howto/3_classification/classification_training_guide.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Train gesture classification model\n",
    "\n",
    "This script contains all necessary code to train a gesture classification model based on the\n",
    "MobileNet architecture. Some lines of code like paths have to be adjusted for your case.\n",
    "All needed adjustments are marked with \"Todo\".\n",
    "\n",
    "Before executing this script the necessary dataset called LaRED has to be loaded. A complete data preprocessing\n",
    "script can be found under data_preprocessing/data_preprocessing_lared.ipynb."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.preprocessing import image\n",
    "import numpy as np\n",
    "import cv2\n",
    "\n",
    "import csv\n",
    "import pandas as pd\n",
    "\n",
    "import tensorflow as tf\n",
    "from keras_preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.callbacks import ReduceLROnPlateau, CSVLogger, ModelCheckpoint\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ToDo: Only necessary if you changed the selected gestures in the script data_preprocessing_lared.ipynb.\n",
    "# adjust the names and quantity of the gestures here too.\n",
    "\n",
    "INDEX_TO_LABEL = {\n",
    "    0 :\"crawl\",\n",
    "    1 :\"fist\",\n",
    "    2 :\"five\",\n",
    "    3 :\"palm\",\n",
    "    4 :\"peace\",\n",
    "\n",
    "}\n",
    "\n",
    "# Todo: If you changed the amount of gestures in data_preprocessing_lared.ipynb change the number here too.\n",
    "NUMBER_OF_CLASSES = 5\n",
    "NUMBER_OF_PROBANDS_IN_TESTSET = 9\n",
    "\n",
    "BATCH_SIZE=32\n",
    "\n",
    "IMAGE_WIDTH = 64\n",
    "IMAGE_HEIGHT = 64\n",
    "\n",
    "TRAINING_DIR = \"C:/datasets/lared/cropped_gesture64x64/train\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Load data\n",
    "\n",
    "The training time is reduced considerably when loading images into memory instead of reading them from disk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "crawl\n",
      "fist\n",
      "five\n",
      "palm\n",
      "peace\n",
      "Train images      (12150, 64, 64, 3) Train labels 12150\n"
     ]
    }
   ],
   "source": [
    "def images_to_array(path):\n",
    "    \"\"\" This method takes a path containing images in different folders representing the classes.\n",
    "    Parameters:\n",
    "        path (str): path to image folder\n",
    "    Returns:\n",
    "        Two arrays containing the images and corresponding labels\n",
    "     \"\"\"\n",
    "    images = []\n",
    "    labels = []\n",
    "\n",
    "    curr_label = 0\n",
    "    \n",
    "    # iterate over gesture folders\n",
    "    for dir_gesture in os.listdir(path):\n",
    "        curr_dir = os.path.join(path, dir_gesture)\n",
    "        curr_files = os.listdir(curr_dir)\n",
    "        \n",
    "        print(dir_gesture)\n",
    "        \n",
    "        for file in curr_files:\n",
    "            img_path = os.path.join(curr_dir, file)\n",
    "            image_as_array = cv2.imread(img_path)\n",
    "            image_as_array = cv2.resize(image_as_array, (IMAGE_HEIGHT,IMAGE_WIDTH))\n",
    "            image_as_array = cv2.cvtColor(image_as_array, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "            images.append(image_as_array)\n",
    "            # labels have to be saved in categorical format for training\n",
    "            labels.append(tf.keras.utils.to_categorical(curr_label,NUMBER_OF_CLASSES))\n",
    "  \n",
    "        curr_label = curr_label + 1\n",
    "        \n",
    "    return np.array(images), labels\n",
    "\n",
    "train_images, train_labels =  images_to_array(TRAINING_DIR)\n",
    "print(\"Train images     \",  np.array(train_images).shape, \"Train labels\",  len(train_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Setup and test data augmentation\n",
    "\n",
    "The image is being randomly flipped horizontally and rotated up to 20 degrees to recognize slightly \n",
    "rotated hands and also left hands, since the dataset contains only right-hand gestures. \n",
    "In addition, the images are being slightly and randomly shifted in width and zoomed in or \n",
    "out for the case where the detection model does not align the bounding box perfectly. \n",
    "And lastly the brightness of the input images is being randomly manipulated to allow \n",
    "to recognize gestures under challenging conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_datagen = ImageDataGenerator(\n",
    "      rescale = 1./255,\n",
    "      rotation_range=20,\n",
    "      width_shift_range=0.1,\n",
    "      zoom_range=(0.7, 1.1),\n",
    "      horizontal_flip=True,\n",
    "      brightness_range=(0.5,1.5),\n",
    "      fill_mode='nearest',\n",
    ")\n",
    "\n",
    "# validation data is not augmented\n",
    "val_datagen = ImageDataGenerator(rescale = 1./255)\n",
    "\n",
    "# test data augmentation by plotting some example augmentations\n",
    "i = 0\n",
    "plt.figure(figsize=(16,16))\n",
    "for batch in train_datagen.flow(np.array(train_images), batch_size=32):\n",
    "    plot_position = \"1\"+str(NUMBER_OF_CLASSES)+str(i+1)\n",
    "    plt.subplot(plot_position)\n",
    "    imgplot = plt.imshow(image.array_to_img(batch[0]))\n",
    "    i += 1\n",
    "    if i % NUMBER_OF_CLASSES == 0:\n",
    "        break\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_fold(current_fold, train_images, train_labels):\n",
    "    \"\"\"\n",
    "    The training set is structured as follows: gesture1 [subject1, subject2 .. subject9], gesture2 [subject1, ..\n",
    "    This method takes one dedicated subject for validation and the rest for training. current fold defines,\n",
    "    which subject is used for validation.\n",
    "    \n",
    "    Parameters:\n",
    "        current_fold (int): number form 0 to total number of folds \n",
    "        train_images [array]: images as array \n",
    "        train_labels [array]: labels as array \n",
    "    Returns:\n",
    "         images of 8 subjects for training and 1 for validation.\n",
    "    \"\"\"\n",
    "    \n",
    "    k = NUMBER_OF_PROBANDS_IN_TESTSET\n",
    "    num_samples_per_gesture = round(len(train_images)/NUMBER_OF_CLASSES)\n",
    "\n",
    "    fold_size = round(num_samples_per_gesture/k)\n",
    "\n",
    "    partial_train_images, val_images, partial_train_labels, val_labels = [], [], [], []\n",
    "    \n",
    "    # patch together all gestures by subject\n",
    "    for i in range(NUMBER_OF_CLASSES):\n",
    "        \n",
    "        start_index = num_samples_per_gesture * i\n",
    "        train_images_current_gesture = train_images[start_index:start_index+num_samples_per_gesture]\n",
    "        train_labels_current_gesture = train_labels[start_index:start_index+num_samples_per_gesture]\n",
    "\n",
    "        val_images.extend(train_images_current_gesture[current_fold*fold_size:(current_fold+1)*fold_size])\n",
    "        partial_train_images.extend(train_images_current_gesture[:current_fold*fold_size])\n",
    "        partial_train_images.extend(train_images_current_gesture[(current_fold+1)*fold_size:])\n",
    "  \n",
    "        val_labels.extend(train_labels_current_gesture[current_fold*fold_size:(current_fold+1)*fold_size])\n",
    "        partial_train_labels.extend(train_labels_current_gesture[:current_fold*fold_size])\n",
    "        partial_train_labels.extend(train_labels_current_gesture[(current_fold+1)*fold_size:])\n",
    "\n",
    "    return partial_train_images, val_images, partial_train_labels, val_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Test fold generation\n",
    "\n",
    "Test if the fold has been created correctly by showing the boundaries of the fold.\n",
    "We plot the last index of the fold and the first index of the ramaining subjects. There subject should differ.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10800 1350\n",
      "10800 1350\n",
      "first index val\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "last index val\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first index train\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "last index train\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "FOLD_TO_TEST = 0\n",
    "\n",
    "plt.rc('font', size=12)\n",
    "\n",
    "partial_train_images, val_images, partial_train_labels, val_labels = get_fold(FOLD_TO_TEST, train_images, train_labels)\n",
    "print(len(partial_train_images), len(val_images))\n",
    "print(len(partial_train_labels), len(val_labels))\n",
    "\n",
    "\n",
    "plt.figure(figsize=(16,16))\n",
    "number_samles_per_gesture_val = int(len(val_images)/NUMBER_OF_CLASSES)\n",
    "print(\"first index val\")\n",
    "for i in range(NUMBER_OF_CLASSES):\n",
    "    index = number_samles_per_gesture_val * i\n",
    "    plot_position = \"1\"+str(NUMBER_OF_CLASSES)+str(i+1)\n",
    "    plt.subplot(plot_position)\n",
    "    text = INDEX_TO_LABEL[np.argmax(val_labels[index])]\n",
    "    plt.text(5,5,text, bbox={'facecolor': 'red', 'alpha': 0.5, 'pad': 2})\n",
    "    plt.imshow(val_images[index])\n",
    "plt.show()\n",
    "\n",
    "\n",
    "plt.figure(figsize=(16,16))\n",
    "print(\"last index val\")\n",
    "for i in range(NUMBER_OF_CLASSES):\n",
    "    index = number_samles_per_gesture_val * i + number_samles_per_gesture_val-1\n",
    "    plot_position = \"1\"+str(NUMBER_OF_CLASSES)+str(i+1)\n",
    "    \n",
    "    plt.subplot(plot_position)\n",
    "    text = INDEX_TO_LABEL[np.argmax(val_labels[index])]\n",
    "    plt.text(10,5,text, bbox={'facecolor': 'red', 'alpha': 0.5, 'pad': 2})\n",
    "    plt.imshow(val_images[index])\n",
    "\n",
    "plt.show()\n",
    "\n",
    "number_samles_per_gesture_train = int(len(partial_train_images)/NUMBER_OF_CLASSES)\n",
    "plt.figure(figsize=(16,16))\n",
    "print(\"first index train\")\n",
    "for i in range(NUMBER_OF_CLASSES):\n",
    "    index = number_samles_per_gesture_train * i\n",
    "    plot_position = \"1\"+str(NUMBER_OF_CLASSES)+str(i+1)\n",
    "    \n",
    "    plt.subplot(plot_position)\n",
    "    text = INDEX_TO_LABEL[np.argmax(partial_train_labels[index])]\n",
    "    plt.text(5,5,text, bbox={'facecolor': 'red', 'alpha': 0.5, 'pad': 2})\n",
    "    plt.imshow(partial_train_images[index])\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(16,16))\n",
    "print(\"last index train\")\n",
    "for i in range(NUMBER_OF_CLASSES):\n",
    "    index = number_samles_per_gesture_train * i + number_samles_per_gesture_train-1\n",
    "    plot_position = \"1\"+str(NUMBER_OF_CLASSES)+str(i+1)\n",
    "    \n",
    "    plt.subplot(plot_position)\n",
    "    text = INDEX_TO_LABEL[np.argmax(partial_train_labels[index])]\n",
    "    plt.text(5,5,text, bbox={'facecolor': 'red', 'alpha': 0.5, 'pad': 2})\n",
    "    plt.imshow(partial_train_images[index])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"mobilenet_0.50_64\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 64, 64, 3)]       0         \n",
      "_________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)    (None, 65, 65, 3)         0         \n",
      "_________________________________________________________________\n",
      "conv1 (Conv2D)               (None, 32, 32, 16)        432       \n",
      "_________________________________________________________________\n",
      "conv1_bn (BatchNormalization (None, 32, 32, 16)        64        \n",
      "_________________________________________________________________\n",
      "conv1_relu (ReLU)            (None, 32, 32, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv_dw_1 (DepthwiseConv2D)  (None, 32, 32, 16)        144       \n",
      "_________________________________________________________________\n",
      "conv_dw_1_bn (BatchNormaliza (None, 32, 32, 16)        64        \n",
      "_________________________________________________________________\n",
      "conv_dw_1_relu (ReLU)        (None, 32, 32, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv_pw_1 (Conv2D)           (None, 32, 32, 32)        512       \n",
      "_________________________________________________________________\n",
      "conv_pw_1_bn (BatchNormaliza (None, 32, 32, 32)        128       \n",
      "_________________________________________________________________\n",
      "conv_pw_1_relu (ReLU)        (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv_pad_2 (ZeroPadding2D)   (None, 33, 33, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv_dw_2 (DepthwiseConv2D)  (None, 16, 16, 32)        288       \n",
      "_________________________________________________________________\n",
      "conv_dw_2_bn (BatchNormaliza (None, 16, 16, 32)        128       \n",
      "_________________________________________________________________\n",
      "conv_dw_2_relu (ReLU)        (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv_pw_2 (Conv2D)           (None, 16, 16, 64)        2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_2_bn (BatchNormaliza (None, 16, 16, 64)        256       \n",
      "_________________________________________________________________\n",
      "conv_pw_2_relu (ReLU)        (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv_dw_3 (DepthwiseConv2D)  (None, 16, 16, 64)        576       \n",
      "_________________________________________________________________\n",
      "conv_dw_3_bn (BatchNormaliza (None, 16, 16, 64)        256       \n",
      "_________________________________________________________________\n",
      "conv_dw_3_relu (ReLU)        (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv_pw_3 (Conv2D)           (None, 16, 16, 64)        4096      \n",
      "_________________________________________________________________\n",
      "conv_pw_3_bn (BatchNormaliza (None, 16, 16, 64)        256       \n",
      "_________________________________________________________________\n",
      "conv_pw_3_relu (ReLU)        (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv_pad_4 (ZeroPadding2D)   (None, 17, 17, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv_dw_4 (DepthwiseConv2D)  (None, 8, 8, 64)          576       \n",
      "_________________________________________________________________\n",
      "conv_dw_4_bn (BatchNormaliza (None, 8, 8, 64)          256       \n",
      "_________________________________________________________________\n",
      "conv_dw_4_relu (ReLU)        (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv_pw_4 (Conv2D)           (None, 8, 8, 128)         8192      \n",
      "_________________________________________________________________\n",
      "conv_pw_4_bn (BatchNormaliza (None, 8, 8, 128)         512       \n",
      "_________________________________________________________________\n",
      "conv_pw_4_relu (ReLU)        (None, 8, 8, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv_dw_5 (DepthwiseConv2D)  (None, 8, 8, 128)         1152      \n",
      "_________________________________________________________________\n",
      "conv_dw_5_bn (BatchNormaliza (None, 8, 8, 128)         512       \n",
      "_________________________________________________________________\n",
      "conv_dw_5_relu (ReLU)        (None, 8, 8, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv_pw_5 (Conv2D)           (None, 8, 8, 128)         16384     \n",
      "_________________________________________________________________\n",
      "conv_pw_5_bn (BatchNormaliza (None, 8, 8, 128)         512       \n",
      "_________________________________________________________________\n",
      "conv_pw_5_relu (ReLU)        (None, 8, 8, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv_pad_6 (ZeroPadding2D)   (None, 9, 9, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv_dw_6 (DepthwiseConv2D)  (None, 4, 4, 128)         1152      \n",
      "_________________________________________________________________\n",
      "conv_dw_6_bn (BatchNormaliza (None, 4, 4, 128)         512       \n",
      "_________________________________________________________________\n",
      "conv_dw_6_relu (ReLU)        (None, 4, 4, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv_pw_6 (Conv2D)           (None, 4, 4, 256)         32768     \n",
      "_________________________________________________________________\n",
      "conv_pw_6_bn (BatchNormaliza (None, 4, 4, 256)         1024      \n",
      "_________________________________________________________________\n",
      "conv_pw_6_relu (ReLU)        (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "conv_dw_7 (DepthwiseConv2D)  (None, 4, 4, 256)         2304      \n",
      "_________________________________________________________________\n",
      "conv_dw_7_bn (BatchNormaliza (None, 4, 4, 256)         1024      \n",
      "_________________________________________________________________\n",
      "conv_dw_7_relu (ReLU)        (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "conv_pw_7 (Conv2D)           (None, 4, 4, 256)         65536     \n",
      "_________________________________________________________________\n",
      "conv_pw_7_bn (BatchNormaliza (None, 4, 4, 256)         1024      \n",
      "_________________________________________________________________\n",
      "conv_pw_7_relu (ReLU)        (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "conv_dw_8 (DepthwiseConv2D)  (None, 4, 4, 256)         2304      \n",
      "_________________________________________________________________\n",
      "conv_dw_8_bn (BatchNormaliza (None, 4, 4, 256)         1024      \n",
      "_________________________________________________________________\n",
      "conv_dw_8_relu (ReLU)        (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "conv_pw_8 (Conv2D)           (None, 4, 4, 256)         65536     \n",
      "_________________________________________________________________\n",
      "conv_pw_8_bn (BatchNormaliza (None, 4, 4, 256)         1024      \n",
      "_________________________________________________________________\n",
      "conv_pw_8_relu (ReLU)        (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "conv_dw_9 (DepthwiseConv2D)  (None, 4, 4, 256)         2304      \n",
      "_________________________________________________________________\n",
      "conv_dw_9_bn (BatchNormaliza (None, 4, 4, 256)         1024      \n",
      "_________________________________________________________________\n",
      "conv_dw_9_relu (ReLU)        (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "conv_pw_9 (Conv2D)           (None, 4, 4, 256)         65536     \n",
      "_________________________________________________________________\n",
      "conv_pw_9_bn (BatchNormaliza (None, 4, 4, 256)         1024      \n",
      "_________________________________________________________________\n",
      "conv_pw_9_relu (ReLU)        (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "conv_dw_10 (DepthwiseConv2D) (None, 4, 4, 256)         2304      \n",
      "_________________________________________________________________\n",
      "conv_dw_10_bn (BatchNormaliz (None, 4, 4, 256)         1024      \n",
      "_________________________________________________________________\n",
      "conv_dw_10_relu (ReLU)       (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "conv_pw_10 (Conv2D)          (None, 4, 4, 256)         65536     \n",
      "_________________________________________________________________\n",
      "conv_pw_10_bn (BatchNormaliz (None, 4, 4, 256)         1024      \n",
      "_________________________________________________________________\n",
      "conv_pw_10_relu (ReLU)       (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "conv_dw_11 (DepthwiseConv2D) (None, 4, 4, 256)         2304      \n",
      "_________________________________________________________________\n",
      "conv_dw_11_bn (BatchNormaliz (None, 4, 4, 256)         1024      \n",
      "_________________________________________________________________\n",
      "conv_dw_11_relu (ReLU)       (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "conv_pw_11 (Conv2D)          (None, 4, 4, 256)         65536     \n",
      "_________________________________________________________________\n",
      "conv_pw_11_bn (BatchNormaliz (None, 4, 4, 256)         1024      \n",
      "_________________________________________________________________\n",
      "conv_pw_11_relu (ReLU)       (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "conv_pad_12 (ZeroPadding2D)  (None, 5, 5, 256)         0         \n",
      "_________________________________________________________________\n",
      "conv_dw_12 (DepthwiseConv2D) (None, 2, 2, 256)         2304      \n",
      "_________________________________________________________________\n",
      "conv_dw_12_bn (BatchNormaliz (None, 2, 2, 256)         1024      \n",
      "_________________________________________________________________\n",
      "conv_dw_12_relu (ReLU)       (None, 2, 2, 256)         0         \n",
      "_________________________________________________________________\n",
      "conv_pw_12 (Conv2D)          (None, 2, 2, 512)         131072    \n",
      "_________________________________________________________________\n",
      "conv_pw_12_bn (BatchNormaliz (None, 2, 2, 512)         2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_12_relu (ReLU)       (None, 2, 2, 512)         0         \n",
      "_________________________________________________________________\n",
      "conv_dw_13 (DepthwiseConv2D) (None, 2, 2, 512)         4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_13_bn (BatchNormaliz (None, 2, 2, 512)         2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_13_relu (ReLU)       (None, 2, 2, 512)         0         \n",
      "_________________________________________________________________\n",
      "conv_pw_13 (Conv2D)          (None, 2, 2, 512)         262144    \n",
      "_________________________________________________________________\n",
      "conv_pw_13_bn (BatchNormaliz (None, 2, 2, 512)         2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_13_relu (ReLU)       (None, 2, 2, 512)         0         \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d (Gl (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "reshape_1 (Reshape)          (None, 1, 1, 512)         0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 1, 1, 512)         0         \n",
      "_________________________________________________________________\n",
      "conv_preds (Conv2D)          (None, 1, 1, 5)           2565      \n",
      "_________________________________________________________________\n",
      "reshape_2 (Reshape)          (None, 5)                 0         \n",
      "_________________________________________________________________\n",
      "act_softmax (Activation)     (None, 5)                 0         \n",
      "=================================================================\n",
      "Total params: 832,101\n",
      "Trainable params: 821,157\n",
      "Non-trainable params: 10,944\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def get_model():\n",
    "\n",
    "    model = tf.keras.applications.MobileNet(include_top=True, \n",
    "                                            input_shape=(IMAGE_HEIGHT,IMAGE_WIDTH,3),\n",
    "                                            weights=None, # there are no weights available for input size 64x64\n",
    "                                            classes=NUMBER_OF_CLASSES,\n",
    "                                            dropout=0.5,\n",
    "                                            # loads the mobilenet_0.50_64 with only 0.8M params, instead 4.2M\n",
    "                                            alpha=0.5,\n",
    "                                            )\n",
    "\n",
    "    optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)\n",
    "    model.compile(loss = 'categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n",
    "    \n",
    "    return model\n",
    "\n",
    "model = get_model()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Define callbacks\n",
    "ReduceLROnPlateau: \n",
    "The learning rate is beeing reduces when the validation loss does not imporove for a defined number uf epochs.\n",
    "\n",
    "CSVLogger:\n",
    "During training the folds the metrics \"epoch,accuracy,loss,val_accuracy,val_loss\" are beeng logged\n",
    "to a csv file. Those metrics are used to define the best hyperparameters for the final trainng run.\n",
    "\n",
    "Todo: If you start the training on your own, delete the previously generated log file \"mobilenet_training_log.csv\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_callbacks():\n",
    "    rlrop = ReduceLROnPlateau(monitor='val_loss', factor=0.1, verbose=1, patience=33)\n",
    "\n",
    "    csv_logger = CSVLogger(filename='mobilenet_training_log.csv',\n",
    "                           separator=',',\n",
    "                           append=True)\n",
    "    \n",
    "    return [csv_logger, rlrop]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Start K-Fold training\n",
    "\n",
    "The training set was split into 9 partitions/folds, since there are 9 subjects in the training set. \n",
    "This allows to train on images from 8 subjects and validating on the remaining one, \n",
    "which wasn't  used during training. \n",
    "In each fold the a new untrained model is loaded and trained and valildated on another training subset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing fold # 0\n",
      "Epoch 1/100\n",
      "338/338 [==============================] - 56s 166ms/step - loss: 1.5616 - accuracy: 0.3327 - val_loss: 1.7542 - val_accuracy: 0.2000\n",
      "Epoch 2/100\n",
      "338/338 [==============================] - 52s 153ms/step - loss: 0.9730 - accuracy: 0.6105 - val_loss: 1.9121 - val_accuracy: 0.2881\n",
      "Epoch 3/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.5979 - accuracy: 0.7809 - val_loss: 1.1986 - val_accuracy: 0.5437\n",
      "Epoch 4/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.4098 - accuracy: 0.8544 - val_loss: 1.0391 - val_accuracy: 0.6252\n",
      "Epoch 5/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.2840 - accuracy: 0.8976 - val_loss: 1.6259 - val_accuracy: 0.5267\n",
      "Epoch 6/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.2142 - accuracy: 0.9260 - val_loss: 1.6729 - val_accuracy: 0.5259\n",
      "Epoch 7/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.1666 - accuracy: 0.9427 - val_loss: 1.3512 - val_accuracy: 0.5393\n",
      "Epoch 8/100\n",
      "338/338 [==============================] - 51s 149ms/step - loss: 0.1357 - accuracy: 0.9567 - val_loss: 1.4873 - val_accuracy: 0.6000\n",
      "Epoch 9/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.1178 - accuracy: 0.9614 - val_loss: 0.8343 - val_accuracy: 0.7089\n",
      "Epoch 10/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.1050 - accuracy: 0.9646 - val_loss: 0.7170 - val_accuracy: 0.7674\n",
      "Epoch 11/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0925 - accuracy: 0.9697 - val_loss: 1.8453 - val_accuracy: 0.5726\n",
      "Epoch 12/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0751 - accuracy: 0.9763 - val_loss: 1.1429 - val_accuracy: 0.6407\n",
      "Epoch 13/100\n",
      "338/338 [==============================] - 53s 158ms/step - loss: 0.0658 - accuracy: 0.9783 - val_loss: 0.6546 - val_accuracy: 0.8222\n",
      "Epoch 14/100\n",
      "338/338 [==============================] - 59s 175ms/step - loss: 0.0604 - accuracy: 0.9805 - val_loss: 0.5382 - val_accuracy: 0.8363\n",
      "Epoch 15/100\n",
      "338/338 [==============================] - 55s 162ms/step - loss: 0.0535 - accuracy: 0.9835 - val_loss: 0.5201 - val_accuracy: 0.8659\n",
      "Epoch 16/100\n",
      "338/338 [==============================] - 55s 163ms/step - loss: 0.0643 - accuracy: 0.9813 - val_loss: 0.7004 - val_accuracy: 0.7659\n",
      "Epoch 17/100\n",
      "338/338 [==============================] - 53s 156ms/step - loss: 0.0488 - accuracy: 0.9840 - val_loss: 0.2568 - val_accuracy: 0.9170\n",
      "Epoch 18/100\n",
      "338/338 [==============================] - 53s 156ms/step - loss: 0.0327 - accuracy: 0.9908 - val_loss: 0.4364 - val_accuracy: 0.8696\n",
      "Epoch 19/100\n",
      "338/338 [==============================] - 56s 164ms/step - loss: 0.0408 - accuracy: 0.9875 - val_loss: 0.2718 - val_accuracy: 0.8978\n",
      "Epoch 20/100\n",
      "338/338 [==============================] - 60s 176ms/step - loss: 0.0392 - accuracy: 0.9876 - val_loss: 0.5271 - val_accuracy: 0.8926\n",
      "Epoch 21/100\n",
      "338/338 [==============================] - 54s 160ms/step - loss: 0.0407 - accuracy: 0.9885 - val_loss: 0.5145 - val_accuracy: 0.8356\n",
      "Epoch 22/100\n",
      "338/338 [==============================] - 54s 160ms/step - loss: 0.0272 - accuracy: 0.9919 - val_loss: 1.0444 - val_accuracy: 0.7370\n",
      "Epoch 23/100\n",
      "338/338 [==============================] - 66s 196ms/step - loss: 0.0474 - accuracy: 0.9861 - val_loss: 0.2669 - val_accuracy: 0.9237\n",
      "Epoch 24/100\n",
      "338/338 [==============================] - 58s 172ms/step - loss: 0.0320 - accuracy: 0.9902 - val_loss: 0.2872 - val_accuracy: 0.8970\n",
      "Epoch 25/100\n",
      "338/338 [==============================] - 58s 171ms/step - loss: 0.0278 - accuracy: 0.9916 - val_loss: 0.2684 - val_accuracy: 0.9348\n",
      "Epoch 26/100\n",
      "338/338 [==============================] - 52s 154ms/step - loss: 0.0196 - accuracy: 0.9941 - val_loss: 0.6291 - val_accuracy: 0.8244\n",
      "Epoch 27/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0261 - accuracy: 0.9923 - val_loss: 2.9592 - val_accuracy: 0.4207\n",
      "Epoch 28/100\n",
      "338/338 [==============================] - 51s 149ms/step - loss: 0.0417 - accuracy: 0.9889 - val_loss: 0.4963 - val_accuracy: 0.8526\n",
      "Epoch 29/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0187 - accuracy: 0.9951 - val_loss: 0.2922 - val_accuracy: 0.9296\n",
      "Epoch 30/100\n",
      "338/338 [==============================] - 52s 153ms/step - loss: 0.0259 - accuracy: 0.9928 - val_loss: 0.4138 - val_accuracy: 0.8785\n",
      "Epoch 31/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0163 - accuracy: 0.9950 - val_loss: 0.5795 - val_accuracy: 0.8533\n",
      "Epoch 32/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0301 - accuracy: 0.9906 - val_loss: 1.1980 - val_accuracy: 0.7711\n",
      "Epoch 33/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0335 - accuracy: 0.9903 - val_loss: 0.4235 - val_accuracy: 0.8763\n",
      "Epoch 34/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0140 - accuracy: 0.9953 - val_loss: 0.3049 - val_accuracy: 0.9267\n",
      "Epoch 35/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0199 - accuracy: 0.9939 - val_loss: 0.3726 - val_accuracy: 0.9215\n",
      "Epoch 36/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0096 - accuracy: 0.9979 - val_loss: 0.3536 - val_accuracy: 0.9000\n",
      "Epoch 37/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0264 - accuracy: 0.9926 - val_loss: 0.2161 - val_accuracy: 0.9304\n",
      "Epoch 38/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0200 - accuracy: 0.9949 - val_loss: 0.6431 - val_accuracy: 0.8363\n",
      "Epoch 39/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0190 - accuracy: 0.9942 - val_loss: 0.4197 - val_accuracy: 0.9193\n",
      "Epoch 40/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0208 - accuracy: 0.9951 - val_loss: 0.3114 - val_accuracy: 0.9370\n",
      "Epoch 41/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0121 - accuracy: 0.9962 - val_loss: 0.2365 - val_accuracy: 0.9104\n",
      "Epoch 42/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0113 - accuracy: 0.9961 - val_loss: 0.1803 - val_accuracy: 0.9356\n",
      "Epoch 43/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0164 - accuracy: 0.9956 - val_loss: 2.2149 - val_accuracy: 0.6030\n",
      "Epoch 44/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0191 - accuracy: 0.9954 - val_loss: 0.2332 - val_accuracy: 0.9267\n",
      "Epoch 45/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0123 - accuracy: 0.9966 - val_loss: 0.1605 - val_accuracy: 0.9496\n",
      "Epoch 46/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0119 - accuracy: 0.9962 - val_loss: 0.5431 - val_accuracy: 0.8585\n",
      "Epoch 47/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0176 - accuracy: 0.9946 - val_loss: 0.6235 - val_accuracy: 0.7933\n",
      "Epoch 48/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0152 - accuracy: 0.9949 - val_loss: 0.2805 - val_accuracy: 0.9333\n",
      "Epoch 49/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0140 - accuracy: 0.9967 - val_loss: 0.3526 - val_accuracy: 0.9089\n",
      "Epoch 50/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0095 - accuracy: 0.9975 - val_loss: 0.2352 - val_accuracy: 0.9415\n",
      "Epoch 51/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0138 - accuracy: 0.9961 - val_loss: 0.1685 - val_accuracy: 0.9556\n",
      "Epoch 52/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0117 - accuracy: 0.9964 - val_loss: 0.2938 - val_accuracy: 0.9230\n",
      "Epoch 53/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0122 - accuracy: 0.9960 - val_loss: 0.1143 - val_accuracy: 0.9630\n",
      "Epoch 54/100\n",
      "338/338 [==============================] - 52s 154ms/step - loss: 0.0145 - accuracy: 0.9961 - val_loss: 0.3633 - val_accuracy: 0.8867\n",
      "Epoch 55/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0073 - accuracy: 0.9980 - val_loss: 0.1054 - val_accuracy: 0.9652\n",
      "Epoch 56/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0099 - accuracy: 0.9970 - val_loss: 0.3633 - val_accuracy: 0.9148\n",
      "Epoch 57/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0090 - accuracy: 0.9980 - val_loss: 1.0153 - val_accuracy: 0.6985\n",
      "Epoch 58/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0104 - accuracy: 0.9968 - val_loss: 0.7853 - val_accuracy: 0.8163\n",
      "Epoch 59/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0076 - accuracy: 0.9977 - val_loss: 0.1361 - val_accuracy: 0.9563\n",
      "Epoch 60/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0181 - accuracy: 0.9944 - val_loss: 0.1512 - val_accuracy: 0.9556\n",
      "Epoch 61/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0048 - accuracy: 0.9986 - val_loss: 0.2258 - val_accuracy: 0.9415\n",
      "Epoch 62/100\n",
      "338/338 [==============================] - 51s 149ms/step - loss: 0.0087 - accuracy: 0.9972 - val_loss: 0.3768 - val_accuracy: 0.9222\n",
      "Epoch 63/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0102 - accuracy: 0.9969 - val_loss: 0.0988 - val_accuracy: 0.9667\n",
      "Epoch 64/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0101 - accuracy: 0.9969 - val_loss: 0.1426 - val_accuracy: 0.9511\n",
      "Epoch 65/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0061 - accuracy: 0.9984 - val_loss: 0.1529 - val_accuracy: 0.9474\n",
      "Epoch 66/100\n",
      "338/338 [==============================] - 51s 149ms/step - loss: 0.0052 - accuracy: 0.9986 - val_loss: 0.2266 - val_accuracy: 0.9541\n",
      "Epoch 67/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0098 - accuracy: 0.9971 - val_loss: 0.2106 - val_accuracy: 0.9496\n",
      "Epoch 68/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0064 - accuracy: 0.9981 - val_loss: 0.1413 - val_accuracy: 0.9563\n",
      "Epoch 69/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0039 - accuracy: 0.9991 - val_loss: 0.0955 - val_accuracy: 0.9733\n",
      "Epoch 70/100\n",
      "338/338 [==============================] - 51s 149ms/step - loss: 0.0071 - accuracy: 0.9981 - val_loss: 0.1105 - val_accuracy: 0.9674\n",
      "Epoch 71/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0145 - accuracy: 0.9961 - val_loss: 0.5023 - val_accuracy: 0.8733\n",
      "Epoch 72/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0111 - accuracy: 0.9969 - val_loss: 0.1639 - val_accuracy: 0.9585\n",
      "Epoch 73/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0068 - accuracy: 0.9984 - val_loss: 0.1151 - val_accuracy: 0.9704\n",
      "Epoch 74/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0091 - accuracy: 0.9974 - val_loss: 0.1981 - val_accuracy: 0.9519\n",
      "Epoch 75/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0095 - accuracy: 0.9976 - val_loss: 0.2357 - val_accuracy: 0.9363\n",
      "Epoch 76/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0091 - accuracy: 0.9976 - val_loss: 0.1112 - val_accuracy: 0.9607\n",
      "Epoch 77/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0050 - accuracy: 0.9984 - val_loss: 0.1102 - val_accuracy: 0.9563\n",
      "Epoch 78/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0050 - accuracy: 0.9992 - val_loss: 0.0279 - val_accuracy: 0.9889\n",
      "Epoch 79/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0041 - accuracy: 0.9988 - val_loss: 0.1235 - val_accuracy: 0.9593\n",
      "Epoch 80/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0085 - accuracy: 0.9969 - val_loss: 0.2125 - val_accuracy: 0.9452\n",
      "Epoch 81/100\n",
      "338/338 [==============================] - 54s 160ms/step - loss: 0.0061 - accuracy: 0.9978 - val_loss: 0.3176 - val_accuracy: 0.9385\n",
      "Epoch 82/100\n",
      "338/338 [==============================] - 53s 156ms/step - loss: 0.0080 - accuracy: 0.9975 - val_loss: 0.1691 - val_accuracy: 0.9467\n",
      "Epoch 83/100\n",
      "338/338 [==============================] - 53s 158ms/step - loss: 0.0022 - accuracy: 0.9994 - val_loss: 0.2929 - val_accuracy: 0.9400\n",
      "Epoch 84/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0074 - accuracy: 0.9980 - val_loss: 0.1365 - val_accuracy: 0.9681\n",
      "Epoch 85/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0081 - accuracy: 0.9981 - val_loss: 0.4278 - val_accuracy: 0.8741\n",
      "Epoch 86/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0053 - accuracy: 0.9981 - val_loss: 0.0424 - val_accuracy: 0.9815\n",
      "Epoch 87/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0083 - accuracy: 0.9981 - val_loss: 0.0754 - val_accuracy: 0.9711\n",
      "Epoch 88/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0011 - accuracy: 0.9994 - val_loss: 0.1385 - val_accuracy: 0.9526\n",
      "Epoch 89/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0097 - accuracy: 0.9972 - val_loss: 0.0847 - val_accuracy: 0.9793\n",
      "Epoch 90/100\n",
      "338/338 [==============================] - 52s 152ms/step - loss: 0.0092 - accuracy: 0.9977 - val_loss: 0.1868 - val_accuracy: 0.9548\n",
      "Epoch 91/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0119 - accuracy: 0.9964 - val_loss: 0.1648 - val_accuracy: 0.9444\n",
      "Epoch 92/100\n",
      "338/338 [==============================] - 52s 153ms/step - loss: 0.0033 - accuracy: 0.9991 - val_loss: 0.1981 - val_accuracy: 0.9489\n",
      "Epoch 93/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0016 - accuracy: 0.9994 - val_loss: 0.1602 - val_accuracy: 0.9541\n",
      "Epoch 94/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0083 - accuracy: 0.9984 - val_loss: 0.2532 - val_accuracy: 0.9541\n",
      "Epoch 95/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0017 - accuracy: 0.9993 - val_loss: 0.3600 - val_accuracy: 0.9459\n",
      "Epoch 96/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0058 - accuracy: 0.9983 - val_loss: 0.5316 - val_accuracy: 0.9289\n",
      "Epoch 97/100\n",
      "338/338 [==============================] - 52s 153ms/step - loss: 0.0095 - accuracy: 0.9975 - val_loss: 0.1319 - val_accuracy: 0.9622\n",
      "Epoch 98/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0053 - accuracy: 0.9983 - val_loss: 0.1546 - val_accuracy: 0.9600\n",
      "Epoch 99/100\n",
      "338/338 [==============================] - 52s 153ms/step - loss: 0.0027 - accuracy: 0.9996 - val_loss: 0.1910 - val_accuracy: 0.9415\n",
      "Epoch 100/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0084 - accuracy: 0.9980 - val_loss: 0.1640 - val_accuracy: 0.9289\n",
      "processing fold # 1\n",
      "Epoch 1/100\n",
      "338/338 [==============================] - 52s 153ms/step - loss: 1.6593 - accuracy: 0.2783 - val_loss: 1.7397 - val_accuracy: 0.2000\n",
      "Epoch 2/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 1.1560 - accuracy: 0.5215 - val_loss: 1.2093 - val_accuracy: 0.4985\n",
      "Epoch 3/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.6922 - accuracy: 0.7427 - val_loss: 0.6445 - val_accuracy: 0.7904\n",
      "Epoch 4/100\n",
      "338/338 [==============================] - 52s 153ms/step - loss: 0.4146 - accuracy: 0.8550 - val_loss: 0.7424 - val_accuracy: 0.7407\n",
      "Epoch 5/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.3107 - accuracy: 0.8951 - val_loss: 0.5408 - val_accuracy: 0.8230\n",
      "Epoch 6/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.2277 - accuracy: 0.9210 - val_loss: 0.8646 - val_accuracy: 0.7548\n",
      "Epoch 7/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.1782 - accuracy: 0.9381 - val_loss: 0.7737 - val_accuracy: 0.8200\n",
      "Epoch 8/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.1502 - accuracy: 0.9498 - val_loss: 0.6527 - val_accuracy: 0.8452\n",
      "Epoch 9/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.1254 - accuracy: 0.9591 - val_loss: 0.1978 - val_accuracy: 0.9452\n",
      "Epoch 10/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0931 - accuracy: 0.9689 - val_loss: 0.4000 - val_accuracy: 0.8859\n",
      "Epoch 11/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0974 - accuracy: 0.9676 - val_loss: 0.6062 - val_accuracy: 0.8193\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 12/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0735 - accuracy: 0.9767 - val_loss: 0.1689 - val_accuracy: 0.9593\n",
      "Epoch 13/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0671 - accuracy: 0.9786 - val_loss: 0.5418 - val_accuracy: 0.8689\n",
      "Epoch 14/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0608 - accuracy: 0.9816 - val_loss: 0.3391 - val_accuracy: 0.9126\n",
      "Epoch 15/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0547 - accuracy: 0.9840 - val_loss: 0.6088 - val_accuracy: 0.8533\n",
      "Epoch 16/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0587 - accuracy: 0.9823 - val_loss: 0.8425 - val_accuracy: 0.7704\n",
      "Epoch 17/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0577 - accuracy: 0.9818 - val_loss: 0.2673 - val_accuracy: 0.9178\n",
      "Epoch 18/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0475 - accuracy: 0.9846 - val_loss: 0.3330 - val_accuracy: 0.9333\n",
      "Epoch 19/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0485 - accuracy: 0.9844 - val_loss: 0.2694 - val_accuracy: 0.9319\n",
      "Epoch 20/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0466 - accuracy: 0.9857 - val_loss: 0.2430 - val_accuracy: 0.9378\n",
      "Epoch 21/100\n",
      "338/338 [==============================] - 52s 154ms/step - loss: 0.0284 - accuracy: 0.9914 - val_loss: 0.2415 - val_accuracy: 0.9311\n",
      "Epoch 22/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0440 - accuracy: 0.9866 - val_loss: 0.9025 - val_accuracy: 0.7778\n",
      "Epoch 23/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0424 - accuracy: 0.9879 - val_loss: 0.6339 - val_accuracy: 0.7970\n",
      "Epoch 24/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0315 - accuracy: 0.9898 - val_loss: 0.7678 - val_accuracy: 0.8281\n",
      "Epoch 25/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0431 - accuracy: 0.9881 - val_loss: 0.2237 - val_accuracy: 0.9148\n",
      "Epoch 26/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0267 - accuracy: 0.9921 - val_loss: 0.0628 - val_accuracy: 0.9733\n",
      "Epoch 27/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0305 - accuracy: 0.9906 - val_loss: 0.2046 - val_accuracy: 0.9304\n",
      "Epoch 28/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0262 - accuracy: 0.9919 - val_loss: 0.1985 - val_accuracy: 0.9363\n",
      "Epoch 29/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0268 - accuracy: 0.9914 - val_loss: 0.4199 - val_accuracy: 0.8978\n",
      "Epoch 30/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0225 - accuracy: 0.9933 - val_loss: 0.1139 - val_accuracy: 0.9600\n",
      "Epoch 31/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0179 - accuracy: 0.9940 - val_loss: 0.2903 - val_accuracy: 0.9333\n",
      "Epoch 32/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0213 - accuracy: 0.9942 - val_loss: 0.1635 - val_accuracy: 0.9681\n",
      "Epoch 33/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0191 - accuracy: 0.9939 - val_loss: 0.3031 - val_accuracy: 0.9244\n",
      "Epoch 34/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0149 - accuracy: 0.9953 - val_loss: 0.2150 - val_accuracy: 0.9511\n",
      "Epoch 35/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0306 - accuracy: 0.9916 - val_loss: 0.2151 - val_accuracy: 0.9400\n",
      "Epoch 36/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0159 - accuracy: 0.9955 - val_loss: 0.0874 - val_accuracy: 0.9704\n",
      "Epoch 37/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0143 - accuracy: 0.9962 - val_loss: 0.1452 - val_accuracy: 0.9659\n",
      "Epoch 38/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0196 - accuracy: 0.9928 - val_loss: 0.2882 - val_accuracy: 0.9133\n",
      "Epoch 39/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0166 - accuracy: 0.9958 - val_loss: 0.3261 - val_accuracy: 0.9356\n",
      "Epoch 40/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0210 - accuracy: 0.9942 - val_loss: 0.0610 - val_accuracy: 0.9800\n",
      "Epoch 41/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0117 - accuracy: 0.9968 - val_loss: 0.2361 - val_accuracy: 0.9267\n",
      "Epoch 42/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0140 - accuracy: 0.9962 - val_loss: 0.3062 - val_accuracy: 0.9370\n",
      "Epoch 43/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0166 - accuracy: 0.9957 - val_loss: 0.0873 - val_accuracy: 0.9852\n",
      "Epoch 44/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0250 - accuracy: 0.9929 - val_loss: 0.1911 - val_accuracy: 0.9407\n",
      "Epoch 45/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0145 - accuracy: 0.9961 - val_loss: 0.0691 - val_accuracy: 0.9822\n",
      "Epoch 46/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0133 - accuracy: 0.9956 - val_loss: 0.1031 - val_accuracy: 0.9793\n",
      "Epoch 47/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0116 - accuracy: 0.9968 - val_loss: 0.0863 - val_accuracy: 0.9778\n",
      "Epoch 48/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0173 - accuracy: 0.9944 - val_loss: 0.2593 - val_accuracy: 0.9267\n",
      "Epoch 49/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0133 - accuracy: 0.9959 - val_loss: 0.0571 - val_accuracy: 0.9815\n",
      "Epoch 50/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0130 - accuracy: 0.9957 - val_loss: 0.0860 - val_accuracy: 0.9770\n",
      "Epoch 51/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0156 - accuracy: 0.9961 - val_loss: 0.1605 - val_accuracy: 0.9622\n",
      "Epoch 52/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0108 - accuracy: 0.9969 - val_loss: 0.8151 - val_accuracy: 0.8311\n",
      "Epoch 53/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0162 - accuracy: 0.9950 - val_loss: 0.1880 - val_accuracy: 0.9363\n",
      "Epoch 54/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0062 - accuracy: 0.9984 - val_loss: 0.2853 - val_accuracy: 0.9237\n",
      "Epoch 55/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0115 - accuracy: 0.9966 - val_loss: 0.1410 - val_accuracy: 0.9644\n",
      "Epoch 56/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0108 - accuracy: 0.9970 - val_loss: 0.3915 - val_accuracy: 0.8711\n",
      "Epoch 57/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0172 - accuracy: 0.9956 - val_loss: 0.4593 - val_accuracy: 0.8785\n",
      "Epoch 58/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0134 - accuracy: 0.9964 - val_loss: 0.2979 - val_accuracy: 0.9252\n",
      "Epoch 59/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0078 - accuracy: 0.9970 - val_loss: 0.2855 - val_accuracy: 0.9519\n",
      "Epoch 60/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0077 - accuracy: 0.9978 - val_loss: 0.1709 - val_accuracy: 0.9652\n",
      "Epoch 61/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0132 - accuracy: 0.9959 - val_loss: 0.1895 - val_accuracy: 0.9526\n",
      "Epoch 62/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0064 - accuracy: 0.9977 - val_loss: 0.1216 - val_accuracy: 0.9659\n",
      "Epoch 63/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0112 - accuracy: 0.9973 - val_loss: 0.1785 - val_accuracy: 0.9504\n",
      "Epoch 64/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0102 - accuracy: 0.9968 - val_loss: 0.3010 - val_accuracy: 0.9363\n",
      "Epoch 65/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0120 - accuracy: 0.9965 - val_loss: 0.1018 - val_accuracy: 0.9852\n",
      "Epoch 66/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0121 - accuracy: 0.9966 - val_loss: 0.1257 - val_accuracy: 0.9689\n",
      "Epoch 67/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0078 - accuracy: 0.9978 - val_loss: 0.0993 - val_accuracy: 0.9741\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 68/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0056 - accuracy: 0.9989 - val_loss: 0.0609 - val_accuracy: 0.9770\n",
      "Epoch 69/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0066 - accuracy: 0.9983 - val_loss: 4.7393 - val_accuracy: 0.6570\n",
      "Epoch 70/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0086 - accuracy: 0.9977 - val_loss: 0.0850 - val_accuracy: 0.9778\n",
      "Epoch 71/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0075 - accuracy: 0.9983 - val_loss: 0.1297 - val_accuracy: 0.9637\n",
      "Epoch 72/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0093 - accuracy: 0.9971 - val_loss: 0.1391 - val_accuracy: 0.9778\n",
      "Epoch 73/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0050 - accuracy: 0.9987 - val_loss: 0.0929 - val_accuracy: 0.9815\n",
      "Epoch 74/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0058 - accuracy: 0.9980 - val_loss: 0.1551 - val_accuracy: 0.9600\n",
      "Epoch 75/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0054 - accuracy: 0.9985 - val_loss: 0.0919 - val_accuracy: 0.9785\n",
      "Epoch 76/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0116 - accuracy: 0.9979 - val_loss: 0.2629 - val_accuracy: 0.9185\n",
      "Epoch 77/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0070 - accuracy: 0.9982 - val_loss: 0.4926 - val_accuracy: 0.8978\n",
      "Epoch 78/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0067 - accuracy: 0.9981 - val_loss: 0.9225 - val_accuracy: 0.8385\n",
      "Epoch 79/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0106 - accuracy: 0.9967 - val_loss: 0.3199 - val_accuracy: 0.9089\n",
      "Epoch 80/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0065 - accuracy: 0.9985 - val_loss: 0.4333 - val_accuracy: 0.8919\n",
      "Epoch 81/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0041 - accuracy: 0.9986 - val_loss: 0.1621 - val_accuracy: 0.9652\n",
      "Epoch 82/100\n",
      "337/338 [============================>.] - ETA: 0s - loss: 0.0067 - accuracy: 0.9982\n",
      "Epoch 00082: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0066 - accuracy: 0.9982 - val_loss: 0.6927 - val_accuracy: 0.8719\n",
      "Epoch 83/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0029 - accuracy: 0.9993 - val_loss: 0.4533 - val_accuracy: 0.8941\n",
      "Epoch 84/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0013 - accuracy: 0.9995 - val_loss: 0.2085 - val_accuracy: 0.9459\n",
      "Epoch 85/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0026 - accuracy: 0.9993 - val_loss: 0.2555 - val_accuracy: 0.9333\n",
      "Epoch 86/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 8.9917e-04 - accuracy: 0.9997 - val_loss: 0.2536 - val_accuracy: 0.9333\n",
      "Epoch 87/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 8.8786e-04 - accuracy: 0.9996 - val_loss: 0.2923 - val_accuracy: 0.9252\n",
      "Epoch 88/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 0.2756 - val_accuracy: 0.9274\n",
      "Epoch 89/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0010 - accuracy: 0.9997 - val_loss: 0.1968 - val_accuracy: 0.9444\n",
      "Epoch 90/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 8.9228e-04 - accuracy: 0.9998 - val_loss: 0.2113 - val_accuracy: 0.9385\n",
      "Epoch 91/100\n",
      "338/338 [==============================] - 53s 157ms/step - loss: 4.8453e-04 - accuracy: 0.9999 - val_loss: 0.1528 - val_accuracy: 0.9556\n",
      "Epoch 92/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 2.1153e-04 - accuracy: 0.9999 - val_loss: 0.1239 - val_accuracy: 0.9637\n",
      "Epoch 93/100\n",
      "338/338 [==============================] - 52s 152ms/step - loss: 5.3488e-04 - accuracy: 0.9999 - val_loss: 0.1405 - val_accuracy: 0.9600\n",
      "Epoch 94/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0012 - accuracy: 0.9995 - val_loss: 0.1126 - val_accuracy: 0.9652\n",
      "Epoch 95/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 4.2332e-04 - accuracy: 0.9998 - val_loss: 0.1504 - val_accuracy: 0.9593\n",
      "Epoch 96/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 3.1424e-04 - accuracy: 0.9999 - val_loss: 0.1026 - val_accuracy: 0.9719\n",
      "Epoch 97/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0012 - accuracy: 0.9996 - val_loss: 0.0518 - val_accuracy: 0.9800\n",
      "Epoch 98/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 4.5414e-04 - accuracy: 0.9999 - val_loss: 0.0618 - val_accuracy: 0.9785\n",
      "Epoch 99/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0016 - accuracy: 0.9996 - val_loss: 0.0371 - val_accuracy: 0.9852\n",
      "Epoch 100/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 2.9236e-04 - accuracy: 0.9999 - val_loss: 0.0446 - val_accuracy: 0.9844\n",
      "processing fold # 2\n",
      "Epoch 1/100\n",
      "338/338 [==============================] - 52s 154ms/step - loss: 1.6373 - accuracy: 0.2978 - val_loss: 1.6998 - val_accuracy: 0.2000\n",
      "Epoch 2/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.9802 - accuracy: 0.6110 - val_loss: 1.1544 - val_accuracy: 0.4400\n",
      "Epoch 3/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.5689 - accuracy: 0.7894 - val_loss: 0.8291 - val_accuracy: 0.7104\n",
      "Epoch 4/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.3538 - accuracy: 0.8731 - val_loss: 0.7541 - val_accuracy: 0.7837\n",
      "Epoch 5/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.2522 - accuracy: 0.9089 - val_loss: 0.7764 - val_accuracy: 0.7778\n",
      "Epoch 6/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.1780 - accuracy: 0.9394 - val_loss: 1.0076 - val_accuracy: 0.7333\n",
      "Epoch 7/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.1474 - accuracy: 0.9540 - val_loss: 0.8635 - val_accuracy: 0.7170\n",
      "Epoch 8/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.1151 - accuracy: 0.9608 - val_loss: 0.5828 - val_accuracy: 0.8193\n",
      "Epoch 9/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.1010 - accuracy: 0.9676 - val_loss: 0.5134 - val_accuracy: 0.8763\n",
      "Epoch 10/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0868 - accuracy: 0.9719 - val_loss: 0.6789 - val_accuracy: 0.8348\n",
      "Epoch 11/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0792 - accuracy: 0.9768 - val_loss: 0.4807 - val_accuracy: 0.8800\n",
      "Epoch 12/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0690 - accuracy: 0.9791 - val_loss: 1.4739 - val_accuracy: 0.6348\n",
      "Epoch 13/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0689 - accuracy: 0.9789 - val_loss: 0.6240 - val_accuracy: 0.8259\n",
      "Epoch 14/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0562 - accuracy: 0.9823 - val_loss: 0.8335 - val_accuracy: 0.7830\n",
      "Epoch 15/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0564 - accuracy: 0.9833 - val_loss: 0.3740 - val_accuracy: 0.8941\n",
      "Epoch 16/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0506 - accuracy: 0.9839 - val_loss: 0.8753 - val_accuracy: 0.8052\n",
      "Epoch 17/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0389 - accuracy: 0.9876 - val_loss: 0.6782 - val_accuracy: 0.8356\n",
      "Epoch 18/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0461 - accuracy: 0.9857 - val_loss: 0.8412 - val_accuracy: 0.8111\n",
      "Epoch 19/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0487 - accuracy: 0.9842 - val_loss: 0.4525 - val_accuracy: 0.8600\n",
      "Epoch 20/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0340 - accuracy: 0.9900 - val_loss: 0.3657 - val_accuracy: 0.9044\n",
      "Epoch 21/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0390 - accuracy: 0.9883 - val_loss: 0.5875 - val_accuracy: 0.8800\n",
      "Epoch 22/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0379 - accuracy: 0.9890 - val_loss: 0.5511 - val_accuracy: 0.8867\n",
      "Epoch 23/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0321 - accuracy: 0.9905 - val_loss: 1.0454 - val_accuracy: 0.7867\n",
      "Epoch 24/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0302 - accuracy: 0.9917 - val_loss: 0.5284 - val_accuracy: 0.8444\n",
      "Epoch 25/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0345 - accuracy: 0.9903 - val_loss: 0.6261 - val_accuracy: 0.8622\n",
      "Epoch 26/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0297 - accuracy: 0.9915 - val_loss: 0.5462 - val_accuracy: 0.8548\n",
      "Epoch 27/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0291 - accuracy: 0.9917 - val_loss: 0.2584 - val_accuracy: 0.9474\n",
      "Epoch 28/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0276 - accuracy: 0.9929 - val_loss: 0.3824 - val_accuracy: 0.8933\n",
      "Epoch 29/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0282 - accuracy: 0.9918 - val_loss: 0.3242 - val_accuracy: 0.9170\n",
      "Epoch 30/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0312 - accuracy: 0.9914 - val_loss: 0.3860 - val_accuracy: 0.9096\n",
      "Epoch 31/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0325 - accuracy: 0.9913 - val_loss: 1.5561 - val_accuracy: 0.7630\n",
      "Epoch 32/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0253 - accuracy: 0.9926 - val_loss: 0.9133 - val_accuracy: 0.7837\n",
      "Epoch 33/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0173 - accuracy: 0.9945 - val_loss: 0.6638 - val_accuracy: 0.8474\n",
      "Epoch 34/100\n",
      "338/338 [==============================] - 51s 149ms/step - loss: 0.0159 - accuracy: 0.9958 - val_loss: 0.4917 - val_accuracy: 0.8548\n",
      "Epoch 35/100\n",
      "338/338 [==============================] - 51s 149ms/step - loss: 0.0284 - accuracy: 0.9932 - val_loss: 0.4651 - val_accuracy: 0.9326\n",
      "Epoch 36/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0240 - accuracy: 0.9930 - val_loss: 0.8865 - val_accuracy: 0.7874\n",
      "Epoch 37/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0193 - accuracy: 0.9939 - val_loss: 0.3064 - val_accuracy: 0.9452\n",
      "Epoch 38/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0204 - accuracy: 0.9935 - val_loss: 0.6679 - val_accuracy: 0.8370\n",
      "Epoch 39/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0129 - accuracy: 0.9960 - val_loss: 0.7943 - val_accuracy: 0.8896\n",
      "Epoch 40/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0112 - accuracy: 0.9968 - val_loss: 0.8124 - val_accuracy: 0.8704\n",
      "Epoch 41/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0218 - accuracy: 0.9944 - val_loss: 0.3463 - val_accuracy: 0.8689\n",
      "Epoch 42/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0138 - accuracy: 0.9960 - val_loss: 0.7429 - val_accuracy: 0.8526\n",
      "Epoch 43/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0131 - accuracy: 0.9961 - val_loss: 0.5714 - val_accuracy: 0.8763\n",
      "Epoch 44/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0138 - accuracy: 0.9962 - val_loss: 0.7582 - val_accuracy: 0.7978\n",
      "Epoch 45/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0172 - accuracy: 0.9947 - val_loss: 0.3975 - val_accuracy: 0.9378\n",
      "Epoch 46/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0225 - accuracy: 0.9944 - val_loss: 0.6751 - val_accuracy: 0.8733\n",
      "Epoch 47/100\n",
      "338/338 [==============================] - 51s 149ms/step - loss: 0.0121 - accuracy: 0.9964 - val_loss: 0.2711 - val_accuracy: 0.9363\n",
      "Epoch 48/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0140 - accuracy: 0.9966 - val_loss: 0.5239 - val_accuracy: 0.8756\n",
      "Epoch 49/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0072 - accuracy: 0.9978 - val_loss: 0.1099 - val_accuracy: 0.9733\n",
      "Epoch 50/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0179 - accuracy: 0.9944 - val_loss: 0.5084 - val_accuracy: 0.8948\n",
      "Epoch 51/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0121 - accuracy: 0.9965 - val_loss: 0.3506 - val_accuracy: 0.9326\n",
      "Epoch 52/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0116 - accuracy: 0.9970 - val_loss: 0.2862 - val_accuracy: 0.9237\n",
      "Epoch 53/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0088 - accuracy: 0.9974 - val_loss: 0.2396 - val_accuracy: 0.9304\n",
      "Epoch 54/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0171 - accuracy: 0.9953 - val_loss: 0.4780 - val_accuracy: 0.9170\n",
      "Epoch 55/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0094 - accuracy: 0.9973 - val_loss: 0.2959 - val_accuracy: 0.9252\n",
      "Epoch 56/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0075 - accuracy: 0.9979 - val_loss: 0.5304 - val_accuracy: 0.9059\n",
      "Epoch 57/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0065 - accuracy: 0.9979 - val_loss: 0.6095 - val_accuracy: 0.9126\n",
      "Epoch 58/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0088 - accuracy: 0.9983 - val_loss: 0.3833 - val_accuracy: 0.9044\n",
      "Epoch 59/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0151 - accuracy: 0.9953 - val_loss: 0.4076 - val_accuracy: 0.9311\n",
      "Epoch 60/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0095 - accuracy: 0.9977 - val_loss: 0.2239 - val_accuracy: 0.9363\n",
      "Epoch 61/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0071 - accuracy: 0.9981 - val_loss: 0.4557 - val_accuracy: 0.8793\n",
      "Epoch 62/100\n",
      "338/338 [==============================] - 51s 149ms/step - loss: 0.0161 - accuracy: 0.9952 - val_loss: 0.2840 - val_accuracy: 0.9207\n",
      "Epoch 63/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0112 - accuracy: 0.9973 - val_loss: 0.3993 - val_accuracy: 0.9252\n",
      "Epoch 64/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0088 - accuracy: 0.9969 - val_loss: 0.4853 - val_accuracy: 0.8815\n",
      "Epoch 65/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0079 - accuracy: 0.9973 - val_loss: 0.4150 - val_accuracy: 0.9089\n",
      "Epoch 66/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0055 - accuracy: 0.9983 - val_loss: 0.5406 - val_accuracy: 0.9015\n",
      "Epoch 67/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0078 - accuracy: 0.9980 - val_loss: 0.4160 - val_accuracy: 0.9452\n",
      "Epoch 68/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0085 - accuracy: 0.9973 - val_loss: 0.2438 - val_accuracy: 0.9348\n",
      "Epoch 69/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0041 - accuracy: 0.9991 - val_loss: 0.3338 - val_accuracy: 0.9363\n",
      "Epoch 70/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0091 - accuracy: 0.9975 - val_loss: 0.3649 - val_accuracy: 0.9200\n",
      "Epoch 71/100\n",
      "338/338 [==============================] - 51s 149ms/step - loss: 0.0013 - accuracy: 0.9994 - val_loss: 0.4374 - val_accuracy: 0.9363\n",
      "Epoch 72/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0059 - accuracy: 0.9982 - val_loss: 0.7209 - val_accuracy: 0.8978\n",
      "Epoch 73/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0148 - accuracy: 0.9958 - val_loss: 0.5121 - val_accuracy: 0.9081\n",
      "Epoch 74/100\n",
      "338/338 [==============================] - 51s 149ms/step - loss: 0.0062 - accuracy: 0.9981 - val_loss: 0.4472 - val_accuracy: 0.9037\n",
      "Epoch 75/100\n",
      "338/338 [==============================] - 51s 149ms/step - loss: 0.0102 - accuracy: 0.9975 - val_loss: 0.5834 - val_accuracy: 0.8867\n",
      "Epoch 76/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0057 - accuracy: 0.9986 - val_loss: 0.2747 - val_accuracy: 0.9326\n",
      "Epoch 77/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0029 - accuracy: 0.9988 - val_loss: 0.2950 - val_accuracy: 0.9370\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 78/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0115 - accuracy: 0.9972 - val_loss: 0.6122 - val_accuracy: 0.8459\n",
      "Epoch 79/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0152 - accuracy: 0.9961 - val_loss: 0.9002 - val_accuracy: 0.8289\n",
      "Epoch 80/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0039 - accuracy: 0.9989 - val_loss: 0.3475 - val_accuracy: 0.9215\n",
      "Epoch 81/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0057 - accuracy: 0.9985 - val_loss: 0.3466 - val_accuracy: 0.9274\n",
      "Epoch 82/100\n",
      "337/338 [============================>.] - ETA: 0s - loss: 0.0035 - accuracy: 0.9992\n",
      "Epoch 00082: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0035 - accuracy: 0.9992 - val_loss: 1.2878 - val_accuracy: 0.7881\n",
      "Epoch 83/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0066 - accuracy: 0.9986 - val_loss: 0.3455 - val_accuracy: 0.9104\n",
      "Epoch 84/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0034 - accuracy: 0.9992 - val_loss: 0.2830 - val_accuracy: 0.9148\n",
      "Epoch 85/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0016 - accuracy: 0.9994 - val_loss: 0.2848 - val_accuracy: 0.9296\n",
      "Epoch 86/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0034 - accuracy: 0.9993 - val_loss: 0.2629 - val_accuracy: 0.9333\n",
      "Epoch 87/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 7.8810e-04 - accuracy: 0.9996 - val_loss: 0.2153 - val_accuracy: 0.9430\n",
      "Epoch 88/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 7.3123e-04 - accuracy: 0.9998 - val_loss: 0.2073 - val_accuracy: 0.9430\n",
      "Epoch 89/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.2091 - val_accuracy: 0.9400\n",
      "Epoch 90/100\n",
      "338/338 [==============================] - 51s 149ms/step - loss: 8.3971e-04 - accuracy: 0.9998 - val_loss: 0.2164 - val_accuracy: 0.9385\n",
      "Epoch 91/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0011 - accuracy: 0.9996 - val_loss: 0.2726 - val_accuracy: 0.9378\n",
      "Epoch 92/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 0.2249 - val_accuracy: 0.9400\n",
      "Epoch 93/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0013 - accuracy: 0.9997 - val_loss: 0.2175 - val_accuracy: 0.9452\n",
      "Epoch 94/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 2.9668e-04 - accuracy: 0.9999 - val_loss: 0.1828 - val_accuracy: 0.9578\n",
      "Epoch 95/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 6.1631e-04 - accuracy: 0.9999 - val_loss: 0.1966 - val_accuracy: 0.9526\n",
      "Epoch 96/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 6.2022e-04 - accuracy: 0.9998 - val_loss: 0.2072 - val_accuracy: 0.9570\n",
      "Epoch 97/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 1.9271e-04 - accuracy: 1.0000 - val_loss: 0.2070 - val_accuracy: 0.9563\n",
      "Epoch 98/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 4.6028e-04 - accuracy: 0.9999 - val_loss: 0.2107 - val_accuracy: 0.9585\n",
      "Epoch 99/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 4.6567e-04 - accuracy: 0.9999 - val_loss: 0.1934 - val_accuracy: 0.9570\n",
      "Epoch 100/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 5.4523e-04 - accuracy: 0.9998 - val_loss: 0.2017 - val_accuracy: 0.9519\n",
      "processing fold # 3\n",
      "Epoch 1/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 1.6696 - accuracy: 0.2739 - val_loss: 1.6992 - val_accuracy: 0.2000\n",
      "Epoch 2/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 1.1171 - accuracy: 0.5423 - val_loss: 1.2836 - val_accuracy: 0.4533\n",
      "Epoch 3/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.6378 - accuracy: 0.7630 - val_loss: 0.6131 - val_accuracy: 0.7615\n",
      "Epoch 4/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.4179 - accuracy: 0.8480 - val_loss: 0.8291 - val_accuracy: 0.7059\n",
      "Epoch 5/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.3032 - accuracy: 0.8939 - val_loss: 0.3202 - val_accuracy: 0.8822\n",
      "Epoch 6/100\n",
      "338/338 [==============================] - 53s 157ms/step - loss: 0.2085 - accuracy: 0.9291 - val_loss: 1.6658 - val_accuracy: 0.6022\n",
      "Epoch 7/100\n",
      "338/338 [==============================] - 56s 166ms/step - loss: 0.1704 - accuracy: 0.9422 - val_loss: 0.1747 - val_accuracy: 0.9504\n",
      "Epoch 8/100\n",
      "338/338 [==============================] - 56s 166ms/step - loss: 0.1363 - accuracy: 0.9549 - val_loss: 0.1343 - val_accuracy: 0.9659\n",
      "Epoch 9/100\n",
      "338/338 [==============================] - 55s 163ms/step - loss: 0.1027 - accuracy: 0.9681 - val_loss: 0.2045 - val_accuracy: 0.9370\n",
      "Epoch 10/100\n",
      "338/338 [==============================] - 54s 160ms/step - loss: 0.0974 - accuracy: 0.9707 - val_loss: 0.1251 - val_accuracy: 0.9652\n",
      "Epoch 11/100\n",
      "338/338 [==============================] - 52s 152ms/step - loss: 0.0768 - accuracy: 0.9743 - val_loss: 0.2012 - val_accuracy: 0.9393\n",
      "Epoch 12/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0788 - accuracy: 0.9756 - val_loss: 0.0806 - val_accuracy: 0.9770\n",
      "Epoch 13/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0722 - accuracy: 0.9780 - val_loss: 0.1067 - val_accuracy: 0.9689\n",
      "Epoch 14/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0534 - accuracy: 0.9831 - val_loss: 0.1107 - val_accuracy: 0.9667\n",
      "Epoch 15/100\n",
      "338/338 [==============================] - 52s 152ms/step - loss: 0.0666 - accuracy: 0.9801 - val_loss: 0.2655 - val_accuracy: 0.9089\n",
      "Epoch 16/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0495 - accuracy: 0.9855 - val_loss: 0.4216 - val_accuracy: 0.8778\n",
      "Epoch 17/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0466 - accuracy: 0.9864 - val_loss: 1.1767 - val_accuracy: 0.7296\n",
      "Epoch 18/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0497 - accuracy: 0.9842 - val_loss: 0.4008 - val_accuracy: 0.9022\n",
      "Epoch 19/100\n",
      "338/338 [==============================] - 52s 153ms/step - loss: 0.0425 - accuracy: 0.9882 - val_loss: 0.2845 - val_accuracy: 0.9267\n",
      "Epoch 20/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0368 - accuracy: 0.9894 - val_loss: 0.0385 - val_accuracy: 0.9926\n",
      "Epoch 21/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0368 - accuracy: 0.9882 - val_loss: 1.0631 - val_accuracy: 0.8170\n",
      "Epoch 22/100\n",
      "338/338 [==============================] - 52s 153ms/step - loss: 0.0380 - accuracy: 0.9886 - val_loss: 0.0196 - val_accuracy: 0.9919\n",
      "Epoch 23/100\n",
      "338/338 [==============================] - 52s 153ms/step - loss: 0.0284 - accuracy: 0.9915 - val_loss: 0.0533 - val_accuracy: 0.9815\n",
      "Epoch 24/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0346 - accuracy: 0.9893 - val_loss: 0.3706 - val_accuracy: 0.9207\n",
      "Epoch 25/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0201 - accuracy: 0.9937 - val_loss: 0.0332 - val_accuracy: 0.9911\n",
      "Epoch 26/100\n",
      "338/338 [==============================] - 52s 153ms/step - loss: 0.0365 - accuracy: 0.9895 - val_loss: 0.1647 - val_accuracy: 0.9459\n",
      "Epoch 27/100\n",
      "338/338 [==============================] - 52s 153ms/step - loss: 0.0287 - accuracy: 0.9914 - val_loss: 0.0808 - val_accuracy: 0.9770\n",
      "Epoch 28/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0302 - accuracy: 0.9919 - val_loss: 0.0415 - val_accuracy: 0.9874\n",
      "Epoch 29/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0307 - accuracy: 0.9918 - val_loss: 0.0058 - val_accuracy: 0.9970\n",
      "Epoch 30/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 0.0295 - accuracy: 0.9919 - val_loss: 0.2654 - val_accuracy: 0.9185\n",
      "Epoch 31/100\n",
      "338/338 [==============================] - 52s 154ms/step - loss: 0.0245 - accuracy: 0.9933 - val_loss: 0.0421 - val_accuracy: 0.9896\n",
      "Epoch 32/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0184 - accuracy: 0.9942 - val_loss: 0.1234 - val_accuracy: 0.9585\n",
      "Epoch 33/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0138 - accuracy: 0.9962 - val_loss: 1.3038 - val_accuracy: 0.7200\n",
      "Epoch 34/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0283 - accuracy: 0.9919 - val_loss: 0.0766 - val_accuracy: 0.9770\n",
      "Epoch 35/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0228 - accuracy: 0.9927 - val_loss: 0.0188 - val_accuracy: 0.9956\n",
      "Epoch 36/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0116 - accuracy: 0.9971 - val_loss: 0.0987 - val_accuracy: 0.9793\n",
      "Epoch 37/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0152 - accuracy: 0.9956 - val_loss: 0.0413 - val_accuracy: 0.9844\n",
      "Epoch 38/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0167 - accuracy: 0.9956 - val_loss: 0.2320 - val_accuracy: 0.9607\n",
      "Epoch 39/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0297 - accuracy: 0.9927 - val_loss: 0.7760 - val_accuracy: 0.7904\n",
      "Epoch 40/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0203 - accuracy: 0.9939 - val_loss: 0.0334 - val_accuracy: 0.9881\n",
      "Epoch 41/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0161 - accuracy: 0.9959 - val_loss: 0.2133 - val_accuracy: 0.9400\n",
      "Epoch 42/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0152 - accuracy: 0.9952 - val_loss: 0.0378 - val_accuracy: 0.9859\n",
      "Epoch 43/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0158 - accuracy: 0.9957 - val_loss: 0.0099 - val_accuracy: 0.9970\n",
      "Epoch 44/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0110 - accuracy: 0.9972 - val_loss: 0.0156 - val_accuracy: 0.9956\n",
      "Epoch 45/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0193 - accuracy: 0.9951 - val_loss: 0.2253 - val_accuracy: 0.9674\n",
      "Epoch 46/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0204 - accuracy: 0.9937 - val_loss: 0.2412 - val_accuracy: 0.9615\n",
      "Epoch 47/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0090 - accuracy: 0.9973 - val_loss: 0.0484 - val_accuracy: 0.9822\n",
      "Epoch 48/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0072 - accuracy: 0.9981 - val_loss: 0.0273 - val_accuracy: 0.9881\n",
      "Epoch 49/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0242 - accuracy: 0.9937 - val_loss: 0.0309 - val_accuracy: 0.9867\n",
      "Epoch 50/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0093 - accuracy: 0.9969 - val_loss: 0.0792 - val_accuracy: 0.9704\n",
      "Epoch 51/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0109 - accuracy: 0.9971 - val_loss: 0.2094 - val_accuracy: 0.9081\n",
      "Epoch 52/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0067 - accuracy: 0.9983 - val_loss: 0.1206 - val_accuracy: 0.9696\n",
      "Epoch 53/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0098 - accuracy: 0.9971 - val_loss: 0.0041 - val_accuracy: 0.9985\n",
      "Epoch 54/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0094 - accuracy: 0.9972 - val_loss: 0.0399 - val_accuracy: 0.9815\n",
      "Epoch 55/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0129 - accuracy: 0.9954 - val_loss: 0.0756 - val_accuracy: 0.9815\n",
      "Epoch 56/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0162 - accuracy: 0.9948 - val_loss: 0.0556 - val_accuracy: 0.9874\n",
      "Epoch 57/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0155 - accuracy: 0.9958 - val_loss: 0.1855 - val_accuracy: 0.9415\n",
      "Epoch 58/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0068 - accuracy: 0.9986 - val_loss: 0.1047 - val_accuracy: 0.9763\n",
      "Epoch 59/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0081 - accuracy: 0.9976 - val_loss: 0.2133 - val_accuracy: 0.9489\n",
      "Epoch 60/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0094 - accuracy: 0.9973 - val_loss: 0.1464 - val_accuracy: 0.9593\n",
      "Epoch 61/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0124 - accuracy: 0.9966 - val_loss: 0.6074 - val_accuracy: 0.8926\n",
      "Epoch 62/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0107 - accuracy: 0.9973 - val_loss: 0.0261 - val_accuracy: 0.9970\n",
      "Epoch 63/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0108 - accuracy: 0.9968 - val_loss: 0.0151 - val_accuracy: 0.9948\n",
      "Epoch 64/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0138 - accuracy: 0.9961 - val_loss: 0.0385 - val_accuracy: 0.9874\n",
      "Epoch 65/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0059 - accuracy: 0.9984 - val_loss: 0.0364 - val_accuracy: 0.9896\n",
      "Epoch 66/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0047 - accuracy: 0.9987 - val_loss: 0.0030 - val_accuracy: 0.9993\n",
      "Epoch 67/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0094 - accuracy: 0.9979 - val_loss: 0.2716 - val_accuracy: 0.9363\n",
      "Epoch 68/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0086 - accuracy: 0.9980 - val_loss: 0.0278 - val_accuracy: 0.9874\n",
      "Epoch 69/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0082 - accuracy: 0.9979 - val_loss: 0.0198 - val_accuracy: 0.9933\n",
      "Epoch 70/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0093 - accuracy: 0.9973 - val_loss: 0.4153 - val_accuracy: 0.9259\n",
      "Epoch 71/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0115 - accuracy: 0.9963 - val_loss: 0.9506 - val_accuracy: 0.7022\n",
      "Epoch 72/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0052 - accuracy: 0.9987 - val_loss: 0.1734 - val_accuracy: 0.9607\n",
      "Epoch 73/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0103 - accuracy: 0.9973 - val_loss: 0.0114 - val_accuracy: 0.9948\n",
      "Epoch 74/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0105 - accuracy: 0.9970 - val_loss: 0.0457 - val_accuracy: 0.9837\n",
      "Epoch 75/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0048 - accuracy: 0.9985 - val_loss: 0.0286 - val_accuracy: 0.9896\n",
      "Epoch 76/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0103 - accuracy: 0.9969 - val_loss: 0.1019 - val_accuracy: 0.9600\n",
      "Epoch 77/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0050 - accuracy: 0.9989 - val_loss: 0.0813 - val_accuracy: 0.9859\n",
      "Epoch 78/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0101 - accuracy: 0.9971 - val_loss: 0.0131 - val_accuracy: 0.9963\n",
      "Epoch 79/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0053 - accuracy: 0.9987 - val_loss: 0.0035 - val_accuracy: 0.9985\n",
      "Epoch 80/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0040 - accuracy: 0.9988 - val_loss: 0.0940 - val_accuracy: 0.9704\n",
      "Epoch 81/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0060 - accuracy: 0.9983 - val_loss: 0.0498 - val_accuracy: 0.9874\n",
      "Epoch 82/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0087 - accuracy: 0.9976 - val_loss: 0.0111 - val_accuracy: 0.9956\n",
      "Epoch 83/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0054 - accuracy: 0.9985 - val_loss: 0.3133 - val_accuracy: 0.9000\n",
      "Epoch 84/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0078 - accuracy: 0.9978 - val_loss: 0.1396 - val_accuracy: 0.9607\n",
      "Epoch 85/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0096 - accuracy: 0.9979 - val_loss: 0.0060 - val_accuracy: 0.9993\n",
      "Epoch 86/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0038 - accuracy: 0.9990 - val_loss: 0.0041 - val_accuracy: 0.9985\n",
      "Epoch 87/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0066 - accuracy: 0.9982 - val_loss: 0.0063 - val_accuracy: 0.9978\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 88/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0076 - accuracy: 0.9977 - val_loss: 0.0034 - val_accuracy: 0.9993\n",
      "Epoch 89/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0037 - accuracy: 0.9991 - val_loss: 0.0494 - val_accuracy: 0.9896\n",
      "Epoch 90/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0128 - accuracy: 0.9969 - val_loss: 0.0050 - val_accuracy: 0.9985\n",
      "Epoch 91/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0055 - accuracy: 0.9981 - val_loss: 8.1379e-04 - val_accuracy: 0.9993\n",
      "Epoch 92/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 0.0045 - val_accuracy: 0.9978\n",
      "Epoch 93/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0110 - accuracy: 0.9974 - val_loss: 0.0113 - val_accuracy: 0.9978\n",
      "Epoch 94/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0089 - accuracy: 0.9972 - val_loss: 0.0040 - val_accuracy: 0.9985\n",
      "Epoch 95/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0018 - accuracy: 0.9993 - val_loss: 0.0038 - val_accuracy: 0.9978\n",
      "Epoch 96/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0016 - accuracy: 0.9993 - val_loss: 0.0311 - val_accuracy: 0.9867\n",
      "Epoch 97/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0023 - accuracy: 0.9992 - val_loss: 0.0054 - val_accuracy: 0.9985\n",
      "Epoch 98/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0033 - accuracy: 0.9989 - val_loss: 0.0061 - val_accuracy: 0.9970\n",
      "Epoch 99/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0119 - accuracy: 0.9969 - val_loss: 0.0067 - val_accuracy: 0.9970\n",
      "Epoch 100/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0058 - accuracy: 0.9989 - val_loss: 5.9825e-04 - val_accuracy: 1.0000\n",
      "processing fold # 4\n",
      "Epoch 1/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 1.5545 - accuracy: 0.3429 - val_loss: 1.7420 - val_accuracy: 0.2000\n",
      "Epoch 2/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 1.0089 - accuracy: 0.6005 - val_loss: 1.0520 - val_accuracy: 0.5037\n",
      "Epoch 3/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.6197 - accuracy: 0.7702 - val_loss: 0.9215 - val_accuracy: 0.6711\n",
      "Epoch 4/100\n",
      "338/338 [==============================] - 49s 146ms/step - loss: 0.4065 - accuracy: 0.8535 - val_loss: 0.4591 - val_accuracy: 0.8215\n",
      "Epoch 5/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.2951 - accuracy: 0.8984 - val_loss: 0.4864 - val_accuracy: 0.8096\n",
      "Epoch 6/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.2317 - accuracy: 0.9225 - val_loss: 0.2666 - val_accuracy: 0.9067\n",
      "Epoch 7/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.1792 - accuracy: 0.9391 - val_loss: 0.2555 - val_accuracy: 0.9104\n",
      "Epoch 8/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.1463 - accuracy: 0.9504 - val_loss: 0.4934 - val_accuracy: 0.8489\n",
      "Epoch 9/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.1066 - accuracy: 0.9645 - val_loss: 0.1958 - val_accuracy: 0.9385\n",
      "Epoch 10/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0936 - accuracy: 0.9705 - val_loss: 0.8756 - val_accuracy: 0.7015\n",
      "Epoch 11/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0848 - accuracy: 0.9725 - val_loss: 0.5462 - val_accuracy: 0.8341\n",
      "Epoch 12/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0611 - accuracy: 0.9804 - val_loss: 0.1718 - val_accuracy: 0.9304\n",
      "Epoch 13/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0682 - accuracy: 0.9790 - val_loss: 0.4466 - val_accuracy: 0.8452\n",
      "Epoch 14/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0605 - accuracy: 0.9799 - val_loss: 0.1437 - val_accuracy: 0.9496\n",
      "Epoch 15/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0468 - accuracy: 0.9867 - val_loss: 0.2208 - val_accuracy: 0.9296\n",
      "Epoch 16/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0482 - accuracy: 0.9848 - val_loss: 0.3514 - val_accuracy: 0.9148\n",
      "Epoch 17/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0545 - accuracy: 0.9826 - val_loss: 0.0454 - val_accuracy: 0.9844\n",
      "Epoch 18/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0422 - accuracy: 0.9865 - val_loss: 0.0357 - val_accuracy: 0.9889\n",
      "Epoch 19/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0414 - accuracy: 0.9879 - val_loss: 0.1220 - val_accuracy: 0.9548\n",
      "Epoch 20/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0309 - accuracy: 0.9903 - val_loss: 0.0767 - val_accuracy: 0.9704\n",
      "Epoch 21/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0456 - accuracy: 0.9867 - val_loss: 0.0679 - val_accuracy: 0.9793\n",
      "Epoch 22/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0395 - accuracy: 0.9892 - val_loss: 0.0217 - val_accuracy: 0.9919\n",
      "Epoch 23/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0330 - accuracy: 0.9908 - val_loss: 0.1524 - val_accuracy: 0.9637\n",
      "Epoch 24/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0323 - accuracy: 0.9908 - val_loss: 0.0355 - val_accuracy: 0.9874\n",
      "Epoch 25/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0298 - accuracy: 0.9903 - val_loss: 0.1187 - val_accuracy: 0.9570\n",
      "Epoch 26/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0282 - accuracy: 0.9924 - val_loss: 0.5592 - val_accuracy: 0.8089\n",
      "Epoch 27/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0199 - accuracy: 0.9945 - val_loss: 0.0352 - val_accuracy: 0.9904\n",
      "Epoch 28/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0277 - accuracy: 0.9917 - val_loss: 0.0139 - val_accuracy: 0.9970\n",
      "Epoch 29/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0362 - accuracy: 0.9899 - val_loss: 0.0706 - val_accuracy: 0.9726\n",
      "Epoch 30/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0238 - accuracy: 0.9923 - val_loss: 0.0406 - val_accuracy: 0.9874\n",
      "Epoch 31/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0214 - accuracy: 0.9944 - val_loss: 0.0115 - val_accuracy: 0.9963\n",
      "Epoch 32/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0218 - accuracy: 0.9939 - val_loss: 0.0150 - val_accuracy: 0.9948\n",
      "Epoch 33/100\n",
      "338/338 [==============================] - 51s 149ms/step - loss: 0.0110 - accuracy: 0.9968 - val_loss: 0.1187 - val_accuracy: 0.9644\n",
      "Epoch 34/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0218 - accuracy: 0.9943 - val_loss: 0.4815 - val_accuracy: 0.8756\n",
      "Epoch 35/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0213 - accuracy: 0.9939 - val_loss: 0.0171 - val_accuracy: 0.9970\n",
      "Epoch 36/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0286 - accuracy: 0.9919 - val_loss: 0.0110 - val_accuracy: 0.9970\n",
      "Epoch 37/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0172 - accuracy: 0.9945 - val_loss: 0.0011 - val_accuracy: 1.0000\n",
      "Epoch 38/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0149 - accuracy: 0.9956 - val_loss: 0.1261 - val_accuracy: 0.9570\n",
      "Epoch 39/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0184 - accuracy: 0.9950 - val_loss: 0.6787 - val_accuracy: 0.8630\n",
      "Epoch 40/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0125 - accuracy: 0.9971 - val_loss: 0.1051 - val_accuracy: 0.9667\n",
      "Epoch 41/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0212 - accuracy: 0.9939 - val_loss: 0.0086 - val_accuracy: 1.0000\n",
      "Epoch 42/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0158 - accuracy: 0.9948 - val_loss: 0.0997 - val_accuracy: 0.9719\n",
      "Epoch 43/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0132 - accuracy: 0.9961 - val_loss: 0.0997 - val_accuracy: 0.9815\n",
      "Epoch 44/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0115 - accuracy: 0.9963 - val_loss: 0.0676 - val_accuracy: 0.9719\n",
      "Epoch 45/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0123 - accuracy: 0.9966 - val_loss: 0.0904 - val_accuracy: 0.9785\n",
      "Epoch 46/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0155 - accuracy: 0.9956 - val_loss: 0.0446 - val_accuracy: 0.9919\n",
      "Epoch 47/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0121 - accuracy: 0.9970 - val_loss: 0.0028 - val_accuracy: 1.0000\n",
      "Epoch 48/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0212 - accuracy: 0.9941 - val_loss: 0.0378 - val_accuracy: 0.9844\n",
      "Epoch 49/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0069 - accuracy: 0.9981 - val_loss: 0.0013 - val_accuracy: 1.0000\n",
      "Epoch 50/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0157 - accuracy: 0.9958 - val_loss: 1.5210 - val_accuracy: 0.7237\n",
      "Epoch 51/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0134 - accuracy: 0.9958 - val_loss: 0.0037 - val_accuracy: 1.0000\n",
      "Epoch 52/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0145 - accuracy: 0.9962 - val_loss: 0.0021 - val_accuracy: 0.9993\n",
      "Epoch 53/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0083 - accuracy: 0.9975 - val_loss: 0.0301 - val_accuracy: 0.9911\n",
      "Epoch 54/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0171 - accuracy: 0.9952 - val_loss: 0.0461 - val_accuracy: 0.9874\n",
      "Epoch 55/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0099 - accuracy: 0.9973 - val_loss: 0.0408 - val_accuracy: 0.9889\n",
      "Epoch 56/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0098 - accuracy: 0.9975 - val_loss: 0.2088 - val_accuracy: 0.9526\n",
      "Epoch 57/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0165 - accuracy: 0.9961 - val_loss: 0.0119 - val_accuracy: 0.9970\n",
      "Epoch 58/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0107 - accuracy: 0.9969 - val_loss: 0.0579 - val_accuracy: 0.9889\n",
      "Epoch 59/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0074 - accuracy: 0.9977 - val_loss: 0.0089 - val_accuracy: 0.9963\n",
      "Epoch 60/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0080 - accuracy: 0.9979 - val_loss: 0.1836 - val_accuracy: 0.9622\n",
      "Epoch 61/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0159 - accuracy: 0.9961 - val_loss: 0.0014 - val_accuracy: 1.0000\n",
      "Epoch 62/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0091 - accuracy: 0.9975 - val_loss: 0.6011 - val_accuracy: 0.9207\n",
      "Epoch 63/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0107 - accuracy: 0.9971 - val_loss: 0.0750 - val_accuracy: 0.9807\n",
      "Epoch 64/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0126 - accuracy: 0.9975 - val_loss: 0.0047 - val_accuracy: 0.9985\n",
      "Epoch 65/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0066 - accuracy: 0.9980 - val_loss: 0.0384 - val_accuracy: 0.9919\n",
      "Epoch 66/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0071 - accuracy: 0.9982 - val_loss: 1.7713e-04 - val_accuracy: 1.0000\n",
      "Epoch 67/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0074 - accuracy: 0.9981 - val_loss: 0.0080 - val_accuracy: 0.9970\n",
      "Epoch 68/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0097 - accuracy: 0.9978 - val_loss: 0.0076 - val_accuracy: 0.9993\n",
      "Epoch 69/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0097 - accuracy: 0.9976 - val_loss: 0.0313 - val_accuracy: 0.9881\n",
      "Epoch 70/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0055 - accuracy: 0.9984 - val_loss: 0.0296 - val_accuracy: 0.9919\n",
      "Epoch 71/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0082 - accuracy: 0.9987 - val_loss: 5.7858e-04 - val_accuracy: 1.0000\n",
      "Epoch 72/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0079 - accuracy: 0.9976 - val_loss: 0.0169 - val_accuracy: 0.9963\n",
      "Epoch 73/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0042 - accuracy: 0.9992 - val_loss: 1.1768 - val_accuracy: 0.7311\n",
      "Epoch 74/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0150 - accuracy: 0.9957 - val_loss: 0.2413 - val_accuracy: 0.9444\n",
      "Epoch 75/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0113 - accuracy: 0.9968 - val_loss: 0.0607 - val_accuracy: 0.9785\n",
      "Epoch 76/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0091 - accuracy: 0.9973 - val_loss: 1.7989e-04 - val_accuracy: 1.0000\n",
      "Epoch 77/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0124 - accuracy: 0.9970 - val_loss: 7.9416e-04 - val_accuracy: 0.9993\n",
      "Epoch 78/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0061 - accuracy: 0.9981 - val_loss: 0.0235 - val_accuracy: 0.9926\n",
      "Epoch 79/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0071 - accuracy: 0.9982 - val_loss: 0.0023 - val_accuracy: 1.0000\n",
      "Epoch 80/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0079 - accuracy: 0.9976 - val_loss: 0.0030 - val_accuracy: 1.0000\n",
      "Epoch 81/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0066 - accuracy: 0.9982 - val_loss: 8.8867e-04 - val_accuracy: 1.0000\n",
      "Epoch 82/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0033 - accuracy: 0.9994 - val_loss: 1.5002e-04 - val_accuracy: 1.0000\n",
      "Epoch 83/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0096 - accuracy: 0.9963 - val_loss: 3.2787e-04 - val_accuracy: 1.0000\n",
      "Epoch 84/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0092 - accuracy: 0.9975 - val_loss: 0.0038 - val_accuracy: 0.9985\n",
      "Epoch 85/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0034 - accuracy: 0.9992 - val_loss: 0.0011 - val_accuracy: 1.0000\n",
      "Epoch 86/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0062 - accuracy: 0.9982 - val_loss: 1.1514e-04 - val_accuracy: 1.0000\n",
      "Epoch 87/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0040 - accuracy: 0.9991 - val_loss: 0.0027 - val_accuracy: 0.9993\n",
      "Epoch 88/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0137 - accuracy: 0.9969 - val_loss: 0.1142 - val_accuracy: 0.9674\n",
      "Epoch 89/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0054 - accuracy: 0.9987 - val_loss: 0.0842 - val_accuracy: 0.9867\n",
      "Epoch 90/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0093 - accuracy: 0.9975 - val_loss: 4.3417e-04 - val_accuracy: 1.0000\n",
      "Epoch 91/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0049 - accuracy: 0.9986 - val_loss: 7.7187e-04 - val_accuracy: 1.0000\n",
      "Epoch 92/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0079 - accuracy: 0.9981 - val_loss: 0.0068 - val_accuracy: 0.9985\n",
      "Epoch 93/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0075 - accuracy: 0.9980 - val_loss: 0.0282 - val_accuracy: 0.9941\n",
      "Epoch 94/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0054 - accuracy: 0.9979 - val_loss: 0.0028 - val_accuracy: 0.9993\n",
      "Epoch 95/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0057 - accuracy: 0.9982 - val_loss: 0.2507 - val_accuracy: 0.9504\n",
      "Epoch 96/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0042 - accuracy: 0.9992 - val_loss: 0.0048 - val_accuracy: 0.9970\n",
      "Epoch 97/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0103 - accuracy: 0.9973 - val_loss: 0.0160 - val_accuracy: 0.9926\n",
      "Epoch 98/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0041 - accuracy: 0.9991 - val_loss: 0.0342 - val_accuracy: 0.9889\n",
      "Epoch 99/100\n",
      "337/338 [============================>.] - ETA: 0s - loss: 8.3241e-04 - accuracy: 0.9998\n",
      "Epoch 00099: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 8.3433e-04 - accuracy: 0.9998 - val_loss: 0.0087 - val_accuracy: 0.9970\n",
      "Epoch 100/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0019 - accuracy: 0.9995 - val_loss: 0.0211 - val_accuracy: 0.9941\n",
      "processing fold # 5\n",
      "Epoch 1/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 1.6860 - accuracy: 0.2698 - val_loss: 1.6598 - val_accuracy: 0.2000\n",
      "Epoch 2/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 1.2928 - accuracy: 0.4481 - val_loss: 1.1524 - val_accuracy: 0.4526\n",
      "Epoch 3/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.9221 - accuracy: 0.6339 - val_loss: 1.8892 - val_accuracy: 0.4741\n",
      "Epoch 4/100\n",
      "338/338 [==============================] - 51s 149ms/step - loss: 0.5701 - accuracy: 0.7886 - val_loss: 0.7733 - val_accuracy: 0.7185\n",
      "Epoch 5/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.3845 - accuracy: 0.8562 - val_loss: 0.3747 - val_accuracy: 0.8644\n",
      "Epoch 6/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.2564 - accuracy: 0.9134 - val_loss: 0.1811 - val_accuracy: 0.9348\n",
      "Epoch 7/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.2008 - accuracy: 0.9306 - val_loss: 0.6156 - val_accuracy: 0.8067\n",
      "Epoch 8/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.1588 - accuracy: 0.9473 - val_loss: 0.8248 - val_accuracy: 0.8052\n",
      "Epoch 9/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.1399 - accuracy: 0.9536 - val_loss: 0.1131 - val_accuracy: 0.9570\n",
      "Epoch 10/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.1093 - accuracy: 0.9649 - val_loss: 0.2865 - val_accuracy: 0.8911\n",
      "Epoch 11/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0909 - accuracy: 0.9702 - val_loss: 0.3344 - val_accuracy: 0.9170\n",
      "Epoch 12/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0801 - accuracy: 0.9756 - val_loss: 0.2922 - val_accuracy: 0.9089\n",
      "Epoch 13/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0793 - accuracy: 0.9740 - val_loss: 0.2237 - val_accuracy: 0.9289\n",
      "Epoch 14/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0587 - accuracy: 0.9814 - val_loss: 0.3946 - val_accuracy: 0.8711\n",
      "Epoch 15/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0713 - accuracy: 0.9781 - val_loss: 0.2558 - val_accuracy: 0.9230\n",
      "Epoch 16/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0559 - accuracy: 0.9819 - val_loss: 0.1550 - val_accuracy: 0.9578\n",
      "Epoch 17/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0548 - accuracy: 0.9825 - val_loss: 0.1004 - val_accuracy: 0.9681\n",
      "Epoch 18/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0551 - accuracy: 0.9836 - val_loss: 0.1690 - val_accuracy: 0.9400\n",
      "Epoch 19/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0466 - accuracy: 0.9869 - val_loss: 0.0565 - val_accuracy: 0.9807\n",
      "Epoch 20/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0382 - accuracy: 0.9887 - val_loss: 0.4715 - val_accuracy: 0.8837\n",
      "Epoch 21/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0392 - accuracy: 0.9885 - val_loss: 0.3838 - val_accuracy: 0.9022\n",
      "Epoch 22/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0409 - accuracy: 0.9875 - val_loss: 0.1624 - val_accuracy: 0.9711\n",
      "Epoch 23/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0497 - accuracy: 0.9857 - val_loss: 0.1016 - val_accuracy: 0.9637\n",
      "Epoch 24/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0405 - accuracy: 0.9892 - val_loss: 0.1113 - val_accuracy: 0.9570\n",
      "Epoch 25/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0331 - accuracy: 0.9906 - val_loss: 0.6397 - val_accuracy: 0.8652\n",
      "Epoch 26/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0300 - accuracy: 0.9914 - val_loss: 0.0880 - val_accuracy: 0.9644\n",
      "Epoch 27/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0385 - accuracy: 0.9882 - val_loss: 0.1346 - val_accuracy: 0.9659\n",
      "Epoch 28/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0297 - accuracy: 0.9921 - val_loss: 0.0666 - val_accuracy: 0.9815\n",
      "Epoch 29/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0299 - accuracy: 0.9905 - val_loss: 0.2327 - val_accuracy: 0.9185\n",
      "Epoch 30/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0205 - accuracy: 0.9939 - val_loss: 0.1443 - val_accuracy: 0.9407\n",
      "Epoch 31/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0314 - accuracy: 0.9906 - val_loss: 0.2671 - val_accuracy: 0.9119\n",
      "Epoch 32/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0253 - accuracy: 0.9934 - val_loss: 0.5547 - val_accuracy: 0.9052\n",
      "Epoch 33/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0231 - accuracy: 0.9932 - val_loss: 0.0666 - val_accuracy: 0.9785\n",
      "Epoch 34/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0216 - accuracy: 0.9938 - val_loss: 0.1018 - val_accuracy: 0.9600\n",
      "Epoch 35/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0198 - accuracy: 0.9949 - val_loss: 0.0780 - val_accuracy: 0.9800\n",
      "Epoch 36/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0196 - accuracy: 0.9944 - val_loss: 0.6402 - val_accuracy: 0.8644\n",
      "Epoch 37/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0206 - accuracy: 0.9944 - val_loss: 0.1226 - val_accuracy: 0.9615\n",
      "Epoch 38/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0200 - accuracy: 0.9950 - val_loss: 0.6077 - val_accuracy: 0.9111\n",
      "Epoch 39/100\n",
      "338/338 [==============================] - 51s 149ms/step - loss: 0.0181 - accuracy: 0.9947 - val_loss: 0.0637 - val_accuracy: 0.9785\n",
      "Epoch 40/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0136 - accuracy: 0.9958 - val_loss: 0.0772 - val_accuracy: 0.9800\n",
      "Epoch 41/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0277 - accuracy: 0.9921 - val_loss: 0.1114 - val_accuracy: 0.9600\n",
      "Epoch 42/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0172 - accuracy: 0.9948 - val_loss: 0.4352 - val_accuracy: 0.9230\n",
      "Epoch 43/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0175 - accuracy: 0.9954 - val_loss: 0.0335 - val_accuracy: 0.9881\n",
      "Epoch 44/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0152 - accuracy: 0.9959 - val_loss: 0.1159 - val_accuracy: 0.9393\n",
      "Epoch 45/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0102 - accuracy: 0.9966 - val_loss: 0.1980 - val_accuracy: 0.9563\n",
      "Epoch 46/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0143 - accuracy: 0.9960 - val_loss: 0.1831 - val_accuracy: 0.9459\n",
      "Epoch 47/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0118 - accuracy: 0.9965 - val_loss: 0.1697 - val_accuracy: 0.9578\n",
      "Epoch 48/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0111 - accuracy: 0.9967 - val_loss: 0.0613 - val_accuracy: 0.9822\n",
      "Epoch 49/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0189 - accuracy: 0.9949 - val_loss: 0.0230 - val_accuracy: 0.9970\n",
      "Epoch 50/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0116 - accuracy: 0.9968 - val_loss: 0.1069 - val_accuracy: 0.9741\n",
      "Epoch 51/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0160 - accuracy: 0.9956 - val_loss: 0.0798 - val_accuracy: 0.9711\n",
      "Epoch 52/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0130 - accuracy: 0.9960 - val_loss: 0.0953 - val_accuracy: 0.9637\n",
      "Epoch 53/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0155 - accuracy: 0.9951 - val_loss: 0.3145 - val_accuracy: 0.9415\n",
      "Epoch 54/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0186 - accuracy: 0.9946 - val_loss: 0.2120 - val_accuracy: 0.9296\n",
      "Epoch 55/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0157 - accuracy: 0.9954 - val_loss: 0.0280 - val_accuracy: 0.9889\n",
      "Epoch 56/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0029 - accuracy: 0.9991 - val_loss: 0.1279 - val_accuracy: 0.9533\n",
      "Epoch 57/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0129 - accuracy: 0.9964 - val_loss: 0.0529 - val_accuracy: 0.9867\n",
      "Epoch 58/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0104 - accuracy: 0.9978 - val_loss: 0.0869 - val_accuracy: 0.9800\n",
      "Epoch 59/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0044 - accuracy: 0.9985 - val_loss: 0.0350 - val_accuracy: 0.9896\n",
      "Epoch 60/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0186 - accuracy: 0.9945 - val_loss: 0.0241 - val_accuracy: 0.9896\n",
      "Epoch 61/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0086 - accuracy: 0.9976 - val_loss: 0.0595 - val_accuracy: 0.9889\n",
      "Epoch 62/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0106 - accuracy: 0.9974 - val_loss: 0.0344 - val_accuracy: 0.9896\n",
      "Epoch 63/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0062 - accuracy: 0.9981 - val_loss: 0.0652 - val_accuracy: 0.9748\n",
      "Epoch 64/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0133 - accuracy: 0.9969 - val_loss: 0.0314 - val_accuracy: 0.9919\n",
      "Epoch 65/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0094 - accuracy: 0.9972 - val_loss: 0.1513 - val_accuracy: 0.9689\n",
      "Epoch 66/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0104 - accuracy: 0.9968 - val_loss: 0.0229 - val_accuracy: 0.9956\n",
      "Epoch 67/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0038 - accuracy: 0.9986 - val_loss: 0.3516 - val_accuracy: 0.9333\n",
      "Epoch 68/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0106 - accuracy: 0.9973 - val_loss: 0.0397 - val_accuracy: 0.9881\n",
      "Epoch 69/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0105 - accuracy: 0.9970 - val_loss: 0.2064 - val_accuracy: 0.9630\n",
      "Epoch 70/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0097 - accuracy: 0.9977 - val_loss: 0.0360 - val_accuracy: 0.9867\n",
      "Epoch 71/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0039 - accuracy: 0.9990 - val_loss: 0.0770 - val_accuracy: 0.9830\n",
      "Epoch 72/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0117 - accuracy: 0.9973 - val_loss: 0.1623 - val_accuracy: 0.9644\n",
      "Epoch 73/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0076 - accuracy: 0.9973 - val_loss: 0.0340 - val_accuracy: 0.9956\n",
      "Epoch 74/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0164 - accuracy: 0.9968 - val_loss: 0.0326 - val_accuracy: 0.9941\n",
      "Epoch 75/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0063 - accuracy: 0.9984 - val_loss: 0.1350 - val_accuracy: 0.9630\n",
      "Epoch 76/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0088 - accuracy: 0.9970 - val_loss: 0.1395 - val_accuracy: 0.9563\n",
      "Epoch 77/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0051 - accuracy: 0.9987 - val_loss: 0.0646 - val_accuracy: 0.9881\n",
      "Epoch 78/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0106 - accuracy: 0.9971 - val_loss: 0.1416 - val_accuracy: 0.9659\n",
      "Epoch 79/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0061 - accuracy: 0.9982 - val_loss: 0.0726 - val_accuracy: 0.9822\n",
      "Epoch 80/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0077 - accuracy: 0.9978 - val_loss: 0.1634 - val_accuracy: 0.9363\n",
      "Epoch 81/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0042 - accuracy: 0.9993 - val_loss: 0.0242 - val_accuracy: 0.9941\n",
      "Epoch 82/100\n",
      "337/338 [============================>.] - ETA: 0s - loss: 0.0098 - accuracy: 0.9968\n",
      "Epoch 00082: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0098 - accuracy: 0.9969 - val_loss: 0.0489 - val_accuracy: 0.9889\n",
      "Epoch 83/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0030 - accuracy: 0.9994 - val_loss: 0.0263 - val_accuracy: 0.9956\n",
      "Epoch 84/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0015 - accuracy: 0.9996 - val_loss: 0.0268 - val_accuracy: 0.9926\n",
      "Epoch 85/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0027 - accuracy: 0.9992 - val_loss: 0.0358 - val_accuracy: 0.9896\n",
      "Epoch 86/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0011 - accuracy: 0.9996 - val_loss: 0.0447 - val_accuracy: 0.9881\n",
      "Epoch 87/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0011 - accuracy: 0.9996 - val_loss: 0.0408 - val_accuracy: 0.9881\n",
      "Epoch 88/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0022 - accuracy: 0.9995 - val_loss: 0.0246 - val_accuracy: 0.9956\n",
      "Epoch 89/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0014 - accuracy: 0.9994 - val_loss: 0.0600 - val_accuracy: 0.9800\n",
      "Epoch 90/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 6.8273e-04 - accuracy: 0.9999 - val_loss: 0.0399 - val_accuracy: 0.9896\n",
      "Epoch 91/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 3.9715e-04 - accuracy: 0.9998 - val_loss: 0.0625 - val_accuracy: 0.9807\n",
      "Epoch 92/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 5.1308e-04 - accuracy: 0.9998 - val_loss: 0.0697 - val_accuracy: 0.9800\n",
      "Epoch 93/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 4.2269e-04 - accuracy: 0.9998 - val_loss: 0.0670 - val_accuracy: 0.9807\n",
      "Epoch 94/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 4.4844e-04 - accuracy: 0.9998 - val_loss: 0.0647 - val_accuracy: 0.9837\n",
      "Epoch 95/100\n",
      "338/338 [==============================] - 51s 149ms/step - loss: 5.1277e-04 - accuracy: 0.9998 - val_loss: 0.0679 - val_accuracy: 0.9822\n",
      "Epoch 96/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 5.6186e-04 - accuracy: 0.9998 - val_loss: 0.1023 - val_accuracy: 0.9681\n",
      "Epoch 97/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 5.1547e-04 - accuracy: 0.9998 - val_loss: 0.0769 - val_accuracy: 0.9785\n",
      "Epoch 98/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 1.8699e-04 - accuracy: 0.9999 - val_loss: 0.0665 - val_accuracy: 0.9830\n",
      "Epoch 99/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 1.5462e-04 - accuracy: 1.0000 - val_loss: 0.0561 - val_accuracy: 0.9874\n",
      "Epoch 100/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 5.7570e-04 - accuracy: 0.9999 - val_loss: 0.1023 - val_accuracy: 0.9704\n",
      "processing fold # 6\n",
      "Epoch 1/100\n",
      "338/338 [==============================] - 51s 149ms/step - loss: 1.5787 - accuracy: 0.3222 - val_loss: 1.6640 - val_accuracy: 0.2000\n",
      "Epoch 2/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 1.1565 - accuracy: 0.5206 - val_loss: 1.0313 - val_accuracy: 0.5733\n",
      "Epoch 3/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.7116 - accuracy: 0.7355 - val_loss: 1.2952 - val_accuracy: 0.5200\n",
      "Epoch 4/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.4449 - accuracy: 0.8387 - val_loss: 0.8489 - val_accuracy: 0.7059\n",
      "Epoch 5/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.3164 - accuracy: 0.8862 - val_loss: 0.8310 - val_accuracy: 0.7000\n",
      "Epoch 6/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "338/338 [==============================] - 51s 150ms/step - loss: 0.2253 - accuracy: 0.9234 - val_loss: 1.0303 - val_accuracy: 0.7356\n",
      "Epoch 7/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.1696 - accuracy: 0.9429 - val_loss: 0.7573 - val_accuracy: 0.7333\n",
      "Epoch 8/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.1439 - accuracy: 0.9544 - val_loss: 1.0213 - val_accuracy: 0.6600\n",
      "Epoch 9/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.1260 - accuracy: 0.9592 - val_loss: 0.5940 - val_accuracy: 0.7519\n",
      "Epoch 10/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0987 - accuracy: 0.9679 - val_loss: 0.4106 - val_accuracy: 0.8570\n",
      "Epoch 11/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0783 - accuracy: 0.9743 - val_loss: 0.3535 - val_accuracy: 0.8800\n",
      "Epoch 12/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0714 - accuracy: 0.9776 - val_loss: 0.2439 - val_accuracy: 0.9222\n",
      "Epoch 13/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0678 - accuracy: 0.9790 - val_loss: 1.0900 - val_accuracy: 0.7244\n",
      "Epoch 14/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0644 - accuracy: 0.9810 - val_loss: 0.3240 - val_accuracy: 0.8941\n",
      "Epoch 15/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0714 - accuracy: 0.9781 - val_loss: 1.1641 - val_accuracy: 0.7341\n",
      "Epoch 16/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0450 - accuracy: 0.9868 - val_loss: 0.9407 - val_accuracy: 0.7170\n",
      "Epoch 17/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0561 - accuracy: 0.9838 - val_loss: 0.7063 - val_accuracy: 0.8111\n",
      "Epoch 18/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0605 - accuracy: 0.9826 - val_loss: 0.0136 - val_accuracy: 0.9956\n",
      "Epoch 19/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0380 - accuracy: 0.9884 - val_loss: 0.0943 - val_accuracy: 0.9622\n",
      "Epoch 20/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0444 - accuracy: 0.9863 - val_loss: 1.2389 - val_accuracy: 0.6237\n",
      "Epoch 21/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0464 - accuracy: 0.9858 - val_loss: 0.0998 - val_accuracy: 0.9652\n",
      "Epoch 22/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0350 - accuracy: 0.9910 - val_loss: 0.2459 - val_accuracy: 0.9356\n",
      "Epoch 23/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0303 - accuracy: 0.9902 - val_loss: 0.2488 - val_accuracy: 0.9207\n",
      "Epoch 24/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0366 - accuracy: 0.9906 - val_loss: 0.1328 - val_accuracy: 0.9519\n",
      "Epoch 25/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0334 - accuracy: 0.9890 - val_loss: 0.0787 - val_accuracy: 0.9711\n",
      "Epoch 26/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0209 - accuracy: 0.9936 - val_loss: 1.3272 - val_accuracy: 0.7889\n",
      "Epoch 27/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0379 - accuracy: 0.9884 - val_loss: 0.2303 - val_accuracy: 0.9289\n",
      "Epoch 28/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0225 - accuracy: 0.9928 - val_loss: 0.0494 - val_accuracy: 0.9800\n",
      "Epoch 29/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0251 - accuracy: 0.9931 - val_loss: 0.3290 - val_accuracy: 0.9148\n",
      "Epoch 30/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0192 - accuracy: 0.9945 - val_loss: 0.2248 - val_accuracy: 0.9274\n",
      "Epoch 31/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0223 - accuracy: 0.9947 - val_loss: 0.0339 - val_accuracy: 0.9874\n",
      "Epoch 32/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0294 - accuracy: 0.9915 - val_loss: 0.1740 - val_accuracy: 0.9444\n",
      "Epoch 33/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0208 - accuracy: 0.9947 - val_loss: 0.0918 - val_accuracy: 0.9726\n",
      "Epoch 34/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0278 - accuracy: 0.9923 - val_loss: 0.1305 - val_accuracy: 0.9556\n",
      "Epoch 35/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0203 - accuracy: 0.9935 - val_loss: 0.0176 - val_accuracy: 0.9933\n",
      "Epoch 36/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0132 - accuracy: 0.9966 - val_loss: 0.0710 - val_accuracy: 0.9741\n",
      "Epoch 37/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0149 - accuracy: 0.9952 - val_loss: 0.0767 - val_accuracy: 0.9726\n",
      "Epoch 38/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0205 - accuracy: 0.9936 - val_loss: 0.0567 - val_accuracy: 0.9770\n",
      "Epoch 39/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0139 - accuracy: 0.9961 - val_loss: 6.0701e-04 - val_accuracy: 1.0000\n",
      "Epoch 40/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0181 - accuracy: 0.9943 - val_loss: 0.4044 - val_accuracy: 0.9037\n",
      "Epoch 41/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0143 - accuracy: 0.9956 - val_loss: 0.2130 - val_accuracy: 0.9333\n",
      "Epoch 42/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0173 - accuracy: 0.9952 - val_loss: 0.0128 - val_accuracy: 0.9933\n",
      "Epoch 43/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0130 - accuracy: 0.9961 - val_loss: 0.5930 - val_accuracy: 0.8296\n",
      "Epoch 44/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0208 - accuracy: 0.9934 - val_loss: 0.0868 - val_accuracy: 0.9793\n",
      "Epoch 45/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0182 - accuracy: 0.9946 - val_loss: 0.0247 - val_accuracy: 0.9896\n",
      "Epoch 46/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0165 - accuracy: 0.9955 - val_loss: 0.0139 - val_accuracy: 0.9985\n",
      "Epoch 47/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0120 - accuracy: 0.9967 - val_loss: 0.2211 - val_accuracy: 0.9341\n",
      "Epoch 48/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0078 - accuracy: 0.9980 - val_loss: 0.1642 - val_accuracy: 0.9481\n",
      "Epoch 49/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0136 - accuracy: 0.9965 - val_loss: 0.2377 - val_accuracy: 0.9170\n",
      "Epoch 50/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0240 - accuracy: 0.9929 - val_loss: 0.8699 - val_accuracy: 0.7926\n",
      "Epoch 51/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0101 - accuracy: 0.9969 - val_loss: 0.0443 - val_accuracy: 0.9815\n",
      "Epoch 52/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0083 - accuracy: 0.9978 - val_loss: 1.4372 - val_accuracy: 0.7800\n",
      "Epoch 53/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0130 - accuracy: 0.9967 - val_loss: 1.6009 - val_accuracy: 0.8222\n",
      "Epoch 54/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0154 - accuracy: 0.9965 - val_loss: 0.3387 - val_accuracy: 0.9311\n",
      "Epoch 55/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0098 - accuracy: 0.9976 - val_loss: 0.0173 - val_accuracy: 0.9926\n",
      "Epoch 56/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0106 - accuracy: 0.9970 - val_loss: 0.0685 - val_accuracy: 0.9763\n",
      "Epoch 57/100\n",
      "338/338 [==============================] - 51s 149ms/step - loss: 0.0120 - accuracy: 0.9978 - val_loss: 0.7587 - val_accuracy: 0.8681\n",
      "Epoch 58/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0118 - accuracy: 0.9965 - val_loss: 0.1357 - val_accuracy: 0.9615\n",
      "Epoch 59/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0072 - accuracy: 0.9982 - val_loss: 0.0297 - val_accuracy: 0.9896\n",
      "Epoch 60/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0096 - accuracy: 0.9976 - val_loss: 0.1115 - val_accuracy: 0.9607\n",
      "Epoch 61/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0157 - accuracy: 0.9951 - val_loss: 0.0713 - val_accuracy: 0.9815\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 62/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0112 - accuracy: 0.9973 - val_loss: 0.0162 - val_accuracy: 0.9926\n",
      "Epoch 63/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0125 - accuracy: 0.9971 - val_loss: 0.0170 - val_accuracy: 0.9911\n",
      "Epoch 64/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0060 - accuracy: 0.9979 - val_loss: 0.9248 - val_accuracy: 0.8630\n",
      "Epoch 65/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0089 - accuracy: 0.9971 - val_loss: 0.1546 - val_accuracy: 0.9704\n",
      "Epoch 66/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0083 - accuracy: 0.9975 - val_loss: 0.1039 - val_accuracy: 0.9778\n",
      "Epoch 67/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0107 - accuracy: 0.9970 - val_loss: 0.0386 - val_accuracy: 0.9844\n",
      "Epoch 68/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0101 - accuracy: 0.9970 - val_loss: 0.0159 - val_accuracy: 0.9933\n",
      "Epoch 69/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0097 - accuracy: 0.9979 - val_loss: 0.0591 - val_accuracy: 0.9800\n",
      "Epoch 70/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0121 - accuracy: 0.9976 - val_loss: 0.0802 - val_accuracy: 0.9778\n",
      "Epoch 71/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0059 - accuracy: 0.9984 - val_loss: 0.0663 - val_accuracy: 0.9748\n",
      "Epoch 72/100\n",
      "337/338 [============================>.] - ETA: 0s - loss: 0.0100 - accuracy: 0.9972\n",
      "Epoch 00072: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0100 - accuracy: 0.9972 - val_loss: 0.2423 - val_accuracy: 0.9422\n",
      "Epoch 73/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0048 - accuracy: 0.9985 - val_loss: 0.2090 - val_accuracy: 0.9467\n",
      "Epoch 74/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0025 - accuracy: 0.9993 - val_loss: 0.1555 - val_accuracy: 0.9570\n",
      "Epoch 75/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0019 - accuracy: 0.9995 - val_loss: 0.1294 - val_accuracy: 0.9593\n",
      "Epoch 76/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 9.2219e-04 - accuracy: 0.9998 - val_loss: 0.1546 - val_accuracy: 0.9585\n",
      "Epoch 77/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0015 - accuracy: 0.9996 - val_loss: 0.1821 - val_accuracy: 0.9556\n",
      "Epoch 78/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 7.3460e-04 - accuracy: 0.9997 - val_loss: 0.1924 - val_accuracy: 0.9570\n",
      "Epoch 79/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 8.0915e-04 - accuracy: 0.9997 - val_loss: 0.2039 - val_accuracy: 0.9563\n",
      "Epoch 80/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0012 - accuracy: 0.9998 - val_loss: 0.1717 - val_accuracy: 0.9600\n",
      "Epoch 81/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 7.1168e-04 - accuracy: 0.9997 - val_loss: 0.2952 - val_accuracy: 0.9437\n",
      "Epoch 82/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0012 - accuracy: 0.9995 - val_loss: 0.2496 - val_accuracy: 0.9504\n",
      "Epoch 83/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 8.8952e-04 - accuracy: 0.9997 - val_loss: 0.1700 - val_accuracy: 0.9607\n",
      "Epoch 84/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.2344 - val_accuracy: 0.9541\n",
      "Epoch 85/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0017 - accuracy: 0.9993 - val_loss: 0.2976 - val_accuracy: 0.9430\n",
      "Epoch 86/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 4.6637e-04 - accuracy: 0.9999 - val_loss: 0.2625 - val_accuracy: 0.9489\n",
      "Epoch 87/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.1873 - val_accuracy: 0.9570\n",
      "Epoch 88/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 4.3043e-04 - accuracy: 0.9999 - val_loss: 0.1817 - val_accuracy: 0.9593\n",
      "Epoch 89/100\n",
      "338/338 [==============================] - 51s 149ms/step - loss: 4.8698e-04 - accuracy: 0.9998 - val_loss: 0.2509 - val_accuracy: 0.9504\n",
      "Epoch 90/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0014 - accuracy: 0.9996 - val_loss: 0.1821 - val_accuracy: 0.9593\n",
      "Epoch 91/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 7.8094e-04 - accuracy: 0.9998 - val_loss: 0.2086 - val_accuracy: 0.9533\n",
      "Epoch 92/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 4.8027e-04 - accuracy: 0.9999 - val_loss: 0.2216 - val_accuracy: 0.9548\n",
      "Epoch 93/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 5.9373e-04 - accuracy: 0.9998 - val_loss: 0.2310 - val_accuracy: 0.9526\n",
      "Epoch 94/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 1.0457e-04 - accuracy: 1.0000 - val_loss: 0.2183 - val_accuracy: 0.9548\n",
      "Epoch 95/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 1.4512e-04 - accuracy: 1.0000 - val_loss: 0.2258 - val_accuracy: 0.9533\n",
      "Epoch 96/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 6.0650e-05 - accuracy: 1.0000 - val_loss: 0.2180 - val_accuracy: 0.9548\n",
      "Epoch 97/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 1.9119e-04 - accuracy: 0.9999 - val_loss: 0.2804 - val_accuracy: 0.9481\n",
      "Epoch 98/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 2.6570e-04 - accuracy: 0.9999 - val_loss: 0.2799 - val_accuracy: 0.9489\n",
      "Epoch 99/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0025 - accuracy: 0.9996 - val_loss: 0.3010 - val_accuracy: 0.9437\n",
      "Epoch 100/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 1.7086e-04 - accuracy: 1.0000 - val_loss: 0.2828 - val_accuracy: 0.9474\n",
      "processing fold # 7\n",
      "Epoch 1/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 1.6171 - accuracy: 0.3037 - val_loss: 1.6565 - val_accuracy: 0.2000\n",
      "Epoch 2/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 1.1419 - accuracy: 0.5356 - val_loss: 1.0622 - val_accuracy: 0.6163\n",
      "Epoch 3/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.7128 - accuracy: 0.7307 - val_loss: 0.8310 - val_accuracy: 0.7193\n",
      "Epoch 4/100\n",
      "338/338 [==============================] - 51s 149ms/step - loss: 0.4592 - accuracy: 0.8336 - val_loss: 0.5288 - val_accuracy: 0.7993\n",
      "Epoch 5/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.3278 - accuracy: 0.8836 - val_loss: 1.0306 - val_accuracy: 0.6748\n",
      "Epoch 6/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.2481 - accuracy: 0.9150 - val_loss: 0.4349 - val_accuracy: 0.8422\n",
      "Epoch 7/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.1972 - accuracy: 0.9320 - val_loss: 1.3656 - val_accuracy: 0.5993\n",
      "Epoch 8/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.1632 - accuracy: 0.9453 - val_loss: 0.5063 - val_accuracy: 0.8178\n",
      "Epoch 9/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.1212 - accuracy: 0.9602 - val_loss: 0.5005 - val_accuracy: 0.8252\n",
      "Epoch 10/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.1112 - accuracy: 0.9605 - val_loss: 0.2515 - val_accuracy: 0.9148\n",
      "Epoch 11/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.1041 - accuracy: 0.9652 - val_loss: 0.7683 - val_accuracy: 0.7867\n",
      "Epoch 12/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0796 - accuracy: 0.9755 - val_loss: 0.2072 - val_accuracy: 0.9133\n",
      "Epoch 13/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0753 - accuracy: 0.9774 - val_loss: 0.0294 - val_accuracy: 0.9941\n",
      "Epoch 14/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0672 - accuracy: 0.9794 - val_loss: 0.3278 - val_accuracy: 0.8793\n",
      "Epoch 15/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0577 - accuracy: 0.9822 - val_loss: 0.4359 - val_accuracy: 0.8496\n",
      "Epoch 16/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0639 - accuracy: 0.9801 - val_loss: 0.0869 - val_accuracy: 0.9652\n",
      "Epoch 17/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0532 - accuracy: 0.9841 - val_loss: 0.3833 - val_accuracy: 0.9215\n",
      "Epoch 18/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0418 - accuracy: 0.9873 - val_loss: 0.1665 - val_accuracy: 0.9437\n",
      "Epoch 19/100\n",
      "338/338 [==============================] - 51s 149ms/step - loss: 0.0464 - accuracy: 0.9856 - val_loss: 0.1656 - val_accuracy: 0.9496\n",
      "Epoch 20/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0442 - accuracy: 0.9862 - val_loss: 0.7375 - val_accuracy: 0.8763\n",
      "Epoch 21/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0388 - accuracy: 0.9884 - val_loss: 0.1563 - val_accuracy: 0.9437\n",
      "Epoch 22/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0315 - accuracy: 0.9897 - val_loss: 0.2605 - val_accuracy: 0.9170\n",
      "Epoch 23/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0424 - accuracy: 0.9869 - val_loss: 0.0973 - val_accuracy: 0.9741\n",
      "Epoch 24/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0416 - accuracy: 0.9877 - val_loss: 0.0282 - val_accuracy: 0.9933\n",
      "Epoch 25/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0276 - accuracy: 0.9918 - val_loss: 0.0427 - val_accuracy: 0.9837\n",
      "Epoch 26/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0353 - accuracy: 0.9889 - val_loss: 0.2465 - val_accuracy: 0.9185\n",
      "Epoch 27/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0305 - accuracy: 0.9909 - val_loss: 0.0324 - val_accuracy: 0.9919\n",
      "Epoch 28/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0224 - accuracy: 0.9928 - val_loss: 0.2858 - val_accuracy: 0.9296\n",
      "Epoch 29/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0279 - accuracy: 0.9919 - val_loss: 0.0081 - val_accuracy: 0.9970\n",
      "Epoch 30/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0320 - accuracy: 0.9905 - val_loss: 0.0100 - val_accuracy: 0.9970\n",
      "Epoch 31/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0234 - accuracy: 0.9926 - val_loss: 0.0242 - val_accuracy: 0.9889\n",
      "Epoch 32/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0192 - accuracy: 0.9951 - val_loss: 0.0244 - val_accuracy: 0.9933\n",
      "Epoch 33/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0229 - accuracy: 0.9938 - val_loss: 0.0900 - val_accuracy: 0.9689\n",
      "Epoch 34/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0184 - accuracy: 0.9940 - val_loss: 0.0302 - val_accuracy: 0.9896\n",
      "Epoch 35/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0227 - accuracy: 0.9937 - val_loss: 0.0613 - val_accuracy: 0.9837\n",
      "Epoch 36/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0193 - accuracy: 0.9949 - val_loss: 0.1139 - val_accuracy: 0.9615\n",
      "Epoch 37/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0187 - accuracy: 0.9949 - val_loss: 0.2713 - val_accuracy: 0.9104\n",
      "Epoch 38/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0174 - accuracy: 0.9956 - val_loss: 0.0247 - val_accuracy: 0.9933\n",
      "Epoch 39/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0200 - accuracy: 0.9944 - val_loss: 0.0258 - val_accuracy: 0.9941\n",
      "Epoch 40/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0186 - accuracy: 0.9950 - val_loss: 0.0292 - val_accuracy: 0.9904\n",
      "Epoch 41/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0093 - accuracy: 0.9968 - val_loss: 0.0098 - val_accuracy: 0.9970\n",
      "Epoch 42/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0199 - accuracy: 0.9943 - val_loss: 0.0807 - val_accuracy: 0.9748\n",
      "Epoch 43/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0185 - accuracy: 0.9949 - val_loss: 0.0049 - val_accuracy: 0.9978\n",
      "Epoch 44/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0171 - accuracy: 0.9950 - val_loss: 0.1036 - val_accuracy: 0.9696\n",
      "Epoch 45/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0208 - accuracy: 0.9936 - val_loss: 0.0254 - val_accuracy: 0.9889\n",
      "Epoch 46/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0091 - accuracy: 0.9971 - val_loss: 0.0703 - val_accuracy: 0.9807\n",
      "Epoch 47/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0197 - accuracy: 0.9944 - val_loss: 0.1015 - val_accuracy: 0.9511\n",
      "Epoch 48/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0131 - accuracy: 0.9962 - val_loss: 0.0500 - val_accuracy: 0.9844\n",
      "Epoch 49/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0133 - accuracy: 0.9960 - val_loss: 0.0388 - val_accuracy: 0.9933\n",
      "Epoch 50/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0161 - accuracy: 0.9950 - val_loss: 0.0397 - val_accuracy: 0.9852\n",
      "Epoch 51/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0132 - accuracy: 0.9968 - val_loss: 0.0011 - val_accuracy: 1.0000\n",
      "Epoch 52/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0096 - accuracy: 0.9974 - val_loss: 0.0049 - val_accuracy: 0.9978\n",
      "Epoch 53/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0116 - accuracy: 0.9971 - val_loss: 0.0191 - val_accuracy: 0.9941\n",
      "Epoch 54/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0139 - accuracy: 0.9964 - val_loss: 0.0124 - val_accuracy: 0.9948\n",
      "Epoch 55/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0104 - accuracy: 0.9968 - val_loss: 0.0212 - val_accuracy: 0.9919\n",
      "Epoch 56/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0121 - accuracy: 0.9966 - val_loss: 0.0202 - val_accuracy: 0.9926\n",
      "Epoch 57/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0108 - accuracy: 0.9968 - val_loss: 0.0029 - val_accuracy: 0.9993\n",
      "Epoch 58/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0095 - accuracy: 0.9969 - val_loss: 0.0059 - val_accuracy: 0.9993\n",
      "Epoch 59/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0138 - accuracy: 0.9956 - val_loss: 0.0273 - val_accuracy: 0.9941\n",
      "Epoch 60/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0110 - accuracy: 0.9969 - val_loss: 0.0024 - val_accuracy: 1.0000\n",
      "Epoch 61/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0079 - accuracy: 0.9981 - val_loss: 0.0296 - val_accuracy: 0.9941\n",
      "Epoch 62/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0104 - accuracy: 0.9969 - val_loss: 0.0096 - val_accuracy: 0.9948\n",
      "Epoch 63/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0053 - accuracy: 0.9984 - val_loss: 0.0345 - val_accuracy: 0.9933\n",
      "Epoch 64/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0166 - accuracy: 0.9950 - val_loss: 0.0294 - val_accuracy: 0.9941\n",
      "Epoch 65/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0119 - accuracy: 0.9974 - val_loss: 0.0271 - val_accuracy: 0.9919\n",
      "Epoch 66/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0071 - accuracy: 0.9981 - val_loss: 0.0177 - val_accuracy: 0.9941\n",
      "Epoch 67/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0083 - accuracy: 0.9978 - val_loss: 0.0168 - val_accuracy: 0.9941\n",
      "Epoch 68/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0040 - accuracy: 0.9987 - val_loss: 0.0095 - val_accuracy: 0.9948\n",
      "Epoch 69/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0059 - accuracy: 0.9982 - val_loss: 0.0166 - val_accuracy: 0.9941\n",
      "Epoch 70/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0108 - accuracy: 0.9968 - val_loss: 0.0491 - val_accuracy: 0.9830\n",
      "Epoch 71/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0073 - accuracy: 0.9981 - val_loss: 0.0037 - val_accuracy: 0.9993\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 72/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0109 - accuracy: 0.9969 - val_loss: 0.0237 - val_accuracy: 0.9933\n",
      "Epoch 73/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0089 - accuracy: 0.9977 - val_loss: 0.0124 - val_accuracy: 0.9941\n",
      "Epoch 74/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0062 - accuracy: 0.9986 - val_loss: 0.0165 - val_accuracy: 0.9933\n",
      "Epoch 75/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0082 - accuracy: 0.9974 - val_loss: 0.0041 - val_accuracy: 0.9993\n",
      "Epoch 76/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0053 - accuracy: 0.9981 - val_loss: 0.0139 - val_accuracy: 0.9941\n",
      "Epoch 77/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0087 - accuracy: 0.9974 - val_loss: 0.0318 - val_accuracy: 0.9941\n",
      "Epoch 78/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0080 - accuracy: 0.9976 - val_loss: 0.0282 - val_accuracy: 0.9933\n",
      "Epoch 79/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0094 - accuracy: 0.9976 - val_loss: 0.0180 - val_accuracy: 0.9941\n",
      "Epoch 80/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0122 - accuracy: 0.9970 - val_loss: 0.0847 - val_accuracy: 0.9867\n",
      "Epoch 81/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0043 - accuracy: 0.9988 - val_loss: 0.0369 - val_accuracy: 0.9941\n",
      "Epoch 82/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0084 - accuracy: 0.9974 - val_loss: 0.0286 - val_accuracy: 0.9941\n",
      "Epoch 83/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0057 - accuracy: 0.9981 - val_loss: 0.0511 - val_accuracy: 0.9933\n",
      "Epoch 84/100\n",
      "337/338 [============================>.] - ETA: 0s - loss: 0.0110 - accuracy: 0.9961\n",
      "Epoch 00084: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0110 - accuracy: 0.9961 - val_loss: 0.0349 - val_accuracy: 0.9919\n",
      "Epoch 85/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0045 - accuracy: 0.9985 - val_loss: 0.0515 - val_accuracy: 0.9933\n",
      "Epoch 86/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0017 - accuracy: 0.9994 - val_loss: 0.0561 - val_accuracy: 0.9933\n",
      "Epoch 87/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0032 - accuracy: 0.9991 - val_loss: 0.0364 - val_accuracy: 0.9933\n",
      "Epoch 88/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 0.0369 - val_accuracy: 0.9941\n",
      "Epoch 89/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 6.3756e-04 - accuracy: 0.9997 - val_loss: 0.0382 - val_accuracy: 0.9933\n",
      "Epoch 90/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 7.9228e-04 - accuracy: 0.9997 - val_loss: 0.0426 - val_accuracy: 0.9941\n",
      "Epoch 91/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 6.9255e-04 - accuracy: 0.9997 - val_loss: 0.0411 - val_accuracy: 0.9941\n",
      "Epoch 92/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 5.5213e-04 - accuracy: 0.9999 - val_loss: 0.0412 - val_accuracy: 0.9941\n",
      "Epoch 93/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 0.0456 - val_accuracy: 0.9941\n",
      "Epoch 94/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0011 - accuracy: 0.9996 - val_loss: 0.0340 - val_accuracy: 0.9941\n",
      "Epoch 95/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 8.3929e-04 - accuracy: 0.9997 - val_loss: 0.0433 - val_accuracy: 0.9941\n",
      "Epoch 96/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 6.4612e-04 - accuracy: 0.9998 - val_loss: 0.0416 - val_accuracy: 0.9941\n",
      "Epoch 97/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 9.1281e-04 - accuracy: 0.9998 - val_loss: 0.0497 - val_accuracy: 0.9941\n",
      "Epoch 98/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 4.1203e-04 - accuracy: 0.9999 - val_loss: 0.0400 - val_accuracy: 0.9941\n",
      "Epoch 99/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 1.8282e-04 - accuracy: 1.0000 - val_loss: 0.0460 - val_accuracy: 0.9941\n",
      "Epoch 100/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 1.5629e-04 - accuracy: 1.0000 - val_loss: 0.0509 - val_accuracy: 0.9941\n",
      "processing fold # 8\n",
      "Epoch 1/100\n",
      "338/338 [==============================] - 51s 152ms/step - loss: 1.5289 - accuracy: 0.3467 - val_loss: 1.6631 - val_accuracy: 0.2000\n",
      "Epoch 2/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.8949 - accuracy: 0.6444 - val_loss: 1.5597 - val_accuracy: 0.3719\n",
      "Epoch 3/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.5646 - accuracy: 0.7872 - val_loss: 1.3838 - val_accuracy: 0.5459\n",
      "Epoch 4/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.3860 - accuracy: 0.8624 - val_loss: 2.3473 - val_accuracy: 0.4059\n",
      "Epoch 5/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.2897 - accuracy: 0.9003 - val_loss: 1.1856 - val_accuracy: 0.6556\n",
      "Epoch 6/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.2162 - accuracy: 0.9258 - val_loss: 1.7216 - val_accuracy: 0.5230\n",
      "Epoch 7/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.1647 - accuracy: 0.9437 - val_loss: 1.4857 - val_accuracy: 0.5348\n",
      "Epoch 8/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.1373 - accuracy: 0.9529 - val_loss: 1.3914 - val_accuracy: 0.6259\n",
      "Epoch 9/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.1183 - accuracy: 0.9614 - val_loss: 1.8247 - val_accuracy: 0.6504\n",
      "Epoch 10/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0927 - accuracy: 0.9702 - val_loss: 0.9708 - val_accuracy: 0.7356\n",
      "Epoch 11/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0836 - accuracy: 0.9737 - val_loss: 2.6070 - val_accuracy: 0.5385\n",
      "Epoch 12/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0687 - accuracy: 0.9761 - val_loss: 1.4147 - val_accuracy: 0.6948\n",
      "Epoch 13/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0622 - accuracy: 0.9805 - val_loss: 0.7742 - val_accuracy: 0.7756\n",
      "Epoch 14/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0568 - accuracy: 0.9815 - val_loss: 0.7763 - val_accuracy: 0.7444\n",
      "Epoch 15/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0546 - accuracy: 0.9812 - val_loss: 1.7331 - val_accuracy: 0.6415\n",
      "Epoch 16/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0556 - accuracy: 0.9826 - val_loss: 0.8788 - val_accuracy: 0.7326\n",
      "Epoch 17/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0526 - accuracy: 0.9838 - val_loss: 0.7475 - val_accuracy: 0.7622\n",
      "Epoch 18/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0439 - accuracy: 0.9856 - val_loss: 1.2878 - val_accuracy: 0.6289\n",
      "Epoch 19/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0358 - accuracy: 0.9899 - val_loss: 1.0175 - val_accuracy: 0.6911\n",
      "Epoch 20/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0374 - accuracy: 0.9893 - val_loss: 0.8873 - val_accuracy: 0.8156\n",
      "Epoch 21/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0353 - accuracy: 0.9901 - val_loss: 2.3288 - val_accuracy: 0.5896\n",
      "Epoch 22/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0433 - accuracy: 0.9867 - val_loss: 0.5888 - val_accuracy: 0.8037\n",
      "Epoch 23/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0336 - accuracy: 0.9905 - val_loss: 0.9570 - val_accuracy: 0.7252\n",
      "Epoch 24/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0390 - accuracy: 0.9880 - val_loss: 1.0683 - val_accuracy: 0.6985\n",
      "Epoch 25/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0225 - accuracy: 0.9934 - val_loss: 0.7507 - val_accuracy: 0.7296\n",
      "Epoch 26/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0248 - accuracy: 0.9927 - val_loss: 1.0403 - val_accuracy: 0.7674\n",
      "Epoch 27/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0255 - accuracy: 0.9928 - val_loss: 1.2625 - val_accuracy: 0.6259\n",
      "Epoch 28/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0255 - accuracy: 0.9919 - val_loss: 0.6571 - val_accuracy: 0.7844\n",
      "Epoch 29/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0308 - accuracy: 0.9917 - val_loss: 0.8638 - val_accuracy: 0.7785\n",
      "Epoch 30/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0262 - accuracy: 0.9927 - val_loss: 1.0321 - val_accuracy: 0.7200\n",
      "Epoch 31/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0178 - accuracy: 0.9944 - val_loss: 0.9979 - val_accuracy: 0.7526\n",
      "Epoch 32/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0162 - accuracy: 0.9955 - val_loss: 1.0257 - val_accuracy: 0.7830\n",
      "Epoch 33/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0214 - accuracy: 0.9932 - val_loss: 2.4082 - val_accuracy: 0.5519\n",
      "Epoch 34/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0337 - accuracy: 0.9908 - val_loss: 0.8374 - val_accuracy: 0.7304\n",
      "Epoch 35/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0216 - accuracy: 0.9942 - val_loss: 0.9682 - val_accuracy: 0.8311\n",
      "Epoch 36/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0183 - accuracy: 0.9942 - val_loss: 0.5017 - val_accuracy: 0.8452\n",
      "Epoch 37/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0106 - accuracy: 0.9968 - val_loss: 2.4488 - val_accuracy: 0.6726\n",
      "Epoch 38/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0145 - accuracy: 0.9950 - val_loss: 1.4972 - val_accuracy: 0.7467\n",
      "Epoch 39/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0154 - accuracy: 0.9959 - val_loss: 1.0284 - val_accuracy: 0.7519\n",
      "Epoch 40/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0233 - accuracy: 0.9936 - val_loss: 0.7504 - val_accuracy: 0.8141\n",
      "Epoch 41/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0157 - accuracy: 0.9954 - val_loss: 0.9294 - val_accuracy: 0.8511\n",
      "Epoch 42/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0184 - accuracy: 0.9952 - val_loss: 1.1458 - val_accuracy: 0.7696\n",
      "Epoch 43/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0107 - accuracy: 0.9972 - val_loss: 0.1087 - val_accuracy: 0.9778\n",
      "Epoch 44/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0117 - accuracy: 0.9964 - val_loss: 2.0658 - val_accuracy: 0.6570\n",
      "Epoch 45/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 0.0150 - accuracy: 0.9959 - val_loss: 0.9674 - val_accuracy: 0.7526\n",
      "Epoch 46/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0159 - accuracy: 0.9962 - val_loss: 0.2922 - val_accuracy: 0.8941\n",
      "Epoch 47/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 0.0166 - accuracy: 0.9957 - val_loss: 1.4942 - val_accuracy: 0.7089\n",
      "Epoch 48/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0104 - accuracy: 0.9973 - val_loss: 1.1274 - val_accuracy: 0.7326\n",
      "Epoch 49/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0165 - accuracy: 0.9953 - val_loss: 0.9701 - val_accuracy: 0.6926\n",
      "Epoch 50/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0114 - accuracy: 0.9969 - val_loss: 0.6473 - val_accuracy: 0.7822\n",
      "Epoch 51/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0094 - accuracy: 0.9971 - val_loss: 0.7224 - val_accuracy: 0.8393\n",
      "Epoch 52/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0224 - accuracy: 0.9943 - val_loss: 1.1446 - val_accuracy: 0.6822\n",
      "Epoch 53/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0056 - accuracy: 0.9985 - val_loss: 0.5505 - val_accuracy: 0.8548\n",
      "Epoch 54/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0119 - accuracy: 0.9971 - val_loss: 0.5623 - val_accuracy: 0.8259\n",
      "Epoch 55/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0104 - accuracy: 0.9968 - val_loss: 1.2524 - val_accuracy: 0.7000\n",
      "Epoch 56/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0115 - accuracy: 0.9965 - val_loss: 0.2878 - val_accuracy: 0.9074\n",
      "Epoch 57/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0085 - accuracy: 0.9977 - val_loss: 0.6069 - val_accuracy: 0.8393\n",
      "Epoch 58/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0056 - accuracy: 0.9978 - val_loss: 0.5372 - val_accuracy: 0.8822\n",
      "Epoch 59/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0075 - accuracy: 0.9975 - val_loss: 2.4645 - val_accuracy: 0.4852\n",
      "Epoch 60/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0125 - accuracy: 0.9962 - val_loss: 0.5045 - val_accuracy: 0.8422\n",
      "Epoch 61/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0095 - accuracy: 0.9971 - val_loss: 1.9103 - val_accuracy: 0.7067\n",
      "Epoch 62/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0102 - accuracy: 0.9975 - val_loss: 1.8112 - val_accuracy: 0.6341\n",
      "Epoch 63/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0065 - accuracy: 0.9982 - val_loss: 0.7251 - val_accuracy: 0.8341\n",
      "Epoch 64/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0094 - accuracy: 0.9973 - val_loss: 1.2034 - val_accuracy: 0.6926\n",
      "Epoch 65/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0097 - accuracy: 0.9975 - val_loss: 0.8161 - val_accuracy: 0.7578\n",
      "Epoch 66/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0096 - accuracy: 0.9975 - val_loss: 0.3232 - val_accuracy: 0.9022\n",
      "Epoch 67/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0093 - accuracy: 0.9974 - val_loss: 0.1213 - val_accuracy: 0.9541\n",
      "Epoch 68/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0066 - accuracy: 0.9981 - val_loss: 0.1376 - val_accuracy: 0.9496\n",
      "Epoch 69/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0036 - accuracy: 0.9987 - val_loss: 0.6262 - val_accuracy: 0.7919\n",
      "Epoch 70/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 0.0186 - accuracy: 0.9949 - val_loss: 1.0742 - val_accuracy: 0.7415\n",
      "Epoch 71/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0112 - accuracy: 0.9971 - val_loss: 0.3100 - val_accuracy: 0.8852\n",
      "Epoch 72/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0087 - accuracy: 0.9974 - val_loss: 0.5066 - val_accuracy: 0.8548\n",
      "Epoch 73/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0072 - accuracy: 0.9980 - val_loss: 1.4702 - val_accuracy: 0.7074\n",
      "Epoch 74/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0064 - accuracy: 0.9980 - val_loss: 0.2176 - val_accuracy: 0.9378\n",
      "Epoch 75/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0022 - accuracy: 0.9992 - val_loss: 0.2847 - val_accuracy: 0.8822\n",
      "Epoch 76/100\n",
      "337/338 [============================>.] - ETA: 0s - loss: 0.0046 - accuracy: 0.9991\n",
      "Epoch 00076: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0046 - accuracy: 0.9991 - val_loss: 0.4481 - val_accuracy: 0.8437\n",
      "Epoch 77/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 8.4871e-04 - accuracy: 0.9998 - val_loss: 0.3927 - val_accuracy: 0.8563\n",
      "Epoch 78/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0019 - accuracy: 0.9996 - val_loss: 0.4207 - val_accuracy: 0.8519\n",
      "Epoch 79/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 8.1756e-04 - accuracy: 0.9996 - val_loss: 0.3592 - val_accuracy: 0.8593\n",
      "Epoch 80/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.3319 - val_accuracy: 0.8696\n",
      "Epoch 81/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 9.6770e-04 - accuracy: 0.9997 - val_loss: 0.3540 - val_accuracy: 0.8659\n",
      "Epoch 82/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0010 - accuracy: 0.9996 - val_loss: 0.3470 - val_accuracy: 0.8644\n",
      "Epoch 83/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 4.3592e-04 - accuracy: 0.9998 - val_loss: 0.3209 - val_accuracy: 0.8815\n",
      "Epoch 84/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 1.8530e-04 - accuracy: 1.0000 - val_loss: 0.3204 - val_accuracy: 0.8815\n",
      "Epoch 85/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 4.1253e-04 - accuracy: 0.9999 - val_loss: 0.3165 - val_accuracy: 0.8704\n",
      "Epoch 86/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 1.7859e-04 - accuracy: 0.9999 - val_loss: 0.4152 - val_accuracy: 0.8496\n",
      "Epoch 87/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 5.2871e-04 - accuracy: 0.9998 - val_loss: 0.3890 - val_accuracy: 0.8548\n",
      "Epoch 88/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 3.6143e-04 - accuracy: 0.9998 - val_loss: 0.4387 - val_accuracy: 0.8474\n",
      "Epoch 89/100\n",
      "338/338 [==============================] - 51s 151ms/step - loss: 8.8086e-05 - accuracy: 1.0000 - val_loss: 0.4440 - val_accuracy: 0.8474\n",
      "Epoch 90/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 1.5173e-04 - accuracy: 1.0000 - val_loss: 0.5361 - val_accuracy: 0.8267\n",
      "Epoch 91/100\n",
      "338/338 [==============================] - 50s 149ms/step - loss: 3.1547e-04 - accuracy: 0.9998 - val_loss: 0.7548 - val_accuracy: 0.7926\n",
      "Epoch 92/100\n",
      "338/338 [==============================] - 51s 149ms/step - loss: 3.7459e-04 - accuracy: 0.9998 - val_loss: 0.7807 - val_accuracy: 0.7948\n",
      "Epoch 93/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.7538 - val_accuracy: 0.8096\n",
      "Epoch 94/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 1.8484e-04 - accuracy: 0.9999 - val_loss: 0.7582 - val_accuracy: 0.8096\n",
      "Epoch 95/100\n",
      "338/338 [==============================] - 50s 147ms/step - loss: 5.4822e-05 - accuracy: 1.0000 - val_loss: 0.7424 - val_accuracy: 0.8170\n",
      "Epoch 96/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 3.9325e-04 - accuracy: 0.9998 - val_loss: 0.5443 - val_accuracy: 0.8393\n",
      "Epoch 97/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 0.0014 - accuracy: 0.9996 - val_loss: 0.7245 - val_accuracy: 0.8067\n",
      "Epoch 98/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 8.4227e-04 - accuracy: 0.9999 - val_loss: 0.6348 - val_accuracy: 0.8133\n",
      "Epoch 99/100\n",
      "338/338 [==============================] - 50s 148ms/step - loss: 0.0011 - accuracy: 0.9998 - val_loss: 0.6216 - val_accuracy: 0.8133\n",
      "Epoch 100/100\n",
      "338/338 [==============================] - 51s 150ms/step - loss: 4.4486e-04 - accuracy: 0.9997 - val_loss: 0.6744 - val_accuracy: 0.8044\n"
     ]
    }
   ],
   "source": [
    "k = NUMBER_OF_PROBANDS_IN_TESTSET\n",
    "\n",
    "for i in range(k):\n",
    "    print(\"processing fold #\", i)\n",
    "    partial_train_images, val_images, partial_train_labels, val_labels = get_fold(i, train_images, train_labels)\n",
    "\n",
    "    train_generator = train_datagen.flow(np.array(partial_train_images), \n",
    "                                         partial_train_labels, \n",
    "                                         batch_size=BATCH_SIZE, \n",
    "                                         shuffle=True,)\n",
    "    val_generator   = val_datagen.flow(np.array(val_images),           \n",
    "                                       val_labels,   \n",
    "                                       batch_size=BATCH_SIZE, \n",
    "                                       shuffle=False,)\n",
    "\n",
    "    model = get_model()\n",
    "    \n",
    "    model.fit_generator(train_generator,\n",
    "                        epochs=100, # validation accuracy does not improve after 100 epochs\n",
    "                        validation_data = val_generator,\n",
    "                        verbose = 1,\n",
    "                        workers=2, # allows the loading of images in two parallel processes\n",
    "                        callbacks=get_callbacks())\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select best hyper parameters after k-fold training\n",
    "\n",
    "The validation and training accuracy of each fold has been saved to the 'mobilenet_training_log.csv' file.\n",
    "Here this data is being loaded and evaluated. The average of the results from all folds is being calculated and plotted.\n",
    "1) The best validation accuracy is calculated and printed.\n",
    "2) Average training and validation over all epochs is plotted.\n",
    "3) For better visualization the average training and validation over all epochs after the 50th epoch is plotted.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best average val_accuracy: \n",
      "     epoch  accuracy      loss  val_accuracy  val_loss\n",
      "67     67  0.997912  0.007794      0.975638   0.07602\n",
      "Average training and validation accuracy over all epochs.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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OS/J6/EpXikEwjvs/cqmw30+24BFpDHMzayJ4LMlOpAwV6+6XdDmHIqVCndbxKHbtLZs4O98zaet3SA9QFfbbiXKH+RTgBuBhgnE2HiC49+Medx9fsBzGRDUP6bQyvRO4pFTY2XdeKuS7jeMO8+8Ax7n7KcCG8O9pwKaY8ihSGirtTuBiKLeRFQuhkPdzlIAoNY/33L02fL0SaHD3LWa2yt3rC5nJOKjmUQLK7UysFJ/XVC4qvXxVJI6ax1IzawxfvwScZGajCcYUF8mtHM+yK+RO4KIop5EVJS9Rah5fAt5x9wfMbCzwO2AH4Dx3v6lwWYyHah5FpLPQbVXLPim32qZso0tXW5mZAY8BbwCEAaQfsIO7r835YdlWtf1D6eqlbVXJFTk0NFRemQToZLNVeJfhc8CWlLQPFDjyUI7NN12lR25kls8jw0VKRJQ+j78BwwqVkapQrU9MVft3dnH2q5TbGCBS1qLcJPgo8KCZ3UbwaJL2zhJ3vzXebFWoam6+KfSNWdUu1w151dZMKomI0mE+L8ssd/f0p+2WnJLoMK+WTlJJVq7f1dy58d7lrUBUdbp8qa67fzrLVPKBo2So+aa4KrVZJ9tlv3/7W7zNpNXYXydZdTp4mNl22aZCZrDiqJO0OCr5wJftggSI716Sau2vk6yiHPg/JHgUSaapQ2ZWb2ZzzGydmS0xs4zPwzKzOjO73cyaw+nSLMsdYWZuZldEKENpyNZJWqlnxsVW6Qe+bDXa/feP7yq3Sr+pUSKLEjz2APZMmQ4D7gWmdPLzPye4G30gMAG4ycz2y7DcDUBPoBE4CJhoZmemLmBm3YH/Ap6KkP/SVslnxsVWDQe+TDXaOJtJdbm1pOnSeB5m1hd42t1zXsJrZr2A1cBwd38pTJsOvOXuF6Yt+y4wtm2QKTP7bvh+dMoyFwL1wABgqbt/r6O8lkSHeTbqSC+sat+/cXVyF3ocDClJcTzbKpNaoDO/xmHA5rbAEXoWyFTzgJSxQsLXw9vfmA0hGM3wso42amZTzGyBmS1YUcpNFNVwZlxM1X6hQlz3kqi/TlJ0+j6PsKaQWk3pCXwKmNGJj/cGWtPSWoE+GZZ9ELjQzM4gaOI6K9xWm58AF7v72uCpKdm5+y3ALRDUPDqRz+JQk0Dh6T6TeOhxIxKKcpPgK2nv1wG/dPe5nfjsWoJaSqpaYE2GZc8Dfgq8DKwEZgHjAMzsBKCPu/8mQr5LX1eec6Tr7jtPBz6R2HQ6eLj7D7qwnZeAbma2l7u/HKaNABZl2M4qgg51AMzsSmB++PYoYJSZLQ/f9wU2m9m/uPtJXchf/uI6eOdzZlyqw3xKYemEQUpAlPs8fmJmh6alHWpmN3b0WXdfB9wFXGZmvczsMOAkYHqG7Qw1s/5mtn346PcpQNvluBcT9J80hdPvgf8GzkxfTyLivkIqStt0pV9+KpnpqjwpEVE6zMcB6ZcrPQN0dvzyc4AaoJmgKepsd19kZqPNLPXpvCMJnuC7BrgKmODuiwDcfY27L2+bgA3AurC2kqxiH7zVyV59iv2bE0kRpc/D2TbYbJ8hLfOHgwP8yRnSHyfoUG97PxuY3cl1fqkzyxVEsR9yqE726lPs35xIiig1j8eBK9oeRxL+vTRMrz7FPnhX++Wn1ajYvzmRFFGCx/nAGOBtM5sPLAOOBs4tRMZKXikcvHXdfXUphd+cSCjSHeZhbeMgYBDBmB7z3X1L7k+VhoLdYa4rXyRpSfzm9LuWUJfGMA9X0ASsdPcngSfDtEFmVu/uz8aX1TKjewckaYX+zekScOmEKM1WM4DuaWk7kOFyWxEpU7qiSzopSvAY7O6vpSa4+6sET78VkUqgS8Clk6IEj6VmdkBqQvh+WbxZqiAan0PKja7okk6KEjxuAO4xs3PN7HgzOxeYA1xfmKyVOd0JLOVIV3RJJ0W92upzwGSCq63eAKa6++8KlLdYJTqeR7WPHyHlT1dbSajLV1uFHgM2AjuF72vN7Cx3v7WrGawouhNYyp2uIpQORLlU92SCK6teIRjEaRHBIE1PAAoeqdRuLCIVLkqfxxXAWe6+P8HDCPcneOLtMwXJWTlTu7GIVLhO93mY2XvuXhu+Xu3u/cI7zpe7+4BCZjIORRnDXO3GIlLm4ujzaDazge7+DrDYzA4B3iV4sq5konZjEalQUZqt/hs4PHx9AzAPeBb4RdyZkiLIdU+K7lcRkTSdDh7ufo273xm+voNgRL+R7n5xoTInCcl1T4ruVxGRDCLd51HOitLnUQ5y3ZMCul9FpMpl6/OI0mwllSjXs4z0nCMRySLqTYJSaTq6J6Ua7lfRVXEikanmUe1y3ZNSDferqE9HJC/q85BArrPvSj0z1zPIRDoU17OtpFLluielUu9X0TPIRPKmZiuJX7ncF6JnkInkTcFD4lVOfQjV0KcjUiDq85D4lGsfQqX26YjEoOj3eZhZvZnNMbN1ZrbEzMZnWa7OzG43s+ZwujRl3gAzm2Vmy8ys1cz+bGafTKoM0oFSuC8knyazhgY48EAFDpEIkmy2+jnwATAQmADcZGb7ZVjuBqAn0AgcBEw0szPDeb2Bp4GRQD1wO3CfmfUubNalU4rdh1BOTWYiZS6R4GFmvYBTgYvdfa27PwH8HpiYYfETgGvdfb27LwamAmcBuPtr7n69u7/t7pvd/RZgB2DvJMohHShmH8KKFTB5ctBk1toa/J08ufQ77UXKVFKX6g4DNrv7SylpzwJHZFne0l4Pz7iQWRNB8Hgly/wpBANWMXjw4IhZlryMGwdjxiTfh6DLbkUSlVSzVW+gNS2tFeiTYdkHgQvNrI+ZfYyg1tEzfSEzqyUYFvcH7p6+bgDc/RZ3H+Xuoxp0AElOMfoQit1kJlJlkgoea4HatLRaYE2GZc8DNgAvA/cAs4ClqQuYWQ1wL/Cku18Ve26l/OiyW5FEJdVs9RLQzcz2cveXw7QRwKL0Bd19FUGHOgBmdiUwP+X9jsDdwFvA/ytkpqXMFKvJTKQKJRI83H2dmd0FXGZmXwaagJOAQ9OXNbOhQEs4HUPQZ3FEOK878DuCmskkd9+SRP7LUrXeu1Cpj1IRKTFJXqp7DlADNBM0RZ3t7ovMbLSZrU1ZbiTwHEGT1lXABHdvq6EcCnyWIKi0mNnacBqdWCnKgS5ZFZEC0x3mlaZc7/IWkZJU9DvMJSGlcJd3pSuXBz+KFJCCR6XRJauFpSZBEUDBo/LoktXC0V3sIu00GFQl0iWrhaG72EXaKXhUKl2yGj81CYq0U7OVSGepSVCknWoeIlGoSVAEUPAQiU5NgiJqthIRkegUPEREJDIFDxERiUzBQ0REIlPwEBGRyHS1lUiV27JlC0uXLmXdunXFzooUQffu3RkwYAC1temDveam4CFS5d59913MjL333pvttlNjRDVxdzZs2MBbb70FECmA6JciUuVaWloYOHCgAkcVMjN69uzJbrvtRnNzc6TP6tciUuU2b95M9+7di50NKaKamho2bdoU6TMKHiKCmRU7C1JE+Xz/Ch4iIhKZgoeIVJxHH32U3XffvdjZqGgKHuVAY2aLSIlR8Ch1GjNbpKJ8+OGHxc5CLBQ8SpnGzJZyUKCa8dVXX81pp522Vdr555/PeeedB8C0adPYZ5996NOnD3vuuSc333xzp9d9/vnnM2jQIGpraxk5ciSPP/54+7zNmzdz5ZVXMnToUPr06cPIkSN58803AVi0aBFHH3009fX1DBw4kCuvvBKAL33pS3zve99rX0d6s1ljYyPXXHMNn/jEJ+jVqxcffvghV199dfs29t13X+bMmQPAxo0bqa+v57nnnmv/fHNzMzU1Nawoof99BY9S1jZmdqq2MbNFSkEBa8bjxo3j/vvv57333gOCg/rs2bMZP348AAMGDOAPf/gD7733HtOmTeOCCy7gr3/9a6fWfeCBB7Jw4UJWrVrF+PHj+dznPsf7778PwPXXX8+sWbPat33rrbfSs2dP1qxZw5gxYzjuuONYtmwZr7zyCkcddVSnyzNr1izuu+8+Wlpa6NatG0OHDuXxxx+ntbWV73//+3zxi1/k7bffZscdd+QLX/gCM2bM2OqzY8aMoaGUxpFx90QmoB6YA6wDlgDjsyxXB9wONIfTpWnzG4F5wHrgRWBMZ7Y/cuRILzvNze41Ne7w0VRTE6SLxOT555/P74MJ/D4PO+wwv/32293d/eGHH/Y999wz67InnXSS33jjje7uPm/ePN9tt906vZ26ujpfuHChu7sPGzbM77777m2WmTlzpjc1NWX8/BlnnOEXXXRR+/v07Q8ZMsSnTp2aMw8jRoxo3+6TTz7pu+++u2/evNnd3UeOHOm/+c1vOl2efGT7HQALPMMxNcmax8+BD4CBwATgJjPbL8NyNwA9CYLEQcBEMzszZf4s4G9Af+Ai4HdmVkLhOEYaM1tKWQI14/HjxzMrrM3MnDmzvdYB8MADD3DwwQdTX19PXV0d999/P++++26n1nvdddexzz770LdvX+rq6mhtbW3/7JtvvsnQoUO3+Uy29M4aNGjQVu/vuOMOmpqaqKuro66ujn/84x/tefjkJz9Jr169+NOf/sSLL77IK6+8woknnpj3tgshkeBhZr2AU4GL3X2tuz8B/B6YmGHxE4Br3X29uy8GpgJnhesZBhwAfN/dN7j7ncBz4bor07hxsGQJzJ0b/B03rtg5Egk0NsIHH2ydtmlTkB6Tz33uczz66KMsXbqUOXPmtAePjRs3cuqpp/Ktb32Ld955h5aWFo4//vi21omcHn/8ca655hpmz57N6tWraWlpoW/fvu2fHTRoEK+++uo2n8uWDtCrVy/Wr1/f/n758uXbLJN6I96SJUv493//d372s5+xcuVKWlpaGD58+Fb5P+OMM5gxYwbTp0/ntNNOo0ePHh2WLUlJ1TyGAZvd/aWUtGeBTDUPAEt7PTx8vR/wmruv6cx6zGyKmS0wswWl1NEUWUMDHHigahxSWhKoGTc0NHDkkUdy5plnsscee7DPPvsA8MEHH7Bx40YaGhro1q0bDzzwAA8//HCn1rlmzRq6detGQ0MDH374IZdddll7vwrAl7/8ZS6++GJefvll3J2///3vrFy5ks9+9rMsX76cG2+8kY0bN7JmzRqeeuopAJqamrj//vtZtWpV+zK5rFu3DjNr78OYNm0a//jHP7ZaZuLEicyZM4cZM2YwadKkTu+zpCQVPHoDrWlprUCfDMs+CFxoZn3M7GMEtY6eeawHd7/F3Ue5+6iS6mgSqRQJ1IzHjx/P3Llzt2qy6tOnDz/5yU84/fTT6devHzNnzux0s86xxx7L2LFjGTZsGEOGDKFHjx5bNSl94xvf4PTTT+eYY46htraWyZMns2HDBvr06cMjjzzCvffey84778xee+3FvHnzgOBAP2LECBobGznmmGP4/Oc/nzMP++67L9/85jc55JBDGDhwIM899xyHHXbYVsvsvvvuHHDAAZgZo0eP7uzuSox1pprX5Y2Y7Q/82d17pqR9EzjS3U9IW7Ye+ClwFLCSoJN9nLsPNbNTgB+6+74py/8UwN3PzZWHUaNG+YIFC+IqkkjFeOGFF9rP6KW0nHXWWey6665cccUVBd9Wtt+BmT3j7qPS05OqebwEdDOzvVLSRgCL0hd091XuPsHdd3b3/cI8zg9nLwL2NLM+Ha0ndrrLW0QStHjxYu666y4mT55c7KxklEjwcPd1wF3AZWbWy8wOA04Cpqcva2ZDzay/mW1vZmOBKcAV4XpeAhYC3zezHmFN5BPAnQUtgO7yFpEEXXzxxQwfPpxvf/vb7LHHHsXOTkZJXqp7DlBDcO/GLOBsd19kZqPNbG3KciMJrqBaA1wFTHD31JrFF4BRwGrgauA0dy9cdUB3eYtIwi6//HLWrl3LRRddVOysZJXYMLTuvgo4OUP64wQd4W3vZwOzc6xnMXBk/DnMou1a9g0bPkpru5ZdnfAiUqX0eJKOJHAtu4hIuVHw6Iju8hYR2UZizVZlbdw4GDMmaKpqbFTgEJGqp+DRWQ0NChoiIiE1W4lIRfvKV77C5ZdfXuxsVBzVPESkZDU2NvKrX/2KMWPG5L2OX/7ylzHmSNqo5iEiZatShnTNZvPmzeOYQmMAAAzBSURBVMXOQlYKHiLSJYV6cs/EiRN54403OOGEE+jduzfXXnstixcvxsyYOnUqgwcP5jOf+QwQPLp95513pm/fvnzqU59i0aKP7itOHSK2bXjY6667jgEDBrDLLrswbdq0rHnoaKjbe+65h6amJmpraxk6dCgPPvggAKtWreLMM89k1113pV+/fpx8cnCL22233cbhhx++1TrMjFdeeaU9r2effTbHH388vXr1Yt68edx3333sv//+1NbWMmjQIC699NKtPv/EE09w6KGHUldXx6BBg7jtttt4+umnGThw4FbB9c4776SpqSnKV5CTgoeI5K2QT+6ZPn06gwcP5t5772Xt2rV85zvfaZ/3pz/9iRdeeIGHHnoIgLFjx/Lyyy/T3NzMAQccwIQJE7Kud/ny5bS2tvLWW28xdepUvvrVr7J69eqMy+Ya6nb+/PlMmjSJH/3oR7S0tPDYY4/RGN7/NXHiRNavX8+iRYtobm7mggsu6HS5Z86cyUUXXcSaNWs4/PDD6dWrF3fccQctLS3cd9993HTTTdx9990AvPHGG4wdO5Zzzz2XFStWsHDhQpqamjjwwAPp378/jzzySPt6Z8yYwcSJmYZQylOm4QUrcSrLYWhFEpDvMLRJjJI8ZMgQf+SRR9rfv/766w74q6++mvUzq1evdsBbWlrcfeshYufNm+c9evTwTZs2tS/f0NDgf/nLXzqVn9ShbqdMmeJf//rXt1lm2bJlbma+atWqbeZNmzbNDzvssK3SAH/55Zfb8zpx4sSceTj//PPbt3vllVf6ySefnHG5q6++2sePH+/u7itXrvSamhpftmxZ1vWW8jC0IlJBEhiFNqvU8Tc2b97MhRdeyNChQ6mtrW0/+882JG3//v3p1u2ja4V69uzJ2rVrMy6ba6jbXMPV1tfX069fvy6XDeCpp57i05/+NA0NDfTt25df/vKXHeYB4Itf/GJ7rW327NmMHj2aXXbZJa88ZaLgISJ5SeLJPalDt2ZLnzlzJvfccw9z586ltbWVxWH08i6OVdTRULe5hqtdtWoVLS0t28yLOlwtBINhnXjiibz55pu0trbyla98pcM8AOy2224ccsghzJkzh+nTp8fbZIWCh4jkKYkn9wwcOJDXXnst5zJr1qxhxx13pH///qxfv57vfve7sWy7o6FuJ0+ezLRp0/jjH//Ili1beOutt3jxxRfZZZddGDt2LOeccw6rV69m06ZNPPbYYwCMGDGCRYsWsXDhQt5///1tOr+zla++vp4ePXowf/58Zs6c2T5vwoQJzJ07l9mzZ/Phhx+ycuVKFi5c2D5/0qRJXHvttTz33HOccsopseyXNgoeIpK3Qo9C+5//+Z9cccUV1NXV8eMf/zjjMpMmTWLIkCHstttu7Lvvvhx88MGxbLujoW4POuig9k70vn37csQRR7BkyRIg6Ozv3r07H//4xxkwYED7mObDhg3jkksuYcyYMey1117bXHmVyS9+8QsuueQS+vTpw2WXXcbpp5/ePm/w4MHcf//9XHfdddTX19PU1MSzzz7bPv+UU05hyZIlnHLKKfTq1SuW/dImkWFoS4GGoRXJTMPQVrahQ4dy8803d3ijZakOQysiIgm78847MbP2+2HipMeTiIhUoCOPPJLnn3+e6dOns9128dcTFDxERCrQo48+WtD1q9lKRLp8WauUt3y+fwUPkSq3/fbbs2nTpmJnQ4pow4YNdO/ePdJnFDxEqlxdXR3vvPMOW7ZsKXZWJGHuzvr163nrrbcYMGBApM+qz0Okyu20004sXbqUf/7zn8XOihRB9+7dGThwILW1tZE+p+AhUuW22247Bg8eXOxsSJlRs5WIiESm4CEiIpEpeIiISGQKHiIiElnVPBjRzFYASzq5+E5A5pFkKlu1lhuqt+zVWm6o3rJHLfcQd9/mQftVEzyiMLMFmZ4iWemqtdxQvWWv1nJD9ZY9rnKr2UpERCJT8BARkcgUPDK7pdgZKJJqLTdUb9mrtdxQvWWPpdzq8xARkchU8xARkcgUPEREJDIFDxERiayqg4eZPWpm75vZ2nD6Z8q88Wa2xMzWmdndZlZfzLwWgpl9wcxeCMv4qpmNDtOPMrMXzWy9mc0zsyHFzmscUr7ntmmzmf00ZX5FlruNmTWa2f1mttrMlpvZz8ysWzivycyeCcv+jJk1FTu/cTGzfczsf82s1cxeMbNTUuZVzHduZl8zswVmttHMbkubl7WcZrajmd1qZu+Fv4tvdGZ7VR08Ql9z997htDeAme0H3AxMBAYC64FfFDGPsTOzo4FrgDOBPsCngNfMbCfgLuBioB5YAPymWPmMU8r33Jvge90A/Bagksud4hdAM7AL0AQcAZxjZjsA9wAzgH7A7cA9YXpZC4PjPcAfCL7XKcAMMxtWgd/5MuAK4NbUxE6U81JgL2AI8GngO2Z2XIdbc/eqnYBHgS9nSL8SmJnyfijwAdCn2HmOsez/B0zOkD4F+L+U970IDrIfL3aeYy7/GcBrfHTFYcWXG3gBOD7l/Y8ITpKOAd5q2xfhvDeA44qd5xjKPBxYm1a2h4HLK/U7DwPIbSnvc5Yz/O6PSZl/OfDrjrajmgdcZWbvmtmfzezIMG0/4Nm2Bdz9VYLgMawI+YudmW0PjAIawmr80rAJo4Zty74OeDVMryRnAHd4+N9CdZT7v4AvmFlPM9sNGAs8SFDGv6fsC4C/Uxlltyxpw6mO7xxylNPM+gG7ps4PX3e4D6o9ePwHsCewG8GNM/ea2VCgN9CatmwrQfNOJRgIdAdOA0YTNGHsD3yPyi87ZjaYoMnm9pTkii838CeCg8J7wFKC5ou7qeyyv0jQVPdtM+tuZscQfPc9qexyp8pVzt4p79Pn5VTVwcPdn3L3Ne6+0d1vB/4MHE9QzU0f0LcWWJN0HgtkQ/j3p+7+tru/C1xPdZQdYBLwhLu/npJW0eU2s+2AhwjavnsRPFm1H0G/V8WW3d03AScD/wosB74JzCYInhVb7jS5yrk25X36vJyqOnhk4ARV2kXAiLZEM9sT2BF4qUj5ipW7ryb458n0eIH0svci6PNZlEzuEjGJrWsdUPnlrgcGAT8LT5ZWAtMIThgWAZ8ws9Qmnk9QIWV397+7+xHu3t/djyVobZhP5X/nbbKWMzwWvJ06P3zd8T4odudOETuV6oBjgR5AN2ACsA7Ym4+q9qMJztJm0IkOpHKagMuAp4EBBGegjxN0lDUQVFtPDffNNcCTxc5vjOU+NPye+6SlV3S5wzK+BlwY/t7rgDnA/wA7EIx1cz7BSdLXwvc7FDvPMZX7E+F32hP4FvB6WM6K+s7D77UHcBUwPeXYlrOcwNUETZr9gI+HwaTDiyWKXuAi7uiG8OC5BmgBngSOTpk/nuCKk3UEl/rVFzvPMZe/O8Glmy0E1fmfAD3CeWMI2oo3EFyR1ljs/MZY7puB6VnmVWy5w/I1heVaTTAY0G+BAeG8/YFnwrL/Fdi/2PmNsdw/Csu8FngA+FglfucEl9x62nRpR+UMA+mtBCfM7wDf6Mz29GBEERGJTH0eIiISmYKHiIhEpuAhIiKRKXiIiEhkCh4iIhKZgoeIiESm4CFSRsIxObxtHA6RYlHwEBGRyBQ8REQkMgUPkS4ys13N7E4zW2Fmr5vZeWH6pWb2OzP7jZmtMbO/mlnqA+r2CYdCbjGzRWZ2Ysq8GjO7LhwKudXMngjHW2kzwczeCMeiuSjB4ooACh4iXRI+6vxeggF0dgOOAr5uZseGi5xE8AypemAmcHc4rkT38HMPEzyc8lzgf8xs7/BzPwZGEjzIsR74DrAlZdOHEzzE8yjgEjPbp2CFFMlAz7YS6QIz+yTwW3cfnJL2nwSjTi4heDrpwWH6dgRDfp4eLvpbYFd33xLOnwX8k+CJx+uAg909dYQ3zKyR4Kmwg9x9aZg2H7je3X9doGKKbENXbIh0zRBgVzNrSUnbnuAR90uAN9sS3X2LmS0lGPYT4M22wBFaQlB72Yng0dmv5tju8pTX6/loRDiRRKjZSqRr3gRed/e6lKmPux8fzh/UtmBY89gdWBZOg8K0NoMJaibvAu8TDNgjUpIUPES6Zj7wnpn9R9jJvb2ZDTezA8P5I83s38L7Mr4ObCQYO+Ypgqap74R9IEcCJxAMOraFYHyF68PO+O3N7BAz2zHx0olkoeAh0gXuvpngoN9E0BfxLvAroG+4yD3A5wkGI5oI/Ju7b3L3D4ATgbHhZ34BTHL3F8PPfQt4jmDAslUEo7/p/1VKhjrMRQrEzC4lGLXui8XOi0jcdCYjIiKRKXiIiEhkarYSEZHIVPMQEZHIFDxERCQyBQ8REYlMwUNERCJT8BARkcj+P9lBQp5q0xgEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def read_csv(filename):\n",
    "    with open(filename, 'r') as f:\n",
    "        reader = csv.reader(f)\n",
    "        return list(reader)\n",
    "    \n",
    "history_all = pd.read_csv('mobilenet_training_log.csv')\n",
    "history_mean = history_all.groupby(['epoch']).mean().reset_index()\n",
    "history_mean_over50 = history_mean[history_mean['epoch'] > 50]\n",
    "history_mean_over80 = history_mean[history_mean['epoch'] > 80]\n",
    "\n",
    "best_val_accuracy = history_mean[history_mean['val_accuracy']==history_mean['val_accuracy'].max()]\n",
    "print(\"Best average val_accuracy: \\n\", best_val_accuracy)\n",
    "\n",
    "print(\"Average training and validation accuracy over all epochs.\")\n",
    "ax = plt.gca()\n",
    "history_mean.plot(kind='scatter', x='epoch',y='val_accuracy',color='red', ax=ax, label=\"val accuray\")\n",
    "history_mean.plot(kind='scatter', x='epoch',y='accuracy',color='blue', ax=ax, label=\"train accuracy\")\n",
    "plt.show()\n",
    "\n",
    "print(\"\")\n",
    "ax = plt.gca()\n",
    "history_mean_over50.plot(kind='scatter', x='epoch',y='val_accuracy',color='red', ax=ax, label=\"val accuray\")\n",
    "history_mean_over50.plot(kind='scatter', x='epoch',y='accuracy',color='blue', ax=ax, label=\"train accuracy\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "print(\"\")\n",
    "ax = plt.gca()\n",
    "history_mean_over80.plot(kind='scatter', x='epoch',y='val_accuracy',color='red', ax=ax, label=\"val accuray\")\n",
    "history_mean_over80.plot(kind='scatter', x='epoch',y='accuracy',color='blue', ax=ax, label=\"train accuracy\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To select the best learning rate and especially the learning rate decay all occurred learning rate reduces on plateau\n",
    "are recorded in the DataFrame below. This data has been collected manually from the training progress printed in the \n",
    "notebook during k-fold training.\n",
    "\n",
    "The average epoch for the first and second reduce are printed and plotted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   time_reduced   fold  epoch_reduced\n",
      "0             1  3.875         86.875\n",
      "1             2  5.000         82.000\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_rate = pd.DataFrame([\n",
    "                   [0, 100, 1], \n",
    "                   [1, 82, 1], \n",
    "                   [2, 82, 1],\n",
    "                   [3, 100, 1],  \n",
    "                   [4, 99, 1], \n",
    "                   [5, 82, 2],\n",
    "                   [6, 72, 1],\n",
    "                   [7, 84, 1],\n",
    "                   [8, 76, 1],\n",
    "                  ],\n",
    "                  columns=['fold', 'epoch_reduced', 'time_reduced'])\n",
    "\n",
    "cmap = {1: 'DarkBlue', 2: 'LightBlue'}\n",
    "\n",
    "ax1 = learning_rate.plot.scatter(x='epoch_reduced',\n",
    "                      y='fold',\n",
    "                      alpha=0.70,\n",
    "                      color=[cmap.get(c) for c in learning_rate.time_reduced])\n",
    "\n",
    "\n",
    "learning_rate_mean = learning_rate.groupby(['time_reduced']).mean().reset_index()\n",
    "print(learning_rate_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final training run\n",
    "\n",
    "Here the final model is being trained on all training data using the best hyper parameters according to the k-fold\n",
    "training.\n",
    "In our case the best average validaton accuracy occurred in epoch 67. The average learning rate reduce has occured\n",
    "later then in epoch 67. Therefore, no learning rate reduce on schedule is applied. A fixes learning rate is kept during\n",
    "training.\n",
    "For test purposes a modelCheckpoint is saved every 10 epochs. Those checkpoints are not used for the final model and\n",
    "can be deleted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/86\n",
      "379/380 [============================>.] - ETA: 0s - loss: 1.5777 - accuracy: 0.3189\n",
      "Epoch 00001: saving model to mobilenet_epoch-01_loss-1.57632.h5\n",
      "380/380 [==============================] - 58s 153ms/step - loss: 1.5764 - accuracy: 0.3193\n",
      "Epoch 2/86\n",
      "379/380 [============================>.] - ETA: 0s - loss: 0.9179 - accuracy: 0.6413\n",
      "Epoch 00002: saving model to mobilenet_epoch-02_loss-0.91610.h5\n",
      "380/380 [==============================] - 58s 153ms/step - loss: 0.9164 - accuracy: 0.6418\n",
      "Epoch 3/86\n",
      "379/380 [============================>.] - ETA: 0s - loss: 0.5029 - accuracy: 0.8190\n",
      "Epoch 00003: saving model to mobilenet_epoch-03_loss-0.50264.h5\n",
      "380/380 [==============================] - 57s 150ms/step - loss: 0.5026 - accuracy: 0.8192\n",
      "Epoch 4/86\n",
      "379/380 [============================>.] - ETA: 0s - loss: 0.3186 - accuracy: 0.8910\n",
      "Epoch 00004: saving model to mobilenet_epoch-04_loss-0.31825.h5\n",
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     ]
    },
    {
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     "output_type": "stream",
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      "380/380 [==============================] - 53s 139ms/step - loss: 0.0082 - accuracy: 0.9974\n",
      "Epoch 75/86\n",
      "379/380 [============================>.] - ETA: 0s - loss: 0.0090 - accuracy: 0.9973\n",
      "Epoch 00075: saving model to mobilenet_epoch-75_loss-0.00898.h5\n",
      "380/380 [==============================] - 52s 138ms/step - loss: 0.0090 - accuracy: 0.9973\n",
      "Epoch 76/86\n",
      "379/380 [============================>.] - ETA: 0s - loss: 0.0079 - accuracy: 0.9981\n",
      "Epoch 00076: saving model to mobilenet_epoch-76_loss-0.00793.h5\n",
      "380/380 [==============================] - 57s 150ms/step - loss: 0.0079 - accuracy: 0.9981\n",
      "Epoch 77/86\n",
      "379/380 [============================>.] - ETA: 0s - loss: 0.0064 - accuracy: 0.9985\n",
      "Epoch 00077: saving model to mobilenet_epoch-77_loss-0.00638.h5\n",
      "380/380 [==============================] - 55s 146ms/step - loss: 0.0064 - accuracy: 0.9985\n",
      "Epoch 78/86\n",
      "379/380 [============================>.] - ETA: 0s - loss: 0.0055 - accuracy: 0.9989\n",
      "Epoch 00078: saving model to mobilenet_epoch-78_loss-0.00550.h5\n",
      "380/380 [==============================] - 53s 139ms/step - loss: 0.0055 - accuracy: 0.9989\n",
      "Epoch 79/86\n",
      "379/380 [============================>.] - ETA: 0s - loss: 0.0060 - accuracy: 0.9984\n",
      "Epoch 00079: saving model to mobilenet_epoch-79_loss-0.00656.h5\n",
      "380/380 [==============================] - 53s 139ms/step - loss: 0.0066 - accuracy: 0.9984\n",
      "Epoch 80/86\n",
      "379/380 [============================>.] - ETA: 0s - loss: 0.0073 - accuracy: 0.9982\n",
      "Epoch 00080: saving model to mobilenet_epoch-80_loss-0.00729.h5\n",
      "380/380 [==============================] - 53s 140ms/step - loss: 0.0073 - accuracy: 0.9982\n",
      "Epoch 81/86\n",
      "379/380 [============================>.] - ETA: 0s - loss: 0.0066 - accuracy: 0.9983\n",
      "Epoch 00081: saving model to mobilenet_epoch-81_loss-0.00661.h5\n",
      "380/380 [==============================] - 57s 150ms/step - loss: 0.0066 - accuracy: 0.9983\n",
      "Epoch 82/86\n",
      "379/380 [============================>.] - ETA: 0s - loss: 0.0040 - accuracy: 0.9990\n",
      "Epoch 00082: saving model to mobilenet_epoch-82_loss-0.00398.h5\n",
      "380/380 [==============================] - 53s 140ms/step - loss: 0.0040 - accuracy: 0.9990\n",
      "Epoch 83/86\n",
      "379/380 [============================>.] - ETA: 0s - loss: 0.0018 - accuracy: 0.9995\n",
      "Epoch 00083: saving model to mobilenet_epoch-83_loss-0.00179.h5\n",
      "380/380 [==============================] - 54s 142ms/step - loss: 0.0018 - accuracy: 0.9995\n",
      "Epoch 84/86\n",
      "379/380 [============================>.] - ETA: 0s - loss: 0.0108 - accuracy: 0.9974\n",
      "Epoch 00084: saving model to mobilenet_epoch-84_loss-0.01076.h5\n",
      "380/380 [==============================] - 55s 145ms/step - loss: 0.0108 - accuracy: 0.9974\n",
      "Epoch 85/86\n",
      "379/380 [============================>.] - ETA: 0s - loss: 0.0083 - accuracy: 0.9979\n",
      "Epoch 00085: saving model to mobilenet_epoch-85_loss-0.00824.h5\n",
      "380/380 [==============================] - 57s 149ms/step - loss: 0.0082 - accuracy: 0.9979\n",
      "Epoch 86/86\n",
      "379/380 [============================>.] - ETA: 0s - loss: 0.0081 - accuracy: 0.9977\n",
      "Epoch 00086: saving model to mobilenet_epoch-86_loss-0.00804.h5\n",
      "380/380 [==============================] - 53s 138ms/step - loss: 0.0080 - accuracy: 0.9977\n"
     ]
    }
   ],
   "source": [
    "model_checkpoint = ModelCheckpoint(filepath='mobilenet_epoch-{epoch:02d}_loss-{loss:.5f}.h5',\n",
    "                                    verbose=1,\n",
    "                                    save_best_only=False,\n",
    "                                    save_weights_only=False\n",
    "                                  )\n",
    "    \n",
    "\n",
    "train_generator = train_datagen.flow(np.array(train_images),\n",
    "                                     train_labels, \n",
    "                                     batch_size=BATCH_SIZE, \n",
    "                                     shuffle=True,)\n",
    "\n",
    "model = get_model()\n",
    "\n",
    "history = model.fit_generator(train_generator, \n",
    "                              epochs=86,\n",
    "                              verbose = 1,\n",
    "                              workers=2,\n",
    "                              callbacks=[model_checkpoint]\n",
    "                             )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the model\n",
    "\n",
    "The final model is being saved in tensorflow format for inference.\n",
    "Optionnaly the model can be also saved in Keras format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\Dimitri\\Anaconda3\\envs\\jetbot\\lib\\site-packages\\tensorflow_core\\python\\ops\\resource_variable_ops.py:1781: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "INFO:tensorflow:Assets written to: model_mobilenet\\assets\n"
     ]
    }
   ],
   "source": [
    "# Save in tensorflow format\n",
    "model.save('model_mobilenet', save_format='tf')\n",
    "\n",
    "# Save in keras format\n",
    "# model.save('model_mobilenet', )"
   ]
  }
 ],
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