brain_model.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hazuIhcojhdm"
      },
      "source": [
        "## Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "AdkD-EfnjnuA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c966a4b-c171-42b1-fbf9-4e20d75babd5"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "6Z84XdATjhd4"
      },
      "outputs": [],
      "source": [
        "from matplotlib.pyplot import imshow\n",
        "import matplotlib.cm as cm\n",
        "import matplotlib.pylab as plt\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "import numpy as np\n",
        "import PIL\n",
        "from PIL import ImageFilter\n",
        "import cv2\n",
        "import itertools\n",
        "import random\n",
        "import keras\n",
        "from keras import utils as np_utils\n",
        "import tensorflow as tf\n",
        "import imutils\n",
        "from imutils import paths\n",
        "import os\n",
        "from keras import optimizers\n",
        "from tensorflow.keras.utils import img_to_array\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from keras import callbacks\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Flatten, BatchNormalization, ZeroPadding2D\n",
        "from keras.layers import Conv2D, MaxPooling2D , UpSampling2D , Conv1D, MaxPooling1D, UpSampling1D\n",
        "from keras import backend as K\n",
        "import pandas as pd\n",
        "# %matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "4tWcvEAPjhd_"
      },
      "outputs": [],
      "source": [
        "def pil_image(img_path):\n",
        "    # convert image into gray scale\n",
        "    pil_im =PIL.Image.open(img_path).convert('L')\n",
        "    pil_im=pil_im.resize((105,105))\n",
        "    #imshow(np.asarray(pil_im))\n",
        "    return pil_im"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XD2Odr_bjheB"
      },
      "source": [
        "## Augumentation Steps\n",
        "1) Noise 2) Blur 3) Perpective Rotation 4) Shading 5) Variable Character Spacing 6) Variable Aspect Ratio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "_80c3yrvjheC"
      },
      "outputs": [],
      "source": [
        "def noise_image(pil_im):\n",
        "    # Adding Noise to image\n",
        "    img_array = np.asarray(pil_im)\n",
        "    mean = 0.0   # some constant\n",
        "    std = 5   # some constant (standard deviation)\n",
        "    noisy_img = img_array + np.random.normal(mean, std, img_array.shape)\n",
        "    noisy_img_clipped = np.clip(noisy_img, 0, 255)\n",
        "    noise_img = PIL.Image.fromarray(np.uint8(noisy_img_clipped)) # output\n",
        "    #imshow((noisy_img_clipped ).astype(np.uint8))\n",
        "    noise_img=noise_img.resize((105,105))\n",
        "    return noise_img"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "GQwNla5mjheD"
      },
      "outputs": [],
      "source": [
        "def blur_image(pil_im):\n",
        "    #Adding Blur to image\n",
        "    blur_img = pil_im.filter(ImageFilter.GaussianBlur(radius=3)) # ouput\n",
        "    #imshow(blur_img)\n",
        "    blur_img=blur_img.resize((105,105))\n",
        "    return blur_img"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "Zcrc6i6bjheF"
      },
      "outputs": [],
      "source": [
        "def affine_rotation(img):\n",
        "\n",
        "    #img=cv2.imread(img_path,0)\n",
        "    rows, columns = img.shape\n",
        "\n",
        "    point1 = np.float32([[10, 10], [30, 10], [10, 30]])\n",
        "    point2 = np.float32([[20, 15], [40, 10], [20, 40]])\n",
        "\n",
        "    A = cv2.getAffineTransform(point1, point2)\n",
        "\n",
        "    output = cv2.warpAffine(img, A, (columns, rows))\n",
        "    affine_img = PIL.Image.fromarray(np.uint8(output)) # affine rotated output\n",
        "    #imshow(output)\n",
        "    affine_img=affine_img.resize((105,105))\n",
        "    return affine_img"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "Kmas4zOLjheI"
      },
      "outputs": [],
      "source": [
        "def gradient_fill(image):\n",
        "    #image=cv2.imread(img_path,0)\n",
        "    laplacian = cv2.Laplacian(image,cv2.CV_64F)\n",
        "    laplacian = cv2.resize(laplacian, (105, 105))\n",
        "    return laplacian"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C7lmLDtojheK"
      },
      "source": [
        "## Preparing Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "Pws2tAjBjheL"
      },
      "outputs": [],
      "source": [
        "data_path = \"/content/drive/MyDrive/master/augmented data\"\n",
        "data=[]\n",
        "labels=[]\n",
        "imagePaths = sorted(list(paths.list_images(data_path)))\n",
        "random.seed(42)\n",
        "random.shuffle(imagePaths)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "tynVZRjNjheO"
      },
      "outputs": [],
      "source": [
        "def conv_label(label):\n",
        "    if label == 'no':\n",
        "        return 0\n",
        "    elif label == 'yes':\n",
        "        return 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "_Xl0OTa9jheP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "613b471c-b3b4-45e0-c706-389eeb0741a4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['blur', 'noise', 'affine', 'gradient']\n",
            "910\n"
          ]
        }
      ],
      "source": [
        "augument=[\"blur\",\"noise\",\"affine\",\"gradient\"]\n",
        "a=itertools.combinations(augument, 4)\n",
        "\n",
        "for i in list(a):\n",
        "    print(list(i))\n",
        "\n",
        "print(len(imagePaths))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "UFauffuSjheR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f5c68f7f-0f30-461d-a5a6-441d29d711b3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'no'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "imagePaths[0].split(os.path.sep)[-2]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Augment Function for Images"
      ],
      "metadata": {
        "id": "cr-WRZxJdL-f"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "zHCd5ArljheS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "outputId": "ffd4bd31-320f-4773-971e-fb8ca1ba9770"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14560\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "counter=0\n",
        "for imagePath in imagePaths:\n",
        "    label = imagePath.split(os.path.sep)[-2]\n",
        "    label = conv_label(label)\n",
        "    pil_img = pil_image(imagePath)\n",
        "    imshow(pil_img)\n",
        "\n",
        "    # Adding original image\n",
        "    org_img = img_to_array(pil_img)\n",
        "    #print(org_img.shape)\n",
        "    data.append(org_img)\n",
        "    labels.append(label)\n",
        "\n",
        "    augument=[\"noise\",\"blur\",\"affine\",\"gradient\"]\n",
        "    for l in range(0,len(augument)):\n",
        "\n",
        "        a=itertools.combinations(augument, l+1)\n",
        "\n",
        "        for i in list(a):\n",
        "            combinations=list(i)\n",
        "            #print(len(combinations))\n",
        "            temp_img = pil_img\n",
        "            for j in combinations:\n",
        "\n",
        "                if j == 'noise':\n",
        "                    # Adding Noise image\n",
        "                    temp_img = noise_image(temp_img)\n",
        "\n",
        "                elif j == 'blur':\n",
        "                    # Adding Blur image\n",
        "                    temp_img = blur_image(temp_img)\n",
        "                    #imshow(blur_img)\n",
        "\n",
        "                elif j == 'affine':\n",
        "                    open_cv_affine = np.array(pil_img)\n",
        "                    # Adding affine rotation image\n",
        "                    temp_img = affine_rotation(open_cv_affine)\n",
        "\n",
        "                elif j == 'gradient':\n",
        "                    open_cv_gradient = np.array(pil_img)\n",
        "                    # Adding gradient image\n",
        "                    temp_img = gradient_fill(open_cv_gradient)\n",
        "\n",
        "            temp_img = img_to_array(temp_img)\n",
        "            data.append(temp_img)\n",
        "            labels.append(label)\n",
        "\n",
        "print(len(data))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "meFknBBFjheS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "25c7e9a7-94c1-410c-83df-510fa10aeb40"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Success\n"
          ]
        }
      ],
      "source": [
        "data = np.asarray(data, dtype=\"int\") / 255.0\n",
        "labels = np.array(labels)\n",
        "print(\"Success\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "GHTQj7TJjheT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22be2edf-cb49-46d5-b0b6-f9347261cb68"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 1, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "qveBRv6mjheT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9be60bb6-57a8-4bd0-ce81-4b87f55dc8ae"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1. 0.]\n",
            " [0. 1.]\n",
            " [1. 0.]\n",
            " ...\n",
            " [1. 0.]\n",
            " [1. 0.]\n",
            " [1. 0.]]\n",
            "[[0. 1.]\n",
            " [1. 0.]\n",
            " [1. 0.]\n",
            " ...\n",
            " [0. 1.]\n",
            " [0. 1.]\n",
            " [1. 0.]]\n"
          ]
        }
      ],
      "source": [
        "# partition the data into training and testing splits using 75% of\n",
        "# the data for training and the remaining 25% for testing\n",
        "(trainX, testX, trainY, testY) =  train_test_split(data,labels, test_size=0.25, random_state=42)\n",
        "# convert the labels from integers to vectors\n",
        "try:\n",
        "    trainY = to_categorical(trainY, num_classes=2) ## input of to_categorial is class vector of integers\n",
        "    print(trainY)\n",
        "except Exception as ex:\n",
        "    trainY = None\n",
        "    print(ex)\n",
        "try:\n",
        "    testY = to_categorical(testY, num_classes=2)\n",
        "    print(testY)\n",
        "except Exception as ex:\n",
        "    testY = None\n",
        "    print(ex)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(trainY.shape, trainX.shape)\n",
        "print(testY.shape, testX.shape)"
      ],
      "metadata": {
        "id": "DdDVE_BCgGpb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2024c96b-fab4-469f-cd89-07ca8aada813"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(10920, 2) (10920, 105, 105, 1)\n",
            "(3640, 2) (3640, 105, 105, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "s_AuLxyJjheU"
      },
      "outputs": [],
      "source": [
        "aug = ImageDataGenerator(rotation_range=30, width_shift_range=0.1,height_shift_range=0.1, shear_range=0.2, zoom_range=0.2,horizontal_flip=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zh-N-w-ZjheU"
      },
      "source": [
        "## LPQ Feature Calculation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "KiBpX_xSjheU"
      },
      "outputs": [],
      "source": [
        "from scipy.signal import convolve2d\n",
        "\n",
        "class LPQ(object):\n",
        "    \"\"\" This implementation of Local Phase Quantization (LPQ) is a\n",
        "\n",
        "      Reference:\n",
        "        Ojansivu V & Heikkilä J (2008) Blur insensitive texture classification\n",
        "        using local phase quantization. Proc. Image and Signal Processing\n",
        "\t(ICISP 2008), Cherbourg-Octeville, France, 5099:236-243.\n",
        "        Copyright 2008 by Heikkilä & Ojansivu\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, radius=3):\n",
        "        self.radius = radius\n",
        "\n",
        "    def euc_dist(self, X):\n",
        "        Y = X = X.astype(np.int32)\n",
        "        XX = np.sum(X * X, axis=1)[:, np.newaxis]\n",
        "        YY = XX.T\n",
        "        distances = np.dot(X,Y.T)\n",
        "        distances *= -2\n",
        "        distances += XX\n",
        "        distances += YY\n",
        "        np.maximum(distances, 0, distances)\n",
        "        distances.flat[::distances.shape[0] + 1] = 0.0\n",
        "        return np.sqrt(distances)\n",
        "\n",
        "    def __call__(self,X):\n",
        "        f = 1.0\n",
        "        x = np.arange(-self.radius,self.radius+1)\n",
        "        n = len(x)\n",
        "        rho = 0.95\n",
        "        [xp, yp] = np.meshgrid(np.arange(1,(n+1)),np.arange(1,(n+1)))\n",
        "        pp = np.concatenate((xp,yp)).reshape(2,-1)\n",
        "        dd = self.euc_dist(pp.T) # squareform(pdist(...)) would do the job, too...\n",
        "        C = np.power(rho,dd)\n",
        "\n",
        "        w0 = (x*0.0+1.0)\n",
        "        w1 = np.exp(-2*np.pi*1j*x*f/n)\n",
        "        w2 = np.conj(w1)\n",
        "\n",
        "        q1 = w0.reshape(-1,1)*w1\n",
        "        q2 = w1.reshape(-1,1)*w0\n",
        "        q3 = w1.reshape(-1,1)*w1\n",
        "        q4 = w1.reshape(-1,1)*w2\n",
        "\n",
        "        u1 = np.real(q1)\n",
        "        u2 = np.imag(q1)\n",
        "        u3 = np.real(q2)\n",
        "        u4 = np.imag(q2)\n",
        "        u5 = np.real(q3)\n",
        "        u6 = np.imag(q3)\n",
        "        u7 = np.real(q4)\n",
        "        u8 = np.imag(q4)\n",
        "\n",
        "        M = np.matrix([u1.flatten(), u2.flatten(), u3.flatten(), u4.flatten(), u5.flatten(), u6.flatten(), u7.flatten(), u8.flatten()])\n",
        "\n",
        "        D = np.dot(np.dot(M,C), M.T)\n",
        "        U,S,V = np.linalg.svd(D)\n",
        "\n",
        "        Qa = convolve2d(convolve2d(X,w0.reshape(-1,1),mode='same'),w1.reshape(1,-1),mode='same')\n",
        "        Qb = convolve2d(convolve2d(X,w1.reshape(-1,1),mode='same'),w0.reshape(1,-1),mode='same')\n",
        "        Qc = convolve2d(convolve2d(X,w1.reshape(-1,1),mode='same'),w1.reshape(1,-1),mode='same')\n",
        "        Qd = convolve2d(convolve2d(X, w1.reshape(-1,1),mode='same'),w2.reshape(1,-1),mode='same')\n",
        "\n",
        "        Fa = np.real(Qa)\n",
        "        Ga = np.imag(Qa)\n",
        "        Fb = np.real(Qb)\n",
        "        Gb = np.imag(Qb)\n",
        "        Fc = np.real(Qc)\n",
        "        Gc = np.imag(Qc)\n",
        "        Fd = np.real(Qd)\n",
        "        Gd = np.imag(Qd)\n",
        "\n",
        "        F = np.array([Fa.flatten(), Ga.flatten(), Fb.flatten(), Gb.flatten(), Fc.flatten(), Gc.flatten(), Fd.flatten(), Gd.flatten()])\n",
        "        G = np.dot(V.T, F)\n",
        "\n",
        "        t = 0\n",
        "\n",
        "        # Calculate the LPQ Patterns:\n",
        "        B = (G[0,:]>=t)*1 + (G[1,:]>=t)*2 + (G[2,:]>=t)*4 + (G[3,:]>=t)*8 + (G[4,:]>=t)*16 + (G[5,:]>=t)*32 + (G[6,:]>=t)*64 + (G[7,:]>=t)*128\n",
        "        B = np.reshape(B, np.shape(Fa))\n",
        "\n",
        "        # And finally build the histogram:\n",
        "        h, b  = np.histogram(B, bins=256, range = (0,255))\n",
        "\n",
        "        return h\n",
        "\n",
        "    def __repr__(self):\n",
        "        return \"LPQ (radius=%s)\" % (self.radius)\n",
        "\n",
        "#len(trainX)\n",
        "#\n",
        "#trainX.shape\n",
        "\n",
        "lpq = LPQ(3)\n",
        "\n",
        "#lpq"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "buExf_efjheV"
      },
      "source": [
        "# Calculate Testing LPQ Features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "JkeE39v3jheW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bcd61e31-dd26-4d4b-c4ee-fe72e94e7b74"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(10920, 256)\n",
            "(3640, 256)\n",
            "10920\n",
            "3640\n"
          ]
        }
      ],
      "source": [
        "dim = (testX.shape[1],testX.shape[1])\n",
        "test_feat =[]\n",
        "for i in range(len(testX)):\n",
        "    X = testX[i]\n",
        "    X = X.reshape(dim)\n",
        "    lpq_feat = lpq(X)\n",
        "    test_feat.append(lpq_feat)\n",
        "\n",
        "train_feat =[]\n",
        "for i in range(len(trainX)):\n",
        "    X = trainX[i]\n",
        "    X = X.reshape(dim)\n",
        "    lpq_feat = lpq(X)\n",
        "    train_feat.append(lpq_feat)\n",
        "\n",
        "train_feat = np.array(train_feat)\n",
        "print(train_feat.shape)\n",
        "test_feat = np.array(test_feat)\n",
        "print(test_feat.shape)\n",
        "numTrain = int(pd.to_numeric(trainX.shape[0]))\n",
        "print(numTrain)\n",
        "numTest = int(pd.to_numeric(testX.shape[0]))\n",
        "print(numTest)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sgvqx6AAjheW"
      },
      "source": [
        "# Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "gUXYm18fjheX"
      },
      "outputs": [],
      "source": [
        "model = Sequential()\n",
        "model.add(Conv1D(filters=16, kernel_size=12, input_shape=(256,1)))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling1D(pool_size=2))\n",
        "\n",
        "model.add(Conv1D(32, kernel_size=24, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling1D(pool_size=2))\n",
        "\n",
        "model.add(Conv1D(64, kernel_size=12, activation='relu'))\n",
        "model.add(Conv1D(128, kernel_size=12, activation='relu'))\n",
        "model.add(Conv1D(128, kernel_size=12, activation='relu'))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(256,activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(320,activation='relu'))\n",
        "model.add(Dense(64, activation='relu'))\n",
        "model.add(Dense(2, activation='sigmoid'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jMMyuDbGjheX"
      },
      "source": [
        "## Model Compilation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "cO0fbbFzjheY"
      },
      "outputs": [],
      "source": [
        "# reshaping LPQ training and testing features according to input of CNN here 256 is no of features per image\n",
        "train_feat = train_feat.reshape((len(train_feat),256,1))\n",
        "test_feat = test_feat.reshape((len(test_feat),256,1))\n",
        "batch_size = 16\n",
        "epochs = 30\n",
        "sgd = optimizers.Adam()\n",
        "model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['accuracy'])\n",
        "\n",
        "early_stopping=callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1, mode='min')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VNv-9m82jheY"
      },
      "source": [
        "## Model Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "scrolled": false,
        "id": "OrV5GSLYjheZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed0a585f-5693-47b3-87eb-478c1405287a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv1d (Conv1D)             (None, 245, 16)           208       \n",
            "                                                                 \n",
            " batch_normalization (Batch  (None, 245, 16)           64        \n",
            " Normalization)                                                  \n",
            "                                                                 \n",
            " max_pooling1d (MaxPooling1  (None, 122, 16)           0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " conv1d_1 (Conv1D)           (None, 99, 32)            12320     \n",
            "                                                                 \n",
            " batch_normalization_1 (Bat  (None, 99, 32)            128       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " max_pooling1d_1 (MaxPoolin  (None, 49, 32)            0         \n",
            " g1D)                                                            \n",
            "                                                                 \n",
            " conv1d_2 (Conv1D)           (None, 38, 64)            24640     \n",
            "                                                                 \n",
            " conv1d_3 (Conv1D)           (None, 27, 128)           98432     \n",
            "                                                                 \n",
            " conv1d_4 (Conv1D)           (None, 16, 128)           196736    \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 2048)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               262272    \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 128)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 256)               33024     \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 320)               82240     \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 64)                20544     \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 2)                 130       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 730738 (2.79 MB)\n",
            "Trainable params: 730642 (2.79 MB)\n",
            "Non-trainable params: 96 (384.00 Byte)\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/30\n",
            "\n",
            "Epoch 1: val_loss improved from inf to 0.50422, saving model to model.h5\n",
            "683/683 - 21s - loss: 0.6082 - accuracy: 0.6509 - val_loss: 0.5042 - val_accuracy: 0.7379 - 21s/epoch - 31ms/step\n",
            "Epoch 2/30\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Epoch 2: val_loss improved from 0.50422 to 0.43114, saving model to model.h5\n",
            "683/683 - 5s - loss: 0.4528 - accuracy: 0.7807 - val_loss: 0.4311 - val_accuracy: 0.8022 - 5s/epoch - 8ms/step\n",
            "Epoch 3/30\n",
            "\n",
            "Epoch 3: val_loss improved from 0.43114 to 0.29140, saving model to model.h5\n",
            "683/683 - 6s - loss: 0.3281 - accuracy: 0.8431 - val_loss: 0.2914 - val_accuracy: 0.8698 - 6s/epoch - 9ms/step\n",
            "Epoch 4/30\n",
            "\n",
            "Epoch 4: val_loss improved from 0.29140 to 0.20758, saving model to model.h5\n",
            "683/683 - 5s - loss: 0.2545 - accuracy: 0.8807 - val_loss: 0.2076 - val_accuracy: 0.8975 - 5s/epoch - 8ms/step\n",
            "Epoch 5/30\n",
            "\n",
            "Epoch 5: val_loss improved from 0.20758 to 0.19256, saving model to model.h5\n",
            "683/683 - 6s - loss: 0.2073 - accuracy: 0.9117 - val_loss: 0.1926 - val_accuracy: 0.9190 - 6s/epoch - 9ms/step\n",
            "Epoch 6/30\n",
            "\n",
            "Epoch 6: val_loss improved from 0.19256 to 0.15639, saving model to model.h5\n",
            "683/683 - 6s - loss: 0.1817 - accuracy: 0.9249 - val_loss: 0.1564 - val_accuracy: 0.9310 - 6s/epoch - 8ms/step\n",
            "Epoch 7/30\n",
            "\n",
            "Epoch 7: val_loss did not improve from 0.15639\n",
            "683/683 - 5s - loss: 0.1648 - accuracy: 0.9309 - val_loss: 0.1637 - val_accuracy: 0.9294 - 5s/epoch - 7ms/step\n",
            "Epoch 8/30\n",
            "\n",
            "Epoch 8: val_loss did not improve from 0.15639\n",
            "683/683 - 6s - loss: 0.1375 - accuracy: 0.9434 - val_loss: 0.1722 - val_accuracy: 0.9228 - 6s/epoch - 9ms/step\n",
            "Epoch 9/30\n",
            "\n",
            "Epoch 9: val_loss did not improve from 0.15639\n",
            "683/683 - 5s - loss: 0.1318 - accuracy: 0.9457 - val_loss: 0.2186 - val_accuracy: 0.9245 - 5s/epoch - 7ms/step\n",
            "Epoch 10/30\n",
            "\n",
            "Epoch 10: val_loss did not improve from 0.15639\n",
            "683/683 - 6s - loss: 0.1243 - accuracy: 0.9526 - val_loss: 0.1706 - val_accuracy: 0.9297 - 6s/epoch - 9ms/step\n",
            "Epoch 11/30\n",
            "\n",
            "Epoch 11: val_loss improved from 0.15639 to 0.14108, saving model to model.h5\n",
            "683/683 - 5s - loss: 0.1034 - accuracy: 0.9590 - val_loss: 0.1411 - val_accuracy: 0.9365 - 5s/epoch - 8ms/step\n",
            "Epoch 12/30\n",
            "\n",
            "Epoch 12: val_loss did not improve from 0.14108\n",
            "683/683 - 5s - loss: 0.0923 - accuracy: 0.9644 - val_loss: 0.1436 - val_accuracy: 0.9525 - 5s/epoch - 8ms/step\n",
            "Epoch 13/30\n",
            "\n",
            "Epoch 13: val_loss did not improve from 0.14108\n",
            "683/683 - 6s - loss: 0.0796 - accuracy: 0.9698 - val_loss: 0.1514 - val_accuracy: 0.9456 - 6s/epoch - 9ms/step\n",
            "Epoch 14/30\n",
            "\n",
            "Epoch 14: val_loss did not improve from 0.14108\n",
            "683/683 - 5s - loss: 0.0768 - accuracy: 0.9714 - val_loss: 0.1776 - val_accuracy: 0.9382 - 5s/epoch - 7ms/step\n",
            "Epoch 15/30\n",
            "\n",
            "Epoch 15: val_loss improved from 0.14108 to 0.13574, saving model to model.h5\n",
            "683/683 - 6s - loss: 0.0926 - accuracy: 0.9639 - val_loss: 0.1357 - val_accuracy: 0.9593 - 6s/epoch - 9ms/step\n",
            "Epoch 16/30\n",
            "\n",
            "Epoch 16: val_loss did not improve from 0.13574\n",
            "683/683 - 5s - loss: 0.1024 - accuracy: 0.9647 - val_loss: 0.2638 - val_accuracy: 0.9283 - 5s/epoch - 8ms/step\n",
            "Epoch 17/30\n",
            "\n",
            "Epoch 17: val_loss improved from 0.13574 to 0.10771, saving model to model.h5\n",
            "683/683 - 6s - loss: 0.0736 - accuracy: 0.9735 - val_loss: 0.1077 - val_accuracy: 0.9629 - 6s/epoch - 9ms/step\n",
            "Epoch 18/30\n",
            "\n",
            "Epoch 18: val_loss did not improve from 0.10771\n",
            "683/683 - 5s - loss: 0.0583 - accuracy: 0.9781 - val_loss: 0.1390 - val_accuracy: 0.9538 - 5s/epoch - 7ms/step\n",
            "Epoch 19/30\n",
            "\n",
            "Epoch 19: val_loss did not improve from 0.10771\n",
            "683/683 - 5s - loss: 0.0593 - accuracy: 0.9794 - val_loss: 0.1568 - val_accuracy: 0.9593 - 5s/epoch - 7ms/step\n",
            "Epoch 20/30\n",
            "\n",
            "Epoch 20: val_loss did not improve from 0.10771\n",
            "683/683 - 6s - loss: 0.0711 - accuracy: 0.9772 - val_loss: 0.2112 - val_accuracy: 0.9330 - 6s/epoch - 9ms/step\n",
            "Epoch 21/30\n",
            "\n",
            "Epoch 21: val_loss did not improve from 0.10771\n",
            "683/683 - 5s - loss: 0.0636 - accuracy: 0.9790 - val_loss: 0.1618 - val_accuracy: 0.9563 - 5s/epoch - 7ms/step\n",
            "Epoch 22/30\n",
            "\n",
            "Epoch 22: val_loss did not improve from 0.10771\n",
            "683/683 - 6s - loss: 0.0634 - accuracy: 0.9781 - val_loss: 0.1147 - val_accuracy: 0.9615 - 6s/epoch - 9ms/step\n",
            "Epoch 23/30\n",
            "\n",
            "Epoch 23: val_loss did not improve from 0.10771\n",
            "683/683 - 5s - loss: 0.0498 - accuracy: 0.9829 - val_loss: 0.1267 - val_accuracy: 0.9657 - 5s/epoch - 8ms/step\n",
            "Epoch 24/30\n",
            "\n",
            "Epoch 24: val_loss did not improve from 0.10771\n",
            "683/683 - 8s - loss: 0.0968 - accuracy: 0.9693 - val_loss: 0.2056 - val_accuracy: 0.9168 - 8s/epoch - 11ms/step\n",
            "Epoch 25/30\n",
            "\n",
            "Epoch 25: val_loss did not improve from 0.10771\n",
            "683/683 - 8s - loss: 0.0897 - accuracy: 0.9676 - val_loss: 0.1793 - val_accuracy: 0.9505 - 8s/epoch - 11ms/step\n",
            "Epoch 26/30\n",
            "\n",
            "Epoch 26: val_loss did not improve from 0.10771\n",
            "683/683 - 6s - loss: 0.0568 - accuracy: 0.9827 - val_loss: 0.1416 - val_accuracy: 0.9629 - 6s/epoch - 9ms/step\n",
            "Epoch 27/30\n",
            "\n",
            "Epoch 27: val_loss did not improve from 0.10771\n",
            "683/683 - 6s - loss: 0.0327 - accuracy: 0.9889 - val_loss: 0.1651 - val_accuracy: 0.9533 - 6s/epoch - 9ms/step\n",
            "Epoch 27: early stopping\n"
          ]
        }
      ],
      "source": [
        "# file path where the model weights will be stored\n",
        "filepath=\"model.h5\"\n",
        "\n",
        "checkpoint = callbacks.ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n",
        "\n",
        "callbacks_list = [early_stopping,checkpoint]\n",
        "\n",
        "print(model.summary())\n",
        "\n",
        "history = model.fit(train_feat, trainY,shuffle=True,\n",
        "          batch_size=batch_size,\n",
        "          epochs=epochs,\n",
        "          verbose=2,\n",
        "          validation_data=(test_feat, testY),callbacks=callbacks_list)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JXxRpq6njhea"
      },
      "source": [
        "## Loss and Accuracy Curve of Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "zHaO6YX4jhec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "outputId": "d0acd9b6-5475-4bb8-c28d-71f272a196ce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# list all data in history\n",
        "print(history.history.keys())\n",
        "# summarize history for accuracy\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "ZtmlZBB0jhee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "f4a9b893-da2d-4031-d15f-a655d4037175"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# summarize history for loss\n",
        "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(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bMpvDzvDjhef"
      },
      "source": [
        "## Load Save Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "e9sjvP-Yjhef",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e02783b5-22f3-4260-e0c1-940e3fc30199"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test loss: 0.10770899802446365\n",
            "Test accuracy: 0.9629120826721191\n"
          ]
        }
      ],
      "source": [
        "from keras.models import load_model\n",
        "model = load_model('model.h5')\n",
        "\n",
        "score = model.evaluate(test_feat, testY, verbose=0)\n",
        "print('Test loss:', score[0])\n",
        "print('Test accuracy:', score[1])"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kSPgQOnOIpdq"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.10.2"
    },
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}