VGG16withPytorch_Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "p9K2JhtVJzwq"
   },
   "source": [
    "# Brain Tumor MR Image Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yIVmQvx6J3qg"
   },
   "source": [
    "In this model we have used VGG16 Network as CNN for constructing Brain Image Classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 72
    },
    "colab_type": "code",
    "id": "--NS9MS7NaBN",
    "outputId": "ac6725fd-85c7-4997-d150-6254cd9729e3"
   },
   "outputs": [],
   "source": [
    "# Import resources\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import cv2\n",
    "import imutils\n",
    "import shutil\n",
    "import torch\n",
    "from PIL import Image\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "from torchvision import datasets, models, transforms\n",
    "import torchvision.models as models\n",
    "import torch.utils.model_zoo\n",
    "import seaborn as sns\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7sZHsxBQD9Bu"
   },
   "outputs": [],
   "source": [
    "# shutil.rmtree('./train')\n",
    "# shutil.rmtree('./valid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WFhot8C_KPFT"
   },
   "source": [
    "Import the data from MRI_data.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "w1zldSHDMS7v"
   },
   "source": [
    "Here the data is provided in two folders namely 'Train' and 'Valid' which specifies training and validation set of images. These two folders contain two subfolders namely 'Yes' and 'No' specifying whether the brain images have tumor or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3PSfm8pANebi"
   },
   "outputs": [],
   "source": [
    "!unzip -qq MRI_data.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bGRh4m5LNHF-"
   },
   "source": [
    "Sample Brain MR Image with Tumor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 273
    },
    "colab_type": "code",
    "id": "Ccr9JGjRFIOm",
    "outputId": "430d548b-74fb-4773-87ac-80b40e9b896e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=256x256 at 0x13AE1F990>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image.open('./train/yes/Y102.jpg').resize((256,256))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tCDdXpR_NK1R"
   },
   "source": [
    "Sample Brain MR Image without Tumor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 273
    },
    "colab_type": "code",
    "id": "5O4oYt0CFsKL",
    "outputId": "1214fdc1-fc56-4d88-85f2-e9af12f7f10e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=256x256 at 0x13A4FEE50>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image.open('./valid/no/no 89.jpg').resize((256,256))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2L20saurNNA2"
   },
   "source": [
    "Distribution of Classes in Brain Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 313
    },
    "colab_type": "code",
    "id": "zWlx1C8YMvvk",
    "outputId": "dc87c418-29c6-42d2-da14-f2e5843e2a27"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Classes in Brain Images')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def fcount(path):\n",
    "    #Counts the number of files in a directory\n",
    "    count = 0\n",
    "    for f in os.listdir(path):\n",
    "        if os.path.isfile(os.path.join(path, f)):\n",
    "            count += 1\n",
    "    return count\n",
    "\n",
    "path_tr_no = r\"./train/no\"\n",
    "path_tr_yes = r\"./train/yes\"\n",
    "path_va_no = r\"./valid/no\"\n",
    "path_va_yes = r\"./valid/yes\"\n",
    "\n",
    "tr_no = fcount(path_tr_no)\n",
    "tr_yes = fcount(path_tr_yes)\n",
    "va_no = fcount(path_va_no)\n",
    "va_yes= fcount(path_va_yes)\n",
    "\n",
    "df = pd.DataFrame() \n",
    "cnt_data = {'x':['Train Set No','Train Set Yes' ,'Valid Set No','Valid Set Yes'], \n",
    "            'y':[tr_no,tr_yes,va_no,va_yes],\n",
    "            'z':['no','yes','no','yes']}\n",
    "df = pd.DataFrame(cnt_data) \n",
    "\n",
    "ax=sns.barplot(x='x',y='y',hue='z',data=df)\n",
    "ax.set(xlabel=\"Image Classes\", ylabel = \"Image Count\")\n",
    "ax.set_title(\"Distribution of Classes in Brain Images\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "d66-1NAtNRzg"
   },
   "source": [
    "# Image Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "udhDfiS9NXEA"
   },
   "source": [
    "Step 1: Cropping the Images along the contours and Gaussian Blurring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2A8Ya1M01Q0r"
   },
   "outputs": [],
   "source": [
    "# Cropping MRI Brain Images \n",
    "def load_edge_crop(filename):\n",
    "\n",
    "    images = list()\n",
    "    for fname in os.listdir(filename):\n",
    "        img = cv2.imread(filename + '/' + fname,0)\n",
    "        img = cv2.GaussianBlur(img, (5, 5), 0)\n",
    "\n",
    "        # threshold the image, then perform a series of erosions +\n",
    "        # dilations to remove any small regions of noise\n",
    "        thresh = cv2.threshold(img, 45, 255, cv2.THRESH_BINARY)[1]\n",
    "        thresh = cv2.erode(thresh, None, iterations=2)\n",
    "        thresh = cv2.dilate(thresh, None, iterations=2)\n",
    "\n",
    "        # find contours in thresholded image, then grab the largest one\n",
    "        cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "        cnts = imutils.grab_contours(cnts)\n",
    "        c = max(cnts, key=cv2.contourArea)\n",
    "\n",
    "        # find the extreme points\n",
    "        extLeft = tuple(c[c[:, :, 0].argmin()][0])\n",
    "        extRight = tuple(c[c[:, :, 0].argmax()][0])\n",
    "        extTop = tuple(c[c[:, :, 1].argmin()][0])\n",
    "        extBot = tuple(c[c[:, :, 1].argmax()][0])\n",
    "\n",
    "        new_img = img[extTop[1]:extBot[1], extLeft[0]:extRight[0]].copy()\n",
    "        new_img = cv2.resize(new_img,dsize=(32,32))\n",
    "        images.append(new_img)\n",
    "\n",
    "    shutil.rmtree(filename)\n",
    "    os.mkdir(filename)\n",
    "\n",
    "    for index,img in enumerate(images):\n",
    "        cv2.imwrite(filename + '/' + str(index) + '.jpg',img)\n",
    "\n",
    "load_edge_crop('./train/no')\n",
    "load_edge_crop('./train/yes')\n",
    "load_edge_crop('./valid/no')\n",
    "load_edge_crop('./valid/yes')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qOntFKUFNai3"
   },
   "source": [
    "Step 2: Data transformation such as image rotation, flipping, normalization and conversion of images into tensors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vowMNXWxNed7"
   },
   "outputs": [],
   "source": [
    "data_transforms = {\n",
    "    'train': transforms.Compose([\n",
    "        transforms.RandomRotation(15),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406],\n",
    "                              [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "    'valid': transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], \n",
    "                              [0.229, 0.224, 0.225])\n",
    "    ])\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pC8X0wk0Nupw"
   },
   "source": [
    "Loading the images into dataloaders as trainloaders and testloaders for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JlcurqhbN3SB"
   },
   "outputs": [],
   "source": [
    "data = {x: datasets.ImageFolder(os.path.join(\"\",x),\n",
    "                                          data_transforms[x])\n",
    "                  for x in ['train', 'valid']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rwXIM3_1YeOi"
   },
   "outputs": [],
   "source": [
    "dataloaders = {\n",
    "    'train': torch.utils.data.DataLoader(data['train'], batch_size=16, shuffle=True),\n",
    "    'valid': torch.utils.data.DataLoader(data['valid'], batch_size=4, shuffle=True)\n",
    "} "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uaRwza3oN9eN"
   },
   "source": [
    "Checking the train data loaded in dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "aV5Ma1jL09cW",
    "outputId": "5d49e1be-c592-4d15-a5ac-18802cd7ad90"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[[[-2.1008, -2.0665, -0.6109,  ..., -2.0837, -2.0837, -2.1008],\n",
      "          [-2.1179, -0.6965, -0.9363,  ..., -2.1008, -2.1179, -2.1008],\n",
      "          [-1.1760, -0.0287, -1.7583,  ..., -2.0665, -2.0665, -2.1008],\n",
      "          ...,\n",
      "          [-1.2103, -1.3815, -1.5357,  ..., -1.5185, -1.3302, -1.1932],\n",
      "          [-1.4329, -1.6213, -1.6727,  ..., -1.0904, -0.9192, -1.2445],\n",
      "          [-1.5014, -1.4158, -1.4843,  ..., -1.4329, -1.2617, -1.1589]],\n",
      "\n",
      "         [[-2.0182, -1.9832, -0.4951,  ..., -2.0007, -2.0007, -2.0182],\n",
      "          [-2.0357, -0.5826, -0.8277,  ..., -2.0182, -2.0357, -2.0182],\n",
      "          [-1.0728,  0.1001, -1.6681,  ..., -1.9832, -1.9832, -2.0182],\n",
      "          ...,\n",
      "          [-1.1078, -1.2829, -1.4405,  ..., -1.4230, -1.2304, -1.0903],\n",
      "          [-1.3354, -1.5280, -1.5805,  ..., -0.9853, -0.8102, -1.1429],\n",
      "          [-1.4055, -1.3179, -1.3880,  ..., -1.3354, -1.1604, -1.0553]],\n",
      "\n",
      "         [[-1.7870, -1.7522, -0.2707,  ..., -1.7696, -1.7696, -1.7870],\n",
      "          [-1.8044, -0.3578, -0.6018,  ..., -1.7870, -1.8044, -1.7870],\n",
      "          [-0.8458,  0.3219, -1.4384,  ..., -1.7522, -1.7522, -1.7870],\n",
      "          ...,\n",
      "          [-0.8807, -1.0550, -1.2119,  ..., -1.1944, -1.0027, -0.8633],\n",
      "          [-1.1073, -1.2990, -1.3513,  ..., -0.7587, -0.5844, -0.9156],\n",
      "          [-1.1770, -1.0898, -1.1596,  ..., -1.1073, -0.9330, -0.8284]]],\n",
      "\n",
      "\n",
      "        [[[-2.0494, -2.0494, -2.0665,  ..., -2.0494, -2.0152, -2.0323],\n",
      "          [-2.0665, -2.0837, -1.9980,  ..., -2.0494, -1.9980, -2.0837],\n",
      "          [-2.0665, -2.1008, -2.0152,  ..., -2.0152, -2.0837, -1.9809],\n",
      "          ...,\n",
      "          [-2.0323, -2.0837, -2.1179,  ..., -1.9809, -2.1179, -2.0494],\n",
      "          [-2.0837, -2.0837, -2.0152,  ..., -2.0837, -2.0665, -2.0494],\n",
      "          [-2.0665, -2.1179, -2.0323,  ..., -2.0837, -2.0837, -2.1008]],\n",
      "\n",
      "         [[-1.9657, -1.9657, -1.9832,  ..., -1.9657, -1.9307, -1.9482],\n",
      "          [-1.9832, -2.0007, -1.9132,  ..., -1.9657, -1.9132, -2.0007],\n",
      "          [-1.9832, -2.0182, -1.9307,  ..., -1.9307, -2.0007, -1.8957],\n",
      "          ...,\n",
      "          [-1.9482, -2.0007, -2.0357,  ..., -1.8957, -2.0357, -1.9657],\n",
      "          [-2.0007, -2.0007, -1.9307,  ..., -2.0007, -1.9832, -1.9657],\n",
      "          [-1.9832, -2.0357, -1.9482,  ..., -2.0007, -2.0007, -2.0182]],\n",
      "\n",
      "         [[-1.7347, -1.7347, -1.7522,  ..., -1.7347, -1.6999, -1.7173],\n",
      "          [-1.7522, -1.7696, -1.6824,  ..., -1.7347, -1.6824, -1.7696],\n",
      "          [-1.7522, -1.7870, -1.6999,  ..., -1.6999, -1.7696, -1.6650],\n",
      "          ...,\n",
      "          [-1.7173, -1.7696, -1.8044,  ..., -1.6650, -1.8044, -1.7347],\n",
      "          [-1.7696, -1.7696, -1.6999,  ..., -1.7696, -1.7522, -1.7347],\n",
      "          [-1.7522, -1.8044, -1.7173,  ..., -1.7696, -1.7696, -1.7870]]],\n",
      "\n",
      "\n",
      "        [[[-2.0494, -2.0494, -1.9980,  ..., -1.9980, -2.0837, -2.0152],\n",
      "          [-2.0494, -2.0665, -2.0665,  ..., -1.9980, -1.9809, -2.0665],\n",
      "          [-2.0152, -2.0152, -2.0152,  ..., -2.0494, -2.0152, -2.0494],\n",
      "          ...,\n",
      "          [-2.0152, -2.0152, -2.0152,  ..., -1.1932, -1.9638, -2.0152],\n",
      "          [-2.0837, -1.9980, -2.0152,  ..., -2.0152, -1.9980, -2.0665],\n",
      "          [-1.9809, -2.1008, -2.0152,  ..., -2.0152, -2.0494, -1.9809]],\n",
      "\n",
      "         [[-1.9657, -1.9657, -1.9132,  ..., -1.9132, -2.0007, -1.9307],\n",
      "          [-1.9657, -1.9832, -1.9832,  ..., -1.9132, -1.8957, -1.9832],\n",
      "          [-1.9307, -1.9307, -1.9307,  ..., -1.9657, -1.9307, -1.9657],\n",
      "          ...,\n",
      "          [-1.9307, -1.9307, -1.9307,  ..., -1.0903, -1.8782, -1.9307],\n",
      "          [-2.0007, -1.9132, -1.9307,  ..., -1.9307, -1.9132, -1.9832],\n",
      "          [-1.8957, -2.0182, -1.9307,  ..., -1.9307, -1.9657, -1.8957]],\n",
      "\n",
      "         [[-1.7347, -1.7347, -1.6824,  ..., -1.6824, -1.7696, -1.6999],\n",
      "          [-1.7347, -1.7522, -1.7522,  ..., -1.6824, -1.6650, -1.7522],\n",
      "          [-1.6999, -1.6999, -1.6999,  ..., -1.7347, -1.6999, -1.7347],\n",
      "          ...,\n",
      "          [-1.6999, -1.6999, -1.6999,  ..., -0.8633, -1.6476, -1.6999],\n",
      "          [-1.7696, -1.6824, -1.6999,  ..., -1.6999, -1.6824, -1.7522],\n",
      "          [-1.6650, -1.7870, -1.6999,  ..., -1.6999, -1.7347, -1.6650]]],\n",
      "\n",
      "\n",
      "        [[[-1.0733,  0.7077,  2.2489,  ...,  2.2489,  2.2318,  2.2489],\n",
      "          [ 1.9749,  0.6049,  2.1804,  ...,  2.2489,  2.2489,  2.1975],\n",
      "          [ 2.2489,  2.2318,  2.2318,  ...,  2.1804,  2.2489,  2.2489],\n",
      "          ...,\n",
      "          [ 2.2489,  2.2318,  2.2147,  ...,  2.2489,  2.2318,  2.2489],\n",
      "          [ 2.2318,  2.2489,  2.2489,  ...,  2.2489,  2.2147,  2.2489],\n",
      "          [ 2.2489,  2.2318,  2.2318,  ...,  2.2489,  2.2489,  2.2489]],\n",
      "\n",
      "         [[-0.9678,  0.8529,  2.4286,  ...,  2.4286,  2.4111,  2.4286],\n",
      "          [ 2.1485,  0.7479,  2.3585,  ...,  2.4286,  2.4286,  2.3761],\n",
      "          [ 2.4286,  2.4111,  2.4111,  ...,  2.3585,  2.4286,  2.4286],\n",
      "          ...,\n",
      "          [ 2.4286,  2.4111,  2.3936,  ...,  2.4286,  2.4111,  2.4286],\n",
      "          [ 2.4111,  2.4286,  2.4286,  ...,  2.4286,  2.3936,  2.4286],\n",
      "          [ 2.4286,  2.4111,  2.4111,  ...,  2.4286,  2.4286,  2.4286]],\n",
      "\n",
      "         [[-0.7413,  1.0714,  2.6400,  ...,  2.6400,  2.6226,  2.6400],\n",
      "          [ 2.3611,  0.9668,  2.5703,  ...,  2.6400,  2.6400,  2.5877],\n",
      "          [ 2.6400,  2.6226,  2.6226,  ...,  2.5703,  2.6400,  2.6400],\n",
      "          ...,\n",
      "          [ 2.6400,  2.6226,  2.6051,  ...,  2.6400,  2.6226,  2.6400],\n",
      "          [ 2.6226,  2.6400,  2.6400,  ...,  2.6400,  2.6051,  2.6400],\n",
      "          [ 2.6400,  2.6226,  2.6226,  ...,  2.6400,  2.6400,  2.6400]]]])\n",
      "tensor([1, 1, 1, 1])\n"
     ]
    }
   ],
   "source": [
    "for data, targets in dataloaders['valid']:\n",
    "    print(data)\n",
    "    print(targets)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "14kD-NDeOB0I"
   },
   "source": [
    "# Defining the VGG16 Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 104,
     "referenced_widgets": [
      "ff7c362c1d944584bf1de9e12b27b866",
      "6526eb95b6af47b88eed400fa5995417",
      "a24c04d9df9344ba9bf65456706ab3e8",
      "6e802702873942ebae2cd652642e59b7",
      "406deaf67155471f9245325d96691dc5",
      "84b6b408dbc44872b12a8660f3aaa2f6",
      "ff7d45d2f30941eb8aa22ed317c67164",
      "3b15fedaf33d49b9bd671cf59589146e"
     ]
    },
    "colab_type": "code",
    "id": "Yvunmm5oNekr",
    "outputId": "93598cee-8d02-484e-87f1-531a471ef780"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/vgg16-397923af.pth\" to /Users/Pranjal/.cache/torch/checkpoints/vgg16-397923af.pth\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cf32574868c64cb9988a36db2bb90889",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=553433881), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "model = models.vgg16(pretrained=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "otvbmqLwOHBZ"
   },
   "source": [
    "Setting the parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Vpv4EjsnNeoP"
   },
   "outputs": [],
   "source": [
    "for param in model.parameters():\n",
    "    param.requires_grad = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iEIGrVNgOMcn"
   },
   "source": [
    "Constructing the model with VGG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dA1EvOJMNwz4"
   },
   "outputs": [],
   "source": [
    "model.classifier[6] = nn.Sequential(\n",
    "                      nn.Linear(4096, 256), \n",
    "                      nn.ReLU(), \n",
    "                      nn.Dropout(0.4),\n",
    "                      nn.Linear(256, 2),                   \n",
    "                      nn.LogSoftmax(dim=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tMcjvLmXOShP"
   },
   "source": [
    "Summary of Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 277
    },
    "colab_type": "code",
    "id": "_4hmjlGuORhG",
    "outputId": "377ada3e-8aa0-4607-ad0b-17698a270307"
   },
   "outputs": [],
   "source": [
    "model.classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "trtBxBsROVix"
   },
   "source": [
    "Declaring the parameters for model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "id": "Ec9s1ypGNw4M",
    "outputId": "860203e8-ef77-4e64-ebd2-9f0df95de38f"
   },
   "outputs": [],
   "source": [
    "total_params = sum(p.numel() for p in model.parameters())\n",
    "print(f'{total_params:,} total parameters.')\n",
    "total_trainable_params = sum(\n",
    "    p.numel() for p in model.parameters() if p.requires_grad)\n",
    "print(f'{total_trainable_params:,} training parameters.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3VYYdxTWOaO4"
   },
   "source": [
    "Specifying Loss and optimizer Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "23VIkNBwNw8w"
   },
   "outputs": [],
   "source": [
    "criterion = nn.NLLLoss()\n",
    "optimizer = optim.Adam(model.parameters())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "N-lTJhP_OgI9"
   },
   "source": [
    "Training the model with brain train images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6LwPeuOYNw_-"
   },
   "outputs": [],
   "source": [
    "for epoch in range(50):\n",
    "  for data, targets in dataloaders['train']:\n",
    "    # Generate predictions\n",
    "    out = model(data)\n",
    "    # Calculate loss\n",
    "    loss = criterion(out, targets)\n",
    "    # Backpropagation\n",
    "    loss.backward()\n",
    "    # Update model parameters\n",
    "    optimizer.step()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "G9rk-RJSOk3a"
   },
   "source": [
    "Testing the model on test data and calculating accuarcy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "id": "Ze4R94lO9IWv",
    "outputId": "4d993fb1-5732-4200-c826-c0466dba0103"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set: Average loss: 0.0680\n",
      "Accuracy: 29/51 (57%)\n"
     ]
    }
   ],
   "source": [
    "val_loss=0\n",
    "correct=0\n",
    "for data, targets in dataloaders['valid']:\n",
    "  # Generate predictions \n",
    "  out = model(data)\n",
    "  # Calculate loss\n",
    "  loss = criterion(out, targets)\n",
    "  val_loss += loss\n",
    "  val_loss = val_loss / len(dataloaders['train'])\n",
    "\n",
    "  testloader=dataloaders['valid']\n",
    "  pred = out.max(1, keepdim=True)[1] \n",
    "  correct += pred.eq(targets.view_as(pred)).sum().item()\n",
    "  accuracy=100. * correct / len(testloader.dataset)\n",
    "  #print(\"Accuracy:\",accuracy)\n",
    "print('Test set: Average loss: {:.4f}\\nAccuracy: {}/{} ({:.0f}%)'.format(\n",
    "        val_loss, correct, len(testloader.dataset),\n",
    "        accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cxHs5BiWZujx"
   },
   "source": [
    "Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GnGl-p3LYSwC"
   },
   "outputs": [],
   "source": [
    "# printing accuracy and loss for each value in train set\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "acc_train=[]\n",
    "loss_train=[]\n",
    "pred_train=[]\n",
    "trainloader=dataloaders['train']\n",
    "model.eval()\n",
    "test_loss = 0\n",
    "correct = 0\n",
    "with torch.no_grad():\n",
    "    for data, target in (trainloader):\n",
    "        data, target = data.to(device), target.to(device)\n",
    "        output = model(data)\n",
    "        test_loss = criterion(output, target).item()\n",
    "        pred = output.max(1, keepdim=True)[1] \n",
    "        pred_train.append(pred)\n",
    "        correct += pred.eq(target.view_as(pred)).sum().item()\n",
    "        accuracy=100. * correct / len(trainloader.dataset)\n",
    "        acc_train.append(accuracy)\n",
    "        loss_train.append(test_loss)\n",
    "epoch_count=range(1,14)\n",
    "\n",
    "# printing accuracy and loss for each value in validation set\n",
    "acc_valid=[]\n",
    "loss_valid=[]\n",
    "pred_valid=[]\n",
    "testloader=dataloaders['valid']\n",
    "model.eval()\n",
    "test_loss = 0\n",
    "correct = 0\n",
    "with torch.no_grad():\n",
    "    for data, target in (testloader):\n",
    "        data, target = data.to(device), target.to(device)\n",
    "        output = model(data)\n",
    "\n",
    "        test_loss = criterion(output, target).item()\n",
    "        pred = output.max(1, keepdim=True)[1] \n",
    "        pred_valid.append(pred)\n",
    "        correct += pred.eq(target.view_as(pred)).sum().item()\n",
    "        accuracy=100. * correct / len(testloader.dataset)\n",
    "        acc_valid.append(accuracy)\n",
    "        loss_valid.append(test_loss)\n",
    "epoch_count2=range(1,14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "O7uc1HM0ysM6"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "a6By6cXWZzqZ"
   },
   "source": [
    "Accuracy Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 299
    },
    "colab_type": "code",
    "id": "jbSm4A5aYmAq",
    "outputId": "3b96864f-2872-4919-ef35-57e5b6d4d4c2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f4010e7eb70>"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(epoch_count, acc_train, label='Train Set')\n",
    "plt.title(\"Model Accuracy Comparison\")\n",
    "plt.plot(epoch_count2, acc_valid, label='Valid Set')\n",
    "plt.legend(loc='lower right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iylCgcYYZ3sO"
   },
   "source": [
    "Loss Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 299
    },
    "colab_type": "code",
    "id": "drmZ6vIzYqMS",
    "outputId": "3b9ea316-075f-49eb-ce9f-5989fcb8656c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Model Loss Comparison')"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(epoch_count, loss_train, label='Train Set')\n",
    "plt.plot(epoch_count2, loss_valid, label='Valid Set')\n",
    "plt.legend(loc='best')\n",
    "plt.title(\"Model Loss Comparison\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RyxHWdRiZ7O-"
   },
   "source": [
    "Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "colab_type": "code",
    "id": "ruglyMc7Rz_-",
    "outputId": "cd418e78-7c24-421c-b660-f15af93dba47"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "classes=['no', 'yes']\n",
    "nb_classes = 2   \n",
    "confusion_matrix = torch.zeros(nb_classes, nb_classes)\n",
    "\n",
    "with torch.no_grad():\n",
    "    for i, (inputs, target) in enumerate(dataloaders['valid']):\n",
    "\n",
    "        inputs = inputs.to(device)\n",
    "        target = target.to(device)\n",
    "\n",
    "        outputs = model(inputs)\n",
    "        _, preds = torch.max(outputs, 1)\n",
    "\n",
    "        for t, p in zip(target.view(-1), preds.view(-1)):\n",
    "                confusion_matrix[t.long(), p.long()] += 1\n",
    "\n",
    "num_classes = 2\n",
    "class_names = classes                \n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(confusion_matrix, interpolation='nearest', cmap=plt.cm.Blues)\n",
    "\n",
    "tick_marks = np.arange(num_classes)\n",
    "classNames = class_names\n",
    "\n",
    "thresh = confusion_matrix.max() / 2.\n",
    "for i in range(confusion_matrix.shape[0]):\n",
    "    for j in range(confusion_matrix.shape[1]):\n",
    "        plt.text(j, i, format(confusion_matrix[i, j]),\n",
    "                ha=\"center\", va=\"center\",\n",
    "                color=\"white\" if  confusion_matrix[i, j] == 0 or confusion_matrix[i, j] > thresh else \"black\") \n",
    "plt.tight_layout()\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  }
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