“CNN_last_run_ipynb”的副本.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuClass": "premium",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "premium"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mmmyr/Human-Voice-Audio-Classification/blob/main/%E2%80%9CCNN_last_run_ipynb%E2%80%9D%E7%9A%84%E5%89%AF%E6%9C%AC.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YUgoHRCRd1vn",
        "outputId": "71976b51-0389-48fb-ebb3-cac128e9e4db"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "from scipy.io import wavfile\n",
        "import re\n",
        "import seaborn as sns\n",
        "import random\n",
        "from sklearn import svm\n",
        "import librosa\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.metrics import accuracy_score\n",
        "import re\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from keras.utils.np_utils import to_categorical\n",
        "import pywt\n",
        "from scipy import signal\n",
        "\n",
        "\n",
        "import keras\n",
        "from keras.layers import Conv1D, Embedding, Dropout, MaxPooling1D, GlobalMaxPooling1D, Dense\n",
        "from keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense, concatenate, Dropout, BatchNormalization\n",
        "from keras.optimizers import Adam, SGD, nadam\n",
        "from keras import Model\n",
        "from keras.callbacks import EarlyStopping\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "\n",
        "# test data and test data directory\n",
        "\n",
        "data_dir = \"/content/drive/MyDrive/ELEC378_FinalProject/data/data\"\n",
        "\n",
        "test_dir = \"/content/drive/MyDrive/ELEC378_FinalProject/test/test\"\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#SVM\n"
      ],
      "metadata": {
        "id": "9VLMvdVH_Tq_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def FeatureExtraction (dir, split=True):\n",
        "\n",
        "  '''\n",
        "  Function for feature extraction. Change this section to change what features we are using.\n",
        "\n",
        "  input: a directory of the data. Will split to test and train, a boolean: if split is true, will extract features and labels and store them in 4 arrays:\n",
        "  train_data, train_label, test_data, test_label. if split is false, function will extract all data provided in the directory and output 2 arrays with values 2 arrays that are empty.\n",
        "  Use labeled data and split = false will give you  (train_data, train_label); use unlabled data and split = false will give you (test_data, test_label).\n",
        "\n",
        "  output: data matrix and lables for both test and train.\n",
        "\n",
        "  '''\n",
        "  audio_files = [f for f in os.listdir(dir) if f.endswith(\".wav\")]\n",
        "\n",
        "  train_files, test_files = train_test_split(audio_files, test_size=0.2)\n",
        "\n",
        "  train_wt = []\n",
        "  train_mfccs = []\n",
        "  train_label= []\n",
        "\n",
        "  test_mfccs = []\n",
        "  test_wt  = []\n",
        "  test_label = []\n",
        "\n",
        "\n",
        "  for file_name in audio_files:\n",
        "\n",
        "      # Load audio\n",
        "      file_path = os.path.join(dir, file_name)\n",
        "      raw_audio, sr = librosa.load(file_path)\n",
        "\n",
        "      #if audio is too short, append zeros after it.\n",
        "      #noise = np.random.normal(loc=0, scale=0.3)\n",
        "\n",
        "      if np.shape(raw_audio)[0] < 80000:\n",
        "          padded_audio = np.pad(raw_audio, [(0, 80000 - np.shape(raw_audio)[0])], mode='constant')\n",
        "      else:\n",
        "          padded_audio = raw_audio\n",
        "\n",
        "      # now slice so that we guarantee that each array has the same length\n",
        "      audio = padded_audio[20000:80000]\n",
        "\n",
        "      # feature extraction\n",
        "      mfccs  = librosa.feature.mfcc(y = audio, sr = 22050, n_mfcc = 28)\n",
        "      #delta_mfccs = librosa.feature.delta(y = mfccs)\n",
        "      spec_centroid = librosa.feature.spectral_centroid(y = audio)\n",
        "\n",
        "      coeffs = pywt.wavedec(audio, 'db1', level =4)\n",
        "\n",
        "      mfccs_wt = []\n",
        "\n",
        "\n",
        "      for i in coeffs:\n",
        "          mfccsw = librosa.feature.mfcc(y=i, sr=22050, n_mfcc=18)\n",
        "          # pad the MFCCs to max_len\n",
        "\n",
        "          mfccs_wt.append(mfccsw)\n",
        "\n",
        "      #mfccs_wt = np.array(mfccs_wt)\n",
        "      mfccs_wt = np.hstack(mfccs_wt)\n",
        "\n",
        "\n",
        "\n",
        "      #spec_centroid = librosa.feature.spectral_centroid(y = audio)\n",
        "\n",
        "      #spec_contrast = librosa.feature.spectral_contrast (y = audio)\n",
        "\n",
        "      #morl = morl_wavelet_pca(audio)\n",
        "\n",
        "      # Concatenate\n",
        "\n",
        "      wavelet_features = mfccs_wt\n",
        "      mfccs_features = np.concatenate([mfccs, spec_centroid])\n",
        "      #features = morl\n",
        "\n",
        "      label_map = {'angry': 0, 'calm': 1, 'disgust': 2, 'fearful': 3, 'happy': 4, 'neutral': 5, 'sad': 6, 'surprised': 7}\n",
        "      label = file_name.split(\".\")[0]\n",
        "\n",
        "      #unlabeled data\n",
        "      if label[:-3] == \"sample\":\n",
        "        test_wt.append(wavelet_features)\n",
        "        test_mfccs.append(mfccs_features)\n",
        "        test_label.append(label)\n",
        "\n",
        "      #labeled data\n",
        "      else:\n",
        "        if split:\n",
        "          if file_name in train_files:\n",
        "            train_wt.append(wavelet_features)\n",
        "            train_mfccs.append(mfccs_features)\n",
        "            label = re.sub('[^a-z]', \"\", label)\n",
        "            train_label.append(label_map[label])\n",
        "\n",
        "          elif file_name in test_files:\n",
        "            test_wt.append(wavelet_features)\n",
        "            test_mfccs.append(mfccs_features)\n",
        "            label = re.sub('[^a-z]', \"\", label)\n",
        "            test_label.append(label_map[label])\n",
        "\n",
        "        elif not split:\n",
        "\n",
        "          train_wt.append(wavelet_features)\n",
        "          train_mfccs.append(mfccs_features)\n",
        "          label = re.sub('[^a-z]', \"\", label)\n",
        "          train_label.append(label_map[label])\n",
        "\n",
        "\n",
        "  train_wt = np.array(train_wt)\n",
        "  train_mfccs = np.array(train_mfccs)\n",
        "  train_label = np.array(train_label)\n",
        "\n",
        "\n",
        "  test_mfccs = np.array(test_mfccs)\n",
        "  test_wt    = np.array(test_wt)\n",
        "  test_label = np.array(test_label)\n",
        "\n",
        "  #print(\"feature size is:\", np.shape(features))\n",
        "  #print(\"spec centroid:\", np.shape(spec_centroid))\n",
        "  return train_mfccs, train_wt, train_label, test_mfccs, test_wt, test_label"
      ],
      "metadata": {
        "id": "7T_1zX-FeBfD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "SVM"
      ],
      "metadata": {
        "id": "Rr3iLM65_X6W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#SVM implementation\n",
        "import sklearn\n",
        "from sklearn import svm\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sk\n",
        "\n",
        "clf=make_pipeline(StandardScaler(),SVC(gamma='scale',kernel=\"linear\"))\n",
        "clf.fit(X_train, y_train)\n",
        "predictions_svm = clf.predict(X_test)"
      ],
      "metadata": {
        "id": "LTTsck5n_cfY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_mfccs, X_train_wt, y_train, X_test_mfccs, X_test_wt, y_test = FeatureExtraction(data_dir, split = True)\n",
        "\n",
        "y_train_ohe = to_categorical(y_train)\n",
        "y_test_ohe = to_categorical(y_test)\n",
        "\n",
        "print(X_train_mfccs.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qUiNMT7deJBP",
        "outputId": "ab5b7166-b1df-48ce-8033-fa7093e4051c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(900, 29, 118)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train_wt.shape)\n",
        "plt.imshow(X_train_wt[1], cmap = \"magma\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "lFGXSsDYfrm-",
        "outputId": "ead5e534-e656-4a71-a026-03f48131345b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(900, 18, 120)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7efc9f928430>"
            ]
          },
          "metadata": {},
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def two_branch(mfcc_input_shape, dwt_input_shape, num_classes):\n",
        "\n",
        "    # Define the input layers for each feature set\n",
        "    mfcc_input = Input(shape=mfcc_input_shape, name='mfcc_input')\n",
        "    dwt_input = Input(shape=dwt_input_shape, name='dwt_input')\n",
        "\n",
        "    # Define the convolutional layers for each feature set\n",
        "    mfcc_conv1 = Conv2D(filters=64, kernel_size=(3, 3), activation='relu')(mfcc_input)\n",
        "    mfcc_batch1 = BatchNormalization()(mfcc_conv1)\n",
        "    mfcc_pool1 = MaxPooling2D(pool_size=(2, 2))(mfcc_batch1)\n",
        "    mfcc_conv2 = Conv2D(filters=128, kernel_size=(3, 3), activation='relu')(mfcc_pool1)\n",
        "    mfcc_batch2 = BatchNormalization()(mfcc_conv2)\n",
        "    mfcc_conv3 = Conv2D(filters=256, kernel_size=(3, 3), activation='relu')(mfcc_batch2)\n",
        "    mfcc_batch3 = BatchNormalization()(mfcc_conv3)\n",
        "    mfcc_pool3 = MaxPooling2D(pool_size=(2, 2))(mfcc_batch3)\n",
        "    mfcc_flat = Flatten()(mfcc_pool3)\n",
        "\n",
        "    dwt_conv1 = Conv2D(filters=64, kernel_size=(3, 3), activation='relu')(dwt_input)\n",
        "    dwt_batch1 = BatchNormalization()(dwt_conv1)\n",
        "    dwt_pool1 = MaxPooling2D(pool_size=(2, 2))(dwt_batch1)\n",
        "    dwt_conv2 = Conv2D(filters=128, kernel_size=(3, 3), activation='relu')(dwt_pool1)\n",
        "    dwt_batch2 = BatchNormalization()(dwt_conv2)\n",
        "    dwt_pool3 = MaxPooling2D(pool_size=(2, 2))(dwt_batch2)\n",
        "    dwt_flat = Flatten()(dwt_pool3)\n",
        "\n",
        "    # Merge the feature sets by concatenating the flattened feature vectors\n",
        "    merged = concatenate([mfcc_flat, dwt_flat])\n",
        "\n",
        "    # Define the dense layers for classification\n",
        "\n",
        "    dense1 = Dense(units=1024, activation='relu')(merged)\n",
        "    dropout1 = Dropout(0.5)(dense1) # add dropout with rate of 0.5\n",
        "    dense2 = Dense(units=num_classes, activation='softmax')(dropout1)\n",
        "    # Define the model with two inputs and one output\n",
        "    model = Model(inputs=[mfcc_input, dwt_input], outputs=dense2)\n",
        "\n",
        "    return model"
      ],
      "metadata": {
        "id": "_PO3I5-neSns"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#simple grid search to find best model parameters\n",
        "def grid_search(model, learning_rates, batch_sizes):\n",
        "    \"\"\"\n",
        "    Perform grid search hyper-parameter tuning\n",
        "    \"\"\"\n",
        "    best_acc = float('-inf')\n",
        "    best_batch = None\n",
        "    best_lr = None\n",
        "    empty_model_weights = model.get_weights()\n",
        "    #iterate through param space\n",
        "\n",
        "    for lr in learning_rates:\n",
        "        for batch in batch_sizes:\n",
        "\n",
        "            #build model with params\n",
        "            model_results, _ = build_model(model, lr, batch, x_train, y_train, train_small=True)\n",
        "            #store the accuracy results as dict mapping params -> model history\n",
        "            val_acc = model_results.history['val_accuracy'][-1]\n",
        "            train_acc = model_results.history['accuracy'][-1]\n",
        "            if val_acc + train_acc > best_acc:\n",
        "                best_batch = batch\n",
        "                best_lr = lr\n",
        "                best_acc = val_acc + train_acc\n",
        "\n",
        "            #reset model weights for new run\n",
        "            model.set_weights(empty_model_weights)\n",
        "\n",
        "    return best_batch, best_lr\n"
      ],
      "metadata": {
        "id": "Y7pP8hmHLUp7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "es = EarlyStopping(monitor='val_loss', mode='min', restore_best_weights=True, patience=17, verbose=1)\n",
        "\n",
        "checkpoint = ModelCheckpoint('best_model.h5', monitor='val_accuracy', save_best_only=True, verbose=1, initial_value_threshold=0.75)\n",
        "\n",
        "model = two_branch((X_train_mfccs.shape[1], X_train_mfccs.shape[2], 1), (X_train_wt.shape[1], X_train_wt.shape[2], 1), 8)\n",
        "\n",
        "model.compile(loss='categorical_crossentropy', optimizer=\"SGD\",  metrics=['accuracy'])\n",
        "\n",
        "# Train the model with the two input feature sets and corresponding labels\n",
        "history = model.fit( [X_train_mfccs, X_train_wt], y_train_ohe, validation_data=([X_test_mfccs, X_test_wt], y_test_ohe), epochs=60, batch_size=32, callbacks=[checkpoint])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hBu99wpRehD9",
        "outputId": "91ea6743-07fc-43dc-e007-a1ef2f98fec1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/60\n",
            "29/29 [==============================] - ETA: 0s - loss: 7.5555 - accuracy: 0.2356\n",
            "Epoch 1: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 3s 40ms/step - loss: 7.5555 - accuracy: 0.2356 - val_loss: 2.0910 - val_accuracy: 0.1644\n",
            "Epoch 2/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 1.7687 - accuracy: 0.3504\n",
            "Epoch 2: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 24ms/step - loss: 1.7691 - accuracy: 0.3500 - val_loss: 1.9285 - val_accuracy: 0.2267\n",
            "Epoch 3/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 1.5939 - accuracy: 0.3984\n",
            "Epoch 3: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 24ms/step - loss: 1.5928 - accuracy: 0.3989 - val_loss: 2.0062 - val_accuracy: 0.2533\n",
            "Epoch 4/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 1.5644 - accuracy: 0.4342\n",
            "Epoch 4: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 24ms/step - loss: 1.5643 - accuracy: 0.4344 - val_loss: 1.6729 - val_accuracy: 0.3378\n",
            "Epoch 5/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 1.2808 - accuracy: 0.5290\n",
            "Epoch 5: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 24ms/step - loss: 1.2810 - accuracy: 0.5300 - val_loss: 1.9701 - val_accuracy: 0.2800\n",
            "Epoch 6/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 1.1356 - accuracy: 0.5781\n",
            "Epoch 6: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 24ms/step - loss: 1.1317 - accuracy: 0.5800 - val_loss: 1.4940 - val_accuracy: 0.4578\n",
            "Epoch 7/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.9955 - accuracy: 0.6272\n",
            "Epoch 7: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 24ms/step - loss: 0.9933 - accuracy: 0.6278 - val_loss: 1.5668 - val_accuracy: 0.4356\n",
            "Epoch 8/60\n",
            "27/29 [==========================>...] - ETA: 0s - loss: 0.8229 - accuracy: 0.7014\n",
            "Epoch 8: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 25ms/step - loss: 0.8174 - accuracy: 0.7044 - val_loss: 1.5372 - val_accuracy: 0.4533\n",
            "Epoch 9/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.7001 - accuracy: 0.7522\n",
            "Epoch 9: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 28ms/step - loss: 0.7001 - accuracy: 0.7522 - val_loss: 1.7076 - val_accuracy: 0.3911\n",
            "Epoch 10/60\n",
            "29/29 [==============================] - ETA: 0s - loss: 0.7154 - accuracy: 0.7544\n",
            "Epoch 10: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 27ms/step - loss: 0.7154 - accuracy: 0.7544 - val_loss: 1.1697 - val_accuracy: 0.6044\n",
            "Epoch 11/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.4656 - accuracy: 0.8415\n",
            "Epoch 11: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 27ms/step - loss: 0.4658 - accuracy: 0.8411 - val_loss: 1.4261 - val_accuracy: 0.5200\n",
            "Epoch 12/60\n",
            "29/29 [==============================] - ETA: 0s - loss: 0.4105 - accuracy: 0.8778\n",
            "Epoch 12: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 30ms/step - loss: 0.4105 - accuracy: 0.8778 - val_loss: 1.0930 - val_accuracy: 0.6133\n",
            "Epoch 13/60\n",
            "27/29 [==========================>...] - ETA: 0s - loss: 0.3337 - accuracy: 0.8843\n",
            "Epoch 13: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 26ms/step - loss: 0.3324 - accuracy: 0.8833 - val_loss: 1.4122 - val_accuracy: 0.5111\n",
            "Epoch 14/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.3076 - accuracy: 0.8973\n",
            "Epoch 14: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 24ms/step - loss: 0.3086 - accuracy: 0.8967 - val_loss: 1.8298 - val_accuracy: 0.5022\n",
            "Epoch 15/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.2977 - accuracy: 0.9029\n",
            "Epoch 15: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 24ms/step - loss: 0.2987 - accuracy: 0.9022 - val_loss: 2.8248 - val_accuracy: 0.3689\n",
            "Epoch 16/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.4511 - accuracy: 0.8493\n",
            "Epoch 16: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 24ms/step - loss: 0.4504 - accuracy: 0.8500 - val_loss: 1.4109 - val_accuracy: 0.5556\n",
            "Epoch 17/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.2247 - accuracy: 0.9275\n",
            "Epoch 17: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 25ms/step - loss: 0.2282 - accuracy: 0.9267 - val_loss: 4.0136 - val_accuracy: 0.3911\n",
            "Epoch 18/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.4463 - accuracy: 0.8839\n",
            "Epoch 18: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 25ms/step - loss: 0.4459 - accuracy: 0.8833 - val_loss: 1.7603 - val_accuracy: 0.4889\n",
            "Epoch 19/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.2305 - accuracy: 0.9308\n",
            "Epoch 19: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 25ms/step - loss: 0.2301 - accuracy: 0.9311 - val_loss: 1.2557 - val_accuracy: 0.6044\n",
            "Epoch 20/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.1443 - accuracy: 0.9621\n",
            "Epoch 20: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 24ms/step - loss: 0.1442 - accuracy: 0.9622 - val_loss: 1.0730 - val_accuracy: 0.6178\n",
            "Epoch 21/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0884 - accuracy: 0.9788\n",
            "Epoch 21: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 24ms/step - loss: 0.0880 - accuracy: 0.9789 - val_loss: 1.0382 - val_accuracy: 0.6533\n",
            "Epoch 22/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0768 - accuracy: 0.9777\n",
            "Epoch 22: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 24ms/step - loss: 0.0765 - accuracy: 0.9778 - val_loss: 1.0130 - val_accuracy: 0.6622\n",
            "Epoch 23/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0695 - accuracy: 0.9833\n",
            "Epoch 23: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 24ms/step - loss: 0.0698 - accuracy: 0.9833 - val_loss: 1.1773 - val_accuracy: 0.6311\n",
            "Epoch 24/60\n",
            "27/29 [==========================>...] - ETA: 0s - loss: 0.0758 - accuracy: 0.9792\n",
            "Epoch 24: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 25ms/step - loss: 0.0772 - accuracy: 0.9789 - val_loss: 0.9907 - val_accuracy: 0.6711\n",
            "Epoch 25/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0380 - accuracy: 0.9922\n",
            "Epoch 25: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 25ms/step - loss: 0.0382 - accuracy: 0.9922 - val_loss: 1.2125 - val_accuracy: 0.6311\n",
            "Epoch 26/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0590 - accuracy: 0.9833\n",
            "Epoch 26: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 25ms/step - loss: 0.0589 - accuracy: 0.9833 - val_loss: 1.0354 - val_accuracy: 0.6933\n",
            "Epoch 27/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0477 - accuracy: 0.9933\n",
            "Epoch 27: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 24ms/step - loss: 0.0475 - accuracy: 0.9933 - val_loss: 1.0465 - val_accuracy: 0.6978\n",
            "Epoch 28/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0413 - accuracy: 0.9900\n",
            "Epoch 28: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 29ms/step - loss: 0.0413 - accuracy: 0.9900 - val_loss: 1.0552 - val_accuracy: 0.6756\n",
            "Epoch 29/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0343 - accuracy: 0.9922\n",
            "Epoch 29: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 26ms/step - loss: 0.0341 - accuracy: 0.9922 - val_loss: 1.0128 - val_accuracy: 0.6889\n",
            "Epoch 30/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0244 - accuracy: 0.9967\n",
            "Epoch 30: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 27ms/step - loss: 0.0243 - accuracy: 0.9967 - val_loss: 1.0572 - val_accuracy: 0.6800\n",
            "Epoch 31/60\n",
            "29/29 [==============================] - ETA: 0s - loss: 0.0246 - accuracy: 0.9944\n",
            "Epoch 31: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 26ms/step - loss: 0.0246 - accuracy: 0.9944 - val_loss: 1.0592 - val_accuracy: 0.6578\n",
            "Epoch 32/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0246 - accuracy: 0.9967\n",
            "Epoch 32: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 25ms/step - loss: 0.0245 - accuracy: 0.9967 - val_loss: 1.0771 - val_accuracy: 0.6667\n",
            "Epoch 33/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0302 - accuracy: 0.9922\n",
            "Epoch 33: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 24ms/step - loss: 0.0301 - accuracy: 0.9922 - val_loss: 1.0836 - val_accuracy: 0.6667\n",
            "Epoch 34/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0241 - accuracy: 0.9944\n",
            "Epoch 34: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 25ms/step - loss: 0.0242 - accuracy: 0.9944 - val_loss: 1.1429 - val_accuracy: 0.6578\n",
            "Epoch 35/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0398 - accuracy: 0.9911\n",
            "Epoch 35: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 24ms/step - loss: 0.0396 - accuracy: 0.9911 - val_loss: 1.0575 - val_accuracy: 0.6800\n",
            "Epoch 36/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0176 - accuracy: 0.9989\n",
            "Epoch 36: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 24ms/step - loss: 0.0175 - accuracy: 0.9989 - val_loss: 1.0426 - val_accuracy: 0.6889\n",
            "Epoch 37/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0118 - accuracy: 1.0000\n",
            "Epoch 37: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 24ms/step - loss: 0.0118 - accuracy: 1.0000 - val_loss: 1.0566 - val_accuracy: 0.7022\n",
            "Epoch 38/60\n",
            "27/29 [==========================>...] - ETA: 0s - loss: 0.0133 - accuracy: 0.9977\n",
            "Epoch 38: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 25ms/step - loss: 0.0130 - accuracy: 0.9978 - val_loss: 1.0511 - val_accuracy: 0.6889\n",
            "Epoch 39/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0176 - accuracy: 0.9967\n",
            "Epoch 39: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 25ms/step - loss: 0.0176 - accuracy: 0.9967 - val_loss: 1.1597 - val_accuracy: 0.6711\n",
            "Epoch 40/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0183 - accuracy: 0.9978\n",
            "Epoch 40: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 24ms/step - loss: 0.0184 - accuracy: 0.9978 - val_loss: 1.0442 - val_accuracy: 0.6978\n",
            "Epoch 41/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0111 - accuracy: 0.9989\n",
            "Epoch 41: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 24ms/step - loss: 0.0111 - accuracy: 0.9989 - val_loss: 1.0492 - val_accuracy: 0.6889\n",
            "Epoch 42/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0118 - accuracy: 0.9978\n",
            "Epoch 42: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 25ms/step - loss: 0.0118 - accuracy: 0.9978 - val_loss: 1.1609 - val_accuracy: 0.6756\n",
            "Epoch 43/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0156 - accuracy: 0.9978\n",
            "Epoch 43: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 25ms/step - loss: 0.0156 - accuracy: 0.9978 - val_loss: 1.0973 - val_accuracy: 0.6844\n",
            "Epoch 44/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0087 - accuracy: 0.9989\n",
            "Epoch 44: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 24ms/step - loss: 0.0086 - accuracy: 0.9989 - val_loss: 1.0610 - val_accuracy: 0.6800\n",
            "Epoch 45/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0100 - accuracy: 0.9978\n",
            "Epoch 45: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 24ms/step - loss: 0.0107 - accuracy: 0.9978 - val_loss: 1.9713 - val_accuracy: 0.5289\n",
            "Epoch 46/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0428 - accuracy: 0.9877\n",
            "Epoch 46: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 29ms/step - loss: 0.0426 - accuracy: 0.9878 - val_loss: 1.1982 - val_accuracy: 0.6800\n",
            "Epoch 47/60\n",
            "27/29 [==========================>...] - ETA: 0s - loss: 0.0205 - accuracy: 0.9965\n",
            "Epoch 47: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 26ms/step - loss: 0.0217 - accuracy: 0.9967 - val_loss: 1.3156 - val_accuracy: 0.6400\n",
            "Epoch 48/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0236 - accuracy: 0.9955\n",
            "Epoch 48: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 26ms/step - loss: 0.0236 - accuracy: 0.9956 - val_loss: 1.1342 - val_accuracy: 0.6844\n",
            "Epoch 49/60\n",
            "29/29 [==============================] - ETA: 0s - loss: 0.0139 - accuracy: 0.9978\n",
            "Epoch 49: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 27ms/step - loss: 0.0139 - accuracy: 0.9978 - val_loss: 1.0576 - val_accuracy: 0.6889\n",
            "Epoch 50/60\n",
            "27/29 [==========================>...] - ETA: 0s - loss: 0.0101 - accuracy: 1.0000\n",
            "Epoch 50: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 25ms/step - loss: 0.0099 - accuracy: 1.0000 - val_loss: 1.0855 - val_accuracy: 0.6756\n",
            "Epoch 51/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0099 - accuracy: 0.9989\n",
            "Epoch 51: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 25ms/step - loss: 0.0100 - accuracy: 0.9989 - val_loss: 1.1153 - val_accuracy: 0.6622\n",
            "Epoch 52/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0090 - accuracy: 1.0000\n",
            "Epoch 52: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 25ms/step - loss: 0.0089 - accuracy: 1.0000 - val_loss: 1.0509 - val_accuracy: 0.6800\n",
            "Epoch 53/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0071 - accuracy: 0.9989\n",
            "Epoch 53: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 25ms/step - loss: 0.0071 - accuracy: 0.9989 - val_loss: 1.0722 - val_accuracy: 0.6800\n",
            "Epoch 54/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0070 - accuracy: 0.9989\n",
            "Epoch 54: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 25ms/step - loss: 0.0070 - accuracy: 0.9989 - val_loss: 1.0262 - val_accuracy: 0.7200\n",
            "Epoch 55/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0103 - accuracy: 0.9989\n",
            "Epoch 55: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 25ms/step - loss: 0.0102 - accuracy: 0.9989 - val_loss: 1.0319 - val_accuracy: 0.6800\n",
            "Epoch 56/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0061 - accuracy: 1.0000\n",
            "Epoch 56: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 24ms/step - loss: 0.0061 - accuracy: 1.0000 - val_loss: 1.0106 - val_accuracy: 0.6800\n",
            "Epoch 57/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0065 - accuracy: 1.0000\n",
            "Epoch 57: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 25ms/step - loss: 0.0065 - accuracy: 1.0000 - val_loss: 1.0439 - val_accuracy: 0.6844\n",
            "Epoch 58/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0043 - accuracy: 1.0000\n",
            "Epoch 58: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 24ms/step - loss: 0.0043 - accuracy: 1.0000 - val_loss: 1.0489 - val_accuracy: 0.6844\n",
            "Epoch 59/60\n",
            "27/29 [==========================>...] - ETA: 0s - loss: 0.0085 - accuracy: 0.9988\n",
            "Epoch 59: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 24ms/step - loss: 0.0083 - accuracy: 0.9989 - val_loss: 1.0093 - val_accuracy: 0.7067\n",
            "Epoch 60/60\n",
            "28/29 [===========================>..] - ETA: 0s - loss: 0.0047 - accuracy: 1.0000\n",
            "Epoch 60: val_accuracy did not improve from 0.75000\n",
            "29/29 [==============================] - 1s 25ms/step - loss: 0.0047 - accuracy: 1.0000 - val_loss: 1.0443 - val_accuracy: 0.6933\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Training and validation error')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Error')\n",
        "plt.legend(['Training', 'Validation'], loc='upper right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "qYJLl21dg3BG",
        "outputId": "738e20bf-c506-46a8-a5ec-31e58c534ff8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train_mfccs.shape[1], X_train_mfccs.shape[2], 1)\n",
        "print(X_train_wt.shape[1], X_train_wt.shape[2], 1)\n",
        "print(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lmIxyxLQec_7",
        "outputId": "2a575801-73fb-4b85-c2a1-26283dce262e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "29 118 1\n",
            "18 120 1\n",
            "['sample014' 'sample024' 'sample015' 'sample012' 'sample018' 'sample029'\n",
            " 'sample003' 'sample004' 'sample000' 'sample013' 'sample017' 'sample006'\n",
            " 'sample007' 'sample025' 'sample020' 'sample010' 'sample030' 'sample009'\n",
            " 'sample022' 'sample008' 'sample021' 'sample019' 'sample002' 'sample026'\n",
            " 'sample016' 'sample027' 'sample028' 'sample001' 'sample011' 'sample023'\n",
            " 'sample005' 'sample096' 'sample051' 'sample068' 'sample061' 'sample050'\n",
            " 'sample074' 'sample046' 'sample065' 'sample052' 'sample069' 'sample084'\n",
            " 'sample053' 'sample081' 'sample082' 'sample080' 'sample040' 'sample101'\n",
            " 'sample056' 'sample067' 'sample038' 'sample097' 'sample063' 'sample088'\n",
            " 'sample100' 'sample077' 'sample059' 'sample060' 'sample045' 'sample090'\n",
            " 'sample044' 'sample057' 'sample066' 'sample093' 'sample031' 'sample072'\n",
            " 'sample047' 'sample043' 'sample099' 'sample098' 'sample086' 'sample048'\n",
            " 'sample049' 'sample083' 'sample058' 'sample062' 'sample071' 'sample035'\n",
            " 'sample079' 'sample089' 'sample076' 'sample054' 'sample033' 'sample085'\n",
            " 'sample102' 'sample042' 'sample092' 'sample087' 'sample075' 'sample095'\n",
            " 'sample064' 'sample039' 'sample032' 'sample036' 'sample041' 'sample037'\n",
            " 'sample073' 'sample091' 'sample094' 'sample055' 'sample070' 'sample078'\n",
            " 'sample034' 'sample146' 'sample107' 'sample161' 'sample103' 'sample174'\n",
            " 'sample178' 'sample154' 'sample117' 'sample111' 'sample119' 'sample124'\n",
            " 'sample128' 'sample152' 'sample130' 'sample160' 'sample153' 'sample171'\n",
            " 'sample172' 'sample165' 'sample166' 'sample129' 'sample141' 'sample168'\n",
            " 'sample150' 'sample139' 'sample164' 'sample163' 'sample169' 'sample162'\n",
            " 'sample140' 'sample131' 'sample167' 'sample125' 'sample137' 'sample170'\n",
            " 'sample106' 'sample144' 'sample113' 'sample112' 'sample110' 'sample108'\n",
            " 'sample120' 'sample145' 'sample133' 'sample155' 'sample118' 'sample114'\n",
            " 'sample156' 'sample138' 'sample151' 'sample149' 'sample147' 'sample122'\n",
            " 'sample143' 'sample158' 'sample104' 'sample177' 'sample127' 'sample121'\n",
            " 'sample116' 'sample115' 'sample176' 'sample148' 'sample123' 'sample135'\n",
            " 'sample173' 'sample126' 'sample175' 'sample109' 'sample105' 'sample179'\n",
            " 'sample132' 'sample136' 'sample134' 'sample159' 'sample157' 'sample142'\n",
            " 'sample236' 'sample229' 'sample216' 'sample235' 'sample184' 'sample180'\n",
            " 'sample254' 'sample256' 'sample255' 'sample191' 'sample203' 'sample246'\n",
            " 'sample206' 'sample227' 'sample252' 'sample224' 'sample207' 'sample202'\n",
            " 'sample242' 'sample194' 'sample210' 'sample244' 'sample251' 'sample212'\n",
            " 'sample199' 'sample189' 'sample185' 'sample247' 'sample223' 'sample205'\n",
            " 'sample241' 'sample217' 'sample192' 'sample234' 'sample186' 'sample220'\n",
            " 'sample243' 'sample211' 'sample258' 'sample218' 'sample195' 'sample183'\n",
            " 'sample232' 'sample208' 'sample197' 'sample181' 'sample215' 'sample238'\n",
            " 'sample188' 'sample228' 'sample187' 'sample250' 'sample257' 'sample182'\n",
            " 'sample226' 'sample248' 'sample193' 'sample222' 'sample221' 'sample240'\n",
            " 'sample213' 'sample225' 'sample239' 'sample209' 'sample196' 'sample253'\n",
            " 'sample204' 'sample200' 'sample237' 'sample214' 'sample249' 'sample219'\n",
            " 'sample233' 'sample190' 'sample230' 'sample201' 'sample245' 'sample231'\n",
            " 'sample198' 'sample305' 'sample282' 'sample308' 'sample300' 'sample278'\n",
            " 'sample265' 'sample285' 'sample261' 'sample277' 'sample276' 'sample296'\n",
            " 'sample267' 'sample313' 'sample281' 'sample293' 'sample279' 'sample270'\n",
            " 'sample298' 'sample268' 'sample302' 'sample273' 'sample271' 'sample299'\n",
            " 'sample263' 'sample292' 'sample262' 'sample297' 'sample295' 'sample272'\n",
            " 'sample303' 'sample284' 'sample269' 'sample310' 'sample259' 'sample294'\n",
            " 'sample311' 'sample266' 'sample291' 'sample283' 'sample264' 'sample304'\n",
            " 'sample274' 'sample290' 'sample286' 'sample288' 'sample314' 'sample275'\n",
            " 'sample260' 'sample301' 'sample307' 'sample287' 'sample309' 'sample289'\n",
            " 'sample280' 'sample306' 'sample312']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dummy1, dummy2, dummy3, X_test_mfccs, X_test_wt, y_test = FeatureExtraction(test_dir, split = False)\n"
      ],
      "metadata": {
        "id": "32iPE6tKemSn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.load_weights('best_model.h5')\n",
        "y_predicted = model.predict([X_test_mfccs, X_test_wt])\n",
        "\n",
        "y_predicted_ohv = np.argmax(y_predicted, axis = 1)\n",
        "\n",
        "print(y_predicted_ohv)\n",
        "label_map = {'angry': 0, 'calm': 1, 'disgust': 2, 'fearful': 3, 'happy': 4, 'neutral': 5, 'sad': 6, 'surprised': 7}\n",
        "\n",
        "def dict_search(dictionary, value):\n",
        "    for key, val in dictionary.items():\n",
        "        if val == value:\n",
        "            return key\n",
        "\n",
        "y_predicted_label = []\n",
        "for i in y_predicted_ohv:\n",
        "    val = dict_search(label_map, i)\n",
        "    y_predicted_label.append(val)\n",
        "\n",
        "\n",
        "print(y_predicted_label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3wuwcA29em5E",
        "outputId": "f05601be-a8fa-4819-999a-af0ea3a22e35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10/10 [==============================] - 1s 38ms/step\n",
            "[1 3 6 1 6 4 5 0 4 2 3 1 2 2 2 1 2 2 6 2 7 5 0 6 0 3 1 5 7 0 5 5 6 1 0 0 3\n",
            " 3 4 0 3 2 1 2 2 1 6 2 2 1 1 2 2 0 1 1 0 5 2 0 7 3 2 3 4 4 2 0 6 0 3 2 7 7\n",
            " 1 7 7 2 3 6 5 2 4 7 3 4 6 2 0 6 4 7 2 3 1 7 2 5 1 3 2 1 2 3 7 7 5 1 3 4 6\n",
            " 1 3 6 4 7 3 3 3 0 2 0 7 7 7 3 6 3 7 1 4 4 7 7 0 7 4 3 4 6 2 1 0 2 1 7 4 5\n",
            " 7 4 5 7 2 0 3 1 3 4 7 3 5 4 1 7 1 1 1 7 2 2 3 5 3 0 4 0 7 7 5 1 2 0 7 1 2\n",
            " 5 6 7 2 0 4 1 0 6 0 3 3 0 4 2 7 5 4 0 2 1 7 6 7 1 1 6 3 1 4 5 0 6 3 4 3 3\n",
            " 4 3 6 1 1 2 6 4 6 3 4 6 3 6 0 1 2 1 7 3 4 1 7 1 3 4 0 3 7 2 4 2 7 0 7 4 3\n",
            " 6 6 4 4 1 7 0 2 6 0 7 0 3 2 3 6 2 7 7 1 4 4 1 4 4 1 2 0 7 4 4 6 2 5 7 0 7\n",
            " 1 7 1 1 5 7 1 2 0 0 4 2 7 0 1 3 0 1 0]\n",
            "['calm', 'fearful', 'sad', 'calm', 'sad', 'happy', 'neutral', 'angry', 'happy', 'disgust', 'fearful', 'calm', 'disgust', 'disgust', 'disgust', 'calm', 'disgust', 'disgust', 'sad', 'disgust', 'surprised', 'neutral', 'angry', 'sad', 'angry', 'fearful', 'calm', 'neutral', 'surprised', 'angry', 'neutral', 'neutral', 'sad', 'calm', 'angry', 'angry', 'fearful', 'fearful', 'happy', 'angry', 'fearful', 'disgust', 'calm', 'disgust', 'disgust', 'calm', 'sad', 'disgust', 'disgust', 'calm', 'calm', 'disgust', 'disgust', 'angry', 'calm', 'calm', 'angry', 'neutral', 'disgust', 'angry', 'surprised', 'fearful', 'disgust', 'fearful', 'happy', 'happy', 'disgust', 'angry', 'sad', 'angry', 'fearful', 'disgust', 'surprised', 'surprised', 'calm', 'surprised', 'surprised', 'disgust', 'fearful', 'sad', 'neutral', 'disgust', 'happy', 'surprised', 'fearful', 'happy', 'sad', 'disgust', 'angry', 'sad', 'happy', 'surprised', 'disgust', 'fearful', 'calm', 'surprised', 'disgust', 'neutral', 'calm', 'fearful', 'disgust', 'calm', 'disgust', 'fearful', 'surprised', 'surprised', 'neutral', 'calm', 'fearful', 'happy', 'sad', 'calm', 'fearful', 'sad', 'happy', 'surprised', 'fearful', 'fearful', 'fearful', 'angry', 'disgust', 'angry', 'surprised', 'surprised', 'surprised', 'fearful', 'sad', 'fearful', 'surprised', 'calm', 'happy', 'happy', 'surprised', 'surprised', 'angry', 'surprised', 'happy', 'fearful', 'happy', 'sad', 'disgust', 'calm', 'angry', 'disgust', 'calm', 'surprised', 'happy', 'neutral', 'surprised', 'happy', 'neutral', 'surprised', 'disgust', 'angry', 'fearful', 'calm', 'fearful', 'happy', 'surprised', 'fearful', 'neutral', 'happy', 'calm', 'surprised', 'calm', 'calm', 'calm', 'surprised', 'disgust', 'disgust', 'fearful', 'neutral', 'fearful', 'angry', 'happy', 'angry', 'surprised', 'surprised', 'neutral', 'calm', 'disgust', 'angry', 'surprised', 'calm', 'disgust', 'neutral', 'sad', 'surprised', 'disgust', 'angry', 'happy', 'calm', 'angry', 'sad', 'angry', 'fearful', 'fearful', 'angry', 'happy', 'disgust', 'surprised', 'neutral', 'happy', 'angry', 'disgust', 'calm', 'surprised', 'sad', 'surprised', 'calm', 'calm', 'sad', 'fearful', 'calm', 'happy', 'neutral', 'angry', 'sad', 'fearful', 'happy', 'fearful', 'fearful', 'happy', 'fearful', 'sad', 'calm', 'calm', 'disgust', 'sad', 'happy', 'sad', 'fearful', 'happy', 'sad', 'fearful', 'sad', 'angry', 'calm', 'disgust', 'calm', 'surprised', 'fearful', 'happy', 'calm', 'surprised', 'calm', 'fearful', 'happy', 'angry', 'fearful', 'surprised', 'disgust', 'happy', 'disgust', 'surprised', 'angry', 'surprised', 'happy', 'fearful', 'sad', 'sad', 'happy', 'happy', 'calm', 'surprised', 'angry', 'disgust', 'sad', 'angry', 'surprised', 'angry', 'fearful', 'disgust', 'fearful', 'sad', 'disgust', 'surprised', 'surprised', 'calm', 'happy', 'happy', 'calm', 'happy', 'happy', 'calm', 'disgust', 'angry', 'surprised', 'happy', 'happy', 'sad', 'disgust', 'neutral', 'surprised', 'angry', 'surprised', 'calm', 'surprised', 'calm', 'calm', 'neutral', 'surprised', 'calm', 'disgust', 'angry', 'angry', 'happy', 'disgust', 'surprised', 'angry', 'calm', 'fearful', 'angry', 'calm', 'angry']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import csv\n",
        "\n",
        "def make_csv(unlabeled_file_name, y_predicted_label):\n",
        "  with open('prediction.csv', mode='w', newline='') as file:\n",
        "      writer = csv.writer(file)\n",
        "      writer.writerow(['filename', 'label'])\n",
        "      for i in range(len(unlabeled_file_name)):\n",
        "        writer.writerow([unlabeled_file_name[i], y_predicted_label[i]])\n",
        "  file.close()\n",
        "\n",
        "make_csv(y_test, y_predicted_label)"
      ],
      "metadata": {
        "id": "2LXRuDD8erE4"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}