3_Spectrograms_CI4R_Datasets_77GHz_Multi_Class_LSTM_with_Attention.ipynb

{
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  "metadata": {
    "colab": {
      "name": "3_Spectrograms_CI4R_Datasets_77GHz_Multi_Class_LSTM_with_Attention.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mertege/FMCW-Data-Classification-/blob/main/3_Spectrograms_CI4R_Datasets_77GHz_Multi_Class_LSTM_with_Attention.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kN4ixvH39HW6"
      },
      "source": [
        "# Burada CI4R dataseti ile Multi Class Classification yapıldı.\n",
        "# Sadece 77GHz train ve test edildi.\n",
        "# Bidirectional(GRU) ve Bidirectional(LSTM) ile paralel olarak koşuyor ve ayrı ayrı attention yapılıyor. Sonrasında concat ile birleştiliyor. \n",
        "# Çözünürlük 216x216\n",
        "# Mean test accuracy is 0.858, max test accuracy is 0.863,\n",
        "# Min test accuracy is 0.855,  std of test accuracy is 0.003.\n",
        "# Time elapsed through all process: 1799.72 sec"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lzJwN6GfkN8Z"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from tensorflow.keras import layers\n",
        "from keras.models import Model\n",
        "from keras.layers import Conv1D,Activation, Dropout, Flatten, Dense, BatchNormalization, Normalization, Input, Conv2D, MaxPooling2D, Concatenate, GRU, LSTM, TimeDistributed, Bidirectional, ReLU\n",
        "import tensorflow as tf\n",
        "import scipy.io\n",
        "import numpy as np\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "from numpy.random import seed\n",
        "from sklearn.model_selection import KFold, StratifiedKFold\n",
        "import time\n",
        "from sklearn.metrics import precision_recall_fscore_support\n",
        "from keras.callbacks import EarlyStopping\n",
        "from numpy import array\n",
        "from numpy import argmax\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from keras import backend as K \n",
        "import gc\n",
        "import os"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8FERVnWrkrwE",
        "outputId": "abaab86d-3ec8-43aa-f967-970afd4f022c"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o19JHieQ81cS"
      },
      "source": [
        "# 77 GHz\n",
        "bending_77GHz                 = np.load('/content/drive/MyDrive/bending_77Ghz.npy')\n",
        "bending_77GHz = np.float32(bending_77GHz)\n",
        "label_bending_77GHz           = np.zeros((bending_77GHz.shape[0],1))\n",
        "label_bending_77GHz[:]        = 1\n",
        "crawling_77GHz                = np.load('/content/drive/MyDrive/crawling_77Ghz.npy')\n",
        "crawling_77GHz = np.float32(crawling_77GHz)\n",
        "label_crawling_77GHz          = np.zeros((crawling_77GHz.shape[0],1))\n",
        "label_crawling_77GHz[:]       = 2\n",
        "kneeling_77GHz                = np.load('/content/drive/MyDrive/kneeling_77Ghz.npy')\n",
        "kneeling_77GHz = np.float32(kneeling_77GHz)\n",
        "label_kneeling_77GHz          = np.zeros((kneeling_77GHz.shape[0],1))\n",
        "label_kneeling_77GHz[:]       = 3\n",
        "limping_with_RL_Stiff_77GHz   = np.load('/content/drive/MyDrive/limping_with_RL_Stiff_77Ghz.npy')\n",
        "limping_with_RL_Stiff_77GHz = np.float32(limping_with_RL_Stiff_77GHz)\n",
        "label_limping_with_RL_Stiff_77GHz           = np.zeros((limping_with_RL_Stiff_77GHz.shape[0],1))\n",
        "label_limping_with_RL_Stiff_77GHz[:]        = 4\n",
        "picking_up_an_object_77GHz    = np.load('/content/drive/MyDrive/picking_up_an_object_77Ghz.npy')\n",
        "picking_up_an_object_77GHz = np.float32(picking_up_an_object_77GHz)\n",
        "label_picking_up_an_object_77GHz          = np.zeros((picking_up_an_object_77GHz.shape[0],1))\n",
        "label_picking_up_an_object_77GHz[:]       = 5\n",
        "scissors_gait_77GHz           = np.load('/content/drive/MyDrive/scissors_gait_77Ghz.npy')\n",
        "scissors_gait_77GHz = np.float32(scissors_gait_77GHz)\n",
        "label_scissors_gait_77GHz          = np.zeros((scissors_gait_77GHz.shape[0],1))\n",
        "label_scissors_gait_77GHz[:]       = 6\n",
        "short_steps_77GHz             = np.load('/content/drive/MyDrive/short_steps_77Ghz.npy')\n",
        "short_steps_77GHz = np.float32(short_steps_77GHz)\n",
        "label_short_steps_77GHz          = np.zeros((short_steps_77GHz.shape[0],1))\n",
        "label_short_steps_77GHz[:]       = 7\n",
        "sitting_77GHz                 = np.load('/content/drive/MyDrive/sitting_77Ghz.npy')\n",
        "sitting_77GHz = np.float32(sitting_77GHz)\n",
        "label_sitting_77GHz          = np.zeros((sitting_77GHz.shape[0],1))\n",
        "label_sitting_77GHz[:]       = 8\n",
        "walking_away_from_Radar_77GHz = np.load('/content/drive/MyDrive/walking_away_from_Radar_77Ghz.npy')\n",
        "walking_away_from_Radar_77GHz = np.float32(walking_away_from_Radar_77GHz)\n",
        "label_walking_away_from_Radar_77GHz         = np.zeros((walking_away_from_Radar_77GHz.shape[0],1))\n",
        "label_walking_away_from_Radar_77GHz[:]       = 9\n",
        "Walking_on_both_toes_77GHz    = np.load('/content/drive/MyDrive/Walking_on_both_toes_77Ghz.npy')\n",
        "Walking_on_both_toes_77GHz = np.float32(Walking_on_both_toes_77GHz)\n",
        "label_Walking_on_both_toes_77GHz         = np.zeros((Walking_on_both_toes_77GHz.shape[0],1))\n",
        "label_Walking_on_both_toes_77GHz[:]       = 10\n",
        "Walking_towards_radar_77GHz   = np.load('/content/drive/MyDrive/Walking_towards_radar_77Ghz.npy')\n",
        "Walking_towards_radar_77GHz = np.float32(Walking_towards_radar_77GHz)\n",
        "label_Walking_towards_radar_77GHz          = np.zeros((Walking_towards_radar_77GHz.shape[0],1))\n",
        "label_Walking_towards_radar_77GHz[:]       = 11\n",
        "# # 24 GHz\n",
        "# bending_24GHz                 = np.load('/content/drive/MyDrive/bending_24GHz.npy')\n",
        "# label_bending_24GHz           = np.zeros((bending_24GHz.shape[0],1))\n",
        "# label_bending_24GHz[:]        = 1\n",
        "# crawling_24GHz                = np.load('/content/drive/MyDrive/crawling_24GHz.npy')\n",
        "# label_crawling_24GHz          = np.zeros((crawling_24GHz.shape[0],1))\n",
        "# label_crawling_24GHz[:]       = 2\n",
        "# kneeling_24GHz                = np.load('/content/drive/MyDrive/kneeling_24GHz.npy')\n",
        "# label_kneeling_24GHz          = np.zeros((kneeling_24GHz.shape[0],1))\n",
        "# label_kneeling_24GHz[:]       = 3\n",
        "# limping_with_RL_Stiff_24GHz   = np.load('/content/drive/MyDrive/limping with right stiff_24GHz.npy')\n",
        "# label_limping_with_RL_Stiff_24GHz           = np.zeros((limping_with_RL_Stiff_24GHz.shape[0],1))\n",
        "# label_limping_with_RL_Stiff_24GHz[:]        = 4\n",
        "# picking_up_an_object_24GHz    = np.load('/content/drive/MyDrive/picking up object_24GHz.npy')\n",
        "# label_picking_up_an_object_24GHz          = np.zeros((picking_up_an_object_24GHz.shape[0],1))\n",
        "# label_picking_up_an_object_24GHz[:]       = 5\n",
        "# scissors_gait_24GHz           = np.load('/content/drive/MyDrive/scissors gait_24GHz.npy')\n",
        "# label_scissors_gait_24GHz          = np.zeros((scissors_gait_24GHz.shape[0],1))\n",
        "# label_scissors_gait_24GHz[:]       = 6\n",
        "# short_steps_24GHz             = np.load('/content/drive/MyDrive/short steps_24GHz.npy')\n",
        "# label_short_steps_24GHz          = np.zeros((short_steps_24GHz.shape[0],1))\n",
        "# label_short_steps_24GHz[:]       = 7\n",
        "# sitting_24GHz                 = np.load('/content/drive/MyDrive/sitting_24GHz.npy')\n",
        "# label_sitting_24GHz          = np.zeros((sitting_24GHz.shape[0],1))\n",
        "# label_sitting_24GHz[:]       = 8\n",
        "# walking_away_from_Radar_24GHz = np.load('/content/drive/MyDrive/walking away from radar_24GHz.npy')\n",
        "# label_walking_away_from_Radar_24GHz         = np.zeros((walking_away_from_Radar_24GHz.shape[0],1))\n",
        "# label_walking_away_from_Radar_24GHz[:]       = 9\n",
        "# Walking_on_both_toes_24GHz    = np.load('/content/drive/MyDrive/walking on toes both_24GHz.npy')\n",
        "# label_Walking_on_both_toes_24GHz         = np.zeros((Walking_on_both_toes_24GHz.shape[0],1))\n",
        "# label_Walking_on_both_toes_24GHz[:]       = 10\n",
        "# Walking_towards_radar_24GHz   = np.load('/content/drive/MyDrive/walking towards radar_24GHz.npy')\n",
        "# label_Walking_towards_radar_24GHz          = np.zeros((Walking_towards_radar_24GHz.shape[0],1))\n",
        "# label_Walking_towards_radar_24GHz[:]       = 11\n",
        "# # Xethru\n",
        "# bending_Xethru                 = np.load('/content/drive/MyDrive/bending_Xethru.npy')\n",
        "# label_bending_Xethru           = np.zeros((bending_Xethru.shape[0],1))\n",
        "# label_bending_Xethru[:]        = 1\n",
        "# crawling_Xethru                = np.load('/content/drive/MyDrive/crawling_Xethru.npy')\n",
        "# label_crawling_Xethru          = np.zeros((crawling_Xethru.shape[0],1))\n",
        "# label_crawling_Xethru[:]       = 2\n",
        "# kneeling_Xethru                = np.load('/content/drive/MyDrive/kneeling_Xethru.npy')\n",
        "# label_kneeling_Xethru          = np.zeros((kneeling_Xethru.shape[0],1))\n",
        "# label_kneeling_Xethru[:]       = 3\n",
        "# limping_with_RL_Stiff_Xethru   = np.load('/content/drive/MyDrive/limping with right leg stiff_Xethru.npy')\n",
        "# label_limping_with_RL_Stiff_Xethru           = np.zeros((limping_with_RL_Stiff_Xethru.shape[0],1))\n",
        "# label_limping_with_RL_Stiff_Xethru[:]        = 4\n",
        "# picking_up_an_object_Xethru    = np.load('/content/drive/MyDrive/ picking up an object_Xethru.npy')\n",
        "# label_picking_up_an_object_Xethru          = np.zeros((picking_up_an_object_Xethru.shape[0],1))\n",
        "# label_picking_up_an_object_Xethru[:]       = 5\n",
        "# scissors_gait_Xethru           = np.load('/content/drive/MyDrive/scissor Gait_Xethru.npy')\n",
        "# label_scissors_gait_Xethru          = np.zeros((scissors_gait_Xethru.shape[0],1))\n",
        "# label_scissors_gait_Xethru[:]       = 6\n",
        "# short_steps_Xethru             = np.load('/content/drive/MyDrive/short Step_Xethru.npy')\n",
        "# label_short_steps_Xethru          = np.zeros((short_steps_Xethru.shape[0],1))\n",
        "# label_short_steps_Xethru[:]       = 7\n",
        "# sitting_Xethru                 = np.load('/content/drive/MyDrive/sitting_Xethru.npy')\n",
        "# label_sitting_Xethru          = np.zeros((sitting_Xethru.shape[0],1))\n",
        "# label_sitting_Xethru[:]       = 8\n",
        "# walking_away_from_Radar_Xethru = np.load('/content/drive/MyDrive/Walking away from radar_Xethru.npy')\n",
        "# label_walking_away_from_Radar_Xethru         = np.zeros((walking_away_from_Radar_Xethru.shape[0],1))\n",
        "# label_walking_away_from_Radar_Xethru[:]       = 9\n",
        "# Walking_on_both_toes_Xethru    = np.load('/content/drive/MyDrive/walking on both toes_Xethru.npy')\n",
        "# label_Walking_on_both_toes_Xethru         = np.zeros((Walking_on_both_toes_Xethru.shape[0],1))\n",
        "# label_Walking_on_both_toes_Xethru[:]       = 10\n",
        "# Walking_towards_radar_Xethru   = np.load('/content/drive/MyDrive/Walking towards the radar_Xethru.npy')\n",
        "# label_Walking_towards_radar_Xethru          = np.zeros((Walking_towards_radar_Xethru.shape[0],1))\n",
        "# label_Walking_towards_radar_Xethru[:]       = 11"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fchoRm_5UtO2"
      },
      "source": [
        "label_concat = label_bending_77GHz\n",
        "del label_bending_77GHz\n",
        "label_concat = np.concatenate((label_concat,label_crawling_77GHz),axis=0)  \n",
        "del label_crawling_77GHz\n",
        "label_concat = np.concatenate((label_concat,label_kneeling_77GHz),axis=0)  \n",
        "del label_kneeling_77GHz\n",
        "label_concat = np.concatenate((label_concat,label_limping_with_RL_Stiff_77GHz),axis=0)  \n",
        "del label_limping_with_RL_Stiff_77GHz\n",
        "label_concat = np.concatenate((label_concat,label_picking_up_an_object_77GHz),axis=0)  \n",
        "del label_picking_up_an_object_77GHz\n",
        "label_concat = np.concatenate((label_concat,label_scissors_gait_77GHz),axis=0)  \n",
        "del label_scissors_gait_77GHz\n",
        "label_concat = np.concatenate((label_concat,label_short_steps_77GHz),axis=0)  \n",
        "del label_short_steps_77GHz\n",
        "label_concat = np.concatenate((label_concat,label_sitting_77GHz),axis=0)  \n",
        "del label_sitting_77GHz\n",
        "label_concat = np.concatenate((label_concat,label_walking_away_from_Radar_77GHz),axis=0)  \n",
        "del label_walking_away_from_Radar_77GHz\n",
        "label_concat = np.concatenate((label_concat,label_Walking_on_both_toes_77GHz),axis=0)  \n",
        "del label_Walking_on_both_toes_77GHz\n",
        "label_concat = np.concatenate((label_concat,label_Walking_towards_radar_77GHz),axis=0)  \n",
        "del label_Walking_towards_radar_77GHz\n",
        "\n",
        "# label_concat = label_bending_24GHz\n",
        "# del label_bending_24GHz\n",
        "# label_concat = np.concatenate((label_concat,label_crawling_24GHz),axis=0)  \n",
        "# del label_crawling_24GHz\n",
        "# label_concat = np.concatenate((label_concat,label_kneeling_24GHz),axis=0)  \n",
        "# del label_kneeling_24GHz\n",
        "# label_concat = np.concatenate((label_concat,label_limping_with_RL_Stiff_24GHz),axis=0)  \n",
        "# del label_limping_with_RL_Stiff_24GHz\n",
        "# label_concat = np.concatenate((label_concat,label_picking_up_an_object_24GHz),axis=0)  \n",
        "# del label_picking_up_an_object_24GHz\n",
        "# label_concat = np.concatenate((label_concat,label_scissors_gait_24GHz),axis=0)  \n",
        "# del label_scissors_gait_24GHz\n",
        "# label_concat = np.concatenate((label_concat,label_short_steps_24GHz),axis=0)  \n",
        "# del label_short_steps_24GHz\n",
        "# label_concat = np.concatenate((label_concat,label_sitting_24GHz),axis=0)  \n",
        "# del label_sitting_24GHz\n",
        "# label_concat = np.concatenate((label_concat,label_walking_away_from_Radar_24GHz),axis=0)  \n",
        "# del label_walking_away_from_Radar_24GHz\n",
        "# label_concat = np.concatenate((label_concat,label_Walking_on_both_toes_24GHz),axis=0)  \n",
        "# del label_Walking_on_both_toes_24GHz\n",
        "# label_concat = np.concatenate((label_concat,label_Walking_towards_radar_24GHz),axis=0)  \n",
        "# del label_Walking_towards_radar_24GHz\n",
        "\n",
        "\n",
        "# label_concat = label_bending_Xethru\n",
        "# del label_bending_Xethru\n",
        "# label_concat = np.concatenate((label_concat,label_crawling_Xethru),axis=0)  \n",
        "# del label_crawling_Xethru\n",
        "# label_concat = np.concatenate((label_concat,label_kneeling_Xethru),axis=0)  \n",
        "# del label_kneeling_Xethru\n",
        "# label_concat = np.concatenate((label_concat,label_limping_with_RL_Stiff_Xethru),axis=0)  \n",
        "# del label_limping_with_RL_Stiff_Xethru\n",
        "# label_concat = np.concatenate((label_concat,label_picking_up_an_object_Xethru),axis=0)  \n",
        "# del label_picking_up_an_object_Xethru\n",
        "# label_concat = np.concatenate((label_concat,label_scissors_gait_Xethru),axis=0)  \n",
        "# del label_scissors_gait_Xethru\n",
        "# label_concat = np.concatenate((label_concat,label_short_steps_Xethru),axis=0)  \n",
        "# del label_short_steps_Xethru\n",
        "# label_concat = np.concatenate((label_concat,label_sitting_Xethru),axis=0)  \n",
        "# del label_sitting_Xethru\n",
        "# label_concat = np.concatenate((label_concat,label_walking_away_from_Radar_Xethru),axis=0)  \n",
        "# del label_walking_away_from_Radar_Xethru\n",
        "# label_concat = np.concatenate((label_concat,label_Walking_on_both_toes_Xethru),axis=0)  \n",
        "# del label_Walking_on_both_toes_Xethru\n",
        "# label_concat = np.concatenate((label_concat,label_Walking_towards_radar_Xethru),axis=0)  \n",
        "# del label_Walking_towards_radar_Xethru"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_upOlnklMNhD"
      },
      "source": [
        "# 77 GHz\n",
        "spectrogram_77GHz_concat = np.concatenate((bending_77GHz,crawling_77GHz),axis=0)\n",
        "del bending_77GHz\n",
        "del crawling_77GHz\n",
        "spectrogram_77GHz_concat = np.concatenate((spectrogram_77GHz_concat,kneeling_77GHz               ),axis=0)\n",
        "del kneeling_77GHz\n",
        "spectrogram_77GHz_concat = np.concatenate((spectrogram_77GHz_concat,limping_with_RL_Stiff_77GHz  ),axis=0)\n",
        "del limping_with_RL_Stiff_77GHz\n",
        "spectrogram_77GHz_concat = np.concatenate((spectrogram_77GHz_concat,picking_up_an_object_77GHz   ),axis=0)\n",
        "del picking_up_an_object_77GHz\n",
        "spectrogram_77GHz_concat = np.concatenate((spectrogram_77GHz_concat,scissors_gait_77GHz          ),axis=0)\n",
        "del scissors_gait_77GHz\n",
        "spectrogram_77GHz_concat = np.concatenate((spectrogram_77GHz_concat,short_steps_77GHz            ),axis=0)\n",
        "del short_steps_77GHz\n",
        "spectrogram_77GHz_concat = np.concatenate((spectrogram_77GHz_concat,sitting_77GHz                ),axis=0)\n",
        "del sitting_77GHz\n",
        "spectrogram_77GHz_concat = np.concatenate((spectrogram_77GHz_concat,walking_away_from_Radar_77GHz),axis=0)\n",
        "del walking_away_from_Radar_77GHz\n",
        "spectrogram_77GHz_concat = np.concatenate((spectrogram_77GHz_concat,Walking_on_both_toes_77GHz   ),axis=0)\n",
        "del Walking_on_both_toes_77GHz\n",
        "spectrogram_77GHz_concat = np.concatenate((spectrogram_77GHz_concat,Walking_towards_radar_77GHz  ),axis=0)\n",
        "del Walking_towards_radar_77GHz\n",
        "# # 24 GHz\n",
        "# spectrogram_24GHz_concat = np.concatenate((bending_24GHz,crawling_24GHz),axis=0)\n",
        "# del bending_24GHz\n",
        "# del crawling_24GHz\n",
        "# spectrogram_24GHz_concat = np.concatenate((spectrogram_24GHz_concat,kneeling_24GHz               ),axis=0)\n",
        "# del kneeling_24GHz\n",
        "# spectrogram_24GHz_concat = np.concatenate((spectrogram_24GHz_concat,limping_with_RL_Stiff_24GHz  ),axis=0)\n",
        "# del limping_with_RL_Stiff_24GHz\n",
        "# spectrogram_24GHz_concat = np.concatenate((spectrogram_24GHz_concat,picking_up_an_object_24GHz   ),axis=0)\n",
        "# del picking_up_an_object_24GHz\n",
        "# spectrogram_24GHz_concat = np.concatenate((spectrogram_24GHz_concat,scissors_gait_24GHz          ),axis=0)\n",
        "# del scissors_gait_24GHz\n",
        "# spectrogram_24GHz_concat = np.concatenate((spectrogram_24GHz_concat,short_steps_24GHz            ),axis=0)\n",
        "# del short_steps_24GHz\n",
        "# spectrogram_24GHz_concat = np.concatenate((spectrogram_24GHz_concat,sitting_24GHz                ),axis=0)\n",
        "# del sitting_24GHz\n",
        "# spectrogram_24GHz_concat = np.concatenate((spectrogram_24GHz_concat,walking_away_from_Radar_24GHz),axis=0)\n",
        "# del walking_away_from_Radar_24GHz\n",
        "# spectrogram_24GHz_concat = np.concatenate((spectrogram_24GHz_concat,Walking_on_both_toes_24GHz   ),axis=0)\n",
        "# del Walking_on_both_toes_24GHz\n",
        "# spectrogram_24GHz_concat = np.concatenate((spectrogram_24GHz_concat,Walking_towards_radar_24GHz  ),axis=0)\n",
        "# del Walking_towards_radar_24GHz\n",
        "\n",
        "\n",
        "# # Xethru\n",
        "# spectrogram_Xethru_concat = np.concatenate((bending_Xethru,crawling_Xethru),axis=0)\n",
        "# del bending_Xethru\n",
        "# del crawling_Xethru\n",
        "# spectrogram_Xethru_concat = np.concatenate((spectrogram_Xethru_concat,kneeling_Xethru               ),axis=0)\n",
        "# del kneeling_Xethru\n",
        "# spectrogram_Xethru_concat = np.concatenate((spectrogram_Xethru_concat,limping_with_RL_Stiff_Xethru  ),axis=0)\n",
        "# del limping_with_RL_Stiff_Xethru\n",
        "# spectrogram_Xethru_concat = np.concatenate((spectrogram_Xethru_concat,picking_up_an_object_Xethru   ),axis=0)\n",
        "# del picking_up_an_object_Xethru\n",
        "# spectrogram_Xethru_concat = np.concatenate((spectrogram_Xethru_concat,scissors_gait_Xethru          ),axis=0)\n",
        "# del scissors_gait_Xethru\n",
        "# spectrogram_Xethru_concat = np.concatenate((spectrogram_Xethru_concat,short_steps_Xethru            ),axis=0)\n",
        "# del short_steps_Xethru\n",
        "# spectrogram_Xethru_concat = np.concatenate((spectrogram_Xethru_concat,sitting_Xethru                ),axis=0)\n",
        "# del sitting_Xethru\n",
        "# spectrogram_Xethru_concat = np.concatenate((spectrogram_Xethru_concat,walking_away_from_Radar_Xethru),axis=0)\n",
        "# del walking_away_from_Radar_Xethru\n",
        "# spectrogram_Xethru_concat = np.concatenate((spectrogram_Xethru_concat,Walking_on_both_toes_Xethru   ),axis=0)\n",
        "# del Walking_on_both_toes_Xethru\n",
        "# spectrogram_Xethru_concat = np.concatenate((spectrogram_Xethru_concat,Walking_towards_radar_Xethru  ),axis=0)\n",
        "# del Walking_towards_radar_Xethru\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yq7lpIJPLnXL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "60aeb4ef-b76c-4f2a-8c71-47a33c390204"
      },
      "source": [
        "# One-Hot Encode Label\n",
        "# integer encode\n",
        "label_encoder = LabelEncoder()\n",
        "integer_encoded = label_encoder.fit_transform(label_concat)\n",
        "# binary encode\n",
        "onehot_encoder = OneHotEncoder(sparse=False)\n",
        "integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)\n",
        "onehot_encoded_labels = onehot_encoder.fit_transform(integer_encoded)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_label.py:115: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wFp_PErnMLN2"
      },
      "source": [
        "# Concat range-doppler data\n",
        "range_doppler_concat = spectrogram_77GHz_concat\n",
        "range_doppler_concat_label = label_concat\n",
        "# Shuffle concat range doppler\n",
        "shuffle_indx = random.sample(range(0, range_doppler_concat.shape[0]), range_doppler_concat.shape[0]) # split validation data\n",
        "range_doppler_concat_shuffle = range_doppler_concat[shuffle_indx,:,:,:]\n",
        "range_doppler_concat_label_shuffle = range_doppler_concat_label[shuffle_indx,:]\n",
        "# Concat range-doppler data\n",
        "# spectrogram_concat = spectrogram_77GHz_concat\n",
        "spectrogram_concat_label = onehot_encoded_labels\n",
        "# Shuffle concat range doppler\n",
        "# spectrogram_concat_shuffle = spectrogram_concat[shuffle_indx,:,:,:]\n",
        "spectrogram_concat_label_shuffle = spectrogram_concat_label[shuffle_indx,:]\n",
        "# Third Spectrogram\n",
        "# third_spectrogram_concat = spectrogram_77GHz_concat\n",
        "# third_spectrogram_concat_shuffle = third_spectrogram_concat[shuffle_indx,:,:,:]\n",
        "del range_doppler_concat\n",
        "del spectrogram_77GHz_concat"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# range_doppler_concat_shuffle_new = np.zeros((735,201,368,1))\n",
        "# for ii in range(range_doppler_concat_shuffle.shape[0]):\n",
        "#   aa = range_doppler_concat_shuffle[ii,:,:,0]\n",
        "#   range_doppler_concat_shuffle_new[ii,:,:,0] = cv2.resize(aa, (368, 201),interpolation = cv2.INTER_CUBIC)\n",
        "# range_doppler_concat_shuffle = range_doppler_concat_shuffle_new\n",
        "# del aa\n",
        "# del range_doppler_concat_shuffle_new"
      ],
      "metadata": {
        "id": "TG8i5jexgAbq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_1aBNkjoLCvI"
      },
      "source": [
        "# ---------------- Augmente and shuffle (train and test) data data ----------------\n",
        "def mixup_augmentation(range_doppler_training_data, labels, repeat_of_mixup, alpha, beta):\n",
        "    batch_size = range_doppler_training_data.shape[0]\n",
        "    concat_images_range_doppler = np.zeros((batch_size*(repeat_of_mixup+1),range_doppler_training_data.shape[1],range_doppler_training_data.shape[2],range_doppler_training_data.shape[3]))\n",
        "    concat_label = np.zeros((batch_size*(repeat_of_mixup+1),labels.shape[1]))\n",
        "\n",
        "    # integer_training_labels = np.argmax(labels, axis=1)\n",
        "    # np.where(integer_training_labels == 0)\n",
        "\n",
        "    if repeat_of_mixup == 0:\n",
        "      concat_images_range_doppler = range_doppler_training_data\n",
        "      concat_label = labels      \n",
        "    else:\n",
        "      for ii in range(repeat_of_mixup):\n",
        "        # shuffle train dataset\n",
        "        shuffle_indx_1 = random.sample(range(0, range_doppler_training_data.shape[0]), range_doppler_training_data.shape[0]) # split validation data\n",
        "        range_doppler_training_data_shuffled_1 = range_doppler_training_data[shuffle_indx_1,:,:,:]\n",
        "        labels_shuffled_1 = labels[shuffle_indx_1,:]\n",
        "\n",
        "        shuffle_indx_2 = random.sample(range(0, range_doppler_training_data.shape[0]), range_doppler_training_data.shape[0]) # split validation data\n",
        "        range_doppler_training_data_shuffled_2 = range_doppler_training_data[shuffle_indx_2,:,:,:]\n",
        "        labels_shuffled_2 = labels[shuffle_indx_2,:]\n",
        "\n",
        "        # shuffle_indx_3 = random.sample(range(0, range_doppler_training_data.shape[0]), range_doppler_training_data.shape[0]) # split validation data\n",
        "        # range_doppler_training_data_shuffled_3 = range_doppler_training_data[shuffle_indx_3,:,:,:]\n",
        "        # labels_shuffled_3 = labels[shuffle_indx_3,:]\n",
        "\n",
        "        # shuffle_indx_4 = random.sample(range(0, range_doppler_training_data.shape[0]), range_doppler_training_data.shape[0]) # split validation data\n",
        "        # range_doppler_training_data_shuffled_4 = range_doppler_training_data[shuffle_indx_4,:,:,:]\n",
        "        # labels_shuffled_4 = labels[shuffle_indx_4,:]\n",
        "\n",
        "        # Sample lambda and reshape it to do the mixup\n",
        "        # ll = np.random.beta(alpha, beta, (batch_size,1,1,1))\n",
        "        gaussian_mean = 0.2\n",
        "        gaussian_std = 0.02\n",
        "        ll = np.random.normal(gaussian_mean, gaussian_std, (batch_size,1,1,1))\n",
        "        x_l = np.reshape(ll, (batch_size,1,1,1))\n",
        "        y_l = np.reshape(ll, (batch_size,1))\n",
        "      \n",
        "        ll_2 = np.random.normal(gaussian_mean, gaussian_std, (batch_size,1,1,1))\n",
        "        x_l_2 = np.reshape(ll_2, (batch_size,1,1,1))\n",
        "        y_l_2 = np.reshape(ll_2, (batch_size,1))\n",
        "\n",
        "        ll_3 = np.random.normal(gaussian_mean, gaussian_std, (batch_size,1,1,1))\n",
        "        x_l_3 = np.reshape(ll_3, (batch_size,1,1,1))\n",
        "        y_l_3 = np.reshape(ll_3, (batch_size,1))\n",
        "\n",
        "        \n",
        "        # Perform mixup on both images and labels by combining a pair of images/labels\n",
        "        # images_mixup_range_doppler = range_doppler_training_data_shuffled_1 * x_l + range_doppler_training_data_shuffled_2 * x_l_2 +\\\n",
        "        # range_doppler_training_data_shuffled_3 * x_l_3 + range_doppler_training_data_shuffled_4 * (1 - x_l - x_l_2 - x_l_3)\n",
        "        # labels_mixup = labels_shuffled_1 * y_l + labels_shuffled_2 * y_l_2  + labels_shuffled_3 * y_l_3 + labels_shuffled_4 * (1 - y_l - y_l_2 - y_l_3)\n",
        "        \n",
        "        images_mixup_range_doppler = range_doppler_training_data_shuffled_1 * x_l + range_doppler_training_data_shuffled_2 * (1 - x_l)\n",
        "        labels_mixup = labels_shuffled_1 * y_l + labels_shuffled_2 * (1 - y_l)\n",
        "\n",
        "        concat_images_range_doppler[ii*batch_size:(ii+1)*batch_size,:,:,:] = images_mixup_range_doppler\n",
        "        concat_label[ii*batch_size:(ii+1)*batch_size,:] = labels_mixup\n",
        "\n",
        "      concat_images_range_doppler[repeat_of_mixup*batch_size:,:,:,:] = range_doppler_training_data\n",
        "      concat_label[repeat_of_mixup*batch_size:,:] = labels\n",
        "    return (concat_images_range_doppler, concat_label)\n",
        "def vertical_mixup_augmentation(range_doppler_training_data, labels, repeat_of_mixup, alpha, beta):\n",
        "    batch_size = range_doppler_training_data.shape[0]\n",
        "    concat_images_range_doppler = np.zeros((batch_size*(repeat_of_mixup+1),range_doppler_training_data.shape[1],range_doppler_training_data.shape[2],range_doppler_training_data.shape[3]))\n",
        "    concat_label = np.zeros((batch_size*(repeat_of_mixup+1),labels.shape[1]))\n",
        "\n",
        "    # integer_training_labels = np.argmax(labels, axis=1)\n",
        "    # np.where(integer_training_labels == 0)\n",
        "\n",
        "    if repeat_of_mixup == 0:\n",
        "      concat_images_range_doppler = range_doppler_training_data\n",
        "      concat_label = labels      \n",
        "    else:\n",
        "      for ii in range(repeat_of_mixup):\n",
        "        # shuffle train dataset\n",
        "        shuffle_indx_1 = random.sample(range(0, range_doppler_training_data.shape[0]), range_doppler_training_data.shape[0]) # split validation data\n",
        "        range_doppler_training_data_shuffled_1 = range_doppler_training_data[shuffle_indx_1,:,:,:]\n",
        "        labels_shuffled_1 = labels[shuffle_indx_1,:]\n",
        "\n",
        "        shuffle_indx_2 = random.sample(range(0, range_doppler_training_data.shape[0]), range_doppler_training_data.shape[0]) # split validation data\n",
        "        range_doppler_training_data_shuffled_2 = range_doppler_training_data[shuffle_indx_2,:,:,:]\n",
        "        labels_shuffled_2 = labels[shuffle_indx_2,:]\n",
        "\n",
        "        images_mixup_range_doppler = np.zeros((range_doppler_training_data.shape))\n",
        "\n",
        "        gaussian_mean = 0.2\n",
        "        gaussian_std = 0.02\n",
        "        ll_1 = np.random.normal(gaussian_mean, gaussian_std, (batch_size,1))\n",
        "        vertical_resolution = range_doppler_training_data_shuffled_1.shape[2]\n",
        "        index_number_1 = int(vertical_resolution*ll_1[0])\n",
        "        index_number_2 = vertical_resolution - index_number_1\n",
        "        shuffle_indx_1 = random.sample(range(0, vertical_resolution), index_number_1) # split validation data\n",
        "        shuffle_indx_2 = np.delete(range(0, vertical_resolution), shuffle_indx_1) # split training data\n",
        "\n",
        "        images_mixup_range_doppler[:,:,shuffle_indx_1,0] = range_doppler_training_data_shuffled_1[:,:,shuffle_indx_1,0]\n",
        "        images_mixup_range_doppler[:,:,shuffle_indx_2,0] = range_doppler_training_data_shuffled_2[:,:,shuffle_indx_2,0]\n",
        "        \n",
        "        # images_mixup_range_doppler = range_doppler_training_data_shuffled_1 * x_l + range_doppler_training_data_shuffled_2 * (1 - x_l)\n",
        "\n",
        "        labels_mixup = labels_shuffled_1 * ll_1 + labels_shuffled_2 * (1 - ll_1)\n",
        "\n",
        "        concat_images_range_doppler[ii*batch_size:(ii+1)*batch_size,:,:,:] = images_mixup_range_doppler\n",
        "        concat_label[ii*batch_size:(ii+1)*batch_size,:] = labels_mixup\n",
        "\n",
        "      concat_images_range_doppler[repeat_of_mixup*batch_size:,:,:,:] = range_doppler_training_data\n",
        "      concat_label[repeat_of_mixup*batch_size:,:] = labels\n",
        "    return (concat_images_range_doppler, concat_label)\n",
        "def split_and_augmentation_of_training(range_doppler_concat_shuffle_train,range_doppler_concat_label_shuffle_train,\\\n",
        "                                       repeat_of_mixup, augmentation_enable):\n",
        "  # ---------------- Parameters ----------------\n",
        "  # to split train and validation with same distribution\n",
        "  alpha = 0.1\n",
        "  beta = 5\n",
        "  dummy_label = np.zeros((range_doppler_concat_shuffle_train.shape[0],1))\n",
        "  for randomlist_for_train_indx, randomlist_for_validation_indx in kfold.split(range_doppler_concat_shuffle_train,dummy_label):   \n",
        "    randomlist_for_validation_indx\n",
        "  # size_of_validation = int(0.2*range_doppler_concat_shuffle_train.shape[0])\n",
        "  # # Split validation\n",
        "  # randomlist_for_validation_indx = random.sample(range(0, range_doppler_concat_shuffle_train.shape[0]), size_of_validation) # split validation data\n",
        "  # randomlist_for_train_indx = np.delete(range(0, range_doppler_concat_shuffle_train.shape[0]), randomlist_for_validation_indx) # split training data\n",
        "  # get validation data\n",
        "  spectrogram_validation_labels = range_doppler_concat_label_shuffle_train[randomlist_for_validation_indx,:]\n",
        "  range_doppler_validation_data = range_doppler_concat_shuffle_train[randomlist_for_validation_indx,:,:,:]\n",
        "  # get training data\n",
        "  spectrogram_training_labels = spectrogram_concat_label_shuffle_train[randomlist_for_train_indx,:]\n",
        "  range_doppler_training_data = range_doppler_concat_shuffle_train[randomlist_for_train_indx,:,:,:]\n",
        "\n",
        "  # (range_doppler_augmented_image,spectrograms_label)=\\\n",
        "  #  mixup_augmentation(range_doppler_training_data, spectrogram_training_labels, repeat_of_mixup, alpha, beta)\n",
        "\n",
        "  (range_doppler_augmented_image,spectrograms_label)=\\\n",
        "   vertical_mixup_augmentation(range_doppler_training_data, spectrogram_training_labels, repeat_of_mixup, alpha, beta)\n",
        "\n",
        "  return (range_doppler_augmented_image,spectrograms_label,\\\n",
        "     range_doppler_validation_data, spectrogram_validation_labels)\n",
        "\n",
        "\n",
        "\n",
        "def normalize_inputs(range_doppler_concat_shuffle, normalize_inputs_enable):\n",
        "  # ---------------- Normalize Inputs ----------------\n",
        "  if normalize_inputs_enable == True:\n",
        "    layer = Normalization(axis=None)\n",
        "    layer.adapt(range_doppler_concat_shuffle)\n",
        "    range_doppler_concat_shuffle = layer(range_doppler_concat_shuffle)\n",
        "  else:\n",
        "    range_doppler_concat_shuffle = range_doppler_concat_shuffle\n",
        "  return(range_doppler_concat_shuffle)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "range_doppler_concat_shuffle_new = np.zeros((range_doppler_concat_shuffle.shape[0],216,216,1))\n",
        "for ii in range(range_doppler_concat_shuffle.shape[0]):\n",
        "  aa = range_doppler_concat_shuffle[ii,:,:,0]\n",
        "  range_doppler_concat_shuffle_new[ii,:,:,0] = cv2.resize(aa, (216, 216),interpolation = cv2.INTER_CUBIC)\n",
        "range_doppler_concat_shuffle = range_doppler_concat_shuffle_new\n",
        "del aa\n",
        "del range_doppler_concat_shuffle_new"
      ],
      "metadata": {
        "id": "8xs6NIs4VvId"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9l7kn8DBfgBh"
      },
      "source": [
        "normalize_inputs_enable = 1\n",
        "range_doppler_concat_shuffle = normalize_inputs(range_doppler_concat_shuffle, normalize_inputs_enable)\n",
        "# spectrogram_concat_shuffle = normalize_inputs(spectrogram_concat_shuffle, normalize_inputs_enable)\n",
        "# third_spectrogram_concat_shuffle = normalize_inputs(third_spectrogram_concat_shuffle, normalize_inputs_enable)\n",
        "range_doppler_concat_shuffle = np.float32(range_doppler_concat_shuffle)\n",
        "# spectrogram_concat_shuffle = np.float32(spectrogram_concat_shuffle)\n",
        "# third_spectrogram_concat_shuffle = np.float32(third_spectrogram_concat_shuffle)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n_features = range_doppler_concat_shuffle.shape[1]\n",
        "n_steps = range_doppler_concat_shuffle.shape[2]\n",
        "range_doppler_concat_shuffle = np.transpose(range_doppler_concat_shuffle, axes = (0,2,1,3)) \n",
        "# spectrogram_concat_shuffle = np.transpose(spectrogram_concat_shuffle, axes = (0,2,1,3)) \n",
        "# third_spectrogram_concat_shuffle = np.transpose(third_spectrogram_concat_shuffle, axes = (0,2,1,3)) "
      ],
      "metadata": {
        "id": "FJ8DgKewawvT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# integer_training_labels = np.argmax(spectrogram_concat_label_shuffle_train, axis=1)\n",
        "# np.where(integer_training_labels == 1)"
      ],
      "metadata": {
        "id": "oJBq1S-NkRXO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.layers import *\n",
        "from keras.models import *\n",
        "from keras import backend as K\n",
        "\n",
        "class attention(Layer):\n",
        "    \n",
        "    def __init__(self, return_sequences=True):\n",
        "        self.return_sequences = return_sequences\n",
        "        super(attention,self).__init__()\n",
        "        \n",
        "    def build(self, input_shape):\n",
        "        \n",
        "        self.W=self.add_weight(name=\"att_weight\", shape=(input_shape[-1],1),\n",
        "                               initializer=\"normal\")\n",
        "        self.b=self.add_weight(name=\"att_bias\", shape=(input_shape[1],1),\n",
        "                               initializer=\"zeros\")\n",
        "        \n",
        "        super(attention,self).build(input_shape)\n",
        "        \n",
        "    def call(self, x):\n",
        "        \n",
        "        e = K.tanh(K.dot(x,self.W)+self.b)\n",
        "        a = K.softmax(e, axis=1)\n",
        "        # a = K.sigmoid(e)\n",
        "        output = x*a\n",
        "        \n",
        "        if self.return_sequences:\n",
        "            return output\n",
        "        \n",
        "        return K.sum(output, axis=1)"
      ],
      "metadata": {
        "id": "5G_sjtd-ynjy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def transformer_encoder(inputs, head_size, num_heads, ff_dim, dropout=0):\n",
        "    # Normalization and Attention\n",
        "    x = layers.LayerNormalization(epsilon=1e-6)(inputs)\n",
        "    x = layers.MultiHeadAttention(\n",
        "        key_dim=head_size, num_heads=num_heads, dropout=dropout\n",
        "    )(x, x)\n",
        "    x = layers.Dropout(dropout)(x)\n",
        "    res = x + inputs\n",
        "\n",
        "    # Feed Forward Part\n",
        "    x = layers.LayerNormalization(epsilon=1e-6)(res)\n",
        "    x = layers.Conv1D(filters=ff_dim, kernel_size=1, activation=\"relu\")(x)\n",
        "    x = layers.Dropout(dropout)(x)\n",
        "    x = layers.Conv1D(filters=inputs.shape[-1], kernel_size=1)(x)\n",
        "    return x + res"
      ],
      "metadata": {
        "id": "ZpnV7V-f1Ycf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rNpkBBIadpPo",
        "cellView": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "94d169f6-df16-4cc5-d8b3-ee1883049325"
      },
      "source": [
        "t = time.time()\n",
        "# ---------- Parameters ----------------\n",
        "augmentation_enable = True\n",
        "normalize_inputs_enable = True\n",
        "num_folds = 5\n",
        "kfold = StratifiedKFold(n_splits=num_folds, shuffle=True, random_state = None) # random_state = 1 ile split run'dan run'a sabit.\n",
        "test_accuracy_per_run = []\n",
        "f1_score_per_run = []\n",
        "epoch_number = 100\n",
        "batch_size = 32\n",
        "dense_size = 128\n",
        "dropout_prob_dense = 0.4\n",
        "repeat_of_mixup = 0\n",
        "number_of_repeat = 5\n",
        "lstm_dropout_rate = 0.4\n",
        "unit_number_of_lstm = 64\n",
        "dense_unit_of_lstm_function = 256\n",
        "decoder_dense = 1024\n",
        "for repeat_run_number in range(number_of_repeat):\n",
        "  test_accuracy_per_fold = []\n",
        "  f1_score_per_fold = []\n",
        "  for train, test in kfold.split(range_doppler_concat_shuffle,range_doppler_concat_label_shuffle):   \n",
        "    gc.collect()\n",
        "    K.clear_session()\n",
        "\n",
        "    randomlist_for_test_indx = test\n",
        "    randomlist_for_train_indx = train\n",
        "    # test data\n",
        "    spectrogram_concat_label_shuffle_test = spectrogram_concat_label_shuffle[randomlist_for_test_indx,:]\n",
        "    range_doppler_concat_shuffle_test = range_doppler_concat_shuffle[randomlist_for_test_indx,:,:,:]\n",
        "    #train data\n",
        "    spectrogram_concat_label_shuffle_train = spectrogram_concat_label_shuffle[randomlist_for_train_indx,:]\n",
        "    range_doppler_concat_shuffle_train = range_doppler_concat_shuffle[randomlist_for_train_indx,:,:,:]\n",
        "    # ---------------- Split labels to equal them during augmentation for Validation ----------------\n",
        "    (range_doppler_augmented_image,spectrogram_concat_label_shuffle_concat,\\\n",
        "     validation_range_doppler, spectrogram_validation_labels)  =\\\n",
        "      split_and_augmentation_of_training(range_doppler_concat_shuffle_train,\\\n",
        "                                         spectrogram_concat_label_shuffle_train,\\\n",
        "                                         repeat_of_mixup, augmentation_enable)\n",
        "\n",
        "\n",
        "    def lstm_encoder_network_1(input_shape):\n",
        "      input = Input(shape=input_shape)\n",
        "      x = Bidirectional(LSTM(unit_number_of_lstm, return_sequences=True, dropout = 0.))(input)       \n",
        "      x = tf.transpose(x, perm=[0,2,1])\n",
        "      x = attention(return_sequences=True)(x)\n",
        "      x = tf.transpose(x, perm=[0,2,1])        \n",
        "      x = GlobalAveragePooling1D()(x)\n",
        "\n",
        "      return Model(input, x)\n",
        "\n",
        "    # def lstm_encoder_network_1_reversed(input_shape):\n",
        "    #   input = Input(shape=input_shape)\n",
        "    #   x = tf.image.flip_up_down(input)\n",
        "    #   x = (LSTM(unit_number_of_lstm, return_sequences=True, dropout = 0.))(x)       \n",
        "    #   x = tf.transpose(x, perm=[0,2,1])\n",
        "    #   x = attention(return_sequences=True)(x)\n",
        "    #   x = tf.transpose(x, perm=[0,2,1])        \n",
        "    #   x = GlobalAveragePooling1D()(x)\n",
        "\n",
        "    #   return Model(input, x)\n",
        "\n",
        "\n",
        "    def lstm_encoder_network_2(input_shape):\n",
        "      input = Input(shape=input_shape)\n",
        "      x = Bidirectional(GRU(unit_number_of_lstm, return_sequences=True, dropout = 0.))(input)\n",
        "      x = tf.transpose(x, perm=[0,2,1])\n",
        "      x = attention(return_sequences=True)(x)\n",
        "      x = tf.transpose(x, perm=[0,2,1])        \n",
        "      x = GlobalAveragePooling1D()(x)\n",
        "      return Model(input, x)\n",
        "    \n",
        "    # def lstm_encoder_network_2_reversed(input_shape):\n",
        "    #   input = Input(shape=input_shape)\n",
        "    #   x = tf.image.flip_up_down(input)\n",
        "    #   x = (GRU(unit_number_of_lstm, return_sequences=True, dropout = 0.))(x)\n",
        "    #   x = tf.transpose(x, perm=[0,2,1])\n",
        "    #   x = attention(return_sequences=True)(x)\n",
        "    #   x = tf.transpose(x, perm=[0,2,1])        \n",
        "    #   x = GlobalAveragePooling1D()(x)\n",
        "    #   return Model(input, x)\n",
        "\n",
        "    # def lstm_encoder_network_3(input_shape):\n",
        "    #   input = Input(shape=input_shape)\n",
        "    #   conv1 = Conv1D(filters=64, kernel_size=3, padding=\"same\")(input)\n",
        "    #   conv1 = BatchNormalization()(conv1)\n",
        "    #   conv1 = ReLU()(conv1)\n",
        "    #   conv2 = Conv1D(filters=64, kernel_size=3, padding=\"same\")(conv1)\n",
        "    #   conv2 = BatchNormalization()(conv2)\n",
        "    #   conv2 = ReLU()(conv2)\n",
        "\n",
        "    #   conv3 = Conv1D(filters=64, kernel_size=3, padding=\"same\")(conv2)\n",
        "    #   conv3 = BatchNormalization()(conv3)\n",
        "    #   x = ReLU()(conv3)\n",
        "    #   x = tf.transpose(x, perm=[0,2,1])\n",
        "    #   x = attention(return_sequences=True)(x)\n",
        "    #   x = tf.transpose(x, perm=[0,2,1])        \n",
        "    #   x = GlobalAveragePooling1D()(x)\n",
        "\n",
        "    #   return Model(input, x)\n",
        "\n",
        "    def decoder_for_concat(input_shape):\n",
        "      input = Input(shape=input_shape)\n",
        "      # x = Dense(1024)(input)\n",
        "      # x = BatchNormalization()(x)\n",
        "      # x = Activation('LeakyReLU')(x)\n",
        "      # x = Dropout(0.5)(x)\n",
        "      x = Dense(64)(input)\n",
        "      x = BatchNormalization()(x)\n",
        "      x = Activation('LeakyReLU')(x)\n",
        "      x = Dropout(0.45)(x)\n",
        "      x = Dense(11, activation=\"softmax\")(x)\n",
        "      return Model(input, x)\n",
        "\n",
        "    # def build_model(\n",
        "    #   input_shape,\n",
        "    #   head_size,\n",
        "    #   num_heads,\n",
        "    #   ff_dim,\n",
        "    #   num_transformer_blocks,\n",
        "    #   mlp_units,\n",
        "    #   dropout=0,\n",
        "    #   mlp_dropout=0,\n",
        "    # ):\n",
        "    #   inputs = Input(shape=input_shape)\n",
        "    #   x = inputs\n",
        "    #   for _ in range(num_transformer_blocks):\n",
        "    #       x = transformer_encoder(x, head_size, num_heads, ff_dim, dropout)\n",
        "\n",
        "    #   x = layers.GlobalAveragePooling1D(data_format=\"channels_first\")(x)\n",
        "    #   for dim in mlp_units:\n",
        "    #       x = layers.Dense(dim, activation=\"relu\")(x)\n",
        "    #       outputs = layers.Dropout(mlp_dropout)(x)\n",
        "    #   # outputs = layers.Dense(11, activation=\"softmax\")(x)\n",
        "    #   return Model(inputs, outputs)\n",
        "\n",
        "    # input_shape = range_doppler_concat_shuffle.shape[1:3]\n",
        "\n",
        "    # model_t = build_model(\n",
        "    #     input_shape,\n",
        "    #     head_size=256,\n",
        "    #     num_heads=4,\n",
        "    #     ff_dim=4,\n",
        "    #     num_transformer_blocks=4,\n",
        "    #     mlp_units=[128],\n",
        "    #     mlp_dropout=0.4,\n",
        "    #     dropout=0.25,\n",
        "    # )\n",
        "\n",
        "    # lstm_input_t  = Input(shape=input_shape)\n",
        "    # processed_lstm_1_t  = model_t(lstm_input_t)\n",
        "\n",
        "    input_shape = range_doppler_concat_shuffle.shape[1:3]\n",
        "\n",
        "    base_network_lstm_1 = lstm_encoder_network_1(input_shape)\n",
        "    lstm_input_1  = Input(shape=input_shape)\n",
        "    processed_lstm_1  = base_network_lstm_1(lstm_input_1)\n",
        "\n",
        "\n",
        "    base_network_lstm_2 = lstm_encoder_network_2(input_shape)\n",
        "    lstm_input_2  = Input(shape=input_shape)\n",
        "    processed_lstm_2  = base_network_lstm_2(lstm_input_1)\n",
        "\n",
        "    # base_network_lstm_1_reversed = lstm_encoder_network_1_reversed(input_shape)\n",
        "    # lstm_input_1_reversed  = Input(shape=input_shape)\n",
        "    # processed_lstm_1_reversed  = base_network_lstm_1(lstm_input_1_reversed)\n",
        "\n",
        "\n",
        "    # base_network_lstm_2_reversed = lstm_encoder_network_2_reversed(input_shape)\n",
        "    # lstm_input_2_reversed  = Input(shape=input_shape)\n",
        "    # processed_lstm_2_reversed  = base_network_lstm_2(lstm_input_2_reversed)\n",
        "\n",
        "\n",
        "    # base_network_lstm_3 = lstm_encoder_network_3(input_shape)\n",
        "    # lstm_input_3  = Input(shape=input_shape)\n",
        "    # processed_lstm_3  = base_network_lstm_3(lstm_input_1)\n",
        "\n",
        "    concat_layer = Concatenate()([processed_lstm_1, processed_lstm_2])\n",
        "\n",
        "\n",
        "\n",
        "    base_decoder_network = decoder_for_concat((concat_layer.shape[1]))\n",
        "    out = base_decoder_network(concat_layer)\n",
        "\n",
        "    model = Model(inputs=[lstm_input_1], outputs=[out])\n",
        "\n",
        "    print(base_network_lstm_1.summary()) \n",
        "    print(base_network_lstm_2.summary()) \n",
        "    # print(model_t.summary()) \n",
        "    print(base_decoder_network.summary())\n",
        "    # ---------------- Compile and Fit ----------------\n",
        "    model.compile(loss='categorical_crossentropy',\n",
        "                  optimizer='adam',\n",
        "                  metrics=['accuracy'])\n",
        "    \n",
        "    earlyStopping = EarlyStopping(monitor='val_loss', patience=30, verbose=0,restore_best_weights=True, mode='min')\n",
        "    # earlyStopping = EarlyStopping(monitor='val_accuracy', patience=15, verbose=0,restore_best_weights=True, mode='max')\n",
        "    history = model.fit((range_doppler_augmented_image),(spectrogram_concat_label_shuffle_concat),\n",
        "                    epochs=epoch_number,\n",
        "                    batch_size=batch_size,\n",
        "                    shuffle = True,\n",
        "                    callbacks=[earlyStopping],\n",
        "                    validation_data = ((validation_range_doppler) , (spectrogram_validation_labels)))\n",
        "    tf.keras.models.load_model\n",
        "    test_loss, test_accuracy  = model.evaluate([range_doppler_concat_shuffle_test],\\\n",
        "                                               [spectrogram_concat_label_shuffle_test],\n",
        "                  batch_size=batch_size)\n",
        "    # ---------------- Get Test Results ----------------\n",
        "    # y_test_predicted = model.predict((range_doppler_concat_shuffle_test,spectrogram_concat_shuffle_test,third_spectrogram_concat_shuffle_test), batch_size=batch_size)\n",
        "    # # ----- Binarize y_test_predicted values -----\n",
        "    # y_test_predicted_binary = np.zeros(y_test_predicted.shape[0])\n",
        "    # for ii in range(y_test_predicted.shape[0]):\n",
        "    #   if argmax(y_test_predicted[ii,:]) == 0:\n",
        "    #     y_test_predicted_binary[ii] = 0\n",
        "    #   elif argmax(y_test_predicted[ii,:]) == 1:\n",
        "    #     y_test_predicted_binary[ii] = 1\n",
        "    #   else:\n",
        "    #     y_test_predicted_binary[ii] = 2\n",
        "    # range_doppler_concat_label_shuffle_test1 = range_doppler_concat_label_shuffle[randomlist_for_test_indx,:]\n",
        "    # test_precision, test_recall, test_f1_score, support = precision_recall_fscore_support(range_doppler_concat_label_shuffle_test1, y_test_predicted_binary, average='macro')\n",
        "    # ----------------------------------------------------------------------------------------------------------\n",
        "    test_accuracy_per_fold.append(test_accuracy)\n",
        "    # f1_score_per_fold.append(test_f1_score)\n",
        "    del model\n",
        "  test_accuracy_per_run.append(sum(test_accuracy_per_fold)/num_folds)\n",
        "  # f1_score_per_run.append(sum(f1_score_per_fold)/num_folds)\n",
        "  print(test_accuracy_per_run)\n",
        "  # print(f1_score_per_run)\n",
        "\n",
        "print(f'Mean test accuracy is {\"{:.3f}\".format(sum(test_accuracy_per_run)/number_of_repeat)},\\\n",
        " max test accuracy is {\"{:.3f}\".format(max(test_accuracy_per_run))},\\\n",
        " min test accuracy is {\"{:.3f}\".format(min(test_accuracy_per_run))}, \\\n",
        " std of test accuracy is {\"{:.3f}\".format(np.std(test_accuracy_per_run, axis=0))}.')\n",
        "elapsed = time.time() - t\n",
        "print(f'Time elapsed through all process: {\"{:.2f}\".format(elapsed)}, sec')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 216, 216)]        0         \n",
            "                                                                 \n",
            " bidirectional (Bidirectiona  (None, 216, 128)         143872    \n",
            " l)                                                              \n",
            "                                                                 \n",
            " tf.compat.v1.transpose (TFO  (None, 128, 216)         0         \n",
            " pLambda)                                                        \n",
            "                                                                 \n",
            " attention (attention)       (None, 128, 216)          344       \n",
            "                                                                 \n",
            " tf.compat.v1.transpose_1 (T  (None, 216, 128)         0         \n",
            " FOpLambda)                                                      \n",
            "                                                                 \n",
            " global_average_pooling1d (G  (None, 128)              0         \n",
            " lobalAveragePooling1D)                                          \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 144,216\n",
            "Trainable params: 144,216\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_3 (InputLayer)        [(None, 216, 216)]        0         \n",
            "                                                                 \n",
            " bidirectional_1 (Bidirectio  (None, 216, 128)         108288    \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " tf.compat.v1.transpose_2 (T  (None, 128, 216)         0         \n",
            " FOpLambda)                                                      \n",
            "                                                                 \n",
            " attention_1 (attention)     (None, 128, 216)          344       \n",
            "                                                                 \n",
            " tf.compat.v1.transpose_3 (T  (None, 216, 128)         0         \n",
            " FOpLambda)                                                      \n",
            "                                                                 \n",
            " global_average_pooling1d_1   (None, 128)              0         \n",
            " (GlobalAveragePooling1D)                                        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 108,632\n",
            "Trainable params: 108,632\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_5 (InputLayer)        [(None, 256)]             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 64)                16448     \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 64)               256       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " activation (Activation)     (None, 64)                0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 64)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 11)                715       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 17,419\n",
            "Trainable params: 17,291\n",
            "Non-trainable params: 128\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/100\n",
            "13/13 [==============================] - 8s 152ms/step - loss: 2.2027 - accuracy: 0.3687 - val_loss: 2.3797 - val_accuracy: 0.2816\n",
            "Epoch 2/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 1.7368 - accuracy: 0.4699 - val_loss: 2.3503 - val_accuracy: 0.3107\n",
            "Epoch 3/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 1.4426 - accuracy: 0.5301 - val_loss: 2.3176 - val_accuracy: 0.3495\n",
            "Epoch 4/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 1.2168 - accuracy: 0.5711 - val_loss: 2.2883 - val_accuracy: 0.3398\n",
            "Epoch 5/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 1.0668 - accuracy: 0.6410 - val_loss: 2.2512 - val_accuracy: 0.4175\n",
            "Epoch 6/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 1.0165 - accuracy: 0.6265 - val_loss: 2.2188 - val_accuracy: 0.3981\n",
            "Epoch 7/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.8678 - accuracy: 0.7301 - val_loss: 2.1849 - val_accuracy: 0.4854\n",
            "Epoch 8/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.8536 - accuracy: 0.7012 - val_loss: 2.1578 - val_accuracy: 0.5534\n",
            "Epoch 9/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.7510 - accuracy: 0.7542 - val_loss: 2.1113 - val_accuracy: 0.6214\n",
            "Epoch 10/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.7441 - accuracy: 0.7373 - val_loss: 2.0931 - val_accuracy: 0.6019\n",
            "Epoch 11/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.6897 - accuracy: 0.7518 - val_loss: 2.0578 - val_accuracy: 0.6311\n",
            "Epoch 12/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.6416 - accuracy: 0.7735 - val_loss: 2.0141 - val_accuracy: 0.6796\n",
            "Epoch 13/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.6207 - accuracy: 0.7735 - val_loss: 1.9807 - val_accuracy: 0.6990\n",
            "Epoch 14/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.5528 - accuracy: 0.7976 - val_loss: 1.9299 - val_accuracy: 0.6990\n",
            "Epoch 15/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.6134 - accuracy: 0.8120 - val_loss: 1.9097 - val_accuracy: 0.6796\n",
            "Epoch 16/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.5702 - accuracy: 0.7831 - val_loss: 1.8538 - val_accuracy: 0.7379\n",
            "Epoch 17/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.5658 - accuracy: 0.7855 - val_loss: 1.8144 - val_accuracy: 0.7282\n",
            "Epoch 18/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.5085 - accuracy: 0.8313 - val_loss: 1.7656 - val_accuracy: 0.7573\n",
            "Epoch 19/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.4798 - accuracy: 0.8386 - val_loss: 1.7261 - val_accuracy: 0.7282\n",
            "Epoch 20/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.4895 - accuracy: 0.8217 - val_loss: 1.6824 - val_accuracy: 0.7184\n",
            "Epoch 21/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.4482 - accuracy: 0.8337 - val_loss: 1.6197 - val_accuracy: 0.7573\n",
            "Epoch 22/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.4355 - accuracy: 0.8482 - val_loss: 1.5665 - val_accuracy: 0.7282\n",
            "Epoch 23/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.4283 - accuracy: 0.8313 - val_loss: 1.4942 - val_accuracy: 0.8058\n",
            "Epoch 24/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.4039 - accuracy: 0.8795 - val_loss: 1.4548 - val_accuracy: 0.7670\n",
            "Epoch 25/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.4171 - accuracy: 0.8675 - val_loss: 1.4044 - val_accuracy: 0.7961\n",
            "Epoch 26/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.3895 - accuracy: 0.8795 - val_loss: 1.3551 - val_accuracy: 0.7961\n",
            "Epoch 27/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.3432 - accuracy: 0.9036 - val_loss: 1.2743 - val_accuracy: 0.7767\n",
            "Epoch 28/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.3213 - accuracy: 0.8795 - val_loss: 1.2067 - val_accuracy: 0.8155\n",
            "Epoch 29/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.3139 - accuracy: 0.9133 - val_loss: 1.1658 - val_accuracy: 0.8058\n",
            "Epoch 30/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.2866 - accuracy: 0.9229 - val_loss: 1.1017 - val_accuracy: 0.8155\n",
            "Epoch 31/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.2901 - accuracy: 0.9181 - val_loss: 1.0712 - val_accuracy: 0.7767\n",
            "Epoch 32/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.2576 - accuracy: 0.9325 - val_loss: 0.9708 - val_accuracy: 0.8252\n",
            "Epoch 33/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.2331 - accuracy: 0.9470 - val_loss: 0.9247 - val_accuracy: 0.7864\n",
            "Epoch 34/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.2505 - accuracy: 0.9398 - val_loss: 0.8582 - val_accuracy: 0.8155\n",
            "Epoch 35/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.2176 - accuracy: 0.9349 - val_loss: 0.8076 - val_accuracy: 0.8155\n",
            "Epoch 36/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.2088 - accuracy: 0.9566 - val_loss: 0.7875 - val_accuracy: 0.8155\n",
            "Epoch 37/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1875 - accuracy: 0.9639 - val_loss: 0.7121 - val_accuracy: 0.8155\n",
            "Epoch 38/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1883 - accuracy: 0.9590 - val_loss: 0.6646 - val_accuracy: 0.8252\n",
            "Epoch 39/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1738 - accuracy: 0.9614 - val_loss: 0.6297 - val_accuracy: 0.8155\n",
            "Epoch 40/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1443 - accuracy: 0.9783 - val_loss: 0.5802 - val_accuracy: 0.8252\n",
            "Epoch 41/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1360 - accuracy: 0.9783 - val_loss: 0.5726 - val_accuracy: 0.8252\n",
            "Epoch 42/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1187 - accuracy: 0.9783 - val_loss: 0.5464 - val_accuracy: 0.8350\n",
            "Epoch 43/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1261 - accuracy: 0.9880 - val_loss: 0.4914 - val_accuracy: 0.8544\n",
            "Epoch 44/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1284 - accuracy: 0.9831 - val_loss: 0.5299 - val_accuracy: 0.8155\n",
            "Epoch 45/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.1236 - accuracy: 0.9783 - val_loss: 0.5122 - val_accuracy: 0.8058\n",
            "Epoch 46/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1304 - accuracy: 0.9711 - val_loss: 0.4852 - val_accuracy: 0.8058\n",
            "Epoch 47/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0938 - accuracy: 0.9904 - val_loss: 0.4527 - val_accuracy: 0.8350\n",
            "Epoch 48/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0816 - accuracy: 0.9928 - val_loss: 0.4716 - val_accuracy: 0.8155\n",
            "Epoch 49/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0713 - accuracy: 0.9928 - val_loss: 0.4584 - val_accuracy: 0.8058\n",
            "Epoch 50/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0699 - accuracy: 0.9952 - val_loss: 0.4594 - val_accuracy: 0.8058\n",
            "Epoch 51/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0734 - accuracy: 0.9880 - val_loss: 0.4755 - val_accuracy: 0.8252\n",
            "Epoch 52/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0557 - accuracy: 1.0000 - val_loss: 0.3891 - val_accuracy: 0.8447\n",
            "Epoch 53/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0564 - accuracy: 0.9928 - val_loss: 0.4600 - val_accuracy: 0.8155\n",
            "Epoch 54/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0567 - accuracy: 0.9976 - val_loss: 0.3848 - val_accuracy: 0.8544\n",
            "Epoch 55/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0565 - accuracy: 0.9952 - val_loss: 0.4693 - val_accuracy: 0.8155\n",
            "Epoch 56/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0376 - accuracy: 0.9976 - val_loss: 0.3999 - val_accuracy: 0.8350\n",
            "Epoch 57/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0443 - accuracy: 1.0000 - val_loss: 0.4630 - val_accuracy: 0.8447\n",
            "Epoch 58/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0505 - accuracy: 0.9928 - val_loss: 0.4035 - val_accuracy: 0.8350\n",
            "Epoch 59/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0515 - accuracy: 0.9952 - val_loss: 0.3939 - val_accuracy: 0.8641\n",
            "Epoch 60/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0448 - accuracy: 0.9928 - val_loss: 0.5389 - val_accuracy: 0.8058\n",
            "Epoch 61/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0503 - accuracy: 0.9952 - val_loss: 0.5005 - val_accuracy: 0.8252\n",
            "Epoch 62/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0455 - accuracy: 0.9928 - val_loss: 0.4699 - val_accuracy: 0.8252\n",
            "Epoch 63/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0424 - accuracy: 0.9976 - val_loss: 0.4356 - val_accuracy: 0.8350\n",
            "Epoch 64/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0383 - accuracy: 0.9976 - val_loss: 0.4792 - val_accuracy: 0.8447\n",
            "Epoch 65/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0357 - accuracy: 0.9952 - val_loss: 0.4849 - val_accuracy: 0.8155\n",
            "Epoch 66/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0527 - accuracy: 0.9952 - val_loss: 0.4381 - val_accuracy: 0.8252\n",
            "Epoch 67/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0406 - accuracy: 1.0000 - val_loss: 0.4038 - val_accuracy: 0.8641\n",
            "Epoch 68/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0362 - accuracy: 0.9976 - val_loss: 0.4570 - val_accuracy: 0.8350\n",
            "Epoch 69/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0385 - accuracy: 0.9928 - val_loss: 0.4284 - val_accuracy: 0.8544\n",
            "Epoch 70/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0325 - accuracy: 0.9952 - val_loss: 0.4517 - val_accuracy: 0.8544\n",
            "Epoch 71/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0458 - accuracy: 0.9880 - val_loss: 0.4339 - val_accuracy: 0.8641\n",
            "Epoch 72/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0306 - accuracy: 0.9976 - val_loss: 0.4795 - val_accuracy: 0.8252\n",
            "Epoch 73/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0361 - accuracy: 0.9976 - val_loss: 0.4386 - val_accuracy: 0.8155\n",
            "Epoch 74/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0292 - accuracy: 0.9976 - val_loss: 0.4055 - val_accuracy: 0.8350\n",
            "Epoch 75/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0224 - accuracy: 1.0000 - val_loss: 0.4803 - val_accuracy: 0.8155\n",
            "Epoch 76/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0190 - accuracy: 1.0000 - val_loss: 0.4807 - val_accuracy: 0.8058\n",
            "Epoch 77/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0196 - accuracy: 1.0000 - val_loss: 0.4209 - val_accuracy: 0.8350\n",
            "Epoch 78/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0166 - accuracy: 1.0000 - val_loss: 0.4739 - val_accuracy: 0.8641\n",
            "Epoch 79/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0144 - accuracy: 1.0000 - val_loss: 0.4347 - val_accuracy: 0.8350\n",
            "Epoch 80/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0140 - accuracy: 1.0000 - val_loss: 0.4745 - val_accuracy: 0.8447\n",
            "Epoch 81/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0135 - accuracy: 1.0000 - val_loss: 0.4487 - val_accuracy: 0.8544\n",
            "Epoch 82/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0109 - accuracy: 1.0000 - val_loss: 0.4145 - val_accuracy: 0.8738\n",
            "Epoch 83/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0143 - accuracy: 1.0000 - val_loss: 0.4377 - val_accuracy: 0.8544\n",
            "Epoch 84/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0159 - accuracy: 1.0000 - val_loss: 0.4280 - val_accuracy: 0.8738\n",
            "5/5 [==============================] - 2s 18ms/step - loss: 0.3585 - accuracy: 0.8769\n",
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 216, 216)]        0         \n",
            "                                                                 \n",
            " bidirectional (Bidirectiona  (None, 216, 128)         143872    \n",
            " l)                                                              \n",
            "                                                                 \n",
            " tf.compat.v1.transpose (TFO  (None, 128, 216)         0         \n",
            " pLambda)                                                        \n",
            "                                                                 \n",
            " attention (attention)       (None, 128, 216)          344       \n",
            "                                                                 \n",
            " tf.compat.v1.transpose_1 (T  (None, 216, 128)         0         \n",
            " FOpLambda)                                                      \n",
            "                                                                 \n",
            " global_average_pooling1d (G  (None, 128)              0         \n",
            " lobalAveragePooling1D)                                          \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 144,216\n",
            "Trainable params: 144,216\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_3 (InputLayer)        [(None, 216, 216)]        0         \n",
            "                                                                 \n",
            " bidirectional_1 (Bidirectio  (None, 216, 128)         108288    \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " tf.compat.v1.transpose_2 (T  (None, 128, 216)         0         \n",
            " FOpLambda)                                                      \n",
            "                                                                 \n",
            " attention_1 (attention)     (None, 128, 216)          344       \n",
            "                                                                 \n",
            " tf.compat.v1.transpose_3 (T  (None, 216, 128)         0         \n",
            " FOpLambda)                                                      \n",
            "                                                                 \n",
            " global_average_pooling1d_1   (None, 128)              0         \n",
            " (GlobalAveragePooling1D)                                        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 108,632\n",
            "Trainable params: 108,632\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_5 (InputLayer)        [(None, 256)]             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 64)                16448     \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 64)               256       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " activation (Activation)     (None, 64)                0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 64)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 11)                715       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 17,419\n",
            "Trainable params: 17,291\n",
            "Non-trainable params: 128\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/100\n",
            "13/13 [==============================] - 7s 153ms/step - loss: 2.2189 - accuracy: 0.2940 - val_loss: 2.3813 - val_accuracy: 0.1845\n",
            "Epoch 2/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 1.8937 - accuracy: 0.4434 - val_loss: 2.3583 - val_accuracy: 0.3786\n",
            "Epoch 3/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 1.5683 - accuracy: 0.5108 - val_loss: 2.3283 - val_accuracy: 0.4757\n",
            "Epoch 4/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 1.3208 - accuracy: 0.6024 - val_loss: 2.2948 - val_accuracy: 0.5049\n",
            "Epoch 5/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 1.1467 - accuracy: 0.6265 - val_loss: 2.2592 - val_accuracy: 0.4854\n",
            "Epoch 6/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 1.0223 - accuracy: 0.6482 - val_loss: 2.2211 - val_accuracy: 0.5631\n",
            "Epoch 7/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.8938 - accuracy: 0.7301 - val_loss: 2.1797 - val_accuracy: 0.6117\n",
            "Epoch 8/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.8735 - accuracy: 0.6916 - val_loss: 2.1525 - val_accuracy: 0.6214\n",
            "Epoch 9/100\n",
            "13/13 [==============================] - 1s 46ms/step - loss: 0.7992 - accuracy: 0.7181 - val_loss: 2.1289 - val_accuracy: 0.7184\n",
            "Epoch 10/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.7515 - accuracy: 0.7373 - val_loss: 2.0892 - val_accuracy: 0.6893\n",
            "Epoch 11/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.7073 - accuracy: 0.7470 - val_loss: 2.0549 - val_accuracy: 0.7379\n",
            "Epoch 12/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.6587 - accuracy: 0.7566 - val_loss: 2.0141 - val_accuracy: 0.7379\n",
            "Epoch 13/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.6152 - accuracy: 0.7904 - val_loss: 1.9795 - val_accuracy: 0.7282\n",
            "Epoch 14/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.5936 - accuracy: 0.7759 - val_loss: 1.9457 - val_accuracy: 0.7476\n",
            "Epoch 15/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.5743 - accuracy: 0.7783 - val_loss: 1.8999 - val_accuracy: 0.7476\n",
            "Epoch 16/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.5261 - accuracy: 0.8289 - val_loss: 1.8482 - val_accuracy: 0.7670\n",
            "Epoch 17/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.5106 - accuracy: 0.8072 - val_loss: 1.8010 - val_accuracy: 0.8252\n",
            "Epoch 18/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.4754 - accuracy: 0.8289 - val_loss: 1.7483 - val_accuracy: 0.7184\n",
            "Epoch 19/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.4852 - accuracy: 0.8120 - val_loss: 1.7137 - val_accuracy: 0.8155\n",
            "Epoch 20/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.4299 - accuracy: 0.8506 - val_loss: 1.6804 - val_accuracy: 0.7282\n",
            "Epoch 21/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.4185 - accuracy: 0.8602 - val_loss: 1.6463 - val_accuracy: 0.8155\n",
            "Epoch 22/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.3945 - accuracy: 0.8675 - val_loss: 1.5715 - val_accuracy: 0.7573\n",
            "Epoch 23/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.3649 - accuracy: 0.8940 - val_loss: 1.5065 - val_accuracy: 0.8155\n",
            "Epoch 24/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.3121 - accuracy: 0.9133 - val_loss: 1.4620 - val_accuracy: 0.8058\n",
            "Epoch 25/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.3183 - accuracy: 0.9036 - val_loss: 1.3809 - val_accuracy: 0.7864\n",
            "Epoch 26/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.2640 - accuracy: 0.9446 - val_loss: 1.3181 - val_accuracy: 0.8447\n",
            "Epoch 27/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.2437 - accuracy: 0.9446 - val_loss: 1.2493 - val_accuracy: 0.8155\n",
            "Epoch 28/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.2271 - accuracy: 0.9470 - val_loss: 1.1931 - val_accuracy: 0.8447\n",
            "Epoch 29/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.2289 - accuracy: 0.9446 - val_loss: 1.1338 - val_accuracy: 0.8447\n",
            "Epoch 30/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1973 - accuracy: 0.9590 - val_loss: 1.0468 - val_accuracy: 0.8252\n",
            "Epoch 31/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1894 - accuracy: 0.9566 - val_loss: 0.9804 - val_accuracy: 0.8252\n",
            "Epoch 32/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1673 - accuracy: 0.9687 - val_loss: 0.9599 - val_accuracy: 0.8350\n",
            "Epoch 33/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1615 - accuracy: 0.9711 - val_loss: 0.8492 - val_accuracy: 0.8544\n",
            "Epoch 34/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1441 - accuracy: 0.9735 - val_loss: 0.8243 - val_accuracy: 0.8350\n",
            "Epoch 35/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1401 - accuracy: 0.9759 - val_loss: 0.7742 - val_accuracy: 0.7961\n",
            "Epoch 36/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1157 - accuracy: 0.9807 - val_loss: 0.7101 - val_accuracy: 0.8447\n",
            "Epoch 37/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1073 - accuracy: 0.9880 - val_loss: 0.6844 - val_accuracy: 0.8252\n",
            "Epoch 38/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1240 - accuracy: 0.9783 - val_loss: 0.5941 - val_accuracy: 0.8544\n",
            "Epoch 39/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1222 - accuracy: 0.9711 - val_loss: 0.6068 - val_accuracy: 0.8252\n",
            "Epoch 40/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0888 - accuracy: 0.9952 - val_loss: 0.5510 - val_accuracy: 0.8350\n",
            "Epoch 41/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0695 - accuracy: 0.9952 - val_loss: 0.5161 - val_accuracy: 0.8350\n",
            "Epoch 42/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0666 - accuracy: 0.9952 - val_loss: 0.4693 - val_accuracy: 0.8641\n",
            "Epoch 43/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0605 - accuracy: 0.9976 - val_loss: 0.4376 - val_accuracy: 0.8641\n",
            "Epoch 44/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0613 - accuracy: 0.9952 - val_loss: 0.4449 - val_accuracy: 0.8544\n",
            "Epoch 45/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0544 - accuracy: 1.0000 - val_loss: 0.4403 - val_accuracy: 0.7961\n",
            "Epoch 46/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0516 - accuracy: 0.9976 - val_loss: 0.4465 - val_accuracy: 0.8155\n",
            "Epoch 47/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0610 - accuracy: 0.9928 - val_loss: 0.3788 - val_accuracy: 0.8544\n",
            "Epoch 48/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0569 - accuracy: 0.9952 - val_loss: 0.4257 - val_accuracy: 0.8155\n",
            "Epoch 49/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0407 - accuracy: 0.9952 - val_loss: 0.3875 - val_accuracy: 0.8447\n",
            "Epoch 50/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0412 - accuracy: 0.9976 - val_loss: 0.4049 - val_accuracy: 0.8447\n",
            "Epoch 51/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0353 - accuracy: 0.9976 - val_loss: 0.4234 - val_accuracy: 0.8447\n",
            "Epoch 52/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0311 - accuracy: 1.0000 - val_loss: 0.3839 - val_accuracy: 0.8641\n",
            "Epoch 53/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0310 - accuracy: 0.9976 - val_loss: 0.3867 - val_accuracy: 0.8350\n",
            "Epoch 54/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0296 - accuracy: 1.0000 - val_loss: 0.3881 - val_accuracy: 0.8350\n",
            "Epoch 55/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0269 - accuracy: 1.0000 - val_loss: 0.4220 - val_accuracy: 0.8058\n",
            "Epoch 56/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0234 - accuracy: 1.0000 - val_loss: 0.3808 - val_accuracy: 0.8738\n",
            "Epoch 57/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0299 - accuracy: 0.9976 - val_loss: 0.3783 - val_accuracy: 0.8544\n",
            "Epoch 58/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0225 - accuracy: 1.0000 - val_loss: 0.3908 - val_accuracy: 0.8641\n",
            "Epoch 59/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0267 - accuracy: 0.9976 - val_loss: 0.4256 - val_accuracy: 0.8350\n",
            "Epoch 60/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0329 - accuracy: 0.9952 - val_loss: 0.4014 - val_accuracy: 0.8350\n",
            "Epoch 61/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0314 - accuracy: 1.0000 - val_loss: 0.4145 - val_accuracy: 0.8155\n",
            "Epoch 62/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0203 - accuracy: 1.0000 - val_loss: 0.4422 - val_accuracy: 0.8447\n",
            "Epoch 63/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0303 - accuracy: 0.9952 - val_loss: 0.4516 - val_accuracy: 0.8447\n",
            "Epoch 64/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0223 - accuracy: 1.0000 - val_loss: 0.4997 - val_accuracy: 0.8058\n",
            "Epoch 65/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0237 - accuracy: 1.0000 - val_loss: 0.4014 - val_accuracy: 0.8544\n",
            "Epoch 66/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0180 - accuracy: 1.0000 - val_loss: 0.4074 - val_accuracy: 0.8447\n",
            "Epoch 67/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0158 - accuracy: 1.0000 - val_loss: 0.4260 - val_accuracy: 0.8350\n",
            "Epoch 68/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0178 - accuracy: 1.0000 - val_loss: 0.4785 - val_accuracy: 0.8350\n",
            "Epoch 69/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0168 - accuracy: 1.0000 - val_loss: 0.4454 - val_accuracy: 0.8252\n",
            "Epoch 70/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0170 - accuracy: 1.0000 - val_loss: 0.4623 - val_accuracy: 0.8350\n",
            "Epoch 71/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0138 - accuracy: 1.0000 - val_loss: 0.4371 - val_accuracy: 0.8544\n",
            "Epoch 72/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0180 - accuracy: 1.0000 - val_loss: 0.4937 - val_accuracy: 0.8155\n",
            "Epoch 73/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0182 - accuracy: 1.0000 - val_loss: 0.3781 - val_accuracy: 0.8544\n",
            "Epoch 74/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0197 - accuracy: 1.0000 - val_loss: 0.4620 - val_accuracy: 0.8544\n",
            "Epoch 75/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0139 - accuracy: 1.0000 - val_loss: 0.4403 - val_accuracy: 0.8544\n",
            "Epoch 76/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0139 - accuracy: 0.9976 - val_loss: 0.4590 - val_accuracy: 0.8350\n",
            "Epoch 77/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0146 - accuracy: 1.0000 - val_loss: 0.4811 - val_accuracy: 0.8641\n",
            "Epoch 78/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0113 - accuracy: 1.0000 - val_loss: 0.4259 - val_accuracy: 0.8544\n",
            "Epoch 79/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0100 - accuracy: 1.0000 - val_loss: 0.4402 - val_accuracy: 0.8447\n",
            "Epoch 80/100\n",
            "13/13 [==============================] - 1s 51ms/step - loss: 0.0107 - accuracy: 1.0000 - val_loss: 0.4438 - val_accuracy: 0.8350\n",
            "Epoch 81/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0075 - accuracy: 1.0000 - val_loss: 0.4461 - val_accuracy: 0.8350\n",
            "Epoch 82/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0100 - accuracy: 1.0000 - val_loss: 0.4488 - val_accuracy: 0.8155\n",
            "Epoch 83/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0111 - accuracy: 1.0000 - val_loss: 0.5908 - val_accuracy: 0.7961\n",
            "Epoch 84/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0104 - accuracy: 1.0000 - val_loss: 0.4868 - val_accuracy: 0.8252\n",
            "Epoch 85/100\n",
            "13/13 [==============================] - 1s 51ms/step - loss: 0.0089 - accuracy: 1.0000 - val_loss: 0.4707 - val_accuracy: 0.8350\n",
            "Epoch 86/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0072 - accuracy: 1.0000 - val_loss: 0.4665 - val_accuracy: 0.8155\n",
            "Epoch 87/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0093 - accuracy: 1.0000 - val_loss: 0.4466 - val_accuracy: 0.8641\n",
            "Epoch 88/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0077 - accuracy: 1.0000 - val_loss: 0.4221 - val_accuracy: 0.8544\n",
            "Epoch 89/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0090 - accuracy: 1.0000 - val_loss: 0.4253 - val_accuracy: 0.8641\n",
            "Epoch 90/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0088 - accuracy: 1.0000 - val_loss: 0.4070 - val_accuracy: 0.8447\n",
            "Epoch 91/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0069 - accuracy: 1.0000 - val_loss: 0.4147 - val_accuracy: 0.8447\n",
            "Epoch 92/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0068 - accuracy: 1.0000 - val_loss: 0.4358 - val_accuracy: 0.8544\n",
            "Epoch 93/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0080 - accuracy: 1.0000 - val_loss: 0.5088 - val_accuracy: 0.8447\n",
            "Epoch 94/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0067 - accuracy: 1.0000 - val_loss: 0.4887 - val_accuracy: 0.8447\n",
            "Epoch 95/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0074 - accuracy: 1.0000 - val_loss: 0.5562 - val_accuracy: 0.8447\n",
            "Epoch 96/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0056 - accuracy: 1.0000 - val_loss: 0.5745 - val_accuracy: 0.8058\n",
            "Epoch 97/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0066 - accuracy: 1.0000 - val_loss: 0.5787 - val_accuracy: 0.8155\n",
            "Epoch 98/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0046 - accuracy: 1.0000 - val_loss: 0.4432 - val_accuracy: 0.8350\n",
            "Epoch 99/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 0.4618 - val_accuracy: 0.8252\n",
            "Epoch 100/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0050 - accuracy: 1.0000 - val_loss: 0.4722 - val_accuracy: 0.8350\n",
            "5/5 [==============================] - 2s 18ms/step - loss: 0.5762 - accuracy: 0.8538\n",
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 216, 216)]        0         \n",
            "                                                                 \n",
            " bidirectional (Bidirectiona  (None, 216, 128)         143872    \n",
            " l)                                                              \n",
            "                                                                 \n",
            " tf.compat.v1.transpose (TFO  (None, 128, 216)         0         \n",
            " pLambda)                                                        \n",
            "                                                                 \n",
            " attention (attention)       (None, 128, 216)          344       \n",
            "                                                                 \n",
            " tf.compat.v1.transpose_1 (T  (None, 216, 128)         0         \n",
            " FOpLambda)                                                      \n",
            "                                                                 \n",
            " global_average_pooling1d (G  (None, 128)              0         \n",
            " lobalAveragePooling1D)                                          \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 144,216\n",
            "Trainable params: 144,216\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_3 (InputLayer)        [(None, 216, 216)]        0         \n",
            "                                                                 \n",
            " bidirectional_1 (Bidirectio  (None, 216, 128)         108288    \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " tf.compat.v1.transpose_2 (T  (None, 128, 216)         0         \n",
            " FOpLambda)                                                      \n",
            "                                                                 \n",
            " attention_1 (attention)     (None, 128, 216)          344       \n",
            "                                                                 \n",
            " tf.compat.v1.transpose_3 (T  (None, 216, 128)         0         \n",
            " FOpLambda)                                                      \n",
            "                                                                 \n",
            " global_average_pooling1d_1   (None, 128)              0         \n",
            " (GlobalAveragePooling1D)                                        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 108,632\n",
            "Trainable params: 108,632\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_5 (InputLayer)        [(None, 256)]             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 64)                16448     \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 64)               256       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " activation (Activation)     (None, 64)                0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 64)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 11)                715       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 17,419\n",
            "Trainable params: 17,291\n",
            "Non-trainable params: 128\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/100\n",
            "13/13 [==============================] - 7s 155ms/step - loss: 2.1808 - accuracy: 0.3133 - val_loss: 2.3757 - val_accuracy: 0.1650\n",
            "Epoch 2/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 1.7485 - accuracy: 0.4434 - val_loss: 2.3439 - val_accuracy: 0.4175\n",
            "Epoch 3/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 1.4857 - accuracy: 0.4964 - val_loss: 2.3165 - val_accuracy: 0.3010\n",
            "Epoch 4/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 1.2647 - accuracy: 0.5807 - val_loss: 2.2840 - val_accuracy: 0.4563\n",
            "Epoch 5/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 1.1709 - accuracy: 0.6241 - val_loss: 2.2589 - val_accuracy: 0.4951\n",
            "Epoch 6/100\n",
            "13/13 [==============================] - 1s 46ms/step - loss: 1.0287 - accuracy: 0.6627 - val_loss: 2.2270 - val_accuracy: 0.5825\n",
            "Epoch 7/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.9517 - accuracy: 0.6554 - val_loss: 2.1973 - val_accuracy: 0.6214\n",
            "Epoch 8/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.8611 - accuracy: 0.7325 - val_loss: 2.1632 - val_accuracy: 0.7573\n",
            "Epoch 9/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.8012 - accuracy: 0.7349 - val_loss: 2.1228 - val_accuracy: 0.7476\n",
            "Epoch 10/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.7380 - accuracy: 0.7494 - val_loss: 2.0808 - val_accuracy: 0.7476\n",
            "Epoch 11/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.6871 - accuracy: 0.7663 - val_loss: 2.0496 - val_accuracy: 0.7767\n",
            "Epoch 12/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.6608 - accuracy: 0.7590 - val_loss: 2.0134 - val_accuracy: 0.7767\n",
            "Epoch 13/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.6342 - accuracy: 0.7807 - val_loss: 1.9713 - val_accuracy: 0.8058\n",
            "Epoch 14/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.5597 - accuracy: 0.8169 - val_loss: 1.9296 - val_accuracy: 0.7864\n",
            "Epoch 15/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.5645 - accuracy: 0.7976 - val_loss: 1.8864 - val_accuracy: 0.7864\n",
            "Epoch 16/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.5256 - accuracy: 0.8386 - val_loss: 1.8384 - val_accuracy: 0.7961\n",
            "Epoch 17/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.5102 - accuracy: 0.8434 - val_loss: 1.7959 - val_accuracy: 0.7961\n",
            "Epoch 18/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.4833 - accuracy: 0.8458 - val_loss: 1.7501 - val_accuracy: 0.7379\n",
            "Epoch 19/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.4861 - accuracy: 0.8289 - val_loss: 1.7376 - val_accuracy: 0.7961\n",
            "Epoch 20/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.4447 - accuracy: 0.8482 - val_loss: 1.6863 - val_accuracy: 0.8058\n",
            "Epoch 21/100\n",
            "13/13 [==============================] - 1s 46ms/step - loss: 0.3896 - accuracy: 0.8843 - val_loss: 1.6312 - val_accuracy: 0.8058\n",
            "Epoch 22/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.3988 - accuracy: 0.8892 - val_loss: 1.5921 - val_accuracy: 0.8058\n",
            "Epoch 23/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.3677 - accuracy: 0.9036 - val_loss: 1.5259 - val_accuracy: 0.8252\n",
            "Epoch 24/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.3402 - accuracy: 0.9060 - val_loss: 1.4668 - val_accuracy: 0.7961\n",
            "Epoch 25/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.3482 - accuracy: 0.8964 - val_loss: 1.4314 - val_accuracy: 0.7961\n",
            "Epoch 26/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.3108 - accuracy: 0.9157 - val_loss: 1.3447 - val_accuracy: 0.8155\n",
            "Epoch 27/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.2945 - accuracy: 0.9108 - val_loss: 1.2548 - val_accuracy: 0.8544\n",
            "Epoch 28/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.2847 - accuracy: 0.9157 - val_loss: 1.1888 - val_accuracy: 0.8350\n",
            "Epoch 29/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.2533 - accuracy: 0.9349 - val_loss: 1.1484 - val_accuracy: 0.7961\n",
            "Epoch 30/100\n",
            "13/13 [==============================] - 1s 46ms/step - loss: 0.2329 - accuracy: 0.9470 - val_loss: 1.0650 - val_accuracy: 0.8350\n",
            "Epoch 31/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.2190 - accuracy: 0.9373 - val_loss: 0.9871 - val_accuracy: 0.8252\n",
            "Epoch 32/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.2374 - accuracy: 0.9373 - val_loss: 0.9419 - val_accuracy: 0.8252\n",
            "Epoch 33/100\n",
            "13/13 [==============================] - 1s 50ms/step - loss: 0.1986 - accuracy: 0.9566 - val_loss: 0.8956 - val_accuracy: 0.8155\n",
            "Epoch 34/100\n",
            "13/13 [==============================] - 1s 51ms/step - loss: 0.1854 - accuracy: 0.9590 - val_loss: 0.8221 - val_accuracy: 0.8350\n",
            "Epoch 35/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.1688 - accuracy: 0.9590 - val_loss: 0.7436 - val_accuracy: 0.8544\n",
            "Epoch 36/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.1427 - accuracy: 0.9783 - val_loss: 0.7027 - val_accuracy: 0.8155\n",
            "Epoch 37/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.1377 - accuracy: 0.9759 - val_loss: 0.6369 - val_accuracy: 0.8544\n",
            "Epoch 38/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.1256 - accuracy: 0.9807 - val_loss: 0.6537 - val_accuracy: 0.8447\n",
            "Epoch 39/100\n",
            "13/13 [==============================] - 1s 50ms/step - loss: 0.1100 - accuracy: 0.9759 - val_loss: 0.5783 - val_accuracy: 0.8252\n",
            "Epoch 40/100\n",
            "13/13 [==============================] - 1s 46ms/step - loss: 0.1026 - accuracy: 0.9855 - val_loss: 0.5407 - val_accuracy: 0.8641\n",
            "Epoch 41/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1069 - accuracy: 0.9783 - val_loss: 0.5200 - val_accuracy: 0.8447\n",
            "Epoch 42/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1064 - accuracy: 0.9831 - val_loss: 0.4828 - val_accuracy: 0.8350\n",
            "Epoch 43/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1049 - accuracy: 0.9831 - val_loss: 0.4523 - val_accuracy: 0.8544\n",
            "Epoch 44/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0834 - accuracy: 0.9904 - val_loss: 0.4682 - val_accuracy: 0.8447\n",
            "Epoch 45/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0788 - accuracy: 0.9928 - val_loss: 0.4344 - val_accuracy: 0.8544\n",
            "Epoch 46/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0764 - accuracy: 0.9904 - val_loss: 0.4322 - val_accuracy: 0.8350\n",
            "Epoch 47/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0919 - accuracy: 0.9831 - val_loss: 0.3950 - val_accuracy: 0.8544\n",
            "Epoch 48/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0800 - accuracy: 0.9880 - val_loss: 0.4726 - val_accuracy: 0.7864\n",
            "Epoch 49/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0676 - accuracy: 0.9976 - val_loss: 0.4056 - val_accuracy: 0.8447\n",
            "Epoch 50/100\n",
            "13/13 [==============================] - 1s 46ms/step - loss: 0.0507 - accuracy: 1.0000 - val_loss: 0.4000 - val_accuracy: 0.8544\n",
            "Epoch 51/100\n",
            "13/13 [==============================] - 1s 46ms/step - loss: 0.0544 - accuracy: 0.9952 - val_loss: 0.3778 - val_accuracy: 0.8641\n",
            "Epoch 52/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0468 - accuracy: 0.9952 - val_loss: 0.3784 - val_accuracy: 0.8641\n",
            "Epoch 53/100\n",
            "13/13 [==============================] - 1s 46ms/step - loss: 0.0448 - accuracy: 1.0000 - val_loss: 0.3804 - val_accuracy: 0.8544\n",
            "Epoch 54/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0362 - accuracy: 1.0000 - val_loss: 0.3780 - val_accuracy: 0.8738\n",
            "Epoch 55/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0354 - accuracy: 0.9976 - val_loss: 0.4202 - val_accuracy: 0.8835\n",
            "Epoch 56/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0381 - accuracy: 0.9976 - val_loss: 0.3885 - val_accuracy: 0.8641\n",
            "Epoch 57/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0407 - accuracy: 0.9976 - val_loss: 0.3839 - val_accuracy: 0.8835\n",
            "Epoch 58/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0246 - accuracy: 1.0000 - val_loss: 0.3965 - val_accuracy: 0.8641\n",
            "Epoch 59/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0282 - accuracy: 1.0000 - val_loss: 0.3816 - val_accuracy: 0.8544\n",
            "Epoch 60/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0241 - accuracy: 1.0000 - val_loss: 0.3611 - val_accuracy: 0.8544\n",
            "Epoch 61/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0298 - accuracy: 1.0000 - val_loss: 0.3859 - val_accuracy: 0.8738\n",
            "Epoch 62/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0243 - accuracy: 1.0000 - val_loss: 0.3692 - val_accuracy: 0.8835\n",
            "Epoch 63/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0230 - accuracy: 1.0000 - val_loss: 0.3992 - val_accuracy: 0.8544\n",
            "Epoch 64/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0270 - accuracy: 0.9952 - val_loss: 0.4197 - val_accuracy: 0.8544\n",
            "Epoch 65/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0254 - accuracy: 1.0000 - val_loss: 0.4255 - val_accuracy: 0.8447\n",
            "Epoch 66/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0276 - accuracy: 1.0000 - val_loss: 0.4888 - val_accuracy: 0.8641\n",
            "Epoch 67/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0296 - accuracy: 1.0000 - val_loss: 0.4111 - val_accuracy: 0.8738\n",
            "Epoch 68/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0261 - accuracy: 0.9976 - val_loss: 0.4029 - val_accuracy: 0.8544\n",
            "Epoch 69/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0207 - accuracy: 1.0000 - val_loss: 0.4329 - val_accuracy: 0.8641\n",
            "Epoch 70/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0136 - accuracy: 1.0000 - val_loss: 0.4551 - val_accuracy: 0.8447\n",
            "Epoch 71/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0171 - accuracy: 1.0000 - val_loss: 0.4341 - val_accuracy: 0.8544\n",
            "Epoch 72/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0162 - accuracy: 1.0000 - val_loss: 0.4029 - val_accuracy: 0.8447\n",
            "Epoch 73/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0126 - accuracy: 1.0000 - val_loss: 0.3998 - val_accuracy: 0.8932\n",
            "Epoch 74/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0138 - accuracy: 1.0000 - val_loss: 0.4261 - val_accuracy: 0.8835\n",
            "Epoch 75/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0176 - accuracy: 1.0000 - val_loss: 0.3982 - val_accuracy: 0.8835\n",
            "Epoch 76/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0166 - accuracy: 1.0000 - val_loss: 0.3817 - val_accuracy: 0.8641\n",
            "Epoch 77/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0131 - accuracy: 1.0000 - val_loss: 0.4624 - val_accuracy: 0.8544\n",
            "Epoch 78/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0152 - accuracy: 0.9976 - val_loss: 0.4264 - val_accuracy: 0.8738\n",
            "Epoch 79/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0138 - accuracy: 1.0000 - val_loss: 0.4406 - val_accuracy: 0.8544\n",
            "Epoch 80/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0141 - accuracy: 1.0000 - val_loss: 0.3771 - val_accuracy: 0.8544\n",
            "Epoch 81/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0112 - accuracy: 1.0000 - val_loss: 0.3561 - val_accuracy: 0.8835\n",
            "Epoch 82/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0125 - accuracy: 1.0000 - val_loss: 0.3800 - val_accuracy: 0.8738\n",
            "Epoch 83/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0124 - accuracy: 1.0000 - val_loss: 0.4021 - val_accuracy: 0.8544\n",
            "Epoch 84/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0125 - accuracy: 1.0000 - val_loss: 0.4793 - val_accuracy: 0.8544\n",
            "Epoch 85/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0099 - accuracy: 1.0000 - val_loss: 0.4137 - val_accuracy: 0.8738\n",
            "Epoch 86/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0097 - accuracy: 1.0000 - val_loss: 0.3918 - val_accuracy: 0.8835\n",
            "Epoch 87/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0110 - accuracy: 1.0000 - val_loss: 0.3875 - val_accuracy: 0.8932\n",
            "Epoch 88/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0094 - accuracy: 1.0000 - val_loss: 0.4242 - val_accuracy: 0.8641\n",
            "Epoch 89/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0107 - accuracy: 1.0000 - val_loss: 0.4199 - val_accuracy: 0.8641\n",
            "Epoch 90/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0116 - accuracy: 1.0000 - val_loss: 0.3894 - val_accuracy: 0.8641\n",
            "Epoch 91/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0140 - accuracy: 1.0000 - val_loss: 0.4746 - val_accuracy: 0.8641\n",
            "Epoch 92/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0212 - accuracy: 0.9976 - val_loss: 0.5755 - val_accuracy: 0.8252\n",
            "Epoch 93/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0362 - accuracy: 0.9904 - val_loss: 0.5505 - val_accuracy: 0.8544\n",
            "Epoch 94/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0453 - accuracy: 0.9928 - val_loss: 0.6207 - val_accuracy: 0.8350\n",
            "Epoch 95/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0810 - accuracy: 0.9735 - val_loss: 0.3809 - val_accuracy: 0.8738\n",
            "Epoch 96/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0766 - accuracy: 0.9807 - val_loss: 0.4932 - val_accuracy: 0.8447\n",
            "Epoch 97/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0577 - accuracy: 0.9880 - val_loss: 0.4810 - val_accuracy: 0.8350\n",
            "Epoch 98/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0519 - accuracy: 0.9928 - val_loss: 0.5350 - val_accuracy: 0.8058\n",
            "Epoch 99/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0658 - accuracy: 0.9807 - val_loss: 0.4199 - val_accuracy: 0.8641\n",
            "Epoch 100/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0347 - accuracy: 0.9976 - val_loss: 0.6001 - val_accuracy: 0.8252\n",
            "5/5 [==============================] - 2s 18ms/step - loss: 0.8004 - accuracy: 0.8077\n",
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 216, 216)]        0         \n",
            "                                                                 \n",
            " bidirectional (Bidirectiona  (None, 216, 128)         143872    \n",
            " l)                                                              \n",
            "                                                                 \n",
            " tf.compat.v1.transpose (TFO  (None, 128, 216)         0         \n",
            " pLambda)                                                        \n",
            "                                                                 \n",
            " attention (attention)       (None, 128, 216)          344       \n",
            "                                                                 \n",
            " tf.compat.v1.transpose_1 (T  (None, 216, 128)         0         \n",
            " FOpLambda)                                                      \n",
            "                                                                 \n",
            " global_average_pooling1d (G  (None, 128)              0         \n",
            " lobalAveragePooling1D)                                          \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 144,216\n",
            "Trainable params: 144,216\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_3 (InputLayer)        [(None, 216, 216)]        0         \n",
            "                                                                 \n",
            " bidirectional_1 (Bidirectio  (None, 216, 128)         108288    \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " tf.compat.v1.transpose_2 (T  (None, 128, 216)         0         \n",
            " FOpLambda)                                                      \n",
            "                                                                 \n",
            " attention_1 (attention)     (None, 128, 216)          344       \n",
            "                                                                 \n",
            " tf.compat.v1.transpose_3 (T  (None, 216, 128)         0         \n",
            " FOpLambda)                                                      \n",
            "                                                                 \n",
            " global_average_pooling1d_1   (None, 128)              0         \n",
            " (GlobalAveragePooling1D)                                        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 108,632\n",
            "Trainable params: 108,632\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_5 (InputLayer)        [(None, 256)]             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 64)                16448     \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 64)               256       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " activation (Activation)     (None, 64)                0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 64)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 11)                715       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 17,419\n",
            "Trainable params: 17,291\n",
            "Non-trainable params: 128\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/100\n",
            "13/13 [==============================] - 6s 159ms/step - loss: 2.2272 - accuracy: 0.3654 - val_loss: 2.3809 - val_accuracy: 0.2524\n",
            "Epoch 2/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 1.8292 - accuracy: 0.4375 - val_loss: 2.3514 - val_accuracy: 0.3107\n",
            "Epoch 3/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 1.5086 - accuracy: 0.5481 - val_loss: 2.3206 - val_accuracy: 0.3495\n",
            "Epoch 4/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 1.3118 - accuracy: 0.5601 - val_loss: 2.2874 - val_accuracy: 0.5146\n",
            "Epoch 5/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 1.1166 - accuracy: 0.6442 - val_loss: 2.2614 - val_accuracy: 0.5049\n",
            "Epoch 6/100\n",
            "13/13 [==============================] - 1s 49ms/step - loss: 1.0142 - accuracy: 0.6442 - val_loss: 2.2230 - val_accuracy: 0.6602\n",
            "Epoch 7/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.9018 - accuracy: 0.6995 - val_loss: 2.1901 - val_accuracy: 0.6505\n",
            "Epoch 8/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.8425 - accuracy: 0.7019 - val_loss: 2.1635 - val_accuracy: 0.6796\n",
            "Epoch 9/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.7917 - accuracy: 0.7356 - val_loss: 2.1298 - val_accuracy: 0.6796\n",
            "Epoch 10/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.7784 - accuracy: 0.7452 - val_loss: 2.0985 - val_accuracy: 0.6699\n",
            "Epoch 11/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.6846 - accuracy: 0.7668 - val_loss: 2.0660 - val_accuracy: 0.6893\n",
            "Epoch 12/100\n",
            "13/13 [==============================] - 1s 49ms/step - loss: 0.6347 - accuracy: 0.7861 - val_loss: 2.0190 - val_accuracy: 0.7087\n",
            "Epoch 13/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.6200 - accuracy: 0.7933 - val_loss: 1.9846 - val_accuracy: 0.7087\n",
            "Epoch 14/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.6015 - accuracy: 0.7909 - val_loss: 1.9266 - val_accuracy: 0.7282\n",
            "Epoch 15/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.5037 - accuracy: 0.8462 - val_loss: 1.8873 - val_accuracy: 0.7282\n",
            "Epoch 16/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.5127 - accuracy: 0.8413 - val_loss: 1.8508 - val_accuracy: 0.7184\n",
            "Epoch 17/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.4579 - accuracy: 0.8582 - val_loss: 1.8018 - val_accuracy: 0.7184\n",
            "Epoch 18/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.4614 - accuracy: 0.8486 - val_loss: 1.7782 - val_accuracy: 0.7379\n",
            "Epoch 19/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.4569 - accuracy: 0.8462 - val_loss: 1.7237 - val_accuracy: 0.7379\n",
            "Epoch 20/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.3987 - accuracy: 0.8822 - val_loss: 1.6704 - val_accuracy: 0.7573\n",
            "Epoch 21/100\n",
            "13/13 [==============================] - 1s 53ms/step - loss: 0.4184 - accuracy: 0.8630 - val_loss: 1.6148 - val_accuracy: 0.7379\n",
            "Epoch 22/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.3675 - accuracy: 0.8870 - val_loss: 1.5768 - val_accuracy: 0.7767\n",
            "Epoch 23/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.3149 - accuracy: 0.9135 - val_loss: 1.4911 - val_accuracy: 0.7864\n",
            "Epoch 24/100\n",
            "13/13 [==============================] - 1s 49ms/step - loss: 0.3036 - accuracy: 0.9183 - val_loss: 1.4614 - val_accuracy: 0.7379\n",
            "Epoch 25/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.3041 - accuracy: 0.9231 - val_loss: 1.3573 - val_accuracy: 0.7864\n",
            "Epoch 26/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.2968 - accuracy: 0.9062 - val_loss: 1.3153 - val_accuracy: 0.7476\n",
            "Epoch 27/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.3099 - accuracy: 0.9038 - val_loss: 1.2800 - val_accuracy: 0.7476\n",
            "Epoch 28/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.2575 - accuracy: 0.9399 - val_loss: 1.2123 - val_accuracy: 0.7670\n",
            "Epoch 29/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.2779 - accuracy: 0.9231 - val_loss: 1.2002 - val_accuracy: 0.7670\n",
            "Epoch 30/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.2487 - accuracy: 0.9375 - val_loss: 1.0911 - val_accuracy: 0.7961\n",
            "Epoch 31/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.2290 - accuracy: 0.9567 - val_loss: 1.0429 - val_accuracy: 0.7864\n",
            "Epoch 32/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.2008 - accuracy: 0.9471 - val_loss: 0.9734 - val_accuracy: 0.7670\n",
            "Epoch 33/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.1877 - accuracy: 0.9543 - val_loss: 0.9131 - val_accuracy: 0.8155\n",
            "Epoch 34/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.1670 - accuracy: 0.9712 - val_loss: 0.8490 - val_accuracy: 0.8155\n",
            "Epoch 35/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.1718 - accuracy: 0.9639 - val_loss: 0.8461 - val_accuracy: 0.7767\n",
            "Epoch 36/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.1575 - accuracy: 0.9663 - val_loss: 0.7532 - val_accuracy: 0.7670\n",
            "Epoch 37/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.1388 - accuracy: 0.9760 - val_loss: 0.6951 - val_accuracy: 0.8350\n",
            "Epoch 38/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.1421 - accuracy: 0.9663 - val_loss: 0.6745 - val_accuracy: 0.8155\n",
            "Epoch 39/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.1461 - accuracy: 0.9663 - val_loss: 0.6724 - val_accuracy: 0.7670\n",
            "Epoch 40/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.1541 - accuracy: 0.9495 - val_loss: 0.6446 - val_accuracy: 0.7864\n",
            "Epoch 41/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.1147 - accuracy: 0.9832 - val_loss: 0.6215 - val_accuracy: 0.7379\n",
            "Epoch 42/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.1067 - accuracy: 0.9760 - val_loss: 0.5704 - val_accuracy: 0.8058\n",
            "Epoch 43/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.1151 - accuracy: 0.9808 - val_loss: 0.5248 - val_accuracy: 0.8350\n",
            "Epoch 44/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.1001 - accuracy: 0.9784 - val_loss: 0.5241 - val_accuracy: 0.8252\n",
            "Epoch 45/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0950 - accuracy: 0.9808 - val_loss: 0.5424 - val_accuracy: 0.7864\n",
            "Epoch 46/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0708 - accuracy: 0.9976 - val_loss: 0.5044 - val_accuracy: 0.7864\n",
            "Epoch 47/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0860 - accuracy: 0.9952 - val_loss: 0.4739 - val_accuracy: 0.7961\n",
            "Epoch 48/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0814 - accuracy: 0.9904 - val_loss: 0.4620 - val_accuracy: 0.7961\n",
            "Epoch 49/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0773 - accuracy: 0.9904 - val_loss: 0.4780 - val_accuracy: 0.8058\n",
            "Epoch 50/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0779 - accuracy: 0.9880 - val_loss: 0.5409 - val_accuracy: 0.7670\n",
            "Epoch 51/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0862 - accuracy: 0.9784 - val_loss: 0.4581 - val_accuracy: 0.8350\n",
            "Epoch 52/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0873 - accuracy: 0.9784 - val_loss: 0.5152 - val_accuracy: 0.7864\n",
            "Epoch 53/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.1189 - accuracy: 0.9688 - val_loss: 0.6145 - val_accuracy: 0.7476\n",
            "Epoch 54/100\n",
            "13/13 [==============================] - 1s 53ms/step - loss: 0.1001 - accuracy: 0.9808 - val_loss: 0.4995 - val_accuracy: 0.8058\n",
            "Epoch 55/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0720 - accuracy: 0.9952 - val_loss: 0.5102 - val_accuracy: 0.7767\n",
            "Epoch 56/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0566 - accuracy: 0.9904 - val_loss: 0.4875 - val_accuracy: 0.8252\n",
            "Epoch 57/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0662 - accuracy: 0.9880 - val_loss: 0.5049 - val_accuracy: 0.8058\n",
            "Epoch 58/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0616 - accuracy: 0.9952 - val_loss: 0.4641 - val_accuracy: 0.8641\n",
            "Epoch 59/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0431 - accuracy: 0.9976 - val_loss: 0.5032 - val_accuracy: 0.7961\n",
            "Epoch 60/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0448 - accuracy: 0.9976 - val_loss: 0.4798 - val_accuracy: 0.8252\n",
            "Epoch 61/100\n",
            "13/13 [==============================] - 1s 46ms/step - loss: 0.0287 - accuracy: 0.9976 - val_loss: 0.5158 - val_accuracy: 0.8447\n",
            "Epoch 62/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0294 - accuracy: 1.0000 - val_loss: 0.4921 - val_accuracy: 0.8350\n",
            "Epoch 63/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0230 - accuracy: 1.0000 - val_loss: 0.5167 - val_accuracy: 0.8447\n",
            "Epoch 64/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0247 - accuracy: 1.0000 - val_loss: 0.5288 - val_accuracy: 0.8155\n",
            "Epoch 65/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0227 - accuracy: 0.9976 - val_loss: 0.5164 - val_accuracy: 0.8350\n",
            "Epoch 66/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0235 - accuracy: 0.9976 - val_loss: 0.5603 - val_accuracy: 0.8252\n",
            "Epoch 67/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0195 - accuracy: 1.0000 - val_loss: 0.5630 - val_accuracy: 0.8252\n",
            "Epoch 68/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0184 - accuracy: 1.0000 - val_loss: 0.5621 - val_accuracy: 0.8252\n",
            "Epoch 69/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0162 - accuracy: 1.0000 - val_loss: 0.5892 - val_accuracy: 0.7961\n",
            "Epoch 70/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0154 - accuracy: 1.0000 - val_loss: 0.6141 - val_accuracy: 0.8252\n",
            "Epoch 71/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0143 - accuracy: 1.0000 - val_loss: 0.6092 - val_accuracy: 0.8350\n",
            "Epoch 72/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0158 - accuracy: 1.0000 - val_loss: 0.5976 - val_accuracy: 0.8350\n",
            "Epoch 73/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0128 - accuracy: 1.0000 - val_loss: 0.5948 - val_accuracy: 0.8155\n",
            "Epoch 74/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0148 - accuracy: 1.0000 - val_loss: 0.6341 - val_accuracy: 0.8350\n",
            "Epoch 75/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0087 - accuracy: 1.0000 - val_loss: 0.6658 - val_accuracy: 0.8155\n",
            "Epoch 76/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0125 - accuracy: 1.0000 - val_loss: 0.6105 - val_accuracy: 0.8252\n",
            "Epoch 77/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0136 - accuracy: 1.0000 - val_loss: 0.6013 - val_accuracy: 0.8155\n",
            "Epoch 78/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0123 - accuracy: 1.0000 - val_loss: 0.6106 - val_accuracy: 0.8252\n",
            "Epoch 79/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0128 - accuracy: 0.9976 - val_loss: 0.6393 - val_accuracy: 0.8252\n",
            "Epoch 80/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0130 - accuracy: 1.0000 - val_loss: 0.6205 - val_accuracy: 0.8252\n",
            "Epoch 81/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0095 - accuracy: 1.0000 - val_loss: 0.6937 - val_accuracy: 0.8155\n",
            "5/5 [==============================] - 1s 17ms/step - loss: 0.4414 - accuracy: 0.8372\n",
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 216, 216)]        0         \n",
            "                                                                 \n",
            " bidirectional (Bidirectiona  (None, 216, 128)         143872    \n",
            " l)                                                              \n",
            "                                                                 \n",
            " tf.compat.v1.transpose (TFO  (None, 128, 216)         0         \n",
            " pLambda)                                                        \n",
            "                                                                 \n",
            " attention (attention)       (None, 128, 216)          344       \n",
            "                                                                 \n",
            " tf.compat.v1.transpose_1 (T  (None, 216, 128)         0         \n",
            " FOpLambda)                                                      \n",
            "                                                                 \n",
            " global_average_pooling1d (G  (None, 128)              0         \n",
            " lobalAveragePooling1D)                                          \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 144,216\n",
            "Trainable params: 144,216\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_3 (InputLayer)        [(None, 216, 216)]        0         \n",
            "                                                                 \n",
            " bidirectional_1 (Bidirectio  (None, 216, 128)         108288    \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " tf.compat.v1.transpose_2 (T  (None, 128, 216)         0         \n",
            " FOpLambda)                                                      \n",
            "                                                                 \n",
            " attention_1 (attention)     (None, 128, 216)          344       \n",
            "                                                                 \n",
            " tf.compat.v1.transpose_3 (T  (None, 216, 128)         0         \n",
            " FOpLambda)                                                      \n",
            "                                                                 \n",
            " global_average_pooling1d_1   (None, 128)              0         \n",
            " (GlobalAveragePooling1D)                                        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 108,632\n",
            "Trainable params: 108,632\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_5 (InputLayer)        [(None, 256)]             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 64)                16448     \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 64)               256       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " activation (Activation)     (None, 64)                0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 64)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 11)                715       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 17,419\n",
            "Trainable params: 17,291\n",
            "Non-trainable params: 128\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/100\n",
            "13/13 [==============================] - 6s 158ms/step - loss: 2.2046 - accuracy: 0.3438 - val_loss: 2.3798 - val_accuracy: 0.3204\n",
            "Epoch 2/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 1.7730 - accuracy: 0.4688 - val_loss: 2.3469 - val_accuracy: 0.4175\n",
            "Epoch 3/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 1.4657 - accuracy: 0.5048 - val_loss: 2.3138 - val_accuracy: 0.4854\n",
            "Epoch 4/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 1.2455 - accuracy: 0.5938 - val_loss: 2.2771 - val_accuracy: 0.4951\n",
            "Epoch 5/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 1.1092 - accuracy: 0.5938 - val_loss: 2.2425 - val_accuracy: 0.5146\n",
            "Epoch 6/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 1.0345 - accuracy: 0.6346 - val_loss: 2.2119 - val_accuracy: 0.6408\n",
            "Epoch 7/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.9529 - accuracy: 0.6370 - val_loss: 2.1862 - val_accuracy: 0.7184\n",
            "Epoch 8/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.8730 - accuracy: 0.6875 - val_loss: 2.1526 - val_accuracy: 0.7087\n",
            "Epoch 9/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.7931 - accuracy: 0.7139 - val_loss: 2.1105 - val_accuracy: 0.7184\n",
            "Epoch 10/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.7888 - accuracy: 0.7115 - val_loss: 2.0824 - val_accuracy: 0.7282\n",
            "Epoch 11/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.7553 - accuracy: 0.7212 - val_loss: 2.0438 - val_accuracy: 0.7670\n",
            "Epoch 12/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.6877 - accuracy: 0.7524 - val_loss: 1.9997 - val_accuracy: 0.8058\n",
            "Epoch 13/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.6473 - accuracy: 0.7909 - val_loss: 1.9671 - val_accuracy: 0.7670\n",
            "Epoch 14/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.6460 - accuracy: 0.7548 - val_loss: 1.9334 - val_accuracy: 0.8252\n",
            "Epoch 15/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.6099 - accuracy: 0.7620 - val_loss: 1.8975 - val_accuracy: 0.8155\n",
            "Epoch 16/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.5720 - accuracy: 0.7885 - val_loss: 1.8605 - val_accuracy: 0.8641\n",
            "Epoch 17/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.5349 - accuracy: 0.8125 - val_loss: 1.8075 - val_accuracy: 0.8447\n",
            "Epoch 18/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.5441 - accuracy: 0.7981 - val_loss: 1.7667 - val_accuracy: 0.8350\n",
            "Epoch 19/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.5322 - accuracy: 0.8125 - val_loss: 1.7320 - val_accuracy: 0.8447\n",
            "Epoch 20/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.4798 - accuracy: 0.8293 - val_loss: 1.6676 - val_accuracy: 0.8447\n",
            "Epoch 21/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.4694 - accuracy: 0.8630 - val_loss: 1.6394 - val_accuracy: 0.8447\n",
            "Epoch 22/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.4021 - accuracy: 0.8870 - val_loss: 1.5759 - val_accuracy: 0.8447\n",
            "Epoch 23/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.4101 - accuracy: 0.8726 - val_loss: 1.5338 - val_accuracy: 0.8544\n",
            "Epoch 24/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.3607 - accuracy: 0.8990 - val_loss: 1.4607 - val_accuracy: 0.8835\n",
            "Epoch 25/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.3989 - accuracy: 0.8774 - val_loss: 1.4094 - val_accuracy: 0.8835\n",
            "Epoch 26/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.3561 - accuracy: 0.8966 - val_loss: 1.3603 - val_accuracy: 0.8058\n",
            "Epoch 27/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.3133 - accuracy: 0.9327 - val_loss: 1.2967 - val_accuracy: 0.8544\n",
            "Epoch 28/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.2789 - accuracy: 0.9375 - val_loss: 1.2159 - val_accuracy: 0.8641\n",
            "Epoch 29/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.2575 - accuracy: 0.9519 - val_loss: 1.2077 - val_accuracy: 0.8447\n",
            "Epoch 30/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.2388 - accuracy: 0.9231 - val_loss: 1.0927 - val_accuracy: 0.8447\n",
            "Epoch 31/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.2223 - accuracy: 0.9567 - val_loss: 1.0741 - val_accuracy: 0.8641\n",
            "Epoch 32/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.1897 - accuracy: 0.9663 - val_loss: 0.9722 - val_accuracy: 0.8544\n",
            "Epoch 33/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.1707 - accuracy: 0.9688 - val_loss: 0.9312 - val_accuracy: 0.8738\n",
            "Epoch 34/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.1560 - accuracy: 0.9760 - val_loss: 0.8328 - val_accuracy: 0.8447\n",
            "Epoch 35/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.1468 - accuracy: 0.9808 - val_loss: 0.7801 - val_accuracy: 0.8738\n",
            "Epoch 36/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.1340 - accuracy: 0.9808 - val_loss: 0.7792 - val_accuracy: 0.8350\n",
            "Epoch 37/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.1178 - accuracy: 0.9880 - val_loss: 0.7224 - val_accuracy: 0.8447\n",
            "Epoch 38/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.1058 - accuracy: 0.9880 - val_loss: 0.6546 - val_accuracy: 0.8350\n",
            "Epoch 39/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0928 - accuracy: 0.9952 - val_loss: 0.6150 - val_accuracy: 0.8447\n",
            "Epoch 40/100\n",
            "13/13 [==============================] - 1s 46ms/step - loss: 0.0835 - accuracy: 0.9952 - val_loss: 0.5806 - val_accuracy: 0.8155\n",
            "Epoch 41/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0722 - accuracy: 0.9952 - val_loss: 0.5035 - val_accuracy: 0.8738\n",
            "Epoch 42/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0560 - accuracy: 1.0000 - val_loss: 0.4934 - val_accuracy: 0.8641\n",
            "Epoch 43/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0506 - accuracy: 1.0000 - val_loss: 0.4714 - val_accuracy: 0.8447\n",
            "Epoch 44/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0516 - accuracy: 0.9952 - val_loss: 0.4393 - val_accuracy: 0.8544\n",
            "Epoch 45/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0505 - accuracy: 1.0000 - val_loss: 0.4347 - val_accuracy: 0.8447\n",
            "Epoch 46/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0455 - accuracy: 1.0000 - val_loss: 0.4221 - val_accuracy: 0.8447\n",
            "Epoch 47/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0437 - accuracy: 1.0000 - val_loss: 0.3955 - val_accuracy: 0.8544\n",
            "Epoch 48/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0329 - accuracy: 1.0000 - val_loss: 0.4188 - val_accuracy: 0.8350\n",
            "Epoch 49/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0344 - accuracy: 1.0000 - val_loss: 0.3749 - val_accuracy: 0.8544\n",
            "Epoch 50/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0354 - accuracy: 1.0000 - val_loss: 0.3694 - val_accuracy: 0.8641\n",
            "Epoch 51/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0305 - accuracy: 0.9976 - val_loss: 0.3687 - val_accuracy: 0.8544\n",
            "Epoch 52/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0280 - accuracy: 1.0000 - val_loss: 0.4117 - val_accuracy: 0.8447\n",
            "Epoch 53/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0279 - accuracy: 0.9976 - val_loss: 0.4126 - val_accuracy: 0.8544\n",
            "Epoch 54/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0284 - accuracy: 1.0000 - val_loss: 0.3812 - val_accuracy: 0.8544\n",
            "Epoch 55/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0267 - accuracy: 0.9976 - val_loss: 0.3513 - val_accuracy: 0.8544\n",
            "Epoch 56/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0242 - accuracy: 1.0000 - val_loss: 0.3384 - val_accuracy: 0.8544\n",
            "Epoch 57/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0203 - accuracy: 1.0000 - val_loss: 0.3572 - val_accuracy: 0.8350\n",
            "Epoch 58/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0233 - accuracy: 1.0000 - val_loss: 0.3634 - val_accuracy: 0.8350\n",
            "Epoch 59/100\n",
            "13/13 [==============================] - 1s 46ms/step - loss: 0.0197 - accuracy: 1.0000 - val_loss: 0.3659 - val_accuracy: 0.8544\n",
            "Epoch 60/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0193 - accuracy: 1.0000 - val_loss: 0.3638 - val_accuracy: 0.8738\n",
            "Epoch 61/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0171 - accuracy: 1.0000 - val_loss: 0.3916 - val_accuracy: 0.8641\n",
            "Epoch 62/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0217 - accuracy: 1.0000 - val_loss: 0.4041 - val_accuracy: 0.8544\n",
            "Epoch 63/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0203 - accuracy: 1.0000 - val_loss: 0.3921 - val_accuracy: 0.8447\n",
            "Epoch 64/100\n",
            "13/13 [==============================] - 1s 46ms/step - loss: 0.0199 - accuracy: 1.0000 - val_loss: 0.4372 - val_accuracy: 0.8350\n",
            "Epoch 65/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0188 - accuracy: 1.0000 - val_loss: 0.4398 - val_accuracy: 0.8544\n",
            "Epoch 66/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0174 - accuracy: 1.0000 - val_loss: 0.4264 - val_accuracy: 0.8350\n",
            "Epoch 67/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0150 - accuracy: 1.0000 - val_loss: 0.3528 - val_accuracy: 0.8835\n",
            "Epoch 68/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0130 - accuracy: 1.0000 - val_loss: 0.3582 - val_accuracy: 0.8544\n",
            "Epoch 69/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0147 - accuracy: 1.0000 - val_loss: 0.4274 - val_accuracy: 0.8544\n",
            "Epoch 70/100\n",
            "13/13 [==============================] - 1s 46ms/step - loss: 0.0129 - accuracy: 1.0000 - val_loss: 0.4990 - val_accuracy: 0.8447\n",
            "Epoch 71/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0150 - accuracy: 1.0000 - val_loss: 0.4517 - val_accuracy: 0.8544\n",
            "Epoch 72/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0110 - accuracy: 1.0000 - val_loss: 0.4707 - val_accuracy: 0.8544\n",
            "Epoch 73/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0109 - accuracy: 1.0000 - val_loss: 0.4577 - val_accuracy: 0.8447\n",
            "Epoch 74/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0107 - accuracy: 1.0000 - val_loss: 0.3891 - val_accuracy: 0.8835\n",
            "Epoch 75/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0116 - accuracy: 1.0000 - val_loss: 0.4096 - val_accuracy: 0.8544\n",
            "Epoch 76/100\n",
            "13/13 [==============================] - 1s 46ms/step - loss: 0.0110 - accuracy: 1.0000 - val_loss: 0.4226 - val_accuracy: 0.8544\n",
            "Epoch 77/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0096 - accuracy: 1.0000 - val_loss: 0.4847 - val_accuracy: 0.8447\n",
            "Epoch 78/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0113 - accuracy: 1.0000 - val_loss: 0.4582 - val_accuracy: 0.8641\n",
            "Epoch 79/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0152 - accuracy: 1.0000 - val_loss: 0.3773 - val_accuracy: 0.8641\n",
            "Epoch 80/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0107 - accuracy: 1.0000 - val_loss: 0.4467 - val_accuracy: 0.8544\n",
            "Epoch 81/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0088 - accuracy: 1.0000 - val_loss: 0.4423 - val_accuracy: 0.8544\n",
            "Epoch 82/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0128 - accuracy: 1.0000 - val_loss: 0.4141 - val_accuracy: 0.8544\n",
            "Epoch 83/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0113 - accuracy: 1.0000 - val_loss: 0.4574 - val_accuracy: 0.8641\n",
            "Epoch 84/100\n",
            "13/13 [==============================] - 1s 47ms/step - loss: 0.0070 - accuracy: 1.0000 - val_loss: 0.4620 - val_accuracy: 0.8544\n",
            "Epoch 85/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0098 - accuracy: 1.0000 - val_loss: 0.4752 - val_accuracy: 0.8447\n",
            "Epoch 86/100\n",
            "13/13 [==============================] - 1s 48ms/step - loss: 0.0094 - accuracy: 1.0000 - val_loss: 0.4317 - val_accuracy: 0.8544\n",
            "5/5 [==============================] - 1s 19ms/step - loss: 0.2840 - accuracy: 0.8915\n",
            "[0.8534287333488464]\n",
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 216, 216)]        0         \n",
            "                                                                 \n",
            " bidirectional (Bidirectiona  (None, 216, 128)         143872    \n",
            " l)                                                              \n",
            "                                                                 \n",
            " tf.compat.v1.transpose (TFO  (None, 128, 216)         0         \n",
            " pLambda)                                                        \n",
            "                                                                 \n",
            " attention (attention)       (None, 128, 216)          344       \n",
            "                                                                 \n",
            " tf.compat.v1.transpose_1 (T  (None, 216, 128)         0         \n",
            " FOpLambda)                                                      \n",
            "                                                                 \n",
            " global_average_pooling1d (G  (None, 128)              0         \n",
            " lobalAveragePooling1D)                                          \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 144,216\n",
            "Trainable params: 144,216\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_3 (InputLayer)        [(None, 216, 216)]        0         \n",
            "                                                                 \n",
            " bidirectional_1 (Bidirectio  (None, 216, 128)         108288    \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " tf.compat.v1.transpose_2 (T  (None, 128, 216)         0         \n",
            " FOpLambda)                                                      \n",
            "                                                                 \n",
            " attention_1 (attention)     (None, 128, 216)          344       \n",
            "                                                                 \n",
            " tf.compat.v1.transpose_3 (T  (None, 216, 128)         0         \n",
            " FOpLambda)                                                      \n",
            "                                                                 \n",
            " global_average_pooling1d_1   (None, 128)              0         \n",
            " (GlobalAveragePooling1D)                                        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 108,632\n",
            "Trainable params: 108,632\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_5 (InputLayer)        [(None, 256)]             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 64)                16448     \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 64)               256       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " activation (Activation)     (None, 64)                0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 64)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 11)                715       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 17,419\n",
            "Trainable params: 17,291\n",
            "Non-trainable params: 128\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/100\n",
            "13/13 [==============================] - 7s 152ms/step - loss: 2.1947 - accuracy: 0.3181 - val_loss: 2.3765 - val_accuracy: 0.3592\n",
            "Epoch 2/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 1.7724 - accuracy: 0.4434 - val_loss: 2.3439 - val_accuracy: 0.5049\n",
            "Epoch 3/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 1.4645 - accuracy: 0.5084 - val_loss: 2.3086 - val_accuracy: 0.5340\n",
            "Epoch 4/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 1.2541 - accuracy: 0.5807 - val_loss: 2.2769 - val_accuracy: 0.5631\n",
            "Epoch 5/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 1.1171 - accuracy: 0.6386 - val_loss: 2.2453 - val_accuracy: 0.6117\n",
            "Epoch 6/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 1.0288 - accuracy: 0.6530 - val_loss: 2.2105 - val_accuracy: 0.5728\n",
            "Epoch 7/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.9347 - accuracy: 0.6627 - val_loss: 2.1730 - val_accuracy: 0.5922\n",
            "Epoch 8/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.8304 - accuracy: 0.7470 - val_loss: 2.1349 - val_accuracy: 0.5728\n",
            "Epoch 9/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.7990 - accuracy: 0.7277 - val_loss: 2.1011 - val_accuracy: 0.5922\n",
            "Epoch 10/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.7388 - accuracy: 0.7494 - val_loss: 2.0710 - val_accuracy: 0.5728\n",
            "Epoch 11/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.6905 - accuracy: 0.7614 - val_loss: 2.0338 - val_accuracy: 0.6408\n",
            "Epoch 12/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.6427 - accuracy: 0.8024 - val_loss: 1.9952 - val_accuracy: 0.6408\n",
            "Epoch 13/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.5966 - accuracy: 0.7855 - val_loss: 1.9586 - val_accuracy: 0.6893\n",
            "Epoch 14/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.5636 - accuracy: 0.8048 - val_loss: 1.9096 - val_accuracy: 0.7087\n",
            "Epoch 15/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.5767 - accuracy: 0.7880 - val_loss: 1.8878 - val_accuracy: 0.6990\n",
            "Epoch 16/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.5263 - accuracy: 0.8337 - val_loss: 1.8388 - val_accuracy: 0.6990\n",
            "Epoch 17/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.5035 - accuracy: 0.8361 - val_loss: 1.7880 - val_accuracy: 0.6990\n",
            "Epoch 18/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.4839 - accuracy: 0.8313 - val_loss: 1.7280 - val_accuracy: 0.7476\n",
            "Epoch 19/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.4646 - accuracy: 0.8265 - val_loss: 1.6632 - val_accuracy: 0.7767\n",
            "Epoch 20/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.4382 - accuracy: 0.8482 - val_loss: 1.6139 - val_accuracy: 0.7670\n",
            "Epoch 21/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.4386 - accuracy: 0.8578 - val_loss: 1.5541 - val_accuracy: 0.7767\n",
            "Epoch 22/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.4034 - accuracy: 0.8747 - val_loss: 1.5385 - val_accuracy: 0.7379\n",
            "Epoch 23/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.3930 - accuracy: 0.8602 - val_loss: 1.4832 - val_accuracy: 0.7184\n",
            "Epoch 24/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.3736 - accuracy: 0.8843 - val_loss: 1.4153 - val_accuracy: 0.7767\n",
            "Epoch 25/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.3370 - accuracy: 0.9012 - val_loss: 1.3399 - val_accuracy: 0.7379\n",
            "Epoch 26/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.3326 - accuracy: 0.8988 - val_loss: 1.2786 - val_accuracy: 0.7670\n",
            "Epoch 27/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.3184 - accuracy: 0.9060 - val_loss: 1.2153 - val_accuracy: 0.7864\n",
            "Epoch 28/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.2946 - accuracy: 0.9060 - val_loss: 1.1506 - val_accuracy: 0.7767\n",
            "Epoch 29/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.2581 - accuracy: 0.9205 - val_loss: 1.0778 - val_accuracy: 0.7670\n",
            "Epoch 30/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.2505 - accuracy: 0.9229 - val_loss: 1.0264 - val_accuracy: 0.8058\n",
            "Epoch 31/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.2389 - accuracy: 0.9349 - val_loss: 0.9887 - val_accuracy: 0.7961\n",
            "Epoch 32/100\n",
            "13/13 [==============================] - 1s 46ms/step - loss: 0.2419 - accuracy: 0.9398 - val_loss: 0.9274 - val_accuracy: 0.7767\n",
            "Epoch 33/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.2332 - accuracy: 0.9349 - val_loss: 0.8816 - val_accuracy: 0.8155\n",
            "Epoch 34/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1979 - accuracy: 0.9639 - val_loss: 0.8023 - val_accuracy: 0.7670\n",
            "Epoch 35/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.1794 - accuracy: 0.9614 - val_loss: 0.7772 - val_accuracy: 0.7573\n",
            "Epoch 36/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.1781 - accuracy: 0.9590 - val_loss: 0.7247 - val_accuracy: 0.7767\n",
            "Epoch 37/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1601 - accuracy: 0.9614 - val_loss: 0.7021 - val_accuracy: 0.7670\n",
            "Epoch 38/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.1582 - accuracy: 0.9735 - val_loss: 0.6552 - val_accuracy: 0.7864\n",
            "Epoch 39/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.1201 - accuracy: 0.9735 - val_loss: 0.6393 - val_accuracy: 0.7573\n",
            "Epoch 40/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1283 - accuracy: 0.9807 - val_loss: 0.5637 - val_accuracy: 0.8252\n",
            "Epoch 41/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.1288 - accuracy: 0.9735 - val_loss: 0.6244 - val_accuracy: 0.7767\n",
            "Epoch 42/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.1079 - accuracy: 0.9807 - val_loss: 0.5672 - val_accuracy: 0.7573\n",
            "Epoch 43/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0968 - accuracy: 0.9928 - val_loss: 0.6110 - val_accuracy: 0.7476\n",
            "Epoch 44/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.1245 - accuracy: 0.9783 - val_loss: 0.7831 - val_accuracy: 0.7087\n",
            "Epoch 45/100\n",
            "13/13 [==============================] - 1s 46ms/step - loss: 0.1477 - accuracy: 0.9711 - val_loss: 0.5362 - val_accuracy: 0.7864\n",
            "Epoch 46/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1250 - accuracy: 0.9735 - val_loss: 0.5587 - val_accuracy: 0.7961\n",
            "Epoch 47/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1089 - accuracy: 0.9831 - val_loss: 0.4748 - val_accuracy: 0.7864\n",
            "Epoch 48/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0898 - accuracy: 0.9928 - val_loss: 0.5344 - val_accuracy: 0.7670\n",
            "Epoch 49/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0740 - accuracy: 0.9952 - val_loss: 0.4686 - val_accuracy: 0.7767\n",
            "Epoch 50/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0676 - accuracy: 0.9976 - val_loss: 0.5601 - val_accuracy: 0.7670\n",
            "Epoch 51/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0520 - accuracy: 0.9976 - val_loss: 0.4463 - val_accuracy: 0.7864\n",
            "Epoch 52/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0507 - accuracy: 0.9976 - val_loss: 0.5181 - val_accuracy: 0.7767\n",
            "Epoch 53/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0415 - accuracy: 0.9976 - val_loss: 0.4524 - val_accuracy: 0.8058\n",
            "Epoch 54/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0403 - accuracy: 0.9976 - val_loss: 0.5601 - val_accuracy: 0.7476\n",
            "Epoch 55/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0361 - accuracy: 0.9976 - val_loss: 0.5305 - val_accuracy: 0.7767\n",
            "Epoch 56/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0314 - accuracy: 1.0000 - val_loss: 0.5021 - val_accuracy: 0.8350\n",
            "Epoch 57/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0342 - accuracy: 1.0000 - val_loss: 0.6213 - val_accuracy: 0.7767\n",
            "Epoch 58/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0315 - accuracy: 0.9976 - val_loss: 0.5533 - val_accuracy: 0.7864\n",
            "Epoch 59/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0254 - accuracy: 1.0000 - val_loss: 0.5554 - val_accuracy: 0.8058\n",
            "Epoch 60/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0261 - accuracy: 1.0000 - val_loss: 0.5387 - val_accuracy: 0.8058\n",
            "Epoch 61/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0247 - accuracy: 1.0000 - val_loss: 0.5168 - val_accuracy: 0.8544\n",
            "Epoch 62/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0323 - accuracy: 0.9952 - val_loss: 0.5401 - val_accuracy: 0.8058\n",
            "Epoch 63/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0370 - accuracy: 0.9976 - val_loss: 0.6012 - val_accuracy: 0.8155\n",
            "Epoch 64/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0437 - accuracy: 0.9952 - val_loss: 0.6698 - val_accuracy: 0.7961\n",
            "Epoch 65/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0362 - accuracy: 0.9952 - val_loss: 0.5079 - val_accuracy: 0.8058\n",
            "Epoch 66/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0325 - accuracy: 0.9976 - val_loss: 0.5350 - val_accuracy: 0.8155\n",
            "Epoch 67/100\n",
            "13/13 [==============================] - 1s 46ms/step - loss: 0.0202 - accuracy: 1.0000 - val_loss: 0.5989 - val_accuracy: 0.7961\n",
            "Epoch 68/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0233 - accuracy: 0.9976 - val_loss: 0.5600 - val_accuracy: 0.8058\n",
            "Epoch 69/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0186 - accuracy: 1.0000 - val_loss: 0.5373 - val_accuracy: 0.8252\n",
            "Epoch 70/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0184 - accuracy: 1.0000 - val_loss: 0.6065 - val_accuracy: 0.8058\n",
            "Epoch 71/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0175 - accuracy: 1.0000 - val_loss: 0.6495 - val_accuracy: 0.7961\n",
            "Epoch 72/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0141 - accuracy: 1.0000 - val_loss: 0.5669 - val_accuracy: 0.8058\n",
            "Epoch 73/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0148 - accuracy: 1.0000 - val_loss: 0.6131 - val_accuracy: 0.8058\n",
            "Epoch 74/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0169 - accuracy: 1.0000 - val_loss: 0.6461 - val_accuracy: 0.7864\n",
            "Epoch 75/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0125 - accuracy: 1.0000 - val_loss: 0.6049 - val_accuracy: 0.8155\n",
            "Epoch 76/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0125 - accuracy: 1.0000 - val_loss: 0.6810 - val_accuracy: 0.7864\n",
            "Epoch 77/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0122 - accuracy: 1.0000 - val_loss: 0.5857 - val_accuracy: 0.8155\n",
            "Epoch 78/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0124 - accuracy: 1.0000 - val_loss: 0.6415 - val_accuracy: 0.8058\n",
            "Epoch 79/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0126 - accuracy: 1.0000 - val_loss: 0.6346 - val_accuracy: 0.8058\n",
            "Epoch 80/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0092 - accuracy: 1.0000 - val_loss: 0.6372 - val_accuracy: 0.8155\n",
            "Epoch 81/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0112 - accuracy: 1.0000 - val_loss: 0.6182 - val_accuracy: 0.8155\n",
            "5/5 [==============================] - 2s 17ms/step - loss: 0.5058 - accuracy: 0.7923\n",
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 216, 216)]        0         \n",
            "                                                                 \n",
            " bidirectional (Bidirectiona  (None, 216, 128)         143872    \n",
            " l)                                                              \n",
            "                                                                 \n",
            " tf.compat.v1.transpose (TFO  (None, 128, 216)         0         \n",
            " pLambda)                                                        \n",
            "                                                                 \n",
            " attention (attention)       (None, 128, 216)          344       \n",
            "                                                                 \n",
            " tf.compat.v1.transpose_1 (T  (None, 216, 128)         0         \n",
            " FOpLambda)                                                      \n",
            "                                                                 \n",
            " global_average_pooling1d (G  (None, 128)              0         \n",
            " lobalAveragePooling1D)                                          \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 144,216\n",
            "Trainable params: 144,216\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_3 (InputLayer)        [(None, 216, 216)]        0         \n",
            "                                                                 \n",
            " bidirectional_1 (Bidirectio  (None, 216, 128)         108288    \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " tf.compat.v1.transpose_2 (T  (None, 128, 216)         0         \n",
            " FOpLambda)                                                      \n",
            "                                                                 \n",
            " attention_1 (attention)     (None, 128, 216)          344       \n",
            "                                                                 \n",
            " tf.compat.v1.transpose_3 (T  (None, 216, 128)         0         \n",
            " FOpLambda)                                                      \n",
            "                                                                 \n",
            " global_average_pooling1d_1   (None, 128)              0         \n",
            " (GlobalAveragePooling1D)                                        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 108,632\n",
            "Trainable params: 108,632\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_5 (InputLayer)        [(None, 256)]             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 64)                16448     \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 64)               256       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " activation (Activation)     (None, 64)                0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 64)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 11)                715       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 17,419\n",
            "Trainable params: 17,291\n",
            "Non-trainable params: 128\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/100\n",
            "13/13 [==============================] - 7s 155ms/step - loss: 2.1991 - accuracy: 0.2964 - val_loss: 2.3771 - val_accuracy: 0.3883\n",
            "Epoch 2/100\n",
            "13/13 [==============================] - 1s 46ms/step - loss: 1.7827 - accuracy: 0.4916 - val_loss: 2.3441 - val_accuracy: 0.5437\n",
            "Epoch 3/100\n",
            "13/13 [==============================] - 1s 46ms/step - loss: 1.4627 - accuracy: 0.5566 - val_loss: 2.3069 - val_accuracy: 0.5049\n",
            "Epoch 4/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 1.2145 - accuracy: 0.6217 - val_loss: 2.2690 - val_accuracy: 0.6408\n",
            "Epoch 5/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 1.0773 - accuracy: 0.6434 - val_loss: 2.2313 - val_accuracy: 0.7087\n",
            "Epoch 6/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.9923 - accuracy: 0.6506 - val_loss: 2.2019 - val_accuracy: 0.7379\n",
            "Epoch 7/100\n",
            "13/13 [==============================] - 1s 46ms/step - loss: 0.8805 - accuracy: 0.7060 - val_loss: 2.1682 - val_accuracy: 0.7476\n",
            "Epoch 8/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.8212 - accuracy: 0.7133 - val_loss: 2.1316 - val_accuracy: 0.7379\n",
            "Epoch 9/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.7518 - accuracy: 0.7181 - val_loss: 2.1060 - val_accuracy: 0.7573\n",
            "Epoch 10/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.7064 - accuracy: 0.7349 - val_loss: 2.0594 - val_accuracy: 0.7670\n",
            "Epoch 11/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.6865 - accuracy: 0.7277 - val_loss: 2.0250 - val_accuracy: 0.7573\n",
            "Epoch 12/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.6294 - accuracy: 0.7759 - val_loss: 1.9849 - val_accuracy: 0.7864\n",
            "Epoch 13/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.5918 - accuracy: 0.8000 - val_loss: 1.9598 - val_accuracy: 0.7864\n",
            "Epoch 14/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.5822 - accuracy: 0.8072 - val_loss: 1.9066 - val_accuracy: 0.7961\n",
            "Epoch 15/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.5290 - accuracy: 0.8193 - val_loss: 1.8815 - val_accuracy: 0.7961\n",
            "Epoch 16/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.5765 - accuracy: 0.7807 - val_loss: 1.8288 - val_accuracy: 0.7767\n",
            "Epoch 17/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.5211 - accuracy: 0.8313 - val_loss: 1.8072 - val_accuracy: 0.8252\n",
            "Epoch 18/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.4971 - accuracy: 0.8048 - val_loss: 1.7614 - val_accuracy: 0.7961\n",
            "Epoch 19/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.4438 - accuracy: 0.8554 - val_loss: 1.6933 - val_accuracy: 0.8447\n",
            "Epoch 20/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.4472 - accuracy: 0.8386 - val_loss: 1.6424 - val_accuracy: 0.7961\n",
            "Epoch 21/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.4126 - accuracy: 0.8699 - val_loss: 1.5930 - val_accuracy: 0.7961\n",
            "Epoch 22/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.4101 - accuracy: 0.8578 - val_loss: 1.5593 - val_accuracy: 0.8155\n",
            "Epoch 23/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.3872 - accuracy: 0.8675 - val_loss: 1.4948 - val_accuracy: 0.8252\n",
            "Epoch 24/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.3886 - accuracy: 0.8723 - val_loss: 1.4509 - val_accuracy: 0.8350\n",
            "Epoch 25/100\n",
            "13/13 [==============================] - 1s 46ms/step - loss: 0.3486 - accuracy: 0.9012 - val_loss: 1.3818 - val_accuracy: 0.8544\n",
            "Epoch 26/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.3352 - accuracy: 0.9060 - val_loss: 1.3047 - val_accuracy: 0.8447\n",
            "Epoch 27/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.3241 - accuracy: 0.9012 - val_loss: 1.2653 - val_accuracy: 0.8350\n",
            "Epoch 28/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.3048 - accuracy: 0.9108 - val_loss: 1.1865 - val_accuracy: 0.8252\n",
            "Epoch 29/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.2858 - accuracy: 0.9036 - val_loss: 1.1369 - val_accuracy: 0.8641\n",
            "Epoch 30/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.2626 - accuracy: 0.9277 - val_loss: 1.0388 - val_accuracy: 0.8835\n",
            "Epoch 31/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.2581 - accuracy: 0.9253 - val_loss: 1.0262 - val_accuracy: 0.7961\n",
            "Epoch 32/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.2272 - accuracy: 0.9373 - val_loss: 0.9367 - val_accuracy: 0.8447\n",
            "Epoch 33/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.2315 - accuracy: 0.9301 - val_loss: 0.8939 - val_accuracy: 0.8641\n",
            "Epoch 34/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1803 - accuracy: 0.9542 - val_loss: 0.8127 - val_accuracy: 0.8447\n",
            "Epoch 35/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1766 - accuracy: 0.9542 - val_loss: 0.7564 - val_accuracy: 0.8738\n",
            "Epoch 36/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.1743 - accuracy: 0.9590 - val_loss: 0.6954 - val_accuracy: 0.8544\n",
            "Epoch 37/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1459 - accuracy: 0.9783 - val_loss: 0.6481 - val_accuracy: 0.8544\n",
            "Epoch 38/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1192 - accuracy: 0.9855 - val_loss: 0.6076 - val_accuracy: 0.8544\n",
            "Epoch 39/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1433 - accuracy: 0.9711 - val_loss: 0.5610 - val_accuracy: 0.8544\n",
            "Epoch 40/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.1152 - accuracy: 0.9735 - val_loss: 0.5620 - val_accuracy: 0.8544\n",
            "Epoch 41/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1127 - accuracy: 0.9807 - val_loss: 0.5279 - val_accuracy: 0.8447\n",
            "Epoch 42/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1045 - accuracy: 0.9904 - val_loss: 0.4718 - val_accuracy: 0.8544\n",
            "Epoch 43/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0907 - accuracy: 0.9928 - val_loss: 0.4577 - val_accuracy: 0.8641\n",
            "Epoch 44/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0864 - accuracy: 0.9904 - val_loss: 0.4048 - val_accuracy: 0.8641\n",
            "Epoch 45/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0818 - accuracy: 0.9928 - val_loss: 0.4074 - val_accuracy: 0.8252\n",
            "Epoch 46/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0765 - accuracy: 0.9904 - val_loss: 0.3863 - val_accuracy: 0.8641\n",
            "Epoch 47/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0523 - accuracy: 1.0000 - val_loss: 0.3756 - val_accuracy: 0.8447\n",
            "Epoch 48/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0590 - accuracy: 0.9976 - val_loss: 0.3756 - val_accuracy: 0.8544\n",
            "Epoch 49/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0533 - accuracy: 0.9976 - val_loss: 0.3818 - val_accuracy: 0.8641\n",
            "Epoch 50/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0591 - accuracy: 0.9952 - val_loss: 0.3560 - val_accuracy: 0.8447\n",
            "Epoch 51/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0534 - accuracy: 0.9952 - val_loss: 0.3629 - val_accuracy: 0.8641\n",
            "Epoch 52/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0543 - accuracy: 0.9976 - val_loss: 0.3337 - val_accuracy: 0.8641\n",
            "Epoch 53/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0407 - accuracy: 1.0000 - val_loss: 0.3649 - val_accuracy: 0.8544\n",
            "Epoch 54/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0343 - accuracy: 1.0000 - val_loss: 0.3493 - val_accuracy: 0.8544\n",
            "Epoch 55/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0313 - accuracy: 1.0000 - val_loss: 0.3634 - val_accuracy: 0.8447\n",
            "Epoch 56/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0307 - accuracy: 1.0000 - val_loss: 0.3851 - val_accuracy: 0.8544\n",
            "Epoch 57/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0283 - accuracy: 1.0000 - val_loss: 0.3914 - val_accuracy: 0.8544\n",
            "Epoch 58/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0223 - accuracy: 1.0000 - val_loss: 0.3599 - val_accuracy: 0.8641\n",
            "Epoch 59/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0262 - accuracy: 0.9976 - val_loss: 0.3548 - val_accuracy: 0.8738\n",
            "Epoch 60/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0280 - accuracy: 1.0000 - val_loss: 0.3844 - val_accuracy: 0.8447\n",
            "Epoch 61/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0239 - accuracy: 1.0000 - val_loss: 0.3593 - val_accuracy: 0.8447\n",
            "Epoch 62/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0227 - accuracy: 1.0000 - val_loss: 0.3968 - val_accuracy: 0.8544\n",
            "Epoch 63/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0209 - accuracy: 1.0000 - val_loss: 0.3921 - val_accuracy: 0.8447\n",
            "Epoch 64/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0205 - accuracy: 1.0000 - val_loss: 0.3659 - val_accuracy: 0.8544\n",
            "Epoch 65/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0187 - accuracy: 1.0000 - val_loss: 0.3876 - val_accuracy: 0.8447\n",
            "Epoch 66/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0209 - accuracy: 1.0000 - val_loss: 0.3863 - val_accuracy: 0.8544\n",
            "Epoch 67/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0174 - accuracy: 1.0000 - val_loss: 0.3664 - val_accuracy: 0.8641\n",
            "Epoch 68/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0147 - accuracy: 1.0000 - val_loss: 0.3833 - val_accuracy: 0.8544\n",
            "Epoch 69/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0221 - accuracy: 1.0000 - val_loss: 0.3889 - val_accuracy: 0.8544\n",
            "Epoch 70/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0219 - accuracy: 1.0000 - val_loss: 0.4104 - val_accuracy: 0.8641\n",
            "Epoch 71/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0184 - accuracy: 1.0000 - val_loss: 0.4172 - val_accuracy: 0.8641\n",
            "Epoch 72/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0185 - accuracy: 1.0000 - val_loss: 0.4199 - val_accuracy: 0.8350\n",
            "Epoch 73/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0225 - accuracy: 0.9976 - val_loss: 0.4207 - val_accuracy: 0.8544\n",
            "Epoch 74/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0125 - accuracy: 1.0000 - val_loss: 0.4191 - val_accuracy: 0.8544\n",
            "Epoch 75/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0121 - accuracy: 1.0000 - val_loss: 0.4124 - val_accuracy: 0.8544\n",
            "Epoch 76/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0119 - accuracy: 1.0000 - val_loss: 0.4001 - val_accuracy: 0.8641\n",
            "Epoch 77/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0137 - accuracy: 1.0000 - val_loss: 0.3960 - val_accuracy: 0.8641\n",
            "Epoch 78/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0131 - accuracy: 1.0000 - val_loss: 0.3436 - val_accuracy: 0.8544\n",
            "Epoch 79/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0109 - accuracy: 1.0000 - val_loss: 0.3752 - val_accuracy: 0.8641\n",
            "Epoch 80/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0125 - accuracy: 0.9976 - val_loss: 0.3685 - val_accuracy: 0.8738\n",
            "Epoch 81/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0117 - accuracy: 1.0000 - val_loss: 0.4258 - val_accuracy: 0.8641\n",
            "Epoch 82/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0099 - accuracy: 1.0000 - val_loss: 0.4203 - val_accuracy: 0.8350\n",
            "5/5 [==============================] - 2s 17ms/step - loss: 0.3491 - accuracy: 0.8769\n",
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 216, 216)]        0         \n",
            "                                                                 \n",
            " bidirectional (Bidirectiona  (None, 216, 128)         143872    \n",
            " l)                                                              \n",
            "                                                                 \n",
            " tf.compat.v1.transpose (TFO  (None, 128, 216)         0         \n",
            " pLambda)                                                        \n",
            "                                                                 \n",
            " attention (attention)       (None, 128, 216)          344       \n",
            "                                                                 \n",
            " tf.compat.v1.transpose_1 (T  (None, 216, 128)         0         \n",
            " FOpLambda)                                                      \n",
            "                                                                 \n",
            " global_average_pooling1d (G  (None, 128)              0         \n",
            " lobalAveragePooling1D)                                          \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 144,216\n",
            "Trainable params: 144,216\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_3 (InputLayer)        [(None, 216, 216)]        0         \n",
            "                                                                 \n",
            " bidirectional_1 (Bidirectio  (None, 216, 128)         108288    \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " tf.compat.v1.transpose_2 (T  (None, 128, 216)         0         \n",
            " FOpLambda)                                                      \n",
            "                                                                 \n",
            " attention_1 (attention)     (None, 128, 216)          344       \n",
            "                                                                 \n",
            " tf.compat.v1.transpose_3 (T  (None, 216, 128)         0         \n",
            " FOpLambda)                                                      \n",
            "                                                                 \n",
            " global_average_pooling1d_1   (None, 128)              0         \n",
            " (GlobalAveragePooling1D)                                        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 108,632\n",
            "Trainable params: 108,632\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_5 (InputLayer)        [(None, 256)]             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 64)                16448     \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 64)               256       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " activation (Activation)     (None, 64)                0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 64)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 11)                715       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 17,419\n",
            "Trainable params: 17,291\n",
            "Non-trainable params: 128\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/100\n",
            "13/13 [==============================] - 7s 153ms/step - loss: 2.1802 - accuracy: 0.3470 - val_loss: 2.3757 - val_accuracy: 0.3010\n",
            "Epoch 2/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 1.7343 - accuracy: 0.4819 - val_loss: 2.3433 - val_accuracy: 0.5049\n",
            "Epoch 3/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 1.4505 - accuracy: 0.5470 - val_loss: 2.3077 - val_accuracy: 0.5146\n",
            "Epoch 4/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 1.2272 - accuracy: 0.5855 - val_loss: 2.2727 - val_accuracy: 0.5340\n",
            "Epoch 5/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 1.1177 - accuracy: 0.6000 - val_loss: 2.2360 - val_accuracy: 0.5437\n",
            "Epoch 6/100\n",
            "13/13 [==============================] - 1s 46ms/step - loss: 0.9935 - accuracy: 0.6506 - val_loss: 2.2051 - val_accuracy: 0.7087\n",
            "Epoch 7/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.9271 - accuracy: 0.6867 - val_loss: 2.1774 - val_accuracy: 0.7379\n",
            "Epoch 8/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.8610 - accuracy: 0.6988 - val_loss: 2.1429 - val_accuracy: 0.7379\n",
            "Epoch 9/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.7834 - accuracy: 0.6867 - val_loss: 2.1123 - val_accuracy: 0.7573\n",
            "Epoch 10/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.7507 - accuracy: 0.7253 - val_loss: 2.0713 - val_accuracy: 0.7767\n",
            "Epoch 11/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.7036 - accuracy: 0.7446 - val_loss: 2.0281 - val_accuracy: 0.7864\n",
            "Epoch 12/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.6540 - accuracy: 0.7590 - val_loss: 1.9920 - val_accuracy: 0.8252\n",
            "Epoch 13/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.6150 - accuracy: 0.7855 - val_loss: 1.9652 - val_accuracy: 0.8155\n",
            "Epoch 14/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.6180 - accuracy: 0.7614 - val_loss: 1.9209 - val_accuracy: 0.8544\n",
            "Epoch 15/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.5842 - accuracy: 0.8024 - val_loss: 1.8676 - val_accuracy: 0.8544\n",
            "Epoch 16/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.5508 - accuracy: 0.7855 - val_loss: 1.8396 - val_accuracy: 0.8447\n",
            "Epoch 17/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.5439 - accuracy: 0.8120 - val_loss: 1.8037 - val_accuracy: 0.8155\n",
            "Epoch 18/100\n",
            "13/13 [==============================] - 1s 46ms/step - loss: 0.4730 - accuracy: 0.8482 - val_loss: 1.7678 - val_accuracy: 0.8544\n",
            "Epoch 19/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.4769 - accuracy: 0.8506 - val_loss: 1.7191 - val_accuracy: 0.8544\n",
            "Epoch 20/100\n",
            "13/13 [==============================] - 1s 46ms/step - loss: 0.4526 - accuracy: 0.8458 - val_loss: 1.6734 - val_accuracy: 0.8447\n",
            "Epoch 21/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.4420 - accuracy: 0.8651 - val_loss: 1.6503 - val_accuracy: 0.8350\n",
            "Epoch 22/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.3762 - accuracy: 0.8988 - val_loss: 1.5668 - val_accuracy: 0.8544\n",
            "Epoch 23/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.3571 - accuracy: 0.8892 - val_loss: 1.4967 - val_accuracy: 0.8738\n",
            "Epoch 24/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.3051 - accuracy: 0.9229 - val_loss: 1.4458 - val_accuracy: 0.8835\n",
            "Epoch 25/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.3047 - accuracy: 0.9277 - val_loss: 1.3846 - val_accuracy: 0.8738\n",
            "Epoch 26/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.2676 - accuracy: 0.9373 - val_loss: 1.3130 - val_accuracy: 0.8544\n",
            "Epoch 27/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.2420 - accuracy: 0.9446 - val_loss: 1.2523 - val_accuracy: 0.8252\n",
            "Epoch 28/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.2247 - accuracy: 0.9542 - val_loss: 1.1837 - val_accuracy: 0.8350\n",
            "Epoch 29/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.2006 - accuracy: 0.9687 - val_loss: 1.1589 - val_accuracy: 0.8835\n",
            "Epoch 30/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1753 - accuracy: 0.9687 - val_loss: 1.0552 - val_accuracy: 0.8641\n",
            "Epoch 31/100\n",
            "13/13 [==============================] - 1s 46ms/step - loss: 0.1877 - accuracy: 0.9590 - val_loss: 0.9746 - val_accuracy: 0.8544\n",
            "Epoch 32/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1552 - accuracy: 0.9735 - val_loss: 0.9119 - val_accuracy: 0.8738\n",
            "Epoch 33/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1290 - accuracy: 0.9880 - val_loss: 0.8447 - val_accuracy: 0.8835\n",
            "Epoch 34/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1036 - accuracy: 0.9952 - val_loss: 0.7684 - val_accuracy: 0.8738\n",
            "Epoch 35/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.1218 - accuracy: 0.9831 - val_loss: 0.7550 - val_accuracy: 0.8641\n",
            "Epoch 36/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0932 - accuracy: 0.9976 - val_loss: 0.6696 - val_accuracy: 0.8835\n",
            "Epoch 37/100\n",
            "13/13 [==============================] - 1s 46ms/step - loss: 0.1028 - accuracy: 0.9928 - val_loss: 0.6466 - val_accuracy: 0.8738\n",
            "Epoch 38/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0910 - accuracy: 0.9880 - val_loss: 0.5855 - val_accuracy: 0.8835\n",
            "Epoch 39/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0843 - accuracy: 0.9928 - val_loss: 0.5387 - val_accuracy: 0.8835\n",
            "Epoch 40/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0742 - accuracy: 0.9928 - val_loss: 0.6102 - val_accuracy: 0.8350\n",
            "Epoch 41/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0817 - accuracy: 0.9904 - val_loss: 0.4893 - val_accuracy: 0.9029\n",
            "Epoch 42/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.1131 - accuracy: 0.9759 - val_loss: 0.5193 - val_accuracy: 0.8932\n",
            "Epoch 43/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.1201 - accuracy: 0.9807 - val_loss: 0.5461 - val_accuracy: 0.8350\n",
            "Epoch 44/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0779 - accuracy: 1.0000 - val_loss: 0.4982 - val_accuracy: 0.8544\n",
            "Epoch 45/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0726 - accuracy: 0.9952 - val_loss: 0.4390 - val_accuracy: 0.8738\n",
            "Epoch 46/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0564 - accuracy: 0.9976 - val_loss: 0.4077 - val_accuracy: 0.8641\n",
            "Epoch 47/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0494 - accuracy: 1.0000 - val_loss: 0.3702 - val_accuracy: 0.8738\n",
            "Epoch 48/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0428 - accuracy: 0.9976 - val_loss: 0.3967 - val_accuracy: 0.8738\n",
            "Epoch 49/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0450 - accuracy: 0.9976 - val_loss: 0.3792 - val_accuracy: 0.8544\n",
            "Epoch 50/100\n",
            "13/13 [==============================] - 1s 44ms/step - loss: 0.0395 - accuracy: 0.9976 - val_loss: 0.3715 - val_accuracy: 0.8641\n",
            "Epoch 51/100\n",
            "13/13 [==============================] - 1s 45ms/step - loss: 0.0363 - accuracy: 0.9976 - val_loss: 0.4076 - val_accuracy: 0.8544\n",
            "Epoch 52/100\n",
            " 1/13 [=>............................] - ETA: 0s - loss: 0.0245 - accuracy: 1.0000"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-203-07b77b3e7c15>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    197\u001b[0m                     \u001b[0mshuffle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m                     \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mearlyStopping\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m                     validation_data = ((validation_range_doppler) , (spectrogram_validation_labels)))\n\u001b[0m\u001b[1;32m    200\u001b[0m     \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m     test_loss, test_accuracy  = model.evaluate([range_doppler_concat_shuffle_test],                                               [spectrogram_concat_label_shuffle_test],\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1219\u001b[0m               \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtmp_logs\u001b[0m  \u001b[0;31m# No error, now safe to assign to logs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1220\u001b[0m               \u001b[0mend_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep_increment\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1221\u001b[0;31m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mend_step\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1222\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_training\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1223\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36mon_train_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m    434\u001b[0m     \"\"\"\n\u001b[1;32m    435\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_call_train_batch_hooks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 436\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mModeKeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'end'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    437\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mon_test_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_call_batch_hook\u001b[0;34m(self, mode, hook, batch, logs)\u001b[0m\n\u001b[1;32m    293\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_begin_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'end'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_end_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    296\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m       raise ValueError(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_call_batch_end_hook\u001b[0;34m(self, mode, batch, logs)\u001b[0m\n\u001b[1;32m    314\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_times\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_time\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 316\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_hook_helper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhook_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    317\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    318\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_times\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_batches_for_timing_check\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_call_batch_hook_helper\u001b[0;34m(self, hook_name, batch, logs)\u001b[0m\n\u001b[1;32m    352\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mcallback\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    353\u001b[0m       \u001b[0mhook\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m       \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_timing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36mon_train_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m   1030\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1031\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mon_train_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1032\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_update_progbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1033\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1034\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mon_test_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_batch_update_progbar\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m   1102\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1103\u001b[0m       \u001b[0;31m# Only block async when verbose = 1.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1104\u001b[0;31m       \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msync_to_numpy_or_python_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1105\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinalize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/tf_utils.py\u001b[0m in \u001b[0;36msync_to_numpy_or_python_type\u001b[0;34m(tensors)\u001b[0m\n\u001b[1;32m    552\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m  \u001b[0;31m# Don't turn ragged or sparse tensors to NumPy.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 554\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_to_single_numpy_or_python_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    555\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    556\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/nest.py\u001b[0m in \u001b[0;36mmap_structure\u001b[0;34m(func, *structure, **kwargs)\u001b[0m\n\u001b[1;32m    867\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    868\u001b[0m   return pack_sequence_as(\n\u001b[0;32m--> 869\u001b[0;31m       \u001b[0mstructure\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mentries\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    870\u001b[0m       expand_composites=expand_composites)\n\u001b[1;32m    871\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/tf_utils.py\u001b[0m in \u001b[0;36m_to_single_numpy_or_python_type\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m    548\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_to_single_numpy_or_python_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    549\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m       \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    551\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    552\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m  \u001b[0;31m# Don't turn ragged or sparse tensors to NumPy.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mnumpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1147\u001b[0m     \"\"\"\n\u001b[1;32m   1148\u001b[0m     \u001b[0;31m# TODO(slebedev): Consider avoiding a copy for non-CPU or remote tensors.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1149\u001b[0;31m     \u001b[0mmaybe_arr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1150\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_arr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m_numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1113\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1114\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1115\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numpy_internal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1116\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1117\u001b[0m       \u001b[0;32mraise\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0N7v4PDP4Dv2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee5d2334-a420-4df5-9aa2-4c088feb0b99"
      },
      "source": [
        "print(f'Mean test accuracy is {\"{:.3f}\".format(sum(test_accuracy_per_run)/number_of_repeat)},\\\n",
        " max test accuracy is {\"{:.3f}\".format(max(test_accuracy_per_run))},')\n",
        "print(f'Min test accuracy is {\"{:.3f}\".format(min(test_accuracy_per_run))}, \\\n",
        " std of test accuracy is {\"{:.3f}\".format(np.std(test_accuracy_per_run, axis=0))}.')\n",
        "print(f'Time elapsed through all process: {\"{:.2f}\".format(elapsed)} sec')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean test accuracy is 0.858, max test accuracy is 0.863,\n",
            "Min test accuracy is 0.855,  std of test accuracy is 0.003.\n",
            "Time elapsed through all process: 1799.72 sec\n"
          ]
        }
      ]
    }
  ]
}