Speech_Emotion_Recognition.ipynb

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    "colab": {
      "name": "Speech Emotion Recognition.ipynb",
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    "kernelspec": {
      "name": "python3",
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    "language_info": {
      "name": "python"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/OmarMoghazy/CNN-Speech-Emotion-Recognition/blob/main/Speech_Emotion_Recognition.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r-BgzluxNxLN"
      },
      "source": [
        "import numpy as np\n",
        "import IPython\n",
        "from IPython.core.display import display\n",
        "import matplotlib.pyplot as plt\n",
        "import librosa\n",
        "import librosa.display\n",
        "import os\n",
        "from sklearn.model_selection import train_test_split\n",
        "import shutil\n",
        "from PIL import Image\n",
        "from sklearn import preprocessing\n",
        "import math\n",
        "import gc\n",
        "import pandas as pd\n",
        "\n",
        "import keras\n",
        "from keras.models import Sequential,Input,Model\n",
        "from keras.layers import Dense, Dropout, Flatten, Activation, BatchNormalization, concatenate\n",
        "from keras.layers import Conv2D, MaxPooling2D, Conv1D, MaxPool1D, MaxPooling1D\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "from keras.layers.advanced_activations import LeakyReLU\n",
        "from keras.callbacks import LearningRateScheduler, ReduceLROnPlateau\n",
        "from keras.regularizers import l2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kHqtpB5QdKRK"
      },
      "source": [
        "# Speech Emotion Recognition\n",
        "\n",
        "Speech is the most natural way of expressing ourselves as humans. It is only natural\n",
        "then to extend this communication medium to computer applications. We define\n",
        "speech emotion recognition (SER) systems as a collection of methodologies that\n",
        "process and classify speech signals to detect the embedded emotions."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IcYLXzJod0Rh"
      },
      "source": [
        "## The Dataset (CREMA-D)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iue9fO1leo4x"
      },
      "source": [
        "https://github.com/CheyneyComputerScience/CREMA-D\n",
        "\n",
        "CREMA-D is a data set of 7,442 original clips from 91 actors. These clips were from 48 male and 43 female actors between the ages of 20 and 74 coming from a variety of races and ethnicities (African America, Asian, Caucasian, Hispanic, and Unspecified).\n",
        "\n",
        "Actors spoke from a selection of 12 sentences. The sentences were presented using one of six different emotions (Anger, Disgust, Fear, Happy, Neutral and Sad) and four different emotion levels (Low, Medium, High and Unspecified).\n",
        "\n",
        "Participants rated the emotion and emotion levels based on the combined audiovisual presentation, the video alone, and the audio alone. Due to the large number of ratings needed, this effort was crowd-sourced and a total of 2443 participants each rated 90 unique clips, 30 audio, 30 visual, and 30 audio-visual. 95% of the clips have more than 7 ratings."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fhKHS101eVRp"
      },
      "source": [
        "### Filename Labeling Conventions\n",
        "\n",
        "The Actor id is a 4 digit number at the start of the file. Each subsequent identifier is separated by an underscore (_)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wFrJ4ZRqes74"
      },
      "source": [
        "\n",
        "Actors spoke from a selection of 12 sentences (in parentheses is the three letter acronym used in the second part of the filename):\n",
        "\n",
        "It's eleven o'clock (IEO).\n",
        "\n",
        "That is exactly what happened (TIE).\n",
        "\n",
        "I'm on my way to the meeting (IOM).\n",
        "\n",
        "I wonder what this is about (IWW).\n",
        "\n",
        "The airplane is almost full (TAI).\n",
        "\n",
        "Maybe tomorrow it will be cold (MTI).\n",
        "\n",
        "I would like a new alarm clock (IWL)\n",
        "\n",
        "I think I have a doctor's appointment (ITH).\n",
        "\n",
        "Don't forget a jacket (DFA).\n",
        "\n",
        "I think I've seen this before (ITS).\n",
        "\n",
        "The surface is slick (TSI).\n",
        "\n",
        "We'll stop in a couple of minutes (WSI)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w8Cxaz_TfreK"
      },
      "source": [
        "The sentences were presented using different emotion (in parentheses is the three letter code used in the third part of the filename):\n",
        "\n",
        "Anger (ANG)\n",
        "\n",
        "Disgust (DIS)\n",
        "\n",
        "Fear (FEA)\n",
        "\n",
        "Happy/Joy (HAP)\n",
        "\n",
        "Neutral (NEU)\n",
        "\n",
        "Sad (SAD)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CMoNlO37fxKW"
      },
      "source": [
        "and emotion level (in parentheses is the two letter code used in the fourth part of the filename):\n",
        "\n",
        "Low (LO)\n",
        "\n",
        "Medium (MD)\n",
        "\n",
        "High (HI)\n",
        "\n",
        "Unspecified (XX)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wkpj6PbMgBHI"
      },
      "source": [
        "The suffix of the filename is based on the type of file, flv for flash video used for presentation of both the video only, and the audio-visual clips. mp3 is used for the audio files used for the audio-only presentation of the clips. wav is used for files used for computational audio processing.\n",
        "\n",
        "For our task, we are only going to use the .wav files."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6HWUJPBZhAJa"
      },
      "source": [
        "## Data Preparation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-3MMBwL1GBit"
      },
      "source": [
        "The first step is reading the data and splitting it into training, testing and validation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "da4XsZU88wHa"
      },
      "source": [
        "dataset_path = './drive/MyDrive/Speech Emotion Recognition/Dataset/Crema'\n",
        "files = os.listdir(dataset_path)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GFOnnuc_-GiX"
      },
      "source": [
        "We will split the dataset into training data and testing data by actor. There are 91 actors. Actor IDs start from 1001 up to 1091. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eb8BNo-6Crwc"
      },
      "source": [
        "IDs = np.arange(1001,1092)\n",
        "training_IDs, testing_IDs = train_test_split(IDs, test_size=0.3, random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2VJ277Iw-OLF"
      },
      "source": [
        "training_IDs = set(training_IDs)\n",
        "testing_IDs = set(testing_IDs)\n",
        "\n",
        "for filename in files:\n",
        "  if int(filename[0:4]) in training_IDs:\n",
        "    shutil.copyfile(dataset_path + '/' + filename, dataset_path + \"/..\" + \"/training/\" + filename)\n",
        "  else:\n",
        "    shutil.copyfile(dataset_path + '/' + filename, dataset_path + \"/..\" + \"/testing/\" + filename)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "amq0Q73CD6eW"
      },
      "source": [
        "Next, we will split the training data into training and validation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4RAEQIsGD-_s"
      },
      "source": [
        "training_IDs_2, validation_IDs = train_test_split(list(training_IDs), test_size=0.05, random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_-2M1JYnETiK"
      },
      "source": [
        "validation_IDs = set(validation_IDs)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WRHrkJyXEs87"
      },
      "source": [
        "for filename in files:\n",
        "  if int(filename[0:4]) in validation_IDs:\n",
        "    shutil.move(dataset_path + \"/..\" + \"/training/\" + filename, dataset_path + \"/..\" + \"/validation/\" + filename)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s9rVKk83hEqw"
      },
      "source": [
        "We need the filename of each of the audio files, which will be used:\n",
        " \n",
        "*   to read the wav file\n",
        "*   to get the actor id, emotion and emotion level.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bcjD3HY1XPtG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16db6067-d6e6-4286-af8b-b8f888e12f1e"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EEOhXlZUHKj9"
      },
      "source": [
        "training_data_path = './drive/MyDrive/Speech Emotion Recognition/Dataset/training'\n",
        "training_files = os.listdir(training_data_path)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wq8Gv-GT_IUF"
      },
      "source": [
        "testing_data_path = './drive/MyDrive/Speech Emotion Recognition/Dataset/testing'\n",
        "testing_files = os.listdir(testing_data_path)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RtEMKBkQ_Joa"
      },
      "source": [
        "validation_data_path = './drive/MyDrive/Speech Emotion Recognition/Dataset/validation'\n",
        "validation_files = os.listdir(validation_data_path)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MTy_hnR2iclQ"
      },
      "source": [
        "The following function takes a single parameter which is the path to an audio file. It displays a player for the audio file, plots its waveform and displays its Mel spectogram. It also displays some information about the file such as duration in seconds, sampling rate and number of samples."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UXA7ioFDDA_h"
      },
      "source": [
        "def el3abat(path):\n",
        "  '''Displays player for the audio file.\n",
        "  Plots the waveform of the audio file.\n",
        "  Plots the Mel spectogram of the audio file'''\n",
        "\n",
        "  display(IPython.display.Audio(path))\n",
        "\n",
        "  data, fs = librosa.load(path, sr=None)\n",
        "\n",
        "  duration = data.size/fs\n",
        "\n",
        "  print(\"Duration:\\t\" + str(duration) + \" seconds\")\n",
        "  print(\"Sampling rate:\\t\" + str(fs) + \" samples per second\")\n",
        "  print(\"Signal size:\\t\" + str(data.size) + \" samples\")\n",
        "  print()\n",
        "\n",
        "  librosa.display.waveplot(data, sr=fs)\n",
        "  plt.title(\"Time Domain Waveform\")\n",
        "  plt.show()\n",
        "  print()\n",
        "  sgram = librosa.stft(data)\n",
        "  # librosa.display.specshow(sgram)\n",
        "  print('Mel spectogram')\n",
        "  sgram_mag, _ = librosa.magphase(sgram)\n",
        "  mel_scale_sgram = librosa.feature.melspectrogram(S=sgram_mag, sr=fs)\n",
        "  librosa.display.specshow(mel_scale_sgram)\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 695
        },
        "id": "lGTejU5DKwZz",
        "outputId": "9cf08c79-cc98-4996-9fc4-5578a02ece39"
      },
      "source": [
        "path = training_data_path + '/' + training_files[8]\n",
        "el3abat(path)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" >\n",
              "                    <source 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\" type=\"audio/x-wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Duration:\t2.93625 seconds\n",
            "Sampling rate:\t16000 samples per second\n",
            "Signal size:\t46980 samples\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Mel Spectogram\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2bS_uPtV2GCF"
      },
      "source": [
        "We need to make sure that all the audio files have the same duration. We will add padding (of silence) to all audio files that have less samples than the maximum. But first, we need to find the maximum."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FSgJfO0PpOaJ",
        "outputId": "32869317-beb1-43f8-ff27-5145cffd6221"
      },
      "source": [
        "# max = 0\n",
        "# for f in files:\n",
        "#   path = path = dataset_path + '/' + f\n",
        "#   # data, fs = librosa.load(path, sr=None)\n",
        "#   s = librosa.get_duration(filename=path)\n",
        "#   if s > max:\n",
        "#     max = s\n",
        "#     print(max, fs, filename) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3.536875 16000 1037_IEO_ANG_HI.wav\n",
            "3.603625 16000 1037_IEO_ANG_HI.wav\n",
            "3.6703125 16000 1037_IEO_ANG_HI.wav\n",
            "3.970625 16000 1037_IEO_ANG_HI.wav\n",
            "4.2041875 16000 1037_IEO_ANG_HI.wav\n",
            "4.3376875 16000 1037_IEO_ANG_HI.wav\n",
            "4.404375 16000 1037_IEO_ANG_HI.wav\n",
            "4.537875 16000 1037_IEO_ANG_HI.wav\n",
            "4.7714375 16000 1037_IEO_ANG_HI.wav\n",
            "4.8048125 16000 1037_IEO_ANG_HI.wav\n",
            "4.8715625 16000 1037_IEO_ANG_HI.wav\n",
            "4.971625 16000 1037_IEO_ANG_HI.wav\n",
            "5.005 16000 1037_IEO_ANG_HI.wav\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HySyPzNe1NVs"
      },
      "source": [
        "From the outout of the previous cell, we can see that the Longest duration is 5.005 seconds. Therefore, the maximum number of samples in the dataset is 80080 samples.\n",
        "\n",
        "For every audio file in the dataset, if it has less than 80100 samples, we will add zero-padding to get it to 80100 samples. Then, we are going to normalize it then generate its Mel spectogram and save it to google drive."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qGaesGlTKB0i"
      },
      "source": [
        "### First Feature Space"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Su_Tlz-Q-Pg5"
      },
      "source": [
        "#### Waveform"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ua9OiqRT2wpo"
      },
      "source": [
        "Z = []\n",
        "count = 0\n",
        "for filename in training_files:\n",
        "  count = count + 1\n",
        "  #load file\n",
        "  path = training_data_path + '/' + filename\n",
        "  data, fs = librosa.load(path, sr=None)\n",
        "  \n",
        "  add padding\n",
        "  if data.size < 80100:\n",
        "    padding = 80100 - data.size\n",
        "    if padding%2 == 0:\n",
        "      data = np.hstack((data, np.zeros(padding//2)))\n",
        "      data = np.hstack((np.zeros(padding//2), data))\n",
        "    else:\n",
        "      data = np.hstack((data, np.zeros(padding//2)))\n",
        "      data = np.hstack((np.zeros(padding//2 + 1), data))\n",
        "  Z.append(data)\n",
        "  librosa.display.waveplot(data, sr=fs)\n",
        "  np.save(\"drive/MyDrive/Speech Emotion Recognition/Training padded waveforms/\"+ filename[:-4] + \".npy\", data)\n",
        "  if count % 100 == 0:\n",
        "    print(count, end=' ')\n",
        "\n",
        "  # produce Mel spectogram\n",
        "  # sgram = librosa.stft(data)\n",
        "  # sgram_mag, _ = librosa.magphase(sgram)\n",
        "  # mel_scale_sgram = librosa.feature.melspectogram(S=sgram_mag, sr=fs)\n",
        "  # librosa.display.specshow(mel_scale_sgram)\n",
        "\n",
        "  # save to google drive\n",
        "  # np.save(\"drive/MyDrive/Speech Emotion Recognition/Training Mel spectograms/\"+ filename[:-4] + \".npy\", mel_scale_sgram)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bhdaUEC_4PCe"
      },
      "source": [
        "Z = []\n",
        "for filename in training_files:\n",
        "  Z.append(np.load(\"drive/MyDrive/Speech Emotion Recognition/Training padded waveforms/\"+ filename[:-4] + \".npy\"))\n",
        "\n",
        "Z = np.array(Z)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "BIUWiwpy4qmE",
        "outputId": "4412744f-8e2c-4c50-8c59-6b51e99f4307"
      },
      "source": [
        "plt.plot(Z[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f31baa5e110>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD4CAYAAADvsV2wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAdF0lEQVR4nO3de3hV9Z3v8feXhHCVOyL3YEUrWPASUeutCCgXR5xjbbXTiq0+2lZHO3R0sHiodZx5EDvW8dRTZaxTW2u9jR05ijCAimgrEBQUkEuEcL+EO4JcQn7nj70Sd5K9k+y9VrJW9vq8nicP676+kJ1PFr+11u9nzjlERCT3tQi7ABERaRoKfBGRmFDgi4jEhAJfRCQmFPgiIjGRH3YB6XTr1s0VFhaGXYaISLOyZMmSXc657qnWRTbwCwsLKS4uDrsMEZFmxcw2pFunJh0RkZhQ4IuIxIQCX0QkJhT4IiIxocAXEYkJBb6ISEwo8EVEYkKBL7GycusBPty4N+wyREIR2RevRBrD2McXAFA6dVzIlYg0PV3hi4jERCCBb2ajzWy1mZWY2aQU639oZp+Y2VIze8/MBgVxXhERaTjfgW9mecATwBhgEHBjikB/3jn3Nefc2cA04FG/5xURkcwEcYU/DChxzq1zzh0DXgDGJ2/gnDuQNNsO0EC6IiJNLIibtr2BTUnzm4ELam5kZncAE4EC4IoAzisiIhlospu2zrknnHNfAf4JuD/VNmZ2m5kVm1lxWVlZU5UmIhILQQT+FqBv0nwfb1k6LwDXplrhnJvunCtyzhV1756y/34REclSEIG/GBhoZgPMrAC4AZiRvIGZDUyaHQesDeC8IiKSAd9t+M65cjO7E5gN5AHPOOdWmNmDQLFzbgZwp5mNBI4De4EJfs8rIiKZCeRNW+fcTGBmjWVTkqbvDuI8IiKSPb1pKyISEwp8EZGYUOCLiMSEAl9EJCYU+CIiMaHAFxGJCQW+iEhMKPBFRGJCgS8iEhMKfImNI8dPhF2CSKgU+BIbLxd/OWxD+YmKECsRCYcCX2LjwJHyqumDSdMicaHAl9h4ZPbqqmmNsSlxpMAXEYkJBb7EknO6xpf4UeCLiMSEAl9iSdf3EkcKfBGRmFDgSyypCV/iSIEvIhITCnyJpa37vgi7BJEmp8CXWBr/xPthlyDS5BT4IiIxocAXEYkJBb6ISEwo8EVEYkKBLyISE4EEvpmNNrPVZlZiZpNSrJ9oZivN7GMzm2dm/YM4r4iINJzvwDezPOAJYAwwCLjRzAbV2OwjoMg5NwR4BZjm97wiIpKZIK7whwElzrl1zrljwAvA+OQNnHNvO+cOe7MfAH0COK+IiGQgiMDvDWxKmt/sLUvnFuDNVCvM7DYzKzaz4rKysgBKExGRSk1609bMvgsUAY+kWu+cm+6cK3LOFXXv3r0pSxMRyXn5ARxjC9A3ab6Pt6waMxsJTAYud84dDeC8IiKSgSCu8BcDA81sgJkVADcAM5I3MLNzgKeAa5xzOwM4p4iIZMh34DvnyoE7gdnAp8BLzrkVZvagmV3jbfYI0B542cyWmtmMNIcTEZFGEkSTDs65mcDMGsumJE2PDOI8IiKSPb1pKyISEwp8iYW5K3fUWrZkw54QKhEJjwJfYqF096Fay677zV9DqEQkPAp8iYWj5RVhlyASOgW+xMIjs1eHXYJI6BT4IiIxocAXEYkJBb6ISEwo8EVEYkKBLznvvbW7wi5BJBIU+JLzdh9S56wioMAXEYkNBb6ISEwo8EVEYkKBLyISEwp8adZ2HjzCj55bwu7Pj+KcS7nNiYrUy+uzcN1uKrLcVySKFPjSrP1qzlreXL6d8x6ay5TXVqTcZs+hYxkfd8HaMr49/QOefPczvyWKRIYCX3LGHz7YkHL5Q298mvGxtu0/AsC6strdKjfErOXb+M5/fJDVviKNJZAhDkVyyfETFSxenxgc5ViW3Sr/8LkPgyxJJBAKfJEkuz8/ynkPza2aX78ruyt8kShSk45IksqmnEqfbNkfUiUiwVPgS7O263N1myDSUAp8adbm1Bic/N01ZSFVIhJ9CnzJKTc9syjt8/hN5S8l6p1TokmBLzkn7HelvvP0wnALEElDgS8iEhOBBL6ZjTaz1WZWYmaTUqy/zMw+NLNyM/tmEOcUEZHM+A58M8sDngDGAIOAG81sUI3NNgI3A8/7PZ9IY/o7NcdIDgviCn8YUOKcW+ecOwa8AIxP3sA5V+qc+xjI7rVFibVZy7cz+rF3OX6iYR8fPzdt939xPOt9Uymc9EaD6xZpbEEEfm9gU9L8Zm+ZSCB++NwSVm0/yMY9h0M5/2dln/vaX2PqSlRE6qatmd1mZsVmVlxWpuepJRqefMdfj5nf/93igCoR8SeIwN8C9E2a7+Mty5hzbrpzrsg5V9S9e/cASpNcUrNv+qbqqz6Ts6zceqDR6hDxK4jAXwwMNLMBZlYA3ADMCOC4ItU8Nndttfmr/897gR6/PE1beya3BNI1/2zd90U2JYkEynfgO+fKgTuB2cCnwEvOuRVm9qCZXQNgZueb2WbgeuApM0s9UoVIHT7dVv3qeeW2YK+m0zW9/NeHmynZ6a8dv3jDXl/7iwQhkO6RnXMzgZk1lk1Jml5MoqlHpNHt++I43dq3yni/BXXcXB356HxKp47Luqawu3sQgYjdtBWpkzVss6Kk/uz3Hc58eMPGUKHAlwhQ4Euz0cC8r6a+nJ365qqsakl7vjTLH59XkvVg6iJBUeBLs2GWTeTX7cn5TTNI+fpdh2p15SzS1BT40mxkEveV4bp0077GKSYLdz6vcW4lXAp8aTYyucCvfKrm938tbZRa0qmrxHI16UjIFPjSbFgG1/iZ/HJ4YdHGLKpJTUMuSpQp8CXSvjh2omp6TwZP3GTS/DPp1U8Ce0P2F/9vZSDHEWkMCnyJtDOnzKqaLjvYeFfPYx9f0GjHFokKBb7kpLmf7sA5x9ur1QmfSCUFvuSkxaV7GTj5zbDLEIkUBb5E1tHyE/VvVAc9FSNSnQJfImvarNVhl1BN8g3kVLbt99cjZkWFY4t61ZRGpMCXyEo1wtUfF24IoZKEg0fqHv5wXdkhX8d//K21XDz1LTbuDmdkL8l9CnxpVib/eXlo5w6igaiu8W3fL0n01rn9wJEAziRSmwJfIqu5dTB5rLz+wcrfWrWzCSoRSU2BL5GV7qbt+l3+mk4ay/3/Xf//PoLv/k2k4RT4ElnlJ1Jf4g//5TtNW4hn3+G62/AbcsN1/xfHeal4U1AliWREgS+RdaKONp3K9u6m9ODr/kfmvOeVj7n3lY9Zu+Ng2m00OpY0FgW+RNbhY+Vp1/3d0wubsJKEI8fTt9FnOrLWsRQ3b497/6PZ/0Xd/5MQyZYCXyJr+ZZgByn3a8mGvcxesZ23VtUeyCTTl7xS9fxZ2Xf/w7OCHYVLpFIgg5iLxMXtf1gCUGtA8yBbYT7z+Ty/SDq6wpcm8eqHmylN8XTNsfIK9hyKxkDjmVi0fk+1v4/L8Cn9FvrJkxDoYyeNbv/h40x8aRkjHp0PQPmJCqbNWsUz763n/H+Zy7n/PCfkCjP3raf+yjeSnxbK8Ap/9GMLqrpq2PX5UWav2N6g/d5etZMlG/ZmdjIRjwJfAvXsX0oZ5QX7D363mMJJb/D4W2sBOFHhOHL8BL955zP+7zuf8eDrK6tuUDbXJ1Omvplob8+m+h//MdE8NOGZRVVNRZVuemYRq7d/+STPkeOJXw7f/91irvvNX4DEjeJDR8v580ebKZz0Bk8vWJdFFalt2nOYA/V0JSHNj9rwJRAnKhyb9x7m5zMSjy4eOlpe9VZpcp84w3/5Dtv21+46YM7KHfTv2o4Zy7awuHQvFw7o0jSF+/Tk/M+Yv6aMpycUZbzv26vLeHjWKlakGG3r3TVlvLumjGnXDeHF4k0s2bCXOf9wWdX6J94u4ZHZq+nYpmXVL82H3viUWy89tWqbsoNHKa+ooGfHNlXLdh44Ql4Lo0u7Ajbv/YKPN+/na7070q9rW9bsOEj7Vvn06tSGS6e9Tf+ubZl/z3D2HT7Ga0u30qNDa0afdUrGf0+JDovqlVVRUZErLi4OuwxpoAnPLGL+Gn+DjeS3MHVpHJChfTqybPN+AKZdN4Srh/akbUE+hZPeAOCaob2YsWxr1fZTrh7Eg68nhmcs7NqWUq8Dt9Kp4xj16HzWeoPC17xZLdFjZkuccymvQBT44suG3Ye4/JF3wi5D6tGtfSvm/fRyhv7ifzLab2jfTizzHhcFBX5zUFfgB9KGb2ajzWy1mZWY2aQU61uZ2Yve+oVmVhjEeSVcx8orFPbNxK7Pj2Yc9kC1sJfmz3fgm1ke8AQwBhgE3Ghmg2psdguw1zl3GvAr4GG/55XwnX6/hhAUaU6CuGk7DChxzq0DMLMXgPHAyqRtxgMPeNOvAL82M3ON0J508MhxHpixsv4NRSRjRQ/N4fLTTw67jJzXv2tb7hoxMPDjBhH4vYHk7v82Axek28Y5V25m+4GuQLUesMzsNuA2gH79+mVVTPkJxwfrdme1r4jUbdfnx/Tz1QTqG10tW5F6LNM5Nx2YDombttkco3O7At6fdEWgdUlqlU98SHzopm3zFsRN2y1A36T5Pt6ylNuYWT7QEdBlgohIEwoi8BcDA81sgJkVADcAM2psMwOY4E1/E3irMdrvRUQkPd+B75wrB+4EZgOfAi8551aY2YNmdo232W+BrmZWAkwEaj26Kc3X6MHp37786iknZXSsadcN8VtOzrv98lPr3yjJXSMGUjp1XK3mmKsG90i5/ccPXMn7k67gw/89KusaJZoCacN3zs0EZtZYNiVp+ghwfRDnkui44fy+vLB4E7dcOoBPtuxPOcTfrJ9c1qC2/r5d2vDqjy7m86PpBz2JqtUPjeaqX71b9XZqY+jRoRU7DhwF4L4xZ3LfmDM5fqKCgZOrPxq74N7h9OrUhq/87Msfx4mjTq91vImjTueO4afx8KxV3HLJADbuOczKrQfYd/g4HVq3pEPrlgC8e89wDh8vZ/RjCxrt7yZNJ1I3baV5+de//RrfvbA/Z/XuyJWDe/Cf75cyd+LlFHZtSwuzqg7Fiu8fyfMLN/LonDW1jlHzqrM5dtjVKj+Pl26/iGH/Oi+j/f752rPo0Dqfo8crGDukJ2f9fHa19Ysmj2DYvySO+e69w7npt4v41bfPrlrfMq8F5/TrxEcb93HXiIFcfno3+nZpC8Cdw09jaN9ODO3bsdoxX7vjYjbuOczVQ3piZvxs7JkA9OjQmvMLa/df1K9r4niDenZgcK8OGf39JHoU+JK1Fi2Ms3onAmXy2DO5+euF9O/artZ23dq34u+vOI1lm/Yxz+tQLZ3uJ7UC4PbLTuWpd4Pr/bGxndyhdUbbty3I43sX9q/7mCe1piC/BcfKK2iVn8eLt19Ua5tXfvh1KpyjZV711tl/vOqMlMdM/BLolFGtADPvvjTjfSR6FPgSiPy8FinDvpKZ8dubz2fF1v08vWA9BXkt+OmVtZsaOrRuWXXVH+XA/7frh/LTl5dxarf0f+cgLJ0yqs7RtPJaGHkphksUSUWBL01qcK+O1ZolmqszvJvRrVvmZbX/jcNqv1h41xWn8fhbJd76xJPObQv0IyrB0adJmp33/mk4lzz8dqg19Omc6GP+u/U0y6TTz2trTzbxyjOYeOUZLC7dw5A+HVPsJeKPAl+anT6da4dlUzr5pFZ0alvg663Tul5DSXXzVCQICnyRDDx3ywUM7NHe93EuPq1bANWIZEZj2opk4JKB3eiR5omchl7xf//iQgb2yOyFNJEgKPAlsup6S7dlXvN9MuUfr0z9yKRIY1PgS2RV1NHOPW/iN5qukIC1ytePnYRDnzyJrFRdAlSqfAO0uSmdOo78PP3YSTj0yZPIOrNntF7lf+Bvao7cmZlp31THcBIuBb5EVhAdaN8/7kz/B/HcfPEAX/sX6MpeQqZPoERWEAMmnJFh98yNqa57EiJNQYEvkZXq5aRrhvbK6BgXndo1qHIa5I27Lkm7TnkvYVPgS2SlysfHbzynwftPHHV6k98gPaWOXjMrewIVCYsCXyKr5hVx3y5tqs0v/NmIOvfv1LZl0CX5MmyAukyQcCnwJbJqvlx1yWndq82ne+M1qrLtWVMkKAp8iay6+tdviEsHdq9/I5EYUeBLpL0/6Yqs9x3QyIOTiDQ3CnyJtN6d2tS/UT3GDekZQCX+3D1iYNgliCjwRYKUboSq71xQe4QrkaamwJfY++X1QwM7VpuC2jdm2xXkNbsbzJKbFPiSk757YcOvqDu1qf/xzctOz/4GsFnz7cpZcotGvJKc1CIpZP3G7bRvDuFbRX2z3l9xL1GhK3xpNjK5UA4yZDM91vXn9fF3AJFG4ivwzayLmc0xs7Xen53TbDfLzPaZ2et+zifx1iKD4Px6gGPGZtoB2yM17gko7yUq/F7hTwLmOecGAvO8+VQeAb7n81wSc2PPavjjlVcNPqXB23ZtX1Dn+iF9OjX4WKmoDV+iwm/gjwee9aafBa5NtZFzbh5w0Oe5JOYaetU+/55vVJtvUU/gntOvM8/fekG2ZdVLeS9R4TfwezjntnnT24EePo8n4pvVaES5qwEvPQXZBFRTfb9wRJpKvU/pmNlcINX/jycnzzjnnJn56vHbzG4DbgPo108vqkjC738wjGWb9jV4+5r52tBuif/Xub159cMtmZTWIIN7RWuoRomvegPfOTcy3Toz22FmPZ1z28ysJ7DTTzHOuenAdICioiINFyFA4hn4dM/BX356d+avKQvkPIU+O2tL5+d/M7hRjiuSKb9NOjOACd70BOA1n8cTycjTE4rCLqFerfL19LNEg99P4lRglJmtBUZ685hZkZk9XbmRmS0AXgZGmNlmM7vK53lFAGjpc0SrHh2+bO4Zf3YvhbPkNF9v2jrndgO1hh1yzhUDtybNX+rnPCKZqNmG39B7pv27tmP1Q2MonPRG8EWJRIAuZyTWaj7RAzCop26ySm5S4EvOyeRFpzuvOK3Wspl36z+kkpsU+JJzMnnq/UxdzUuMKPAlZ1TecA3yPadLBzbeC1kiTU2BL83egnuHs3TKKDq1rb9f+0x1bVd3PzvptG6pHy2JHn0qpdnr26UtndoWcErHxPi3+S2C+1hn+/bfr288N7AaRIKiAVAkZ/x2QhHvl+yq1ZVC25a1hx2sVODzOf50Rg5St1ISPbrCl5zRrX0rxp/du9by/LwWlE4dl3Kfs3rXfdPW+ejgo3enNtnvLNIIFPgSa/U9wlnhJ/FFIkaBL1IHxb3kEgW+SF0CSHx1hy9RocAXqYPTNb7kEAW+SB3UhC+5RIEvUgcFvuQSBb7Exmknt894H7W/Sy5R4EtsjP1azwZv29nrpqEhA6Cnc89VZwCJ9wNEokBv2kpsZHKx3qZlHns5Toc22ffPc+05vbn2nNovgomERVf4EhstMmifGdQr8QauhjyUXKIrfImNTNrj//2Gc1ix9YCaYySn6PJFYiOTJp12rfIZNqBLo9UiEgYFvsSGnriRuFPgi4jEhAJfYiOTwc1FcpECX2KjZZ4CX+JNgS+xcdNFhWGXIBIqBb7ERus6hjoUiQMFvohITPgKfDPrYmZzzGyt92fnFNucbWZ/NbMVZvaxmX3bzzlFRCQ7fq/wJwHznHMDgXnefE2HgZucc4OB0cBjZtbJ53lFRCRDfgN/PPCsN/0scG3NDZxza5xza73prcBOoLvP84qISIb8Bn4P59w2b3o70KOujc1sGFAAfJZm/W1mVmxmxWVlZT5LExGRZPV2nmZmc4FTUqyanDzjnHNmlnZ8IDPrCfwBmOCcq0i1jXNuOjAdoKioSGMNiYgEqN7Ad86NTLfOzHaYWU/n3DYv0Hem2a4D8AYw2Tn3QdbViohI1vw26cwAJnjTE4DXam5gZgXAn4HfO+de8Xk+ERHJkt/AnwqMMrO1wEhvHjMrMrOnvW2+BVwG3GxmS72vs32eV0REMuRrABTn3G5gRIrlxcCt3vRzwHN+ziMiIv7pTVsRkZhQ4IuIxIQCX0QkJhT4IiIxocAXEYkJBb6ISEwo8EVEYkKBLyISEwp8iaWvf6Vr2CWINDlfb9qKNDfTv3ceZsaIr55MhVOHrBIvCnyJlSsHf9nTdwssxEpEmp6adEREYkKBLyISEwp8EZGYUOCLiMSEAl9EJCYU+CIiMaHAFxGJCQW+iEhMmIvo24ZmVgZs8HGIbsCugMoJkurKjOrKjOrKTC7W1d851z3VisgGvl9mVuycKwq7jppUV2ZUV2ZUV2biVpeadEREYkKBLyISE7kc+NPDLiAN1ZUZ1ZUZ1ZWZWNWVs234IiJSXS5f4YuISBIFvohITORc4JvZaDNbbWYlZjapkc7xjJntNLPlScu6mNkcM1vr/dnZW25m9rhXz8dmdm7SPhO87dea2YSk5eeZ2SfePo+bWYNG6jCzvmb2tpmtNLMVZnZ3FGozs9ZmtsjMlnl1/cJbPsDMFnrHetHMCrzlrbz5Em99YdKx7vOWrzazq5KWZ/V9N7M8M/vIzF6PSk3evqXev/NSMyv2lkXhM9bJzF4xs1Vm9qmZXRR2XWZ2hvfvVPl1wMx+EnZd3n7/4H3ml5vZnyzxsxDeZ8w5lzNfQB7wGXAqUAAsAwY1wnkuA84FlictmwZM8qYnAQ9702OBNwEDLgQWesu7AOu8Pzt70529dYu8bc3bd0wD6+oJnOtNnwSsAQaFXZu3bXtvuiWw0DvGS8AN3vIngR950z8GnvSmbwBe9KYHed/TVsAA73ud5+f7DkwEngde9+ZDr8k7binQrcayKHzGngVu9aYLgE5RqKtGBmwH+oddF9AbWA+0Sfps3RzmZyz0kA7yC7gImJ00fx9wXyOdq5Dqgb8a6OlN9wRWe9NPATfW3A64EXgqaflT3rKewKqk5dW2y7DG14BRUaoNaAt8CFxA4k3C/JrfO2A2cJE3ne9tZzW/n5XbZft9B/oA84ArgNe9c4RaU9L2pdQO/FC/j0BHEgFmUaqrRi1XAu9HoS4Sgb+JxC+QfO8zdlWYn7Fca9Kp/AeutNlb1hR6OOe2edPbgR711FTX8s0plmfE++/gOSSupkOvzRJNJ0uBncAcElcm+5xz5SmOVXV+b/1+oGsW9dbnMeBeoMKb7xqBmio54H/MbImZ3eYtC/v7OAAoA/7TEs1gT5tZuwjUlewG4E/edKh1Oee2AL8ENgLbSHxmlhDiZyzXAj8SXOLXbWjPu5pZe+C/gJ845w4krwurNufcCefc2SSuqocBX23qGpKZ2dXATufckjDrqMMlzrlzgTHAHWZ2WfLKkL6P+SSaMn/jnDsHOESiqSTsugDw2sKvAV6uuS6Murx7BuNJ/KLsBbQDRjdlDTXlWuBvAfomzffxljWFHWbWE8D7c2c9NdW1vE+K5Q1iZi1JhP0fnXOvRqk2AOfcPuBtEv8d7WRm+SmOVXV+b31HYHcW9dblYuAaMysFXiDRrPPvIddUxbs6xDm3E/gziV+SYX8fNwObnXMLvflXSPwCCLuuSmOAD51zO7z5sOsaCax3zpU5544Dr5L43IX3GcukfSzqXySuQNaR+I1aeRNjcCOdq5DqbfiPUP0G0TRvehzVbxAt8pZ3IdEe2tn7Wg908dbVvEE0toE1GfB74LEay0OtDegOdPKm2wALgKtJXIkl37z6sTd9B9VvXr3kTQ+m+s2rdSRuXPn6vgPf4MubtqHXROJK8KSk6b+QuDKMwmdsAXCGN/2AV1PodXn7vgB8P0Kf+wuAFSTuWxmJG95/H+ZnLPSQDvqLxB34NSTaiCc30jn+RKJN7jiJq55bSLS1zQPWAnOTPigGPOHV8wlQlHScHwAl3lfyB7UIWO7t82tq3CSro65LSPy39WNgqfc1NuzagCHAR15dy4Ep3vJTvR+kEu+HoJW3vLU3X+KtPzXpWJO9c68m6UkJP993qgd+6DV5NSzzvlZU7hv299Hb72yg2Pte/jeJYIxCXe1IXA13TFoWhbp+Aazy9v0DidAO7TOmrhVERGIi19rwRUQkDQW+iEhMKPBFRGJCgS8iEhMKfBGRmFDgi4jEhAJfRCQm/j/fOF0vYnKIFAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hWk-tgejxGrv"
      },
      "source": [
        "mean = np.mean(Z, axis=0)\n",
        "std = np.std(Z, axis=0)\n",
        "\n",
        "np.save('drive/MyDrive/Speech Emotion Recognition/training_mean.npy', mean)\n",
        "np.save('drive/MyDrive/Speech Emotion Recognition/training_std.npy', std)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "32DGEPQo9wxt"
      },
      "source": [
        "mean = np.load('drive/MyDrive/Speech Emotion Recognition/training_mean.npy')\n",
        "std = np.load('drive/MyDrive/Speech Emotion Recognition/training_std.npy')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 660
        },
        "id": "QAc6Vy2yxcQX",
        "outputId": "e7507475-a014-4930-d4f4-9bc4a833f1c7"
      },
      "source": [
        "print('Mean of the dataset: ' + str(mean))\n",
        "print('Standard deviation for the dataset: ' + str(std))\n",
        "\n",
        "fig, axes = plt.subplots(2,1)\n",
        "fig.set_size_inches(10,10)\n",
        "axes[0].plot(mean)\n",
        "axes[0].set_title('Mean')\n",
        "\n",
        "axes[1].plot(std)\n",
        "axes[1].set_title('Standard Deviation')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of the dataset: [0. 0. 0. ... 0. 0. 0.]\n",
            "Standard deviation for the dataset: [0. 0. 0. ... 0. 0. 0.]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Standard Deviation')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_wQsF2HxyqfF"
      },
      "source": [
        "There are some zeros in the standard deviation, which will affect our normalization process because we divide by the standard deviation.\n",
        "\n",
        "We will set all 0 values of standard deviation to 1 instead, which makes sense because 0 standard deviation means all values for this feature are equal to the mean, so the normalized (centered and scales) value for this feature will be 0, which means it does not deviate from the mean."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "27wqrSfqyqFf"
      },
      "source": [
        "zeros = np.where(std == 0)\n",
        "std[zeros] = 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wlh-pSqNWZP1"
      },
      "source": [
        "Normalizing the data after calculating the mean and variance."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3pArrg5nVmSG"
      },
      "source": [
        "for filename in training_files:\n",
        "  temp = np.load('drive/MyDrive/Speech Emotion Recognition/Training padded waveforms/' + filename[:-4] + '.npy')\n",
        "  temp = (temp-mean)/std\n",
        "  np.save('drive/MyDrive/Speech Emotion Recognition/Training normalized waveforms/' + filename[:-4] + '.npy', temp)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TQgOwOGIKf4J"
      },
      "source": [
        "#### Zero Crossing Rate and Energy\n",
        "ZCR: The rate of sign-changes of the signal during\n",
        "the duration of a particular frame.\n",
        "\n",
        "Energy: The sum of squares of the signal values, normalized by\n",
        "the respective frame length."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LXwYeZ7KLpvu"
      },
      "source": [
        "zcrs = []\n",
        "energy = []\n",
        "count = 0\n",
        "for filename in training_files:\n",
        "  count = count + 1\n",
        "  if count % 100 == 0:\n",
        "    print(count, end=' ')\n",
        "  data, fs = librosa.load('drive/MyDrive/Speech Emotion Recognition/Dataset/training/' + filename[:-4] + '.wav', sr=None)\n",
        "  zcrs.append(np.sum(librosa.feature.zero_crossing_rate(data, pad=False)))\n",
        "  energy.append(np.sum(np.square(data))/data.size)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LU-D8x_aMfKJ"
      },
      "source": [
        "np.save('drive/MyDrive/Speech Emotion Recognition/ZCR.npy', zcrs)\n",
        "np.save('drive/MyDrive/Speech Emotion Recognition/energy.npy', energy)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "FD6T50G2RTQE",
        "outputId": "5812c6e9-288c-42e7-d165-98c323e22d0f"
      },
      "source": [
        "fig, axes = plt.subplots(1,2)\n",
        "fig.set_size_inches(10,4)\n",
        "\n",
        "axes[0].set_title('Zero Crossing Rate')\n",
        "axes[0].plot(zcrs)\n",
        "axes[1].set_title('Energy')\n",
        "axes[1].plot(energy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f2feac67550>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J6DlO58p-x-x"
      },
      "source": [
        "### Second Feature Space"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L_xTG1kZKK5J"
      },
      "source": [
        "#### Mel spectograms"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TmZ_SHywpOzx"
      },
      "source": [
        "Next, we are going to read the entire dataset and produce a Mel spectogram for each audio file which we will save to google drive as a numpy array using the same naming conventions.\n",
        "\n",
        "We will use these Mel spectograms as the second feature space.\n",
        "\n",
        "[Read more about Mel spectograms and why they are using for deep learning here](https://towardsdatascience.com/audio-deep-learning-made-simple-part-2-why-mel-spectograms-perform-better-aad889a93505)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wzz__LA0_Bhh"
      },
      "source": [
        "fs = 16000\n",
        "S = []\n",
        "count = 0\n",
        "\n",
        "for filename in training_files:\n",
        "  count = count + 1\n",
        "  if count % 100 ==0:\n",
        "    print(count)\n",
        "  temp = np.load('drive/MyDrive/Speech Emotion Recognition/Training normalized waveforms/' + filename[:-4] + '.npy')\n",
        "\n",
        "  # produce Mel spectogram\n",
        "  sgram = librosa.stft(temp)\n",
        "  sgram_mag, _ = librosa.magphase(sgram)\n",
        "  mel_scale_sgram = librosa.feature.melspectogram(S=sgram_mag, sr=fs)\n",
        "  \n",
        "  S.append(mel_scale_sgram)\n",
        "  # librosa.display.specshow(mel_scale_sgram)\n",
        "\n",
        "  # save to google drive\n",
        "  # np.save(\"drive/MyDrive/Speech Emotion Recognition/Training Mel spectograms/\"+ filename[:-4] + \".npy\", mel_scale_sgram)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dsy73ZT1D99e"
      },
      "source": [
        "Next, we will calculate the normalization statistics for the mel spectograms"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sKOawYk0EGRh"
      },
      "source": [
        "mean = np.mean(S, axis=0)\n",
        "std = np.std(S, axis=0)\n",
        "\n",
        "np.save('drive/MyDrive/Speech Emotion Recognition/training_spectogram_mean.npy', mean)\n",
        "np.save('drive/MyDrive/Speech Emotion Recognition/training_spectogram_std.npy', std)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i7B-518FE4v7"
      },
      "source": [
        "zeros = np.where(std==0)\n",
        "std[zeros] = 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U1sSN73zHCUL"
      },
      "source": [
        "Now we will normalize the spectograms using the normalization statistics that we just calculated."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ntROvYaSG8P2"
      },
      "source": [
        "fs = 16000\n",
        "count = 0\n",
        "for filename in training_files:\n",
        "  count = count + 1\n",
        "  if count % 100 == 0:\n",
        "    print(count, end=' ')\n",
        "  temp = np.load('drive/MyDrive/Speech Emotion Recognition/Training normalized waveforms/' + filename[:-4] + '.npy')\n",
        "\n",
        "   # produce Mel spectogram\n",
        "  sgram = librosa.stft(temp)\n",
        "  sgram_mag, _ = librosa.magphase(sgram)\n",
        "  mel_scale_sgram = librosa.feature.melspectogram(S=sgram_mag, sr=fs)\n",
        "\n",
        "  # normalization\n",
        "  temp = (mel_scale_sgram - mean) / std\n",
        "\n",
        "  # save to google drive\n",
        "  np.save('drive/MyDrive/Speech Emotion Recognition/Training normalized Mel Spectograms/' + filename[:-4] + '.npy', temp)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NA2OY3OMTZUf"
      },
      "source": [
        "## Feature Extraction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "afb0DmmcTexl"
      },
      "source": [
        "### Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HM3UZUZ1TdPH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cd77f841-6570-491b-91cf-e907b3858d72"
      },
      "source": [
        "df = pd.DataFrame(columns=['power_max', 'power_mean', 'power_min', 'power_median', 'power_range', 'power_std', 'power_var',\n",
        "                           'zcr_max', 'zcr_mean', 'zcr_min', 'zcr_median', 'zcr_range', 'zcr_std', 'zcr_var', 'spectogram', 'filename',\n",
        "                           'label'])\n",
        "count = 0\n",
        "spectograms = []\n",
        "labels = []\n",
        "for i, filename in zip(range(len(training_files)),training_files):\n",
        "  count = count + 1\n",
        "  if count % 100 == 0:\n",
        "    print(count, end=' ')\n",
        "  powers = []\n",
        "  power_max = float('-inf')\n",
        "  power_mean = 0\n",
        "  power_min = float('inf')\n",
        "  power_median = 0\n",
        "  power_range = 0\n",
        "  power_std = 0\n",
        "  power_var = 0\n",
        "\n",
        "  zcrs = []\n",
        "  zcr_max = float('-inf')\n",
        "  zcr_mean = 0\n",
        "  zcr_min = float('inf')\n",
        "  zcr_median = 0\n",
        "  zcr_range = 0\n",
        "  zcr_std = 0\n",
        "  zcr_var = 0\n",
        "\n",
        "  data, fs = librosa.load('drive/MyDrive/Speech Emotion Recognition/Dataset/training/' + filename[:-4] + '.wav', sr=None)\n",
        "  chunk_size = 256\n",
        "  chuggs = [data[x:x+chunk_size] for x in range(0, len(data), chunk_size)]\n",
        "  for chunk in chuggs:\n",
        "    chunk_power = np.mean(np.sum(chunk**2))\n",
        "    powers.append(chunk_power)\n",
        "    if power_max < chunk_power:\n",
        "      power_max = chunk_power\n",
        "    if power_min > chunk_power:\n",
        "      power_min = chunk_power\n",
        "    \n",
        "    chunk_zcr = np.sum(librosa.feature.zero_crossing_rate(chunk, pad=False))\n",
        "    zcrs.append(chunk_zcr)\n",
        "    if zcr_max < chunk_zcr:\n",
        "      zcr_max = chunk_zcr\n",
        "    if zcr_min > chunk_zcr:\n",
        "      zcr_min = chunk_zcr\n",
        "\n",
        "  power_mean = np.mean(powers)\n",
        "  power_median = np.median(powers)\n",
        "  power_range = power_max - power_median\n",
        "  power_std = np.std(powers)\n",
        "  power_var = np.var(powers)\n",
        "\n",
        "  zcr_mean = np.mean(zcrs)\n",
        "  zcr_median = np.median(zcrs)\n",
        "  zcr_range = zcr_max - zcr_min\n",
        "  zcr_std = np.std(zcrs)\n",
        "  zcr_var = np.var(zcrs)\n",
        "\n",
        "\n",
        "  s = np.load('/content/drive/MyDrive/Speech Emotion Recognition/Training normalized Mel Spectograms/'+filename[:-4]+'.npy')\n",
        "\n",
        "  spectograms.append(s)\n",
        "\n",
        "  \n",
        "  label = filename.split('_')[2]\n",
        "\n",
        "  labels.append(label)\n",
        "\n",
        "  if label == 'ANG':\n",
        "    label = 0\n",
        "  elif label == 'DIS':\n",
        "    label = 1\n",
        "  elif label == 'FEA':\n",
        "    label = 2\n",
        "  elif label == 'HAP':\n",
        "    label = 3\n",
        "  elif label == 'NEU':\n",
        "    label = 4\n",
        "  elif label == 'SAD':\n",
        "    label = 5\n",
        "\n",
        "\n",
        "  df.loc[i] = {'power_max':power_max,'power_mean':power_mean,'power_min':power_min,\n",
        "               'power_median':power_median,'power_range':power_range,'power_std':power_std,\n",
        "               'power_var':power_var, 'zcr_max':zcr_max, 'zcr_mean':zcr_mean,'zcr_min':zcr_min,\n",
        "               'zcr_median':zcr_median, 'zcr_range':zcr_range, 'zcr_std':zcr_std,\n",
        "               'zcr_var':zcr_var, 'spectogram':s, 'filename':filename,'label':label}"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100 3200 3300 3400 3500 3600 3700 3800 3900 4000 4100 4200 4300 4400 4500 4600 4700 4800 "
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FZM7OnA6UxbC"
      },
      "source": [
        "_1d_mean = np.mean(df.drop(columns = ['spectogram', 'filename', 'label']), axis=0)\n",
        "_1d_std = np.std(df.drop(columns = ['spectogram', 'filename', 'label']), axis=0)\n",
        "\n",
        "np.save('drive/MyDrive/Speech Emotion Recognition/1d_mean.npy', _1d_mean)\n",
        "np.save('drive/MyDrive/Speech Emotion Recognition/1d_std.npy', _1d_std)\n",
        "\n",
        "df = pd.concat(((df.drop(columns = ['spectogram', 'filename', 'label']) - _1d_mean) / _1d_std, df[['spectogram', 'filename', 'label']]), axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jzhbi1pGThCU"
      },
      "source": [
        "### Validation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qAlmlU2PTjg3",
        "outputId": "e8a4a5eb-cbd0-422c-8692-739167f1bcf5"
      },
      "source": [
        "df_v = pd.DataFrame(columns=['power_max', 'power_mean', 'power_min', 'power_median', 'power_range', 'power_std', 'power_var',\n",
        "                           'zcr_max', 'zcr_mean', 'zcr_min', 'zcr_median', 'zcr_range', 'zcr_std', 'zcr_var', 'spectogram', 'filename',\n",
        "                           'label'])\n",
        "count = 0\n",
        "spectograms = []\n",
        "labels = []\n",
        "\n",
        "mean_aaa = np.load('/content/drive/MyDrive/Speech Emotion Recognition/1d_mean.npy')\n",
        "std_aaa = np.load('/content/drive/MyDrive/Speech Emotion Recognition/1d_std.npy')\n",
        "\n",
        "_1d_mean = np.load('/content/drive/MyDrive/Speech Emotion Recognition/training_mean.npy')\n",
        "_1d_std = np.load('/content/drive/MyDrive/Speech Emotion Recognition/training_std.npy')\n",
        "\n",
        "_2d_mean = np.load('drive/MyDrive/Speech Emotion Recognition/training_spectogram_mean.npy')\n",
        "_2d_std = np.load('drive/MyDrive/Speech Emotion Recognition/training_spectogram_std.npy')\n",
        "\n",
        "for i, filename in zip(range(len(validation_files)),validation_files):\n",
        "  count = count + 1\n",
        "  if count % 100 == 0:\n",
        "    print(count, end=' ')\n",
        "  powers = []\n",
        "  power_max = float('-inf')\n",
        "  power_mean = 0\n",
        "  power_min = float('inf')\n",
        "  power_median = 0\n",
        "  power_range = 0\n",
        "  power_std = 0\n",
        "  power_var = 0\n",
        "\n",
        "  zcrs = []\n",
        "  zcr_max = float('-inf')\n",
        "  zcr_mean = 0\n",
        "  zcr_min = float('inf')\n",
        "  zcr_median = 0\n",
        "  zcr_range = 0\n",
        "  zcr_std = 0\n",
        "  zcr_var = 0\n",
        "\n",
        "  data, fs = librosa.load('/content/drive/MyDrive/Speech Emotion Recognition/Dataset/validation/'+filename, sr=None)\n",
        "\n",
        "  chunk_size = 256\n",
        "  chuggs = [data[x:x+chunk_size] for x in range(0, len(data), chunk_size)]\n",
        "  for chunk in chuggs:\n",
        "    chunk_power = np.mean(np.sum(chunk**2))\n",
        "    powers.append(chunk_power)\n",
        "    if power_max < chunk_power:\n",
        "      power_max = chunk_power\n",
        "    if power_min > chunk_power:\n",
        "      power_min = chunk_power\n",
        "    \n",
        "    chunk_zcr = np.sum(librosa.feature.zero_crossing_rate(chunk, pad=False))\n",
        "    zcrs.append(chunk_zcr)\n",
        "    if zcr_max < chunk_zcr:\n",
        "      zcr_max = chunk_zcr\n",
        "    if zcr_min > chunk_zcr:\n",
        "      zcr_min = chunk_zcr\n",
        "\n",
        "  power_mean = np.mean(powers)\n",
        "  power_median = np.median(powers)\n",
        "  power_range = power_max - power_median\n",
        "  power_std = np.std(powers)\n",
        "  power_var = np.var(powers)\n",
        "\n",
        "  zcr_mean = np.mean(zcrs)\n",
        "  zcr_median = np.median(zcrs)\n",
        "  zcr_range = zcr_max - zcr_min\n",
        "  zcr_std = np.std(zcrs)\n",
        "  zcr_var = np.var(zcrs)\n",
        "\n",
        "  if data.size < 80100:\n",
        "    padding = 80100 - data.size\n",
        "    if padding%2 == 0:\n",
        "      data = np.hstack((data, np.zeros(padding//2)))\n",
        "      data = np.hstack((np.zeros(padding//2), data))\n",
        "    else:\n",
        "      data = np.hstack((data, np.zeros(padding//2)))\n",
        "      data = np.hstack((np.zeros(padding//2 + 1), data))\n",
        "\n",
        "  zeros = np.where(_1d_std == 0)\n",
        "  _1d_std[zeros] = 1\n",
        "  \n",
        "  data = (data-_1d_mean)/_1d_std\n",
        "\n",
        "  sgram = librosa.stft(data)\n",
        "  sgram_mag, _ = librosa.magphase(sgram)\n",
        "  s = librosa.feature.melspectrogram(S=sgram_mag, sr=fs)\n",
        "\n",
        "  s = (s - _2d_mean) / _2d_std \n",
        "  s = s.reshape(-1,157,128,1)\n",
        "  spectograms.append(s)\n",
        "  \n",
        "  label = filename.split('_')[2]\n",
        "\n",
        "  labels.append(label)\n",
        "\n",
        "  if label == 'ANG':\n",
        "    label = 0\n",
        "  elif label == 'DIS':\n",
        "    label = 1\n",
        "  elif label == 'FEA':\n",
        "    label = 2\n",
        "  elif label == 'HAP':\n",
        "    label = 3\n",
        "  elif label == 'NEU':\n",
        "    label = 4\n",
        "  elif label == 'SAD':\n",
        "    label = 5\n",
        "\n",
        "\n",
        "  df_v.loc[i] = {'power_max':power_max,'power_mean':power_mean,'power_min':power_min,\n",
        "               'power_median':power_median,'power_range':power_range,'power_std':power_std,\n",
        "               'power_var':power_var, 'zcr_max':zcr_max, 'zcr_mean':zcr_mean,'zcr_min':zcr_min,\n",
        "               'zcr_median':zcr_median, 'zcr_range':zcr_range, 'zcr_std':zcr_std,\n",
        "               'zcr_var':zcr_var, 'spectogram':s, 'filename':filename,'label':label}\n",
        "\n",
        "df_v = pd.concat(((df_v.drop(columns = ['spectogram', 'filename', 'label']) - mean_aaa) / std_aaa, df_v[['spectogram', 'filename', 'label']]), axis=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100 200 300 "
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z2U_zztYUjAH"
      },
      "source": [
        "### Testing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZraeLyfnUlW5",
        "outputId": "d781af35-9310-4f06-8afd-78d4c982d2b7"
      },
      "source": [
        "df_t = pd.DataFrame(columns=['power_max', 'power_mean', 'power_min', 'power_median', 'power_range', 'power_std', 'power_var',\n",
        "                           'zcr_max', 'zcr_mean', 'zcr_min', 'zcr_median', 'zcr_range', 'zcr_std', 'zcr_var', 'spectogram', 'filename',\n",
        "                           'label'])\n",
        "count = 0\n",
        "spectograms = []\n",
        "labels = []\n",
        "\n",
        "mean_aaa = np.load('/content/drive/MyDrive/Speech Emotion Recognition/1d_mean.npy')\n",
        "std_aaa = np.load('/content/drive/MyDrive/Speech Emotion Recognition/1d_std.npy')\n",
        "\n",
        "_1d_mean = np.load('/content/drive/MyDrive/Speech Emotion Recognition/training_mean.npy')\n",
        "_1d_std = np.load('/content/drive/MyDrive/Speech Emotion Recognition/training_std.npy')\n",
        "\n",
        "_2d_mean = np.load('drive/MyDrive/Speech Emotion Recognition/training_spectogram_mean.npy')\n",
        "_2d_std = np.load('drive/MyDrive/Speech Emotion Recognition/training_spectogram_std.npy')\n",
        "\n",
        "for i, filename in zip(range(len(testing_files)),testing_files):\n",
        "  count = count + 1\n",
        "  if count % 100 == 0:\n",
        "    print(count, end=' ')\n",
        "  powers = []\n",
        "  power_max = float('-inf')\n",
        "  power_mean = 0\n",
        "  power_min = float('inf')\n",
        "  power_median = 0\n",
        "  power_range = 0\n",
        "  power_std = 0\n",
        "  power_var = 0\n",
        "\n",
        "  zcrs = []\n",
        "  zcr_max = float('-inf')\n",
        "  zcr_mean = 0\n",
        "  zcr_min = float('inf')\n",
        "  zcr_median = 0\n",
        "  zcr_range = 0\n",
        "  zcr_std = 0\n",
        "  zcr_var = 0\n",
        "\n",
        "  data, fs = librosa.load('/content/drive/MyDrive/Speech Emotion Recognition/Dataset/testing/'+filename, sr=None)\n",
        "\n",
        "  chunk_size = 256\n",
        "  chuggs = [data[x:x+chunk_size] for x in range(0, len(data), chunk_size)]\n",
        "  for chunk in chuggs:\n",
        "    chunk_power = np.mean(np.sum(chunk**2))\n",
        "    powers.append(chunk_power)\n",
        "    if power_max < chunk_power:\n",
        "      power_max = chunk_power\n",
        "    if power_min > chunk_power:\n",
        "      power_min = chunk_power\n",
        "    \n",
        "    chunk_zcr = np.sum(librosa.feature.zero_crossing_rate(chunk, pad=False))\n",
        "    zcrs.append(chunk_zcr)\n",
        "    if zcr_max < chunk_zcr:\n",
        "      zcr_max = chunk_zcr\n",
        "    if zcr_min > chunk_zcr:\n",
        "      zcr_min = chunk_zcr\n",
        "\n",
        "  power_mean = np.mean(powers)\n",
        "  power_median = np.median(powers)\n",
        "  power_range = power_max - power_median\n",
        "  power_std = np.std(powers)\n",
        "  power_var = np.var(powers)\n",
        "\n",
        "  zcr_mean = np.mean(zcrs)\n",
        "  zcr_median = np.median(zcrs)\n",
        "  zcr_range = zcr_max - zcr_min\n",
        "  zcr_std = np.std(zcrs)\n",
        "  zcr_var = np.var(zcrs)\n",
        "\n",
        "  if data.size < 80100:\n",
        "    padding = 80100 - data.size\n",
        "    if padding%2 == 0:\n",
        "      data = np.hstack((data, np.zeros(padding//2)))\n",
        "      data = np.hstack((np.zeros(padding//2), data))\n",
        "    else:\n",
        "      data = np.hstack((data, np.zeros(padding//2)))\n",
        "      data = np.hstack((np.zeros(padding//2 + 1), data))\n",
        "\n",
        "  zeros = np.where(_1d_std == 0)\n",
        "  _1d_std[zeros] = 1\n",
        "  \n",
        "  data = (data-_1d_mean)/_1d_std\n",
        "\n",
        "  sgram = librosa.stft(data)\n",
        "  sgram_mag, _ = librosa.magphase(sgram)\n",
        "  s = librosa.feature.melspectrogram(S=sgram_mag, sr=fs)\n",
        "\n",
        "  s = (s - _2d_mean) / _2d_std \n",
        "  s = s.reshape(-1,157,128,1)\n",
        "  spectograms.append(s)\n",
        "  \n",
        "  label = filename.split('_')[2]\n",
        "\n",
        "  labels.append(label)\n",
        "\n",
        "  if label == 'ANG':\n",
        "    label = 0\n",
        "  elif label == 'DIS':\n",
        "    label = 1\n",
        "  elif label == 'FEA':\n",
        "    label = 2\n",
        "  elif label == 'HAP':\n",
        "    label = 3\n",
        "  elif label == 'NEU':\n",
        "    label = 4\n",
        "  elif label == 'SAD':\n",
        "    label = 5\n",
        "\n",
        "\n",
        "  df_t.loc[i] = {'power_max':power_max,'power_mean':power_mean,'power_min':power_min,\n",
        "               'power_median':power_median,'power_range':power_range,'power_std':power_std,\n",
        "               'power_var':power_var, 'zcr_max':zcr_max, 'zcr_mean':zcr_mean,'zcr_min':zcr_min,\n",
        "               'zcr_median':zcr_median, 'zcr_range':zcr_range, 'zcr_std':zcr_std,\n",
        "               'zcr_var':zcr_var, 'spectogram':s, 'filename':filename,'label':label}\n",
        "\n",
        "df_t = pd.concat(((df_t.drop(columns = ['spectogram', 'filename', 'label']) - mean_aaa) / std_aaa, df_t[['spectogram', 'filename', 'label']]), axis=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 "
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yaee_fWRlL9L"
      },
      "source": [
        "x1_training = np.asarray(df.drop(columns=['filename', 'label', 'spectogram','zcr_median','power_median','zcr_var','power_var'])).astype('float32').reshape(-1,10,1)\n",
        "x2_training = np.array([x for x in df['spectogram']]).reshape(-1,157,128,1)\n",
        "y_training = np.array(df['label']).astype('int32').reshape((-1,1))\n",
        "\n",
        "x1_validation = np.asarray(df_v.drop(columns=['filename', 'label', 'spectogram','zcr_median','power_median','zcr_var','power_var'])).astype('float32').reshape(-1,10,1)\n",
        "x2_validation = np.array([x for x in df_v['spectogram']]).reshape(-1,157,128,1)\n",
        "y_validation = np.array(df_v['label']).astype('int32').reshape((-1,1))\n",
        "\n",
        "x1_testing = np.asarray(df_t.drop(columns=['filename', 'label', 'spectogram','zcr_median','power_median','zcr_var','power_var'])).astype('float32').reshape(-1,10,1)\n",
        "x2_testing = np.array([x for x in df_t['spectogram']]).reshape(-1,157,128,1)\n",
        "y_testing = np.array(df_t['label']).astype('int32').reshape((-1,1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dm78YUWFkgpu"
      },
      "source": [
        "## CNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A0h70xxuldVQ"
      },
      "source": [
        "batch_size = 64\n",
        "epochs = 20\n",
        "num_classes = 6"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b5R2tGgOkieX"
      },
      "source": [
        "### 1D Convolution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8KzyH8DakkYL"
      },
      "source": [
        "i = Input(shape=(10,1))\n",
        "x = Dropout(0.2)(i)\n",
        "x = Conv1D(filters=128, kernel_size=3, activation='linear', kernel_regularizer=l2(), bias_regularizer=l2())(x)\n",
        "x = LeakyReLU(alpha=0.1)(x)\n",
        "x = Dropout(0.4)(x)\n",
        "x = Conv1D(filters=256, kernel_size=3, activation='linear', kernel_regularizer=l2(), bias_regularizer=l2())(x)\n",
        "x = LeakyReLU(alpha=0.1)(x)\n",
        "x = MaxPooling1D(pool_size=2)(x)\n",
        "x = Flatten()(x)\n",
        "x = Dropout(0.5)(x)\n",
        "x = Dense(256, activation='linear', kernel_regularizer=l2(), bias_regularizer=l2())(x)\n",
        "x1 = LeakyReLU(alpha=0.1)(x)\n",
        "x = Dense(6, activation='softmax')(x1)\n",
        "\n",
        "x = Model(inputs=[i],outputs=[x])\n",
        "x2 = Model(inputs=[i],outputs=[x1])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dmcPxjiBkxxc"
      },
      "source": [
        "x.compile(loss=keras.losses.sparse_categorical_crossentropy, optimizer=keras.optimizers.Adam(),metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1bbZZOfPaqxn",
        "outputId": "767ade52-96ed-4968-b8f4-01a69bc17fa5"
      },
      "source": [
        "x.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_34\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_16 (InputLayer)        [(None, 10, 1)]           0         \n",
            "_________________________________________________________________\n",
            "dropout_49 (Dropout)         (None, 10, 1)             0         \n",
            "_________________________________________________________________\n",
            "conv1d_20 (Conv1D)           (None, 8, 128)            512       \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_58 (LeakyReLU)   (None, 8, 128)            0         \n",
            "_________________________________________________________________\n",
            "dropout_50 (Dropout)         (None, 8, 128)            0         \n",
            "_________________________________________________________________\n",
            "conv1d_21 (Conv1D)           (None, 6, 256)            98560     \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_59 (LeakyReLU)   (None, 6, 256)            0         \n",
            "_________________________________________________________________\n",
            "max_pooling1d_10 (MaxPooling (None, 3, 256)            0         \n",
            "_________________________________________________________________\n",
            "flatten_15 (Flatten)         (None, 768)               0         \n",
            "_________________________________________________________________\n",
            "dropout_51 (Dropout)         (None, 768)               0         \n",
            "_________________________________________________________________\n",
            "dense_35 (Dense)             (None, 256)               196864    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_60 (LeakyReLU)   (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_36 (Dense)             (None, 6)                 1542      \n",
            "=================================================================\n",
            "Total params: 297,478\n",
            "Trainable params: 297,478\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9-i4eFmrlDeY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "105eb7ef-12b6-4cc3-dfae-73a057fc135a"
      },
      "source": [
        "x_history = x.fit(x1_training, y_training, batch_size=batch_size,epochs=200,verbose=1,validation_data=(x1_validation, y_validation))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/200\n",
            "76/76 [==============================] - 2s 9ms/step - loss: 1.6128 - accuracy: 0.3425 - val_loss: 1.6247 - val_accuracy: 0.3506\n",
            "Epoch 2/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5949 - accuracy: 0.3569 - val_loss: 1.6561 - val_accuracy: 0.3354\n",
            "Epoch 3/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6036 - accuracy: 0.3515 - val_loss: 1.6423 - val_accuracy: 0.3323\n",
            "Epoch 4/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5854 - accuracy: 0.3569 - val_loss: 1.6437 - val_accuracy: 0.3537\n",
            "Epoch 5/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6059 - accuracy: 0.3533 - val_loss: 1.6378 - val_accuracy: 0.3445\n",
            "Epoch 6/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6023 - accuracy: 0.3466 - val_loss: 1.6357 - val_accuracy: 0.3476\n",
            "Epoch 7/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6069 - accuracy: 0.3634 - val_loss: 1.6435 - val_accuracy: 0.3079\n",
            "Epoch 8/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5859 - accuracy: 0.3613 - val_loss: 1.6671 - val_accuracy: 0.2866\n",
            "Epoch 9/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6075 - accuracy: 0.3461 - val_loss: 1.6351 - val_accuracy: 0.3354\n",
            "Epoch 10/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6062 - accuracy: 0.3487 - val_loss: 1.6772 - val_accuracy: 0.3232\n",
            "Epoch 11/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6152 - accuracy: 0.3407 - val_loss: 1.6227 - val_accuracy: 0.3659\n",
            "Epoch 12/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6009 - accuracy: 0.3469 - val_loss: 1.6274 - val_accuracy: 0.3567\n",
            "Epoch 13/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5980 - accuracy: 0.3479 - val_loss: 1.6292 - val_accuracy: 0.3384\n",
            "Epoch 14/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5988 - accuracy: 0.3407 - val_loss: 1.6364 - val_accuracy: 0.3354\n",
            "Epoch 15/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5926 - accuracy: 0.3489 - val_loss: 1.6431 - val_accuracy: 0.3384\n",
            "Epoch 16/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5984 - accuracy: 0.3529 - val_loss: 1.6352 - val_accuracy: 0.3720\n",
            "Epoch 17/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6128 - accuracy: 0.3551 - val_loss: 1.6354 - val_accuracy: 0.3415\n",
            "Epoch 18/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5916 - accuracy: 0.3446 - val_loss: 1.6413 - val_accuracy: 0.3506\n",
            "Epoch 19/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5848 - accuracy: 0.3576 - val_loss: 1.6380 - val_accuracy: 0.2896\n",
            "Epoch 20/200\n",
            "76/76 [==============================] - 0s 3ms/step - loss: 1.5971 - accuracy: 0.3405 - val_loss: 1.6502 - val_accuracy: 0.3384\n",
            "Epoch 21/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5997 - accuracy: 0.3517 - val_loss: 1.6377 - val_accuracy: 0.3293\n",
            "Epoch 22/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5837 - accuracy: 0.3591 - val_loss: 1.6401 - val_accuracy: 0.3415\n",
            "Epoch 23/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5817 - accuracy: 0.3593 - val_loss: 1.6544 - val_accuracy: 0.2896\n",
            "Epoch 24/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6027 - accuracy: 0.3489 - val_loss: 1.6384 - val_accuracy: 0.3567\n",
            "Epoch 25/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5912 - accuracy: 0.3538 - val_loss: 1.6575 - val_accuracy: 0.3018\n",
            "Epoch 26/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5913 - accuracy: 0.3557 - val_loss: 1.6320 - val_accuracy: 0.3476\n",
            "Epoch 27/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5916 - accuracy: 0.3581 - val_loss: 1.6251 - val_accuracy: 0.3262\n",
            "Epoch 28/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5960 - accuracy: 0.3555 - val_loss: 1.6327 - val_accuracy: 0.3445\n",
            "Epoch 29/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5846 - accuracy: 0.3563 - val_loss: 1.6841 - val_accuracy: 0.2561\n",
            "Epoch 30/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5942 - accuracy: 0.3439 - val_loss: 1.6297 - val_accuracy: 0.3384\n",
            "Epoch 31/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5834 - accuracy: 0.3684 - val_loss: 1.6431 - val_accuracy: 0.3384\n",
            "Epoch 32/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5942 - accuracy: 0.3551 - val_loss: 1.6233 - val_accuracy: 0.3598\n",
            "Epoch 33/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5824 - accuracy: 0.3442 - val_loss: 1.6359 - val_accuracy: 0.3506\n",
            "Epoch 34/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6114 - accuracy: 0.3415 - val_loss: 1.6381 - val_accuracy: 0.3445\n",
            "Epoch 35/200\n",
            "76/76 [==============================] - 0s 3ms/step - loss: 1.5921 - accuracy: 0.3565 - val_loss: 1.6416 - val_accuracy: 0.3445\n",
            "Epoch 36/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5957 - accuracy: 0.3391 - val_loss: 1.6530 - val_accuracy: 0.3354\n",
            "Epoch 37/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6094 - accuracy: 0.3445 - val_loss: 1.6374 - val_accuracy: 0.3354\n",
            "Epoch 38/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5847 - accuracy: 0.3451 - val_loss: 1.6343 - val_accuracy: 0.3323\n",
            "Epoch 39/200\n",
            "76/76 [==============================] - 0s 3ms/step - loss: 1.5963 - accuracy: 0.3558 - val_loss: 1.6430 - val_accuracy: 0.3323\n",
            "Epoch 40/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5874 - accuracy: 0.3401 - val_loss: 1.6235 - val_accuracy: 0.3506\n",
            "Epoch 41/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5937 - accuracy: 0.3502 - val_loss: 1.6402 - val_accuracy: 0.3598\n",
            "Epoch 42/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6014 - accuracy: 0.3496 - val_loss: 1.6532 - val_accuracy: 0.2866\n",
            "Epoch 43/200\n",
            "76/76 [==============================] - 1s 9ms/step - loss: 1.5983 - accuracy: 0.3574 - val_loss: 1.6695 - val_accuracy: 0.2744\n",
            "Epoch 44/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6140 - accuracy: 0.3332 - val_loss: 1.6506 - val_accuracy: 0.3262\n",
            "Epoch 45/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6067 - accuracy: 0.3484 - val_loss: 1.6422 - val_accuracy: 0.3171\n",
            "Epoch 46/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6007 - accuracy: 0.3453 - val_loss: 1.6389 - val_accuracy: 0.3598\n",
            "Epoch 47/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6151 - accuracy: 0.3351 - val_loss: 1.6374 - val_accuracy: 0.3506\n",
            "Epoch 48/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5911 - accuracy: 0.3541 - val_loss: 1.6615 - val_accuracy: 0.3354\n",
            "Epoch 49/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5973 - accuracy: 0.3452 - val_loss: 1.6525 - val_accuracy: 0.3232\n",
            "Epoch 50/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5781 - accuracy: 0.3593 - val_loss: 1.6575 - val_accuracy: 0.3506\n",
            "Epoch 51/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5956 - accuracy: 0.3518 - val_loss: 1.6470 - val_accuracy: 0.3476\n",
            "Epoch 52/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5854 - accuracy: 0.3493 - val_loss: 1.6458 - val_accuracy: 0.3201\n",
            "Epoch 53/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5963 - accuracy: 0.3458 - val_loss: 1.6376 - val_accuracy: 0.3079\n",
            "Epoch 54/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6022 - accuracy: 0.3489 - val_loss: 1.6320 - val_accuracy: 0.3537\n",
            "Epoch 55/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5797 - accuracy: 0.3521 - val_loss: 1.6489 - val_accuracy: 0.3201\n",
            "Epoch 56/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5923 - accuracy: 0.3524 - val_loss: 1.6359 - val_accuracy: 0.3628\n",
            "Epoch 57/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5989 - accuracy: 0.3433 - val_loss: 1.6325 - val_accuracy: 0.3354\n",
            "Epoch 58/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5952 - accuracy: 0.3589 - val_loss: 1.6237 - val_accuracy: 0.3628\n",
            "Epoch 59/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6043 - accuracy: 0.3410 - val_loss: 1.6511 - val_accuracy: 0.3354\n",
            "Epoch 60/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5864 - accuracy: 0.3428 - val_loss: 1.6407 - val_accuracy: 0.3201\n",
            "Epoch 61/200\n",
            "76/76 [==============================] - 0s 3ms/step - loss: 1.5962 - accuracy: 0.3548 - val_loss: 1.6128 - val_accuracy: 0.3415\n",
            "Epoch 62/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5965 - accuracy: 0.3531 - val_loss: 1.6328 - val_accuracy: 0.3476\n",
            "Epoch 63/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5863 - accuracy: 0.3576 - val_loss: 1.6383 - val_accuracy: 0.3720\n",
            "Epoch 64/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6066 - accuracy: 0.3439 - val_loss: 1.6328 - val_accuracy: 0.3567\n",
            "Epoch 65/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5895 - accuracy: 0.3473 - val_loss: 1.6291 - val_accuracy: 0.3537\n",
            "Epoch 66/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5806 - accuracy: 0.3440 - val_loss: 1.6504 - val_accuracy: 0.3018\n",
            "Epoch 67/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5929 - accuracy: 0.3445 - val_loss: 1.6237 - val_accuracy: 0.3628\n",
            "Epoch 68/200\n",
            "76/76 [==============================] - 0s 3ms/step - loss: 1.5931 - accuracy: 0.3555 - val_loss: 1.6272 - val_accuracy: 0.3537\n",
            "Epoch 69/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5980 - accuracy: 0.3591 - val_loss: 1.6375 - val_accuracy: 0.3171\n",
            "Epoch 70/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5924 - accuracy: 0.3522 - val_loss: 1.6445 - val_accuracy: 0.3079\n",
            "Epoch 71/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5818 - accuracy: 0.3555 - val_loss: 1.6643 - val_accuracy: 0.3415\n",
            "Epoch 72/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6045 - accuracy: 0.3411 - val_loss: 1.6307 - val_accuracy: 0.3415\n",
            "Epoch 73/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5707 - accuracy: 0.3624 - val_loss: 1.6592 - val_accuracy: 0.3140\n",
            "Epoch 74/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5768 - accuracy: 0.3572 - val_loss: 1.6547 - val_accuracy: 0.3476\n",
            "Epoch 75/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5912 - accuracy: 0.3490 - val_loss: 1.6357 - val_accuracy: 0.3598\n",
            "Epoch 76/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5912 - accuracy: 0.3597 - val_loss: 1.6453 - val_accuracy: 0.3354\n",
            "Epoch 77/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5881 - accuracy: 0.3546 - val_loss: 1.6225 - val_accuracy: 0.3567\n",
            "Epoch 78/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5896 - accuracy: 0.3456 - val_loss: 1.6373 - val_accuracy: 0.3537\n",
            "Epoch 79/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5943 - accuracy: 0.3466 - val_loss: 1.6548 - val_accuracy: 0.2988\n",
            "Epoch 80/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5902 - accuracy: 0.3535 - val_loss: 1.6169 - val_accuracy: 0.3628\n",
            "Epoch 81/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5862 - accuracy: 0.3537 - val_loss: 1.6215 - val_accuracy: 0.3537\n",
            "Epoch 82/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5930 - accuracy: 0.3493 - val_loss: 1.6410 - val_accuracy: 0.3110\n",
            "Epoch 83/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5974 - accuracy: 0.3577 - val_loss: 1.6346 - val_accuracy: 0.3415\n",
            "Epoch 84/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5899 - accuracy: 0.3517 - val_loss: 1.6408 - val_accuracy: 0.3354\n",
            "Epoch 85/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5975 - accuracy: 0.3379 - val_loss: 1.6799 - val_accuracy: 0.2622\n",
            "Epoch 86/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5971 - accuracy: 0.3497 - val_loss: 1.6271 - val_accuracy: 0.3689\n",
            "Epoch 87/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6039 - accuracy: 0.3469 - val_loss: 1.6215 - val_accuracy: 0.3445\n",
            "Epoch 88/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5867 - accuracy: 0.3634 - val_loss: 1.6243 - val_accuracy: 0.3659\n",
            "Epoch 89/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5779 - accuracy: 0.3640 - val_loss: 1.6582 - val_accuracy: 0.2866\n",
            "Epoch 90/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5834 - accuracy: 0.3463 - val_loss: 1.6514 - val_accuracy: 0.3232\n",
            "Epoch 91/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6002 - accuracy: 0.3375 - val_loss: 1.6305 - val_accuracy: 0.3415\n",
            "Epoch 92/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5989 - accuracy: 0.3497 - val_loss: 1.6392 - val_accuracy: 0.3415\n",
            "Epoch 93/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5921 - accuracy: 0.3566 - val_loss: 1.6467 - val_accuracy: 0.3079\n",
            "Epoch 94/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5838 - accuracy: 0.3494 - val_loss: 1.6351 - val_accuracy: 0.3354\n",
            "Epoch 95/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6000 - accuracy: 0.3391 - val_loss: 1.6355 - val_accuracy: 0.3201\n",
            "Epoch 96/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5982 - accuracy: 0.3482 - val_loss: 1.6236 - val_accuracy: 0.3506\n",
            "Epoch 97/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5974 - accuracy: 0.3456 - val_loss: 1.6294 - val_accuracy: 0.3415\n",
            "Epoch 98/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5990 - accuracy: 0.3477 - val_loss: 1.6403 - val_accuracy: 0.3323\n",
            "Epoch 99/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5932 - accuracy: 0.3528 - val_loss: 1.6357 - val_accuracy: 0.3598\n",
            "Epoch 100/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5917 - accuracy: 0.3617 - val_loss: 1.6350 - val_accuracy: 0.3506\n",
            "Epoch 101/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5885 - accuracy: 0.3605 - val_loss: 1.6216 - val_accuracy: 0.3354\n",
            "Epoch 102/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5870 - accuracy: 0.3532 - val_loss: 1.6295 - val_accuracy: 0.3415\n",
            "Epoch 103/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5992 - accuracy: 0.3333 - val_loss: 1.6323 - val_accuracy: 0.3537\n",
            "Epoch 104/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5843 - accuracy: 0.3571 - val_loss: 1.6390 - val_accuracy: 0.3415\n",
            "Epoch 105/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5871 - accuracy: 0.3578 - val_loss: 1.6225 - val_accuracy: 0.3506\n",
            "Epoch 106/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5967 - accuracy: 0.3479 - val_loss: 1.6310 - val_accuracy: 0.3476\n",
            "Epoch 107/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5789 - accuracy: 0.3652 - val_loss: 1.6298 - val_accuracy: 0.3445\n",
            "Epoch 108/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5788 - accuracy: 0.3620 - val_loss: 1.6270 - val_accuracy: 0.3415\n",
            "Epoch 109/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5766 - accuracy: 0.3564 - val_loss: 1.6424 - val_accuracy: 0.3262\n",
            "Epoch 110/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6016 - accuracy: 0.3434 - val_loss: 1.6182 - val_accuracy: 0.3445\n",
            "Epoch 111/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6003 - accuracy: 0.3554 - val_loss: 1.6441 - val_accuracy: 0.3262\n",
            "Epoch 112/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5868 - accuracy: 0.3463 - val_loss: 1.6179 - val_accuracy: 0.3476\n",
            "Epoch 113/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6167 - accuracy: 0.3490 - val_loss: 1.6391 - val_accuracy: 0.3384\n",
            "Epoch 114/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5957 - accuracy: 0.3462 - val_loss: 1.6443 - val_accuracy: 0.3445\n",
            "Epoch 115/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5978 - accuracy: 0.3370 - val_loss: 1.6304 - val_accuracy: 0.3445\n",
            "Epoch 116/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5824 - accuracy: 0.3504 - val_loss: 1.6330 - val_accuracy: 0.3476\n",
            "Epoch 117/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5987 - accuracy: 0.3575 - val_loss: 1.6286 - val_accuracy: 0.3476\n",
            "Epoch 118/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6027 - accuracy: 0.3415 - val_loss: 1.6108 - val_accuracy: 0.3445\n",
            "Epoch 119/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5856 - accuracy: 0.3524 - val_loss: 1.6227 - val_accuracy: 0.3567\n",
            "Epoch 120/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6064 - accuracy: 0.3413 - val_loss: 1.6280 - val_accuracy: 0.3384\n",
            "Epoch 121/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5780 - accuracy: 0.3600 - val_loss: 1.6327 - val_accuracy: 0.3323\n",
            "Epoch 122/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5888 - accuracy: 0.3541 - val_loss: 1.6357 - val_accuracy: 0.3476\n",
            "Epoch 123/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5869 - accuracy: 0.3454 - val_loss: 1.6591 - val_accuracy: 0.2866\n",
            "Epoch 124/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5934 - accuracy: 0.3575 - val_loss: 1.6663 - val_accuracy: 0.2805\n",
            "Epoch 125/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6046 - accuracy: 0.3469 - val_loss: 1.6290 - val_accuracy: 0.3506\n",
            "Epoch 126/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5883 - accuracy: 0.3500 - val_loss: 1.6392 - val_accuracy: 0.3506\n",
            "Epoch 127/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6021 - accuracy: 0.3546 - val_loss: 1.6416 - val_accuracy: 0.3354\n",
            "Epoch 128/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5941 - accuracy: 0.3567 - val_loss: 1.6486 - val_accuracy: 0.3445\n",
            "Epoch 129/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5860 - accuracy: 0.3538 - val_loss: 1.6451 - val_accuracy: 0.3323\n",
            "Epoch 130/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6043 - accuracy: 0.3529 - val_loss: 1.6163 - val_accuracy: 0.3537\n",
            "Epoch 131/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5851 - accuracy: 0.3649 - val_loss: 1.6186 - val_accuracy: 0.3628\n",
            "Epoch 132/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5804 - accuracy: 0.3625 - val_loss: 1.6239 - val_accuracy: 0.3567\n",
            "Epoch 133/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5864 - accuracy: 0.3648 - val_loss: 1.6915 - val_accuracy: 0.2988\n",
            "Epoch 134/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5967 - accuracy: 0.3580 - val_loss: 1.6450 - val_accuracy: 0.3537\n",
            "Epoch 135/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5955 - accuracy: 0.3430 - val_loss: 1.6332 - val_accuracy: 0.3354\n",
            "Epoch 136/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6073 - accuracy: 0.3339 - val_loss: 1.6255 - val_accuracy: 0.3445\n",
            "Epoch 137/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5933 - accuracy: 0.3419 - val_loss: 1.6281 - val_accuracy: 0.3567\n",
            "Epoch 138/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6028 - accuracy: 0.3431 - val_loss: 1.6158 - val_accuracy: 0.3476\n",
            "Epoch 139/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5799 - accuracy: 0.3605 - val_loss: 1.6426 - val_accuracy: 0.3201\n",
            "Epoch 140/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5854 - accuracy: 0.3544 - val_loss: 1.6319 - val_accuracy: 0.3445\n",
            "Epoch 141/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5800 - accuracy: 0.3625 - val_loss: 1.6357 - val_accuracy: 0.3445\n",
            "Epoch 142/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5865 - accuracy: 0.3504 - val_loss: 1.6510 - val_accuracy: 0.3293\n",
            "Epoch 143/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5876 - accuracy: 0.3430 - val_loss: 1.6319 - val_accuracy: 0.3323\n",
            "Epoch 144/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5755 - accuracy: 0.3590 - val_loss: 1.6322 - val_accuracy: 0.3354\n",
            "Epoch 145/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5900 - accuracy: 0.3515 - val_loss: 1.6857 - val_accuracy: 0.3049\n",
            "Epoch 146/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5743 - accuracy: 0.3474 - val_loss: 1.6419 - val_accuracy: 0.3262\n",
            "Epoch 147/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5862 - accuracy: 0.3428 - val_loss: 1.6381 - val_accuracy: 0.3171\n",
            "Epoch 148/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5837 - accuracy: 0.3558 - val_loss: 1.6246 - val_accuracy: 0.3354\n",
            "Epoch 149/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5983 - accuracy: 0.3426 - val_loss: 1.6438 - val_accuracy: 0.3323\n",
            "Epoch 150/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5851 - accuracy: 0.3573 - val_loss: 1.6364 - val_accuracy: 0.3720\n",
            "Epoch 151/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5872 - accuracy: 0.3491 - val_loss: 1.6246 - val_accuracy: 0.3476\n",
            "Epoch 152/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5878 - accuracy: 0.3446 - val_loss: 1.6232 - val_accuracy: 0.3476\n",
            "Epoch 153/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6020 - accuracy: 0.3399 - val_loss: 1.6174 - val_accuracy: 0.3567\n",
            "Epoch 154/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5875 - accuracy: 0.3495 - val_loss: 1.6367 - val_accuracy: 0.3445\n",
            "Epoch 155/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5867 - accuracy: 0.3587 - val_loss: 1.6503 - val_accuracy: 0.3476\n",
            "Epoch 156/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5973 - accuracy: 0.3379 - val_loss: 1.6446 - val_accuracy: 0.3018\n",
            "Epoch 157/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5866 - accuracy: 0.3595 - val_loss: 1.6327 - val_accuracy: 0.3293\n",
            "Epoch 158/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5947 - accuracy: 0.3572 - val_loss: 1.6522 - val_accuracy: 0.3171\n",
            "Epoch 159/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5948 - accuracy: 0.3407 - val_loss: 1.6214 - val_accuracy: 0.3537\n",
            "Epoch 160/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5957 - accuracy: 0.3533 - val_loss: 1.7054 - val_accuracy: 0.2744\n",
            "Epoch 161/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5921 - accuracy: 0.3464 - val_loss: 1.6498 - val_accuracy: 0.3079\n",
            "Epoch 162/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5870 - accuracy: 0.3490 - val_loss: 1.6433 - val_accuracy: 0.3445\n",
            "Epoch 163/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6026 - accuracy: 0.3461 - val_loss: 1.6185 - val_accuracy: 0.3689\n",
            "Epoch 164/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5905 - accuracy: 0.3448 - val_loss: 1.6555 - val_accuracy: 0.3110\n",
            "Epoch 165/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5767 - accuracy: 0.3562 - val_loss: 1.6207 - val_accuracy: 0.3415\n",
            "Epoch 166/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5912 - accuracy: 0.3601 - val_loss: 1.6346 - val_accuracy: 0.3445\n",
            "Epoch 167/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5768 - accuracy: 0.3558 - val_loss: 1.6461 - val_accuracy: 0.3384\n",
            "Epoch 168/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5848 - accuracy: 0.3423 - val_loss: 1.6274 - val_accuracy: 0.3476\n",
            "Epoch 169/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5989 - accuracy: 0.3442 - val_loss: 1.6429 - val_accuracy: 0.3506\n",
            "Epoch 170/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5867 - accuracy: 0.3429 - val_loss: 1.6396 - val_accuracy: 0.3537\n",
            "Epoch 171/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5853 - accuracy: 0.3501 - val_loss: 1.6512 - val_accuracy: 0.2988\n",
            "Epoch 172/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5962 - accuracy: 0.3523 - val_loss: 1.6377 - val_accuracy: 0.3537\n",
            "Epoch 173/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5899 - accuracy: 0.3483 - val_loss: 1.6485 - val_accuracy: 0.3415\n",
            "Epoch 174/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5951 - accuracy: 0.3549 - val_loss: 1.6487 - val_accuracy: 0.2957\n",
            "Epoch 175/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5867 - accuracy: 0.3559 - val_loss: 1.6546 - val_accuracy: 0.3140\n",
            "Epoch 176/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5980 - accuracy: 0.3407 - val_loss: 1.6405 - val_accuracy: 0.3323\n",
            "Epoch 177/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5963 - accuracy: 0.3439 - val_loss: 1.6434 - val_accuracy: 0.3537\n",
            "Epoch 178/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5843 - accuracy: 0.3613 - val_loss: 1.6274 - val_accuracy: 0.3476\n",
            "Epoch 179/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5810 - accuracy: 0.3533 - val_loss: 1.6600 - val_accuracy: 0.2530\n",
            "Epoch 180/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5802 - accuracy: 0.3517 - val_loss: 1.6273 - val_accuracy: 0.3445\n",
            "Epoch 181/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5903 - accuracy: 0.3585 - val_loss: 1.6351 - val_accuracy: 0.3445\n",
            "Epoch 182/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5922 - accuracy: 0.3463 - val_loss: 1.6294 - val_accuracy: 0.3476\n",
            "Epoch 183/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5841 - accuracy: 0.3553 - val_loss: 1.6273 - val_accuracy: 0.3445\n",
            "Epoch 184/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5967 - accuracy: 0.3523 - val_loss: 1.6359 - val_accuracy: 0.3506\n",
            "Epoch 185/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5851 - accuracy: 0.3554 - val_loss: 1.6440 - val_accuracy: 0.3506\n",
            "Epoch 186/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5988 - accuracy: 0.3484 - val_loss: 1.6497 - val_accuracy: 0.3293\n",
            "Epoch 187/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5789 - accuracy: 0.3587 - val_loss: 1.6281 - val_accuracy: 0.3354\n",
            "Epoch 188/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5841 - accuracy: 0.3645 - val_loss: 1.6652 - val_accuracy: 0.3293\n",
            "Epoch 189/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6043 - accuracy: 0.3428 - val_loss: 1.6391 - val_accuracy: 0.3384\n",
            "Epoch 190/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5780 - accuracy: 0.3607 - val_loss: 1.6176 - val_accuracy: 0.3384\n",
            "Epoch 191/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5738 - accuracy: 0.3491 - val_loss: 1.6408 - val_accuracy: 0.3384\n",
            "Epoch 192/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5936 - accuracy: 0.3513 - val_loss: 1.6584 - val_accuracy: 0.2835\n",
            "Epoch 193/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6119 - accuracy: 0.3337 - val_loss: 1.6598 - val_accuracy: 0.3201\n",
            "Epoch 194/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6018 - accuracy: 0.3464 - val_loss: 1.6279 - val_accuracy: 0.3476\n",
            "Epoch 195/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5923 - accuracy: 0.3642 - val_loss: 1.6448 - val_accuracy: 0.3171\n",
            "Epoch 196/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.6021 - accuracy: 0.3454 - val_loss: 1.6662 - val_accuracy: 0.3262\n",
            "Epoch 197/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5900 - accuracy: 0.3529 - val_loss: 1.6343 - val_accuracy: 0.3506\n",
            "Epoch 198/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5895 - accuracy: 0.3579 - val_loss: 1.6394 - val_accuracy: 0.3476\n",
            "Epoch 199/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5902 - accuracy: 0.3454 - val_loss: 1.6147 - val_accuracy: 0.3537\n",
            "Epoch 200/200\n",
            "76/76 [==============================] - 0s 4ms/step - loss: 1.5669 - accuracy: 0.3660 - val_loss: 1.6120 - val_accuracy: 0.3567\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "almO5KGGmH6X",
        "outputId": "273521f4-5933-4749-d498-976cc99d0d68"
      },
      "source": [
        "accuracy = x_history.history['accuracy']\n",
        "val_accuracy = x_history.history['val_accuracy']\n",
        "loss = x_history.history['loss']\n",
        "val_loss = x_history.history['val_loss']\n",
        "epochs = range(len(accuracy))\n",
        "plt.plot(epochs, accuracy, 'bo', label='Training accuracy')\n",
        "plt.plot(epochs, val_accuracy, 'b', label='Validation accuracy')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.legend()\n",
        "plt.figure()\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xdTVbo2Bmenq"
      },
      "source": [
        "### 2D Convolution "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vDitOGPLmTZH"
      },
      "source": [
        "i1 = Input(shape=(157,128,1))\n",
        "c1 = Conv2D(32, kernel_size=(3, 3),activation='linear',padding='same')(i1)\n",
        "a1 = LeakyReLU(alpha=0.1)(c1)\n",
        "p1 = MaxPooling2D((2, 2),padding='same')(a1)\n",
        "\n",
        "d1 = Dropout(0.5)(p1)\n",
        "c2 = Conv2D(64, (3, 3), activation='linear',padding='same')(d1)\n",
        "a2 = LeakyReLU(alpha=0.1)(c2)\n",
        "p2 = MaxPooling2D(pool_size=(2, 2),padding='same')(a2)\n",
        "\n",
        "d2 = Dropout(0.5)(p2)\n",
        "c3 = Conv2D(128, (3, 3), activation='linear',padding='same')(d2)\n",
        "a3 = LeakyReLU(alpha=0.1)(c3)\n",
        "p3 = MaxPooling2D(pool_size=(2, 2),padding='same')(a3)\n",
        "\n",
        "d3 = Dropout(0.7)(p2)\n",
        "c4 = Conv2D(256, (3, 3), activation='linear',padding='same')(d3)\n",
        "a4 = LeakyReLU(alpha=0.1)(c3)\n",
        "p4 = MaxPooling2D(pool_size=(2, 2),padding='same')(a4)\n",
        "\n",
        "f1 = Flatten()(p4)\n",
        "\n",
        "d5 = Dropout(0.7)(f1)\n",
        "a5 = LeakyReLU(alpha=0.1)(d5)\n",
        "fc1 = Dense(256, activation='linear')(a5)\n",
        "fc2 = Dense(num_classes, activation='softmax')(fc1)\n",
        "\n",
        "fashion_model = Model(inputs=[i1],outputs=[fc2])\n",
        "\n",
        "\n",
        "y = Model(inputs=[i1],outputs=[fc2])\n",
        "y2 = Model(inputs=[i1], outputs=[a5])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cn5skAoVmwFD"
      },
      "source": [
        "y.compile(loss=keras.losses.sparse_categorical_crossentropy, optimizer=keras.optimizers.Adam(),metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cvoe2nSea_5j",
        "outputId": "8426ef61-ee33-4b18-a30e-6f437ff1b72d"
      },
      "source": [
        "y.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_12\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_6 (InputLayer)         [(None, 157, 128, 1)]     0         \n",
            "_________________________________________________________________\n",
            "conv2d_16 (Conv2D)           (None, 157, 128, 32)      320       \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_24 (LeakyReLU)   (None, 157, 128, 32)      0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_16 (MaxPooling (None, 79, 64, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_18 (Dropout)         (None, 79, 64, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_17 (Conv2D)           (None, 79, 64, 64)        18496     \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_25 (LeakyReLU)   (None, 79, 64, 64)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_17 (MaxPooling (None, 40, 32, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_19 (Dropout)         (None, 40, 32, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_18 (Conv2D)           (None, 40, 32, 128)       73856     \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_27 (LeakyReLU)   (None, 40, 32, 128)       0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_19 (MaxPooling (None, 20, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "flatten_5 (Flatten)          (None, 40960)             0         \n",
            "_________________________________________________________________\n",
            "dropout_21 (Dropout)         (None, 40960)             0         \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_28 (LeakyReLU)   (None, 40960)             0         \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 256)               10486016  \n",
            "_________________________________________________________________\n",
            "dense_12 (Dense)             (None, 6)                 1542      \n",
            "=================================================================\n",
            "Total params: 10,580,230\n",
            "Trainable params: 10,580,230\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pVCjZzaGmdpD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "90483273-b942-4c04-cc3f-abe891b814d9"
      },
      "source": [
        "y_history = y.fit(x2_training, y_training, batch_size=batch_size,epochs=50,verbose=1,validation_data=(x2_validation, y_validation))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "76/76 [==============================] - 5s 54ms/step - loss: 6.4586 - accuracy: 0.1984 - val_loss: 1.9299 - val_accuracy: 0.2012\n",
            "Epoch 2/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 1.6719 - accuracy: 0.3011 - val_loss: 2.1510 - val_accuracy: 0.2348\n",
            "Epoch 3/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 1.6124 - accuracy: 0.3498 - val_loss: 2.1613 - val_accuracy: 0.2165\n",
            "Epoch 4/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 1.5806 - accuracy: 0.3603 - val_loss: 2.6933 - val_accuracy: 0.2530\n",
            "Epoch 5/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 1.4970 - accuracy: 0.3897 - val_loss: 1.8611 - val_accuracy: 0.3354\n",
            "Epoch 6/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 1.4835 - accuracy: 0.3837 - val_loss: 1.7476 - val_accuracy: 0.3994\n",
            "Epoch 7/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 1.4531 - accuracy: 0.4208 - val_loss: 2.1387 - val_accuracy: 0.3537\n",
            "Epoch 8/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 1.4326 - accuracy: 0.4185 - val_loss: 2.6304 - val_accuracy: 0.2957\n",
            "Epoch 9/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 1.4406 - accuracy: 0.4261 - val_loss: 1.7881 - val_accuracy: 0.2957\n",
            "Epoch 10/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 1.4027 - accuracy: 0.4316 - val_loss: 2.1307 - val_accuracy: 0.3506\n",
            "Epoch 11/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 1.3796 - accuracy: 0.4493 - val_loss: 1.7489 - val_accuracy: 0.3780\n",
            "Epoch 12/50\n",
            "76/76 [==============================] - 4s 48ms/step - loss: 1.3571 - accuracy: 0.4665 - val_loss: 1.7313 - val_accuracy: 0.4238\n",
            "Epoch 13/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 1.3121 - accuracy: 0.4768 - val_loss: 1.8097 - val_accuracy: 0.3811\n",
            "Epoch 14/50\n",
            "76/76 [==============================] - 4s 48ms/step - loss: 1.3212 - accuracy: 0.4878 - val_loss: 1.7521 - val_accuracy: 0.4146\n",
            "Epoch 15/50\n",
            "76/76 [==============================] - 4s 48ms/step - loss: 1.3229 - accuracy: 0.4667 - val_loss: 1.7981 - val_accuracy: 0.4360\n",
            "Epoch 16/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 1.2826 - accuracy: 0.4883 - val_loss: 1.6108 - val_accuracy: 0.4238\n",
            "Epoch 17/50\n",
            "76/76 [==============================] - 4s 48ms/step - loss: 1.2950 - accuracy: 0.4908 - val_loss: 1.9989 - val_accuracy: 0.4177\n",
            "Epoch 18/50\n",
            "76/76 [==============================] - 4s 48ms/step - loss: 1.2761 - accuracy: 0.4900 - val_loss: 1.9783 - val_accuracy: 0.3750\n",
            "Epoch 19/50\n",
            "76/76 [==============================] - 4s 48ms/step - loss: 1.2646 - accuracy: 0.5024 - val_loss: 1.7372 - val_accuracy: 0.3902\n",
            "Epoch 20/50\n",
            "76/76 [==============================] - 4s 48ms/step - loss: 1.2582 - accuracy: 0.5004 - val_loss: 2.0720 - val_accuracy: 0.3933\n",
            "Epoch 21/50\n",
            "76/76 [==============================] - 4s 48ms/step - loss: 1.2463 - accuracy: 0.5109 - val_loss: 1.8903 - val_accuracy: 0.4055\n",
            "Epoch 22/50\n",
            "76/76 [==============================] - 4s 48ms/step - loss: 1.2237 - accuracy: 0.5265 - val_loss: 1.6258 - val_accuracy: 0.4085\n",
            "Epoch 23/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 1.2074 - accuracy: 0.5257 - val_loss: 1.7946 - val_accuracy: 0.4055\n",
            "Epoch 24/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 1.1677 - accuracy: 0.5369 - val_loss: 1.8134 - val_accuracy: 0.4116\n",
            "Epoch 25/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 1.1786 - accuracy: 0.5362 - val_loss: 1.5789 - val_accuracy: 0.4268\n",
            "Epoch 26/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 1.1558 - accuracy: 0.5395 - val_loss: 1.4459 - val_accuracy: 0.4878\n",
            "Epoch 27/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 1.1597 - accuracy: 0.5503 - val_loss: 1.5260 - val_accuracy: 0.4634\n",
            "Epoch 28/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 1.1491 - accuracy: 0.5487 - val_loss: 1.5970 - val_accuracy: 0.4665\n",
            "Epoch 29/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 1.1280 - accuracy: 0.5522 - val_loss: 1.5258 - val_accuracy: 0.4482\n",
            "Epoch 30/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 1.1123 - accuracy: 0.5724 - val_loss: 1.7577 - val_accuracy: 0.4177\n",
            "Epoch 31/50\n",
            "76/76 [==============================] - 4s 48ms/step - loss: 1.1146 - accuracy: 0.5609 - val_loss: 1.5960 - val_accuracy: 0.4207\n",
            "Epoch 32/50\n",
            "76/76 [==============================] - 4s 48ms/step - loss: 1.0773 - accuracy: 0.5712 - val_loss: 1.5961 - val_accuracy: 0.4604\n",
            "Epoch 33/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 1.0587 - accuracy: 0.5843 - val_loss: 1.5720 - val_accuracy: 0.4634\n",
            "Epoch 34/50\n",
            "76/76 [==============================] - 4s 48ms/step - loss: 1.0704 - accuracy: 0.5972 - val_loss: 1.5872 - val_accuracy: 0.4512\n",
            "Epoch 35/50\n",
            "76/76 [==============================] - 4s 48ms/step - loss: 1.0307 - accuracy: 0.6068 - val_loss: 1.7139 - val_accuracy: 0.4116\n",
            "Epoch 36/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 1.0369 - accuracy: 0.6106 - val_loss: 1.7437 - val_accuracy: 0.4390\n",
            "Epoch 37/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 1.0281 - accuracy: 0.5956 - val_loss: 1.5696 - val_accuracy: 0.4787\n",
            "Epoch 38/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 1.0209 - accuracy: 0.6062 - val_loss: 1.5937 - val_accuracy: 0.4451\n",
            "Epoch 39/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 1.0126 - accuracy: 0.6055 - val_loss: 1.6232 - val_accuracy: 0.4329\n",
            "Epoch 40/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 0.9905 - accuracy: 0.6162 - val_loss: 1.5447 - val_accuracy: 0.4573\n",
            "Epoch 41/50\n",
            "76/76 [==============================] - 4s 48ms/step - loss: 0.9769 - accuracy: 0.6307 - val_loss: 1.5928 - val_accuracy: 0.4665\n",
            "Epoch 42/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 0.9747 - accuracy: 0.6083 - val_loss: 1.6490 - val_accuracy: 0.4573\n",
            "Epoch 43/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 0.9341 - accuracy: 0.6470 - val_loss: 1.5553 - val_accuracy: 0.4695\n",
            "Epoch 44/50\n",
            "76/76 [==============================] - 4s 48ms/step - loss: 0.9561 - accuracy: 0.6340 - val_loss: 1.5247 - val_accuracy: 0.4848\n",
            "Epoch 45/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 0.9045 - accuracy: 0.6502 - val_loss: 1.5984 - val_accuracy: 0.4756\n",
            "Epoch 46/50\n",
            "76/76 [==============================] - 4s 48ms/step - loss: 0.9188 - accuracy: 0.6509 - val_loss: 1.4827 - val_accuracy: 0.4756\n",
            "Epoch 47/50\n",
            "76/76 [==============================] - 4s 48ms/step - loss: 0.9393 - accuracy: 0.6358 - val_loss: 1.6034 - val_accuracy: 0.4848\n",
            "Epoch 48/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 0.9293 - accuracy: 0.6522 - val_loss: 1.7778 - val_accuracy: 0.4482\n",
            "Epoch 49/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 0.8897 - accuracy: 0.6562 - val_loss: 1.5955 - val_accuracy: 0.4909\n",
            "Epoch 50/50\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 0.8905 - accuracy: 0.6643 - val_loss: 1.7453 - val_accuracy: 0.4604\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "FFhNpgbanpwb",
        "outputId": "5e789316-45e7-4c17-9ab5-449d83e397b6"
      },
      "source": [
        "accuracy = y_history.history['accuracy']\n",
        "val_accuracy = y_history.history['val_accuracy']\n",
        "loss = y_history.history['loss']\n",
        "val_loss = y_history.history['val_loss']\n",
        "epochs = range(len(accuracy))\n",
        "plt.plot(epochs, accuracy, 'bo', label='Training accuracy')\n",
        "plt.plot(epochs, val_accuracy, 'b', label='Validation accuracy')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.legend()\n",
        "plt.figure()\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BcHHNshAn_rs"
      },
      "source": [
        "### Combined Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vLA9CUt1oDjF"
      },
      "source": [
        "combined = concatenate([x2.output, y2.output])\n",
        "\n",
        "z = Dense(128, kernel_regularizer=l2(), bias_regularizer=l2())(combined)\n",
        "z = LeakyReLU(alpha=0.1)(z)\n",
        "z = Dense(num_classes, activation='softmax')(z)\n",
        "\n",
        "model = Model(inputs=[x2.input, y2.input], outputs=z)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rac6_k16oNXK"
      },
      "source": [
        "model.compile(loss=keras.losses.sparse_categorical_crossentropy, optimizer=keras.optimizers.Adam(learning_rate=0.001),metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dq7iOqHdbM-P",
        "outputId": "aa3c22e0-c223-4db8-bbca-c34989227e53"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_16\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_6 (InputLayer)            [(None, 157, 128, 1) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_16 (Conv2D)              (None, 157, 128, 32) 320         input_6[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_24 (LeakyReLU)      (None, 157, 128, 32) 0           conv2d_16[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "input_7 (InputLayer)            [(None, 14, 1)]      0                                            \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_16 (MaxPooling2D) (None, 79, 64, 32)   0           leaky_re_lu_24[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "dropout_22 (Dropout)            (None, 14, 1)        0           input_7[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dropout_18 (Dropout)            (None, 79, 64, 32)   0           max_pooling2d_16[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv1d_2 (Conv1D)               (None, 12, 128)      512         dropout_22[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_17 (Conv2D)              (None, 79, 64, 64)   18496       dropout_18[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_29 (LeakyReLU)      (None, 12, 128)      0           conv1d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_25 (LeakyReLU)      (None, 79, 64, 64)   0           conv2d_17[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dropout_23 (Dropout)            (None, 12, 128)      0           leaky_re_lu_29[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_17 (MaxPooling2D) (None, 40, 32, 64)   0           leaky_re_lu_25[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv1d_3 (Conv1D)               (None, 10, 256)      98560       dropout_23[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "dropout_19 (Dropout)            (None, 40, 32, 64)   0           max_pooling2d_17[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_30 (LeakyReLU)      (None, 10, 256)      0           conv1d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_18 (Conv2D)              (None, 40, 32, 128)  73856       dropout_19[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling1d_1 (MaxPooling1D)  (None, 5, 256)       0           leaky_re_lu_30[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_27 (LeakyReLU)      (None, 40, 32, 128)  0           conv2d_18[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "flatten_6 (Flatten)             (None, 1280)         0           max_pooling1d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_19 (MaxPooling2D) (None, 20, 16, 128)  0           leaky_re_lu_27[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "dropout_24 (Dropout)            (None, 1280)         0           flatten_6[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "flatten_5 (Flatten)             (None, 40960)        0           max_pooling2d_19[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "dense_13 (Dense)                (None, 256)          327936      dropout_24[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "dropout_21 (Dropout)            (None, 40960)        0           flatten_5[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_31 (LeakyReLU)      (None, 256)          0           dense_13[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_28 (LeakyReLU)      (None, 40960)        0           dropout_21[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_1 (Concatenate)     (None, 41216)        0           leaky_re_lu_31[0][0]             \n",
            "                                                                 leaky_re_lu_28[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "dense_15 (Dense)                (None, 128)          5275776     concatenate_1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_32 (LeakyReLU)      (None, 128)          0           dense_15[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dense_16 (Dense)                (None, 6)            774         leaky_re_lu_32[0][0]             \n",
            "==================================================================================================\n",
            "Total params: 5,796,230\n",
            "Trainable params: 5,796,230\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bn0trDVtoTs3",
        "outputId": "7a957467-600a-4b2f-9a51-7a18b11a0fdc"
      },
      "source": [
        "model_history = model.fit(x=[x1_training,x2_training], y=y_training, batch_size=batch_size,epochs=30,verbose=1,validation_data=([x1_validation, x2_validation], y_validation))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "76/76 [==============================] - 6s 59ms/step - loss: 3.0202 - accuracy: 0.4125 - val_loss: 2.1888 - val_accuracy: 0.4695\n",
            "Epoch 2/30\n",
            "76/76 [==============================] - 4s 50ms/step - loss: 1.9925 - accuracy: 0.5328 - val_loss: 2.1572 - val_accuracy: 0.4848\n",
            "Epoch 3/30\n",
            "76/76 [==============================] - 4s 50ms/step - loss: 2.0378 - accuracy: 0.5651 - val_loss: 2.3376 - val_accuracy: 0.4726\n",
            "Epoch 4/30\n",
            "76/76 [==============================] - 4s 50ms/step - loss: 2.0523 - accuracy: 0.5453 - val_loss: 2.2608 - val_accuracy: 0.4756\n",
            "Epoch 5/30\n",
            "76/76 [==============================] - 4s 51ms/step - loss: 1.9913 - accuracy: 0.5833 - val_loss: 2.3309 - val_accuracy: 0.4817\n",
            "Epoch 6/30\n",
            "76/76 [==============================] - 4s 50ms/step - loss: 2.0024 - accuracy: 0.5673 - val_loss: 2.2205 - val_accuracy: 0.5274\n",
            "Epoch 7/30\n",
            "76/76 [==============================] - 4s 51ms/step - loss: 1.9950 - accuracy: 0.5901 - val_loss: 2.3175 - val_accuracy: 0.4939\n",
            "Epoch 8/30\n",
            "76/76 [==============================] - 4s 50ms/step - loss: 2.0489 - accuracy: 0.5879 - val_loss: 2.3295 - val_accuracy: 0.5061\n",
            "Epoch 9/30\n",
            "76/76 [==============================] - 4s 50ms/step - loss: 1.9868 - accuracy: 0.5938 - val_loss: 2.2416 - val_accuracy: 0.4909\n",
            "Epoch 10/30\n",
            "76/76 [==============================] - 4s 50ms/step - loss: 1.9589 - accuracy: 0.5966 - val_loss: 2.2793 - val_accuracy: 0.4970\n",
            "Epoch 11/30\n",
            "76/76 [==============================] - 4s 50ms/step - loss: 1.9648 - accuracy: 0.5937 - val_loss: 2.3277 - val_accuracy: 0.5061\n",
            "Epoch 12/30\n",
            "76/76 [==============================] - 4s 50ms/step - loss: 1.9867 - accuracy: 0.5985 - val_loss: 2.2212 - val_accuracy: 0.5183\n",
            "Epoch 13/30\n",
            "76/76 [==============================] - 4s 50ms/step - loss: 1.9563 - accuracy: 0.6040 - val_loss: 2.2302 - val_accuracy: 0.5366\n",
            "Epoch 14/30\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 1.9413 - accuracy: 0.6160 - val_loss: 2.2939 - val_accuracy: 0.5091\n",
            "Epoch 15/30\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 1.8980 - accuracy: 0.6284 - val_loss: 2.2936 - val_accuracy: 0.5091\n",
            "Epoch 16/30\n",
            "76/76 [==============================] - 4s 50ms/step - loss: 1.9679 - accuracy: 0.6075 - val_loss: 2.4096 - val_accuracy: 0.4939\n",
            "Epoch 17/30\n",
            "76/76 [==============================] - 4s 50ms/step - loss: 1.9415 - accuracy: 0.6206 - val_loss: 2.3269 - val_accuracy: 0.4970\n",
            "Epoch 18/30\n",
            "76/76 [==============================] - 4s 49ms/step - loss: 1.9010 - accuracy: 0.6206 - val_loss: 2.3401 - val_accuracy: 0.4543\n",
            "Epoch 19/30\n",
            "76/76 [==============================] - 4s 50ms/step - loss: 1.8539 - accuracy: 0.6271 - val_loss: 2.3594 - val_accuracy: 0.4848\n",
            "Epoch 20/30\n",
            "76/76 [==============================] - 4s 50ms/step - loss: 1.9503 - accuracy: 0.6057 - val_loss: 2.2897 - val_accuracy: 0.5091\n",
            "Epoch 21/30\n",
            "76/76 [==============================] - 4s 50ms/step - loss: 1.9538 - accuracy: 0.6231 - val_loss: 2.2824 - val_accuracy: 0.4970\n",
            "Epoch 22/30\n",
            "76/76 [==============================] - 4s 50ms/step - loss: 1.8867 - accuracy: 0.6365 - val_loss: 2.2272 - val_accuracy: 0.5213\n",
            "Epoch 23/30\n",
            "76/76 [==============================] - 4s 50ms/step - loss: 1.8458 - accuracy: 0.6243 - val_loss: 2.2764 - val_accuracy: 0.5061\n",
            "Epoch 24/30\n",
            "76/76 [==============================] - 4s 50ms/step - loss: 1.8733 - accuracy: 0.6293 - val_loss: 2.2567 - val_accuracy: 0.5061\n",
            "Epoch 25/30\n",
            "76/76 [==============================] - 4s 50ms/step - loss: 1.9088 - accuracy: 0.6368 - val_loss: 2.2434 - val_accuracy: 0.5122\n",
            "Epoch 26/30\n",
            "76/76 [==============================] - 4s 50ms/step - loss: 1.7935 - accuracy: 0.6524 - val_loss: 2.4937 - val_accuracy: 0.4817\n",
            "Epoch 27/30\n",
            "76/76 [==============================] - 4s 50ms/step - loss: 1.8355 - accuracy: 0.6476 - val_loss: 2.4550 - val_accuracy: 0.4695\n",
            "Epoch 28/30\n",
            "76/76 [==============================] - 4s 50ms/step - loss: 1.8587 - accuracy: 0.6487 - val_loss: 2.3830 - val_accuracy: 0.4756\n",
            "Epoch 29/30\n",
            "76/76 [==============================] - 4s 50ms/step - loss: 1.8083 - accuracy: 0.6549 - val_loss: 2.3316 - val_accuracy: 0.4970\n",
            "Epoch 30/30\n",
            "76/76 [==============================] - 4s 50ms/step - loss: 1.8836 - accuracy: 0.6402 - val_loss: 2.2956 - val_accuracy: 0.4939\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "JU2FzLiopmWu",
        "outputId": "10eb9b3f-c446-40bc-c28a-f5f20ea425aa"
      },
      "source": [
        "accuracy = model_history.history['accuracy']\n",
        "val_accuracy = model_history.history['val_accuracy']\n",
        "loss = model_history.history['loss']\n",
        "val_loss = model_history.history['val_loss']\n",
        "epochs = range(len(accuracy))\n",
        "plt.plot(epochs, accuracy, 'bo', label='Training accuracy')\n",
        "plt.plot(epochs, val_accuracy, 'b', label='Validation accuracy')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.legend()\n",
        "plt.figure()\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CIrA1LDWrM6G"
      },
      "source": [
        "### Testing and Evaluation\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VvmOz5pmu4H5"
      },
      "source": [
        "from sklearn.metrics import accuracy_score,f1_score,confusion_matrix"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IrDIaSKaczz7"
      },
      "source": [
        "#### 1D CNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p3adZ7BJc2fN",
        "outputId": "9e0df5ac-7431-44fe-df7b-eb1a598d3f52"
      },
      "source": [
        "y_prob = x.predict(x=x1_testing, batch_size=64, verbose=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "36/36 [==============================] - 0s 1ms/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eOVQUlnyc9fC",
        "outputId": "28b26a99-b46e-4121-b415-e3e2b13e3d1f"
      },
      "source": [
        "y_pred = np.zeros(y_prob.shape[0])\n",
        "for i in range(y_prob.shape[0]):\n",
        "  y_pred[i]=np.argmax(y_prob[i])\n",
        "y_pred = np.int32(y_pred)\n",
        "y_pred"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 0, 3, ..., 1, 3, 3], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 241
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4RmuGsRCdEfc",
        "outputId": "e902c4e8-3de2-4392-ad0e-689b90ff587e"
      },
      "source": [
        "accuracy_score(y_testing,y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.36375545851528385"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 242
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w2yJGE0XdHE4",
        "outputId": "dce07de9-aa50-4150-d240-17c5bfb75d42"
      },
      "source": [
        "f1_score(y_testing,y_pred, average='macro')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.31840294111387446"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 243
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "OY5oZ65BdSGs",
        "outputId": "d1119767-8776-466b-f8a0-fa14546028ae"
      },
      "source": [
        "a = confusion_matrix(y_testing,y_pred)\n",
        "lbls =['ANG', 'DIS','FEA', 'HAP','NEU', 'SAD']\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "im = ax.imshow(a, cmap='hot', interpolation='nearest')\n",
        "ax.set_xticks(range(6))\n",
        "ax.set_xticklabels(lbls, rotation='horizontal')\n",
        "ax.set_yticks(range(6))\n",
        "ax.set_yticklabels(lbls, rotation='horizontal')\n",
        "\n",
        "plt.colorbar(im)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IcHuHUkpc1dl"
      },
      "source": [
        "#### 2D CNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dTesBixpdVbP",
        "outputId": "48b0c840-8e28-4c5e-8420-04399b05b8d5"
      },
      "source": [
        "y_prob = y.predict(x=x2_testing, batch_size=64, verbose=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "36/36 [==============================] - 1s 17ms/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uElRdbUNdtVP",
        "outputId": "7084431c-1eb3-44dd-9589-97d6c71f5c31"
      },
      "source": [
        "y_pred = np.zeros(y_prob.shape[0])\n",
        "for i in range(y_prob.shape[0]):\n",
        "  y_pred[i]=np.argmax(y_prob[i])\n",
        "y_pred = np.int32(y_pred)\n",
        "y_pred"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4, 3, 1, ..., 2, 3, 3], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 191
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pc0C1KFbdvJ_",
        "outputId": "c778c49a-27be-44bb-e5f9-b395f1a78f14"
      },
      "source": [
        "accuracy_score(y_testing,y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.46943231441048033"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 192
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bVGYZlPxdxX4",
        "outputId": "3aec4fad-bdc0-4b6c-e302-cc9a976e6605"
      },
      "source": [
        "f1_score(y_testing,y_pred, average='macro')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.45136286459303115"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 193
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "cEr1XVqOd1Di",
        "outputId": "428b8987-5157-4d9e-e08b-2ee87fe7f278"
      },
      "source": [
        "a = confusion_matrix(y_testing,y_pred)\n",
        "lbls =['ANG', 'DIS','FEA', 'HAP','NEU', 'SAD']\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "im = ax.imshow(a, cmap='hot', interpolation='nearest')\n",
        "ax.set_xticks(range(6))\n",
        "ax.set_xticklabels(lbls, rotation='horizontal')\n",
        "ax.set_yticks(range(6))\n",
        "ax.set_yticklabels(lbls, rotation='horizontal')\n",
        "\n",
        "plt.colorbar(im)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aF1UrK3OcyWv"
      },
      "source": [
        "#### Combined"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sSoSx9vJrQnw",
        "outputId": "aea67a21-9b8a-4b65-9562-b0153e84c6bc"
      },
      "source": [
        "y_prob = model.predict(x=[x1_testing, x2_testing], batch_size=64, verbose=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "36/36 [==============================] - 1s 18ms/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f3Drjk2UvdPK",
        "outputId": "4327774e-89db-41ba-9cac-ca96db2825e9"
      },
      "source": [
        "y_pred = np.zeros(y_prob.shape[0])\n",
        "for i in range(y_prob.shape[0]):\n",
        "  y_pred[i]=np.argmax(y_prob[i])\n",
        "y_pred = np.int32(y_pred)\n",
        "y_pred"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4, 0, 1, ..., 4, 3, 4], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 202
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8vtXISHQuzdJ",
        "outputId": "013aed89-5b82-4853-dfc7-133cb4dfe874"
      },
      "source": [
        "accuracy_score(y_testing,y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5004366812227075"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 203
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JJkwB1-LvNeC",
        "outputId": "daa101ea-8b18-4c58-a868-97d41722bd29"
      },
      "source": [
        "f1_score(y_testing,y_pred, average='macro')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.48498373610012196"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 204
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "j_FEhRnqvP23",
        "outputId": "c74ed4d8-d486-4cbc-9069-69714fb960db"
      },
      "source": [
        "a = confusion_matrix(y_testing,y_pred)\n",
        "lbls =['ANG', 'DIS','FEA', 'HAP','NEU', 'SAD']\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "im = ax.imshow(a, cmap='hot', interpolation='nearest')\n",
        "ax.set_xticks(range(6))\n",
        "ax.set_xticklabels(lbls, rotation='horizontal')\n",
        "ax.set_yticks(range(6))\n",
        "ax.set_yticklabels(lbls, rotation='horizontal')\n",
        "\n",
        "plt.colorbar(im)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}