supervised-learning/neural-networks/Neural_Netwokr_Cat_NotCat.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementing CNN for cat or non-Cat\n",
    "\n",
    "## Import some Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import some libraries\n",
    "from keras.models import Sequential\n",
    "import glob\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "from skimage import transform\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Colleting Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "width = 68\n",
    "height = 68\n",
    "X = []\n",
    "y = []\n",
    "def get_dataset(path_image, Cat=True):\n",
    "    images = glob.glob(path_image+\"*.png\")\n",
    "    \n",
    "    for image in images:\n",
    "        img = Image.open(image)\n",
    "        img = np.asarray(img)\n",
    "        if Cat:\n",
    "            y.append(1)\n",
    "        else:\n",
    "            y.append(0)\n",
    "        img = transform.resize(img,(width, height))\n",
    "        X.append(img)\n",
    "        \n",
    "path_images_cat = 'train/cat/'\n",
    "path_images_n_cat = 'train/noncat/'\n",
    "\n",
    "get_dataset(path_images_cat)\n",
    "get_dataset(path_images_n_cat, False)\n",
    "\n",
    "X = np.asarray(X)\n",
    "y = np.asarray(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X: (209, 68, 68, 3)\n",
      "Shape of y: (209,)\n"
     ]
    }
   ],
   "source": [
    "# check the shape of data set and labels\n",
    "print(\"Shape of X: {}\".format(X.shape))\n",
    "print(\"Shape of y: {}\".format(y.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build the CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 500\n",
    "batch_size = 128\n",
    "BATCH_NORM = False\n",
    "num_class = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sequential model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Stacking layes\n",
    "from keras.models import Sequential\n",
    "from keras.layers.convolutional import Convolution2D, MaxPooling2D, Conv2D\n",
    "from keras.layers import Flatten, Dense, Activation, Dropout\n",
    "from keras.optimizers import Adam, RMSprop, SGD\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(128, (3, 3), padding='same', input_shape=X.shape[1:]))\n",
    "model.add(BatchNormalization()) if BATCH_NORM else None\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "model.add(Conv2D(32, (3, 3), padding='same'))\n",
    "model.add(BatchNormalization()) if BATCH_NORM else None\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "model.add(Flatten())\n",
    "\n",
    "model.add(Dense(256))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(num_class))\n",
    "model.add(Activation('softmax'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set the optimizer & compile the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set learning rate\n",
    "lr = 0.001\n",
    "opt = RMSprop(lr)\n",
    "model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_9 (Conv2D)            (None, 68, 68, 128)       3584      \n",
      "_________________________________________________________________\n",
      "activation_7 (Activation)    (None, 68, 68, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 68, 68, 32)        36896     \n",
      "_________________________________________________________________\n",
      "activation_8 (Activation)    (None, 68, 68, 32)        0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 147968)            0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 256)               37880064  \n",
      "_________________________________________________________________\n",
      "activation_9 (Activation)    (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 2)                 514       \n",
      "_________________________________________________________________\n",
      "activation_10 (Activation)   (None, 2)                 0         \n",
      "=================================================================\n",
      "Total params: 37,921,058\n",
      "Trainable params: 37,921,058\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=5)\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(\n",
    "    X_train, y_train, test_size=0.2, random_state=5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transfor Vector to Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of y_train: (133,)\t Shape of y_val: (34,)\tShape of y_test: (42,)\n",
      "Shape of y_train: (133, 2)\t Shape of y_val: (34, 2)\tShape of y_test: (42, 2)\n"
     ]
    }
   ],
   "source": [
    "from keras.utils import np_utils\n",
    "print(\"Shape of y_train: {}\\t Shape of y_val: {}\\tShape of y_test: {}\".format(y_train.shape, y_val.shape, y_test.shape))\n",
    "\n",
    "y_train = np_utils.to_categorical(y_train)\n",
    "y_test = np_utils.to_categorical(y_test)\n",
    "y_val = np_utils.to_categorical(y_val)\n",
    "\n",
    "print(\"Shape of y_train: {}\\t Shape of y_val: {}\\tShape of y_test: {}\".format(y_train.shape, y_val.shape, y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/neemiasbsilva/venv/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "Train on 133 samples, validate on 34 samples\n",
      "Epoch 1/500\n",
      "133/133 [==============================] - 5s 35ms/step - loss: 8.8025 - accuracy: 0.6241 - val_loss: 7.7855 - val_accuracy: 0.3235\n",
      "Epoch 2/500\n",
      "133/133 [==============================] - 3s 25ms/step - loss: 7.2400 - accuracy: 0.3759 - val_loss: 3.3855 - val_accuracy: 0.6765\n",
      "Epoch 3/500\n",
      "133/133 [==============================] - 3s 25ms/step - loss: 3.4480 - accuracy: 0.6241 - val_loss: 0.6965 - val_accuracy: 0.6765\n",
      "Epoch 4/500\n",
      "133/133 [==============================] - 3s 25ms/step - loss: 0.6637 - accuracy: 0.6316 - val_loss: 0.6547 - val_accuracy: 0.6765\n",
      "Epoch 5/500\n",
      "133/133 [==============================] - 3s 26ms/step - loss: 0.6433 - accuracy: 0.6541 - val_loss: 0.6424 - val_accuracy: 0.6765\n",
      "Epoch 6/500\n",
      "133/133 [==============================] - 4s 29ms/step - loss: 0.6349 - accuracy: 0.6316 - val_loss: 1.0825 - val_accuracy: 0.6765\n",
      "Epoch 7/500\n",
      "133/133 [==============================] - 4s 26ms/step - loss: 1.4591 - accuracy: 0.6316 - val_loss: 0.6565 - val_accuracy: 0.6765\n",
      "Epoch 8/500\n",
      "133/133 [==============================] - 4s 29ms/step - loss: 0.6654 - accuracy: 0.6316 - val_loss: 0.6422 - val_accuracy: 0.6765\n",
      "Epoch 9/500\n",
      "133/133 [==============================] - 4s 29ms/step - loss: 0.6506 - accuracy: 0.6165 - val_loss: 0.6232 - val_accuracy: 0.6765\n",
      "Epoch 10/500\n",
      "133/133 [==============================] - 4s 29ms/step - loss: 0.6219 - accuracy: 0.6617 - val_loss: 0.6018 - val_accuracy: 0.6765\n",
      "Epoch 11/500\n",
      "133/133 [==============================] - 4s 29ms/step - loss: 0.5539 - accuracy: 0.7594 - val_loss: 9.2963 - val_accuracy: 0.6765\n",
      "Epoch 12/500\n",
      "133/133 [==============================] - 4s 28ms/step - loss: 9.4395 - accuracy: 0.6316 - val_loss: 0.5987 - val_accuracy: 0.6765\n",
      "Epoch 13/500\n",
      "133/133 [==============================] - 4s 28ms/step - loss: 0.5659 - accuracy: 0.6692 - val_loss: 0.5712 - val_accuracy: 0.6765\n",
      "Epoch 14/500\n",
      "133/133 [==============================] - 4s 29ms/step - loss: 0.5086 - accuracy: 0.7444 - val_loss: 0.4935 - val_accuracy: 0.7353\n",
      "Epoch 15/500\n",
      "133/133 [==============================] - 4s 29ms/step - loss: 0.4727 - accuracy: 0.7519 - val_loss: 0.4711 - val_accuracy: 0.7353\n",
      "Epoch 16/500\n",
      "133/133 [==============================] - 4s 29ms/step - loss: 0.4341 - accuracy: 0.7744 - val_loss: 0.5405 - val_accuracy: 0.6765\n",
      "Epoch 17/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 0.5313 - accuracy: 0.6692 - val_loss: 0.4716 - val_accuracy: 0.7941\n",
      "Epoch 18/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 0.4087 - accuracy: 0.8722 - val_loss: 0.7467 - val_accuracy: 0.6765\n",
      "Epoch 19/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.7440 - accuracy: 0.6316 - val_loss: 1.6536 - val_accuracy: 0.6765\n",
      "Epoch 20/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.4766 - accuracy: 0.6617 - val_loss: 0.5308 - val_accuracy: 0.6765\n",
      "Epoch 21/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.4358 - accuracy: 0.8195 - val_loss: 0.5587 - val_accuracy: 0.7353\n",
      "Epoch 22/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.4460 - accuracy: 0.7594 - val_loss: 0.4832 - val_accuracy: 0.7647\n",
      "Epoch 23/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.4435 - accuracy: 0.8797 - val_loss: 2.5151 - val_accuracy: 0.3529\n",
      "Epoch 24/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 2.1745 - accuracy: 0.4286 - val_loss: 0.6111 - val_accuracy: 0.7059\n",
      "Epoch 25/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.5285 - accuracy: 0.8346 - val_loss: 0.5993 - val_accuracy: 0.7353\n",
      "Epoch 26/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.5070 - accuracy: 0.8496 - val_loss: 0.5840 - val_accuracy: 0.7059\n",
      "Epoch 27/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.4782 - accuracy: 0.8120 - val_loss: 0.5663 - val_accuracy: 0.7059\n",
      "Epoch 28/500\n",
      "133/133 [==============================] - 5s 37ms/step - loss: 0.4310 - accuracy: 0.8421 - val_loss: 0.8277 - val_accuracy: 0.6765\n",
      "Epoch 29/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 0.8079 - accuracy: 0.6466 - val_loss: 0.4731 - val_accuracy: 0.7353\n",
      "Epoch 30/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.3787 - accuracy: 0.8722 - val_loss: 0.3929 - val_accuracy: 0.8235\n",
      "Epoch 31/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 0.3245 - accuracy: 0.9098 - val_loss: 0.3860 - val_accuracy: 0.8529\n",
      "Epoch 32/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 0.2857 - accuracy: 0.8571 - val_loss: 0.4237 - val_accuracy: 0.7353\n",
      "Epoch 33/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.3151 - accuracy: 0.8647 - val_loss: 0.4228 - val_accuracy: 0.7647\n",
      "Epoch 34/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.3127 - accuracy: 0.8346 - val_loss: 0.7711 - val_accuracy: 0.4118\n",
      "Epoch 35/500\n",
      "133/133 [==============================] - 5s 37ms/step - loss: 0.4491 - accuracy: 0.7368 - val_loss: 0.4936 - val_accuracy: 0.7059\n",
      "Epoch 36/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 0.3461 - accuracy: 0.9023 - val_loss: 0.4129 - val_accuracy: 0.7353\n",
      "Epoch 37/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 0.2630 - accuracy: 0.8797 - val_loss: 0.3894 - val_accuracy: 0.7647\n",
      "Epoch 38/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 0.2598 - accuracy: 0.8872 - val_loss: 0.5112 - val_accuracy: 0.7353\n",
      "Epoch 39/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 0.3139 - accuracy: 0.8496 - val_loss: 0.4494 - val_accuracy: 0.7059\n",
      "Epoch 40/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 0.2790 - accuracy: 0.8571 - val_loss: 0.6677 - val_accuracy: 0.6471\n",
      "Epoch 41/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.3101 - accuracy: 0.8195 - val_loss: 0.3907 - val_accuracy: 0.7353\n",
      "Epoch 42/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 0.1944 - accuracy: 0.9173 - val_loss: 0.3671 - val_accuracy: 0.7941\n",
      "Epoch 43/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 0.1481 - accuracy: 0.9323 - val_loss: 1.3350 - val_accuracy: 0.7059\n",
      "Epoch 44/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 1.5534 - accuracy: 0.7143 - val_loss: 2.1899 - val_accuracy: 0.6765\n",
      "Epoch 45/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 2.1354 - accuracy: 0.6466 - val_loss: 0.8322 - val_accuracy: 0.6765\n",
      "Epoch 46/500\n",
      "133/133 [==============================] - 5s 34ms/step - loss: 0.7024 - accuracy: 0.7218 - val_loss: 0.5794 - val_accuracy: 0.5882\n",
      "Epoch 47/500\n",
      "133/133 [==============================] - 5s 34ms/step - loss: 0.3659 - accuracy: 0.8722 - val_loss: 0.5511 - val_accuracy: 0.6176\n",
      "Epoch 48/500\n",
      "133/133 [==============================] - 4s 34ms/step - loss: 0.3236 - accuracy: 0.8571 - val_loss: 0.5060 - val_accuracy: 0.6765\n",
      "Epoch 49/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 0.2799 - accuracy: 0.8947 - val_loss: 1.3929 - val_accuracy: 0.7059\n",
      "Epoch 50/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 1.0154 - accuracy: 0.7368 - val_loss: 0.3182 - val_accuracy: 0.8235\n",
      "Epoch 51/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 0.2019 - accuracy: 0.9248 - val_loss: 0.9294 - val_accuracy: 0.5000\n",
      "Epoch 52/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.4548 - accuracy: 0.7820 - val_loss: 0.3525 - val_accuracy: 0.8529\n",
      "Epoch 53/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.2296 - accuracy: 0.9248 - val_loss: 0.3341 - val_accuracy: 0.8235\n",
      "Epoch 54/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 0.1810 - accuracy: 0.9098 - val_loss: 0.4897 - val_accuracy: 0.7647\n",
      "Epoch 55/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.3098 - accuracy: 0.8647 - val_loss: 0.6214 - val_accuracy: 0.5588\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 56/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.2753 - accuracy: 0.8647 - val_loss: 0.5074 - val_accuracy: 0.6765\n",
      "Epoch 57/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.2280 - accuracy: 0.8722 - val_loss: 0.4822 - val_accuracy: 0.6765\n",
      "Epoch 58/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.2028 - accuracy: 0.8872 - val_loss: 2.0177 - val_accuracy: 0.6765\n",
      "Epoch 59/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 1.4885 - accuracy: 0.6767 - val_loss: 4.1250 - val_accuracy: 0.6471\n",
      "Epoch 60/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 2.8262 - accuracy: 0.6767 - val_loss: 0.6102 - val_accuracy: 0.7059\n",
      "Epoch 61/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.4385 - accuracy: 0.7895 - val_loss: 0.4704 - val_accuracy: 0.7647\n",
      "Epoch 62/500\n",
      "133/133 [==============================] - 5s 35ms/step - loss: 0.1832 - accuracy: 0.8947 - val_loss: 1.7824 - val_accuracy: 0.6765\n",
      "Epoch 63/500\n",
      "133/133 [==============================] - 4s 34ms/step - loss: 0.8796 - accuracy: 0.7594 - val_loss: 0.6987 - val_accuracy: 0.6765\n",
      "Epoch 64/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.3089 - accuracy: 0.8195 - val_loss: 0.5030 - val_accuracy: 0.7647\n",
      "Epoch 65/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 0.1994 - accuracy: 0.8797 - val_loss: 0.4458 - val_accuracy: 0.7353\n",
      "Epoch 66/500\n",
      "133/133 [==============================] - 5s 35ms/step - loss: 0.1358 - accuracy: 0.9323 - val_loss: 0.5573 - val_accuracy: 0.7353\n",
      "Epoch 67/500\n",
      "133/133 [==============================] - 4s 34ms/step - loss: 0.1616 - accuracy: 0.9248 - val_loss: 0.5497 - val_accuracy: 0.7647\n",
      "Epoch 68/500\n",
      "133/133 [==============================] - 5s 34ms/step - loss: 0.1227 - accuracy: 0.9699 - val_loss: 0.4723 - val_accuracy: 0.7647\n",
      "Epoch 69/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 0.0813 - accuracy: 0.9925 - val_loss: 0.6362 - val_accuracy: 0.7059\n",
      "Epoch 70/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.1286 - accuracy: 0.9474 - val_loss: 2.6663 - val_accuracy: 0.6765\n",
      "Epoch 71/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 1.3920 - accuracy: 0.7143 - val_loss: 0.4816 - val_accuracy: 0.6765\n",
      "Epoch 72/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.1850 - accuracy: 0.9173 - val_loss: 0.4189 - val_accuracy: 0.7059\n",
      "Epoch 73/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.1386 - accuracy: 0.9699 - val_loss: 4.3554 - val_accuracy: 0.6765\n",
      "Epoch 74/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 3.1558 - accuracy: 0.6917 - val_loss: 0.8833 - val_accuracy: 0.5882\n",
      "Epoch 75/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.3409 - accuracy: 0.8947 - val_loss: 0.3867 - val_accuracy: 0.7647\n",
      "Epoch 76/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.1325 - accuracy: 0.9624 - val_loss: 0.3841 - val_accuracy: 0.7647\n",
      "Epoch 77/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.0984 - accuracy: 0.9774 - val_loss: 0.4058 - val_accuracy: 0.7647\n",
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      "Epoch 82/500\n",
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      "Epoch 84/500\n",
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      "Epoch 85/500\n",
      "133/133 [==============================] - 5s 38ms/step - loss: 0.0727 - accuracy: 0.9774 - val_loss: 0.4102 - val_accuracy: 0.8235\n",
      "Epoch 86/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 0.0489 - accuracy: 0.9850 - val_loss: 0.4275 - val_accuracy: 0.7941\n",
      "Epoch 87/500\n",
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      "Epoch 88/500\n",
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      "Epoch 90/500\n",
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      "Epoch 91/500\n",
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      "Epoch 93/500\n",
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      "Epoch 95/500\n",
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      "Epoch 96/500\n",
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      "Epoch 97/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.0708 - accuracy: 0.9850 - val_loss: 13.6562 - val_accuracy: 0.6765\n",
      "Epoch 98/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 10.8460 - accuracy: 0.6466 - val_loss: 0.6094 - val_accuracy: 0.6471\n",
      "Epoch 99/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.4578 - accuracy: 0.8872 - val_loss: 0.4215 - val_accuracy: 0.7941\n",
      "Epoch 100/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.1602 - accuracy: 0.9850 - val_loss: 1.2766 - val_accuracy: 0.7059\n",
      "Epoch 101/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.5706 - accuracy: 0.7970 - val_loss: 0.4084 - val_accuracy: 0.7941\n",
      "Epoch 102/500\n",
      "133/133 [==============================] - 5s 37ms/step - loss: 0.0853 - accuracy: 0.9925 - val_loss: 0.3119 - val_accuracy: 0.8824\n",
      "Epoch 103/500\n",
      "133/133 [==============================] - 5s 35ms/step - loss: 0.0488 - accuracy: 0.9925 - val_loss: 2.8462 - val_accuracy: 0.5882\n",
      "Epoch 104/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.5256 - accuracy: 0.7444 - val_loss: 1.2442 - val_accuracy: 0.7941\n",
      "Epoch 105/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.7646 - accuracy: 0.8797 - val_loss: 0.6894 - val_accuracy: 0.7059\n",
      "Epoch 106/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.1390 - accuracy: 0.9699 - val_loss: 0.3301 - val_accuracy: 0.8529\n",
      "Epoch 107/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0343 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.8235\n",
      "Epoch 108/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.0318 - accuracy: 0.9925 - val_loss: 0.4824 - val_accuracy: 0.7059\n",
      "Epoch 109/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 0.0454 - accuracy: 0.9925 - val_loss: 0.3845 - val_accuracy: 0.7941\n",
      "Epoch 110/500\n",
      "133/133 [==============================] - 5s 34ms/step - loss: 0.0225 - accuracy: 0.9925 - val_loss: 0.3311 - val_accuracy: 0.8824\n",
      "Epoch 111/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 0.0074 - accuracy: 1.0000 - val_loss: 0.3263 - val_accuracy: 0.8824\n",
      "Epoch 112/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "133/133 [==============================] - 4s 33ms/step - loss: 0.0104 - accuracy: 1.0000 - val_loss: 0.3246 - val_accuracy: 0.8529\n",
      "Epoch 113/500\n",
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      "Epoch 118/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.0043 - accuracy: 1.0000 - val_loss: 0.3248 - val_accuracy: 0.8529\n",
      "Epoch 119/500\n",
      "133/133 [==============================] - 4s 34ms/step - loss: 0.0059 - accuracy: 1.0000 - val_loss: 0.3201 - val_accuracy: 0.8824\n",
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      "133/133 [==============================] - 5s 38ms/step - loss: 0.0036 - accuracy: 1.0000 - val_loss: 0.3448 - val_accuracy: 0.8529\n",
      "Epoch 121/500\n",
      "133/133 [==============================] - 5s 37ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.3692 - val_accuracy: 0.8529\n",
      "Epoch 122/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.3957 - val_accuracy: 0.8529\n",
      "Epoch 123/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 0.0063 - accuracy: 1.0000 - val_loss: 0.5275 - val_accuracy: 0.7353\n",
      "Epoch 124/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 0.0192 - accuracy: 1.0000 - val_loss: 0.3394 - val_accuracy: 0.8529\n",
      "Epoch 125/500\n",
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      "Epoch 126/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.4487 - val_accuracy: 0.8529\n",
      "Epoch 127/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 5.4215e-04 - accuracy: 1.0000 - val_loss: 0.3984 - val_accuracy: 0.8529\n",
      "Epoch 128/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 1.3559 - val_accuracy: 0.7059\n",
      "Epoch 129/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0493 - accuracy: 0.9850 - val_loss: 0.6205 - val_accuracy: 0.7647\n",
      "Epoch 130/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.0497 - accuracy: 0.9699 - val_loss: 0.3199 - val_accuracy: 0.8529\n",
      "Epoch 131/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0230 - accuracy: 0.9850 - val_loss: 0.3908 - val_accuracy: 0.8824\n",
      "Epoch 132/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0055 - accuracy: 1.0000 - val_loss: 0.3028 - val_accuracy: 0.9118\n",
      "Epoch 133/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0060 - accuracy: 1.0000 - val_loss: 0.3150 - val_accuracy: 0.9118\n",
      "Epoch 134/500\n",
      "133/133 [==============================] - 4s 34ms/step - loss: 8.0178e-04 - accuracy: 1.0000 - val_loss: 0.3108 - val_accuracy: 0.9118\n",
      "Epoch 135/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.3546 - val_accuracy: 0.8824\n",
      "Epoch 136/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.3357 - val_accuracy: 0.8824\n",
      "Epoch 137/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 4.3187e-04 - accuracy: 1.0000 - val_loss: 0.3588 - val_accuracy: 0.9118\n",
      "Epoch 138/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 3.6081e-04 - accuracy: 1.0000 - val_loss: 0.3869 - val_accuracy: 0.8235\n",
      "Epoch 139/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 3.0262e-04 - accuracy: 1.0000 - val_loss: 0.4757 - val_accuracy: 0.8529\n",
      "Epoch 140/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.4357 - val_accuracy: 0.7941\n",
      "Epoch 141/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.3367 - val_accuracy: 0.8235\n",
      "Epoch 142/500\n",
      "133/133 [==============================] - 5s 35ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.3251 - val_accuracy: 0.9118\n",
      "Epoch 143/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 1.6404e-04 - accuracy: 1.0000 - val_loss: 0.3225 - val_accuracy: 0.9118\n",
      "Epoch 144/500\n",
      "133/133 [==============================] - 5s 34ms/step - loss: 1.3918e-04 - accuracy: 1.0000 - val_loss: 0.3356 - val_accuracy: 0.9118\n",
      "Epoch 145/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 8.5387e-05 - accuracy: 1.0000 - val_loss: 0.3493 - val_accuracy: 0.8824\n",
      "Epoch 146/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 1.5714e-04 - accuracy: 1.0000 - val_loss: 0.3333 - val_accuracy: 0.8824\n",
      "Epoch 147/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 1.0354e-04 - accuracy: 1.0000 - val_loss: 0.3402 - val_accuracy: 0.9118\n",
      "Epoch 148/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.0300e-04 - accuracy: 1.0000 - val_loss: 0.3574 - val_accuracy: 0.9118\n",
      "Epoch 149/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 9.2670e-05 - accuracy: 1.0000 - val_loss: 0.6481 - val_accuracy: 0.8529\n",
      "Epoch 150/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.1421 - accuracy: 0.9549 - val_loss: 1.4824 - val_accuracy: 0.6471\n",
      "Epoch 151/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 0.7899 - accuracy: 0.7970 - val_loss: 0.5845 - val_accuracy: 0.7647\n",
      "Epoch 152/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 0.0723 - accuracy: 0.9925 - val_loss: 0.4076 - val_accuracy: 0.7647\n",
      "Epoch 153/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0615 - accuracy: 0.9850 - val_loss: 0.3980 - val_accuracy: 0.7941\n",
      "Epoch 154/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0478 - accuracy: 0.9925 - val_loss: 0.3904 - val_accuracy: 0.7353\n",
      "Epoch 155/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0373 - accuracy: 0.9925 - val_loss: 0.3802 - val_accuracy: 0.7353\n",
      "Epoch 156/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0290 - accuracy: 1.0000 - val_loss: 0.3675 - val_accuracy: 0.7941\n",
      "Epoch 157/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0187 - accuracy: 1.0000 - val_loss: 0.3508 - val_accuracy: 0.8235\n",
      "Epoch 158/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 0.0176 - accuracy: 1.0000 - val_loss: 0.3433 - val_accuracy: 0.8235\n",
      "Epoch 159/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 0.0123 - accuracy: 1.0000 - val_loss: 0.3438 - val_accuracy: 0.8235\n",
      "Epoch 160/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0075 - accuracy: 1.0000 - val_loss: 0.3998 - val_accuracy: 0.8235\n",
      "Epoch 161/500\n",
      "133/133 [==============================] - 4s 29ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 1.3528 - val_accuracy: 0.7059\n",
      "Epoch 162/500\n",
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      "Epoch 163/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.0044 - accuracy: 1.0000 - val_loss: 0.3321 - val_accuracy: 0.8529\n",
      "Epoch 164/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 0.0038 - accuracy: 1.0000 - val_loss: 0.3526 - val_accuracy: 0.8529\n",
      "Epoch 165/500\n",
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      "Epoch 166/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 8.6079 - val_accuracy: 0.4706\n",
      "Epoch 167/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 3.3499 - accuracy: 0.7594 - val_loss: 0.4618 - val_accuracy: 0.7059\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 168/500\n",
      "133/133 [==============================] - 5s 35ms/step - loss: 0.1028 - accuracy: 0.9850 - val_loss: 0.4224 - val_accuracy: 0.7647\n",
      "Epoch 169/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 0.0885 - accuracy: 0.9774 - val_loss: 0.4372 - val_accuracy: 0.7059\n",
      "Epoch 170/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.0441 - accuracy: 1.0000 - val_loss: 0.5367 - val_accuracy: 0.7059\n",
      "Epoch 171/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0390 - accuracy: 0.9925 - val_loss: 0.4795 - val_accuracy: 0.7059\n",
      "Epoch 172/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0270 - accuracy: 1.0000 - val_loss: 0.3783 - val_accuracy: 0.7647\n",
      "Epoch 173/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0240 - accuracy: 1.0000 - val_loss: 0.4561 - val_accuracy: 0.7059\n",
      "Epoch 174/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0184 - accuracy: 1.0000 - val_loss: 0.3917 - val_accuracy: 0.7647\n",
      "Epoch 175/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0139 - accuracy: 1.0000 - val_loss: 0.3951 - val_accuracy: 0.7647\n",
      "Epoch 176/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0118 - accuracy: 1.0000 - val_loss: 0.3249 - val_accuracy: 0.8235\n",
      "Epoch 177/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0094 - accuracy: 1.0000 - val_loss: 0.3761 - val_accuracy: 0.7647\n",
      "Epoch 178/500\n",
      "133/133 [==============================] - 4s 29ms/step - loss: 0.0076 - accuracy: 1.0000 - val_loss: 0.4030 - val_accuracy: 0.7353\n",
      "Epoch 179/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 0.3969 - val_accuracy: 0.7647\n",
      "Epoch 180/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0042 - accuracy: 1.0000 - val_loss: 0.3934 - val_accuracy: 0.7353\n",
      "Epoch 181/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0037 - accuracy: 1.0000 - val_loss: 0.4481 - val_accuracy: 0.7059\n",
      "Epoch 182/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0031 - accuracy: 1.0000 - val_loss: 0.3104 - val_accuracy: 0.8235\n",
      "Epoch 183/500\n",
      "133/133 [==============================] - 4s 29ms/step - loss: 0.0037 - accuracy: 1.0000 - val_loss: 2.1526 - val_accuracy: 0.7059\n",
      "Epoch 184/500\n",
      "133/133 [==============================] - 4s 29ms/step - loss: 0.2312 - accuracy: 0.9323 - val_loss: 2.0975 - val_accuracy: 0.5588\n",
      "Epoch 185/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.4628 - accuracy: 0.9023 - val_loss: 0.9220 - val_accuracy: 0.7353\n",
      "Epoch 186/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0822 - accuracy: 0.9850 - val_loss: 0.3221 - val_accuracy: 0.7647\n",
      "Epoch 187/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0185 - accuracy: 0.9925 - val_loss: 0.3260 - val_accuracy: 0.8529\n",
      "Epoch 188/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 0.0101 - accuracy: 1.0000 - val_loss: 0.3326 - val_accuracy: 0.8235\n",
      "Epoch 189/500\n",
      "133/133 [==============================] - 5s 38ms/step - loss: 0.0100 - accuracy: 1.0000 - val_loss: 0.3517 - val_accuracy: 0.8529\n",
      "Epoch 190/500\n",
      "133/133 [==============================] - 5s 34ms/step - loss: 0.0064 - accuracy: 1.0000 - val_loss: 0.3944 - val_accuracy: 0.7941\n",
      "Epoch 191/500\n",
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      "Epoch 192/500\n",
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      "Epoch 193/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0031 - accuracy: 1.0000 - val_loss: 0.5338 - val_accuracy: 0.7941\n",
      "Epoch 194/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.6236 - val_accuracy: 0.7941\n",
      "Epoch 195/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 7.4151e-04 - accuracy: 1.0000 - val_loss: 0.3191 - val_accuracy: 0.8529\n",
      "Epoch 196/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 5.4775e-04 - accuracy: 1.0000 - val_loss: 0.3622 - val_accuracy: 0.8235\n",
      "Epoch 197/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 2.2158e-04 - accuracy: 1.0000 - val_loss: 0.6085 - val_accuracy: 0.7941\n",
      "Epoch 198/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.7546e-04 - accuracy: 1.0000 - val_loss: 0.6714 - val_accuracy: 0.7941\n",
      "Epoch 199/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 4.7884e-04 - accuracy: 1.0000 - val_loss: 0.5032 - val_accuracy: 0.7647\n",
      "Epoch 200/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 9.5139e-05 - accuracy: 1.0000 - val_loss: 0.4988 - val_accuracy: 0.7647\n",
      "Epoch 201/500\n",
      "133/133 [==============================] - 4s 29ms/step - loss: 7.1579e-05 - accuracy: 1.0000 - val_loss: 0.4680 - val_accuracy: 0.8235\n",
      "Epoch 202/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 4.0878e-05 - accuracy: 1.0000 - val_loss: 0.4675 - val_accuracy: 0.8235\n",
      "Epoch 203/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.1283e-04 - accuracy: 1.0000 - val_loss: 0.4594 - val_accuracy: 0.7941\n",
      "Epoch 204/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.0300e-04 - accuracy: 1.0000 - val_loss: 0.3660 - val_accuracy: 0.8235\n",
      "Epoch 205/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 9.5300e-05 - accuracy: 1.0000 - val_loss: 0.3890 - val_accuracy: 0.7941\n",
      "Epoch 206/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 4.2009e-05 - accuracy: 1.0000 - val_loss: 0.4138 - val_accuracy: 0.7941\n",
      "Epoch 207/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 6.3434e-05 - accuracy: 1.0000 - val_loss: 0.4135 - val_accuracy: 0.7941\n",
      "Epoch 208/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.1425e-04 - accuracy: 1.0000 - val_loss: 0.4301 - val_accuracy: 0.7941\n",
      "Epoch 209/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 5.7881e-05 - accuracy: 1.0000 - val_loss: 0.4170 - val_accuracy: 0.7941\n",
      "Epoch 210/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 2.6437e-05 - accuracy: 1.0000 - val_loss: 0.4116 - val_accuracy: 0.7941\n",
      "Epoch 211/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 3.8008e-05 - accuracy: 1.0000 - val_loss: 0.4111 - val_accuracy: 0.7941\n",
      "Epoch 212/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 4.4108e-05 - accuracy: 1.0000 - val_loss: 0.4359 - val_accuracy: 0.7941\n",
      "Epoch 213/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 2.7636e-04 - accuracy: 1.0000 - val_loss: 0.3168 - val_accuracy: 0.8824\n",
      "Epoch 214/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 8.4570e-05 - accuracy: 1.0000 - val_loss: 0.3874 - val_accuracy: 0.8235\n",
      "Epoch 215/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 5.3768e-05 - accuracy: 1.0000 - val_loss: 0.3911 - val_accuracy: 0.8235\n",
      "Epoch 216/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.1536e-04 - accuracy: 1.0000 - val_loss: 0.4531 - val_accuracy: 0.7941\n",
      "Epoch 217/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 9.2040e-06 - accuracy: 1.0000 - val_loss: 0.4551 - val_accuracy: 0.7941\n",
      "Epoch 218/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 2.9218e-05 - accuracy: 1.0000 - val_loss: 0.4220 - val_accuracy: 0.8529\n",
      "Epoch 219/500\n",
      "133/133 [==============================] - 4s 29ms/step - loss: 1.0417e-05 - accuracy: 1.0000 - val_loss: 0.4335 - val_accuracy: 0.8529\n",
      "Epoch 220/500\n",
      "133/133 [==============================] - 4s 29ms/step - loss: 7.8673e-06 - accuracy: 1.0000 - val_loss: 0.4362 - val_accuracy: 0.8529\n",
      "Epoch 221/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.8614e-04 - accuracy: 1.0000 - val_loss: 0.4955 - val_accuracy: 0.8235\n",
      "Epoch 222/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 6.2015e-05 - accuracy: 1.0000 - val_loss: 0.3505 - val_accuracy: 0.9118\n",
      "Epoch 223/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "133/133 [==============================] - 4s 30ms/step - loss: 4.0788e-05 - accuracy: 1.0000 - val_loss: 0.3836 - val_accuracy: 0.8235\n",
      "Epoch 224/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 3.8299e-05 - accuracy: 1.0000 - val_loss: 0.4561 - val_accuracy: 0.7941\n",
      "Epoch 225/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 2.4565e-05 - accuracy: 1.0000 - val_loss: 0.3907 - val_accuracy: 0.8529\n",
      "Epoch 226/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.3121e-05 - accuracy: 1.0000 - val_loss: 1.2379 - val_accuracy: 0.7941\n",
      "Epoch 227/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.6935e-04 - accuracy: 1.0000 - val_loss: 0.5442 - val_accuracy: 0.8824\n",
      "Epoch 228/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 6.2713e-05 - accuracy: 1.0000 - val_loss: 0.8842 - val_accuracy: 0.7941\n",
      "Epoch 229/500\n",
      "133/133 [==============================] - 5s 35ms/step - loss: 5.4308e-06 - accuracy: 1.0000 - val_loss: 0.8367 - val_accuracy: 0.7941\n",
      "Epoch 230/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 2.9093e-06 - accuracy: 1.0000 - val_loss: 1.0618 - val_accuracy: 0.8235\n",
      "Epoch 231/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 1.1697e-06 - accuracy: 1.0000 - val_loss: 1.0517 - val_accuracy: 0.8235\n",
      "Epoch 232/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 1.0316e-05 - accuracy: 1.0000 - val_loss: 0.9327 - val_accuracy: 0.7941\n",
      "Epoch 233/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.2663e-05 - accuracy: 1.0000 - val_loss: 0.7338 - val_accuracy: 0.8235\n",
      "Epoch 234/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 2.1663e-06 - accuracy: 1.0000 - val_loss: 0.7158 - val_accuracy: 0.8235\n",
      "Epoch 235/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 6.3637e-07 - accuracy: 1.0000 - val_loss: 0.7113 - val_accuracy: 0.8235\n",
      "Epoch 236/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 2.7174e-06 - accuracy: 1.0000 - val_loss: 0.7071 - val_accuracy: 0.8235\n",
      "Epoch 237/500\n",
      "133/133 [==============================] - 4s 34ms/step - loss: 6.1195e-06 - accuracy: 1.0000 - val_loss: 0.6237 - val_accuracy: 0.8235\n",
      "Epoch 238/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 7.0581e-06 - accuracy: 1.0000 - val_loss: 0.6566 - val_accuracy: 0.8235\n",
      "Epoch 239/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 3.6928e-07 - accuracy: 1.0000 - val_loss: 0.6585 - val_accuracy: 0.8235\n",
      "Epoch 240/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 5.2882e-07 - accuracy: 1.0000 - val_loss: 0.6631 - val_accuracy: 0.8235\n",
      "Epoch 241/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 3.4508e-07 - accuracy: 1.0000 - val_loss: 0.6646 - val_accuracy: 0.8235\n",
      "Epoch 242/500\n",
      "133/133 [==============================] - 4s 34ms/step - loss: 1.2996e-06 - accuracy: 1.0000 - val_loss: 0.6599 - val_accuracy: 0.8235\n",
      "Epoch 243/500\n",
      "133/133 [==============================] - 5s 34ms/step - loss: 1.4637e-05 - accuracy: 1.0000 - val_loss: 0.5415 - val_accuracy: 0.8529\n",
      "Epoch 244/500\n",
      "133/133 [==============================] - 5s 34ms/step - loss: 1.5864e-06 - accuracy: 1.0000 - val_loss: 0.5414 - val_accuracy: 0.8824\n",
      "Epoch 245/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 3.5673e-07 - accuracy: 1.0000 - val_loss: 0.5435 - val_accuracy: 0.8824\n",
      "Epoch 246/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 2.5455e-07 - accuracy: 1.0000 - val_loss: 0.5458 - val_accuracy: 0.8824\n",
      "Epoch 247/500\n",
      "133/133 [==============================] - 5s 36ms/step - loss: 1.2279e-06 - accuracy: 1.0000 - val_loss: 0.5520 - val_accuracy: 0.8824\n",
      "Epoch 248/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.2583e-06 - accuracy: 1.0000 - val_loss: 0.5848 - val_accuracy: 0.8824\n",
      "Epoch 249/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.3714e-07 - accuracy: 1.0000 - val_loss: 0.5866 - val_accuracy: 0.8824\n",
      "Epoch 250/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 2.2318e-07 - accuracy: 1.0000 - val_loss: 0.5862 - val_accuracy: 0.8824\n",
      "Epoch 251/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 4.0692e-07 - accuracy: 1.0000 - val_loss: 0.5890 - val_accuracy: 0.8824\n",
      "Epoch 252/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 2.4648e-07 - accuracy: 1.0000 - val_loss: 0.5912 - val_accuracy: 0.8529\n",
      "Epoch 253/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 7.4302e-07 - accuracy: 1.0000 - val_loss: 0.6145 - val_accuracy: 0.8529\n",
      "Epoch 254/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 4.9655e-07 - accuracy: 1.0000 - val_loss: 0.6212 - val_accuracy: 0.8529\n",
      "Epoch 255/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 5.4675e-08 - accuracy: 1.0000 - val_loss: 0.6245 - val_accuracy: 0.8529\n",
      "Epoch 256/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.0459e-06 - accuracy: 1.0000 - val_loss: 0.6405 - val_accuracy: 0.8235\n",
      "Epoch 257/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 4.1230e-08 - accuracy: 1.0000 - val_loss: 0.6481 - val_accuracy: 0.8235\n",
      "Epoch 258/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.2548e-08 - accuracy: 1.0000 - val_loss: 0.6494 - val_accuracy: 0.8235\n",
      "Epoch 259/500\n",
      "133/133 [==============================] - 5s 36ms/step - loss: 7.2062e-07 - accuracy: 1.0000 - val_loss: 0.6381 - val_accuracy: 0.8235\n",
      "Epoch 260/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 9.1020e-06 - accuracy: 1.0000 - val_loss: 19.7959 - val_accuracy: 0.2941\n",
      "Epoch 261/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 9.2895 - accuracy: 0.6617 - val_loss: 117.9201 - val_accuracy: 0.6765\n",
      "Epoch 262/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 103.2535 - accuracy: 0.6391 - val_loss: 0.7946 - val_accuracy: 0.6765\n",
      "Epoch 263/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.3384 - accuracy: 0.8722 - val_loss: 0.7435 - val_accuracy: 0.7941\n",
      "Epoch 264/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.1115 - accuracy: 0.9699 - val_loss: 1.1734 - val_accuracy: 0.7647\n",
      "Epoch 265/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 0.0894 - accuracy: 0.9774 - val_loss: 0.4653 - val_accuracy: 0.7353\n",
      "Epoch 266/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.0121 - accuracy: 1.0000 - val_loss: 0.4699 - val_accuracy: 0.7353\n",
      "Epoch 267/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.0096 - accuracy: 1.0000 - val_loss: 0.4669 - val_accuracy: 0.7353\n",
      "Epoch 268/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0122 - accuracy: 1.0000 - val_loss: 0.4759 - val_accuracy: 0.7353\n",
      "Epoch 269/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0115 - accuracy: 1.0000 - val_loss: 0.4601 - val_accuracy: 0.7353\n",
      "Epoch 270/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0074 - accuracy: 1.0000 - val_loss: 0.4603 - val_accuracy: 0.7353\n",
      "Epoch 271/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0120 - accuracy: 1.0000 - val_loss: 0.9482 - val_accuracy: 0.8235\n",
      "Epoch 272/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.1503 - accuracy: 0.9624 - val_loss: 0.8032 - val_accuracy: 0.7353\n",
      "Epoch 273/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0150 - accuracy: 0.9925 - val_loss: 0.6668 - val_accuracy: 0.7647\n",
      "Epoch 274/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0030 - accuracy: 1.0000 - val_loss: 0.6597 - val_accuracy: 0.7647\n",
      "Epoch 275/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 0.0036 - accuracy: 1.0000 - val_loss: 0.6451 - val_accuracy: 0.7941\n",
      "Epoch 276/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 0.6510 - val_accuracy: 0.7941\n",
      "Epoch 277/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0032 - accuracy: 1.0000 - val_loss: 0.6480 - val_accuracy: 0.7941\n",
      "Epoch 278/500\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.6418 - val_accuracy: 0.8235\n",
      "Epoch 279/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.4897 - val_accuracy: 0.7647\n",
      "Epoch 280/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.5060 - val_accuracy: 0.7941\n",
      "Epoch 281/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.4966 - val_accuracy: 0.7647\n",
      "Epoch 282/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 9.6795e-04 - accuracy: 1.0000 - val_loss: 0.4887 - val_accuracy: 0.7353\n",
      "Epoch 283/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.5058 - val_accuracy: 0.7353\n",
      "Epoch 284/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 8.3324e-04 - accuracy: 1.0000 - val_loss: 0.5096 - val_accuracy: 0.7353\n",
      "Epoch 285/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 7.3822e-04 - accuracy: 1.0000 - val_loss: 0.5207 - val_accuracy: 0.7353\n",
      "Epoch 286/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 7.7089e-04 - accuracy: 1.0000 - val_loss: 0.5240 - val_accuracy: 0.7353\n",
      "Epoch 287/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 7.3890e-04 - accuracy: 1.0000 - val_loss: 0.7850 - val_accuracy: 0.7647\n",
      "Epoch 288/500\n",
      "133/133 [==============================] - 4s 34ms/step - loss: 6.1223e-04 - accuracy: 1.0000 - val_loss: 0.7245 - val_accuracy: 0.7647\n",
      "Epoch 289/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 5.8360e-04 - accuracy: 1.0000 - val_loss: 0.7554 - val_accuracy: 0.7647\n",
      "Epoch 290/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 3.9945e-04 - accuracy: 1.0000 - val_loss: 0.7226 - val_accuracy: 0.7647\n",
      "Epoch 291/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 2.2579e-04 - accuracy: 1.0000 - val_loss: 0.7205 - val_accuracy: 0.7647\n",
      "Epoch 292/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 6.0777e-04 - accuracy: 1.0000 - val_loss: 0.7392 - val_accuracy: 0.7647\n",
      "Epoch 293/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 2.7648e-04 - accuracy: 1.0000 - val_loss: 0.7328 - val_accuracy: 0.7647\n",
      "Epoch 294/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 2.1204e-04 - accuracy: 1.0000 - val_loss: 0.7201 - val_accuracy: 0.7647\n",
      "Epoch 295/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.4873e-04 - accuracy: 1.0000 - val_loss: 0.7788 - val_accuracy: 0.7647\n",
      "Epoch 296/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 5.9229e-04 - accuracy: 1.0000 - val_loss: 0.6453 - val_accuracy: 0.7647\n",
      "Epoch 297/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 8.5772e-05 - accuracy: 1.0000 - val_loss: 0.6187 - val_accuracy: 0.7647\n",
      "Epoch 298/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.4649e-04 - accuracy: 1.0000 - val_loss: 0.6373 - val_accuracy: 0.7647\n",
      "Epoch 299/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 2.3731e-04 - accuracy: 1.0000 - val_loss: 0.6239 - val_accuracy: 0.7647\n",
      "Epoch 300/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.9869e-05 - accuracy: 1.0000 - val_loss: 0.6241 - val_accuracy: 0.7647\n",
      "Epoch 301/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 9.8422e-05 - accuracy: 1.0000 - val_loss: 0.7096 - val_accuracy: 0.7647\n",
      "Epoch 302/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 3.9686e-05 - accuracy: 1.0000 - val_loss: 0.6747 - val_accuracy: 0.7647\n",
      "Epoch 303/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 1.1260e-04 - accuracy: 1.0000 - val_loss: 0.7192 - val_accuracy: 0.7941\n",
      "Epoch 304/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 1.1439e-05 - accuracy: 1.0000 - val_loss: 0.6326 - val_accuracy: 0.7941\n",
      "Epoch 305/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 2.8968e-06 - accuracy: 1.0000 - val_loss: 0.5984 - val_accuracy: 0.8529\n",
      "Epoch 306/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 1.5290e-05 - accuracy: 1.0000 - val_loss: 0.5855 - val_accuracy: 0.8529\n",
      "Epoch 307/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.5005e-05 - accuracy: 1.0000 - val_loss: 0.6138 - val_accuracy: 0.8235\n",
      "Epoch 308/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 2.0895e-05 - accuracy: 1.0000 - val_loss: 0.6438 - val_accuracy: 0.8235\n",
      "Epoch 309/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.5292e-05 - accuracy: 1.0000 - val_loss: 0.6569 - val_accuracy: 0.8235\n",
      "Epoch 310/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 1.2387e-05 - accuracy: 1.0000 - val_loss: 0.6959 - val_accuracy: 0.7941\n",
      "Epoch 311/500\n",
      "133/133 [==============================] - 5s 35ms/step - loss: 3.3081e-06 - accuracy: 1.0000 - val_loss: 0.6868 - val_accuracy: 0.7941\n",
      "Epoch 312/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 5.6643e-06 - accuracy: 1.0000 - val_loss: 0.6721 - val_accuracy: 0.8235\n",
      "Epoch 313/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 6.2657e-06 - accuracy: 1.0000 - val_loss: 0.6513 - val_accuracy: 0.8235\n",
      "Epoch 314/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 4.5171e-06 - accuracy: 1.0000 - val_loss: 0.6675 - val_accuracy: 0.8235\n",
      "Epoch 315/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 3.7992e-06 - accuracy: 1.0000 - val_loss: 0.6573 - val_accuracy: 0.8235\n",
      "Epoch 316/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 2.6421e-06 - accuracy: 1.0000 - val_loss: 0.6471 - val_accuracy: 0.8529\n",
      "Epoch 317/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 6.1841e-06 - accuracy: 1.0000 - val_loss: 0.6176 - val_accuracy: 0.8235\n",
      "Epoch 318/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 5.5885e-06 - accuracy: 1.0000 - val_loss: 0.6137 - val_accuracy: 0.8235\n",
      "Epoch 319/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 4.1314e-06 - accuracy: 1.0000 - val_loss: 0.6199 - val_accuracy: 0.8235\n",
      "Epoch 320/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 8.1535e-06 - accuracy: 1.0000 - val_loss: 0.6506 - val_accuracy: 0.8235\n",
      "Epoch 321/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.5072e-05 - accuracy: 1.0000 - val_loss: 0.6216 - val_accuracy: 0.8529\n",
      "Epoch 322/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 9.7713e-06 - accuracy: 1.0000 - val_loss: 0.7809 - val_accuracy: 0.7941\n",
      "Epoch 323/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 8.8787e-06 - accuracy: 1.0000 - val_loss: 0.6655 - val_accuracy: 0.8235\n",
      "Epoch 324/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.4699e-06 - accuracy: 1.0000 - val_loss: 0.6526 - val_accuracy: 0.7941\n",
      "Epoch 325/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 8.4073e-07 - accuracy: 1.0000 - val_loss: 0.7044 - val_accuracy: 0.8235\n",
      "Epoch 326/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 2.4450e-06 - accuracy: 1.0000 - val_loss: 0.8530 - val_accuracy: 0.8235\n",
      "Epoch 327/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.3377 - accuracy: 0.9850 - val_loss: 2.4770 - val_accuracy: 0.5000\n",
      "Epoch 328/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 5.6469 - accuracy: 0.7820 - val_loss: 0.3730 - val_accuracy: 0.7647\n",
      "Epoch 329/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0822 - accuracy: 0.9925 - val_loss: 0.2975 - val_accuracy: 0.7941\n",
      "Epoch 330/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0101 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.7941\n",
      "Epoch 331/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0072 - accuracy: 1.0000 - val_loss: 0.2839 - val_accuracy: 0.7941\n",
      "Epoch 332/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0079 - accuracy: 1.0000 - val_loss: 0.2821 - val_accuracy: 0.8235\n",
      "Epoch 333/500\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0055 - accuracy: 1.0000 - val_loss: 0.2779 - val_accuracy: 0.8529\n",
      "Epoch 334/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0030 - accuracy: 1.0000 - val_loss: 0.2866 - val_accuracy: 0.7941\n",
      "Epoch 335/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.0030 - accuracy: 1.0000 - val_loss: 0.2963 - val_accuracy: 0.7941\n",
      "Epoch 336/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.2797 - val_accuracy: 0.8824\n",
      "Epoch 337/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.2837 - val_accuracy: 0.8824\n",
      "Epoch 338/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.2838 - val_accuracy: 0.8824\n",
      "Epoch 339/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.2948 - val_accuracy: 0.8824\n",
      "Epoch 340/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.3074 - val_accuracy: 0.8529\n",
      "Epoch 341/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 7.9691e-04 - accuracy: 1.0000 - val_loss: 0.2780 - val_accuracy: 0.8824\n",
      "Epoch 342/500\n",
      "133/133 [==============================] - 5s 36ms/step - loss: 8.7298e-04 - accuracy: 1.0000 - val_loss: 0.2802 - val_accuracy: 0.8824\n",
      "Epoch 343/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 6.3516e-04 - accuracy: 1.0000 - val_loss: 0.2880 - val_accuracy: 0.8824\n",
      "Epoch 344/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 5.1154e-04 - accuracy: 1.0000 - val_loss: 0.2854 - val_accuracy: 0.8824\n",
      "Epoch 345/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 6.5739e-04 - accuracy: 1.0000 - val_loss: 0.2854 - val_accuracy: 0.8824\n",
      "Epoch 346/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 8.3908e-04 - accuracy: 1.0000 - val_loss: 0.2939 - val_accuracy: 0.8824\n",
      "Epoch 347/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 3.7424e-04 - accuracy: 1.0000 - val_loss: 0.3086 - val_accuracy: 0.8824\n",
      "Epoch 348/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 3.0172e-04 - accuracy: 1.0000 - val_loss: 0.3235 - val_accuracy: 0.8824\n",
      "Epoch 349/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 3.0678e-04 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.8824\n",
      "Epoch 350/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.3381e-04 - accuracy: 1.0000 - val_loss: 0.3034 - val_accuracy: 0.9118\n",
      "Epoch 351/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.8145e-04 - accuracy: 1.0000 - val_loss: 0.3322 - val_accuracy: 0.8529\n",
      "Epoch 352/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 6.3250e-05 - accuracy: 1.0000 - val_loss: 0.3081 - val_accuracy: 0.9118\n",
      "Epoch 353/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 3.6315e-04 - accuracy: 1.0000 - val_loss: 0.2950 - val_accuracy: 0.8824\n",
      "Epoch 354/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 6.2941e-05 - accuracy: 1.0000 - val_loss: 0.2967 - val_accuracy: 0.8824\n",
      "Epoch 355/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 3.1325e-04 - accuracy: 1.0000 - val_loss: 0.2916 - val_accuracy: 0.9118\n",
      "Epoch 356/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.5190e-04 - accuracy: 1.0000 - val_loss: 0.3325 - val_accuracy: 0.8824\n",
      "Epoch 357/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 1.7602e-04 - accuracy: 1.0000 - val_loss: 0.2906 - val_accuracy: 0.8824\n",
      "Epoch 358/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 3.8925e-05 - accuracy: 1.0000 - val_loss: 0.2945 - val_accuracy: 0.8824\n",
      "Epoch 359/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 5.1561e-05 - accuracy: 1.0000 - val_loss: 0.3086 - val_accuracy: 0.8824\n",
      "Epoch 360/500\n",
      "133/133 [==============================] - 6s 45ms/step - loss: 1.0871e-04 - accuracy: 1.0000 - val_loss: 0.3975 - val_accuracy: 0.8824\n",
      "Epoch 361/500\n",
      "133/133 [==============================] - 5s 36ms/step - loss: 1.8813e-05 - accuracy: 1.0000 - val_loss: 0.3523 - val_accuracy: 0.8529\n",
      "Epoch 362/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 3.6424e-05 - accuracy: 1.0000 - val_loss: 0.3212 - val_accuracy: 0.8824\n",
      "Epoch 363/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 6.2526e-05 - accuracy: 1.0000 - val_loss: 0.2737 - val_accuracy: 0.8824\n",
      "Epoch 364/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 5.0560e-04 - accuracy: 1.0000 - val_loss: 0.4086 - val_accuracy: 0.8824\n",
      "Epoch 365/500\n",
      "133/133 [==============================] - 5s 36ms/step - loss: 2.1878e-05 - accuracy: 1.0000 - val_loss: 0.3577 - val_accuracy: 0.8824\n",
      "Epoch 366/500\n",
      "133/133 [==============================] - 5s 35ms/step - loss: 3.1428e-05 - accuracy: 1.0000 - val_loss: 0.3373 - val_accuracy: 0.8824\n",
      "Epoch 367/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 3.0425e-05 - accuracy: 1.0000 - val_loss: 0.3345 - val_accuracy: 0.8824\n",
      "Epoch 368/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 1.5828e-05 - accuracy: 1.0000 - val_loss: 0.3339 - val_accuracy: 0.8824\n",
      "Epoch 369/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 5.4161e-06 - accuracy: 1.0000 - val_loss: 0.3333 - val_accuracy: 0.8824\n",
      "Epoch 370/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 7.7497e-06 - accuracy: 1.0000 - val_loss: 0.3351 - val_accuracy: 0.8824\n",
      "Epoch 371/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.1647e-05 - accuracy: 1.0000 - val_loss: 0.3422 - val_accuracy: 0.8824\n",
      "Epoch 372/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 4.5687e-05 - accuracy: 1.0000 - val_loss: 0.3396 - val_accuracy: 0.8824\n",
      "Epoch 373/500\n",
      "133/133 [==============================] - 5s 35ms/step - loss: 9.7965e-06 - accuracy: 1.0000 - val_loss: 0.3436 - val_accuracy: 0.8824\n",
      "Epoch 374/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 2.1224e-06 - accuracy: 1.0000 - val_loss: 0.3442 - val_accuracy: 0.8824\n",
      "Epoch 375/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 4.0324e-06 - accuracy: 1.0000 - val_loss: 0.3451 - val_accuracy: 0.8824\n",
      "Epoch 376/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 6.7720e-06 - accuracy: 1.0000 - val_loss: 0.3464 - val_accuracy: 0.8824\n",
      "Epoch 377/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 3.0567e-05 - accuracy: 1.0000 - val_loss: 0.3451 - val_accuracy: 0.8824\n",
      "Epoch 378/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.2233e-04 - accuracy: 1.0000 - val_loss: 0.4142 - val_accuracy: 0.7941\n",
      "Epoch 379/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 3.5503e-05 - accuracy: 1.0000 - val_loss: 0.3855 - val_accuracy: 0.8824\n",
      "Epoch 380/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 6.4944e-06 - accuracy: 1.0000 - val_loss: 0.3895 - val_accuracy: 0.8824\n",
      "Epoch 381/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 5.4357e-05 - accuracy: 1.0000 - val_loss: 0.4113 - val_accuracy: 0.8824\n",
      "Epoch 382/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 4.0260e-06 - accuracy: 1.0000 - val_loss: 0.4191 - val_accuracy: 0.8824\n",
      "Epoch 383/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 4.3925e-06 - accuracy: 1.0000 - val_loss: 0.4231 - val_accuracy: 0.8824\n",
      "Epoch 384/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 2.5335e-05 - accuracy: 1.0000 - val_loss: 0.4595 - val_accuracy: 0.8824\n",
      "Epoch 385/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 3.3966e-06 - accuracy: 1.0000 - val_loss: 0.4617 - val_accuracy: 0.8824\n",
      "Epoch 386/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 2.7381e-06 - accuracy: 1.0000 - val_loss: 0.4381 - val_accuracy: 0.8824\n",
      "Epoch 387/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 2.6036e-06 - accuracy: 1.0000 - val_loss: 0.4354 - val_accuracy: 0.8824\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 388/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 2.4154e-06 - accuracy: 1.0000 - val_loss: 0.4381 - val_accuracy: 0.8824\n",
      "Epoch 389/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 2.1457e-06 - accuracy: 1.0000 - val_loss: 0.5376 - val_accuracy: 0.8529\n",
      "Epoch 390/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.2620e-06 - accuracy: 1.0000 - val_loss: 0.5268 - val_accuracy: 0.8529\n",
      "Epoch 391/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 2.9711e-06 - accuracy: 1.0000 - val_loss: 0.4700 - val_accuracy: 0.8235\n",
      "Epoch 392/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 4.4647e-06 - accuracy: 1.0000 - val_loss: 0.4583 - val_accuracy: 0.8824\n",
      "Epoch 393/500\n",
      "133/133 [==============================] - 5s 36ms/step - loss: 2.6941e-06 - accuracy: 1.0000 - val_loss: 0.4510 - val_accuracy: 0.8824\n",
      "Epoch 394/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 3.9975e-07 - accuracy: 1.0000 - val_loss: 0.4478 - val_accuracy: 0.8824\n",
      "Epoch 395/500\n",
      "133/133 [==============================] - 5s 35ms/step - loss: 2.0576e-05 - accuracy: 1.0000 - val_loss: 0.8198 - val_accuracy: 0.7647\n",
      "Epoch 396/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 3.1140e-04 - accuracy: 1.0000 - val_loss: 1.6474 - val_accuracy: 0.7353\n",
      "Epoch 397/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.7937 - accuracy: 0.9023 - val_loss: 0.7143 - val_accuracy: 0.7647\n",
      "Epoch 398/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 0.2854 - accuracy: 0.9173 - val_loss: 1.5547 - val_accuracy: 0.7353\n",
      "Epoch 399/500\n",
      "133/133 [==============================] - 5s 36ms/step - loss: 0.3300 - accuracy: 0.9549 - val_loss: 2.5259 - val_accuracy: 0.6471\n",
      "Epoch 400/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 0.3539 - accuracy: 0.9173 - val_loss: 0.6225 - val_accuracy: 0.7647\n",
      "Epoch 401/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.4709 - accuracy: 0.9850 - val_loss: 0.5193 - val_accuracy: 0.7647\n",
      "Epoch 402/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 5.8327e-04 - accuracy: 1.0000 - val_loss: 0.5237 - val_accuracy: 0.7647\n",
      "Epoch 403/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.5200 - val_accuracy: 0.7647\n",
      "Epoch 404/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 3.0703e-04 - accuracy: 1.0000 - val_loss: 0.5204 - val_accuracy: 0.7647\n",
      "Epoch 405/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 0.0061 - accuracy: 1.0000 - val_loss: 0.5294 - val_accuracy: 0.7647\n",
      "Epoch 406/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 3.5905e-04 - accuracy: 1.0000 - val_loss: 0.5301 - val_accuracy: 0.7647\n",
      "Epoch 407/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 1.1169e-04 - accuracy: 1.0000 - val_loss: 0.5309 - val_accuracy: 0.7647\n",
      "Epoch 408/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.0931e-04 - accuracy: 1.0000 - val_loss: 0.5437 - val_accuracy: 0.7647\n",
      "Epoch 409/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 1.7289e-04 - accuracy: 1.0000 - val_loss: 1.6190 - val_accuracy: 0.7059\n",
      "Epoch 410/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.0172 - accuracy: 0.9850 - val_loss: 0.7315 - val_accuracy: 0.7941\n",
      "Epoch 411/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 8.4211e-05 - accuracy: 1.0000 - val_loss: 0.7323 - val_accuracy: 0.7941\n",
      "Epoch 412/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 3.4024e-05 - accuracy: 1.0000 - val_loss: 0.7334 - val_accuracy: 0.7941\n",
      "Epoch 413/500\n",
      "133/133 [==============================] - 5s 34ms/step - loss: 1.3733e-05 - accuracy: 1.0000 - val_loss: 0.7326 - val_accuracy: 0.7941\n",
      "Epoch 414/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 1.7292e-05 - accuracy: 1.0000 - val_loss: 0.7316 - val_accuracy: 0.7941\n",
      "Epoch 415/500\n",
      "133/133 [==============================] - 5s 35ms/step - loss: 6.3724e-05 - accuracy: 1.0000 - val_loss: 0.7326 - val_accuracy: 0.7941\n",
      "Epoch 416/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 2.3553e-04 - accuracy: 1.0000 - val_loss: 0.7349 - val_accuracy: 0.7941\n",
      "Epoch 417/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 2.9963e-04 - accuracy: 1.0000 - val_loss: 0.7432 - val_accuracy: 0.7647\n",
      "Epoch 418/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 1.9083e-04 - accuracy: 1.0000 - val_loss: 0.7429 - val_accuracy: 0.7647\n",
      "Epoch 419/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 6.0325e-06 - accuracy: 1.0000 - val_loss: 0.7431 - val_accuracy: 0.7647\n",
      "Epoch 420/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 1.8359e-05 - accuracy: 1.0000 - val_loss: 0.7396 - val_accuracy: 0.7647\n",
      "Epoch 421/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 2.3257e-05 - accuracy: 1.0000 - val_loss: 0.7412 - val_accuracy: 0.7647\n",
      "Epoch 422/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 1.8908e-05 - accuracy: 1.0000 - val_loss: 0.7435 - val_accuracy: 0.7647\n",
      "Epoch 423/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 8.7236e-05 - accuracy: 1.0000 - val_loss: 0.6915 - val_accuracy: 0.7941\n",
      "Epoch 424/500\n",
      "133/133 [==============================] - 5s 34ms/step - loss: 8.9357e-06 - accuracy: 1.0000 - val_loss: 0.6925 - val_accuracy: 0.7941\n",
      "Epoch 425/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.3449e-05 - accuracy: 1.0000 - val_loss: 0.6928 - val_accuracy: 0.7941\n",
      "Epoch 426/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 2.2996e-05 - accuracy: 1.0000 - val_loss: 0.6919 - val_accuracy: 0.7941\n",
      "Epoch 427/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 8.6478e-06 - accuracy: 1.0000 - val_loss: 0.6919 - val_accuracy: 0.7941\n",
      "Epoch 428/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 3.4102e-06 - accuracy: 1.0000 - val_loss: 0.6924 - val_accuracy: 0.7941\n",
      "Epoch 429/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 1.0773e-06 - accuracy: 1.0000 - val_loss: 0.6926 - val_accuracy: 0.7941\n",
      "Epoch 430/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 6.4338e-06 - accuracy: 1.0000 - val_loss: 0.6932 - val_accuracy: 0.7941\n",
      "Epoch 431/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 1.9729e-05 - accuracy: 1.0000 - val_loss: 0.6718 - val_accuracy: 0.7941\n",
      "Epoch 432/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 2.3029e-05 - accuracy: 1.0000 - val_loss: 0.6787 - val_accuracy: 0.7941\n",
      "Epoch 433/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 5.2160e-06 - accuracy: 1.0000 - val_loss: 0.6806 - val_accuracy: 0.7941\n",
      "Epoch 434/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 6.3059e-06 - accuracy: 1.0000 - val_loss: 0.6837 - val_accuracy: 0.7941\n",
      "Epoch 435/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 2.9442e-06 - accuracy: 1.0000 - val_loss: 0.6850 - val_accuracy: 0.7941\n",
      "Epoch 436/500\n",
      "133/133 [==============================] - 5s 37ms/step - loss: 7.4609e-06 - accuracy: 1.0000 - val_loss: 0.6881 - val_accuracy: 0.7941\n",
      "Epoch 437/500\n",
      "133/133 [==============================] - 5s 39ms/step - loss: 5.1898e-06 - accuracy: 1.0000 - val_loss: 0.6900 - val_accuracy: 0.7941\n",
      "Epoch 438/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 6.1575e-07 - accuracy: 1.0000 - val_loss: 0.6915 - val_accuracy: 0.7941\n",
      "Epoch 439/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 2.1770e-06 - accuracy: 1.0000 - val_loss: 0.6935 - val_accuracy: 0.7941\n",
      "Epoch 440/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 5.5999e-06 - accuracy: 1.0000 - val_loss: 0.6953 - val_accuracy: 0.7941\n",
      "Epoch 441/500\n",
      "133/133 [==============================] - 5s 38ms/step - loss: 1.0345e-05 - accuracy: 1.0000 - val_loss: 0.7057 - val_accuracy: 0.7941\n",
      "Epoch 442/500\n",
      "133/133 [==============================] - 5s 35ms/step - loss: 4.5787e-06 - accuracy: 1.0000 - val_loss: 0.7029 - val_accuracy: 0.8235\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 443/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 3.8541e-07 - accuracy: 1.0000 - val_loss: 0.7033 - val_accuracy: 0.8235\n",
      "Epoch 444/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 4.8042e-07 - accuracy: 1.0000 - val_loss: 0.7038 - val_accuracy: 0.8235\n",
      "Epoch 445/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 7.0802e-06 - accuracy: 1.0000 - val_loss: 0.6915 - val_accuracy: 0.8235\n",
      "Epoch 446/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 2.5928e-06 - accuracy: 1.0000 - val_loss: 0.6953 - val_accuracy: 0.8235\n",
      "Epoch 447/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 9.6170e-07 - accuracy: 1.0000 - val_loss: 0.6981 - val_accuracy: 0.8235\n",
      "Epoch 448/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 3.2536e-07 - accuracy: 1.0000 - val_loss: 0.6992 - val_accuracy: 0.8235\n",
      "Epoch 449/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 2.4181e-06 - accuracy: 1.0000 - val_loss: 0.7054 - val_accuracy: 0.8235\n",
      "Epoch 450/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 1.7845e-06 - accuracy: 1.0000 - val_loss: 0.7105 - val_accuracy: 0.7941\n",
      "Epoch 451/500\n",
      "133/133 [==============================] - 4s 34ms/step - loss: 3.9078e-07 - accuracy: 1.0000 - val_loss: 0.7129 - val_accuracy: 0.7941\n",
      "Epoch 452/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 1.7128e-06 - accuracy: 1.0000 - val_loss: 0.7157 - val_accuracy: 0.8235\n",
      "Epoch 453/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 1.6868e-06 - accuracy: 1.0000 - val_loss: 0.7202 - val_accuracy: 0.7941\n",
      "Epoch 454/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 2.4673e-06 - accuracy: 1.0000 - val_loss: 0.7308 - val_accuracy: 0.7941\n",
      "Epoch 455/500\n",
      "133/133 [==============================] - 5s 34ms/step - loss: 2.0873e-06 - accuracy: 1.0000 - val_loss: 0.7306 - val_accuracy: 0.7941\n",
      "Epoch 456/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 8.6133e-07 - accuracy: 1.0000 - val_loss: 0.7384 - val_accuracy: 0.7941\n",
      "Epoch 457/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 2.3901e-06 - accuracy: 1.0000 - val_loss: 0.7523 - val_accuracy: 0.7941\n",
      "Epoch 458/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 4.2485e-07 - accuracy: 1.0000 - val_loss: 0.7520 - val_accuracy: 0.7941\n",
      "Epoch 459/500\n",
      "133/133 [==============================] - 4s 34ms/step - loss: 5.0731e-07 - accuracy: 1.0000 - val_loss: 0.7571 - val_accuracy: 0.7941\n",
      "Epoch 460/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 5.2344e-07 - accuracy: 1.0000 - val_loss: 0.7544 - val_accuracy: 0.7941\n",
      "Epoch 461/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 1.8230e-06 - accuracy: 1.0000 - val_loss: 0.7761 - val_accuracy: 0.7941\n",
      "Epoch 462/500\n",
      "133/133 [==============================] - 5s 36ms/step - loss: 1.2817e-07 - accuracy: 1.0000 - val_loss: 0.7775 - val_accuracy: 0.7941\n",
      "Epoch 463/500\n",
      "133/133 [==============================] - 4s 34ms/step - loss: 9.5009e-08 - accuracy: 1.0000 - val_loss: 0.7810 - val_accuracy: 0.7941\n",
      "Epoch 464/500\n",
      "133/133 [==============================] - 4s 30ms/step - loss: 1.6940e-07 - accuracy: 1.0000 - val_loss: 0.7858 - val_accuracy: 0.7941\n",
      "Epoch 465/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 5.7364e-08 - accuracy: 1.0000 - val_loss: 0.7893 - val_accuracy: 0.7941\n",
      "Epoch 466/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 3.0385e-07 - accuracy: 1.0000 - val_loss: 0.7912 - val_accuracy: 0.7941\n",
      "Epoch 467/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 1.3355e-07 - accuracy: 1.0000 - val_loss: 0.7916 - val_accuracy: 0.7941\n",
      "Epoch 468/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 4.3918e-07 - accuracy: 1.0000 - val_loss: 0.7928 - val_accuracy: 0.7941\n",
      "Epoch 469/500\n",
      "133/133 [==============================] - 4s 34ms/step - loss: 1.8733e-07 - accuracy: 1.0000 - val_loss: 0.8012 - val_accuracy: 0.7941\n",
      "Epoch 470/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 1.8554e-07 - accuracy: 1.0000 - val_loss: 0.8056 - val_accuracy: 0.7941\n",
      "Epoch 471/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 1.3445e-07 - accuracy: 1.0000 - val_loss: 0.8109 - val_accuracy: 0.7941\n",
      "Epoch 472/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 0.0642 - accuracy: 0.9925 - val_loss: 25.0851 - val_accuracy: 0.6765\n",
      "Epoch 473/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 15.0698 - accuracy: 0.6992 - val_loss: 16.8159 - val_accuracy: 0.6765\n",
      "Epoch 474/500\n",
      "133/133 [==============================] - 4s 34ms/step - loss: 11.5163 - accuracy: 0.6541 - val_loss: 2.4838 - val_accuracy: 0.6765\n",
      "Epoch 475/500\n",
      "133/133 [==============================] - 4s 33ms/step - loss: 0.5860 - accuracy: 0.8947 - val_loss: 1.2039 - val_accuracy: 0.7647\n",
      "Epoch 476/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 0.5858 - accuracy: 0.9098 - val_loss: 0.5286 - val_accuracy: 0.7941\n",
      "Epoch 477/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.0040 - accuracy: 1.0000 - val_loss: 0.8839 - val_accuracy: 0.7353\n",
      "Epoch 478/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.0350 - accuracy: 0.9850 - val_loss: 0.3598 - val_accuracy: 0.8824\n",
      "Epoch 479/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.0170 - accuracy: 0.9925 - val_loss: 0.2649 - val_accuracy: 0.9412\n",
      "Epoch 480/500\n",
      "133/133 [==============================] - 4s 31ms/step - loss: 0.0050 - accuracy: 1.0000 - val_loss: 0.2654 - val_accuracy: 0.9118\n",
      "Epoch 481/500\n",
      "133/133 [==============================] - 4s 32ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.2682 - val_accuracy: 0.9118\n",
      "Epoch 482/500\n",
      "133/133 [==============================] - 5s 39ms/step - loss: 0.0132 - accuracy: 0.9925 - val_loss: 0.3376 - val_accuracy: 0.8824\n",
      "Epoch 483/500\n",
      "133/133 [==============================] - 5s 39ms/step - loss: 0.0056 - accuracy: 1.0000 - val_loss: 0.3183 - val_accuracy: 0.9118\n",
      "Epoch 484/500\n",
      "133/133 [==============================] - 6s 47ms/step - loss: 2.1650e-04 - accuracy: 1.0000 - val_loss: 0.3215 - val_accuracy: 0.9118\n",
      "Epoch 485/500\n",
      "133/133 [==============================] - 7s 52ms/step - loss: 2.4926e-04 - accuracy: 1.0000 - val_loss: 0.3348 - val_accuracy: 0.8824\n",
      "Epoch 486/500\n",
      "133/133 [==============================] - 7s 55ms/step - loss: 6.5207e-04 - accuracy: 1.0000 - val_loss: 0.2806 - val_accuracy: 0.9412\n",
      "Epoch 487/500\n",
      "133/133 [==============================] - 9s 71ms/step - loss: 4.8533e-04 - accuracy: 1.0000 - val_loss: 0.2906 - val_accuracy: 0.9412\n",
      "Epoch 488/500\n",
      "133/133 [==============================] - 8s 58ms/step - loss: 0.0056 - accuracy: 1.0000 - val_loss: 0.3229 - val_accuracy: 0.9118\n",
      "Epoch 489/500\n",
      "133/133 [==============================] - 6s 42ms/step - loss: 4.5960e-04 - accuracy: 1.0000 - val_loss: 0.3381 - val_accuracy: 0.9118\n",
      "Epoch 490/500\n",
      "133/133 [==============================] - 5s 41ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.3711 - val_accuracy: 0.8824\n",
      "Epoch 491/500\n",
      "133/133 [==============================] - 6s 48ms/step - loss: 4.2314e-04 - accuracy: 1.0000 - val_loss: 0.3669 - val_accuracy: 0.8824\n",
      "Epoch 492/500\n",
      "133/133 [==============================] - 6s 43ms/step - loss: 7.8079e-05 - accuracy: 1.0000 - val_loss: 0.3906 - val_accuracy: 0.8824\n",
      "Epoch 493/500\n",
      "133/133 [==============================] - 4s 34ms/step - loss: 2.2425e-04 - accuracy: 1.0000 - val_loss: 0.3507 - val_accuracy: 0.9118\n",
      "Epoch 494/500\n",
      "133/133 [==============================] - 5s 37ms/step - loss: 0.0032 - accuracy: 1.0000 - val_loss: 0.4281 - val_accuracy: 0.8824\n",
      "Epoch 495/500\n",
      "133/133 [==============================] - 5s 41ms/step - loss: 0.0146 - accuracy: 0.9925 - val_loss: 0.6277 - val_accuracy: 0.8235\n",
      "Epoch 496/500\n",
      "133/133 [==============================] - 5s 39ms/step - loss: 2.6207e-04 - accuracy: 1.0000 - val_loss: 0.8809 - val_accuracy: 0.8824\n",
      "Epoch 497/500\n",
      "133/133 [==============================] - 5s 37ms/step - loss: 0.0049 - accuracy: 1.0000 - val_loss: 0.1787 - val_accuracy: 0.9412\n",
      "Epoch 498/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "133/133 [==============================] - 5s 37ms/step - loss: 4.0206e-05 - accuracy: 1.0000 - val_loss: 0.1777 - val_accuracy: 0.9412\n",
      "Epoch 499/500\n",
      "133/133 [==============================] - 5s 34ms/step - loss: 0.0033 - accuracy: 1.0000 - val_loss: 0.1968 - val_accuracy: 0.9706\n",
      "Epoch 500/500\n",
      "133/133 [==============================] - 4s 34ms/step - loss: 3.5495e-05 - accuracy: 1.0000 - val_loss: 0.1969 - val_accuracy: 0.9706\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x13631c750>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, \n",
    "          validation_data=(X_val, y_val), shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42/42 [==============================] - 0s 5ms/step\n"
     ]
    }
   ],
   "source": [
    "predicted = model.predict(X_test, verbose=1)\n",
    "\n",
    "predictionsPlot = np.zeros(y_test.shape[0],)\n",
    "\n",
    "y_testPlot = np.zeros(y_test.shape[0],)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(42, 2)\n",
      "(42, 2)\n",
      "Accuracy: 1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(y_test.shape)\n",
    "print(predicted.shape)\n",
    "y_testPlot *= 100\n",
    "predictionsPlot *= 100\n",
    "acc = accuracy_score(y_testPlot, predictionsPlot)\n",
    "print(\"Accuracy: {}\".format(acc))"
   ]
  }
 ],
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