{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t6qNdKItY7AX"
      },
      "source": [
        "# Import Statements"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install keras\n",
        "!pip install tensorflow\n",
        "!pip numpy\n",
        "!pip pandas\n",
        "!pip matplotlib.pyplot\n"
      ],
      "metadata": {
        "id": "JDPTBXQL0Lu-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "146bb08f-07d3-4f87-fba2-4c6b6e6d7b40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: keras in /usr/local/lib/python3.10/dist-packages (2.12.0)\n",
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.12.0)\n",
            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.4.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=2.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (23.5.26)\n",
            "Requirement already satisfied: gast<=0.4.0,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.4.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.2.0)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.56.2)\n",
            "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.8.0)\n",
            "Requirement already satisfied: jax>=0.3.15 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.4.13)\n",
            "Requirement already satisfied: keras<2.13,>=2.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.12.0)\n",
            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (16.0.6)\n",
            "Requirement already satisfied: numpy<1.24,>=1.22 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.22.4)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.3.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow) (23.1)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.20.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow) (67.7.2)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.16.0)\n",
            "Requirement already satisfied: tensorboard<2.13,>=2.12 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.12.3)\n",
            "Requirement already satisfied: tensorflow-estimator<2.13,>=2.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.12.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.3.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (4.7.1)\n",
            "Requirement already satisfied: wrapt<1.15,>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.14.1)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.32.0)\n",
            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow) (0.41.0)\n",
            "Requirement already satisfied: ml-dtypes>=0.1.0 in /usr/local/lib/python3.10/dist-packages (from jax>=0.3.15->tensorflow) (0.2.0)\n",
            "Requirement already satisfied: scipy>=1.7 in /usr/local/lib/python3.10/dist-packages (from jax>=0.3.15->tensorflow) (1.10.1)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.13,>=2.12->tensorflow) (2.17.3)\n",
            "Requirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.13,>=2.12->tensorflow) (1.0.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.13,>=2.12->tensorflow) (3.4.4)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.13,>=2.12->tensorflow) (2.27.1)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.13,>=2.12->tensorflow) (0.7.1)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.13,>=2.12->tensorflow) (2.3.6)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow) (5.3.1)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow) (0.3.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow) (4.9)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tensorflow) (1.3.1)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow) (1.26.16)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow) (2023.7.22)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow) (3.4)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.13,>=2.12->tensorflow) (2.1.3)\n",
            "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow) (0.5.0)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tensorflow) (3.2.2)\n",
            "ERROR: unknown command \"numpy\"\n",
            "ERROR: unknown command \"pandas\"\n",
            "ERROR: unknown command \"matplotlib.pyplot\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xccV3KTrUCt4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d6435e2d-0d68-41bf-9b36-d6936c480fb1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "done\n"
          ]
        }
      ],
      "source": [
        "from zipfile import ZipFile\n",
        "file_name =\"/content/brain_tumor_dataset_kaggle.zip\"\n",
        "\n",
        "with ZipFile(file_name,'r') as zip:\n",
        "  zip.extractall()\n",
        "  print(\"done\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CwivZk7AY7AZ"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization\n",
        "from PIL import Image\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "2b3DV2eqY7Ab",
        "outputId": "47acd320-920b-47c9-9189-fc991451bbd5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OneHotEncoder()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneHotEncoder()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "encoder = OneHotEncoder()\n",
        "encoder.fit([[0], [1]])\n",
        "\n",
        "# 0 - Tumor\n",
        "# 1 - Normal"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vDjUc-IDY7Ac"
      },
      "source": [
        "\n",
        "1. data list for storing image data in numpy array form\n",
        "2. paths list for storing paths of all images\n",
        "3. result list for storing one hot encoded form of target class whether normal or tumor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vRl4R1R-Y7Ac"
      },
      "outputs": [],
      "source": [
        "# This cell updates result list for images with tumor\n",
        "\n",
        "data = []\n",
        "paths = []\n",
        "result = []\n",
        "\n",
        "for r, d, f in os.walk(r'../content/brain_tumor_dataset/yes'): #r-read, d-directory, f-file\n",
        "    for file in f:\n",
        "        if '.jpg' in file:\n",
        "            paths.append(os.path.join(r, file))\n",
        "\n",
        "for path in paths:\n",
        "    img = Image.open(path)\n",
        "    img = img.resize((128,128))\n",
        "    img = np.array(img)\n",
        "    if(img.shape == (128,128,3)):\n",
        "        data.append(np.array(img))\n",
        "        result.append(encoder.transform([[0]]).toarray())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uzi2vsLBY7Ac"
      },
      "outputs": [],
      "source": [
        "# This cell updates result list for images without tumor\n",
        "\n",
        "paths = []\n",
        "for r, d, f in os.walk(r\"../content/brain_tumor_dataset/no\"):\n",
        "    for file in f:\n",
        "        if '.jpg' in file:\n",
        "            paths.append(os.path.join(r, file))\n",
        "\n",
        "for path in paths:\n",
        "    img = Image.open(path)\n",
        "    img = img.resize((128,128))\n",
        "    img = np.array(img)\n",
        "    if(img.shape == (128,128,3)):\n",
        "        data.append(np.array(img))\n",
        "        result.append(encoder.transform([[1]]).toarray())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1d91mu8wY7Ac",
        "outputId": "c877722c-ab33-4fec-b2e2-8387fa35fc11"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(139, 128, 128, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "data = np.array(data)\n",
        "data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jRa51-JOY7Ad"
      },
      "outputs": [],
      "source": [
        "result = np.array(result)\n",
        "result = result.reshape(139,2)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Data shape:\", data.shape)\n",
        "print(\"Result shape:\", result.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s6w9s5xffqBx",
        "outputId": "fca5e973-a123-4f12-d221-01de1d90bb07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data shape: (139, 128, 128, 3)\n",
            "Result shape: (139, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FxSJzRgYY7Ad"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test = train_test_split(data, result, test_size=0.1, shuffle=True, random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"y_train shape:\", y_train.shape)\n",
        "print(\"y_test shape:\", y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NDxQgmmQfyYV",
        "outputId": "804bb66f-69d1-4235-ce81-d479e90f3a4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "y_train shape: (125, 2)\n",
            "y_test shape: (14, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hTAcuwXhY7Ad"
      },
      "source": [
        "# Model Building"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qx7sbte_Y7Ad"
      },
      "source": [
        "Batch normalization is a technique for training very deep neural networks that standardizes the inputs to a layer for each mini-batch. This has the effect of stabilizing the learning process and dramatically reducing the number of training epochs required to train deep networks."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M7sjq9_VY7Ad",
        "outputId": "a7e5c66d-ec78-41cb-99fd-0972e0deb8bd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_12 (Conv2D)          (None, 128, 128, 32)      416       \n",
            "                                                                 \n",
            " conv2d_13 (Conv2D)          (None, 128, 128, 32)      4128      \n",
            "                                                                 \n",
            " batch_normalization_4 (Batc  (None, 128, 128, 32)     128       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_4 (MaxPooling  (None, 64, 64, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_6 (Dropout)         (None, 64, 64, 32)        0         \n",
            "                                                                 \n",
            " conv2d_14 (Conv2D)          (None, 64, 64, 64)        8256      \n",
            "                                                                 \n",
            " conv2d_15 (Conv2D)          (None, 64, 64, 64)        16448     \n",
            "                                                                 \n",
            " conv2d_16 (Conv2D)          (None, 64, 64, 64)        16448     \n",
            "                                                                 \n",
            " conv2d_17 (Conv2D)          (None, 64, 64, 64)        16448     \n",
            "                                                                 \n",
            " batch_normalization_5 (Batc  (None, 64, 64, 64)       256       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_5 (MaxPooling  (None, 32, 32, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_7 (Dropout)         (None, 32, 32, 64)        0         \n",
            "                                                                 \n",
            " flatten_2 (Flatten)         (None, 65536)             0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 512)               33554944  \n",
            "                                                                 \n",
            " dropout_8 (Dropout)         (None, 512)               0         \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 2)                 1026      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 33,618,498\n",
            "Trainable params: 33,618,306\n",
            "Non-trainable params: 192\n",
            "_________________________________________________________________\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(32, kernel_size=(2, 2), input_shape=(128, 128, 3), padding = 'Same'))\n",
        "model.add(Conv2D(32, kernel_size=(2, 2),  activation ='relu', padding = 'Same'))\n",
        "\n",
        "\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Conv2D(64, kernel_size = (2,2), activation ='relu', padding = 'Same'))\n",
        "model.add(Conv2D(64, kernel_size = (2,2), activation ='relu', padding = 'Same'))\n",
        "model.add(Conv2D(64, kernel_size = (2,2), activation ='relu', padding = 'Same'))\n",
        "model.add(Conv2D(64, kernel_size = (2,2), activation ='relu', padding = 'Same'))\n",
        "\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2))) # the number of pixels shifts over the input matrix\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(512, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(2, activation='softmax'))\n",
        "\n",
        "model.compile(loss = \"categorical_crossentropy\", optimizer='Adamax')\n",
        "print(model.summary())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ryQr77EAY7Ae",
        "outputId": "189fe7f9-c4c2-4f6d-caf0-bf09ee570c76"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "4/4 [==============================] - 14s 385ms/step - loss: 30.5690 - val_loss: 30.6870\n",
            "Epoch 2/30\n",
            "4/4 [==============================] - 0s 82ms/step - loss: 9.0089 - val_loss: 11.6961\n",
            "Epoch 3/30\n",
            "4/4 [==============================] - 0s 82ms/step - loss: 3.2237 - val_loss: 17.0948\n",
            "Epoch 4/30\n",
            "4/4 [==============================] - 0s 79ms/step - loss: 1.8086 - val_loss: 6.9118\n",
            "Epoch 5/30\n",
            "4/4 [==============================] - 0s 82ms/step - loss: 1.0439 - val_loss: 2.4904\n",
            "Epoch 6/30\n",
            "4/4 [==============================] - 0s 76ms/step - loss: 1.2211 - val_loss: 3.5656\n",
            "Epoch 7/30\n",
            "4/4 [==============================] - 0s 78ms/step - loss: 0.4776 - val_loss: 4.4536\n",
            "Epoch 8/30\n",
            "4/4 [==============================] - 0s 76ms/step - loss: 1.1406 - val_loss: 3.6863\n",
            "Epoch 9/30\n",
            "4/4 [==============================] - 0s 77ms/step - loss: 0.6065 - val_loss: 3.2749\n",
            "Epoch 10/30\n",
            "4/4 [==============================] - 0s 74ms/step - loss: 0.0580 - val_loss: 3.2851\n",
            "Epoch 11/30\n",
            "4/4 [==============================] - 0s 75ms/step - loss: 0.1268 - val_loss: 3.1472\n",
            "Epoch 12/30\n",
            "4/4 [==============================] - 0s 75ms/step - loss: 0.2179 - val_loss: 2.9552\n",
            "Epoch 13/30\n",
            "4/4 [==============================] - 0s 75ms/step - loss: 0.0047 - val_loss: 3.0580\n",
            "Epoch 14/30\n",
            "4/4 [==============================] - 0s 75ms/step - loss: 0.1939 - val_loss: 3.3269\n",
            "Epoch 15/30\n",
            "4/4 [==============================] - 0s 76ms/step - loss: 0.1033 - val_loss: 3.3863\n",
            "Epoch 16/30\n",
            "4/4 [==============================] - 0s 75ms/step - loss: 0.0181 - val_loss: 3.3323\n",
            "Epoch 17/30\n",
            "4/4 [==============================] - 0s 76ms/step - loss: 0.0633 - val_loss: 3.2015\n",
            "Epoch 18/30\n",
            "4/4 [==============================] - 0s 76ms/step - loss: 0.0746 - val_loss: 3.0326\n",
            "Epoch 19/30\n",
            "4/4 [==============================] - 0s 81ms/step - loss: 0.0057 - val_loss: 2.8986\n",
            "Epoch 20/30\n",
            "4/4 [==============================] - 0s 75ms/step - loss: 0.0462 - val_loss: 2.7526\n",
            "Epoch 21/30\n",
            "4/4 [==============================] - 0s 74ms/step - loss: 0.1711 - val_loss: 2.5150\n",
            "Epoch 22/30\n",
            "4/4 [==============================] - 0s 78ms/step - loss: 0.0025 - val_loss: 2.4670\n",
            "Epoch 23/30\n",
            "4/4 [==============================] - 0s 76ms/step - loss: 0.0810 - val_loss: 2.4541\n",
            "Epoch 24/30\n",
            "4/4 [==============================] - 0s 76ms/step - loss: 0.0817 - val_loss: 2.5989\n",
            "Epoch 25/30\n",
            "4/4 [==============================] - 0s 75ms/step - loss: 0.1040 - val_loss: 2.8758\n",
            "Epoch 26/30\n",
            "4/4 [==============================] - 0s 77ms/step - loss: 0.0127 - val_loss: 3.2134\n",
            "Epoch 27/30\n",
            "4/4 [==============================] - 0s 75ms/step - loss: 2.4793e-04 - val_loss: 3.5426\n",
            "Epoch 28/30\n",
            "4/4 [==============================] - 0s 77ms/step - loss: 0.0019 - val_loss: 3.7811\n",
            "Epoch 29/30\n",
            "4/4 [==============================] - 0s 77ms/step - loss: 0.0042 - val_loss: 3.9601\n",
            "Epoch 30/30\n",
            "4/4 [==============================] - 0s 77ms/step - loss: 0.0023 - val_loss: 4.1010\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(x_train, y_train, epochs = 30, batch_size = 40, verbose = 1,validation_data = (x_test, y_test))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nznazd-4Y7Ae"
      },
      "source": [
        "# Plotting Losses"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "6lLTzirhY7Ae",
        "outputId": "657c7ee8-e067-4a39-e779-6a20c1454479"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Test', 'Validation'], loc='upper right')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RB5vCOBZY7Af"
      },
      "source": [
        "# Just Checking the Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ICbaWtWFY7Af"
      },
      "outputs": [],
      "source": [
        "def names(number):\n",
        "    if number==0:\n",
        "        return 'Its a Tumor'\n",
        "    else:\n",
        "        return 'No, Its not a tumor'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "KnDlyxU1Y7Af",
        "outputId": "9e731453-9931-4ea1-d073-7d396c85ffb3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "99.56170320510864% Confidence This Is A No, Its not a tumor\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from matplotlib.pyplot import imshow\n",
        "img = Image.open(r\"../content/brain_tumor_dataset/no/11 no.jpg\")\n",
        "x = np.array(img.resize((128,128)))\n",
        "x = x.reshape(1,128,128,3)\n",
        "res = model.predict_on_batch(x)\n",
        "classification = np.where(res == np.amax(res))[1][0]\n",
        "imshow(img)\n",
        "print(str(res[0][classification]*100) + '% Confidence This Is A ' + names(classification))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "fMb1Kbe0Y7Af",
        "outputId": "77033d14-edff-4d60-b69a-8d578398be78"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "62.54897117614746% Confidence This Is A No, Its not a tumor\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from matplotlib.pyplot import imshow\n",
        "img = Image.open(r\"../content/brain_tumor_dataset/no/18 no.jpg\")\n",
        "x = np.array(img.resize((128,128)))\n",
        "x = x.reshape(1,128,128,3)\n",
        "res = model.predict_on_batch(x)\n",
        "classification = np.where(res == np.amax(res))[1][0]\n",
        "imshow(img)\n",
        "print(str(res[0][classification]*100) + '% Confidence This Is A ' + names(classification))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eIrl8m2vY7Af"
      },
      "source": [
        "# Thats It !!"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "JCoFHSIzLqEh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('my_model.h5')"
      ],
      "metadata": {
        "id": "7PEWtDMBLqoo"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
    },
    "papermill": {
      "duration": 163.388087,
      "end_time": "2021-01-13T19:06:40.018519",
      "environment_variables": {},
      "exception": null,
      "input_path": "__notebook__.ipynb",
      "output_path": "__notebook__.ipynb",
      "parameters": {},
      "start_time": "2021-01-13T19:03:56.630432",
      "version": "2.1.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}