Speaker_Classification_gfcc_v1_10.ipynb

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      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.9"
    },
    "colab": {
      "name": "Speaker_Classification_gfcc_v1_10.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "qlUm3WDCPrt_"
      ],
      "toc_visible": true,
      "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_gfcc_v1_10.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",
        "outputId": "b3140fd4-22e1-421a-c987-917bd30ae648",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        }
      },
      "source": [
        "!pip install soundfile\n",
        "!pip install spafe"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting soundfile\n",
            "  Downloading https://files.pythonhosted.org/packages/eb/f2/3cbbbf3b96fb9fa91582c438b574cff3f45b29c772f94c400e2c99ef5db9/SoundFile-0.10.3.post1-py2.py3-none-any.whl\n",
            "Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.6/dist-packages (from soundfile) (1.14.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.6/dist-packages (from cffi>=1.0->soundfile) (2.20)\n",
            "Installing collected packages: soundfile\n",
            "Successfully installed soundfile-0.10.3.post1\n",
            "Collecting spafe\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a9/d9/d92807657a5125318639aa1636c0a76fb87222d48b5c290996ed9d5be1b6/spafe-0.1.2-py3-none-any.whl (77kB)\n",
            "\u001b[K     |████████████████████████████████| 81kB 2.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17.2 in /usr/local/lib/python3.6/dist-packages (from spafe) (1.18.4)\n",
            "Requirement already satisfied: scipy>=1.3.1 in /usr/local/lib/python3.6/dist-packages (from spafe) (1.4.1)\n",
            "Installing collected packages: spafe\n",
            "Successfully installed spafe-0.1.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rBgxeaV6jtiB",
        "colab_type": "code",
        "outputId": "fbf4b4d3-9817-4bdb-f37a-bc46335e385f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import tensorflow\n",
        "tensorflow.test.gpu_device_name() "
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/device:GPU:0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "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",
        "outputId": "caca7915-2738-4a78-e927-6c0f0b608478",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "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": 5,
      "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",
        "outputId": "6a06efd0-e712-47f8-c726-92e6f7b53d2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "!pip install patool"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting patool\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/43/94/52243ddff508780dd2d8110964320ab4851134a55ab102285b46e740f76a/patool-1.12-py2.py3-none-any.whl (77kB)\n",
            "\u001b[K     |████████████████████████████████| 81kB 2.4MB/s \n",
            "\u001b[?25hInstalling collected packages: patool\n",
            "Successfully installed patool-1.12\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ffyDpxaCSgDx",
        "colab_type": "code",
        "outputId": "bc2efdca-eb8e-416f-97e4-e79cc327dc93",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import os\n",
        "os.listdir()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['.config', 'LibriSpeech.zip', 'adc.json', 'sample_data']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CH0Ug5BPShB8",
        "colab_type": "code",
        "outputId": "09d225a2-3b88-4ca0-cf3b-2471935e2152",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "%%time\n",
        "import patoolib\n",
        "patoolib.extract_archive(\"LibriSpeech.zip\")"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "patool: Extracting LibriSpeech.zip ...\n",
            "patool: running /usr/bin/7z x -o./Unpack_l2tiuh9y -- LibriSpeech.zip\n",
            "patool: ... LibriSpeech.zip extracted to `LibriSpeech'.\n",
            "CPU times: user 9.53 ms, sys: 21 ms, total: 30.6 ms\n",
            "Wall time: 600 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": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "08e5e610-e949-45b7-e9e5-ae5e9413734b"
      },
      "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": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "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",
        "cep = 20\n",
        "def mfcc_(sig,fs):\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 lpcc_(sig,fs):\n",
        "  lpccs = lpc(sig, fs=fs, num_ceps=cep, pre_emph=0, \n",
        "                       pre_emph_coeff=0.97, win_type='hamming', \n",
        "                       win_len=0.025, win_hop=0.01, do_rasta=True, \n",
        "                       dither=1)\n",
        "  return lpccs\n",
        "\n",
        "def gfcc_(sig,fs):\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"
      ],
      "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": "aV0iqD15me3D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from pathlib import Path\n",
        "len_audio = 148\n",
        "def s2c(mfcc=False,lpcc=False,gfcc=False):\n",
        "    x,y=[],[]\n",
        "    data = np.empty([1,len_audio,cep])\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",
        "        \n",
        "        if mfcc:\n",
        "            feature = mfcc_(signal,sr)\n",
        "            #print(feature.shape[0])\n",
        "        if lpcc:\n",
        "            feature = lpcc_(signal,sr)   \n",
        "        #if feature.shape[0]>256:\n",
        "        if gfcc:\n",
        "            feature = gfcc_(signal,sr)\n",
        "\n",
        "        quotient = int(feature.shape[0]/len_audio)\n",
        "        end_index = feature.shape[0]-(feature.shape[0]%len_audio)\n",
        "        r = feature[0:end_index,:]\n",
        "        re_data = np.reshape(r,(-1,len_audio,cep)) #b = np.reshape(a, (8, 3, -1))\n",
        "        data=np.concatenate((data,re_data),axis=0)\n",
        "        \n",
        "        re_label = label(user,quotient)\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",
        "outputId": "710d24b0-51e1-4b3a-c072-e4cffc7ad7cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "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": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 9 µs, sys: 2 µs, total: 11 µs\n",
            "Wall time: 15.7 µs\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dAmw3NFOEp8p",
        "colab_type": "code",
        "outputId": "92bb3216-70d4-49b7-de35-69685fdc37b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "%%time\n",
        "x,y = s2c(mfcc=False,lpcc=False,gfcc=True)\n",
        "\n",
        "#write_(x,y)\n",
        "print(x.shape,y.shape)\n",
        "\n",
        "\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": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "661it [00:22, 29.48it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "(2950, 148, 20) (2950,)\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 24.3 s, sys: 18.4 s, total: 42.7 s\n",
            "Wall time: 22.4 s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gbqqVr_6Ep9d",
        "colab_type": "code",
        "outputId": "ce8328ee-d377-44d2-b93e-8d508e45b89c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "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": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2950, 148, 20, 1)\n",
            "[[0. 0. 0. ... 0. 1. 0.]\n",
            " [0. 0. 0. ... 0. 1. 0.]\n",
            " [0. 0. 0. ... 0. 1. 0.]\n",
            " ...\n",
            " [0. 0. 0. ... 1. 0. 0.]\n",
            " [0. 0. 0. ... 1. 0. 0.]\n",
            " [0. 0. 0. ... 1. 0. 0.]]\n",
            "Training shape: (2360, 148, 20, 1), Training label shape : (2360, 10)\n",
            "Training shape: (590, 148, 20, 1), Training label shape : (590, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d1HwaUks6gKY",
        "colab_type": "text"
      },
      "source": [
        "# Creating CNN Architecture :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cqpW8dQTnKt-",
        "colab_type": "code",
        "outputId": "15e0b943-cff7-464f-9df3-eeb4b3f7ac42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "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": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found GPU at: /device:GPU:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EulSDA-nS7lu",
        "colab_type": "code",
        "outputId": "68bf5852-57de-4519-adbd-233211440477",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        }
      },
      "source": [
        "from tensorflow.keras.optimizers import SGD\n",
        "opt = SGD(lr=0.001, momentum=0.9)\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",
        "                                                           #optimizer=keras.optimizers.Adam()\n",
        "model.compile(loss=keras.losses.categorical_crossentropy, optimizer=opt, metrics=['accuracy'])\n",
        "print(model.summary())"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 146, 18, 32)       320       \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 144, 16, 32)       9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 72, 8, 32)         0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 72, 8, 32)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 70, 6, 64)         18496     \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 68, 4, 64)         36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 34, 2, 64)         0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 34, 2, 64)         0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 4352)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 256)               1114368   \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                2570      \n",
            "=================================================================\n",
            "Total params: 1,181,930\n",
            "Trainable params: 1,181,930\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0C4e0DA7Ep_K",
        "colab_type": "code",
        "outputId": "1e390491-fcf5-4348-930a-82c92728e52a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "%%time\n",
        "epoch = 500\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=1)\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=0, callbacks=[checkpoint])"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00320: val_loss did not improve from 0.07652\n",
            "\n",
            "CPU times: user 7min 55s, sys: 59.3 s, total: 8min 54s\n",
            "Wall time: 12min 6s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RwEThfog4WjM",
        "colab_type": "code",
        "outputId": "e1d91a82-c204-438a-9604-7486bb98e333",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "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": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "37/37 [==============================] - 0s 5ms/step - loss: 0.0574 - accuracy: 0.9847\n",
            "Accuracy on test data: 98.47%\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=(12,8))\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=(12,8))\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": "code",
      "metadata": {
        "id": "kzuVliqNEqBd",
        "colab_type": "code",
        "outputId": "9b96c2a2-3875-4d41-86ea-933a0cc1c03e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        }
      },
      "source": [
        "\n",
        "accuracy_plot(history)\n",
        "loss_plot(history)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xth20FsnOd3g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os, signal\n",
        "os.kill(os.getpid(), signal.SIGKILL)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NgNfqbzP8D18",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mNwKuBxHgY0d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qlUm3WDCPrt_",
        "colab_type": "text"
      },
      "source": [
        "# VGG16"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BiyIHBtrPu2B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\"\"\"model1 = Sequential()\n",
        "model1.add(Conv2D(input_shape=(x.shape[1],x.shape[2],1),filters=64,kernel_size=(3,3),padding=\"same\", activation=\"relu\"))\n",
        "model1.add(Conv2D(filters=64,kernel_size=(3,3),padding=\"same\", activation=\"relu\"))\n",
        "\n",
        "model1.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))\n",
        "\n",
        "model1.add(Conv2D(filters=128, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "model1.add(Conv2D(filters=128, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "\n",
        "model1.add(MaxPooling2D(pool_size=(2,2),strides=(1,1)))\n",
        "\n",
        "model1.add(Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "model1.add(Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "model1.add(Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "\n",
        "model1.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))\n",
        "\n",
        "model1.add(Conv2D(filters=512, kernel_size=(3,3), pāadding=\"same\", activation=\"relu\"))\n",
        "model1.add(Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "model1.add(Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "\n",
        "model1.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))\n",
        "\n",
        "model1.add(Conv2D(filters=128, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "model1.add(Conv2D(filters=128, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "model1.add(Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "\n",
        "model1.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))\n",
        "\n",
        "model1.add(Flatten())\n",
        "model1.add(Dense(units=128,activation=\"relu\"))\n",
        "model1.add(Dense(units=128,activation=\"relu\"))\n",
        "model1.add(Dense(units=40, activation=\"softmax\"))\n",
        "print(model1.summary())\n",
        "model1.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"adam\", metrics=['accuracy'])\n",
        "\"\"\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PQXJf-duQKpE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\"\"\"%%time\n",
        "from 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",
        "# Training the model for 40 epochs\n",
        "history = model1.fit(x_train, y_train, batch_size=32, epochs=200, \n",
        "                    validation_data=(x_test, y_test), verbose=1, callbacks=[checkpoint])\"\"\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mwac_IauQMVC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\"\"\"# Loading the model that performed the best on the validation set\n",
        "model1.load_weights('Model.v_2.hdf5')\n",
        "\n",
        "# Testing the model on the Test data\n",
        "(loss, accuracy) = model1.evaluate(x_test, y_test, batch_size=128, verbose=1)\n",
        "\n",
        "print('Accuracy on test data: {:.2f}%'.format(accuracy * 100))\"\"\""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}