DEG_machine_learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ==================================== Import Libraries ============================================\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from collections import OrderedDict\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso, RidgeCV, LassoCV\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from random import shuffle\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from tqdm import tqdm\n",
    "import time\n",
    "from hyperopt import fmin, tpe, hp\n",
    "import tensorflow as tf\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Input, Dense\n",
    "from keras.models import Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gene    A_Wt.1    A_Wt.2    A_Wt.3    A_Wt.4    A_Wt.5    A_Wt.6  \\\n",
      "0     A1bg  3.070662  3.439500  3.156002  3.090952  2.995979  3.082066   \n",
      "1     A1cf  4.203059  4.163411  4.090821  4.063431  4.244164  4.304573   \n",
      "2    A2ld1  5.688889  5.985790  6.008787  7.747365  7.666683  7.691302   \n",
      "3      A2m  5.643947  6.761445  5.575969  5.979892  5.427408  6.560586   \n",
      "4  A3galt2  4.848963  4.872971  4.875028  4.532527  4.888920  4.799635   \n",
      "5   A4galt  7.968874  7.840223  8.045192  7.943682  7.862246  7.870894   \n",
      "6    A4gnt  4.766360  4.508001  4.703926  4.669750  4.655340  4.617132   \n",
      "7     Aaas  6.955212  7.195978  6.989937  7.357529  7.634138  7.262624   \n",
      "8     Aacs  7.917138  8.385624  8.005533  6.794933  7.004142  7.143298   \n",
      "9    Aadac  3.028571  2.869241  2.999758  2.924827  3.060868  3.189684   \n",
      "\n",
      "     A_Wt.7    A_Wt.8    A_Wt.9  ...  G_Ther_Cim.1  G_Ther_Cim.2  \\\n",
      "0  3.056484  4.211936  4.176905  ...      2.953014      2.972845   \n",
      "1  4.019474  4.075409  4.092110  ...      4.293593      4.315601   \n",
      "2  7.858077  8.027757  8.240062  ...      7.488242      7.525415   \n",
      "3  5.986411  6.934267  6.496496  ...      5.227837      6.544025   \n",
      "4  4.830187  5.179198  5.345951  ...      4.770949      4.813948   \n",
      "5  7.690078  8.552827  8.555463  ...      8.180562      8.262546   \n",
      "6  4.739260  3.961076  3.966581  ...      4.767250      4.765568   \n",
      "7  7.284300  7.849359  7.822357  ...      7.695891      7.950718   \n",
      "8  6.717079  7.022004  6.945533  ...      7.054353      6.773098   \n",
      "9  3.110301  3.316294  3.242006  ...      3.199159      2.998891   \n",
      "\n",
      "   G_Ther_Cim.3  G_Ther_Cim.4  G_Ther_Cim.5  G_Ther_Cim.6  G_Ther_Cim.7  \\\n",
      "0      2.919909      2.918813      4.287650      4.145197      4.284299   \n",
      "1      4.354033      4.202757      3.825536      4.089815      3.929056   \n",
      "2      7.474313      7.468092      7.221335      7.252869      7.100726   \n",
      "3      6.002996      5.074160      5.075109      5.059333      6.735612   \n",
      "4      4.779167      4.860330      5.123214      5.060895      4.974007   \n",
      "5      8.321367      8.102045      9.619675      9.600157      9.522049   \n",
      "6      4.740331      4.760670      4.411882      4.441106      4.556694   \n",
      "7      7.694418      7.626025      6.876995      7.277305      7.038310   \n",
      "8      6.988879      6.730798      6.795776      6.976342      6.967932   \n",
      "9      3.186709      3.085837      2.988743      3.060018      3.180197   \n",
      "\n",
      "   G_Ther_Cim.8  G_Ther_Cim.9  G_Ther_Cim.10  \n",
      "0      4.451450      4.063431       4.587729  \n",
      "1      3.973460      3.937325       4.045415  \n",
      "2      7.456668      7.041198       7.357232  \n",
      "3      5.473457      5.297770       4.798315  \n",
      "4      5.122402      4.950228       5.096405  \n",
      "5      9.638612      9.709996       9.446098  \n",
      "6      4.477549      4.569458       4.802614  \n",
      "7      7.037981      6.915920       7.014394  \n",
      "8      7.166091      7.118318       6.723467  \n",
      "9      3.054603      3.126782       3.038146  \n",
      "\n",
      "[10 rows x 67 columns]\n"
     ]
    }
   ],
   "source": [
    "# Read data of initial dataset\n",
    "tsv_file = \"Raw_common18704genes_antiTNF_normalized.tsv\"\n",
    "df = pd.read_csv(tsv_file, sep=\"\\t\")\n",
    "print(df.head(10))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['A1bg' 'A1cf' 'A2ld1' ... 'Zyx' 'Zzef1' 'Zzz3']\n",
      "['A_Wt.1', 'A_Wt.2', 'A_Wt.3', 'A_Wt.4', 'A_Wt.5', 'A_Wt.6', 'A_Wt.7', 'A_Wt.8', 'A_Wt.9', 'A_Wt.10', 'B_Tg.1', 'B_Tg.2', 'B_Tg.3', 'B_Tg.4', 'B_Tg.5', 'B_Tg.6', 'B_Tg.7', 'B_Tg.8', 'B_Tg.9', 'B_Tg.10', 'B_Tg.11', 'B_Tg.12', 'B_Tg.13', 'C_Proph_Ther_Rem.1', 'C_Proph_Ther_Rem.2', 'C_Proph_Ther_Rem.3', 'D_Ther_Rem.1', 'D_Ther_Rem.2', 'D_Ther_Rem.3', 'D_Ther_Rem.4', 'D_Ther_Rem.5', 'D_Ther_Rem.6', 'D_Ther_Rem.7', 'D_Ther_Rem.8', 'D_Ther_Rem.9', 'D_Ther_Rem.10', 'E_Ther_Hum.1', 'E_Ther_Hum.2', 'E_Ther_Hum.3', 'E_Ther_Hum.4', 'E_Ther_Hum.5', 'E_Ther_Hum.6', 'E_Ther_Hum.7', 'E_Ther_Hum.8', 'E_Ther_Hum.9', 'E_Ther_Hum.10', 'F_Ther_Enb.1', 'F_Ther_Enb.2', 'F_Ther_Enb.3', 'F_Ther_Enb.4', 'F_Ther_Enb.5', 'F_Ther_Enb.6', 'F_Ther_Enb.7', 'F_Ther_Enb.8', 'F_Ther_Enb.9', 'F_Ther_Enb.10', 'G_Ther_Cim.1', 'G_Ther_Cim.2', 'G_Ther_Cim.3', 'G_Ther_Cim.4', 'G_Ther_Cim.5', 'G_Ther_Cim.6', 'G_Ther_Cim.7', 'G_Ther_Cim.8', 'G_Ther_Cim.9', 'G_Ther_Cim.10']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alexa\\AppData\\Local\\Temp\\ipykernel_17176\\3390165842.py:21: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  final_df = final_df._append(pd.DataFrame(new_data), ignore_index=True)\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1bg</th>\n",
       "      <th>A1cf</th>\n",
       "      <th>A2ld1</th>\n",
       "      <th>A2m</th>\n",
       "      <th>A3galt2</th>\n",
       "      <th>A4galt</th>\n",
       "      <th>A4gnt</th>\n",
       "      <th>Aaas</th>\n",
       "      <th>Aacs</th>\n",
       "      <th>Aadac</th>\n",
       "      <th>...</th>\n",
       "      <th>Zwint</th>\n",
       "      <th>Zxda</th>\n",
       "      <th>Zxdb</th>\n",
       "      <th>Zxdc</th>\n",
       "      <th>Zyg11a</th>\n",
       "      <th>Zyg11b</th>\n",
       "      <th>Zyx</th>\n",
       "      <th>Zzef1</th>\n",
       "      <th>Zzz3</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.321070</td>\n",
       "      <td>4.021391</td>\n",
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       "      <td>4.682857</td>\n",
       "      <td>6.962096</td>\n",
       "      <td>6.852633</td>\n",
       "      <td>3.196332</td>\n",
       "      <td>...</td>\n",
       "      <td>7.763792</td>\n",
       "      <td>4.622556</td>\n",
       "      <td>6.151181</td>\n",
       "      <td>8.119445</td>\n",
       "      <td>4.354939</td>\n",
       "      <td>8.772263</td>\n",
       "      <td>8.774053</td>\n",
       "      <td>8.267863</td>\n",
       "      <td>8.159677</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.919909</td>\n",
       "      <td>4.354033</td>\n",
       "      <td>7.474313</td>\n",
       "      <td>6.002996</td>\n",
       "      <td>4.779167</td>\n",
       "      <td>8.321367</td>\n",
       "      <td>4.740331</td>\n",
       "      <td>7.694418</td>\n",
       "      <td>6.988879</td>\n",
       "      <td>3.186709</td>\n",
       "      <td>...</td>\n",
       "      <td>8.125422</td>\n",
       "      <td>6.525314</td>\n",
       "      <td>7.214950</td>\n",
       "      <td>7.679649</td>\n",
       "      <td>4.684149</td>\n",
       "      <td>8.911109</td>\n",
       "      <td>8.567431</td>\n",
       "      <td>8.192386</td>\n",
       "      <td>8.862444</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.206897</td>\n",
       "      <td>4.073325</td>\n",
       "      <td>7.571640</td>\n",
       "      <td>5.777709</td>\n",
       "      <td>4.939243</td>\n",
       "      <td>7.909050</td>\n",
       "      <td>4.710475</td>\n",
       "      <td>7.804725</td>\n",
       "      <td>7.004807</td>\n",
       "      <td>3.160626</td>\n",
       "      <td>...</td>\n",
       "      <td>8.096005</td>\n",
       "      <td>6.545766</td>\n",
       "      <td>7.083403</td>\n",
       "      <td>7.623476</td>\n",
       "      <td>4.477810</td>\n",
       "      <td>8.999331</td>\n",
       "      <td>8.539705</td>\n",
       "      <td>8.004594</td>\n",
       "      <td>8.658114</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.972845</td>\n",
       "      <td>4.315601</td>\n",
       "      <td>7.525415</td>\n",
       "      <td>6.544025</td>\n",
       "      <td>4.813948</td>\n",
       "      <td>8.262546</td>\n",
       "      <td>4.765568</td>\n",
       "      <td>7.950718</td>\n",
       "      <td>6.773098</td>\n",
       "      <td>2.998891</td>\n",
       "      <td>...</td>\n",
       "      <td>8.166265</td>\n",
       "      <td>6.605128</td>\n",
       "      <td>7.169980</td>\n",
       "      <td>7.472457</td>\n",
       "      <td>4.534786</td>\n",
       "      <td>8.796447</td>\n",
       "      <td>8.509368</td>\n",
       "      <td>7.969855</td>\n",
       "      <td>8.860479</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.587729</td>\n",
       "      <td>4.045415</td>\n",
       "      <td>7.357232</td>\n",
       "      <td>4.798315</td>\n",
       "      <td>5.096405</td>\n",
       "      <td>9.446098</td>\n",
       "      <td>4.802614</td>\n",
       "      <td>7.014394</td>\n",
       "      <td>6.723467</td>\n",
       "      <td>3.038146</td>\n",
       "      <td>...</td>\n",
       "      <td>7.911696</td>\n",
       "      <td>4.689726</td>\n",
       "      <td>6.434438</td>\n",
       "      <td>7.937090</td>\n",
       "      <td>4.246788</td>\n",
       "      <td>9.232032</td>\n",
       "      <td>8.474880</td>\n",
       "      <td>8.148693</td>\n",
       "      <td>8.176003</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.221542</td>\n",
       "      <td>4.014900</td>\n",
       "      <td>8.189553</td>\n",
       "      <td>6.411558</td>\n",
       "      <td>5.145728</td>\n",
       "      <td>9.073336</td>\n",
       "      <td>4.246255</td>\n",
       "      <td>7.951498</td>\n",
       "      <td>7.118635</td>\n",
       "      <td>3.345147</td>\n",
       "      <td>...</td>\n",
       "      <td>8.483164</td>\n",
       "      <td>5.696999</td>\n",
       "      <td>7.139869</td>\n",
       "      <td>8.094630</td>\n",
       "      <td>4.410693</td>\n",
       "      <td>8.927373</td>\n",
       "      <td>8.792674</td>\n",
       "      <td>8.044391</td>\n",
       "      <td>8.235345</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.232535</td>\n",
       "      <td>4.254009</td>\n",
       "      <td>7.269357</td>\n",
       "      <td>5.429067</td>\n",
       "      <td>4.710475</td>\n",
       "      <td>7.989371</td>\n",
       "      <td>4.811270</td>\n",
       "      <td>7.922743</td>\n",
       "      <td>7.020712</td>\n",
       "      <td>3.072391</td>\n",
       "      <td>...</td>\n",
       "      <td>8.164049</td>\n",
       "      <td>6.594374</td>\n",
       "      <td>7.176467</td>\n",
       "      <td>7.481956</td>\n",
       "      <td>4.080114</td>\n",
       "      <td>8.683916</td>\n",
       "      <td>8.246039</td>\n",
       "      <td>8.113242</td>\n",
       "      <td>8.779520</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.224174</td>\n",
       "      <td>4.016924</td>\n",
       "      <td>7.612807</td>\n",
       "      <td>6.044607</td>\n",
       "      <td>5.060565</td>\n",
       "      <td>8.913594</td>\n",
       "      <td>3.998422</td>\n",
       "      <td>8.379063</td>\n",
       "      <td>7.132631</td>\n",
       "      <td>3.275255</td>\n",
       "      <td>...</td>\n",
       "      <td>8.502794</td>\n",
       "      <td>5.762630</td>\n",
       "      <td>6.980724</td>\n",
       "      <td>7.866077</td>\n",
       "      <td>4.308622</td>\n",
       "      <td>8.563490</td>\n",
       "      <td>9.262654</td>\n",
       "      <td>8.266389</td>\n",
       "      <td>8.349752</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.145197</td>\n",
       "      <td>4.089815</td>\n",
       "      <td>7.252869</td>\n",
       "      <td>5.059333</td>\n",
       "      <td>5.060895</td>\n",
       "      <td>9.600157</td>\n",
       "      <td>4.441106</td>\n",
       "      <td>7.277305</td>\n",
       "      <td>6.976342</td>\n",
       "      <td>3.060018</td>\n",
       "      <td>...</td>\n",
       "      <td>7.814398</td>\n",
       "      <td>4.690094</td>\n",
       "      <td>6.861327</td>\n",
       "      <td>7.954723</td>\n",
       "      <td>4.022942</td>\n",
       "      <td>8.830288</td>\n",
       "      <td>8.707521</td>\n",
       "      <td>8.191723</td>\n",
       "      <td>8.009167</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.156002</td>\n",
       "      <td>4.090821</td>\n",
       "      <td>6.008787</td>\n",
       "      <td>5.575969</td>\n",
       "      <td>4.875028</td>\n",
       "      <td>8.045192</td>\n",
       "      <td>4.703926</td>\n",
       "      <td>6.989937</td>\n",
       "      <td>8.005533</td>\n",
       "      <td>2.999758</td>\n",
       "      <td>...</td>\n",
       "      <td>8.244772</td>\n",
       "      <td>5.653223</td>\n",
       "      <td>7.061028</td>\n",
       "      <td>7.937860</td>\n",
       "      <td>6.376859</td>\n",
       "      <td>9.284346</td>\n",
       "      <td>8.051423</td>\n",
       "      <td>8.702022</td>\n",
       "      <td>9.122607</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.201218</td>\n",
       "      <td>3.724559</td>\n",
       "      <td>5.512228</td>\n",
       "      <td>4.909472</td>\n",
       "      <td>4.936379</td>\n",
       "      <td>7.509391</td>\n",
       "      <td>4.766212</td>\n",
       "      <td>7.561179</td>\n",
       "      <td>7.074273</td>\n",
       "      <td>3.222948</td>\n",
       "      <td>...</td>\n",
       "      <td>8.009167</td>\n",
       "      <td>5.489534</td>\n",
       "      <td>7.187070</td>\n",
       "      <td>7.755945</td>\n",
       "      <td>4.289164</td>\n",
       "      <td>8.990053</td>\n",
       "      <td>8.351559</td>\n",
       "      <td>8.598761</td>\n",
       "      <td>9.207717</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.179957</td>\n",
       "      <td>4.350461</td>\n",
       "      <td>8.103844</td>\n",
       "      <td>5.732425</td>\n",
       "      <td>5.164518</td>\n",
       "      <td>8.968576</td>\n",
       "      <td>3.966876</td>\n",
       "      <td>8.165871</td>\n",
       "      <td>6.939805</td>\n",
       "      <td>3.240073</td>\n",
       "      <td>...</td>\n",
       "      <td>8.479211</td>\n",
       "      <td>5.585838</td>\n",
       "      <td>6.964042</td>\n",
       "      <td>8.135678</td>\n",
       "      <td>4.112204</td>\n",
       "      <td>9.033732</td>\n",
       "      <td>9.179323</td>\n",
       "      <td>8.170249</td>\n",
       "      <td>8.272346</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.211936</td>\n",
       "      <td>4.163802</td>\n",
       "      <td>8.161904</td>\n",
       "      <td>6.092865</td>\n",
       "      <td>5.000407</td>\n",
       "      <td>8.641947</td>\n",
       "      <td>4.070842</td>\n",
       "      <td>7.917748</td>\n",
       "      <td>6.994640</td>\n",
       "      <td>3.257637</td>\n",
       "      <td>...</td>\n",
       "      <td>8.582680</td>\n",
       "      <td>5.821607</td>\n",
       "      <td>6.959950</td>\n",
       "      <td>8.127945</td>\n",
       "      <td>4.304869</td>\n",
       "      <td>9.018530</td>\n",
       "      <td>9.180087</td>\n",
       "      <td>8.035402</td>\n",
       "      <td>8.269289</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.082066</td>\n",
       "      <td>4.304573</td>\n",
       "      <td>7.691302</td>\n",
       "      <td>6.560586</td>\n",
       "      <td>4.799635</td>\n",
       "      <td>7.870894</td>\n",
       "      <td>4.617132</td>\n",
       "      <td>7.262624</td>\n",
       "      <td>7.143298</td>\n",
       "      <td>3.189684</td>\n",
       "      <td>...</td>\n",
       "      <td>8.187328</td>\n",
       "      <td>6.601643</td>\n",
       "      <td>7.136452</td>\n",
       "      <td>7.736575</td>\n",
       "      <td>4.546413</td>\n",
       "      <td>9.275169</td>\n",
       "      <td>8.188497</td>\n",
       "      <td>7.973546</td>\n",
       "      <td>8.600527</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.143843</td>\n",
       "      <td>3.968376</td>\n",
       "      <td>7.884686</td>\n",
       "      <td>6.353967</td>\n",
       "      <td>4.940052</td>\n",
       "      <td>8.697356</td>\n",
       "      <td>4.042833</td>\n",
       "      <td>8.435114</td>\n",
       "      <td>6.922320</td>\n",
       "      <td>3.477845</td>\n",
       "      <td>...</td>\n",
       "      <td>8.552160</td>\n",
       "      <td>5.737108</td>\n",
       "      <td>6.988215</td>\n",
       "      <td>8.076065</td>\n",
       "      <td>4.462451</td>\n",
       "      <td>8.803856</td>\n",
       "      <td>8.831274</td>\n",
       "      <td>8.190259</td>\n",
       "      <td>8.089339</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.923576</td>\n",
       "      <td>4.444097</td>\n",
       "      <td>7.744628</td>\n",
       "      <td>5.966971</td>\n",
       "      <td>4.985238</td>\n",
       "      <td>8.072300</td>\n",
       "      <td>4.583266</td>\n",
       "      <td>7.330320</td>\n",
       "      <td>6.784067</td>\n",
       "      <td>2.955474</td>\n",
       "      <td>...</td>\n",
       "      <td>8.059501</td>\n",
       "      <td>6.565963</td>\n",
       "      <td>7.198939</td>\n",
       "      <td>7.798314</td>\n",
       "      <td>3.896716</td>\n",
       "      <td>9.446659</td>\n",
       "      <td>8.166974</td>\n",
       "      <td>7.930131</td>\n",
       "      <td>8.801594</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4.168870</td>\n",
       "      <td>4.106948</td>\n",
       "      <td>7.257537</td>\n",
       "      <td>5.840330</td>\n",
       "      <td>5.356050</td>\n",
       "      <td>9.785303</td>\n",
       "      <td>4.459981</td>\n",
       "      <td>7.125786</td>\n",
       "      <td>7.155845</td>\n",
       "      <td>3.745433</td>\n",
       "      <td>...</td>\n",
       "      <td>7.799171</td>\n",
       "      <td>4.600233</td>\n",
       "      <td>6.636210</td>\n",
       "      <td>8.011567</td>\n",
       "      <td>4.398981</td>\n",
       "      <td>8.772689</td>\n",
       "      <td>8.358845</td>\n",
       "      <td>8.193473</td>\n",
       "      <td>8.210883</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.889848</td>\n",
       "      <td>4.108383</td>\n",
       "      <td>7.437147</td>\n",
       "      <td>5.692869</td>\n",
       "      <td>4.667796</td>\n",
       "      <td>8.038859</td>\n",
       "      <td>4.594191</td>\n",
       "      <td>7.789981</td>\n",
       "      <td>6.679193</td>\n",
       "      <td>3.362119</td>\n",
       "      <td>...</td>\n",
       "      <td>8.191427</td>\n",
       "      <td>6.723750</td>\n",
       "      <td>7.226902</td>\n",
       "      <td>7.609943</td>\n",
       "      <td>4.135177</td>\n",
       "      <td>8.805689</td>\n",
       "      <td>8.513486</td>\n",
       "      <td>8.164049</td>\n",
       "      <td>8.742647</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>4.353070</td>\n",
       "      <td>4.080114</td>\n",
       "      <td>7.399227</td>\n",
       "      <td>5.857244</td>\n",
       "      <td>4.995655</td>\n",
       "      <td>9.707450</td>\n",
       "      <td>4.420678</td>\n",
       "      <td>7.162189</td>\n",
       "      <td>6.860641</td>\n",
       "      <td>3.199159</td>\n",
       "      <td>...</td>\n",
       "      <td>7.526748</td>\n",
       "      <td>4.614155</td>\n",
       "      <td>6.577314</td>\n",
       "      <td>7.946963</td>\n",
       "      <td>3.966876</td>\n",
       "      <td>8.866846</td>\n",
       "      <td>8.764438</td>\n",
       "      <td>8.316751</td>\n",
       "      <td>8.146425</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4.211045</td>\n",
       "      <td>4.059337</td>\n",
       "      <td>7.724071</td>\n",
       "      <td>6.278023</td>\n",
       "      <td>5.058792</td>\n",
       "      <td>8.721773</td>\n",
       "      <td>3.988819</td>\n",
       "      <td>8.408037</td>\n",
       "      <td>7.110992</td>\n",
       "      <td>3.335297</td>\n",
       "      <td>...</td>\n",
       "      <td>8.464348</td>\n",
       "      <td>5.581255</td>\n",
       "      <td>6.929832</td>\n",
       "      <td>7.929144</td>\n",
       "      <td>4.399590</td>\n",
       "      <td>8.683522</td>\n",
       "      <td>8.882459</td>\n",
       "      <td>8.194287</td>\n",
       "      <td>8.222634</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 18704 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        A1bg      A1cf     A2ld1       A2m   A3galt2    A4galt     A4gnt  \\\n",
       "0   4.321070  4.021391  7.369673  6.121501  5.147899  9.822775  4.682857   \n",
       "1   2.919909  4.354033  7.474313  6.002996  4.779167  8.321367  4.740331   \n",
       "2   3.206897  4.073325  7.571640  5.777709  4.939243  7.909050  4.710475   \n",
       "3   2.972845  4.315601  7.525415  6.544025  4.813948  8.262546  4.765568   \n",
       "4   4.587729  4.045415  7.357232  4.798315  5.096405  9.446098  4.802614   \n",
       "5   4.221542  4.014900  8.189553  6.411558  5.145728  9.073336  4.246255   \n",
       "6   3.232535  4.254009  7.269357  5.429067  4.710475  7.989371  4.811270   \n",
       "7   4.224174  4.016924  7.612807  6.044607  5.060565  8.913594  3.998422   \n",
       "8   4.145197  4.089815  7.252869  5.059333  5.060895  9.600157  4.441106   \n",
       "9   3.156002  4.090821  6.008787  5.575969  4.875028  8.045192  4.703926   \n",
       "10  3.201218  3.724559  5.512228  4.909472  4.936379  7.509391  4.766212   \n",
       "11  4.179957  4.350461  8.103844  5.732425  5.164518  8.968576  3.966876   \n",
       "12  4.211936  4.163802  8.161904  6.092865  5.000407  8.641947  4.070842   \n",
       "13  3.082066  4.304573  7.691302  6.560586  4.799635  7.870894  4.617132   \n",
       "14  4.143843  3.968376  7.884686  6.353967  4.940052  8.697356  4.042833   \n",
       "15  2.923576  4.444097  7.744628  5.966971  4.985238  8.072300  4.583266   \n",
       "16  4.168870  4.106948  7.257537  5.840330  5.356050  9.785303  4.459981   \n",
       "17  2.889848  4.108383  7.437147  5.692869  4.667796  8.038859  4.594191   \n",
       "18  4.353070  4.080114  7.399227  5.857244  4.995655  9.707450  4.420678   \n",
       "19  4.211045  4.059337  7.724071  6.278023  5.058792  8.721773  3.988819   \n",
       "\n",
       "        Aaas      Aacs     Aadac  ...     Zwint      Zxda      Zxdb      Zxdc  \\\n",
       "0   6.962096  6.852633  3.196332  ...  7.763792  4.622556  6.151181  8.119445   \n",
       "1   7.694418  6.988879  3.186709  ...  8.125422  6.525314  7.214950  7.679649   \n",
       "2   7.804725  7.004807  3.160626  ...  8.096005  6.545766  7.083403  7.623476   \n",
       "3   7.950718  6.773098  2.998891  ...  8.166265  6.605128  7.169980  7.472457   \n",
       "4   7.014394  6.723467  3.038146  ...  7.911696  4.689726  6.434438  7.937090   \n",
       "5   7.951498  7.118635  3.345147  ...  8.483164  5.696999  7.139869  8.094630   \n",
       "6   7.922743  7.020712  3.072391  ...  8.164049  6.594374  7.176467  7.481956   \n",
       "7   8.379063  7.132631  3.275255  ...  8.502794  5.762630  6.980724  7.866077   \n",
       "8   7.277305  6.976342  3.060018  ...  7.814398  4.690094  6.861327  7.954723   \n",
       "9   6.989937  8.005533  2.999758  ...  8.244772  5.653223  7.061028  7.937860   \n",
       "10  7.561179  7.074273  3.222948  ...  8.009167  5.489534  7.187070  7.755945   \n",
       "11  8.165871  6.939805  3.240073  ...  8.479211  5.585838  6.964042  8.135678   \n",
       "12  7.917748  6.994640  3.257637  ...  8.582680  5.821607  6.959950  8.127945   \n",
       "13  7.262624  7.143298  3.189684  ...  8.187328  6.601643  7.136452  7.736575   \n",
       "14  8.435114  6.922320  3.477845  ...  8.552160  5.737108  6.988215  8.076065   \n",
       "15  7.330320  6.784067  2.955474  ...  8.059501  6.565963  7.198939  7.798314   \n",
       "16  7.125786  7.155845  3.745433  ...  7.799171  4.600233  6.636210  8.011567   \n",
       "17  7.789981  6.679193  3.362119  ...  8.191427  6.723750  7.226902  7.609943   \n",
       "18  7.162189  6.860641  3.199159  ...  7.526748  4.614155  6.577314  7.946963   \n",
       "19  8.408037  7.110992  3.335297  ...  8.464348  5.581255  6.929832  7.929144   \n",
       "\n",
       "      Zyg11a    Zyg11b       Zyx     Zzef1      Zzz3  label  \n",
       "0   4.354939  8.772263  8.774053  8.267863  8.159677      5  \n",
       "1   4.684149  8.911109  8.567431  8.192386  8.862444      6  \n",
       "2   4.477810  8.999331  8.539705  8.004594  8.658114      4  \n",
       "3   4.534786  8.796447  8.509368  7.969855  8.860479      6  \n",
       "4   4.246788  9.232032  8.474880  8.148693  8.176003      6  \n",
       "5   4.410693  8.927373  8.792674  8.044391  8.235345      4  \n",
       "6   4.080114  8.683916  8.246039  8.113242  8.779520      1  \n",
       "7   4.308622  8.563490  9.262654  8.266389  8.349752      3  \n",
       "8   4.022942  8.830288  8.707521  8.191723  8.009167      6  \n",
       "9   6.376859  9.284346  8.051423  8.702022  9.122607      0  \n",
       "10  4.289164  8.990053  8.351559  8.598761  9.207717      1  \n",
       "11  4.112204  9.033732  9.179323  8.170249  8.272346      4  \n",
       "12  4.304869  9.018530  9.180087  8.035402  8.269289      3  \n",
       "13  4.546413  9.275169  8.188497  7.973546  8.600527      0  \n",
       "14  4.462451  8.803856  8.831274  8.190259  8.089339      1  \n",
       "15  3.896716  9.446659  8.166974  7.930131  8.801594      4  \n",
       "16  4.398981  8.772689  8.358845  8.193473  8.210883      4  \n",
       "17  4.135177  8.805689  8.513486  8.164049  8.742647      5  \n",
       "18  3.966876  8.866846  8.764438  8.316751  8.146425      4  \n",
       "19  4.399590  8.683522  8.882459  8.194287  8.222634      1  \n",
       "\n",
       "[20 rows x 18704 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ---------------------------------------- PREPARE & ORGANIZE DATA INTO NEW DATASET --------------------------------\n",
    "genes = df['Gene'].values\n",
    "print(genes)\n",
    "\n",
    "classes = df.columns.to_list()\n",
    "# Exclude \"Gene\" class\n",
    "classes = classes[1:]\n",
    "print(classes)\n",
    "\n",
    "# Modify class names \n",
    "for i in range(0, len(classes)):\n",
    "    if '.' in classes[i]:\n",
    "        parts = classes[i].split('.')\n",
    "        classes[i] = parts[0]\n",
    "\n",
    "# Create final dataframe with gene names as column headers\n",
    "final_df = pd.DataFrame(columns=genes)\n",
    "for i in range(1, len(classes)+1):\n",
    "    values = df.iloc[:, i].tolist()\n",
    "    new_data = {column: [value] for column, value in zip(genes, values)}\n",
    "    final_df = final_df._append(pd.DataFrame(new_data), ignore_index=True)\n",
    "\n",
    "# Create class of numeric labels\n",
    "final_df['label']=classes\n",
    "final_df['label'] = pd.factorize(final_df['label'])[0]\n",
    "\n",
    "# Create list of row indices\n",
    "rows = list(final_df.index)\n",
    "\n",
    "# Randomly shuffle list of row indices\n",
    "shuffle(rows)\n",
    "\n",
    "# Reorder rows and reset index\n",
    "final_df = final_df.loc[rows].reset_index(drop=True)\n",
    "\n",
    "# Display first 20 rows of new dataframe\n",
    "final_df.head(20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ------------------------------------- SPLIT DATASET INTO TRAINING & TESTING DATA ---------------------------------------\n",
    "y=final_df['label']\n",
    "\n",
    "# Remove label column from dataframe\n",
    "final_df.drop(['label'], \n",
    "              axis=1,  # Removing a column\n",
    "              inplace=True) # Modification happens in-place --> original dataframe is changed\n",
    "\n",
    "''' Assign remaining data to x:\n",
    "x contains gene expression values for all the genes across samples. \n",
    "Each row represents a sample / Each column represents a gene.\n",
    "'''\n",
    "x = final_df\n",
    "\n",
    "''' Split data into training and validation sets:\n",
    "x_train: Training data for the features (gene expression values)\n",
    "x_val: Validation data for the features (gene expression values)\n",
    "y_train: Training data for the labels (class labels)\n",
    "y_val: Validation data for the labels (class labels)\n",
    "'''\n",
    "x_train, x_val, y_train, y_val = train_test_split(x, y, \n",
    "                                                  test_size=0.3, # 30% of data will be used as testing/validation set\n",
    "                                                  random_state=10) # Seed for the random number generator -> Makes split reproducible\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1bg</th>\n",
       "      <th>A1cf</th>\n",
       "      <th>A2ld1</th>\n",
       "      <th>A2m</th>\n",
       "      <th>A3galt2</th>\n",
       "      <th>A4galt</th>\n",
       "      <th>A4gnt</th>\n",
       "      <th>Aaas</th>\n",
       "      <th>Aacs</th>\n",
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       "      <th>...</th>\n",
       "      <th>Zwilch</th>\n",
       "      <th>Zwint</th>\n",
       "      <th>Zxda</th>\n",
       "      <th>Zxdb</th>\n",
       "      <th>Zxdc</th>\n",
       "      <th>Zyg11a</th>\n",
       "      <th>Zyg11b</th>\n",
       "      <th>Zyx</th>\n",
       "      <th>Zzef1</th>\n",
       "      <th>Zzz3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>4.310112</td>\n",
       "      <td>4.058462</td>\n",
       "      <td>7.664548</td>\n",
       "      <td>6.591627</td>\n",
       "      <td>4.839540</td>\n",
       "      <td>8.732336</td>\n",
       "      <td>4.107264</td>\n",
       "      <td>8.555904</td>\n",
       "      <td>7.296280</td>\n",
       "      <td>3.348826</td>\n",
       "      <td>...</td>\n",
       "      <td>5.628507</td>\n",
       "      <td>8.495026</td>\n",
       "      <td>5.772443</td>\n",
       "      <td>7.070543</td>\n",
       "      <td>7.832033</td>\n",
       "      <td>4.210723</td>\n",
       "      <td>8.501608</td>\n",
       "      <td>8.959582</td>\n",
       "      <td>8.241116</td>\n",
       "      <td>8.230142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.201218</td>\n",
       "      <td>3.724559</td>\n",
       "      <td>5.512228</td>\n",
       "      <td>4.909472</td>\n",
       "      <td>4.936379</td>\n",
       "      <td>7.509391</td>\n",
       "      <td>4.766212</td>\n",
       "      <td>7.561179</td>\n",
       "      <td>7.074273</td>\n",
       "      <td>3.222948</td>\n",
       "      <td>...</td>\n",
       "      <td>6.415518</td>\n",
       "      <td>8.009167</td>\n",
       "      <td>5.489534</td>\n",
       "      <td>7.187070</td>\n",
       "      <td>7.755945</td>\n",
       "      <td>4.289164</td>\n",
       "      <td>8.990053</td>\n",
       "      <td>8.351559</td>\n",
       "      <td>8.598761</td>\n",
       "      <td>9.207717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2.734864</td>\n",
       "      <td>3.928354</td>\n",
       "      <td>5.959974</td>\n",
       "      <td>6.163791</td>\n",
       "      <td>4.811149</td>\n",
       "      <td>7.825811</td>\n",
       "      <td>4.943579</td>\n",
       "      <td>7.033396</td>\n",
       "      <td>7.397792</td>\n",
       "      <td>3.105009</td>\n",
       "      <td>...</td>\n",
       "      <td>5.852874</td>\n",
       "      <td>8.101346</td>\n",
       "      <td>5.784241</td>\n",
       "      <td>7.121693</td>\n",
       "      <td>7.926408</td>\n",
       "      <td>5.524635</td>\n",
       "      <td>9.334896</td>\n",
       "      <td>8.358463</td>\n",
       "      <td>8.669588</td>\n",
       "      <td>9.039516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>3.086808</td>\n",
       "      <td>4.161198</td>\n",
       "      <td>6.092865</td>\n",
       "      <td>5.717453</td>\n",
       "      <td>4.827016</td>\n",
       "      <td>7.701078</td>\n",
       "      <td>4.868709</td>\n",
       "      <td>6.993167</td>\n",
       "      <td>7.977590</td>\n",
       "      <td>2.912311</td>\n",
       "      <td>...</td>\n",
       "      <td>5.983840</td>\n",
       "      <td>8.114938</td>\n",
       "      <td>5.594973</td>\n",
       "      <td>7.158818</td>\n",
       "      <td>7.950397</td>\n",
       "      <td>5.556018</td>\n",
       "      <td>9.380916</td>\n",
       "      <td>7.934088</td>\n",
       "      <td>8.623265</td>\n",
       "      <td>9.006445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>3.009204</td>\n",
       "      <td>4.073736</td>\n",
       "      <td>7.518545</td>\n",
       "      <td>5.986411</td>\n",
       "      <td>4.804974</td>\n",
       "      <td>7.867430</td>\n",
       "      <td>4.605314</td>\n",
       "      <td>7.722711</td>\n",
       "      <td>6.978855</td>\n",
       "      <td>3.070662</td>\n",
       "      <td>...</td>\n",
       "      <td>6.827716</td>\n",
       "      <td>8.241116</td>\n",
       "      <td>6.633596</td>\n",
       "      <td>7.151413</td>\n",
       "      <td>7.648888</td>\n",
       "      <td>4.553253</td>\n",
       "      <td>8.939947</td>\n",
       "      <td>8.580009</td>\n",
       "      <td>7.951099</td>\n",
       "      <td>8.688412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>4.353070</td>\n",
       "      <td>4.080114</td>\n",
       "      <td>7.399227</td>\n",
       "      <td>5.857244</td>\n",
       "      <td>4.995655</td>\n",
       "      <td>9.707450</td>\n",
       "      <td>4.420678</td>\n",
       "      <td>7.162189</td>\n",
       "      <td>6.860641</td>\n",
       "      <td>3.199159</td>\n",
       "      <td>...</td>\n",
       "      <td>5.083333</td>\n",
       "      <td>7.526748</td>\n",
       "      <td>4.614155</td>\n",
       "      <td>6.577314</td>\n",
       "      <td>7.946963</td>\n",
       "      <td>3.966876</td>\n",
       "      <td>8.866846</td>\n",
       "      <td>8.764438</td>\n",
       "      <td>8.316751</td>\n",
       "      <td>8.146425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.587729</td>\n",
       "      <td>4.045415</td>\n",
       "      <td>7.357232</td>\n",
       "      <td>4.798315</td>\n",
       "      <td>5.096405</td>\n",
       "      <td>9.446098</td>\n",
       "      <td>4.802614</td>\n",
       "      <td>7.014394</td>\n",
       "      <td>6.723467</td>\n",
       "      <td>3.038146</td>\n",
       "      <td>...</td>\n",
       "      <td>5.399890</td>\n",
       "      <td>7.911696</td>\n",
       "      <td>4.689726</td>\n",
       "      <td>6.434438</td>\n",
       "      <td>7.937090</td>\n",
       "      <td>4.246788</td>\n",
       "      <td>9.232032</td>\n",
       "      <td>8.474880</td>\n",
       "      <td>8.148693</td>\n",
       "      <td>8.176003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2.995979</td>\n",
       "      <td>4.244164</td>\n",
       "      <td>7.666683</td>\n",
       "      <td>5.427408</td>\n",
       "      <td>4.888920</td>\n",
       "      <td>7.862246</td>\n",
       "      <td>4.655340</td>\n",
       "      <td>7.634138</td>\n",
       "      <td>7.004142</td>\n",
       "      <td>3.060868</td>\n",
       "      <td>...</td>\n",
       "      <td>6.381893</td>\n",
       "      <td>8.174538</td>\n",
       "      <td>6.567327</td>\n",
       "      <td>7.101713</td>\n",
       "      <td>7.685325</td>\n",
       "      <td>4.293126</td>\n",
       "      <td>9.134554</td>\n",
       "      <td>8.274243</td>\n",
       "      <td>8.046331</td>\n",
       "      <td>8.786375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.919909</td>\n",
       "      <td>4.354033</td>\n",
       "      <td>7.474313</td>\n",
       "      <td>6.002996</td>\n",
       "      <td>4.779167</td>\n",
       "      <td>8.321367</td>\n",
       "      <td>4.740331</td>\n",
       "      <td>7.694418</td>\n",
       "      <td>6.988879</td>\n",
       "      <td>3.186709</td>\n",
       "      <td>...</td>\n",
       "      <td>6.151492</td>\n",
       "      <td>8.125422</td>\n",
       "      <td>6.525314</td>\n",
       "      <td>7.214950</td>\n",
       "      <td>7.679649</td>\n",
       "      <td>4.684149</td>\n",
       "      <td>8.911109</td>\n",
       "      <td>8.567431</td>\n",
       "      <td>8.192386</td>\n",
       "      <td>8.862444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.211936</td>\n",
       "      <td>4.163802</td>\n",
       "      <td>8.161904</td>\n",
       "      <td>6.092865</td>\n",
       "      <td>5.000407</td>\n",
       "      <td>8.641947</td>\n",
       "      <td>4.070842</td>\n",
       "      <td>7.917748</td>\n",
       "      <td>6.994640</td>\n",
       "      <td>3.257637</td>\n",
       "      <td>...</td>\n",
       "      <td>5.413710</td>\n",
       "      <td>8.582680</td>\n",
       "      <td>5.821607</td>\n",
       "      <td>6.959950</td>\n",
       "      <td>8.127945</td>\n",
       "      <td>4.304869</td>\n",
       "      <td>9.018530</td>\n",
       "      <td>9.180087</td>\n",
       "      <td>8.035402</td>\n",
       "      <td>8.269289</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 18703 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        A1bg      A1cf     A2ld1       A2m   A3galt2    A4galt     A4gnt  \\\n",
       "53  4.310112  4.058462  7.664548  6.591627  4.839540  8.732336  4.107264   \n",
       "10  3.201218  3.724559  5.512228  4.909472  4.936379  7.509391  4.766212   \n",
       "46  2.734864  3.928354  5.959974  6.163791  4.811149  7.825811  4.943579   \n",
       "44  3.086808  4.161198  6.092865  5.717453  4.827016  7.701078  4.868709   \n",
       "35  3.009204  4.073736  7.518545  5.986411  4.804974  7.867430  4.605314   \n",
       "18  4.353070  4.080114  7.399227  5.857244  4.995655  9.707450  4.420678   \n",
       "4   4.587729  4.045415  7.357232  4.798315  5.096405  9.446098  4.802614   \n",
       "31  2.995979  4.244164  7.666683  5.427408  4.888920  7.862246  4.655340   \n",
       "1   2.919909  4.354033  7.474313  6.002996  4.779167  8.321367  4.740331   \n",
       "12  4.211936  4.163802  8.161904  6.092865  5.000407  8.641947  4.070842   \n",
       "\n",
       "        Aaas      Aacs     Aadac  ...    Zwilch     Zwint      Zxda      Zxdb  \\\n",
       "53  8.555904  7.296280  3.348826  ...  5.628507  8.495026  5.772443  7.070543   \n",
       "10  7.561179  7.074273  3.222948  ...  6.415518  8.009167  5.489534  7.187070   \n",
       "46  7.033396  7.397792  3.105009  ...  5.852874  8.101346  5.784241  7.121693   \n",
       "44  6.993167  7.977590  2.912311  ...  5.983840  8.114938  5.594973  7.158818   \n",
       "35  7.722711  6.978855  3.070662  ...  6.827716  8.241116  6.633596  7.151413   \n",
       "18  7.162189  6.860641  3.199159  ...  5.083333  7.526748  4.614155  6.577314   \n",
       "4   7.014394  6.723467  3.038146  ...  5.399890  7.911696  4.689726  6.434438   \n",
       "31  7.634138  7.004142  3.060868  ...  6.381893  8.174538  6.567327  7.101713   \n",
       "1   7.694418  6.988879  3.186709  ...  6.151492  8.125422  6.525314  7.214950   \n",
       "12  7.917748  6.994640  3.257637  ...  5.413710  8.582680  5.821607  6.959950   \n",
       "\n",
       "        Zxdc    Zyg11a    Zyg11b       Zyx     Zzef1      Zzz3  \n",
       "53  7.832033  4.210723  8.501608  8.959582  8.241116  8.230142  \n",
       "10  7.755945  4.289164  8.990053  8.351559  8.598761  9.207717  \n",
       "46  7.926408  5.524635  9.334896  8.358463  8.669588  9.039516  \n",
       "44  7.950397  5.556018  9.380916  7.934088  8.623265  9.006445  \n",
       "35  7.648888  4.553253  8.939947  8.580009  7.951099  8.688412  \n",
       "18  7.946963  3.966876  8.866846  8.764438  8.316751  8.146425  \n",
       "4   7.937090  4.246788  9.232032  8.474880  8.148693  8.176003  \n",
       "31  7.685325  4.293126  9.134554  8.274243  8.046331  8.786375  \n",
       "1   7.679649  4.684149  8.911109  8.567431  8.192386  8.862444  \n",
       "12  8.127945  4.304869  9.018530  9.180087  8.035402  8.269289  \n",
       "\n",
       "[10 rows x 18703 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GaussianNB<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.naive_bayes.GaussianNB.html\">?<span>Documentation for GaussianNB</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GaussianNB()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "''' Gaussian Naive Bayes Classifier\n",
    "Based on applying Bayes' theorem with the “naive” assumption of independence between every pair of features.\n",
    "'''\n",
    "nbc = GaussianNB()\n",
    "\n",
    "# Train model\n",
    "nbc.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "''' Support Vector Classifier\n",
    "Aims to find the hyperplane in an N-dimensional space that distinctly classifies the data points.\n",
    "To separate two classes, the SVM finds the hyperplane with the maximum margin,\n",
    "which is the maximum distance between data points of both classes. \n",
    "'''\n",
    "svc = SVC()\n",
    "\n",
    "# Train model\n",
    "svc.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "''' Desicion Tree Classifier\n",
    "Uses a decision tree to go from observations about an item to conclusions about the item's target value.\n",
    "It's a simple flowchart-like structure where each internal node represents a “test” on an attribute,\n",
    "each branch represents the outcome of the test,and each leaf node represents a class label.\n",
    "The paths from root to leaf represent classification rules.\n",
    "'''\n",
    "tree = DecisionTreeClassifier()\n",
    "\n",
    "# Train model\n",
    "tree.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "''' Random Forest Classifier\n",
    "Builds multiple decision trees and merges them together to get a more accurate and stable prediction. \n",
    "Often more accurate than the prediction of any individual tree,\n",
    "because the forest corrects for the overfitting of individual trees to their training set.\n",
    "'''\n",
    "rf = RandomForestClassifier()\n",
    "\n",
    "# Train model\n",
    "rf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions using models\n",
    "y_pred_nbc = nbc.predict(x_val)\n",
    "y_pred_svc = svc.predict(x_val)\n",
    "y_pred_tree = tree.predict(x_val)\n",
    "y_pred_rf = rf.predict(x_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GaussianNB F1 score: 0.3 \n",
      "GaussianNB accuracy: 0.3\n"
     ]
    }
   ],
   "source": [
    "# Evaluate predictions and decide which model is best fit for our dataset\n",
    "\n",
    "''' Calculate F1 score between true labels and labels from model's prediction.\n",
    "The average=parameter argument uses the value 'micro' to calculate the F1 score by aggregating the contributions of all classes.\n",
    "[Calculating metrics globally by counting the total true positives, false negatives, and false positives.]\n",
    "It's useful when you have a multi-class classification problem and want to get an overall F1 score across all classes.\n",
    "'''\n",
    "parameter = 'micro'\n",
    "s1 = f1_score(y_val, y_pred_nbc, average = parameter)\n",
    "\n",
    "# Calculate accuracy of model prediction\n",
    "accuracy = accuracy_score(y_val, y_pred_nbc)\n",
    "\n",
    "print(f\"GaussianNB F1 score: {s1} \\nGaussianNB accuracy: {accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC F1 score: 0.25 \n",
      "SVC accuracy: 0.25\n"
     ]
    }
   ],
   "source": [
    "# Calculate F1 & accuracy scores for SVC\n",
    "s2=f1_score(y_val, y_pred_svc, average=parameter)\n",
    "accuracy2 = accuracy_score(y_val, y_pred_svc)\n",
    "print(f\"SVC F1 score: {s2} \\nSVC accuracy: {accuracy2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree F1 score: 0.5 \n",
      "Decision Tree accuracy: 0.25\n"
     ]
    }
   ],
   "source": [
    "# Calculate F1 & accuracy scores for Decision Tree Classifier\n",
    "s3=f1_score(y_val, y_pred_tree, average=parameter)\n",
    "accuracy3 = accuracy_score(y_val, y_pred_svc)\n",
    "print(f\"Decision Tree F1 score: {s3} \\nDecision Tree accuracy: {accuracy3}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest F1 score: 0.3 \n",
      "Random Forest accuracy: 0.25\n"
     ]
    }
   ],
   "source": [
    "# Calculate F1 & accuracy scores for Random Forest Classifier\n",
    "s4=f1_score(y_val, y_pred_rf, average=parameter)\n",
    "accuracy4 = accuracy_score(y_val, y_pred_svc)\n",
    "print(f\"Random Forest F1 score: {s4} \\nRandom Forest accuracy: {accuracy4}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 2 0 0 0]\n",
      " [0 4 0 0 0 1 0]\n",
      " [1 0 0 0 0 0 0]\n",
      " [0 2 0 1 0 0 0]\n",
      " [1 1 0 1 0 0 0]\n",
      " [0 1 0 1 1 1 1]\n",
      " [0 1 0 0 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "'''Confusion Matrix Calculation for Random Forest Classifier model predictions:\n",
    "A confusion matrix is a table used to evaluate the performance of a classification model\n",
    "by showing how well the predicted values match the actual values.\n",
    "'''\n",
    "cnf_matrix = confusion_matrix(y_val, y_pred_rf)\n",
    "print(cnf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Because of the small amount of data that we have, if we execute again the above cells, we may get slightly different results.\n",
    "SOLUTION: run these experiments multiple times to get average results.\n",
    "'''\n",
    "# Define function for trials\n",
    "def average_trainer(trials):\n",
    "    '''The function calculates the minimum, maximum, and average F1 scores for each classifier based on the predictions made on a validation dataset.\n",
    "    The F1 score ranges from 0 (worst performance) to 1 (perfect performance).\n",
    "    '''\n",
    "    # Initialize values for min variable for each classifier\n",
    "    min1 = 1\n",
    "    min2 = 1\n",
    "    min3 = 1\n",
    "    min4 = 1\n",
    "    \n",
    "    # Initialize values for max variable for each classifier\n",
    "    max1 = 0\n",
    "    max2 = 0\n",
    "    max3 = 0\n",
    "    max4 = 0\n",
    "\n",
    "    # Initialize values for avg variable for each classifier\n",
    "    avg1 = 0\n",
    "    avg2 = 0\n",
    "    avg3 = 0\n",
    "    avg4 = 0\n",
    "\n",
    "    # Set parameter to 'micro'\n",
    "    parameter = 'micro'\n",
    "\n",
    "    for i in tqdm(range(trials), desc=\"Processing\", unit=\"iteration\"):\n",
    "\n",
    "        # Split dataset to training and validation sets for features and labels\n",
    "        x_train, x_val, y_train, y_val = train_test_split(x, y, test_size=0.3, random_state=10)\n",
    "\n",
    "        # Define & Train Classifiers\n",
    "        nbc = GaussianNB()\n",
    "        nbc.fit(x_train,y_train)\n",
    "        svc = SVC()\n",
    "        svc.fit(x_train, y_train)\n",
    "        tree = DecisionTreeClassifier()\n",
    "        tree.fit(x_train, y_train)\n",
    "        rf = RandomForestClassifier()\n",
    "        rf.fit(x_train, y_train)\n",
    "\n",
    "        # Make predictions using classifiers\n",
    "        y_pred_nbc = nbc.predict(x_val)\n",
    "        y_pred_svc = svc.predict(x_val)\n",
    "        y_pred_tree = tree.predict(x_val)\n",
    "        y_pred_rf = rf.predict(x_val)\n",
    "\n",
    "        # Calculate F1 scores for GaussianNB classifier\n",
    "        s1=f1_score(y_val, y_pred_nbc, average=parameter)\n",
    "        # Assign f1 score to min or max value for classifier\n",
    "        if(s1<min1):\n",
    "            min1=s1\n",
    "        if(s1>max1):\n",
    "            max1=s1\n",
    "        # Add to average of f1 values for classifier\n",
    "        avg1 += s1\n",
    "\n",
    "\n",
    "        # Calculate F1 scores for SVM classifier\n",
    "        s2=f1_score(y_val, y_pred_svc, average=parameter)\n",
    "        # Assign f1 score to min or max value for classifier\n",
    "        if(s2<min2):\n",
    "            min2=s2\n",
    "        if(s2>max2):\n",
    "            max2=s2\n",
    "        # Add to average of f1 values for classifier\n",
    "        avg2 += s2\n",
    "\n",
    "        # Calculate F1 scores for Decission Tree classifier\n",
    "        s3=f1_score(y_val, y_pred_tree, average=parameter)\n",
    "        # Assign f1 score to min or max value for classifier\n",
    "        if(s3<min3):\n",
    "            min3=s3\n",
    "        if(s3>max3):\n",
    "            max3=s3\n",
    "        # Add to average of f1 values for classifier\n",
    "        avg3 += s3\n",
    "\n",
    "        # Calculate F1 scores for Random Forest classifier\n",
    "        s4=f1_score(y_val, y_pred_rf, average=parameter)\n",
    "        # Assign f1 score to min or max value for classifier\n",
    "        if(s4<min4):\n",
    "            min4=s4\n",
    "        if(s4>max4):\n",
    "            max4=s4\n",
    "        # Add to average of f1 values for classifier\n",
    "        avg4 += s4\n",
    "\n",
    "    # Fix averages\n",
    "    avg1=avg1/trials\n",
    "    avg2=avg2/trials\n",
    "    avg3=avg3/trials\n",
    "    avg4=avg4/trials   \n",
    "\n",
    "    # Print metrics of classifiers' performance\n",
    "    print(f\"GNB: max: {max1}, min: {min1}, average: {avg1}\")\n",
    "    print(f\"SVC: max: {max2}, min: {min2}, average: {avg2}\")\n",
    "    print(f\"DecisionTree: max: {max3}, min: {min3}, average: {avg3}\")\n",
    "    print(f\"RandomForest: max: {max4}, min: {min4}, average: {avg4}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing: 100%|██████████| 50/50 [05:57<00:00,  7.15s/iteration]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GNB: max: 0.3, min: 0.3, average: 0.30000000000000027\n",
      "SVC: max: 0.25, min: 0.25, average: 0.25\n",
      "DecisionTree: max: 0.6, min: 0.3, average: 0.4379999999999999\n",
      "RandomForest: max: 0.5, min: 0.25, average: 0.36200000000000004\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "average_trainer(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check how different model parameters change results --> Select the best possible random forest classifier\n",
    "\n",
    "def objective(params : dict) -> int :\n",
    "    ''' Function that evaluates the performance of a Random Forest Classifier\n",
    "    with a specific set of hyperparameters.\n",
    "    It returns a negative score.\n",
    "    '''\n",
    "    # Dictionary of hyperparameters\n",
    "    n_estimators = params['n_estimators'] # Number of trees in the Random Forest\n",
    "    max_depth = params['max_depth'] # Maximum depth of each tree\n",
    "    min_samples_split = params['min_samples_split']  # Minimum number of samples required to split an internal node\n",
    "    min_samples_leaf = params['min_samples_leaf'] # Minimum numbere of samples required to be at a leaf node\n",
    "\n",
    "    # Initialize Classifier\n",
    "    clf = RandomForestClassifier(\n",
    "        n_estimators = n_estimators,\n",
    "        max_depth = max_depth, \n",
    "        min_samples_split = min_samples_split,\n",
    "        min_samples_leaf = min_samples_leaf,\n",
    "        random_state = 42\n",
    "    )\n",
    "\n",
    "    # Initialize final_score variable\n",
    "    final_score = 0\n",
    "    \n",
    "    for i in range(0,20):\n",
    "\n",
    "        # Split data into training and validation sets of features and labels\n",
    "        x_train, x_val, y_train, y_val = train_test_split(x, y, test_size=0.3, random_state=10)\n",
    "\n",
    "        # Fit model\n",
    "        clf.fit(x_train, y_train)\n",
    "\n",
    "        # Model prediction of labels\n",
    "        y_pred = clf.predict(x_val)\n",
    "\n",
    "        # Calculate F1 scores\n",
    "        parameter = 'micro'\n",
    "        score = f1_score(y_val, y_pred, average = parameter)\n",
    "        final_score += score\n",
    "    \n",
    "    final_score = -(final_score / 20) # Negative value because it will be used for the fmin() function which minimizes the score for optimization purposes.\n",
    "\n",
    "    return final_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [27:18<00:00, 16.38s/trial, best loss: -0.4000000000000001]\n",
      "Best hyperparameters: {'max_depth': 7, 'min_samples_leaf': 2, 'min_samples_split': 1, 'n_estimators': 6}\n"
     ]
    }
   ],
   "source": [
    "''' Define search space (space dictionary) for possible hyperparameter values the objective() function will use.\n",
    "hp.choice() function allows for discrete choices within a specific range.\n",
    "'''\n",
    "space = {\n",
    "    'n_estimators': hp.choice('n_estimators', range(10, 101)),\n",
    "    'max_depth' : hp.choice('max_depth', range(1,21)),\n",
    "    'min_samples_split' : hp.choice('min_samples_split', range(2,11)),\n",
    "    'min_samples_leaf' : hp.choice('min_samples_leaf', range(1, 11))\n",
    "}\n",
    "\n",
    "''' Hyperparameter Optimization:\n",
    "The fmin() function aims to minimize the output of the objective() function, \n",
    "which is negative, so it will lead to the highest F1 score.\n",
    "'''\n",
    "best = fmin(fn = objective, # Function being minimized: Trains a RF using the hyperparameters defined by space and returns a negative F1 score\n",
    "            space = space,\n",
    "            algo = tpe.suggest, # Optimization algorithm: Tree-structured Parzen Estimator [a Bayesian optimization algorithm]\n",
    "            max_evals = 100, # Maximum number of evaluations / trials for the optimization\n",
    "            ) # fmin will try 100 combinations of hyperparameters before it finishes\n",
    "\n",
    "print(f\"Best hyperparameters: {best}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [26:09<00:00, 15.70s/trial, best loss: -0.45]             \n"
     ]
    }
   ],
   "source": [
    "''' Try PCA for dimensionality reduction to check if we get better results:\n",
    "The number of principal components (n_components) is a new hyperparameter that is added to the search space.\n",
    "The model will search for the best number of components to retain during PCA by exploring values between 2 and 100.\n",
    "'''\n",
    "def pca_objective(params):\n",
    "    n_components = params['n_components']\n",
    "    n_estimators = params['n_estimators']\n",
    "    max_depth = params['max_depth']\n",
    "    min_samples_split = params['min_samples_split']\n",
    "    min_samples_leaf = params['min_samples_leaf']\n",
    "\n",
    "    clf = RandomForestClassifier(\n",
    "        n_estimators=n_estimators,\n",
    "        max_depth=max_depth,\n",
    "        min_samples_split=min_samples_split,\n",
    "        min_samples_leaf=min_samples_leaf,\n",
    "        random_state=42\n",
    "    )\n",
    "\n",
    "    final_score=0\n",
    "\n",
    "    for i in range(0,20):\n",
    "      x_train, x_val, y_train, y_val = train_test_split(x, y, test_size=0.3, random_state=10)\n",
    "\n",
    "      # Scale training and validation data to normalize features\n",
    "      scaler = StandardScaler()\n",
    "      x_train_scaled = scaler.fit_transform(x_train) # Learns scaling parameters from training data and applies them\n",
    "      x_val_scaled = scaler.transform(x_val) # Only applies learned parameters to validation data (!Model should not learn from validation data)\n",
    "\n",
    "      pca = PCA(n_components=n_components)\n",
    "      x_train_pca = pca.fit_transform(x_train_scaled)\n",
    "      x_val_pca = pca.transform(x_val_scaled)\n",
    "      clf.fit(x_train_pca, y_train)\n",
    "      y_pred = clf.predict(x_val_pca)\n",
    "      parameter='micro'\n",
    "      score=f1_score(y_val, y_pred, average=parameter)\n",
    "      final_score=final_score+score\n",
    "    final_score=final_score/20\n",
    "\n",
    "    return -final_score\n",
    "\n",
    "# Define the search space for hyperparameters\n",
    "space = {\n",
    "    'n_components': hp.choice('n_components', range(2, 101)),\n",
    "    'n_estimators': hp.choice('n_estimators', range(10, 101)),\n",
    "    'max_depth': hp.choice('max_depth', range(1, 21)),\n",
    "    'min_samples_split': hp.choice('min_samples_split', range(2, 11)),\n",
    "    'min_samples_leaf': hp.choice('min_samples_leaf', range(1, 11))\n",
    "}\n",
    "\n",
    "# Use Tree-structured Parzen Estimator (TPE) as the optimization algorithm\n",
    "best = fmin(fn=objective, space=space, algo=tpe.suggest, max_evals=100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best hyperparameters: {'max_depth': 4, 'min_samples_leaf': 2, 'min_samples_split': 0, 'n_components': 22, 'n_estimators': 17}\n"
     ]
    }
   ],
   "source": [
    "print(f\"Best hyperparameters: {best}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOTE: We do not get any better results using PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\alexa\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# Using Neural Networks \n",
    "\n",
    "# Split dataset into training and testing data\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=10)\n",
    "\n",
    "# Split training dataset into training and validation data\n",
    "x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=0.3, random_state=10)\n",
    "\n",
    "# Define model\n",
    "model = Sequential([\n",
    "    Dense(64, activation = 'relu', input_shape = (18703,)),\n",
    "    Dense(32, activation = 'relu'),\n",
    "    Dense(7, activation = 'softmax')\n",
    "])\n",
    "\n",
    "# Compile model\n",
    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "16/16 - 8s - 509ms/step - accuracy: 0.0938 - loss: 127.0492 - val_accuracy: 0.1429 - val_loss: 28.6411\n",
      "Epoch 2/100\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.0625 - loss: 34.8420 - val_accuracy: 0.2143 - val_loss: 9.3806\n",
      "Epoch 3/100\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1562 - loss: 14.1368 - val_accuracy: 0.2143 - val_loss: 10.3651\n",
      "Epoch 4/100\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 10.0614 - val_accuracy: 0.1429 - val_loss: 6.0540\n",
      "Epoch 5/100\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.2188 - loss: 5.0448 - val_accuracy: 0.1429 - val_loss: 6.8176\n",
      "Epoch 6/100\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.0625 - loss: 6.6973 - val_accuracy: 0.2143 - val_loss: 7.1860\n",
      "Epoch 7/100\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.2500 - loss: 7.4285 - val_accuracy: 0.3571 - val_loss: 6.1970\n",
      "Epoch 8/100\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.0938 - loss: 6.6395 - val_accuracy: 0.2143 - val_loss: 6.5573\n",
      "Epoch 9/100\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1250 - loss: 6.3141 - val_accuracy: 0.1429 - val_loss: 5.2240\n",
      "Epoch 10/100\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1562 - loss: 6.8201 - val_accuracy: 0.1429 - val_loss: 10.9122\n",
      "Epoch 11/100\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1250 - loss: 7.1962 - val_accuracy: 0.2143 - val_loss: 5.0032\n",
      "Epoch 12/100\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.0938 - loss: 4.1479 - val_accuracy: 0.2857 - val_loss: 2.2702\n",
      "Epoch 13/100\n",
      "16/16 - 1s - 80ms/step - accuracy: 0.1250 - loss: 3.5808 - val_accuracy: 0.1429 - val_loss: 2.5384\n",
      "Epoch 14/100\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.3750 - loss: 3.5573 - val_accuracy: 0.0714 - val_loss: 3.1620\n",
      "Epoch 15/100\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1562 - loss: 4.1105 - val_accuracy: 0.2143 - val_loss: 2.8963\n",
      "Epoch 16/100\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.0938 - loss: 3.3986 - val_accuracy: 0.2143 - val_loss: 4.4777\n",
      "Epoch 17/100\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.0625 - loss: 4.1646 - val_accuracy: 0.2143 - val_loss: 3.7541\n",
      "Epoch 18/100\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.2812 - loss: 3.9723 - val_accuracy: 0.0714 - val_loss: 6.0857\n",
      "Epoch 19/100\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 4.0686 - val_accuracy: 0.0714 - val_loss: 8.0351\n",
      "Epoch 20/100\n",
      "16/16 - 1s - 55ms/step - accuracy: 0.1562 - loss: 5.8910 - val_accuracy: 0.4286 - val_loss: 2.5138\n",
      "Epoch 21/100\n",
      "16/16 - 1s - 77ms/step - accuracy: 0.2812 - loss: 5.2050 - val_accuracy: 0.1429 - val_loss: 4.5124\n",
      "Epoch 22/100\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.0625 - loss: 9.1062 - val_accuracy: 0.0714 - val_loss: 7.1465\n",
      "Epoch 23/100\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.2188 - loss: 7.2391 - val_accuracy: 0.2143 - val_loss: 12.1711\n",
      "Epoch 24/100\n",
      "16/16 - 1s - 50ms/step - accuracy: 0.0938 - loss: 6.5940 - val_accuracy: 0.0000e+00 - val_loss: 4.8249\n",
      "Epoch 25/100\n",
      "16/16 - 1s - 86ms/step - accuracy: 0.2500 - loss: 5.1578 - val_accuracy: 0.0714 - val_loss: 5.3978\n",
      "Epoch 26/100\n",
      "16/16 - 2s - 111ms/step - accuracy: 0.1250 - loss: 4.0382 - val_accuracy: 0.0714 - val_loss: 2.5464\n",
      "Epoch 27/100\n",
      "16/16 - 1s - 71ms/step - accuracy: 0.1562 - loss: 2.4637 - val_accuracy: 0.0714 - val_loss: 2.9411\n",
      "Epoch 28/100\n",
      "16/16 - 1s - 51ms/step - accuracy: 0.2188 - loss: 2.7469 - val_accuracy: 0.2143 - val_loss: 2.3305\n",
      "Epoch 29/100\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1562 - loss: 3.4731 - val_accuracy: 0.1429 - val_loss: 3.4375\n",
      "Epoch 30/100\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.0625 - loss: 3.5385 - val_accuracy: 0.0714 - val_loss: 3.1589\n",
      "Epoch 31/100\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.2188 - loss: 2.6287 - val_accuracy: 0.1429 - val_loss: 5.0894\n",
      "Epoch 32/100\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1250 - loss: 2.4963 - val_accuracy: 0.0000e+00 - val_loss: 1.9764\n",
      "Epoch 33/100\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.2812 - loss: 2.6960 - val_accuracy: 0.1429 - val_loss: 3.9818\n",
      "Epoch 34/100\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.2188 - loss: 2.8017 - val_accuracy: 0.0714 - val_loss: 2.3550\n",
      "Epoch 35/100\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.0938 - loss: 2.3442 - val_accuracy: 0.0714 - val_loss: 2.5743\n",
      "Epoch 36/100\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.0625 - loss: 2.3882 - val_accuracy: 0.0000e+00 - val_loss: 3.1186\n",
      "Epoch 37/100\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1250 - loss: 3.0768 - val_accuracy: 0.0714 - val_loss: 2.2679\n",
      "Epoch 38/100\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.2188 - loss: 2.2885 - val_accuracy: 0.2143 - val_loss: 2.7360\n",
      "Epoch 39/100\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1562 - loss: 1.9569 - val_accuracy: 0.2143 - val_loss: 2.2695\n",
      "Epoch 40/100\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 2.5990 - val_accuracy: 0.2857 - val_loss: 2.8610\n",
      "Epoch 41/100\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1562 - loss: 3.8781 - val_accuracy: 0.1429 - val_loss: 4.2287\n",
      "Epoch 42/100\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.2188 - loss: 3.4479 - val_accuracy: 0.2857 - val_loss: 2.3430\n",
      "Epoch 43/100\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.0625 - loss: 2.7814 - val_accuracy: 0.2857 - val_loss: 2.5732\n",
      "Epoch 44/100\n",
      "16/16 - 1s - 57ms/step - accuracy: 0.0312 - loss: 2.9543 - val_accuracy: 0.0714 - val_loss: 2.5688\n",
      "Epoch 45/100\n",
      "16/16 - 1s - 91ms/step - accuracy: 0.1250 - loss: 2.2955 - val_accuracy: 0.2143 - val_loss: 2.0769\n",
      "Epoch 46/100\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1562 - loss: 1.8975 - val_accuracy: 0.2857 - val_loss: 2.3908\n",
      "Epoch 47/100\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.2188 - loss: 2.4271 - val_accuracy: 0.0714 - val_loss: 2.7404\n",
      "Epoch 48/100\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1562 - loss: 2.4107 - val_accuracy: 0.2143 - val_loss: 3.5848\n",
      "Epoch 49/100\n",
      "16/16 - 1s - 77ms/step - accuracy: 0.1875 - loss: 2.7904 - val_accuracy: 0.2143 - val_loss: 2.5712\n",
      "Epoch 50/100\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1562 - loss: 2.7051 - val_accuracy: 0.2143 - val_loss: 2.2968\n",
      "Epoch 51/100\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 2.0868 - val_accuracy: 0.1429 - val_loss: 2.5482\n",
      "Epoch 52/100\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.2188 - loss: 1.9867 - val_accuracy: 0.3571 - val_loss: 2.1819\n",
      "Epoch 53/100\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 2.1483 - val_accuracy: 0.1429 - val_loss: 2.1558\n",
      "Epoch 54/100\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.3125 - loss: 2.0437 - val_accuracy: 0.2857 - val_loss: 2.8065\n",
      "Epoch 55/100\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.2500 - loss: 2.0425 - val_accuracy: 0.2857 - val_loss: 1.9006\n",
      "Epoch 56/100\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.8958 - val_accuracy: 0.1429 - val_loss: 2.0485\n",
      "Epoch 57/100\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.2188 - loss: 2.0007 - val_accuracy: 0.1429 - val_loss: 1.9556\n",
      "Epoch 58/100\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.2188 - loss: 2.3412 - val_accuracy: 0.0714 - val_loss: 1.9598\n",
      "Epoch 59/100\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1250 - loss: 2.0111 - val_accuracy: 0.2857 - val_loss: 1.8099\n",
      "Epoch 60/100\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.2188 - loss: 1.8195 - val_accuracy: 0.2143 - val_loss: 1.9173\n",
      "Epoch 61/100\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.2188 - loss: 1.9715 - val_accuracy: 0.2857 - val_loss: 1.9700\n",
      "Epoch 62/100\n",
      "16/16 - 1s - 53ms/step - accuracy: 0.2188 - loss: 2.0563 - val_accuracy: 0.2857 - val_loss: 1.8401\n",
      "Epoch 63/100\n",
      "16/16 - 1s - 75ms/step - accuracy: 0.2188 - loss: 2.0657 - val_accuracy: 0.1429 - val_loss: 1.9302\n",
      "Epoch 64/100\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1250 - loss: 1.9815 - val_accuracy: 0.3571 - val_loss: 1.8904\n",
      "Epoch 65/100\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.4062 - loss: 1.9282 - val_accuracy: 0.2857 - val_loss: 2.1682\n",
      "Epoch 66/100\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.7657 - val_accuracy: 0.0714 - val_loss: 3.8956\n",
      "Epoch 67/100\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.2500 - loss: 2.2423 - val_accuracy: 0.1429 - val_loss: 3.0208\n",
      "Epoch 68/100\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 2.4388 - val_accuracy: 0.2143 - val_loss: 1.9913\n",
      "Epoch 69/100\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.2812 - loss: 2.2701 - val_accuracy: 0.1429 - val_loss: 2.3629\n",
      "Epoch 70/100\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.2188 - loss: 2.3434 - val_accuracy: 0.0000e+00 - val_loss: 2.4228\n",
      "Epoch 71/100\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.2500 - loss: 2.2319 - val_accuracy: 0.1429 - val_loss: 2.3635\n",
      "Epoch 72/100\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.2812 - loss: 1.9496 - val_accuracy: 0.3571 - val_loss: 1.7683\n",
      "Epoch 73/100\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1562 - loss: 2.0513 - val_accuracy: 0.3571 - val_loss: 3.1484\n",
      "Epoch 74/100\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.2500 - loss: 2.0320 - val_accuracy: 0.0714 - val_loss: 2.9328\n",
      "Epoch 75/100\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.2188 - loss: 2.1531 - val_accuracy: 0.1429 - val_loss: 2.2525\n",
      "Epoch 76/100\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1562 - loss: 2.4334 - val_accuracy: 0.2143 - val_loss: 2.2518\n",
      "Epoch 77/100\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.2812 - loss: 2.1407 - val_accuracy: 0.1429 - val_loss: 2.2713\n",
      "Epoch 78/100\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.2500 - loss: 1.9433 - val_accuracy: 0.1429 - val_loss: 2.0314\n",
      "Epoch 79/100\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.2500 - loss: 1.8971 - val_accuracy: 0.2143 - val_loss: 1.7716\n",
      "Epoch 80/100\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.8720 - val_accuracy: 0.0714 - val_loss: 2.8922\n",
      "Epoch 81/100\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.2188 - loss: 1.8836 - val_accuracy: 0.1429 - val_loss: 2.1194\n",
      "Epoch 82/100\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.3125 - loss: 1.9057 - val_accuracy: 0.1429 - val_loss: 1.9566\n",
      "Epoch 83/100\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.3125 - loss: 1.7368 - val_accuracy: 0.0714 - val_loss: 3.1077\n",
      "Epoch 84/100\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1250 - loss: 2.1844 - val_accuracy: 0.2143 - val_loss: 2.1199\n",
      "Epoch 85/100\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 2.0204 - val_accuracy: 0.0714 - val_loss: 5.3334\n",
      "Epoch 86/100\n",
      "16/16 - 1s - 53ms/step - accuracy: 0.2188 - loss: 4.5503 - val_accuracy: 0.1429 - val_loss: 2.7162\n",
      "Epoch 87/100\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.2500 - loss: 3.2338 - val_accuracy: 0.2857 - val_loss: 1.7661\n",
      "Epoch 88/100\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.2188 - loss: 2.6103 - val_accuracy: 0.2143 - val_loss: 2.3859\n",
      "Epoch 89/100\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.2500 - loss: 1.9029 - val_accuracy: 0.1429 - val_loss: 2.0082\n",
      "Epoch 90/100\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.2812 - loss: 1.8486 - val_accuracy: 0.2143 - val_loss: 1.9531\n",
      "Epoch 91/100\n",
      "16/16 - 1s - 51ms/step - accuracy: 0.1562 - loss: 1.8634 - val_accuracy: 0.2143 - val_loss: 1.8591\n",
      "Epoch 92/100\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9847 - val_accuracy: 0.0714 - val_loss: 2.6678\n",
      "Epoch 93/100\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 3.4774 - val_accuracy: 0.0000e+00 - val_loss: 4.4186\n",
      "Epoch 94/100\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.0312 - loss: 3.9194 - val_accuracy: 0.3571 - val_loss: 3.1764\n",
      "Epoch 95/100\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.2500 - loss: 3.4591 - val_accuracy: 0.0714 - val_loss: 2.6739\n",
      "Epoch 96/100\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.2500 - loss: 2.3212 - val_accuracy: 0.2143 - val_loss: 3.2362\n",
      "Epoch 97/100\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1250 - loss: 3.0554 - val_accuracy: 0.1429 - val_loss: 2.5790\n",
      "Epoch 98/100\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.2188 - loss: 2.5674 - val_accuracy: 0.1429 - val_loss: 2.3600\n",
      "Epoch 99/100\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 2.2211 - val_accuracy: 0.2143 - val_loss: 2.1651\n",
      "Epoch 100/100\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1562 - loss: 2.7306 - val_accuracy: 0.2857 - val_loss: 2.2584\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x29250f65490>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train model\n",
    "model.fit(x_train, y_train, epochs=100, batch_size=2, validation_data=(x_val, y_val), verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 379ms/step - accuracy: 0.2000 - loss: 2.3527\n",
      "Test Accuracy: 20.00 %\n"
     ]
    }
   ],
   "source": [
    "# Evaluate model on test set\n",
    "test_loss, test_accuracy = model.evaluate(x_test, y_test)\n",
    "print(f\"Test Accuracy: {test_accuracy * 100:.2f} %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use objective() function and PCA for potential optimization of the neural network model \n",
    "\n",
    "# Define function that returns a neural network model\n",
    "def neural_network(input_size, n_hidden, hidden_size):\n",
    "    # Define input shape as a first layer in the form of an Input(shape) object \n",
    "    input_layer = Input(shape = (input_size,))\n",
    "\n",
    "    # Define architecture of hidden layers as it is defined from the Input(shape) object layer\n",
    "    hidden_layer = input_layer\n",
    "\n",
    "    # Define hidden layers\n",
    "    for _ in range(0, n_hidden):\n",
    "        hidden_layer = Dense(hidden_size, activation='relu')(hidden_layer)\n",
    "\n",
    "    # Define output layer\n",
    "    output_layer = Dense(7, activation = 'softmax')(hidden_layer)\n",
    "\n",
    "    # Define model\n",
    "    model = Model(inputs=input_layer, outputs=output_layer)\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def neural_pca_objective(params):\n",
    "    ''' Optimization function with dimensionality reduction of data with PCA.\n",
    "    Returns negative test accuracy result of model evaluation for further use in the optimization function.\n",
    "    '''\n",
    "    n_components = params['n_components']\n",
    "    n_hidden = params['n_hidden']\n",
    "    hidden_size = params['hidden_size']\n",
    "\n",
    "    # Split dataset into training and testing sets\n",
    "    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=10)\n",
    "\n",
    "    # Split training dataset into training and validation sets\n",
    "    x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=0.3, random_state=10)\n",
    "\n",
    "    # Normalize datasets\n",
    "    scaler = StandardScaler()\n",
    "    x_train_scaled = scaler.fit_transform(x_train)\n",
    "    x_val_scaled = scaler.transform(x_val)\n",
    "    x_test_scaled = scaler.transform(x_test)\n",
    "\n",
    "    # Perform PCA on training and validation data\n",
    "    pca = PCA(n_components=n_components)\n",
    "    x_train_pca = pca.fit_transform(x_train_scaled)\n",
    "    x_val_pca = pca.transform(x_val_scaled)\n",
    "\n",
    "    # Call neural_network function to create model \n",
    "    clf=neural_network(n_components, n_hidden, hidden_size)\n",
    "\n",
    "    # Compile model\n",
    "    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "    # Train model\n",
    "    model.fit(x_train, y_train, epochs=20, batch_size=2, validation_data=(x_val, y_val), verbose=2)\n",
    "\n",
    "    # Test model's prediction\n",
    "    test_loss, test_accuracy = model.evaluate(x_test, y_test)\n",
    "\n",
    "    return -test_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20                                             \n",
      "\n",
      "16/16 - 5s - 287ms/step - accuracy: 0.1875 - loss: 4.7214 - val_accuracy: 0.0714 - val_loss: 3.6927\n",
      "\n",
      "Epoch 2/20                                             \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1250 - loss: 4.0582 - val_accuracy: 0.2143 - val_loss: 2.8100\n",
      "\n",
      "Epoch 3/20                                             \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.2188 - loss: 2.3580 - val_accuracy: 0.1429 - val_loss: 3.7164\n",
      "\n",
      "Epoch 4/20                                             \n",
      "\n",
      "16/16 - 1s - 77ms/step - accuracy: 0.2500 - loss: 2.5986 - val_accuracy: 0.2143 - val_loss: 3.4354\n",
      "\n",
      "Epoch 5/20                                             \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 2.3727 - val_accuracy: 0.2857 - val_loss: 2.4380\n",
      "\n",
      "Epoch 6/20                                             \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1562 - loss: 2.8887 - val_accuracy: 0.2857 - val_loss: 2.4802\n",
      "\n",
      "Epoch 7/20                                             \n",
      "\n",
      "16/16 - 1s - 58ms/step - accuracy: 0.3438 - loss: 2.1374 - val_accuracy: 0.2143 - val_loss: 2.9305\n",
      "\n",
      "Epoch 8/20                                             \n",
      "\n",
      "16/16 - 1s - 90ms/step - accuracy: 0.1875 - loss: 2.3256 - val_accuracy: 0.2143 - val_loss: 3.4309\n",
      "\n",
      "Epoch 9/20                                             \n",
      "\n",
      "16/16 - 1s - 69ms/step - accuracy: 0.2500 - loss: 2.4773 - val_accuracy: 0.2143 - val_loss: 2.3345\n",
      "\n",
      "Epoch 10/20                                            \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.3125 - loss: 1.7303 - val_accuracy: 0.2143 - val_loss: 2.3104\n",
      "\n",
      "Epoch 11/20                                            \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.3750 - loss: 1.7225 - val_accuracy: 0.2857 - val_loss: 2.1460\n",
      "\n",
      "Epoch 12/20                                            \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.3438 - loss: 1.8735 - val_accuracy: 0.0714 - val_loss: 2.4927\n",
      "\n",
      "Epoch 13/20                                            \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.2500 - loss: 1.6695 - val_accuracy: 0.0714 - val_loss: 4.8068\n",
      "\n",
      "Epoch 14/20                                            \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.0938 - loss: 3.8642 - val_accuracy: 0.2143 - val_loss: 1.9374\n",
      "\n",
      "Epoch 15/20                                            \n",
      "\n",
      "16/16 - 1s - 61ms/step - accuracy: 0.1875 - loss: 1.9423 - val_accuracy: 0.2143 - val_loss: 1.9361\n",
      "\n",
      "Epoch 16/20                                            \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9412 - val_accuracy: 0.2143 - val_loss: 1.9346\n",
      "\n",
      "Epoch 17/20                                            \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9395 - val_accuracy: 0.2143 - val_loss: 1.9328\n",
      "\n",
      "Epoch 18/20                                            \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9388 - val_accuracy: 0.2143 - val_loss: 1.9306\n",
      "\n",
      "Epoch 19/20                                            \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9374 - val_accuracy: 0.2143 - val_loss: 1.9291\n",
      "\n",
      "Epoch 20/20                                            \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9364 - val_accuracy: 0.2143 - val_loss: 1.9276\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 416ms/step - accuracy: 0.0500 - loss: 1.9425\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 429ms/step - accuracy: 0.0500 - loss: 1.9425\n",
      "\n",
      "Epoch 1/20                                                                         \n",
      "\n",
      "16/16 - 3s - 181ms/step - accuracy: 0.1875 - loss: 1.9363 - val_accuracy: 0.2143 - val_loss: 1.9255\n",
      "\n",
      "Epoch 2/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9351 - val_accuracy: 0.2143 - val_loss: 1.9241\n",
      "\n",
      "Epoch 3/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1250 - loss: 1.9335 - val_accuracy: 0.1429 - val_loss: 1.9221\n",
      "\n",
      "Epoch 4/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9319 - val_accuracy: 0.1429 - val_loss: 1.9209\n",
      "\n",
      "Epoch 5/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1250 - loss: 1.9316 - val_accuracy: 0.2143 - val_loss: 1.9186\n",
      "\n",
      "Epoch 6/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1562 - loss: 1.9302 - val_accuracy: 0.1429 - val_loss: 1.9174\n",
      "\n",
      "Epoch 7/20                                                                         \n",
      "\n",
      "16/16 - 1s - 57ms/step - accuracy: 0.1875 - loss: 1.9290 - val_accuracy: 0.1429 - val_loss: 1.9159\n",
      "\n",
      "Epoch 8/20                                                                         \n",
      "\n",
      "16/16 - 1s - 64ms/step - accuracy: 0.1875 - loss: 1.9286 - val_accuracy: 0.1429 - val_loss: 1.9142\n",
      "\n",
      "Epoch 9/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9269 - val_accuracy: 0.1429 - val_loss: 1.9128\n",
      "\n",
      "Epoch 10/20                                                                        \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9263 - val_accuracy: 0.1429 - val_loss: 1.9116\n",
      "\n",
      "Epoch 11/20                                                                        \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1875 - loss: 1.9254 - val_accuracy: 0.1429 - val_loss: 1.9103\n",
      "\n",
      "Epoch 12/20                                                                        \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9246 - val_accuracy: 0.1429 - val_loss: 1.9083\n",
      "\n",
      "Epoch 13/20                                                                        \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9232 - val_accuracy: 0.1429 - val_loss: 1.9069\n",
      "\n",
      "Epoch 14/20                                                                        \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9229 - val_accuracy: 0.1429 - val_loss: 1.9056\n",
      "\n",
      "Epoch 15/20                                                                        \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9216 - val_accuracy: 0.1429 - val_loss: 1.9043\n",
      "\n",
      "Epoch 16/20                                                                        \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9209 - val_accuracy: 0.1429 - val_loss: 1.9027\n",
      "\n",
      "Epoch 17/20                                                                        \n",
      "\n",
      "16/16 - 1s - 52ms/step - accuracy: 0.1875 - loss: 1.9198 - val_accuracy: 0.1429 - val_loss: 1.9019\n",
      "\n",
      "Epoch 18/20                                                                        \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9195 - val_accuracy: 0.1429 - val_loss: 1.9002\n",
      "\n",
      "Epoch 19/20                                                                        \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9186 - val_accuracy: 0.1429 - val_loss: 1.8995\n",
      "\n",
      "Epoch 20/20                                                                        \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9176 - val_accuracy: 0.1429 - val_loss: 1.8981\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 491ms/step - accuracy: 0.1000 - loss: 1.9439\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 511ms/step - accuracy: 0.1000 - loss: 1.9439\n",
      "\n",
      "Epoch 1/20                                                                         \n",
      "\n",
      "16/16 - 3s - 171ms/step - accuracy: 0.1875 - loss: 1.9185 - val_accuracy: 0.1429 - val_loss: 1.8944\n",
      "\n",
      "Epoch 2/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9160 - val_accuracy: 0.1429 - val_loss: 1.8941\n",
      "\n",
      "Epoch 3/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9156 - val_accuracy: 0.1429 - val_loss: 1.8929\n",
      "\n",
      "Epoch 4/20                                                                         \n",
      "\n",
      "16/16 - 1s - 80ms/step - accuracy: 0.1875 - loss: 1.9148 - val_accuracy: 0.1429 - val_loss: 1.8916\n",
      "\n",
      "Epoch 5/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9143 - val_accuracy: 0.1429 - val_loss: 1.8909\n",
      "\n",
      "Epoch 6/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9148 - val_accuracy: 0.1429 - val_loss: 1.8884\n",
      "\n",
      "Epoch 7/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9130 - val_accuracy: 0.1429 - val_loss: 1.8885\n",
      "\n",
      "Epoch 8/20                                                                         \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9127 - val_accuracy: 0.1429 - val_loss: 1.8877\n",
      "\n",
      "Epoch 9/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9120 - val_accuracy: 0.1429 - val_loss: 1.8870\n",
      "\n",
      "Epoch 10/20                                                                        \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9114 - val_accuracy: 0.1429 - val_loss: 1.8863\n",
      "\n",
      "Epoch 11/20                                                                        \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9110 - val_accuracy: 0.1429 - val_loss: 1.8858\n",
      "\n",
      "Epoch 12/20                                                                        \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9108 - val_accuracy: 0.1429 - val_loss: 1.8844\n",
      "\n",
      "Epoch 13/20                                                                        \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1875 - loss: 1.9102 - val_accuracy: 0.1429 - val_loss: 1.8833\n",
      "\n",
      "Epoch 14/20                                                                        \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9099 - val_accuracy: 0.1429 - val_loss: 1.8833\n",
      "\n",
      "Epoch 15/20                                                                        \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9097 - val_accuracy: 0.1429 - val_loss: 1.8823\n",
      "\n",
      "Epoch 16/20                                                                        \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9091 - val_accuracy: 0.1429 - val_loss: 1.8815\n",
      "\n",
      "Epoch 17/20                                                                        \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9088 - val_accuracy: 0.1429 - val_loss: 1.8804\n",
      "\n",
      "Epoch 18/20                                                                        \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9086 - val_accuracy: 0.1429 - val_loss: 1.8805\n",
      "\n",
      "Epoch 19/20                                                                        \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9086 - val_accuracy: 0.1429 - val_loss: 1.8793\n",
      "\n",
      "Epoch 20/20                                                                        \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9077 - val_accuracy: 0.1429 - val_loss: 1.8787\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 531ms/step - accuracy: 0.1000 - loss: 1.9499\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 543ms/step - accuracy: 0.1000 - loss: 1.9499\n",
      "\n",
      "Epoch 1/20                                                                         \n",
      "\n",
      "16/16 - 3s - 171ms/step - accuracy: 0.1875 - loss: 1.9094 - val_accuracy: 0.1429 - val_loss: 1.8803\n",
      "\n",
      "Epoch 2/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9077 - val_accuracy: 0.1429 - val_loss: 1.8794\n",
      "\n",
      "Epoch 3/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9072 - val_accuracy: 0.1429 - val_loss: 1.8786\n",
      "\n",
      "Epoch 4/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9071 - val_accuracy: 0.1429 - val_loss: 1.8784\n",
      "\n",
      "Epoch 5/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9066 - val_accuracy: 0.1429 - val_loss: 1.8779\n",
      "\n",
      "Epoch 6/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9063 - val_accuracy: 0.1429 - val_loss: 1.8769\n",
      "\n",
      "Epoch 7/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9064 - val_accuracy: 0.1429 - val_loss: 1.8762\n",
      "\n",
      "Epoch 8/20                                                                         \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9064 - val_accuracy: 0.1429 - val_loss: 1.8755\n",
      "\n",
      "Epoch 9/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9056 - val_accuracy: 0.1429 - val_loss: 1.8752\n",
      "\n",
      "Epoch 10/20                                                                        \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9055 - val_accuracy: 0.1429 - val_loss: 1.8751\n",
      "\n",
      "Epoch 11/20                                                                        \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9058 - val_accuracy: 0.1429 - val_loss: 1.8735\n",
      "\n",
      "Epoch 12/20                                                                        \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9053 - val_accuracy: 0.1429 - val_loss: 1.8738\n",
      "\n",
      "Epoch 13/20                                                                        \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9053 - val_accuracy: 0.1429 - val_loss: 1.8731\n",
      "\n",
      "Epoch 14/20                                                                        \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9050 - val_accuracy: 0.1429 - val_loss: 1.8729\n",
      "\n",
      "Epoch 15/20                                                                        \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9047 - val_accuracy: 0.1429 - val_loss: 1.8727\n",
      "\n",
      "Epoch 16/20                                                                        \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9049 - val_accuracy: 0.1429 - val_loss: 1.8711\n",
      "\n",
      "Epoch 17/20                                                                        \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9044 - val_accuracy: 0.1429 - val_loss: 1.8709\n",
      "\n",
      "Epoch 18/20                                                                        \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9045 - val_accuracy: 0.1429 - val_loss: 1.8711\n",
      "\n",
      "Epoch 19/20                                                                        \n",
      "\n",
      "16/16 - 1s - 50ms/step - accuracy: 0.1875 - loss: 1.9049 - val_accuracy: 0.1429 - val_loss: 1.8709\n",
      "\n",
      "Epoch 20/20                                                                        \n",
      "\n",
      "16/16 - 1s - 77ms/step - accuracy: 0.1875 - loss: 1.9039 - val_accuracy: 0.1429 - val_loss: 1.8706\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 259ms/step - accuracy: 0.1000 - loss: 1.9558\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 272ms/step - accuracy: 0.1000 - loss: 1.9558\n",
      "\n",
      "Epoch 1/20                                                                         \n",
      "\n",
      "16/16 - 3s - 172ms/step - accuracy: 0.1875 - loss: 1.9048 - val_accuracy: 0.1429 - val_loss: 1.8702\n",
      "\n",
      "Epoch 2/20                                                                         \n",
      "\n",
      "16/16 - 1s - 68ms/step - accuracy: 0.1875 - loss: 1.9041 - val_accuracy: 0.1429 - val_loss: 1.8703\n",
      "\n",
      "Epoch 3/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9038 - val_accuracy: 0.1429 - val_loss: 1.8698\n",
      "\n",
      "Epoch 4/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9034 - val_accuracy: 0.1429 - val_loss: 1.8691\n",
      "\n",
      "Epoch 5/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9037 - val_accuracy: 0.1429 - val_loss: 1.8689\n",
      "\n",
      "Epoch 6/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9036 - val_accuracy: 0.1429 - val_loss: 1.8686\n",
      "\n",
      "Epoch 7/20                                                                         \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1875 - loss: 1.9034 - val_accuracy: 0.1429 - val_loss: 1.8686\n",
      "\n",
      "Epoch 8/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9035 - val_accuracy: 0.1429 - val_loss: 1.8678\n",
      "\n",
      "Epoch 9/20                                                                         \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.1875 - loss: 1.9033 - val_accuracy: 0.1429 - val_loss: 1.8669\n",
      "\n",
      "Epoch 10/20                                                                        \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9033 - val_accuracy: 0.1429 - val_loss: 1.8660\n",
      "\n",
      "Epoch 11/20                                                                        \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9035 - val_accuracy: 0.1429 - val_loss: 1.8674\n",
      "\n",
      "Epoch 12/20                                                                        \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9032 - val_accuracy: 0.1429 - val_loss: 1.8663\n",
      "\n",
      "Epoch 13/20                                                                        \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9029 - val_accuracy: 0.1429 - val_loss: 1.8663\n",
      "\n",
      "Epoch 14/20                                                                        \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9029 - val_accuracy: 0.1429 - val_loss: 1.8665\n",
      "\n",
      "Epoch 15/20                                                                        \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9027 - val_accuracy: 0.1429 - val_loss: 1.8657\n",
      "\n",
      "Epoch 16/20                                                                        \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1875 - loss: 1.9026 - val_accuracy: 0.1429 - val_loss: 1.8658\n",
      "\n",
      "Epoch 17/20                                                                        \n",
      "\n",
      "16/16 - 1s - 65ms/step - accuracy: 0.1875 - loss: 1.9025 - val_accuracy: 0.1429 - val_loss: 1.8655\n",
      "\n",
      "Epoch 18/20                                                                        \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9032 - val_accuracy: 0.1429 - val_loss: 1.8651\n",
      "\n",
      "Epoch 19/20                                                                        \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9030 - val_accuracy: 0.1429 - val_loss: 1.8647\n",
      "\n",
      "Epoch 20/20                                                                        \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9025 - val_accuracy: 0.1429 - val_loss: 1.8642\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 447ms/step - accuracy: 0.1000 - loss: 1.9611\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 461ms/step - accuracy: 0.1000 - loss: 1.9611\n",
      "\n",
      "Epoch 1/20                                                                         \n",
      "\n",
      "16/16 - 3s - 168ms/step - accuracy: 0.1875 - loss: 1.9038 - val_accuracy: 0.1429 - val_loss: 1.8650\n",
      "\n",
      "Epoch 2/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1562 - loss: 1.9025 - val_accuracy: 0.1429 - val_loss: 1.8644\n",
      "\n",
      "Epoch 3/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9024 - val_accuracy: 0.1429 - val_loss: 1.8639\n",
      "\n",
      "Epoch 4/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9026 - val_accuracy: 0.1429 - val_loss: 1.8637\n",
      "\n",
      "Epoch 5/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9025 - val_accuracy: 0.1429 - val_loss: 1.8639\n",
      "\n",
      "Epoch 6/20                                                                         \n",
      "\n",
      "16/16 - 1s - 31ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.1429 - val_loss: 1.8634\n",
      "\n",
      "Epoch 7/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9024 - val_accuracy: 0.1429 - val_loss: 1.8635\n",
      "\n",
      "Epoch 8/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.1429 - val_loss: 1.8637\n",
      "\n",
      "Epoch 9/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9024 - val_accuracy: 0.1429 - val_loss: 1.8628\n",
      "\n",
      "Epoch 10/20                                                                        \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9025 - val_accuracy: 0.1429 - val_loss: 1.8630\n",
      "\n",
      "Epoch 11/20                                                                        \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9026 - val_accuracy: 0.1429 - val_loss: 1.8632\n",
      "\n",
      "Epoch 12/20                                                                        \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8629\n",
      "\n",
      "Epoch 13/20                                                                        \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1875 - loss: 1.9026 - val_accuracy: 0.1429 - val_loss: 1.8630\n",
      "\n",
      "Epoch 14/20                                                                        \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9026 - val_accuracy: 0.1429 - val_loss: 1.8628\n",
      "\n",
      "Epoch 15/20                                                                        \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8620\n",
      "\n",
      "Epoch 16/20                                                                        \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8619\n",
      "\n",
      "Epoch 17/20                                                                        \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1250 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8618\n",
      "\n",
      "Epoch 18/20                                                                        \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8616\n",
      "\n",
      "Epoch 19/20                                                                        \n",
      "\n",
      "16/16 - 1s - 75ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8614\n",
      "\n",
      "Epoch 20/20                                                                        \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8611\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 271ms/step - accuracy: 0.1000 - loss: 1.9635\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 286ms/step - accuracy: 0.1000 - loss: 1.9635\n",
      "\n",
      "Epoch 1/20                                                                         \n",
      "\n",
      "16/16 - 3s - 189ms/step - accuracy: 0.1875 - loss: 1.9045 - val_accuracy: 0.1429 - val_loss: 1.8591\n",
      "\n",
      "Epoch 2/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8593\n",
      "\n",
      "Epoch 3/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8589\n",
      "\n",
      "Epoch 4/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.1429 - val_loss: 1.8596\n",
      "\n",
      "Epoch 5/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8593\n",
      "\n",
      "Epoch 6/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8590\n",
      "\n",
      "Epoch 7/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.1429 - val_loss: 1.8590\n",
      "\n",
      "Epoch 8/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8592\n",
      "\n",
      "Epoch 9/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8593\n",
      "\n",
      "Epoch 10/20                                                                        \n",
      "\n",
      "16/16 - 1s - 79ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8589\n",
      "\n",
      "Epoch 11/20                                                                        \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8584\n",
      "\n",
      "Epoch 12/20                                                                        \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8585\n",
      "\n",
      "Epoch 13/20                                                                        \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8583\n",
      "\n",
      "Epoch 14/20                                                                        \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8586\n",
      "\n",
      "Epoch 15/20                                                                        \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8587\n",
      "\n",
      "Epoch 16/20                                                                        \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9025 - val_accuracy: 0.1429 - val_loss: 1.8578\n",
      "\n",
      "Epoch 17/20                                                                        \n",
      "\n",
      "16/16 - 1s - 51ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8578\n",
      "\n",
      "Epoch 18/20                                                                        \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8580\n",
      "\n",
      "Epoch 19/20                                                                        \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8576\n",
      "\n",
      "Epoch 20/20                                                                        \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8579\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 278ms/step - accuracy: 0.1000 - loss: 1.9651\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 291ms/step - accuracy: 0.1000 - loss: 1.9651\n",
      "\n",
      "Epoch 1/20                                                                         \n",
      "\n",
      "16/16 - 7s - 434ms/step - accuracy: 0.1875 - loss: 1.9029 - val_accuracy: 0.1429 - val_loss: 1.8582\n",
      "\n",
      "Epoch 2/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.1429 - val_loss: 1.8581\n",
      "\n",
      "Epoch 3/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8587\n",
      "\n",
      "Epoch 4/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8585\n",
      "\n",
      "Epoch 5/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8577\n",
      "\n",
      "Epoch 6/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8580\n",
      "\n",
      "Epoch 7/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.1429 - val_loss: 1.8578\n",
      "\n",
      "Epoch 8/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8568\n",
      "\n",
      "Epoch 9/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8579\n",
      "\n",
      "Epoch 10/20                                                                        \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8579\n",
      "\n",
      "Epoch 11/20                                                                        \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8578\n",
      "\n",
      "Epoch 12/20                                                                        \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8572\n",
      "\n",
      "Epoch 13/20                                                                        \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8579\n",
      "\n",
      "Epoch 14/20                                                                        \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8574\n",
      "\n",
      "Epoch 15/20                                                                        \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8579\n",
      "\n",
      "Epoch 16/20                                                                        \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8574\n",
      "\n",
      "Epoch 17/20                                                                        \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8576\n",
      "\n",
      "Epoch 18/20                                                                        \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8579\n",
      "\n",
      "Epoch 19/20                                                                        \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8575\n",
      "\n",
      "Epoch 20/20                                                                        \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8574\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 283ms/step - accuracy: 0.1000 - loss: 1.9653\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 296ms/step - accuracy: 0.1000 - loss: 1.9653\n",
      "\n",
      "Epoch 1/20                                                                         \n",
      "\n",
      "16/16 - 3s - 218ms/step - accuracy: 0.1875 - loss: 1.9041 - val_accuracy: 0.1429 - val_loss: 1.8574\n",
      "\n",
      "Epoch 2/20                                                                         \n",
      "\n",
      "16/16 - 1s - 65ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8577\n",
      "\n",
      "Epoch 3/20                                                                         \n",
      "\n",
      "16/16 - 1s - 84ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8573\n",
      "\n",
      "Epoch 4/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.1429 - val_loss: 1.8572\n",
      "\n",
      "Epoch 5/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8574\n",
      "\n",
      "Epoch 6/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8569\n",
      "\n",
      "Epoch 7/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8571\n",
      "\n",
      "Epoch 8/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9025 - val_accuracy: 0.1429 - val_loss: 1.8577\n",
      "\n",
      "Epoch 9/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8572\n",
      "\n",
      "Epoch 10/20                                                                        \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8572\n",
      "\n",
      "Epoch 11/20                                                                        \n",
      "\n",
      "16/16 - 1s - 62ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8563\n",
      "\n",
      "Epoch 12/20                                                                        \n",
      "\n",
      "16/16 - 1s - 74ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8562\n",
      "\n",
      "Epoch 13/20                                                                        \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8566\n",
      "\n",
      "Epoch 14/20                                                                        \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8562\n",
      "\n",
      "Epoch 15/20                                                                        \n",
      "\n",
      "16/16 - 1s - 76ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8570\n",
      "\n",
      "Epoch 16/20                                                                        \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.1875 - loss: 1.9025 - val_accuracy: 0.1429 - val_loss: 1.8563\n",
      "\n",
      "Epoch 17/20                                                                        \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9028 - val_accuracy: 0.1429 - val_loss: 1.8571\n",
      "\n",
      "Epoch 18/20                                                                        \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.1429 - val_loss: 1.8565\n",
      "\n",
      "Epoch 19/20                                                                        \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8567\n",
      "\n",
      "Epoch 20/20                                                                        \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8568\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 255ms/step - accuracy: 0.1000 - loss: 1.9668\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 268ms/step - accuracy: 0.1000 - loss: 1.9668\n",
      "\n",
      "Epoch 1/20                                                                         \n",
      "\n",
      "16/16 - 3s - 182ms/step - accuracy: 0.1875 - loss: 1.9035 - val_accuracy: 0.1429 - val_loss: 1.8576\n",
      "\n",
      "Epoch 2/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8572\n",
      "\n",
      "Epoch 3/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8571\n",
      "\n",
      "Epoch 4/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8575\n",
      "\n",
      "Epoch 5/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8571\n",
      "\n",
      "Epoch 6/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8573\n",
      "\n",
      "Epoch 7/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9026 - val_accuracy: 0.1429 - val_loss: 1.8572\n",
      "\n",
      "Epoch 8/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8571\n",
      "\n",
      "Epoch 9/20                                                                         \n",
      "\n",
      "16/16 - 1s - 58ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8566\n",
      "\n",
      "Epoch 10/20                                                                        \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8567\n",
      "\n",
      "Epoch 11/20                                                                        \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8568\n",
      "\n",
      "Epoch 12/20                                                                        \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8567\n",
      "\n",
      "Epoch 13/20                                                                        \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8560\n",
      "\n",
      "Epoch 14/20                                                                        \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8563\n",
      "\n",
      "Epoch 15/20                                                                        \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8565\n",
      "\n",
      "Epoch 16/20                                                                        \n",
      "\n",
      "16/16 - 1s - 52ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8568\n",
      "\n",
      "Epoch 17/20                                                                        \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8567\n",
      "\n",
      "Epoch 18/20                                                                        \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8567\n",
      "\n",
      "Epoch 19/20                                                                        \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1562 - loss: 1.9024 - val_accuracy: 0.1429 - val_loss: 1.8567\n",
      "\n",
      "Epoch 20/20                                                                        \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.1429 - val_loss: 1.8561\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 291ms/step - accuracy: 0.1000 - loss: 1.9666\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 304ms/step - accuracy: 0.1000 - loss: 1.9666\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 217ms/step - accuracy: 0.1875 - loss: 1.9039 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 64ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8547\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8556\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8553\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8550\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8552\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 59ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8552\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 75ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8555\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8556\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8557\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8553\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8556\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9024 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 80ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8554\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8554\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8553\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 292ms/step - accuracy: 0.1000 - loss: 1.9672\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 305ms/step - accuracy: 0.1000 - loss: 1.9672\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 173ms/step - accuracy: 0.1875 - loss: 1.9038 - val_accuracy: 0.1429 - val_loss: 1.8566\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8556\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8558\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8558\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8557\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8561\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8558\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 57ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8557\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8559\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8561\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8565\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8554\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8557\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8557\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8552\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8555\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8562\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8558\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8558\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.0938 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8554\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 264ms/step - accuracy: 0.1000 - loss: 1.9643\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 278ms/step - accuracy: 0.1000 - loss: 1.9643\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 212ms/step - accuracy: 0.1562 - loss: 1.9031 - val_accuracy: 0.2143 - val_loss: 1.8565\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8563\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1250 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8556\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8555\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8554\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8558\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8559\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8554\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8561\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 51ms/step - accuracy: 0.0312 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8557\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8553\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8556\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8553\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8553\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8561\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8552\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8556\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.0312 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8557\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1250 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8558\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1562 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8558\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 593ms/step - accuracy: 0.1000 - loss: 1.9638\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 607ms/step - accuracy: 0.1000 - loss: 1.9638\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 210ms/step - accuracy: 0.1250 - loss: 1.9035 - val_accuracy: 0.1429 - val_loss: 1.8530\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8532\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8536\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8537\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8535\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8534\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.0625 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1562 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8535\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8538\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.0938 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1562 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8536\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.0938 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8534\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 31ms/step - accuracy: 0.1250 - loss: 1.9024 - val_accuracy: 0.1429 - val_loss: 1.8540\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1250 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8534\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1562 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8538\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1250 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8535\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 316ms/step - accuracy: 0.1000 - loss: 1.9645\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 330ms/step - accuracy: 0.1000 - loss: 1.9645\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 5s - 306ms/step - accuracy: 0.0938 - loss: 1.9041 - val_accuracy: 0.2143 - val_loss: 1.8555\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8555\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 80ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8556\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8552\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 79ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8551\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.0938 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8553\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 83ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8553\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1250 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8556\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1562 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 65ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8549\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 272ms/step - accuracy: 0.0500 - loss: 1.9640\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 292ms/step - accuracy: 0.0500 - loss: 1.9640\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 171ms/step - accuracy: 0.1875 - loss: 1.9033 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8546\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 54ms/step - accuracy: 0.1875 - loss: 1.9025 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 81ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8551\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9025 - val_accuracy: 0.2143 - val_loss: 1.8545\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.0938 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8544\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8555\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1250 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8545\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 74ms/step - accuracy: 0.0625 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8551\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8539\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1562 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 425ms/step - accuracy: 0.0500 - loss: 1.9630\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 439ms/step - accuracy: 0.0500 - loss: 1.9630\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 191ms/step - accuracy: 0.1562 - loss: 1.9038 - val_accuracy: 0.1429 - val_loss: 1.8529\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 70ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8533\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8531\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 56ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8532\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 61ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8535\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8537\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8530\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8534\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8537\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8535\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8529\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9025 - val_accuracy: 0.1429 - val_loss: 1.8527\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 57ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8538\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8541\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.1429 - val_loss: 1.8534\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8542\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 74ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8533\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 411ms/step - accuracy: 0.1000 - loss: 1.9643\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 423ms/step - accuracy: 0.1000 - loss: 1.9643\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 198ms/step - accuracy: 0.1875 - loss: 1.9027 - val_accuracy: 0.1429 - val_loss: 1.8521\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8522\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8523\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8524\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 57ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8531\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8526\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9025 - val_accuracy: 0.1429 - val_loss: 1.8533\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8530\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8527\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8525\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8532\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8530\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8533\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8531\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8531\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8532\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8533\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.0625 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8532\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8533\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 31ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8529\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 251ms/step - accuracy: 0.1000 - loss: 1.9635\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 263ms/step - accuracy: 0.1000 - loss: 1.9635\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 4s - 227ms/step - accuracy: 0.1875 - loss: 1.9036 - val_accuracy: 0.1429 - val_loss: 1.8538\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 63ms/step - accuracy: 0.1875 - loss: 1.9033 - val_accuracy: 0.1429 - val_loss: 1.8537\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8532\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8536\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8538\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8532\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.1429 - val_loss: 1.8535\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8538\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8540\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8537\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.1429 - val_loss: 1.8540\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.1429 - val_loss: 1.8536\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8535\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9025 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 92ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8542\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8540\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 251ms/step - accuracy: 0.1000 - loss: 1.9648\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 267ms/step - accuracy: 0.1000 - loss: 1.9648\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 170ms/step - accuracy: 0.1875 - loss: 1.9027 - val_accuracy: 0.1429 - val_loss: 1.8552\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8552\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 58ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8552\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 57ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8552\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 51ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8547\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8547\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 59ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8545\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 256ms/step - accuracy: 0.1000 - loss: 1.9637\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 269ms/step - accuracy: 0.1000 - loss: 1.9637\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 170ms/step - accuracy: 0.1875 - loss: 1.9028 - val_accuracy: 0.1429 - val_loss: 1.8536\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.1429 - val_loss: 1.8537\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8536\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 52ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8541\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8538\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8538\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.1429 - val_loss: 1.8538\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8542\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8540\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 80ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8538\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 53ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8540\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8540\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 526ms/step - accuracy: 0.1000 - loss: 1.9649\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 539ms/step - accuracy: 0.1000 - loss: 1.9649\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 186ms/step - accuracy: 0.1875 - loss: 1.9036 - val_accuracy: 0.1429 - val_loss: 1.8515\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 73ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8527\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8522\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8525\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 57ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8531\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8533\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8528\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8529\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8523\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8524\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9024 - val_accuracy: 0.1429 - val_loss: 1.8531\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8527\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8533\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 51ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8532\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8527\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8533\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8528\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8536\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8533\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8536\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 364ms/step - accuracy: 0.1000 - loss: 1.9657\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 376ms/step - accuracy: 0.1000 - loss: 1.9657\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 175ms/step - accuracy: 0.1875 - loss: 1.9036 - val_accuracy: 0.1429 - val_loss: 1.8532\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8533\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8535\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 52ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8538\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8537\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8541\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8532\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 54ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8537\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8534\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8535\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8538\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8534\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8535\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8537\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8535\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.1429 - val_loss: 1.8540\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8536\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 55ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8542\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 266ms/step - accuracy: 0.1000 - loss: 1.9658\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 280ms/step - accuracy: 0.1000 - loss: 1.9658\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 187ms/step - accuracy: 0.1875 - loss: 1.9028 - val_accuracy: 0.1429 - val_loss: 1.8526\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 65ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8528\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 56ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.1429 - val_loss: 1.8531\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9024 - val_accuracy: 0.1429 - val_loss: 1.8533\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9025 - val_accuracy: 0.1429 - val_loss: 1.8530\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8528\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8535\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 57ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8528\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8529\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8538\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8537\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8532\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8542\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8534\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8540\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8534\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8541\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 80ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8528\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8534\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 401ms/step - accuracy: 0.1000 - loss: 1.9665\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 416ms/step - accuracy: 0.1000 - loss: 1.9665\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 174ms/step - accuracy: 0.0938 - loss: 1.9038 - val_accuracy: 0.2143 - val_loss: 1.8521\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1250 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8524\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9025 - val_accuracy: 0.1429 - val_loss: 1.8525\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 54ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8525\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.0938 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8528\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1250 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8527\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 58ms/step - accuracy: 0.1562 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8529\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 59ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8530\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9024 - val_accuracy: 0.1429 - val_loss: 1.8521\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8525\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1250 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8528\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8530\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1250 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8533\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1562 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8521\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8530\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1562 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8532\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 53ms/step - accuracy: 0.1562 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8530\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1250 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8530\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.0938 - loss: 1.9023 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 374ms/step - accuracy: 0.0500 - loss: 1.9662\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 390ms/step - accuracy: 0.0500 - loss: 1.9662\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 166ms/step - accuracy: 0.1875 - loss: 1.9029 - val_accuracy: 0.2143 - val_loss: 1.8530\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 72ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8531\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8530\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 58ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8534\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8532\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8526\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8530\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8532\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8533\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8531\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 53ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8534\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8532\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.0312 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8541\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 480ms/step - accuracy: 0.1000 - loss: 1.9642\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 491ms/step - accuracy: 0.1000 - loss: 1.9642\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 168ms/step - accuracy: 0.1875 - loss: 1.9030 - val_accuracy: 0.1429 - val_loss: 1.8560\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 76ms/step - accuracy: 0.1875 - loss: 1.9027 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8550\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8560\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 68ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.1429 - val_loss: 1.8555\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 56ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8552\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8558\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8553\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8552\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8550\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8553\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8552\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8550\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 57ms/step - accuracy: 0.0938 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8552\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 74ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8553\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8553\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8556\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8552\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 465ms/step - accuracy: 0.1000 - loss: 1.9638\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 480ms/step - accuracy: 0.1000 - loss: 1.9638\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 4s - 251ms/step - accuracy: 0.1875 - loss: 1.9029 - val_accuracy: 0.1429 - val_loss: 1.8545\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8544\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8544\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9026 - val_accuracy: 0.1429 - val_loss: 1.8540\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8545\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8540\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8550\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.1429 - val_loss: 1.8556\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9025 - val_accuracy: 0.1429 - val_loss: 1.8550\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8540\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.1429 - val_loss: 1.8542\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8547\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8550\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 262ms/step - accuracy: 0.1000 - loss: 1.9633\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 275ms/step - accuracy: 0.1000 - loss: 1.9633\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 4s - 225ms/step - accuracy: 0.1875 - loss: 1.9030 - val_accuracy: 0.1429 - val_loss: 1.8547\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 66ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8545\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8547\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.1429 - val_loss: 1.8545\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8547\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 64ms/step - accuracy: 0.1875 - loss: 1.9024 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8541\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.0625 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8540\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8547\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8547\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 446ms/step - accuracy: 0.1000 - loss: 1.9632\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 461ms/step - accuracy: 0.1000 - loss: 1.9632\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 4s - 220ms/step - accuracy: 0.1875 - loss: 1.9028 - val_accuracy: 0.1429 - val_loss: 1.8556\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 61ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8555\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8553\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8553\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1250 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8553\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.0938 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8557\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1562 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8561\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8556\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 52ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8555\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8554\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8555\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8558\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1250 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8550\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8555\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.0938 - loss: 1.9024 - val_accuracy: 0.2143 - val_loss: 1.8556\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.0938 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8558\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 526ms/step - accuracy: 0.1000 - loss: 1.9648\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 540ms/step - accuracy: 0.1000 - loss: 1.9648\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 214ms/step - accuracy: 0.1562 - loss: 1.9034 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 58ms/step - accuracy: 0.1875 - loss: 1.9025 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9026 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 82ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8539\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 52ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 52ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8546\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9025 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8545\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 63ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 352ms/step - accuracy: 0.0500 - loss: 1.9649\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 364ms/step - accuracy: 0.0500 - loss: 1.9649\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 211ms/step - accuracy: 0.1875 - loss: 1.9030 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8546\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8539\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 54ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 63ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8545\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 78ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8539\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 59ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8549\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8552\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 258ms/step - accuracy: 0.0500 - loss: 1.9644\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 272ms/step - accuracy: 0.0500 - loss: 1.9644\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 177ms/step - accuracy: 0.1875 - loss: 1.9039 - val_accuracy: 0.2143 - val_loss: 1.8581\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 69ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8574\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8571\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 57ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8570\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8571\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8569\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8573\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 73ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8574\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9025 - val_accuracy: 0.2143 - val_loss: 1.8574\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8565\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 54ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8573\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8573\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8568\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8564\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8571\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8563\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 57ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8566\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8567\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8572\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8565\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 389ms/step - accuracy: 0.0500 - loss: 1.9656\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 399ms/step - accuracy: 0.0500 - loss: 1.9656\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 203ms/step - accuracy: 0.1875 - loss: 1.9030 - val_accuracy: 0.2143 - val_loss: 1.8552\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 60ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8551\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8556\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8551\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 58ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8552\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 81ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.2143 - val_loss: 1.8555\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8549\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 80ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8552\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 53ms/step - accuracy: 0.1875 - loss: 1.9026 - val_accuracy: 0.2143 - val_loss: 1.8550\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8559\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8550\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8559\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8551\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 67ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8557\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 72ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8560\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8553\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 54ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8557\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8558\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8556\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8554\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 776ms/step - accuracy: 0.0500 - loss: 1.9650\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 789ms/step - accuracy: 0.0500 - loss: 1.9650\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 4s - 225ms/step - accuracy: 0.1875 - loss: 1.9037 - val_accuracy: 0.2143 - val_loss: 1.8531\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8530\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8536\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8541\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8545\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.0625 - loss: 1.9023 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8539\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8545\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 259ms/step - accuracy: 0.0500 - loss: 1.9644\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 272ms/step - accuracy: 0.0500 - loss: 1.9644\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 216ms/step - accuracy: 0.0938 - loss: 1.9030 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 65ms/step - accuracy: 0.0625 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1562 - loss: 1.9024 - val_accuracy: 0.1429 - val_loss: 1.8541\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 51ms/step - accuracy: 0.1250 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8539\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1250 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1250 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8545\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1250 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.0312 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 60ms/step - accuracy: 0.1562 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1562 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1562 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8547\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8544\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8544\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1562 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.0938 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8546\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1250 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8545\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 637ms/step - accuracy: 0.0500 - loss: 1.9650\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 652ms/step - accuracy: 0.0500 - loss: 1.9650\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 210ms/step - accuracy: 0.1875 - loss: 1.9037 - val_accuracy: 0.2143 - val_loss: 1.8555\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8551\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8554\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9026 - val_accuracy: 0.2143 - val_loss: 1.8554\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8554\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8546\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8550\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8557\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8554\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8554\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 60ms/step - accuracy: 0.0938 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8550\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8551\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 61ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8550\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.0938 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8553\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8550\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8551\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8552\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 554ms/step - accuracy: 0.0500 - loss: 1.9635\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 568ms/step - accuracy: 0.0500 - loss: 1.9635\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 4s - 226ms/step - accuracy: 0.1875 - loss: 1.9028 - val_accuracy: 0.2143 - val_loss: 1.8561\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 65ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8560\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8559\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8556\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8560\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8557\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.0938 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8557\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1250 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8556\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1562 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8555\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 56ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8551\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8554\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8553\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1250 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8554\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 60ms/step - accuracy: 0.0938 - loss: 1.9023 - val_accuracy: 0.1429 - val_loss: 1.8558\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1250 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8555\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8556\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8550\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8555\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.0938 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8559\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 53ms/step - accuracy: 0.1250 - loss: 1.9023 - val_accuracy: 0.2143 - val_loss: 1.8550\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 305ms/step - accuracy: 0.0500 - loss: 1.9635\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 317ms/step - accuracy: 0.0500 - loss: 1.9635\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 211ms/step - accuracy: 0.1875 - loss: 1.9036 - val_accuracy: 0.2143 - val_loss: 1.8556\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8561\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8557\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8564\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8559\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8558\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8551\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8560\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8559\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.2143 - val_loss: 1.8555\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 56ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8553\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8552\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.2143 - val_loss: 1.8557\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8554\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8554\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8557\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8559\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8554\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8558\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 57ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8553\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 287ms/step - accuracy: 0.0500 - loss: 1.9652\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 302ms/step - accuracy: 0.0500 - loss: 1.9652\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 4s - 239ms/step - accuracy: 0.0312 - loss: 1.9027 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 67ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8552\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8555\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.0938 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8553\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1562 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8549\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 54ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 76ms/step - accuracy: 0.1250 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8545\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.0938 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8554\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8557\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8556\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8557\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 52ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8553\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 83ms/step - accuracy: 0.1250 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8551\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8554\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 55ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8556\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.0625 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 316ms/step - accuracy: 0.1000 - loss: 1.9653\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 335ms/step - accuracy: 0.1000 - loss: 1.9653\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 187ms/step - accuracy: 0.1562 - loss: 1.9045 - val_accuracy: 0.2143 - val_loss: 1.8523\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 71ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8531\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8524\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8530\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8523\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 60ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8531\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 76ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9024 - val_accuracy: 0.2143 - val_loss: 1.8533\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8532\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8534\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8529\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8534\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8533\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 64ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8536\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 68ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8533\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8532\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8532\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8534\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 443ms/step - accuracy: 0.0500 - loss: 1.9650\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 456ms/step - accuracy: 0.0500 - loss: 1.9650\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 189ms/step - accuracy: 0.1562 - loss: 1.9034 - val_accuracy: 0.2143 - val_loss: 1.8530\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8531\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8527\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 57ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8526\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.0938 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8528\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8531\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.2143 - val_loss: 1.8533\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8530\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8533\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8532\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9024 - val_accuracy: 0.2143 - val_loss: 1.8531\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1250 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8534\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8534\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8541\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 258ms/step - accuracy: 0.0500 - loss: 1.9649\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 271ms/step - accuracy: 0.0500 - loss: 1.9649\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 4s - 250ms/step - accuracy: 0.0938 - loss: 1.9026 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 55ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1562 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8545\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 53ms/step - accuracy: 0.1562 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8538\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1562 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8541\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8539\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 31ms/step - accuracy: 0.0938 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8544\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 61ms/step - accuracy: 0.1250 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8538\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8542\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.0938 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.0625 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1250 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8552\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 79ms/step - accuracy: 0.1250 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.0938 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 51ms/step - accuracy: 0.1250 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8549\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 56ms/step - accuracy: 0.0938 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8547\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1250 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 429ms/step - accuracy: 0.0500 - loss: 1.9624\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 447ms/step - accuracy: 0.0500 - loss: 1.9624\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 175ms/step - accuracy: 0.1562 - loss: 1.9029 - val_accuracy: 0.1429 - val_loss: 1.8538\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 71ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8540\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8533\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 80ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8541\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8542\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8534\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8542\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8544\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 56ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8542\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8545\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8542\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8542\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8544\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 681ms/step - accuracy: 0.1000 - loss: 1.9643\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 702ms/step - accuracy: 0.1000 - loss: 1.9643\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 190ms/step - accuracy: 0.1562 - loss: 1.9038 - val_accuracy: 0.2143 - val_loss: 1.8527\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 50ms/step - accuracy: 0.1250 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8528\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8532\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 75ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8530\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.0625 - loss: 1.9025 - val_accuracy: 0.1429 - val_loss: 1.8540\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1562 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8539\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 79ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8533\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.0938 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 60ms/step - accuracy: 0.0938 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8532\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 67ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8541\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8533\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 50ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8539\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 79ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 58ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8539\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 75ms/step - accuracy: 0.0625 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8540\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8538\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.0938 - loss: 1.9025 - val_accuracy: 0.2143 - val_loss: 1.8533\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 265ms/step - accuracy: 0.0500 - loss: 1.9663\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 279ms/step - accuracy: 0.0500 - loss: 1.9663\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 182ms/step - accuracy: 0.1875 - loss: 1.9026 - val_accuracy: 0.2143 - val_loss: 1.8530\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 67ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8529\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8527\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 52ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.2143 - val_loss: 1.8528\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8532\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8530\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8529\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 54ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8532\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8531\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 80ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8533\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8532\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 53ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8533\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8536\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8530\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8531\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 55ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8533\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 76ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8536\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 64ms/step - accuracy: 0.0938 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8532\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8532\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 546ms/step - accuracy: 0.0500 - loss: 1.9638\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 556ms/step - accuracy: 0.0500 - loss: 1.9638\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 4s - 223ms/step - accuracy: 0.0938 - loss: 1.9032 - val_accuracy: 0.1429 - val_loss: 1.8511\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 58ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8517\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1562 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8516\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8519\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8518\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1250 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8516\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1562 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8517\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1562 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8517\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 51ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8511\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8522\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.0938 - loss: 1.9024 - val_accuracy: 0.1429 - val_loss: 1.8516\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 52ms/step - accuracy: 0.1562 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8522\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 70ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8524\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1562 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8522\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1250 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8522\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.0312 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8525\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1562 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8520\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8524\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8525\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8527\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 550ms/step - accuracy: 0.0500 - loss: 1.9638\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 565ms/step - accuracy: 0.0500 - loss: 1.9638\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 210ms/step - accuracy: 0.1875 - loss: 1.9034 - val_accuracy: 0.2143 - val_loss: 1.8515\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 58ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8518\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8523\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8523\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 51ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8521\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8517\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8519\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8519\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8520\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8518\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8519\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 51ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8523\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8518\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.0938 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8530\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 50ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8525\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.2143 - val_loss: 1.8525\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1562 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8525\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1562 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8522\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8525\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8522\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 264ms/step - accuracy: 0.0500 - loss: 1.9658\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 278ms/step - accuracy: 0.0500 - loss: 1.9658\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 215ms/step - accuracy: 0.1875 - loss: 1.9033 - val_accuracy: 0.2143 - val_loss: 1.8513\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 66ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8514\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8515\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8517\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8518\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8516\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8517\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8519\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8521\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8517\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8523\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8520\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8521\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8520\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1250 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8518\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8519\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.0625 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8528\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8526\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8522\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8526\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 292ms/step - accuracy: 0.0500 - loss: 1.9644\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 309ms/step - accuracy: 0.0500 - loss: 1.9644\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 218ms/step - accuracy: 0.1562 - loss: 1.9040 - val_accuracy: 0.1429 - val_loss: 1.8514\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 67ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8516\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8518\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1250 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8512\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 55ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8520\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 74ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8521\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1562 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8518\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 55ms/step - accuracy: 0.0938 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8517\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.2143 - val_loss: 1.8523\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8517\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8516\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8519\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8523\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8523\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8527\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.0938 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8526\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 54ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8527\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1250 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8523\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8527\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1250 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8526\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 268ms/step - accuracy: 0.0500 - loss: 1.9661\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 281ms/step - accuracy: 0.0500 - loss: 1.9661\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 185ms/step - accuracy: 0.1562 - loss: 1.9035 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 67ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8528\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 53ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8534\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8531\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8535\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1250 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8530\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.2188 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8537\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8534\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 60ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8537\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8535\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8542\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8535\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 54ms/step - accuracy: 0.0938 - loss: 1.9023 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8541\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1562 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8542\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8535\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.0625 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8532\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 59ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8541\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1250 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8540\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8535\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 390ms/step - accuracy: 0.1000 - loss: 1.9656\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 403ms/step - accuracy: 0.1000 - loss: 1.9656\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 170ms/step - accuracy: 0.1875 - loss: 1.9035 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 73ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8542\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 80ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8542\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 52ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8545\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 73ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8545\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8544\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8542\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8544\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8544\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8547\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8552\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 68ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9029 - val_accuracy: 0.1429 - val_loss: 1.8550\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 85ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8544\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8550\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8545\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8547\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 456ms/step - accuracy: 0.1000 - loss: 1.9657\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 473ms/step - accuracy: 0.1000 - loss: 1.9657\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 214ms/step - accuracy: 0.1875 - loss: 1.9025 - val_accuracy: 0.1429 - val_loss: 1.8553\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 63ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8545\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8554\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.1429 - val_loss: 1.8550\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8545\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8552\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 53ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8547\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 52ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 63ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.1429 - val_loss: 1.8547\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8552\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 653ms/step - accuracy: 0.1000 - loss: 1.9644\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 666ms/step - accuracy: 0.1000 - loss: 1.9644\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 193ms/step - accuracy: 0.1875 - loss: 1.9031 - val_accuracy: 0.1429 - val_loss: 1.8552\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8552\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 55ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8552\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8554\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 51ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8560\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.1429 - val_loss: 1.8554\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 56ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8558\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8554\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8553\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 55ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8558\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8555\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8553\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1250 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 65ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 407ms/step - accuracy: 0.1000 - loss: 1.9640\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 419ms/step - accuracy: 0.1000 - loss: 1.9640\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 194ms/step - accuracy: 0.1875 - loss: 1.9033 - val_accuracy: 0.1429 - val_loss: 1.8537\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8534\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 71ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8537\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8536\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8540\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8534\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8538\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8538\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.1429 - val_loss: 1.8542\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 70ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8540\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 50ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8541\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8540\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8542\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 52ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8544\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8541\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8542\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 686ms/step - accuracy: 0.1000 - loss: 1.9634\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 702ms/step - accuracy: 0.1000 - loss: 1.9634\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 204ms/step - accuracy: 0.1875 - loss: 1.9048 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 63ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8541\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 88ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8540\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 82ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8540\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 75ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8535\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8538\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 71ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8545\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8545\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8542\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8547\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8544\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8544\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8545\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 56ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 63ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8547\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8545\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 52ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8541\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 434ms/step - accuracy: 0.1000 - loss: 1.9638\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 445ms/step - accuracy: 0.1000 - loss: 1.9638\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 182ms/step - accuracy: 0.1875 - loss: 1.9045 - val_accuracy: 0.1429 - val_loss: 1.8557\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 68ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8554\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8553\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 80ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8550\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 56ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8557\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8552\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8550\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8547\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.0938 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8552\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 74ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8555\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8555\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.0938 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8552\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 55ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 85ms/step - accuracy: 0.1250 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 359ms/step - accuracy: 0.0500 - loss: 1.9616\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 373ms/step - accuracy: 0.0500 - loss: 1.9616\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 4s - 241ms/step - accuracy: 0.1250 - loss: 1.9029 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 52ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.1429 - val_loss: 1.8541\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 87ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8544\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8550\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8545\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 66ms/step - accuracy: 0.1875 - loss: 1.9024 - val_accuracy: 0.1429 - val_loss: 1.8544\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 70ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.1429 - val_loss: 1.8545\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8550\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 53ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 59ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 263ms/step - accuracy: 0.1000 - loss: 1.9620\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 278ms/step - accuracy: 0.1000 - loss: 1.9620\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 200ms/step - accuracy: 0.1875 - loss: 1.9027 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8550\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8545\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8544\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 70ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8554\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8550\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8547\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 62ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8550\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 266ms/step - accuracy: 0.1000 - loss: 1.9616\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 278ms/step - accuracy: 0.1000 - loss: 1.9616\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 211ms/step - accuracy: 0.0625 - loss: 1.9033 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 55ms/step - accuracy: 0.1250 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1562 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.0938 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 63ms/step - accuracy: 0.1250 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8545\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 68ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8544\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9013 - val_accuracy: 0.1429 - val_loss: 1.8545\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8541\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8544\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 50ms/step - accuracy: 0.0938 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.0625 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8542\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1250 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8546\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 58ms/step - accuracy: 0.1562 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 70ms/step - accuracy: 0.0625 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8541\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 254ms/step - accuracy: 0.0500 - loss: 1.9627\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 268ms/step - accuracy: 0.0500 - loss: 1.9627\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 186ms/step - accuracy: 0.1562 - loss: 1.9032 - val_accuracy: 0.1429 - val_loss: 1.8538\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 70ms/step - accuracy: 0.1875 - loss: 1.9013 - val_accuracy: 0.1429 - val_loss: 1.8536\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8542\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 51ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8538\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8537\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8538\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 61ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8540\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8540\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8540\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 51ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8540\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8540\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 65ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8540\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 406ms/step - accuracy: 0.1000 - loss: 1.9621\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 421ms/step - accuracy: 0.1000 - loss: 1.9621\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 178ms/step - accuracy: 0.1562 - loss: 1.9029 - val_accuracy: 0.1429 - val_loss: 1.8511\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 50ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8520\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9026 - val_accuracy: 0.1429 - val_loss: 1.8520\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 60ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8524\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.0938 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8520\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8525\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 60ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8521\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.0938 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8518\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1562 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8525\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.0938 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8517\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 52ms/step - accuracy: 0.1562 - loss: 1.9014 - val_accuracy: 0.1429 - val_loss: 1.8523\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 77ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8524\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.0938 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8528\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8533\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 50ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8528\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8526\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8527\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8528\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 53ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8526\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 74ms/step - accuracy: 0.1250 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8530\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 462ms/step - accuracy: 0.0500 - loss: 1.9629\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 476ms/step - accuracy: 0.0500 - loss: 1.9629\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 190ms/step - accuracy: 0.1875 - loss: 1.9028 - val_accuracy: 0.2143 - val_loss: 1.8545\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1250 - loss: 1.9026 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 57ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8541\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 76ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8536\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1250 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 65ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 70ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.0938 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8544\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 58ms/step - accuracy: 0.1250 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8544\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8547\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1562 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8550\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 761ms/step - accuracy: 0.0500 - loss: 1.9627\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 778ms/step - accuracy: 0.0500 - loss: 1.9627\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 219ms/step - accuracy: 0.1875 - loss: 1.9028 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 65ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8545\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8541\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8539\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 64ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8541\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 78ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8546\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8551\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 82ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 346ms/step - accuracy: 0.0500 - loss: 1.9633\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 365ms/step - accuracy: 0.0500 - loss: 1.9633\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 205ms/step - accuracy: 0.1875 - loss: 1.9026 - val_accuracy: 0.2143 - val_loss: 1.8553\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 64ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8552\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.2143 - val_loss: 1.8554\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1250 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1562 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8553\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 60ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8554\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8554\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8550\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 0s - 31ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8553\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.0938 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8552\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8550\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 55ms/step - accuracy: 0.1250 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8551\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 0s - 31ms/step - accuracy: 0.1250 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8554\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 31ms/step - accuracy: 0.1250 - loss: 1.9014 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 62ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8546\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1250 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8550\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1250 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8552\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.0938 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 465ms/step - accuracy: 0.1000 - loss: 1.9649\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 476ms/step - accuracy: 0.1000 - loss: 1.9649\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 158ms/step - accuracy: 0.1250 - loss: 1.9034 - val_accuracy: 0.2143 - val_loss: 1.8541\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 74ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 50ms/step - accuracy: 0.1562 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8540\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.0938 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8539\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 61ms/step - accuracy: 0.1562 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1250 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8536\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1562 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8536\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 0s - 31ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 51ms/step - accuracy: 0.1562 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8534\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1250 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1250 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8540\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 0s - 30ms/step - accuracy: 0.1562 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 0s - 30ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 0s - 30ms/step - accuracy: 0.1562 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 52ms/step - accuracy: 0.1250 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1250 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8545\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1250 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1562 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 250ms/step - accuracy: 0.0500 - loss: 1.9642\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 262ms/step - accuracy: 0.0500 - loss: 1.9642\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 165ms/step - accuracy: 0.1250 - loss: 1.9031 - val_accuracy: 0.2143 - val_loss: 1.8541\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8539\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.0938 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8544\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1250 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8541\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8546\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 0s - 30ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 60ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8545\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8541\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1562 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8542\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1562 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8545\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 0s - 30ms/step - accuracy: 0.0938 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8546\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 62ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 313ms/step - accuracy: 0.0500 - loss: 1.9637\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 323ms/step - accuracy: 0.0500 - loss: 1.9637\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 182ms/step - accuracy: 0.1562 - loss: 1.9028 - val_accuracy: 0.1429 - val_loss: 1.8545\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8545\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 52ms/step - accuracy: 0.0625 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8542\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8544\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 31ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 0s - 30ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8542\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 0s - 30ms/step - accuracy: 0.1250 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8546\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 53ms/step - accuracy: 0.1250 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8550\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 65ms/step - accuracy: 0.0938 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8545\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1562 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8547\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8544\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8550\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 59ms/step - accuracy: 0.0938 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8545\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1250 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8541\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 0s - 31ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8538\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 409ms/step - accuracy: 0.1000 - loss: 1.9641\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 423ms/step - accuracy: 0.1000 - loss: 1.9641\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 2s - 156ms/step - accuracy: 0.1562 - loss: 1.9026 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.0938 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 62ms/step - accuracy: 0.1562 - loss: 1.9026 - val_accuracy: 0.1429 - val_loss: 1.8542\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8540\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 0s - 31ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8537\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 60ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8541\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 67ms/step - accuracy: 0.1250 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8539\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8541\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.0938 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 54ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.2143 - val_loss: 1.8539\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1562 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1562 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.0938 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8545\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 57ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8542\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8538\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 287ms/step - accuracy: 0.1000 - loss: 1.9640\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 300ms/step - accuracy: 0.1000 - loss: 1.9640\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 4s - 252ms/step - accuracy: 0.1562 - loss: 1.9031 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 0s - 30ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 0s - 30ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 0s - 30ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 59ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8533\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 69ms/step - accuracy: 0.1250 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8534\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1250 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 0s - 30ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8536\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 0s - 31ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8536\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.0938 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 0s - 31ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.2143 - val_loss: 1.8536\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.0625 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 0s - 31ms/step - accuracy: 0.0938 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8539\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 61ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8541\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 367ms/step - accuracy: 0.0500 - loss: 1.9643\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 380ms/step - accuracy: 0.0500 - loss: 1.9643\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 5s - 300ms/step - accuracy: 0.1875 - loss: 1.9026 - val_accuracy: 0.2143 - val_loss: 1.8527\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 74ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 76ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8525\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8527\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8533\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8533\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8533\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.2143 - val_loss: 1.8532\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 64ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8536\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 76ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8531\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8539\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 57ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8536\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8534\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 51ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8534\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 64ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8533\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8530\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 461ms/step - accuracy: 0.0500 - loss: 1.9646\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 471ms/step - accuracy: 0.0500 - loss: 1.9646\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 176ms/step - accuracy: 0.1875 - loss: 1.9032 - val_accuracy: 0.2143 - val_loss: 1.8545\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 51ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8539\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 56ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8541\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 62ms/step - accuracy: 0.1562 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8546\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8546\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8545\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 51ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 70ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.0312 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8546\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 61ms/step - accuracy: 0.1250 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8547\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.2188 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.2143 - val_loss: 1.8545\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8549\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 461ms/step - accuracy: 0.0500 - loss: 1.9656\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 473ms/step - accuracy: 0.0500 - loss: 1.9656\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 173ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8546\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 51ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8550\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 74ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8545\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 60ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 51ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8545\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8539\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9024 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8551\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 52ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8549\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 57ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8551\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 58ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8554\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 72ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8546\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 546ms/step - accuracy: 0.0500 - loss: 1.9655\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 559ms/step - accuracy: 0.0500 - loss: 1.9655\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 209ms/step - accuracy: 0.1875 - loss: 1.9039 - val_accuracy: 0.2143 - val_loss: 1.8523\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 60ms/step - accuracy: 0.1875 - loss: 1.9024 - val_accuracy: 0.2143 - val_loss: 1.8527\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8528\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8533\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8527\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 56ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8528\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8532\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 58ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8534\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8530\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8532\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 57ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8533\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8530\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 51ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8531\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 58ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8536\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8530\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 58ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8534\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 346ms/step - accuracy: 0.0500 - loss: 1.9645\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 359ms/step - accuracy: 0.0500 - loss: 1.9645\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 212ms/step - accuracy: 0.1250 - loss: 1.9036 - val_accuracy: 0.1429 - val_loss: 1.8526\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.0938 - loss: 1.9029 - val_accuracy: 0.1429 - val_loss: 1.8534\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8531\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1250 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8530\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8528\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 73ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8530\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8531\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8529\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 65ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8532\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1250 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8534\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8533\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1562 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8536\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.0938 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8535\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 74ms/step - accuracy: 0.0312 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8532\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.0938 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8538\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8537\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 55ms/step - accuracy: 0.1250 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.0938 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8539\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1250 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8538\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1562 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8533\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 465ms/step - accuracy: 0.0500 - loss: 1.9641\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 477ms/step - accuracy: 0.0500 - loss: 1.9641\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 182ms/step - accuracy: 0.1562 - loss: 1.9027 - val_accuracy: 0.1429 - val_loss: 1.8531\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.1429 - val_loss: 1.8536\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 53ms/step - accuracy: 0.0625 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8527\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1250 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8532\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8534\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8532\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1250 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8531\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 70ms/step - accuracy: 0.1250 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8535\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 71ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8533\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8530\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 52ms/step - accuracy: 0.0625 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8529\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 72ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8529\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.0938 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8533\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 61ms/step - accuracy: 0.1562 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8535\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.0625 - loss: 1.9026 - val_accuracy: 0.2143 - val_loss: 1.8534\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1562 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8536\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8537\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8536\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1250 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 53ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8539\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 365ms/step - accuracy: 0.0500 - loss: 1.9620\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 375ms/step - accuracy: 0.0500 - loss: 1.9620\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 181ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8530\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8531\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 63ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8536\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8536\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8533\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8534\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8532\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8527\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 86ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8531\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8532\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 64ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8531\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 75ms/step - accuracy: 0.0938 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8533\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1250 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8536\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8534\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 55ms/step - accuracy: 0.0625 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8534\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 437ms/step - accuracy: 0.1000 - loss: 1.9624\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 450ms/step - accuracy: 0.1000 - loss: 1.9624\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 194ms/step - accuracy: 0.1562 - loss: 1.9029 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8545\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 56ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8539\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8536\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 78ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8541\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 59ms/step - accuracy: 0.0625 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8542\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 76ms/step - accuracy: 0.1250 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 51ms/step - accuracy: 0.0938 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8541\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 51ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1562 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 702ms/step - accuracy: 0.0500 - loss: 1.9640\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 711ms/step - accuracy: 0.0500 - loss: 1.9640\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 197ms/step - accuracy: 0.1875 - loss: 1.9031 - val_accuracy: 0.2143 - val_loss: 1.8539\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 69ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8545\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8536\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 53ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8539\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 85ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8545\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8545\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 660ms/step - accuracy: 0.0500 - loss: 1.9635\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 679ms/step - accuracy: 0.0500 - loss: 1.9635\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 4s - 253ms/step - accuracy: 0.1875 - loss: 1.9034 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 56ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 55ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8546\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8541\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 56ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8539\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 54ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8541\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8545\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 79ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8545\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 266ms/step - accuracy: 0.0500 - loss: 1.9635\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 278ms/step - accuracy: 0.0500 - loss: 1.9635\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 186ms/step - accuracy: 0.1875 - loss: 1.9025 - val_accuracy: 0.2143 - val_loss: 1.8524\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8521\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 55ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8523\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 50ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8525\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8526\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8527\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8529\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 59ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8528\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 70ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8534\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8527\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8534\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 53ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8529\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8532\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9012 - val_accuracy: 0.2143 - val_loss: 1.8532\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 68ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8534\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 51ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8530\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8525\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 475ms/step - accuracy: 0.0500 - loss: 1.9641\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 486ms/step - accuracy: 0.0500 - loss: 1.9641\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 193ms/step - accuracy: 0.1875 - loss: 1.9031 - val_accuracy: 0.2143 - val_loss: 1.8530\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 70ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8526\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8532\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8532\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8527\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 62ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8529\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8532\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8529\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 55ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8525\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8530\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 52ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8528\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.2143 - val_loss: 1.8531\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8530\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8531\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 57ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8534\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8529\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8536\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 53ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8530\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8532\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 345ms/step - accuracy: 0.0500 - loss: 1.9647\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 354ms/step - accuracy: 0.0500 - loss: 1.9647\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 171ms/step - accuracy: 0.1875 - loss: 1.9034 - val_accuracy: 0.2143 - val_loss: 1.8528\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8522\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 55ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8526\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8529\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8526\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8528\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.2143 - val_loss: 1.8528\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 70ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8526\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8527\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 55ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8524\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8532\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8533\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8528\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8533\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8529\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8534\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 58ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8534\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 70ms/step - accuracy: 0.1875 - loss: 1.9013 - val_accuracy: 0.2143 - val_loss: 1.8534\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 528ms/step - accuracy: 0.0500 - loss: 1.9633\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 540ms/step - accuracy: 0.0500 - loss: 1.9633\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 177ms/step - accuracy: 0.1875 - loss: 1.9026 - val_accuracy: 0.2143 - val_loss: 1.8530\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8525\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 57ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8525\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8531\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8530\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8533\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8531\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 64ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8528\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8538\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8536\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8533\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 64ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 52ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 304ms/step - accuracy: 0.0500 - loss: 1.9635\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 319ms/step - accuracy: 0.0500 - loss: 1.9635\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 175ms/step - accuracy: 0.0625 - loss: 1.9033 - val_accuracy: 0.2143 - val_loss: 1.8559\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8550\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 74ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8553\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 51ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8553\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8550\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 77ms/step - accuracy: 0.0938 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8551\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9013 - val_accuracy: 0.2143 - val_loss: 1.8550\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8546\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1250 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8553\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 62ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8550\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 72ms/step - accuracy: 0.0625 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8553\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1250 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8558\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.1562 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8553\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1250 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8554\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8553\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8550\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 59ms/step - accuracy: 0.0625 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8553\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 344ms/step - accuracy: 0.1000 - loss: 1.9638\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 353ms/step - accuracy: 0.1000 - loss: 1.9638\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 199ms/step - accuracy: 0.1250 - loss: 1.9025 - val_accuracy: 0.2143 - val_loss: 1.8552\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 53ms/step - accuracy: 0.1250 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1562 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8552\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8552\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8552\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8551\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8551\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 84ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8552\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1250 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8552\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 60ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8550\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8553\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.2143 - val_loss: 1.8550\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8553\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 66ms/step - accuracy: 0.0938 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 60ms/step - accuracy: 0.0938 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8552\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.2143 - val_loss: 1.8553\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 524ms/step - accuracy: 0.0500 - loss: 1.9634\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 535ms/step - accuracy: 0.0500 - loss: 1.9634\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 179ms/step - accuracy: 0.1562 - loss: 1.9036 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 53ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8550\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 69ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8544\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 70ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 67ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8545\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8545\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 77ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9024 - val_accuracy: 0.1429 - val_loss: 1.8552\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 58ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8547\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 74ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8544\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 50ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.0938 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8546\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 858ms/step - accuracy: 0.1000 - loss: 1.9611\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 876ms/step - accuracy: 0.1000 - loss: 1.9611\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 198ms/step - accuracy: 0.1875 - loss: 1.9025 - val_accuracy: 0.1429 - val_loss: 1.8563\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 50ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8556\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 63ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8560\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8554\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8558\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.1429 - val_loss: 1.8559\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.0938 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 61ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8557\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 69ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8557\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8556\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 53ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8550\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 70ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8547\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 57ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8554\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8561\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 57ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8558\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 76ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8559\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8554\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8555\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 69ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8553\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 72ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8556\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 619ms/step - accuracy: 0.1000 - loss: 1.9626\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 640ms/step - accuracy: 0.1000 - loss: 1.9626\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 7s - 430ms/step - accuracy: 0.1875 - loss: 1.9024 - val_accuracy: 0.1429 - val_loss: 1.8574\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 63ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8575\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8571\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8570\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8568\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8567\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8570\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8567\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8564\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8562\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8564\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8564\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.1429 - val_loss: 1.8565\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8563\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8566\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8565\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8564\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.1429 - val_loss: 1.8560\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8566\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8567\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 266ms/step - accuracy: 0.1000 - loss: 1.9626\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 278ms/step - accuracy: 0.1000 - loss: 1.9626\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 4s - 225ms/step - accuracy: 0.1875 - loss: 1.9029 - val_accuracy: 0.1429 - val_loss: 1.8581\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 66ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8576\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.1429 - val_loss: 1.8580\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8578\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8583\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8581\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 52ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8580\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8575\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8573\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8574\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8573\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8575\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8577\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8573\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8569\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 55ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8575\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8573\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8570\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8571\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8568\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 273ms/step - accuracy: 0.1000 - loss: 1.9637\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 285ms/step - accuracy: 0.1000 - loss: 1.9637\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 213ms/step - accuracy: 0.1875 - loss: 1.9036 - val_accuracy: 0.1429 - val_loss: 1.8556\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8553\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8558\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8556\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8560\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8559\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8557\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8557\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8560\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8550\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1562 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8554\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8554\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8559\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8555\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8552\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8561\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8557\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8558\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8557\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 271ms/step - accuracy: 0.1000 - loss: 1.9629\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 283ms/step - accuracy: 0.1000 - loss: 1.9629\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 201ms/step - accuracy: 0.1875 - loss: 1.9028 - val_accuracy: 0.1429 - val_loss: 1.8564\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8564\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9025 - val_accuracy: 0.1429 - val_loss: 1.8556\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8560\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 62ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8558\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 71ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8563\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8564\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8563\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8564\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 61ms/step - accuracy: 0.1250 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8556\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8559\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8564\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8558\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8561\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 58ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.1429 - val_loss: 1.8560\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 51ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8560\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8552\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 87ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8559\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 76ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8556\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8558\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 673ms/step - accuracy: 0.1000 - loss: 1.9627\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 688ms/step - accuracy: 0.1000 - loss: 1.9627\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 214ms/step - accuracy: 0.1875 - loss: 1.9040 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 62ms/step - accuracy: 0.1250 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8549\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1250 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8549\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1562 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8552\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8547\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8548\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8552\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 74ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8553\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 68ms/step - accuracy: 0.1250 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8553\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1250 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1562 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8553\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 51ms/step - accuracy: 0.1562 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8553\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.1562 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8552\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8555\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 598ms/step - accuracy: 0.1000 - loss: 1.9652\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 612ms/step - accuracy: 0.1000 - loss: 1.9652\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 213ms/step - accuracy: 0.1562 - loss: 1.9035 - val_accuracy: 0.2143 - val_loss: 1.8551\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8551\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.0938 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8554\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1562 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8552\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1250 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8555\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1250 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8551\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1562 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.0625 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8553\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8554\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8552\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 55ms/step - accuracy: 0.0938 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8550\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.1250 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8550\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1562 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8551\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1562 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8552\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1562 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8551\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 57ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8551\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 72ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8553\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8553\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 458ms/step - accuracy: 0.0500 - loss: 1.9629\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 469ms/step - accuracy: 0.0500 - loss: 1.9629\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 172ms/step - accuracy: 0.1875 - loss: 1.9029 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 72ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9023 - val_accuracy: 0.2143 - val_loss: 1.8532\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8535\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 64ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8539\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 76ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8537\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8545\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 60ms/step - accuracy: 0.0938 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8541\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 62ms/step - accuracy: 0.1875 - loss: 1.9025 - val_accuracy: 0.2143 - val_loss: 1.8541\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.2143 - val_loss: 1.8541\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9024 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 571ms/step - accuracy: 0.0500 - loss: 1.9620\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 582ms/step - accuracy: 0.0500 - loss: 1.9620\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 189ms/step - accuracy: 0.1875 - loss: 1.9027 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 87ms/step - accuracy: 0.1562 - loss: 1.9023 - val_accuracy: 0.2143 - val_loss: 1.8550\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 50ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.2143 - val_loss: 1.8546\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8546\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 54ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8546\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8550\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 63ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8545\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.2143 - val_loss: 1.8547\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8549\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8551\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8545\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.2143 - val_loss: 1.8549\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 51ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8550\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 608ms/step - accuracy: 0.0500 - loss: 1.9641\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 621ms/step - accuracy: 0.0500 - loss: 1.9641\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 195ms/step - accuracy: 0.0938 - loss: 1.9034 - val_accuracy: 0.1429 - val_loss: 1.8560\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1562 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8555\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 56ms/step - accuracy: 0.1250 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8557\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8556\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1250 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8558\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8554\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1250 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8555\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1562 - loss: 1.9014 - val_accuracy: 0.2143 - val_loss: 1.8555\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 52ms/step - accuracy: 0.1562 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8553\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1562 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8550\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8555\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 32ms/step - accuracy: 0.1250 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8556\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.0938 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8553\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 62ms/step - accuracy: 0.0938 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8557\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 66ms/step - accuracy: 0.1562 - loss: 1.9014 - val_accuracy: 0.1429 - val_loss: 1.8559\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8557\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 51ms/step - accuracy: 0.1562 - loss: 1.9013 - val_accuracy: 0.2143 - val_loss: 1.8554\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8560\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.0938 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8555\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1875 - loss: 1.9014 - val_accuracy: 0.2143 - val_loss: 1.8552\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 342ms/step - accuracy: 0.0500 - loss: 1.9648\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 352ms/step - accuracy: 0.0500 - loss: 1.9648\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 187ms/step - accuracy: 0.1250 - loss: 1.9040 - val_accuracy: 0.1429 - val_loss: 1.8544\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.0625 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.2143 - val_loss: 1.8536\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 50ms/step - accuracy: 0.1250 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8544\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1562 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8542\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8539\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 72ms/step - accuracy: 0.1562 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8545\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1562 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8544\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1250 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8543\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 61ms/step - accuracy: 0.1562 - loss: 1.9021 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 40ms/step - accuracy: 0.1562 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8542\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8548\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 60ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8543\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8539\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8540\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.1250 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8550\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 41ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8550\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8550\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 53ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8549\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 273ms/step - accuracy: 0.1000 - loss: 1.9632\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 287ms/step - accuracy: 0.1000 - loss: 1.9632\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 4s - 220ms/step - accuracy: 0.1250 - loss: 1.9030 - val_accuracy: 0.1429 - val_loss: 1.8559\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8557\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.0625 - loss: 1.9020 - val_accuracy: 0.2143 - val_loss: 1.8551\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8550\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1562 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8557\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1562 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8555\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 46ms/step - accuracy: 0.1562 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8555\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 71ms/step - accuracy: 0.1562 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8551\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.0938 - loss: 1.9023 - val_accuracy: 0.2143 - val_loss: 1.8555\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.2143 - val_loss: 1.8555\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 45ms/step - accuracy: 0.1562 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8554\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 35ms/step - accuracy: 0.1250 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8556\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.0938 - loss: 1.9017 - val_accuracy: 0.2143 - val_loss: 1.8554\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1562 - loss: 1.9015 - val_accuracy: 0.2143 - val_loss: 1.8549\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1562 - loss: 1.9014 - val_accuracy: 0.1429 - val_loss: 1.8553\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 53ms/step - accuracy: 0.1250 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8553\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8559\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 36ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8555\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8557\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 47ms/step - accuracy: 0.0938 - loss: 1.9016 - val_accuracy: 0.2143 - val_loss: 1.8554\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 268ms/step - accuracy: 0.0500 - loss: 1.9634\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 282ms/step - accuracy: 0.0500 - loss: 1.9634\n",
      "\n",
      "Epoch 1/20                                                                          \n",
      "\n",
      "16/16 - 3s - 206ms/step - accuracy: 0.1250 - loss: 1.9036 - val_accuracy: 0.1429 - val_loss: 1.8556\n",
      "\n",
      "Epoch 2/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9018 - val_accuracy: 0.1429 - val_loss: 1.8559\n",
      "\n",
      "Epoch 3/20                                                                          \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8558\n",
      "\n",
      "Epoch 4/20                                                                          \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8563\n",
      "\n",
      "Epoch 5/20                                                                          \n",
      "\n",
      "16/16 - 1s - 69ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8561\n",
      "\n",
      "Epoch 6/20                                                                          \n",
      "\n",
      "16/16 - 1s - 65ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8555\n",
      "\n",
      "Epoch 7/20                                                                          \n",
      "\n",
      "16/16 - 1s - 48ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8557\n",
      "\n",
      "Epoch 8/20                                                                          \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9015 - val_accuracy: 0.1429 - val_loss: 1.8554\n",
      "\n",
      "Epoch 9/20                                                                          \n",
      "\n",
      "16/16 - 1s - 37ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8553\n",
      "\n",
      "Epoch 10/20                                                                         \n",
      "\n",
      "16/16 - 1s - 44ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8560\n",
      "\n",
      "Epoch 11/20                                                                         \n",
      "\n",
      "16/16 - 1s - 43ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8562\n",
      "\n",
      "Epoch 12/20                                                                         \n",
      "\n",
      "16/16 - 1s - 53ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8560\n",
      "\n",
      "Epoch 13/20                                                                         \n",
      "\n",
      "16/16 - 1s - 39ms/step - accuracy: 0.1875 - loss: 1.9017 - val_accuracy: 0.1429 - val_loss: 1.8561\n",
      "\n",
      "Epoch 14/20                                                                         \n",
      "\n",
      "16/16 - 1s - 38ms/step - accuracy: 0.1875 - loss: 1.9022 - val_accuracy: 0.1429 - val_loss: 1.8560\n",
      "\n",
      "Epoch 15/20                                                                         \n",
      "\n",
      "16/16 - 1s - 33ms/step - accuracy: 0.1875 - loss: 1.9021 - val_accuracy: 0.1429 - val_loss: 1.8555\n",
      "\n",
      "Epoch 16/20                                                                         \n",
      "\n",
      "16/16 - 1s - 34ms/step - accuracy: 0.1875 - loss: 1.9016 - val_accuracy: 0.1429 - val_loss: 1.8559\n",
      "\n",
      "Epoch 17/20                                                                         \n",
      "\n",
      "16/16 - 1s - 74ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8556\n",
      "\n",
      "Epoch 18/20                                                                         \n",
      "\n",
      "16/16 - 1s - 60ms/step - accuracy: 0.1875 - loss: 1.9020 - val_accuracy: 0.1429 - val_loss: 1.8559\n",
      "\n",
      "Epoch 19/20                                                                         \n",
      "\n",
      "16/16 - 1s - 42ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8560\n",
      "\n",
      "Epoch 20/20                                                                         \n",
      "\n",
      "16/16 - 1s - 49ms/step - accuracy: 0.1875 - loss: 1.9019 - val_accuracy: 0.1429 - val_loss: 1.8556\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 462ms/step - accuracy: 0.1000 - loss: 1.9634\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 474ms/step - accuracy: 0.1000 - loss: 1.9634\n",
      "\n",
      "100%|██████████| 100/100 [32:07<00:00, 19.28s/trial, best loss: -0.10000000149011612]\n"
     ]
    }
   ],
   "source": [
    "# Define the search space for hyperparameters\n",
    "space = {\n",
    "    'n_components': hp.choice('n_components', range(2, 32)),\n",
    "    'n_hidden': hp.choice('n_hidden', range(1, 10)),\n",
    "    'hidden_size': hp.choice('hidden_size', [32, 64, 128, 256, 512, 1024]),\n",
    "}\n",
    "\n",
    "# Use Tree-structured Parzen Estimator (TPE) as the optimization algorithm\n",
    "best = fmin(fn=neural_pca_objective, space=space, algo=tpe.suggest, max_evals=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best hyperparameters: {'hidden_size': 4, 'n_components': 3, 'n_hidden': 3}\n"
     ]
    }
   ],
   "source": [
    "print(f\"Best hyperparameters: {best}\")"
   ]
  }
 ],
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