Speaker_Classification_mfcc&gfcc_1s_20cep.ipynb

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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.9"
    },
    "colab": {
      "name": "Speaker_Classification_mfcc&gfcc_1s_20cep.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ritikraj660/Speaker-recognition/blob/master/Speaker_Classification_mfcc%26gfcc_1s_20cep.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CpNLfJBO1-WH",
        "colab_type": "text"
      },
      "source": [
        "# Installing Packages:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wj01p9v5K199",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#!pip install --upgrade tensorflow-gpu"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9RXw9shFmGBg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install soundfile\n",
        "!pip install spafe"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rBgxeaV6jtiB",
        "colab_type": "code",
        "outputId": "68aefc19-4f66-42f3-b9d8-f3c18838c658",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import tensorflow\n",
        "tensorflow.test.gpu_device_name() "
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/device:GPU:0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x0XYclYb2NlS",
        "colab_type": "text"
      },
      "source": [
        "# Import Data from Drive :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o7LFfT5dOyO6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ulBEeHGOPVb5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bFtoL6s2PVFJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bd51d858-bd08-440c-8e79-3031d1c81539"
      },
      "source": [
        "link = 'https://drive.google.com/file/d/1qxZWgx3vttou1HSvyxRDiOYWnaLAK9fj/view?usp=sharing'\n",
        "#fluff, id = link.split('=')\n",
        "id = '1qxZWgx3vttou1HSvyxRDiOYWnaLAK9fj'\n",
        "print (id) "
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1qxZWgx3vttou1HSvyxRDiOYWnaLAK9fj\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GpzvACTgPyj0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "downloaded = drive.CreateFile({'id':id}) \n",
        "downloaded.GetContentFile('LibriSpeech.zip')  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MCRUyoks2WwX",
        "colab_type": "text"
      },
      "source": [
        "# Extracting Data from (.)rar"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PUsfP_J5PySI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install patool\n",
        "import os\n",
        "os.listdir()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ffyDpxaCSgDx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CH0Ug5BPShB8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "0de950e6-3048-4f6e-c8cb-c118e368be3e"
      },
      "source": [
        "%%time\n",
        "import patoolib\n",
        "patoolib.extract_archive(\"LibriSpeech.zip\")"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "patool: Extracting LibriSpeech.zip ...\n",
            "patool: running /usr/bin/7z x -o./Unpack_dxqpc932 -- LibriSpeech.zip\n",
            "patool: ... LibriSpeech.zip extracted to `LibriSpeech'.\n",
            "CPU times: user 8.21 ms, sys: 16.5 ms, total: 24.7 ms\n",
            "Wall time: 525 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fcQzszcy2htw",
        "colab_type": "text"
      },
      "source": [
        "# Audio Data Preprocessing :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Von9Wv-VP_1g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import librosa\n",
        "import soundfile\n",
        "import tqdm\n",
        "from scipy.io import wavfile\n",
        "import glob\n",
        "import os,pickle\n",
        "import numpy as np\n",
        "from spafe.features.mfcc import mfcc, imfcc\n",
        "import csv\n",
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "from sklearn.model_selection import train_test_split\n",
        "import numpy as np\n",
        "import tensorflow.python.keras\n",
        "from tensorflow.python.keras.models import Sequential\n",
        "from tensorflow.python.keras.layers import Dense, Dropout, Flatten, BatchNormalization\n",
        "from tensorflow.python.keras.layers import Conv2D, MaxPooling2D\n",
        "from keras.optimizers import SGD"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "73C5dhr-me2y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def norm_(x,mean = False):\n",
        "    if mean == True:\n",
        "        return (x - np.mean(x)) / (np.max(x) - np.min(x)) #normalization                             \n",
        "    else:\n",
        "        return (x-np.mean(x)/np.std(x)) #standardization\n",
        "\n",
        "def zero_handling(x):\n",
        "    \"\"\"\n",
        "    handle the issue with zero values if they are exposed to become an argument\n",
        "    for any log function.\n",
        "\n",
        "    Returns:\n",
        "        vector with zeros substituted with epsilon values.\n",
        "    \"\"\"\n",
        "    return np.where(x == float(0), np.finfo(float).eps, x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mSPPghULme23",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from spafe.features.lpc import lpc\n",
        "from spafe.features.gfcc import gfcc\n",
        "\n",
        "def mfcc_(sig,fs,cep):\n",
        "    mfccs  = mfcc(sig, fs=fs, num_ceps=cep, pre_emph=1, pre_emph_coeff=0.95, \n",
        "              win_len=0.025, win_hop=0.01, win_type='hamming', nfilts=26, \n",
        "              nfft=512, low_freq=None, high_freq=None, scale='constant', \n",
        "              dct_type=2, use_energy=False, lifter=22)\n",
        "    #print(fs)\n",
        "    return mfccs\n",
        "\n",
        "def gfcc_(sig,fs,cep):\n",
        "  gfccs = gfcc(sig, fs=fs, num_ceps=cep, pre_emph=0, \n",
        "             pre_emph_coeff=0.97, win_len=0.025, win_hop=0.01, win_type='hamming', \n",
        "             nfilts=26, nfft=512, low_freq=None, high_freq=None, scale='constant', \n",
        "             dct_type=2, use_energy=False, lifter=22, normalize=1)\n",
        "  return gfccs\n",
        "\n",
        "# combine mfcc and gfcc ceptral features\n",
        "def gfcc_mfcc(y,sr,cep):\n",
        "    g = gfcc_(y,sr,cep)\n",
        "    m = mfcc_(y,sr,cep)\n",
        "    if g.shape[0] != m.shape[0]:\n",
        "        pass\n",
        "    result = np.zeros([g.shape[1]+m.shape[1]])\n",
        "    for i in range(g.shape[0]):\n",
        "        merge = np.concatenate((m[i],g[i]))\n",
        "        result = np.vstack((result,merge))\n",
        "    return result[1:,:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xaE6275lme26",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def label(user,total): # its creating lable for splited audio data\n",
        "    k_train = []\n",
        "    for i in range(total):\n",
        "        k_train.append(user)\n",
        "    y_train=np.array(k_train).T\n",
        "    return y_train"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mjoMCRgoFcvW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def audioLengthInSecond(sr,m,t_s):#t_s will gives time in second[1 for 1sec, 2 for 2sec]\n",
        "    t = round(sr/512) # Number of frames in one second\n",
        "    totalFrame = t*t_s # Number of frames in one second * time(second)\n",
        "    t1 = round(m.shape[0]/totalFrame) # total_audio_time/ t_s\n",
        "    #print(time_in_sec)\n",
        "    #print(\" Actual audio length in second :\",t1,\"\\n\",\"Number of frames in one second :\",t)\n",
        "    j=0\n",
        "    total = totalFrame*m.shape[1]\n",
        "    result = np.zeros([1,total])\n",
        "    #print(result.shape)\n",
        "    for i in range(t1):\n",
        "            temp = m[j:j+totalFrame,:].reshape(1,-1)\n",
        "            diff = total-temp.shape[1]\n",
        "            if round(total*0.3)>diff: # if more 30% padding need done then we will not add that audio file\n",
        "                #print(diff)\n",
        "                if diff!=0:\n",
        "                    temp = np.concatenate((temp[0,:],temp[0,diff:diff+diff])).reshape(1,-1)\n",
        "                #print(\"temp\",temp.shape)\n",
        "                result = np.concatenate((result,temp))\n",
        "                j += totalFrame\n",
        "            else:\n",
        "                pass\n",
        "    return result[1:,:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aV0iqD15me3D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from pathlib import Path\n",
        "len_audio = 148\n",
        "def s2c(mfcc=False,gmfcc=False,gfcc=False,cep=20,t_s=1):\n",
        "    x,y=[],[]\n",
        "    if gmfcc==True:\n",
        "      ceps=cep+cep\n",
        "    else:\n",
        "      ceps=cep\n",
        "    t = round(16000/512)\n",
        "    totalFrame = t*t_s\n",
        "    total = totalFrame*ceps    \n",
        "    data = np.empty([1,total])\n",
        "    data_label = np.asarray([])\n",
        "    count =0  # E:\\dataset\\LibriSpeech\\dev-clean\n",
        "    #for file in tqdm(glob.iglob('/LibriSpeech/test-clean//**/*.flac', recursive=True)):\n",
        "    for file in tqdm(Path('LibriSpeech/dev-clean').glob('**/*.flac')):\n",
        "        file_name=os.path.basename(file)\n",
        "        user = file_name.split(\"-\")[0]\n",
        "        #print(f)\n",
        "        with soundfile.SoundFile(file) as sound_file:\n",
        "                signal = sound_file.read(dtype=\"float32\")\n",
        "                sr=sound_file.samplerate\n",
        "        #signal = norm_(signal,mean = False)\n",
        "        #signal = zero_handling(signal)\n",
        "        #print(signal.shape,sr)\n",
        "        if mfcc:\n",
        "            feature = mfcc_(signal,sr,cep)\n",
        "            #print(feature.shape[0])\n",
        "        if gmfcc:\n",
        "            feature = gfcc_mfcc(signal,sr,cep)\n",
        "            ceps = feature.shape[1]   \n",
        "        #if feature.shape[0]>256:\n",
        "        if gfcc:\n",
        "            feature = gfcc_(signal,sr,cep)\n",
        "        t_s = 1\n",
        "        re_data = audioLengthInSecond(sr,feature,t_s) # return (3,400)\n",
        "        #print(\"shape of feature return \",re_data.shape)\n",
        "        data=np.concatenate((data,re_data),axis=0)\n",
        "        #print(\"Updated data length\",data.shape)\n",
        "        re_label = label(user,re_data.shape[0])\n",
        "        data_label = np.concatenate((data_label,re_label),axis=0)\n",
        "    return data[1:,:],data_label"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "atS_es2Ame3Z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "9f058d5c-84d4-4e23-d99f-9eea3b15271f"
      },
      "source": [
        "%%time\n",
        "def write_(x,y):\n",
        "    with open('mfcc_data.csv', 'a') as mfcc_dataFile:\n",
        "        writer = csv.writer(mfcc_dataFile)\n",
        "        writer.writerows(x)\n",
        "    mfcc_dataFile.close()\n",
        "    with open('mfcc_label.csv', 'a') as mfcc_labelFile:\n",
        "        writer = csv.writer(mfcc_labelFile)\n",
        "        writer.writerows(y)\n",
        "    mfcc_labelFile.close()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs\n",
            "Wall time: 5.01 µs\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dAmw3NFOEp8p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def pre_pro(mfcc=False,gmfcc=True,gfcc=False,cep=20,dim=1,t_s=1):\n",
        "  \n",
        "  x,y = s2c(mfcc=mfcc,gmfcc=gmfcc,gfcc=gfcc,cep=cep,t_s=t_s)\n",
        "  #write_(x,y)\n",
        "  print(x.shape,y.shape,type(x),type(y))\n",
        "  if dim ==2:\n",
        "    if gmfcc == True:\n",
        "      ceps = cep+cep\n",
        "    else:\n",
        "      ceps = cep   \n",
        "    d = np.empty([1,round(x.shape[1]/ceps),ceps])\n",
        "    for i in tqdm(range(x.shape[0])):\n",
        "      t = np.reshape(x[i],(1,-1,40))\n",
        "      d = np.concatenate((d,t),axis=0)\n",
        "    print(d.shape)\n",
        "    x=d[1:,:,:]\n",
        "  return x,y  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v9L22P6mnuFn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "d5a9a019-d2b9-447b-b798-22e9e99ceebc"
      },
      "source": [
        "%%time\n",
        "x,y = pre_pro(mfcc=False,gmfcc=True,gfcc=False,cep=20,dim=2,t_s=2)\n",
        "u = np.unique(y)\n",
        "num_classes = len(u)\n",
        "#print(u)\n",
        "d = dict()\n",
        "for enu,key in enumerate(u,0):\n",
        "    d[key]=enu\n",
        "print(d)\n",
        "for i in range(len(y)):\n",
        "    y[i] = d[y[i]]"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "661it [00:49, 13.37it/s]\n",
            "  2%|▏         | 333/15548 [00:00<00:04, 3325.59it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "(15548, 1240) (15548,) <class 'numpy.ndarray'> <class 'numpy.ndarray'>\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 15548/15548 [04:54<00:00, 52.78it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "(15549, 31, 40)\n",
            "{'1272': 0, '1462': 1, '1673': 2, '174': 3, '1919': 4, '251': 5, '422': 6, '652': 7, '777': 8, '84': 9}\n",
            "CPU times: user 5min 44s, sys: 45.6 s, total: 6min 29s\n",
            "Wall time: 5min 44s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gbqqVr_6Ep9d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "d0aea7ab-949f-4787-de3b-08d58431395e"
      },
      "source": [
        "X  = x.reshape(x.shape[0],x.shape[1],x.shape[2],1)\n",
        "print(X.shape)\n",
        "import keras\n",
        "\n",
        "y_binary = keras.utils.to_categorical(y, num_classes = 10)\n",
        "#y_binary = np.asarray(list(map(int,y)))\n",
        "print(y_binary)\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(X,y_binary ,test_size=0.20, shuffle= True)\n",
        "\n",
        "print(\"Training shape: {0}, Training label shape : {1}\".format(x_train.shape,y_train.shape))\n",
        "print(\"Training shape: {0}, Training label shape : {1}\".format(x_test.shape,y_test.shape))"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(15548, 31, 40, 1)\n",
            "[[0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " ...\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]]\n",
            "Training shape: (12438, 31, 40, 1), Training label shape : (12438, 10)\n",
            "Training shape: (3110, 31, 40, 1), Training label shape : (3110, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FF6XbgMm7MGZ",
        "colab_type": "text"
      },
      "source": [
        "# Plotting plot :"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mMj-6gUL7UYB",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "1.   **accuracy_plot()** : This function plot graph between train accuracy vs validation/test accuracy\n",
        "2.   **loss_plot():** This function plot graph between train loss vs validation loss\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xpHMjtZGEp_9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import keras\n",
        "from matplotlib import pyplot as plt\n",
        "def accuracy_plot(history):\n",
        "    plt.figure(figsize=(15,8))\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', 'val'], loc='upper left')\n",
        "    plt.grid()\n",
        "    plt.show()\n",
        "\n",
        "def loss_plot(history):\n",
        "    plt.figure(figsize=(15,8))\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', 'val'], loc='upper left')\n",
        "    plt.grid()\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d1HwaUks6gKY",
        "colab_type": "text"
      },
      "source": [
        "# Creating CNN Architecture :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cqpW8dQTnKt-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "aa053b76-5255-4a13-a1e6-7d6d20f44bcf"
      },
      "source": [
        "%tensorflow_version 2.x\n",
        "import tensorflow \n",
        "device_name = tensorflow.test.gpu_device_name()\n",
        "if device_name != '/device:GPU:0':\n",
        "  raise SystemError('GPU device not found')\n",
        "print('Found GPU at: {}'.format(device_name))"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found GPU at: /device:GPU:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EulSDA-nS7lu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "outputId": "11bd1d25-e47b-4f8b-c624-a739b6260cad"
      },
      "source": [
        "from tensorflow.keras.optimizers import SGD\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(x.shape[1],x.shape[2],1)))\n",
        "\n",
        "model.add(Conv2D(32, (3, 3), activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "\n",
        "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(256, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "opt = SGD(lr=0.001, momentum=0.9)                        #optimizer=keras.optimizers.Adam()\n",
        "model.compile(loss=keras.losses.categorical_crossentropy, optimizer=opt, metrics=['accuracy'])\n",
        "print(model.summary())"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 29, 38, 32)        320       \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 27, 36, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 13, 18, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 13, 18, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 11, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 9, 14, 64)         36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 4, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 4, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 1792)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 256)               459008    \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                2570      \n",
            "=================================================================\n",
            "Total params: 526,570\n",
            "Trainable params: 526,570\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0C4e0DA7Ep_K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%%time\n",
        "epoch = 300\n",
        "batch_sizes = 16\n",
        "from tensorflow.python.keras.callbacks import ModelCheckpoint\n",
        "# Saving the model that performed the best on the validation set\n",
        "checkpoint = ModelCheckpoint(filepath='Model.v_2.hdf5', save_best_only=True, verbose=0)\n",
        "\n",
        "\n",
        "\n",
        "history = model.fit(x_train, y_train, batch_size = batch_sizes, epochs=epoch, \n",
        "                    validation_data=(x_test, y_test), verbose=1, callbacks=[checkpoint])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RwEThfog4WjM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "870e6d96-8675-467a-8421-8ab5074bfcd3"
      },
      "source": [
        "#Loading the model that performed the best on the validation set\n",
        "model.load_weights('Model.v_2.hdf5')\n",
        "\n",
        "# Testing the model on the Test data\n",
        "(loss, accuracy) = model.evaluate(x_test, y_test, batch_size=16, verbose=1)\n",
        "\n",
        "print('Accuracy on test data: {:.2f}%'.format(accuracy * 100))"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "195/195 [==============================] - 0s 2ms/step - loss: 0.0521 - accuracy: 0.9842\n",
            "Accuracy on test data: 98.42%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UGOIOWkoZBRf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e48145a9-0f38-40a9-f555-41bd15728968"
      },
      "source": [
        "accuracy_plot(history)\n",
        "loss_plot(history)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gdSAF2cy3E0X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#import os, signal\n",
        "#os.kill(os.getpid(), signal.SIGKILL)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}