Speaker_Classification_v4_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_v4_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_v4_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",
        "outputId": "5a1cd4bb-00f0-4d80-c50c-9b65e4138c62",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        }
      },
      "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",
            "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: 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: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.11.2)\n",
            "Requirement already satisfied, skipping upgrade: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.15.0)\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: 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: 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: six>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.12.0)\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: 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: wheel>=0.26; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (0.33.6)\n",
            "Requirement already satisfied, skipping upgrade: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.17.5)\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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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",
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            "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: 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: 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",
            "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"
          ],
          "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": "7f8d5d00-36e3-43d5-e6a1-89688a4bc835",
        "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: 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": "47344983-7c83-4132-86b0-bf70fd4133ec",
        "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": "c69d7498-6e6d-49c2-9af4-1ac7bb887243",
        "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": "735eadcf-edf2-42f0-cec6-32db8a903c00",
        "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": "656be9ae-620c-43ca-ed00-b8ec3301703a",
        "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": "cf841c40-cb0d-4a84-c7b8-fa77ba987323",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "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",
              " '.ipynb_checkpoints',\n",
              " 'sample_data']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CH0Ug5BPShB8",
        "colab_type": "code",
        "outputId": "6f1478bb-b001-4b5d-aa77-af77e83af894",
        "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_btmorvds'\n",
            "patool: ... LibriSpeech.rar extracted to `LibriSpeech3' (local file exists).\n",
            "CPU times: user 18.7 ms, sys: 31.5 ms, total: 50.2 ms\n",
            "Wall time: 4.05 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": "daa72e22-0241-4a1e-c37c-25c405d88bde",
        "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 2 µs, sys: 0 ns, total: 2 µs\n",
            "Wall time: 4.77 µs\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dAmw3NFOEp8p",
        "colab_type": "code",
        "outputId": "dfa1b46a-c803-42d7-9564-bc1b482fb8f6",
        "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 [02:48, 11.21it/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 2s, sys: 2min 22s, total: 5min 25s\n",
            "Wall time: 2min 48s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gbqqVr_6Ep9d",
        "colab_type": "code",
        "outputId": "6c2a5540-a91c-4ad1-fefd-dca0f6f20be3",
        "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": "45eaea20-da66-4349-f9c2-b1423fa9db74",
        "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": "91ba3304-4ff4-4ffc-987f-df7710772ca5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "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",
        "model2 = Sequential()\n",
        "# input: 100x100 images with 3 channels -> (100, 100, 3) tensors.\n",
        "# this applies 32 convolution filters of size 3x3 each.\n",
        "model2.add(Conv2D(32, (3, 3), activation='relu', input_shape=(x.shape[1],x.shape[2],1)))\n",
        "\n",
        "model2.add(Conv2D(32, (3, 3), activation='relu'))\n",
        "model2.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model2.add(Dropout(0.5))\n",
        "\n",
        "model2.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "\n",
        "model2.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "model2.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model2.add(Dropout(0.5))\n",
        "model2.add(Flatten())\n",
        "\n",
        "model2.add(Dense(256, activation='relu'))\n",
        "model2.add(Dropout(0.5))\n",
        "\n",
        "model2.add(Dense(40, activation='softmax'))\n",
        "\n",
        "model2.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=['accuracy'])\n",
        "print(model2.summary())#Train and Test The Model\n",
        "\"\"\""
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'%%time\\nimport numpy as np\\n#import tensorflow.device(\\'/device:GPU:0\\')\\nimport tensorflow.python.keras\\nfrom tensorflow.python.keras.models import Sequential\\nfrom tensorflow.python.keras.layers import Dense, Dropout, Flatten\\nfrom tensorflow.python.keras.layers import Conv2D, MaxPooling2D\\nfrom keras.optimizers import Adam\\n\\nmodel2 = Sequential()\\n# input: 100x100 images with 3 channels -> (100, 100, 3) tensors.\\n# this applies 32 convolution filters of size 3x3 each.\\nmodel2.add(Conv2D(32, (3, 3), activation=\\'relu\\', input_shape=(x.shape[1],x.shape[2],1)))\\n\\nmodel2.add(Conv2D(32, (3, 3), activation=\\'relu\\'))\\nmodel2.add(MaxPooling2D(pool_size=(2, 2)))\\nmodel2.add(Dropout(0.5))\\n\\nmodel2.add(Conv2D(64, (3, 3), activation=\\'relu\\'))\\n\\nmodel2.add(Conv2D(64, (3, 3), activation=\\'relu\\'))\\nmodel2.add(MaxPooling2D(pool_size=(2, 2)))\\nmodel2.add(Dropout(0.5))\\nmodel2.add(Flatten())\\n\\nmodel2.add(Dense(256, activation=\\'relu\\'))\\nmodel2.add(Dropout(0.5))\\n\\nmodel2.add(Dense(40, activation=\\'softmax\\'))\\n\\nmodel2.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\\'accuracy\\'])\\nprint(model2.summary())#Train and Test The Model\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EulSDA-nS7lu",
        "colab_type": "code",
        "outputId": "1ca4b79a-026a-45eb-ccfb-88d49f679d68",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        }
      },
      "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": "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, 256)               65792     \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 256)               65792     \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 40)                10280     \n",
            "=================================================================\n",
            "Total params: 1,321,224\n",
            "Trainable params: 1,321,224\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0C4e0DA7Ep_K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%%time\n",
        "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",
        "outputId": "481e1d5b-3413-4453-c3dd-0a1f23d4c535",
        "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": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2362/2362 [==============================] - 0s 86us/sample - loss: 0.3479 - accuracy: 0.9064\n",
            "Accuracy on test data: 90.64%\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": "c8740b2e-86ff-48f3-9b34-ea0310d4e35f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "\n",
        "accuracy_plot(history)\n",
        "loss_plot(history)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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begWSXVLF5M+38tndfRjR0Y+EnFLWJuQRHeLB3G3puNjb4uNiR2phBc9c05Ej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\nnK56xr22egh5UDp12I+TJGFLQxtLZhigJUmSjkc6V6DPBjYnSbIlSZJ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qB8L5BmhJkqQx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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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FQE+3AVqSJKlYGKCzqTx9G+/e7q6EC5EkSdJwMUBnU3k1AAM9BmhJkqRiYYDOpswK9GDf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\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": []
    }
  ]
}