Model_creation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "SER_CNN1D_MFCC_ZCR (1).ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "weHAn3pKILU-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "45c8e91e-2d7e-48da-92af-e37cb69b0bc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gsmGH39Zskas"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd \n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import librosa as lb\n",
        "import librosa.display as  ldisplay\n",
        "from IPython.display import Audio\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Conv1D,MaxPooling1D,Flatten, Dropout, BatchNormalization\n",
        "from keras import Input\n",
        "from tensorflow.keras.optimizers import Adam,RMSprop\n",
        "from keras.utils import np_utils\n",
        "from tensorflow.python.keras.callbacks import EarlyStopping, ReduceLROnPlateau"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_emotion_df=pd.read_csv('/content/drive/MyDrive/praneeth/Feature_extracted_dataset (3).csv')"
      ],
      "metadata": {
        "id": "zAQEO2EfuYkY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "feature_emotion_df.head()"
      ],
      "metadata": {
        "id": "sMc_sfgEusBz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "7f3ca990-9acd-4033-ae86-ca782c18ec22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          0         1         2         3         4         5         6  \\\n",
              "0  0.065430  0.071289  0.071289  0.052246  0.048340  0.106445  0.171387   \n",
              "1  0.262207  0.386230  0.518555  0.517090  0.508789  0.520020  0.510254   \n",
              "2  0.063965  0.068359  0.068359  0.058594  0.073242  0.135742  0.201172   \n",
              "3  0.209473  0.254883  0.254883  0.176758  0.132324  0.174316  0.263184   \n",
              "4  0.044922  0.061035  0.076172  0.066406  0.069336  0.065918  0.070801   \n",
              "\n",
              "          7         8         9  ...       2259       2260       2261  \\\n",
              "0  0.228027  0.248047  0.235840  ...  24.838633  12.986765 -14.747004   \n",
              "1  0.519043  0.515625  0.512695  ...  15.930484   9.471877  -1.277585   \n",
              "2  0.257324  0.236816  0.213379  ...  28.065681   3.222074 -19.927702   \n",
              "3  0.332520  0.323730  0.304688  ...  25.137230   4.358447 -16.416542   \n",
              "4  0.055664  0.052734  0.063477  ...   2.206598   9.609125 -10.786152   \n",
              "\n",
              "       2262       2263       2264       2265      2266       2267  Emotion  \n",
              "0  4.591933  -2.150724 -12.089533 -12.665945 -0.801496 -16.449780    angry  \n",
              "1 -0.975825  -2.921517 -10.849216  -8.569133 -8.426794 -10.460051    angry  \n",
              "2 -0.588958  -7.595579  -5.199802  -9.166500  0.022583  -5.449619    angry  \n",
              "3  2.289107  -5.057943 -11.831305  -7.739297 -2.064261 -14.117687    angry  \n",
              "4  4.056137  14.608828   8.215265   1.501894  8.435717   6.355412     fear  \n",
              "\n",
              "[5 rows x 2269 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-691a3ce8-e1cd-415f-aeaa-ab2da2f172fd\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>...</th>\n",
              "      <th>2259</th>\n",
              "      <th>2260</th>\n",
              "      <th>2261</th>\n",
              "      <th>2262</th>\n",
              "      <th>2263</th>\n",
              "      <th>2264</th>\n",
              "      <th>2265</th>\n",
              "      <th>2266</th>\n",
              "      <th>2267</th>\n",
              "      <th>Emotion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.065430</td>\n",
              "      <td>0.071289</td>\n",
              "      <td>0.071289</td>\n",
              "      <td>0.052246</td>\n",
              "      <td>0.048340</td>\n",
              "      <td>0.106445</td>\n",
              "      <td>0.171387</td>\n",
              "      <td>0.228027</td>\n",
              "      <td>0.248047</td>\n",
              "      <td>0.235840</td>\n",
              "      <td>...</td>\n",
              "      <td>24.838633</td>\n",
              "      <td>12.986765</td>\n",
              "      <td>-14.747004</td>\n",
              "      <td>4.591933</td>\n",
              "      <td>-2.150724</td>\n",
              "      <td>-12.089533</td>\n",
              "      <td>-12.665945</td>\n",
              "      <td>-0.801496</td>\n",
              "      <td>-16.449780</td>\n",
              "      <td>angry</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.262207</td>\n",
              "      <td>0.386230</td>\n",
              "      <td>0.518555</td>\n",
              "      <td>0.517090</td>\n",
              "      <td>0.508789</td>\n",
              "      <td>0.520020</td>\n",
              "      <td>0.510254</td>\n",
              "      <td>0.519043</td>\n",
              "      <td>0.515625</td>\n",
              "      <td>0.512695</td>\n",
              "      <td>...</td>\n",
              "      <td>15.930484</td>\n",
              "      <td>9.471877</td>\n",
              "      <td>-1.277585</td>\n",
              "      <td>-0.975825</td>\n",
              "      <td>-2.921517</td>\n",
              "      <td>-10.849216</td>\n",
              "      <td>-8.569133</td>\n",
              "      <td>-8.426794</td>\n",
              "      <td>-10.460051</td>\n",
              "      <td>angry</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.063965</td>\n",
              "      <td>0.068359</td>\n",
              "      <td>0.068359</td>\n",
              "      <td>0.058594</td>\n",
              "      <td>0.073242</td>\n",
              "      <td>0.135742</td>\n",
              "      <td>0.201172</td>\n",
              "      <td>0.257324</td>\n",
              "      <td>0.236816</td>\n",
              "      <td>0.213379</td>\n",
              "      <td>...</td>\n",
              "      <td>28.065681</td>\n",
              "      <td>3.222074</td>\n",
              "      <td>-19.927702</td>\n",
              "      <td>-0.588958</td>\n",
              "      <td>-7.595579</td>\n",
              "      <td>-5.199802</td>\n",
              "      <td>-9.166500</td>\n",
              "      <td>0.022583</td>\n",
              "      <td>-5.449619</td>\n",
              "      <td>angry</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.209473</td>\n",
              "      <td>0.254883</td>\n",
              "      <td>0.254883</td>\n",
              "      <td>0.176758</td>\n",
              "      <td>0.132324</td>\n",
              "      <td>0.174316</td>\n",
              "      <td>0.263184</td>\n",
              "      <td>0.332520</td>\n",
              "      <td>0.323730</td>\n",
              "      <td>0.304688</td>\n",
              "      <td>...</td>\n",
              "      <td>25.137230</td>\n",
              "      <td>4.358447</td>\n",
              "      <td>-16.416542</td>\n",
              "      <td>2.289107</td>\n",
              "      <td>-5.057943</td>\n",
              "      <td>-11.831305</td>\n",
              "      <td>-7.739297</td>\n",
              "      <td>-2.064261</td>\n",
              "      <td>-14.117687</td>\n",
              "      <td>angry</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.044922</td>\n",
              "      <td>0.061035</td>\n",
              "      <td>0.076172</td>\n",
              "      <td>0.066406</td>\n",
              "      <td>0.069336</td>\n",
              "      <td>0.065918</td>\n",
              "      <td>0.070801</td>\n",
              "      <td>0.055664</td>\n",
              "      <td>0.052734</td>\n",
              "      <td>0.063477</td>\n",
              "      <td>...</td>\n",
              "      <td>2.206598</td>\n",
              "      <td>9.609125</td>\n",
              "      <td>-10.786152</td>\n",
              "      <td>4.056137</td>\n",
              "      <td>14.608828</td>\n",
              "      <td>8.215265</td>\n",
              "      <td>1.501894</td>\n",
              "      <td>8.435717</td>\n",
              "      <td>6.355412</td>\n",
              "      <td>fear</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 2269 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-691a3ce8-e1cd-415f-aeaa-ab2da2f172fd')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-691a3ce8-e1cd-415f-aeaa-ab2da2f172fd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-691a3ce8-e1cd-415f-aeaa-ab2da2f172fd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_emotion_df.shape"
      ],
      "metadata": {
        "id": "odorIWzHur9o",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "159d64f7-1497-4b8f-a2b4-eb99db8971db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(38968, 2269)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_emotion_df= feature_emotion_df.fillna(0)\n",
        "feature_emotion_df.shape"
      ],
      "metadata": {
        "id": "L0Mvbehrur5N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "101cf3a3-cfb8-4dae-9d08-7cbe5457b197"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(38968, 2269)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X=feature_emotion_df.iloc[:,:-1].values\n",
        "Y=feature_emotion_df.iloc[:,-1].values\n",
        "X"
      ],
      "metadata": {
        "id": "PzBDDHZEur10",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fbcc65cb-28e9-4230-fcf3-e8a9ab70603f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 6.54296875e-02,  7.12890625e-02,  7.12890625e-02, ...,\n",
              "        -1.26659451e+01, -8.01496029e-01, -1.64497795e+01],\n",
              "       [ 2.62207031e-01,  3.86230469e-01,  5.18554688e-01, ...,\n",
              "        -8.56913298e+00, -8.42679413e+00, -1.04600506e+01],\n",
              "       [ 6.39648438e-02,  6.83593750e-02,  6.83593750e-02, ...,\n",
              "        -9.16650009e+00,  2.25828886e-02, -5.44961882e+00],\n",
              "       ...,\n",
              "       [ 2.63671875e-02,  4.19921875e-02,  5.61523438e-02, ...,\n",
              "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
              "       [ 1.46484375e-02,  1.90429688e-02,  2.44140625e-02, ...,\n",
              "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
              "       [ 1.46484375e-02,  2.00195312e-02,  2.53906250e-02, ...,\n",
              "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00]])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "id": "7P4ZAQ4Nu3Eh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "142afadd-7fb6-459b-857e-9e372c7272f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 6.54296875e-02,  7.12890625e-02,  7.12890625e-02, ...,\n",
              "        -1.26659451e+01, -8.01496029e-01, -1.64497795e+01],\n",
              "       [ 2.62207031e-01,  3.86230469e-01,  5.18554688e-01, ...,\n",
              "        -8.56913298e+00, -8.42679413e+00, -1.04600506e+01],\n",
              "       [ 6.39648438e-02,  6.83593750e-02,  6.83593750e-02, ...,\n",
              "        -9.16650009e+00,  2.25828886e-02, -5.44961882e+00],\n",
              "       ...,\n",
              "       [ 2.63671875e-02,  4.19921875e-02,  5.61523438e-02, ...,\n",
              "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
              "       [ 1.46484375e-02,  1.90429688e-02,  2.44140625e-02, ...,\n",
              "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
              "       [ 1.46484375e-02,  2.00195312e-02,  2.53906250e-02, ...,\n",
              "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00]])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.utils import np_utils\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "LE = LabelEncoder()\n",
        "Y = np_utils.to_categorical(LE.fit_transform(Y))"
      ],
      "metadata": {
        "id": "qEm936eDu3B0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y"
      ],
      "metadata": {
        "id": "_7PprAWJu8Cp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "434cae51-6d6b-4ac7-cb5b-2c583a5a589d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 0., 0., 0., 0.],\n",
              "       [1., 0., 0., 0., 0., 0.],\n",
              "       [1., 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.]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(X,Y,test_size=0.07,random_state=40)\n",
        "x_train,x_validation,y_train,y_validation=train_test_split(x_train,y_train,test_size=0.25,random_state=40)"
      ],
      "metadata": {
        "id": "IE3_b37su8AE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape,y_train.shape,x_validation.shape,y_validation.shape,x_test.shape,y_test.shape"
      ],
      "metadata": {
        "id": "7LKt6jf_u79r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c35b90f6-9fcf-4c57-cd65-d5400ee00512"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((27180, 2268), (27180, 6), (9060, 2268), (9060, 6), (2728, 2268), (2728, 6))"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sc=StandardScaler()\n",
        "x_train=sc.fit_transform(x_train)\n",
        "x_test=sc.transform(x_test)\n",
        "x_validation=sc.transform(x_validation)"
      ],
      "metadata": {
        "id": "EeU9ckGHu766"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train=np.expand_dims(x_train,axis=2)\n",
        "x_test=np.expand_dims(x_test,axis=2)\n",
        "x_validation=np.expand_dims(x_validation,axis=2)"
      ],
      "metadata": {
        "id": "5qeKROZIu2--"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape,y_train.shape,x_validation.shape,y_validation.shape,x_test.shape,y_test.shape"
      ],
      "metadata": {
        "id": "GE7rS4YXury-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16e4a6b9-83f0-4adf-b277-5e3d965eeba5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((27180, 2268, 1),\n",
              " (27180, 6),\n",
              " (9060, 2268, 1),\n",
              " (9060, 6),\n",
              " (2728, 2268, 1),\n",
              " (2728, 6))"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape[1]"
      ],
      "metadata": {
        "id": "U9_9MYHnurwA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "721d4c18-fb7c-4095-d079-010110f4c027"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2268"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras import backend as K\n",
        "\n",
        "def recall_m(y_true, y_pred):\n",
        "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
        "    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
        "    recall = true_positives / (possible_positives + K.epsilon())\n",
        "    return recall\n",
        "\n",
        "def precision_m(y_true, y_pred):\n",
        "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
        "    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
        "    precision = true_positives / (predicted_positives + K.epsilon())\n",
        "    return precision\n",
        "\n",
        "def f1_m(y_true, y_pred):\n",
        "    precision = precision_m(y_true, y_pred)\n",
        "    recall = recall_m(y_true, y_pred)\n",
        "    return 2*((precision*recall)/(precision+recall+K.epsilon()))"
      ],
      "metadata": {
        "id": "vi9z_QClbGV0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=Sequential()"
      ],
      "metadata": {
        "id": "B0vjT31uvPGM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(Conv1D(128,5,activation='relu',input_shape=(x_train.shape[1],x_train.shape[2])))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling1D(pool_size=4,strides=2,padding='same'))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Conv1D(256,5,activation='relu',padding='same'))\n",
        "model.add(Conv1D(256,5,activation='relu',padding='same'))\n",
        "model.add(Conv1D(256,5,activation='relu',padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling1D(pool_size=4,strides=2))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Conv1D(512,5,activation='relu',padding='same'))\n",
        "model.add(Conv1D(512,5,activation='relu',padding='same'))\n",
        "model.add(Conv1D(512,5,activation='relu',padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling1D(pool_size=4,strides=2))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Conv1D(128,3,activation='relu',padding='same'))\n",
        "model.add(Conv1D(128,3,activation='relu',padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling1D(pool_size=4,strides=2))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(512,activation='relu'))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(128,activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dense(6,activation='softmax'))"
      ],
      "metadata": {
        "id": "icTumdeyvPCZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer=\"Adam\",loss='categorical_crossentropy',metrics=['accuracy',f1_m])"
      ],
      "metadata": {
        "id": "D4N-plzbNfTD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "id": "LYaK_MUCOeis",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c56ee04b-7c28-46b6-9db4-d9e83471cf6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv1d (Conv1D)             (None, 2264, 128)         768       \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 2264, 128)        512       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " max_pooling1d (MaxPooling1D  (None, 1132, 128)        0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 1132, 128)         0         \n",
            "                                                                 \n",
            " conv1d_1 (Conv1D)           (None, 1132, 256)         164096    \n",
            "                                                                 \n",
            " conv1d_2 (Conv1D)           (None, 1132, 256)         327936    \n",
            "                                                                 \n",
            " conv1d_3 (Conv1D)           (None, 1132, 256)         327936    \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 1132, 256)        1024      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling1d_1 (MaxPooling  (None, 565, 256)         0         \n",
            " 1D)                                                             \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 565, 256)          0         \n",
            "                                                                 \n",
            " conv1d_4 (Conv1D)           (None, 565, 512)          655872    \n",
            "                                                                 \n",
            " conv1d_5 (Conv1D)           (None, 565, 512)          1311232   \n",
            "                                                                 \n",
            " conv1d_6 (Conv1D)           (None, 565, 512)          1311232   \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 565, 512)         2048      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling1d_2 (MaxPooling  (None, 281, 512)         0         \n",
            " 1D)                                                             \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 281, 512)          0         \n",
            "                                                                 \n",
            " conv1d_7 (Conv1D)           (None, 281, 128)          196736    \n",
            "                                                                 \n",
            " conv1d_8 (Conv1D)           (None, 281, 128)          49280     \n",
            "                                                                 \n",
            " batch_normalization_3 (Batc  (None, 281, 128)         512       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling1d_3 (MaxPooling  (None, 139, 128)         0         \n",
            " 1D)                                                             \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 139, 128)          0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 17792)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 512)               9110016   \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 512)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 128)               65664     \n",
            "                                                                 \n",
            " batch_normalization_4 (Batc  (None, 128)              512       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 6)                 774       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 13,526,150\n",
            "Trainable params: 13,523,846\n",
            "Non-trainable params: 2,304\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "earlystopping = EarlyStopping(monitor =\"val_accuracy\",\n",
        "                              mode = 'auto', patience = 5,\n",
        "                              restore_best_weights = True)\n",
        "\n",
        "learning_rate_reduction = ReduceLROnPlateau(monitor='val_accuracy',\n",
        "                                            patience=3,\n",
        "                                            verbose=1,\n",
        "                                            factor=0.5,\n",
        "                                            min_lr=0.00001)"
      ],
      "metadata": {
        "id": "_2fygplNSs9H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history=model.fit(x_train,y_train,batch_size=64,epochs=18,validation_data=(x_validation,y_validation),\n",
        "          validation_batch_size=32, callbacks=[earlystopping, learning_rate_reduction])"
      ],
      "metadata": {
        "id": "U7U0wrVjOhUU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7e8503a4-72ed-446d-8436-11bdf573d510"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/18\n",
            "425/425 [==============================] - 170s 370ms/step - loss: 1.4733 - accuracy: 0.4048 - f1_m: 0.2768 - val_loss: 2.1243 - val_accuracy: 0.3113 - val_f1_m: 0.2701 - lr: 0.0010\n",
            "Epoch 2/18\n",
            "425/425 [==============================] - 167s 393ms/step - loss: 1.2707 - accuracy: 0.4924 - f1_m: 0.4036 - val_loss: 1.2745 - val_accuracy: 0.5045 - val_f1_m: 0.4499 - lr: 0.0010\n",
            "Epoch 3/18\n",
            "425/425 [==============================] - 167s 392ms/step - loss: 1.1707 - accuracy: 0.5389 - f1_m: 0.4650 - val_loss: 1.2793 - val_accuracy: 0.5105 - val_f1_m: 0.4736 - lr: 0.0010\n",
            "Epoch 4/18\n",
            "425/425 [==============================] - 168s 395ms/step - loss: 1.0794 - accuracy: 0.5744 - f1_m: 0.5212 - val_loss: 1.0635 - val_accuracy: 0.5777 - val_f1_m: 0.5165 - lr: 0.0010\n",
            "Epoch 5/18\n",
            "425/425 [==============================] - 168s 396ms/step - loss: 0.9965 - accuracy: 0.6152 - f1_m: 0.5691 - val_loss: 1.0739 - val_accuracy: 0.5911 - val_f1_m: 0.5498 - lr: 0.0010\n",
            "Epoch 6/18\n",
            "425/425 [==============================] - 168s 395ms/step - loss: 0.8561 - accuracy: 0.6713 - f1_m: 0.6429 - val_loss: 0.8425 - val_accuracy: 0.6785 - val_f1_m: 0.6377 - lr: 0.0010\n",
            "Epoch 7/18\n",
            "425/425 [==============================] - 168s 396ms/step - loss: 0.7068 - accuracy: 0.7358 - f1_m: 0.7227 - val_loss: 0.7345 - val_accuracy: 0.7310 - val_f1_m: 0.7136 - lr: 0.0010\n",
            "Epoch 8/18\n",
            "425/425 [==============================] - 168s 394ms/step - loss: 0.5439 - accuracy: 0.8001 - f1_m: 0.7939 - val_loss: 0.9060 - val_accuracy: 0.6712 - val_f1_m: 0.6600 - lr: 0.0010\n",
            "Epoch 9/18\n",
            "425/425 [==============================] - 168s 395ms/step - loss: 0.4168 - accuracy: 0.8515 - f1_m: 0.8485 - val_loss: 0.6082 - val_accuracy: 0.7817 - val_f1_m: 0.7808 - lr: 0.0010\n",
            "Epoch 10/18\n",
            "425/425 [==============================] - 168s 395ms/step - loss: 0.3035 - accuracy: 0.8926 - f1_m: 0.8924 - val_loss: 0.4165 - val_accuracy: 0.8596 - val_f1_m: 0.8589 - lr: 0.0010\n",
            "Epoch 11/18\n",
            "425/425 [==============================] - 167s 394ms/step - loss: 0.2382 - accuracy: 0.9146 - f1_m: 0.9149 - val_loss: 0.5678 - val_accuracy: 0.8050 - val_f1_m: 0.8055 - lr: 0.0010\n",
            "Epoch 12/18\n",
            "425/425 [==============================] - 167s 393ms/step - loss: 0.2046 - accuracy: 0.9294 - f1_m: 0.9286 - val_loss: 0.5900 - val_accuracy: 0.7989 - val_f1_m: 0.8000 - lr: 0.0010\n",
            "Epoch 13/18\n",
            "425/425 [==============================] - ETA: 0s - loss: 0.1749 - accuracy: 0.9398 - f1_m: 0.9393\n",
            "Epoch 00013: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
            "425/425 [==============================] - 167s 394ms/step - loss: 0.1749 - accuracy: 0.9398 - f1_m: 0.9393 - val_loss: 0.4684 - val_accuracy: 0.8453 - val_f1_m: 0.8472 - lr: 0.0010\n",
            "Epoch 14/18\n",
            "425/425 [==============================] - 166s 391ms/step - loss: 0.0974 - accuracy: 0.9674 - f1_m: 0.9679 - val_loss: 0.2100 - val_accuracy: 0.9383 - val_f1_m: 0.9382 - lr: 5.0000e-04\n",
            "Epoch 15/18\n",
            "425/425 [==============================] - 167s 393ms/step - loss: 0.0691 - accuracy: 0.9777 - f1_m: 0.9780 - val_loss: 0.2130 - val_accuracy: 0.9400 - val_f1_m: 0.9413 - lr: 5.0000e-04\n",
            "Epoch 16/18\n",
            "425/425 [==============================] - 167s 394ms/step - loss: 0.0661 - accuracy: 0.9795 - f1_m: 0.9796 - val_loss: 0.2092 - val_accuracy: 0.9429 - val_f1_m: 0.9433 - lr: 5.0000e-04\n",
            "Epoch 17/18\n",
            "425/425 [==============================] - 167s 393ms/step - loss: 0.0562 - accuracy: 0.9813 - f1_m: 0.9817 - val_loss: 0.2125 - val_accuracy: 0.9391 - val_f1_m: 0.9396 - lr: 5.0000e-04\n",
            "Epoch 18/18\n",
            "425/425 [==============================] - 167s 393ms/step - loss: 0.0498 - accuracy: 0.9844 - f1_m: 0.9844 - val_loss: 0.2570 - val_accuracy: 0.9279 - val_f1_m: 0.9286 - lr: 5.0000e-04\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('ser_cnn1d.h5')"
      ],
      "metadata": {
        "id": "5QlEdqFPPd5A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy of our model on test data : \" , model.evaluate(x_test,y_test)[1]*100 , \"%\")\n",
        "\n",
        "fig , ax = plt.subplots(1,2)\n",
        "train_acc = history.history['accuracy']\n",
        "train_loss = history.history['loss']\n",
        "test_acc = history.history['val_accuracy']\n",
        "test_loss = history.history['val_loss']\n",
        "\n",
        "fig.set_size_inches(20,6)\n",
        "ax[0].plot(train_loss, label = 'Training Loss')\n",
        "ax[0].plot(test_loss , label = 'Validation Loss')\n",
        "ax[0].set_title('Training & Validation Loss')\n",
        "ax[0].legend()\n",
        "ax[0].set_xlabel(\"Epochs\")\n",
        "\n",
        "ax[1].plot(train_acc, label = 'Training Accuracy')\n",
        "ax[1].plot(test_acc , label = 'Validation Accuracy')\n",
        "ax[1].set_title('Training & Validation Accuracy')\n",
        "ax[1].legend()\n",
        "ax[1].set_xlabel(\"Epochs\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "3FG98LPCjDUX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "outputId": "bc6bb096-2f54-4585-b5e8-073321c39490"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "86/86 [==============================] - 5s 59ms/step - loss: 0.2511 - accuracy: 0.9373 - f1_m: 0.9389\n",
            "Accuracy of our model on test data :  93.73167157173157 %\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x432 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('best_model.hdf5')"
      ],
      "metadata": {
        "id": "pA4Xo1bZnSLn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "rykTTo9TbUHg"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}