Runway_Classification_LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:90% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:90% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "#Set some numpy print options for displaying numpy arrays to fit maximum width of cell\n",
    "np.set_printoptions(precision=8, edgeitems=30, linewidth=1000,formatter=dict(float=lambda x: \"%.8g\" % x))\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler,LabelEncoder,label_binarize\n",
    "from sklearn.metrics import accuracy_score,classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Flatten,Dropout,LSTM\n",
    "from keras.utils import np_utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from cmplotter import plot_confusion_matrix\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Landing_Time</th>\n",
       "      <th>hourofday</th>\n",
       "      <th>aircraft_registration</th>\n",
       "      <th>wind_direction_degrees</th>\n",
       "      <th>Wind_Speed_kt</th>\n",
       "      <th>Wind_And_Gust_kt</th>\n",
       "      <th>Landing_RVR_or_Vis_m</th>\n",
       "      <th>temperature_c</th>\n",
       "      <th>dew_point_c</th>\n",
       "      <th>relative_humidity</th>\n",
       "      <th>pressure_altimiter_in</th>\n",
       "      <th>RunwayTrueTrack</th>\n",
       "      <th>Landing_Visibilty_Class_RVR</th>\n",
       "      <th>present_weather_codes</th>\n",
       "      <th>Landing_RWY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 04:34:38</td>\n",
       "      <td>4</td>\n",
       "      <td>GXLEJ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>93.08</td>\n",
       "      <td>30.21</td>\n",
       "      <td>89.713523</td>\n",
       "      <td>VMC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RW09R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-01 04:38:22</td>\n",
       "      <td>4</td>\n",
       "      <td>GSTBH</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>VMC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RW09R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-01 04:40:38</td>\n",
       "      <td>4</td>\n",
       "      <td>GCIVH</td>\n",
       "      <td>110.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>30.21</td>\n",
       "      <td>89.713523</td>\n",
       "      <td>VMC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RW09R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-01 04:42:36</td>\n",
       "      <td>4</td>\n",
       "      <td>GVYUM</td>\n",
       "      <td>110.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>93.08</td>\n",
       "      <td>30.21</td>\n",
       "      <td>89.713523</td>\n",
       "      <td>VMC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RW09R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-01 04:44:43</td>\n",
       "      <td>4</td>\n",
       "      <td>GBYGB</td>\n",
       "      <td>110.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>93.08</td>\n",
       "      <td>30.21</td>\n",
       "      <td>89.713523</td>\n",
       "      <td>VMC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RW09R</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Landing_Time  hourofday aircraft_registration  \\\n",
       "0 2016-01-01 04:34:38          4                 GXLEJ   \n",
       "1 2016-01-01 04:38:22          4                 GSTBH   \n",
       "2 2016-01-01 04:40:38          4                 GCIVH   \n",
       "3 2016-01-01 04:42:36          4                 GVYUM   \n",
       "4 2016-01-01 04:44:43          4                 GBYGB   \n",
       "\n",
       "   wind_direction_degrees  Wind_Speed_kt  Wind_And_Gust_kt  \\\n",
       "0                     NaN            1.0               1.0   \n",
       "1                     NaN            1.0               1.0   \n",
       "2                   110.0            1.0               1.0   \n",
       "3                   110.0            1.0               1.0   \n",
       "4                   110.0            1.0               1.0   \n",
       "\n",
       "   Landing_RVR_or_Vis_m  temperature_c  dew_point_c  relative_humidity  \\\n",
       "0                6000.0            2.0          1.0              93.08   \n",
       "1                6000.0            2.0          1.0              93.08   \n",
       "2                6000.0            2.0          1.0              93.08   \n",
       "3                6000.0            2.0          1.0              93.08   \n",
       "4                6000.0            2.0          1.0              93.08   \n",
       "\n",
       "   pressure_altimiter_in  RunwayTrueTrack Landing_Visibilty_Class_RVR  \\\n",
       "0                  30.21        89.713523                         VMC   \n",
       "1                  30.21        89.713523                         VMC   \n",
       "2                  30.21        89.713523                         VMC   \n",
       "3                  30.21        89.713523                         VMC   \n",
       "4                  30.21        89.713523                         VMC   \n",
       "\n",
       "  present_weather_codes Landing_RWY  \n",
       "0                   NaN       RW09R  \n",
       "1                   NaN       RW09R  \n",
       "2                   NaN       RW09R  \n",
       "3                   NaN       RW09R  \n",
       "4                   NaN       RW09R  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load Dataset\n",
    "data = pd.read_csv('data/2016_EGLL_Testdata_cleaned.csv', parse_dates = ['Landing_Time'], infer_datetime_format = True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 220940 entries, 0 to 220939\n",
      "Data columns (total 15 columns):\n",
      "Landing_Time                   220940 non-null datetime64[ns]\n",
      "hourofday                      220940 non-null int64\n",
      "aircraft_registration          220799 non-null object\n",
      "wind_direction_degrees         203226 non-null float64\n",
      "Wind_Speed_kt                  220940 non-null float64\n",
      "Wind_And_Gust_kt               220940 non-null float64\n",
      "Landing_RVR_or_Vis_m           220940 non-null float64\n",
      "temperature_c                  220940 non-null float64\n",
      "dew_point_c                    220940 non-null float64\n",
      "relative_humidity              220940 non-null float64\n",
      "pressure_altimiter_in          220940 non-null float64\n",
      "RunwayTrueTrack                220940 non-null float64\n",
      "Landing_Visibilty_Class_RVR    220940 non-null object\n",
      "present_weather_codes          33015 non-null object\n",
      "Landing_RWY                    220940 non-null object\n",
      "dtypes: datetime64[ns](1), float64(9), int64(1), object(4)\n",
      "memory usage: 25.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# One-Hot Encoding Categorical Variables\n",
    "data.set_index('Landing_Time',inplace = True)\n",
    "data.drop(columns=['aircraft_registration'],inplace = True)\n",
    "data = pd.get_dummies(data, columns=['Landing_Visibilty_Class_RVR', 'present_weather_codes'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 220940 entries, 2016-01-01 04:34:38 to 2016-12-30 23:57:49\n",
      "Data columns (total 65 columns):\n",
      "hourofday                                   220940 non-null int64\n",
      "wind_direction_degrees                      203226 non-null float64\n",
      "Wind_Speed_kt                               220940 non-null float64\n",
      "Wind_And_Gust_kt                            220940 non-null float64\n",
      "Landing_RVR_or_Vis_m                        220940 non-null float64\n",
      "temperature_c                               220940 non-null float64\n",
      "dew_point_c                                 220940 non-null float64\n",
      "relative_humidity                           220940 non-null float64\n",
      "pressure_altimiter_in                       220940 non-null float64\n",
      "RunwayTrueTrack                             220940 non-null float64\n",
      "Landing_RWY                                 220940 non-null object\n",
      "Landing_Visibilty_Class_RVR_CAT1            220940 non-null uint8\n",
      "Landing_Visibilty_Class_RVR_CAT2            220940 non-null uint8\n",
      "Landing_Visibilty_Class_RVR_CAT3            220940 non-null uint8\n",
      "Landing_Visibilty_Class_RVR_Marginal VMC    220940 non-null uint8\n",
      "Landing_Visibilty_Class_RVR_VMC             220940 non-null uint8\n",
      "present_weather_codes_+RA                   220940 non-null uint8\n",
      "present_weather_codes_+RA VCTS              220940 non-null uint8\n",
      "present_weather_codes_+SHRA                 220940 non-null uint8\n",
      "present_weather_codes_-DZ                   220940 non-null uint8\n",
      "present_weather_codes_-DZ BR                220940 non-null uint8\n",
      "present_weather_codes_-DZ FG                220940 non-null uint8\n",
      "present_weather_codes_-RA                   220940 non-null uint8\n",
      "present_weather_codes_-RA VCTS              220940 non-null uint8\n",
      "present_weather_codes_-RADZ                 220940 non-null uint8\n",
      "present_weather_codes_-RADZ BR              220940 non-null uint8\n",
      "present_weather_codes_-RADZ FG              220940 non-null uint8\n",
      "present_weather_codes_-RADZ HZ              220940 non-null uint8\n",
      "present_weather_codes_-SHRA                 220940 non-null uint8\n",
      "present_weather_codes_-SHRA TS              220940 non-null uint8\n",
      "present_weather_codes_-SHRA VCTS            220940 non-null uint8\n",
      "present_weather_codes_-SHRASN               220940 non-null uint8\n",
      "present_weather_codes_-SN                   220940 non-null uint8\n",
      "present_weather_codes_-TSRA                 220940 non-null uint8\n",
      "present_weather_codes_//////                220940 non-null uint8\n",
      "present_weather_codes_BCFG                  220940 non-null uint8\n",
      "present_weather_codes_BR                    220940 non-null uint8\n",
      "present_weather_codes_BR BCFG               220940 non-null uint8\n",
      "present_weather_codes_BR MIFG               220940 non-null uint8\n",
      "present_weather_codes_BR PRFG               220940 non-null uint8\n",
      "present_weather_codes_BR VCFG               220940 non-null uint8\n",
      "present_weather_codes_BR VCTS               220940 non-null uint8\n",
      "present_weather_codes_DZ                    220940 non-null uint8\n",
      "present_weather_codes_FG                    220940 non-null uint8\n",
      "present_weather_codes_FZFG                  220940 non-null uint8\n",
      "present_weather_codes_HZ                    220940 non-null uint8\n",
      "present_weather_codes_MIFG                  220940 non-null uint8\n",
      "present_weather_codes_MIFG BR               220940 non-null uint8\n",
      "present_weather_codes_PRFG                  220940 non-null uint8\n",
      "present_weather_codes_PRFG VCFG             220940 non-null uint8\n",
      "present_weather_codes_RA                    220940 non-null uint8\n",
      "present_weather_codes_RA HZ                 220940 non-null uint8\n",
      "present_weather_codes_RADZ                  220940 non-null uint8\n",
      "present_weather_codes_RADZ FG               220940 non-null uint8\n",
      "present_weather_codes_SG                    220940 non-null uint8\n",
      "present_weather_codes_SHRA                  220940 non-null uint8\n",
      "present_weather_codes_SHRA HZ               220940 non-null uint8\n",
      "present_weather_codes_SHRA VCTS             220940 non-null uint8\n",
      "present_weather_codes_TS                    220940 non-null uint8\n",
      "present_weather_codes_TSRA                  220940 non-null uint8\n",
      "present_weather_codes_VCFG                  220940 non-null uint8\n",
      "present_weather_codes_VCSH                  220940 non-null uint8\n",
      "present_weather_codes_VCTS                  220940 non-null uint8\n",
      "present_weather_codes_VCTS -SHRA            220940 non-null uint8\n",
      "present_weather_codes_VCTS VCSH             220940 non-null uint8\n",
      "dtypes: float64(9), int64(1), object(1), uint8(54)\n",
      "memory usage: 31.6+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Wind_And_Gust_kt</th>\n",
       "      <th>Landing_RVR_or_Vis_m</th>\n",
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       "      <th>relative_humidity</th>\n",
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       "      <th>RunwayTrueTrack</th>\n",
       "      <th>...</th>\n",
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       "      <th>Landing_Time</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01 04:34:38</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>93.08</td>\n",
       "      <td>30.21</td>\n",
       "      <td>89.713523</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2016-01-01 04:38:22</th>\n",
       "      <td>4</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>93.08</td>\n",
       "      <td>30.21</td>\n",
       "      <td>89.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04 06:10:11</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93.30</td>\n",
       "      <td>29.03</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2016-01-04 06:10:50</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93.30</td>\n",
       "      <td>29.03</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04 06:12:36</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93.30</td>\n",
       "      <td>29.03</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04 06:13:39</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93.30</td>\n",
       "      <td>29.03</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04 06:15:03</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93.30</td>\n",
       "      <td>29.03</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04 06:16:04</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93.30</td>\n",
       "      <td>29.03</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04 06:17:32</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93.30</td>\n",
       "      <td>29.03</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04 06:18:40</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93.30</td>\n",
       "      <td>29.03</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04 06:20:04</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93.30</td>\n",
       "      <td>29.03</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04 06:21:05</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93.30</td>\n",
       "      <td>29.03</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04 06:22:32</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93.30</td>\n",
       "      <td>29.03</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04 06:23:14</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93.30</td>\n",
       "      <td>29.03</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04 06:24:35</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93.30</td>\n",
       "      <td>29.03</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04 06:25:42</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93.30</td>\n",
       "      <td>29.03</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04 06:27:06</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93.30</td>\n",
       "      <td>29.03</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04 06:28:14</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93.30</td>\n",
       "      <td>29.03</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04 06:29:33</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93.30</td>\n",
       "      <td>29.03</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04 06:30:32</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93.30</td>\n",
       "      <td>29.03</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04 06:32:06</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93.30</td>\n",
       "      <td>29.03</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04 06:33:03</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93.30</td>\n",
       "      <td>29.03</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04 06:34:46</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93.30</td>\n",
       "      <td>29.03</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04 06:35:55</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93.30</td>\n",
       "      <td>29.03</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04 06:37:19</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93.30</td>\n",
       "      <td>29.03</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04 06:38:22</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93.30</td>\n",
       "      <td>29.03</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04 06:39:52</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93.30</td>\n",
       "      <td>29.03</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05 06:10:40</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93.30</td>\n",
       "      <td>29.00</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05 06:12:06</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93.30</td>\n",
       "      <td>29.00</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05 06:13:39</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93.30</td>\n",
       "      <td>29.00</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "      <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>2016-12-30 19:03:13</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>30.56</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:05:36</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>30.56</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:07:18</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>30.56</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:09:31</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>30.56</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:11:28</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>30.56</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:13:33</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>30.56</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:16:43</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>30.56</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:18:48</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>30.56</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:21:21</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>30.56</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:22:38</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>30.56</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:25:36</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>30.56</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:27:03</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>30.56</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:29:28</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>30.56</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:31:09</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>30.56</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:33:23</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>30.56</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:35:12</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>30.56</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:37:33</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>30.56</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:39:48</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>30.56</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:41:48</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>30.59</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:43:38</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>30.59</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:46:25</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>30.59</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:49:03</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>30.59</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:50:53</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>30.59</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:53:13</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>30.59</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:58:00</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>30.59</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:59:43</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>30.59</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 20:02:11</th>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>30.59</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 20:04:08</th>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>30.59</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 20:07:07</th>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>30.59</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 20:09:00</th>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>30.59</td>\n",
       "      <td>269.713523</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17714 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     hourofday  wind_direction_degrees  Wind_Speed_kt  \\\n",
       "Landing_Time                                                            \n",
       "2016-01-01 04:34:38          4                     NaN            1.0   \n",
       "2016-01-01 04:38:22          4                     NaN            1.0   \n",
       "2016-01-04 06:10:11          6                     NaN            7.0   \n",
       "2016-01-04 06:10:50          6                     NaN            7.0   \n",
       "2016-01-04 06:12:36          6                     NaN            7.0   \n",
       "2016-01-04 06:13:39          6                     NaN            7.0   \n",
       "2016-01-04 06:15:03          6                     NaN            7.0   \n",
       "2016-01-04 06:16:04          6                     NaN            7.0   \n",
       "2016-01-04 06:17:32          6                     NaN            7.0   \n",
       "2016-01-04 06:18:40          6                     NaN            7.0   \n",
       "2016-01-04 06:20:04          6                     NaN            7.0   \n",
       "2016-01-04 06:21:05          6                     NaN            7.0   \n",
       "2016-01-04 06:22:32          6                     NaN            7.0   \n",
       "2016-01-04 06:23:14          6                     NaN            7.0   \n",
       "2016-01-04 06:24:35          6                     NaN            7.0   \n",
       "2016-01-04 06:25:42          6                     NaN            7.0   \n",
       "2016-01-04 06:27:06          6                     NaN            7.0   \n",
       "2016-01-04 06:28:14          6                     NaN            7.0   \n",
       "2016-01-04 06:29:33          6                     NaN            7.0   \n",
       "2016-01-04 06:30:32          6                     NaN            7.0   \n",
       "2016-01-04 06:32:06          6                     NaN            7.0   \n",
       "2016-01-04 06:33:03          6                     NaN            7.0   \n",
       "2016-01-04 06:34:46          6                     NaN            7.0   \n",
       "2016-01-04 06:35:55          6                     NaN            7.0   \n",
       "2016-01-04 06:37:19          6                     NaN            7.0   \n",
       "2016-01-04 06:38:22          6                     NaN            7.0   \n",
       "2016-01-04 06:39:52          6                     NaN            7.0   \n",
       "2016-01-05 06:10:40          6                     NaN            5.0   \n",
       "2016-01-05 06:12:06          6                     NaN            5.0   \n",
       "2016-01-05 06:13:39          6                     NaN            5.0   \n",
       "...                        ...                     ...            ...   \n",
       "2016-12-30 19:03:13         19                     NaN            2.0   \n",
       "2016-12-30 19:05:36         19                     NaN            2.0   \n",
       "2016-12-30 19:07:18         19                     NaN            2.0   \n",
       "2016-12-30 19:09:31         19                     NaN            2.0   \n",
       "2016-12-30 19:11:28         19                     NaN            3.0   \n",
       "2016-12-30 19:13:33         19                     NaN            3.0   \n",
       "2016-12-30 19:16:43         19                     NaN            3.0   \n",
       "2016-12-30 19:18:48         19                     NaN            3.0   \n",
       "2016-12-30 19:21:21         19                     NaN            3.0   \n",
       "2016-12-30 19:22:38         19                     NaN            3.0   \n",
       "2016-12-30 19:25:36         19                     NaN            3.0   \n",
       "2016-12-30 19:27:03         19                     NaN            3.0   \n",
       "2016-12-30 19:29:28         19                     NaN            3.0   \n",
       "2016-12-30 19:31:09         19                     NaN            3.0   \n",
       "2016-12-30 19:33:23         19                     NaN            3.0   \n",
       "2016-12-30 19:35:12         19                     NaN            3.0   \n",
       "2016-12-30 19:37:33         19                     NaN            3.0   \n",
       "2016-12-30 19:39:48         19                     NaN            3.0   \n",
       "2016-12-30 19:41:48         19                     NaN            3.0   \n",
       "2016-12-30 19:43:38         19                     NaN            3.0   \n",
       "2016-12-30 19:46:25         19                     NaN            3.0   \n",
       "2016-12-30 19:49:03         19                     NaN            3.0   \n",
       "2016-12-30 19:50:53         19                     NaN            3.0   \n",
       "2016-12-30 19:53:13         19                     NaN            3.0   \n",
       "2016-12-30 19:58:00         19                     NaN            3.0   \n",
       "2016-12-30 19:59:43         19                     NaN            3.0   \n",
       "2016-12-30 20:02:11         20                     NaN            3.0   \n",
       "2016-12-30 20:04:08         20                     NaN            3.0   \n",
       "2016-12-30 20:07:07         20                     NaN            3.0   \n",
       "2016-12-30 20:09:00         20                     NaN            3.0   \n",
       "\n",
       "                     Wind_And_Gust_kt  Landing_RVR_or_Vis_m  temperature_c  \\\n",
       "Landing_Time                                                                 \n",
       "2016-01-01 04:34:38               1.0                6000.0            2.0   \n",
       "2016-01-01 04:38:22               1.0                6000.0            2.0   \n",
       "2016-01-04 06:10:11               7.0               10000.0            6.0   \n",
       "2016-01-04 06:10:50               7.0               10000.0            6.0   \n",
       "2016-01-04 06:12:36               7.0               10000.0            6.0   \n",
       "2016-01-04 06:13:39               7.0               10000.0            6.0   \n",
       "2016-01-04 06:15:03               7.0               10000.0            6.0   \n",
       "2016-01-04 06:16:04               7.0               10000.0            6.0   \n",
       "2016-01-04 06:17:32               7.0               10000.0            6.0   \n",
       "2016-01-04 06:18:40               7.0               10000.0            6.0   \n",
       "2016-01-04 06:20:04               7.0               10000.0            6.0   \n",
       "2016-01-04 06:21:05               7.0               10000.0            6.0   \n",
       "2016-01-04 06:22:32               7.0               10000.0            6.0   \n",
       "2016-01-04 06:23:14               7.0               10000.0            6.0   \n",
       "2016-01-04 06:24:35               7.0               10000.0            6.0   \n",
       "2016-01-04 06:25:42               7.0               10000.0            6.0   \n",
       "2016-01-04 06:27:06               7.0               10000.0            6.0   \n",
       "2016-01-04 06:28:14               7.0               10000.0            6.0   \n",
       "2016-01-04 06:29:33               7.0               10000.0            6.0   \n",
       "2016-01-04 06:30:32               7.0               10000.0            6.0   \n",
       "2016-01-04 06:32:06               7.0               10000.0            6.0   \n",
       "2016-01-04 06:33:03               7.0               10000.0            6.0   \n",
       "2016-01-04 06:34:46               7.0               10000.0            6.0   \n",
       "2016-01-04 06:35:55               7.0               10000.0            6.0   \n",
       "2016-01-04 06:37:19               7.0               10000.0            6.0   \n",
       "2016-01-04 06:38:22               7.0               10000.0            6.0   \n",
       "2016-01-04 06:39:52               7.0               10000.0            6.0   \n",
       "2016-01-05 06:10:40               5.0                6000.0            6.0   \n",
       "2016-01-05 06:12:06               5.0                6000.0            6.0   \n",
       "2016-01-05 06:13:39               5.0                6000.0            6.0   \n",
       "...                               ...                   ...            ...   \n",
       "2016-12-30 19:03:13               2.0                 600.0            3.0   \n",
       "2016-12-30 19:05:36               2.0                 600.0            3.0   \n",
       "2016-12-30 19:07:18               2.0                 600.0            3.0   \n",
       "2016-12-30 19:09:31               2.0                 600.0            3.0   \n",
       "2016-12-30 19:11:28               3.0                 550.0            2.0   \n",
       "2016-12-30 19:13:33               3.0                 550.0            2.0   \n",
       "2016-12-30 19:16:43               3.0                 550.0            2.0   \n",
       "2016-12-30 19:18:48               3.0                 550.0            2.0   \n",
       "2016-12-30 19:21:21               3.0                 550.0            2.0   \n",
       "2016-12-30 19:22:38               3.0                 550.0            2.0   \n",
       "2016-12-30 19:25:36               3.0                 550.0            2.0   \n",
       "2016-12-30 19:27:03               3.0                 550.0            2.0   \n",
       "2016-12-30 19:29:28               3.0                 550.0            2.0   \n",
       "2016-12-30 19:31:09               3.0                 550.0            2.0   \n",
       "2016-12-30 19:33:23               3.0                 550.0            2.0   \n",
       "2016-12-30 19:35:12               3.0                 550.0            2.0   \n",
       "2016-12-30 19:37:33               3.0                 550.0            2.0   \n",
       "2016-12-30 19:39:48               3.0                 550.0            2.0   \n",
       "2016-12-30 19:41:48               3.0                 550.0            2.0   \n",
       "2016-12-30 19:43:38               3.0                 550.0            2.0   \n",
       "2016-12-30 19:46:25               3.0                 550.0            2.0   \n",
       "2016-12-30 19:49:03               3.0                 550.0            2.0   \n",
       "2016-12-30 19:50:53               3.0                 550.0            2.0   \n",
       "2016-12-30 19:53:13               3.0                 550.0            2.0   \n",
       "2016-12-30 19:58:00               3.0                 550.0            2.0   \n",
       "2016-12-30 19:59:43               3.0                 550.0            2.0   \n",
       "2016-12-30 20:02:11               3.0                 550.0            2.0   \n",
       "2016-12-30 20:04:08               3.0                 550.0            2.0   \n",
       "2016-12-30 20:07:07               3.0                 550.0            2.0   \n",
       "2016-12-30 20:09:00               3.0                 550.0            2.0   \n",
       "\n",
       "                     dew_point_c  relative_humidity  pressure_altimiter_in  \\\n",
       "Landing_Time                                                                 \n",
       "2016-01-01 04:34:38          1.0              93.08                  30.21   \n",
       "2016-01-01 04:38:22          1.0              93.08                  30.21   \n",
       "2016-01-04 06:10:11          5.0              93.30                  29.03   \n",
       "2016-01-04 06:10:50          5.0              93.30                  29.03   \n",
       "2016-01-04 06:12:36          5.0              93.30                  29.03   \n",
       "2016-01-04 06:13:39          5.0              93.30                  29.03   \n",
       "2016-01-04 06:15:03          5.0              93.30                  29.03   \n",
       "2016-01-04 06:16:04          5.0              93.30                  29.03   \n",
       "2016-01-04 06:17:32          5.0              93.30                  29.03   \n",
       "2016-01-04 06:18:40          5.0              93.30                  29.03   \n",
       "2016-01-04 06:20:04          5.0              93.30                  29.03   \n",
       "2016-01-04 06:21:05          5.0              93.30                  29.03   \n",
       "2016-01-04 06:22:32          5.0              93.30                  29.03   \n",
       "2016-01-04 06:23:14          5.0              93.30                  29.03   \n",
       "2016-01-04 06:24:35          5.0              93.30                  29.03   \n",
       "2016-01-04 06:25:42          5.0              93.30                  29.03   \n",
       "2016-01-04 06:27:06          5.0              93.30                  29.03   \n",
       "2016-01-04 06:28:14          5.0              93.30                  29.03   \n",
       "2016-01-04 06:29:33          5.0              93.30                  29.03   \n",
       "2016-01-04 06:30:32          5.0              93.30                  29.03   \n",
       "2016-01-04 06:32:06          5.0              93.30                  29.03   \n",
       "2016-01-04 06:33:03          5.0              93.30                  29.03   \n",
       "2016-01-04 06:34:46          5.0              93.30                  29.03   \n",
       "2016-01-04 06:35:55          5.0              93.30                  29.03   \n",
       "2016-01-04 06:37:19          5.0              93.30                  29.03   \n",
       "2016-01-04 06:38:22          5.0              93.30                  29.03   \n",
       "2016-01-04 06:39:52          5.0              93.30                  29.03   \n",
       "2016-01-05 06:10:40          5.0              93.30                  29.00   \n",
       "2016-01-05 06:12:06          5.0              93.30                  29.00   \n",
       "2016-01-05 06:13:39          5.0              93.30                  29.00   \n",
       "...                          ...                ...                    ...   \n",
       "2016-12-30 19:03:13          3.0             100.00                  30.56   \n",
       "2016-12-30 19:05:36          3.0             100.00                  30.56   \n",
       "2016-12-30 19:07:18          3.0             100.00                  30.56   \n",
       "2016-12-30 19:09:31          3.0             100.00                  30.56   \n",
       "2016-12-30 19:11:28          2.0             100.00                  30.56   \n",
       "2016-12-30 19:13:33          2.0             100.00                  30.56   \n",
       "2016-12-30 19:16:43          2.0             100.00                  30.56   \n",
       "2016-12-30 19:18:48          2.0             100.00                  30.56   \n",
       "2016-12-30 19:21:21          2.0             100.00                  30.56   \n",
       "2016-12-30 19:22:38          2.0             100.00                  30.56   \n",
       "2016-12-30 19:25:36          2.0             100.00                  30.56   \n",
       "2016-12-30 19:27:03          2.0             100.00                  30.56   \n",
       "2016-12-30 19:29:28          2.0             100.00                  30.56   \n",
       "2016-12-30 19:31:09          2.0             100.00                  30.56   \n",
       "2016-12-30 19:33:23          2.0             100.00                  30.56   \n",
       "2016-12-30 19:35:12          2.0             100.00                  30.56   \n",
       "2016-12-30 19:37:33          2.0             100.00                  30.56   \n",
       "2016-12-30 19:39:48          2.0             100.00                  30.56   \n",
       "2016-12-30 19:41:48          2.0             100.00                  30.59   \n",
       "2016-12-30 19:43:38          2.0             100.00                  30.59   \n",
       "2016-12-30 19:46:25          2.0             100.00                  30.59   \n",
       "2016-12-30 19:49:03          2.0             100.00                  30.59   \n",
       "2016-12-30 19:50:53          2.0             100.00                  30.59   \n",
       "2016-12-30 19:53:13          2.0             100.00                  30.59   \n",
       "2016-12-30 19:58:00          2.0             100.00                  30.59   \n",
       "2016-12-30 19:59:43          2.0             100.00                  30.59   \n",
       "2016-12-30 20:02:11          2.0             100.00                  30.59   \n",
       "2016-12-30 20:04:08          2.0             100.00                  30.59   \n",
       "2016-12-30 20:07:07          2.0             100.00                  30.59   \n",
       "2016-12-30 20:09:00          2.0             100.00                  30.59   \n",
       "\n",
       "                     RunwayTrueTrack               ...                 \\\n",
       "Landing_Time                                       ...                  \n",
       "2016-01-01 04:34:38        89.713523               ...                  \n",
       "2016-01-01 04:38:22        89.713523               ...                  \n",
       "2016-01-04 06:10:11       269.713523               ...                  \n",
       "2016-01-04 06:10:50       269.713523               ...                  \n",
       "2016-01-04 06:12:36       269.713523               ...                  \n",
       "2016-01-04 06:13:39       269.713523               ...                  \n",
       "2016-01-04 06:15:03       269.713523               ...                  \n",
       "2016-01-04 06:16:04       269.713523               ...                  \n",
       "2016-01-04 06:17:32       269.713523               ...                  \n",
       "2016-01-04 06:18:40       269.713523               ...                  \n",
       "2016-01-04 06:20:04       269.713523               ...                  \n",
       "2016-01-04 06:21:05       269.713523               ...                  \n",
       "2016-01-04 06:22:32       269.713523               ...                  \n",
       "2016-01-04 06:23:14       269.713523               ...                  \n",
       "2016-01-04 06:24:35       269.713523               ...                  \n",
       "2016-01-04 06:25:42       269.713523               ...                  \n",
       "2016-01-04 06:27:06       269.713523               ...                  \n",
       "2016-01-04 06:28:14       269.713523               ...                  \n",
       "2016-01-04 06:29:33       269.713523               ...                  \n",
       "2016-01-04 06:30:32       269.713523               ...                  \n",
       "2016-01-04 06:32:06       269.713523               ...                  \n",
       "2016-01-04 06:33:03       269.713523               ...                  \n",
       "2016-01-04 06:34:46       269.713523               ...                  \n",
       "2016-01-04 06:35:55       269.713523               ...                  \n",
       "2016-01-04 06:37:19       269.713523               ...                  \n",
       "2016-01-04 06:38:22       269.713523               ...                  \n",
       "2016-01-04 06:39:52       269.713523               ...                  \n",
       "2016-01-05 06:10:40       269.713523               ...                  \n",
       "2016-01-05 06:12:06       269.713523               ...                  \n",
       "2016-01-05 06:13:39       269.713523               ...                  \n",
       "...                              ...               ...                  \n",
       "2016-12-30 19:03:13       269.713523               ...                  \n",
       "2016-12-30 19:05:36       269.713523               ...                  \n",
       "2016-12-30 19:07:18       269.713523               ...                  \n",
       "2016-12-30 19:09:31       269.713523               ...                  \n",
       "2016-12-30 19:11:28       269.713523               ...                  \n",
       "2016-12-30 19:13:33       269.713523               ...                  \n",
       "2016-12-30 19:16:43       269.713523               ...                  \n",
       "2016-12-30 19:18:48       269.713523               ...                  \n",
       "2016-12-30 19:21:21       269.713523               ...                  \n",
       "2016-12-30 19:22:38       269.713523               ...                  \n",
       "2016-12-30 19:25:36       269.713523               ...                  \n",
       "2016-12-30 19:27:03       269.713523               ...                  \n",
       "2016-12-30 19:29:28       269.713523               ...                  \n",
       "2016-12-30 19:31:09       269.713523               ...                  \n",
       "2016-12-30 19:33:23       269.713523               ...                  \n",
       "2016-12-30 19:35:12       269.713523               ...                  \n",
       "2016-12-30 19:37:33       269.713523               ...                  \n",
       "2016-12-30 19:39:48       269.713523               ...                  \n",
       "2016-12-30 19:41:48       269.713523               ...                  \n",
       "2016-12-30 19:43:38       269.713523               ...                  \n",
       "2016-12-30 19:46:25       269.713523               ...                  \n",
       "2016-12-30 19:49:03       269.713523               ...                  \n",
       "2016-12-30 19:50:53       269.713523               ...                  \n",
       "2016-12-30 19:53:13       269.713523               ...                  \n",
       "2016-12-30 19:58:00       269.713523               ...                  \n",
       "2016-12-30 19:59:43       269.713523               ...                  \n",
       "2016-12-30 20:02:11       269.713523               ...                  \n",
       "2016-12-30 20:04:08       269.713523               ...                  \n",
       "2016-12-30 20:07:07       269.713523               ...                  \n",
       "2016-12-30 20:09:00       269.713523               ...                  \n",
       "\n",
       "                    present_weather_codes_SHRA  present_weather_codes_SHRA HZ  \\\n",
       "Landing_Time                                                                    \n",
       "2016-01-01 04:34:38                          0                              0   \n",
       "2016-01-01 04:38:22                          0                              0   \n",
       "2016-01-04 06:10:11                          0                              0   \n",
       "2016-01-04 06:10:50                          0                              0   \n",
       "2016-01-04 06:12:36                          0                              0   \n",
       "2016-01-04 06:13:39                          0                              0   \n",
       "2016-01-04 06:15:03                          0                              0   \n",
       "2016-01-04 06:16:04                          0                              0   \n",
       "2016-01-04 06:17:32                          0                              0   \n",
       "2016-01-04 06:18:40                          0                              0   \n",
       "2016-01-04 06:20:04                          0                              0   \n",
       "2016-01-04 06:21:05                          0                              0   \n",
       "2016-01-04 06:22:32                          0                              0   \n",
       "2016-01-04 06:23:14                          0                              0   \n",
       "2016-01-04 06:24:35                          0                              0   \n",
       "2016-01-04 06:25:42                          0                              0   \n",
       "2016-01-04 06:27:06                          0                              0   \n",
       "2016-01-04 06:28:14                          0                              0   \n",
       "2016-01-04 06:29:33                          0                              0   \n",
       "2016-01-04 06:30:32                          0                              0   \n",
       "2016-01-04 06:32:06                          0                              0   \n",
       "2016-01-04 06:33:03                          0                              0   \n",
       "2016-01-04 06:34:46                          0                              0   \n",
       "2016-01-04 06:35:55                          0                              0   \n",
       "2016-01-04 06:37:19                          0                              0   \n",
       "2016-01-04 06:38:22                          0                              0   \n",
       "2016-01-04 06:39:52                          0                              0   \n",
       "2016-01-05 06:10:40                          0                              0   \n",
       "2016-01-05 06:12:06                          0                              0   \n",
       "2016-01-05 06:13:39                          0                              0   \n",
       "...                                        ...                            ...   \n",
       "2016-12-30 19:03:13                          0                              0   \n",
       "2016-12-30 19:05:36                          0                              0   \n",
       "2016-12-30 19:07:18                          0                              0   \n",
       "2016-12-30 19:09:31                          0                              0   \n",
       "2016-12-30 19:11:28                          0                              0   \n",
       "2016-12-30 19:13:33                          0                              0   \n",
       "2016-12-30 19:16:43                          0                              0   \n",
       "2016-12-30 19:18:48                          0                              0   \n",
       "2016-12-30 19:21:21                          0                              0   \n",
       "2016-12-30 19:22:38                          0                              0   \n",
       "2016-12-30 19:25:36                          0                              0   \n",
       "2016-12-30 19:27:03                          0                              0   \n",
       "2016-12-30 19:29:28                          0                              0   \n",
       "2016-12-30 19:31:09                          0                              0   \n",
       "2016-12-30 19:33:23                          0                              0   \n",
       "2016-12-30 19:35:12                          0                              0   \n",
       "2016-12-30 19:37:33                          0                              0   \n",
       "2016-12-30 19:39:48                          0                              0   \n",
       "2016-12-30 19:41:48                          0                              0   \n",
       "2016-12-30 19:43:38                          0                              0   \n",
       "2016-12-30 19:46:25                          0                              0   \n",
       "2016-12-30 19:49:03                          0                              0   \n",
       "2016-12-30 19:50:53                          0                              0   \n",
       "2016-12-30 19:53:13                          0                              0   \n",
       "2016-12-30 19:58:00                          0                              0   \n",
       "2016-12-30 19:59:43                          0                              0   \n",
       "2016-12-30 20:02:11                          0                              0   \n",
       "2016-12-30 20:04:08                          0                              0   \n",
       "2016-12-30 20:07:07                          0                              0   \n",
       "2016-12-30 20:09:00                          0                              0   \n",
       "\n",
       "                     present_weather_codes_SHRA VCTS  \\\n",
       "Landing_Time                                           \n",
       "2016-01-01 04:34:38                                0   \n",
       "2016-01-01 04:38:22                                0   \n",
       "2016-01-04 06:10:11                                0   \n",
       "2016-01-04 06:10:50                                0   \n",
       "2016-01-04 06:12:36                                0   \n",
       "2016-01-04 06:13:39                                0   \n",
       "2016-01-04 06:15:03                                0   \n",
       "2016-01-04 06:16:04                                0   \n",
       "2016-01-04 06:17:32                                0   \n",
       "2016-01-04 06:18:40                                0   \n",
       "2016-01-04 06:20:04                                0   \n",
       "2016-01-04 06:21:05                                0   \n",
       "2016-01-04 06:22:32                                0   \n",
       "2016-01-04 06:23:14                                0   \n",
       "2016-01-04 06:24:35                                0   \n",
       "2016-01-04 06:25:42                                0   \n",
       "2016-01-04 06:27:06                                0   \n",
       "2016-01-04 06:28:14                                0   \n",
       "2016-01-04 06:29:33                                0   \n",
       "2016-01-04 06:30:32                                0   \n",
       "2016-01-04 06:32:06                                0   \n",
       "2016-01-04 06:33:03                                0   \n",
       "2016-01-04 06:34:46                                0   \n",
       "2016-01-04 06:35:55                                0   \n",
       "2016-01-04 06:37:19                                0   \n",
       "2016-01-04 06:38:22                                0   \n",
       "2016-01-04 06:39:52                                0   \n",
       "2016-01-05 06:10:40                                0   \n",
       "2016-01-05 06:12:06                                0   \n",
       "2016-01-05 06:13:39                                0   \n",
       "...                                              ...   \n",
       "2016-12-30 19:03:13                                0   \n",
       "2016-12-30 19:05:36                                0   \n",
       "2016-12-30 19:07:18                                0   \n",
       "2016-12-30 19:09:31                                0   \n",
       "2016-12-30 19:11:28                                0   \n",
       "2016-12-30 19:13:33                                0   \n",
       "2016-12-30 19:16:43                                0   \n",
       "2016-12-30 19:18:48                                0   \n",
       "2016-12-30 19:21:21                                0   \n",
       "2016-12-30 19:22:38                                0   \n",
       "2016-12-30 19:25:36                                0   \n",
       "2016-12-30 19:27:03                                0   \n",
       "2016-12-30 19:29:28                                0   \n",
       "2016-12-30 19:31:09                                0   \n",
       "2016-12-30 19:33:23                                0   \n",
       "2016-12-30 19:35:12                                0   \n",
       "2016-12-30 19:37:33                                0   \n",
       "2016-12-30 19:39:48                                0   \n",
       "2016-12-30 19:41:48                                0   \n",
       "2016-12-30 19:43:38                                0   \n",
       "2016-12-30 19:46:25                                0   \n",
       "2016-12-30 19:49:03                                0   \n",
       "2016-12-30 19:50:53                                0   \n",
       "2016-12-30 19:53:13                                0   \n",
       "2016-12-30 19:58:00                                0   \n",
       "2016-12-30 19:59:43                                0   \n",
       "2016-12-30 20:02:11                                0   \n",
       "2016-12-30 20:04:08                                0   \n",
       "2016-12-30 20:07:07                                0   \n",
       "2016-12-30 20:09:00                                0   \n",
       "\n",
       "                     present_weather_codes_TS  present_weather_codes_TSRA  \\\n",
       "Landing_Time                                                                \n",
       "2016-01-01 04:34:38                         0                           0   \n",
       "2016-01-01 04:38:22                         0                           0   \n",
       "2016-01-04 06:10:11                         0                           0   \n",
       "2016-01-04 06:10:50                         0                           0   \n",
       "2016-01-04 06:12:36                         0                           0   \n",
       "2016-01-04 06:13:39                         0                           0   \n",
       "2016-01-04 06:15:03                         0                           0   \n",
       "2016-01-04 06:16:04                         0                           0   \n",
       "2016-01-04 06:17:32                         0                           0   \n",
       "2016-01-04 06:18:40                         0                           0   \n",
       "2016-01-04 06:20:04                         0                           0   \n",
       "2016-01-04 06:21:05                         0                           0   \n",
       "2016-01-04 06:22:32                         0                           0   \n",
       "2016-01-04 06:23:14                         0                           0   \n",
       "2016-01-04 06:24:35                         0                           0   \n",
       "2016-01-04 06:25:42                         0                           0   \n",
       "2016-01-04 06:27:06                         0                           0   \n",
       "2016-01-04 06:28:14                         0                           0   \n",
       "2016-01-04 06:29:33                         0                           0   \n",
       "2016-01-04 06:30:32                         0                           0   \n",
       "2016-01-04 06:32:06                         0                           0   \n",
       "2016-01-04 06:33:03                         0                           0   \n",
       "2016-01-04 06:34:46                         0                           0   \n",
       "2016-01-04 06:35:55                         0                           0   \n",
       "2016-01-04 06:37:19                         0                           0   \n",
       "2016-01-04 06:38:22                         0                           0   \n",
       "2016-01-04 06:39:52                         0                           0   \n",
       "2016-01-05 06:10:40                         0                           0   \n",
       "2016-01-05 06:12:06                         0                           0   \n",
       "2016-01-05 06:13:39                         0                           0   \n",
       "...                                       ...                         ...   \n",
       "2016-12-30 19:03:13                         0                           0   \n",
       "2016-12-30 19:05:36                         0                           0   \n",
       "2016-12-30 19:07:18                         0                           0   \n",
       "2016-12-30 19:09:31                         0                           0   \n",
       "2016-12-30 19:11:28                         0                           0   \n",
       "2016-12-30 19:13:33                         0                           0   \n",
       "2016-12-30 19:16:43                         0                           0   \n",
       "2016-12-30 19:18:48                         0                           0   \n",
       "2016-12-30 19:21:21                         0                           0   \n",
       "2016-12-30 19:22:38                         0                           0   \n",
       "2016-12-30 19:25:36                         0                           0   \n",
       "2016-12-30 19:27:03                         0                           0   \n",
       "2016-12-30 19:29:28                         0                           0   \n",
       "2016-12-30 19:31:09                         0                           0   \n",
       "2016-12-30 19:33:23                         0                           0   \n",
       "2016-12-30 19:35:12                         0                           0   \n",
       "2016-12-30 19:37:33                         0                           0   \n",
       "2016-12-30 19:39:48                         0                           0   \n",
       "2016-12-30 19:41:48                         0                           0   \n",
       "2016-12-30 19:43:38                         0                           0   \n",
       "2016-12-30 19:46:25                         0                           0   \n",
       "2016-12-30 19:49:03                         0                           0   \n",
       "2016-12-30 19:50:53                         0                           0   \n",
       "2016-12-30 19:53:13                         0                           0   \n",
       "2016-12-30 19:58:00                         0                           0   \n",
       "2016-12-30 19:59:43                         0                           0   \n",
       "2016-12-30 20:02:11                         0                           0   \n",
       "2016-12-30 20:04:08                         0                           0   \n",
       "2016-12-30 20:07:07                         0                           0   \n",
       "2016-12-30 20:09:00                         0                           0   \n",
       "\n",
       "                     present_weather_codes_VCFG  present_weather_codes_VCSH  \\\n",
       "Landing_Time                                                                  \n",
       "2016-01-01 04:34:38                           0                           0   \n",
       "2016-01-01 04:38:22                           0                           0   \n",
       "2016-01-04 06:10:11                           0                           0   \n",
       "2016-01-04 06:10:50                           0                           0   \n",
       "2016-01-04 06:12:36                           0                           0   \n",
       "2016-01-04 06:13:39                           0                           0   \n",
       "2016-01-04 06:15:03                           0                           0   \n",
       "2016-01-04 06:16:04                           0                           0   \n",
       "2016-01-04 06:17:32                           0                           0   \n",
       "2016-01-04 06:18:40                           0                           0   \n",
       "2016-01-04 06:20:04                           0                           0   \n",
       "2016-01-04 06:21:05                           0                           0   \n",
       "2016-01-04 06:22:32                           0                           0   \n",
       "2016-01-04 06:23:14                           0                           0   \n",
       "2016-01-04 06:24:35                           0                           0   \n",
       "2016-01-04 06:25:42                           0                           0   \n",
       "2016-01-04 06:27:06                           0                           0   \n",
       "2016-01-04 06:28:14                           0                           0   \n",
       "2016-01-04 06:29:33                           0                           0   \n",
       "2016-01-04 06:30:32                           0                           0   \n",
       "2016-01-04 06:32:06                           0                           0   \n",
       "2016-01-04 06:33:03                           0                           0   \n",
       "2016-01-04 06:34:46                           0                           0   \n",
       "2016-01-04 06:35:55                           0                           0   \n",
       "2016-01-04 06:37:19                           0                           0   \n",
       "2016-01-04 06:38:22                           0                           0   \n",
       "2016-01-04 06:39:52                           0                           0   \n",
       "2016-01-05 06:10:40                           0                           0   \n",
       "2016-01-05 06:12:06                           0                           0   \n",
       "2016-01-05 06:13:39                           0                           0   \n",
       "...                                         ...                         ...   \n",
       "2016-12-30 19:03:13                           0                           0   \n",
       "2016-12-30 19:05:36                           0                           0   \n",
       "2016-12-30 19:07:18                           0                           0   \n",
       "2016-12-30 19:09:31                           0                           0   \n",
       "2016-12-30 19:11:28                           0                           0   \n",
       "2016-12-30 19:13:33                           0                           0   \n",
       "2016-12-30 19:16:43                           0                           0   \n",
       "2016-12-30 19:18:48                           0                           0   \n",
       "2016-12-30 19:21:21                           0                           0   \n",
       "2016-12-30 19:22:38                           0                           0   \n",
       "2016-12-30 19:25:36                           0                           0   \n",
       "2016-12-30 19:27:03                           0                           0   \n",
       "2016-12-30 19:29:28                           0                           0   \n",
       "2016-12-30 19:31:09                           0                           0   \n",
       "2016-12-30 19:33:23                           0                           0   \n",
       "2016-12-30 19:35:12                           0                           0   \n",
       "2016-12-30 19:37:33                           0                           0   \n",
       "2016-12-30 19:39:48                           0                           0   \n",
       "2016-12-30 19:41:48                           0                           0   \n",
       "2016-12-30 19:43:38                           0                           0   \n",
       "2016-12-30 19:46:25                           0                           0   \n",
       "2016-12-30 19:49:03                           0                           0   \n",
       "2016-12-30 19:50:53                           0                           0   \n",
       "2016-12-30 19:53:13                           0                           0   \n",
       "2016-12-30 19:58:00                           0                           0   \n",
       "2016-12-30 19:59:43                           0                           0   \n",
       "2016-12-30 20:02:11                           0                           0   \n",
       "2016-12-30 20:04:08                           0                           0   \n",
       "2016-12-30 20:07:07                           0                           0   \n",
       "2016-12-30 20:09:00                           0                           0   \n",
       "\n",
       "                     present_weather_codes_VCTS  \\\n",
       "Landing_Time                                      \n",
       "2016-01-01 04:34:38                           0   \n",
       "2016-01-01 04:38:22                           0   \n",
       "2016-01-04 06:10:11                           0   \n",
       "2016-01-04 06:10:50                           0   \n",
       "2016-01-04 06:12:36                           0   \n",
       "2016-01-04 06:13:39                           0   \n",
       "2016-01-04 06:15:03                           0   \n",
       "2016-01-04 06:16:04                           0   \n",
       "2016-01-04 06:17:32                           0   \n",
       "2016-01-04 06:18:40                           0   \n",
       "2016-01-04 06:20:04                           0   \n",
       "2016-01-04 06:21:05                           0   \n",
       "2016-01-04 06:22:32                           0   \n",
       "2016-01-04 06:23:14                           0   \n",
       "2016-01-04 06:24:35                           0   \n",
       "2016-01-04 06:25:42                           0   \n",
       "2016-01-04 06:27:06                           0   \n",
       "2016-01-04 06:28:14                           0   \n",
       "2016-01-04 06:29:33                           0   \n",
       "2016-01-04 06:30:32                           0   \n",
       "2016-01-04 06:32:06                           0   \n",
       "2016-01-04 06:33:03                           0   \n",
       "2016-01-04 06:34:46                           0   \n",
       "2016-01-04 06:35:55                           0   \n",
       "2016-01-04 06:37:19                           0   \n",
       "2016-01-04 06:38:22                           0   \n",
       "2016-01-04 06:39:52                           0   \n",
       "2016-01-05 06:10:40                           0   \n",
       "2016-01-05 06:12:06                           0   \n",
       "2016-01-05 06:13:39                           0   \n",
       "...                                         ...   \n",
       "2016-12-30 19:03:13                           0   \n",
       "2016-12-30 19:05:36                           0   \n",
       "2016-12-30 19:07:18                           0   \n",
       "2016-12-30 19:09:31                           0   \n",
       "2016-12-30 19:11:28                           0   \n",
       "2016-12-30 19:13:33                           0   \n",
       "2016-12-30 19:16:43                           0   \n",
       "2016-12-30 19:18:48                           0   \n",
       "2016-12-30 19:21:21                           0   \n",
       "2016-12-30 19:22:38                           0   \n",
       "2016-12-30 19:25:36                           0   \n",
       "2016-12-30 19:27:03                           0   \n",
       "2016-12-30 19:29:28                           0   \n",
       "2016-12-30 19:31:09                           0   \n",
       "2016-12-30 19:33:23                           0   \n",
       "2016-12-30 19:35:12                           0   \n",
       "2016-12-30 19:37:33                           0   \n",
       "2016-12-30 19:39:48                           0   \n",
       "2016-12-30 19:41:48                           0   \n",
       "2016-12-30 19:43:38                           0   \n",
       "2016-12-30 19:46:25                           0   \n",
       "2016-12-30 19:49:03                           0   \n",
       "2016-12-30 19:50:53                           0   \n",
       "2016-12-30 19:53:13                           0   \n",
       "2016-12-30 19:58:00                           0   \n",
       "2016-12-30 19:59:43                           0   \n",
       "2016-12-30 20:02:11                           0   \n",
       "2016-12-30 20:04:08                           0   \n",
       "2016-12-30 20:07:07                           0   \n",
       "2016-12-30 20:09:00                           0   \n",
       "\n",
       "                     present_weather_codes_VCTS -SHRA  \\\n",
       "Landing_Time                                            \n",
       "2016-01-01 04:34:38                                 0   \n",
       "2016-01-01 04:38:22                                 0   \n",
       "2016-01-04 06:10:11                                 0   \n",
       "2016-01-04 06:10:50                                 0   \n",
       "2016-01-04 06:12:36                                 0   \n",
       "2016-01-04 06:13:39                                 0   \n",
       "2016-01-04 06:15:03                                 0   \n",
       "2016-01-04 06:16:04                                 0   \n",
       "2016-01-04 06:17:32                                 0   \n",
       "2016-01-04 06:18:40                                 0   \n",
       "2016-01-04 06:20:04                                 0   \n",
       "2016-01-04 06:21:05                                 0   \n",
       "2016-01-04 06:22:32                                 0   \n",
       "2016-01-04 06:23:14                                 0   \n",
       "2016-01-04 06:24:35                                 0   \n",
       "2016-01-04 06:25:42                                 0   \n",
       "2016-01-04 06:27:06                                 0   \n",
       "2016-01-04 06:28:14                                 0   \n",
       "2016-01-04 06:29:33                                 0   \n",
       "2016-01-04 06:30:32                                 0   \n",
       "2016-01-04 06:32:06                                 0   \n",
       "2016-01-04 06:33:03                                 0   \n",
       "2016-01-04 06:34:46                                 0   \n",
       "2016-01-04 06:35:55                                 0   \n",
       "2016-01-04 06:37:19                                 0   \n",
       "2016-01-04 06:38:22                                 0   \n",
       "2016-01-04 06:39:52                                 0   \n",
       "2016-01-05 06:10:40                                 0   \n",
       "2016-01-05 06:12:06                                 0   \n",
       "2016-01-05 06:13:39                                 0   \n",
       "...                                               ...   \n",
       "2016-12-30 19:03:13                                 0   \n",
       "2016-12-30 19:05:36                                 0   \n",
       "2016-12-30 19:07:18                                 0   \n",
       "2016-12-30 19:09:31                                 0   \n",
       "2016-12-30 19:11:28                                 0   \n",
       "2016-12-30 19:13:33                                 0   \n",
       "2016-12-30 19:16:43                                 0   \n",
       "2016-12-30 19:18:48                                 0   \n",
       "2016-12-30 19:21:21                                 0   \n",
       "2016-12-30 19:22:38                                 0   \n",
       "2016-12-30 19:25:36                                 0   \n",
       "2016-12-30 19:27:03                                 0   \n",
       "2016-12-30 19:29:28                                 0   \n",
       "2016-12-30 19:31:09                                 0   \n",
       "2016-12-30 19:33:23                                 0   \n",
       "2016-12-30 19:35:12                                 0   \n",
       "2016-12-30 19:37:33                                 0   \n",
       "2016-12-30 19:39:48                                 0   \n",
       "2016-12-30 19:41:48                                 0   \n",
       "2016-12-30 19:43:38                                 0   \n",
       "2016-12-30 19:46:25                                 0   \n",
       "2016-12-30 19:49:03                                 0   \n",
       "2016-12-30 19:50:53                                 0   \n",
       "2016-12-30 19:53:13                                 0   \n",
       "2016-12-30 19:58:00                                 0   \n",
       "2016-12-30 19:59:43                                 0   \n",
       "2016-12-30 20:02:11                                 0   \n",
       "2016-12-30 20:04:08                                 0   \n",
       "2016-12-30 20:07:07                                 0   \n",
       "2016-12-30 20:09:00                                 0   \n",
       "\n",
       "                     present_weather_codes_VCTS VCSH  \n",
       "Landing_Time                                          \n",
       "2016-01-01 04:34:38                                0  \n",
       "2016-01-01 04:38:22                                0  \n",
       "2016-01-04 06:10:11                                0  \n",
       "2016-01-04 06:10:50                                0  \n",
       "2016-01-04 06:12:36                                0  \n",
       "2016-01-04 06:13:39                                0  \n",
       "2016-01-04 06:15:03                                0  \n",
       "2016-01-04 06:16:04                                0  \n",
       "2016-01-04 06:17:32                                0  \n",
       "2016-01-04 06:18:40                                0  \n",
       "2016-01-04 06:20:04                                0  \n",
       "2016-01-04 06:21:05                                0  \n",
       "2016-01-04 06:22:32                                0  \n",
       "2016-01-04 06:23:14                                0  \n",
       "2016-01-04 06:24:35                                0  \n",
       "2016-01-04 06:25:42                                0  \n",
       "2016-01-04 06:27:06                                0  \n",
       "2016-01-04 06:28:14                                0  \n",
       "2016-01-04 06:29:33                                0  \n",
       "2016-01-04 06:30:32                                0  \n",
       "2016-01-04 06:32:06                                0  \n",
       "2016-01-04 06:33:03                                0  \n",
       "2016-01-04 06:34:46                                0  \n",
       "2016-01-04 06:35:55                                0  \n",
       "2016-01-04 06:37:19                                0  \n",
       "2016-01-04 06:38:22                                0  \n",
       "2016-01-04 06:39:52                                0  \n",
       "2016-01-05 06:10:40                                0  \n",
       "2016-01-05 06:12:06                                0  \n",
       "2016-01-05 06:13:39                                0  \n",
       "...                                              ...  \n",
       "2016-12-30 19:03:13                                0  \n",
       "2016-12-30 19:05:36                                0  \n",
       "2016-12-30 19:07:18                                0  \n",
       "2016-12-30 19:09:31                                0  \n",
       "2016-12-30 19:11:28                                0  \n",
       "2016-12-30 19:13:33                                0  \n",
       "2016-12-30 19:16:43                                0  \n",
       "2016-12-30 19:18:48                                0  \n",
       "2016-12-30 19:21:21                                0  \n",
       "2016-12-30 19:22:38                                0  \n",
       "2016-12-30 19:25:36                                0  \n",
       "2016-12-30 19:27:03                                0  \n",
       "2016-12-30 19:29:28                                0  \n",
       "2016-12-30 19:31:09                                0  \n",
       "2016-12-30 19:33:23                                0  \n",
       "2016-12-30 19:35:12                                0  \n",
       "2016-12-30 19:37:33                                0  \n",
       "2016-12-30 19:39:48                                0  \n",
       "2016-12-30 19:41:48                                0  \n",
       "2016-12-30 19:43:38                                0  \n",
       "2016-12-30 19:46:25                                0  \n",
       "2016-12-30 19:49:03                                0  \n",
       "2016-12-30 19:50:53                                0  \n",
       "2016-12-30 19:53:13                                0  \n",
       "2016-12-30 19:58:00                                0  \n",
       "2016-12-30 19:59:43                                0  \n",
       "2016-12-30 20:02:11                                0  \n",
       "2016-12-30 20:04:08                                0  \n",
       "2016-12-30 20:07:07                                0  \n",
       "2016-12-30 20:09:00                                0  \n",
       "\n",
       "[17714 rows x 65 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 203226 entries, 2016-01-01 04:40:38 to 2016-12-30 23:57:49\n",
      "Data columns (total 65 columns):\n",
      "hourofday                                   203226 non-null int64\n",
      "wind_direction_degrees                      203226 non-null float64\n",
      "Wind_Speed_kt                               203226 non-null float64\n",
      "Wind_And_Gust_kt                            203226 non-null float64\n",
      "Landing_RVR_or_Vis_m                        203226 non-null float64\n",
      "temperature_c                               203226 non-null float64\n",
      "dew_point_c                                 203226 non-null float64\n",
      "relative_humidity                           203226 non-null float64\n",
      "pressure_altimiter_in                       203226 non-null float64\n",
      "RunwayTrueTrack                             203226 non-null float64\n",
      "Landing_RWY                                 203226 non-null object\n",
      "Landing_Visibilty_Class_RVR_CAT1            203226 non-null uint8\n",
      "Landing_Visibilty_Class_RVR_CAT2            203226 non-null uint8\n",
      "Landing_Visibilty_Class_RVR_CAT3            203226 non-null uint8\n",
      "Landing_Visibilty_Class_RVR_Marginal VMC    203226 non-null uint8\n",
      "Landing_Visibilty_Class_RVR_VMC             203226 non-null uint8\n",
      "present_weather_codes_+RA                   203226 non-null uint8\n",
      "present_weather_codes_+RA VCTS              203226 non-null uint8\n",
      "present_weather_codes_+SHRA                 203226 non-null uint8\n",
      "present_weather_codes_-DZ                   203226 non-null uint8\n",
      "present_weather_codes_-DZ BR                203226 non-null uint8\n",
      "present_weather_codes_-DZ FG                203226 non-null uint8\n",
      "present_weather_codes_-RA                   203226 non-null uint8\n",
      "present_weather_codes_-RA VCTS              203226 non-null uint8\n",
      "present_weather_codes_-RADZ                 203226 non-null uint8\n",
      "present_weather_codes_-RADZ BR              203226 non-null uint8\n",
      "present_weather_codes_-RADZ FG              203226 non-null uint8\n",
      "present_weather_codes_-RADZ HZ              203226 non-null uint8\n",
      "present_weather_codes_-SHRA                 203226 non-null uint8\n",
      "present_weather_codes_-SHRA TS              203226 non-null uint8\n",
      "present_weather_codes_-SHRA VCTS            203226 non-null uint8\n",
      "present_weather_codes_-SHRASN               203226 non-null uint8\n",
      "present_weather_codes_-SN                   203226 non-null uint8\n",
      "present_weather_codes_-TSRA                 203226 non-null uint8\n",
      "present_weather_codes_//////                203226 non-null uint8\n",
      "present_weather_codes_BCFG                  203226 non-null uint8\n",
      "present_weather_codes_BR                    203226 non-null uint8\n",
      "present_weather_codes_BR BCFG               203226 non-null uint8\n",
      "present_weather_codes_BR MIFG               203226 non-null uint8\n",
      "present_weather_codes_BR PRFG               203226 non-null uint8\n",
      "present_weather_codes_BR VCFG               203226 non-null uint8\n",
      "present_weather_codes_BR VCTS               203226 non-null uint8\n",
      "present_weather_codes_DZ                    203226 non-null uint8\n",
      "present_weather_codes_FG                    203226 non-null uint8\n",
      "present_weather_codes_FZFG                  203226 non-null uint8\n",
      "present_weather_codes_HZ                    203226 non-null uint8\n",
      "present_weather_codes_MIFG                  203226 non-null uint8\n",
      "present_weather_codes_MIFG BR               203226 non-null uint8\n",
      "present_weather_codes_PRFG                  203226 non-null uint8\n",
      "present_weather_codes_PRFG VCFG             203226 non-null uint8\n",
      "present_weather_codes_RA                    203226 non-null uint8\n",
      "present_weather_codes_RA HZ                 203226 non-null uint8\n",
      "present_weather_codes_RADZ                  203226 non-null uint8\n",
      "present_weather_codes_RADZ FG               203226 non-null uint8\n",
      "present_weather_codes_SG                    203226 non-null uint8\n",
      "present_weather_codes_SHRA                  203226 non-null uint8\n",
      "present_weather_codes_SHRA HZ               203226 non-null uint8\n",
      "present_weather_codes_SHRA VCTS             203226 non-null uint8\n",
      "present_weather_codes_TS                    203226 non-null uint8\n",
      "present_weather_codes_TSRA                  203226 non-null uint8\n",
      "present_weather_codes_VCFG                  203226 non-null uint8\n",
      "present_weather_codes_VCSH                  203226 non-null uint8\n",
      "present_weather_codes_VCTS                  203226 non-null uint8\n",
      "present_weather_codes_VCTS -SHRA            203226 non-null uint8\n",
      "present_weather_codes_VCTS VCSH             203226 non-null uint8\n",
      "dtypes: float64(9), int64(1), object(1), uint8(54)\n",
      "memory usage: 29.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data.dropna(axis=0, how='any', thresh=None, subset=None, inplace=True)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# One Hot Encode categorical variable output\n",
    "encoder = LabelEncoder()\n",
    "# normalize features\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_for_output = data.copy()\n",
    "X_data = data_for_output.drop('Landing_RWY', axis=1)\n",
    "X_data = scaler.fit_transform(X_data.values)\n",
    "y = data_for_output.Landing_RWY\n",
    "\n",
    "# Encode class values as integers\n",
    "# encoded_Y = encoder.fit_transform(y)\n",
    "encoded_Y = encoder.fit(y).transform(y)\n",
    "\n",
    "# convert integers to dummy variables (i.e. one hot encoded)\n",
    "y_data = np_utils.to_categorical(encoded_Y)\n",
    "\n",
    "# print(encoded_Y)\n",
    "# print(y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of train data: (142258, 64) (142258, 4)\n",
      "Shape of test data: (60968, 64) (60968, 4)\n"
     ]
    }
   ],
   "source": [
    "#Split the dataset into train and Test\n",
    "seed = 42\n",
    "test_size = 0.3\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_data, y_data, test_size=test_size, random_state=seed)\n",
    "print('Shape of train data:',X_train.shape, y_train.shape)\n",
    "print('Shape of test data:',X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "lag_steps = 1\n",
    "# reshape input to be 3D [samples, timesteps, features]\n",
    "X_train = X_train.reshape((X_train.shape[0], lag_steps, X_train.shape[1]))\n",
    "X_test = X_test.reshape((X_test.shape[0], lag_steps, X_test.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(X_train)\n",
    "# print(X_test)\n",
    "# print(y_test)\n",
    "# print(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit and evaluate a model\n",
    "def evaluate_model(trainX, trainy, testX, testy):    \n",
    "    n_timesteps, n_features, n_outputs = trainX.shape[1], trainX.shape[2], trainy.shape[1]\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(100, input_shape=(n_timesteps,n_features)))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(100, activation='relu'))\n",
    "    model.add(Dense(n_outputs, activation='softmax'))\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    # fit network\n",
    "    history = model.fit(trainX, trainy, epochs=1000, batch_size=64,validation_split=0.25, verbose=2)\n",
    "    # evaluate model\n",
    "    _, accuracy = model.evaluate(testX, testy, batch_size=64, verbose=2)    \n",
    "    return history,accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 106693 samples, validate on 35565 samples\n",
      "Epoch 1/1000\n",
      " - 14s - loss: 0.5968 - acc: 0.6225 - val_loss: 0.5594 - val_acc: 0.6433\n",
      "Epoch 2/1000\n",
      " - 12s - loss: 0.5627 - acc: 0.6367 - val_loss: 0.5595 - val_acc: 0.6269\n",
      "Epoch 3/1000\n",
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      " - 7s - loss: 0.5293 - acc: 0.6924 - val_loss: 0.5228 - val_acc: 0.7012\n",
      "Epoch 28/1000\n",
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      "Epoch 30/1000\n",
      " - 7s - loss: 0.5266 - acc: 0.6955 - val_loss: 0.5181 - val_acc: 0.7049\n",
      "Epoch 31/1000\n",
      " - 7s - loss: 0.5258 - acc: 0.6984 - val_loss: 0.5183 - val_acc: 0.7106\n",
      "Epoch 32/1000\n",
      " - 7s - loss: 0.5244 - acc: 0.6977 - val_loss: 0.5163 - val_acc: 0.7140\n",
      "Epoch 33/1000\n",
      " - 7s - loss: 0.5238 - acc: 0.6989 - val_loss: 0.5145 - val_acc: 0.7128\n",
      "Epoch 34/1000\n",
      " - 8s - loss: 0.5220 - acc: 0.7007 - val_loss: 0.5152 - val_acc: 0.7095\n",
      "Epoch 35/1000\n",
      " - 8s - loss: 0.5215 - acc: 0.7018 - val_loss: 0.5135 - val_acc: 0.7158\n",
      "Epoch 36/1000\n",
      " - 7s - loss: 0.5206 - acc: 0.7039 - val_loss: 0.5136 - val_acc: 0.7104\n",
      "Epoch 37/1000\n",
      " - 7s - loss: 0.5196 - acc: 0.7040 - val_loss: 0.5109 - val_acc: 0.7199\n",
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      " - 7s - loss: 0.5186 - acc: 0.7069 - val_loss: 0.5086 - val_acc: 0.7257\n",
      "Epoch 39/1000\n",
      " - 7s - loss: 0.5172 - acc: 0.7082 - val_loss: 0.5086 - val_acc: 0.7191\n",
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      " - 7s - loss: 0.5166 - acc: 0.7081 - val_loss: 0.5031 - val_acc: 0.7263\n",
      "Epoch 41/1000\n",
      " - 7s - loss: 0.5156 - acc: 0.7101 - val_loss: 0.5048 - val_acc: 0.7264\n",
      "Epoch 42/1000\n",
      " - 7s - loss: 0.5142 - acc: 0.7106 - val_loss: 0.5028 - val_acc: 0.7252\n",
      "Epoch 43/1000\n",
      " - 7s - loss: 0.5134 - acc: 0.7118 - val_loss: 0.5009 - val_acc: 0.7310\n",
      "Epoch 44/1000\n",
      " - 7s - loss: 0.5128 - acc: 0.7120 - val_loss: 0.5033 - val_acc: 0.7279\n",
      "Epoch 45/1000\n",
      " - 7s - loss: 0.5102 - acc: 0.7154 - val_loss: 0.4996 - val_acc: 0.7348\n",
      "Epoch 46/1000\n",
      " - 7s - loss: 0.5096 - acc: 0.7163 - val_loss: 0.4996 - val_acc: 0.7323\n",
      "Epoch 47/1000\n",
      " - 7s - loss: 0.5096 - acc: 0.7167 - val_loss: 0.4991 - val_acc: 0.7360\n",
      "Epoch 48/1000\n",
      " - 7s - loss: 0.5090 - acc: 0.7172 - val_loss: 0.4943 - val_acc: 0.7395\n",
      "Epoch 49/1000\n",
      " - 7s - loss: 0.5068 - acc: 0.7211 - val_loss: 0.4942 - val_acc: 0.7372\n",
      "Epoch 50/1000\n",
      " - 7s - loss: 0.5060 - acc: 0.7227 - val_loss: 0.4916 - val_acc: 0.7442\n",
      "Epoch 51/1000\n",
      " - 7s - loss: 0.5055 - acc: 0.7226 - val_loss: 0.4903 - val_acc: 0.7391\n",
      "Epoch 52/1000\n",
      " - 7s - loss: 0.5041 - acc: 0.7226 - val_loss: 0.4866 - val_acc: 0.7455\n",
      "Epoch 53/1000\n",
      " - 7s - loss: 0.5035 - acc: 0.7243 - val_loss: 0.4833 - val_acc: 0.7467\n",
      "Epoch 54/1000\n",
      " - 7s - loss: 0.5021 - acc: 0.7281 - val_loss: 0.4856 - val_acc: 0.7433\n",
      "Epoch 55/1000\n",
      " - 7s - loss: 0.5014 - acc: 0.7268 - val_loss: 0.4817 - val_acc: 0.7445\n",
      "Epoch 56/1000\n",
      " - 7s - loss: 0.5000 - acc: 0.7285 - val_loss: 0.4804 - val_acc: 0.7525\n",
      "Epoch 57/1000\n",
      " - 7s - loss: 0.4988 - acc: 0.7291 - val_loss: 0.4784 - val_acc: 0.7609\n",
      "Epoch 58/1000\n",
      " - 7s - loss: 0.4990 - acc: 0.7304 - val_loss: 0.4776 - val_acc: 0.7551\n",
      "Epoch 59/1000\n",
      " - 7s - loss: 0.4963 - acc: 0.7325 - val_loss: 0.4793 - val_acc: 0.7560\n",
      "Epoch 60/1000\n",
      " - 7s - loss: 0.4965 - acc: 0.7339 - val_loss: 0.4775 - val_acc: 0.7588\n",
      "Epoch 61/1000\n",
      " - 7s - loss: 0.4953 - acc: 0.7337 - val_loss: 0.4720 - val_acc: 0.7616\n",
      "Epoch 62/1000\n",
      " - 7s - loss: 0.4938 - acc: 0.7349 - val_loss: 0.4757 - val_acc: 0.7587\n",
      "Epoch 63/1000\n",
      " - 7s - loss: 0.4936 - acc: 0.7349 - val_loss: 0.4703 - val_acc: 0.7701\n",
      "Epoch 64/1000\n",
      " - 7s - loss: 0.4926 - acc: 0.7358 - val_loss: 0.4690 - val_acc: 0.7613\n",
      "Epoch 65/1000\n",
      " - 7s - loss: 0.4917 - acc: 0.7380 - val_loss: 0.4678 - val_acc: 0.7652\n",
      "Epoch 66/1000\n",
      " - 7s - loss: 0.4903 - acc: 0.7401 - val_loss: 0.4675 - val_acc: 0.7681\n",
      "Epoch 67/1000\n",
      " - 7s - loss: 0.4900 - acc: 0.7390 - val_loss: 0.4658 - val_acc: 0.7699\n",
      "Epoch 68/1000\n",
      " - 7s - loss: 0.4889 - acc: 0.7410 - val_loss: 0.4635 - val_acc: 0.7694\n",
      "Epoch 69/1000\n",
      " - 7s - loss: 0.4874 - acc: 0.7414 - val_loss: 0.4619 - val_acc: 0.7696\n",
      "Epoch 70/1000\n",
      " - 7s - loss: 0.4869 - acc: 0.7426 - val_loss: 0.4587 - val_acc: 0.7739\n",
      "Epoch 71/1000\n",
      " - 7s - loss: 0.4844 - acc: 0.7440 - val_loss: 0.4613 - val_acc: 0.7680\n",
      "Epoch 72/1000\n",
      " - 7s - loss: 0.4855 - acc: 0.7436 - val_loss: 0.4624 - val_acc: 0.7723\n",
      "Epoch 73/1000\n",
      " - 7s - loss: 0.4834 - acc: 0.7460 - val_loss: 0.4599 - val_acc: 0.7737\n",
      "Epoch 74/1000\n",
      " - 7s - loss: 0.4836 - acc: 0.7460 - val_loss: 0.4572 - val_acc: 0.7756\n",
      "Epoch 75/1000\n",
      " - 7s - loss: 0.4824 - acc: 0.7470 - val_loss: 0.4574 - val_acc: 0.7750\n",
      "Epoch 76/1000\n",
      " - 7s - loss: 0.4804 - acc: 0.7483 - val_loss: 0.4538 - val_acc: 0.7767\n",
      "Epoch 77/1000\n",
      " - 8s - loss: 0.4788 - acc: 0.7495 - val_loss: 0.4501 - val_acc: 0.7813\n",
      "Epoch 78/1000\n",
      " - 8s - loss: 0.4793 - acc: 0.7485 - val_loss: 0.4486 - val_acc: 0.7787\n",
      "Epoch 79/1000\n",
      " - 7s - loss: 0.4781 - acc: 0.7512 - val_loss: 0.4514 - val_acc: 0.7764\n",
      "Epoch 80/1000\n",
      " - 7s - loss: 0.4782 - acc: 0.7497 - val_loss: 0.4538 - val_acc: 0.7726\n",
      "Epoch 81/1000\n",
      " - 7s - loss: 0.4770 - acc: 0.7516 - val_loss: 0.4464 - val_acc: 0.7871\n",
      "Epoch 82/1000\n",
      " - 7s - loss: 0.4760 - acc: 0.7531 - val_loss: 0.4486 - val_acc: 0.7787\n",
      "Epoch 83/1000\n",
      " - 7s - loss: 0.4750 - acc: 0.7536 - val_loss: 0.4490 - val_acc: 0.7780\n",
      "Epoch 84/1000\n",
      " - 7s - loss: 0.4743 - acc: 0.7538 - val_loss: 0.4487 - val_acc: 0.7800\n",
      "Epoch 85/1000\n",
      " - 7s - loss: 0.4740 - acc: 0.7553 - val_loss: 0.4405 - val_acc: 0.7846\n",
      "Epoch 86/1000\n",
      " - 7s - loss: 0.4740 - acc: 0.7525 - val_loss: 0.4407 - val_acc: 0.7891\n",
      "Epoch 87/1000\n",
      " - 7s - loss: 0.4730 - acc: 0.7550 - val_loss: 0.4398 - val_acc: 0.7852\n",
      "Epoch 88/1000\n",
      " - 7s - loss: 0.4725 - acc: 0.7544 - val_loss: 0.4447 - val_acc: 0.7874\n",
      "Epoch 89/1000\n",
      " - 7s - loss: 0.4716 - acc: 0.7546 - val_loss: 0.4373 - val_acc: 0.7880\n",
      "Epoch 90/1000\n",
      " - 7s - loss: 0.4704 - acc: 0.7573 - val_loss: 0.4367 - val_acc: 0.7888\n",
      "Epoch 91/1000\n",
      " - 7s - loss: 0.4706 - acc: 0.7565 - val_loss: 0.4369 - val_acc: 0.7907\n",
      "Epoch 92/1000\n",
      " - 7s - loss: 0.4688 - acc: 0.7580 - val_loss: 0.4336 - val_acc: 0.7939\n",
      "Epoch 93/1000\n",
      " - 7s - loss: 0.4684 - acc: 0.7602 - val_loss: 0.4332 - val_acc: 0.7907\n",
      "Epoch 94/1000\n",
      " - 7s - loss: 0.4668 - acc: 0.7598 - val_loss: 0.4365 - val_acc: 0.7907\n",
      "Epoch 95/1000\n",
      " - 7s - loss: 0.4667 - acc: 0.7588 - val_loss: 0.4328 - val_acc: 0.7936\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 96/1000\n",
      " - 7s - loss: 0.4658 - acc: 0.7610 - val_loss: 0.4305 - val_acc: 0.7944\n",
      "Epoch 97/1000\n",
      " - 7s - loss: 0.4662 - acc: 0.7610 - val_loss: 0.4324 - val_acc: 0.7949\n",
      "Epoch 98/1000\n",
      " - 7s - loss: 0.4657 - acc: 0.7609 - val_loss: 0.4302 - val_acc: 0.7964\n",
      "Epoch 99/1000\n",
      " - 7s - loss: 0.4645 - acc: 0.7618 - val_loss: 0.4321 - val_acc: 0.7994\n",
      "Epoch 100/1000\n",
      " - 7s - loss: 0.4640 - acc: 0.7627 - val_loss: 0.4268 - val_acc: 0.7981\n",
      "Epoch 101/1000\n",
      " - 7s - loss: 0.4644 - acc: 0.7630 - val_loss: 0.4335 - val_acc: 0.7930\n",
      "Epoch 102/1000\n",
      " - 7s - loss: 0.4642 - acc: 0.7626 - val_loss: 0.4322 - val_acc: 0.7884\n",
      "Epoch 103/1000\n",
      " - 7s - loss: 0.4628 - acc: 0.7640 - val_loss: 0.4251 - val_acc: 0.7985\n",
      "Epoch 104/1000\n",
      " - 7s - loss: 0.4627 - acc: 0.7636 - val_loss: 0.4232 - val_acc: 0.8008\n",
      "Epoch 105/1000\n",
      " - 7s - loss: 0.4615 - acc: 0.7649 - val_loss: 0.4241 - val_acc: 0.7985\n",
      "Epoch 106/1000\n",
      " - 7s - loss: 0.4613 - acc: 0.7641 - val_loss: 0.4219 - val_acc: 0.8040\n",
      "Epoch 107/1000\n",
      " - 7s - loss: 0.4615 - acc: 0.7656 - val_loss: 0.4211 - val_acc: 0.8066\n",
      "Epoch 108/1000\n",
      " - 7s - loss: 0.4595 - acc: 0.7668 - val_loss: 0.4271 - val_acc: 0.8017\n",
      "Epoch 109/1000\n",
      " - 7s - loss: 0.4603 - acc: 0.7649 - val_loss: 0.4222 - val_acc: 0.8051\n",
      "Epoch 110/1000\n",
      " - 7s - loss: 0.4599 - acc: 0.7668 - val_loss: 0.4195 - val_acc: 0.8039\n",
      "Epoch 111/1000\n",
      " - 7s - loss: 0.4584 - acc: 0.7679 - val_loss: 0.4215 - val_acc: 0.8040\n",
      "Epoch 112/1000\n",
      " - 7s - loss: 0.4582 - acc: 0.7670 - val_loss: 0.4185 - val_acc: 0.8009\n",
      "Epoch 113/1000\n",
      " - 7s - loss: 0.4583 - acc: 0.7684 - val_loss: 0.4184 - val_acc: 0.8047\n",
      "Epoch 114/1000\n",
      " - 7s - loss: 0.4572 - acc: 0.7684 - val_loss: 0.4179 - val_acc: 0.8043\n",
      "Epoch 115/1000\n",
      " - 7s - loss: 0.4560 - acc: 0.7688 - val_loss: 0.4197 - val_acc: 0.8009\n",
      "Epoch 116/1000\n",
      " - 7s - loss: 0.4546 - acc: 0.7720 - val_loss: 0.4176 - val_acc: 0.8046\n",
      "Epoch 117/1000\n",
      " - 7s - loss: 0.4535 - acc: 0.7702 - val_loss: 0.4157 - val_acc: 0.8073\n",
      "Epoch 118/1000\n",
      " - 7s - loss: 0.4539 - acc: 0.7711 - val_loss: 0.4170 - val_acc: 0.8067\n",
      "Epoch 119/1000\n",
      " - 7s - loss: 0.4553 - acc: 0.7704 - val_loss: 0.4132 - val_acc: 0.8080\n",
      "Epoch 120/1000\n",
      " - 7s - loss: 0.4539 - acc: 0.7701 - val_loss: 0.4183 - val_acc: 0.8042\n",
      "Epoch 121/1000\n",
      " - 9s - loss: 0.4527 - acc: 0.7724 - val_loss: 0.4165 - val_acc: 0.8062\n",
      "Epoch 122/1000\n",
      " - 7s - loss: 0.4535 - acc: 0.7706 - val_loss: 0.4135 - val_acc: 0.8108\n",
      "Epoch 123/1000\n",
      " - 7s - loss: 0.4516 - acc: 0.7733 - val_loss: 0.4160 - val_acc: 0.8049\n",
      "Epoch 124/1000\n",
      " - 7s - loss: 0.4526 - acc: 0.7724 - val_loss: 0.4109 - val_acc: 0.8105\n",
      "Epoch 125/1000\n",
      " - 7s - loss: 0.4512 - acc: 0.7726 - val_loss: 0.4160 - val_acc: 0.8037\n",
      "Epoch 126/1000\n",
      " - 7s - loss: 0.4516 - acc: 0.7721 - val_loss: 0.4084 - val_acc: 0.8130\n",
      "Epoch 127/1000\n",
      " - 7s - loss: 0.4532 - acc: 0.7718 - val_loss: 0.4098 - val_acc: 0.8108\n",
      "Epoch 128/1000\n",
      " - 7s - loss: 0.4524 - acc: 0.7727 - val_loss: 0.4123 - val_acc: 0.8055\n",
      "Epoch 129/1000\n",
      " - 7s - loss: 0.4507 - acc: 0.7735 - val_loss: 0.4103 - val_acc: 0.8095\n",
      "Epoch 130/1000\n",
      " - 7s - loss: 0.4514 - acc: 0.7728 - val_loss: 0.4096 - val_acc: 0.8074\n",
      "Epoch 131/1000\n",
      " - 7s - loss: 0.4511 - acc: 0.7736 - val_loss: 0.4139 - val_acc: 0.8010\n",
      "Epoch 132/1000\n",
      " - 7s - loss: 0.4486 - acc: 0.7741 - val_loss: 0.4039 - val_acc: 0.8138\n",
      "Epoch 133/1000\n",
      " - 7s - loss: 0.4495 - acc: 0.7732 - val_loss: 0.4039 - val_acc: 0.8154\n",
      "Epoch 134/1000\n",
      " - 7s - loss: 0.4476 - acc: 0.7759 - val_loss: 0.4069 - val_acc: 0.8117\n",
      "Epoch 135/1000\n",
      " - 7s - loss: 0.4479 - acc: 0.7767 - val_loss: 0.4069 - val_acc: 0.8075\n",
      "Epoch 136/1000\n",
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      "Epoch 139/1000\n",
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      "Epoch 142/1000\n",
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      "Epoch 143/1000\n",
      " - 7s - loss: 0.4452 - acc: 0.7788 - val_loss: 0.3993 - val_acc: 0.8178\n",
      "Epoch 144/1000\n",
      " - 7s - loss: 0.4443 - acc: 0.7787 - val_loss: 0.4009 - val_acc: 0.8198\n",
      "Epoch 145/1000\n",
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      "Epoch 146/1000\n",
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      "Epoch 147/1000\n",
      " - 7s - loss: 0.4438 - acc: 0.7793 - val_loss: 0.3992 - val_acc: 0.8159\n",
      "Epoch 148/1000\n",
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      "Epoch 149/1000\n",
      " - 7s - loss: 0.4425 - acc: 0.7802 - val_loss: 0.3985 - val_acc: 0.8223\n",
      "Epoch 150/1000\n",
      " - 7s - loss: 0.4423 - acc: 0.7802 - val_loss: 0.3990 - val_acc: 0.8172\n",
      "Epoch 151/1000\n",
      " - 7s - loss: 0.4435 - acc: 0.7797 - val_loss: 0.3954 - val_acc: 0.8220\n",
      "Epoch 152/1000\n",
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      " - 7s - loss: 0.4356 - acc: 0.7863 - val_loss: 0.3926 - val_acc: 0.8228\n",
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      " - 7s - loss: 0.4333 - acc: 0.7875 - val_loss: 0.3854 - val_acc: 0.8219\n",
      "Epoch 181/1000\n",
      " - 7s - loss: 0.4328 - acc: 0.7886 - val_loss: 0.3855 - val_acc: 0.8265\n",
      "Epoch 182/1000\n",
      " - 7s - loss: 0.4328 - acc: 0.7869 - val_loss: 0.3854 - val_acc: 0.8265\n",
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      " - 7s - loss: 0.4324 - acc: 0.7888 - val_loss: 0.3857 - val_acc: 0.8262\n",
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      " - 7s - loss: 0.4313 - acc: 0.7902 - val_loss: 0.3885 - val_acc: 0.8252\n",
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      " - 7s - loss: 0.4327 - acc: 0.7868 - val_loss: 0.3867 - val_acc: 0.8260\n",
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      " - 7s - loss: 0.4320 - acc: 0.7887 - val_loss: 0.3827 - val_acc: 0.8304\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 191/1000\n",
      " - 7s - loss: 0.4312 - acc: 0.7889 - val_loss: 0.3872 - val_acc: 0.8284\n",
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      "Epoch 209/1000\n",
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      " - 7s - loss: 0.4268 - acc: 0.7935 - val_loss: 0.3793 - val_acc: 0.8291\n",
      "Epoch 216/1000\n",
      " - 7s - loss: 0.4262 - acc: 0.7929 - val_loss: 0.3776 - val_acc: 0.8322\n",
      "Epoch 217/1000\n",
      " - 7s - loss: 0.4252 - acc: 0.7938 - val_loss: 0.3814 - val_acc: 0.8308\n",
      "Epoch 218/1000\n",
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      "Epoch 224/1000\n",
      " - 7s - loss: 0.4245 - acc: 0.7950 - val_loss: 0.3715 - val_acc: 0.8374\n",
      "Epoch 225/1000\n",
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      " - 7s - loss: 0.4252 - acc: 0.7948 - val_loss: 0.3751 - val_acc: 0.8349\n",
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      " - 7s - loss: 0.4218 - acc: 0.7957 - val_loss: 0.3716 - val_acc: 0.8361\n",
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      " - 7s - loss: 0.4171 - acc: 0.7993 - val_loss: 0.3629 - val_acc: 0.8448\n",
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      " - 7s - loss: 0.4168 - acc: 0.7998 - val_loss: 0.3643 - val_acc: 0.8411\n",
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      " - 7s - loss: 0.4156 - acc: 0.8003 - val_loss: 0.3641 - val_acc: 0.8410\n",
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      " - 7s - loss: 0.4170 - acc: 0.7998 - val_loss: 0.3639 - val_acc: 0.8409\n",
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      " - 7s - loss: 0.4145 - acc: 0.8008 - val_loss: 0.3649 - val_acc: 0.8413\n",
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      " - 7s - loss: 0.4158 - acc: 0.8017 - val_loss: 0.3650 - val_acc: 0.8423\n",
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      " - 7s - loss: 0.4161 - acc: 0.8006 - val_loss: 0.3679 - val_acc: 0.8421\n",
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      " - 7s - loss: 0.4147 - acc: 0.8016 - val_loss: 0.3606 - val_acc: 0.8444\n",
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      " - 7s - loss: 0.4135 - acc: 0.8013 - val_loss: 0.3621 - val_acc: 0.8456\n",
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      " - 7s - loss: 0.4151 - acc: 0.8011 - val_loss: 0.3595 - val_acc: 0.8449\n",
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      " - 7s - loss: 0.4147 - acc: 0.8013 - val_loss: 0.3663 - val_acc: 0.8390\n",
      "Epoch 285/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - 7s - loss: 0.4140 - acc: 0.8036 - val_loss: 0.3669 - val_acc: 0.8382\n",
      "Epoch 286/1000\n",
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      "Epoch 289/1000\n",
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      "Epoch 290/1000\n",
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      "Epoch 291/1000\n",
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      "Epoch 292/1000\n",
      " - 7s - loss: 0.4124 - acc: 0.8024 - val_loss: 0.3601 - val_acc: 0.8438\n",
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      "Epoch 297/1000\n",
      " - 7s - loss: 0.4131 - acc: 0.8037 - val_loss: 0.3585 - val_acc: 0.8457\n",
      "Epoch 298/1000\n",
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      "Epoch 299/1000\n",
      " - 7s - loss: 0.4127 - acc: 0.8038 - val_loss: 0.3612 - val_acc: 0.8465\n",
      "Epoch 300/1000\n",
      " - 7s - loss: 0.4120 - acc: 0.8037 - val_loss: 0.3616 - val_acc: 0.8412\n",
      "Epoch 301/1000\n",
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      "Epoch 302/1000\n",
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      " - 7s - loss: 0.4035 - acc: 0.8089 - val_loss: 0.3488 - val_acc: 0.8473\n",
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      " - 7s - loss: 0.4063 - acc: 0.8070 - val_loss: 0.3522 - val_acc: 0.8445\n",
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      " - 7s - loss: 0.4054 - acc: 0.8078 - val_loss: 0.3498 - val_acc: 0.8506\n",
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      " - 7s - loss: 0.4046 - acc: 0.8100 - val_loss: 0.3468 - val_acc: 0.8514\n",
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      " - 7s - loss: 0.4033 - acc: 0.8102 - val_loss: 0.3459 - val_acc: 0.8545\n",
      "Epoch 367/1000\n",
      " - 7s - loss: 0.4049 - acc: 0.8094 - val_loss: 0.3483 - val_acc: 0.8509\n",
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      " - 7s - loss: 0.4045 - acc: 0.8077 - val_loss: 0.3530 - val_acc: 0.8466\n",
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      " - 7s - loss: 0.4035 - acc: 0.8103 - val_loss: 0.3473 - val_acc: 0.8524\n",
      "Epoch 370/1000\n",
      " - 7s - loss: 0.4043 - acc: 0.8095 - val_loss: 0.3480 - val_acc: 0.8534\n",
      "Epoch 371/1000\n",
      " - 7s - loss: 0.4055 - acc: 0.8087 - val_loss: 0.3506 - val_acc: 0.8511\n",
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      " - 7s - loss: 0.4027 - acc: 0.8106 - val_loss: 0.3464 - val_acc: 0.8537\n",
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      " - 7s - loss: 0.4028 - acc: 0.8098 - val_loss: 0.3441 - val_acc: 0.8564\n",
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      " - 7s - loss: 0.4017 - acc: 0.8108 - val_loss: 0.3455 - val_acc: 0.8536\n",
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      " - 7s - loss: 0.4024 - acc: 0.8107 - val_loss: 0.3441 - val_acc: 0.8569\n",
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      " - 7s - loss: 0.4043 - acc: 0.8101 - val_loss: 0.3445 - val_acc: 0.8562\n",
      "Epoch 377/1000\n",
      " - 7s - loss: 0.4048 - acc: 0.8099 - val_loss: 0.3462 - val_acc: 0.8538\n",
      "Epoch 378/1000\n",
      " - 7s - loss: 0.4028 - acc: 0.8101 - val_loss: 0.3445 - val_acc: 0.8539\n",
      "Epoch 379/1000\n",
      " - 7s - loss: 0.4038 - acc: 0.8100 - val_loss: 0.3462 - val_acc: 0.8520\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 380/1000\n",
      " - 7s - loss: 0.4036 - acc: 0.8090 - val_loss: 0.3487 - val_acc: 0.8540\n",
      "Epoch 381/1000\n",
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      "Epoch 382/1000\n",
      " - 7s - loss: 0.4030 - acc: 0.8097 - val_loss: 0.3456 - val_acc: 0.8555\n",
      "Epoch 383/1000\n",
      " - 8s - loss: 0.4006 - acc: 0.8116 - val_loss: 0.3464 - val_acc: 0.8511\n",
      "Epoch 384/1000\n",
      " - 8s - loss: 0.4018 - acc: 0.8106 - val_loss: 0.3522 - val_acc: 0.8491\n",
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      " - 7s - loss: 0.4019 - acc: 0.8121 - val_loss: 0.3461 - val_acc: 0.8524\n",
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      "Epoch 387/1000\n",
      " - 7s - loss: 0.4022 - acc: 0.8123 - val_loss: 0.3451 - val_acc: 0.8550\n",
      "Epoch 388/1000\n",
      " - 7s - loss: 0.4037 - acc: 0.8108 - val_loss: 0.3462 - val_acc: 0.8567\n",
      "Epoch 389/1000\n",
      " - 7s - loss: 0.4018 - acc: 0.8105 - val_loss: 0.3469 - val_acc: 0.8520\n",
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      "Epoch 397/1000\n",
      " - 7s - loss: 0.3998 - acc: 0.8111 - val_loss: 0.3481 - val_acc: 0.8483\n",
      "Epoch 398/1000\n",
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      " - 7s - loss: 0.4005 - acc: 0.8112 - val_loss: 0.3456 - val_acc: 0.8529\n",
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      " - 7s - loss: 0.3990 - acc: 0.8128 - val_loss: 0.3439 - val_acc: 0.8530\n",
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      "Epoch 447/1000\n",
      " - 7s - loss: 0.3969 - acc: 0.8147 - val_loss: 0.3373 - val_acc: 0.8590\n",
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      " - 7s - loss: 0.3955 - acc: 0.8155 - val_loss: 0.3363 - val_acc: 0.8614\n",
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      "Epoch 451/1000\n",
      " - 7s - loss: 0.3967 - acc: 0.8156 - val_loss: 0.3401 - val_acc: 0.8570\n",
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      " - 7s - loss: 0.3945 - acc: 0.8164 - val_loss: 0.3381 - val_acc: 0.8599\n",
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      " - 7s - loss: 0.3950 - acc: 0.8161 - val_loss: 0.3358 - val_acc: 0.8596\n",
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      " - 7s - loss: 0.3942 - acc: 0.8179 - val_loss: 0.3402 - val_acc: 0.8592\n",
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      " - 7s - loss: 0.3959 - acc: 0.8149 - val_loss: 0.3351 - val_acc: 0.8614\n",
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      " - 7s - loss: 0.3955 - acc: 0.8160 - val_loss: 0.3405 - val_acc: 0.8581\n",
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      " - 7s - loss: 0.3919 - acc: 0.8186 - val_loss: 0.3356 - val_acc: 0.8621\n",
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      " - 7s - loss: 0.3943 - acc: 0.8172 - val_loss: 0.3367 - val_acc: 0.8619\n",
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      " - 7s - loss: 0.3939 - acc: 0.8178 - val_loss: 0.3356 - val_acc: 0.8597\n",
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      " - 7s - loss: 0.3934 - acc: 0.8178 - val_loss: 0.3388 - val_acc: 0.8572\n",
      "Epoch 474/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - 7s - loss: 0.3937 - acc: 0.8173 - val_loss: 0.3350 - val_acc: 0.8603\n",
      "Epoch 475/1000\n",
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      " - 7s - loss: 0.3910 - acc: 0.8185 - val_loss: 0.3350 - val_acc: 0.8646\n",
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      " - 7s - loss: 0.3924 - acc: 0.8177 - val_loss: 0.3328 - val_acc: 0.8632\n",
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      " - 7s - loss: 0.3912 - acc: 0.8180 - val_loss: 0.3362 - val_acc: 0.8608\n",
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      " - 7s - loss: 0.3912 - acc: 0.8181 - val_loss: 0.3355 - val_acc: 0.8592\n",
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      " - 7s - loss: 0.3933 - acc: 0.8178 - val_loss: 0.3368 - val_acc: 0.8597\n",
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      " - 7s - loss: 0.3927 - acc: 0.8180 - val_loss: 0.3345 - val_acc: 0.8608\n",
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      " - 7s - loss: 0.3910 - acc: 0.8186 - val_loss: 0.3316 - val_acc: 0.8607\n",
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      " - 7s - loss: 0.3924 - acc: 0.8171 - val_loss: 0.3328 - val_acc: 0.8657\n",
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      " - 7s - loss: 0.3908 - acc: 0.8185 - val_loss: 0.3327 - val_acc: 0.8614\n",
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      " - 7s - loss: 0.3906 - acc: 0.8192 - val_loss: 0.3346 - val_acc: 0.8614\n",
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      " - 7s - loss: 0.3909 - acc: 0.8193 - val_loss: 0.3329 - val_acc: 0.8608\n",
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      " - 7s - loss: 0.3911 - acc: 0.8193 - val_loss: 0.3330 - val_acc: 0.8635\n",
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      " - 7s - loss: 0.3913 - acc: 0.8175 - val_loss: 0.3336 - val_acc: 0.8625\n",
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      " - 7s - loss: 0.3911 - acc: 0.8179 - val_loss: 0.3318 - val_acc: 0.8617\n",
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      " - 7s - loss: 0.3907 - acc: 0.8186 - val_loss: 0.3325 - val_acc: 0.8595\n",
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      " - 7s - loss: 0.3908 - acc: 0.8196 - val_loss: 0.3325 - val_acc: 0.8622\n",
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      " - 7s - loss: 0.3908 - acc: 0.8182 - val_loss: 0.3322 - val_acc: 0.8642\n",
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      " - 7s - loss: 0.3867 - acc: 0.8210 - val_loss: 0.3261 - val_acc: 0.8663\n",
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      " - 7s - loss: 0.3874 - acc: 0.8205 - val_loss: 0.3306 - val_acc: 0.8625\n",
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      " - 7s - loss: 0.3874 - acc: 0.8209 - val_loss: 0.3298 - val_acc: 0.8641\n",
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      " - 7s - loss: 0.3885 - acc: 0.8218 - val_loss: 0.3325 - val_acc: 0.8626\n",
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      " - 7s - loss: 0.3896 - acc: 0.8198 - val_loss: 0.3319 - val_acc: 0.8614\n",
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      " - 7s - loss: 0.3871 - acc: 0.8207 - val_loss: 0.3318 - val_acc: 0.8627\n",
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      " - 8s - loss: 0.3854 - acc: 0.8241 - val_loss: 0.3280 - val_acc: 0.8645\n",
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      " - 8s - loss: 0.3870 - acc: 0.8215 - val_loss: 0.3316 - val_acc: 0.8632\n",
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      " - 7s - loss: 0.3874 - acc: 0.8213 - val_loss: 0.3271 - val_acc: 0.8659\n",
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      " - 7s - loss: 0.3867 - acc: 0.8212 - val_loss: 0.3288 - val_acc: 0.8670\n",
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      " - 7s - loss: 0.3875 - acc: 0.8208 - val_loss: 0.3287 - val_acc: 0.8634\n",
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      " - 7s - loss: 0.3884 - acc: 0.8198 - val_loss: 0.3301 - val_acc: 0.8642\n",
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      " - 7s - loss: 0.3882 - acc: 0.8205 - val_loss: 0.3281 - val_acc: 0.8648\n",
      "Epoch 566/1000\n",
      " - 7s - loss: 0.3880 - acc: 0.8204 - val_loss: 0.3312 - val_acc: 0.8668\n",
      "Epoch 567/1000\n",
      " - 7s - loss: 0.3879 - acc: 0.8227 - val_loss: 0.3262 - val_acc: 0.8671\n",
      "Epoch 568/1000\n",
      " - 7s - loss: 0.3871 - acc: 0.8205 - val_loss: 0.3276 - val_acc: 0.8659\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 569/1000\n",
      " - 7s - loss: 0.3874 - acc: 0.8216 - val_loss: 0.3280 - val_acc: 0.8660\n",
      "Epoch 570/1000\n",
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      "Epoch 571/1000\n",
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      "Epoch 572/1000\n",
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      "Epoch 573/1000\n",
      " - 7s - loss: 0.3854 - acc: 0.8221 - val_loss: 0.3254 - val_acc: 0.8669\n",
      "Epoch 574/1000\n",
      " - 7s - loss: 0.3869 - acc: 0.8221 - val_loss: 0.3301 - val_acc: 0.8621\n",
      "Epoch 575/1000\n",
      " - 7s - loss: 0.3879 - acc: 0.8208 - val_loss: 0.3302 - val_acc: 0.8641\n",
      "Epoch 576/1000\n",
      " - 7s - loss: 0.3854 - acc: 0.8225 - val_loss: 0.3273 - val_acc: 0.8677\n",
      "Epoch 577/1000\n",
      " - 7s - loss: 0.3869 - acc: 0.8213 - val_loss: 0.3317 - val_acc: 0.8623\n",
      "Epoch 578/1000\n",
      " - 7s - loss: 0.3859 - acc: 0.8223 - val_loss: 0.3261 - val_acc: 0.8679\n",
      "Epoch 579/1000\n",
      " - 7s - loss: 0.3873 - acc: 0.8210 - val_loss: 0.3278 - val_acc: 0.8636\n",
      "Epoch 580/1000\n",
      " - 7s - loss: 0.3847 - acc: 0.8212 - val_loss: 0.3271 - val_acc: 0.8647\n",
      "Epoch 581/1000\n",
      " - 7s - loss: 0.3850 - acc: 0.8217 - val_loss: 0.3268 - val_acc: 0.8645\n",
      "Epoch 582/1000\n",
      " - 7s - loss: 0.3864 - acc: 0.8229 - val_loss: 0.3293 - val_acc: 0.8646\n",
      "Epoch 583/1000\n",
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      "Epoch 584/1000\n",
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      "Epoch 585/1000\n",
      " - 7s - loss: 0.3848 - acc: 0.8232 - val_loss: 0.3278 - val_acc: 0.8664\n",
      "Epoch 586/1000\n",
      " - 7s - loss: 0.3863 - acc: 0.8231 - val_loss: 0.3284 - val_acc: 0.8641\n",
      "Epoch 587/1000\n",
      " - 7s - loss: 0.3848 - acc: 0.8235 - val_loss: 0.3307 - val_acc: 0.8643\n",
      "Epoch 588/1000\n",
      " - 7s - loss: 0.3857 - acc: 0.8218 - val_loss: 0.3269 - val_acc: 0.8670\n",
      "Epoch 589/1000\n",
      " - 7s - loss: 0.3858 - acc: 0.8220 - val_loss: 0.3281 - val_acc: 0.8652\n",
      "Epoch 590/1000\n",
      " - 7s - loss: 0.3850 - acc: 0.8231 - val_loss: 0.3286 - val_acc: 0.8651\n",
      "Epoch 591/1000\n",
      " - 7s - loss: 0.3854 - acc: 0.8231 - val_loss: 0.3303 - val_acc: 0.8629\n",
      "Epoch 592/1000\n",
      " - 7s - loss: 0.3856 - acc: 0.8230 - val_loss: 0.3303 - val_acc: 0.8626\n",
      "Epoch 593/1000\n",
      " - 7s - loss: 0.3862 - acc: 0.8207 - val_loss: 0.3265 - val_acc: 0.8653\n",
      "Epoch 594/1000\n",
      " - 7s - loss: 0.3851 - acc: 0.8224 - val_loss: 0.3296 - val_acc: 0.8624\n",
      "Epoch 595/1000\n",
      " - 7s - loss: 0.3857 - acc: 0.8212 - val_loss: 0.3252 - val_acc: 0.8658\n",
      "Epoch 596/1000\n",
      " - 7s - loss: 0.3867 - acc: 0.8218 - val_loss: 0.3317 - val_acc: 0.8637\n",
      "Epoch 597/1000\n",
      " - 7s - loss: 0.3861 - acc: 0.8208 - val_loss: 0.3263 - val_acc: 0.8675\n",
      "Epoch 598/1000\n",
      " - 7s - loss: 0.3862 - acc: 0.8216 - val_loss: 0.3274 - val_acc: 0.8677\n",
      "Epoch 599/1000\n",
      " - 7s - loss: 0.3837 - acc: 0.8238 - val_loss: 0.3286 - val_acc: 0.8649\n",
      "Epoch 600/1000\n",
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      " - 7s - loss: 0.3822 - acc: 0.8245 - val_loss: 0.3259 - val_acc: 0.8663\n",
      "Epoch 618/1000\n",
      " - 7s - loss: 0.3847 - acc: 0.8220 - val_loss: 0.3258 - val_acc: 0.8671\n",
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      " - 7s - loss: 0.3847 - acc: 0.8222 - val_loss: 0.3245 - val_acc: 0.8688\n",
      "Epoch 621/1000\n",
      " - 7s - loss: 0.3822 - acc: 0.8230 - val_loss: 0.3250 - val_acc: 0.8677\n",
      "Epoch 622/1000\n",
      " - 7s - loss: 0.3837 - acc: 0.8237 - val_loss: 0.3283 - val_acc: 0.8648\n",
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      " - 7s - loss: 0.3829 - acc: 0.8227 - val_loss: 0.3232 - val_acc: 0.8691\n",
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      " - 7s - loss: 0.3833 - acc: 0.8235 - val_loss: 0.3232 - val_acc: 0.8670\n",
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      " - 7s - loss: 0.3821 - acc: 0.8239 - val_loss: 0.3252 - val_acc: 0.8695\n",
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      " - 7s - loss: 0.3822 - acc: 0.8243 - val_loss: 0.3229 - val_acc: 0.8679\n",
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      "Epoch 628/1000\n",
      " - 7s - loss: 0.3827 - acc: 0.8240 - val_loss: 0.3221 - val_acc: 0.8677\n",
      "Epoch 629/1000\n",
      " - 7s - loss: 0.3836 - acc: 0.8240 - val_loss: 0.3283 - val_acc: 0.8664\n",
      "Epoch 630/1000\n",
      " - 7s - loss: 0.3818 - acc: 0.8248 - val_loss: 0.3241 - val_acc: 0.8665\n",
      "Epoch 631/1000\n",
      " - 7s - loss: 0.3848 - acc: 0.8238 - val_loss: 0.3217 - val_acc: 0.8668\n",
      "Epoch 632/1000\n",
      " - 7s - loss: 0.3835 - acc: 0.8236 - val_loss: 0.3231 - val_acc: 0.8686\n",
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      " - 7s - loss: 0.3811 - acc: 0.8237 - val_loss: 0.3261 - val_acc: 0.8658\n",
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      " - 7s - loss: 0.3833 - acc: 0.8244 - val_loss: 0.3256 - val_acc: 0.8675\n",
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      " - 7s - loss: 0.3836 - acc: 0.8235 - val_loss: 0.3229 - val_acc: 0.8692\n",
      "Epoch 638/1000\n",
      " - 7s - loss: 0.3832 - acc: 0.8233 - val_loss: 0.3269 - val_acc: 0.8669\n",
      "Epoch 639/1000\n",
      " - 7s - loss: 0.3803 - acc: 0.8265 - val_loss: 0.3275 - val_acc: 0.8653\n",
      "Epoch 640/1000\n",
      " - 7s - loss: 0.3821 - acc: 0.8231 - val_loss: 0.3272 - val_acc: 0.8641\n",
      "Epoch 641/1000\n",
      " - 7s - loss: 0.3832 - acc: 0.8236 - val_loss: 0.3245 - val_acc: 0.8679\n",
      "Epoch 642/1000\n",
      " - 7s - loss: 0.3834 - acc: 0.8229 - val_loss: 0.3243 - val_acc: 0.8682\n",
      "Epoch 643/1000\n",
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      "Epoch 644/1000\n",
      " - 7s - loss: 0.3814 - acc: 0.8244 - val_loss: 0.3247 - val_acc: 0.8675\n",
      "Epoch 645/1000\n",
      " - 7s - loss: 0.3837 - acc: 0.8240 - val_loss: 0.3265 - val_acc: 0.8655\n",
      "Epoch 646/1000\n",
      " - 9s - loss: 0.3810 - acc: 0.8254 - val_loss: 0.3224 - val_acc: 0.8657\n",
      "Epoch 647/1000\n",
      " - 7s - loss: 0.3820 - acc: 0.8246 - val_loss: 0.3262 - val_acc: 0.8671\n",
      "Epoch 648/1000\n",
      " - 7s - loss: 0.3844 - acc: 0.8224 - val_loss: 0.3217 - val_acc: 0.8675\n",
      "Epoch 649/1000\n",
      " - 7s - loss: 0.3824 - acc: 0.8247 - val_loss: 0.3253 - val_acc: 0.8659\n",
      "Epoch 650/1000\n",
      " - 7s - loss: 0.3812 - acc: 0.8255 - val_loss: 0.3239 - val_acc: 0.8677\n",
      "Epoch 651/1000\n",
      " - 7s - loss: 0.3847 - acc: 0.8235 - val_loss: 0.3247 - val_acc: 0.8668\n",
      "Epoch 652/1000\n",
      " - 7s - loss: 0.3818 - acc: 0.8241 - val_loss: 0.3230 - val_acc: 0.8671\n",
      "Epoch 653/1000\n",
      " - 7s - loss: 0.3809 - acc: 0.8251 - val_loss: 0.3261 - val_acc: 0.8648\n",
      "Epoch 654/1000\n",
      " - 7s - loss: 0.3810 - acc: 0.8250 - val_loss: 0.3231 - val_acc: 0.8697\n",
      "Epoch 655/1000\n",
      " - 7s - loss: 0.3809 - acc: 0.8235 - val_loss: 0.3279 - val_acc: 0.8666\n",
      "Epoch 656/1000\n",
      " - 7s - loss: 0.3832 - acc: 0.8246 - val_loss: 0.3214 - val_acc: 0.8706\n",
      "Epoch 657/1000\n",
      " - 7s - loss: 0.3819 - acc: 0.8252 - val_loss: 0.3291 - val_acc: 0.8622\n",
      "Epoch 658/1000\n",
      " - 7s - loss: 0.3807 - acc: 0.8254 - val_loss: 0.3251 - val_acc: 0.8681\n",
      "Epoch 659/1000\n",
      " - 7s - loss: 0.3824 - acc: 0.8239 - val_loss: 0.3202 - val_acc: 0.8716\n",
      "Epoch 660/1000\n",
      " - 7s - loss: 0.3815 - acc: 0.8261 - val_loss: 0.3234 - val_acc: 0.8678\n",
      "Epoch 661/1000\n",
      " - 7s - loss: 0.3803 - acc: 0.8255 - val_loss: 0.3215 - val_acc: 0.8710\n",
      "Epoch 662/1000\n",
      " - 7s - loss: 0.3820 - acc: 0.8239 - val_loss: 0.3249 - val_acc: 0.8685\n",
      "Epoch 663/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - 7s - loss: 0.3818 - acc: 0.8246 - val_loss: 0.3253 - val_acc: 0.8657\n",
      "Epoch 664/1000\n",
      " - 7s - loss: 0.3802 - acc: 0.8265 - val_loss: 0.3226 - val_acc: 0.8689\n",
      "Epoch 665/1000\n",
      " - 7s - loss: 0.3798 - acc: 0.8258 - val_loss: 0.3233 - val_acc: 0.8677\n",
      "Epoch 666/1000\n",
      " - 7s - loss: 0.3822 - acc: 0.8252 - val_loss: 0.3236 - val_acc: 0.8704\n",
      "Epoch 667/1000\n",
      " - 7s - loss: 0.3803 - acc: 0.8247 - val_loss: 0.3237 - val_acc: 0.8699\n",
      "Epoch 668/1000\n",
      " - 7s - loss: 0.3816 - acc: 0.8238 - val_loss: 0.3233 - val_acc: 0.8676\n",
      "Epoch 669/1000\n",
      " - 7s - loss: 0.3806 - acc: 0.8262 - val_loss: 0.3226 - val_acc: 0.8704\n",
      "Epoch 670/1000\n",
      " - 7s - loss: 0.3802 - acc: 0.8260 - val_loss: 0.3240 - val_acc: 0.8653\n",
      "Epoch 671/1000\n",
      " - 7s - loss: 0.3833 - acc: 0.8235 - val_loss: 0.3242 - val_acc: 0.8673\n",
      "Epoch 672/1000\n",
      " - 7s - loss: 0.3810 - acc: 0.8241 - val_loss: 0.3238 - val_acc: 0.8680\n",
      "Epoch 673/1000\n",
      " - 7s - loss: 0.3804 - acc: 0.8256 - val_loss: 0.3233 - val_acc: 0.8702\n",
      "Epoch 674/1000\n",
      " - 7s - loss: 0.3818 - acc: 0.8251 - val_loss: 0.3218 - val_acc: 0.8709\n",
      "Epoch 675/1000\n",
      " - 7s - loss: 0.3810 - acc: 0.8241 - val_loss: 0.3215 - val_acc: 0.8673\n",
      "Epoch 676/1000\n",
      " - 7s - loss: 0.3812 - acc: 0.8254 - val_loss: 0.3216 - val_acc: 0.8667\n",
      "Epoch 677/1000\n",
      " - 7s - loss: 0.3802 - acc: 0.8262 - val_loss: 0.3248 - val_acc: 0.8647\n",
      "Epoch 678/1000\n",
      " - 7s - loss: 0.3809 - acc: 0.8246 - val_loss: 0.3207 - val_acc: 0.8672\n",
      "Epoch 679/1000\n",
      " - 7s - loss: 0.3810 - acc: 0.8265 - val_loss: 0.3213 - val_acc: 0.8687\n",
      "Epoch 680/1000\n",
      " - 7s - loss: 0.3812 - acc: 0.8261 - val_loss: 0.3200 - val_acc: 0.8689\n",
      "Epoch 681/1000\n",
      " - 7s - loss: 0.3804 - acc: 0.8253 - val_loss: 0.3203 - val_acc: 0.8690\n",
      "Epoch 682/1000\n",
      " - 7s - loss: 0.3822 - acc: 0.8248 - val_loss: 0.3214 - val_acc: 0.8695\n",
      "Epoch 683/1000\n",
      " - 7s - loss: 0.3826 - acc: 0.8253 - val_loss: 0.3236 - val_acc: 0.8659\n",
      "Epoch 684/1000\n",
      " - 7s - loss: 0.3827 - acc: 0.8238 - val_loss: 0.3254 - val_acc: 0.8660\n",
      "Epoch 685/1000\n",
      " - 7s - loss: 0.3794 - acc: 0.8273 - val_loss: 0.3225 - val_acc: 0.8670\n",
      "Epoch 686/1000\n",
      " - 7s - loss: 0.3828 - acc: 0.8245 - val_loss: 0.3221 - val_acc: 0.8676\n",
      "Epoch 687/1000\n",
      " - 7s - loss: 0.3798 - acc: 0.8257 - val_loss: 0.3247 - val_acc: 0.8668\n",
      "Epoch 688/1000\n",
      " - 7s - loss: 0.3816 - acc: 0.8259 - val_loss: 0.3227 - val_acc: 0.8691\n",
      "Epoch 689/1000\n",
      " - 7s - loss: 0.3798 - acc: 0.8267 - val_loss: 0.3224 - val_acc: 0.8649\n",
      "Epoch 690/1000\n",
      " - 9s - loss: 0.3809 - acc: 0.8255 - val_loss: 0.3207 - val_acc: 0.8688\n",
      "Epoch 691/1000\n",
      " - 8s - loss: 0.3797 - acc: 0.8265 - val_loss: 0.3268 - val_acc: 0.8672\n",
      "Epoch 692/1000\n",
      " - 7s - loss: 0.3814 - acc: 0.8247 - val_loss: 0.3211 - val_acc: 0.8695\n",
      "Epoch 693/1000\n",
      " - 7s - loss: 0.3809 - acc: 0.8253 - val_loss: 0.3197 - val_acc: 0.8692\n",
      "Epoch 694/1000\n",
      " - 7s - loss: 0.3801 - acc: 0.8273 - val_loss: 0.3212 - val_acc: 0.8689\n",
      "Epoch 695/1000\n",
      " - 7s - loss: 0.3830 - acc: 0.8240 - val_loss: 0.3247 - val_acc: 0.8671\n",
      "Epoch 696/1000\n",
      " - 7s - loss: 0.3796 - acc: 0.8262 - val_loss: 0.3209 - val_acc: 0.8684\n",
      "Epoch 697/1000\n",
      " - 7s - loss: 0.3790 - acc: 0.8263 - val_loss: 0.3191 - val_acc: 0.8730\n",
      "Epoch 698/1000\n",
      " - 7s - loss: 0.3812 - acc: 0.8248 - val_loss: 0.3231 - val_acc: 0.8686\n",
      "Epoch 699/1000\n",
      " - 7s - loss: 0.3794 - acc: 0.8252 - val_loss: 0.3241 - val_acc: 0.8663\n",
      "Epoch 700/1000\n",
      " - 7s - loss: 0.3787 - acc: 0.8271 - val_loss: 0.3196 - val_acc: 0.8681\n",
      "Epoch 701/1000\n",
      " - 7s - loss: 0.3804 - acc: 0.8260 - val_loss: 0.3205 - val_acc: 0.8694\n",
      "Epoch 702/1000\n",
      " - 7s - loss: 0.3793 - acc: 0.8266 - val_loss: 0.3195 - val_acc: 0.8684\n",
      "Epoch 703/1000\n",
      " - 7s - loss: 0.3782 - acc: 0.8262 - val_loss: 0.3219 - val_acc: 0.8676\n",
      "Epoch 704/1000\n",
      " - 7s - loss: 0.3787 - acc: 0.8262 - val_loss: 0.3224 - val_acc: 0.8671\n",
      "Epoch 705/1000\n",
      " - 7s - loss: 0.3791 - acc: 0.8257 - val_loss: 0.3224 - val_acc: 0.8673\n",
      "Epoch 706/1000\n",
      " - 7s - loss: 0.3777 - acc: 0.8267 - val_loss: 0.3169 - val_acc: 0.8734\n",
      "Epoch 707/1000\n",
      " - 7s - loss: 0.3785 - acc: 0.8269 - val_loss: 0.3214 - val_acc: 0.8682\n",
      "Epoch 708/1000\n",
      " - 7s - loss: 0.3791 - acc: 0.8259 - val_loss: 0.3191 - val_acc: 0.8699\n",
      "Epoch 709/1000\n",
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      "Epoch 713/1000\n",
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      "Epoch 714/1000\n",
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      "Epoch 715/1000\n",
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      "Epoch 716/1000\n",
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      "Epoch 717/1000\n",
      " - 7s - loss: 0.3801 - acc: 0.8256 - val_loss: 0.3195 - val_acc: 0.8702\n",
      "Epoch 718/1000\n",
      " - 7s - loss: 0.3803 - acc: 0.8265 - val_loss: 0.3213 - val_acc: 0.8687\n",
      "Epoch 719/1000\n",
      " - 7s - loss: 0.3774 - acc: 0.8278 - val_loss: 0.3228 - val_acc: 0.8657\n",
      "Epoch 720/1000\n",
      " - 7s - loss: 0.3820 - acc: 0.8246 - val_loss: 0.3190 - val_acc: 0.8691\n",
      "Epoch 721/1000\n",
      " - 7s - loss: 0.3776 - acc: 0.8283 - val_loss: 0.3177 - val_acc: 0.8696\n",
      "Epoch 722/1000\n",
      " - 7s - loss: 0.3787 - acc: 0.8270 - val_loss: 0.3199 - val_acc: 0.8681\n",
      "Epoch 723/1000\n",
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      "Epoch 724/1000\n",
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      "Epoch 725/1000\n",
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      "Epoch 726/1000\n",
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      "Epoch 727/1000\n",
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      "Epoch 728/1000\n",
      " - 7s - loss: 0.3799 - acc: 0.8265 - val_loss: 0.3180 - val_acc: 0.8715\n",
      "Epoch 729/1000\n",
      " - 7s - loss: 0.3772 - acc: 0.8273 - val_loss: 0.3179 - val_acc: 0.8722\n",
      "Epoch 730/1000\n",
      " - 7s - loss: 0.3787 - acc: 0.8275 - val_loss: 0.3221 - val_acc: 0.8681\n",
      "Epoch 731/1000\n",
      " - 7s - loss: 0.3775 - acc: 0.8273 - val_loss: 0.3166 - val_acc: 0.8729\n",
      "Epoch 732/1000\n",
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      "Epoch 733/1000\n",
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      "Epoch 734/1000\n",
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      "Epoch 735/1000\n",
      " - 8s - loss: 0.3779 - acc: 0.8267 - val_loss: 0.3211 - val_acc: 0.8684\n",
      "Epoch 736/1000\n",
      " - 7s - loss: 0.3759 - acc: 0.8288 - val_loss: 0.3176 - val_acc: 0.8718\n",
      "Epoch 737/1000\n",
      " - 7s - loss: 0.3789 - acc: 0.8278 - val_loss: 0.3202 - val_acc: 0.8713\n",
      "Epoch 738/1000\n",
      " - 7s - loss: 0.3791 - acc: 0.8253 - val_loss: 0.3204 - val_acc: 0.8695\n",
      "Epoch 739/1000\n",
      " - 7s - loss: 0.3802 - acc: 0.8262 - val_loss: 0.3233 - val_acc: 0.8677\n",
      "Epoch 740/1000\n",
      " - 7s - loss: 0.3785 - acc: 0.8278 - val_loss: 0.3200 - val_acc: 0.8686\n",
      "Epoch 741/1000\n",
      " - 7s - loss: 0.3762 - acc: 0.8268 - val_loss: 0.3175 - val_acc: 0.8717\n",
      "Epoch 742/1000\n",
      " - 7s - loss: 0.3774 - acc: 0.8276 - val_loss: 0.3179 - val_acc: 0.8703\n",
      "Epoch 743/1000\n",
      " - 7s - loss: 0.3754 - acc: 0.8290 - val_loss: 0.3199 - val_acc: 0.8693\n",
      "Epoch 744/1000\n",
      " - 7s - loss: 0.3756 - acc: 0.8290 - val_loss: 0.3161 - val_acc: 0.8736\n",
      "Epoch 745/1000\n",
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      "Epoch 746/1000\n",
      " - 7s - loss: 0.3786 - acc: 0.8269 - val_loss: 0.3186 - val_acc: 0.8703\n",
      "Epoch 747/1000\n",
      " - 7s - loss: 0.3773 - acc: 0.8280 - val_loss: 0.3158 - val_acc: 0.8729\n",
      "Epoch 748/1000\n",
      " - 7s - loss: 0.3787 - acc: 0.8260 - val_loss: 0.3208 - val_acc: 0.8700\n",
      "Epoch 749/1000\n",
      " - 7s - loss: 0.3773 - acc: 0.8280 - val_loss: 0.3199 - val_acc: 0.8694\n",
      "Epoch 750/1000\n",
      " - 7s - loss: 0.3787 - acc: 0.8273 - val_loss: 0.3178 - val_acc: 0.8718\n",
      "Epoch 751/1000\n",
      " - 7s - loss: 0.3758 - acc: 0.8279 - val_loss: 0.3173 - val_acc: 0.8723\n",
      "Epoch 752/1000\n",
      " - 7s - loss: 0.3778 - acc: 0.8268 - val_loss: 0.3177 - val_acc: 0.8699\n",
      "Epoch 753/1000\n",
      " - 7s - loss: 0.3765 - acc: 0.8285 - val_loss: 0.3168 - val_acc: 0.8712\n",
      "Epoch 754/1000\n",
      " - 7s - loss: 0.3769 - acc: 0.8280 - val_loss: 0.3150 - val_acc: 0.8743\n",
      "Epoch 755/1000\n",
      " - 7s - loss: 0.3758 - acc: 0.8291 - val_loss: 0.3174 - val_acc: 0.8737\n",
      "Epoch 756/1000\n",
      " - 7s - loss: 0.3778 - acc: 0.8280 - val_loss: 0.3205 - val_acc: 0.8698\n",
      "Epoch 757/1000\n",
      " - 7s - loss: 0.3773 - acc: 0.8272 - val_loss: 0.3200 - val_acc: 0.8698\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 758/1000\n",
      " - 7s - loss: 0.3780 - acc: 0.8269 - val_loss: 0.3210 - val_acc: 0.8709\n",
      "Epoch 759/1000\n",
      " - 7s - loss: 0.3772 - acc: 0.8277 - val_loss: 0.3198 - val_acc: 0.8693\n",
      "Epoch 760/1000\n",
      " - 7s - loss: 0.3773 - acc: 0.8277 - val_loss: 0.3173 - val_acc: 0.8724\n",
      "Epoch 761/1000\n",
      " - 7s - loss: 0.3762 - acc: 0.8287 - val_loss: 0.3185 - val_acc: 0.8710\n",
      "Epoch 762/1000\n",
      " - 7s - loss: 0.3762 - acc: 0.8276 - val_loss: 0.3199 - val_acc: 0.8681\n",
      "Epoch 763/1000\n",
      " - 7s - loss: 0.3769 - acc: 0.8276 - val_loss: 0.3177 - val_acc: 0.8730\n",
      "Epoch 764/1000\n",
      " - 7s - loss: 0.3782 - acc: 0.8266 - val_loss: 0.3169 - val_acc: 0.8718\n",
      "Epoch 765/1000\n",
      " - 7s - loss: 0.3781 - acc: 0.8275 - val_loss: 0.3197 - val_acc: 0.8702\n",
      "Epoch 766/1000\n",
      " - 7s - loss: 0.3768 - acc: 0.8267 - val_loss: 0.3157 - val_acc: 0.8744\n",
      "Epoch 767/1000\n",
      " - 7s - loss: 0.3763 - acc: 0.8279 - val_loss: 0.3156 - val_acc: 0.8729\n",
      "Epoch 768/1000\n",
      " - 7s - loss: 0.3764 - acc: 0.8280 - val_loss: 0.3175 - val_acc: 0.8712\n",
      "Epoch 769/1000\n",
      " - 7s - loss: 0.3756 - acc: 0.8288 - val_loss: 0.3160 - val_acc: 0.8729\n",
      "Epoch 770/1000\n",
      " - 7s - loss: 0.3757 - acc: 0.8288 - val_loss: 0.3144 - val_acc: 0.8722\n",
      "Epoch 771/1000\n",
      " - 7s - loss: 0.3758 - acc: 0.8281 - val_loss: 0.3142 - val_acc: 0.8716\n",
      "Epoch 772/1000\n",
      " - 7s - loss: 0.3764 - acc: 0.8289 - val_loss: 0.3201 - val_acc: 0.8688\n",
      "Epoch 773/1000\n",
      " - 7s - loss: 0.3759 - acc: 0.8285 - val_loss: 0.3183 - val_acc: 0.8710\n",
      "Epoch 774/1000\n",
      " - 7s - loss: 0.3769 - acc: 0.8287 - val_loss: 0.3180 - val_acc: 0.8672\n",
      "Epoch 775/1000\n",
      " - 7s - loss: 0.3755 - acc: 0.8288 - val_loss: 0.3174 - val_acc: 0.8718\n",
      "Epoch 776/1000\n",
      " - 7s - loss: 0.3782 - acc: 0.8285 - val_loss: 0.3168 - val_acc: 0.8712\n",
      "Epoch 777/1000\n",
      " - 8s - loss: 0.3757 - acc: 0.8282 - val_loss: 0.3183 - val_acc: 0.8733\n",
      "Epoch 778/1000\n",
      " - 7s - loss: 0.3764 - acc: 0.8283 - val_loss: 0.3140 - val_acc: 0.8748\n",
      "Epoch 779/1000\n",
      " - 7s - loss: 0.3768 - acc: 0.8280 - val_loss: 0.3163 - val_acc: 0.8750\n",
      "Epoch 780/1000\n",
      " - 7s - loss: 0.3779 - acc: 0.8265 - val_loss: 0.3152 - val_acc: 0.8729\n",
      "Epoch 781/1000\n",
      " - 7s - loss: 0.3782 - acc: 0.8274 - val_loss: 0.3134 - val_acc: 0.8751\n",
      "Epoch 782/1000\n",
      " - 7s - loss: 0.3749 - acc: 0.8289 - val_loss: 0.3192 - val_acc: 0.8709\n",
      "Epoch 783/1000\n",
      " - 7s - loss: 0.3753 - acc: 0.8297 - val_loss: 0.3151 - val_acc: 0.8738\n",
      "Epoch 784/1000\n",
      " - 7s - loss: 0.3757 - acc: 0.8267 - val_loss: 0.3181 - val_acc: 0.8716\n",
      "Epoch 785/1000\n",
      " - 7s - loss: 0.3758 - acc: 0.8296 - val_loss: 0.3168 - val_acc: 0.8700\n",
      "Epoch 786/1000\n",
      " - 7s - loss: 0.3749 - acc: 0.8286 - val_loss: 0.3146 - val_acc: 0.8710\n",
      "Epoch 787/1000\n",
      " - 7s - loss: 0.3753 - acc: 0.8280 - val_loss: 0.3153 - val_acc: 0.8720\n",
      "Epoch 788/1000\n",
      " - 7s - loss: 0.3781 - acc: 0.8270 - val_loss: 0.3164 - val_acc: 0.8721\n",
      "Epoch 789/1000\n",
      " - 7s - loss: 0.3770 - acc: 0.8289 - val_loss: 0.3153 - val_acc: 0.8750\n",
      "Epoch 790/1000\n",
      " - 7s - loss: 0.3756 - acc: 0.8291 - val_loss: 0.3145 - val_acc: 0.8718\n",
      "Epoch 791/1000\n",
      " - 7s - loss: 0.3757 - acc: 0.8297 - val_loss: 0.3175 - val_acc: 0.8711\n",
      "Epoch 792/1000\n",
      " - 7s - loss: 0.3751 - acc: 0.8297 - val_loss: 0.3173 - val_acc: 0.8717\n",
      "Epoch 793/1000\n",
      " - 7s - loss: 0.3767 - acc: 0.8276 - val_loss: 0.3165 - val_acc: 0.8711\n",
      "Epoch 794/1000\n",
      " - 7s - loss: 0.3740 - acc: 0.8298 - val_loss: 0.3153 - val_acc: 0.8717\n",
      "Epoch 795/1000\n",
      " - 7s - loss: 0.3773 - acc: 0.8273 - val_loss: 0.3201 - val_acc: 0.8692\n",
      "Epoch 796/1000\n",
      " - 7s - loss: 0.3753 - acc: 0.8293 - val_loss: 0.3157 - val_acc: 0.8725\n",
      "Epoch 797/1000\n",
      " - 7s - loss: 0.3760 - acc: 0.8291 - val_loss: 0.3115 - val_acc: 0.8752\n",
      "Epoch 798/1000\n",
      " - 7s - loss: 0.3746 - acc: 0.8284 - val_loss: 0.3177 - val_acc: 0.8716\n",
      "Epoch 799/1000\n",
      " - 7s - loss: 0.3756 - acc: 0.8281 - val_loss: 0.3172 - val_acc: 0.8702\n",
      "Epoch 800/1000\n",
      " - 7s - loss: 0.3749 - acc: 0.8283 - val_loss: 0.3172 - val_acc: 0.8723\n",
      "Epoch 801/1000\n",
      " - 7s - loss: 0.3751 - acc: 0.8284 - val_loss: 0.3123 - val_acc: 0.8763\n",
      "Epoch 802/1000\n",
      " - 7s - loss: 0.3749 - acc: 0.8282 - val_loss: 0.3164 - val_acc: 0.8705\n",
      "Epoch 803/1000\n",
      " - 7s - loss: 0.3747 - acc: 0.8287 - val_loss: 0.3160 - val_acc: 0.8710\n",
      "Epoch 804/1000\n",
      " - 7s - loss: 0.3744 - acc: 0.8298 - val_loss: 0.3129 - val_acc: 0.8744\n",
      "Epoch 805/1000\n",
      " - 7s - loss: 0.3742 - acc: 0.8295 - val_loss: 0.3129 - val_acc: 0.8759\n",
      "Epoch 806/1000\n",
      " - 7s - loss: 0.3728 - acc: 0.8308 - val_loss: 0.3155 - val_acc: 0.8732\n",
      "Epoch 807/1000\n",
      " - 7s - loss: 0.3742 - acc: 0.8291 - val_loss: 0.3165 - val_acc: 0.8749\n",
      "Epoch 808/1000\n",
      " - 7s - loss: 0.3758 - acc: 0.8287 - val_loss: 0.3128 - val_acc: 0.8761\n",
      "Epoch 809/1000\n",
      " - 7s - loss: 0.3740 - acc: 0.8299 - val_loss: 0.3158 - val_acc: 0.8713\n",
      "Epoch 810/1000\n",
      " - 7s - loss: 0.3755 - acc: 0.8279 - val_loss: 0.3159 - val_acc: 0.8698\n",
      "Epoch 811/1000\n",
      " - 7s - loss: 0.3738 - acc: 0.8304 - val_loss: 0.3188 - val_acc: 0.8677\n",
      "Epoch 812/1000\n",
      " - 7s - loss: 0.3745 - acc: 0.8285 - val_loss: 0.3124 - val_acc: 0.8768\n",
      "Epoch 813/1000\n",
      " - 7s - loss: 0.3749 - acc: 0.8289 - val_loss: 0.3154 - val_acc: 0.8726\n",
      "Epoch 814/1000\n",
      " - 7s - loss: 0.3745 - acc: 0.8294 - val_loss: 0.3148 - val_acc: 0.8742\n",
      "Epoch 815/1000\n",
      " - 7s - loss: 0.3760 - acc: 0.8296 - val_loss: 0.3168 - val_acc: 0.8730\n",
      "Epoch 816/1000\n",
      " - 7s - loss: 0.3747 - acc: 0.8298 - val_loss: 0.3163 - val_acc: 0.8716\n",
      "Epoch 817/1000\n",
      " - 7s - loss: 0.3723 - acc: 0.8301 - val_loss: 0.3143 - val_acc: 0.8728\n",
      "Epoch 818/1000\n",
      " - 7s - loss: 0.3759 - acc: 0.8295 - val_loss: 0.3122 - val_acc: 0.8740\n",
      "Epoch 819/1000\n",
      " - 7s - loss: 0.3757 - acc: 0.8274 - val_loss: 0.3129 - val_acc: 0.8739\n",
      "Epoch 820/1000\n",
      " - 7s - loss: 0.3729 - acc: 0.8309 - val_loss: 0.3165 - val_acc: 0.8711\n",
      "Epoch 821/1000\n",
      " - 8s - loss: 0.3737 - acc: 0.8311 - val_loss: 0.3118 - val_acc: 0.8752\n",
      "Epoch 822/1000\n",
      " - 7s - loss: 0.3743 - acc: 0.8288 - val_loss: 0.3149 - val_acc: 0.8752\n",
      "Epoch 823/1000\n",
      " - 7s - loss: 0.3748 - acc: 0.8295 - val_loss: 0.3119 - val_acc: 0.8768\n",
      "Epoch 824/1000\n",
      " - 7s - loss: 0.3726 - acc: 0.8302 - val_loss: 0.3125 - val_acc: 0.8758\n",
      "Epoch 825/1000\n",
      " - 7s - loss: 0.3743 - acc: 0.8297 - val_loss: 0.3123 - val_acc: 0.8755\n",
      "Epoch 826/1000\n",
      " - 7s - loss: 0.3742 - acc: 0.8296 - val_loss: 0.3154 - val_acc: 0.8741\n",
      "Epoch 827/1000\n",
      " - 7s - loss: 0.3755 - acc: 0.8281 - val_loss: 0.3151 - val_acc: 0.8731\n",
      "Epoch 828/1000\n",
      " - 7s - loss: 0.3738 - acc: 0.8297 - val_loss: 0.3170 - val_acc: 0.8736\n",
      "Epoch 829/1000\n",
      " - 7s - loss: 0.3722 - acc: 0.8310 - val_loss: 0.3172 - val_acc: 0.8697\n",
      "Epoch 830/1000\n",
      " - 7s - loss: 0.3749 - acc: 0.8296 - val_loss: 0.3117 - val_acc: 0.8761\n",
      "Epoch 831/1000\n",
      " - 7s - loss: 0.3741 - acc: 0.8289 - val_loss: 0.3141 - val_acc: 0.8754\n",
      "Epoch 832/1000\n",
      " - 7s - loss: 0.3743 - acc: 0.8291 - val_loss: 0.3120 - val_acc: 0.8761\n",
      "Epoch 833/1000\n",
      " - 7s - loss: 0.3758 - acc: 0.8293 - val_loss: 0.3122 - val_acc: 0.8759\n",
      "Epoch 834/1000\n",
      " - 8s - loss: 0.3738 - acc: 0.8288 - val_loss: 0.3139 - val_acc: 0.8753\n",
      "Epoch 835/1000\n",
      " - 7s - loss: 0.3739 - acc: 0.8310 - val_loss: 0.3143 - val_acc: 0.8755\n",
      "Epoch 836/1000\n",
      " - 7s - loss: 0.3740 - acc: 0.8307 - val_loss: 0.3150 - val_acc: 0.8747\n",
      "Epoch 837/1000\n",
      " - 7s - loss: 0.3758 - acc: 0.8299 - val_loss: 0.3159 - val_acc: 0.8725\n",
      "Epoch 838/1000\n",
      " - 7s - loss: 0.3726 - acc: 0.8304 - val_loss: 0.3147 - val_acc: 0.8732\n",
      "Epoch 839/1000\n",
      " - 7s - loss: 0.3735 - acc: 0.8299 - val_loss: 0.3160 - val_acc: 0.8738\n",
      "Epoch 840/1000\n",
      " - 7s - loss: 0.3729 - acc: 0.8309 - val_loss: 0.3121 - val_acc: 0.8752\n",
      "Epoch 841/1000\n",
      " - 7s - loss: 0.3737 - acc: 0.8307 - val_loss: 0.3136 - val_acc: 0.8735\n",
      "Epoch 842/1000\n",
      " - 7s - loss: 0.3740 - acc: 0.8294 - val_loss: 0.3154 - val_acc: 0.8753\n",
      "Epoch 843/1000\n",
      " - 7s - loss: 0.3721 - acc: 0.8315 - val_loss: 0.3140 - val_acc: 0.8729\n",
      "Epoch 844/1000\n",
      " - 7s - loss: 0.3732 - acc: 0.8296 - val_loss: 0.3115 - val_acc: 0.8739\n",
      "Epoch 845/1000\n",
      " - 7s - loss: 0.3741 - acc: 0.8298 - val_loss: 0.3147 - val_acc: 0.8730\n",
      "Epoch 846/1000\n",
      " - 7s - loss: 0.3733 - acc: 0.8311 - val_loss: 0.3137 - val_acc: 0.8724\n",
      "Epoch 847/1000\n",
      " - 7s - loss: 0.3735 - acc: 0.8308 - val_loss: 0.3127 - val_acc: 0.8737\n",
      "Epoch 848/1000\n",
      " - 7s - loss: 0.3732 - acc: 0.8290 - val_loss: 0.3159 - val_acc: 0.8709\n",
      "Epoch 849/1000\n",
      " - 7s - loss: 0.3748 - acc: 0.8299 - val_loss: 0.3163 - val_acc: 0.8736\n",
      "Epoch 850/1000\n",
      " - 7s - loss: 0.3717 - acc: 0.8310 - val_loss: 0.3095 - val_acc: 0.8749\n",
      "Epoch 851/1000\n",
      " - 7s - loss: 0.3737 - acc: 0.8310 - val_loss: 0.3119 - val_acc: 0.8751\n",
      "Epoch 852/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - 7s - loss: 0.3696 - acc: 0.8327 - val_loss: 0.3141 - val_acc: 0.8722\n",
      "Epoch 853/1000\n",
      " - 7s - loss: 0.3732 - acc: 0.8297 - val_loss: 0.3128 - val_acc: 0.8734\n",
      "Epoch 854/1000\n",
      " - 7s - loss: 0.3726 - acc: 0.8313 - val_loss: 0.3127 - val_acc: 0.8752\n",
      "Epoch 855/1000\n",
      " - 7s - loss: 0.3746 - acc: 0.8301 - val_loss: 0.3146 - val_acc: 0.8729\n",
      "Epoch 856/1000\n",
      " - 7s - loss: 0.3740 - acc: 0.8297 - val_loss: 0.3121 - val_acc: 0.8769\n",
      "Epoch 857/1000\n",
      " - 7s - loss: 0.3737 - acc: 0.8305 - val_loss: 0.3148 - val_acc: 0.8715\n",
      "Epoch 858/1000\n",
      " - 7s - loss: 0.3725 - acc: 0.8309 - val_loss: 0.3143 - val_acc: 0.8711\n",
      "Epoch 859/1000\n",
      " - 7s - loss: 0.3719 - acc: 0.8310 - val_loss: 0.3123 - val_acc: 0.8753\n",
      "Epoch 860/1000\n",
      " - 7s - loss: 0.3721 - acc: 0.8312 - val_loss: 0.3121 - val_acc: 0.8751\n",
      "Epoch 861/1000\n",
      " - 7s - loss: 0.3724 - acc: 0.8318 - val_loss: 0.3094 - val_acc: 0.8766\n",
      "Epoch 862/1000\n",
      " - 7s - loss: 0.3714 - acc: 0.8319 - val_loss: 0.3114 - val_acc: 0.8746\n",
      "Epoch 863/1000\n",
      " - 7s - loss: 0.3746 - acc: 0.8291 - val_loss: 0.3129 - val_acc: 0.8739\n",
      "Epoch 864/1000\n",
      " - 7s - loss: 0.3727 - acc: 0.8307 - val_loss: 0.3127 - val_acc: 0.8734\n",
      "Epoch 865/1000\n",
      " - 9s - loss: 0.3716 - acc: 0.8309 - val_loss: 0.3088 - val_acc: 0.8787\n",
      "Epoch 866/1000\n",
      " - 8s - loss: 0.3729 - acc: 0.8318 - val_loss: 0.3114 - val_acc: 0.8751\n",
      "Epoch 867/1000\n",
      " - 7s - loss: 0.3716 - acc: 0.8317 - val_loss: 0.3130 - val_acc: 0.8755\n",
      "Epoch 868/1000\n",
      " - 7s - loss: 0.3742 - acc: 0.8311 - val_loss: 0.3135 - val_acc: 0.8745\n",
      "Epoch 869/1000\n",
      " - 7s - loss: 0.3723 - acc: 0.8309 - val_loss: 0.3094 - val_acc: 0.8793\n",
      "Epoch 870/1000\n",
      " - 7s - loss: 0.3721 - acc: 0.8321 - val_loss: 0.3087 - val_acc: 0.8793\n",
      "Epoch 871/1000\n",
      " - 7s - loss: 0.3741 - acc: 0.8311 - val_loss: 0.3123 - val_acc: 0.8738\n",
      "Epoch 872/1000\n",
      " - 7s - loss: 0.3715 - acc: 0.8306 - val_loss: 0.3106 - val_acc: 0.8749\n",
      "Epoch 873/1000\n",
      " - 7s - loss: 0.3722 - acc: 0.8315 - val_loss: 0.3126 - val_acc: 0.8763\n",
      "Epoch 874/1000\n",
      " - 7s - loss: 0.3719 - acc: 0.8323 - val_loss: 0.3123 - val_acc: 0.8747\n",
      "Epoch 875/1000\n",
      " - 7s - loss: 0.3713 - acc: 0.8313 - val_loss: 0.3137 - val_acc: 0.8763\n",
      "Epoch 876/1000\n",
      " - 7s - loss: 0.3725 - acc: 0.8313 - val_loss: 0.3122 - val_acc: 0.8742\n",
      "Epoch 877/1000\n",
      " - 7s - loss: 0.3735 - acc: 0.8309 - val_loss: 0.3100 - val_acc: 0.8758\n",
      "Epoch 878/1000\n",
      " - 7s - loss: 0.3714 - acc: 0.8317 - val_loss: 0.3094 - val_acc: 0.8772\n",
      "Epoch 879/1000\n",
      " - 7s - loss: 0.3703 - acc: 0.8325 - val_loss: 0.3150 - val_acc: 0.8721\n",
      "Epoch 880/1000\n",
      " - 7s - loss: 0.3717 - acc: 0.8326 - val_loss: 0.3081 - val_acc: 0.8767\n",
      "Epoch 881/1000\n",
      " - 7s - loss: 0.3706 - acc: 0.8318 - val_loss: 0.3124 - val_acc: 0.8743\n",
      "Epoch 882/1000\n",
      " - 7s - loss: 0.3730 - acc: 0.8313 - val_loss: 0.3087 - val_acc: 0.8779\n",
      "Epoch 883/1000\n",
      " - 7s - loss: 0.3711 - acc: 0.8319 - val_loss: 0.3069 - val_acc: 0.8785\n",
      "Epoch 884/1000\n",
      " - 7s - loss: 0.3718 - acc: 0.8316 - val_loss: 0.3092 - val_acc: 0.8772\n",
      "Epoch 885/1000\n",
      " - 7s - loss: 0.3721 - acc: 0.8322 - val_loss: 0.3106 - val_acc: 0.8752\n",
      "Epoch 886/1000\n",
      " - 7s - loss: 0.3720 - acc: 0.8310 - val_loss: 0.3121 - val_acc: 0.8763\n",
      "Epoch 887/1000\n",
      " - 7s - loss: 0.3720 - acc: 0.8318 - val_loss: 0.3131 - val_acc: 0.8745\n",
      "Epoch 888/1000\n",
      " - 7s - loss: 0.3715 - acc: 0.8325 - val_loss: 0.3128 - val_acc: 0.8774\n",
      "Epoch 889/1000\n",
      " - 7s - loss: 0.3716 - acc: 0.8313 - val_loss: 0.3107 - val_acc: 0.8742\n",
      "Epoch 890/1000\n",
      " - 7s - loss: 0.3713 - acc: 0.8319 - val_loss: 0.3129 - val_acc: 0.8739\n",
      "Epoch 891/1000\n",
      " - 7s - loss: 0.3717 - acc: 0.8320 - val_loss: 0.3107 - val_acc: 0.8748\n",
      "Epoch 892/1000\n",
      " - 7s - loss: 0.3706 - acc: 0.8338 - val_loss: 0.3095 - val_acc: 0.8769\n",
      "Epoch 893/1000\n",
      " - 7s - loss: 0.3693 - acc: 0.8334 - val_loss: 0.3099 - val_acc: 0.8778\n",
      "Epoch 894/1000\n",
      " - 7s - loss: 0.3710 - acc: 0.8317 - val_loss: 0.3113 - val_acc: 0.8786\n",
      "Epoch 895/1000\n",
      " - 7s - loss: 0.3727 - acc: 0.8314 - val_loss: 0.3146 - val_acc: 0.8713\n",
      "Epoch 896/1000\n",
      " - 7s - loss: 0.3710 - acc: 0.8330 - val_loss: 0.3138 - val_acc: 0.8733\n",
      "Epoch 897/1000\n",
      " - 7s - loss: 0.3707 - acc: 0.8312 - val_loss: 0.3094 - val_acc: 0.8780\n",
      "Epoch 898/1000\n",
      " - 7s - loss: 0.3717 - acc: 0.8316 - val_loss: 0.3116 - val_acc: 0.8755\n",
      "Epoch 899/1000\n",
      " - 7s - loss: 0.3720 - acc: 0.8315 - val_loss: 0.3103 - val_acc: 0.8754\n",
      "Epoch 900/1000\n",
      " - 7s - loss: 0.3694 - acc: 0.8321 - val_loss: 0.3097 - val_acc: 0.8767\n",
      "Epoch 901/1000\n",
      " - 7s - loss: 0.3700 - acc: 0.8317 - val_loss: 0.3094 - val_acc: 0.8786\n",
      "Epoch 902/1000\n",
      " - 7s - loss: 0.3739 - acc: 0.8311 - val_loss: 0.3136 - val_acc: 0.8768\n",
      "Epoch 903/1000\n",
      " - 7s - loss: 0.3701 - acc: 0.8331 - val_loss: 0.3104 - val_acc: 0.8778\n",
      "Epoch 904/1000\n",
      " - 7s - loss: 0.3696 - acc: 0.8331 - val_loss: 0.3109 - val_acc: 0.8755\n",
      "Epoch 905/1000\n",
      " - 7s - loss: 0.3725 - acc: 0.8307 - val_loss: 0.3089 - val_acc: 0.8770\n",
      "Epoch 906/1000\n",
      " - 7s - loss: 0.3700 - acc: 0.8317 - val_loss: 0.3080 - val_acc: 0.8789\n",
      "Epoch 907/1000\n",
      " - 7s - loss: 0.3724 - acc: 0.8316 - val_loss: 0.3084 - val_acc: 0.8791\n",
      "Epoch 908/1000\n",
      " - 8s - loss: 0.3703 - acc: 0.8303 - val_loss: 0.3083 - val_acc: 0.8781\n",
      "Epoch 909/1000\n",
      " - 7s - loss: 0.3695 - acc: 0.8320 - val_loss: 0.3107 - val_acc: 0.8746\n",
      "Epoch 910/1000\n",
      " - 7s - loss: 0.3691 - acc: 0.8332 - val_loss: 0.3090 - val_acc: 0.8756\n",
      "Epoch 911/1000\n",
      " - 7s - loss: 0.3714 - acc: 0.8321 - val_loss: 0.3096 - val_acc: 0.8763\n",
      "Epoch 912/1000\n",
      " - 7s - loss: 0.3712 - acc: 0.8319 - val_loss: 0.3118 - val_acc: 0.8767\n",
      "Epoch 913/1000\n",
      " - 7s - loss: 0.3716 - acc: 0.8312 - val_loss: 0.3110 - val_acc: 0.8752\n",
      "Epoch 914/1000\n",
      " - 7s - loss: 0.3710 - acc: 0.8322 - val_loss: 0.3079 - val_acc: 0.8782\n",
      "Epoch 915/1000\n",
      " - 7s - loss: 0.3710 - acc: 0.8324 - val_loss: 0.3105 - val_acc: 0.8748\n",
      "Epoch 916/1000\n",
      " - 7s - loss: 0.3695 - acc: 0.8327 - val_loss: 0.3123 - val_acc: 0.8734\n",
      "Epoch 917/1000\n",
      " - 7s - loss: 0.3702 - acc: 0.8326 - val_loss: 0.3094 - val_acc: 0.8777\n",
      "Epoch 918/1000\n",
      " - 7s - loss: 0.3702 - acc: 0.8334 - val_loss: 0.3131 - val_acc: 0.8754\n",
      "Epoch 919/1000\n",
      " - 7s - loss: 0.3697 - acc: 0.8341 - val_loss: 0.3093 - val_acc: 0.8771\n",
      "Epoch 920/1000\n",
      " - 7s - loss: 0.3690 - acc: 0.8330 - val_loss: 0.3098 - val_acc: 0.8778\n",
      "Epoch 921/1000\n",
      " - 7s - loss: 0.3686 - acc: 0.8340 - val_loss: 0.3067 - val_acc: 0.8770\n",
      "Epoch 922/1000\n",
      " - 7s - loss: 0.3711 - acc: 0.8324 - val_loss: 0.3094 - val_acc: 0.8776\n",
      "Epoch 923/1000\n",
      " - 7s - loss: 0.3709 - acc: 0.8309 - val_loss: 0.3109 - val_acc: 0.8757\n",
      "Epoch 924/1000\n",
      " - 7s - loss: 0.3712 - acc: 0.8321 - val_loss: 0.3074 - val_acc: 0.8776\n",
      "Epoch 925/1000\n",
      " - 7s - loss: 0.3700 - acc: 0.8327 - val_loss: 0.3097 - val_acc: 0.8779\n",
      "Epoch 926/1000\n",
      " - 7s - loss: 0.3701 - acc: 0.8335 - val_loss: 0.3085 - val_acc: 0.8764\n",
      "Epoch 927/1000\n",
      " - 7s - loss: 0.3706 - acc: 0.8336 - val_loss: 0.3100 - val_acc: 0.8745\n",
      "Epoch 928/1000\n",
      " - 7s - loss: 0.3697 - acc: 0.8319 - val_loss: 0.3131 - val_acc: 0.8723\n",
      "Epoch 929/1000\n",
      " - 7s - loss: 0.3681 - acc: 0.8334 - val_loss: 0.3096 - val_acc: 0.8748\n",
      "Epoch 930/1000\n",
      " - 7s - loss: 0.3694 - acc: 0.8329 - val_loss: 0.3075 - val_acc: 0.8768\n",
      "Epoch 931/1000\n",
      " - 7s - loss: 0.3702 - acc: 0.8318 - val_loss: 0.3079 - val_acc: 0.8772\n",
      "Epoch 932/1000\n",
      " - 7s - loss: 0.3697 - acc: 0.8318 - val_loss: 0.3059 - val_acc: 0.8792\n",
      "Epoch 933/1000\n",
      " - 7s - loss: 0.3679 - acc: 0.8347 - val_loss: 0.3079 - val_acc: 0.8782\n",
      "Epoch 934/1000\n",
      " - 7s - loss: 0.3693 - acc: 0.8330 - val_loss: 0.3065 - val_acc: 0.8786\n",
      "Epoch 935/1000\n",
      " - 7s - loss: 0.3698 - acc: 0.8321 - val_loss: 0.3091 - val_acc: 0.8773\n",
      "Epoch 936/1000\n",
      " - 7s - loss: 0.3697 - acc: 0.8328 - val_loss: 0.3076 - val_acc: 0.8784\n",
      "Epoch 937/1000\n",
      " - 7s - loss: 0.3695 - acc: 0.8327 - val_loss: 0.3060 - val_acc: 0.8776\n",
      "Epoch 938/1000\n",
      " - 7s - loss: 0.3697 - acc: 0.8322 - val_loss: 0.3066 - val_acc: 0.8782\n",
      "Epoch 939/1000\n",
      " - 7s - loss: 0.3696 - acc: 0.8333 - val_loss: 0.3072 - val_acc: 0.8810\n",
      "Epoch 940/1000\n",
      " - 7s - loss: 0.3695 - acc: 0.8323 - val_loss: 0.3106 - val_acc: 0.8771\n",
      "Epoch 941/1000\n",
      " - 7s - loss: 0.3692 - acc: 0.8339 - val_loss: 0.3115 - val_acc: 0.8758\n",
      "Epoch 942/1000\n",
      " - 7s - loss: 0.3689 - acc: 0.8340 - val_loss: 0.3102 - val_acc: 0.8754\n",
      "Epoch 943/1000\n",
      " - 7s - loss: 0.3691 - acc: 0.8317 - val_loss: 0.3153 - val_acc: 0.8724\n",
      "Epoch 944/1000\n",
      " - 7s - loss: 0.3711 - acc: 0.8311 - val_loss: 0.3068 - val_acc: 0.8787\n",
      "Epoch 945/1000\n",
      " - 7s - loss: 0.3695 - acc: 0.8346 - val_loss: 0.3070 - val_acc: 0.8797\n",
      "Epoch 946/1000\n",
      " - 7s - loss: 0.3671 - acc: 0.8342 - val_loss: 0.3105 - val_acc: 0.8770\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 947/1000\n",
      " - 7s - loss: 0.3688 - acc: 0.8331 - val_loss: 0.3109 - val_acc: 0.8760\n",
      "Epoch 948/1000\n",
      " - 7s - loss: 0.3683 - acc: 0.8341 - val_loss: 0.3086 - val_acc: 0.8788\n",
      "Epoch 949/1000\n",
      " - 7s - loss: 0.3681 - acc: 0.8345 - val_loss: 0.3054 - val_acc: 0.8800\n",
      "Epoch 950/1000\n",
      " - 7s - loss: 0.3693 - acc: 0.8342 - val_loss: 0.3078 - val_acc: 0.8791\n",
      "Epoch 951/1000\n",
      " - 7s - loss: 0.3678 - acc: 0.8349 - val_loss: 0.3100 - val_acc: 0.8774\n",
      "Epoch 952/1000\n",
      " - 8s - loss: 0.3675 - acc: 0.8356 - val_loss: 0.3080 - val_acc: 0.8784\n",
      "Epoch 953/1000\n",
      " - 7s - loss: 0.3673 - acc: 0.8345 - val_loss: 0.3084 - val_acc: 0.8763\n",
      "Epoch 954/1000\n",
      " - 7s - loss: 0.3696 - acc: 0.8333 - val_loss: 0.3102 - val_acc: 0.8772\n",
      "Epoch 955/1000\n",
      " - 7s - loss: 0.3690 - acc: 0.8339 - val_loss: 0.3074 - val_acc: 0.8781\n",
      "Epoch 956/1000\n",
      " - 7s - loss: 0.3665 - acc: 0.8338 - val_loss: 0.3070 - val_acc: 0.8796\n",
      "Epoch 957/1000\n",
      " - 7s - loss: 0.3671 - acc: 0.8337 - val_loss: 0.3053 - val_acc: 0.8781\n",
      "Epoch 958/1000\n",
      " - 7s - loss: 0.3682 - acc: 0.8334 - val_loss: 0.3060 - val_acc: 0.8789\n",
      "Epoch 959/1000\n",
      " - 7s - loss: 0.3677 - acc: 0.8337 - val_loss: 0.3098 - val_acc: 0.8765\n",
      "Epoch 960/1000\n",
      " - 7s - loss: 0.3695 - acc: 0.8340 - val_loss: 0.3098 - val_acc: 0.8766\n",
      "Epoch 961/1000\n",
      " - 7s - loss: 0.3676 - acc: 0.8331 - val_loss: 0.3070 - val_acc: 0.8784\n",
      "Epoch 962/1000\n",
      " - 7s - loss: 0.3693 - acc: 0.8331 - val_loss: 0.3096 - val_acc: 0.8785\n",
      "Epoch 963/1000\n",
      " - 7s - loss: 0.3676 - acc: 0.8337 - val_loss: 0.3067 - val_acc: 0.8778\n",
      "Epoch 964/1000\n",
      " - 7s - loss: 0.3675 - acc: 0.8347 - val_loss: 0.3053 - val_acc: 0.8790\n",
      "Epoch 965/1000\n",
      " - 7s - loss: 0.3676 - acc: 0.8343 - val_loss: 0.3114 - val_acc: 0.8749\n",
      "Epoch 966/1000\n",
      " - 7s - loss: 0.3691 - acc: 0.8336 - val_loss: 0.3066 - val_acc: 0.8784\n",
      "Epoch 967/1000\n",
      " - 7s - loss: 0.3704 - acc: 0.8335 - val_loss: 0.3071 - val_acc: 0.8780\n",
      "Epoch 968/1000\n",
      " - 7s - loss: 0.3687 - acc: 0.8334 - val_loss: 0.3072 - val_acc: 0.8777\n",
      "Epoch 969/1000\n",
      " - 7s - loss: 0.3670 - acc: 0.8339 - val_loss: 0.3059 - val_acc: 0.8806\n",
      "Epoch 970/1000\n",
      " - 7s - loss: 0.3663 - acc: 0.8349 - val_loss: 0.3067 - val_acc: 0.8772\n",
      "Epoch 971/1000\n",
      " - 7s - loss: 0.3691 - acc: 0.8335 - val_loss: 0.3090 - val_acc: 0.8769\n",
      "Epoch 972/1000\n",
      " - 7s - loss: 0.3673 - acc: 0.8338 - val_loss: 0.3086 - val_acc: 0.8770\n",
      "Epoch 973/1000\n",
      " - 7s - loss: 0.3697 - acc: 0.8335 - val_loss: 0.3066 - val_acc: 0.8786\n",
      "Epoch 974/1000\n",
      " - 7s - loss: 0.3688 - acc: 0.8343 - val_loss: 0.3082 - val_acc: 0.8770\n",
      "Epoch 975/1000\n",
      " - 7s - loss: 0.3679 - acc: 0.8334 - val_loss: 0.3056 - val_acc: 0.8800\n",
      "Epoch 976/1000\n",
      " - 7s - loss: 0.3662 - acc: 0.8350 - val_loss: 0.3069 - val_acc: 0.8802\n",
      "Epoch 977/1000\n",
      " - 7s - loss: 0.3686 - acc: 0.8338 - val_loss: 0.3065 - val_acc: 0.8783\n",
      "Epoch 978/1000\n",
      " - 7s - loss: 0.3663 - acc: 0.8344 - val_loss: 0.3040 - val_acc: 0.8820\n",
      "Epoch 979/1000\n",
      " - 7s - loss: 0.3661 - acc: 0.8353 - val_loss: 0.3060 - val_acc: 0.8788\n",
      "Epoch 980/1000\n",
      " - 7s - loss: 0.3697 - acc: 0.8334 - val_loss: 0.3064 - val_acc: 0.8757\n",
      "Epoch 981/1000\n",
      " - 7s - loss: 0.3687 - acc: 0.8337 - val_loss: 0.3076 - val_acc: 0.8786\n",
      "Epoch 982/1000\n",
      " - 7s - loss: 0.3672 - acc: 0.8348 - val_loss: 0.3074 - val_acc: 0.8775\n",
      "Epoch 983/1000\n",
      " - 7s - loss: 0.3674 - acc: 0.8346 - val_loss: 0.3064 - val_acc: 0.8783\n",
      "Epoch 984/1000\n",
      " - 7s - loss: 0.3672 - acc: 0.8339 - val_loss: 0.3072 - val_acc: 0.8797\n",
      "Epoch 985/1000\n",
      " - 7s - loss: 0.3670 - acc: 0.8348 - val_loss: 0.3072 - val_acc: 0.8779\n",
      "Epoch 986/1000\n",
      " - 7s - loss: 0.3690 - acc: 0.8329 - val_loss: 0.3102 - val_acc: 0.8767\n",
      "Epoch 987/1000\n",
      " - 7s - loss: 0.3694 - acc: 0.8339 - val_loss: 0.3075 - val_acc: 0.8786\n",
      "Epoch 988/1000\n",
      " - 7s - loss: 0.3666 - acc: 0.8340 - val_loss: 0.3064 - val_acc: 0.8803\n",
      "Epoch 989/1000\n",
      " - 7s - loss: 0.3688 - acc: 0.8335 - val_loss: 0.3102 - val_acc: 0.8778\n",
      "Epoch 990/1000\n",
      " - 7s - loss: 0.3693 - acc: 0.8340 - val_loss: 0.3069 - val_acc: 0.8778\n",
      "Epoch 991/1000\n",
      " - 7s - loss: 0.3665 - acc: 0.8351 - val_loss: 0.3048 - val_acc: 0.8802\n",
      "Epoch 992/1000\n",
      " - 7s - loss: 0.3652 - acc: 0.8350 - val_loss: 0.3033 - val_acc: 0.8811\n",
      "Epoch 993/1000\n",
      " - 7s - loss: 0.3706 - acc: 0.8320 - val_loss: 0.3040 - val_acc: 0.8817\n",
      "Epoch 994/1000\n",
      " - 7s - loss: 0.3676 - acc: 0.8337 - val_loss: 0.3060 - val_acc: 0.8799\n",
      "Epoch 995/1000\n",
      " - 7s - loss: 0.3664 - acc: 0.8353 - val_loss: 0.3050 - val_acc: 0.8798\n",
      "Epoch 996/1000\n",
      " - 8s - loss: 0.3666 - acc: 0.8353 - val_loss: 0.3055 - val_acc: 0.8803\n",
      "Epoch 997/1000\n",
      " - 7s - loss: 0.3677 - acc: 0.8342 - val_loss: 0.3079 - val_acc: 0.8790\n",
      "Epoch 998/1000\n",
      " - 7s - loss: 0.3683 - acc: 0.8344 - val_loss: 0.3041 - val_acc: 0.8799\n",
      "Epoch 999/1000\n",
      " - 7s - loss: 0.3669 - acc: 0.8339 - val_loss: 0.3052 - val_acc: 0.8802\n",
      "Epoch 1000/1000\n",
      " - 7s - loss: 0.3672 - acc: 0.8350 - val_loss: 0.3069 - val_acc: 0.8802\n",
      "Iteration >#1: 88.169 \n",
      "\n",
      "Train on 106693 samples, validate on 35565 samples\n",
      "Epoch 1/1000\n",
      " - 8s - loss: 0.5930 - acc: 0.6251 - val_loss: 0.5592 - val_acc: 0.6466\n",
      "Epoch 2/1000\n",
      " - 7s - loss: 0.5626 - acc: 0.6388 - val_loss: 0.5567 - val_acc: 0.6500\n",
      "Epoch 3/1000\n",
      " - 7s - loss: 0.5592 - acc: 0.6419 - val_loss: 0.5553 - val_acc: 0.6436\n",
      "Epoch 4/1000\n",
      " - 7s - loss: 0.5555 - acc: 0.6526 - val_loss: 0.5536 - val_acc: 0.6566\n",
      "Epoch 5/1000\n",
      " - 7s - loss: 0.5530 - acc: 0.6565 - val_loss: 0.5511 - val_acc: 0.6608\n",
      "Epoch 6/1000\n",
      " - 7s - loss: 0.5521 - acc: 0.6596 - val_loss: 0.5486 - val_acc: 0.6660\n",
      "Epoch 7/1000\n",
      " - 7s - loss: 0.5511 - acc: 0.6601 - val_loss: 0.5482 - val_acc: 0.6639\n",
      "Epoch 8/1000\n",
      " - 7s - loss: 0.5500 - acc: 0.6605 - val_loss: 0.5498 - val_acc: 0.6585\n",
      "Epoch 9/1000\n",
      " - 7s - loss: 0.5488 - acc: 0.6625 - val_loss: 0.5470 - val_acc: 0.6653\n",
      "Epoch 10/1000\n",
      " - 7s - loss: 0.5481 - acc: 0.6635 - val_loss: 0.5445 - val_acc: 0.6711\n",
      "Epoch 11/1000\n",
      " - 7s - loss: 0.5467 - acc: 0.6671 - val_loss: 0.5445 - val_acc: 0.6666\n",
      "Epoch 12/1000\n",
      " - 7s - loss: 0.5453 - acc: 0.6682 - val_loss: 0.5429 - val_acc: 0.6702\n",
      "Epoch 13/1000\n",
      " - 7s - loss: 0.5444 - acc: 0.6698 - val_loss: 0.5398 - val_acc: 0.6798\n",
      "Epoch 14/1000\n",
      " - 7s - loss: 0.5425 - acc: 0.6733 - val_loss: 0.5398 - val_acc: 0.6843\n",
      "Epoch 15/1000\n",
      " - 7s - loss: 0.5414 - acc: 0.6755 - val_loss: 0.5382 - val_acc: 0.6888\n",
      "Epoch 16/1000\n",
      " - 7s - loss: 0.5404 - acc: 0.6767 - val_loss: 0.5369 - val_acc: 0.6836\n",
      "Epoch 17/1000\n",
      " - 7s - loss: 0.5391 - acc: 0.6790 - val_loss: 0.5370 - val_acc: 0.6815\n",
      "Epoch 18/1000\n",
      " - 7s - loss: 0.5382 - acc: 0.6824 - val_loss: 0.5344 - val_acc: 0.6852\n",
      "Epoch 19/1000\n",
      " - 7s - loss: 0.5370 - acc: 0.6836 - val_loss: 0.5321 - val_acc: 0.6929\n",
      "Epoch 20/1000\n",
      " - 7s - loss: 0.5361 - acc: 0.6838 - val_loss: 0.5302 - val_acc: 0.6913\n",
      "Epoch 21/1000\n",
      " - 7s - loss: 0.5349 - acc: 0.6858 - val_loss: 0.5285 - val_acc: 0.6975\n",
      "Epoch 22/1000\n",
      " - 7s - loss: 0.5337 - acc: 0.6885 - val_loss: 0.5304 - val_acc: 0.6951\n",
      "Epoch 23/1000\n",
      " - 7s - loss: 0.5328 - acc: 0.6884 - val_loss: 0.5278 - val_acc: 0.6987\n",
      "Epoch 24/1000\n",
      " - 7s - loss: 0.5318 - acc: 0.6896 - val_loss: 0.5271 - val_acc: 0.6973\n",
      "Epoch 25/1000\n",
      " - 7s - loss: 0.5312 - acc: 0.6907 - val_loss: 0.5239 - val_acc: 0.7073\n",
      "Epoch 26/1000\n",
      " - 7s - loss: 0.5295 - acc: 0.6935 - val_loss: 0.5231 - val_acc: 0.7001\n",
      "Epoch 27/1000\n",
      " - 7s - loss: 0.5282 - acc: 0.6947 - val_loss: 0.5222 - val_acc: 0.7049\n",
      "Epoch 28/1000\n",
      " - 7s - loss: 0.5279 - acc: 0.6943 - val_loss: 0.5181 - val_acc: 0.7119\n",
      "Epoch 29/1000\n",
      " - 7s - loss: 0.5269 - acc: 0.6967 - val_loss: 0.5215 - val_acc: 0.7029\n",
      "Epoch 30/1000\n",
      " - 7s - loss: 0.5254 - acc: 0.6986 - val_loss: 0.5170 - val_acc: 0.7127\n",
      "Epoch 31/1000\n",
      " - 7s - loss: 0.5243 - acc: 0.6990 - val_loss: 0.5145 - val_acc: 0.7183\n",
      "Epoch 32/1000\n",
      " - 7s - loss: 0.5234 - acc: 0.7002 - val_loss: 0.5170 - val_acc: 0.7130\n",
      "Epoch 33/1000\n",
      " - 7s - loss: 0.5235 - acc: 0.6997 - val_loss: 0.5134 - val_acc: 0.7149\n",
      "Epoch 34/1000\n",
      " - 7s - loss: 0.5211 - acc: 0.7035 - val_loss: 0.5130 - val_acc: 0.7162\n",
      "Epoch 35/1000\n",
      " - 7s - loss: 0.5205 - acc: 0.7039 - val_loss: 0.5092 - val_acc: 0.7167\n",
      "Epoch 36/1000\n",
      " - 7s - loss: 0.5191 - acc: 0.7048 - val_loss: 0.5084 - val_acc: 0.7199\n",
      "Epoch 37/1000\n",
      " - 7s - loss: 0.5184 - acc: 0.7055 - val_loss: 0.5102 - val_acc: 0.7178\n",
      "Epoch 38/1000\n",
      " - 7s - loss: 0.5170 - acc: 0.7086 - val_loss: 0.5030 - val_acc: 0.7267\n",
      "Epoch 39/1000\n",
      " - 9s - loss: 0.5162 - acc: 0.7078 - val_loss: 0.5009 - val_acc: 0.7309\n",
      "Epoch 40/1000\n",
      " - 8s - loss: 0.5154 - acc: 0.7097 - val_loss: 0.5011 - val_acc: 0.7303\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 41/1000\n",
      " - 7s - loss: 0.5128 - acc: 0.7125 - val_loss: 0.5010 - val_acc: 0.7317\n",
      "Epoch 42/1000\n",
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      "Epoch 43/1000\n",
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      "Epoch 44/1000\n",
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      "Epoch 45/1000\n",
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      "Epoch 46/1000\n",
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      "Epoch 47/1000\n",
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      "Epoch 48/1000\n",
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      "Epoch 49/1000\n",
      " - 7s - loss: 0.5035 - acc: 0.7231 - val_loss: 0.4847 - val_acc: 0.7474\n",
      "Epoch 50/1000\n",
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      "Epoch 52/1000\n",
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      "Epoch 54/1000\n",
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      "Epoch 55/1000\n",
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      "Epoch 56/1000\n",
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      "Epoch 57/1000\n",
      " - 7s - loss: 0.4943 - acc: 0.7315 - val_loss: 0.4692 - val_acc: 0.7616\n",
      "Epoch 58/1000\n",
      " - 7s - loss: 0.4925 - acc: 0.7333 - val_loss: 0.4726 - val_acc: 0.7551\n",
      "Epoch 59/1000\n",
      " - 7s - loss: 0.4926 - acc: 0.7333 - val_loss: 0.4677 - val_acc: 0.7654\n",
      "Epoch 60/1000\n",
      " - 7s - loss: 0.4921 - acc: 0.7340 - val_loss: 0.4665 - val_acc: 0.7635\n",
      "Epoch 61/1000\n",
      " - 7s - loss: 0.4909 - acc: 0.7344 - val_loss: 0.4648 - val_acc: 0.7635\n",
      "Epoch 62/1000\n",
      " - 7s - loss: 0.4896 - acc: 0.7381 - val_loss: 0.4635 - val_acc: 0.7615\n",
      "Epoch 63/1000\n",
      " - 7s - loss: 0.4875 - acc: 0.7390 - val_loss: 0.4607 - val_acc: 0.7713\n",
      "Epoch 64/1000\n",
      " - 7s - loss: 0.4879 - acc: 0.7393 - val_loss: 0.4586 - val_acc: 0.7747\n",
      "Epoch 65/1000\n",
      " - 7s - loss: 0.4872 - acc: 0.7404 - val_loss: 0.4583 - val_acc: 0.7746\n",
      "Epoch 66/1000\n",
      " - 7s - loss: 0.4846 - acc: 0.7422 - val_loss: 0.4612 - val_acc: 0.7661\n",
      "Epoch 67/1000\n",
      " - 7s - loss: 0.4856 - acc: 0.7424 - val_loss: 0.4582 - val_acc: 0.7709\n",
      "Epoch 68/1000\n",
      " - 7s - loss: 0.4827 - acc: 0.7447 - val_loss: 0.4551 - val_acc: 0.7747\n",
      "Epoch 69/1000\n",
      " - 7s - loss: 0.4829 - acc: 0.7431 - val_loss: 0.4514 - val_acc: 0.7801\n",
      "Epoch 70/1000\n",
      " - 7s - loss: 0.4814 - acc: 0.7446 - val_loss: 0.4579 - val_acc: 0.7665\n",
      "Epoch 71/1000\n",
      " - 7s - loss: 0.4823 - acc: 0.7444 - val_loss: 0.4525 - val_acc: 0.7803\n",
      "Epoch 72/1000\n",
      " - 7s - loss: 0.4798 - acc: 0.7468 - val_loss: 0.4521 - val_acc: 0.7761\n",
      "Epoch 73/1000\n",
      " - 7s - loss: 0.4797 - acc: 0.7469 - val_loss: 0.4528 - val_acc: 0.7729\n",
      "Epoch 74/1000\n",
      " - 7s - loss: 0.4782 - acc: 0.7489 - val_loss: 0.4484 - val_acc: 0.7773\n",
      "Epoch 75/1000\n",
      " - 7s - loss: 0.4793 - acc: 0.7475 - val_loss: 0.4472 - val_acc: 0.7809\n",
      "Epoch 76/1000\n",
      " - 7s - loss: 0.4769 - acc: 0.7506 - val_loss: 0.4426 - val_acc: 0.7852\n",
      "Epoch 77/1000\n",
      " - 7s - loss: 0.4781 - acc: 0.7475 - val_loss: 0.4506 - val_acc: 0.7769\n",
      "Epoch 78/1000\n",
      " - 7s - loss: 0.4749 - acc: 0.7503 - val_loss: 0.4474 - val_acc: 0.7779\n",
      "Epoch 79/1000\n",
      " - 7s - loss: 0.4749 - acc: 0.7510 - val_loss: 0.4414 - val_acc: 0.7827\n",
      "Epoch 80/1000\n",
      " - 7s - loss: 0.4753 - acc: 0.7506 - val_loss: 0.4414 - val_acc: 0.7857\n",
      "Epoch 81/1000\n",
      " - 7s - loss: 0.4740 - acc: 0.7524 - val_loss: 0.4433 - val_acc: 0.7828\n",
      "Epoch 82/1000\n",
      " - 8s - loss: 0.4741 - acc: 0.7526 - val_loss: 0.4377 - val_acc: 0.7876\n",
      "Epoch 83/1000\n",
      " - 8s - loss: 0.4720 - acc: 0.7549 - val_loss: 0.4370 - val_acc: 0.7866\n",
      "Epoch 84/1000\n",
      " - 7s - loss: 0.4725 - acc: 0.7542 - val_loss: 0.4368 - val_acc: 0.7872\n",
      "Epoch 85/1000\n",
      " - 7s - loss: 0.4709 - acc: 0.7546 - val_loss: 0.4418 - val_acc: 0.7807\n",
      "Epoch 86/1000\n",
      " - 7s - loss: 0.4711 - acc: 0.7549 - val_loss: 0.4374 - val_acc: 0.7879\n",
      "Epoch 87/1000\n",
      " - 7s - loss: 0.4704 - acc: 0.7550 - val_loss: 0.4331 - val_acc: 0.7899\n",
      "Epoch 88/1000\n",
      " - 7s - loss: 0.4685 - acc: 0.7566 - val_loss: 0.4348 - val_acc: 0.7930\n",
      "Epoch 89/1000\n",
      " - 7s - loss: 0.4690 - acc: 0.7568 - val_loss: 0.4401 - val_acc: 0.7875\n",
      "Epoch 90/1000\n",
      " - 7s - loss: 0.4688 - acc: 0.7562 - val_loss: 0.4401 - val_acc: 0.7822\n",
      "Epoch 91/1000\n",
      " - 7s - loss: 0.4676 - acc: 0.7564 - val_loss: 0.4314 - val_acc: 0.7958\n",
      "Epoch 92/1000\n",
      " - 7s - loss: 0.4671 - acc: 0.7575 - val_loss: 0.4320 - val_acc: 0.7945\n",
      "Epoch 93/1000\n",
      " - 7s - loss: 0.4664 - acc: 0.7592 - val_loss: 0.4306 - val_acc: 0.7918\n",
      "Epoch 94/1000\n",
      " - 7s - loss: 0.4673 - acc: 0.7569 - val_loss: 0.4345 - val_acc: 0.7853\n",
      "Epoch 95/1000\n",
      " - 7s - loss: 0.4640 - acc: 0.7620 - val_loss: 0.4295 - val_acc: 0.7931\n",
      "Epoch 96/1000\n",
      " - 7s - loss: 0.4650 - acc: 0.7593 - val_loss: 0.4275 - val_acc: 0.7958\n",
      "Epoch 97/1000\n",
      " - 7s - loss: 0.4623 - acc: 0.7623 - val_loss: 0.4263 - val_acc: 0.7972\n",
      "Epoch 98/1000\n",
      " - 7s - loss: 0.4645 - acc: 0.7605 - val_loss: 0.4272 - val_acc: 0.7946\n",
      "Epoch 99/1000\n",
      " - 7s - loss: 0.4620 - acc: 0.7626 - val_loss: 0.4252 - val_acc: 0.7977\n",
      "Epoch 100/1000\n",
      " - 7s - loss: 0.4628 - acc: 0.7605 - val_loss: 0.4257 - val_acc: 0.7983\n",
      "Epoch 101/1000\n",
      " - 7s - loss: 0.4606 - acc: 0.7640 - val_loss: 0.4219 - val_acc: 0.8026\n",
      "Epoch 102/1000\n",
      " - 7s - loss: 0.4619 - acc: 0.7638 - val_loss: 0.4271 - val_acc: 0.7945\n",
      "Epoch 103/1000\n",
      " - 7s - loss: 0.4618 - acc: 0.7630 - val_loss: 0.4267 - val_acc: 0.7945\n",
      "Epoch 104/1000\n",
      " - 7s - loss: 0.4611 - acc: 0.7643 - val_loss: 0.4213 - val_acc: 0.8021\n",
      "Epoch 105/1000\n",
      " - 7s - loss: 0.4596 - acc: 0.7649 - val_loss: 0.4233 - val_acc: 0.8005\n",
      "Epoch 106/1000\n",
      " - 7s - loss: 0.4591 - acc: 0.7653 - val_loss: 0.4244 - val_acc: 0.7968\n",
      "Epoch 107/1000\n",
      " - 7s - loss: 0.4581 - acc: 0.7649 - val_loss: 0.4153 - val_acc: 0.8057\n",
      "Epoch 108/1000\n",
      " - 7s - loss: 0.4586 - acc: 0.7654 - val_loss: 0.4167 - val_acc: 0.8013\n",
      "Epoch 109/1000\n",
      " - 7s - loss: 0.4571 - acc: 0.7670 - val_loss: 0.4162 - val_acc: 0.8057\n",
      "Epoch 110/1000\n",
      " - 7s - loss: 0.4581 - acc: 0.7672 - val_loss: 0.4190 - val_acc: 0.7992\n",
      "Epoch 111/1000\n",
      " - 7s - loss: 0.4573 - acc: 0.7672 - val_loss: 0.4140 - val_acc: 0.8086\n",
      "Epoch 112/1000\n",
      " - 7s - loss: 0.4577 - acc: 0.7658 - val_loss: 0.4160 - val_acc: 0.8030\n",
      "Epoch 113/1000\n",
      " - 7s - loss: 0.4574 - acc: 0.7663 - val_loss: 0.4192 - val_acc: 0.7997\n",
      "Epoch 114/1000\n",
      " - 7s - loss: 0.4558 - acc: 0.7675 - val_loss: 0.4135 - val_acc: 0.8096\n",
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      " - 7s - loss: 0.4553 - acc: 0.7672 - val_loss: 0.4127 - val_acc: 0.8082\n",
      "Epoch 116/1000\n",
      " - 8s - loss: 0.4537 - acc: 0.7695 - val_loss: 0.4152 - val_acc: 0.8018\n",
      "Epoch 117/1000\n",
      " - 7s - loss: 0.4568 - acc: 0.7676 - val_loss: 0.4129 - val_acc: 0.8093\n",
      "Epoch 118/1000\n",
      " - 7s - loss: 0.4538 - acc: 0.7687 - val_loss: 0.4186 - val_acc: 0.7979\n",
      "Epoch 119/1000\n",
      " - 7s - loss: 0.4533 - acc: 0.7687 - val_loss: 0.4140 - val_acc: 0.8037\n",
      "Epoch 120/1000\n",
      " - 7s - loss: 0.4546 - acc: 0.7693 - val_loss: 0.4122 - val_acc: 0.8113\n",
      "Epoch 121/1000\n",
      " - 7s - loss: 0.4545 - acc: 0.7698 - val_loss: 0.4144 - val_acc: 0.8067\n",
      "Epoch 122/1000\n",
      " - 7s - loss: 0.4520 - acc: 0.7718 - val_loss: 0.4098 - val_acc: 0.8114\n",
      "Epoch 123/1000\n",
      " - 7s - loss: 0.4525 - acc: 0.7691 - val_loss: 0.4132 - val_acc: 0.8048\n",
      "Epoch 124/1000\n",
      " - 7s - loss: 0.4502 - acc: 0.7722 - val_loss: 0.4098 - val_acc: 0.8084\n",
      "Epoch 125/1000\n",
      " - 8s - loss: 0.4523 - acc: 0.7713 - val_loss: 0.4088 - val_acc: 0.8105\n",
      "Epoch 126/1000\n",
      " - 8s - loss: 0.4516 - acc: 0.7716 - val_loss: 0.4078 - val_acc: 0.8103\n",
      "Epoch 127/1000\n",
      " - 7s - loss: 0.4512 - acc: 0.7714 - val_loss: 0.4070 - val_acc: 0.8057\n",
      "Epoch 128/1000\n",
      " - 7s - loss: 0.4515 - acc: 0.7706 - val_loss: 0.4099 - val_acc: 0.8077\n",
      "Epoch 129/1000\n",
      " - 7s - loss: 0.4515 - acc: 0.7703 - val_loss: 0.4083 - val_acc: 0.8093\n",
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      " - 7s - loss: 0.4505 - acc: 0.7722 - val_loss: 0.4054 - val_acc: 0.8152\n",
      "Epoch 131/1000\n",
      " - 7s - loss: 0.4510 - acc: 0.7730 - val_loss: 0.4062 - val_acc: 0.8102\n",
      "Epoch 132/1000\n",
      " - 7s - loss: 0.4495 - acc: 0.7723 - val_loss: 0.4084 - val_acc: 0.8119\n",
      "Epoch 133/1000\n",
      " - 7s - loss: 0.4500 - acc: 0.7728 - val_loss: 0.4056 - val_acc: 0.8103\n",
      "Epoch 134/1000\n",
      " - 7s - loss: 0.4489 - acc: 0.7729 - val_loss: 0.4084 - val_acc: 0.8105\n",
      "Epoch 135/1000\n",
      " - 7s - loss: 0.4484 - acc: 0.7728 - val_loss: 0.4098 - val_acc: 0.8087\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 136/1000\n",
      " - 7s - loss: 0.4496 - acc: 0.7728 - val_loss: 0.4059 - val_acc: 0.8139\n",
      "Epoch 137/1000\n",
      " - 7s - loss: 0.4488 - acc: 0.7728 - val_loss: 0.4042 - val_acc: 0.8114\n",
      "Epoch 138/1000\n",
      " - 7s - loss: 0.4471 - acc: 0.7753 - val_loss: 0.4070 - val_acc: 0.8144\n",
      "Epoch 139/1000\n",
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      "Epoch 140/1000\n",
      " - 7s - loss: 0.4466 - acc: 0.7766 - val_loss: 0.4049 - val_acc: 0.8143\n",
      "Epoch 141/1000\n",
      " - 7s - loss: 0.4485 - acc: 0.7734 - val_loss: 0.4016 - val_acc: 0.8140\n",
      "Epoch 142/1000\n",
      " - 7s - loss: 0.4470 - acc: 0.7749 - val_loss: 0.4038 - val_acc: 0.8117\n",
      "Epoch 143/1000\n",
      " - 7s - loss: 0.4468 - acc: 0.7758 - val_loss: 0.4021 - val_acc: 0.8125\n",
      "Epoch 144/1000\n",
      " - 7s - loss: 0.4461 - acc: 0.7758 - val_loss: 0.3994 - val_acc: 0.8164\n",
      "Epoch 145/1000\n",
      " - 7s - loss: 0.4443 - acc: 0.7779 - val_loss: 0.3991 - val_acc: 0.8186\n",
      "Epoch 146/1000\n",
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      "Epoch 147/1000\n",
      " - 7s - loss: 0.4454 - acc: 0.7756 - val_loss: 0.3988 - val_acc: 0.8195\n",
      "Epoch 148/1000\n",
      " - 7s - loss: 0.4455 - acc: 0.7758 - val_loss: 0.4033 - val_acc: 0.8150\n",
      "Epoch 149/1000\n",
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      "Epoch 150/1000\n",
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      "Epoch 151/1000\n",
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      "Epoch 152/1000\n",
      " - 7s - loss: 0.4427 - acc: 0.7772 - val_loss: 0.4024 - val_acc: 0.8160\n",
      "Epoch 153/1000\n",
      " - 7s - loss: 0.4445 - acc: 0.7765 - val_loss: 0.3984 - val_acc: 0.8204\n",
      "Epoch 154/1000\n",
      " - 7s - loss: 0.4426 - acc: 0.7779 - val_loss: 0.3970 - val_acc: 0.8143\n",
      "Epoch 155/1000\n",
      " - 7s - loss: 0.4428 - acc: 0.7787 - val_loss: 0.3956 - val_acc: 0.8219\n",
      "Epoch 156/1000\n",
      " - 7s - loss: 0.4442 - acc: 0.7778 - val_loss: 0.3974 - val_acc: 0.8200\n",
      "Epoch 157/1000\n",
      " - 7s - loss: 0.4414 - acc: 0.7786 - val_loss: 0.3990 - val_acc: 0.8142\n",
      "Epoch 158/1000\n",
      " - 7s - loss: 0.4432 - acc: 0.7783 - val_loss: 0.3980 - val_acc: 0.8179\n",
      "Epoch 159/1000\n",
      " - 7s - loss: 0.4423 - acc: 0.7789 - val_loss: 0.3938 - val_acc: 0.8226\n",
      "Epoch 160/1000\n",
      " - 7s - loss: 0.4427 - acc: 0.7782 - val_loss: 0.3953 - val_acc: 0.8213\n",
      "Epoch 161/1000\n",
      " - 7s - loss: 0.4428 - acc: 0.7783 - val_loss: 0.3962 - val_acc: 0.8170\n",
      "Epoch 162/1000\n",
      " - 7s - loss: 0.4416 - acc: 0.7791 - val_loss: 0.3942 - val_acc: 0.8209\n",
      "Epoch 163/1000\n",
      " - 7s - loss: 0.4406 - acc: 0.7809 - val_loss: 0.3932 - val_acc: 0.8214\n",
      "Epoch 164/1000\n",
      " - 7s - loss: 0.4423 - acc: 0.7786 - val_loss: 0.3963 - val_acc: 0.8194\n",
      "Epoch 165/1000\n",
      " - 7s - loss: 0.4405 - acc: 0.7805 - val_loss: 0.3927 - val_acc: 0.8219\n",
      "Epoch 166/1000\n",
      " - 7s - loss: 0.4396 - acc: 0.7818 - val_loss: 0.3957 - val_acc: 0.8197\n",
      "Epoch 167/1000\n",
      " - 7s - loss: 0.4412 - acc: 0.7803 - val_loss: 0.3957 - val_acc: 0.8214\n",
      "Epoch 168/1000\n",
      " - 7s - loss: 0.4402 - acc: 0.7809 - val_loss: 0.3922 - val_acc: 0.8224\n",
      "Epoch 169/1000\n",
      " - 9s - loss: 0.4396 - acc: 0.7822 - val_loss: 0.3929 - val_acc: 0.8199\n",
      "Epoch 170/1000\n",
      " - 8s - loss: 0.4384 - acc: 0.7801 - val_loss: 0.3917 - val_acc: 0.8223\n",
      "Epoch 171/1000\n",
      " - 7s - loss: 0.4372 - acc: 0.7830 - val_loss: 0.3912 - val_acc: 0.8212\n",
      "Epoch 172/1000\n",
      " - 7s - loss: 0.4391 - acc: 0.7819 - val_loss: 0.3926 - val_acc: 0.8182\n",
      "Epoch 173/1000\n",
      " - 7s - loss: 0.4397 - acc: 0.7795 - val_loss: 0.3879 - val_acc: 0.8269\n",
      "Epoch 174/1000\n",
      " - 7s - loss: 0.4383 - acc: 0.7825 - val_loss: 0.3858 - val_acc: 0.8267\n",
      "Epoch 175/1000\n",
      " - 7s - loss: 0.4377 - acc: 0.7814 - val_loss: 0.3876 - val_acc: 0.8238\n",
      "Epoch 176/1000\n",
      " - 7s - loss: 0.4376 - acc: 0.7819 - val_loss: 0.3892 - val_acc: 0.8237\n",
      "Epoch 177/1000\n",
      " - 7s - loss: 0.4377 - acc: 0.7824 - val_loss: 0.3902 - val_acc: 0.8257\n",
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      "Epoch 179/1000\n",
      " - 7s - loss: 0.4378 - acc: 0.7826 - val_loss: 0.3882 - val_acc: 0.8237\n",
      "Epoch 180/1000\n",
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      "Epoch 181/1000\n",
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      "Epoch 182/1000\n",
      " - 7s - loss: 0.4356 - acc: 0.7845 - val_loss: 0.3883 - val_acc: 0.8249\n",
      "Epoch 183/1000\n",
      " - 7s - loss: 0.4357 - acc: 0.7846 - val_loss: 0.3891 - val_acc: 0.8214\n",
      "Epoch 184/1000\n",
      " - 7s - loss: 0.4357 - acc: 0.7852 - val_loss: 0.3913 - val_acc: 0.8212\n",
      "Epoch 185/1000\n",
      " - 7s - loss: 0.4357 - acc: 0.7852 - val_loss: 0.3870 - val_acc: 0.8268\n",
      "Epoch 186/1000\n",
      " - 7s - loss: 0.4357 - acc: 0.7833 - val_loss: 0.3864 - val_acc: 0.8262\n",
      "Epoch 187/1000\n",
      " - 7s - loss: 0.4364 - acc: 0.7844 - val_loss: 0.3896 - val_acc: 0.8243\n",
      "Epoch 188/1000\n",
      " - 7s - loss: 0.4368 - acc: 0.7835 - val_loss: 0.3863 - val_acc: 0.8248\n",
      "Epoch 189/1000\n",
      " - 7s - loss: 0.4353 - acc: 0.7836 - val_loss: 0.3830 - val_acc: 0.8307\n",
      "Epoch 190/1000\n",
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      "Epoch 191/1000\n",
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      "Epoch 230/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - 7s - loss: 0.4267 - acc: 0.7918 - val_loss: 0.3761 - val_acc: 0.8336\n",
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      " - 7s - loss: 0.4154 - acc: 0.7997 - val_loss: 0.3620 - val_acc: 0.8430\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 325/1000\n",
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      " - 7s - loss: 0.4068 - acc: 0.8062 - val_loss: 0.3531 - val_acc: 0.8456\n",
      "Epoch 419/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - 7s - loss: 0.4045 - acc: 0.8080 - val_loss: 0.3473 - val_acc: 0.8517\n",
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      " - 7s - loss: 0.3979 - acc: 0.8129 - val_loss: 0.3412 - val_acc: 0.8544\n",
      "Epoch 490/1000\n",
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      " - 7s - loss: 0.3969 - acc: 0.8138 - val_loss: 0.3423 - val_acc: 0.8578\n",
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      " - 7s - loss: 0.3992 - acc: 0.8120 - val_loss: 0.3389 - val_acc: 0.8579\n",
      "Epoch 496/1000\n",
      " - 7s - loss: 0.3990 - acc: 0.8130 - val_loss: 0.3419 - val_acc: 0.8561\n",
      "Epoch 497/1000\n",
      " - 7s - loss: 0.4010 - acc: 0.8107 - val_loss: 0.3398 - val_acc: 0.8596\n",
      "Epoch 498/1000\n",
      " - 7s - loss: 0.3993 - acc: 0.8110 - val_loss: 0.3407 - val_acc: 0.8581\n",
      "Epoch 499/1000\n",
      " - 7s - loss: 0.4009 - acc: 0.8102 - val_loss: 0.3396 - val_acc: 0.8605\n",
      "Epoch 500/1000\n",
      " - 7s - loss: 0.3998 - acc: 0.8110 - val_loss: 0.3370 - val_acc: 0.8596\n",
      "Epoch 501/1000\n",
      " - 7s - loss: 0.3983 - acc: 0.8126 - val_loss: 0.3440 - val_acc: 0.8514\n",
      "Epoch 502/1000\n",
      " - 7s - loss: 0.3990 - acc: 0.8120 - val_loss: 0.3403 - val_acc: 0.8582\n",
      "Epoch 503/1000\n",
      " - 7s - loss: 0.3996 - acc: 0.8122 - val_loss: 0.3387 - val_acc: 0.8594\n",
      "Epoch 504/1000\n",
      " - 7s - loss: 0.3991 - acc: 0.8119 - val_loss: 0.3404 - val_acc: 0.8561\n",
      "Epoch 505/1000\n",
      " - 7s - loss: 0.3983 - acc: 0.8119 - val_loss: 0.3408 - val_acc: 0.8579\n",
      "Epoch 506/1000\n",
      " - 7s - loss: 0.3970 - acc: 0.8135 - val_loss: 0.3373 - val_acc: 0.8573\n",
      "Epoch 507/1000\n",
      " - 7s - loss: 0.3989 - acc: 0.8126 - val_loss: 0.3414 - val_acc: 0.8569\n",
      "Epoch 508/1000\n",
      " - 7s - loss: 0.3984 - acc: 0.8132 - val_loss: 0.3390 - val_acc: 0.8585\n",
      "Epoch 509/1000\n",
      " - 7s - loss: 0.3972 - acc: 0.8145 - val_loss: 0.3377 - val_acc: 0.8579\n",
      "Epoch 510/1000\n",
      " - 7s - loss: 0.3975 - acc: 0.8129 - val_loss: 0.3402 - val_acc: 0.8588\n",
      "Epoch 511/1000\n",
      " - 7s - loss: 0.3968 - acc: 0.8128 - val_loss: 0.3410 - val_acc: 0.8583\n",
      "Epoch 512/1000\n",
      " - 7s - loss: 0.3998 - acc: 0.8121 - val_loss: 0.3400 - val_acc: 0.8573\n",
      "Epoch 513/1000\n",
      " - 7s - loss: 0.3988 - acc: 0.8124 - val_loss: 0.3381 - val_acc: 0.8587\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 514/1000\n",
      " - 7s - loss: 0.3986 - acc: 0.8116 - val_loss: 0.3404 - val_acc: 0.8573\n",
      "Epoch 515/1000\n",
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      "Epoch 516/1000\n",
      " - 7s - loss: 0.3961 - acc: 0.8151 - val_loss: 0.3382 - val_acc: 0.8580\n",
      "Epoch 517/1000\n",
      " - 7s - loss: 0.3961 - acc: 0.8144 - val_loss: 0.3363 - val_acc: 0.8589\n",
      "Epoch 518/1000\n",
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      "Epoch 519/1000\n",
      " - 7s - loss: 0.3987 - acc: 0.8108 - val_loss: 0.3372 - val_acc: 0.8589\n",
      "Epoch 520/1000\n",
      " - 7s - loss: 0.3976 - acc: 0.8132 - val_loss: 0.3421 - val_acc: 0.8572\n",
      "Epoch 521/1000\n",
      " - 7s - loss: 0.3980 - acc: 0.8128 - val_loss: 0.3387 - val_acc: 0.8588\n",
      "Epoch 522/1000\n",
      " - 7s - loss: 0.3964 - acc: 0.8141 - val_loss: 0.3405 - val_acc: 0.8573\n",
      "Epoch 523/1000\n",
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      "Epoch 524/1000\n",
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      "Epoch 526/1000\n",
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      "Epoch 527/1000\n",
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      "Epoch 528/1000\n",
      " - 7s - loss: 0.3971 - acc: 0.8136 - val_loss: 0.3432 - val_acc: 0.8583\n",
      "Epoch 529/1000\n",
      " - 7s - loss: 0.3965 - acc: 0.8137 - val_loss: 0.3383 - val_acc: 0.8597\n",
      "Epoch 530/1000\n",
      " - 7s - loss: 0.3975 - acc: 0.8128 - val_loss: 0.3386 - val_acc: 0.8575\n",
      "Epoch 531/1000\n",
      " - 7s - loss: 0.3985 - acc: 0.8131 - val_loss: 0.3367 - val_acc: 0.8613\n",
      "Epoch 532/1000\n",
      " - 7s - loss: 0.3967 - acc: 0.8148 - val_loss: 0.3365 - val_acc: 0.8593\n",
      "Epoch 533/1000\n",
      " - 7s - loss: 0.3963 - acc: 0.8137 - val_loss: 0.3353 - val_acc: 0.8620\n",
      "Epoch 534/1000\n",
      " - 7s - loss: 0.3961 - acc: 0.8139 - val_loss: 0.3390 - val_acc: 0.8579\n",
      "Epoch 535/1000\n",
      " - 7s - loss: 0.3968 - acc: 0.8139 - val_loss: 0.3381 - val_acc: 0.8563\n",
      "Epoch 536/1000\n",
      " - 7s - loss: 0.3975 - acc: 0.8129 - val_loss: 0.3357 - val_acc: 0.8579\n",
      "Epoch 537/1000\n",
      " - 7s - loss: 0.3973 - acc: 0.8142 - val_loss: 0.3358 - val_acc: 0.8585\n",
      "Epoch 538/1000\n",
      " - 8s - loss: 0.3957 - acc: 0.8143 - val_loss: 0.3395 - val_acc: 0.8582\n",
      "Epoch 539/1000\n",
      " - 7s - loss: 0.3955 - acc: 0.8133 - val_loss: 0.3369 - val_acc: 0.8580\n",
      "Epoch 540/1000\n",
      " - 7s - loss: 0.3963 - acc: 0.8153 - val_loss: 0.3374 - val_acc: 0.8586\n",
      "Epoch 541/1000\n",
      " - 7s - loss: 0.3966 - acc: 0.8125 - val_loss: 0.3358 - val_acc: 0.8613\n",
      "Epoch 542/1000\n",
      " - 7s - loss: 0.3955 - acc: 0.8146 - val_loss: 0.3362 - val_acc: 0.8614\n",
      "Epoch 543/1000\n",
      " - 7s - loss: 0.3973 - acc: 0.8130 - val_loss: 0.3369 - val_acc: 0.8600\n",
      "Epoch 544/1000\n",
      " - 7s - loss: 0.3963 - acc: 0.8132 - val_loss: 0.3396 - val_acc: 0.8557\n",
      "Epoch 545/1000\n",
      " - 7s - loss: 0.3952 - acc: 0.8151 - val_loss: 0.3337 - val_acc: 0.8634\n",
      "Epoch 546/1000\n",
      " - 7s - loss: 0.3948 - acc: 0.8135 - val_loss: 0.3337 - val_acc: 0.8617\n",
      "Epoch 547/1000\n",
      " - 7s - loss: 0.3947 - acc: 0.8141 - val_loss: 0.3359 - val_acc: 0.8584\n",
      "Epoch 548/1000\n",
      " - 8s - loss: 0.3949 - acc: 0.8141 - val_loss: 0.3365 - val_acc: 0.8613\n",
      "Epoch 549/1000\n",
      " - 7s - loss: 0.3951 - acc: 0.8153 - val_loss: 0.3351 - val_acc: 0.8607\n",
      "Epoch 550/1000\n",
      " - 7s - loss: 0.3945 - acc: 0.8154 - val_loss: 0.3338 - val_acc: 0.8617\n",
      "Epoch 551/1000\n",
      " - 7s - loss: 0.3958 - acc: 0.8142 - val_loss: 0.3360 - val_acc: 0.8573\n",
      "Epoch 552/1000\n",
      " - 7s - loss: 0.3952 - acc: 0.8149 - val_loss: 0.3334 - val_acc: 0.8637\n",
      "Epoch 553/1000\n",
      " - 7s - loss: 0.3955 - acc: 0.8135 - val_loss: 0.3395 - val_acc: 0.8558\n",
      "Epoch 554/1000\n",
      " - 7s - loss: 0.3947 - acc: 0.8135 - val_loss: 0.3347 - val_acc: 0.8592\n",
      "Epoch 555/1000\n",
      " - 7s - loss: 0.3967 - acc: 0.8143 - val_loss: 0.3350 - val_acc: 0.8617\n",
      "Epoch 556/1000\n",
      " - 7s - loss: 0.3936 - acc: 0.8148 - val_loss: 0.3342 - val_acc: 0.8574\n",
      "Epoch 557/1000\n",
      " - 7s - loss: 0.3953 - acc: 0.8151 - val_loss: 0.3358 - val_acc: 0.8625\n",
      "Epoch 558/1000\n",
      " - 9s - loss: 0.3957 - acc: 0.8148 - val_loss: 0.3364 - val_acc: 0.8607\n",
      "Epoch 559/1000\n",
      " - 7s - loss: 0.3952 - acc: 0.8144 - val_loss: 0.3338 - val_acc: 0.8605\n",
      "Epoch 560/1000\n",
      " - 7s - loss: 0.3955 - acc: 0.8140 - val_loss: 0.3387 - val_acc: 0.8571\n",
      "Epoch 561/1000\n",
      " - 7s - loss: 0.3963 - acc: 0.8143 - val_loss: 0.3339 - val_acc: 0.8587\n",
      "Epoch 562/1000\n",
      " - 7s - loss: 0.3964 - acc: 0.8133 - val_loss: 0.3361 - val_acc: 0.8581\n",
      "Epoch 563/1000\n",
      " - 7s - loss: 0.3944 - acc: 0.8142 - val_loss: 0.3358 - val_acc: 0.8621\n",
      "Epoch 564/1000\n",
      " - 7s - loss: 0.3951 - acc: 0.8137 - val_loss: 0.3336 - val_acc: 0.8630\n",
      "Epoch 565/1000\n",
      " - 7s - loss: 0.3956 - acc: 0.8145 - val_loss: 0.3319 - val_acc: 0.8655\n",
      "Epoch 566/1000\n",
      " - 7s - loss: 0.3954 - acc: 0.8141 - val_loss: 0.3361 - val_acc: 0.8605\n",
      "Epoch 567/1000\n",
      " - 7s - loss: 0.3945 - acc: 0.8154 - val_loss: 0.3353 - val_acc: 0.8628\n",
      "Epoch 568/1000\n",
      " - 7s - loss: 0.3944 - acc: 0.8156 - val_loss: 0.3365 - val_acc: 0.8601\n",
      "Epoch 569/1000\n",
      " - 7s - loss: 0.3955 - acc: 0.8157 - val_loss: 0.3356 - val_acc: 0.8581\n",
      "Epoch 570/1000\n",
      " - 7s - loss: 0.3951 - acc: 0.8150 - val_loss: 0.3361 - val_acc: 0.8604\n",
      "Epoch 571/1000\n",
      " - 7s - loss: 0.3947 - acc: 0.8164 - val_loss: 0.3327 - val_acc: 0.8645\n",
      "Epoch 572/1000\n",
      " - 7s - loss: 0.3931 - acc: 0.8159 - val_loss: 0.3333 - val_acc: 0.8601\n",
      "Epoch 573/1000\n",
      " - 7s - loss: 0.3936 - acc: 0.8158 - val_loss: 0.3348 - val_acc: 0.8605\n",
      "Epoch 574/1000\n",
      " - 7s - loss: 0.3936 - acc: 0.8166 - val_loss: 0.3354 - val_acc: 0.8641\n",
      "Epoch 575/1000\n",
      " - 7s - loss: 0.3949 - acc: 0.8137 - val_loss: 0.3331 - val_acc: 0.8616\n",
      "Epoch 576/1000\n",
      " - 7s - loss: 0.3942 - acc: 0.8148 - val_loss: 0.3332 - val_acc: 0.8630\n",
      "Epoch 577/1000\n",
      " - 7s - loss: 0.3942 - acc: 0.8153 - val_loss: 0.3354 - val_acc: 0.8574\n",
      "Epoch 578/1000\n",
      " - 7s - loss: 0.3930 - acc: 0.8158 - val_loss: 0.3324 - val_acc: 0.8662\n",
      "Epoch 579/1000\n",
      " - 7s - loss: 0.3947 - acc: 0.8154 - val_loss: 0.3348 - val_acc: 0.8610\n",
      "Epoch 580/1000\n",
      " - 7s - loss: 0.3929 - acc: 0.8168 - val_loss: 0.3314 - val_acc: 0.8645\n",
      "Epoch 581/1000\n",
      " - 7s - loss: 0.3927 - acc: 0.8164 - val_loss: 0.3340 - val_acc: 0.8604\n",
      "Epoch 582/1000\n",
      " - 7s - loss: 0.3937 - acc: 0.8159 - val_loss: 0.3334 - val_acc: 0.8611\n",
      "Epoch 583/1000\n",
      " - 7s - loss: 0.3942 - acc: 0.8151 - val_loss: 0.3347 - val_acc: 0.8612\n",
      "Epoch 584/1000\n",
      " - 7s - loss: 0.3920 - acc: 0.8171 - val_loss: 0.3294 - val_acc: 0.8686\n",
      "Epoch 585/1000\n",
      " - 7s - loss: 0.3935 - acc: 0.8165 - val_loss: 0.3303 - val_acc: 0.8620\n",
      "Epoch 586/1000\n",
      " - 7s - loss: 0.3921 - acc: 0.8165 - val_loss: 0.3331 - val_acc: 0.8635\n",
      "Epoch 587/1000\n",
      " - 7s - loss: 0.3940 - acc: 0.8159 - val_loss: 0.3309 - val_acc: 0.8629\n",
      "Epoch 588/1000\n",
      " - 7s - loss: 0.3929 - acc: 0.8175 - val_loss: 0.3317 - val_acc: 0.8651\n",
      "Epoch 589/1000\n",
      " - 7s - loss: 0.3925 - acc: 0.8162 - val_loss: 0.3321 - val_acc: 0.8635\n",
      "Epoch 590/1000\n",
      " - 7s - loss: 0.3923 - acc: 0.8162 - val_loss: 0.3338 - val_acc: 0.8581\n",
      "Epoch 591/1000\n",
      " - 7s - loss: 0.3928 - acc: 0.8168 - val_loss: 0.3323 - val_acc: 0.8592\n",
      "Epoch 592/1000\n",
      " - 7s - loss: 0.3933 - acc: 0.8166 - val_loss: 0.3283 - val_acc: 0.8630\n",
      "Epoch 593/1000\n",
      " - 7s - loss: 0.3912 - acc: 0.8172 - val_loss: 0.3326 - val_acc: 0.8639\n",
      "Epoch 594/1000\n",
      " - 7s - loss: 0.3933 - acc: 0.8160 - val_loss: 0.3325 - val_acc: 0.8598\n",
      "Epoch 595/1000\n",
      " - 7s - loss: 0.3934 - acc: 0.8154 - val_loss: 0.3325 - val_acc: 0.8626\n",
      "Epoch 596/1000\n",
      " - 7s - loss: 0.3935 - acc: 0.8161 - val_loss: 0.3338 - val_acc: 0.8623\n",
      "Epoch 597/1000\n",
      " - 7s - loss: 0.3934 - acc: 0.8168 - val_loss: 0.3332 - val_acc: 0.8623\n",
      "Epoch 598/1000\n",
      " - 7s - loss: 0.3932 - acc: 0.8160 - val_loss: 0.3347 - val_acc: 0.8597\n",
      "Epoch 599/1000\n",
      " - 7s - loss: 0.3916 - acc: 0.8169 - val_loss: 0.3347 - val_acc: 0.8603\n",
      "Epoch 600/1000\n",
      " - 7s - loss: 0.3919 - acc: 0.8177 - val_loss: 0.3336 - val_acc: 0.8609\n",
      "Epoch 601/1000\n",
      " - 8s - loss: 0.3908 - acc: 0.8182 - val_loss: 0.3309 - val_acc: 0.8653\n",
      "Epoch 602/1000\n",
      " - 8s - loss: 0.3914 - acc: 0.8167 - val_loss: 0.3321 - val_acc: 0.8617\n",
      "Epoch 603/1000\n",
      " - 7s - loss: 0.3919 - acc: 0.8177 - val_loss: 0.3318 - val_acc: 0.8614\n",
      "Epoch 604/1000\n",
      " - 7s - loss: 0.3908 - acc: 0.8169 - val_loss: 0.3340 - val_acc: 0.8626\n",
      "Epoch 605/1000\n",
      " - 7s - loss: 0.3910 - acc: 0.8171 - val_loss: 0.3335 - val_acc: 0.8618\n",
      "Epoch 606/1000\n",
      " - 7s - loss: 0.3917 - acc: 0.8186 - val_loss: 0.3321 - val_acc: 0.8597\n",
      "Epoch 607/1000\n",
      " - 7s - loss: 0.3909 - acc: 0.8169 - val_loss: 0.3322 - val_acc: 0.8614\n",
      "Epoch 608/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - 7s - loss: 0.3922 - acc: 0.8177 - val_loss: 0.3320 - val_acc: 0.8593\n",
      "Epoch 609/1000\n",
      " - 7s - loss: 0.3908 - acc: 0.8173 - val_loss: 0.3301 - val_acc: 0.8645\n",
      "Epoch 610/1000\n",
      " - 7s - loss: 0.3920 - acc: 0.8175 - val_loss: 0.3269 - val_acc: 0.8643\n",
      "Epoch 611/1000\n",
      " - 7s - loss: 0.3917 - acc: 0.8183 - val_loss: 0.3322 - val_acc: 0.8624\n",
      "Epoch 612/1000\n",
      " - 7s - loss: 0.3915 - acc: 0.8176 - val_loss: 0.3291 - val_acc: 0.8691\n",
      "Epoch 613/1000\n",
      " - 7s - loss: 0.3912 - acc: 0.8180 - val_loss: 0.3309 - val_acc: 0.8617\n",
      "Epoch 614/1000\n",
      " - 7s - loss: 0.3900 - acc: 0.8188 - val_loss: 0.3294 - val_acc: 0.8602\n",
      "Epoch 615/1000\n",
      " - 7s - loss: 0.3911 - acc: 0.8178 - val_loss: 0.3289 - val_acc: 0.8641\n",
      "Epoch 616/1000\n",
      " - 7s - loss: 0.3928 - acc: 0.8168 - val_loss: 0.3293 - val_acc: 0.8629\n",
      "Epoch 617/1000\n",
      " - 7s - loss: 0.3908 - acc: 0.8179 - val_loss: 0.3307 - val_acc: 0.8655\n",
      "Epoch 618/1000\n",
      " - 7s - loss: 0.3908 - acc: 0.8170 - val_loss: 0.3279 - val_acc: 0.8658\n",
      "Epoch 619/1000\n",
      " - 7s - loss: 0.3894 - acc: 0.8182 - val_loss: 0.3258 - val_acc: 0.8662\n",
      "Epoch 620/1000\n",
      " - 7s - loss: 0.3905 - acc: 0.8169 - val_loss: 0.3328 - val_acc: 0.8597\n",
      "Epoch 621/1000\n",
      " - 7s - loss: 0.3925 - acc: 0.8171 - val_loss: 0.3292 - val_acc: 0.8654\n",
      "Epoch 622/1000\n",
      " - 7s - loss: 0.3885 - acc: 0.8195 - val_loss: 0.3297 - val_acc: 0.8633\n",
      "Epoch 623/1000\n",
      " - 7s - loss: 0.3914 - acc: 0.8174 - val_loss: 0.3302 - val_acc: 0.8636\n",
      "Epoch 624/1000\n",
      " - 7s - loss: 0.3903 - acc: 0.8191 - val_loss: 0.3303 - val_acc: 0.8663\n",
      "Epoch 625/1000\n",
      " - 7s - loss: 0.3902 - acc: 0.8173 - val_loss: 0.3299 - val_acc: 0.8653\n",
      "Epoch 626/1000\n",
      " - 7s - loss: 0.3932 - acc: 0.8179 - val_loss: 0.3333 - val_acc: 0.8601\n",
      "Epoch 627/1000\n",
      " - 7s - loss: 0.3895 - acc: 0.8195 - val_loss: 0.3294 - val_acc: 0.8620\n",
      "Epoch 628/1000\n",
      " - 7s - loss: 0.3898 - acc: 0.8177 - val_loss: 0.3285 - val_acc: 0.8586\n",
      "Epoch 629/1000\n",
      " - 7s - loss: 0.3900 - acc: 0.8179 - val_loss: 0.3307 - val_acc: 0.8637\n",
      "Epoch 630/1000\n",
      " - 7s - loss: 0.3922 - acc: 0.8175 - val_loss: 0.3315 - val_acc: 0.8615\n",
      "Epoch 631/1000\n",
      " - 7s - loss: 0.3902 - acc: 0.8180 - val_loss: 0.3270 - val_acc: 0.8692\n",
      "Epoch 632/1000\n",
      " - 7s - loss: 0.3898 - acc: 0.8196 - val_loss: 0.3332 - val_acc: 0.8625\n",
      "Epoch 633/1000\n",
      " - 7s - loss: 0.3881 - acc: 0.8200 - val_loss: 0.3296 - val_acc: 0.8664\n",
      "Epoch 634/1000\n",
      " - 7s - loss: 0.3893 - acc: 0.8192 - val_loss: 0.3269 - val_acc: 0.8676\n",
      "Epoch 635/1000\n",
      " - 7s - loss: 0.3898 - acc: 0.8187 - val_loss: 0.3329 - val_acc: 0.8623\n",
      "Epoch 636/1000\n",
      " - 7s - loss: 0.3905 - acc: 0.8174 - val_loss: 0.3314 - val_acc: 0.8621\n",
      "Epoch 637/1000\n",
      " - 7s - loss: 0.3908 - acc: 0.8186 - val_loss: 0.3328 - val_acc: 0.8634\n",
      "Epoch 638/1000\n",
      " - 7s - loss: 0.3910 - acc: 0.8173 - val_loss: 0.3263 - val_acc: 0.8669\n",
      "Epoch 639/1000\n",
      " - 7s - loss: 0.3903 - acc: 0.8179 - val_loss: 0.3288 - val_acc: 0.8670\n",
      "Epoch 640/1000\n",
      " - 7s - loss: 0.3895 - acc: 0.8187 - val_loss: 0.3287 - val_acc: 0.8652\n",
      "Epoch 641/1000\n",
      " - 7s - loss: 0.3882 - acc: 0.8205 - val_loss: 0.3304 - val_acc: 0.8637\n",
      "Epoch 642/1000\n",
      " - 7s - loss: 0.3906 - acc: 0.8181 - val_loss: 0.3304 - val_acc: 0.8657\n",
      "Epoch 643/1000\n",
      " - 7s - loss: 0.3899 - acc: 0.8169 - val_loss: 0.3272 - val_acc: 0.8662\n",
      "Epoch 644/1000\n",
      " - 7s - loss: 0.3905 - acc: 0.8190 - val_loss: 0.3280 - val_acc: 0.8658\n",
      "Epoch 645/1000\n",
      " - 8s - loss: 0.3886 - acc: 0.8195 - val_loss: 0.3285 - val_acc: 0.8664\n",
      "Epoch 646/1000\n",
      " - 8s - loss: 0.3887 - acc: 0.8202 - val_loss: 0.3295 - val_acc: 0.8669\n",
      "Epoch 647/1000\n",
      " - 7s - loss: 0.3910 - acc: 0.8182 - val_loss: 0.3276 - val_acc: 0.8675\n",
      "Epoch 648/1000\n",
      " - 7s - loss: 0.3895 - acc: 0.8187 - val_loss: 0.3273 - val_acc: 0.8639\n",
      "Epoch 649/1000\n",
      " - 7s - loss: 0.3895 - acc: 0.8187 - val_loss: 0.3281 - val_acc: 0.8644\n",
      "Epoch 650/1000\n",
      " - 7s - loss: 0.3905 - acc: 0.8175 - val_loss: 0.3338 - val_acc: 0.8592\n",
      "Epoch 651/1000\n",
      " - 7s - loss: 0.3899 - acc: 0.8185 - val_loss: 0.3299 - val_acc: 0.8658\n",
      "Epoch 652/1000\n",
      " - 7s - loss: 0.3879 - acc: 0.8202 - val_loss: 0.3274 - val_acc: 0.8666\n",
      "Epoch 653/1000\n",
      " - 7s - loss: 0.3884 - acc: 0.8200 - val_loss: 0.3309 - val_acc: 0.8611\n",
      "Epoch 654/1000\n",
      " - 7s - loss: 0.3883 - acc: 0.8187 - val_loss: 0.3282 - val_acc: 0.8642\n",
      "Epoch 655/1000\n",
      " - 7s - loss: 0.3897 - acc: 0.8185 - val_loss: 0.3251 - val_acc: 0.8669\n",
      "Epoch 656/1000\n",
      " - 7s - loss: 0.3874 - acc: 0.8194 - val_loss: 0.3271 - val_acc: 0.8622\n",
      "Epoch 657/1000\n",
      " - 7s - loss: 0.3893 - acc: 0.8183 - val_loss: 0.3274 - val_acc: 0.8676\n",
      "Epoch 658/1000\n",
      " - 7s - loss: 0.3892 - acc: 0.8186 - val_loss: 0.3286 - val_acc: 0.8627\n",
      "Epoch 659/1000\n",
      " - 7s - loss: 0.3890 - acc: 0.8201 - val_loss: 0.3293 - val_acc: 0.8631\n",
      "Epoch 660/1000\n",
      " - 7s - loss: 0.3881 - acc: 0.8203 - val_loss: 0.3247 - val_acc: 0.8655\n",
      "Epoch 661/1000\n",
      " - 7s - loss: 0.3920 - acc: 0.8172 - val_loss: 0.3248 - val_acc: 0.8665\n",
      "Epoch 662/1000\n",
      " - 7s - loss: 0.3883 - acc: 0.8195 - val_loss: 0.3270 - val_acc: 0.8679\n",
      "Epoch 663/1000\n",
      " - 7s - loss: 0.3885 - acc: 0.8190 - val_loss: 0.3299 - val_acc: 0.8643\n",
      "Epoch 664/1000\n",
      " - 7s - loss: 0.3874 - acc: 0.8185 - val_loss: 0.3250 - val_acc: 0.8658\n",
      "Epoch 665/1000\n",
      " - 7s - loss: 0.3876 - acc: 0.8193 - val_loss: 0.3339 - val_acc: 0.8587\n",
      "Epoch 666/1000\n",
      " - 7s - loss: 0.3883 - acc: 0.8196 - val_loss: 0.3286 - val_acc: 0.8646\n",
      "Epoch 667/1000\n",
      " - 7s - loss: 0.3880 - acc: 0.8190 - val_loss: 0.3266 - val_acc: 0.8652\n",
      "Epoch 668/1000\n",
      " - 7s - loss: 0.3897 - acc: 0.8193 - val_loss: 0.3279 - val_acc: 0.8612\n",
      "Epoch 669/1000\n",
      " - 7s - loss: 0.3887 - acc: 0.8198 - val_loss: 0.3258 - val_acc: 0.8678\n",
      "Epoch 670/1000\n",
      " - 7s - loss: 0.3864 - acc: 0.8213 - val_loss: 0.3287 - val_acc: 0.8657\n",
      "Epoch 671/1000\n",
      " - 7s - loss: 0.3900 - acc: 0.8187 - val_loss: 0.3274 - val_acc: 0.8636\n",
      "Epoch 672/1000\n",
      " - 7s - loss: 0.3872 - acc: 0.8192 - val_loss: 0.3264 - val_acc: 0.8664\n",
      "Epoch 673/1000\n",
      " - 7s - loss: 0.3898 - acc: 0.8196 - val_loss: 0.3262 - val_acc: 0.8653\n",
      "Epoch 674/1000\n",
      " - 7s - loss: 0.3886 - acc: 0.8187 - val_loss: 0.3272 - val_acc: 0.8659\n",
      "Epoch 675/1000\n",
      " - 7s - loss: 0.3893 - acc: 0.8186 - val_loss: 0.3249 - val_acc: 0.8657\n",
      "Epoch 676/1000\n",
      " - 7s - loss: 0.3876 - acc: 0.8188 - val_loss: 0.3238 - val_acc: 0.8679\n",
      "Epoch 677/1000\n",
      " - 7s - loss: 0.3870 - acc: 0.8211 - val_loss: 0.3271 - val_acc: 0.8655\n",
      "Epoch 678/1000\n",
      " - 7s - loss: 0.3878 - acc: 0.8185 - val_loss: 0.3289 - val_acc: 0.8660\n",
      "Epoch 679/1000\n",
      " - 7s - loss: 0.3882 - acc: 0.8204 - val_loss: 0.3276 - val_acc: 0.8637\n",
      "Epoch 680/1000\n",
      " - 7s - loss: 0.3901 - acc: 0.8186 - val_loss: 0.3280 - val_acc: 0.8641\n",
      "Epoch 681/1000\n",
      " - 7s - loss: 0.3861 - acc: 0.8206 - val_loss: 0.3269 - val_acc: 0.8652\n",
      "Epoch 682/1000\n",
      " - 7s - loss: 0.3878 - acc: 0.8197 - val_loss: 0.3239 - val_acc: 0.8692\n",
      "Epoch 683/1000\n",
      " - 7s - loss: 0.3881 - acc: 0.8197 - val_loss: 0.3281 - val_acc: 0.8638\n",
      "Epoch 684/1000\n",
      " - 7s - loss: 0.3878 - acc: 0.8201 - val_loss: 0.3234 - val_acc: 0.8713\n",
      "Epoch 685/1000\n",
      " - 7s - loss: 0.3869 - acc: 0.8205 - val_loss: 0.3227 - val_acc: 0.8681\n",
      "Epoch 686/1000\n",
      " - 7s - loss: 0.3888 - acc: 0.8194 - val_loss: 0.3297 - val_acc: 0.8612\n",
      "Epoch 687/1000\n",
      " - 7s - loss: 0.3870 - acc: 0.8199 - val_loss: 0.3259 - val_acc: 0.8665\n",
      "Epoch 688/1000\n",
      " - 8s - loss: 0.3860 - acc: 0.8216 - val_loss: 0.3243 - val_acc: 0.8665\n",
      "Epoch 689/1000\n",
      " - 7s - loss: 0.3886 - acc: 0.8204 - val_loss: 0.3288 - val_acc: 0.8627\n",
      "Epoch 690/1000\n",
      " - 7s - loss: 0.3871 - acc: 0.8200 - val_loss: 0.3248 - val_acc: 0.8668\n",
      "Epoch 691/1000\n",
      " - 7s - loss: 0.3863 - acc: 0.8212 - val_loss: 0.3246 - val_acc: 0.8660\n",
      "Epoch 692/1000\n",
      " - 7s - loss: 0.3861 - acc: 0.8201 - val_loss: 0.3278 - val_acc: 0.8630\n",
      "Epoch 693/1000\n",
      " - 7s - loss: 0.3867 - acc: 0.8203 - val_loss: 0.3229 - val_acc: 0.8667\n",
      "Epoch 694/1000\n",
      " - 7s - loss: 0.3863 - acc: 0.8218 - val_loss: 0.3254 - val_acc: 0.8641\n",
      "Epoch 695/1000\n",
      " - 7s - loss: 0.3896 - acc: 0.8182 - val_loss: 0.3240 - val_acc: 0.8666\n",
      "Epoch 696/1000\n",
      " - 7s - loss: 0.3863 - acc: 0.8195 - val_loss: 0.3220 - val_acc: 0.8662\n",
      "Epoch 697/1000\n",
      " - 7s - loss: 0.3869 - acc: 0.8213 - val_loss: 0.3254 - val_acc: 0.8678\n",
      "Epoch 698/1000\n",
      " - 7s - loss: 0.3881 - acc: 0.8200 - val_loss: 0.3208 - val_acc: 0.8688\n",
      "Epoch 699/1000\n",
      " - 7s - loss: 0.3897 - acc: 0.8182 - val_loss: 0.3247 - val_acc: 0.8670\n",
      "Epoch 700/1000\n",
      " - 7s - loss: 0.3852 - acc: 0.8209 - val_loss: 0.3244 - val_acc: 0.8654\n",
      "Epoch 701/1000\n",
      " - 7s - loss: 0.3865 - acc: 0.8199 - val_loss: 0.3244 - val_acc: 0.8647\n",
      "Epoch 702/1000\n",
      " - 7s - loss: 0.3853 - acc: 0.8225 - val_loss: 0.3235 - val_acc: 0.8690\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 703/1000\n",
      " - 7s - loss: 0.3863 - acc: 0.8211 - val_loss: 0.3247 - val_acc: 0.8685\n",
      "Epoch 704/1000\n",
      " - 7s - loss: 0.3856 - acc: 0.8206 - val_loss: 0.3254 - val_acc: 0.8694\n",
      "Epoch 705/1000\n",
      " - 7s - loss: 0.3863 - acc: 0.8201 - val_loss: 0.3237 - val_acc: 0.8670\n",
      "Epoch 706/1000\n",
      " - 7s - loss: 0.3860 - acc: 0.8223 - val_loss: 0.3291 - val_acc: 0.8632\n",
      "Epoch 707/1000\n",
      " - 7s - loss: 0.3865 - acc: 0.8209 - val_loss: 0.3231 - val_acc: 0.8678\n",
      "Epoch 708/1000\n",
      " - 7s - loss: 0.3868 - acc: 0.8211 - val_loss: 0.3238 - val_acc: 0.8664\n",
      "Epoch 709/1000\n",
      " - 7s - loss: 0.3866 - acc: 0.8218 - val_loss: 0.3237 - val_acc: 0.8667\n",
      "Epoch 710/1000\n",
      " - 7s - loss: 0.3859 - acc: 0.8202 - val_loss: 0.3253 - val_acc: 0.8669\n",
      "Epoch 711/1000\n",
      " - 7s - loss: 0.3854 - acc: 0.8216 - val_loss: 0.3295 - val_acc: 0.8614\n",
      "Epoch 712/1000\n",
      " - 7s - loss: 0.3876 - acc: 0.8205 - val_loss: 0.3235 - val_acc: 0.8663\n",
      "Epoch 713/1000\n",
      " - 7s - loss: 0.3869 - acc: 0.8206 - val_loss: 0.3255 - val_acc: 0.8655\n",
      "Epoch 714/1000\n",
      " - 7s - loss: 0.3851 - acc: 0.8203 - val_loss: 0.3223 - val_acc: 0.8680\n",
      "Epoch 715/1000\n",
      " - 7s - loss: 0.3869 - acc: 0.8209 - val_loss: 0.3253 - val_acc: 0.8642\n",
      "Epoch 716/1000\n",
      " - 7s - loss: 0.3868 - acc: 0.8202 - val_loss: 0.3236 - val_acc: 0.8665\n",
      "Epoch 717/1000\n",
      " - 7s - loss: 0.3843 - acc: 0.8214 - val_loss: 0.3225 - val_acc: 0.8687\n",
      "Epoch 718/1000\n",
      " - 7s - loss: 0.3846 - acc: 0.8214 - val_loss: 0.3267 - val_acc: 0.8655\n",
      "Epoch 719/1000\n",
      " - 7s - loss: 0.3853 - acc: 0.8214 - val_loss: 0.3220 - val_acc: 0.8671\n",
      "Epoch 720/1000\n",
      " - 7s - loss: 0.3847 - acc: 0.8219 - val_loss: 0.3236 - val_acc: 0.8705\n",
      "Epoch 721/1000\n",
      " - 7s - loss: 0.3859 - acc: 0.8221 - val_loss: 0.3250 - val_acc: 0.8633\n",
      "Epoch 722/1000\n",
      " - 7s - loss: 0.3837 - acc: 0.8228 - val_loss: 0.3225 - val_acc: 0.8675\n",
      "Epoch 723/1000\n",
      " - 7s - loss: 0.3856 - acc: 0.8214 - val_loss: 0.3238 - val_acc: 0.8689\n",
      "Epoch 724/1000\n",
      " - 7s - loss: 0.3851 - acc: 0.8212 - val_loss: 0.3232 - val_acc: 0.8652\n",
      "Epoch 725/1000\n",
      " - 7s - loss: 0.3852 - acc: 0.8224 - val_loss: 0.3229 - val_acc: 0.8647\n",
      "Epoch 726/1000\n",
      " - 7s - loss: 0.3872 - acc: 0.8204 - val_loss: 0.3237 - val_acc: 0.8684\n",
      "Epoch 727/1000\n",
      " - 7s - loss: 0.3863 - acc: 0.8212 - val_loss: 0.3277 - val_acc: 0.8607\n",
      "Epoch 728/1000\n",
      " - 7s - loss: 0.3856 - acc: 0.8211 - val_loss: 0.3227 - val_acc: 0.8671\n",
      "Epoch 729/1000\n",
      " - 7s - loss: 0.3852 - acc: 0.8213 - val_loss: 0.3241 - val_acc: 0.8684\n",
      "Epoch 730/1000\n",
      " - 7s - loss: 0.3861 - acc: 0.8205 - val_loss: 0.3221 - val_acc: 0.8685\n",
      "Epoch 731/1000\n",
      " - 8s - loss: 0.3866 - acc: 0.8205 - val_loss: 0.3269 - val_acc: 0.8660\n",
      "Epoch 732/1000\n",
      " - 8s - loss: 0.3868 - acc: 0.8214 - val_loss: 0.3240 - val_acc: 0.8685\n",
      "Epoch 733/1000\n",
      " - 7s - loss: 0.3859 - acc: 0.8212 - val_loss: 0.3261 - val_acc: 0.8657\n",
      "Epoch 734/1000\n",
      " - 7s - loss: 0.3839 - acc: 0.8223 - val_loss: 0.3256 - val_acc: 0.8641\n",
      "Epoch 735/1000\n",
      " - 7s - loss: 0.3856 - acc: 0.8211 - val_loss: 0.3221 - val_acc: 0.8673\n",
      "Epoch 736/1000\n",
      " - 7s - loss: 0.3854 - acc: 0.8211 - val_loss: 0.3245 - val_acc: 0.8662\n",
      "Epoch 737/1000\n",
      " - 7s - loss: 0.3844 - acc: 0.8226 - val_loss: 0.3209 - val_acc: 0.8693\n",
      "Epoch 738/1000\n",
      " - 7s - loss: 0.3862 - acc: 0.8202 - val_loss: 0.3233 - val_acc: 0.8654\n",
      "Epoch 739/1000\n",
      " - 7s - loss: 0.3843 - acc: 0.8211 - val_loss: 0.3212 - val_acc: 0.8678\n",
      "Epoch 740/1000\n",
      " - 7s - loss: 0.3856 - acc: 0.8214 - val_loss: 0.3226 - val_acc: 0.8679\n",
      "Epoch 741/1000\n",
      " - 7s - loss: 0.3839 - acc: 0.8227 - val_loss: 0.3227 - val_acc: 0.8684\n",
      "Epoch 742/1000\n",
      " - 7s - loss: 0.3846 - acc: 0.8230 - val_loss: 0.3235 - val_acc: 0.8674\n",
      "Epoch 743/1000\n",
      " - 7s - loss: 0.3849 - acc: 0.8210 - val_loss: 0.3219 - val_acc: 0.8710\n",
      "Epoch 744/1000\n",
      " - 7s - loss: 0.3846 - acc: 0.8213 - val_loss: 0.3268 - val_acc: 0.8641\n",
      "Epoch 745/1000\n",
      " - 7s - loss: 0.3841 - acc: 0.8223 - val_loss: 0.3254 - val_acc: 0.8668\n",
      "Epoch 746/1000\n",
      " - 7s - loss: 0.3837 - acc: 0.8216 - val_loss: 0.3225 - val_acc: 0.8705\n",
      "Epoch 747/1000\n",
      " - 7s - loss: 0.3848 - acc: 0.8217 - val_loss: 0.3219 - val_acc: 0.8673\n",
      "Epoch 748/1000\n",
      " - 7s - loss: 0.3834 - acc: 0.8235 - val_loss: 0.3256 - val_acc: 0.8645\n",
      "Epoch 749/1000\n",
      " - 7s - loss: 0.3833 - acc: 0.8229 - val_loss: 0.3239 - val_acc: 0.8677\n",
      "Epoch 750/1000\n",
      " - 7s - loss: 0.3862 - acc: 0.8214 - val_loss: 0.3222 - val_acc: 0.8668\n",
      "Epoch 751/1000\n",
      " - 7s - loss: 0.3852 - acc: 0.8214 - val_loss: 0.3189 - val_acc: 0.8703\n",
      "Epoch 752/1000\n",
      " - 7s - loss: 0.3830 - acc: 0.8229 - val_loss: 0.3188 - val_acc: 0.8682\n",
      "Epoch 753/1000\n",
      " - 7s - loss: 0.3832 - acc: 0.8230 - val_loss: 0.3245 - val_acc: 0.8653\n",
      "Epoch 754/1000\n",
      " - 7s - loss: 0.3818 - acc: 0.8230 - val_loss: 0.3221 - val_acc: 0.8694\n",
      "Epoch 755/1000\n",
      " - 7s - loss: 0.3830 - acc: 0.8223 - val_loss: 0.3212 - val_acc: 0.8664\n",
      "Epoch 756/1000\n",
      " - 7s - loss: 0.3832 - acc: 0.8247 - val_loss: 0.3195 - val_acc: 0.8710\n",
      "Epoch 757/1000\n",
      " - 7s - loss: 0.3837 - acc: 0.8228 - val_loss: 0.3249 - val_acc: 0.8654\n",
      "Epoch 758/1000\n",
      " - 7s - loss: 0.3829 - acc: 0.8229 - val_loss: 0.3200 - val_acc: 0.8720\n",
      "Epoch 759/1000\n",
      " - 7s - loss: 0.3846 - acc: 0.8217 - val_loss: 0.3228 - val_acc: 0.8688\n",
      "Epoch 760/1000\n",
      " - 7s - loss: 0.3833 - acc: 0.8221 - val_loss: 0.3224 - val_acc: 0.8682\n",
      "Epoch 761/1000\n",
      " - 7s - loss: 0.3834 - acc: 0.8224 - val_loss: 0.3201 - val_acc: 0.8700\n",
      "Epoch 762/1000\n",
      " - 7s - loss: 0.3824 - acc: 0.8235 - val_loss: 0.3278 - val_acc: 0.8601\n",
      "Epoch 763/1000\n",
      " - 7s - loss: 0.3836 - acc: 0.8235 - val_loss: 0.3184 - val_acc: 0.8714\n",
      "Epoch 764/1000\n",
      " - 7s - loss: 0.3819 - acc: 0.8234 - val_loss: 0.3203 - val_acc: 0.8696\n",
      "Epoch 765/1000\n",
      " - 7s - loss: 0.3840 - acc: 0.8230 - val_loss: 0.3243 - val_acc: 0.8655\n",
      "Epoch 766/1000\n",
      " - 7s - loss: 0.3837 - acc: 0.8229 - val_loss: 0.3196 - val_acc: 0.8661\n",
      "Epoch 767/1000\n",
      " - 7s - loss: 0.3831 - acc: 0.8230 - val_loss: 0.3227 - val_acc: 0.8684\n",
      "Epoch 768/1000\n",
      " - 7s - loss: 0.3839 - acc: 0.8223 - val_loss: 0.3212 - val_acc: 0.8695\n",
      "Epoch 769/1000\n",
      " - 7s - loss: 0.3828 - acc: 0.8239 - val_loss: 0.3173 - val_acc: 0.8719\n",
      "Epoch 770/1000\n",
      " - 7s - loss: 0.3833 - acc: 0.8233 - val_loss: 0.3235 - val_acc: 0.8676\n",
      "Epoch 771/1000\n",
      " - 7s - loss: 0.3852 - acc: 0.8226 - val_loss: 0.3211 - val_acc: 0.8680\n",
      "Epoch 772/1000\n",
      " - 7s - loss: 0.3839 - acc: 0.8238 - val_loss: 0.3180 - val_acc: 0.8690\n",
      "Epoch 773/1000\n",
      " - 7s - loss: 0.3814 - acc: 0.8237 - val_loss: 0.3230 - val_acc: 0.8684\n",
      "Epoch 774/1000\n",
      " - 7s - loss: 0.3815 - acc: 0.8239 - val_loss: 0.3261 - val_acc: 0.8639\n",
      "Epoch 775/1000\n",
      " - 8s - loss: 0.3844 - acc: 0.8224 - val_loss: 0.3171 - val_acc: 0.8718\n",
      "Epoch 776/1000\n",
      " - 7s - loss: 0.3829 - acc: 0.8234 - val_loss: 0.3232 - val_acc: 0.8650\n",
      "Epoch 777/1000\n",
      " - 7s - loss: 0.3810 - acc: 0.8250 - val_loss: 0.3210 - val_acc: 0.8709\n",
      "Epoch 778/1000\n",
      " - 7s - loss: 0.3821 - acc: 0.8237 - val_loss: 0.3213 - val_acc: 0.8676\n",
      "Epoch 779/1000\n",
      " - 7s - loss: 0.3815 - acc: 0.8244 - val_loss: 0.3247 - val_acc: 0.8647\n",
      "Epoch 780/1000\n",
      " - 7s - loss: 0.3831 - acc: 0.8235 - val_loss: 0.3231 - val_acc: 0.8654\n",
      "Epoch 781/1000\n",
      " - 7s - loss: 0.3820 - acc: 0.8231 - val_loss: 0.3217 - val_acc: 0.8683\n",
      "Epoch 782/1000\n",
      " - 7s - loss: 0.3847 - acc: 0.8225 - val_loss: 0.3231 - val_acc: 0.8704\n",
      "Epoch 783/1000\n",
      " - 7s - loss: 0.3812 - acc: 0.8229 - val_loss: 0.3224 - val_acc: 0.8683\n",
      "Epoch 784/1000\n",
      " - 7s - loss: 0.3819 - acc: 0.8242 - val_loss: 0.3184 - val_acc: 0.8718\n",
      "Epoch 785/1000\n",
      " - 7s - loss: 0.3809 - acc: 0.8239 - val_loss: 0.3204 - val_acc: 0.8689\n",
      "Epoch 786/1000\n",
      " - 7s - loss: 0.3821 - acc: 0.8234 - val_loss: 0.3168 - val_acc: 0.8728\n",
      "Epoch 787/1000\n",
      " - 7s - loss: 0.3836 - acc: 0.8225 - val_loss: 0.3223 - val_acc: 0.8686\n",
      "Epoch 788/1000\n",
      " - 7s - loss: 0.3811 - acc: 0.8238 - val_loss: 0.3216 - val_acc: 0.8678\n",
      "Epoch 789/1000\n",
      " - 7s - loss: 0.3840 - acc: 0.8235 - val_loss: 0.3228 - val_acc: 0.8678\n",
      "Epoch 790/1000\n",
      " - 7s - loss: 0.3826 - acc: 0.8224 - val_loss: 0.3183 - val_acc: 0.8730\n",
      "Epoch 791/1000\n",
      " - 7s - loss: 0.3820 - acc: 0.8255 - val_loss: 0.3206 - val_acc: 0.8696\n",
      "Epoch 792/1000\n",
      " - 7s - loss: 0.3821 - acc: 0.8243 - val_loss: 0.3241 - val_acc: 0.8671\n",
      "Epoch 793/1000\n",
      " - 7s - loss: 0.3825 - acc: 0.8236 - val_loss: 0.3188 - val_acc: 0.8714\n",
      "Epoch 794/1000\n",
      " - 7s - loss: 0.3830 - acc: 0.8225 - val_loss: 0.3222 - val_acc: 0.8702\n",
      "Epoch 795/1000\n",
      " - 7s - loss: 0.3821 - acc: 0.8231 - val_loss: 0.3199 - val_acc: 0.8677\n",
      "Epoch 796/1000\n",
      " - 7s - loss: 0.3812 - acc: 0.8250 - val_loss: 0.3181 - val_acc: 0.8727\n",
      "Epoch 797/1000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      " - 7s - loss: 0.3818 - acc: 0.8245 - val_loss: 0.3181 - val_acc: 0.8727\n",
      "Epoch 798/1000\n",
      " - 7s - loss: 0.3816 - acc: 0.8235 - val_loss: 0.3186 - val_acc: 0.8693\n",
      "Epoch 799/1000\n",
      " - 7s - loss: 0.3830 - acc: 0.8218 - val_loss: 0.3191 - val_acc: 0.8680\n",
      "Epoch 800/1000\n",
      " - 7s - loss: 0.3841 - acc: 0.8227 - val_loss: 0.3176 - val_acc: 0.8692\n",
      "Epoch 801/1000\n",
      " - 7s - loss: 0.3806 - acc: 0.8245 - val_loss: 0.3177 - val_acc: 0.8723\n",
      "Epoch 802/1000\n",
      " - 7s - loss: 0.3830 - acc: 0.8240 - val_loss: 0.3161 - val_acc: 0.8720\n",
      "Epoch 803/1000\n",
      " - 7s - loss: 0.3809 - acc: 0.8258 - val_loss: 0.3188 - val_acc: 0.8701\n",
      "Epoch 804/1000\n",
      " - 7s - loss: 0.3808 - acc: 0.8240 - val_loss: 0.3239 - val_acc: 0.8668\n",
      "Epoch 805/1000\n",
      " - 7s - loss: 0.3815 - acc: 0.8253 - val_loss: 0.3174 - val_acc: 0.8705\n",
      "Epoch 806/1000\n",
      " - 7s - loss: 0.3797 - acc: 0.8258 - val_loss: 0.3196 - val_acc: 0.8687\n",
      "Epoch 807/1000\n",
      " - 7s - loss: 0.3799 - acc: 0.8258 - val_loss: 0.3207 - val_acc: 0.8685\n",
      "Epoch 808/1000\n",
      " - 7s - loss: 0.3816 - acc: 0.8244 - val_loss: 0.3205 - val_acc: 0.8701\n",
      "Epoch 809/1000\n",
      " - 7s - loss: 0.3794 - acc: 0.8260 - val_loss: 0.3181 - val_acc: 0.8718\n",
      "Epoch 810/1000\n",
      " - 7s - loss: 0.3799 - acc: 0.8250 - val_loss: 0.3203 - val_acc: 0.8715\n",
      "Epoch 811/1000\n",
      " - 7s - loss: 0.3816 - acc: 0.8247 - val_loss: 0.3170 - val_acc: 0.8710\n",
      "Epoch 812/1000\n",
      " - 7s - loss: 0.3814 - acc: 0.8241 - val_loss: 0.3180 - val_acc: 0.8699\n",
      "Epoch 813/1000\n",
      " - 7s - loss: 0.3801 - acc: 0.8241 - val_loss: 0.3177 - val_acc: 0.8719\n",
      "Epoch 814/1000\n",
      " - 7s - loss: 0.3785 - acc: 0.8250 - val_loss: 0.3194 - val_acc: 0.8700\n",
      "Epoch 815/1000\n",
      " - 7s - loss: 0.3799 - acc: 0.8248 - val_loss: 0.3162 - val_acc: 0.8726\n",
      "Epoch 816/1000\n",
      " - 7s - loss: 0.3809 - acc: 0.8237 - val_loss: 0.3185 - val_acc: 0.8686\n",
      "Epoch 817/1000\n",
      " - 7s - loss: 0.3801 - acc: 0.8244 - val_loss: 0.3233 - val_acc: 0.8641\n",
      "Epoch 818/1000\n",
      " - 8s - loss: 0.3807 - acc: 0.8245 - val_loss: 0.3190 - val_acc: 0.8734\n",
      "Epoch 819/1000\n",
      " - 8s - loss: 0.3811 - acc: 0.8236 - val_loss: 0.3210 - val_acc: 0.8700\n",
      "Epoch 820/1000\n",
      " - 7s - loss: 0.3808 - acc: 0.8242 - val_loss: 0.3177 - val_acc: 0.8721\n",
      "Epoch 821/1000\n",
      " - 7s - loss: 0.3800 - acc: 0.8250 - val_loss: 0.3186 - val_acc: 0.8704\n",
      "Epoch 822/1000\n",
      " - 7s - loss: 0.3799 - acc: 0.8252 - val_loss: 0.3183 - val_acc: 0.8714\n",
      "Epoch 823/1000\n",
      " - 7s - loss: 0.3823 - acc: 0.8234 - val_loss: 0.3207 - val_acc: 0.8687\n",
      "Epoch 824/1000\n",
      " - 7s - loss: 0.3812 - acc: 0.8241 - val_loss: 0.3205 - val_acc: 0.8674\n",
      "Epoch 825/1000\n",
      " - 7s - loss: 0.3807 - acc: 0.8241 - val_loss: 0.3215 - val_acc: 0.8697\n",
      "Epoch 826/1000\n",
      " - 7s - loss: 0.3807 - acc: 0.8247 - val_loss: 0.3177 - val_acc: 0.8700\n",
      "Epoch 827/1000\n",
      " - 7s - loss: 0.3799 - acc: 0.8246 - val_loss: 0.3195 - val_acc: 0.8711\n",
      "Epoch 828/1000\n",
      " - 7s - loss: 0.3824 - acc: 0.8241 - val_loss: 0.3182 - val_acc: 0.8723\n",
      "Epoch 829/1000\n",
      " - 7s - loss: 0.3804 - acc: 0.8249 - val_loss: 0.3170 - val_acc: 0.8720\n",
      "Epoch 830/1000\n",
      " - 7s - loss: 0.3807 - acc: 0.8240 - val_loss: 0.3217 - val_acc: 0.8659\n",
      "Epoch 831/1000\n",
      " - 7s - loss: 0.3785 - acc: 0.8250 - val_loss: 0.3163 - val_acc: 0.8724\n",
      "Epoch 832/1000\n",
      " - 7s - loss: 0.3800 - acc: 0.8252 - val_loss: 0.3222 - val_acc: 0.8665\n",
      "Epoch 833/1000\n",
      " - 7s - loss: 0.3810 - acc: 0.8242 - val_loss: 0.3203 - val_acc: 0.8691\n",
      "Epoch 834/1000\n",
      " - 7s - loss: 0.3799 - acc: 0.8243 - val_loss: 0.3176 - val_acc: 0.8704\n",
      "Epoch 835/1000\n",
      " - 7s - loss: 0.3799 - acc: 0.8257 - val_loss: 0.3177 - val_acc: 0.8705\n",
      "Epoch 836/1000\n",
      " - 7s - loss: 0.3788 - acc: 0.8267 - val_loss: 0.3177 - val_acc: 0.8708\n",
      "Epoch 837/1000\n",
      " - 7s - loss: 0.3804 - acc: 0.8251 - val_loss: 0.3202 - val_acc: 0.8682\n",
      "Epoch 838/1000\n",
      " - 7s - loss: 0.3805 - acc: 0.8242 - val_loss: 0.3212 - val_acc: 0.8690\n",
      "Epoch 839/1000\n",
      " - 7s - loss: 0.3788 - acc: 0.8256 - val_loss: 0.3171 - val_acc: 0.8709\n",
      "Epoch 840/1000\n",
      " - 7s - loss: 0.3811 - acc: 0.8241 - val_loss: 0.3227 - val_acc: 0.8683\n",
      "Epoch 841/1000\n",
      " - 7s - loss: 0.3800 - acc: 0.8246 - val_loss: 0.3189 - val_acc: 0.8705\n",
      "Epoch 842/1000\n",
      " - 7s - loss: 0.3813 - acc: 0.8241 - val_loss: 0.3168 - val_acc: 0.8698\n",
      "Epoch 843/1000\n",
      " - 7s - loss: 0.3800 - acc: 0.8253 - val_loss: 0.3189 - val_acc: 0.8690\n",
      "Epoch 844/1000\n",
      " - 7s - loss: 0.3792 - acc: 0.8262 - val_loss: 0.3202 - val_acc: 0.8689\n",
      "Epoch 845/1000\n",
      " - 7s - loss: 0.3795 - acc: 0.8254 - val_loss: 0.3195 - val_acc: 0.8669\n",
      "Epoch 846/1000\n",
      " - 7s - loss: 0.3816 - acc: 0.8237 - val_loss: 0.3163 - val_acc: 0.8721\n",
      "Epoch 847/1000\n",
      " - 7s - loss: 0.3804 - acc: 0.8241 - val_loss: 0.3185 - val_acc: 0.8713\n",
      "Epoch 848/1000\n",
      " - 7s - loss: 0.3798 - acc: 0.8243 - val_loss: 0.3194 - val_acc: 0.8702\n",
      "Epoch 849/1000\n",
      " - 7s - loss: 0.3803 - acc: 0.8250 - val_loss: 0.3172 - val_acc: 0.8712\n",
      "Epoch 850/1000\n",
      " - 7s - loss: 0.3796 - acc: 0.8246 - val_loss: 0.3188 - val_acc: 0.8714\n",
      "Epoch 851/1000\n",
      " - 7s - loss: 0.3804 - acc: 0.8252 - val_loss: 0.3231 - val_acc: 0.8677\n",
      "Epoch 852/1000\n",
      " - 7s - loss: 0.3797 - acc: 0.8253 - val_loss: 0.3213 - val_acc: 0.8686\n",
      "Epoch 853/1000\n",
      " - 7s - loss: 0.3794 - acc: 0.8243 - val_loss: 0.3171 - val_acc: 0.8728\n",
      "Epoch 854/1000\n",
      " - 7s - loss: 0.3783 - acc: 0.8252 - val_loss: 0.3167 - val_acc: 0.8716\n",
      "Epoch 855/1000\n",
      " - 7s - loss: 0.3772 - acc: 0.8262 - val_loss: 0.3166 - val_acc: 0.8730\n",
      "Epoch 856/1000\n",
      " - 7s - loss: 0.3793 - acc: 0.8255 - val_loss: 0.3164 - val_acc: 0.8704\n",
      "Epoch 857/1000\n",
      " - 7s - loss: 0.3794 - acc: 0.8254 - val_loss: 0.3166 - val_acc: 0.8714\n",
      "Epoch 858/1000\n",
      " - 7s - loss: 0.3784 - acc: 0.8265 - val_loss: 0.3194 - val_acc: 0.8719\n",
      "Epoch 859/1000\n",
      " - 7s - loss: 0.3788 - acc: 0.8260 - val_loss: 0.3152 - val_acc: 0.8737\n",
      "Epoch 860/1000\n",
      " - 7s - loss: 0.3774 - acc: 0.8260 - val_loss: 0.3141 - val_acc: 0.8745\n",
      "Epoch 861/1000\n",
      " - 8s - loss: 0.3792 - acc: 0.8259 - val_loss: 0.3140 - val_acc: 0.8747\n",
      "Epoch 862/1000\n",
      " - 7s - loss: 0.3791 - acc: 0.8261 - val_loss: 0.3149 - val_acc: 0.8739\n",
      "Epoch 863/1000\n",
      " - 7s - loss: 0.3789 - acc: 0.8258 - val_loss: 0.3210 - val_acc: 0.8692\n",
      "Epoch 864/1000\n",
      " - 7s - loss: 0.3792 - acc: 0.8248 - val_loss: 0.3146 - val_acc: 0.8725\n",
      "Epoch 865/1000\n",
      " - 7s - loss: 0.3777 - acc: 0.8265 - val_loss: 0.3143 - val_acc: 0.8761\n",
      "Epoch 866/1000\n",
      " - 7s - loss: 0.3799 - acc: 0.8255 - val_loss: 0.3143 - val_acc: 0.8744\n",
      "Epoch 867/1000\n",
      " - 7s - loss: 0.3779 - acc: 0.8254 - val_loss: 0.3140 - val_acc: 0.8721\n",
      "Epoch 868/1000\n",
      " - 7s - loss: 0.3793 - acc: 0.8256 - val_loss: 0.3142 - val_acc: 0.8719\n",
      "Epoch 869/1000\n",
      " - 7s - loss: 0.3776 - acc: 0.8258 - val_loss: 0.3153 - val_acc: 0.8711\n",
      "Epoch 870/1000\n",
      " - 7s - loss: 0.3799 - acc: 0.8245 - val_loss: 0.3163 - val_acc: 0.8727\n",
      "Epoch 871/1000\n",
      " - 7s - loss: 0.3797 - acc: 0.8240 - val_loss: 0.3149 - val_acc: 0.8737\n",
      "Epoch 872/1000\n",
      " - 7s - loss: 0.3776 - acc: 0.8274 - val_loss: 0.3163 - val_acc: 0.8723\n",
      "Epoch 873/1000\n",
      " - 7s - loss: 0.3772 - acc: 0.8252 - val_loss: 0.3178 - val_acc: 0.8691\n",
      "Epoch 874/1000\n",
      " - 7s - loss: 0.3797 - acc: 0.8257 - val_loss: 0.3181 - val_acc: 0.8708\n",
      "Epoch 875/1000\n",
      " - 7s - loss: 0.3790 - acc: 0.8260 - val_loss: 0.3186 - val_acc: 0.8702\n",
      "Epoch 876/1000\n",
      " - 7s - loss: 0.3811 - acc: 0.8247 - val_loss: 0.3141 - val_acc: 0.8734\n",
      "Epoch 877/1000\n",
      " - 7s - loss: 0.3793 - acc: 0.8253 - val_loss: 0.3167 - val_acc: 0.8706\n",
      "Epoch 878/1000\n",
      " - 7s - loss: 0.3781 - acc: 0.8271 - val_loss: 0.3149 - val_acc: 0.8729\n",
      "Epoch 879/1000\n",
      " - 7s - loss: 0.3788 - acc: 0.8258 - val_loss: 0.3171 - val_acc: 0.8734\n",
      "Epoch 880/1000\n",
      " - 7s - loss: 0.3781 - acc: 0.8270 - val_loss: 0.3157 - val_acc: 0.8709\n",
      "Epoch 881/1000\n",
      " - 7s - loss: 0.3793 - acc: 0.8255 - val_loss: 0.3186 - val_acc: 0.8702\n",
      "Epoch 882/1000\n",
      " - 7s - loss: 0.3780 - acc: 0.8273 - val_loss: 0.3198 - val_acc: 0.8704\n",
      "Epoch 883/1000\n",
      " - 7s - loss: 0.3784 - acc: 0.8270 - val_loss: 0.3182 - val_acc: 0.8704\n",
      "Epoch 884/1000\n",
      " - 7s - loss: 0.3777 - acc: 0.8272 - val_loss: 0.3178 - val_acc: 0.8725\n",
      "Epoch 885/1000\n",
      " - 7s - loss: 0.3780 - acc: 0.8266 - val_loss: 0.3181 - val_acc: 0.8690\n",
      "Epoch 886/1000\n",
      " - 7s - loss: 0.3786 - acc: 0.8264 - val_loss: 0.3125 - val_acc: 0.8762\n",
      "Epoch 887/1000\n",
      " - 7s - loss: 0.3785 - acc: 0.8268 - val_loss: 0.3120 - val_acc: 0.8779\n",
      "Epoch 888/1000\n",
      " - 7s - loss: 0.3762 - acc: 0.8274 - val_loss: 0.3146 - val_acc: 0.8739\n",
      "Epoch 889/1000\n",
      " - 7s - loss: 0.3776 - acc: 0.8268 - val_loss: 0.3140 - val_acc: 0.8735\n",
      "Epoch 890/1000\n",
      " - 7s - loss: 0.3768 - acc: 0.8266 - val_loss: 0.3134 - val_acc: 0.8731\n",
      "Epoch 891/1000\n",
      " - 7s - loss: 0.3780 - acc: 0.8264 - val_loss: 0.3168 - val_acc: 0.8724\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 892/1000\n",
      " - 7s - loss: 0.3773 - acc: 0.8271 - val_loss: 0.3163 - val_acc: 0.8714\n",
      "Epoch 893/1000\n",
      " - 7s - loss: 0.3779 - acc: 0.8275 - val_loss: 0.3138 - val_acc: 0.8746\n",
      "Epoch 894/1000\n",
      " - 7s - loss: 0.3775 - acc: 0.8279 - val_loss: 0.3161 - val_acc: 0.8712\n",
      "Epoch 895/1000\n",
      " - 7s - loss: 0.3781 - acc: 0.8254 - val_loss: 0.3181 - val_acc: 0.8723\n",
      "Epoch 896/1000\n",
      " - 7s - loss: 0.3753 - acc: 0.8279 - val_loss: 0.3152 - val_acc: 0.8730\n",
      "Epoch 897/1000\n",
      " - 7s - loss: 0.3772 - acc: 0.8260 - val_loss: 0.3146 - val_acc: 0.8722\n",
      "Epoch 898/1000\n",
      " - 7s - loss: 0.3750 - acc: 0.8281 - val_loss: 0.3170 - val_acc: 0.8721\n",
      "Epoch 899/1000\n",
      " - 7s - loss: 0.3761 - acc: 0.8275 - val_loss: 0.3134 - val_acc: 0.8765\n",
      "Epoch 900/1000\n",
      " - 7s - loss: 0.3790 - acc: 0.8256 - val_loss: 0.3132 - val_acc: 0.8764\n",
      "Epoch 901/1000\n",
      " - 7s - loss: 0.3765 - acc: 0.8261 - val_loss: 0.3129 - val_acc: 0.8735\n",
      "Epoch 902/1000\n",
      " - 7s - loss: 0.3795 - acc: 0.8255 - val_loss: 0.3124 - val_acc: 0.8758\n",
      "Epoch 903/1000\n",
      " - 7s - loss: 0.3759 - acc: 0.8280 - val_loss: 0.3142 - val_acc: 0.8745\n",
      "Epoch 904/1000\n",
      " - 8s - loss: 0.3777 - acc: 0.8262 - val_loss: 0.3133 - val_acc: 0.8765\n",
      "Epoch 905/1000\n",
      " - 8s - loss: 0.3773 - acc: 0.8281 - val_loss: 0.3198 - val_acc: 0.8700\n",
      "Epoch 906/1000\n",
      " - 7s - loss: 0.3758 - acc: 0.8286 - val_loss: 0.3160 - val_acc: 0.8727\n",
      "Epoch 907/1000\n",
      " - 7s - loss: 0.3750 - acc: 0.8287 - val_loss: 0.3153 - val_acc: 0.8711\n",
      "Epoch 908/1000\n",
      " - 7s - loss: 0.3768 - acc: 0.8290 - val_loss: 0.3133 - val_acc: 0.8725\n",
      "Epoch 909/1000\n",
      " - 7s - loss: 0.3753 - acc: 0.8277 - val_loss: 0.3128 - val_acc: 0.8744\n",
      "Epoch 910/1000\n",
      " - 7s - loss: 0.3768 - acc: 0.8278 - val_loss: 0.3161 - val_acc: 0.8726\n",
      "Epoch 911/1000\n",
      " - 7s - loss: 0.3780 - acc: 0.8270 - val_loss: 0.3145 - val_acc: 0.8744\n",
      "Epoch 912/1000\n",
      " - 7s - loss: 0.3753 - acc: 0.8282 - val_loss: 0.3143 - val_acc: 0.8719\n",
      "Epoch 913/1000\n",
      " - 7s - loss: 0.3775 - acc: 0.8266 - val_loss: 0.3168 - val_acc: 0.8743\n",
      "Epoch 914/1000\n",
      " - 7s - loss: 0.3771 - acc: 0.8260 - val_loss: 0.3113 - val_acc: 0.8775\n",
      "Epoch 915/1000\n",
      " - 7s - loss: 0.3764 - acc: 0.8273 - val_loss: 0.3145 - val_acc: 0.8737\n",
      "Epoch 916/1000\n",
      " - 7s - loss: 0.3778 - acc: 0.8273 - val_loss: 0.3166 - val_acc: 0.8705\n",
      "Epoch 917/1000\n",
      " - 7s - loss: 0.3763 - acc: 0.8279 - val_loss: 0.3126 - val_acc: 0.8738\n",
      "Epoch 918/1000\n",
      " - 7s - loss: 0.3768 - acc: 0.8274 - val_loss: 0.3134 - val_acc: 0.8756\n",
      "Epoch 919/1000\n",
      " - 7s - loss: 0.3780 - acc: 0.8264 - val_loss: 0.3130 - val_acc: 0.8765\n",
      "Epoch 920/1000\n",
      " - 7s - loss: 0.3762 - acc: 0.8279 - val_loss: 0.3224 - val_acc: 0.8658\n",
      "Epoch 921/1000\n",
      " - 7s - loss: 0.3777 - acc: 0.8272 - val_loss: 0.3124 - val_acc: 0.8754\n",
      "Epoch 922/1000\n",
      " - 7s - loss: 0.3756 - acc: 0.8283 - val_loss: 0.3172 - val_acc: 0.8731\n",
      "Epoch 923/1000\n",
      " - 7s - loss: 0.3756 - acc: 0.8281 - val_loss: 0.3152 - val_acc: 0.8722\n",
      "Epoch 924/1000\n",
      " - 7s - loss: 0.3769 - acc: 0.8279 - val_loss: 0.3164 - val_acc: 0.8728\n",
      "Epoch 925/1000\n",
      " - 7s - loss: 0.3762 - acc: 0.8277 - val_loss: 0.3148 - val_acc: 0.8741\n",
      "Epoch 926/1000\n",
      " - 7s - loss: 0.3773 - acc: 0.8267 - val_loss: 0.3127 - val_acc: 0.8757\n",
      "Epoch 927/1000\n",
      " - 7s - loss: 0.3775 - acc: 0.8274 - val_loss: 0.3133 - val_acc: 0.8750\n",
      "Epoch 928/1000\n",
      " - 7s - loss: 0.3764 - acc: 0.8270 - val_loss: 0.3122 - val_acc: 0.8757\n",
      "Epoch 929/1000\n",
      " - 7s - loss: 0.3748 - acc: 0.8277 - val_loss: 0.3124 - val_acc: 0.8746\n",
      "Epoch 930/1000\n",
      " - 7s - loss: 0.3769 - acc: 0.8282 - val_loss: 0.3129 - val_acc: 0.8740\n",
      "Epoch 931/1000\n",
      " - 7s - loss: 0.3756 - acc: 0.8288 - val_loss: 0.3137 - val_acc: 0.8722\n",
      "Epoch 932/1000\n",
      " - 7s - loss: 0.3782 - acc: 0.8276 - val_loss: 0.3121 - val_acc: 0.8769\n",
      "Epoch 933/1000\n",
      " - 7s - loss: 0.3749 - acc: 0.8281 - val_loss: 0.3163 - val_acc: 0.8741\n",
      "Epoch 934/1000\n",
      " - 7s - loss: 0.3767 - acc: 0.8276 - val_loss: 0.3119 - val_acc: 0.8753\n",
      "Epoch 935/1000\n",
      " - 7s - loss: 0.3763 - acc: 0.8266 - val_loss: 0.3178 - val_acc: 0.8706\n",
      "Epoch 936/1000\n",
      " - 7s - loss: 0.3759 - acc: 0.8280 - val_loss: 0.3128 - val_acc: 0.8757\n",
      "Epoch 937/1000\n",
      " - 7s - loss: 0.3764 - acc: 0.8276 - val_loss: 0.3149 - val_acc: 0.8738\n",
      "Epoch 938/1000\n",
      " - 7s - loss: 0.3755 - acc: 0.8279 - val_loss: 0.3134 - val_acc: 0.8717\n",
      "Epoch 939/1000\n",
      " - 7s - loss: 0.3754 - acc: 0.8275 - val_loss: 0.3142 - val_acc: 0.8721\n",
      "Epoch 940/1000\n",
      " - 7s - loss: 0.3755 - acc: 0.8286 - val_loss: 0.3171 - val_acc: 0.8718\n",
      "Epoch 941/1000\n",
      " - 7s - loss: 0.3748 - acc: 0.8280 - val_loss: 0.3130 - val_acc: 0.8718\n",
      "Epoch 942/1000\n",
      " - 7s - loss: 0.3763 - acc: 0.8270 - val_loss: 0.3101 - val_acc: 0.8774\n",
      "Epoch 943/1000\n",
      " - 7s - loss: 0.3766 - acc: 0.8277 - val_loss: 0.3104 - val_acc: 0.8770\n",
      "Epoch 944/1000\n",
      " - 7s - loss: 0.3752 - acc: 0.8291 - val_loss: 0.3116 - val_acc: 0.8754\n",
      "Epoch 945/1000\n",
      " - 7s - loss: 0.3747 - acc: 0.8292 - val_loss: 0.3137 - val_acc: 0.8746\n",
      "Epoch 946/1000\n",
      " - 7s - loss: 0.3755 - acc: 0.8280 - val_loss: 0.3139 - val_acc: 0.8736\n",
      "Epoch 947/1000\n",
      " - 7s - loss: 0.3739 - acc: 0.8275 - val_loss: 0.3090 - val_acc: 0.8753\n",
      "Epoch 948/1000\n",
      " - 8s - loss: 0.3742 - acc: 0.8289 - val_loss: 0.3140 - val_acc: 0.8736\n",
      "Epoch 949/1000\n",
      " - 8s - loss: 0.3749 - acc: 0.8288 - val_loss: 0.3119 - val_acc: 0.8755\n",
      "Epoch 950/1000\n",
      " - 7s - loss: 0.3754 - acc: 0.8284 - val_loss: 0.3139 - val_acc: 0.8709\n",
      "Epoch 951/1000\n",
      " - 7s - loss: 0.3766 - acc: 0.8273 - val_loss: 0.3120 - val_acc: 0.8756\n",
      "Epoch 952/1000\n",
      " - 7s - loss: 0.3758 - acc: 0.8274 - val_loss: 0.3127 - val_acc: 0.8754\n",
      "Epoch 953/1000\n",
      " - 7s - loss: 0.3743 - acc: 0.8288 - val_loss: 0.3146 - val_acc: 0.8733\n",
      "Epoch 954/1000\n",
      " - 7s - loss: 0.3752 - acc: 0.8290 - val_loss: 0.3125 - val_acc: 0.8748\n",
      "Epoch 955/1000\n",
      " - 7s - loss: 0.3732 - acc: 0.8308 - val_loss: 0.3152 - val_acc: 0.8729\n",
      "Epoch 956/1000\n",
      " - 7s - loss: 0.3730 - acc: 0.8294 - val_loss: 0.3103 - val_acc: 0.8735\n",
      "Epoch 957/1000\n",
      " - 7s - loss: 0.3770 - acc: 0.8280 - val_loss: 0.3148 - val_acc: 0.8735\n",
      "Epoch 958/1000\n",
      " - 7s - loss: 0.3753 - acc: 0.8284 - val_loss: 0.3166 - val_acc: 0.8742\n",
      "Epoch 959/1000\n",
      " - 7s - loss: 0.3731 - acc: 0.8302 - val_loss: 0.3091 - val_acc: 0.8779\n",
      "Epoch 960/1000\n",
      " - 7s - loss: 0.3738 - acc: 0.8290 - val_loss: 0.3095 - val_acc: 0.8755\n",
      "Epoch 961/1000\n",
      " - 7s - loss: 0.3731 - acc: 0.8289 - val_loss: 0.3136 - val_acc: 0.8755\n",
      "Epoch 962/1000\n",
      " - 7s - loss: 0.3754 - acc: 0.8286 - val_loss: 0.3111 - val_acc: 0.8767\n",
      "Epoch 963/1000\n",
      " - 7s - loss: 0.3750 - acc: 0.8298 - val_loss: 0.3118 - val_acc: 0.8741\n",
      "Epoch 964/1000\n",
      " - 7s - loss: 0.3747 - acc: 0.8295 - val_loss: 0.3146 - val_acc: 0.8730\n",
      "Epoch 965/1000\n",
      " - 7s - loss: 0.3737 - acc: 0.8298 - val_loss: 0.3118 - val_acc: 0.8764\n",
      "Epoch 966/1000\n",
      " - 7s - loss: 0.3736 - acc: 0.8286 - val_loss: 0.3133 - val_acc: 0.8732\n",
      "Epoch 967/1000\n",
      " - 7s - loss: 0.3738 - acc: 0.8292 - val_loss: 0.3113 - val_acc: 0.8742\n",
      "Epoch 968/1000\n",
      " - 7s - loss: 0.3744 - acc: 0.8288 - val_loss: 0.3135 - val_acc: 0.8751\n",
      "Epoch 969/1000\n",
      " - 7s - loss: 0.3744 - acc: 0.8308 - val_loss: 0.3168 - val_acc: 0.8703\n",
      "Epoch 970/1000\n",
      " - 7s - loss: 0.3753 - acc: 0.8278 - val_loss: 0.3107 - val_acc: 0.8766\n",
      "Epoch 971/1000\n",
      " - 7s - loss: 0.3747 - acc: 0.8288 - val_loss: 0.3154 - val_acc: 0.8717\n",
      "Epoch 972/1000\n",
      " - 7s - loss: 0.3743 - acc: 0.8304 - val_loss: 0.3128 - val_acc: 0.8760\n",
      "Epoch 973/1000\n",
      " - 7s - loss: 0.3748 - acc: 0.8290 - val_loss: 0.3121 - val_acc: 0.8760\n",
      "Epoch 974/1000\n",
      " - 7s - loss: 0.3735 - acc: 0.8291 - val_loss: 0.3078 - val_acc: 0.8770\n",
      "Epoch 975/1000\n",
      " - 7s - loss: 0.3753 - acc: 0.8277 - val_loss: 0.3111 - val_acc: 0.8775\n",
      "Epoch 976/1000\n",
      " - 7s - loss: 0.3741 - acc: 0.8292 - val_loss: 0.3138 - val_acc: 0.8741\n",
      "Epoch 977/1000\n",
      " - 7s - loss: 0.3758 - acc: 0.8294 - val_loss: 0.3103 - val_acc: 0.8769\n",
      "Epoch 978/1000\n",
      " - 7s - loss: 0.3730 - acc: 0.8302 - val_loss: 0.3116 - val_acc: 0.8771\n",
      "Epoch 979/1000\n",
      " - 7s - loss: 0.3722 - acc: 0.8301 - val_loss: 0.3078 - val_acc: 0.8765\n",
      "Epoch 980/1000\n",
      " - 7s - loss: 0.3717 - acc: 0.8310 - val_loss: 0.3133 - val_acc: 0.8719\n",
      "Epoch 981/1000\n",
      " - 7s - loss: 0.3729 - acc: 0.8300 - val_loss: 0.3102 - val_acc: 0.8793\n",
      "Epoch 982/1000\n",
      " - 7s - loss: 0.3735 - acc: 0.8299 - val_loss: 0.3100 - val_acc: 0.8766\n",
      "Epoch 983/1000\n",
      " - 7s - loss: 0.3741 - acc: 0.8291 - val_loss: 0.3131 - val_acc: 0.8748\n",
      "Epoch 984/1000\n",
      " - 7s - loss: 0.3740 - acc: 0.8296 - val_loss: 0.3121 - val_acc: 0.8736\n",
      "Epoch 985/1000\n",
      " - 7s - loss: 0.3729 - acc: 0.8291 - val_loss: 0.3115 - val_acc: 0.8748\n",
      "Epoch 986/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - 7s - loss: 0.3721 - acc: 0.8309 - val_loss: 0.3096 - val_acc: 0.8777\n",
      "Epoch 987/1000\n",
      " - 7s - loss: 0.3745 - acc: 0.8286 - val_loss: 0.3157 - val_acc: 0.8739\n",
      "Epoch 988/1000\n",
      " - 7s - loss: 0.3736 - acc: 0.8296 - val_loss: 0.3100 - val_acc: 0.8771\n",
      "Epoch 989/1000\n",
      " - 7s - loss: 0.3725 - acc: 0.8301 - val_loss: 0.3107 - val_acc: 0.8747\n",
      "Epoch 990/1000\n",
      " - 7s - loss: 0.3719 - acc: 0.8313 - val_loss: 0.3088 - val_acc: 0.8762\n",
      "Epoch 991/1000\n",
      " - 8s - loss: 0.3720 - acc: 0.8312 - val_loss: 0.3079 - val_acc: 0.8796\n",
      "Epoch 992/1000\n",
      " - 7s - loss: 0.3738 - acc: 0.8298 - val_loss: 0.3107 - val_acc: 0.8745\n",
      "Epoch 993/1000\n",
      " - 7s - loss: 0.3719 - acc: 0.8292 - val_loss: 0.3085 - val_acc: 0.8787\n",
      "Epoch 994/1000\n",
      " - 7s - loss: 0.3713 - acc: 0.8313 - val_loss: 0.3114 - val_acc: 0.8766\n",
      "Epoch 995/1000\n",
      " - 7s - loss: 0.3738 - acc: 0.8290 - val_loss: 0.3115 - val_acc: 0.8766\n",
      "Epoch 996/1000\n",
      " - 7s - loss: 0.3742 - acc: 0.8308 - val_loss: 0.3129 - val_acc: 0.8722\n",
      "Epoch 997/1000\n",
      " - 7s - loss: 0.3717 - acc: 0.8305 - val_loss: 0.3085 - val_acc: 0.8764\n",
      "Epoch 998/1000\n",
      " - 7s - loss: 0.3748 - acc: 0.8293 - val_loss: 0.3096 - val_acc: 0.8783\n",
      "Epoch 999/1000\n",
      " - 7s - loss: 0.3743 - acc: 0.8292 - val_loss: 0.3091 - val_acc: 0.8768\n",
      "Epoch 1000/1000\n",
      " - 7s - loss: 0.3736 - acc: 0.8302 - val_loss: 0.3103 - val_acc: 0.8772\n",
      "Iteration >#2: 87.972 \n",
      "\n",
      "Train on 106693 samples, validate on 35565 samples\n",
      "Epoch 1/1000\n",
      " - 8s - loss: 0.5938 - acc: 0.6241 - val_loss: 0.5607 - val_acc: 0.6347\n",
      "Epoch 2/1000\n",
      " - 7s - loss: 0.5621 - acc: 0.6401 - val_loss: 0.5587 - val_acc: 0.6460\n",
      "Epoch 3/1000\n",
      " - 7s - loss: 0.5595 - acc: 0.6450 - val_loss: 0.5582 - val_acc: 0.6506\n",
      "Epoch 4/1000\n",
      " - 7s - loss: 0.5566 - acc: 0.6525 - val_loss: 0.5522 - val_acc: 0.6610\n",
      "Epoch 5/1000\n",
      " - 7s - loss: 0.5547 - acc: 0.6554 - val_loss: 0.5528 - val_acc: 0.6559\n",
      "Epoch 6/1000\n",
      " - 7s - loss: 0.5527 - acc: 0.6587 - val_loss: 0.5490 - val_acc: 0.6693\n",
      "Epoch 7/1000\n",
      " - 7s - loss: 0.5514 - acc: 0.6604 - val_loss: 0.5501 - val_acc: 0.6588\n",
      "Epoch 8/1000\n",
      " - 7s - loss: 0.5499 - acc: 0.6637 - val_loss: 0.5456 - val_acc: 0.6719\n",
      "Epoch 9/1000\n",
      " - 7s - loss: 0.5486 - acc: 0.6646 - val_loss: 0.5452 - val_acc: 0.6790\n",
      "Epoch 10/1000\n",
      " - 7s - loss: 0.5475 - acc: 0.6654 - val_loss: 0.5433 - val_acc: 0.6687\n",
      "Epoch 11/1000\n",
      " - 7s - loss: 0.5466 - acc: 0.6666 - val_loss: 0.5418 - val_acc: 0.6780\n",
      "Epoch 12/1000\n",
      " - 7s - loss: 0.5454 - acc: 0.6694 - val_loss: 0.5446 - val_acc: 0.6667\n",
      "Epoch 13/1000\n",
      " - 7s - loss: 0.5441 - acc: 0.6705 - val_loss: 0.5413 - val_acc: 0.6739\n",
      "Epoch 14/1000\n",
      " - 7s - loss: 0.5433 - acc: 0.6711 - val_loss: 0.5424 - val_acc: 0.6861\n",
      "Epoch 15/1000\n",
      " - 7s - loss: 0.5426 - acc: 0.6734 - val_loss: 0.5404 - val_acc: 0.6822\n",
      "Epoch 16/1000\n",
      " - 7s - loss: 0.5418 - acc: 0.6728 - val_loss: 0.5378 - val_acc: 0.6793\n",
      "Epoch 17/1000\n",
      " - 7s - loss: 0.5401 - acc: 0.6757 - val_loss: 0.5355 - val_acc: 0.6880\n",
      "Epoch 18/1000\n",
      " - 7s - loss: 0.5389 - acc: 0.6790 - val_loss: 0.5377 - val_acc: 0.6825\n",
      "Epoch 19/1000\n",
      " - 7s - loss: 0.5386 - acc: 0.6784 - val_loss: 0.5344 - val_acc: 0.6834\n",
      "Epoch 20/1000\n",
      " - 7s - loss: 0.5373 - acc: 0.6812 - val_loss: 0.5384 - val_acc: 0.6788\n",
      "Epoch 21/1000\n",
      " - 7s - loss: 0.5362 - acc: 0.6840 - val_loss: 0.5318 - val_acc: 0.7001\n",
      "Epoch 22/1000\n",
      " - 7s - loss: 0.5355 - acc: 0.6854 - val_loss: 0.5331 - val_acc: 0.6923\n",
      "Epoch 23/1000\n",
      " - 7s - loss: 0.5342 - acc: 0.6867 - val_loss: 0.5293 - val_acc: 0.7024\n",
      "Epoch 24/1000\n",
      " - 7s - loss: 0.5338 - acc: 0.6871 - val_loss: 0.5319 - val_acc: 0.6966\n",
      "Epoch 25/1000\n",
      " - 7s - loss: 0.5331 - acc: 0.6898 - val_loss: 0.5292 - val_acc: 0.6974\n",
      "Epoch 26/1000\n",
      " - 7s - loss: 0.5326 - acc: 0.6903 - val_loss: 0.5272 - val_acc: 0.7040\n",
      "Epoch 27/1000\n",
      " - 7s - loss: 0.5309 - acc: 0.6917 - val_loss: 0.5256 - val_acc: 0.7027\n",
      "Epoch 28/1000\n",
      " - 7s - loss: 0.5302 - acc: 0.6928 - val_loss: 0.5242 - val_acc: 0.7114\n",
      "Epoch 29/1000\n",
      " - 7s - loss: 0.5294 - acc: 0.6943 - val_loss: 0.5226 - val_acc: 0.7142\n",
      "Epoch 30/1000\n",
      " - 7s - loss: 0.5280 - acc: 0.6970 - val_loss: 0.5219 - val_acc: 0.7129\n",
      "Epoch 31/1000\n",
      " - 7s - loss: 0.5269 - acc: 0.6980 - val_loss: 0.5196 - val_acc: 0.7033\n",
      "Epoch 32/1000\n",
      " - 7s - loss: 0.5265 - acc: 0.6985 - val_loss: 0.5184 - val_acc: 0.7189\n",
      "Epoch 33/1000\n",
      " - 9s - loss: 0.5242 - acc: 0.7010 - val_loss: 0.5162 - val_acc: 0.7138\n",
      "Epoch 34/1000\n",
      " - 8s - loss: 0.5229 - acc: 0.7037 - val_loss: 0.5135 - val_acc: 0.7187\n",
      "Epoch 35/1000\n",
      " - 7s - loss: 0.5223 - acc: 0.7036 - val_loss: 0.5169 - val_acc: 0.7140\n",
      "Epoch 36/1000\n",
      " - 7s - loss: 0.5218 - acc: 0.7057 - val_loss: 0.5107 - val_acc: 0.7197\n",
      "Epoch 37/1000\n",
      " - 7s - loss: 0.5196 - acc: 0.7054 - val_loss: 0.5106 - val_acc: 0.7226\n",
      "Epoch 38/1000\n",
      " - 7s - loss: 0.5187 - acc: 0.7070 - val_loss: 0.5089 - val_acc: 0.7229\n",
      "Epoch 39/1000\n",
      " - 7s - loss: 0.5174 - acc: 0.7091 - val_loss: 0.5057 - val_acc: 0.7289\n",
      "Epoch 40/1000\n",
      " - 7s - loss: 0.5157 - acc: 0.7114 - val_loss: 0.5039 - val_acc: 0.7292\n",
      "Epoch 41/1000\n",
      " - 7s - loss: 0.5136 - acc: 0.7124 - val_loss: 0.5022 - val_acc: 0.7313\n",
      "Epoch 42/1000\n",
      " - 7s - loss: 0.5128 - acc: 0.7135 - val_loss: 0.4991 - val_acc: 0.7315\n",
      "Epoch 43/1000\n",
      " - 7s - loss: 0.5117 - acc: 0.7162 - val_loss: 0.4968 - val_acc: 0.7359\n",
      "Epoch 44/1000\n",
      " - 7s - loss: 0.5102 - acc: 0.7186 - val_loss: 0.4930 - val_acc: 0.7380\n",
      "Epoch 45/1000\n",
      " - 7s - loss: 0.5093 - acc: 0.7172 - val_loss: 0.4930 - val_acc: 0.7379\n",
      "Epoch 46/1000\n",
      " - 7s - loss: 0.5082 - acc: 0.7186 - val_loss: 0.4897 - val_acc: 0.7412\n",
      "Epoch 47/1000\n",
      " - 7s - loss: 0.5067 - acc: 0.7206 - val_loss: 0.4900 - val_acc: 0.7415\n",
      "Epoch 48/1000\n",
      " - 7s - loss: 0.5054 - acc: 0.7221 - val_loss: 0.4871 - val_acc: 0.7418\n",
      "Epoch 49/1000\n",
      " - 7s - loss: 0.5048 - acc: 0.7232 - val_loss: 0.4853 - val_acc: 0.7445\n",
      "Epoch 50/1000\n",
      " - 7s - loss: 0.5028 - acc: 0.7250 - val_loss: 0.4856 - val_acc: 0.7464\n",
      "Epoch 51/1000\n",
      " - 7s - loss: 0.5015 - acc: 0.7275 - val_loss: 0.4889 - val_acc: 0.7401\n",
      "Epoch 52/1000\n",
      " - 7s - loss: 0.5008 - acc: 0.7280 - val_loss: 0.4829 - val_acc: 0.7482\n",
      "Epoch 53/1000\n",
      " - 7s - loss: 0.4998 - acc: 0.7298 - val_loss: 0.4772 - val_acc: 0.7553\n",
      "Epoch 54/1000\n",
      " - 7s - loss: 0.4999 - acc: 0.7289 - val_loss: 0.4824 - val_acc: 0.7480\n",
      "Epoch 55/1000\n",
      " - 7s - loss: 0.4974 - acc: 0.7304 - val_loss: 0.4725 - val_acc: 0.7586\n",
      "Epoch 56/1000\n",
      " - 7s - loss: 0.4976 - acc: 0.7304 - val_loss: 0.4734 - val_acc: 0.7596\n",
      "Epoch 57/1000\n",
      " - 7s - loss: 0.4956 - acc: 0.7332 - val_loss: 0.4746 - val_acc: 0.7568\n",
      "Epoch 58/1000\n",
      " - 7s - loss: 0.4948 - acc: 0.7343 - val_loss: 0.4744 - val_acc: 0.7535\n",
      "Epoch 59/1000\n",
      " - 7s - loss: 0.4939 - acc: 0.7357 - val_loss: 0.4720 - val_acc: 0.7571\n",
      "Epoch 60/1000\n",
      " - 7s - loss: 0.4942 - acc: 0.7343 - val_loss: 0.4722 - val_acc: 0.7574\n",
      "Epoch 61/1000\n",
      " - 7s - loss: 0.4923 - acc: 0.7364 - val_loss: 0.4762 - val_acc: 0.7490\n",
      "Epoch 62/1000\n",
      " - 7s - loss: 0.4917 - acc: 0.7362 - val_loss: 0.4732 - val_acc: 0.7514\n",
      "Epoch 63/1000\n",
      " - 7s - loss: 0.4911 - acc: 0.7382 - val_loss: 0.4651 - val_acc: 0.7688\n",
      "Epoch 64/1000\n",
      " - 7s - loss: 0.4897 - acc: 0.7383 - val_loss: 0.4642 - val_acc: 0.7654\n",
      "Epoch 65/1000\n",
      " - 7s - loss: 0.4904 - acc: 0.7379 - val_loss: 0.4647 - val_acc: 0.7622\n",
      "Epoch 66/1000\n",
      " - 7s - loss: 0.4889 - acc: 0.7398 - val_loss: 0.4638 - val_acc: 0.7650\n",
      "Epoch 67/1000\n",
      " - 7s - loss: 0.4874 - acc: 0.7402 - val_loss: 0.4683 - val_acc: 0.7575\n",
      "Epoch 68/1000\n",
      " - 7s - loss: 0.4870 - acc: 0.7428 - val_loss: 0.4656 - val_acc: 0.7651\n",
      "Epoch 69/1000\n",
      " - 7s - loss: 0.4858 - acc: 0.7427 - val_loss: 0.4564 - val_acc: 0.7709\n",
      "Epoch 70/1000\n",
      " - 7s - loss: 0.4849 - acc: 0.7438 - val_loss: 0.4565 - val_acc: 0.7693\n",
      "Epoch 71/1000\n",
      " - 7s - loss: 0.4845 - acc: 0.7441 - val_loss: 0.4548 - val_acc: 0.7754\n",
      "Epoch 72/1000\n",
      " - 7s - loss: 0.4835 - acc: 0.7447 - val_loss: 0.4552 - val_acc: 0.7742\n",
      "Epoch 73/1000\n",
      " - 7s - loss: 0.4824 - acc: 0.7465 - val_loss: 0.4544 - val_acc: 0.7767\n",
      "Epoch 74/1000\n",
      " - 7s - loss: 0.4824 - acc: 0.7459 - val_loss: 0.4569 - val_acc: 0.7718\n",
      "Epoch 75/1000\n",
      " - 9s - loss: 0.4809 - acc: 0.7464 - val_loss: 0.4567 - val_acc: 0.7696\n",
      "Epoch 76/1000\n",
      " - 8s - loss: 0.4816 - acc: 0.7474 - val_loss: 0.4538 - val_acc: 0.7725\n",
      "Epoch 77/1000\n",
      " - 7s - loss: 0.4807 - acc: 0.7470 - val_loss: 0.4484 - val_acc: 0.7820\n",
      "Epoch 78/1000\n",
      " - 7s - loss: 0.4793 - acc: 0.7490 - val_loss: 0.4472 - val_acc: 0.7765\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 81/1000\n",
      " - 7s - loss: 0.4775 - acc: 0.7515 - val_loss: 0.4451 - val_acc: 0.7823\n",
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      "Epoch 99/1000\n",
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      "Epoch 106/1000\n",
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      "Epoch 107/1000\n",
      " - 7s - loss: 0.4643 - acc: 0.7630 - val_loss: 0.4260 - val_acc: 0.7956\n",
      "Epoch 108/1000\n",
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      "Epoch 109/1000\n",
      " - 7s - loss: 0.4643 - acc: 0.7614 - val_loss: 0.4257 - val_acc: 0.8001\n",
      "Epoch 110/1000\n",
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      "Epoch 111/1000\n",
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      "Epoch 113/1000\n",
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      "Epoch 114/1000\n",
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      " - 9s - loss: 0.4581 - acc: 0.7684 - val_loss: 0.4206 - val_acc: 0.8008\n",
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      "Epoch 130/1000\n",
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      "Epoch 133/1000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 176/1000\n",
      " - 7s - loss: 0.4370 - acc: 0.7835 - val_loss: 0.3905 - val_acc: 0.8218\n",
      "Epoch 177/1000\n",
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      "Epoch 182/1000\n",
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      "Epoch 183/1000\n",
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      "Epoch 193/1000\n",
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      "Epoch 201/1000\n",
      " - 7s - loss: 0.4309 - acc: 0.7890 - val_loss: 0.3804 - val_acc: 0.8310\n",
      "Epoch 202/1000\n",
      " - 9s - loss: 0.4309 - acc: 0.7903 - val_loss: 0.3819 - val_acc: 0.8244\n",
      "Epoch 203/1000\n",
      " - 8s - loss: 0.4306 - acc: 0.7892 - val_loss: 0.3811 - val_acc: 0.8297\n",
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      "Epoch 224/1000\n",
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      "Epoch 270/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - 7s - loss: 0.4188 - acc: 0.7974 - val_loss: 0.3672 - val_acc: 0.8410\n",
      "Epoch 271/1000\n",
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      " - 7s - loss: 0.4066 - acc: 0.8075 - val_loss: 0.3466 - val_acc: 0.8561\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 365/1000\n",
      " - 7s - loss: 0.4070 - acc: 0.8084 - val_loss: 0.3501 - val_acc: 0.8505\n",
      "Epoch 366/1000\n",
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      "Epoch 371/1000\n",
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      "Epoch 373/1000\n",
      " - 7s - loss: 0.4037 - acc: 0.8101 - val_loss: 0.3449 - val_acc: 0.8542\n",
      "Epoch 374/1000\n",
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      "Epoch 375/1000\n",
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      "Epoch 376/1000\n",
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      " - 7s - loss: 0.3997 - acc: 0.8139 - val_loss: 0.3395 - val_acc: 0.8568\n",
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      " - 7s - loss: 0.3983 - acc: 0.8140 - val_loss: 0.3383 - val_acc: 0.8588\n",
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      " - 7s - loss: 0.3980 - acc: 0.8146 - val_loss: 0.3378 - val_acc: 0.8580\n",
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      " - 7s - loss: 0.3972 - acc: 0.8149 - val_loss: 0.3378 - val_acc: 0.8612\n",
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      " - 7s - loss: 0.3981 - acc: 0.8145 - val_loss: 0.3413 - val_acc: 0.8591\n",
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      " - 7s - loss: 0.3963 - acc: 0.8142 - val_loss: 0.3366 - val_acc: 0.8585\n",
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      " - 7s - loss: 0.3965 - acc: 0.8147 - val_loss: 0.3370 - val_acc: 0.8604\n",
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      " - 8s - loss: 0.3972 - acc: 0.8142 - val_loss: 0.3373 - val_acc: 0.8596\n",
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      " - 7s - loss: 0.3972 - acc: 0.8152 - val_loss: 0.3350 - val_acc: 0.8618\n",
      "Epoch 459/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - 7s - loss: 0.3966 - acc: 0.8160 - val_loss: 0.3352 - val_acc: 0.8599\n",
      "Epoch 460/1000\n",
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      " - 7s - loss: 0.3911 - acc: 0.8193 - val_loss: 0.3290 - val_acc: 0.8659\n",
      "Epoch 526/1000\n",
      " - 7s - loss: 0.3931 - acc: 0.8177 - val_loss: 0.3281 - val_acc: 0.8630\n",
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      " - 7s - loss: 0.3914 - acc: 0.8176 - val_loss: 0.3322 - val_acc: 0.8651\n",
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      " - 7s - loss: 0.3915 - acc: 0.8189 - val_loss: 0.3277 - val_acc: 0.8658\n",
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      " - 7s - loss: 0.3903 - acc: 0.8190 - val_loss: 0.3310 - val_acc: 0.8606\n",
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      " - 7s - loss: 0.3890 - acc: 0.8208 - val_loss: 0.3265 - val_acc: 0.8666\n",
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      " - 7s - loss: 0.3902 - acc: 0.8193 - val_loss: 0.3290 - val_acc: 0.8646\n",
      "Epoch 537/1000\n",
      " - 7s - loss: 0.3895 - acc: 0.8194 - val_loss: 0.3281 - val_acc: 0.8658\n",
      "Epoch 538/1000\n",
      " - 7s - loss: 0.3893 - acc: 0.8205 - val_loss: 0.3309 - val_acc: 0.8615\n",
      "Epoch 539/1000\n",
      " - 7s - loss: 0.3910 - acc: 0.8184 - val_loss: 0.3314 - val_acc: 0.8599\n",
      "Epoch 540/1000\n",
      " - 9s - loss: 0.3895 - acc: 0.8201 - val_loss: 0.3281 - val_acc: 0.8647\n",
      "Epoch 541/1000\n",
      " - 8s - loss: 0.3892 - acc: 0.8197 - val_loss: 0.3281 - val_acc: 0.8654\n",
      "Epoch 542/1000\n",
      " - 7s - loss: 0.3878 - acc: 0.8208 - val_loss: 0.3274 - val_acc: 0.8686\n",
      "Epoch 543/1000\n",
      " - 7s - loss: 0.3889 - acc: 0.8213 - val_loss: 0.3310 - val_acc: 0.8597\n",
      "Epoch 544/1000\n",
      " - 7s - loss: 0.3889 - acc: 0.8196 - val_loss: 0.3295 - val_acc: 0.8640\n",
      "Epoch 545/1000\n",
      " - 7s - loss: 0.3903 - acc: 0.8210 - val_loss: 0.3266 - val_acc: 0.8660\n",
      "Epoch 546/1000\n",
      " - 7s - loss: 0.3867 - acc: 0.8200 - val_loss: 0.3307 - val_acc: 0.8637\n",
      "Epoch 547/1000\n",
      " - 7s - loss: 0.3871 - acc: 0.8211 - val_loss: 0.3322 - val_acc: 0.8613\n",
      "Epoch 548/1000\n",
      " - 7s - loss: 0.3911 - acc: 0.8187 - val_loss: 0.3320 - val_acc: 0.8620\n",
      "Epoch 549/1000\n",
      " - 7s - loss: 0.3901 - acc: 0.8200 - val_loss: 0.3326 - val_acc: 0.8605\n",
      "Epoch 550/1000\n",
      " - 7s - loss: 0.3883 - acc: 0.8195 - val_loss: 0.3278 - val_acc: 0.8638\n",
      "Epoch 551/1000\n",
      " - 7s - loss: 0.3880 - acc: 0.8200 - val_loss: 0.3274 - val_acc: 0.8646\n",
      "Epoch 552/1000\n",
      " - 7s - loss: 0.3896 - acc: 0.8197 - val_loss: 0.3239 - val_acc: 0.8666\n",
      "Epoch 553/1000\n",
      " - 7s - loss: 0.3882 - acc: 0.8206 - val_loss: 0.3251 - val_acc: 0.8665\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 554/1000\n",
      " - 7s - loss: 0.3898 - acc: 0.8205 - val_loss: 0.3314 - val_acc: 0.8634\n",
      "Epoch 555/1000\n",
      " - 7s - loss: 0.3871 - acc: 0.8219 - val_loss: 0.3274 - val_acc: 0.8646\n",
      "Epoch 556/1000\n",
      " - 7s - loss: 0.3899 - acc: 0.8198 - val_loss: 0.3276 - val_acc: 0.8668\n",
      "Epoch 557/1000\n",
      " - 7s - loss: 0.3878 - acc: 0.8203 - val_loss: 0.3313 - val_acc: 0.8607\n",
      "Epoch 558/1000\n",
      " - 7s - loss: 0.3881 - acc: 0.8193 - val_loss: 0.3284 - val_acc: 0.8653\n",
      "Epoch 559/1000\n",
      " - 7s - loss: 0.3899 - acc: 0.8207 - val_loss: 0.3277 - val_acc: 0.8635\n",
      "Epoch 560/1000\n",
      " - 7s - loss: 0.3882 - acc: 0.8216 - val_loss: 0.3272 - val_acc: 0.8663\n",
      "Epoch 561/1000\n",
      " - 7s - loss: 0.3870 - acc: 0.8218 - val_loss: 0.3240 - val_acc: 0.8668\n",
      "Epoch 562/1000\n",
      " - 7s - loss: 0.3875 - acc: 0.8214 - val_loss: 0.3260 - val_acc: 0.8654\n",
      "Epoch 563/1000\n",
      " - 7s - loss: 0.3898 - acc: 0.8194 - val_loss: 0.3264 - val_acc: 0.8674\n",
      "Epoch 564/1000\n",
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      "Epoch 565/1000\n",
      " - 7s - loss: 0.3903 - acc: 0.8199 - val_loss: 0.3277 - val_acc: 0.8633\n",
      "Epoch 566/1000\n",
      " - 7s - loss: 0.3885 - acc: 0.8196 - val_loss: 0.3283 - val_acc: 0.8630\n",
      "Epoch 567/1000\n",
      " - 7s - loss: 0.3881 - acc: 0.8213 - val_loss: 0.3262 - val_acc: 0.8673\n",
      "Epoch 568/1000\n",
      " - 7s - loss: 0.3890 - acc: 0.8206 - val_loss: 0.3258 - val_acc: 0.8646\n",
      "Epoch 569/1000\n",
      " - 7s - loss: 0.3860 - acc: 0.8229 - val_loss: 0.3276 - val_acc: 0.8641\n",
      "Epoch 570/1000\n",
      " - 7s - loss: 0.3859 - acc: 0.8218 - val_loss: 0.3232 - val_acc: 0.8650\n",
      "Epoch 571/1000\n",
      " - 7s - loss: 0.3869 - acc: 0.8221 - val_loss: 0.3288 - val_acc: 0.8646\n",
      "Epoch 572/1000\n",
      " - 7s - loss: 0.3879 - acc: 0.8214 - val_loss: 0.3251 - val_acc: 0.8677\n",
      "Epoch 573/1000\n",
      " - 7s - loss: 0.3895 - acc: 0.8200 - val_loss: 0.3260 - val_acc: 0.8647\n",
      "Epoch 574/1000\n",
      " - 7s - loss: 0.3844 - acc: 0.8225 - val_loss: 0.3261 - val_acc: 0.8659\n",
      "Epoch 575/1000\n",
      " - 7s - loss: 0.3867 - acc: 0.8215 - val_loss: 0.3259 - val_acc: 0.8651\n",
      "Epoch 576/1000\n",
      " - 7s - loss: 0.3861 - acc: 0.8222 - val_loss: 0.3250 - val_acc: 0.8671\n",
      "Epoch 577/1000\n",
      " - 7s - loss: 0.3853 - acc: 0.8238 - val_loss: 0.3272 - val_acc: 0.8657\n",
      "Epoch 578/1000\n",
      " - 7s - loss: 0.3860 - acc: 0.8219 - val_loss: 0.3312 - val_acc: 0.8650\n",
      "Epoch 579/1000\n",
      " - 7s - loss: 0.3866 - acc: 0.8224 - val_loss: 0.3262 - val_acc: 0.8628\n",
      "Epoch 580/1000\n",
      " - 7s - loss: 0.3843 - acc: 0.8231 - val_loss: 0.3273 - val_acc: 0.8655\n",
      "Epoch 581/1000\n",
      " - 7s - loss: 0.3866 - acc: 0.8210 - val_loss: 0.3287 - val_acc: 0.8647\n",
      "Epoch 582/1000\n",
      " - 7s - loss: 0.3871 - acc: 0.8222 - val_loss: 0.3250 - val_acc: 0.8646\n",
      "Epoch 583/1000\n",
      " - 8s - loss: 0.3858 - acc: 0.8218 - val_loss: 0.3261 - val_acc: 0.8676\n",
      "Epoch 584/1000\n",
      " - 8s - loss: 0.3843 - acc: 0.8229 - val_loss: 0.3221 - val_acc: 0.8696\n",
      "Epoch 585/1000\n",
      " - 7s - loss: 0.3873 - acc: 0.8217 - val_loss: 0.3294 - val_acc: 0.8636\n",
      "Epoch 586/1000\n",
      " - 7s - loss: 0.3850 - acc: 0.8219 - val_loss: 0.3238 - val_acc: 0.8660\n",
      "Epoch 587/1000\n",
      " - 7s - loss: 0.3876 - acc: 0.8221 - val_loss: 0.3232 - val_acc: 0.8687\n",
      "Epoch 588/1000\n",
      " - 7s - loss: 0.3865 - acc: 0.8223 - val_loss: 0.3309 - val_acc: 0.8640\n",
      "Epoch 589/1000\n",
      " - 7s - loss: 0.3839 - acc: 0.8234 - val_loss: 0.3260 - val_acc: 0.8652\n",
      "Epoch 590/1000\n",
      " - 7s - loss: 0.3853 - acc: 0.8213 - val_loss: 0.3285 - val_acc: 0.8633\n",
      "Epoch 591/1000\n",
      " - 7s - loss: 0.3854 - acc: 0.8223 - val_loss: 0.3265 - val_acc: 0.8641\n",
      "Epoch 592/1000\n",
      " - 7s - loss: 0.3847 - acc: 0.8234 - val_loss: 0.3216 - val_acc: 0.8682\n",
      "Epoch 593/1000\n",
      " - 7s - loss: 0.3843 - acc: 0.8237 - val_loss: 0.3224 - val_acc: 0.8689\n",
      "Epoch 594/1000\n",
      " - 7s - loss: 0.3862 - acc: 0.8231 - val_loss: 0.3267 - val_acc: 0.8654\n",
      "Epoch 595/1000\n",
      " - 7s - loss: 0.3849 - acc: 0.8224 - val_loss: 0.3259 - val_acc: 0.8653\n",
      "Epoch 596/1000\n",
      " - 7s - loss: 0.3848 - acc: 0.8223 - val_loss: 0.3278 - val_acc: 0.8652\n",
      "Epoch 597/1000\n",
      " - 7s - loss: 0.3871 - acc: 0.8221 - val_loss: 0.3237 - val_acc: 0.8662\n",
      "Epoch 598/1000\n",
      " - 7s - loss: 0.3840 - acc: 0.8228 - val_loss: 0.3237 - val_acc: 0.8699\n",
      "Epoch 599/1000\n",
      " - 7s - loss: 0.3859 - acc: 0.8218 - val_loss: 0.3269 - val_acc: 0.8644\n",
      "Epoch 600/1000\n",
      " - 7s - loss: 0.3846 - acc: 0.8231 - val_loss: 0.3212 - val_acc: 0.8691\n",
      "Epoch 601/1000\n",
      " - 7s - loss: 0.3858 - acc: 0.8223 - val_loss: 0.3247 - val_acc: 0.8647\n",
      "Epoch 602/1000\n",
      " - 7s - loss: 0.3840 - acc: 0.8237 - val_loss: 0.3216 - val_acc: 0.8696\n",
      "Epoch 603/1000\n",
      " - 7s - loss: 0.3849 - acc: 0.8226 - val_loss: 0.3234 - val_acc: 0.8696\n",
      "Epoch 604/1000\n",
      " - 7s - loss: 0.3833 - acc: 0.8246 - val_loss: 0.3250 - val_acc: 0.8646\n",
      "Epoch 605/1000\n",
      " - 7s - loss: 0.3844 - acc: 0.8229 - val_loss: 0.3258 - val_acc: 0.8668\n",
      "Epoch 606/1000\n",
      " - 7s - loss: 0.3829 - acc: 0.8235 - val_loss: 0.3249 - val_acc: 0.8653\n",
      "Epoch 607/1000\n",
      " - 7s - loss: 0.3845 - acc: 0.8224 - val_loss: 0.3232 - val_acc: 0.8678\n",
      "Epoch 608/1000\n",
      " - 7s - loss: 0.3838 - acc: 0.8226 - val_loss: 0.3255 - val_acc: 0.8668\n",
      "Epoch 609/1000\n",
      " - 7s - loss: 0.3867 - acc: 0.8230 - val_loss: 0.3238 - val_acc: 0.8669\n",
      "Epoch 610/1000\n",
      " - 7s - loss: 0.3835 - acc: 0.8223 - val_loss: 0.3198 - val_acc: 0.8702\n",
      "Epoch 611/1000\n",
      " - 7s - loss: 0.3831 - acc: 0.8228 - val_loss: 0.3225 - val_acc: 0.8686\n",
      "Epoch 612/1000\n",
      " - 7s - loss: 0.3824 - acc: 0.8237 - val_loss: 0.3226 - val_acc: 0.8670\n",
      "Epoch 613/1000\n",
      " - 7s - loss: 0.3849 - acc: 0.8222 - val_loss: 0.3217 - val_acc: 0.8688\n",
      "Epoch 614/1000\n",
      " - 7s - loss: 0.3842 - acc: 0.8218 - val_loss: 0.3225 - val_acc: 0.8684\n",
      "Epoch 615/1000\n",
      " - 7s - loss: 0.3845 - acc: 0.8245 - val_loss: 0.3265 - val_acc: 0.8628\n",
      "Epoch 616/1000\n",
      " - 7s - loss: 0.3831 - acc: 0.8245 - val_loss: 0.3226 - val_acc: 0.8682\n",
      "Epoch 617/1000\n",
      " - 7s - loss: 0.3823 - acc: 0.8236 - val_loss: 0.3269 - val_acc: 0.8628\n",
      "Epoch 618/1000\n",
      " - 7s - loss: 0.3838 - acc: 0.8237 - val_loss: 0.3220 - val_acc: 0.8678\n",
      "Epoch 619/1000\n",
      " - 7s - loss: 0.3826 - acc: 0.8237 - val_loss: 0.3199 - val_acc: 0.8675\n",
      "Epoch 620/1000\n",
      " - 7s - loss: 0.3853 - acc: 0.8227 - val_loss: 0.3218 - val_acc: 0.8673\n",
      "Epoch 621/1000\n",
      " - 7s - loss: 0.3828 - acc: 0.8249 - val_loss: 0.3248 - val_acc: 0.8691\n",
      "Epoch 622/1000\n",
      " - 7s - loss: 0.3833 - acc: 0.8236 - val_loss: 0.3242 - val_acc: 0.8672\n",
      "Epoch 623/1000\n",
      " - 7s - loss: 0.3856 - acc: 0.8226 - val_loss: 0.3207 - val_acc: 0.8683\n",
      "Epoch 624/1000\n",
      " - 7s - loss: 0.3839 - acc: 0.8236 - val_loss: 0.3240 - val_acc: 0.8691\n",
      "Epoch 625/1000\n",
      " - 9s - loss: 0.3840 - acc: 0.8235 - val_loss: 0.3262 - val_acc: 0.8668\n",
      "Epoch 626/1000\n",
      " - 8s - loss: 0.3846 - acc: 0.8219 - val_loss: 0.3218 - val_acc: 0.8680\n",
      "Epoch 627/1000\n",
      " - 7s - loss: 0.3833 - acc: 0.8243 - val_loss: 0.3210 - val_acc: 0.8686\n",
      "Epoch 628/1000\n",
      " - 7s - loss: 0.3825 - acc: 0.8240 - val_loss: 0.3255 - val_acc: 0.8638\n",
      "Epoch 629/1000\n",
      " - 7s - loss: 0.3826 - acc: 0.8237 - val_loss: 0.3206 - val_acc: 0.8679\n",
      "Epoch 630/1000\n",
      " - 7s - loss: 0.3825 - acc: 0.8255 - val_loss: 0.3210 - val_acc: 0.8690\n",
      "Epoch 631/1000\n",
      " - 7s - loss: 0.3840 - acc: 0.8244 - val_loss: 0.3259 - val_acc: 0.8649\n",
      "Epoch 632/1000\n",
      " - 7s - loss: 0.3834 - acc: 0.8231 - val_loss: 0.3218 - val_acc: 0.8683\n",
      "Epoch 633/1000\n",
      " - 7s - loss: 0.3820 - acc: 0.8249 - val_loss: 0.3236 - val_acc: 0.8663\n",
      "Epoch 634/1000\n",
      " - 7s - loss: 0.3837 - acc: 0.8252 - val_loss: 0.3235 - val_acc: 0.8668\n",
      "Epoch 635/1000\n",
      " - 7s - loss: 0.3838 - acc: 0.8249 - val_loss: 0.3223 - val_acc: 0.8674\n",
      "Epoch 636/1000\n",
      " - 7s - loss: 0.3830 - acc: 0.8250 - val_loss: 0.3218 - val_acc: 0.8687\n",
      "Epoch 637/1000\n",
      " - 7s - loss: 0.3813 - acc: 0.8246 - val_loss: 0.3210 - val_acc: 0.8709\n",
      "Epoch 638/1000\n",
      " - 7s - loss: 0.3814 - acc: 0.8254 - val_loss: 0.3192 - val_acc: 0.8655\n",
      "Epoch 639/1000\n",
      " - 7s - loss: 0.3828 - acc: 0.8246 - val_loss: 0.3206 - val_acc: 0.8692\n",
      "Epoch 640/1000\n",
      " - 7s - loss: 0.3818 - acc: 0.8252 - val_loss: 0.3209 - val_acc: 0.8677\n",
      "Epoch 641/1000\n",
      " - 7s - loss: 0.3810 - acc: 0.8260 - val_loss: 0.3235 - val_acc: 0.8667\n",
      "Epoch 642/1000\n",
      " - 7s - loss: 0.3829 - acc: 0.8231 - val_loss: 0.3212 - val_acc: 0.8670\n",
      "Epoch 643/1000\n",
      " - 7s - loss: 0.3828 - acc: 0.8253 - val_loss: 0.3202 - val_acc: 0.8718\n",
      "Epoch 644/1000\n",
      " - 7s - loss: 0.3828 - acc: 0.8240 - val_loss: 0.3200 - val_acc: 0.8704\n",
      "Epoch 645/1000\n",
      " - 7s - loss: 0.3822 - acc: 0.8249 - val_loss: 0.3228 - val_acc: 0.8683\n",
      "Epoch 646/1000\n",
      " - 8s - loss: 0.3811 - acc: 0.8247 - val_loss: 0.3197 - val_acc: 0.8693\n",
      "Epoch 647/1000\n",
      " - 7s - loss: 0.3825 - acc: 0.8232 - val_loss: 0.3203 - val_acc: 0.8680\n",
      "Epoch 648/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - 7s - loss: 0.3818 - acc: 0.8255 - val_loss: 0.3196 - val_acc: 0.8691\n",
      "Epoch 649/1000\n",
      " - 7s - loss: 0.3808 - acc: 0.8258 - val_loss: 0.3207 - val_acc: 0.8699\n",
      "Epoch 650/1000\n",
      " - 7s - loss: 0.3819 - acc: 0.8250 - val_loss: 0.3223 - val_acc: 0.8677\n",
      "Epoch 651/1000\n",
      " - 7s - loss: 0.3803 - acc: 0.8270 - val_loss: 0.3228 - val_acc: 0.8652\n",
      "Epoch 652/1000\n",
      " - 7s - loss: 0.3827 - acc: 0.8232 - val_loss: 0.3184 - val_acc: 0.8722\n",
      "Epoch 653/1000\n",
      " - 7s - loss: 0.3827 - acc: 0.8242 - val_loss: 0.3236 - val_acc: 0.8670\n",
      "Epoch 654/1000\n",
      " - 7s - loss: 0.3816 - acc: 0.8253 - val_loss: 0.3219 - val_acc: 0.8684\n",
      "Epoch 655/1000\n",
      " - 7s - loss: 0.3805 - acc: 0.8238 - val_loss: 0.3178 - val_acc: 0.8716\n",
      "Epoch 656/1000\n",
      " - 7s - loss: 0.3825 - acc: 0.8249 - val_loss: 0.3239 - val_acc: 0.8639\n",
      "Epoch 657/1000\n",
      " - 7s - loss: 0.3803 - acc: 0.8254 - val_loss: 0.3220 - val_acc: 0.8665\n",
      "Epoch 658/1000\n",
      " - 7s - loss: 0.3811 - acc: 0.8267 - val_loss: 0.3219 - val_acc: 0.8689\n",
      "Epoch 659/1000\n",
      " - 7s - loss: 0.3794 - acc: 0.8268 - val_loss: 0.3203 - val_acc: 0.8696\n",
      "Epoch 660/1000\n",
      " - 7s - loss: 0.3816 - acc: 0.8240 - val_loss: 0.3207 - val_acc: 0.8689\n",
      "Epoch 661/1000\n",
      " - 7s - loss: 0.3783 - acc: 0.8271 - val_loss: 0.3197 - val_acc: 0.8688\n",
      "Epoch 662/1000\n",
      " - 7s - loss: 0.3812 - acc: 0.8253 - val_loss: 0.3227 - val_acc: 0.8675\n",
      "Epoch 663/1000\n",
      " - 7s - loss: 0.3803 - acc: 0.8250 - val_loss: 0.3189 - val_acc: 0.8716\n",
      "Epoch 664/1000\n",
      " - 7s - loss: 0.3811 - acc: 0.8266 - val_loss: 0.3189 - val_acc: 0.8696\n",
      "Epoch 665/1000\n",
      " - 7s - loss: 0.3837 - acc: 0.8237 - val_loss: 0.3195 - val_acc: 0.8704\n",
      "Epoch 666/1000\n",
      " - 7s - loss: 0.3812 - acc: 0.8260 - val_loss: 0.3197 - val_acc: 0.8696\n",
      "Epoch 667/1000\n",
      " - 9s - loss: 0.3807 - acc: 0.8257 - val_loss: 0.3227 - val_acc: 0.8682\n",
      "Epoch 668/1000\n",
      " - 8s - loss: 0.3804 - acc: 0.8251 - val_loss: 0.3198 - val_acc: 0.8685\n",
      "Epoch 669/1000\n",
      " - 7s - loss: 0.3790 - acc: 0.8263 - val_loss: 0.3203 - val_acc: 0.8682\n",
      "Epoch 670/1000\n",
      " - 7s - loss: 0.3815 - acc: 0.8250 - val_loss: 0.3205 - val_acc: 0.8686\n",
      "Epoch 671/1000\n",
      " - 7s - loss: 0.3782 - acc: 0.8270 - val_loss: 0.3194 - val_acc: 0.8693\n",
      "Epoch 672/1000\n",
      " - 7s - loss: 0.3818 - acc: 0.8245 - val_loss: 0.3226 - val_acc: 0.8682\n",
      "Epoch 673/1000\n",
      " - 7s - loss: 0.3809 - acc: 0.8250 - val_loss: 0.3215 - val_acc: 0.8681\n",
      "Epoch 674/1000\n",
      " - 7s - loss: 0.3807 - acc: 0.8255 - val_loss: 0.3197 - val_acc: 0.8696\n",
      "Epoch 675/1000\n",
      " - 7s - loss: 0.3806 - acc: 0.8265 - val_loss: 0.3177 - val_acc: 0.8725\n",
      "Epoch 676/1000\n",
      " - 7s - loss: 0.3796 - acc: 0.8260 - val_loss: 0.3168 - val_acc: 0.8715\n",
      "Epoch 677/1000\n",
      " - 7s - loss: 0.3800 - acc: 0.8261 - val_loss: 0.3176 - val_acc: 0.8719\n",
      "Epoch 678/1000\n",
      " - 7s - loss: 0.3810 - acc: 0.8261 - val_loss: 0.3194 - val_acc: 0.8715\n",
      "Epoch 679/1000\n",
      " - 7s - loss: 0.3809 - acc: 0.8263 - val_loss: 0.3210 - val_acc: 0.8681\n",
      "Epoch 680/1000\n",
      " - 7s - loss: 0.3802 - acc: 0.8278 - val_loss: 0.3211 - val_acc: 0.8700\n",
      "Epoch 681/1000\n",
      " - 7s - loss: 0.3829 - acc: 0.8238 - val_loss: 0.3178 - val_acc: 0.8701\n",
      "Epoch 682/1000\n",
      " - 7s - loss: 0.3805 - acc: 0.8258 - val_loss: 0.3190 - val_acc: 0.8733\n",
      "Epoch 683/1000\n",
      " - 7s - loss: 0.3805 - acc: 0.8266 - val_loss: 0.3212 - val_acc: 0.8715\n",
      "Epoch 684/1000\n",
      " - 7s - loss: 0.3803 - acc: 0.8267 - val_loss: 0.3205 - val_acc: 0.8687\n",
      "Epoch 685/1000\n",
      " - 7s - loss: 0.3777 - acc: 0.8271 - val_loss: 0.3191 - val_acc: 0.8718\n",
      "Epoch 686/1000\n",
      " - 7s - loss: 0.3809 - acc: 0.8262 - val_loss: 0.3185 - val_acc: 0.8701\n",
      "Epoch 687/1000\n",
      " - 7s - loss: 0.3807 - acc: 0.8263 - val_loss: 0.3195 - val_acc: 0.8713\n",
      "Epoch 688/1000\n",
      " - 7s - loss: 0.3804 - acc: 0.8263 - val_loss: 0.3183 - val_acc: 0.8673\n",
      "Epoch 689/1000\n",
      " - 7s - loss: 0.3802 - acc: 0.8266 - val_loss: 0.3231 - val_acc: 0.8677\n",
      "Epoch 690/1000\n",
      " - 7s - loss: 0.3802 - acc: 0.8257 - val_loss: 0.3185 - val_acc: 0.8688\n",
      "Epoch 691/1000\n",
      " - 7s - loss: 0.3784 - acc: 0.8273 - val_loss: 0.3184 - val_acc: 0.8716\n",
      "Epoch 692/1000\n",
      " - 7s - loss: 0.3774 - acc: 0.8282 - val_loss: 0.3167 - val_acc: 0.8708\n",
      "Epoch 693/1000\n",
      " - 7s - loss: 0.3810 - acc: 0.8242 - val_loss: 0.3150 - val_acc: 0.8723\n",
      "Epoch 694/1000\n",
      " - 7s - loss: 0.3792 - acc: 0.8266 - val_loss: 0.3207 - val_acc: 0.8696\n",
      "Epoch 695/1000\n",
      " - 7s - loss: 0.3804 - acc: 0.8273 - val_loss: 0.3220 - val_acc: 0.8658\n",
      "Epoch 696/1000\n",
      " - 7s - loss: 0.3804 - acc: 0.8263 - val_loss: 0.3196 - val_acc: 0.8681\n",
      "Epoch 697/1000\n",
      " - 7s - loss: 0.3784 - acc: 0.8276 - val_loss: 0.3187 - val_acc: 0.8685\n",
      "Epoch 698/1000\n",
      " - 7s - loss: 0.3786 - acc: 0.8263 - val_loss: 0.3180 - val_acc: 0.8692\n",
      "Epoch 699/1000\n",
      " - 7s - loss: 0.3780 - acc: 0.8266 - val_loss: 0.3168 - val_acc: 0.8683\n",
      "Epoch 700/1000\n",
      " - 7s - loss: 0.3782 - acc: 0.8272 - val_loss: 0.3174 - val_acc: 0.8719\n",
      "Epoch 701/1000\n",
      " - 7s - loss: 0.3801 - acc: 0.8256 - val_loss: 0.3163 - val_acc: 0.8735\n",
      "Epoch 702/1000\n",
      " - 7s - loss: 0.3790 - acc: 0.8274 - val_loss: 0.3175 - val_acc: 0.8698\n",
      "Epoch 703/1000\n",
      " - 7s - loss: 0.3791 - acc: 0.8264 - val_loss: 0.3177 - val_acc: 0.8694\n",
      "Epoch 704/1000\n",
      " - 7s - loss: 0.3783 - acc: 0.8280 - val_loss: 0.3190 - val_acc: 0.8664\n",
      "Epoch 705/1000\n",
      " - 7s - loss: 0.3789 - acc: 0.8257 - val_loss: 0.3200 - val_acc: 0.8693\n",
      "Epoch 706/1000\n",
      " - 7s - loss: 0.3769 - acc: 0.8277 - val_loss: 0.3179 - val_acc: 0.8696\n",
      "Epoch 707/1000\n",
      " - 7s - loss: 0.3773 - acc: 0.8279 - val_loss: 0.3162 - val_acc: 0.8725\n",
      "Epoch 708/1000\n",
      " - 7s - loss: 0.3792 - acc: 0.8262 - val_loss: 0.3144 - val_acc: 0.8721\n",
      "Epoch 709/1000\n",
      " - 8s - loss: 0.3780 - acc: 0.8276 - val_loss: 0.3211 - val_acc: 0.8684\n",
      "Epoch 710/1000\n",
      " - 8s - loss: 0.3795 - acc: 0.8268 - val_loss: 0.3189 - val_acc: 0.8694\n",
      "Epoch 711/1000\n",
      " - 8s - loss: 0.3761 - acc: 0.8284 - val_loss: 0.3147 - val_acc: 0.8707\n",
      "Epoch 712/1000\n",
      " - 7s - loss: 0.3766 - acc: 0.8274 - val_loss: 0.3196 - val_acc: 0.8684\n",
      "Epoch 713/1000\n",
      " - 7s - loss: 0.3782 - acc: 0.8272 - val_loss: 0.3177 - val_acc: 0.8693\n",
      "Epoch 714/1000\n",
      " - 7s - loss: 0.3780 - acc: 0.8269 - val_loss: 0.3205 - val_acc: 0.8689\n",
      "Epoch 715/1000\n",
      " - 7s - loss: 0.3777 - acc: 0.8271 - val_loss: 0.3168 - val_acc: 0.8682\n",
      "Epoch 716/1000\n",
      " - 7s - loss: 0.3790 - acc: 0.8273 - val_loss: 0.3190 - val_acc: 0.8695\n",
      "Epoch 717/1000\n",
      " - 7s - loss: 0.3765 - acc: 0.8280 - val_loss: 0.3162 - val_acc: 0.8715\n",
      "Epoch 718/1000\n",
      " - 7s - loss: 0.3798 - acc: 0.8257 - val_loss: 0.3178 - val_acc: 0.8700\n",
      "Epoch 719/1000\n",
      " - 7s - loss: 0.3768 - acc: 0.8279 - val_loss: 0.3182 - val_acc: 0.8716\n",
      "Epoch 720/1000\n",
      " - 7s - loss: 0.3783 - acc: 0.8277 - val_loss: 0.3151 - val_acc: 0.8733\n",
      "Epoch 721/1000\n",
      " - 7s - loss: 0.3786 - acc: 0.8276 - val_loss: 0.3173 - val_acc: 0.8689\n",
      "Epoch 722/1000\n",
      " - 7s - loss: 0.3760 - acc: 0.8277 - val_loss: 0.3168 - val_acc: 0.8707\n",
      "Epoch 723/1000\n",
      " - 7s - loss: 0.3790 - acc: 0.8270 - val_loss: 0.3222 - val_acc: 0.8678\n",
      "Epoch 724/1000\n",
      " - 7s - loss: 0.3789 - acc: 0.8273 - val_loss: 0.3178 - val_acc: 0.8700\n",
      "Epoch 725/1000\n",
      " - 7s - loss: 0.3773 - acc: 0.8266 - val_loss: 0.3180 - val_acc: 0.8711\n",
      "Epoch 726/1000\n",
      " - 7s - loss: 0.3775 - acc: 0.8286 - val_loss: 0.3179 - val_acc: 0.8693\n",
      "Epoch 727/1000\n",
      " - 7s - loss: 0.3773 - acc: 0.8279 - val_loss: 0.3162 - val_acc: 0.8706\n",
      "Epoch 728/1000\n",
      " - 7s - loss: 0.3770 - acc: 0.8287 - val_loss: 0.3138 - val_acc: 0.8741\n",
      "Epoch 729/1000\n",
      " - 7s - loss: 0.3761 - acc: 0.8292 - val_loss: 0.3161 - val_acc: 0.8699\n",
      "Epoch 730/1000\n",
      " - 7s - loss: 0.3771 - acc: 0.8282 - val_loss: 0.3180 - val_acc: 0.8708\n",
      "Epoch 731/1000\n",
      " - 7s - loss: 0.3765 - acc: 0.8280 - val_loss: 0.3142 - val_acc: 0.8722\n",
      "Epoch 732/1000\n",
      " - 7s - loss: 0.3761 - acc: 0.8282 - val_loss: 0.3151 - val_acc: 0.8724\n",
      "Epoch 733/1000\n",
      " - 7s - loss: 0.3776 - acc: 0.8281 - val_loss: 0.3172 - val_acc: 0.8715\n",
      "Epoch 734/1000\n",
      " - 7s - loss: 0.3780 - acc: 0.8280 - val_loss: 0.3175 - val_acc: 0.8717\n",
      "Epoch 735/1000\n",
      " - 7s - loss: 0.3770 - acc: 0.8284 - val_loss: 0.3183 - val_acc: 0.8687\n",
      "Epoch 736/1000\n",
      " - 7s - loss: 0.3774 - acc: 0.8284 - val_loss: 0.3157 - val_acc: 0.8726\n",
      "Epoch 737/1000\n",
      " - 7s - loss: 0.3758 - acc: 0.8290 - val_loss: 0.3169 - val_acc: 0.8719\n",
      "Epoch 738/1000\n",
      " - 7s - loss: 0.3778 - acc: 0.8275 - val_loss: 0.3226 - val_acc: 0.8659\n",
      "Epoch 739/1000\n",
      " - 7s - loss: 0.3753 - acc: 0.8297 - val_loss: 0.3140 - val_acc: 0.8715\n",
      "Epoch 740/1000\n",
      " - 7s - loss: 0.3780 - acc: 0.8261 - val_loss: 0.3196 - val_acc: 0.8686\n",
      "Epoch 741/1000\n",
      " - 7s - loss: 0.3757 - acc: 0.8291 - val_loss: 0.3183 - val_acc: 0.8682\n",
      "Epoch 742/1000\n",
      " - 7s - loss: 0.3753 - acc: 0.8287 - val_loss: 0.3152 - val_acc: 0.8706\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 743/1000\n",
      " - 7s - loss: 0.3762 - acc: 0.8286 - val_loss: 0.3155 - val_acc: 0.8745\n",
      "Epoch 744/1000\n",
      " - 7s - loss: 0.3776 - acc: 0.8274 - val_loss: 0.3164 - val_acc: 0.8715\n",
      "Epoch 745/1000\n",
      " - 7s - loss: 0.3757 - acc: 0.8287 - val_loss: 0.3148 - val_acc: 0.8739\n",
      "Epoch 746/1000\n",
      " - 7s - loss: 0.3764 - acc: 0.8285 - val_loss: 0.3132 - val_acc: 0.8738\n",
      "Epoch 747/1000\n",
      " - 7s - loss: 0.3766 - acc: 0.8285 - val_loss: 0.3174 - val_acc: 0.8678\n",
      "Epoch 748/1000\n",
      " - 7s - loss: 0.3777 - acc: 0.8277 - val_loss: 0.3211 - val_acc: 0.8686\n",
      "Epoch 749/1000\n",
      " - 7s - loss: 0.3763 - acc: 0.8281 - val_loss: 0.3168 - val_acc: 0.8694\n",
      "Epoch 750/1000\n",
      " - 7s - loss: 0.3774 - acc: 0.8284 - val_loss: 0.3203 - val_acc: 0.8714\n",
      "Epoch 751/1000\n",
      " - 7s - loss: 0.3767 - acc: 0.8273 - val_loss: 0.3134 - val_acc: 0.8725\n",
      "Epoch 752/1000\n",
      " - 9s - loss: 0.3783 - acc: 0.8280 - val_loss: 0.3194 - val_acc: 0.8679\n",
      "Epoch 753/1000\n",
      " - 8s - loss: 0.3768 - acc: 0.8287 - val_loss: 0.3199 - val_acc: 0.8683\n",
      "Epoch 754/1000\n",
      " - 7s - loss: 0.3762 - acc: 0.8281 - val_loss: 0.3148 - val_acc: 0.8715\n",
      "Epoch 755/1000\n",
      " - 7s - loss: 0.3770 - acc: 0.8289 - val_loss: 0.3178 - val_acc: 0.8679\n",
      "Epoch 756/1000\n",
      " - 7s - loss: 0.3757 - acc: 0.8294 - val_loss: 0.3137 - val_acc: 0.8734\n",
      "Epoch 757/1000\n",
      " - 7s - loss: 0.3770 - acc: 0.8285 - val_loss: 0.3156 - val_acc: 0.8732\n",
      "Epoch 758/1000\n",
      " - 7s - loss: 0.3765 - acc: 0.8286 - val_loss: 0.3129 - val_acc: 0.8728\n",
      "Epoch 759/1000\n",
      " - 7s - loss: 0.3766 - acc: 0.8283 - val_loss: 0.3144 - val_acc: 0.8719\n",
      "Epoch 760/1000\n",
      " - 7s - loss: 0.3748 - acc: 0.8293 - val_loss: 0.3150 - val_acc: 0.8734\n",
      "Epoch 761/1000\n",
      " - 7s - loss: 0.3754 - acc: 0.8285 - val_loss: 0.3148 - val_acc: 0.8720\n",
      "Epoch 762/1000\n",
      " - 7s - loss: 0.3770 - acc: 0.8288 - val_loss: 0.3156 - val_acc: 0.8715\n",
      "Epoch 763/1000\n",
      " - 7s - loss: 0.3764 - acc: 0.8287 - val_loss: 0.3177 - val_acc: 0.8701\n",
      "Epoch 764/1000\n",
      " - 7s - loss: 0.3740 - acc: 0.8304 - val_loss: 0.3192 - val_acc: 0.8725\n",
      "Epoch 765/1000\n",
      " - 7s - loss: 0.3747 - acc: 0.8297 - val_loss: 0.3134 - val_acc: 0.8718\n",
      "Epoch 766/1000\n",
      " - 7s - loss: 0.3750 - acc: 0.8309 - val_loss: 0.3147 - val_acc: 0.8749\n",
      "Epoch 767/1000\n",
      " - 7s - loss: 0.3774 - acc: 0.8282 - val_loss: 0.3136 - val_acc: 0.8743\n",
      "Epoch 768/1000\n",
      " - 7s - loss: 0.3750 - acc: 0.8311 - val_loss: 0.3160 - val_acc: 0.8720\n",
      "Epoch 769/1000\n",
      " - 7s - loss: 0.3758 - acc: 0.8288 - val_loss: 0.3136 - val_acc: 0.8723\n",
      "Epoch 770/1000\n",
      " - 7s - loss: 0.3754 - acc: 0.8292 - val_loss: 0.3138 - val_acc: 0.8733\n",
      "Epoch 771/1000\n",
      " - 7s - loss: 0.3749 - acc: 0.8298 - val_loss: 0.3141 - val_acc: 0.8738\n",
      "Epoch 772/1000\n",
      " - 7s - loss: 0.3752 - acc: 0.8297 - val_loss: 0.3172 - val_acc: 0.8713\n",
      "Epoch 773/1000\n",
      " - 7s - loss: 0.3752 - acc: 0.8292 - val_loss: 0.3160 - val_acc: 0.8707\n",
      "Epoch 774/1000\n",
      " - 7s - loss: 0.3771 - acc: 0.8287 - val_loss: 0.3154 - val_acc: 0.8728\n",
      "Epoch 775/1000\n",
      " - 7s - loss: 0.3761 - acc: 0.8290 - val_loss: 0.3164 - val_acc: 0.8729\n",
      "Epoch 776/1000\n",
      " - 7s - loss: 0.3762 - acc: 0.8291 - val_loss: 0.3150 - val_acc: 0.8712\n",
      "Epoch 777/1000\n",
      " - 7s - loss: 0.3743 - acc: 0.8304 - val_loss: 0.3146 - val_acc: 0.8706\n",
      "Epoch 778/1000\n",
      " - 7s - loss: 0.3745 - acc: 0.8297 - val_loss: 0.3134 - val_acc: 0.8718\n",
      "Epoch 779/1000\n",
      " - 7s - loss: 0.3742 - acc: 0.8312 - val_loss: 0.3174 - val_acc: 0.8713\n",
      "Epoch 780/1000\n",
      " - 7s - loss: 0.3745 - acc: 0.8296 - val_loss: 0.3157 - val_acc: 0.8705\n",
      "Epoch 781/1000\n",
      " - 7s - loss: 0.3756 - acc: 0.8286 - val_loss: 0.3141 - val_acc: 0.8724\n",
      "Epoch 782/1000\n",
      " - 7s - loss: 0.3732 - acc: 0.8312 - val_loss: 0.3175 - val_acc: 0.8695\n",
      "Epoch 783/1000\n",
      " - 7s - loss: 0.3758 - acc: 0.8295 - val_loss: 0.3192 - val_acc: 0.8694\n",
      "Epoch 784/1000\n",
      " - 7s - loss: 0.3758 - acc: 0.8286 - val_loss: 0.3163 - val_acc: 0.8689\n",
      "Epoch 785/1000\n",
      " - 7s - loss: 0.3762 - acc: 0.8292 - val_loss: 0.3109 - val_acc: 0.8783\n",
      "Epoch 786/1000\n",
      " - 7s - loss: 0.3767 - acc: 0.8284 - val_loss: 0.3141 - val_acc: 0.8729\n",
      "Epoch 787/1000\n",
      " - 7s - loss: 0.3724 - acc: 0.8297 - val_loss: 0.3150 - val_acc: 0.8740\n",
      "Epoch 788/1000\n",
      " - 7s - loss: 0.3729 - acc: 0.8314 - val_loss: 0.3146 - val_acc: 0.8725\n",
      "Epoch 789/1000\n",
      " - 7s - loss: 0.3733 - acc: 0.8310 - val_loss: 0.3148 - val_acc: 0.8705\n",
      "Epoch 790/1000\n",
      " - 7s - loss: 0.3739 - acc: 0.8314 - val_loss: 0.3181 - val_acc: 0.8704\n",
      "Epoch 791/1000\n",
      " - 7s - loss: 0.3732 - acc: 0.8307 - val_loss: 0.3118 - val_acc: 0.8742\n",
      "Epoch 792/1000\n",
      " - 7s - loss: 0.3724 - acc: 0.8305 - val_loss: 0.3158 - val_acc: 0.8716\n",
      "Epoch 793/1000\n",
      " - 7s - loss: 0.3743 - acc: 0.8295 - val_loss: 0.3128 - val_acc: 0.8745\n",
      "Epoch 794/1000\n",
      " - 9s - loss: 0.3729 - acc: 0.8297 - val_loss: 0.3145 - val_acc: 0.8718\n",
      "Epoch 795/1000\n",
      " - 8s - loss: 0.3749 - acc: 0.8301 - val_loss: 0.3124 - val_acc: 0.8739\n",
      "Epoch 796/1000\n",
      " - 7s - loss: 0.3746 - acc: 0.8290 - val_loss: 0.3117 - val_acc: 0.8738\n",
      "Epoch 797/1000\n",
      " - 7s - loss: 0.3753 - acc: 0.8296 - val_loss: 0.3111 - val_acc: 0.8741\n",
      "Epoch 798/1000\n",
      " - 7s - loss: 0.3750 - acc: 0.8289 - val_loss: 0.3141 - val_acc: 0.8732\n",
      "Epoch 799/1000\n",
      " - 7s - loss: 0.3754 - acc: 0.8285 - val_loss: 0.3127 - val_acc: 0.8748\n",
      "Epoch 800/1000\n",
      " - 7s - loss: 0.3743 - acc: 0.8302 - val_loss: 0.3134 - val_acc: 0.8739\n",
      "Epoch 801/1000\n",
      " - 7s - loss: 0.3745 - acc: 0.8310 - val_loss: 0.3134 - val_acc: 0.8732\n",
      "Epoch 802/1000\n",
      " - 7s - loss: 0.3742 - acc: 0.8289 - val_loss: 0.3136 - val_acc: 0.8746\n",
      "Epoch 803/1000\n",
      " - 7s - loss: 0.3741 - acc: 0.8295 - val_loss: 0.3121 - val_acc: 0.8728\n",
      "Epoch 804/1000\n",
      " - 7s - loss: 0.3731 - acc: 0.8306 - val_loss: 0.3134 - val_acc: 0.8727\n",
      "Epoch 805/1000\n",
      " - 7s - loss: 0.3742 - acc: 0.8303 - val_loss: 0.3174 - val_acc: 0.8720\n",
      "Epoch 806/1000\n",
      " - 7s - loss: 0.3743 - acc: 0.8293 - val_loss: 0.3134 - val_acc: 0.8721\n",
      "Epoch 807/1000\n",
      " - 7s - loss: 0.3736 - acc: 0.8306 - val_loss: 0.3151 - val_acc: 0.8721\n",
      "Epoch 808/1000\n",
      " - 7s - loss: 0.3727 - acc: 0.8310 - val_loss: 0.3150 - val_acc: 0.8730\n",
      "Epoch 809/1000\n",
      " - 7s - loss: 0.3728 - acc: 0.8322 - val_loss: 0.3122 - val_acc: 0.8743\n",
      "Epoch 810/1000\n",
      " - 7s - loss: 0.3736 - acc: 0.8307 - val_loss: 0.3117 - val_acc: 0.8722\n",
      "Epoch 811/1000\n",
      " - 7s - loss: 0.3740 - acc: 0.8308 - val_loss: 0.3091 - val_acc: 0.8756\n",
      "Epoch 812/1000\n",
      " - 7s - loss: 0.3751 - acc: 0.8288 - val_loss: 0.3137 - val_acc: 0.8749\n",
      "Epoch 813/1000\n",
      " - 7s - loss: 0.3754 - acc: 0.8301 - val_loss: 0.3119 - val_acc: 0.8739\n",
      "Epoch 814/1000\n",
      " - 7s - loss: 0.3756 - acc: 0.8296 - val_loss: 0.3101 - val_acc: 0.8763\n",
      "Epoch 815/1000\n",
      " - 7s - loss: 0.3724 - acc: 0.8314 - val_loss: 0.3142 - val_acc: 0.8720\n",
      "Epoch 816/1000\n",
      " - 7s - loss: 0.3735 - acc: 0.8313 - val_loss: 0.3153 - val_acc: 0.8726\n",
      "Epoch 817/1000\n",
      " - 7s - loss: 0.3742 - acc: 0.8305 - val_loss: 0.3123 - val_acc: 0.8765\n",
      "Epoch 818/1000\n",
      " - 7s - loss: 0.3725 - acc: 0.8321 - val_loss: 0.3132 - val_acc: 0.8714\n",
      "Epoch 819/1000\n",
      " - 7s - loss: 0.3731 - acc: 0.8307 - val_loss: 0.3134 - val_acc: 0.8711\n",
      "Epoch 820/1000\n",
      " - 7s - loss: 0.3738 - acc: 0.8308 - val_loss: 0.3102 - val_acc: 0.8746\n",
      "Epoch 821/1000\n",
      " - 7s - loss: 0.3742 - acc: 0.8292 - val_loss: 0.3095 - val_acc: 0.8766\n",
      "Epoch 822/1000\n",
      " - 7s - loss: 0.3748 - acc: 0.8296 - val_loss: 0.3115 - val_acc: 0.8736\n",
      "Epoch 823/1000\n",
      " - 7s - loss: 0.3714 - acc: 0.8318 - val_loss: 0.3100 - val_acc: 0.8723\n",
      "Epoch 824/1000\n",
      " - 7s - loss: 0.3725 - acc: 0.8310 - val_loss: 0.3104 - val_acc: 0.8734\n",
      "Epoch 825/1000\n",
      " - 7s - loss: 0.3738 - acc: 0.8312 - val_loss: 0.3130 - val_acc: 0.8719\n",
      "Epoch 826/1000\n",
      " - 7s - loss: 0.3737 - acc: 0.8288 - val_loss: 0.3151 - val_acc: 0.8706\n",
      "Epoch 827/1000\n",
      " - 7s - loss: 0.3716 - acc: 0.8321 - val_loss: 0.3111 - val_acc: 0.8735\n",
      "Epoch 828/1000\n",
      " - 7s - loss: 0.3731 - acc: 0.8305 - val_loss: 0.3145 - val_acc: 0.8718\n",
      "Epoch 829/1000\n",
      " - 7s - loss: 0.3722 - acc: 0.8304 - val_loss: 0.3168 - val_acc: 0.8723\n",
      "Epoch 830/1000\n",
      " - 7s - loss: 0.3728 - acc: 0.8303 - val_loss: 0.3137 - val_acc: 0.8742\n",
      "Epoch 831/1000\n",
      " - 7s - loss: 0.3734 - acc: 0.8307 - val_loss: 0.3109 - val_acc: 0.8736\n",
      "Epoch 832/1000\n",
      " - 7s - loss: 0.3714 - acc: 0.8320 - val_loss: 0.3119 - val_acc: 0.8757\n",
      "Epoch 833/1000\n",
      " - 7s - loss: 0.3716 - acc: 0.8316 - val_loss: 0.3131 - val_acc: 0.8753\n",
      "Epoch 834/1000\n",
      " - 7s - loss: 0.3735 - acc: 0.8302 - val_loss: 0.3111 - val_acc: 0.8726\n",
      "Epoch 835/1000\n",
      " - 7s - loss: 0.3746 - acc: 0.8309 - val_loss: 0.3120 - val_acc: 0.8741\n",
      "Epoch 836/1000\n",
      " - 8s - loss: 0.3718 - acc: 0.8320 - val_loss: 0.3103 - val_acc: 0.8767\n",
      "Epoch 837/1000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      " - 8s - loss: 0.3728 - acc: 0.8315 - val_loss: 0.3122 - val_acc: 0.8733\n",
      "Epoch 838/1000\n",
      " - 8s - loss: 0.3732 - acc: 0.8315 - val_loss: 0.3112 - val_acc: 0.8725\n",
      "Epoch 839/1000\n",
      " - 7s - loss: 0.3716 - acc: 0.8316 - val_loss: 0.3113 - val_acc: 0.8741\n",
      "Epoch 840/1000\n",
      " - 7s - loss: 0.3719 - acc: 0.8320 - val_loss: 0.3094 - val_acc: 0.8761\n",
      "Epoch 841/1000\n",
      " - 7s - loss: 0.3709 - acc: 0.8315 - val_loss: 0.3105 - val_acc: 0.8756\n",
      "Epoch 842/1000\n",
      " - 7s - loss: 0.3720 - acc: 0.8304 - val_loss: 0.3130 - val_acc: 0.8724\n",
      "Epoch 843/1000\n",
      " - 7s - loss: 0.3720 - acc: 0.8310 - val_loss: 0.3108 - val_acc: 0.8755\n",
      "Epoch 844/1000\n",
      " - 7s - loss: 0.3702 - acc: 0.8333 - val_loss: 0.3094 - val_acc: 0.8759\n",
      "Epoch 845/1000\n",
      " - 7s - loss: 0.3720 - acc: 0.8307 - val_loss: 0.3151 - val_acc: 0.8729\n",
      "Epoch 846/1000\n",
      " - 7s - loss: 0.3728 - acc: 0.8319 - val_loss: 0.3148 - val_acc: 0.8708\n",
      "Epoch 847/1000\n",
      " - 7s - loss: 0.3724 - acc: 0.8313 - val_loss: 0.3116 - val_acc: 0.8754\n",
      "Epoch 848/1000\n",
      " - 7s - loss: 0.3724 - acc: 0.8303 - val_loss: 0.3100 - val_acc: 0.8749\n",
      "Epoch 849/1000\n",
      " - 7s - loss: 0.3719 - acc: 0.8314 - val_loss: 0.3123 - val_acc: 0.8739\n",
      "Epoch 850/1000\n",
      " - 7s - loss: 0.3716 - acc: 0.8319 - val_loss: 0.3101 - val_acc: 0.8771\n",
      "Epoch 851/1000\n",
      " - 7s - loss: 0.3715 - acc: 0.8316 - val_loss: 0.3118 - val_acc: 0.8745\n",
      "Epoch 852/1000\n",
      " - 7s - loss: 0.3728 - acc: 0.8301 - val_loss: 0.3091 - val_acc: 0.8750\n",
      "Epoch 853/1000\n",
      " - 7s - loss: 0.3712 - acc: 0.8314 - val_loss: 0.3101 - val_acc: 0.8747\n",
      "Epoch 854/1000\n",
      " - 7s - loss: 0.3684 - acc: 0.8336 - val_loss: 0.3082 - val_acc: 0.8769\n",
      "Epoch 855/1000\n",
      " - 7s - loss: 0.3738 - acc: 0.8308 - val_loss: 0.3133 - val_acc: 0.8743\n",
      "Epoch 856/1000\n",
      " - 7s - loss: 0.3700 - acc: 0.8326 - val_loss: 0.3121 - val_acc: 0.8739\n",
      "Epoch 857/1000\n",
      " - 7s - loss: 0.3717 - acc: 0.8312 - val_loss: 0.3105 - val_acc: 0.8745\n",
      "Epoch 858/1000\n",
      " - 7s - loss: 0.3725 - acc: 0.8312 - val_loss: 0.3155 - val_acc: 0.8728\n",
      "Epoch 859/1000\n",
      " - 7s - loss: 0.3724 - acc: 0.8316 - val_loss: 0.3134 - val_acc: 0.8739\n",
      "Epoch 860/1000\n",
      " - 7s - loss: 0.3712 - acc: 0.8319 - val_loss: 0.3109 - val_acc: 0.8739\n",
      "Epoch 861/1000\n",
      " - 7s - loss: 0.3716 - acc: 0.8322 - val_loss: 0.3150 - val_acc: 0.8736\n",
      "Epoch 862/1000\n",
      " - 7s - loss: 0.3704 - acc: 0.8319 - val_loss: 0.3076 - val_acc: 0.8785\n",
      "Epoch 863/1000\n",
      " - 7s - loss: 0.3676 - acc: 0.8336 - val_loss: 0.3131 - val_acc: 0.8717\n",
      "Epoch 864/1000\n",
      " - 7s - loss: 0.3723 - acc: 0.8323 - val_loss: 0.3153 - val_acc: 0.8721\n",
      "Epoch 865/1000\n",
      " - 7s - loss: 0.3713 - acc: 0.8304 - val_loss: 0.3101 - val_acc: 0.8730\n",
      "Epoch 866/1000\n",
      " - 7s - loss: 0.3721 - acc: 0.8318 - val_loss: 0.3123 - val_acc: 0.8757\n",
      "Epoch 867/1000\n",
      " - 7s - loss: 0.3717 - acc: 0.8311 - val_loss: 0.3112 - val_acc: 0.8763\n",
      "Epoch 868/1000\n",
      " - 7s - loss: 0.3709 - acc: 0.8316 - val_loss: 0.3135 - val_acc: 0.8736\n",
      "Epoch 869/1000\n",
      " - 7s - loss: 0.3712 - acc: 0.8313 - val_loss: 0.3131 - val_acc: 0.8760\n",
      "Epoch 870/1000\n",
      " - 7s - loss: 0.3693 - acc: 0.8338 - val_loss: 0.3132 - val_acc: 0.8729\n",
      "Epoch 871/1000\n",
      " - 7s - loss: 0.3717 - acc: 0.8315 - val_loss: 0.3108 - val_acc: 0.8733\n",
      "Epoch 872/1000\n",
      " - 7s - loss: 0.3723 - acc: 0.8309 - val_loss: 0.3104 - val_acc: 0.8739\n",
      "Epoch 873/1000\n",
      " - 7s - loss: 0.3700 - acc: 0.8321 - val_loss: 0.3090 - val_acc: 0.8768\n",
      "Epoch 874/1000\n",
      " - 7s - loss: 0.3715 - acc: 0.8321 - val_loss: 0.3082 - val_acc: 0.8764\n",
      "Epoch 875/1000\n",
      " - 7s - loss: 0.3708 - acc: 0.8324 - val_loss: 0.3123 - val_acc: 0.8728\n",
      "Epoch 876/1000\n",
      " - 7s - loss: 0.3695 - acc: 0.8343 - val_loss: 0.3121 - val_acc: 0.8757\n",
      "Epoch 877/1000\n",
      " - 7s - loss: 0.3726 - acc: 0.8316 - val_loss: 0.3115 - val_acc: 0.8736\n",
      "Epoch 878/1000\n",
      " - 7s - loss: 0.3728 - acc: 0.8312 - val_loss: 0.3120 - val_acc: 0.8752\n",
      "Epoch 879/1000\n",
      " - 9s - loss: 0.3711 - acc: 0.8320 - val_loss: 0.3084 - val_acc: 0.8770\n",
      "Epoch 880/1000\n",
      " - 8s - loss: 0.3699 - acc: 0.8318 - val_loss: 0.3080 - val_acc: 0.8763\n",
      "Epoch 881/1000\n",
      " - 7s - loss: 0.3713 - acc: 0.8321 - val_loss: 0.3102 - val_acc: 0.8771\n",
      "Epoch 882/1000\n",
      " - 7s - loss: 0.3700 - acc: 0.8331 - val_loss: 0.3150 - val_acc: 0.8714\n",
      "Epoch 883/1000\n",
      " - 7s - loss: 0.3694 - acc: 0.8329 - val_loss: 0.3123 - val_acc: 0.8729\n",
      "Epoch 884/1000\n",
      " - 7s - loss: 0.3702 - acc: 0.8326 - val_loss: 0.3114 - val_acc: 0.8741\n",
      "Epoch 885/1000\n",
      " - 7s - loss: 0.3687 - acc: 0.8333 - val_loss: 0.3110 - val_acc: 0.8740\n",
      "Epoch 886/1000\n",
      " - 7s - loss: 0.3719 - acc: 0.8322 - val_loss: 0.3113 - val_acc: 0.8738\n",
      "Epoch 887/1000\n",
      " - 7s - loss: 0.3692 - acc: 0.8346 - val_loss: 0.3099 - val_acc: 0.8745\n",
      "Epoch 888/1000\n",
      " - 7s - loss: 0.3710 - acc: 0.8300 - val_loss: 0.3125 - val_acc: 0.8718\n",
      "Epoch 889/1000\n",
      " - 7s - loss: 0.3700 - acc: 0.8315 - val_loss: 0.3097 - val_acc: 0.8769\n",
      "Epoch 890/1000\n",
      " - 7s - loss: 0.3697 - acc: 0.8338 - val_loss: 0.3100 - val_acc: 0.8745\n",
      "Epoch 891/1000\n",
      " - 7s - loss: 0.3695 - acc: 0.8333 - val_loss: 0.3155 - val_acc: 0.8715\n",
      "Epoch 892/1000\n",
      " - 7s - loss: 0.3706 - acc: 0.8334 - val_loss: 0.3072 - val_acc: 0.8770\n",
      "Epoch 893/1000\n",
      " - 7s - loss: 0.3698 - acc: 0.8317 - val_loss: 0.3074 - val_acc: 0.8743\n",
      "Epoch 894/1000\n",
      " - 7s - loss: 0.3690 - acc: 0.8330 - val_loss: 0.3113 - val_acc: 0.8769\n",
      "Epoch 895/1000\n",
      " - 7s - loss: 0.3690 - acc: 0.8338 - val_loss: 0.3098 - val_acc: 0.8775\n",
      "Epoch 896/1000\n",
      " - 7s - loss: 0.3702 - acc: 0.8311 - val_loss: 0.3101 - val_acc: 0.8757\n",
      "Epoch 897/1000\n",
      " - 7s - loss: 0.3720 - acc: 0.8309 - val_loss: 0.3097 - val_acc: 0.8758\n",
      "Epoch 898/1000\n",
      " - 7s - loss: 0.3678 - acc: 0.8329 - val_loss: 0.3079 - val_acc: 0.8756\n",
      "Epoch 899/1000\n",
      " - 7s - loss: 0.3693 - acc: 0.8323 - val_loss: 0.3046 - val_acc: 0.8787\n",
      "Epoch 900/1000\n",
      " - 7s - loss: 0.3714 - acc: 0.8319 - val_loss: 0.3101 - val_acc: 0.8749\n",
      "Epoch 901/1000\n",
      " - 7s - loss: 0.3701 - acc: 0.8331 - val_loss: 0.3110 - val_acc: 0.8738\n",
      "Epoch 902/1000\n",
      " - 7s - loss: 0.3709 - acc: 0.8318 - val_loss: 0.3150 - val_acc: 0.8724\n",
      "Epoch 903/1000\n",
      " - 7s - loss: 0.3693 - acc: 0.8319 - val_loss: 0.3063 - val_acc: 0.8774\n",
      "Epoch 904/1000\n",
      " - 7s - loss: 0.3698 - acc: 0.8332 - val_loss: 0.3070 - val_acc: 0.8764\n",
      "Epoch 905/1000\n",
      " - 7s - loss: 0.3676 - acc: 0.8344 - val_loss: 0.3069 - val_acc: 0.8781\n",
      "Epoch 906/1000\n",
      " - 7s - loss: 0.3702 - acc: 0.8320 - val_loss: 0.3068 - val_acc: 0.8792\n",
      "Epoch 907/1000\n",
      " - 7s - loss: 0.3703 - acc: 0.8331 - val_loss: 0.3081 - val_acc: 0.8767\n",
      "Epoch 908/1000\n",
      " - 7s - loss: 0.3705 - acc: 0.8326 - val_loss: 0.3085 - val_acc: 0.8788\n",
      "Epoch 909/1000\n",
      " - 7s - loss: 0.3691 - acc: 0.8337 - val_loss: 0.3123 - val_acc: 0.8731\n",
      "Epoch 910/1000\n",
      " - 7s - loss: 0.3685 - acc: 0.8329 - val_loss: 0.3089 - val_acc: 0.8756\n",
      "Epoch 911/1000\n",
      " - 7s - loss: 0.3694 - acc: 0.8335 - val_loss: 0.3110 - val_acc: 0.8719\n",
      "Epoch 912/1000\n",
      " - 7s - loss: 0.3695 - acc: 0.8331 - val_loss: 0.3115 - val_acc: 0.8730\n",
      "Epoch 913/1000\n",
      " - 7s - loss: 0.3698 - acc: 0.8316 - val_loss: 0.3100 - val_acc: 0.8752\n",
      "Epoch 914/1000\n",
      " - 7s - loss: 0.3693 - acc: 0.8327 - val_loss: 0.3081 - val_acc: 0.8769\n",
      "Epoch 915/1000\n",
      " - 7s - loss: 0.3672 - acc: 0.8330 - val_loss: 0.3132 - val_acc: 0.8754\n",
      "Epoch 916/1000\n",
      " - 7s - loss: 0.3681 - acc: 0.8340 - val_loss: 0.3089 - val_acc: 0.8739\n",
      "Epoch 917/1000\n",
      " - 7s - loss: 0.3701 - acc: 0.8337 - val_loss: 0.3158 - val_acc: 0.8718\n",
      "Epoch 918/1000\n",
      " - 7s - loss: 0.3690 - acc: 0.8332 - val_loss: 0.3143 - val_acc: 0.8729\n",
      "Epoch 919/1000\n",
      " - 7s - loss: 0.3700 - acc: 0.8325 - val_loss: 0.3103 - val_acc: 0.8742\n",
      "Epoch 920/1000\n",
      " - 7s - loss: 0.3698 - acc: 0.8324 - val_loss: 0.3097 - val_acc: 0.8763\n",
      "Epoch 921/1000\n",
      " - 9s - loss: 0.3710 - acc: 0.8319 - val_loss: 0.3093 - val_acc: 0.8734\n",
      "Epoch 922/1000\n",
      " - 8s - loss: 0.3674 - acc: 0.8346 - val_loss: 0.3080 - val_acc: 0.8789\n",
      "Epoch 923/1000\n",
      " - 7s - loss: 0.3695 - acc: 0.8329 - val_loss: 0.3077 - val_acc: 0.8757\n",
      "Epoch 924/1000\n",
      " - 7s - loss: 0.3704 - acc: 0.8333 - val_loss: 0.3067 - val_acc: 0.8766\n",
      "Epoch 925/1000\n",
      " - 7s - loss: 0.3694 - acc: 0.8336 - val_loss: 0.3077 - val_acc: 0.8760\n",
      "Epoch 926/1000\n",
      " - 7s - loss: 0.3682 - acc: 0.8330 - val_loss: 0.3079 - val_acc: 0.8757\n",
      "Epoch 927/1000\n",
      " - 7s - loss: 0.3687 - acc: 0.8332 - val_loss: 0.3081 - val_acc: 0.8767\n",
      "Epoch 928/1000\n",
      " - 7s - loss: 0.3687 - acc: 0.8334 - val_loss: 0.3106 - val_acc: 0.8747\n",
      "Epoch 929/1000\n",
      " - 7s - loss: 0.3689 - acc: 0.8346 - val_loss: 0.3091 - val_acc: 0.8718\n",
      "Epoch 930/1000\n",
      " - 8s - loss: 0.3675 - acc: 0.8329 - val_loss: 0.3062 - val_acc: 0.8774\n",
      "Epoch 931/1000\n",
      " - 7s - loss: 0.3672 - acc: 0.8348 - val_loss: 0.3094 - val_acc: 0.8734\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 932/1000\n",
      " - 7s - loss: 0.3681 - acc: 0.8336 - val_loss: 0.3086 - val_acc: 0.8756\n",
      "Epoch 933/1000\n",
      " - 7s - loss: 0.3692 - acc: 0.8327 - val_loss: 0.3073 - val_acc: 0.8775\n",
      "Epoch 934/1000\n",
      " - 7s - loss: 0.3681 - acc: 0.8338 - val_loss: 0.3084 - val_acc: 0.8749\n",
      "Epoch 935/1000\n",
      " - 7s - loss: 0.3700 - acc: 0.8330 - val_loss: 0.3083 - val_acc: 0.8752\n",
      "Epoch 936/1000\n",
      " - 7s - loss: 0.3687 - acc: 0.8328 - val_loss: 0.3056 - val_acc: 0.8781\n",
      "Epoch 937/1000\n",
      " - 7s - loss: 0.3683 - acc: 0.8332 - val_loss: 0.3052 - val_acc: 0.8788\n",
      "Epoch 938/1000\n",
      " - 7s - loss: 0.3677 - acc: 0.8341 - val_loss: 0.3070 - val_acc: 0.8758\n",
      "Epoch 939/1000\n",
      " - 7s - loss: 0.3699 - acc: 0.8327 - val_loss: 0.3086 - val_acc: 0.8750\n",
      "Epoch 940/1000\n",
      " - 7s - loss: 0.3671 - acc: 0.8345 - val_loss: 0.3063 - val_acc: 0.8772\n",
      "Epoch 941/1000\n",
      " - 7s - loss: 0.3688 - acc: 0.8337 - val_loss: 0.3076 - val_acc: 0.8753\n",
      "Epoch 942/1000\n",
      " - 7s - loss: 0.3688 - acc: 0.8327 - val_loss: 0.3076 - val_acc: 0.8758\n",
      "Epoch 943/1000\n",
      " - 7s - loss: 0.3681 - acc: 0.8337 - val_loss: 0.3065 - val_acc: 0.8762\n",
      "Epoch 944/1000\n",
      " - 7s - loss: 0.3677 - acc: 0.8334 - val_loss: 0.3075 - val_acc: 0.8751\n",
      "Epoch 945/1000\n",
      " - 7s - loss: 0.3661 - acc: 0.8370 - val_loss: 0.3087 - val_acc: 0.8771\n",
      "Epoch 946/1000\n",
      " - 7s - loss: 0.3685 - acc: 0.8331 - val_loss: 0.3087 - val_acc: 0.8747\n",
      "Epoch 947/1000\n",
      " - 7s - loss: 0.3681 - acc: 0.8338 - val_loss: 0.3063 - val_acc: 0.8783\n",
      "Epoch 948/1000\n",
      " - 7s - loss: 0.3677 - acc: 0.8338 - val_loss: 0.3078 - val_acc: 0.8757\n",
      "Epoch 949/1000\n",
      " - 7s - loss: 0.3680 - acc: 0.8334 - val_loss: 0.3082 - val_acc: 0.8756\n",
      "Epoch 950/1000\n",
      " - 7s - loss: 0.3658 - acc: 0.8351 - val_loss: 0.3054 - val_acc: 0.8789\n",
      "Epoch 951/1000\n",
      " - 7s - loss: 0.3683 - acc: 0.8318 - val_loss: 0.3096 - val_acc: 0.8740\n",
      "Epoch 952/1000\n",
      " - 7s - loss: 0.3679 - acc: 0.8339 - val_loss: 0.3061 - val_acc: 0.8770\n",
      "Epoch 953/1000\n",
      " - 7s - loss: 0.3670 - acc: 0.8344 - val_loss: 0.3073 - val_acc: 0.8765\n",
      "Epoch 954/1000\n",
      " - 7s - loss: 0.3666 - acc: 0.8343 - val_loss: 0.3079 - val_acc: 0.8752\n",
      "Epoch 955/1000\n",
      " - 7s - loss: 0.3673 - acc: 0.8352 - val_loss: 0.3060 - val_acc: 0.8781\n",
      "Epoch 956/1000\n",
      " - 7s - loss: 0.3686 - acc: 0.8336 - val_loss: 0.3066 - val_acc: 0.8774\n",
      "Epoch 957/1000\n",
      " - 7s - loss: 0.3659 - acc: 0.8359 - val_loss: 0.3072 - val_acc: 0.8763\n",
      "Epoch 958/1000\n",
      " - 7s - loss: 0.3682 - acc: 0.8331 - val_loss: 0.3054 - val_acc: 0.8788\n",
      "Epoch 959/1000\n",
      " - 7s - loss: 0.3680 - acc: 0.8336 - val_loss: 0.3051 - val_acc: 0.8785\n",
      "Epoch 960/1000\n",
      " - 7s - loss: 0.3655 - acc: 0.8360 - val_loss: 0.3049 - val_acc: 0.8777\n",
      "Epoch 961/1000\n",
      " - 7s - loss: 0.3670 - acc: 0.8338 - val_loss: 0.3064 - val_acc: 0.8784\n",
      "Epoch 962/1000\n",
      " - 7s - loss: 0.3682 - acc: 0.8341 - val_loss: 0.3107 - val_acc: 0.8748\n",
      "Epoch 963/1000\n",
      " - 9s - loss: 0.3680 - acc: 0.8351 - val_loss: 0.3072 - val_acc: 0.8772\n",
      "Epoch 964/1000\n",
      " - 8s - loss: 0.3652 - acc: 0.8359 - val_loss: 0.3092 - val_acc: 0.8754\n",
      "Epoch 965/1000\n",
      " - 7s - loss: 0.3682 - acc: 0.8338 - val_loss: 0.3037 - val_acc: 0.8800\n",
      "Epoch 966/1000\n",
      " - 7s - loss: 0.3678 - acc: 0.8334 - val_loss: 0.3087 - val_acc: 0.8750\n",
      "Epoch 967/1000\n",
      " - 7s - loss: 0.3667 - acc: 0.8345 - val_loss: 0.3060 - val_acc: 0.8798\n",
      "Epoch 968/1000\n",
      " - 7s - loss: 0.3662 - acc: 0.8339 - val_loss: 0.3046 - val_acc: 0.8784\n",
      "Epoch 969/1000\n",
      " - 7s - loss: 0.3660 - acc: 0.8364 - val_loss: 0.3068 - val_acc: 0.8778\n",
      "Epoch 970/1000\n",
      " - 7s - loss: 0.3649 - acc: 0.8358 - val_loss: 0.3057 - val_acc: 0.8758\n",
      "Epoch 971/1000\n",
      " - 7s - loss: 0.3676 - acc: 0.8337 - val_loss: 0.3061 - val_acc: 0.8783\n",
      "Epoch 972/1000\n",
      " - 7s - loss: 0.3677 - acc: 0.8346 - val_loss: 0.3085 - val_acc: 0.8783\n",
      "Epoch 973/1000\n",
      " - 7s - loss: 0.3674 - acc: 0.8344 - val_loss: 0.3024 - val_acc: 0.8780\n",
      "Epoch 974/1000\n",
      " - 7s - loss: 0.3659 - acc: 0.8343 - val_loss: 0.3032 - val_acc: 0.8792\n",
      "Epoch 975/1000\n",
      " - 7s - loss: 0.3655 - acc: 0.8346 - val_loss: 0.3055 - val_acc: 0.8753\n",
      "Epoch 976/1000\n",
      " - 7s - loss: 0.3666 - acc: 0.8339 - val_loss: 0.3094 - val_acc: 0.8748\n",
      "Epoch 977/1000\n",
      " - 7s - loss: 0.3666 - acc: 0.8357 - val_loss: 0.3078 - val_acc: 0.8741\n",
      "Epoch 978/1000\n",
      " - 7s - loss: 0.3671 - acc: 0.8345 - val_loss: 0.3044 - val_acc: 0.8768\n",
      "Epoch 979/1000\n",
      " - 7s - loss: 0.3672 - acc: 0.8354 - val_loss: 0.3085 - val_acc: 0.8758\n",
      "Epoch 980/1000\n",
      " - 7s - loss: 0.3672 - acc: 0.8343 - val_loss: 0.3046 - val_acc: 0.8776\n",
      "Epoch 981/1000\n",
      " - 7s - loss: 0.3664 - acc: 0.8342 - val_loss: 0.3096 - val_acc: 0.8757\n",
      "Epoch 982/1000\n",
      " - 7s - loss: 0.3658 - acc: 0.8346 - val_loss: 0.3032 - val_acc: 0.8766\n",
      "Epoch 983/1000\n",
      " - 7s - loss: 0.3659 - acc: 0.8346 - val_loss: 0.3064 - val_acc: 0.8766\n",
      "Epoch 984/1000\n",
      " - 7s - loss: 0.3677 - acc: 0.8345 - val_loss: 0.3059 - val_acc: 0.8783\n",
      "Epoch 985/1000\n",
      " - 7s - loss: 0.3675 - acc: 0.8343 - val_loss: 0.3073 - val_acc: 0.8732\n",
      "Epoch 986/1000\n",
      " - 7s - loss: 0.3673 - acc: 0.8350 - val_loss: 0.3083 - val_acc: 0.8772\n",
      "Epoch 987/1000\n",
      " - 7s - loss: 0.3671 - acc: 0.8346 - val_loss: 0.3071 - val_acc: 0.8761\n",
      "Epoch 988/1000\n",
      " - 7s - loss: 0.3637 - acc: 0.8369 - val_loss: 0.3061 - val_acc: 0.8759\n",
      "Epoch 989/1000\n",
      " - 7s - loss: 0.3683 - acc: 0.8330 - val_loss: 0.3067 - val_acc: 0.8749\n",
      "Epoch 990/1000\n",
      " - 7s - loss: 0.3678 - acc: 0.8347 - val_loss: 0.3064 - val_acc: 0.8787\n",
      "Epoch 991/1000\n",
      " - 7s - loss: 0.3672 - acc: 0.8349 - val_loss: 0.3086 - val_acc: 0.8739\n",
      "Epoch 992/1000\n",
      " - 7s - loss: 0.3659 - acc: 0.8345 - val_loss: 0.3039 - val_acc: 0.8782\n",
      "Epoch 993/1000\n",
      " - 7s - loss: 0.3651 - acc: 0.8369 - val_loss: 0.3089 - val_acc: 0.8746\n",
      "Epoch 994/1000\n",
      " - 7s - loss: 0.3685 - acc: 0.8337 - val_loss: 0.3042 - val_acc: 0.8801\n",
      "Epoch 995/1000\n",
      " - 7s - loss: 0.3656 - acc: 0.8361 - val_loss: 0.3071 - val_acc: 0.8765\n",
      "Epoch 996/1000\n",
      " - 7s - loss: 0.3668 - acc: 0.8352 - val_loss: 0.3038 - val_acc: 0.8793\n",
      "Epoch 997/1000\n",
      " - 7s - loss: 0.3684 - acc: 0.8333 - val_loss: 0.3050 - val_acc: 0.8769\n",
      "Epoch 998/1000\n",
      " - 7s - loss: 0.3662 - acc: 0.8341 - val_loss: 0.3055 - val_acc: 0.8790\n",
      "Epoch 999/1000\n",
      " - 7s - loss: 0.3669 - acc: 0.8346 - val_loss: 0.3048 - val_acc: 0.8769\n",
      "Epoch 1000/1000\n",
      " - 7s - loss: 0.3683 - acc: 0.8334 - val_loss: 0.3087 - val_acc: 0.8754\n",
      "Iteration >#3: 87.715 \n",
      "\n",
      "Wall time: 5h 48min 55s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# summarize results\n",
    "repeats=3\n",
    "scores = list()\n",
    "for r in range(repeats):\n",
    "    history,score = evaluate_model(X_train, y_train, X_test, y_test)    \n",
    "    print('Iteration >#%d: %.3f' % (r+1, score*100),'\\n')\n",
    "    scores.append(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot history\n",
    "plt.figure(figsize=(20,12),dpi=150)\n",
    "plt.plot(history.history['loss'],  label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.plot(history.history['acc'], '--', markersize=0.25, linewidth=1, label='Training Accuracy')\n",
    "plt.plot(history.history['val_acc'], '--', markersize=0.25, linewidth=1, label='Validation Accuracy')\n",
    "plt.legend()\n",
    "plt.title('Training & Validation Loss over each Training Iteration')\n",
    "plt.xlabel('Epochs')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum Training Accuracy: 0.837018361092664\n",
      "Maximum Validation Accuracy: 0.8801349641602032\n"
     ]
    }
   ],
   "source": [
    "print('Maximum Training Accuracy:',max(history.history['acc']))\n",
    "print('Maximum Validation Accuracy:',max(history.history['val_acc']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.8816920351503466, 0.8797237895211121, 0.8771486681616067] \n",
      "\n",
      "Average Accuracy: 87.952% (+/-0.186)\n"
     ]
    }
   ],
   "source": [
    "# Average Accuracy over multiple runs of the Training Iterations\n",
    "print(scores,'\\n')\n",
    "m, s = np.mean(scores), np.std(scores)\n",
    "print('Average Accuracy: %.3f%% (+/-%.3f)' % (m*100, s*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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