Speaker_Classification_v6_40.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "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_v6_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_v6_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": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "outputId": "09b9717b-3237-4e8b-dfd5-7d353cff4b6f"
      },
      "source": [
        "!pip install --upgrade tensorflow-gpu"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already up-to-date: tensorflow-gpu in /usr/local/lib/python3.6/dist-packages (2.1.0)\n",
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            "Requirement already satisfied, skipping upgrade: protobuf>=3.8.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (3.10.0)\n",
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            "Requirement already satisfied, skipping upgrade: tensorboard<2.2.0,>=2.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (2.1.0)\n",
            "Requirement already satisfied, skipping upgrade: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (0.1.8)\n",
            "Requirement already satisfied, skipping upgrade: keras-applications>=1.0.8 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.0.8)\n",
            "Requirement already satisfied, skipping upgrade: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.1.0)\n",
            "Requirement already satisfied, skipping upgrade: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (0.9.0)\n",
            "Requirement already satisfied, skipping upgrade: keras-preprocessing>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.1.0)\n",
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            "Requirement already satisfied, skipping upgrade: tensorflow-estimator<2.2.0,>=2.1.0rc0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (2.1.0)\n",
            "Requirement already satisfied, skipping upgrade: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (3.1.0)\n",
            "Requirement already satisfied, skipping upgrade: scipy==1.4.1; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.4.1)\n",
            "Requirement already satisfied, skipping upgrade: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.11.2)\n",
            "Requirement already satisfied, skipping upgrade: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.8.0->tensorflow-gpu) (42.0.2)\n",
            "Requirement already satisfied, skipping upgrade: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (0.4.1)\n",
            "Requirement already satisfied, skipping upgrade: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (1.11.0)\n",
            "Requirement already satisfied, skipping upgrade: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (3.1.1)\n",
            "Requirement already satisfied, skipping upgrade: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (0.16.0)\n",
            "Requirement already satisfied, skipping upgrade: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (2.21.0)\n",
            "Requirement already satisfied, skipping upgrade: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tensorflow-gpu) (2.8.0)\n",
            "Requirement already satisfied, skipping upgrade: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (1.3.0)\n",
            "Requirement already satisfied, skipping upgrade: rsa<4.1,>=3.1.4 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (4.0)\n",
            "Requirement already satisfied, skipping upgrade: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (0.2.7)\n",
            "Requirement already satisfied, skipping upgrade: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (4.0.0)\n",
            "Requirement already satisfied, skipping upgrade: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (1.24.3)\n",
            "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (2019.11.28)\n",
            "Requirement already satisfied, skipping upgrade: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (2.8)\n",
            "Requirement already satisfied, skipping upgrade: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (3.0.4)\n",
            "Requirement already satisfied, skipping upgrade: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (3.1.0)\n",
            "Requirement already satisfied, skipping upgrade: pyasn1>=0.1.3 in /usr/local/lib/python3.6/dist-packages (from rsa<4.1,>=3.1.4->google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (0.4.8)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": "c4a639e3-ce18-4202-df40-fa0d0f672210",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        }
      },
      "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: scipy>=1.3.1 in /usr/local/lib/python3.6/dist-packages (from spafe) (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.17.2 in /usr/local/lib/python3.6/dist-packages (from spafe) (1.17.5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rBgxeaV6jtiB",
        "colab_type": "code",
        "outputId": "1e0855bb-3c96-4db9-b9e7-149ebbedd6ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "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": "b376bb3c-029d-4421-ae04-7acc4bbe3b0c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "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": "f6b10dc5-cebc-462f-8edf-67aa08264d31",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "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": "000d563b-903c-4de5-965f-30d7205b7d97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "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": "7c46a6d2-56b7-438e-c7cc-6926b7fc2d3b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        }
      },
      "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",
              " 'Model.v_2.hdf5',\n",
              " 'LibriSpeech',\n",
              " 'sample_data']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CH0Ug5BPShB8",
        "colab_type": "code",
        "outputId": "f07062f5-9398-4a7e-a685-e12015deb82c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "%%time\n",
        "import patoolib\n",
        "#patoolib.extract_archive(\"LibriSpeech.rar\")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 3.73 ms, sys: 148 µs, total: 3.88 ms\n",
            "Wall time: 4.65 ms\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": "80d97111-16e3-47a0-9f5e-e0d0b3cb6d57",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "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 2 µs, sys: 0 ns, total: 2 µs\n",
            "Wall time: 4.05 µs\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dAmw3NFOEp8p",
        "colab_type": "code",
        "outputId": "b846fb11-9e9d-4e9d-83b6-492f16f4c8d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "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 [02:57,  8.86it/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 12s, sys: 2min 30s, total: 5min 42s\n",
            "Wall time: 2min 57s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gbqqVr_6Ep9d",
        "colab_type": "code",
        "outputId": "761e6233-f646-4eb3-bcb5-3ed02ac1244c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        }
      },
      "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",
        "#y_binary = np.reshape(y,(-1,1))\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": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(11806, 148, 20, 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. 1. 0.]\n",
            " [0. 0. 0. ... 0. 1. 0.]\n",
            " [0. 0. 0. ... 0. 1. 0.]]\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": "efe4f7de-270c-4917-fcb6-94acc9582f4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "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": "9924bcc2-e3c0-4ede-c569-3911bfbb9d3d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 586
        }
      },
      "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 545 ms, sys: 87.7 ms, total: 633 ms\n",
            "Wall time: 633 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EulSDA-nS7lu",
        "colab_type": "code",
        "outputId": "41957d19-d40e-45dd-8157-1a7e7031794b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "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",
        "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 = model.fit(x_train, y_train, batch_size=128, epochs=500, \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": 50
        },
        "outputId": "4c38a9ed-2cc4-406b-a0a4-3b4499e6c59c"
      },
      "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 60us/sample - loss: 0.2320 - accuracy: 0.9348\n",
            "Accuracy on test data: 93.48%\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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "302035d1-ed3c-4eaa-e3e8-9fb89f7597bf"
      },
      "source": [
        "\n",
        "accuracy_plot(history)\n",
        "loss_plot(history)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jWJZUQLiPG/ed34l3V6bx445c/Nyd8XAxMff2BOosNlalFPH8okRK7XO6dg/3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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GONq6eotcjCRJkgplsB1oYoxXAlcWsJbxLVtLNZ0ArG/vKXIxkiRJKpQdBugQ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\n1NsczBgSx7xf9jO2WxgHymo4UFbD70Z3YumuApJzy3E3m+gfF8iGjBK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            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NgNfqbzP8D18",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c9c5754b-ceba-4543-9fd2-9431b223603b"
      },
      "source": [
        "history.history"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mNwKuBxHgY0d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}