Курсовая, пульсары итоговый вариант.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import seaborn as sns\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import pyplot\n",
    "import featuretools as ft\n",
    "import featuretools.variable_types as vtypes\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'c:\\\\Users\\\\Artem\\\\Desktop\\\\neural networks\\\\HTRU2'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.chdir('c:\\\\Users\\\\Artem\\\\Desktop\\\\neural networks\\\\HTRU2')\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('HTRU_2.csv', header=None)\n",
    "column_names = pd.read_csv('columns_names.txt',sep=\" \", header=None)\n",
    "data.columns = [\"MeanIP\",\"StdIP\",\"ExckurtIP\",\"SkewIP\",\"MeanDMSNR\",\"StdDMSNR\",\"ExckurtDMSNR\",\"SkewDMSNR\", \"class\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.columns = data.columns.map(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_metrics(history): #для графиков функции потерь и точности в зависимости от эпохи\n",
    "  metrics =  ['loss', 'accuracy']\n",
    "  for n, metric in enumerate(metrics):\n",
    "    name = metric.replace(\"_\",\" \").capitalize()\n",
    "    plt.subplot(2,2,n+2)\n",
    "    plt.plot(history.epoch,  history.history[metric], label='Train')\n",
    "    plt.plot(history.epoch, history.history['val_'+metric], linestyle=\"--\", label='Val')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel(name)\n",
    "    if metric == 'loss':\n",
    "      plt.ylim([0, plt.ylim()[1]])\n",
    "    else:\n",
    "      plt.ylim([0,1.2])\n",
    "\n",
    "    plt.legend()\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_cm(labels, predictions, p=0.5): #для confusion_matrix\n",
    "  cm = confusion_matrix(labels, predictions > p)\n",
    "  plt.figure(figsize=(5,5))\n",
    "  sns.heatmap(cm, annot=True, fmt=\"d\")\n",
    "  plt.title('Confusion matrix @{:.2f}'.format(p))\n",
    "  plt.ylabel('Actual label')\n",
    "  plt.xlabel('Predicted label')\n",
    "\n",
    "  print('всего 27 \"пульсаров\"')\n",
    "  print('всего 1763 \"не пульсаров\"')\n",
    "  print('не пульсар предсказанный как не пульсар: ', cm[0][0])\n",
    "  print('не пульсар предсказанный как пульсар :', cm[0][1])\n",
    "  print('пульсар предсказанный как не пульсар :', cm[1][0])\n",
    "  print('пульсар предсказанный как пульсар : ', cm[1][1])\n",
    "  print('количество правильных ответов', np.sum([cm[0][0],cm[1][1]]), 'из 1790')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_loss(history, label, n):\n",
    "  # Use a log scale to show the wide range of values.\n",
    "  plt.semilogy(history.epoch,  history.history['loss'],\n",
    "               color=colors[n], label='Train '+label)\n",
    "  plt.semilogy(history.epoch,  history.history['val_loss'],\n",
    "          color=colors[n], label='Val '+label,\n",
    "          linestyle=\"--\")\n",
    "  plt.xlabel('Epoch')\n",
    "  plt.ylabel('Loss')\n",
    "  \n",
    "  plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "plt.rcParams['figure.figsize'] = (12, 10)\n",
    "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "del data['class'] #удаляется последняя колонка с ответами для feature engeneering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>time_since_first</td>\n",
       "      <td>aggregation</td>\n",
       "      <td>Calculates the time elapsed since the first da...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>num_unique</td>\n",
       "      <td>aggregation</td>\n",
       "      <td>Determines the number of distinct values, igno...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>median</td>\n",
       "      <td>aggregation</td>\n",
       "      <td>Determines the middlemost number in a list of ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>any</td>\n",
       "      <td>aggregation</td>\n",
       "      <td>Determines if any value is 'True' in a list.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>percent_true</td>\n",
       "      <td>aggregation</td>\n",
       "      <td>Determines the percent of `True` values.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mode</td>\n",
       "      <td>aggregation</td>\n",
       "      <td>Determines the most commonly repeated value.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>entropy</td>\n",
       "      <td>aggregation</td>\n",
       "      <td>Calculates the entropy for a categorical variable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>max</td>\n",
       "      <td>aggregation</td>\n",
       "      <td>Calculates the highest value, ignoring `NaN` v...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>std</td>\n",
       "      <td>aggregation</td>\n",
       "      <td>Computes the dispersion relative to the mean v...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sum</td>\n",
       "      <td>aggregation</td>\n",
       "      <td>Calculates the total addition, ignoring `NaN`.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>skew</td>\n",
       "      <td>aggregation</td>\n",
       "      <td>Computes the extent to which a distribution di...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>trend</td>\n",
       "      <td>aggregation</td>\n",
       "      <td>Calculates the trend of a variable over time.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>min</td>\n",
       "      <td>aggregation</td>\n",
       "      <td>Calculates the smallest value, ignoring `NaN` ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>time_since_last</td>\n",
       "      <td>aggregation</td>\n",
       "      <td>Calculates the time elapsed since the last dat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>count</td>\n",
       "      <td>aggregation</td>\n",
       "      <td>Determines the total number of values, excludi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>num_true</td>\n",
       "      <td>aggregation</td>\n",
       "      <td>Counts the number of `True` values.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>last</td>\n",
       "      <td>aggregation</td>\n",
       "      <td>Determines the last value in a list.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>mean</td>\n",
       "      <td>aggregation</td>\n",
       "      <td>Computes the average for a list of values.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>all</td>\n",
       "      <td>aggregation</td>\n",
       "      <td>Calculates if all values are 'True' in a list.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>first</td>\n",
       "      <td>aggregation</td>\n",
       "      <td>Determines the first value in a list.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>avg_time_between</td>\n",
       "      <td>aggregation</td>\n",
       "      <td>Computes the average number of seconds between...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>n_most_common</td>\n",
       "      <td>aggregation</td>\n",
       "      <td>Determines the `n` most common elements.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>day</td>\n",
       "      <td>transform</td>\n",
       "      <td>Determines the day of the month from a datetime.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>month</td>\n",
       "      <td>transform</td>\n",
       "      <td>Determines the month value of a datetime.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>not_equal_scalar</td>\n",
       "      <td>transform</td>\n",
       "      <td>Determines if values in a list are not equal t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>subtract_numeric</td>\n",
       "      <td>transform</td>\n",
       "      <td>Element-wise subtraction of two lists.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>week</td>\n",
       "      <td>transform</td>\n",
       "      <td>Determines the week of the year from a datetime.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>absolute</td>\n",
       "      <td>transform</td>\n",
       "      <td>Computes the absolute value of a number.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>modulo_numeric_scalar</td>\n",
       "      <td>transform</td>\n",
       "      <td>Return the modulo of each element in the list ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>greater_than_equal_to_scalar</td>\n",
       "      <td>transform</td>\n",
       "      <td>Determines if values are greater than or equal...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>is_weekend</td>\n",
       "      <td>transform</td>\n",
       "      <td>Determines if a date falls on a weekend.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>not</td>\n",
       "      <td>transform</td>\n",
       "      <td>Negates a boolean value.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>add_numeric</td>\n",
       "      <td>transform</td>\n",
       "      <td>Element-wise addition of two lists.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>subtract_numeric_scalar</td>\n",
       "      <td>transform</td>\n",
       "      <td>Subtract a scalar from each element in the list.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>less_than</td>\n",
       "      <td>transform</td>\n",
       "      <td>Determines if values in one list are less than...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>year</td>\n",
       "      <td>transform</td>\n",
       "      <td>Determines the year value of a datetime.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>hour</td>\n",
       "      <td>transform</td>\n",
       "      <td>Determines the hour value of a datetime.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>not_equal</td>\n",
       "      <td>transform</td>\n",
       "      <td>Determines if values in one list are not equal...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>less_than_equal_to_scalar</td>\n",
       "      <td>transform</td>\n",
       "      <td>Determines if values are less than or equal to...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>latitude</td>\n",
       "      <td>transform</td>\n",
       "      <td>Returns the first tuple value in a list of Lat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>cum_min</td>\n",
       "      <td>transform</td>\n",
       "      <td>Calculates the cumulative minimum.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>divide_numeric</td>\n",
       "      <td>transform</td>\n",
       "      <td>Element-wise division of two lists.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>greater_than_equal_to</td>\n",
       "      <td>transform</td>\n",
       "      <td>Determines if values in one list are greater t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>less_than_scalar</td>\n",
       "      <td>transform</td>\n",
       "      <td>Determines if values are less than a given sca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>weekday</td>\n",
       "      <td>transform</td>\n",
       "      <td>Determines the day of the week from a datetime.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>cum_max</td>\n",
       "      <td>transform</td>\n",
       "      <td>Calculates the cumulative maximum.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>multiply_numeric</td>\n",
       "      <td>transform</td>\n",
       "      <td>Element-wise multiplication of two lists.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>multiply_boolean</td>\n",
       "      <td>transform</td>\n",
       "      <td>Element-wise multiplication of two lists of bo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>is_null</td>\n",
       "      <td>transform</td>\n",
       "      <td>Determines if a value is null.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>less_than_equal_to</td>\n",
       "      <td>transform</td>\n",
       "      <td>Determines if values in one list are less than...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>divide_by_feature</td>\n",
       "      <td>transform</td>\n",
       "      <td>Divide a scalar by each value in the list.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>greater_than</td>\n",
       "      <td>transform</td>\n",
       "      <td>Determines if values in one list are greater t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>percentile</td>\n",
       "      <td>transform</td>\n",
       "      <td>Determines the percentile rank for each value ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>time_since</td>\n",
       "      <td>transform</td>\n",
       "      <td>Calculates time from a value to a specified cu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>add_numeric_scalar</td>\n",
       "      <td>transform</td>\n",
       "      <td>Add a scalar to each value in the list.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>or</td>\n",
       "      <td>transform</td>\n",
       "      <td>Element-wise logical OR of two lists.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>num_characters</td>\n",
       "      <td>transform</td>\n",
       "      <td>Calculates the number of characters in a string.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>negate</td>\n",
       "      <td>transform</td>\n",
       "      <td>Negates a numeric value.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>multiply_numeric_scalar</td>\n",
       "      <td>transform</td>\n",
       "      <td>Multiply each element in the list by a scalar.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>divide_numeric_scalar</td>\n",
       "      <td>transform</td>\n",
       "      <td>Divide each element in the list by a scalar.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            name         type  \\\n",
       "0               time_since_first  aggregation   \n",
       "1                     num_unique  aggregation   \n",
       "2                         median  aggregation   \n",
       "3                            any  aggregation   \n",
       "4                   percent_true  aggregation   \n",
       "5                           mode  aggregation   \n",
       "6                        entropy  aggregation   \n",
       "7                            max  aggregation   \n",
       "8                            std  aggregation   \n",
       "9                            sum  aggregation   \n",
       "10                          skew  aggregation   \n",
       "11                         trend  aggregation   \n",
       "12                           min  aggregation   \n",
       "13               time_since_last  aggregation   \n",
       "14                         count  aggregation   \n",
       "15                      num_true  aggregation   \n",
       "16                          last  aggregation   \n",
       "17                          mean  aggregation   \n",
       "18                           all  aggregation   \n",
       "19                         first  aggregation   \n",
       "20              avg_time_between  aggregation   \n",
       "21                 n_most_common  aggregation   \n",
       "22                           day    transform   \n",
       "23                         month    transform   \n",
       "24              not_equal_scalar    transform   \n",
       "25              subtract_numeric    transform   \n",
       "26                          week    transform   \n",
       "27                      absolute    transform   \n",
       "28         modulo_numeric_scalar    transform   \n",
       "29  greater_than_equal_to_scalar    transform   \n",
       "..                           ...          ...   \n",
       "48                    is_weekend    transform   \n",
       "49                           not    transform   \n",
       "50                   add_numeric    transform   \n",
       "51       subtract_numeric_scalar    transform   \n",
       "52                     less_than    transform   \n",
       "53                          year    transform   \n",
       "54                          hour    transform   \n",
       "55                     not_equal    transform   \n",
       "56     less_than_equal_to_scalar    transform   \n",
       "57                      latitude    transform   \n",
       "58                       cum_min    transform   \n",
       "59                divide_numeric    transform   \n",
       "60         greater_than_equal_to    transform   \n",
       "61              less_than_scalar    transform   \n",
       "62                       weekday    transform   \n",
       "63                       cum_max    transform   \n",
       "64              multiply_numeric    transform   \n",
       "65              multiply_boolean    transform   \n",
       "66                       is_null    transform   \n",
       "67            less_than_equal_to    transform   \n",
       "68             divide_by_feature    transform   \n",
       "69                  greater_than    transform   \n",
       "70                    percentile    transform   \n",
       "71                    time_since    transform   \n",
       "72            add_numeric_scalar    transform   \n",
       "73                            or    transform   \n",
       "74                num_characters    transform   \n",
       "75                        negate    transform   \n",
       "76       multiply_numeric_scalar    transform   \n",
       "77         divide_numeric_scalar    transform   \n",
       "\n",
       "                                          description  \n",
       "0   Calculates the time elapsed since the first da...  \n",
       "1   Determines the number of distinct values, igno...  \n",
       "2   Determines the middlemost number in a list of ...  \n",
       "3        Determines if any value is 'True' in a list.  \n",
       "4            Determines the percent of `True` values.  \n",
       "5        Determines the most commonly repeated value.  \n",
       "6   Calculates the entropy for a categorical variable  \n",
       "7   Calculates the highest value, ignoring `NaN` v...  \n",
       "8   Computes the dispersion relative to the mean v...  \n",
       "9      Calculates the total addition, ignoring `NaN`.  \n",
       "10  Computes the extent to which a distribution di...  \n",
       "11      Calculates the trend of a variable over time.  \n",
       "12  Calculates the smallest value, ignoring `NaN` ...  \n",
       "13  Calculates the time elapsed since the last dat...  \n",
       "14  Determines the total number of values, excludi...  \n",
       "15                Counts the number of `True` values.  \n",
       "16               Determines the last value in a list.  \n",
       "17         Computes the average for a list of values.  \n",
       "18     Calculates if all values are 'True' in a list.  \n",
       "19              Determines the first value in a list.  \n",
       "20  Computes the average number of seconds between...  \n",
       "21           Determines the `n` most common elements.  \n",
       "22   Determines the day of the month from a datetime.  \n",
       "23          Determines the month value of a datetime.  \n",
       "24  Determines if values in a list are not equal t...  \n",
       "25             Element-wise subtraction of two lists.  \n",
       "26   Determines the week of the year from a datetime.  \n",
       "27           Computes the absolute value of a number.  \n",
       "28  Return the modulo of each element in the list ...  \n",
       "29  Determines if values are greater than or equal...  \n",
       "..                                                ...  \n",
       "48           Determines if a date falls on a weekend.  \n",
       "49                           Negates a boolean value.  \n",
       "50                Element-wise addition of two lists.  \n",
       "51   Subtract a scalar from each element in the list.  \n",
       "52  Determines if values in one list are less than...  \n",
       "53           Determines the year value of a datetime.  \n",
       "54           Determines the hour value of a datetime.  \n",
       "55  Determines if values in one list are not equal...  \n",
       "56  Determines if values are less than or equal to...  \n",
       "57  Returns the first tuple value in a list of Lat...  \n",
       "58                 Calculates the cumulative minimum.  \n",
       "59                Element-wise division of two lists.  \n",
       "60  Determines if values in one list are greater t...  \n",
       "61  Determines if values are less than a given sca...  \n",
       "62    Determines the day of the week from a datetime.  \n",
       "63                 Calculates the cumulative maximum.  \n",
       "64          Element-wise multiplication of two lists.  \n",
       "65  Element-wise multiplication of two lists of bo...  \n",
       "66                     Determines if a value is null.  \n",
       "67  Determines if values in one list are less than...  \n",
       "68         Divide a scalar by each value in the list.  \n",
       "69  Determines if values in one list are greater t...  \n",
       "70  Determines the percentile rank for each value ...  \n",
       "71  Calculates time from a value to a specified cu...  \n",
       "72            Add a scalar to each value in the list.  \n",
       "73              Element-wise logical OR of two lists.  \n",
       "74   Calculates the number of characters in a string.  \n",
       "75                           Negates a numeric value.  \n",
       "76     Multiply each element in the list by a scalar.  \n",
       "77       Divide each element in the list by a scalar.  \n",
       "\n",
       "[78 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ft.list_primitives()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-05-22 23:08:23,224 featuretools.entityset - WARNING    index number not found in dataframe, creating new integer column\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Entityset: pulsars\n",
       "  Entities:\n",
       "    signals [Rows: 17898, Columns: 9]\n",
       "  Relationships:\n",
       "    No relationships"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating and entity set 'es' #начинается работа над генерацией признаков с помощью feature engineering.\n",
    "es = ft.EntitySet(id = 'pulsars')\n",
    "# adding a dataframe \n",
    "es.entity_from_dataframe(entity_id = 'signals', dataframe = data, index='number')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Variable: number (dtype = index)>,\n",
       " <Variable: MeanIP (dtype = numeric)>,\n",
       " <Variable: StdIP (dtype = numeric)>,\n",
       " <Variable: ExckurtIP (dtype = numeric)>,\n",
       " <Variable: SkewIP (dtype = numeric)>,\n",
       " <Variable: MeanDMSNR (dtype = numeric)>,\n",
       " <Variable: StdDMSNR (dtype = numeric)>,\n",
       " <Variable: ExckurtDMSNR (dtype = numeric)>,\n",
       " <Variable: SkewDMSNR (dtype = numeric)>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "es[\"signals\"].variables "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Built 8 features\n",
      "Elapsed: 00:00 | Progress: 100%|██████████\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MeanIP</th>\n",
       "      <th>StdIP</th>\n",
       "      <th>ExckurtIP</th>\n",
       "      <th>SkewIP</th>\n",
       "      <th>MeanDMSNR</th>\n",
       "      <th>StdDMSNR</th>\n",
       "      <th>ExckurtDMSNR</th>\n",
       "      <th>SkewDMSNR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>140.562500</td>\n",
       "      <td>55.683782</td>\n",
       "      <td>-0.234571</td>\n",
       "      <td>-0.699648</td>\n",
       "      <td>3.199833</td>\n",
       "      <td>19.110426</td>\n",
       "      <td>7.975532</td>\n",
       "      <td>74.242225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>102.507812</td>\n",
       "      <td>58.882430</td>\n",
       "      <td>0.465318</td>\n",
       "      <td>-0.515088</td>\n",
       "      <td>1.677258</td>\n",
       "      <td>14.860146</td>\n",
       "      <td>10.576487</td>\n",
       "      <td>127.393580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>103.015625</td>\n",
       "      <td>39.341649</td>\n",
       "      <td>0.323328</td>\n",
       "      <td>1.051164</td>\n",
       "      <td>3.121237</td>\n",
       "      <td>21.744669</td>\n",
       "      <td>7.735822</td>\n",
       "      <td>63.171909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>136.750000</td>\n",
       "      <td>57.178449</td>\n",
       "      <td>-0.068415</td>\n",
       "      <td>-0.636238</td>\n",
       "      <td>3.642977</td>\n",
       "      <td>20.959280</td>\n",
       "      <td>6.896499</td>\n",
       "      <td>53.593661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>88.726562</td>\n",
       "      <td>40.672225</td>\n",
       "      <td>0.600866</td>\n",
       "      <td>1.123492</td>\n",
       "      <td>1.178930</td>\n",
       "      <td>11.468720</td>\n",
       "      <td>14.269573</td>\n",
       "      <td>252.567306</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            MeanIP      StdIP  ExckurtIP    SkewIP  MeanDMSNR   StdDMSNR  \\\n",
       "number                                                                     \n",
       "0       140.562500  55.683782  -0.234571 -0.699648   3.199833  19.110426   \n",
       "1       102.507812  58.882430   0.465318 -0.515088   1.677258  14.860146   \n",
       "2       103.015625  39.341649   0.323328  1.051164   3.121237  21.744669   \n",
       "3       136.750000  57.178449  -0.068415 -0.636238   3.642977  20.959280   \n",
       "4        88.726562  40.672225   0.600866  1.123492   1.178930  11.468720   \n",
       "\n",
       "        ExckurtDMSNR   SkewDMSNR  \n",
       "number                            \n",
       "0           7.975532   74.242225  \n",
       "1          10.576487  127.393580  \n",
       "2           7.735822   63.171909  \n",
       "3           6.896499   53.593661  \n",
       "4          14.269573  252.567306  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_matrix, feature_names=ft.dfs(entityset=es, target_entity = 'signals',max_depth = 3, verbose = 3, n_jobs = 1)\n",
    "feature_matrix.head() #при первой попытке генрации признаков(при \"no relationships\" ничего не сгенерировалось)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "es = es.normalize_entity(base_entity_id='signals', new_entity_id='new_signals', index='MeanIP') #создаём relationship: выделяем столбец, на основе которого будут строиться общие признаки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entityset: pulsars\n",
      "  Entities:\n",
      "    signals [Rows: 17898, Columns: 9]\n",
      "    new_signals [Rows: 8626, Columns: 1]\n",
      "  Relationships:\n",
      "    signals.MeanIP -> new_signals.MeanIP\n"
     ]
    }
   ],
   "source": [
    "print(es)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>MeanIP</th>\n",
       "      <th>StdIP</th>\n",
       "      <th>ExckurtIP</th>\n",
       "      <th>SkewIP</th>\n",
       "      <th>MeanDMSNR</th>\n",
       "      <th>StdDMSNR</th>\n",
       "      <th>ExckurtDMSNR</th>\n",
       "      <th>SkewDMSNR</th>\n",
       "      <th>new_signals.MEAN(signals.MeanDMSNR)</th>\n",
       "      <th>new_signals.MEAN(signals.SkewDMSNR)</th>\n",
       "      <th>...</th>\n",
       "      <th>new_signals.SUM(signals.StdIP)</th>\n",
       "      <th>new_signals.SUM(signals.SkewIP)</th>\n",
       "      <th>new_signals.SUM(signals.StdDMSNR)</th>\n",
       "      <th>new_signals.MEDIAN(signals.MeanDMSNR)</th>\n",
       "      <th>new_signals.MEDIAN(signals.SkewDMSNR)</th>\n",
       "      <th>new_signals.MEDIAN(signals.ExckurtDMSNR)</th>\n",
       "      <th>new_signals.MEDIAN(signals.ExckurtIP)</th>\n",
       "      <th>new_signals.MEDIAN(signals.StdIP)</th>\n",
       "      <th>new_signals.MEDIAN(signals.SkewIP)</th>\n",
       "      <th>new_signals.MEDIAN(signals.StdDMSNR)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>140.562500</td>\n",
       "      <td>55.683782</td>\n",
       "      <td>-0.234571</td>\n",
       "      <td>-0.699648</td>\n",
       "      <td>3.199833</td>\n",
       "      <td>19.110426</td>\n",
       "      <td>7.975532</td>\n",
       "      <td>74.242225</td>\n",
       "      <td>3.199833</td>\n",
       "      <td>74.242225</td>\n",
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       "      <td>55.683782</td>\n",
       "      <td>-0.699648</td>\n",
       "      <td>19.110426</td>\n",
       "      <td>3.199833</td>\n",
       "      <td>74.242225</td>\n",
       "      <td>7.975532</td>\n",
       "      <td>-0.234571</td>\n",
       "      <td>55.683782</td>\n",
       "      <td>-0.699648</td>\n",
       "      <td>19.110426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>102.507812</td>\n",
       "      <td>58.882430</td>\n",
       "      <td>0.465318</td>\n",
       "      <td>-0.515088</td>\n",
       "      <td>1.677258</td>\n",
       "      <td>14.860146</td>\n",
       "      <td>10.576487</td>\n",
       "      <td>127.393580</td>\n",
       "      <td>2.066332</td>\n",
       "      <td>110.547849</td>\n",
       "      <td>...</td>\n",
       "      <td>160.533606</td>\n",
       "      <td>-0.404816</td>\n",
       "      <td>45.756319</td>\n",
       "      <td>1.836957</td>\n",
       "      <td>127.393580</td>\n",
       "      <td>10.360940</td>\n",
       "      <td>0.452962</td>\n",
       "      <td>52.275860</td>\n",
       "      <td>-0.246135</td>\n",
       "      <td>14.860146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>103.015625</td>\n",
       "      <td>39.341649</td>\n",
       "      <td>0.323328</td>\n",
       "      <td>1.051164</td>\n",
       "      <td>3.121237</td>\n",
       "      <td>21.744669</td>\n",
       "      <td>7.735822</td>\n",
       "      <td>63.171909</td>\n",
       "      <td>2.757107</td>\n",
       "      <td>85.309060</td>\n",
       "      <td>...</td>\n",
       "      <td>88.082191</td>\n",
       "      <td>1.273260</td>\n",
       "      <td>36.393436</td>\n",
       "      <td>2.757107</td>\n",
       "      <td>85.309060</td>\n",
       "      <td>8.400171</td>\n",
       "      <td>0.466137</td>\n",
       "      <td>44.041095</td>\n",
       "      <td>0.636630</td>\n",
       "      <td>18.196718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>136.750000</td>\n",
       "      <td>57.178449</td>\n",
       "      <td>-0.068415</td>\n",
       "      <td>-0.636238</td>\n",
       "      <td>3.642977</td>\n",
       "      <td>20.959280</td>\n",
       "      <td>6.896499</td>\n",
       "      <td>53.593661</td>\n",
       "      <td>3.258361</td>\n",
       "      <td>71.589360</td>\n",
       "      <td>...</td>\n",
       "      <td>157.041468</td>\n",
       "      <td>-1.103265</td>\n",
       "      <td>58.706194</td>\n",
       "      <td>3.326923</td>\n",
       "      <td>67.242135</td>\n",
       "      <td>7.752153</td>\n",
       "      <td>-0.068415</td>\n",
       "      <td>49.955605</td>\n",
       "      <td>-0.375633</td>\n",
       "      <td>20.725051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>88.726562</td>\n",
       "      <td>40.672225</td>\n",
       "      <td>0.600866</td>\n",
       "      <td>1.123492</td>\n",
       "      <td>1.178930</td>\n",
       "      <td>11.468720</td>\n",
       "      <td>14.269573</td>\n",
       "      <td>252.567306</td>\n",
       "      <td>1.178930</td>\n",
       "      <td>252.567306</td>\n",
       "      <td>...</td>\n",
       "      <td>40.672225</td>\n",
       "      <td>1.123492</td>\n",
       "      <td>11.468720</td>\n",
       "      <td>1.178930</td>\n",
       "      <td>252.567306</td>\n",
       "      <td>14.269573</td>\n",
       "      <td>0.600866</td>\n",
       "      <td>40.672225</td>\n",
       "      <td>1.123492</td>\n",
       "      <td>11.468720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>93.570312</td>\n",
       "      <td>46.698114</td>\n",
       "      <td>0.531905</td>\n",
       "      <td>0.416721</td>\n",
       "      <td>1.636288</td>\n",
       "      <td>14.545074</td>\n",
       "      <td>10.621748</td>\n",
       "      <td>131.394004</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>72.641038</td>\n",
       "      <td>...</td>\n",
       "      <td>90.217808</td>\n",
       "      <td>2.066170</td>\n",
       "      <td>66.592926</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>72.641038</td>\n",
       "      <td>7.301360</td>\n",
       "      <td>0.619374</td>\n",
       "      <td>45.108904</td>\n",
       "      <td>1.033085</td>\n",
       "      <td>33.296463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>119.484375</td>\n",
       "      <td>48.765059</td>\n",
       "      <td>0.031460</td>\n",
       "      <td>-0.112168</td>\n",
       "      <td>0.999164</td>\n",
       "      <td>9.279612</td>\n",
       "      <td>19.206230</td>\n",
       "      <td>479.756567</td>\n",
       "      <td>38.548495</td>\n",
       "      <td>165.400459</td>\n",
       "      <td>...</td>\n",
       "      <td>189.047439</td>\n",
       "      <td>0.170001</td>\n",
       "      <td>129.733021</td>\n",
       "      <td>2.663462</td>\n",
       "      <td>91.395097</td>\n",
       "      <td>8.464385</td>\n",
       "      <td>0.054479</td>\n",
       "      <td>47.513018</td>\n",
       "      <td>0.066080</td>\n",
       "      <td>16.192914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>130.382812</td>\n",
       "      <td>39.844056</td>\n",
       "      <td>-0.158323</td>\n",
       "      <td>0.389540</td>\n",
       "      <td>1.220736</td>\n",
       "      <td>14.378941</td>\n",
       "      <td>13.539456</td>\n",
       "      <td>198.236457</td>\n",
       "      <td>2.247492</td>\n",
       "      <td>125.717906</td>\n",
       "      <td>...</td>\n",
       "      <td>141.668487</td>\n",
       "      <td>-0.452515</td>\n",
       "      <td>51.905838</td>\n",
       "      <td>2.678094</td>\n",
       "      <td>106.652628</td>\n",
       "      <td>9.391858</td>\n",
       "      <td>-0.158323</td>\n",
       "      <td>50.379206</td>\n",
       "      <td>-0.117718</td>\n",
       "      <td>16.535941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>107.250000</td>\n",
       "      <td>52.627078</td>\n",
       "      <td>0.452688</td>\n",
       "      <td>0.170347</td>\n",
       "      <td>2.331940</td>\n",
       "      <td>14.486853</td>\n",
       "      <td>9.001004</td>\n",
       "      <td>107.972506</td>\n",
       "      <td>2.886407</td>\n",
       "      <td>128.005504</td>\n",
       "      <td>...</td>\n",
       "      <td>348.057879</td>\n",
       "      <td>0.240581</td>\n",
       "      <td>124.083662</td>\n",
       "      <td>2.331940</td>\n",
       "      <td>107.972506</td>\n",
       "      <td>9.001004</td>\n",
       "      <td>0.329787</td>\n",
       "      <td>48.306153</td>\n",
       "      <td>0.123002</td>\n",
       "      <td>14.486853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>107.257812</td>\n",
       "      <td>39.496488</td>\n",
       "      <td>0.465882</td>\n",
       "      <td>1.162877</td>\n",
       "      <td>4.079431</td>\n",
       "      <td>24.980418</td>\n",
       "      <td>7.397080</td>\n",
       "      <td>57.784738</td>\n",
       "      <td>3.438127</td>\n",
       "      <td>80.598816</td>\n",
       "      <td>...</td>\n",
       "      <td>172.235183</td>\n",
       "      <td>2.712742</td>\n",
       "      <td>86.149156</td>\n",
       "      <td>3.544732</td>\n",
       "      <td>68.064048</td>\n",
       "      <td>7.809346</td>\n",
       "      <td>0.449450</td>\n",
       "      <td>42.456847</td>\n",
       "      <td>0.788828</td>\n",
       "      <td>21.853419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>142.078125</td>\n",
       "      <td>45.288073</td>\n",
       "      <td>-0.320328</td>\n",
       "      <td>0.283953</td>\n",
       "      <td>5.376254</td>\n",
       "      <td>29.009897</td>\n",
       "      <td>6.076266</td>\n",
       "      <td>37.831393</td>\n",
       "      <td>5.376254</td>\n",
       "      <td>37.831393</td>\n",
       "      <td>...</td>\n",
       "      <td>45.288073</td>\n",
       "      <td>0.283953</td>\n",
       "      <td>29.009897</td>\n",
       "      <td>5.376254</td>\n",
       "      <td>37.831393</td>\n",
       "      <td>6.076266</td>\n",
       "      <td>-0.320328</td>\n",
       "      <td>45.288073</td>\n",
       "      <td>0.283953</td>\n",
       "      <td>29.009897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>133.257812</td>\n",
       "      <td>44.058244</td>\n",
       "      <td>-0.081060</td>\n",
       "      <td>0.115362</td>\n",
       "      <td>1.632107</td>\n",
       "      <td>12.007806</td>\n",
       "      <td>11.972067</td>\n",
       "      <td>195.543448</td>\n",
       "      <td>7.340301</td>\n",
       "      <td>194.169547</td>\n",
       "      <td>...</td>\n",
       "      <td>151.189064</td>\n",
       "      <td>-0.833638</td>\n",
       "      <td>75.701908</td>\n",
       "      <td>1.632107</td>\n",
       "      <td>195.543448</td>\n",
       "      <td>11.972067</td>\n",
       "      <td>-0.047957</td>\n",
       "      <td>50.598025</td>\n",
       "      <td>-0.314778</td>\n",
       "      <td>12.007806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>134.960938</td>\n",
       "      <td>49.554327</td>\n",
       "      <td>-0.135304</td>\n",
       "      <td>-0.080470</td>\n",
       "      <td>10.696488</td>\n",
       "      <td>41.342044</td>\n",
       "      <td>3.893934</td>\n",
       "      <td>14.131206</td>\n",
       "      <td>6.377717</td>\n",
       "      <td>36.311523</td>\n",
       "      <td>...</td>\n",
       "      <td>212.683196</td>\n",
       "      <td>-1.074346</td>\n",
       "      <td>121.942456</td>\n",
       "      <td>5.525920</td>\n",
       "      <td>36.325598</td>\n",
       "      <td>5.865141</td>\n",
       "      <td>0.060802</td>\n",
       "      <td>52.555330</td>\n",
       "      <td>-0.289093</td>\n",
       "      <td>29.035078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>117.945312</td>\n",
       "      <td>45.506577</td>\n",
       "      <td>0.325438</td>\n",
       "      <td>0.661459</td>\n",
       "      <td>2.836120</td>\n",
       "      <td>23.118350</td>\n",
       "      <td>8.943212</td>\n",
       "      <td>82.475592</td>\n",
       "      <td>15.166388</td>\n",
       "      <td>127.562018</td>\n",
       "      <td>...</td>\n",
       "      <td>323.744570</td>\n",
       "      <td>1.179586</td>\n",
       "      <td>187.504603</td>\n",
       "      <td>2.090301</td>\n",
       "      <td>144.047147</td>\n",
       "      <td>10.238807</td>\n",
       "      <td>0.074769</td>\n",
       "      <td>45.506577</td>\n",
       "      <td>0.244345</td>\n",
       "      <td>15.503351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>138.179688</td>\n",
       "      <td>51.524484</td>\n",
       "      <td>-0.031852</td>\n",
       "      <td>0.046797</td>\n",
       "      <td>6.330268</td>\n",
       "      <td>31.576347</td>\n",
       "      <td>5.155940</td>\n",
       "      <td>26.143310</td>\n",
       "      <td>3.763020</td>\n",
       "      <td>83.095065</td>\n",
       "      <td>...</td>\n",
       "      <td>333.929495</td>\n",
       "      <td>1.159069</td>\n",
       "      <td>151.045168</td>\n",
       "      <td>2.955686</td>\n",
       "      <td>63.457027</td>\n",
       "      <td>7.786672</td>\n",
       "      <td>-0.152906</td>\n",
       "      <td>47.848464</td>\n",
       "      <td>0.145101</td>\n",
       "      <td>21.243465</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            MeanIP      StdIP  ExckurtIP    SkewIP  MeanDMSNR   StdDMSNR  \\\n",
       "number                                                                     \n",
       "0       140.562500  55.683782  -0.234571 -0.699648   3.199833  19.110426   \n",
       "1       102.507812  58.882430   0.465318 -0.515088   1.677258  14.860146   \n",
       "2       103.015625  39.341649   0.323328  1.051164   3.121237  21.744669   \n",
       "3       136.750000  57.178449  -0.068415 -0.636238   3.642977  20.959280   \n",
       "4        88.726562  40.672225   0.600866  1.123492   1.178930  11.468720   \n",
       "5        93.570312  46.698114   0.531905  0.416721   1.636288  14.545074   \n",
       "6       119.484375  48.765059   0.031460 -0.112168   0.999164   9.279612   \n",
       "7       130.382812  39.844056  -0.158323  0.389540   1.220736  14.378941   \n",
       "8       107.250000  52.627078   0.452688  0.170347   2.331940  14.486853   \n",
       "9       107.257812  39.496488   0.465882  1.162877   4.079431  24.980418   \n",
       "10      142.078125  45.288073  -0.320328  0.283953   5.376254  29.009897   \n",
       "11      133.257812  44.058244  -0.081060  0.115362   1.632107  12.007806   \n",
       "12      134.960938  49.554327  -0.135304 -0.080470  10.696488  41.342044   \n",
       "13      117.945312  45.506577   0.325438  0.661459   2.836120  23.118350   \n",
       "14      138.179688  51.524484  -0.031852  0.046797   6.330268  31.576347   \n",
       "\n",
       "        ExckurtDMSNR   SkewDMSNR  new_signals.MEAN(signals.MeanDMSNR)  \\\n",
       "number                                                                  \n",
       "0           7.975532   74.242225                             3.199833   \n",
       "1          10.576487  127.393580                             2.066332   \n",
       "2           7.735822   63.171909                             2.757107   \n",
       "3           6.896499   53.593661                             3.258361   \n",
       "4          14.269573  252.567306                             1.178930   \n",
       "5          10.621748  131.394004                             7.000000   \n",
       "6          19.206230  479.756567                            38.548495   \n",
       "7          13.539456  198.236457                             2.247492   \n",
       "8           9.001004  107.972506                             2.886407   \n",
       "9           7.397080   57.784738                             3.438127   \n",
       "10          6.076266   37.831393                             5.376254   \n",
       "11         11.972067  195.543448                             7.340301   \n",
       "12          3.893934   14.131206                             6.377717   \n",
       "13          8.943212   82.475592                            15.166388   \n",
       "14          5.155940   26.143310                             3.763020   \n",
       "\n",
       "        new_signals.MEAN(signals.SkewDMSNR)  ...  \\\n",
       "number                                       ...   \n",
       "0                                 74.242225  ...   \n",
       "1                                110.547849  ...   \n",
       "2                                 85.309060  ...   \n",
       "3                                 71.589360  ...   \n",
       "4                                252.567306  ...   \n",
       "5                                 72.641038  ...   \n",
       "6                                165.400459  ...   \n",
       "7                                125.717906  ...   \n",
       "8                                128.005504  ...   \n",
       "9                                 80.598816  ...   \n",
       "10                                37.831393  ...   \n",
       "11                               194.169547  ...   \n",
       "12                                36.311523  ...   \n",
       "13                               127.562018  ...   \n",
       "14                                83.095065  ...   \n",
       "\n",
       "        new_signals.SUM(signals.StdIP)  new_signals.SUM(signals.SkewIP)  \\\n",
       "number                                                                    \n",
       "0                            55.683782                        -0.699648   \n",
       "1                           160.533606                        -0.404816   \n",
       "2                            88.082191                         1.273260   \n",
       "3                           157.041468                        -1.103265   \n",
       "4                            40.672225                         1.123492   \n",
       "5                            90.217808                         2.066170   \n",
       "6                           189.047439                         0.170001   \n",
       "7                           141.668487                        -0.452515   \n",
       "8                           348.057879                         0.240581   \n",
       "9                           172.235183                         2.712742   \n",
       "10                           45.288073                         0.283953   \n",
       "11                          151.189064                        -0.833638   \n",
       "12                          212.683196                        -1.074346   \n",
       "13                          323.744570                         1.179586   \n",
       "14                          333.929495                         1.159069   \n",
       "\n",
       "        new_signals.SUM(signals.StdDMSNR)  \\\n",
       "number                                      \n",
       "0                               19.110426   \n",
       "1                               45.756319   \n",
       "2                               36.393436   \n",
       "3                               58.706194   \n",
       "4                               11.468720   \n",
       "5                               66.592926   \n",
       "6                              129.733021   \n",
       "7                               51.905838   \n",
       "8                              124.083662   \n",
       "9                               86.149156   \n",
       "10                              29.009897   \n",
       "11                              75.701908   \n",
       "12                             121.942456   \n",
       "13                             187.504603   \n",
       "14                             151.045168   \n",
       "\n",
       "        new_signals.MEDIAN(signals.MeanDMSNR)  \\\n",
       "number                                          \n",
       "0                                    3.199833   \n",
       "1                                    1.836957   \n",
       "2                                    2.757107   \n",
       "3                                    3.326923   \n",
       "4                                    1.178930   \n",
       "5                                    7.000000   \n",
       "6                                    2.663462   \n",
       "7                                    2.678094   \n",
       "8                                    2.331940   \n",
       "9                                    3.544732   \n",
       "10                                   5.376254   \n",
       "11                                   1.632107   \n",
       "12                                   5.525920   \n",
       "13                                   2.090301   \n",
       "14                                   2.955686   \n",
       "\n",
       "        new_signals.MEDIAN(signals.SkewDMSNR)  \\\n",
       "number                                          \n",
       "0                                   74.242225   \n",
       "1                                  127.393580   \n",
       "2                                   85.309060   \n",
       "3                                   67.242135   \n",
       "4                                  252.567306   \n",
       "5                                   72.641038   \n",
       "6                                   91.395097   \n",
       "7                                  106.652628   \n",
       "8                                  107.972506   \n",
       "9                                   68.064048   \n",
       "10                                  37.831393   \n",
       "11                                 195.543448   \n",
       "12                                  36.325598   \n",
       "13                                 144.047147   \n",
       "14                                  63.457027   \n",
       "\n",
       "        new_signals.MEDIAN(signals.ExckurtDMSNR)  \\\n",
       "number                                             \n",
       "0                                       7.975532   \n",
       "1                                      10.360940   \n",
       "2                                       8.400171   \n",
       "3                                       7.752153   \n",
       "4                                      14.269573   \n",
       "5                                       7.301360   \n",
       "6                                       8.464385   \n",
       "7                                       9.391858   \n",
       "8                                       9.001004   \n",
       "9                                       7.809346   \n",
       "10                                      6.076266   \n",
       "11                                     11.972067   \n",
       "12                                      5.865141   \n",
       "13                                     10.238807   \n",
       "14                                      7.786672   \n",
       "\n",
       "        new_signals.MEDIAN(signals.ExckurtIP)  \\\n",
       "number                                          \n",
       "0                                   -0.234571   \n",
       "1                                    0.452962   \n",
       "2                                    0.466137   \n",
       "3                                   -0.068415   \n",
       "4                                    0.600866   \n",
       "5                                    0.619374   \n",
       "6                                    0.054479   \n",
       "7                                   -0.158323   \n",
       "8                                    0.329787   \n",
       "9                                    0.449450   \n",
       "10                                  -0.320328   \n",
       "11                                  -0.047957   \n",
       "12                                   0.060802   \n",
       "13                                   0.074769   \n",
       "14                                  -0.152906   \n",
       "\n",
       "        new_signals.MEDIAN(signals.StdIP)  new_signals.MEDIAN(signals.SkewIP)  \\\n",
       "number                                                                          \n",
       "0                               55.683782                           -0.699648   \n",
       "1                               52.275860                           -0.246135   \n",
       "2                               44.041095                            0.636630   \n",
       "3                               49.955605                           -0.375633   \n",
       "4                               40.672225                            1.123492   \n",
       "5                               45.108904                            1.033085   \n",
       "6                               47.513018                            0.066080   \n",
       "7                               50.379206                           -0.117718   \n",
       "8                               48.306153                            0.123002   \n",
       "9                               42.456847                            0.788828   \n",
       "10                              45.288073                            0.283953   \n",
       "11                              50.598025                           -0.314778   \n",
       "12                              52.555330                           -0.289093   \n",
       "13                              45.506577                            0.244345   \n",
       "14                              47.848464                            0.145101   \n",
       "\n",
       "        new_signals.MEDIAN(signals.StdDMSNR)  \n",
       "number                                        \n",
       "0                                  19.110426  \n",
       "1                                  14.860146  \n",
       "2                                  18.196718  \n",
       "3                                  20.725051  \n",
       "4                                  11.468720  \n",
       "5                                  33.296463  \n",
       "6                                  16.192914  \n",
       "7                                  16.535941  \n",
       "8                                  14.486853  \n",
       "9                                  21.853419  \n",
       "10                                 29.009897  \n",
       "11                                 12.007806  \n",
       "12                                 29.035078  \n",
       "13                                 15.503351  \n",
       "14                                 21.243465  \n",
       "\n",
       "[15 rows x 43 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_matrix, feature_names=ft.dfs(entityset=es, target_entity = 'signals',agg_primitives=[\"mean\", \"max\",\"min\", \"sum\", \"median\"], max_depth=5)\n",
    "feature_matrix.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_matrix=feature_matrix.to_numpy() # начинается подготовка данных для сети"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "xf_train, xf_valid, xf_test=np.split(feature_matrix, [14318,16108]) # подборка данных с feature engeneering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "weight_for_0 = (1 / 16259)*(17898)/2.0 #замена значений весов\n",
    "weight_for_1 = (1 / 1639)*(17898)/2.0\n",
    "\n",
    "class_weight = {0: weight_for_0, 1: weight_for_1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ещё раз приходится считывать массив исходных данных data, поскольку до этого нужно было удалить столбец\n",
    "\n",
    "data = pd.read_csv('HTRU_2.csv', header=None) \n",
    "column_names = pd.read_csv('columns_names.txt',sep=\" \", header=None)\n",
    "data.columns = [\"MeanIP\",\"StdIP\",\"ExckurtIP\",\"SkewIP\",\"MeanDMSNR\",\"StdDMSNR\",\"ExckurtDMSNR\",\"SkewDMSNR\", \"class\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y, z=np.split(data, [14318,16108])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, y_train=np.hsplit(x, [8]) #нарезаем на тренировку\n",
    "x_valid, y_valid=np.hsplit(y, [8]) #нарезаем на валидацию\n",
    "x_test, y_test=np.hsplit(z,[8]) #нарезаем на тест"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 14318 samples, validate on 1790 samples\n",
      "Epoch 1/100\n",
      "14318/14318 [==============================] - ETA: 5:12 - loss: 38.5240 - accuracy: 0.375 - ETA: 7s - loss: 9.7142 - accuracy: 0.6599  - ETA: 3s - loss: 7.3697 - accuracy: 0.72 - ETA: 1s - loss: 5.9057 - accuracy: 0.75 - ETA: 1s - loss: 4.9900 - accuracy: 0.77 - ETA: 0s - loss: 4.3967 - accuracy: 0.78 - ETA: 0s - loss: 3.9045 - accuracy: 0.80 - ETA: 0s - loss: 3.5206 - accuracy: 0.81 - ETA: 0s - loss: 3.2173 - accuracy: 0.82 - ETA: 0s - loss: 2.9995 - accuracy: 0.82 - 1s 89us/sample - loss: 2.9886 - accuracy: 0.8262 - val_loss: 0.0729 - val_accuracy: 0.9911\n",
      "Epoch 2/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 2.1490 - accuracy: 0.90 - ETA: 0s - loss: 0.9256 - accuracy: 0.88 - ETA: 0s - loss: 0.8445 - accuracy: 0.88 - ETA: 0s - loss: 0.8108 - accuracy: 0.87 - ETA: 0s - loss: 0.7398 - accuracy: 0.87 - ETA: 0s - loss: 0.7073 - accuracy: 0.88 - ETA: 0s - loss: 0.6701 - accuracy: 0.88 - ETA: 0s - loss: 0.6413 - accuracy: 0.88 - ETA: 0s - loss: 0.6188 - accuracy: 0.88 - ETA: 0s - loss: 0.5910 - accuracy: 0.88 - 1s 38us/sample - loss: 0.5699 - accuracy: 0.8894 - val_loss: 0.0488 - val_accuracy: 0.9916\n",
      "Epoch 3/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.5099 - accuracy: 0.84 - ETA: 0s - loss: 0.3646 - accuracy: 0.91 - ETA: 0s - loss: 0.3236 - accuracy: 0.91 - ETA: 0s - loss: 0.3896 - accuracy: 0.90 - ETA: 0s - loss: 0.3653 - accuracy: 0.90 - ETA: 0s - loss: 0.3585 - accuracy: 0.90 - ETA: 0s - loss: 0.3446 - accuracy: 0.90 - ETA: 0s - loss: 0.3384 - accuracy: 0.90 - ETA: 0s - loss: 0.3288 - accuracy: 0.90 - ETA: 0s - loss: 0.3245 - accuracy: 0.90 - 1s 36us/sample - loss: 0.3248 - accuracy: 0.9089 - val_loss: 0.0462 - val_accuracy: 0.9916\n",
      "Epoch 4/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.1555 - accuracy: 0.93 - ETA: 0s - loss: 0.2255 - accuracy: 0.91 - ETA: 0s - loss: 0.2741 - accuracy: 0.90 - ETA: 0s - loss: 0.2693 - accuracy: 0.90 - ETA: 0s - loss: 0.2839 - accuracy: 0.90 - ETA: 0s - loss: 0.2741 - accuracy: 0.91 - ETA: 0s - loss: 0.2604 - accuracy: 0.91 - ETA: 0s - loss: 0.2563 - accuracy: 0.91 - ETA: 0s - loss: 0.2581 - accuracy: 0.91 - ETA: 0s - loss: 0.2563 - accuracy: 0.91 - 1s 37us/sample - loss: 0.2525 - accuracy: 0.9169 - val_loss: 0.0448 - val_accuracy: 0.9922\n",
      "Epoch 5/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0858 - accuracy: 0.96 - ETA: 0s - loss: 0.2053 - accuracy: 0.92 - ETA: 0s - loss: 0.2024 - accuracy: 0.92 - ETA: 0s - loss: 0.2398 - accuracy: 0.92 - ETA: 0s - loss: 0.2303 - accuracy: 0.92 - ETA: 0s - loss: 0.2220 - accuracy: 0.92 - ETA: 0s - loss: 0.2233 - accuracy: 0.92 - ETA: 0s - loss: 0.2212 - accuracy: 0.92 - ETA: 0s - loss: 0.2212 - accuracy: 0.92 - ETA: 0s - loss: 0.2191 - accuracy: 0.92 - 1s 36us/sample - loss: 0.2184 - accuracy: 0.9267 - val_loss: 0.0406 - val_accuracy: 0.9916\n",
      "Epoch 6/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.4594 - accuracy: 0.90 - ETA: 0s - loss: 0.2056 - accuracy: 0.92 - ETA: 0s - loss: 0.2123 - accuracy: 0.93 - ETA: 0s - loss: 0.2132 - accuracy: 0.92 - ETA: 0s - loss: 0.2075 - accuracy: 0.93 - ETA: 0s - loss: 0.2047 - accuracy: 0.93 - ETA: 0s - loss: 0.2084 - accuracy: 0.93 - ETA: 0s - loss: 0.2107 - accuracy: 0.93 - ETA: 0s - loss: 0.2098 - accuracy: 0.93 - ETA: 0s - loss: 0.2073 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2065 - accuracy: 0.9330 - val_loss: 0.0388 - val_accuracy: 0.9916\n",
      "Epoch 7/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2042 - accuracy: 0.90 - ETA: 0s - loss: 0.1845 - accuracy: 0.93 - ETA: 0s - loss: 0.1960 - accuracy: 0.93 - ETA: 0s - loss: 0.1883 - accuracy: 0.93 - ETA: 0s - loss: 0.1879 - accuracy: 0.93 - ETA: 0s - loss: 0.1969 - accuracy: 0.93 - ETA: 0s - loss: 0.1968 - accuracy: 0.93 - ETA: 0s - loss: 0.1895 - accuracy: 0.94 - ETA: 0s - loss: 0.1865 - accuracy: 0.94 - ETA: 0s - loss: 0.1867 - accuracy: 0.94 - 1s 37us/sample - loss: 0.1872 - accuracy: 0.9413 - val_loss: 0.0487 - val_accuracy: 0.9916\n",
      "Epoch 8/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.4111 - accuracy: 0.84 - ETA: 0s - loss: 0.1768 - accuracy: 0.94 - ETA: 0s - loss: 0.1599 - accuracy: 0.94 - ETA: 0s - loss: 0.1544 - accuracy: 0.94 - ETA: 0s - loss: 0.1536 - accuracy: 0.94 - ETA: 0s - loss: 0.1577 - accuracy: 0.94 - ETA: 0s - loss: 0.1573 - accuracy: 0.94 - ETA: 0s - loss: 0.1676 - accuracy: 0.94 - ETA: 0s - loss: 0.1679 - accuracy: 0.94 - ETA: 0s - loss: 0.1686 - accuracy: 0.94 - 1s 37us/sample - loss: 0.1676 - accuracy: 0.9450 - val_loss: 0.0368 - val_accuracy: 0.9916\n",
      "Epoch 9/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0760 - accuracy: 0.96 - ETA: 0s - loss: 0.1499 - accuracy: 0.95 - ETA: 0s - loss: 0.1609 - accuracy: 0.94 - ETA: 0s - loss: 0.1583 - accuracy: 0.94 - ETA: 0s - loss: 0.1580 - accuracy: 0.94 - ETA: 0s - loss: 0.1605 - accuracy: 0.94 - ETA: 0s - loss: 0.1603 - accuracy: 0.94 - ETA: 0s - loss: 0.1579 - accuracy: 0.94 - ETA: 0s - loss: 0.1572 - accuracy: 0.94 - ETA: 0s - loss: 0.1620 - accuracy: 0.94 - 1s 36us/sample - loss: 0.1618 - accuracy: 0.9476 - val_loss: 0.0521 - val_accuracy: 0.9911\n",
      "Epoch 10/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1603 - accuracy: 0.90 - ETA: 0s - loss: 0.1822 - accuracy: 0.93 - ETA: 0s - loss: 0.1678 - accuracy: 0.94 - ETA: 0s - loss: 0.1612 - accuracy: 0.94 - ETA: 0s - loss: 0.1639 - accuracy: 0.94 - ETA: 0s - loss: 0.1614 - accuracy: 0.94 - ETA: 0s - loss: 0.1629 - accuracy: 0.94 - ETA: 0s - loss: 0.1648 - accuracy: 0.94 - ETA: 0s - loss: 0.1634 - accuracy: 0.94 - 0s 35us/sample - loss: 0.1616 - accuracy: 0.9481 - val_loss: 0.0439 - val_accuracy: 0.9916\n",
      "Epoch 11/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0426 - accuracy: 1.00 - ETA: 0s - loss: 0.1655 - accuracy: 0.93 - ETA: 0s - loss: 0.1587 - accuracy: 0.94 - ETA: 0s - loss: 0.1560 - accuracy: 0.94 - ETA: 0s - loss: 0.1592 - accuracy: 0.94 - ETA: 0s - loss: 0.1542 - accuracy: 0.94 - ETA: 0s - loss: 0.1571 - accuracy: 0.94 - ETA: 0s - loss: 0.1606 - accuracy: 0.94 - ETA: 0s - loss: 0.1610 - accuracy: 0.94 - 0s 35us/sample - loss: 0.1605 - accuracy: 0.9459 - val_loss: 0.0433 - val_accuracy: 0.9916\n",
      "Epoch 12/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.4969 - accuracy: 0.84 - ETA: 0s - loss: 0.1604 - accuracy: 0.94 - ETA: 0s - loss: 0.1622 - accuracy: 0.95 - ETA: 0s - loss: 0.1583 - accuracy: 0.95 - ETA: 0s - loss: 0.1547 - accuracy: 0.95 - ETA: 0s - loss: 0.1562 - accuracy: 0.95 - ETA: 0s - loss: 0.1523 - accuracy: 0.95 - ETA: 0s - loss: 0.1507 - accuracy: 0.95 - ETA: 0s - loss: 0.1509 - accuracy: 0.95 - ETA: 0s - loss: 0.1503 - accuracy: 0.95 - 1s 36us/sample - loss: 0.1507 - accuracy: 0.9519 - val_loss: 0.0436 - val_accuracy: 0.9911\n",
      "Epoch 13/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2220 - accuracy: 0.93 - ETA: 0s - loss: 0.1466 - accuracy: 0.95 - ETA: 0s - loss: 0.1516 - accuracy: 0.95 - ETA: 0s - loss: 0.1530 - accuracy: 0.94 - ETA: 0s - loss: 0.1562 - accuracy: 0.94 - ETA: 0s - loss: 0.1545 - accuracy: 0.94 - ETA: 0s - loss: 0.1537 - accuracy: 0.94 - ETA: 0s - loss: 0.1543 - accuracy: 0.94 - ETA: 0s - loss: 0.1571 - accuracy: 0.94 - 1s 35us/sample - loss: 0.1560 - accuracy: 0.9479 - val_loss: 0.0392 - val_accuracy: 0.9911\n",
      "Epoch 14/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.0325 - accuracy: 1.00 - ETA: 0s - loss: 0.1679 - accuracy: 0.94 - ETA: 0s - loss: 0.1550 - accuracy: 0.94 - ETA: 0s - loss: 0.1580 - accuracy: 0.94 - ETA: 0s - loss: 0.1567 - accuracy: 0.94 - ETA: 0s - loss: 0.1500 - accuracy: 0.95 - ETA: 0s - loss: 0.1493 - accuracy: 0.94 - ETA: 0s - loss: 0.1513 - accuracy: 0.94 - ETA: 0s - loss: 0.1529 - accuracy: 0.94 - ETA: 0s - loss: 0.1546 - accuracy: 0.94 - 1s 36us/sample - loss: 0.1537 - accuracy: 0.9481 - val_loss: 0.0481 - val_accuracy: 0.9916\n",
      "Epoch 15/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0809 - accuracy: 1.00 - ETA: 0s - loss: 0.1369 - accuracy: 0.95 - ETA: 0s - loss: 0.1505 - accuracy: 0.94 - ETA: 0s - loss: 0.1524 - accuracy: 0.94 - ETA: 0s - loss: 0.1534 - accuracy: 0.94 - ETA: 0s - loss: 0.1555 - accuracy: 0.94 - ETA: 0s - loss: 0.1523 - accuracy: 0.94 - ETA: 0s - loss: 0.1495 - accuracy: 0.94 - ETA: 0s - loss: 0.1524 - accuracy: 0.94 - ETA: 0s - loss: 0.1522 - accuracy: 0.94 - 1s 36us/sample - loss: 0.1520 - accuracy: 0.9489 - val_loss: 0.0465 - val_accuracy: 0.9922\n",
      "Epoch 16/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.1069 - accuracy: 0.93 - ETA: 0s - loss: 0.1607 - accuracy: 0.94 - ETA: 0s - loss: 0.1644 - accuracy: 0.94 - ETA: 0s - loss: 0.1630 - accuracy: 0.94 - ETA: 0s - loss: 0.1589 - accuracy: 0.94 - ETA: 0s - loss: 0.1568 - accuracy: 0.94 - ETA: 0s - loss: 0.1566 - accuracy: 0.94 - ETA: 0s - loss: 0.1549 - accuracy: 0.94 - ETA: 0s - loss: 0.1550 - accuracy: 0.94 - 0s 35us/sample - loss: 0.1546 - accuracy: 0.9478 - val_loss: 0.0456 - val_accuracy: 0.9916\n",
      "Epoch 17/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2916 - accuracy: 0.90 - ETA: 0s - loss: 0.1410 - accuracy: 0.95 - ETA: 0s - loss: 0.1294 - accuracy: 0.95 - ETA: 0s - loss: 0.1345 - accuracy: 0.95 - ETA: 0s - loss: 0.1345 - accuracy: 0.95 - ETA: 0s - loss: 0.1372 - accuracy: 0.95 - ETA: 0s - loss: 0.1440 - accuracy: 0.95 - ETA: 0s - loss: 0.1461 - accuracy: 0.95 - ETA: 0s - loss: 0.1472 - accuracy: 0.94 - ETA: 0s - loss: 0.1475 - accuracy: 0.94 - 1s 36us/sample - loss: 0.1475 - accuracy: 0.9484 - val_loss: 0.0401 - val_accuracy: 0.9922\n",
      "Epoch 18/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2218 - accuracy: 0.87 - ETA: 0s - loss: 0.1595 - accuracy: 0.94 - ETA: 0s - loss: 0.1431 - accuracy: 0.95 - ETA: 0s - loss: 0.1410 - accuracy: 0.95 - ETA: 0s - loss: 0.1484 - accuracy: 0.95 - ETA: 0s - loss: 0.1479 - accuracy: 0.95 - ETA: 0s - loss: 0.1503 - accuracy: 0.94 - ETA: 0s - loss: 0.1481 - accuracy: 0.94 - ETA: 0s - loss: 0.1471 - accuracy: 0.95 - ETA: 0s - loss: 0.1480 - accuracy: 0.95 - 1s 36us/sample - loss: 0.1480 - accuracy: 0.9506 - val_loss: 0.0502 - val_accuracy: 0.9922\n",
      "Epoch 19/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1158 - accuracy: 0.93 - ETA: 0s - loss: 0.1558 - accuracy: 0.95 - ETA: 0s - loss: 0.1608 - accuracy: 0.94 - ETA: 0s - loss: 0.1509 - accuracy: 0.94 - ETA: 0s - loss: 0.1502 - accuracy: 0.94 - ETA: 0s - loss: 0.1524 - accuracy: 0.94 - ETA: 0s - loss: 0.1527 - accuracy: 0.94 - ETA: 0s - loss: 0.1496 - accuracy: 0.94 - ETA: 0s - loss: 0.1483 - accuracy: 0.94 - 0s 35us/sample - loss: 0.1456 - accuracy: 0.9496 - val_loss: 0.0394 - val_accuracy: 0.9911\n",
      "Epoch 20/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1591 - accuracy: 0.90 - ETA: 0s - loss: 0.1542 - accuracy: 0.94 - ETA: 0s - loss: 0.1425 - accuracy: 0.94 - ETA: 0s - loss: 0.1449 - accuracy: 0.94 - ETA: 0s - loss: 0.1373 - accuracy: 0.95 - ETA: 0s - loss: 0.1392 - accuracy: 0.94 - ETA: 0s - loss: 0.1432 - accuracy: 0.94 - ETA: 0s - loss: 0.1442 - accuracy: 0.94 - ETA: 0s - loss: 0.1434 - accuracy: 0.94 - 0s 35us/sample - loss: 0.1431 - accuracy: 0.9480 - val_loss: 0.0539 - val_accuracy: 0.9916\n",
      "Epoch 21/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1411 - accuracy: 0.93 - ETA: 0s - loss: 0.1253 - accuracy: 0.95 - ETA: 0s - loss: 0.1272 - accuracy: 0.95 - ETA: 0s - loss: 0.1371 - accuracy: 0.95 - ETA: 0s - loss: 0.1417 - accuracy: 0.95 - ETA: 0s - loss: 0.1389 - accuracy: 0.95 - ETA: 0s - loss: 0.1408 - accuracy: 0.95 - ETA: 0s - loss: 0.1392 - accuracy: 0.95 - ETA: 0s - loss: 0.1405 - accuracy: 0.95 - ETA: 0s - loss: 0.1397 - accuracy: 0.95 - 1s 35us/sample - loss: 0.1394 - accuracy: 0.9508 - val_loss: 0.0543 - val_accuracy: 0.9922\n",
      "Epoch 22/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0922 - accuracy: 0.93 - ETA: 0s - loss: 0.1608 - accuracy: 0.94 - ETA: 0s - loss: 0.1511 - accuracy: 0.94 - ETA: 0s - loss: 0.1469 - accuracy: 0.94 - ETA: 0s - loss: 0.1417 - accuracy: 0.94 - ETA: 0s - loss: 0.1396 - accuracy: 0.95 - ETA: 0s - loss: 0.1370 - accuracy: 0.95 - ETA: 0s - loss: 0.1389 - accuracy: 0.94 - ETA: 0s - loss: 0.1372 - accuracy: 0.94 - ETA: 0s - loss: 0.1381 - accuracy: 0.94 - 1s 36us/sample - loss: 0.1373 - accuracy: 0.9499 - val_loss: 0.0472 - val_accuracy: 0.9922\n",
      "Epoch 23/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1582 - accuracy: 0.93 - ETA: 0s - loss: 0.1199 - accuracy: 0.94 - ETA: 0s - loss: 0.1381 - accuracy: 0.94 - ETA: 0s - loss: 0.1390 - accuracy: 0.94 - ETA: 0s - loss: 0.1366 - accuracy: 0.94 - ETA: 0s - loss: 0.1305 - accuracy: 0.94 - ETA: 0s - loss: 0.1350 - accuracy: 0.94 - ETA: 0s - loss: 0.1362 - accuracy: 0.94 - ETA: 0s - loss: 0.1394 - accuracy: 0.94 - 0s 34us/sample - loss: 0.1378 - accuracy: 0.9478 - val_loss: 0.0483 - val_accuracy: 0.9916\n",
      "Epoch 24/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2023 - accuracy: 0.96 - ETA: 0s - loss: 0.1209 - accuracy: 0.95 - ETA: 0s - loss: 0.1307 - accuracy: 0.95 - ETA: 0s - loss: 0.1328 - accuracy: 0.95 - ETA: 0s - loss: 0.1322 - accuracy: 0.95 - ETA: 0s - loss: 0.1297 - accuracy: 0.95 - ETA: 0s - loss: 0.1298 - accuracy: 0.95 - ETA: 0s - loss: 0.1332 - accuracy: 0.95 - ETA: 0s - loss: 0.1343 - accuracy: 0.95 - ETA: 0s - loss: 0.1333 - accuracy: 0.95 - 1s 35us/sample - loss: 0.1341 - accuracy: 0.9515 - val_loss: 0.0476 - val_accuracy: 0.9922\n",
      "Epoch 25/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1158 - accuracy: 0.93 - ETA: 0s - loss: 0.1342 - accuracy: 0.95 - ETA: 0s - loss: 0.1192 - accuracy: 0.95 - ETA: 0s - loss: 0.1196 - accuracy: 0.95 - ETA: 0s - loss: 0.1297 - accuracy: 0.95 - ETA: 0s - loss: 0.1293 - accuracy: 0.95 - ETA: 0s - loss: 0.1287 - accuracy: 0.95 - ETA: 0s - loss: 0.1322 - accuracy: 0.95 - ETA: 0s - loss: 0.1336 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1320 - accuracy: 0.9525 - val_loss: 0.0441 - val_accuracy: 0.9916\n",
      "Epoch 26/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0545 - accuracy: 0.96 - ETA: 0s - loss: 0.1172 - accuracy: 0.95 - ETA: 0s - loss: 0.1341 - accuracy: 0.95 - ETA: 0s - loss: 0.1288 - accuracy: 0.95 - ETA: 0s - loss: 0.1343 - accuracy: 0.95 - ETA: 0s - loss: 0.1297 - accuracy: 0.95 - ETA: 0s - loss: 0.1286 - accuracy: 0.95 - ETA: 0s - loss: 0.1298 - accuracy: 0.95 - ETA: 0s - loss: 0.1307 - accuracy: 0.95 - ETA: 0s - loss: 0.1319 - accuracy: 0.95 - 1s 36us/sample - loss: 0.1319 - accuracy: 0.9510 - val_loss: 0.0538 - val_accuracy: 0.9911\n",
      "Epoch 27/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1015 - accuracy: 0.93 - ETA: 0s - loss: 0.1355 - accuracy: 0.95 - ETA: 0s - loss: 0.1358 - accuracy: 0.95 - ETA: 0s - loss: 0.1291 - accuracy: 0.95 - ETA: 0s - loss: 0.1325 - accuracy: 0.95 - ETA: 0s - loss: 0.1313 - accuracy: 0.95 - ETA: 0s - loss: 0.1284 - accuracy: 0.95 - ETA: 0s - loss: 0.1322 - accuracy: 0.95 - ETA: 0s - loss: 0.1314 - accuracy: 0.95 - ETA: 0s - loss: 0.1317 - accuracy: 0.95 - 1s 38us/sample - loss: 0.1317 - accuracy: 0.9530 - val_loss: 0.0408 - val_accuracy: 0.9922\n",
      "Epoch 28/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0198 - accuracy: 1.00 - ETA: 0s - loss: 0.1260 - accuracy: 0.95 - ETA: 0s - loss: 0.1216 - accuracy: 0.95 - ETA: 0s - loss: 0.1324 - accuracy: 0.95 - ETA: 0s - loss: 0.1296 - accuracy: 0.95 - ETA: 0s - loss: 0.1222 - accuracy: 0.95 - ETA: 0s - loss: 0.1258 - accuracy: 0.95 - ETA: 0s - loss: 0.1266 - accuracy: 0.95 - ETA: 0s - loss: 0.1289 - accuracy: 0.95 - ETA: 0s - loss: 0.1308 - accuracy: 0.95 - ETA: 0s - loss: 0.1306 - accuracy: 0.95 - 1s 39us/sample - loss: 0.1315 - accuracy: 0.9532 - val_loss: 0.0514 - val_accuracy: 0.9922\n",
      "Epoch 29/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0241 - accuracy: 1.00 - ETA: 0s - loss: 0.1291 - accuracy: 0.94 - ETA: 0s - loss: 0.1297 - accuracy: 0.95 - ETA: 0s - loss: 0.1290 - accuracy: 0.95 - ETA: 0s - loss: 0.1341 - accuracy: 0.95 - ETA: 0s - loss: 0.1326 - accuracy: 0.95 - ETA: 0s - loss: 0.1303 - accuracy: 0.95 - ETA: 0s - loss: 0.1309 - accuracy: 0.95 - ETA: 0s - loss: 0.1324 - accuracy: 0.95 - ETA: 0s - loss: 0.1303 - accuracy: 0.95 - 1s 36us/sample - loss: 0.1305 - accuracy: 0.9547 - val_loss: 0.0533 - val_accuracy: 0.9911\n",
      "Epoch 30/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1942 - accuracy: 0.96 - ETA: 0s - loss: 0.1480 - accuracy: 0.94 - ETA: 0s - loss: 0.1334 - accuracy: 0.94 - ETA: 0s - loss: 0.1361 - accuracy: 0.95 - ETA: 0s - loss: 0.1308 - accuracy: 0.95 - ETA: 0s - loss: 0.1289 - accuracy: 0.95 - ETA: 0s - loss: 0.1303 - accuracy: 0.95 - ETA: 0s - loss: 0.1280 - accuracy: 0.95 - ETA: 0s - loss: 0.1297 - accuracy: 0.95 - ETA: 0s - loss: 0.1305 - accuracy: 0.95 - ETA: 0s - loss: 0.1298 - accuracy: 0.95 - 1s 41us/sample - loss: 0.1316 - accuracy: 0.9533 - val_loss: 0.0535 - val_accuracy: 0.9911\n",
      "Epoch 31/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.1441 - accuracy: 0.93 - ETA: 0s - loss: 0.1243 - accuracy: 0.95 - ETA: 0s - loss: 0.1332 - accuracy: 0.95 - ETA: 0s - loss: 0.1412 - accuracy: 0.95 - ETA: 0s - loss: 0.1390 - accuracy: 0.95 - ETA: 0s - loss: 0.1393 - accuracy: 0.95 - ETA: 0s - loss: 0.1386 - accuracy: 0.95 - ETA: 0s - loss: 0.1382 - accuracy: 0.95 - ETA: 0s - loss: 0.1357 - accuracy: 0.95 - ETA: 0s - loss: 0.1349 - accuracy: 0.95 - ETA: 0s - loss: 0.1337 - accuracy: 0.95 - 1s 39us/sample - loss: 0.1338 - accuracy: 0.9520 - val_loss: 0.0453 - val_accuracy: 0.9911\n",
      "Epoch 32/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0670 - accuracy: 0.96 - ETA: 0s - loss: 0.1240 - accuracy: 0.96 - ETA: 0s - loss: 0.1235 - accuracy: 0.95 - ETA: 0s - loss: 0.1283 - accuracy: 0.95 - ETA: 0s - loss: 0.1313 - accuracy: 0.95 - ETA: 0s - loss: 0.1285 - accuracy: 0.95 - ETA: 0s - loss: 0.1301 - accuracy: 0.95 - ETA: 0s - loss: 0.1328 - accuracy: 0.95 - ETA: 0s - loss: 0.1353 - accuracy: 0.95 - ETA: 0s - loss: 0.1341 - accuracy: 0.95 - 1s 37us/sample - loss: 0.1334 - accuracy: 0.9520 - val_loss: 0.0466 - val_accuracy: 0.9916\n",
      "Epoch 33/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2799 - accuracy: 0.96 - ETA: 0s - loss: 0.1287 - accuracy: 0.95 - ETA: 0s - loss: 0.1286 - accuracy: 0.95 - ETA: 0s - loss: 0.1254 - accuracy: 0.95 - ETA: 0s - loss: 0.1245 - accuracy: 0.95 - ETA: 0s - loss: 0.1250 - accuracy: 0.95 - ETA: 0s - loss: 0.1265 - accuracy: 0.95 - ETA: 0s - loss: 0.1284 - accuracy: 0.95 - ETA: 0s - loss: 0.1293 - accuracy: 0.95 - ETA: 0s - loss: 0.1300 - accuracy: 0.95 - 1s 37us/sample - loss: 0.1311 - accuracy: 0.9545 - val_loss: 0.0576 - val_accuracy: 0.9916\n",
      "Epoch 34/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1356 - accuracy: 0.93 - ETA: 0s - loss: 0.1214 - accuracy: 0.95 - ETA: 0s - loss: 0.1203 - accuracy: 0.95 - ETA: 0s - loss: 0.1285 - accuracy: 0.95 - ETA: 0s - loss: 0.1240 - accuracy: 0.95 - ETA: 0s - loss: 0.1238 - accuracy: 0.95 - ETA: 0s - loss: 0.1264 - accuracy: 0.95 - ETA: 0s - loss: 0.1294 - accuracy: 0.95 - ETA: 0s - loss: 0.1275 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1280 - accuracy: 0.9556 - val_loss: 0.0511 - val_accuracy: 0.9916\n",
      "Epoch 35/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0721 - accuracy: 0.96 - ETA: 0s - loss: 0.1362 - accuracy: 0.95 - ETA: 0s - loss: 0.1236 - accuracy: 0.95 - ETA: 0s - loss: 0.1228 - accuracy: 0.95 - ETA: 0s - loss: 0.1221 - accuracy: 0.95 - ETA: 0s - loss: 0.1207 - accuracy: 0.95 - ETA: 0s - loss: 0.1240 - accuracy: 0.95 - ETA: 0s - loss: 0.1256 - accuracy: 0.95 - ETA: 0s - loss: 0.1262 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1259 - accuracy: 0.9560 - val_loss: 0.0482 - val_accuracy: 0.9916\n",
      "Epoch 36/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0203 - accuracy: 1.00 - ETA: 0s - loss: 0.1309 - accuracy: 0.95 - ETA: 0s - loss: 0.1266 - accuracy: 0.95 - ETA: 0s - loss: 0.1284 - accuracy: 0.95 - ETA: 0s - loss: 0.1332 - accuracy: 0.95 - ETA: 0s - loss: 0.1297 - accuracy: 0.95 - ETA: 0s - loss: 0.1307 - accuracy: 0.95 - ETA: 0s - loss: 0.1283 - accuracy: 0.95 - ETA: 0s - loss: 0.1273 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1277 - accuracy: 0.9563 - val_loss: 0.0380 - val_accuracy: 0.9927\n",
      "Epoch 37/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1272 - accuracy: 0.96 - ETA: 0s - loss: 0.1389 - accuracy: 0.95 - ETA: 0s - loss: 0.1385 - accuracy: 0.95 - ETA: 0s - loss: 0.1364 - accuracy: 0.95 - ETA: 0s - loss: 0.1334 - accuracy: 0.95 - ETA: 0s - loss: 0.1313 - accuracy: 0.95 - ETA: 0s - loss: 0.1278 - accuracy: 0.95 - ETA: 0s - loss: 0.1290 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1270 - accuracy: 0.9556 - val_loss: 0.0430 - val_accuracy: 0.9922\n",
      "Epoch 38/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0962 - accuracy: 0.96 - ETA: 0s - loss: 0.1284 - accuracy: 0.95 - ETA: 0s - loss: 0.1251 - accuracy: 0.95 - ETA: 0s - loss: 0.1314 - accuracy: 0.95 - ETA: 0s - loss: 0.1250 - accuracy: 0.95 - ETA: 0s - loss: 0.1265 - accuracy: 0.95 - ETA: 0s - loss: 0.1294 - accuracy: 0.95 - ETA: 0s - loss: 0.1271 - accuracy: 0.95 - ETA: 0s - loss: 0.1256 - accuracy: 0.95 - ETA: 0s - loss: 0.1253 - accuracy: 0.95 - 1s 35us/sample - loss: 0.1259 - accuracy: 0.9562 - val_loss: 0.0438 - val_accuracy: 0.9922\n",
      "Epoch 39/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0604 - accuracy: 0.96 - ETA: 0s - loss: 0.1289 - accuracy: 0.95 - ETA: 0s - loss: 0.1265 - accuracy: 0.95 - ETA: 0s - loss: 0.1318 - accuracy: 0.95 - ETA: 0s - loss: 0.1343 - accuracy: 0.95 - ETA: 0s - loss: 0.1328 - accuracy: 0.95 - ETA: 0s - loss: 0.1304 - accuracy: 0.95 - ETA: 0s - loss: 0.1321 - accuracy: 0.95 - 0s 33us/sample - loss: 0.1313 - accuracy: 0.9550 - val_loss: 0.0597 - val_accuracy: 0.9916\n",
      "Epoch 40/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0388 - accuracy: 1.00 - ETA: 0s - loss: 0.1318 - accuracy: 0.95 - ETA: 0s - loss: 0.1365 - accuracy: 0.95 - ETA: 0s - loss: 0.1397 - accuracy: 0.95 - ETA: 0s - loss: 0.1334 - accuracy: 0.95 - ETA: 0s - loss: 0.1295 - accuracy: 0.95 - ETA: 0s - loss: 0.1261 - accuracy: 0.95 - ETA: 0s - loss: 0.1267 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1264 - accuracy: 0.9564 - val_loss: 0.0529 - val_accuracy: 0.9916\n",
      "Epoch 41/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0476 - accuracy: 1.00 - ETA: 0s - loss: 0.1308 - accuracy: 0.95 - ETA: 0s - loss: 0.1290 - accuracy: 0.95 - ETA: 0s - loss: 0.1253 - accuracy: 0.95 - ETA: 0s - loss: 0.1244 - accuracy: 0.95 - ETA: 0s - loss: 0.1234 - accuracy: 0.95 - ETA: 0s - loss: 0.1282 - accuracy: 0.95 - ETA: 0s - loss: 0.1288 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1272 - accuracy: 0.9566 - val_loss: 0.0493 - val_accuracy: 0.9916\n",
      "Epoch 42/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0638 - accuracy: 0.96 - ETA: 0s - loss: 0.1249 - accuracy: 0.95 - ETA: 0s - loss: 0.1194 - accuracy: 0.95 - ETA: 0s - loss: 0.1201 - accuracy: 0.95 - ETA: 0s - loss: 0.1204 - accuracy: 0.95 - ETA: 0s - loss: 0.1225 - accuracy: 0.95 - ETA: 0s - loss: 0.1210 - accuracy: 0.95 - ETA: 0s - loss: 0.1233 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1241 - accuracy: 0.9592 - val_loss: 0.0538 - val_accuracy: 0.9927\n",
      "Epoch 43/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.3708 - accuracy: 0.87 - ETA: 0s - loss: 0.1222 - accuracy: 0.96 - ETA: 0s - loss: 0.1163 - accuracy: 0.96 - ETA: 0s - loss: 0.1188 - accuracy: 0.96 - ETA: 0s - loss: 0.1212 - accuracy: 0.96 - ETA: 0s - loss: 0.1212 - accuracy: 0.95 - ETA: 0s - loss: 0.1244 - accuracy: 0.95 - ETA: 0s - loss: 0.1264 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1263 - accuracy: 0.9574 - val_loss: 0.0429 - val_accuracy: 0.9922\n",
      "Epoch 44/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0204 - accuracy: 1.00 - ETA: 0s - loss: 0.1380 - accuracy: 0.95 - ETA: 0s - loss: 0.1272 - accuracy: 0.95 - ETA: 0s - loss: 0.1264 - accuracy: 0.95 - ETA: 0s - loss: 0.1241 - accuracy: 0.95 - ETA: 0s - loss: 0.1251 - accuracy: 0.95 - ETA: 0s - loss: 0.1264 - accuracy: 0.95 - ETA: 0s - loss: 0.1259 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1251 - accuracy: 0.9570 - val_loss: 0.0579 - val_accuracy: 0.9927\n",
      "Epoch 45/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1047 - accuracy: 0.93 - ETA: 0s - loss: 0.1289 - accuracy: 0.95 - ETA: 0s - loss: 0.1247 - accuracy: 0.95 - ETA: 0s - loss: 0.1249 - accuracy: 0.95 - ETA: 0s - loss: 0.1255 - accuracy: 0.95 - ETA: 0s - loss: 0.1239 - accuracy: 0.95 - ETA: 0s - loss: 0.1286 - accuracy: 0.95 - ETA: 0s - loss: 0.1294 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1295 - accuracy: 0.9559 - val_loss: 0.0564 - val_accuracy: 0.9911\n",
      "Epoch 46/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1086 - accuracy: 0.93 - ETA: 0s - loss: 0.1256 - accuracy: 0.95 - ETA: 0s - loss: 0.1347 - accuracy: 0.95 - ETA: 0s - loss: 0.1335 - accuracy: 0.95 - ETA: 0s - loss: 0.1279 - accuracy: 0.95 - ETA: 0s - loss: 0.1306 - accuracy: 0.95 - ETA: 0s - loss: 0.1299 - accuracy: 0.95 - ETA: 0s - loss: 0.1287 - accuracy: 0.95 - ETA: 0s - loss: 0.1283 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1267 - accuracy: 0.9549 - val_loss: 0.0440 - val_accuracy: 0.9916\n",
      "Epoch 47/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0983 - accuracy: 0.90 - ETA: 0s - loss: 0.1244 - accuracy: 0.95 - ETA: 0s - loss: 0.1245 - accuracy: 0.95 - ETA: 0s - loss: 0.1261 - accuracy: 0.95 - ETA: 0s - loss: 0.1217 - accuracy: 0.95 - ETA: 0s - loss: 0.1255 - accuracy: 0.95 - ETA: 0s - loss: 0.1255 - accuracy: 0.95 - ETA: 0s - loss: 0.1264 - accuracy: 0.95 - ETA: 0s - loss: 0.1272 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1282 - accuracy: 0.9569 - val_loss: 0.0560 - val_accuracy: 0.9927\n",
      "Epoch 48/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0519 - accuracy: 1.00 - ETA: 0s - loss: 0.1197 - accuracy: 0.96 - ETA: 0s - loss: 0.1343 - accuracy: 0.95 - ETA: 0s - loss: 0.1358 - accuracy: 0.95 - ETA: 0s - loss: 0.1309 - accuracy: 0.95 - ETA: 0s - loss: 0.1303 - accuracy: 0.95 - ETA: 0s - loss: 0.1291 - accuracy: 0.95 - ETA: 0s - loss: 0.1296 - accuracy: 0.95 - ETA: 0s - loss: 0.1273 - accuracy: 0.95 - 1s 36us/sample - loss: 0.1275 - accuracy: 0.9576 - val_loss: 0.0403 - val_accuracy: 0.9922\n",
      "Epoch 49/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1219 - accuracy: 0.90 - ETA: 0s - loss: 0.1346 - accuracy: 0.95 - ETA: 0s - loss: 0.1400 - accuracy: 0.95 - ETA: 0s - loss: 0.1367 - accuracy: 0.95 - ETA: 0s - loss: 0.1312 - accuracy: 0.95 - ETA: 0s - loss: 0.1279 - accuracy: 0.95 - ETA: 0s - loss: 0.1274 - accuracy: 0.95 - ETA: 0s - loss: 0.1284 - accuracy: 0.95 - 0s 32us/sample - loss: 0.1282 - accuracy: 0.9552 - val_loss: 0.0456 - val_accuracy: 0.9922\n",
      "Epoch 50/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2006 - accuracy: 0.90 - ETA: 0s - loss: 0.1043 - accuracy: 0.96 - ETA: 0s - loss: 0.1132 - accuracy: 0.95 - ETA: 0s - loss: 0.1178 - accuracy: 0.95 - ETA: 0s - loss: 0.1241 - accuracy: 0.95 - ETA: 0s - loss: 0.1248 - accuracy: 0.95 - ETA: 0s - loss: 0.1247 - accuracy: 0.95 - ETA: 0s - loss: 0.1251 - accuracy: 0.95 - ETA: 0s - loss: 0.1268 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1257 - accuracy: 0.9571 - val_loss: 0.0498 - val_accuracy: 0.9927\n",
      "Epoch 51/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1242 - accuracy: 0.96 - ETA: 0s - loss: 0.1118 - accuracy: 0.96 - ETA: 0s - loss: 0.1277 - accuracy: 0.95 - ETA: 0s - loss: 0.1256 - accuracy: 0.95 - ETA: 0s - loss: 0.1290 - accuracy: 0.95 - ETA: 0s - loss: 0.1339 - accuracy: 0.95 - ETA: 0s - loss: 0.1322 - accuracy: 0.95 - ETA: 0s - loss: 0.1312 - accuracy: 0.95 - ETA: 0s - loss: 0.1310 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1317 - accuracy: 0.9538 - val_loss: 0.0493 - val_accuracy: 0.9911\n",
      "Epoch 52/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0644 - accuracy: 1.00 - ETA: 0s - loss: 0.1330 - accuracy: 0.95 - ETA: 0s - loss: 0.1334 - accuracy: 0.95 - ETA: 0s - loss: 0.1274 - accuracy: 0.95 - ETA: 0s - loss: 0.1207 - accuracy: 0.95 - ETA: 0s - loss: 0.1225 - accuracy: 0.95 - ETA: 0s - loss: 0.1236 - accuracy: 0.95 - ETA: 0s - loss: 0.1253 - accuracy: 0.95 - ETA: 0s - loss: 0.1252 - accuracy: 0.95 - 1s 35us/sample - loss: 0.1275 - accuracy: 0.9550 - val_loss: 0.0656 - val_accuracy: 0.9916\n",
      "Epoch 53/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2218 - accuracy: 0.93 - ETA: 0s - loss: 0.1230 - accuracy: 0.95 - ETA: 0s - loss: 0.1279 - accuracy: 0.95 - ETA: 0s - loss: 0.1291 - accuracy: 0.95 - ETA: 0s - loss: 0.1290 - accuracy: 0.95 - ETA: 0s - loss: 0.1297 - accuracy: 0.95 - ETA: 0s - loss: 0.1308 - accuracy: 0.95 - ETA: 0s - loss: 0.1302 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1301 - accuracy: 0.9563 - val_loss: 0.0588 - val_accuracy: 0.9911\n",
      "Epoch 54/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1471 - accuracy: 0.93 - ETA: 0s - loss: 0.1161 - accuracy: 0.95 - ETA: 0s - loss: 0.1174 - accuracy: 0.95 - ETA: 0s - loss: 0.1186 - accuracy: 0.95 - ETA: 0s - loss: 0.1223 - accuracy: 0.95 - ETA: 0s - loss: 0.1269 - accuracy: 0.95 - ETA: 0s - loss: 0.1286 - accuracy: 0.95 - ETA: 0s - loss: 0.1280 - accuracy: 0.95 - ETA: 0s - loss: 0.1272 - accuracy: 0.95 - ETA: 0s - loss: 0.1262 - accuracy: 0.95 - 1s 37us/sample - loss: 0.1250 - accuracy: 0.9570 - val_loss: 0.0573 - val_accuracy: 0.9927\n",
      "Epoch 55/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0855 - accuracy: 0.96 - ETA: 0s - loss: 0.1358 - accuracy: 0.95 - ETA: 0s - loss: 0.1242 - accuracy: 0.95 - ETA: 0s - loss: 0.1232 - accuracy: 0.95 - ETA: 0s - loss: 0.1266 - accuracy: 0.95 - ETA: 0s - loss: 0.1280 - accuracy: 0.95 - ETA: 0s - loss: 0.1278 - accuracy: 0.95 - ETA: 0s - loss: 0.1288 - accuracy: 0.95 - ETA: 0s - loss: 0.1270 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1273 - accuracy: 0.9559 - val_loss: 0.0426 - val_accuracy: 0.9916\n",
      "Epoch 56/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0387 - accuracy: 1.00 - ETA: 0s - loss: 0.1077 - accuracy: 0.96 - ETA: 0s - loss: 0.1239 - accuracy: 0.95 - ETA: 0s - loss: 0.1278 - accuracy: 0.95 - ETA: 0s - loss: 0.1232 - accuracy: 0.95 - ETA: 0s - loss: 0.1225 - accuracy: 0.95 - ETA: 0s - loss: 0.1231 - accuracy: 0.95 - ETA: 0s - loss: 0.1230 - accuracy: 0.95 - ETA: 0s - loss: 0.1230 - accuracy: 0.95 - 0s 35us/sample - loss: 0.1231 - accuracy: 0.9568 - val_loss: 0.0677 - val_accuracy: 0.9916\n",
      "Epoch 57/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.4015 - accuracy: 0.93 - ETA: 0s - loss: 0.1585 - accuracy: 0.94 - ETA: 0s - loss: 0.1346 - accuracy: 0.95 - ETA: 0s - loss: 0.1277 - accuracy: 0.95 - ETA: 0s - loss: 0.1275 - accuracy: 0.95 - ETA: 0s - loss: 0.1261 - accuracy: 0.95 - ETA: 0s - loss: 0.1260 - accuracy: 0.95 - ETA: 0s - loss: 0.1248 - accuracy: 0.95 - ETA: 0s - loss: 0.1247 - accuracy: 0.95 - 0s 35us/sample - loss: 0.1253 - accuracy: 0.9585 - val_loss: 0.0635 - val_accuracy: 0.9911\n",
      "Epoch 58/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1571 - accuracy: 0.96 - ETA: 0s - loss: 0.1186 - accuracy: 0.95 - ETA: 0s - loss: 0.1209 - accuracy: 0.95 - ETA: 0s - loss: 0.1120 - accuracy: 0.95 - ETA: 0s - loss: 0.1183 - accuracy: 0.95 - ETA: 0s - loss: 0.1189 - accuracy: 0.95 - ETA: 0s - loss: 0.1222 - accuracy: 0.95 - ETA: 0s - loss: 0.1246 - accuracy: 0.95 - ETA: 0s - loss: 0.1270 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1274 - accuracy: 0.9575 - val_loss: 0.0599 - val_accuracy: 0.9905\n",
      "Epoch 59/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.0600 - accuracy: 1.00 - ETA: 0s - loss: 0.1173 - accuracy: 0.95 - ETA: 0s - loss: 0.1211 - accuracy: 0.95 - ETA: 0s - loss: 0.1160 - accuracy: 0.95 - ETA: 0s - loss: 0.1173 - accuracy: 0.95 - ETA: 0s - loss: 0.1150 - accuracy: 0.95 - ETA: 0s - loss: 0.1192 - accuracy: 0.95 - ETA: 0s - loss: 0.1227 - accuracy: 0.95 - ETA: 0s - loss: 0.1241 - accuracy: 0.95 - 0s 33us/sample - loss: 0.1240 - accuracy: 0.9566 - val_loss: 0.0564 - val_accuracy: 0.9933\n",
      "Epoch 60/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0845 - accuracy: 0.96 - ETA: 0s - loss: 0.1395 - accuracy: 0.95 - ETA: 0s - loss: 0.1362 - accuracy: 0.95 - ETA: 0s - loss: 0.1304 - accuracy: 0.95 - ETA: 0s - loss: 0.1266 - accuracy: 0.95 - ETA: 0s - loss: 0.1222 - accuracy: 0.95 - ETA: 0s - loss: 0.1257 - accuracy: 0.95 - ETA: 0s - loss: 0.1242 - accuracy: 0.95 - 0s 35us/sample - loss: 0.1284 - accuracy: 0.9544 - val_loss: 0.0725 - val_accuracy: 0.9922\n",
      "Epoch 61/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.3339 - accuracy: 0.90 - ETA: 0s - loss: 0.0943 - accuracy: 0.96 - ETA: 0s - loss: 0.1353 - accuracy: 0.95 - ETA: 0s - loss: 0.1328 - accuracy: 0.95 - ETA: 0s - loss: 0.1304 - accuracy: 0.95 - ETA: 0s - loss: 0.1290 - accuracy: 0.95 - ETA: 0s - loss: 0.1272 - accuracy: 0.95 - ETA: 0s - loss: 0.1255 - accuracy: 0.95 - ETA: 0s - loss: 0.1251 - accuracy: 0.95 - ETA: 0s - loss: 0.1257 - accuracy: 0.95 - 1s 38us/sample - loss: 0.1253 - accuracy: 0.9591 - val_loss: 0.0559 - val_accuracy: 0.9922\n",
      "Epoch 62/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0403 - accuracy: 1.00 - ETA: 0s - loss: 0.1149 - accuracy: 0.96 - ETA: 0s - loss: 0.1102 - accuracy: 0.96 - ETA: 0s - loss: 0.1147 - accuracy: 0.96 - ETA: 0s - loss: 0.1198 - accuracy: 0.96 - ETA: 0s - loss: 0.1183 - accuracy: 0.96 - ETA: 0s - loss: 0.1234 - accuracy: 0.96 - ETA: 0s - loss: 0.1247 - accuracy: 0.95 - ETA: 0s - loss: 0.1250 - accuracy: 0.95 - ETA: 0s - loss: 0.1237 - accuracy: 0.95 - ETA: 0s - loss: 0.1246 - accuracy: 0.95 - ETA: 0s - loss: 0.1247 - accuracy: 0.95 - 1s 43us/sample - loss: 0.1243 - accuracy: 0.9584 - val_loss: 0.0479 - val_accuracy: 0.9927\n",
      "Epoch 63/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1906 - accuracy: 0.93 - ETA: 0s - loss: 0.1276 - accuracy: 0.95 - ETA: 0s - loss: 0.1295 - accuracy: 0.95 - ETA: 0s - loss: 0.1314 - accuracy: 0.95 - ETA: 0s - loss: 0.1348 - accuracy: 0.95 - ETA: 0s - loss: 0.1326 - accuracy: 0.95 - ETA: 0s - loss: 0.1306 - accuracy: 0.95 - ETA: 0s - loss: 0.1319 - accuracy: 0.95 - ETA: 0s - loss: 0.1295 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1282 - accuracy: 0.9554 - val_loss: 0.0461 - val_accuracy: 0.9916\n",
      "Epoch 64/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1294 - accuracy: 0.93 - ETA: 0s - loss: 0.0997 - accuracy: 0.96 - ETA: 0s - loss: 0.1198 - accuracy: 0.95 - ETA: 0s - loss: 0.1196 - accuracy: 0.95 - ETA: 0s - loss: 0.1239 - accuracy: 0.95 - ETA: 0s - loss: 0.1217 - accuracy: 0.95 - ETA: 0s - loss: 0.1276 - accuracy: 0.95 - ETA: 0s - loss: 0.1301 - accuracy: 0.95 - ETA: 0s - loss: 0.1278 - accuracy: 0.95 - ETA: 0s - loss: 0.1268 - accuracy: 0.95 - 1s 36us/sample - loss: 0.1275 - accuracy: 0.9567 - val_loss: 0.0563 - val_accuracy: 0.9911\n",
      "Epoch 65/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0210 - accuracy: 1.00 - ETA: 0s - loss: 0.1289 - accuracy: 0.95 - ETA: 0s - loss: 0.1269 - accuracy: 0.95 - ETA: 0s - loss: 0.1217 - accuracy: 0.95 - ETA: 0s - loss: 0.1239 - accuracy: 0.95 - ETA: 0s - loss: 0.1232 - accuracy: 0.95 - ETA: 0s - loss: 0.1207 - accuracy: 0.95 - ETA: 0s - loss: 0.1204 - accuracy: 0.95 - ETA: 0s - loss: 0.1250 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1246 - accuracy: 0.9552 - val_loss: 0.0546 - val_accuracy: 0.9927\n",
      "Epoch 66/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0965 - accuracy: 0.93 - ETA: 0s - loss: 0.1210 - accuracy: 0.95 - ETA: 0s - loss: 0.1241 - accuracy: 0.95 - ETA: 0s - loss: 0.1287 - accuracy: 0.95 - ETA: 0s - loss: 0.1291 - accuracy: 0.95 - ETA: 0s - loss: 0.1304 - accuracy: 0.95 - ETA: 0s - loss: 0.1271 - accuracy: 0.95 - ETA: 0s - loss: 0.1253 - accuracy: 0.95 - ETA: 0s - loss: 0.1264 - accuracy: 0.95 - 0s 35us/sample - loss: 0.1258 - accuracy: 0.9557 - val_loss: 0.0488 - val_accuracy: 0.9911\n",
      "Epoch 67/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1095 - accuracy: 0.96 - ETA: 0s - loss: 0.1045 - accuracy: 0.96 - ETA: 0s - loss: 0.1145 - accuracy: 0.96 - ETA: 0s - loss: 0.1191 - accuracy: 0.95 - ETA: 0s - loss: 0.1162 - accuracy: 0.95 - ETA: 0s - loss: 0.1193 - accuracy: 0.95 - ETA: 0s - loss: 0.1188 - accuracy: 0.95 - ETA: 0s - loss: 0.1193 - accuracy: 0.95 - ETA: 0s - loss: 0.1192 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1220 - accuracy: 0.9575 - val_loss: 0.0721 - val_accuracy: 0.9911\n",
      "Epoch 68/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1643 - accuracy: 0.96 - ETA: 0s - loss: 0.1221 - accuracy: 0.95 - ETA: 0s - loss: 0.1178 - accuracy: 0.95 - ETA: 0s - loss: 0.1177 - accuracy: 0.95 - ETA: 0s - loss: 0.1206 - accuracy: 0.95 - ETA: 0s - loss: 0.1222 - accuracy: 0.95 - ETA: 0s - loss: 0.1232 - accuracy: 0.95 - ETA: 0s - loss: 0.1256 - accuracy: 0.95 - ETA: 0s - loss: 0.1249 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1225 - accuracy: 0.9591 - val_loss: 0.0455 - val_accuracy: 0.9911\n",
      "Epoch 69/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1899 - accuracy: 0.93 - ETA: 0s - loss: 0.1254 - accuracy: 0.96 - ETA: 0s - loss: 0.1099 - accuracy: 0.96 - ETA: 0s - loss: 0.1221 - accuracy: 0.96 - ETA: 0s - loss: 0.1256 - accuracy: 0.96 - ETA: 0s - loss: 0.1236 - accuracy: 0.96 - ETA: 0s - loss: 0.1240 - accuracy: 0.96 - ETA: 0s - loss: 0.1238 - accuracy: 0.96 - ETA: 0s - loss: 0.1222 - accuracy: 0.96 - ETA: 0s - loss: 0.1239 - accuracy: 0.96 - 1s 37us/sample - loss: 0.1240 - accuracy: 0.9598 - val_loss: 0.0659 - val_accuracy: 0.9916\n",
      "Epoch 70/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.2801 - accuracy: 0.96 - ETA: 0s - loss: 0.1242 - accuracy: 0.96 - ETA: 0s - loss: 0.1151 - accuracy: 0.96 - ETA: 0s - loss: 0.1182 - accuracy: 0.96 - ETA: 0s - loss: 0.1174 - accuracy: 0.96 - ETA: 0s - loss: 0.1186 - accuracy: 0.96 - ETA: 0s - loss: 0.1208 - accuracy: 0.96 - ETA: 0s - loss: 0.1248 - accuracy: 0.96 - ETA: 0s - loss: 0.1278 - accuracy: 0.96 - 0s 34us/sample - loss: 0.1263 - accuracy: 0.9608 - val_loss: 0.0524 - val_accuracy: 0.9911\n",
      "Epoch 71/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1593 - accuracy: 0.90 - ETA: 0s - loss: 0.1453 - accuracy: 0.95 - ETA: 0s - loss: 0.1315 - accuracy: 0.95 - ETA: 0s - loss: 0.1260 - accuracy: 0.95 - ETA: 0s - loss: 0.1274 - accuracy: 0.95 - ETA: 0s - loss: 0.1266 - accuracy: 0.95 - ETA: 0s - loss: 0.1264 - accuracy: 0.95 - ETA: 0s - loss: 0.1275 - accuracy: 0.95 - ETA: 0s - loss: 0.1270 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1268 - accuracy: 0.9587 - val_loss: 0.0537 - val_accuracy: 0.9922\n",
      "Epoch 72/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0935 - accuracy: 0.96 - ETA: 0s - loss: 0.1145 - accuracy: 0.96 - ETA: 0s - loss: 0.1333 - accuracy: 0.95 - ETA: 0s - loss: 0.1290 - accuracy: 0.95 - ETA: 0s - loss: 0.1296 - accuracy: 0.95 - ETA: 0s - loss: 0.1266 - accuracy: 0.95 - ETA: 0s - loss: 0.1230 - accuracy: 0.95 - ETA: 0s - loss: 0.1199 - accuracy: 0.96 - ETA: 0s - loss: 0.1219 - accuracy: 0.96 - 0s 33us/sample - loss: 0.1213 - accuracy: 0.9608 - val_loss: 0.0564 - val_accuracy: 0.9916\n",
      "Epoch 73/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1786 - accuracy: 0.90 - ETA: 0s - loss: 0.1496 - accuracy: 0.94 - ETA: 0s - loss: 0.1316 - accuracy: 0.95 - ETA: 0s - loss: 0.1338 - accuracy: 0.95 - ETA: 0s - loss: 0.1334 - accuracy: 0.95 - ETA: 0s - loss: 0.1265 - accuracy: 0.95 - ETA: 0s - loss: 0.1260 - accuracy: 0.95 - ETA: 0s - loss: 0.1265 - accuracy: 0.95 - ETA: 0s - loss: 0.1240 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1238 - accuracy: 0.9594 - val_loss: 0.0399 - val_accuracy: 0.9916\n",
      "Epoch 74/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0421 - accuracy: 1.00 - ETA: 0s - loss: 0.1351 - accuracy: 0.95 - ETA: 0s - loss: 0.1269 - accuracy: 0.95 - ETA: 0s - loss: 0.1178 - accuracy: 0.96 - ETA: 0s - loss: 0.1199 - accuracy: 0.96 - ETA: 0s - loss: 0.1175 - accuracy: 0.96 - ETA: 0s - loss: 0.1203 - accuracy: 0.96 - ETA: 0s - loss: 0.1199 - accuracy: 0.96 - ETA: 0s - loss: 0.1238 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1239 - accuracy: 0.9599 - val_loss: 0.0492 - val_accuracy: 0.9911\n",
      "Epoch 75/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2767 - accuracy: 0.93 - ETA: 0s - loss: 0.1194 - accuracy: 0.96 - ETA: 0s - loss: 0.1125 - accuracy: 0.96 - ETA: 0s - loss: 0.1193 - accuracy: 0.96 - ETA: 0s - loss: 0.1193 - accuracy: 0.95 - ETA: 0s - loss: 0.1223 - accuracy: 0.95 - ETA: 0s - loss: 0.1229 - accuracy: 0.96 - ETA: 0s - loss: 0.1216 - accuracy: 0.96 - ETA: 0s - loss: 0.1222 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1221 - accuracy: 0.9599 - val_loss: 0.0593 - val_accuracy: 0.9916\n",
      "Epoch 76/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0525 - accuracy: 1.00 - ETA: 0s - loss: 0.0910 - accuracy: 0.96 - ETA: 0s - loss: 0.1088 - accuracy: 0.96 - ETA: 0s - loss: 0.1105 - accuracy: 0.96 - ETA: 0s - loss: 0.1167 - accuracy: 0.96 - ETA: 0s - loss: 0.1217 - accuracy: 0.95 - ETA: 0s - loss: 0.1229 - accuracy: 0.95 - ETA: 0s - loss: 0.1236 - accuracy: 0.95 - 0s 33us/sample - loss: 0.1236 - accuracy: 0.9582 - val_loss: 0.0486 - val_accuracy: 0.9911\n",
      "Epoch 77/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.3870 - accuracy: 0.84 - ETA: 0s - loss: 0.1123 - accuracy: 0.95 - ETA: 0s - loss: 0.1073 - accuracy: 0.96 - ETA: 0s - loss: 0.1160 - accuracy: 0.96 - ETA: 0s - loss: 0.1162 - accuracy: 0.96 - ETA: 0s - loss: 0.1184 - accuracy: 0.95 - ETA: 0s - loss: 0.1217 - accuracy: 0.95 - ETA: 0s - loss: 0.1218 - accuracy: 0.95 - 0s 33us/sample - loss: 0.1241 - accuracy: 0.9587 - val_loss: 0.0579 - val_accuracy: 0.9911\n",
      "Epoch 78/100\n",
      "14318/14318 [==============================] - ETA: 7s - loss: 0.2866 - accuracy: 0.87 - ETA: 0s - loss: 0.1280 - accuracy: 0.95 - ETA: 0s - loss: 0.1337 - accuracy: 0.95 - ETA: 0s - loss: 0.1314 - accuracy: 0.95 - ETA: 0s - loss: 0.1210 - accuracy: 0.95 - ETA: 0s - loss: 0.1247 - accuracy: 0.95 - ETA: 0s - loss: 0.1285 - accuracy: 0.95 - ETA: 0s - loss: 0.1269 - accuracy: 0.95 - ETA: 0s - loss: 0.1264 - accuracy: 0.95 - 0s 35us/sample - loss: 0.1256 - accuracy: 0.9578 - val_loss: 0.0507 - val_accuracy: 0.9916\n",
      "Epoch 79/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.4008 - accuracy: 0.93 - ETA: 0s - loss: 0.1384 - accuracy: 0.95 - ETA: 0s - loss: 0.1344 - accuracy: 0.95 - ETA: 0s - loss: 0.1280 - accuracy: 0.95 - ETA: 0s - loss: 0.1261 - accuracy: 0.95 - ETA: 0s - loss: 0.1265 - accuracy: 0.95 - ETA: 0s - loss: 0.1266 - accuracy: 0.95 - ETA: 0s - loss: 0.1237 - accuracy: 0.95 - ETA: 0s - loss: 0.1247 - accuracy: 0.95 - ETA: 0s - loss: 0.1263 - accuracy: 0.95 - 1s 36us/sample - loss: 0.1277 - accuracy: 0.9578 - val_loss: 0.0572 - val_accuracy: 0.9927\n",
      "Epoch 80/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0326 - accuracy: 1.00 - ETA: 0s - loss: 0.1272 - accuracy: 0.95 - ETA: 0s - loss: 0.1312 - accuracy: 0.95 - ETA: 0s - loss: 0.1245 - accuracy: 0.95 - ETA: 0s - loss: 0.1263 - accuracy: 0.95 - ETA: 0s - loss: 0.1225 - accuracy: 0.95 - ETA: 0s - loss: 0.1242 - accuracy: 0.95 - ETA: 0s - loss: 0.1231 - accuracy: 0.95 - ETA: 0s - loss: 0.1210 - accuracy: 0.95 - ETA: 0s - loss: 0.1223 - accuracy: 0.95 - 1s 35us/sample - loss: 0.1222 - accuracy: 0.9589 - val_loss: 0.0463 - val_accuracy: 0.9916\n",
      "Epoch 81/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2222 - accuracy: 0.96 - ETA: 0s - loss: 0.1461 - accuracy: 0.95 - ETA: 0s - loss: 0.1394 - accuracy: 0.95 - ETA: 0s - loss: 0.1297 - accuracy: 0.95 - ETA: 0s - loss: 0.1259 - accuracy: 0.96 - ETA: 0s - loss: 0.1226 - accuracy: 0.95 - ETA: 0s - loss: 0.1216 - accuracy: 0.95 - ETA: 0s - loss: 0.1223 - accuracy: 0.95 - ETA: 0s - loss: 0.1229 - accuracy: 0.95 - ETA: 0s - loss: 0.1269 - accuracy: 0.95 - 1s 37us/sample - loss: 0.1256 - accuracy: 0.9585 - val_loss: 0.0518 - val_accuracy: 0.9916\n",
      "Epoch 82/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.1282 - accuracy: 0.96 - ETA: 0s - loss: 0.1270 - accuracy: 0.96 - ETA: 0s - loss: 0.1175 - accuracy: 0.96 - ETA: 0s - loss: 0.1170 - accuracy: 0.96 - ETA: 0s - loss: 0.1168 - accuracy: 0.96 - ETA: 0s - loss: 0.1160 - accuracy: 0.96 - ETA: 0s - loss: 0.1217 - accuracy: 0.96 - ETA: 0s - loss: 0.1260 - accuracy: 0.95 - ETA: 0s - loss: 0.1293 - accuracy: 0.95 - ETA: 0s - loss: 0.1258 - accuracy: 0.95 - 1s 35us/sample - loss: 0.1255 - accuracy: 0.9587 - val_loss: 0.0473 - val_accuracy: 0.9922\n",
      "Epoch 83/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0498 - accuracy: 1.00 - ETA: 0s - loss: 0.1057 - accuracy: 0.97 - ETA: 0s - loss: 0.1093 - accuracy: 0.96 - ETA: 0s - loss: 0.1074 - accuracy: 0.96 - ETA: 0s - loss: 0.1111 - accuracy: 0.96 - ETA: 0s - loss: 0.1166 - accuracy: 0.96 - ETA: 0s - loss: 0.1183 - accuracy: 0.96 - ETA: 0s - loss: 0.1207 - accuracy: 0.96 - ETA: 0s - loss: 0.1222 - accuracy: 0.96 - ETA: 0s - loss: 0.1226 - accuracy: 0.96 - 1s 37us/sample - loss: 0.1223 - accuracy: 0.9614 - val_loss: 0.0475 - val_accuracy: 0.9916\n",
      "Epoch 84/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0631 - accuracy: 0.96 - ETA: 0s - loss: 0.1274 - accuracy: 0.95 - ETA: 0s - loss: 0.1319 - accuracy: 0.95 - ETA: 0s - loss: 0.1273 - accuracy: 0.95 - ETA: 0s - loss: 0.1208 - accuracy: 0.95 - ETA: 0s - loss: 0.1202 - accuracy: 0.96 - ETA: 0s - loss: 0.1226 - accuracy: 0.95 - ETA: 0s - loss: 0.1220 - accuracy: 0.95 - ETA: 0s - loss: 0.1236 - accuracy: 0.95 - 0s 35us/sample - loss: 0.1264 - accuracy: 0.9590 - val_loss: 0.0797 - val_accuracy: 0.9911\n",
      "Epoch 85/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2910 - accuracy: 0.90 - ETA: 0s - loss: 0.1120 - accuracy: 0.96 - ETA: 0s - loss: 0.1206 - accuracy: 0.96 - ETA: 0s - loss: 0.1254 - accuracy: 0.95 - ETA: 0s - loss: 0.1250 - accuracy: 0.95 - ETA: 0s - loss: 0.1212 - accuracy: 0.96 - ETA: 0s - loss: 0.1230 - accuracy: 0.96 - ETA: 0s - loss: 0.1214 - accuracy: 0.96 - ETA: 0s - loss: 0.1216 - accuracy: 0.96 - ETA: 0s - loss: 0.1206 - accuracy: 0.96 - 1s 37us/sample - loss: 0.1192 - accuracy: 0.9609 - val_loss: 0.0421 - val_accuracy: 0.9922\n",
      "Epoch 86/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.3946 - accuracy: 0.87 - ETA: 0s - loss: 0.1291 - accuracy: 0.95 - ETA: 0s - loss: 0.1258 - accuracy: 0.95 - ETA: 0s - loss: 0.1202 - accuracy: 0.95 - ETA: 0s - loss: 0.1239 - accuracy: 0.95 - ETA: 0s - loss: 0.1230 - accuracy: 0.95 - ETA: 0s - loss: 0.1203 - accuracy: 0.95 - ETA: 0s - loss: 0.1212 - accuracy: 0.95 - ETA: 0s - loss: 0.1258 - accuracy: 0.95 - 0s 35us/sample - loss: 0.1264 - accuracy: 0.9571 - val_loss: 0.0570 - val_accuracy: 0.9922\n",
      "Epoch 87/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0720 - accuracy: 0.93 - ETA: 0s - loss: 0.1129 - accuracy: 0.96 - ETA: 0s - loss: 0.1290 - accuracy: 0.95 - ETA: 0s - loss: 0.1287 - accuracy: 0.95 - ETA: 0s - loss: 0.1243 - accuracy: 0.95 - ETA: 0s - loss: 0.1218 - accuracy: 0.95 - ETA: 0s - loss: 0.1252 - accuracy: 0.95 - ETA: 0s - loss: 0.1244 - accuracy: 0.95 - ETA: 0s - loss: 0.1265 - accuracy: 0.95 - 1s 35us/sample - loss: 0.1260 - accuracy: 0.9590 - val_loss: 0.0635 - val_accuracy: 0.9911\n",
      "Epoch 88/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2446 - accuracy: 0.90 - ETA: 0s - loss: 0.1230 - accuracy: 0.95 - ETA: 0s - loss: 0.1265 - accuracy: 0.95 - ETA: 0s - loss: 0.1236 - accuracy: 0.95 - ETA: 0s - loss: 0.1247 - accuracy: 0.95 - ETA: 0s - loss: 0.1257 - accuracy: 0.95 - ETA: 0s - loss: 0.1248 - accuracy: 0.95 - ETA: 0s - loss: 0.1254 - accuracy: 0.95 - ETA: 0s - loss: 0.1234 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1228 - accuracy: 0.9580 - val_loss: 0.0460 - val_accuracy: 0.9916\n",
      "Epoch 89/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2322 - accuracy: 0.93 - ETA: 0s - loss: 0.1147 - accuracy: 0.96 - ETA: 0s - loss: 0.1130 - accuracy: 0.96 - ETA: 0s - loss: 0.1225 - accuracy: 0.95 - ETA: 0s - loss: 0.1214 - accuracy: 0.95 - ETA: 0s - loss: 0.1202 - accuracy: 0.96 - ETA: 0s - loss: 0.1168 - accuracy: 0.96 - ETA: 0s - loss: 0.1171 - accuracy: 0.96 - ETA: 0s - loss: 0.1228 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1219 - accuracy: 0.9597 - val_loss: 0.0695 - val_accuracy: 0.9911\n",
      "Epoch 90/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0254 - accuracy: 1.00 - ETA: 0s - loss: 0.1261 - accuracy: 0.95 - ETA: 0s - loss: 0.1236 - accuracy: 0.96 - ETA: 0s - loss: 0.1277 - accuracy: 0.95 - ETA: 0s - loss: 0.1228 - accuracy: 0.95 - ETA: 0s - loss: 0.1223 - accuracy: 0.96 - ETA: 0s - loss: 0.1206 - accuracy: 0.96 - ETA: 0s - loss: 0.1222 - accuracy: 0.96 - ETA: 0s - loss: 0.1214 - accuracy: 0.96 - 0s 34us/sample - loss: 0.1217 - accuracy: 0.9603 - val_loss: 0.0507 - val_accuracy: 0.9922\n",
      "Epoch 91/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1556 - accuracy: 0.90 - ETA: 0s - loss: 0.1403 - accuracy: 0.95 - ETA: 0s - loss: 0.1262 - accuracy: 0.96 - ETA: 0s - loss: 0.1160 - accuracy: 0.96 - ETA: 0s - loss: 0.1171 - accuracy: 0.96 - ETA: 0s - loss: 0.1229 - accuracy: 0.96 - ETA: 0s - loss: 0.1238 - accuracy: 0.95 - ETA: 0s - loss: 0.1238 - accuracy: 0.95 - ETA: 0s - loss: 0.1229 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1235 - accuracy: 0.9583 - val_loss: 0.0581 - val_accuracy: 0.9911\n",
      "Epoch 92/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0613 - accuracy: 1.00 - ETA: 0s - loss: 0.1318 - accuracy: 0.96 - ETA: 0s - loss: 0.1156 - accuracy: 0.96 - ETA: 0s - loss: 0.1217 - accuracy: 0.96 - ETA: 0s - loss: 0.1275 - accuracy: 0.95 - ETA: 0s - loss: 0.1258 - accuracy: 0.95 - ETA: 0s - loss: 0.1209 - accuracy: 0.96 - ETA: 0s - loss: 0.1215 - accuracy: 0.96 - 0s 34us/sample - loss: 0.1212 - accuracy: 0.9605 - val_loss: 0.0760 - val_accuracy: 0.9922\n",
      "Epoch 93/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1132 - accuracy: 0.96 - ETA: 0s - loss: 0.1229 - accuracy: 0.96 - ETA: 0s - loss: 0.1322 - accuracy: 0.95 - ETA: 0s - loss: 0.1279 - accuracy: 0.95 - ETA: 0s - loss: 0.1264 - accuracy: 0.96 - ETA: 0s - loss: 0.1268 - accuracy: 0.95 - ETA: 0s - loss: 0.1262 - accuracy: 0.95 - ETA: 0s - loss: 0.1265 - accuracy: 0.95 - 0s 35us/sample - loss: 0.1240 - accuracy: 0.9599 - val_loss: 0.0487 - val_accuracy: 0.9922\n",
      "Epoch 94/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.0636 - accuracy: 0.96 - ETA: 0s - loss: 0.1122 - accuracy: 0.95 - ETA: 0s - loss: 0.1148 - accuracy: 0.96 - ETA: 0s - loss: 0.1215 - accuracy: 0.96 - ETA: 0s - loss: 0.1245 - accuracy: 0.95 - ETA: 0s - loss: 0.1212 - accuracy: 0.96 - ETA: 0s - loss: 0.1189 - accuracy: 0.96 - ETA: 0s - loss: 0.1165 - accuracy: 0.96 - ETA: 0s - loss: 0.1182 - accuracy: 0.96 - ETA: 0s - loss: 0.1181 - accuracy: 0.96 - 1s 37us/sample - loss: 0.1190 - accuracy: 0.9620 - val_loss: 0.0513 - val_accuracy: 0.9916\n",
      "Epoch 95/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0572 - accuracy: 0.96 - ETA: 0s - loss: 0.1094 - accuracy: 0.96 - ETA: 0s - loss: 0.1229 - accuracy: 0.95 - ETA: 0s - loss: 0.1243 - accuracy: 0.95 - ETA: 0s - loss: 0.1248 - accuracy: 0.95 - ETA: 0s - loss: 0.1270 - accuracy: 0.95 - ETA: 0s - loss: 0.1267 - accuracy: 0.95 - ETA: 0s - loss: 0.1245 - accuracy: 0.95 - ETA: 0s - loss: 0.1232 - accuracy: 0.95 - 0s 35us/sample - loss: 0.1241 - accuracy: 0.9575 - val_loss: 0.0759 - val_accuracy: 0.9911\n",
      "Epoch 96/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2597 - accuracy: 0.93 - ETA: 0s - loss: 0.1092 - accuracy: 0.96 - ETA: 0s - loss: 0.1149 - accuracy: 0.96 - ETA: 0s - loss: 0.1209 - accuracy: 0.96 - ETA: 0s - loss: 0.1193 - accuracy: 0.96 - ETA: 0s - loss: 0.1179 - accuracy: 0.96 - ETA: 0s - loss: 0.1176 - accuracy: 0.96 - ETA: 0s - loss: 0.1204 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1234 - accuracy: 0.9593 - val_loss: 0.0927 - val_accuracy: 0.9927\n",
      "Epoch 97/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0439 - accuracy: 1.00 - ETA: 0s - loss: 0.1314 - accuracy: 0.95 - ETA: 0s - loss: 0.1263 - accuracy: 0.95 - ETA: 0s - loss: 0.1239 - accuracy: 0.96 - ETA: 0s - loss: 0.1204 - accuracy: 0.96 - ETA: 0s - loss: 0.1202 - accuracy: 0.96 - ETA: 0s - loss: 0.1212 - accuracy: 0.96 - ETA: 0s - loss: 0.1230 - accuracy: 0.96 - 0s 34us/sample - loss: 0.1231 - accuracy: 0.9606 - val_loss: 0.0650 - val_accuracy: 0.9927\n",
      "Epoch 98/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1162 - accuracy: 0.96 - ETA: 0s - loss: 0.1432 - accuracy: 0.95 - ETA: 0s - loss: 0.1381 - accuracy: 0.95 - ETA: 0s - loss: 0.1328 - accuracy: 0.95 - ETA: 0s - loss: 0.1281 - accuracy: 0.95 - ETA: 0s - loss: 0.1250 - accuracy: 0.95 - ETA: 0s - loss: 0.1275 - accuracy: 0.95 - ETA: 0s - loss: 0.1259 - accuracy: 0.95 - ETA: 0s - loss: 0.1247 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1223 - accuracy: 0.9600 - val_loss: 0.0446 - val_accuracy: 0.9922\n",
      "Epoch 99/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0974 - accuracy: 0.96 - ETA: 0s - loss: 0.1147 - accuracy: 0.96 - ETA: 0s - loss: 0.1088 - accuracy: 0.96 - ETA: 0s - loss: 0.1101 - accuracy: 0.96 - ETA: 0s - loss: 0.1200 - accuracy: 0.96 - ETA: 0s - loss: 0.1202 - accuracy: 0.95 - ETA: 0s - loss: 0.1270 - accuracy: 0.95 - ETA: 0s - loss: 0.1268 - accuracy: 0.95 - ETA: 0s - loss: 0.1248 - accuracy: 0.95 - 0s 34us/sample - loss: 0.1249 - accuracy: 0.9587 - val_loss: 0.0457 - val_accuracy: 0.9911\n",
      "Epoch 100/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0342 - accuracy: 1.00 - ETA: 0s - loss: 0.1224 - accuracy: 0.95 - ETA: 0s - loss: 0.1218 - accuracy: 0.95 - ETA: 0s - loss: 0.1271 - accuracy: 0.95 - ETA: 0s - loss: 0.1252 - accuracy: 0.95 - ETA: 0s - loss: 0.1230 - accuracy: 0.95 - ETA: 0s - loss: 0.1238 - accuracy: 0.95 - ETA: 0s - loss: 0.1233 - accuracy: 0.95 - 0s 33us/sample - loss: 0.1223 - accuracy: 0.9578 - val_loss: 0.0453 - val_accuracy: 0.9922\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.05196929413346605, 0.98826814]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Для данных без feature engeneering и без пересчёта веса класса.\n",
    "\n",
    "model = tf.keras.models.Sequential([\n",
    "tf.keras.layers.Dense(16, activation=tf.nn.relu),\n",
    "tf.keras.layers.Dropout(0.5),\n",
    "tf.keras.layers.Dense(32, activation=tf.nn.relu),\n",
    "tf.keras.layers.Dropout(0.5),\n",
    "tf.keras.layers.Dense(1, activation=tf.nn.sigmoid)\n",
    "])\n",
    "model.compile(optimizer='adam',\n",
    "loss='binary_crossentropy',\n",
    "metrics=['accuracy'])\n",
    "baseline_history=model.fit(x_train, y_train, epochs=100, validation_data=(x_valid, y_valid), shuffle=True)\n",
    "model.evaluate(x_test, y_test, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                multiple                  144       \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            multiple                  0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              multiple                  544       \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          multiple                  0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              multiple                  33        \n",
      "=================================================================\n",
      "Total params: 721\n",
      "Trainable params: 721\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(baseline_history, 'loss', 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(baseline_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss :  0.05196929413346605\n",
      "accuracy :  0.98826814\n",
      "\n",
      "всего 27 \"пульсаров\"\n",
      "всего 1763 \"не пульсаров\"\n",
      "не пульсар предсказанный как не пульсар:  1755\n",
      "не пульсар предсказанный как пульсар : 8\n",
      "пульсар предсказанный как не пульсар : 13\n",
      "пульсар предсказанный как пульсар :  14\n",
      "количество правильных ответов 1769 из 1790\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#для данных с feature engeneering\n",
    "\n",
    "test_predictions_baseline = model.predict(x_test)\n",
    "\n",
    "baseline_results = model.evaluate(x_test, y_test, verbose=0)\n",
    "for name, value in zip(model.metrics_names, baseline_results):\n",
    "  print(name, ': ', value)\n",
    "print()\n",
    "\n",
    "plot_cm(y_test, test_predictions_baseline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# далее всё тоже самое, только с данными без feature engeneering, но с пересчётом веса класса"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 14318 samples, validate on 1790 samples\n",
      "Epoch 1/100\n",
      "14318/14318 [==============================] - ETA: 4:38 - loss: 16.4579 - accuracy: 0.406 - ETA: 5s - loss: 13.3686 - accuracy: 0.5006  - ETA: 2s - loss: 11.3777 - accuracy: 0.535 - ETA: 1s - loss: 10.3007 - accuracy: 0.567 - ETA: 1s - loss: 9.3039 - accuracy: 0.580 - ETA: 0s - loss: 8.2828 - accuracy: 0.59 - ETA: 0s - loss: 7.6772 - accuracy: 0.61 - ETA: 0s - loss: 7.1004 - accuracy: 0.61 - ETA: 0s - loss: 6.5474 - accuracy: 0.62 - ETA: 0s - loss: 6.1171 - accuracy: 0.62 - 1s 91us/sample - loss: 5.7946 - accuracy: 0.6338 - val_loss: 0.2369 - val_accuracy: 0.9425\n",
      "Epoch 2/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 2.7238 - accuracy: 0.68 - ETA: 0s - loss: 1.7358 - accuracy: 0.67 - ETA: 0s - loss: 1.9813 - accuracy: 0.69 - ETA: 0s - loss: 1.7968 - accuracy: 0.69 - ETA: 0s - loss: 1.7986 - accuracy: 0.69 - ETA: 0s - loss: 1.6762 - accuracy: 0.69 - ETA: 0s - loss: 1.5773 - accuracy: 0.69 - ETA: 0s - loss: 1.4745 - accuracy: 0.70 - ETA: 0s - loss: 1.4220 - accuracy: 0.70 - ETA: 0s - loss: 1.3882 - accuracy: 0.70 - 1s 38us/sample - loss: 1.3602 - accuracy: 0.7012 - val_loss: 0.2097 - val_accuracy: 0.9553\n",
      "Epoch 3/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2536 - accuracy: 0.68 - ETA: 0s - loss: 1.0148 - accuracy: 0.68 - ETA: 0s - loss: 1.0189 - accuracy: 0.68 - ETA: 0s - loss: 0.9108 - accuracy: 0.69 - ETA: 0s - loss: 0.8299 - accuracy: 0.71 - ETA: 0s - loss: 0.7998 - accuracy: 0.72 - ETA: 0s - loss: 0.7672 - accuracy: 0.73 - ETA: 0s - loss: 0.7690 - accuracy: 0.74 - 0s 34us/sample - loss: 0.7677 - accuracy: 0.7391 - val_loss: 0.2235 - val_accuracy: 0.9592\n",
      "Epoch 4/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.3370 - accuracy: 0.81 - ETA: 0s - loss: 0.6023 - accuracy: 0.75 - ETA: 0s - loss: 0.5566 - accuracy: 0.77 - ETA: 0s - loss: 0.5070 - accuracy: 0.78 - ETA: 0s - loss: 0.5287 - accuracy: 0.78 - ETA: 0s - loss: 0.5171 - accuracy: 0.78 - ETA: 0s - loss: 0.5240 - accuracy: 0.78 - ETA: 0s - loss: 0.5328 - accuracy: 0.78 - ETA: 0s - loss: 0.5363 - accuracy: 0.78 - ETA: 0s - loss: 0.5442 - accuracy: 0.78 - 1s 37us/sample - loss: 0.5437 - accuracy: 0.7820 - val_loss: 0.2430 - val_accuracy: 0.9620\n",
      "Epoch 5/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.3608 - accuracy: 0.90 - ETA: 0s - loss: 0.5037 - accuracy: 0.79 - ETA: 0s - loss: 0.4983 - accuracy: 0.78 - ETA: 0s - loss: 0.4924 - accuracy: 0.79 - ETA: 0s - loss: 0.4945 - accuracy: 0.80 - ETA: 0s - loss: 0.4915 - accuracy: 0.79 - ETA: 0s - loss: 0.4905 - accuracy: 0.79 - ETA: 0s - loss: 0.4849 - accuracy: 0.79 - 0s 35us/sample - loss: 0.4889 - accuracy: 0.7970 - val_loss: 0.2279 - val_accuracy: 0.9603\n",
      "Epoch 6/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.3811 - accuracy: 0.71 - ETA: 0s - loss: 0.4619 - accuracy: 0.78 - ETA: 0s - loss: 0.4579 - accuracy: 0.79 - ETA: 0s - loss: 0.4598 - accuracy: 0.79 - ETA: 0s - loss: 0.4604 - accuracy: 0.80 - ETA: 0s - loss: 0.4672 - accuracy: 0.80 - ETA: 0s - loss: 0.4671 - accuracy: 0.80 - ETA: 0s - loss: 0.4690 - accuracy: 0.80 - ETA: 0s - loss: 0.4588 - accuracy: 0.80 - ETA: 0s - loss: 0.4492 - accuracy: 0.80 - 1s 37us/sample - loss: 0.4487 - accuracy: 0.8068 - val_loss: 0.1601 - val_accuracy: 0.9665\n",
      "Epoch 7/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.3183 - accuracy: 0.75 - ETA: 0s - loss: 0.4152 - accuracy: 0.82 - ETA: 0s - loss: 0.4217 - accuracy: 0.81 - ETA: 0s - loss: 0.4531 - accuracy: 0.80 - ETA: 0s - loss: 0.4512 - accuracy: 0.79 - ETA: 0s - loss: 0.4445 - accuracy: 0.80 - ETA: 0s - loss: 0.4304 - accuracy: 0.80 - ETA: 0s - loss: 0.4304 - accuracy: 0.80 - 0s 34us/sample - loss: 0.4312 - accuracy: 0.8091 - val_loss: 0.1954 - val_accuracy: 0.9520\n",
      "Epoch 8/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2033 - accuracy: 0.78 - ETA: 0s - loss: 0.4099 - accuracy: 0.81 - ETA: 0s - loss: 0.4033 - accuracy: 0.81 - ETA: 0s - loss: 0.4126 - accuracy: 0.81 - ETA: 0s - loss: 0.4093 - accuracy: 0.81 - ETA: 0s - loss: 0.4058 - accuracy: 0.82 - ETA: 0s - loss: 0.4114 - accuracy: 0.82 - ETA: 0s - loss: 0.4181 - accuracy: 0.82 - ETA: 0s - loss: 0.4181 - accuracy: 0.82 - ETA: 0s - loss: 0.4177 - accuracy: 0.82 - 1s 36us/sample - loss: 0.4182 - accuracy: 0.8230 - val_loss: 0.1928 - val_accuracy: 0.9631\n",
      "Epoch 9/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.4645 - accuracy: 0.84 - ETA: 0s - loss: 0.4231 - accuracy: 0.83 - ETA: 0s - loss: 0.3775 - accuracy: 0.84 - ETA: 0s - loss: 0.4184 - accuracy: 0.83 - ETA: 0s - loss: 0.4141 - accuracy: 0.83 - ETA: 0s - loss: 0.4093 - accuracy: 0.84 - ETA: 0s - loss: 0.4130 - accuracy: 0.84 - ETA: 0s - loss: 0.4058 - accuracy: 0.84 - 0s 34us/sample - loss: 0.4006 - accuracy: 0.8480 - val_loss: 0.1812 - val_accuracy: 0.9721\n",
      "Epoch 10/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0945 - accuracy: 0.96 - ETA: 0s - loss: 0.3393 - accuracy: 0.87 - ETA: 0s - loss: 0.4186 - accuracy: 0.86 - ETA: 0s - loss: 0.3995 - accuracy: 0.86 - ETA: 0s - loss: 0.3811 - accuracy: 0.86 - ETA: 0s - loss: 0.3843 - accuracy: 0.86 - ETA: 0s - loss: 0.3840 - accuracy: 0.86 - ETA: 0s - loss: 0.3842 - accuracy: 0.86 - 1s 36us/sample - loss: 0.3820 - accuracy: 0.8637 - val_loss: 0.1972 - val_accuracy: 0.9570\n",
      "Epoch 11/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2071 - accuracy: 0.87 - ETA: 0s - loss: 0.4366 - accuracy: 0.84 - ETA: 0s - loss: 0.3946 - accuracy: 0.84 - ETA: 0s - loss: 0.3684 - accuracy: 0.85 - ETA: 0s - loss: 0.3597 - accuracy: 0.86 - ETA: 0s - loss: 0.3547 - accuracy: 0.86 - ETA: 0s - loss: 0.3555 - accuracy: 0.86 - ETA: 0s - loss: 0.3525 - accuracy: 0.86 - 0s 35us/sample - loss: 0.3518 - accuracy: 0.8676 - val_loss: 0.1887 - val_accuracy: 0.9704\n",
      "Epoch 12/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1995 - accuracy: 0.93 - ETA: 0s - loss: 0.3832 - accuracy: 0.86 - ETA: 0s - loss: 0.3672 - accuracy: 0.85 - ETA: 0s - loss: 0.3855 - accuracy: 0.85 - ETA: 0s - loss: 0.3771 - accuracy: 0.85 - ETA: 0s - loss: 0.3837 - accuracy: 0.86 - ETA: 0s - loss: 0.3756 - accuracy: 0.86 - ETA: 0s - loss: 0.3674 - accuracy: 0.86 - ETA: 0s - loss: 0.3599 - accuracy: 0.86 - ETA: 0s - loss: 0.3568 - accuracy: 0.86 - 1s 36us/sample - loss: 0.3635 - accuracy: 0.8698 - val_loss: 0.2113 - val_accuracy: 0.9620\n",
      "Epoch 13/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.4737 - accuracy: 0.84 - ETA: 0s - loss: 0.3932 - accuracy: 0.86 - ETA: 0s - loss: 0.3548 - accuracy: 0.86 - ETA: 0s - loss: 0.3498 - accuracy: 0.86 - ETA: 0s - loss: 0.3299 - accuracy: 0.87 - ETA: 0s - loss: 0.3250 - accuracy: 0.87 - ETA: 0s - loss: 0.3319 - accuracy: 0.87 - ETA: 0s - loss: 0.3288 - accuracy: 0.87 - 0s 34us/sample - loss: 0.3309 - accuracy: 0.8756 - val_loss: 0.1688 - val_accuracy: 0.9687\n",
      "Epoch 14/100\n",
      "14318/14318 [==============================] - ETA: 6s - loss: 0.2692 - accuracy: 0.93 - ETA: 0s - loss: 0.3123 - accuracy: 0.88 - ETA: 0s - loss: 0.3479 - accuracy: 0.86 - ETA: 0s - loss: 0.3511 - accuracy: 0.86 - ETA: 0s - loss: 0.3592 - accuracy: 0.85 - ETA: 0s - loss: 0.3441 - accuracy: 0.86 - ETA: 0s - loss: 0.3415 - accuracy: 0.86 - ETA: 0s - loss: 0.3402 - accuracy: 0.86 - ETA: 0s - loss: 0.3353 - accuracy: 0.87 - 1s 36us/sample - loss: 0.3324 - accuracy: 0.8715 - val_loss: 0.1528 - val_accuracy: 0.9642\n",
      "Epoch 15/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.3573 - accuracy: 0.81 - ETA: 0s - loss: 0.3417 - accuracy: 0.89 - ETA: 0s - loss: 0.3127 - accuracy: 0.87 - ETA: 0s - loss: 0.3314 - accuracy: 0.87 - ETA: 0s - loss: 0.3312 - accuracy: 0.87 - ETA: 0s - loss: 0.3311 - accuracy: 0.87 - ETA: 0s - loss: 0.3286 - accuracy: 0.87 - ETA: 0s - loss: 0.3299 - accuracy: 0.87 - ETA: 0s - loss: 0.3294 - accuracy: 0.87 - 1s 36us/sample - loss: 0.3297 - accuracy: 0.8782 - val_loss: 0.1885 - val_accuracy: 0.9765\n",
      "Epoch 16/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1497 - accuracy: 0.87 - ETA: 0s - loss: 0.2873 - accuracy: 0.88 - ETA: 0s - loss: 0.3106 - accuracy: 0.88 - ETA: 0s - loss: 0.3128 - accuracy: 0.88 - ETA: 0s - loss: 0.3240 - accuracy: 0.88 - ETA: 0s - loss: 0.3269 - accuracy: 0.88 - ETA: 0s - loss: 0.3253 - accuracy: 0.88 - ETA: 0s - loss: 0.3333 - accuracy: 0.88 - ETA: 0s - loss: 0.3353 - accuracy: 0.88 - 0s 35us/sample - loss: 0.3295 - accuracy: 0.8848 - val_loss: 0.1219 - val_accuracy: 0.9760\n",
      "Epoch 17/100\n",
      "14318/14318 [==============================] - ETA: 6s - loss: 0.1738 - accuracy: 0.87 - ETA: 0s - loss: 0.3506 - accuracy: 0.88 - ETA: 0s - loss: 0.3196 - accuracy: 0.88 - ETA: 0s - loss: 0.3226 - accuracy: 0.88 - ETA: 0s - loss: 0.3219 - accuracy: 0.88 - ETA: 0s - loss: 0.3119 - accuracy: 0.88 - ETA: 0s - loss: 0.3228 - accuracy: 0.88 - ETA: 0s - loss: 0.3259 - accuracy: 0.88 - 0s 34us/sample - loss: 0.3260 - accuracy: 0.8832 - val_loss: 0.1576 - val_accuracy: 0.9782\n",
      "Epoch 18/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1910 - accuracy: 0.93 - ETA: 0s - loss: 0.2981 - accuracy: 0.89 - ETA: 0s - loss: 0.2756 - accuracy: 0.89 - ETA: 0s - loss: 0.2795 - accuracy: 0.89 - ETA: 0s - loss: 0.2947 - accuracy: 0.89 - ETA: 0s - loss: 0.3061 - accuracy: 0.89 - ETA: 0s - loss: 0.3027 - accuracy: 0.89 - ETA: 0s - loss: 0.3021 - accuracy: 0.88 - ETA: 0s - loss: 0.3107 - accuracy: 0.88 - ETA: 0s - loss: 0.3102 - accuracy: 0.88 - 1s 36us/sample - loss: 0.3083 - accuracy: 0.8875 - val_loss: 0.1566 - val_accuracy: 0.9777\n",
      "Epoch 19/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.3409 - accuracy: 0.93 - ETA: 0s - loss: 0.3092 - accuracy: 0.90 - ETA: 0s - loss: 0.3358 - accuracy: 0.88 - ETA: 0s - loss: 0.3183 - accuracy: 0.88 - ETA: 0s - loss: 0.3100 - accuracy: 0.88 - ETA: 0s - loss: 0.3002 - accuracy: 0.88 - ETA: 0s - loss: 0.3061 - accuracy: 0.88 - ETA: 0s - loss: 0.3063 - accuracy: 0.88 - 0s 34us/sample - loss: 0.3048 - accuracy: 0.8891 - val_loss: 0.1607 - val_accuracy: 0.9670\n",
      "Epoch 20/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1663 - accuracy: 0.84 - ETA: 0s - loss: 0.3032 - accuracy: 0.90 - ETA: 0s - loss: 0.3035 - accuracy: 0.89 - ETA: 0s - loss: 0.3093 - accuracy: 0.89 - ETA: 0s - loss: 0.3086 - accuracy: 0.88 - ETA: 0s - loss: 0.3074 - accuracy: 0.88 - ETA: 0s - loss: 0.3039 - accuracy: 0.88 - ETA: 0s - loss: 0.3072 - accuracy: 0.88 - ETA: 0s - loss: 0.3013 - accuracy: 0.88 - ETA: 0s - loss: 0.3073 - accuracy: 0.88 - 0s 34us/sample - loss: 0.3068 - accuracy: 0.8882 - val_loss: 0.1636 - val_accuracy: 0.9687\n",
      "Epoch 21/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.3964 - accuracy: 0.87 - ETA: 0s - loss: 0.2528 - accuracy: 0.90 - ETA: 0s - loss: 0.2823 - accuracy: 0.89 - ETA: 0s - loss: 0.2897 - accuracy: 0.89 - ETA: 0s - loss: 0.2907 - accuracy: 0.89 - ETA: 0s - loss: 0.3003 - accuracy: 0.89 - ETA: 0s - loss: 0.3014 - accuracy: 0.89 - ETA: 0s - loss: 0.3010 - accuracy: 0.89 - 0s 35us/sample - loss: 0.3010 - accuracy: 0.8936 - val_loss: 0.1691 - val_accuracy: 0.9777\n",
      "Epoch 22/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1748 - accuracy: 0.96 - ETA: 0s - loss: 0.2574 - accuracy: 0.91 - ETA: 0s - loss: 0.2991 - accuracy: 0.90 - ETA: 0s - loss: 0.2883 - accuracy: 0.90 - ETA: 0s - loss: 0.2950 - accuracy: 0.90 - ETA: 0s - loss: 0.3060 - accuracy: 0.90 - ETA: 0s - loss: 0.3040 - accuracy: 0.90 - ETA: 0s - loss: 0.3041 - accuracy: 0.89 - ETA: 0s - loss: 0.3080 - accuracy: 0.89 - ETA: 0s - loss: 0.3081 - accuracy: 0.89 - ETA: 0s - loss: 0.3012 - accuracy: 0.89 - 1s 40us/sample - loss: 0.3043 - accuracy: 0.8948 - val_loss: 0.1270 - val_accuracy: 0.9709\n",
      "Epoch 23/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.9295 - accuracy: 0.87 - ETA: 0s - loss: 0.3280 - accuracy: 0.88 - ETA: 0s - loss: 0.3141 - accuracy: 0.88 - ETA: 0s - loss: 0.3162 - accuracy: 0.88 - ETA: 0s - loss: 0.2943 - accuracy: 0.88 - ETA: 0s - loss: 0.2902 - accuracy: 0.89 - ETA: 0s - loss: 0.2937 - accuracy: 0.89 - ETA: 0s - loss: 0.2877 - accuracy: 0.89 - ETA: 0s - loss: 0.2863 - accuracy: 0.89 - 1s 38us/sample - loss: 0.2834 - accuracy: 0.8934 - val_loss: 0.1097 - val_accuracy: 0.9726\n",
      "Epoch 24/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.3517 - accuracy: 0.84 - ETA: 0s - loss: 0.2833 - accuracy: 0.90 - ETA: 0s - loss: 0.2952 - accuracy: 0.89 - ETA: 0s - loss: 0.3021 - accuracy: 0.89 - ETA: 0s - loss: 0.3028 - accuracy: 0.89 - ETA: 0s - loss: 0.3031 - accuracy: 0.89 - ETA: 0s - loss: 0.2967 - accuracy: 0.89 - ETA: 0s - loss: 0.2989 - accuracy: 0.89 - 1s 35us/sample - loss: 0.3007 - accuracy: 0.8926 - val_loss: 0.1398 - val_accuracy: 0.9709\n",
      "Epoch 25/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1716 - accuracy: 0.93 - ETA: 0s - loss: 0.3497 - accuracy: 0.87 - ETA: 0s - loss: 0.3091 - accuracy: 0.88 - ETA: 0s - loss: 0.3026 - accuracy: 0.89 - ETA: 0s - loss: 0.2919 - accuracy: 0.89 - ETA: 0s - loss: 0.2929 - accuracy: 0.89 - ETA: 0s - loss: 0.2867 - accuracy: 0.89 - ETA: 0s - loss: 0.2953 - accuracy: 0.89 - 1s 35us/sample - loss: 0.2933 - accuracy: 0.8968 - val_loss: 0.1380 - val_accuracy: 0.9709\n",
      "Epoch 26/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1341 - accuracy: 0.87 - ETA: 0s - loss: 0.2782 - accuracy: 0.87 - ETA: 0s - loss: 0.3018 - accuracy: 0.88 - ETA: 0s - loss: 0.2863 - accuracy: 0.88 - ETA: 0s - loss: 0.2834 - accuracy: 0.89 - ETA: 0s - loss: 0.2873 - accuracy: 0.89 - ETA: 0s - loss: 0.2860 - accuracy: 0.89 - ETA: 0s - loss: 0.2862 - accuracy: 0.90 - ETA: 0s - loss: 0.2903 - accuracy: 0.89 - 1s 35us/sample - loss: 0.2882 - accuracy: 0.9003 - val_loss: 0.1202 - val_accuracy: 0.9810\n",
      "Epoch 27/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2590 - accuracy: 0.93 - ETA: 0s - loss: 0.3111 - accuracy: 0.91 - ETA: 0s - loss: 0.3028 - accuracy: 0.91 - ETA: 0s - loss: 0.3027 - accuracy: 0.91 - ETA: 0s - loss: 0.2960 - accuracy: 0.91 - ETA: 0s - loss: 0.2888 - accuracy: 0.91 - ETA: 0s - loss: 0.2949 - accuracy: 0.91 - ETA: 0s - loss: 0.2901 - accuracy: 0.91 - ETA: 0s - loss: 0.2917 - accuracy: 0.91 - 1s 36us/sample - loss: 0.2911 - accuracy: 0.9123 - val_loss: 0.1268 - val_accuracy: 0.9749\n",
      "Epoch 28/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1332 - accuracy: 0.93 - ETA: 0s - loss: 0.3277 - accuracy: 0.90 - ETA: 0s - loss: 0.3158 - accuracy: 0.91 - ETA: 0s - loss: 0.2922 - accuracy: 0.91 - ETA: 0s - loss: 0.2871 - accuracy: 0.91 - ETA: 0s - loss: 0.2936 - accuracy: 0.91 - ETA: 0s - loss: 0.2829 - accuracy: 0.91 - ETA: 0s - loss: 0.2831 - accuracy: 0.91 - ETA: 0s - loss: 0.2857 - accuracy: 0.91 - 1s 35us/sample - loss: 0.2886 - accuracy: 0.9172 - val_loss: 0.1530 - val_accuracy: 0.9682\n",
      "Epoch 29/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2687 - accuracy: 0.87 - ETA: 0s - loss: 0.3058 - accuracy: 0.90 - ETA: 0s - loss: 0.2713 - accuracy: 0.91 - ETA: 0s - loss: 0.2713 - accuracy: 0.91 - ETA: 0s - loss: 0.2761 - accuracy: 0.91 - ETA: 0s - loss: 0.2893 - accuracy: 0.91 - ETA: 0s - loss: 0.2885 - accuracy: 0.91 - ETA: 0s - loss: 0.2838 - accuracy: 0.91 - 1s 35us/sample - loss: 0.2776 - accuracy: 0.9173 - val_loss: 0.1029 - val_accuracy: 0.9777\n",
      "Epoch 30/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.3557 - accuracy: 0.90 - ETA: 0s - loss: 0.3081 - accuracy: 0.91 - ETA: 0s - loss: 0.2977 - accuracy: 0.91 - ETA: 0s - loss: 0.2867 - accuracy: 0.91 - ETA: 0s - loss: 0.2897 - accuracy: 0.91 - ETA: 0s - loss: 0.2851 - accuracy: 0.92 - ETA: 0s - loss: 0.2803 - accuracy: 0.92 - ETA: 0s - loss: 0.2792 - accuracy: 0.91 - ETA: 0s - loss: 0.2734 - accuracy: 0.91 - 0s 35us/sample - loss: 0.2762 - accuracy: 0.9183 - val_loss: 0.1335 - val_accuracy: 0.9749\n",
      "Epoch 31/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.4841 - accuracy: 0.90 - ETA: 0s - loss: 0.3250 - accuracy: 0.91 - ETA: 0s - loss: 0.2926 - accuracy: 0.91 - ETA: 0s - loss: 0.2854 - accuracy: 0.91 - ETA: 0s - loss: 0.2755 - accuracy: 0.92 - ETA: 0s - loss: 0.2813 - accuracy: 0.92 - ETA: 0s - loss: 0.2818 - accuracy: 0.92 - ETA: 0s - loss: 0.2777 - accuracy: 0.92 - 1s 35us/sample - loss: 0.2745 - accuracy: 0.9211 - val_loss: 0.1207 - val_accuracy: 0.9810\n",
      "Epoch 32/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2613 - accuracy: 0.87 - ETA: 0s - loss: 0.2775 - accuracy: 0.92 - ETA: 0s - loss: 0.2519 - accuracy: 0.92 - ETA: 0s - loss: 0.2674 - accuracy: 0.92 - ETA: 0s - loss: 0.2674 - accuracy: 0.92 - ETA: 0s - loss: 0.2652 - accuracy: 0.92 - ETA: 0s - loss: 0.2714 - accuracy: 0.92 - ETA: 0s - loss: 0.2759 - accuracy: 0.92 - 1s 35us/sample - loss: 0.2773 - accuracy: 0.9209 - val_loss: 0.1611 - val_accuracy: 0.9687\n",
      "Epoch 33/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1555 - accuracy: 0.87 - ETA: 0s - loss: 0.2549 - accuracy: 0.92 - ETA: 0s - loss: 0.2608 - accuracy: 0.92 - ETA: 0s - loss: 0.2712 - accuracy: 0.91 - ETA: 0s - loss: 0.2753 - accuracy: 0.91 - ETA: 0s - loss: 0.2741 - accuracy: 0.92 - ETA: 0s - loss: 0.2707 - accuracy: 0.92 - ETA: 0s - loss: 0.2746 - accuracy: 0.92 - 1s 35us/sample - loss: 0.2703 - accuracy: 0.9219 - val_loss: 0.1088 - val_accuracy: 0.9760\n",
      "Epoch 34/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.3329 - accuracy: 0.84 - ETA: 0s - loss: 0.2824 - accuracy: 0.92 - ETA: 0s - loss: 0.2936 - accuracy: 0.91 - ETA: 0s - loss: 0.2876 - accuracy: 0.91 - ETA: 0s - loss: 0.2785 - accuracy: 0.91 - ETA: 0s - loss: 0.2770 - accuracy: 0.91 - ETA: 0s - loss: 0.2776 - accuracy: 0.91 - ETA: 0s - loss: 0.2810 - accuracy: 0.91 - 1s 35us/sample - loss: 0.2782 - accuracy: 0.9176 - val_loss: 0.1177 - val_accuracy: 0.9832\n",
      "Epoch 35/100\n",
      "14318/14318 [==============================] - ETA: 6s - loss: 0.2883 - accuracy: 0.87 - ETA: 0s - loss: 0.2554 - accuracy: 0.93 - ETA: 0s - loss: 0.2577 - accuracy: 0.92 - ETA: 0s - loss: 0.2396 - accuracy: 0.93 - ETA: 0s - loss: 0.2579 - accuracy: 0.92 - ETA: 0s - loss: 0.2592 - accuracy: 0.92 - ETA: 0s - loss: 0.2608 - accuracy: 0.92 - ETA: 0s - loss: 0.2661 - accuracy: 0.92 - ETA: 0s - loss: 0.2643 - accuracy: 0.92 - 1s 37us/sample - loss: 0.2658 - accuracy: 0.9251 - val_loss: 0.1315 - val_accuracy: 0.9721\n",
      "Epoch 36/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.6062 - accuracy: 0.81 - ETA: 0s - loss: 0.2927 - accuracy: 0.91 - ETA: 0s - loss: 0.2792 - accuracy: 0.92 - ETA: 0s - loss: 0.2817 - accuracy: 0.92 - ETA: 0s - loss: 0.2735 - accuracy: 0.92 - ETA: 0s - loss: 0.2643 - accuracy: 0.92 - ETA: 0s - loss: 0.2652 - accuracy: 0.92 - ETA: 0s - loss: 0.2632 - accuracy: 0.92 - ETA: 0s - loss: 0.2692 - accuracy: 0.92 - 1s 35us/sample - loss: 0.2652 - accuracy: 0.9264 - val_loss: 0.1077 - val_accuracy: 0.9804\n",
      "Epoch 37/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1994 - accuracy: 0.96 - ETA: 0s - loss: 0.2252 - accuracy: 0.92 - ETA: 0s - loss: 0.2662 - accuracy: 0.91 - ETA: 0s - loss: 0.2721 - accuracy: 0.91 - ETA: 0s - loss: 0.2704 - accuracy: 0.91 - ETA: 0s - loss: 0.2735 - accuracy: 0.91 - ETA: 0s - loss: 0.2738 - accuracy: 0.91 - ETA: 0s - loss: 0.2764 - accuracy: 0.91 - ETA: 0s - loss: 0.2718 - accuracy: 0.92 - 1s 36us/sample - loss: 0.2722 - accuracy: 0.9203 - val_loss: 0.1089 - val_accuracy: 0.9788\n",
      "Epoch 38/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1037 - accuracy: 0.93 - ETA: 0s - loss: 0.2538 - accuracy: 0.93 - ETA: 0s - loss: 0.2529 - accuracy: 0.93 - ETA: 0s - loss: 0.2669 - accuracy: 0.93 - ETA: 0s - loss: 0.2669 - accuracy: 0.93 - ETA: 0s - loss: 0.2594 - accuracy: 0.93 - ETA: 0s - loss: 0.2540 - accuracy: 0.93 - ETA: 0s - loss: 0.2673 - accuracy: 0.93 - ETA: 0s - loss: 0.2672 - accuracy: 0.93 - ETA: 0s - loss: 0.2692 - accuracy: 0.93 - 1s 37us/sample - loss: 0.2693 - accuracy: 0.9308 - val_loss: 0.1446 - val_accuracy: 0.9749\n",
      "Epoch 39/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2007 - accuracy: 0.90 - ETA: 0s - loss: 0.2886 - accuracy: 0.91 - ETA: 0s - loss: 0.2695 - accuracy: 0.92 - ETA: 0s - loss: 0.2757 - accuracy: 0.92 - ETA: 0s - loss: 0.2686 - accuracy: 0.92 - ETA: 0s - loss: 0.2762 - accuracy: 0.92 - ETA: 0s - loss: 0.2696 - accuracy: 0.92 - ETA: 0s - loss: 0.2737 - accuracy: 0.92 - ETA: 0s - loss: 0.2687 - accuracy: 0.92 - 1s 36us/sample - loss: 0.2660 - accuracy: 0.9278 - val_loss: 0.1073 - val_accuracy: 0.9777\n",
      "Epoch 40/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.9048 - accuracy: 0.90 - ETA: 0s - loss: 0.2165 - accuracy: 0.94 - ETA: 0s - loss: 0.2414 - accuracy: 0.94 - ETA: 0s - loss: 0.2524 - accuracy: 0.93 - ETA: 0s - loss: 0.2583 - accuracy: 0.93 - ETA: 0s - loss: 0.2732 - accuracy: 0.92 - ETA: 0s - loss: 0.2736 - accuracy: 0.92 - ETA: 0s - loss: 0.2653 - accuracy: 0.93 - ETA: 0s - loss: 0.2688 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2686 - accuracy: 0.9306 - val_loss: 0.1457 - val_accuracy: 0.9760\n",
      "Epoch 41/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1425 - accuracy: 0.90 - ETA: 0s - loss: 0.2507 - accuracy: 0.93 - ETA: 0s - loss: 0.2531 - accuracy: 0.93 - ETA: 0s - loss: 0.2626 - accuracy: 0.93 - ETA: 0s - loss: 0.2703 - accuracy: 0.93 - ETA: 0s - loss: 0.2758 - accuracy: 0.93 - ETA: 0s - loss: 0.2668 - accuracy: 0.93 - ETA: 0s - loss: 0.2778 - accuracy: 0.93 - ETA: 0s - loss: 0.2749 - accuracy: 0.93 - ETA: 0s - loss: 0.2735 - accuracy: 0.93 - 1s 37us/sample - loss: 0.2736 - accuracy: 0.9339 - val_loss: 0.1283 - val_accuracy: 0.9654\n",
      "Epoch 42/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.4297 - accuracy: 0.84 - ETA: 0s - loss: 0.2627 - accuracy: 0.91 - ETA: 0s - loss: 0.2634 - accuracy: 0.92 - ETA: 0s - loss: 0.2672 - accuracy: 0.92 - ETA: 0s - loss: 0.2695 - accuracy: 0.92 - ETA: 0s - loss: 0.2550 - accuracy: 0.92 - ETA: 0s - loss: 0.2627 - accuracy: 0.92 - ETA: 0s - loss: 0.2620 - accuracy: 0.92 - ETA: 0s - loss: 0.2697 - accuracy: 0.92 - ETA: 0s - loss: 0.2697 - accuracy: 0.92 - 1s 37us/sample - loss: 0.2669 - accuracy: 0.9272 - val_loss: 0.1398 - val_accuracy: 0.9804\n",
      "Epoch 43/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.4444 - accuracy: 0.90 - ETA: 0s - loss: 0.2400 - accuracy: 0.94 - ETA: 0s - loss: 0.2369 - accuracy: 0.94 - ETA: 0s - loss: 0.2420 - accuracy: 0.94 - ETA: 0s - loss: 0.2385 - accuracy: 0.93 - ETA: 0s - loss: 0.2428 - accuracy: 0.93 - ETA: 0s - loss: 0.2582 - accuracy: 0.93 - ETA: 0s - loss: 0.2636 - accuracy: 0.93 - 1s 35us/sample - loss: 0.2633 - accuracy: 0.9339 - val_loss: 0.1217 - val_accuracy: 0.9771\n",
      "Epoch 44/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2142 - accuracy: 0.90 - ETA: 0s - loss: 0.2579 - accuracy: 0.92 - ETA: 0s - loss: 0.2555 - accuracy: 0.93 - ETA: 0s - loss: 0.2721 - accuracy: 0.93 - ETA: 0s - loss: 0.2740 - accuracy: 0.92 - ETA: 0s - loss: 0.2749 - accuracy: 0.92 - ETA: 0s - loss: 0.2691 - accuracy: 0.92 - ETA: 0s - loss: 0.2655 - accuracy: 0.92 - 1s 35us/sample - loss: 0.2693 - accuracy: 0.9282 - val_loss: 0.1479 - val_accuracy: 0.9765\n",
      "Epoch 45/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2126 - accuracy: 0.93 - ETA: 0s - loss: 0.2532 - accuracy: 0.94 - ETA: 0s - loss: 0.2515 - accuracy: 0.93 - ETA: 0s - loss: 0.2550 - accuracy: 0.93 - ETA: 0s - loss: 0.2645 - accuracy: 0.93 - ETA: 0s - loss: 0.2654 - accuracy: 0.93 - ETA: 0s - loss: 0.2715 - accuracy: 0.93 - ETA: 0s - loss: 0.2699 - accuracy: 0.93 - ETA: 0s - loss: 0.2726 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2702 - accuracy: 0.9304 - val_loss: 0.1533 - val_accuracy: 0.9821\n",
      "Epoch 46/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1886 - accuracy: 0.93 - ETA: 0s - loss: 0.2226 - accuracy: 0.94 - ETA: 0s - loss: 0.2437 - accuracy: 0.94 - ETA: 0s - loss: 0.2493 - accuracy: 0.93 - ETA: 0s - loss: 0.2622 - accuracy: 0.93 - ETA: 0s - loss: 0.2655 - accuracy: 0.93 - ETA: 0s - loss: 0.2694 - accuracy: 0.93 - ETA: 0s - loss: 0.2663 - accuracy: 0.93 - 0s 35us/sample - loss: 0.2669 - accuracy: 0.9334 - val_loss: 0.1394 - val_accuracy: 0.9816\n",
      "Epoch 47/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1125 - accuracy: 0.93 - ETA: 0s - loss: 0.2578 - accuracy: 0.93 - ETA: 0s - loss: 0.2695 - accuracy: 0.93 - ETA: 0s - loss: 0.2671 - accuracy: 0.93 - ETA: 0s - loss: 0.2710 - accuracy: 0.93 - ETA: 0s - loss: 0.2633 - accuracy: 0.93 - ETA: 0s - loss: 0.2609 - accuracy: 0.93 - ETA: 0s - loss: 0.2659 - accuracy: 0.93 - ETA: 0s - loss: 0.2695 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2632 - accuracy: 0.9348 - val_loss: 0.1228 - val_accuracy: 0.9838\n",
      "Epoch 48/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.9186 - accuracy: 0.93 - ETA: 0s - loss: 0.2926 - accuracy: 0.93 - ETA: 0s - loss: 0.2751 - accuracy: 0.92 - ETA: 0s - loss: 0.2779 - accuracy: 0.92 - ETA: 0s - loss: 0.2638 - accuracy: 0.92 - ETA: 0s - loss: 0.2611 - accuracy: 0.92 - ETA: 0s - loss: 0.2602 - accuracy: 0.92 - ETA: 0s - loss: 0.2660 - accuracy: 0.92 - 1s 35us/sample - loss: 0.2639 - accuracy: 0.9286 - val_loss: 0.1175 - val_accuracy: 0.9838\n",
      "Epoch 49/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1450 - accuracy: 0.96 - ETA: 0s - loss: 0.2408 - accuracy: 0.93 - ETA: 0s - loss: 0.2536 - accuracy: 0.94 - ETA: 0s - loss: 0.2569 - accuracy: 0.93 - ETA: 0s - loss: 0.2523 - accuracy: 0.93 - ETA: 0s - loss: 0.2525 - accuracy: 0.94 - ETA: 0s - loss: 0.2540 - accuracy: 0.94 - ETA: 0s - loss: 0.2548 - accuracy: 0.94 - ETA: 0s - loss: 0.2549 - accuracy: 0.93 - 1s 35us/sample - loss: 0.2575 - accuracy: 0.9392 - val_loss: 0.1502 - val_accuracy: 0.9827\n",
      "Epoch 50/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0579 - accuracy: 1.00 - ETA: 0s - loss: 0.2313 - accuracy: 0.94 - ETA: 0s - loss: 0.2512 - accuracy: 0.94 - ETA: 0s - loss: 0.2431 - accuracy: 0.94 - ETA: 0s - loss: 0.2533 - accuracy: 0.93 - ETA: 0s - loss: 0.2558 - accuracy: 0.93 - ETA: 0s - loss: 0.2643 - accuracy: 0.93 - ETA: 0s - loss: 0.2642 - accuracy: 0.93 - 0s 35us/sample - loss: 0.2637 - accuracy: 0.9328 - val_loss: 0.1314 - val_accuracy: 0.9810\n",
      "Epoch 51/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1489 - accuracy: 0.87 - ETA: 0s - loss: 0.3129 - accuracy: 0.92 - ETA: 0s - loss: 0.2617 - accuracy: 0.93 - ETA: 0s - loss: 0.2652 - accuracy: 0.93 - ETA: 0s - loss: 0.2721 - accuracy: 0.93 - ETA: 0s - loss: 0.2649 - accuracy: 0.93 - ETA: 0s - loss: 0.2585 - accuracy: 0.93 - ETA: 0s - loss: 0.2653 - accuracy: 0.93 - ETA: 0s - loss: 0.2670 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2667 - accuracy: 0.9326 - val_loss: 0.1242 - val_accuracy: 0.9788\n",
      "Epoch 52/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.4368 - accuracy: 0.93 - ETA: 0s - loss: 0.2754 - accuracy: 0.93 - ETA: 0s - loss: 0.2652 - accuracy: 0.93 - ETA: 0s - loss: 0.2627 - accuracy: 0.93 - ETA: 0s - loss: 0.2678 - accuracy: 0.93 - ETA: 0s - loss: 0.2661 - accuracy: 0.93 - ETA: 0s - loss: 0.2708 - accuracy: 0.93 - ETA: 0s - loss: 0.2697 - accuracy: 0.93 - 0s 35us/sample - loss: 0.2650 - accuracy: 0.9343 - val_loss: 0.1157 - val_accuracy: 0.9777\n",
      "Epoch 53/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0998 - accuracy: 0.93 - ETA: 0s - loss: 0.2330 - accuracy: 0.93 - ETA: 0s - loss: 0.2311 - accuracy: 0.93 - ETA: 0s - loss: 0.2335 - accuracy: 0.93 - ETA: 0s - loss: 0.2336 - accuracy: 0.93 - ETA: 0s - loss: 0.2319 - accuracy: 0.93 - ETA: 0s - loss: 0.2495 - accuracy: 0.93 - ETA: 0s - loss: 0.2556 - accuracy: 0.93 - ETA: 0s - loss: 0.2610 - accuracy: 0.93 - ETA: 0s - loss: 0.2612 - accuracy: 0.93 - 1s 39us/sample - loss: 0.2621 - accuracy: 0.9309 - val_loss: 0.1200 - val_accuracy: 0.9793\n",
      "Epoch 54/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1021 - accuracy: 0.96 - ETA: 0s - loss: 0.2520 - accuracy: 0.93 - ETA: 0s - loss: 0.2439 - accuracy: 0.92 - ETA: 0s - loss: 0.2661 - accuracy: 0.92 - ETA: 0s - loss: 0.2568 - accuracy: 0.92 - ETA: 0s - loss: 0.2651 - accuracy: 0.92 - ETA: 0s - loss: 0.2637 - accuracy: 0.92 - ETA: 0s - loss: 0.2570 - accuracy: 0.92 - ETA: 0s - loss: 0.2580 - accuracy: 0.92 - 1s 38us/sample - loss: 0.2581 - accuracy: 0.9290 - val_loss: 0.1314 - val_accuracy: 0.9804\n",
      "Epoch 55/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2002 - accuracy: 0.96 - ETA: 0s - loss: 0.2462 - accuracy: 0.93 - ETA: 0s - loss: 0.2498 - accuracy: 0.94 - ETA: 0s - loss: 0.2543 - accuracy: 0.93 - ETA: 0s - loss: 0.2593 - accuracy: 0.93 - ETA: 0s - loss: 0.2567 - accuracy: 0.93 - ETA: 0s - loss: 0.2636 - accuracy: 0.93 - ETA: 0s - loss: 0.2662 - accuracy: 0.92 - ETA: 0s - loss: 0.2658 - accuracy: 0.92 - 1s 36us/sample - loss: 0.2660 - accuracy: 0.9286 - val_loss: 0.1368 - val_accuracy: 0.9816\n",
      "Epoch 56/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.4160 - accuracy: 0.90 - ETA: 0s - loss: 0.2811 - accuracy: 0.93 - ETA: 0s - loss: 0.2531 - accuracy: 0.93 - ETA: 0s - loss: 0.2459 - accuracy: 0.93 - ETA: 0s - loss: 0.2529 - accuracy: 0.92 - ETA: 0s - loss: 0.2609 - accuracy: 0.92 - ETA: 0s - loss: 0.2491 - accuracy: 0.93 - ETA: 0s - loss: 0.2492 - accuracy: 0.93 - ETA: 0s - loss: 0.2543 - accuracy: 0.93 - ETA: 0s - loss: 0.2589 - accuracy: 0.92 - 1s 37us/sample - loss: 0.2603 - accuracy: 0.9293 - val_loss: 0.1416 - val_accuracy: 0.9709\n",
      "Epoch 57/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1789 - accuracy: 0.87 - ETA: 0s - loss: 0.2785 - accuracy: 0.91 - ETA: 0s - loss: 0.3095 - accuracy: 0.91 - ETA: 0s - loss: 0.3038 - accuracy: 0.91 - ETA: 0s - loss: 0.2936 - accuracy: 0.91 - ETA: 0s - loss: 0.2796 - accuracy: 0.92 - ETA: 0s - loss: 0.2732 - accuracy: 0.92 - ETA: 0s - loss: 0.2650 - accuracy: 0.92 - ETA: 0s - loss: 0.2676 - accuracy: 0.93 - 1s 35us/sample - loss: 0.2719 - accuracy: 0.9300 - val_loss: 0.1588 - val_accuracy: 0.9793\n",
      "Epoch 58/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1061 - accuracy: 0.96 - ETA: 0s - loss: 0.2050 - accuracy: 0.94 - ETA: 0s - loss: 0.2478 - accuracy: 0.93 - ETA: 0s - loss: 0.2524 - accuracy: 0.92 - ETA: 0s - loss: 0.2495 - accuracy: 0.93 - ETA: 0s - loss: 0.2689 - accuracy: 0.93 - ETA: 0s - loss: 0.2666 - accuracy: 0.93 - ETA: 0s - loss: 0.2643 - accuracy: 0.93 - ETA: 0s - loss: 0.2657 - accuracy: 0.93 - ETA: 0s - loss: 0.2674 - accuracy: 0.93 - 1s 37us/sample - loss: 0.2678 - accuracy: 0.9304 - val_loss: 0.1335 - val_accuracy: 0.9821\n",
      "Epoch 59/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0836 - accuracy: 1.00 - ETA: 0s - loss: 0.2326 - accuracy: 0.92 - ETA: 0s - loss: 0.2520 - accuracy: 0.92 - ETA: 0s - loss: 0.2626 - accuracy: 0.92 - ETA: 0s - loss: 0.2674 - accuracy: 0.92 - ETA: 0s - loss: 0.2569 - accuracy: 0.93 - ETA: 0s - loss: 0.2649 - accuracy: 0.93 - ETA: 0s - loss: 0.2628 - accuracy: 0.92 - ETA: 0s - loss: 0.2641 - accuracy: 0.92 - 0s 35us/sample - loss: 0.2660 - accuracy: 0.9296 - val_loss: 0.1461 - val_accuracy: 0.9799\n",
      "Epoch 60/100\n",
      "14318/14318 [==============================] - ETA: 6s - loss: 0.1717 - accuracy: 0.96 - ETA: 0s - loss: 0.2478 - accuracy: 0.94 - ETA: 0s - loss: 0.2388 - accuracy: 0.94 - ETA: 0s - loss: 0.2530 - accuracy: 0.94 - ETA: 0s - loss: 0.2605 - accuracy: 0.93 - ETA: 0s - loss: 0.2599 - accuracy: 0.93 - ETA: 0s - loss: 0.2548 - accuracy: 0.93 - ETA: 0s - loss: 0.2547 - accuracy: 0.93 - ETA: 0s - loss: 0.2581 - accuracy: 0.93 - 1s 37us/sample - loss: 0.2586 - accuracy: 0.9359 - val_loss: 0.1319 - val_accuracy: 0.9782\n",
      "Epoch 61/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1463 - accuracy: 0.90 - ETA: 0s - loss: 0.2326 - accuracy: 0.93 - ETA: 0s - loss: 0.2541 - accuracy: 0.93 - ETA: 0s - loss: 0.2625 - accuracy: 0.93 - ETA: 0s - loss: 0.2645 - accuracy: 0.93 - ETA: 0s - loss: 0.2696 - accuracy: 0.92 - ETA: 0s - loss: 0.2692 - accuracy: 0.92 - ETA: 0s - loss: 0.2704 - accuracy: 0.92 - ETA: 0s - loss: 0.2670 - accuracy: 0.93 - ETA: 0s - loss: 0.2603 - accuracy: 0.93 - 1s 37us/sample - loss: 0.2615 - accuracy: 0.9337 - val_loss: 0.1200 - val_accuracy: 0.9810\n",
      "Epoch 62/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0985 - accuracy: 0.96 - ETA: 0s - loss: 0.2562 - accuracy: 0.93 - ETA: 0s - loss: 0.2469 - accuracy: 0.93 - ETA: 0s - loss: 0.2599 - accuracy: 0.93 - ETA: 0s - loss: 0.2488 - accuracy: 0.93 - ETA: 0s - loss: 0.2445 - accuracy: 0.93 - ETA: 0s - loss: 0.2454 - accuracy: 0.93 - ETA: 0s - loss: 0.2521 - accuracy: 0.93 - ETA: 0s - loss: 0.2544 - accuracy: 0.93 - ETA: 0s - loss: 0.2533 - accuracy: 0.93 - 1s 39us/sample - loss: 0.2565 - accuracy: 0.9326 - val_loss: 0.1326 - val_accuracy: 0.9765\n",
      "Epoch 63/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1025 - accuracy: 0.96 - ETA: 0s - loss: 0.2278 - accuracy: 0.93 - ETA: 0s - loss: 0.2265 - accuracy: 0.93 - ETA: 0s - loss: 0.2307 - accuracy: 0.93 - ETA: 0s - loss: 0.2485 - accuracy: 0.94 - ETA: 0s - loss: 0.2630 - accuracy: 0.93 - ETA: 0s - loss: 0.2648 - accuracy: 0.93 - ETA: 0s - loss: 0.2675 - accuracy: 0.93 - ETA: 0s - loss: 0.2629 - accuracy: 0.93 - ETA: 0s - loss: 0.2616 - accuracy: 0.93 - 1s 37us/sample - loss: 0.2617 - accuracy: 0.9366 - val_loss: 0.1186 - val_accuracy: 0.9804\n",
      "Epoch 64/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1765 - accuracy: 0.93 - ETA: 0s - loss: 0.2639 - accuracy: 0.94 - ETA: 0s - loss: 0.2739 - accuracy: 0.93 - ETA: 0s - loss: 0.2852 - accuracy: 0.93 - ETA: 0s - loss: 0.2820 - accuracy: 0.93 - ETA: 0s - loss: 0.2776 - accuracy: 0.93 - ETA: 0s - loss: 0.2706 - accuracy: 0.93 - ETA: 0s - loss: 0.2648 - accuracy: 0.93 - ETA: 0s - loss: 0.2657 - accuracy: 0.93 - 1s 35us/sample - loss: 0.2631 - accuracy: 0.9348 - val_loss: 0.1099 - val_accuracy: 0.9832\n",
      "Epoch 65/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1286 - accuracy: 1.00 - ETA: 0s - loss: 0.2794 - accuracy: 0.92 - ETA: 0s - loss: 0.2720 - accuracy: 0.92 - ETA: 0s - loss: 0.2818 - accuracy: 0.92 - ETA: 0s - loss: 0.2822 - accuracy: 0.92 - ETA: 0s - loss: 0.2738 - accuracy: 0.92 - ETA: 0s - loss: 0.2662 - accuracy: 0.93 - ETA: 0s - loss: 0.2654 - accuracy: 0.93 - ETA: 0s - loss: 0.2614 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2611 - accuracy: 0.9341 - val_loss: 0.1230 - val_accuracy: 0.9799\n",
      "Epoch 66/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.9808 - accuracy: 0.84 - ETA: 0s - loss: 0.2860 - accuracy: 0.92 - ETA: 0s - loss: 0.2713 - accuracy: 0.92 - ETA: 0s - loss: 0.2792 - accuracy: 0.92 - ETA: 0s - loss: 0.2750 - accuracy: 0.92 - ETA: 0s - loss: 0.2631 - accuracy: 0.92 - ETA: 0s - loss: 0.2606 - accuracy: 0.93 - ETA: 0s - loss: 0.2642 - accuracy: 0.93 - ETA: 0s - loss: 0.2636 - accuracy: 0.93 - ETA: 0s - loss: 0.2618 - accuracy: 0.93 - 1s 37us/sample - loss: 0.2605 - accuracy: 0.9316 - val_loss: 0.1047 - val_accuracy: 0.9838\n",
      "Epoch 67/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.5109 - accuracy: 0.93 - ETA: 0s - loss: 0.3297 - accuracy: 0.91 - ETA: 0s - loss: 0.2964 - accuracy: 0.92 - ETA: 0s - loss: 0.2863 - accuracy: 0.92 - ETA: 0s - loss: 0.2788 - accuracy: 0.92 - ETA: 0s - loss: 0.2681 - accuracy: 0.92 - ETA: 0s - loss: 0.2693 - accuracy: 0.92 - ETA: 0s - loss: 0.2682 - accuracy: 0.93 - ETA: 0s - loss: 0.2652 - accuracy: 0.93 - ETA: 0s - loss: 0.2609 - accuracy: 0.93 - 1s 37us/sample - loss: 0.2571 - accuracy: 0.9333 - val_loss: 0.0904 - val_accuracy: 0.9810\n",
      "Epoch 68/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0778 - accuracy: 1.00 - ETA: 0s - loss: 0.3020 - accuracy: 0.93 - ETA: 0s - loss: 0.2741 - accuracy: 0.93 - ETA: 0s - loss: 0.2641 - accuracy: 0.93 - ETA: 0s - loss: 0.2566 - accuracy: 0.93 - ETA: 0s - loss: 0.2469 - accuracy: 0.93 - ETA: 0s - loss: 0.2431 - accuracy: 0.93 - ETA: 0s - loss: 0.2518 - accuracy: 0.93 - ETA: 0s - loss: 0.2495 - accuracy: 0.93 - ETA: 0s - loss: 0.2528 - accuracy: 0.93 - 1s 38us/sample - loss: 0.2569 - accuracy: 0.9327 - val_loss: 0.1402 - val_accuracy: 0.9827\n",
      "Epoch 69/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.3098 - accuracy: 0.90 - ETA: 0s - loss: 0.2828 - accuracy: 0.92 - ETA: 0s - loss: 0.2651 - accuracy: 0.92 - ETA: 0s - loss: 0.2673 - accuracy: 0.93 - ETA: 0s - loss: 0.2687 - accuracy: 0.93 - ETA: 0s - loss: 0.2537 - accuracy: 0.93 - ETA: 0s - loss: 0.2559 - accuracy: 0.93 - ETA: 0s - loss: 0.2564 - accuracy: 0.93 - ETA: 0s - loss: 0.2580 - accuracy: 0.93 - 1s 35us/sample - loss: 0.2585 - accuracy: 0.9309 - val_loss: 0.1332 - val_accuracy: 0.9799\n",
      "Epoch 70/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1484 - accuracy: 0.93 - ETA: 0s - loss: 0.2684 - accuracy: 0.91 - ETA: 0s - loss: 0.2527 - accuracy: 0.93 - ETA: 0s - loss: 0.2594 - accuracy: 0.93 - ETA: 0s - loss: 0.2591 - accuracy: 0.93 - ETA: 0s - loss: 0.2604 - accuracy: 0.93 - ETA: 0s - loss: 0.2647 - accuracy: 0.92 - ETA: 0s - loss: 0.2616 - accuracy: 0.92 - ETA: 0s - loss: 0.2635 - accuracy: 0.92 - 1s 36us/sample - loss: 0.2639 - accuracy: 0.9258 - val_loss: 0.1028 - val_accuracy: 0.9810\n",
      "Epoch 71/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1102 - accuracy: 0.93 - ETA: 0s - loss: 0.2431 - accuracy: 0.92 - ETA: 0s - loss: 0.2559 - accuracy: 0.93 - ETA: 0s - loss: 0.2445 - accuracy: 0.93 - ETA: 0s - loss: 0.2499 - accuracy: 0.93 - ETA: 0s - loss: 0.2524 - accuracy: 0.93 - ETA: 0s - loss: 0.2509 - accuracy: 0.93 - ETA: 0s - loss: 0.2493 - accuracy: 0.93 - 0s 35us/sample - loss: 0.2621 - accuracy: 0.9327 - val_loss: 0.1632 - val_accuracy: 0.9754\n",
      "Epoch 72/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1052 - accuracy: 1.00 - ETA: 0s - loss: 0.2680 - accuracy: 0.92 - ETA: 0s - loss: 0.2438 - accuracy: 0.93 - ETA: 0s - loss: 0.2458 - accuracy: 0.93 - ETA: 0s - loss: 0.2584 - accuracy: 0.93 - ETA: 0s - loss: 0.2628 - accuracy: 0.93 - ETA: 0s - loss: 0.2665 - accuracy: 0.92 - ETA: 0s - loss: 0.2603 - accuracy: 0.93 - 0s 35us/sample - loss: 0.2561 - accuracy: 0.9309 - val_loss: 0.1032 - val_accuracy: 0.9793\n",
      "Epoch 73/100\n",
      "14318/14318 [==============================] - ETA: 6s - loss: 0.0511 - accuracy: 0.96 - ETA: 0s - loss: 0.2455 - accuracy: 0.92 - ETA: 0s - loss: 0.2763 - accuracy: 0.91 - ETA: 0s - loss: 0.2492 - accuracy: 0.92 - ETA: 0s - loss: 0.2512 - accuracy: 0.92 - ETA: 0s - loss: 0.2503 - accuracy: 0.93 - ETA: 0s - loss: 0.2516 - accuracy: 0.93 - ETA: 0s - loss: 0.2559 - accuracy: 0.93 - 1s 35us/sample - loss: 0.2578 - accuracy: 0.9309 - val_loss: 0.1255 - val_accuracy: 0.9821\n",
      "Epoch 74/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2155 - accuracy: 0.90 - ETA: 0s - loss: 0.3032 - accuracy: 0.92 - ETA: 0s - loss: 0.2976 - accuracy: 0.92 - ETA: 0s - loss: 0.2823 - accuracy: 0.92 - ETA: 0s - loss: 0.2765 - accuracy: 0.92 - ETA: 0s - loss: 0.2686 - accuracy: 0.92 - ETA: 0s - loss: 0.2549 - accuracy: 0.92 - ETA: 0s - loss: 0.2564 - accuracy: 0.92 - ETA: 0s - loss: 0.2524 - accuracy: 0.92 - 1s 36us/sample - loss: 0.2547 - accuracy: 0.9276 - val_loss: 0.1153 - val_accuracy: 0.9821\n",
      "Epoch 75/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1465 - accuracy: 0.93 - ETA: 0s - loss: 0.2659 - accuracy: 0.93 - ETA: 0s - loss: 0.2538 - accuracy: 0.93 - ETA: 0s - loss: 0.2749 - accuracy: 0.92 - ETA: 0s - loss: 0.2742 - accuracy: 0.92 - ETA: 0s - loss: 0.2770 - accuracy: 0.92 - ETA: 0s - loss: 0.2689 - accuracy: 0.92 - ETA: 0s - loss: 0.2620 - accuracy: 0.92 - 0s 35us/sample - loss: 0.2644 - accuracy: 0.9281 - val_loss: 0.1234 - val_accuracy: 0.9715\n",
      "Epoch 76/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1368 - accuracy: 0.93 - ETA: 0s - loss: 0.2569 - accuracy: 0.92 - ETA: 0s - loss: 0.2510 - accuracy: 0.92 - ETA: 0s - loss: 0.2566 - accuracy: 0.92 - ETA: 0s - loss: 0.2580 - accuracy: 0.92 - ETA: 0s - loss: 0.2593 - accuracy: 0.92 - ETA: 0s - loss: 0.2601 - accuracy: 0.93 - ETA: 0s - loss: 0.2646 - accuracy: 0.93 - 1s 35us/sample - loss: 0.2633 - accuracy: 0.9316 - val_loss: 0.1270 - val_accuracy: 0.9827\n",
      "Epoch 77/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0848 - accuracy: 0.96 - ETA: 0s - loss: 0.2410 - accuracy: 0.93 - ETA: 0s - loss: 0.2595 - accuracy: 0.93 - ETA: 0s - loss: 0.2592 - accuracy: 0.93 - ETA: 0s - loss: 0.2580 - accuracy: 0.93 - ETA: 0s - loss: 0.2646 - accuracy: 0.93 - ETA: 0s - loss: 0.2592 - accuracy: 0.93 - ETA: 0s - loss: 0.2593 - accuracy: 0.93 - 1s 35us/sample - loss: 0.2576 - accuracy: 0.9306 - val_loss: 0.1057 - val_accuracy: 0.9804\n",
      "Epoch 78/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2287 - accuracy: 0.90 - ETA: 0s - loss: 0.2831 - accuracy: 0.93 - ETA: 0s - loss: 0.2618 - accuracy: 0.93 - ETA: 0s - loss: 0.2593 - accuracy: 0.93 - ETA: 0s - loss: 0.2574 - accuracy: 0.93 - ETA: 0s - loss: 0.2668 - accuracy: 0.92 - ETA: 0s - loss: 0.2688 - accuracy: 0.92 - ETA: 0s - loss: 0.2679 - accuracy: 0.92 - ETA: 0s - loss: 0.2593 - accuracy: 0.92 - 1s 36us/sample - loss: 0.2641 - accuracy: 0.9265 - val_loss: 0.0842 - val_accuracy: 0.9838\n",
      "Epoch 79/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1014 - accuracy: 0.93 - ETA: 0s - loss: 0.2562 - accuracy: 0.93 - ETA: 0s - loss: 0.2667 - accuracy: 0.92 - ETA: 0s - loss: 0.2578 - accuracy: 0.93 - ETA: 0s - loss: 0.2651 - accuracy: 0.93 - ETA: 0s - loss: 0.2578 - accuracy: 0.93 - ETA: 0s - loss: 0.2597 - accuracy: 0.93 - ETA: 0s - loss: 0.2631 - accuracy: 0.93 - 0s 34us/sample - loss: 0.2628 - accuracy: 0.9347 - val_loss: 0.1430 - val_accuracy: 0.9821\n",
      "Epoch 80/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0996 - accuracy: 0.96 - ETA: 0s - loss: 0.2862 - accuracy: 0.92 - ETA: 0s - loss: 0.2420 - accuracy: 0.93 - ETA: 0s - loss: 0.2468 - accuracy: 0.93 - ETA: 0s - loss: 0.2437 - accuracy: 0.93 - ETA: 0s - loss: 0.2478 - accuracy: 0.93 - ETA: 0s - loss: 0.2436 - accuracy: 0.93 - ETA: 0s - loss: 0.2439 - accuracy: 0.93 - ETA: 0s - loss: 0.2506 - accuracy: 0.93 - ETA: 0s - loss: 0.2559 - accuracy: 0.93 - 1s 37us/sample - loss: 0.2561 - accuracy: 0.9322 - val_loss: 0.1346 - val_accuracy: 0.9827\n",
      "Epoch 81/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1636 - accuracy: 0.96 - ETA: 0s - loss: 0.2948 - accuracy: 0.93 - ETA: 0s - loss: 0.2849 - accuracy: 0.93 - ETA: 0s - loss: 0.2694 - accuracy: 0.93 - ETA: 0s - loss: 0.2632 - accuracy: 0.93 - ETA: 0s - loss: 0.2569 - accuracy: 0.93 - ETA: 0s - loss: 0.2549 - accuracy: 0.93 - ETA: 0s - loss: 0.2536 - accuracy: 0.93 - 0s 34us/sample - loss: 0.2533 - accuracy: 0.9296 - val_loss: 0.1270 - val_accuracy: 0.9804\n",
      "Epoch 82/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2735 - accuracy: 0.93 - ETA: 0s - loss: 0.3165 - accuracy: 0.93 - ETA: 0s - loss: 0.2793 - accuracy: 0.93 - ETA: 0s - loss: 0.2744 - accuracy: 0.93 - ETA: 0s - loss: 0.2746 - accuracy: 0.93 - ETA: 0s - loss: 0.2601 - accuracy: 0.93 - ETA: 0s - loss: 0.2557 - accuracy: 0.93 - ETA: 0s - loss: 0.2587 - accuracy: 0.93 - ETA: 0s - loss: 0.2565 - accuracy: 0.93 - ETA: 0s - loss: 0.2543 - accuracy: 0.93 - 1s 38us/sample - loss: 0.2532 - accuracy: 0.9367 - val_loss: 0.1290 - val_accuracy: 0.9782\n",
      "Epoch 83/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0998 - accuracy: 0.96 - ETA: 0s - loss: 0.2216 - accuracy: 0.93 - ETA: 0s - loss: 0.2653 - accuracy: 0.92 - ETA: 0s - loss: 0.2651 - accuracy: 0.92 - ETA: 0s - loss: 0.2555 - accuracy: 0.92 - ETA: 0s - loss: 0.2523 - accuracy: 0.93 - ETA: 0s - loss: 0.2532 - accuracy: 0.93 - ETA: 0s - loss: 0.2465 - accuracy: 0.93 - ETA: 0s - loss: 0.2521 - accuracy: 0.93 - ETA: 0s - loss: 0.2584 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2586 - accuracy: 0.9310 - val_loss: 0.1539 - val_accuracy: 0.9788\n",
      "Epoch 84/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1319 - accuracy: 1.00 - ETA: 0s - loss: 0.1951 - accuracy: 0.93 - ETA: 0s - loss: 0.2689 - accuracy: 0.93 - ETA: 0s - loss: 0.2706 - accuracy: 0.93 - ETA: 0s - loss: 0.2633 - accuracy: 0.93 - ETA: 0s - loss: 0.2596 - accuracy: 0.93 - ETA: 0s - loss: 0.2594 - accuracy: 0.93 - ETA: 0s - loss: 0.2526 - accuracy: 0.93 - ETA: 0s - loss: 0.2533 - accuracy: 0.93 - ETA: 0s - loss: 0.2489 - accuracy: 0.93 - ETA: 0s - loss: 0.2568 - accuracy: 0.93 - 1s 42us/sample - loss: 0.2538 - accuracy: 0.9369 - val_loss: 0.1214 - val_accuracy: 0.9793\n",
      "Epoch 85/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1301 - accuracy: 0.90 - ETA: 0s - loss: 0.3004 - accuracy: 0.92 - ETA: 0s - loss: 0.2717 - accuracy: 0.92 - ETA: 0s - loss: 0.2640 - accuracy: 0.92 - ETA: 0s - loss: 0.2571 - accuracy: 0.92 - ETA: 0s - loss: 0.2525 - accuracy: 0.92 - ETA: 0s - loss: 0.2519 - accuracy: 0.92 - ETA: 0s - loss: 0.2529 - accuracy: 0.92 - ETA: 0s - loss: 0.2546 - accuracy: 0.92 - ETA: 0s - loss: 0.2521 - accuracy: 0.92 - 1s 38us/sample - loss: 0.2546 - accuracy: 0.9286 - val_loss: 0.1245 - val_accuracy: 0.9765\n",
      "Epoch 86/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1249 - accuracy: 0.93 - ETA: 0s - loss: 0.2373 - accuracy: 0.92 - ETA: 0s - loss: 0.2632 - accuracy: 0.92 - ETA: 0s - loss: 0.2678 - accuracy: 0.91 - ETA: 0s - loss: 0.2612 - accuracy: 0.92 - ETA: 0s - loss: 0.2569 - accuracy: 0.92 - ETA: 0s - loss: 0.2488 - accuracy: 0.92 - ETA: 0s - loss: 0.2514 - accuracy: 0.92 - ETA: 0s - loss: 0.2539 - accuracy: 0.92 - ETA: 0s - loss: 0.2563 - accuracy: 0.92 - 1s 36us/sample - loss: 0.2569 - accuracy: 0.9295 - val_loss: 0.1407 - val_accuracy: 0.9832\n",
      "Epoch 87/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2115 - accuracy: 0.90 - ETA: 0s - loss: 0.2654 - accuracy: 0.93 - ETA: 0s - loss: 0.2767 - accuracy: 0.93 - ETA: 0s - loss: 0.2607 - accuracy: 0.93 - ETA: 0s - loss: 0.2503 - accuracy: 0.93 - ETA: 0s - loss: 0.2557 - accuracy: 0.93 - ETA: 0s - loss: 0.2689 - accuracy: 0.93 - ETA: 0s - loss: 0.2684 - accuracy: 0.93 - ETA: 0s - loss: 0.2634 - accuracy: 0.93 - ETA: 0s - loss: 0.2585 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2559 - accuracy: 0.9376 - val_loss: 0.1065 - val_accuracy: 0.9838\n",
      "Epoch 88/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2400 - accuracy: 0.93 - ETA: 0s - loss: 0.3062 - accuracy: 0.94 - ETA: 0s - loss: 0.2908 - accuracy: 0.92 - ETA: 0s - loss: 0.2769 - accuracy: 0.93 - ETA: 0s - loss: 0.2714 - accuracy: 0.93 - ETA: 0s - loss: 0.2681 - accuracy: 0.93 - ETA: 0s - loss: 0.2596 - accuracy: 0.93 - ETA: 0s - loss: 0.2625 - accuracy: 0.93 - ETA: 0s - loss: 0.2582 - accuracy: 0.93 - ETA: 0s - loss: 0.2606 - accuracy: 0.93 - 1s 37us/sample - loss: 0.2593 - accuracy: 0.9370 - val_loss: 0.1303 - val_accuracy: 0.9793\n",
      "Epoch 89/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.5415 - accuracy: 0.90 - ETA: 0s - loss: 0.3185 - accuracy: 0.92 - ETA: 0s - loss: 0.2951 - accuracy: 0.92 - ETA: 0s - loss: 0.2766 - accuracy: 0.93 - ETA: 0s - loss: 0.2652 - accuracy: 0.93 - ETA: 0s - loss: 0.2617 - accuracy: 0.93 - ETA: 0s - loss: 0.2629 - accuracy: 0.93 - ETA: 0s - loss: 0.2593 - accuracy: 0.93 - ETA: 0s - loss: 0.2552 - accuracy: 0.93 - ETA: 0s - loss: 0.2540 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2522 - accuracy: 0.9346 - val_loss: 0.1072 - val_accuracy: 0.9816\n",
      "Epoch 90/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0465 - accuracy: 1.00 - ETA: 0s - loss: 0.2185 - accuracy: 0.94 - ETA: 0s - loss: 0.2712 - accuracy: 0.93 - ETA: 0s - loss: 0.2672 - accuracy: 0.92 - ETA: 0s - loss: 0.2670 - accuracy: 0.92 - ETA: 0s - loss: 0.2597 - accuracy: 0.93 - ETA: 0s - loss: 0.2561 - accuracy: 0.93 - ETA: 0s - loss: 0.2554 - accuracy: 0.93 - ETA: 0s - loss: 0.2518 - accuracy: 0.93 - ETA: 0s - loss: 0.2491 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2489 - accuracy: 0.9384 - val_loss: 0.1135 - val_accuracy: 0.9804\n",
      "Epoch 91/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0753 - accuracy: 0.96 - ETA: 0s - loss: 0.2304 - accuracy: 0.94 - ETA: 0s - loss: 0.2518 - accuracy: 0.93 - ETA: 0s - loss: 0.2536 - accuracy: 0.92 - ETA: 0s - loss: 0.2503 - accuracy: 0.93 - ETA: 0s - loss: 0.2510 - accuracy: 0.92 - ETA: 0s - loss: 0.2580 - accuracy: 0.92 - ETA: 0s - loss: 0.2560 - accuracy: 0.93 - ETA: 0s - loss: 0.2610 - accuracy: 0.92 - 1s 35us/sample - loss: 0.2564 - accuracy: 0.9298 - val_loss: 0.1165 - val_accuracy: 0.9816\n",
      "Epoch 92/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0651 - accuracy: 1.00 - ETA: 0s - loss: 0.3130 - accuracy: 0.93 - ETA: 0s - loss: 0.2943 - accuracy: 0.93 - ETA: 0s - loss: 0.2762 - accuracy: 0.93 - ETA: 0s - loss: 0.2664 - accuracy: 0.93 - ETA: 0s - loss: 0.2623 - accuracy: 0.93 - ETA: 0s - loss: 0.2634 - accuracy: 0.93 - ETA: 0s - loss: 0.2572 - accuracy: 0.93 - ETA: 0s - loss: 0.2547 - accuracy: 0.93 - 0s 35us/sample - loss: 0.2554 - accuracy: 0.9357 - val_loss: 0.1080 - val_accuracy: 0.9844\n",
      "Epoch 93/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1605 - accuracy: 0.93 - ETA: 0s - loss: 0.2784 - accuracy: 0.93 - ETA: 0s - loss: 0.2677 - accuracy: 0.93 - ETA: 0s - loss: 0.2626 - accuracy: 0.93 - ETA: 0s - loss: 0.2581 - accuracy: 0.93 - ETA: 0s - loss: 0.2636 - accuracy: 0.93 - ETA: 0s - loss: 0.2561 - accuracy: 0.93 - ETA: 0s - loss: 0.2577 - accuracy: 0.93 - ETA: 0s - loss: 0.2621 - accuracy: 0.93 - 1s 37us/sample - loss: 0.2591 - accuracy: 0.9351 - val_loss: 0.1300 - val_accuracy: 0.9816\n",
      "Epoch 94/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.3805 - accuracy: 0.96 - ETA: 0s - loss: 0.2380 - accuracy: 0.94 - ETA: 0s - loss: 0.2584 - accuracy: 0.94 - ETA: 0s - loss: 0.2617 - accuracy: 0.93 - ETA: 0s - loss: 0.2666 - accuracy: 0.93 - ETA: 0s - loss: 0.2554 - accuracy: 0.93 - ETA: 0s - loss: 0.2526 - accuracy: 0.93 - ETA: 0s - loss: 0.2570 - accuracy: 0.93 - ETA: 0s - loss: 0.2605 - accuracy: 0.93 - ETA: 0s - loss: 0.2574 - accuracy: 0.93 - ETA: 0s - loss: 0.2552 - accuracy: 0.93 - 1s 41us/sample - loss: 0.2496 - accuracy: 0.9357 - val_loss: 0.0803 - val_accuracy: 0.9849\n",
      "Epoch 95/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1444 - accuracy: 0.90 - ETA: 0s - loss: 0.2575 - accuracy: 0.94 - ETA: 0s - loss: 0.2582 - accuracy: 0.94 - ETA: 0s - loss: 0.2691 - accuracy: 0.94 - ETA: 0s - loss: 0.2692 - accuracy: 0.93 - ETA: 0s - loss: 0.2695 - accuracy: 0.93 - ETA: 0s - loss: 0.2647 - accuracy: 0.93 - ETA: 0s - loss: 0.2564 - accuracy: 0.93 - ETA: 0s - loss: 0.2568 - accuracy: 0.93 - ETA: 0s - loss: 0.2573 - accuracy: 0.93 - 1s 35us/sample - loss: 0.2588 - accuracy: 0.9364 - val_loss: 0.1206 - val_accuracy: 0.9816\n",
      "Epoch 96/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1153 - accuracy: 0.93 - ETA: 0s - loss: 0.3000 - accuracy: 0.92 - ETA: 0s - loss: 0.2671 - accuracy: 0.92 - ETA: 0s - loss: 0.2488 - accuracy: 0.92 - ETA: 0s - loss: 0.2442 - accuracy: 0.93 - ETA: 0s - loss: 0.2441 - accuracy: 0.93 - ETA: 0s - loss: 0.2463 - accuracy: 0.93 - ETA: 0s - loss: 0.2503 - accuracy: 0.93 - ETA: 0s - loss: 0.2463 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2431 - accuracy: 0.9367 - val_loss: 0.1046 - val_accuracy: 0.9838\n",
      "Epoch 97/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0864 - accuracy: 1.00 - ETA: 0s - loss: 0.2341 - accuracy: 0.94 - ETA: 0s - loss: 0.2239 - accuracy: 0.94 - ETA: 0s - loss: 0.2433 - accuracy: 0.93 - ETA: 0s - loss: 0.2427 - accuracy: 0.94 - ETA: 0s - loss: 0.2462 - accuracy: 0.94 - ETA: 0s - loss: 0.2416 - accuracy: 0.94 - ETA: 0s - loss: 0.2433 - accuracy: 0.94 - ETA: 0s - loss: 0.2438 - accuracy: 0.94 - ETA: 0s - loss: 0.2501 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2540 - accuracy: 0.9387 - val_loss: 0.1430 - val_accuracy: 0.9793\n",
      "Epoch 98/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2547 - accuracy: 0.96 - ETA: 0s - loss: 0.2823 - accuracy: 0.92 - ETA: 0s - loss: 0.2800 - accuracy: 0.92 - ETA: 0s - loss: 0.2712 - accuracy: 0.92 - ETA: 0s - loss: 0.2723 - accuracy: 0.92 - ETA: 0s - loss: 0.2672 - accuracy: 0.92 - ETA: 0s - loss: 0.2517 - accuracy: 0.93 - ETA: 0s - loss: 0.2614 - accuracy: 0.93 - ETA: 0s - loss: 0.2655 - accuracy: 0.93 - ETA: 0s - loss: 0.2622 - accuracy: 0.92 - 1s 37us/sample - loss: 0.2601 - accuracy: 0.9299 - val_loss: 0.0950 - val_accuracy: 0.9810\n",
      "Epoch 99/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0599 - accuracy: 0.96 - ETA: 0s - loss: 0.2213 - accuracy: 0.95 - ETA: 0s - loss: 0.2407 - accuracy: 0.93 - ETA: 0s - loss: 0.2360 - accuracy: 0.93 - ETA: 0s - loss: 0.2453 - accuracy: 0.93 - ETA: 0s - loss: 0.2436 - accuracy: 0.93 - ETA: 0s - loss: 0.2448 - accuracy: 0.93 - ETA: 0s - loss: 0.2470 - accuracy: 0.93 - ETA: 0s - loss: 0.2531 - accuracy: 0.93 - ETA: 0s - loss: 0.2603 - accuracy: 0.93 - 1s 35us/sample - loss: 0.2607 - accuracy: 0.9308 - val_loss: 0.1281 - val_accuracy: 0.9799\n",
      "Epoch 100/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.7002 - accuracy: 0.93 - ETA: 0s - loss: 0.2609 - accuracy: 0.92 - ETA: 0s - loss: 0.2772 - accuracy: 0.92 - ETA: 0s - loss: 0.2638 - accuracy: 0.93 - ETA: 0s - loss: 0.2688 - accuracy: 0.92 - ETA: 0s - loss: 0.2647 - accuracy: 0.92 - ETA: 0s - loss: 0.2603 - accuracy: 0.93 - ETA: 0s - loss: 0.2620 - accuracy: 0.93 - ETA: 0s - loss: 0.2600 - accuracy: 0.93 - ETA: 0s - loss: 0.2603 - accuracy: 0.93 - 1s 37us/sample - loss: 0.2595 - accuracy: 0.9318 - val_loss: 0.1251 - val_accuracy: 0.9749\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.20917624711324384, 0.975419]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Для данных без feature engeneering, но с пересчётом веса класса\n",
    "\n",
    "model = tf.keras.models.Sequential([\n",
    "tf.keras.layers.Dense(16, activation=tf.nn.relu),\n",
    "tf.keras.layers.Dropout(0.5),\n",
    "tf.keras.layers.Dense(32, activation=tf.nn.relu),\n",
    "tf.keras.layers.Dropout(0.5),\n",
    "tf.keras.layers.Dense(1, activation=tf.nn.sigmoid)\n",
    "])\n",
    "model.compile(optimizer='adam',\n",
    "loss='binary_crossentropy',\n",
    "metrics=['accuracy'])\n",
    "baseline_history1=model.fit(x_train, y_train, epochs=100, validation_data=(x_valid, y_valid), shuffle=True, class_weight=class_weight)\n",
    "model.evaluate(x_test, y_test, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_3 (Dense)              multiple                  144       \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          multiple                  0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              multiple                  544       \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          multiple                  0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              multiple                  33        \n",
      "=================================================================\n",
      "Total params: 721\n",
      "Trainable params: 721\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(baseline_history1, 'loss', 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(baseline_history1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss :  0.20917624711324384\n",
      "accuracy :  0.975419\n",
      "\n",
      "всего 27 \"пульсаров\"\n",
      "всего 1763 \"не пульсаров\"\n",
      "не пульсар предсказанный как не пульсар:  1722\n",
      "не пульсар предсказанный как пульсар : 41\n",
      "пульсар предсказанный как не пульсар : 3\n",
      "пульсар предсказанный как пульсар :  24\n",
      "количество правильных ответов 1746 из 1790\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#для данных без feature engeneering\n",
    "\n",
    "\n",
    "test_predictions_baseline = model.predict(x_test)\n",
    "\n",
    "baseline_results = model.evaluate(x_test, y_test, verbose=0)\n",
    "for name, value in zip(model.metrics_names, baseline_results):\n",
    "  print(name, ': ', value)\n",
    "print()\n",
    "\n",
    "plot_cm(y_test, test_predictions_baseline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# далее всё тоже самое, только с центрированными данными"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "datanorm = pd.read_csv('пульсары центрированные данные.csv', header=None)\n",
    "#path = 'пульсары нормализованные.csv'\n",
    "#myfile = open(path, mode = 'a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17898, 9)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datanorm.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "datanorm=datanorm.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "a, b, c=np.split(datanorm, [14318,16108])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "a_train, b_train=np.hsplit(a, [8]) #нарезаем на тренировку\n",
    "a_valid, b_valid=np.hsplit(b, [8]) #нарезаем на валидацию\n",
    "a_test, b_test=np.hsplit(c,[8]) #нарезаем на тест"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 14318 samples, validate on 1790 samples\n",
      "Epoch 1/100\n",
      "14318/14318 [==============================] - ETA: 4:28 - loss: 3.8886 - accuracy: 0.56 - ETA: 5s - loss: 11.4964 - accuracy: 0.5449 - ETA: 2s - loss: 9.5684 - accuracy: 0.570 - ETA: 1s - loss: 8.1080 - accuracy: 0.59 - ETA: 1s - loss: 7.2809 - accuracy: 0.59 - ETA: 0s - loss: 6.5388 - accuracy: 0.60 - ETA: 0s - loss: 5.9847 - accuracy: 0.60 - ETA: 0s - loss: 5.4862 - accuracy: 0.61 - ETA: 0s - loss: 5.0134 - accuracy: 0.61 - ETA: 0s - loss: 4.6138 - accuracy: 0.62 - 1s 88us/sample - loss: 4.4600 - accuracy: 0.6266 - val_loss: 0.1424 - val_accuracy: 0.9743\n",
      "Epoch 2/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.6524 - accuracy: 0.78 - ETA: 0s - loss: 1.0486 - accuracy: 0.70 - ETA: 0s - loss: 0.9988 - accuracy: 0.69 - ETA: 0s - loss: 0.9731 - accuracy: 0.69 - ETA: 0s - loss: 0.9182 - accuracy: 0.70 - ETA: 0s - loss: 0.8881 - accuracy: 0.70 - ETA: 0s - loss: 0.8614 - accuracy: 0.70 - ETA: 0s - loss: 0.8299 - accuracy: 0.71 - ETA: 0s - loss: 0.7953 - accuracy: 0.72 - ETA: 0s - loss: 0.7637 - accuracy: 0.72 - 1s 38us/sample - loss: 0.7498 - accuracy: 0.7322 - val_loss: 0.3062 - val_accuracy: 0.9620\n",
      "Epoch 3/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.3382 - accuracy: 0.81 - ETA: 0s - loss: 0.5431 - accuracy: 0.77 - ETA: 0s - loss: 0.5220 - accuracy: 0.76 - ETA: 0s - loss: 0.5259 - accuracy: 0.77 - ETA: 0s - loss: 0.5306 - accuracy: 0.77 - ETA: 0s - loss: 0.5052 - accuracy: 0.78 - ETA: 0s - loss: 0.4916 - accuracy: 0.79 - ETA: 0s - loss: 0.4905 - accuracy: 0.79 - ETA: 0s - loss: 0.4805 - accuracy: 0.79 - ETA: 0s - loss: 0.4679 - accuracy: 0.80 - 1s 35us/sample - loss: 0.4685 - accuracy: 0.8045 - val_loss: 0.2639 - val_accuracy: 0.9749\n",
      "Epoch 4/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.3014 - accuracy: 0.71 - ETA: 0s - loss: 0.4119 - accuracy: 0.83 - ETA: 0s - loss: 0.3755 - accuracy: 0.85 - ETA: 0s - loss: 0.3892 - accuracy: 0.85 - ETA: 0s - loss: 0.3913 - accuracy: 0.85 - ETA: 0s - loss: 0.3963 - accuracy: 0.85 - ETA: 0s - loss: 0.4040 - accuracy: 0.85 - ETA: 0s - loss: 0.4057 - accuracy: 0.85 - 0s 35us/sample - loss: 0.4105 - accuracy: 0.8563 - val_loss: 0.2882 - val_accuracy: 0.9665\n",
      "Epoch 5/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.7200 - accuracy: 0.87 - ETA: 0s - loss: 0.3936 - accuracy: 0.87 - ETA: 0s - loss: 0.3835 - accuracy: 0.87 - ETA: 0s - loss: 0.4130 - accuracy: 0.86 - ETA: 0s - loss: 0.4099 - accuracy: 0.86 - ETA: 0s - loss: 0.4068 - accuracy: 0.86 - ETA: 0s - loss: 0.3978 - accuracy: 0.86 - ETA: 0s - loss: 0.3880 - accuracy: 0.87 - ETA: 0s - loss: 0.3875 - accuracy: 0.87 - 0s 35us/sample - loss: 0.3824 - accuracy: 0.8767 - val_loss: 0.2303 - val_accuracy: 0.9832\n",
      "Epoch 6/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.5940 - accuracy: 0.90 - ETA: 0s - loss: 0.3414 - accuracy: 0.88 - ETA: 0s - loss: 0.3637 - accuracy: 0.87 - ETA: 0s - loss: 0.3630 - accuracy: 0.88 - ETA: 0s - loss: 0.3666 - accuracy: 0.88 - ETA: 0s - loss: 0.3692 - accuracy: 0.88 - ETA: 0s - loss: 0.3739 - accuracy: 0.88 - ETA: 0s - loss: 0.3692 - accuracy: 0.88 - 1s 35us/sample - loss: 0.3600 - accuracy: 0.8878 - val_loss: 0.2069 - val_accuracy: 0.9793\n",
      "Epoch 7/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1071 - accuracy: 0.90 - ETA: 0s - loss: 0.3539 - accuracy: 0.90 - ETA: 0s - loss: 0.3731 - accuracy: 0.90 - ETA: 0s - loss: 0.3673 - accuracy: 0.89 - ETA: 0s - loss: 0.3559 - accuracy: 0.90 - ETA: 0s - loss: 0.3566 - accuracy: 0.89 - ETA: 0s - loss: 0.3501 - accuracy: 0.89 - ETA: 0s - loss: 0.3488 - accuracy: 0.89 - ETA: 0s - loss: 0.3492 - accuracy: 0.89 - 0s 34us/sample - loss: 0.3483 - accuracy: 0.8980 - val_loss: 0.2470 - val_accuracy: 0.9654\n",
      "Epoch 8/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 1.0395 - accuracy: 0.87 - ETA: 0s - loss: 0.3389 - accuracy: 0.90 - ETA: 0s - loss: 0.3541 - accuracy: 0.89 - ETA: 0s - loss: 0.3429 - accuracy: 0.90 - ETA: 0s - loss: 0.3434 - accuracy: 0.90 - ETA: 0s - loss: 0.3464 - accuracy: 0.90 - ETA: 0s - loss: 0.3406 - accuracy: 0.90 - ETA: 0s - loss: 0.3403 - accuracy: 0.90 - 0s 35us/sample - loss: 0.3388 - accuracy: 0.9050 - val_loss: 0.2188 - val_accuracy: 0.9709\n",
      "Epoch 9/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.6245 - accuracy: 0.75 - ETA: 0s - loss: 0.3499 - accuracy: 0.90 - ETA: 0s - loss: 0.3289 - accuracy: 0.90 - ETA: 0s - loss: 0.3047 - accuracy: 0.91 - ETA: 0s - loss: 0.3124 - accuracy: 0.90 - ETA: 0s - loss: 0.3107 - accuracy: 0.90 - ETA: 0s - loss: 0.3170 - accuracy: 0.90 - ETA: 0s - loss: 0.3141 - accuracy: 0.90 - ETA: 0s - loss: 0.3124 - accuracy: 0.90 - 0s 34us/sample - loss: 0.3169 - accuracy: 0.9093 - val_loss: 0.2186 - val_accuracy: 0.9670\n",
      "Epoch 10/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1757 - accuracy: 0.93 - ETA: 0s - loss: 0.3101 - accuracy: 0.89 - ETA: 0s - loss: 0.3179 - accuracy: 0.91 - ETA: 0s - loss: 0.3197 - accuracy: 0.91 - ETA: 0s - loss: 0.3145 - accuracy: 0.91 - ETA: 0s - loss: 0.3093 - accuracy: 0.91 - ETA: 0s - loss: 0.3139 - accuracy: 0.91 - ETA: 0s - loss: 0.3064 - accuracy: 0.91 - 1s 36us/sample - loss: 0.3099 - accuracy: 0.9160 - val_loss: 0.1990 - val_accuracy: 0.9547\n",
      "Epoch 11/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1695 - accuracy: 0.90 - ETA: 0s - loss: 0.2852 - accuracy: 0.92 - ETA: 0s - loss: 0.3174 - accuracy: 0.91 - ETA: 0s - loss: 0.3062 - accuracy: 0.91 - ETA: 0s - loss: 0.3044 - accuracy: 0.91 - ETA: 0s - loss: 0.3180 - accuracy: 0.91 - ETA: 0s - loss: 0.3202 - accuracy: 0.91 - ETA: 0s - loss: 0.3117 - accuracy: 0.91 - ETA: 0s - loss: 0.3134 - accuracy: 0.91 - 1s 38us/sample - loss: 0.3157 - accuracy: 0.9128 - val_loss: 0.1737 - val_accuracy: 0.9771\n",
      "Epoch 12/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1777 - accuracy: 0.90 - ETA: 0s - loss: 0.3224 - accuracy: 0.89 - ETA: 0s - loss: 0.2877 - accuracy: 0.91 - ETA: 0s - loss: 0.2961 - accuracy: 0.91 - ETA: 0s - loss: 0.3012 - accuracy: 0.91 - ETA: 0s - loss: 0.3030 - accuracy: 0.91 - ETA: 0s - loss: 0.3015 - accuracy: 0.91 - ETA: 0s - loss: 0.3030 - accuracy: 0.91 - 0s 35us/sample - loss: 0.3067 - accuracy: 0.9135 - val_loss: 0.1772 - val_accuracy: 0.9704\n",
      "Epoch 13/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1527 - accuracy: 0.90 - ETA: 0s - loss: 0.3114 - accuracy: 0.91 - ETA: 0s - loss: 0.3085 - accuracy: 0.91 - ETA: 0s - loss: 0.3052 - accuracy: 0.91 - ETA: 0s - loss: 0.3110 - accuracy: 0.91 - ETA: 0s - loss: 0.3080 - accuracy: 0.91 - ETA: 0s - loss: 0.3036 - accuracy: 0.91 - ETA: 0s - loss: 0.3004 - accuracy: 0.91 - 1s 36us/sample - loss: 0.2969 - accuracy: 0.9163 - val_loss: 0.1631 - val_accuracy: 0.9704\n",
      "Epoch 14/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2305 - accuracy: 0.93 - ETA: 0s - loss: 0.3196 - accuracy: 0.91 - ETA: 0s - loss: 0.3165 - accuracy: 0.91 - ETA: 0s - loss: 0.2896 - accuracy: 0.91 - ETA: 0s - loss: 0.2922 - accuracy: 0.91 - ETA: 0s - loss: 0.2926 - accuracy: 0.92 - ETA: 0s - loss: 0.2901 - accuracy: 0.91 - ETA: 0s - loss: 0.2911 - accuracy: 0.91 - ETA: 0s - loss: 0.2920 - accuracy: 0.91 - 1s 35us/sample - loss: 0.2926 - accuracy: 0.9180 - val_loss: 0.2087 - val_accuracy: 0.9346\n",
      "Epoch 15/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2818 - accuracy: 0.90 - ETA: 0s - loss: 0.2956 - accuracy: 0.91 - ETA: 0s - loss: 0.2963 - accuracy: 0.90 - ETA: 0s - loss: 0.2950 - accuracy: 0.91 - ETA: 0s - loss: 0.2828 - accuracy: 0.91 - ETA: 0s - loss: 0.2826 - accuracy: 0.91 - ETA: 0s - loss: 0.2891 - accuracy: 0.91 - ETA: 0s - loss: 0.2834 - accuracy: 0.91 - ETA: 0s - loss: 0.2845 - accuracy: 0.91 - 0s 35us/sample - loss: 0.2837 - accuracy: 0.9172 - val_loss: 0.1598 - val_accuracy: 0.9810\n",
      "Epoch 16/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.4944 - accuracy: 0.87 - ETA: 0s - loss: 0.2384 - accuracy: 0.93 - ETA: 0s - loss: 0.2606 - accuracy: 0.92 - ETA: 0s - loss: 0.2660 - accuracy: 0.91 - ETA: 0s - loss: 0.2739 - accuracy: 0.91 - ETA: 0s - loss: 0.2764 - accuracy: 0.91 - ETA: 0s - loss: 0.2756 - accuracy: 0.91 - ETA: 0s - loss: 0.2741 - accuracy: 0.91 - 1s 35us/sample - loss: 0.2788 - accuracy: 0.9156 - val_loss: 0.1698 - val_accuracy: 0.9642\n",
      "Epoch 17/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1946 - accuracy: 0.81 - ETA: 0s - loss: 0.2477 - accuracy: 0.93 - ETA: 0s - loss: 0.2928 - accuracy: 0.92 - ETA: 0s - loss: 0.2877 - accuracy: 0.92 - ETA: 0s - loss: 0.2846 - accuracy: 0.92 - ETA: 0s - loss: 0.2910 - accuracy: 0.92 - ETA: 0s - loss: 0.2902 - accuracy: 0.92 - ETA: 0s - loss: 0.2891 - accuracy: 0.92 - 1s 35us/sample - loss: 0.2838 - accuracy: 0.9226 - val_loss: 0.1379 - val_accuracy: 0.9866\n",
      "Epoch 18/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1264 - accuracy: 0.87 - ETA: 0s - loss: 0.2413 - accuracy: 0.93 - ETA: 0s - loss: 0.2522 - accuracy: 0.92 - ETA: 0s - loss: 0.2609 - accuracy: 0.92 - ETA: 0s - loss: 0.2637 - accuracy: 0.92 - ETA: 0s - loss: 0.2789 - accuracy: 0.92 - ETA: 0s - loss: 0.2731 - accuracy: 0.92 - ETA: 0s - loss: 0.2735 - accuracy: 0.92 - ETA: 0s - loss: 0.2789 - accuracy: 0.91 - 0s 35us/sample - loss: 0.2802 - accuracy: 0.9182 - val_loss: 0.1877 - val_accuracy: 0.9715\n",
      "Epoch 19/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1371 - accuracy: 0.93 - ETA: 0s - loss: 0.2532 - accuracy: 0.91 - ETA: 0s - loss: 0.2565 - accuracy: 0.92 - ETA: 0s - loss: 0.2598 - accuracy: 0.92 - ETA: 0s - loss: 0.2629 - accuracy: 0.92 - ETA: 0s - loss: 0.2574 - accuracy: 0.92 - ETA: 0s - loss: 0.2647 - accuracy: 0.92 - ETA: 0s - loss: 0.2701 - accuracy: 0.92 - ETA: 0s - loss: 0.2738 - accuracy: 0.92 - 0s 34us/sample - loss: 0.2758 - accuracy: 0.9260 - val_loss: 0.1711 - val_accuracy: 0.9782\n",
      "Epoch 20/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1534 - accuracy: 0.93 - ETA: 0s - loss: 0.2294 - accuracy: 0.93 - ETA: 0s - loss: 0.2447 - accuracy: 0.93 - ETA: 0s - loss: 0.2570 - accuracy: 0.93 - ETA: 0s - loss: 0.2630 - accuracy: 0.92 - ETA: 0s - loss: 0.2652 - accuracy: 0.92 - ETA: 0s - loss: 0.2669 - accuracy: 0.92 - ETA: 0s - loss: 0.2715 - accuracy: 0.92 - ETA: 0s - loss: 0.2732 - accuracy: 0.92 - 1s 36us/sample - loss: 0.2738 - accuracy: 0.9242 - val_loss: 0.1673 - val_accuracy: 0.9804\n",
      "Epoch 21/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1126 - accuracy: 0.93 - ETA: 0s - loss: 0.2486 - accuracy: 0.92 - ETA: 0s - loss: 0.2431 - accuracy: 0.93 - ETA: 0s - loss: 0.2503 - accuracy: 0.92 - ETA: 0s - loss: 0.2528 - accuracy: 0.92 - ETA: 0s - loss: 0.2559 - accuracy: 0.92 - ETA: 0s - loss: 0.2596 - accuracy: 0.92 - ETA: 0s - loss: 0.2700 - accuracy: 0.91 - ETA: 0s - loss: 0.2735 - accuracy: 0.91 - 0s 34us/sample - loss: 0.2771 - accuracy: 0.9165 - val_loss: 0.1757 - val_accuracy: 0.9631\n",
      "Epoch 22/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1629 - accuracy: 0.87 - ETA: 0s - loss: 0.2552 - accuracy: 0.91 - ETA: 0s - loss: 0.2510 - accuracy: 0.92 - ETA: 0s - loss: 0.2538 - accuracy: 0.92 - ETA: 0s - loss: 0.2636 - accuracy: 0.92 - ETA: 0s - loss: 0.2632 - accuracy: 0.91 - ETA: 0s - loss: 0.2649 - accuracy: 0.92 - ETA: 0s - loss: 0.2641 - accuracy: 0.92 - 0s 33us/sample - loss: 0.2616 - accuracy: 0.9230 - val_loss: 0.1281 - val_accuracy: 0.9782\n",
      "Epoch 23/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.5143 - accuracy: 0.93 - ETA: 0s - loss: 0.2499 - accuracy: 0.94 - ETA: 0s - loss: 0.2547 - accuracy: 0.93 - ETA: 0s - loss: 0.2651 - accuracy: 0.92 - ETA: 0s - loss: 0.2674 - accuracy: 0.92 - ETA: 0s - loss: 0.2709 - accuracy: 0.92 - ETA: 0s - loss: 0.2691 - accuracy: 0.92 - ETA: 0s - loss: 0.2674 - accuracy: 0.92 - 1s 35us/sample - loss: 0.2663 - accuracy: 0.9270 - val_loss: 0.1434 - val_accuracy: 0.9844\n",
      "Epoch 24/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1703 - accuracy: 0.96 - ETA: 0s - loss: 0.2796 - accuracy: 0.93 - ETA: 0s - loss: 0.2623 - accuracy: 0.92 - ETA: 0s - loss: 0.2637 - accuracy: 0.92 - ETA: 0s - loss: 0.2524 - accuracy: 0.92 - ETA: 0s - loss: 0.2585 - accuracy: 0.92 - ETA: 0s - loss: 0.2567 - accuracy: 0.92 - ETA: 0s - loss: 0.2619 - accuracy: 0.92 - ETA: 0s - loss: 0.2621 - accuracy: 0.92 - 1s 35us/sample - loss: 0.2612 - accuracy: 0.9248 - val_loss: 0.1445 - val_accuracy: 0.9844\n",
      "Epoch 25/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1031 - accuracy: 0.93 - ETA: 0s - loss: 0.2790 - accuracy: 0.92 - ETA: 0s - loss: 0.2846 - accuracy: 0.92 - ETA: 0s - loss: 0.2721 - accuracy: 0.92 - ETA: 0s - loss: 0.2547 - accuracy: 0.93 - ETA: 0s - loss: 0.2555 - accuracy: 0.93 - ETA: 0s - loss: 0.2576 - accuracy: 0.93 - ETA: 0s - loss: 0.2579 - accuracy: 0.92 - ETA: 0s - loss: 0.2621 - accuracy: 0.92 - 0s 35us/sample - loss: 0.2644 - accuracy: 0.9267 - val_loss: 0.1589 - val_accuracy: 0.9654\n",
      "Epoch 26/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.5800 - accuracy: 0.90 - ETA: 0s - loss: 0.2637 - accuracy: 0.92 - ETA: 0s - loss: 0.2841 - accuracy: 0.92 - ETA: 0s - loss: 0.2922 - accuracy: 0.91 - ETA: 0s - loss: 0.2879 - accuracy: 0.91 - ETA: 0s - loss: 0.2774 - accuracy: 0.92 - ETA: 0s - loss: 0.2754 - accuracy: 0.92 - ETA: 0s - loss: 0.2755 - accuracy: 0.92 - ETA: 0s - loss: 0.2682 - accuracy: 0.92 - 1s 36us/sample - loss: 0.2654 - accuracy: 0.9246 - val_loss: 0.1134 - val_accuracy: 0.9844\n",
      "Epoch 27/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.5584 - accuracy: 0.93 - ETA: 0s - loss: 0.3149 - accuracy: 0.90 - ETA: 0s - loss: 0.3098 - accuracy: 0.90 - ETA: 0s - loss: 0.2889 - accuracy: 0.91 - ETA: 0s - loss: 0.2811 - accuracy: 0.91 - ETA: 0s - loss: 0.2716 - accuracy: 0.91 - ETA: 0s - loss: 0.2646 - accuracy: 0.92 - ETA: 0s - loss: 0.2592 - accuracy: 0.92 - 1s 36us/sample - loss: 0.2589 - accuracy: 0.9236 - val_loss: 0.1410 - val_accuracy: 0.9743\n",
      "Epoch 28/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2379 - accuracy: 0.93 - ETA: 0s - loss: 0.2600 - accuracy: 0.91 - ETA: 0s - loss: 0.2686 - accuracy: 0.92 - ETA: 0s - loss: 0.2628 - accuracy: 0.92 - ETA: 0s - loss: 0.2817 - accuracy: 0.92 - ETA: 0s - loss: 0.2741 - accuracy: 0.92 - ETA: 0s - loss: 0.2688 - accuracy: 0.92 - ETA: 0s - loss: 0.2683 - accuracy: 0.92 - ETA: 0s - loss: 0.2622 - accuracy: 0.92 - 1s 37us/sample - loss: 0.2615 - accuracy: 0.9244 - val_loss: 0.1253 - val_accuracy: 0.9832\n",
      "Epoch 29/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.1078 - accuracy: 0.96 - ETA: 0s - loss: 0.1995 - accuracy: 0.94 - ETA: 0s - loss: 0.2127 - accuracy: 0.93 - ETA: 0s - loss: 0.2431 - accuracy: 0.92 - ETA: 0s - loss: 0.2420 - accuracy: 0.92 - ETA: 0s - loss: 0.2542 - accuracy: 0.92 - ETA: 0s - loss: 0.2570 - accuracy: 0.92 - ETA: 0s - loss: 0.2623 - accuracy: 0.92 - ETA: 0s - loss: 0.2627 - accuracy: 0.92 - ETA: 0s - loss: 0.2627 - accuracy: 0.92 - 1s 38us/sample - loss: 0.2605 - accuracy: 0.9260 - val_loss: 0.1275 - val_accuracy: 0.9754\n",
      "Epoch 30/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1251 - accuracy: 0.87 - ETA: 0s - loss: 0.2494 - accuracy: 0.92 - ETA: 0s - loss: 0.2321 - accuracy: 0.93 - ETA: 0s - loss: 0.2349 - accuracy: 0.92 - ETA: 0s - loss: 0.2375 - accuracy: 0.93 - ETA: 0s - loss: 0.2402 - accuracy: 0.93 - ETA: 0s - loss: 0.2560 - accuracy: 0.93 - ETA: 0s - loss: 0.2576 - accuracy: 0.92 - ETA: 0s - loss: 0.2572 - accuracy: 0.92 - ETA: 0s - loss: 0.2615 - accuracy: 0.92 - 1s 36us/sample - loss: 0.2647 - accuracy: 0.9263 - val_loss: 0.1954 - val_accuracy: 0.9603\n",
      "Epoch 31/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0986 - accuracy: 1.00 - ETA: 0s - loss: 0.2000 - accuracy: 0.92 - ETA: 0s - loss: 0.2219 - accuracy: 0.93 - ETA: 0s - loss: 0.2343 - accuracy: 0.93 - ETA: 0s - loss: 0.2377 - accuracy: 0.93 - ETA: 0s - loss: 0.2406 - accuracy: 0.93 - ETA: 0s - loss: 0.2467 - accuracy: 0.93 - ETA: 0s - loss: 0.2582 - accuracy: 0.93 - ETA: 0s - loss: 0.2599 - accuracy: 0.93 - 1s 37us/sample - loss: 0.2618 - accuracy: 0.9297 - val_loss: 0.1450 - val_accuracy: 0.9721\n",
      "Epoch 32/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1800 - accuracy: 0.93 - ETA: 0s - loss: 0.2255 - accuracy: 0.93 - ETA: 0s - loss: 0.2383 - accuracy: 0.93 - ETA: 0s - loss: 0.2487 - accuracy: 0.92 - ETA: 0s - loss: 0.2676 - accuracy: 0.92 - ETA: 0s - loss: 0.2697 - accuracy: 0.92 - ETA: 0s - loss: 0.2709 - accuracy: 0.92 - ETA: 0s - loss: 0.2648 - accuracy: 0.92 - ETA: 0s - loss: 0.2671 - accuracy: 0.92 - ETA: 0s - loss: 0.2603 - accuracy: 0.92 - 1s 37us/sample - loss: 0.2631 - accuracy: 0.9246 - val_loss: 0.1442 - val_accuracy: 0.9777\n",
      "Epoch 33/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2613 - accuracy: 0.93 - ETA: 0s - loss: 0.2689 - accuracy: 0.93 - ETA: 0s - loss: 0.2610 - accuracy: 0.93 - ETA: 0s - loss: 0.2685 - accuracy: 0.92 - ETA: 0s - loss: 0.2738 - accuracy: 0.92 - ETA: 0s - loss: 0.2673 - accuracy: 0.92 - ETA: 0s - loss: 0.2718 - accuracy: 0.92 - ETA: 0s - loss: 0.2701 - accuracy: 0.92 - ETA: 0s - loss: 0.2687 - accuracy: 0.92 - ETA: 0s - loss: 0.2676 - accuracy: 0.92 - ETA: 0s - loss: 0.2644 - accuracy: 0.92 - 1s 40us/sample - loss: 0.2631 - accuracy: 0.9304 - val_loss: 0.1335 - val_accuracy: 0.9782\n",
      "Epoch 34/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0842 - accuracy: 0.96 - ETA: 0s - loss: 0.2002 - accuracy: 0.94 - ETA: 0s - loss: 0.2345 - accuracy: 0.93 - ETA: 0s - loss: 0.2453 - accuracy: 0.93 - ETA: 0s - loss: 0.2449 - accuracy: 0.93 - ETA: 0s - loss: 0.2427 - accuracy: 0.93 - ETA: 0s - loss: 0.2471 - accuracy: 0.93 - ETA: 0s - loss: 0.2568 - accuracy: 0.92 - ETA: 0s - loss: 0.2550 - accuracy: 0.92 - ETA: 0s - loss: 0.2624 - accuracy: 0.92 - 1s 37us/sample - loss: 0.2606 - accuracy: 0.9276 - val_loss: 0.1459 - val_accuracy: 0.9760\n",
      "Epoch 35/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.8168 - accuracy: 0.87 - ETA: 0s - loss: 0.2972 - accuracy: 0.91 - ETA: 0s - loss: 0.2621 - accuracy: 0.92 - ETA: 0s - loss: 0.2514 - accuracy: 0.93 - ETA: 0s - loss: 0.2543 - accuracy: 0.93 - ETA: 0s - loss: 0.2581 - accuracy: 0.93 - ETA: 0s - loss: 0.2614 - accuracy: 0.93 - ETA: 0s - loss: 0.2577 - accuracy: 0.92 - ETA: 0s - loss: 0.2588 - accuracy: 0.93 - ETA: 0s - loss: 0.2588 - accuracy: 0.93 - 1s 37us/sample - loss: 0.2590 - accuracy: 0.9306 - val_loss: 0.1311 - val_accuracy: 0.9832\n",
      "Epoch 36/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0779 - accuracy: 1.00 - ETA: 0s - loss: 0.2362 - accuracy: 0.94 - ETA: 0s - loss: 0.2796 - accuracy: 0.92 - ETA: 0s - loss: 0.2711 - accuracy: 0.92 - ETA: 0s - loss: 0.2614 - accuracy: 0.92 - ETA: 0s - loss: 0.2593 - accuracy: 0.92 - ETA: 0s - loss: 0.2561 - accuracy: 0.92 - ETA: 0s - loss: 0.2544 - accuracy: 0.93 - ETA: 0s - loss: 0.2578 - accuracy: 0.92 - ETA: 0s - loss: 0.2593 - accuracy: 0.92 - 1s 37us/sample - loss: 0.2604 - accuracy: 0.9300 - val_loss: 0.1406 - val_accuracy: 0.9642\n",
      "Epoch 37/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0962 - accuracy: 0.96 - ETA: 0s - loss: 0.2312 - accuracy: 0.93 - ETA: 0s - loss: 0.2453 - accuracy: 0.93 - ETA: 0s - loss: 0.2320 - accuracy: 0.93 - ETA: 0s - loss: 0.2385 - accuracy: 0.93 - ETA: 0s - loss: 0.2457 - accuracy: 0.93 - ETA: 0s - loss: 0.2470 - accuracy: 0.93 - ETA: 0s - loss: 0.2516 - accuracy: 0.93 - ETA: 0s - loss: 0.2563 - accuracy: 0.93 - ETA: 0s - loss: 0.2615 - accuracy: 0.93 - 1s 38us/sample - loss: 0.2582 - accuracy: 0.9314 - val_loss: 0.1124 - val_accuracy: 0.9810\n",
      "Epoch 38/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.3145 - accuracy: 0.93 - ETA: 0s - loss: 0.3184 - accuracy: 0.91 - ETA: 0s - loss: 0.2713 - accuracy: 0.92 - ETA: 0s - loss: 0.2792 - accuracy: 0.92 - ETA: 0s - loss: 0.2747 - accuracy: 0.92 - ETA: 0s - loss: 0.2845 - accuracy: 0.92 - ETA: 0s - loss: 0.2848 - accuracy: 0.92 - ETA: 0s - loss: 0.2777 - accuracy: 0.92 - ETA: 0s - loss: 0.2742 - accuracy: 0.92 - ETA: 0s - loss: 0.2696 - accuracy: 0.92 - 1s 37us/sample - loss: 0.2680 - accuracy: 0.9302 - val_loss: 0.1141 - val_accuracy: 0.9788\n",
      "Epoch 39/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.4612 - accuracy: 0.90 - ETA: 0s - loss: 0.2230 - accuracy: 0.93 - ETA: 0s - loss: 0.2302 - accuracy: 0.93 - ETA: 0s - loss: 0.2314 - accuracy: 0.93 - ETA: 0s - loss: 0.2311 - accuracy: 0.93 - ETA: 0s - loss: 0.2544 - accuracy: 0.92 - ETA: 0s - loss: 0.2563 - accuracy: 0.92 - ETA: 0s - loss: 0.2609 - accuracy: 0.92 - ETA: 0s - loss: 0.2581 - accuracy: 0.92 - ETA: 0s - loss: 0.2551 - accuracy: 0.92 - ETA: 0s - loss: 0.2526 - accuracy: 0.92 - 1s 39us/sample - loss: 0.2528 - accuracy: 0.9295 - val_loss: 0.1209 - val_accuracy: 0.9760\n",
      "Epoch 40/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2808 - accuracy: 0.93 - ETA: 0s - loss: 0.2508 - accuracy: 0.92 - ETA: 0s - loss: 0.2553 - accuracy: 0.91 - ETA: 0s - loss: 0.2519 - accuracy: 0.91 - ETA: 0s - loss: 0.2497 - accuracy: 0.91 - ETA: 0s - loss: 0.2477 - accuracy: 0.92 - ETA: 0s - loss: 0.2517 - accuracy: 0.92 - ETA: 0s - loss: 0.2497 - accuracy: 0.92 - ETA: 0s - loss: 0.2547 - accuracy: 0.92 - ETA: 0s - loss: 0.2591 - accuracy: 0.92 - 1s 37us/sample - loss: 0.2583 - accuracy: 0.9260 - val_loss: 0.1225 - val_accuracy: 0.9804\n",
      "Epoch 41/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1893 - accuracy: 0.96 - ETA: 0s - loss: 0.3048 - accuracy: 0.91 - ETA: 0s - loss: 0.2824 - accuracy: 0.92 - ETA: 0s - loss: 0.2844 - accuracy: 0.91 - ETA: 0s - loss: 0.2728 - accuracy: 0.92 - ETA: 0s - loss: 0.2722 - accuracy: 0.92 - ETA: 0s - loss: 0.2611 - accuracy: 0.92 - ETA: 0s - loss: 0.2664 - accuracy: 0.92 - ETA: 0s - loss: 0.2681 - accuracy: 0.92 - ETA: 0s - loss: 0.2625 - accuracy: 0.92 - ETA: 0s - loss: 0.2613 - accuracy: 0.93 - 1s 41us/sample - loss: 0.2613 - accuracy: 0.9301 - val_loss: 0.1314 - val_accuracy: 0.9687\n",
      "Epoch 42/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0589 - accuracy: 1.00 - ETA: 0s - loss: 0.2372 - accuracy: 0.93 - ETA: 0s - loss: 0.2381 - accuracy: 0.93 - ETA: 0s - loss: 0.2519 - accuracy: 0.93 - ETA: 0s - loss: 0.2532 - accuracy: 0.93 - ETA: 0s - loss: 0.2499 - accuracy: 0.93 - ETA: 0s - loss: 0.2443 - accuracy: 0.93 - ETA: 0s - loss: 0.2426 - accuracy: 0.94 - ETA: 0s - loss: 0.2449 - accuracy: 0.94 - ETA: 0s - loss: 0.2481 - accuracy: 0.93 - ETA: 0s - loss: 0.2493 - accuracy: 0.93 - 1s 40us/sample - loss: 0.2519 - accuracy: 0.9392 - val_loss: 0.1429 - val_accuracy: 0.9788\n",
      "Epoch 43/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.3608 - accuracy: 0.84 - ETA: 0s - loss: 0.2532 - accuracy: 0.93 - ETA: 0s - loss: 0.2638 - accuracy: 0.93 - ETA: 0s - loss: 0.2530 - accuracy: 0.93 - ETA: 0s - loss: 0.2523 - accuracy: 0.93 - ETA: 0s - loss: 0.2522 - accuracy: 0.93 - ETA: 0s - loss: 0.2603 - accuracy: 0.93 - ETA: 0s - loss: 0.2619 - accuracy: 0.93 - ETA: 0s - loss: 0.2615 - accuracy: 0.93 - ETA: 0s - loss: 0.2561 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2591 - accuracy: 0.9349 - val_loss: 0.1376 - val_accuracy: 0.9810\n",
      "Epoch 44/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1749 - accuracy: 0.90 - ETA: 0s - loss: 0.2351 - accuracy: 0.93 - ETA: 0s - loss: 0.2463 - accuracy: 0.93 - ETA: 0s - loss: 0.2374 - accuracy: 0.93 - ETA: 0s - loss: 0.2409 - accuracy: 0.93 - ETA: 0s - loss: 0.2476 - accuracy: 0.93 - ETA: 0s - loss: 0.2531 - accuracy: 0.93 - ETA: 0s - loss: 0.2541 - accuracy: 0.93 - ETA: 0s - loss: 0.2524 - accuracy: 0.93 - ETA: 0s - loss: 0.2525 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2518 - accuracy: 0.9346 - val_loss: 0.1237 - val_accuracy: 0.9827\n",
      "Epoch 45/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0473 - accuracy: 1.00 - ETA: 0s - loss: 0.2413 - accuracy: 0.93 - ETA: 0s - loss: 0.2144 - accuracy: 0.93 - ETA: 0s - loss: 0.2381 - accuracy: 0.93 - ETA: 0s - loss: 0.2511 - accuracy: 0.93 - ETA: 0s - loss: 0.2433 - accuracy: 0.93 - ETA: 0s - loss: 0.2491 - accuracy: 0.93 - ETA: 0s - loss: 0.2514 - accuracy: 0.93 - ETA: 0s - loss: 0.2548 - accuracy: 0.93 - ETA: 0s - loss: 0.2552 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2560 - accuracy: 0.9345 - val_loss: 0.1400 - val_accuracy: 0.9721\n",
      "Epoch 46/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0744 - accuracy: 0.96 - ETA: 0s - loss: 0.2647 - accuracy: 0.93 - ETA: 0s - loss: 0.2921 - accuracy: 0.92 - ETA: 0s - loss: 0.2875 - accuracy: 0.92 - ETA: 0s - loss: 0.2743 - accuracy: 0.92 - ETA: 0s - loss: 0.2666 - accuracy: 0.93 - ETA: 0s - loss: 0.2600 - accuracy: 0.93 - ETA: 0s - loss: 0.2548 - accuracy: 0.93 - ETA: 0s - loss: 0.2548 - accuracy: 0.93 - ETA: 0s - loss: 0.2586 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2576 - accuracy: 0.9366 - val_loss: 0.1499 - val_accuracy: 0.9782\n",
      "Epoch 47/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.1161 - accuracy: 0.96 - ETA: 0s - loss: 0.2731 - accuracy: 0.93 - ETA: 0s - loss: 0.2674 - accuracy: 0.93 - ETA: 0s - loss: 0.2484 - accuracy: 0.93 - ETA: 0s - loss: 0.2644 - accuracy: 0.93 - ETA: 0s - loss: 0.2617 - accuracy: 0.93 - ETA: 0s - loss: 0.2590 - accuracy: 0.93 - ETA: 0s - loss: 0.2554 - accuracy: 0.93 - ETA: 0s - loss: 0.2632 - accuracy: 0.92 - ETA: 0s - loss: 0.2607 - accuracy: 0.92 - 1s 36us/sample - loss: 0.2577 - accuracy: 0.9304 - val_loss: 0.1000 - val_accuracy: 0.9866\n",
      "Epoch 48/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.7416 - accuracy: 0.90 - ETA: 0s - loss: 0.2201 - accuracy: 0.93 - ETA: 0s - loss: 0.2420 - accuracy: 0.93 - ETA: 0s - loss: 0.2532 - accuracy: 0.92 - ETA: 0s - loss: 0.2528 - accuracy: 0.92 - ETA: 0s - loss: 0.2613 - accuracy: 0.92 - ETA: 0s - loss: 0.2579 - accuracy: 0.92 - ETA: 0s - loss: 0.2624 - accuracy: 0.92 - ETA: 0s - loss: 0.2552 - accuracy: 0.92 - ETA: 0s - loss: 0.2555 - accuracy: 0.92 - 1s 36us/sample - loss: 0.2572 - accuracy: 0.9284 - val_loss: 0.1092 - val_accuracy: 0.9827\n",
      "Epoch 49/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1586 - accuracy: 0.96 - ETA: 0s - loss: 0.2749 - accuracy: 0.94 - ETA: 0s - loss: 0.2515 - accuracy: 0.94 - ETA: 0s - loss: 0.2491 - accuracy: 0.94 - ETA: 0s - loss: 0.2486 - accuracy: 0.93 - ETA: 0s - loss: 0.2598 - accuracy: 0.94 - ETA: 0s - loss: 0.2683 - accuracy: 0.93 - ETA: 0s - loss: 0.2591 - accuracy: 0.93 - ETA: 0s - loss: 0.2533 - accuracy: 0.93 - ETA: 0s - loss: 0.2565 - accuracy: 0.94 - 1s 36us/sample - loss: 0.2563 - accuracy: 0.9405 - val_loss: 0.1396 - val_accuracy: 0.9743\n",
      "Epoch 50/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1191 - accuracy: 0.96 - ETA: 0s - loss: 0.2670 - accuracy: 0.93 - ETA: 0s - loss: 0.2468 - accuracy: 0.94 - ETA: 0s - loss: 0.2432 - accuracy: 0.94 - ETA: 0s - loss: 0.2486 - accuracy: 0.94 - ETA: 0s - loss: 0.2425 - accuracy: 0.94 - ETA: 0s - loss: 0.2475 - accuracy: 0.94 - ETA: 0s - loss: 0.2434 - accuracy: 0.94 - ETA: 0s - loss: 0.2438 - accuracy: 0.94 - ETA: 0s - loss: 0.2486 - accuracy: 0.94 - 1s 36us/sample - loss: 0.2516 - accuracy: 0.9426 - val_loss: 0.1593 - val_accuracy: 0.9665\n",
      "Epoch 51/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1284 - accuracy: 0.96 - ETA: 0s - loss: 0.2742 - accuracy: 0.94 - ETA: 0s - loss: 0.2811 - accuracy: 0.94 - ETA: 0s - loss: 0.2700 - accuracy: 0.94 - ETA: 0s - loss: 0.2567 - accuracy: 0.94 - ETA: 0s - loss: 0.2524 - accuracy: 0.94 - ETA: 0s - loss: 0.2583 - accuracy: 0.94 - ETA: 0s - loss: 0.2514 - accuracy: 0.94 - ETA: 0s - loss: 0.2509 - accuracy: 0.94 - ETA: 0s - loss: 0.2550 - accuracy: 0.94 - 1s 36us/sample - loss: 0.2561 - accuracy: 0.9459 - val_loss: 0.1378 - val_accuracy: 0.9648\n",
      "Epoch 52/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1270 - accuracy: 0.96 - ETA: 0s - loss: 0.2337 - accuracy: 0.93 - ETA: 0s - loss: 0.2687 - accuracy: 0.93 - ETA: 0s - loss: 0.2715 - accuracy: 0.92 - ETA: 0s - loss: 0.2699 - accuracy: 0.93 - ETA: 0s - loss: 0.2657 - accuracy: 0.93 - ETA: 0s - loss: 0.2638 - accuracy: 0.93 - ETA: 0s - loss: 0.2577 - accuracy: 0.93 - ETA: 0s - loss: 0.2572 - accuracy: 0.93 - ETA: 0s - loss: 0.2559 - accuracy: 0.93 - 1s 38us/sample - loss: 0.2581 - accuracy: 0.9355 - val_loss: 0.1323 - val_accuracy: 0.9732\n",
      "Epoch 53/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1561 - accuracy: 0.90 - ETA: 0s - loss: 0.2496 - accuracy: 0.92 - ETA: 0s - loss: 0.2404 - accuracy: 0.92 - ETA: 0s - loss: 0.2450 - accuracy: 0.93 - ETA: 0s - loss: 0.2536 - accuracy: 0.93 - ETA: 0s - loss: 0.2507 - accuracy: 0.93 - ETA: 0s - loss: 0.2466 - accuracy: 0.93 - ETA: 0s - loss: 0.2511 - accuracy: 0.93 - ETA: 0s - loss: 0.2497 - accuracy: 0.93 - ETA: 0s - loss: 0.2538 - accuracy: 0.93 - 1s 37us/sample - loss: 0.2491 - accuracy: 0.9370 - val_loss: 0.1207 - val_accuracy: 0.9816\n",
      "Epoch 54/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1811 - accuracy: 0.93 - ETA: 0s - loss: 0.2902 - accuracy: 0.93 - ETA: 0s - loss: 0.2873 - accuracy: 0.93 - ETA: 0s - loss: 0.2856 - accuracy: 0.93 - ETA: 0s - loss: 0.2644 - accuracy: 0.93 - ETA: 0s - loss: 0.2572 - accuracy: 0.93 - ETA: 0s - loss: 0.2642 - accuracy: 0.93 - ETA: 0s - loss: 0.2572 - accuracy: 0.93 - ETA: 0s - loss: 0.2501 - accuracy: 0.93 - ETA: 0s - loss: 0.2478 - accuracy: 0.93 - 1s 37us/sample - loss: 0.2465 - accuracy: 0.9385 - val_loss: 0.1095 - val_accuracy: 0.9765\n",
      "Epoch 55/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.3729 - accuracy: 0.90 - ETA: 0s - loss: 0.3060 - accuracy: 0.91 - ETA: 0s - loss: 0.2714 - accuracy: 0.93 - ETA: 0s - loss: 0.2761 - accuracy: 0.93 - ETA: 0s - loss: 0.2639 - accuracy: 0.93 - ETA: 0s - loss: 0.2607 - accuracy: 0.93 - ETA: 0s - loss: 0.2604 - accuracy: 0.93 - ETA: 0s - loss: 0.2677 - accuracy: 0.93 - ETA: 0s - loss: 0.2603 - accuracy: 0.93 - ETA: 0s - loss: 0.2577 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2588 - accuracy: 0.9345 - val_loss: 0.1228 - val_accuracy: 0.9765\n",
      "Epoch 56/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0792 - accuracy: 0.96 - ETA: 0s - loss: 0.2643 - accuracy: 0.92 - ETA: 0s - loss: 0.2530 - accuracy: 0.92 - ETA: 0s - loss: 0.2530 - accuracy: 0.93 - ETA: 0s - loss: 0.2502 - accuracy: 0.93 - ETA: 0s - loss: 0.2465 - accuracy: 0.93 - ETA: 0s - loss: 0.2494 - accuracy: 0.93 - ETA: 0s - loss: 0.2476 - accuracy: 0.93 - ETA: 0s - loss: 0.2485 - accuracy: 0.93 - ETA: 0s - loss: 0.2480 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2503 - accuracy: 0.9400 - val_loss: 0.1335 - val_accuracy: 0.9804\n",
      "Epoch 57/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.2147 - accuracy: 0.93 - ETA: 0s - loss: 0.2221 - accuracy: 0.94 - ETA: 0s - loss: 0.2582 - accuracy: 0.93 - ETA: 0s - loss: 0.2619 - accuracy: 0.93 - ETA: 0s - loss: 0.2584 - accuracy: 0.93 - ETA: 0s - loss: 0.2584 - accuracy: 0.93 - ETA: 0s - loss: 0.2566 - accuracy: 0.93 - ETA: 0s - loss: 0.2564 - accuracy: 0.93 - ETA: 0s - loss: 0.2559 - accuracy: 0.93 - ETA: 0s - loss: 0.2532 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2514 - accuracy: 0.9368 - val_loss: 0.1348 - val_accuracy: 0.9849\n",
      "Epoch 58/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.3788 - accuracy: 0.90 - ETA: 0s - loss: 0.2670 - accuracy: 0.94 - ETA: 0s - loss: 0.2606 - accuracy: 0.93 - ETA: 0s - loss: 0.2634 - accuracy: 0.93 - ETA: 0s - loss: 0.2588 - accuracy: 0.93 - ETA: 0s - loss: 0.2595 - accuracy: 0.93 - ETA: 0s - loss: 0.2509 - accuracy: 0.93 - ETA: 0s - loss: 0.2522 - accuracy: 0.93 - ETA: 0s - loss: 0.2494 - accuracy: 0.93 - ETA: 0s - loss: 0.2447 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2466 - accuracy: 0.9348 - val_loss: 0.1211 - val_accuracy: 0.9782\n",
      "Epoch 59/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0644 - accuracy: 1.00 - ETA: 0s - loss: 0.2852 - accuracy: 0.93 - ETA: 0s - loss: 0.2759 - accuracy: 0.93 - ETA: 0s - loss: 0.2610 - accuracy: 0.92 - ETA: 0s - loss: 0.2748 - accuracy: 0.92 - ETA: 0s - loss: 0.2647 - accuracy: 0.92 - ETA: 0s - loss: 0.2633 - accuracy: 0.92 - ETA: 0s - loss: 0.2591 - accuracy: 0.92 - ETA: 0s - loss: 0.2531 - accuracy: 0.92 - ETA: 0s - loss: 0.2554 - accuracy: 0.92 - 1s 36us/sample - loss: 0.2563 - accuracy: 0.9242 - val_loss: 0.1135 - val_accuracy: 0.9777\n",
      "Epoch 60/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0945 - accuracy: 0.93 - ETA: 0s - loss: 0.2559 - accuracy: 0.94 - ETA: 0s - loss: 0.2610 - accuracy: 0.93 - ETA: 0s - loss: 0.2776 - accuracy: 0.93 - ETA: 0s - loss: 0.2773 - accuracy: 0.93 - ETA: 0s - loss: 0.2675 - accuracy: 0.93 - ETA: 0s - loss: 0.2636 - accuracy: 0.93 - ETA: 0s - loss: 0.2639 - accuracy: 0.93 - ETA: 0s - loss: 0.2578 - accuracy: 0.93 - ETA: 0s - loss: 0.2571 - accuracy: 0.93 - 1s 37us/sample - loss: 0.2553 - accuracy: 0.9361 - val_loss: 0.1150 - val_accuracy: 0.9732\n",
      "Epoch 61/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1306 - accuracy: 0.87 - ETA: 0s - loss: 0.2400 - accuracy: 0.93 - ETA: 0s - loss: 0.2436 - accuracy: 0.93 - ETA: 0s - loss: 0.2627 - accuracy: 0.93 - ETA: 0s - loss: 0.2487 - accuracy: 0.93 - ETA: 0s - loss: 0.2497 - accuracy: 0.93 - ETA: 0s - loss: 0.2520 - accuracy: 0.93 - ETA: 0s - loss: 0.2447 - accuracy: 0.93 - ETA: 0s - loss: 0.2448 - accuracy: 0.93 - ETA: 0s - loss: 0.2512 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2528 - accuracy: 0.9356 - val_loss: 0.1543 - val_accuracy: 0.9749\n",
      "Epoch 62/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.4492 - accuracy: 0.93 - ETA: 0s - loss: 0.3019 - accuracy: 0.91 - ETA: 0s - loss: 0.2962 - accuracy: 0.92 - ETA: 0s - loss: 0.2687 - accuracy: 0.92 - ETA: 0s - loss: 0.2570 - accuracy: 0.93 - ETA: 0s - loss: 0.2606 - accuracy: 0.93 - ETA: 0s - loss: 0.2605 - accuracy: 0.93 - ETA: 0s - loss: 0.2590 - accuracy: 0.93 - ETA: 0s - loss: 0.2580 - accuracy: 0.93 - ETA: 0s - loss: 0.2540 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2550 - accuracy: 0.9355 - val_loss: 0.1111 - val_accuracy: 0.9765\n",
      "Epoch 63/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0801 - accuracy: 0.96 - ETA: 0s - loss: 0.2849 - accuracy: 0.93 - ETA: 0s - loss: 0.2474 - accuracy: 0.93 - ETA: 0s - loss: 0.2356 - accuracy: 0.94 - ETA: 0s - loss: 0.2384 - accuracy: 0.93 - ETA: 0s - loss: 0.2467 - accuracy: 0.93 - ETA: 0s - loss: 0.2432 - accuracy: 0.93 - ETA: 0s - loss: 0.2516 - accuracy: 0.93 - ETA: 0s - loss: 0.2520 - accuracy: 0.93 - ETA: 0s - loss: 0.2494 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2486 - accuracy: 0.9371 - val_loss: 0.1237 - val_accuracy: 0.9810\n",
      "Epoch 64/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1401 - accuracy: 0.90 - ETA: 0s - loss: 0.2655 - accuracy: 0.94 - ETA: 0s - loss: 0.2618 - accuracy: 0.93 - ETA: 0s - loss: 0.2614 - accuracy: 0.93 - ETA: 0s - loss: 0.2542 - accuracy: 0.93 - ETA: 0s - loss: 0.2576 - accuracy: 0.93 - ETA: 0s - loss: 0.2467 - accuracy: 0.93 - ETA: 0s - loss: 0.2453 - accuracy: 0.93 - ETA: 0s - loss: 0.2380 - accuracy: 0.93 - ETA: 0s - loss: 0.2425 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2443 - accuracy: 0.9362 - val_loss: 0.1302 - val_accuracy: 0.9821\n",
      "Epoch 65/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.6428 - accuracy: 0.87 - ETA: 0s - loss: 0.2399 - accuracy: 0.94 - ETA: 0s - loss: 0.2547 - accuracy: 0.94 - ETA: 0s - loss: 0.2815 - accuracy: 0.93 - ETA: 0s - loss: 0.2831 - accuracy: 0.93 - ETA: 0s - loss: 0.2733 - accuracy: 0.93 - ETA: 0s - loss: 0.2655 - accuracy: 0.93 - ETA: 0s - loss: 0.2577 - accuracy: 0.93 - ETA: 0s - loss: 0.2538 - accuracy: 0.93 - ETA: 0s - loss: 0.2547 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2544 - accuracy: 0.9378 - val_loss: 0.1612 - val_accuracy: 0.9575\n",
      "Epoch 66/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1237 - accuracy: 1.00 - ETA: 0s - loss: 0.2510 - accuracy: 0.93 - ETA: 0s - loss: 0.2626 - accuracy: 0.92 - ETA: 0s - loss: 0.2604 - accuracy: 0.93 - ETA: 0s - loss: 0.2581 - accuracy: 0.93 - ETA: 0s - loss: 0.2576 - accuracy: 0.93 - ETA: 0s - loss: 0.2561 - accuracy: 0.93 - ETA: 0s - loss: 0.2493 - accuracy: 0.93 - ETA: 0s - loss: 0.2592 - accuracy: 0.93 - ETA: 0s - loss: 0.2569 - accuracy: 0.93 - 1s 37us/sample - loss: 0.2533 - accuracy: 0.9304 - val_loss: 0.1006 - val_accuracy: 0.9799\n",
      "Epoch 67/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0939 - accuracy: 0.93 - ETA: 0s - loss: 0.2672 - accuracy: 0.92 - ETA: 0s - loss: 0.2804 - accuracy: 0.91 - ETA: 0s - loss: 0.2760 - accuracy: 0.91 - ETA: 0s - loss: 0.2622 - accuracy: 0.92 - ETA: 0s - loss: 0.2586 - accuracy: 0.92 - ETA: 0s - loss: 0.2605 - accuracy: 0.92 - ETA: 0s - loss: 0.2584 - accuracy: 0.92 - ETA: 0s - loss: 0.2524 - accuracy: 0.92 - ETA: 0s - loss: 0.2518 - accuracy: 0.92 - 1s 36us/sample - loss: 0.2512 - accuracy: 0.9267 - val_loss: 0.1074 - val_accuracy: 0.9827\n",
      "Epoch 68/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0938 - accuracy: 0.93 - ETA: 0s - loss: 0.2155 - accuracy: 0.94 - ETA: 0s - loss: 0.2391 - accuracy: 0.93 - ETA: 0s - loss: 0.2575 - accuracy: 0.92 - ETA: 0s - loss: 0.2590 - accuracy: 0.92 - ETA: 0s - loss: 0.2577 - accuracy: 0.92 - ETA: 0s - loss: 0.2523 - accuracy: 0.92 - ETA: 0s - loss: 0.2528 - accuracy: 0.92 - ETA: 0s - loss: 0.2603 - accuracy: 0.92 - ETA: 0s - loss: 0.2554 - accuracy: 0.92 - 1s 36us/sample - loss: 0.2538 - accuracy: 0.9216 - val_loss: 0.0896 - val_accuracy: 0.9849\n",
      "Epoch 69/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2761 - accuracy: 0.84 - ETA: 0s - loss: 0.2300 - accuracy: 0.94 - ETA: 0s - loss: 0.2564 - accuracy: 0.93 - ETA: 0s - loss: 0.2612 - accuracy: 0.92 - ETA: 0s - loss: 0.2587 - accuracy: 0.92 - ETA: 0s - loss: 0.2581 - accuracy: 0.92 - ETA: 0s - loss: 0.2545 - accuracy: 0.92 - ETA: 0s - loss: 0.2546 - accuracy: 0.92 - ETA: 0s - loss: 0.2596 - accuracy: 0.92 - ETA: 0s - loss: 0.2550 - accuracy: 0.92 - 1s 37us/sample - loss: 0.2575 - accuracy: 0.9289 - val_loss: 0.1490 - val_accuracy: 0.9721\n",
      "Epoch 70/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2389 - accuracy: 0.84 - ETA: 0s - loss: 0.1978 - accuracy: 0.93 - ETA: 0s - loss: 0.2362 - accuracy: 0.93 - ETA: 0s - loss: 0.2488 - accuracy: 0.93 - ETA: 0s - loss: 0.2603 - accuracy: 0.93 - ETA: 0s - loss: 0.2684 - accuracy: 0.93 - ETA: 0s - loss: 0.2644 - accuracy: 0.93 - ETA: 0s - loss: 0.2643 - accuracy: 0.93 - ETA: 0s - loss: 0.2650 - accuracy: 0.93 - ETA: 0s - loss: 0.2593 - accuracy: 0.93 - 1s 38us/sample - loss: 0.2586 - accuracy: 0.9321 - val_loss: 0.1250 - val_accuracy: 0.9793\n",
      "Epoch 71/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.4188 - accuracy: 0.87 - ETA: 0s - loss: 0.2494 - accuracy: 0.93 - ETA: 0s - loss: 0.2334 - accuracy: 0.93 - ETA: 0s - loss: 0.2504 - accuracy: 0.93 - ETA: 0s - loss: 0.2528 - accuracy: 0.93 - ETA: 0s - loss: 0.2579 - accuracy: 0.93 - ETA: 0s - loss: 0.2634 - accuracy: 0.93 - ETA: 0s - loss: 0.2583 - accuracy: 0.93 - ETA: 0s - loss: 0.2555 - accuracy: 0.93 - ETA: 0s - loss: 0.2574 - accuracy: 0.93 - ETA: 0s - loss: 0.2589 - accuracy: 0.93 - 1s 41us/sample - loss: 0.2567 - accuracy: 0.9324 - val_loss: 0.1136 - val_accuracy: 0.9804\n",
      "Epoch 72/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.4374 - accuracy: 0.87 - ETA: 0s - loss: 0.2638 - accuracy: 0.94 - ETA: 0s - loss: 0.2274 - accuracy: 0.94 - ETA: 0s - loss: 0.2640 - accuracy: 0.93 - ETA: 0s - loss: 0.2718 - accuracy: 0.93 - ETA: 0s - loss: 0.2658 - accuracy: 0.93 - ETA: 0s - loss: 0.2607 - accuracy: 0.93 - ETA: 0s - loss: 0.2637 - accuracy: 0.93 - ETA: 0s - loss: 0.2584 - accuracy: 0.93 - ETA: 0s - loss: 0.2583 - accuracy: 0.93 - ETA: 0s - loss: 0.2542 - accuracy: 0.93 - ETA: 0s - loss: 0.2494 - accuracy: 0.93 - 1s 43us/sample - loss: 0.2476 - accuracy: 0.9367 - val_loss: 0.1010 - val_accuracy: 0.9838\n",
      "Epoch 73/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2506 - accuracy: 0.93 - ETA: 0s - loss: 0.2877 - accuracy: 0.94 - ETA: 0s - loss: 0.2978 - accuracy: 0.92 - ETA: 0s - loss: 0.2829 - accuracy: 0.92 - ETA: 0s - loss: 0.2580 - accuracy: 0.93 - ETA: 0s - loss: 0.2526 - accuracy: 0.93 - ETA: 0s - loss: 0.2368 - accuracy: 0.93 - ETA: 0s - loss: 0.2382 - accuracy: 0.93 - ETA: 0s - loss: 0.2433 - accuracy: 0.93 - ETA: 0s - loss: 0.2468 - accuracy: 0.93 - 1s 37us/sample - loss: 0.2445 - accuracy: 0.9372 - val_loss: 0.1167 - val_accuracy: 0.9821\n",
      "Epoch 74/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.5800 - accuracy: 0.93 - ETA: 0s - loss: 0.2147 - accuracy: 0.95 - ETA: 0s - loss: 0.2314 - accuracy: 0.95 - ETA: 0s - loss: 0.2348 - accuracy: 0.94 - ETA: 0s - loss: 0.2328 - accuracy: 0.94 - ETA: 0s - loss: 0.2411 - accuracy: 0.94 - ETA: 0s - loss: 0.2471 - accuracy: 0.93 - ETA: 0s - loss: 0.2437 - accuracy: 0.93 - ETA: 0s - loss: 0.2441 - accuracy: 0.93 - ETA: 0s - loss: 0.2471 - accuracy: 0.93 - 1s 37us/sample - loss: 0.2466 - accuracy: 0.9396 - val_loss: 0.1157 - val_accuracy: 0.9799\n",
      "Epoch 75/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2095 - accuracy: 0.90 - ETA: 0s - loss: 0.2787 - accuracy: 0.91 - ETA: 0s - loss: 0.2541 - accuracy: 0.93 - ETA: 0s - loss: 0.2777 - accuracy: 0.92 - ETA: 0s - loss: 0.2678 - accuracy: 0.92 - ETA: 0s - loss: 0.2602 - accuracy: 0.93 - ETA: 0s - loss: 0.2568 - accuracy: 0.93 - ETA: 0s - loss: 0.2491 - accuracy: 0.93 - ETA: 0s - loss: 0.2514 - accuracy: 0.93 - ETA: 0s - loss: 0.2522 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2505 - accuracy: 0.9352 - val_loss: 0.1499 - val_accuracy: 0.9665\n",
      "Epoch 76/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.3405 - accuracy: 0.87 - ETA: 0s - loss: 0.2642 - accuracy: 0.94 - ETA: 0s - loss: 0.2617 - accuracy: 0.94 - ETA: 0s - loss: 0.2706 - accuracy: 0.93 - ETA: 0s - loss: 0.2580 - accuracy: 0.93 - ETA: 0s - loss: 0.2573 - accuracy: 0.93 - ETA: 0s - loss: 0.2557 - accuracy: 0.93 - ETA: 0s - loss: 0.2526 - accuracy: 0.93 - ETA: 0s - loss: 0.2451 - accuracy: 0.93 - ETA: 0s - loss: 0.2476 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2464 - accuracy: 0.9375 - val_loss: 0.1315 - val_accuracy: 0.9754\n",
      "Epoch 77/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.8941 - accuracy: 0.93 - ETA: 0s - loss: 0.2678 - accuracy: 0.93 - ETA: 0s - loss: 0.2764 - accuracy: 0.92 - ETA: 0s - loss: 0.2615 - accuracy: 0.93 - ETA: 0s - loss: 0.2552 - accuracy: 0.93 - ETA: 0s - loss: 0.2586 - accuracy: 0.93 - ETA: 0s - loss: 0.2554 - accuracy: 0.93 - ETA: 0s - loss: 0.2506 - accuracy: 0.93 - ETA: 0s - loss: 0.2479 - accuracy: 0.93 - ETA: 0s - loss: 0.2506 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2489 - accuracy: 0.9352 - val_loss: 0.1117 - val_accuracy: 0.9810\n",
      "Epoch 78/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.3598 - accuracy: 0.93 - ETA: 0s - loss: 0.2298 - accuracy: 0.95 - ETA: 0s - loss: 0.2499 - accuracy: 0.93 - ETA: 0s - loss: 0.2310 - accuracy: 0.93 - ETA: 0s - loss: 0.2309 - accuracy: 0.93 - ETA: 0s - loss: 0.2432 - accuracy: 0.93 - ETA: 0s - loss: 0.2449 - accuracy: 0.93 - ETA: 0s - loss: 0.2490 - accuracy: 0.93 - ETA: 0s - loss: 0.2508 - accuracy: 0.93 - ETA: 0s - loss: 0.2495 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2487 - accuracy: 0.9313 - val_loss: 0.1043 - val_accuracy: 0.9765\n",
      "Epoch 79/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.0474 - accuracy: 1.00 - ETA: 0s - loss: 0.2783 - accuracy: 0.93 - ETA: 0s - loss: 0.2440 - accuracy: 0.93 - ETA: 0s - loss: 0.2556 - accuracy: 0.93 - ETA: 0s - loss: 0.2569 - accuracy: 0.93 - ETA: 0s - loss: 0.2599 - accuracy: 0.92 - ETA: 0s - loss: 0.2578 - accuracy: 0.92 - ETA: 0s - loss: 0.2540 - accuracy: 0.92 - ETA: 0s - loss: 0.2457 - accuracy: 0.93 - ETA: 0s - loss: 0.2477 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2488 - accuracy: 0.9307 - val_loss: 0.1357 - val_accuracy: 0.9782\n",
      "Epoch 80/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.6553 - accuracy: 0.87 - ETA: 0s - loss: 0.2613 - accuracy: 0.92 - ETA: 0s - loss: 0.2472 - accuracy: 0.92 - ETA: 0s - loss: 0.2698 - accuracy: 0.92 - ETA: 0s - loss: 0.2631 - accuracy: 0.92 - ETA: 0s - loss: 0.2579 - accuracy: 0.93 - ETA: 0s - loss: 0.2588 - accuracy: 0.93 - ETA: 0s - loss: 0.2567 - accuracy: 0.93 - ETA: 0s - loss: 0.2556 - accuracy: 0.93 - ETA: 0s - loss: 0.2482 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2531 - accuracy: 0.9343 - val_loss: 0.1727 - val_accuracy: 0.9715\n",
      "Epoch 81/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.3269 - accuracy: 0.87 - ETA: 0s - loss: 0.2355 - accuracy: 0.93 - ETA: 0s - loss: 0.2298 - accuracy: 0.93 - ETA: 0s - loss: 0.2343 - accuracy: 0.93 - ETA: 0s - loss: 0.2271 - accuracy: 0.93 - ETA: 0s - loss: 0.2299 - accuracy: 0.93 - ETA: 0s - loss: 0.2365 - accuracy: 0.93 - ETA: 0s - loss: 0.2374 - accuracy: 0.93 - ETA: 0s - loss: 0.2427 - accuracy: 0.93 - ETA: 0s - loss: 0.2478 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2477 - accuracy: 0.9323 - val_loss: 0.1313 - val_accuracy: 0.9788\n",
      "Epoch 82/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.5019 - accuracy: 0.84 - ETA: 0s - loss: 0.2517 - accuracy: 0.93 - ETA: 0s - loss: 0.2462 - accuracy: 0.93 - ETA: 0s - loss: 0.2529 - accuracy: 0.93 - ETA: 0s - loss: 0.2577 - accuracy: 0.93 - ETA: 0s - loss: 0.2597 - accuracy: 0.93 - ETA: 0s - loss: 0.2642 - accuracy: 0.93 - ETA: 0s - loss: 0.2612 - accuracy: 0.93 - ETA: 0s - loss: 0.2613 - accuracy: 0.93 - ETA: 0s - loss: 0.2566 - accuracy: 0.93 - 1s 37us/sample - loss: 0.2552 - accuracy: 0.9352 - val_loss: 0.1178 - val_accuracy: 0.9832\n",
      "Epoch 83/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0669 - accuracy: 0.96 - ETA: 0s - loss: 0.2233 - accuracy: 0.94 - ETA: 0s - loss: 0.2410 - accuracy: 0.94 - ETA: 0s - loss: 0.2498 - accuracy: 0.93 - ETA: 0s - loss: 0.2510 - accuracy: 0.93 - ETA: 0s - loss: 0.2517 - accuracy: 0.93 - ETA: 0s - loss: 0.2409 - accuracy: 0.93 - ETA: 0s - loss: 0.2406 - accuracy: 0.93 - ETA: 0s - loss: 0.2395 - accuracy: 0.93 - ETA: 0s - loss: 0.2459 - accuracy: 0.93 - 1s 37us/sample - loss: 0.2476 - accuracy: 0.9375 - val_loss: 0.1540 - val_accuracy: 0.9816\n",
      "Epoch 84/100\n",
      "14318/14318 [==============================] - ETA: 2s - loss: 0.0878 - accuracy: 1.00 - ETA: 0s - loss: 0.2393 - accuracy: 0.94 - ETA: 0s - loss: 0.2152 - accuracy: 0.94 - ETA: 0s - loss: 0.2452 - accuracy: 0.94 - ETA: 0s - loss: 0.2431 - accuracy: 0.94 - ETA: 0s - loss: 0.2536 - accuracy: 0.93 - ETA: 0s - loss: 0.2567 - accuracy: 0.93 - ETA: 0s - loss: 0.2531 - accuracy: 0.93 - ETA: 0s - loss: 0.2542 - accuracy: 0.93 - ETA: 0s - loss: 0.2498 - accuracy: 0.93 - 1s 38us/sample - loss: 0.2493 - accuracy: 0.9341 - val_loss: 0.1222 - val_accuracy: 0.9793\n",
      "Epoch 85/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.3532 - accuracy: 0.93 - ETA: 0s - loss: 0.2574 - accuracy: 0.94 - ETA: 0s - loss: 0.2394 - accuracy: 0.94 - ETA: 0s - loss: 0.2359 - accuracy: 0.94 - ETA: 0s - loss: 0.2473 - accuracy: 0.94 - ETA: 0s - loss: 0.2420 - accuracy: 0.94 - ETA: 0s - loss: 0.2552 - accuracy: 0.93 - ETA: 0s - loss: 0.2570 - accuracy: 0.93 - ETA: 0s - loss: 0.2573 - accuracy: 0.93 - 1s 35us/sample - loss: 0.2496 - accuracy: 0.9360 - val_loss: 0.0897 - val_accuracy: 0.9832\n",
      "Epoch 86/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.4547 - accuracy: 0.93 - ETA: 0s - loss: 0.2851 - accuracy: 0.94 - ETA: 0s - loss: 0.2727 - accuracy: 0.93 - ETA: 0s - loss: 0.2700 - accuracy: 0.93 - ETA: 0s - loss: 0.2669 - accuracy: 0.94 - ETA: 0s - loss: 0.2615 - accuracy: 0.94 - ETA: 0s - loss: 0.2602 - accuracy: 0.94 - ETA: 0s - loss: 0.2532 - accuracy: 0.94 - ETA: 0s - loss: 0.2571 - accuracy: 0.94 - ETA: 0s - loss: 0.2548 - accuracy: 0.94 - 1s 36us/sample - loss: 0.2514 - accuracy: 0.9413 - val_loss: 0.1095 - val_accuracy: 0.9799\n",
      "Epoch 87/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.3826 - accuracy: 0.87 - ETA: 0s - loss: 0.2671 - accuracy: 0.94 - ETA: 0s - loss: 0.2801 - accuracy: 0.93 - ETA: 0s - loss: 0.2617 - accuracy: 0.93 - ETA: 0s - loss: 0.2559 - accuracy: 0.93 - ETA: 0s - loss: 0.2527 - accuracy: 0.93 - ETA: 0s - loss: 0.2466 - accuracy: 0.93 - ETA: 0s - loss: 0.2478 - accuracy: 0.93 - ETA: 0s - loss: 0.2483 - accuracy: 0.93 - ETA: 0s - loss: 0.2511 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2494 - accuracy: 0.9364 - val_loss: 0.1017 - val_accuracy: 0.9816\n",
      "Epoch 88/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.3421 - accuracy: 0.90 - ETA: 0s - loss: 0.2609 - accuracy: 0.93 - ETA: 0s - loss: 0.2565 - accuracy: 0.93 - ETA: 0s - loss: 0.2706 - accuracy: 0.93 - ETA: 0s - loss: 0.2776 - accuracy: 0.93 - ETA: 0s - loss: 0.2673 - accuracy: 0.93 - ETA: 0s - loss: 0.2607 - accuracy: 0.93 - ETA: 0s - loss: 0.2627 - accuracy: 0.93 - ETA: 0s - loss: 0.2556 - accuracy: 0.93 - ETA: 0s - loss: 0.2556 - accuracy: 0.93 - 1s 37us/sample - loss: 0.2519 - accuracy: 0.9359 - val_loss: 0.1128 - val_accuracy: 0.9788\n",
      "Epoch 89/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1743 - accuracy: 0.96 - ETA: 0s - loss: 0.2388 - accuracy: 0.93 - ETA: 0s - loss: 0.2357 - accuracy: 0.93 - ETA: 0s - loss: 0.2385 - accuracy: 0.94 - ETA: 0s - loss: 0.2419 - accuracy: 0.93 - ETA: 0s - loss: 0.2535 - accuracy: 0.93 - ETA: 0s - loss: 0.2563 - accuracy: 0.93 - ETA: 0s - loss: 0.2485 - accuracy: 0.93 - ETA: 0s - loss: 0.2534 - accuracy: 0.93 - ETA: 0s - loss: 0.2505 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2505 - accuracy: 0.9383 - val_loss: 0.1329 - val_accuracy: 0.9726\n",
      "Epoch 90/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1971 - accuracy: 0.93 - ETA: 0s - loss: 0.2541 - accuracy: 0.93 - ETA: 0s - loss: 0.2470 - accuracy: 0.94 - ETA: 0s - loss: 0.2553 - accuracy: 0.93 - ETA: 0s - loss: 0.2525 - accuracy: 0.94 - ETA: 0s - loss: 0.2531 - accuracy: 0.94 - ETA: 0s - loss: 0.2518 - accuracy: 0.94 - ETA: 0s - loss: 0.2482 - accuracy: 0.93 - ETA: 0s - loss: 0.2444 - accuracy: 0.93 - ETA: 0s - loss: 0.2458 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2450 - accuracy: 0.9388 - val_loss: 0.1370 - val_accuracy: 0.9682\n",
      "Epoch 91/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.8270 - accuracy: 0.87 - ETA: 0s - loss: 0.2358 - accuracy: 0.94 - ETA: 0s - loss: 0.2051 - accuracy: 0.94 - ETA: 0s - loss: 0.2062 - accuracy: 0.94 - ETA: 0s - loss: 0.2206 - accuracy: 0.94 - ETA: 0s - loss: 0.2316 - accuracy: 0.94 - ETA: 0s - loss: 0.2359 - accuracy: 0.94 - ETA: 0s - loss: 0.2367 - accuracy: 0.94 - ETA: 0s - loss: 0.2454 - accuracy: 0.93 - ETA: 0s - loss: 0.2480 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2497 - accuracy: 0.9368 - val_loss: 0.1152 - val_accuracy: 0.9782\n",
      "Epoch 92/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.1462 - accuracy: 0.93 - ETA: 0s - loss: 0.2677 - accuracy: 0.93 - ETA: 0s - loss: 0.2658 - accuracy: 0.93 - ETA: 0s - loss: 0.2691 - accuracy: 0.93 - ETA: 0s - loss: 0.2556 - accuracy: 0.93 - ETA: 0s - loss: 0.2528 - accuracy: 0.93 - ETA: 0s - loss: 0.2570 - accuracy: 0.93 - ETA: 0s - loss: 0.2520 - accuracy: 0.93 - ETA: 0s - loss: 0.2520 - accuracy: 0.93 - ETA: 0s - loss: 0.2416 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2451 - accuracy: 0.9387 - val_loss: 0.1107 - val_accuracy: 0.9855\n",
      "Epoch 93/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1423 - accuracy: 0.96 - ETA: 0s - loss: 0.3242 - accuracy: 0.94 - ETA: 0s - loss: 0.3008 - accuracy: 0.92 - ETA: 0s - loss: 0.2848 - accuracy: 0.93 - ETA: 0s - loss: 0.2782 - accuracy: 0.93 - ETA: 0s - loss: 0.2605 - accuracy: 0.93 - ETA: 0s - loss: 0.2551 - accuracy: 0.93 - ETA: 0s - loss: 0.2478 - accuracy: 0.93 - ETA: 0s - loss: 0.2513 - accuracy: 0.93 - ETA: 0s - loss: 0.2522 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2510 - accuracy: 0.9390 - val_loss: 0.1502 - val_accuracy: 0.9777\n",
      "Epoch 94/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1036 - accuracy: 0.96 - ETA: 0s - loss: 0.2321 - accuracy: 0.94 - ETA: 0s - loss: 0.2388 - accuracy: 0.93 - ETA: 0s - loss: 0.2551 - accuracy: 0.93 - ETA: 0s - loss: 0.2570 - accuracy: 0.93 - ETA: 0s - loss: 0.2571 - accuracy: 0.93 - ETA: 0s - loss: 0.2619 - accuracy: 0.93 - ETA: 0s - loss: 0.2620 - accuracy: 0.93 - ETA: 0s - loss: 0.2569 - accuracy: 0.93 - ETA: 0s - loss: 0.2513 - accuracy: 0.94 - 1s 36us/sample - loss: 0.2500 - accuracy: 0.9399 - val_loss: 0.1335 - val_accuracy: 0.9687\n",
      "Epoch 95/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.3026 - accuracy: 0.90 - ETA: 0s - loss: 0.1899 - accuracy: 0.95 - ETA: 0s - loss: 0.2092 - accuracy: 0.95 - ETA: 0s - loss: 0.2369 - accuracy: 0.94 - ETA: 0s - loss: 0.2394 - accuracy: 0.94 - ETA: 0s - loss: 0.2436 - accuracy: 0.94 - ETA: 0s - loss: 0.2447 - accuracy: 0.93 - ETA: 0s - loss: 0.2360 - accuracy: 0.94 - ETA: 0s - loss: 0.2390 - accuracy: 0.94 - ETA: 0s - loss: 0.2431 - accuracy: 0.94 - 1s 37us/sample - loss: 0.2409 - accuracy: 0.9404 - val_loss: 0.1072 - val_accuracy: 0.9760\n",
      "Epoch 96/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0760 - accuracy: 1.00 - ETA: 0s - loss: 0.2508 - accuracy: 0.94 - ETA: 0s - loss: 0.2611 - accuracy: 0.93 - ETA: 0s - loss: 0.2556 - accuracy: 0.93 - ETA: 0s - loss: 0.2526 - accuracy: 0.93 - ETA: 0s - loss: 0.2540 - accuracy: 0.93 - ETA: 0s - loss: 0.2496 - accuracy: 0.93 - ETA: 0s - loss: 0.2495 - accuracy: 0.93 - ETA: 0s - loss: 0.2472 - accuracy: 0.93 - ETA: 0s - loss: 0.2536 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2553 - accuracy: 0.9336 - val_loss: 0.1517 - val_accuracy: 0.9816\n",
      "Epoch 97/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.4026 - accuracy: 0.93 - ETA: 0s - loss: 0.2510 - accuracy: 0.92 - ETA: 0s - loss: 0.2845 - accuracy: 0.91 - ETA: 0s - loss: 0.2922 - accuracy: 0.91 - ETA: 0s - loss: 0.2893 - accuracy: 0.92 - ETA: 0s - loss: 0.2720 - accuracy: 0.92 - ETA: 0s - loss: 0.2656 - accuracy: 0.93 - ETA: 0s - loss: 0.2635 - accuracy: 0.93 - ETA: 0s - loss: 0.2604 - accuracy: 0.93 - ETA: 0s - loss: 0.2611 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2596 - accuracy: 0.9336 - val_loss: 0.1134 - val_accuracy: 0.9844\n",
      "Epoch 98/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.1379 - accuracy: 0.93 - ETA: 0s - loss: 0.2550 - accuracy: 0.93 - ETA: 0s - loss: 0.2637 - accuracy: 0.92 - ETA: 0s - loss: 0.2698 - accuracy: 0.92 - ETA: 0s - loss: 0.2621 - accuracy: 0.92 - ETA: 0s - loss: 0.2557 - accuracy: 0.93 - ETA: 0s - loss: 0.2515 - accuracy: 0.93 - ETA: 0s - loss: 0.2454 - accuracy: 0.93 - ETA: 0s - loss: 0.2474 - accuracy: 0.93 - ETA: 0s - loss: 0.2441 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2420 - accuracy: 0.9371 - val_loss: 0.1010 - val_accuracy: 0.9838\n",
      "Epoch 99/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1207 - accuracy: 0.93 - ETA: 0s - loss: 0.2355 - accuracy: 0.93 - ETA: 0s - loss: 0.2372 - accuracy: 0.93 - ETA: 0s - loss: 0.2582 - accuracy: 0.93 - ETA: 0s - loss: 0.2514 - accuracy: 0.93 - ETA: 0s - loss: 0.2485 - accuracy: 0.93 - ETA: 0s - loss: 0.2352 - accuracy: 0.94 - ETA: 0s - loss: 0.2399 - accuracy: 0.94 - ETA: 0s - loss: 0.2436 - accuracy: 0.93 - ETA: 0s - loss: 0.2423 - accuracy: 0.93 - 1s 37us/sample - loss: 0.2459 - accuracy: 0.9385 - val_loss: 0.1433 - val_accuracy: 0.9670\n",
      "Epoch 100/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0759 - accuracy: 0.96 - ETA: 0s - loss: 0.2405 - accuracy: 0.93 - ETA: 0s - loss: 0.2411 - accuracy: 0.93 - ETA: 0s - loss: 0.2312 - accuracy: 0.93 - ETA: 0s - loss: 0.2290 - accuracy: 0.94 - ETA: 0s - loss: 0.2390 - accuracy: 0.94 - ETA: 0s - loss: 0.2384 - accuracy: 0.93 - ETA: 0s - loss: 0.2432 - accuracy: 0.93 - ETA: 0s - loss: 0.2397 - accuracy: 0.93 - ETA: 0s - loss: 0.2442 - accuracy: 0.93 - 1s 36us/sample - loss: 0.2450 - accuracy: 0.9369 - val_loss: 0.1280 - val_accuracy: 0.9782\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.2177614570329975, 0.9743017]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#модель с центрированными данными\n",
    "model = tf.keras.models.Sequential([\n",
    "\n",
    "tf.keras.layers.Dense(16, activation=tf.nn.relu),\n",
    "tf.keras.layers.Dropout(0.5),\n",
    "tf.keras.layers.Dense(32, activation=tf.nn.relu),\n",
    "tf.keras.layers.Dropout(0.5),\n",
    "tf.keras.layers.Dense(1, activation=tf.nn.sigmoid)\n",
    "])\n",
    "model.compile(optimizer='adam',\n",
    "loss='binary_crossentropy',\n",
    "metrics=['accuracy'])\n",
    "baseline_history2=model.fit(a_train, b_train, epochs=100, validation_data=(a_valid, b_valid), shuffle=True, class_weight=class_weight)\n",
    "model.evaluate(a_test, b_test, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(baseline_history2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss :  0.2177614570329975\n",
      "accuracy :  0.9743017\n",
      "\n",
      "всего 27 \"пульсаров\"\n",
      "всего 1763 \"не пульсаров\"\n",
      "не пульсар предсказанный как не пульсар:  1720\n",
      "не пульсар предсказанный как пульсар : 43\n",
      "пульсар предсказанный как не пульсар : 3\n",
      "пульсар предсказанный как пульсар :  24\n",
      "количество правильных ответов 1744 из 1790\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_predictions_baseline = model.predict(a_test)\n",
    "baseline_results = model.evaluate(a_test, b_test, verbose=0)\n",
    "for name, value in zip(model.metrics_names, baseline_results):\n",
    "  print(name, ': ', value)\n",
    "print()\n",
    "\n",
    "plot_cm(b_test, test_predictions_baseline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 14318 samples, validate on 1790 samples\n",
      "Epoch 1/100\n",
      "14318/14318 [==============================] - ETA: 4:39 - loss: 60.8960 - accuracy: 0.437 - ETA: 8s - loss: 13.7579 - accuracy: 0.7783  - ETA: 4s - loss: 9.1839 - accuracy: 0.827 - ETA: 2s - loss: 8.4659 - accuracy: 0.80 - ETA: 1s - loss: 8.1503 - accuracy: 0.79 - ETA: 1s - loss: 7.4424 - accuracy: 0.80 - ETA: 1s - loss: 7.0805 - accuracy: 0.79 - ETA: 0s - loss: 6.4310 - accuracy: 0.80 - ETA: 0s - loss: 5.9524 - accuracy: 0.79 - ETA: 0s - loss: 5.5688 - accuracy: 0.80 - ETA: 0s - loss: 5.1883 - accuracy: 0.80 - ETA: 0s - loss: 4.8843 - accuracy: 0.80 - ETA: 0s - loss: 4.5923 - accuracy: 0.80 - 1s 101us/sample - loss: 4.4949 - accuracy: 0.8079 - val_loss: 0.1774 - val_accuracy: 0.9458\n",
      "Epoch 2/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.5952 - accuracy: 0.87 - ETA: 0s - loss: 1.8749 - accuracy: 0.86 - ETA: 0s - loss: 1.8315 - accuracy: 0.82 - ETA: 0s - loss: 1.6225 - accuracy: 0.82 - ETA: 0s - loss: 1.5487 - accuracy: 0.82 - ETA: 0s - loss: 1.6249 - accuracy: 0.81 - ETA: 0s - loss: 1.4645 - accuracy: 0.83 - ETA: 0s - loss: 1.4086 - accuracy: 0.82 - ETA: 0s - loss: 1.3424 - accuracy: 0.83 - ETA: 0s - loss: 1.2676 - accuracy: 0.83 - ETA: 0s - loss: 1.2352 - accuracy: 0.83 - ETA: 0s - loss: 1.1900 - accuracy: 0.83 - ETA: 0s - loss: 1.1391 - accuracy: 0.83 - 1s 50us/sample - loss: 1.0940 - accuracy: 0.8385 - val_loss: 0.1224 - val_accuracy: 0.9715\n",
      "Epoch 3/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1880 - accuracy: 0.90 - ETA: 0s - loss: 0.5303 - accuracy: 0.83 - ETA: 0s - loss: 0.4942 - accuracy: 0.86 - ETA: 0s - loss: 0.5557 - accuracy: 0.84 - ETA: 0s - loss: 0.5926 - accuracy: 0.84 - ETA: 0s - loss: 0.5556 - accuracy: 0.85 - ETA: 0s - loss: 0.5440 - accuracy: 0.85 - ETA: 0s - loss: 0.5510 - accuracy: 0.86 - ETA: 0s - loss: 0.5476 - accuracy: 0.86 - ETA: 0s - loss: 0.5631 - accuracy: 0.85 - ETA: 0s - loss: 0.5618 - accuracy: 0.85 - ETA: 0s - loss: 0.5607 - accuracy: 0.85 - ETA: 0s - loss: 0.5620 - accuracy: 0.85 - ETA: 0s - loss: 0.5562 - accuracy: 0.85 - 1s 52us/sample - loss: 0.5633 - accuracy: 0.8507 - val_loss: 0.1803 - val_accuracy: 0.9682\n",
      "Epoch 4/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.2455 - accuracy: 0.84 - ETA: 0s - loss: 0.5968 - accuracy: 0.82 - ETA: 0s - loss: 0.6265 - accuracy: 0.83 - ETA: 0s - loss: 0.5567 - accuracy: 0.83 - ETA: 0s - loss: 0.5321 - accuracy: 0.83 - ETA: 0s - loss: 0.5119 - accuracy: 0.84 - ETA: 0s - loss: 0.5189 - accuracy: 0.84 - ETA: 0s - loss: 0.5046 - accuracy: 0.84 - ETA: 0s - loss: 0.4861 - accuracy: 0.85 - ETA: 0s - loss: 0.4725 - accuracy: 0.85 - ETA: 0s - loss: 0.4693 - accuracy: 0.86 - ETA: 0s - loss: 0.4643 - accuracy: 0.86 - ETA: 0s - loss: 0.4546 - accuracy: 0.86 - 1s 49us/sample - loss: 0.4515 - accuracy: 0.8667 - val_loss: 0.1653 - val_accuracy: 0.9793\n",
      "Epoch 5/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2241 - accuracy: 0.96 - ETA: 0s - loss: 0.4394 - accuracy: 0.87 - ETA: 0s - loss: 0.3982 - accuracy: 0.87 - ETA: 0s - loss: 0.3770 - accuracy: 0.88 - ETA: 0s - loss: 0.3733 - accuracy: 0.88 - ETA: 0s - loss: 0.3563 - accuracy: 0.88 - ETA: 0s - loss: 0.3633 - accuracy: 0.88 - ETA: 0s - loss: 0.3570 - accuracy: 0.88 - ETA: 0s - loss: 0.3621 - accuracy: 0.88 - ETA: 0s - loss: 0.3658 - accuracy: 0.88 - 1s 45us/sample - loss: 0.3644 - accuracy: 0.8916 - val_loss: 0.1410 - val_accuracy: 0.9737\n",
      "Epoch 6/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2392 - accuracy: 0.78 - ETA: 0s - loss: 0.3393 - accuracy: 0.90 - ETA: 0s - loss: 0.3437 - accuracy: 0.89 - ETA: 0s - loss: 0.3602 - accuracy: 0.89 - ETA: 0s - loss: 0.3410 - accuracy: 0.90 - ETA: 0s - loss: 0.3431 - accuracy: 0.90 - ETA: 0s - loss: 0.3446 - accuracy: 0.90 - ETA: 0s - loss: 0.3510 - accuracy: 0.90 - ETA: 0s - loss: 0.3455 - accuracy: 0.91 - ETA: 0s - loss: 0.3522 - accuracy: 0.91 - ETA: 0s - loss: 0.3411 - accuracy: 0.91 - ETA: 0s - loss: 0.3391 - accuracy: 0.91 - 1s 47us/sample - loss: 0.3405 - accuracy: 0.9143 - val_loss: 0.1179 - val_accuracy: 0.9821\n",
      "Epoch 7/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1639 - accuracy: 0.90 - ETA: 0s - loss: 0.2458 - accuracy: 0.93 - ETA: 0s - loss: 0.2642 - accuracy: 0.92 - ETA: 0s - loss: 0.3363 - accuracy: 0.92 - ETA: 0s - loss: 0.3575 - accuracy: 0.91 - ETA: 0s - loss: 0.3751 - accuracy: 0.91 - ETA: 0s - loss: 0.3557 - accuracy: 0.91 - ETA: 0s - loss: 0.3488 - accuracy: 0.91 - ETA: 0s - loss: 0.3379 - accuracy: 0.91 - ETA: 0s - loss: 0.3380 - accuracy: 0.91 - ETA: 0s - loss: 0.3325 - accuracy: 0.92 - 1s 45us/sample - loss: 0.3316 - accuracy: 0.9215 - val_loss: 0.1576 - val_accuracy: 0.9799\n",
      "Epoch 8/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2653 - accuracy: 0.87 - ETA: 0s - loss: 0.3154 - accuracy: 0.92 - ETA: 0s - loss: 0.3016 - accuracy: 0.93 - ETA: 0s - loss: 0.3064 - accuracy: 0.93 - ETA: 0s - loss: 0.3088 - accuracy: 0.93 - ETA: 0s - loss: 0.3059 - accuracy: 0.93 - ETA: 0s - loss: 0.3079 - accuracy: 0.93 - ETA: 0s - loss: 0.3212 - accuracy: 0.92 - ETA: 0s - loss: 0.3159 - accuracy: 0.93 - ETA: 0s - loss: 0.3126 - accuracy: 0.93 - 1s 45us/sample - loss: 0.3163 - accuracy: 0.9313 - val_loss: 0.1459 - val_accuracy: 0.9709\n",
      "Epoch 9/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1292 - accuracy: 0.93 - ETA: 0s - loss: 0.3264 - accuracy: 0.94 - ETA: 0s - loss: 0.2827 - accuracy: 0.95 - ETA: 0s - loss: 0.3117 - accuracy: 0.94 - ETA: 0s - loss: 0.2962 - accuracy: 0.94 - ETA: 0s - loss: 0.3013 - accuracy: 0.94 - ETA: 0s - loss: 0.3062 - accuracy: 0.94 - ETA: 0s - loss: 0.3050 - accuracy: 0.94 - ETA: 0s - loss: 0.3078 - accuracy: 0.93 - ETA: 0s - loss: 0.3064 - accuracy: 0.94 - ETA: 0s - loss: 0.3112 - accuracy: 0.93 - ETA: 0s - loss: 0.3087 - accuracy: 0.93 - 1s 46us/sample - loss: 0.3088 - accuracy: 0.9364 - val_loss: 0.1318 - val_accuracy: 0.9821\n",
      "Epoch 10/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1109 - accuracy: 0.96 - ETA: 0s - loss: 0.2561 - accuracy: 0.94 - ETA: 0s - loss: 0.2736 - accuracy: 0.93 - ETA: 0s - loss: 0.2810 - accuracy: 0.94 - ETA: 0s - loss: 0.2790 - accuracy: 0.94 - ETA: 0s - loss: 0.2834 - accuracy: 0.94 - ETA: 0s - loss: 0.2821 - accuracy: 0.94 - ETA: 0s - loss: 0.2795 - accuracy: 0.94 - ETA: 0s - loss: 0.2774 - accuracy: 0.94 - ETA: 0s - loss: 0.2795 - accuracy: 0.94 - ETA: 0s - loss: 0.2826 - accuracy: 0.93 - 1s 45us/sample - loss: 0.2820 - accuracy: 0.9395 - val_loss: 0.1583 - val_accuracy: 0.9520\n",
      "Epoch 11/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1298 - accuracy: 0.96 - ETA: 0s - loss: 0.3202 - accuracy: 0.93 - ETA: 0s - loss: 0.3040 - accuracy: 0.93 - ETA: 0s - loss: 0.2976 - accuracy: 0.92 - ETA: 0s - loss: 0.2860 - accuracy: 0.92 - ETA: 0s - loss: 0.3023 - accuracy: 0.92 - ETA: 0s - loss: 0.2898 - accuracy: 0.92 - ETA: 0s - loss: 0.2921 - accuracy: 0.92 - ETA: 0s - loss: 0.2922 - accuracy: 0.92 - ETA: 0s - loss: 0.2909 - accuracy: 0.92 - ETA: 0s - loss: 0.2972 - accuracy: 0.92 - ETA: 0s - loss: 0.2974 - accuracy: 0.92 - ETA: 0s - loss: 0.2982 - accuracy: 0.92 - 1s 47us/sample - loss: 0.2976 - accuracy: 0.9270 - val_loss: 0.1577 - val_accuracy: 0.9754\n",
      "Epoch 12/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1888 - accuracy: 0.96 - ETA: 0s - loss: 0.2511 - accuracy: 0.94 - ETA: 0s - loss: 0.3065 - accuracy: 0.92 - ETA: 0s - loss: 0.3142 - accuracy: 0.93 - ETA: 0s - loss: 0.3176 - accuracy: 0.93 - ETA: 0s - loss: 0.3045 - accuracy: 0.93 - ETA: 0s - loss: 0.3108 - accuracy: 0.92 - ETA: 0s - loss: 0.3118 - accuracy: 0.92 - ETA: 0s - loss: 0.3023 - accuracy: 0.93 - ETA: 0s - loss: 0.2990 - accuracy: 0.93 - ETA: 0s - loss: 0.2899 - accuracy: 0.93 - ETA: 0s - loss: 0.2878 - accuracy: 0.93 - 1s 47us/sample - loss: 0.2878 - accuracy: 0.9316 - val_loss: 0.1396 - val_accuracy: 0.9782\n",
      "Epoch 13/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2538 - accuracy: 0.87 - ETA: 0s - loss: 0.2364 - accuracy: 0.93 - ETA: 0s - loss: 0.2892 - accuracy: 0.92 - ETA: 0s - loss: 0.2793 - accuracy: 0.93 - ETA: 0s - loss: 0.3006 - accuracy: 0.93 - ETA: 0s - loss: 0.2898 - accuracy: 0.93 - ETA: 0s - loss: 0.2990 - accuracy: 0.93 - ETA: 0s - loss: 0.2969 - accuracy: 0.93 - ETA: 0s - loss: 0.2935 - accuracy: 0.93 - ETA: 0s - loss: 0.2904 - accuracy: 0.93 - ETA: 0s - loss: 0.2924 - accuracy: 0.93 - ETA: 0s - loss: 0.2928 - accuracy: 0.93 - 1s 46us/sample - loss: 0.2918 - accuracy: 0.9333 - val_loss: 0.1532 - val_accuracy: 0.9749\n",
      "Epoch 14/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1588 - accuracy: 0.87 - ETA: 0s - loss: 0.3416 - accuracy: 0.91 - ETA: 0s - loss: 0.2901 - accuracy: 0.92 - ETA: 0s - loss: 0.2956 - accuracy: 0.92 - ETA: 0s - loss: 0.2899 - accuracy: 0.93 - ETA: 0s - loss: 0.2824 - accuracy: 0.93 - ETA: 0s - loss: 0.2767 - accuracy: 0.93 - ETA: 0s - loss: 0.2767 - accuracy: 0.94 - ETA: 0s - loss: 0.2840 - accuracy: 0.93 - ETA: 0s - loss: 0.2792 - accuracy: 0.93 - ETA: 0s - loss: 0.2805 - accuracy: 0.93 - ETA: 0s - loss: 0.2742 - accuracy: 0.94 - ETA: 0s - loss: 0.2758 - accuracy: 0.94 - ETA: 0s - loss: 0.2775 - accuracy: 0.94 - 1s 51us/sample - loss: 0.2774 - accuracy: 0.9431 - val_loss: 0.1254 - val_accuracy: 0.9804\n",
      "Epoch 15/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1152 - accuracy: 0.96 - ETA: 0s - loss: 0.2840 - accuracy: 0.95 - ETA: 0s - loss: 0.3187 - accuracy: 0.94 - ETA: 0s - loss: 0.3301 - accuracy: 0.93 - ETA: 0s - loss: 0.3059 - accuracy: 0.94 - ETA: 0s - loss: 0.2994 - accuracy: 0.94 - ETA: 0s - loss: 0.2856 - accuracy: 0.94 - ETA: 0s - loss: 0.2836 - accuracy: 0.93 - ETA: 0s - loss: 0.2775 - accuracy: 0.93 - ETA: 0s - loss: 0.2754 - accuracy: 0.94 - ETA: 0s - loss: 0.2769 - accuracy: 0.93 - ETA: 0s - loss: 0.2789 - accuracy: 0.93 - ETA: 0s - loss: 0.2774 - accuracy: 0.93 - 1s 47us/sample - loss: 0.2774 - accuracy: 0.9381 - val_loss: 0.1299 - val_accuracy: 0.9827\n",
      "Epoch 16/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.0938 - accuracy: 0.96 - ETA: 0s - loss: 0.2816 - accuracy: 0.94 - ETA: 0s - loss: 0.2667 - accuracy: 0.95 - ETA: 0s - loss: 0.2637 - accuracy: 0.94 - ETA: 0s - loss: 0.2730 - accuracy: 0.93 - ETA: 0s - loss: 0.2673 - accuracy: 0.93 - ETA: 0s - loss: 0.2706 - accuracy: 0.93 - ETA: 0s - loss: 0.2740 - accuracy: 0.93 - ETA: 0s - loss: 0.2679 - accuracy: 0.93 - ETA: 0s - loss: 0.2708 - accuracy: 0.93 - ETA: 0s - loss: 0.2709 - accuracy: 0.93 - ETA: 0s - loss: 0.2719 - accuracy: 0.93 - ETA: 0s - loss: 0.2696 - accuracy: 0.93 - ETA: 0s - loss: 0.2743 - accuracy: 0.93 - 1s 52us/sample - loss: 0.2741 - accuracy: 0.9369 - val_loss: 0.1525 - val_accuracy: 0.9782\n",
      "Epoch 17/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.6951 - accuracy: 0.87 - ETA: 0s - loss: 0.2442 - accuracy: 0.95 - ETA: 0s - loss: 0.2776 - accuracy: 0.94 - ETA: 0s - loss: 0.2574 - accuracy: 0.94 - ETA: 0s - loss: 0.2535 - accuracy: 0.94 - ETA: 0s - loss: 0.2630 - accuracy: 0.94 - ETA: 0s - loss: 0.2670 - accuracy: 0.93 - ETA: 0s - loss: 0.2721 - accuracy: 0.93 - ETA: 0s - loss: 0.2708 - accuracy: 0.93 - ETA: 0s - loss: 0.2674 - accuracy: 0.93 - ETA: 0s - loss: 0.2600 - accuracy: 0.94 - ETA: 0s - loss: 0.2655 - accuracy: 0.94 - ETA: 0s - loss: 0.2651 - accuracy: 0.93 - ETA: 0s - loss: 0.2677 - accuracy: 0.93 - 1s 54us/sample - loss: 0.2679 - accuracy: 0.9398 - val_loss: 0.1143 - val_accuracy: 0.9754\n",
      "Epoch 18/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2153 - accuracy: 0.93 - ETA: 0s - loss: 0.3538 - accuracy: 0.93 - ETA: 0s - loss: 0.2976 - accuracy: 0.94 - ETA: 0s - loss: 0.2940 - accuracy: 0.93 - ETA: 0s - loss: 0.2904 - accuracy: 0.93 - ETA: 0s - loss: 0.2732 - accuracy: 0.93 - ETA: 0s - loss: 0.2816 - accuracy: 0.93 - ETA: 0s - loss: 0.2836 - accuracy: 0.93 - ETA: 0s - loss: 0.2845 - accuracy: 0.93 - ETA: 0s - loss: 0.2830 - accuracy: 0.93 - ETA: 0s - loss: 0.2827 - accuracy: 0.93 - ETA: 0s - loss: 0.2794 - accuracy: 0.93 - ETA: 0s - loss: 0.2771 - accuracy: 0.93 - 1s 48us/sample - loss: 0.2769 - accuracy: 0.9347 - val_loss: 0.1092 - val_accuracy: 0.9838\n",
      "Epoch 19/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.1406 - accuracy: 1.00 - ETA: 0s - loss: 0.2875 - accuracy: 0.93 - ETA: 0s - loss: 0.2731 - accuracy: 0.94 - ETA: 0s - loss: 0.2858 - accuracy: 0.94 - ETA: 0s - loss: 0.2711 - accuracy: 0.94 - ETA: 0s - loss: 0.2828 - accuracy: 0.94 - ETA: 0s - loss: 0.2811 - accuracy: 0.94 - ETA: 0s - loss: 0.2809 - accuracy: 0.94 - ETA: 0s - loss: 0.2769 - accuracy: 0.94 - ETA: 0s - loss: 0.2756 - accuracy: 0.94 - ETA: 0s - loss: 0.2725 - accuracy: 0.94 - ETA: 0s - loss: 0.2752 - accuracy: 0.94 - ETA: 0s - loss: 0.2737 - accuracy: 0.94 - 1s 50us/sample - loss: 0.2766 - accuracy: 0.9453 - val_loss: 0.1301 - val_accuracy: 0.9799\n",
      "Epoch 20/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.1727 - accuracy: 0.96 - ETA: 0s - loss: 0.2046 - accuracy: 0.95 - ETA: 0s - loss: 0.2530 - accuracy: 0.93 - ETA: 0s - loss: 0.2358 - accuracy: 0.94 - ETA: 0s - loss: 0.2581 - accuracy: 0.94 - ETA: 0s - loss: 0.2554 - accuracy: 0.94 - ETA: 0s - loss: 0.2626 - accuracy: 0.93 - ETA: 0s - loss: 0.2724 - accuracy: 0.93 - ETA: 0s - loss: 0.2676 - accuracy: 0.93 - ETA: 0s - loss: 0.2714 - accuracy: 0.93 - ETA: 0s - loss: 0.2679 - accuracy: 0.93 - ETA: 0s - loss: 0.2651 - accuracy: 0.94 - 1s 47us/sample - loss: 0.2697 - accuracy: 0.9396 - val_loss: 0.1321 - val_accuracy: 0.9849\n",
      "Epoch 21/100\n",
      "14318/14318 [==============================] - ETA: 2s - loss: 0.4996 - accuracy: 0.93 - ETA: 1s - loss: 0.2104 - accuracy: 0.94 - ETA: 1s - loss: 0.2466 - accuracy: 0.94 - ETA: 0s - loss: 0.2555 - accuracy: 0.95 - ETA: 0s - loss: 0.2677 - accuracy: 0.94 - ETA: 0s - loss: 0.2638 - accuracy: 0.94 - ETA: 0s - loss: 0.2665 - accuracy: 0.94 - ETA: 0s - loss: 0.2746 - accuracy: 0.94 - ETA: 0s - loss: 0.2731 - accuracy: 0.94 - ETA: 0s - loss: 0.2645 - accuracy: 0.94 - ETA: 0s - loss: 0.2619 - accuracy: 0.94 - ETA: 0s - loss: 0.2585 - accuracy: 0.94 - ETA: 0s - loss: 0.2593 - accuracy: 0.94 - 1s 51us/sample - loss: 0.2612 - accuracy: 0.9438 - val_loss: 0.1062 - val_accuracy: 0.9860\n",
      "Epoch 22/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.2088 - accuracy: 0.96 - ETA: 0s - loss: 0.2529 - accuracy: 0.94 - ETA: 0s - loss: 0.2290 - accuracy: 0.94 - ETA: 0s - loss: 0.2537 - accuracy: 0.95 - ETA: 0s - loss: 0.2704 - accuracy: 0.94 - ETA: 0s - loss: 0.2650 - accuracy: 0.94 - ETA: 0s - loss: 0.2497 - accuracy: 0.94 - ETA: 0s - loss: 0.2514 - accuracy: 0.94 - ETA: 0s - loss: 0.2577 - accuracy: 0.94 - ETA: 0s - loss: 0.2634 - accuracy: 0.94 - ETA: 0s - loss: 0.2595 - accuracy: 0.94 - ETA: 0s - loss: 0.2628 - accuracy: 0.94 - 1s 47us/sample - loss: 0.2639 - accuracy: 0.9442 - val_loss: 0.1227 - val_accuracy: 0.9860\n",
      "Epoch 23/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.4649 - accuracy: 0.90 - ETA: 0s - loss: 0.2017 - accuracy: 0.95 - ETA: 0s - loss: 0.2506 - accuracy: 0.95 - ETA: 0s - loss: 0.2716 - accuracy: 0.94 - ETA: 0s - loss: 0.2735 - accuracy: 0.94 - ETA: 0s - loss: 0.2551 - accuracy: 0.95 - ETA: 0s - loss: 0.2515 - accuracy: 0.95 - ETA: 0s - loss: 0.2547 - accuracy: 0.94 - ETA: 0s - loss: 0.2516 - accuracy: 0.94 - ETA: 0s - loss: 0.2477 - accuracy: 0.94 - ETA: 0s - loss: 0.2563 - accuracy: 0.94 - 1s 45us/sample - loss: 0.2597 - accuracy: 0.9478 - val_loss: 0.0827 - val_accuracy: 0.9872\n",
      "Epoch 24/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0833 - accuracy: 0.96 - ETA: 0s - loss: 0.2056 - accuracy: 0.96 - ETA: 0s - loss: 0.2286 - accuracy: 0.95 - ETA: 0s - loss: 0.2358 - accuracy: 0.95 - ETA: 0s - loss: 0.2366 - accuracy: 0.95 - ETA: 0s - loss: 0.2519 - accuracy: 0.94 - ETA: 0s - loss: 0.2596 - accuracy: 0.94 - ETA: 0s - loss: 0.2633 - accuracy: 0.94 - ETA: 0s - loss: 0.2652 - accuracy: 0.94 - ETA: 0s - loss: 0.2664 - accuracy: 0.94 - ETA: 0s - loss: 0.2695 - accuracy: 0.94 - ETA: 0s - loss: 0.2672 - accuracy: 0.94 - 1s 45us/sample - loss: 0.2671 - accuracy: 0.9459 - val_loss: 0.1070 - val_accuracy: 0.9832\n",
      "Epoch 25/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1431 - accuracy: 1.00 - ETA: 0s - loss: 0.2145 - accuracy: 0.95 - ETA: 0s - loss: 0.2616 - accuracy: 0.94 - ETA: 0s - loss: 0.2508 - accuracy: 0.94 - ETA: 0s - loss: 0.2497 - accuracy: 0.94 - ETA: 0s - loss: 0.2437 - accuracy: 0.94 - ETA: 0s - loss: 0.2482 - accuracy: 0.94 - ETA: 0s - loss: 0.2539 - accuracy: 0.94 - ETA: 0s - loss: 0.2580 - accuracy: 0.94 - ETA: 0s - loss: 0.2567 - accuracy: 0.94 - ETA: 0s - loss: 0.2569 - accuracy: 0.94 - ETA: 0s - loss: 0.2576 - accuracy: 0.94 - 1s 45us/sample - loss: 0.2573 - accuracy: 0.9431 - val_loss: 0.1490 - val_accuracy: 0.9721\n",
      "Epoch 26/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2222 - accuracy: 0.87 - ETA: 0s - loss: 0.2372 - accuracy: 0.94 - ETA: 0s - loss: 0.2485 - accuracy: 0.94 - ETA: 0s - loss: 0.2530 - accuracy: 0.94 - ETA: 0s - loss: 0.2701 - accuracy: 0.94 - ETA: 0s - loss: 0.2589 - accuracy: 0.94 - ETA: 0s - loss: 0.2548 - accuracy: 0.94 - ETA: 0s - loss: 0.2640 - accuracy: 0.94 - ETA: 0s - loss: 0.2611 - accuracy: 0.94 - ETA: 0s - loss: 0.2616 - accuracy: 0.94 - ETA: 0s - loss: 0.2633 - accuracy: 0.94 - ETA: 0s - loss: 0.2646 - accuracy: 0.94 - 1s 46us/sample - loss: 0.2642 - accuracy: 0.9467 - val_loss: 0.0936 - val_accuracy: 0.9860\n",
      "Epoch 27/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.0563 - accuracy: 1.00 - ETA: 0s - loss: 0.3097 - accuracy: 0.92 - ETA: 0s - loss: 0.2666 - accuracy: 0.93 - ETA: 0s - loss: 0.2789 - accuracy: 0.93 - ETA: 0s - loss: 0.2751 - accuracy: 0.94 - ETA: 0s - loss: 0.2856 - accuracy: 0.93 - ETA: 0s - loss: 0.2842 - accuracy: 0.94 - ETA: 0s - loss: 0.2743 - accuracy: 0.94 - ETA: 0s - loss: 0.2690 - accuracy: 0.94 - ETA: 0s - loss: 0.2586 - accuracy: 0.94 - ETA: 0s - loss: 0.2574 - accuracy: 0.94 - ETA: 0s - loss: 0.2622 - accuracy: 0.94 - 1s 45us/sample - loss: 0.2593 - accuracy: 0.9434 - val_loss: 0.1151 - val_accuracy: 0.9911\n",
      "Epoch 28/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.4960 - accuracy: 0.93 - ETA: 0s - loss: 0.2841 - accuracy: 0.94 - ETA: 0s - loss: 0.2928 - accuracy: 0.94 - ETA: 0s - loss: 0.2683 - accuracy: 0.94 - ETA: 0s - loss: 0.2623 - accuracy: 0.94 - ETA: 0s - loss: 0.2567 - accuracy: 0.94 - ETA: 0s - loss: 0.2515 - accuracy: 0.94 - ETA: 0s - loss: 0.2440 - accuracy: 0.95 - ETA: 0s - loss: 0.2495 - accuracy: 0.94 - ETA: 0s - loss: 0.2519 - accuracy: 0.94 - ETA: 0s - loss: 0.2497 - accuracy: 0.94 - 1s 45us/sample - loss: 0.2565 - accuracy: 0.9473 - val_loss: 0.1485 - val_accuracy: 0.9855\n",
      "Epoch 29/100\n",
      "14318/14318 [==============================] - ETA: 6s - loss: 0.1642 - accuracy: 0.93 - ETA: 0s - loss: 0.2629 - accuracy: 0.94 - ETA: 0s - loss: 0.2819 - accuracy: 0.95 - ETA: 0s - loss: 0.2598 - accuracy: 0.95 - ETA: 0s - loss: 0.2545 - accuracy: 0.95 - ETA: 0s - loss: 0.2600 - accuracy: 0.95 - ETA: 0s - loss: 0.2630 - accuracy: 0.94 - ETA: 0s - loss: 0.2599 - accuracy: 0.94 - ETA: 0s - loss: 0.2603 - accuracy: 0.94 - ETA: 0s - loss: 0.2586 - accuracy: 0.94 - ETA: 0s - loss: 0.2599 - accuracy: 0.94 - 1s 47us/sample - loss: 0.2601 - accuracy: 0.9455 - val_loss: 0.1259 - val_accuracy: 0.9855\n",
      "Epoch 30/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0943 - accuracy: 0.96 - ETA: 0s - loss: 0.2105 - accuracy: 0.95 - ETA: 0s - loss: 0.2434 - accuracy: 0.95 - ETA: 0s - loss: 0.2482 - accuracy: 0.95 - ETA: 0s - loss: 0.2473 - accuracy: 0.95 - ETA: 0s - loss: 0.2623 - accuracy: 0.94 - ETA: 0s - loss: 0.2540 - accuracy: 0.94 - ETA: 0s - loss: 0.2652 - accuracy: 0.94 - ETA: 0s - loss: 0.2633 - accuracy: 0.94 - ETA: 0s - loss: 0.2562 - accuracy: 0.94 - ETA: 0s - loss: 0.2563 - accuracy: 0.94 - ETA: 0s - loss: 0.2544 - accuracy: 0.94 - 1s 45us/sample - loss: 0.2607 - accuracy: 0.9466 - val_loss: 0.1321 - val_accuracy: 0.9872\n",
      "Epoch 31/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2492 - accuracy: 0.87 - ETA: 0s - loss: 0.2658 - accuracy: 0.94 - ETA: 0s - loss: 0.2472 - accuracy: 0.95 - ETA: 0s - loss: 0.2755 - accuracy: 0.94 - ETA: 0s - loss: 0.2737 - accuracy: 0.95 - ETA: 0s - loss: 0.2669 - accuracy: 0.95 - ETA: 0s - loss: 0.2548 - accuracy: 0.95 - ETA: 0s - loss: 0.2594 - accuracy: 0.94 - ETA: 0s - loss: 0.2558 - accuracy: 0.94 - ETA: 0s - loss: 0.2526 - accuracy: 0.94 - ETA: 0s - loss: 0.2537 - accuracy: 0.94 - ETA: 0s - loss: 0.2485 - accuracy: 0.94 - 1s 45us/sample - loss: 0.2504 - accuracy: 0.9498 - val_loss: 0.0878 - val_accuracy: 0.9866\n",
      "Epoch 32/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0996 - accuracy: 0.96 - ETA: 0s - loss: 0.2102 - accuracy: 0.95 - ETA: 0s - loss: 0.2246 - accuracy: 0.94 - ETA: 0s - loss: 0.2237 - accuracy: 0.94 - ETA: 0s - loss: 0.2379 - accuracy: 0.94 - ETA: 0s - loss: 0.2404 - accuracy: 0.94 - ETA: 0s - loss: 0.2438 - accuracy: 0.94 - ETA: 0s - loss: 0.2454 - accuracy: 0.93 - ETA: 0s - loss: 0.2493 - accuracy: 0.93 - ETA: 0s - loss: 0.2539 - accuracy: 0.94 - ETA: 0s - loss: 0.2530 - accuracy: 0.94 - 1s 46us/sample - loss: 0.2508 - accuracy: 0.9411 - val_loss: 0.1040 - val_accuracy: 0.9899\n",
      "Epoch 33/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0610 - accuracy: 1.00 - ETA: 0s - loss: 0.2975 - accuracy: 0.94 - ETA: 0s - loss: 0.2812 - accuracy: 0.95 - ETA: 0s - loss: 0.2666 - accuracy: 0.95 - ETA: 0s - loss: 0.2695 - accuracy: 0.94 - ETA: 0s - loss: 0.2701 - accuracy: 0.94 - ETA: 0s - loss: 0.2684 - accuracy: 0.94 - ETA: 0s - loss: 0.2670 - accuracy: 0.94 - ETA: 0s - loss: 0.2647 - accuracy: 0.94 - ETA: 0s - loss: 0.2600 - accuracy: 0.94 - ETA: 0s - loss: 0.2560 - accuracy: 0.94 - ETA: 0s - loss: 0.2519 - accuracy: 0.95 - 1s 45us/sample - loss: 0.2582 - accuracy: 0.9479 - val_loss: 0.1325 - val_accuracy: 0.9777\n",
      "Epoch 34/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1669 - accuracy: 0.90 - ETA: 0s - loss: 0.2912 - accuracy: 0.94 - ETA: 0s - loss: 0.2875 - accuracy: 0.94 - ETA: 0s - loss: 0.2681 - accuracy: 0.94 - ETA: 0s - loss: 0.2599 - accuracy: 0.94 - ETA: 0s - loss: 0.2598 - accuracy: 0.94 - ETA: 0s - loss: 0.2661 - accuracy: 0.94 - ETA: 0s - loss: 0.2672 - accuracy: 0.94 - ETA: 0s - loss: 0.2640 - accuracy: 0.94 - ETA: 0s - loss: 0.2584 - accuracy: 0.94 - ETA: 0s - loss: 0.2655 - accuracy: 0.94 - 1s 45us/sample - loss: 0.2645 - accuracy: 0.9455 - val_loss: 0.1188 - val_accuracy: 0.9894\n",
      "Epoch 35/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0717 - accuracy: 0.96 - ETA: 0s - loss: 0.4109 - accuracy: 0.93 - ETA: 0s - loss: 0.3292 - accuracy: 0.93 - ETA: 0s - loss: 0.3158 - accuracy: 0.94 - ETA: 0s - loss: 0.2988 - accuracy: 0.94 - ETA: 0s - loss: 0.2921 - accuracy: 0.94 - ETA: 0s - loss: 0.2898 - accuracy: 0.93 - ETA: 0s - loss: 0.2799 - accuracy: 0.94 - ETA: 0s - loss: 0.2744 - accuracy: 0.94 - ETA: 0s - loss: 0.2723 - accuracy: 0.94 - ETA: 0s - loss: 0.2679 - accuracy: 0.94 - 1s 46us/sample - loss: 0.2673 - accuracy: 0.9454 - val_loss: 0.0675 - val_accuracy: 0.9844\n",
      "Epoch 36/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0675 - accuracy: 0.96 - ETA: 0s - loss: 0.2591 - accuracy: 0.95 - ETA: 0s - loss: 0.2594 - accuracy: 0.95 - ETA: 0s - loss: 0.2590 - accuracy: 0.95 - ETA: 0s - loss: 0.2659 - accuracy: 0.94 - ETA: 0s - loss: 0.2546 - accuracy: 0.94 - ETA: 0s - loss: 0.2623 - accuracy: 0.94 - ETA: 0s - loss: 0.2637 - accuracy: 0.94 - ETA: 0s - loss: 0.2568 - accuracy: 0.94 - ETA: 0s - loss: 0.2496 - accuracy: 0.94 - ETA: 0s - loss: 0.2539 - accuracy: 0.94 - ETA: 0s - loss: 0.2541 - accuracy: 0.94 - 1s 46us/sample - loss: 0.2539 - accuracy: 0.9439 - val_loss: 0.0835 - val_accuracy: 0.9883\n",
      "Epoch 37/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.0945 - accuracy: 0.96 - ETA: 0s - loss: 0.2736 - accuracy: 0.94 - ETA: 0s - loss: 0.2719 - accuracy: 0.94 - ETA: 0s - loss: 0.2489 - accuracy: 0.95 - ETA: 0s - loss: 0.2548 - accuracy: 0.94 - ETA: 0s - loss: 0.2546 - accuracy: 0.94 - ETA: 0s - loss: 0.2617 - accuracy: 0.94 - ETA: 0s - loss: 0.2580 - accuracy: 0.94 - ETA: 0s - loss: 0.2608 - accuracy: 0.94 - ETA: 0s - loss: 0.2616 - accuracy: 0.94 - ETA: 0s - loss: 0.2551 - accuracy: 0.94 - ETA: 0s - loss: 0.2543 - accuracy: 0.94 - ETA: 0s - loss: 0.2517 - accuracy: 0.94 - 1s 48us/sample - loss: 0.2490 - accuracy: 0.9473 - val_loss: 0.1032 - val_accuracy: 0.9899\n",
      "Epoch 38/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0687 - accuracy: 0.96 - ETA: 0s - loss: 0.2446 - accuracy: 0.94 - ETA: 0s - loss: 0.2484 - accuracy: 0.95 - ETA: 0s - loss: 0.2543 - accuracy: 0.94 - ETA: 0s - loss: 0.2392 - accuracy: 0.94 - ETA: 0s - loss: 0.2363 - accuracy: 0.94 - ETA: 0s - loss: 0.2442 - accuracy: 0.94 - ETA: 0s - loss: 0.2499 - accuracy: 0.94 - ETA: 0s - loss: 0.2535 - accuracy: 0.94 - ETA: 0s - loss: 0.2563 - accuracy: 0.94 - ETA: 0s - loss: 0.2521 - accuracy: 0.94 - ETA: 0s - loss: 0.2482 - accuracy: 0.94 - ETA: 0s - loss: 0.2461 - accuracy: 0.94 - 1s 49us/sample - loss: 0.2502 - accuracy: 0.9471 - val_loss: 0.0959 - val_accuracy: 0.9827\n",
      "Epoch 39/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1543 - accuracy: 0.90 - ETA: 0s - loss: 0.1927 - accuracy: 0.93 - ETA: 0s - loss: 0.2123 - accuracy: 0.94 - ETA: 0s - loss: 0.2336 - accuracy: 0.94 - ETA: 0s - loss: 0.2472 - accuracy: 0.94 - ETA: 0s - loss: 0.2509 - accuracy: 0.94 - ETA: 0s - loss: 0.2616 - accuracy: 0.94 - ETA: 0s - loss: 0.2579 - accuracy: 0.94 - ETA: 0s - loss: 0.2612 - accuracy: 0.94 - ETA: 0s - loss: 0.2626 - accuracy: 0.94 - ETA: 0s - loss: 0.2621 - accuracy: 0.94 - ETA: 0s - loss: 0.2630 - accuracy: 0.94 - ETA: 0s - loss: 0.2589 - accuracy: 0.94 - 1s 48us/sample - loss: 0.2610 - accuracy: 0.9471 - val_loss: 0.1219 - val_accuracy: 0.9821\n",
      "Epoch 40/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.1048 - accuracy: 0.96 - ETA: 0s - loss: 0.2726 - accuracy: 0.93 - ETA: 0s - loss: 0.2615 - accuracy: 0.93 - ETA: 0s - loss: 0.2537 - accuracy: 0.93 - ETA: 0s - loss: 0.2449 - accuracy: 0.94 - ETA: 0s - loss: 0.2469 - accuracy: 0.94 - ETA: 0s - loss: 0.2506 - accuracy: 0.94 - ETA: 0s - loss: 0.2483 - accuracy: 0.94 - ETA: 0s - loss: 0.2560 - accuracy: 0.94 - ETA: 0s - loss: 0.2746 - accuracy: 0.94 - ETA: 0s - loss: 0.2712 - accuracy: 0.94 - ETA: 0s - loss: 0.2661 - accuracy: 0.94 - ETA: 0s - loss: 0.2631 - accuracy: 0.94 - ETA: 0s - loss: 0.2666 - accuracy: 0.94 - 1s 51us/sample - loss: 0.2654 - accuracy: 0.9480 - val_loss: 0.0991 - val_accuracy: 0.9844\n",
      "Epoch 41/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.1930 - accuracy: 0.96 - ETA: 0s - loss: 0.2448 - accuracy: 0.94 - ETA: 0s - loss: 0.2317 - accuracy: 0.94 - ETA: 0s - loss: 0.2335 - accuracy: 0.94 - ETA: 0s - loss: 0.2567 - accuracy: 0.94 - ETA: 0s - loss: 0.2569 - accuracy: 0.94 - ETA: 0s - loss: 0.2513 - accuracy: 0.94 - ETA: 0s - loss: 0.2437 - accuracy: 0.94 - ETA: 0s - loss: 0.2428 - accuracy: 0.94 - ETA: 0s - loss: 0.2525 - accuracy: 0.94 - ETA: 0s - loss: 0.2477 - accuracy: 0.94 - ETA: 0s - loss: 0.2424 - accuracy: 0.94 - ETA: 0s - loss: 0.2488 - accuracy: 0.94 - 1s 49us/sample - loss: 0.2503 - accuracy: 0.9440 - val_loss: 0.0854 - val_accuracy: 0.9939\n",
      "Epoch 42/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0715 - accuracy: 0.96 - ETA: 0s - loss: 0.2918 - accuracy: 0.95 - ETA: 0s - loss: 0.2646 - accuracy: 0.94 - ETA: 0s - loss: 0.2794 - accuracy: 0.94 - ETA: 0s - loss: 0.2762 - accuracy: 0.94 - ETA: 0s - loss: 0.2742 - accuracy: 0.94 - ETA: 0s - loss: 0.2686 - accuracy: 0.94 - ETA: 0s - loss: 0.2632 - accuracy: 0.94 - ETA: 0s - loss: 0.2683 - accuracy: 0.94 - ETA: 0s - loss: 0.2635 - accuracy: 0.94 - ETA: 0s - loss: 0.2614 - accuracy: 0.94 - ETA: 0s - loss: 0.2603 - accuracy: 0.94 - 1s 45us/sample - loss: 0.2535 - accuracy: 0.9482 - val_loss: 0.0765 - val_accuracy: 0.9821\n",
      "Epoch 43/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0271 - accuracy: 1.00 - ETA: 0s - loss: 0.2739 - accuracy: 0.94 - ETA: 0s - loss: 0.2457 - accuracy: 0.94 - ETA: 0s - loss: 0.2378 - accuracy: 0.94 - ETA: 0s - loss: 0.2383 - accuracy: 0.94 - ETA: 0s - loss: 0.2538 - accuracy: 0.94 - ETA: 0s - loss: 0.2582 - accuracy: 0.94 - ETA: 0s - loss: 0.2583 - accuracy: 0.94 - ETA: 0s - loss: 0.2577 - accuracy: 0.94 - ETA: 0s - loss: 0.2573 - accuracy: 0.94 - ETA: 0s - loss: 0.2630 - accuracy: 0.94 - ETA: 0s - loss: 0.2578 - accuracy: 0.94 - ETA: 0s - loss: 0.2591 - accuracy: 0.94 - 1s 50us/sample - loss: 0.2584 - accuracy: 0.9441 - val_loss: 0.1132 - val_accuracy: 0.9832\n",
      "Epoch 44/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.1179 - accuracy: 0.96 - ETA: 0s - loss: 0.2726 - accuracy: 0.92 - ETA: 0s - loss: 0.2701 - accuracy: 0.92 - ETA: 0s - loss: 0.2514 - accuracy: 0.93 - ETA: 0s - loss: 0.2434 - accuracy: 0.93 - ETA: 0s - loss: 0.2460 - accuracy: 0.93 - ETA: 0s - loss: 0.2485 - accuracy: 0.93 - ETA: 0s - loss: 0.2477 - accuracy: 0.93 - ETA: 0s - loss: 0.2514 - accuracy: 0.94 - ETA: 0s - loss: 0.2501 - accuracy: 0.93 - ETA: 0s - loss: 0.2456 - accuracy: 0.94 - ETA: 0s - loss: 0.2424 - accuracy: 0.94 - ETA: 0s - loss: 0.2482 - accuracy: 0.94 - 1s 49us/sample - loss: 0.2471 - accuracy: 0.9427 - val_loss: 0.0971 - val_accuracy: 0.9922\n",
      "Epoch 45/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0994 - accuracy: 0.96 - ETA: 0s - loss: 0.2676 - accuracy: 0.94 - ETA: 0s - loss: 0.2411 - accuracy: 0.94 - ETA: 0s - loss: 0.2621 - accuracy: 0.94 - ETA: 0s - loss: 0.2622 - accuracy: 0.94 - ETA: 0s - loss: 0.2697 - accuracy: 0.94 - ETA: 0s - loss: 0.2622 - accuracy: 0.94 - ETA: 0s - loss: 0.2607 - accuracy: 0.94 - ETA: 0s - loss: 0.2523 - accuracy: 0.94 - ETA: 0s - loss: 0.2527 - accuracy: 0.94 - ETA: 0s - loss: 0.2504 - accuracy: 0.94 - ETA: 0s - loss: 0.2496 - accuracy: 0.94 - 1s 46us/sample - loss: 0.2525 - accuracy: 0.9440 - val_loss: 0.1113 - val_accuracy: 0.9866\n",
      "Epoch 46/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0721 - accuracy: 1.00 - ETA: 0s - loss: 0.2773 - accuracy: 0.96 - ETA: 0s - loss: 0.2300 - accuracy: 0.96 - ETA: 0s - loss: 0.2627 - accuracy: 0.95 - ETA: 0s - loss: 0.2600 - accuracy: 0.95 - ETA: 0s - loss: 0.2587 - accuracy: 0.95 - ETA: 0s - loss: 0.2673 - accuracy: 0.95 - ETA: 0s - loss: 0.2627 - accuracy: 0.95 - ETA: 0s - loss: 0.2570 - accuracy: 0.95 - ETA: 0s - loss: 0.2538 - accuracy: 0.95 - ETA: 0s - loss: 0.2605 - accuracy: 0.95 - ETA: 0s - loss: 0.2585 - accuracy: 0.95 - 1s 46us/sample - loss: 0.2505 - accuracy: 0.9512 - val_loss: 0.0752 - val_accuracy: 0.9855\n",
      "Epoch 47/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0559 - accuracy: 1.00 - ETA: 0s - loss: 0.2506 - accuracy: 0.93 - ETA: 0s - loss: 0.2608 - accuracy: 0.94 - ETA: 0s - loss: 0.2559 - accuracy: 0.94 - ETA: 0s - loss: 0.2610 - accuracy: 0.94 - ETA: 0s - loss: 0.2644 - accuracy: 0.94 - ETA: 0s - loss: 0.2628 - accuracy: 0.94 - ETA: 0s - loss: 0.2499 - accuracy: 0.94 - ETA: 0s - loss: 0.2551 - accuracy: 0.94 - ETA: 0s - loss: 0.2518 - accuracy: 0.94 - ETA: 0s - loss: 0.2475 - accuracy: 0.94 - ETA: 0s - loss: 0.2455 - accuracy: 0.94 - ETA: 0s - loss: 0.2432 - accuracy: 0.94 - 1s 48us/sample - loss: 0.2453 - accuracy: 0.9444 - val_loss: 0.1002 - val_accuracy: 0.9872\n",
      "Epoch 48/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1159 - accuracy: 0.93 - ETA: 0s - loss: 0.2576 - accuracy: 0.93 - ETA: 0s - loss: 0.2723 - accuracy: 0.93 - ETA: 0s - loss: 0.2714 - accuracy: 0.93 - ETA: 0s - loss: 0.2637 - accuracy: 0.94 - ETA: 0s - loss: 0.2581 - accuracy: 0.94 - ETA: 0s - loss: 0.2620 - accuracy: 0.94 - ETA: 0s - loss: 0.2746 - accuracy: 0.94 - ETA: 0s - loss: 0.2672 - accuracy: 0.94 - ETA: 0s - loss: 0.2666 - accuracy: 0.94 - ETA: 0s - loss: 0.2668 - accuracy: 0.94 - ETA: 0s - loss: 0.2621 - accuracy: 0.94 - 1s 46us/sample - loss: 0.2600 - accuracy: 0.9466 - val_loss: 0.1111 - val_accuracy: 0.9916\n",
      "Epoch 49/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.6940 - accuracy: 0.93 - ETA: 0s - loss: 0.2684 - accuracy: 0.94 - ETA: 0s - loss: 0.2523 - accuracy: 0.94 - ETA: 0s - loss: 0.2791 - accuracy: 0.95 - ETA: 0s - loss: 0.2674 - accuracy: 0.95 - ETA: 0s - loss: 0.2559 - accuracy: 0.95 - ETA: 0s - loss: 0.2659 - accuracy: 0.95 - ETA: 0s - loss: 0.2842 - accuracy: 0.95 - ETA: 0s - loss: 0.2844 - accuracy: 0.95 - ETA: 0s - loss: 0.2797 - accuracy: 0.94 - ETA: 0s - loss: 0.2741 - accuracy: 0.94 - ETA: 0s - loss: 0.2709 - accuracy: 0.94 - 1s 46us/sample - loss: 0.2697 - accuracy: 0.9495 - val_loss: 0.1157 - val_accuracy: 0.9872\n",
      "Epoch 50/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1498 - accuracy: 0.93 - ETA: 0s - loss: 0.2685 - accuracy: 0.94 - ETA: 0s - loss: 0.2488 - accuracy: 0.94 - ETA: 0s - loss: 0.2395 - accuracy: 0.94 - ETA: 0s - loss: 0.2608 - accuracy: 0.94 - ETA: 0s - loss: 0.2583 - accuracy: 0.94 - ETA: 0s - loss: 0.2571 - accuracy: 0.94 - ETA: 0s - loss: 0.2467 - accuracy: 0.94 - ETA: 0s - loss: 0.2578 - accuracy: 0.94 - ETA: 0s - loss: 0.2622 - accuracy: 0.94 - ETA: 0s - loss: 0.2627 - accuracy: 0.94 - ETA: 0s - loss: 0.2595 - accuracy: 0.94 - 1s 46us/sample - loss: 0.2591 - accuracy: 0.9445 - val_loss: 0.1016 - val_accuracy: 0.9911\n",
      "Epoch 51/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0993 - accuracy: 0.93 - ETA: 0s - loss: 0.2402 - accuracy: 0.94 - ETA: 0s - loss: 0.2630 - accuracy: 0.93 - ETA: 0s - loss: 0.2420 - accuracy: 0.94 - ETA: 0s - loss: 0.2449 - accuracy: 0.94 - ETA: 0s - loss: 0.2504 - accuracy: 0.94 - ETA: 0s - loss: 0.2501 - accuracy: 0.94 - ETA: 0s - loss: 0.2534 - accuracy: 0.94 - ETA: 0s - loss: 0.2522 - accuracy: 0.94 - ETA: 0s - loss: 0.2532 - accuracy: 0.94 - ETA: 0s - loss: 0.2501 - accuracy: 0.94 - ETA: 0s - loss: 0.2510 - accuracy: 0.94 - 1s 45us/sample - loss: 0.2490 - accuracy: 0.9438 - val_loss: 0.0928 - val_accuracy: 0.9888\n",
      "Epoch 52/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.6277 - accuracy: 0.90 - ETA: 0s - loss: 0.2708 - accuracy: 0.93 - ETA: 0s - loss: 0.2725 - accuracy: 0.94 - ETA: 0s - loss: 0.2529 - accuracy: 0.94 - ETA: 0s - loss: 0.2456 - accuracy: 0.94 - ETA: 0s - loss: 0.2379 - accuracy: 0.95 - ETA: 0s - loss: 0.2477 - accuracy: 0.94 - ETA: 0s - loss: 0.2498 - accuracy: 0.94 - ETA: 0s - loss: 0.2451 - accuracy: 0.94 - ETA: 0s - loss: 0.2487 - accuracy: 0.94 - ETA: 0s - loss: 0.2470 - accuracy: 0.94 - ETA: 0s - loss: 0.2482 - accuracy: 0.94 - 1s 45us/sample - loss: 0.2467 - accuracy: 0.9463 - val_loss: 0.0870 - val_accuracy: 0.9872\n",
      "Epoch 53/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0754 - accuracy: 0.96 - ETA: 0s - loss: 0.2594 - accuracy: 0.94 - ETA: 0s - loss: 0.2465 - accuracy: 0.94 - ETA: 0s - loss: 0.2479 - accuracy: 0.94 - ETA: 0s - loss: 0.2429 - accuracy: 0.94 - ETA: 0s - loss: 0.2448 - accuracy: 0.94 - ETA: 0s - loss: 0.2509 - accuracy: 0.94 - ETA: 0s - loss: 0.2500 - accuracy: 0.94 - ETA: 0s - loss: 0.2426 - accuracy: 0.94 - ETA: 0s - loss: 0.2490 - accuracy: 0.94 - ETA: 0s - loss: 0.2520 - accuracy: 0.94 - 1s 45us/sample - loss: 0.2533 - accuracy: 0.9475 - val_loss: 0.1178 - val_accuracy: 0.9894\n",
      "Epoch 54/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.1519 - accuracy: 0.93 - ETA: 0s - loss: 0.2911 - accuracy: 0.92 - ETA: 0s - loss: 0.2531 - accuracy: 0.94 - ETA: 0s - loss: 0.2523 - accuracy: 0.94 - ETA: 0s - loss: 0.2453 - accuracy: 0.95 - ETA: 0s - loss: 0.2591 - accuracy: 0.95 - ETA: 0s - loss: 0.2527 - accuracy: 0.95 - ETA: 0s - loss: 0.2484 - accuracy: 0.95 - ETA: 0s - loss: 0.2486 - accuracy: 0.95 - ETA: 0s - loss: 0.2471 - accuracy: 0.95 - ETA: 0s - loss: 0.2459 - accuracy: 0.95 - ETA: 0s - loss: 0.2441 - accuracy: 0.95 - 1s 45us/sample - loss: 0.2448 - accuracy: 0.9505 - val_loss: 0.1193 - val_accuracy: 0.9838\n",
      "Epoch 55/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.6618 - accuracy: 0.93 - ETA: 0s - loss: 0.2970 - accuracy: 0.90 - ETA: 0s - loss: 0.2600 - accuracy: 0.92 - ETA: 0s - loss: 0.2560 - accuracy: 0.93 - ETA: 0s - loss: 0.2418 - accuracy: 0.93 - ETA: 0s - loss: 0.2388 - accuracy: 0.94 - ETA: 0s - loss: 0.2380 - accuracy: 0.94 - ETA: 0s - loss: 0.2389 - accuracy: 0.94 - ETA: 0s - loss: 0.2456 - accuracy: 0.94 - ETA: 0s - loss: 0.2475 - accuracy: 0.94 - ETA: 0s - loss: 0.2377 - accuracy: 0.94 - ETA: 0s - loss: 0.2468 - accuracy: 0.94 - ETA: 0s - loss: 0.2462 - accuracy: 0.94 - 1s 47us/sample - loss: 0.2464 - accuracy: 0.9427 - val_loss: 0.0932 - val_accuracy: 0.9821\n",
      "Epoch 56/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0788 - accuracy: 1.00 - ETA: 0s - loss: 0.2450 - accuracy: 0.94 - ETA: 0s - loss: 0.2512 - accuracy: 0.94 - ETA: 0s - loss: 0.2526 - accuracy: 0.94 - ETA: 0s - loss: 0.2558 - accuracy: 0.94 - ETA: 0s - loss: 0.2596 - accuracy: 0.94 - ETA: 0s - loss: 0.2546 - accuracy: 0.94 - ETA: 0s - loss: 0.2481 - accuracy: 0.94 - ETA: 0s - loss: 0.2493 - accuracy: 0.94 - ETA: 0s - loss: 0.2445 - accuracy: 0.94 - ETA: 0s - loss: 0.2514 - accuracy: 0.94 - ETA: 0s - loss: 0.2537 - accuracy: 0.94 - 1s 46us/sample - loss: 0.2517 - accuracy: 0.9465 - val_loss: 0.1060 - val_accuracy: 0.9877\n",
      "Epoch 57/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.1786 - accuracy: 0.96 - ETA: 0s - loss: 0.2121 - accuracy: 0.95 - ETA: 0s - loss: 0.2430 - accuracy: 0.94 - ETA: 0s - loss: 0.2596 - accuracy: 0.94 - ETA: 0s - loss: 0.2441 - accuracy: 0.94 - ETA: 0s - loss: 0.2557 - accuracy: 0.94 - ETA: 0s - loss: 0.2558 - accuracy: 0.94 - ETA: 0s - loss: 0.2487 - accuracy: 0.94 - ETA: 0s - loss: 0.2511 - accuracy: 0.94 - ETA: 0s - loss: 0.2469 - accuracy: 0.94 - ETA: 0s - loss: 0.2550 - accuracy: 0.94 - ETA: 0s - loss: 0.2567 - accuracy: 0.94 - ETA: 0s - loss: 0.2567 - accuracy: 0.94 - 1s 47us/sample - loss: 0.2571 - accuracy: 0.9455 - val_loss: 0.1153 - val_accuracy: 0.9866\n",
      "Epoch 58/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1184 - accuracy: 0.93 - ETA: 0s - loss: 0.2579 - accuracy: 0.93 - ETA: 0s - loss: 0.2437 - accuracy: 0.94 - ETA: 0s - loss: 0.2267 - accuracy: 0.95 - ETA: 0s - loss: 0.2290 - accuracy: 0.94 - ETA: 0s - loss: 0.2356 - accuracy: 0.94 - ETA: 0s - loss: 0.2341 - accuracy: 0.94 - ETA: 0s - loss: 0.2432 - accuracy: 0.94 - ETA: 0s - loss: 0.2409 - accuracy: 0.94 - ETA: 0s - loss: 0.2463 - accuracy: 0.94 - ETA: 0s - loss: 0.2490 - accuracy: 0.94 - ETA: 0s - loss: 0.2432 - accuracy: 0.94 - 1s 45us/sample - loss: 0.2480 - accuracy: 0.9478 - val_loss: 0.1106 - val_accuracy: 0.9754\n",
      "Epoch 59/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2384 - accuracy: 0.90 - ETA: 0s - loss: 0.2366 - accuracy: 0.94 - ETA: 0s - loss: 0.2634 - accuracy: 0.93 - ETA: 0s - loss: 0.2715 - accuracy: 0.93 - ETA: 0s - loss: 0.2552 - accuracy: 0.94 - ETA: 0s - loss: 0.2508 - accuracy: 0.94 - ETA: 0s - loss: 0.2479 - accuracy: 0.94 - ETA: 0s - loss: 0.2483 - accuracy: 0.94 - ETA: 0s - loss: 0.2481 - accuracy: 0.94 - ETA: 0s - loss: 0.2474 - accuracy: 0.94 - ETA: 0s - loss: 0.2426 - accuracy: 0.94 - ETA: 0s - loss: 0.2400 - accuracy: 0.94 - 1s 45us/sample - loss: 0.2427 - accuracy: 0.9464 - val_loss: 0.0873 - val_accuracy: 0.9810\n",
      "Epoch 60/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2476 - accuracy: 0.81 - ETA: 0s - loss: 0.2288 - accuracy: 0.93 - ETA: 0s - loss: 0.2416 - accuracy: 0.94 - ETA: 0s - loss: 0.2515 - accuracy: 0.94 - ETA: 0s - loss: 0.2382 - accuracy: 0.94 - ETA: 0s - loss: 0.2435 - accuracy: 0.94 - ETA: 0s - loss: 0.2485 - accuracy: 0.94 - ETA: 0s - loss: 0.2500 - accuracy: 0.94 - ETA: 0s - loss: 0.2462 - accuracy: 0.94 - ETA: 0s - loss: 0.2518 - accuracy: 0.94 - ETA: 0s - loss: 0.2520 - accuracy: 0.94 - ETA: 0s - loss: 0.2495 - accuracy: 0.94 - 1s 44us/sample - loss: 0.2512 - accuracy: 0.9399 - val_loss: 0.1383 - val_accuracy: 0.9916\n",
      "Epoch 61/100\n",
      "14318/14318 [==============================] - ETA: 6s - loss: 0.3355 - accuracy: 0.87 - ETA: 0s - loss: 0.2465 - accuracy: 0.94 - ETA: 0s - loss: 0.2556 - accuracy: 0.94 - ETA: 0s - loss: 0.2597 - accuracy: 0.94 - ETA: 0s - loss: 0.2496 - accuracy: 0.94 - ETA: 0s - loss: 0.2525 - accuracy: 0.95 - ETA: 0s - loss: 0.2458 - accuracy: 0.95 - ETA: 0s - loss: 0.2448 - accuracy: 0.94 - ETA: 0s - loss: 0.2456 - accuracy: 0.95 - ETA: 0s - loss: 0.2469 - accuracy: 0.95 - ETA: 0s - loss: 0.2459 - accuracy: 0.94 - 1s 46us/sample - loss: 0.2480 - accuracy: 0.9489 - val_loss: 0.1062 - val_accuracy: 0.9877\n",
      "Epoch 62/100\n",
      "14318/14318 [==============================] - ETA: 4s - loss: 0.0640 - accuracy: 1.00 - ETA: 0s - loss: 0.2598 - accuracy: 0.93 - ETA: 0s - loss: 0.2395 - accuracy: 0.94 - ETA: 0s - loss: 0.2479 - accuracy: 0.94 - ETA: 0s - loss: 0.2680 - accuracy: 0.94 - ETA: 0s - loss: 0.2576 - accuracy: 0.94 - ETA: 0s - loss: 0.2514 - accuracy: 0.94 - ETA: 0s - loss: 0.2444 - accuracy: 0.94 - ETA: 0s - loss: 0.2456 - accuracy: 0.94 - ETA: 0s - loss: 0.2444 - accuracy: 0.94 - ETA: 0s - loss: 0.2487 - accuracy: 0.94 - 1s 45us/sample - loss: 0.2521 - accuracy: 0.9449 - val_loss: 0.0929 - val_accuracy: 0.9888\n",
      "Epoch 63/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.5676 - accuracy: 0.90 - ETA: 0s - loss: 0.2307 - accuracy: 0.95 - ETA: 0s - loss: 0.2176 - accuracy: 0.94 - ETA: 0s - loss: 0.2142 - accuracy: 0.95 - ETA: 0s - loss: 0.2174 - accuracy: 0.95 - ETA: 0s - loss: 0.2210 - accuracy: 0.94 - ETA: 0s - loss: 0.2319 - accuracy: 0.94 - ETA: 0s - loss: 0.2471 - accuracy: 0.94 - ETA: 0s - loss: 0.2476 - accuracy: 0.94 - ETA: 0s - loss: 0.2455 - accuracy: 0.94 - ETA: 0s - loss: 0.2487 - accuracy: 0.94 - ETA: 0s - loss: 0.2494 - accuracy: 0.94 - 1s 45us/sample - loss: 0.2474 - accuracy: 0.9463 - val_loss: 0.1125 - val_accuracy: 0.9816\n",
      "Epoch 64/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1851 - accuracy: 0.87 - ETA: 0s - loss: 0.2852 - accuracy: 0.94 - ETA: 0s - loss: 0.2792 - accuracy: 0.94 - ETA: 0s - loss: 0.2774 - accuracy: 0.94 - ETA: 0s - loss: 0.2586 - accuracy: 0.94 - ETA: 0s - loss: 0.2603 - accuracy: 0.94 - ETA: 0s - loss: 0.2596 - accuracy: 0.94 - ETA: 0s - loss: 0.2660 - accuracy: 0.94 - ETA: 0s - loss: 0.2624 - accuracy: 0.94 - ETA: 0s - loss: 0.2612 - accuracy: 0.94 - ETA: 0s - loss: 0.2634 - accuracy: 0.94 - ETA: 0s - loss: 0.2620 - accuracy: 0.94 - ETA: 0s - loss: 0.2597 - accuracy: 0.94 - 1s 50us/sample - loss: 0.2571 - accuracy: 0.9453 - val_loss: 0.1018 - val_accuracy: 0.9911\n",
      "Epoch 65/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1080 - accuracy: 0.93 - ETA: 0s - loss: 0.2378 - accuracy: 0.96 - ETA: 0s - loss: 0.2431 - accuracy: 0.95 - ETA: 0s - loss: 0.2457 - accuracy: 0.95 - ETA: 0s - loss: 0.2491 - accuracy: 0.95 - ETA: 0s - loss: 0.2413 - accuracy: 0.94 - ETA: 0s - loss: 0.2463 - accuracy: 0.94 - ETA: 0s - loss: 0.2453 - accuracy: 0.95 - ETA: 0s - loss: 0.2454 - accuracy: 0.94 - ETA: 0s - loss: 0.2556 - accuracy: 0.94 - ETA: 0s - loss: 0.2481 - accuracy: 0.94 - ETA: 0s - loss: 0.2479 - accuracy: 0.94 - ETA: 0s - loss: 0.2422 - accuracy: 0.94 - 1s 48us/sample - loss: 0.2515 - accuracy: 0.9492 - val_loss: 0.1275 - val_accuracy: 0.9860\n",
      "Epoch 66/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.7831 - accuracy: 0.93 - ETA: 0s - loss: 0.2901 - accuracy: 0.92 - ETA: 0s - loss: 0.2416 - accuracy: 0.94 - ETA: 0s - loss: 0.2709 - accuracy: 0.93 - ETA: 0s - loss: 0.2580 - accuracy: 0.94 - ETA: 0s - loss: 0.2612 - accuracy: 0.93 - ETA: 0s - loss: 0.2572 - accuracy: 0.94 - ETA: 0s - loss: 0.2521 - accuracy: 0.94 - ETA: 0s - loss: 0.2546 - accuracy: 0.94 - ETA: 0s - loss: 0.2518 - accuracy: 0.94 - ETA: 0s - loss: 0.2463 - accuracy: 0.94 - ETA: 0s - loss: 0.2556 - accuracy: 0.94 - ETA: 0s - loss: 0.2640 - accuracy: 0.94 - 1s 47us/sample - loss: 0.2620 - accuracy: 0.9485 - val_loss: 0.1239 - val_accuracy: 0.9883\n",
      "Epoch 67/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2185 - accuracy: 0.87 - ETA: 0s - loss: 0.2748 - accuracy: 0.93 - ETA: 0s - loss: 0.2602 - accuracy: 0.94 - ETA: 0s - loss: 0.2654 - accuracy: 0.93 - ETA: 0s - loss: 0.2555 - accuracy: 0.94 - ETA: 0s - loss: 0.2517 - accuracy: 0.94 - ETA: 0s - loss: 0.2570 - accuracy: 0.94 - ETA: 0s - loss: 0.2561 - accuracy: 0.94 - ETA: 0s - loss: 0.2573 - accuracy: 0.94 - ETA: 0s - loss: 0.2531 - accuracy: 0.94 - ETA: 0s - loss: 0.2528 - accuracy: 0.94 - ETA: 0s - loss: 0.2487 - accuracy: 0.94 - ETA: 0s - loss: 0.2559 - accuracy: 0.94 - 1s 48us/sample - loss: 0.2555 - accuracy: 0.9466 - val_loss: 0.1048 - val_accuracy: 0.9922\n",
      "Epoch 68/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2784 - accuracy: 0.96 - ETA: 0s - loss: 0.2420 - accuracy: 0.94 - ETA: 0s - loss: 0.2392 - accuracy: 0.93 - ETA: 0s - loss: 0.2418 - accuracy: 0.93 - ETA: 0s - loss: 0.2350 - accuracy: 0.94 - ETA: 0s - loss: 0.2277 - accuracy: 0.94 - ETA: 0s - loss: 0.2417 - accuracy: 0.94 - ETA: 0s - loss: 0.2377 - accuracy: 0.94 - ETA: 0s - loss: 0.2421 - accuracy: 0.94 - ETA: 0s - loss: 0.2406 - accuracy: 0.94 - ETA: 0s - loss: 0.2422 - accuracy: 0.94 - ETA: 0s - loss: 0.2416 - accuracy: 0.94 - 1s 47us/sample - loss: 0.2450 - accuracy: 0.9462 - val_loss: 0.0968 - val_accuracy: 0.9927\n",
      "Epoch 69/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0785 - accuracy: 0.96 - ETA: 0s - loss: 0.2932 - accuracy: 0.94 - ETA: 0s - loss: 0.2928 - accuracy: 0.93 - ETA: 0s - loss: 0.2825 - accuracy: 0.94 - ETA: 0s - loss: 0.2737 - accuracy: 0.94 - ETA: 0s - loss: 0.2685 - accuracy: 0.94 - ETA: 0s - loss: 0.2652 - accuracy: 0.94 - ETA: 0s - loss: 0.2689 - accuracy: 0.94 - ETA: 0s - loss: 0.2614 - accuracy: 0.94 - ETA: 0s - loss: 0.2625 - accuracy: 0.94 - ETA: 0s - loss: 0.2584 - accuracy: 0.94 - ETA: 0s - loss: 0.2539 - accuracy: 0.94 - ETA: 0s - loss: 0.2574 - accuracy: 0.94 - ETA: 0s - loss: 0.2531 - accuracy: 0.94 - 1s 51us/sample - loss: 0.2512 - accuracy: 0.9489 - val_loss: 0.0955 - val_accuracy: 0.9927\n",
      "Epoch 70/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0677 - accuracy: 0.96 - ETA: 0s - loss: 0.1977 - accuracy: 0.94 - ETA: 0s - loss: 0.2148 - accuracy: 0.94 - ETA: 0s - loss: 0.2359 - accuracy: 0.94 - ETA: 0s - loss: 0.2465 - accuracy: 0.94 - ETA: 0s - loss: 0.2502 - accuracy: 0.94 - ETA: 0s - loss: 0.2611 - accuracy: 0.93 - ETA: 0s - loss: 0.2480 - accuracy: 0.94 - ETA: 0s - loss: 0.2376 - accuracy: 0.94 - ETA: 0s - loss: 0.2427 - accuracy: 0.94 - ETA: 0s - loss: 0.2536 - accuracy: 0.94 - ETA: 0s - loss: 0.2488 - accuracy: 0.94 - ETA: 0s - loss: 0.2495 - accuracy: 0.94 - 1s 49us/sample - loss: 0.2500 - accuracy: 0.9461 - val_loss: 0.1047 - val_accuracy: 0.9911\n",
      "Epoch 71/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0926 - accuracy: 0.96 - ETA: 0s - loss: 0.2614 - accuracy: 0.95 - ETA: 0s - loss: 0.2447 - accuracy: 0.94 - ETA: 0s - loss: 0.2495 - accuracy: 0.94 - ETA: 0s - loss: 0.2527 - accuracy: 0.94 - ETA: 0s - loss: 0.2430 - accuracy: 0.94 - ETA: 0s - loss: 0.2413 - accuracy: 0.94 - ETA: 0s - loss: 0.2565 - accuracy: 0.94 - ETA: 0s - loss: 0.2593 - accuracy: 0.94 - ETA: 0s - loss: 0.2613 - accuracy: 0.94 - ETA: 0s - loss: 0.2683 - accuracy: 0.94 - ETA: 0s - loss: 0.2684 - accuracy: 0.94 - ETA: 0s - loss: 0.2669 - accuracy: 0.94 - 1s 48us/sample - loss: 0.2691 - accuracy: 0.9470 - val_loss: 0.1144 - val_accuracy: 0.9911\n",
      "Epoch 72/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.1163 - accuracy: 0.93 - ETA: 0s - loss: 0.2535 - accuracy: 0.94 - ETA: 0s - loss: 0.2374 - accuracy: 0.94 - ETA: 0s - loss: 0.2350 - accuracy: 0.94 - ETA: 0s - loss: 0.2359 - accuracy: 0.94 - ETA: 0s - loss: 0.2426 - accuracy: 0.94 - ETA: 0s - loss: 0.2449 - accuracy: 0.94 - ETA: 0s - loss: 0.2531 - accuracy: 0.94 - ETA: 0s - loss: 0.2565 - accuracy: 0.93 - ETA: 0s - loss: 0.2546 - accuracy: 0.93 - ETA: 0s - loss: 0.2545 - accuracy: 0.94 - ETA: 0s - loss: 0.2529 - accuracy: 0.94 - 1s 46us/sample - loss: 0.2520 - accuracy: 0.9415 - val_loss: 0.1078 - val_accuracy: 0.9916\n",
      "Epoch 73/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0761 - accuracy: 1.00 - ETA: 0s - loss: 0.2611 - accuracy: 0.95 - ETA: 0s - loss: 0.2524 - accuracy: 0.93 - ETA: 0s - loss: 0.2383 - accuracy: 0.94 - ETA: 0s - loss: 0.2405 - accuracy: 0.94 - ETA: 0s - loss: 0.2470 - accuracy: 0.94 - ETA: 0s - loss: 0.2453 - accuracy: 0.94 - ETA: 0s - loss: 0.2472 - accuracy: 0.94 - ETA: 0s - loss: 0.2523 - accuracy: 0.94 - ETA: 0s - loss: 0.2514 - accuracy: 0.94 - ETA: 0s - loss: 0.2532 - accuracy: 0.94 - ETA: 0s - loss: 0.2522 - accuracy: 0.94 - 1s 46us/sample - loss: 0.2509 - accuracy: 0.9481 - val_loss: 0.1008 - val_accuracy: 0.9855\n",
      "Epoch 74/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2964 - accuracy: 0.90 - ETA: 0s - loss: 0.2240 - accuracy: 0.94 - ETA: 0s - loss: 0.2207 - accuracy: 0.95 - ETA: 0s - loss: 0.2255 - accuracy: 0.94 - ETA: 0s - loss: 0.2251 - accuracy: 0.94 - ETA: 0s - loss: 0.2367 - accuracy: 0.94 - ETA: 0s - loss: 0.2368 - accuracy: 0.94 - ETA: 0s - loss: 0.2352 - accuracy: 0.94 - ETA: 0s - loss: 0.2345 - accuracy: 0.94 - ETA: 0s - loss: 0.2361 - accuracy: 0.94 - ETA: 0s - loss: 0.2389 - accuracy: 0.94 - ETA: 0s - loss: 0.2447 - accuracy: 0.94 - 1s 46us/sample - loss: 0.2444 - accuracy: 0.9457 - val_loss: 0.1002 - val_accuracy: 0.9916\n",
      "Epoch 75/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.4765 - accuracy: 0.93 - ETA: 0s - loss: 0.2569 - accuracy: 0.94 - ETA: 0s - loss: 0.2453 - accuracy: 0.95 - ETA: 0s - loss: 0.2469 - accuracy: 0.95 - ETA: 0s - loss: 0.2473 - accuracy: 0.95 - ETA: 0s - loss: 0.2464 - accuracy: 0.95 - ETA: 0s - loss: 0.2443 - accuracy: 0.94 - ETA: 0s - loss: 0.2515 - accuracy: 0.94 - ETA: 0s - loss: 0.2568 - accuracy: 0.94 - ETA: 0s - loss: 0.2622 - accuracy: 0.94 - ETA: 0s - loss: 0.2585 - accuracy: 0.94 - ETA: 0s - loss: 0.2528 - accuracy: 0.94 - 1s 44us/sample - loss: 0.2515 - accuracy: 0.9460 - val_loss: 0.0686 - val_accuracy: 0.9888\n",
      "Epoch 76/100\n",
      "14318/14318 [==============================] - ETA: 7s - loss: 0.0491 - accuracy: 1.00 - ETA: 0s - loss: 0.2220 - accuracy: 0.95 - ETA: 0s - loss: 0.2175 - accuracy: 0.95 - ETA: 0s - loss: 0.2358 - accuracy: 0.95 - ETA: 0s - loss: 0.2230 - accuracy: 0.95 - ETA: 0s - loss: 0.2238 - accuracy: 0.95 - ETA: 0s - loss: 0.2292 - accuracy: 0.95 - ETA: 0s - loss: 0.2280 - accuracy: 0.95 - ETA: 0s - loss: 0.2286 - accuracy: 0.95 - ETA: 0s - loss: 0.2315 - accuracy: 0.95 - ETA: 0s - loss: 0.2442 - accuracy: 0.94 - ETA: 0s - loss: 0.2499 - accuracy: 0.94 - 1s 46us/sample - loss: 0.2494 - accuracy: 0.9471 - val_loss: 0.1103 - val_accuracy: 0.9911\n",
      "Epoch 77/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2043 - accuracy: 0.96 - ETA: 0s - loss: 0.2408 - accuracy: 0.95 - ETA: 0s - loss: 0.2204 - accuracy: 0.95 - ETA: 0s - loss: 0.2313 - accuracy: 0.95 - ETA: 0s - loss: 0.2458 - accuracy: 0.95 - ETA: 0s - loss: 0.2496 - accuracy: 0.95 - ETA: 0s - loss: 0.2505 - accuracy: 0.94 - ETA: 0s - loss: 0.2530 - accuracy: 0.94 - ETA: 0s - loss: 0.2487 - accuracy: 0.95 - ETA: 0s - loss: 0.2478 - accuracy: 0.95 - ETA: 0s - loss: 0.2482 - accuracy: 0.95 - ETA: 0s - loss: 0.2476 - accuracy: 0.95 - 1s 45us/sample - loss: 0.2471 - accuracy: 0.9520 - val_loss: 0.0998 - val_accuracy: 0.9905\n",
      "Epoch 78/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1755 - accuracy: 0.96 - ETA: 0s - loss: 0.2307 - accuracy: 0.95 - ETA: 0s - loss: 0.2527 - accuracy: 0.94 - ETA: 0s - loss: 0.2445 - accuracy: 0.94 - ETA: 0s - loss: 0.2393 - accuracy: 0.95 - ETA: 0s - loss: 0.2385 - accuracy: 0.95 - ETA: 0s - loss: 0.2329 - accuracy: 0.95 - ETA: 0s - loss: 0.2454 - accuracy: 0.95 - ETA: 0s - loss: 0.2492 - accuracy: 0.95 - ETA: 0s - loss: 0.2486 - accuracy: 0.95 - ETA: 0s - loss: 0.2508 - accuracy: 0.95 - ETA: 0s - loss: 0.2524 - accuracy: 0.95 - 1s 45us/sample - loss: 0.2516 - accuracy: 0.9513 - val_loss: 0.1111 - val_accuracy: 0.9922\n",
      "Epoch 79/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0966 - accuracy: 1.00 - ETA: 0s - loss: 0.2954 - accuracy: 0.94 - ETA: 0s - loss: 0.2529 - accuracy: 0.95 - ETA: 0s - loss: 0.2382 - accuracy: 0.95 - ETA: 0s - loss: 0.2382 - accuracy: 0.95 - ETA: 0s - loss: 0.2459 - accuracy: 0.95 - ETA: 0s - loss: 0.2373 - accuracy: 0.95 - ETA: 0s - loss: 0.2396 - accuracy: 0.95 - ETA: 0s - loss: 0.2404 - accuracy: 0.95 - ETA: 0s - loss: 0.2387 - accuracy: 0.95 - ETA: 0s - loss: 0.2450 - accuracy: 0.95 - ETA: 0s - loss: 0.2443 - accuracy: 0.95 - 1s 45us/sample - loss: 0.2460 - accuracy: 0.9502 - val_loss: 0.1040 - val_accuracy: 0.9933\n",
      "Epoch 80/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.2988 - accuracy: 0.93 - ETA: 0s - loss: 0.2175 - accuracy: 0.95 - ETA: 0s - loss: 0.2050 - accuracy: 0.94 - ETA: 0s - loss: 0.2284 - accuracy: 0.94 - ETA: 0s - loss: 0.2331 - accuracy: 0.94 - ETA: 0s - loss: 0.2274 - accuracy: 0.94 - ETA: 0s - loss: 0.2278 - accuracy: 0.94 - ETA: 0s - loss: 0.2429 - accuracy: 0.94 - ETA: 0s - loss: 0.2401 - accuracy: 0.94 - ETA: 0s - loss: 0.2426 - accuracy: 0.94 - ETA: 0s - loss: 0.2467 - accuracy: 0.94 - 1s 45us/sample - loss: 0.2450 - accuracy: 0.9436 - val_loss: 0.1037 - val_accuracy: 0.9888\n",
      "Epoch 81/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.6813 - accuracy: 0.93 - ETA: 0s - loss: 0.2583 - accuracy: 0.94 - ETA: 0s - loss: 0.2586 - accuracy: 0.94 - ETA: 0s - loss: 0.2552 - accuracy: 0.94 - ETA: 0s - loss: 0.2491 - accuracy: 0.95 - ETA: 0s - loss: 0.2429 - accuracy: 0.94 - ETA: 0s - loss: 0.2426 - accuracy: 0.94 - ETA: 0s - loss: 0.2464 - accuracy: 0.94 - ETA: 0s - loss: 0.2473 - accuracy: 0.94 - ETA: 0s - loss: 0.2470 - accuracy: 0.94 - 1s 45us/sample - loss: 0.2463 - accuracy: 0.9474 - val_loss: 0.0923 - val_accuracy: 0.9916\n",
      "Epoch 82/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.8135 - accuracy: 0.93 - ETA: 0s - loss: 0.2391 - accuracy: 0.95 - ETA: 0s - loss: 0.2330 - accuracy: 0.94 - ETA: 0s - loss: 0.2434 - accuracy: 0.95 - ETA: 0s - loss: 0.2499 - accuracy: 0.94 - ETA: 0s - loss: 0.2511 - accuracy: 0.94 - ETA: 0s - loss: 0.2564 - accuracy: 0.94 - ETA: 0s - loss: 0.2583 - accuracy: 0.94 - ETA: 0s - loss: 0.2509 - accuracy: 0.95 - ETA: 0s - loss: 0.2510 - accuracy: 0.95 - ETA: 0s - loss: 0.2481 - accuracy: 0.95 - ETA: 0s - loss: 0.2437 - accuracy: 0.95 - 1s 46us/sample - loss: 0.2461 - accuracy: 0.9501 - val_loss: 0.0776 - val_accuracy: 0.9899\n",
      "Epoch 83/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1513 - accuracy: 0.90 - ETA: 0s - loss: 0.2747 - accuracy: 0.92 - ETA: 0s - loss: 0.2683 - accuracy: 0.93 - ETA: 0s - loss: 0.2652 - accuracy: 0.94 - ETA: 0s - loss: 0.2609 - accuracy: 0.94 - ETA: 0s - loss: 0.2522 - accuracy: 0.94 - ETA: 0s - loss: 0.2469 - accuracy: 0.94 - ETA: 0s - loss: 0.2506 - accuracy: 0.94 - ETA: 0s - loss: 0.2477 - accuracy: 0.94 - ETA: 0s - loss: 0.2411 - accuracy: 0.94 - ETA: 0s - loss: 0.2393 - accuracy: 0.95 - 1s 45us/sample - loss: 0.2397 - accuracy: 0.9502 - val_loss: 0.0963 - val_accuracy: 0.9905\n",
      "Epoch 84/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1624 - accuracy: 0.96 - ETA: 0s - loss: 0.2039 - accuracy: 0.96 - ETA: 0s - loss: 0.2305 - accuracy: 0.95 - ETA: 0s - loss: 0.2225 - accuracy: 0.95 - ETA: 0s - loss: 0.2410 - accuracy: 0.94 - ETA: 0s - loss: 0.2456 - accuracy: 0.94 - ETA: 0s - loss: 0.2400 - accuracy: 0.94 - ETA: 0s - loss: 0.2453 - accuracy: 0.94 - ETA: 0s - loss: 0.2460 - accuracy: 0.94 - ETA: 0s - loss: 0.2394 - accuracy: 0.94 - ETA: 0s - loss: 0.2409 - accuracy: 0.94 - 1s 46us/sample - loss: 0.2431 - accuracy: 0.9488 - val_loss: 0.1106 - val_accuracy: 0.9866\n",
      "Epoch 85/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.3819 - accuracy: 0.93 - ETA: 0s - loss: 0.2678 - accuracy: 0.93 - ETA: 0s - loss: 0.2452 - accuracy: 0.94 - ETA: 0s - loss: 0.2522 - accuracy: 0.94 - ETA: 0s - loss: 0.2497 - accuracy: 0.93 - ETA: 0s - loss: 0.2427 - accuracy: 0.94 - ETA: 0s - loss: 0.2332 - accuracy: 0.94 - ETA: 0s - loss: 0.2289 - accuracy: 0.94 - ETA: 0s - loss: 0.2323 - accuracy: 0.94 - ETA: 0s - loss: 0.2392 - accuracy: 0.94 - ETA: 0s - loss: 0.2421 - accuracy: 0.94 - ETA: 0s - loss: 0.2413 - accuracy: 0.94 - 1s 45us/sample - loss: 0.2424 - accuracy: 0.9462 - val_loss: 0.0860 - val_accuracy: 0.9883\n",
      "Epoch 86/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1084 - accuracy: 0.96 - ETA: 0s - loss: 0.2130 - accuracy: 0.95 - ETA: 0s - loss: 0.2266 - accuracy: 0.95 - ETA: 0s - loss: 0.2286 - accuracy: 0.95 - ETA: 0s - loss: 0.2280 - accuracy: 0.95 - ETA: 0s - loss: 0.2415 - accuracy: 0.95 - ETA: 0s - loss: 0.2513 - accuracy: 0.95 - ETA: 0s - loss: 0.2513 - accuracy: 0.95 - ETA: 0s - loss: 0.2560 - accuracy: 0.95 - ETA: 0s - loss: 0.2521 - accuracy: 0.95 - ETA: 0s - loss: 0.2497 - accuracy: 0.95 - ETA: 0s - loss: 0.2498 - accuracy: 0.94 - 1s 46us/sample - loss: 0.2466 - accuracy: 0.9501 - val_loss: 0.0820 - val_accuracy: 0.9916\n",
      "Epoch 87/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1974 - accuracy: 0.96 - ETA: 0s - loss: 0.2575 - accuracy: 0.95 - ETA: 0s - loss: 0.2653 - accuracy: 0.94 - ETA: 0s - loss: 0.2536 - accuracy: 0.94 - ETA: 0s - loss: 0.2411 - accuracy: 0.94 - ETA: 0s - loss: 0.2497 - accuracy: 0.95 - ETA: 0s - loss: 0.2508 - accuracy: 0.94 - ETA: 0s - loss: 0.2390 - accuracy: 0.95 - ETA: 0s - loss: 0.2449 - accuracy: 0.95 - ETA: 0s - loss: 0.2593 - accuracy: 0.94 - ETA: 0s - loss: 0.2562 - accuracy: 0.94 - ETA: 0s - loss: 0.2514 - accuracy: 0.94 - 1s 45us/sample - loss: 0.2505 - accuracy: 0.9490 - val_loss: 0.0868 - val_accuracy: 0.9927\n",
      "Epoch 88/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0838 - accuracy: 0.93 - ETA: 0s - loss: 0.2841 - accuracy: 0.94 - ETA: 0s - loss: 0.2698 - accuracy: 0.94 - ETA: 0s - loss: 0.2543 - accuracy: 0.94 - ETA: 0s - loss: 0.2579 - accuracy: 0.94 - ETA: 0s - loss: 0.2549 - accuracy: 0.94 - ETA: 0s - loss: 0.2539 - accuracy: 0.94 - ETA: 0s - loss: 0.2475 - accuracy: 0.95 - ETA: 0s - loss: 0.2457 - accuracy: 0.95 - ETA: 0s - loss: 0.2529 - accuracy: 0.95 - ETA: 0s - loss: 0.2521 - accuracy: 0.95 - ETA: 0s - loss: 0.2501 - accuracy: 0.95 - ETA: 0s - loss: 0.2438 - accuracy: 0.95 - 1s 50us/sample - loss: 0.2431 - accuracy: 0.9517 - val_loss: 0.0729 - val_accuracy: 0.9855\n",
      "Epoch 89/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.0685 - accuracy: 0.93 - ETA: 0s - loss: 0.2438 - accuracy: 0.93 - ETA: 0s - loss: 0.2188 - accuracy: 0.94 - ETA: 0s - loss: 0.2237 - accuracy: 0.95 - ETA: 0s - loss: 0.2271 - accuracy: 0.95 - ETA: 0s - loss: 0.2145 - accuracy: 0.95 - ETA: 0s - loss: 0.2205 - accuracy: 0.95 - ETA: 0s - loss: 0.2213 - accuracy: 0.95 - ETA: 0s - loss: 0.2287 - accuracy: 0.95 - ETA: 0s - loss: 0.2323 - accuracy: 0.95 - ETA: 0s - loss: 0.2364 - accuracy: 0.95 - ETA: 0s - loss: 0.2365 - accuracy: 0.95 - 1s 48us/sample - loss: 0.2387 - accuracy: 0.9508 - val_loss: 0.1181 - val_accuracy: 0.9804\n",
      "Epoch 90/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.0866 - accuracy: 1.00 - ETA: 0s - loss: 0.2540 - accuracy: 0.94 - ETA: 0s - loss: 0.2407 - accuracy: 0.94 - ETA: 0s - loss: 0.2364 - accuracy: 0.95 - ETA: 0s - loss: 0.2371 - accuracy: 0.94 - ETA: 0s - loss: 0.2462 - accuracy: 0.94 - ETA: 0s - loss: 0.2432 - accuracy: 0.94 - ETA: 0s - loss: 0.2408 - accuracy: 0.94 - ETA: 0s - loss: 0.2448 - accuracy: 0.94 - ETA: 0s - loss: 0.2400 - accuracy: 0.94 - ETA: 0s - loss: 0.2446 - accuracy: 0.94 - ETA: 0s - loss: 0.2362 - accuracy: 0.95 - ETA: 0s - loss: 0.2451 - accuracy: 0.94 - 1s 46us/sample - loss: 0.2449 - accuracy: 0.9477 - val_loss: 0.1167 - val_accuracy: 0.9922\n",
      "Epoch 91/100\n",
      "14318/14318 [==============================] - ETA: 6s - loss: 0.2662 - accuracy: 0.93 - ETA: 0s - loss: 0.2492 - accuracy: 0.95 - ETA: 0s - loss: 0.2279 - accuracy: 0.95 - ETA: 0s - loss: 0.2290 - accuracy: 0.96 - ETA: 0s - loss: 0.2364 - accuracy: 0.95 - ETA: 0s - loss: 0.2378 - accuracy: 0.95 - ETA: 0s - loss: 0.2408 - accuracy: 0.95 - ETA: 0s - loss: 0.2378 - accuracy: 0.95 - ETA: 0s - loss: 0.2389 - accuracy: 0.95 - ETA: 0s - loss: 0.2380 - accuracy: 0.95 - ETA: 0s - loss: 0.2420 - accuracy: 0.95 - ETA: 0s - loss: 0.2466 - accuracy: 0.95 - 1s 48us/sample - loss: 0.2471 - accuracy: 0.9515 - val_loss: 0.1005 - val_accuracy: 0.9922\n",
      "Epoch 92/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.0696 - accuracy: 1.00 - ETA: 0s - loss: 0.1855 - accuracy: 0.95 - ETA: 0s - loss: 0.2001 - accuracy: 0.95 - ETA: 0s - loss: 0.2409 - accuracy: 0.95 - ETA: 0s - loss: 0.2345 - accuracy: 0.95 - ETA: 0s - loss: 0.2392 - accuracy: 0.95 - ETA: 0s - loss: 0.2403 - accuracy: 0.95 - ETA: 0s - loss: 0.2429 - accuracy: 0.95 - ETA: 0s - loss: 0.2397 - accuracy: 0.95 - ETA: 0s - loss: 0.2422 - accuracy: 0.95 - ETA: 0s - loss: 0.2446 - accuracy: 0.95 - ETA: 0s - loss: 0.2469 - accuracy: 0.95 - ETA: 0s - loss: 0.2477 - accuracy: 0.94 - 1s 47us/sample - loss: 0.2468 - accuracy: 0.9501 - val_loss: 0.1031 - val_accuracy: 0.9927\n",
      "Epoch 93/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.3141 - accuracy: 0.96 - ETA: 0s - loss: 0.2656 - accuracy: 0.95 - ETA: 0s - loss: 0.2391 - accuracy: 0.94 - ETA: 0s - loss: 0.2530 - accuracy: 0.94 - ETA: 0s - loss: 0.2433 - accuracy: 0.94 - ETA: 0s - loss: 0.2541 - accuracy: 0.94 - ETA: 0s - loss: 0.2491 - accuracy: 0.94 - ETA: 0s - loss: 0.2504 - accuracy: 0.94 - ETA: 0s - loss: 0.2509 - accuracy: 0.94 - ETA: 0s - loss: 0.2502 - accuracy: 0.94 - ETA: 0s - loss: 0.2463 - accuracy: 0.94 - ETA: 0s - loss: 0.2473 - accuracy: 0.94 - 1s 45us/sample - loss: 0.2491 - accuracy: 0.9491 - val_loss: 0.0878 - val_accuracy: 0.9883\n",
      "Epoch 94/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.1960 - accuracy: 0.96 - ETA: 0s - loss: 0.2725 - accuracy: 0.93 - ETA: 0s - loss: 0.2424 - accuracy: 0.94 - ETA: 0s - loss: 0.2591 - accuracy: 0.94 - ETA: 0s - loss: 0.2476 - accuracy: 0.94 - ETA: 0s - loss: 0.2564 - accuracy: 0.94 - ETA: 0s - loss: 0.2561 - accuracy: 0.94 - ETA: 0s - loss: 0.2518 - accuracy: 0.94 - ETA: 0s - loss: 0.2566 - accuracy: 0.94 - ETA: 0s - loss: 0.2517 - accuracy: 0.94 - ETA: 0s - loss: 0.2520 - accuracy: 0.94 - ETA: 0s - loss: 0.2512 - accuracy: 0.94 - 1s 46us/sample - loss: 0.2460 - accuracy: 0.9482 - val_loss: 0.0902 - val_accuracy: 0.9911\n",
      "Epoch 95/100\n",
      "14318/14318 [==============================] - ETA: 1s - loss: 0.0853 - accuracy: 0.96 - ETA: 0s - loss: 0.2202 - accuracy: 0.96 - ETA: 0s - loss: 0.2318 - accuracy: 0.95 - ETA: 0s - loss: 0.2227 - accuracy: 0.95 - ETA: 0s - loss: 0.2171 - accuracy: 0.95 - ETA: 0s - loss: 0.2319 - accuracy: 0.95 - ETA: 0s - loss: 0.2388 - accuracy: 0.94 - ETA: 0s - loss: 0.2440 - accuracy: 0.94 - ETA: 0s - loss: 0.2442 - accuracy: 0.94 - ETA: 0s - loss: 0.2409 - accuracy: 0.94 - ETA: 0s - loss: 0.2429 - accuracy: 0.94 - 1s 45us/sample - loss: 0.2386 - accuracy: 0.9469 - val_loss: 0.0970 - val_accuracy: 0.9916\n",
      "Epoch 96/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0894 - accuracy: 0.93 - ETA: 0s - loss: 0.1812 - accuracy: 0.96 - ETA: 0s - loss: 0.2079 - accuracy: 0.96 - ETA: 0s - loss: 0.2359 - accuracy: 0.95 - ETA: 0s - loss: 0.2385 - accuracy: 0.95 - ETA: 0s - loss: 0.2367 - accuracy: 0.95 - ETA: 0s - loss: 0.2419 - accuracy: 0.94 - ETA: 0s - loss: 0.2400 - accuracy: 0.94 - ETA: 0s - loss: 0.2353 - accuracy: 0.94 - ETA: 0s - loss: 0.2383 - accuracy: 0.94 - ETA: 0s - loss: 0.2412 - accuracy: 0.94 - ETA: 0s - loss: 0.2381 - accuracy: 0.94 - 1s 47us/sample - loss: 0.2388 - accuracy: 0.9493 - val_loss: 0.0948 - val_accuracy: 0.9883\n",
      "Epoch 97/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.5294 - accuracy: 0.93 - ETA: 0s - loss: 0.2398 - accuracy: 0.95 - ETA: 0s - loss: 0.2321 - accuracy: 0.94 - ETA: 0s - loss: 0.2294 - accuracy: 0.94 - ETA: 0s - loss: 0.2310 - accuracy: 0.95 - ETA: 0s - loss: 0.2216 - accuracy: 0.95 - ETA: 0s - loss: 0.2323 - accuracy: 0.95 - ETA: 0s - loss: 0.2398 - accuracy: 0.95 - ETA: 0s - loss: 0.2451 - accuracy: 0.94 - ETA: 0s - loss: 0.2469 - accuracy: 0.94 - ETA: 0s - loss: 0.2484 - accuracy: 0.94 - ETA: 0s - loss: 0.2516 - accuracy: 0.94 - ETA: 0s - loss: 0.2558 - accuracy: 0.94 - ETA: 0s - loss: 0.2528 - accuracy: 0.94 - 1s 51us/sample - loss: 0.2524 - accuracy: 0.9480 - val_loss: 0.1030 - val_accuracy: 0.9894\n",
      "Epoch 98/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0957 - accuracy: 1.00 - ETA: 0s - loss: 0.1885 - accuracy: 0.95 - ETA: 0s - loss: 0.2300 - accuracy: 0.94 - ETA: 0s - loss: 0.2417 - accuracy: 0.94 - ETA: 0s - loss: 0.2465 - accuracy: 0.94 - ETA: 0s - loss: 0.2535 - accuracy: 0.94 - ETA: 0s - loss: 0.2476 - accuracy: 0.94 - ETA: 0s - loss: 0.2476 - accuracy: 0.94 - ETA: 0s - loss: 0.2536 - accuracy: 0.94 - ETA: 0s - loss: 0.2514 - accuracy: 0.94 - ETA: 0s - loss: 0.2452 - accuracy: 0.94 - ETA: 0s - loss: 0.2467 - accuracy: 0.94 - ETA: 0s - loss: 0.2524 - accuracy: 0.94 - 1s 47us/sample - loss: 0.2506 - accuracy: 0.9487 - val_loss: 0.0921 - val_accuracy: 0.9883\n",
      "Epoch 99/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.0956 - accuracy: 0.96 - ETA: 0s - loss: 0.1912 - accuracy: 0.96 - ETA: 0s - loss: 0.2030 - accuracy: 0.95 - ETA: 0s - loss: 0.2266 - accuracy: 0.94 - ETA: 0s - loss: 0.2339 - accuracy: 0.94 - ETA: 0s - loss: 0.2361 - accuracy: 0.94 - ETA: 0s - loss: 0.2454 - accuracy: 0.94 - ETA: 0s - loss: 0.2439 - accuracy: 0.94 - ETA: 0s - loss: 0.2395 - accuracy: 0.94 - ETA: 0s - loss: 0.2449 - accuracy: 0.94 - ETA: 0s - loss: 0.2543 - accuracy: 0.94 - ETA: 0s - loss: 0.2508 - accuracy: 0.94 - 1s 45us/sample - loss: 0.2506 - accuracy: 0.9472 - val_loss: 0.1014 - val_accuracy: 0.9927\n",
      "Epoch 100/100\n",
      "14318/14318 [==============================] - ETA: 0s - loss: 0.4360 - accuracy: 0.96 - ETA: 0s - loss: 0.2396 - accuracy: 0.94 - ETA: 0s - loss: 0.2486 - accuracy: 0.94 - ETA: 0s - loss: 0.2320 - accuracy: 0.94 - ETA: 0s - loss: 0.2396 - accuracy: 0.94 - ETA: 0s - loss: 0.2358 - accuracy: 0.94 - ETA: 0s - loss: 0.2291 - accuracy: 0.94 - ETA: 0s - loss: 0.2410 - accuracy: 0.94 - ETA: 0s - loss: 0.2405 - accuracy: 0.94 - ETA: 0s - loss: 0.2449 - accuracy: 0.94 - ETA: 0s - loss: 0.2465 - accuracy: 0.94 - ETA: 0s - loss: 0.2425 - accuracy: 0.94 - ETA: 0s - loss: 0.2432 - accuracy: 0.94 - 1s 48us/sample - loss: 0.2407 - accuracy: 0.9475 - val_loss: 0.1010 - val_accuracy: 0.9894\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.14739838046068585, 0.98324025]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Для данных с feature engeneering\n",
    "\n",
    "model = tf.keras.models.Sequential([\n",
    "\n",
    "tf.keras.layers.Dense(100, activation=tf.nn.relu),\n",
    "tf.keras.layers.Dropout(0.5),\n",
    "tf.keras.layers.Dense(200, activation=tf.nn.relu),\n",
    "tf.keras.layers.Dropout(0.5),\n",
    "tf.keras.layers.Dense(1, activation=tf.nn.sigmoid)\n",
    "])\n",
    "model.compile(optimizer='adam',\n",
    "loss='binary_crossentropy',\n",
    "metrics=['accuracy'])\n",
    "baseline_history3=model.fit(xf_train, y_train, epochs=100, validation_data=(xf_valid, y_valid), shuffle=True, class_weight = class_weight)\n",
    "model.evaluate(xf_test, y_test, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_9 (Dense)              multiple                  4400      \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          multiple                  0         \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             multiple                  20200     \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          multiple                  0         \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             multiple                  201       \n",
      "=================================================================\n",
      "Total params: 24,801\n",
      "Trainable params: 24,801\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(baseline_history3, 'loss', 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(baseline_history3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss :  0.14739838046068585\n",
      "accuracy :  0.98324025\n",
      "\n",
      "всего 27 \"пульсаров\"\n",
      "всего 1763 \"не пульсаров\"\n",
      "не пульсар предсказанный как не пульсар:  1743\n",
      "не пульсар предсказанный как пульсар : 20\n",
      "пульсар предсказанный как не пульсар : 10\n",
      "пульсар предсказанный как пульсар :  17\n",
      "количество правильных ответов 1760 из 1790\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#для данных с feature engeneering\n",
    "\n",
    "test_predictions_baseline = model.predict(xf_test)\n",
    "\n",
    "baseline_results = model.evaluate(xf_test, y_test, verbose=0)\n",
    "for name, value in zip(model.metrics_names, baseline_results):\n",
    "  print(name, ': ', value)\n",
    "print()\n",
    "\n",
    "plot_cm(y_test, test_predictions_baseline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(baseline_history1, 'loss Обычные данные', 0)\n",
    "plot_loss(baseline_history2, 'loss Центрированные данные', 1)\n",
    "plot_loss(baseline_history3, 'loss Данные с feature engineering', 2)\n",
    "\n",
    "#график для обучения со всеми типами данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}