mIe8lm+2vLkDbyBzAij7IdWM3MrKa8bJyZmVmOvGycmZlZjtxiNTMzy5FbrGZmZjlyi9XMzCxHbrGamZnlyC1WMzOzHEU017oKuXJgNTOzmip6dZtFzYHVzMxqqpz1VeuJA6uZmdWUW6xmZmY5covVzMwsR37cxszMLEd+3MbMzCxH7go2MzPLkQcvmZmZ5ajRWqyL1boCZmZmjcQtVjMzqymPCjYzM8tRo3UFO7CamVlNefCSmZlZjtxiNTMzy5HvsZqZmeXIMy+ZmZnlyC1WMzOzHPkeq5mZWY7cFWxmZpYjt1jNzMxy5MBqZmaWo8YKq6BG+0vByidpWESMrnU9zD4vf5etM/HqNl3bsFpXwCwn/i5bp+HAamZmliMHVjMzsxw5sHZtvidljcLfZes0PHjJzMwsR26xmpmZ5ciBtROS1E/SY63STpD0k3bOGSjpzDKufbikJyVdWkW9jpC0ZIXnDJZ0Q6VlWWOQNE/SFEmPSbqq0u9PhWX9XVLvtA0vSf+SpPE5XL/df4NmLRxYG0RETIqIw8vIOhzYOiJ+WEUxRwCF/WK0hvRRRGwQEesCs4FDyjlJUsWT10TE9hHxDtCb7Hvekv5yRHy30uuZVcuBtc5IukvS7yQ9KOnfkjZL6fNbhukv6/NT3mclHZ7SzwNWA26SdKSkpVK+ByU9ImmXlK9J0mmplTFV0sh0jS8Bd0q6M+XbRtI/JT2cWiNLp/RtJT0l6WFg90X+Q7LO6h/AGpJ2kvRA+s7dJmklmP+9vUTSfcAlqefmH+n79bCkTVO+PpLuKWkJt/wbeF7SisDJwOrp/VNLe4AkjUnpUyS9LmlUSv+ppIfS9/3ElgpLOj79O7sXWHOR/rSsbnlKw/rULSI2lrQ9MArYqo08awFbAL2ApyWdGxGHSNoW2CIi3pD0W+COiNhfUuCtPxsAAAUrSURBVG/gQUm3AfsC/YANImKupOUj4i1JR5WcuyLwC2CriJgl6VjgKEmnAH8BtgSmAVcU+pOwupBaoNsBNwP3AptEREg6EDgGODplXRv4VkR8lLqNt46IjyUNAMYBA4EfALdExG8kNfHZXpTjgHUjYoNUdr+WNyLiwJS2aqrLhZK2AQYAGwMCJkjaHJgF7AVsQPa78mFgcn4/FWtUDqyd08KGarekX51eJ5MFwLbcGBGfAJ9Ieg1YCXipVZ5tgJ1L7hv1AL5MFqjPi4i5ABHxVhvX34Tsl+B9kgC6A/8kC+jPRcQzAJL+imfF6cp6SpqS9v8BjCVr+V0hqQ/Z9+a5kvwTIuKjtL84cLakDYB5wFdS+kPA+ZIWB66NiClUQFIP4CpgZES8IGkk2b+FR1KWpckCbS/gmoj4MJ03oZJyrOtyYO2c3gSWa5W2PJ/+Avokvc5j4f8PPynZX1g+AXtExNMLJGaBsiMCJkbE91udu0E5J1uX8VFLy7GFpLOA0yNigqTBwAklb88q2T8SeBVYn+y21ccAEXFPalHuQNbiPD0iLq6gTucBV0fEbS1VAv43Iv7cqp5HVHBNs/l8j7UTiogPgJmStgSQtDywLVkXWp5uAUYqRVJJX0vpE4GDWwaQpPIB3if7Kx7gfmCQpDVSnqUkfQV4CugnafWUb4HAawYsC8xI+0M7yDczIpqBfYAmmN+N+2pE/AUYA2zY6rzS7+kCJI0AekXEySXJtwD7l4wR6Cvpi8A9wK6SekrqBexUwWe0LsyBtfPaF/hl6ka7AzgxIv6Tcxm/Jutumyrp8XQM2S+rF1P6o2T3tCCb3eZmSXdGxOvAj4BxkqaSuoEj4mOyrt8b0+Cl13Kus9W/E4CrJE0G3mgn35+Aoek7uBaftmYHA49KegT4HnBG6UkR8SbZLYrHJJ3a6po/Ab5aMoDpkIi4FbgM+KekfwHjyYLvw2RjBB4FbiLrgjbrkGdeMjMzy5FbrGZmZjlyYDUzM8uRA6uZmVmOHFjNzMxy5MBqZmaWIwdWM/JdhUXShZK+m/bHSFq7nbyDW+bArbCMlnlxzayTcWA1y7S7CouqWG0FsrlpI+KJdrIMBioOrGbWeTmwmn1Wyyosg9PqKhOAJ5St+nNqySooBwMoc7akp9MiBl9suZCyFYYGpv1tla3S8qik29Pk8IcAR6bW8maSviDpb6mMhyQNSueuIOlWSY9LGkM2DZ+ZdUKeK9isRKtVWCCbLm/diHhO0jDg3Yj4uqQlyGb3uRX4GtnE8muTLXbwBHB+q+t+gWzVn83TtVpWDDoP+CAiTkv5LgP+EBH3Svoy2XR7/49sFaN7I+JXknYADij0B2FmVXNgNcu0tQrLpsCDEdGy+ME2wHot90/J5rIdAGwOjIuIecDLku5o4/qbAPe0XGshKwZBtrLQ2iULISyT5rDdnLS2bUTcKOntKj+nmRXMgdUs09YqLLDgaisiW2rsllb5ts+xHouRrVX6cRt1MbM64HusZuW7BTg0rQOKpK9IWopsFZTvpXuwfcgWmG/tfmBzSf3TuW2tGARwKzCy5aBkGb57SIshSNqOzy4raGadhAOrWfnGkN0/fVjSY8CfyXp9rgGeSe9dTLbSzwLSakDDgKvTai1XpLeuB3ZrGbwEHA4MTIOjnuDT0cknkgXmx8m6hF8s6DOa2efk1W3MzMxy5BarmZlZjhxYzczMcuTAamZmliMHVjMzsxw5sJqZmeXIgdXMzCxHDqxmZmY5cmA1MzPL0f8HdAQDZhoIA+YAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eWq4jyPL7f5a"
      },
      "source": [
        "<b>Plotting the train and validation curves"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U3h5gAwyW05u"
      },
      "source": [
        "# function to plot train and validation accuracy \n",
        "def plot_accuracy(history):\n",
        "    N = len(history.history[\"accuracy\"])\n",
        "    plt.figure(figsize=(7,7))\n",
        "    plt.plot(np.arange(0, N), history.history[\"accuracy\"], label=\"train_accuracy\", ls='--')\n",
        "    plt.plot(np.arange(0, N), history.history[\"val_accuracy\"], label=\"val_accuracy\", ls='--')\n",
        "    plt.title(\"Accuracy vs Epoch\")\n",
        "    plt.xlabel(\"Epochs\")\n",
        "    plt.ylabel(\"Accuracy\")\n",
        "    plt.legend(loc=\"upper left\")"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "0sO-cNujONBm",
        "outputId": "6f4d5be7-b4be-4470-cc8c-e0475a212203"
      },
      "source": [
        "plot_accuracy(history)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AGOtxZ10fEIn"
      },
      "source": [
        "\n",
        "\n",
        "* Here we can clearly observe that the training and valiation accuracy are increasing \n",
        "* And we can also notice that validation accuracy is slightly higher than the train accuracy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dCDgxiFnXSek"
      },
      "source": [
        "So now let's try to build another model with few more add on layers and try to check if we can try to improve the model. Therefore try to build a model by adding few layers if required and altering the activation functions."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2tJeklwR77rs"
      },
      "source": [
        "###<b> Model 1\n",
        "####<b> Trying to improve the performance of our model by adding new layers\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v3in8FDCOND9"
      },
      "source": [
        "#Clearing backend\n",
        "from tensorflow.keras import backend\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D,MaxPooling2D,Dense,Flatten,Dropout  \n",
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "from random import shuffle\n",
        "\n",
        "backend.clear_session()\n",
        "#Fixing the seed for random number generators so that we can ensure we receive the same output everytime\n",
        "np.random.seed(42)\n",
        "import random\n",
        "random.seed(42)\n",
        "tf.random.set_seed(42)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DT99NdnzZW3n"
      },
      "source": [
        "####<b> Building the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "42163105-ee1e-44ee-b764-e0731779f355",
        "id": "ibyMsOxGQ6wV"
      },
      "source": [
        "#creating sequential model\n",
        "model1=Sequential()\n",
        "model1.add(Conv2D(filters=32,kernel_size=2,padding=\"same\",activation=\"relu\",input_shape=(64,64,3)))\n",
        "model1.add(MaxPooling2D(pool_size=2))\n",
        "model1.add(Dropout(0.2))\n",
        "model1.add(Conv2D(filters=32,kernel_size=2,padding=\"same\",activation=\"relu\"))\n",
        "model1.add(MaxPooling2D(pool_size=2))\n",
        "model1.add(Dropout(0.2))\n",
        "model1.add(Conv2D(filters=32,kernel_size=2,padding=\"same\",activation=\"relu\"))\n",
        "model1.add(MaxPooling2D(pool_size=2))\n",
        "model1.add(Conv2D(filters=32,kernel_size=2,padding=\"same\",activation=\"relu\"))\n",
        "model1.add(MaxPooling2D(pool_size=2))\n",
        "model1.add(Conv2D(filters=32,kernel_size=2,padding=\"same\",activation=\"relu\"))\n",
        "model1.add(MaxPooling2D(pool_size=2))\n",
        "\n",
        "model1.add(Dropout(0.2))\n",
        "\n",
        "\n",
        "model1.add(Flatten())\n",
        "\n",
        "model1.add(Dense(512,activation=\"relu\"))\n",
        "model1.add(Dropout(0.4))\n",
        "model1.add(Dense(2,activation=\"softmax\"))#2 represent output layer neurons \n",
        "model1.summary()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 64, 64, 32)        416       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 32, 32, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 32, 32, 32)        0         \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 32, 32, 32)        4128      \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 16, 16, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 16, 16, 32)        0         \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 16, 16, 32)        4128      \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 8, 8, 32)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 8, 8, 32)          4128      \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPooling  (None, 4, 4, 32)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 4, 4, 32)          4128      \n",
            "                                                                 \n",
            " max_pooling2d_4 (MaxPooling  (None, 2, 2, 32)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 2, 2, 32)          0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 128)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 512)               66048     \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 512)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 2)                 1026      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 84,002\n",
            "Trainable params: 84,002\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S6cTCOAVZZpI"
      },
      "source": [
        "####<b> Compiling the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JWcQhKidONIr"
      },
      "source": [
        "\n",
        "model1.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2PSskadUZhUt"
      },
      "source": [
        "<b> Using Callbacks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z4_T2_sKONMA"
      },
      "source": [
        "callbacks = [EarlyStopping(monitor='val_loss', patience=2),\n",
        "             ModelCheckpoint('.mdl_wts.hdf5', monitor='val_loss', save_best_only=True)]"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VYLgQK9DZkoc"
      },
      "source": [
        "<b>Fit and Train the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O7S7LmVTZjrd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "abf66621-2f65-4292-ab7e-ee0b91ba448f"
      },
      "source": [
        "history=model1.fit(train_images,train_labels,batch_size=32,callbacks=callbacks,validation_split=0.2,epochs=6,verbose=1)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/6\n",
            "624/624 [==============================] - 84s 133ms/step - loss: 0.3276 - accuracy: 0.8443 - val_loss: 0.2041 - val_accuracy: 0.9551\n",
            "Epoch 2/6\n",
            "624/624 [==============================] - 82s 132ms/step - loss: 0.1583 - accuracy: 0.9503 - val_loss: 0.1245 - val_accuracy: 0.9665\n",
            "Epoch 3/6\n",
            "624/624 [==============================] - 82s 131ms/step - loss: 0.1466 - accuracy: 0.9538 - val_loss: 0.1739 - val_accuracy: 0.9613\n",
            "Epoch 4/6\n",
            "624/624 [==============================] - 81s 130ms/step - loss: 0.1449 - accuracy: 0.9519 - val_loss: 0.2536 - val_accuracy: 0.9221\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bHABkGRzZwyt"
      },
      "source": [
        "####<b> Evaluating the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yie1Z-R6Zjt_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b884e536-e77f-4d3d-b0ea-8ca0fa87f907"
      },
      "source": [
        "accuracy = model1.evaluate(test_images, test_labels, verbose=1)\n",
        "print('\\n', 'Test_Accuracy:-', accuracy[1])\n"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "82/82 [==============================] - 3s 35ms/step - loss: 0.1784 - accuracy: 0.9392\n",
            "\n",
            " Test_Accuracy:- 0.939230740070343\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cCJEZ6cqZywk"
      },
      "source": [
        "<b> Plotting the confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YmuJFfUjZjws",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "outputId": "46350e75-a7f5-4fcd-c3af-e1f23349f6e5"
      },
      "source": [
        "pred = model1.predict(test_images)\n",
        "pred = np.argmax(pred,axis = 1) \n",
        "y_true = np.argmax(test_labels,axis = 1)\n",
        "\n",
        "#Printing the classification report\n",
        "print(classification_report(y_true,pred))\n",
        "\n",
        "#Plotting the heatmap using confusion matrix\n",
        "cm = confusion_matrix(y_true,pred)\n",
        "plt.figure(figsize=(8,5))\n",
        "sns.heatmap(cm, annot=True,  fmt='.0f', xticklabels=['Uninfected', 'Parasitized'], yticklabels=['Uninfected', 'Parasitized'])\n",
        "plt.ylabel('Actual')\n",
        "plt.xlabel('Predicted')\n",
        "plt.show()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.91      0.94      1300\n",
            "           1       0.92      0.96      0.94      1300\n",
            "\n",
            "    accuracy                           0.94      2600\n",
            "   macro avg       0.94      0.94      0.94      2600\n",
            "weighted avg       0.94      0.94      0.94      2600\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "roDugWeKaVDW"
      },
      "source": [
        "<b> Plotting the Train and validation curves"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JakeVNnbZjzh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "c93b19c3-151f-42d7-d339-f27845956250"
      },
      "source": [
        "plot_accuracy(history)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zsx7jVjaiO9V"
      },
      "source": [
        "####<b>Think about it:</b><br>\n",
        "Now let's build a model with LeakyRelu as the activation function  \n",
        "\n",
        "*  Can the model performance be improved if we change our activation function to LeakyRelu?\n",
        "*  Can BatchNormalization improve our model?\n",
        "\n",
        "Let us try to build a model using BatchNormalization and using LeakyRelu as our activation function."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JU4qr7amiXds"
      },
      "source": [
        "###<b> Model 2 with Batch Normalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fOXd_ciyZj2n"
      },
      "source": [
        "#Clearing backend\n",
        "from tensorflow.keras import backend\n",
        "backend.clear_session()\n",
        "\n",
        "#Fixing the seed for random number generators\n",
        "np.random.seed(42)\n",
        "import random\n",
        "random.seed(42)\n",
        "tf.random.set_seed(42)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1kDMUu-U8vV3"
      },
      "source": [
        "####<b> Building the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uJBcwhtbiu0l",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d48c4188-99c1-4f33-98c7-f0b107122b50"
      },
      "source": [
        "model2 = Sequential()\n",
        "model2.add(Conv2D(32, (3,3), input_shape=(64, 64, 3), padding='same'))\n",
        "\n",
        "model2.add(LeakyReLU(alpha=0.1))\n",
        "\n",
        "model2.add(Conv2D(32, (3,3), input_shape=(64, 64, 3), padding='same'))\n",
        "model2.add(LeakyReLU(alpha=0.1))\n",
        "model2.add(MaxPool2D(pool_size=(2, 2)))    \n",
        "model2.add(BatchNormalization())\n",
        "model2.add(Conv2D(32, (3,3), input_shape=(64, 64, 3), padding='same'))\n",
        "model2.add(LeakyReLU(alpha=0.1))\n",
        "model2.add(MaxPool2D(pool_size=(2, 2)))    \n",
        "model2.add(BatchNormalization())\n",
        "model2.add(Flatten())    \n",
        "model2.add(Dense(32))\n",
        "model2.add(LeakyReLU(alpha=0.1))\n",
        "model2.add(Dropout(0.5))\n",
        "model2.add(Dense(2, activation='softmax'))\n",
        "\n",
        "'''\n",
        "\n",
        "Complete this model using BatchNormalization layers and by using LeakyRelu as the activation function\n",
        "\n",
        "\n",
        "'''\n",
        "\n",
        "adam = optimizers.Adam(learning_rate=0.001)\n",
        "model2.summary()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 64, 64, 32)        896       \n",
            "                                                                 \n",
            " leaky_re_lu (LeakyReLU)     (None, 64, 64, 32)        0         \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 64, 64, 32)        9248      \n",
            "                                                                 \n",
            " leaky_re_lu_1 (LeakyReLU)   (None, 64, 64, 32)        0         \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 32, 32, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 32, 32, 32)       128       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 32, 32, 32)        9248      \n",
            "                                                                 \n",
            " leaky_re_lu_2 (LeakyReLU)   (None, 32, 32, 32)        0         \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 16, 16, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 16, 16, 32)       128       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 8192)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 32)                262176    \n",
            "                                                                 \n",
            " leaky_re_lu_3 (LeakyReLU)   (None, 32)                0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 32)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 2)                 66        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 281,890\n",
            "Trainable params: 281,762\n",
            "Non-trainable params: 128\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t9WQ7-cOLIQy"
      },
      "source": [
        "####<b>Compiling the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6SiRoyZ7LHSL"
      },
      "source": [
        "model2.compile(loss=\"binary_crossentropy\", optimizer=adam, metrics = ['accuracy'])"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XOVzX0WTDUEy"
      },
      "source": [
        "<b> Using callbacks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jC0FcITongxx"
      },
      "source": [
        "'''\n",
        "\n",
        "create the callbacks similarly as done in the base model\n",
        "As callbacks will help us in saving our checkpoints and stopping at an accuracy where the model doesnot seem to improve\n",
        "\n",
        "'''\n",
        "\n",
        "callbacks = [EarlyStopping(monitor='val_loss', patience=2),\n",
        "             ModelCheckpoint('.mdl_wts.hdf5', monitor='val_loss', save_best_only=True)]"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rxj3AcS0Dqm8"
      },
      "source": [
        "<b>Fit and train the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fMy-4IGajL31",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6cb6aea0-2e30-4da2-a79a-031d4d869add"
      },
      "source": [
        "history2 = model2.fit(train_images,train_labels,batch_size=32,callbacks=callbacks,validation_split=0.2,epochs=4,verbose=1)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/4\n",
            "624/624 [==============================] - 263s 420ms/step - loss: 0.5505 - accuracy: 0.7450 - val_loss: 0.0555 - val_accuracy: 0.9902\n",
            "Epoch 2/4\n",
            "624/624 [==============================] - 262s 419ms/step - loss: 0.2162 - accuracy: 0.9276 - val_loss: 0.2490 - val_accuracy: 0.9337\n",
            "Epoch 3/4\n",
            "624/624 [==============================] - 261s 418ms/step - loss: 0.1793 - accuracy: 0.9395 - val_loss: 0.1514 - val_accuracy: 0.9681\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j9DkuwVYDtc3"
      },
      "source": [
        "<b>Plotting the train and validation accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ILu-oHB_jxGY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "84c82f72-4588-445f-8767-19be29efa5b9"
      },
      "source": [
        "# plotting the accuracies\n",
        "\n",
        "plot_accuracy(history2)\n"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mSREnGkgDz6N"
      },
      "source": [
        "####<b>Evaluating the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pZGH8gE089Gx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4346e83f-c191-4d02-f73b-1935cc7bd6f6"
      },
      "source": [
        "# Evaluate the model to calculate the accuracy\n",
        "accuracy = model2.evaluate(test_images, test_labels, verbose=1)\n",
        "print('\\n', 'Test_Accuracy:-', accuracy[1])"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "82/82 [==============================] - 9s 104ms/step - loss: 0.1578 - accuracy: 0.9538\n",
            "\n",
            " Test_Accuracy:- 0.9538461565971375\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RiIQnwKaJ27O"
      },
      "source": [
        "#####<b>Observations and insights: ____"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YDL1BQZ7_JxS"
      },
      "source": [
        "<b> Generate the classification report and confusion matrix "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pB-CYdwC9V8P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "outputId": "79187153-bca5-42d0-9c65-6819516066bb"
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "pred = model2.predict(test_images)\n",
        "pred = np.argmax(pred,axis = 1) \n",
        "y_true = np.argmax(test_labels,axis = 1)\n",
        "#Printing the classification report\n",
        "print(classification_report(y_true,pred))\n",
        "\n",
        "#Plotting the heatmap using confusion matrix\n",
        "cm = confusion_matrix(y_true,pred)\n",
        "plt.figure(figsize=(8,5))\n",
        "sns.heatmap(cm, annot=True,  fmt='.0f', xticklabels=['Uninfected', 'Parasitized'], yticklabels=['Uninfected', 'Parasitized'])\n",
        "plt.ylabel('Actual')\n",
        "plt.xlabel('Predicted')\n",
        "plt.show()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.97      0.95      1300\n",
            "           1       0.97      0.94      0.95      1300\n",
            "\n",
            "    accuracy                           0.95      2600\n",
            "   macro avg       0.95      0.95      0.95      2600\n",
            "weighted avg       0.95      0.95      0.95      2600\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aUwBBVzuijlU"
      },
      "source": [
        "####**Think About It :**<br>\n",
        "\n",
        "* Can we improve the model with Image Data Augmentation?\n",
        "* References to image data augmentation can be seen below:\n",
        "  *   [Image Augmentation for Computer Vision](https://www.mygreatlearning.com/blog/understanding-data-augmentation/)\n",
        "  *   [How to Configure Image Data Augmentation in Keras?](https://machinelearningmastery.com/how-to-configure-image-data-augmentation-when-training-deep-learning-neural-networks/)\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DYVJZ0Psi0D0"
      },
      "source": [
        "###<b>Model 3 with Data Augmentation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ddfrhcLihpz"
      },
      "source": [
        "#Clearing backend\n",
        "from tensorflow.keras import backend\n",
        "backend.clear_session()\n",
        "#Fixing the seed for random number generators\n",
        "np.random.seed(42)\n",
        "import random\n",
        "random.seed(42)\n",
        "tf.random.set_seed(42)"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NqOnCq3FZ6JG"
      },
      "source": [
        "####<b> Using image data generator"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MnvWygv4aAZc"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_val, y_train, y_val = train_test_split(train_images, train_labels, test_size=0.2, random_state=42)\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "# Using ImageDataGenerator to generate images\n",
        "\n",
        "train_datagen = ImageDataGenerator(horizontal_flip=True, \n",
        "                                  zoom_range = 0.5, rotation_range=30)\n",
        "val_datagen  = ImageDataGenerator()\n",
        "\n",
        "# Flowing training images using train_datagen generator\n",
        "train_generator = train_datagen.flow(x =X_train, y = y_train, batch_size=64, seed=42, shuffle=True)     \n",
        "\n",
        "# Flowing validation images using val_datagen generator\n",
        "val_generator =  val_datagen.flow(x= X_val, y = y_val, batch_size=64, seed=42, shuffle=True)"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_2cJN07Bbofx"
      },
      "source": [
        "####**Think About It :**<br>\n",
        "\n",
        "*  Check if the performance of the model can be improved by changing different parameters in the ImageDataGenerator.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "341Ilg5McowX"
      },
      "source": [
        "####<B>Visualizing Augmented images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ws3BE91RbFBy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 917
        },
        "outputId": "20394ad9-e840-437a-e8cc-65c36516ac5b"
      },
      "source": [
        "#Creating an iterable for images and labels from the training data\n",
        "images, labels = next(train_generator)\n",
        "\n",
        "#Plotting 16 images from the training data\n",
        "fig, axes = plt.subplots(4, 4, figsize = (16, 8))\n",
        "fig.set_size_inches(16, 16)\n",
        "for (image, label, ax) in zip(images, labels, axes.flatten()):\n",
        "    ax.imshow(image)\n",
        "    if label[1] == 1: \n",
        "        ax.set_title('parasitized')\n",
        "    else:\n",
        "        ax.set_title('uninfected')\n",
        "    ax.axis('off')"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x1152 with 16 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZfCMCaAZdK3o"
      },
      "source": [
        "#####<b>Observations and insights: ____"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BrpS4Azamjs4"
      },
      "source": [
        "####<b>Building the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YVELJI8uihsz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4bc28b18-6e8e-47f8-9044-32588799e77a"
      },
      "source": [
        "\n",
        "# Build the model here\n",
        "\n",
        "model3 = Sequential()\n",
        "model3.add(Conv2D(32, (3,3), input_shape=(64, 64, 3), padding='same'))\n",
        "\n",
        "model3.add(LeakyReLU(alpha=0.1))\n",
        "\n",
        "model3.add(Conv2D(32, (3,3), input_shape=(64, 64, 3), padding='same'))\n",
        "model3.add(LeakyReLU(alpha=0.1))\n",
        "model3.add(MaxPool2D(pool_size=(2, 2)))    \n",
        "model3.add(BatchNormalization())\n",
        "model3.add(Conv2D(32, (3,3), input_shape=(64, 64, 3), padding='same'))\n",
        "model3.add(LeakyReLU(alpha=0.1))\n",
        "model3.add(MaxPool2D(pool_size=(2, 2)))    \n",
        "model3.add(BatchNormalization())\n",
        "model3.add(Flatten())    \n",
        "model3.add(Dense(32))\n",
        "model3.add(LeakyReLU(alpha=0.1))\n",
        "model3.add(Dropout(0.5))\n",
        "model3.add(Dense(2, activation='softmax'))\n",
        "\n",
        "\n",
        "adam = optimizers.Adam(learning_rate=0.001)\n",
        "model2.summary()\n",
        "\n",
        "\n",
        "model3.compile(loss='binary_crossentropy', optimizer=adam, metrics = ['accuracy'])\n",
        "model3.summary()\n"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 64, 64, 32)        896       \n",
            "                                                                 \n",
            " leaky_re_lu (LeakyReLU)     (None, 64, 64, 32)        0         \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 64, 64, 32)        9248      \n",
            "                                                                 \n",
            " leaky_re_lu_1 (LeakyReLU)   (None, 64, 64, 32)        0         \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 32, 32, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 32, 32, 32)       128       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 32, 32, 32)        9248      \n",
            "                                                                 \n",
            " leaky_re_lu_2 (LeakyReLU)   (None, 32, 32, 32)        0         \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 16, 16, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 16, 16, 32)       128       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 8192)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 32)                262176    \n",
            "                                                                 \n",
            " leaky_re_lu_3 (LeakyReLU)   (None, 32)                0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 32)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 2)                 66        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 281,890\n",
            "Trainable params: 281,762\n",
            "Non-trainable params: 128\n",
            "_________________________________________________________________\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 64, 64, 32)        896       \n",
            "                                                                 \n",
            " leaky_re_lu (LeakyReLU)     (None, 64, 64, 32)        0         \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 64, 64, 32)        9248      \n",
            "                                                                 \n",
            " leaky_re_lu_1 (LeakyReLU)   (None, 64, 64, 32)        0         \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 32, 32, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 32, 32, 32)       128       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 32, 32, 32)        9248      \n",
            "                                                                 \n",
            " leaky_re_lu_2 (LeakyReLU)   (None, 32, 32, 32)        0         \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 16, 16, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 16, 16, 32)       128       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 8192)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 32)                262176    \n",
            "                                                                 \n",
            " leaky_re_lu_3 (LeakyReLU)   (None, 32)                0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 32)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 2)                 66        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 281,890\n",
            "Trainable params: 281,762\n",
            "Non-trainable params: 128\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j-HrtTMJnEPl"
      },
      "source": [
        "<b>Using Callbacks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xMWPxLvdihvf"
      },
      "source": [
        "callbacks = [EarlyStopping(monitor='val_loss', patience=2),\n",
        "             ModelCheckpoint('.mdl_wts.hdf5', monitor='val_loss', save_best_only=True)]"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SEeOIRrCnMBh"
      },
      "source": [
        "<b> Fit and Train the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u_2dSGYMihyk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "42258825-8a5f-4236-a476-4cdd7e970316"
      },
      "source": [
        "history3 = model3.fit(train_generator, \n",
        "                                  validation_data=val_generator,\n",
        "                                  batch_size=32,callbacks=callbacks,\n",
        "                                  epochs=20,verbose=1)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "312/312 [==============================] - 273s 872ms/step - loss: 0.6865 - accuracy: 0.6424 - val_loss: 1.4625 - val_accuracy: 0.5102\n",
            "Epoch 2/20\n",
            "312/312 [==============================] - 271s 869ms/step - loss: 0.4101 - accuracy: 0.8351 - val_loss: 0.6696 - val_accuracy: 0.6388\n",
            "Epoch 3/20\n",
            "312/312 [==============================] - 271s 870ms/step - loss: 0.3009 - accuracy: 0.8974 - val_loss: 0.1862 - val_accuracy: 0.9503\n",
            "Epoch 4/20\n",
            "312/312 [==============================] - 272s 873ms/step - loss: 0.2716 - accuracy: 0.9049 - val_loss: 0.1571 - val_accuracy: 0.9409\n",
            "Epoch 5/20\n",
            "312/312 [==============================] - 271s 868ms/step - loss: 0.2515 - accuracy: 0.9126 - val_loss: 0.1570 - val_accuracy: 0.9503\n",
            "Epoch 6/20\n",
            "312/312 [==============================] - 275s 878ms/step - loss: 0.2407 - accuracy: 0.9173 - val_loss: 0.1457 - val_accuracy: 0.9507\n",
            "Epoch 7/20\n",
            "312/312 [==============================] - 273s 875ms/step - loss: 0.2337 - accuracy: 0.9188 - val_loss: 0.4743 - val_accuracy: 0.8716\n",
            "Epoch 8/20\n",
            "312/312 [==============================] - 270s 866ms/step - loss: 0.2319 - accuracy: 0.9174 - val_loss: 0.1486 - val_accuracy: 0.9485\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8EFWVEUKoM1U"
      },
      "source": [
        "####<B>Evaluating the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8twGIoxWnrCb"
      },
      "source": [
        "<b>Plot the train and validation accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HjB4IecBih2K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "f5db2302-8880-49a5-b7f5-5903f9ae784a"
      },
      "source": [
        "# plotting the accuracies\n",
        "plot_accuracy(history3)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i9sBTSvL_Uz-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a1359e1d-7e24-4929-d8e8-7abad0541573"
      },
      "source": [
        "# Evaluating the model on test data\n",
        "accuracy3 = model3.evaluate(test_images, test_labels, verbose=1)\n",
        "print('\\n', 'Test_Accuracy:-', accuracy3[1])\n"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "82/82 [==============================] - 9s 104ms/step - loss: 0.1496 - accuracy: 0.9446\n",
            "\n",
            " Test_Accuracy:- 0.944615364074707\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x_VDQxXwoe6u"
      },
      "source": [
        "<B>Plotting the classification report and confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RxdOpuzJoj8l",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "outputId": "0d25d906-452e-43de-c51c-99972c8f2ab4"
      },
      "source": [
        "# Plotting the heatmap using confusion matrix\n",
        "pred = model3.predict(test_images)\n",
        "pred = np.argmax(pred,axis = 1) \n",
        "y_true = np.argmax(test_labels,axis = 1)\n",
        "\n",
        "#Printing the classification report\n",
        "print(classification_report(y_true,pred))\n",
        "\n",
        "#Plotting the heatmap using confusion matrix\n",
        "cm = confusion_matrix(y_true,pred)\n",
        "plt.figure(figsize=(8,5))\n",
        "sns.heatmap(cm, annot=True,  fmt='.0f', xticklabels=['Uninfected', 'Parasitized'], yticklabels=['Uninfected', 'Parasitized'])\n",
        "plt.ylabel('Actual')\n",
        "plt.xlabel('Predicted')\n",
        "plt.show()\n"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.96      0.95      1300\n",
            "           1       0.96      0.93      0.94      1300\n",
            "\n",
            "    accuracy                           0.94      2600\n",
            "   macro avg       0.94      0.94      0.94      2600\n",
            "weighted avg       0.94      0.94      0.94      2600\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "obzJSEF5ypj-"
      },
      "source": [
        "<b> Now let us try to use a pretrained model like VGG16 and check how it performs on our data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ra-GUCjbLHGg"
      },
      "source": [
        "### **Pre-trained model (VGG16)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5dH2_XWNLyac"
      },
      "source": [
        "#Clearing backend\n",
        "from tensorflow.keras import backend\n",
        "backend.clear_session()\n",
        "#Fixing the seed for random number generators\n",
        "np.random.seed(42)\n",
        "import random\n",
        "random.seed(42)\n",
        "tf.random.set_seed(42)"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bCtAgs1FAxhp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "44c1bda7-af9e-4a25-b7f9-cadbf20f62c8"
      },
      "source": [
        "from tensorflow.keras.applications.vgg16 import VGG16\n",
        "from tensorflow.keras import Model\n",
        "vgg = VGG16(include_top=False, weights='imagenet', input_shape=(64,64,3))\n",
        "vgg.summary()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "58892288/58889256 [==============================] - 1s 0us/step\n",
            "58900480/58889256 [==============================] - 1s 0us/step\n",
            "Model: \"vgg16\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 64, 64, 3)]       0         \n",
            "                                                                 \n",
            " block1_conv1 (Conv2D)       (None, 64, 64, 64)        1792      \n",
            "                                                                 \n",
            " block1_conv2 (Conv2D)       (None, 64, 64, 64)        36928     \n",
            "                                                                 \n",
            " block1_pool (MaxPooling2D)  (None, 32, 32, 64)        0         \n",
            "                                                                 \n",
            " block2_conv1 (Conv2D)       (None, 32, 32, 128)       73856     \n",
            "                                                                 \n",
            " block2_conv2 (Conv2D)       (None, 32, 32, 128)       147584    \n",
            "                                                                 \n",
            " block2_pool (MaxPooling2D)  (None, 16, 16, 128)       0         \n",
            "                                                                 \n",
            " block3_conv1 (Conv2D)       (None, 16, 16, 256)       295168    \n",
            "                                                                 \n",
            " block3_conv2 (Conv2D)       (None, 16, 16, 256)       590080    \n",
            "                                                                 \n",
            " block3_conv3 (Conv2D)       (None, 16, 16, 256)       590080    \n",
            "                                                                 \n",
            " block3_pool (MaxPooling2D)  (None, 8, 8, 256)         0         \n",
            "                                                                 \n",
            " block4_conv1 (Conv2D)       (None, 8, 8, 512)         1180160   \n",
            "                                                                 \n",
            " block4_conv2 (Conv2D)       (None, 8, 8, 512)         2359808   \n",
            "                                                                 \n",
            " block4_conv3 (Conv2D)       (None, 8, 8, 512)         2359808   \n",
            "                                                                 \n",
            " block4_pool (MaxPooling2D)  (None, 4, 4, 512)         0         \n",
            "                                                                 \n",
            " block5_conv1 (Conv2D)       (None, 4, 4, 512)         2359808   \n",
            "                                                                 \n",
            " block5_conv2 (Conv2D)       (None, 4, 4, 512)         2359808   \n",
            "                                                                 \n",
            " block5_conv3 (Conv2D)       (None, 4, 4, 512)         2359808   \n",
            "                                                                 \n",
            " block5_pool (MaxPooling2D)  (None, 2, 2, 512)         0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 14,714,688\n",
            "Trainable params: 14,714,688\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iDQ7RswtA0jf"
      },
      "source": [
        "transfer_layer = vgg.get_layer('block5_pool')\n",
        "vgg.trainable=False\n",
        "\n",
        "# Add classification layers on top of it  \n",
        " \n",
        "x = Flatten()(transfer_layer.output) #Flatten the output from the 3rd block of the VGG16 model\n",
        "x = Dense(256, activation='relu')(x)\n",
        "\n",
        "x = Dense(128, activation='relu')(x)\n",
        "\n",
        "# Similarly add a dense layer with 128 neurons\n",
        "x = Dropout(0.3)(x)\n",
        "\n",
        "# Add a dense layer with 64 neurons\n",
        "\n",
        "x = BatchNormalization()(x)\n",
        "pred = Dense(2, activation='softmax')(x)\n",
        "\n",
        "\n",
        "model4 = Model(vgg.input, pred) #Initializing the model\n"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mhoug92KFWPa"
      },
      "source": [
        "####<b>Compiling the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CdJ8C0L5Lkyt"
      },
      "source": [
        "# Compiling the model \n",
        "\n",
        "model4.compile(loss = \"binary_crossentropy\", optimizer = adam , metrics=['accuracy'])"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rBPzjtxcFLQt"
      },
      "source": [
        "<b> using callbacks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qS3skmQBR4if"
      },
      "source": [
        "# Adding Callbacks to the model\n",
        "\n",
        "callbacks = [EarlyStopping(monitor='val_loss', patience=2),\n",
        "             ModelCheckpoint('.mdl_wts.hdf5', monitor='val_loss', save_best_only=True)]\n"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4_ZeobGmFIOF"
      },
      "source": [
        "<b>Fit and Train the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V6sSx39uL1mS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22b6f53e-e6c7-44c2-8c69-1a7dcd019066"
      },
      "source": [
        "#Fitting the model and running the model for 10 epochs\n",
        "history4 = model4.fit(\n",
        "            train_images, train_labels, \n",
        "            epochs=4,\n",
        "            callbacks=callbacks,\n",
        "            batch_size=64,\n",
        "            validation_split=0.2,\n",
        "            verbose=1\n",
        ")"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/4\n",
            "312/312 [==============================] - 904s 3s/step - loss: 0.1761 - accuracy: 0.9307 - val_loss: 0.1923 - val_accuracy: 0.9299\n",
            "Epoch 2/4\n",
            "312/312 [==============================] - 904s 3s/step - loss: 0.1631 - accuracy: 0.9371 - val_loss: 0.2556 - val_accuracy: 0.9000\n",
            "Epoch 3/4\n",
            "312/312 [==============================] - 904s 3s/step - loss: 0.1544 - accuracy: 0.9401 - val_loss: 0.1982 - val_accuracy: 0.9217\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O0WI9dvDFBaR"
      },
      "source": [
        "<b>Plot the train and validation accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mKmS2XyiMBqb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "5faafcc0-4c10-44de-9a88-41d85f46526f"
      },
      "source": [
        "# plotting the accuracies\n",
        "plot_accuracy(history4)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mb3jUduI0BNs"
      },
      "source": [
        "#####**Observations and insights: _____**\n",
        "\n",
        "*   What can be observed from the validation and train curves?\n",
        "\n",
        "The training accuracy is steadily growing. while the validation is showing abnormal drop initially and gradually improving not up to the mark of Training Accuracy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ykl7xLODEoix"
      },
      "source": [
        "####<b> Evaluating the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RxcgkSoivwWf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a77778d9-7f32-41ff-e887-375f618c08ed"
      },
      "source": [
        "# Evaluating the model on test data\n",
        "accuracy = model4.evaluate(test_images, test_labels, verbose=1)\n",
        "print('\\n', 'Test_Accuracy:-', accuracy[1])"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "82/82 [==============================] - 96s 1s/step - loss: 0.1754 - accuracy: 0.9362\n",
            "\n",
            " Test_Accuracy:- 0.9361538290977478\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r_lr-dUHE77F"
      },
      "source": [
        "<b>Plotting the classification report and confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jns2wf2HMBto",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "outputId": "cf2e9812-ba29-48a2-f7f3-fa4efa93d470"
      },
      "source": [
        "# Plot the confusion matrix and generate a classification report for the model\n",
        "\n",
        "pred = model4.predict(test_images)\n",
        "pred = np.argmax(pred,axis = 1) \n",
        "y_true = np.argmax(test_labels,axis = 1).\n",
        "\n",
        "#Printing the classification report\n",
        "print(classification_report(y_true,pred))\n",
        "\n",
        "#Plotting the heatmap using confusion matrix\n",
        "cm = confusion_matrix(y_true,pred)\n",
        "plt.figure(figsize=(8,5))\n",
        "sns.heatmap(cm, annot=True,  fmt='.0f', xticklabels=['Uninfected', 'Parasitized'], yticklabels=['Uninfected', 'Parasitized'])\n",
        "plt.ylabel('Actual')\n",
        "plt.xlabel('Predicted')\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.92      0.94      1300\n",
            "           1       0.92      0.95      0.94      1300\n",
            "\n",
            "    accuracy                           0.94      2600\n",
            "   macro avg       0.94      0.94      0.94      2600\n",
            "weighted avg       0.94      0.94      0.94      2600\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MEZPA_mN0tUo"
      },
      "source": [
        "####<b>Think about it:</b>\n",
        "*  What observations and insights can be drawn from the confusion matrix and classification report?\n",
        "*  Choosw the model with the best accuracy scores from all the above models and save it as a final model.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Smzi8y5AEYIi"
      },
      "source": [
        "###<b> Observations and Conclusions drawn from the final model: _____\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yo6IpgOZ1n-g"
      },
      "source": [
        "**Improvements that can be done:**<br>\n",
        "\n",
        "\n",
        "*  Can the model performance be improved using other pre-trained models or different CNN architecture?\n",
        "*  You can try to build a model using these HSV images and compare them with your other models."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vhWMt7jKdTlT"
      },
      "source": [
        "###<b>Insights"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mJB6yf2EczFk"
      },
      "source": [
        "### **Insights**\n",
        "\n",
        "####**Refined insights**:\n",
        "- What are the most meaningful insights from the data relevant to the problem?\n",
        "\n",
        "####**Comparison of various techniques and their relative performance**:\n",
        "- How do different techniques perform? Which one is performing relatively better? Is there scope to improve the performance further?\n",
        "\n",
        "####**Proposal for the final solution design**:\n",
        "- What model do you propose to be adopted? Why is this the best solution to adopt?"
      ]
    }
  ]
}