Range_Doppler_LSTM_v2_datalarla.ipynb

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      "name": "Range_Doppler_LSTM_v2_datalarla.ipynb",
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    "language_info": {
      "name": "python"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mertege/FMCW-Data-Classification-/blob/main/Range_Doppler_LSTM_v2_datalarla.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 sadece range-doppler 2D image datası LSTM ile train edildi.\n",
        "# 2D image'ın column'ları lstm'e sokuldu ve çıktıları dense layer ile classification moduna getirildi.\n",
        "# 3 defa run edildi ve değerler aşağıda paylaşıldı.\n",
        "# Mean test accuracy is 0.971, mean test f1 score is 0.964, max test accuracy is 0.982, max test f1 score is 0.980\n",
        "# Min test accuracy is 0.959, min test f1 score is 0.949, std of test accuracy is 0.010, std of test f1 score is 0.013"
      ],
      "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 Activation, Dropout, Flatten, Dense, BatchNormalization, Normalization, Input, Conv2D, MaxPooling2D, Concatenate, GRU, LSTM, GRU, TimeDistributed, Bidirectional\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": "a35ea361-20f3-4236-8d2a-1d1a80f60daa"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o19JHieQ81cS"
      },
      "source": [
        "# Get Range-Doppler data from\n",
        "range_doppler_fast_resized = scipy.io.loadmat('/content/drive/MyDrive/data/range_doppler_fast_resizedv2.mat')\n",
        "range_doppler_fast_resized = range_doppler_fast_resized['range_doppler_fast_resized']\n",
        "range_doppler_fast_resized = np.transpose(range_doppler_fast_resized, (2, 0, 1))\n",
        "# range_doppler_fast_resized = np.delete(range_doppler_fast_resized,(49), axis=0) # 50th row is deleted since there is no 50th row in spectrogram fast data.\n",
        "range_doppler_fast_label = scipy.io.loadmat('/content/drive/MyDrive/data/range_doppler_fast_label.mat')\n",
        "range_doppler_fast_label = range_doppler_fast_label['range_doppler_fast_label']  \n",
        "\n",
        "range_doppler_slow_resized = scipy.io.loadmat('/content/drive/MyDrive/data/range_doppler_slow_resizedv2.mat')\n",
        "range_doppler_slow_resized = range_doppler_slow_resized['range_doppler_slow_resized']\n",
        "range_doppler_slow_resized = np.transpose(range_doppler_slow_resized, (2, 0, 1))\n",
        "range_doppler_slow_label = scipy.io.loadmat('/content/drive/MyDrive/data/range_doppler_slow_label.mat')\n",
        "range_doppler_slow_label = range_doppler_slow_label['range_doppler_slow_label']  \n",
        "\n",
        "range_doppler_slow_pocket_resized = scipy.io.loadmat('/content/drive/MyDrive/data/range_doppler_slow_pocket_resizedv2.mat')\n",
        "range_doppler_slow_pocket_resized = range_doppler_slow_pocket_resized['range_doppler_slow_pocket_resized']\n",
        "range_doppler_slow_pocket_resized = np.transpose(range_doppler_slow_pocket_resized, (2, 0, 1))\n",
        "range_doppler_pocket_label = scipy.io.loadmat('/content/drive/MyDrive/data/range_doppler_pocket_label.mat')\n",
        "range_doppler_pocket_label = range_doppler_pocket_label['range_doppler_pocket_label']  \n",
        "# Get Range-Doppler data from\n",
        "spectrogram_fast_resized = scipy.io.loadmat('/content/drive/MyDrive/data/spectrogram_fast_resized.mat')\n",
        "spectrogram_fast_resized = spectrogram_fast_resized['spectrogram_fast_resized']\n",
        "spectrogram_fast_resized = np.transpose(spectrogram_fast_resized, (2, 0, 1))\n",
        "spectrogram_fast_label = scipy.io.loadmat('/content/drive/MyDrive/data/spectrogram_fast_label.mat')\n",
        "spectrogram_fast_label = spectrogram_fast_label['spectrogram_fast_label']  \n",
        "\n",
        "spectrogram_slow_resized = scipy.io.loadmat('/content/drive/MyDrive/data/spectrogram_slow_resized.mat')\n",
        "spectrogram_slow_resized = spectrogram_slow_resized['spectrogram_slow_resized']\n",
        "spectrogram_slow_resized = np.transpose(spectrogram_slow_resized, (2, 0, 1))\n",
        "spectrogram_slow_label = scipy.io.loadmat('/content/drive/MyDrive/data/spectrogram_slow_label.mat')\n",
        "spectrogram_slow_label = spectrogram_slow_label['spectrogram_slow_label']  \n",
        "\n",
        "spectrogram_slow_pocket_resized = scipy.io.loadmat('/content/drive/MyDrive/data/spectrogram_slow_pocket_resized.mat')\n",
        "spectrogram_slow_pocket_resized = spectrogram_slow_pocket_resized['spectrogram_slow_pocket_resized']\n",
        "spectrogram_slow_pocket_resized = np.transpose(spectrogram_slow_pocket_resized, (2, 0, 1))\n",
        "spectrogram_slow_pocket_label = scipy.io.loadmat('/content/drive/MyDrive/data/spectrogram_slow_pocket_label.mat')\n",
        "spectrogram_slow_pocket_label = spectrogram_slow_pocket_label['spectrogram_slow_pocket_label']  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wFp_PErnMLN2"
      },
      "source": [
        "# Concat range-doppler data\n",
        "range_doppler_concat = np.concatenate((range_doppler_fast_resized,range_doppler_slow_resized),axis=0)\n",
        "range_doppler_concat = np.concatenate((range_doppler_concat,range_doppler_slow_pocket_resized),axis=0)\n",
        "range_doppler_concat = range_doppler_concat[:,:,:,np.newaxis] \n",
        "range_doppler_concat_label = np.zeros((range_doppler_concat.shape[0],1))\n",
        "range_doppler_concat_label[:range_doppler_fast_resized.shape[0],:] = 1\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 = np.concatenate((spectrogram_fast_resized,spectrogram_slow_resized),axis=0)\n",
        "spectrogram_concat = np.concatenate((spectrogram_concat,spectrogram_slow_pocket_resized),axis=0)\n",
        "spectrogram_concat = spectrogram_concat[:,:,:,np.newaxis] \n",
        "spectrogram_concat_label = np.zeros((spectrogram_concat.shape[0],1))\n",
        "spectrogram_concat_label[:spectrogram_fast_resized.shape[0],:] = 1\n",
        "# Shuffle concat range doppler\n",
        "# shuffle_indx = random.sample(range(0, spectrogram_concat.shape[0]), spectrogram_concat.shape[0]) # split validation data\n",
        "spectrogram_concat_shuffle = spectrogram_concat[shuffle_indx,:,:,:]\n",
        "spectrogram_concat_label_shuffle = spectrogram_concat_label[shuffle_indx,:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_1aBNkjoLCvI"
      },
      "source": [
        "# ---------------- Augmente and shuffle (train and test) data data ----------------\n",
        "data_augmentation = tf.keras.Sequential([\n",
        "  layers.RandomFlip(\"horizontal_and_vertical\"),\n",
        "  layers.RandomRotation(0.1),\n",
        "])\n",
        "\n",
        "\n",
        "def mixup_augmentation(images,range_doppler_training_data, labels, repeat_of_mixup, alpha=0.2):\n",
        "    batch_size = images.shape[0]\n",
        "    concat_images = np.zeros((batch_size*(repeat_of_mixup+1),images.shape[1],images.shape[2],images.shape[3]))\n",
        "    concat_images_range_doppler = np.zeros((batch_size*(repeat_of_mixup+1),range_doppler_training_data.shape[1],\\\n",
        "                                            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",
        "    for ii in range(repeat_of_mixup):\n",
        "      # shuffle train dataset\n",
        "      shuffle_indx_1 = random.sample(range(0, images.shape[0]), images.shape[0]) # split validation data\n",
        "      images_shuffled_1 = images[shuffle_indx_1,:,:,:]\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, images.shape[0]), images.shape[0]) # split validation data\n",
        "      images_shuffled_2 = images[shuffle_indx_2,:,:,:]\n",
        "      range_doppler_training_data_shuffled_2 = range_doppler_training_data[shuffle_indx_2,:,:,:]\n",
        "      labels_shuffled_2 = labels[shuffle_indx_2,:]\n",
        "\n",
        "      # Sample lambda and reshape it to do the mixup\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",
        "      # Perform mixup on both images and labels by combining a pair of images/labels\n",
        "      images_mixup = images_shuffled_1 * x_l + images_shuffled_2 * (1 - x_l)\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",
        "      concat_images[ii*batch_size:(ii+1)*batch_size,:,:,:] = images_mixup\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[repeat_of_mixup*batch_size:,:,:,:] = images\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,concat_images_range_doppler, concat_label)\n",
        "def split_and_augmentation_of_training(spectrogram_concat_shuffle_train,range_doppler_concat_shuffle_train,range_doppler_concat_label_shuffle_train,\\\n",
        "                                       repeat_of_mixup, augmentation_enable):\n",
        "  # ---------------- Parameters ----------------\n",
        "  repeat_of_augmentation_for_fast = 1\n",
        "  repeat_of_augmentation_for_slow = np.floor(repeat_of_augmentation_for_fast/2)\n",
        "  repeat_of_augmentation_for_slow = int(repeat_of_augmentation_for_slow)\n",
        "  # size_of_validation = 30\n",
        "  alpha = 0.2\n",
        "  dummy_label = np.zeros((spectrogram_concat_shuffle_train.shape[0],1))\n",
        "  for randomlist_for_train_indx, randomlist_for_validation_indx in kfold.split(spectrogram_concat_shuffle_train,dummy_label):   \n",
        "    randomlist_for_validation_indx\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_data = spectrogram_concat_shuffle_train[randomlist_for_validation_indx,:,:,:]\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_data = spectrogram_concat_shuffle_train[randomlist_for_train_indx,:,:,:]\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",
        "  # Rotate Augmentation\n",
        "  # get slow and fast indexes of training data\n",
        "  slow_indexes = np.where(spectrogram_training_labels == 0)[0]\n",
        "  fast_indexes = np.delete(range(0, spectrogram_training_labels.shape[0]), slow_indexes)  \n",
        "\n",
        "  slow_spectrograms_train = spectrogram_training_data[slow_indexes,:,:,:]\n",
        "  size_of_samples_slow = slow_spectrograms_train.shape[0]\n",
        "\n",
        "  fast_spectrograms_train = spectrogram_training_data[fast_indexes,:,:,:]  \n",
        "  size_of_samples_fast = fast_spectrograms_train.shape[0]\n",
        "\n",
        "  slow_range_train = range_doppler_training_data[slow_indexes,:,:,:]\n",
        "  fast_range_train = range_doppler_training_data[fast_indexes,:,:,:]  \n",
        "\n",
        "  if augmentation_enable == True: \n",
        "    # ---------------- Augmente Train Data for Fast ----------------\n",
        "    augmented_image_fast = np.zeros((size_of_samples_fast*repeat_of_augmentation_for_fast,fast_spectrograms_train.shape[1],fast_spectrograms_train.shape[2],1))\n",
        "    spectrograms_fast_label = np.ones((size_of_samples_fast*(repeat_of_augmentation_for_fast+1),1))\n",
        "    # augmented_image_fast = np.flip(fast_spectrograms_train,axis=2)\n",
        "    for jj in range(repeat_of_augmentation_for_fast):\n",
        "      for ii in range(size_of_samples_fast):\n",
        "        augmented_image_fast[size_of_samples_fast*jj+ii,:,:,:] = data_augmentation(fast_spectrograms_train[ii,:,:,:])\n",
        "    augmented_image_fast = np.concatenate((augmented_image_fast,fast_spectrograms_train),axis=0)   \n",
        "\n",
        "    augmented_image_fast_range = np.zeros((size_of_samples_fast*repeat_of_augmentation_for_fast,fast_range_train.shape[1],fast_range_train.shape[2],1))\n",
        "    # augmented_image_fast_range  = np.flip(fast_range_train,axis=2)\n",
        "    for jj in range(repeat_of_augmentation_for_fast):\n",
        "      for ii in range(size_of_samples_fast):\n",
        "        augmented_image_fast_range[size_of_samples_fast*jj+ii,:,:,:]  =data_augmentation(fast_range_train[ii,:,:,:])\n",
        "    augmented_image_fast_range = np.concatenate((augmented_image_fast_range,fast_range_train),axis=0)   \n",
        "\n",
        "    # ---------------- Augmente Train Data for Slow ----------------\n",
        "    augmented_image_slow = slow_spectrograms_train\n",
        "    augmented_image_slow_range = slow_range_train\n",
        "\n",
        "  else:\n",
        "    augmented_image_fast = fast_spectrograms_train\n",
        "    augmented_image_slow = slow_spectrograms_train\n",
        "    augmented_image_fast_range = fast_range_train\n",
        "    augmented_image_slow_range = slow_range_train\n",
        "    spectrograms_fast_label = np.ones((size_of_samples_fast,1))\n",
        "    \n",
        "  spectrograms_slow_label = np.zeros((size_of_samples_slow,1))\n",
        "\n",
        "  spectrogram_training_data = np.concatenate((augmented_image_fast,augmented_image_slow),axis=0)\n",
        "  range_doppler_training_data = np.concatenate((augmented_image_fast_range,augmented_image_slow_range),axis=0)\n",
        "  spectrogram_training_labels = np.concatenate((spectrograms_fast_label,spectrograms_slow_label),axis=0)\n",
        "\n",
        "  (spectrogram_augmented_image,range_doppler_augmented_image,spectrograms_label)=\\\n",
        "   mixup_augmentation(spectrogram_training_data,range_doppler_training_data, spectrogram_training_labels, repeat_of_mixup, alpha=0.2)\n",
        "\n",
        "  return (spectrogram_augmented_image,range_doppler_augmented_image,spectrograms_label,\\\n",
        "     spectrogram_validation_data,range_doppler_validation_data, spectrogram_validation_labels)\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)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8CUvxggth7ZQ"
      },
      "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",
        "range_doppler_concat_shuffle = np.float32(range_doppler_concat_shuffle)\n",
        "spectrogram_concat_shuffle = np.float32(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)) "
      ],
      "metadata": {
        "id": "38jkSYXM4aey"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rNpkBBIadpPo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5dd43dd5-d3c4-4f69-df94-e9b97ae8bf66"
      },
      "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 = 64\n",
        "dropout_prob_dense = 0.5\n",
        "repeat_of_mixup = 5\n",
        "number_of_repeat = 5\n",
        "lstm_dropout_rate = 0.4\n",
        "unit_number_of_lstm = 16\n",
        "dense_unit_of_lstm_function = 256\n",
        "decoder_dense = 256\n",
        "\n",
        "\n",
        "for repeat_run_number in range(number_of_repeat):\n",
        "  test_accuracy_per_fold = []\n",
        "  f1_score_per_fold = []\n",
        "  if repeat_run_number > 0:\n",
        "    del range_doppler_concat_shuffle_test\n",
        "    del spectrogram_concat_shuffle_test\n",
        "    del range_doppler_augmented_image\n",
        "    del range_doppler_concat_shuffle_train\n",
        "    del spectrogram_concat_shuffle_train\n",
        "    del spectrogram_augmented_image\n",
        "   \n",
        "  for randomlist_for_train_indx, randomlist_for_test_indx in kfold.split(range_doppler_concat_shuffle,range_doppler_concat_label_shuffle):   \n",
        "    gc.collect()\n",
        "    K.clear_session()\n",
        "    \n",
        "    # test data\n",
        "    range_doppler_concat_shuffle_test = range_doppler_concat_shuffle[randomlist_for_test_indx,:,:,:]\n",
        "    spectrogram_concat_shuffle_test = spectrogram_concat_shuffle[randomlist_for_test_indx,:,:,:]\n",
        "    range_doppler_concat_label_shuffle_test = range_doppler_concat_label_shuffle[randomlist_for_test_indx,:]\n",
        "    #train data\n",
        "    range_doppler_concat_shuffle_train = range_doppler_concat_shuffle[randomlist_for_train_indx,:,:,:]\n",
        "    spectrogram_concat_shuffle_train = spectrogram_concat_shuffle[randomlist_for_train_indx,:,:,:]\n",
        "    spectrogram_concat_label_shuffle_train = spectrogram_concat_label_shuffle[randomlist_for_train_indx,:]\n",
        "      # ---------------- MixUp Augmentation ----------------\n",
        "    (spectrogram_augmented_image,range_doppler_augmented_image,spectrogram_concat_label_shuffle_concat,\\\n",
        "     validation_spectrogram,validation_range_doppler, spectrogram_validation_labels)  =\\\n",
        "      split_and_augmentation_of_training(spectrogram_concat_shuffle_train,range_doppler_concat_shuffle_train,\\\n",
        "                                         spectrogram_concat_label_shuffle_train,\\\n",
        "                                         repeat_of_mixup, augmentation_enable)\n",
        "    \n",
        "    # ---------------- Neural Network Architecture ----------------\n",
        "\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 = 0.5))(input)\n",
        "        x = Flatten()(x)\n",
        "        x = BatchNormalization()(x)\n",
        "        x = Dropout(0.5)(x)\n",
        "        x = Dense(dense_unit_of_lstm_function)(x)\n",
        "        x = BatchNormalization()(x)\n",
        "        x = Activation('LeakyReLU')(x)\n",
        "        x = Dropout(dropout_prob_dense)(x)\n",
        "        return Model(input, x)\n",
        "\n",
        "\n",
        "    def decoder_for_concat(input_shape):\n",
        "      input = Input(shape=input_shape)\n",
        "      x = Dense(decoder_dense)(input)\n",
        "      # x = BatchNormalization()(x)\n",
        "      x = Activation('LeakyReLU')(x)\n",
        "      x = Dropout(0.3)(x)\n",
        "      x = Dense(dense_size)(x)\n",
        "      # x = BatchNormalization()(x)\n",
        "      x = Activation('LeakyReLU')(x)\n",
        "      x = Dropout(dropout_prob_dense)(x)\n",
        "      x = Dense(1, activation=\"sigmoid\")(x)\n",
        "      return Model(input, x)\n",
        "\n",
        "    # input_shape = range_doppler_concat_shuffle.shape[1:]\n",
        "    # base_network_range_doppler = encoder_for_range_doppler(input_shape)\n",
        "    # range_doppler_input  = Input(shape=input_shape)\n",
        "    # processed_range_doppler  = base_network_range_doppler(range_doppler_input)\n",
        "\n",
        "    input_shape = range_doppler_concat_shuffle.shape[1:3]\n",
        "    base_network_lstm = lstm_encoder_network_1(input_shape)\n",
        "    range_doppler_input  = Input(shape=input_shape)\n",
        "    processed_lstm  = base_network_lstm(range_doppler_input)\n",
        "\n",
        "    base_decoder_network = decoder_for_concat((processed_lstm.shape[1]))\n",
        "    out = base_decoder_network(processed_lstm)\n",
        "\n",
        "    model = Model(inputs=[range_doppler_input], outputs=[out]) \n",
        "    if repeat_run_number == 0:\n",
        "      print(base_network_lstm.summary())\n",
        "      print(model.summary())\n",
        "    # ---------------- Compile and Fit ----------------\n",
        "    model.compile(loss='binary_crossentropy',\n",
        "                  optimizer='adam',\n",
        "                  metrics=['accuracy'])\n",
        "    \n",
        "    earlyStopping = EarlyStopping(monitor='val_loss', patience=15, verbose=0,restore_best_weights=True, mode='min')\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",
        "    test_loss, test_accuracy  = model.evaluate([range_doppler_concat_shuffle_test],\\\n",
        "                                               [range_doppler_concat_label_shuffle_test],\n",
        "                  batch_size=batch_size)\n",
        "    gc.collect()\n",
        "    # ---------------- Get Test Results ----------------\n",
        "    y_test_predicted = model.predict((range_doppler_concat_shuffle_test), batch_size=batch_size)\n",
        "    # ----- Binarize y_test_predicted values -----\n",
        "    y_test_predicted_binary = np.zeros(y_test_predicted.size)\n",
        "    for ii in range(y_test_predicted.size):\n",
        "      if y_test_predicted[ii] < 0.5:\n",
        "        y_test_predicted_binary[ii] = 0\n",
        "      else:\n",
        "        y_test_predicted_binary[ii] = 1\n",
        "    \n",
        "    test_precision, test_recall, test_f1_score, support = precision_recall_fscore_support(range_doppler_concat_label_shuffle_test, 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",
        "print(f'Mean test accuracy is {\"{:.2f}\".format(sum(test_accuracy_per_run)/number_of_repeat)}, mean test f1 score is {\"{:.2f}\".format(sum(f1_score_per_run)/number_of_repeat)}, \\\n",
        "max test accuracy is {\"{:.2f}\".format(max(test_accuracy_per_run))}, max test f1 score is {\"{:.2f}\".format(max(f1_score_per_run))}, \\\n",
        "min test accuracy is {\"{:.2f}\".format(min(test_accuracy_per_run))}, min test f1 score is {\"{:.2f}\".format(min(f1_score_per_run))}, \\\n",
        "std of test accuracy is {\"{:.2f}\".format(np.std(test_accuracy_per_run, axis=0))}, std of test f1 score is {\"{:.2f}\".format(np.std(f1_score_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, 5120, 52)]        0         \n",
            "                                                                 \n",
            " bidirectional (Bidirectiona  (None, 5120, 32)         8832      \n",
            " l)                                                              \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 163840)            0         \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 163840)           655360    \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 163840)            0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 256)               41943296  \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 256)              1024      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation (Activation)     (None, 256)               0         \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 42,608,512\n",
            "Trainable params: 42,280,320\n",
            "Non-trainable params: 328,192\n",
            "_________________________________________________________________\n",
            "None\n",
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_2 (InputLayer)        [(None, 5120, 52)]        0         \n",
            "                                                                 \n",
            " model (Functional)          (None, 256)               42608512  \n",
            "                                                                 \n",
            " model_1 (Functional)        (None, 1)                 82305     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 42,690,817\n",
            "Trainable params: 42,362,625\n",
            "Non-trainable params: 328,192\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/100\n",
            "28/28 [==============================] - 15s 341ms/step - loss: 0.5668 - accuracy: 0.4566 - val_loss: 0.3469 - val_accuracy: 0.8519\n",
            "Epoch 2/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.4105 - accuracy: 0.5240 - val_loss: 0.2530 - val_accuracy: 0.8889\n",
            "Epoch 3/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.3896 - accuracy: 0.5308 - val_loss: 0.2902 - val_accuracy: 0.9259\n",
            "Epoch 4/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.3453 - accuracy: 0.5422 - val_loss: 0.2659 - val_accuracy: 0.9259\n",
            "Epoch 5/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.3467 - accuracy: 0.5514 - val_loss: 0.2556 - val_accuracy: 0.8889\n",
            "Epoch 6/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.3394 - accuracy: 0.5525 - val_loss: 0.2885 - val_accuracy: 0.8148\n",
            "Epoch 7/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.3257 - accuracy: 0.5537 - val_loss: 0.2111 - val_accuracy: 0.9630\n",
            "Epoch 8/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.3172 - accuracy: 0.5548 - val_loss: 0.2988 - val_accuracy: 0.8519\n",
            "Epoch 9/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.3177 - accuracy: 0.5559 - val_loss: 0.2482 - val_accuracy: 0.9630\n",
            "Epoch 10/100\n",
            "28/28 [==============================] - 8s 300ms/step - loss: 0.3088 - accuracy: 0.5582 - val_loss: 0.1643 - val_accuracy: 0.9259\n",
            "Epoch 11/100\n",
            "28/28 [==============================] - 8s 292ms/step - loss: 0.3276 - accuracy: 0.5582 - val_loss: 0.3950 - val_accuracy: 0.8148\n",
            "Epoch 12/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.3033 - accuracy: 0.5571 - val_loss: 0.2872 - val_accuracy: 0.8519\n",
            "Epoch 13/100\n",
            "28/28 [==============================] - 8s 292ms/step - loss: 0.3038 - accuracy: 0.5582 - val_loss: 0.3074 - val_accuracy: 0.8519\n",
            "Epoch 14/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2916 - accuracy: 0.5582 - val_loss: 0.1824 - val_accuracy: 0.9630\n",
            "Epoch 15/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2885 - accuracy: 0.5594 - val_loss: 0.2048 - val_accuracy: 0.9630\n",
            "Epoch 16/100\n",
            "28/28 [==============================] - 8s 292ms/step - loss: 0.2925 - accuracy: 0.5571 - val_loss: 0.2221 - val_accuracy: 0.9259\n",
            "Epoch 17/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2847 - accuracy: 0.5594 - val_loss: 0.1869 - val_accuracy: 0.9259\n",
            "Epoch 18/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2858 - accuracy: 0.5594 - val_loss: 0.1763 - val_accuracy: 0.9630\n",
            "Epoch 19/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2874 - accuracy: 0.5582 - val_loss: 0.1572 - val_accuracy: 0.9630\n",
            "Epoch 20/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2844 - accuracy: 0.5594 - val_loss: 0.1949 - val_accuracy: 0.9630\n",
            "Epoch 21/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.2874 - accuracy: 0.5594 - val_loss: 0.1436 - val_accuracy: 0.9630\n",
            "Epoch 22/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2744 - accuracy: 0.5594 - val_loss: 0.1938 - val_accuracy: 0.9259\n",
            "Epoch 23/100\n",
            "28/28 [==============================] - 8s 292ms/step - loss: 0.2811 - accuracy: 0.5594 - val_loss: 0.1615 - val_accuracy: 0.9630\n",
            "Epoch 24/100\n",
            "28/28 [==============================] - 8s 292ms/step - loss: 0.2743 - accuracy: 0.5594 - val_loss: 0.1630 - val_accuracy: 0.9630\n",
            "Epoch 25/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2873 - accuracy: 0.5594 - val_loss: 0.1469 - val_accuracy: 0.9630\n",
            "Epoch 26/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2849 - accuracy: 0.5582 - val_loss: 0.2160 - val_accuracy: 0.9630\n",
            "Epoch 27/100\n",
            "28/28 [==============================] - 8s 292ms/step - loss: 0.2767 - accuracy: 0.5594 - val_loss: 0.1497 - val_accuracy: 0.9630\n",
            "Epoch 28/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2746 - accuracy: 0.5594 - val_loss: 0.1979 - val_accuracy: 0.9259\n",
            "Epoch 29/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2715 - accuracy: 0.5594 - val_loss: 0.2179 - val_accuracy: 0.9259\n",
            "Epoch 30/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2735 - accuracy: 0.5594 - val_loss: 0.2069 - val_accuracy: 0.9630\n",
            "Epoch 31/100\n",
            "28/28 [==============================] - 8s 292ms/step - loss: 0.2859 - accuracy: 0.5594 - val_loss: 0.2575 - val_accuracy: 0.9259\n",
            "Epoch 32/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2764 - accuracy: 0.5594 - val_loss: 0.1779 - val_accuracy: 0.9630\n",
            "Epoch 33/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2772 - accuracy: 0.5594 - val_loss: 0.1571 - val_accuracy: 0.9630\n",
            "Epoch 34/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2703 - accuracy: 0.5594 - val_loss: 0.2121 - val_accuracy: 0.9630\n",
            "Epoch 35/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2729 - accuracy: 0.5594 - val_loss: 0.2293 - val_accuracy: 0.9259\n",
            "Epoch 36/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2790 - accuracy: 0.5582 - val_loss: 0.1808 - val_accuracy: 0.9630\n",
            "2/2 [==============================] - 1s 120ms/step - loss: 0.0815 - accuracy: 1.0000\n",
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 5120, 52)]        0         \n",
            "                                                                 \n",
            " bidirectional (Bidirectiona  (None, 5120, 32)         8832      \n",
            " l)                                                              \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 163840)            0         \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 163840)           655360    \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 163840)            0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 256)               41943296  \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 256)              1024      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation (Activation)     (None, 256)               0         \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 42,608,512\n",
            "Trainable params: 42,280,320\n",
            "Non-trainable params: 328,192\n",
            "_________________________________________________________________\n",
            "None\n",
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_2 (InputLayer)        [(None, 5120, 52)]        0         \n",
            "                                                                 \n",
            " model (Functional)          (None, 256)               42608512  \n",
            "                                                                 \n",
            " model_1 (Functional)        (None, 1)                 82305     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 42,690,817\n",
            "Trainable params: 42,362,625\n",
            "Non-trainable params: 328,192\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/100\n",
            "27/27 [==============================] - 12s 328ms/step - loss: 0.5245 - accuracy: 0.4894 - val_loss: 0.3184 - val_accuracy: 0.8519\n",
            "Epoch 2/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.4191 - accuracy: 0.5387 - val_loss: 0.4941 - val_accuracy: 0.7778\n",
            "Epoch 3/100\n",
            "27/27 [==============================] - 8s 298ms/step - loss: 0.3818 - accuracy: 0.5516 - val_loss: 0.2017 - val_accuracy: 0.9630\n",
            "Epoch 4/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.3768 - accuracy: 0.5599 - val_loss: 0.2907 - val_accuracy: 0.8519\n",
            "Epoch 5/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.3336 - accuracy: 0.5704 - val_loss: 0.1714 - val_accuracy: 0.9630\n",
            "Epoch 6/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.3351 - accuracy: 0.5692 - val_loss: 0.1261 - val_accuracy: 0.9630\n",
            "Epoch 7/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.3160 - accuracy: 0.5751 - val_loss: 0.1916 - val_accuracy: 0.8889\n",
            "Epoch 8/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.3190 - accuracy: 0.5704 - val_loss: 0.1302 - val_accuracy: 0.9630\n",
            "Epoch 9/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.3027 - accuracy: 0.5763 - val_loss: 0.2926 - val_accuracy: 0.8519\n",
            "Epoch 10/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.3089 - accuracy: 0.5739 - val_loss: 0.1335 - val_accuracy: 0.9630\n",
            "Epoch 11/100\n",
            "27/27 [==============================] - 8s 298ms/step - loss: 0.3039 - accuracy: 0.5739 - val_loss: 0.0710 - val_accuracy: 1.0000\n",
            "Epoch 12/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.2949 - accuracy: 0.5775 - val_loss: 0.0726 - val_accuracy: 1.0000\n",
            "Epoch 13/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.2857 - accuracy: 0.5763 - val_loss: 0.0797 - val_accuracy: 0.9630\n",
            "Epoch 14/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2842 - accuracy: 0.5775 - val_loss: 0.0625 - val_accuracy: 1.0000\n",
            "Epoch 15/100\n",
            "27/27 [==============================] - 8s 293ms/step - loss: 0.2863 - accuracy: 0.5763 - val_loss: 0.0808 - val_accuracy: 1.0000\n",
            "Epoch 16/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2856 - accuracy: 0.5775 - val_loss: 0.1437 - val_accuracy: 0.9259\n",
            "Epoch 17/100\n",
            "27/27 [==============================] - 8s 293ms/step - loss: 0.2846 - accuracy: 0.5763 - val_loss: 0.0891 - val_accuracy: 1.0000\n",
            "Epoch 18/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.2807 - accuracy: 0.5763 - val_loss: 0.1280 - val_accuracy: 0.9259\n",
            "Epoch 19/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2780 - accuracy: 0.5775 - val_loss: 0.0659 - val_accuracy: 1.0000\n",
            "Epoch 20/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.2807 - accuracy: 0.5775 - val_loss: 0.0913 - val_accuracy: 0.9630\n",
            "Epoch 21/100\n",
            "27/27 [==============================] - 8s 293ms/step - loss: 0.2712 - accuracy: 0.5775 - val_loss: 0.0997 - val_accuracy: 0.9630\n",
            "Epoch 22/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.2746 - accuracy: 0.5775 - val_loss: 0.1067 - val_accuracy: 0.9630\n",
            "Epoch 23/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.2742 - accuracy: 0.5739 - val_loss: 0.0988 - val_accuracy: 0.9630\n",
            "Epoch 24/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.2722 - accuracy: 0.5775 - val_loss: 0.0880 - val_accuracy: 0.9630\n",
            "Epoch 25/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.2645 - accuracy: 0.5775 - val_loss: 0.0835 - val_accuracy: 0.9630\n",
            "Epoch 26/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.2704 - accuracy: 0.5763 - val_loss: 0.0862 - val_accuracy: 0.9630\n",
            "Epoch 27/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.2693 - accuracy: 0.5775 - val_loss: 0.0700 - val_accuracy: 1.0000\n",
            "Epoch 28/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.2669 - accuracy: 0.5775 - val_loss: 0.0767 - val_accuracy: 0.9630\n",
            "Epoch 29/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2590 - accuracy: 0.5775 - val_loss: 0.1544 - val_accuracy: 0.9259\n",
            "2/2 [==============================] - 1s 115ms/step - loss: 0.1080 - accuracy: 1.0000\n",
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 5120, 52)]        0         \n",
            "                                                                 \n",
            " bidirectional (Bidirectiona  (None, 5120, 32)         8832      \n",
            " l)                                                              \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 163840)            0         \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 163840)           655360    \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 163840)            0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 256)               41943296  \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 256)              1024      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation (Activation)     (None, 256)               0         \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 42,608,512\n",
            "Trainable params: 42,280,320\n",
            "Non-trainable params: 328,192\n",
            "_________________________________________________________________\n",
            "None\n",
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_2 (InputLayer)        [(None, 5120, 52)]        0         \n",
            "                                                                 \n",
            " model (Functional)          (None, 256)               42608512  \n",
            "                                                                 \n",
            " model_1 (Functional)        (None, 1)                 82305     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 42,690,817\n",
            "Trainable params: 42,362,625\n",
            "Non-trainable params: 328,192\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/100\n",
            "27/27 [==============================] - 12s 329ms/step - loss: 0.4856 - accuracy: 0.5186 - val_loss: 0.3979 - val_accuracy: 0.7778\n",
            "Epoch 2/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.3798 - accuracy: 0.5664 - val_loss: 0.2849 - val_accuracy: 0.8889\n",
            "Epoch 3/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.3686 - accuracy: 0.5758 - val_loss: 0.4626 - val_accuracy: 0.7037\n",
            "Epoch 4/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.3234 - accuracy: 0.5816 - val_loss: 0.2890 - val_accuracy: 0.8889\n",
            "Epoch 5/100\n",
            "27/27 [==============================] - 8s 293ms/step - loss: 0.3198 - accuracy: 0.5862 - val_loss: 0.4014 - val_accuracy: 0.7037\n",
            "Epoch 6/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.3241 - accuracy: 0.5781 - val_loss: 0.2280 - val_accuracy: 0.8889\n",
            "Epoch 7/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.3046 - accuracy: 0.5886 - val_loss: 0.2619 - val_accuracy: 0.8519\n",
            "Epoch 8/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2990 - accuracy: 0.5932 - val_loss: 0.2218 - val_accuracy: 0.9259\n",
            "Epoch 9/100\n",
            "27/27 [==============================] - 8s 293ms/step - loss: 0.2999 - accuracy: 0.5921 - val_loss: 0.2521 - val_accuracy: 0.8889\n",
            "Epoch 10/100\n",
            "27/27 [==============================] - 8s 298ms/step - loss: 0.2959 - accuracy: 0.5897 - val_loss: 0.2101 - val_accuracy: 0.8889\n",
            "Epoch 11/100\n",
            "27/27 [==============================] - 8s 298ms/step - loss: 0.2844 - accuracy: 0.5944 - val_loss: 0.1848 - val_accuracy: 1.0000\n",
            "Epoch 12/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2809 - accuracy: 0.5944 - val_loss: 0.1744 - val_accuracy: 0.9630\n",
            "Epoch 13/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.2784 - accuracy: 0.5932 - val_loss: 0.1780 - val_accuracy: 0.9630\n",
            "Epoch 14/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2705 - accuracy: 0.5944 - val_loss: 0.1672 - val_accuracy: 0.9259\n",
            "Epoch 15/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2654 - accuracy: 0.5944 - val_loss: 0.2669 - val_accuracy: 0.8519\n",
            "Epoch 16/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2691 - accuracy: 0.5944 - val_loss: 0.2372 - val_accuracy: 0.8889\n",
            "Epoch 17/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.2654 - accuracy: 0.5932 - val_loss: 0.2309 - val_accuracy: 0.8889\n",
            "Epoch 18/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.2682 - accuracy: 0.5932 - val_loss: 0.1845 - val_accuracy: 0.9259\n",
            "Epoch 19/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.2716 - accuracy: 0.5944 - val_loss: 0.2185 - val_accuracy: 0.8889\n",
            "Epoch 20/100\n",
            "27/27 [==============================] - 8s 298ms/step - loss: 0.2662 - accuracy: 0.5944 - val_loss: 0.1628 - val_accuracy: 0.9259\n",
            "Epoch 21/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.2613 - accuracy: 0.5944 - val_loss: 0.2393 - val_accuracy: 0.8889\n",
            "Epoch 22/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.2568 - accuracy: 0.5944 - val_loss: 0.2031 - val_accuracy: 0.9259\n",
            "Epoch 23/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.2559 - accuracy: 0.5944 - val_loss: 0.3280 - val_accuracy: 0.8889\n",
            "Epoch 24/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2596 - accuracy: 0.5932 - val_loss: 0.2635 - val_accuracy: 0.8519\n",
            "Epoch 25/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.2835 - accuracy: 0.5932 - val_loss: 0.3858 - val_accuracy: 0.8519\n",
            "Epoch 26/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2605 - accuracy: 0.5944 - val_loss: 0.2004 - val_accuracy: 0.8889\n",
            "Epoch 27/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.2597 - accuracy: 0.5944 - val_loss: 0.1792 - val_accuracy: 0.9259\n",
            "Epoch 28/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.2541 - accuracy: 0.5944 - val_loss: 0.2451 - val_accuracy: 0.8889\n",
            "Epoch 29/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.2516 - accuracy: 0.5944 - val_loss: 0.2074 - val_accuracy: 0.9259\n",
            "Epoch 30/100\n",
            "27/27 [==============================] - 8s 293ms/step - loss: 0.2517 - accuracy: 0.5944 - val_loss: 0.1729 - val_accuracy: 0.9259\n",
            "Epoch 31/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.2490 - accuracy: 0.5944 - val_loss: 0.2429 - val_accuracy: 0.9259\n",
            "Epoch 32/100\n",
            "27/27 [==============================] - 8s 293ms/step - loss: 0.2528 - accuracy: 0.5944 - val_loss: 0.2485 - val_accuracy: 0.8889\n",
            "Epoch 33/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.2498 - accuracy: 0.5944 - val_loss: 0.2975 - val_accuracy: 0.8519\n",
            "Epoch 34/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2494 - accuracy: 0.5944 - val_loss: 0.3251 - val_accuracy: 0.8519\n",
            "Epoch 35/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2496 - accuracy: 0.5944 - val_loss: 0.2634 - val_accuracy: 0.8519\n",
            "2/2 [==============================] - 1s 114ms/step - loss: 0.1594 - accuracy: 0.9412\n",
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 5120, 52)]        0         \n",
            "                                                                 \n",
            " bidirectional (Bidirectiona  (None, 5120, 32)         8832      \n",
            " l)                                                              \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 163840)            0         \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 163840)           655360    \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 163840)            0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 256)               41943296  \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 256)              1024      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation (Activation)     (None, 256)               0         \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 42,608,512\n",
            "Trainable params: 42,280,320\n",
            "Non-trainable params: 328,192\n",
            "_________________________________________________________________\n",
            "None\n",
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_2 (InputLayer)        [(None, 5120, 52)]        0         \n",
            "                                                                 \n",
            " model (Functional)          (None, 256)               42608512  \n",
            "                                                                 \n",
            " model_1 (Functional)        (None, 1)                 82305     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 42,690,817\n",
            "Trainable params: 42,362,625\n",
            "Non-trainable params: 328,192\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/100\n",
            "28/28 [==============================] - 12s 325ms/step - loss: 0.6110 - accuracy: 0.4326 - val_loss: 0.3394 - val_accuracy: 0.8148\n",
            "Epoch 2/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.4259 - accuracy: 0.5046 - val_loss: 0.2602 - val_accuracy: 0.9259\n",
            "Epoch 3/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.3882 - accuracy: 0.5263 - val_loss: 0.2731 - val_accuracy: 0.9630\n",
            "Epoch 4/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.3632 - accuracy: 0.5297 - val_loss: 0.2070 - val_accuracy: 1.0000\n",
            "Epoch 5/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.3556 - accuracy: 0.5308 - val_loss: 0.2031 - val_accuracy: 1.0000\n",
            "Epoch 6/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.3552 - accuracy: 0.5320 - val_loss: 0.2408 - val_accuracy: 0.9259\n",
            "Epoch 7/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.3413 - accuracy: 0.5342 - val_loss: 0.1819 - val_accuracy: 0.9630\n",
            "Epoch 8/100\n",
            "28/28 [==============================] - 8s 292ms/step - loss: 0.3378 - accuracy: 0.5342 - val_loss: 0.2237 - val_accuracy: 0.9259\n",
            "Epoch 9/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.3365 - accuracy: 0.5365 - val_loss: 0.1699 - val_accuracy: 1.0000\n",
            "Epoch 10/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.3170 - accuracy: 0.5342 - val_loss: 0.1443 - val_accuracy: 1.0000\n",
            "Epoch 11/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.3213 - accuracy: 0.5354 - val_loss: 0.1519 - val_accuracy: 1.0000\n",
            "Epoch 12/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.3131 - accuracy: 0.5365 - val_loss: 0.2622 - val_accuracy: 0.9259\n",
            "Epoch 13/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.3141 - accuracy: 0.5354 - val_loss: 0.1025 - val_accuracy: 1.0000\n",
            "Epoch 14/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.3128 - accuracy: 0.5377 - val_loss: 0.1021 - val_accuracy: 1.0000\n",
            "Epoch 15/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.3041 - accuracy: 0.5388 - val_loss: 0.1224 - val_accuracy: 1.0000\n",
            "Epoch 16/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.3069 - accuracy: 0.5388 - val_loss: 0.0801 - val_accuracy: 1.0000\n",
            "Epoch 17/100\n",
            "28/28 [==============================] - 8s 292ms/step - loss: 0.3213 - accuracy: 0.5342 - val_loss: 0.0964 - val_accuracy: 1.0000\n",
            "Epoch 18/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.3073 - accuracy: 0.5365 - val_loss: 0.0957 - val_accuracy: 1.0000\n",
            "Epoch 19/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2997 - accuracy: 0.5388 - val_loss: 0.1524 - val_accuracy: 0.9630\n",
            "Epoch 20/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.3018 - accuracy: 0.5388 - val_loss: 0.1709 - val_accuracy: 0.9259\n",
            "Epoch 21/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.3144 - accuracy: 0.5388 - val_loss: 0.0581 - val_accuracy: 1.0000\n",
            "Epoch 22/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.3005 - accuracy: 0.5388 - val_loss: 0.1055 - val_accuracy: 1.0000\n",
            "Epoch 23/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2919 - accuracy: 0.5388 - val_loss: 0.1419 - val_accuracy: 1.0000\n",
            "Epoch 24/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2886 - accuracy: 0.5388 - val_loss: 0.1289 - val_accuracy: 1.0000\n",
            "Epoch 25/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2894 - accuracy: 0.5388 - val_loss: 0.0990 - val_accuracy: 1.0000\n",
            "Epoch 26/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2974 - accuracy: 0.5388 - val_loss: 0.0960 - val_accuracy: 1.0000\n",
            "Epoch 27/100\n",
            "28/28 [==============================] - 8s 292ms/step - loss: 0.2848 - accuracy: 0.5388 - val_loss: 0.1134 - val_accuracy: 1.0000\n",
            "Epoch 28/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2906 - accuracy: 0.5388 - val_loss: 0.1079 - val_accuracy: 1.0000\n",
            "Epoch 29/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2907 - accuracy: 0.5388 - val_loss: 0.1970 - val_accuracy: 0.9630\n",
            "Epoch 30/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2923 - accuracy: 0.5388 - val_loss: 0.1318 - val_accuracy: 1.0000\n",
            "Epoch 31/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2879 - accuracy: 0.5388 - val_loss: 0.1011 - val_accuracy: 1.0000\n",
            "Epoch 32/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2855 - accuracy: 0.5388 - val_loss: 0.0785 - val_accuracy: 1.0000\n",
            "Epoch 33/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2857 - accuracy: 0.5388 - val_loss: 0.1087 - val_accuracy: 1.0000\n",
            "Epoch 34/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2870 - accuracy: 0.5388 - val_loss: 0.1189 - val_accuracy: 1.0000\n",
            "Epoch 35/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2895 - accuracy: 0.5388 - val_loss: 0.0961 - val_accuracy: 0.9630\n",
            "Epoch 36/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2914 - accuracy: 0.5377 - val_loss: 0.0871 - val_accuracy: 1.0000\n",
            "2/2 [==============================] - 1s 113ms/step - loss: 0.1168 - accuracy: 0.9412\n",
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 5120, 52)]        0         \n",
            "                                                                 \n",
            " bidirectional (Bidirectiona  (None, 5120, 32)         8832      \n",
            " l)                                                              \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 163840)            0         \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 163840)           655360    \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 163840)            0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 256)               41943296  \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 256)              1024      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation (Activation)     (None, 256)               0         \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 42,608,512\n",
            "Trainable params: 42,280,320\n",
            "Non-trainable params: 328,192\n",
            "_________________________________________________________________\n",
            "None\n",
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_2 (InputLayer)        [(None, 5120, 52)]        0         \n",
            "                                                                 \n",
            " model (Functional)          (None, 256)               42608512  \n",
            "                                                                 \n",
            " model_1 (Functional)        (None, 1)                 82305     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 42,690,817\n",
            "Trainable params: 42,362,625\n",
            "Non-trainable params: 328,192\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/100\n",
            "28/28 [==============================] - 12s 327ms/step - loss: 0.5158 - accuracy: 0.5138 - val_loss: 0.2041 - val_accuracy: 0.8889\n",
            "Epoch 2/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.3819 - accuracy: 0.5632 - val_loss: 0.1685 - val_accuracy: 0.9259\n",
            "Epoch 3/100\n",
            "28/28 [==============================] - 8s 292ms/step - loss: 0.3592 - accuracy: 0.5828 - val_loss: 0.2053 - val_accuracy: 0.8889\n",
            "Epoch 4/100\n",
            "28/28 [==============================] - 8s 292ms/step - loss: 0.3468 - accuracy: 0.5793 - val_loss: 0.2044 - val_accuracy: 0.9259\n",
            "Epoch 5/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.3253 - accuracy: 0.5839 - val_loss: 0.1775 - val_accuracy: 0.9630\n",
            "Epoch 6/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.3145 - accuracy: 0.5885 - val_loss: 0.1675 - val_accuracy: 1.0000\n",
            "Epoch 7/100\n",
            "28/28 [==============================] - 8s 292ms/step - loss: 0.3108 - accuracy: 0.5920 - val_loss: 0.1706 - val_accuracy: 0.9630\n",
            "Epoch 8/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.3128 - accuracy: 0.5839 - val_loss: 0.1249 - val_accuracy: 1.0000\n",
            "Epoch 9/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2995 - accuracy: 0.5920 - val_loss: 0.1062 - val_accuracy: 1.0000\n",
            "Epoch 10/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2950 - accuracy: 0.5908 - val_loss: 0.1290 - val_accuracy: 1.0000\n",
            "Epoch 11/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2931 - accuracy: 0.5920 - val_loss: 0.1197 - val_accuracy: 1.0000\n",
            "Epoch 12/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2893 - accuracy: 0.5920 - val_loss: 0.1418 - val_accuracy: 1.0000\n",
            "Epoch 13/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2910 - accuracy: 0.5954 - val_loss: 0.0776 - val_accuracy: 1.0000\n",
            "Epoch 14/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2911 - accuracy: 0.5920 - val_loss: 0.0951 - val_accuracy: 1.0000\n",
            "Epoch 15/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2798 - accuracy: 0.5943 - val_loss: 0.0655 - val_accuracy: 1.0000\n",
            "Epoch 16/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2881 - accuracy: 0.5931 - val_loss: 0.1017 - val_accuracy: 1.0000\n",
            "Epoch 17/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2741 - accuracy: 0.5954 - val_loss: 0.0864 - val_accuracy: 1.0000\n",
            "Epoch 18/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2733 - accuracy: 0.5954 - val_loss: 0.0988 - val_accuracy: 1.0000\n",
            "Epoch 19/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2680 - accuracy: 0.5954 - val_loss: 0.0802 - val_accuracy: 1.0000\n",
            "Epoch 20/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2747 - accuracy: 0.5943 - val_loss: 0.0667 - val_accuracy: 1.0000\n",
            "Epoch 21/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.2761 - accuracy: 0.5943 - val_loss: 0.0613 - val_accuracy: 1.0000\n",
            "Epoch 22/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2653 - accuracy: 0.5943 - val_loss: 0.1147 - val_accuracy: 1.0000\n",
            "Epoch 23/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2702 - accuracy: 0.5954 - val_loss: 0.0696 - val_accuracy: 1.0000\n",
            "Epoch 24/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2599 - accuracy: 0.5943 - val_loss: 0.0737 - val_accuracy: 1.0000\n",
            "Epoch 25/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2632 - accuracy: 0.5954 - val_loss: 0.1410 - val_accuracy: 1.0000\n",
            "Epoch 26/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2684 - accuracy: 0.5954 - val_loss: 0.1278 - val_accuracy: 1.0000\n",
            "Epoch 27/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2601 - accuracy: 0.5954 - val_loss: 0.1024 - val_accuracy: 1.0000\n",
            "Epoch 28/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2626 - accuracy: 0.5954 - val_loss: 0.0925 - val_accuracy: 1.0000\n",
            "Epoch 29/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2647 - accuracy: 0.5954 - val_loss: 0.0811 - val_accuracy: 1.0000\n",
            "Epoch 30/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2623 - accuracy: 0.5954 - val_loss: 0.0763 - val_accuracy: 1.0000\n",
            "Epoch 31/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2539 - accuracy: 0.5954 - val_loss: 0.1212 - val_accuracy: 1.0000\n",
            "Epoch 32/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2591 - accuracy: 0.5954 - val_loss: 0.1129 - val_accuracy: 1.0000\n",
            "Epoch 33/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2667 - accuracy: 0.5931 - val_loss: 0.1307 - val_accuracy: 1.0000\n",
            "Epoch 34/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2671 - accuracy: 0.5954 - val_loss: 0.1345 - val_accuracy: 1.0000\n",
            "Epoch 35/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2648 - accuracy: 0.5954 - val_loss: 0.0648 - val_accuracy: 1.0000\n",
            "Epoch 36/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2660 - accuracy: 0.5943 - val_loss: 0.1093 - val_accuracy: 1.0000\n",
            "2/2 [==============================] - 1s 112ms/step - loss: 0.1278 - accuracy: 0.9706\n",
            "WARNING:tensorflow:5 out of the last 9 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f4e1051ef80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "[0.970588231086731]\n",
            "[0.9647771023302939]\n",
            "Epoch 1/100\n",
            "28/28 [==============================] - 12s 328ms/step - loss: 0.5508 - accuracy: 0.4772 - val_loss: 0.2377 - val_accuracy: 0.8519\n",
            "Epoch 2/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.4116 - accuracy: 0.5240 - val_loss: 0.1321 - val_accuracy: 0.9630\n",
            "Epoch 3/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.3879 - accuracy: 0.5411 - val_loss: 0.1547 - val_accuracy: 1.0000\n",
            "Epoch 4/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.3707 - accuracy: 0.5400 - val_loss: 0.1310 - val_accuracy: 1.0000\n",
            "Epoch 5/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.3603 - accuracy: 0.5457 - val_loss: 0.1671 - val_accuracy: 1.0000\n",
            "Epoch 6/100\n",
            "28/28 [==============================] - 8s 300ms/step - loss: 0.3432 - accuracy: 0.5491 - val_loss: 0.1025 - val_accuracy: 1.0000\n",
            "Epoch 7/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.3404 - accuracy: 0.5525 - val_loss: 0.1167 - val_accuracy: 1.0000\n",
            "Epoch 8/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.3197 - accuracy: 0.5548 - val_loss: 0.0861 - val_accuracy: 1.0000\n",
            "Epoch 9/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.3222 - accuracy: 0.5525 - val_loss: 0.0972 - val_accuracy: 1.0000\n",
            "Epoch 10/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.3183 - accuracy: 0.5525 - val_loss: 0.0971 - val_accuracy: 1.0000\n",
            "Epoch 11/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.3113 - accuracy: 0.5525 - val_loss: 0.1203 - val_accuracy: 0.9630\n",
            "Epoch 12/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.3080 - accuracy: 0.5559 - val_loss: 0.1245 - val_accuracy: 1.0000\n",
            "Epoch 13/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.2983 - accuracy: 0.5559 - val_loss: 0.0549 - val_accuracy: 1.0000\n",
            "Epoch 14/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.2977 - accuracy: 0.5571 - val_loss: 0.0499 - val_accuracy: 1.0000\n",
            "Epoch 15/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.3100 - accuracy: 0.5559 - val_loss: 0.0506 - val_accuracy: 1.0000\n",
            "Epoch 16/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2942 - accuracy: 0.5571 - val_loss: 0.0677 - val_accuracy: 1.0000\n",
            "Epoch 17/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2959 - accuracy: 0.5559 - val_loss: 0.0825 - val_accuracy: 1.0000\n",
            "Epoch 18/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.2908 - accuracy: 0.5571 - val_loss: 0.0475 - val_accuracy: 1.0000\n",
            "Epoch 19/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2900 - accuracy: 0.5559 - val_loss: 0.1423 - val_accuracy: 0.9259\n",
            "Epoch 20/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.3016 - accuracy: 0.5559 - val_loss: 0.0705 - val_accuracy: 1.0000\n",
            "Epoch 21/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2901 - accuracy: 0.5559 - val_loss: 0.1001 - val_accuracy: 1.0000\n",
            "Epoch 22/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2877 - accuracy: 0.5571 - val_loss: 0.0528 - val_accuracy: 1.0000\n",
            "Epoch 23/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.2809 - accuracy: 0.5571 - val_loss: 0.0342 - val_accuracy: 1.0000\n",
            "Epoch 24/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2805 - accuracy: 0.5571 - val_loss: 0.0780 - val_accuracy: 0.9630\n",
            "Epoch 25/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2811 - accuracy: 0.5559 - val_loss: 0.0557 - val_accuracy: 1.0000\n",
            "Epoch 26/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2808 - accuracy: 0.5571 - val_loss: 0.0902 - val_accuracy: 0.9630\n",
            "Epoch 27/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2839 - accuracy: 0.5571 - val_loss: 0.0581 - val_accuracy: 1.0000\n",
            "Epoch 28/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2776 - accuracy: 0.5559 - val_loss: 0.0571 - val_accuracy: 1.0000\n",
            "Epoch 29/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2766 - accuracy: 0.5571 - val_loss: 0.0711 - val_accuracy: 1.0000\n",
            "Epoch 30/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2878 - accuracy: 0.5571 - val_loss: 0.0496 - val_accuracy: 1.0000\n",
            "Epoch 31/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2862 - accuracy: 0.5571 - val_loss: 0.0791 - val_accuracy: 0.9630\n",
            "Epoch 32/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2788 - accuracy: 0.5571 - val_loss: 0.0505 - val_accuracy: 1.0000\n",
            "Epoch 33/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2779 - accuracy: 0.5571 - val_loss: 0.0442 - val_accuracy: 1.0000\n",
            "Epoch 34/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2739 - accuracy: 0.5571 - val_loss: 0.0672 - val_accuracy: 1.0000\n",
            "Epoch 35/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2729 - accuracy: 0.5571 - val_loss: 0.0575 - val_accuracy: 1.0000\n",
            "Epoch 36/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2712 - accuracy: 0.5571 - val_loss: 0.0888 - val_accuracy: 1.0000\n",
            "Epoch 37/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2722 - accuracy: 0.5571 - val_loss: 0.0977 - val_accuracy: 1.0000\n",
            "Epoch 38/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2729 - accuracy: 0.5571 - val_loss: 0.0909 - val_accuracy: 1.0000\n",
            "2/2 [==============================] - 1s 114ms/step - loss: 0.1874 - accuracy: 0.9118\n",
            "WARNING:tensorflow:6 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f4d7b43d320> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "Epoch 1/100\n",
            "28/28 [==============================] - 12s 329ms/step - loss: 0.5600 - accuracy: 0.4886 - val_loss: 0.3167 - val_accuracy: 0.8519\n",
            "Epoch 2/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.4234 - accuracy: 0.5434 - val_loss: 0.4964 - val_accuracy: 0.7037\n",
            "Epoch 3/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.3903 - accuracy: 0.5479 - val_loss: 0.2511 - val_accuracy: 0.9259\n",
            "Epoch 4/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.3670 - accuracy: 0.5628 - val_loss: 0.3264 - val_accuracy: 0.8889\n",
            "Epoch 5/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.3369 - accuracy: 0.5662 - val_loss: 0.2585 - val_accuracy: 0.8889\n",
            "Epoch 6/100\n",
            "28/28 [==============================] - 8s 300ms/step - loss: 0.3360 - accuracy: 0.5685 - val_loss: 0.2476 - val_accuracy: 0.9259\n",
            "Epoch 7/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.3260 - accuracy: 0.5708 - val_loss: 0.2536 - val_accuracy: 0.9259\n",
            "Epoch 8/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.3189 - accuracy: 0.5685 - val_loss: 0.1888 - val_accuracy: 0.9630\n",
            "Epoch 9/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.3196 - accuracy: 0.5674 - val_loss: 0.3594 - val_accuracy: 0.8148\n",
            "Epoch 10/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.3104 - accuracy: 0.5731 - val_loss: 0.2334 - val_accuracy: 0.8889\n",
            "Epoch 11/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.3060 - accuracy: 0.5719 - val_loss: 0.2124 - val_accuracy: 0.9259\n",
            "Epoch 12/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2952 - accuracy: 0.5731 - val_loss: 0.1922 - val_accuracy: 0.9630\n",
            "Epoch 13/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2898 - accuracy: 0.5753 - val_loss: 0.2722 - val_accuracy: 0.9259\n",
            "Epoch 14/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.2887 - accuracy: 0.5731 - val_loss: 0.1711 - val_accuracy: 0.9259\n",
            "Epoch 15/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2952 - accuracy: 0.5731 - val_loss: 0.1811 - val_accuracy: 0.9630\n",
            "Epoch 16/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2824 - accuracy: 0.5731 - val_loss: 0.1883 - val_accuracy: 0.9630\n",
            "Epoch 17/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2795 - accuracy: 0.5742 - val_loss: 0.2291 - val_accuracy: 0.9259\n",
            "Epoch 18/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2877 - accuracy: 0.5731 - val_loss: 0.1930 - val_accuracy: 0.9259\n",
            "Epoch 19/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2810 - accuracy: 0.5753 - val_loss: 0.1925 - val_accuracy: 0.9259\n",
            "Epoch 20/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2757 - accuracy: 0.5753 - val_loss: 0.2121 - val_accuracy: 0.9630\n",
            "Epoch 21/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2864 - accuracy: 0.5753 - val_loss: 0.2830 - val_accuracy: 0.8889\n",
            "Epoch 22/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.2826 - accuracy: 0.5753 - val_loss: 0.1288 - val_accuracy: 1.0000\n",
            "Epoch 23/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2709 - accuracy: 0.5742 - val_loss: 0.1680 - val_accuracy: 0.9259\n",
            "Epoch 24/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2664 - accuracy: 0.5753 - val_loss: 0.2008 - val_accuracy: 0.9259\n",
            "Epoch 25/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2701 - accuracy: 0.5753 - val_loss: 0.1731 - val_accuracy: 0.9259\n",
            "Epoch 26/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2686 - accuracy: 0.5753 - val_loss: 0.2466 - val_accuracy: 0.9259\n",
            "Epoch 27/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2744 - accuracy: 0.5753 - val_loss: 0.2924 - val_accuracy: 0.8889\n",
            "Epoch 28/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2654 - accuracy: 0.5753 - val_loss: 0.2807 - val_accuracy: 0.9259\n",
            "Epoch 29/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2699 - accuracy: 0.5753 - val_loss: 0.1571 - val_accuracy: 0.9630\n",
            "Epoch 30/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2710 - accuracy: 0.5753 - val_loss: 0.1468 - val_accuracy: 0.9630\n",
            "Epoch 31/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2760 - accuracy: 0.5753 - val_loss: 0.1450 - val_accuracy: 0.9630\n",
            "Epoch 32/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2617 - accuracy: 0.5753 - val_loss: 0.2245 - val_accuracy: 0.8889\n",
            "Epoch 33/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2715 - accuracy: 0.5753 - val_loss: 0.2108 - val_accuracy: 0.9259\n",
            "Epoch 34/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2624 - accuracy: 0.5753 - val_loss: 0.2848 - val_accuracy: 0.8889\n",
            "Epoch 35/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2629 - accuracy: 0.5753 - val_loss: 0.1617 - val_accuracy: 0.9630\n",
            "Epoch 36/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2653 - accuracy: 0.5753 - val_loss: 0.2072 - val_accuracy: 0.8889\n",
            "Epoch 37/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2702 - accuracy: 0.5753 - val_loss: 0.1766 - val_accuracy: 0.9630\n",
            "2/2 [==============================] - 1s 113ms/step - loss: 0.0909 - accuracy: 1.0000\n",
            "Epoch 1/100\n",
            "28/28 [==============================] - 12s 327ms/step - loss: 0.5633 - accuracy: 0.4852 - val_loss: 0.2244 - val_accuracy: 0.9630\n",
            "Epoch 2/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.4019 - accuracy: 0.5525 - val_loss: 0.2750 - val_accuracy: 0.8519\n",
            "Epoch 3/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.3469 - accuracy: 0.5616 - val_loss: 0.2199 - val_accuracy: 0.9259\n",
            "Epoch 4/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.3578 - accuracy: 0.5696 - val_loss: 0.3016 - val_accuracy: 0.8148\n",
            "Epoch 5/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.3381 - accuracy: 0.5696 - val_loss: 0.2877 - val_accuracy: 0.8148\n",
            "Epoch 6/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.3475 - accuracy: 0.5731 - val_loss: 0.1728 - val_accuracy: 0.9259\n",
            "Epoch 7/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.3225 - accuracy: 0.5799 - val_loss: 0.1515 - val_accuracy: 0.9630\n",
            "Epoch 8/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.3103 - accuracy: 0.5776 - val_loss: 0.1879 - val_accuracy: 0.9630\n",
            "Epoch 9/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.3084 - accuracy: 0.5799 - val_loss: 0.1981 - val_accuracy: 0.9259\n",
            "Epoch 10/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.3027 - accuracy: 0.5799 - val_loss: 0.1701 - val_accuracy: 0.9259\n",
            "Epoch 11/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.2968 - accuracy: 0.5822 - val_loss: 0.1371 - val_accuracy: 1.0000\n",
            "Epoch 12/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2949 - accuracy: 0.5811 - val_loss: 0.2493 - val_accuracy: 0.8889\n",
            "Epoch 13/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2966 - accuracy: 0.5822 - val_loss: 0.2045 - val_accuracy: 0.8889\n",
            "Epoch 14/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2879 - accuracy: 0.5845 - val_loss: 0.2230 - val_accuracy: 0.8889\n",
            "Epoch 15/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2898 - accuracy: 0.5845 - val_loss: 0.1587 - val_accuracy: 0.9630\n",
            "Epoch 16/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2868 - accuracy: 0.5833 - val_loss: 0.1827 - val_accuracy: 0.9259\n",
            "Epoch 17/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2739 - accuracy: 0.5833 - val_loss: 0.1517 - val_accuracy: 0.9630\n",
            "Epoch 18/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2735 - accuracy: 0.5833 - val_loss: 0.1878 - val_accuracy: 0.8889\n",
            "Epoch 19/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2801 - accuracy: 0.5822 - val_loss: 0.1855 - val_accuracy: 0.9630\n",
            "Epoch 20/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2767 - accuracy: 0.5845 - val_loss: 0.1400 - val_accuracy: 0.9630\n",
            "Epoch 21/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.2788 - accuracy: 0.5845 - val_loss: 0.1273 - val_accuracy: 0.9630\n",
            "Epoch 22/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2739 - accuracy: 0.5845 - val_loss: 0.1454 - val_accuracy: 0.9259\n",
            "Epoch 23/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2683 - accuracy: 0.5845 - val_loss: 0.1960 - val_accuracy: 0.9259\n",
            "Epoch 24/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2751 - accuracy: 0.5845 - val_loss: 0.1396 - val_accuracy: 0.9259\n",
            "Epoch 25/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.2754 - accuracy: 0.5845 - val_loss: 0.1148 - val_accuracy: 0.9259\n",
            "Epoch 26/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2662 - accuracy: 0.5845 - val_loss: 0.1155 - val_accuracy: 1.0000\n",
            "Epoch 27/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2630 - accuracy: 0.5845 - val_loss: 0.1535 - val_accuracy: 0.9259\n",
            "Epoch 28/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2640 - accuracy: 0.5845 - val_loss: 0.1554 - val_accuracy: 0.9259\n",
            "Epoch 29/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2689 - accuracy: 0.5845 - val_loss: 0.1502 - val_accuracy: 1.0000\n",
            "Epoch 30/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2640 - accuracy: 0.5845 - val_loss: 0.1193 - val_accuracy: 1.0000\n",
            "Epoch 31/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2729 - accuracy: 0.5822 - val_loss: 0.1617 - val_accuracy: 0.9630\n",
            "Epoch 32/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2671 - accuracy: 0.5845 - val_loss: 0.1498 - val_accuracy: 0.9259\n",
            "Epoch 33/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2593 - accuracy: 0.5845 - val_loss: 0.1427 - val_accuracy: 1.0000\n",
            "Epoch 34/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2578 - accuracy: 0.5845 - val_loss: 0.2047 - val_accuracy: 0.9630\n",
            "Epoch 35/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2608 - accuracy: 0.5845 - val_loss: 0.1516 - val_accuracy: 1.0000\n",
            "Epoch 36/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2538 - accuracy: 0.5845 - val_loss: 0.1517 - val_accuracy: 1.0000\n",
            "Epoch 37/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.2597 - accuracy: 0.5845 - val_loss: 0.0879 - val_accuracy: 1.0000\n",
            "Epoch 38/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2524 - accuracy: 0.5845 - val_loss: 0.1241 - val_accuracy: 1.0000\n",
            "Epoch 39/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2545 - accuracy: 0.5845 - val_loss: 0.1396 - val_accuracy: 1.0000\n",
            "Epoch 40/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2549 - accuracy: 0.5845 - val_loss: 0.1460 - val_accuracy: 1.0000\n",
            "Epoch 41/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2541 - accuracy: 0.5833 - val_loss: 0.1437 - val_accuracy: 0.9630\n",
            "Epoch 42/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2525 - accuracy: 0.5845 - val_loss: 0.1048 - val_accuracy: 1.0000\n",
            "Epoch 43/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2510 - accuracy: 0.5845 - val_loss: 0.1448 - val_accuracy: 0.9630\n",
            "Epoch 44/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2568 - accuracy: 0.5845 - val_loss: 0.1363 - val_accuracy: 1.0000\n",
            "Epoch 45/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2473 - accuracy: 0.5845 - val_loss: 0.1262 - val_accuracy: 0.9630\n",
            "Epoch 46/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2481 - accuracy: 0.5845 - val_loss: 0.1383 - val_accuracy: 1.0000\n",
            "Epoch 47/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2512 - accuracy: 0.5845 - val_loss: 0.0992 - val_accuracy: 1.0000\n",
            "Epoch 48/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2537 - accuracy: 0.5845 - val_loss: 0.1166 - val_accuracy: 1.0000\n",
            "Epoch 49/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2516 - accuracy: 0.5845 - val_loss: 0.1220 - val_accuracy: 0.9630\n",
            "Epoch 50/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2507 - accuracy: 0.5845 - val_loss: 0.1440 - val_accuracy: 1.0000\n",
            "Epoch 51/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2476 - accuracy: 0.5845 - val_loss: 0.1523 - val_accuracy: 0.9630\n",
            "Epoch 52/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.2488 - accuracy: 0.5845 - val_loss: 0.1607 - val_accuracy: 1.0000\n",
            "2/2 [==============================] - 1s 111ms/step - loss: 0.0934 - accuracy: 1.0000\n",
            "Epoch 1/100\n",
            "28/28 [==============================] - 12s 330ms/step - loss: 0.5528 - accuracy: 0.5134 - val_loss: 0.3570 - val_accuracy: 0.8889\n",
            "Epoch 2/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.4024 - accuracy: 0.5593 - val_loss: 0.2813 - val_accuracy: 0.8889\n",
            "Epoch 3/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.3570 - accuracy: 0.5772 - val_loss: 0.3075 - val_accuracy: 0.8889\n",
            "Epoch 4/100\n",
            "28/28 [==============================] - 8s 301ms/step - loss: 0.3462 - accuracy: 0.5783 - val_loss: 0.2244 - val_accuracy: 0.9630\n",
            "Epoch 5/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.3152 - accuracy: 0.5850 - val_loss: 0.2427 - val_accuracy: 0.9630\n",
            "Epoch 6/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.3102 - accuracy: 0.5906 - val_loss: 0.1958 - val_accuracy: 1.0000\n",
            "Epoch 7/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.3033 - accuracy: 0.5928 - val_loss: 0.2776 - val_accuracy: 0.8519\n",
            "Epoch 8/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2921 - accuracy: 0.5951 - val_loss: 0.2064 - val_accuracy: 0.9630\n",
            "Epoch 9/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2944 - accuracy: 0.5928 - val_loss: 0.2663 - val_accuracy: 0.8889\n",
            "Epoch 10/100\n",
            "28/28 [==============================] - 8s 300ms/step - loss: 0.2831 - accuracy: 0.5962 - val_loss: 0.1583 - val_accuracy: 1.0000\n",
            "Epoch 11/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.2857 - accuracy: 0.5951 - val_loss: 0.1423 - val_accuracy: 1.0000\n",
            "Epoch 12/100\n",
            "28/28 [==============================] - 8s 302ms/step - loss: 0.2833 - accuracy: 0.5940 - val_loss: 0.1384 - val_accuracy: 0.9630\n",
            "Epoch 13/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2674 - accuracy: 0.5962 - val_loss: 0.2199 - val_accuracy: 0.8519\n",
            "Epoch 14/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2777 - accuracy: 0.5973 - val_loss: 0.1989 - val_accuracy: 0.9259\n",
            "Epoch 15/100\n",
            "28/28 [==============================] - 8s 301ms/step - loss: 0.2819 - accuracy: 0.5973 - val_loss: 0.1383 - val_accuracy: 1.0000\n",
            "Epoch 16/100\n",
            "28/28 [==============================] - 8s 301ms/step - loss: 0.2724 - accuracy: 0.5973 - val_loss: 0.1030 - val_accuracy: 0.9630\n",
            "Epoch 17/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2651 - accuracy: 0.5962 - val_loss: 0.1322 - val_accuracy: 0.9259\n",
            "Epoch 18/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2679 - accuracy: 0.5951 - val_loss: 0.2196 - val_accuracy: 0.8889\n",
            "Epoch 19/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2653 - accuracy: 0.5973 - val_loss: 0.1266 - val_accuracy: 0.9259\n",
            "Epoch 20/100\n",
            "28/28 [==============================] - 8s 301ms/step - loss: 0.2602 - accuracy: 0.5973 - val_loss: 0.1026 - val_accuracy: 0.9630\n",
            "Epoch 21/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2644 - accuracy: 0.5973 - val_loss: 0.1028 - val_accuracy: 0.9630\n",
            "Epoch 22/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.2572 - accuracy: 0.5973 - val_loss: 0.1133 - val_accuracy: 0.9630\n",
            "Epoch 23/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2524 - accuracy: 0.5973 - val_loss: 0.1345 - val_accuracy: 0.9630\n",
            "Epoch 24/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2514 - accuracy: 0.5973 - val_loss: 0.1044 - val_accuracy: 0.9630\n",
            "Epoch 25/100\n",
            "28/28 [==============================] - 8s 301ms/step - loss: 0.2529 - accuracy: 0.5973 - val_loss: 0.0969 - val_accuracy: 0.9630\n",
            "Epoch 26/100\n",
            "28/28 [==============================] - 8s 300ms/step - loss: 0.2586 - accuracy: 0.5973 - val_loss: 0.0817 - val_accuracy: 0.9630\n",
            "Epoch 27/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2472 - accuracy: 0.5973 - val_loss: 0.1455 - val_accuracy: 0.9259\n",
            "Epoch 28/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2489 - accuracy: 0.5973 - val_loss: 0.1531 - val_accuracy: 0.9630\n",
            "Epoch 29/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2536 - accuracy: 0.5962 - val_loss: 0.1388 - val_accuracy: 0.9630\n",
            "Epoch 30/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2521 - accuracy: 0.5973 - val_loss: 0.1366 - val_accuracy: 0.9630\n",
            "Epoch 31/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2548 - accuracy: 0.5973 - val_loss: 0.1912 - val_accuracy: 0.9259\n",
            "Epoch 32/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2485 - accuracy: 0.5973 - val_loss: 0.1110 - val_accuracy: 0.9259\n",
            "Epoch 33/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2441 - accuracy: 0.5973 - val_loss: 0.1395 - val_accuracy: 0.9630\n",
            "Epoch 34/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2464 - accuracy: 0.5973 - val_loss: 0.1173 - val_accuracy: 0.9630\n",
            "Epoch 35/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2465 - accuracy: 0.5973 - val_loss: 0.0990 - val_accuracy: 0.9630\n",
            "Epoch 36/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2472 - accuracy: 0.5973 - val_loss: 0.1022 - val_accuracy: 1.0000\n",
            "Epoch 37/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2519 - accuracy: 0.5962 - val_loss: 0.1468 - val_accuracy: 0.9630\n",
            "Epoch 38/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2414 - accuracy: 0.5973 - val_loss: 0.1174 - val_accuracy: 0.9630\n",
            "Epoch 39/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2510 - accuracy: 0.5973 - val_loss: 0.1734 - val_accuracy: 0.9630\n",
            "Epoch 40/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2498 - accuracy: 0.5973 - val_loss: 0.1342 - val_accuracy: 0.9630\n",
            "Epoch 41/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.2418 - accuracy: 0.5973 - val_loss: 0.1379 - val_accuracy: 0.9630\n",
            "2/2 [==============================] - 1s 113ms/step - loss: 0.1527 - accuracy: 0.9118\n",
            "Epoch 1/100\n",
            "28/28 [==============================] - 12s 327ms/step - loss: 0.5037 - accuracy: 0.5251 - val_loss: 0.3714 - val_accuracy: 0.8148\n",
            "Epoch 2/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.4102 - accuracy: 0.5605 - val_loss: 0.2798 - val_accuracy: 0.8889\n",
            "Epoch 3/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.3456 - accuracy: 0.5856 - val_loss: 0.1957 - val_accuracy: 0.9630\n",
            "Epoch 4/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.3515 - accuracy: 0.5833 - val_loss: 0.1869 - val_accuracy: 0.9630\n",
            "Epoch 5/100\n",
            "28/28 [==============================] - 8s 300ms/step - loss: 0.3266 - accuracy: 0.5970 - val_loss: 0.1801 - val_accuracy: 0.9630\n",
            "Epoch 6/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.3093 - accuracy: 0.5959 - val_loss: 0.2257 - val_accuracy: 0.9630\n",
            "Epoch 7/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.3009 - accuracy: 0.5993 - val_loss: 0.2045 - val_accuracy: 1.0000\n",
            "Epoch 8/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.3181 - accuracy: 0.5936 - val_loss: 0.1843 - val_accuracy: 0.9259\n",
            "Epoch 9/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.2912 - accuracy: 0.6005 - val_loss: 0.1368 - val_accuracy: 0.9630\n",
            "Epoch 10/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2869 - accuracy: 0.5993 - val_loss: 0.1712 - val_accuracy: 0.8889\n",
            "Epoch 11/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2813 - accuracy: 0.6027 - val_loss: 0.1430 - val_accuracy: 0.9259\n",
            "Epoch 12/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2784 - accuracy: 0.6016 - val_loss: 0.1465 - val_accuracy: 0.9630\n",
            "Epoch 13/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2870 - accuracy: 0.6005 - val_loss: 0.1778 - val_accuracy: 0.9259\n",
            "Epoch 14/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2847 - accuracy: 0.6005 - val_loss: 0.2132 - val_accuracy: 0.8519\n",
            "Epoch 15/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2743 - accuracy: 0.6016 - val_loss: 0.1837 - val_accuracy: 0.8889\n",
            "Epoch 16/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2697 - accuracy: 0.6027 - val_loss: 0.1660 - val_accuracy: 0.8889\n",
            "Epoch 17/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2750 - accuracy: 0.6027 - val_loss: 0.4533 - val_accuracy: 0.7778\n",
            "Epoch 18/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2682 - accuracy: 0.6027 - val_loss: 0.2955 - val_accuracy: 0.8519\n",
            "Epoch 19/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2649 - accuracy: 0.6016 - val_loss: 0.1562 - val_accuracy: 0.9259\n",
            "Epoch 20/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2622 - accuracy: 0.6016 - val_loss: 0.1841 - val_accuracy: 0.8889\n",
            "Epoch 21/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.2658 - accuracy: 0.6027 - val_loss: 0.1244 - val_accuracy: 0.9630\n",
            "Epoch 22/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2662 - accuracy: 0.6027 - val_loss: 0.1438 - val_accuracy: 0.9630\n",
            "Epoch 23/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2589 - accuracy: 0.6027 - val_loss: 0.2272 - val_accuracy: 0.8889\n",
            "Epoch 24/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2623 - accuracy: 0.6016 - val_loss: 0.1369 - val_accuracy: 0.9630\n",
            "Epoch 25/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2674 - accuracy: 0.6027 - val_loss: 0.1296 - val_accuracy: 0.9630\n",
            "Epoch 26/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.2652 - accuracy: 0.6016 - val_loss: 0.1226 - val_accuracy: 0.9630\n",
            "Epoch 27/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.2542 - accuracy: 0.6016 - val_loss: 0.1224 - val_accuracy: 0.9630\n",
            "Epoch 28/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2529 - accuracy: 0.6027 - val_loss: 0.1557 - val_accuracy: 0.9259\n",
            "Epoch 29/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2553 - accuracy: 0.6027 - val_loss: 0.1651 - val_accuracy: 0.8889\n",
            "Epoch 30/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.2596 - accuracy: 0.6027 - val_loss: 0.1219 - val_accuracy: 0.9630\n",
            "Epoch 31/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2628 - accuracy: 0.6016 - val_loss: 0.2530 - val_accuracy: 0.8889\n",
            "Epoch 32/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2630 - accuracy: 0.6016 - val_loss: 0.1408 - val_accuracy: 0.9630\n",
            "Epoch 33/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.2534 - accuracy: 0.6027 - val_loss: 0.0982 - val_accuracy: 0.9630\n",
            "Epoch 34/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2488 - accuracy: 0.6027 - val_loss: 0.1430 - val_accuracy: 0.9259\n",
            "Epoch 35/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2521 - accuracy: 0.6027 - val_loss: 0.2053 - val_accuracy: 0.9259\n",
            "Epoch 36/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2570 - accuracy: 0.6016 - val_loss: 0.1259 - val_accuracy: 0.9630\n",
            "Epoch 37/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2501 - accuracy: 0.6027 - val_loss: 0.1200 - val_accuracy: 0.9630\n",
            "Epoch 38/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2505 - accuracy: 0.6027 - val_loss: 0.1218 - val_accuracy: 0.9630\n",
            "Epoch 39/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2533 - accuracy: 0.6027 - val_loss: 0.1059 - val_accuracy: 0.9630\n",
            "Epoch 40/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.2440 - accuracy: 0.6027 - val_loss: 0.0771 - val_accuracy: 0.9630\n",
            "Epoch 41/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2508 - accuracy: 0.6027 - val_loss: 0.0930 - val_accuracy: 0.9630\n",
            "Epoch 42/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2462 - accuracy: 0.6027 - val_loss: 0.1011 - val_accuracy: 0.9630\n",
            "Epoch 43/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2465 - accuracy: 0.6027 - val_loss: 0.1224 - val_accuracy: 0.9630\n",
            "Epoch 44/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2504 - accuracy: 0.6027 - val_loss: 0.1620 - val_accuracy: 0.9630\n",
            "Epoch 45/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2459 - accuracy: 0.6027 - val_loss: 0.1367 - val_accuracy: 0.9630\n",
            "Epoch 46/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2451 - accuracy: 0.6027 - val_loss: 0.1839 - val_accuracy: 0.9630\n",
            "Epoch 47/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2465 - accuracy: 0.6027 - val_loss: 0.1301 - val_accuracy: 0.9630\n",
            "Epoch 48/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2480 - accuracy: 0.6027 - val_loss: 0.1280 - val_accuracy: 0.9630\n",
            "Epoch 49/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2441 - accuracy: 0.6027 - val_loss: 0.1240 - val_accuracy: 0.9630\n",
            "Epoch 50/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2449 - accuracy: 0.6027 - val_loss: 0.1189 - val_accuracy: 0.9630\n",
            "Epoch 51/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2498 - accuracy: 0.6016 - val_loss: 0.1278 - val_accuracy: 0.9630\n",
            "Epoch 52/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2466 - accuracy: 0.6027 - val_loss: 0.1193 - val_accuracy: 0.9630\n",
            "Epoch 53/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2439 - accuracy: 0.6027 - val_loss: 0.1497 - val_accuracy: 0.9630\n",
            "Epoch 54/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2401 - accuracy: 0.6027 - val_loss: 0.1329 - val_accuracy: 0.9630\n",
            "Epoch 55/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2440 - accuracy: 0.6027 - val_loss: 0.1176 - val_accuracy: 0.9630\n",
            "2/2 [==============================] - 1s 112ms/step - loss: 0.0586 - accuracy: 0.9706\n",
            "[0.970588231086731, 0.9588235139846801]\n",
            "[0.9647771023302939, 0.9505298199907063]\n",
            "Epoch 1/100\n",
            "27/27 [==============================] - 12s 330ms/step - loss: 0.5718 - accuracy: 0.4613 - val_loss: 0.2518 - val_accuracy: 0.8889\n",
            "Epoch 2/100\n",
            "27/27 [==============================] - 8s 299ms/step - loss: 0.4236 - accuracy: 0.5258 - val_loss: 0.1823 - val_accuracy: 0.9630\n",
            "Epoch 3/100\n",
            "27/27 [==============================] - 8s 300ms/step - loss: 0.3804 - accuracy: 0.5528 - val_loss: 0.1534 - val_accuracy: 0.9630\n",
            "Epoch 4/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.3478 - accuracy: 0.5669 - val_loss: 0.1726 - val_accuracy: 0.9630\n",
            "Epoch 5/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.3363 - accuracy: 0.5610 - val_loss: 0.1816 - val_accuracy: 0.9259\n",
            "Epoch 6/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.3199 - accuracy: 0.5681 - val_loss: 0.2038 - val_accuracy: 0.8889\n",
            "Epoch 7/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.3150 - accuracy: 0.5692 - val_loss: 0.2088 - val_accuracy: 0.8889\n",
            "Epoch 8/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.3160 - accuracy: 0.5716 - val_loss: 0.2182 - val_accuracy: 0.8889\n",
            "Epoch 9/100\n",
            "27/27 [==============================] - 8s 299ms/step - loss: 0.3044 - accuracy: 0.5751 - val_loss: 0.1505 - val_accuracy: 0.9630\n",
            "Epoch 10/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2986 - accuracy: 0.5728 - val_loss: 0.2165 - val_accuracy: 0.9259\n",
            "Epoch 11/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2988 - accuracy: 0.5739 - val_loss: 0.1977 - val_accuracy: 0.9259\n",
            "Epoch 12/100\n",
            "27/27 [==============================] - 8s 300ms/step - loss: 0.2883 - accuracy: 0.5728 - val_loss: 0.1303 - val_accuracy: 0.9630\n",
            "Epoch 13/100\n",
            "27/27 [==============================] - 8s 300ms/step - loss: 0.2915 - accuracy: 0.5728 - val_loss: 0.1199 - val_accuracy: 0.9630\n",
            "Epoch 14/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2876 - accuracy: 0.5751 - val_loss: 0.1447 - val_accuracy: 0.9630\n",
            "Epoch 15/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2818 - accuracy: 0.5751 - val_loss: 0.2277 - val_accuracy: 0.9259\n",
            "Epoch 16/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2813 - accuracy: 0.5751 - val_loss: 0.1967 - val_accuracy: 0.9259\n",
            "Epoch 17/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2801 - accuracy: 0.5751 - val_loss: 0.2959 - val_accuracy: 0.9259\n",
            "Epoch 18/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2792 - accuracy: 0.5751 - val_loss: 0.2975 - val_accuracy: 0.9259\n",
            "Epoch 19/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2855 - accuracy: 0.5751 - val_loss: 0.1737 - val_accuracy: 0.9259\n",
            "Epoch 20/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2787 - accuracy: 0.5739 - val_loss: 0.2193 - val_accuracy: 0.9259\n",
            "Epoch 21/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2729 - accuracy: 0.5751 - val_loss: 0.1263 - val_accuracy: 0.9630\n",
            "Epoch 22/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2700 - accuracy: 0.5739 - val_loss: 0.1391 - val_accuracy: 0.9630\n",
            "Epoch 23/100\n",
            "27/27 [==============================] - 8s 300ms/step - loss: 0.2805 - accuracy: 0.5751 - val_loss: 0.0985 - val_accuracy: 0.9630\n",
            "Epoch 24/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2726 - accuracy: 0.5751 - val_loss: 0.1074 - val_accuracy: 0.9630\n",
            "Epoch 25/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2708 - accuracy: 0.5751 - val_loss: 0.1012 - val_accuracy: 0.9630\n",
            "Epoch 26/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2690 - accuracy: 0.5751 - val_loss: 0.1321 - val_accuracy: 0.9630\n",
            "Epoch 27/100\n",
            "27/27 [==============================] - 8s 298ms/step - loss: 0.2670 - accuracy: 0.5751 - val_loss: 0.1609 - val_accuracy: 0.9259\n",
            "Epoch 28/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2628 - accuracy: 0.5751 - val_loss: 0.1749 - val_accuracy: 0.9259\n",
            "Epoch 29/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2588 - accuracy: 0.5751 - val_loss: 0.1717 - val_accuracy: 0.9259\n",
            "Epoch 30/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2627 - accuracy: 0.5751 - val_loss: 0.1907 - val_accuracy: 0.9259\n",
            "Epoch 31/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2733 - accuracy: 0.5751 - val_loss: 0.1271 - val_accuracy: 0.9630\n",
            "Epoch 32/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2718 - accuracy: 0.5751 - val_loss: 0.1818 - val_accuracy: 0.9259\n",
            "Epoch 33/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2671 - accuracy: 0.5751 - val_loss: 0.1817 - val_accuracy: 0.9630\n",
            "Epoch 34/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2653 - accuracy: 0.5751 - val_loss: 0.1496 - val_accuracy: 0.9259\n",
            "Epoch 35/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2620 - accuracy: 0.5751 - val_loss: 0.1454 - val_accuracy: 0.9630\n",
            "Epoch 36/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2608 - accuracy: 0.5751 - val_loss: 0.3124 - val_accuracy: 0.8889\n",
            "Epoch 37/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2542 - accuracy: 0.5751 - val_loss: 0.1265 - val_accuracy: 0.9630\n",
            "Epoch 38/100\n",
            "27/27 [==============================] - 8s 299ms/step - loss: 0.2658 - accuracy: 0.5739 - val_loss: 0.0872 - val_accuracy: 0.9630\n",
            "Epoch 39/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2647 - accuracy: 0.5751 - val_loss: 0.0938 - val_accuracy: 0.9630\n",
            "Epoch 40/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2624 - accuracy: 0.5751 - val_loss: 0.1627 - val_accuracy: 0.9630\n",
            "Epoch 41/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2659 - accuracy: 0.5751 - val_loss: 0.2005 - val_accuracy: 0.9259\n",
            "Epoch 42/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2603 - accuracy: 0.5751 - val_loss: 0.1744 - val_accuracy: 0.9630\n",
            "Epoch 43/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2632 - accuracy: 0.5751 - val_loss: 0.1477 - val_accuracy: 0.9630\n",
            "Epoch 44/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2579 - accuracy: 0.5751 - val_loss: 0.2605 - val_accuracy: 0.8889\n",
            "Epoch 45/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2599 - accuracy: 0.5751 - val_loss: 0.1691 - val_accuracy: 0.9630\n",
            "Epoch 46/100\n",
            "27/27 [==============================] - 8s 298ms/step - loss: 0.2612 - accuracy: 0.5751 - val_loss: 0.1490 - val_accuracy: 0.9630\n",
            "Epoch 47/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2601 - accuracy: 0.5751 - val_loss: 0.2045 - val_accuracy: 0.9259\n",
            "Epoch 48/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2610 - accuracy: 0.5751 - val_loss: 0.1653 - val_accuracy: 0.9630\n",
            "Epoch 49/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2616 - accuracy: 0.5751 - val_loss: 0.2099 - val_accuracy: 0.9259\n",
            "Epoch 50/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2604 - accuracy: 0.5751 - val_loss: 0.1671 - val_accuracy: 0.9259\n",
            "Epoch 51/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2546 - accuracy: 0.5751 - val_loss: 0.1880 - val_accuracy: 0.9259\n",
            "Epoch 52/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2504 - accuracy: 0.5751 - val_loss: 0.1862 - val_accuracy: 0.9259\n",
            "Epoch 53/100\n",
            "27/27 [==============================] - 8s 299ms/step - loss: 0.2506 - accuracy: 0.5751 - val_loss: 0.2762 - val_accuracy: 0.8889\n",
            "2/2 [==============================] - 1s 115ms/step - loss: 0.2250 - accuracy: 0.8824\n",
            "Epoch 1/100\n",
            "27/27 [==============================] - 12s 330ms/step - loss: 0.5609 - accuracy: 0.4872 - val_loss: 0.5604 - val_accuracy: 0.7037\n",
            "Epoch 2/100\n",
            "27/27 [==============================] - 8s 301ms/step - loss: 0.3960 - accuracy: 0.5420 - val_loss: 0.5269 - val_accuracy: 0.6667\n",
            "Epoch 3/100\n",
            "27/27 [==============================] - 8s 301ms/step - loss: 0.3511 - accuracy: 0.5583 - val_loss: 0.4432 - val_accuracy: 0.6667\n",
            "Epoch 4/100\n",
            "27/27 [==============================] - 8s 300ms/step - loss: 0.3329 - accuracy: 0.5594 - val_loss: 0.3216 - val_accuracy: 0.7778\n",
            "Epoch 5/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.3404 - accuracy: 0.5618 - val_loss: 0.4100 - val_accuracy: 0.7037\n",
            "Epoch 6/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.3176 - accuracy: 0.5653 - val_loss: 0.5087 - val_accuracy: 0.7037\n",
            "Epoch 7/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.3228 - accuracy: 0.5653 - val_loss: 0.3243 - val_accuracy: 0.8519\n",
            "Epoch 8/100\n",
            "27/27 [==============================] - 8s 299ms/step - loss: 0.3186 - accuracy: 0.5618 - val_loss: 0.2201 - val_accuracy: 0.9259\n",
            "Epoch 9/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2953 - accuracy: 0.5688 - val_loss: 0.3271 - val_accuracy: 0.8519\n",
            "Epoch 10/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.3119 - accuracy: 0.5629 - val_loss: 0.3222 - val_accuracy: 0.8148\n",
            "Epoch 11/100\n",
            "27/27 [==============================] - 8s 300ms/step - loss: 0.3037 - accuracy: 0.5676 - val_loss: 0.1889 - val_accuracy: 1.0000\n",
            "Epoch 12/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2919 - accuracy: 0.5676 - val_loss: 0.2821 - val_accuracy: 0.8519\n",
            "Epoch 13/100\n",
            "27/27 [==============================] - 8s 301ms/step - loss: 0.2857 - accuracy: 0.5688 - val_loss: 0.1815 - val_accuracy: 1.0000\n",
            "Epoch 14/100\n",
            "27/27 [==============================] - 8s 301ms/step - loss: 0.2952 - accuracy: 0.5676 - val_loss: 0.1775 - val_accuracy: 0.9259\n",
            "Epoch 15/100\n",
            "27/27 [==============================] - 8s 302ms/step - loss: 0.2865 - accuracy: 0.5688 - val_loss: 0.1756 - val_accuracy: 0.9630\n",
            "Epoch 16/100\n",
            "27/27 [==============================] - 8s 302ms/step - loss: 0.2835 - accuracy: 0.5688 - val_loss: 0.1669 - val_accuracy: 0.9259\n",
            "Epoch 17/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2777 - accuracy: 0.5676 - val_loss: 0.1868 - val_accuracy: 0.8889\n",
            "Epoch 18/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2795 - accuracy: 0.5688 - val_loss: 0.1863 - val_accuracy: 0.8889\n",
            "Epoch 19/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2762 - accuracy: 0.5688 - val_loss: 0.1883 - val_accuracy: 0.9259\n",
            "Epoch 20/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2866 - accuracy: 0.5676 - val_loss: 0.2388 - val_accuracy: 0.8889\n",
            "Epoch 21/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2795 - accuracy: 0.5688 - val_loss: 0.2472 - val_accuracy: 0.8519\n",
            "Epoch 22/100\n",
            "27/27 [==============================] - 8s 300ms/step - loss: 0.2805 - accuracy: 0.5676 - val_loss: 0.1431 - val_accuracy: 0.9630\n",
            "Epoch 23/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2778 - accuracy: 0.5676 - val_loss: 0.1447 - val_accuracy: 0.9259\n",
            "Epoch 24/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2738 - accuracy: 0.5676 - val_loss: 0.1802 - val_accuracy: 1.0000\n",
            "Epoch 25/100\n",
            "27/27 [==============================] - 8s 301ms/step - loss: 0.2776 - accuracy: 0.5688 - val_loss: 0.1345 - val_accuracy: 0.9259\n",
            "Epoch 26/100\n",
            "27/27 [==============================] - 8s 298ms/step - loss: 0.2710 - accuracy: 0.5688 - val_loss: 0.1579 - val_accuracy: 0.8889\n",
            "Epoch 27/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2666 - accuracy: 0.5688 - val_loss: 0.1921 - val_accuracy: 0.9630\n",
            "Epoch 28/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2609 - accuracy: 0.5688 - val_loss: 0.1553 - val_accuracy: 0.9630\n",
            "Epoch 29/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2683 - accuracy: 0.5688 - val_loss: 0.1510 - val_accuracy: 0.9630\n",
            "Epoch 30/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2648 - accuracy: 0.5688 - val_loss: 0.1696 - val_accuracy: 1.0000\n",
            "Epoch 31/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2654 - accuracy: 0.5688 - val_loss: 0.1936 - val_accuracy: 0.9259\n",
            "Epoch 32/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2676 - accuracy: 0.5688 - val_loss: 0.1542 - val_accuracy: 1.0000\n",
            "Epoch 33/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2636 - accuracy: 0.5688 - val_loss: 0.1579 - val_accuracy: 1.0000\n",
            "Epoch 34/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2610 - accuracy: 0.5688 - val_loss: 0.1643 - val_accuracy: 0.9630\n",
            "Epoch 35/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2614 - accuracy: 0.5688 - val_loss: 0.1537 - val_accuracy: 0.9630\n",
            "Epoch 36/100\n",
            "27/27 [==============================] - 8s 298ms/step - loss: 0.2663 - accuracy: 0.5688 - val_loss: 0.1364 - val_accuracy: 0.9630\n",
            "Epoch 37/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2555 - accuracy: 0.5688 - val_loss: 0.1752 - val_accuracy: 0.8889\n",
            "Epoch 38/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2550 - accuracy: 0.5688 - val_loss: 0.1754 - val_accuracy: 0.9630\n",
            "Epoch 39/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2641 - accuracy: 0.5688 - val_loss: 0.2052 - val_accuracy: 0.8889\n",
            "Epoch 40/100\n",
            "27/27 [==============================] - 8s 298ms/step - loss: 0.2551 - accuracy: 0.5688 - val_loss: 0.2181 - val_accuracy: 0.8519\n",
            "2/2 [==============================] - 1s 114ms/step - loss: 0.1906 - accuracy: 0.9412\n",
            "Epoch 1/100\n",
            "28/28 [==============================] - 12s 330ms/step - loss: 0.5524 - accuracy: 0.4887 - val_loss: 0.2710 - val_accuracy: 0.8519\n",
            "Epoch 2/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.4100 - accuracy: 0.5383 - val_loss: 0.1925 - val_accuracy: 0.9630\n",
            "Epoch 3/100\n",
            "28/28 [==============================] - 8s 300ms/step - loss: 0.3639 - accuracy: 0.5529 - val_loss: 0.1488 - val_accuracy: 1.0000\n",
            "Epoch 4/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.3484 - accuracy: 0.5597 - val_loss: 0.1834 - val_accuracy: 1.0000\n",
            "Epoch 5/100\n",
            "28/28 [==============================] - 8s 300ms/step - loss: 0.3409 - accuracy: 0.5574 - val_loss: 0.1324 - val_accuracy: 1.0000\n",
            "Epoch 6/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.3289 - accuracy: 0.5619 - val_loss: 0.1516 - val_accuracy: 1.0000\n",
            "Epoch 7/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.3137 - accuracy: 0.5676 - val_loss: 0.1913 - val_accuracy: 1.0000\n",
            "Epoch 8/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.3170 - accuracy: 0.5687 - val_loss: 0.1378 - val_accuracy: 1.0000\n",
            "Epoch 9/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.3080 - accuracy: 0.5664 - val_loss: 0.1212 - val_accuracy: 0.9630\n",
            "Epoch 10/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.3091 - accuracy: 0.5676 - val_loss: 0.1696 - val_accuracy: 0.9630\n",
            "Epoch 11/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.3072 - accuracy: 0.5687 - val_loss: 0.1290 - val_accuracy: 0.9630\n",
            "Epoch 12/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2932 - accuracy: 0.5664 - val_loss: 0.1876 - val_accuracy: 0.9630\n",
            "Epoch 13/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2912 - accuracy: 0.5698 - val_loss: 0.2037 - val_accuracy: 0.9630\n",
            "Epoch 14/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2870 - accuracy: 0.5698 - val_loss: 0.1345 - val_accuracy: 0.9630\n",
            "Epoch 15/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.2901 - accuracy: 0.5698 - val_loss: 0.0876 - val_accuracy: 1.0000\n",
            "Epoch 16/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2863 - accuracy: 0.5664 - val_loss: 0.2719 - val_accuracy: 0.9630\n",
            "Epoch 17/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2811 - accuracy: 0.5698 - val_loss: 0.1937 - val_accuracy: 0.9630\n",
            "Epoch 18/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2831 - accuracy: 0.5698 - val_loss: 0.2924 - val_accuracy: 0.9259\n",
            "Epoch 19/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2779 - accuracy: 0.5698 - val_loss: 0.1659 - val_accuracy: 0.9630\n",
            "Epoch 20/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2791 - accuracy: 0.5698 - val_loss: 0.2005 - val_accuracy: 0.9630\n",
            "Epoch 21/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2843 - accuracy: 0.5698 - val_loss: 0.1074 - val_accuracy: 0.9630\n",
            "Epoch 22/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2835 - accuracy: 0.5698 - val_loss: 0.1042 - val_accuracy: 0.9630\n",
            "Epoch 23/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2714 - accuracy: 0.5698 - val_loss: 0.1490 - val_accuracy: 0.9630\n",
            "Epoch 24/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2827 - accuracy: 0.5698 - val_loss: 0.2517 - val_accuracy: 0.9630\n",
            "Epoch 25/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.2761 - accuracy: 0.5698 - val_loss: 0.2503 - val_accuracy: 0.8889\n",
            "Epoch 26/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2693 - accuracy: 0.5698 - val_loss: 0.2818 - val_accuracy: 0.8889\n",
            "Epoch 27/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2761 - accuracy: 0.5687 - val_loss: 0.3089 - val_accuracy: 0.9259\n",
            "Epoch 28/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2702 - accuracy: 0.5698 - val_loss: 0.2001 - val_accuracy: 0.9630\n",
            "Epoch 29/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2756 - accuracy: 0.5698 - val_loss: 0.2159 - val_accuracy: 0.9630\n",
            "Epoch 30/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.2742 - accuracy: 0.5698 - val_loss: 0.1249 - val_accuracy: 0.9630\n",
            "2/2 [==============================] - 1s 113ms/step - loss: 0.1307 - accuracy: 0.9412\n",
            "Epoch 1/100\n",
            "28/28 [==============================] - 12s 329ms/step - loss: 0.5126 - accuracy: 0.5125 - val_loss: 0.2616 - val_accuracy: 0.8889\n",
            "Epoch 2/100\n",
            "28/28 [==============================] - 8s 300ms/step - loss: 0.3776 - accuracy: 0.5476 - val_loss: 0.2184 - val_accuracy: 0.9259\n",
            "Epoch 3/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.3773 - accuracy: 0.5556 - val_loss: 0.2729 - val_accuracy: 1.0000\n",
            "Epoch 4/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.3727 - accuracy: 0.5567 - val_loss: 0.2338 - val_accuracy: 0.9259\n",
            "Epoch 5/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.3374 - accuracy: 0.5635 - val_loss: 0.1880 - val_accuracy: 1.0000\n",
            "Epoch 6/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.3252 - accuracy: 0.5692 - val_loss: 0.2349 - val_accuracy: 0.9259\n",
            "Epoch 7/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.3168 - accuracy: 0.5760 - val_loss: 0.1949 - val_accuracy: 0.9259\n",
            "Epoch 8/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.3143 - accuracy: 0.5726 - val_loss: 0.1525 - val_accuracy: 1.0000\n",
            "Epoch 9/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.2971 - accuracy: 0.5782 - val_loss: 0.1574 - val_accuracy: 0.9630\n",
            "Epoch 10/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2972 - accuracy: 0.5782 - val_loss: 0.1699 - val_accuracy: 1.0000\n",
            "Epoch 11/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2911 - accuracy: 0.5782 - val_loss: 0.1615 - val_accuracy: 0.9630\n",
            "Epoch 12/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.2911 - accuracy: 0.5771 - val_loss: 0.1189 - val_accuracy: 0.9630\n",
            "Epoch 13/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2838 - accuracy: 0.5782 - val_loss: 0.1437 - val_accuracy: 0.9630\n",
            "Epoch 14/100\n",
            "28/28 [==============================] - 8s 300ms/step - loss: 0.2896 - accuracy: 0.5760 - val_loss: 0.1014 - val_accuracy: 1.0000\n",
            "Epoch 15/100\n",
            "28/28 [==============================] - 8s 300ms/step - loss: 0.2830 - accuracy: 0.5771 - val_loss: 0.0813 - val_accuracy: 1.0000\n",
            "Epoch 16/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2854 - accuracy: 0.5760 - val_loss: 0.1539 - val_accuracy: 0.9630\n",
            "Epoch 17/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2798 - accuracy: 0.5771 - val_loss: 0.1607 - val_accuracy: 1.0000\n",
            "Epoch 18/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2822 - accuracy: 0.5782 - val_loss: 0.1504 - val_accuracy: 1.0000\n",
            "Epoch 19/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.2811 - accuracy: 0.5771 - val_loss: 0.1152 - val_accuracy: 1.0000\n",
            "Epoch 20/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2837 - accuracy: 0.5771 - val_loss: 0.1171 - val_accuracy: 1.0000\n",
            "Epoch 21/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2838 - accuracy: 0.5771 - val_loss: 0.2037 - val_accuracy: 0.9259\n",
            "Epoch 22/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2764 - accuracy: 0.5771 - val_loss: 0.2033 - val_accuracy: 0.8889\n",
            "Epoch 23/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2721 - accuracy: 0.5782 - val_loss: 0.1388 - val_accuracy: 0.9259\n",
            "Epoch 24/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2644 - accuracy: 0.5771 - val_loss: 0.1114 - val_accuracy: 1.0000\n",
            "Epoch 25/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2685 - accuracy: 0.5782 - val_loss: 0.1450 - val_accuracy: 0.9259\n",
            "Epoch 26/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2737 - accuracy: 0.5771 - val_loss: 0.1192 - val_accuracy: 1.0000\n",
            "Epoch 27/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.2709 - accuracy: 0.5782 - val_loss: 0.0973 - val_accuracy: 1.0000\n",
            "Epoch 28/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2652 - accuracy: 0.5782 - val_loss: 0.1403 - val_accuracy: 0.9630\n",
            "Epoch 29/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2650 - accuracy: 0.5782 - val_loss: 0.1407 - val_accuracy: 0.8889\n",
            "Epoch 30/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.2574 - accuracy: 0.5782 - val_loss: 0.1339 - val_accuracy: 0.8889\n",
            "2/2 [==============================] - 1s 112ms/step - loss: 0.0448 - accuracy: 1.0000\n",
            "Epoch 1/100\n",
            "27/27 [==============================] - 12s 330ms/step - loss: 0.6017 - accuracy: 0.4695 - val_loss: 0.4987 - val_accuracy: 0.7778\n",
            "Epoch 2/100\n",
            "27/27 [==============================] - 8s 300ms/step - loss: 0.4146 - accuracy: 0.5552 - val_loss: 0.2836 - val_accuracy: 0.8889\n",
            "Epoch 3/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.3839 - accuracy: 0.5634 - val_loss: 0.4717 - val_accuracy: 0.8148\n",
            "Epoch 4/100\n",
            "27/27 [==============================] - 8s 298ms/step - loss: 0.3626 - accuracy: 0.5751 - val_loss: 0.2972 - val_accuracy: 0.8889\n",
            "Epoch 5/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.3450 - accuracy: 0.5657 - val_loss: 0.4014 - val_accuracy: 0.7778\n",
            "Epoch 6/100\n",
            "27/27 [==============================] - 8s 299ms/step - loss: 0.3336 - accuracy: 0.5716 - val_loss: 0.2322 - val_accuracy: 0.8889\n",
            "Epoch 7/100\n",
            "27/27 [==============================] - 8s 299ms/step - loss: 0.3133 - accuracy: 0.5845 - val_loss: 0.2125 - val_accuracy: 0.9259\n",
            "Epoch 8/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.3191 - accuracy: 0.5822 - val_loss: 0.3080 - val_accuracy: 0.8519\n",
            "Epoch 9/100\n",
            "27/27 [==============================] - 8s 299ms/step - loss: 0.3094 - accuracy: 0.5763 - val_loss: 0.1943 - val_accuracy: 0.8889\n",
            "Epoch 10/100\n",
            "27/27 [==============================] - 8s 299ms/step - loss: 0.3113 - accuracy: 0.5810 - val_loss: 0.1690 - val_accuracy: 0.9259\n",
            "Epoch 11/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.3246 - accuracy: 0.5845 - val_loss: 0.2401 - val_accuracy: 0.8889\n",
            "Epoch 12/100\n",
            "27/27 [==============================] - 8s 299ms/step - loss: 0.2949 - accuracy: 0.5845 - val_loss: 0.1291 - val_accuracy: 0.9630\n",
            "Epoch 13/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2839 - accuracy: 0.5869 - val_loss: 0.1309 - val_accuracy: 0.9630\n",
            "Epoch 14/100\n",
            "27/27 [==============================] - 8s 300ms/step - loss: 0.2808 - accuracy: 0.5869 - val_loss: 0.1262 - val_accuracy: 0.9630\n",
            "Epoch 15/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2819 - accuracy: 0.5869 - val_loss: 0.1955 - val_accuracy: 0.8889\n",
            "Epoch 16/100\n",
            "27/27 [==============================] - 8s 299ms/step - loss: 0.2802 - accuracy: 0.5869 - val_loss: 0.1209 - val_accuracy: 0.9630\n",
            "Epoch 17/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2832 - accuracy: 0.5845 - val_loss: 0.1330 - val_accuracy: 0.9630\n",
            "Epoch 18/100\n",
            "27/27 [==============================] - 8s 299ms/step - loss: 0.2783 - accuracy: 0.5869 - val_loss: 0.1128 - val_accuracy: 0.9630\n",
            "Epoch 19/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2801 - accuracy: 0.5833 - val_loss: 0.1521 - val_accuracy: 0.9259\n",
            "Epoch 20/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2741 - accuracy: 0.5869 - val_loss: 0.1976 - val_accuracy: 0.9259\n",
            "Epoch 21/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2796 - accuracy: 0.5869 - val_loss: 0.2617 - val_accuracy: 0.8519\n",
            "Epoch 22/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2748 - accuracy: 0.5857 - val_loss: 0.2683 - val_accuracy: 0.8889\n",
            "Epoch 23/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2721 - accuracy: 0.5857 - val_loss: 0.2682 - val_accuracy: 0.8148\n",
            "Epoch 24/100\n",
            "27/27 [==============================] - 8s 298ms/step - loss: 0.2701 - accuracy: 0.5869 - val_loss: 0.1271 - val_accuracy: 0.9630\n",
            "Epoch 25/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2757 - accuracy: 0.5857 - val_loss: 0.2309 - val_accuracy: 0.9259\n",
            "Epoch 26/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2697 - accuracy: 0.5869 - val_loss: 0.1833 - val_accuracy: 0.8889\n",
            "Epoch 27/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2703 - accuracy: 0.5869 - val_loss: 0.1377 - val_accuracy: 0.9630\n",
            "Epoch 28/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2651 - accuracy: 0.5869 - val_loss: 0.1658 - val_accuracy: 0.9630\n",
            "Epoch 29/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2584 - accuracy: 0.5869 - val_loss: 0.1731 - val_accuracy: 0.8889\n",
            "Epoch 30/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2621 - accuracy: 0.5869 - val_loss: 0.1827 - val_accuracy: 0.9259\n",
            "Epoch 31/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2636 - accuracy: 0.5869 - val_loss: 0.1605 - val_accuracy: 0.9259\n",
            "Epoch 32/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2667 - accuracy: 0.5869 - val_loss: 0.1363 - val_accuracy: 0.9630\n",
            "Epoch 33/100\n",
            "27/27 [==============================] - 8s 298ms/step - loss: 0.2647 - accuracy: 0.5869 - val_loss: 0.1200 - val_accuracy: 0.9630\n",
            "2/2 [==============================] - 1s 114ms/step - loss: 0.0909 - accuracy: 0.9706\n",
            "[0.970588231086731, 0.9588235139846801, 0.9470588207244873]\n",
            "[0.9647771023302939, 0.9505298199907063, 0.9378996319745626]\n",
            "Epoch 1/100\n",
            "28/28 [==============================] - 12s 326ms/step - loss: 0.5835 - accuracy: 0.4726 - val_loss: 0.1716 - val_accuracy: 0.9259\n",
            "Epoch 2/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.4238 - accuracy: 0.5468 - val_loss: 0.1575 - val_accuracy: 0.9259\n",
            "Epoch 3/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.3863 - accuracy: 0.5559 - val_loss: 0.1913 - val_accuracy: 0.9259\n",
            "Epoch 4/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.3709 - accuracy: 0.5731 - val_loss: 0.1783 - val_accuracy: 1.0000\n",
            "Epoch 5/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.3582 - accuracy: 0.5776 - val_loss: 0.1692 - val_accuracy: 1.0000\n",
            "Epoch 6/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.3362 - accuracy: 0.5765 - val_loss: 0.2190 - val_accuracy: 0.8889\n",
            "Epoch 7/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.3214 - accuracy: 0.5856 - val_loss: 0.2159 - val_accuracy: 0.9259\n",
            "Epoch 8/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.3167 - accuracy: 0.5833 - val_loss: 0.1691 - val_accuracy: 0.9630\n",
            "Epoch 9/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.3135 - accuracy: 0.5868 - val_loss: 0.1760 - val_accuracy: 0.9259\n",
            "Epoch 10/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.3097 - accuracy: 0.5902 - val_loss: 0.1692 - val_accuracy: 0.9630\n",
            "Epoch 11/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.3030 - accuracy: 0.5902 - val_loss: 0.2454 - val_accuracy: 0.8889\n",
            "Epoch 12/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.3043 - accuracy: 0.5868 - val_loss: 0.2659 - val_accuracy: 0.8519\n",
            "Epoch 13/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2985 - accuracy: 0.5868 - val_loss: 0.1425 - val_accuracy: 0.9630\n",
            "Epoch 14/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2923 - accuracy: 0.5913 - val_loss: 0.2045 - val_accuracy: 0.9259\n",
            "Epoch 15/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2943 - accuracy: 0.5890 - val_loss: 0.1143 - val_accuracy: 1.0000\n",
            "Epoch 16/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2884 - accuracy: 0.5913 - val_loss: 0.1361 - val_accuracy: 0.9630\n",
            "Epoch 17/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2865 - accuracy: 0.5913 - val_loss: 0.1887 - val_accuracy: 0.9259\n",
            "Epoch 18/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2785 - accuracy: 0.5902 - val_loss: 0.1403 - val_accuracy: 0.9259\n",
            "Epoch 19/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2824 - accuracy: 0.5902 - val_loss: 0.1291 - val_accuracy: 0.9630\n",
            "Epoch 20/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2805 - accuracy: 0.5902 - val_loss: 0.1153 - val_accuracy: 0.9630\n",
            "Epoch 21/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2846 - accuracy: 0.5913 - val_loss: 0.0839 - val_accuracy: 1.0000\n",
            "Epoch 22/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.2727 - accuracy: 0.5913 - val_loss: 0.0808 - val_accuracy: 0.9630\n",
            "Epoch 23/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2667 - accuracy: 0.5913 - val_loss: 0.1672 - val_accuracy: 0.9630\n",
            "Epoch 24/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2673 - accuracy: 0.5902 - val_loss: 0.1194 - val_accuracy: 0.9630\n",
            "Epoch 25/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2680 - accuracy: 0.5913 - val_loss: 0.0857 - val_accuracy: 1.0000\n",
            "Epoch 26/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2728 - accuracy: 0.5902 - val_loss: 0.1053 - val_accuracy: 1.0000\n",
            "Epoch 27/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2618 - accuracy: 0.5913 - val_loss: 0.1212 - val_accuracy: 0.9630\n",
            "Epoch 28/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2636 - accuracy: 0.5913 - val_loss: 0.0848 - val_accuracy: 1.0000\n",
            "Epoch 29/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2668 - accuracy: 0.5913 - val_loss: 0.1066 - val_accuracy: 0.9630\n",
            "Epoch 30/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2604 - accuracy: 0.5913 - val_loss: 0.1004 - val_accuracy: 1.0000\n",
            "Epoch 31/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2605 - accuracy: 0.5913 - val_loss: 0.0957 - val_accuracy: 0.9630\n",
            "Epoch 32/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.2636 - accuracy: 0.5913 - val_loss: 0.0688 - val_accuracy: 0.9630\n",
            "Epoch 33/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2607 - accuracy: 0.5913 - val_loss: 0.0919 - val_accuracy: 0.9630\n",
            "Epoch 34/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2516 - accuracy: 0.5913 - val_loss: 0.0953 - val_accuracy: 0.9630\n",
            "Epoch 35/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2606 - accuracy: 0.5913 - val_loss: 0.1097 - val_accuracy: 1.0000\n",
            "Epoch 36/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2559 - accuracy: 0.5913 - val_loss: 0.1038 - val_accuracy: 0.9630\n",
            "Epoch 37/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2573 - accuracy: 0.5913 - val_loss: 0.0941 - val_accuracy: 0.9630\n",
            "Epoch 38/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2540 - accuracy: 0.5913 - val_loss: 0.1046 - val_accuracy: 0.9630\n",
            "Epoch 39/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2586 - accuracy: 0.5913 - val_loss: 0.1100 - val_accuracy: 0.9630\n",
            "Epoch 40/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2577 - accuracy: 0.5913 - val_loss: 0.1943 - val_accuracy: 0.9630\n",
            "Epoch 41/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2590 - accuracy: 0.5913 - val_loss: 0.1277 - val_accuracy: 0.9259\n",
            "Epoch 42/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2506 - accuracy: 0.5913 - val_loss: 0.1752 - val_accuracy: 0.9630\n",
            "Epoch 43/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2525 - accuracy: 0.5913 - val_loss: 0.1081 - val_accuracy: 0.9630\n",
            "Epoch 44/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2517 - accuracy: 0.5913 - val_loss: 0.1130 - val_accuracy: 0.9630\n",
            "Epoch 45/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2531 - accuracy: 0.5913 - val_loss: 0.1517 - val_accuracy: 0.9630\n",
            "Epoch 46/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2526 - accuracy: 0.5913 - val_loss: 0.1641 - val_accuracy: 1.0000\n",
            "Epoch 47/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.2492 - accuracy: 0.5913 - val_loss: 0.1643 - val_accuracy: 0.9630\n",
            "2/2 [==============================] - 1s 115ms/step - loss: 0.0574 - accuracy: 1.0000\n",
            "Epoch 1/100\n",
            "27/27 [==============================] - 12s 330ms/step - loss: 0.6110 - accuracy: 0.4744 - val_loss: 0.5341 - val_accuracy: 0.7407\n",
            "Epoch 2/100\n",
            "27/27 [==============================] - 8s 300ms/step - loss: 0.4317 - accuracy: 0.5303 - val_loss: 0.3309 - val_accuracy: 0.8148\n",
            "Epoch 3/100\n",
            "27/27 [==============================] - 8s 300ms/step - loss: 0.3635 - accuracy: 0.5676 - val_loss: 0.2650 - val_accuracy: 0.9259\n",
            "Epoch 4/100\n",
            "27/27 [==============================] - 8s 300ms/step - loss: 0.3460 - accuracy: 0.5664 - val_loss: 0.2605 - val_accuracy: 0.8519\n",
            "Epoch 5/100\n",
            "27/27 [==============================] - 8s 301ms/step - loss: 0.3288 - accuracy: 0.5711 - val_loss: 0.2388 - val_accuracy: 0.9259\n",
            "Epoch 6/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.3194 - accuracy: 0.5711 - val_loss: 0.2782 - val_accuracy: 0.8519\n",
            "Epoch 7/100\n",
            "27/27 [==============================] - 8s 300ms/step - loss: 0.3054 - accuracy: 0.5793 - val_loss: 0.2222 - val_accuracy: 0.9259\n",
            "Epoch 8/100\n",
            "27/27 [==============================] - 8s 300ms/step - loss: 0.3081 - accuracy: 0.5746 - val_loss: 0.2188 - val_accuracy: 0.8519\n",
            "Epoch 9/100\n",
            "27/27 [==============================] - 8s 300ms/step - loss: 0.3016 - accuracy: 0.5828 - val_loss: 0.2163 - val_accuracy: 0.8519\n",
            "Epoch 10/100\n",
            "27/27 [==============================] - 8s 300ms/step - loss: 0.2946 - accuracy: 0.5793 - val_loss: 0.1885 - val_accuracy: 0.8889\n",
            "Epoch 11/100\n",
            "27/27 [==============================] - 8s 299ms/step - loss: 0.2944 - accuracy: 0.5816 - val_loss: 0.1817 - val_accuracy: 0.9630\n",
            "Epoch 12/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2864 - accuracy: 0.5828 - val_loss: 0.2335 - val_accuracy: 0.8519\n",
            "Epoch 13/100\n",
            "27/27 [==============================] - 8s 300ms/step - loss: 0.2797 - accuracy: 0.5804 - val_loss: 0.1658 - val_accuracy: 0.8889\n",
            "Epoch 14/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2767 - accuracy: 0.5816 - val_loss: 0.2467 - val_accuracy: 0.8148\n",
            "Epoch 15/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2717 - accuracy: 0.5828 - val_loss: 0.1906 - val_accuracy: 0.8889\n",
            "Epoch 16/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2801 - accuracy: 0.5828 - val_loss: 0.3328 - val_accuracy: 0.8148\n",
            "Epoch 17/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2817 - accuracy: 0.5816 - val_loss: 0.2787 - val_accuracy: 0.7778\n",
            "Epoch 18/100\n",
            "27/27 [==============================] - 8s 299ms/step - loss: 0.2734 - accuracy: 0.5828 - val_loss: 0.1470 - val_accuracy: 0.9630\n",
            "Epoch 19/100\n",
            "27/27 [==============================] - 8s 298ms/step - loss: 0.2813 - accuracy: 0.5828 - val_loss: 0.1131 - val_accuracy: 1.0000\n",
            "Epoch 20/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2767 - accuracy: 0.5828 - val_loss: 0.1187 - val_accuracy: 1.0000\n",
            "Epoch 21/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2688 - accuracy: 0.5828 - val_loss: 0.1518 - val_accuracy: 0.9259\n",
            "Epoch 22/100\n",
            "27/27 [==============================] - 8s 298ms/step - loss: 0.2651 - accuracy: 0.5828 - val_loss: 0.1215 - val_accuracy: 1.0000\n",
            "Epoch 23/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2671 - accuracy: 0.5828 - val_loss: 0.1527 - val_accuracy: 0.9259\n",
            "Epoch 24/100\n",
            "27/27 [==============================] - 8s 298ms/step - loss: 0.2640 - accuracy: 0.5828 - val_loss: 0.1842 - val_accuracy: 0.9259\n",
            "Epoch 25/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2716 - accuracy: 0.5828 - val_loss: 0.1291 - val_accuracy: 0.9630\n",
            "Epoch 26/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2619 - accuracy: 0.5828 - val_loss: 0.1559 - val_accuracy: 0.9630\n",
            "Epoch 27/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2653 - accuracy: 0.5828 - val_loss: 0.1718 - val_accuracy: 0.8889\n",
            "Epoch 28/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2638 - accuracy: 0.5828 - val_loss: 0.1662 - val_accuracy: 0.9259\n",
            "Epoch 29/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2691 - accuracy: 0.5828 - val_loss: 0.1457 - val_accuracy: 0.9259\n",
            "Epoch 30/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2660 - accuracy: 0.5828 - val_loss: 0.2020 - val_accuracy: 0.8889\n",
            "Epoch 31/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2587 - accuracy: 0.5828 - val_loss: 0.2174 - val_accuracy: 0.8148\n",
            "Epoch 32/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2642 - accuracy: 0.5816 - val_loss: 0.2428 - val_accuracy: 0.7778\n",
            "Epoch 33/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2655 - accuracy: 0.5828 - val_loss: 0.1380 - val_accuracy: 1.0000\n",
            "Epoch 34/100\n",
            "27/27 [==============================] - 8s 300ms/step - loss: 0.2561 - accuracy: 0.5828 - val_loss: 0.0941 - val_accuracy: 1.0000\n",
            "Epoch 35/100\n",
            "27/27 [==============================] - 8s 300ms/step - loss: 0.2573 - accuracy: 0.5828 - val_loss: 0.0733 - val_accuracy: 1.0000\n",
            "Epoch 36/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2632 - accuracy: 0.5828 - val_loss: 0.1240 - val_accuracy: 0.9630\n",
            "Epoch 37/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2578 - accuracy: 0.5828 - val_loss: 0.2066 - val_accuracy: 0.8148\n",
            "Epoch 38/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2654 - accuracy: 0.5828 - val_loss: 0.1763 - val_accuracy: 0.9259\n",
            "Epoch 39/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2644 - accuracy: 0.5828 - val_loss: 0.1052 - val_accuracy: 1.0000\n",
            "Epoch 40/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2555 - accuracy: 0.5828 - val_loss: 0.1108 - val_accuracy: 1.0000\n",
            "Epoch 41/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2542 - accuracy: 0.5828 - val_loss: 0.1332 - val_accuracy: 1.0000\n",
            "Epoch 42/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2506 - accuracy: 0.5828 - val_loss: 0.1414 - val_accuracy: 0.9630\n",
            "Epoch 43/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2519 - accuracy: 0.5828 - val_loss: 0.1585 - val_accuracy: 0.9630\n",
            "Epoch 44/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2587 - accuracy: 0.5828 - val_loss: 0.1142 - val_accuracy: 1.0000\n",
            "Epoch 45/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2517 - accuracy: 0.5828 - val_loss: 0.1207 - val_accuracy: 1.0000\n",
            "Epoch 46/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2533 - accuracy: 0.5828 - val_loss: 0.0836 - val_accuracy: 1.0000\n",
            "Epoch 47/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2489 - accuracy: 0.5828 - val_loss: 0.1288 - val_accuracy: 0.9630\n",
            "Epoch 48/100\n",
            "27/27 [==============================] - 8s 299ms/step - loss: 0.2631 - accuracy: 0.5828 - val_loss: 0.1517 - val_accuracy: 0.9259\n",
            "Epoch 49/100\n",
            "27/27 [==============================] - 8s 298ms/step - loss: 0.2524 - accuracy: 0.5828 - val_loss: 0.1441 - val_accuracy: 0.9630\n",
            "Epoch 50/100\n",
            "27/27 [==============================] - 8s 299ms/step - loss: 0.2535 - accuracy: 0.5828 - val_loss: 0.1254 - val_accuracy: 1.0000\n",
            "2/2 [==============================] - 1s 115ms/step - loss: 0.0799 - accuracy: 0.9706\n",
            "Epoch 1/100\n",
            "28/28 [==============================] - 12s 329ms/step - loss: 0.5565 - accuracy: 0.4863 - val_loss: 0.2523 - val_accuracy: 0.9259\n",
            "Epoch 2/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.4096 - accuracy: 0.5388 - val_loss: 0.2306 - val_accuracy: 0.9630\n",
            "Epoch 3/100\n",
            "28/28 [==============================] - 8s 300ms/step - loss: 0.3732 - accuracy: 0.5571 - val_loss: 0.2274 - val_accuracy: 0.8889\n",
            "Epoch 4/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.3500 - accuracy: 0.5616 - val_loss: 0.2111 - val_accuracy: 0.9259\n",
            "Epoch 5/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.3477 - accuracy: 0.5662 - val_loss: 0.2013 - val_accuracy: 0.9259\n",
            "Epoch 6/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.3249 - accuracy: 0.5674 - val_loss: 0.1502 - val_accuracy: 0.9630\n",
            "Epoch 7/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.3287 - accuracy: 0.5742 - val_loss: 0.1372 - val_accuracy: 0.9630\n",
            "Epoch 8/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.3120 - accuracy: 0.5753 - val_loss: 0.1414 - val_accuracy: 0.9630\n",
            "Epoch 9/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.3058 - accuracy: 0.5765 - val_loss: 0.1397 - val_accuracy: 0.9630\n",
            "Epoch 10/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.2926 - accuracy: 0.5765 - val_loss: 0.1278 - val_accuracy: 0.9630\n",
            "Epoch 11/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2990 - accuracy: 0.5765 - val_loss: 0.1527 - val_accuracy: 0.9630\n",
            "Epoch 12/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.2873 - accuracy: 0.5753 - val_loss: 0.1112 - val_accuracy: 0.9630\n",
            "Epoch 13/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.2954 - accuracy: 0.5742 - val_loss: 0.1001 - val_accuracy: 0.9630\n",
            "Epoch 14/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.2843 - accuracy: 0.5765 - val_loss: 0.0833 - val_accuracy: 0.9630\n",
            "Epoch 15/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.3001 - accuracy: 0.5765 - val_loss: 0.1072 - val_accuracy: 0.9630\n",
            "Epoch 16/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2917 - accuracy: 0.5765 - val_loss: 0.1528 - val_accuracy: 0.9259\n",
            "Epoch 17/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2873 - accuracy: 0.5776 - val_loss: 0.2741 - val_accuracy: 0.8889\n",
            "Epoch 18/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2827 - accuracy: 0.5776 - val_loss: 0.1582 - val_accuracy: 0.9259\n",
            "Epoch 19/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2819 - accuracy: 0.5765 - val_loss: 0.1798 - val_accuracy: 0.9259\n",
            "Epoch 20/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2767 - accuracy: 0.5765 - val_loss: 0.1510 - val_accuracy: 0.9259\n",
            "Epoch 21/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2799 - accuracy: 0.5753 - val_loss: 0.1571 - val_accuracy: 0.9259\n",
            "Epoch 22/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2776 - accuracy: 0.5776 - val_loss: 0.1632 - val_accuracy: 0.9259\n",
            "Epoch 23/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2879 - accuracy: 0.5776 - val_loss: 0.1389 - val_accuracy: 0.9630\n",
            "Epoch 24/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2743 - accuracy: 0.5776 - val_loss: 0.0949 - val_accuracy: 0.9630\n",
            "Epoch 25/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2736 - accuracy: 0.5776 - val_loss: 0.0975 - val_accuracy: 0.9630\n",
            "Epoch 26/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.2673 - accuracy: 0.5776 - val_loss: 0.0833 - val_accuracy: 1.0000\n",
            "Epoch 27/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2658 - accuracy: 0.5776 - val_loss: 0.1447 - val_accuracy: 0.9630\n",
            "Epoch 28/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2666 - accuracy: 0.5776 - val_loss: 0.1003 - val_accuracy: 0.9630\n",
            "Epoch 29/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2555 - accuracy: 0.5776 - val_loss: 0.1464 - val_accuracy: 0.9630\n",
            "Epoch 30/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2618 - accuracy: 0.5776 - val_loss: 0.1287 - val_accuracy: 0.9630\n",
            "Epoch 31/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2713 - accuracy: 0.5776 - val_loss: 0.1208 - val_accuracy: 0.9630\n",
            "Epoch 32/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2660 - accuracy: 0.5765 - val_loss: 0.1157 - val_accuracy: 0.9630\n",
            "Epoch 33/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2638 - accuracy: 0.5776 - val_loss: 0.1172 - val_accuracy: 0.9630\n",
            "Epoch 34/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2674 - accuracy: 0.5776 - val_loss: 0.1215 - val_accuracy: 0.9630\n",
            "Epoch 35/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2681 - accuracy: 0.5765 - val_loss: 0.1227 - val_accuracy: 0.9630\n",
            "Epoch 36/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2680 - accuracy: 0.5776 - val_loss: 0.1499 - val_accuracy: 0.9630\n",
            "Epoch 37/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.2727 - accuracy: 0.5765 - val_loss: 0.0665 - val_accuracy: 1.0000\n",
            "Epoch 38/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2633 - accuracy: 0.5776 - val_loss: 0.1072 - val_accuracy: 0.9630\n",
            "Epoch 39/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2606 - accuracy: 0.5776 - val_loss: 0.1113 - val_accuracy: 0.9630\n",
            "Epoch 40/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2650 - accuracy: 0.5776 - val_loss: 0.1355 - val_accuracy: 0.9630\n",
            "Epoch 41/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2702 - accuracy: 0.5776 - val_loss: 0.1112 - val_accuracy: 0.9630\n",
            "Epoch 42/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2625 - accuracy: 0.5776 - val_loss: 0.1487 - val_accuracy: 0.9630\n",
            "Epoch 43/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2624 - accuracy: 0.5776 - val_loss: 0.1660 - val_accuracy: 0.9630\n",
            "Epoch 44/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2614 - accuracy: 0.5776 - val_loss: 0.1618 - val_accuracy: 0.9630\n",
            "Epoch 45/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2569 - accuracy: 0.5776 - val_loss: 0.1100 - val_accuracy: 0.9630\n",
            "Epoch 46/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2562 - accuracy: 0.5776 - val_loss: 0.1568 - val_accuracy: 0.9630\n",
            "Epoch 47/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2580 - accuracy: 0.5776 - val_loss: 0.1207 - val_accuracy: 0.9259\n",
            "Epoch 48/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2601 - accuracy: 0.5776 - val_loss: 0.1388 - val_accuracy: 0.9259\n",
            "Epoch 49/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2595 - accuracy: 0.5776 - val_loss: 0.1504 - val_accuracy: 0.9630\n",
            "Epoch 50/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2543 - accuracy: 0.5776 - val_loss: 0.0998 - val_accuracy: 0.9630\n",
            "Epoch 51/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2551 - accuracy: 0.5776 - val_loss: 0.1309 - val_accuracy: 0.9630\n",
            "Epoch 52/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2568 - accuracy: 0.5776 - val_loss: 0.1307 - val_accuracy: 0.9630\n",
            "2/2 [==============================] - 1s 112ms/step - loss: 0.1185 - accuracy: 0.9706\n",
            "Epoch 1/100\n",
            "28/28 [==============================] - 12s 327ms/step - loss: 0.5351 - accuracy: 0.5069 - val_loss: 0.2230 - val_accuracy: 0.9630\n",
            "Epoch 2/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.4118 - accuracy: 0.5529 - val_loss: 0.2158 - val_accuracy: 0.9630\n",
            "Epoch 3/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.3826 - accuracy: 0.5598 - val_loss: 0.2289 - val_accuracy: 0.9630\n",
            "Epoch 4/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.3516 - accuracy: 0.5690 - val_loss: 0.2394 - val_accuracy: 0.9630\n",
            "Epoch 5/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.3314 - accuracy: 0.5816 - val_loss: 0.2012 - val_accuracy: 0.9630\n",
            "Epoch 6/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.3291 - accuracy: 0.5793 - val_loss: 0.1589 - val_accuracy: 1.0000\n",
            "Epoch 7/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.3146 - accuracy: 0.5851 - val_loss: 0.1830 - val_accuracy: 0.9630\n",
            "Epoch 8/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.3191 - accuracy: 0.5805 - val_loss: 0.1870 - val_accuracy: 0.9259\n",
            "Epoch 9/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.3134 - accuracy: 0.5839 - val_loss: 0.1337 - val_accuracy: 0.9630\n",
            "Epoch 10/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2984 - accuracy: 0.5862 - val_loss: 0.2728 - val_accuracy: 0.8148\n",
            "Epoch 11/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2881 - accuracy: 0.5897 - val_loss: 0.1930 - val_accuracy: 0.8889\n",
            "Epoch 12/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2856 - accuracy: 0.5897 - val_loss: 0.1471 - val_accuracy: 0.9630\n",
            "Epoch 13/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2827 - accuracy: 0.5908 - val_loss: 0.1310 - val_accuracy: 0.9630\n",
            "Epoch 14/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2846 - accuracy: 0.5908 - val_loss: 0.1443 - val_accuracy: 0.9630\n",
            "Epoch 15/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2772 - accuracy: 0.5897 - val_loss: 0.1428 - val_accuracy: 0.9259\n",
            "Epoch 16/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2804 - accuracy: 0.5897 - val_loss: 0.1091 - val_accuracy: 1.0000\n",
            "Epoch 17/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.2854 - accuracy: 0.5897 - val_loss: 0.1080 - val_accuracy: 0.9630\n",
            "Epoch 18/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2842 - accuracy: 0.5908 - val_loss: 0.0710 - val_accuracy: 1.0000\n",
            "Epoch 19/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2796 - accuracy: 0.5908 - val_loss: 0.1484 - val_accuracy: 0.8889\n",
            "Epoch 20/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2769 - accuracy: 0.5897 - val_loss: 0.1316 - val_accuracy: 0.9259\n",
            "Epoch 21/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2686 - accuracy: 0.5908 - val_loss: 0.1384 - val_accuracy: 0.9259\n",
            "Epoch 22/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2636 - accuracy: 0.5885 - val_loss: 0.0593 - val_accuracy: 1.0000\n",
            "Epoch 23/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2693 - accuracy: 0.5908 - val_loss: 0.0961 - val_accuracy: 1.0000\n",
            "Epoch 24/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2775 - accuracy: 0.5885 - val_loss: 0.0977 - val_accuracy: 1.0000\n",
            "Epoch 25/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2780 - accuracy: 0.5874 - val_loss: 0.2054 - val_accuracy: 0.8889\n",
            "Epoch 26/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2771 - accuracy: 0.5897 - val_loss: 0.0763 - val_accuracy: 0.9630\n",
            "Epoch 27/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2672 - accuracy: 0.5908 - val_loss: 0.0840 - val_accuracy: 0.9630\n",
            "Epoch 28/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2628 - accuracy: 0.5908 - val_loss: 0.0900 - val_accuracy: 1.0000\n",
            "Epoch 29/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2571 - accuracy: 0.5908 - val_loss: 0.1653 - val_accuracy: 0.9259\n",
            "Epoch 30/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2666 - accuracy: 0.5908 - val_loss: 0.1267 - val_accuracy: 0.9259\n",
            "Epoch 31/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2660 - accuracy: 0.5908 - val_loss: 0.0912 - val_accuracy: 1.0000\n",
            "Epoch 32/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2602 - accuracy: 0.5908 - val_loss: 0.1215 - val_accuracy: 1.0000\n",
            "Epoch 33/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2602 - accuracy: 0.5908 - val_loss: 0.0651 - val_accuracy: 1.0000\n",
            "Epoch 34/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2536 - accuracy: 0.5908 - val_loss: 0.1085 - val_accuracy: 1.0000\n",
            "Epoch 35/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2661 - accuracy: 0.5908 - val_loss: 0.1286 - val_accuracy: 0.9259\n",
            "Epoch 36/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2570 - accuracy: 0.5908 - val_loss: 0.1094 - val_accuracy: 1.0000\n",
            "Epoch 37/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2612 - accuracy: 0.5908 - val_loss: 0.1724 - val_accuracy: 0.8889\n",
            "2/2 [==============================] - 1s 113ms/step - loss: 0.1066 - accuracy: 0.9706\n",
            "Epoch 1/100\n",
            "27/27 [==============================] - 12s 330ms/step - loss: 0.5916 - accuracy: 0.4838 - val_loss: 0.7139 - val_accuracy: 0.6296\n",
            "Epoch 2/100\n",
            "27/27 [==============================] - 8s 298ms/step - loss: 0.4237 - accuracy: 0.5475 - val_loss: 0.3656 - val_accuracy: 0.8148\n",
            "Epoch 3/100\n",
            "27/27 [==============================] - 8s 299ms/step - loss: 0.3585 - accuracy: 0.5706 - val_loss: 0.2780 - val_accuracy: 0.8519\n",
            "Epoch 4/100\n",
            "27/27 [==============================] - 8s 298ms/step - loss: 0.3643 - accuracy: 0.5660 - val_loss: 0.2733 - val_accuracy: 0.9630\n",
            "Epoch 5/100\n",
            "27/27 [==============================] - 8s 298ms/step - loss: 0.3403 - accuracy: 0.5764 - val_loss: 0.1940 - val_accuracy: 1.0000\n",
            "Epoch 6/100\n",
            "27/27 [==============================] - 8s 300ms/step - loss: 0.3145 - accuracy: 0.5868 - val_loss: 0.1824 - val_accuracy: 0.9259\n",
            "Epoch 7/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.3261 - accuracy: 0.5833 - val_loss: 0.2152 - val_accuracy: 1.0000\n",
            "Epoch 8/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.3122 - accuracy: 0.5868 - val_loss: 0.1927 - val_accuracy: 0.9630\n",
            "Epoch 9/100\n",
            "27/27 [==============================] - 8s 300ms/step - loss: 0.3055 - accuracy: 0.5868 - val_loss: 0.1424 - val_accuracy: 1.0000\n",
            "Epoch 10/100\n",
            "27/27 [==============================] - 8s 299ms/step - loss: 0.3043 - accuracy: 0.5880 - val_loss: 0.1135 - val_accuracy: 1.0000\n",
            "Epoch 11/100\n",
            "27/27 [==============================] - 8s 299ms/step - loss: 0.2883 - accuracy: 0.5903 - val_loss: 0.1059 - val_accuracy: 1.0000\n",
            "Epoch 12/100\n",
            "27/27 [==============================] - 8s 300ms/step - loss: 0.2872 - accuracy: 0.5891 - val_loss: 0.0958 - val_accuracy: 1.0000\n",
            "Epoch 13/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2876 - accuracy: 0.5903 - val_loss: 0.1157 - val_accuracy: 1.0000\n",
            "Epoch 14/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2775 - accuracy: 0.5914 - val_loss: 0.0978 - val_accuracy: 1.0000\n",
            "Epoch 15/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2757 - accuracy: 0.5914 - val_loss: 0.1720 - val_accuracy: 0.9630\n",
            "Epoch 16/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2829 - accuracy: 0.5914 - val_loss: 0.4408 - val_accuracy: 0.7778\n",
            "Epoch 17/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2741 - accuracy: 0.5926 - val_loss: 0.1147 - val_accuracy: 1.0000\n",
            "Epoch 18/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2684 - accuracy: 0.5903 - val_loss: 0.1234 - val_accuracy: 1.0000\n",
            "Epoch 19/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2726 - accuracy: 0.5926 - val_loss: 0.1252 - val_accuracy: 0.9630\n",
            "Epoch 20/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2696 - accuracy: 0.5914 - val_loss: 0.1276 - val_accuracy: 0.9630\n",
            "Epoch 21/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2662 - accuracy: 0.5926 - val_loss: 0.1395 - val_accuracy: 1.0000\n",
            "Epoch 22/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.2703 - accuracy: 0.5914 - val_loss: 0.1388 - val_accuracy: 1.0000\n",
            "Epoch 23/100\n",
            "27/27 [==============================] - 8s 298ms/step - loss: 0.2627 - accuracy: 0.5926 - val_loss: 0.0883 - val_accuracy: 1.0000\n",
            "Epoch 24/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2581 - accuracy: 0.5926 - val_loss: 0.0921 - val_accuracy: 1.0000\n",
            "Epoch 25/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2597 - accuracy: 0.5926 - val_loss: 0.1434 - val_accuracy: 0.9630\n",
            "Epoch 26/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2587 - accuracy: 0.5926 - val_loss: 0.1448 - val_accuracy: 0.9630\n",
            "Epoch 27/100\n",
            "27/27 [==============================] - 8s 300ms/step - loss: 0.2568 - accuracy: 0.5926 - val_loss: 0.0865 - val_accuracy: 1.0000\n",
            "Epoch 28/100\n",
            "27/27 [==============================] - 8s 299ms/step - loss: 0.2590 - accuracy: 0.5926 - val_loss: 0.0719 - val_accuracy: 1.0000\n",
            "Epoch 29/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2640 - accuracy: 0.5926 - val_loss: 0.2001 - val_accuracy: 0.9630\n",
            "Epoch 30/100\n",
            "27/27 [==============================] - 8s 299ms/step - loss: 0.2597 - accuracy: 0.5926 - val_loss: 0.0579 - val_accuracy: 1.0000\n",
            "Epoch 31/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2487 - accuracy: 0.5926 - val_loss: 0.1341 - val_accuracy: 1.0000\n",
            "Epoch 32/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2592 - accuracy: 0.5926 - val_loss: 0.2090 - val_accuracy: 0.9259\n",
            "Epoch 33/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2581 - accuracy: 0.5926 - val_loss: 0.1795 - val_accuracy: 0.9259\n",
            "Epoch 34/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2559 - accuracy: 0.5926 - val_loss: 0.1272 - val_accuracy: 1.0000\n",
            "Epoch 35/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2498 - accuracy: 0.5926 - val_loss: 0.0736 - val_accuracy: 1.0000\n",
            "Epoch 36/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2580 - accuracy: 0.5926 - val_loss: 0.0805 - val_accuracy: 1.0000\n",
            "Epoch 37/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2521 - accuracy: 0.5926 - val_loss: 0.2025 - val_accuracy: 0.8889\n",
            "Epoch 38/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2510 - accuracy: 0.5914 - val_loss: 0.2067 - val_accuracy: 0.9259\n",
            "Epoch 39/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2558 - accuracy: 0.5926 - val_loss: 0.1626 - val_accuracy: 0.9630\n",
            "Epoch 40/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2485 - accuracy: 0.5926 - val_loss: 0.1387 - val_accuracy: 0.9630\n",
            "Epoch 41/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2468 - accuracy: 0.5926 - val_loss: 0.1085 - val_accuracy: 0.9630\n",
            "Epoch 42/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2451 - accuracy: 0.5926 - val_loss: 0.1501 - val_accuracy: 1.0000\n",
            "Epoch 43/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2466 - accuracy: 0.5926 - val_loss: 0.1315 - val_accuracy: 1.0000\n",
            "Epoch 44/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2472 - accuracy: 0.5926 - val_loss: 0.0849 - val_accuracy: 0.9630\n",
            "Epoch 45/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2491 - accuracy: 0.5926 - val_loss: 0.1027 - val_accuracy: 0.9630\n",
            "2/2 [==============================] - 1s 115ms/step - loss: 0.0903 - accuracy: 0.9706\n",
            "[0.970588231086731, 0.9588235139846801, 0.9470588207244873, 0.9764705657958984]\n",
            "[0.9647771023302939, 0.9505298199907063, 0.9378996319745626, 0.9727612586817027]\n",
            "Epoch 1/100\n",
            "28/28 [==============================] - 12s 327ms/step - loss: 0.5406 - accuracy: 0.4989 - val_loss: 0.4494 - val_accuracy: 0.7407\n",
            "Epoch 2/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.4175 - accuracy: 0.5388 - val_loss: 0.3675 - val_accuracy: 0.7407\n",
            "Epoch 3/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.3792 - accuracy: 0.5616 - val_loss: 0.3167 - val_accuracy: 0.8519\n",
            "Epoch 4/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.3679 - accuracy: 0.5616 - val_loss: 0.2886 - val_accuracy: 0.8889\n",
            "Epoch 5/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.3240 - accuracy: 0.5822 - val_loss: 0.2980 - val_accuracy: 0.8889\n",
            "Epoch 6/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.3235 - accuracy: 0.5799 - val_loss: 0.2202 - val_accuracy: 0.9259\n",
            "Epoch 7/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.3175 - accuracy: 0.5833 - val_loss: 0.2128 - val_accuracy: 0.9259\n",
            "Epoch 8/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.3258 - accuracy: 0.5731 - val_loss: 0.3496 - val_accuracy: 0.8148\n",
            "Epoch 9/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.3105 - accuracy: 0.5833 - val_loss: 0.2869 - val_accuracy: 0.8889\n",
            "Epoch 10/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.3051 - accuracy: 0.5868 - val_loss: 0.1716 - val_accuracy: 1.0000\n",
            "Epoch 11/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2904 - accuracy: 0.5845 - val_loss: 0.4102 - val_accuracy: 0.7778\n",
            "Epoch 12/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2879 - accuracy: 0.5856 - val_loss: 0.2348 - val_accuracy: 0.8889\n",
            "Epoch 13/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2869 - accuracy: 0.5856 - val_loss: 0.1723 - val_accuracy: 0.8889\n",
            "Epoch 14/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2861 - accuracy: 0.5845 - val_loss: 0.2013 - val_accuracy: 0.8889\n",
            "Epoch 15/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.2918 - accuracy: 0.5856 - val_loss: 0.1680 - val_accuracy: 0.9259\n",
            "Epoch 16/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.2839 - accuracy: 0.5845 - val_loss: 0.1668 - val_accuracy: 0.9259\n",
            "Epoch 17/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2709 - accuracy: 0.5868 - val_loss: 0.2134 - val_accuracy: 0.9259\n",
            "Epoch 18/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2749 - accuracy: 0.5868 - val_loss: 0.2006 - val_accuracy: 0.8889\n",
            "Epoch 19/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2749 - accuracy: 0.5868 - val_loss: 0.1975 - val_accuracy: 0.8889\n",
            "Epoch 20/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2732 - accuracy: 0.5868 - val_loss: 0.2134 - val_accuracy: 0.8889\n",
            "Epoch 21/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2828 - accuracy: 0.5822 - val_loss: 0.2033 - val_accuracy: 0.9259\n",
            "Epoch 22/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2685 - accuracy: 0.5868 - val_loss: 0.1963 - val_accuracy: 0.8889\n",
            "Epoch 23/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2688 - accuracy: 0.5868 - val_loss: 0.1537 - val_accuracy: 0.9630\n",
            "Epoch 24/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2692 - accuracy: 0.5856 - val_loss: 0.2075 - val_accuracy: 0.9259\n",
            "Epoch 25/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2648 - accuracy: 0.5868 - val_loss: 0.1797 - val_accuracy: 0.9259\n",
            "Epoch 26/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2676 - accuracy: 0.5868 - val_loss: 0.2541 - val_accuracy: 0.8889\n",
            "Epoch 27/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2659 - accuracy: 0.5856 - val_loss: 0.2654 - val_accuracy: 0.8889\n",
            "Epoch 28/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2724 - accuracy: 0.5856 - val_loss: 0.1864 - val_accuracy: 0.9259\n",
            "Epoch 29/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2583 - accuracy: 0.5868 - val_loss: 0.1625 - val_accuracy: 0.9259\n",
            "Epoch 30/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2530 - accuracy: 0.5868 - val_loss: 0.1643 - val_accuracy: 0.9259\n",
            "Epoch 31/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2558 - accuracy: 0.5868 - val_loss: 0.1976 - val_accuracy: 0.8889\n",
            "Epoch 32/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2620 - accuracy: 0.5868 - val_loss: 0.1907 - val_accuracy: 0.9630\n",
            "Epoch 33/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2580 - accuracy: 0.5845 - val_loss: 0.1706 - val_accuracy: 0.9259\n",
            "Epoch 34/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2578 - accuracy: 0.5868 - val_loss: 0.1731 - val_accuracy: 0.9259\n",
            "Epoch 35/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2591 - accuracy: 0.5868 - val_loss: 0.1884 - val_accuracy: 0.8889\n",
            "Epoch 36/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2555 - accuracy: 0.5868 - val_loss: 0.2246 - val_accuracy: 0.9630\n",
            "Epoch 37/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2569 - accuracy: 0.5868 - val_loss: 0.1909 - val_accuracy: 0.9630\n",
            "Epoch 38/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2532 - accuracy: 0.5868 - val_loss: 0.2395 - val_accuracy: 0.8889\n",
            "2/2 [==============================] - 1s 113ms/step - loss: 0.0707 - accuracy: 0.9706\n",
            "Epoch 1/100\n",
            "28/28 [==============================] - 12s 327ms/step - loss: 0.5972 - accuracy: 0.4521 - val_loss: 0.2835 - val_accuracy: 0.9259\n",
            "Epoch 2/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.4688 - accuracy: 0.4977 - val_loss: 0.2389 - val_accuracy: 0.9630\n",
            "Epoch 3/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.3928 - accuracy: 0.5263 - val_loss: 0.3442 - val_accuracy: 0.8889\n",
            "Epoch 4/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.3767 - accuracy: 0.5331 - val_loss: 0.2587 - val_accuracy: 0.8519\n",
            "Epoch 5/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.3539 - accuracy: 0.5377 - val_loss: 0.2166 - val_accuracy: 0.9630\n",
            "Epoch 6/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.3548 - accuracy: 0.5331 - val_loss: 0.2678 - val_accuracy: 0.9259\n",
            "Epoch 7/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.3359 - accuracy: 0.5479 - val_loss: 0.2871 - val_accuracy: 0.9259\n",
            "Epoch 8/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.3353 - accuracy: 0.5445 - val_loss: 0.2156 - val_accuracy: 0.9259\n",
            "Epoch 9/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.3247 - accuracy: 0.5502 - val_loss: 0.2342 - val_accuracy: 0.9259\n",
            "Epoch 10/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.3227 - accuracy: 0.5468 - val_loss: 0.1798 - val_accuracy: 0.9259\n",
            "Epoch 11/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.3220 - accuracy: 0.5491 - val_loss: 0.1583 - val_accuracy: 0.9630\n",
            "Epoch 12/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.3128 - accuracy: 0.5491 - val_loss: 0.1651 - val_accuracy: 0.9259\n",
            "Epoch 13/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.3079 - accuracy: 0.5502 - val_loss: 0.1304 - val_accuracy: 0.9630\n",
            "Epoch 14/100\n",
            "28/28 [==============================] - 8s 301ms/step - loss: 0.3170 - accuracy: 0.5502 - val_loss: 0.1511 - val_accuracy: 0.9259\n",
            "Epoch 15/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.3079 - accuracy: 0.5491 - val_loss: 0.1993 - val_accuracy: 0.9259\n",
            "Epoch 16/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.3005 - accuracy: 0.5502 - val_loss: 0.1352 - val_accuracy: 0.9259\n",
            "Epoch 17/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2907 - accuracy: 0.5502 - val_loss: 0.1693 - val_accuracy: 0.9259\n",
            "Epoch 18/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.3010 - accuracy: 0.5502 - val_loss: 0.1217 - val_accuracy: 0.9630\n",
            "Epoch 19/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.2939 - accuracy: 0.5502 - val_loss: 0.1020 - val_accuracy: 0.9630\n",
            "Epoch 20/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2952 - accuracy: 0.5502 - val_loss: 0.1434 - val_accuracy: 0.9630\n",
            "Epoch 21/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2898 - accuracy: 0.5502 - val_loss: 0.1102 - val_accuracy: 0.9630\n",
            "Epoch 22/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2889 - accuracy: 0.5502 - val_loss: 0.1654 - val_accuracy: 0.9259\n",
            "Epoch 23/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2877 - accuracy: 0.5502 - val_loss: 0.1507 - val_accuracy: 0.9259\n",
            "Epoch 24/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2880 - accuracy: 0.5502 - val_loss: 0.1197 - val_accuracy: 0.9630\n",
            "Epoch 25/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2881 - accuracy: 0.5502 - val_loss: 0.1152 - val_accuracy: 0.9259\n",
            "Epoch 26/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2826 - accuracy: 0.5502 - val_loss: 0.1098 - val_accuracy: 0.9630\n",
            "Epoch 27/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2846 - accuracy: 0.5491 - val_loss: 0.1946 - val_accuracy: 0.9259\n",
            "Epoch 28/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2866 - accuracy: 0.5502 - val_loss: 0.1089 - val_accuracy: 0.9630\n",
            "Epoch 29/100\n",
            "28/28 [==============================] - 8s 293ms/step - loss: 0.2843 - accuracy: 0.5502 - val_loss: 0.1531 - val_accuracy: 0.9259\n",
            "Epoch 30/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2841 - accuracy: 0.5502 - val_loss: 0.1267 - val_accuracy: 0.9630\n",
            "Epoch 31/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2759 - accuracy: 0.5502 - val_loss: 0.1349 - val_accuracy: 1.0000\n",
            "Epoch 32/100\n",
            "28/28 [==============================] - 8s 294ms/step - loss: 0.2805 - accuracy: 0.5502 - val_loss: 0.2444 - val_accuracy: 0.9259\n",
            "Epoch 33/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2827 - accuracy: 0.5502 - val_loss: 0.1253 - val_accuracy: 0.9259\n",
            "Epoch 34/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2790 - accuracy: 0.5502 - val_loss: 0.1255 - val_accuracy: 0.9630\n",
            "2/2 [==============================] - 1s 113ms/step - loss: 0.0855 - accuracy: 1.0000\n",
            "Epoch 1/100\n",
            "28/28 [==============================] - 12s 329ms/step - loss: 0.5622 - accuracy: 0.5011 - val_loss: 0.4147 - val_accuracy: 0.8148\n",
            "Epoch 2/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.4014 - accuracy: 0.5473 - val_loss: 0.3677 - val_accuracy: 0.8889\n",
            "Epoch 3/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.3887 - accuracy: 0.5563 - val_loss: 0.2190 - val_accuracy: 0.9259\n",
            "Epoch 4/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.3490 - accuracy: 0.5631 - val_loss: 0.2861 - val_accuracy: 0.9259\n",
            "Epoch 5/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.3266 - accuracy: 0.5721 - val_loss: 0.2495 - val_accuracy: 0.9259\n",
            "Epoch 6/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.3164 - accuracy: 0.5721 - val_loss: 0.1970 - val_accuracy: 0.9630\n",
            "Epoch 7/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.3146 - accuracy: 0.5732 - val_loss: 0.2330 - val_accuracy: 0.9630\n",
            "Epoch 8/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.3100 - accuracy: 0.5709 - val_loss: 0.1498 - val_accuracy: 0.9630\n",
            "Epoch 9/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.3091 - accuracy: 0.5721 - val_loss: 0.1304 - val_accuracy: 1.0000\n",
            "Epoch 10/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2975 - accuracy: 0.5721 - val_loss: 0.2437 - val_accuracy: 0.9259\n",
            "Epoch 11/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2872 - accuracy: 0.5755 - val_loss: 0.1345 - val_accuracy: 0.9630\n",
            "Epoch 12/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2985 - accuracy: 0.5766 - val_loss: 0.1452 - val_accuracy: 0.9630\n",
            "Epoch 13/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.2872 - accuracy: 0.5755 - val_loss: 0.1261 - val_accuracy: 0.9630\n",
            "Epoch 14/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2930 - accuracy: 0.5766 - val_loss: 0.1802 - val_accuracy: 0.9259\n",
            "Epoch 15/100\n",
            "28/28 [==============================] - 8s 298ms/step - loss: 0.2827 - accuracy: 0.5766 - val_loss: 0.1161 - val_accuracy: 0.9630\n",
            "Epoch 16/100\n",
            "28/28 [==============================] - 8s 299ms/step - loss: 0.2807 - accuracy: 0.5766 - val_loss: 0.0986 - val_accuracy: 0.9630\n",
            "Epoch 17/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2713 - accuracy: 0.5766 - val_loss: 0.1308 - val_accuracy: 0.9630\n",
            "Epoch 18/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2739 - accuracy: 0.5755 - val_loss: 0.1327 - val_accuracy: 0.9259\n",
            "Epoch 19/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2737 - accuracy: 0.5766 - val_loss: 0.1528 - val_accuracy: 0.9630\n",
            "Epoch 20/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2850 - accuracy: 0.5743 - val_loss: 0.1001 - val_accuracy: 0.9630\n",
            "Epoch 21/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2807 - accuracy: 0.5766 - val_loss: 0.0988 - val_accuracy: 1.0000\n",
            "Epoch 22/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2674 - accuracy: 0.5743 - val_loss: 0.1326 - val_accuracy: 0.9630\n",
            "Epoch 23/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2682 - accuracy: 0.5766 - val_loss: 0.1280 - val_accuracy: 0.9259\n",
            "Epoch 24/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2629 - accuracy: 0.5766 - val_loss: 0.1264 - val_accuracy: 0.9259\n",
            "Epoch 25/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2713 - accuracy: 0.5766 - val_loss: 0.1123 - val_accuracy: 0.9630\n",
            "Epoch 26/100\n",
            "28/28 [==============================] - 8s 300ms/step - loss: 0.2731 - accuracy: 0.5766 - val_loss: 0.0669 - val_accuracy: 0.9630\n",
            "Epoch 27/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2714 - accuracy: 0.5766 - val_loss: 0.1439 - val_accuracy: 0.9630\n",
            "Epoch 28/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2584 - accuracy: 0.5766 - val_loss: 0.1123 - val_accuracy: 0.9630\n",
            "Epoch 29/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2621 - accuracy: 0.5766 - val_loss: 0.1365 - val_accuracy: 0.9259\n",
            "Epoch 30/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2623 - accuracy: 0.5766 - val_loss: 0.1696 - val_accuracy: 0.9259\n",
            "Epoch 31/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2620 - accuracy: 0.5766 - val_loss: 0.1110 - val_accuracy: 0.9630\n",
            "Epoch 32/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2578 - accuracy: 0.5766 - val_loss: 0.1173 - val_accuracy: 0.9630\n",
            "Epoch 33/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2696 - accuracy: 0.5755 - val_loss: 0.1066 - val_accuracy: 1.0000\n",
            "Epoch 34/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2624 - accuracy: 0.5766 - val_loss: 0.1187 - val_accuracy: 0.9630\n",
            "Epoch 35/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2616 - accuracy: 0.5766 - val_loss: 0.1017 - val_accuracy: 0.9630\n",
            "Epoch 36/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2617 - accuracy: 0.5766 - val_loss: 0.1532 - val_accuracy: 0.9630\n",
            "Epoch 37/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2588 - accuracy: 0.5766 - val_loss: 0.1263 - val_accuracy: 0.9630\n",
            "Epoch 38/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2576 - accuracy: 0.5766 - val_loss: 0.1276 - val_accuracy: 0.9630\n",
            "Epoch 39/100\n",
            "28/28 [==============================] - 8s 295ms/step - loss: 0.2547 - accuracy: 0.5766 - val_loss: 0.1535 - val_accuracy: 0.9259\n",
            "Epoch 40/100\n",
            "28/28 [==============================] - 8s 296ms/step - loss: 0.2554 - accuracy: 0.5766 - val_loss: 0.1614 - val_accuracy: 0.9259\n",
            "Epoch 41/100\n",
            "28/28 [==============================] - 8s 297ms/step - loss: 0.2641 - accuracy: 0.5766 - val_loss: 0.1645 - val_accuracy: 0.9630\n",
            "2/2 [==============================] - 1s 107ms/step - loss: 0.1027 - accuracy: 0.9706\n",
            "Epoch 1/100\n",
            "27/27 [==============================] - 12s 332ms/step - loss: 0.5538 - accuracy: 0.5047 - val_loss: 0.6471 - val_accuracy: 0.5926\n",
            "Epoch 2/100\n",
            "27/27 [==============================] - 8s 300ms/step - loss: 0.3947 - accuracy: 0.5540 - val_loss: 0.2650 - val_accuracy: 0.9259\n",
            "Epoch 3/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.3693 - accuracy: 0.5587 - val_loss: 0.3173 - val_accuracy: 0.8889\n",
            "Epoch 4/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.3256 - accuracy: 0.5716 - val_loss: 0.3800 - val_accuracy: 0.8148\n",
            "Epoch 5/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.3314 - accuracy: 0.5681 - val_loss: 0.3229 - val_accuracy: 0.8889\n",
            "Epoch 6/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.3228 - accuracy: 0.5634 - val_loss: 0.3165 - val_accuracy: 0.8519\n",
            "Epoch 7/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.3214 - accuracy: 0.5669 - val_loss: 0.3082 - val_accuracy: 0.9259\n",
            "Epoch 8/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.3083 - accuracy: 0.5728 - val_loss: 0.2737 - val_accuracy: 0.9259\n",
            "Epoch 9/100\n",
            "27/27 [==============================] - 8s 299ms/step - loss: 0.3153 - accuracy: 0.5692 - val_loss: 0.2565 - val_accuracy: 0.9630\n",
            "Epoch 10/100\n",
            "27/27 [==============================] - 8s 299ms/step - loss: 0.3051 - accuracy: 0.5751 - val_loss: 0.2246 - val_accuracy: 0.9630\n",
            "Epoch 11/100\n",
            "27/27 [==============================] - 8s 300ms/step - loss: 0.3015 - accuracy: 0.5739 - val_loss: 0.2225 - val_accuracy: 0.8889\n",
            "Epoch 12/100\n",
            "27/27 [==============================] - 8s 300ms/step - loss: 0.2929 - accuracy: 0.5728 - val_loss: 0.2087 - val_accuracy: 0.9630\n",
            "Epoch 13/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2876 - accuracy: 0.5751 - val_loss: 0.2372 - val_accuracy: 0.9259\n",
            "Epoch 14/100\n",
            "27/27 [==============================] - 8s 298ms/step - loss: 0.2869 - accuracy: 0.5751 - val_loss: 0.1976 - val_accuracy: 0.9630\n",
            "Epoch 15/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2862 - accuracy: 0.5751 - val_loss: 0.2864 - val_accuracy: 0.9259\n",
            "Epoch 16/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2927 - accuracy: 0.5751 - val_loss: 0.2317 - val_accuracy: 0.9630\n",
            "Epoch 17/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2844 - accuracy: 0.5751 - val_loss: 0.2274 - val_accuracy: 0.9630\n",
            "Epoch 18/100\n",
            "27/27 [==============================] - 8s 299ms/step - loss: 0.2879 - accuracy: 0.5739 - val_loss: 0.1915 - val_accuracy: 0.9630\n",
            "Epoch 19/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.2739 - accuracy: 0.5751 - val_loss: 0.2174 - val_accuracy: 0.9259\n",
            "Epoch 20/100\n",
            "27/27 [==============================] - 8s 299ms/step - loss: 0.2802 - accuracy: 0.5751 - val_loss: 0.1769 - val_accuracy: 0.9630\n",
            "Epoch 21/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2725 - accuracy: 0.5751 - val_loss: 0.2220 - val_accuracy: 0.9630\n",
            "Epoch 22/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2732 - accuracy: 0.5751 - val_loss: 0.2025 - val_accuracy: 0.9259\n",
            "Epoch 23/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2668 - accuracy: 0.5751 - val_loss: 0.2194 - val_accuracy: 0.9630\n",
            "Epoch 24/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2764 - accuracy: 0.5751 - val_loss: 0.2560 - val_accuracy: 0.8889\n",
            "Epoch 25/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2639 - accuracy: 0.5751 - val_loss: 0.2818 - val_accuracy: 0.9259\n",
            "Epoch 26/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2714 - accuracy: 0.5751 - val_loss: 0.2484 - val_accuracy: 0.9259\n",
            "Epoch 27/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2719 - accuracy: 0.5751 - val_loss: 0.3326 - val_accuracy: 0.8519\n",
            "Epoch 28/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2740 - accuracy: 0.5751 - val_loss: 0.3494 - val_accuracy: 0.8519\n",
            "Epoch 29/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2611 - accuracy: 0.5751 - val_loss: 0.2535 - val_accuracy: 0.9259\n",
            "Epoch 30/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2686 - accuracy: 0.5751 - val_loss: 0.2601 - val_accuracy: 0.9259\n",
            "Epoch 31/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2629 - accuracy: 0.5751 - val_loss: 0.3018 - val_accuracy: 0.8519\n",
            "Epoch 32/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2672 - accuracy: 0.5751 - val_loss: 0.2345 - val_accuracy: 0.9630\n",
            "Epoch 33/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2650 - accuracy: 0.5751 - val_loss: 0.2168 - val_accuracy: 0.9630\n",
            "Epoch 34/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2621 - accuracy: 0.5739 - val_loss: 0.2996 - val_accuracy: 0.8889\n",
            "Epoch 35/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2729 - accuracy: 0.5751 - val_loss: 0.3173 - val_accuracy: 0.8889\n",
            "2/2 [==============================] - 1s 114ms/step - loss: 0.2830 - accuracy: 0.8824\n",
            "Epoch 1/100\n",
            "27/27 [==============================] - 12s 331ms/step - loss: 0.5215 - accuracy: 0.4977 - val_loss: 0.2199 - val_accuracy: 0.8889\n",
            "Epoch 2/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.4196 - accuracy: 0.5315 - val_loss: 0.2979 - val_accuracy: 0.9259\n",
            "Epoch 3/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.3818 - accuracy: 0.5431 - val_loss: 0.2271 - val_accuracy: 1.0000\n",
            "Epoch 4/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.3626 - accuracy: 0.5490 - val_loss: 0.2241 - val_accuracy: 0.9630\n",
            "Epoch 5/100\n",
            "27/27 [==============================] - 8s 299ms/step - loss: 0.3540 - accuracy: 0.5478 - val_loss: 0.2117 - val_accuracy: 0.9630\n",
            "Epoch 6/100\n",
            "27/27 [==============================] - 8s 300ms/step - loss: 0.3597 - accuracy: 0.5548 - val_loss: 0.2086 - val_accuracy: 0.9630\n",
            "Epoch 7/100\n",
            "27/27 [==============================] - 8s 299ms/step - loss: 0.3357 - accuracy: 0.5536 - val_loss: 0.1825 - val_accuracy: 0.9630\n",
            "Epoch 8/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.3257 - accuracy: 0.5559 - val_loss: 0.2118 - val_accuracy: 0.9259\n",
            "Epoch 9/100\n",
            "27/27 [==============================] - 8s 299ms/step - loss: 0.3149 - accuracy: 0.5606 - val_loss: 0.1580 - val_accuracy: 0.9630\n",
            "Epoch 10/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.3213 - accuracy: 0.5583 - val_loss: 0.1615 - val_accuracy: 0.9630\n",
            "Epoch 11/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.3206 - accuracy: 0.5583 - val_loss: 0.1601 - val_accuracy: 0.9630\n",
            "Epoch 12/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.3013 - accuracy: 0.5641 - val_loss: 0.1623 - val_accuracy: 0.9630\n",
            "Epoch 13/100\n",
            "27/27 [==============================] - 8s 300ms/step - loss: 0.3030 - accuracy: 0.5618 - val_loss: 0.1329 - val_accuracy: 0.9630\n",
            "Epoch 14/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2937 - accuracy: 0.5618 - val_loss: 0.1546 - val_accuracy: 0.9630\n",
            "Epoch 15/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.3011 - accuracy: 0.5641 - val_loss: 0.1581 - val_accuracy: 0.9630\n",
            "Epoch 16/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2890 - accuracy: 0.5629 - val_loss: 0.1686 - val_accuracy: 0.9630\n",
            "Epoch 17/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2908 - accuracy: 0.5629 - val_loss: 0.1771 - val_accuracy: 0.9630\n",
            "Epoch 18/100\n",
            "27/27 [==============================] - 8s 300ms/step - loss: 0.2879 - accuracy: 0.5618 - val_loss: 0.1192 - val_accuracy: 0.9630\n",
            "Epoch 19/100\n",
            "27/27 [==============================] - 8s 299ms/step - loss: 0.2836 - accuracy: 0.5641 - val_loss: 0.1076 - val_accuracy: 0.9630\n",
            "Epoch 20/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2878 - accuracy: 0.5629 - val_loss: 0.1206 - val_accuracy: 0.9630\n",
            "Epoch 21/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2831 - accuracy: 0.5629 - val_loss: 0.1472 - val_accuracy: 0.9630\n",
            "Epoch 22/100\n",
            "27/27 [==============================] - 8s 299ms/step - loss: 0.2785 - accuracy: 0.5629 - val_loss: 0.0900 - val_accuracy: 0.9630\n",
            "Epoch 23/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2728 - accuracy: 0.5641 - val_loss: 0.1702 - val_accuracy: 0.9259\n",
            "Epoch 24/100\n",
            "27/27 [==============================] - 8s 297ms/step - loss: 0.2776 - accuracy: 0.5641 - val_loss: 0.1696 - val_accuracy: 0.9259\n",
            "Epoch 25/100\n",
            "27/27 [==============================] - 8s 301ms/step - loss: 0.2830 - accuracy: 0.5641 - val_loss: 0.0745 - val_accuracy: 1.0000\n",
            "Epoch 26/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2847 - accuracy: 0.5641 - val_loss: 0.1300 - val_accuracy: 0.9630\n",
            "Epoch 27/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2767 - accuracy: 0.5641 - val_loss: 0.1291 - val_accuracy: 0.9630\n",
            "Epoch 28/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2875 - accuracy: 0.5641 - val_loss: 0.1525 - val_accuracy: 0.9630\n",
            "Epoch 29/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2811 - accuracy: 0.5629 - val_loss: 0.1734 - val_accuracy: 0.9630\n",
            "Epoch 30/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2742 - accuracy: 0.5641 - val_loss: 0.1314 - val_accuracy: 0.9630\n",
            "Epoch 31/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2710 - accuracy: 0.5641 - val_loss: 0.1151 - val_accuracy: 0.9630\n",
            "Epoch 32/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2672 - accuracy: 0.5641 - val_loss: 0.1431 - val_accuracy: 0.9630\n",
            "Epoch 33/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2700 - accuracy: 0.5641 - val_loss: 0.1359 - val_accuracy: 0.9630\n",
            "Epoch 34/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2674 - accuracy: 0.5641 - val_loss: 0.1196 - val_accuracy: 0.9630\n",
            "Epoch 35/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2817 - accuracy: 0.5641 - val_loss: 0.1540 - val_accuracy: 0.9630\n",
            "Epoch 36/100\n",
            "27/27 [==============================] - 8s 294ms/step - loss: 0.2680 - accuracy: 0.5641 - val_loss: 0.1142 - val_accuracy: 0.9630\n",
            "Epoch 37/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2685 - accuracy: 0.5641 - val_loss: 0.1153 - val_accuracy: 0.9630\n",
            "Epoch 38/100\n",
            "27/27 [==============================] - 8s 295ms/step - loss: 0.2648 - accuracy: 0.5641 - val_loss: 0.1011 - val_accuracy: 0.9630\n",
            "Epoch 39/100\n",
            "27/27 [==============================] - 8s 296ms/step - loss: 0.2777 - accuracy: 0.5641 - val_loss: 0.1147 - val_accuracy: 0.9630\n",
            "Epoch 40/100\n",
            "27/27 [==============================] - 8s 298ms/step - loss: 0.2675 - accuracy: 0.5641 - val_loss: 0.1627 - val_accuracy: 0.9630\n",
            "2/2 [==============================] - 1s 111ms/step - loss: 0.1129 - accuracy: 0.9706\n",
            "[0.970588231086731, 0.9588235139846801, 0.9470588207244873, 0.9764705657958984, 0.9588235139846801]\n",
            "[0.9647771023302939, 0.9505298199907063, 0.9378996319745626, 0.9727612586817027, 0.9530928686895382]\n",
            "Mean test accuracy is 0.96, mean test f1 score is 0.96, max test accuracy is 0.98, max test f1 score is 0.97, min test accuracy is 0.95, min test f1 score is 0.94, std of test accuracy is 0.01, std of test f1 score is 0.01\n",
            "Time elapsed through all process: 8529.44, sec\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0N7v4PDP4Dv2"
      },
      "source": [
        "print(f'Mean test accuracy is {\"{:.3f}\".format(sum(test_accuracy_per_run)/number_of_repeat)}, mean test f1 score is {\"{:.3f}\".format(sum(f1_score_per_run)/number_of_repeat)}, \\\n",
        "max test accuracy is {\"{:.3f}\".format(max(test_accuracy_per_run))}, max test f1 score is {\"{:.3f}\".format(max(f1_score_per_run))}, \\\n",
        "min test accuracy is {\"{:.3f}\".format(min(test_accuracy_per_run))}, min test f1 score is {\"{:.3f}\".format(min(f1_score_per_run))}, \\\n",
        "std of test accuracy is {\"{:.3f}\".format(np.std(test_accuracy_per_run, axis=0))}, std of test f1 score is {\"{:.3f}\".format(np.std(f1_score_per_run, axis=0))}')\n",
        "# print(f'Time elapsed through all process: {\"{:.3f}\".format(elapsed)}, sec')"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}