Mod1_Wk3 Programming Assignment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Programming Assignment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model validation on the Iris dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Instructions\n",
    "\n",
    "In this notebook, you will build, compile and fit a neural network model to the Iris dataset. You will also implement validation, regularisation and callbacks to improve your model.\n",
    "\n",
    "Some code cells are provided you in the notebook. You should avoid editing provided code, and make sure to execute the cells in order to avoid unexpected errors. Some cells begin with the line: \n",
    "\n",
    "`#### GRADED CELL ####`\n",
    "\n",
    "Don't move or edit this first line - this is what the automatic grader looks for to recognise graded cells. These cells require you to write your own code to complete them, and are automatically graded when you submit the notebook. Don't edit the function name or signature provided in these cells, otherwise the automatic grader might not function properly. Inside these graded cells, you can use any functions or classes that are imported below, but make sure you don't use any variables that are outside the scope of the function.\n",
    "\n",
    "### How to submit\n",
    "\n",
    "Complete all the tasks you are asked for in the worksheet. When you have finished and are happy with your code, press the **Submit Assignment** button at the top of this notebook.\n",
    "\n",
    "### Let's get started!\n",
    "\n",
    "We'll start running some imports, and loading the dataset. Do not edit the existing imports in the following cell. If you would like to make further Tensorflow imports, you should add them here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### PACKAGE IMPORTS ####\n",
    "\n",
    "# Run this cell first to import all required packages. Do not make any imports elsewhere in the notebook\n",
    "from numpy.random import seed\n",
    "seed(8)\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets, model_selection \n",
    "%matplotlib inline\n",
    "\n",
    "# If you would like to make further imports from tensorflow, add them here\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<tr>\n",
    "<td><img src=\"data/iris_setosa.jpg\" alt=\"Drawing\" style=\"height: 270px;\"/></td>\n",
    "<td><img src=\"data/iris_versicolor.jpg\" alt=\"Drawing\" style=\"height: 270px;\"/></td>\n",
    "<td><img src=\"data/iris_virginica.jpg\" alt=\"Drawing\" style=\"height: 270px;\"/></td>\n",
    "</tr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Iris dataset\n",
    "\n",
    "In this assignment, you will use the [Iris dataset](https://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html). It consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimeters. For a reference, see the following papers:\n",
    "\n",
    "- R. A. Fisher. \"The use of multiple measurements in taxonomic problems\". Annals of Eugenics. 7 (2): 179–188, 1936.\n",
    "\n",
    "Your goal is to construct a neural network that classifies each sample into the correct class, as well as applying validation and regularisation techniques."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load and preprocess the data\n",
    "\n",
    "First read in the Iris dataset using `datasets.load_iris()`, and split the dataset into training and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### GRADED CELL ####\n",
    "\n",
    "# Complete the following function. \n",
    "# Make sure to not change the function name or arguments.\n",
    "\n",
    "def read_in_and_split_data(iris_data):\n",
    "    \"\"\"\n",
    "    This function takes the Iris dataset as loaded by sklearn.datasets.load_iris(), and then \n",
    "    splits so that the training set includes 90% of the full dataset, with the test set \n",
    "    making up the remaining 10%.\n",
    "    Your function should return a tuple (train_data, test_data, train_targets, test_targets) \n",
    "    of appropriately split training and test data and targets.\n",
    "    \n",
    "    If you would like to import any further packages to aid you in this task, please do so in the \n",
    "    Package Imports cell above.\n",
    "    \"\"\"\n",
    "    from sklearn.model_selection import train_test_split\n",
    "    return model_selection.train_test_split(iris_data[\"data\"], iris_data[\"target\"], test_size=0.1)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run your function to generate the test and training data.\n",
    "\n",
    "iris_data = datasets.load_iris()\n",
    "train_data, test_data, train_targets, test_targets = read_in_and_split_data(iris_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now convert the training and test targets using a one hot encoder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert targets to a one-hot encoding\n",
    "\n",
    "train_targets = tf.keras.utils.to_categorical(np.array(train_targets))\n",
    "test_targets = tf.keras.utils.to_categorical(np.array(test_targets))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Build the neural network model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can now construct a model to fit to the data. Using the Sequential API, build your model according to the following specifications:\n",
    "\n",
    "* The model should use the `input_shape` in the function argument to set the input size in the first layer.\n",
    "* The first layer should be a dense layer with 64 units.\n",
    "* The weights of the first layer should be initialised with the He uniform initializer.\n",
    "* The biases of the first layer should be all initially equal to one.\n",
    "* There should then be a further four dense layers, each with 128 units.\n",
    "* This should be followed with four dense layers, each with 64 units.\n",
    "* All of these Dense layers should use the ReLU activation function.\n",
    "* The output Dense layer should have 3 units and the softmax activation function.\n",
    "\n",
    "In total, the network should have 10 layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### GRADED CELL ####\n",
    "\n",
    "# Complete the following function. \n",
    "# Make sure to not change the function name or arguments.\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras import initializers\n",
    "\n",
    "# initializer = tf.keras.initializers.he_uniform\n",
    "# bias_initializer=initializers.Zeros()\n",
    "\n",
    "def get_model(input_shape):\n",
    "    \"\"\"\n",
    "    This function should build a Sequential model according to the above specification. Ensure the \n",
    "    weights are initialised by providing the input_shape argument in the first layer, given by the\n",
    "    function argument.\n",
    "    Your function should return the model.\n",
    "    \"\"\"\n",
    "    model = Sequential([\n",
    "        Dense(64, activation='relu', input_shape=input_shape,kernel_initializer='he_uniform', bias_initializer='ones'),\n",
    "        Dense(128, activation='relu'),\n",
    "        Dense(128, activation='relu'),\n",
    "        Dense(128, activation='relu'),\n",
    "        Dense(128, activation='relu'),\n",
    "        Dense(64, activation='relu'),\n",
    "        Dense(64, activation='relu'),\n",
    "        Dense(64, activation='relu'),\n",
    "        Dense(64, activation='relu'),\n",
    "        Dense(3, activation='softmax')\n",
    "    ])\n",
    "    \n",
    "    return model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run your function to get the model\n",
    "\n",
    "model = get_model(train_data[0].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compile the model\n",
    "\n",
    "You should now compile the model using the `compile` method. Remember that you need to specify an optimizer, a loss function and a metric to judge the performance of your model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### GRADED CELL ####\n",
    "\n",
    "# Complete the following function. \n",
    "# Make sure to not change the function name or arguments.\n",
    "\n",
    "adam = tf.keras.optimizers.Adam(learning_rate=0.0001)\n",
    "def compile_model(model):\n",
    "    \"\"\"\n",
    "    This function takes in the model returned from your get_model function, and compiles it with an optimiser,\n",
    "    loss function and metric.\n",
    "    Compile the model using the Adam optimiser (with learning rate set to 0.0001), \n",
    "    the categorical crossentropy loss function and accuracy as the only metric. \n",
    "    Your function doesn't need to return anything; the model will be compiled in-place.\n",
    "    \"\"\"\n",
    "    model.compile(optimizer=adam, loss='categorical_crossentropy',metrics=['accuracy'])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run your function to compile the model\n",
    "\n",
    "compile_model(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fit the model to the training data\n",
    "\n",
    "Now you should train the model on the Iris dataset, using the model's `fit` method. \n",
    "* Run the training for a fixed number of epochs, given by the function's `epochs` argument.\n",
    "* Return the training history to be used for plotting the learning curves.\n",
    "* Set the batch size to 40.\n",
    "* Set the validation set to be 15% of the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### GRADED CELL ####\n",
    "\n",
    "# Complete the following function. \n",
    "# Make sure to not change the function name or arguments.\n",
    "\n",
    "def train_model(model, train_data, train_targets, epochs):\n",
    "    \"\"\"\n",
    "    This function should train the model for the given number of epochs on the \n",
    "    train_data and train_targets. \n",
    "    Your function should return the training history, as returned by model.fit.\n",
    "    \"\"\"\n",
    "    history = model.fit(train_data, train_targets, batch_size=40, validation_split=0.15, epochs=epochs)\n",
    "    return history\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following cell to run the training for 800 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 114 samples, validate on 21 samples\n",
      "Epoch 1/800\n",
      "114/114 [==============================] - 2s 17ms/sample - loss: 1.1583 - accuracy: 0.3333 - val_loss: 1.0904 - val_accuracy: 0.3810\n",
      "Epoch 2/800\n",
      "114/114 [==============================] - 0s 909us/sample - loss: 1.0948 - accuracy: 0.3333 - val_loss: 1.0503 - val_accuracy: 0.3810\n",
      "Epoch 3/800\n",
      "114/114 [==============================] - 0s 882us/sample - loss: 1.0616 - accuracy: 0.3333 - val_loss: 1.0191 - val_accuracy: 0.3810\n",
      "Epoch 4/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 1.0361 - accuracy: 0.3333 - val_loss: 0.9955 - val_accuracy: 0.3810\n",
      "Epoch 5/800\n",
      "114/114 [==============================] - 0s 921us/sample - loss: 1.0136 - accuracy: 0.3333 - val_loss: 0.9730 - val_accuracy: 0.3810\n",
      "Epoch 6/800\n",
      "114/114 [==============================] - 0s 959us/sample - loss: 0.9932 - accuracy: 0.3333 - val_loss: 0.9541 - val_accuracy: 0.3810\n",
      "Epoch 7/800\n",
      "114/114 [==============================] - 0s 876us/sample - loss: 0.9730 - accuracy: 0.3333 - val_loss: 0.9362 - val_accuracy: 0.3810\n",
      "Epoch 8/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.9529 - accuracy: 0.3333 - val_loss: 0.9177 - val_accuracy: 0.3810\n",
      "Epoch 9/800\n",
      "114/114 [==============================] - 0s 895us/sample - loss: 0.9351 - accuracy: 0.3333 - val_loss: 0.9001 - val_accuracy: 0.3810\n",
      "Epoch 10/800\n",
      "114/114 [==============================] - 0s 913us/sample - loss: 0.9142 - accuracy: 0.3333 - val_loss: 0.8800 - val_accuracy: 0.3810\n",
      "Epoch 11/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.8958 - accuracy: 0.3333 - val_loss: 0.8598 - val_accuracy: 0.3810\n",
      "Epoch 12/800\n",
      "114/114 [==============================] - 0s 958us/sample - loss: 0.8769 - accuracy: 0.4737 - val_loss: 0.8414 - val_accuracy: 0.5238\n",
      "Epoch 13/800\n",
      "114/114 [==============================] - 0s 927us/sample - loss: 0.8590 - accuracy: 0.6491 - val_loss: 0.8262 - val_accuracy: 0.5714\n",
      "Epoch 14/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.8387 - accuracy: 0.6754 - val_loss: 0.8056 - val_accuracy: 0.6667\n",
      "Epoch 15/800\n",
      "114/114 [==============================] - 0s 924us/sample - loss: 0.8216 - accuracy: 0.8246 - val_loss: 0.7882 - val_accuracy: 0.8095\n",
      "Epoch 16/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.8021 - accuracy: 0.8596 - val_loss: 0.7715 - val_accuracy: 0.8095\n",
      "Epoch 17/800\n",
      "114/114 [==============================] - 0s 922us/sample - loss: 0.7827 - accuracy: 0.8333 - val_loss: 0.7549 - val_accuracy: 0.8095\n",
      "Epoch 18/800\n",
      "114/114 [==============================] - 0s 874us/sample - loss: 0.7624 - accuracy: 0.8421 - val_loss: 0.7343 - val_accuracy: 0.9048\n",
      "Epoch 19/800\n",
      "114/114 [==============================] - 0s 922us/sample - loss: 0.7403 - accuracy: 0.8596 - val_loss: 0.7135 - val_accuracy: 0.9048\n",
      "Epoch 20/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.7172 - accuracy: 0.9298 - val_loss: 0.6918 - val_accuracy: 0.9048\n",
      "Epoch 21/800\n",
      "114/114 [==============================] - 0s 905us/sample - loss: 0.6928 - accuracy: 0.9386 - val_loss: 0.6731 - val_accuracy: 0.9048\n",
      "Epoch 22/800\n",
      "114/114 [==============================] - 0s 918us/sample - loss: 0.6700 - accuracy: 0.9386 - val_loss: 0.6523 - val_accuracy: 0.9524\n",
      "Epoch 23/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6413 - accuracy: 0.9474 - val_loss: 0.6328 - val_accuracy: 0.9048\n",
      "Epoch 24/800\n",
      "114/114 [==============================] - 0s 935us/sample - loss: 0.6165 - accuracy: 0.9035 - val_loss: 0.6122 - val_accuracy: 0.9048\n",
      "Epoch 25/800\n",
      "114/114 [==============================] - 0s 910us/sample - loss: 0.5875 - accuracy: 0.9386 - val_loss: 0.5869 - val_accuracy: 0.9524\n",
      "Epoch 26/800\n",
      "114/114 [==============================] - 0s 875us/sample - loss: 0.5612 - accuracy: 0.9561 - val_loss: 0.5640 - val_accuracy: 0.9524\n",
      "Epoch 27/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5341 - accuracy: 0.9825 - val_loss: 0.5419 - val_accuracy: 0.9524\n",
      "Epoch 28/800\n",
      "114/114 [==============================] - 0s 954us/sample - loss: 0.5081 - accuracy: 0.9474 - val_loss: 0.5210 - val_accuracy: 0.9524\n",
      "Epoch 29/800\n",
      "114/114 [==============================] - 0s 880us/sample - loss: 0.4821 - accuracy: 0.9737 - val_loss: 0.4971 - val_accuracy: 0.9524\n",
      "Epoch 30/800\n",
      "114/114 [==============================] - 0s 916us/sample - loss: 0.4627 - accuracy: 0.9561 - val_loss: 0.4747 - val_accuracy: 0.9524\n",
      "Epoch 31/800\n",
      "114/114 [==============================] - 0s 962us/sample - loss: 0.4370 - accuracy: 0.9298 - val_loss: 0.4646 - val_accuracy: 0.9524\n",
      "Epoch 32/800\n",
      "114/114 [==============================] - 0s 888us/sample - loss: 0.4135 - accuracy: 0.9386 - val_loss: 0.4403 - val_accuracy: 0.9524\n",
      "Epoch 33/800\n",
      "114/114 [==============================] - 0s 954us/sample - loss: 0.3976 - accuracy: 0.9474 - val_loss: 0.4197 - val_accuracy: 0.9524\n",
      "Epoch 34/800\n",
      "114/114 [==============================] - 0s 901us/sample - loss: 0.3749 - accuracy: 0.9737 - val_loss: 0.4040 - val_accuracy: 0.9524\n",
      "Epoch 35/800\n",
      "114/114 [==============================] - 0s 885us/sample - loss: 0.3560 - accuracy: 0.9561 - val_loss: 0.3897 - val_accuracy: 0.9524\n",
      "Epoch 36/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.3404 - accuracy: 0.9474 - val_loss: 0.3709 - val_accuracy: 0.9524\n",
      "Epoch 37/800\n",
      "114/114 [==============================] - 0s 903us/sample - loss: 0.3235 - accuracy: 0.9649 - val_loss: 0.3544 - val_accuracy: 0.9524\n",
      "Epoch 38/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.3094 - accuracy: 0.9737 - val_loss: 0.3392 - val_accuracy: 0.9524\n",
      "Epoch 39/800\n",
      "114/114 [==============================] - 0s 950us/sample - loss: 0.2932 - accuracy: 0.9649 - val_loss: 0.3254 - val_accuracy: 0.9524\n",
      "Epoch 40/800\n",
      "114/114 [==============================] - 0s 897us/sample - loss: 0.2814 - accuracy: 0.9386 - val_loss: 0.3090 - val_accuracy: 0.9524\n",
      "Epoch 41/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.2662 - accuracy: 0.9737 - val_loss: 0.2961 - val_accuracy: 0.9524\n",
      "Epoch 42/800\n",
      "114/114 [==============================] - 0s 931us/sample - loss: 0.2538 - accuracy: 0.9737 - val_loss: 0.2800 - val_accuracy: 0.9524\n",
      "Epoch 43/800\n",
      "114/114 [==============================] - 0s 873us/sample - loss: 0.2385 - accuracy: 0.9561 - val_loss: 0.2707 - val_accuracy: 0.9524\n",
      "Epoch 44/800\n",
      "114/114 [==============================] - 0s 897us/sample - loss: 0.2303 - accuracy: 0.9474 - val_loss: 0.2574 - val_accuracy: 0.9524\n",
      "Epoch 45/800\n",
      "114/114 [==============================] - 0s 905us/sample - loss: 0.2208 - accuracy: 0.9649 - val_loss: 0.2540 - val_accuracy: 0.9524\n",
      "Epoch 46/800\n",
      "114/114 [==============================] - 0s 956us/sample - loss: 0.2180 - accuracy: 0.9649 - val_loss: 0.2397 - val_accuracy: 0.9524\n",
      "Epoch 47/800\n",
      "114/114 [==============================] - 0s 902us/sample - loss: 0.2027 - accuracy: 0.9386 - val_loss: 0.2354 - val_accuracy: 0.9524\n",
      "Epoch 48/800\n",
      "114/114 [==============================] - 0s 904us/sample - loss: 0.1934 - accuracy: 0.9386 - val_loss: 0.2249 - val_accuracy: 0.9524\n",
      "Epoch 49/800\n",
      "114/114 [==============================] - 0s 951us/sample - loss: 0.1890 - accuracy: 0.9737 - val_loss: 0.2288 - val_accuracy: 0.9524\n",
      "Epoch 50/800\n",
      "114/114 [==============================] - 0s 908us/sample - loss: 0.1803 - accuracy: 0.9649 - val_loss: 0.2107 - val_accuracy: 0.9524\n",
      "Epoch 51/800\n",
      "114/114 [==============================] - 0s 895us/sample - loss: 0.1710 - accuracy: 0.9474 - val_loss: 0.2051 - val_accuracy: 0.9524\n",
      "Epoch 52/800\n",
      "114/114 [==============================] - 0s 862us/sample - loss: 0.1613 - accuracy: 0.9737 - val_loss: 0.2085 - val_accuracy: 0.9524\n",
      "Epoch 53/800\n",
      "114/114 [==============================] - 0s 956us/sample - loss: 0.1618 - accuracy: 0.9825 - val_loss: 0.2039 - val_accuracy: 0.9524\n",
      "Epoch 54/800\n",
      "114/114 [==============================] - 0s 923us/sample - loss: 0.1473 - accuracy: 0.9649 - val_loss: 0.1909 - val_accuracy: 0.9524\n",
      "Epoch 55/800\n",
      "114/114 [==============================] - 0s 870us/sample - loss: 0.1570 - accuracy: 0.9386 - val_loss: 0.1865 - val_accuracy: 0.9524\n",
      "Epoch 56/800\n",
      "114/114 [==============================] - 0s 932us/sample - loss: 0.1437 - accuracy: 0.9561 - val_loss: 0.2084 - val_accuracy: 0.9524\n",
      "Epoch 57/800\n",
      "114/114 [==============================] - 0s 933us/sample - loss: 0.1445 - accuracy: 0.9737 - val_loss: 0.1913 - val_accuracy: 0.9524\n",
      "Epoch 58/800\n",
      "114/114 [==============================] - 0s 880us/sample - loss: 0.1347 - accuracy: 0.9561 - val_loss: 0.1759 - val_accuracy: 0.9524\n",
      "Epoch 59/800\n",
      "114/114 [==============================] - 0s 965us/sample - loss: 0.1376 - accuracy: 0.9386 - val_loss: 0.1779 - val_accuracy: 0.9524\n",
      "Epoch 60/800\n",
      "114/114 [==============================] - 0s 905us/sample - loss: 0.1281 - accuracy: 0.9737 - val_loss: 0.2139 - val_accuracy: 0.9524\n",
      "Epoch 61/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.1362 - accuracy: 0.9737 - val_loss: 0.1763 - val_accuracy: 0.9524\n",
      "Epoch 62/800\n",
      "114/114 [==============================] - 0s 945us/sample - loss: 0.1184 - accuracy: 0.9561 - val_loss: 0.1712 - val_accuracy: 0.9524\n",
      "Epoch 63/800\n",
      "114/114 [==============================] - 0s 923us/sample - loss: 0.1163 - accuracy: 0.9649 - val_loss: 0.1856 - val_accuracy: 0.9524\n",
      "Epoch 64/800\n",
      "114/114 [==============================] - 0s 863us/sample - loss: 0.1133 - accuracy: 0.9825 - val_loss: 0.1859 - val_accuracy: 0.9524\n",
      "Epoch 65/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.1064 - accuracy: 0.9737 - val_loss: 0.1703 - val_accuracy: 0.9524\n",
      "Epoch 66/800\n",
      "114/114 [==============================] - 0s 943us/sample - loss: 0.1110 - accuracy: 0.9474 - val_loss: 0.1734 - val_accuracy: 0.9524\n",
      "Epoch 67/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.1015 - accuracy: 0.9737 - val_loss: 0.1875 - val_accuracy: 0.9524\n",
      "Epoch 68/800\n",
      "114/114 [==============================] - 0s 925us/sample - loss: 0.1014 - accuracy: 0.9825 - val_loss: 0.1876 - val_accuracy: 0.9524\n",
      "Epoch 69/800\n",
      "114/114 [==============================] - 0s 972us/sample - loss: 0.0965 - accuracy: 0.9825 - val_loss: 0.1706 - val_accuracy: 0.9524\n",
      "Epoch 70/800\n",
      "114/114 [==============================] - 0s 930us/sample - loss: 0.0966 - accuracy: 0.9474 - val_loss: 0.1744 - val_accuracy: 0.9524\n",
      "Epoch 71/800\n",
      "114/114 [==============================] - 0s 928us/sample - loss: 0.0950 - accuracy: 0.9737 - val_loss: 0.1930 - val_accuracy: 0.9524\n",
      "Epoch 72/800\n",
      "114/114 [==============================] - 0s 903us/sample - loss: 0.0916 - accuracy: 0.9825 - val_loss: 0.1779 - val_accuracy: 0.9524\n",
      "Epoch 73/800\n",
      "114/114 [==============================] - 0s 969us/sample - loss: 0.0933 - accuracy: 0.9561 - val_loss: 0.1692 - val_accuracy: 0.9524\n",
      "Epoch 74/800\n",
      "114/114 [==============================] - 0s 898us/sample - loss: 0.0910 - accuracy: 0.9474 - val_loss: 0.1862 - val_accuracy: 0.9524\n",
      "Epoch 75/800\n",
      "114/114 [==============================] - 0s 897us/sample - loss: 0.0948 - accuracy: 0.9825 - val_loss: 0.1993 - val_accuracy: 0.9524\n",
      "Epoch 76/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0875 - accuracy: 0.9825 - val_loss: 0.1671 - val_accuracy: 0.9524\n",
      "Epoch 77/800\n",
      "114/114 [==============================] - 0s 927us/sample - loss: 0.0877 - accuracy: 0.9474 - val_loss: 0.1743 - val_accuracy: 0.9524\n",
      "Epoch 78/800\n",
      "114/114 [==============================] - 0s 910us/sample - loss: 0.0827 - accuracy: 0.9825 - val_loss: 0.1964 - val_accuracy: 0.9524\n",
      "Epoch 79/800\n",
      "114/114 [==============================] - 0s 963us/sample - loss: 0.0850 - accuracy: 0.9825 - val_loss: 0.1895 - val_accuracy: 0.9524\n",
      "Epoch 80/800\n",
      "114/114 [==============================] - 0s 896us/sample - loss: 0.0857 - accuracy: 0.9825 - val_loss: 0.1962 - val_accuracy: 0.9524\n",
      "Epoch 81/800\n",
      "114/114 [==============================] - 0s 912us/sample - loss: 0.0840 - accuracy: 0.9649 - val_loss: 0.1698 - val_accuracy: 0.9524\n",
      "Epoch 82/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0833 - accuracy: 0.9561 - val_loss: 0.1935 - val_accuracy: 0.9524\n",
      "Epoch 83/800\n",
      "114/114 [==============================] - 0s 925us/sample - loss: 0.0763 - accuracy: 0.9825 - val_loss: 0.1911 - val_accuracy: 0.9524\n",
      "Epoch 84/800\n",
      "114/114 [==============================] - 0s 886us/sample - loss: 0.0747 - accuracy: 0.9825 - val_loss: 0.1875 - val_accuracy: 0.9524\n",
      "Epoch 85/800\n",
      "114/114 [==============================] - 0s 941us/sample - loss: 0.0760 - accuracy: 0.9737 - val_loss: 0.1841 - val_accuracy: 0.9524\n",
      "Epoch 86/800\n",
      "114/114 [==============================] - 0s 954us/sample - loss: 0.0729 - accuracy: 0.9825 - val_loss: 0.1950 - val_accuracy: 0.9524\n",
      "Epoch 87/800\n",
      "114/114 [==============================] - 0s 890us/sample - loss: 0.0766 - accuracy: 0.9825 - val_loss: 0.1954 - val_accuracy: 0.9524\n",
      "Epoch 88/800\n",
      "114/114 [==============================] - 0s 979us/sample - loss: 0.0758 - accuracy: 0.9737 - val_loss: 0.1805 - val_accuracy: 0.9524\n",
      "Epoch 89/800\n",
      "114/114 [==============================] - 0s 904us/sample - loss: 0.0712 - accuracy: 0.9737 - val_loss: 0.2005 - val_accuracy: 0.9524\n",
      "Epoch 90/800\n",
      "114/114 [==============================] - 0s 881us/sample - loss: 0.0728 - accuracy: 0.9825 - val_loss: 0.2056 - val_accuracy: 0.9524\n",
      "Epoch 91/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0718 - accuracy: 0.9825 - val_loss: 0.1899 - val_accuracy: 0.9524\n",
      "Epoch 92/800\n",
      "114/114 [==============================] - 0s 921us/sample - loss: 0.0684 - accuracy: 0.9825 - val_loss: 0.1943 - val_accuracy: 0.9524\n",
      "Epoch 93/800\n",
      "114/114 [==============================] - 0s 874us/sample - loss: 0.0715 - accuracy: 0.9825 - val_loss: 0.2130 - val_accuracy: 0.9524\n",
      "Epoch 94/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0710 - accuracy: 0.9825 - val_loss: 0.1913 - val_accuracy: 0.9524\n",
      "Epoch 95/800\n",
      "114/114 [==============================] - 0s 906us/sample - loss: 0.0685 - accuracy: 0.9825 - val_loss: 0.2011 - val_accuracy: 0.9524\n",
      "Epoch 96/800\n",
      "114/114 [==============================] - 0s 897us/sample - loss: 0.0685 - accuracy: 0.9825 - val_loss: 0.2107 - val_accuracy: 0.9524\n",
      "Epoch 97/800\n",
      "114/114 [==============================] - 0s 897us/sample - loss: 0.0682 - accuracy: 0.9737 - val_loss: 0.1847 - val_accuracy: 0.9524\n",
      "Epoch 98/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0657 - accuracy: 0.9737 - val_loss: 0.1992 - val_accuracy: 0.9524\n",
      "Epoch 99/800\n",
      "114/114 [==============================] - 0s 936us/sample - loss: 0.0633 - accuracy: 0.9825 - val_loss: 0.2068 - val_accuracy: 0.9524\n",
      "Epoch 100/800\n",
      "114/114 [==============================] - 0s 904us/sample - loss: 0.0630 - accuracy: 0.9825 - val_loss: 0.2168 - val_accuracy: 0.9524\n",
      "Epoch 101/800\n",
      "114/114 [==============================] - 0s 883us/sample - loss: 0.0688 - accuracy: 0.9825 - val_loss: 0.1943 - val_accuracy: 0.9524\n",
      "Epoch 102/800\n",
      "114/114 [==============================] - 0s 957us/sample - loss: 0.0617 - accuracy: 0.9825 - val_loss: 0.2079 - val_accuracy: 0.9524\n",
      "Epoch 103/800\n",
      "114/114 [==============================] - 0s 889us/sample - loss: 0.0622 - accuracy: 0.9825 - val_loss: 0.2097 - val_accuracy: 0.9524\n",
      "Epoch 104/800\n",
      "114/114 [==============================] - 0s 902us/sample - loss: 0.0598 - accuracy: 0.9825 - val_loss: 0.2137 - val_accuracy: 0.9524\n",
      "Epoch 105/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0591 - accuracy: 0.9825 - val_loss: 0.2058 - val_accuracy: 0.9524\n",
      "Epoch 106/800\n",
      "114/114 [==============================] - 0s 944us/sample - loss: 0.0628 - accuracy: 0.9825 - val_loss: 0.2077 - val_accuracy: 0.9524\n",
      "Epoch 107/800\n",
      "114/114 [==============================] - 0s 880us/sample - loss: 0.0570 - accuracy: 0.9825 - val_loss: 0.1973 - val_accuracy: 0.9524\n",
      "Epoch 108/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0624 - accuracy: 0.9825 - val_loss: 0.2083 - val_accuracy: 0.9524\n",
      "Epoch 109/800\n",
      "114/114 [==============================] - 0s 920us/sample - loss: 0.0621 - accuracy: 0.9737 - val_loss: 0.1992 - val_accuracy: 0.9524\n",
      "Epoch 110/800\n",
      "114/114 [==============================] - 0s 933us/sample - loss: 0.0583 - accuracy: 0.9825 - val_loss: 0.2250 - val_accuracy: 0.9524\n",
      "Epoch 111/800\n",
      "114/114 [==============================] - 0s 890us/sample - loss: 0.0585 - accuracy: 0.9825 - val_loss: 0.2170 - val_accuracy: 0.9524\n",
      "Epoch 112/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0564 - accuracy: 0.9912 - val_loss: 0.1954 - val_accuracy: 0.9524\n",
      "Epoch 113/800\n",
      "114/114 [==============================] - 0s 927us/sample - loss: 0.0582 - accuracy: 0.9737 - val_loss: 0.2126 - val_accuracy: 0.9524\n",
      "Epoch 114/800\n",
      "114/114 [==============================] - 0s 904us/sample - loss: 0.0545 - accuracy: 0.9825 - val_loss: 0.2447 - val_accuracy: 0.9524\n",
      "Epoch 115/800\n",
      "114/114 [==============================] - 0s 878us/sample - loss: 0.0591 - accuracy: 0.9737 - val_loss: 0.2226 - val_accuracy: 0.9524\n",
      "Epoch 116/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0607 - accuracy: 0.9737 - val_loss: 0.1995 - val_accuracy: 0.9524\n",
      "Epoch 117/800\n",
      "114/114 [==============================] - 0s 909us/sample - loss: 0.0562 - accuracy: 0.9825 - val_loss: 0.2260 - val_accuracy: 0.9524\n",
      "Epoch 118/800\n",
      "114/114 [==============================] - 0s 897us/sample - loss: 0.0540 - accuracy: 0.9825 - val_loss: 0.2274 - val_accuracy: 0.9524\n",
      "Epoch 119/800\n",
      "114/114 [==============================] - 0s 875us/sample - loss: 0.0573 - accuracy: 0.9825 - val_loss: 0.2063 - val_accuracy: 0.9524\n",
      "Epoch 120/800\n",
      "114/114 [==============================] - 0s 910us/sample - loss: 0.0538 - accuracy: 0.9825 - val_loss: 0.2271 - val_accuracy: 0.9524\n",
      "Epoch 121/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0536 - accuracy: 0.9825 - val_loss: 0.2315 - val_accuracy: 0.9524\n",
      "Epoch 122/800\n",
      "114/114 [==============================] - 0s 905us/sample - loss: 0.0516 - accuracy: 0.9825 - val_loss: 0.2088 - val_accuracy: 0.9524\n",
      "Epoch 123/800\n",
      "114/114 [==============================] - 0s 890us/sample - loss: 0.0536 - accuracy: 0.9825 - val_loss: 0.2239 - val_accuracy: 0.9524\n",
      "Epoch 124/800\n",
      "114/114 [==============================] - 0s 896us/sample - loss: 0.0512 - accuracy: 0.9825 - val_loss: 0.2257 - val_accuracy: 0.9524\n",
      "Epoch 125/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0537 - accuracy: 0.9825 - val_loss: 0.2125 - val_accuracy: 0.9524\n",
      "Epoch 126/800\n",
      "114/114 [==============================] - 0s 913us/sample - loss: 0.0480 - accuracy: 0.9912 - val_loss: 0.2389 - val_accuracy: 0.9524\n",
      "Epoch 127/800\n",
      "114/114 [==============================] - 0s 907us/sample - loss: 0.0525 - accuracy: 0.9737 - val_loss: 0.2482 - val_accuracy: 0.9524\n",
      "Epoch 128/800\n",
      "114/114 [==============================] - 0s 921us/sample - loss: 0.0485 - accuracy: 0.9825 - val_loss: 0.2121 - val_accuracy: 0.9524\n",
      "Epoch 129/800\n",
      "114/114 [==============================] - 0s 905us/sample - loss: 0.0551 - accuracy: 0.9825 - val_loss: 0.2133 - val_accuracy: 0.9524\n",
      "Epoch 130/800\n",
      "114/114 [==============================] - 0s 900us/sample - loss: 0.0553 - accuracy: 0.9912 - val_loss: 0.2576 - val_accuracy: 0.9524\n",
      "Epoch 131/800\n",
      "114/114 [==============================] - 0s 898us/sample - loss: 0.0569 - accuracy: 0.9737 - val_loss: 0.2246 - val_accuracy: 0.9524\n",
      "Epoch 132/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0473 - accuracy: 0.9912 - val_loss: 0.2263 - val_accuracy: 0.9524\n",
      "Epoch 133/800\n",
      "114/114 [==============================] - 0s 946us/sample - loss: 0.0583 - accuracy: 0.9737 - val_loss: 0.2508 - val_accuracy: 0.9524\n",
      "Epoch 134/800\n",
      "114/114 [==============================] - 0s 919us/sample - loss: 0.0559 - accuracy: 0.9649 - val_loss: 0.2081 - val_accuracy: 0.9524\n",
      "Epoch 135/800\n",
      "114/114 [==============================] - 0s 863us/sample - loss: 0.0528 - accuracy: 0.9825 - val_loss: 0.2390 - val_accuracy: 0.9524\n",
      "Epoch 136/800\n",
      "114/114 [==============================] - 0s 877us/sample - loss: 0.0528 - accuracy: 0.9737 - val_loss: 0.2487 - val_accuracy: 0.9524\n",
      "Epoch 137/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0462 - accuracy: 0.9825 - val_loss: 0.2094 - val_accuracy: 0.9048\n",
      "Epoch 138/800\n",
      "114/114 [==============================] - 0s 929us/sample - loss: 0.0547 - accuracy: 0.9737 - val_loss: 0.2238 - val_accuracy: 0.9524\n",
      "Epoch 139/800\n",
      "114/114 [==============================] - 0s 895us/sample - loss: 0.0421 - accuracy: 0.9912 - val_loss: 0.2799 - val_accuracy: 0.9524\n",
      "Epoch 140/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0635 - accuracy: 0.9737 - val_loss: 0.2671 - val_accuracy: 0.9524\n",
      "Epoch 141/800\n",
      "114/114 [==============================] - 0s 937us/sample - loss: 0.0380 - accuracy: 0.9912 - val_loss: 0.2069 - val_accuracy: 0.9048\n",
      "Epoch 142/800\n",
      "114/114 [==============================] - 0s 877us/sample - loss: 0.0728 - accuracy: 0.9474 - val_loss: 0.2147 - val_accuracy: 0.9524\n",
      "Epoch 143/800\n",
      "114/114 [==============================] - 0s 903us/sample - loss: 0.0585 - accuracy: 0.9825 - val_loss: 0.2954 - val_accuracy: 0.9524\n",
      "Epoch 144/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0624 - accuracy: 0.9737 - val_loss: 0.2466 - val_accuracy: 0.9524\n",
      "Epoch 145/800\n",
      "114/114 [==============================] - 0s 931us/sample - loss: 0.0495 - accuracy: 0.9825 - val_loss: 0.2111 - val_accuracy: 0.9048\n",
      "Epoch 146/800\n",
      "114/114 [==============================] - 0s 870us/sample - loss: 0.0507 - accuracy: 0.9825 - val_loss: 0.2381 - val_accuracy: 0.9524\n",
      "Epoch 147/800\n",
      "114/114 [==============================] - 0s 892us/sample - loss: 0.0390 - accuracy: 0.9825 - val_loss: 0.2837 - val_accuracy: 0.9524\n",
      "Epoch 148/800\n",
      "114/114 [==============================] - 0s 884us/sample - loss: 0.0557 - accuracy: 0.9737 - val_loss: 0.2624 - val_accuracy: 0.9524\n",
      "Epoch 149/800\n",
      "114/114 [==============================] - 0s 918us/sample - loss: 0.0382 - accuracy: 0.9737 - val_loss: 0.2184 - val_accuracy: 0.9048\n",
      "Epoch 150/800\n",
      "114/114 [==============================] - 0s 914us/sample - loss: 0.0542 - accuracy: 0.9737 - val_loss: 0.2157 - val_accuracy: 0.9048\n",
      "Epoch 151/800\n",
      "114/114 [==============================] - 0s 891us/sample - loss: 0.0414 - accuracy: 0.9912 - val_loss: 0.2628 - val_accuracy: 0.9524\n",
      "Epoch 152/800\n",
      "114/114 [==============================] - 0s 910us/sample - loss: 0.0537 - accuracy: 0.9737 - val_loss: 0.2819 - val_accuracy: 0.9524\n",
      "Epoch 153/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0444 - accuracy: 0.9737 - val_loss: 0.2316 - val_accuracy: 0.9524\n",
      "Epoch 154/800\n",
      "114/114 [==============================] - 0s 956us/sample - loss: 0.0436 - accuracy: 0.9825 - val_loss: 0.2254 - val_accuracy: 0.9524\n",
      "Epoch 155/800\n",
      "114/114 [==============================] - 0s 871us/sample - loss: 0.0432 - accuracy: 0.9825 - val_loss: 0.2595 - val_accuracy: 0.9524\n",
      "Epoch 156/800\n",
      "114/114 [==============================] - 0s 894us/sample - loss: 0.0425 - accuracy: 0.9825 - val_loss: 0.2633 - val_accuracy: 0.9524\n",
      "Epoch 157/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0419 - accuracy: 0.9825 - val_loss: 0.2475 - val_accuracy: 0.9524\n",
      "Epoch 158/800\n",
      "114/114 [==============================] - 0s 892us/sample - loss: 0.0528 - accuracy: 0.9737 - val_loss: 0.2289 - val_accuracy: 0.9524\n",
      "Epoch 159/800\n",
      "114/114 [==============================] - 0s 902us/sample - loss: 0.0391 - accuracy: 0.9912 - val_loss: 0.2861 - val_accuracy: 0.9524\n",
      "Epoch 160/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0488 - accuracy: 0.9737 - val_loss: 0.2790 - val_accuracy: 0.9524\n",
      "Epoch 161/800\n",
      "114/114 [==============================] - 0s 948us/sample - loss: 0.0426 - accuracy: 0.9737 - val_loss: 0.2355 - val_accuracy: 0.9524\n",
      "Epoch 162/800\n",
      "114/114 [==============================] - 0s 869us/sample - loss: 0.0421 - accuracy: 0.9912 - val_loss: 0.2350 - val_accuracy: 0.9524\n",
      "Epoch 163/800\n",
      "114/114 [==============================] - 0s 901us/sample - loss: 0.0406 - accuracy: 0.9912 - val_loss: 0.2599 - val_accuracy: 0.9524\n",
      "Epoch 164/800\n",
      "114/114 [==============================] - 0s 889us/sample - loss: 0.0386 - accuracy: 0.9825 - val_loss: 0.2598 - val_accuracy: 0.9524\n",
      "Epoch 165/800\n",
      "114/114 [==============================] - 0s 868us/sample - loss: 0.0392 - accuracy: 0.9825 - val_loss: 0.2558 - val_accuracy: 0.9524\n",
      "Epoch 166/800\n",
      "114/114 [==============================] - 0s 953us/sample - loss: 0.0364 - accuracy: 0.9912 - val_loss: 0.2438 - val_accuracy: 0.9524\n",
      "Epoch 167/800\n",
      "114/114 [==============================] - 0s 895us/sample - loss: 0.0391 - accuracy: 0.9912 - val_loss: 0.2465 - val_accuracy: 0.9524\n",
      "Epoch 168/800\n",
      "114/114 [==============================] - 0s 881us/sample - loss: 0.0374 - accuracy: 0.9912 - val_loss: 0.2639 - val_accuracy: 0.9524\n",
      "Epoch 169/800\n",
      "114/114 [==============================] - 0s 928us/sample - loss: 0.0396 - accuracy: 0.9825 - val_loss: 0.2633 - val_accuracy: 0.9524\n",
      "Epoch 170/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0407 - accuracy: 0.9912 - val_loss: 0.2437 - val_accuracy: 0.9524\n",
      "Epoch 171/800\n",
      "114/114 [==============================] - 0s 897us/sample - loss: 0.0374 - accuracy: 0.9912 - val_loss: 0.2638 - val_accuracy: 0.9524\n",
      "Epoch 172/800\n",
      "114/114 [==============================] - 0s 927us/sample - loss: 0.0370 - accuracy: 0.9825 - val_loss: 0.2646 - val_accuracy: 0.9524\n",
      "Epoch 173/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0388 - accuracy: 0.9825 - val_loss: 0.2528 - val_accuracy: 0.9524\n",
      "Epoch 174/800\n",
      "114/114 [==============================] - 0s 936us/sample - loss: 0.0380 - accuracy: 0.9912 - val_loss: 0.2618 - val_accuracy: 0.9524\n",
      "Epoch 175/800\n",
      "114/114 [==============================] - 0s 900us/sample - loss: 0.0390 - accuracy: 0.9912 - val_loss: 0.2844 - val_accuracy: 0.9524\n",
      "Epoch 176/800\n",
      "114/114 [==============================] - 0s 874us/sample - loss: 0.0380 - accuracy: 0.9825 - val_loss: 0.2586 - val_accuracy: 0.9524\n",
      "Epoch 177/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0345 - accuracy: 0.9912 - val_loss: 0.2426 - val_accuracy: 0.9048\n",
      "Epoch 178/800\n",
      "114/114 [==============================] - 0s 908us/sample - loss: 0.0482 - accuracy: 0.9825 - val_loss: 0.2633 - val_accuracy: 0.9524\n",
      "Epoch 179/800\n",
      "114/114 [==============================] - 0s 897us/sample - loss: 0.0381 - accuracy: 0.9825 - val_loss: 0.2837 - val_accuracy: 0.9524\n",
      "Epoch 180/800\n",
      "114/114 [==============================] - 0s 891us/sample - loss: 0.0430 - accuracy: 0.9825 - val_loss: 0.2558 - val_accuracy: 0.9524\n",
      "Epoch 181/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0356 - accuracy: 0.9912 - val_loss: 0.2696 - val_accuracy: 0.9524\n",
      "Epoch 182/800\n",
      "114/114 [==============================] - 0s 885us/sample - loss: 0.0386 - accuracy: 0.9825 - val_loss: 0.2714 - val_accuracy: 0.9524\n",
      "Epoch 183/800\n",
      "114/114 [==============================] - 0s 932us/sample - loss: 0.0360 - accuracy: 0.9825 - val_loss: 0.2627 - val_accuracy: 0.9524\n",
      "Epoch 184/800\n",
      "114/114 [==============================] - 0s 873us/sample - loss: 0.0347 - accuracy: 0.9912 - val_loss: 0.2594 - val_accuracy: 0.9524\n",
      "Epoch 185/800\n",
      "114/114 [==============================] - 0s 963us/sample - loss: 0.0347 - accuracy: 0.9912 - val_loss: 0.2739 - val_accuracy: 0.9524\n",
      "Epoch 186/800\n",
      "114/114 [==============================] - 0s 918us/sample - loss: 0.0339 - accuracy: 0.9825 - val_loss: 0.2723 - val_accuracy: 0.9524\n",
      "Epoch 187/800\n",
      "114/114 [==============================] - 0s 882us/sample - loss: 0.0334 - accuracy: 0.9912 - val_loss: 0.2680 - val_accuracy: 0.9524\n",
      "Epoch 188/800\n",
      "114/114 [==============================] - 0s 985us/sample - loss: 0.0331 - accuracy: 0.9912 - val_loss: 0.2706 - val_accuracy: 0.9524\n",
      "Epoch 189/800\n",
      "114/114 [==============================] - 0s 915us/sample - loss: 0.0359 - accuracy: 0.9912 - val_loss: 0.2669 - val_accuracy: 0.9524\n",
      "Epoch 190/800\n",
      "114/114 [==============================] - 0s 841us/sample - loss: 0.0367 - accuracy: 0.9825 - val_loss: 0.2781 - val_accuracy: 0.9524\n",
      "Epoch 191/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0326 - accuracy: 0.9825 - val_loss: 0.2615 - val_accuracy: 0.9524\n",
      "Epoch 192/800\n",
      "114/114 [==============================] - 0s 940us/sample - loss: 0.0347 - accuracy: 0.9912 - val_loss: 0.2677 - val_accuracy: 0.9524\n",
      "Epoch 193/800\n",
      "114/114 [==============================] - 0s 879us/sample - loss: 0.0323 - accuracy: 0.9912 - val_loss: 0.2728 - val_accuracy: 0.9524\n",
      "Epoch 194/800\n",
      "114/114 [==============================] - 0s 969us/sample - loss: 0.0343 - accuracy: 0.9825 - val_loss: 0.2757 - val_accuracy: 0.9524\n",
      "Epoch 195/800\n",
      "114/114 [==============================] - 0s 901us/sample - loss: 0.0401 - accuracy: 0.9825 - val_loss: 0.2880 - val_accuracy: 0.9524\n",
      "Epoch 196/800\n",
      "114/114 [==============================] - 0s 901us/sample - loss: 0.0330 - accuracy: 0.9825 - val_loss: 0.2596 - val_accuracy: 0.9524\n",
      "Epoch 197/800\n",
      "114/114 [==============================] - 0s 853us/sample - loss: 0.0340 - accuracy: 1.0000 - val_loss: 0.2725 - val_accuracy: 0.9524\n",
      "Epoch 198/800\n",
      "114/114 [==============================] - 0s 965us/sample - loss: 0.0446 - accuracy: 0.9825 - val_loss: 0.3056 - val_accuracy: 0.9524\n",
      "Epoch 199/800\n",
      "114/114 [==============================] - 0s 894us/sample - loss: 0.0442 - accuracy: 0.9825 - val_loss: 0.2516 - val_accuracy: 0.9048\n",
      "Epoch 200/800\n",
      "114/114 [==============================] - 0s 894us/sample - loss: 0.0405 - accuracy: 0.9912 - val_loss: 0.2719 - val_accuracy: 0.9524\n",
      "Epoch 201/800\n",
      "114/114 [==============================] - 0s 963us/sample - loss: 0.0411 - accuracy: 0.9825 - val_loss: 0.3144 - val_accuracy: 0.9524\n",
      "Epoch 202/800\n",
      "114/114 [==============================] - 0s 864us/sample - loss: 0.0382 - accuracy: 0.9825 - val_loss: 0.2735 - val_accuracy: 0.9524\n",
      "Epoch 203/800\n",
      "114/114 [==============================] - 0s 936us/sample - loss: 0.0450 - accuracy: 0.9825 - val_loss: 0.2665 - val_accuracy: 0.9524\n",
      "Epoch 204/800\n",
      "114/114 [==============================] - 0s 851us/sample - loss: 0.0283 - accuracy: 0.9912 - val_loss: 0.2989 - val_accuracy: 0.9524\n",
      "Epoch 205/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0500 - accuracy: 0.9825 - val_loss: 0.3123 - val_accuracy: 0.9524\n",
      "Epoch 206/800\n",
      "114/114 [==============================] - 0s 903us/sample - loss: 0.0445 - accuracy: 0.9825 - val_loss: 0.2527 - val_accuracy: 0.9048\n",
      "Epoch 207/800\n",
      "114/114 [==============================] - 0s 884us/sample - loss: 0.0438 - accuracy: 0.9825 - val_loss: 0.2863 - val_accuracy: 0.9524\n",
      "Epoch 208/800\n",
      "114/114 [==============================] - 0s 910us/sample - loss: 0.0400 - accuracy: 0.9825 - val_loss: 0.3164 - val_accuracy: 0.9524\n",
      "Epoch 209/800\n",
      "114/114 [==============================] - 0s 939us/sample - loss: 0.0380 - accuracy: 0.9825 - val_loss: 0.2764 - val_accuracy: 0.9524\n",
      "Epoch 210/800\n",
      "114/114 [==============================] - 0s 904us/sample - loss: 0.0478 - accuracy: 0.9825 - val_loss: 0.2695 - val_accuracy: 0.9524\n",
      "Epoch 211/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0256 - accuracy: 1.0000 - val_loss: 0.3124 - val_accuracy: 0.9524\n",
      "Epoch 212/800\n",
      "114/114 [==============================] - 0s 935us/sample - loss: 0.0383 - accuracy: 0.9825 - val_loss: 0.3125 - val_accuracy: 0.9524\n",
      "Epoch 213/800\n",
      "114/114 [==============================] - 0s 905us/sample - loss: 0.0372 - accuracy: 0.9825 - val_loss: 0.2728 - val_accuracy: 0.9524\n",
      "Epoch 214/800\n",
      "114/114 [==============================] - 0s 885us/sample - loss: 0.0336 - accuracy: 1.0000 - val_loss: 0.2752 - val_accuracy: 0.9524\n",
      "Epoch 215/800\n",
      "114/114 [==============================] - 0s 910us/sample - loss: 0.0295 - accuracy: 0.9912 - val_loss: 0.3014 - val_accuracy: 0.9524\n",
      "Epoch 216/800\n",
      "114/114 [==============================] - 0s 916us/sample - loss: 0.0344 - accuracy: 0.9825 - val_loss: 0.2968 - val_accuracy: 0.9524\n",
      "Epoch 217/800\n",
      "114/114 [==============================] - 0s 911us/sample - loss: 0.0271 - accuracy: 0.9912 - val_loss: 0.2736 - val_accuracy: 0.9524\n",
      "Epoch 218/800\n",
      "114/114 [==============================] - 0s 887us/sample - loss: 0.0358 - accuracy: 0.9912 - val_loss: 0.2712 - val_accuracy: 0.9048\n",
      "Epoch 219/800\n",
      "114/114 [==============================] - 0s 967us/sample - loss: 0.0276 - accuracy: 1.0000 - val_loss: 0.3069 - val_accuracy: 0.9524\n",
      "Epoch 220/800\n",
      "114/114 [==============================] - 0s 887us/sample - loss: 0.0390 - accuracy: 0.9825 - val_loss: 0.3176 - val_accuracy: 0.9524\n",
      "Epoch 221/800\n",
      "114/114 [==============================] - 0s 895us/sample - loss: 0.0426 - accuracy: 0.9825 - val_loss: 0.2666 - val_accuracy: 0.9048\n",
      "Epoch 222/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0417 - accuracy: 0.9825 - val_loss: 0.2915 - val_accuracy: 0.9524\n",
      "Epoch 223/800\n",
      "114/114 [==============================] - 0s 908us/sample - loss: 0.0276 - accuracy: 0.9912 - val_loss: 0.3008 - val_accuracy: 0.9524\n",
      "Epoch 224/800\n",
      "114/114 [==============================] - 0s 928us/sample - loss: 0.0298 - accuracy: 0.9825 - val_loss: 0.2942 - val_accuracy: 0.9524\n",
      "Epoch 225/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0279 - accuracy: 0.9825 - val_loss: 0.2856 - val_accuracy: 0.9524\n",
      "Epoch 226/800\n",
      "114/114 [==============================] - 0s 926us/sample - loss: 0.0295 - accuracy: 1.0000 - val_loss: 0.2790 - val_accuracy: 0.9524\n",
      "Epoch 227/800\n",
      "114/114 [==============================] - 0s 913us/sample - loss: 0.0290 - accuracy: 1.0000 - val_loss: 0.2937 - val_accuracy: 0.9524\n",
      "Epoch 228/800\n",
      "114/114 [==============================] - 0s 885us/sample - loss: 0.0325 - accuracy: 0.9912 - val_loss: 0.3171 - val_accuracy: 0.9524\n",
      "Epoch 229/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0329 - accuracy: 0.9825 - val_loss: 0.2871 - val_accuracy: 0.9524\n",
      "Epoch 230/800\n",
      "114/114 [==============================] - 0s 905us/sample - loss: 0.0421 - accuracy: 0.9825 - val_loss: 0.2768 - val_accuracy: 0.9048\n",
      "Epoch 231/800\n",
      "114/114 [==============================] - 0s 903us/sample - loss: 0.0323 - accuracy: 0.9912 - val_loss: 0.3144 - val_accuracy: 0.9524\n",
      "Epoch 232/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0329 - accuracy: 0.9825 - val_loss: 0.3094 - val_accuracy: 0.9524\n",
      "Epoch 233/800\n",
      "114/114 [==============================] - 0s 922us/sample - loss: 0.0376 - accuracy: 0.9825 - val_loss: 0.2783 - val_accuracy: 0.9048\n",
      "Epoch 234/800\n",
      "114/114 [==============================] - 0s 908us/sample - loss: 0.0303 - accuracy: 1.0000 - val_loss: 0.3026 - val_accuracy: 0.9524\n",
      "Epoch 235/800\n",
      "114/114 [==============================] - 0s 878us/sample - loss: 0.0265 - accuracy: 0.9825 - val_loss: 0.3157 - val_accuracy: 0.9524\n",
      "Epoch 236/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0316 - accuracy: 0.9825 - val_loss: 0.3058 - val_accuracy: 0.9524\n",
      "Epoch 237/800\n",
      "114/114 [==============================] - 0s 992us/sample - loss: 0.0306 - accuracy: 0.9912 - val_loss: 0.2806 - val_accuracy: 0.9048\n",
      "Epoch 238/800\n",
      "114/114 [==============================] - 0s 889us/sample - loss: 0.0300 - accuracy: 1.0000 - val_loss: 0.2948 - val_accuracy: 0.9524\n",
      "Epoch 239/800\n",
      "114/114 [==============================] - 0s 922us/sample - loss: 0.0255 - accuracy: 0.9912 - val_loss: 0.3044 - val_accuracy: 0.9524\n",
      "Epoch 240/800\n",
      "114/114 [==============================] - 0s 935us/sample - loss: 0.0303 - accuracy: 0.9825 - val_loss: 0.3075 - val_accuracy: 0.9524\n",
      "Epoch 241/800\n",
      "114/114 [==============================] - 0s 891us/sample - loss: 0.0270 - accuracy: 0.9912 - val_loss: 0.2853 - val_accuracy: 0.9048\n",
      "Epoch 242/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0293 - accuracy: 1.0000 - val_loss: 0.2939 - val_accuracy: 0.9524\n",
      "Epoch 243/800\n",
      "114/114 [==============================] - 0s 881us/sample - loss: 0.0257 - accuracy: 0.9912 - val_loss: 0.3052 - val_accuracy: 0.9524\n",
      "Epoch 244/800\n",
      "114/114 [==============================] - 0s 893us/sample - loss: 0.0307 - accuracy: 0.9825 - val_loss: 0.3157 - val_accuracy: 0.9524\n",
      "Epoch 245/800\n",
      "114/114 [==============================] - 0s 902us/sample - loss: 0.0291 - accuracy: 0.9825 - val_loss: 0.2955 - val_accuracy: 0.9524\n",
      "Epoch 246/800\n",
      "114/114 [==============================] - 0s 964us/sample - loss: 0.0257 - accuracy: 1.0000 - val_loss: 0.3016 - val_accuracy: 0.9524\n",
      "Epoch 247/800\n",
      "114/114 [==============================] - 0s 868us/sample - loss: 0.0367 - accuracy: 0.9825 - val_loss: 0.3154 - val_accuracy: 0.9524\n",
      "Epoch 248/800\n",
      "114/114 [==============================] - 0s 915us/sample - loss: 0.0227 - accuracy: 0.9825 - val_loss: 0.2885 - val_accuracy: 0.9048\n",
      "Epoch 249/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0364 - accuracy: 0.9912 - val_loss: 0.2939 - val_accuracy: 0.9524\n",
      "Epoch 250/800\n",
      "114/114 [==============================] - 0s 942us/sample - loss: 0.0262 - accuracy: 0.9912 - val_loss: 0.3260 - val_accuracy: 0.9524\n",
      "Epoch 251/800\n",
      "114/114 [==============================] - 0s 898us/sample - loss: 0.0321 - accuracy: 0.9825 - val_loss: 0.3173 - val_accuracy: 0.9524\n",
      "Epoch 252/800\n",
      "114/114 [==============================] - 0s 901us/sample - loss: 0.0312 - accuracy: 0.9912 - val_loss: 0.2883 - val_accuracy: 0.9048\n",
      "Epoch 253/800\n",
      "114/114 [==============================] - 0s 974us/sample - loss: 0.0335 - accuracy: 1.0000 - val_loss: 0.3107 - val_accuracy: 0.9524\n",
      "Epoch 254/800\n",
      "114/114 [==============================] - 0s 851us/sample - loss: 0.0285 - accuracy: 0.9825 - val_loss: 0.3162 - val_accuracy: 0.9524\n",
      "Epoch 255/800\n",
      "114/114 [==============================] - 0s 870us/sample - loss: 0.0270 - accuracy: 0.9825 - val_loss: 0.3079 - val_accuracy: 0.9524\n",
      "Epoch 256/800\n",
      "114/114 [==============================] - 0s 919us/sample - loss: 0.0291 - accuracy: 0.9912 - val_loss: 0.2906 - val_accuracy: 0.9048\n",
      "Epoch 257/800\n",
      "114/114 [==============================] - 0s 868us/sample - loss: 0.0297 - accuracy: 1.0000 - val_loss: 0.3149 - val_accuracy: 0.9524\n",
      "Epoch 258/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0308 - accuracy: 0.9825 - val_loss: 0.3216 - val_accuracy: 0.9524\n",
      "Epoch 259/800\n",
      "114/114 [==============================] - 0s 897us/sample - loss: 0.0307 - accuracy: 0.9825 - val_loss: 0.2925 - val_accuracy: 0.9048\n",
      "Epoch 260/800\n",
      "114/114 [==============================] - 0s 890us/sample - loss: 0.0303 - accuracy: 1.0000 - val_loss: 0.3069 - val_accuracy: 0.9524\n",
      "Epoch 261/800\n",
      "114/114 [==============================] - 0s 925us/sample - loss: 0.0219 - accuracy: 0.9912 - val_loss: 0.3242 - val_accuracy: 0.9524\n",
      "Epoch 262/800\n",
      "114/114 [==============================] - 0s 899us/sample - loss: 0.0309 - accuracy: 0.9825 - val_loss: 0.3203 - val_accuracy: 0.9524\n",
      "Epoch 263/800\n",
      "114/114 [==============================] - 0s 909us/sample - loss: 0.0277 - accuracy: 0.9912 - val_loss: 0.2977 - val_accuracy: 0.9048\n",
      "Epoch 264/800\n",
      "114/114 [==============================] - 0s 898us/sample - loss: 0.0273 - accuracy: 1.0000 - val_loss: 0.3123 - val_accuracy: 0.9524\n",
      "Epoch 265/800\n",
      "114/114 [==============================] - 0s 923us/sample - loss: 0.0305 - accuracy: 0.9825 - val_loss: 0.3275 - val_accuracy: 0.9524\n",
      "Epoch 266/800\n",
      "114/114 [==============================] - 0s 927us/sample - loss: 0.0251 - accuracy: 0.9825 - val_loss: 0.3042 - val_accuracy: 0.9524\n",
      "Epoch 267/800\n",
      "114/114 [==============================] - 0s 885us/sample - loss: 0.0313 - accuracy: 1.0000 - val_loss: 0.3032 - val_accuracy: 0.9524\n",
      "Epoch 268/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0264 - accuracy: 0.9912 - val_loss: 0.3269 - val_accuracy: 0.9524\n",
      "Epoch 269/800\n",
      "114/114 [==============================] - 0s 888us/sample - loss: 0.0322 - accuracy: 0.9825 - val_loss: 0.3202 - val_accuracy: 0.9524\n",
      "Epoch 270/800\n",
      "114/114 [==============================] - 0s 888us/sample - loss: 0.0223 - accuracy: 1.0000 - val_loss: 0.3055 - val_accuracy: 0.9524\n",
      "Epoch 271/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0248 - accuracy: 1.0000 - val_loss: 0.3110 - val_accuracy: 0.9524\n",
      "Epoch 272/800\n",
      "114/114 [==============================] - 0s 943us/sample - loss: 0.0230 - accuracy: 0.9912 - val_loss: 0.3167 - val_accuracy: 0.9524\n",
      "Epoch 273/800\n",
      "114/114 [==============================] - 0s 897us/sample - loss: 0.0235 - accuracy: 0.9912 - val_loss: 0.3169 - val_accuracy: 0.9524\n",
      "Epoch 274/800\n",
      "114/114 [==============================] - 0s 890us/sample - loss: 0.0219 - accuracy: 1.0000 - val_loss: 0.3109 - val_accuracy: 0.9524\n",
      "Epoch 275/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0229 - accuracy: 1.0000 - val_loss: 0.3132 - val_accuracy: 0.9524\n",
      "Epoch 276/800\n",
      "114/114 [==============================] - 0s 955us/sample - loss: 0.0224 - accuracy: 1.0000 - val_loss: 0.3184 - val_accuracy: 0.9524\n",
      "Epoch 277/800\n",
      "114/114 [==============================] - 0s 866us/sample - loss: 0.0220 - accuracy: 0.9825 - val_loss: 0.3236 - val_accuracy: 0.9524\n",
      "Epoch 278/800\n",
      "114/114 [==============================] - 0s 923us/sample - loss: 0.0249 - accuracy: 0.9825 - val_loss: 0.3199 - val_accuracy: 0.9524\n",
      "Epoch 279/800\n",
      "114/114 [==============================] - 0s 895us/sample - loss: 0.0254 - accuracy: 0.9912 - val_loss: 0.3073 - val_accuracy: 0.9048\n",
      "Epoch 280/800\n",
      "114/114 [==============================] - 0s 939us/sample - loss: 0.0247 - accuracy: 1.0000 - val_loss: 0.3234 - val_accuracy: 0.9524\n",
      "Epoch 281/800\n",
      "114/114 [==============================] - 0s 896us/sample - loss: 0.0246 - accuracy: 0.9825 - val_loss: 0.3329 - val_accuracy: 0.9524\n",
      "Epoch 282/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0257 - accuracy: 0.9825 - val_loss: 0.3150 - val_accuracy: 0.9524\n",
      "Epoch 283/800\n",
      "114/114 [==============================] - 0s 918us/sample - loss: 0.0289 - accuracy: 1.0000 - val_loss: 0.3133 - val_accuracy: 0.9048\n",
      "Epoch 284/800\n",
      "114/114 [==============================] - 0s 914us/sample - loss: 0.0238 - accuracy: 0.9912 - val_loss: 0.3276 - val_accuracy: 0.9524\n",
      "Epoch 285/800\n",
      "114/114 [==============================] - 0s 906us/sample - loss: 0.0228 - accuracy: 0.9825 - val_loss: 0.3213 - val_accuracy: 0.9524\n",
      "Epoch 286/800\n",
      "114/114 [==============================] - 0s 985us/sample - loss: 0.0217 - accuracy: 1.0000 - val_loss: 0.3204 - val_accuracy: 0.9524\n",
      "Epoch 287/800\n",
      "114/114 [==============================] - 0s 920us/sample - loss: 0.0232 - accuracy: 0.9912 - val_loss: 0.3244 - val_accuracy: 0.9524\n",
      "Epoch 288/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0205 - accuracy: 1.0000 - val_loss: 0.3202 - val_accuracy: 0.9524\n",
      "Epoch 289/800\n",
      "114/114 [==============================] - 0s 985us/sample - loss: 0.0212 - accuracy: 1.0000 - val_loss: 0.3215 - val_accuracy: 0.9524\n",
      "Epoch 290/800\n",
      "114/114 [==============================] - 0s 885us/sample - loss: 0.0216 - accuracy: 0.9912 - val_loss: 0.3243 - val_accuracy: 0.9524\n",
      "Epoch 291/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0221 - accuracy: 0.9912 - val_loss: 0.3260 - val_accuracy: 0.9524\n",
      "Epoch 292/800\n",
      "114/114 [==============================] - 0s 979us/sample - loss: 0.0210 - accuracy: 0.9912 - val_loss: 0.3229 - val_accuracy: 0.9524\n",
      "Epoch 293/800\n",
      "114/114 [==============================] - 0s 864us/sample - loss: 0.0221 - accuracy: 1.0000 - val_loss: 0.3270 - val_accuracy: 0.9524\n",
      "Epoch 294/800\n",
      "114/114 [==============================] - 0s 892us/sample - loss: 0.0209 - accuracy: 0.9912 - val_loss: 0.3358 - val_accuracy: 0.9524\n",
      "Epoch 295/800\n",
      "114/114 [==============================] - 0s 982us/sample - loss: 0.0255 - accuracy: 0.9825 - val_loss: 0.3259 - val_accuracy: 0.9524\n",
      "Epoch 296/800\n",
      "114/114 [==============================] - 0s 872us/sample - loss: 0.0205 - accuracy: 1.0000 - val_loss: 0.3260 - val_accuracy: 0.9524\n",
      "Epoch 297/800\n",
      "114/114 [==============================] - 0s 923us/sample - loss: 0.0212 - accuracy: 1.0000 - val_loss: 0.3279 - val_accuracy: 0.9524\n",
      "Epoch 298/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0202 - accuracy: 0.9912 - val_loss: 0.3353 - val_accuracy: 0.9524\n",
      "Epoch 299/800\n",
      "114/114 [==============================] - 0s 917us/sample - loss: 0.0233 - accuracy: 0.9825 - val_loss: 0.3315 - val_accuracy: 0.9524\n",
      "Epoch 300/800\n",
      "114/114 [==============================] - 0s 920us/sample - loss: 0.0217 - accuracy: 1.0000 - val_loss: 0.3242 - val_accuracy: 0.9048\n",
      "Epoch 301/800\n",
      "114/114 [==============================] - 0s 975us/sample - loss: 0.0219 - accuracy: 1.0000 - val_loss: 0.3340 - val_accuracy: 0.9524\n",
      "Epoch 302/800\n",
      "114/114 [==============================] - 0s 889us/sample - loss: 0.0263 - accuracy: 0.9825 - val_loss: 0.3373 - val_accuracy: 0.9524\n",
      "Epoch 303/800\n",
      "114/114 [==============================] - 0s 883us/sample - loss: 0.0228 - accuracy: 0.9825 - val_loss: 0.3284 - val_accuracy: 0.9524\n",
      "Epoch 304/800\n",
      "114/114 [==============================] - 0s 979us/sample - loss: 0.0207 - accuracy: 1.0000 - val_loss: 0.3304 - val_accuracy: 0.9524\n",
      "Epoch 305/800\n",
      "114/114 [==============================] - 0s 880us/sample - loss: 0.0193 - accuracy: 1.0000 - val_loss: 0.3356 - val_accuracy: 0.9524\n",
      "Epoch 306/800\n",
      "114/114 [==============================] - 0s 891us/sample - loss: 0.0235 - accuracy: 0.9825 - val_loss: 0.3380 - val_accuracy: 0.9524\n",
      "Epoch 307/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0207 - accuracy: 0.9912 - val_loss: 0.3303 - val_accuracy: 0.9048\n",
      "Epoch 308/800\n",
      "114/114 [==============================] - 0s 941us/sample - loss: 0.0199 - accuracy: 1.0000 - val_loss: 0.3363 - val_accuracy: 0.9524\n",
      "Epoch 309/800\n",
      "114/114 [==============================] - 0s 890us/sample - loss: 0.0237 - accuracy: 0.9825 - val_loss: 0.3404 - val_accuracy: 0.9524\n",
      "Epoch 310/800\n",
      "114/114 [==============================] - 0s 969us/sample - loss: 0.0209 - accuracy: 1.0000 - val_loss: 0.3319 - val_accuracy: 0.9048\n",
      "Epoch 311/800\n",
      "114/114 [==============================] - 0s 894us/sample - loss: 0.0239 - accuracy: 1.0000 - val_loss: 0.3411 - val_accuracy: 0.9524\n",
      "Epoch 312/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0287 - accuracy: 0.9825 - val_loss: 0.3484 - val_accuracy: 0.9524\n",
      "Epoch 313/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.0210 - accuracy: 0.9825 - val_loss: 0.3365 - val_accuracy: 0.9524\n",
      "Epoch 314/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0296 - accuracy: 0.9912 - val_loss: 0.3357 - val_accuracy: 0.9048\n",
      "Epoch 315/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0289 - accuracy: 0.9912 - val_loss: 0.3593 - val_accuracy: 0.9524\n",
      "Epoch 316/800\n",
      "114/114 [==============================] - 0s 953us/sample - loss: 0.0370 - accuracy: 0.9825 - val_loss: 0.3364 - val_accuracy: 0.9048\n",
      "Epoch 317/800\n",
      "114/114 [==============================] - 0s 913us/sample - loss: 0.0230 - accuracy: 1.0000 - val_loss: 0.3368 - val_accuracy: 0.9048\n",
      "Epoch 318/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0187 - accuracy: 1.0000 - val_loss: 0.3455 - val_accuracy: 0.9524\n",
      "Epoch 319/800\n",
      "114/114 [==============================] - 0s 925us/sample - loss: 0.0319 - accuracy: 0.9825 - val_loss: 0.3451 - val_accuracy: 0.9524\n",
      "Epoch 320/800\n",
      "114/114 [==============================] - 0s 908us/sample - loss: 0.0285 - accuracy: 0.9912 - val_loss: 0.3374 - val_accuracy: 0.9048\n",
      "Epoch 321/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0224 - accuracy: 1.0000 - val_loss: 0.3466 - val_accuracy: 0.9524\n",
      "Epoch 322/800\n",
      "114/114 [==============================] - 0s 917us/sample - loss: 0.0217 - accuracy: 0.9825 - val_loss: 0.3443 - val_accuracy: 0.9524\n",
      "Epoch 323/800\n",
      "114/114 [==============================] - 0s 901us/sample - loss: 0.0211 - accuracy: 0.9912 - val_loss: 0.3393 - val_accuracy: 0.9048\n",
      "Epoch 324/800\n",
      "114/114 [==============================] - 0s 988us/sample - loss: 0.0206 - accuracy: 1.0000 - val_loss: 0.3432 - val_accuracy: 0.9524\n",
      "Epoch 325/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0176 - accuracy: 1.0000 - val_loss: 0.3457 - val_accuracy: 0.9524\n",
      "Epoch 326/800\n",
      "114/114 [==============================] - 0s 949us/sample - loss: 0.0192 - accuracy: 0.9912 - val_loss: 0.3427 - val_accuracy: 0.9524\n",
      "Epoch 327/800\n",
      "114/114 [==============================] - 0s 924us/sample - loss: 0.0174 - accuracy: 1.0000 - val_loss: 0.3438 - val_accuracy: 0.9524\n",
      "Epoch 328/800\n",
      "114/114 [==============================] - 0s 886us/sample - loss: 0.0174 - accuracy: 1.0000 - val_loss: 0.3462 - val_accuracy: 0.9524\n",
      "Epoch 329/800\n",
      "114/114 [==============================] - 0s 957us/sample - loss: 0.0189 - accuracy: 0.9912 - val_loss: 0.3447 - val_accuracy: 0.9524\n",
      "Epoch 330/800\n",
      "114/114 [==============================] - 0s 897us/sample - loss: 0.0190 - accuracy: 0.9912 - val_loss: 0.3479 - val_accuracy: 0.9524\n",
      "Epoch 331/800\n",
      "114/114 [==============================] - 0s 889us/sample - loss: 0.0172 - accuracy: 1.0000 - val_loss: 0.3459 - val_accuracy: 0.9524\n",
      "Epoch 332/800\n",
      "114/114 [==============================] - 0s 953us/sample - loss: 0.0172 - accuracy: 1.0000 - val_loss: 0.3464 - val_accuracy: 0.9524\n",
      "Epoch 333/800\n",
      "114/114 [==============================] - 0s 916us/sample - loss: 0.0170 - accuracy: 1.0000 - val_loss: 0.3475 - val_accuracy: 0.9524\n",
      "Epoch 334/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0176 - accuracy: 0.9912 - val_loss: 0.3489 - val_accuracy: 0.9524\n",
      "Epoch 335/800\n",
      "114/114 [==============================] - 0s 921us/sample - loss: 0.0179 - accuracy: 1.0000 - val_loss: 0.3483 - val_accuracy: 0.9524\n",
      "Epoch 336/800\n",
      "114/114 [==============================] - 0s 913us/sample - loss: 0.0165 - accuracy: 1.0000 - val_loss: 0.3496 - val_accuracy: 0.9524\n",
      "Epoch 337/800\n",
      "114/114 [==============================] - 0s 905us/sample - loss: 0.0238 - accuracy: 0.9825 - val_loss: 0.3518 - val_accuracy: 0.9524\n",
      "Epoch 338/800\n",
      "114/114 [==============================] - 0s 996us/sample - loss: 0.0170 - accuracy: 1.0000 - val_loss: 0.3509 - val_accuracy: 0.9048\n",
      "Epoch 339/800\n",
      "114/114 [==============================] - 0s 906us/sample - loss: 0.0258 - accuracy: 1.0000 - val_loss: 0.3526 - val_accuracy: 0.9524\n",
      "Epoch 340/800\n",
      "114/114 [==============================] - 0s 859us/sample - loss: 0.0197 - accuracy: 0.9825 - val_loss: 0.3575 - val_accuracy: 0.9524\n",
      "Epoch 341/800\n",
      "114/114 [==============================] - 0s 961us/sample - loss: 0.0218 - accuracy: 0.9912 - val_loss: 0.3515 - val_accuracy: 0.9524\n",
      "Epoch 342/800\n",
      "114/114 [==============================] - 0s 901us/sample - loss: 0.0156 - accuracy: 1.0000 - val_loss: 0.3524 - val_accuracy: 0.9048\n",
      "Epoch 343/800\n",
      "114/114 [==============================] - 0s 904us/sample - loss: 0.0228 - accuracy: 1.0000 - val_loss: 0.3529 - val_accuracy: 0.9524\n",
      "Epoch 344/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.0195 - accuracy: 0.9912 - val_loss: 0.3574 - val_accuracy: 0.9524\n",
      "Epoch 345/800\n",
      "114/114 [==============================] - 0s 894us/sample - loss: 0.0201 - accuracy: 0.9912 - val_loss: 0.3533 - val_accuracy: 0.9524\n",
      "Epoch 346/800\n",
      "114/114 [==============================] - 0s 949us/sample - loss: 0.0227 - accuracy: 1.0000 - val_loss: 0.3541 - val_accuracy: 0.9048\n",
      "Epoch 347/800\n",
      "114/114 [==============================] - 0s 891us/sample - loss: 0.0202 - accuracy: 0.9912 - val_loss: 0.3563 - val_accuracy: 0.9524\n",
      "Epoch 348/800\n",
      "114/114 [==============================] - 0s 904us/sample - loss: 0.0184 - accuracy: 0.9912 - val_loss: 0.3542 - val_accuracy: 0.9524\n",
      "Epoch 349/800\n",
      "114/114 [==============================] - 0s 874us/sample - loss: 0.0155 - accuracy: 1.0000 - val_loss: 0.3553 - val_accuracy: 0.9048\n",
      "Epoch 350/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0178 - accuracy: 1.0000 - val_loss: 0.3556 - val_accuracy: 0.9524\n",
      "Epoch 351/800\n",
      "114/114 [==============================] - 0s 919us/sample - loss: 0.0165 - accuracy: 0.9912 - val_loss: 0.3555 - val_accuracy: 0.9524\n",
      "Epoch 352/800\n",
      "114/114 [==============================] - 0s 886us/sample - loss: 0.0158 - accuracy: 1.0000 - val_loss: 0.3559 - val_accuracy: 0.9524\n",
      "Epoch 353/800\n",
      "114/114 [==============================] - 0s 874us/sample - loss: 0.0149 - accuracy: 1.0000 - val_loss: 0.3567 - val_accuracy: 0.9524\n",
      "Epoch 354/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0154 - accuracy: 1.0000 - val_loss: 0.3570 - val_accuracy: 0.9524\n",
      "Epoch 355/800\n",
      "114/114 [==============================] - 0s 869us/sample - loss: 0.0167 - accuracy: 0.9912 - val_loss: 0.3576 - val_accuracy: 0.9524\n",
      "Epoch 356/800\n",
      "114/114 [==============================] - 0s 900us/sample - loss: 0.0159 - accuracy: 1.0000 - val_loss: 0.3585 - val_accuracy: 0.9524\n",
      "Epoch 357/800\n",
      "114/114 [==============================] - 0s 889us/sample - loss: 0.0196 - accuracy: 1.0000 - val_loss: 0.3585 - val_accuracy: 0.9524\n",
      "Epoch 358/800\n",
      "114/114 [==============================] - 0s 948us/sample - loss: 0.0147 - accuracy: 1.0000 - val_loss: 0.3601 - val_accuracy: 0.9524\n",
      "Epoch 359/800\n",
      "114/114 [==============================] - 0s 932us/sample - loss: 0.0235 - accuracy: 0.9825 - val_loss: 0.3599 - val_accuracy: 0.9524\n",
      "Epoch 360/800\n",
      "114/114 [==============================] - 0s 872us/sample - loss: 0.0154 - accuracy: 1.0000 - val_loss: 0.3602 - val_accuracy: 0.9524\n",
      "Epoch 361/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0160 - accuracy: 1.0000 - val_loss: 0.3595 - val_accuracy: 0.9524\n",
      "Epoch 362/800\n",
      "114/114 [==============================] - 0s 947us/sample - loss: 0.0153 - accuracy: 1.0000 - val_loss: 0.3621 - val_accuracy: 0.9524\n",
      "Epoch 363/800\n",
      "114/114 [==============================] - 0s 889us/sample - loss: 0.0161 - accuracy: 1.0000 - val_loss: 0.3617 - val_accuracy: 0.9524\n",
      "Epoch 364/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0137 - accuracy: 1.0000 - val_loss: 0.3614 - val_accuracy: 0.9524\n",
      "Epoch 365/800\n",
      "114/114 [==============================] - 0s 916us/sample - loss: 0.0200 - accuracy: 0.9825 - val_loss: 0.3627 - val_accuracy: 0.9524\n",
      "Epoch 366/800\n",
      "114/114 [==============================] - 0s 901us/sample - loss: 0.0171 - accuracy: 1.0000 - val_loss: 0.3726 - val_accuracy: 0.9048\n",
      "Epoch 367/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0193 - accuracy: 1.0000 - val_loss: 0.3636 - val_accuracy: 0.9524\n",
      "Epoch 368/800\n",
      "114/114 [==============================] - 0s 959us/sample - loss: 0.0200 - accuracy: 0.9825 - val_loss: 0.3631 - val_accuracy: 0.9524\n",
      "Epoch 369/800\n",
      "114/114 [==============================] - 0s 896us/sample - loss: 0.0162 - accuracy: 0.9912 - val_loss: 0.3707 - val_accuracy: 0.9048\n",
      "Epoch 370/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0161 - accuracy: 1.0000 - val_loss: 0.3661 - val_accuracy: 0.9524\n",
      "Epoch 371/800\n",
      "114/114 [==============================] - 0s 935us/sample - loss: 0.0125 - accuracy: 1.0000 - val_loss: 0.3642 - val_accuracy: 0.9524\n",
      "Epoch 372/800\n",
      "114/114 [==============================] - 0s 900us/sample - loss: 0.0213 - accuracy: 0.9825 - val_loss: 0.3648 - val_accuracy: 0.9524\n",
      "Epoch 373/800\n",
      "114/114 [==============================] - 0s 911us/sample - loss: 0.0158 - accuracy: 1.0000 - val_loss: 0.3782 - val_accuracy: 0.9048\n",
      "Epoch 374/800\n",
      "114/114 [==============================] - 0s 941us/sample - loss: 0.0236 - accuracy: 0.9912 - val_loss: 0.3653 - val_accuracy: 0.9524\n",
      "Epoch 375/800\n",
      "114/114 [==============================] - 0s 925us/sample - loss: 0.0240 - accuracy: 0.9912 - val_loss: 0.3748 - val_accuracy: 0.9524\n",
      "Epoch 376/800\n",
      "114/114 [==============================] - 0s 892us/sample - loss: 0.0237 - accuracy: 0.9912 - val_loss: 0.3708 - val_accuracy: 0.9048\n",
      "Epoch 377/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0228 - accuracy: 1.0000 - val_loss: 0.3755 - val_accuracy: 0.9048\n",
      "Epoch 378/800\n",
      "114/114 [==============================] - 0s 924us/sample - loss: 0.0116 - accuracy: 1.0000 - val_loss: 0.3672 - val_accuracy: 0.9524\n",
      "Epoch 379/800\n",
      "114/114 [==============================] - 0s 937us/sample - loss: 0.0236 - accuracy: 0.9825 - val_loss: 0.3650 - val_accuracy: 0.9524\n",
      "Epoch 380/800\n",
      "114/114 [==============================] - 0s 886us/sample - loss: 0.0187 - accuracy: 0.9912 - val_loss: 0.3741 - val_accuracy: 0.9048\n",
      "Epoch 381/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0151 - accuracy: 1.0000 - val_loss: 0.3662 - val_accuracy: 0.9524\n",
      "Epoch 382/800\n",
      "114/114 [==============================] - 0s 888us/sample - loss: 0.0210 - accuracy: 0.9825 - val_loss: 0.3659 - val_accuracy: 0.9524\n",
      "Epoch 383/800\n",
      "114/114 [==============================] - 0s 922us/sample - loss: 0.0158 - accuracy: 1.0000 - val_loss: 0.3760 - val_accuracy: 0.9048\n",
      "Epoch 384/800\n",
      "114/114 [==============================] - 0s 916us/sample - loss: 0.0171 - accuracy: 1.0000 - val_loss: 0.3694 - val_accuracy: 0.9524\n",
      "Epoch 385/800\n",
      "114/114 [==============================] - 0s 906us/sample - loss: 0.0153 - accuracy: 0.9912 - val_loss: 0.3680 - val_accuracy: 0.9524\n",
      "Epoch 386/800\n",
      "114/114 [==============================] - 0s 950us/sample - loss: 0.0168 - accuracy: 0.9912 - val_loss: 0.3729 - val_accuracy: 0.9524\n",
      "Epoch 387/800\n",
      "114/114 [==============================] - 0s 911us/sample - loss: 0.0175 - accuracy: 1.0000 - val_loss: 0.3772 - val_accuracy: 0.9048\n",
      "Epoch 388/800\n",
      "114/114 [==============================] - 0s 895us/sample - loss: 0.0125 - accuracy: 1.0000 - val_loss: 0.3689 - val_accuracy: 0.9524\n",
      "Epoch 389/800\n",
      "114/114 [==============================] - 0s 894us/sample - loss: 0.0212 - accuracy: 0.9825 - val_loss: 0.3679 - val_accuracy: 0.9524\n",
      "Epoch 390/800\n",
      "114/114 [==============================] - 0s 953us/sample - loss: 0.0141 - accuracy: 1.0000 - val_loss: 0.3830 - val_accuracy: 0.9048\n",
      "Epoch 391/800\n",
      "114/114 [==============================] - 0s 874us/sample - loss: 0.0216 - accuracy: 1.0000 - val_loss: 0.3696 - val_accuracy: 0.9524\n",
      "Epoch 392/800\n",
      "114/114 [==============================] - 0s 911us/sample - loss: 0.0130 - accuracy: 1.0000 - val_loss: 0.3726 - val_accuracy: 0.9524\n",
      "Epoch 393/800\n",
      "114/114 [==============================] - 0s 874us/sample - loss: 0.0130 - accuracy: 1.0000 - val_loss: 0.3756 - val_accuracy: 0.9524\n",
      "Epoch 394/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0122 - accuracy: 1.0000 - val_loss: 0.3724 - val_accuracy: 0.9524\n",
      "Epoch 395/800\n",
      "114/114 [==============================] - 0s 862us/sample - loss: 0.0120 - accuracy: 1.0000 - val_loss: 0.3715 - val_accuracy: 0.9524\n",
      "Epoch 396/800\n",
      "114/114 [==============================] - 0s 910us/sample - loss: 0.0128 - accuracy: 1.0000 - val_loss: 0.3711 - val_accuracy: 0.9524\n",
      "Epoch 397/800\n",
      "114/114 [==============================] - 0s 873us/sample - loss: 0.0139 - accuracy: 1.0000 - val_loss: 0.3757 - val_accuracy: 0.9524\n",
      "Epoch 398/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0122 - accuracy: 1.0000 - val_loss: 0.3859 - val_accuracy: 0.9048\n",
      "Epoch 399/800\n",
      "114/114 [==============================] - 0s 905us/sample - loss: 0.0146 - accuracy: 1.0000 - val_loss: 0.3709 - val_accuracy: 0.9524\n",
      "Epoch 400/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0157 - accuracy: 0.9912 - val_loss: 0.3722 - val_accuracy: 0.9524\n",
      "Epoch 401/800\n",
      "114/114 [==============================] - 0s 943us/sample - loss: 0.0131 - accuracy: 1.0000 - val_loss: 0.3791 - val_accuracy: 0.9524\n",
      "Epoch 402/800\n",
      "114/114 [==============================] - 0s 902us/sample - loss: 0.0161 - accuracy: 1.0000 - val_loss: 0.3862 - val_accuracy: 0.9048\n",
      "Epoch 403/800\n",
      "114/114 [==============================] - 0s 896us/sample - loss: 0.0106 - accuracy: 1.0000 - val_loss: 0.3721 - val_accuracy: 0.9524\n",
      "Epoch 404/800\n",
      "114/114 [==============================] - 0s 920us/sample - loss: 0.0173 - accuracy: 0.9825 - val_loss: 0.3724 - val_accuracy: 0.9524\n",
      "Epoch 405/800\n",
      "114/114 [==============================] - 0s 924us/sample - loss: 0.0103 - accuracy: 1.0000 - val_loss: 0.3860 - val_accuracy: 0.9048\n",
      "Epoch 406/800\n",
      "114/114 [==============================] - 0s 896us/sample - loss: 0.0182 - accuracy: 1.0000 - val_loss: 0.3816 - val_accuracy: 0.9048\n",
      "Epoch 407/800\n",
      "114/114 [==============================] - 0s 887us/sample - loss: 0.0114 - accuracy: 1.0000 - val_loss: 0.3742 - val_accuracy: 0.9524\n",
      "Epoch 408/800\n",
      "114/114 [==============================] - 0s 915us/sample - loss: 0.0173 - accuracy: 0.9912 - val_loss: 0.3751 - val_accuracy: 0.9524\n",
      "Epoch 409/800\n",
      "114/114 [==============================] - 0s 925us/sample - loss: 0.0096 - accuracy: 1.0000 - val_loss: 0.3864 - val_accuracy: 0.9048\n",
      "Epoch 410/800\n",
      "114/114 [==============================] - 0s 930us/sample - loss: 0.0130 - accuracy: 1.0000 - val_loss: 0.3777 - val_accuracy: 0.9524\n",
      "Epoch 411/800\n",
      "114/114 [==============================] - 0s 947us/sample - loss: 0.0117 - accuracy: 1.0000 - val_loss: 0.3771 - val_accuracy: 0.9524\n",
      "Epoch 412/800\n",
      "114/114 [==============================] - 0s 931us/sample - loss: 0.0116 - accuracy: 1.0000 - val_loss: 0.3848 - val_accuracy: 0.9048\n",
      "Epoch 413/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0124 - accuracy: 1.0000 - val_loss: 0.3766 - val_accuracy: 0.9524\n",
      "Epoch 414/800\n",
      "114/114 [==============================] - 0s 960us/sample - loss: 0.0121 - accuracy: 1.0000 - val_loss: 0.3810 - val_accuracy: 0.9524\n",
      "Epoch 415/800\n",
      "114/114 [==============================] - 0s 924us/sample - loss: 0.0106 - accuracy: 1.0000 - val_loss: 0.3796 - val_accuracy: 0.9524\n",
      "Epoch 416/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0116 - accuracy: 1.0000 - val_loss: 0.3798 - val_accuracy: 0.9524\n",
      "Epoch 417/800\n",
      "114/114 [==============================] - 0s 884us/sample - loss: 0.0100 - accuracy: 1.0000 - val_loss: 0.3859 - val_accuracy: 0.9048\n",
      "Epoch 418/800\n",
      "114/114 [==============================] - 0s 883us/sample - loss: 0.0109 - accuracy: 1.0000 - val_loss: 0.3820 - val_accuracy: 0.9524\n",
      "Epoch 419/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0107 - accuracy: 1.0000 - val_loss: 0.3814 - val_accuracy: 0.9524\n",
      "Epoch 420/800\n",
      "114/114 [==============================] - 0s 951us/sample - loss: 0.0108 - accuracy: 1.0000 - val_loss: 0.3848 - val_accuracy: 0.9524\n",
      "Epoch 421/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0103 - accuracy: 1.0000 - val_loss: 0.3869 - val_accuracy: 0.9524\n",
      "Epoch 422/800\n",
      "114/114 [==============================] - 0s 987us/sample - loss: 0.0113 - accuracy: 1.0000 - val_loss: 0.3799 - val_accuracy: 0.9524\n",
      "Epoch 423/800\n",
      "114/114 [==============================] - 0s 946us/sample - loss: 0.0117 - accuracy: 1.0000 - val_loss: 0.3846 - val_accuracy: 0.9524\n",
      "Epoch 424/800\n",
      "114/114 [==============================] - 0s 891us/sample - loss: 0.0138 - accuracy: 1.0000 - val_loss: 0.3876 - val_accuracy: 0.9524\n",
      "Epoch 425/800\n",
      "114/114 [==============================] - 0s 914us/sample - loss: 0.0102 - accuracy: 1.0000 - val_loss: 0.3804 - val_accuracy: 0.9524\n",
      "Epoch 426/800\n",
      "114/114 [==============================] - 0s 873us/sample - loss: 0.0130 - accuracy: 0.9912 - val_loss: 0.3844 - val_accuracy: 0.9524\n",
      "Epoch 427/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0091 - accuracy: 1.0000 - val_loss: 0.4031 - val_accuracy: 0.9048\n",
      "Epoch 428/800\n",
      "114/114 [==============================] - 0s 907us/sample - loss: 0.0144 - accuracy: 1.0000 - val_loss: 0.3814 - val_accuracy: 0.9524\n",
      "Epoch 429/800\n",
      "114/114 [==============================] - 0s 873us/sample - loss: 0.0188 - accuracy: 0.9825 - val_loss: 0.3816 - val_accuracy: 0.9524\n",
      "Epoch 430/800\n",
      "114/114 [==============================] - 0s 963us/sample - loss: 0.0194 - accuracy: 1.0000 - val_loss: 0.4249 - val_accuracy: 0.9048\n",
      "Epoch 431/800\n",
      "114/114 [==============================] - 0s 878us/sample - loss: 0.0187 - accuracy: 1.0000 - val_loss: 0.3817 - val_accuracy: 0.9524\n",
      "Epoch 432/800\n",
      "114/114 [==============================] - 0s 887us/sample - loss: 0.0394 - accuracy: 0.9825 - val_loss: 0.3864 - val_accuracy: 0.9524\n",
      "Epoch 433/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0326 - accuracy: 0.9825 - val_loss: 0.4310 - val_accuracy: 0.9048\n",
      "Epoch 434/800\n",
      "114/114 [==============================] - 0s 930us/sample - loss: 0.0339 - accuracy: 0.9737 - val_loss: 0.3841 - val_accuracy: 0.9524\n",
      "Epoch 435/800\n",
      "114/114 [==============================] - 0s 895us/sample - loss: 0.0643 - accuracy: 0.9825 - val_loss: 0.3955 - val_accuracy: 0.9524\n",
      "Epoch 436/800\n",
      "114/114 [==============================] - 0s 903us/sample - loss: 0.0329 - accuracy: 0.9825 - val_loss: 0.4356 - val_accuracy: 0.9048\n",
      "Epoch 437/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0449 - accuracy: 0.9737 - val_loss: 0.3787 - val_accuracy: 0.9524\n",
      "Epoch 438/800\n",
      "114/114 [==============================] - 0s 947us/sample - loss: 0.0236 - accuracy: 0.9825 - val_loss: 0.3784 - val_accuracy: 0.9524\n",
      "Epoch 439/800\n",
      "114/114 [==============================] - 0s 902us/sample - loss: 0.0124 - accuracy: 0.9912 - val_loss: 0.3925 - val_accuracy: 0.9048\n",
      "Epoch 440/800\n",
      "114/114 [==============================] - 0s 891us/sample - loss: 0.0135 - accuracy: 1.0000 - val_loss: 0.3892 - val_accuracy: 0.9048\n",
      "Epoch 441/800\n",
      "114/114 [==============================] - 0s 969us/sample - loss: 0.0126 - accuracy: 1.0000 - val_loss: 0.3779 - val_accuracy: 0.9524\n",
      "Epoch 442/800\n",
      "114/114 [==============================] - 0s 900us/sample - loss: 0.0130 - accuracy: 0.9912 - val_loss: 0.3850 - val_accuracy: 0.9524\n",
      "Epoch 443/800\n",
      "114/114 [==============================] - 0s 894us/sample - loss: 0.0102 - accuracy: 1.0000 - val_loss: 0.3856 - val_accuracy: 0.9524\n",
      "Epoch 444/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0091 - accuracy: 1.0000 - val_loss: 0.3819 - val_accuracy: 0.9524\n",
      "Epoch 445/800\n",
      "114/114 [==============================] - 0s 944us/sample - loss: 0.0109 - accuracy: 1.0000 - val_loss: 0.3807 - val_accuracy: 0.9524\n",
      "Epoch 446/800\n",
      "114/114 [==============================] - 0s 873us/sample - loss: 0.0115 - accuracy: 1.0000 - val_loss: 0.3967 - val_accuracy: 0.9048\n",
      "Epoch 447/800\n",
      "114/114 [==============================] - 0s 943us/sample - loss: 0.0106 - accuracy: 1.0000 - val_loss: 0.3848 - val_accuracy: 0.9524\n",
      "Epoch 448/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0116 - accuracy: 1.0000 - val_loss: 0.3800 - val_accuracy: 0.9524\n",
      "Epoch 449/800\n",
      "114/114 [==============================] - 0s 914us/sample - loss: 0.0108 - accuracy: 0.9912 - val_loss: 0.3940 - val_accuracy: 0.9048\n",
      "Epoch 450/800\n",
      "114/114 [==============================] - 0s 918us/sample - loss: 0.0101 - accuracy: 1.0000 - val_loss: 0.3931 - val_accuracy: 0.9048\n",
      "Epoch 451/800\n",
      "114/114 [==============================] - 0s 877us/sample - loss: 0.0090 - accuracy: 1.0000 - val_loss: 0.3845 - val_accuracy: 0.9524\n",
      "Epoch 452/800\n",
      "114/114 [==============================] - 0s 950us/sample - loss: 0.0093 - accuracy: 1.0000 - val_loss: 0.3854 - val_accuracy: 0.9524\n",
      "Epoch 453/800\n",
      "114/114 [==============================] - 0s 917us/sample - loss: 0.0094 - accuracy: 1.0000 - val_loss: 0.3931 - val_accuracy: 0.9048\n",
      "Epoch 454/800\n",
      "114/114 [==============================] - 0s 892us/sample - loss: 0.0099 - accuracy: 1.0000 - val_loss: 0.3933 - val_accuracy: 0.9048\n",
      "Epoch 455/800\n",
      "114/114 [==============================] - 0s 967us/sample - loss: 0.0086 - accuracy: 1.0000 - val_loss: 0.3832 - val_accuracy: 0.9524\n",
      "Epoch 456/800\n",
      "114/114 [==============================] - 0s 908us/sample - loss: 0.0112 - accuracy: 1.0000 - val_loss: 0.3898 - val_accuracy: 0.9524\n",
      "Epoch 457/800\n",
      "114/114 [==============================] - 0s 900us/sample - loss: 0.0089 - accuracy: 1.0000 - val_loss: 0.3887 - val_accuracy: 0.9524\n",
      "Epoch 458/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0089 - accuracy: 1.0000 - val_loss: 0.3914 - val_accuracy: 0.9524\n",
      "Epoch 459/800\n",
      "114/114 [==============================] - 0s 911us/sample - loss: 0.0088 - accuracy: 1.0000 - val_loss: 0.3914 - val_accuracy: 0.9524\n",
      "Epoch 460/800\n",
      "114/114 [==============================] - 0s 905us/sample - loss: 0.0085 - accuracy: 1.0000 - val_loss: 0.3874 - val_accuracy: 0.9524\n",
      "Epoch 461/800\n",
      "114/114 [==============================] - 0s 965us/sample - loss: 0.0102 - accuracy: 1.0000 - val_loss: 0.3865 - val_accuracy: 0.9524\n",
      "Epoch 462/800\n",
      "114/114 [==============================] - 0s 899us/sample - loss: 0.0080 - accuracy: 1.0000 - val_loss: 0.3992 - val_accuracy: 0.9048\n",
      "Epoch 463/800\n",
      "114/114 [==============================] - 0s 875us/sample - loss: 0.0111 - accuracy: 1.0000 - val_loss: 0.3960 - val_accuracy: 0.9524\n",
      "Epoch 464/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0086 - accuracy: 1.0000 - val_loss: 0.3909 - val_accuracy: 0.9524\n",
      "Epoch 465/800\n",
      "114/114 [==============================] - 0s 911us/sample - loss: 0.0094 - accuracy: 1.0000 - val_loss: 0.3902 - val_accuracy: 0.9524\n",
      "Epoch 466/800\n",
      "114/114 [==============================] - 0s 891us/sample - loss: 0.0078 - accuracy: 1.0000 - val_loss: 0.4062 - val_accuracy: 0.9048\n",
      "Epoch 467/800\n",
      "114/114 [==============================] - 0s 886us/sample - loss: 0.0098 - accuracy: 1.0000 - val_loss: 0.3940 - val_accuracy: 0.9524\n",
      "Epoch 468/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0103 - accuracy: 1.0000 - val_loss: 0.3894 - val_accuracy: 0.9524\n",
      "Epoch 469/800\n",
      "114/114 [==============================] - 0s 910us/sample - loss: 0.0110 - accuracy: 1.0000 - val_loss: 0.4017 - val_accuracy: 0.9048\n",
      "Epoch 470/800\n",
      "114/114 [==============================] - 0s 877us/sample - loss: 0.0095 - accuracy: 1.0000 - val_loss: 0.3893 - val_accuracy: 0.9524\n",
      "Epoch 471/800\n",
      "114/114 [==============================] - 0s 954us/sample - loss: 0.0115 - accuracy: 1.0000 - val_loss: 0.3945 - val_accuracy: 0.9524\n",
      "Epoch 472/800\n",
      "114/114 [==============================] - 0s 921us/sample - loss: 0.0177 - accuracy: 1.0000 - val_loss: 0.4050 - val_accuracy: 0.9048\n",
      "Epoch 473/800\n",
      "114/114 [==============================] - 0s 886us/sample - loss: 0.0077 - accuracy: 1.0000 - val_loss: 0.3887 - val_accuracy: 0.9524\n",
      "Epoch 474/800\n",
      "114/114 [==============================] - 0s 958us/sample - loss: 0.0144 - accuracy: 0.9825 - val_loss: 0.3914 - val_accuracy: 0.9524\n",
      "Epoch 475/800\n",
      "114/114 [==============================] - 0s 881us/sample - loss: 0.0119 - accuracy: 1.0000 - val_loss: 0.4128 - val_accuracy: 0.9048\n",
      "Epoch 476/800\n",
      "114/114 [==============================] - 0s 916us/sample - loss: 0.0090 - accuracy: 1.0000 - val_loss: 0.3904 - val_accuracy: 0.9524\n",
      "Epoch 477/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0120 - accuracy: 0.9912 - val_loss: 0.3929 - val_accuracy: 0.9524\n",
      "Epoch 478/800\n",
      "114/114 [==============================] - 0s 946us/sample - loss: 0.0066 - accuracy: 1.0000 - val_loss: 0.4145 - val_accuracy: 0.9048\n",
      "Epoch 479/800\n",
      "114/114 [==============================] - 0s 898us/sample - loss: 0.0103 - accuracy: 1.0000 - val_loss: 0.3998 - val_accuracy: 0.9524\n",
      "Epoch 480/800\n",
      "114/114 [==============================] - 0s 894us/sample - loss: 0.0104 - accuracy: 1.0000 - val_loss: 0.3929 - val_accuracy: 0.9524\n",
      "Epoch 481/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0076 - accuracy: 1.0000 - val_loss: 0.4117 - val_accuracy: 0.9048\n",
      "Epoch 482/800\n",
      "114/114 [==============================] - 0s 909us/sample - loss: 0.0095 - accuracy: 1.0000 - val_loss: 0.4000 - val_accuracy: 0.9524\n",
      "Epoch 483/800\n",
      "114/114 [==============================] - 0s 897us/sample - loss: 0.0087 - accuracy: 1.0000 - val_loss: 0.3940 - val_accuracy: 0.9524\n",
      "Epoch 484/800\n",
      "114/114 [==============================] - 0s 887us/sample - loss: 0.0085 - accuracy: 1.0000 - val_loss: 0.4095 - val_accuracy: 0.9048\n",
      "Epoch 485/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0085 - accuracy: 1.0000 - val_loss: 0.4030 - val_accuracy: 0.9524\n",
      "Epoch 486/800\n",
      "114/114 [==============================] - 0s 884us/sample - loss: 0.0119 - accuracy: 1.0000 - val_loss: 0.3955 - val_accuracy: 0.9524\n",
      "Epoch 487/800\n",
      "114/114 [==============================] - 0s 891us/sample - loss: 0.0071 - accuracy: 1.0000 - val_loss: 0.4130 - val_accuracy: 0.9048\n",
      "Epoch 488/800\n",
      "114/114 [==============================] - 0s 867us/sample - loss: 0.0085 - accuracy: 1.0000 - val_loss: 0.4001 - val_accuracy: 0.9524\n",
      "Epoch 489/800\n",
      "114/114 [==============================] - 0s 942us/sample - loss: 0.0079 - accuracy: 1.0000 - val_loss: 0.4000 - val_accuracy: 0.9524\n",
      "Epoch 490/800\n",
      "114/114 [==============================] - 0s 894us/sample - loss: 0.0074 - accuracy: 1.0000 - val_loss: 0.4063 - val_accuracy: 0.9048\n",
      "Epoch 491/800\n",
      "114/114 [==============================] - 0s 898us/sample - loss: 0.0070 - accuracy: 1.0000 - val_loss: 0.4002 - val_accuracy: 0.9524\n",
      "Epoch 492/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0073 - accuracy: 1.0000 - val_loss: 0.4006 - val_accuracy: 0.9524\n",
      "Epoch 493/800\n",
      "114/114 [==============================] - 0s 922us/sample - loss: 0.0072 - accuracy: 1.0000 - val_loss: 0.4069 - val_accuracy: 0.9524\n",
      "Epoch 494/800\n",
      "114/114 [==============================] - 0s 888us/sample - loss: 0.0070 - accuracy: 1.0000 - val_loss: 0.4026 - val_accuracy: 0.9524\n",
      "Epoch 495/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0076 - accuracy: 1.0000 - val_loss: 0.4012 - val_accuracy: 0.9524\n",
      "Epoch 496/800\n",
      "114/114 [==============================] - 0s 933us/sample - loss: 0.0083 - accuracy: 1.0000 - val_loss: 0.4111 - val_accuracy: 0.9048\n",
      "Epoch 497/800\n",
      "114/114 [==============================] - 0s 917us/sample - loss: 0.0070 - accuracy: 1.0000 - val_loss: 0.3998 - val_accuracy: 0.9524\n",
      "Epoch 498/800\n",
      "114/114 [==============================] - 0s 885us/sample - loss: 0.0080 - accuracy: 1.0000 - val_loss: 0.4089 - val_accuracy: 0.9524\n",
      "Epoch 499/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0073 - accuracy: 1.0000 - val_loss: 0.4090 - val_accuracy: 0.9524\n",
      "Epoch 500/800\n",
      "114/114 [==============================] - 0s 934us/sample - loss: 0.0070 - accuracy: 1.0000 - val_loss: 0.4046 - val_accuracy: 0.9524\n",
      "Epoch 501/800\n",
      "114/114 [==============================] - 0s 922us/sample - loss: 0.0068 - accuracy: 1.0000 - val_loss: 0.4119 - val_accuracy: 0.9048\n",
      "Epoch 502/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0066 - accuracy: 1.0000 - val_loss: 0.4050 - val_accuracy: 0.9524\n",
      "Epoch 503/800\n",
      "114/114 [==============================] - 0s 893us/sample - loss: 0.0074 - accuracy: 1.0000 - val_loss: 0.4054 - val_accuracy: 0.9524\n",
      "Epoch 504/800\n",
      "114/114 [==============================] - 0s 869us/sample - loss: 0.0067 - accuracy: 1.0000 - val_loss: 0.4068 - val_accuracy: 0.9524\n",
      "Epoch 505/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0072 - accuracy: 1.0000 - val_loss: 0.4102 - val_accuracy: 0.9524\n",
      "Epoch 506/800\n",
      "114/114 [==============================] - 0s 941us/sample - loss: 0.0060 - accuracy: 1.0000 - val_loss: 0.4037 - val_accuracy: 0.9524\n",
      "Epoch 507/800\n",
      "114/114 [==============================] - 0s 913us/sample - loss: 0.0076 - accuracy: 1.0000 - val_loss: 0.4040 - val_accuracy: 0.9524\n",
      "Epoch 508/800\n",
      "114/114 [==============================] - 0s 877us/sample - loss: 0.0059 - accuracy: 1.0000 - val_loss: 0.4165 - val_accuracy: 0.9048\n",
      "Epoch 509/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0072 - accuracy: 1.0000 - val_loss: 0.4161 - val_accuracy: 0.9048\n",
      "Epoch 510/800\n",
      "114/114 [==============================] - 0s 872us/sample - loss: 0.0064 - accuracy: 1.0000 - val_loss: 0.4028 - val_accuracy: 0.9524\n",
      "Epoch 511/800\n",
      "114/114 [==============================] - 0s 904us/sample - loss: 0.0085 - accuracy: 1.0000 - val_loss: 0.4041 - val_accuracy: 0.9524\n",
      "Epoch 512/800\n",
      "114/114 [==============================] - 0s 879us/sample - loss: 0.0102 - accuracy: 1.0000 - val_loss: 0.4426 - val_accuracy: 0.9048\n",
      "Epoch 513/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0095 - accuracy: 1.0000 - val_loss: 0.4058 - val_accuracy: 0.9524\n",
      "Epoch 514/800\n",
      "114/114 [==============================] - 0s 909us/sample - loss: 0.0118 - accuracy: 1.0000 - val_loss: 0.4059 - val_accuracy: 0.9524\n",
      "Epoch 515/800\n",
      "114/114 [==============================] - 0s 897us/sample - loss: 0.0067 - accuracy: 1.0000 - val_loss: 0.4265 - val_accuracy: 0.9048\n",
      "Epoch 516/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0076 - accuracy: 1.0000 - val_loss: 0.4120 - val_accuracy: 0.9524\n",
      "Epoch 517/800\n",
      "114/114 [==============================] - 0s 914us/sample - loss: 0.0060 - accuracy: 1.0000 - val_loss: 0.4065 - val_accuracy: 0.9524\n",
      "Epoch 518/800\n",
      "114/114 [==============================] - 0s 916us/sample - loss: 0.0069 - accuracy: 1.0000 - val_loss: 0.4067 - val_accuracy: 0.9524\n",
      "Epoch 519/800\n",
      "114/114 [==============================] - 0s 950us/sample - loss: 0.0067 - accuracy: 1.0000 - val_loss: 0.4116 - val_accuracy: 0.9524\n",
      "Epoch 520/800\n",
      "114/114 [==============================] - 0s 924us/sample - loss: 0.0058 - accuracy: 1.0000 - val_loss: 0.4204 - val_accuracy: 0.9048\n",
      "Epoch 521/800\n",
      "114/114 [==============================] - 0s 910us/sample - loss: 0.0068 - accuracy: 1.0000 - val_loss: 0.4113 - val_accuracy: 0.9524\n",
      "Epoch 522/800\n",
      "114/114 [==============================] - 0s 932us/sample - loss: 0.0055 - accuracy: 1.0000 - val_loss: 0.4052 - val_accuracy: 0.9524\n",
      "Epoch 523/800\n",
      "114/114 [==============================] - 0s 925us/sample - loss: 0.0080 - accuracy: 1.0000 - val_loss: 0.4107 - val_accuracy: 0.9524\n",
      "Epoch 524/800\n",
      "114/114 [==============================] - 0s 887us/sample - loss: 0.0048 - accuracy: 1.0000 - val_loss: 0.4300 - val_accuracy: 0.9048\n",
      "Epoch 525/800\n",
      "114/114 [==============================] - 0s 870us/sample - loss: 0.0078 - accuracy: 1.0000 - val_loss: 0.4151 - val_accuracy: 0.9524\n",
      "Epoch 526/800\n",
      "114/114 [==============================] - 0s 969us/sample - loss: 0.0066 - accuracy: 1.0000 - val_loss: 0.4062 - val_accuracy: 0.9524\n",
      "Epoch 527/800\n",
      "114/114 [==============================] - 0s 889us/sample - loss: 0.0094 - accuracy: 1.0000 - val_loss: 0.4165 - val_accuracy: 0.9524\n",
      "Epoch 528/800\n",
      "114/114 [==============================] - 0s 900us/sample - loss: 0.0081 - accuracy: 1.0000 - val_loss: 0.4611 - val_accuracy: 0.9048\n",
      "Epoch 529/800\n",
      "114/114 [==============================] - 0s 920us/sample - loss: 0.0107 - accuracy: 1.0000 - val_loss: 0.4099 - val_accuracy: 0.9524\n",
      "Epoch 530/800\n",
      "114/114 [==============================] - 0s 912us/sample - loss: 0.0155 - accuracy: 0.9825 - val_loss: 0.4099 - val_accuracy: 0.9524\n",
      "Epoch 531/800\n",
      "114/114 [==============================] - 0s 912us/sample - loss: 0.0051 - accuracy: 1.0000 - val_loss: 0.4258 - val_accuracy: 0.9048\n",
      "Epoch 532/800\n",
      "114/114 [==============================] - 0s 865us/sample - loss: 0.0064 - accuracy: 1.0000 - val_loss: 0.4175 - val_accuracy: 0.9524\n",
      "Epoch 533/800\n",
      "114/114 [==============================] - 0s 967us/sample - loss: 0.0053 - accuracy: 1.0000 - val_loss: 0.4110 - val_accuracy: 0.9524\n",
      "Epoch 534/800\n",
      "114/114 [==============================] - 0s 889us/sample - loss: 0.0068 - accuracy: 1.0000 - val_loss: 0.4208 - val_accuracy: 0.9524\n",
      "Epoch 535/800\n",
      "114/114 [==============================] - 0s 920us/sample - loss: 0.0059 - accuracy: 1.0000 - val_loss: 0.4214 - val_accuracy: 0.9524\n",
      "Epoch 536/800\n",
      "114/114 [==============================] - 0s 960us/sample - loss: 0.0057 - accuracy: 1.0000 - val_loss: 0.4156 - val_accuracy: 0.9524\n",
      "Epoch 537/800\n",
      "114/114 [==============================] - 0s 897us/sample - loss: 0.0052 - accuracy: 1.0000 - val_loss: 0.4180 - val_accuracy: 0.9524\n",
      "Epoch 538/800\n",
      "114/114 [==============================] - 0s 893us/sample - loss: 0.0066 - accuracy: 1.0000 - val_loss: 0.4185 - val_accuracy: 0.9524\n",
      "Epoch 539/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0055 - accuracy: 1.0000 - val_loss: 0.4250 - val_accuracy: 0.9048\n",
      "Epoch 540/800\n",
      "114/114 [==============================] - 0s 942us/sample - loss: 0.0053 - accuracy: 1.0000 - val_loss: 0.4173 - val_accuracy: 0.9524\n",
      "Epoch 541/800\n",
      "114/114 [==============================] - 0s 871us/sample - loss: 0.0053 - accuracy: 1.0000 - val_loss: 0.4173 - val_accuracy: 0.9524\n",
      "Epoch 542/800\n",
      "114/114 [==============================] - 0s 910us/sample - loss: 0.0051 - accuracy: 1.0000 - val_loss: 0.4252 - val_accuracy: 0.9048\n",
      "Epoch 543/800\n",
      "114/114 [==============================] - 0s 887us/sample - loss: 0.0074 - accuracy: 1.0000 - val_loss: 0.4215 - val_accuracy: 0.9524\n",
      "Epoch 544/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0076 - accuracy: 1.0000 - val_loss: 0.4136 - val_accuracy: 0.9524\n",
      "Epoch 545/800\n",
      "114/114 [==============================] - 0s 892us/sample - loss: 0.0053 - accuracy: 1.0000 - val_loss: 0.4286 - val_accuracy: 0.9048\n",
      "Epoch 546/800\n",
      "114/114 [==============================] - 0s 886us/sample - loss: 0.0051 - accuracy: 1.0000 - val_loss: 0.4495 - val_accuracy: 0.9048\n",
      "Epoch 547/800\n",
      "114/114 [==============================] - 0s 882us/sample - loss: 0.0092 - accuracy: 1.0000 - val_loss: 0.4178 - val_accuracy: 0.9524\n",
      "Epoch 548/800\n",
      "114/114 [==============================] - 0s 974us/sample - loss: 0.0094 - accuracy: 0.9912 - val_loss: 0.4127 - val_accuracy: 0.9524\n",
      "Epoch 549/800\n",
      "114/114 [==============================] - 0s 864us/sample - loss: 0.0125 - accuracy: 0.9912 - val_loss: 0.4313 - val_accuracy: 0.9048\n",
      "Epoch 550/800\n",
      "114/114 [==============================] - 0s 897us/sample - loss: 0.0139 - accuracy: 1.0000 - val_loss: 0.4190 - val_accuracy: 0.9524\n",
      "Epoch 551/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0246 - accuracy: 0.9825 - val_loss: 0.4125 - val_accuracy: 0.9524\n",
      "Epoch 552/800\n",
      "114/114 [==============================] - 0s 878us/sample - loss: 0.0240 - accuracy: 0.9825 - val_loss: 0.4431 - val_accuracy: 0.9048\n",
      "Epoch 553/800\n",
      "114/114 [==============================] - 0s 900us/sample - loss: 0.0049 - accuracy: 1.0000 - val_loss: 0.4121 - val_accuracy: 0.9524\n",
      "Epoch 554/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0406 - accuracy: 0.9825 - val_loss: 0.4123 - val_accuracy: 0.9524\n",
      "Epoch 555/800\n",
      "114/114 [==============================] - 0s 945us/sample - loss: 0.0542 - accuracy: 0.9737 - val_loss: 0.4283 - val_accuracy: 0.9048\n",
      "Epoch 556/800\n",
      "114/114 [==============================] - 0s 907us/sample - loss: 0.0355 - accuracy: 0.9912 - val_loss: 0.4453 - val_accuracy: 0.9524\n",
      "Epoch 557/800\n",
      "114/114 [==============================] - 0s 900us/sample - loss: 0.0940 - accuracy: 0.9825 - val_loss: 0.4104 - val_accuracy: 0.9524\n",
      "Epoch 558/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0222 - accuracy: 0.9912 - val_loss: 0.4801 - val_accuracy: 0.9048\n",
      "Epoch 559/800\n",
      "114/114 [==============================] - 0s 899us/sample - loss: 0.0453 - accuracy: 0.9737 - val_loss: 0.4232 - val_accuracy: 0.9524\n",
      "Epoch 560/800\n",
      "114/114 [==============================] - 0s 897us/sample - loss: 0.0855 - accuracy: 0.9825 - val_loss: 0.4139 - val_accuracy: 0.9524\n",
      "Epoch 561/800\n",
      "114/114 [==============================] - 0s 884us/sample - loss: 0.0327 - accuracy: 0.9912 - val_loss: 0.4638 - val_accuracy: 0.9048\n",
      "Epoch 562/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0358 - accuracy: 0.9737 - val_loss: 0.4011 - val_accuracy: 0.9524\n",
      "Epoch 563/800\n",
      "114/114 [==============================] - 0s 918us/sample - loss: 0.0228 - accuracy: 0.9912 - val_loss: 0.4020 - val_accuracy: 0.9524\n",
      "Epoch 564/800\n",
      "114/114 [==============================] - 0s 920us/sample - loss: 0.0145 - accuracy: 0.9912 - val_loss: 0.4311 - val_accuracy: 0.9048\n",
      "Epoch 565/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0235 - accuracy: 0.9825 - val_loss: 0.4029 - val_accuracy: 0.9524\n",
      "Epoch 566/800\n",
      "114/114 [==============================] - 0s 947us/sample - loss: 0.0145 - accuracy: 0.9912 - val_loss: 0.3968 - val_accuracy: 0.9524\n",
      "Epoch 567/800\n",
      "114/114 [==============================] - 0s 874us/sample - loss: 0.0149 - accuracy: 0.9912 - val_loss: 0.4594 - val_accuracy: 0.9048\n",
      "Epoch 568/800\n",
      "114/114 [==============================] - 0s 896us/sample - loss: 0.0154 - accuracy: 0.9912 - val_loss: 0.4037 - val_accuracy: 0.9524\n",
      "Epoch 569/800\n",
      "114/114 [==============================] - 0s 862us/sample - loss: 0.0078 - accuracy: 1.0000 - val_loss: 0.3965 - val_accuracy: 0.9524\n",
      "Epoch 570/800\n",
      "114/114 [==============================] - 0s 957us/sample - loss: 0.0100 - accuracy: 1.0000 - val_loss: 0.4155 - val_accuracy: 0.9048\n",
      "Epoch 571/800\n",
      "114/114 [==============================] - 0s 910us/sample - loss: 0.0110 - accuracy: 1.0000 - val_loss: 0.4276 - val_accuracy: 0.9048\n",
      "Epoch 572/800\n",
      "114/114 [==============================] - 0s 884us/sample - loss: 0.0079 - accuracy: 1.0000 - val_loss: 0.3966 - val_accuracy: 0.9524\n",
      "Epoch 573/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0148 - accuracy: 0.9912 - val_loss: 0.3988 - val_accuracy: 0.9524\n",
      "Epoch 574/800\n",
      "114/114 [==============================] - 0s 917us/sample - loss: 0.0042 - accuracy: 1.0000 - val_loss: 0.4490 - val_accuracy: 0.9048\n",
      "Epoch 575/800\n",
      "114/114 [==============================] - 0s 917us/sample - loss: 0.0123 - accuracy: 1.0000 - val_loss: 0.4011 - val_accuracy: 0.9524\n",
      "Epoch 576/800\n",
      "114/114 [==============================] - 0s 879us/sample - loss: 0.0047 - accuracy: 1.0000 - val_loss: 0.3967 - val_accuracy: 0.9524\n",
      "Epoch 577/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0110 - accuracy: 0.9912 - val_loss: 0.4096 - val_accuracy: 0.9524\n",
      "Epoch 578/800\n",
      "114/114 [==============================] - 0s 890us/sample - loss: 0.0049 - accuracy: 1.0000 - val_loss: 0.4197 - val_accuracy: 0.9048\n",
      "Epoch 579/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0059 - accuracy: 1.0000 - val_loss: 0.4076 - val_accuracy: 0.9524\n",
      "Epoch 580/800\n",
      "114/114 [==============================] - 0s 889us/sample - loss: 0.0057 - accuracy: 1.0000 - val_loss: 0.4008 - val_accuracy: 0.9524\n",
      "Epoch 581/800\n",
      "114/114 [==============================] - 0s 897us/sample - loss: 0.0051 - accuracy: 1.0000 - val_loss: 0.4111 - val_accuracy: 0.9524\n",
      "Epoch 582/800\n",
      "114/114 [==============================] - 0s 905us/sample - loss: 0.0049 - accuracy: 1.0000 - val_loss: 0.4126 - val_accuracy: 0.9048\n",
      "Epoch 583/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0045 - accuracy: 1.0000 - val_loss: 0.4039 - val_accuracy: 0.9524\n",
      "Epoch 584/800\n",
      "114/114 [==============================] - 0s 952us/sample - loss: 0.0064 - accuracy: 1.0000 - val_loss: 0.4022 - val_accuracy: 0.9524\n",
      "Epoch 585/800\n",
      "114/114 [==============================] - 0s 896us/sample - loss: 0.0057 - accuracy: 1.0000 - val_loss: 0.4270 - val_accuracy: 0.9048\n",
      "Epoch 586/800\n",
      "114/114 [==============================] - 0s 869us/sample - loss: 0.0084 - accuracy: 1.0000 - val_loss: 0.4156 - val_accuracy: 0.9048\n",
      "Epoch 587/800\n",
      "114/114 [==============================] - 0s 921us/sample - loss: 0.0054 - accuracy: 1.0000 - val_loss: 0.3997 - val_accuracy: 0.9524\n",
      "Epoch 588/800\n",
      "114/114 [==============================] - 0s 936us/sample - loss: 0.0132 - accuracy: 0.9912 - val_loss: 0.4140 - val_accuracy: 0.9524\n",
      "Epoch 589/800\n",
      "114/114 [==============================] - 0s 903us/sample - loss: 0.0059 - accuracy: 1.0000 - val_loss: 0.4227 - val_accuracy: 0.9048\n",
      "Epoch 590/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0045 - accuracy: 1.0000 - val_loss: 0.4029 - val_accuracy: 0.9524\n",
      "Epoch 591/800\n",
      "114/114 [==============================] - 0s 912us/sample - loss: 0.0058 - accuracy: 1.0000 - val_loss: 0.4034 - val_accuracy: 0.9524\n",
      "Epoch 592/800\n",
      "114/114 [==============================] - 0s 875us/sample - loss: 0.0066 - accuracy: 1.0000 - val_loss: 0.4206 - val_accuracy: 0.9048\n",
      "Epoch 593/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0055 - accuracy: 1.0000 - val_loss: 0.4102 - val_accuracy: 0.9524\n",
      "Epoch 594/800\n",
      "114/114 [==============================] - 0s 929us/sample - loss: 0.0060 - accuracy: 1.0000 - val_loss: 0.4055 - val_accuracy: 0.9524\n",
      "Epoch 595/800\n",
      "114/114 [==============================] - 0s 867us/sample - loss: 0.0054 - accuracy: 1.0000 - val_loss: 0.4280 - val_accuracy: 0.9048\n",
      "Epoch 596/800\n",
      "114/114 [==============================] - 0s 926us/sample - loss: 0.0053 - accuracy: 1.0000 - val_loss: 0.4174 - val_accuracy: 0.9048\n",
      "Epoch 597/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0038 - accuracy: 1.0000 - val_loss: 0.4052 - val_accuracy: 0.9524\n",
      "Epoch 598/800\n",
      "114/114 [==============================] - 0s 886us/sample - loss: 0.0059 - accuracy: 1.0000 - val_loss: 0.4069 - val_accuracy: 0.9524\n",
      "Epoch 599/800\n",
      "114/114 [==============================] - 0s 892us/sample - loss: 0.0040 - accuracy: 1.0000 - val_loss: 0.4255 - val_accuracy: 0.9048\n",
      "Epoch 600/800\n",
      "114/114 [==============================] - 0s 893us/sample - loss: 0.0049 - accuracy: 1.0000 - val_loss: 0.4208 - val_accuracy: 0.9048\n",
      "Epoch 601/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0045 - accuracy: 1.0000 - val_loss: 0.4076 - val_accuracy: 0.9524\n",
      "Epoch 602/800\n",
      "114/114 [==============================] - 0s 924us/sample - loss: 0.0046 - accuracy: 1.0000 - val_loss: 0.4082 - val_accuracy: 0.9524\n",
      "Epoch 603/800\n",
      "114/114 [==============================] - 0s 899us/sample - loss: 0.0054 - accuracy: 1.0000 - val_loss: 0.4298 - val_accuracy: 0.9048\n",
      "Epoch 604/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0050 - accuracy: 1.0000 - val_loss: 0.4179 - val_accuracy: 0.9524\n",
      "Epoch 605/800\n",
      "114/114 [==============================] - 0s 920us/sample - loss: 0.0042 - accuracy: 1.0000 - val_loss: 0.4088 - val_accuracy: 0.9524\n",
      "Epoch 606/800\n",
      "114/114 [==============================] - 0s 884us/sample - loss: 0.0054 - accuracy: 1.0000 - val_loss: 0.4201 - val_accuracy: 0.9048\n",
      "Epoch 607/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0041 - accuracy: 1.0000 - val_loss: 0.4171 - val_accuracy: 0.9524\n",
      "Epoch 608/800\n",
      "114/114 [==============================] - 0s 907us/sample - loss: 0.0039 - accuracy: 1.0000 - val_loss: 0.4163 - val_accuracy: 0.9524\n",
      "Epoch 609/800\n",
      "114/114 [==============================] - 0s 889us/sample - loss: 0.0046 - accuracy: 1.0000 - val_loss: 0.4149 - val_accuracy: 0.9524\n",
      "Epoch 610/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0044 - accuracy: 1.0000 - val_loss: 0.4252 - val_accuracy: 0.9048\n",
      "Epoch 611/800\n",
      "114/114 [==============================] - 0s 932us/sample - loss: 0.0039 - accuracy: 1.0000 - val_loss: 0.4154 - val_accuracy: 0.9524\n",
      "Epoch 612/800\n",
      "114/114 [==============================] - 0s 897us/sample - loss: 0.0041 - accuracy: 1.0000 - val_loss: 0.4096 - val_accuracy: 0.9524\n",
      "Epoch 613/800\n",
      "114/114 [==============================] - 0s 879us/sample - loss: 0.0054 - accuracy: 1.0000 - val_loss: 0.4138 - val_accuracy: 0.9524\n",
      "Epoch 614/800\n",
      "114/114 [==============================] - 0s 976us/sample - loss: 0.0065 - accuracy: 1.0000 - val_loss: 0.4492 - val_accuracy: 0.9048\n",
      "Epoch 615/800\n",
      "114/114 [==============================] - 0s 907us/sample - loss: 0.0064 - accuracy: 1.0000 - val_loss: 0.4101 - val_accuracy: 0.9524\n",
      "Epoch 616/800\n",
      "114/114 [==============================] - 0s 898us/sample - loss: 0.0106 - accuracy: 0.9912 - val_loss: 0.4089 - val_accuracy: 0.9524\n",
      "Epoch 617/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.4490 - val_accuracy: 0.9048\n",
      "Epoch 618/800\n",
      "114/114 [==============================] - 0s 938us/sample - loss: 0.0094 - accuracy: 1.0000 - val_loss: 0.4315 - val_accuracy: 0.9048\n",
      "Epoch 619/800\n",
      "114/114 [==============================] - 0s 903us/sample - loss: 0.0058 - accuracy: 1.0000 - val_loss: 0.4084 - val_accuracy: 0.9524\n",
      "Epoch 620/800\n",
      "114/114 [==============================] - 0s 879us/sample - loss: 0.0105 - accuracy: 0.9912 - val_loss: 0.4188 - val_accuracy: 0.9524\n",
      "Epoch 621/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0049 - accuracy: 1.0000 - val_loss: 0.4455 - val_accuracy: 0.9048\n",
      "Epoch 622/800\n",
      "114/114 [==============================] - 0s 898us/sample - loss: 0.0060 - accuracy: 1.0000 - val_loss: 0.4159 - val_accuracy: 0.9524\n",
      "Epoch 623/800\n",
      "114/114 [==============================] - 0s 880us/sample - loss: 0.0050 - accuracy: 1.0000 - val_loss: 0.4107 - val_accuracy: 0.9524\n",
      "Epoch 624/800\n",
      "114/114 [==============================] - 0s 883us/sample - loss: 0.0040 - accuracy: 1.0000 - val_loss: 0.4207 - val_accuracy: 0.9524\n",
      "Epoch 625/800\n",
      "114/114 [==============================] - 0s 962us/sample - loss: 0.0037 - accuracy: 1.0000 - val_loss: 0.4298 - val_accuracy: 0.9048\n",
      "Epoch 626/800\n",
      "114/114 [==============================] - 0s 924us/sample - loss: 0.0044 - accuracy: 1.0000 - val_loss: 0.4191 - val_accuracy: 0.9524\n",
      "Epoch 627/800\n",
      "114/114 [==============================] - 0s 867us/sample - loss: 0.0045 - accuracy: 1.0000 - val_loss: 0.4138 - val_accuracy: 0.9524\n",
      "Epoch 628/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0043 - accuracy: 1.0000 - val_loss: 0.4231 - val_accuracy: 0.9524\n",
      "Epoch 629/800\n",
      "114/114 [==============================] - 0s 917us/sample - loss: 0.0035 - accuracy: 1.0000 - val_loss: 0.4231 - val_accuracy: 0.9524\n",
      "Epoch 630/800\n",
      "114/114 [==============================] - 0s 889us/sample - loss: 0.0035 - accuracy: 1.0000 - val_loss: 0.4223 - val_accuracy: 0.9524\n",
      "Epoch 631/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0034 - accuracy: 1.0000 - val_loss: 0.4188 - val_accuracy: 0.9524\n",
      "Epoch 632/800\n",
      "114/114 [==============================] - 0s 915us/sample - loss: 0.0051 - accuracy: 1.0000 - val_loss: 0.4180 - val_accuracy: 0.9524\n",
      "Epoch 633/800\n",
      "114/114 [==============================] - 0s 920us/sample - loss: 0.0030 - accuracy: 1.0000 - val_loss: 0.4351 - val_accuracy: 0.9048\n",
      "Epoch 634/800\n",
      "114/114 [==============================] - 0s 889us/sample - loss: 0.0047 - accuracy: 1.0000 - val_loss: 0.4295 - val_accuracy: 0.9048\n",
      "Epoch 635/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0076 - accuracy: 1.0000 - val_loss: 0.4141 - val_accuracy: 0.9524\n",
      "Epoch 636/800\n",
      "114/114 [==============================] - 0s 892us/sample - loss: 0.0034 - accuracy: 1.0000 - val_loss: 0.4293 - val_accuracy: 0.9048\n",
      "Epoch 637/800\n",
      "114/114 [==============================] - 0s 869us/sample - loss: 0.0044 - accuracy: 1.0000 - val_loss: 0.4372 - val_accuracy: 0.9048\n",
      "Epoch 638/800\n",
      "114/114 [==============================] - 0s 902us/sample - loss: 0.0035 - accuracy: 1.0000 - val_loss: 0.4164 - val_accuracy: 0.9524\n",
      "Epoch 639/800\n",
      "114/114 [==============================] - 0s 979us/sample - loss: 0.0043 - accuracy: 1.0000 - val_loss: 0.4149 - val_accuracy: 0.9524\n",
      "Epoch 640/800\n",
      "114/114 [==============================] - 0s 900us/sample - loss: 0.0049 - accuracy: 1.0000 - val_loss: 0.4428 - val_accuracy: 0.9048\n",
      "Epoch 641/800\n",
      "114/114 [==============================] - 0s 870us/sample - loss: 0.0046 - accuracy: 1.0000 - val_loss: 0.4341 - val_accuracy: 0.9048\n",
      "Epoch 642/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0034 - accuracy: 1.0000 - val_loss: 0.4202 - val_accuracy: 0.9524\n",
      "Epoch 643/800\n",
      "114/114 [==============================] - 0s 938us/sample - loss: 0.0033 - accuracy: 1.0000 - val_loss: 0.4176 - val_accuracy: 0.9524\n",
      "Epoch 644/800\n",
      "114/114 [==============================] - 0s 915us/sample - loss: 0.0036 - accuracy: 1.0000 - val_loss: 0.4257 - val_accuracy: 0.9524\n",
      "Epoch 645/800\n",
      "114/114 [==============================] - 0s 876us/sample - loss: 0.0030 - accuracy: 1.0000 - val_loss: 0.4302 - val_accuracy: 0.9524\n",
      "Epoch 646/800\n",
      "114/114 [==============================] - 0s 959us/sample - loss: 0.0033 - accuracy: 1.0000 - val_loss: 0.4282 - val_accuracy: 0.9524\n",
      "Epoch 647/800\n",
      "114/114 [==============================] - 0s 895us/sample - loss: 0.0031 - accuracy: 1.0000 - val_loss: 0.4281 - val_accuracy: 0.9524\n",
      "Epoch 648/800\n",
      "114/114 [==============================] - 0s 886us/sample - loss: 0.0031 - accuracy: 1.0000 - val_loss: 0.4265 - val_accuracy: 0.9524\n",
      "Epoch 649/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0030 - accuracy: 1.0000 - val_loss: 0.4243 - val_accuracy: 0.9524\n",
      "Epoch 650/800\n",
      "114/114 [==============================] - 0s 974us/sample - loss: 0.0038 - accuracy: 1.0000 - val_loss: 0.4236 - val_accuracy: 0.9524\n",
      "Epoch 651/800\n",
      "114/114 [==============================] - 0s 878us/sample - loss: 0.0033 - accuracy: 1.0000 - val_loss: 0.4418 - val_accuracy: 0.9048\n",
      "Epoch 652/800\n",
      "114/114 [==============================] - 0s 871us/sample - loss: 0.0042 - accuracy: 1.0000 - val_loss: 0.4319 - val_accuracy: 0.9524\n",
      "Epoch 653/800\n",
      "114/114 [==============================] - 0s 964us/sample - loss: 0.0045 - accuracy: 1.0000 - val_loss: 0.4218 - val_accuracy: 0.9524\n",
      "Epoch 654/800\n",
      "114/114 [==============================] - 0s 886us/sample - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.4309 - val_accuracy: 0.9524\n",
      "Epoch 655/800\n",
      "114/114 [==============================] - 0s 899us/sample - loss: 0.0036 - accuracy: 1.0000 - val_loss: 0.4416 - val_accuracy: 0.9048\n",
      "Epoch 656/800\n",
      "114/114 [==============================] - 0s 872us/sample - loss: 0.0040 - accuracy: 1.0000 - val_loss: 0.4229 - val_accuracy: 0.9524\n",
      "Epoch 657/800\n",
      "114/114 [==============================] - 0s 975us/sample - loss: 0.0033 - accuracy: 1.0000 - val_loss: 0.4247 - val_accuracy: 0.9524\n",
      "Epoch 658/800\n",
      "114/114 [==============================] - 0s 883us/sample - loss: 0.0030 - accuracy: 1.0000 - val_loss: 0.4359 - val_accuracy: 0.9048\n",
      "Epoch 659/800\n",
      "114/114 [==============================] - 0s 901us/sample - loss: 0.0036 - accuracy: 1.0000 - val_loss: 0.4421 - val_accuracy: 0.9048\n",
      "Epoch 660/800\n",
      "114/114 [==============================] - 0s 914us/sample - loss: 0.0035 - accuracy: 1.0000 - val_loss: 0.4244 - val_accuracy: 0.9524\n",
      "Epoch 661/800\n",
      "114/114 [==============================] - 0s 923us/sample - loss: 0.0031 - accuracy: 1.0000 - val_loss: 0.4268 - val_accuracy: 0.9524\n",
      "Epoch 662/800\n",
      "114/114 [==============================] - 0s 899us/sample - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.4301 - val_accuracy: 0.9524\n",
      "Epoch 663/800\n",
      "114/114 [==============================] - 0s 883us/sample - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.4324 - val_accuracy: 0.9524\n",
      "Epoch 664/800\n",
      "114/114 [==============================] - 0s 961us/sample - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.4356 - val_accuracy: 0.9524\n",
      "Epoch 665/800\n",
      "114/114 [==============================] - 0s 897us/sample - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.4298 - val_accuracy: 0.9524\n",
      "Epoch 666/800\n",
      "114/114 [==============================] - 0s 885us/sample - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.4305 - val_accuracy: 0.9524\n",
      "Epoch 667/800\n",
      "114/114 [==============================] - 0s 878us/sample - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.4316 - val_accuracy: 0.9524\n",
      "Epoch 668/800\n",
      "114/114 [==============================] - 0s 918us/sample - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.4352 - val_accuracy: 0.9524\n",
      "Epoch 669/800\n",
      "114/114 [==============================] - 0s 948us/sample - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.4354 - val_accuracy: 0.9524\n",
      "Epoch 670/800\n",
      "114/114 [==============================] - 0s 855us/sample - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.4342 - val_accuracy: 0.9524\n",
      "Epoch 671/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.4369 - val_accuracy: 0.9524\n",
      "Epoch 672/800\n",
      "114/114 [==============================] - 0s 927us/sample - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.4340 - val_accuracy: 0.9524\n",
      "Epoch 673/800\n",
      "114/114 [==============================] - 0s 921us/sample - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.4303 - val_accuracy: 0.9524\n",
      "Epoch 674/800\n",
      "114/114 [==============================] - 0s 907us/sample - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.4340 - val_accuracy: 0.9524\n",
      "Epoch 675/800\n",
      "114/114 [==============================] - 0s 858us/sample - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.4340 - val_accuracy: 0.9524\n",
      "Epoch 676/800\n",
      "114/114 [==============================] - 0s 963us/sample - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.4381 - val_accuracy: 0.9524\n",
      "Epoch 677/800\n",
      "114/114 [==============================] - 0s 891us/sample - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.4319 - val_accuracy: 0.9524\n",
      "Epoch 678/800\n",
      "114/114 [==============================] - 0s 912us/sample - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.4347 - val_accuracy: 0.9524\n",
      "Epoch 679/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0030 - accuracy: 1.0000 - val_loss: 0.4473 - val_accuracy: 0.9048\n",
      "Epoch 680/800\n",
      "114/114 [==============================] - 0s 921us/sample - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.4343 - val_accuracy: 0.9524\n",
      "Epoch 681/800\n",
      "114/114 [==============================] - 0s 889us/sample - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.4310 - val_accuracy: 0.9524\n",
      "Epoch 682/800\n",
      "114/114 [==============================] - 0s 902us/sample - loss: 0.0033 - accuracy: 1.0000 - val_loss: 0.4405 - val_accuracy: 0.9524\n",
      "Epoch 683/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.4349 - val_accuracy: 0.9524\n",
      "Epoch 684/800\n",
      "114/114 [==============================] - 0s 924us/sample - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.4341 - val_accuracy: 0.9524\n",
      "Epoch 685/800\n",
      "114/114 [==============================] - 0s 886us/sample - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.4403 - val_accuracy: 0.9524\n",
      "Epoch 686/800\n",
      "114/114 [==============================] - 0s 892us/sample - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.4378 - val_accuracy: 0.9524\n",
      "Epoch 687/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.4404 - val_accuracy: 0.9524\n",
      "Epoch 688/800\n",
      "114/114 [==============================] - 0s 890us/sample - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.4387 - val_accuracy: 0.9524\n",
      "Epoch 689/800\n",
      "114/114 [==============================] - 0s 897us/sample - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.4393 - val_accuracy: 0.9524\n",
      "Epoch 690/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.4366 - val_accuracy: 0.9524\n",
      "Epoch 691/800\n",
      "114/114 [==============================] - 0s 958us/sample - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.4363 - val_accuracy: 0.9524\n",
      "Epoch 692/800\n",
      "114/114 [==============================] - 0s 899us/sample - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.4444 - val_accuracy: 0.9048\n",
      "Epoch 693/800\n",
      "114/114 [==============================] - 0s 878us/sample - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.4456 - val_accuracy: 0.9048\n",
      "Epoch 694/800\n",
      "114/114 [==============================] - 0s 928us/sample - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.4359 - val_accuracy: 0.9524\n",
      "Epoch 695/800\n",
      "114/114 [==============================] - 0s 934us/sample - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.4374 - val_accuracy: 0.9524\n",
      "Epoch 696/800\n",
      "114/114 [==============================] - 0s 890us/sample - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.4470 - val_accuracy: 0.9048\n",
      "Epoch 697/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.4450 - val_accuracy: 0.9524\n",
      "Epoch 698/800\n",
      "114/114 [==============================] - 0s 881us/sample - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.4369 - val_accuracy: 0.9524\n",
      "Epoch 699/800\n",
      "114/114 [==============================] - 0s 897us/sample - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.4369 - val_accuracy: 0.9524\n",
      "Epoch 700/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.4459 - val_accuracy: 0.9524\n",
      "Epoch 701/800\n",
      "114/114 [==============================] - 0s 903us/sample - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.4410 - val_accuracy: 0.9524\n",
      "Epoch 702/800\n",
      "114/114 [==============================] - 0s 893us/sample - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.4439 - val_accuracy: 0.9524\n",
      "Epoch 703/800\n",
      "114/114 [==============================] - 0s 902us/sample - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.4412 - val_accuracy: 0.9524\n",
      "Epoch 704/800\n",
      "114/114 [==============================] - 0s 902us/sample - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.4423 - val_accuracy: 0.9524\n",
      "Epoch 705/800\n",
      "114/114 [==============================] - 0s 948us/sample - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.4455 - val_accuracy: 0.9524\n",
      "Epoch 706/800\n",
      "114/114 [==============================] - 0s 933us/sample - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.4411 - val_accuracy: 0.9524\n",
      "Epoch 707/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.4440 - val_accuracy: 0.9524\n",
      "Epoch 708/800\n",
      "114/114 [==============================] - 0s 941us/sample - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.4435 - val_accuracy: 0.9524\n",
      "Epoch 709/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.4412 - val_accuracy: 0.9524\n",
      "Epoch 710/800\n",
      "114/114 [==============================] - 0s 949us/sample - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.4427 - val_accuracy: 0.9524\n",
      "Epoch 711/800\n",
      "114/114 [==============================] - 0s 858us/sample - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.4453 - val_accuracy: 0.9524\n",
      "Epoch 712/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.4456 - val_accuracy: 0.9524\n",
      "Epoch 713/800\n",
      "114/114 [==============================] - 0s 985us/sample - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.4509 - val_accuracy: 0.9048\n",
      "Epoch 714/800\n",
      "114/114 [==============================] - 0s 901us/sample - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.4426 - val_accuracy: 0.9524\n",
      "Epoch 715/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.4400 - val_accuracy: 0.9524\n",
      "Epoch 716/800\n",
      "114/114 [==============================] - 0s 943us/sample - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.4459 - val_accuracy: 0.9524\n",
      "Epoch 717/800\n",
      "114/114 [==============================] - 0s 896us/sample - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.4505 - val_accuracy: 0.9524\n",
      "Epoch 718/800\n",
      "114/114 [==============================] - 0s 901us/sample - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.4456 - val_accuracy: 0.9524\n",
      "Epoch 719/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.4486 - val_accuracy: 0.9524\n",
      "Epoch 720/800\n",
      "114/114 [==============================] - 0s 932us/sample - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.4491 - val_accuracy: 0.9524\n",
      "Epoch 721/800\n",
      "114/114 [==============================] - 0s 877us/sample - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.4455 - val_accuracy: 0.9524\n",
      "Epoch 722/800\n",
      "114/114 [==============================] - 0s 890us/sample - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.4452 - val_accuracy: 0.9524\n",
      "Epoch 723/800\n",
      "114/114 [==============================] - 0s 940us/sample - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.4466 - val_accuracy: 0.9524\n",
      "Epoch 724/800\n",
      "114/114 [==============================] - 0s 928us/sample - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.4476 - val_accuracy: 0.9524\n",
      "Epoch 725/800\n",
      "114/114 [==============================] - 0s 882us/sample - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.4524 - val_accuracy: 0.9524\n",
      "Epoch 726/800\n",
      "114/114 [==============================] - 0s 980us/sample - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.4510 - val_accuracy: 0.9524\n",
      "Epoch 727/800\n",
      "114/114 [==============================] - 0s 909us/sample - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.4429 - val_accuracy: 0.9524\n",
      "Epoch 728/800\n",
      "114/114 [==============================] - 0s 872us/sample - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.4475 - val_accuracy: 0.9524\n",
      "Epoch 729/800\n",
      "114/114 [==============================] - 0s 963us/sample - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.4555 - val_accuracy: 0.9048\n",
      "Epoch 730/800\n",
      "114/114 [==============================] - 0s 881us/sample - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.4554 - val_accuracy: 0.9048\n",
      "Epoch 731/800\n",
      "114/114 [==============================] - 0s 896us/sample - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.4459 - val_accuracy: 0.9524\n",
      "Epoch 732/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.4446 - val_accuracy: 0.9524\n",
      "Epoch 733/800\n",
      "114/114 [==============================] - 0s 920us/sample - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.4546 - val_accuracy: 0.9524\n",
      "Epoch 734/800\n",
      "114/114 [==============================] - 0s 889us/sample - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.4533 - val_accuracy: 0.9524\n",
      "Epoch 735/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.4522 - val_accuracy: 0.9524\n",
      "Epoch 736/800\n",
      "114/114 [==============================] - 0s 935us/sample - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.4458 - val_accuracy: 0.9524\n",
      "Epoch 737/800\n",
      "114/114 [==============================] - 0s 884us/sample - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.4576 - val_accuracy: 0.9048\n",
      "Epoch 738/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.4551 - val_accuracy: 0.9524\n",
      "Epoch 739/800\n",
      "114/114 [==============================] - 0s 927us/sample - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.4470 - val_accuracy: 0.9524\n",
      "Epoch 740/800\n",
      "114/114 [==============================] - 0s 881us/sample - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.4526 - val_accuracy: 0.9524\n",
      "Epoch 741/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.4563 - val_accuracy: 0.9524\n",
      "Epoch 742/800\n",
      "114/114 [==============================] - 0s 940us/sample - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.4507 - val_accuracy: 0.9524\n",
      "Epoch 743/800\n",
      "114/114 [==============================] - 0s 878us/sample - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.4513 - val_accuracy: 0.9524\n",
      "Epoch 744/800\n",
      "114/114 [==============================] - 0s 909us/sample - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.4517 - val_accuracy: 0.9524\n",
      "Epoch 745/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.4542 - val_accuracy: 0.9524\n",
      "Epoch 746/800\n",
      "114/114 [==============================] - 0s 937us/sample - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.4543 - val_accuracy: 0.9524\n",
      "Epoch 747/800\n",
      "114/114 [==============================] - 0s 911us/sample - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.4503 - val_accuracy: 0.9524\n",
      "Epoch 748/800\n",
      "114/114 [==============================] - 0s 901us/sample - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.4487 - val_accuracy: 0.9524\n",
      "Epoch 749/800\n",
      "114/114 [==============================] - 0s 966us/sample - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.4639 - val_accuracy: 0.9048\n",
      "Epoch 750/800\n",
      "114/114 [==============================] - 0s 890us/sample - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.4523 - val_accuracy: 0.9524\n",
      "Epoch 751/800\n",
      "114/114 [==============================] - 0s 885us/sample - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.4469 - val_accuracy: 0.9524\n",
      "Epoch 752/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.4525 - val_accuracy: 0.9524\n",
      "Epoch 753/800\n",
      "114/114 [==============================] - 0s 942us/sample - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.4566 - val_accuracy: 0.9524\n",
      "Epoch 754/800\n",
      "114/114 [==============================] - 0s 889us/sample - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.4560 - val_accuracy: 0.9524\n",
      "Epoch 755/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.4543 - val_accuracy: 0.9524\n",
      "Epoch 756/800\n",
      "114/114 [==============================] - 0s 941us/sample - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.4530 - val_accuracy: 0.9524\n",
      "Epoch 757/800\n",
      "114/114 [==============================] - 0s 891us/sample - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.4555 - val_accuracy: 0.9524\n",
      "Epoch 758/800\n",
      "114/114 [==============================] - 0s 914us/sample - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.4571 - val_accuracy: 0.9524\n",
      "Epoch 759/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.4532 - val_accuracy: 0.9524\n",
      "Epoch 760/800\n",
      "114/114 [==============================] - 0s 890us/sample - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.4500 - val_accuracy: 0.9524\n",
      "Epoch 761/800\n",
      "114/114 [==============================] - 0s 908us/sample - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.4671 - val_accuracy: 0.9048\n",
      "Epoch 762/800\n",
      "114/114 [==============================] - 0s 878us/sample - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.4663 - val_accuracy: 0.9048\n",
      "Epoch 763/800\n",
      "114/114 [==============================] - 0s 962us/sample - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.4535 - val_accuracy: 0.9524\n",
      "Epoch 764/800\n",
      "114/114 [==============================] - 0s 918us/sample - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.4511 - val_accuracy: 0.9524\n",
      "Epoch 765/800\n",
      "114/114 [==============================] - 0s 882us/sample - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.4797 - val_accuracy: 0.9048\n",
      "Epoch 766/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.4646 - val_accuracy: 0.9048\n",
      "Epoch 767/800\n",
      "114/114 [==============================] - 0s 941us/sample - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.4519 - val_accuracy: 0.9524\n",
      "Epoch 768/800\n",
      "114/114 [==============================] - 0s 899us/sample - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.4510 - val_accuracy: 0.9524\n",
      "Epoch 769/800\n",
      "114/114 [==============================] - 0s 972us/sample - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.4700 - val_accuracy: 0.9048\n",
      "Epoch 770/800\n",
      "114/114 [==============================] - 0s 884us/sample - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.4545 - val_accuracy: 0.9524\n",
      "Epoch 771/800\n",
      "114/114 [==============================] - 0s 885us/sample - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.4530 - val_accuracy: 0.9524\n",
      "Epoch 772/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.4568 - val_accuracy: 0.9524\n",
      "Epoch 773/800\n",
      "114/114 [==============================] - 0s 901us/sample - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.4622 - val_accuracy: 0.9524\n",
      "Epoch 774/800\n",
      "114/114 [==============================] - 0s 896us/sample - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.4627 - val_accuracy: 0.9524\n",
      "Epoch 775/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.4572 - val_accuracy: 0.9524\n",
      "Epoch 776/800\n",
      "114/114 [==============================] - 0s 938us/sample - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.4601 - val_accuracy: 0.9524\n",
      "Epoch 777/800\n",
      "114/114 [==============================] - 0s 897us/sample - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.4613 - val_accuracy: 0.9524\n",
      "Epoch 778/800\n",
      "114/114 [==============================] - 0s 889us/sample - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.4669 - val_accuracy: 0.9048\n",
      "Epoch 779/800\n",
      "114/114 [==============================] - 0s 874us/sample - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.4564 - val_accuracy: 0.9524\n",
      "Epoch 780/800\n",
      "114/114 [==============================] - 0s 959us/sample - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.4527 - val_accuracy: 0.9524\n",
      "Epoch 781/800\n",
      "114/114 [==============================] - 0s 899us/sample - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.4833 - val_accuracy: 0.9048\n",
      "Epoch 782/800\n",
      "114/114 [==============================] - 0s 895us/sample - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.4614 - val_accuracy: 0.9524\n",
      "Epoch 783/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.4525 - val_accuracy: 0.9524\n",
      "Epoch 784/800\n",
      "114/114 [==============================] - 0s 881us/sample - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.4616 - val_accuracy: 0.9524\n",
      "Epoch 785/800\n",
      "114/114 [==============================] - 0s 880us/sample - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.4724 - val_accuracy: 0.9048\n",
      "Epoch 786/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.4576 - val_accuracy: 0.9524\n",
      "Epoch 787/800\n",
      "114/114 [==============================] - 0s 973us/sample - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.4557 - val_accuracy: 0.9524\n",
      "Epoch 788/800\n",
      "114/114 [==============================] - 0s 866us/sample - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.4568 - val_accuracy: 0.9524\n",
      "Epoch 789/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.4683 - val_accuracy: 0.9524\n",
      "Epoch 790/800\n",
      "114/114 [==============================] - 0s 948us/sample - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.4680 - val_accuracy: 0.9524\n",
      "Epoch 791/800\n",
      "114/114 [==============================] - 0s 882us/sample - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.4659 - val_accuracy: 0.9524\n",
      "Epoch 792/800\n",
      "114/114 [==============================] - 0s 884us/sample - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.4595 - val_accuracy: 0.9524\n",
      "Epoch 793/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.4605 - val_accuracy: 0.9524\n",
      "Epoch 794/800\n",
      "114/114 [==============================] - 0s 896us/sample - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.4678 - val_accuracy: 0.9524\n",
      "Epoch 795/800\n",
      "114/114 [==============================] - 0s 892us/sample - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.4611 - val_accuracy: 0.9524\n",
      "Epoch 796/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.4599 - val_accuracy: 0.9524\n",
      "Epoch 797/800\n",
      "114/114 [==============================] - 0s 953us/sample - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.4725 - val_accuracy: 0.9048\n",
      "Epoch 798/800\n",
      "114/114 [==============================] - 0s 884us/sample - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.4673 - val_accuracy: 0.9524\n",
      "Epoch 799/800\n",
      "114/114 [==============================] - 0s 911us/sample - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.4662 - val_accuracy: 0.9524\n",
      "Epoch 800/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.4602 - val_accuracy: 0.9524\n"
     ]
    }
   ],
   "source": [
    "# Run your function to train the model\n",
    "\n",
    "history = train_model(model, train_data, train_targets, epochs=800)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot the learning curves\n",
    "\n",
    "We will now plot two graphs:\n",
    "* Epoch vs accuracy\n",
    "* Epoch vs loss\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this cell to plot the epoch vs accuracy graph\n",
    "\n",
    "try:\n",
    "    plt.plot(history.history['accuracy'])\n",
    "    plt.plot(history.history['val_accuracy'])\n",
    "except KeyError:\n",
    "    plt.plot(history.history['acc'])\n",
    "    plt.plot(history.history['val_acc'])\n",
    "plt.title('Accuracy vs. epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Training', 'Validation'], loc='lower right')\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Run this cell to plot the epoch vs loss graph\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Loss vs. epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Training', 'Validation'], loc='upper right')\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oh no! We have overfit our dataset. You should now try to now try to mitigate this overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reducing overfitting in the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should now define a new regularised model.\n",
    "The specs for the regularised model are the same as our original model, with the addition of two dropout layers, weight decay, and a batch normalisation layer. \n",
    "\n",
    "In particular:\n",
    "\n",
    "* Add a dropout layer after the 3rd Dense layer\n",
    "* Then there should be two more Dense layers with 128 units before a batch normalisation layer\n",
    "* Following this, two more Dense layers with 64 units and then another Dropout layer\n",
    "* Two more Dense layers with 64 units and then the final 3-way softmax layer\n",
    "* Add weight decay (l2 kernel regularisation) in all Dense layers except the final softmax layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### GRADED CELL ####\n",
    "\n",
    "# Complete the following function. \n",
    "# Make sure to not change the function name or arguments.\n",
    "from tensorflow.keras import regularizers\n",
    "from tensorflow.keras.layers import Dropout\n",
    "from tensorflow.keras.layers import BatchNormalization\n",
    "\n",
    "\n",
    "def get_regularised_model(input_shape, dropout_rate, weight_decay):\n",
    "    \"\"\"\n",
    "    This function should build a regularised Sequential model according to the above specification. \n",
    "    The dropout_rate argument in the function should be used to set the Dropout rate for all Dropout layers.\n",
    "    L2 kernel regularisation (weight decay) should be added using the weight_decay argument to \n",
    "    set the weight decay coefficient in all Dense layers that use L2 regularisation.\n",
    "    Ensure the weights are initialised by providing the input_shape argument in the first layer, given by the\n",
    "    function argument input_shape.\n",
    "    Your function should return the model.\n",
    "    \"\"\"\n",
    "    model = Sequential([\n",
    "                      Dense(64, activation='relu', kernel_regularizer = regularizers.l2(weight_decay), kernel_initializer='he_uniform', bias_initializer='ones', input_shape=input_shape),\n",
    "                      Dense(128, activation='relu', kernel_regularizer = regularizers.l2(weight_decay)),\n",
    "                      Dense(128, activation='relu', kernel_regularizer = regularizers.l2(weight_decay)),\n",
    "                      Dropout(dropout_rate),\n",
    "                      Dense(128, activation='relu', kernel_regularizer = regularizers.l2(weight_decay)),\n",
    "                      Dense(128, activation='relu', kernel_regularizer = regularizers.l2(weight_decay)),\n",
    "                      BatchNormalization(),\n",
    "                      Dense(64, activation='relu', kernel_regularizer = regularizers.l2(weight_decay)),\n",
    "                      Dense(64, activation='relu', kernel_regularizer = regularizers.l2(weight_decay)),\n",
    "                      Dropout(dropout_rate),\n",
    "                      Dense(64, activation='relu', kernel_regularizer = regularizers.l2(weight_decay)),\n",
    "                      Dense(64, activation='relu', kernel_regularizer = regularizers.l2(weight_decay)),\n",
    "                      Dense(3, activation='softmax')\n",
    "    ])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Instantiate, compile and train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate the model, using a dropout rate of 0.3 and weight decay coefficient of 0.001\n",
    "\n",
    "reg_model = get_regularised_model(train_data[0].shape, 0.3, 0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "\n",
    "compile_model(reg_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 114 samples, validate on 21 samples\n",
      "Epoch 1/800\n",
      "114/114 [==============================] - 3s 23ms/sample - loss: 2.0906 - accuracy: 0.3333 - val_loss: 2.0383 - val_accuracy: 0.1905\n",
      "Epoch 2/800\n",
      "114/114 [==============================] - 0s 736us/sample - loss: 1.9855 - accuracy: 0.3684 - val_loss: 1.9701 - val_accuracy: 0.1905\n",
      "Epoch 3/800\n",
      "114/114 [==============================] - 0s 980us/sample - loss: 1.9117 - accuracy: 0.4737 - val_loss: 1.8982 - val_accuracy: 0.6190\n",
      "Epoch 4/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 1.8732 - accuracy: 0.4561 - val_loss: 1.8676 - val_accuracy: 0.6190\n",
      "Epoch 5/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 1.8442 - accuracy: 0.4649 - val_loss: 1.8254 - val_accuracy: 0.5714\n",
      "Epoch 6/800\n",
      "114/114 [==============================] - 0s 904us/sample - loss: 1.7814 - accuracy: 0.5877 - val_loss: 1.7543 - val_accuracy: 0.6190\n",
      "Epoch 7/800\n",
      "114/114 [==============================] - 0s 970us/sample - loss: 1.7282 - accuracy: 0.5965 - val_loss: 1.7003 - val_accuracy: 0.6190\n",
      "Epoch 8/800\n",
      "114/114 [==============================] - 0s 940us/sample - loss: 1.6443 - accuracy: 0.6053 - val_loss: 1.6446 - val_accuracy: 0.6190\n",
      "Epoch 9/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 1.5675 - accuracy: 0.6316 - val_loss: 1.5739 - val_accuracy: 0.6190\n",
      "Epoch 10/800\n",
      "114/114 [==============================] - 0s 892us/sample - loss: 1.4859 - accuracy: 0.6930 - val_loss: 1.5111 - val_accuracy: 0.6190\n",
      "Epoch 11/800\n",
      "114/114 [==============================] - 0s 974us/sample - loss: 1.4424 - accuracy: 0.6667 - val_loss: 1.4609 - val_accuracy: 0.6190\n",
      "Epoch 12/800\n",
      "114/114 [==============================] - 0s 907us/sample - loss: 1.4168 - accuracy: 0.7193 - val_loss: 1.4180 - val_accuracy: 0.6190\n",
      "Epoch 13/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 1.3769 - accuracy: 0.6842 - val_loss: 1.3849 - val_accuracy: 0.6190\n",
      "Epoch 14/800\n",
      "114/114 [==============================] - 0s 913us/sample - loss: 1.3594 - accuracy: 0.6667 - val_loss: 1.3617 - val_accuracy: 0.6190\n",
      "Epoch 15/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 1.3140 - accuracy: 0.6842 - val_loss: 1.3451 - val_accuracy: 0.6190\n",
      "Epoch 16/800\n",
      "114/114 [==============================] - 0s 903us/sample - loss: 1.2587 - accuracy: 0.7368 - val_loss: 1.3253 - val_accuracy: 0.6190\n",
      "Epoch 17/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 1.2629 - accuracy: 0.7281 - val_loss: 1.3087 - val_accuracy: 0.6190\n",
      "Epoch 18/800\n",
      "114/114 [==============================] - 0s 936us/sample - loss: 1.2733 - accuracy: 0.7105 - val_loss: 1.2937 - val_accuracy: 0.6190\n",
      "Epoch 19/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 1.2664 - accuracy: 0.6930 - val_loss: 1.2753 - val_accuracy: 0.6667\n",
      "Epoch 20/800\n",
      "114/114 [==============================] - 0s 952us/sample - loss: 1.2115 - accuracy: 0.7368 - val_loss: 1.2657 - val_accuracy: 0.6667\n",
      "Epoch 21/800\n",
      "114/114 [==============================] - 0s 944us/sample - loss: 1.2555 - accuracy: 0.7281 - val_loss: 1.2538 - val_accuracy: 0.6667\n",
      "Epoch 22/800\n",
      "114/114 [==============================] - 0s 958us/sample - loss: 1.1801 - accuracy: 0.8333 - val_loss: 1.2380 - val_accuracy: 0.6667\n",
      "Epoch 23/800\n",
      "114/114 [==============================] - 0s 928us/sample - loss: 1.1656 - accuracy: 0.7719 - val_loss: 1.2194 - val_accuracy: 0.6667\n",
      "Epoch 24/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 1.1410 - accuracy: 0.7895 - val_loss: 1.2024 - val_accuracy: 0.6667\n",
      "Epoch 25/800\n",
      "114/114 [==============================] - 0s 896us/sample - loss: 1.1239 - accuracy: 0.8596 - val_loss: 1.1885 - val_accuracy: 0.7143\n",
      "Epoch 26/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 1.0887 - accuracy: 0.8596 - val_loss: 1.1638 - val_accuracy: 0.7143\n",
      "Epoch 27/800\n",
      "114/114 [==============================] - 0s 943us/sample - loss: 1.1382 - accuracy: 0.7982 - val_loss: 1.1428 - val_accuracy: 0.8095\n",
      "Epoch 28/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 1.0920 - accuracy: 0.8772 - val_loss: 1.1262 - val_accuracy: 0.8571\n",
      "Epoch 29/800\n",
      "114/114 [==============================] - 0s 953us/sample - loss: 1.0631 - accuracy: 0.9035 - val_loss: 1.1143 - val_accuracy: 0.8571\n",
      "Epoch 30/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 1.0683 - accuracy: 0.8684 - val_loss: 1.1027 - val_accuracy: 0.8571\n",
      "Epoch 31/800\n",
      "114/114 [==============================] - 0s 937us/sample - loss: 1.0221 - accuracy: 0.9035 - val_loss: 1.0991 - val_accuracy: 0.9048\n",
      "Epoch 32/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 1.0579 - accuracy: 0.8684 - val_loss: 1.0889 - val_accuracy: 0.9048\n",
      "Epoch 33/800\n",
      "114/114 [==============================] - 0s 927us/sample - loss: 1.0313 - accuracy: 0.9035 - val_loss: 1.0727 - val_accuracy: 0.9048\n",
      "Epoch 34/800\n",
      "114/114 [==============================] - 0s 982us/sample - loss: 1.0422 - accuracy: 0.8684 - val_loss: 1.0431 - val_accuracy: 0.9048\n",
      "Epoch 35/800\n",
      "114/114 [==============================] - 0s 889us/sample - loss: 0.9462 - accuracy: 0.9386 - val_loss: 1.0266 - val_accuracy: 0.9048\n",
      "Epoch 36/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.9784 - accuracy: 0.9123 - val_loss: 1.0160 - val_accuracy: 0.9048\n",
      "Epoch 37/800\n",
      "114/114 [==============================] - 0s 994us/sample - loss: 0.9531 - accuracy: 0.9035 - val_loss: 1.0145 - val_accuracy: 0.9048\n",
      "Epoch 38/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.9473 - accuracy: 0.9211 - val_loss: 0.9963 - val_accuracy: 0.9048\n",
      "Epoch 39/800\n",
      "114/114 [==============================] - 0s 942us/sample - loss: 0.8832 - accuracy: 0.9474 - val_loss: 0.9870 - val_accuracy: 0.9048\n",
      "Epoch 40/800\n",
      "114/114 [==============================] - 0s 945us/sample - loss: 0.8672 - accuracy: 0.9561 - val_loss: 0.9684 - val_accuracy: 0.9048\n",
      "Epoch 41/800\n",
      "114/114 [==============================] - 0s 965us/sample - loss: 0.8565 - accuracy: 0.9474 - val_loss: 0.9484 - val_accuracy: 0.9524\n",
      "Epoch 42/800\n",
      "114/114 [==============================] - 0s 902us/sample - loss: 0.8783 - accuracy: 0.9561 - val_loss: 0.9417 - val_accuracy: 0.9524\n",
      "Epoch 43/800\n",
      "114/114 [==============================] - 0s 907us/sample - loss: 0.8674 - accuracy: 0.9386 - val_loss: 0.9313 - val_accuracy: 0.9524\n",
      "Epoch 44/800\n",
      "114/114 [==============================] - 0s 966us/sample - loss: 0.8752 - accuracy: 0.9386 - val_loss: 0.9244 - val_accuracy: 0.9048\n",
      "Epoch 45/800\n",
      "114/114 [==============================] - 0s 909us/sample - loss: 0.8418 - accuracy: 0.9561 - val_loss: 0.9672 - val_accuracy: 0.8571\n",
      "Epoch 46/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.8106 - accuracy: 0.9561 - val_loss: 0.9774 - val_accuracy: 0.8571\n",
      "Epoch 47/800\n",
      "114/114 [==============================] - 0s 932us/sample - loss: 0.8326 - accuracy: 0.9386 - val_loss: 0.9485 - val_accuracy: 0.8571\n",
      "Epoch 48/800\n",
      "114/114 [==============================] - 0s 907us/sample - loss: 0.7972 - accuracy: 0.9737 - val_loss: 0.9296 - val_accuracy: 0.9048\n",
      "Epoch 49/800\n",
      "114/114 [==============================] - 0s 994us/sample - loss: 0.8195 - accuracy: 0.9561 - val_loss: 0.9317 - val_accuracy: 0.9048\n",
      "Epoch 50/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.7607 - accuracy: 0.9737 - val_loss: 0.9281 - val_accuracy: 0.9048\n",
      "Epoch 51/800\n",
      "114/114 [==============================] - 0s 964us/sample - loss: 0.7520 - accuracy: 0.9561 - val_loss: 0.9128 - val_accuracy: 0.9048\n",
      "Epoch 52/800\n",
      "114/114 [==============================] - 0s 895us/sample - loss: 0.7879 - accuracy: 0.9474 - val_loss: 0.9108 - val_accuracy: 0.9048\n",
      "Epoch 53/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.7449 - accuracy: 0.9825 - val_loss: 0.9642 - val_accuracy: 0.8571\n",
      "Epoch 54/800\n",
      "114/114 [==============================] - 0s 894us/sample - loss: 0.7888 - accuracy: 0.9561 - val_loss: 0.9545 - val_accuracy: 0.8571\n",
      "Epoch 55/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.7650 - accuracy: 0.9649 - val_loss: 0.8817 - val_accuracy: 0.9048\n",
      "Epoch 56/800\n",
      "114/114 [==============================] - 0s 978us/sample - loss: 0.7587 - accuracy: 0.9649 - val_loss: 0.8717 - val_accuracy: 0.9524\n",
      "Epoch 57/800\n",
      "114/114 [==============================] - 0s 882us/sample - loss: 0.7392 - accuracy: 0.9825 - val_loss: 0.8666 - val_accuracy: 0.9524\n",
      "Epoch 58/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.7872 - accuracy: 0.9561 - val_loss: 0.8656 - val_accuracy: 0.9048\n",
      "Epoch 59/800\n",
      "114/114 [==============================] - 0s 924us/sample - loss: 0.7654 - accuracy: 0.9825 - val_loss: 0.8852 - val_accuracy: 0.8571\n",
      "Epoch 60/800\n",
      "114/114 [==============================] - 0s 942us/sample - loss: 0.8084 - accuracy: 0.9561 - val_loss: 0.8687 - val_accuracy: 0.9048\n",
      "Epoch 61/800\n",
      "114/114 [==============================] - 0s 927us/sample - loss: 0.7183 - accuracy: 0.9825 - val_loss: 0.8465 - val_accuracy: 0.9524\n",
      "Epoch 62/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.8031 - accuracy: 0.9561 - val_loss: 0.8632 - val_accuracy: 0.9524\n",
      "Epoch 63/800\n",
      "114/114 [==============================] - 0s 927us/sample - loss: 0.7494 - accuracy: 0.9649 - val_loss: 0.8456 - val_accuracy: 0.9524\n",
      "Epoch 64/800\n",
      "114/114 [==============================] - 0s 901us/sample - loss: 0.7454 - accuracy: 0.9737 - val_loss: 0.8361 - val_accuracy: 0.9524\n",
      "Epoch 65/800\n",
      "114/114 [==============================] - 0s 971us/sample - loss: 0.7439 - accuracy: 0.9737 - val_loss: 0.8307 - val_accuracy: 0.9048\n",
      "Epoch 66/800\n",
      "114/114 [==============================] - 0s 900us/sample - loss: 0.7425 - accuracy: 0.9649 - val_loss: 0.8647 - val_accuracy: 0.9048\n",
      "Epoch 67/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.7321 - accuracy: 0.9561 - val_loss: 0.8690 - val_accuracy: 0.9048\n",
      "Epoch 68/800\n",
      "114/114 [==============================] - 0s 961us/sample - loss: 0.8014 - accuracy: 0.9386 - val_loss: 0.8538 - val_accuracy: 0.9048\n",
      "Epoch 69/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.7002 - accuracy: 0.9825 - val_loss: 0.8532 - val_accuracy: 0.9048\n",
      "Epoch 70/800\n",
      "114/114 [==============================] - 0s 950us/sample - loss: 0.7745 - accuracy: 0.9737 - val_loss: 0.8429 - val_accuracy: 0.9048\n",
      "Epoch 71/800\n",
      "114/114 [==============================] - 0s 913us/sample - loss: 0.7027 - accuracy: 0.9912 - val_loss: 0.8196 - val_accuracy: 0.9048\n",
      "Epoch 72/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6996 - accuracy: 0.9825 - val_loss: 0.8172 - val_accuracy: 0.9048\n",
      "Epoch 73/800\n",
      "114/114 [==============================] - 0s 905us/sample - loss: 0.7234 - accuracy: 0.9825 - val_loss: 0.8425 - val_accuracy: 0.9048\n",
      "Epoch 74/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.7741 - accuracy: 0.9561 - val_loss: 0.8625 - val_accuracy: 0.9048\n",
      "Epoch 75/800\n",
      "114/114 [==============================] - 0s 937us/sample - loss: 0.7050 - accuracy: 0.9825 - val_loss: 0.8644 - val_accuracy: 0.9048\n",
      "Epoch 76/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.7281 - accuracy: 0.9649 - val_loss: 0.8610 - val_accuracy: 0.9048\n",
      "Epoch 77/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.7469 - accuracy: 0.9649 - val_loss: 0.8426 - val_accuracy: 0.9048\n",
      "Epoch 78/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.7215 - accuracy: 0.9737 - val_loss: 0.8146 - val_accuracy: 0.9048\n",
      "Epoch 79/800\n",
      "114/114 [==============================] - 0s 967us/sample - loss: 0.7049 - accuracy: 0.9737 - val_loss: 0.8221 - val_accuracy: 0.9048\n",
      "Epoch 80/800\n",
      "114/114 [==============================] - 0s 902us/sample - loss: 0.7199 - accuracy: 0.9561 - val_loss: 0.8414 - val_accuracy: 0.9048\n",
      "Epoch 81/800\n",
      "114/114 [==============================] - 0s 964us/sample - loss: 0.7468 - accuracy: 0.9561 - val_loss: 0.8252 - val_accuracy: 0.9048\n",
      "Epoch 82/800\n",
      "114/114 [==============================] - 0s 911us/sample - loss: 0.7641 - accuracy: 0.9561 - val_loss: 0.8294 - val_accuracy: 0.9048\n",
      "Epoch 83/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.7091 - accuracy: 0.9825 - val_loss: 0.8080 - val_accuracy: 0.9048\n",
      "Epoch 84/800\n",
      "114/114 [==============================] - 0s 913us/sample - loss: 0.7089 - accuracy: 0.9825 - val_loss: 0.8053 - val_accuracy: 0.9524\n",
      "Epoch 85/800\n",
      "114/114 [==============================] - 0s 893us/sample - loss: 0.7127 - accuracy: 0.9737 - val_loss: 0.8245 - val_accuracy: 0.9048\n",
      "Epoch 86/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6774 - accuracy: 0.9825 - val_loss: 0.8492 - val_accuracy: 0.9048\n",
      "Epoch 87/800\n",
      "114/114 [==============================] - 0s 969us/sample - loss: 0.6873 - accuracy: 0.9912 - val_loss: 0.8558 - val_accuracy: 0.9048\n",
      "Epoch 88/800\n",
      "114/114 [==============================] - 0s 958us/sample - loss: 0.6890 - accuracy: 0.9825 - val_loss: 0.8499 - val_accuracy: 0.9048\n",
      "Epoch 89/800\n",
      "114/114 [==============================] - 0s 918us/sample - loss: 0.6708 - accuracy: 0.9825 - val_loss: 0.8264 - val_accuracy: 0.9048\n",
      "Epoch 90/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6813 - accuracy: 0.9825 - val_loss: 0.8214 - val_accuracy: 0.9048\n",
      "Epoch 91/800\n",
      "114/114 [==============================] - 0s 950us/sample - loss: 0.7048 - accuracy: 0.9825 - val_loss: 0.8315 - val_accuracy: 0.9048\n",
      "Epoch 92/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6937 - accuracy: 0.9649 - val_loss: 0.8510 - val_accuracy: 0.9048\n",
      "Epoch 93/800\n",
      "114/114 [==============================] - 0s 972us/sample - loss: 0.6604 - accuracy: 0.9912 - val_loss: 0.8470 - val_accuracy: 0.9048\n",
      "Epoch 94/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.7314 - accuracy: 0.9737 - val_loss: 0.8197 - val_accuracy: 0.9048\n",
      "Epoch 95/800\n",
      "114/114 [==============================] - 0s 983us/sample - loss: 0.6471 - accuracy: 1.0000 - val_loss: 0.7997 - val_accuracy: 0.9524\n",
      "Epoch 96/800\n",
      "114/114 [==============================] - 0s 879us/sample - loss: 0.6784 - accuracy: 0.9825 - val_loss: 0.8044 - val_accuracy: 0.9524\n",
      "Epoch 97/800\n",
      "114/114 [==============================] - 0s 969us/sample - loss: 0.6781 - accuracy: 0.9825 - val_loss: 0.8029 - val_accuracy: 0.9048\n",
      "Epoch 98/800\n",
      "114/114 [==============================] - 0s 910us/sample - loss: 0.6764 - accuracy: 0.9649 - val_loss: 0.8490 - val_accuracy: 0.9048\n",
      "Epoch 99/800\n",
      "114/114 [==============================] - 0s 958us/sample - loss: 0.6675 - accuracy: 0.9825 - val_loss: 0.8561 - val_accuracy: 0.9048\n",
      "Epoch 100/800\n",
      "114/114 [==============================] - 0s 925us/sample - loss: 0.7145 - accuracy: 0.9737 - val_loss: 0.8476 - val_accuracy: 0.9048\n",
      "Epoch 101/800\n",
      "114/114 [==============================] - 0s 975us/sample - loss: 0.6656 - accuracy: 0.9912 - val_loss: 0.8507 - val_accuracy: 0.9048\n",
      "Epoch 102/800\n",
      "114/114 [==============================] - 0s 898us/sample - loss: 0.6870 - accuracy: 0.9737 - val_loss: 0.8514 - val_accuracy: 0.9048\n",
      "Epoch 103/800\n",
      "114/114 [==============================] - 0s 888us/sample - loss: 0.6559 - accuracy: 1.0000 - val_loss: 0.8515 - val_accuracy: 0.9048\n",
      "Epoch 104/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.6946 - accuracy: 0.9649 - val_loss: 0.8482 - val_accuracy: 0.9048\n",
      "Epoch 105/800\n",
      "114/114 [==============================] - 0s 878us/sample - loss: 0.7282 - accuracy: 0.9561 - val_loss: 0.8597 - val_accuracy: 0.9048\n",
      "Epoch 106/800\n",
      "114/114 [==============================] - 0s 968us/sample - loss: 0.7294 - accuracy: 0.9561 - val_loss: 0.8498 - val_accuracy: 0.9048\n",
      "Epoch 107/800\n",
      "114/114 [==============================] - 0s 934us/sample - loss: 0.6730 - accuracy: 0.9737 - val_loss: 0.8414 - val_accuracy: 0.9048\n",
      "Epoch 108/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.7054 - accuracy: 0.9737 - val_loss: 0.8344 - val_accuracy: 0.9048\n",
      "Epoch 109/800\n",
      "114/114 [==============================] - 0s 893us/sample - loss: 0.6530 - accuracy: 0.9825 - val_loss: 0.8250 - val_accuracy: 0.9048\n",
      "Epoch 110/800\n",
      "114/114 [==============================] - 0s 913us/sample - loss: 0.6866 - accuracy: 0.9825 - val_loss: 0.8242 - val_accuracy: 0.9048\n",
      "Epoch 111/800\n",
      "114/114 [==============================] - 0s 949us/sample - loss: 0.6429 - accuracy: 0.9912 - val_loss: 0.8252 - val_accuracy: 0.9048\n",
      "Epoch 112/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.7205 - accuracy: 0.9474 - val_loss: 0.8302 - val_accuracy: 0.9048\n",
      "Epoch 113/800\n",
      "114/114 [==============================] - 0s 943us/sample - loss: 0.6558 - accuracy: 0.9737 - val_loss: 0.8432 - val_accuracy: 0.9048\n",
      "Epoch 114/800\n",
      "114/114 [==============================] - 0s 932us/sample - loss: 0.6787 - accuracy: 0.9737 - val_loss: 0.8526 - val_accuracy: 0.9048\n",
      "Epoch 115/800\n",
      "114/114 [==============================] - 0s 980us/sample - loss: 0.7011 - accuracy: 0.9561 - val_loss: 0.8536 - val_accuracy: 0.9048\n",
      "Epoch 116/800\n",
      "114/114 [==============================] - 0s 899us/sample - loss: 0.6434 - accuracy: 0.9912 - val_loss: 0.8305 - val_accuracy: 0.9048\n",
      "Epoch 117/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6931 - accuracy: 0.9737 - val_loss: 0.8291 - val_accuracy: 0.9048\n",
      "Epoch 118/800\n",
      "114/114 [==============================] - 0s 929us/sample - loss: 0.6673 - accuracy: 0.9737 - val_loss: 0.8451 - val_accuracy: 0.9048\n",
      "Epoch 119/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6418 - accuracy: 0.9825 - val_loss: 0.8548 - val_accuracy: 0.9048\n",
      "Epoch 120/800\n",
      "114/114 [==============================] - 0s 959us/sample - loss: 0.7092 - accuracy: 0.9737 - val_loss: 0.8525 - val_accuracy: 0.9048\n",
      "Epoch 121/800\n",
      "114/114 [==============================] - 0s 906us/sample - loss: 0.6888 - accuracy: 0.9561 - val_loss: 0.8480 - val_accuracy: 0.9048\n",
      "Epoch 122/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6499 - accuracy: 0.9912 - val_loss: 0.8367 - val_accuracy: 0.9048\n",
      "Epoch 123/800\n",
      "114/114 [==============================] - 0s 906us/sample - loss: 0.6623 - accuracy: 0.9825 - val_loss: 0.8118 - val_accuracy: 0.9048\n",
      "Epoch 124/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6495 - accuracy: 0.9737 - val_loss: 0.7946 - val_accuracy: 0.9048\n",
      "Epoch 125/800\n",
      "114/114 [==============================] - 0s 923us/sample - loss: 0.6683 - accuracy: 0.9912 - val_loss: 0.7893 - val_accuracy: 0.9048\n",
      "Epoch 126/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6505 - accuracy: 0.9825 - val_loss: 0.7941 - val_accuracy: 0.9048\n",
      "Epoch 127/800\n",
      "114/114 [==============================] - 0s 956us/sample - loss: 0.6510 - accuracy: 0.9737 - val_loss: 0.8194 - val_accuracy: 0.9048\n",
      "Epoch 128/800\n",
      "114/114 [==============================] - 0s 902us/sample - loss: 0.6464 - accuracy: 0.9825 - val_loss: 0.8336 - val_accuracy: 0.9048\n",
      "Epoch 129/800\n",
      "114/114 [==============================] - 0s 985us/sample - loss: 0.6566 - accuracy: 0.9649 - val_loss: 0.8391 - val_accuracy: 0.9048\n",
      "Epoch 130/800\n",
      "114/114 [==============================] - 0s 926us/sample - loss: 0.6668 - accuracy: 0.9737 - val_loss: 0.8362 - val_accuracy: 0.9048\n",
      "Epoch 131/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6887 - accuracy: 0.9737 - val_loss: 0.8394 - val_accuracy: 0.9048\n",
      "Epoch 132/800\n",
      "114/114 [==============================] - 0s 929us/sample - loss: 0.6296 - accuracy: 0.9912 - val_loss: 0.8371 - val_accuracy: 0.9048\n",
      "Epoch 133/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6490 - accuracy: 0.9912 - val_loss: 0.8348 - val_accuracy: 0.9048\n",
      "Epoch 134/800\n",
      "114/114 [==============================] - 0s 922us/sample - loss: 0.6264 - accuracy: 0.9825 - val_loss: 0.8151 - val_accuracy: 0.9048\n",
      "Epoch 135/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6577 - accuracy: 0.9912 - val_loss: 0.8035 - val_accuracy: 0.9048\n",
      "Epoch 136/800\n",
      "114/114 [==============================] - 0s 951us/sample - loss: 0.6482 - accuracy: 0.9737 - val_loss: 0.8186 - val_accuracy: 0.9048\n",
      "Epoch 137/800\n",
      "114/114 [==============================] - 0s 935us/sample - loss: 0.6324 - accuracy: 0.9912 - val_loss: 0.8446 - val_accuracy: 0.9048\n",
      "Epoch 138/800\n",
      "114/114 [==============================] - 0s 966us/sample - loss: 0.6986 - accuracy: 0.9649 - val_loss: 0.8583 - val_accuracy: 0.9048\n",
      "Epoch 139/800\n",
      "114/114 [==============================] - 0s 925us/sample - loss: 0.6810 - accuracy: 0.9649 - val_loss: 0.8548 - val_accuracy: 0.9048\n",
      "Epoch 140/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6653 - accuracy: 0.9649 - val_loss: 0.8475 - val_accuracy: 0.9048\n",
      "Epoch 141/800\n",
      "114/114 [==============================] - 0s 894us/sample - loss: 0.6372 - accuracy: 0.9825 - val_loss: 0.8198 - val_accuracy: 0.9048\n",
      "Epoch 142/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6232 - accuracy: 0.9825 - val_loss: 0.8059 - val_accuracy: 0.9048\n",
      "Epoch 143/800\n",
      "114/114 [==============================] - 0s 890us/sample - loss: 0.6511 - accuracy: 0.9825 - val_loss: 0.8193 - val_accuracy: 0.9048\n",
      "Epoch 144/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6316 - accuracy: 0.9737 - val_loss: 0.8299 - val_accuracy: 0.9048\n",
      "Epoch 145/800\n",
      "114/114 [==============================] - 0s 983us/sample - loss: 0.6366 - accuracy: 0.9737 - val_loss: 0.8306 - val_accuracy: 0.9048\n",
      "Epoch 146/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6695 - accuracy: 0.9649 - val_loss: 0.8278 - val_accuracy: 0.9048\n",
      "Epoch 147/800\n",
      "114/114 [==============================] - 0s 937us/sample - loss: 0.6216 - accuracy: 0.9912 - val_loss: 0.8264 - val_accuracy: 0.9048\n",
      "Epoch 148/800\n",
      "114/114 [==============================] - 0s 914us/sample - loss: 0.6676 - accuracy: 0.9737 - val_loss: 0.8152 - val_accuracy: 0.9048\n",
      "Epoch 149/800\n",
      "114/114 [==============================] - 0s 980us/sample - loss: 0.6321 - accuracy: 0.9912 - val_loss: 0.7979 - val_accuracy: 0.9048\n",
      "Epoch 150/800\n",
      "114/114 [==============================] - 0s 910us/sample - loss: 0.6438 - accuracy: 0.9825 - val_loss: 0.8054 - val_accuracy: 0.9048\n",
      "Epoch 151/800\n",
      "114/114 [==============================] - 0s 962us/sample - loss: 0.6848 - accuracy: 0.9737 - val_loss: 0.8171 - val_accuracy: 0.9048\n",
      "Epoch 152/800\n",
      "114/114 [==============================] - 0s 922us/sample - loss: 0.6088 - accuracy: 0.9912 - val_loss: 0.8392 - val_accuracy: 0.9048\n",
      "Epoch 153/800\n",
      "114/114 [==============================] - 0s 870us/sample - loss: 0.6490 - accuracy: 0.9561 - val_loss: 0.8407 - val_accuracy: 0.9048\n",
      "Epoch 154/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6701 - accuracy: 0.9649 - val_loss: 0.8393 - val_accuracy: 0.9048\n",
      "Epoch 155/800\n",
      "114/114 [==============================] - 0s 930us/sample - loss: 0.6271 - accuracy: 0.9737 - val_loss: 0.8441 - val_accuracy: 0.9048\n",
      "Epoch 156/800\n",
      "114/114 [==============================] - 0s 950us/sample - loss: 0.6648 - accuracy: 0.9561 - val_loss: 0.8415 - val_accuracy: 0.9048\n",
      "Epoch 157/800\n",
      "114/114 [==============================] - 0s 910us/sample - loss: 0.6200 - accuracy: 0.9737 - val_loss: 0.8287 - val_accuracy: 0.9048\n",
      "Epoch 158/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6442 - accuracy: 0.9737 - val_loss: 0.8218 - val_accuracy: 0.9048\n",
      "Epoch 159/800\n",
      "114/114 [==============================] - 0s 958us/sample - loss: 0.6391 - accuracy: 0.9825 - val_loss: 0.8170 - val_accuracy: 0.9048\n",
      "Epoch 160/800\n",
      "114/114 [==============================] - 0s 914us/sample - loss: 0.6126 - accuracy: 0.9825 - val_loss: 0.8420 - val_accuracy: 0.9048\n",
      "Epoch 161/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6096 - accuracy: 0.9912 - val_loss: 0.8693 - val_accuracy: 0.9048\n",
      "Epoch 162/800\n",
      "114/114 [==============================] - 0s 911us/sample - loss: 0.6180 - accuracy: 0.9825 - val_loss: 0.8708 - val_accuracy: 0.9048\n",
      "Epoch 163/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6556 - accuracy: 0.9561 - val_loss: 0.8588 - val_accuracy: 0.9048\n",
      "Epoch 164/800\n",
      "114/114 [==============================] - 0s 955us/sample - loss: 0.6245 - accuracy: 0.9825 - val_loss: 0.8128 - val_accuracy: 0.9048\n",
      "Epoch 165/800\n",
      "114/114 [==============================] - 0s 912us/sample - loss: 0.5994 - accuracy: 0.9912 - val_loss: 0.7899 - val_accuracy: 0.9048\n",
      "Epoch 166/800\n",
      "114/114 [==============================] - 0s 971us/sample - loss: 0.6091 - accuracy: 0.9825 - val_loss: 0.7891 - val_accuracy: 0.9048\n",
      "Epoch 167/800\n",
      "114/114 [==============================] - 0s 908us/sample - loss: 0.6462 - accuracy: 0.9737 - val_loss: 0.8210 - val_accuracy: 0.9048\n",
      "Epoch 168/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6192 - accuracy: 0.9825 - val_loss: 0.8485 - val_accuracy: 0.9048\n",
      "Epoch 169/800\n",
      "114/114 [==============================] - 0s 950us/sample - loss: 0.6779 - accuracy: 0.9649 - val_loss: 0.8210 - val_accuracy: 0.9048\n",
      "Epoch 170/800\n",
      "114/114 [==============================] - 0s 924us/sample - loss: 0.6329 - accuracy: 0.9825 - val_loss: 0.7825 - val_accuracy: 0.9048\n",
      "Epoch 171/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6429 - accuracy: 0.9737 - val_loss: 0.7769 - val_accuracy: 0.9524\n",
      "Epoch 172/800\n",
      "114/114 [==============================] - 0s 953us/sample - loss: 0.6437 - accuracy: 0.9912 - val_loss: 0.7838 - val_accuracy: 0.9048\n",
      "Epoch 173/800\n",
      "114/114 [==============================] - 0s 918us/sample - loss: 0.6030 - accuracy: 0.9912 - val_loss: 0.8218 - val_accuracy: 0.9048\n",
      "Epoch 174/800\n",
      "114/114 [==============================] - 0s 974us/sample - loss: 0.6234 - accuracy: 0.9825 - val_loss: 0.8503 - val_accuracy: 0.9048\n",
      "Epoch 175/800\n",
      "114/114 [==============================] - 0s 928us/sample - loss: 0.6209 - accuracy: 0.9737 - val_loss: 0.8614 - val_accuracy: 0.9048\n",
      "Epoch 176/800\n",
      "114/114 [==============================] - 0s 969us/sample - loss: 0.6095 - accuracy: 0.9737 - val_loss: 0.8425 - val_accuracy: 0.9048\n",
      "Epoch 177/800\n",
      "114/114 [==============================] - 0s 932us/sample - loss: 0.6024 - accuracy: 0.9912 - val_loss: 0.7870 - val_accuracy: 0.9048\n",
      "Epoch 178/800\n",
      "114/114 [==============================] - 0s 987us/sample - loss: 0.6275 - accuracy: 0.9912 - val_loss: 0.7762 - val_accuracy: 0.9524\n",
      "Epoch 179/800\n",
      "114/114 [==============================] - 0s 905us/sample - loss: 0.6222 - accuracy: 0.9825 - val_loss: 0.7766 - val_accuracy: 0.9524\n",
      "Epoch 180/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6383 - accuracy: 0.9737 - val_loss: 0.7809 - val_accuracy: 0.9524\n",
      "Epoch 181/800\n",
      "114/114 [==============================] - 0s 971us/sample - loss: 0.6080 - accuracy: 0.9912 - val_loss: 0.8493 - val_accuracy: 0.9048\n",
      "Epoch 182/800\n",
      "114/114 [==============================] - 0s 851us/sample - loss: 0.6134 - accuracy: 0.9912 - val_loss: 0.8891 - val_accuracy: 0.9048\n",
      "Epoch 183/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.6067 - accuracy: 0.9737 - val_loss: 0.8832 - val_accuracy: 0.9048\n",
      "Epoch 184/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6577 - accuracy: 0.9649 - val_loss: 0.8128 - val_accuracy: 0.9048\n",
      "Epoch 185/800\n",
      "114/114 [==============================] - 0s 929us/sample - loss: 0.6086 - accuracy: 0.9825 - val_loss: 0.7816 - val_accuracy: 0.9048\n",
      "Epoch 186/800\n",
      "114/114 [==============================] - 0s 960us/sample - loss: 0.5800 - accuracy: 1.0000 - val_loss: 0.7784 - val_accuracy: 0.9048\n",
      "Epoch 187/800\n",
      "114/114 [==============================] - 0s 910us/sample - loss: 0.6417 - accuracy: 0.9737 - val_loss: 0.8342 - val_accuracy: 0.9048\n",
      "Epoch 188/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5914 - accuracy: 0.9825 - val_loss: 0.8794 - val_accuracy: 0.9048\n",
      "Epoch 189/800\n",
      "114/114 [==============================] - 0s 930us/sample - loss: 0.5935 - accuracy: 0.9912 - val_loss: 0.8732 - val_accuracy: 0.9048\n",
      "Epoch 190/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6239 - accuracy: 0.9825 - val_loss: 0.8811 - val_accuracy: 0.9048\n",
      "Epoch 191/800\n",
      "114/114 [==============================] - 0s 923us/sample - loss: 0.6134 - accuracy: 0.9825 - val_loss: 0.8444 - val_accuracy: 0.9048\n",
      "Epoch 192/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6042 - accuracy: 0.9825 - val_loss: 0.7681 - val_accuracy: 0.9524\n",
      "Epoch 193/800\n",
      "114/114 [==============================] - 0s 903us/sample - loss: 0.6149 - accuracy: 0.9825 - val_loss: 0.8098 - val_accuracy: 0.9524\n",
      "Epoch 194/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6508 - accuracy: 0.9737 - val_loss: 0.7929 - val_accuracy: 0.9524\n",
      "Epoch 195/800\n",
      "114/114 [==============================] - 0s 985us/sample - loss: 0.6362 - accuracy: 0.9825 - val_loss: 0.7690 - val_accuracy: 0.9524\n",
      "Epoch 196/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6358 - accuracy: 0.9649 - val_loss: 0.7679 - val_accuracy: 0.9048\n",
      "Epoch 197/800\n",
      "114/114 [==============================] - 0s 981us/sample - loss: 0.5965 - accuracy: 0.9912 - val_loss: 0.8295 - val_accuracy: 0.9048\n",
      "Epoch 198/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5946 - accuracy: 0.9912 - val_loss: 0.8507 - val_accuracy: 0.9048\n",
      "Epoch 199/800\n",
      "114/114 [==============================] - 0s 915us/sample - loss: 0.6177 - accuracy: 0.9737 - val_loss: 0.8155 - val_accuracy: 0.9048\n",
      "Epoch 200/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5929 - accuracy: 0.9825 - val_loss: 0.7853 - val_accuracy: 0.9048\n",
      "Epoch 201/800\n",
      "114/114 [==============================] - 0s 988us/sample - loss: 0.5806 - accuracy: 0.9912 - val_loss: 0.8044 - val_accuracy: 0.9048\n",
      "Epoch 202/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6017 - accuracy: 0.9825 - val_loss: 0.8282 - val_accuracy: 0.9048\n",
      "Epoch 203/800\n",
      "114/114 [==============================] - 0s 940us/sample - loss: 0.6556 - accuracy: 0.9649 - val_loss: 0.8210 - val_accuracy: 0.9048\n",
      "Epoch 204/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6064 - accuracy: 0.9912 - val_loss: 0.7651 - val_accuracy: 0.9048\n",
      "Epoch 205/800\n",
      "114/114 [==============================] - 0s 940us/sample - loss: 0.6070 - accuracy: 0.9737 - val_loss: 0.7572 - val_accuracy: 0.9524\n",
      "Epoch 206/800\n",
      "114/114 [==============================] - 0s 912us/sample - loss: 0.6251 - accuracy: 0.9825 - val_loss: 0.7556 - val_accuracy: 0.9524\n",
      "Epoch 207/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.6004 - accuracy: 0.9912 - val_loss: 0.7558 - val_accuracy: 0.9524\n",
      "Epoch 208/800\n",
      "114/114 [==============================] - 0s 899us/sample - loss: 0.5986 - accuracy: 0.9825 - val_loss: 0.8028 - val_accuracy: 0.9048\n",
      "Epoch 209/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.5689 - accuracy: 1.0000 - val_loss: 0.8481 - val_accuracy: 0.9048\n",
      "Epoch 210/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6103 - accuracy: 0.9737 - val_loss: 0.8521 - val_accuracy: 0.9048\n",
      "Epoch 211/800\n",
      "114/114 [==============================] - 0s 957us/sample - loss: 0.5874 - accuracy: 0.9825 - val_loss: 0.8579 - val_accuracy: 0.9048\n",
      "Epoch 212/800\n",
      "114/114 [==============================] - 0s 905us/sample - loss: 0.6231 - accuracy: 0.9737 - val_loss: 0.8599 - val_accuracy: 0.9048\n",
      "Epoch 213/800\n",
      "114/114 [==============================] - 0s 959us/sample - loss: 0.6232 - accuracy: 0.9737 - val_loss: 0.8164 - val_accuracy: 0.9048\n",
      "Epoch 214/800\n",
      "114/114 [==============================] - 0s 941us/sample - loss: 0.6037 - accuracy: 0.9737 - val_loss: 0.7787 - val_accuracy: 0.9048\n",
      "Epoch 215/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.6052 - accuracy: 0.9649 - val_loss: 0.7438 - val_accuracy: 0.9048\n",
      "Epoch 216/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5995 - accuracy: 0.9825 - val_loss: 0.7407 - val_accuracy: 0.9524\n",
      "Epoch 217/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.6007 - accuracy: 0.9737 - val_loss: 0.7461 - val_accuracy: 0.9048\n",
      "Epoch 218/800\n",
      "114/114 [==============================] - 0s 893us/sample - loss: 0.5807 - accuracy: 0.9912 - val_loss: 0.7672 - val_accuracy: 0.9048\n",
      "Epoch 219/800\n",
      "114/114 [==============================] - 0s 975us/sample - loss: 0.5745 - accuracy: 0.9912 - val_loss: 0.7951 - val_accuracy: 0.9048\n",
      "Epoch 220/800\n",
      "114/114 [==============================] - 0s 932us/sample - loss: 0.6130 - accuracy: 0.9737 - val_loss: 0.8207 - val_accuracy: 0.9048\n",
      "Epoch 221/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5727 - accuracy: 1.0000 - val_loss: 0.8301 - val_accuracy: 0.9048\n",
      "Epoch 222/800\n",
      "114/114 [==============================] - 0s 913us/sample - loss: 0.5718 - accuracy: 1.0000 - val_loss: 0.8160 - val_accuracy: 0.9048\n",
      "Epoch 223/800\n",
      "114/114 [==============================] - 0s 975us/sample - loss: 0.5755 - accuracy: 0.9825 - val_loss: 0.8096 - val_accuracy: 0.9048\n",
      "Epoch 224/800\n",
      "114/114 [==============================] - 0s 946us/sample - loss: 0.5896 - accuracy: 0.9737 - val_loss: 0.8105 - val_accuracy: 0.9048\n",
      "Epoch 225/800\n",
      "114/114 [==============================] - 0s 960us/sample - loss: 0.5785 - accuracy: 0.9825 - val_loss: 0.8060 - val_accuracy: 0.9048\n",
      "Epoch 226/800\n",
      "114/114 [==============================] - 0s 934us/sample - loss: 0.6199 - accuracy: 0.9737 - val_loss: 0.8164 - val_accuracy: 0.9048\n",
      "Epoch 227/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5754 - accuracy: 1.0000 - val_loss: 0.8318 - val_accuracy: 0.9048\n",
      "Epoch 228/800\n",
      "114/114 [==============================] - 0s 956us/sample - loss: 0.5587 - accuracy: 1.0000 - val_loss: 0.8192 - val_accuracy: 0.9048\n",
      "Epoch 229/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5792 - accuracy: 0.9912 - val_loss: 0.7947 - val_accuracy: 0.9048\n",
      "Epoch 230/800\n",
      "114/114 [==============================] - 0s 949us/sample - loss: 0.6135 - accuracy: 0.9737 - val_loss: 0.7839 - val_accuracy: 0.9048\n",
      "Epoch 231/800\n",
      "114/114 [==============================] - 0s 918us/sample - loss: 0.5813 - accuracy: 0.9912 - val_loss: 0.7975 - val_accuracy: 0.9048\n",
      "Epoch 232/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5818 - accuracy: 0.9825 - val_loss: 0.8297 - val_accuracy: 0.9048\n",
      "Epoch 233/800\n",
      "114/114 [==============================] - 0s 901us/sample - loss: 0.5854 - accuracy: 0.9825 - val_loss: 0.8624 - val_accuracy: 0.9048\n",
      "Epoch 234/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5833 - accuracy: 0.9825 - val_loss: 0.8483 - val_accuracy: 0.9048\n",
      "Epoch 235/800\n",
      "114/114 [==============================] - 0s 939us/sample - loss: 0.5794 - accuracy: 0.9912 - val_loss: 0.8474 - val_accuracy: 0.9048\n",
      "Epoch 236/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5581 - accuracy: 1.0000 - val_loss: 0.8463 - val_accuracy: 0.9048\n",
      "Epoch 237/800\n",
      "114/114 [==============================] - 0s 967us/sample - loss: 0.6065 - accuracy: 0.9825 - val_loss: 0.8456 - val_accuracy: 0.9048\n",
      "Epoch 238/800\n",
      "114/114 [==============================] - 0s 887us/sample - loss: 0.6218 - accuracy: 0.9649 - val_loss: 0.7952 - val_accuracy: 0.9048\n",
      "Epoch 239/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5744 - accuracy: 0.9912 - val_loss: 0.7647 - val_accuracy: 0.9524\n",
      "Epoch 240/800\n",
      "114/114 [==============================] - 0s 914us/sample - loss: 0.6037 - accuracy: 0.9649 - val_loss: 0.7620 - val_accuracy: 0.9524\n",
      "Epoch 241/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6018 - accuracy: 0.9825 - val_loss: 0.7817 - val_accuracy: 0.9048\n",
      "Epoch 242/800\n",
      "114/114 [==============================] - 0s 944us/sample - loss: 0.5760 - accuracy: 0.9825 - val_loss: 0.7886 - val_accuracy: 0.9048\n",
      "Epoch 243/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5733 - accuracy: 0.9912 - val_loss: 0.8044 - val_accuracy: 0.9048\n",
      "Epoch 244/800\n",
      "114/114 [==============================] - 0s 940us/sample - loss: 0.5838 - accuracy: 0.9825 - val_loss: 0.8161 - val_accuracy: 0.9048\n",
      "Epoch 245/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5877 - accuracy: 0.9825 - val_loss: 0.8426 - val_accuracy: 0.9048\n",
      "Epoch 246/800\n",
      "114/114 [==============================] - 0s 984us/sample - loss: 0.5752 - accuracy: 0.9912 - val_loss: 0.8253 - val_accuracy: 0.9048\n",
      "Epoch 247/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5670 - accuracy: 0.9912 - val_loss: 0.8345 - val_accuracy: 0.9048\n",
      "Epoch 248/800\n",
      "114/114 [==============================] - 0s 960us/sample - loss: 0.5996 - accuracy: 0.9825 - val_loss: 0.7928 - val_accuracy: 0.9048\n",
      "Epoch 249/800\n",
      "114/114 [==============================] - 0s 905us/sample - loss: 0.5694 - accuracy: 0.9912 - val_loss: 0.7856 - val_accuracy: 0.9048\n",
      "Epoch 250/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5861 - accuracy: 0.9825 - val_loss: 0.8073 - val_accuracy: 0.9048\n",
      "Epoch 251/800\n",
      "114/114 [==============================] - 0s 960us/sample - loss: 0.5745 - accuracy: 0.9825 - val_loss: 0.8190 - val_accuracy: 0.9048\n",
      "Epoch 252/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5556 - accuracy: 1.0000 - val_loss: 0.8109 - val_accuracy: 0.9048\n",
      "Epoch 253/800\n",
      "114/114 [==============================] - 0s 920us/sample - loss: 0.5947 - accuracy: 0.9561 - val_loss: 0.8211 - val_accuracy: 0.9048\n",
      "Epoch 254/800\n",
      "114/114 [==============================] - 0s 909us/sample - loss: 0.5816 - accuracy: 0.9825 - val_loss: 0.8517 - val_accuracy: 0.9048\n",
      "Epoch 255/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5838 - accuracy: 0.9649 - val_loss: 0.8604 - val_accuracy: 0.9048\n",
      "Epoch 256/800\n",
      "114/114 [==============================] - 0s 975us/sample - loss: 0.6502 - accuracy: 0.9561 - val_loss: 0.8536 - val_accuracy: 0.9048\n",
      "Epoch 257/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5588 - accuracy: 1.0000 - val_loss: 0.8201 - val_accuracy: 0.9048\n",
      "Epoch 258/800\n",
      "114/114 [==============================] - 0s 954us/sample - loss: 0.5801 - accuracy: 0.9825 - val_loss: 0.7546 - val_accuracy: 0.9048\n",
      "Epoch 259/800\n",
      "114/114 [==============================] - 0s 912us/sample - loss: 0.5951 - accuracy: 0.9912 - val_loss: 0.7387 - val_accuracy: 0.9524\n",
      "Epoch 260/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5562 - accuracy: 0.9912 - val_loss: 0.7394 - val_accuracy: 0.9524\n",
      "Epoch 261/800\n",
      "114/114 [==============================] - 0s 897us/sample - loss: 0.5989 - accuracy: 0.9737 - val_loss: 0.7459 - val_accuracy: 0.9048\n",
      "Epoch 262/800\n",
      "114/114 [==============================] - 0s 893us/sample - loss: 0.5520 - accuracy: 1.0000 - val_loss: 0.7739 - val_accuracy: 0.9048\n",
      "Epoch 263/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5979 - accuracy: 0.9737 - val_loss: 0.7848 - val_accuracy: 0.9048\n",
      "Epoch 264/800\n",
      "114/114 [==============================] - 0s 898us/sample - loss: 0.6638 - accuracy: 0.9474 - val_loss: 0.7806 - val_accuracy: 0.9048\n",
      "Epoch 265/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6350 - accuracy: 0.9561 - val_loss: 0.7596 - val_accuracy: 0.9048\n",
      "Epoch 266/800\n",
      "114/114 [==============================] - 0s 965us/sample - loss: 0.5798 - accuracy: 0.9825 - val_loss: 0.7454 - val_accuracy: 0.9048\n",
      "Epoch 267/800\n",
      "114/114 [==============================] - 0s 927us/sample - loss: 0.5568 - accuracy: 0.9912 - val_loss: 0.7474 - val_accuracy: 0.9048\n",
      "Epoch 268/800\n",
      "114/114 [==============================] - 0s 971us/sample - loss: 0.5529 - accuracy: 0.9912 - val_loss: 0.7542 - val_accuracy: 0.9048\n",
      "Epoch 269/800\n",
      "114/114 [==============================] - 0s 912us/sample - loss: 0.5514 - accuracy: 0.9912 - val_loss: 0.7726 - val_accuracy: 0.9048\n",
      "Epoch 270/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5683 - accuracy: 0.9825 - val_loss: 0.7839 - val_accuracy: 0.9048\n",
      "Epoch 271/800\n",
      "114/114 [==============================] - 0s 900us/sample - loss: 0.5652 - accuracy: 0.9825 - val_loss: 0.7992 - val_accuracy: 0.9048\n",
      "Epoch 272/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5856 - accuracy: 0.9737 - val_loss: 0.7883 - val_accuracy: 0.9048\n",
      "Epoch 273/800\n",
      "114/114 [==============================] - 0s 947us/sample - loss: 0.5619 - accuracy: 0.9825 - val_loss: 0.7572 - val_accuracy: 0.9048\n",
      "Epoch 274/800\n",
      "114/114 [==============================] - 0s 877us/sample - loss: 0.5596 - accuracy: 0.9912 - val_loss: 0.7406 - val_accuracy: 0.9048\n",
      "Epoch 275/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5596 - accuracy: 0.9825 - val_loss: 0.7276 - val_accuracy: 0.9048\n",
      "Epoch 276/800\n",
      "114/114 [==============================] - 0s 911us/sample - loss: 0.5650 - accuracy: 0.9825 - val_loss: 0.7248 - val_accuracy: 0.9048\n",
      "Epoch 277/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.6171 - accuracy: 0.9737 - val_loss: 0.7330 - val_accuracy: 0.9048\n",
      "Epoch 278/800\n",
      "114/114 [==============================] - 0s 931us/sample - loss: 0.5685 - accuracy: 0.9825 - val_loss: 0.7533 - val_accuracy: 0.9048\n",
      "Epoch 279/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5799 - accuracy: 0.9825 - val_loss: 0.7795 - val_accuracy: 0.9048\n",
      "Epoch 280/800\n",
      "114/114 [==============================] - 0s 990us/sample - loss: 0.5550 - accuracy: 0.9912 - val_loss: 0.7916 - val_accuracy: 0.9048\n",
      "Epoch 281/800\n",
      "114/114 [==============================] - 0s 890us/sample - loss: 0.5567 - accuracy: 0.9912 - val_loss: 0.8022 - val_accuracy: 0.9048\n",
      "Epoch 282/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5589 - accuracy: 0.9825 - val_loss: 0.7773 - val_accuracy: 0.9048\n",
      "Epoch 283/800\n",
      "114/114 [==============================] - 0s 932us/sample - loss: 0.5435 - accuracy: 1.0000 - val_loss: 0.7477 - val_accuracy: 0.9048\n",
      "Epoch 284/800\n",
      "114/114 [==============================] - 0s 969us/sample - loss: 0.5643 - accuracy: 0.9825 - val_loss: 0.7401 - val_accuracy: 0.9048\n",
      "Epoch 285/800\n",
      "114/114 [==============================] - 0s 916us/sample - loss: 0.5534 - accuracy: 0.9825 - val_loss: 0.7362 - val_accuracy: 0.9048\n",
      "Epoch 286/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5591 - accuracy: 0.9825 - val_loss: 0.7610 - val_accuracy: 0.9048\n",
      "Epoch 287/800\n",
      "114/114 [==============================] - 0s 967us/sample - loss: 0.5376 - accuracy: 1.0000 - val_loss: 0.7859 - val_accuracy: 0.9048\n",
      "Epoch 288/800\n",
      "114/114 [==============================] - 0s 871us/sample - loss: 0.5379 - accuracy: 0.9912 - val_loss: 0.8116 - val_accuracy: 0.9048\n",
      "Epoch 289/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5539 - accuracy: 0.9912 - val_loss: 0.8197 - val_accuracy: 0.9048\n",
      "Epoch 290/800\n",
      "114/114 [==============================] - 0s 911us/sample - loss: 0.5369 - accuracy: 0.9912 - val_loss: 0.8282 - val_accuracy: 0.9048\n",
      "Epoch 291/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5545 - accuracy: 0.9912 - val_loss: 0.8362 - val_accuracy: 0.9048\n",
      "Epoch 292/800\n",
      "114/114 [==============================] - 0s 937us/sample - loss: 0.5756 - accuracy: 0.9825 - val_loss: 0.8214 - val_accuracy: 0.9048\n",
      "Epoch 293/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5873 - accuracy: 0.9737 - val_loss: 0.7938 - val_accuracy: 0.9048\n",
      "Epoch 294/800\n",
      "114/114 [==============================] - 0s 970us/sample - loss: 0.5393 - accuracy: 0.9912 - val_loss: 0.7865 - val_accuracy: 0.9048\n",
      "Epoch 295/800\n",
      "114/114 [==============================] - 0s 890us/sample - loss: 0.6022 - accuracy: 0.9825 - val_loss: 0.7795 - val_accuracy: 0.9048\n",
      "Epoch 296/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5460 - accuracy: 0.9825 - val_loss: 0.7631 - val_accuracy: 0.9048\n",
      "Epoch 297/800\n",
      "114/114 [==============================] - 0s 882us/sample - loss: 0.5637 - accuracy: 0.9825 - val_loss: 0.7526 - val_accuracy: 0.9048\n",
      "Epoch 298/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5430 - accuracy: 0.9912 - val_loss: 0.7589 - val_accuracy: 0.9048\n",
      "Epoch 299/800\n",
      "114/114 [==============================] - 0s 943us/sample - loss: 0.5305 - accuracy: 0.9912 - val_loss: 0.7902 - val_accuracy: 0.9048\n",
      "Epoch 300/800\n",
      "114/114 [==============================] - 0s 895us/sample - loss: 0.5246 - accuracy: 1.0000 - val_loss: 0.8095 - val_accuracy: 0.9048\n",
      "Epoch 301/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5440 - accuracy: 0.9825 - val_loss: 0.8129 - val_accuracy: 0.9048\n",
      "Epoch 302/800\n",
      "114/114 [==============================] - 0s 958us/sample - loss: 0.5643 - accuracy: 0.9825 - val_loss: 0.8442 - val_accuracy: 0.9048\n",
      "Epoch 303/800\n",
      "114/114 [==============================] - 0s 945us/sample - loss: 0.5569 - accuracy: 0.9825 - val_loss: 0.8623 - val_accuracy: 0.9048\n",
      "Epoch 304/800\n",
      "114/114 [==============================] - 0s 938us/sample - loss: 0.5598 - accuracy: 0.9737 - val_loss: 0.8342 - val_accuracy: 0.9048\n",
      "Epoch 305/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5218 - accuracy: 1.0000 - val_loss: 0.7812 - val_accuracy: 0.9048\n",
      "Epoch 306/800\n",
      "114/114 [==============================] - 0s 948us/sample - loss: 0.5433 - accuracy: 0.9825 - val_loss: 0.7566 - val_accuracy: 0.9048\n",
      "Epoch 307/800\n",
      "114/114 [==============================] - 0s 885us/sample - loss: 0.5223 - accuracy: 1.0000 - val_loss: 0.7481 - val_accuracy: 0.9048\n",
      "Epoch 308/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5212 - accuracy: 1.0000 - val_loss: 0.7532 - val_accuracy: 0.9048\n",
      "Epoch 309/800\n",
      "114/114 [==============================] - 0s 915us/sample - loss: 0.5477 - accuracy: 0.9825 - val_loss: 0.7970 - val_accuracy: 0.9048\n",
      "Epoch 310/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5462 - accuracy: 0.9825 - val_loss: 0.8640 - val_accuracy: 0.9048\n",
      "Epoch 311/800\n",
      "114/114 [==============================] - 0s 945us/sample - loss: 0.5451 - accuracy: 0.9912 - val_loss: 0.8784 - val_accuracy: 0.9048\n",
      "Epoch 312/800\n",
      "114/114 [==============================] - 0s 910us/sample - loss: 0.5496 - accuracy: 0.9737 - val_loss: 0.8468 - val_accuracy: 0.9048\n",
      "Epoch 313/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5332 - accuracy: 0.9912 - val_loss: 0.8318 - val_accuracy: 0.9048\n",
      "Epoch 314/800\n",
      "114/114 [==============================] - 0s 919us/sample - loss: 0.5283 - accuracy: 0.9912 - val_loss: 0.7735 - val_accuracy: 0.9048\n",
      "Epoch 315/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5234 - accuracy: 0.9912 - val_loss: 0.7491 - val_accuracy: 0.9048\n",
      "Epoch 316/800\n",
      "114/114 [==============================] - 0s 923us/sample - loss: 0.5318 - accuracy: 0.9912 - val_loss: 0.7545 - val_accuracy: 0.9048\n",
      "Epoch 317/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5437 - accuracy: 0.9912 - val_loss: 0.7898 - val_accuracy: 0.9048\n",
      "Epoch 318/800\n",
      "114/114 [==============================] - 0s 952us/sample - loss: 0.5401 - accuracy: 0.9825 - val_loss: 0.8227 - val_accuracy: 0.9048\n",
      "Epoch 319/800\n",
      "114/114 [==============================] - 0s 924us/sample - loss: 0.5345 - accuracy: 0.9912 - val_loss: 0.8666 - val_accuracy: 0.9048\n",
      "Epoch 320/800\n",
      "114/114 [==============================] - 0s 989us/sample - loss: 0.5357 - accuracy: 0.9912 - val_loss: 0.8618 - val_accuracy: 0.9048\n",
      "Epoch 321/800\n",
      "114/114 [==============================] - 0s 912us/sample - loss: 0.5460 - accuracy: 0.9912 - val_loss: 0.8349 - val_accuracy: 0.9048\n",
      "Epoch 322/800\n",
      "114/114 [==============================] - 0s 966us/sample - loss: 0.5247 - accuracy: 1.0000 - val_loss: 0.8096 - val_accuracy: 0.9048\n",
      "Epoch 323/800\n",
      "114/114 [==============================] - 0s 911us/sample - loss: 0.5517 - accuracy: 0.9912 - val_loss: 0.7828 - val_accuracy: 0.9048\n",
      "Epoch 324/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5488 - accuracy: 0.9825 - val_loss: 0.7739 - val_accuracy: 0.9048\n",
      "Epoch 325/800\n",
      "114/114 [==============================] - 0s 934us/sample - loss: 0.5169 - accuracy: 1.0000 - val_loss: 0.7797 - val_accuracy: 0.9048\n",
      "Epoch 326/800\n",
      "114/114 [==============================] - 0s 908us/sample - loss: 0.5227 - accuracy: 1.0000 - val_loss: 0.7913 - val_accuracy: 0.9048\n",
      "Epoch 327/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.5246 - accuracy: 0.9912 - val_loss: 0.8238 - val_accuracy: 0.9048\n",
      "Epoch 328/800\n",
      "114/114 [==============================] - 0s 875us/sample - loss: 0.5332 - accuracy: 0.9825 - val_loss: 0.8628 - val_accuracy: 0.9048\n",
      "Epoch 329/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5288 - accuracy: 0.9912 - val_loss: 0.8618 - val_accuracy: 0.9048\n",
      "Epoch 330/800\n",
      "114/114 [==============================] - 0s 883us/sample - loss: 0.5257 - accuracy: 0.9912 - val_loss: 0.8815 - val_accuracy: 0.9048\n",
      "Epoch 331/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5435 - accuracy: 0.9912 - val_loss: 0.8416 - val_accuracy: 0.9048\n",
      "Epoch 332/800\n",
      "114/114 [==============================] - 0s 912us/sample - loss: 0.5109 - accuracy: 1.0000 - val_loss: 0.7760 - val_accuracy: 0.9048\n",
      "Epoch 333/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5265 - accuracy: 0.9912 - val_loss: 0.7807 - val_accuracy: 0.9048\n",
      "Epoch 334/800\n",
      "114/114 [==============================] - 0s 937us/sample - loss: 0.5277 - accuracy: 0.9825 - val_loss: 0.8194 - val_accuracy: 0.9048\n",
      "Epoch 335/800\n",
      "114/114 [==============================] - 0s 899us/sample - loss: 0.5272 - accuracy: 0.9912 - val_loss: 0.8624 - val_accuracy: 0.9048\n",
      "Epoch 336/800\n",
      "114/114 [==============================] - 0s 968us/sample - loss: 0.5274 - accuracy: 0.9912 - val_loss: 0.9011 - val_accuracy: 0.9048\n",
      "Epoch 337/800\n",
      "114/114 [==============================] - 0s 923us/sample - loss: 0.5342 - accuracy: 0.9825 - val_loss: 0.8910 - val_accuracy: 0.9048\n",
      "Epoch 338/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5348 - accuracy: 0.9825 - val_loss: 0.8673 - val_accuracy: 0.9048\n",
      "Epoch 339/800\n",
      "114/114 [==============================] - 0s 994us/sample - loss: 0.5618 - accuracy: 0.9737 - val_loss: 0.8033 - val_accuracy: 0.9048\n",
      "Epoch 340/800\n",
      "114/114 [==============================] - 0s 857us/sample - loss: 0.5284 - accuracy: 0.9825 - val_loss: 0.7971 - val_accuracy: 0.9048\n",
      "Epoch 341/800\n",
      "114/114 [==============================] - 0s 955us/sample - loss: 0.5584 - accuracy: 0.9737 - val_loss: 0.7932 - val_accuracy: 0.9048\n",
      "Epoch 342/800\n",
      "114/114 [==============================] - 0s 890us/sample - loss: 0.5066 - accuracy: 1.0000 - val_loss: 0.8079 - val_accuracy: 0.9048\n",
      "Epoch 343/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5228 - accuracy: 0.9912 - val_loss: 0.8059 - val_accuracy: 0.9048\n",
      "Epoch 344/800\n",
      "114/114 [==============================] - 0s 966us/sample - loss: 0.5151 - accuracy: 0.9912 - val_loss: 0.8028 - val_accuracy: 0.9048\n",
      "Epoch 345/800\n",
      "114/114 [==============================] - 0s 883us/sample - loss: 0.5376 - accuracy: 0.9825 - val_loss: 0.8102 - val_accuracy: 0.9048\n",
      "Epoch 346/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.5150 - accuracy: 1.0000 - val_loss: 0.8455 - val_accuracy: 0.9048\n",
      "Epoch 347/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5135 - accuracy: 1.0000 - val_loss: 0.8529 - val_accuracy: 0.9048\n",
      "Epoch 348/800\n",
      "114/114 [==============================] - 0s 961us/sample - loss: 0.5345 - accuracy: 0.9912 - val_loss: 0.8599 - val_accuracy: 0.9048\n",
      "Epoch 349/800\n",
      "114/114 [==============================] - 0s 913us/sample - loss: 0.5052 - accuracy: 1.0000 - val_loss: 0.8598 - val_accuracy: 0.9048\n",
      "Epoch 350/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5508 - accuracy: 0.9737 - val_loss: 0.8565 - val_accuracy: 0.9048\n",
      "Epoch 351/800\n",
      "114/114 [==============================] - 0s 901us/sample - loss: 0.5872 - accuracy: 0.9737 - val_loss: 0.8061 - val_accuracy: 0.9048\n",
      "Epoch 352/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5388 - accuracy: 0.9825 - val_loss: 0.8066 - val_accuracy: 0.9048\n",
      "Epoch 353/800\n",
      "114/114 [==============================] - 0s 937us/sample - loss: 0.5449 - accuracy: 0.9737 - val_loss: 0.8344 - val_accuracy: 0.9048\n",
      "Epoch 354/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5185 - accuracy: 1.0000 - val_loss: 0.8501 - val_accuracy: 0.9048\n",
      "Epoch 355/800\n",
      "114/114 [==============================] - 0s 905us/sample - loss: 0.5320 - accuracy: 0.9825 - val_loss: 0.8489 - val_accuracy: 0.9048\n",
      "Epoch 356/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5089 - accuracy: 1.0000 - val_loss: 0.8267 - val_accuracy: 0.9048\n",
      "Epoch 357/800\n",
      "114/114 [==============================] - 0s 928us/sample - loss: 0.6092 - accuracy: 0.9561 - val_loss: 0.8197 - val_accuracy: 0.9048\n",
      "Epoch 358/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5329 - accuracy: 0.9825 - val_loss: 0.8353 - val_accuracy: 0.9048\n",
      "Epoch 359/800\n",
      "114/114 [==============================] - 0s 956us/sample - loss: 0.5171 - accuracy: 0.9912 - val_loss: 0.8326 - val_accuracy: 0.9048\n",
      "Epoch 360/800\n",
      "114/114 [==============================] - 0s 912us/sample - loss: 0.5131 - accuracy: 0.9912 - val_loss: 0.8173 - val_accuracy: 0.9048\n",
      "Epoch 361/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5112 - accuracy: 0.9912 - val_loss: 0.8305 - val_accuracy: 0.9048\n",
      "Epoch 362/800\n",
      "114/114 [==============================] - 0s 897us/sample - loss: 0.5415 - accuracy: 0.9825 - val_loss: 0.8009 - val_accuracy: 0.9048\n",
      "Epoch 363/800\n",
      "114/114 [==============================] - 0s 895us/sample - loss: 0.5345 - accuracy: 0.9825 - val_loss: 0.7945 - val_accuracy: 0.9048\n",
      "Epoch 364/800\n",
      "114/114 [==============================] - 0s 945us/sample - loss: 0.5468 - accuracy: 0.9825 - val_loss: 0.8426 - val_accuracy: 0.9048\n",
      "Epoch 365/800\n",
      "114/114 [==============================] - 0s 932us/sample - loss: 0.5504 - accuracy: 0.9737 - val_loss: 0.8636 - val_accuracy: 0.9048\n",
      "Epoch 366/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5214 - accuracy: 0.9912 - val_loss: 0.8229 - val_accuracy: 0.9048\n",
      "Epoch 367/800\n",
      "114/114 [==============================] - 0s 951us/sample - loss: 0.5268 - accuracy: 0.9912 - val_loss: 0.7580 - val_accuracy: 0.9048\n",
      "Epoch 368/800\n",
      "114/114 [==============================] - 0s 915us/sample - loss: 0.5338 - accuracy: 0.9912 - val_loss: 0.7571 - val_accuracy: 0.9048\n",
      "Epoch 369/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5531 - accuracy: 0.9737 - val_loss: 0.7760 - val_accuracy: 0.9048\n",
      "Epoch 370/800\n",
      "114/114 [==============================] - 0s 965us/sample - loss: 0.5379 - accuracy: 0.9825 - val_loss: 0.8271 - val_accuracy: 0.9048\n",
      "Epoch 371/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5025 - accuracy: 1.0000 - val_loss: 0.8542 - val_accuracy: 0.9048\n",
      "Epoch 372/800\n",
      "114/114 [==============================] - 0s 972us/sample - loss: 0.5308 - accuracy: 0.9737 - val_loss: 0.8554 - val_accuracy: 0.9048\n",
      "Epoch 373/800\n",
      "114/114 [==============================] - 0s 882us/sample - loss: 0.5422 - accuracy: 0.9912 - val_loss: 0.8174 - val_accuracy: 0.9048\n",
      "Epoch 374/800\n",
      "114/114 [==============================] - 0s 1000us/sample - loss: 0.5331 - accuracy: 0.9825 - val_loss: 0.8257 - val_accuracy: 0.9048\n",
      "Epoch 375/800\n",
      "114/114 [==============================] - 0s 906us/sample - loss: 0.5025 - accuracy: 0.9912 - val_loss: 0.8430 - val_accuracy: 0.9048\n",
      "Epoch 376/800\n",
      "114/114 [==============================] - 0s 956us/sample - loss: 0.5015 - accuracy: 1.0000 - val_loss: 0.8530 - val_accuracy: 0.9048\n",
      "Epoch 377/800\n",
      "114/114 [==============================] - 0s 900us/sample - loss: 0.5202 - accuracy: 0.9912 - val_loss: 0.8370 - val_accuracy: 0.9048\n",
      "Epoch 378/800\n",
      "114/114 [==============================] - 0s 894us/sample - loss: 0.5119 - accuracy: 0.9912 - val_loss: 0.8074 - val_accuracy: 0.9048\n",
      "Epoch 379/800\n",
      "114/114 [==============================] - 0s 972us/sample - loss: 0.5186 - accuracy: 0.9825 - val_loss: 0.7866 - val_accuracy: 0.9048\n",
      "Epoch 380/800\n",
      "114/114 [==============================] - 0s 893us/sample - loss: 0.5012 - accuracy: 1.0000 - val_loss: 0.7746 - val_accuracy: 0.9048\n",
      "Epoch 381/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5217 - accuracy: 0.9912 - val_loss: 0.7820 - val_accuracy: 0.9048\n",
      "Epoch 382/800\n",
      "114/114 [==============================] - 0s 929us/sample - loss: 0.5350 - accuracy: 0.9825 - val_loss: 0.8190 - val_accuracy: 0.9048\n",
      "Epoch 383/800\n",
      "114/114 [==============================] - 0s 907us/sample - loss: 0.5104 - accuracy: 0.9825 - val_loss: 0.8548 - val_accuracy: 0.9048\n",
      "Epoch 384/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5047 - accuracy: 0.9912 - val_loss: 0.8647 - val_accuracy: 0.9048\n",
      "Epoch 385/800\n",
      "114/114 [==============================] - 0s 896us/sample - loss: 0.5077 - accuracy: 0.9912 - val_loss: 0.8500 - val_accuracy: 0.9048\n",
      "Epoch 386/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5228 - accuracy: 0.9912 - val_loss: 0.8343 - val_accuracy: 0.9048\n",
      "Epoch 387/800\n",
      "114/114 [==============================] - 0s 972us/sample - loss: 0.5338 - accuracy: 0.9912 - val_loss: 0.7816 - val_accuracy: 0.9048\n",
      "Epoch 388/800\n",
      "114/114 [==============================] - 0s 912us/sample - loss: 0.5079 - accuracy: 0.9912 - val_loss: 0.7400 - val_accuracy: 0.9524\n",
      "Epoch 389/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4932 - accuracy: 1.0000 - val_loss: 0.7365 - val_accuracy: 0.9524\n",
      "Epoch 390/800\n",
      "114/114 [==============================] - 0s 944us/sample - loss: 0.5028 - accuracy: 0.9912 - val_loss: 0.7372 - val_accuracy: 0.9524\n",
      "Epoch 391/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5107 - accuracy: 0.9825 - val_loss: 0.7540 - val_accuracy: 0.9048\n",
      "Epoch 392/800\n",
      "114/114 [==============================] - 0s 927us/sample - loss: 0.5497 - accuracy: 0.9825 - val_loss: 0.8158 - val_accuracy: 0.9048\n",
      "Epoch 393/800\n",
      "114/114 [==============================] - 0s 980us/sample - loss: 0.5113 - accuracy: 0.9825 - val_loss: 0.8524 - val_accuracy: 0.9048\n",
      "Epoch 394/800\n",
      "114/114 [==============================] - 0s 927us/sample - loss: 0.5201 - accuracy: 0.9912 - val_loss: 0.8542 - val_accuracy: 0.9048\n",
      "Epoch 395/800\n",
      "114/114 [==============================] - 0s 939us/sample - loss: 0.5345 - accuracy: 0.9912 - val_loss: 0.8052 - val_accuracy: 0.9048\n",
      "Epoch 396/800\n",
      "114/114 [==============================] - 0s 950us/sample - loss: 0.5355 - accuracy: 0.9825 - val_loss: 0.7401 - val_accuracy: 0.9048\n",
      "Epoch 397/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5118 - accuracy: 0.9825 - val_loss: 0.7329 - val_accuracy: 0.9524\n",
      "Epoch 398/800\n",
      "114/114 [==============================] - 0s 955us/sample - loss: 0.5100 - accuracy: 0.9825 - val_loss: 0.7308 - val_accuracy: 0.9524\n",
      "Epoch 399/800\n",
      "114/114 [==============================] - 0s 890us/sample - loss: 0.5435 - accuracy: 0.9825 - val_loss: 0.7470 - val_accuracy: 0.9048\n",
      "Epoch 400/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4894 - accuracy: 1.0000 - val_loss: 0.7846 - val_accuracy: 0.9048\n",
      "Epoch 401/800\n",
      "114/114 [==============================] - 0s 891us/sample - loss: 0.5030 - accuracy: 1.0000 - val_loss: 0.8007 - val_accuracy: 0.9048\n",
      "Epoch 402/800\n",
      "114/114 [==============================] - 0s 917us/sample - loss: 0.4927 - accuracy: 1.0000 - val_loss: 0.8150 - val_accuracy: 0.9048\n",
      "Epoch 403/800\n",
      "114/114 [==============================] - 0s 964us/sample - loss: 0.5005 - accuracy: 1.0000 - val_loss: 0.8209 - val_accuracy: 0.9048\n",
      "Epoch 404/800\n",
      "114/114 [==============================] - 0s 897us/sample - loss: 0.4977 - accuracy: 1.0000 - val_loss: 0.8263 - val_accuracy: 0.9048\n",
      "Epoch 405/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.4942 - accuracy: 0.9912 - val_loss: 0.8372 - val_accuracy: 0.9048\n",
      "Epoch 406/800\n",
      "114/114 [==============================] - 0s 895us/sample - loss: 0.5125 - accuracy: 0.9825 - val_loss: 0.8316 - val_accuracy: 0.9048\n",
      "Epoch 407/800\n",
      "114/114 [==============================] - 0s 1000us/sample - loss: 0.5073 - accuracy: 0.9912 - val_loss: 0.8161 - val_accuracy: 0.9048\n",
      "Epoch 408/800\n",
      "114/114 [==============================] - 0s 895us/sample - loss: 0.5233 - accuracy: 0.9825 - val_loss: 0.8188 - val_accuracy: 0.9048\n",
      "Epoch 409/800\n",
      "114/114 [==============================] - 0s 975us/sample - loss: 0.4877 - accuracy: 1.0000 - val_loss: 0.8343 - val_accuracy: 0.9048\n",
      "Epoch 410/800\n",
      "114/114 [==============================] - 0s 903us/sample - loss: 0.4885 - accuracy: 1.0000 - val_loss: 0.8406 - val_accuracy: 0.9048\n",
      "Epoch 411/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4933 - accuracy: 1.0000 - val_loss: 0.8266 - val_accuracy: 0.9048\n",
      "Epoch 412/800\n",
      "114/114 [==============================] - 0s 932us/sample - loss: 0.4868 - accuracy: 1.0000 - val_loss: 0.7968 - val_accuracy: 0.9048\n",
      "Epoch 413/800\n",
      "114/114 [==============================] - 0s 893us/sample - loss: 0.5439 - accuracy: 0.9825 - val_loss: 0.8032 - val_accuracy: 0.9048\n",
      "Epoch 414/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.5165 - accuracy: 0.9912 - val_loss: 0.8454 - val_accuracy: 0.9048\n",
      "Epoch 415/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4990 - accuracy: 0.9912 - val_loss: 0.8653 - val_accuracy: 0.9048\n",
      "Epoch 416/800\n",
      "114/114 [==============================] - 0s 927us/sample - loss: 0.5002 - accuracy: 1.0000 - val_loss: 0.8689 - val_accuracy: 0.9048\n",
      "Epoch 417/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5220 - accuracy: 0.9825 - val_loss: 0.8489 - val_accuracy: 0.9048\n",
      "Epoch 418/800\n",
      "114/114 [==============================] - 0s 995us/sample - loss: 0.5017 - accuracy: 0.9912 - val_loss: 0.8205 - val_accuracy: 0.9048\n",
      "Epoch 419/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5051 - accuracy: 0.9912 - val_loss: 0.8282 - val_accuracy: 0.9048\n",
      "Epoch 420/800\n",
      "114/114 [==============================] - 0s 962us/sample - loss: 0.4942 - accuracy: 0.9912 - val_loss: 0.8354 - val_accuracy: 0.9048\n",
      "Epoch 421/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5156 - accuracy: 0.9825 - val_loss: 0.8335 - val_accuracy: 0.9048\n",
      "Epoch 422/800\n",
      "114/114 [==============================] - 0s 940us/sample - loss: 0.5203 - accuracy: 0.9825 - val_loss: 0.8434 - val_accuracy: 0.9048\n",
      "Epoch 423/800\n",
      "114/114 [==============================] - 0s 900us/sample - loss: 0.5089 - accuracy: 0.9825 - val_loss: 0.8390 - val_accuracy: 0.9048\n",
      "Epoch 424/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5202 - accuracy: 0.9737 - val_loss: 0.8188 - val_accuracy: 0.9048\n",
      "Epoch 425/800\n",
      "114/114 [==============================] - 0s 959us/sample - loss: 0.4842 - accuracy: 1.0000 - val_loss: 0.8230 - val_accuracy: 0.9048\n",
      "Epoch 426/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5031 - accuracy: 0.9825 - val_loss: 0.8409 - val_accuracy: 0.9048\n",
      "Epoch 427/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.4843 - accuracy: 1.0000 - val_loss: 0.8365 - val_accuracy: 0.9048\n",
      "Epoch 428/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4883 - accuracy: 0.9912 - val_loss: 0.8354 - val_accuracy: 0.9048\n",
      "Epoch 429/800\n",
      "114/114 [==============================] - 0s 996us/sample - loss: 0.5612 - accuracy: 0.9825 - val_loss: 0.8180 - val_accuracy: 0.9048\n",
      "Epoch 430/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4816 - accuracy: 1.0000 - val_loss: 0.7347 - val_accuracy: 0.9048\n",
      "Epoch 431/800\n",
      "114/114 [==============================] - 0s 971us/sample - loss: 0.5191 - accuracy: 0.9912 - val_loss: 0.7048 - val_accuracy: 0.9524\n",
      "Epoch 432/800\n",
      "114/114 [==============================] - 0s 934us/sample - loss: 0.4915 - accuracy: 0.9912 - val_loss: 0.7260 - val_accuracy: 0.9048\n",
      "Epoch 433/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.4900 - accuracy: 0.9912 - val_loss: 0.7384 - val_accuracy: 0.9048\n",
      "Epoch 434/800\n",
      "114/114 [==============================] - 0s 875us/sample - loss: 0.4837 - accuracy: 0.9912 - val_loss: 0.7322 - val_accuracy: 0.9048\n",
      "Epoch 435/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5180 - accuracy: 0.9825 - val_loss: 0.7342 - val_accuracy: 0.9048\n",
      "Epoch 436/800\n",
      "114/114 [==============================] - 0s 932us/sample - loss: 0.5124 - accuracy: 0.9737 - val_loss: 0.7623 - val_accuracy: 0.9048\n",
      "Epoch 437/800\n",
      "114/114 [==============================] - 0s 922us/sample - loss: 0.5076 - accuracy: 0.9912 - val_loss: 0.7868 - val_accuracy: 0.9048\n",
      "Epoch 438/800\n",
      "114/114 [==============================] - 0s 978us/sample - loss: 0.4821 - accuracy: 1.0000 - val_loss: 0.8027 - val_accuracy: 0.9048\n",
      "Epoch 439/800\n",
      "114/114 [==============================] - 0s 900us/sample - loss: 0.4808 - accuracy: 0.9912 - val_loss: 0.8136 - val_accuracy: 0.9048\n",
      "Epoch 440/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4861 - accuracy: 0.9912 - val_loss: 0.8015 - val_accuracy: 0.9048\n",
      "Epoch 441/800\n",
      "114/114 [==============================] - 0s 928us/sample - loss: 0.4855 - accuracy: 0.9912 - val_loss: 0.7858 - val_accuracy: 0.9048\n",
      "Epoch 442/800\n",
      "114/114 [==============================] - 0s 978us/sample - loss: 0.4858 - accuracy: 0.9912 - val_loss: 0.7855 - val_accuracy: 0.9048\n",
      "Epoch 443/800\n",
      "114/114 [==============================] - 0s 897us/sample - loss: 0.4814 - accuracy: 1.0000 - val_loss: 0.7951 - val_accuracy: 0.9048\n",
      "Epoch 444/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5033 - accuracy: 0.9825 - val_loss: 0.8062 - val_accuracy: 0.9048\n",
      "Epoch 445/800\n",
      "114/114 [==============================] - 0s 929us/sample - loss: 0.4749 - accuracy: 1.0000 - val_loss: 0.8110 - val_accuracy: 0.9048\n",
      "Epoch 446/800\n",
      "114/114 [==============================] - 0s 899us/sample - loss: 0.4805 - accuracy: 1.0000 - val_loss: 0.8120 - val_accuracy: 0.9048\n",
      "Epoch 447/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4937 - accuracy: 0.9912 - val_loss: 0.7952 - val_accuracy: 0.9048\n",
      "Epoch 448/800\n",
      "114/114 [==============================] - 0s 932us/sample - loss: 0.5511 - accuracy: 0.9737 - val_loss: 0.8023 - val_accuracy: 0.9048\n",
      "Epoch 449/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4882 - accuracy: 0.9825 - val_loss: 0.8219 - val_accuracy: 0.9048\n",
      "Epoch 450/800\n",
      "114/114 [==============================] - 0s 993us/sample - loss: 0.5221 - accuracy: 0.9825 - val_loss: 0.8461 - val_accuracy: 0.9048\n",
      "Epoch 451/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4885 - accuracy: 0.9912 - val_loss: 0.8464 - val_accuracy: 0.9048\n",
      "Epoch 452/800\n",
      "114/114 [==============================] - 0s 926us/sample - loss: 0.4935 - accuracy: 0.9912 - val_loss: 0.8180 - val_accuracy: 0.9048\n",
      "Epoch 453/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4993 - accuracy: 0.9912 - val_loss: 0.7900 - val_accuracy: 0.9048\n",
      "Epoch 454/800\n",
      "114/114 [==============================] - 0s 951us/sample - loss: 0.4879 - accuracy: 0.9825 - val_loss: 0.8318 - val_accuracy: 0.9048\n",
      "Epoch 455/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4942 - accuracy: 0.9912 - val_loss: 0.8306 - val_accuracy: 0.9048\n",
      "Epoch 456/800\n",
      "114/114 [==============================] - 0s 914us/sample - loss: 0.4919 - accuracy: 0.9912 - val_loss: 0.8073 - val_accuracy: 0.9048\n",
      "Epoch 457/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4911 - accuracy: 0.9825 - val_loss: 0.7887 - val_accuracy: 0.9048\n",
      "Epoch 458/800\n",
      "114/114 [==============================] - 0s 938us/sample - loss: 0.4878 - accuracy: 0.9912 - val_loss: 0.7520 - val_accuracy: 0.9048\n",
      "Epoch 459/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4785 - accuracy: 0.9912 - val_loss: 0.7156 - val_accuracy: 0.9048\n",
      "Epoch 460/800\n",
      "114/114 [==============================] - 0s 946us/sample - loss: 0.4893 - accuracy: 0.9912 - val_loss: 0.7062 - val_accuracy: 0.9048\n",
      "Epoch 461/800\n",
      "114/114 [==============================] - 0s 926us/sample - loss: 0.5110 - accuracy: 0.9825 - val_loss: 0.7023 - val_accuracy: 0.9048\n",
      "Epoch 462/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5053 - accuracy: 0.9825 - val_loss: 0.7193 - val_accuracy: 0.9048\n",
      "Epoch 463/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4883 - accuracy: 0.9912 - val_loss: 0.7698 - val_accuracy: 0.9048\n",
      "Epoch 464/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4855 - accuracy: 0.9912 - val_loss: 0.7963 - val_accuracy: 0.9048\n",
      "Epoch 465/800\n",
      "114/114 [==============================] - 0s 923us/sample - loss: 0.6144 - accuracy: 0.9561 - val_loss: 0.7987 - val_accuracy: 0.9048\n",
      "Epoch 466/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4941 - accuracy: 0.9825 - val_loss: 0.7995 - val_accuracy: 0.9048\n",
      "Epoch 467/800\n",
      "114/114 [==============================] - 0s 926us/sample - loss: 0.4720 - accuracy: 1.0000 - val_loss: 0.7863 - val_accuracy: 0.9048\n",
      "Epoch 468/800\n",
      "114/114 [==============================] - 0s 905us/sample - loss: 0.4865 - accuracy: 0.9825 - val_loss: 0.7752 - val_accuracy: 0.9048\n",
      "Epoch 469/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4757 - accuracy: 0.9912 - val_loss: 0.7543 - val_accuracy: 0.9048\n",
      "Epoch 470/800\n",
      "114/114 [==============================] - 0s 967us/sample - loss: 0.5226 - accuracy: 0.9825 - val_loss: 0.7372 - val_accuracy: 0.9048\n",
      "Epoch 471/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4767 - accuracy: 1.0000 - val_loss: 0.7097 - val_accuracy: 0.9048\n",
      "Epoch 472/800\n",
      "114/114 [==============================] - 0s 976us/sample - loss: 0.4759 - accuracy: 0.9912 - val_loss: 0.7182 - val_accuracy: 0.9048\n",
      "Epoch 473/800\n",
      "114/114 [==============================] - 0s 892us/sample - loss: 0.5038 - accuracy: 0.9825 - val_loss: 0.7341 - val_accuracy: 0.9048\n",
      "Epoch 474/800\n",
      "114/114 [==============================] - 0s 947us/sample - loss: 0.4758 - accuracy: 0.9912 - val_loss: 0.7642 - val_accuracy: 0.9048\n",
      "Epoch 475/800\n",
      "114/114 [==============================] - 0s 918us/sample - loss: 0.5016 - accuracy: 0.9737 - val_loss: 0.7834 - val_accuracy: 0.9048\n",
      "Epoch 476/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4775 - accuracy: 0.9912 - val_loss: 0.7957 - val_accuracy: 0.9048\n",
      "Epoch 477/800\n",
      "114/114 [==============================] - 0s 892us/sample - loss: 0.5104 - accuracy: 0.9737 - val_loss: 0.7829 - val_accuracy: 0.9048\n",
      "Epoch 478/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5170 - accuracy: 0.9649 - val_loss: 0.7490 - val_accuracy: 0.9048\n",
      "Epoch 479/800\n",
      "114/114 [==============================] - 0s 927us/sample - loss: 0.4864 - accuracy: 0.9912 - val_loss: 0.7548 - val_accuracy: 0.9048\n",
      "Epoch 480/800\n",
      "114/114 [==============================] - 0s 916us/sample - loss: 0.4690 - accuracy: 1.0000 - val_loss: 0.7436 - val_accuracy: 0.9048\n",
      "Epoch 481/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4728 - accuracy: 1.0000 - val_loss: 0.7367 - val_accuracy: 0.9048\n",
      "Epoch 482/800\n",
      "114/114 [==============================] - 0s 919us/sample - loss: 0.4771 - accuracy: 0.9912 - val_loss: 0.7353 - val_accuracy: 0.9048\n",
      "Epoch 483/800\n",
      "114/114 [==============================] - 0s 888us/sample - loss: 0.4819 - accuracy: 0.9825 - val_loss: 0.7283 - val_accuracy: 0.9048\n",
      "Epoch 484/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5400 - accuracy: 0.9912 - val_loss: 0.7229 - val_accuracy: 0.9048\n",
      "Epoch 485/800\n",
      "114/114 [==============================] - 0s 914us/sample - loss: 0.4771 - accuracy: 0.9912 - val_loss: 0.7265 - val_accuracy: 0.9048\n",
      "Epoch 486/800\n",
      "114/114 [==============================] - 0s 957us/sample - loss: 0.4803 - accuracy: 0.9912 - val_loss: 0.7157 - val_accuracy: 0.9048\n",
      "Epoch 487/800\n",
      "114/114 [==============================] - 0s 906us/sample - loss: 0.4785 - accuracy: 0.9912 - val_loss: 0.7427 - val_accuracy: 0.9048\n",
      "Epoch 488/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.5071 - accuracy: 0.9825 - val_loss: 0.7544 - val_accuracy: 0.9048\n",
      "Epoch 489/800\n",
      "114/114 [==============================] - 0s 925us/sample - loss: 0.4728 - accuracy: 0.9912 - val_loss: 0.7663 - val_accuracy: 0.9048\n",
      "Epoch 490/800\n",
      "114/114 [==============================] - 0s 906us/sample - loss: 0.4850 - accuracy: 0.9825 - val_loss: 0.7071 - val_accuracy: 0.9048\n",
      "Epoch 491/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4743 - accuracy: 0.9912 - val_loss: 0.6576 - val_accuracy: 0.9524\n",
      "Epoch 492/800\n",
      "114/114 [==============================] - 0s 913us/sample - loss: 0.5005 - accuracy: 0.9912 - val_loss: 0.6591 - val_accuracy: 0.9524\n",
      "Epoch 493/800\n",
      "114/114 [==============================] - 0s 936us/sample - loss: 0.5009 - accuracy: 0.9825 - val_loss: 0.6619 - val_accuracy: 0.9524\n",
      "Epoch 494/800\n",
      "114/114 [==============================] - 0s 962us/sample - loss: 0.5003 - accuracy: 0.9825 - val_loss: 0.6504 - val_accuracy: 0.9524\n",
      "Epoch 495/800\n",
      "114/114 [==============================] - 0s 914us/sample - loss: 0.4880 - accuracy: 0.9912 - val_loss: 0.6534 - val_accuracy: 0.9048\n",
      "Epoch 496/800\n",
      "114/114 [==============================] - 0s 876us/sample - loss: 0.4588 - accuracy: 1.0000 - val_loss: 0.6828 - val_accuracy: 0.9048\n",
      "Epoch 497/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4736 - accuracy: 0.9912 - val_loss: 0.7485 - val_accuracy: 0.9048\n",
      "Epoch 498/800\n",
      "114/114 [==============================] - 0s 907us/sample - loss: 0.4813 - accuracy: 0.9912 - val_loss: 0.7665 - val_accuracy: 0.9048\n",
      "Epoch 499/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4901 - accuracy: 0.9825 - val_loss: 0.7540 - val_accuracy: 0.9048\n",
      "Epoch 500/800\n",
      "114/114 [==============================] - 0s 913us/sample - loss: 0.4950 - accuracy: 0.9737 - val_loss: 0.6862 - val_accuracy: 0.9048\n",
      "Epoch 501/800\n",
      "114/114 [==============================] - 0s 901us/sample - loss: 0.4629 - accuracy: 1.0000 - val_loss: 0.6622 - val_accuracy: 0.9048\n",
      "Epoch 502/800\n",
      "114/114 [==============================] - 0s 888us/sample - loss: 0.4733 - accuracy: 0.9912 - val_loss: 0.6502 - val_accuracy: 0.9048\n",
      "Epoch 503/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4739 - accuracy: 0.9912 - val_loss: 0.6827 - val_accuracy: 0.9048\n",
      "Epoch 504/800\n",
      "114/114 [==============================] - 0s 920us/sample - loss: 0.4794 - accuracy: 0.9825 - val_loss: 0.7454 - val_accuracy: 0.9048\n",
      "Epoch 505/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4631 - accuracy: 1.0000 - val_loss: 0.7786 - val_accuracy: 0.9048\n",
      "Epoch 506/800\n",
      "114/114 [==============================] - 0s 924us/sample - loss: 0.4716 - accuracy: 0.9912 - val_loss: 0.7838 - val_accuracy: 0.9048\n",
      "Epoch 507/800\n",
      "114/114 [==============================] - 0s 962us/sample - loss: 0.4833 - accuracy: 0.9912 - val_loss: 0.7878 - val_accuracy: 0.9048\n",
      "Epoch 508/800\n",
      "114/114 [==============================] - 0s 914us/sample - loss: 0.4967 - accuracy: 0.9825 - val_loss: 0.7603 - val_accuracy: 0.9048\n",
      "Epoch 509/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4683 - accuracy: 0.9912 - val_loss: 0.7200 - val_accuracy: 0.9048\n",
      "Epoch 510/800\n",
      "114/114 [==============================] - 0s 952us/sample - loss: 0.4679 - accuracy: 0.9912 - val_loss: 0.6964 - val_accuracy: 0.9048\n",
      "Epoch 511/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4793 - accuracy: 0.9825 - val_loss: 0.7097 - val_accuracy: 0.9048\n",
      "Epoch 512/800\n",
      "114/114 [==============================] - 0s 977us/sample - loss: 0.4920 - accuracy: 0.9912 - val_loss: 0.7424 - val_accuracy: 0.9048\n",
      "Epoch 513/800\n",
      "114/114 [==============================] - 0s 911us/sample - loss: 0.4812 - accuracy: 0.9912 - val_loss: 0.7826 - val_accuracy: 0.9048\n",
      "Epoch 514/800\n",
      "114/114 [==============================] - 0s 982us/sample - loss: 0.5112 - accuracy: 0.9737 - val_loss: 0.7775 - val_accuracy: 0.9048\n",
      "Epoch 515/800\n",
      "114/114 [==============================] - 0s 917us/sample - loss: 0.4589 - accuracy: 1.0000 - val_loss: 0.6911 - val_accuracy: 0.9048\n",
      "Epoch 516/800\n",
      "114/114 [==============================] - 0s 962us/sample - loss: 0.4897 - accuracy: 0.9912 - val_loss: 0.6589 - val_accuracy: 0.9524\n",
      "Epoch 517/800\n",
      "114/114 [==============================] - 0s 916us/sample - loss: 0.4654 - accuracy: 0.9912 - val_loss: 0.6575 - val_accuracy: 0.9524\n",
      "Epoch 518/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4838 - accuracy: 0.9825 - val_loss: 0.6561 - val_accuracy: 0.9524\n",
      "Epoch 519/800\n",
      "114/114 [==============================] - 0s 960us/sample - loss: 0.5567 - accuracy: 0.9649 - val_loss: 0.6740 - val_accuracy: 0.9048\n",
      "Epoch 520/800\n",
      "114/114 [==============================] - 0s 862us/sample - loss: 0.4983 - accuracy: 0.9737 - val_loss: 0.6944 - val_accuracy: 0.9048\n",
      "Epoch 521/800\n",
      "114/114 [==============================] - 0s 953us/sample - loss: 0.4719 - accuracy: 0.9912 - val_loss: 0.7251 - val_accuracy: 0.9048\n",
      "Epoch 522/800\n",
      "114/114 [==============================] - 0s 929us/sample - loss: 0.4706 - accuracy: 1.0000 - val_loss: 0.7340 - val_accuracy: 0.9048\n",
      "Epoch 523/800\n",
      "114/114 [==============================] - 0s 953us/sample - loss: 0.4602 - accuracy: 1.0000 - val_loss: 0.7337 - val_accuracy: 0.9048\n",
      "Epoch 524/800\n",
      "114/114 [==============================] - 0s 907us/sample - loss: 0.4566 - accuracy: 1.0000 - val_loss: 0.7321 - val_accuracy: 0.9048\n",
      "Epoch 525/800\n",
      "114/114 [==============================] - 0s 921us/sample - loss: 0.4899 - accuracy: 0.9737 - val_loss: 0.7241 - val_accuracy: 0.9048\n",
      "Epoch 526/800\n",
      "114/114 [==============================] - 0s 960us/sample - loss: 0.4629 - accuracy: 0.9912 - val_loss: 0.7031 - val_accuracy: 0.9048\n",
      "Epoch 527/800\n",
      "114/114 [==============================] - 0s 928us/sample - loss: 0.4567 - accuracy: 0.9912 - val_loss: 0.6999 - val_accuracy: 0.9048\n",
      "Epoch 528/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4716 - accuracy: 0.9912 - val_loss: 0.7073 - val_accuracy: 0.9048\n",
      "Epoch 529/800\n",
      "114/114 [==============================] - 0s 933us/sample - loss: 0.4903 - accuracy: 0.9825 - val_loss: 0.6992 - val_accuracy: 0.9048\n",
      "Epoch 530/800\n",
      "114/114 [==============================] - 0s 902us/sample - loss: 0.4699 - accuracy: 0.9912 - val_loss: 0.7166 - val_accuracy: 0.9048\n",
      "Epoch 531/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4642 - accuracy: 0.9912 - val_loss: 0.7484 - val_accuracy: 0.9048\n",
      "Epoch 532/800\n",
      "114/114 [==============================] - 0s 860us/sample - loss: 0.4553 - accuracy: 1.0000 - val_loss: 0.7537 - val_accuracy: 0.9048\n",
      "Epoch 533/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4604 - accuracy: 0.9912 - val_loss: 0.7634 - val_accuracy: 0.9048\n",
      "Epoch 534/800\n",
      "114/114 [==============================] - 0s 906us/sample - loss: 0.4928 - accuracy: 0.9649 - val_loss: 0.7189 - val_accuracy: 0.9048\n",
      "Epoch 535/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4786 - accuracy: 0.9912 - val_loss: 0.6570 - val_accuracy: 0.9048\n",
      "Epoch 536/800\n",
      "114/114 [==============================] - 0s 951us/sample - loss: 0.4572 - accuracy: 1.0000 - val_loss: 0.6490 - val_accuracy: 0.9524\n",
      "Epoch 537/800\n",
      "114/114 [==============================] - 0s 889us/sample - loss: 0.4888 - accuracy: 0.9737 - val_loss: 0.6577 - val_accuracy: 0.9048\n",
      "Epoch 538/800\n",
      "114/114 [==============================] - 0s 970us/sample - loss: 0.4699 - accuracy: 0.9912 - val_loss: 0.7011 - val_accuracy: 0.9048\n",
      "Epoch 539/800\n",
      "114/114 [==============================] - 0s 916us/sample - loss: 0.4445 - accuracy: 1.0000 - val_loss: 0.7333 - val_accuracy: 0.9048\n",
      "Epoch 540/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4749 - accuracy: 0.9825 - val_loss: 0.7594 - val_accuracy: 0.9048\n",
      "Epoch 541/800\n",
      "114/114 [==============================] - 0s 952us/sample - loss: 0.4475 - accuracy: 1.0000 - val_loss: 0.7574 - val_accuracy: 0.9048\n",
      "Epoch 542/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4574 - accuracy: 1.0000 - val_loss: 0.7404 - val_accuracy: 0.9048\n",
      "Epoch 543/800\n",
      "114/114 [==============================] - 0s 930us/sample - loss: 0.4526 - accuracy: 1.0000 - val_loss: 0.7119 - val_accuracy: 0.9048\n",
      "Epoch 544/800\n",
      "114/114 [==============================] - 0s 913us/sample - loss: 0.4580 - accuracy: 0.9912 - val_loss: 0.7111 - val_accuracy: 0.9048\n",
      "Epoch 545/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4479 - accuracy: 1.0000 - val_loss: 0.7168 - val_accuracy: 0.9048\n",
      "Epoch 546/800\n",
      "114/114 [==============================] - 0s 924us/sample - loss: 0.4495 - accuracy: 1.0000 - val_loss: 0.7228 - val_accuracy: 0.9048\n",
      "Epoch 547/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4806 - accuracy: 0.9825 - val_loss: 0.7570 - val_accuracy: 0.9048\n",
      "Epoch 548/800\n",
      "114/114 [==============================] - 0s 969us/sample - loss: 0.4410 - accuracy: 1.0000 - val_loss: 0.7645 - val_accuracy: 0.9048\n",
      "Epoch 549/800\n",
      "114/114 [==============================] - 0s 891us/sample - loss: 0.4470 - accuracy: 1.0000 - val_loss: 0.7690 - val_accuracy: 0.9048\n",
      "Epoch 550/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4500 - accuracy: 1.0000 - val_loss: 0.7758 - val_accuracy: 0.9048\n",
      "Epoch 551/800\n",
      "114/114 [==============================] - 0s 944us/sample - loss: 0.4480 - accuracy: 1.0000 - val_loss: 0.7749 - val_accuracy: 0.9048\n",
      "Epoch 552/800\n",
      "114/114 [==============================] - 0s 892us/sample - loss: 0.4696 - accuracy: 0.9825 - val_loss: 0.7343 - val_accuracy: 0.9048\n",
      "Epoch 553/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4441 - accuracy: 1.0000 - val_loss: 0.7115 - val_accuracy: 0.9048\n",
      "Epoch 554/800\n",
      "114/114 [==============================] - 0s 949us/sample - loss: 0.4492 - accuracy: 0.9912 - val_loss: 0.7009 - val_accuracy: 0.9048\n",
      "Epoch 555/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4708 - accuracy: 0.9912 - val_loss: 0.7126 - val_accuracy: 0.9048\n",
      "Epoch 556/800\n",
      "114/114 [==============================] - 0s 924us/sample - loss: 0.4434 - accuracy: 1.0000 - val_loss: 0.7338 - val_accuracy: 0.9048\n",
      "Epoch 557/800\n",
      "114/114 [==============================] - 0s 922us/sample - loss: 0.4537 - accuracy: 0.9912 - val_loss: 0.7673 - val_accuracy: 0.9048\n",
      "Epoch 558/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4741 - accuracy: 0.9825 - val_loss: 0.7929 - val_accuracy: 0.9048\n",
      "Epoch 559/800\n",
      "114/114 [==============================] - 0s 904us/sample - loss: 0.4459 - accuracy: 0.9912 - val_loss: 0.7951 - val_accuracy: 0.9048\n",
      "Epoch 560/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4438 - accuracy: 1.0000 - val_loss: 0.7925 - val_accuracy: 0.9048\n",
      "Epoch 561/800\n",
      "114/114 [==============================] - 0s 988us/sample - loss: 0.4430 - accuracy: 1.0000 - val_loss: 0.7895 - val_accuracy: 0.9048\n",
      "Epoch 562/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4426 - accuracy: 1.0000 - val_loss: 0.7725 - val_accuracy: 0.9048\n",
      "Epoch 563/800\n",
      "114/114 [==============================] - 0s 898us/sample - loss: 0.4621 - accuracy: 0.9912 - val_loss: 0.7514 - val_accuracy: 0.9048\n",
      "Epoch 564/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4408 - accuracy: 1.0000 - val_loss: 0.7271 - val_accuracy: 0.9048\n",
      "Epoch 565/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4661 - accuracy: 0.9737 - val_loss: 0.7065 - val_accuracy: 0.9048\n",
      "Epoch 566/800\n",
      "114/114 [==============================] - 0s 924us/sample - loss: 0.4375 - accuracy: 1.0000 - val_loss: 0.6800 - val_accuracy: 0.9048\n",
      "Epoch 567/800\n",
      "114/114 [==============================] - 0s 935us/sample - loss: 0.4381 - accuracy: 1.0000 - val_loss: 0.6695 - val_accuracy: 0.9524\n",
      "Epoch 568/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.4603 - accuracy: 0.9912 - val_loss: 0.6827 - val_accuracy: 0.9048\n",
      "Epoch 569/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4462 - accuracy: 0.9912 - val_loss: 0.7224 - val_accuracy: 0.9048\n",
      "Epoch 570/800\n",
      "114/114 [==============================] - 0s 967us/sample - loss: 0.5250 - accuracy: 0.9737 - val_loss: 0.8272 - val_accuracy: 0.9048\n",
      "Epoch 571/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4448 - accuracy: 1.0000 - val_loss: 0.8454 - val_accuracy: 0.9048\n",
      "Epoch 572/800\n",
      "114/114 [==============================] - 0s 994us/sample - loss: 0.4381 - accuracy: 1.0000 - val_loss: 0.8477 - val_accuracy: 0.9048\n",
      "Epoch 573/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4522 - accuracy: 0.9912 - val_loss: 0.7655 - val_accuracy: 0.9048\n",
      "Epoch 574/800\n",
      "114/114 [==============================] - 0s 963us/sample - loss: 0.4400 - accuracy: 0.9912 - val_loss: 0.7327 - val_accuracy: 0.9048\n",
      "Epoch 575/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4588 - accuracy: 0.9912 - val_loss: 0.7532 - val_accuracy: 0.9048\n",
      "Epoch 576/800\n",
      "114/114 [==============================] - 0s 872us/sample - loss: 0.4396 - accuracy: 1.0000 - val_loss: 0.8018 - val_accuracy: 0.9048\n",
      "Epoch 577/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4528 - accuracy: 0.9912 - val_loss: 0.8456 - val_accuracy: 0.9048\n",
      "Epoch 578/800\n",
      "114/114 [==============================] - 0s 937us/sample - loss: 0.4403 - accuracy: 1.0000 - val_loss: 0.8565 - val_accuracy: 0.9048\n",
      "Epoch 579/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4786 - accuracy: 0.9825 - val_loss: 0.8336 - val_accuracy: 0.9048\n",
      "Epoch 580/800\n",
      "114/114 [==============================] - 0s 917us/sample - loss: 0.4327 - accuracy: 1.0000 - val_loss: 0.7512 - val_accuracy: 0.9048\n",
      "Epoch 581/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4435 - accuracy: 0.9912 - val_loss: 0.6970 - val_accuracy: 0.9048\n",
      "Epoch 582/800\n",
      "114/114 [==============================] - 0s 896us/sample - loss: 0.4393 - accuracy: 1.0000 - val_loss: 0.6986 - val_accuracy: 0.9048\n",
      "Epoch 583/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4378 - accuracy: 0.9912 - val_loss: 0.7242 - val_accuracy: 0.9048\n",
      "Epoch 584/800\n",
      "114/114 [==============================] - 0s 974us/sample - loss: 0.4455 - accuracy: 1.0000 - val_loss: 0.7481 - val_accuracy: 0.9048\n",
      "Epoch 585/800\n",
      "114/114 [==============================] - 0s 893us/sample - loss: 0.4443 - accuracy: 0.9912 - val_loss: 0.7709 - val_accuracy: 0.9048\n",
      "Epoch 586/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.4328 - accuracy: 1.0000 - val_loss: 0.7861 - val_accuracy: 0.9048\n",
      "Epoch 587/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4501 - accuracy: 0.9912 - val_loss: 0.7440 - val_accuracy: 0.9048\n",
      "Epoch 588/800\n",
      "114/114 [==============================] - 0s 966us/sample - loss: 0.4360 - accuracy: 1.0000 - val_loss: 0.7344 - val_accuracy: 0.9048\n",
      "Epoch 589/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4516 - accuracy: 0.9912 - val_loss: 0.7498 - val_accuracy: 0.9048\n",
      "Epoch 590/800\n",
      "114/114 [==============================] - 0s 961us/sample - loss: 0.4494 - accuracy: 0.9912 - val_loss: 0.7589 - val_accuracy: 0.9048\n",
      "Epoch 591/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4387 - accuracy: 0.9912 - val_loss: 0.7673 - val_accuracy: 0.9048\n",
      "Epoch 592/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.4294 - accuracy: 1.0000 - val_loss: 0.7936 - val_accuracy: 0.9048\n",
      "Epoch 593/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4348 - accuracy: 1.0000 - val_loss: 0.8172 - val_accuracy: 0.9048\n",
      "Epoch 594/800\n",
      "114/114 [==============================] - 0s 984us/sample - loss: 0.4463 - accuracy: 0.9912 - val_loss: 0.8024 - val_accuracy: 0.9048\n",
      "Epoch 595/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4337 - accuracy: 1.0000 - val_loss: 0.7613 - val_accuracy: 0.9048\n",
      "Epoch 596/800\n",
      "114/114 [==============================] - 0s 983us/sample - loss: 0.4345 - accuracy: 0.9912 - val_loss: 0.7249 - val_accuracy: 0.9048\n",
      "Epoch 597/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4538 - accuracy: 0.9825 - val_loss: 0.7455 - val_accuracy: 0.9048\n",
      "Epoch 598/800\n",
      "114/114 [==============================] - 0s 900us/sample - loss: 0.4629 - accuracy: 0.9912 - val_loss: 0.7717 - val_accuracy: 0.9048\n",
      "Epoch 599/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4341 - accuracy: 0.9912 - val_loss: 0.8204 - val_accuracy: 0.9048\n",
      "Epoch 600/800\n",
      "114/114 [==============================] - 0s 921us/sample - loss: 0.4510 - accuracy: 0.9825 - val_loss: 0.8227 - val_accuracy: 0.9048\n",
      "Epoch 601/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4325 - accuracy: 1.0000 - val_loss: 0.8190 - val_accuracy: 0.9048\n",
      "Epoch 602/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.4308 - accuracy: 1.0000 - val_loss: 0.8234 - val_accuracy: 0.9048\n",
      "Epoch 603/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4321 - accuracy: 1.0000 - val_loss: 0.8347 - val_accuracy: 0.9048\n",
      "Epoch 604/800\n",
      "114/114 [==============================] - 0s 924us/sample - loss: 0.4281 - accuracy: 1.0000 - val_loss: 0.8468 - val_accuracy: 0.9048\n",
      "Epoch 605/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4372 - accuracy: 0.9912 - val_loss: 0.8547 - val_accuracy: 0.9048\n",
      "Epoch 606/800\n",
      "114/114 [==============================] - 0s 862us/sample - loss: 0.4471 - accuracy: 0.9912 - val_loss: 0.8421 - val_accuracy: 0.9048\n",
      "Epoch 607/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4367 - accuracy: 0.9912 - val_loss: 0.7635 - val_accuracy: 0.9048\n",
      "Epoch 608/800\n",
      "114/114 [==============================] - 0s 933us/sample - loss: 0.4415 - accuracy: 0.9912 - val_loss: 0.7363 - val_accuracy: 0.9048\n",
      "Epoch 609/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4474 - accuracy: 0.9825 - val_loss: 0.7502 - val_accuracy: 0.9048\n",
      "Epoch 610/800\n",
      "114/114 [==============================] - 0s 904us/sample - loss: 0.4256 - accuracy: 1.0000 - val_loss: 0.7867 - val_accuracy: 0.9048\n",
      "Epoch 611/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4814 - accuracy: 0.9737 - val_loss: 0.7503 - val_accuracy: 0.9048\n",
      "Epoch 612/800\n",
      "114/114 [==============================] - 0s 951us/sample - loss: 0.4244 - accuracy: 1.0000 - val_loss: 0.7033 - val_accuracy: 0.9048\n",
      "Epoch 613/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4508 - accuracy: 0.9912 - val_loss: 0.6801 - val_accuracy: 0.9048\n",
      "Epoch 614/800\n",
      "114/114 [==============================] - 0s 900us/sample - loss: 0.4452 - accuracy: 0.9825 - val_loss: 0.6795 - val_accuracy: 0.9524\n",
      "Epoch 615/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4460 - accuracy: 0.9912 - val_loss: 0.6947 - val_accuracy: 0.9048\n",
      "Epoch 616/800\n",
      "114/114 [==============================] - 0s 899us/sample - loss: 0.4439 - accuracy: 0.9912 - val_loss: 0.7485 - val_accuracy: 0.9048\n",
      "Epoch 617/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4488 - accuracy: 0.9912 - val_loss: 0.8170 - val_accuracy: 0.9048\n",
      "Epoch 618/800\n",
      "114/114 [==============================] - 0s 981us/sample - loss: 0.4357 - accuracy: 0.9912 - val_loss: 0.8289 - val_accuracy: 0.9048\n",
      "Epoch 619/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4247 - accuracy: 1.0000 - val_loss: 0.7984 - val_accuracy: 0.9048\n",
      "Epoch 620/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.4403 - accuracy: 0.9912 - val_loss: 0.7779 - val_accuracy: 0.9048\n",
      "Epoch 621/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4190 - accuracy: 1.0000 - val_loss: 0.7875 - val_accuracy: 0.9048\n",
      "Epoch 622/800\n",
      "114/114 [==============================] - 0s 906us/sample - loss: 0.4435 - accuracy: 0.9912 - val_loss: 0.8009 - val_accuracy: 0.9048\n",
      "Epoch 623/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4355 - accuracy: 0.9912 - val_loss: 0.7680 - val_accuracy: 0.9048\n",
      "Epoch 624/800\n",
      "114/114 [==============================] - 0s 963us/sample - loss: 0.4254 - accuracy: 1.0000 - val_loss: 0.7759 - val_accuracy: 0.9048\n",
      "Epoch 625/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4431 - accuracy: 0.9912 - val_loss: 0.7600 - val_accuracy: 0.9048\n",
      "Epoch 626/800\n",
      "114/114 [==============================] - 0s 956us/sample - loss: 0.4474 - accuracy: 0.9825 - val_loss: 0.7585 - val_accuracy: 0.9048\n",
      "Epoch 627/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4296 - accuracy: 0.9912 - val_loss: 0.7614 - val_accuracy: 0.9048\n",
      "Epoch 628/800\n",
      "114/114 [==============================] - 0s 958us/sample - loss: 0.4511 - accuracy: 0.9825 - val_loss: 0.7670 - val_accuracy: 0.9048\n",
      "Epoch 629/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4225 - accuracy: 1.0000 - val_loss: 0.7598 - val_accuracy: 0.9048\n",
      "Epoch 630/800\n",
      "114/114 [==============================] - 0s 906us/sample - loss: 0.4201 - accuracy: 1.0000 - val_loss: 0.7535 - val_accuracy: 0.9048\n",
      "Epoch 631/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4246 - accuracy: 1.0000 - val_loss: 0.7667 - val_accuracy: 0.9048\n",
      "Epoch 632/800\n",
      "114/114 [==============================] - 0s 930us/sample - loss: 0.4236 - accuracy: 0.9912 - val_loss: 0.7869 - val_accuracy: 0.9048\n",
      "Epoch 633/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4377 - accuracy: 0.9912 - val_loss: 0.8313 - val_accuracy: 0.9048\n",
      "Epoch 634/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.4874 - accuracy: 0.9561 - val_loss: 0.7911 - val_accuracy: 0.9048\n",
      "Epoch 635/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4221 - accuracy: 1.0000 - val_loss: 0.6951 - val_accuracy: 0.9048\n",
      "Epoch 636/800\n",
      "114/114 [==============================] - 0s 933us/sample - loss: 0.4489 - accuracy: 0.9825 - val_loss: 0.6799 - val_accuracy: 0.9048\n",
      "Epoch 637/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4431 - accuracy: 0.9912 - val_loss: 0.7048 - val_accuracy: 0.9048\n",
      "Epoch 638/800\n",
      "114/114 [==============================] - 0s 987us/sample - loss: 0.4387 - accuracy: 0.9912 - val_loss: 0.7394 - val_accuracy: 0.9048\n",
      "Epoch 639/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.4412 - accuracy: 0.9825 - val_loss: 0.7693 - val_accuracy: 0.9048\n",
      "Epoch 640/800\n",
      "114/114 [==============================] - 0s 890us/sample - loss: 0.4261 - accuracy: 1.0000 - val_loss: 0.7820 - val_accuracy: 0.9048\n",
      "Epoch 641/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4350 - accuracy: 0.9912 - val_loss: 0.7587 - val_accuracy: 0.9048\n",
      "Epoch 642/800\n",
      "114/114 [==============================] - 0s 915us/sample - loss: 0.4456 - accuracy: 0.9912 - val_loss: 0.7415 - val_accuracy: 0.9048\n",
      "Epoch 643/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4205 - accuracy: 1.0000 - val_loss: 0.6893 - val_accuracy: 0.9048\n",
      "Epoch 644/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4232 - accuracy: 0.9912 - val_loss: 0.6658 - val_accuracy: 0.9048\n",
      "Epoch 645/800\n",
      "114/114 [==============================] - 0s 1000us/sample - loss: 0.4181 - accuracy: 1.0000 - val_loss: 0.6659 - val_accuracy: 0.9048\n",
      "Epoch 646/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4537 - accuracy: 0.9737 - val_loss: 0.7568 - val_accuracy: 0.9048\n",
      "Epoch 647/800\n",
      "114/114 [==============================] - 0s 961us/sample - loss: 0.4389 - accuracy: 0.9912 - val_loss: 0.8217 - val_accuracy: 0.9048\n",
      "Epoch 648/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4506 - accuracy: 0.9912 - val_loss: 0.7922 - val_accuracy: 0.9048\n",
      "Epoch 649/800\n",
      "114/114 [==============================] - 0s 946us/sample - loss: 0.4178 - accuracy: 1.0000 - val_loss: 0.7509 - val_accuracy: 0.9048\n",
      "Epoch 650/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.4220 - accuracy: 0.9912 - val_loss: 0.7387 - val_accuracy: 0.9048\n",
      "Epoch 651/800\n",
      "114/114 [==============================] - 0s 932us/sample - loss: 0.4178 - accuracy: 1.0000 - val_loss: 0.7333 - val_accuracy: 0.9048\n",
      "Epoch 652/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4206 - accuracy: 0.9912 - val_loss: 0.7373 - val_accuracy: 0.9048\n",
      "Epoch 653/800\n",
      "114/114 [==============================] - 0s 928us/sample - loss: 0.4343 - accuracy: 0.9912 - val_loss: 0.7553 - val_accuracy: 0.9048\n",
      "Epoch 654/800\n",
      "114/114 [==============================] - 0s 998us/sample - loss: 0.4354 - accuracy: 0.9912 - val_loss: 0.7286 - val_accuracy: 0.9048\n",
      "Epoch 655/800\n",
      "114/114 [==============================] - 0s 905us/sample - loss: 0.4125 - accuracy: 1.0000 - val_loss: 0.6967 - val_accuracy: 0.9048\n",
      "Epoch 656/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4458 - accuracy: 0.9912 - val_loss: 0.6894 - val_accuracy: 0.9048\n",
      "Epoch 657/800\n",
      "114/114 [==============================] - 0s 935us/sample - loss: 0.4496 - accuracy: 0.9912 - val_loss: 0.7074 - val_accuracy: 0.9048\n",
      "Epoch 658/800\n",
      "114/114 [==============================] - 0s 952us/sample - loss: 0.4570 - accuracy: 0.9825 - val_loss: 0.7857 - val_accuracy: 0.9048\n",
      "Epoch 659/800\n",
      "114/114 [==============================] - 0s 899us/sample - loss: 0.4131 - accuracy: 1.0000 - val_loss: 0.7974 - val_accuracy: 0.9048\n",
      "Epoch 660/800\n",
      "114/114 [==============================] - 0s 998us/sample - loss: 0.4263 - accuracy: 0.9912 - val_loss: 0.7729 - val_accuracy: 0.9048\n",
      "Epoch 661/800\n",
      "114/114 [==============================] - 0s 902us/sample - loss: 0.4247 - accuracy: 0.9912 - val_loss: 0.7467 - val_accuracy: 0.9048\n",
      "Epoch 662/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4449 - accuracy: 0.9912 - val_loss: 0.7199 - val_accuracy: 0.9048\n",
      "Epoch 663/800\n",
      "114/114 [==============================] - 0s 937us/sample - loss: 0.4344 - accuracy: 0.9912 - val_loss: 0.7469 - val_accuracy: 0.9048\n",
      "Epoch 664/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4167 - accuracy: 1.0000 - val_loss: 0.7923 - val_accuracy: 0.9048\n",
      "Epoch 665/800\n",
      "114/114 [==============================] - 0s 981us/sample - loss: 0.4225 - accuracy: 1.0000 - val_loss: 0.7866 - val_accuracy: 0.9048\n",
      "Epoch 666/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4208 - accuracy: 0.9912 - val_loss: 0.8016 - val_accuracy: 0.9048\n",
      "Epoch 667/800\n",
      "114/114 [==============================] - 0s 917us/sample - loss: 0.4171 - accuracy: 1.0000 - val_loss: 0.8023 - val_accuracy: 0.9048\n",
      "Epoch 668/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4520 - accuracy: 0.9912 - val_loss: 0.7387 - val_accuracy: 0.9048\n",
      "Epoch 669/800\n",
      "114/114 [==============================] - 0s 954us/sample - loss: 0.4129 - accuracy: 1.0000 - val_loss: 0.6548 - val_accuracy: 0.9524\n",
      "Epoch 670/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4585 - accuracy: 0.9737 - val_loss: 0.6594 - val_accuracy: 0.9524\n",
      "Epoch 671/800\n",
      "114/114 [==============================] - 0s 985us/sample - loss: 0.4314 - accuracy: 0.9912 - val_loss: 0.6779 - val_accuracy: 0.9048\n",
      "Epoch 672/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4177 - accuracy: 0.9912 - val_loss: 0.7905 - val_accuracy: 0.9048\n",
      "Epoch 673/800\n",
      "114/114 [==============================] - 0s 893us/sample - loss: 0.4124 - accuracy: 1.0000 - val_loss: 0.8338 - val_accuracy: 0.9048\n",
      "Epoch 674/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.4257 - accuracy: 1.0000 - val_loss: 0.8364 - val_accuracy: 0.9048\n",
      "Epoch 675/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4183 - accuracy: 0.9912 - val_loss: 0.8006 - val_accuracy: 0.9048\n",
      "Epoch 676/800\n",
      "114/114 [==============================] - 0s 891us/sample - loss: 0.4110 - accuracy: 1.0000 - val_loss: 0.6953 - val_accuracy: 0.9048\n",
      "Epoch 677/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4175 - accuracy: 0.9912 - val_loss: 0.6529 - val_accuracy: 0.9524\n",
      "Epoch 678/800\n",
      "114/114 [==============================] - 0s 905us/sample - loss: 0.4270 - accuracy: 0.9912 - val_loss: 0.6554 - val_accuracy: 0.9524\n",
      "Epoch 679/800\n",
      "114/114 [==============================] - 0s 907us/sample - loss: 0.4139 - accuracy: 1.0000 - val_loss: 0.7305 - val_accuracy: 0.9048\n",
      "Epoch 680/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.4157 - accuracy: 1.0000 - val_loss: 0.7953 - val_accuracy: 0.9048\n",
      "Epoch 681/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4106 - accuracy: 1.0000 - val_loss: 0.8231 - val_accuracy: 0.9048\n",
      "Epoch 682/800\n",
      "114/114 [==============================] - 0s 983us/sample - loss: 0.4279 - accuracy: 0.9912 - val_loss: 0.8104 - val_accuracy: 0.9048\n",
      "Epoch 683/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4144 - accuracy: 1.0000 - val_loss: 0.7191 - val_accuracy: 0.9048\n",
      "Epoch 684/800\n",
      "114/114 [==============================] - 0s 908us/sample - loss: 0.4265 - accuracy: 0.9912 - val_loss: 0.6672 - val_accuracy: 0.9048\n",
      "Epoch 685/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4223 - accuracy: 0.9912 - val_loss: 0.6996 - val_accuracy: 0.9048\n",
      "Epoch 686/800\n",
      "114/114 [==============================] - 0s 927us/sample - loss: 0.4086 - accuracy: 1.0000 - val_loss: 0.7850 - val_accuracy: 0.9048\n",
      "Epoch 687/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4056 - accuracy: 1.0000 - val_loss: 0.8212 - val_accuracy: 0.9048\n",
      "Epoch 688/800\n",
      "114/114 [==============================] - 0s 975us/sample - loss: 0.4101 - accuracy: 1.0000 - val_loss: 0.7888 - val_accuracy: 0.9048\n",
      "Epoch 689/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4112 - accuracy: 1.0000 - val_loss: 0.7421 - val_accuracy: 0.9048\n",
      "Epoch 690/800\n",
      "114/114 [==============================] - 0s 916us/sample - loss: 0.4074 - accuracy: 1.0000 - val_loss: 0.7043 - val_accuracy: 0.9048\n",
      "Epoch 691/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4081 - accuracy: 1.0000 - val_loss: 0.6934 - val_accuracy: 0.9048\n",
      "Epoch 692/800\n",
      "114/114 [==============================] - 0s 989us/sample - loss: 0.4059 - accuracy: 1.0000 - val_loss: 0.6892 - val_accuracy: 0.9048\n",
      "Epoch 693/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4108 - accuracy: 0.9912 - val_loss: 0.6709 - val_accuracy: 0.9048\n",
      "Epoch 694/800\n",
      "114/114 [==============================] - 0s 988us/sample - loss: 0.4110 - accuracy: 0.9912 - val_loss: 0.6484 - val_accuracy: 0.9524\n",
      "Epoch 695/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4218 - accuracy: 0.9912 - val_loss: 0.6820 - val_accuracy: 0.9048\n",
      "Epoch 696/800\n",
      "114/114 [==============================] - 0s 916us/sample - loss: 0.4017 - accuracy: 1.0000 - val_loss: 0.8033 - val_accuracy: 0.9048\n",
      "Epoch 697/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4076 - accuracy: 1.0000 - val_loss: 0.8563 - val_accuracy: 0.9048\n",
      "Epoch 698/800\n",
      "114/114 [==============================] - 0s 932us/sample - loss: 0.4399 - accuracy: 0.9825 - val_loss: 0.8636 - val_accuracy: 0.9048\n",
      "Epoch 699/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4089 - accuracy: 0.9912 - val_loss: 0.7392 - val_accuracy: 0.9048\n",
      "Epoch 700/800\n",
      "114/114 [==============================] - 0s 971us/sample - loss: 0.4070 - accuracy: 1.0000 - val_loss: 0.6674 - val_accuracy: 0.9524\n",
      "Epoch 701/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4125 - accuracy: 0.9912 - val_loss: 0.6724 - val_accuracy: 0.9524\n",
      "Epoch 702/800\n",
      "114/114 [==============================] - 0s 963us/sample - loss: 0.4300 - accuracy: 0.9912 - val_loss: 0.6699 - val_accuracy: 0.9524\n",
      "Epoch 703/800\n",
      "114/114 [==============================] - 0s 905us/sample - loss: 0.4215 - accuracy: 0.9912 - val_loss: 0.7071 - val_accuracy: 0.9048\n",
      "Epoch 704/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.4135 - accuracy: 0.9912 - val_loss: 0.7885 - val_accuracy: 0.9048\n",
      "Epoch 705/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4086 - accuracy: 1.0000 - val_loss: 0.8071 - val_accuracy: 0.9048\n",
      "Epoch 706/800\n",
      "114/114 [==============================] - 0s 933us/sample - loss: 0.4110 - accuracy: 0.9912 - val_loss: 0.8005 - val_accuracy: 0.9048\n",
      "Epoch 707/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4031 - accuracy: 1.0000 - val_loss: 0.7727 - val_accuracy: 0.9048\n",
      "Epoch 708/800\n",
      "114/114 [==============================] - 0s 965us/sample - loss: 0.4005 - accuracy: 1.0000 - val_loss: 0.7337 - val_accuracy: 0.9048\n",
      "Epoch 709/800\n",
      "114/114 [==============================] - 0s 952us/sample - loss: 0.4037 - accuracy: 1.0000 - val_loss: 0.7238 - val_accuracy: 0.9048\n",
      "Epoch 710/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.3973 - accuracy: 1.0000 - val_loss: 0.7568 - val_accuracy: 0.9048\n",
      "Epoch 711/800\n",
      "114/114 [==============================] - 0s 978us/sample - loss: 0.4030 - accuracy: 1.0000 - val_loss: 0.7931 - val_accuracy: 0.9048\n",
      "Epoch 712/800\n",
      "114/114 [==============================] - 0s 899us/sample - loss: 0.4053 - accuracy: 0.9912 - val_loss: 0.7961 - val_accuracy: 0.9048\n",
      "Epoch 713/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.4097 - accuracy: 0.9912 - val_loss: 0.7667 - val_accuracy: 0.9048\n",
      "Epoch 714/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4066 - accuracy: 1.0000 - val_loss: 0.8050 - val_accuracy: 0.9048\n",
      "Epoch 715/800\n",
      "114/114 [==============================] - 0s 948us/sample - loss: 0.3989 - accuracy: 1.0000 - val_loss: 0.8307 - val_accuracy: 0.9048\n",
      "Epoch 716/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4048 - accuracy: 1.0000 - val_loss: 0.8330 - val_accuracy: 0.9048\n",
      "Epoch 717/800\n",
      "114/114 [==============================] - 0s 933us/sample - loss: 0.3982 - accuracy: 1.0000 - val_loss: 0.7997 - val_accuracy: 0.9048\n",
      "Epoch 718/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.3992 - accuracy: 1.0000 - val_loss: 0.8082 - val_accuracy: 0.9048\n",
      "Epoch 719/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4444 - accuracy: 0.9825 - val_loss: 0.7778 - val_accuracy: 0.9048\n",
      "Epoch 720/800\n",
      "114/114 [==============================] - 0s 988us/sample - loss: 0.4564 - accuracy: 0.9825 - val_loss: 0.7080 - val_accuracy: 0.9048\n",
      "Epoch 721/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4065 - accuracy: 0.9912 - val_loss: 0.7251 - val_accuracy: 0.9048\n",
      "Epoch 722/800\n",
      "114/114 [==============================] - 0s 973us/sample - loss: 0.3973 - accuracy: 1.0000 - val_loss: 0.7580 - val_accuracy: 0.9048\n",
      "Epoch 723/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4574 - accuracy: 0.9737 - val_loss: 0.8876 - val_accuracy: 0.9048\n",
      "Epoch 724/800\n",
      "114/114 [==============================] - 0s 985us/sample - loss: 0.4197 - accuracy: 0.9825 - val_loss: 0.8824 - val_accuracy: 0.9048\n",
      "Epoch 725/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4194 - accuracy: 0.9912 - val_loss: 0.8445 - val_accuracy: 0.9048\n",
      "Epoch 726/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.4061 - accuracy: 0.9912 - val_loss: 0.7460 - val_accuracy: 0.9048\n",
      "Epoch 727/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.3956 - accuracy: 1.0000 - val_loss: 0.6701 - val_accuracy: 0.9524\n",
      "Epoch 728/800\n",
      "114/114 [==============================] - 0s 914us/sample - loss: 0.4081 - accuracy: 0.9912 - val_loss: 0.6728 - val_accuracy: 0.9524\n",
      "Epoch 729/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4452 - accuracy: 0.9912 - val_loss: 0.6687 - val_accuracy: 0.9524\n",
      "Epoch 730/800\n",
      "114/114 [==============================] - 0s 890us/sample - loss: 0.4030 - accuracy: 0.9912 - val_loss: 0.6656 - val_accuracy: 0.9524\n",
      "Epoch 731/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.3961 - accuracy: 1.0000 - val_loss: 0.6918 - val_accuracy: 0.9048\n",
      "Epoch 732/800\n",
      "114/114 [==============================] - 0s 944us/sample - loss: 0.4095 - accuracy: 0.9912 - val_loss: 0.7665 - val_accuracy: 0.9048\n",
      "Epoch 733/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4030 - accuracy: 0.9912 - val_loss: 0.8301 - val_accuracy: 0.9048\n",
      "Epoch 734/800\n",
      "114/114 [==============================] - 0s 949us/sample - loss: 0.3955 - accuracy: 1.0000 - val_loss: 0.8408 - val_accuracy: 0.9048\n",
      "Epoch 735/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4376 - accuracy: 0.9912 - val_loss: 0.8409 - val_accuracy: 0.9048\n",
      "Epoch 736/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.3966 - accuracy: 1.0000 - val_loss: 0.8254 - val_accuracy: 0.9048\n",
      "Epoch 737/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.3963 - accuracy: 1.0000 - val_loss: 0.7550 - val_accuracy: 0.9048\n",
      "Epoch 738/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4101 - accuracy: 0.9825 - val_loss: 0.7235 - val_accuracy: 0.9048\n",
      "Epoch 739/800\n",
      "114/114 [==============================] - 0s 943us/sample - loss: 0.3943 - accuracy: 1.0000 - val_loss: 0.8217 - val_accuracy: 0.9048\n",
      "Epoch 740/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.3971 - accuracy: 1.0000 - val_loss: 0.8485 - val_accuracy: 0.9048\n",
      "Epoch 741/800\n",
      "114/114 [==============================] - 0s 937us/sample - loss: 0.3916 - accuracy: 1.0000 - val_loss: 0.8570 - val_accuracy: 0.9048\n",
      "Epoch 742/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4174 - accuracy: 0.9825 - val_loss: 0.7563 - val_accuracy: 0.9048\n",
      "Epoch 743/800\n",
      "114/114 [==============================] - 0s 966us/sample - loss: 0.4692 - accuracy: 0.9737 - val_loss: 0.6679 - val_accuracy: 0.9524\n",
      "Epoch 744/800\n",
      "114/114 [==============================] - 0s 903us/sample - loss: 0.3961 - accuracy: 1.0000 - val_loss: 0.6771 - val_accuracy: 0.9524\n",
      "Epoch 745/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.4337 - accuracy: 0.9825 - val_loss: 0.6751 - val_accuracy: 0.9524\n",
      "Epoch 746/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4218 - accuracy: 0.9825 - val_loss: 0.6395 - val_accuracy: 0.9524\n",
      "Epoch 747/800\n",
      "114/114 [==============================] - 0s 958us/sample - loss: 0.4114 - accuracy: 0.9912 - val_loss: 0.7245 - val_accuracy: 0.9048\n",
      "Epoch 748/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4126 - accuracy: 0.9912 - val_loss: 0.8059 - val_accuracy: 0.9048\n",
      "Epoch 749/800\n",
      "114/114 [==============================] - 0s 951us/sample - loss: 0.3941 - accuracy: 1.0000 - val_loss: 0.7804 - val_accuracy: 0.9524\n",
      "Epoch 750/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4273 - accuracy: 0.9825 - val_loss: 0.7774 - val_accuracy: 0.9524\n",
      "Epoch 751/800\n",
      "114/114 [==============================] - 0s 939us/sample - loss: 0.4245 - accuracy: 0.9825 - val_loss: 0.7341 - val_accuracy: 0.9048\n",
      "Epoch 752/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.3933 - accuracy: 1.0000 - val_loss: 0.6347 - val_accuracy: 0.9524\n",
      "Epoch 753/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4271 - accuracy: 0.9825 - val_loss: 0.6559 - val_accuracy: 0.9524\n",
      "Epoch 754/800\n",
      "114/114 [==============================] - 0s 963us/sample - loss: 0.4201 - accuracy: 0.9737 - val_loss: 0.6421 - val_accuracy: 0.9524\n",
      "Epoch 755/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4302 - accuracy: 0.9825 - val_loss: 0.6594 - val_accuracy: 0.9048\n",
      "Epoch 756/800\n",
      "114/114 [==============================] - 0s 982us/sample - loss: 0.4385 - accuracy: 0.9737 - val_loss: 0.7242 - val_accuracy: 0.9048\n",
      "Epoch 757/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.4159 - accuracy: 0.9912 - val_loss: 0.7290 - val_accuracy: 0.9048\n",
      "Epoch 758/800\n",
      "114/114 [==============================] - 0s 913us/sample - loss: 0.3955 - accuracy: 1.0000 - val_loss: 0.7462 - val_accuracy: 0.9048\n",
      "Epoch 759/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.4073 - accuracy: 0.9912 - val_loss: 0.7615 - val_accuracy: 0.9048\n",
      "Epoch 760/800\n",
      "114/114 [==============================] - 0s 928us/sample - loss: 0.4331 - accuracy: 0.9825 - val_loss: 0.7511 - val_accuracy: 0.9048\n",
      "Epoch 761/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.4091 - accuracy: 0.9912 - val_loss: 0.7469 - val_accuracy: 0.9048\n",
      "Epoch 762/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4168 - accuracy: 0.9825 - val_loss: 0.7491 - val_accuracy: 0.9048\n",
      "Epoch 763/800\n",
      "114/114 [==============================] - 0s 976us/sample - loss: 0.4015 - accuracy: 0.9912 - val_loss: 0.7402 - val_accuracy: 0.9048\n",
      "Epoch 764/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.3952 - accuracy: 0.9912 - val_loss: 0.7274 - val_accuracy: 0.9048\n",
      "Epoch 765/800\n",
      "114/114 [==============================] - 0s 996us/sample - loss: 0.3962 - accuracy: 1.0000 - val_loss: 0.7100 - val_accuracy: 0.9048\n",
      "Epoch 766/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.3900 - accuracy: 1.0000 - val_loss: 0.6997 - val_accuracy: 0.9048\n",
      "Epoch 767/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.4109 - accuracy: 0.9912 - val_loss: 0.6760 - val_accuracy: 0.9048\n",
      "Epoch 768/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.3906 - accuracy: 1.0000 - val_loss: 0.6377 - val_accuracy: 0.9524\n",
      "Epoch 769/800\n",
      "114/114 [==============================] - 0s 953us/sample - loss: 0.4107 - accuracy: 0.9825 - val_loss: 0.6383 - val_accuracy: 0.9524\n",
      "Epoch 770/800\n",
      "114/114 [==============================] - 0s 900us/sample - loss: 0.3981 - accuracy: 0.9912 - val_loss: 0.6348 - val_accuracy: 0.9524\n",
      "Epoch 771/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.4060 - accuracy: 0.9825 - val_loss: 0.6475 - val_accuracy: 0.9048\n",
      "Epoch 772/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.3909 - accuracy: 1.0000 - val_loss: 0.7025 - val_accuracy: 0.9048\n",
      "Epoch 773/800\n",
      "114/114 [==============================] - 0s 974us/sample - loss: 0.3847 - accuracy: 1.0000 - val_loss: 0.7398 - val_accuracy: 0.9048\n",
      "Epoch 774/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4202 - accuracy: 0.9912 - val_loss: 0.7692 - val_accuracy: 0.9048\n",
      "Epoch 775/800\n",
      "114/114 [==============================] - 0s 940us/sample - loss: 0.3992 - accuracy: 0.9912 - val_loss: 0.7830 - val_accuracy: 0.9048\n",
      "Epoch 776/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4168 - accuracy: 0.9737 - val_loss: 0.7455 - val_accuracy: 0.9048\n",
      "Epoch 777/800\n",
      "114/114 [==============================] - 0s 892us/sample - loss: 0.4089 - accuracy: 0.9825 - val_loss: 0.6853 - val_accuracy: 0.9048\n",
      "Epoch 778/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4089 - accuracy: 0.9825 - val_loss: 0.6980 - val_accuracy: 0.9048\n",
      "Epoch 779/800\n",
      "114/114 [==============================] - 0s 976us/sample - loss: 0.3925 - accuracy: 0.9912 - val_loss: 0.7169 - val_accuracy: 0.9048\n",
      "Epoch 780/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4044 - accuracy: 0.9912 - val_loss: 0.7209 - val_accuracy: 0.9048\n",
      "Epoch 781/800\n",
      "114/114 [==============================] - 0s 949us/sample - loss: 0.3865 - accuracy: 1.0000 - val_loss: 0.7491 - val_accuracy: 0.9048\n",
      "Epoch 782/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.3807 - accuracy: 1.0000 - val_loss: 0.7685 - val_accuracy: 0.9048\n",
      "Epoch 783/800\n",
      "114/114 [==============================] - 0s 895us/sample - loss: 0.4550 - accuracy: 0.9737 - val_loss: 0.7725 - val_accuracy: 0.9048\n",
      "Epoch 784/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4100 - accuracy: 0.9912 - val_loss: 0.7178 - val_accuracy: 0.9048\n",
      "Epoch 785/800\n",
      "114/114 [==============================] - 0s 902us/sample - loss: 0.3869 - accuracy: 1.0000 - val_loss: 0.6690 - val_accuracy: 0.9048\n",
      "Epoch 786/800\n",
      "114/114 [==============================] - 0s 1ms/sample - loss: 0.3973 - accuracy: 0.9825 - val_loss: 0.6427 - val_accuracy: 0.9524\n",
      "Epoch 787/800\n",
      "114/114 [==============================] - 0s 901us/sample - loss: 0.3828 - accuracy: 1.0000 - val_loss: 0.6371 - val_accuracy: 0.9524\n",
      "Epoch 788/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.3995 - accuracy: 0.9912 - val_loss: 0.6510 - val_accuracy: 0.9048\n",
      "Epoch 789/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.3830 - accuracy: 1.0000 - val_loss: 0.6930 - val_accuracy: 0.9048\n",
      "Epoch 790/800\n",
      "114/114 [==============================] - 0s 945us/sample - loss: 0.4274 - accuracy: 0.9825 - val_loss: 0.7402 - val_accuracy: 0.9048\n",
      "Epoch 791/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.3939 - accuracy: 0.9912 - val_loss: 0.8023 - val_accuracy: 0.9048\n",
      "Epoch 792/800\n",
      "114/114 [==============================] - 0s 941us/sample - loss: 0.4192 - accuracy: 0.9825 - val_loss: 0.7407 - val_accuracy: 0.9048\n",
      "Epoch 793/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.3807 - accuracy: 1.0000 - val_loss: 0.6511 - val_accuracy: 0.9048\n",
      "Epoch 794/800\n",
      "114/114 [==============================] - 0s 963us/sample - loss: 0.4045 - accuracy: 0.9912 - val_loss: 0.6430 - val_accuracy: 0.9524\n",
      "Epoch 795/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.4180 - accuracy: 0.9825 - val_loss: 0.6634 - val_accuracy: 0.9048\n",
      "Epoch 796/800\n",
      "114/114 [==============================] - 0s 894us/sample - loss: 0.3825 - accuracy: 1.0000 - val_loss: 0.7321 - val_accuracy: 0.9048\n",
      "Epoch 797/800\n",
      "114/114 [==============================] - 0s 2ms/sample - loss: 0.3860 - accuracy: 1.0000 - val_loss: 0.7647 - val_accuracy: 0.9048\n",
      "Epoch 798/800\n",
      "114/114 [==============================] - 0s 928us/sample - loss: 0.3969 - accuracy: 0.9912 - val_loss: 0.7960 - val_accuracy: 0.9048\n",
      "Epoch 799/800\n",
      "114/114 [==============================] - 0s 987us/sample - loss: 0.3825 - accuracy: 1.0000 - val_loss: 0.8049 - val_accuracy: 0.9048\n",
      "Epoch 800/800\n",
      "114/114 [==============================] - 0s 929us/sample - loss: 0.3939 - accuracy: 1.0000 - val_loss: 0.7819 - val_accuracy: 0.9048\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "\n",
    "reg_history = train_model(reg_model, train_data, train_targets, epochs=800)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot the learning curves\n",
    "\n",
    "Let's now plot the loss and accuracy for the training and validation sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Run this cell to plot the new accuracy vs epoch graph\n",
    "\n",
    "try:\n",
    "    plt.plot(reg_history.history['accuracy'])\n",
    "    plt.plot(reg_history.history['val_accuracy'])\n",
    "except KeyError:\n",
    "    plt.plot(reg_history.history['acc'])\n",
    "    plt.plot(reg_history.history['val_acc'])\n",
    "plt.title('Accuracy vs. epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Training', 'Validation'], loc='lower right')\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Run this cell to plot the new loss vs epoch graph\n",
    "\n",
    "plt.plot(reg_history.history['loss'])\n",
    "plt.plot(reg_history.history['val_loss'])\n",
    "plt.title('Loss vs. epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Training', 'Validation'], loc='upper right')\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the regularisation has helped to reduce the overfitting of the network.\n",
    "You will now incorporate callbacks into a new training run that implements early stopping and learning rate reduction on plateaux.\n",
    "\n",
    "Fill in the function below so that:\n",
    "\n",
    "* It creates an `EarlyStopping` callback object and a `ReduceLROnPlateau` callback object\n",
    "* The early stopping callback is used and monitors validation loss with the mode set to `\"min\"` and patience of 30.\n",
    "* The learning rate reduction on plateaux is used with a learning rate factor of 0.2 and a patience of 20."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### GRADED CELL ####\n",
    "\n",
    "# Complete the following function. \n",
    "# Make sure to not change the function name or arguments.\n",
    "\n",
    "def get_callbacks():\n",
    "    \"\"\"\n",
    "    This function should create and return a tuple (early_stopping, learning_rate_reduction) callbacks.\n",
    "    The callbacks should be instantiated according to the above requirements.\n",
    "    \"\"\"\n",
    "    early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=30, mode='min')\n",
    "    lr_reduce = tf.keras.callbacks.ReduceLROnPlateau(patience=20, factor=0.2)\n",
    "    \n",
    "    return early_stopping, lr_reduce\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the cell below to instantiate and train the regularised model with the callbacks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "call_model = get_regularised_model(train_data[0].shape, 0.3, 0.0001)\n",
    "compile_model(call_model)\n",
    "early_stopping, learning_rate_reduction = get_callbacks()\n",
    "call_history = call_model.fit(train_data, train_targets, epochs=800, validation_split=0.15,\n",
    "                         callbacks=[early_stopping, learning_rate_reduction], verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learning_rate_reduction.patience"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's replot the accuracy and loss graphs for our new model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "try:\n",
    "    plt.plot(call_history.history['accuracy'])\n",
    "    plt.plot(call_history.history['val_accuracy'])\n",
    "except KeyError:\n",
    "    plt.plot(call_history.history['acc'])\n",
    "    plt.plot(call_history.history['val_acc'])\n",
    "plt.title('Accuracy vs. epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Training', 'Validation'], loc='lower right')\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(call_history.history['loss'])\n",
    "plt.plot(call_history.history['val_loss'])\n",
    "plt.title('Loss vs. epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Training', 'Validation'], loc='upper right')\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.099\n",
      "Test accuracy: 100.00%\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the test set\n",
    "\n",
    "test_loss, test_acc = call_model.evaluate(test_data, test_targets, verbose=0)\n",
    "print(\"Test loss: {:.3f}\\nTest accuracy: {:.2f}%\".format(test_loss, 100 * test_acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congratulations for completing this programming assignment! In the next week of the course we will learn how to save and load pre-trained models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "coursera": {
   "course_slug": "tensor-flow-2-1",
   "graded_item_id": "mtZ4n",
   "launcher_item_id": "WphgK"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}