nnmike.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import ssl\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "import sqlalchemy\n",
    "#import datetime\n",
    "\n",
    "from utils.buildnn import buildnn\n",
    "\n",
    "#import tensorflow as tf\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "database_connection_string = 'sqlite:///SP500.db'\n",
    "engine = sqlalchemy.create_engine(database_connection_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SPY Open</th>\n",
       "      <th>SPY Close</th>\n",
       "      <th>Industrials</th>\n",
       "      <th>Health Care</th>\n",
       "      <th>Information Technology</th>\n",
       "      <th>Communication Services</th>\n",
       "      <th>Consumer Staples</th>\n",
       "      <th>Consumer Discretionary</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>Financials</th>\n",
       "      <th>Materials</th>\n",
       "      <th>Real Estate</th>\n",
       "      <th>Energy</th>\n",
       "      <th>SPY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>413.79</td>\n",
       "      <td>415.87</td>\n",
       "      <td>-0.000804</td>\n",
       "      <td>0.000509</td>\n",
       "      <td>-0.004617</td>\n",
       "      <td>-0.004491</td>\n",
       "      <td>-0.001597</td>\n",
       "      <td>-0.002539</td>\n",
       "      <td>0.007733</td>\n",
       "      <td>0.010694</td>\n",
       "      <td>0.009282</td>\n",
       "      <td>-0.004979</td>\n",
       "      <td>0.020342</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>417.29</td>\n",
       "      <td>417.26</td>\n",
       "      <td>0.000453</td>\n",
       "      <td>0.011926</td>\n",
       "      <td>0.004855</td>\n",
       "      <td>0.003417</td>\n",
       "      <td>0.005316</td>\n",
       "      <td>-0.004535</td>\n",
       "      <td>0.011355</td>\n",
       "      <td>-0.001082</td>\n",
       "      <td>0.004324</td>\n",
       "      <td>0.011913</td>\n",
       "      <td>-0.006888</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>416.26</td>\n",
       "      <td>415.21</td>\n",
       "      <td>-0.003498</td>\n",
       "      <td>0.002819</td>\n",
       "      <td>-0.002692</td>\n",
       "      <td>-0.003881</td>\n",
       "      <td>0.001754</td>\n",
       "      <td>0.002421</td>\n",
       "      <td>0.002735</td>\n",
       "      <td>-0.004246</td>\n",
       "      <td>-0.001499</td>\n",
       "      <td>-0.003487</td>\n",
       "      <td>-0.018957</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>413.93</td>\n",
       "      <td>412.17</td>\n",
       "      <td>-0.004149</td>\n",
       "      <td>-0.000356</td>\n",
       "      <td>-0.009452</td>\n",
       "      <td>-0.002727</td>\n",
       "      <td>-0.002429</td>\n",
       "      <td>-0.007847</td>\n",
       "      <td>-0.005995</td>\n",
       "      <td>-0.003404</td>\n",
       "      <td>-0.005562</td>\n",
       "      <td>0.002253</td>\n",
       "      <td>-0.001944</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>411.55</td>\n",
       "      <td>416.07</td>\n",
       "      <td>-0.008910</td>\n",
       "      <td>0.002328</td>\n",
       "      <td>-0.005653</td>\n",
       "      <td>-0.007387</td>\n",
       "      <td>0.009700</td>\n",
       "      <td>-0.015166</td>\n",
       "      <td>0.012631</td>\n",
       "      <td>-0.011951</td>\n",
       "      <td>-0.008709</td>\n",
       "      <td>0.011858</td>\n",
       "      <td>-0.025533</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>448.04</td>\n",
       "      <td>447.57</td>\n",
       "      <td>0.005856</td>\n",
       "      <td>0.021049</td>\n",
       "      <td>0.005208</td>\n",
       "      <td>-0.009337</td>\n",
       "      <td>0.008555</td>\n",
       "      <td>0.004695</td>\n",
       "      <td>-0.003217</td>\n",
       "      <td>-0.001480</td>\n",
       "      <td>0.004416</td>\n",
       "      <td>-0.004456</td>\n",
       "      <td>0.002626</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>444.11</td>\n",
       "      <td>439.92</td>\n",
       "      <td>-0.005791</td>\n",
       "      <td>0.005426</td>\n",
       "      <td>-0.007141</td>\n",
       "      <td>0.001863</td>\n",
       "      <td>0.001890</td>\n",
       "      <td>0.004482</td>\n",
       "      <td>0.001318</td>\n",
       "      <td>0.002899</td>\n",
       "      <td>0.002020</td>\n",
       "      <td>0.001696</td>\n",
       "      <td>0.027976</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>443.02</td>\n",
       "      <td>438.29</td>\n",
       "      <td>-0.001636</td>\n",
       "      <td>-0.016449</td>\n",
       "      <td>-0.008222</td>\n",
       "      <td>-0.001345</td>\n",
       "      <td>-0.002333</td>\n",
       "      <td>0.006947</td>\n",
       "      <td>-0.014041</td>\n",
       "      <td>-0.002284</td>\n",
       "      <td>-0.001462</td>\n",
       "      <td>-0.010802</td>\n",
       "      <td>-0.018648</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>437.96</td>\n",
       "      <td>443.31</td>\n",
       "      <td>-0.005875</td>\n",
       "      <td>-0.009887</td>\n",
       "      <td>-0.015830</td>\n",
       "      <td>-0.014729</td>\n",
       "      <td>0.000453</td>\n",
       "      <td>-0.009666</td>\n",
       "      <td>0.006666</td>\n",
       "      <td>-0.009787</td>\n",
       "      <td>-0.003684</td>\n",
       "      <td>0.001091</td>\n",
       "      <td>-0.003254</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>443.44</td>\n",
       "      <td>437.79</td>\n",
       "      <td>0.009596</td>\n",
       "      <td>0.009621</td>\n",
       "      <td>0.013019</td>\n",
       "      <td>0.016400</td>\n",
       "      <td>0.006615</td>\n",
       "      <td>0.019986</td>\n",
       "      <td>-0.002671</td>\n",
       "      <td>0.013958</td>\n",
       "      <td>0.011097</td>\n",
       "      <td>0.005808</td>\n",
       "      <td>0.002591</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>254 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     SPY Open  SPY Close  Industrials  Health Care  Information Technology  \\\n",
       "0      413.79     415.87    -0.000804     0.000509               -0.004617   \n",
       "1      417.29     417.26     0.000453     0.011926                0.004855   \n",
       "2      416.26     415.21    -0.003498     0.002819               -0.002692   \n",
       "3      413.93     412.17    -0.004149    -0.000356               -0.009452   \n",
       "4      411.55     416.07    -0.008910     0.002328               -0.005653   \n",
       "..        ...        ...          ...          ...                     ...   \n",
       "249    448.04     447.57     0.005856     0.021049                0.005208   \n",
       "250    444.11     439.92    -0.005791     0.005426               -0.007141   \n",
       "251    443.02     438.29    -0.001636    -0.016449               -0.008222   \n",
       "252    437.96     443.31    -0.005875    -0.009887               -0.015830   \n",
       "253    443.44     437.79     0.009596     0.009621                0.013019   \n",
       "\n",
       "     Communication Services  Consumer Staples  Consumer Discretionary  \\\n",
       "0                 -0.004491         -0.001597               -0.002539   \n",
       "1                  0.003417          0.005316               -0.004535   \n",
       "2                 -0.003881          0.001754                0.002421   \n",
       "3                 -0.002727         -0.002429               -0.007847   \n",
       "4                 -0.007387          0.009700               -0.015166   \n",
       "..                      ...               ...                     ...   \n",
       "249               -0.009337          0.008555                0.004695   \n",
       "250                0.001863          0.001890                0.004482   \n",
       "251               -0.001345         -0.002333                0.006947   \n",
       "252               -0.014729          0.000453               -0.009666   \n",
       "253                0.016400          0.006615                0.019986   \n",
       "\n",
       "     Utilities  Financials  Materials  Real Estate    Energy  SPY  \n",
       "0     0.007733    0.010694   0.009282    -0.004979  0.020342  1.0  \n",
       "1     0.011355   -0.001082   0.004324     0.011913 -0.006888  0.0  \n",
       "2     0.002735   -0.004246  -0.001499    -0.003487 -0.018957  0.0  \n",
       "3    -0.005995   -0.003404  -0.005562     0.002253 -0.001944  0.0  \n",
       "4     0.012631   -0.011951  -0.008709     0.011858 -0.025533  1.0  \n",
       "..         ...         ...        ...          ...       ...  ...  \n",
       "249  -0.003217   -0.001480   0.004416    -0.004456  0.002626  0.0  \n",
       "250   0.001318    0.002899   0.002020     0.001696  0.027976  0.0  \n",
       "251  -0.014041   -0.002284  -0.001462    -0.010802 -0.018648  0.0  \n",
       "252   0.006666   -0.009787  -0.003684     0.001091 -0.003254  1.0  \n",
       "253  -0.002671    0.013958   0.011097     0.005808  0.002591  0.0  \n",
       "\n",
       "[254 rows x 14 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SectorDF = pd.read_sql_table('SectorDF3Y', engine)\n",
    "SectorDF = SectorDF.drop(columns=['timestamp'])\n",
    "SectorDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_8\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_39 (Dense)            (None, 16)                192       \n",
      "                                                                 \n",
      " dense_40 (Dense)            (None, 16)                272       \n",
      "                                                                 \n",
      " dense_41 (Dense)            (None, 8)                 136       \n",
      "                                                                 \n",
      " dense_42 (Dense)            (None, 1)                 9         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 609\n",
      "Trainable params: 609\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\canav\\anaconda3\\envs\\dev\\envs\\dev\\lib\\site-packages\\sklearn\\utils\\validation.py:1679: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['quoted_name']. An error will be raised in 1.2.\n",
      "  FutureWarning,\n",
      "C:\\Users\\canav\\anaconda3\\envs\\dev\\envs\\dev\\lib\\site-packages\\sklearn\\utils\\validation.py:1679: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['quoted_name']. An error will be raised in 1.2.\n",
      "  FutureWarning,\n",
      "C:\\Users\\canav\\anaconda3\\envs\\dev\\envs\\dev\\lib\\site-packages\\sklearn\\utils\\validation.py:1679: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['quoted_name']. An error will be raised in 1.2.\n",
      "  FutureWarning,\n"
     ]
    }
   ],
   "source": [
    "y = SectorDF[\"SPY\"]\n",
    "X = SectorDF.drop(columns=[\"SPY\",'SPY Open','SPY Close'])\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\n",
    "scaler = StandardScaler()\n",
    "X_scaler = scaler.fit(X_train)\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)\n",
    "\n",
    "number_input_features = len(X.iloc[0])\n",
    "number_output_neurons = 1\n",
    "nn = Sequential()\n",
    "\n",
    "actfunction = 'relu'\n",
    "\n",
    "nn.add(Dense(units=16 , input_dim=number_input_features, activation=actfunction))\n",
    "\n",
    "layers = [16,8]\n",
    "\n",
    "for layer in layers:\n",
    "    nn.add(Dense(units=layer, activation=actfunction))\n",
    "nn.add(Dense(units=number_output_neurons, activation=actfunction))\n",
    "nn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y = SectorDF[\"SPY\"]\n",
    "# X = SectorDF.drop(columns=[\"SPY\",'SPY Open','SPY Close'])\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\n",
    "# scaler = StandardScaler()\n",
    "# X_scaler = scaler.fit(X_train)\n",
    "# X_train_scaled = X_scaler.transform(X_train)\n",
    "# X_test_scaled = X_scaler.transform(X_test)\n",
    "# number_input_features = len(X.iloc[0])\n",
    "# number_input_features\n",
    "# number_output_neurons = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# layers = [6,5,3]\n",
    "# nn = Sequential()\n",
    "# count = 0\n",
    "# nn.add(Dense(units=(number_input_features + 1) // 2 , input_dim=number_input_features, activation=\"relu\"))\n",
    "# for layer in layers:\n",
    "#     nn.add(Dense(units=layer, activation=\"relu\"))\n",
    "# nn.add(Dense(units=number_output_neurons, activation=\"sigmoid\"))\n",
    "# nn.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the Sequential model\n",
    "nn.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 4.8984 - accuracy: 0.5105\n",
      "Epoch 2/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 4.2418 - accuracy: 0.5263\n",
      "Epoch 3/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 3.6708 - accuracy: 0.5158\n",
      "Epoch 4/700\n",
      "6/6 [==============================] - 0s 804us/step - loss: 3.3208 - accuracy: 0.5368\n",
      "Epoch 5/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 3.1631 - accuracy: 0.5421\n",
      "Epoch 6/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 3.0878 - accuracy: 0.5421\n",
      "Epoch 7/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 2.9960 - accuracy: 0.5579\n",
      "Epoch 8/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 2.8618 - accuracy: 0.5632\n",
      "Epoch 9/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 2.7820 - accuracy: 0.5632\n",
      "Epoch 10/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 2.4947 - accuracy: 0.5632\n",
      "Epoch 11/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 2.3511 - accuracy: 0.5632\n",
      "Epoch 12/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 2.1478 - accuracy: 0.5632\n",
      "Epoch 13/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 1.6758 - accuracy: 0.5579\n",
      "Epoch 14/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 1.4301 - accuracy: 0.5526\n",
      "Epoch 15/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 1.1150 - accuracy: 0.5579\n",
      "Epoch 16/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.9194 - accuracy: 0.5632\n",
      "Epoch 17/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.8520 - accuracy: 0.5474\n",
      "Epoch 18/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.8260 - accuracy: 0.5579\n",
      "Epoch 19/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.8141 - accuracy: 0.5632\n",
      "Epoch 20/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.8059 - accuracy: 0.5737\n",
      "Epoch 21/700\n",
      "6/6 [==============================] - 0s 996us/step - loss: 0.7987 - accuracy: 0.5737\n",
      "Epoch 22/700\n",
      "6/6 [==============================] - 0s 805us/step - loss: 0.7940 - accuracy: 0.5737\n",
      "Epoch 23/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.7906 - accuracy: 0.5737\n",
      "Epoch 24/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.7869 - accuracy: 0.5737\n",
      "Epoch 25/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.7831 - accuracy: 0.5737\n",
      "Epoch 26/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.7800 - accuracy: 0.5789\n",
      "Epoch 27/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.7776 - accuracy: 0.5737\n",
      "Epoch 28/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.7744 - accuracy: 0.5842\n",
      "Epoch 29/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.7717 - accuracy: 0.5842\n",
      "Epoch 30/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.7695 - accuracy: 0.5842\n",
      "Epoch 31/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.7664 - accuracy: 0.6000\n",
      "Epoch 32/700\n",
      "6/6 [==============================] - 0s 998us/step - loss: 0.7640 - accuracy: 0.6000\n",
      "Epoch 33/700\n",
      "6/6 [==============================] - 0s 804us/step - loss: 0.7621 - accuracy: 0.6000\n",
      "Epoch 34/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.7596 - accuracy: 0.6000\n",
      "Epoch 35/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.7574 - accuracy: 0.6000\n",
      "Epoch 36/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.7557 - accuracy: 0.6053\n",
      "Epoch 37/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.7534 - accuracy: 0.6053\n",
      "Epoch 38/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.7518 - accuracy: 0.6000\n",
      "Epoch 39/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.7500 - accuracy: 0.5947\n",
      "Epoch 40/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.7482 - accuracy: 0.5947\n",
      "Epoch 41/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.7469 - accuracy: 0.5947\n",
      "Epoch 42/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.7449 - accuracy: 0.6000\n",
      "Epoch 43/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.7432 - accuracy: 0.5947\n",
      "Epoch 44/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.7420 - accuracy: 0.5947\n",
      "Epoch 45/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.7402 - accuracy: 0.5947\n",
      "Epoch 46/700\n",
      "6/6 [==============================] - 0s 806us/step - loss: 0.7383 - accuracy: 0.6000\n",
      "Epoch 47/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.7373 - accuracy: 0.5947\n",
      "Epoch 48/700\n",
      "6/6 [==============================] - 0s 805us/step - loss: 0.7354 - accuracy: 0.5947\n",
      "Epoch 49/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.7341 - accuracy: 0.5947\n",
      "Epoch 50/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.7337 - accuracy: 0.5947\n",
      "Epoch 51/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.7313 - accuracy: 0.6000\n",
      "Epoch 52/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.7299 - accuracy: 0.6000\n",
      "Epoch 53/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.7283 - accuracy: 0.6000\n",
      "Epoch 54/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.7272 - accuracy: 0.6000\n",
      "Epoch 55/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.7259 - accuracy: 0.5947\n",
      "Epoch 56/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.7246 - accuracy: 0.5947\n",
      "Epoch 57/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.7236 - accuracy: 0.5895\n",
      "Epoch 58/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.7219 - accuracy: 0.6000\n",
      "Epoch 59/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.7207 - accuracy: 0.5947\n",
      "Epoch 60/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.7193 - accuracy: 0.5947\n",
      "Epoch 61/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.7182 - accuracy: 0.5947\n",
      "Epoch 62/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.7170 - accuracy: 0.5947\n",
      "Epoch 63/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.7153 - accuracy: 0.5895\n",
      "Epoch 64/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.7144 - accuracy: 0.5895\n",
      "Epoch 65/700\n",
      "6/6 [==============================] - 0s 801us/step - loss: 0.7134 - accuracy: 0.5947\n",
      "Epoch 66/700\n",
      "6/6 [==============================] - 0s 805us/step - loss: 0.7120 - accuracy: 0.5947\n",
      "Epoch 67/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.7105 - accuracy: 0.5947\n",
      "Epoch 68/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.7093 - accuracy: 0.5947\n",
      "Epoch 69/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.7083 - accuracy: 0.5947\n",
      "Epoch 70/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.7068 - accuracy: 0.6000\n",
      "Epoch 71/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.7057 - accuracy: 0.6053\n",
      "Epoch 72/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.7045 - accuracy: 0.6000\n",
      "Epoch 73/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.7029 - accuracy: 0.6000\n",
      "Epoch 74/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.7021 - accuracy: 0.6053\n",
      "Epoch 75/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.7009 - accuracy: 0.6105\n",
      "Epoch 76/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.6996 - accuracy: 0.6158\n",
      "Epoch 77/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6984 - accuracy: 0.6158\n",
      "Epoch 78/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.6973 - accuracy: 0.6158\n",
      "Epoch 79/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.6957 - accuracy: 0.6263\n",
      "Epoch 80/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6946 - accuracy: 0.6211\n",
      "Epoch 81/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6935 - accuracy: 0.6211\n",
      "Epoch 82/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.6927 - accuracy: 0.6211\n",
      "Epoch 83/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6911 - accuracy: 0.6316\n",
      "Epoch 84/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6898 - accuracy: 0.6316\n",
      "Epoch 85/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6889 - accuracy: 0.6263\n",
      "Epoch 86/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6880 - accuracy: 0.6316\n",
      "Epoch 87/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.6863 - accuracy: 0.6368\n",
      "Epoch 88/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6849 - accuracy: 0.6368\n",
      "Epoch 89/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6843 - accuracy: 0.6316\n",
      "Epoch 90/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.6829 - accuracy: 0.6316\n",
      "Epoch 91/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6818 - accuracy: 0.6263\n",
      "Epoch 92/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.6806 - accuracy: 0.6263\n",
      "Epoch 93/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6795 - accuracy: 0.6263\n",
      "Epoch 94/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6781 - accuracy: 0.6263\n",
      "Epoch 95/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6770 - accuracy: 0.6316\n",
      "Epoch 96/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.6762 - accuracy: 0.6316\n",
      "Epoch 97/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6750 - accuracy: 0.6368\n",
      "Epoch 98/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6743 - accuracy: 0.6368\n",
      "Epoch 99/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6730 - accuracy: 0.6368\n",
      "Epoch 100/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.6718 - accuracy: 0.6316\n",
      "Epoch 101/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6708 - accuracy: 0.6368\n",
      "Epoch 102/700\n",
      "6/6 [==============================] - 0s 952us/step - loss: 0.6697 - accuracy: 0.6368\n",
      "Epoch 103/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6685 - accuracy: 0.6368\n",
      "Epoch 104/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.6680 - accuracy: 0.6421\n",
      "Epoch 105/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.6669 - accuracy: 0.6421\n",
      "Epoch 106/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.6654 - accuracy: 0.6474\n",
      "Epoch 107/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6648 - accuracy: 0.6368\n",
      "Epoch 108/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.6633 - accuracy: 0.6474\n",
      "Epoch 109/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.6620 - accuracy: 0.6421\n",
      "Epoch 110/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.6612 - accuracy: 0.6474\n",
      "Epoch 111/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6601 - accuracy: 0.6526\n",
      "Epoch 112/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.6589 - accuracy: 0.6526\n",
      "Epoch 113/700\n",
      "6/6 [==============================] - 0s 805us/step - loss: 0.6579 - accuracy: 0.6579\n",
      "Epoch 114/700\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.6566 - accuracy: 0.6579\n",
      "Epoch 115/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.6556 - accuracy: 0.6579\n",
      "Epoch 116/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.6545 - accuracy: 0.6579\n",
      "Epoch 117/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.6541 - accuracy: 0.6579\n",
      "Epoch 118/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6524 - accuracy: 0.6579\n",
      "Epoch 119/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6513 - accuracy: 0.6579\n",
      "Epoch 120/700\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.6505 - accuracy: 0.6632\n",
      "Epoch 121/700\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.6502 - accuracy: 0.6737\n",
      "Epoch 122/700\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.6483 - accuracy: 0.6737\n",
      "Epoch 123/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6474 - accuracy: 0.6684\n",
      "Epoch 124/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.6463 - accuracy: 0.6737\n",
      "Epoch 125/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.6450 - accuracy: 0.6789\n",
      "Epoch 126/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.6439 - accuracy: 0.6789\n",
      "Epoch 127/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6429 - accuracy: 0.6842\n",
      "Epoch 128/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6417 - accuracy: 0.6895\n",
      "Epoch 129/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6406 - accuracy: 0.6895\n",
      "Epoch 130/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6394 - accuracy: 0.6895\n",
      "Epoch 131/700\n",
      "6/6 [==============================] - 0s 799us/step - loss: 0.6383 - accuracy: 0.6895\n",
      "Epoch 132/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6374 - accuracy: 0.6895\n",
      "Epoch 133/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6359 - accuracy: 0.6895\n",
      "Epoch 134/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.6357 - accuracy: 0.6842\n",
      "Epoch 135/700\n",
      "6/6 [==============================] - 0s 802us/step - loss: 0.6360 - accuracy: 0.6947\n",
      "Epoch 136/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.6333 - accuracy: 0.6947\n",
      "Epoch 137/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.6319 - accuracy: 0.6842\n",
      "Epoch 138/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6306 - accuracy: 0.6842\n",
      "Epoch 139/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.6301 - accuracy: 0.6842\n",
      "Epoch 140/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.6287 - accuracy: 0.6895\n",
      "Epoch 141/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6269 - accuracy: 0.7000\n",
      "Epoch 142/700\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.6261 - accuracy: 0.7000\n",
      "Epoch 143/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.6251 - accuracy: 0.7000\n",
      "Epoch 144/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.6241 - accuracy: 0.7000\n",
      "Epoch 145/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6230 - accuracy: 0.7000\n",
      "Epoch 146/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6215 - accuracy: 0.7000\n",
      "Epoch 147/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.6204 - accuracy: 0.7053\n",
      "Epoch 148/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.6194 - accuracy: 0.7000\n",
      "Epoch 149/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6182 - accuracy: 0.7000\n",
      "Epoch 150/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6179 - accuracy: 0.7000\n",
      "Epoch 151/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6163 - accuracy: 0.7000\n",
      "Epoch 152/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.6148 - accuracy: 0.7053\n",
      "Epoch 153/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6132 - accuracy: 0.7105\n",
      "Epoch 154/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.6119 - accuracy: 0.7105\n",
      "Epoch 155/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6103 - accuracy: 0.7105\n",
      "Epoch 156/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.6089 - accuracy: 0.7105\n",
      "Epoch 157/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6084 - accuracy: 0.7105\n",
      "Epoch 158/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6065 - accuracy: 0.7105\n",
      "Epoch 159/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6049 - accuracy: 0.7158\n",
      "Epoch 160/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6046 - accuracy: 0.7158\n",
      "Epoch 161/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.6022 - accuracy: 0.7158\n",
      "Epoch 162/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.6010 - accuracy: 0.7158\n",
      "Epoch 163/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5997 - accuracy: 0.7158\n",
      "Epoch 164/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.5984 - accuracy: 0.7105\n",
      "Epoch 165/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.5970 - accuracy: 0.7158\n",
      "Epoch 166/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5958 - accuracy: 0.7158\n",
      "Epoch 167/700\n",
      "6/6 [==============================] - 0s 804us/step - loss: 0.5941 - accuracy: 0.7263\n",
      "Epoch 168/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5930 - accuracy: 0.7263\n",
      "Epoch 169/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5917 - accuracy: 0.7263\n",
      "Epoch 170/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.5905 - accuracy: 0.7211\n",
      "Epoch 171/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5901 - accuracy: 0.7211\n",
      "Epoch 172/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5881 - accuracy: 0.7263\n",
      "Epoch 173/700\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.5866 - accuracy: 0.7263\n",
      "Epoch 174/700\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.5858 - accuracy: 0.7211\n",
      "Epoch 175/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5840 - accuracy: 0.7263\n",
      "Epoch 176/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5828 - accuracy: 0.7316\n",
      "Epoch 177/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.5811 - accuracy: 0.7316\n",
      "Epoch 178/700\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.5795 - accuracy: 0.7316\n",
      "Epoch 179/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.5784 - accuracy: 0.7263\n",
      "Epoch 180/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5770 - accuracy: 0.7316\n",
      "Epoch 181/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5760 - accuracy: 0.7316\n",
      "Epoch 182/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5751 - accuracy: 0.7316\n",
      "Epoch 183/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.5755 - accuracy: 0.7368\n",
      "Epoch 184/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5725 - accuracy: 0.7421\n",
      "Epoch 185/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.5715 - accuracy: 0.7316\n",
      "Epoch 186/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5693 - accuracy: 0.7316\n",
      "Epoch 187/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.5687 - accuracy: 0.7368\n",
      "Epoch 188/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5669 - accuracy: 0.7368\n",
      "Epoch 189/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5653 - accuracy: 0.7368\n",
      "Epoch 190/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.5636 - accuracy: 0.7368\n",
      "Epoch 191/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.5626 - accuracy: 0.7421\n",
      "Epoch 192/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.5609 - accuracy: 0.7474\n",
      "Epoch 193/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.5596 - accuracy: 0.7421\n",
      "Epoch 194/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5579 - accuracy: 0.7421\n",
      "Epoch 195/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5564 - accuracy: 0.7421\n",
      "Epoch 196/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5556 - accuracy: 0.7474\n",
      "Epoch 197/700\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.5548 - accuracy: 0.7421\n",
      "Epoch 198/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5516 - accuracy: 0.7421\n",
      "Epoch 199/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5511 - accuracy: 0.7368\n",
      "Epoch 200/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5496 - accuracy: 0.7421\n",
      "Epoch 201/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5472 - accuracy: 0.7474\n",
      "Epoch 202/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5456 - accuracy: 0.7579\n",
      "Epoch 203/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5446 - accuracy: 0.7579\n",
      "Epoch 204/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.5430 - accuracy: 0.7579\n",
      "Epoch 205/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5442 - accuracy: 0.7579\n",
      "Epoch 206/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5415 - accuracy: 0.7526\n",
      "Epoch 207/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5385 - accuracy: 0.7579\n",
      "Epoch 208/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5372 - accuracy: 0.7474\n",
      "Epoch 209/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5367 - accuracy: 0.7474\n",
      "Epoch 210/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5357 - accuracy: 0.7474\n",
      "Epoch 211/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.5344 - accuracy: 0.7526\n",
      "Epoch 212/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.5324 - accuracy: 0.7579\n",
      "Epoch 213/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.5302 - accuracy: 0.7579\n",
      "Epoch 214/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5294 - accuracy: 0.7579\n",
      "Epoch 215/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.5276 - accuracy: 0.7526\n",
      "Epoch 216/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.5269 - accuracy: 0.7526\n",
      "Epoch 217/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.5247 - accuracy: 0.7579\n",
      "Epoch 218/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.5237 - accuracy: 0.7684\n",
      "Epoch 219/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5232 - accuracy: 0.7684\n",
      "Epoch 220/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.5246 - accuracy: 0.7579\n",
      "Epoch 221/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.5198 - accuracy: 0.7579\n",
      "Epoch 222/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5182 - accuracy: 0.7632\n",
      "Epoch 223/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5165 - accuracy: 0.7684\n",
      "Epoch 224/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5142 - accuracy: 0.7684\n",
      "Epoch 225/700\n",
      "6/6 [==============================] - 0s 803us/step - loss: 0.5121 - accuracy: 0.7684\n",
      "Epoch 226/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.5112 - accuracy: 0.7684\n",
      "Epoch 227/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5094 - accuracy: 0.7684\n",
      "Epoch 228/700\n",
      "6/6 [==============================] - 0s 801us/step - loss: 0.5077 - accuracy: 0.7737\n",
      "Epoch 229/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.5057 - accuracy: 0.7737\n",
      "Epoch 230/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.5038 - accuracy: 0.7789\n",
      "Epoch 231/700\n",
      "6/6 [==============================] - 0s 804us/step - loss: 0.5018 - accuracy: 0.7789\n",
      "Epoch 232/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.5010 - accuracy: 0.7789\n",
      "Epoch 233/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.4997 - accuracy: 0.7842\n",
      "Epoch 234/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.4964 - accuracy: 0.7842\n",
      "Epoch 235/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.4951 - accuracy: 0.7842\n",
      "Epoch 236/700\n",
      "6/6 [==============================] - 0s 801us/step - loss: 0.4936 - accuracy: 0.7842\n",
      "Epoch 237/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.4942 - accuracy: 0.7842\n",
      "Epoch 238/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.4907 - accuracy: 0.7947\n",
      "Epoch 239/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.4884 - accuracy: 0.7842\n",
      "Epoch 240/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.4863 - accuracy: 0.7895\n",
      "Epoch 241/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.4853 - accuracy: 0.8000\n",
      "Epoch 242/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.4831 - accuracy: 0.7947\n",
      "Epoch 243/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.4807 - accuracy: 0.7947\n",
      "Epoch 244/700\n",
      "6/6 [==============================] - 0s 801us/step - loss: 0.4778 - accuracy: 0.7947\n",
      "Epoch 245/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.4759 - accuracy: 0.8000\n",
      "Epoch 246/700\n",
      "6/6 [==============================] - 0s 799us/step - loss: 0.4741 - accuracy: 0.8000\n",
      "Epoch 247/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.4723 - accuracy: 0.7947\n",
      "Epoch 248/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.4708 - accuracy: 0.8000\n",
      "Epoch 249/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.4689 - accuracy: 0.8000\n",
      "Epoch 250/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.4675 - accuracy: 0.8000\n",
      "Epoch 251/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.4653 - accuracy: 0.7947\n",
      "Epoch 252/700\n",
      "6/6 [==============================] - 0s 804us/step - loss: 0.4647 - accuracy: 0.7947\n",
      "Epoch 253/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.4619 - accuracy: 0.8000\n",
      "Epoch 254/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.4592 - accuracy: 0.7947\n",
      "Epoch 255/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.4578 - accuracy: 0.7947\n",
      "Epoch 256/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.4560 - accuracy: 0.8000\n",
      "Epoch 257/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.4552 - accuracy: 0.8000\n",
      "Epoch 258/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.4522 - accuracy: 0.8000\n",
      "Epoch 259/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.4508 - accuracy: 0.8105\n",
      "Epoch 260/700\n",
      "6/6 [==============================] - 0s 795us/step - loss: 0.4474 - accuracy: 0.8053\n",
      "Epoch 261/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.4460 - accuracy: 0.8105\n",
      "Epoch 262/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.4440 - accuracy: 0.8105\n",
      "Epoch 263/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.4427 - accuracy: 0.8053\n",
      "Epoch 264/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.4402 - accuracy: 0.8053\n",
      "Epoch 265/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.4381 - accuracy: 0.8053\n",
      "Epoch 266/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.4355 - accuracy: 0.8053\n",
      "Epoch 267/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.4350 - accuracy: 0.8000\n",
      "Epoch 268/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.4318 - accuracy: 0.8105\n",
      "Epoch 269/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.4298 - accuracy: 0.8053\n",
      "Epoch 270/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.4290 - accuracy: 0.8053\n",
      "Epoch 271/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.4267 - accuracy: 0.8053\n",
      "Epoch 272/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.4247 - accuracy: 0.8105\n",
      "Epoch 273/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.4224 - accuracy: 0.8158\n",
      "Epoch 274/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.4215 - accuracy: 0.8211\n",
      "Epoch 275/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.4194 - accuracy: 0.8263\n",
      "Epoch 276/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.4175 - accuracy: 0.8211\n",
      "Epoch 277/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.4157 - accuracy: 0.8263\n",
      "Epoch 278/700\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.4143 - accuracy: 0.8263\n",
      "Epoch 279/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.4128 - accuracy: 0.8263\n",
      "Epoch 280/700\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.4096 - accuracy: 0.8368\n",
      "Epoch 281/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.4083 - accuracy: 0.8368\n",
      "Epoch 282/700\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.4058 - accuracy: 0.8368\n",
      "Epoch 283/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.4041 - accuracy: 0.8368\n",
      "Epoch 284/700\n",
      "6/6 [==============================] - 0s 799us/step - loss: 0.4027 - accuracy: 0.8368\n",
      "Epoch 285/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.4016 - accuracy: 0.8368\n",
      "Epoch 286/700\n",
      "6/6 [==============================] - 0s 904us/step - loss: 0.4025 - accuracy: 0.8263\n",
      "Epoch 287/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.3980 - accuracy: 0.8263\n",
      "Epoch 288/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.3963 - accuracy: 0.8368\n",
      "Epoch 289/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.3953 - accuracy: 0.8316\n",
      "Epoch 290/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3934 - accuracy: 0.8316\n",
      "Epoch 291/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3903 - accuracy: 0.8421\n",
      "Epoch 292/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.3891 - accuracy: 0.8368\n",
      "Epoch 293/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.3862 - accuracy: 0.8368\n",
      "Epoch 294/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3859 - accuracy: 0.8368\n",
      "Epoch 295/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3843 - accuracy: 0.8368\n",
      "Epoch 296/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3835 - accuracy: 0.8368\n",
      "Epoch 297/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.3807 - accuracy: 0.8421\n",
      "Epoch 298/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.3799 - accuracy: 0.8421\n",
      "Epoch 299/700\n",
      "6/6 [==============================] - 0s 802us/step - loss: 0.3780 - accuracy: 0.8368\n",
      "Epoch 300/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3763 - accuracy: 0.8368\n",
      "Epoch 301/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.3764 - accuracy: 0.8368\n",
      "Epoch 302/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.3780 - accuracy: 0.8368\n",
      "Epoch 303/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3734 - accuracy: 0.8368\n",
      "Epoch 304/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3743 - accuracy: 0.8421\n",
      "Epoch 305/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.3706 - accuracy: 0.8368\n",
      "Epoch 306/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3695 - accuracy: 0.8421\n",
      "Epoch 307/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.3666 - accuracy: 0.8474\n",
      "Epoch 308/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3658 - accuracy: 0.8421\n",
      "Epoch 309/700\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.3648 - accuracy: 0.8421\n",
      "Epoch 310/700\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.3641 - accuracy: 0.8421\n",
      "Epoch 311/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3623 - accuracy: 0.8632\n",
      "Epoch 312/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3605 - accuracy: 0.8421\n",
      "Epoch 313/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.3594 - accuracy: 0.8526\n",
      "Epoch 314/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3593 - accuracy: 0.8526\n",
      "Epoch 315/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3568 - accuracy: 0.8632\n",
      "Epoch 316/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3553 - accuracy: 0.8684\n",
      "Epoch 317/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3531 - accuracy: 0.8579\n",
      "Epoch 318/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3536 - accuracy: 0.8579\n",
      "Epoch 319/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3511 - accuracy: 0.8632\n",
      "Epoch 320/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.3474 - accuracy: 0.8632\n",
      "Epoch 321/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.3472 - accuracy: 0.8737\n",
      "Epoch 322/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.3460 - accuracy: 0.8737\n",
      "Epoch 323/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.3440 - accuracy: 0.8737\n",
      "Epoch 324/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3422 - accuracy: 0.8737\n",
      "Epoch 325/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3406 - accuracy: 0.8789\n",
      "Epoch 326/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3400 - accuracy: 0.8737\n",
      "Epoch 327/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.3389 - accuracy: 0.8737\n",
      "Epoch 328/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.3372 - accuracy: 0.8737\n",
      "Epoch 329/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.3352 - accuracy: 0.8737\n",
      "Epoch 330/700\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.3349 - accuracy: 0.8684\n",
      "Epoch 331/700\n",
      "6/6 [==============================] - 0s 998us/step - loss: 0.3332 - accuracy: 0.8737\n",
      "Epoch 332/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.3318 - accuracy: 0.8684\n",
      "Epoch 333/700\n",
      "6/6 [==============================] - 0s 995us/step - loss: 0.3296 - accuracy: 0.8684\n",
      "Epoch 334/700\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.3295 - accuracy: 0.8789\n",
      "Epoch 335/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.3282 - accuracy: 0.8737\n",
      "Epoch 336/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.3258 - accuracy: 0.8842\n",
      "Epoch 337/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3279 - accuracy: 0.8789\n",
      "Epoch 338/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3216 - accuracy: 0.8842\n",
      "Epoch 339/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3214 - accuracy: 0.8842\n",
      "Epoch 340/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.3202 - accuracy: 0.8789\n",
      "Epoch 341/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3174 - accuracy: 0.8789\n",
      "Epoch 342/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3163 - accuracy: 0.8842\n",
      "Epoch 343/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3156 - accuracy: 0.8895\n",
      "Epoch 344/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3122 - accuracy: 0.8947\n",
      "Epoch 345/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.3124 - accuracy: 0.8895\n",
      "Epoch 346/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.3123 - accuracy: 0.8842\n",
      "Epoch 347/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.3079 - accuracy: 0.9000\n",
      "Epoch 348/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.3068 - accuracy: 0.9053\n",
      "Epoch 349/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3047 - accuracy: 0.9000\n",
      "Epoch 350/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3028 - accuracy: 0.9000\n",
      "Epoch 351/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3020 - accuracy: 0.9053\n",
      "Epoch 352/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2996 - accuracy: 0.9053\n",
      "Epoch 353/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2992 - accuracy: 0.9053\n",
      "Epoch 354/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.2991 - accuracy: 0.9000\n",
      "Epoch 355/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.3003 - accuracy: 0.9000\n",
      "Epoch 356/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.2965 - accuracy: 0.9000\n",
      "Epoch 357/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.2938 - accuracy: 0.8947\n",
      "Epoch 358/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2916 - accuracy: 0.9000\n",
      "Epoch 359/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2907 - accuracy: 0.9000\n",
      "Epoch 360/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2884 - accuracy: 0.9000\n",
      "Epoch 361/700\n",
      "6/6 [==============================] - 0s 976us/step - loss: 0.2878 - accuracy: 0.9000\n",
      "Epoch 362/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.2875 - accuracy: 0.9000\n",
      "Epoch 363/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.2860 - accuracy: 0.9000\n",
      "Epoch 364/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2873 - accuracy: 0.9000\n",
      "Epoch 365/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2826 - accuracy: 0.9000\n",
      "Epoch 366/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2836 - accuracy: 0.9000\n",
      "Epoch 367/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2796 - accuracy: 0.9000\n",
      "Epoch 368/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2790 - accuracy: 0.9053\n",
      "Epoch 369/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2775 - accuracy: 0.9053\n",
      "Epoch 370/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.2792 - accuracy: 0.9105\n",
      "Epoch 371/700\n",
      "6/6 [==============================] - 0s 799us/step - loss: 0.2741 - accuracy: 0.9053\n",
      "Epoch 372/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2729 - accuracy: 0.9053\n",
      "Epoch 373/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.2725 - accuracy: 0.9053\n",
      "Epoch 374/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.2702 - accuracy: 0.9000\n",
      "Epoch 375/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2721 - accuracy: 0.9053\n",
      "Epoch 376/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2677 - accuracy: 0.9053\n",
      "Epoch 377/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.2676 - accuracy: 0.9053\n",
      "Epoch 378/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2649 - accuracy: 0.9053\n",
      "Epoch 379/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.2670 - accuracy: 0.9105\n",
      "Epoch 380/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2637 - accuracy: 0.9105\n",
      "Epoch 381/700\n",
      "6/6 [==============================] - 0s 997us/step - loss: 0.2639 - accuracy: 0.8947\n",
      "Epoch 382/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2626 - accuracy: 0.9000\n",
      "Epoch 383/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.2611 - accuracy: 0.9105\n",
      "Epoch 384/700\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.2585 - accuracy: 0.9053\n",
      "Epoch 385/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2575 - accuracy: 0.9158\n",
      "Epoch 386/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2573 - accuracy: 0.9105\n",
      "Epoch 387/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.2548 - accuracy: 0.9105\n",
      "Epoch 388/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2544 - accuracy: 0.9053\n",
      "Epoch 389/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2535 - accuracy: 0.9105\n",
      "Epoch 390/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2514 - accuracy: 0.9053\n",
      "Epoch 391/700\n",
      "6/6 [==============================] - 0s 806us/step - loss: 0.2512 - accuracy: 0.9105\n",
      "Epoch 392/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2509 - accuracy: 0.9105\n",
      "Epoch 393/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2511 - accuracy: 0.9211\n",
      "Epoch 394/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.2489 - accuracy: 0.9263\n",
      "Epoch 395/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2475 - accuracy: 0.9263\n",
      "Epoch 396/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.2460 - accuracy: 0.9211\n",
      "Epoch 397/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.2463 - accuracy: 0.9158\n",
      "Epoch 398/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.2434 - accuracy: 0.9211\n",
      "Epoch 399/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.2436 - accuracy: 0.9158\n",
      "Epoch 400/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.2405 - accuracy: 0.9158\n",
      "Epoch 401/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.2405 - accuracy: 0.9211\n",
      "Epoch 402/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2405 - accuracy: 0.9158\n",
      "Epoch 403/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2372 - accuracy: 0.9263\n",
      "Epoch 404/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.2370 - accuracy: 0.9211\n",
      "Epoch 405/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.2378 - accuracy: 0.9211\n",
      "Epoch 406/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.2341 - accuracy: 0.9316\n",
      "Epoch 407/700\n",
      "6/6 [==============================] - 0s 804us/step - loss: 0.2336 - accuracy: 0.9211\n",
      "Epoch 408/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.2334 - accuracy: 0.9263\n",
      "Epoch 409/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2305 - accuracy: 0.9263\n",
      "Epoch 410/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2300 - accuracy: 0.9211\n",
      "Epoch 411/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.2303 - accuracy: 0.9211\n",
      "Epoch 412/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.2335 - accuracy: 0.9211\n",
      "Epoch 413/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.2276 - accuracy: 0.9316\n",
      "Epoch 414/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2271 - accuracy: 0.9263\n",
      "Epoch 415/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.2263 - accuracy: 0.9316\n",
      "Epoch 416/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.2229 - accuracy: 0.9316\n",
      "Epoch 417/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.2223 - accuracy: 0.9368\n",
      "Epoch 418/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.2207 - accuracy: 0.9316\n",
      "Epoch 419/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2193 - accuracy: 0.9368\n",
      "Epoch 420/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.2175 - accuracy: 0.9368\n",
      "Epoch 421/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.2179 - accuracy: 0.9316\n",
      "Epoch 422/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.2176 - accuracy: 0.9368\n",
      "Epoch 423/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.2192 - accuracy: 0.9368\n",
      "Epoch 424/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2127 - accuracy: 0.9421\n",
      "Epoch 425/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2151 - accuracy: 0.9316\n",
      "Epoch 426/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2133 - accuracy: 0.9368\n",
      "Epoch 427/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.2114 - accuracy: 0.9421\n",
      "Epoch 428/700\n",
      "6/6 [==============================] - 0s 799us/step - loss: 0.2112 - accuracy: 0.9368\n",
      "Epoch 429/700\n",
      "6/6 [==============================] - 0s 799us/step - loss: 0.2088 - accuracy: 0.9368\n",
      "Epoch 430/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.2082 - accuracy: 0.9316\n",
      "Epoch 431/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.2107 - accuracy: 0.9316\n",
      "Epoch 432/700\n",
      "6/6 [==============================] - 0s 805us/step - loss: 0.2045 - accuracy: 0.9421\n",
      "Epoch 433/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2054 - accuracy: 0.9368\n",
      "Epoch 434/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2032 - accuracy: 0.9368\n",
      "Epoch 435/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2023 - accuracy: 0.9368\n",
      "Epoch 436/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.2048 - accuracy: 0.9368\n",
      "Epoch 437/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.1995 - accuracy: 0.9368\n",
      "Epoch 438/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.2014 - accuracy: 0.9421\n",
      "Epoch 439/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.1994 - accuracy: 0.9474\n",
      "Epoch 440/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.1994 - accuracy: 0.9421\n",
      "Epoch 441/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1984 - accuracy: 0.9368\n",
      "Epoch 442/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.1978 - accuracy: 0.9368\n",
      "Epoch 443/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1967 - accuracy: 0.9421\n",
      "Epoch 444/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.1945 - accuracy: 0.9421\n",
      "Epoch 445/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1955 - accuracy: 0.9421\n",
      "Epoch 446/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1931 - accuracy: 0.9421\n",
      "Epoch 447/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.1914 - accuracy: 0.9474\n",
      "Epoch 448/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.1914 - accuracy: 0.9474\n",
      "Epoch 449/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1904 - accuracy: 0.9421\n",
      "Epoch 450/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1894 - accuracy: 0.9474\n",
      "Epoch 451/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1890 - accuracy: 0.9421\n",
      "Epoch 452/700\n",
      "6/6 [==============================] - 0s 805us/step - loss: 0.1874 - accuracy: 0.9474\n",
      "Epoch 453/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.1870 - accuracy: 0.9474\n",
      "Epoch 454/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1875 - accuracy: 0.9474\n",
      "Epoch 455/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.1855 - accuracy: 0.9474\n",
      "Epoch 456/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.1857 - accuracy: 0.9474\n",
      "Epoch 457/700\n",
      "6/6 [==============================] - 0s 806us/step - loss: 0.1835 - accuracy: 0.9474\n",
      "Epoch 458/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.1831 - accuracy: 0.9421\n",
      "Epoch 459/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1847 - accuracy: 0.9474\n",
      "Epoch 460/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.1822 - accuracy: 0.9526\n",
      "Epoch 461/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.1818 - accuracy: 0.9474\n",
      "Epoch 462/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1796 - accuracy: 0.9474\n",
      "Epoch 463/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.1794 - accuracy: 0.9474\n",
      "Epoch 464/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1804 - accuracy: 0.9474\n",
      "Epoch 465/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.1796 - accuracy: 0.9526\n",
      "Epoch 466/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1786 - accuracy: 0.9526\n",
      "Epoch 467/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.1761 - accuracy: 0.9632\n",
      "Epoch 468/700\n",
      "6/6 [==============================] - 0s 799us/step - loss: 0.1759 - accuracy: 0.9579\n",
      "Epoch 469/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1739 - accuracy: 0.9526\n",
      "Epoch 470/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1730 - accuracy: 0.9579\n",
      "Epoch 471/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1736 - accuracy: 0.9632\n",
      "Epoch 472/700\n",
      "6/6 [==============================] - 0s 995us/step - loss: 0.1724 - accuracy: 0.9579\n",
      "Epoch 473/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1708 - accuracy: 0.9579\n",
      "Epoch 474/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.1692 - accuracy: 0.9579\n",
      "Epoch 475/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1682 - accuracy: 0.9632\n",
      "Epoch 476/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.1692 - accuracy: 0.9632\n",
      "Epoch 477/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1668 - accuracy: 0.9684\n",
      "Epoch 478/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.1664 - accuracy: 0.9684\n",
      "Epoch 479/700\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.1662 - accuracy: 0.9632\n",
      "Epoch 480/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1662 - accuracy: 0.9684\n",
      "Epoch 481/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.1641 - accuracy: 0.9684\n",
      "Epoch 482/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1632 - accuracy: 0.9632\n",
      "Epoch 483/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1625 - accuracy: 0.9684\n",
      "Epoch 484/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.1616 - accuracy: 0.9684\n",
      "Epoch 485/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.1625 - accuracy: 0.9632\n",
      "Epoch 486/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1616 - accuracy: 0.9684\n",
      "Epoch 487/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.1607 - accuracy: 0.9684\n",
      "Epoch 488/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.1596 - accuracy: 0.9632\n",
      "Epoch 489/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1594 - accuracy: 0.9684\n",
      "Epoch 490/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1601 - accuracy: 0.9684\n",
      "Epoch 491/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1580 - accuracy: 0.9684\n",
      "Epoch 492/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1574 - accuracy: 0.9684\n",
      "Epoch 493/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1573 - accuracy: 0.9684\n",
      "Epoch 494/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1560 - accuracy: 0.9684\n",
      "Epoch 495/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1564 - accuracy: 0.9684\n",
      "Epoch 496/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1545 - accuracy: 0.9684\n",
      "Epoch 497/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1547 - accuracy: 0.9684\n",
      "Epoch 498/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.1532 - accuracy: 0.9684\n",
      "Epoch 499/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.1535 - accuracy: 0.9684\n",
      "Epoch 500/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.1525 - accuracy: 0.9684\n",
      "Epoch 501/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1547 - accuracy: 0.9684\n",
      "Epoch 502/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.1534 - accuracy: 0.9684\n",
      "Epoch 503/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1542 - accuracy: 0.9684\n",
      "Epoch 504/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1542 - accuracy: 0.9684\n",
      "Epoch 505/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1530 - accuracy: 0.9684\n",
      "Epoch 506/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1506 - accuracy: 0.9684\n",
      "Epoch 507/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1498 - accuracy: 0.9684\n",
      "Epoch 508/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.1480 - accuracy: 0.9684\n",
      "Epoch 509/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1479 - accuracy: 0.9737\n",
      "Epoch 510/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1479 - accuracy: 0.9684\n",
      "Epoch 511/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1468 - accuracy: 0.9684\n",
      "Epoch 512/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1457 - accuracy: 0.9684\n",
      "Epoch 513/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1459 - accuracy: 0.9684\n",
      "Epoch 514/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1444 - accuracy: 0.9737\n",
      "Epoch 515/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1439 - accuracy: 0.9684\n",
      "Epoch 516/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1448 - accuracy: 0.9684\n",
      "Epoch 517/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.1435 - accuracy: 0.9737\n",
      "Epoch 518/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1425 - accuracy: 0.9737\n",
      "Epoch 519/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.1417 - accuracy: 0.9737\n",
      "Epoch 520/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1429 - accuracy: 0.9684\n",
      "Epoch 521/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1399 - accuracy: 0.9737\n",
      "Epoch 522/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1440 - accuracy: 0.9737\n",
      "Epoch 523/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1413 - accuracy: 0.9737\n",
      "Epoch 524/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.1400 - accuracy: 0.9737\n",
      "Epoch 525/700\n",
      "6/6 [==============================] - 0s 801us/step - loss: 0.1381 - accuracy: 0.9737\n",
      "Epoch 526/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1382 - accuracy: 0.9737\n",
      "Epoch 527/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1406 - accuracy: 0.9737\n",
      "Epoch 528/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1374 - accuracy: 0.9789\n",
      "Epoch 529/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1385 - accuracy: 0.9737\n",
      "Epoch 530/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1364 - accuracy: 0.9737\n",
      "Epoch 531/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1368 - accuracy: 0.9789\n",
      "Epoch 532/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.1364 - accuracy: 0.9737\n",
      "Epoch 533/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1349 - accuracy: 0.9737\n",
      "Epoch 534/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1342 - accuracy: 0.9789\n",
      "Epoch 535/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.1353 - accuracy: 0.9737\n",
      "Epoch 536/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1349 - accuracy: 0.9789\n",
      "Epoch 537/700\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 0.1345 - accuracy: 0.9789\n",
      "Epoch 538/700\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 0.1339 - accuracy: 0.9737\n",
      "Epoch 539/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1346 - accuracy: 0.9789\n",
      "Epoch 540/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1332 - accuracy: 0.9737\n",
      "Epoch 541/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.1321 - accuracy: 0.9789\n",
      "Epoch 542/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.1327 - accuracy: 0.9789\n",
      "Epoch 543/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.1321 - accuracy: 0.9737\n",
      "Epoch 544/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.1306 - accuracy: 0.9842\n",
      "Epoch 545/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1305 - accuracy: 0.9842\n",
      "Epoch 546/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1295 - accuracy: 0.9789\n",
      "Epoch 547/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1286 - accuracy: 0.9842\n",
      "Epoch 548/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1281 - accuracy: 0.9842\n",
      "Epoch 549/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1278 - accuracy: 0.9842\n",
      "Epoch 550/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1275 - accuracy: 0.9842\n",
      "Epoch 551/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.1267 - accuracy: 0.9842\n",
      "Epoch 552/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1268 - accuracy: 0.9842\n",
      "Epoch 553/700\n",
      "6/6 [==============================] - 0s 934us/step - loss: 0.1275 - accuracy: 0.9789\n",
      "Epoch 554/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1259 - accuracy: 0.9842\n",
      "Epoch 555/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.1257 - accuracy: 0.9842\n",
      "Epoch 556/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.1251 - accuracy: 0.9842\n",
      "Epoch 557/700\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.1254 - accuracy: 0.9842\n",
      "Epoch 558/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1255 - accuracy: 0.9842\n",
      "Epoch 559/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1248 - accuracy: 0.9789\n",
      "Epoch 560/700\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.1242 - accuracy: 0.9842\n",
      "Epoch 561/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.1235 - accuracy: 0.9842\n",
      "Epoch 562/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.1244 - accuracy: 0.9842\n",
      "Epoch 563/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.1225 - accuracy: 0.9842\n",
      "Epoch 564/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1228 - accuracy: 0.9842\n",
      "Epoch 565/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.1230 - accuracy: 0.9842\n",
      "Epoch 566/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1241 - accuracy: 0.9842\n",
      "Epoch 567/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1215 - accuracy: 0.9842\n",
      "Epoch 568/700\n",
      "6/6 [==============================] - 0s 961us/step - loss: 0.1216 - accuracy: 0.9789\n",
      "Epoch 569/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.1205 - accuracy: 0.9842\n",
      "Epoch 570/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1210 - accuracy: 0.9842\n",
      "Epoch 571/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.1201 - accuracy: 0.9842\n",
      "Epoch 572/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.1193 - accuracy: 0.9895\n",
      "Epoch 573/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.1212 - accuracy: 0.9895\n",
      "Epoch 574/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1203 - accuracy: 0.9842\n",
      "Epoch 575/700\n",
      "6/6 [==============================] - 0s 803us/step - loss: 0.1197 - accuracy: 0.9842\n",
      "Epoch 576/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.1195 - accuracy: 0.9842\n",
      "Epoch 577/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1190 - accuracy: 0.9842\n",
      "Epoch 578/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1185 - accuracy: 0.9842\n",
      "Epoch 579/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1175 - accuracy: 0.9842\n",
      "Epoch 580/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.1174 - accuracy: 0.9895\n",
      "Epoch 581/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1165 - accuracy: 0.9842\n",
      "Epoch 582/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.1166 - accuracy: 0.9895\n",
      "Epoch 583/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1162 - accuracy: 0.9895\n",
      "Epoch 584/700\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.1178 - accuracy: 0.9895\n",
      "Epoch 585/700\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 0.1158 - accuracy: 0.9895\n",
      "Epoch 586/700\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.1175 - accuracy: 0.9842\n",
      "Epoch 587/700\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.1180 - accuracy: 0.9895\n",
      "Epoch 588/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.1163 - accuracy: 0.9895\n",
      "Epoch 589/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.1152 - accuracy: 0.9895\n",
      "Epoch 590/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.1140 - accuracy: 0.9895\n",
      "Epoch 591/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1139 - accuracy: 0.9895\n",
      "Epoch 592/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1132 - accuracy: 0.9895\n",
      "Epoch 593/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.1139 - accuracy: 0.9895\n",
      "Epoch 594/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1134 - accuracy: 0.9895\n",
      "Epoch 595/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1135 - accuracy: 0.9895\n",
      "Epoch 596/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1120 - accuracy: 0.9895\n",
      "Epoch 597/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.1119 - accuracy: 0.9895\n",
      "Epoch 598/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.1112 - accuracy: 0.9895\n",
      "Epoch 599/700\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.1108 - accuracy: 0.9895\n",
      "Epoch 600/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.1106 - accuracy: 0.9895\n",
      "Epoch 601/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1101 - accuracy: 0.9895\n",
      "Epoch 602/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1102 - accuracy: 0.9895\n",
      "Epoch 603/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1094 - accuracy: 0.9895\n",
      "Epoch 604/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.1089 - accuracy: 0.9895\n",
      "Epoch 605/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1095 - accuracy: 0.9895\n",
      "Epoch 606/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1089 - accuracy: 0.9895\n",
      "Epoch 607/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1089 - accuracy: 0.9895\n",
      "Epoch 608/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1092 - accuracy: 0.9895\n",
      "Epoch 609/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.1076 - accuracy: 0.9895\n",
      "Epoch 610/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.1089 - accuracy: 0.9895\n",
      "Epoch 611/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.1067 - accuracy: 0.9895\n",
      "Epoch 612/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1084 - accuracy: 0.9895\n",
      "Epoch 613/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1068 - accuracy: 0.9895\n",
      "Epoch 614/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1062 - accuracy: 0.9895\n",
      "Epoch 615/700\n",
      "6/6 [==============================] - 0s 799us/step - loss: 0.1058 - accuracy: 0.9895\n",
      "Epoch 616/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1060 - accuracy: 0.9895\n",
      "Epoch 617/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.1046 - accuracy: 0.9895\n",
      "Epoch 618/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1051 - accuracy: 0.9895\n",
      "Epoch 619/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.1047 - accuracy: 0.9895\n",
      "Epoch 620/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1048 - accuracy: 0.9895\n",
      "Epoch 621/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1046 - accuracy: 0.9895\n",
      "Epoch 622/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.1047 - accuracy: 0.9895\n",
      "Epoch 623/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.1031 - accuracy: 0.9895\n",
      "Epoch 624/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1029 - accuracy: 0.9895\n",
      "Epoch 625/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1054 - accuracy: 0.9895\n",
      "Epoch 626/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1025 - accuracy: 0.9895\n",
      "Epoch 627/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.1027 - accuracy: 0.9895\n",
      "Epoch 628/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.1020 - accuracy: 0.9895\n",
      "Epoch 629/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.1025 - accuracy: 0.9895\n",
      "Epoch 630/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1017 - accuracy: 0.9895\n",
      "Epoch 631/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.1015 - accuracy: 0.9895\n",
      "Epoch 632/700\n",
      "6/6 [==============================] - 0s 856us/step - loss: 0.1015 - accuracy: 0.9895\n",
      "Epoch 633/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.1012 - accuracy: 0.9895\n",
      "Epoch 634/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.1002 - accuracy: 0.9895\n",
      "Epoch 635/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.1007 - accuracy: 0.9895\n",
      "Epoch 636/700\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.1011 - accuracy: 0.9895\n",
      "Epoch 637/700\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.1023 - accuracy: 0.9895\n",
      "Epoch 638/700\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.1004 - accuracy: 0.9895\n",
      "Epoch 639/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.0994 - accuracy: 0.9895\n",
      "Epoch 640/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.0994 - accuracy: 0.9895\n",
      "Epoch 641/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.0991 - accuracy: 0.9895\n",
      "Epoch 642/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.0999 - accuracy: 0.9895\n",
      "Epoch 643/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.0985 - accuracy: 0.9895\n",
      "Epoch 644/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.0991 - accuracy: 0.9895\n",
      "Epoch 645/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.0994 - accuracy: 0.9895\n",
      "Epoch 646/700\n",
      "6/6 [==============================] - 0s 798us/step - loss: 0.0975 - accuracy: 0.9895\n",
      "Epoch 647/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.0997 - accuracy: 0.9895\n",
      "Epoch 648/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.0971 - accuracy: 0.9895\n",
      "Epoch 649/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.0982 - accuracy: 0.9895\n",
      "Epoch 650/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.0985 - accuracy: 0.9895\n",
      "Epoch 651/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.0967 - accuracy: 0.9895\n",
      "Epoch 652/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.0971 - accuracy: 0.9895\n",
      "Epoch 653/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.0981 - accuracy: 0.9895\n",
      "Epoch 654/700\n",
      "6/6 [==============================] - 0s 794us/step - loss: 0.0986 - accuracy: 0.9895\n",
      "Epoch 655/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.0964 - accuracy: 0.9895\n",
      "Epoch 656/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.1002 - accuracy: 0.9895\n",
      "Epoch 657/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.0984 - accuracy: 0.9895\n",
      "Epoch 658/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.0970 - accuracy: 0.9895\n",
      "Epoch 659/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.0968 - accuracy: 0.9895\n",
      "Epoch 660/700\n",
      "6/6 [==============================] - 0s 801us/step - loss: 0.0953 - accuracy: 0.9895\n",
      "Epoch 661/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.0953 - accuracy: 0.9895\n",
      "Epoch 662/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.0948 - accuracy: 0.9895\n",
      "Epoch 663/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.0949 - accuracy: 0.9895\n",
      "Epoch 664/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.0941 - accuracy: 0.9895\n",
      "Epoch 665/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.0951 - accuracy: 0.9895\n",
      "Epoch 666/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.0941 - accuracy: 0.9895\n",
      "Epoch 667/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.0958 - accuracy: 0.9895\n",
      "Epoch 668/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.0960 - accuracy: 0.9895\n",
      "Epoch 669/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.0952 - accuracy: 0.9895\n",
      "Epoch 670/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.0969 - accuracy: 0.9895\n",
      "Epoch 671/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.0956 - accuracy: 0.9895\n",
      "Epoch 672/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.0938 - accuracy: 0.9895\n",
      "Epoch 673/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.0932 - accuracy: 0.9895\n",
      "Epoch 674/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.0936 - accuracy: 0.9895\n",
      "Epoch 675/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.0926 - accuracy: 0.9895\n",
      "Epoch 676/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.0931 - accuracy: 0.9895\n",
      "Epoch 677/700\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.0924 - accuracy: 0.9895\n",
      "Epoch 678/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.0919 - accuracy: 0.9895\n",
      "Epoch 679/700\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.0922 - accuracy: 0.9895\n",
      "Epoch 680/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.0919 - accuracy: 0.9895\n",
      "Epoch 681/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.0920 - accuracy: 0.9895\n",
      "Epoch 682/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.0912 - accuracy: 0.9895\n",
      "Epoch 683/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.0918 - accuracy: 0.9895\n",
      "Epoch 684/700\n",
      "6/6 [==============================] - 0s 913us/step - loss: 0.0921 - accuracy: 0.9895\n",
      "Epoch 685/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.0910 - accuracy: 0.9895\n",
      "Epoch 686/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.0910 - accuracy: 0.9895\n",
      "Epoch 687/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.0924 - accuracy: 0.9947\n",
      "Epoch 688/700\n",
      "6/6 [==============================] - 0s 799us/step - loss: 0.0909 - accuracy: 0.9895\n",
      "Epoch 689/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.0909 - accuracy: 0.9895\n",
      "Epoch 690/700\n",
      "6/6 [==============================] - 0s 800us/step - loss: 0.0907 - accuracy: 0.9895\n",
      "Epoch 691/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.0906 - accuracy: 0.9895\n",
      "Epoch 692/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.0912 - accuracy: 0.9895\n",
      "Epoch 693/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.0898 - accuracy: 0.9895\n",
      "Epoch 694/700\n",
      "6/6 [==============================] - 0s 999us/step - loss: 0.0904 - accuracy: 0.9947\n",
      "Epoch 695/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.0921 - accuracy: 0.9895\n",
      "Epoch 696/700\n",
      "6/6 [==============================] - 0s 1000us/step - loss: 0.0906 - accuracy: 0.9895\n",
      "Epoch 697/700\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.0907 - accuracy: 0.9947\n",
      "Epoch 698/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.0917 - accuracy: 0.9895\n",
      "Epoch 699/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.0918 - accuracy: 0.9947\n",
      "Epoch 700/700\n",
      "6/6 [==============================] - 0s 1ms/step - loss: 0.0901 - accuracy: 0.9895\n"
     ]
    }
   ],
   "source": [
    "# Fit the model using 500 epochs and the training data\n",
    "fit_model = nn.fit(X_train_scaled, y_train, epochs=700)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 11 calls to <function Model.make_test_function.<locals>.test_function at 0x00000166118269D8> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "2/2 - 0s - loss: 4.7489 - accuracy: 0.4688 - 75ms/epoch - 38ms/step\n",
      "Loss: 4.748878479003906, Accuracy: 0.46875\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model loss and accuracy metrics using the evaluate method and the test data\n",
    "model_loss, model_accuracy = nn.evaluate(X_test_scaled,y_test,verbose=2)\n",
    "\n",
    "# Display the model loss and accuracy results\n",
    "print(f\"Loss: {model_loss}, Accuracy: {model_accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a DataFrame using the model history and an index parameter\n",
    "model_plot = pd.DataFrame(fit_model.history, index=range(1, len(fit_model.history[\"loss\"]) + 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vizualize the model plot where the y-axis displays the loss metric\n",
    "model_plot.plot(y=\"loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAAuhElEQVR4nO3deXRc5Xn48e8zm2TLBi8Ib7KxSQzBgDeMw5IEszvEhEChcRZO4EdCSaFNfik/lrSkTdL20CUUEkhdHyCUkyY0YQuhTigmEBLCYpvdGIOxASsGLGxjWbal2Z7fH3Pv6Gp0Z3QljTR3rp7POTqauds8M9I8887zvve9oqoYY4yJrlitAzDGGDO0LNEbY0zEWaI3xpiIs0RvjDERZ4neGGMizhK9McZEXKBELyJLRWSjiGwSkWt81i8Rkd0i8rzz862S9XEReU5EHqxW4MYYY4JJ9LWBiMSBW4DTgVZgjYg8oKqvlGz6O1VdVuYwXwM2AAcMJlhjjDH9F6RFvxjYpKqbVTUN3AWcE/QBRKQF+BRw68BCNMYYMxh9tuiBacBWz/1W4KM+2x0vIi8A24ArVXW9s/xG4CpgbKUHEZFLgUsBmpqajvnIRz4SIDRjjDEA69ate19Vm/3WBUn04rOsdN6EZ4FDVLVDRM4C7gdmi8gyYLuqrhORJZUeRFVXAisBFi1apGvXrg0QmjHGGAAReavcuiClm1Zguud+C4VWe5Gqtqtqh3N7FZAUkYOAE4FPi8ibFEo+p4jIj/sXvjHGmMEIkujXUGidzxKRFLAceMC7gYhMFhFxbi92jrtDVa9V1RZVnens9xtV/WJVn4ExxpiK+izdqGpWRK4AHgLiwO2qul5ELnPWrwDOB74qIllgP7BcbVpMY4wJBQljPvar0WcyGVpbW+ns7KxRVPWtsbGRlpYWkslkrUMxxgwBEVmnqov81gXpjA2F1tZWxo4dy8yZM3GqRCYgVWXHjh20trYya9asWodjjBlmdTMFQmdnJxMnTrQkPwAiwsSJE+3bkDEjVN0kesCS/CDYa2fMyFU3pRtj6llnJscvX9jG+ce02IduRPz3mrf54679VT3m6IYEl530oaoeEyzRGzMs/u3h1/iPxzczbnSK0+dMqnU4ZpD2dGa4+p6XAKjm5/ZBYxos0Y8E2WyWRML+LFHzXnuhf6R9f6bGkZhqyOQKoxX/7uw5XHRi+Ac41FWNvtY+85nPcMwxx3DkkUeycuVKAH7961+zcOFC5s2bx6mnngpAR0cHF198MUcffTRz587lnnvuAWDMmDHFY919991cdNFFAFx00UV84xvf4OSTT+bqq6/mmWee4YQTTmDBggWccMIJbNy4EYBcLseVV15ZPO4PfvADHnnkEc4999zicR9++GHOO++84Xg5zABY1SYacvlCoo/H6uMPWpdNx2//cj2vbGuv6jHnTD2Avz37yIrb3H777UyYMIH9+/dz7LHHcs455/CVr3yFxx9/nFmzZrFz504Avvvd73LggQfy0kuFr3a7du3q8/Ffe+01Vq9eTTwep729nccff5xEIsHq1av55je/yT333MPKlSvZsmULzz33HIlEgp07dzJ+/Hguv/xy2traaG5u5kc/+hEXX3zx4F8QU1VWl4+W7kRfH23lukz0tfL973+f++67D4CtW7eycuVKPvGJTxTHpk+YMAGA1atXc9dddxX3Gz9+fJ/HvuCCC4jH4wDs3r2bL33pS7z++uuICJlMpnjcyy67rFjacR/vwgsv5Mc//jEXX3wxTz75JHfeeWeVnrGpljCemGgGLqduoq9xIAHVZaLvq+U9FB577DFWr17Nk08+yejRo1myZAnz5s0rllW8VNW3BeddVjqmvampqXj7uuuu4+STT+a+++7jzTffZMmSJRWPe/HFF3P22WfT2NjIBRdcYDV+Y4ZYzqnRx+rkm1qdfB7V3u7duxk/fjyjR4/m1Vdf5amnnqKrq4vf/va3bNmyBaBYujnjjDO4+eabi/u6pZtJkyaxYcMG8vl88ZtBuceaNm0aAHfccUdx+RlnnMGKFSvIZrM9Hm/q1KlMnTqVv//7vy/W/Y0xQ8dt0SfilugjZenSpWSzWebOnct1113HcccdR3NzMytXruS8885j3rx5fPaznwXgb/7mb9i1axdHHXUU8+bN49FHHwXg+uuvZ9myZZxyyilMmTKl7GNdddVVXHvttZx44onkcrni8i9/+cvMmDGDuXPnMm/ePH7yk58U133hC19g+vTpzJkzZ4heATMY7jcxt7Zr6pv7d6yXFn3dTGq2YcMGjjjiiBpFFH5XXHEFCxYs4JJLLim7jb2GtfP1u57j/ue38Q/nHsUXPnpIrcMxg7Tx3T2ceePj3PL5hXxqbvlG23CKxKRmprxjjjmGpqYmvve979U6FNOHTDZf6xBMFXSPuqlxIAFZoo+AdevW1ToEE1A6Z4k+CuqtdFMnn0cFYSwz1Qt77WrLrdGnrUUfCdYZO0QaGxvZsWOHJawBcOejb2xsrHUoI1be+b+1RB8N9dair5vSTUtLC62trbS1tdU6lLrkXmHK1Iab4NM5a6hEgZvoE3ZmbHUlk0m7OpKpW8VEby36SCi26Osjz9dPojcmjDozubLrUvEY6VyepPMbYH8m67tPIiYkAgzhyOe1eKyGRKxqc+ioKl1V+hAK+lyqoStbeC0bEvFheTyXW4qLW+nGmOE185r/4f+cOItvnT08J43d8PBrfP+R1/vcbvqEUUw5cBQAP31mKz99ZmuvbcY2JHj8qpMZ35SqeKw//Y8nWftW4Uzrz8yfyo3LFwwg8t6+++AGbn9iS1WONaEpxe+vPpnRqaFNL4++up1L/nMNAD+6eDEnHdY8pI/nlc3XV2esJXoTKbc/sWXYEv0bbR1MbErx5Y8f2mvdAy9sY8M7hRlWt+7cz4GjkojAVWd+pNe2G99t5/7nt9HW0dVnon+jrYOFM8bxwb4MW3bsq84TcY479cBGLjx+5qCO82LrB/zq5Xf5YF9myBP9lvf34p5o/NaOvcDwJfp8FDtjRWQpcBMQB25V1etL1i8BfgG4TYJ7VfU7ItIIPA40OI91t6r+bXVCN6a20tk8zWMb+OqS3lcEen37nmKiB9jbleOUww/23fZ/17/L/c9vC1S/T2fzLJgxni3v72X7nupd7D2dzdMyfrRvfP1x77Ot/Orld4elL8J7TsJw931kozYfvYjEgVuA04FWYI2IPKCqr5Rs+jtVXVayrAs4RVU7RCQJ/F5EfqWqT1UjeGNqKZ3N05Dwr0WXLt/TmSVZpm6dcrYNUiNP5/KkEjFiIlTz3Kt0Ls+o5ODr3O5zGY4Tw7zJvVr9C0HV24VHgvSYLAY2qepmVU0DdwHnBDm4FnQ4d5POj40vM5GQzuaLia1Uyknq7jf7vV3Z8tu6ybGPZJXPK5mckorHiMcgl69ecqv0XPrDfd7D0cLOeD5MMsN8xnGxMzZCiX4a4O09anWWlTpeRF4QkV+JSHHCeBGJi8jzwHbgYVV9ejABG+MnX4NZId3WtR93+RinTr0/kyu7bUPAVrC7PpWIkYjFqjoTZiaXJ1mFjsX+fDsZLPcbVSImNSvdJCKU6P2eSel/2LPAIao6D/gBcH9xQ9Wcqs4HWoDFInKU74OIXCoia0VkrZ0UZforV4MzpjPO0Ek/7vKmhu7qaLlE727b14Rnbqs1FY8RiwnV/GwrtOirULpxn8swtLC7nG8hqURs2BN9vXXGBkn0rcB0z/0WYJt3A1Vtd0s0qroKSIrIQSXbfAA8Biz1exBVXamqi1R1UXPz8PWem2ioxTzv6Wy+mNhKuUl9dEN38uxr2z5b9Flvi17IVrF001XhufRH0DJUNaRzhRZ9KhEb9sni6q0zNshfdg0wW0RmiUgKWA484N1ARCaLc+aGiCx2jrtDRJpFZJyzfBRwGvBqFeM3Bqhhou+jle49kaeven5fydFbuomJUMU8X7EM1R/DmegzzodTKh4b/hp9nSX6PkfdqGpWRK4AHqIwvPJ2VV0vIpc561cA5wNfFZEssB9YrqoqIlOA/3RG7sSAn6nqg0P1ZMzIla1Bou8K0IHpXd9ni76vRJ/tLt3EY1S1RZ/J5UlVsUY/LKNucnmSiRiSq95ZvUHl6qwzNtA4eqccs6pk2QrP7ZuBm332exGozql7xlRQq87YcsMrXd7k2VfHbVc/SjfxWKy6wyvrcNSNWzqLi9o4+j7YmbEmEsLWGevyri87jj5gZ6zbSk46Lfp8FZ9ztRK9+xyHaxx9KlEYfVSrzlib68aYYRS2zliXt8Xf5zj6gC36Bmd4ZbZKyTSfV7J5JRUf/KibhmHujE3GY8REh71GX28nTFmiN5EQts5YVypIog/aGZst6Yyt0lP2dvIO1rCOuvG26C3RV1QnsykbU9lwJ/piK7iP5Ogt1zSUaf0n4jFi0r9RN4UzY6vznLtLQvXXGduQKIy6Ge7STb11xlqiN5Ew3IneWy+vxFvaqfSh4J2zvuxjZr01+uqdGestCQ1WsgadsbU4YcouJWjMMNjTmeHfH3uD/Zkcc6YcwMJDxhfX/c+L7/CpuVPI55VbHt3Ezn3pqj++WxPuKzk2JPvujIXCh8DvX3+fb/9yfdlt3namJS7OdVOlzlhvSWiwEjFBBB55dTu7huB199r2wX6mjx+NiLJ11/6Kr121Pb/1A6B+pkCwRG/q0tObd/LDx94gHhMaEzHuu/zE4rp/XLWBT82dwub39/K9h1+jMRnrs+U9EBOaUhwx5QDfdcvmTuGGh1/jSyfMZO2bu9i5N82HDx5T9liLZ07gmTd3snVd5Tnmp08YxbRxo4iLkMsrqjroq0xVM9GLCItnTuCVd9rZ3NbR9w6DtGDGOHKqPL1lB3evax3yx/OaP31c3ZRuLNGbutTpXELulI8czKOvbi9+lR4/OllsbbuX7Ltp+QLOPHLysMZ3yMQm3vjHswB4+Bsn9bn9bRcd26/jx52LleYVBltazwQsQwX13392fFWO0x9/vuTDw/6Y9cRq9KYuua3QMQ0Jsnklmysk+sZkvFjrruZokrBxc3I16vRdnjNuTTTZX9bUJbcV2uRMGrbfab2PSsaLHwKZCCcwt0VfjUQf5Q9EU2B/WVOXulv0ScCT6FPx4odAlBNYsUVfhQ7ZatboTTjZX9bUJbfcMLbRubBHurtFn8kpec9p8VFs0bvD+nK5wSd67zz3JprsL2vqkttab0oVSjednha9uz7KLVV3WJ+16E0Q9pc1dSmTLSQ49wpO+5wWfWPSk+irPJokTNxhfVWp0Vuijzz7y5q6lM7liMek2IL3dsZCIXlV84zPsBmSztgIfiCaAvvLmrrknv7uttY7SxJ9xtOij2JLtZqdsV3Woo88+8uaupTJafHC0ODpjE11t+ijPLzS7YytxgVXrDM2+uwva+pSV7YwF7k7I6Rbumn0lm7cGn0EW6oJ53TYalxC0Wr00Wd/WVOX0llnilonObmdsaOdFn2Xp0YfxZZqcXilJXoTgP1lTV1K5woXnSit0Tcmu+dD757Wtz4mnuqPIRl1E8EPRFNgf1lTlzLZPMm49K7Ru52x2TzpnJKKxwY9u2MYJaqY6DO5PCL1cxEN0382e6UJjZ17076Ja2JTilhJEvpgf7pHZ2x7ZwaAUanCv3RbRxe792ciW45wSzc79nbRtqdrUMfavT8T2Q9EU2CJ3oTCf695m6vvecl33UUnzOTvPn1k8f72PZ08tXknc6YcQJOT2P/wxg6g8KEAcMVPngPg4LENQxl2zbidzhfe9kxVjjfBed1MNAVK9CKyFLgJiAO3qur1JeuXAL8AtjiL7lXV74jIdOBOYDKQB1aq6k3VCd1Eydad+xGB75xzVI/lP3x0E627el6M4/09hSsXnXHkJCYf2MiKLy6krSNN85gUJx3WzE3L59PemQXg8Eljh+cJDLPjDp3I9y6Yxz6nb2KwZle4KIqpf30mehGJA7cApwOtwBoReUBVXynZ9HequqxkWRb4K1V9VkTGAutE5GGffc0Il8kVToC68LhDeiy/e10r6ZKJu/LOSUJznKs7LT1qSo/158yfNoSRhkMqEeNPjmmpdRimTgQpYC4GNqnqZlVNA3cB5wQ5uKq+o6rPOrf3ABuA6L8LTb91ZfO+9fSGeIx0tmer1a3jJyI4msaYoRAk0U8Dtnrut+KfrI8XkRdE5FcicmTpShGZCSwAnvZ7EBG5VETWisjatra2AGGZKEnn8r5z0qQSseLwP5d7klDMOg+NCSRIovd7N5UOjXgWOERV5wE/AO7vcQCRMcA9wNdVtd3vQVR1paouUtVFzc3NAcIyUeLOXVMqlYgVz3B1uaUbGw5oTDBBEn0rMN1zvwXY5t1AVdtVtcO5vQpIishBACKSpJDk/0tV761K1CZyMrm871QFqbhPiz5nid6Y/giS6NcAs0VkloikgOXAA94NRGSyOINwRWSxc9wdzrLbgA2qekN1QzdRUq5Fn0zEyJTpjI1b6caYQPocdaOqWRG5AniIwvDK21V1vYhc5qxfAZwPfFVEssB+YLmqqoh8DLgQeElEnncO+U2n1W9MUbpMZ6xfi946Y43pn0Dj6J3EvKpk2QrP7ZuBm332+z3+NX5jenDnrimVSsSK86W7ctYZa0y/RPP8cFN30s60w6UaEuWHV1qN3phgLNGbUCg3vDIZl141+pyNujGmXyzRm1Doz/BKa9Eb0z+W6E0oZMrV6ONxcnntMatlMdFbjd6YQCzRm1AoV6N3k7935I2dMGVM/1iiN6FQbnile3Uob/nGTpgypn9sPvoAdu1N82+rX+Oc+VM55pAJtQ6nrt3y6CbefH9vr+U79qb9JzVzlu3pzHDT6tfZ05nhvuf+CFiiNyYoS/QBPLV5B3c++RavvrOHn112fK3DqVudmRz/8tBGxjYmGNvQ81/voDENLJ7Z+0PUTf7Pvf0Btz+xhQlNqeKkZpbojQnGEn0AbtlgXyZb40jqm/s6fu3U2Xz544cG2sdN9B1dhdf+5s8t4PO3FiZAtc5YY4KxGn0ApWdmmoFxO1T7cx3XVLxwybwO54pRDc4l9MBa9MYEZYk+ADdBae/rVpt+KCZ6n9E15bidsXucFr33pCpL9MYEY4k+gNJJtczAZJzSjd8wynLc1v9eJ9F7941ZojcmEEv0Abi1ZSsJD86ASjclid67b8ISvTGBWKIPIGMt+qroGkCiLw6v9En0NnulMcFYog/AbdFnc1akHwz3dexPondLNW5nrLe+bzV6Y4KxRB+AW3LI5KxlPxiZAXTG9irdeBO9teiNCcQSfQBuyaF0FkXTPwNp0buJvcOvdGMtemMCsUQfgNuSt9E3gzOQ4ZWlJ0z150PCGFNg75oA3ARliX5wBnbCVHeij8fE6vLGDIAl+gDckkPplY5M/6QHOY4+aRcDN2ZALNEHYC366nBfP79LBpbjJvpMTvtV8jHGdLN3TgBpT2es2jwIAzaYztjCfvEKWxpjygk0e6WILAVuAuLArap6fcn6JcAvgC3OontV9TvOutuBZcB2VT2qOmEPjqqyded+UokYkw9srLjtB/vStHdmivd37cswoSlFe2eGHR1p333GjUoyvilVfKy3d+7DcyU8msc2MKYhwe79GXbu7T5GIia0jB+F1PGwwXd3d7I/k/Nd997uTqB/nbHxmCBSmGeoP98EjDHd+kz0IhIHbgFOB1qBNSLygKq+UrLp71R1mc8h7gBuBu4cZKxVc8cf3uTbvyyEf89Xjy97MZHOTI4Tr/8Ne9PdievsH/yeJ645hTP/7XHecRJXqVHJOM9edzqjUnFuf+JNvvtgz5dq9sFjePgbJ3HaDb+lbU9Xj3U3/Ok8zlvYMpinVzPPvr2L8374h4rbxAQak8Fb5iLC2IYE7Z1ZmhoK+40fnWTXvkwfexpjXEFa9IuBTaq6GUBE7gLOAUoTvS9VfVxEZg44wiHwfkd3ct3e3lV2u71dWfamc/zJwhYu+dgsvnLn2uK+77V3cvqcSXzq6Ck99nli0/v8fF0re7oyjErF2d7eSTIu/Mv58wC497k/8vzbu8jnlbY9XXzyqMmceeRk0rk8V939Iu9ViCfstrcXPvj+35mHM23cKN9tphzYyKhU/0owd17yUd58fy9HTDkAgEf+akmPb0LGmMqCJPppwFbP/Vbgoz7bHS8iLwDbgCtVdX1/AhGRS4FLAWbMmNGfXfvNW0apdBKUu27RzPHMmXoAn54/lVt/t5lsLk9e4ehpB/KZBdN67pPN8/N1rcW6flc2T2MyXtzulXfaeWbLjuKxj24pHCOXV666+8W67vB1Tyw788jJfPjgMVU77vzp45g/fVzx/oSmFBOc0pgxpm9Bip5+BePSHslngUNUdR7wA+D+/gaiqitVdZGqLmpubu7v7v2S93SoVkqspSf4pOIxMjmtODmXu8w7bYK3tuweo9gx6RzbHSNez9MsuMNPrZZuTLgEeUe2AtM991sotNqLVLVdVTuc26uApIgcVLUoq0yDtuidZJ10EldxTHe699zoLndZ2nM2rXe7ZDxGLq90OnV/74dFMi51Pc1C8fWyYZDGhEqQd+QaYLaIzBKRFLAceMC7gYhMFmeoiIgsdo67o9rBVksuH7BFn+vdogfPTIoVWvSZrBaP4d2u1yn98Z6t/Xou3aSzvT+8jDG11+c7UlWzwBXAQ8AG4Gequl5ELhORy5zNzgdedmr03weWqzPgXER+CjwJHC4irSJyyVA8kf7Iq+KeSR+kdNNQ2qLvKiS0Bp+Wa7F0k8sVj9FzLHjPY/T8EIjX9fVpBzJO3hgz9AKNo3fKMatKlq3w3L6ZwhBKv30/N5gAh4JqYYjfvnSuYk28dG4W9/eersLQvmSid/eFm9S7PDX6ZI9Wu3sNVOcYJeuiUKO3qQqMCZcR2fTKq/bqNPVT2kJ1k3ixNR7vPUyw9LhdWf/SjX+Lvr5LN10DmJ3SGDP0RuQ7MpdX4iKkEjG6KrSgSy9mnSzW1wutcd8afbx7bhZwSje+Nfrex6j3RO+Wqer5zF5jomhEJvq8Fs64bOij89NveCVAh09r3FXaok/3Gl4Z73GMhpL6fb2PurH6vDHhMyLflep0xiYTsYo18dLx8m7Cdkfd+NWiSztjS2v07j7FY/QYXlk5nrArPFdrzRsTNoE6Y6Mmr0o8JsREBjjqJttjuVevFn3ZUTf+wyvretSNteiNCaUR+a7M5SHm1Oj71Rnbawy8T2ds3CfRVxpHH6Uafc4SvTFhNCLflaqKSN818UzJmZ7u7z2VTpgqnhnr3xnrfgvwO0ZDvSf6km8vxphwGJHvysIJU0IyHiOdLX8hkfLDKwPU6IudsdprCgTvMVLx6NTo0yX9EcaYcIh0jX7dWzvZ+G4HybjQMn40x39oIlAYdROPSdkWfUdXlr97YD0vtn4AeEbdOEl87Vu7etz3cpfd+2wrL7Z+wO796Z6jbiocI5WI8ccP9vPVH6/jwuMO4YQPh2e6oDue2MLTW3b6rhOBd3Z3snXnfqaNq3whF2PM8It0ov+Tf3+yx/03r/8UUGjRizhnovqUSl5q3c3d61ppGT+KpUdOLrbcp40bxXGHTmDn3jSHTx7DwWN7J7V4TPjU0VN4ffse3mjr4EPNYzjB+YABmD5+NItnTeCDfWmOmDK2x3S7Jx3WzIZ32lm94T1GpeKhSvT/8fhm9nZlfa/I9dp7HQAcNKaB0+dMGu7QjDF9iHSiL8ct3cREyPlcA9Zt5d+0fH6Pq0+NSsW569Lj+zz+LV9YWHZdU0OCn/2Z/zHOW9jCeQtbOOVfHwtdrT6dzXP2vKn8w7lH91p35Ld+zd50jitO/hAXnTirBtEZYyoZkQXVfL5wSbt4TMjneyf60k7Y4RbGWn2Q+nvSRtwYE0oj8p3ptujjscot+loNFQzjMMt0Nl/2giLulAc24saYcBqR78y80p3ofVr0pVMfDLewTYWgqoHGyNsYemPCaUS+M/OqxGIQlzKJvtYt+pBdgCSbV1T7/uCzSwgaE04j8p3Zo3QT2hZ9+fH9w610Xv5ybAy9MeE0It+ZgUs3NWqhJkPWoi+drrkcK90YE04j8p3pzl4Z1s7YwlQIuZo8tp+gH3zWGWtMOI3Id2YuX7l0kwlB6SYTotJN6XTN5ViL3phwGpHvzGKNvkJnrDgt/loIW2es+w2n7PBK57fV6I0JpxH5zswrhVE3FWr0tbwkXtiGV/bVOe2+gjbqxphwGpHvTO1j1E3pBb2Hm3XGGmOqKdA7U0SWishGEdkkItf4rF8iIrtF5Hnn51tB960Fb40+79MZm8nVdl710LbobXilMXWpz0nNRCQO3AKcDrQCa0TkAVV9pWTT36nqsgHuO6wKFwcvlG6y5Uo3NWydulMgFC6QUvtrsPaV6N0IrUVvTDgFmb1yMbBJVTcDiMhdwDlAkGQ9mH0H7M3393LwAQ1l16vnmrG5vPJeeydte7qK69s6umqatNxa93vtXb2mBd7R0UU2r0w6oJEdHV1MHFP+eXr3OWBUktfe24PPFxigkKQ/3DyG17d39JpQ7fXtHcVtKrEWvTHhFCTRTwO2eu63Ah/12e54EXkB2AZcqarr+7Fv1WzavofTbnicEz40kVHJOPszvcejuydMJWKFi4Of/K+PsS/dc7ujpx04lGFWdEBj4c9y7g+f4MlrT+2x7hP//Ch70zm+d8E8/urnL/CLy09k3vRxZY/1Py++w+U/eZaTDmvmt6+1VXzcz8yfyv3Pb6sQV9J3+ccPO4hVL71rnbHGhFSQRO9XOyhtFz4LHKKqHSJyFnA/MDvgvoUHEbkUuBRgxowZAcLyt6MjDcBTm3dwaPMYdu/POCNourcp1OgLpRt3jPgFx7RwxpGTi9scPmnsgGMYrAsWTefOJ99i6659vdbtdT6QfrNxOwAb3mmvmOif3Pw+AL99rY2mVJwbly/otc3OvV1cfc9LxQuI3PjZ+TQ19PzXOKAxwYeam3wf44Y/nc+VZ+ynMdn7YunGmNoLkuhbgeme+y0UWu1Fqtruub1KRH4oIgcF2dez30pgJcCiRYsGfLaQ24npzjV/3KETaUjE+MOm94vbFC884hknf+TUA0JzdaTGZJzT5kzitt9tKbuNe1JXXyV88XzWjm5I+D7H7e2dAOxNF65je/qcSb0SfV/xHto8JvD2xpjhFeS79hpgtojMEpEUsBx4wLuBiEwWp9dQRBY7x90RZN9qczsO3Y7WuECiZKoD9ZRuXKlEuFqjqXhh5I32iLv79kBG5ZQbSeTW3js6sz3uG2Oioc9mm6pmReQK4CEgDtyuqutF5DJn/QrgfOCrIpIF9gPLtZCVfPcdoucCeBK909Eaj8WIlYyXd6cpjok30YcrubnxpHN5GpwPIe+0CO7zLNe56qdcDd19rD1dWcT5YDTGREeg7+equgpYVbJshef2zcDNQfcdSm5L103ucZ955/POsEVvQnMvAB4Wbuu7cGWnQqL3tuIHcqnBch9mPR+rdmcEG2OGRriasVXgtnTdck081vsM2LwWkr+3Rh+2ESNuUva24jOes2UHcuZsueGP8ZgUa/02A6Ux0RO5d3VpZ6x/ou8edeMKbenGm9w9rfiuAST6sic8iRQTfNheB2PM4EXuXd27M7b3RcDdUTc9OmPj4euMhZJE75P0/c7s7euYvusSluiNiarIvau9nbF5pzO2V4s+X2jFejtjw1ajTxY7Y7tP5PK24t0afX9KOJWSeIMlemMiK3LvajfxxdwWfZnO2HgMEvEQl26c1ndXmRZ9Jlt4Pv0ZZlnpORZLN1ajNyZyIveudlu6MSmUa2Ixd5bK7nHoxROmQjy8ssGvM7ZHjb7Q0s/00aJXz4nIlZK4+w3C5qsxJnoi967ucpJhLq/k80rCSfTuMnBnr5QenbFhG3WT9KvRexJ9Zybfa5mfrOeDIlCLPmSvgzFm8CL3rnYTYy6vPTpjgWKHrPqMuglbS9Z31I3ntjtZW181eu9664w1ZmSK3LvaTWzZvDv6JlZM6M4i50SqwjVjXWFLcCmfzli/pN7XMMsuT4u/YoveWRe2bzbGmMGL3LvaTYZdxWGWFBO6m/zdaYp7jKMPW4vep3Tjl9T7Kt30aNFbZ6wxI1LwKQrrhNthmfFMheCeAXvhbc8wtjFBhzOnS4/STchasqlEIbYbV7/Ofz39NkCPi6O4HtnwHhfu7D2dsWv9tuLEohXLUynrjDUmsiKX6N0TiNzOymQsVhwj//zWD1gwYxzzp49jyeEHs7crW9wvGQtXgmsZP5rTjjiYHXvTdDhxjkrFOeUjB5OKx3hvTyeb3utg6rhRxfV+Dpk4mkw2z2GTx3LSYc1lt1s2dwodXVk+efTkstsYY+pT5BJ96WyOqUSsRznivj8/sXj7ofXvFm/HQzZjY2Myzq1fOnbYHu+zx87gs8cO/IIvxpjwClcztgryJZk+lYiVrU17O2Ntal5jTFRFPtEn4xUSvSe5xyzRG2MiKnKJ3q90U2l6XmOMibrIJfrSyRxTAVv0xhgTVZFL9FrSpG9IxGiwFr0xZgSLXqIvuR+0Rm+MMVEVuUTfn1E3Mbs2qjFmBIhgou95v1JnrA2pNMaMBJFL9KU1euuMNcaMdBFM9D3vpxJSdqIuK90YY0aCQIleRJaKyEYR2SQi11TY7lgRyYnI+Z5lXxORl0VkvYh8vQoxV9SrRh+Pl516NxGy68QaY8xQ6DPRi0gcuAX4JDAH+JyIzCmz3T8BD3mWHQV8BVgMzAOWicjs6oTuz3euG+uMNcaMYEFa9IuBTaq6WVXTwF3AOT7b/QVwD7Dds+wI4ClV3aeqWeC3wLmDjLkiv1E31hlrjBnJgiT6acBWz/1WZ1mRiEyjkMBXlOz7MvAJEZkoIqOBs4Dpfg8iIpeKyFoRWdvW1hY0/l5KW/TJuFhnrDFmRAuS6P2yYel5STcCV6tqrsdGqhsolHMeBn4NvAD4Tp6uqitVdZGqLmpuLj9vel+0JLTGZLzYcj9jzqQe62wiM2PMSBBkPvpWerbCW4BtJdssAu6SQs37IOAsEcmq6v2qehtwG4CI/KNzvCGTVzjhQxO58szDGT86VSzbPP3NUxk3OtljWyvdGGNGgiCJfg0wW0RmAX8ElgOf926gqrPc2yJyB/Cgqt7v3D9YVbeLyAzgPOD46oTuL6+FC38vnDG+x/JJBzT22tY6Y40xI0GfiV5VsyJyBYXRNHHgdlVdLyKXOetL6/Kl7hGRiUAGuFxVdw026MrxggRM4NaiN8aMBIEuJaiqq4BVJct8E7yqXlRy/+MDDW4gVJWg+dtq9MaYkSByZ8bm1b/32I+NujHGjAQRTPQauPZupRtjzEgQuUTfnxq9dcYaY0aCyCX6fD9q9Fa6McaMBJFL9IUWfbBtLc8bY0aC6CV6gtfog5Z4jDGmnkUu0efVau/GGOMVwUTfj/GVxhgzAkQu0WMtemOM6SFyib4/o26MMWYkiGCit8qNMcZ4RSrRL1/5JG/v3GelG2OM8Qg0qVm9eGrzTqB/wyavWzaHIyaPHaqQjDGm5iLVov/LUwvXHe9Pg/6Sj83ihA8fNEQRGWNM7UUq0buTlJVeN9YYY0aySCX6eDHRW6Y3xhhXpBK92wlrad4YY7pFKtG7pZu8teiNMaYoUok+Vkz0NQ7EGGNCJFKJPmE1emOM6SVSiT5mo26MMaaXSCX6uFiN3hhjSgVK9CKyVEQ2isgmEbmmwnbHikhORM73LPu/IrJeRF4WkZ+KSGM1Avdj4+iNMaa3PhO9iMSBW4BPAnOAz4nInDLb/RPwkGfZNOAvgUWqehQQB5ZXJ/TeYjbqxhhjegnSol8MbFLVzaqaBu4CzvHZ7i+Ae4DtJcsTwCgRSQCjgW2DiLeiuPNsbNSNMcZ0C5LopwFbPfdbnWVFTsv9XGCFd7mq/hH4V+Bt4B1gt6r+r9+DiMilIrJWRNa2tbUFfwYe8Zj7dCzTG2OMK0ii95sirDST3ghcraq5HjuKjKfQ+p8FTAWaROSLfg+iqitVdZGqLmpubg4QVm/dnbED2t0YYyIpyDTFrcB0z/0WepdfFgF3OdMDHwScJSJZIAlsUdU2ABG5FzgB+PEg4/bVXbqxTG+MMa4giX4NMFtEZgF/pNCZ+nnvBqo6y70tIncAD6rq/SLyUeA4ERkN7AdOBdZWKfZe3NKNteiNMaZbn4leVbMicgWF0TRx4HZVXS8ilznrV1TY92kRuRt4FsgCzwErqxK5D7dFb2fGGmNMt0BXmFLVVcCqkmW+CV5VLyq5/7fA3w4wvn5xW/SW540xppudGWuMMREXqUTvnC9lLXpjjPGIVKIXa9EbY0wvEUv0hd+W540xplukEn33pQQt0xtjjCtiib7w28bRG2NMt0gleikmesv0xhjjiliit/nojTGmVKQSfbFGb5neGGOKIpXo3Wk2rUZvjDHdIpXobdSNMcb0FqlEX+yMzdc2DmOMCZNoJnqr0RtjTFGkEn3Smae4IRmvcSTGGBMegaYprhezDx7D10+bzQWLpve9sTHGjBCRSvQiwtdPO6zWYRhjTKhEqnRjjDGmN0v0xhgTcZbojTEm4izRG2NMxFmiN8aYiLNEb4wxEWeJ3hhjIs4SvTHGRJyEce52EWkD3hrArgcB71c5nKFUT/HWU6xg8Q6leooV6ivewcR6iKo2+60IZaIfKBFZq6qLah1HUPUUbz3FChbvUKqnWKG+4h2qWK10Y4wxEWeJ3hhjIi5qiX5lrQPop3qKt55iBYt3KNVTrFBf8Q5JrJGq0RtjjOktai16Y4wxJSzRG2NMxEUm0YvIUhHZKCKbROSaEMRzu4hsF5GXPcsmiMjDIvK683u8Z921TuwbReTMGsQ7XUQeFZENIrJeRL4W1phFpFFEnhGRF5xYvx3WWD2PHxeR50TkwTqI9U0ReUlEnheRtXUQ7zgRuVtEXnX+f48PY7wicrjzmro/7SLy9WGJVVXr/geIA28AhwIp4AVgTo1j+gSwEHjZs+yfgWuc29cA/+TcnuPE3ADMcp5LfJjjnQIsdG6PBV5z4gpdzIAAY5zbSeBp4LgwxuqJ+RvAT4AH6+B/4U3goJJlYY73P4EvO7dTwLgwx+vEEQfeBQ4ZjliH9ckN4Yt2PPCQ5/61wLUhiGsmPRP9RmCKc3sKsNEvXuAh4Pgax/4L4PSwxwyMBp4FPhrWWIEW4BHgFE+iD2WszmP6JfpQxgscAGzBGVgS9ng9j3sG8MRwxRqV0s00YKvnfquzLGwmqeo7AM7vg53loYpfRGYCCyi0lEMZs1MKeR7YDjysqqGNFbgRuArIe5aFNVYABf5XRNaJyKXOsrDGeyjQBvzIKY3dKiJNIY7XtRz4qXN7yGONSqIXn2X1NG40NPGLyBjgHuDrqtpeaVOfZcMWs6rmVHU+hdbyYhE5qsLmNYtVRJYB21V1XdBdfJYN9//Ciaq6EPgkcLmIfKLCtrWON0GhRPrvqroA2Euh/FFOreNFRFLAp4Gf97Wpz7IBxRqVRN8KTPfcbwG21SiWSt4TkSkAzu/tzvJQxC8iSQpJ/r9U9V5ncahjVtUPgMeApYQz1hOBT4vIm8BdwCki8uOQxgqAqm5zfm8H7gMWE954W4FW5xsdwN0UEn9Y44XCB+izqvqec3/IY41Kol8DzBaRWc6n5XLggRrH5OcB4EvO7S9RqIO7y5eLSIOIzAJmA88MZ2AiIsBtwAZVvcGzKnQxi0iziIxzbo8CTgNeDWOsqnqtqrao6kwK/5e/UdUvhjFWABFpEpGx7m0KteSXwxqvqr4LbBWRw51FpwKvhDVex+foLtu4MQ1trMPdCTGEnRtnURgp8gbw1yGI56fAO0CGwifzJcBECp1yrzu/J3i2/2sn9o3AJ2sQ78cofC18EXje+TkrjDEDc4HnnFhfBr7lLA9drCVxL6G7MzaUsVKoeb/g/Kx330thjdd5/PnAWuf/4X5gfFjjpTB4YAdwoGfZkMdqUyAYY0zERaV0Y4wxpgxL9MYYE3GW6I0xJuIs0RtjTMRZojfGmIizRG+MMRFnid4YYyLu/wOWehPu2WSCcAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vizualize the model plot where the y-axis displays the accuracy metric\n",
    "model_plot.plot(y=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the model's file path\n",
    "file_path = Path(\"resources/nnmike.h5\")\n",
    "\n",
    "# Export your model to an HDF5 file\n",
    "nn.save(file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "2be5faf79681da6f2a61fdfdd5405d65d042280f7fba6178067603e3a2925119"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}