Train_CNN_Analog-Readout_Version-Small3-long.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CNN Training\n",
    "\n",
    "Target of this code is to train a CNN network to extract the needle position of an analog needle device.\n",
    "\n",
    "### Preparing the training\n",
    "* First all libraries are loaded\n",
    "    * It is assumed, that they are installed during the Python setup\n",
    "* matplotlib is set to print the output inline in the jupyter notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "########### Basic Parameters for Running: ################################\n",
    "    \n",
    "TFlite_Version  = \"1210\"   \n",
    "TFlite_MainType = \"ana-cont\"\n",
    "TFlite_Size     = \"s3_long\"\n",
    "Training_Percentage = 0.05              # 0.0 = Use all Images for Training\n",
    "Epoch_Anz = 100\n",
    "\n",
    "##########################################################################\n",
    "\n",
    "\n",
    "\n",
    "import os\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import glob\n",
    "import numpy as np\n",
    "from sklearn.utils import shuffle\n",
    "from tensorflow.python import keras\n",
    "from tensorflow.python.keras import Sequential\n",
    "from tensorflow.keras.layers import Dense, InputLayer, Conv2D, MaxPool2D, Flatten, BatchNormalization\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from sklearn.model_selection import train_test_split\n",
    "import tensorflow.keras.backend as K\n",
    "from tensorflow.keras.callbacks import History \n",
    "import math\n",
    "from PIL import Image \n",
    "\n",
    "loss_ges = np.array([])\n",
    "val_loss_ges = np.array([])\n",
    "\n",
    "%matplotlib inline\n",
    "np.set_printoptions(precision=4)\n",
    "np.set_printoptions(suppress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load training data\n",
    "* The data is expected in the \"Input_dir\"\n",
    "* Picture size must be 32x32 with 3 color channels (RGB)\n",
    "* The filename contains the informations needed for training in the first 3 digits::\n",
    "* Typical filename: \n",
    "    * x.y-zzzz.jpg \n",
    "    * e.g. \"4.6_Lfd-1406_zeiger3_2019-06-02T050011\"\n",
    "\n",
    "|Place holder | Meaning                     | Usage        |\n",
    "|------------- |-----------------------------|--------------|\n",
    "| **x.y**          | readout value               | **to be learned** |\n",
    "| zzzz        | additional information              | not needed   |\n",
    "\n",
    "* The images are stored in the x_data[]\n",
    "* The expected output for each image in the corresponding y_data[]\n",
    "    * The periodic nature is reflected in a **sin/cos coding**, which allows to restore the angle/counter value with an arctan later on.\n",
    "\n",
    "* The last step is a shuffle (from sklearn.utils) as the filenames are on order due to the encoding of the expected analog readout in the filename "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7684, 32, 32, 3)\n",
      "(7684, 2)\n"
     ]
    }
   ],
   "source": [
    "Input_dir='data_resize_all'\n",
    "\n",
    "files = glob.glob(Input_dir + '/*.*')\n",
    "x_data = []\n",
    "y_data = []\n",
    "\n",
    "for aktfile in files:\n",
    "    test_image = Image.open(aktfile)\n",
    "    test_image = np.array(test_image, dtype=\"float32\")\n",
    "    test_image = np.reshape(test_image, (32,32,3))\n",
    "    base = os.path.basename(aktfile)\n",
    "    target_number = (float(base[0:3])) / 10\n",
    "    target_sin = math.sin(target_number * math.pi * 2)\n",
    "    target_cos = math.cos(target_number * math.pi * 2)\n",
    "\n",
    "    x_data.append(test_image)\n",
    "    zw = np.array([target_sin, target_cos])\n",
    "    y_data.append(zw)\n",
    "\n",
    "x_data = np.array(x_data)\n",
    "y_data = np.array(y_data)\n",
    "print(x_data.shape)\n",
    "print(y_data.shape)\n",
    "\n",
    "x_data, y_data = shuffle(x_data, y_data)\n",
    "\n",
    "if (Training_Percentage > 0):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(x_data, y_data, test_size=Training_Percentage)\n",
    "else:\n",
    "    X_train = x_data\n",
    "    y_train = y_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the model\n",
    "\n",
    "The layout of the network ist a typcial CNN network with alternating **Conv2D** and **MaxPool2D** layers. Finished after **flattening** with additional **Dense** layer.\n",
    "\n",
    "#### Important\n",
    "* Shape of the input layer: (32, 32, 3)\n",
    "* Shape of the output layer: (2) - sin and cos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 32, 32, 3)]       0         \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 32, 32, 3)        12        \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " conv2d (Conv2D)             (None, 32, 32, 32)        2432      \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 8, 8, 32)         0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 8, 8, 16)          12816     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 4, 4, 16)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 4, 4, 32)          4640      \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 2, 2, 32)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 128)               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 64)                8256      \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 32)                2080      \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 2)                 66        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 30,302\n",
      "Trainable params: 30,296\n",
      "Non-trainable params: 6\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "inputs = tf.keras.Input(shape=(32, 32, 3))\n",
    "inputs2 = tf.keras.layers.BatchNormalization()(inputs)\n",
    "inputs3 = tf.keras.layers.Conv2D(32, (5, 5), padding='same', activation=\"relu\")(inputs2)\n",
    "inputs4 = tf.keras.layers.MaxPool2D(pool_size=(4,4))(inputs3)\n",
    "inputs5 = tf.keras.layers.Conv2D(16, (5, 5), padding='same', activation=\"relu\")(inputs4)\n",
    "inputs6 = tf.keras.layers.MaxPool2D(pool_size=(2,2))(inputs5)\n",
    "inputs7 = tf.keras.layers.Conv2D(32, (3, 3), padding='same', activation=\"relu\")(inputs6)\n",
    "inputs8 = tf.keras.layers.MaxPool2D(pool_size=(2,2))(inputs7)\n",
    "inputs9 = tf.keras.layers.Flatten()(inputs8)\n",
    "inputs10 = tf.keras.layers.Dense(64,activation=\"relu\")(inputs9)\n",
    "inputs11 = tf.keras.layers.Dense(32,activation=\"relu\")(inputs10)\n",
    "output = tf.keras.layers.Dense(2)(inputs11)\n",
    "\n",
    "model = tf.keras.Model(inputs=inputs, outputs=output)\n",
    "\n",
    "model.compile(loss=keras.losses.mean_squared_error, optimizer=tf.keras.optimizers.Adadelta(learning_rate=1.0, rho=0.95), metrics = [\"accuracy\"])\n",
    "\n",
    "model.summary()\n",
    "\n",
    "#model = Sequential()\n",
    "#model.add(BatchNormalization(input_shape=(32,32,3)))\n",
    "#model.add(Conv2D(32, (5, 5), input_shape=(32,32,3), padding='same', activation=\"relu\"))\n",
    "#model.add(MaxPool2D(pool_size=(4,4)))\n",
    "#model.add(Conv2D(16, (5, 5), padding='same', activation=\"relu\"))\n",
    "#model.add(MaxPool2D(pool_size=(4,4)))\n",
    "#model.add(Conv2D(32, (3, 3), padding='same', activation=\"relu\"))\n",
    "#model.add(MaxPool2D(pool_size=(2,2)))\n",
    "#model.add(Flatten())\n",
    "#model.add(Dense(64,activation=\"relu\"))\n",
    "#model.add(Dense(32,activation=\"relu\"))\n",
    "#model.add(Dense(2))\n",
    "\n",
    "#model.compile(loss=keras.losses.mean_squared_error, optimizer=tf.keras.optimizers.Adadelta(learning_rate=1.0, rho=0.95), metrics = [\"accuracy\"])\n",
    "\n",
    "#model.summary()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training\n",
    "The input pictures are randomly scattered for brightness and pixel shift variations. These is implemented with a ImageDataGenerator.\n",
    "\n",
    "\n",
    "The training is splitted into two steps:\n",
    "1. Variation of the brightness only\n",
    "2. Variation of brightness and Pixel Shift"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Brigthness scattering only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Muell\\AppData\\Local\\Temp\\ipykernel_10080\\65716224.py:11: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  history = model.fit_generator(train_iterator, validation_data = validation_iterator, epochs = Epoch_Anz)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "961/961 [==============================] - 6s 6ms/step - loss: 0.1860 - accuracy: 0.8393 - val_loss: 0.0635 - val_accuracy: 0.9299\n",
      "Epoch 2/100\n",
      "961/961 [==============================] - 5s 6ms/step - loss: 0.0568 - accuracy: 0.9383 - val_loss: 0.0466 - val_accuracy: 0.9351\n",
      "Epoch 3/100\n",
      "961/961 [==============================] - 5s 6ms/step - loss: 0.0377 - accuracy: 0.9504 - val_loss: 0.0497 - val_accuracy: 0.9403\n",
      "Epoch 4/100\n",
      "961/961 [==============================] - 5s 6ms/step - loss: 0.0299 - accuracy: 0.9563 - val_loss: 0.0306 - val_accuracy: 0.9377\n",
      "Epoch 5/100\n",
      "961/961 [==============================] - 5s 6ms/step - loss: 0.0239 - accuracy: 0.9610 - val_loss: 0.0306 - val_accuracy: 0.9506\n",
      "Epoch 6/100\n",
      "961/961 [==============================] - 5s 6ms/step - loss: 0.0192 - accuracy: 0.9668 - val_loss: 0.0158 - val_accuracy: 0.9714\n",
      "Epoch 7/100\n",
      "961/961 [==============================] - 5s 6ms/step - loss: 0.0150 - accuracy: 0.9693 - val_loss: 0.0276 - val_accuracy: 0.9610\n",
      "Epoch 8/100\n",
      "961/961 [==============================] - 5s 6ms/step - loss: 0.0127 - accuracy: 0.9735 - val_loss: 0.0097 - val_accuracy: 0.9584\n",
      "Epoch 9/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0118 - accuracy: 0.9721 - val_loss: 0.0060 - val_accuracy: 0.9688\n",
      "Epoch 10/100\n",
      "961/961 [==============================] - 5s 6ms/step - loss: 0.0097 - accuracy: 0.9759 - val_loss: 0.0071 - val_accuracy: 0.9792\n",
      "Epoch 11/100\n",
      "961/961 [==============================] - 5s 6ms/step - loss: 0.0086 - accuracy: 0.9745 - val_loss: 0.0067 - val_accuracy: 0.9844\n",
      "Epoch 12/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0080 - accuracy: 0.9780 - val_loss: 0.0045 - val_accuracy: 0.9844\n",
      "Epoch 13/100\n",
      "961/961 [==============================] - 5s 6ms/step - loss: 0.0077 - accuracy: 0.9775 - val_loss: 0.0038 - val_accuracy: 0.9870\n",
      "Epoch 14/100\n",
      "961/961 [==============================] - 5s 6ms/step - loss: 0.0066 - accuracy: 0.9771 - val_loss: 0.0067 - val_accuracy: 0.9688\n",
      "Epoch 15/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0067 - accuracy: 0.9755 - val_loss: 0.0039 - val_accuracy: 0.9870\n",
      "Epoch 16/100\n",
      "961/961 [==============================] - 5s 6ms/step - loss: 0.0056 - accuracy: 0.9777 - val_loss: 0.0102 - val_accuracy: 0.9688\n",
      "Epoch 17/100\n",
      "961/961 [==============================] - 5s 6ms/step - loss: 0.0054 - accuracy: 0.9790 - val_loss: 0.0032 - val_accuracy: 0.9922\n",
      "Epoch 18/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0050 - accuracy: 0.9803 - val_loss: 0.0085 - val_accuracy: 0.9792\n",
      "Epoch 19/100\n",
      "961/961 [==============================] - 5s 6ms/step - loss: 0.0050 - accuracy: 0.9806 - val_loss: 0.0031 - val_accuracy: 0.9870\n",
      "Epoch 20/100\n",
      "961/961 [==============================] - 5s 6ms/step - loss: 0.0048 - accuracy: 0.9822 - val_loss: 0.0032 - val_accuracy: 0.9792\n",
      "Epoch 21/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0043 - accuracy: 0.9813 - val_loss: 0.0039 - val_accuracy: 0.9740\n",
      "Epoch 22/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0042 - accuracy: 0.9848 - val_loss: 0.0033 - val_accuracy: 0.9818\n",
      "Epoch 23/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0042 - accuracy: 0.9822 - val_loss: 0.0018 - val_accuracy: 0.9766\n",
      "Epoch 24/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0038 - accuracy: 0.9831 - val_loss: 0.0032 - val_accuracy: 0.9844\n",
      "Epoch 25/100\n",
      "961/961 [==============================] - 5s 6ms/step - loss: 0.0038 - accuracy: 0.9833 - val_loss: 0.0021 - val_accuracy: 0.9818\n",
      "Epoch 26/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0034 - accuracy: 0.9833 - val_loss: 0.0026 - val_accuracy: 0.9844\n",
      "Epoch 27/100\n",
      "961/961 [==============================] - 5s 6ms/step - loss: 0.0034 - accuracy: 0.9814 - val_loss: 0.0021 - val_accuracy: 0.9896\n",
      "Epoch 28/100\n",
      "961/961 [==============================] - 5s 6ms/step - loss: 0.0033 - accuracy: 0.9836 - val_loss: 0.0025 - val_accuracy: 0.9740\n",
      "Epoch 29/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0033 - accuracy: 0.9839 - val_loss: 0.0020 - val_accuracy: 0.9870\n",
      "Epoch 30/100\n",
      "961/961 [==============================] - 5s 6ms/step - loss: 0.0032 - accuracy: 0.9836 - val_loss: 0.0022 - val_accuracy: 0.9922\n",
      "Epoch 31/100\n",
      "961/961 [==============================] - 5s 6ms/step - loss: 0.0031 - accuracy: 0.9843 - val_loss: 0.0026 - val_accuracy: 0.9532\n",
      "Epoch 32/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0030 - accuracy: 0.9852 - val_loss: 0.0024 - val_accuracy: 0.9818\n",
      "Epoch 33/100\n",
      "961/961 [==============================] - 5s 6ms/step - loss: 0.0028 - accuracy: 0.9858 - val_loss: 0.0031 - val_accuracy: 0.9766\n",
      "Epoch 34/100\n",
      "961/961 [==============================] - 5s 6ms/step - loss: 0.0031 - accuracy: 0.9845 - val_loss: 0.0017 - val_accuracy: 0.9922\n",
      "Epoch 35/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0032 - accuracy: 0.9841 - val_loss: 0.0016 - val_accuracy: 0.9948\n",
      "Epoch 36/100\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0026 - accuracy: 0.9840 - val_loss: 0.0022 - val_accuracy: 0.9870\n",
      "Epoch 37/100\n",
      "961/961 [==============================] - 10s 10ms/step - loss: 0.0025 - accuracy: 0.9856 - val_loss: 0.0020 - val_accuracy: 0.9844\n",
      "Epoch 38/100\n",
      "961/961 [==============================] - 9s 9ms/step - loss: 0.0026 - accuracy: 0.9856 - val_loss: 0.0027 - val_accuracy: 0.9948\n",
      "Epoch 39/100\n",
      "961/961 [==============================] - 8s 8ms/step - loss: 0.0026 - accuracy: 0.9861 - val_loss: 0.0021 - val_accuracy: 0.9818\n",
      "Epoch 40/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0024 - accuracy: 0.9862 - val_loss: 0.0020 - val_accuracy: 0.9870\n",
      "Epoch 41/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0025 - accuracy: 0.9858 - val_loss: 0.0023 - val_accuracy: 0.9922\n",
      "Epoch 42/100\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0022 - accuracy: 0.9856 - val_loss: 0.0022 - val_accuracy: 0.9792\n",
      "Epoch 43/100\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0023 - accuracy: 0.9866 - val_loss: 0.0015 - val_accuracy: 0.9896\n",
      "Epoch 44/100\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0023 - accuracy: 0.9850 - val_loss: 0.0026 - val_accuracy: 0.9896\n",
      "Epoch 45/100\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0022 - accuracy: 0.9874 - val_loss: 0.0021 - val_accuracy: 0.9818\n",
      "Epoch 46/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0022 - accuracy: 0.9879 - val_loss: 0.0017 - val_accuracy: 0.9922\n",
      "Epoch 47/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0023 - accuracy: 0.9866 - val_loss: 0.0014 - val_accuracy: 0.9948\n",
      "Epoch 48/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0024 - accuracy: 0.9852 - val_loss: 0.0012 - val_accuracy: 0.9870\n",
      "Epoch 49/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0018 - accuracy: 0.9872 - val_loss: 0.0017 - val_accuracy: 0.9922\n",
      "Epoch 50/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0020 - accuracy: 0.9867 - val_loss: 0.0014 - val_accuracy: 0.9844\n",
      "Epoch 51/100\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0019 - accuracy: 0.9871 - val_loss: 0.0017 - val_accuracy: 0.9948\n",
      "Epoch 52/100\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0020 - accuracy: 0.9887 - val_loss: 0.0011 - val_accuracy: 0.9948\n",
      "Epoch 53/100\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0018 - accuracy: 0.9865 - val_loss: 0.0015 - val_accuracy: 0.9740\n",
      "Epoch 54/100\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0019 - accuracy: 0.9889 - val_loss: 0.0016 - val_accuracy: 0.9896\n",
      "Epoch 55/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0020 - accuracy: 0.9858 - val_loss: 0.0022 - val_accuracy: 0.9818\n",
      "Epoch 56/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0017 - accuracy: 0.9888 - val_loss: 0.0016 - val_accuracy: 0.9870\n",
      "Epoch 57/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0017 - accuracy: 0.9876 - val_loss: 0.0016 - val_accuracy: 0.9922\n",
      "Epoch 58/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0016 - accuracy: 0.9874 - val_loss: 0.0016 - val_accuracy: 0.9948\n",
      "Epoch 59/100\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0015 - accuracy: 0.9888 - val_loss: 0.0017 - val_accuracy: 0.9740\n",
      "Epoch 60/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0014 - accuracy: 0.9895 - val_loss: 0.0013 - val_accuracy: 0.9896\n",
      "Epoch 61/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0016 - accuracy: 0.9887 - val_loss: 0.0013 - val_accuracy: 0.9896\n",
      "Epoch 62/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0015 - accuracy: 0.9906 - val_loss: 0.0012 - val_accuracy: 0.9896\n",
      "Epoch 63/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0015 - accuracy: 0.9879 - val_loss: 0.0012 - val_accuracy: 0.9896\n",
      "Epoch 64/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0018 - accuracy: 0.9871 - val_loss: 0.0013 - val_accuracy: 0.9896\n",
      "Epoch 65/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0016 - accuracy: 0.9930 - val_loss: 0.0011 - val_accuracy: 0.9922\n",
      "Epoch 66/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0014 - accuracy: 0.9895 - val_loss: 0.0013 - val_accuracy: 0.9844\n",
      "Epoch 67/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0013 - accuracy: 0.9902 - val_loss: 0.0014 - val_accuracy: 0.9922\n",
      "Epoch 68/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0015 - accuracy: 0.9891 - val_loss: 0.0011 - val_accuracy: 0.9974\n",
      "Epoch 69/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0014 - accuracy: 0.9891 - val_loss: 0.0014 - val_accuracy: 0.9896\n",
      "Epoch 70/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0015 - accuracy: 0.9883 - val_loss: 0.0010 - val_accuracy: 0.9948\n",
      "Epoch 71/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9900 - val_loss: 0.0014 - val_accuracy: 0.9948\n",
      "Epoch 72/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0015 - accuracy: 0.9885 - val_loss: 0.0011 - val_accuracy: 1.0000\n",
      "Epoch 73/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0013 - accuracy: 0.9906 - val_loss: 0.0011 - val_accuracy: 0.9896\n",
      "Epoch 74/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9908 - val_loss: 0.0016 - val_accuracy: 0.9844\n",
      "Epoch 75/100\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0012 - accuracy: 0.9913 - val_loss: 0.0013 - val_accuracy: 0.9870\n",
      "Epoch 76/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9898 - val_loss: 0.0013 - val_accuracy: 0.9870\n",
      "Epoch 77/100\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0012 - accuracy: 0.9888 - val_loss: 9.0083e-04 - val_accuracy: 0.9896\n",
      "Epoch 78/100\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0012 - accuracy: 0.9896 - val_loss: 0.0014 - val_accuracy: 0.9974\n",
      "Epoch 79/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9892 - val_loss: 8.6252e-04 - val_accuracy: 1.0000\n",
      "Epoch 80/100\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0012 - accuracy: 0.9917 - val_loss: 0.0013 - val_accuracy: 0.9870\n",
      "Epoch 81/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9909 - val_loss: 0.0012 - val_accuracy: 0.9896\n",
      "Epoch 82/100\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0012 - accuracy: 0.9897 - val_loss: 8.8188e-04 - val_accuracy: 0.9922\n",
      "Epoch 83/100\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0011 - accuracy: 0.9895 - val_loss: 9.9559e-04 - val_accuracy: 0.9922\n",
      "Epoch 84/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9913 - val_loss: 0.0011 - val_accuracy: 0.9922\n",
      "Epoch 85/100\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0011 - accuracy: 0.9912 - val_loss: 9.5548e-04 - val_accuracy: 0.9870\n",
      "Epoch 86/100\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0011 - accuracy: 0.9925 - val_loss: 0.0011 - val_accuracy: 0.9974\n",
      "Epoch 87/100\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0010 - accuracy: 0.9896 - val_loss: 0.0012 - val_accuracy: 0.9896\n",
      "Epoch 88/100\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 9.7842e-04 - accuracy: 0.9904 - val_loss: 0.0012 - val_accuracy: 0.9948\n",
      "Epoch 89/100\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0011 - accuracy: 0.9910 - val_loss: 9.5144e-04 - val_accuracy: 0.9948\n",
      "Epoch 90/100\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0010 - accuracy: 0.9910 - val_loss: 7.4254e-04 - val_accuracy: 0.9974\n",
      "Epoch 91/100\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0010 - accuracy: 0.9909 - val_loss: 9.4283e-04 - val_accuracy: 0.9948\n",
      "Epoch 92/100\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0010 - accuracy: 0.9902 - val_loss: 9.7162e-04 - val_accuracy: 0.9974\n",
      "Epoch 93/100\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0011 - accuracy: 0.9914 - val_loss: 0.0011 - val_accuracy: 0.9922\n",
      "Epoch 94/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 9.3514e-04 - accuracy: 0.9906 - val_loss: 0.0011 - val_accuracy: 0.9844\n",
      "Epoch 95/100\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 9.8842e-04 - accuracy: 0.9930 - val_loss: 7.4440e-04 - val_accuracy: 0.9896\n",
      "Epoch 96/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 9.2870e-04 - accuracy: 0.9904 - val_loss: 8.8252e-04 - val_accuracy: 0.9922\n",
      "Epoch 97/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 9.9892e-04 - accuracy: 0.9913 - val_loss: 8.8538e-04 - val_accuracy: 0.9948\n",
      "Epoch 98/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9910 - val_loss: 8.1323e-04 - val_accuracy: 1.0000\n",
      "Epoch 99/100\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9921 - val_loss: 9.2189e-04 - val_accuracy: 0.9922\n",
      "Epoch 100/100\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0011 - accuracy: 0.9917 - val_loss: 8.2908e-04 - val_accuracy: 1.0000\n"
     ]
    }
   ],
   "source": [
    "Batch_Size = 8\n",
    "Epoch_Anz = 100\n",
    "Shift_Range = 0\n",
    "Brightness_Range = 0.3\n",
    "\n",
    "datagen = ImageDataGenerator(width_shift_range=[-Shift_Range,Shift_Range], height_shift_range=[-Shift_Range,Shift_Range],brightness_range=[1-Brightness_Range,1+Brightness_Range])\n",
    "\n",
    "if (Training_Percentage > 0):\n",
    "    train_iterator = datagen.flow(x_data, y_data, batch_size=Batch_Size)\n",
    "    validation_iterator = datagen.flow(X_test, y_test, batch_size=Batch_Size)\n",
    "    history = model.fit_generator(train_iterator, validation_data = validation_iterator, epochs = Epoch_Anz)\n",
    "else:\n",
    "    train_iterator = datagen.flow(x_data, y_data, batch_size=Batch_Size)\n",
    "    history = model.fit_generator(train_iterator, epochs = Epoch_Anz)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Learing result\n",
    " \n",
    "* Visualization of the training and validation results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_ges = np.append(loss_ges, history.history['loss'])\n",
    "\n",
    "if (Training_Percentage > 0):\n",
    "    val_loss_ges = np.append(val_loss_ges, history.history['val_loss'])\n",
    "    plt.semilogy(val_loss_ges)\n",
    "\n",
    "plt.semilogy(history.history['loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train','eval'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Brigthness and Pixel Shift scattering\n",
    "Here a higher number of epochs is used to reach the minimum loss function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/600\n",
      " 24/961 [..............................] - ETA: 6s - loss: 0.3970 - accuracy: 0.7292"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Muell\\AppData\\Local\\Temp\\ipykernel_10080\\3747815558.py:11: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  history = model.fit_generator(train_iterator, validation_data = validation_iterator, epochs = Epoch_Anz)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "961/961 [==============================] - 7s 7ms/step - loss: 0.1184 - accuracy: 0.8894 - val_loss: 0.0715 - val_accuracy: 0.8935\n",
      "Epoch 2/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0550 - accuracy: 0.9373 - val_loss: 0.0505 - val_accuracy: 0.9532\n",
      "Epoch 3/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0416 - accuracy: 0.9464 - val_loss: 0.0353 - val_accuracy: 0.9221\n",
      "Epoch 4/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0354 - accuracy: 0.9535 - val_loss: 0.0299 - val_accuracy: 0.9558\n",
      "Epoch 5/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0299 - accuracy: 0.9555 - val_loss: 0.0260 - val_accuracy: 0.9506\n",
      "Epoch 6/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0257 - accuracy: 0.9595 - val_loss: 0.0236 - val_accuracy: 0.9506\n",
      "Epoch 7/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0221 - accuracy: 0.9621 - val_loss: 0.0211 - val_accuracy: 0.9532\n",
      "Epoch 8/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0197 - accuracy: 0.9660 - val_loss: 0.0210 - val_accuracy: 0.9610\n",
      "Epoch 9/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0164 - accuracy: 0.9694 - val_loss: 0.0124 - val_accuracy: 0.9662\n",
      "Epoch 10/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0160 - accuracy: 0.9659 - val_loss: 0.0119 - val_accuracy: 0.9558\n",
      "Epoch 11/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0143 - accuracy: 0.9694 - val_loss: 0.0122 - val_accuracy: 0.9481\n",
      "Epoch 12/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0132 - accuracy: 0.9712 - val_loss: 0.0143 - val_accuracy: 0.9584\n",
      "Epoch 13/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0138 - accuracy: 0.9729 - val_loss: 0.0085 - val_accuracy: 0.9610\n",
      "Epoch 14/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0111 - accuracy: 0.9727 - val_loss: 0.0055 - val_accuracy: 0.9584\n",
      "Epoch 15/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0106 - accuracy: 0.9750 - val_loss: 0.0109 - val_accuracy: 0.9532\n",
      "Epoch 16/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0109 - accuracy: 0.9736 - val_loss: 0.0078 - val_accuracy: 0.9714\n",
      "Epoch 17/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0108 - accuracy: 0.9714 - val_loss: 0.0090 - val_accuracy: 0.9584\n",
      "Epoch 18/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0089 - accuracy: 0.9758 - val_loss: 0.0075 - val_accuracy: 0.9714\n",
      "Epoch 19/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0081 - accuracy: 0.9771 - val_loss: 0.0069 - val_accuracy: 0.9766\n",
      "Epoch 20/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0076 - accuracy: 0.9761 - val_loss: 0.0049 - val_accuracy: 0.9636\n",
      "Epoch 21/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0089 - accuracy: 0.9746 - val_loss: 0.0072 - val_accuracy: 0.9558\n",
      "Epoch 22/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0085 - accuracy: 0.9759 - val_loss: 0.0076 - val_accuracy: 0.9481\n",
      "Epoch 23/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0072 - accuracy: 0.9780 - val_loss: 0.0082 - val_accuracy: 0.9714\n",
      "Epoch 24/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0078 - accuracy: 0.9798 - val_loss: 0.0061 - val_accuracy: 0.9610\n",
      "Epoch 25/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0068 - accuracy: 0.9802 - val_loss: 0.0083 - val_accuracy: 0.9688\n",
      "Epoch 26/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0068 - accuracy: 0.9781 - val_loss: 0.0055 - val_accuracy: 0.9766\n",
      "Epoch 27/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0067 - accuracy: 0.9787 - val_loss: 0.0107 - val_accuracy: 0.9766\n",
      "Epoch 28/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0075 - accuracy: 0.9767 - val_loss: 0.0054 - val_accuracy: 0.9766\n",
      "Epoch 29/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0073 - accuracy: 0.9762 - val_loss: 0.0048 - val_accuracy: 0.9844\n",
      "Epoch 30/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0056 - accuracy: 0.9781 - val_loss: 0.0058 - val_accuracy: 0.9792\n",
      "Epoch 31/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0063 - accuracy: 0.9809 - val_loss: 0.0045 - val_accuracy: 0.9870\n",
      "Epoch 32/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0067 - accuracy: 0.9788 - val_loss: 0.0057 - val_accuracy: 0.9688\n",
      "Epoch 33/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0056 - accuracy: 0.9810 - val_loss: 0.0034 - val_accuracy: 0.9870\n",
      "Epoch 34/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0058 - accuracy: 0.9801 - val_loss: 0.0037 - val_accuracy: 0.9792\n",
      "Epoch 35/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0053 - accuracy: 0.9805 - val_loss: 0.0054 - val_accuracy: 0.9714\n",
      "Epoch 36/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0055 - accuracy: 0.9798 - val_loss: 0.0028 - val_accuracy: 0.9714\n",
      "Epoch 37/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0050 - accuracy: 0.9802 - val_loss: 0.0056 - val_accuracy: 0.9636\n",
      "Epoch 38/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0053 - accuracy: 0.9815 - val_loss: 0.0028 - val_accuracy: 0.9766\n",
      "Epoch 39/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0053 - accuracy: 0.9810 - val_loss: 0.0048 - val_accuracy: 0.9766\n",
      "Epoch 40/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0048 - accuracy: 0.9827 - val_loss: 0.0036 - val_accuracy: 0.9766\n",
      "Epoch 41/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0055 - accuracy: 0.9801 - val_loss: 0.0039 - val_accuracy: 0.9688\n",
      "Epoch 42/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0048 - accuracy: 0.9807 - val_loss: 0.0028 - val_accuracy: 0.9662\n",
      "Epoch 43/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0046 - accuracy: 0.9813 - val_loss: 0.0039 - val_accuracy: 0.9870\n",
      "Epoch 44/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0047 - accuracy: 0.9830 - val_loss: 0.0029 - val_accuracy: 0.9740\n",
      "Epoch 45/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0042 - accuracy: 0.9811 - val_loss: 0.0082 - val_accuracy: 0.9688\n",
      "Epoch 46/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0043 - accuracy: 0.9819 - val_loss: 0.0041 - val_accuracy: 0.9662\n",
      "Epoch 47/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0044 - accuracy: 0.9822 - val_loss: 0.0033 - val_accuracy: 0.9870\n",
      "Epoch 48/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0043 - accuracy: 0.9824 - val_loss: 0.0035 - val_accuracy: 0.9818\n",
      "Epoch 49/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0044 - accuracy: 0.9832 - val_loss: 0.0031 - val_accuracy: 0.9870\n",
      "Epoch 50/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0040 - accuracy: 0.9837 - val_loss: 0.0029 - val_accuracy: 0.9818\n",
      "Epoch 51/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0046 - accuracy: 0.9846 - val_loss: 0.0030 - val_accuracy: 0.9662\n",
      "Epoch 52/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0044 - accuracy: 0.9820 - val_loss: 0.0030 - val_accuracy: 0.9818\n",
      "Epoch 53/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0039 - accuracy: 0.9824 - val_loss: 0.0034 - val_accuracy: 0.9740\n",
      "Epoch 54/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0041 - accuracy: 0.9819 - val_loss: 0.0038 - val_accuracy: 0.9740\n",
      "Epoch 55/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0041 - accuracy: 0.9832 - val_loss: 0.0053 - val_accuracy: 0.9740\n",
      "Epoch 56/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0041 - accuracy: 0.9833 - val_loss: 0.0025 - val_accuracy: 0.9662\n",
      "Epoch 57/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0049 - accuracy: 0.9814 - val_loss: 0.0031 - val_accuracy: 0.9740\n",
      "Epoch 58/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0042 - accuracy: 0.9845 - val_loss: 0.0034 - val_accuracy: 0.9532\n",
      "Epoch 59/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0035 - accuracy: 0.9832 - val_loss: 0.0026 - val_accuracy: 0.9740\n",
      "Epoch 60/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0038 - accuracy: 0.9824 - val_loss: 0.0025 - val_accuracy: 0.9740\n",
      "Epoch 61/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0038 - accuracy: 0.9837 - val_loss: 0.0021 - val_accuracy: 0.9818\n",
      "Epoch 62/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0037 - accuracy: 0.9830 - val_loss: 0.0038 - val_accuracy: 0.9766\n",
      "Epoch 63/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0038 - accuracy: 0.9828 - val_loss: 0.0028 - val_accuracy: 0.9844\n",
      "Epoch 64/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0036 - accuracy: 0.9845 - val_loss: 0.0030 - val_accuracy: 0.9844\n",
      "Epoch 65/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0032 - accuracy: 0.9846 - val_loss: 0.0041 - val_accuracy: 0.9688\n",
      "Epoch 66/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0037 - accuracy: 0.9854 - val_loss: 0.0025 - val_accuracy: 0.9818\n",
      "Epoch 67/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0035 - accuracy: 0.9832 - val_loss: 0.0026 - val_accuracy: 0.9870\n",
      "Epoch 68/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0038 - accuracy: 0.9833 - val_loss: 0.0028 - val_accuracy: 0.9740\n",
      "Epoch 69/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0034 - accuracy: 0.9844 - val_loss: 0.0024 - val_accuracy: 0.9844\n",
      "Epoch 70/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0032 - accuracy: 0.9858 - val_loss: 0.0023 - val_accuracy: 0.9818\n",
      "Epoch 71/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0031 - accuracy: 0.9836 - val_loss: 0.0022 - val_accuracy: 0.9896\n",
      "Epoch 72/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0035 - accuracy: 0.9840 - val_loss: 0.0022 - val_accuracy: 0.9714\n",
      "Epoch 73/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0033 - accuracy: 0.9850 - val_loss: 0.0029 - val_accuracy: 0.9636\n",
      "Epoch 74/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0032 - accuracy: 0.9844 - val_loss: 0.0042 - val_accuracy: 0.9818\n",
      "Epoch 75/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0031 - accuracy: 0.9862 - val_loss: 0.0024 - val_accuracy: 0.9870\n",
      "Epoch 76/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0034 - accuracy: 0.9841 - val_loss: 0.0027 - val_accuracy: 0.9818\n",
      "Epoch 77/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0036 - accuracy: 0.9832 - val_loss: 0.0032 - val_accuracy: 0.9844\n",
      "Epoch 78/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0033 - accuracy: 0.9830 - val_loss: 0.0025 - val_accuracy: 0.9818\n",
      "Epoch 79/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0033 - accuracy: 0.9836 - val_loss: 0.0021 - val_accuracy: 0.9870\n",
      "Epoch 80/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0029 - accuracy: 0.9854 - val_loss: 0.0024 - val_accuracy: 0.9792\n",
      "Epoch 81/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0031 - accuracy: 0.9856 - val_loss: 0.0030 - val_accuracy: 0.9844\n",
      "Epoch 82/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0030 - accuracy: 0.9845 - val_loss: 0.0027 - val_accuracy: 0.9922\n",
      "Epoch 83/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0027 - accuracy: 0.9853 - val_loss: 0.0025 - val_accuracy: 0.9818\n",
      "Epoch 84/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0034 - accuracy: 0.9849 - val_loss: 0.0023 - val_accuracy: 0.9870\n",
      "Epoch 85/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0029 - accuracy: 0.9858 - val_loss: 0.0033 - val_accuracy: 0.9896\n",
      "Epoch 86/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0028 - accuracy: 0.9861 - val_loss: 0.0023 - val_accuracy: 0.9740\n",
      "Epoch 87/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0029 - accuracy: 0.9850 - val_loss: 0.0022 - val_accuracy: 0.9818\n",
      "Epoch 88/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0027 - accuracy: 0.9849 - val_loss: 0.0022 - val_accuracy: 0.9792\n",
      "Epoch 89/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0029 - accuracy: 0.9849 - val_loss: 0.0023 - val_accuracy: 0.9870\n",
      "Epoch 90/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0027 - accuracy: 0.9857 - val_loss: 0.0019 - val_accuracy: 0.9870\n",
      "Epoch 91/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0028 - accuracy: 0.9862 - val_loss: 0.0022 - val_accuracy: 0.9922\n",
      "Epoch 92/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0027 - accuracy: 0.9846 - val_loss: 0.0021 - val_accuracy: 0.9792\n",
      "Epoch 93/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0031 - accuracy: 0.9859 - val_loss: 0.0035 - val_accuracy: 0.9766\n",
      "Epoch 94/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0029 - accuracy: 0.9870 - val_loss: 0.0021 - val_accuracy: 0.9844\n",
      "Epoch 95/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0028 - accuracy: 0.9862 - val_loss: 0.0024 - val_accuracy: 0.9844\n",
      "Epoch 96/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0027 - accuracy: 0.9875 - val_loss: 0.0048 - val_accuracy: 0.9844\n",
      "Epoch 97/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0024 - accuracy: 0.9852 - val_loss: 0.0017 - val_accuracy: 0.9870\n",
      "Epoch 98/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0026 - accuracy: 0.9831 - val_loss: 0.0036 - val_accuracy: 0.9740\n",
      "Epoch 99/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0031 - accuracy: 0.9857 - val_loss: 0.0022 - val_accuracy: 0.9844\n",
      "Epoch 100/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0026 - accuracy: 0.9879 - val_loss: 0.0019 - val_accuracy: 0.9922\n",
      "Epoch 101/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0025 - accuracy: 0.9859 - val_loss: 0.0019 - val_accuracy: 0.9740\n",
      "Epoch 102/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0025 - accuracy: 0.9862 - val_loss: 0.0018 - val_accuracy: 0.9818\n",
      "Epoch 103/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0025 - accuracy: 0.9869 - val_loss: 0.0025 - val_accuracy: 0.9870\n",
      "Epoch 104/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0027 - accuracy: 0.9840 - val_loss: 0.0029 - val_accuracy: 0.9766\n",
      "Epoch 105/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0025 - accuracy: 0.9862 - val_loss: 0.0022 - val_accuracy: 0.9844\n",
      "Epoch 106/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0025 - accuracy: 0.9840 - val_loss: 0.0018 - val_accuracy: 0.9870\n",
      "Epoch 107/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0025 - accuracy: 0.9871 - val_loss: 0.0015 - val_accuracy: 0.9922\n",
      "Epoch 108/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0024 - accuracy: 0.9862 - val_loss: 0.0021 - val_accuracy: 0.9766\n",
      "Epoch 109/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0023 - accuracy: 0.9872 - val_loss: 0.0015 - val_accuracy: 0.9870\n",
      "Epoch 110/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0024 - accuracy: 0.9866 - val_loss: 0.0020 - val_accuracy: 0.9766\n",
      "Epoch 111/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0024 - accuracy: 0.9854 - val_loss: 0.0022 - val_accuracy: 0.9766\n",
      "Epoch 112/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0028 - accuracy: 0.9852 - val_loss: 0.0018 - val_accuracy: 0.9922\n",
      "Epoch 113/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0023 - accuracy: 0.9869 - val_loss: 0.0018 - val_accuracy: 0.9792\n",
      "Epoch 114/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0023 - accuracy: 0.9856 - val_loss: 0.0018 - val_accuracy: 0.9766\n",
      "Epoch 115/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0024 - accuracy: 0.9866 - val_loss: 0.0020 - val_accuracy: 0.9948\n",
      "Epoch 116/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0025 - accuracy: 0.9862 - val_loss: 0.0016 - val_accuracy: 0.9896\n",
      "Epoch 117/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0022 - accuracy: 0.9897 - val_loss: 0.0018 - val_accuracy: 0.9948\n",
      "Epoch 118/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0022 - accuracy: 0.9874 - val_loss: 0.0021 - val_accuracy: 0.9844\n",
      "Epoch 119/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0024 - accuracy: 0.9857 - val_loss: 0.0019 - val_accuracy: 0.9896\n",
      "Epoch 120/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0026 - accuracy: 0.9866 - val_loss: 0.0026 - val_accuracy: 0.9844\n",
      "Epoch 121/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0023 - accuracy: 0.9871 - val_loss: 0.0014 - val_accuracy: 0.9870\n",
      "Epoch 122/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0024 - accuracy: 0.9866 - val_loss: 0.0022 - val_accuracy: 0.9740\n",
      "Epoch 123/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0021 - accuracy: 0.9856 - val_loss: 0.0017 - val_accuracy: 0.9844\n",
      "Epoch 124/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0022 - accuracy: 0.9859 - val_loss: 0.0017 - val_accuracy: 0.9870\n",
      "Epoch 125/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0022 - accuracy: 0.9883 - val_loss: 0.0019 - val_accuracy: 0.9818\n",
      "Epoch 126/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0023 - accuracy: 0.9871 - val_loss: 0.0014 - val_accuracy: 0.9948\n",
      "Epoch 127/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0022 - accuracy: 0.9871 - val_loss: 0.0021 - val_accuracy: 0.9844\n",
      "Epoch 128/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0025 - accuracy: 0.9878 - val_loss: 0.0017 - val_accuracy: 0.9896\n",
      "Epoch 129/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0022 - accuracy: 0.9856 - val_loss: 0.0024 - val_accuracy: 0.9896\n",
      "Epoch 130/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0024 - accuracy: 0.9859 - val_loss: 0.0023 - val_accuracy: 0.9740\n",
      "Epoch 131/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0024 - accuracy: 0.9880 - val_loss: 0.0015 - val_accuracy: 0.9818\n",
      "Epoch 132/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0021 - accuracy: 0.9872 - val_loss: 0.0030 - val_accuracy: 0.9818\n",
      "Epoch 133/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0022 - accuracy: 0.9883 - val_loss: 0.0020 - val_accuracy: 0.9766\n",
      "Epoch 134/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0022 - accuracy: 0.9876 - val_loss: 0.0020 - val_accuracy: 0.9714\n",
      "Epoch 135/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0023 - accuracy: 0.9884 - val_loss: 0.0019 - val_accuracy: 0.9870\n",
      "Epoch 136/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0025 - accuracy: 0.9857 - val_loss: 0.0026 - val_accuracy: 0.9792\n",
      "Epoch 137/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0021 - accuracy: 0.9870 - val_loss: 0.0015 - val_accuracy: 0.9922\n",
      "Epoch 138/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0023 - accuracy: 0.9875 - val_loss: 0.0018 - val_accuracy: 0.9844\n",
      "Epoch 139/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0022 - accuracy: 0.9869 - val_loss: 0.0018 - val_accuracy: 0.9818\n",
      "Epoch 140/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0020 - accuracy: 0.9852 - val_loss: 0.0021 - val_accuracy: 0.9792\n",
      "Epoch 141/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0021 - accuracy: 0.9869 - val_loss: 0.0017 - val_accuracy: 0.9818\n",
      "Epoch 142/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0021 - accuracy: 0.9879 - val_loss: 0.0017 - val_accuracy: 0.9844\n",
      "Epoch 143/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0023 - accuracy: 0.9867 - val_loss: 0.0022 - val_accuracy: 0.9870\n",
      "Epoch 144/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0022 - accuracy: 0.9878 - val_loss: 0.0021 - val_accuracy: 0.9792\n",
      "Epoch 145/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0019 - accuracy: 0.9887 - val_loss: 0.0015 - val_accuracy: 0.9844\n",
      "Epoch 146/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0021 - accuracy: 0.9856 - val_loss: 0.0014 - val_accuracy: 0.9948\n",
      "Epoch 147/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0020 - accuracy: 0.9867 - val_loss: 0.0016 - val_accuracy: 0.9870\n",
      "Epoch 148/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0020 - accuracy: 0.9878 - val_loss: 0.0016 - val_accuracy: 0.9870\n",
      "Epoch 149/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0021 - accuracy: 0.9887 - val_loss: 0.0014 - val_accuracy: 0.9870\n",
      "Epoch 150/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0020 - accuracy: 0.9882 - val_loss: 0.0022 - val_accuracy: 0.9584\n",
      "Epoch 151/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0020 - accuracy: 0.9880 - val_loss: 0.0025 - val_accuracy: 0.9818\n",
      "Epoch 152/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0023 - accuracy: 0.9872 - val_loss: 0.0015 - val_accuracy: 0.9870\n",
      "Epoch 153/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0019 - accuracy: 0.9888 - val_loss: 0.0020 - val_accuracy: 0.9792\n",
      "Epoch 154/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0022 - accuracy: 0.9888 - val_loss: 0.0016 - val_accuracy: 0.9870\n",
      "Epoch 155/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0019 - accuracy: 0.9871 - val_loss: 0.0016 - val_accuracy: 0.9948\n",
      "Epoch 156/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0019 - accuracy: 0.9883 - val_loss: 0.0015 - val_accuracy: 0.9844\n",
      "Epoch 157/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0018 - accuracy: 0.9878 - val_loss: 0.0022 - val_accuracy: 0.9584\n",
      "Epoch 158/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0020 - accuracy: 0.9892 - val_loss: 0.0022 - val_accuracy: 0.9610\n",
      "Epoch 159/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0022 - accuracy: 0.9883 - val_loss: 0.0013 - val_accuracy: 0.9896\n",
      "Epoch 160/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0019 - accuracy: 0.9884 - val_loss: 0.0013 - val_accuracy: 0.9844\n",
      "Epoch 161/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0020 - accuracy: 0.9896 - val_loss: 0.0014 - val_accuracy: 0.9818\n",
      "Epoch 162/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0018 - accuracy: 0.9883 - val_loss: 0.0011 - val_accuracy: 0.9922\n",
      "Epoch 163/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0021 - accuracy: 0.9883 - val_loss: 0.0017 - val_accuracy: 0.9818\n",
      "Epoch 164/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0018 - accuracy: 0.9888 - val_loss: 0.0015 - val_accuracy: 0.9792\n",
      "Epoch 165/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0020 - accuracy: 0.9889 - val_loss: 0.0016 - val_accuracy: 0.9896\n",
      "Epoch 166/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0018 - accuracy: 0.9880 - val_loss: 0.0018 - val_accuracy: 0.9792\n",
      "Epoch 167/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0020 - accuracy: 0.9893 - val_loss: 0.0017 - val_accuracy: 0.9896\n",
      "Epoch 168/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0019 - accuracy: 0.9893 - val_loss: 0.0022 - val_accuracy: 0.9844\n",
      "Epoch 169/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0022 - accuracy: 0.9875 - val_loss: 0.0015 - val_accuracy: 0.9896\n",
      "Epoch 170/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0022 - accuracy: 0.9879 - val_loss: 0.0034 - val_accuracy: 0.9844\n",
      "Epoch 171/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0020 - accuracy: 0.9876 - val_loss: 0.0016 - val_accuracy: 0.9922\n",
      "Epoch 172/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0017 - accuracy: 0.9880 - val_loss: 0.0024 - val_accuracy: 0.9870\n",
      "Epoch 173/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0018 - accuracy: 0.9872 - val_loss: 0.0014 - val_accuracy: 0.9922\n",
      "Epoch 174/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0017 - accuracy: 0.9884 - val_loss: 0.0015 - val_accuracy: 0.9844\n",
      "Epoch 175/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0017 - accuracy: 0.9888 - val_loss: 0.0016 - val_accuracy: 0.9896\n",
      "Epoch 176/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0017 - accuracy: 0.9891 - val_loss: 0.0012 - val_accuracy: 0.9792\n",
      "Epoch 177/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0020 - accuracy: 0.9895 - val_loss: 0.0015 - val_accuracy: 0.9896\n",
      "Epoch 178/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0019 - accuracy: 0.9906 - val_loss: 0.0014 - val_accuracy: 0.9922\n",
      "Epoch 179/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0019 - accuracy: 0.9884 - val_loss: 0.0016 - val_accuracy: 0.9870\n",
      "Epoch 180/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0018 - accuracy: 0.9893 - val_loss: 0.0015 - val_accuracy: 0.9948\n",
      "Epoch 181/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0020 - accuracy: 0.9871 - val_loss: 0.0017 - val_accuracy: 0.9844\n",
      "Epoch 182/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0018 - accuracy: 0.9875 - val_loss: 0.0026 - val_accuracy: 0.9766\n",
      "Epoch 183/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0019 - accuracy: 0.9863 - val_loss: 0.0017 - val_accuracy: 0.9740\n",
      "Epoch 184/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0019 - accuracy: 0.9872 - val_loss: 0.0014 - val_accuracy: 0.9792\n",
      "Epoch 185/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0018 - accuracy: 0.9882 - val_loss: 0.0014 - val_accuracy: 0.9870\n",
      "Epoch 186/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0018 - accuracy: 0.9875 - val_loss: 0.0013 - val_accuracy: 0.9974\n",
      "Epoch 187/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0017 - accuracy: 0.9898 - val_loss: 0.0016 - val_accuracy: 0.9844\n",
      "Epoch 188/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0017 - accuracy: 0.9880 - val_loss: 0.0013 - val_accuracy: 0.9870\n",
      "Epoch 189/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0017 - accuracy: 0.9882 - val_loss: 0.0016 - val_accuracy: 0.9792\n",
      "Epoch 190/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0017 - accuracy: 0.9915 - val_loss: 0.0012 - val_accuracy: 0.9896\n",
      "Epoch 191/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0016 - accuracy: 0.9875 - val_loss: 0.0014 - val_accuracy: 0.9896\n",
      "Epoch 192/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0018 - accuracy: 0.9882 - val_loss: 0.0015 - val_accuracy: 0.9844\n",
      "Epoch 193/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0015 - accuracy: 0.9896 - val_loss: 0.0013 - val_accuracy: 0.9870\n",
      "Epoch 194/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0016 - accuracy: 0.9875 - val_loss: 0.0018 - val_accuracy: 0.9922\n",
      "Epoch 195/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0018 - accuracy: 0.9882 - val_loss: 0.0012 - val_accuracy: 0.9792\n",
      "Epoch 196/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0016 - accuracy: 0.9883 - val_loss: 0.0013 - val_accuracy: 0.9844\n",
      "Epoch 197/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0016 - accuracy: 0.9859 - val_loss: 0.0015 - val_accuracy: 0.9792\n",
      "Epoch 198/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0019 - accuracy: 0.9887 - val_loss: 0.0021 - val_accuracy: 0.9948\n",
      "Epoch 199/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0016 - accuracy: 0.9906 - val_loss: 0.0016 - val_accuracy: 0.9922\n",
      "Epoch 200/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0016 - accuracy: 0.9891 - val_loss: 0.0013 - val_accuracy: 0.9870\n",
      "Epoch 201/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0018 - accuracy: 0.9882 - val_loss: 0.0012 - val_accuracy: 0.9844\n",
      "Epoch 202/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0017 - accuracy: 0.9891 - val_loss: 0.0012 - val_accuracy: 0.9844\n",
      "Epoch 203/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0019 - accuracy: 0.9878 - val_loss: 0.0014 - val_accuracy: 0.9896\n",
      "Epoch 204/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0017 - accuracy: 0.9885 - val_loss: 0.0014 - val_accuracy: 0.9766\n",
      "Epoch 205/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0018 - accuracy: 0.9876 - val_loss: 0.0014 - val_accuracy: 0.9922\n",
      "Epoch 206/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0017 - accuracy: 0.9884 - val_loss: 0.0011 - val_accuracy: 0.9896\n",
      "Epoch 207/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0015 - accuracy: 0.9892 - val_loss: 0.0017 - val_accuracy: 0.9818\n",
      "Epoch 208/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0016 - accuracy: 0.9880 - val_loss: 0.0011 - val_accuracy: 0.9896\n",
      "Epoch 209/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0016 - accuracy: 0.9885 - val_loss: 0.0021 - val_accuracy: 0.9818\n",
      "Epoch 210/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0016 - accuracy: 0.9874 - val_loss: 0.0017 - val_accuracy: 0.9870\n",
      "Epoch 211/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0016 - accuracy: 0.9888 - val_loss: 0.0011 - val_accuracy: 0.9870\n",
      "Epoch 212/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0015 - accuracy: 0.9905 - val_loss: 0.0013 - val_accuracy: 0.9948\n",
      "Epoch 213/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0016 - accuracy: 0.9904 - val_loss: 0.0011 - val_accuracy: 0.9896\n",
      "Epoch 214/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0016 - accuracy: 0.9895 - val_loss: 0.0016 - val_accuracy: 0.9870\n",
      "Epoch 215/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0016 - accuracy: 0.9887 - val_loss: 0.0015 - val_accuracy: 0.9922\n",
      "Epoch 216/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0015 - accuracy: 0.9879 - val_loss: 0.0014 - val_accuracy: 0.9922\n",
      "Epoch 217/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0016 - accuracy: 0.9908 - val_loss: 0.0015 - val_accuracy: 0.9922\n",
      "Epoch 218/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0016 - accuracy: 0.9865 - val_loss: 0.0018 - val_accuracy: 0.9818\n",
      "Epoch 219/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0016 - accuracy: 0.9888 - val_loss: 0.0015 - val_accuracy: 0.9922\n",
      "Epoch 220/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0017 - accuracy: 0.9892 - val_loss: 0.0015 - val_accuracy: 0.9896\n",
      "Epoch 221/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0016 - accuracy: 0.9884 - val_loss: 0.0014 - val_accuracy: 0.9922\n",
      "Epoch 222/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0015 - accuracy: 0.9889 - val_loss: 0.0022 - val_accuracy: 0.9844\n",
      "Epoch 223/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0015 - accuracy: 0.9892 - val_loss: 0.0013 - val_accuracy: 0.9844\n",
      "Epoch 224/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0015 - accuracy: 0.9910 - val_loss: 0.0012 - val_accuracy: 0.9870\n",
      "Epoch 225/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0015 - accuracy: 0.9909 - val_loss: 0.0020 - val_accuracy: 0.9948\n",
      "Epoch 226/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0015 - accuracy: 0.9882 - val_loss: 0.0013 - val_accuracy: 0.9870\n",
      "Epoch 227/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0018 - accuracy: 0.9885 - val_loss: 0.0014 - val_accuracy: 0.9870\n",
      "Epoch 228/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0014 - accuracy: 0.9889 - val_loss: 0.0012 - val_accuracy: 0.9870\n",
      "Epoch 229/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0016 - accuracy: 0.9895 - val_loss: 0.0011 - val_accuracy: 0.9948\n",
      "Epoch 230/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0016 - accuracy: 0.9915 - val_loss: 0.0011 - val_accuracy: 0.9922\n",
      "Epoch 231/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0016 - accuracy: 0.9888 - val_loss: 0.0011 - val_accuracy: 0.9896\n",
      "Epoch 232/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0014 - accuracy: 0.9896 - val_loss: 0.0013 - val_accuracy: 0.9974\n",
      "Epoch 233/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0015 - accuracy: 0.9901 - val_loss: 0.0015 - val_accuracy: 0.9870\n",
      "Epoch 234/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0014 - accuracy: 0.9885 - val_loss: 0.0015 - val_accuracy: 0.9818\n",
      "Epoch 235/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0016 - accuracy: 0.9901 - val_loss: 9.7776e-04 - val_accuracy: 0.9922\n",
      "Epoch 236/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0016 - accuracy: 0.9887 - val_loss: 0.0013 - val_accuracy: 0.9896\n",
      "Epoch 237/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0014 - accuracy: 0.9897 - val_loss: 0.0025 - val_accuracy: 0.9766\n",
      "Epoch 238/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0017 - accuracy: 0.9900 - val_loss: 0.0011 - val_accuracy: 0.9896\n",
      "Epoch 239/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0015 - accuracy: 0.9893 - val_loss: 0.0021 - val_accuracy: 0.9922\n",
      "Epoch 240/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0018 - accuracy: 0.9889 - val_loss: 0.0013 - val_accuracy: 0.9896\n",
      "Epoch 241/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0015 - accuracy: 0.9893 - val_loss: 0.0013 - val_accuracy: 0.9870\n",
      "Epoch 242/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0015 - accuracy: 0.9889 - val_loss: 0.0013 - val_accuracy: 0.9766\n",
      "Epoch 243/600\n",
      "961/961 [==============================] - 8s 8ms/step - loss: 0.0015 - accuracy: 0.9891 - val_loss: 0.0014 - val_accuracy: 0.9974\n",
      "Epoch 244/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0015 - accuracy: 0.9909 - val_loss: 0.0014 - val_accuracy: 0.9896\n",
      "Epoch 245/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0014 - accuracy: 0.9879 - val_loss: 0.0014 - val_accuracy: 0.9896\n",
      "Epoch 246/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0016 - accuracy: 0.9908 - val_loss: 0.0024 - val_accuracy: 0.9792\n",
      "Epoch 247/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0016 - accuracy: 0.9900 - val_loss: 0.0013 - val_accuracy: 0.9896\n",
      "Epoch 248/600\n",
      "961/961 [==============================] - 7s 8ms/step - loss: 0.0015 - accuracy: 0.9904 - val_loss: 0.0011 - val_accuracy: 0.9844\n",
      "Epoch 249/600\n",
      "961/961 [==============================] - 9s 9ms/step - loss: 0.0014 - accuracy: 0.9885 - val_loss: 0.0014 - val_accuracy: 0.9870\n",
      "Epoch 250/600\n",
      "961/961 [==============================] - 9s 9ms/step - loss: 0.0014 - accuracy: 0.9909 - val_loss: 0.0011 - val_accuracy: 0.9922\n",
      "Epoch 251/600\n",
      "961/961 [==============================] - 12s 12ms/step - loss: 0.0014 - accuracy: 0.9892 - val_loss: 0.0011 - val_accuracy: 0.9896\n",
      "Epoch 252/600\n",
      "961/961 [==============================] - 14s 14ms/step - loss: 0.0015 - accuracy: 0.9871 - val_loss: 0.0014 - val_accuracy: 0.9896\n",
      "Epoch 253/600\n",
      "961/961 [==============================] - 13s 13ms/step - loss: 0.0014 - accuracy: 0.9908 - val_loss: 0.0011 - val_accuracy: 0.9870\n",
      "Epoch 254/600\n",
      "961/961 [==============================] - 13s 13ms/step - loss: 0.0012 - accuracy: 0.9896 - val_loss: 0.0012 - val_accuracy: 0.9974\n",
      "Epoch 255/600\n",
      "961/961 [==============================] - 14s 15ms/step - loss: 0.0013 - accuracy: 0.9927 - val_loss: 0.0014 - val_accuracy: 0.9818\n",
      "Epoch 256/600\n",
      "961/961 [==============================] - 16s 17ms/step - loss: 0.0014 - accuracy: 0.9893 - val_loss: 0.0012 - val_accuracy: 0.9896\n",
      "Epoch 257/600\n",
      "961/961 [==============================] - 14s 14ms/step - loss: 0.0012 - accuracy: 0.9909 - val_loss: 0.0012 - val_accuracy: 0.9896\n",
      "Epoch 258/600\n",
      "961/961 [==============================] - 17s 17ms/step - loss: 0.0018 - accuracy: 0.9904 - val_loss: 9.0444e-04 - val_accuracy: 0.9870\n",
      "Epoch 259/600\n",
      "961/961 [==============================] - 16s 17ms/step - loss: 0.0013 - accuracy: 0.9900 - val_loss: 0.0015 - val_accuracy: 0.9844\n",
      "Epoch 260/600\n",
      "961/961 [==============================] - 16s 16ms/step - loss: 0.0013 - accuracy: 0.9918 - val_loss: 0.0012 - val_accuracy: 0.9870\n",
      "Epoch 261/600\n",
      "961/961 [==============================] - 12s 12ms/step - loss: 0.0014 - accuracy: 0.9906 - val_loss: 7.6314e-04 - val_accuracy: 0.9974\n",
      "Epoch 262/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0018 - accuracy: 0.9882 - val_loss: 0.0011 - val_accuracy: 0.9870\n",
      "Epoch 263/600\n",
      "961/961 [==============================] - 7s 8ms/step - loss: 0.0015 - accuracy: 0.9900 - val_loss: 0.0017 - val_accuracy: 0.9922\n",
      "Epoch 264/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0013 - accuracy: 0.9901 - val_loss: 0.0017 - val_accuracy: 0.9948\n",
      "Epoch 265/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0014 - accuracy: 0.9905 - val_loss: 0.0011 - val_accuracy: 0.9870\n",
      "Epoch 266/600\n",
      "961/961 [==============================] - 12s 12ms/step - loss: 0.0014 - accuracy: 0.9895 - val_loss: 0.0012 - val_accuracy: 0.9896\n",
      "Epoch 267/600\n",
      "961/961 [==============================] - 15s 16ms/step - loss: 0.0014 - accuracy: 0.9895 - val_loss: 0.0013 - val_accuracy: 0.9792\n",
      "Epoch 268/600\n",
      "961/961 [==============================] - 15s 16ms/step - loss: 0.0013 - accuracy: 0.9906 - val_loss: 0.0011 - val_accuracy: 0.9870\n",
      "Epoch 269/600\n",
      "961/961 [==============================] - 15s 16ms/step - loss: 0.0013 - accuracy: 0.9905 - val_loss: 0.0016 - val_accuracy: 0.9844\n",
      "Epoch 270/600\n",
      "961/961 [==============================] - 15s 16ms/step - loss: 0.0014 - accuracy: 0.9910 - val_loss: 9.7163e-04 - val_accuracy: 0.9922\n",
      "Epoch 271/600\n",
      "961/961 [==============================] - 15s 16ms/step - loss: 0.0013 - accuracy: 0.9879 - val_loss: 9.2512e-04 - val_accuracy: 0.9896\n",
      "Epoch 272/600\n",
      "961/961 [==============================] - 10s 11ms/step - loss: 0.0014 - accuracy: 0.9897 - val_loss: 0.0012 - val_accuracy: 0.9792\n",
      "Epoch 273/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0013 - accuracy: 0.9896 - val_loss: 9.2523e-04 - val_accuracy: 0.9922\n",
      "Epoch 274/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0013 - accuracy: 0.9883 - val_loss: 0.0012 - val_accuracy: 0.9948\n",
      "Epoch 275/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0014 - accuracy: 0.9914 - val_loss: 9.4763e-04 - val_accuracy: 0.9922\n",
      "Epoch 276/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0013 - accuracy: 0.9905 - val_loss: 0.0013 - val_accuracy: 0.9948\n",
      "Epoch 277/600\n",
      "961/961 [==============================] - 13s 14ms/step - loss: 0.0015 - accuracy: 0.9902 - val_loss: 9.6676e-04 - val_accuracy: 0.9792\n",
      "Epoch 278/600\n",
      "961/961 [==============================] - 14s 15ms/step - loss: 0.0015 - accuracy: 0.9887 - val_loss: 0.0033 - val_accuracy: 0.9974\n",
      "Epoch 279/600\n",
      "961/961 [==============================] - 14s 15ms/step - loss: 0.0013 - accuracy: 0.9872 - val_loss: 0.0011 - val_accuracy: 0.9896\n",
      "Epoch 280/600\n",
      "961/961 [==============================] - 14s 15ms/step - loss: 0.0012 - accuracy: 0.9891 - val_loss: 0.0011 - val_accuracy: 0.9870\n",
      "Epoch 281/600\n",
      "961/961 [==============================] - 14s 15ms/step - loss: 0.0014 - accuracy: 0.9901 - val_loss: 0.0012 - val_accuracy: 0.9870\n",
      "Epoch 282/600\n",
      "961/961 [==============================] - 14s 15ms/step - loss: 0.0012 - accuracy: 0.9893 - val_loss: 0.0011 - val_accuracy: 0.9766\n",
      "Epoch 283/600\n",
      "961/961 [==============================] - 15s 15ms/step - loss: 0.0013 - accuracy: 0.9902 - val_loss: 0.0012 - val_accuracy: 0.9870\n",
      "Epoch 284/600\n",
      "961/961 [==============================] - 15s 16ms/step - loss: 0.0014 - accuracy: 0.9912 - val_loss: 0.0012 - val_accuracy: 0.9896\n",
      "Epoch 285/600\n",
      "961/961 [==============================] - 9s 9ms/step - loss: 0.0013 - accuracy: 0.9898 - val_loss: 0.0011 - val_accuracy: 0.9948\n",
      "Epoch 286/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0015 - accuracy: 0.9917 - val_loss: 9.7813e-04 - val_accuracy: 0.9922\n",
      "Epoch 287/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0016 - accuracy: 0.9904 - val_loss: 0.0011 - val_accuracy: 0.9974\n",
      "Epoch 288/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0012 - accuracy: 0.9910 - val_loss: 0.0012 - val_accuracy: 0.9844\n",
      "Epoch 289/600\n",
      "961/961 [==============================] - 10s 10ms/step - loss: 0.0013 - accuracy: 0.9887 - val_loss: 0.0011 - val_accuracy: 0.9896\n",
      "Epoch 290/600\n",
      "961/961 [==============================] - 15s 16ms/step - loss: 0.0013 - accuracy: 0.9910 - val_loss: 0.0011 - val_accuracy: 0.9844\n",
      "Epoch 291/600\n",
      "961/961 [==============================] - 16s 16ms/step - loss: 0.0013 - accuracy: 0.9891 - val_loss: 9.4952e-04 - val_accuracy: 0.9896\n",
      "Epoch 292/600\n",
      "961/961 [==============================] - 16s 16ms/step - loss: 0.0012 - accuracy: 0.9927 - val_loss: 0.0011 - val_accuracy: 0.9922\n",
      "Epoch 293/600\n",
      "961/961 [==============================] - 16s 17ms/step - loss: 0.0015 - accuracy: 0.9882 - val_loss: 0.0011 - val_accuracy: 0.9922\n",
      "Epoch 294/600\n",
      "961/961 [==============================] - 16s 17ms/step - loss: 0.0015 - accuracy: 0.9893 - val_loss: 0.0012 - val_accuracy: 0.9818\n",
      "Epoch 295/600\n",
      "961/961 [==============================] - 16s 17ms/step - loss: 0.0013 - accuracy: 0.9918 - val_loss: 0.0013 - val_accuracy: 0.9844\n",
      "Epoch 296/600\n",
      "961/961 [==============================] - 16s 17ms/step - loss: 0.0013 - accuracy: 0.9902 - val_loss: 9.4342e-04 - val_accuracy: 0.9948\n",
      "Epoch 297/600\n",
      "961/961 [==============================] - 17s 17ms/step - loss: 0.0013 - accuracy: 0.9919 - val_loss: 0.0010 - val_accuracy: 0.9922\n",
      "Epoch 298/600\n",
      "961/961 [==============================] - 17s 18ms/step - loss: 0.0013 - accuracy: 0.9906 - val_loss: 0.0010 - val_accuracy: 0.9922\n",
      "Epoch 299/600\n",
      "961/961 [==============================] - 20s 21ms/step - loss: 0.0013 - accuracy: 0.9906 - val_loss: 0.0012 - val_accuracy: 0.9844\n",
      "Epoch 300/600\n",
      "961/961 [==============================] - 23s 24ms/step - loss: 0.0012 - accuracy: 0.9905 - val_loss: 0.0011 - val_accuracy: 0.9896\n",
      "Epoch 301/600\n",
      "961/961 [==============================] - 23s 24ms/step - loss: 0.0012 - accuracy: 0.9888 - val_loss: 0.0011 - val_accuracy: 0.9896\n",
      "Epoch 302/600\n",
      "961/961 [==============================] - 25s 26ms/step - loss: 0.0013 - accuracy: 0.9921 - val_loss: 9.7889e-04 - val_accuracy: 0.9922\n",
      "Epoch 303/600\n",
      "961/961 [==============================] - 26s 27ms/step - loss: 0.0015 - accuracy: 0.9904 - val_loss: 0.0013 - val_accuracy: 0.9948\n",
      "Epoch 304/600\n",
      "961/961 [==============================] - 26s 27ms/step - loss: 0.0013 - accuracy: 0.9885 - val_loss: 0.0012 - val_accuracy: 0.9922\n",
      "Epoch 305/600\n",
      "961/961 [==============================] - 26s 27ms/step - loss: 0.0012 - accuracy: 0.9898 - val_loss: 0.0017 - val_accuracy: 0.9740\n",
      "Epoch 306/600\n",
      "961/961 [==============================] - 19s 20ms/step - loss: 0.0014 - accuracy: 0.9912 - val_loss: 0.0011 - val_accuracy: 0.9870\n",
      "Epoch 307/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0012 - accuracy: 0.9908 - val_loss: 0.0012 - val_accuracy: 0.9818\n",
      "Epoch 308/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0013 - accuracy: 0.9904 - val_loss: 0.0013 - val_accuracy: 0.9896\n",
      "Epoch 309/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0013 - accuracy: 0.9906 - val_loss: 0.0011 - val_accuracy: 0.9792\n",
      "Epoch 310/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0012 - accuracy: 0.9905 - val_loss: 0.0013 - val_accuracy: 0.9870\n",
      "Epoch 311/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9908 - val_loss: 0.0011 - val_accuracy: 0.9948\n",
      "Epoch 312/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0012 - accuracy: 0.9900 - val_loss: 0.0011 - val_accuracy: 0.9896\n",
      "Epoch 313/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0012 - accuracy: 0.9891 - val_loss: 0.0015 - val_accuracy: 0.9844\n",
      "Epoch 314/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9884 - val_loss: 0.0024 - val_accuracy: 0.9844\n",
      "Epoch 315/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9898 - val_loss: 0.0020 - val_accuracy: 0.9896\n",
      "Epoch 316/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0013 - accuracy: 0.9917 - val_loss: 7.9138e-04 - val_accuracy: 0.9870\n",
      "Epoch 317/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0013 - accuracy: 0.9906 - val_loss: 0.0013 - val_accuracy: 0.9870\n",
      "Epoch 318/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0013 - accuracy: 0.9917 - val_loss: 9.5446e-04 - val_accuracy: 0.9948\n",
      "Epoch 319/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9898 - val_loss: 8.3882e-04 - val_accuracy: 0.9922\n",
      "Epoch 320/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9901 - val_loss: 0.0012 - val_accuracy: 0.9818\n",
      "Epoch 321/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0013 - accuracy: 0.9901 - val_loss: 0.0013 - val_accuracy: 0.9922\n",
      "Epoch 322/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9908 - val_loss: 0.0010 - val_accuracy: 0.9896\n",
      "Epoch 323/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0015 - accuracy: 0.9893 - val_loss: 0.0014 - val_accuracy: 0.9844\n",
      "Epoch 324/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0013 - accuracy: 0.9909 - val_loss: 0.0011 - val_accuracy: 0.9948\n",
      "Epoch 325/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0013 - accuracy: 0.9898 - val_loss: 0.0013 - val_accuracy: 0.9896\n",
      "Epoch 326/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9914 - val_loss: 0.0010 - val_accuracy: 0.9896\n",
      "Epoch 327/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0015 - accuracy: 0.9913 - val_loss: 8.1656e-04 - val_accuracy: 0.9870\n",
      "Epoch 328/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9893 - val_loss: 0.0010 - val_accuracy: 0.9818\n",
      "Epoch 329/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9905 - val_loss: 0.0011 - val_accuracy: 0.9948\n",
      "Epoch 330/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9885 - val_loss: 9.2085e-04 - val_accuracy: 0.9948\n",
      "Epoch 331/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0013 - accuracy: 0.9921 - val_loss: 9.8620e-04 - val_accuracy: 0.9948\n",
      "Epoch 332/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9897 - val_loss: 8.1838e-04 - val_accuracy: 0.9844\n",
      "Epoch 333/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0013 - accuracy: 0.9901 - val_loss: 0.0013 - val_accuracy: 0.9844\n",
      "Epoch 334/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9901 - val_loss: 0.0016 - val_accuracy: 0.9896\n",
      "Epoch 335/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9906 - val_loss: 0.0011 - val_accuracy: 0.9896\n",
      "Epoch 336/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9904 - val_loss: 9.6546e-04 - val_accuracy: 0.9896\n",
      "Epoch 337/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9906 - val_loss: 0.0010 - val_accuracy: 0.9896\n",
      "Epoch 338/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9909 - val_loss: 8.9580e-04 - val_accuracy: 0.9896\n",
      "Epoch 339/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9901 - val_loss: 0.0011 - val_accuracy: 0.9818\n",
      "Epoch 340/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0013 - accuracy: 0.9889 - val_loss: 0.0011 - val_accuracy: 0.9948\n",
      "Epoch 341/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0013 - accuracy: 0.9921 - val_loss: 0.0011 - val_accuracy: 0.9896\n",
      "Epoch 342/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0013 - accuracy: 0.9912 - val_loss: 9.8082e-04 - val_accuracy: 0.9948\n",
      "Epoch 343/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0011 - accuracy: 0.9901 - val_loss: 0.0017 - val_accuracy: 0.9792\n",
      "Epoch 344/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0012 - accuracy: 0.9909 - val_loss: 0.0010 - val_accuracy: 0.9948\n",
      "Epoch 345/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0011 - accuracy: 0.9917 - val_loss: 9.6818e-04 - val_accuracy: 0.9792\n",
      "Epoch 346/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9914 - val_loss: 8.6126e-04 - val_accuracy: 0.9948\n",
      "Epoch 347/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9901 - val_loss: 0.0011 - val_accuracy: 0.9870\n",
      "Epoch 348/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0013 - accuracy: 0.9901 - val_loss: 8.0580e-04 - val_accuracy: 0.9896\n",
      "Epoch 349/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9915 - val_loss: 8.8782e-04 - val_accuracy: 0.9766\n",
      "Epoch 350/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9902 - val_loss: 8.0043e-04 - val_accuracy: 0.9974\n",
      "Epoch 351/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0014 - accuracy: 0.9915 - val_loss: 9.4057e-04 - val_accuracy: 0.9870\n",
      "Epoch 352/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0013 - accuracy: 0.9918 - val_loss: 9.9273e-04 - val_accuracy: 0.9896\n",
      "Epoch 353/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9902 - val_loss: 0.0011 - val_accuracy: 0.9818\n",
      "Epoch 354/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9888 - val_loss: 0.0013 - val_accuracy: 0.9922\n",
      "Epoch 355/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9910 - val_loss: 0.0012 - val_accuracy: 0.9896\n",
      "Epoch 356/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0014 - accuracy: 0.9923 - val_loss: 7.6201e-04 - val_accuracy: 0.9948\n",
      "Epoch 357/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9901 - val_loss: 8.6582e-04 - val_accuracy: 0.9948\n",
      "Epoch 358/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9898 - val_loss: 9.2423e-04 - val_accuracy: 0.9922\n",
      "Epoch 359/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0010 - accuracy: 0.9898 - val_loss: 0.0011 - val_accuracy: 0.9896\n",
      "Epoch 360/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9904 - val_loss: 9.9628e-04 - val_accuracy: 0.9948\n",
      "Epoch 361/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0011 - accuracy: 0.9904 - val_loss: 0.0012 - val_accuracy: 0.9844\n",
      "Epoch 362/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0011 - accuracy: 0.9891 - val_loss: 0.0015 - val_accuracy: 0.9922\n",
      "Epoch 363/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9912 - val_loss: 9.2564e-04 - val_accuracy: 0.9922\n",
      "Epoch 364/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9912 - val_loss: 0.0010 - val_accuracy: 0.9948\n",
      "Epoch 365/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0012 - accuracy: 0.9922 - val_loss: 0.0015 - val_accuracy: 0.9896\n",
      "Epoch 366/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9910 - val_loss: 0.0010 - val_accuracy: 0.9870\n",
      "Epoch 367/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0011 - accuracy: 0.9904 - val_loss: 0.0012 - val_accuracy: 0.9870\n",
      "Epoch 368/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9901 - val_loss: 9.3373e-04 - val_accuracy: 0.9870\n",
      "Epoch 369/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9901 - val_loss: 8.0714e-04 - val_accuracy: 0.9870\n",
      "Epoch 370/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9919 - val_loss: 8.3645e-04 - val_accuracy: 0.9922\n",
      "Epoch 371/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9908 - val_loss: 0.0011 - val_accuracy: 1.0000\n",
      "Epoch 372/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9900 - val_loss: 0.0013 - val_accuracy: 0.9792\n",
      "Epoch 373/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9902 - val_loss: 8.5201e-04 - val_accuracy: 0.9870\n",
      "Epoch 374/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0011 - accuracy: 0.9921 - val_loss: 0.0011 - val_accuracy: 0.9844\n",
      "Epoch 375/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0014 - accuracy: 0.9908 - val_loss: 0.0010 - val_accuracy: 0.9844\n",
      "Epoch 376/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0010 - accuracy: 0.9900 - val_loss: 0.0011 - val_accuracy: 0.9870\n",
      "Epoch 377/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0014 - accuracy: 0.9922 - val_loss: 8.9070e-04 - val_accuracy: 0.9948\n",
      "Epoch 378/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9895 - val_loss: 0.0013 - val_accuracy: 0.9844\n",
      "Epoch 379/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9921 - val_loss: 9.5738e-04 - val_accuracy: 0.9818\n",
      "Epoch 380/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9915 - val_loss: 0.0011 - val_accuracy: 0.9922\n",
      "Epoch 381/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9906 - val_loss: 8.8341e-04 - val_accuracy: 1.0000\n",
      "Epoch 382/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9918 - val_loss: 7.8857e-04 - val_accuracy: 0.9896\n",
      "Epoch 383/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9913 - val_loss: 8.0681e-04 - val_accuracy: 0.9896\n",
      "Epoch 384/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9897 - val_loss: 0.0014 - val_accuracy: 0.9870\n",
      "Epoch 385/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0013 - accuracy: 0.9908 - val_loss: 0.0011 - val_accuracy: 0.9974\n",
      "Epoch 386/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9897 - val_loss: 8.9448e-04 - val_accuracy: 0.9792\n",
      "Epoch 387/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9906 - val_loss: 8.7855e-04 - val_accuracy: 0.9948\n",
      "Epoch 388/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9898 - val_loss: 9.9862e-04 - val_accuracy: 0.9974\n",
      "Epoch 389/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9913 - val_loss: 0.0010 - val_accuracy: 0.9896\n",
      "Epoch 390/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0010 - accuracy: 0.9909 - val_loss: 0.0014 - val_accuracy: 0.9922\n",
      "Epoch 391/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9910 - val_loss: 0.0010 - val_accuracy: 0.9948\n",
      "Epoch 392/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9921 - val_loss: 0.0010 - val_accuracy: 0.9948\n",
      "Epoch 393/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9917 - val_loss: 8.3669e-04 - val_accuracy: 0.9922\n",
      "Epoch 394/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0013 - accuracy: 0.9900 - val_loss: 9.7676e-04 - val_accuracy: 0.9818\n",
      "Epoch 395/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0011 - accuracy: 0.9904 - val_loss: 9.2054e-04 - val_accuracy: 0.9922\n",
      "Epoch 396/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9908 - val_loss: 8.8333e-04 - val_accuracy: 0.9922\n",
      "Epoch 397/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9918 - val_loss: 7.9402e-04 - val_accuracy: 0.9922\n",
      "Epoch 398/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 9.8006e-04 - accuracy: 0.9896 - val_loss: 0.0011 - val_accuracy: 0.9922\n",
      "Epoch 399/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0013 - accuracy: 0.9921 - val_loss: 8.3945e-04 - val_accuracy: 0.9922\n",
      "Epoch 400/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0014 - accuracy: 0.9918 - val_loss: 0.0011 - val_accuracy: 0.9870\n",
      "Epoch 401/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9919 - val_loss: 0.0010 - val_accuracy: 0.9818\n",
      "Epoch 402/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9906 - val_loss: 8.7271e-04 - val_accuracy: 0.9896\n",
      "Epoch 403/600\n",
      "961/961 [==============================] - -3s -3504us/step - loss: 0.0011 - accuracy: 0.9906 - val_loss: 0.0013 - val_accuracy: 0.9922\n",
      "Epoch 404/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9910 - val_loss: 7.6547e-04 - val_accuracy: 0.9896\n",
      "Epoch 405/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9909 - val_loss: 7.6439e-04 - val_accuracy: 0.9922\n",
      "Epoch 406/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9900 - val_loss: 0.0013 - val_accuracy: 0.9870\n",
      "Epoch 407/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9922 - val_loss: 0.0010 - val_accuracy: 1.0000\n",
      "Epoch 408/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0010 - accuracy: 0.9909 - val_loss: 0.0010 - val_accuracy: 0.9844\n",
      "Epoch 409/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9908 - val_loss: 9.1870e-04 - val_accuracy: 0.9948\n",
      "Epoch 410/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0010 - accuracy: 0.9910 - val_loss: 9.7824e-04 - val_accuracy: 0.9974\n",
      "Epoch 411/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0010 - accuracy: 0.9912 - val_loss: 9.3912e-04 - val_accuracy: 0.9896\n",
      "Epoch 412/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0011 - accuracy: 0.9904 - val_loss: 8.0227e-04 - val_accuracy: 0.9922\n",
      "Epoch 413/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9889 - val_loss: 0.0010 - val_accuracy: 0.9896\n",
      "Epoch 414/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9904 - val_loss: 0.0025 - val_accuracy: 0.9844\n",
      "Epoch 415/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9922 - val_loss: 9.8003e-04 - val_accuracy: 0.9922\n",
      "Epoch 416/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0010 - accuracy: 0.9914 - val_loss: 8.7050e-04 - val_accuracy: 0.9922\n",
      "Epoch 417/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9913 - val_loss: 7.6061e-04 - val_accuracy: 0.9922\n",
      "Epoch 418/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0011 - accuracy: 0.9918 - val_loss: 9.3669e-04 - val_accuracy: 0.9922\n",
      "Epoch 419/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9904 - val_loss: 0.0012 - val_accuracy: 0.9948\n",
      "Epoch 420/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9909 - val_loss: 0.0011 - val_accuracy: 0.9844\n",
      "Epoch 421/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0011 - accuracy: 0.9904 - val_loss: 9.9129e-04 - val_accuracy: 0.9870\n",
      "Epoch 422/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0011 - accuracy: 0.9904 - val_loss: 7.5064e-04 - val_accuracy: 0.9896\n",
      "Epoch 423/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0010 - accuracy: 0.9921 - val_loss: 9.9664e-04 - val_accuracy: 0.9844\n",
      "Epoch 424/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9912 - val_loss: 0.0011 - val_accuracy: 0.9896\n",
      "Epoch 425/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9921 - val_loss: 9.1848e-04 - val_accuracy: 0.9974\n",
      "Epoch 426/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 9.9123e-04 - accuracy: 0.9918 - val_loss: 8.9550e-04 - val_accuracy: 0.9870\n",
      "Epoch 427/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0011 - accuracy: 0.9915 - val_loss: 9.3756e-04 - val_accuracy: 0.9896\n",
      "Epoch 428/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0013 - accuracy: 0.9919 - val_loss: 9.1250e-04 - val_accuracy: 0.9948\n",
      "Epoch 429/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9917 - val_loss: 8.6945e-04 - val_accuracy: 0.9896\n",
      "Epoch 430/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 9.7018e-04 - accuracy: 0.9923 - val_loss: 0.0012 - val_accuracy: 0.9818\n",
      "Epoch 431/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0010 - accuracy: 0.9912 - val_loss: 8.9810e-04 - val_accuracy: 0.9922\n",
      "Epoch 432/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 9.9965e-04 - accuracy: 0.9919 - val_loss: 8.3994e-04 - val_accuracy: 0.9922\n",
      "Epoch 433/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0010 - accuracy: 0.9928 - val_loss: 7.6815e-04 - val_accuracy: 0.9922\n",
      "Epoch 434/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0011 - accuracy: 0.9914 - val_loss: 9.3607e-04 - val_accuracy: 0.9896\n",
      "Epoch 435/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0011 - accuracy: 0.9918 - val_loss: 8.8102e-04 - val_accuracy: 0.9896\n",
      "Epoch 436/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0011 - accuracy: 0.9909 - val_loss: 8.5608e-04 - val_accuracy: 0.9870\n",
      "Epoch 437/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0011 - accuracy: 0.9923 - val_loss: 8.5591e-04 - val_accuracy: 0.9896\n",
      "Epoch 438/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0011 - accuracy: 0.9906 - val_loss: 9.9790e-04 - val_accuracy: 0.9922\n",
      "Epoch 439/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9915 - val_loss: 9.6900e-04 - val_accuracy: 0.9844\n",
      "Epoch 440/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0011 - accuracy: 0.9928 - val_loss: 6.6960e-04 - val_accuracy: 0.9974\n",
      "Epoch 441/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0013 - accuracy: 0.9905 - val_loss: 0.0011 - val_accuracy: 0.9896\n",
      "Epoch 442/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9906 - val_loss: 8.5893e-04 - val_accuracy: 0.9948\n",
      "Epoch 443/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9917 - val_loss: 9.3202e-04 - val_accuracy: 0.9948\n",
      "Epoch 444/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9909 - val_loss: 7.3578e-04 - val_accuracy: 0.9922\n",
      "Epoch 445/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9928 - val_loss: 9.1063e-04 - val_accuracy: 0.9896\n",
      "Epoch 446/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0011 - accuracy: 0.9897 - val_loss: 9.0727e-04 - val_accuracy: 0.9870\n",
      "Epoch 447/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9918 - val_loss: 8.6787e-04 - val_accuracy: 0.9896\n",
      "Epoch 448/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0010 - accuracy: 0.9912 - val_loss: 0.0013 - val_accuracy: 0.9922\n",
      "Epoch 449/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0011 - accuracy: 0.9913 - val_loss: 7.3468e-04 - val_accuracy: 0.9922\n",
      "Epoch 450/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0012 - accuracy: 0.9927 - val_loss: 0.0010 - val_accuracy: 0.9844\n",
      "Epoch 451/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 9.9947e-04 - accuracy: 0.9913 - val_loss: 9.4158e-04 - val_accuracy: 0.9948\n",
      "Epoch 452/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 9.6469e-04 - accuracy: 0.9904 - val_loss: 0.0012 - val_accuracy: 0.9792\n",
      "Epoch 453/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9909 - val_loss: 0.0011 - val_accuracy: 0.9948\n",
      "Epoch 454/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0010 - accuracy: 0.9938 - val_loss: 8.1878e-04 - val_accuracy: 0.9974\n",
      "Epoch 455/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 9.9456e-04 - accuracy: 0.9922 - val_loss: 9.3054e-04 - val_accuracy: 0.9870\n",
      "Epoch 456/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 9.9718e-04 - accuracy: 0.9910 - val_loss: 9.4311e-04 - val_accuracy: 0.9948\n",
      "Epoch 457/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0011 - accuracy: 0.9913 - val_loss: 8.4622e-04 - val_accuracy: 0.9948\n",
      "Epoch 458/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 9.6740e-04 - accuracy: 0.9925 - val_loss: 0.0016 - val_accuracy: 0.9870\n",
      "Epoch 459/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0011 - accuracy: 0.9897 - val_loss: 0.0010 - val_accuracy: 0.9948\n",
      "Epoch 460/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9905 - val_loss: 0.0011 - val_accuracy: 0.9922\n",
      "Epoch 461/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 9.8075e-04 - accuracy: 0.9912 - val_loss: 0.0018 - val_accuracy: 0.9870\n",
      "Epoch 462/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 9.9118e-04 - accuracy: 0.9915 - val_loss: 9.7053e-04 - val_accuracy: 0.9792\n",
      "Epoch 463/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 9.9913e-04 - accuracy: 0.9913 - val_loss: 9.6032e-04 - val_accuracy: 0.9922\n",
      "Epoch 464/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0011 - accuracy: 0.9914 - val_loss: 0.0011 - val_accuracy: 0.9766\n",
      "Epoch 465/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 9.5900e-04 - accuracy: 0.9918 - val_loss: 8.0060e-04 - val_accuracy: 0.9922\n",
      "Epoch 466/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0010 - accuracy: 0.9898 - val_loss: 0.0013 - val_accuracy: 0.9870\n",
      "Epoch 467/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9906 - val_loss: 7.7088e-04 - val_accuracy: 0.9896\n",
      "Epoch 468/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 9.4990e-04 - accuracy: 0.9923 - val_loss: 9.5349e-04 - val_accuracy: 0.9844\n",
      "Epoch 469/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 9.6138e-04 - accuracy: 0.9910 - val_loss: 8.0636e-04 - val_accuracy: 0.9740\n",
      "Epoch 470/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 9.8438e-04 - accuracy: 0.9909 - val_loss: 8.5633e-04 - val_accuracy: 0.9948\n",
      "Epoch 471/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 9.2842e-04 - accuracy: 0.9901 - val_loss: 9.2143e-04 - val_accuracy: 0.9844\n",
      "Epoch 472/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9908 - val_loss: 7.0864e-04 - val_accuracy: 0.9974\n",
      "Epoch 473/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 9.5019e-04 - accuracy: 0.9921 - val_loss: 9.4601e-04 - val_accuracy: 0.9844\n",
      "Epoch 474/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 9.8156e-04 - accuracy: 0.9921 - val_loss: 0.0011 - val_accuracy: 0.9896\n",
      "Epoch 475/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 9.5626e-04 - accuracy: 0.9910 - val_loss: 0.0011 - val_accuracy: 0.9792\n",
      "Epoch 476/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0011 - accuracy: 0.9922 - val_loss: 8.4522e-04 - val_accuracy: 0.9896\n",
      "Epoch 477/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0011 - accuracy: 0.9902 - val_loss: 7.4024e-04 - val_accuracy: 0.9870\n",
      "Epoch 478/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 9.4995e-04 - accuracy: 0.9914 - val_loss: 9.0365e-04 - val_accuracy: 0.9870\n",
      "Epoch 479/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0010 - accuracy: 0.9908 - val_loss: 8.9583e-04 - val_accuracy: 0.9896\n",
      "Epoch 480/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0011 - accuracy: 0.9909 - val_loss: 0.0011 - val_accuracy: 0.9870\n",
      "Epoch 481/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0012 - accuracy: 0.9914 - val_loss: 8.9493e-04 - val_accuracy: 0.9870\n",
      "Epoch 482/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0010 - accuracy: 0.9909 - val_loss: 9.7540e-04 - val_accuracy: 0.9922\n",
      "Epoch 483/600\n",
      "961/961 [==============================] - 6s 6ms/step - loss: 0.0010 - accuracy: 0.9930 - val_loss: 7.4885e-04 - val_accuracy: 0.9896\n",
      "Epoch 484/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0010 - accuracy: 0.9918 - val_loss: 9.0286e-04 - val_accuracy: 0.9870\n",
      "Epoch 485/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0011 - accuracy: 0.9922 - val_loss: 8.6717e-04 - val_accuracy: 1.0000\n",
      "Epoch 486/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 9.1934e-04 - accuracy: 0.9921 - val_loss: 7.8851e-04 - val_accuracy: 0.9818\n",
      "Epoch 487/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 9.4223e-04 - accuracy: 0.9918 - val_loss: 0.0018 - val_accuracy: 0.9818\n",
      "Epoch 488/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 0.0010 - accuracy: 0.9923 - val_loss: 7.8626e-04 - val_accuracy: 0.9974\n",
      "Epoch 489/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 9.6837e-04 - accuracy: 0.9914 - val_loss: 0.0015 - val_accuracy: 0.9896\n",
      "Epoch 490/600\n",
      "961/961 [==============================] - 6s 7ms/step - loss: 9.3629e-04 - accuracy: 0.9914 - val_loss: 7.8456e-04 - val_accuracy: 0.9844\n",
      "Epoch 491/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0010 - accuracy: 0.9896 - val_loss: 7.8809e-04 - val_accuracy: 0.9948\n",
      "Epoch 492/600\n",
      "961/961 [==============================] - 8s 9ms/step - loss: 0.0011 - accuracy: 0.9913 - val_loss: 7.7840e-04 - val_accuracy: 0.9922\n",
      "Epoch 493/600\n",
      "961/961 [==============================] - 24s 25ms/step - loss: 9.5383e-04 - accuracy: 0.9908 - val_loss: 0.0010 - val_accuracy: 0.9922\n",
      "Epoch 494/600\n",
      "961/961 [==============================] - 20s 21ms/step - loss: 0.0011 - accuracy: 0.9919 - val_loss: 7.8136e-04 - val_accuracy: 0.9896\n",
      "Epoch 495/600\n",
      "961/961 [==============================] - 20s 21ms/step - loss: 9.2621e-04 - accuracy: 0.9914 - val_loss: 9.3899e-04 - val_accuracy: 0.9792\n",
      "Epoch 496/600\n",
      "961/961 [==============================] - 19s 20ms/step - loss: 0.0010 - accuracy: 0.9913 - val_loss: 8.0448e-04 - val_accuracy: 0.9896\n",
      "Epoch 497/600\n",
      "961/961 [==============================] - 19s 20ms/step - loss: 9.9531e-04 - accuracy: 0.9921 - val_loss: 8.3176e-04 - val_accuracy: 0.9844\n",
      "Epoch 498/600\n",
      "961/961 [==============================] - 20s 21ms/step - loss: 0.0012 - accuracy: 0.9915 - val_loss: 9.2320e-04 - val_accuracy: 0.9922\n",
      "Epoch 499/600\n",
      "961/961 [==============================] - 19s 20ms/step - loss: 0.0011 - accuracy: 0.9905 - val_loss: 7.4137e-04 - val_accuracy: 0.9948\n",
      "Epoch 500/600\n",
      "961/961 [==============================] - 12s 13ms/step - loss: 9.2776e-04 - accuracy: 0.9912 - val_loss: 0.0011 - val_accuracy: 0.9870\n",
      "Epoch 501/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 9.7526e-04 - accuracy: 0.9928 - val_loss: 7.1120e-04 - val_accuracy: 0.9974\n",
      "Epoch 502/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0010 - accuracy: 0.9908 - val_loss: 9.5373e-04 - val_accuracy: 0.9948\n",
      "Epoch 503/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0010 - accuracy: 0.9912 - val_loss: 7.8975e-04 - val_accuracy: 0.9896\n",
      "Epoch 504/600\n",
      "961/961 [==============================] - 11s 12ms/step - loss: 9.2108e-04 - accuracy: 0.9913 - val_loss: 8.4803e-04 - val_accuracy: 0.9922\n",
      "Epoch 505/600\n",
      "961/961 [==============================] - 20s 21ms/step - loss: 9.6839e-04 - accuracy: 0.9928 - val_loss: 6.9248e-04 - val_accuracy: 0.9896\n",
      "Epoch 506/600\n",
      "961/961 [==============================] - 19s 20ms/step - loss: 9.2621e-04 - accuracy: 0.9917 - val_loss: 7.6735e-04 - val_accuracy: 0.9922\n",
      "Epoch 507/600\n",
      "961/961 [==============================] - 19s 20ms/step - loss: 9.5541e-04 - accuracy: 0.9930 - val_loss: 9.7672e-04 - val_accuracy: 0.9948\n",
      "Epoch 508/600\n",
      "961/961 [==============================] - 20s 21ms/step - loss: 9.7187e-04 - accuracy: 0.9913 - val_loss: 7.6072e-04 - val_accuracy: 0.9870\n",
      "Epoch 509/600\n",
      "961/961 [==============================] - 20s 21ms/step - loss: 9.6718e-04 - accuracy: 0.9913 - val_loss: 0.0026 - val_accuracy: 0.9922\n",
      "Epoch 510/600\n",
      "961/961 [==============================] - 20s 21ms/step - loss: 9.9012e-04 - accuracy: 0.9917 - val_loss: 7.8845e-04 - val_accuracy: 0.9896\n",
      "Epoch 511/600\n",
      "961/961 [==============================] - 20s 21ms/step - loss: 9.2624e-04 - accuracy: 0.9908 - val_loss: 7.8933e-04 - val_accuracy: 0.9844\n",
      "Epoch 512/600\n",
      "961/961 [==============================] - 20s 21ms/step - loss: 9.8493e-04 - accuracy: 0.9928 - val_loss: 6.9943e-04 - val_accuracy: 0.9896\n",
      "Epoch 513/600\n",
      "961/961 [==============================] - 21s 22ms/step - loss: 9.9337e-04 - accuracy: 0.9905 - val_loss: 7.6201e-04 - val_accuracy: 0.9792\n",
      "Epoch 514/600\n",
      "961/961 [==============================] - 22s 22ms/step - loss: 9.9061e-04 - accuracy: 0.9926 - val_loss: 8.4114e-04 - val_accuracy: 0.9922\n",
      "Epoch 515/600\n",
      "961/961 [==============================] - 26s 27ms/step - loss: 0.0010 - accuracy: 0.9915 - val_loss: 0.0010 - val_accuracy: 0.9896\n",
      "Epoch 516/600\n",
      "961/961 [==============================] - 26s 27ms/step - loss: 8.8426e-04 - accuracy: 0.9910 - val_loss: 9.1254e-04 - val_accuracy: 0.9922\n",
      "Epoch 517/600\n",
      "961/961 [==============================] - 22s 22ms/step - loss: 0.0011 - accuracy: 0.9910 - val_loss: 8.9858e-04 - val_accuracy: 0.9844\n",
      "Epoch 518/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0010 - accuracy: 0.9913 - val_loss: 9.0401e-04 - val_accuracy: 0.9844\n",
      "Epoch 519/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 9.0587e-04 - accuracy: 0.9914 - val_loss: 9.1817e-04 - val_accuracy: 1.0000\n",
      "Epoch 520/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 9.1294e-04 - accuracy: 0.9930 - val_loss: 8.3805e-04 - val_accuracy: 0.9948\n",
      "Epoch 521/600\n",
      "961/961 [==============================] - 15s 16ms/step - loss: 0.0010 - accuracy: 0.9921 - val_loss: 6.4656e-04 - val_accuracy: 0.9974\n",
      "Epoch 522/600\n",
      "961/961 [==============================] - 20s 21ms/step - loss: 0.0010 - accuracy: 0.9931 - val_loss: 7.1675e-04 - val_accuracy: 0.9896\n",
      "Epoch 523/600\n",
      "961/961 [==============================] - 19s 20ms/step - loss: 0.0011 - accuracy: 0.9913 - val_loss: 0.0010 - val_accuracy: 0.9974\n",
      "Epoch 524/600\n",
      "961/961 [==============================] - 21s 21ms/step - loss: 9.8993e-04 - accuracy: 0.9921 - val_loss: 7.8046e-04 - val_accuracy: 0.9974\n",
      "Epoch 525/600\n",
      "961/961 [==============================] - 20s 20ms/step - loss: 8.9191e-04 - accuracy: 0.9909 - val_loss: 7.1024e-04 - val_accuracy: 0.9922\n",
      "Epoch 526/600\n",
      "961/961 [==============================] - 21s 22ms/step - loss: 9.3764e-04 - accuracy: 0.9908 - val_loss: 8.2271e-04 - val_accuracy: 0.9870\n",
      "Epoch 527/600\n",
      "961/961 [==============================] - 22s 23ms/step - loss: 9.6745e-04 - accuracy: 0.9913 - val_loss: 8.2257e-04 - val_accuracy: 0.9896\n",
      "Epoch 528/600\n",
      "961/961 [==============================] - 23s 24ms/step - loss: 8.8730e-04 - accuracy: 0.9921 - val_loss: 8.6724e-04 - val_accuracy: 0.9896\n",
      "Epoch 529/600\n",
      "961/961 [==============================] - 28s 29ms/step - loss: 9.0546e-04 - accuracy: 0.9917 - val_loss: 8.7776e-04 - val_accuracy: 0.9844\n",
      "Epoch 530/600\n",
      "961/961 [==============================] - 16s 16ms/step - loss: 8.9195e-04 - accuracy: 0.9938 - val_loss: 7.8054e-04 - val_accuracy: 0.9974\n",
      "Epoch 531/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 8.8240e-04 - accuracy: 0.9928 - val_loss: 9.7806e-04 - val_accuracy: 0.9818\n",
      "Epoch 532/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 9.7442e-04 - accuracy: 0.9919 - val_loss: 8.3211e-04 - val_accuracy: 0.9870\n",
      "Epoch 533/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0011 - accuracy: 0.9926 - val_loss: 9.3291e-04 - val_accuracy: 0.9896\n",
      "Epoch 534/600\n",
      "961/961 [==============================] - 13s 13ms/step - loss: 9.6824e-04 - accuracy: 0.9915 - val_loss: 6.7067e-04 - val_accuracy: 0.9948\n",
      "Epoch 535/600\n",
      "961/961 [==============================] - 21s 22ms/step - loss: 9.9209e-04 - accuracy: 0.9921 - val_loss: 7.9049e-04 - val_accuracy: 0.9844\n",
      "Epoch 536/600\n",
      "961/961 [==============================] - 20s 21ms/step - loss: 9.8084e-04 - accuracy: 0.9909 - val_loss: 0.0011 - val_accuracy: 0.9974\n",
      "Epoch 537/600\n",
      "961/961 [==============================] - 20s 21ms/step - loss: 9.7516e-04 - accuracy: 0.9921 - val_loss: 0.0011 - val_accuracy: 0.9818\n",
      "Epoch 538/600\n",
      "961/961 [==============================] - 20s 21ms/step - loss: 0.0010 - accuracy: 0.9923 - val_loss: 9.7019e-04 - val_accuracy: 0.9922\n",
      "Epoch 539/600\n",
      "961/961 [==============================] - 20s 21ms/step - loss: 9.8136e-04 - accuracy: 0.9910 - val_loss: 7.8142e-04 - val_accuracy: 0.9922\n",
      "Epoch 540/600\n",
      "961/961 [==============================] - 20s 21ms/step - loss: 9.4728e-04 - accuracy: 0.9922 - val_loss: 6.2498e-04 - val_accuracy: 0.9974\n",
      "Epoch 541/600\n",
      "961/961 [==============================] - 22s 22ms/step - loss: 9.0222e-04 - accuracy: 0.9921 - val_loss: 6.2532e-04 - val_accuracy: 0.9948\n",
      "Epoch 542/600\n",
      "961/961 [==============================] - 22s 23ms/step - loss: 8.8947e-04 - accuracy: 0.9918 - val_loss: 0.0012 - val_accuracy: 0.9974\n",
      "Epoch 543/600\n",
      "961/961 [==============================] - 26s 27ms/step - loss: 9.0225e-04 - accuracy: 0.9921 - val_loss: 8.3847e-04 - val_accuracy: 0.9974\n",
      "Epoch 544/600\n",
      "961/961 [==============================] - 27s 28ms/step - loss: 0.0010 - accuracy: 0.9918 - val_loss: 7.7367e-04 - val_accuracy: 0.9870\n",
      "Epoch 545/600\n",
      "961/961 [==============================] - 11s 11ms/step - loss: 9.5131e-04 - accuracy: 0.9919 - val_loss: 8.7453e-04 - val_accuracy: 0.9922\n",
      "Epoch 546/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 9.7518e-04 - accuracy: 0.9927 - val_loss: 0.0010 - val_accuracy: 0.9896\n",
      "Epoch 547/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0010 - accuracy: 0.9934 - val_loss: 8.4015e-04 - val_accuracy: 0.9948\n",
      "Epoch 548/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 9.8013e-04 - accuracy: 0.9926 - val_loss: 6.7645e-04 - val_accuracy: 0.9974\n",
      "Epoch 549/600\n",
      "961/961 [==============================] - 20s 21ms/step - loss: 9.3572e-04 - accuracy: 0.9919 - val_loss: 7.3352e-04 - val_accuracy: 0.9922\n",
      "Epoch 550/600\n",
      "961/961 [==============================] - 20s 20ms/step - loss: 0.0010 - accuracy: 0.9901 - val_loss: 8.2292e-04 - val_accuracy: 0.9896\n",
      "Epoch 551/600\n",
      "961/961 [==============================] - 20s 21ms/step - loss: 9.5258e-04 - accuracy: 0.9906 - val_loss: 6.6418e-04 - val_accuracy: 0.9974\n",
      "Epoch 552/600\n",
      "961/961 [==============================] - 20s 21ms/step - loss: 8.8258e-04 - accuracy: 0.9927 - val_loss: 9.7753e-04 - val_accuracy: 0.9974\n",
      "Epoch 553/600\n",
      "961/961 [==============================] - 12s 13ms/step - loss: 8.5175e-04 - accuracy: 0.9926 - val_loss: 7.0676e-04 - val_accuracy: 0.9948\n",
      "Epoch 554/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0010 - accuracy: 0.9915 - val_loss: 8.6822e-04 - val_accuracy: 0.9896\n",
      "Epoch 555/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 9.1687e-04 - accuracy: 0.9915 - val_loss: 0.0022 - val_accuracy: 0.9896\n",
      "Epoch 556/600\n",
      "961/961 [==============================] - 7s 8ms/step - loss: 8.8173e-04 - accuracy: 0.9932 - val_loss: 0.0011 - val_accuracy: 0.9766\n",
      "Epoch 557/600\n",
      "961/961 [==============================] - 16s 16ms/step - loss: 8.8572e-04 - accuracy: 0.9913 - val_loss: 7.0784e-04 - val_accuracy: 0.9844\n",
      "Epoch 558/600\n",
      "961/961 [==============================] - 21s 22ms/step - loss: 9.5403e-04 - accuracy: 0.9931 - val_loss: 6.9528e-04 - val_accuracy: 0.9896\n",
      "Epoch 559/600\n",
      "961/961 [==============================] - 24s 24ms/step - loss: 9.0609e-04 - accuracy: 0.9943 - val_loss: 8.4899e-04 - val_accuracy: 0.9974\n",
      "Epoch 560/600\n",
      "961/961 [==============================] - 9s 10ms/step - loss: 0.0011 - accuracy: 0.9909 - val_loss: 7.9791e-04 - val_accuracy: 0.9948\n",
      "Epoch 561/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 9.1760e-04 - accuracy: 0.9904 - val_loss: 8.4901e-04 - val_accuracy: 0.9870\n",
      "Epoch 562/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 9.8252e-04 - accuracy: 0.9906 - val_loss: 8.6334e-04 - val_accuracy: 0.9844\n",
      "Epoch 563/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 8.7320e-04 - accuracy: 0.9902 - val_loss: 0.0017 - val_accuracy: 0.9974\n",
      "Epoch 564/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 0.0010 - accuracy: 0.9925 - val_loss: 8.8431e-04 - val_accuracy: 0.9974\n",
      "Epoch 565/600\n",
      "961/961 [==============================] - 13s 13ms/step - loss: 9.4468e-04 - accuracy: 0.9927 - val_loss: 8.7645e-04 - val_accuracy: 0.9870\n",
      "Epoch 566/600\n",
      "961/961 [==============================] - 21s 22ms/step - loss: 9.9222e-04 - accuracy: 0.9921 - val_loss: 8.4613e-04 - val_accuracy: 0.9896\n",
      "Epoch 567/600\n",
      "961/961 [==============================] - 21s 22ms/step - loss: 8.7128e-04 - accuracy: 0.9917 - val_loss: 9.4499e-04 - val_accuracy: 0.9896\n",
      "Epoch 568/600\n",
      "961/961 [==============================] - 20s 21ms/step - loss: 9.1180e-04 - accuracy: 0.9925 - val_loss: 8.0376e-04 - val_accuracy: 0.9948\n",
      "Epoch 569/600\n",
      "961/961 [==============================] - 21s 22ms/step - loss: 9.3865e-04 - accuracy: 0.9913 - val_loss: 5.9713e-04 - val_accuracy: 0.9896\n",
      "Epoch 570/600\n",
      "961/961 [==============================] - 20s 21ms/step - loss: 8.8316e-04 - accuracy: 0.9917 - val_loss: 8.2320e-04 - val_accuracy: 0.9896\n",
      "Epoch 571/600\n",
      "961/961 [==============================] - 20s 21ms/step - loss: 0.0011 - accuracy: 0.9904 - val_loss: 9.3507e-04 - val_accuracy: 0.9818\n",
      "Epoch 572/600\n",
      "961/961 [==============================] - 22s 22ms/step - loss: 9.2953e-04 - accuracy: 0.9905 - val_loss: 9.7515e-04 - val_accuracy: 0.9740\n",
      "Epoch 573/600\n",
      "961/961 [==============================] - 23s 24ms/step - loss: 9.4387e-04 - accuracy: 0.9914 - val_loss: 6.8873e-04 - val_accuracy: 0.9948\n",
      "Epoch 574/600\n",
      "961/961 [==============================] - 26s 27ms/step - loss: 9.4230e-04 - accuracy: 0.9917 - val_loss: 6.8746e-04 - val_accuracy: 0.9948\n",
      "Epoch 575/600\n",
      "961/961 [==============================] - 12s 13ms/step - loss: 9.9325e-04 - accuracy: 0.9944 - val_loss: 8.9951e-04 - val_accuracy: 0.9896\n",
      "Epoch 576/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 8.6360e-04 - accuracy: 0.9927 - val_loss: 7.9187e-04 - val_accuracy: 0.9948\n",
      "Epoch 577/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 9.7725e-04 - accuracy: 0.9923 - val_loss: 9.1454e-04 - val_accuracy: 0.9922\n",
      "Epoch 578/600\n",
      "961/961 [==============================] - 7s 7ms/step - loss: 9.7750e-04 - accuracy: 0.9918 - val_loss: 7.3346e-04 - val_accuracy: 0.9948\n",
      "Epoch 579/600\n",
      "961/961 [==============================] - 19s 20ms/step - loss: 9.5056e-04 - accuracy: 0.9922 - val_loss: 7.6291e-04 - val_accuracy: 0.9974\n",
      "Epoch 580/600\n",
      "961/961 [==============================] - 20s 21ms/step - loss: 9.3150e-04 - accuracy: 0.9910 - val_loss: 7.5555e-04 - val_accuracy: 0.9870\n",
      "Epoch 581/600\n",
      "961/961 [==============================] - 20s 21ms/step - loss: 9.4564e-04 - accuracy: 0.9910 - val_loss: 0.0010 - val_accuracy: 0.9896\n",
      "Epoch 582/600\n",
      "961/961 [==============================] - 20s 21ms/step - loss: 9.5832e-04 - accuracy: 0.9922 - val_loss: 8.7981e-04 - val_accuracy: 0.9896\n",
      "Epoch 583/600\n",
      "961/961 [==============================] - 20s 21ms/step - loss: 9.3630e-04 - accuracy: 0.9931 - val_loss: 7.2957e-04 - val_accuracy: 0.9896\n",
      "Epoch 584/600\n",
      "961/961 [==============================] - 20s 21ms/step - loss: 8.3900e-04 - accuracy: 0.9922 - val_loss: 0.0011 - val_accuracy: 0.9948\n",
      "Epoch 585/600\n",
      "961/961 [==============================] - 22s 22ms/step - loss: 9.6142e-04 - accuracy: 0.9913 - val_loss: 8.0016e-04 - val_accuracy: 0.9870\n",
      "Epoch 586/600\n",
      "961/961 [==============================] - 23s 24ms/step - loss: 8.6526e-04 - accuracy: 0.9902 - val_loss: 8.4281e-04 - val_accuracy: 0.9922\n",
      "Epoch 587/600\n",
      "961/961 [==============================] - 25s 26ms/step - loss: 9.3169e-04 - accuracy: 0.9927 - val_loss: 0.0012 - val_accuracy: 0.9948\n",
      "Epoch 588/600\n",
      "961/961 [==============================] - 28s 29ms/step - loss: 8.8954e-04 - accuracy: 0.9928 - val_loss: 7.5422e-04 - val_accuracy: 0.9948\n",
      "Epoch 589/600\n",
      "961/961 [==============================] - 26s 27ms/step - loss: 9.7124e-04 - accuracy: 0.9921 - val_loss: 8.1874e-04 - val_accuracy: 0.9766\n",
      "Epoch 590/600\n",
      "961/961 [==============================] - 22s 23ms/step - loss: 0.0010 - accuracy: 0.9927 - val_loss: 6.7845e-04 - val_accuracy: 0.9948\n",
      "Epoch 591/600\n",
      "961/961 [==============================] - 21s 22ms/step - loss: 8.4035e-04 - accuracy: 0.9930 - val_loss: 6.9524e-04 - val_accuracy: 0.9948\n",
      "Epoch 592/600\n",
      "961/961 [==============================] - 23s 24ms/step - loss: 8.6362e-04 - accuracy: 0.9925 - val_loss: 0.0012 - val_accuracy: 0.9974\n",
      "Epoch 593/600\n",
      "961/961 [==============================] - 28s 29ms/step - loss: 0.0012 - accuracy: 0.9909 - val_loss: 8.2116e-04 - val_accuracy: 0.9948\n",
      "Epoch 594/600\n",
      "961/961 [==============================] - 27s 28ms/step - loss: 9.3114e-04 - accuracy: 0.9910 - val_loss: 0.0012 - val_accuracy: 0.9818\n",
      "Epoch 595/600\n",
      "961/961 [==============================] - 22s 23ms/step - loss: 9.6841e-04 - accuracy: 0.9923 - val_loss: 0.0010 - val_accuracy: 0.9818\n",
      "Epoch 596/600\n",
      "961/961 [==============================] - 20s 21ms/step - loss: 9.1093e-04 - accuracy: 0.9934 - val_loss: 6.3713e-04 - val_accuracy: 0.9922\n",
      "Epoch 597/600\n",
      "961/961 [==============================] - 21s 22ms/step - loss: 0.0010 - accuracy: 0.9930 - val_loss: 9.5494e-04 - val_accuracy: 0.9922\n",
      "Epoch 598/600\n",
      "961/961 [==============================] - 20s 21ms/step - loss: 9.6398e-04 - accuracy: 0.9918 - val_loss: 8.1957e-04 - val_accuracy: 0.9948\n",
      "Epoch 599/600\n",
      "961/961 [==============================] - 20s 20ms/step - loss: 9.0098e-04 - accuracy: 0.9918 - val_loss: 7.6973e-04 - val_accuracy: 0.9870\n",
      "Epoch 600/600\n",
      "961/961 [==============================] - 20s 20ms/step - loss: 9.3654e-04 - accuracy: 0.9919 - val_loss: 9.0871e-04 - val_accuracy: 0.9948\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "721/721 [==============================] - 9s 13ms/step - loss: 5.0155e-04 - accuracy: 0.9962 - val_loss: 4.6241e-04 - val_accuracy: 0.9974\n",
      "Epoch 163/600\n",
      "721/721 [==============================] - 10s 13ms/step - loss: 5.0047e-04 - accuracy: 0.9970 - val_loss: 4.9512e-04 - val_accuracy: 0.9991\n",
      "Epoch 164/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.7911e-04 - accuracy: 0.9958 - val_loss: 4.8826e-04 - val_accuracy: 0.9887\n",
      "Epoch 165/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 5.0449e-04 - accuracy: 0.9953 - val_loss: 4.9133e-04 - val_accuracy: 0.9931\n",
      "Epoch 166/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.9675e-04 - accuracy: 0.9962 - val_loss: 5.1841e-04 - val_accuracy: 0.9957\n",
      "Epoch 167/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 5.0528e-04 - accuracy: 0.9965 - val_loss: 5.4107e-04 - val_accuracy: 0.9913\n",
      "Epoch 168/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.8258e-04 - accuracy: 0.9957 - val_loss: 5.2363e-04 - val_accuracy: 0.9948\n",
      "Epoch 169/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.9333e-04 - accuracy: 0.9950 - val_loss: 5.4954e-04 - val_accuracy: 0.9879\n",
      "Epoch 170/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.9555e-04 - accuracy: 0.9967 - val_loss: 4.7386e-04 - val_accuracy: 0.9957\n",
      "Epoch 171/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.6926e-04 - accuracy: 0.9958 - val_loss: 4.6867e-04 - val_accuracy: 0.9948\n",
      "Epoch 172/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.9741e-04 - accuracy: 0.9950 - val_loss: 4.6184e-04 - val_accuracy: 0.9983\n",
      "Epoch 173/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.6460e-04 - accuracy: 0.9962 - val_loss: 5.4861e-04 - val_accuracy: 0.9939\n",
      "Epoch 174/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 5.0546e-04 - accuracy: 0.9957 - val_loss: 4.7492e-04 - val_accuracy: 0.9965\n",
      "Epoch 175/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.6991e-04 - accuracy: 0.9964 - val_loss: 4.4356e-04 - val_accuracy: 0.9974\n",
      "Epoch 176/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.8421e-04 - accuracy: 0.9967 - val_loss: 5.1032e-04 - val_accuracy: 0.9922\n",
      "Epoch 177/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.7477e-04 - accuracy: 0.9944 - val_loss: 5.1298e-04 - val_accuracy: 0.9939\n",
      "Epoch 178/600\n",
      "721/721 [==============================] - 10s 13ms/step - loss: 4.8124e-04 - accuracy: 0.9939 - val_loss: 4.1122e-04 - val_accuracy: 0.9922\n",
      "Epoch 179/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.7569e-04 - accuracy: 0.9965 - val_loss: 4.3510e-04 - val_accuracy: 0.9957\n",
      "Epoch 180/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.6698e-04 - accuracy: 0.9957 - val_loss: 5.3288e-04 - val_accuracy: 0.9939\n",
      "Epoch 181/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 4.5698e-04 - accuracy: 0.9969 - val_loss: 5.9505e-04 - val_accuracy: 0.9957\n",
      "Epoch 182/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.4907e-04 - accuracy: 0.9960 - val_loss: 5.2186e-04 - val_accuracy: 0.9957\n",
      "Epoch 183/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.4862e-04 - accuracy: 0.9960 - val_loss: 4.1868e-04 - val_accuracy: 0.9939\n",
      "Epoch 184/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.7630e-04 - accuracy: 0.9943 - val_loss: 4.9642e-04 - val_accuracy: 0.9974\n",
      "Epoch 185/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.7356e-04 - accuracy: 0.9965 - val_loss: 4.3216e-04 - val_accuracy: 0.9957\n",
      "Epoch 186/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.5645e-04 - accuracy: 0.9960 - val_loss: 4.5152e-04 - val_accuracy: 0.9905\n",
      "Epoch 187/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.5759e-04 - accuracy: 0.9955 - val_loss: 4.2974e-04 - val_accuracy: 0.9974\n",
      "Epoch 188/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.6234e-04 - accuracy: 0.9950 - val_loss: 4.4550e-04 - val_accuracy: 0.9931\n",
      "Epoch 189/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.5405e-04 - accuracy: 0.9970 - val_loss: 4.2442e-04 - val_accuracy: 0.9965\n",
      "Epoch 190/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.4330e-04 - accuracy: 0.9962 - val_loss: 4.7416e-04 - val_accuracy: 0.9974\n",
      "Epoch 191/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.5302e-04 - accuracy: 0.9958 - val_loss: 5.1046e-04 - val_accuracy: 0.9896\n",
      "Epoch 192/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.5092e-04 - accuracy: 0.9955 - val_loss: 4.1212e-04 - val_accuracy: 0.9887\n",
      "Epoch 193/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 4.6281e-04 - accuracy: 0.9955 - val_loss: 4.3498e-04 - val_accuracy: 0.9974\n",
      "Epoch 194/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.5306e-04 - accuracy: 0.9964 - val_loss: 4.0260e-04 - val_accuracy: 0.9957\n",
      "Epoch 195/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.7112e-04 - accuracy: 0.9958 - val_loss: 4.2984e-04 - val_accuracy: 0.9939\n",
      "Epoch 196/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.4503e-04 - accuracy: 0.9957 - val_loss: 4.2281e-04 - val_accuracy: 0.9965\n",
      "Epoch 197/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.4081e-04 - accuracy: 0.9976 - val_loss: 5.0372e-04 - val_accuracy: 0.9974\n",
      "Epoch 198/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.5979e-04 - accuracy: 0.9962 - val_loss: 4.1966e-04 - val_accuracy: 0.9965\n",
      "Epoch 199/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.5358e-04 - accuracy: 0.9965 - val_loss: 4.5571e-04 - val_accuracy: 0.9948\n",
      "Epoch 200/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.5060e-04 - accuracy: 0.9962 - val_loss: 3.7569e-04 - val_accuracy: 0.9965\n",
      "Epoch 201/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.4833e-04 - accuracy: 0.9957 - val_loss: 4.1794e-04 - val_accuracy: 0.9931\n",
      "Epoch 202/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.3797e-04 - accuracy: 0.9960 - val_loss: 9.6062e-04 - val_accuracy: 0.9913\n",
      "Epoch 203/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.3676e-04 - accuracy: 0.9965 - val_loss: 4.5890e-04 - val_accuracy: 0.9965\n",
      "Epoch 204/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.4689e-04 - accuracy: 0.9964 - val_loss: 3.8567e-04 - val_accuracy: 0.9939\n",
      "Epoch 205/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 4.3963e-04 - accuracy: 0.9958 - val_loss: 4.2321e-04 - val_accuracy: 0.9983\n",
      "Epoch 206/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.3918e-04 - accuracy: 0.9962 - val_loss: 3.8616e-04 - val_accuracy: 0.9957\n",
      "Epoch 207/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.4982e-04 - accuracy: 0.9951 - val_loss: 3.9890e-04 - val_accuracy: 0.9948\n",
      "Epoch 208/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.5022e-04 - accuracy: 0.9950 - val_loss: 4.2684e-04 - val_accuracy: 0.9957\n",
      "Epoch 209/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.3423e-04 - accuracy: 0.9960 - val_loss: 4.3354e-04 - val_accuracy: 0.9939\n",
      "Epoch 210/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.3747e-04 - accuracy: 0.9955 - val_loss: 4.0163e-04 - val_accuracy: 0.9931\n",
      "Epoch 211/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.2793e-04 - accuracy: 0.9950 - val_loss: 5.8123e-04 - val_accuracy: 0.9913\n",
      "Epoch 212/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.2231e-04 - accuracy: 0.9967 - val_loss: 3.6892e-04 - val_accuracy: 0.9974\n",
      "Epoch 213/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.5002e-04 - accuracy: 0.9965 - val_loss: 5.2617e-04 - val_accuracy: 0.9931\n",
      "Epoch 214/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.4357e-04 - accuracy: 0.9957 - val_loss: 3.0985e-04 - val_accuracy: 0.9983\n",
      "Epoch 215/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.0733e-04 - accuracy: 0.9964 - val_loss: 4.3648e-04 - val_accuracy: 0.9974\n",
      "Epoch 216/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.3433e-04 - accuracy: 0.9967 - val_loss: 5.1075e-04 - val_accuracy: 0.9948\n",
      "Epoch 217/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.6541e-04 - accuracy: 0.9951 - val_loss: 3.7633e-04 - val_accuracy: 0.9957\n",
      "Epoch 218/600\n",
      "721/721 [==============================] - 10s 13ms/step - loss: 4.2788e-04 - accuracy: 0.9951 - val_loss: 3.8249e-04 - val_accuracy: 0.9948\n",
      "Epoch 219/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 4.3635e-04 - accuracy: 0.9957 - val_loss: 4.1666e-04 - val_accuracy: 0.9974\n",
      "Epoch 220/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.3898e-04 - accuracy: 0.9965 - val_loss: 6.6150e-04 - val_accuracy: 0.9957\n",
      "Epoch 221/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.4098e-04 - accuracy: 0.9943 - val_loss: 3.8564e-04 - val_accuracy: 0.9974\n",
      "Epoch 222/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.3881e-04 - accuracy: 0.9967 - val_loss: 4.3338e-04 - val_accuracy: 0.9965\n",
      "Epoch 223/600\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 4.3347e-04 - accuracy: 0.9960 - val_loss: 4.2087e-04 - val_accuracy: 0.9957\n",
      "Epoch 224/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 4.2408e-04 - accuracy: 0.9953 - val_loss: 3.7493e-04 - val_accuracy: 0.9965\n",
      "Epoch 225/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.2127e-04 - accuracy: 0.9955 - val_loss: 4.2348e-04 - val_accuracy: 0.9948\n",
      "Epoch 226/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.3136e-04 - accuracy: 0.9965 - val_loss: 3.8869e-04 - val_accuracy: 0.9983\n",
      "Epoch 227/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.2726e-04 - accuracy: 0.9965 - val_loss: 3.6792e-04 - val_accuracy: 0.9965\n",
      "Epoch 228/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.2678e-04 - accuracy: 0.9948 - val_loss: 3.8434e-04 - val_accuracy: 0.9948\n",
      "Epoch 229/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 4.2656e-04 - accuracy: 0.9967 - val_loss: 3.6224e-04 - val_accuracy: 0.9957\n",
      "Epoch 230/600\n",
      "721/721 [==============================] - 10s 13ms/step - loss: 4.3707e-04 - accuracy: 0.9957 - val_loss: 3.7596e-04 - val_accuracy: 0.9965\n",
      "Epoch 231/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 4.2108e-04 - accuracy: 0.9951 - val_loss: 4.2783e-04 - val_accuracy: 0.9957\n",
      "Epoch 232/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.3945e-04 - accuracy: 0.9953 - val_loss: 5.1060e-04 - val_accuracy: 0.9983\n",
      "Epoch 233/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.2312e-04 - accuracy: 0.9964 - val_loss: 3.9681e-04 - val_accuracy: 0.9948\n",
      "Epoch 234/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.3107e-04 - accuracy: 0.9955 - val_loss: 5.3508e-04 - val_accuracy: 0.9957\n",
      "Epoch 235/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.0979e-04 - accuracy: 0.9969 - val_loss: 3.7449e-04 - val_accuracy: 0.9957\n",
      "Epoch 236/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.3601e-04 - accuracy: 0.9970 - val_loss: 4.4834e-04 - val_accuracy: 0.9939\n",
      "Epoch 237/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.2411e-04 - accuracy: 0.9953 - val_loss: 3.6463e-04 - val_accuracy: 0.9957\n",
      "Epoch 238/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.0893e-04 - accuracy: 0.9964 - val_loss: 3.8059e-04 - val_accuracy: 0.9957\n",
      "Epoch 239/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.2081e-04 - accuracy: 0.9960 - val_loss: 3.7423e-04 - val_accuracy: 0.9974\n",
      "Epoch 240/600\n",
      "721/721 [==============================] - 10s 13ms/step - loss: 4.0710e-04 - accuracy: 0.9965 - val_loss: 4.5822e-04 - val_accuracy: 0.9983\n",
      "Epoch 241/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.1225e-04 - accuracy: 0.9958 - val_loss: 3.2684e-04 - val_accuracy: 0.9957\n",
      "Epoch 242/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.0047e-04 - accuracy: 0.9964 - val_loss: 4.4605e-04 - val_accuracy: 0.9965\n",
      "Epoch 243/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.1333e-04 - accuracy: 0.9955 - val_loss: 3.7224e-04 - val_accuracy: 0.9965\n",
      "Epoch 244/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.1501e-04 - accuracy: 0.9967 - val_loss: 3.8403e-04 - val_accuracy: 0.9948\n",
      "Epoch 245/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.0422e-04 - accuracy: 0.9955 - val_loss: 3.2822e-04 - val_accuracy: 0.9983\n",
      "Epoch 246/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.0566e-04 - accuracy: 0.9964 - val_loss: 3.6951e-04 - val_accuracy: 0.9965\n",
      "Epoch 247/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.2440e-04 - accuracy: 0.9962 - val_loss: 3.2771e-04 - val_accuracy: 0.9983\n",
      "Epoch 248/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.1978e-04 - accuracy: 0.9965 - val_loss: 5.0774e-04 - val_accuracy: 0.9974\n",
      "Epoch 249/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.1689e-04 - accuracy: 0.9974 - val_loss: 3.0705e-04 - val_accuracy: 0.9974\n",
      "Epoch 250/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 4.0920e-04 - accuracy: 0.9967 - val_loss: 3.7767e-04 - val_accuracy: 0.9957\n",
      "Epoch 251/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.0607e-04 - accuracy: 0.9960 - val_loss: 4.2339e-04 - val_accuracy: 0.9974\n",
      "Epoch 252/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.1887e-04 - accuracy: 0.9964 - val_loss: 3.5446e-04 - val_accuracy: 0.9957\n",
      "Epoch 253/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.0227e-04 - accuracy: 0.9970 - val_loss: 4.7298e-04 - val_accuracy: 0.9991\n",
      "Epoch 254/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.1416e-04 - accuracy: 0.9969 - val_loss: 4.0470e-04 - val_accuracy: 0.9948\n",
      "Epoch 255/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.8456e-04 - accuracy: 0.9964 - val_loss: 3.3155e-04 - val_accuracy: 0.9948\n",
      "Epoch 256/600\n",
      "721/721 [==============================] - 10s 13ms/step - loss: 3.9567e-04 - accuracy: 0.9969 - val_loss: 3.9880e-04 - val_accuracy: 0.9957\n",
      "Epoch 257/600\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 3.9538e-04 - accuracy: 0.9979 - val_loss: 4.6140e-04 - val_accuracy: 0.9974\n",
      "Epoch 258/600\n",
      "721/721 [==============================] - 10s 13ms/step - loss: 4.1881e-04 - accuracy: 0.9969 - val_loss: 4.0452e-04 - val_accuracy: 0.9983\n",
      "Epoch 259/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.0341e-04 - accuracy: 0.9960 - val_loss: 4.3345e-04 - val_accuracy: 0.9931\n",
      "Epoch 260/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 4.0652e-04 - accuracy: 0.9957 - val_loss: 3.6808e-04 - val_accuracy: 0.9965\n",
      "Epoch 261/600\n",
      "721/721 [==============================] - 10s 13ms/step - loss: 4.0108e-04 - accuracy: 0.9962 - val_loss: 3.1895e-04 - val_accuracy: 0.9983\n",
      "Epoch 262/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.9992e-04 - accuracy: 0.9969 - val_loss: 4.0631e-04 - val_accuracy: 0.9965\n",
      "Epoch 263/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.0320e-04 - accuracy: 0.9955 - val_loss: 3.1983e-04 - val_accuracy: 0.9974\n",
      "Epoch 264/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 4.0785e-04 - accuracy: 0.9957 - val_loss: 5.9185e-04 - val_accuracy: 0.9939\n",
      "Epoch 265/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.9386e-04 - accuracy: 0.9951 - val_loss: 4.1230e-04 - val_accuracy: 0.9931\n",
      "Epoch 266/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.8710e-04 - accuracy: 0.9962 - val_loss: 4.1590e-04 - val_accuracy: 0.9931\n",
      "Epoch 267/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.9464e-04 - accuracy: 0.9967 - val_loss: 4.6494e-04 - val_accuracy: 0.9983\n",
      "Epoch 268/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "721/721 [==============================] - 9s 13ms/step - loss: 3.8756e-04 - accuracy: 0.9958 - val_loss: 3.8555e-04 - val_accuracy: 0.9983\n",
      "Epoch 269/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.9617e-04 - accuracy: 0.9958 - val_loss: 3.5900e-04 - val_accuracy: 0.9957\n",
      "Epoch 270/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.9981e-04 - accuracy: 0.9951 - val_loss: 3.8510e-04 - val_accuracy: 0.9965\n",
      "Epoch 271/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.7818e-04 - accuracy: 0.9967 - val_loss: 3.4793e-04 - val_accuracy: 0.9983\n",
      "Epoch 272/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.8851e-04 - accuracy: 0.9969 - val_loss: 3.5742e-04 - val_accuracy: 0.9913\n",
      "Epoch 273/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.9432e-04 - accuracy: 0.9960 - val_loss: 4.2198e-04 - val_accuracy: 0.9939\n",
      "Epoch 274/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.8020e-04 - accuracy: 0.9967 - val_loss: 3.6763e-04 - val_accuracy: 0.9983\n",
      "Epoch 275/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.9322e-04 - accuracy: 0.9960 - val_loss: 4.4953e-04 - val_accuracy: 0.9957\n",
      "Epoch 276/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.9116e-04 - accuracy: 0.9953 - val_loss: 3.7331e-04 - val_accuracy: 0.9983\n",
      "Epoch 277/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.9260e-04 - accuracy: 0.9976 - val_loss: 4.5990e-04 - val_accuracy: 0.9991\n",
      "Epoch 278/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.8119e-04 - accuracy: 0.9967 - val_loss: 4.7716e-04 - val_accuracy: 0.9965\n",
      "Epoch 279/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 4.1343e-04 - accuracy: 0.9960 - val_loss: 3.4099e-04 - val_accuracy: 0.9965\n",
      "Epoch 280/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.8846e-04 - accuracy: 0.9970 - val_loss: 4.1194e-04 - val_accuracy: 0.9957\n",
      "Epoch 281/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.8791e-04 - accuracy: 0.9960 - val_loss: 3.0678e-04 - val_accuracy: 0.9957\n",
      "Epoch 282/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.8579e-04 - accuracy: 0.9977 - val_loss: 3.6732e-04 - val_accuracy: 0.9939\n",
      "Epoch 283/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.9744e-04 - accuracy: 0.9960 - val_loss: 3.5565e-04 - val_accuracy: 0.9991\n",
      "Epoch 284/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.9331e-04 - accuracy: 0.9969 - val_loss: 3.0717e-04 - val_accuracy: 0.9974\n",
      "Epoch 285/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.9246e-04 - accuracy: 0.9964 - val_loss: 4.0452e-04 - val_accuracy: 0.9939\n",
      "Epoch 286/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.7441e-04 - accuracy: 0.9976 - val_loss: 3.5003e-04 - val_accuracy: 0.9983\n",
      "Epoch 287/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.8305e-04 - accuracy: 0.9976 - val_loss: 3.8648e-04 - val_accuracy: 0.9939\n",
      "Epoch 288/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.8570e-04 - accuracy: 0.9960 - val_loss: 3.6004e-04 - val_accuracy: 0.9957\n",
      "Epoch 289/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.9350e-04 - accuracy: 0.9957 - val_loss: 3.3886e-04 - val_accuracy: 0.9983\n",
      "Epoch 290/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.8696e-04 - accuracy: 0.9972 - val_loss: 5.0769e-04 - val_accuracy: 0.9974\n",
      "Epoch 291/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.7653e-04 - accuracy: 0.9974 - val_loss: 3.8402e-04 - val_accuracy: 0.9965\n",
      "Epoch 292/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.7212e-04 - accuracy: 0.9976 - val_loss: 4.3257e-04 - val_accuracy: 0.9922\n",
      "Epoch 293/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.8990e-04 - accuracy: 0.9964 - val_loss: 3.3732e-04 - val_accuracy: 0.9931\n",
      "Epoch 294/600\n",
      "721/721 [==============================] - 10s 13ms/step - loss: 3.8138e-04 - accuracy: 0.9955 - val_loss: 4.1565e-04 - val_accuracy: 0.9948\n",
      "Epoch 295/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.7235e-04 - accuracy: 0.9967 - val_loss: 3.2715e-04 - val_accuracy: 0.9965\n",
      "Epoch 296/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.6643e-04 - accuracy: 0.9957 - val_loss: 3.8340e-04 - val_accuracy: 0.9939\n",
      "Epoch 297/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.9194e-04 - accuracy: 0.9960 - val_loss: 3.7150e-04 - val_accuracy: 0.9974\n",
      "Epoch 298/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.7326e-04 - accuracy: 0.9960 - val_loss: 3.1569e-04 - val_accuracy: 0.9974\n",
      "Epoch 299/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.6993e-04 - accuracy: 0.9969 - val_loss: 3.0906e-04 - val_accuracy: 0.9965\n",
      "Epoch 300/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.8381e-04 - accuracy: 0.9964 - val_loss: 3.7209e-04 - val_accuracy: 0.9983\n",
      "Epoch 301/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.8589e-04 - accuracy: 0.9979 - val_loss: 3.5919e-04 - val_accuracy: 0.9974\n",
      "Epoch 302/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.5668e-04 - accuracy: 0.9962 - val_loss: 3.7678e-04 - val_accuracy: 0.9983\n",
      "Epoch 303/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.7041e-04 - accuracy: 0.9979 - val_loss: 4.0440e-04 - val_accuracy: 0.9922\n",
      "Epoch 304/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.7053e-04 - accuracy: 0.9957 - val_loss: 3.9222e-04 - val_accuracy: 0.9974\n",
      "Epoch 305/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.7892e-04 - accuracy: 0.9970 - val_loss: 3.3305e-04 - val_accuracy: 0.9965\n",
      "Epoch 306/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.5381e-04 - accuracy: 0.9967 - val_loss: 3.8975e-04 - val_accuracy: 0.9957\n",
      "Epoch 307/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.5958e-04 - accuracy: 0.9965 - val_loss: 3.0045e-04 - val_accuracy: 0.9974\n",
      "Epoch 308/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.7198e-04 - accuracy: 0.9981 - val_loss: 2.8211e-04 - val_accuracy: 0.9939\n",
      "Epoch 309/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.5628e-04 - accuracy: 0.9972 - val_loss: 4.3572e-04 - val_accuracy: 0.9983\n",
      "Epoch 310/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.6256e-04 - accuracy: 0.9969 - val_loss: 3.3489e-04 - val_accuracy: 0.9983\n",
      "Epoch 311/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.6499e-04 - accuracy: 0.9970 - val_loss: 3.2374e-04 - val_accuracy: 0.9974\n",
      "Epoch 312/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.8682e-04 - accuracy: 0.9953 - val_loss: 4.2524e-04 - val_accuracy: 0.9965\n",
      "Epoch 313/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.6028e-04 - accuracy: 0.9967 - val_loss: 3.0031e-04 - val_accuracy: 0.9974\n",
      "Epoch 314/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.5290e-04 - accuracy: 0.9972 - val_loss: 3.0271e-04 - val_accuracy: 0.9974\n",
      "Epoch 315/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.7509e-04 - accuracy: 0.9969 - val_loss: 4.5647e-04 - val_accuracy: 0.9948\n",
      "Epoch 316/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.6362e-04 - accuracy: 0.9957 - val_loss: 3.5118e-04 - val_accuracy: 1.0000\n",
      "Epoch 317/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.7956e-04 - accuracy: 0.9983 - val_loss: 3.5517e-04 - val_accuracy: 0.9974\n",
      "Epoch 318/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.6665e-04 - accuracy: 0.9976 - val_loss: 3.4989e-04 - val_accuracy: 0.9939\n",
      "Epoch 319/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.6516e-04 - accuracy: 0.9960 - val_loss: 3.3159e-04 - val_accuracy: 0.9965\n",
      "Epoch 320/600\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 3.6608e-04 - accuracy: 0.9958 - val_loss: 3.4875e-04 - val_accuracy: 0.9948\n",
      "Epoch 321/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.5782e-04 - accuracy: 0.9969 - val_loss: 3.4687e-04 - val_accuracy: 0.9974\n",
      "Epoch 322/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.6038e-04 - accuracy: 0.9977 - val_loss: 3.4564e-04 - val_accuracy: 0.9957\n",
      "Epoch 323/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.7373e-04 - accuracy: 0.9969 - val_loss: 5.7558e-04 - val_accuracy: 0.9974\n",
      "Epoch 324/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.7069e-04 - accuracy: 0.9969 - val_loss: 3.7659e-04 - val_accuracy: 0.9983\n",
      "Epoch 325/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.5911e-04 - accuracy: 0.9967 - val_loss: 3.4035e-04 - val_accuracy: 0.9957\n",
      "Epoch 326/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.6976e-04 - accuracy: 0.9964 - val_loss: 5.0687e-04 - val_accuracy: 0.9931\n",
      "Epoch 327/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.5256e-04 - accuracy: 0.9967 - val_loss: 3.0660e-04 - val_accuracy: 0.9965\n",
      "Epoch 328/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.5354e-04 - accuracy: 0.9977 - val_loss: 3.4321e-04 - val_accuracy: 0.9974\n",
      "Epoch 329/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.4819e-04 - accuracy: 0.9967 - val_loss: 4.0570e-04 - val_accuracy: 0.9948\n",
      "Epoch 330/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.5575e-04 - accuracy: 0.9958 - val_loss: 4.6406e-04 - val_accuracy: 0.9974\n",
      "Epoch 331/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.5989e-04 - accuracy: 0.9972 - val_loss: 2.9694e-04 - val_accuracy: 0.9983\n",
      "Epoch 332/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.6212e-04 - accuracy: 0.9967 - val_loss: 3.2072e-04 - val_accuracy: 0.9974\n",
      "Epoch 333/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.5563e-04 - accuracy: 0.9979 - val_loss: 2.9517e-04 - val_accuracy: 0.9983\n",
      "Epoch 334/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.5264e-04 - accuracy: 0.9962 - val_loss: 3.4452e-04 - val_accuracy: 0.9965\n",
      "Epoch 335/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.4554e-04 - accuracy: 0.9962 - val_loss: 3.4597e-04 - val_accuracy: 0.9974\n",
      "Epoch 336/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.6073e-04 - accuracy: 0.9972 - val_loss: 3.4295e-04 - val_accuracy: 0.9965\n",
      "Epoch 337/600\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 3.6850e-04 - accuracy: 0.9970 - val_loss: 4.9456e-04 - val_accuracy: 0.9939\n",
      "Epoch 338/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.4589e-04 - accuracy: 0.9967 - val_loss: 3.4747e-04 - val_accuracy: 0.9948\n",
      "Epoch 339/600\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 3.4771e-04 - accuracy: 0.9965 - val_loss: 3.5318e-04 - val_accuracy: 0.9957\n",
      "Epoch 340/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.7086e-04 - accuracy: 0.9960 - val_loss: 3.3126e-04 - val_accuracy: 1.0000\n",
      "Epoch 341/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.5372e-04 - accuracy: 0.9955 - val_loss: 2.9000e-04 - val_accuracy: 0.9974\n",
      "Epoch 342/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.4886e-04 - accuracy: 0.9967 - val_loss: 4.0097e-04 - val_accuracy: 0.9965\n",
      "Epoch 343/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.6427e-04 - accuracy: 0.9970 - val_loss: 3.1413e-04 - val_accuracy: 0.9965\n",
      "Epoch 344/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.7133e-04 - accuracy: 0.9957 - val_loss: 3.3191e-04 - val_accuracy: 0.9957\n",
      "Epoch 345/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.5929e-04 - accuracy: 0.9988 - val_loss: 3.3378e-04 - val_accuracy: 0.9983\n",
      "Epoch 346/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.6286e-04 - accuracy: 0.9977 - val_loss: 3.7105e-04 - val_accuracy: 0.9983\n",
      "Epoch 347/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.4976e-04 - accuracy: 0.9965 - val_loss: 3.4094e-04 - val_accuracy: 0.9974\n",
      "Epoch 348/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.6298e-04 - accuracy: 0.9970 - val_loss: 3.3697e-04 - val_accuracy: 0.9965\n",
      "Epoch 349/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.5595e-04 - accuracy: 0.9974 - val_loss: 5.3357e-04 - val_accuracy: 0.9939\n",
      "Epoch 350/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.6361e-04 - accuracy: 0.9972 - val_loss: 3.4964e-04 - val_accuracy: 0.9983\n",
      "Epoch 351/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.4480e-04 - accuracy: 0.9977 - val_loss: 2.9042e-04 - val_accuracy: 0.9974\n",
      "Epoch 352/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.3788e-04 - accuracy: 0.9962 - val_loss: 3.3646e-04 - val_accuracy: 0.9948\n",
      "Epoch 353/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.4877e-04 - accuracy: 0.9977 - val_loss: 3.2413e-04 - val_accuracy: 0.9983\n",
      "Epoch 354/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.4514e-04 - accuracy: 0.9972 - val_loss: 3.5182e-04 - val_accuracy: 0.9931\n",
      "Epoch 355/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.4707e-04 - accuracy: 0.9969 - val_loss: 2.7812e-04 - val_accuracy: 0.9957\n",
      "Epoch 356/600\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 3.5371e-04 - accuracy: 0.9960 - val_loss: 3.7359e-04 - val_accuracy: 0.9974\n",
      "Epoch 357/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.4583e-04 - accuracy: 0.9965 - val_loss: 3.7229e-04 - val_accuracy: 0.9939\n",
      "Epoch 358/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.4623e-04 - accuracy: 0.9974 - val_loss: 3.3745e-04 - val_accuracy: 0.9965\n",
      "Epoch 359/600\n",
      "721/721 [==============================] - 10s 13ms/step - loss: 3.4388e-04 - accuracy: 0.9969 - val_loss: 3.1328e-04 - val_accuracy: 0.9974\n",
      "Epoch 360/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.3313e-04 - accuracy: 0.9967 - val_loss: 3.2545e-04 - val_accuracy: 0.9983\n",
      "Epoch 361/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.5285e-04 - accuracy: 0.9972 - val_loss: 3.2382e-04 - val_accuracy: 0.9974\n",
      "Epoch 362/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.4449e-04 - accuracy: 0.9972 - val_loss: 3.2740e-04 - val_accuracy: 0.9983\n",
      "Epoch 363/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.5408e-04 - accuracy: 0.9960 - val_loss: 3.6051e-04 - val_accuracy: 0.9974\n",
      "Epoch 364/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.3625e-04 - accuracy: 0.9970 - val_loss: 3.6862e-04 - val_accuracy: 0.9983\n",
      "Epoch 365/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.4843e-04 - accuracy: 0.9981 - val_loss: 3.4240e-04 - val_accuracy: 0.9974\n",
      "Epoch 366/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.3994e-04 - accuracy: 0.9970 - val_loss: 3.1957e-04 - val_accuracy: 0.9939\n",
      "Epoch 367/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.3127e-04 - accuracy: 0.9962 - val_loss: 3.1796e-04 - val_accuracy: 0.9965\n",
      "Epoch 368/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.5647e-04 - accuracy: 0.9965 - val_loss: 3.2484e-04 - val_accuracy: 0.9965\n",
      "Epoch 369/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.4033e-04 - accuracy: 0.9969 - val_loss: 3.3014e-04 - val_accuracy: 0.9939\n",
      "Epoch 370/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.2874e-04 - accuracy: 0.9960 - val_loss: 2.8465e-04 - val_accuracy: 0.9974\n",
      "Epoch 371/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.3700e-04 - accuracy: 0.9984 - val_loss: 2.8025e-04 - val_accuracy: 0.9983\n",
      "Epoch 372/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.2689e-04 - accuracy: 0.9974 - val_loss: 3.1959e-04 - val_accuracy: 0.9948\n",
      "Epoch 373/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.4296e-04 - accuracy: 0.9962 - val_loss: 3.3196e-04 - val_accuracy: 0.9948\n",
      "Epoch 374/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "721/721 [==============================] - 9s 12ms/step - loss: 3.3457e-04 - accuracy: 0.9979 - val_loss: 2.8276e-04 - val_accuracy: 0.9974\n",
      "Epoch 375/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.4083e-04 - accuracy: 0.9967 - val_loss: 2.8647e-04 - val_accuracy: 0.9965\n",
      "Epoch 376/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.2627e-04 - accuracy: 0.9967 - val_loss: 3.3350e-04 - val_accuracy: 0.9965\n",
      "Epoch 377/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.2368e-04 - accuracy: 0.9972 - val_loss: 3.7978e-04 - val_accuracy: 0.9957\n",
      "Epoch 378/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.3972e-04 - accuracy: 0.9969 - val_loss: 3.4141e-04 - val_accuracy: 0.9991\n",
      "Epoch 379/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.4301e-04 - accuracy: 0.9958 - val_loss: 3.0835e-04 - val_accuracy: 0.9983\n",
      "Epoch 380/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.3459e-04 - accuracy: 0.9967 - val_loss: 3.2001e-04 - val_accuracy: 0.9965\n",
      "Epoch 381/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.4355e-04 - accuracy: 0.9972 - val_loss: 4.0830e-04 - val_accuracy: 0.9948\n",
      "Epoch 382/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.3957e-04 - accuracy: 0.9967 - val_loss: 3.1226e-04 - val_accuracy: 0.9974\n",
      "Epoch 383/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.5028e-04 - accuracy: 0.9969 - val_loss: 3.2417e-04 - val_accuracy: 0.9974\n",
      "Epoch 384/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.3872e-04 - accuracy: 0.9969 - val_loss: 3.1318e-04 - val_accuracy: 0.9983\n",
      "Epoch 385/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.3490e-04 - accuracy: 0.9979 - val_loss: 2.7987e-04 - val_accuracy: 0.9983\n",
      "Epoch 386/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.3789e-04 - accuracy: 0.9960 - val_loss: 3.7616e-04 - val_accuracy: 0.9939\n",
      "Epoch 387/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.3004e-04 - accuracy: 0.9974 - val_loss: 2.9622e-04 - val_accuracy: 1.0000\n",
      "Epoch 388/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.2142e-04 - accuracy: 0.9977 - val_loss: 3.3542e-04 - val_accuracy: 0.9965\n",
      "Epoch 389/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.3023e-04 - accuracy: 0.9964 - val_loss: 2.5968e-04 - val_accuracy: 1.0000\n",
      "Epoch 390/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.4010e-04 - accuracy: 0.9974 - val_loss: 3.6326e-04 - val_accuracy: 0.9948\n",
      "Epoch 391/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.3724e-04 - accuracy: 0.9964 - val_loss: 2.9177e-04 - val_accuracy: 0.9983\n",
      "Epoch 392/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.2947e-04 - accuracy: 0.9964 - val_loss: 3.1556e-04 - val_accuracy: 0.9957\n",
      "Epoch 393/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.1907e-04 - accuracy: 0.9969 - val_loss: 3.6975e-04 - val_accuracy: 0.9939\n",
      "Epoch 394/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.2723e-04 - accuracy: 0.9969 - val_loss: 3.8209e-04 - val_accuracy: 0.9974\n",
      "Epoch 395/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.2967e-04 - accuracy: 0.9960 - val_loss: 4.4100e-04 - val_accuracy: 0.9948\n",
      "Epoch 396/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.2507e-04 - accuracy: 0.9967 - val_loss: 2.9373e-04 - val_accuracy: 0.9957\n",
      "Epoch 397/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.3865e-04 - accuracy: 0.9970 - val_loss: 2.6676e-04 - val_accuracy: 0.9974\n",
      "Epoch 398/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.3531e-04 - accuracy: 0.9969 - val_loss: 2.8356e-04 - val_accuracy: 0.9922\n",
      "Epoch 399/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.3374e-04 - accuracy: 0.9970 - val_loss: 3.2616e-04 - val_accuracy: 0.9983\n",
      "Epoch 400/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.3402e-04 - accuracy: 0.9967 - val_loss: 3.2279e-04 - val_accuracy: 0.9983\n",
      "Epoch 401/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.2911e-04 - accuracy: 0.9964 - val_loss: 3.5525e-04 - val_accuracy: 0.9922\n",
      "Epoch 402/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.3209e-04 - accuracy: 0.9964 - val_loss: 3.1283e-04 - val_accuracy: 0.9965\n",
      "Epoch 403/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.3404e-04 - accuracy: 0.9967 - val_loss: 3.3206e-04 - val_accuracy: 0.9965\n",
      "Epoch 404/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.2209e-04 - accuracy: 0.9979 - val_loss: 2.9812e-04 - val_accuracy: 0.9991\n",
      "Epoch 405/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.3201e-04 - accuracy: 0.9979 - val_loss: 3.6024e-04 - val_accuracy: 0.9974\n",
      "Epoch 406/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.4710e-04 - accuracy: 0.9969 - val_loss: 3.1333e-04 - val_accuracy: 0.9957\n",
      "Epoch 407/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.2879e-04 - accuracy: 0.9979 - val_loss: 2.6187e-04 - val_accuracy: 0.9974\n",
      "Epoch 408/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.3498e-04 - accuracy: 0.9976 - val_loss: 2.6391e-04 - val_accuracy: 1.0000\n",
      "Epoch 409/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.1002e-04 - accuracy: 0.9977 - val_loss: 3.3632e-04 - val_accuracy: 0.9957\n",
      "Epoch 410/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.2964e-04 - accuracy: 0.9969 - val_loss: 2.7553e-04 - val_accuracy: 0.9974\n",
      "Epoch 411/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.3253e-04 - accuracy: 0.9974 - val_loss: 2.9823e-04 - val_accuracy: 0.9991\n",
      "Epoch 412/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.2480e-04 - accuracy: 0.9974 - val_loss: 3.4330e-04 - val_accuracy: 0.9957\n",
      "Epoch 413/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.3751e-04 - accuracy: 0.9969 - val_loss: 4.2370e-04 - val_accuracy: 0.9948\n",
      "Epoch 414/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.2195e-04 - accuracy: 0.9977 - val_loss: 3.4100e-04 - val_accuracy: 0.9965\n",
      "Epoch 415/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.1713e-04 - accuracy: 0.9977 - val_loss: 3.1637e-04 - val_accuracy: 0.9974\n",
      "Epoch 416/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.3071e-04 - accuracy: 0.9969 - val_loss: 3.3207e-04 - val_accuracy: 0.9939\n",
      "Epoch 417/600\n",
      "721/721 [==============================] - 10s 13ms/step - loss: 3.2528e-04 - accuracy: 0.9967 - val_loss: 4.1090e-04 - val_accuracy: 0.9974\n",
      "Epoch 418/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.2521e-04 - accuracy: 0.9969 - val_loss: 3.5252e-04 - val_accuracy: 0.9965\n",
      "Epoch 419/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.2325e-04 - accuracy: 0.9970 - val_loss: 3.2972e-04 - val_accuracy: 0.9991\n",
      "Epoch 420/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.2555e-04 - accuracy: 0.9979 - val_loss: 4.1631e-04 - val_accuracy: 0.9957\n",
      "Epoch 421/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.3803e-04 - accuracy: 0.9970 - val_loss: 3.0679e-04 - val_accuracy: 0.9965\n",
      "Epoch 422/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.2813e-04 - accuracy: 0.9967 - val_loss: 2.9997e-04 - val_accuracy: 0.9965\n",
      "Epoch 423/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.2550e-04 - accuracy: 0.9970 - val_loss: 3.7879e-04 - val_accuracy: 0.9948\n",
      "Epoch 424/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.3123e-04 - accuracy: 0.9957 - val_loss: 3.0372e-04 - val_accuracy: 0.9983\n",
      "Epoch 425/600\n",
      "721/721 [==============================] - 10s 13ms/step - loss: 3.2225e-04 - accuracy: 0.9970 - val_loss: 2.9804e-04 - val_accuracy: 0.9991\n",
      "Epoch 426/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.2054e-04 - accuracy: 0.9974 - val_loss: 3.0168e-04 - val_accuracy: 0.9983\n",
      "Epoch 427/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0530e-04 - accuracy: 0.9969 - val_loss: 2.6515e-04 - val_accuracy: 0.9983\n",
      "Epoch 428/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.2771e-04 - accuracy: 0.9972 - val_loss: 2.7787e-04 - val_accuracy: 0.9991\n",
      "Epoch 429/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.2363e-04 - accuracy: 0.9970 - val_loss: 3.8973e-04 - val_accuracy: 0.9974\n",
      "Epoch 430/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.1854e-04 - accuracy: 0.9979 - val_loss: 3.3283e-04 - val_accuracy: 0.9957\n",
      "Epoch 431/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.2704e-04 - accuracy: 0.9979 - val_loss: 2.8177e-04 - val_accuracy: 0.9931\n",
      "Epoch 432/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.2536e-04 - accuracy: 0.9967 - val_loss: 3.2311e-04 - val_accuracy: 0.9974\n",
      "Epoch 433/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.2608e-04 - accuracy: 0.9969 - val_loss: 2.6108e-04 - val_accuracy: 0.9974\n",
      "Epoch 434/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.3147e-04 - accuracy: 0.9976 - val_loss: 2.5709e-04 - val_accuracy: 0.9965\n",
      "Epoch 435/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.2505e-04 - accuracy: 0.9962 - val_loss: 3.1385e-04 - val_accuracy: 0.9939\n",
      "Epoch 436/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.1791e-04 - accuracy: 0.9977 - val_loss: 2.8852e-04 - val_accuracy: 0.9957\n",
      "Epoch 437/600\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 3.3788e-04 - accuracy: 0.9967 - val_loss: 3.0806e-04 - val_accuracy: 0.9983\n",
      "Epoch 438/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.1788e-04 - accuracy: 0.9979 - val_loss: 2.7594e-04 - val_accuracy: 0.9965\n",
      "Epoch 439/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.1779e-04 - accuracy: 0.9970 - val_loss: 3.0915e-04 - val_accuracy: 0.9974\n",
      "Epoch 440/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.2549e-04 - accuracy: 0.9970 - val_loss: 3.4328e-04 - val_accuracy: 0.9974\n",
      "Epoch 441/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.1536e-04 - accuracy: 0.9976 - val_loss: 2.8893e-04 - val_accuracy: 0.9957\n",
      "Epoch 442/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.2127e-04 - accuracy: 0.9976 - val_loss: 2.5221e-04 - val_accuracy: 0.9991\n",
      "Epoch 443/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9469e-04 - accuracy: 0.9967 - val_loss: 3.4108e-04 - val_accuracy: 0.9974\n",
      "Epoch 444/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.2603e-04 - accuracy: 0.9981 - val_loss: 2.7645e-04 - val_accuracy: 0.9939\n",
      "Epoch 445/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.3291e-04 - accuracy: 0.9962 - val_loss: 3.1504e-04 - val_accuracy: 0.9974\n",
      "Epoch 446/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0635e-04 - accuracy: 0.9983 - val_loss: 3.6024e-04 - val_accuracy: 0.9983\n",
      "Epoch 447/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.1987e-04 - accuracy: 0.9984 - val_loss: 2.9871e-04 - val_accuracy: 0.9965\n",
      "Epoch 448/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0789e-04 - accuracy: 0.9977 - val_loss: 2.5857e-04 - val_accuracy: 0.9974\n",
      "Epoch 449/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.1875e-04 - accuracy: 0.9974 - val_loss: 3.0350e-04 - val_accuracy: 0.9965\n",
      "Epoch 450/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0601e-04 - accuracy: 0.9970 - val_loss: 3.2170e-04 - val_accuracy: 0.9974\n",
      "Epoch 451/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.1999e-04 - accuracy: 0.9965 - val_loss: 3.1210e-04 - val_accuracy: 0.9974\n",
      "Epoch 452/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.1453e-04 - accuracy: 0.9972 - val_loss: 2.9813e-04 - val_accuracy: 0.9983\n",
      "Epoch 453/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0330e-04 - accuracy: 0.9976 - val_loss: 3.1579e-04 - val_accuracy: 0.9974\n",
      "Epoch 454/600\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 3.2311e-04 - accuracy: 0.9977 - val_loss: 2.5941e-04 - val_accuracy: 0.9991\n",
      "Epoch 455/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0744e-04 - accuracy: 0.9972 - val_loss: 4.2367e-04 - val_accuracy: 0.9965\n",
      "Epoch 456/600\n",
      "721/721 [==============================] - 10s 13ms/step - loss: 3.1428e-04 - accuracy: 0.9964 - val_loss: 2.8926e-04 - val_accuracy: 0.9974\n",
      "Epoch 457/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.1754e-04 - accuracy: 0.9970 - val_loss: 2.9293e-04 - val_accuracy: 0.9983\n",
      "Epoch 458/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.1445e-04 - accuracy: 0.9981 - val_loss: 3.8812e-04 - val_accuracy: 1.0000\n",
      "Epoch 459/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0757e-04 - accuracy: 0.9977 - val_loss: 3.0676e-04 - val_accuracy: 1.0000\n",
      "Epoch 460/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.2096e-04 - accuracy: 0.9970 - val_loss: 2.6931e-04 - val_accuracy: 0.9974\n",
      "Epoch 461/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.1908e-04 - accuracy: 0.9983 - val_loss: 2.6498e-04 - val_accuracy: 0.9974\n",
      "Epoch 462/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0918e-04 - accuracy: 0.9977 - val_loss: 2.6906e-04 - val_accuracy: 0.9974\n",
      "Epoch 463/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.1421e-04 - accuracy: 0.9972 - val_loss: 2.5374e-04 - val_accuracy: 0.9991\n",
      "Epoch 464/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.0316e-04 - accuracy: 0.9979 - val_loss: 2.7148e-04 - val_accuracy: 0.9965\n",
      "Epoch 465/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0029e-04 - accuracy: 0.9977 - val_loss: 2.9916e-04 - val_accuracy: 0.9983\n",
      "Epoch 466/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.1636e-04 - accuracy: 0.9976 - val_loss: 2.6677e-04 - val_accuracy: 0.9991\n",
      "Epoch 467/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.1782e-04 - accuracy: 0.9972 - val_loss: 3.2341e-04 - val_accuracy: 0.9965\n",
      "Epoch 468/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.1068e-04 - accuracy: 0.9967 - val_loss: 3.7168e-04 - val_accuracy: 0.9965\n",
      "Epoch 469/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.1000e-04 - accuracy: 0.9970 - val_loss: 4.0200e-04 - val_accuracy: 0.9870\n",
      "Epoch 470/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.1730e-04 - accuracy: 0.9972 - val_loss: 3.1020e-04 - val_accuracy: 0.9974\n",
      "Epoch 471/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.1750e-04 - accuracy: 0.9974 - val_loss: 3.1232e-04 - val_accuracy: 0.9974\n",
      "Epoch 472/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.1154e-04 - accuracy: 0.9977 - val_loss: 2.9607e-04 - val_accuracy: 0.9948\n",
      "Epoch 473/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.1701e-04 - accuracy: 0.9981 - val_loss: 2.9439e-04 - val_accuracy: 0.9957\n",
      "Epoch 474/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0266e-04 - accuracy: 0.9976 - val_loss: 2.5236e-04 - val_accuracy: 0.9983\n",
      "Epoch 475/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.1755e-04 - accuracy: 0.9967 - val_loss: 2.5332e-04 - val_accuracy: 1.0000\n",
      "Epoch 476/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.1263e-04 - accuracy: 0.9974 - val_loss: 2.8318e-04 - val_accuracy: 0.9991\n",
      "Epoch 477/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0601e-04 - accuracy: 0.9977 - val_loss: 2.9795e-04 - val_accuracy: 0.9991\n",
      "Epoch 478/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0960e-04 - accuracy: 0.9977 - val_loss: 3.1782e-04 - val_accuracy: 0.9965\n",
      "Epoch 479/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.1082e-04 - accuracy: 0.9970 - val_loss: 2.8249e-04 - val_accuracy: 0.9991\n",
      "Epoch 480/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0061e-04 - accuracy: 0.9981 - val_loss: 2.9628e-04 - val_accuracy: 0.9983\n",
      "Epoch 481/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 2.9393e-04 - accuracy: 0.9979 - val_loss: 3.5981e-04 - val_accuracy: 0.9957\n",
      "Epoch 482/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9896e-04 - accuracy: 0.9967 - val_loss: 2.6984e-04 - val_accuracy: 0.9974\n",
      "Epoch 483/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.0930e-04 - accuracy: 0.9962 - val_loss: 3.3248e-04 - val_accuracy: 0.9983\n",
      "Epoch 484/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.1728e-04 - accuracy: 0.9972 - val_loss: 2.5831e-04 - val_accuracy: 0.9974\n",
      "Epoch 485/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.2625e-04 - accuracy: 0.9988 - val_loss: 3.4847e-04 - val_accuracy: 0.9974\n",
      "Epoch 486/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0585e-04 - accuracy: 0.9977 - val_loss: 3.9607e-04 - val_accuracy: 0.9948\n",
      "Epoch 487/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0053e-04 - accuracy: 0.9970 - val_loss: 2.9187e-04 - val_accuracy: 0.9974\n",
      "Epoch 488/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9984e-04 - accuracy: 0.9976 - val_loss: 2.2807e-04 - val_accuracy: 1.0000\n",
      "Epoch 489/600\n",
      "721/721 [==============================] - 10s 13ms/step - loss: 3.1236e-04 - accuracy: 0.9976 - val_loss: 2.4922e-04 - val_accuracy: 0.9957\n",
      "Epoch 490/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.0701e-04 - accuracy: 0.9972 - val_loss: 2.4166e-04 - val_accuracy: 0.9983\n",
      "Epoch 491/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0203e-04 - accuracy: 0.9983 - val_loss: 2.7515e-04 - val_accuracy: 0.9965\n",
      "Epoch 492/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.1131e-04 - accuracy: 0.9964 - val_loss: 2.7715e-04 - val_accuracy: 0.9983\n",
      "Epoch 493/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0323e-04 - accuracy: 0.9969 - val_loss: 2.5497e-04 - val_accuracy: 0.9991\n",
      "Epoch 494/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0586e-04 - accuracy: 0.9972 - val_loss: 2.7510e-04 - val_accuracy: 0.9974\n",
      "Epoch 495/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0596e-04 - accuracy: 0.9979 - val_loss: 2.6195e-04 - val_accuracy: 1.0000\n",
      "Epoch 496/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.8819e-04 - accuracy: 0.9979 - val_loss: 3.0763e-04 - val_accuracy: 0.9965\n",
      "Epoch 497/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 2.9711e-04 - accuracy: 0.9967 - val_loss: 3.3606e-04 - val_accuracy: 0.9965\n",
      "Epoch 498/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0636e-04 - accuracy: 0.9967 - val_loss: 2.9423e-04 - val_accuracy: 0.9991\n",
      "Epoch 499/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9425e-04 - accuracy: 0.9984 - val_loss: 3.1196e-04 - val_accuracy: 0.9991\n",
      "Epoch 500/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9489e-04 - accuracy: 0.9976 - val_loss: 3.7041e-04 - val_accuracy: 0.9983\n",
      "Epoch 501/600\n",
      "721/721 [==============================] - 10s 13ms/step - loss: 3.1557e-04 - accuracy: 0.9977 - val_loss: 2.6790e-04 - val_accuracy: 0.9957\n",
      "Epoch 502/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9942e-04 - accuracy: 0.9974 - val_loss: 2.9969e-04 - val_accuracy: 0.9974\n",
      "Epoch 503/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9327e-04 - accuracy: 0.9970 - val_loss: 2.7872e-04 - val_accuracy: 0.9948\n",
      "Epoch 504/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0707e-04 - accuracy: 0.9977 - val_loss: 3.0110e-04 - val_accuracy: 0.9931\n",
      "Epoch 505/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0518e-04 - accuracy: 0.9976 - val_loss: 2.9667e-04 - val_accuracy: 0.9974\n",
      "Epoch 506/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0390e-04 - accuracy: 0.9979 - val_loss: 2.6667e-04 - val_accuracy: 0.9974\n",
      "Epoch 507/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.1161e-04 - accuracy: 0.9981 - val_loss: 2.5742e-04 - val_accuracy: 0.9983\n",
      "Epoch 508/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9360e-04 - accuracy: 0.9972 - val_loss: 2.6390e-04 - val_accuracy: 0.9974\n",
      "Epoch 509/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 2.9753e-04 - accuracy: 0.9981 - val_loss: 2.7719e-04 - val_accuracy: 0.9957\n",
      "Epoch 510/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0300e-04 - accuracy: 0.9967 - val_loss: 2.8697e-04 - val_accuracy: 0.9974\n",
      "Epoch 511/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.0830e-04 - accuracy: 0.9977 - val_loss: 2.5937e-04 - val_accuracy: 0.9983\n",
      "Epoch 512/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9889e-04 - accuracy: 0.9965 - val_loss: 5.4267e-04 - val_accuracy: 0.9948\n",
      "Epoch 513/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0359e-04 - accuracy: 0.9972 - val_loss: 3.5292e-04 - val_accuracy: 0.9948\n",
      "Epoch 514/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0514e-04 - accuracy: 0.9970 - val_loss: 2.4290e-04 - val_accuracy: 0.9991\n",
      "Epoch 515/600\n",
      "721/721 [==============================] - 10s 13ms/step - loss: 3.1012e-04 - accuracy: 0.9972 - val_loss: 2.5899e-04 - val_accuracy: 0.9983\n",
      "Epoch 516/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9809e-04 - accuracy: 0.9977 - val_loss: 2.5402e-04 - val_accuracy: 0.9991\n",
      "Epoch 517/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0541e-04 - accuracy: 0.9976 - val_loss: 2.4299e-04 - val_accuracy: 0.9965\n",
      "Epoch 518/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9313e-04 - accuracy: 0.9969 - val_loss: 3.0422e-04 - val_accuracy: 0.9965\n",
      "Epoch 519/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9690e-04 - accuracy: 0.9970 - val_loss: 2.9869e-04 - val_accuracy: 0.9991\n",
      "Epoch 520/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0629e-04 - accuracy: 0.9983 - val_loss: 5.4812e-04 - val_accuracy: 0.9939\n",
      "Epoch 521/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9622e-04 - accuracy: 0.9969 - val_loss: 3.3562e-04 - val_accuracy: 0.9974\n",
      "Epoch 522/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0200e-04 - accuracy: 0.9970 - val_loss: 2.9346e-04 - val_accuracy: 0.9991\n",
      "Epoch 523/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0225e-04 - accuracy: 0.9976 - val_loss: 2.3573e-04 - val_accuracy: 0.9983\n",
      "Epoch 524/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9271e-04 - accuracy: 0.9965 - val_loss: 3.3111e-04 - val_accuracy: 0.9957\n",
      "Epoch 525/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9960e-04 - accuracy: 0.9974 - val_loss: 2.4058e-04 - val_accuracy: 0.9983\n",
      "Epoch 526/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9691e-04 - accuracy: 0.9981 - val_loss: 5.3716e-04 - val_accuracy: 0.9948\n",
      "Epoch 527/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9713e-04 - accuracy: 0.9972 - val_loss: 3.1214e-04 - val_accuracy: 0.9957\n",
      "Epoch 528/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9626e-04 - accuracy: 0.9977 - val_loss: 2.5368e-04 - val_accuracy: 0.9983\n",
      "Epoch 529/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9512e-04 - accuracy: 0.9974 - val_loss: 3.5562e-04 - val_accuracy: 0.9965\n",
      "Epoch 530/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 3.1018e-04 - accuracy: 0.9972 - val_loss: 2.6646e-04 - val_accuracy: 0.9991\n",
      "Epoch 531/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0840e-04 - accuracy: 0.9970 - val_loss: 2.6101e-04 - val_accuracy: 0.9974\n",
      "Epoch 532/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.8530e-04 - accuracy: 0.9986 - val_loss: 2.7290e-04 - val_accuracy: 0.9965\n",
      "Epoch 533/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9441e-04 - accuracy: 0.9969 - val_loss: 2.5800e-04 - val_accuracy: 0.9991\n",
      "Epoch 534/600\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 2.9657e-04 - accuracy: 0.9974 - val_loss: 3.3103e-04 - val_accuracy: 0.9965\n",
      "Epoch 535/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9230e-04 - accuracy: 0.9984 - val_loss: 2.9171e-04 - val_accuracy: 0.9957\n",
      "Epoch 536/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.8967e-04 - accuracy: 0.9979 - val_loss: 2.4739e-04 - val_accuracy: 0.9948\n",
      "Epoch 537/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0925e-04 - accuracy: 0.9979 - val_loss: 2.9165e-04 - val_accuracy: 0.9974\n",
      "Epoch 538/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9537e-04 - accuracy: 0.9965 - val_loss: 3.0216e-04 - val_accuracy: 0.9974\n",
      "Epoch 539/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9890e-04 - accuracy: 0.9986 - val_loss: 3.0681e-04 - val_accuracy: 0.9983\n",
      "Epoch 540/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 2.9345e-04 - accuracy: 0.9970 - val_loss: 2.5709e-04 - val_accuracy: 0.9983\n",
      "Epoch 541/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9066e-04 - accuracy: 0.9974 - val_loss: 2.6696e-04 - val_accuracy: 0.9991\n",
      "Epoch 542/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0001e-04 - accuracy: 0.9977 - val_loss: 2.8744e-04 - val_accuracy: 0.9983\n",
      "Epoch 543/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9124e-04 - accuracy: 0.9976 - val_loss: 2.3240e-04 - val_accuracy: 0.9983\n",
      "Epoch 544/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.7296e-04 - accuracy: 0.9984 - val_loss: 2.6728e-04 - val_accuracy: 0.9991\n",
      "Epoch 545/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 3.0001e-04 - accuracy: 0.9974 - val_loss: 3.0137e-04 - val_accuracy: 0.9974\n",
      "Epoch 546/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9193e-04 - accuracy: 0.9986 - val_loss: 2.3544e-04 - val_accuracy: 0.9965\n",
      "Epoch 547/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 2.9986e-04 - accuracy: 0.9986 - val_loss: 2.7314e-04 - val_accuracy: 0.9991\n",
      "Epoch 548/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.8823e-04 - accuracy: 0.9976 - val_loss: 3.1343e-04 - val_accuracy: 0.9991\n",
      "Epoch 549/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 2.8604e-04 - accuracy: 0.9979 - val_loss: 2.2796e-04 - val_accuracy: 0.9965\n",
      "Epoch 550/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.7836e-04 - accuracy: 0.9972 - val_loss: 2.7828e-04 - val_accuracy: 0.9991\n",
      "Epoch 551/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.8110e-04 - accuracy: 0.9979 - val_loss: 3.0002e-04 - val_accuracy: 0.9948\n",
      "Epoch 552/600\n",
      "721/721 [==============================] - 10s 13ms/step - loss: 3.0354e-04 - accuracy: 0.9977 - val_loss: 2.2516e-04 - val_accuracy: 0.9991\n",
      "Epoch 553/600\n",
      "721/721 [==============================] - 10s 14ms/step - loss: 2.9263e-04 - accuracy: 0.9981 - val_loss: 2.4589e-04 - val_accuracy: 0.9957\n",
      "Epoch 554/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.8258e-04 - accuracy: 0.9972 - val_loss: 2.8264e-04 - val_accuracy: 0.9983\n",
      "Epoch 555/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9528e-04 - accuracy: 0.9969 - val_loss: 2.7172e-04 - val_accuracy: 0.9991\n",
      "Epoch 556/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9692e-04 - accuracy: 0.9972 - val_loss: 2.6726e-04 - val_accuracy: 0.9948\n",
      "Epoch 557/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9024e-04 - accuracy: 0.9967 - val_loss: 2.3065e-04 - val_accuracy: 0.9974\n",
      "Epoch 558/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9418e-04 - accuracy: 0.9970 - val_loss: 2.5955e-04 - val_accuracy: 0.9965\n",
      "Epoch 559/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 2.8746e-04 - accuracy: 0.9976 - val_loss: 3.0718e-04 - val_accuracy: 0.9965\n",
      "Epoch 560/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.8787e-04 - accuracy: 0.9972 - val_loss: 2.6454e-04 - val_accuracy: 1.0000\n",
      "Epoch 561/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 2.9053e-04 - accuracy: 0.9970 - val_loss: 3.3334e-04 - val_accuracy: 1.0000\n",
      "Epoch 562/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.8933e-04 - accuracy: 0.9981 - val_loss: 2.9500e-04 - val_accuracy: 1.0000\n",
      "Epoch 563/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9399e-04 - accuracy: 0.9972 - val_loss: 2.4945e-04 - val_accuracy: 1.0000\n",
      "Epoch 564/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9656e-04 - accuracy: 0.9977 - val_loss: 3.1100e-04 - val_accuracy: 0.9983\n",
      "Epoch 565/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9133e-04 - accuracy: 0.9981 - val_loss: 2.7972e-04 - val_accuracy: 0.9983\n",
      "Epoch 566/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.8247e-04 - accuracy: 0.9979 - val_loss: 2.8192e-04 - val_accuracy: 0.9957\n",
      "Epoch 567/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9139e-04 - accuracy: 0.9986 - val_loss: 2.7314e-04 - val_accuracy: 0.9974\n",
      "Epoch 568/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 2.8936e-04 - accuracy: 0.9970 - val_loss: 3.6033e-04 - val_accuracy: 0.9991\n",
      "Epoch 569/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9561e-04 - accuracy: 0.9965 - val_loss: 3.3624e-04 - val_accuracy: 0.9957\n",
      "Epoch 570/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.8182e-04 - accuracy: 0.9972 - val_loss: 2.6633e-04 - val_accuracy: 0.9991\n",
      "Epoch 571/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9816e-04 - accuracy: 0.9979 - val_loss: 2.5093e-04 - val_accuracy: 1.0000\n",
      "Epoch 572/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.7872e-04 - accuracy: 0.9984 - val_loss: 2.6721e-04 - val_accuracy: 0.9983\n",
      "Epoch 573/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9087e-04 - accuracy: 0.9981 - val_loss: 2.2793e-04 - val_accuracy: 0.9983\n",
      "Epoch 574/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.8329e-04 - accuracy: 0.9983 - val_loss: 2.9752e-04 - val_accuracy: 0.9991\n",
      "Epoch 575/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 2.8250e-04 - accuracy: 0.9979 - val_loss: 3.7137e-04 - val_accuracy: 0.9965\n",
      "Epoch 576/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9035e-04 - accuracy: 0.9979 - val_loss: 2.7583e-04 - val_accuracy: 0.9991\n",
      "Epoch 577/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.8099e-04 - accuracy: 0.9977 - val_loss: 2.4894e-04 - val_accuracy: 0.9974\n",
      "Epoch 578/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.8855e-04 - accuracy: 0.9976 - val_loss: 2.5163e-04 - val_accuracy: 0.9991\n",
      "Epoch 579/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.8694e-04 - accuracy: 0.9964 - val_loss: 2.7822e-04 - val_accuracy: 0.9965\n",
      "Epoch 580/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.8505e-04 - accuracy: 0.9974 - val_loss: 3.0503e-04 - val_accuracy: 0.9957\n",
      "Epoch 581/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9104e-04 - accuracy: 0.9967 - val_loss: 2.4417e-04 - val_accuracy: 0.9965\n",
      "Epoch 582/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.7206e-04 - accuracy: 0.9976 - val_loss: 2.3477e-04 - val_accuracy: 0.9991\n",
      "Epoch 583/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.8492e-04 - accuracy: 0.9965 - val_loss: 2.2644e-04 - val_accuracy: 0.9974\n",
      "Epoch 584/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.9043e-04 - accuracy: 0.9977 - val_loss: 2.8736e-04 - val_accuracy: 0.9965\n",
      "Epoch 585/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 2.8416e-04 - accuracy: 0.9970 - val_loss: 2.7156e-04 - val_accuracy: 0.9974\n",
      "Epoch 586/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "721/721 [==============================] - 9s 13ms/step - loss: 2.8799e-04 - accuracy: 0.9986 - val_loss: 2.2895e-04 - val_accuracy: 0.9983\n",
      "Epoch 587/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 2.7919e-04 - accuracy: 0.9983 - val_loss: 2.2559e-04 - val_accuracy: 0.9974\n",
      "Epoch 588/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.7668e-04 - accuracy: 0.9984 - val_loss: 2.5792e-04 - val_accuracy: 0.9965\n",
      "Epoch 589/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 2.8425e-04 - accuracy: 0.9970 - val_loss: 2.2672e-04 - val_accuracy: 0.9983\n",
      "Epoch 590/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.7706e-04 - accuracy: 0.9981 - val_loss: 2.8228e-04 - val_accuracy: 0.9983\n",
      "Epoch 591/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.8737e-04 - accuracy: 0.9976 - val_loss: 2.7261e-04 - val_accuracy: 0.9974\n",
      "Epoch 592/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 2.9821e-04 - accuracy: 0.9965 - val_loss: 2.3404e-04 - val_accuracy: 0.9965\n",
      "Epoch 593/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.8516e-04 - accuracy: 0.9969 - val_loss: 2.7265e-04 - val_accuracy: 0.9965\n",
      "Epoch 594/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 2.9040e-04 - accuracy: 0.9983 - val_loss: 3.8256e-04 - val_accuracy: 0.9939\n",
      "Epoch 595/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.7775e-04 - accuracy: 0.9981 - val_loss: 2.4445e-04 - val_accuracy: 0.9983\n",
      "Epoch 596/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.8642e-04 - accuracy: 0.9986 - val_loss: 2.6428e-04 - val_accuracy: 0.9983\n",
      "Epoch 597/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.8245e-04 - accuracy: 0.9976 - val_loss: 2.5697e-04 - val_accuracy: 0.9965\n",
      "Epoch 598/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.8566e-04 - accuracy: 0.9974 - val_loss: 2.4131e-04 - val_accuracy: 1.0000\n",
      "Epoch 599/600\n",
      "721/721 [==============================] - 9s 12ms/step - loss: 2.7761e-04 - accuracy: 0.9974 - val_loss: 2.2994e-04 - val_accuracy: 0.9991\n",
      "Epoch 600/600\n",
      "721/721 [==============================] - 9s 13ms/step - loss: 2.8095e-04 - accuracy: 0.9979 - val_loss: 2.3863e-04 - val_accuracy: 0.9991\n"
     ]
    }
   ],
   "source": [
    "Batch_Size = 8\n",
    "Epoch_Anz = 600\n",
    "Shift_Range = 3\n",
    "Brightness_Range = 0.3\n",
    "\n",
    "datagen = ImageDataGenerator(width_shift_range=[-Shift_Range,Shift_Range], height_shift_range=[-Shift_Range,Shift_Range],brightness_range=[1-Brightness_Range,1+Brightness_Range])\n",
    "\n",
    "if (Training_Percentage > 0):\n",
    "    train_iterator = datagen.flow(x_data, y_data, batch_size=Batch_Size)\n",
    "    validation_iterator = datagen.flow(X_test, y_test, batch_size=Batch_Size)\n",
    "    history = model.fit_generator(train_iterator, validation_data = validation_iterator, epochs = Epoch_Anz)\n",
    "else:\n",
    "    train_iterator = datagen.flow(x_data, y_data, batch_size=Batch_Size)\n",
    "    history = model.fit_generator(train_iterator, epochs = Epoch_Anz)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overall Learing results (Step 1 & Step 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkIAAAHHCAYAAABTMjf2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAACfV0lEQVR4nOzdd3xT9frA8c/J7G4pq5S9BYGyEQVFRRER1OsWvXB/ilfFiQu97oV7V3Drve6JAxcggiCyZKjI3rOs7jbz/P5Ik5yT1UHbpM3zfr140ZycJN+Ekjx5vs/3+SqqqqoIIYQQQsQhQ7QHIIQQQggRLRIICSGEECJuSSAkhBBCiLglgZAQQggh4pYEQkIIIYSIWxIICSGEECJuSSAkhBBCiLglgZAQQggh4pYEQkIIIYSIWxIICSEalW3btqEoCm+//Xa1b/vzzz+jKAo///xzxPPefvttFEVh27ZtNRqjECJ2SCAkhBBCiLglgZAQQggh4pYEQkIIIYSIWxIICSFq1f3334+iKGzYsIHLLruM9PR0mjdvzj333IOqquzcuZOzzz6btLQ0srKyePrpp4PuIy8vjyuuuIKWLVuSkJBATk4O77zzTtB5+fn5TJw4kfT0dDIyMpgwYQL5+fkhx7Vu3TrOP/98MjMzSUhIYODAgXz11Ve1+txffvlljj32WKxWK9nZ2UyePDloPBs3buS8884jKyuLhIQE2rRpw8UXX0xBQYHvnNmzZzNs2DAyMjJISUmhe/fu3HXXXbU6ViGEhynaAxBCNE4XXXQRPXr04LHHHmPWrFk8/PDDZGZm8sorr3DKKafw+OOP895773HrrbcyaNAgTjzxRADKysoYMWIEmzZt4rrrrqNjx4588sknTJw4kfz8fG688UYAVFXl7LPPZuHChVx99dX06NGDL774ggkTJgSN5a+//uKEE06gdevWTJ06leTkZD7++GPOOeccPvvsM84999yjfr73338/DzzwACNHjuSaa65h/fr1TJ8+nWXLlrFo0SLMZjN2u51Ro0Zhs9m4/vrrycrKYvfu3XzzzTfk5+eTnp7OX3/9xVlnnUWfPn148MEHsVqtbNq0iUWLFh31GIUQIahCCFGL7rvvPhVQr7rqKt8xp9OptmnTRlUURX3sscd8x48cOaImJiaqEyZM8B177rnnVEB99913fcfsdrs6dOhQNSUlRS0sLFRVVVVnzpypAuoTTzyhe5zhw4ergPrWW2/5jp966qlq79691fLyct8xt9utHn/88WrXrl19x+bNm6cC6rx58yI+x7feeksF1K1bt6qqqqp5eXmqxWJRTz/9dNXlcvnOe+mll1RAffPNN1VVVdWVK1eqgPrJJ5+Eve9nn31WBdQDBw5EHIMQonbI1JgQok5ceeWVvp+NRiMDBw5EVVWuuOIK3/GMjAy6d+/Oli1bfMe+/fZbsrKyuOSSS3zHzGYzN9xwA8XFxcyfP993nslk4pprrtE9zvXXX68bx+HDh/npp5+48MILKSoq4uDBgxw8eJBDhw4xatQoNm7cyO7du4/quc6ZMwe73c5NN92EweB/W500aRJpaWnMmjULgPT0dAB++OEHSktLQ95XRkYGAF9++SVut/uoxiWEqJwEQkKIOtGuXTvd5fT0dBISEmjWrFnQ8SNHjvgub9++na5du+oCCoAePXr4rvf+3apVK1JSUnTnde/eXXd506ZNqKrKPffcQ/PmzXV/7rvvPsBTk3Q0vGMKfGyLxUKnTp1813fs2JEpU6bw+uuv06xZM0aNGkVubq6uPuiiiy7ihBNO4Morr6Rly5ZcfPHFfPzxxxIUCVFHpEZICFEnjEZjlY6Bp96nrngDiFtvvZVRo0aFPKdLly519viBnn76aSZOnMiXX37Jjz/+yA033MC0adP47bffaNOmDYmJiSxYsIB58+Yxa9Ysvv/+ez766CNOOeUUfvzxx7CvoRCiZiQjJISIKe3bt2fjxo1BGZB169b5rvf+vXfvXoqLi3XnrV+/Xne5U6dOgGd6beTIkSH/pKamHvWYQz223W5n69atvuu9evfuzd13382CBQv45Zdf2L17NzNmzPBdbzAYOPXUU3nmmWdYu3YtjzzyCD/99BPz5s07qnEKIYJJICSEiClnnnkm+/bt46OPPvIdczqdvPjii6SkpHDSSSf5znM6nUyfPt13nsvl4sUXX9TdX4sWLRgxYgSvvPIKe/fuDXq8AwcOHPWYR44cicVi4YUXXtBlt9544w0KCgoYM2YMAIWFhTidTt1te/fujcFgwGazAZ6apkB9+/YF8J0jhKg9MjUmhIgpV111Fa+88goTJ05kxYoVdOjQgU8//ZRFixbx3HPP+bI3Y8eO5YQTTmDq1Kls27aNnj178vnnn+vqbbxyc3MZNmwYvXv3ZtKkSXTq1In9+/ezePFidu3axerVq49qzM2bN+fOO+/kgQce4IwzzmDcuHGsX7+el19+mUGDBnHZZZcB8NNPP3HddddxwQUX0K1bN5xOJ//73/8wGo2cd955ADz44IMsWLCAMWPG0L59e/Ly8nj55Zdp06YNw4YNO6pxCiGCSSAkhIgpiYmJ/Pzzz0ydOpV33nmHwsJCunfvzltvvcXEiRN95xkMBr766ituuukm3n33XRRFYdy4cTz99NP069dPd589e/Zk+fLlPPDAA7z99tscOnSIFi1a0K9fP+69995aGff9999P8+bNeemll7j55pvJzMzkqquu4tFHH8VsNgOQk5PDqFGj+Prrr9m9ezdJSUnk5OTw3XffcdxxxwEwbtw4tm3bxptvvsnBgwdp1qwZJ510Eg888IBv1ZkQovYoal1WKQohhBBCxDCpERJCCCFE3JJASAghhBBxSwIhIYQQQsQtCYSEEEIIEbckEBJCCCFE3JJASAghhBBxS/oIVcLtdrNnzx5SU1NRFCXawxFCCCFEFaiqSlFREdnZ2UGbOGtJIFSJPXv20LZt22gPQwghhBA1sHPnTtq0aRP2egmEKuFt579z507S0tKiPBohhBBCVEVhYSFt27atdFNlCYQq4Z0OS0tLk0BICCGEaGAqK2uRYmkhhBBCxC0JhIQQQggRtyQQEkIIIUTckhqhWuB2u7Hb7dEeRoNkNpsxGo3RHoYQQog4JYHQUbLb7WzduhW32x3toTRYGRkZZGVlSZ8mIYQQ9U4CoaOgqip79+7FaDTStm3biA2bRDBVVSktLSUvLw+AVq1aRXlEQggh4o0EQkfB6XRSWlpKdnY2SUlJ0R5Og5SYmAhAXl4eLVq0kGkyIYQQ9UpSGEfB5XIBYLFYojyShs0bRDocjiiPRAghRLyRQKgWSG3L0ZHXTwghRLRIICSEEEKIuCWBUBi5ubn07NmTQYMGRXsoMa1Dhw4899xz0R6GEEIIUSNSLB3G5MmTmTx5MoWFhaSnp0d7OLVqxIgR9O3bt1YCmGXLlpGcnHz0gxJCCCGiQAKhaHHaARWMZlBiKzGnqioulwuTqfJfj+bNm9fDiIQQQoi6EVufwPHk4HrIWwtOW70+7MSJE5k/fz7PP/88iqKgKApvv/02iqLw3XffMWDAAKxWKwsXLmTz5s2cffbZtGzZkpSUFAYNGsScOXN09xc4NaYoCq+//jrnnnsuSUlJdO3ala+++qpen6MQQghRVRII1SJVVSm1O6v2x+H2/LG7qn6bCH9UVa3SGJ9//nmGDh3KpEmT2Lt3L3v37qVt27YATJ06lccee4y///6bPn36UFxczJlnnsncuXNZuXIlZ5xxBmPHjmXHjh0RH+OBBx7gwgsvZM2aNZx55pmMHz+ew4cPH/XrK4QQQtQ2mRqrRWUOFz3v/aGat9pXK4+99sFRJFkq/+dMT0/HYrGQlJREVlYWAOvWrQPgwQcf5LTTTvOdm5mZSU5Oju/yQw89xBdffMFXX33FddddF/YxJk6cyCWXXALAo48+ygsvvMDSpUs544wzavTchBBCiLoiGSHhM3DgQN3l4uJibr31Vnr06EFGRgYpKSn8/ffflWaE+vTp4/s5OTmZtLQ03zYaQgghRCyRjFAtSjQbWfvgqKqdnPc3uOzQtCtYjn57jkTz0W9NEbj669Zbb2X27Nk89dRTdOnShcTERM4//3zsdnvE+zGbzbrLiqLIprRCCCFikgRCtUhRlCpNTwFgNoDBABYjVPU2tcRisfi2B4lk0aJFTJw4kXPPPRfwZIi2bdtWx6MTQggh6o9MjcWhDh06sGTJErZt28bBgwfDZmu6du3K559/zqpVq1i9ejWXXnqpZHaEEEI0KhIIRU3F/lpVXO1Vm2699VaMRiM9e/akefPmYWt+nnnmGZo0acLxxx/P2LFjGTVqFP3796/n0QohhBB1R1Gruu46Tnk7SxcUFJCWlqa7rry8nK1bt9KxY0cSEhKqd8feHkJNu4I1pRZH3PAc1esohBBChBDp81tLMkJR491xXeJQIYQQIlokEBJCCCFE3JJAKGqiVyMkhBBCCA8JhIQQQggRtyQQihZFaoSEEEKIaJNASAghhBBxSwKhaJMaISGEECJqJBCKGqXyU4QQQghRpyQQihaJg4QQQoiok0Aoahp3sfTbb79NRkZGtIchhBBCRCSBULRJjZAQQggRNRIIRY3MjQkhhBDRJoFQtEQ5DnK73UybNo2OHTuSmJhITk4On376KW63mzZt2jB9+nTd+StXrsRgMLB9+3bAszN97969SU5Opm3btlx77bUUFxdH46kIIYQQNWaK9gAaFVUFR2nVznWUg6MM7CVgsh79Y5uTNE0aKzdt2jTeffddZsyYQdeuXVmwYAGXXXYZP/zwA5dccgnvv/8+11xzje/89957jxNOOIH27dsDYDAYeOGFF+jYsSNbtmzh2muv5fbbb+fll18++ucihBBC1BMJhGqToxQezY7OY9+1ByzJVTrVZrPx6KOPMmfOHIYOHQpAp06dWLhwIa+88gq33347Tz/9NDt27KBdu3a43W4+/PBD7r77bt993HTTTb6fO3TowMMPP8zVV18tgZAQQogGRQKhOLRp0yZKS0s57bTTdMftdjv9+vWjb9++9OjRg/fff5+pU6cyf/588vLyuOCCC3znzpkzh2nTprFu3ToKCwtxOp2Ul5dTWlpKUlJSfT8lIYQQokYkEKpN5iRPZqYqjmyD8gJIawPJTWvnsavIW8sza9YsWrdurbvOavVM040fP94XCL3//vucccYZNG3qGee2bds466yzuOaaa3jkkUfIzMxk4cKFXHHFFdjtdgmEhBBCNBgSCNUmRany9BTmRHDZwZJY9dvUkp49e2K1WtmxYwcnnXRSyHMuvfRS7r77blasWMGnn37KjBkzfNetWLECt9vN008/jcHgqbf/+OOPqzcIlxOK9kJSJmCs6VMRQgghjooEQlETvWVjqamp3Hrrrdx888243W6GDRtGQUEBixYtIi0tjQkTJtChQweOP/54rrjiClwuF+PGjfPdvkuXLjgcDl588UXGjh3LokWLdIFSlRTuhrLDUHoQMnvU8jMUQgghqkaWz0dblBoqPvTQQ9xzzz1MmzaNHj16cMYZZzBr1iw6duzoO2f8+PGsXr2ac889l8TERN/xnJwcnnnmGR5//HF69erFe++9x7Rp06o3AGd5bT0VIYQQosYUVZXWxpEUFhaSnp5OQUEBaWlpuuvKy8vZunUrHTt2JCEhoXp3fGQblB2BtNaQ0qL2BtxQHFjvazVQntmj5q+jEEIIEUKkz28tyQiJKJHO2kIIIaIvLgKhc889lyZNmnD++edHeygajXvT1UpJHCSEECIGxEUgdOONN/Lf//432sMILU7jIImEhBBCxIK4CIRGjBhBampqtIehp8R5RkgIIYSIAVEPhBYsWMDYsWPJzs5GURRmzpwZdE5ubi4dOnQgISGBIUOGsHTp0vofaARSb14Dmn3R5PUTQggRLVEPhEpKSsjJySE3Nzfk9R999BFTpkzhvvvu4/fffycnJ4dRo0aRl5fnO6dv37706tUr6M+ePVXs8lxDRqOnEaDdbq/BreM9I+QPhEpLPavHzGZztAYjhBAiTkW9oeLo0aMZPXp02OufeeYZJk2axL/+9S8AZsyYwaxZs3jzzTeZOnUqAKtWraq18dhsNmw2m+9yYWFh2HNNJhNJSUkcOHAAs9ns67JcJXYnOFWwOcAchz117C5Uh0qpA/IK8sjIyPAFlkIIIUR9iXogFIndbmfFihXceeedvmMGg4GRI0eyePHiOnnMadOm8cADD1TpXEVRaNWqFVu3bmX79u3Ve6CyI2ArggQ7JJTUYKQNXMlBTx8hl52MzgPJysqK9oiEEELEoZgOhA4ePIjL5aJly5a64y1btmTdunVVvp+RI0eyevVqSkpKaNOmDZ988glDhw4Nee6dd97JlClTfJcLCwtp27Zt2Pu2WCx07dq1+tNjCz6HNR/AgH/B0MnVu21j8O3LsPknzOWHMA7bF+3RCCGEiFMxHQjVljlz5lT5XKvV6tuBvaoMBkP1OyK7S6B4JzgLIR67KTsLoWRXtEchhBAizkW9WDqSZs2aYTQa2b9/v+74/v37G/5UireeSHVHdxzRYpB6ICGEENEX04GQxWJhwIABzJ0713fM7XYzd+7csFNbDYYS54GQIoGQEEKI6Iv61FhxcTGbNm3yXd66dSurVq0iMzOTdu3aMWXKFCZMmMDAgQMZPHgwzz33HCUlJb5VZA1WvAdCkhESQggRA6IeCC1fvpyTTz7Zd9lbqDxhwgTefvttLrroIg4cOMC9997Lvn376Nu3L99//31QAXVty83NJTc3F5fLVTcP4M2IxGsgJBkhIYQQMUBRpa1vRIWFhaSnp1NQUEBaWlrt3fFPj8CCJ2DQJBjzVO3db0Px1fXwe8X+b/cXRHcsQgghGp2qfn7HdI1QoxbvU2OSERJCCBEDJBCKFkOcT40Zoj4rK4QQQkggFDXeTUfVOqpBinVSLC2EECIGSCAULXFfLC2/ekIIIaJPPo2ixVcjFKe16tqMULy+BkIIIaJOAqEwcnNz6dmzJ4MGDaqbB/AGQu44nRrTFkvHa1ZMCCFE1EkgFMbkyZNZu3Yty5Ytq5sHiPtiaU0g5HZGbxxCCCHimgRC0eKbGpOMUNxmxYQQQkSdBELREu99hCQjJIQQIgZIIBQt8R4IKRIICSGEiD4JhKIl3oulDZpfvXgNBoUQQkSdBELR4iuWjs+l45sPlPgvSEZICCFElEggFC1xPjX266YD/gsSCAkhhIgSCYTCqLc+QnG6asyoaC7E6/SgEEKIqJNAKIw67yMU51tsmLSRkGSEhBBCRIkEQtES51NjJkUbCElGSAghRHRIIBQtcb5qzKjNCMXp9KAQQojok0AoSvYW2QFwuSUjJFNjQgghokUCoSh5Zs4mAGx2e5RHEh0m7W+eBEJCCCGiRAKhKDEaTQCocZoRMhq0GaH4fA2EEEJEnwRCUWIyel56d7wWS0tGSAghRAyQQChKTKaKjJArPoMAfUYoPl8DIYQQ0SeBUBh13VDR4J0ai9uMkD8QcsVpMCiEECL6JBAKo64bKlpMnoaK8VojpJ0aczjjs2BcCCFE9EkgFCUmXyAUnz10tFNjDrsjiiMRQggRzyQQihKj0eL5IU7rY7RbjTkdEggJIYSIDgmEosRo9gRCijs+gwBVVX0/O5zx+RoIIYSIPgmEosRgtnr+dsVnfYyCPxByOmxRHIkQQoh4JoFQlBjNCQAY3PEZCGm5bGXRHoIQQog4JYFQlBgsnoyQMV4DIX9CCLe9JHrjEEIIEdckEIoSU0VGyKjGa32Mv22A214axXEIIYSIZxIIRYnZUhEIxWmxtJYqgZAQQogokUAoSkzeqTFcbN5fEOXRRIFm1ZhqlxohIYQQ0SGBUBh1vcWGxZro+3na12vq5DFimaq94JCMkBBCiOiQQCiMut5iw2xN8P3sdpbXyWM0FIpTMkJCCCGiQwKhKHFh8v3cuYkliiOJEs3UmOKQQEgIIUR0SCAUJYXlTmyqGYAMS3xuvOolGSEhhBDRIoFQlJzdtzW2iqyQ2xmPnZX9GSGDBEJCCCGiRAKhKGmSbMFUsYQeZxw2VdRMjRldEggJIYSIDgmEositeKbGcMVjRsjP6IrvYnEhhBDRI4FQFLkMniJpNQ6nxrSbrpokIySEECJKJBCKIrexYrVYHO5Ar+0jZHLHXyAohBAiNkggFEVqRUYoHmuEtBkhs1umxoQQQkSHBEJR5M8IxWFGRFMsbXHL1JgQQojokEAoirwZIcUdfxkhLbPqAGmqKIQQIgokEIoi1eQJhAxxODUWsNsYlOVHZRRCCCHimwRCYdT1pqsAGOM3I6QGxEGUHYnKOIQQQsQ3CYTCqOtNVwFfIGSIwxohJSgjJIGQEEKI+ieBUDQZrQAobkeUBxINEggJIYSIPgmEosnk2WIjHpePB02NledHYxhCCCHinARCUaRakoH4DIRkakwIIUQskEAomioCIWsc9tEJTAhJICSEECIaJBCKJksKEJ+BkBI4NyaBkBBCiCiQQCiKDFZPRihRjb9AyJsTKlSTPJdKJRASQghR/yQQiiIlIRWAhLgMhDyOqJ6smLPkcJRHIoQQIh5JIBRFBqsnCEgk/oqlvfLxvAbuUgmEhBBC1D8JhKLIYPVkhBLVctSg9eSNXMXTLVA904OyfF4IIUQ0SCAURcYETxCQTBkud5wFQrgBf0bIIIGQEEKIKJBAKIpMCWkAJCs2HK54C4Q8vDVCZkcRuJxRHo0QQoh4I4FQFBkriqWTKMfuckd5NNFRQLL/QnlB9AYihBAiLkkgFEXmRE82JAkbm/bHWRBQURPlUo2+JfTSS0gIIUR9k0AoipSKVWMGReX579ZEeTTRoaKQ7y2YlkBICCFEPZNAKJrMSbhRPD+6SqM8mPrmr4nyTY9JICSEEKKeSSAURm5uLj179mTQoEF19yCKgtvkmRYyxd02G55AyGoxkl9RMC1L6IUQQtQ3CYTCmDx5MmvXrmXZsmV1+jgusycbYom7jJBHgtlEQcUSeskICSGEqG8SCEWZNyMUd4FQRbF0gtkgNUJCCCGiRgKhKHOZPdmQQ4cPsymvKMqjqX+JZpOvqaIEQkIIIeqbBEJRppo9GaEkyhn5zALKHa4oj6i+eDNC/hoh2W9MCCFEfZNAKMrcFTVCyYpn49Uye3wEQorqD4S8q8ZcsgO9EEKIeiaBUJSp3kCoYgd6RYnmaOqfwWDAZvJsNeIqkakxIYQQ9UsCoWizertLewKh+NmE3v9EndYMzw/lEggJIYSoXxIIRZkaMDXmjpNIyPcsFQVjUhPPj9JHSAghRD2TQCjaLPqpMVecBEKK5nkmZzQHwGwviKeUmBBCiBgggVCUKRbP1Jg3EHLH3Sb0CmlNPIGQQXWBLf5aCAghhIgeCYSiLaGiRijOpsZ8FIUWmRmUqRbPZeklJIQQoh5JIBRlBl9GyAaAyx0vgZD/ebbOSJSNV4UQQkSFBEJRpli9xdKeTVfjJiOkeZ7ZGYmy8aoQQoiokEAoygzWVMCfEYqbhJCXYqBlWoK/qWKpZISEEELUHwmEosyQ4AmEvH2E4mVqTNFMjTVLsfgyQiX5B6I1JCGEEHFIAqEoM1Q0VIy7PkL+RkKYjAbKK7pLlxZIICSEEKL+SCAUZcYE/fL5eMsIeXcUcVgyALAXHorOgIQQQsQlCYSizOhbPm/DgDtuMkLeVWNqRSTkTGwGgLtoX7QGJIQQIg5JIBRlSkWxNEAitjhsqOj5FVRTWgBQemRvNAcjhBAizkggFG2mBFwVaZEkyuNmiw1tHyGAtGZtADCV7GfrwZJoDEgIIUQckkAo2hSFEhIASFHK42hqrELF1NioIX0AaKHkk19qj+KAhBBCxBMJhGJAaUUglEQ57jgplvYuG/MWS5vSWwGQoZTgcpRHaVBCCCHijQRCYeTm5tKzZ08GDRpU549VonoCoWTK42bVmK9Y2hsKJTbBjgkApTgvWoMSQggRZyQQCmPy5MmsXbuWZcuW1fljeafGkpTyuOws7flboUDJ8PxYIr2EhBBC1A8JhGKAd2oshfipEVIC/gYoMXhaCSi2wnofjxBCiPgkgVAM8E6NJSnxMzXmjfe0z7a0IhDCVlDv4xFCCBGfJBCKAd6pseQ4Wj4f2FkaoMzg2XjVIBkhIYQQ9UQCoRjgywhhQ42TQMiXC1L8oVCZ0ZMRMtiLojEgIYQQcUgCoRjgqxFSynDFW2dpTSBkqwiEjJIREkIIUU8kEIoBJViBis7ScVIj5N9+3h8IlXszQg7JCAkhhKgfEgjFgBI1EYBkJZ6mxoLZTZ5AyGyXjJAQQoj6IYFQDPAXS5fFTbF04F5jADaTZwNak2SEhBBC1BMJhGJAqeqZGounztJKiGJpe0UgZJZASAghRD2RQCgG+DtL24i/hJA/EHKaPVNjVkd+vQ9HCCFEfJJAKAaUUFEjFEcZIX8k5A+EDiV1xqUqNC3dAnnrojMsIYQQcUUCoRjgnxqLpxqhCpqOimUJLfnJ3d9zYd3X0RmPEEKIuCKBUAzo1q4V4Jkac8dNRiiY2aSwVm3vuVC4J7qDEUIIERckEIoBD5w/GPBMjcVPHFSxxYamWNpsMLBfbeK5ULQvGoMSQggRZyQQigHWpHQAEhU7brczyqOpb/5AyGRUyFMzPBeK9kZnOEIIIeKKBEKxwJri+9FoL47iQOqPEqIWymzUZoT21/OIhBBCxCMJhGKByYpdsXh+dMRZV2Xt1Jg2I1S8H9yu6IxJCCFE3JBAKEaUGSu6KtsKojyS+hKcETIZDBwkHTcKqC7emr0sCuMSQggRTyQQihFlBm9X5TjJCKnBnaXNJgMujL7psa9+/i0aIxNCCBFHJBCKEWUVO6/H3/YS2lVjnp+3qy0B+MJ6HxzaHJVRCSGEiA8SCMWIct8+W/ExNaaEmhozen4dt7tb+g8ueKq+hiSEECIOSSAUI8oraoQscZIR8m81pi+WBjhCqv9Eo7n+BiWEECLuSCAUI8pMaQBY4qRGSAn4GzzL5wF2q039BxPS6m1MQggh4o8EQjHCbvLUCFmc8REIeXNCqrahYkWN0AeuU/yn2UvqdVRCCCHiiwRCMaLc6Ml8WONkasxLCVg1BuDExMOO8Z6DtvhoMCmEECI6JBCKEaWWTABS7HlRHkk9CdVZ2uD/dSwhwfNDnHTaFkIIER01CoTeeecdZs2a5bt8++23k5GRwfHHH8/27dtrbXDxJD+xHQCZZfH2+vkzQslWo+/nEjXR84MtvjJkQggh6leNAqFHH32UxETPB9XixYvJzc3liSeeoFmzZtx88821OsB4cSSxPQDJziNQlh/dwdQD3/J5zdRYz2x/YXRxRUZIlYyQEEKIOlSjQGjnzp106dIFgJkzZ3Leeedx1VVXMW3aNH755ZdaHWC8cJpT2O/dZysumggGT41ZTUY6NksGtBkhCYSEEELUnRoFQikpKRw6dAiAH3/8kdNOOw2AhIQEysrKam90ccSoKL6OyuRvi+pY6pWi6C4+dUEfBnfI9GWEpEZICCFEXapRIHTaaadx5ZVXcuWVV7JhwwbOPPNMAP766y86dOhQm+OLGwYFClTPEvp4qIvxTo0pAccHtM/kvUlD/MXSkhESQghRh2oUCOXm5jJ06FAOHDjAZ599RtOmngZ4K1as4JJLLqnVAcYLg0GhiPgpEPYvGgsMhTz9hHxTY46SkCvMhBBCiNpgqsmNMjIyeOmll4KOP/DAA0c9oHhlVBSKvR/+5Y2/qaIv/FGCAyFFUXAYkzw/q25PU0VrSv0NTgghRNyoUUbo+++/Z+HChb7Lubm59O3bl0svvZQjR47U2uBqw86dOxkxYgQ9e/akT58+fPLJJ9EeUkgGg0JxHGWEQhVLa9mNCRxQK1aRTWsNy16vhzEJIYSINzUKhG677TYKCz1Ziz/++INbbrmFM888k61btzJlypRaHeDRMplMPPfcc6xdu5Yff/yRm266iZKS2Nu2waAoFKmeLEh8BEJewRkhALPRyHp3W/+BWbeA26U7Z/XOfG76cCV7C6RAXwghRM3UaGps69at9OzZE4DPPvuMs846i0cffZTff//dVzgdK1q1akWrVq0AyMrKolmzZhw+fJjk5OQoj0zPaEBTIxQPU2PePkKhrzcbDXxtP55hxr/8B22FkNjEd/Hs3EUA7Css58OrhtbVUIUQQjRiNcoIWSwWSktLAZgzZw6nn346AJmZmb5MUVUtWLCAsWPHkp2djaIozJw5M+ic3NxcOnToQEJCAkOGDGHp0qU1GTYrVqzA5XLRtm3byk+uZwZtjZAmECoodeByN75i4UjF0gAWo8InrpO4wzHJfzBM7dTmA7GX4RNCCNEw1CgQGjZsGFOmTOGhhx5i6dKljBkzBoANGzbQpk2bat1XSUkJOTk55Obmhrz+o48+YsqUKdx33338/vvv5OTkMGrUKPLy/Hty9e3bl169egX92bNnj++cw4cP889//pNXX321Bs+47hmU4BqhHYdKyXnwR/4x/dcojqxu+JfPh5kaMxlwY+Aj18kcUNM9B+NqylAIIUR9qNHU2EsvvcS1117Lp59+yvTp02ndujUA3333HWeccUa17mv06NGMHj067PXPPPMMkyZN4l//+hcAM2bMYNasWbz55ptMnToVgFWrVkV8DJvNxjnnnMPUqVM5/vjjKz3XZrP5Llc3w1VTRkNwjdDXazyB3Oqd+fUyhvoVvMWGltnoj9GL1ESaKwVxMWUohBCiftUoEGrXrh3ffPNN0PFnn332qAekZbfbWbFiBXfeeafvmMFgYOTIkSxevLhK96GqKhMnTuSUU07h8ssvr/T8adOmRaUNQPytGvNQI9QIeRVRESDGQVsBIYQQ9atGgRCAy+Vi5syZ/P333wAce+yxjBs3DqPRWMktq+7gwYO4XC5atmypO96yZUvWrVtXpftYtGgRH330EX369PHVH/3vf/+jd+/eIc+/8847dSvfCgsL66WmyKBoiqUrPvDVRtxIMFxnaS+L0X9NUYjaKSGEEKI21CgQ2rRpE2eeeSa7d++me/fugCeT0rZtW2bNmkXnzp1rdZBHY9iwYbjd7iqfb7VasVqtdTii0MxGA/lqxUo2Rwk44mRJeFWmxnwZoYLQd1HrgxJCCBEvalQsfcMNN9C5c2d27tzJ77//zu+//86OHTvo2LEjN9xwQ60NrlmzZhiNRvbv3687vn//frKysmrtcWKBxWigkGTsmD0Hivc37p0lKnlu2kAo1Go6IYQQojbUKBCaP38+TzzxBJmZmb5jTZs25bHHHmP+/Pm1NjiLxcKAAQOYO3eu75jb7Wbu3LkMHdq4+saYjAqgkG+seE2L9kc8v6HzT42F/hU0aafGiMdGk0IIIepDjabGrFYrRUXBH0rFxcVYLJZq3VdxcTGbNm3yXd66dSurVq0iMzOTdu3aMWXKFCZMmMDAgQMZPHgwzz33HCUlJb5VZI2FNwNyWMmkBfuheB+QGflGDZjq+ztcH6EwxdK2IrCm1vHohBBCxIsaZYTOOussrrrqKpYsWYKqqqiqym+//cbVV1/NuHHjqnVfy5cvp1+/fvTr1w+AKVOm0K9fP+69914ALrroIp566inuvfde+vbty6pVq/j++++DCqhrW25uLj179mTQoEF1+jhe3g/+w4aKzslF+yubPWrQqtJZ2quwoq2AuvJ/MK0NLH2trocnhBAiTtQoI/TCCy8wYcIEhg4ditnsqWlxOBycffbZPPfcc9W6rxEjRlS6Ouq6667juuuuq8lQa2zy5MlMnjyZwsJC0tPT6/zxvB/8B6kIhIr31fljxoJwhc5mkz8QylMzPOe67J4D394KgyeFuJUQQghRPTUKhDIyMvjyyy/ZtGmTb/l8jx496NKlS60OLp6YK2piDpDhOVC4F1UzA3Tjhyt59sK+GAyNZY1U5ODXrKkR2qM2DT6h7EhtD0gIIUQcqnIgVNmu8vPmzfP9/Mwzz9R8RHHKmwHZiWeDWA5vRk31BwtfrtrDJYPbcVynEEFBA6R4s4BK6NlZbY3Q3lCB0P61dTEsIYQQcabKgdDKlSurdJ4Spi+MiMxs8Hzwb1ErAqGDG6G9/hybs+r9kBqKsHuNaQKhPDJwqwoGxR8YPvL5YqCH5z6q+Cs3Z+1+HC43o3u3qvF4hRBCNC5VDoS0GR9R+8wmz6f5ZndFf6Syw1jt+dEbUJRpAyEnJvLIIAv/dNiRQ3l4A6GqsDvdXPnf5QCsuvc0MpKqt7pRCCFE41SjVWOi9nk/+ItcZkj3bOnRtHRLNIdUP8J1ljbpj+9Sm+sup1NSrYdxuf3ZpKJyZ7VuK4QQovGSQCiMaC2fd7pVaDMQgHb5S+vlsaOjoqFimGktbY0QwAZ3G93le8zv0k6petNJVVOc3ag7dgshhKgWCYTCmDx5MmvXrmXZsmX18njejJDD5YaupwPQ4civ9fLY0eDtIxQuJjEHBEI71BZB5zxnzq24r8qLhLQZIbdEQkIIISrUePd5Ubu8y8UdLhW1zSAUILNse3QHVQ+UMKvGtFtsgKaIXKOXspWTDStp43IAp0Z8HO2+uxIGCSGE8JJAKEaYNBkQhzUTC2Bxl2LGiaPin6lRrcerJCujnRrr1jKFufv7syp1BFmFa8hSDgNQhpW3LE+CC9hzDmT3C3t/2ixQZQ08hRBCxA+ZGosR2g9+hznV118ng+JoDal+hCuW1rwerdITcWFkUvn1POwY7ztuQVP0vP77iA/jUmVqTAghRDAJhGKEtpPy4VInJGQAkKH4A6HG9PGtVNpZ2v+r2al5MgAHimwkK+W+44mK3X+DHYsj3p9bUyPkanztmIQQQtSQBEIxwqjZOmP4E/NQEz17jjX6jFDYhor+452ap/h+nu/qE/pu7JFfJ21GyOmWSEgIIYSHBEIxIrAjt92SAUATpSgKo6lHYabGLJpNV1ulJWCtuLyPpoyxPRJ8A3tpxIfRJIRwuhpTbk0IIcTRkEAojPruIxSoUPHsuJquVK9xYEPhnRoLu/u8ZmrMZFRol5nku7xRbRN8A0fk10k7NeZ0SyAkhBDCQwKhMOq7j1CgzUVmAJ40v4oRF9C4inz9z6TyYmmTwUBaotl32Y6ZYjVBfwNbEaz9EkoOhbw/l65GqPG8jkIIIY6OBEIxalO+v46lt7IV0Gc1Gjr/7vOhr9fWCBkNCglm/a/qdOc49qqZ/gNlR+Djf8JH4wnFLTVCQgghQpBAKEZ97Tre97N35VhjymT4p8ZC/wpaAqbGrCaj7vpc1zkMtb3I4PJc/Q3DrB7TBkKN6XUUQghxdCQQilFL1B4sch0LQBqeQuDG9PntfSpqmIyQtsGk0aD4iqX1FEpICHE8mHbJvNQICSGE8JJAKIYV4ikQTqsomG5MNUJKwN+BtFNjZoMhTCDk6S5dFbqMkKwaE0IIUUECoRhWqHoaCXozQo1ySqcKnaU9NULGkOe5A3+FDeaQ57lk1ZgQQogQJBCKYf6MkHdqrPF8gFe2fF7bR8hTI1TFX1XVpd9htYLUCAkhhAhFAqEYVqhWBEIV3aUbUyDkqxKqYkbIqskITTmtW4S7dYfsMq1rqCirxoQQQlSQQCiMaDRUvPV0/Qd8IRVTY4p3aqzehlKPKt9iwxRQLD2wfRMuGdw2/F2W5wcdkj5CQgghQpFAKIxoNFS87pSufHL1UN9lf0aoYmqsEX2A+zddDbPFRoQaIavZgCFMJgmAr28KOqTrIyTF0kIIISpIIBRjtJmPAl9GyLNqzNWopsYqVGFqTFH0GSGL0ajbpDbI5rl8tXwrv+844jskxdJCCCFCkUAoxmiLhPNVz67rLZV8oJFN6VQS1Jk0U2OqquoDIVMlGSEg97Pv+cfLv3oKpw9txq2pC3JJjZAQQogKEgjFGG0H5Q1qG9yqQivlMM3JR21EGSFfH6FwDRUN/l9NVdW/LoGB0FOOC7CpJqY5LvEd+8E6lVYcgtn3wIv9abH+Pd91khESQgjhJYFQjNFmhIpJYqPaGoAcw+bGlRGqoIapEUowGzi5e3OGdMykTZNErJq9xqwmA5cOaee7/JLrXPrYXucV11iWuI/xHb/J9BksfgmATise8R0PfB1VVWXrwZJGFWgKIYSoGgmEYoy2SBhgtbszAL0NW2lcNb4VfYTCpIQUReGtfw3mo38PDa4RMhno0iKFNfef7qsVsmEBoLVy0HfeRaaf/Y+meZzAjNBj363j5Kd+Zvr8zUf3lIQQQjQ4EgjFGEtA48DNajYA7ZV9jXLVmBK2pWLA+ZpAxvsapSWYSbGadOc94bg47CN6BWaEXlmwxXPb79dXaSxCCCEaDwmEYkxgB+XtaksA2it5jXPVWBVpwyVt1izZot964yv3UN5wjo54D7J8XgghhJcEQjEmcGrMGwj1M2yi7cEF0RhSHYncWTqQNiOkDRYTLYF7kCks1dQJ+R9NmxEKvWpM28RRCCFEfJBAKMYYDIruA3mH2sL386g/b4/GkOpUuBqhoPPC3CY5YGoMYKG7F1vdLcPeQahVY6mU8r7pQVj6WpXGI4QQonGQQCiMaGyx4aXNCpWQyGZ3KwBMqh2K9tf7eOpCdXMvKQnBAQ9AYohd6UtI5GT7M7zqHOM7ZnDZ8GahQq2++z/jdwxS/oZvb63myIQQQjRkEgiFEY0tNrysAR/uZ9gfZ6e7uefCjsX1Pp46oUbeYiPQkI6ZXDK4LXeP6aE73rlFSphbKJRVrCQDMKguTjX8Dqg4QtQIpVbs51bXvly1m4UbD1Z+ohBCiHohgVAMCqwTcmBisbun58KhjVEYUR2KtFWGhqIoTPtHH64c3kl3/PZR3WmXmRTyNu86TyNfTfZdfsPyNKMMy0PWCLnr4b/C1oMl3PjhKi57Y0mdP5YQQoiqkUAoBgUuoQfYRxPPD0X76nk0dcO/6erRyUiy8N6VQ0Jed4AMBtqms6vZMN+x0cYl/hqhhc/CH58C4NYVEdlrZWyB9haU1cn9CiGEqDkJhGJQqEAoT21cgZDf0a/UMhvD/xo7MbGq3QTf5WI10VMjlPc3zLkfPruCZMr04ygvOOoxhSSr9oUQIuZIIBSDkoKWhMN+XyC0t55HU1dqLypommKJeP3u9AGs73AZACcbV3H63unw82O+608w/Eki5f4b1FEg1Ij6YQohRKMhgVAMSg2xQsoXCBU2jkDIv+nq0f8Kmo0GFtx2ctjrnW6VfZkDAWitHOKUg+/D2pm+64ca1pKiaKat6igQUjXBX2PqEi6EEA1Z6DXJIqqSLRECoeL94HaBIThrFM/SEsP/KjtcbkrNTcNe31zJx4rTf6D8SG0OzUfbGNytqhhqYVpQCCHE0ZGMUAwKNTV2kHQcigVUF+QOhrK6+bCuL7VVLO1lilAn5HKrlFjCB0JNKSKFqmeEVFXll40HOFBkq9YY3ZpIKJ63SxFCiFgigVAMSgyREXJhZGtKX8+FQ5t8q50aKl8XoSoun69MpO0xHC6VEpM/EJqT9g/d9UONaxlqXOs/UJYf8bFm/bGXy99YyilP/1ytMWpDnzC7fAghhKhnMjUWg0JlhAAWN/0H3YqWei5U8mEd66q7+3xlzIZIGSE3DkMCk+xTMOPE2WQ0Iws/D39nmozQ4RI7VpNBt5XHnLWe7t5F5c6gm0YUMDUmhBAi+iQjFIPCBUJ/pRwPnU/1XCg5UI8jqn3eQEitpUDIYFAwhcku2Z1uXG6V2e6BfOs+jmJnJb/2FYFQQZmD/g/NZuDDc3RX17TOWabGhBAi9kggFIOSQkyNAWzKK2Z/y+GeC8WNZM+xKm66WhWmMNNj5Q63LngpsVWSySnPB2DtnkIAyhwu3dU1zeboiqVl1ZgQQsQECYTCiOamq+EyQr/vyOehnw95LjTwjFBdNBc0hZke+2j5Th7/fp3vcrHNCdf/Dv/w7zQ/wzmWN1Kv9lyoyAhpN2dVVe3PNRuf9mahNn4VQghR/yQQCiOam64mhgmEwLN6DIDtiyBvXdjzGoraqhGqjhKbC5p2hj4XUtqiPwCfuE6kiIp9yYoPULxnHYXlDt9tnJvmw8FNQM2DGO3tJA4SQojYIIFQDDquY/il3gfUdP+Fnx6qh9HUDd/y+VqMg6o6ZVWsmRpbc8rbDLM9x2a1NYXeQGj7QlJeHcKZnx3DZcbZXGich/m9s+GlAdV6nEjjk2JpIYSIDbJqLAa1a5rEnCknkpFkCSrU3atqgqRtC+t5ZLXHVywdhUCoxO5EVVUUReGAzcwutQUAhWrwLvYPm98KeBB3jbM5Ts0NZWpMCCFig2SEYlSXFqk0S7EGHS8lgbUDH/ZcsKbW86hqn1KLv4Lljqo151FVKLV7CqAPFfubIhaSUvmNyw7XOJvj0jQP+sfLv+oyU0IIIaJDAqEGaHuT4zw/FO1t8J35anHRWLV4g5BDJXbfsQI10ffzQ47xrHJ3DnHD/b5AyIod5j8B+9cGnxdg9c58Vu/09yfaV1jOO79uq+HohRBC1BYJhBqgA2oGoIDbCaUHoz2cGqrdPkLVddX/VgBwsFgbCCX7ft6ktmGb2jL4hutmceP+e+io7OU/pvdg3iPw7j+Cz9MosTk5O3cRbwcEPmV2V+gbCCGEqDdSI9QAFdqB5OZQkgeFeyClRbSHVG1KwN/1bfXOfModLg5qpsZKVIvv57/cHTjRsCb4hvMeoR/wsnk33ZRdnmNFeyM+Vn6ZI+TxhlYw7XarLN9+hJ7ZaaRY5a1DCNE4SEaoASoqd0J6G8+FPSujO5ga8m2xodTer2BWWkK1zj9cYtfVCO08Us4ptqf4YvAHHCSdQ2pa2Nv2MOzEqGgCGXuJ5+8f/gMvDYbywkofv6F1l/5w2U4ufGUxF72yONpDEUKIWiOBUANUWO6EY8/1XJj/BBTsju6AjkYtFgl9du3xvHBJvyqff7jErpsaA9iiZnPzAk+AchB/IFTUengld7bF8/fil+Dgelj1nu8qNUzA08DiID773ZMB+2tP5UGeEEI0FBIINUBF5Q4Y+H+Q2QmK9sBX1zW8T9U6aC3dOiORcTnZVT7/cImd/YXl4a/XZITys0+KfGcbf9Tf9shh389OV+jnKttsNA5/7i7gie/XVb51ixAiJkkg1AAV25xgTYHxn4LRApt/go2zoz2smqnnIqGTujX3/bztUAk2Z/Cqu9SK+pcS/FNth9qcGvmOf3lWt4Lvf4s2+X52uEKv7GtoU2PRqueKdWe9uJCXf97Ms7M3RHsoQogakECoAfp5/QGueHuZZ5uIIRX7Y3m7TH83FT7/d8xniKJVLJ1sNTKmTysA/t4beoonuSIQylMzfMeKE9tCQkbQuTNdx3t+sBfpiqZN+LMDDpeKJwPm/zfpouwi0X6kZk9CxKS/98mUoRANkQRCMe7NiQNDHp+7Lg+70w0n3AQosG8N9z7zIiyZDms+hCPb6nOY1eYvlq7fUMjudJOZ5FkdtnZvEQDJAXu7eTM1m9XW3O34F/+234TDrULrAUH3V65aOKxWNGJ8tqfvuEUbCDmdfGG5j48sD6Hgpr2yjznW27nlj3G1+tzqWrR6PgkhRF2SQCjGnXJMS975v8Ehr3O43JDcFFr2AuDK/Of9V9pi+9up6vu7fj9dbU43mcmeQGj1znwAWjdJ1J1TpNls9V3XafzgHux5rUdMBWC5qT9/udsD8Iu7Dwe1+79VSKHM97NSvI9+hk0MMawjiyMMMqwHwEjt9hFSVRWbU3oTCSFEdUgg1AAM79KM5y/uy31je+qO2731LR09K5raGQ74r6zC8u1oUnxTd/WfEWoZsMw+O0MfCIXaqsPhUqHtYLj+d+633sL59vv4p/0OvnUPpoDkoPMzlGLfajF3mf/fop2Sh6rdYM1VewW2/3xzKX3u/5H8UnvlJwshhAAkEAorNzeXnj17MmjQoGgPBYNB4ey+renUXL8Xlt1bhNv7guAblRcEH4tBiqGeAyGXmz5t9Bmcfw5tX+ntfAXPTTtz2JlIGQkscOegYiCB4MCjmVKAw1ER5JTn+463N+zTr5erxczdLxsPYnO6+XHt/lq7T1F1MV6WJ4QIQwKhMCZPnszatWtZtmxZtIfiYw4IGnwZoex+HDS31p8c41NjSh0sn68Ku9NN9yz/ZrVjereif7smld5Ou/LLHrAKLAlb4OkMMmzA9HxPKC9E0QRCHZT9JCiaTtO2QnYdKWX9vqJqPIvIwvUtOlqKrBsTQjRCEgg1ICaj/p/L94GsKOy3tNWfHONTY171/eF6TFYaZqOBoZ2aAjDh+A4YqpCVcmh6AQUuuf9L7RDyNoaSPHisLWn7lviOHatsI4VS/0nlBQx7fB6jnlvAgaLggKomwqzWF0IIEYJsGNSAmIxhMkJAsRJQp9JApsbqcylSz1Zp3HNWDwCmX9afPfnl9MxOo9ReeZ2OU9MjKDAQeshxOcVqIpeafgp5244b3/T9fILhT3arzfz3VeRfQr/rSCnNU61VezIRNLQ9zIQQIpokI9SAmA0BGSHNB/JmyzH6k+N4auzqkzr7frZosmh3j+lBRsXS+YwkCz2zPZ2jjVXICN375V98uHQHLreqe90BDpDBXc4rqzQ2k+JmrNG/V1dRwSH/ddp/341z4NvbwVn9LFGdBUIyMyaEaIQkEGpAAj+wtbUqPySeyVvOUSx3d/Mc0NSlxLK66CM0dfQxfH3dMI7rlMknVw/1HW8VsDrMyxhmDN1apui27Jj6+R+UOaq2PH2G86yw16Uq/qX1JYX+QKhcu/T9vfNg6Suw4u0qPZ6WbN0hhBBVJ4FQA2KOMDVW5jLygHMCX1Z0Oi7IP8Sfu2N3eqyui6V7t0nnw6uGktM2g4//PZTcS/vTsVnwMncInxE6JivN12XaK9J+Un+7PXVa+9QmWHEEXV+qBk97lRfl007ZzzHKDkrtIYKs/B1hH09LG/yE2drsqElCKDKZkRS1pdzh4q89BXW28EHoSSDUgAQWS49/fQl5ReXkl9pZus2zyWehmgTAX5u2c9aLCymvyGB8uWo3K7bHzpYOvi026qFGaHDHTN+2GiHHEmYMJqMSFHzuLQi/Seskxy187DyJy+x38pHrZM/Bjv7NWme7gztTO4sPMtdyK99bp6Lme3Z3132iGjSBWOlhWPslOIOX62v3LZM3TyEatsteX8KYFxYyc9XuaA8lLkgg1ICYQmQuXp63mf/M/NN3eU9FIe7xxrW8an4a24af2Hm4lBs/XMWNH66st7FWXez+CpoNBswBwefuI2VhzoZdagtud/6bTWob1qntWDzuZ7j0Y77p/xofO0/ifsc/salm3W1a7p2HWfEEq6l7f/UctGmW0hv957vevwQ+/ifuX54OemztDvdHSu38662lfLV6T1Wfaq1Ytu0w//7fcnbnh3+NhBCVW17xpfWDpTujPJL4IKvGGpDAVWMANqeLWWv8m31uUv01LacbV8An5+PudiFwTq0tz64dsZ+1MBmVoMLo3fmlYc4OVmBpBeYEdqYN4HGnZ1put9qUTso+3znNSvw7lg9YeRcYNsIJN/jvxOXP/hh3/QZAyZJ3SD35Tt1jaVe15c7bDMC89Qd0NU5Hq7Lk3QUzPEXghWVOPrjquFp73JrKKyonxWoiySJvcw3Zo9/+zYEiG89cmFPvexOK+BC7X8dFEJMh+J8r8BvDEdKCzmmy4WPMOLE53brGgLEg1t7YMpL8GRiz0cC2QyW66yNlhAJ59/3SvuZO/Bu8ukL991vxFuz+3X+5LB9Wvge7V/gOHbEFv2auoALpygPNz3/fxdBpc/ljV+3Wku04XPVgsa7kFZYz+JG5DH5kbrSHIo7Sqwu28MXK3WzYXxztoYhGSgKhBiSwXqU6muCZbolU7FufotVZujJnHJvl+9lkUNgTMM1TnWkfb78hpyYQaqL438z/mzoJ8BRX/+nu4L/h9kX+n//8DL68Fl47xXfIrlj81x/aDB9cCruW0kfZjAknbZQDLLNeyw3GzyOOb8rHq9lbUM6dX6yp0vNpSJ2lvTVzxSF+3xtSDdWREju58zaxtyA+pxu1/1ax9iVONB4SCDUggcXS4awY9go/u3J0x5oqnr5CoT4YosH3kRojGaGHzunF5ce1Z1xf/1SSyWjg7jE9davKdkXICAXWcHkDIbumfucnVz8ANrtb8WXC2dzX5g1Osz3JufYHOZjYyXPSNk0gZA/+FtzFvQ1KKpbdf3E1rJ9Fxvtj+Mp6D5cZ5zDV9D7NlQKmmD+t0nM3xMi/QW0KF+tsOVDM4Efn8vovW2r/MesguL/9szU8+cN6Lnn1t1q/74YgONMZZ+L86dcXCYQakFDF0qFsazKMiY47ONnmL6r1BkKOfevgmylQEBurEWJlauzy49rz0Dm9yEz2Z1vMRoWTj2nBn/eP4vpTugCwLsKeYN1apuou2ypW7GkzQo86L+VJx4X80z4Vh8vNNkM7ikjCgYk1mad5Tjq4vvIBf/qvkOeON86lnZJX+e010hLMlZ8UYNGmg9W+TX0K9/nx0DdrOVBk4+FZf9freGrql40HANh2KPrTjdHgakDZO9FwSSDUgFQ1ECqp2DJiq9qKA809RasTjd/TR9lMh49Pg+VvwOx76mycVRObb3AZif5AyJsJSrQYGdG9he68oZ2aMn18f64Z4e9i3aOVvj7LmxHSpvQLSCHXdQ67aY7D5fbVEQFstQZ0B49k63zYuyZoK5VtaktaKPn+A1XoQ5SWWLViYm3MOv71JeFPjAHhpr+cDSzDYDUZKz+pEYv7jFBsfE9s9GQ5RQNSla0gAIrK/dNfi/cpjDPCSONKehm2oagV1x3ZVgcjrIEYyQh5aYulyx3+AKZ/uwxaplnZX+hZeZedkcjo3q10NUPds1J09/X9n/v4bcuhsFteOFz67TrWWXr7r1QMoFZSE/HK8KBDmUoRWYqmX9RzveH2rZCUqTtPG4DVJCMEng+pqv5O1rfGkkiwmuL7u6o2EIqxt4r60Uh+j2NdfP8va2AiTSMpCqQnej7QCsv9XY0N+D9MdR+QLXrU/gCrwVssHWvvbQlm/zfwYpv/dVQUhZw2Gb7LSRbPedogokVqgu6+/thdwC8bD7Jo0yFCsTvdug1ci50GGH4rWNPg/36A1PBNIMPpqWwPPvj31/DzY5A7BAo9vYUOl3iW5V9j/IqxeTOq/TgQvng1Lj+wqJvgS/v7GI/cUh8t6oEEQo1EqtWEpeLb4yvz/YWgBWpK6BtE+R0m1oqlQymx6be86NU63fdzojcQ0kwrNUm2cN3JXahqksQWEAiVOVxw6j2s/eefnPpxKXNO/BjGvgDH/sN3zn2OCRHvM1EJ7jrNxh/h52lwYB3MewSAQ8V2LDi4w/whJ+x/Dw57fmd+/GsfK7YfDrqLdXsLuGjHA7xhfhIrnseI5jRTZSuItIXLqq7rdp0NqU7EfUaoof2DiQYpvv+XNSJNki0ha4iec/6Dz1zDgm/g8BRfut0qF76ymCvfWVbXQwwpVoqlQ9FOMQL01gZC5uCMUGaShVtHdWf9w6OrdP+F5Q7fFiiAb6+x6z5YyeYDJVz52U4YMAF6X+A7Z427U/WfyPZffT86dnr6ER0usdMMTX3RC/34ecHPPP/uZyS+eTJsmqO7iwuf/56zjb9yqnEl15lmeu7LGZ1g+tZPVtP/wdkRG4Rq4/yGXGdiNcf3W7T2305iorp3qNjGweJYarxbP+L7f1kj0iLVGrLz9AGacIvjWp5wXKi/oiIQ2naohKVbDzPn7zxd3Uhd80+NxW4gFFjbc2K35pw/oA3piWaO69QUgLREfyDUJNnzs9loYHSvLCpjd7opKPNPv3l7PO08ErBCqPtoGHYzX7mGslrtTCjdy98O/0Bl/gyP+eDf8Nop2EsLaK4tqgbS5t/LDPOz9FS2wbvn6a7LUPyNJYcb/gDAEaWs4qcrdlFkc/LRsvCF4Np/uYacVdAWSzek/ke1Rft/sCEHtA2B0+VmwMNzGPjwnHr9LIgFUizdSKQnmjlUHGJapMKbrtFsVVtxZvc0xm59COwVGSHNe4vd6a73VSpqDAZCD53Ti5fnbeKuM/WruIwGhacu0Pdn0u5Fpl16/9QFOSzdephDJeH/TUCfdfIGRY7A7eMVBdcp93HDnG+Dbr9bbcoyd3ds+B+7VLWSpET4Vrd7BSftP4kBFv1hi7uUtoYDuvM4sg16nedryAnQWdkDqMHj9A83olK7k0dm/c3oXq0Y1rVZ5JMjiJRNVBvJB6h2aszmdMddzZD2364hB7QNQYndH/wcLrHTKj0xiqOpX5IRaiTSEs0hM0Je5Vj5zj2Ew1TUDDm8WQf/m4t2lVTdq8gIxV4cxOXHtWfxnafSpUVqped2aJZEsxQrnZon+6bLAJKtJh47r0+1Hnd/YTkv/bQx5HVrduUHHSvN6M4Jthe5yXEdADfYJzPDOZZjbW/wlnNUxMcyO4t1Xa4hRLfv106BT/8Pdq/QnZuqlJHFYV1/pOp4Zf4W3luyg8veCF6CX2Z3ccGMX8mdt6lG9+2lfSZz/q5eX6VYog20A6dq44E2EHI34IC2IdC+F8dbzCmBUCORnmgOuRdZoCJnxVROyUFwObGVl9NR2Usbpb6nxir+jsVIqBqsJiML7ziZH246Mei5pCX4E65j+lS+AszhUnnqxw26Yy63SmG5g3Nf/jXo/OLs43WXv3KfwGPOS1Ax8KDzcoaWv1idp4Ih3FrdA+tJRx80dTXsrvGWBzsj7EX2+cpdLNt2hCd/qLypZMRfHc1TueGDlayo2M27Lro/h3jIWqMNBGKlK3x90mWEJBCqU9puHfH2Sksg1EAN69KMxXf6959qlZ5Qpb3I8r2BUMEOeKgp3d8bxDzrLSy03kS5vT7faL3/1Rp2IASeJc7mENufaOuHzjg2i7UPjtJNn1XGaFAY//pv9Ln/R93xM22P8pLzbPYPvC3sbVUM7KWp73KZasGlRn6tTaoj9BWqm4uNP+sOZSmHPVNjS16Bjy7Haav6XliGCMvqqvOtP9LWIIEBzx8hMmoNgV0TbBbHY0ZIk5qQOKhuOeO4V4EEQmHk5ubSs2dPBg0aFO2hhNUqPZE7zjiGwR0yuey49lVqbpfv0DfPM9n8vYUcZYW1PsZw/BmhenvIepeuCYSaJltIspjCFryGex1+2xK8lH2t2oGnnBdRbqh8Dv9F5zkUqwlcZL8HoxL5k6Q1YaaQvpzMUONa3aE0Sj0Zoe9uh7+/wr3oeb623MUVxln0dq2HsiOh74vIHdKt5qoXB/+0Lo/pP28OeV5gsqqhfohqs25ljvgqYAV9YByuMWljVpcZzEC6oLOh/oepIQmEwpg8eTJr165l2bLoLCuvjPeD85oRnfn46qEkWUxBm7L2ap0WlCU64ghfH+8oya/tYVZB442EtBmhZKvndQ/39tIsxRp0rLKpgMKyMBkcjaedF9LX9iprAlabvTZsIZ3L/8du1Z81SqK80vvzSlNKdMXSlgXT6G3Yxj3m93jZNhW+vinsbQ0GBTOa7MbGObDDs6mothjYFmJ5vjboWbr1MI9/v47FW4IbVroCvt021A9RbSAUj1NDrkZS9N4QNJaWEzUhgVAjog16urRI4fNrTuCdfw3WnXM4QiDkLisIe11tCyrMbYSSLf4P9ewMT/Ym3OexNntUVUdKKw+EAJwhFoeWkoALI+85T6324wKkU4LTFmEj0LUzw17VwraTVdZJTDW9jzt/N7x3Hrw5CtxuLJUUB9tD1CXtKwgO4AKbPdZVHFTXS9odzvjOiMT7qrH6bC+ifX0b2p58R0sCoUZEWyx9yeB2WEwGX7dpr0O28Mtv3aX5dTW0IL5AKEb3qqoNiqLwzfXD+Oiq42ie6sn4hPvg1BZWV9Wtn6wOOlbVTsR2l2ea5Q3XmTzuuJid7uZVut0XrhMASFNKUcryI58c5rmeuv9NkhUbV5u+wX5om/+KsiOVFgeHW7IfyKk5L4tDKM6yoCHVRhBT15/NcZ8RivNVY/U5NeaO48J0CYQaEW1GKKGiI21gIHTQHj4QUgN2Mq+fNZSNNxACz7YcQzr5p5+0r+iA9k18P9ckIxSKxWhg5uQTQl73X+dpABR1PssXUNiwMN01jg9cJ/vOW+HuGvL2N9uv4Td3T894KYlYBwRAceXL1u2FmnNK8nQFmyWhAqEQ02UGR0nQMe832o7KXn5LuJ7zll0adE5tvNnrsjR18N9FmwGLx4yIrBqrP9rXN94KpyUQakS0GSFvY8TAQEg3m5Ki736sC4SKD3h2Lp/zQK2PU6txh0GRZaX5N2lNreEO8IFcqkrfthkha44ecY5nkn0K24c9odv1HmCl6g9+lrqPCbwpAPtpQoGaDEBfwyaUsoraHFNCyPOZW/G743bDD//BtvIjDv/1E8n2g75T0r6c6D//5eMwaIKrwKmxUrszaMn+lcZZnPPdYFinbzbprREaZfDU+DUp2xY0vJDp/7+/gRcHwJ6VoZ9TgLoOTrTPNx4zIm7dqrH6ff6qqlJQxenncHYeLmX5tuAFD1UVramxeAs6JRBqYCaf7Cl6veOM4A8rY6iMUIhl3bYB/4bOp0JWL91xxaZZNbbsNSjYCQufqY1hh6UocfYrqHl/6dAsyfezt5g6ks7Nkys9x/sGZg/RE8qGhdnugZSoCUGFyKvd/mLqcG+9hWoShXjG3FQp4thld3muaNkr9A1WvefpV7Xhe1j8EtYvryLzk3PpVBI+yOiy8XXfz9qpscWbD9Hz3h94do6+4eTd5vc8P3xxte64N+MVqXN5yD5IH42HQ5vgo8vBVuz5E0HdT43F74cT6Ff/1bBtVY09+M1ach78kbl/76/xfQx/Yh7nz1jM+n1FlZ8cQrSmxqRGSMS020Ydw4aHR+t2Qvcya+ptvBkha4iW/AeHPwCXfw7DpuiO6wKhOv4mosRhmh/0syf/Pqkzfdqkc+vp3UgNqBFqkmQOWlLfMi1M5kXD+605UpfwMocrKAgoxX/fm9RsNp34AhgtHD7mUj51nchnrmH8qXb0ZYQAkkp3eX5IbMKf/5jHXFc/AD5yjfDf8ZOd4cNLKh23V3KJf/8w7dTYXV949jf7YGmY/cUCXixP0KCSrfizT4FRS8TAouwIPNUV9+Md+c9nK3Wb42rVdZZCOxV4tI/10bIdXPrab7r97WJdNIul31q0DYDHvlt31Pf1x+76W4hSU5IREg1K4HSXV6LF/2HqLZoNlRG6/dPVXP7GElztjueV/l/5tmPov+klcFbsjWXWfOg6a383Yt+mq425kVAI2gLdtAQzX103jOtO6UqyRR8I9WqdzhfXnsC5/Vr7jnVunlLp/fsyQhG+Ppc73EFTYwBjbI/yiONSZrpOYH+7MXDXHtb0e4BbHVdzi+NaQKGIpOA7tKZQlNyeKxy30qf8Ve5wXIV7wL8qHWsoxgN/+34u0gRClRY2B/weOd0qE40/MME023/QUaoLJiIWXtuLwVGKwW1n7rI/+Gr1npCnaT8vjubbe15ROSOenBe0tYiuRugoMyJ3fPYHv24+xPSfNx/dHdUj3dRYlD6c4+UtSlcjpPm/cbDYxl97Yj+QOxoSCDUirTP8wYu3H0uooGnRpkP8svEgX6zczUFjc1yaX4M7H32U7/7YC4omk1Qa3Kel9sTJu0yFcG/lKQEZIUVR6Ns2gxHd/au5urSoPBAK9VlxTFaqrolheYiMEMBfagdec52FExMOl5tXFu5g4lueGhvvzXepzfnN3YMtbk19WVrrivtTKKzYy87eZXSlYw2luWs/3ldJ20m50s/AgClWp8vN/eb/6s+xFeve4KtaEJqslIf9h6utb865P21i26HSoK1FHHVQLF1Y3kAzQtEKhOLkPSrcaz3w4TmMeWEhf++tv4a79U0CoUakdRN/p2FvRijScupbP1nN57/vZo27k+9YC9sObv9sDdg1K3FKDoS49dFRgn6ID92zQm/kmmLVT2GG2outKhmhUL69YTh/PjCKkT1aAp7ppaVbIxdwOlwq0zRTAm0zPZkgNwYutt/DKfZn+Hz4LDj5PzB0MqUB27OUtz+Zw0Nur9oAz38Lep4NgEVxkYpnqXuZzQEVBfwqKm2UPJ4wvcJU0/uYCFxRpv9FyijdGvw49mIcYb71RpJOSdhsT231EQrVPNLlVnUBYDwWS8dCH6G4zAiF+JKwJETj0sZCAqFGpHWGf9rClxEKMTWmdajEziz3cSypWCnU0bCXVKvJMzXgVXIwzK2PXrwVS790aX/OH9CGb28YrjueYtWvGvO++Wo/aMPtU+YNcMIxGBQSzEYSKxo8Ltl6mEMl9oi3CcwYtUoPrk86Ym0NJ90OadlB9/f+0h30n9+Xxzu8EfFxAGgzEC78L8Wq5zGaKgWAyoCdb8LjHeCPTznV8QsLrTdxoWk+V5u+YZJRv0pMlxGyFXHd2uDl8tgKcWqeV1ULQtOUEuwuFVVV2Xm4VPdvUluxSaj6n8B/g9oqYG1I5Xnx3keoPrmlRkg0Bm00GSFvT6FIG1x6uTDyuvNMADorexjc1KbvAVMHgZChkn2vGqvWGYk8dUEOPbPTdMeTw2SERh2bxfCuzbhz9DG6PlFXnejP4nVuEX41mfbbbEIVmy1CqEAoeF+zXzcdZFOeJ2A+VKwPhJ743jPF8+a68H2rfFKzPfehel6Tn6238Kr5GU7a9YpnS+zPruB+h3714kBDwO702ie65uPQj1MxNZZGCW2UA7qgKJJ0SnA43Uyfv5nhT8zjmdkbfNfpPqiP4lc6VHASWOcVj4GAroC3niK4UruTXzb6s+DxUseoX6EX/Fo35t8+CYQaEe2qokRLFT6ANDapnqLc3oZtPLfnEvhD82FSqg+E3vl1G5e8+lvIzr/VFS9vMpUJXDXmfV0SzEb+d8UQ/n1SZ91ecn3bZvh+jvQBmahZNVid34nAQuJ2mf5sozc7NHddHiOfmc/egjJdcKBlI3QWy2tn+kAwep77YfzB4enGFRFv10HZh4Lmnbt4v2d/swMbPG0fQrEX09axhd+s17HQeiPmXYsjPoZXulKCw+X2BXcv/uQvaFZrqc9NqFsGNo+saiCwemc+k/67nC0HIi/9bwjcUcgI3fDBSi5/Y2m9PFYsiYV6rGiRQKgRMRoU3pw4kKcuyAn5DT6SrWoWO8Jts7Dhe/hmiq+T8H1f/cXiLYd4e1GIOgxRI4F9hEKFh9qC5xTN+ZESG7pAKEQrhXDKAmp+tIFQv3YZuutu+nBVxPv6P/utustvOUex0d2a6c6xfNTlcd/xw2ro+imvP90dONv2IACdDXv5p3G2/oQVb8Frp0DRvtB3YCump+MvkhTPKsjEnfMjPp5XOiWhew5B5BqeLydD7nHgKKv0MULFOIHBaFU/nM7OXcTstfu55t3fq3R+ZdxulS0Hiut8X7VQorGke87f+o7oNf2qFo3X62hoA3npIyQatFOOacn5A9pU+3adm6fwkzI49JVbF8DyN+DHu2HPSjoqewHIr2nX1Qb2BlEfUgICoa4tg4MCk2ZqTFsEHykToc0CBT5GJMU2fd+ctppASNvBHDw1R5H85O7vq0Hb4s7iAecETrM/yePOS8h3+Ttgt1f8jesec1wMwAJXb9+xIjWJNWonylRPlukB8zvBD2YvgtUfhB7I51cyxfGq72LCwb8ijtsrvaJGKBRXuHohpx1WvgsH/oYdlWeeQn1oBgZf1c04bTsUvPVITTzw9V+c8vR8Xv+l/r/46Iul6/3hgZoXS9dG4Fafb5WSERJx7/NrT2DEKWdGPmnlu/DqCOZZbwFq51uD0og3Xa0ObR+hMX1acf0pXYLOMWpeK7MmEHK63Yzu5VnOflafVrrb9Mr2N95skRa87UY4gft8tc30ZxhNEf7NssI0fbzV8W/edp7OPx136o6XagKu3yv2ONulNmOGaxx3NH2R6x3X+65fqnZHxcBKd/BrUxOJh9ZW6byIGSGnk4uM88jmoP7D45CmH5Cx4nVf/x38/DgU7NbfSelh+hf8SCLlusOBK8lCfjipKnz+b5hzf9BV4f+dqvf/9p3F2wGClvbXh4bcR0j7/vjpip1BKytjTTzvPl/9La9Fg2U1GUIu0+3UPJn0RDP5LXtU+b4sOGq+MZ/mP5zUCHlo+wg9fUGOb9WfllmTidF+yLnc8NQFOYzNyeakbp7pzc+uOZ4Pl+5g6mj/ViwtInSmNhqUiDu/t0pP5Jy+2ThcKs1TwwdUrZsksq+wPOj4TrUl9zsnBh0v0Xw4THNewh6a8r+KzWFXuzpQQBGjbI8x2riUl52eJfZTHNdwnTqTy0xzw46jKixl+z3TvYe3QFYfMIbe7y1dKQm52StAwubvedz8GpjhTPdM/xWaxpA4Sj3TYx9c7L/ugrf91392JZftmYvBdAp3Oa/0HbYFbJOiC4QOboJ1X0O742HNh55jp96n+9Q2hgmEqppl2JNfxltRnv7WtjiI2vL5Gk6OaYO437Yc5r4v/+LJC3IqvZ2qe3+s0UPXiH73eXfQWBozyQjFAe8eVRcObBvy+k7NPNebmofedTyUVEqr3IclmPZ2EggBmI0GFt5xMgtuOzlkEAT6veS0b84ut5tkq4kze7fy1RoNaN+EJy/Ioalm89WWqcGB0JPn92H2zSdyXKdM3fFQ316fu7gfueP7s7cgONABOK5TJid2DVNnFkap3fNh//airRwhjeec53MITxbLu+nqerUdzznP55g2zQDYR1Pudl7Bc85/VOkx1rnb8mvKSLCEqEGa84CnrujHu8Pe3lssDdBW2c9Qw1/g9ozbnLfGd15/R0WBt6rCfs20m73Y1w8JgMKALtWbPQHdpaafAP8HUmD3b93U2IxhnizQnPs0j6OfCjNV0jqjMle8s5zXajgdVtUVeZWJhSXdtZERAvh6Teju5IF03cqjNDXmHXu0xlLfJBCKA7NuGM4vt5/MkIAPOy/v1hzJiQn87fYES+7uY6Bp+MAoVSmNvEVBVUlGyKdNkyTaNQ2xhUUFbUZI+7JV9TOnZYipsd5t0unaMpUnz8/h1GNa+ILib//wFxwH9jzaeaQ05P3/74ohZKZEXiUWqNTu4ukf13P/18HTVIUBe2KlJ+ozNtOd4/jMNazSxzjD/jhPJE5hSspjvmM2tSIDt+Itz99LZnDnZ6tD3r69sp/j9/6X4YY1/GK9mQ8sj8CDmbDhR3D6g8JsV8UH3ZeT4Zen/XdgLwGbZtPNikUH4Tgqvo0HT41pLjgrCrC19Ud2/SoxbUaoJtNKNe0kvPlAMX0e+JEnf/A05DxSYue5ORvYcSj0700k2ucctamxGt4ucLxVzSzV9f514YSqEapx1r+BkUAoDiSYjbTNTAq7w3lSRQYiyWLifPv9nGh7lqJz3oFJ4aceUimj3Oni7pl/8O0fe6s3oMb81aIOaT/YFAXaVwRNY/pkhbuJTpMkf5AyulcW0/7Rm2OyPEvWszMSeWPiIE6smFrzTo21TLMG9Tw6NuCyl9lo0G3zUhWb8op1y9G1igKm5wIzAjYs3OK4lhLVE+CpY/S9hrRW7czn2z3+IHO12jnonB0rvgt52yzlCKP2vcL/LI/pr3j/Aoyl/hVGTdwVAc6q9/Tn2UtAu6Fxafji8rmWW3Dt9WSTImaEQrHpAyHtJsxhaz7cLjiw3v9/0h16c1kgdESw4QdY/z3s9C83f/rH9ZTaXeTO8+xpdufnf/DcnI2cN+PXyOMPIRp9hILU8MtaTTNY0cp86WqEKr7k1jzr37BIIBRHUsMEQt6VRRaTAZshiR1qS8rsLrCG/sADT0Zo1pq9vPvbDq59r+bLdKVGqOq0dUEGReHr64fx1XUncHL3FlW6vba55sWD23HJ4HZB5wTuTRdqr7rbTj+Gm0bqs4VXDusIwNBOzaq1TL86O6G73Kpu7zWvobaXOC/pLcr6/JPL7J5i7P1qBhPtt3OGzR+8lGPlevt13OGYhBriU/09y7Qqj8UrdeNM38/Z7r2wd03wSfbigIzQYQjzTbuzYS+Wb28GqlgsrXucIt1F7VSq9pu9Lp6YfS/kDvZkxvashMfawa8vRX4cr4Mb4f0L4YOL4I3TYNdyz+MGrCpctMnTh+xAUYjNm9d+Gb4BJtHpI1RbahrQRCveC50RaliveU1JsXQcCdzY00vb1TjJYqSw3OkpYlUScGDCHLSvk6dGqOY0xYBSI1RlBoPCSd2ac6jERreWqRgNCn3aZFTrPj6YdBx/7SngxK7NQl6v7V4NobdoSU8yc9PIbry5cCuFFXU8d5/VE/AE1Sd1a873f4Xp5VOhabKl0m0+Ak05rRvHZKVx5X+XsWybf3qpkGTsiekUlrtY6O5Nv/IZODBRTPA049fu4wE4qKYxxLIu6PqjcZJrMbwyPPiKwKkx1Q22AkjIAHeIlUQV2aPAjFClH6w2fa8fbZuDsNPYiyuCnlm3QnZfT9D243/g+OsiPxZ4MklaG2dDm4G6TBREWKNmL4WP/+n5uctISAqeuo+JvcZqeLuajjdaU2Oh+ghpX//GHBJJIBRHUhNCr4pJ0izdTraaKCx3+pY171Fa0l7dHXSbNOUoAiHdf3QJhKrj7X8NAmqeSRvauSlDOzcNe31gj6BI78nhPpiHdm5aaSCUnZFY5UBocIdMcsf3961Wm3xyFya+tUx3ToLZ4NtV/QjhM5k3ntqVVTvzmbuhP+NsD/Go+Q16GbZVaRw1pq0X8vriGjiwDtKDe365Fc8Xk8BVY74PqnB1G/Ziyh3+67RTqdri5ZAbyKouT4BWHe6AbJ6jNOhxI9J2ALcVhgyE9MXS1RtebalxsXQNp5WiFfDpt9jwXNBnEhtvKCRTY3EkcBsHL+1URtOKYtcN+z3fYOe4B4S+L/TdcrV781SH9BGqHkVR6nQ6MTugxidUuwWvcGnzgR2aVPo4rTOq3vk8wWLULdk3h8hSFZY5KSqvfJqtWYqFJIsRUFijduYs+6O85gzdP+u/Fcv468SG7+DIVtj2S9BV5kPrYPNP2Bz6QMibIdr77Imh7/PINtS5D9KcfAC0/7WcDv+0VLh/tyJ71T/o/t5byL4jAVt4VARCJqMBK3aOVbaBqob/AD2y3f9zeejCbO1Yo5UpqXGxdA3HW914tLa4Q6wa037ZqZXFMTFKAqE4kmIJUyyt6T586jGencxn/bEXl1vlCds/mOEcyz9s9+tuk6bol+pe/sZSX/BUOe3UmIgl5/Zrw+CO/m/m4RoJQvgP1J6t0kI2hNRq3aQagVBAnVKoRoFHSu0UllXesK5pilWXAQXYrwYHbofVFF50nlul8V1rv6FK51WVggr/O5fm+xb4jrXiEIZFzzH3l19oVfRH6Bt+P5WkJc/xruVRFNz+D665D9Hipa70VTxF6U6XChvnwPP6njY78iKvZvPKL7Uz+vlfePq7gHqoiuX7ZqPCy+bnmWW9C/74JDj/5A0Q8v2B0Kade/h7byFldlfYDscRa4QKdgdP1VVi15FS9uRXvv2JoigUlDp45sf11dq/rab1NVFbNRaiVYGuj1MjXkEmgVAcCbcTvXYbhrE5ns7EP63Lo/Nd32LDwmPOS/hd7aa7TWBGCGDhRv/mrDani70Flb/JSCgUWywmAx9MOs53OVIgFG5qTFEUbjm9Oy9c0i/sbXu0SqNZFZfamwMDIU1G6PSensA9v9RRpcLrzGSLriYOIC9EIHSS7TkOkOG7vNzdjTw1Q3eOmt6O553n8q17SPADZbSH468PPl4duz3Fx+kU84n1Ae4wf8ipc8+q9GbdDbt42fw85d6M0i9PobjKmWm9l1GGZZ7pjvfOgyPbdLdrp2j22IrwYbytYhl8ckAn7C17PLc3GQycalzpuZvfXtbf1dc3wosDPDVTmsefNnMpo5//hZwHfmTMC/4smbuqNULP9vQUfRftD329rRj+/sa371u5w8Wwx+dx/GM/RfwdB8+U0D1f/skLP23irBcXRjxXq6bF3dpAqD5DolB9hJwhjjVGEggJ3TfkLi1SOSYr8uaXAJ2VPSRgo7Oym2mm1/iHYQEb8/zflv75xlKGTvspdJZI20dF4qCYo63xOJp0+LicbN3WHFojujf3Ld2H8NO2ELwLe4LZ/7bl3TbE7nKzuwrf7punBmeEDhH8+15UUWi9ryJI+sg1giG2l+he/jbvO0+G896g6JrfedZ5AaF+iV81XYojrX3Q8e9dg3jKcUGl4wQYmfc2/zG9yxWmb2mjHKz8Bhqjjcto6whuhnih8WcsttCZn1TF//q5bMGZDwXA7cK64Ruakx+0YMK4/w9wu3V74qkup74macXbcHizZ7VYob/20PvFyu5ys26f/z3DG/xkUgiuMIGudsl/XphtU76cDB+Nhx/+A8BhTX1aucPF/sLysFN4LrfK0or99LwNQMNSVV+wVdNaH+3t6rMuJ1TzSm0WqDEvpZdASOimxsD/LTvQjfZrfT+PMK7mB8sdvG5+iktM83jGMoP8vVsAzzch70ac3/0RUDS7ZxU83d1/WZbPxzR7hG/L5/ZrDeDrPRTKCxf3o33TJGZc1p+Xx/cHoFfrNJqlWOnRyh+AZKeHnyorC6iV6d4ylUsGt6V5qpXTe2b5NqDVfoAGemPCQB45txedm6eQHPD7vtzdncWuniFvN872MP+238ynrhNRMWDDwl3OSdD7fH/GBfjIOUL/eLta89fOQ0H3l6EU81/XaazqOImDFn+h9BOOi0I+/iTTt9xgmhn2eUXysfsWyN+pO9ZGOcB1+8J30fYqLQweu83p5sj8l+nxy2QWWm/gFvOnuuvbG/Jg5X91U5eqSztdqfkgdTl0fY9SKxZfGHADqq8eyu1WGaL8zXLrNZy+68XQg9X2aApXYLN2pufv5W94Hl6T3Zi5cjdDHp3LY9+HXkVYrS8D30+FxzvAgQ01Dhy0sU99xh66jJBLMkIiziQGfDD0DrMk+0v3MMbaHvZdbm/Io6PBn4pOKdsFwJaD/je4VoEN9hY9F3CvEgjFskjTBg+d04tnL8rhxQhTYP3aNWH+bSdzRq9WnNm7Fb9OPYVP/u1Zwj6wg6cWqUWqlTN7twp7HzaHfgwmo4Fp/+jDsv+M5MRuzX2F12t25Ye9j1N7tGT8EE+GJimgn5YNC5c47uZq+01Bt8ujCT+4B6GGeKsst/vH9aDzcl5wnkOhmsi3rsHsJ5N5iSNZ7u7Gh02ugtYDAfjCNYxCUnjaeQHLyvzP+Sv3UPqUvxZ2/NvdLVjg6h32eq9DWcP15706Qnd9d8Muutj/pjLlmkCoh7KdF80v0JoDbJv3NgBWJUw91oYfSLH53xOMB//mTBYBkERAHyGHP6OUSimDlb9ZYp3Mm+YnKakofHepKi9ZXsCgqAw9qA+8NIP1/VhSWrUaHm32496vPA0sX5m/JeS5Trc79Eq7UJbM8HQbXzKjxrU+7jrKCG3YX8SIJ+fxxcpdIa93hShMr80aIVVVeey7dXy2IvTjR5MEQiKoAV64zsEA29XwzfueLL4LDqxn9U7/vkpBq47M+t4ukhCKbZHeh1OsJs7t1yZo64tIsjMSfYH36T1b8tk1Q5lzy0m+1YqhlDsjT0d4C6+3V3ELh8CMkNf37kHcYJ/MqbYnq3Q/a/f6f89LSOQZ54X0sb3BtY6bAHh96UHOt9/P1L0j4PLPebfbi3ziOgnw1DR95Toeh2rkafUydqktKCSZj5wj+M41iB9dA5jtGsBv7h7sVTN51DleV7ME6L6UeBWmdWWa81L/gVLPlJotpXWVnpNX4sLHfT+/bnmKscbfeMXyLFYi12Gpu5Zz7cpxumPPGF+kvbKPNDQLLFwO3ZYgqUoZT5tn0Fwp4BTjKlJeGwyzbsHpctNcKdDdX3lAhlCbEbr3w1+Ztz6PyngzTs3J5wzrH0SqxqlyJkQ7dZeQVrXbhfgPpr1ZbRZO3/7pGrYdKuXmj0JvJaOvEXKHOHZ0Y1my9TAz5m/mlk9Wx9xSfAmERFATvVbp4bdJKCRFd7lATWKZW1NI/eVk3TfzssDNO82BTe4kEopXiqIwoH0maQnmkCvBvMoqqctoU40VaBCcEdKMiK/cJ7BZrTxomPz+71z9buSO6iWacavWNFaacnBXvOWW2p185x5CL9sbvGjzL9+/w3kV1zhu5irHLUxy3MLF9nsY4XqZH9yDOKT6v6C86hzDH2on/nLr65BKrC34W23PTNfxuuOlTY4JO053Vp+gYynbfqiYpoLWiic71MuwjYTArE4ApSR0EDLE8Le+95itALfNHxilUkqKpkbJXLANlr2O2eEPlsoMyTz8zVr6PzTb/x7z/V2eDWgrpCklPPxNmDohzwg9D18RCH1rvZPpTOMMw7Kwt3C61Kp1e9a2AzAlVF4sPfchePZYKNRvUaTfBb4Kj1tFQQFkgJCrxmpYI+R2q3y9eg+b8op5/ZctbD1YQn6pg87Kbqabn2XSE2/5+n7FAgmE4kxOm3QAxuZk+44FNtFTFCVoo02t2a4BuFWF0bZpHG97kWVuzZvswY2s3uX/BldmD/ifbNJv/ClhkIDITfi6V1K836ZJ+I1qQ0mqxhYg4cxaU7399b5es5fPfvdPCeRVbDdho/KVc94M2mHV/zocUD3/jy+038sY26M4TpwKrfqyrfVYAA5WXO/10dbwwaKt9dCQx1tyhCz0tUKdDJEbZYbTVjmgK64+cugA+w/591xLV0pCdqtPtvkDq0R3CWOWXs6/3R+hvnaqZxXYb7m689MoDb9cH8DgbVZZkRGqyDa9bH6eXPNzFBcH15k5w0QjbrfKd3/sZdXOfM+Bw5qptfICmmz4hAFKhCX9vzzlKRhfqN8jr66mxkzGyO+27lA1Qq6aZYQ+/X0X13+wkpHPzOfhWX8z8pn5GBR4w/wUo43LeKF0Kp8sj50pMuksHWfenDiI2Wv3c1ZFIFRic/o279QK3GhT61rHjTShiDw8K2oS8K/AUO0lbNx7GG+MXeoIyAg59G92ikFi8Vj09AU53PLJap6/uG+9PJ42EPpH/9Z8/rtnRVHzVCv3jzs24m3bZup/f1MTTBSVh+8plGQNHQgN6tBEt3VHbbrhg5W6y5HGFyjJbCQfBwUk+44td3sWHJSQyF9qBwoGj6TZKXdSvHQHAJaAbXE2q9n8234Tr1ieC7r/wuzhJK54xXdZNSaguMpZnHCUy/81TjGs1C3Pb7L6Fd23oG7KLkxKcMBxzdrxusv9DJvoZ6jYpPej8UHnpyml/lmuX1/0bAh77gz/CYrn/caU95cuu2VQVMYYl3Lg19eA7ii4OdewkOVqd3YUhF488vuOI1xTsc/iGxMGcmrhNv+V67+l85FtfGaFDuXvh7y9T4m+GW1dTY0ZK6lD0HeWVj3NMDUF7dWpEfp1k36Vo8utYlAUOlTUlCYpNvJLq7fFTl2ST6E40zTFysWD25FiNfHiJf14c+KgancqdmDyBUEAi9z+DyrF7WCg29PwzYSTsoAdxHV7LnluUa3HFvXjvAFtWPfQGZzdt3q1JTWlDYSeubCv7+cPrzqOZinWELfwO61HS844NgvwdI6u7PzkMI1FM5Or1teovnkzQk78AdxqtbPunG0HS1i+7bBvld9it34V3BZ3K35wD+Zjp6dGydn5NNznvQknTeVAS33215bRKWgMn7mGBR0LFKrYfGM/z3L1Yw3bOdsYfvf57obayQ6kKyWeOGjLz/Dj3bB9ITzXy3+Cyw6/zSBn1hi+tNwTdHv7Yc8qu4uMP/OMZQafWe7zXWfFTmv8QcveAn8fpfX7i9i2dYP/jgJ6NAGcrcyHF/rB/rX6LFXAe2KoZew19eumg4x//Td255dVuvWJbvd5twqfXclxH/WhveLJAlZnaizUZ0rg4xdWY8PlutboA6H8/HwGDhxI37596dWrF6+9Fn5lhqg6q6bJ3Vx3fybZpzDX5Vk9dKZhCYmUM996M+M33qy/YcB/esVY9UJbUb8SamEKqaoC3yS/u3E4/7tiMJ2bp4S5hV+ixciMywfw252n8s31w6ls1xZtu4ivr/N/wMd6IPSV63hmuQZzm+MqX61Ry4o+SufPWMz5MxazYrsno/WdezBT7Ff77mOL6lmh9oDzn9zj/jeD/jqPCxe2gpPvpNDmxKZ6/h8uch3LxlJ/5snrpSp02f7ePVh3+Tr79exP71vp7crU2nvd0yhBdbthzgPhT/r+DiB08KU6PcHNuUZP48TmSiHDDH/wtPllvrDcx6KEGz2BDPhqXC4w/kyfLa+zZVPoaTBvrdVjhpc902fTh+q6am/dvZd1+/wF39opKl1CaONseLIrbPgh/HMLcOnrS1i06RCPf7cuqAQikL42SYU/Pav0Jhh/BKo3NRbqu3XgsepkRetaow+EUlNTWbBgAatWrWLJkiU8+uijHDoU3CNDBAvX5O6GU7rovrWDwmz3QF51erreXmT6mQXWm2itHKJ7yTJcG2az8/HjuO/ld1ArlrqWq2bOsT2IYpTZWQHDuzTHoPhr2Hq0SmN41/D9iULJSk8gKz2h0m/R2qCrY3P/h/4px4SeAjkmK5WxOdmse+iMao2ntng/wGxYmOy4iU9cI3zXpQQUfnsb/4HC5+4TuSPtccbb7/RtRFtCIv+zn8QR0lheETQVlTuZ4LiDma7jucFxHQXF+unrfWoTtqnBr403eAplv5rBN+6h7DVVnlH0rqSrqlznuLDXpSslZKv7YE/kQvZwTCX7+dFyG0MM/p5C71qmcZ5xIT0NFcHLum+g9DCtN39MBkU8aX6VYTumc4pzQcj7TAlR+8Qr/v3ijCV5XDB9se9y2KmxT6+Akjx4/0LP5T2r4JuboVy/qg6AAxvIP+SfinS63QTGQb76I3sJqGpARsg/Dabgba6oGYvTBnnrwi4rNYSIhFQVnKp/EIUxFAg1+k8ho9FIUpKnhsBms6FG2gRQ6Hxx7Ql8vHwn5Q4X/13s/wYz5fTuITtGL1GPYb27Dd0Nu2iu+L/hGN8/n7bApSVPYGuRQQJwlWMKq9TI+1GJ+JGeZOavB87AYjr672aBcVBgw8emyf6pswSTgfm3jWDzgWJGdAvdGuLL607AavJkZYwGJWKgZTEZfEuzq+vtfw3i9V+2sjCgviLSap+UBH0wEvjx8+nBdrjcbSM+7r6Ccn5z9+S3iuk07equ2a7+POm8KGQfpbnufpxpXBryPrdXBE77bJVne551nsfJhlW0NVRt4+bnnOdzpmGJroeZV3Py6eqqqCHK7g/dR8O8R4LOc5iSMTtLgo5n7Z1LVmW/gtZU+OxKRmyeywxLj0rHuybhKv7nHKk/qAleWir5KLZ8WJwL/S7Drfr/Fb3BiaqqYDDo/31frQggVRXGPuc/fmA95A7GktoR8Dx3k8Ggywg9/M1avv1jL99O7ETGm8fDMWfhTpziuz5wGmy8cQ4pJf0BT1NUPp7g2Tj4wv9Bz+DANFRS1uFy48KIqSJDdm7eS/DffLj0YzBFNxsb9YzQggULGDt2LNnZ2SiKwsyZM4POyc3NpUOHDiQkJDBkyBCWLg39ny+c/Px8cnJyaNOmDbfddhvNmjWrpdE3bl1apHDXmT148OxedG2hn6KwhvzAUrjWcWPY+0tXSlAqpsaK1cSKW0iNkPBItBgrrWOoCm2g8txFfXnpUn3Dx/QkMx//eyhfXHs8JqOB9k2TOeWYliH34lMUsGj2Nou0zH/pXaey/O6RLL3rVN1xb8NHgNvP6K67TrtdyIjuLXj3yiG8P2kIFwxow52jj2FYl2YR67QCeyIdLNYXoFalxmRzwEaiDzoux6aauc1xFZMct7JB9QRSl9nvZL6rD3c7/sVsV3/udvwfq9yeWqUjquf94RL7f/jF3YfbHVcBsDu/jMkVm9I+7TifHSNegOt/p8zgfz85Qhqn2Z/gdNvjXG6fGjS+AeXTdZcdmPhL7RjyuXQy7ONWe0VxdHZfOOl2OP9NXneOZlD5y3ztOg7OfYXPT5nHLrWGnwOlh2DzXACOM1TenBLgctOcsNdZFQffW6fCD3fBz4/pskDexMxtn65hbalmT7wl/uJ29gVsfvv31wAkFW1FqQg6DhbbfL/fJpy8vnALewrK2fjdi54FLH98TOdDP/OJ5X7aKHk4nP76neGGP3jE/CZ37rnO/xgbvvP8/esLIZ9TqKkxh0vFpQk5xpR8AVvm+V7LaIp6IFRSUkJOTg65ubkhr//oo4+YMmUK9913H7///js5OTmMGjWKvDx/2s9b/xP4Z8+ePQBkZGSwevVqtm7dyvvvv8/+/WE25hNhPXF+H8xGxfdGHu6b+2a1NRPtt4e8bo/aFENFX5Biqtf7RYiq0n6QnNOvNWkJwVM4gztm0q9d8Gargawmg67wUxsUBWqRlkBagpkWafo+XNpAqEeWfjVmi9Tgnl3Hd27Gkxfk8O+TOvPulUNoGqJ2aVxONt9cPyxo37RIW6KE4nS5gwKh5eoxdLe9rZuCA1jo7s0Ex1TedZ3GJMetHCaNa+038p7zVM63e4qKF7uP5XL7VLZV1CR9+8deZrmPY1B5Lrmuc5jyd1do2plCiz5LV46VDWpbfnH3DuqgfQh/K4CNbk9QuFkN34k81du4sVVfAAo6j+Nh5+UcIIPrHTdAzsWUqRbOtD3KePudVXuhtEojl1b85q48SxQoW6mY0tzyM9rFWd7ZC3Xl+xyraJbnf+d/jw2KdV3+YLgF+bxkfoFb9t+BVXFixc7P1im8b36Ey40/0jp/ue/cCzdPZZBhA4+bXsPo8GfLOiqaVhGBK8dcwSu//thVQH6pvhC6BUfos/RWkpQQfaiKataSoTZFfWps9OjRjB49Ouz1zzzzDJMmTeJf//oXADNmzGDWrFm8+eabTJ3q+fawatWqKj1Wy5YtycnJ4ZdffuH8888PeY7NZsNm8/9jFRYWhjwv3vRr10Q3deGdKgglsMmbV3/DJrwbVnszQpIQErXtaFfaaAX+nhsr6cUSirZ/S7Kmpicjyczwrs14b8mOiLcPlam69fTutGuaFLRPYHWVO91szgueIqrqf8w9NOM/zivCXu+tAzlQscrUW5dUYmoCBG8KCwpXOG5jBs9yqnElf1tzoBzudvyLG8wzmezwZJdmuwZwo+mLyIPrMZbv/9wbsvGl3eWmkBR2qdWrQwNg359hr7KpJq6yT2HN2QdwzJ2G2Rl+/7uQMtpVBPIq/zZ+Q3tHEbz+GE9bws+C2PZvIFFV/WmYfP/v05KEiiyOE3qV/85BxUkb5SBtjAc53rgWQny8tVPyMDn9wbFR0fx/ensMTPzGf9lRBu9dCJmdYPRjrNxxhHNfDl4d+L7lEdru2hP6CRSGOV6Pop4RisRut7NixQpGjvTPrxoMBkaOHMnixYsj3NJv//79FBV5fhkLCgpYsGAB3bt3D3v+tGnTSE9P9/1p2zby/Ho80WaBQk+NeRxA/037McfFusuqYvRtFyBbbIjaVpt7QwZOhdVkI02TJouknQqbNLwTWWnhu7h7hUpCWSvu52gDoX0FZewr9Hw76dIi/Aq9k7s3p1fr8L3FKjPx+A6+n8+f/iuHTKEL08Ez9TXVMYlHHJdyccFkAN51ncbg8lzfNN2faifOs93HVXbPqlR3arb+Ti78HyRlkjtvc8jH8O5fV6NAaFf4oKSIJApJ5vGCUylIrUEN5IbvefHjb/nU8gB3mj/gUvc3ER8PINFVBDt+gyPb+HXTQdasCV0oPqhkvm9z20gSFLuuo7fOjl89RdpeBzfAxh9gyXR4JBvTtzeHuJFKF0OEYOfQpkrHVNdiOhA6ePAgLpeLli31/2latmzJvn1VS6dt376d4cOHk5OTw/Dhw7n++uvp3Tv85oV33nknBQUFvj87d+4Me248q6yo9SPjWArUJIbZnmem6wT9lSktcUQ/GSkaqWcvygHg7jHVn6IIFNgPpaoLLZ6/uC8Wo4Hp4/tj0WSEtC0Jemancdlx7clKS+CfQ0NnUSH0ChzvF5HAqTGv1IDVZHeccQzHZKUGbZ+zdKsnQ9OxWXLQFNyPN/tXNjVJtpBkrvn/2bP6+Keylm8/wuR9Z7HFncWjjktCnn+ADF5znUUB4YOzFWp3fnQP4nTb4xz65zw2uz2P8anhDF8Bb6hmseCfQnRh9K1+O6imcUvX78hX/SsJ17vb8JTjgio/z0LV83jTf96Mw1RJ64cWPVnoCm4W+nrRNQw0bAhxA7117rYc9G678tYZ8Nop/N/bS8hyhy79yClZRGvlYMjrtBKwM7z85/AnlIdpOuooofe+L7jMOJv/mqfxH9O7LLZexwjDqsgPGAOBUKP/NBo8eHCVp84ArFYrVmvkhmwictEowJaB/6Hf/ItwY8ASuFGjql2aKUTtGt61OeseOqNW+iAF/pq3b5rM2r3B8wnv/J++h87ZfVtzZu9WmI0GftvirynRBjXHZKXSJNnC4jtPidjUNFQBucUUOSP0f8M68vzcjb7LfdtmcM2Izrzz6zbuq9htHWDpVs/YerdO50hAp1/tZrpNky0cKq55J+C2mUkYFH+2Lo8mnGJ/JvKNqmiD2pZnfsljkeN2zjQs4UvLGNpvO0yi2RhyxZ3Lreo2gz7L/giPmV/jKeeFLP7jCH8r/2GS6TvOveVlfv3LxRtf/84U06cYlMqD4L/UDr6fbcbIW7+4TYk01XTbjuRd56lcZtIXFeepGbgw0My7Qrf0EM1cB2hm9KxI+yvzNK7ZeyYmXHxgeZiW5HOOcVGlj5WqlHGJ/dOw12//5knCh+3wsPktAE7E01j3bUslmxjvWwOr3odjxkBCeuRz60hMZ4SaNWuG0WgMKm7ev38/WVlZURqVgNCdQ71euKQfV5zQ0df0zY6+WFXbIbe6Xa2FqIraagYZmI05JsS+Zz/efCIndQueYjFXzGndfFo3Tu7enNxL+9OmSSKpVhOZyRbftFhl/wdCbY3gLdoOt11Is1Qrn13j30MsueK85qn6L3l/7fF8iPZoleYbr5c2EMpMth7VNFx1mlWmhelfFskHS3eyQ23JDNc49pYZuWDGYs56cSEHioKLc2/8cKWuxcFGtQ3n2R9gcUWH/LVqB252XAMZ7fjXCR25aXRfBtleJqf8VT5wnqy7r/edJ3OV/WbutP6HnxnIA44JvutM5YcJ9LDDvy3I34dctFTCb+nizXAB5KnBhf0HSGefmqk7Nsi4AYOiYlNNjNkzkR1qS7ao2fxU0exW2x/pDNtjYR87kvb5v9XodhHNvAaKq9Y+oS7EdCBksVgYMGAAc+f6I2G3283cuXMZOjT0RoEi+sblZJMS8Gb2RMKNOFQjbgxcW/DPKI1MiOoJTMZcOqSd7vJvd55Kt5aRN4XNSLLw1r8GM6ZPKxLMRhbecQqL7oicBdIKdZ637ijcBrJWo4EmSf7gw1ukHRgI7avYJqJpikW3Ii7RbCTBbPQFJSN7tAi5+k4rVJAInqJws9FQ5dqtZqmRM/JNkqrejV67AbTXN2v28vav23THzh/QJux9tEizcoh0CkjhTuckBpXnUqQm8qNrAHc5J/GjexAzS/swsXwKBzUr3Hba/VNj/7bfxF2OK3jdNYZ5Ls/U7VNFp/GAw/NeqA2QAG62X8M056UAHOo5gXKCn/MBtQmHVH3d1sPGNwAqAiT/780KtZvuvAcdl7NObcc7bR7AlRG6FUFVBLYgWNniHFa5O+NQqx80H7KG/zeoa1GfGisuLmbTJv8c4datW1m1ahWZmZm0a9eOKVOmMGHCBAYOHMjgwYN57rnnKCkp8a0iE7HF2+k2IWC1zfKM0XTNH8LNJ7Vh9nx/a3vJB4lYEtgQMTAIGdghk5mTT8BkUDAbDWSlV17sHCi9Gh/kEHnjTW2NUHqimYKK/ZvMJkUXCHmDnOYB+7AVVewFmJ5oxqyp+/MWTv9828kcLLbRtaVnGi+ScAsoqrt1yRnHZnGk1M4HS0PXZzZLsXKktHb3qQrsk6YVuHLwAE0YbMulHP/zKgsxBXf7kXFcY1SY7hrLTk1n7qsdN9PWmccm1fPBv7C8N4dIY7OazQMnWPnXwnQ2q542AaPdz3F/v1HYV08Luv9f3T25zKjvT5SseALbvTTVHV/h1gdCeWoGAIutwxl70nFkvjcq7PP32u5uQXuDfipvuO05/rBeSUrF41669xLKHBcy03I3fbXL/QO84RzNFabvfJc3uFvTPIobcEc9I7R8+XL69etHv36e1N2UKVPo168f9957LwAXXXQRTz31FPfeey99+/Zl1apVfP/990EF1LUtNzeXnj17MmjQoDp9nIbs5pHdgo55t+UwGBRdQXWTZM+b/7OaIAhk1ZiILbOuH8a/Tujguxzq97Nv2wx6tU6ne5gMSG2LGAhppsb+c6a/ONxiNOqmtryZoMCMkFdGohmzpqjbm+XKTLb4fq4sE3Ns69D1HVsOhFqeH16C2ci0f/RheFd/tkE7XabdVLdT8+SIvZ2qKiPCc9Ou9PMqIyFkt22tnWpL7nJeqQuCwLNVijcIAm+fJIV57n6sbjPeFwQBbFNbUew0UoI/4H7IMZ5/2u9ggTuHX9yhF/54i7a9tqpZLHZ5OocfVNN8G2WX2J2UpnQI/QQy2vHOoC99FwNLHABUDBjxf3FwVBShF6rB+9VpPeIczym2p5hov43l7m5c5ZhCWmL1viDUpqgHQiNGjPBte6H98/bbb/vOue6669i+fTs2m40lS5YwZMiQOh/X5MmTWbt2LcuWLavzx2qobhzZNejYtH+E/o8ZqxtaCqHVtWUq9431r+QJtWKrvkXqi6St22mR5g8QLCYDBoPCmvtPZ+U9p/lqppKtoScB0pPMuoxOt5bBGRJthknr82uP58Gzj+XcfvoO2Md39mQlxgdMJ1bGW6ukDXDaZvo/2LXB3KD2mfx82wjfY9VUaoRpv0g90wI9fl74FclVsb+wXHfZraoU25x84zqOnaYOqAOv5BPz2Sxwe6bXPnSdwgOGa1k35jN+cA1ksasnDtXIbPeAgHtWmOy4gRed53CZ/S7f3nPFNie2gNVtLlVhh7s56sRZHLb42xIcwj8NV6wm8LTD04vPoAmEvFuCaOtAAWa59IsJ3BjYombzs7sf59vv55Clba10la+pqE+NiYZtSMdMlmw9zCWD23LXmT30byia9+9wbzSyxYaIZVF8b/aJlBFK1Cxp107TeZf5V1bX45WRaNEVS3dsFvyNPtzUWP92TejfrgnbD+kzP/eO7UlBqYMe2dXrP+TNTGkzyq3SE32F3dpAKNFiJDsjkacvzOHiV39j+6HK++SEkhihuD5Sz7RAXVqk0K1lChv2h+nDU4mHZ+m37HCrKkXlTspI4OH2b/DKWQNpvfkXCitWLjowMZOTUfZl86bDs1eYFTs2zbSdd7XeYdJ42nmh7v5LbE7sTjdb3Fl0Mnha0uTYXqOUBMZ8d4ivV+9hi+E6TknZxv2F43jJ/ALfuYfwoetk32KYV11ncb1pJrNcg317sAYGQlMc1zImzL50QFSzQRADGSHRsL36z4G8eEk/7j3r2IjfqhJqYTNNIepbbGSEwl+nDRa09T+2CBu/fn3dsKBj6YlmXSAUagot1NTY1Sd19v0cuF2IyWBgSKemlQZj3Vum6hou+jJC2ueW6v9g106NeTNdrdITmX/bydxxxjG6+9Zub6KVajUxtJM/ixTpnzlwNZ2WQdHfNsVqrtWGnm7Vk7UBfzYv8Dm53Cqrd+X7LmuDIICmKeGLz0tsLmxONzc7rsWmmnjHeRrFJOHGwNerPU0Q55lPZODVr1FACpc77uJ916m+IAjgBec/uNw+lVsdV/uOleF/zIHl03VjCtU7KV0CIdGQpSeaGZuTTWKIpbWqJiVkDfONKwY+Z4QIKxZ+P92aT9b0RDMTNM0XdR/CmjqaSIFQ7zbpQZmuBLNB94HfLMSHZ4ZmamxcTjar7z2dqaP9gUeixahr5GgO2I7krjM952prfwCevKCP7lzvliTaqbGmyf7xaIO0wMfQZrIuHNiGRVNPCVrpB0BAAFNq9xc73z+2JzMn+5vAVhYMZ2g+xFMSTLW6xYtbVX3TZd7nrZ0CBc82Jiu2h1+GH2qvOq+8onLOfXkRq9UuDLTN4H7nhKBz+rbNoG1mEh9edVzI+3Bg4hd3H8o0dUzPOM+nUE3iJefZvpV0jzku5k93B6Y4rg26j2gHQjI1JuqMNqMfKr1888hutdbvRYi6EAsZIe3U2Iq7R+q27Ai1MgygQ5iOyv771F9WFAWnJvXUNCX4w1ObERretVnI1W/N06wUHfBkMEwBmZRJwzsxulcrXG6VEU/9DMCsG4ZxbHY63//p3ykgVEaoWYol5M+B2jTxZ0tSrJ7xhXrvUdC/ru00NUgTT9AvJ4/0K6DiCX68q9hSrCacgRuTHgVVhV1HygB/Jihcy4RwmqdaWbcv9J5nDs2WMUWE/p3x1mcd16kpJ3ZrzoINlff72a5m0df2qi5zNMM1jhmucSHPl0BINFra91rtm1p2egI3ndaNCwfKPm4itsVCIOTSfGAHBhcdmyVzz1k9aZZiQVEUvrruBDYfKGZgh8zAu9E5p282M1fp938qLPcvSQ+1dUdGkoUmSWaOlDo45ZgWIe9X28AwNaCXmKIotM1MokyTffF+EGuflzeg0+59pp3eibTwQhsIhao18jIYFN1G6r1apzN9fH9dUbZXpEaSqqoPQFOsJlw12I8uklU78wF/IBQq+w4wontzfl5/wDcO75RapIxQVWhX1FUnYHGHmXA6s3cW3/6h3yLraPfMO1oSCIWRm5tLbm4uLldwfwhRNdp9mbTfPObeMiLsf2YhYkkMxEFhGxV6XTHMn8Ho0yaDPm0yKr3PR87tzfCuzZm5ajfjcjwrg4oqdooPx2hQ+PbG4Thdati6k56t0liy9TAdmyWHrQ3S/t/3ZmIsIabGLjuuPbuPlHFit+a+YxB+5RvoP6i9gV2oVV8K+gATYHTvVkHnAXRqnsLE4zv4mjA2T7XqAj7t/RsNCo4qTo2d178Nn/2+q9LzvI+VXUkg1D0r1RcIJVmMvkAo1DRndWh/v6qbjbIYDb593bwGdcgMCoQOltR8+5baIDVCYcjy+aOnfTuwOf0BZai+HELEoljYAmZA+0ymj+/PdzcOr7X7TLaaOG9AG/53xRAuqMjMajNC4bRKTwyZNfF68OxeTBjano//Hbnz/4q7R/LL7Sf7sjvajJB3asxsNHD3WT05sVtz3Yd54IayWtp/L29zyVD7IiqKUq1anvvH+Qt8xwQETIEZJ+39/qNf65Abv47p3Yqh1Vzy37pJ5Kmx0b0847Ka9PVe2WEKxsO9D2tX0D1/cd+A4vTqvXd/cnXw70FgVhP8/1bRIhkhUWe0X7jKHeG79QoRqwa0z4j2EIDw2Yra1C4zmUUcqvzECLpnpfLA2b0qPa9pilXX+7hZJVNf2oLkJE0gFKqzwOk9W/Lj2v38c2gHIHQLhMAaoaq4/pQufLJ8F9eO6KzboiMwENLWWj1zUV/fzweLbfy6+RB9WqfTNjOJb9bopyYjSbWafBm2UNOW4MnGzb9tBGkJZk5/boHveHaGfjVf95apjOubzVuLtlHuCN6LLTPZwu58T11S4PRmQphsVOuMRN9ttEJNS5oD/kHMRoU7zuge8n7riwRCol44I60BFiLGzL75RL5Zs5dJJ3aK9lDqze2jumM0wPkD6r92b1xOtm913MD2wRuMtkhLYNLwjhgNBt82PgBdQzR+fHl8f/YX2Xw1NaG+eClK5EaVodxyenemnNYt6P4Ci7F7Zqfx25bDQZmoZilW3zQkRF6Wryhw5+hjePRbzyaprTW1T+GmxiwmA+2belbN2TRbfqQn6gPLJy/oQ582GXy8fCcHQ7Q70mZ9vAXnvuvCNJdslmoNGQgpCpzQpSmLNvkD7MCM0B/3j4r6ohmZoxB15qFzPN8Mrz+lCxOO70CTJLNu+wIhYlXXlqncfFo33YduY9ck2cLD5/Smb9uMen9si8nAhYPacuGgtmEzxv8Z09O3XP+La4/nnrN6MrZPdtB5JqNB12sn1N11z0qlRSWbu4YSamxn9/V01PYu3X/mwr5cOLANX18f3K9JK/B1TtYEODOvPYGLBvmX/WszZlUpLC7XtE8I/B32dnBODpNZsmiCncDbhgvCtGP/+dYRvp9tDjev/3OQrnYrsOVBtIMgkIyQqEOXH9eekT1akJWWgKIoLL/7tKi2URdCNA792jWhX7vgzFEo2pV/MyefwFuLtjJ19DG4VZj62RpdMXBN/KNfa5qnWulV0UE7OyORJ87PqfR22RmJzLt1BCdXtBJIspooqVhR1yo9Qbe/mnaKqSoLTbQbBydb9ed7M1Hh6n20jxU0NRamMe4JXZpR7nAxsEMmHZoloyieacuOzZNJtBgZ3DGT2Wv36x4/lkggJOpUq3T/NzMJgoQQ9U37ttO3bQbPX9zPd/l/VxzdvpVJFiMGg8JJ3ZrX6PbaBpBpCSbfCrGmKVZd9km7RD9cjVA44TJC4QqUtQmbSBmh9ESz7z6aJFn4/Fp/E8o1951OucPtq2vSBlShitejTQIhIYQQjVZd7Gf4zv8N5oGv/+KJ8/oc9X29cEk/lm09jNVkYPOBrUDwl8bumhYK2qmxE7o0xWI0cLmm23igwHYD3kDkcIgl6/93QkcKyx38viM/5G2101jaQCjRos/ypCaY0e64om2lIBmhBkT6CAkhRMNXF4tUT+rWnJ9uGVEr9zUuJ5txOdn8vuMIry/cSufm/izRf/9vMN/9uU+3p5t2eXvv1hm6bU5CSTQbfVNV4C9W9nbD9mqSZObesT3JL7Wzemc+rTISg1Z9aQOh5qlWdhz2bHKbao3caFEbdHUIsaFvtEkgFMbkyZOZPHkyhYWFpKenR3s4QgghaiAWuoNXRf92TfjhphN1y91P7NacEwOm3bTTU1Up+DYYFJLMRl/9kTcj1Ldthq9rNfgLwTOSLPxw04kYQkxhaQOhf/RvTe/W6dhdboYF7B8XaFCHJny1eg/NUiy66cBYIYGQEEKIRmtE9+Y8+E3kPcpiRfdKuoiDfmosI8R+bwB3j+nBw7P+9k3dJWsKsb3Tbi9e0o83Fm719UTq0tzfiiBUEAT6bFTzFCvjh4SfktO6YGBbrGYjo3pmVen8+iaBkBBCiEarU/MUFt5xsm6D2oZM28sn3N5fVw7vxHn929CkojmlNpPjzQi1zUzi/nHHcv6ANry5cCtTTu9W+WNrVppVZ9l7gtkY03tLSiAkhBCiUWvTJPy2IA2NNlvTu034so0mmg7d2gAmsKFhr9bpug7YkWgzQrHQ/6e2xF75thBCCCHCWnrXqcy7dQQtUhMqP5nQGaGaSNAFQjUPH26v2FKjNlbd1QbJCAkhhBANSIu0qgVAXj1bpbFmVwGZyRZdT6Lq0gZCofYRq6prR3Th0sHtyIiR6UoJhIQQQohG7P5xxzIuJ5tuWalhC6GrQpsFOtrVeLESBIEEQkIIIUSjlmA2cnyXyEvcq0JbI9QwmhJUjQRCYUhDRSGEEMLPZDQwNiebIyV2OmuW2zd0iqp6+02KULwNFQsKCkhLS4v2cIQQQghRBVX9/JZVY0IIIYSIWxIICSGEECJuSSAkhBBCiLglgZAQQggh4pYEQkIIIYSIWxIICSGEECJuSSAkhBBCiLglgZAQQggh4pYEQmHk5ubSs2dPBg0aFO2hCCGEEKKOSGfpSkhnaSGEEKLhkc7SQgghhBCVkEBICCGEEHFLAiEhhBBCxC0JhIQQQggRt0zRHkCs89aSFxYWRnkkQgghhKgq7+d2ZWvCJBCqRFFREQBt27aN8kiEEEIIUV1FRUWkp6eHvV6Wz1fC7XazZ88eUlNTURSl1u63sLCQtm3bsnPnzrhdlh/vr0G8P3+Q1yDenz/IaxDvzx/q7jVQVZWioiKys7MxGMJXAklGqBIGg4E2bdrU2f2npaXF7S+/V7y/BvH+/EFeg3h//iCvQbw/f6ib1yBSJshLiqWFEEIIEbckEBJCCCFE3JJAKEqsViv33XcfVqs12kOJmnh/DeL9+YO8BvH+/EFeg3h//hD910CKpYUQQggRtyQjJIQQQoi4JYGQEEIIIeKWBEJCCCGEiFsSCAkhhBAibkkgFCW5ubl06NCBhIQEhgwZwtKlS6M9pFqxYMECxo4dS3Z2NoqiMHPmTN31qqpy77330qpVKxITExk5ciQbN27UnXP48GHGjx9PWloaGRkZXHHFFRQXF9fjs6i5adOmMWjQIFJTU2nRogXnnHMO69ev151TXl7O5MmTadq0KSkpKZx33nns379fd86OHTsYM2YMSUlJtGjRgttuuw2n01mfT6XGpk+fTp8+fXzN0YYOHcp3333nu76xP/9Ajz32GIqicNNNN/mONfbX4P7770dRFN2fY445xnd9Y3/+ALt37+ayyy6jadOmJCYm0rt3b5YvX+67vrG/F3bo0CHod0BRFCZPngzE2O+AKurdhx9+qFosFvXNN99U//rrL3XSpElqRkaGun///mgP7ah9++236n/+8x/1888/VwH1iy++0F3/2GOPqenp6erMmTPV1atXq+PGjVM7duyolpWV+c4544wz1JycHPW3335Tf/nlF7VLly7qJZdcUs/PpGZGjRqlvvXWW+qff/6prlq1Sj3zzDPVdu3aqcXFxb5zrr76arVt27bq3Llz1eXLl6vHHXecevzxx/uudzqdaq9evdSRI0eqK1euVL/99lu1WbNm6p133hmNp1RtX331lTpr1ix1w4YN6vr169W77rpLNZvN6p9//qmqauN//lpLly5VO3TooPbp00e98cYbfccb+2tw3333qccee6y6d+9e358DBw74rm/sz//w4cNq+/bt1YkTJ6pLlixRt2zZov7www/qpk2bfOc09vfCvLw83b//7NmzVUCdN2+eqqqx9TsggVAUDB48WJ08ebLvssvlUrOzs9Vp06ZFcVS1LzAQcrvdalZWlvrkk0/6juXn56tWq1X94IMPVFVV1bVr16qAumzZMt853333naooirp79+56G3ttycvLUwF1/vz5qqp6nq/ZbFY/+eQT3zl///23CqiLFy9WVdUTTBoMBnXfvn2+c6ZPn66mpaWpNputfp9ALWnSpIn6+uuvx9XzLyoqUrt27arOnj1bPemkk3yBUDy8Bvfdd5+ak5MT8rp4eP533HGHOmzYsLDXx+N74Y033qh27txZdbvdMfc7IFNj9cxut7NixQpGjhzpO2YwGBg5ciSLFy+O4sjq3tatW9m3b5/uuaenpzNkyBDfc1+8eDEZGRkMHDjQd87IkSMxGAwsWbKk3sd8tAoKCgDIzMwEYMWKFTgcDt1rcMwxx9CuXTvda9C7d29atmzpO2fUqFEUFhby119/1ePoj57L5eLDDz+kpKSEoUOHxtXznzx5MmPGjNE9V4if34GNGzeSnZ1Np06dGD9+PDt27ADi4/l/9dVXDBw4kAsuuIAWLVrQr18/XnvtNd/18fZeaLfbeffdd/m///s/FEWJud8BCYTq2cGDB3G5XLp/XICWLVuyb9++KI2qfnifX6Tnvm/fPlq0aKG73mQykZmZ2eBeH7fbzU033cQJJ5xAr169AM/zs1gsZGRk6M4NfA1CvUbe6xqCP/74g5SUFKxWK1dffTVffPEFPXv2jJvn/+GHH/L7778zbdq0oOvi4TUYMmQIb7/9Nt9//z3Tp09n69atDB8+nKKiorh4/lu2bGH69Ol07dqVH374gWuuuYYbbriBd955B4i/98KZM2eSn5/PxIkTgdj7PyC7zwtRRyZPnsyff/7JwoULoz2Uete9e3dWrVpFQUEBn376KRMmTGD+/PnRHla92LlzJzfeeCOzZ88mISEh2sOJitGjR/t+7tOnD0OGDKF9+/Z8/PHHJCYmRnFk9cPtdjNw4EAeffRRAPr168eff/7JjBkzmDBhQpRHV//eeOMNRo8eTXZ2drSHEpJkhOpZs2bNMBqNQdXx+/fvJysrK0qjqh/e5xfpuWdlZZGXl6e73ul0cvjw4Qb1+lx33XV88803zJs3jzZt2viOZ2VlYbfbyc/P150f+BqEeo281zUEFouFLl26MGDAAKZNm0ZOTg7PP/98XDz/FStWkJeXR//+/TGZTJhMJubPn88LL7yAyWSiZcuWjf41CJSRkUG3bt3YtGlTXPwOtGrVip49e+qO9ejRwzc9GE/vhdu3b2fOnDlceeWVvmOx9jsggVA9s1gsDBgwgLlz5/qOud1u5s6dy9ChQ6M4srrXsWNHsrKydM+9sLCQJUuW+J770KFDyc/PZ8WKFb5zfvrpJ9xuN0OGDKn3MVeXqqpcd911fPHFF/z000907NhRd/2AAQMwm82612D9+vXs2LFD9xr88ccfujfB2bNnk5aWFvTm2lC43W5sNltcPP9TTz2VP/74g1WrVvn+DBw4kPHjx/t+buyvQaDi4mI2b95Mq1at4uJ34IQTTghqm7Fhwwbat28PxMd7oddbb71FixYtGDNmjO9YzP0O1GrptaiSDz/8ULVarerbb7+trl27Vr3qqqvUjIwMXXV8Q1VUVKSuXLlSXblypQqozzzzjLpy5Up1+/btqqp6loxmZGSoX375pbpmzRr17LPPDrlktF+/fuqSJUvUhQsXql27dm0wS0avueYaNT09Xf3/9u4vpKk2jgP4d7h23FhitiHDnAaS2R8NqWgZhMyCiKButqLIGhHhTcgqZWKEI9bNbiz6B5FIUURdyaJMal6MpAykkpGrrG6ChUkUE5H260I6b+c1XqrXWtv5fuDAYc+zs+d3GOPLc86zE41GNUtHU6mU2ufAgQPidDrl7t27Mjg4KC6XS1wul9r+ddnoxo0bZWhoSG7duiV2uz1rlg63trZKf3+/jI6OyuPHj6W1tVUMBoP09vaKSO7X/z3frhoTyf1z4Pf7JRqNyujoqMRiMWloaBCbzSbJZFJEcr/+Bw8eiNFolOPHj0sikZDLly+LxWKRS5cuqX1y/bdQZHpFtNPplJaWlhltf9N3gEEoQ06ePClOp1NMJpOsXr1aBgYGMj2kWXHv3j0BMGNrbGwUkello+3t7VJcXCyKoojb7ZZnz55pjjE2NiY7duwQq9UqBQUFsnfvXvn48WMGqvl536sdgFy8eFHtMzExIU1NTTJv3jyxWCyybds2efv2reY4r169kk2bNonZbBabzSZ+v1+mpqb+cDW/xufzSVlZmZhMJrHb7eJ2u9UQJJL79X/Pv4NQrp8Dr9crDodDTCaTlJSUiNfr1fyHTq7XLyLS09Mjy5YtE0VRZPHixXL+/HlNe67/FoqI3L59WwDMqEvk7/oOGEREZneOiYiIiCg78B4hIiIi0i0GISIiItItBiEiIiLSLQYhIiIi0i0GISIiItItBiEiIiLSLQYhIiIi0i0GISKinxCNRmEwGGY8J4mIshODEBEREekWgxARERHpFoMQEWWVdDqNUCiEhQsXwmw2o6amBtevXwfwz2WrSCSC6upq5OfnY82aNXj69KnmGDdu3MDSpUuhKArKy8sRDoc17ZOTk2hpaUFpaSkURUFFRQUuXLig6fPo0SOsXLkSFosFa9eunfG0cSLKDgxCRJRVQqEQuru7cfbsWQwPD6O5uRm7du1Cf3+/2ufw4cMIh8N4+PAh7HY7tmzZgqmpKQDTAcbj8WD79u148uQJjh07hvb2dnR1danv3717N65cuYLOzk7E43GcO3cOVqtVM462tjaEw2EMDg7CaDTC5/P9kfqJaHbxoatElDUmJydRVFSEvr4+uFwu9fV9+/YhlUph//79qK+vx9WrV+H1egEA79+/x4IFC9DV1QWPx4OdO3fi3bt36O3tVd9/5MgRRCIRDA8PY2RkBJWVlbhz5w4aGhpmjCEajaK+vh59fX1wu90AgJs3b2Lz5s2YmJhAfn7+bz4LRDSbOCNERFnj+fPnSKVS2LBhA6xWq7p1d3fjxYsXar9vQ1JRUREqKysRj8cBAPF4HHV1dZrj1tXVIZFI4PPnzxgaGkJeXh7Wr1//n2Oprq5W9x0OBwAgmUz+7xqJ6M8yZnoAREQ/6tOnTwCASCSCkpISTZuiKJow9KvMZvMP9ZszZ466bzAYAEzfv0RE2YUzQkSUNZYsWQJFUfDmzRtUVFRottLSUrXfwMCAuj8+Po6RkRFUVVUBAKqqqhCLxTTHjcViWLRoEfLy8rB8+XKk02nNPUdElLs4I0REWWPu3Lk4dOgQmpubkU6nsW7dOnz48AGxWAwFBQUoKysDAHR0dGD+/PkoLi5GW1sbbDYbtm7dCgDw+/1YtWoVgsEgvF4v7t+/j1OnTuH06dMAgPLycjQ2NsLn86GzsxM1NTV4/fo1kskkPB5Ppkonot+EQYiIskowGITdbkcoFMLLly9RWFiI2tpaBAIB9dLUiRMncPDgQSQSCaxYsQI9PT0wmUwAgNraWly7dg1Hjx5FMBiEw+FAR0cH9uzZo37GmTNnEAgE0NTUhLGxMTidTgQCgUyUS0S/GVeNEVHO+Lqia3x8HIWFhZkeDhFlAd4jRERERLrFIERERES6xUtjREREpFucESIiIiLdYhAiIiIi3WIQIiIiIt1iECIiIiLdYhAiIiIi3WIQIiIiIt1iECIiIiLdYhAiIiIi3WIQIiIiIt36AqT3oVxieiLxAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_ges = np.append(loss_ges, history.history['loss'])\n",
    "\n",
    "\n",
    "if (Training_Percentage > 0):\n",
    "    val_loss_ges = np.append(val_loss_ges, history.history['val_loss'])\n",
    "    plt.semilogy(val_loss_ges)\n",
    "\n",
    "\n",
    "plt.semilogy(loss_ges)\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train','eval'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the model by hand\n",
    "\n",
    "* The following code uses the trained model to check the deviation for each picture.\n",
    "* The evaluation takes the periodic character of the results into account (dev1 ... dev2).\n",
    "* Images, that have a bigger deviation as the parameter \"deviation_max_list\" are printed in a list to check the picture and labeling itself"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data_resize_all\\0.0_759b6e62af23ccf905789eeb80c9f440.jpg 0.0 0.2879394690120767 -0.2879394690120767\n",
      "data_resize_all\\0.0_name_20230216-061847.jpg 0.0 -0.13498130042204004 0.13498130042204004\n",
      "data_resize_all\\0.0_name_20230216-062347.jpg 0.0 -0.053061406013862666 0.053061406013862666\n",
      "data_resize_all\\0.0_name_20230216-132347.jpg 0.0 -0.0931054865116826 0.0931054865116826\n",
      "data_resize_all\\0.0_name_20230216-191846.jpg 0.0 -0.0847970926232976 0.0847970926232976\n",
      "data_resize_all\\0.1_name_20230216-230846.jpg 0.01 -0.05114437112769088 0.06114437112769088\n",
      "data_resize_all\\0.2_0076_zeiger3_2020-04-29_14-38-02.jpg 0.02 -0.47125709801203375 0.49125709801203377\n",
      "data_resize_all\\0.2_71cb15f34eae196cb16f3d7ca457c337.jpg 0.02 0.26306122592471765 -0.24306122592471766\n",
      "data_resize_all\\0.2_b996a82eca0579959c32b800815d045b.jpg 0.02 0.2073705006443632 -0.1873705006443632\n",
      "data_resize_all\\0.2_name_20230216-192346.jpg 0.02 -0.04159301087641043 0.061593010876410434\n",
      "data_resize_all\\0.2_name_20230216-231346.jpg 0.02 -0.07689700175167158 0.09689700175167158\n",
      "data_resize_all\\0.3_0149_zeiger3_2020-04-29_12-06-02.jpg 0.03 -0.028075779546146262 0.05807577954614626\n",
      "data_resize_all\\0.3_0e81959dc0e81e5f79710399becf8440.jpg 0.03 -0.07713081240788444 0.10713081240788444\n",
      "data_resize_all\\0.3_44325e462ba5ecbe224f05b023527f83.jpg 0.03 -0.13782781021776436 0.16782781021776436\n",
      "data_resize_all\\0.3_f38f2eb5263c3f8159ae2d2a7168d5d5.jpg 0.03 0.08715804237365095 -0.05715804237365095\n",
      "data_resize_all\\0.4_5e6981d685bbb4d0baa4c316fc87e95b.jpg 0.04 -0.032669451731559884 0.07266945173155989\n",
      "data_resize_all\\0.4_c5ae6d83b827b85f2264dc70cb00bf65.jpg 0.04 -0.027228992614233172 0.06722899261423318\n",
      "data_resize_all\\0.4_name_20230216-203346.jpg 0.04 -0.043744392398917475 0.08374439239891748\n",
      "data_resize_all\\0.6_name_20230216-063347.jpg 0.06 -0.01133815789670023 0.07133815789670023\n",
      "data_resize_all\\0.7_name_20230216-212846.jpg 0.06999999999999999 -0.024618472969113125 0.09461847296911312\n",
      "data_resize_all\\0.8_name_20230216-092847.jpg 0.08 0.008649598004227496 0.0713504019957725\n",
      "data_resize_all\\0.8_name_20230216-120347.jpg 0.08 -0.009529069644332866 0.08952906964433287\n",
      "data_resize_all\\1.0_name_20230216-100347.jpg 0.1 0.02442285919719615 0.07557714080280385\n",
      "data_resize_all\\1.0_name_20230216-111347.jpg 0.1 0.016622735733172352 0.08337726426682765\n",
      "data_resize_all\\1.0_name_20230216-200346.jpg 0.1 0.04941573088293833 0.050584269117061674\n",
      "data_resize_all\\1.1_ea7521dfe38bb3f5373f0bcced59e61f.jpg 0.11000000000000001 0.01634946057595818 0.09365053942404183\n",
      "data_resize_all\\1.2_653a1e9a9d7e73e9b6cc06b0c03aa572.jpg 0.12 0.23229016123993929 -0.11229016123993929\n",
      "data_resize_all\\1.2_bfa362540bb8a0b1e468fa571cdb4c7c.jpg 0.12 0.22234187687732956 -0.10234187687732957\n",
      "data_resize_all\\1.2_name_20230216-193346.jpg 0.12 0.06179024547753123 0.058209754522468765\n",
      "data_resize_all\\1.3_1fe175f541247c9266791695b0689d43.jpg 0.13 0.1871911051692621 -0.057191105169262096\n",
      "data_resize_all\\1.4_47793c77b74e5c8b0fe164fd16780625.jpg 0.13999999999999999 0.30050449370945675 -0.16050449370945677\n",
      "data_resize_all\\1.9_54d09a0b9395d9e4e8d0a58b78de5fe8.jpg 0.19 0.2431741246005977 -0.0531741246005977\n",
      "data_resize_all\\2.0_5d47b38e08fec2c5af7f98e3023603f5.jpg 0.2 0.1346248309519823 0.06537516904801771\n",
      "data_resize_all\\2.3_a961f9b89c6c69715e50aaf17f444a61.jpg 0.22999999999999998 0.04843911818173882 0.18156088181826116\n",
      "data_resize_all\\2.5_name_20230216-101347.jpg 0.25 0.19223836832018149 0.057761631679818515\n",
      "data_resize_all\\2.5_name_20230216-181346.jpg 0.25 0.15250324953219227 0.09749675046780773\n",
      "data_resize_all\\2.6_1e1287ecc7eb9b4cf62f442342a19816.jpg 0.26 0.3282385906707797 -0.0682385906707797\n",
      "data_resize_all\\2.6_name_20230216-122347.jpg 0.26 0.20624596691576366 0.05375403308423635\n",
      "data_resize_all\\2.7_d5150b41240107ecdde459b4423b345d.jpg 0.27 0.056608731163925695 0.21339126883607432\n",
      "data_resize_all\\2.8_name_20230216-094847.jpg 0.27999999999999997 -0.0373655743829302 0.31736557438293017\n",
      "data_resize_all\\2.9_2ad3dd7d058798612aef75f5f958534d.jpg 0.29 0.04617161395696768 0.2438283860430323\n",
      "data_resize_all\\2.9_name_20230216-181846.jpg 0.29 0.18535065377915907 0.1046493462208409\n",
      "data_resize_all\\3.0_name_20230216-065347.jpg 0.3 0.5930620344920685 -0.2930620344920685\n",
      "data_resize_all\\3.0_name_20230216-101847.jpg 0.3 0.23947612737471893 0.060523872625281055\n",
      "data_resize_all\\3.0_name_20230216-122847.jpg 0.3 0.5346440715943711 -0.23464407159437112\n",
      "data_resize_all\\3.1_5336b0c4e6b7962e96c361b6802ffd82.jpg 0.31 0.11765868848553986 0.19234131151446016\n",
      "data_resize_all\\3.1_692709121e9fbd3679df0180d12494aa.jpg 0.31 0.37452947680612875 -0.06452947680612875\n",
      "data_resize_all\\3.1_name_20230216-100847.jpg 0.31 0.3664413185729812 -0.05644131857298118\n",
      "data_resize_all\\3.1_name_20230216-102347.jpg 0.31 0.4719107091701086 -0.16191070917010858\n",
      "data_resize_all\\3.2_name_20230216-080347.jpg 0.32 0.8030982820784969 -0.48309828207849687\n",
      "data_resize_all\\3.3_deb59dd19c8a3f5b349510e3acc49663.jpg 0.32999999999999996 0.3943648773927812 -0.06436487739278124\n",
      "data_resize_all\\3.3_name_20230216-133847.jpg 0.32999999999999996 0.49677100403814867 -0.1667710040381487\n",
      "data_resize_all\\3.3_name_20230216-233346.jpg 0.32999999999999996 0.5273981136313814 -0.1973981136313815\n",
      "data_resize_all\\3.4_name_20230216-083347.jpg 0.33999999999999997 0.483611658019239 -0.14361165801923903\n",
      "data_resize_all\\3.5_name_20230216-080847.jpg 0.35 0.49695151075971394 -0.14695151075971397\n",
      "data_resize_all\\3.6_name_20230216-134347.jpg 0.36 0.5431366149718788 -0.18313661497187883\n",
      "data_resize_all\\3.7_3.8.jpg 0.37 0.4232333169932617 -0.053233316993261726\n",
      "data_resize_all\\3.7_4f5ca2757d0c887004c2ad983981a078.jpg 0.37 0.49137124620781353 -0.12137124620781353\n",
      "data_resize_all\\3.7_name_20230216-095347.jpg 0.37 0.42465612128836616 -0.054656121288366166\n",
      "data_resize_all\\3.7_name_20230216-102847.jpg 0.37 0.7327352290143365 -0.3627352290143365\n",
      "data_resize_all\\3.7_name_20230216-134847.jpg 0.37 0.5116810894412744 -0.14168108944127444\n",
      "data_resize_all\\3.8_name_20230216-204346.jpg 0.38 -0.013073360340561613 0.3930733603405616\n",
      "data_resize_all\\3.9.jpg 0.39 0.4934473580970297 -0.1034473580970297\n",
      "data_resize_all\\3.9_4.0.jpg 0.39 0.5776388981637489 -0.1876388981637489\n",
      "data_resize_all\\3.9_586cbd728968d79cecec23052196684b.jpg 0.39 0.48488279416927377 -0.09488279416927375\n",
      "data_resize_all\\3.9_name_20230216-065847.jpg 0.39 -0.018130588927207536 0.40813058892720755\n",
      "data_resize_all\\4.0_0fe8c60c7be4e00807ba7c3babef625a.jpg 0.4 0.5526745946928477 -0.15267459469284772\n",
      "data_resize_all\\4.0_419b104a69f39a3c177eb9b8ad55515a.jpg 0.4 0.455091254502466 -0.05509125450246599\n",
      "data_resize_all\\4.0_e9f1959e0bfb6dd27c3436f4f1e58536.jpg 0.4 0.48653429596508746 -0.08653429596508744\n",
      "data_resize_all\\4.0_name_20230216-182346.jpg 0.4 0.4868560358204905 -0.08685603582049045\n",
      "data_resize_all\\4.1_2fc93b2548d98fcb2ff0d2e220bcdbcd.jpg 0.41 0.46762210928124837 -0.05762210928124839\n",
      "data_resize_all\\4.1_6ec36a9ac45f109b44cb9efd44afde51.jpg 0.41 0.4664007955666593 -0.056400795566659334\n",
      "data_resize_all\\4.1_8f81da3e8707b5a546e36eb0021c89ba.jpg 0.41 0.4697373809217059 -0.05973738092170594\n",
      "data_resize_all\\4.1_name_20230216-103347.jpg 0.41 0.5349843288515239 -0.12498432885152394\n",
      "data_resize_all\\4.3_name_20230216-083847.jpg 0.43 0.5426284109521344 -0.11262841095213444\n",
      "data_resize_all\\4.5_1124ad4ecf70dfd294d6448293d8757e.jpg 0.45 0.3784698888292999 0.07153011117070013\n",
      "data_resize_all\\4.5_1d6d188d4f9d9706854c1090dd43d537.jpg 0.45 0.38901346212357163 0.06098653787642838\n",
      "data_resize_all\\4.5_7667a61d729712bb4f450d304dd3aa42.jpg 0.45 0.5976885769628628 -0.1476885769628628\n",
      "data_resize_all\\4.5_f7f57eec572bf8ad1be8b5991689e205.jpg 0.45 0.5390489364811031 -0.08904893648110307\n",
      "data_resize_all\\4.5_name_20230216-204846.jpg 0.45 0.5122955408481078 -0.06229554084810779\n",
      "data_resize_all\\4.5_name_20230216-220346.jpg 0.45 0.5042204358653741 -0.05422043586537412\n",
      "data_resize_all\\4.6_1ea28f634a44dbe1b67cce91d96b08c2.jpg 0.45999999999999996 0.37085639079797283 0.08914360920202713\n",
      "data_resize_all\\4.6_8659e43f4fdda095d3718a7031edb921.jpg 0.45999999999999996 0.5638087516773531 -0.10380875167735315\n",
      "data_resize_all\\4.6_da6df3db58f2a70a3b16708682908274.jpg 0.45999999999999996 0.5924505428338754 -0.13245054283387547\n",
      "data_resize_all\\4.6_da84d790d1a35290e697851970e7aa3f.jpg 0.45999999999999996 0.3889647021619968 0.07103529783800316\n",
      "data_resize_all\\4.8_name_20230216-104347.jpg 0.48 0.5806400832850525 -0.10064008328505247\n",
      "data_resize_all\\5.0_6bd252e1db7c57a0b2174ebe90ae9830.jpg 0.5 0.4297214654678384 0.0702785345321616\n",
      "data_resize_all\\5.0_b2388e52e7f926a8fdbcfc2ac8eaa8e2.jpg 0.5 0.4467147105043624 0.0532852894956376\n",
      "data_resize_all\\5.0_f8a1e8c0e3735abc7ad45bb402f27afd.jpg 0.5 0.4465100704477297 0.05348992955227028\n",
      "data_resize_all\\5.0_main_ana3_20221213-124605.jpg 0.5 0.5896970279609756 -0.08969702796097556\n",
      "data_resize_all\\5.0_main_ana3_20221213-125105.jpg 0.5 0.5604670057349337 -0.06046700573493369\n",
      "data_resize_all\\5.1_1bce6520cfe24b664801c29ffb53cf8f.jpg 0.51 0.4513024347333738 0.05869756526662623\n",
      "data_resize_all\\5.2_aed852b59943f4b92c27e55b5d4c2b67.jpg 0.52 0.19801278099923952 0.3219872190007605\n",
      "data_resize_all\\5.3_2659_zeiger4_2019-06-05T051009.jpg 0.53 0.6075678005238647 -0.0775678005238647\n",
      "data_resize_all\\5.3_2660_zeiger4_2019-06-05T165009.jpg 0.53 0.6107551083000511 -0.08075510830005106\n",
      "data_resize_all\\5.3_2661_zeiger4_2019-06-05T170009.jpg 0.53 0.6177228119453535 -0.08772281194535347\n",
      "data_resize_all\\5.3_2681_zeiger3_2020-04-29_14-36-02.jpg 0.53 0.5876175768681652 -0.057617576868165155\n",
      "data_resize_all\\5.3_84c20b8e22b48cfdb97a445df75a4a50.jpg 0.53 0.3380557354013252 0.1919442645986748\n",
      "data_resize_all\\5.4_2683_zeiger4_2019-06-05T204009.jpg 0.54 0.6129243004894565 -0.07292430048945642\n",
      "data_resize_all\\5.4_4c1d5a2bea926c9966e2182d97be68fe.jpg 0.54 0.33532221780449534 0.2046777821955047\n",
      "data_resize_all\\5.5_2696_zeiger4_2019-06-06T055009.jpg 0.55 0.603907805711618 -0.05390780571161791\n",
      "data_resize_all\\5.6_2753_zeiger4_2019-06-05T053009.jpg 0.5599999999999999 0.6289596873858769 -0.06895968738587699\n",
      "data_resize_all\\5.6_2755_zeiger4_2019-06-06T101009.jpg 0.5599999999999999 0.631215395304769 -0.07121539530476906\n",
      "data_resize_all\\5.6_2792_zeiger3_2019-06-06T052009.jpg 0.5599999999999999 0.6174385242938993 -0.057438524293899396\n",
      "data_resize_all\\5.6_2793_zeiger3_2019-06-06T053009.jpg 0.5599999999999999 0.61692591859669 -0.05692591859669005\n",
      "data_resize_all\\5.6_2795_zeiger4_2019-06-04T064009.jpg 0.5599999999999999 0.6204791727304075 -0.06047917273040759\n",
      "data_resize_all\\5.6_dc8dd0a85433fceb02ffc098dabf591b.jpg 0.5599999999999999 0.3637336418585112 0.19626635814148874\n",
      "data_resize_all\\5.7_2809_zeiger3_2019-06-04T175009.jpg 0.5700000000000001 0.6209256773746494 -0.05092567737464937\n",
      "data_resize_all\\5.7_2843_zeiger4_2019-06-03T210009.jpg 0.5700000000000001 0.6309778185581387 -0.06097781855813866\n",
      "data_resize_all\\5.7_2844_zeiger4_2019-06-03T221010.jpg 0.5700000000000001 0.6291866393937983 -0.0591866393937982\n",
      "data_resize_all\\5.7_2845_zeiger4_2019-06-03T223009.jpg 0.5700000000000001 0.6283791971974166 -0.05837919719741658\n",
      "data_resize_all\\5.7_2846_zeiger4_2019-06-03T224009.jpg 0.5700000000000001 0.6311543410026881 -0.06115434100268802\n",
      "data_resize_all\\5.7_name_20230216-203846.jpg 0.5700000000000001 0.5167566789045016 0.05324332109549845\n",
      "data_resize_all\\5.8_2840_zeiger4_2019-06-02T091009.jpg 0.58 0.6305469854034924 -0.05054698540349245\n",
      "data_resize_all\\5.8_name_20230216-095847.jpg 0.58 0.5273958369016581 0.052604163098341816\n",
      "data_resize_all\\5.9_name_20230216-071347.jpg 0.5900000000000001 0.4538784785243395 0.1361215214756606\n",
      "data_resize_all\\5.9_name_20230216-221346.jpg 0.5900000000000001 0.5203630187862683 0.06963698121373174\n",
      "data_resize_all\\6.0_name_20230216-071847.jpg 0.6 0.5365701090493239 0.06342989095067608\n",
      "data_resize_all\\6.0_name_20230216-112347.jpg 0.6 0.5240599345655286 0.0759400654344714\n",
      "data_resize_all\\6.0_name_20230216-123847.jpg 0.6 0.5229543166663804 0.07704568333361961\n",
      "data_resize_all\\6.1_name_20230216-141847.jpg 0.61 0.534814788378126 0.07518521162187397\n",
      "data_resize_all\\6.2_name_20230216-151847.jpg 0.62 0.5321254393379289 0.08787456066207111\n",
      "data_resize_all\\6.2_name_20230216-183346.jpg 0.62 0.4331570126748594 0.1868429873251406\n",
      "data_resize_all\\6.2_name_20230216-221846.jpg 0.62 0.5588142896832675 0.06118571031673248\n",
      "data_resize_all\\6.3_name_20230216-010348.jpg 0.63 0.5341266847647426 0.09587331523525744\n",
      "data_resize_all\\6.4_c0fa89df519f3e665d7778b437c7d493.jpg 0.64 0.5335317373425391 0.1064682626574609\n",
      "data_resize_all\\6.4_name_20230216-072347.jpg 0.64 0.5270754251385938 0.11292457486140617\n",
      "data_resize_all\\6.4_name_20230216-124347.jpg 0.64 0.9372228206465235 -0.2972228206465235\n",
      "data_resize_all\\6.4_name_20230216-152347.jpg 0.64 0.5758041575707019 0.0641958424292981\n",
      "data_resize_all\\6.4_name_20230216-152847.jpg 0.64 0.5403038319591114 0.09969616804088866\n",
      "data_resize_all\\6.5_ec2a446ccae23b3f97cd54871ef9b42b.jpg 0.65 0.7113937337702008 -0.06139373377020074\n",
      "data_resize_all\\6.5_name_20230216-010848.jpg 0.65 0.7003303641073434 -0.05033036410734337\n",
      "data_resize_all\\6.5_name_20230216-011848.jpg 0.65 0.578167478914517 0.07183252108548299\n",
      "data_resize_all\\6.5_name_20230216-012348.jpg 0.65 0.5506073620495526 0.0993926379504474\n",
      "data_resize_all\\6.5_name_20230216-124847.jpg 0.65 0.34816972774229854 0.3018302722577015\n",
      "data_resize_all\\6.5_name_20230216-194346.jpg 0.65 0.5276307954091066 0.1223692045908934\n",
      "data_resize_all\\6.6_name_20230216-013348.jpg 0.6599999999999999 0.7311730319419207 -0.07117303194192082\n",
      "data_resize_all\\6.6_name_20230216-072847.jpg 0.6599999999999999 0.5949358209998707 0.06506417900012917\n",
      "data_resize_all\\6.6_name_20230216-153347.jpg 0.6599999999999999 0.48018195441850786 0.17981804558149206\n",
      "data_resize_all\\6.6_name_20230216-205846.jpg 0.6599999999999999 0.8361496433631794 -0.1761496433631795\n",
      "data_resize_all\\6.6_name_20230216-222346.jpg 0.6599999999999999 0.45295814847917565 0.20704185152082427\n",
      "data_resize_all\\6.7_name_20230216-013848.jpg 0.67 0.23161418121233268 0.43838581878766736\n",
      "data_resize_all\\6.7_name_20230216-183846.jpg 0.67 0.560969062584425 0.10903093741557501\n",
      "data_resize_all\\6.8_623f331e36885917172f759b16489f84.jpg 0.6799999999999999 0.7332707727635086 -0.05327077276350867\n",
      "data_resize_all\\6.8_name_20230216-014348.jpg 0.6799999999999999 0.8854387643575655 -0.20543876435756558\n",
      "data_resize_all\\6.8_name_20230216-153847.jpg 0.6799999999999999 0.5224144281579863 0.1575855718420136\n",
      "data_resize_all\\6.8_name_20230216-184346.jpg 0.6799999999999999 0.44809239760544933 0.2319076023945506\n",
      "data_resize_all\\6.9_name_20230216-085847.jpg 0.6900000000000001 0.38964920095331823 0.3003507990466818\n",
      "data_resize_all\\6.9_name_20230216-210346.jpg 0.6900000000000001 0.5289425332392075 0.16105746676079258\n",
      "data_resize_all\\6.9_name_20230216-235346.jpg 0.6900000000000001 0.6392649250236105 0.050735074976389516\n",
      "data_resize_all\\7.0_01934391e26e686813c8c94b41ff7de8.jpg 0.7 0.6256658286586383 0.07433417134136167\n",
      "data_resize_all\\7.0_ae79600ba6421797912b70aa4695593a.jpg 0.7 0.7788259716342565 -0.07882597163425653\n",
      "data_resize_all\\7.0_name_20230216-014848.jpg 0.7 0.5982596566684667 0.10174034333153326\n",
      "data_resize_all\\7.0_name_20230216-015348.jpg 0.7 0.6319264179973674 0.06807358200263258\n",
      "data_resize_all\\7.0_name_20230216-073347.jpg 0.7 0.4375970909813124 0.26240290901868757\n",
      "data_resize_all\\7.0_name_20230216-113347.jpg 0.7 0.4745751280198352 0.22542487198016475\n",
      "data_resize_all\\7.1_6f0ce91b122837a42868d985cb24746e.jpg 0.71 0.6495135859277192 0.060486414072280725\n",
      "data_resize_all\\7.1_db8cb6c41d9ddb97f73cf5431b067ebc.jpg 0.71 0.3492792919729671 0.3607207080270329\n",
      "data_resize_all\\7.1_name_20230216-015848.jpg 0.71 0.5504087553266868 0.1595912446733132\n",
      "data_resize_all\\7.1_name_20230216-154347.jpg 0.71 0.6005334645886593 0.10946653541134066\n",
      "data_resize_all\\7.2_a94010eb5796793d04a4560e15542847.jpg 0.72 0.2832412475004926 0.43675875249950735\n",
      "data_resize_all\\7.3_a4accb1ac1e16cc3d5fa1a28ccbe35e8.jpg 0.73 0.26932908182581367 0.4606709181741863\n",
      "data_resize_all\\7.3_name_20230216-022848.jpg 0.73 1.0590575863975344 -0.32905758639753446\n",
      "data_resize_all\\7.3_name_20230216-023848.jpg 0.73 0.5703454703860278 0.1596545296139722\n",
      "data_resize_all\\7.3_name_20230216-024348.jpg 0.73 0.5963992649205367 0.13360073507946324\n",
      "data_resize_all\\7.3_name_20230216-030348.jpg 0.73 0.5798965792484012 0.15010342075159877\n",
      "data_resize_all\\7.3_name_20230216-125347.jpg 0.73 0.5682685616414085 0.1617314383585915\n",
      "data_resize_all\\7.3_name_20230216-154847.jpg 0.73 0.851046978448268 -0.12104697844826806\n",
      "data_resize_all\\7.3_name_20230216-235846.jpg 0.73 0.8475749561338044 -0.11757495613380442\n",
      "data_resize_all\\7.4_08b48ff404c21e063e61fa03e9497068.jpg 0.74 0.7924918520711134 -0.0524918520711134\n",
      "data_resize_all\\7.4_2283df06881f82b289d286cf3f7f3fe0.jpg 0.74 1.2325879195422564 -0.49258791954225645\n",
      "data_resize_all\\7.4_name_20230216-030848.jpg 0.74 0.5795697926421115 0.1604302073578885\n",
      "data_resize_all\\7.4_name_20230216-074847.jpg 0.74 0.8945481781672378 -0.15454817816723776\n",
      "data_resize_all\\7.4_name_20230216-104847.jpg 0.74 0.8054806900064928 -0.06548069000649281\n",
      "data_resize_all\\7.4_pointer_20211010-082610.jpg 0.74 0.7961933402825679 -0.05619334028256795\n",
      "data_resize_all\\7.5_3a03a983cd2b50fa9b4e0f2b42a52980.jpg 0.75 0.6671261393944865 0.0828738606055135\n",
      "data_resize_all\\7.5_name_20230216-031348.jpg 0.75 0.8166530391321072 -0.06665303913210718\n",
      "data_resize_all\\7.5_name_20230216-155847.jpg 0.75 0.9613242032351909 -0.21132420323519086\n",
      "data_resize_all\\7.5_pointer_20211011-123657.jpg 0.75 0.9803180901279926 -0.23031809012799265\n",
      "data_resize_all\\7.6_73d0e536745d447e90a84af0dfbad84c.jpg 0.76 1.0700876494018143 -0.3100876494018143\n",
      "data_resize_all\\7.6_name_20230216-160347.jpg 0.76 0.6982760708779759 0.06172392912202407\n",
      "data_resize_all\\7.7_0920bba85f5c351668f134e1323636ac.jpg 0.77 0.7046839456570573 0.06531605434294274\n",
      "data_resize_all\\7.8_2b130c7c2696fbeeceb957b5707d364d.jpg 0.78 0.7076365984897584 0.07236340151024168\n",
      "data_resize_all\\7.8_2d0b5fa6adf6f3a33bca713ae06b848a.jpg 0.78 0.8349951896114657 -0.054995189611465656\n",
      "data_resize_all\\7.8_34175a9a8e1b6169ad03ab9bc69096fe.jpg 0.78 0.8362158203071377 -0.056215820307137676\n",
      "data_resize_all\\7.8_3a0a680f92b105771bfd229cf1e399e3.jpg 0.78 0.8316136578171308 -0.05161365781713079\n",
      "data_resize_all\\7.8_5ac626c2854a99695d44710926152b8f.jpg 0.78 0.8680710731725433 -0.08807107317254326\n",
      "data_resize_all\\7.8_67cafd9104be569cb64f8d1a0cfb2021.jpg 0.78 0.890642951104666 -0.11064295110466593\n",
      "data_resize_all\\7.8_852995579f20d283c4293503bd632c17.jpg 0.78 0.8707054202171741 -0.0907054202171741\n",
      "data_resize_all\\7.8_895e928c4f954537a320911bed9ed335.jpg 0.78 0.8940781568022285 -0.11407815680222844\n",
      "data_resize_all\\7.8_8a2c1bafca941f828f223a6fa1b852e3.jpg 0.78 0.835430614171054 -0.05543061417105399\n",
      "data_resize_all\\7.8_c1b70b1faee532b85c571815f5463db8.jpg 0.78 0.8850871993427868 -0.10508719934278676\n",
      "data_resize_all\\7.8_edaab8b8a50b50146041cd0b02215d2b.jpg 0.78 0.8386508206917427 -0.058650820691742656\n",
      "data_resize_all\\7.8_fb4e0d0872de121a10609ceb98c83037.jpg 0.78 0.8333323610852784 -0.05333236108527839\n",
      "data_resize_all\\7.9_3966_zeiger1_2020-04-29_13-19-02.jpg 0.79 0.9017719190426771 -0.11177191904267703\n",
      "data_resize_all\\7.9_7ea202456c12d3726ac4d2bfc26c4f49.jpg 0.79 0.5218832671135316 0.2681167328864684\n",
      "data_resize_all\\7.9_8bb3df7709daaee885e6f47535fd425a.jpg 0.79 0.9918828478509851 -0.2018828478509851\n",
      "data_resize_all\\7.9_c2dda5aed2ce7d7a8ff6a073771b62ba.jpg 0.79 0.6026374284714909 0.1873625715285091\n",
      "data_resize_all\\7.9_name_20230216-033348.jpg 0.79 0.7194142731305653 0.07058572686943476\n",
      "data_resize_all\\7.9_name_20230216-041847.jpg 0.79 0.7039081120583346 0.08609188794166545\n",
      "data_resize_all\\7.9_name_20230216-075347.jpg 0.79 0.7358723918183665 0.054127608181633535\n",
      "data_resize_all\\8.0_10379cd7623f772faeea85bf23137b45.jpg 0.8 0.9856978898135499 -0.18569788981354984\n",
      "data_resize_all\\8.0_212a0547c1c6a30fa66ae6c8d37486c9.jpg 0.8 0.9661628444240552 -0.16616284442405516\n",
      "data_resize_all\\8.0_3976_zeiger2_2019-06-04T124009.jpg 0.8 0.7304077661636639 0.06959223383633617\n",
      "data_resize_all\\8.0_412ba4e05023e6aad4ed901edb211454.jpg 0.8 0.8511896914564259 -0.05118969145642582\n",
      "data_resize_all\\8.0_6450ad5f5e78a53fee3907144ea1a22f.jpg 0.8 0.6453551770335386 0.15464482296646143\n",
      "data_resize_all\\8.0_name_20230216-044847.jpg 0.8 0.7427878255437705 0.0572121744562295\n",
      "data_resize_all\\8.0_name_20230216-190346.jpg 0.8 0.7365487323281792 0.06345126767182085\n",
      "data_resize_all\\8.0_name_20230216-224346.jpg 0.8 0.8657431454895521 -0.06574314548955207\n",
      "data_resize_all\\8.1_06077aeb4593725db75dc3b921de9440.jpg 0.8099999999999999 0.9229090303622739 -0.11290903036227395\n",
      "data_resize_all\\8.1_4018_zeiger1_2020-04-29_12-01-02.jpg 0.8099999999999999 0.8630694299934553 -0.05306942999345532\n",
      "data_resize_all\\8.1_4033_zeiger2_2020-04-29_13-57-02.jpg 0.8099999999999999 0.9156963695714881 -0.1056963695714882\n",
      "data_resize_all\\8.1_9804055b6b52aeb1fc0b335b14a472c6.jpg 0.8099999999999999 1.1181634489515777 -0.30816344895157777\n",
      "data_resize_all\\8.1_name_20230216-091347.jpg 0.8099999999999999 0.7314820410836207 0.07851795891637925\n",
      "data_resize_all\\8.1_name_20230216-130347.jpg 0.8099999999999999 0.7405907054697589 0.06940929453024103\n",
      "data_resize_all\\8.2_4094_zeiger3_2020-04-29_12-15-02.jpg 0.82 0.8850491020654163 -0.06504910206541636\n",
      "data_resize_all\\8.2_4110_zeiger1_2019-09-15_06-20-12.jpg 0.82 0.872468064368982 -0.052468064368982104\n",
      "data_resize_all\\8.2_4138_zeiger4_2020-04-29_12-05-02.jpg 0.82 0.8859417793308154 -0.06594177933081546\n",
      "data_resize_all\\8.2_4139_zeiger4_2020-04-29_12-07-02.jpg 0.82 0.8733962301589437 -0.05339623015894379\n",
      "data_resize_all\\8.2_5eae9ebf1970ac573eede029dcafcd2e.jpg 0.82 1.152534877102599 -0.332534877102599\n",
      "data_resize_all\\8.2_8.2.jpg 0.82 0.8788522991044361 -0.05885229910443612\n",
      "data_resize_all\\8.3_dc2b987ee05cda3c841c1976a9e6e004.jpg 0.8300000000000001 0.7087235472051752 0.12127645279482491\n",
      "data_resize_all\\8.3_name_20230216-143347.jpg 0.8300000000000001 0.7277097845232657 0.10229021547673434\n",
      "data_resize_all\\8.4_15c0263a48bb19f58ce3b7b3c0dd3c65.jpg 0.8400000000000001 0.9267098978261522 -0.08670989782615213\n",
      "data_resize_all\\8.4_6fbe46653bdf7a84990ef22c16655022.jpg 0.8400000000000001 0.711194197382698 0.12880580261730212\n",
      "data_resize_all\\8.4_705c8934733595610e08229ba84e96d7.jpg 0.8400000000000001 0.7862712383117766 0.05372876168822349\n",
      "data_resize_all\\8.4_a2614b241520fb496aa8c04f7e52409e.jpg 0.8400000000000001 0.7365437045500403 0.10345629544995982\n",
      "data_resize_all\\8.4_d49c8cceb0ad8435ece362a2c938d076.jpg 0.8400000000000001 0.9059846563998809 -0.06598465639988083\n",
      "data_resize_all\\8.5_d49d083f6645aeafa36cfdce680284be.jpg 0.85 0.9010320198280877 -0.051032019828087694\n",
      "data_resize_all\\8.7_name_20230216-105347.jpg 0.8699999999999999 1.049896400702591 -0.17989640070259116\n",
      "data_resize_all\\8.8_6bc93d79688ceda7a51fe041de3880ff.jpg 0.8800000000000001 0.8217475633975335 0.058252436602466595\n",
      "data_resize_all\\8.9_4409_zeiger2_2019-06-03T191009.jpg 0.89 0.835708451088292 0.054291548911708065\n",
      "data_resize_all\\8.9_8.8.jpg 0.89 0.9654561309167771 -0.07545613091677705\n",
      "data_resize_all\\9.0_4438_zeiger1_2020-04-29_13-26-28.jpg 0.9 0.8343975670318373 0.0656024329681627\n",
      "data_resize_all\\9.0_4474_zeiger3_2020-02-12_16-41-06.jpg 0.9 0.9809086216431702 -0.08090862164317014\n",
      "data_resize_all\\9.0_4490_zeiger4_2020-04-29_13-26-01.jpg 0.9 0.8121321748659712 0.0878678251340288\n",
      "data_resize_all\\9.1_4441_zeiger2_2019-06-02T184009.jpg 0.9099999999999999 1.1915131540123076 -0.2815131540123077\n",
      "data_resize_all\\9.1_4443_zeiger2_2019-06-04T231009.jpg 0.9099999999999999 0.9739963385796802 -0.06399633857968023\n",
      "data_resize_all\\9.1_4446_zeiger2_2019-06-04T234009.jpg 0.9099999999999999 1.012784191949763 -0.10278419194976318\n",
      "data_resize_all\\9.1_4447_zeiger2_2019-06-04T235009.jpg 0.9099999999999999 1.001867407958962 -0.09186740795896209\n",
      "data_resize_all\\9.1_4449_zeiger2_2019-06-05T001009.jpg 0.9099999999999999 0.9944326007497315 -0.08443260074973158\n",
      "data_resize_all\\9.1_4454_zeiger2_2019-06-05T010010.jpg 0.9099999999999999 0.9630005545872974 -0.053000554587297466\n",
      "data_resize_all\\9.1_4455_zeiger2_2019-06-05T011009.jpg 0.9099999999999999 0.9625082165278126 -0.05250821652781268\n",
      "data_resize_all\\9.1_4457_zeiger2_2019-06-05T013009.jpg 0.9099999999999999 0.9614385946742279 -0.051438594674227955\n",
      "data_resize_all\\9.1_4458_zeiger2_2019-06-05T014009.jpg 0.9099999999999999 0.9771426272753512 -0.06714262727535125\n",
      "data_resize_all\\9.1_4459_zeiger2_2019-06-05T015009.jpg 0.9099999999999999 1.007059761195811 -0.09705976119581106\n",
      "data_resize_all\\9.1_4462_zeiger2_2019-06-05T022009.jpg 0.9099999999999999 1.036802970752359 -0.12680297075235902\n",
      "data_resize_all\\9.1_4469_zeiger3_2019-06-06T165009.jpg 0.9099999999999999 0.9640405528669507 -0.05404055286695075\n",
      "data_resize_all\\9.1_4470_zeiger3_2019-06-06T170009.jpg 0.9099999999999999 0.9965047467417811 -0.08650474674178121\n",
      "data_resize_all\\9.1_4532_zeiger2_2019-06-02T183009.jpg 0.9099999999999999 1.1169638685194727 -0.2069638685194728\n",
      "data_resize_all\\9.1_4533_zeiger2_2019-06-05T203009.jpg 0.9099999999999999 1.1926316544071405 -0.28263165440714055\n",
      "data_resize_all\\9.1_4534_zeiger2_2019-06-06T105009.jpg 0.9099999999999999 1.409689013923631 -0.4996890139236312\n",
      "data_resize_all\\9.1_4535_zeiger2_2019-06-06T110009.jpg 0.9099999999999999 1.385583227285905 -0.4755832272859051\n",
      "data_resize_all\\9.1_f305aab69b8d2fda3cceb155e8c97c0c.jpg 0.9099999999999999 0.9831822540654467 -0.07318225406544676\n",
      "data_resize_all\\9.1_main_ana3_20221213-142934.jpg 0.9099999999999999 0.9738554279825533 -0.06385542798255339\n",
      "data_resize_all\\9.2_432b9556f796d3e96077f81c67aa3f6f.jpg 0.9199999999999999 1.0319354056096344 -0.11193540560963444\n",
      "data_resize_all\\9.2_4551_zeiger3_2019-06-04T171009.jpg 0.9199999999999999 0.9761915708652961 -0.05619157086529614\n",
      "data_resize_all\\9.2_4584_zeiger2_2019-09-14_19-20-12.jpg 0.9199999999999999 0.42411084451962683 0.4958891554803731\n",
      "data_resize_all\\9.2_4585_zeiger2_2019-09-14_19-30-13.jpg 0.9199999999999999 1.3801991409451655 -0.46019914094516556\n",
      "data_resize_all\\9.2_8614c6dad3e1e63b993abaf7f26cc863.jpg 0.9199999999999999 0.9816037937908665 -0.06160379379086656\n",
      "data_resize_all\\9.2_name_20230216-144847.jpg 0.9199999999999999 0.9893195312163322 -0.06931953121633228\n",
      "data_resize_all\\9.3_4531_zeiger1_2020-04-29_13-29-01.jpg 0.93 0.8419220069854865 0.08807799301451358\n",
      "data_resize_all\\9.3_4581_zeiger2_2019-06-02T190009.jpg 0.93 0.9809139601000527 -0.05091396010005267\n",
      "data_resize_all\\9.3_4582_zeiger2_2019-06-03T201009.jpg 0.93 0.981014068617466 -0.051014068617465935\n",
      "data_resize_all\\9.3_4637_zeiger2_2019-06-02T211009.jpg 0.93 0.9805835283546123 -0.05058352835461222\n",
      "data_resize_all\\9.3_4642_zeiger2_2019-06-02T220009.jpg 0.93 0.9990549374218582 -0.0690549374218582\n",
      "data_resize_all\\9.3_4674_zeiger2_2019-06-03T193009.jpg 0.93 0.9896102340281004 -0.059610234028100395\n",
      "data_resize_all\\9.3_4677_zeiger2_2019-06-03T200009.jpg 0.93 0.9910423503849327 -0.061042350384932664\n",
      "data_resize_all\\9.3_4682_zeiger2_2019-06-04T140009.jpg 0.93 0.9804657787392334 -0.0504657787392333\n",
      "data_resize_all\\9.3_4684_zeiger2_2019-06-05T145010.jpg 0.93 0.9841532460901417 -0.054153246090141605\n",
      "data_resize_all\\9.3_6f64d8b2bb625fd3e7321cccb75bb25a.jpg 0.93 1.0168786840285546 -0.08687868402855459\n",
      "data_resize_all\\9.3_name_20230216-230346.jpg 0.93 0.8744770032449859 0.05552299675501415\n",
      "data_resize_all\\9.4_3a250eaa40e18b4f19408abe578dabbc.jpg 0.9400000000000001 1.0194414326665733 -0.07944143266657322\n",
      "data_resize_all\\9.4_4604_zeiger4_2019-06-04T194009.jpg 0.9400000000000001 1.0286673166169518 -0.08866731661695171\n",
      "data_resize_all\\9.4_4751_zeiger2_2019-09-15_05-40-13.jpg 0.9400000000000001 1.1236011712500495 -0.1836011712500495\n",
      "data_resize_all\\9.4_e2694e4ad0778aaf8d1c08fa2de4a81d.jpg 0.9400000000000001 1.0830392011447871 -0.14303920114478708\n",
      "data_resize_all\\9.5_0b0cddb87bf3beed1dfaf20770a2742e.jpg 0.95 0.8302332386403759 0.11976676135962405\n",
      "data_resize_all\\9.5_4775_zeiger1_2020-04-29_12-12-01.jpg 0.95 0.606157859751697 0.34384214024830295\n",
      "data_resize_all\\9.5_4793_zeiger1_2020-04-29_13-31-02.jpg 0.95 0.8291789799692367 0.12082102003076323\n",
      "data_resize_all\\9.5_bdeab982fbac878494bf669d45b91020.jpg 0.95 1.3531707163615911 -0.4031707163615912\n",
      "data_resize_all\\9.5_efa1a02f3a65203c2578b4c7e9f76427.jpg 0.95 0.7784594181487992 0.1715405818512008\n",
      "data_resize_all\\9.5_name_20230216-131847.jpg 0.95 0.8060417141558185 0.14395828584418147\n",
      "data_resize_all\\9.5_name_20230216-145347.jpg 0.95 0.8731918936662741 0.07680810633372581\n",
      "data_resize_all\\9.6_10365c543b2d121d348f65b596407ab4.jpg 0.96 1.1706903357446437 -0.2106903357446437\n",
      "data_resize_all\\9.6_name_20230216-082847.jpg 0.96 0.9082594224438321 0.0517405775561679\n",
      "data_resize_all\\9.6_name_20230216-191346.jpg 0.96 0.83595287699684 0.12404712300316001\n",
      "data_resize_all\\9.7_4803_zeiger4_2020-04-29_11-55-02.jpg 0.97 0.9045014333897434 0.06549856661025655\n",
      "data_resize_all\\9.7_94e5a2fab73bcf6f0c36b28fb9ecedc2.jpg 0.97 1.0685170047134875 -0.09851700471348757\n",
      "data_resize_all\\9.7_name_20230216-092347.jpg 0.97 0.8575640129312095 0.11243598706879043\n",
      "data_resize_all\\9.7_name_20230216-110847.jpg 0.97 0.9120900107169737 0.057909989283026264\n",
      "data_resize_all\\9.7_name_20230216-174846.jpg 0.97 0.9187239457848347 0.051276054215165234\n",
      "data_resize_all\\9.8_4829_zeiger1_2020-04-29_10-55-02.jpg 0.9800000000000001 0.920933075608137 0.05906692439186312\n",
      "data_resize_all\\9.8_4853_zeiger4_2020-04-29_11-19-02.jpg 0.9800000000000001 1.3517991005397665 -0.3717991005397664\n",
      "data_resize_all\\9.8_4911_zeiger1_2020-04-29_13-33-02.jpg 0.9800000000000001 0.9272663542864705 0.052733645713529564\n",
      "data_resize_all\\9.8_4926_zeiger2_2020-02-17_17-43-42.jpg 0.9800000000000001 1.202135691882484 -0.22213569188248383\n",
      "data_resize_all\\9.8_63c3d0b4dd73c02866c84aac759a4ef9.jpg 0.9800000000000001 1.0554050099296364 -0.07540500992963628\n",
      "data_resize_all\\9.9_0001_analog1_20200814-090613.jpg 0.99 0.7759434420770341 0.21405655792296585\n",
      "data_resize_all\\9.9_05455b42b0c076190b97f4e9d06f9bec.jpg 0.99 1.290153930177481 -0.30015393017748093\n",
      "data_resize_all\\9.9_4969_zeiger2_2020-02-17_17-42-46.jpg 0.99 1.464300170887422 -0.47430017088742193\n",
      "data_resize_all\\9.9_4970_zeiger2_2020-02-17_17-43-10.jpg 0.99 1.4549271781804043 -0.4649271781804043\n",
      "data_resize_all\\9.9_8fc8e279012dcde2b7556ce8871a8cec.jpg 0.99 0.9176400042499276 0.07235999575007235\n"
     ]
    }
   ],
   "source": [
    "Input_dir='data_resize_all'\n",
    "#Input_dir='test_result'\n",
    "files = glob.glob(Input_dir + '/*.*')\n",
    "res = []\n",
    "stat_Anz = []\n",
    "stat_Abweichung = []\n",
    "i = 0\n",
    "deviation_max_list = 0.05\n",
    "\n",
    "for i in range(100):\n",
    "    stat_Anz.append(0)\n",
    "    stat_Abweichung.append(0)\n",
    "\n",
    "for aktfile in sorted(files):\n",
    "    base = os.path.basename(aktfile)\n",
    "    target = (float(base[0:3])) / 10\n",
    "    \n",
    "    target_sin = math.sin(target * math.pi * 2)\n",
    "    target_cos = math.cos(target * math.pi * 2)\n",
    "\n",
    "    test_image = Image.open(aktfile)\n",
    "    test_image = np.array(test_image, dtype=\"float32\")\n",
    "    img = np.reshape(test_image,[1,32,32,3])\n",
    "    classes = model.predict(img, verbose=0)\n",
    "    \n",
    "    out_sin = classes[0][0]  \n",
    "    out_cos = classes[0][1]\n",
    "    out_target = (np.arctan2(out_sin, out_cos)/(2*math.pi)) % 1\n",
    "\n",
    "    dev_sin = target_sin - out_sin\n",
    "    dev_cos = target_cos - out_cos\n",
    "    dev_target = target - out_target\n",
    "    \n",
    "    if abs(dev_target + 1) < abs(dev_target):\n",
    "        out_target = out_target - 1\n",
    "        dev_target = target - out_target\n",
    "    else:\n",
    "        if abs(dev_target - 1) < abs(dev_target):\n",
    "            out_target = out_target + 1\n",
    "            dev_target = target - out_target\n",
    "            \n",
    "    target_int = int ((float(base[0:3])) * 10)\n",
    "    stat_Abweichung[target_int] = stat_Abweichung[target_int] + dev_target  \n",
    "    stat_Anz[target_int] = stat_Anz[target_int] + 1\n",
    "               \n",
    "    res.append(np.array([target, out_target, dev_target, out_sin, out_cos, i]))\n",
    "    if abs(dev_target) > deviation_max_list:\n",
    "        print(aktfile + \" \" + str(target) + \" \" + str(out_target) +  \" \" + str(dev_target))\n",
    "\n",
    "    \n",
    "for i in range(100):\n",
    "    stat_Abweichung[i] = stat_Abweichung[i] / stat_Anz[i]\n",
    "\n",
    "res = np.asarray(res)\n",
    "res_step_1 = res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(res[:,3])\n",
    "plt.plot(res[:,4])\n",
    "plt.title('Result')\n",
    "plt.ylabel('value')\n",
    "plt.xlabel('#Picture')\n",
    "plt.legend(['sin', 'cos'], loc='lower left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(res[:,0])\n",
    "plt.plot(res[:,1])\n",
    "plt.title('Result')\n",
    "plt.ylabel('Counter Value')\n",
    "plt.xlabel('#Picture')\n",
    "plt.legend(['Orginal', 'Prediction'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(stat_Abweichung)\n",
    "plt.title('Result')\n",
    "plt.ylabel('Counter Value')\n",
    "plt.xlabel('#Picture')\n",
    "plt.legend(['Orginal', 'Prediction'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(stat_Anz)\n",
    "plt.title('Result')\n",
    "plt.ylabel('Counter Value')\n",
    "plt.xlabel('#Picture')\n",
    "plt.legend(['Orginal', 'Prediction'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deviation from Expected Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.0027  0.0366 -0.4997  0.4959]\n"
     ]
    }
   ],
   "source": [
    "plt.plot(res[:,2])\n",
    "plt.title('Deviation')\n",
    "plt.ylabel('Deviation from expected value')\n",
    "plt.xlabel('#Picture')\n",
    "plt.legend(['Deviation'], loc='upper left')\n",
    "#plt.ylim(-0.3, 0.3)\n",
    "plt.show()\n",
    "\n",
    "statistic = np.array([np.mean(res[:,2]), np.std(res[:,2]), np.min(res[:,2]), np.max(res[:,2])])\n",
    "print(statistic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the model\n",
    "\n",
    "* Save the model to the file with the \"h5\" file format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmp4c75klq4\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmp4c75klq4\\assets\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "125772"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FileName = TFlite_MainType + \"_\" + TFlite_Version + \"_\" + TFlite_Size\n",
    "\n",
    "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
    "tflite_model = converter.convert()\n",
    "open(FileName + \".tflite\", \"wb\").write(tflite_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmpbeefza2x\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Muell\\AppData\\Local\\Temp\\tmpbeefza2x\\assets\n",
      "C:\\Users\\Muell\\anaconda3\\envs\\py39-td-opencv\\lib\\site-packages\\tensorflow\\lite\\python\\convert.py:766: UserWarning: Statistics for quantized inputs were expected, but not specified; continuing anyway.\n",
      "  warnings.warn(\"Statistics for quantized inputs were expected, but not \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ana-cont_1210_s3_long_q.tflite\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "38544"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "import tensorflow as tf\n",
    "\n",
    "FileName = FileName = TFlite_MainType + \"_\" + TFlite_Version + \"_\" + TFlite_Size + \"_q.tflite\"\n",
    "\n",
    "def representative_dataset():\n",
    "    for n in range(x_data[0].size):\n",
    "      data = np.expand_dims(x_data[n], axis=0)\n",
    "      yield [data.astype(np.float32)]\n",
    "        \n",
    "converter2 = tf.lite.TFLiteConverter.from_keras_model(model)\n",
    "converter2.optimizations = [tf.lite.Optimize.DEFAULT]\n",
    "converter2.representative_dataset = representative_dataset\n",
    "tflite_quant_model = converter2.convert()\n",
    "\n",
    "open(FileName, \"wb\").write(tflite_quant_model)\n",
    "print(FileName)\n",
    "Path(FileName).stat().st_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[2], line 10\u001b[0m\n\u001b[0;32m      7\u001b[0m       data \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mexpand_dims(x_data[n], axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m      8\u001b[0m       \u001b[38;5;28;01myield\u001b[39;00m [data\u001b[38;5;241m.\u001b[39mastype(np\u001b[38;5;241m.\u001b[39mfloat32)]\n\u001b[1;32m---> 10\u001b[0m converter2 \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mlite\u001b[38;5;241m.\u001b[39mTFLiteConverter\u001b[38;5;241m.\u001b[39mfrom_keras_model(\u001b[43mmodel\u001b[49m)\n\u001b[0;32m     11\u001b[0m converter2\u001b[38;5;241m.\u001b[39mrepresentative_dataset \u001b[38;5;241m=\u001b[39m representative_dataset\n\u001b[0;32m     12\u001b[0m converter2\u001b[38;5;241m.\u001b[39moptimizations \u001b[38;5;241m=\u001b[39m [tf\u001b[38;5;241m.\u001b[39mlite\u001b[38;5;241m.\u001b[39mOptimize\u001b[38;5;241m.\u001b[39mDEFAULT]\n",
      "\u001b[1;31mNameError\u001b[0m: name 'model' is not defined"
     ]
    }
   ],
   "source": [
    "FileName = \"testq\"\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "def representative_dataset():\n",
    "    for n in range(x_data[0].size):\n",
    "      data = np.expand_dims(x_data[n], axis=0)\n",
    "      yield [data.astype(np.float32)]\n",
    "        \n",
    "converter2 = tf.lite.TFLiteConverter.from_keras_model(model)\n",
    "converter2.representative_dataset = representative_dataset\n",
    "converter2.optimizations = [tf.lite.Optimize.DEFAULT]\n",
    "converter2.representative_dataset = representative_dataset\n",
    "tflite_quant_model = converter2.convert()\n",
    "\n",
    "\n",
    "open(FileName + \".tflite\", \"wb\").write(tflite_quant_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  },
  "vscode": {
   "interpreter": {
    "hash": "3431513051aa6ef6a02e4978d18932220794b1f41fa21fbae2ad068757488314"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}