CNN.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "yB_rIECnIETr"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import os\n",
        "import sys\n",
        "\n",
        "# librosa is a Python library for analyzing audio and music. It can be used to extract the data from the audio files we will see it later.\n",
        "import librosa\n",
        "import librosa.display\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# to play the audio files\n",
        "from IPython.display import Audio\n",
        "\n",
        "import keras\n",
        "from keras.callbacks import ReduceLROnPlateau\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Conv1D, MaxPooling1D, Flatten, Dropout, BatchNormalization\n",
        "from keras.utils import np_utils, to_categorical\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "\n",
        "import warnings\n",
        "if not sys.warnoptions:\n",
        "    warnings.simplefilter(\"ignore\")\n",
        "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "cP8mFoe0Jj-w"
      },
      "outputs": [],
      "source": [
        "data=pd.read_csv('/content/drive/MyDrive/modeldata.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "SaZYY2VYIdYl"
      },
      "outputs": [],
      "source": [
        "X = data.iloc[: ,:-1].values\n",
        "Y = data['emo'].values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "7262qm3MJ6YK"
      },
      "outputs": [],
      "source": [
        "encoder = OneHotEncoder()\n",
        "Y = encoder.fit_transform(np.array(Y).reshape(-1,1)).toarray()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "JysY5EFpJ78n",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1660fc70-6b41-4097-b62e-c90f7662d6cf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((7046, 801), (7046, 5), (2349, 801), (2349, 5))"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(X, Y, random_state=0, shuffle=True)\n",
        "x_train.shape, y_train.shape, x_test.shape, y_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "JGVF4LL-KB1R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "716a2ec6-73d0-4313-93af-d795fbca6dd3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((7046, 801), (7046, 5), (2349, 801), (2349, 5))"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# scaling our data with sklearn's Standard scaler\n",
        "scaler = StandardScaler()\n",
        "x_train = scaler.fit_transform(x_train)\n",
        "x_test = scaler.transform(x_test)\n",
        "x_train.shape, y_train.shape, x_test.shape, y_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "hDlTtOTuKd2N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b10768d9-509d-4836-fe48-f198cc150b28"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((7046, 801, 1), (7046, 5), (2349, 801, 1), (2349, 5))"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# making our data compatible to model.\n",
        "x_train = np.expand_dims(x_train, axis=2)\n",
        "x_test = np.expand_dims(x_test, axis=2)\n",
        "x_train.shape, y_train.shape, x_test.shape, y_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "tol1sdsMKjLA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "96708ddc-ccea-4798-df68-7e628924f719"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv1d (Conv1D)             (None, 801, 256)          1536      \n",
            "                                                                 \n",
            " max_pooling1d (MaxPooling1D  (None, 401, 256)         0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv1d_1 (Conv1D)           (None, 401, 256)          327936    \n",
            "                                                                 \n",
            " max_pooling1d_1 (MaxPooling  (None, 201, 256)         0         \n",
            " 1D)                                                             \n",
            "                                                                 \n",
            " conv1d_2 (Conv1D)           (None, 201, 128)          163968    \n",
            "                                                                 \n",
            " max_pooling1d_2 (MaxPooling  (None, 101, 128)         0         \n",
            " 1D)                                                             \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 101, 128)          0         \n",
            "                                                                 \n",
            " conv1d_3 (Conv1D)           (None, 101, 64)           41024     \n",
            "                                                                 \n",
            " max_pooling1d_3 (MaxPooling  (None, 51, 64)           0         \n",
            " 1D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 3264)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 32)                104480    \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 32)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 5)                 165       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 639,109\n",
            "Trainable params: 639,109\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model=Sequential()\n",
        "model.add(Conv1D(256, kernel_size=5, strides=1, padding='same', activation='relu', input_shape=(x_train.shape[1], 1)))\n",
        "model.add(MaxPooling1D(pool_size=5, strides = 2, padding = 'same'))\n",
        "\n",
        "model.add(Conv1D(256, kernel_size=5, strides=1, padding='same', activation='relu'))\n",
        "model.add(MaxPooling1D(pool_size=5, strides = 2, padding = 'same'))\n",
        "\n",
        "model.add(Conv1D(128, kernel_size=5, strides=1, padding='same', activation='relu'))\n",
        "model.add(MaxPooling1D(pool_size=5, strides = 2, padding = 'same'))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Conv1D(64, kernel_size=5, strides=1, padding='same', activation='relu'))\n",
        "model.add(MaxPooling1D(pool_size=5, strides = 2, padding = 'same'))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(units=32, activation='relu'))\n",
        "model.add(Dropout(0.3))\n",
        "\n",
        "model.add(Dense(units=5, activation='softmax'))\n",
        "model.compile(optimizer = 'adam' , loss = 'categorical_crossentropy' , metrics = ['accuracy'])\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras import backend as K\n",
        "K.set_value(model.optimizer.learning_rate, 0.0001)"
      ],
      "metadata": {
        "id": "Qw9NPGWPrPDo"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ofg9DGDjKwBq",
        "outputId": "4284fdb3-3162-4fbb-e658-4fd5b2376236"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/60\n",
            "111/111 [==============================] - 217s 2s/step - loss: 1.5804 - accuracy: 0.2320 - val_loss: 1.5660 - val_accuracy: 0.2520 - lr: 1.0000e-04\n",
            "Epoch 2/60\n",
            "111/111 [==============================] - 216s 2s/step - loss: 1.5634 - accuracy: 0.2563 - val_loss: 1.5603 - val_accuracy: 0.2916 - lr: 1.0000e-04\n",
            "Epoch 3/60\n",
            "111/111 [==============================] - 210s 2s/step - loss: 1.5532 - accuracy: 0.2604 - val_loss: 1.5519 - val_accuracy: 0.2950 - lr: 1.0000e-04\n",
            "Epoch 4/60\n",
            "111/111 [==============================] - 207s 2s/step - loss: 1.5483 - accuracy: 0.2689 - val_loss: 1.5482 - val_accuracy: 0.2908 - lr: 1.0000e-04\n",
            "Epoch 5/60\n",
            "111/111 [==============================] - 205s 2s/step - loss: 1.5417 - accuracy: 0.2716 - val_loss: 1.5488 - val_accuracy: 0.2669 - lr: 1.0000e-04\n",
            "Epoch 6/60\n",
            "111/111 [==============================] - 205s 2s/step - loss: 1.5375 - accuracy: 0.2857 - val_loss: 1.5276 - val_accuracy: 0.3035 - lr: 1.0000e-04\n",
            "Epoch 7/60\n",
            "111/111 [==============================] - 202s 2s/step - loss: 1.5310 - accuracy: 0.2780 - val_loss: 1.5204 - val_accuracy: 0.3078 - lr: 1.0000e-04\n",
            "Epoch 8/60\n",
            "111/111 [==============================] - 202s 2s/step - loss: 1.5224 - accuracy: 0.2983 - val_loss: 1.5138 - val_accuracy: 0.3074 - lr: 1.0000e-04\n",
            "Epoch 9/60\n",
            "111/111 [==============================] - 202s 2s/step - loss: 1.5150 - accuracy: 0.3023 - val_loss: 1.5024 - val_accuracy: 0.3086 - lr: 1.0000e-04\n",
            "Epoch 10/60\n",
            "111/111 [==============================] - 202s 2s/step - loss: 1.5105 - accuracy: 0.3014 - val_loss: 1.5000 - val_accuracy: 0.2997 - lr: 1.0000e-04\n",
            "Epoch 11/60\n",
            "111/111 [==============================] - 200s 2s/step - loss: 1.5052 - accuracy: 0.3111 - val_loss: 1.4916 - val_accuracy: 0.3133 - lr: 1.0000e-04\n",
            "Epoch 12/60\n",
            "111/111 [==============================] - 203s 2s/step - loss: 1.4953 - accuracy: 0.3080 - val_loss: 1.4822 - val_accuracy: 0.3193 - lr: 1.0000e-04\n",
            "Epoch 13/60\n",
            "111/111 [==============================] - 202s 2s/step - loss: 1.4841 - accuracy: 0.3225 - val_loss: 1.4714 - val_accuracy: 0.3346 - lr: 1.0000e-04\n",
            "Epoch 14/60\n",
            "111/111 [==============================] - 203s 2s/step - loss: 1.4735 - accuracy: 0.3229 - val_loss: 1.4710 - val_accuracy: 0.3380 - lr: 1.0000e-04\n",
            "Epoch 15/60\n",
            "111/111 [==============================] - 205s 2s/step - loss: 1.4600 - accuracy: 0.3332 - val_loss: 1.4510 - val_accuracy: 0.3436 - lr: 1.0000e-04\n",
            "Epoch 16/60\n",
            "111/111 [==============================] - 202s 2s/step - loss: 1.4532 - accuracy: 0.3372 - val_loss: 1.4465 - val_accuracy: 0.3457 - lr: 1.0000e-04\n",
            "Epoch 17/60\n",
            "111/111 [==============================] - 202s 2s/step - loss: 1.4324 - accuracy: 0.3464 - val_loss: 1.4529 - val_accuracy: 0.3504 - lr: 1.0000e-04\n",
            "Epoch 18/60\n",
            "111/111 [==============================] - 202s 2s/step - loss: 1.4255 - accuracy: 0.3561 - val_loss: 1.4300 - val_accuracy: 0.3619 - lr: 1.0000e-04\n",
            "Epoch 19/60\n",
            "111/111 [==============================] - 201s 2s/step - loss: 1.4130 - accuracy: 0.3608 - val_loss: 1.4318 - val_accuracy: 0.3597 - lr: 1.0000e-04\n",
            "Epoch 20/60\n",
            "111/111 [==============================] - 202s 2s/step - loss: 1.4049 - accuracy: 0.3669 - val_loss: 1.4183 - val_accuracy: 0.3653 - lr: 1.0000e-04\n",
            "Epoch 21/60\n",
            "111/111 [==============================] - 202s 2s/step - loss: 1.4033 - accuracy: 0.3612 - val_loss: 1.4106 - val_accuracy: 0.3661 - lr: 1.0000e-04\n",
            "Epoch 22/60\n",
            "111/111 [==============================] - 202s 2s/step - loss: 1.3829 - accuracy: 0.3850 - val_loss: 1.4034 - val_accuracy: 0.3848 - lr: 1.0000e-04\n",
            "Epoch 23/60\n",
            "111/111 [==============================] - 202s 2s/step - loss: 1.3700 - accuracy: 0.3872 - val_loss: 1.4133 - val_accuracy: 0.3670 - lr: 1.0000e-04\n",
            "Epoch 24/60\n",
            "111/111 [==============================] - 202s 2s/step - loss: 1.3708 - accuracy: 0.3804 - val_loss: 1.3954 - val_accuracy: 0.3819 - lr: 1.0000e-04\n",
            "Epoch 25/60\n",
            "111/111 [==============================] - 204s 2s/step - loss: 1.3473 - accuracy: 0.4002 - val_loss: 1.4000 - val_accuracy: 0.3895 - lr: 1.0000e-04\n",
            "Epoch 26/60\n",
            "111/111 [==============================] - 201s 2s/step - loss: 1.3495 - accuracy: 0.3953 - val_loss: 1.3885 - val_accuracy: 0.3887 - lr: 1.0000e-04\n",
            "Epoch 27/60\n",
            "111/111 [==============================] - 201s 2s/step - loss: 1.3421 - accuracy: 0.4112 - val_loss: 1.3925 - val_accuracy: 0.3785 - lr: 1.0000e-04\n",
            "Epoch 28/60\n",
            "111/111 [==============================] - 203s 2s/step - loss: 1.3191 - accuracy: 0.4080 - val_loss: 1.3902 - val_accuracy: 0.3891 - lr: 1.0000e-04\n",
            "Epoch 29/60\n",
            "111/111 [==============================] - 203s 2s/step - loss: 1.3171 - accuracy: 0.4174 - val_loss: 1.3799 - val_accuracy: 0.4023 - lr: 1.0000e-04\n",
            "Epoch 30/60\n",
            "111/111 [==============================] - 203s 2s/step - loss: 1.3123 - accuracy: 0.4188 - val_loss: 1.3856 - val_accuracy: 0.3908 - lr: 1.0000e-04\n",
            "Epoch 31/60\n",
            "111/111 [==============================] - 200s 2s/step - loss: 1.3044 - accuracy: 0.4305 - val_loss: 1.3745 - val_accuracy: 0.4083 - lr: 1.0000e-04\n",
            "Epoch 32/60\n",
            "111/111 [==============================] - 200s 2s/step - loss: 1.2962 - accuracy: 0.4305 - val_loss: 1.3845 - val_accuracy: 0.4006 - lr: 1.0000e-04\n",
            "Epoch 33/60\n",
            "111/111 [==============================] - 200s 2s/step - loss: 1.2775 - accuracy: 0.4330 - val_loss: 1.3749 - val_accuracy: 0.4074 - lr: 1.0000e-04\n",
            "Epoch 34/60\n",
            "111/111 [==============================] - 200s 2s/step - loss: 1.2794 - accuracy: 0.4374 - val_loss: 1.3633 - val_accuracy: 0.4061 - lr: 1.0000e-04\n",
            "Epoch 35/60\n",
            "111/111 [==============================] - 199s 2s/step - loss: 1.2594 - accuracy: 0.4454 - val_loss: 1.3582 - val_accuracy: 0.4138 - lr: 1.0000e-04\n",
            "Epoch 36/60\n",
            "111/111 [==============================] - 198s 2s/step - loss: 1.2527 - accuracy: 0.4516 - val_loss: 1.3510 - val_accuracy: 0.4210 - lr: 1.0000e-04\n",
            "Epoch 37/60\n",
            "111/111 [==============================] - 198s 2s/step - loss: 1.2545 - accuracy: 0.4506 - val_loss: 1.3702 - val_accuracy: 0.4070 - lr: 1.0000e-04\n",
            "Epoch 38/60\n",
            "111/111 [==============================] - 193s 2s/step - loss: 1.2419 - accuracy: 0.4564 - val_loss: 1.3775 - val_accuracy: 0.3972 - lr: 1.0000e-04\n",
            "Epoch 39/60\n",
            "111/111 [==============================] - 198s 2s/step - loss: 1.2250 - accuracy: 0.4625 - val_loss: 1.3730 - val_accuracy: 0.4091 - lr: 1.0000e-04\n",
            "Epoch 40/60\n",
            "111/111 [==============================] - 198s 2s/step - loss: 1.2127 - accuracy: 0.4728 - val_loss: 1.3501 - val_accuracy: 0.4236 - lr: 1.0000e-04\n",
            "Epoch 41/60\n",
            "111/111 [==============================] - 194s 2s/step - loss: 1.2093 - accuracy: 0.4725 - val_loss: 1.3535 - val_accuracy: 0.4134 - lr: 1.0000e-04\n",
            "Epoch 42/60\n",
            "111/111 [==============================] - 203s 2s/step - loss: 1.1929 - accuracy: 0.4848 - val_loss: 1.3619 - val_accuracy: 0.4244 - lr: 1.0000e-04\n",
            "Epoch 43/60\n",
            "111/111 [==============================] - 196s 2s/step - loss: 1.1907 - accuracy: 0.4831 - val_loss: 1.3737 - val_accuracy: 0.4014 - lr: 1.0000e-04\n",
            "Epoch 44/60\n",
            "111/111 [==============================] - 201s 2s/step - loss: 1.1864 - accuracy: 0.4861 - val_loss: 1.3637 - val_accuracy: 0.4125 - lr: 1.0000e-04\n",
            "Epoch 45/60\n",
            "111/111 [==============================] - 198s 2s/step - loss: 1.1760 - accuracy: 0.4929 - val_loss: 1.3456 - val_accuracy: 0.4278 - lr: 1.0000e-04\n",
            "Epoch 46/60\n",
            "111/111 [==============================] - 202s 2s/step - loss: 1.1645 - accuracy: 0.4965 - val_loss: 1.3766 - val_accuracy: 0.4108 - lr: 1.0000e-04\n",
            "Epoch 47/60\n",
            "111/111 [==============================] - 197s 2s/step - loss: 1.1533 - accuracy: 0.5014 - val_loss: 1.3673 - val_accuracy: 0.4287 - lr: 1.0000e-04\n",
            "Epoch 48/60\n",
            "111/111 [==============================] - 203s 2s/step - loss: 1.1514 - accuracy: 0.5027 - val_loss: 1.3717 - val_accuracy: 0.4193 - lr: 1.0000e-04\n",
            "Epoch 49/60\n",
            "111/111 [==============================] - 202s 2s/step - loss: 1.1410 - accuracy: 0.5014 - val_loss: 1.3718 - val_accuracy: 0.4321 - lr: 1.0000e-04\n",
            "Epoch 50/60\n",
            "111/111 [==============================] - 199s 2s/step - loss: 1.1399 - accuracy: 0.5070 - val_loss: 1.3505 - val_accuracy: 0.4223 - lr: 1.0000e-04\n",
            "Epoch 51/60\n",
            "111/111 [==============================] - 202s 2s/step - loss: 1.1237 - accuracy: 0.5121 - val_loss: 1.3671 - val_accuracy: 0.4368 - lr: 1.0000e-04\n",
            "Epoch 52/60\n",
            "111/111 [==============================] - 201s 2s/step - loss: 1.1118 - accuracy: 0.5257 - val_loss: 1.3689 - val_accuracy: 0.4347 - lr: 1.0000e-04\n",
            "Epoch 53/60\n",
            "111/111 [==============================] - 196s 2s/step - loss: 1.1115 - accuracy: 0.5199 - val_loss: 1.3986 - val_accuracy: 0.4155 - lr: 1.0000e-04\n",
            "Epoch 54/60\n",
            "111/111 [==============================] - 201s 2s/step - loss: 1.1091 - accuracy: 0.5220 - val_loss: 1.3628 - val_accuracy: 0.4236 - lr: 1.0000e-04\n",
            "Epoch 55/60\n",
            "111/111 [==============================] - 201s 2s/step - loss: 1.0885 - accuracy: 0.5341 - val_loss: 1.3955 - val_accuracy: 0.4100 - lr: 1.0000e-04\n",
            "Epoch 56/60\n",
            "111/111 [==============================] - 196s 2s/step - loss: 1.0940 - accuracy: 0.5301 - val_loss: 1.3939 - val_accuracy: 0.4057 - lr: 1.0000e-04\n",
            "Epoch 57/60\n",
            "111/111 [==============================] - 200s 2s/step - loss: 1.0802 - accuracy: 0.5294 - val_loss: 1.3797 - val_accuracy: 0.4278 - lr: 1.0000e-04\n",
            "Epoch 58/60\n",
            "111/111 [==============================] - 200s 2s/step - loss: 1.0712 - accuracy: 0.5348 - val_loss: 1.3856 - val_accuracy: 0.4172 - lr: 1.0000e-04\n",
            "Epoch 59/60\n",
            "111/111 [==============================] - 200s 2s/step - loss: 1.0553 - accuracy: 0.5475 - val_loss: 1.4199 - val_accuracy: 0.4155 - lr: 1.0000e-04\n",
            "Epoch 60/60\n",
            "111/111 [==============================] - 198s 2s/step - loss: 1.0581 - accuracy: 0.5433 - val_loss: 1.4008 - val_accuracy: 0.4304 - lr: 1.0000e-04\n"
          ]
        }
      ],
      "source": [
        "rlrp = ReduceLROnPlateau(monitor='loss', factor=0.4, verbose=0, patience=2, min_lr=0.0000001)\n",
        "history=model.fit(x_train, y_train, batch_size=64, epochs=60, validation_data=(x_test, y_test), callbacks=[rlrp])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy of our model on test data : \" , model.evaluate(x_test,y_test)[1]*100 , \"%\")\n",
        "\n",
        "epochs = [i for i in range(60)]\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(epochs , train_loss , label = 'Training Loss')\n",
        "ax[0].plot(epochs , test_loss , label = 'Testing Loss')\n",
        "ax[0].set_title('Training & Testing Loss')\n",
        "ax[0].legend()\n",
        "ax[0].set_xlabel(\"Epochs\")\n",
        "\n",
        "ax[1].plot(epochs , train_acc , label = 'Training Accuracy')\n",
        "ax[1].plot(epochs , test_acc , label = 'Testing Accuracy')\n",
        "ax[1].set_title('Training & Testing Accuracy')\n",
        "ax[1].legend()\n",
        "ax[1].set_xlabel(\"Epochs\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "ggmJoXgqpo8p",
        "outputId": "41668130-f471-453d-9683-63bad330b998"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "74/74 [==============================] - 15s 204ms/step - loss: 1.4008 - accuracy: 0.4304\n",
            "Accuracy of our model on test data :  43.03959012031555 %\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": [
        "# predicting on test data.\n",
        "pred_test = model.predict(x_test)\n",
        "y_pred = encoder.inverse_transform(pred_test)\n",
        "\n",
        "y_test = encoder.inverse_transform(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "84RGPchqptID",
        "outputId": "f5514f15-f48e-49ef-f932-e3dd5844deb4"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "74/74 [==============================] - 20s 265ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(columns=['Predicted Labels', 'Actual Labels'])\n",
        "df['Predicted Labels'] = y_pred.flatten()\n",
        "df['Actual Labels'] = y_test.flatten()\n",
        "\n",
        "df.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "l6s12nXvpzc-",
        "outputId": "e3700856-1cb2-492b-d92c-bb285707ac7e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Predicted Labels  Actual Labels\n",
              "0                 3              3\n",
              "1                 0              1\n",
              "2                 4              1\n",
              "3                 4              1\n",
              "4                 5              3\n",
              "5                 3              5\n",
              "6                 1              1\n",
              "7                 5              5\n",
              "8                 3              3\n",
              "9                 3              4"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-92ec3f1f-73e1-4103-bb3f-946e408931e9\">\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>Predicted Labels</th>\n",
              "      <th>Actual Labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-92ec3f1f-73e1-4103-bb3f-946e408931e9')\"\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-92ec3f1f-73e1-4103-bb3f-946e408931e9 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-92ec3f1f-73e1-4103-bb3f-946e408931e9');\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": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_test, y_pred)\n",
        "plt.figure(figsize = (12, 10))\n",
        "cm = pd.DataFrame(cm , index = [i for i in encoder.categories_] , columns = [i for i in encoder.categories_])\n",
        "sns.heatmap(cm, linecolor='white', cmap='Blues', linewidth=1, annot=True, fmt='')\n",
        "plt.title('Confusion Matrix', size=20)\n",
        "plt.xlabel('Predicted Labels', size=14)\n",
        "plt.ylabel('Actual Labels', size=14)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 630
        },
        "id": "g_R37nsDp4sf",
        "outputId": "70724fae-09e8-4575-917f-62a8d2132f77"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fxYwxUEkp-ks",
        "outputId": "8c1a7f84-3d2d-4705-b8c5-6e71fd69d486"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.52      0.39      0.44       459\n",
            "           1       0.37      0.58      0.45       439\n",
            "           3       0.52      0.46      0.49       484\n",
            "           4       0.36      0.32      0.34       468\n",
            "           5       0.44      0.41      0.42       499\n",
            "\n",
            "    accuracy                           0.43      2349\n",
            "   macro avg       0.44      0.43      0.43      2349\n",
            "weighted avg       0.44      0.43      0.43      2349\n",
            "\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}