Brain_Tumer_Prediction_.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of Brain Tumer Prediction (Day 1)",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ridhap/Brain_Tumer_Prediction/blob/main/Brain_Tumer_Prediction_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p1WaCtBHBJP-"
      },
      "source": [
        "# to ignore the warnings \n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kEUskv1fBIxI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "678c917f-bfea-4db1-9546-1083420a3af7"
      },
      "source": [
        "# get the data from \n",
        "\n",
        "!wget https://www.dropbox.com/s/jztol5j7hvm2w96/brain_tumor%20data%20set.zip\n",
        "\n",
        "# unzip the data\n",
        "!unzip \"/content/brain_tumor data set.zip\"\n",
        "!rm \"/content/brain_tumor data set.zip\""
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-08-11 15:12:34--  https://www.dropbox.com/s/jztol5j7hvm2w96/brain_tumor%20data%20set.zip\n",
            "Resolving www.dropbox.com (www.dropbox.com)... 162.125.2.18, 2620:100:6017:18::a27d:212\n",
            "Connecting to www.dropbox.com (www.dropbox.com)|162.125.2.18|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: /s/raw/jztol5j7hvm2w96/brain_tumor%20data%20set.zip [following]\n",
            "--2021-08-11 15:12:35--  https://www.dropbox.com/s/raw/jztol5j7hvm2w96/brain_tumor%20data%20set.zip\n",
            "Reusing existing connection to www.dropbox.com:443.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://uce9e1d3bfaaa4ab457b5538d237.dl.dropboxusercontent.com/cd/0/inline/BUAbg-4vwK0m3fMeySjs-sUWNPqCj4dInxz8o4D18uv_9vPALkF0dms0viIWwyJLE9D0P3ca3syxJ1VxsLuNScYCrG0V3dpAu2UPMX4ybXe8ritqsD2Kuu_tjiqz9Vw0mg4v1M0iFdI953n_Rwimn4pD/file# [following]\n",
            "--2021-08-11 15:12:35--  https://uce9e1d3bfaaa4ab457b5538d237.dl.dropboxusercontent.com/cd/0/inline/BUAbg-4vwK0m3fMeySjs-sUWNPqCj4dInxz8o4D18uv_9vPALkF0dms0viIWwyJLE9D0P3ca3syxJ1VxsLuNScYCrG0V3dpAu2UPMX4ybXe8ritqsD2Kuu_tjiqz9Vw0mg4v1M0iFdI953n_Rwimn4pD/file\n",
            "Resolving uce9e1d3bfaaa4ab457b5538d237.dl.dropboxusercontent.com (uce9e1d3bfaaa4ab457b5538d237.dl.dropboxusercontent.com)... 162.125.64.15, 2620:100:6017:15::a27d:20f\n",
            "Connecting to uce9e1d3bfaaa4ab457b5538d237.dl.dropboxusercontent.com (uce9e1d3bfaaa4ab457b5538d237.dl.dropboxusercontent.com)|162.125.64.15|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: /cd/0/inline2/BUDrc7eCAFB23R-og5YK0E-aW08JC7kTn7hwsWN7W0pqtIsiFIYf-Kyp4hhts6gZhgProkleLMBeGXiO7-gE0bUeltPsp4izyRNeNQzPWNweycRgMlYpEAcyVslm-YOPHMSWvLydtPlMyfb_8A4iCTaze2aJp89tQ-qMGmk-959N5MB1zcyVaJ6pwMdlTsaVVYoPEr1jJok3iZJ4yH71F5xcNykbHRfI3HJdamc2T_6977h1jzoSDNp-iC2UI9tflRsIYl969HvSuT7wBMckZ94lUmpMnNdDova4VSI7KgcqlW8JvcWlYlDSDX_RNz01yRN3r-QpgmkFob0HGY-uk2MZ35juSvzTQ3EZiB2mhxYGAmMPWScckQ_4m0NTDnCv3Jo/file [following]\n",
            "--2021-08-11 15:12:35--  https://uce9e1d3bfaaa4ab457b5538d237.dl.dropboxusercontent.com/cd/0/inline2/BUDrc7eCAFB23R-og5YK0E-aW08JC7kTn7hwsWN7W0pqtIsiFIYf-Kyp4hhts6gZhgProkleLMBeGXiO7-gE0bUeltPsp4izyRNeNQzPWNweycRgMlYpEAcyVslm-YOPHMSWvLydtPlMyfb_8A4iCTaze2aJp89tQ-qMGmk-959N5MB1zcyVaJ6pwMdlTsaVVYoPEr1jJok3iZJ4yH71F5xcNykbHRfI3HJdamc2T_6977h1jzoSDNp-iC2UI9tflRsIYl969HvSuT7wBMckZ94lUmpMnNdDova4VSI7KgcqlW8JvcWlYlDSDX_RNz01yRN3r-QpgmkFob0HGY-uk2MZ35juSvzTQ3EZiB2mhxYGAmMPWScckQ_4m0NTDnCv3Jo/file\n",
            "Reusing existing connection to uce9e1d3bfaaa4ab457b5538d237.dl.dropboxusercontent.com:443.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 112034712 (107M) [application/zip]\n",
            "Saving to: ‘brain_tumor data set.zip’\n",
            "\n",
            "brain_tumor data se 100%[===================>] 106.84M  21.6MB/s    in 6.3s    \n",
            "\n",
            "2021-08-11 15:12:42 (16.9 MB/s) - ‘brain_tumor data set.zip’ saved [112034712/112034712]\n",
            "\n",
            "Archive:  /content/brain_tumor data set.zip\n",
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            " extracting: Brain Tumor Data Set/Healthey/Not Cancer  (550).jpg  \n",
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            " extracting: Brain Tumor Data Set/Healthey/Not Cancer  (565).jpg  \n",
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            " extracting: Brain Tumor Data Set/Healthey/Not Cancer  (616).jpg  \n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "avs4zYYFljw0"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import glob\n",
        "import os \n",
        "import shutil\n",
        "import math\n",
        "import imutils\n",
        "import cv2\n"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eo_YQh8VlPUM",
        "outputId": "cfbc0276-0a31-43ec-ba98-16fdc5416fa6"
      },
      "source": [
        "# count the number of images in the respective classes \n",
        "\n",
        "ROOT_DIR = \"/content/Brain Tumor Data Set\"\n",
        "\n",
        "num_of_images = {}\n",
        "\n",
        "for dir in os.listdir(ROOT_DIR):\n",
        "  num_of_images[dir] = len(os.listdir(os.path.join(ROOT_DIR, dir)))\n",
        "\n",
        "num_of_images"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Brain Tumor': 2513, 'Healthey': 2087}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "26bwrhRPul_y"
      },
      "source": [
        "###we will split our data such that we have \n",
        "    * 70% for Train Data \n",
        "    * 15% for Validation \n",
        "    * 15% for Testing "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "udVaH7ZEmKE-"
      },
      "source": [
        "# we create a training folder \n",
        "\n",
        "if not os.path.exists(\"./train\"):\n",
        "  os.mkdir(\"./train\")\n",
        "\n",
        "  for dir in os.listdir(ROOT_DIR):\n",
        "    os.makedirs(\"./train/\"+dir)\n",
        "    for img in np.random.choice(a=os.listdir(os.path.join(ROOT_DIR, dir) ) , \n",
        "                                size= (math.floor(70/100*num_of_images[dir])-5) , replace=False ):\n",
        "      \n",
        "      O = os.path.join(ROOT_DIR,dir,img)\n",
        "      D = os.path.join('./train', dir)\n",
        "      shutil.copy(O,D)\n",
        "      os.remove(O)\n",
        "else:\n",
        "  print(\"Train Folder exsist\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_eQ3H_r7yc6a"
      },
      "source": [
        "# we create a Validation folder \n",
        "\n",
        "if not os.path.exists(\"./val\"):\n",
        "  os.mkdir(\"./val\")\n",
        "\n",
        "  for dir in os.listdir(ROOT_DIR):\n",
        "    os.makedirs(\"./val/\"+dir)\n",
        "    for img in np.random.choice(a=os.listdir(os.path.join(ROOT_DIR, dir) ) , \n",
        "                                size= (math.floor(15/100*num_of_images[dir])-5) , replace=False ):\n",
        "      \n",
        "      O = os.path.join(ROOT_DIR,dir,img)\n",
        "      D = os.path.join('./val', dir)\n",
        "      shutil.copy(O,D)\n",
        "      os.remove(O)\n",
        "else:\n",
        "  print(\"Train Folder exsist\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P7l47nEWz_s3"
      },
      "source": [
        "# we create a test folder \n",
        "\n",
        "if not os.path.exists(\"./test\"):\n",
        "  os.mkdir(\"./test\")\n",
        "\n",
        "  for dir in os.listdir(ROOT_DIR):\n",
        "    os.makedirs(\"./test/\"+dir)\n",
        "    for img in np.random.choice(a=os.listdir(os.path.join(ROOT_DIR, dir) ) , \n",
        "                                size= (math.floor(15/100*num_of_images[dir])-5) , replace=False ):\n",
        "      \n",
        "      O = os.path.join(ROOT_DIR,dir,img)\n",
        "      D = os.path.join('./test', dir)\n",
        "      shutil.copy(O,D)\n",
        "      os.remove(O)\n",
        "else:\n",
        "  print(\"Train Folder exsist\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p4pjfje91k7D"
      },
      "source": [
        "### WE have now sucessfully devided our data \n",
        "\n",
        "## Lets now build our model "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SFgvuo9q1S9t"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.layers import Dense,Conv2D, Flatten, MaxPool2D, Dropout, BatchNormalization, MaxPooling2D, GlobalAvgPool2D\n",
        "from keras.models import Sequential\n",
        "from keras.preprocessing import image\n",
        "import keras"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZGDgyWUl13Iz"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eCx34xTN_Nic",
        "outputId": "c7340935-8a83-4073-8dfc-05a1edb33207"
      },
      "source": [
        "# Cnn model \n",
        "\n",
        "model = Sequential() \n",
        "model.add(Conv2D(filters=16, kernel_size= (3,3), activation= 'relu', input_shape=(224,224,3)) )\n",
        "\n",
        "model.add(Conv2D(filters=32, kernel_size=(3,3), activation='relu' ))\n",
        "model.add(MaxPool2D(pool_size=(2,2)))\n",
        "\n",
        "\n",
        "model.add(Conv2D(filters=64, kernel_size=(3,3), activation='relu' ))\n",
        "model.add(MaxPool2D(pool_size=(2,2)))\n",
        "\n",
        "\n",
        "model.add(Conv2D(filters=128, kernel_size=(3,3), activation='relu' ))\n",
        "model.add(MaxPool2D(pool_size=(2,2)))\n",
        "\n",
        "model.add(Dropout(rate=0.25))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(units=64, activation='relu'))\n",
        "model.add(Dropout(rate=0.25))\n",
        "model.add(Dense(units=1, activation='sigmoid'))\n",
        "\n",
        "model.compile(loss= keras.losses.binary_crossentropy, optimizer='adam', metrics=['accuracy'] )\n",
        " \n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 222, 222, 16)      448       \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 220, 220, 32)      4640      \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 110, 110, 32)      0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 108, 108, 64)      18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 54, 54, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 52, 52, 128)       73856     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 26, 26, 128)       0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 26, 26, 128)       0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 86528)             0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 64)                5537856   \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 5,635,361\n",
            "Trainable params: 5,635,361\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D20xZkfm4aQj"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jQTLkfM85THf"
      },
      "source": [
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']  )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YmaXaPqI5rkl"
      },
      "source": [
        "## Preparing our data using data generator "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V5Ct0i-35kgH"
      },
      "source": [
        "#train_datagen = image.ImageDataGenerator(preprocessing_function= , )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rlOcG5n-6Jmx"
      },
      "source": [
        "train_datagen = image.ImageDataGenerator(\n",
        "     zoom_range = 0.2, shear_range = 0.2 , rescale = 1./255 , horizontal_flip=True\n",
        ")\n",
        "\n",
        "val_datagen = image.ImageDataGenerator(rescale = 1./255)\n",
        "\n",
        "test_datagen = image.ImageDataGenerator(rescale = 1./255)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ze4QLGT-6RfD",
        "outputId": "36cd8d0a-1bba-4955-b0a5-93936a591e3b"
      },
      "source": [
        "train_data = train_datagen.flow_from_directory(directory= \"/content/train\", target_size=(224,224), batch_size=32,\n",
        "                                  class_mode = 'binary', \n",
        "                                  )\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 3209 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dxd6UwmpHEVm",
        "outputId": "fd633e85-5e60-43f7-b0c5-864fa5c72df5"
      },
      "source": [
        "train_data.class_indices"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Brain Tumor': 0, 'Healthey': 1}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QtdHpvg76czX",
        "outputId": "37c57d59-20ad-4243-80c9-d07a5fb5515f"
      },
      "source": [
        "val_data = val_datagen.flow_from_directory(directory= \"/content/val\", target_size=(224,224), batch_size=32,\n",
        "                                  class_mode = 'binary'\n",
        "                                  )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 679 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E0eGbsVW6oY8",
        "outputId": "aed2cd39-8166-4cae-dd8e-6ef1df4ff56e"
      },
      "source": [
        "test_data = test_datagen.flow_from_directory(directory= \"/content/test\", target_size=(224,224), batch_size=32,\n",
        "                                  class_mode = 'binary'\n",
        "                                  )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 679 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TEQqFPto4viO"
      },
      "source": [
        "## having early stopping and model check point \n",
        "\n",
        "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
        "\n",
        "# early stopping\n",
        "es = EarlyStopping(monitor='val_accuracy', min_delta= 0.01 , patience= 5, verbose= 1, mode='auto')\n",
        "\n",
        "# model check point\n",
        "mc = ModelCheckpoint(filepath=\"best_model.h5\", monitor= 'val_accuracy', verbose= 1, save_best_only= True, mode = 'auto')\n",
        "\n",
        "# puting call back in a list \n",
        "call_back = [es, mc]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BO0XKcIe63SR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2134027b-8caa-4037-a710-a0b9344176ab"
      },
      "source": [
        "hist = model.fit_generator(generator=train_data , \n",
        "                              steps_per_epoch= 8 , \n",
        "                              epochs= 30 , verbose= 1, \n",
        "                              validation_data= val_data , \n",
        "                              validation_steps= 16,\n",
        "                              callbacks = call_back )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:1915: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "8/8 [==============================] - 50s 741ms/step - loss: 0.7908 - accuracy: 0.4945 - val_loss: 0.6874 - val_accuracy: 0.5703\n",
            "\n",
            "Epoch 00001: val_accuracy improved from -inf to 0.57031, saving model to best_model.h5\n",
            "Epoch 2/30\n",
            "8/8 [==============================] - 5s 704ms/step - loss: 0.6853 - accuracy: 0.5577 - val_loss: 0.6611 - val_accuracy: 0.5684\n",
            "\n",
            "Epoch 00002: val_accuracy did not improve from 0.57031\n",
            "Epoch 3/30\n",
            "8/8 [==============================] - 5s 654ms/step - loss: 0.6746 - accuracy: 0.5340 - val_loss: 0.6477 - val_accuracy: 0.6934\n",
            "\n",
            "Epoch 00003: val_accuracy improved from 0.57031 to 0.69336, saving model to best_model.h5\n",
            "Epoch 4/30\n",
            "8/8 [==============================] - 5s 658ms/step - loss: 0.6574 - accuracy: 0.6418 - val_loss: 0.6170 - val_accuracy: 0.7031\n",
            "\n",
            "Epoch 00004: val_accuracy improved from 0.69336 to 0.70312, saving model to best_model.h5\n",
            "Epoch 5/30\n",
            "8/8 [==============================] - 5s 637ms/step - loss: 0.6937 - accuracy: 0.5711 - val_loss: 0.6289 - val_accuracy: 0.7168\n",
            "\n",
            "Epoch 00005: val_accuracy improved from 0.70312 to 0.71680, saving model to best_model.h5\n",
            "Epoch 6/30\n",
            "8/8 [==============================] - 5s 668ms/step - loss: 0.6232 - accuracy: 0.7639 - val_loss: 0.5798 - val_accuracy: 0.6875\n",
            "\n",
            "Epoch 00006: val_accuracy did not improve from 0.71680\n",
            "Epoch 7/30\n",
            "8/8 [==============================] - 5s 647ms/step - loss: 0.6626 - accuracy: 0.6446 - val_loss: 0.6407 - val_accuracy: 0.6016\n",
            "\n",
            "Epoch 00007: val_accuracy did not improve from 0.71680\n",
            "Epoch 8/30\n",
            "8/8 [==============================] - 5s 684ms/step - loss: 0.6459 - accuracy: 0.6163 - val_loss: 0.6150 - val_accuracy: 0.6367\n",
            "\n",
            "Epoch 00008: val_accuracy did not improve from 0.71680\n",
            "Epoch 9/30\n",
            "8/8 [==============================] - 5s 703ms/step - loss: 0.6170 - accuracy: 0.6467 - val_loss: 0.5411 - val_accuracy: 0.7383\n",
            "\n",
            "Epoch 00009: val_accuracy improved from 0.71680 to 0.73828, saving model to best_model.h5\n",
            "Epoch 10/30\n",
            "8/8 [==============================] - 5s 702ms/step - loss: 0.6000 - accuracy: 0.6675 - val_loss: 0.5071 - val_accuracy: 0.7617\n",
            "\n",
            "Epoch 00010: val_accuracy improved from 0.73828 to 0.76172, saving model to best_model.h5\n",
            "Epoch 11/30\n",
            "8/8 [==============================] - 5s 661ms/step - loss: 0.5641 - accuracy: 0.7080 - val_loss: 0.4672 - val_accuracy: 0.7988\n",
            "\n",
            "Epoch 00011: val_accuracy improved from 0.76172 to 0.79883, saving model to best_model.h5\n",
            "Epoch 12/30\n",
            "8/8 [==============================] - 5s 659ms/step - loss: 0.5345 - accuracy: 0.7371 - val_loss: 0.4836 - val_accuracy: 0.7930\n",
            "\n",
            "Epoch 00012: val_accuracy did not improve from 0.79883\n",
            "Epoch 13/30\n",
            "8/8 [==============================] - 5s 646ms/step - loss: 0.5361 - accuracy: 0.7391 - val_loss: 0.5025 - val_accuracy: 0.7441\n",
            "\n",
            "Epoch 00013: val_accuracy did not improve from 0.79883\n",
            "Epoch 14/30\n",
            "8/8 [==============================] - 5s 658ms/step - loss: 0.4629 - accuracy: 0.7835 - val_loss: 0.4430 - val_accuracy: 0.8125\n",
            "\n",
            "Epoch 00014: val_accuracy improved from 0.79883 to 0.81250, saving model to best_model.h5\n",
            "Epoch 15/30\n",
            "8/8 [==============================] - 5s 661ms/step - loss: 0.5408 - accuracy: 0.7358 - val_loss: 0.4348 - val_accuracy: 0.8086\n",
            "\n",
            "Epoch 00015: val_accuracy did not improve from 0.81250\n",
            "Epoch 16/30\n",
            "8/8 [==============================] - 5s 665ms/step - loss: 0.5094 - accuracy: 0.7548 - val_loss: 0.4614 - val_accuracy: 0.7793\n",
            "\n",
            "Epoch 00016: val_accuracy did not improve from 0.81250\n",
            "Epoch 17/30\n",
            "8/8 [==============================] - 5s 652ms/step - loss: 0.4870 - accuracy: 0.8096 - val_loss: 0.4467 - val_accuracy: 0.7871\n",
            "\n",
            "Epoch 00017: val_accuracy did not improve from 0.81250\n",
            "Epoch 18/30\n",
            "8/8 [==============================] - 5s 710ms/step - loss: 0.5015 - accuracy: 0.7914 - val_loss: 0.4046 - val_accuracy: 0.8242\n",
            "\n",
            "Epoch 00018: val_accuracy improved from 0.81250 to 0.82422, saving model to best_model.h5\n",
            "Epoch 19/30\n",
            "8/8 [==============================] - 5s 707ms/step - loss: 0.5007 - accuracy: 0.7928 - val_loss: 0.5044 - val_accuracy: 0.7578\n",
            "\n",
            "Epoch 00019: val_accuracy did not improve from 0.82422\n",
            "Epoch 20/30\n",
            "8/8 [==============================] - 5s 702ms/step - loss: 0.4905 - accuracy: 0.7665 - val_loss: 0.4385 - val_accuracy: 0.8379\n",
            "\n",
            "Epoch 00020: val_accuracy improved from 0.82422 to 0.83789, saving model to best_model.h5\n",
            "Epoch 21/30\n",
            "8/8 [==============================] - 5s 707ms/step - loss: 0.4599 - accuracy: 0.8425 - val_loss: 0.4224 - val_accuracy: 0.8184\n",
            "\n",
            "Epoch 00021: val_accuracy did not improve from 0.83789\n",
            "Epoch 22/30\n",
            "8/8 [==============================] - 5s 677ms/step - loss: 0.4615 - accuracy: 0.7725 - val_loss: 0.4333 - val_accuracy: 0.8242\n",
            "\n",
            "Epoch 00022: val_accuracy did not improve from 0.83789\n",
            "Epoch 23/30\n",
            "8/8 [==============================] - 5s 696ms/step - loss: 0.4106 - accuracy: 0.8537 - val_loss: 0.3917 - val_accuracy: 0.8340\n",
            "\n",
            "Epoch 00023: val_accuracy did not improve from 0.83789\n",
            "Epoch 24/30\n",
            "8/8 [==============================] - 5s 704ms/step - loss: 0.4170 - accuracy: 0.8358 - val_loss: 0.4115 - val_accuracy: 0.8320\n",
            "\n",
            "Epoch 00024: val_accuracy did not improve from 0.83789\n",
            "Epoch 25/30\n",
            "8/8 [==============================] - 5s 652ms/step - loss: 0.5643 - accuracy: 0.7439 - val_loss: 0.3787 - val_accuracy: 0.8535\n",
            "\n",
            "Epoch 00025: val_accuracy improved from 0.83789 to 0.85352, saving model to best_model.h5\n",
            "Epoch 26/30\n",
            "8/8 [==============================] - 5s 665ms/step - loss: 0.4659 - accuracy: 0.8059 - val_loss: 0.3738 - val_accuracy: 0.8438\n",
            "\n",
            "Epoch 00026: val_accuracy did not improve from 0.85352\n",
            "Epoch 27/30\n",
            "8/8 [==============================] - 5s 657ms/step - loss: 0.4276 - accuracy: 0.7834 - val_loss: 0.4237 - val_accuracy: 0.8164\n",
            "\n",
            "Epoch 00027: val_accuracy did not improve from 0.85352\n",
            "Epoch 28/30\n",
            "8/8 [==============================] - 5s 659ms/step - loss: 0.4302 - accuracy: 0.8072 - val_loss: 0.3726 - val_accuracy: 0.8418\n",
            "\n",
            "Epoch 00028: val_accuracy did not improve from 0.85352\n",
            "Epoch 29/30\n",
            "8/8 [==============================] - 5s 653ms/step - loss: 0.4011 - accuracy: 0.8240 - val_loss: 0.3256 - val_accuracy: 0.8574\n",
            "\n",
            "Epoch 00029: val_accuracy improved from 0.85352 to 0.85742, saving model to best_model.h5\n",
            "Epoch 30/30\n",
            "8/8 [==============================] - 5s 658ms/step - loss: 0.4299 - accuracy: 0.8093 - val_loss: 0.3669 - val_accuracy: 0.8652\n",
            "\n",
            "Epoch 00030: val_accuracy improved from 0.85742 to 0.86523, saving model to best_model.h5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MvToQlerJSJV"
      },
      "source": [
        "# Loading the best fit model \n",
        "from keras.models import load_model\n",
        "\n",
        "model = load_model(\"/content/best_model.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7wFfgVLFZ5VW"
      },
      "source": [
        "# Model Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4aObwwCP7ZjK",
        "outputId": "bd81b526-36e0-447f-8fcc-e354e7e9a56a"
      },
      "source": [
        "\n",
        "\n",
        "# checking out the accuracy of our model \n",
        "\n",
        "acc = model.evaluate_generator(generator= test_data)[1] \n",
        "\n",
        "print(f\"The accuracy of your model is = {acc*100} %\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:1948: UserWarning: `Model.evaluate_generator` is deprecated and will be removed in a future version. Please use `Model.evaluate`, which supports generators.\n",
            "  warnings.warn('`Model.evaluate_generator` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The accuracy of your model is = 84.97790694236755 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Izk8XB_QFwz"
      },
      "source": [
        "h =  hist.history\n",
        "h.keys()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E0fTfDVYQNci"
      },
      "source": [
        "plt.plot(h['accuracy'])\n",
        "plt.plot(h['val_accuracy'] , c = \"red\")\n",
        "plt.title(\"acc vs v-acc\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ERL-3yvUQRBL"
      },
      "source": [
        "plt.plot(h['loss'])\n",
        "plt.plot(h['val_loss'] , c = \"red\")\n",
        "plt.title(\"loss vs v-loss\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a03IzW6m948Z"
      },
      "source": [
        "# path for the image to see if it predics correct class\n",
        "\n",
        "path = \"/content/val/Healthey/Not Cancer  (1768).jpg\"\n",
        "img = image.load_img(path, target_size=(224,224),  )\n",
        "\n",
        "i = image.img_to_array(img)/255\n",
        "input_arr = np.array([i])\n",
        "input_arr.shape\n",
        "\n",
        "pred = model.predict_classes(input_arr)[0][0] \n",
        "\n",
        "\n",
        "\n",
        "if pred == 0:\n",
        "  print(\"The MRI image is of BRAIN TUMOR\")\n",
        "else:\n",
        "  print(\"The MRI image is of Healthey brain\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kucxn3sNlcqv"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}