Speaker_Classification_v5_40.ipynb

{
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  "metadata": {
    "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_v5_40.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_v5_40.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": "SjuL4ZnF2F_y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#from google.colab import drive\n",
        "#drive.mount('/content/gdrive')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9RXw9shFmGBg",
        "colab_type": "code",
        "outputId": "1a0967ad-dfb1-44d6-d5eb-21774c520f82",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "!pip install soundfile\n",
        "!pip install spafe"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: soundfile in /usr/local/lib/python3.6/dist-packages (0.10.3.post1)\n",
            "Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.6/dist-packages (from soundfile) (1.13.2)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.6/dist-packages (from cffi>=1.0->soundfile) (2.19)\n",
            "Requirement already satisfied: spafe in /usr/local/lib/python3.6/dist-packages (0.1.1)\n",
            "Requirement already satisfied: numpy>=1.17.2 in /usr/local/lib/python3.6/dist-packages (from spafe) (1.17.5)\n",
            "Requirement already satisfied: scipy>=1.3.1 in /usr/local/lib/python3.6/dist-packages (from spafe) (1.4.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rBgxeaV6jtiB",
        "colab_type": "code",
        "outputId": "7ff502cc-98c6-4990-9627-79bb4773de9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import tensorflow\n",
        "tensorflow.test.gpu_device_name() "
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/device:GPU:0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rmhg7V0a2H9L",
        "colab_type": "text"
      },
      "source": [
        "# Import Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6jYJat4GEpzL",
        "colab_type": "code",
        "outputId": "5622e8cc-7b51-4bed-b7a2-14d6d35eccc0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "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 keras\n",
        "from keras.models import Sequential\n",
        "#from __future__ import print_function\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D,Dropout\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from keras.models import model_from_json\n",
        "from keras import backend as K"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "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": "52216b73-3525-447a-995f-2d841f6a8260",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "link = 'https://drive.google.com/open?id=1-jhCnWc1Pg5PKjY1aME8JwsVHXljmjFF'\n",
        "fluff, id = link.split('=')\n",
        "print (id) "
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1-jhCnWc1Pg5PKjY1aME8JwsVHXljmjFF\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.rar')  "
      ],
      "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": "f1b0b867-e31e-45c6-f6c1-131cfd97366b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!pip install patool"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: patool in /usr/local/lib/python3.6/dist-packages (1.12)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ffyDpxaCSgDx",
        "colab_type": "code",
        "outputId": "0a83c9dc-1225-4d65-b306-0e5cacf42166",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "import os\n",
        "os.listdir()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['.config',\n",
              " 'LibriSpeech.rar',\n",
              " 'adc.json',\n",
              " 'LibriSpeech2',\n",
              " 'Model.v_2.hdf5',\n",
              " 'LibriSpeech1',\n",
              " 'LibriSpeech',\n",
              " 'LibriSpeech3',\n",
              " '.ipynb_checkpoints',\n",
              " 'sample_data']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CH0Ug5BPShB8",
        "colab_type": "code",
        "outputId": "684dbe38-98ce-4c36-9032-4cb84a926006",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "%%time\n",
        "import patoolib\n",
        "patoolib.extract_archive(\"LibriSpeech.rar\")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "patool: Extracting LibriSpeech.rar ...\n",
            "patool: running /usr/bin/unrar x -- /content/LibriSpeech.rar\n",
            "patool:     with cwd='./Unpack_wjtw6la5'\n",
            "patool: ... LibriSpeech.rar extracted to `LibriSpeech4' (local file exists).\n",
            "CPU times: user 27.4 ms, sys: 29.4 ms, total: 56.8 ms\n",
            "Wall time: 4.36 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fcQzszcy2htw",
        "colab_type": "text"
      },
      "source": [
        "# Audio Data Preprocessing :"
      ]
    },
    {
      "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": [
        "def mfcc_(sig,fs):\n",
        "    mfccs  = mfcc(sig, fs=fs, num_ceps=20, 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"
      ],
      "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": "V5hyRZYb0njc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from pathlib import Path\n",
        "\n",
        "#for file_path in Path('LibriSpeech/test-clean').glob('**/*.flac'):\n",
        "#     print(file_path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aV0iqD15me3D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def s2c(mfcc=False,lpcc=False):\n",
        "    x,y=[],[]\n",
        "    data = np.empty([1,148,20])\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/test-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",
        "        \n",
        "        quotient = int(feature.shape[0]/148)\n",
        "        end_index = feature.shape[0]-(feature.shape[0]%148)\n",
        "        r = feature[0:end_index,:]\n",
        "        re_data = np.reshape(r,(-1,148,20))#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": "809793cb-7e63-4899-99fd-14756b6599aa",
        "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": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 3 µs, sys: 0 ns, total: 3 µs\n",
            "Wall time: 5.96 µs\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dAmw3NFOEp8p",
        "colab_type": "code",
        "outputId": "14870a88-cbf8-42ca-a162-a305b3549762",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "%%time\n",
        "x,y = s2c(mfcc=True,lpcc=False)\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": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2620it [03:07,  8.99it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "(11806, 148, 20) (11806,)\n",
            "{'1089': 0, '1188': 1, '121': 2, '1221': 3, '1284': 4, '1320': 5, '1580': 6, '1995': 7, '2094': 8, '2300': 9, '237': 10, '260': 11, '2830': 12, '2961': 13, '3570': 14, '3575': 15, '3729': 16, '4077': 17, '4446': 18, '4507': 19, '4970': 20, '4992': 21, '5105': 22, '5142': 23, '5639': 24, '5683': 25, '61': 26, '672': 27, '6829': 28, '6930': 29, '7021': 30, '7127': 31, '7176': 32, '7729': 33, '8224': 34, '8230': 35, '8455': 36, '8463': 37, '8555': 38, '908': 39}\n",
            "CPU times: user 3min 21s, sys: 2min 37s, total: 5min 59s\n",
            "Wall time: 3min 7s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gbqqVr_6Ep9d",
        "colab_type": "code",
        "outputId": "fb4ba339-a60f-4e72-eff9-ed1ad858a0ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "X  = x.reshape(x.shape[0],x.shape[1],x.shape[2],1)\n",
        "print(X.shape)\n",
        "\n",
        "\n",
        "y_binary = keras.utils.to_categorical(y, num_classes = num_classes)\n",
        "print(y_binary.shape)\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": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(11806, 148, 20, 1)\n",
            "(11806, 40)\n",
            "Training shape: (9444, 148, 20, 1), Training label shape : (9444, 40)\n",
            "Training shape: (2362, 148, 20, 1), Training label shape : (2362, 40)\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": "a7e9e2e1-1b2c-4515-fb80-508736add487",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "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": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow is already loaded. Please restart the runtime to change versions.\n",
            "Found GPU at: /device:GPU:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P6w-KrxMEp-O",
        "colab_type": "code",
        "outputId": "f774cf13-b062-4a9e-cb54-27b56a08b02b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 612
        }
      },
      "source": [
        "%%time\n",
        "import numpy as np\n",
        "#import tensorflow.device('/device:GPU:0')\n",
        "import tensorflow.python.keras\n",
        "from tensorflow.python.keras.models import Sequential\n",
        "from tensorflow.python.keras.layers import Dense, Dropout, Flatten\n",
        "from tensorflow.python.keras.layers import Conv2D, MaxPooling2D\n",
        "from keras.optimizers import Adam\n",
        "\n",
        "model = Sequential()\n",
        "# input: 100x100 images with 3 channels -> (100, 100, 3) tensors.\n",
        "# this applies 32 convolution filters of size 3x3 each.\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(40, activation='softmax'))\n",
        "\n",
        "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=['accuracy'])\n",
        "print(model.summary())#Train and Test The Model\n"
      ],
      "execution_count": 22,
      "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, 40)                10280     \n",
            "=================================================================\n",
            "Total params: 1,189,640\n",
            "Trainable params: 1,189,640\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "CPU times: user 580 ms, sys: 177 ms, total: 757 ms\n",
            "Wall time: 758 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EulSDA-nS7lu",
        "colab_type": "code",
        "outputId": "519a91f5-7e4d-4bff-e852-027351853d91",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "\"\"\"\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 Adam\n",
        "\n",
        "model = Sequential()\n",
        "# input: 100x100 images with 3 channels -> (100, 100, 3) tensors.\n",
        "# this applies 32 convolution filters of size 3x3 each.\n",
        "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(x.shape[1],x.shape[2],1)))\n",
        "model.add(Conv2D(32, (3, 3), activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.5))\n",
        "#model.add(BatchNormalization())\n",
        "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Flatten())\n",
        "#model.add(BatchNormalization())\n",
        "model.add(Dense(256, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "#model.add(BatchNormalization())\n",
        "model.add(Dense(256, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "#model.add(BatchNormalization())\n",
        "model.add(Dense(256, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(40, activation='softmax'))\n",
        "\n",
        "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=['accuracy'])\n",
        "print(model.summary())\n",
        "\"\"\""
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nimport numpy as np\\nimport tensorflow.python.keras\\nfrom tensorflow.python.keras.models import Sequential\\nfrom tensorflow.python.keras.layers import Dense, Dropout, Flatten, BatchNormalization\\nfrom tensorflow.python.keras.layers import Conv2D, MaxPooling2D\\nfrom keras.optimizers import Adam\\n\\nmodel = Sequential()\\n# input: 100x100 images with 3 channels -> (100, 100, 3) tensors.\\n# this applies 32 convolution filters of size 3x3 each.\\nmodel.add(Conv2D(32, (3, 3), activation=\\'relu\\', input_shape=(x.shape[1],x.shape[2],1)))\\nmodel.add(Conv2D(32, (3, 3), activation=\\'relu\\'))\\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\\nmodel.add(Dropout(0.5))\\n#model.add(BatchNormalization())\\nmodel.add(Conv2D(64, (3, 3), activation=\\'relu\\'))\\nmodel.add(Conv2D(64, (3, 3), activation=\\'relu\\'))\\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\\nmodel.add(Dropout(0.5))\\n\\nmodel.add(Flatten())\\n#model.add(BatchNormalization())\\nmodel.add(Dense(256, activation=\\'relu\\'))\\nmodel.add(Dropout(0.5))\\n#model.add(BatchNormalization())\\nmodel.add(Dense(256, activation=\\'relu\\'))\\nmodel.add(Dropout(0.5))\\n#model.add(BatchNormalization())\\nmodel.add(Dense(256, activation=\\'relu\\'))\\nmodel.add(Dropout(0.5))\\nmodel.add(Dense(40, activation=\\'softmax\\'))\\n\\nmodel.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\\'accuracy\\'])\\nprint(model.summary())\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0C4e0DA7Ep_K",
        "colab_type": "code",
        "outputId": "56db8a3c-142b-47bb-aac6-8e7953c3cf01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "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 = model.fit(x_train, y_train, batch_size=128, epochs=500, \n",
        "                    validation_data=(x_test, y_test), verbose=1, callbacks=[checkpoint])"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "9444/9444 [==============================] - 2s 174us/sample - loss: 0.0913 - accuracy: 0.9705 - val_loss: 0.3419 - val_accuracy: 0.9183\n",
            "Epoch 497/500\n",
            "9444/9444 [==============================] - 2s 176us/sample - loss: 0.0875 - accuracy: 0.9726 - val_loss: 0.3797 - val_accuracy: 0.9081\n",
            "Epoch 498/500\n",
            "9444/9444 [==============================] - 2s 177us/sample - loss: 0.1096 - accuracy: 0.9662 - val_loss: 0.3392 - val_accuracy: 0.9141\n",
            "Epoch 499/500\n",
            "9444/9444 [==============================] - 2s 174us/sample - loss: 0.0993 - accuracy: 0.9698 - val_loss: 0.3053 - val_accuracy: 0.9213\n",
            "Epoch 500/500\n",
            "9444/9444 [==============================] - 2s 175us/sample - loss: 0.0948 - accuracy: 0.9697 - val_loss: 0.3166 - val_accuracy: 0.9208\n",
            "CPU times: user 8min 53s, sys: 2min 47s, total: 11min 41s\n",
            "Wall time: 14min 2s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RwEThfog4WjM",
        "colab_type": "code",
        "outputId": "39d92378-2185-4b78-c073-b615d4a6e5d5",
        "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=128, verbose=1)\n",
        "\n",
        "print('Accuracy on test data: {:.2f}%'.format(accuracy * 100))"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2362/2362 [==============================] - 0s 63us/sample - loss: 0.2963 - accuracy: 0.9217\n",
            "Accuracy on test data: 92.17%\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": "a0050ef4-88a8-4cef-d8d8-53540f540f34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "\n",
        "accuracy_plot(history)\n",
        "loss_plot(history)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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k/N3sKCk3klNSwd3dm5KUW8rOM1msfKwvzZvIjP37v97LgfhcpvcP5eYwb+Zt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\ng3S2NPKqU47Yv9NfTUop3zi3/JzRVdMHr8sTGF7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8/X5o7oQTL4aFKytdniRJUs0r5wr0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\nJEmaAAO0JEmSNAH/D9H7v2XRmIYeAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Gm2w1dNiBliRJSoP9BugYo9t190auFtqTGegtze2lrkaSJElF5EoahZCrgfbm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\n8RMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NgNfqbzP8D18",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}