pruebasconANN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# classification es el notebook oficial de clasificación\n",
    "\n",
    "### - Limpiamos los datos según conclusiones del notebook main\n",
    "### - Probaremos distintos algorítmos de clasificación y compararemos los resultados\n",
    "### - Utilizamos el dataset de train ya recortado por la cátedra\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ACA TODOS LOS IMPORTS DEL NOTEBOOK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score, cross_val_predict, validation_curve\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, precision_recall_curve, auc, roc_curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LECTURA Y LIMPIEZA DEL DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(239, 13)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orig = pd.read_csv(r\"DS_G3_HeartFailure_FULL_train.csv\",index_col=0)  #datos de entrenamiento (80% del dataset original)\n",
    "df_orig.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transformaciones necesarias al df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>DEATH_EVENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>95.0</td>\n",
       "      <td>371</td>\n",
       "      <td>30</td>\n",
       "      <td>461000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>40.0</td>\n",
       "      <td>478</td>\n",
       "      <td>30</td>\n",
       "      <td>303000.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>70.0</td>\n",
       "      <td>232</td>\n",
       "      <td>30</td>\n",
       "      <td>173000.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>70.0</td>\n",
       "      <td>2695</td>\n",
       "      <td>40</td>\n",
       "      <td>241000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>59.0</td>\n",
       "      <td>176</td>\n",
       "      <td>25</td>\n",
       "      <td>221000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  creatinine_phosphokinase  ejection_fraction  platelets  \\\n",
       "55   95.0                       371                 30   461000.0   \n",
       "180  40.0                       478                 30   303000.0   \n",
       "237  70.0                       232                 30   173000.0   \n",
       "280  70.0                      2695                 40   241000.0   \n",
       "181  59.0                       176                 25   221000.0   \n",
       "\n",
       "     serum_creatinine  serum_sodium  DEATH_EVENT  \n",
       "55                2.0           132            1  \n",
       "180               0.9           136            0  \n",
       "237               1.2           132            0  \n",
       "280               1.0           137            0  \n",
       "181               1.0           136            1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cleaned = df_orig.drop([\"time\",\"anaemia\",\"diabetes\",\"high_blood_pressure\",\"sex\",\"smoking\"],axis=1)\n",
    "\n",
    "#df_cleaned = df_orig.drop([\"anaemia\",\"diabetes\",\"high_blood_pressure\",\"sex\",\"smoking\"],axis=1)\n",
    "# NOS QUEDAMOS CON AGE, CREATININE_PHOSPHOKINASE, EJECTION_FRACTION, PLATELETS, SERUM_CREATININE, SERUM_SODIUM Y DEATH_EVENT\n",
    "df_cleaned.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_train, targ_train = df_cleaned.drop([\"DEATH_EVENT\"],axis=1),df_cleaned[\"DEATH_EVENT\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(326, 6)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "feat_train = scaler.fit_transform(feat_train)\n",
    "#APLICAMOS SMOTE PARA AUMENTAR LA CANTIDAD DE SAMPLES Y BALANCEAR EL TARGET\n",
    "feat_train, targ_train = SMOTE(random_state=420).fit_resample(feat_train, targ_train)\n",
    "feat_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_train, feat_val, targ_train, targ_val = train_test_split(feat_train,targ_train,random_state=420,test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ACA TENEMOS EL DATASET RECORTADO Y STANDARIZADO "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_metric = \"recall\" # Queremos pocos FN, es decir pacientes q decimos q NO tienen riesgo pero SI."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RED NEURONAL SIMPLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "input = keras.layers.Input(shape=[6,])\n",
    "flatten = keras.layers.Flatten()(input)\n",
    "hidden1 = keras.layers.Dense(100, activation=\"relu\",kernel_initializer='he_uniform')(flatten)\n",
    "dropout = keras.layers.Dropout(rate=0.2)(hidden1)\n",
    "hidden2 = keras.layers.Dense(100, activation=\"relu\",kernel_initializer='he_uniform')(hidden1)\n",
    "output = keras.layers.Dense(1, activation=\"sigmoid\")(hidden2)\n",
    "\n",
    "model = keras.models.Model(inputs=[input], outputs=[output])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 6)]               0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 6)                 0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 100)               700       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 100)               10100     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 10,901\n",
      "Trainable params: 10,901\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss=tf.keras.losses.BinaryCrossentropy(),\n",
    "              optimizer=keras.optimizers.Adam(learning_rate=5e-5),\n",
    "              metrics=[\"accuracy\",tf.keras.metrics.Recall()])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2000\n",
      "11/11 [==============================] - 1s 17ms/step - loss: 0.8309 - accuracy: 0.5031 - recall: 0.9816 - val_loss: 0.6529 - val_accuracy: 0.5909 - val_recall: 0.9744\n",
      "Epoch 2/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.7911 - accuracy: 0.5153 - recall: 0.9571 - val_loss: 0.6314 - val_accuracy: 0.6061 - val_recall: 0.9487\n",
      "Epoch 3/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.7544 - accuracy: 0.5215 - recall: 0.9325 - val_loss: 0.6159 - val_accuracy: 0.6515 - val_recall: 0.9487\n",
      "Epoch 4/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.7290 - accuracy: 0.5460 - recall: 0.9141 - val_loss: 0.6030 - val_accuracy: 0.6515 - val_recall: 0.9487\n",
      "Epoch 5/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.7030 - accuracy: 0.5644 - recall: 0.8957 - val_loss: 0.5950 - val_accuracy: 0.6667 - val_recall: 0.9231\n",
      "Epoch 6/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.6850 - accuracy: 0.5859 - recall: 0.8896 - val_loss: 0.5895 - val_accuracy: 0.6818 - val_recall: 0.9231\n",
      "Epoch 7/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.6687 - accuracy: 0.6074 - recall: 0.8896 - val_loss: 0.5865 - val_accuracy: 0.6818 - val_recall: 0.9231\n",
      "Epoch 8/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.6544 - accuracy: 0.6135 - recall: 0.8834 - val_loss: 0.5852 - val_accuracy: 0.6818 - val_recall: 0.9231\n",
      "Epoch 9/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.6438 - accuracy: 0.6380 - recall: 0.8712 - val_loss: 0.5845 - val_accuracy: 0.6515 - val_recall: 0.8718\n",
      "Epoch 10/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.6346 - accuracy: 0.6503 - recall: 0.8466 - val_loss: 0.5845 - val_accuracy: 0.6515 - val_recall: 0.8462\n",
      "Epoch 11/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.6258 - accuracy: 0.6595 - recall: 0.8405 - val_loss: 0.5844 - val_accuracy: 0.6515 - val_recall: 0.8462\n",
      "Epoch 12/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.6192 - accuracy: 0.6595 - recall: 0.8405 - val_loss: 0.5848 - val_accuracy: 0.6515 - val_recall: 0.8462\n",
      "Epoch 13/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.6128 - accuracy: 0.6656 - recall: 0.8282 - val_loss: 0.5849 - val_accuracy: 0.6515 - val_recall: 0.8462\n",
      "Epoch 14/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.6065 - accuracy: 0.6656 - recall: 0.8160 - val_loss: 0.5852 - val_accuracy: 0.6515 - val_recall: 0.8462\n",
      "Epoch 15/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.6011 - accuracy: 0.6718 - recall: 0.8221 - val_loss: 0.5859 - val_accuracy: 0.6515 - val_recall: 0.8205\n",
      "Epoch 16/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5961 - accuracy: 0.6810 - recall: 0.8221 - val_loss: 0.5865 - val_accuracy: 0.6515 - val_recall: 0.8205\n",
      "Epoch 17/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5918 - accuracy: 0.6779 - recall: 0.8160 - val_loss: 0.5858 - val_accuracy: 0.6364 - val_recall: 0.8205\n",
      "Epoch 18/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5873 - accuracy: 0.6810 - recall: 0.8221 - val_loss: 0.5849 - val_accuracy: 0.6515 - val_recall: 0.8462\n",
      "Epoch 19/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5831 - accuracy: 0.6871 - recall: 0.8282 - val_loss: 0.5842 - val_accuracy: 0.6515 - val_recall: 0.8462\n",
      "Epoch 20/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5798 - accuracy: 0.7025 - recall: 0.8344 - val_loss: 0.5841 - val_accuracy: 0.6515 - val_recall: 0.8462\n",
      "Epoch 21/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.5767 - accuracy: 0.7055 - recall: 0.8282 - val_loss: 0.5854 - val_accuracy: 0.6818 - val_recall: 0.8462\n",
      "Epoch 22/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.5728 - accuracy: 0.7178 - recall: 0.8282 - val_loss: 0.5860 - val_accuracy: 0.6818 - val_recall: 0.8462\n",
      "Epoch 23/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5701 - accuracy: 0.7209 - recall: 0.8282 - val_loss: 0.5861 - val_accuracy: 0.6818 - val_recall: 0.8462\n",
      "Epoch 24/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5676 - accuracy: 0.7239 - recall: 0.8344 - val_loss: 0.5858 - val_accuracy: 0.7121 - val_recall: 0.8718\n",
      "Epoch 25/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5649 - accuracy: 0.7301 - recall: 0.8344 - val_loss: 0.5849 - val_accuracy: 0.7121 - val_recall: 0.8718\n",
      "Epoch 26/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5623 - accuracy: 0.7270 - recall: 0.8344 - val_loss: 0.5834 - val_accuracy: 0.6970 - val_recall: 0.8718\n",
      "Epoch 27/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5598 - accuracy: 0.7270 - recall: 0.8344 - val_loss: 0.5819 - val_accuracy: 0.6970 - val_recall: 0.8718\n",
      "Epoch 28/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5574 - accuracy: 0.7239 - recall: 0.8282 - val_loss: 0.5808 - val_accuracy: 0.7121 - val_recall: 0.8718\n",
      "Epoch 29/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5553 - accuracy: 0.7270 - recall: 0.8344 - val_loss: 0.5804 - val_accuracy: 0.7121 - val_recall: 0.8718\n",
      "Epoch 30/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5536 - accuracy: 0.7270 - recall: 0.8344 - val_loss: 0.5806 - val_accuracy: 0.7121 - val_recall: 0.8718\n",
      "Epoch 31/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5514 - accuracy: 0.7301 - recall: 0.8344 - val_loss: 0.5797 - val_accuracy: 0.7121 - val_recall: 0.8718\n",
      "Epoch 32/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5495 - accuracy: 0.7270 - recall: 0.8344 - val_loss: 0.5789 - val_accuracy: 0.6970 - val_recall: 0.8718\n",
      "Epoch 33/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5476 - accuracy: 0.7393 - recall: 0.8528 - val_loss: 0.5785 - val_accuracy: 0.7121 - val_recall: 0.8718\n",
      "Epoch 34/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5460 - accuracy: 0.7454 - recall: 0.8466 - val_loss: 0.5788 - val_accuracy: 0.7424 - val_recall: 0.8718\n",
      "Epoch 35/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5444 - accuracy: 0.7423 - recall: 0.8466 - val_loss: 0.5773 - val_accuracy: 0.7273 - val_recall: 0.8718\n",
      "Epoch 36/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5427 - accuracy: 0.7485 - recall: 0.8466 - val_loss: 0.5776 - val_accuracy: 0.7424 - val_recall: 0.8718\n",
      "Epoch 37/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5408 - accuracy: 0.7454 - recall: 0.8405 - val_loss: 0.5779 - val_accuracy: 0.7424 - val_recall: 0.8718\n",
      "Epoch 38/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5393 - accuracy: 0.7454 - recall: 0.8405 - val_loss: 0.5775 - val_accuracy: 0.7424 - val_recall: 0.8718\n",
      "Epoch 39/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5379 - accuracy: 0.7454 - recall: 0.8405 - val_loss: 0.5779 - val_accuracy: 0.7424 - val_recall: 0.8718\n",
      "Epoch 40/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5361 - accuracy: 0.7423 - recall: 0.8344 - val_loss: 0.5769 - val_accuracy: 0.7424 - val_recall: 0.8718\n",
      "Epoch 41/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5342 - accuracy: 0.7454 - recall: 0.8405 - val_loss: 0.5768 - val_accuracy: 0.7424 - val_recall: 0.8718\n",
      "Epoch 42/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5327 - accuracy: 0.7454 - recall: 0.8405 - val_loss: 0.5766 - val_accuracy: 0.7424 - val_recall: 0.8718\n",
      "Epoch 43/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5313 - accuracy: 0.7485 - recall: 0.8405 - val_loss: 0.5762 - val_accuracy: 0.7424 - val_recall: 0.8718\n",
      "Epoch 44/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5301 - accuracy: 0.7485 - recall: 0.8405 - val_loss: 0.5756 - val_accuracy: 0.7576 - val_recall: 0.8718\n",
      "Epoch 45/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5285 - accuracy: 0.7515 - recall: 0.8344 - val_loss: 0.5760 - val_accuracy: 0.7576 - val_recall: 0.8718\n",
      "Epoch 46/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5270 - accuracy: 0.7577 - recall: 0.8344 - val_loss: 0.5752 - val_accuracy: 0.7576 - val_recall: 0.8718\n",
      "Epoch 47/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5257 - accuracy: 0.7577 - recall: 0.8344 - val_loss: 0.5750 - val_accuracy: 0.7576 - val_recall: 0.8718\n",
      "Epoch 48/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5245 - accuracy: 0.7577 - recall: 0.8344 - val_loss: 0.5749 - val_accuracy: 0.7576 - val_recall: 0.8718\n",
      "Epoch 49/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5231 - accuracy: 0.7577 - recall: 0.8344 - val_loss: 0.5739 - val_accuracy: 0.7576 - val_recall: 0.8718\n",
      "Epoch 50/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5220 - accuracy: 0.7577 - recall: 0.8344 - val_loss: 0.5743 - val_accuracy: 0.7576 - val_recall: 0.8718\n",
      "Epoch 51/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5208 - accuracy: 0.7607 - recall: 0.8344 - val_loss: 0.5745 - val_accuracy: 0.7879 - val_recall: 0.8718\n",
      "Epoch 52/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5196 - accuracy: 0.7607 - recall: 0.8282 - val_loss: 0.5742 - val_accuracy: 0.7879 - val_recall: 0.8718\n",
      "Epoch 53/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5186 - accuracy: 0.7607 - recall: 0.8282 - val_loss: 0.5732 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 54/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5179 - accuracy: 0.7638 - recall: 0.8282 - val_loss: 0.5721 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 55/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5167 - accuracy: 0.7638 - recall: 0.8282 - val_loss: 0.5708 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 56/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5158 - accuracy: 0.7699 - recall: 0.8282 - val_loss: 0.5709 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 57/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5144 - accuracy: 0.7699 - recall: 0.8282 - val_loss: 0.5702 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 58/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5132 - accuracy: 0.7730 - recall: 0.8282 - val_loss: 0.5694 - val_accuracy: 0.7879 - val_recall: 0.8718\n",
      "Epoch 59/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5123 - accuracy: 0.7669 - recall: 0.8282 - val_loss: 0.5685 - val_accuracy: 0.7879 - val_recall: 0.8718\n",
      "Epoch 60/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5111 - accuracy: 0.7669 - recall: 0.8282 - val_loss: 0.5679 - val_accuracy: 0.7879 - val_recall: 0.8718\n",
      "Epoch 61/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5102 - accuracy: 0.7669 - recall: 0.8221 - val_loss: 0.5684 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 62/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5089 - accuracy: 0.7699 - recall: 0.8282 - val_loss: 0.5662 - val_accuracy: 0.7879 - val_recall: 0.8718\n",
      "Epoch 63/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5080 - accuracy: 0.7699 - recall: 0.8221 - val_loss: 0.5664 - val_accuracy: 0.7727 - val_recall: 0.8462\n",
      "Epoch 64/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5078 - accuracy: 0.7699 - recall: 0.8160 - val_loss: 0.5674 - val_accuracy: 0.7879 - val_recall: 0.8462\n",
      "Epoch 65/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5064 - accuracy: 0.7730 - recall: 0.8160 - val_loss: 0.5656 - val_accuracy: 0.7879 - val_recall: 0.8462\n",
      "Epoch 66/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5054 - accuracy: 0.7822 - recall: 0.8344 - val_loss: 0.5636 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 67/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5042 - accuracy: 0.7791 - recall: 0.8344 - val_loss: 0.5616 - val_accuracy: 0.7879 - val_recall: 0.8718\n",
      "Epoch 68/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5033 - accuracy: 0.7791 - recall: 0.8344 - val_loss: 0.5611 - val_accuracy: 0.7879 - val_recall: 0.8718\n",
      "Epoch 69/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5022 - accuracy: 0.7761 - recall: 0.8344 - val_loss: 0.5598 - val_accuracy: 0.7879 - val_recall: 0.8718\n",
      "Epoch 70/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5014 - accuracy: 0.7822 - recall: 0.8344 - val_loss: 0.5589 - val_accuracy: 0.7879 - val_recall: 0.8718\n",
      "Epoch 71/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.5006 - accuracy: 0.7853 - recall: 0.8344 - val_loss: 0.5587 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 72/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4999 - accuracy: 0.7883 - recall: 0.8344 - val_loss: 0.5595 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 73/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4992 - accuracy: 0.7853 - recall: 0.8282 - val_loss: 0.5596 - val_accuracy: 0.7879 - val_recall: 0.8462\n",
      "Epoch 74/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4979 - accuracy: 0.7853 - recall: 0.8282 - val_loss: 0.5585 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 75/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4968 - accuracy: 0.7853 - recall: 0.8282 - val_loss: 0.5577 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 76/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4959 - accuracy: 0.7853 - recall: 0.8282 - val_loss: 0.5571 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 77/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4951 - accuracy: 0.7853 - recall: 0.8282 - val_loss: 0.5571 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 78/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.4944 - accuracy: 0.7883 - recall: 0.8282 - val_loss: 0.5562 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 79/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.4936 - accuracy: 0.7914 - recall: 0.8344 - val_loss: 0.5562 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 80/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4927 - accuracy: 0.7914 - recall: 0.8344 - val_loss: 0.5557 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 81/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4918 - accuracy: 0.7975 - recall: 0.8344 - val_loss: 0.5550 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 82/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4910 - accuracy: 0.8006 - recall: 0.8344 - val_loss: 0.5546 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 83/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4903 - accuracy: 0.8006 - recall: 0.8344 - val_loss: 0.5534 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 84/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4896 - accuracy: 0.8006 - recall: 0.8344 - val_loss: 0.5523 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 85/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4887 - accuracy: 0.8006 - recall: 0.8344 - val_loss: 0.5517 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 86/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4882 - accuracy: 0.7975 - recall: 0.8282 - val_loss: 0.5501 - val_accuracy: 0.7879 - val_recall: 0.8462\n",
      "Epoch 87/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4874 - accuracy: 0.7975 - recall: 0.8282 - val_loss: 0.5498 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 88/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4866 - accuracy: 0.8037 - recall: 0.8405 - val_loss: 0.5486 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 89/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4858 - accuracy: 0.7945 - recall: 0.8221 - val_loss: 0.5488 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 90/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4850 - accuracy: 0.7975 - recall: 0.8221 - val_loss: 0.5473 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 91/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4844 - accuracy: 0.8006 - recall: 0.8282 - val_loss: 0.5468 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 92/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4837 - accuracy: 0.8037 - recall: 0.8344 - val_loss: 0.5462 - val_accuracy: 0.7879 - val_recall: 0.8462\n",
      "Epoch 93/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4827 - accuracy: 0.8037 - recall: 0.8282 - val_loss: 0.5449 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 94/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4819 - accuracy: 0.8037 - recall: 0.8344 - val_loss: 0.5446 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 95/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4812 - accuracy: 0.8067 - recall: 0.8344 - val_loss: 0.5436 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 96/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4805 - accuracy: 0.8067 - recall: 0.8344 - val_loss: 0.5428 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 97/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4798 - accuracy: 0.8067 - recall: 0.8344 - val_loss: 0.5423 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 98/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4789 - accuracy: 0.8067 - recall: 0.8282 - val_loss: 0.5420 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 99/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4783 - accuracy: 0.8067 - recall: 0.8282 - val_loss: 0.5413 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 100/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4778 - accuracy: 0.8098 - recall: 0.8344 - val_loss: 0.5406 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 101/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4769 - accuracy: 0.8098 - recall: 0.8344 - val_loss: 0.5408 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 102/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4762 - accuracy: 0.8098 - recall: 0.8344 - val_loss: 0.5406 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 103/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4753 - accuracy: 0.8067 - recall: 0.8282 - val_loss: 0.5405 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 104/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4749 - accuracy: 0.8067 - recall: 0.8282 - val_loss: 0.5402 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 105/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4739 - accuracy: 0.8098 - recall: 0.8282 - val_loss: 0.5401 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 106/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4732 - accuracy: 0.8098 - recall: 0.8282 - val_loss: 0.5395 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 107/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4724 - accuracy: 0.8098 - recall: 0.8282 - val_loss: 0.5388 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 108/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4717 - accuracy: 0.8098 - recall: 0.8282 - val_loss: 0.5376 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 109/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4709 - accuracy: 0.8098 - recall: 0.8282 - val_loss: 0.5375 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 110/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4710 - accuracy: 0.8098 - recall: 0.8282 - val_loss: 0.5383 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 111/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4701 - accuracy: 0.8098 - recall: 0.8282 - val_loss: 0.5362 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 112/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4690 - accuracy: 0.8098 - recall: 0.8282 - val_loss: 0.5349 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 113/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4687 - accuracy: 0.8098 - recall: 0.8282 - val_loss: 0.5342 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 114/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4679 - accuracy: 0.8098 - recall: 0.8282 - val_loss: 0.5337 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 115/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4674 - accuracy: 0.8129 - recall: 0.8282 - val_loss: 0.5340 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 116/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4666 - accuracy: 0.8129 - recall: 0.8282 - val_loss: 0.5330 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 117/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4660 - accuracy: 0.8129 - recall: 0.8282 - val_loss: 0.5319 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 118/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4655 - accuracy: 0.8129 - recall: 0.8282 - val_loss: 0.5316 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 119/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4648 - accuracy: 0.8129 - recall: 0.8282 - val_loss: 0.5301 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 120/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4642 - accuracy: 0.8129 - recall: 0.8282 - val_loss: 0.5293 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 121/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4638 - accuracy: 0.8129 - recall: 0.8282 - val_loss: 0.5291 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 122/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4628 - accuracy: 0.8129 - recall: 0.8282 - val_loss: 0.5285 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 123/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4624 - accuracy: 0.8129 - recall: 0.8282 - val_loss: 0.5273 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 124/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4616 - accuracy: 0.8129 - recall: 0.8282 - val_loss: 0.5275 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 125/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4611 - accuracy: 0.8129 - recall: 0.8282 - val_loss: 0.5269 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 126/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4607 - accuracy: 0.8129 - recall: 0.8282 - val_loss: 0.5280 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 127/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4600 - accuracy: 0.8129 - recall: 0.8282 - val_loss: 0.5281 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 128/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4595 - accuracy: 0.8129 - recall: 0.8282 - val_loss: 0.5269 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 129/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4587 - accuracy: 0.8129 - recall: 0.8282 - val_loss: 0.5266 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 130/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4581 - accuracy: 0.8129 - recall: 0.8282 - val_loss: 0.5269 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 131/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4581 - accuracy: 0.8129 - recall: 0.8282 - val_loss: 0.5275 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 132/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4571 - accuracy: 0.8129 - recall: 0.8282 - val_loss: 0.5262 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 133/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4565 - accuracy: 0.8098 - recall: 0.8221 - val_loss: 0.5252 - val_accuracy: 0.7879 - val_recall: 0.8462\n",
      "Epoch 134/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4559 - accuracy: 0.8098 - recall: 0.8221 - val_loss: 0.5238 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 135/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4552 - accuracy: 0.8129 - recall: 0.8282 - val_loss: 0.5234 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 136/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4552 - accuracy: 0.8129 - recall: 0.8282 - val_loss: 0.5208 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 137/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4542 - accuracy: 0.8098 - recall: 0.8282 - val_loss: 0.5198 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 138/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4536 - accuracy: 0.8098 - recall: 0.8282 - val_loss: 0.5190 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 139/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4530 - accuracy: 0.8098 - recall: 0.8282 - val_loss: 0.5192 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 140/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4523 - accuracy: 0.8129 - recall: 0.8282 - val_loss: 0.5197 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 141/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4516 - accuracy: 0.8129 - recall: 0.8282 - val_loss: 0.5189 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 142/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4509 - accuracy: 0.8129 - recall: 0.8282 - val_loss: 0.5198 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 143/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4505 - accuracy: 0.8129 - recall: 0.8282 - val_loss: 0.5194 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 144/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.4498 - accuracy: 0.8129 - recall: 0.8282 - val_loss: 0.5175 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 145/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.4494 - accuracy: 0.8129 - recall: 0.8282 - val_loss: 0.5158 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 146/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4487 - accuracy: 0.8129 - recall: 0.8344 - val_loss: 0.5138 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 147/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4481 - accuracy: 0.8129 - recall: 0.8344 - val_loss: 0.5127 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 148/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4475 - accuracy: 0.8098 - recall: 0.8344 - val_loss: 0.5115 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 149/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4469 - accuracy: 0.8098 - recall: 0.8344 - val_loss: 0.5104 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 150/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4463 - accuracy: 0.8098 - recall: 0.8344 - val_loss: 0.5099 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 151/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4460 - accuracy: 0.8098 - recall: 0.8344 - val_loss: 0.5089 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 152/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4450 - accuracy: 0.8129 - recall: 0.8405 - val_loss: 0.5087 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 153/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4442 - accuracy: 0.8098 - recall: 0.8344 - val_loss: 0.5086 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 154/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4437 - accuracy: 0.8098 - recall: 0.8282 - val_loss: 0.5089 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 155/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4429 - accuracy: 0.8098 - recall: 0.8282 - val_loss: 0.5088 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 156/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4427 - accuracy: 0.8098 - recall: 0.8282 - val_loss: 0.5090 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 157/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4420 - accuracy: 0.8098 - recall: 0.8282 - val_loss: 0.5072 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 158/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4415 - accuracy: 0.8098 - recall: 0.8282 - val_loss: 0.5060 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 159/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4409 - accuracy: 0.8098 - recall: 0.8282 - val_loss: 0.5056 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 160/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4407 - accuracy: 0.8160 - recall: 0.8405 - val_loss: 0.5039 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 161/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4398 - accuracy: 0.8160 - recall: 0.8405 - val_loss: 0.5048 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 162/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4397 - accuracy: 0.8129 - recall: 0.8344 - val_loss: 0.5049 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 163/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4390 - accuracy: 0.8098 - recall: 0.8344 - val_loss: 0.5031 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 164/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4385 - accuracy: 0.8160 - recall: 0.8405 - val_loss: 0.5022 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 165/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4375 - accuracy: 0.8129 - recall: 0.8405 - val_loss: 0.5013 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 166/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4371 - accuracy: 0.8160 - recall: 0.8405 - val_loss: 0.5012 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 167/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4366 - accuracy: 0.8160 - recall: 0.8405 - val_loss: 0.5004 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 168/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4360 - accuracy: 0.8160 - recall: 0.8405 - val_loss: 0.5006 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 169/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4356 - accuracy: 0.8190 - recall: 0.8405 - val_loss: 0.5003 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 170/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4355 - accuracy: 0.8221 - recall: 0.8405 - val_loss: 0.5010 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 171/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4350 - accuracy: 0.8221 - recall: 0.8405 - val_loss: 0.4997 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 172/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4341 - accuracy: 0.8221 - recall: 0.8405 - val_loss: 0.4988 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 173/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4336 - accuracy: 0.8221 - recall: 0.8405 - val_loss: 0.4983 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 174/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4331 - accuracy: 0.8190 - recall: 0.8405 - val_loss: 0.4972 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 175/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4324 - accuracy: 0.8190 - recall: 0.8405 - val_loss: 0.4967 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 176/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4316 - accuracy: 0.8160 - recall: 0.8405 - val_loss: 0.4958 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 177/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4312 - accuracy: 0.8190 - recall: 0.8466 - val_loss: 0.4960 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 178/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4308 - accuracy: 0.8190 - recall: 0.8405 - val_loss: 0.4964 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 179/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4306 - accuracy: 0.8190 - recall: 0.8405 - val_loss: 0.4963 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 180/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4299 - accuracy: 0.8221 - recall: 0.8466 - val_loss: 0.4949 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 181/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4293 - accuracy: 0.8221 - recall: 0.8466 - val_loss: 0.4940 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 182/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4288 - accuracy: 0.8221 - recall: 0.8466 - val_loss: 0.4924 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 183/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4283 - accuracy: 0.8221 - recall: 0.8466 - val_loss: 0.4924 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 184/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4277 - accuracy: 0.8221 - recall: 0.8466 - val_loss: 0.4914 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 185/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4273 - accuracy: 0.8221 - recall: 0.8466 - val_loss: 0.4908 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 186/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4271 - accuracy: 0.8221 - recall: 0.8466 - val_loss: 0.4911 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 187/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4265 - accuracy: 0.8252 - recall: 0.8466 - val_loss: 0.4919 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 188/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4267 - accuracy: 0.8221 - recall: 0.8344 - val_loss: 0.4931 - val_accuracy: 0.7879 - val_recall: 0.8462\n",
      "Epoch 189/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4262 - accuracy: 0.8252 - recall: 0.8405 - val_loss: 0.4919 - val_accuracy: 0.7879 - val_recall: 0.8462\n",
      "Epoch 190/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4255 - accuracy: 0.8190 - recall: 0.8405 - val_loss: 0.4902 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 191/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4248 - accuracy: 0.8221 - recall: 0.8466 - val_loss: 0.4901 - val_accuracy: 0.7879 - val_recall: 0.8462\n",
      "Epoch 192/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4241 - accuracy: 0.8221 - recall: 0.8466 - val_loss: 0.4890 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 193/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4237 - accuracy: 0.8252 - recall: 0.8528 - val_loss: 0.4873 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 194/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4231 - accuracy: 0.8282 - recall: 0.8589 - val_loss: 0.4870 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 195/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4224 - accuracy: 0.8282 - recall: 0.8589 - val_loss: 0.4862 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 196/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4221 - accuracy: 0.8282 - recall: 0.8589 - val_loss: 0.4848 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 197/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4216 - accuracy: 0.8282 - recall: 0.8589 - val_loss: 0.4837 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 198/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4214 - accuracy: 0.8282 - recall: 0.8589 - val_loss: 0.4833 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 199/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4208 - accuracy: 0.8282 - recall: 0.8589 - val_loss: 0.4826 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 200/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4203 - accuracy: 0.8282 - recall: 0.8589 - val_loss: 0.4829 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 201/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4199 - accuracy: 0.8252 - recall: 0.8528 - val_loss: 0.4831 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 202/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4195 - accuracy: 0.8282 - recall: 0.8589 - val_loss: 0.4828 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 203/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4191 - accuracy: 0.8282 - recall: 0.8589 - val_loss: 0.4821 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 204/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4188 - accuracy: 0.8252 - recall: 0.8589 - val_loss: 0.4820 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 205/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4181 - accuracy: 0.8252 - recall: 0.8589 - val_loss: 0.4814 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 206/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4178 - accuracy: 0.8282 - recall: 0.8589 - val_loss: 0.4818 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 207/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4173 - accuracy: 0.8282 - recall: 0.8589 - val_loss: 0.4819 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 208/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4168 - accuracy: 0.8282 - recall: 0.8589 - val_loss: 0.4816 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 209/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4165 - accuracy: 0.8282 - recall: 0.8589 - val_loss: 0.4813 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 210/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4160 - accuracy: 0.8282 - recall: 0.8589 - val_loss: 0.4813 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 211/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4156 - accuracy: 0.8282 - recall: 0.8589 - val_loss: 0.4799 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 212/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4150 - accuracy: 0.8313 - recall: 0.8650 - val_loss: 0.4799 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 213/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4146 - accuracy: 0.8252 - recall: 0.8528 - val_loss: 0.4804 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 214/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4142 - accuracy: 0.8252 - recall: 0.8528 - val_loss: 0.4802 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 215/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4138 - accuracy: 0.8252 - recall: 0.8528 - val_loss: 0.4786 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 216/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4134 - accuracy: 0.8282 - recall: 0.8589 - val_loss: 0.4774 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 217/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4130 - accuracy: 0.8252 - recall: 0.8528 - val_loss: 0.4782 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 218/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4127 - accuracy: 0.8252 - recall: 0.8528 - val_loss: 0.4783 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 219/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4122 - accuracy: 0.8252 - recall: 0.8528 - val_loss: 0.4778 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 220/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4115 - accuracy: 0.8252 - recall: 0.8528 - val_loss: 0.4768 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 221/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4112 - accuracy: 0.8252 - recall: 0.8528 - val_loss: 0.4756 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 222/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4108 - accuracy: 0.8282 - recall: 0.8589 - val_loss: 0.4748 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 223/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4104 - accuracy: 0.8282 - recall: 0.8589 - val_loss: 0.4748 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 224/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4099 - accuracy: 0.8282 - recall: 0.8589 - val_loss: 0.4744 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 225/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4096 - accuracy: 0.8282 - recall: 0.8589 - val_loss: 0.4733 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 226/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4091 - accuracy: 0.8282 - recall: 0.8589 - val_loss: 0.4732 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 227/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4090 - accuracy: 0.8282 - recall: 0.8589 - val_loss: 0.4718 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 228/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4082 - accuracy: 0.8313 - recall: 0.8650 - val_loss: 0.4714 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 229/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4079 - accuracy: 0.8313 - recall: 0.8650 - val_loss: 0.4713 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 230/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4074 - accuracy: 0.8282 - recall: 0.8589 - val_loss: 0.4710 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 231/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4068 - accuracy: 0.8282 - recall: 0.8589 - val_loss: 0.4701 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 232/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4065 - accuracy: 0.8344 - recall: 0.8712 - val_loss: 0.4689 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 233/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4064 - accuracy: 0.8344 - recall: 0.8712 - val_loss: 0.4682 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 234/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4057 - accuracy: 0.8374 - recall: 0.8773 - val_loss: 0.4685 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 235/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4053 - accuracy: 0.8313 - recall: 0.8650 - val_loss: 0.4689 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 236/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4051 - accuracy: 0.8344 - recall: 0.8712 - val_loss: 0.4688 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 237/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4047 - accuracy: 0.8344 - recall: 0.8712 - val_loss: 0.4688 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 238/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4041 - accuracy: 0.8282 - recall: 0.8589 - val_loss: 0.4683 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 239/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4037 - accuracy: 0.8313 - recall: 0.8650 - val_loss: 0.4670 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 240/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4033 - accuracy: 0.8344 - recall: 0.8650 - val_loss: 0.4661 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 241/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4028 - accuracy: 0.8313 - recall: 0.8650 - val_loss: 0.4657 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 242/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4026 - accuracy: 0.8344 - recall: 0.8650 - val_loss: 0.4652 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 243/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4019 - accuracy: 0.8313 - recall: 0.8589 - val_loss: 0.4643 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 244/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4015 - accuracy: 0.8374 - recall: 0.8589 - val_loss: 0.4637 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 245/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4012 - accuracy: 0.8344 - recall: 0.8589 - val_loss: 0.4629 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 246/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4007 - accuracy: 0.8344 - recall: 0.8650 - val_loss: 0.4612 - val_accuracy: 0.8030 - val_recall: 0.8718\n",
      "Epoch 247/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4007 - accuracy: 0.8436 - recall: 0.8834 - val_loss: 0.4607 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 248/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.4000 - accuracy: 0.8436 - recall: 0.8834 - val_loss: 0.4597 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 249/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3996 - accuracy: 0.8436 - recall: 0.8834 - val_loss: 0.4590 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 250/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3991 - accuracy: 0.8436 - recall: 0.8834 - val_loss: 0.4585 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 251/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3987 - accuracy: 0.8436 - recall: 0.8834 - val_loss: 0.4576 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 252/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3984 - accuracy: 0.8405 - recall: 0.8834 - val_loss: 0.4575 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 253/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3982 - accuracy: 0.8405 - recall: 0.8834 - val_loss: 0.4569 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 254/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3977 - accuracy: 0.8436 - recall: 0.8834 - val_loss: 0.4573 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 255/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3971 - accuracy: 0.8436 - recall: 0.8834 - val_loss: 0.4570 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 256/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3967 - accuracy: 0.8436 - recall: 0.8834 - val_loss: 0.4563 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 257/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3963 - accuracy: 0.8436 - recall: 0.8834 - val_loss: 0.4552 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 258/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3958 - accuracy: 0.8436 - recall: 0.8834 - val_loss: 0.4549 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 259/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3954 - accuracy: 0.8436 - recall: 0.8834 - val_loss: 0.4536 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 260/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3951 - accuracy: 0.8436 - recall: 0.8896 - val_loss: 0.4532 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 261/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3947 - accuracy: 0.8436 - recall: 0.8896 - val_loss: 0.4534 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 262/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3943 - accuracy: 0.8466 - recall: 0.8896 - val_loss: 0.4537 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 263/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3939 - accuracy: 0.8497 - recall: 0.8896 - val_loss: 0.4538 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 264/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3937 - accuracy: 0.8436 - recall: 0.8834 - val_loss: 0.4540 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 265/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3930 - accuracy: 0.8436 - recall: 0.8834 - val_loss: 0.4529 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 266/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3927 - accuracy: 0.8497 - recall: 0.8896 - val_loss: 0.4532 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 267/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3922 - accuracy: 0.8497 - recall: 0.8896 - val_loss: 0.4531 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 268/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3918 - accuracy: 0.8497 - recall: 0.8896 - val_loss: 0.4535 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 269/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3912 - accuracy: 0.8497 - recall: 0.8896 - val_loss: 0.4533 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 270/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3910 - accuracy: 0.8466 - recall: 0.8896 - val_loss: 0.4526 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 271/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3905 - accuracy: 0.8436 - recall: 0.8834 - val_loss: 0.4519 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 272/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3902 - accuracy: 0.8528 - recall: 0.8834 - val_loss: 0.4506 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 273/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3901 - accuracy: 0.8528 - recall: 0.8834 - val_loss: 0.4495 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 274/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3896 - accuracy: 0.8558 - recall: 0.8834 - val_loss: 0.4496 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 275/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3888 - accuracy: 0.8558 - recall: 0.8834 - val_loss: 0.4492 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 276/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3888 - accuracy: 0.8528 - recall: 0.8773 - val_loss: 0.4502 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 277/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3882 - accuracy: 0.8528 - recall: 0.8834 - val_loss: 0.4485 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 278/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3876 - accuracy: 0.8528 - recall: 0.8834 - val_loss: 0.4498 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 279/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3871 - accuracy: 0.8466 - recall: 0.8773 - val_loss: 0.4494 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 280/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3870 - accuracy: 0.8497 - recall: 0.8896 - val_loss: 0.4480 - val_accuracy: 0.8333 - val_recall: 0.9231\n",
      "Epoch 281/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3867 - accuracy: 0.8497 - recall: 0.8896 - val_loss: 0.4476 - val_accuracy: 0.8333 - val_recall: 0.9231\n",
      "Epoch 282/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3864 - accuracy: 0.8466 - recall: 0.8896 - val_loss: 0.4468 - val_accuracy: 0.8333 - val_recall: 0.9231\n",
      "Epoch 283/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3862 - accuracy: 0.8466 - recall: 0.8896 - val_loss: 0.4461 - val_accuracy: 0.8333 - val_recall: 0.9231\n",
      "Epoch 284/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.3858 - accuracy: 0.8497 - recall: 0.8896 - val_loss: 0.4455 - val_accuracy: 0.8333 - val_recall: 0.9231\n",
      "Epoch 285/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.3855 - accuracy: 0.8497 - recall: 0.8896 - val_loss: 0.4439 - val_accuracy: 0.8333 - val_recall: 0.9231\n",
      "Epoch 286/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3846 - accuracy: 0.8466 - recall: 0.8896 - val_loss: 0.4439 - val_accuracy: 0.8333 - val_recall: 0.9231\n",
      "Epoch 287/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3839 - accuracy: 0.8558 - recall: 0.8896 - val_loss: 0.4443 - val_accuracy: 0.8333 - val_recall: 0.9231\n",
      "Epoch 288/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3837 - accuracy: 0.8589 - recall: 0.8896 - val_loss: 0.4441 - val_accuracy: 0.8333 - val_recall: 0.9231\n",
      "Epoch 289/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3834 - accuracy: 0.8620 - recall: 0.8896 - val_loss: 0.4441 - val_accuracy: 0.8333 - val_recall: 0.9231\n",
      "Epoch 290/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3831 - accuracy: 0.8620 - recall: 0.8834 - val_loss: 0.4434 - val_accuracy: 0.8333 - val_recall: 0.9231\n",
      "Epoch 291/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3826 - accuracy: 0.8589 - recall: 0.8834 - val_loss: 0.4428 - val_accuracy: 0.8333 - val_recall: 0.9231\n",
      "Epoch 292/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3821 - accuracy: 0.8589 - recall: 0.8896 - val_loss: 0.4428 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 293/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3821 - accuracy: 0.8528 - recall: 0.8834 - val_loss: 0.4435 - val_accuracy: 0.8333 - val_recall: 0.9231\n",
      "Epoch 294/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3814 - accuracy: 0.8558 - recall: 0.8896 - val_loss: 0.4435 - val_accuracy: 0.8333 - val_recall: 0.9231\n",
      "Epoch 295/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3809 - accuracy: 0.8558 - recall: 0.8896 - val_loss: 0.4440 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 296/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3806 - accuracy: 0.8558 - recall: 0.8834 - val_loss: 0.4436 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 297/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3802 - accuracy: 0.8528 - recall: 0.8834 - val_loss: 0.4424 - val_accuracy: 0.8333 - val_recall: 0.9231\n",
      "Epoch 298/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3798 - accuracy: 0.8528 - recall: 0.8896 - val_loss: 0.4420 - val_accuracy: 0.8333 - val_recall: 0.9231\n",
      "Epoch 299/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3795 - accuracy: 0.8497 - recall: 0.8896 - val_loss: 0.4412 - val_accuracy: 0.8333 - val_recall: 0.9231\n",
      "Epoch 300/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3791 - accuracy: 0.8497 - recall: 0.8896 - val_loss: 0.4411 - val_accuracy: 0.8333 - val_recall: 0.9231\n",
      "Epoch 301/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3788 - accuracy: 0.8528 - recall: 0.8896 - val_loss: 0.4410 - val_accuracy: 0.8333 - val_recall: 0.9231\n",
      "Epoch 302/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3784 - accuracy: 0.8497 - recall: 0.8834 - val_loss: 0.4409 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 303/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3779 - accuracy: 0.8528 - recall: 0.8834 - val_loss: 0.4403 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 304/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3776 - accuracy: 0.8589 - recall: 0.8834 - val_loss: 0.4396 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 305/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3771 - accuracy: 0.8620 - recall: 0.8834 - val_loss: 0.4394 - val_accuracy: 0.8182 - val_recall: 0.8974\n",
      "Epoch 306/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3770 - accuracy: 0.8650 - recall: 0.8834 - val_loss: 0.4403 - val_accuracy: 0.8333 - val_recall: 0.8974\n",
      "Epoch 307/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3767 - accuracy: 0.8650 - recall: 0.8834 - val_loss: 0.4397 - val_accuracy: 0.8333 - val_recall: 0.8974\n",
      "Epoch 308/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3761 - accuracy: 0.8620 - recall: 0.8834 - val_loss: 0.4376 - val_accuracy: 0.8333 - val_recall: 0.9231\n",
      "Epoch 309/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3756 - accuracy: 0.8589 - recall: 0.8896 - val_loss: 0.4363 - val_accuracy: 0.8333 - val_recall: 0.9231\n",
      "Epoch 310/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3753 - accuracy: 0.8528 - recall: 0.8896 - val_loss: 0.4349 - val_accuracy: 0.8333 - val_recall: 0.9231\n",
      "Epoch 311/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3749 - accuracy: 0.8497 - recall: 0.8896 - val_loss: 0.4344 - val_accuracy: 0.8333 - val_recall: 0.9231\n",
      "Epoch 312/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3744 - accuracy: 0.8528 - recall: 0.8957 - val_loss: 0.4327 - val_accuracy: 0.8333 - val_recall: 0.9231\n",
      "Epoch 313/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3742 - accuracy: 0.8558 - recall: 0.8957 - val_loss: 0.4323 - val_accuracy: 0.8333 - val_recall: 0.9231\n",
      "Epoch 314/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3737 - accuracy: 0.8589 - recall: 0.8896 - val_loss: 0.4321 - val_accuracy: 0.8333 - val_recall: 0.9231\n",
      "Epoch 315/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3736 - accuracy: 0.8620 - recall: 0.8896 - val_loss: 0.4328 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 316/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3732 - accuracy: 0.8681 - recall: 0.8896 - val_loss: 0.4330 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 317/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3729 - accuracy: 0.8712 - recall: 0.8896 - val_loss: 0.4319 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 318/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3723 - accuracy: 0.8681 - recall: 0.8834 - val_loss: 0.4304 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 319/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3718 - accuracy: 0.8712 - recall: 0.8896 - val_loss: 0.4294 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 320/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3714 - accuracy: 0.8712 - recall: 0.8896 - val_loss: 0.4288 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 321/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3710 - accuracy: 0.8712 - recall: 0.8896 - val_loss: 0.4285 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 322/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3706 - accuracy: 0.8712 - recall: 0.8896 - val_loss: 0.4290 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 323/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3704 - accuracy: 0.8712 - recall: 0.8896 - val_loss: 0.4288 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 324/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3698 - accuracy: 0.8712 - recall: 0.8896 - val_loss: 0.4282 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 325/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3696 - accuracy: 0.8681 - recall: 0.8896 - val_loss: 0.4278 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 326/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3692 - accuracy: 0.8650 - recall: 0.8896 - val_loss: 0.4261 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 327/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3688 - accuracy: 0.8620 - recall: 0.8896 - val_loss: 0.4251 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 328/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3689 - accuracy: 0.8681 - recall: 0.8896 - val_loss: 0.4240 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 329/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3685 - accuracy: 0.8681 - recall: 0.8896 - val_loss: 0.4226 - val_accuracy: 0.8333 - val_recall: 0.9231\n",
      "Epoch 330/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3679 - accuracy: 0.8712 - recall: 0.8896 - val_loss: 0.4227 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 331/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3672 - accuracy: 0.8712 - recall: 0.8896 - val_loss: 0.4229 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 332/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3670 - accuracy: 0.8712 - recall: 0.8896 - val_loss: 0.4234 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 333/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3666 - accuracy: 0.8712 - recall: 0.8896 - val_loss: 0.4225 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 334/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3662 - accuracy: 0.8712 - recall: 0.8896 - val_loss: 0.4233 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 335/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3659 - accuracy: 0.8681 - recall: 0.8896 - val_loss: 0.4229 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 336/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3654 - accuracy: 0.8681 - recall: 0.8896 - val_loss: 0.4228 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 337/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3652 - accuracy: 0.8650 - recall: 0.8896 - val_loss: 0.4221 - val_accuracy: 0.8333 - val_recall: 0.9231\n",
      "Epoch 338/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3650 - accuracy: 0.8620 - recall: 0.8957 - val_loss: 0.4212 - val_accuracy: 0.8333 - val_recall: 0.9231\n",
      "Epoch 339/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3644 - accuracy: 0.8620 - recall: 0.8957 - val_loss: 0.4209 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 340/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3641 - accuracy: 0.8650 - recall: 0.8957 - val_loss: 0.4205 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 341/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3636 - accuracy: 0.8742 - recall: 0.8957 - val_loss: 0.4211 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 342/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3636 - accuracy: 0.8742 - recall: 0.8896 - val_loss: 0.4214 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 343/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3630 - accuracy: 0.8773 - recall: 0.8957 - val_loss: 0.4209 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 344/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3628 - accuracy: 0.8773 - recall: 0.8957 - val_loss: 0.4199 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 345/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3622 - accuracy: 0.8773 - recall: 0.8957 - val_loss: 0.4184 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 346/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3619 - accuracy: 0.8773 - recall: 0.8957 - val_loss: 0.4182 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 347/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3614 - accuracy: 0.8773 - recall: 0.8957 - val_loss: 0.4170 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 348/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3615 - accuracy: 0.8773 - recall: 0.8957 - val_loss: 0.4166 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 349/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3607 - accuracy: 0.8773 - recall: 0.8957 - val_loss: 0.4166 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 350/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3609 - accuracy: 0.8773 - recall: 0.8896 - val_loss: 0.4181 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 351/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3601 - accuracy: 0.8742 - recall: 0.8957 - val_loss: 0.4165 - val_accuracy: 0.8333 - val_recall: 0.9231\n",
      "Epoch 352/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3598 - accuracy: 0.8773 - recall: 0.8957 - val_loss: 0.4155 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 353/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3592 - accuracy: 0.8773 - recall: 0.8957 - val_loss: 0.4157 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 354/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3588 - accuracy: 0.8773 - recall: 0.8957 - val_loss: 0.4154 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 355/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3587 - accuracy: 0.8773 - recall: 0.8957 - val_loss: 0.4149 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 356/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3581 - accuracy: 0.8773 - recall: 0.8957 - val_loss: 0.4144 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 357/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3578 - accuracy: 0.8773 - recall: 0.8957 - val_loss: 0.4143 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 358/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3575 - accuracy: 0.8773 - recall: 0.8957 - val_loss: 0.4135 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 359/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3569 - accuracy: 0.8773 - recall: 0.8957 - val_loss: 0.4136 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 360/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3566 - accuracy: 0.8773 - recall: 0.8957 - val_loss: 0.4135 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 361/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3562 - accuracy: 0.8773 - recall: 0.8957 - val_loss: 0.4133 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 362/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3561 - accuracy: 0.8742 - recall: 0.8896 - val_loss: 0.4125 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 363/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3555 - accuracy: 0.8773 - recall: 0.8957 - val_loss: 0.4117 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 364/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3554 - accuracy: 0.8773 - recall: 0.9018 - val_loss: 0.4102 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 365/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3552 - accuracy: 0.8804 - recall: 0.9018 - val_loss: 0.4114 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 366/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3545 - accuracy: 0.8834 - recall: 0.9018 - val_loss: 0.4111 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 367/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3542 - accuracy: 0.8834 - recall: 0.9018 - val_loss: 0.4095 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 368/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3538 - accuracy: 0.8804 - recall: 0.9018 - val_loss: 0.4083 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 369/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3535 - accuracy: 0.8804 - recall: 0.9018 - val_loss: 0.4068 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 370/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3532 - accuracy: 0.8834 - recall: 0.9018 - val_loss: 0.4073 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 371/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3527 - accuracy: 0.8834 - recall: 0.9018 - val_loss: 0.4064 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 372/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3524 - accuracy: 0.8865 - recall: 0.9080 - val_loss: 0.4055 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 373/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3520 - accuracy: 0.8865 - recall: 0.9080 - val_loss: 0.4047 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 374/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3516 - accuracy: 0.8865 - recall: 0.9080 - val_loss: 0.4048 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 375/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3513 - accuracy: 0.8834 - recall: 0.9018 - val_loss: 0.4052 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 376/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3511 - accuracy: 0.8834 - recall: 0.9018 - val_loss: 0.4052 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 377/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3506 - accuracy: 0.8834 - recall: 0.9018 - val_loss: 0.4046 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 378/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3504 - accuracy: 0.8804 - recall: 0.8957 - val_loss: 0.4038 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 379/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3498 - accuracy: 0.8834 - recall: 0.9018 - val_loss: 0.4026 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 380/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3499 - accuracy: 0.8865 - recall: 0.9080 - val_loss: 0.4021 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 381/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3493 - accuracy: 0.8865 - recall: 0.9080 - val_loss: 0.4029 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 382/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3488 - accuracy: 0.8865 - recall: 0.9080 - val_loss: 0.4023 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 383/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3485 - accuracy: 0.8865 - recall: 0.9080 - val_loss: 0.4020 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 384/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3484 - accuracy: 0.8865 - recall: 0.9080 - val_loss: 0.4013 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 385/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3478 - accuracy: 0.8865 - recall: 0.9080 - val_loss: 0.4004 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 386/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3475 - accuracy: 0.8865 - recall: 0.9080 - val_loss: 0.4002 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 387/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3476 - accuracy: 0.8834 - recall: 0.9080 - val_loss: 0.3992 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 388/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3474 - accuracy: 0.8865 - recall: 0.9080 - val_loss: 0.3995 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 389/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.3468 - accuracy: 0.8896 - recall: 0.9080 - val_loss: 0.3995 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 390/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3466 - accuracy: 0.8865 - recall: 0.9080 - val_loss: 0.3983 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 391/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3460 - accuracy: 0.8865 - recall: 0.9080 - val_loss: 0.3977 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 392/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3455 - accuracy: 0.8865 - recall: 0.9080 - val_loss: 0.3986 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 393/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3453 - accuracy: 0.8896 - recall: 0.9080 - val_loss: 0.3996 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 394/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3449 - accuracy: 0.8896 - recall: 0.9080 - val_loss: 0.4001 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 395/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3446 - accuracy: 0.8896 - recall: 0.9080 - val_loss: 0.4002 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 396/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3443 - accuracy: 0.8896 - recall: 0.9018 - val_loss: 0.4002 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 397/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3440 - accuracy: 0.8896 - recall: 0.9080 - val_loss: 0.3991 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 398/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3436 - accuracy: 0.8896 - recall: 0.9080 - val_loss: 0.3986 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 399/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3435 - accuracy: 0.8896 - recall: 0.9080 - val_loss: 0.3982 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 400/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3429 - accuracy: 0.8896 - recall: 0.9080 - val_loss: 0.3975 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 401/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3427 - accuracy: 0.8896 - recall: 0.9080 - val_loss: 0.3971 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 402/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3424 - accuracy: 0.8896 - recall: 0.9080 - val_loss: 0.3960 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 403/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3419 - accuracy: 0.8896 - recall: 0.9080 - val_loss: 0.3951 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 404/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3418 - accuracy: 0.8896 - recall: 0.9080 - val_loss: 0.3947 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 405/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3414 - accuracy: 0.8865 - recall: 0.9018 - val_loss: 0.3950 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 406/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3411 - accuracy: 0.8896 - recall: 0.9018 - val_loss: 0.3948 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 407/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3414 - accuracy: 0.8865 - recall: 0.8896 - val_loss: 0.3957 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 408/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3407 - accuracy: 0.8865 - recall: 0.8896 - val_loss: 0.3939 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 409/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3402 - accuracy: 0.8926 - recall: 0.9018 - val_loss: 0.3922 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 410/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3397 - accuracy: 0.8896 - recall: 0.9018 - val_loss: 0.3917 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 411/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3395 - accuracy: 0.8896 - recall: 0.9018 - val_loss: 0.3911 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 412/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3393 - accuracy: 0.8926 - recall: 0.9080 - val_loss: 0.3902 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 413/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3387 - accuracy: 0.8896 - recall: 0.9080 - val_loss: 0.3899 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 414/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3383 - accuracy: 0.8926 - recall: 0.9080 - val_loss: 0.3895 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 415/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3381 - accuracy: 0.8896 - recall: 0.9080 - val_loss: 0.3900 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 416/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3377 - accuracy: 0.8926 - recall: 0.9080 - val_loss: 0.3884 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 417/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3376 - accuracy: 0.8926 - recall: 0.9080 - val_loss: 0.3882 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 418/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3370 - accuracy: 0.8896 - recall: 0.9080 - val_loss: 0.3879 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 419/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3368 - accuracy: 0.8896 - recall: 0.9080 - val_loss: 0.3874 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 420/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3365 - accuracy: 0.8896 - recall: 0.9080 - val_loss: 0.3875 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 421/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3363 - accuracy: 0.8865 - recall: 0.9080 - val_loss: 0.3857 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 422/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3359 - accuracy: 0.8896 - recall: 0.9080 - val_loss: 0.3869 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 423/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3356 - accuracy: 0.8957 - recall: 0.9080 - val_loss: 0.3876 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 424/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3352 - accuracy: 0.8957 - recall: 0.9080 - val_loss: 0.3875 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 425/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3350 - accuracy: 0.8957 - recall: 0.9080 - val_loss: 0.3877 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 426/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3346 - accuracy: 0.8957 - recall: 0.9080 - val_loss: 0.3872 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 427/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3347 - accuracy: 0.8926 - recall: 0.9018 - val_loss: 0.3878 - val_accuracy: 0.8636 - val_recall: 0.9231\n",
      "Epoch 428/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3343 - accuracy: 0.8926 - recall: 0.9018 - val_loss: 0.3875 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 429/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3342 - accuracy: 0.8957 - recall: 0.9080 - val_loss: 0.3845 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 430/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3333 - accuracy: 0.8957 - recall: 0.9080 - val_loss: 0.3841 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 431/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3331 - accuracy: 0.8957 - recall: 0.9080 - val_loss: 0.3827 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 432/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3325 - accuracy: 0.8957 - recall: 0.9080 - val_loss: 0.3825 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 433/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3322 - accuracy: 0.8957 - recall: 0.9080 - val_loss: 0.3826 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 434/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3319 - accuracy: 0.8957 - recall: 0.9080 - val_loss: 0.3829 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 435/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3315 - accuracy: 0.8957 - recall: 0.9080 - val_loss: 0.3824 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 436/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3313 - accuracy: 0.8957 - recall: 0.9080 - val_loss: 0.3815 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 437/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3309 - accuracy: 0.8957 - recall: 0.9080 - val_loss: 0.3802 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 438/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3305 - accuracy: 0.8957 - recall: 0.9080 - val_loss: 0.3795 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 439/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3301 - accuracy: 0.8957 - recall: 0.9080 - val_loss: 0.3789 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 440/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3301 - accuracy: 0.8957 - recall: 0.9080 - val_loss: 0.3791 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 441/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3296 - accuracy: 0.8957 - recall: 0.9080 - val_loss: 0.3790 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 442/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3297 - accuracy: 0.8957 - recall: 0.9080 - val_loss: 0.3810 - val_accuracy: 0.8636 - val_recall: 0.9231\n",
      "Epoch 443/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3292 - accuracy: 0.8988 - recall: 0.9080 - val_loss: 0.3794 - val_accuracy: 0.8636 - val_recall: 0.9231\n",
      "Epoch 444/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3291 - accuracy: 0.8957 - recall: 0.9080 - val_loss: 0.3770 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 445/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3284 - accuracy: 0.8957 - recall: 0.9080 - val_loss: 0.3763 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 446/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3281 - accuracy: 0.8957 - recall: 0.9080 - val_loss: 0.3769 - val_accuracy: 0.8636 - val_recall: 0.9231\n",
      "Epoch 447/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3279 - accuracy: 0.8957 - recall: 0.9080 - val_loss: 0.3762 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 448/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3274 - accuracy: 0.8957 - recall: 0.9080 - val_loss: 0.3764 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 449/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3273 - accuracy: 0.8957 - recall: 0.9080 - val_loss: 0.3764 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 450/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3268 - accuracy: 0.8957 - recall: 0.9080 - val_loss: 0.3760 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 451/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3266 - accuracy: 0.8957 - recall: 0.9080 - val_loss: 0.3749 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 452/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3263 - accuracy: 0.8957 - recall: 0.9080 - val_loss: 0.3747 - val_accuracy: 0.8636 - val_recall: 0.9487\n",
      "Epoch 453/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3260 - accuracy: 0.8988 - recall: 0.9141 - val_loss: 0.3745 - val_accuracy: 0.8636 - val_recall: 0.9487\n",
      "Epoch 454/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3257 - accuracy: 0.8988 - recall: 0.9141 - val_loss: 0.3731 - val_accuracy: 0.8636 - val_recall: 0.9487\n",
      "Epoch 455/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3254 - accuracy: 0.8988 - recall: 0.9141 - val_loss: 0.3739 - val_accuracy: 0.8636 - val_recall: 0.9487\n",
      "Epoch 456/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3250 - accuracy: 0.8988 - recall: 0.9141 - val_loss: 0.3739 - val_accuracy: 0.8636 - val_recall: 0.9487\n",
      "Epoch 457/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3251 - accuracy: 0.8957 - recall: 0.9080 - val_loss: 0.3752 - val_accuracy: 0.8485 - val_recall: 0.9231\n",
      "Epoch 458/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3245 - accuracy: 0.8988 - recall: 0.9141 - val_loss: 0.3739 - val_accuracy: 0.8636 - val_recall: 0.9487\n",
      "Epoch 459/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3241 - accuracy: 0.8988 - recall: 0.9141 - val_loss: 0.3737 - val_accuracy: 0.8636 - val_recall: 0.9487\n",
      "Epoch 460/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3238 - accuracy: 0.8988 - recall: 0.9141 - val_loss: 0.3728 - val_accuracy: 0.8636 - val_recall: 0.9487\n",
      "Epoch 461/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3236 - accuracy: 0.8988 - recall: 0.9141 - val_loss: 0.3725 - val_accuracy: 0.8636 - val_recall: 0.9487\n",
      "Epoch 462/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3233 - accuracy: 0.8988 - recall: 0.9141 - val_loss: 0.3726 - val_accuracy: 0.8636 - val_recall: 0.9487\n",
      "Epoch 463/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3230 - accuracy: 0.8988 - recall: 0.9141 - val_loss: 0.3730 - val_accuracy: 0.8636 - val_recall: 0.9487\n",
      "Epoch 464/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3227 - accuracy: 0.8988 - recall: 0.9141 - val_loss: 0.3738 - val_accuracy: 0.8636 - val_recall: 0.9487\n",
      "Epoch 465/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3224 - accuracy: 0.8988 - recall: 0.9141 - val_loss: 0.3737 - val_accuracy: 0.8636 - val_recall: 0.9487\n",
      "Epoch 466/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3221 - accuracy: 0.8988 - recall: 0.9141 - val_loss: 0.3733 - val_accuracy: 0.8636 - val_recall: 0.9487\n",
      "Epoch 467/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3217 - accuracy: 0.8988 - recall: 0.9141 - val_loss: 0.3726 - val_accuracy: 0.8636 - val_recall: 0.9487\n",
      "Epoch 468/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3214 - accuracy: 0.8988 - recall: 0.9141 - val_loss: 0.3712 - val_accuracy: 0.8636 - val_recall: 0.9487\n",
      "Epoch 469/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3210 - accuracy: 0.8988 - recall: 0.9141 - val_loss: 0.3696 - val_accuracy: 0.8636 - val_recall: 0.9487\n",
      "Epoch 470/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3207 - accuracy: 0.8988 - recall: 0.9141 - val_loss: 0.3691 - val_accuracy: 0.8636 - val_recall: 0.9487\n",
      "Epoch 471/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.3208 - accuracy: 0.8988 - recall: 0.9141 - val_loss: 0.3698 - val_accuracy: 0.8636 - val_recall: 0.9487\n",
      "Epoch 472/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3203 - accuracy: 0.8988 - recall: 0.9141 - val_loss: 0.3686 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 473/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3198 - accuracy: 0.9018 - recall: 0.9141 - val_loss: 0.3688 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 474/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3196 - accuracy: 0.9018 - recall: 0.9141 - val_loss: 0.3682 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 475/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3197 - accuracy: 0.9018 - recall: 0.9141 - val_loss: 0.3674 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 476/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3193 - accuracy: 0.9018 - recall: 0.9141 - val_loss: 0.3661 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 477/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3187 - accuracy: 0.9018 - recall: 0.9141 - val_loss: 0.3661 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 478/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3185 - accuracy: 0.9018 - recall: 0.9141 - val_loss: 0.3657 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 479/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3182 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3651 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 480/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3183 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3636 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 481/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3180 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3643 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 482/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3175 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3631 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 483/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3170 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3639 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 484/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3167 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3639 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 485/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3164 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3633 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 486/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3159 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3631 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 487/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3156 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3638 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 488/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3155 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3628 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 489/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3152 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3614 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 490/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3147 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3602 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 491/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3147 - accuracy: 0.9018 - recall: 0.9202 - val_loss: 0.3599 - val_accuracy: 0.8636 - val_recall: 0.9487\n",
      "Epoch 492/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3144 - accuracy: 0.9018 - recall: 0.9202 - val_loss: 0.3595 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 493/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3140 - accuracy: 0.9018 - recall: 0.9202 - val_loss: 0.3596 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 494/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3137 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3596 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 495/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3137 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3581 - val_accuracy: 0.8636 - val_recall: 0.9487\n",
      "Epoch 496/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.3132 - accuracy: 0.9018 - recall: 0.9202 - val_loss: 0.3587 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 497/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3128 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3587 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 498/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3128 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3590 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 499/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3122 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3584 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 500/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3121 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3578 - val_accuracy: 0.8636 - val_recall: 0.9487\n",
      "Epoch 501/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3116 - accuracy: 0.9018 - recall: 0.9202 - val_loss: 0.3579 - val_accuracy: 0.8636 - val_recall: 0.9487\n",
      "Epoch 502/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3115 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3569 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 503/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3112 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3562 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 504/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3107 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3558 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 505/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3105 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3566 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 506/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3100 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3567 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 507/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3098 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3561 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 508/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3095 - accuracy: 0.9018 - recall: 0.9202 - val_loss: 0.3541 - val_accuracy: 0.8636 - val_recall: 0.9487\n",
      "Epoch 509/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3094 - accuracy: 0.9018 - recall: 0.9202 - val_loss: 0.3534 - val_accuracy: 0.8636 - val_recall: 0.9487\n",
      "Epoch 510/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3093 - accuracy: 0.9018 - recall: 0.9202 - val_loss: 0.3543 - val_accuracy: 0.8636 - val_recall: 0.9487\n",
      "Epoch 511/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3088 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3545 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 512/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3085 - accuracy: 0.9018 - recall: 0.9141 - val_loss: 0.3555 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 513/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3083 - accuracy: 0.9018 - recall: 0.9141 - val_loss: 0.3550 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 514/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3080 - accuracy: 0.9018 - recall: 0.9141 - val_loss: 0.3547 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 515/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3076 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3541 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 516/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3072 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3534 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 517/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3069 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3529 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 518/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3068 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3533 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 519/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3064 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3527 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 520/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3060 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3521 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 521/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3058 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3511 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 522/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3055 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3505 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 523/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3052 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3519 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 524/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3052 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3504 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 525/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3046 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3495 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 526/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3044 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3496 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 527/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3042 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3500 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 528/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3037 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3490 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 529/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3036 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3479 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 530/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3033 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3468 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 531/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3029 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3459 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 532/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3026 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3459 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 533/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3023 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3459 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 534/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3019 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3464 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 535/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3019 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3463 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 536/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3014 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3460 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 537/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3011 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3458 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 538/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3011 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3462 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 539/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3008 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3461 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 540/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3004 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3466 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 541/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.3004 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3465 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 542/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2999 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3464 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 543/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2997 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3457 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 544/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2993 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3452 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 545/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2990 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3443 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 546/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2988 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3434 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 547/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2984 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3430 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 548/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2980 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3427 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 549/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2978 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3425 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 550/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2975 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3425 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 551/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2974 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3439 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 552/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2970 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3436 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 553/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2970 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3430 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 554/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2969 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3436 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 555/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2965 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3431 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 556/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2961 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3423 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 557/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2959 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3429 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 558/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2955 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3422 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 559/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2955 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3417 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 560/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2949 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3400 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 561/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2949 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3381 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 562/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2945 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3382 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 563/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2941 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3381 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 564/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2941 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3371 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 565/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2934 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3370 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 566/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2930 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3370 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 567/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2927 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3374 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 568/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2926 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3381 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 569/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2926 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3376 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 570/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2922 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3368 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 571/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2919 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3354 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 572/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2917 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3351 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 573/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2913 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3343 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 574/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2911 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3340 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 575/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2907 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3328 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 576/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2903 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3329 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 577/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2901 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3325 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 578/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2899 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3325 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 579/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2897 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3326 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 580/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2893 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3323 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 581/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2892 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3321 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 582/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2889 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3317 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 583/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2887 - accuracy: 0.9049 - recall: 0.9202 - val_loss: 0.3326 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 584/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2884 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3318 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 585/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2887 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3330 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 586/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2878 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3320 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 587/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2875 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3302 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 588/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2871 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3304 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 589/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2868 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3300 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 590/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2866 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3282 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 591/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2868 - accuracy: 0.9110 - recall: 0.9325 - val_loss: 0.3267 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 592/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2864 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3261 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 593/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2860 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3262 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 594/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2857 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3258 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 595/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2853 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3256 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 596/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2852 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3256 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 597/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2848 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3255 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 598/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2844 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3255 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 599/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2843 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3247 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 600/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2840 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3254 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 601/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2837 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3262 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 602/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2835 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3260 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 603/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2831 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3253 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 604/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2828 - accuracy: 0.9110 - recall: 0.9325 - val_loss: 0.3244 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 605/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2826 - accuracy: 0.9110 - recall: 0.9325 - val_loss: 0.3248 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 606/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2824 - accuracy: 0.9110 - recall: 0.9325 - val_loss: 0.3247 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 607/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2824 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3261 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 608/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2819 - accuracy: 0.9141 - recall: 0.9264 - val_loss: 0.3252 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 609/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2817 - accuracy: 0.9141 - recall: 0.9264 - val_loss: 0.3241 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 610/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2817 - accuracy: 0.9110 - recall: 0.9325 - val_loss: 0.3230 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 611/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2814 - accuracy: 0.9110 - recall: 0.9325 - val_loss: 0.3215 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 612/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2810 - accuracy: 0.9110 - recall: 0.9325 - val_loss: 0.3213 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 613/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2806 - accuracy: 0.9110 - recall: 0.9325 - val_loss: 0.3211 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 614/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2803 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3205 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 615/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2800 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3214 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 616/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2799 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3219 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 617/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2799 - accuracy: 0.9110 - recall: 0.9325 - val_loss: 0.3209 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 618/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2793 - accuracy: 0.9110 - recall: 0.9325 - val_loss: 0.3205 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 619/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2789 - accuracy: 0.9110 - recall: 0.9325 - val_loss: 0.3208 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 620/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2789 - accuracy: 0.9110 - recall: 0.9325 - val_loss: 0.3200 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 621/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2785 - accuracy: 0.9110 - recall: 0.9325 - val_loss: 0.3201 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 622/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2784 - accuracy: 0.9080 - recall: 0.9264 - val_loss: 0.3196 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 623/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2782 - accuracy: 0.9110 - recall: 0.9264 - val_loss: 0.3181 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 624/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2781 - accuracy: 0.9080 - recall: 0.9202 - val_loss: 0.3178 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 625/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2777 - accuracy: 0.9080 - recall: 0.9202 - val_loss: 0.3169 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 626/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2774 - accuracy: 0.9080 - recall: 0.9202 - val_loss: 0.3163 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 627/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2772 - accuracy: 0.9080 - recall: 0.9202 - val_loss: 0.3156 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 628/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2770 - accuracy: 0.9110 - recall: 0.9264 - val_loss: 0.3152 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 629/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2770 - accuracy: 0.9110 - recall: 0.9264 - val_loss: 0.3154 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 630/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2762 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3145 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 631/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2762 - accuracy: 0.9172 - recall: 0.9387 - val_loss: 0.3136 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 632/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2762 - accuracy: 0.9141 - recall: 0.9387 - val_loss: 0.3128 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 633/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2756 - accuracy: 0.9172 - recall: 0.9387 - val_loss: 0.3128 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 634/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2752 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3132 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 635/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2749 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3135 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 636/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2748 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3126 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 637/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2744 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3127 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 638/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2742 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3128 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 639/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2738 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3125 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 640/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2736 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3119 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 641/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2734 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3117 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 642/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2731 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3116 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 643/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2729 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3114 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 644/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2728 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3113 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 645/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2725 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3101 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 646/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2722 - accuracy: 0.9110 - recall: 0.9325 - val_loss: 0.3098 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 647/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2720 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3099 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 648/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2715 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3102 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 649/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2714 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3107 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 650/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2714 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3107 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 651/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2708 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3105 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 652/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2708 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3109 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 653/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2705 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3106 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 654/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2703 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3103 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 655/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2702 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3094 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 656/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2698 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3094 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 657/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2696 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3089 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 658/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2692 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3099 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 659/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2690 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3102 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 660/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2687 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3104 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 661/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2684 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3110 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 662/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2682 - accuracy: 0.9172 - recall: 0.9325 - val_loss: 0.3105 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 663/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2680 - accuracy: 0.9172 - recall: 0.9325 - val_loss: 0.3102 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 664/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2676 - accuracy: 0.9172 - recall: 0.9325 - val_loss: 0.3090 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 665/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2674 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3086 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 666/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2672 - accuracy: 0.9141 - recall: 0.9325 - val_loss: 0.3071 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 667/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2670 - accuracy: 0.9172 - recall: 0.9387 - val_loss: 0.3050 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 668/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2666 - accuracy: 0.9172 - recall: 0.9387 - val_loss: 0.3045 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 669/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2663 - accuracy: 0.9172 - recall: 0.9387 - val_loss: 0.3043 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 670/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2662 - accuracy: 0.9172 - recall: 0.9387 - val_loss: 0.3033 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 671/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2662 - accuracy: 0.9172 - recall: 0.9387 - val_loss: 0.3040 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 672/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2661 - accuracy: 0.9202 - recall: 0.9448 - val_loss: 0.3037 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 673/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2657 - accuracy: 0.9172 - recall: 0.9387 - val_loss: 0.3039 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 674/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2652 - accuracy: 0.9202 - recall: 0.9387 - val_loss: 0.3045 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 675/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2648 - accuracy: 0.9202 - recall: 0.9387 - val_loss: 0.3041 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 676/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2647 - accuracy: 0.9141 - recall: 0.9264 - val_loss: 0.3031 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 677/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2649 - accuracy: 0.9141 - recall: 0.9264 - val_loss: 0.3022 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 678/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2646 - accuracy: 0.9172 - recall: 0.9325 - val_loss: 0.3005 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 679/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2643 - accuracy: 0.9202 - recall: 0.9387 - val_loss: 0.3013 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 680/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2635 - accuracy: 0.9202 - recall: 0.9387 - val_loss: 0.3010 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 681/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2634 - accuracy: 0.9202 - recall: 0.9387 - val_loss: 0.3005 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 682/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2634 - accuracy: 0.9202 - recall: 0.9387 - val_loss: 0.2996 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 683/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2631 - accuracy: 0.9202 - recall: 0.9387 - val_loss: 0.2992 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 684/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2626 - accuracy: 0.9202 - recall: 0.9387 - val_loss: 0.3016 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 685/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2625 - accuracy: 0.9202 - recall: 0.9325 - val_loss: 0.3022 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 686/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2622 - accuracy: 0.9202 - recall: 0.9325 - val_loss: 0.3019 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 687/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2620 - accuracy: 0.9202 - recall: 0.9387 - val_loss: 0.3012 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 688/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2618 - accuracy: 0.9202 - recall: 0.9387 - val_loss: 0.3007 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 689/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2616 - accuracy: 0.9202 - recall: 0.9387 - val_loss: 0.3004 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 690/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2614 - accuracy: 0.9202 - recall: 0.9387 - val_loss: 0.2998 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 691/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2610 - accuracy: 0.9202 - recall: 0.9387 - val_loss: 0.2992 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 692/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2607 - accuracy: 0.9233 - recall: 0.9448 - val_loss: 0.2973 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 693/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2605 - accuracy: 0.9233 - recall: 0.9448 - val_loss: 0.2969 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 694/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2604 - accuracy: 0.9233 - recall: 0.9448 - val_loss: 0.2971 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 695/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2600 - accuracy: 0.9233 - recall: 0.9448 - val_loss: 0.2961 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 696/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2598 - accuracy: 0.9233 - recall: 0.9448 - val_loss: 0.2946 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 697/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2595 - accuracy: 0.9233 - recall: 0.9448 - val_loss: 0.2940 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 698/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2594 - accuracy: 0.9233 - recall: 0.9448 - val_loss: 0.2939 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 699/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2591 - accuracy: 0.9233 - recall: 0.9448 - val_loss: 0.2931 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 700/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2590 - accuracy: 0.9233 - recall: 0.9448 - val_loss: 0.2927 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 701/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2587 - accuracy: 0.9233 - recall: 0.9448 - val_loss: 0.2927 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 702/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2583 - accuracy: 0.9233 - recall: 0.9448 - val_loss: 0.2928 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 703/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2583 - accuracy: 0.9233 - recall: 0.9448 - val_loss: 0.2928 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 704/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2580 - accuracy: 0.9233 - recall: 0.9448 - val_loss: 0.2908 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 705/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2577 - accuracy: 0.9233 - recall: 0.9448 - val_loss: 0.2911 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 706/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2577 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2912 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 707/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2574 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2906 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 708/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2572 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2902 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 709/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2567 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2906 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 710/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2564 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2910 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 711/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2564 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2916 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 712/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2561 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2917 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 713/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2558 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2916 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 714/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2554 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2920 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 715/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2554 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2926 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 716/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2551 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2918 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 717/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2547 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2926 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 718/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2546 - accuracy: 0.9233 - recall: 0.9387 - val_loss: 0.2924 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 719/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2549 - accuracy: 0.9233 - recall: 0.9325 - val_loss: 0.2919 - val_accuracy: 0.8939 - val_recall: 0.9487\n",
      "Epoch 720/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2543 - accuracy: 0.9202 - recall: 0.9325 - val_loss: 0.2909 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 721/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2539 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2904 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 722/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2536 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2900 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 723/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2534 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2891 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 724/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2531 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2884 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 725/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2530 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2883 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 726/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2527 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2882 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 727/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2525 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2877 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 728/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2520 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2877 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 729/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2520 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2877 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 730/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2518 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2877 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 731/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2515 - accuracy: 0.9233 - recall: 0.9387 - val_loss: 0.2873 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 732/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2511 - accuracy: 0.9233 - recall: 0.9387 - val_loss: 0.2861 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 733/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2513 - accuracy: 0.9202 - recall: 0.9387 - val_loss: 0.2850 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 734/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2511 - accuracy: 0.9233 - recall: 0.9387 - val_loss: 0.2870 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 735/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2506 - accuracy: 0.9233 - recall: 0.9387 - val_loss: 0.2871 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 736/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2506 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2874 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 737/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2501 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2883 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 738/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2501 - accuracy: 0.9233 - recall: 0.9387 - val_loss: 0.2875 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 739/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2496 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2865 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 740/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2496 - accuracy: 0.9233 - recall: 0.9387 - val_loss: 0.2848 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 741/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2492 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2853 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 742/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2491 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2854 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 743/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2489 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2853 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 744/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2484 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2839 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 745/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2481 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2837 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 746/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2478 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2837 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 747/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2477 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2832 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 748/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2475 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2834 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 749/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2474 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2823 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 750/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2470 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2821 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 751/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2468 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2819 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 752/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2468 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2819 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 753/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2465 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2820 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 754/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2460 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2821 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 755/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2460 - accuracy: 0.9233 - recall: 0.9387 - val_loss: 0.2825 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 756/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2461 - accuracy: 0.9233 - recall: 0.9387 - val_loss: 0.2824 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 757/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2459 - accuracy: 0.9264 - recall: 0.9387 - val_loss: 0.2814 - val_accuracy: 0.8939 - val_recall: 0.9487\n",
      "Epoch 758/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2455 - accuracy: 0.9264 - recall: 0.9387 - val_loss: 0.2799 - val_accuracy: 0.8939 - val_recall: 0.9487\n",
      "Epoch 759/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2453 - accuracy: 0.9233 - recall: 0.9387 - val_loss: 0.2793 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 760/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2450 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2794 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 761/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2448 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2784 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 762/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2444 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2783 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 763/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2442 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2779 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 764/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2441 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2778 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 765/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2439 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2785 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 766/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2435 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2785 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 767/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2437 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2787 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 768/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2439 - accuracy: 0.9233 - recall: 0.9448 - val_loss: 0.2785 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 769/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2431 - accuracy: 0.9233 - recall: 0.9448 - val_loss: 0.2782 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 770/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2427 - accuracy: 0.9233 - recall: 0.9448 - val_loss: 0.2785 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 771/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2426 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2791 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 772/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2422 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2792 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 773/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2420 - accuracy: 0.9233 - recall: 0.9387 - val_loss: 0.2779 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 774/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2420 - accuracy: 0.9233 - recall: 0.9387 - val_loss: 0.2768 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 775/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2416 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2770 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 776/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2413 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2763 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 777/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2412 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2754 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 778/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2410 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2753 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 779/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2409 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2749 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 780/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2406 - accuracy: 0.9294 - recall: 0.9509 - val_loss: 0.2753 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 781/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2404 - accuracy: 0.9294 - recall: 0.9509 - val_loss: 0.2754 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 782/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2401 - accuracy: 0.9294 - recall: 0.9509 - val_loss: 0.2754 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 783/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2399 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2753 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 784/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2398 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2753 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 785/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2393 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2747 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 786/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2392 - accuracy: 0.9294 - recall: 0.9509 - val_loss: 0.2739 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 787/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2390 - accuracy: 0.9294 - recall: 0.9509 - val_loss: 0.2729 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 788/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2389 - accuracy: 0.9294 - recall: 0.9509 - val_loss: 0.2723 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 789/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2385 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2725 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 790/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2384 - accuracy: 0.9294 - recall: 0.9509 - val_loss: 0.2724 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 791/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2382 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2731 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 792/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2379 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2733 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 793/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2377 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2727 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 794/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2375 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2712 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 795/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2372 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2707 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 796/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2369 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2700 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 797/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2368 - accuracy: 0.9294 - recall: 0.9448 - val_loss: 0.2696 - val_accuracy: 0.8939 - val_recall: 0.9487\n",
      "Epoch 798/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2366 - accuracy: 0.9294 - recall: 0.9448 - val_loss: 0.2697 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 799/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2365 - accuracy: 0.9264 - recall: 0.9448 - val_loss: 0.2692 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 800/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2361 - accuracy: 0.9294 - recall: 0.9448 - val_loss: 0.2687 - val_accuracy: 0.8939 - val_recall: 0.9487\n",
      "Epoch 801/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2359 - accuracy: 0.9294 - recall: 0.9448 - val_loss: 0.2681 - val_accuracy: 0.8939 - val_recall: 0.9487\n",
      "Epoch 802/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2357 - accuracy: 0.9294 - recall: 0.9448 - val_loss: 0.2680 - val_accuracy: 0.8939 - val_recall: 0.9487\n",
      "Epoch 803/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2359 - accuracy: 0.9294 - recall: 0.9509 - val_loss: 0.2671 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 804/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2352 - accuracy: 0.9325 - recall: 0.9509 - val_loss: 0.2674 - val_accuracy: 0.8939 - val_recall: 0.9487\n",
      "Epoch 805/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2349 - accuracy: 0.9294 - recall: 0.9509 - val_loss: 0.2674 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 806/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2348 - accuracy: 0.9294 - recall: 0.9509 - val_loss: 0.2659 - val_accuracy: 0.8788 - val_recall: 0.9487\n",
      "Epoch 807/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2348 - accuracy: 0.9325 - recall: 0.9571 - val_loss: 0.2648 - val_accuracy: 0.8939 - val_recall: 0.9744\n",
      "Epoch 808/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2345 - accuracy: 0.9325 - recall: 0.9571 - val_loss: 0.2649 - val_accuracy: 0.8939 - val_recall: 0.9744\n",
      "Epoch 809/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2343 - accuracy: 0.9294 - recall: 0.9509 - val_loss: 0.2654 - val_accuracy: 0.8939 - val_recall: 0.9744\n",
      "Epoch 810/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2338 - accuracy: 0.9325 - recall: 0.9509 - val_loss: 0.2658 - val_accuracy: 0.8939 - val_recall: 0.9487\n",
      "Epoch 811/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2338 - accuracy: 0.9294 - recall: 0.9448 - val_loss: 0.2660 - val_accuracy: 0.8939 - val_recall: 0.9487\n",
      "Epoch 812/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2336 - accuracy: 0.9294 - recall: 0.9448 - val_loss: 0.2652 - val_accuracy: 0.8939 - val_recall: 0.9487\n",
      "Epoch 813/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2334 - accuracy: 0.9294 - recall: 0.9448 - val_loss: 0.2647 - val_accuracy: 0.8939 - val_recall: 0.9487\n",
      "Epoch 814/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2332 - accuracy: 0.9294 - recall: 0.9448 - val_loss: 0.2645 - val_accuracy: 0.8939 - val_recall: 0.9487\n",
      "Epoch 815/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2329 - accuracy: 0.9294 - recall: 0.9448 - val_loss: 0.2649 - val_accuracy: 0.8939 - val_recall: 0.9487\n",
      "Epoch 816/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2327 - accuracy: 0.9294 - recall: 0.9448 - val_loss: 0.2641 - val_accuracy: 0.8939 - val_recall: 0.9487\n",
      "Epoch 817/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2325 - accuracy: 0.9294 - recall: 0.9448 - val_loss: 0.2642 - val_accuracy: 0.8939 - val_recall: 0.9487\n",
      "Epoch 818/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2322 - accuracy: 0.9294 - recall: 0.9448 - val_loss: 0.2643 - val_accuracy: 0.8939 - val_recall: 0.9487\n",
      "Epoch 819/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2320 - accuracy: 0.9294 - recall: 0.9448 - val_loss: 0.2640 - val_accuracy: 0.8939 - val_recall: 0.9487\n",
      "Epoch 820/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2318 - accuracy: 0.9294 - recall: 0.9448 - val_loss: 0.2632 - val_accuracy: 0.8939 - val_recall: 0.9487\n",
      "Epoch 821/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2315 - accuracy: 0.9294 - recall: 0.9448 - val_loss: 0.2620 - val_accuracy: 0.8939 - val_recall: 0.9487\n",
      "Epoch 822/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2314 - accuracy: 0.9294 - recall: 0.9448 - val_loss: 0.2620 - val_accuracy: 0.8939 - val_recall: 0.9487\n",
      "Epoch 823/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2312 - accuracy: 0.9294 - recall: 0.9448 - val_loss: 0.2621 - val_accuracy: 0.8939 - val_recall: 0.9487\n",
      "Epoch 824/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2311 - accuracy: 0.9294 - recall: 0.9448 - val_loss: 0.2621 - val_accuracy: 0.8939 - val_recall: 0.9487\n",
      "Epoch 825/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2307 - accuracy: 0.9294 - recall: 0.9448 - val_loss: 0.2611 - val_accuracy: 0.8939 - val_recall: 0.9487\n",
      "Epoch 826/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2306 - accuracy: 0.9294 - recall: 0.9448 - val_loss: 0.2607 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 827/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2303 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2604 - val_accuracy: 0.8939 - val_recall: 0.9744\n",
      "Epoch 828/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2304 - accuracy: 0.9325 - recall: 0.9571 - val_loss: 0.2592 - val_accuracy: 0.8939 - val_recall: 0.9744\n",
      "Epoch 829/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2301 - accuracy: 0.9325 - recall: 0.9571 - val_loss: 0.2587 - val_accuracy: 0.8939 - val_recall: 0.9744\n",
      "Epoch 830/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2301 - accuracy: 0.9325 - recall: 0.9509 - val_loss: 0.2592 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 831/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2295 - accuracy: 0.9325 - recall: 0.9509 - val_loss: 0.2589 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 832/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2294 - accuracy: 0.9325 - recall: 0.9509 - val_loss: 0.2593 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 833/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2290 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2579 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 834/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2288 - accuracy: 0.9325 - recall: 0.9571 - val_loss: 0.2573 - val_accuracy: 0.8939 - val_recall: 0.9744\n",
      "Epoch 835/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2288 - accuracy: 0.9325 - recall: 0.9571 - val_loss: 0.2571 - val_accuracy: 0.8939 - val_recall: 0.9744\n",
      "Epoch 836/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2285 - accuracy: 0.9325 - recall: 0.9571 - val_loss: 0.2571 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 837/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2282 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2581 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 838/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2280 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2586 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 839/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2278 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2584 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 840/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2274 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2592 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 841/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2275 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2599 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 842/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2273 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2592 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 843/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2270 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2589 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 844/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2268 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2577 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 845/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2267 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2572 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 846/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2263 - accuracy: 0.9325 - recall: 0.9509 - val_loss: 0.2573 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 847/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2261 - accuracy: 0.9325 - recall: 0.9509 - val_loss: 0.2575 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 848/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2259 - accuracy: 0.9325 - recall: 0.9509 - val_loss: 0.2582 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 849/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2258 - accuracy: 0.9325 - recall: 0.9509 - val_loss: 0.2571 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 850/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2254 - accuracy: 0.9325 - recall: 0.9509 - val_loss: 0.2569 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 851/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2252 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2559 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 852/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2251 - accuracy: 0.9325 - recall: 0.9509 - val_loss: 0.2562 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 853/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2248 - accuracy: 0.9325 - recall: 0.9509 - val_loss: 0.2565 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 854/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2245 - accuracy: 0.9325 - recall: 0.9509 - val_loss: 0.2563 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 855/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2244 - accuracy: 0.9325 - recall: 0.9509 - val_loss: 0.2559 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 856/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2245 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2549 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 857/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2239 - accuracy: 0.9325 - recall: 0.9509 - val_loss: 0.2547 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 858/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2239 - accuracy: 0.9325 - recall: 0.9509 - val_loss: 0.2535 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 859/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2237 - accuracy: 0.9325 - recall: 0.9509 - val_loss: 0.2533 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 860/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2234 - accuracy: 0.9325 - recall: 0.9509 - val_loss: 0.2534 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 861/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2233 - accuracy: 0.9325 - recall: 0.9509 - val_loss: 0.2536 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 862/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2230 - accuracy: 0.9325 - recall: 0.9509 - val_loss: 0.2534 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 863/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2228 - accuracy: 0.9325 - recall: 0.9509 - val_loss: 0.2527 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 864/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2225 - accuracy: 0.9325 - recall: 0.9509 - val_loss: 0.2521 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 865/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2225 - accuracy: 0.9325 - recall: 0.9509 - val_loss: 0.2523 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 866/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2224 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2512 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 867/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2221 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2507 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 868/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2218 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2503 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 869/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2217 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2501 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 870/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2214 - accuracy: 0.9325 - recall: 0.9509 - val_loss: 0.2503 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 871/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2212 - accuracy: 0.9325 - recall: 0.9509 - val_loss: 0.2499 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 872/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2211 - accuracy: 0.9325 - recall: 0.9509 - val_loss: 0.2489 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 873/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2210 - accuracy: 0.9325 - recall: 0.9509 - val_loss: 0.2478 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 874/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2209 - accuracy: 0.9356 - recall: 0.9509 - val_loss: 0.2477 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 875/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2206 - accuracy: 0.9356 - recall: 0.9509 - val_loss: 0.2465 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 876/2000\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.2203 - accuracy: 0.9356 - recall: 0.9509 - val_loss: 0.2467 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 877/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2201 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2471 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 878/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2198 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2467 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 879/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2197 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2457 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 880/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2193 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2459 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 881/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2193 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2456 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 882/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2191 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2461 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 883/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2188 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2454 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 884/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2186 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2454 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 885/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2188 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2446 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 886/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2181 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2443 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 887/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2180 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2450 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 888/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2180 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2456 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 889/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2175 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2458 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 890/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2173 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2459 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 891/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2170 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2459 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 892/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2168 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2458 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 893/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2166 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2456 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 894/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2163 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2454 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 895/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2164 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2449 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 896/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2162 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2451 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 897/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2159 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2457 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 898/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2156 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2448 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 899/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2156 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2443 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 900/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2158 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2435 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 901/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2152 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2441 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 902/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2149 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2447 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 903/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2148 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2444 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 904/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2145 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2440 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 905/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2143 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2440 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 906/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2141 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2441 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 907/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2139 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2440 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 908/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2137 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2438 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 909/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2136 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2437 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 910/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2133 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2432 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 911/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2133 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2440 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 912/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2132 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2430 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 913/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2127 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2425 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 914/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2125 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2424 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 915/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2127 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2435 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 916/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2123 - accuracy: 0.9356 - recall: 0.9509 - val_loss: 0.2429 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 917/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2122 - accuracy: 0.9356 - recall: 0.9509 - val_loss: 0.2425 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 918/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2119 - accuracy: 0.9356 - recall: 0.9509 - val_loss: 0.2421 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 919/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2117 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2416 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 920/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2118 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2414 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 921/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2112 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2408 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 922/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2112 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2413 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 923/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2109 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2423 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 924/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2109 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2417 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 925/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2105 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2411 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 926/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2106 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2401 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 927/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2099 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2396 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 928/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2098 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2392 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 929/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2097 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2391 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 930/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2096 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2394 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 931/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2094 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2389 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 932/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2090 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2386 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 933/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2089 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2386 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 934/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2088 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2378 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 935/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2085 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2378 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 936/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2084 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2368 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 937/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2085 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2377 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 938/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2079 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2373 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 939/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2077 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2374 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 940/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2075 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2367 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 941/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2073 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2353 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 942/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2072 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2347 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 943/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2069 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2346 - val_accuracy: 0.9091 - val_recall: 0.9744\n",
      "Epoch 944/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2066 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2343 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 945/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2064 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2347 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 946/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2062 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2343 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 947/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2061 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2345 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 948/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2059 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2329 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 949/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2058 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2326 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 950/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2056 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2325 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 951/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2055 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2324 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 952/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2055 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2327 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 953/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2050 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2326 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 954/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.2053 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2331 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 955/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2047 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2332 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 956/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2044 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2330 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 957/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2042 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2327 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 958/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2039 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2330 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 959/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2039 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2326 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 960/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2038 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2323 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 961/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2035 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2323 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 962/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2033 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2315 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 963/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2032 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2312 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 964/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2030 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2305 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 965/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2027 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2296 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 966/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2025 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2298 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 967/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2025 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2296 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 968/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2021 - accuracy: 0.9356 - recall: 0.9571 - val_loss: 0.2298 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 969/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2023 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2295 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 970/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2018 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2294 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 971/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2016 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2286 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 972/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2014 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2285 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 973/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2013 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2287 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 974/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2010 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2285 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 975/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2008 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2279 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 976/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2007 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2277 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 977/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2005 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2271 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 978/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2003 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2269 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 979/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.2002 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2273 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 980/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1998 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2265 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 981/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1998 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2259 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 982/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1999 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2273 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 983/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1993 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2282 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 984/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1993 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2282 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 985/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1990 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2280 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 986/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1989 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2272 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 987/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1992 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2260 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 988/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1986 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2262 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 989/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1982 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2257 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 990/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1980 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2258 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 991/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1983 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2256 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 992/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1976 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2252 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 993/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1975 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2247 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 994/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1974 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2252 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 995/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1973 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2249 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 996/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1969 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2247 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 997/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1967 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2245 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 998/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1966 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2235 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 999/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1963 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2226 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1000/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1961 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2226 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1001/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1962 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2230 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1002/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1957 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2227 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1003/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1955 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2218 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1004/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1954 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2221 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1005/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1953 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2223 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1006/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1951 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2218 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1007/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1952 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2204 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1008/2000\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.1952 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2203 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1009/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1950 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2200 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1010/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1946 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2184 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1011/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1942 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2183 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1012/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1939 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2190 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1013/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1938 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2187 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1014/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1936 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2189 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1015/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1935 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2189 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1016/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1933 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2188 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1017/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1931 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2192 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1018/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1931 - accuracy: 0.9479 - recall: 0.9571 - val_loss: 0.2186 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1019/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1928 - accuracy: 0.9479 - recall: 0.9571 - val_loss: 0.2185 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1020/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1927 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2182 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1021/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1923 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2188 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1022/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1924 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2179 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1023/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1921 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2178 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1024/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1920 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2169 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1025/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1916 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2171 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1026/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1915 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2176 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1027/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1914 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2175 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1028/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1912 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2177 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1029/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1911 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2168 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1030/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1908 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2158 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1031/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1906 - accuracy: 0.9387 - recall: 0.9571 - val_loss: 0.2154 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1032/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1905 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2150 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1033/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1903 - accuracy: 0.9479 - recall: 0.9571 - val_loss: 0.2156 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1034/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1900 - accuracy: 0.9479 - recall: 0.9571 - val_loss: 0.2154 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1035/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1899 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2149 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1036/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1898 - accuracy: 0.9479 - recall: 0.9571 - val_loss: 0.2153 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1037/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1894 - accuracy: 0.9479 - recall: 0.9571 - val_loss: 0.2156 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1038/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1894 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2149 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1039/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1889 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2133 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1040/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1891 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2125 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1041/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1891 - accuracy: 0.9417 - recall: 0.9571 - val_loss: 0.2122 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1042/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1886 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2110 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1043/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1885 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2113 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1044/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1884 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2108 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1045/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1882 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2104 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1046/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1879 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2104 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1047/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1879 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2105 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1048/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1875 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2105 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1049/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1874 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2112 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1050/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1871 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2116 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1051/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1870 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2110 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1052/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1868 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2102 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1053/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1866 - accuracy: 0.9479 - recall: 0.9571 - val_loss: 0.2098 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1054/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1864 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2095 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1055/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1863 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2097 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1056/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1862 - accuracy: 0.9479 - recall: 0.9571 - val_loss: 0.2097 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1057/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1859 - accuracy: 0.9479 - recall: 0.9571 - val_loss: 0.2093 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1058/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1856 - accuracy: 0.9479 - recall: 0.9571 - val_loss: 0.2087 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1059/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1857 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2090 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1060/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1854 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2086 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1061/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1852 - accuracy: 0.9479 - recall: 0.9571 - val_loss: 0.2084 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1062/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1850 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2092 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1063/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1849 - accuracy: 0.9448 - recall: 0.9571 - val_loss: 0.2090 - val_accuracy: 0.9242 - val_recall: 0.9744\n",
      "Epoch 1064/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1849 - accuracy: 0.9479 - recall: 0.9571 - val_loss: 0.2079 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1065/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1848 - accuracy: 0.9479 - recall: 0.9571 - val_loss: 0.2072 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1066/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1845 - accuracy: 0.9479 - recall: 0.9571 - val_loss: 0.2073 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1067/2000\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.1844 - accuracy: 0.9479 - recall: 0.9571 - val_loss: 0.2069 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1068/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1841 - accuracy: 0.9479 - recall: 0.9571 - val_loss: 0.2067 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1069/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1840 - accuracy: 0.9479 - recall: 0.9571 - val_loss: 0.2079 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1070/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1837 - accuracy: 0.9479 - recall: 0.9571 - val_loss: 0.2077 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1071/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1834 - accuracy: 0.9479 - recall: 0.9571 - val_loss: 0.2081 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1072/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1833 - accuracy: 0.9479 - recall: 0.9571 - val_loss: 0.2078 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1073/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1832 - accuracy: 0.9479 - recall: 0.9571 - val_loss: 0.2077 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1074/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1833 - accuracy: 0.9479 - recall: 0.9571 - val_loss: 0.2071 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1075/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1830 - accuracy: 0.9479 - recall: 0.9571 - val_loss: 0.2069 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1076/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1826 - accuracy: 0.9479 - recall: 0.9571 - val_loss: 0.2068 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1077/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1827 - accuracy: 0.9479 - recall: 0.9571 - val_loss: 0.2071 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1078/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1823 - accuracy: 0.9479 - recall: 0.9571 - val_loss: 0.2070 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1079/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1822 - accuracy: 0.9479 - recall: 0.9571 - val_loss: 0.2057 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1080/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1819 - accuracy: 0.9479 - recall: 0.9571 - val_loss: 0.2065 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1081/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1818 - accuracy: 0.9479 - recall: 0.9571 - val_loss: 0.2074 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1082/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1819 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.2065 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1083/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1814 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.2062 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1084/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1812 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.2058 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1085/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1810 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.2054 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1086/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1809 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.2047 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1087/2000\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.1808 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.2033 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1088/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1809 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.2027 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1089/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1806 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.2023 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1090/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1801 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.2023 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1091/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1802 - accuracy: 0.9479 - recall: 0.9571 - val_loss: 0.2029 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1092/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1799 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.2037 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1093/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1799 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.2047 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1094/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1796 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.2040 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1095/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1796 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.2037 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1096/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1793 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.2030 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1097/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1790 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.2020 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1098/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1787 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.2015 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1099/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1787 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.2016 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1100/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1787 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.2014 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1101/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1786 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.2018 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1102/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1783 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.2014 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1103/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1781 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.2009 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1104/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1780 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.2015 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1105/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1777 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.2007 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1106/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1774 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.2003 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1107/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1776 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.2002 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1108/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1773 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.2010 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1109/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1771 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.2004 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1110/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1770 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.1997 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1111/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1767 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.1997 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1112/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1765 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.1993 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1113/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1764 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.1986 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1114/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1762 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.1992 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1115/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1762 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.1993 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1116/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1759 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.1992 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1117/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1759 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1996 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1118/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1755 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1984 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1119/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1756 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.1979 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1120/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1752 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.1982 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1121/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1751 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.1989 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1122/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1751 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.1988 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1123/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1747 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.1983 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1124/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1745 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.1971 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1125/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1746 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.1968 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1126/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1741 - accuracy: 0.9509 - recall: 0.9571 - val_loss: 0.1964 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1127/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1743 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1962 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1128/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1739 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1955 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1129/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1739 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1956 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1130/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1738 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1954 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1131/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1736 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1952 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1132/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1733 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1949 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1133/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1732 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1948 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1134/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1730 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1947 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1135/2000\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.1731 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1947 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1136/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1727 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1947 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1137/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1725 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1947 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1138/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1723 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1946 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1139/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1722 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1946 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1140/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1719 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1948 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1141/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1720 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1948 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1142/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1718 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1941 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1143/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1715 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1934 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1144/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1713 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1933 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1145/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1712 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1931 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1146/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1710 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1929 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1147/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1709 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1928 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1148/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1706 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1923 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1149/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1706 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1919 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1150/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1706 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1920 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1151/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1704 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1921 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1152/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1701 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1915 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1153/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1699 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1917 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1154/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1699 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1907 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1155/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1696 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1917 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1156/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1695 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1921 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1157/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1695 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1919 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1158/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1692 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1921 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1159/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1691 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1911 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1160/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1688 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1908 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1161/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1689 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1918 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1162/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1686 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1912 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1163/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1683 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1905 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1164/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1683 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1901 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1165/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1681 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1899 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1166/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1680 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1905 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1167/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1681 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1905 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1168/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1677 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1904 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1169/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1675 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1898 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1170/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1671 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1902 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1171/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1671 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1899 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1172/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1673 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1892 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1173/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1671 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1891 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1174/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1668 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1884 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1175/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1666 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1877 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1176/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1664 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1869 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1177/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1662 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1869 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1178/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1662 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1874 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1179/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1659 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1871 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1180/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1659 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1869 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1181/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1655 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1870 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1182/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1655 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1869 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1183/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1654 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1869 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1184/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1650 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1866 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1185/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1650 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1860 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1186/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1648 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1852 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1187/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1647 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1852 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1188/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1643 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1847 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1189/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1646 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1849 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1190/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1645 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1846 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1191/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1641 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1843 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1192/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1640 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1829 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1193/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1636 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1835 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1194/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1636 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1842 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1195/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1636 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1840 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1196/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1632 - accuracy: 0.9540 - recall: 0.9571 - val_loss: 0.1838 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1197/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1630 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1837 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1198/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1630 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1839 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1199/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1630 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1833 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1200/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1627 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1829 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1201/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1626 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1822 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1202/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1625 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1831 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1203/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1623 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1829 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1204/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1624 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1828 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1205/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1620 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1832 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1206/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1619 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1835 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1207/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1616 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1824 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1208/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1614 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1819 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1209/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1615 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1818 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1210/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1612 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1824 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1211/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1613 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1823 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1212/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1613 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1811 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1213/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1609 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1801 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1214/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1606 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1800 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1215/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1603 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1795 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1216/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1603 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1793 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1217/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1602 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1803 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1218/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1599 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1798 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1219/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1597 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1799 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1220/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1597 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1798 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1221/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1595 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1797 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1222/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1594 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1793 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1223/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1591 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1791 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1224/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1590 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1791 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1225/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1588 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1791 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1226/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1587 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1792 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1227/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1586 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1793 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1228/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1585 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1792 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1229/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1583 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1794 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1230/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1581 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1787 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1231/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1579 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1781 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1232/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1579 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1784 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1233/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1576 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1781 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1234/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1575 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1776 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1235/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1576 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1766 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1236/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1575 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1763 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1237/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1572 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1763 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1238/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1569 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1766 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1239/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1568 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1764 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1240/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1566 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1749 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1241/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1566 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1747 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1242/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1563 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1742 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1243/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1562 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1737 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1244/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1561 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1732 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1245/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1560 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1735 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1246/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1559 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1741 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1247/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1556 - accuracy: 0.9571 - recall: 0.9632 - val_loss: 0.1738 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1248/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1556 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1742 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1249/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1557 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1746 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1250/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1552 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1737 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1251/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1552 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1736 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1252/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1550 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1747 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1253/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1547 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1750 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1254/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1545 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1747 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1255/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1547 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1753 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1256/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1541 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1749 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1257/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1542 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1744 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1258/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1543 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1742 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1259/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1538 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1737 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1260/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1539 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1735 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1261/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1536 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1735 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1262/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1535 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1734 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1263/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1533 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1736 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1264/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1531 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1735 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1265/2000\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.1532 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1738 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1266/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1530 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1723 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1267/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1533 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1714 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1268/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1528 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1710 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1269/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1527 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1706 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1270/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1524 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1705 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1271/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1523 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1715 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1272/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1520 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1720 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1273/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1522 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1726 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1274/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1518 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1722 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1275/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1517 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1719 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1276/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1514 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1717 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1277/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1513 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1721 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1278/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1511 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1721 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1279/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1511 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1716 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1280/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1509 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1701 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1281/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1507 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1696 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1282/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1507 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1691 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1283/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1503 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1693 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1284/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1501 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1684 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1285/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1500 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1676 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1286/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1501 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1674 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1287/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1501 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1663 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1288/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1497 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1660 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1289/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1495 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1666 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1290/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1494 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1662 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1291/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1493 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1670 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1292/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1491 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1663 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1293/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1490 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1663 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1294/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1488 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1665 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1295/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1487 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1664 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1296/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1488 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1662 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1297/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1484 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1660 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1298/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1481 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1657 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1299/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1483 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1648 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1300/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1481 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1654 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1301/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1480 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1653 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1302/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1478 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1657 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1303/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1476 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1654 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1304/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1475 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1652 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1305/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1473 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1650 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1306/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1471 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1650 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1307/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1469 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1649 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1308/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1467 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1649 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1309/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1467 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1649 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1310/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1466 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1648 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1311/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1463 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1642 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1312/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1462 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1642 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1313/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1461 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1635 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1314/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1460 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1632 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1315/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1460 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1636 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1316/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1456 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1631 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1317/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1455 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1629 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1318/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1456 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1625 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1319/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1452 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1630 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1320/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1451 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1622 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1321/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1449 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1625 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1322/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1447 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1627 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1323/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1447 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1629 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1324/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1445 - accuracy: 0.9632 - recall: 0.9693 - val_loss: 0.1631 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1325/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1444 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1632 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1326/2000\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.1443 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1628 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1327/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1443 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1622 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1328/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1440 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1610 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1329/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1441 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1602 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1330/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1437 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1599 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1331/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1434 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1596 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1332/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1433 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1601 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1333/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1431 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1600 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1334/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1431 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1601 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1335/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1429 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1598 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1336/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1426 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1596 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1337/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1429 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1596 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1338/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1422 - accuracy: 0.9632 - recall: 0.9693 - val_loss: 0.1595 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1339/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1423 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1601 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1340/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1421 - accuracy: 0.9632 - recall: 0.9632 - val_loss: 0.1596 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1341/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1420 - accuracy: 0.9632 - recall: 0.9632 - val_loss: 0.1592 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1342/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1420 - accuracy: 0.9632 - recall: 0.9632 - val_loss: 0.1595 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1343/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1418 - accuracy: 0.9632 - recall: 0.9632 - val_loss: 0.1588 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1344/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1416 - accuracy: 0.9632 - recall: 0.9632 - val_loss: 0.1580 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1345/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1414 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1574 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1346/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1412 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1577 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1347/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1410 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1579 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1348/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1409 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1573 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1349/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1408 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1577 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1350/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1407 - accuracy: 0.9632 - recall: 0.9693 - val_loss: 0.1574 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1351/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1406 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1569 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1352/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1405 - accuracy: 0.9601 - recall: 0.9632 - val_loss: 0.1578 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1353/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1404 - accuracy: 0.9632 - recall: 0.9693 - val_loss: 0.1578 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1354/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1402 - accuracy: 0.9632 - recall: 0.9693 - val_loss: 0.1573 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1355/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1401 - accuracy: 0.9632 - recall: 0.9693 - val_loss: 0.1571 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1356/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1400 - accuracy: 0.9632 - recall: 0.9693 - val_loss: 0.1569 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1357/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1398 - accuracy: 0.9632 - recall: 0.9693 - val_loss: 0.1568 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1358/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1395 - accuracy: 0.9632 - recall: 0.9693 - val_loss: 0.1569 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1359/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1394 - accuracy: 0.9632 - recall: 0.9693 - val_loss: 0.1569 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1360/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1393 - accuracy: 0.9632 - recall: 0.9693 - val_loss: 0.1568 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1361/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1390 - accuracy: 0.9632 - recall: 0.9693 - val_loss: 0.1565 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1362/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1389 - accuracy: 0.9632 - recall: 0.9693 - val_loss: 0.1561 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1363/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1387 - accuracy: 0.9632 - recall: 0.9693 - val_loss: 0.1565 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1364/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1386 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1564 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1365/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1386 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1559 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1366/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1386 - accuracy: 0.9632 - recall: 0.9632 - val_loss: 0.1553 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1367/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1383 - accuracy: 0.9632 - recall: 0.9632 - val_loss: 0.1554 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1368/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1382 - accuracy: 0.9632 - recall: 0.9632 - val_loss: 0.1546 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1369/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1382 - accuracy: 0.9632 - recall: 0.9632 - val_loss: 0.1537 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1370/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1380 - accuracy: 0.9632 - recall: 0.9632 - val_loss: 0.1537 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1371/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1377 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1530 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1372/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1376 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1527 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1373/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1374 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1523 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1374/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1374 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1518 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1375/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1371 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1512 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1376/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1373 - accuracy: 0.9632 - recall: 0.9632 - val_loss: 0.1511 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1377/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1369 - accuracy: 0.9632 - recall: 0.9632 - val_loss: 0.1513 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1378/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1367 - accuracy: 0.9632 - recall: 0.9632 - val_loss: 0.1514 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1379/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1366 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1517 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1380/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1364 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1521 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1381/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1363 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1517 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1382/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1359 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1513 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1383/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1364 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1518 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1384/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1358 - accuracy: 0.9632 - recall: 0.9693 - val_loss: 0.1512 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1385/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1357 - accuracy: 0.9632 - recall: 0.9693 - val_loss: 0.1507 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1386/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1356 - accuracy: 0.9632 - recall: 0.9693 - val_loss: 0.1511 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1387/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1355 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1520 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1388/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1352 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1516 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1389/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1350 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1511 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1390/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1351 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1499 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1391/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1350 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1491 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1392/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1348 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1489 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1393/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1345 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1492 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1394/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1345 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1499 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1395/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1345 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1501 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1396/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1341 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1500 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1397/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1339 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1496 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1398/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1338 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1496 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1399/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1337 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1494 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1400/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1335 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1493 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1401/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1335 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1504 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1402/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1334 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1507 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1403/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1333 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1497 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1404/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1332 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1483 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1405/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1331 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1475 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1406/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1330 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1477 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1407/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1327 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1481 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1408/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1325 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1480 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1409/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1323 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1475 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1410/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1324 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1465 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1411/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1322 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1464 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1412/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1321 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1465 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1413/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1320 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1469 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1414/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1318 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1472 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1415/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1318 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1470 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1416/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1316 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1466 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1417/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1315 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1468 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1418/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1313 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1461 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1419/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1311 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1464 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1420/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1309 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1457 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1421/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1310 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1457 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1422/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1307 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1458 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1423/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1306 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1461 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1424/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1304 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1456 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1425/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1302 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1455 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1426/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1302 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1446 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1427/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1303 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1448 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1428/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1303 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1454 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1429/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1299 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1448 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1430/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1299 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1451 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1431/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1299 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1457 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1432/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1297 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1456 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1433/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1293 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1453 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1434/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1293 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1452 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1435/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1291 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1448 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1436/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1289 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1447 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1437/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1286 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1443 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1438/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1285 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1440 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1439/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1288 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1435 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1440/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1284 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1425 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1441/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1282 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1417 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1442/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1281 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1411 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1443/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1281 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1415 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1444/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1280 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1418 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1445/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1277 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1417 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1446/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1274 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1414 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1447/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1278 - accuracy: 0.9724 - recall: 0.9755 - val_loss: 0.1421 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1448/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1274 - accuracy: 0.9724 - recall: 0.9755 - val_loss: 0.1420 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1449/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1272 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1412 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1450/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1270 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1410 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1451/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1268 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1406 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1452/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1267 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1402 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1453/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1267 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1398 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1454/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1264 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1402 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1455/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1263 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1402 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1456/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1263 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1398 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1457/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1261 - accuracy: 0.9724 - recall: 0.9755 - val_loss: 0.1393 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1458/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1261 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1392 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1459/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1258 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1389 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1460/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1255 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1392 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1461/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1257 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1394 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1462/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1255 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1396 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1463/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1253 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1401 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1464/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1251 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1403 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1465/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1250 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1402 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1466/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1248 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1391 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1467/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1248 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1389 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1468/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1247 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1385 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1469/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1247 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1387 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1470/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1244 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1389 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1471/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1242 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1391 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1472/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1242 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1391 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1473/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1241 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1392 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1474/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1237 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1388 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1475/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1237 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1392 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1476/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1237 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1391 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1477/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1235 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1391 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1478/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1234 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1389 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1479/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1233 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1384 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1480/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1233 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1382 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1481/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1229 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1376 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1482/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1231 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1376 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1483/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1229 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1378 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1484/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1228 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1373 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1485/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1225 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1372 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1486/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1224 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1371 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1487/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1223 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1367 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1488/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1221 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1369 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1489/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1221 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1369 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1490/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1217 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1365 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1491/2000\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.1217 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1363 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1492/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1216 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1363 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1493/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1215 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1364 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1494/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1213 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1364 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1495/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1212 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1362 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1496/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1213 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1363 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1497/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1209 - accuracy: 0.9693 - recall: 0.9755 - val_loss: 0.1360 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1498/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1211 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1355 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1499/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1209 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1346 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1500/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1206 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1344 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1501/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1205 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1346 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1502/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1204 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1345 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1503/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1206 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1345 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1504/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1203 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1348 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1505/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1201 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1345 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1506/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1203 - accuracy: 0.9663 - recall: 0.9693 - val_loss: 0.1341 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1507/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1197 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1338 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1508/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1197 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1337 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1509/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1196 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1340 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1510/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1195 - accuracy: 0.9693 - recall: 0.9693 - val_loss: 0.1337 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1511/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1195 - accuracy: 0.9755 - recall: 0.9816 - val_loss: 0.1331 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1512/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1192 - accuracy: 0.9755 - recall: 0.9816 - val_loss: 0.1334 - val_accuracy: 0.9394 - val_recall: 0.9744\n",
      "Epoch 1513/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1193 - accuracy: 0.9724 - recall: 0.9755 - val_loss: 0.1328 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1514/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1190 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1330 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1515/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1189 - accuracy: 0.9724 - recall: 0.9755 - val_loss: 0.1330 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1516/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1187 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1329 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1517/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1185 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1329 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1518/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1185 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1327 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1519/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1183 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1322 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1520/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1182 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1321 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1521/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1180 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1321 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1522/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1178 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1321 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1523/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1178 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1321 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1524/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1178 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1320 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1525/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1177 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1317 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1526/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1174 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1322 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1527/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1173 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1327 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1528/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1173 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1327 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1529/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1172 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1323 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1530/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1172 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1310 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1531/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1168 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1306 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1532/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1167 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1306 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1533/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1167 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1303 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1534/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1166 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1303 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1535/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1164 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1295 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1536/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1163 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1295 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1537/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1161 - accuracy: 0.9724 - recall: 0.9816 - val_loss: 0.1294 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1538/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1160 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1294 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1539/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1159 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1294 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1540/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1159 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1295 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1541/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1158 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1295 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1542/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1154 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1288 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1543/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1157 - accuracy: 0.9785 - recall: 0.9939 - val_loss: 0.1296 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1544/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1155 - accuracy: 0.9785 - recall: 0.9939 - val_loss: 0.1293 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1545/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1152 - accuracy: 0.9785 - recall: 0.9939 - val_loss: 0.1294 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1546/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1151 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1291 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1547/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1150 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1292 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1548/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1148 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1281 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1549/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1146 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1282 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1550/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1145 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1282 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1551/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1146 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1278 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1552/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1143 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1268 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1553/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1142 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1265 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1554/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1141 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1267 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1555/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.1142 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1269 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1556/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1138 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1263 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1557/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1137 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1261 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1558/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1137 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1263 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1559/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1134 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1263 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1560/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1134 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1265 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1561/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1135 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1264 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1562/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1133 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1255 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1563/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1133 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1247 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1564/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1130 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1248 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1565/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1128 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1254 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1566/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1128 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1250 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1567/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1126 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1249 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1568/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1125 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1246 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1569/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1123 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1240 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1570/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1124 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1237 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1571/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1122 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1233 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1572/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1122 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1230 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1573/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1118 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1229 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1574/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1119 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1230 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1575/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1117 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1228 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1576/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1119 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1228 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1577/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1117 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1228 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1578/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1115 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1226 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1579/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1113 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1227 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1580/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1111 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1223 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1581/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1110 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1221 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1582/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1108 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1223 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1583/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1106 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1224 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1584/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1104 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1227 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1585/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1107 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1223 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1586/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1104 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1225 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1587/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1102 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1220 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1588/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1101 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1223 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1589/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1100 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1225 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1590/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1098 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1226 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1591/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1096 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1221 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1592/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1096 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1217 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1593/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1095 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1217 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1594/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1094 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1216 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1595/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1095 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1210 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1596/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1092 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1207 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1597/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1090 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1211 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1598/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1089 - accuracy: 0.9755 - recall: 0.9877 - val_loss: 0.1214 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1599/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1091 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1214 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1600/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1088 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1212 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1601/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1085 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1211 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1602/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1084 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1209 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1603/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1083 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1214 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1604/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1082 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1210 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1605/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1082 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1208 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1606/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1082 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1207 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1607/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1079 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1205 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1608/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1079 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1202 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1609/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1079 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1198 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1610/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1076 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1198 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1611/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1074 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1200 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1612/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1074 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1198 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1613/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1071 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1192 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1614/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1071 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1191 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1615/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1071 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1189 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1616/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1069 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1188 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1617/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1068 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1184 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1618/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1067 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1184 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1619/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1065 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1183 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1620/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1064 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1186 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1621/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1064 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1184 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1622/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1061 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1183 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1623/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1062 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1183 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1624/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1061 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1184 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1625/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1059 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1183 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1626/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1059 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1177 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1627/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1058 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1179 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1628/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1057 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1180 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1629/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1055 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1174 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1630/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1055 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1180 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1631/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1053 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1179 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1632/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1051 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1178 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1633/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1051 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1172 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1634/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1048 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1179 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1635/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1048 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1180 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1636/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1047 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1175 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1637/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1046 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1176 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1638/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1044 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1170 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1639/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1043 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1159 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1640/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1042 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1154 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1641/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1042 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1156 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1642/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1041 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1159 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1643/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1038 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1153 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1644/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1037 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1149 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1645/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1036 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1150 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1646/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1034 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1146 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1647/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1035 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1146 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1648/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1033 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1148 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1649/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1032 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1145 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1650/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1030 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1145 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1651/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1029 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1145 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1652/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1028 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1143 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1653/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1027 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1137 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1654/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1025 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1137 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1655/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1024 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1137 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1656/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1024 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1143 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1657/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1022 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1148 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1658/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1023 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1148 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1659/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1022 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1148 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1660/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1021 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1133 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1661/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1018 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1129 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1662/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1016 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1131 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1663/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1016 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1125 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1664/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1015 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1128 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1665/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1016 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1129 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1666/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1013 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1124 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1667/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1012 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1126 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1668/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1010 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1124 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1669/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1009 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1120 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1670/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1008 - accuracy: 0.9785 - recall: 0.9877 - val_loss: 0.1115 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1671/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1008 - accuracy: 0.9816 - recall: 0.9877 - val_loss: 0.1113 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1672/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1006 - accuracy: 0.9816 - recall: 0.9877 - val_loss: 0.1116 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1673/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1006 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1116 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1674/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1004 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1112 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1675/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1003 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1102 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1676/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1003 - accuracy: 0.9847 - recall: 0.9877 - val_loss: 0.1099 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1677/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1001 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1099 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1678/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.1002 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1099 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1679/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0999 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1095 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1680/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0999 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1092 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1681/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.0996 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1090 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1682/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0997 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1092 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1683/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0996 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1091 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1684/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0994 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1088 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1685/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0991 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1084 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1686/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0992 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1086 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1687/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0991 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1083 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1688/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.0989 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1082 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1689/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.0991 - accuracy: 0.9816 - recall: 0.9939 - val_loss: 0.1090 - val_accuracy: 0.9545 - val_recall: 1.0000\n",
      "Epoch 1690/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0988 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1080 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1691/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0986 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.1079 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1692/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0987 - accuracy: 0.9847 - recall: 0.9877 - val_loss: 0.1073 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1693/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0987 - accuracy: 0.9816 - recall: 0.9877 - val_loss: 0.1075 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1694/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.0984 - accuracy: 0.9816 - recall: 0.9877 - val_loss: 0.1074 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1695/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0981 - accuracy: 0.9816 - recall: 0.9877 - val_loss: 0.1080 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1696/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0981 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1083 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1697/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0979 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1082 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1698/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0979 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1081 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1699/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0977 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1080 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1700/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0977 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1069 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1701/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0978 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1067 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1702/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0976 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.1064 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1703/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0975 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1062 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1704/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0974 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1064 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1705/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0972 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1062 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1706/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0971 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1063 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1707/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0971 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1062 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1708/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0969 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1059 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1709/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0967 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1060 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1710/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0967 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1057 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1711/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0964 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1057 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1712/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.0963 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1052 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1713/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0963 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1052 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1714/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0962 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1052 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1715/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0962 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1052 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1716/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0960 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1050 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1717/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0959 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1042 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1718/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0957 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1039 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1719/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0956 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.1035 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1720/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0955 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.1036 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1721/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0956 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1040 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1722/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0955 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.1036 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1723/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0953 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1037 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1724/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0951 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1035 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1725/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0951 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1038 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1726/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0948 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1040 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1727/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0948 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1039 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1728/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0947 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1038 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1729/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0945 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1036 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1730/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0945 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1034 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1731/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0944 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1031 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1732/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0942 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.1029 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1733/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0942 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.1029 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1734/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0943 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1032 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1735/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0941 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1026 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1736/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0939 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1032 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1737/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0937 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1030 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1738/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0937 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1028 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1739/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0934 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1033 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1740/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0935 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.1032 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1741/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0939 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.1028 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1742/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0935 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1028 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1743/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0933 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1024 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1744/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0932 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1019 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1745/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0930 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1020 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1746/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0929 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1015 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1747/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0928 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1016 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1748/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0926 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1020 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1749/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0926 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1021 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1750/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0924 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1019 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1751/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0925 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1016 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1752/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0924 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1013 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1753/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0922 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1011 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1754/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0921 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1014 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1755/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0920 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1014 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1756/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0919 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1016 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1757/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0918 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1018 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1758/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0917 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1014 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1759/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0915 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.1013 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1760/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0912 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.1011 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1761/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0915 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.1013 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1762/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0914 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.1014 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1763/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0914 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.1012 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1764/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0913 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.1011 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1765/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0910 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1009 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1766/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0909 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1009 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1767/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0908 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.1006 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1768/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0907 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1008 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1769/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0906 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.1003 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1770/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0906 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.1000 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1771/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0904 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.0997 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1772/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0902 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.0993 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1773/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0903 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0993 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1774/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0902 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0989 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1775/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0901 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0980 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1776/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0900 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0975 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1777/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0900 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0971 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1778/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0898 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0973 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1779/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0896 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0974 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1780/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0897 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0978 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1781/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0893 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0977 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1782/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0894 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0973 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1783/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0893 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0971 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1784/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0893 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0970 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1785/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0893 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0971 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1786/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0890 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0976 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1787/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0888 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0978 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1788/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0892 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0984 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1789/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0889 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0979 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1790/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0887 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0977 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1791/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0886 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0976 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1792/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0886 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0969 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1793/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0883 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0976 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1794/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0883 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0982 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1795/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0880 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.0985 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1796/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0880 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.0986 - val_accuracy: 0.9697 - val_recall: 1.0000\n",
      "Epoch 1797/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0879 - accuracy: 0.9847 - recall: 0.9939 - val_loss: 0.0981 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1798/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0879 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0977 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1799/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0878 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0971 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1800/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0876 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0962 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1801/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0875 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0956 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1802/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0873 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0953 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1803/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0873 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0955 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1804/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0872 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0952 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1805/2000\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.0870 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0950 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1806/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.0870 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0945 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1807/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0870 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0949 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1808/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0868 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0948 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1809/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0868 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0949 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1810/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0865 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0951 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1811/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.0866 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0954 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1812/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0864 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0953 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1813/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0864 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0948 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1814/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0863 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0943 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1815/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0861 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0942 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1816/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0861 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0941 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1817/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0860 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0941 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1818/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0858 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0941 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1819/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0857 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0940 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1820/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0856 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0940 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1821/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0856 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0935 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1822/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0854 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0940 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1823/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0856 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0943 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1824/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0852 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0940 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1825/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0852 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0939 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1826/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0850 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0931 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1827/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0850 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0928 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1828/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0851 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0925 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1829/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0849 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0923 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1830/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0849 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0923 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1831/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0846 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0921 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1832/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0848 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0917 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1833/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0847 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0917 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1834/2000\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.0845 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0912 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1835/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0843 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0919 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1836/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0843 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0920 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1837/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0844 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0913 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1838/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0841 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0914 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1839/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0842 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0912 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1840/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0839 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0918 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1841/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0837 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0915 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1842/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0835 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0914 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1843/2000\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 0.0834 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0915 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1844/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0833 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0914 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1845/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0834 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0910 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1846/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0835 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0908 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1847/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0833 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0915 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1848/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0833 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0911 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1849/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0830 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0914 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1850/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0830 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0916 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1851/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0828 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0909 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1852/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0826 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0911 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1853/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0826 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0911 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1854/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0826 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0903 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1855/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0825 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0898 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1856/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0825 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0896 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1857/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0825 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0889 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1858/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0824 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0887 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1859/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0822 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0889 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1860/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0820 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0886 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1861/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0819 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0887 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1862/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0819 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0886 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1863/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0817 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0885 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1864/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0817 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0884 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1865/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.0816 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0887 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1866/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0817 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0886 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1867/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0815 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0887 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1868/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0813 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0883 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1869/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0812 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0890 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1870/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0811 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0888 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1871/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0810 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0884 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1872/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0808 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0882 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1873/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0808 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0880 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1874/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0808 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0879 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1875/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0807 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0880 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1876/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0805 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0879 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1877/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0804 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0875 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1878/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0805 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0876 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1879/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0802 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0875 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1880/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0803 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0874 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1881/2000\n",
      "11/11 [==============================] - 0s 3ms/step - loss: 0.0801 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0873 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1882/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0800 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0873 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1883/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0800 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0872 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1884/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0800 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0865 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1885/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0798 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0864 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1886/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0797 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0865 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1887/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0796 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0867 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1888/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0795 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0868 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1889/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0795 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0868 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1890/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0794 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0867 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1891/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0792 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0863 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1892/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0792 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0860 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1893/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0791 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0862 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1894/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0790 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0855 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1895/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0791 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0857 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1896/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0790 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0857 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1897/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0787 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0854 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1898/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0788 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0854 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1899/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0787 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0856 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1900/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0785 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0852 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1901/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0784 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0851 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1902/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0783 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0848 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1903/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0785 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0848 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1904/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0783 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0848 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1905/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0781 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0845 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1906/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0779 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0837 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1907/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0780 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0840 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1908/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0780 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0843 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1909/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0779 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0843 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1910/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0777 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0844 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1911/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0776 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0841 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1912/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0775 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0838 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1913/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0776 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0836 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1914/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0775 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0836 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1915/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0773 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0831 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1916/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0773 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0829 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1917/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0772 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0832 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1918/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0771 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0833 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1919/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0769 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0828 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1920/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0770 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0830 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1921/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0768 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0830 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1922/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0766 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0829 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1923/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0766 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0833 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1924/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0765 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0832 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1925/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0765 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0832 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1926/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0764 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0829 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1927/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0763 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0830 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1928/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0761 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0829 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1929/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0761 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0832 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1930/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0760 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0830 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1931/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0759 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0827 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1932/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0758 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0821 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1933/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0758 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0819 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1934/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0756 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0816 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1935/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0756 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0816 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1936/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0755 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0810 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1937/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0757 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0807 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1938/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0756 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0811 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1939/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0754 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0807 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1940/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0754 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0809 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1941/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0752 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0806 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1942/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0750 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0808 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1943/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0749 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0809 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1944/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0748 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0810 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1945/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0749 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0812 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1946/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0748 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0810 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1947/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0749 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0806 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1948/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0747 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0805 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1949/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0744 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0802 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1950/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0744 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0802 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1951/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0744 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0805 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1952/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0744 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0806 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1953/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0742 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0804 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1954/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0741 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0802 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1955/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0740 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0806 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1956/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0739 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0805 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1957/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0739 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0806 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1958/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0737 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0803 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1959/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0737 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0799 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1960/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0737 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0795 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1961/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0737 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0796 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1962/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0734 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0797 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1963/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0734 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0796 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1964/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0732 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0798 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1965/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0732 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0800 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1966/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0730 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0796 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1967/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0731 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0794 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1968/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0730 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0789 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1969/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0728 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0789 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1970/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0727 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0788 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1971/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0727 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0787 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1972/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0726 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0785 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1973/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0727 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0785 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1974/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0726 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0784 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1975/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0725 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0781 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1976/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0723 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0777 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1977/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0722 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0778 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1978/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0722 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0777 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1979/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0721 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0776 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1980/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0720 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0776 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1981/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0718 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0773 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1982/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0719 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0770 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1983/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0718 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0772 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1984/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0716 - accuracy: 0.9908 - recall: 0.9939 - val_loss: 0.0770 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1985/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0717 - accuracy: 0.9908 - recall: 0.9939 - val_loss: 0.0772 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1986/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0715 - accuracy: 0.9908 - recall: 0.9939 - val_loss: 0.0771 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1987/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0714 - accuracy: 0.9908 - recall: 0.9939 - val_loss: 0.0770 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1988/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0715 - accuracy: 0.9908 - recall: 0.9939 - val_loss: 0.0764 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1989/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0714 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0770 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1990/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0711 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0771 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1991/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0712 - accuracy: 0.9908 - recall: 0.9939 - val_loss: 0.0772 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1992/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0711 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0771 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1993/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0710 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0767 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1994/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0709 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0767 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1995/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0708 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0769 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1996/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0707 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0767 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1997/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0707 - accuracy: 0.9908 - recall: 0.9939 - val_loss: 0.0771 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1998/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0707 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0769 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 1999/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0705 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0767 - val_accuracy: 0.9848 - val_recall: 1.0000\n",
      "Epoch 2000/2000\n",
      "11/11 [==============================] - 0s 2ms/step - loss: 0.0705 - accuracy: 0.9877 - recall: 0.9939 - val_loss: 0.0765 - val_accuracy: 0.9848 - val_recall: 1.0000\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(feat_train, targ_train, epochs=2000, batch_size=32,\n",
    "                    validation_data=(feat_val,targ_val),\n",
    "                    callbacks=[keras.callbacks.EarlyStopping(patience=100, restore_best_weights=True)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 0s 500us/step - loss: 0.0703 - accuracy: 0.9877 - recall: 0.9939\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.07025530934333801, 0.987730085849762, 0.9938650131225586]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(feat_train,targ_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeMAAAEzCAYAAAACSWsXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAB3WklEQVR4nO3dd3gU1frA8e/Znl5JL/TQQgtNpKM0CxYUwYL92r1ey7Xrz3qvYu967YrYFRUUECIWOgKh90ACJCSkt23z+2OWFEggwIaE5f08T57szpyZOWc32XfPmVOUpmkIIYQQovkYmjsDQgghxKlOgrEQQgjRzCQYCyGEEM1MgrEQQgjRzCQYCyGEEM1MgrEQQgjRzI4YjJVS7ymlcpVSaxrYr5RSLyultiilViulens/m0IIIYTvakzN+ANgzGH2jwU6eH6uB944/mwJIYQQp44jBmNN0xYA+w+TZDzwkaZbBIQqpWK9lUEhhBDC13njnnE8sKvW8yzPNiGEEEI0gulEXkwpdT16UzZ+fn5piYmJXju32+3GYPCN/mhSlpbJV8riK+WAllEWZ0U59tISlMGA5nZjsvnhrKw44nEGkxmU54mm4XY6a/aZzU2U2xND0zSUUkdO2MJpgF9ouNfOt2nTpjxN01rVt88bwTgbqB1VEzzbDqFp2tvA2wB9+vTRli1b5oXL69LT0xk2bJjXztecpCwtk6+UxVfKAS2jLCtm/cD8D97C4uePvaKczoOHs/73+Uc87rpX3yO4VRQAuzdt4LOH7qre989Pv8VoqgnIi/cs5trZ13JTj5u4rMtlDPl8CH5GPxZcsgCToe7H+NivxxLhF8GIpBG8sPwFAMJt4eyvPNzdRlEfP+XHkiuWeO18SqnMhvZ5IxjPAG5RSk0H+gNFmqbt8cJ5hRDiJKIvuuNyO4+QTre+aCP9PcF4Z3lWnX3TN32B0WAEIK8ij++2fAfAT9t/wuF24HQ7KXGX8MHaDwg0B9Y5Nqs0i6zSLNyau3pb7UDcPrQ9Wwq3AHB///vrHPvJuk/YWbITgPHtxtPKFsH/1r4HwG3JZxEY2QlMVijfD9t/hw5nQlEWmG1Qug9iUsFkYdv61bT1L4OqEnBUQEDkQS+VGwxGaGkLFa39ts5Tp9+J6/50xGCslPoMGAZEKqWygEcAM4CmaW8CM4FxwBagHLiqqTIrhBAtjycIe5qZV+9bja0RR92UfjOL2i7HbDBz1593c36trjb/Wfqfeo/JLM7knYx3qp+/tOKlBs+fkZdBqDWUwqrCOtuHJgwlPjCeZTnLmNRpUp19hVWFTP/zW6JLW3PDkJG4372dstIHyIxYRodVdnr5P03IaWezccFm5hb9E+aAIgkNRQ//HzFbV+GO6MT+LWdiM68lx9EeF1ba2f7EhL36OrvtXYm1rEfhpmW5us4zk7I3kM77jhiMNU2bdIT9GnCz13J0DBZkLeDFvS/SvbI74Tbvte8LIcSROJz6B7bL6QCgsKqImEYc5zLA1T9fzZr8Ndjque2dfnE6b69+m2kbpgGwcNJCqlxV4HJgm/Vv3GYb9lFPwkH3Zg0rPkGzBqD5RxK65B3smRk4el2OOyASd/4mwjLmoHavBHspfHB2nWNvyN+Ce/PrACR8eB4Lyq8hpDKK7tnjWAu4NRsj1k5nbtH/qo/R0Gvwq8rP1atjBfr23Y6u1Wm2Vp5OcLje7O6odFLh1iipjKre1nIoUAa95g7YXRUMO0FXPqEduJpKQWUBW6u2UuYok2AshPCackc5fia/w3ZGyirUm3U9FWScbgeexsPDchk0jAYjTrcTtzIesj/CDZckj2HahmmMrKgi8Kl4Ag9OtHI62ELAUOt65Xl1kvgBfis+qj8TbledYK5Kau4wuhIHo7ZH6AHWw95pEvzjKbhh3hHLd7DLnxoMwNYVufz89hrCYvyZ/OiAoz7PiZSenn7CruUTwbhq027O+jOG4mF5EOS9HtpCHIsVM78nM2MlZ99xL2aLld8+eY/s9WubO1vViouL2T3nh+bOhlc0ZVkqXZVsKthEuC2chMAEAHaV7KKgqoDukd0B2Fy4GVVqJ6DWR2n07kMDa30eyc9nwpArSc1Zfsi+9nY7PNuWNkBGQycIaAVl+8AaDB1G1Wxf9q7+22CCc1+FnX+BvRxy1kJ4Gxj0L0CDkj3QZXzdc+ashUdyAHBc8i38tB3m1b2ffbzMtsa9PqcanwjGxkoXrYqslFeUNHdWhGD+h/o9veLcHCISklibPheTxUpEQsv4omisqsIaENDc2fCKYy1LmaMcozJgM9kotpdgM9pQCnLKcqo7TpW5yrCb3Ox15uFPMAA5rnwwwX6KASiiDAIhoNJEdmQF8Xl+5IZVEVRuIrhcr60GhBbSIWQfm4pasU3Z8K8ysi2ujK5uDT4az0yTkW0mM++3DaLc5kJD48M9OTD2Wcj4ArKW6pk+8zG9CbUoG3pMhJjusOln9gcNobzUSUJKmJ7urOdg4yy9c5XRDD0Pe6exjkLaAHowzs8uZfVBgTh3ZwmLZ2w76tfbaKpphzdbPWHHB4Y+eZNPBGOLRe8uUVFVfoSUQpw4Tod+D9HldNBlyHCGXXFdM+dI1xKGA3nLsZYl9cNUAFZevpKeH/ekbUhbLEYLG/ZvaOCI3CM8111RVEwkis9CgnhqXx7nlNZ8Jo3038st0a342d8PgKdMp8HAf5L47Y1ElWRxS6dCAG43xRI8MRdMFuhzNWybD+FtIaLdoRfsdBafeZqMb35zhL5NKeg0rlGvw8EW/1ATaFfO3XXI/pL8SpbP2tGoc6UOjSfjN32U65BJHau3h7Tyw+pvoucZLePLaUvhE8HYatX/uKsqJRiLlsPl0Dv2OO0OjGZLM+em+TlcDh748wHObns2QxKGVG/XNI3/W/h/7CzZydK9S0kOTibKP6pR5ywsKOSjXxq4H9oIV/+i957dVlQThC7WAumcn8n/RUZUb4tyOvkiey8ARsDlp9dCVUUBZk0jKKorFblrsY38Pyjbx80Z0wk58wVIGQuWQL1GW1XMS/OfxrXiQyyRHeCydyAgAv6ZgdXtYP6L/YkYcCVq0D9rMmg06TXcE8ReXjMsq6ywqvpxdaBvpPT0dIYMS2HIpJRD9vkHW7j2+SH1HHVq84lgbLHqNeNKuwRj0XK4HA40txu3y4nbAEVVRbg1NwZlIMQaUpPO7aLCWUGAOYCiqiI0Gh576Wfyo8J55NmdDqfUVUpBZcFxneNYbCvaxqzts1i8ZzHfjf+uentBVQFfb/6aeH+9D3JmcSaRlhDYtwEq9eZgjBZAA5ejzjmDDCbcJcfQ3GkxYtI01K7FYDUT7XRi1GC32cSl2ZsICk7i9PISTAGRuDSNc2wRRLTtAue8DCH1z/brV+txyOgnDk1gC8E49j8Yxx40bMlgAIOVNX1eZNigYUdfliZSXlR15ETCa3wiGNus+j2jqqrKZs6JEDVcDgdOz3CXN9e+zZrKqYek6R/TH7PRzB/ZfzAsYRjpWeknJnOfn5jL1Gd/5X6GfH5ozeiu7RncEd2KaKeTD5b+eOiBfa6GZe/V2ZQX0Y/INt2OPhO7V4IlAOI6wI4/IGUcFO6EvRlwawYEx/FmI0/lcrjZvjqPxM5h7FpfQFLXcApzytm1fj9KKSpLHQRF2DAYFSazAbcbCnPLCY6oOxp570o3K+yZOKtcBIRaj7pIaxZkH/dt2JKCmgBcXnzixtgKXwnGFk8ztf34agxCHA2X28XM7TMpsZeQV5HHzT1vJq+iZljJ/rJ8Yux6MHYb9Nruff3u44XlL1Dp0r84Lt67uDp9elY6EbYIru9+fb3Xe27Zc9jdds5tdy5dI7rWm6YxNm/eTIcOHY75ePK3QlkubJqtj1WN66X30gVY881hD80zGYlwuVAHVf79NI2hsQN5Y/efxDqdeuclzQ3OSnCU6829cb1gwM2w8hP9oPg01uQENfv97y0rcpn7/jpi2gazd1sxfca1ZtnMHcd0roUbth5zPn6btvGYj63PgcmxrP4+ESZaPJ94lf1ses24aNkGfS4w0aT+/OITFn09HYBWyW0Om7a0tJSdM78+EdlqcgeXpdxRzq6Smk4ur/gvweG2Vy+FtuCj91gd+B2gjyntHdWbyZ0nMztzNsvrGc4CkBadxuTOk+vdt6N4B59t+IzJnScfVzBOz0lnWOdh+pO/PwG/cL3Dz7L34K9X4PLvICxZnxpw4Wuwfxt0GK3XJF12WPFh3RNu/B34/dALdT0f0g6akM/t1IfcHMzsBwl9GZS/FfzC9Hup9YlsD2c8WvM8J71RZW5KlWX6F6783WUAFOc1rlJw0X198A/Wa8B5WSX89Nrq6n1XPDXwqBZaqDVPxXGz+BlxuzScdjd+QWYMRun1fCL4RDCOStQDgrNM7hmfCBv/WlD9OLhV9GHT2pWR4MjIw6Zpam7NxaaCTbQJaYPVaEPDzbr89dhddZvhXG4XVa5K/M0BmAxG8ivy0dAIteqdddwWF2ZKcWluHG47Za4y8K85vpTdmE1mjLEGzCUunAElFBgcFMZW0qZHGk+N1iftf2fUO5TaSz15c6OUIsAcQIm9hDDPtepzX7/7uLXXrQRZgo7r9TC4KuHzywEN1nvG6Ia3g/2eWtlL3SH5dMj8s+agVdP0oA36+NYzH4c/X4QRD0Fiv5p0yqj35tXc4B9x9MNXItsfa7Gaj6cG6XYe3TzLIVH+WP30j2BHVd35rIPCGzOhZhPzjdFvJw2fCMaBfsHsjqggwdm4CdrF8XHVep3Pu/vBw6Y9UcNoyh3l+Jv96903Y+sMPv3jK0YmteLR0+5iZ8lOHp75Je1C2tXpSLUidwUA0f7R5JTnVG9vGxKEw+2orgWnRqaSkbepen9MQAx7y/bSo1UPjMrI2e3OZlvhNtblrwNAqSCu7DoBs2eWJLPBTJjt0KBrtdXqca1phwQypZQeiKtKwVprLqZ5T0Lxbhj1eM22b66HLXNqnge00qtP7UYyZNW0Q1+k/Qc1j2b+SYkrgr32ToDC0n8SKmVs3TTDRoEbOGQdGg19bcCmXyWodI/GzrX5TX6dw8nP1r9YuZx61bRkf+P6rpgtNWNvjWbfWNJSHDufCMYmgwmXAdwOCcYngsvhOHKiE+iLjV/w+KLHeX7Y85yZXHcYyN6yvTzwxwMA/LrzV37d+Wv1vsdPf5zUVqnVzw+MPb2hxw3838L/q97+xOlPsLtsN3f9pi9x9/6Y95n4w0S2Fm3lj0v+qBPQ61VZBI5KyFkHFQX6ajWhyeCq0ptknXYozIQ/XoANP8KF78LX10Cfa6DbhXXPte57WPIWpF4MaVfq2xY8o/8+cC+1PvF9YNMsvYYL0P8GWOzpopR2JSz/QH88/jV9ZqYBN/PRbYtqjp8NzF51+HI2k8zfWla+9mwpOmSbwaQOqTkbjDUB2Orf0uZoFieaTwRjpRSaETSHq7mzckpwOk5sL8ucshzyK/PpEtGFxXsWsyJnBRF++j3FxKDE6hrod1u+O2TIzvai7fWec1ybcXSLrL8Xbv+Y/jw16Cm2FG4h3BZOaqtUOoZ3ZFL4JAb3GozVaOWlES+xrXDboYHYUakHy/J8GP6Avtzcm4P0nrqN9fU1+u9l79ZMbXiwjC/0nwMsgTDyYf1xURb89bL+eMRDENYaUidA9grIWsrK3VX0HHsbdBwDpTnQfSKMew42/QydzmqwafnCe9IaX4YTZMWKFfTu3bu5s6F/Bmla9W8Aza2hDIrAMBt+gWb27y3DYjPhqHIREFJ33LnVz0TH8YqhwwfjdrWwZQXFCeETwRgAgwHN1dKW4/JNrhPcAnH+9+dT4ijh78v/5trZ19bZZzKYSIvSg8SCrAUsyFpwyPF+Jj8md5rMu2tqAts/e//zkA4yE1Mm8s3mb4jwi+CcdufU2Wc1WhkYNJDBCfpk98nBySTbWsHcRyF3vR5sB94K391Yc9CfL0G/6+sG4oR+epNweQNNq2OfhVYpUFkIttD605j99DViD1AK4tP0DlYH9L0GguP16RAPiO8N8b0pPDD5fbvhNfuMJuhcdwWfg8W0PUILQDPw36laZL7q0yrx8Pf6zX4Ki813PpLF0fGZd14zKnBKzbipaZrmlZpxhbOCB/54gIFxA5nQcQIrclbwxqo3MCojd/a5k/m75rN4z2KW7F1Sfcw1v1xT/Tg5OJmJKRN5ZukzLN67mKEJQ3l04KP1Xsvf5M/0jXrv7+tSr+Pa1Gtr7i+X5sKMW8FRzgNVJdxZXIbftIlgDtBrimjQRh8TO2z7AkgHkgdB5h+HXqh2ID4g40u941P/G/QA6R+hT1xRWaQHUbdLv5drDaSsoBxraCgmixF7hROLX+P+PSvLHJTn24GyWltbQaUdOPS9qirS2L+n7JDtQojm4zPBGKMBQ6mDrcsX0y6tf3PnxuuWfP8Va9Ln4nI4KN6ndy5q0zMNt9tNcd6+E5cRTasZgHiQUnspTnfdWnOZq4zCykL8zH5U1KrNZeRlMCdzDnMy53BG0hnM2DqDZTnLcLqddN3Rlc83fk5R1aH33npF9aLCWcEdaXcQ7V/Tk7t1cGsi/RrutX1GwnBW7FnK+Phh+JflwccX6AGxzDPHcGRHVN4mfRaliv36EJ4D9qzS0x5wcCAOiNLP4xeu105vWQbmw/SGNVkgsFXd16mwig8eXkVch1C6DY1n9v/WMvHBfkQmHLJo3iE+f3IJpfuPbrakLbMWHzlRLWEx9XeOE0J4h88E45xESN4Bu9at8clgvG3FEqrKSnFU1Xzobl+pj1WNatOOsJi4E5aXmHYdCImOJb5Tl+pti/cs5rrZ19U/leMRZnsa/Lne9Ns5vDO55bm8vfptAFLCUthYsJEH+j/AJZ0uOew5zrTG6rVMw0HLs+3NgD2rSPr+Zl4F+LNWT+K43vo90tAkGHQHvNAVirPhlqXwUg89TfIgmDIDKovIe/cSIrsMhrZDYc7D0P4M/b6wF1afKfXMA7x7c2H1sJa8rJIjBmNN0ygtqKJdr1a0S2vcfM7r1q2jS5cuR0xXVe4kpJUf5cV24jqENurcQohj4zPBuDzCjMsCbpdv9qh22h1Et22Pf3Aoa3+bW2dfjzPH0n3kmBOWl92lu/l2y7esYxkhGzcDsGTvEjQ07upzF6Zakzps2byFrwu+RkPj3Hbn0iWiJgg43U7MBnN1AO8Z1ZMKRwUbCzZiUibGtBnD8pzlDEscVnNxe7k+GYXJCgGRvDf6PfbMvp/Ub26Cnx+CYfdB3iZo1Qly1hwyfSIJfSH1In0N2O4T9XmBD7h5CexcpHd4uuFPfT7kVp7VZvzDWZN6f80wrevTvfNiejiqju0Wi9PuBg2iWgfToc/hx3wfkF26vtFphRAnhs8EY7MyoxkcuH30vrHL6cBoqn/4g+kErwj0yt+v8OO2Q+cOjg+MZ0rXKXW2peeko1opvtz0JVd0uYKU8ENXcTlYn5g+1Y9HJI3Qm4nzt0K3C+DLKbB5dvX+vkGx+lAc0DtFzbyr/pN2Pkef4GLQHXptuD7WQOhwhv445hjmOz4Mt8uNBg32lK0sddRJC/qcxw774f+eK0r05nSzVRZsF+Jk5lPB2K0cPlszdjnsmCz1B12jufFjFF1uFw//9TB2l51iezEut/5hv3jvYnpH9aZnVE9cblf1uq7r8tfhZ/ajTXDNtJfr96+vfjz/4vnVjxuaGeqhAQ9xR9oddfdXleodntoN1xcA2PGnPl5WGfTZnTbOhO0LYEetaRb/egV2r6h78gOBuNfl+jCerb/qUzheMg1MNrD46+e0BBw6WcYJ8ppnvdnG2rxMv4+d/ulG0j9t3HzDMn+wECc3n/kPNhvMuA0abpdv1oydjoZrxo1dK1fTNDYVbGLG1hkAKBS9onqRVZIF6DNQHZiFKj4wnij/KEocJZQ4SkgITKg+T/vQ9lS5qri8y+V6pylNg23p8P3N+tzDpTWzVw0DSIcggMBaTaMH0qyfAen/qXMMWxsIXiYrtB0GZ7+gL7ZelKXf500aqM8+5RcGPSfrP/VphkB8sLAYfzqdFlvvPqfDjcliAA1ydhQT3Tq4Uec0mg207t68U44KIY6P7wRjZcattDpTNfoSl8OByWLGaT909iuTqXFv4+OLHufLTV9WP9fQ+HDsh6TvSufWebcS5R9FbrleK7up502c2+5cUj9MRaH4cOyHUFUCzio9KGYtBQyQ+ZdeE93gabZOvbjuZBS1+YVB0oCa50XZEByr11wB2o3Q16/NXqYvJtD3Wlj/I5x2U90xtAeEJMCjh/a4bkm0g3qeRyYG0Xt0cjPlRgjRUvlcMD6Za8a71mUw+82XKS8vY/NXH9XZV15chNFkRh3cWxhYnvc3YVXt68wGtWTPEn7d+SuJQYnklOeQGJTIl5u+pH1oe85tdy7PL3++Ou3QhKG8NPwl4gPjmfDDBADOSDoD8jbzXtw4/Ew2vSPUj3foBwTFQcnuupkwWmDSdGg/EnpfrjcxR3dj2bZ8+li363MjD7ip7iQUDel9ec3jqM5HTt+COe0yEY0Q4sh8JhjblA0nblwn8T3jPZs3Upizh/COXYiNPagpUym6DBlBWGwcOds2E9+pK46qKnLK9/JE5stsCc7n3/3+Dei9lK+ZfU09V9Cb86/qdhWvrXyNvjF9PadWjEgaQYm9pDqd/5Ox4BdO34p6Jvs/EIgDY+CCt/THUV1rxs62GVI9UUbpvnQYVn9eWpplM7eze3Mh597eq979O39389uejQydVH8ntB9eWcnOtfrrpQwKq5/pkJqxTe7tCiHq4TOfDKGmUBzKRUlFy262PJwDCzC0Hj6GrJhsnl/2PEnBSVzZ9UrcmpsHtvyH/Wv2s7nDZnDN5qIuF7G9aCfOHI1P1n/C4r2LCbeFHzLxBsDLw1/mtvm3YVR6zXrJpUtQKNj4s96RKiaVAEcZ1K64HgjEnc+BcVP1xwcWTvWP0JfLqz006CS3eEb981gfUJINa7KzGwzGBwIx6PMSd+ijj/vdu72Y4Eg/giNtpA5LqPdYIcSpzWeCcZIliS2GhRRXnrzB2OmwYzAaqdAqmbpsKm7Nzbaibby/9n2sRivZpdl1aq+17/8CbC7YTK8ovVZ34PeEjhPYXLCZQQmDOL/9+VzZ4UJ4YxCG4mx9juPibP3g7b9hAC6JCKN97fvSnc+FiR83ablPBgfXcI/EGmBiSANBWwghDuYzwbiNtQ2bFFTZK46cuIXamr+ZKhz8O+vfdbYfWHko0Fx/b+DrUq/jnYx36B/bn/+N+l+D538s6Wx4fUjNhtov1c1LwC+cB948Hc55AzqM0hc66HXZMZfnZOV2axgMdWfVOtp7v2aLjPsVQjSezwRjozJiMBpxnoS9qZfuXUrXiK4UlxfiMmicH3Y+nTp0wt/sT6RfJDfO1RcgKHWU8u253/Lm6jfpFdULi9GC0+1kXJtx9IvtR+fwgzo7bf8d8rfoqwpFdYYf/1l3f3g7OGsqxPYE/3B9212bavYP/lej8r9idiabl9YMTcrbVVr92OwPa6fPIzKx+YcVNdYXTy09ZIZLzV1TM/78ySUciX/wiZ2IRQhxcvOZYAxgMBlPug5cu4p3cfUvV3N++/MJdThwGTWGBw1neOea5e0mdJzAV5u+YmLKRNqHtWfq0KmHnGdA7IC6G4qy4MMGlsS7Y60+dMjsB7bjX35uy7JcyorsRLcOxu2sW4N0lOu/K0sdRB5hCbnm5rS7KdpXUT039MGKC8sIDg0gMKyB/XmV2Cv0v7/BEzs2WT6FEL7Hp4Kx0WjCXaV/GC78+jNWzZ7ZYNqqsrI6SwEGhIZRVlhQ53l9DqRpaH9tmqZRXlR42PQVzkoudsTDr0twuyxoVnXIOrsPD3iYB/o/UN356tCTFMJ/kyG6G0R21DteHVghqd8/YNMsOP9tmHELjHtWH5/rRY4qF/EdQhl9XTccdhdv3/bbIWnadI886e+hpqenM2yY7y1CIoRofj4VjA1GI5pnnHHWugyABldwWv3rz3WeRya1rhOMj3RcUmpPzBbrYfNzYFWlw51v1b5V7CrQJ9qICwxnc2AuQw9Ko5TCpBp4q5xV+pq5oC+MkLOm7v4z/w/GPaM/vnU5TcFe6cRs078omMz19642y6LpQgjRIJ/6hFRGI8ozrMfpcBKRkMSZ199Sb9qDg3HauPFkrv67+vmRjhtx5T+wBR7+PuhPLz/Lhj9/q3O+pxc/zbQN08iYksHK3JV8OOstqK6o7q+zRm+9irL0OZcDImH5B/DDP6Hr+fq+NkP0yTZqM/sd/nxHkJGeRURCIHHtQ1k1bxfLftpBSv8Y1i/cg9XfRGRCIJUlDixW/U/p4Fr9AcYGgrQQQgjwqU9Ig9EInlVxXA77US2gYDAe3fcSo/no0r+x6g3eWf0O0zbo6+kuz1nO9I3TD0nn0g4zg5jbDa/2g+c76/NBr5oOaLD2G31N3tNv19ONf13/HdvjqPJYnwXTN/HtVH2+6j++2ExlmYNV83Zhr3BSkl9JcX4l4fEBJHYNrz6m29B4AALDrIR3gOg2wSR2Dq/3/EIIIXysZmwwGsF9IBg7jm5pwaNcH/5oAj3A6ytfr/P8yp+vrDfd0IShYD9o47L39WAbGA2OMn3bdzfpSwYekNhfX+z+lmUQ0R4S+3mlc9aRXPJgv0O2DZ2UUj0xhn6ftc8haYQQQtTwqWCsagVjp6dm/OHaD2kf2p7T40/36rUM9cwR3Ri39LyFV1e+CkBiUCKfnfUZ/mZ/DBgodZQSZAliwW+1mppz1tYMSRr+YM32VdPqnjjZU77IDnV/HweXo6Zn9NFOeiGEEKLxfCoYG41G3J744XI4MZrMTF2mDwPKmJJx+IO9HGvsLjsOd81MVnek3UG4LZzeUb2rg/HElIl1Fneo/ZjiPfB8p7onnf+E/juqC+Suq96cY+/ApjVdYOsm8neXYrIYsVc49aFEGuxYnUebHke/xJ6r1jCl3z/ffNTHCyGEaBzfCsZmM2a74rmJ+vhak6WmmXpn8U6SgpMaPLZ22uN18683syBrAf12h9EFfU3aq7tdXb2/wS8GLifsWUlI4Tr49dD7ydVu+BMWv6l34Jr4CaveXceWxWVY/KqoKq8ZZ71nS83UoOv+3I3RdOxdBDYt2VvnucGkSB0i8ywLIYQ3+FQwDunbmZIVNUv7dT9jDPz2BgA3zL2BZ4c+S9eIrgCcd8/D5O3cQVzHThiMJuJSOtNr8kQMZhOp3Rtu0r7yudfZvzur+vmu4l24NBdbC7eSWZJJsCWYBVl6M/PWjg4iiisp7RfVuAKs+BB++hf1rhl06dewZQ60Ha4vznDaTfoPYA+sIDLRzsX39+W1G+ZVHxLcyo9WiUFsXZHLiCs606HPEXpqCyGEaBY+FYwT23Til6gfSMz1p//5EwlJjK/et6tkF9f+ci0LJy8EoF1aP9ql1e18dHuhPh43I6HhJu2IhCQiEmpq2OO+HddgWkeAgVmn5fCP7uc1nGlNg2Xv6gsyzHu8Zvuw+yGuF0R10ldKCkmADmfUewp7pROLTeZCFkKIk5VPBeN+Mf2Y5ekV/emmadz4yTN19pc6Srl93u08fNrD+Jn8eOjPhyhzlNEjqgcrc1dWp0v9MBWzwczzw55nWOKwBq/3/PLnD9n27bnfEmQJwt/sz3Wzr2Nt/lpGJI1oONMZX8FPd+o/Hr8P+ozBw+oG+X07S8jaqE9KsndrESGt/AiJ8sNpd1OSX9nip5oUQgjRMJ8IxnPW5fD0wgo+6VVz37fAVVz9+LGBj/HZhs8wKAPzds1jZPJI4gPjmZ05G4A/d/95yDkdbgev/P0KadFph+wzG8xUOCv4YM0HdbZH+0fTLrRd9cQXj5/+OJ+u/5SOgYlQVQIGM6z4CDL/hIs/hKxl8M21dU8eFIfL5H/INf/8ejPZGwsbfA069NWboENa+WEwGSjYW0bq0HjCYgLYuiKX6NbBDR4rhBCieflEMC4ot7OtyI3LZcSo9E5KboPePTrQHMj5Hc7n/A7nU2Iv4fTPTueBPx445BzhtnD2V+6vs21TwSYGfjbwsNceFD+IP7L/AOCVEa/UmYGqQ0AcjwanwjNtwXXQ4OFHGxgDfNvf8OeiQzZXlTtJ6hqB1c/I5mW5dfZd9+IQzFa9mfqyx08DwOVyYzTqr8XNbx6mZi6EEKLZ+UQwtniCjsPlxq3pw3FcBo1nhjxDr6ia7lBBliBeGP4CX236qjqAPtj/QapcVfSL7cdFP1wEwLg240gJT6l3Puhnlz0LwOVdLic5KJlRrUcxJ3MOZoOZlPAU2PQLGIz6UKlPLzxy5vvfqE9nWboXOp2jd86qh6PShTXGhNF46OwklnrmfT4QiIUQQrR8PhGMzZ7A43RrOIx6MG4T0Z6xbcYeknZk0kgSAhOqg3Hv6N50CNMnyPhn73/y4ooXua33bcQHxh9yLMC+in18sPYDrk+9nlBbKAAXp1ys79y/HaZdXH8m79oMe1dDQj/YtQScFRDfB4Jjq5M4qlzsWrePop0aW5bXrf1Wljkw24y4HUe3yL0QQoiWzyeCsclTW7Q73axqX0RxgJPgpNgG06eEp2AymHC6nfiZahZSuLrb1VzS6RICzAENHvuvtH9xQ48b6qZxOeCzSfrQo9qm/AAz74HTbobAKH26SmiwV/T6v/bw++ebAMj6a80h+wNDrYRG+7NhUc2Y3/C4hvMqhBDi5OATwbh2M/XNI+7m6SVPM3PIw4c9ZurQqfy49UeiA2rG3iqlaoKso0IfdrR/G3x+KTj1e76qbB8BB2bWCoqDkt3QdhhsS685+bD79Pmh2wyBmw+9/9uQylL9Gu3GKPr261tnn1KK0Gh/DAZFfMcwlEFvurYFHt0c2UIIIVqeRgVjpdQY4CXACPxP07T/HLQ/CfgQCPWkuVfTtJnezWrDajdTT+48mcmdJx/xmJFJIxmZNPLQHZoGy9+HH++ou73DaL12+/fHNdtKPBOM1A7Et66AiHZHWQKdvcqFyWrEFqoREdfw8oz+wXqvcb/Dr+AohBDiJHHEYKyUMgKvAWcCWcBSpdQMTdPW1Ur2IPCFpmlvKKW6ADOB1k2Q33odaKZ2OI98P7Wy1EFxfgV+QRYMRkVVmZOqcgfKqHDlZ6NWfoJh21zAs9BC7ysgrA20HaI/T74F95oZGKLaQmxPWPcduF3gF6rXhosjobhmGkqDQeFyuFH1dLw6WEleJRarEXAeMa0QQgjf0ZiacT9gi6Zp2wCUUtOB8UDtYKwBBwayhgC7OYEO1IztriMH4x9fW0XO9uLDpDjD8+Mx98CD5bXSHFgScCfQ+6Djl3M8IuIDkGAshBCnFnWkpfGUUhOAMZqmXet5fjnQX9O0W2qliQVmA2FAAHCGpmmHRCWl1PXA9QDR0dFp06cfZjGEo7CjyMWjCyu5vbeVXlGH/36xaYYbR/mh2/sFTmNJaU3zdtKQ+muyOxfor1fCQIXhCF9l8tZplOcd/nwHswaDnTICA32jDbq0tFTK0sL4SjlAytJS+UpZvF2O4cOHL9c0rd4F3r3VgWsS8IGmac8ppU4DPlZKddM0rU5VVdO0t4G3Afr06aMNGzbMKxdfv6cYFv5Op85dGZbacC9qgC0zFlBfzTPGvLHO83MmD6/3+NcW6AsxnD156BFXQZr97lo25+Uc9nz1SU9Px1uvTXOTsrQ8vlIOkLK0VL5SlhNZjsbMDJENJNZ6nuDZVts1wBcAmqYtBGzA0S+ge4wa20ytaVqdJQaPx/EsRyiEEELU1piIshTooJRqo5SyAJcAMw5KsxMYCaCU6owejPd5M6OHc2Bok9N1+Cb3gj31tE972Gw1x1oDGm4wsAU0fihRcISt0WmFEEKcuo7YTK1pmlMpdQvwC/qwpfc0TVurlHoMWKZp2gzgTuAdpdQd6J25rtSOdDPai6p7Ux+hZlxeXAVAn7Nas+ynHQAMCPyYdra/CB12DedG98RsMRJ0mCB6ycP9KC+yN7i/tt6jk1FGRUq/mEalF0IIcWpq1D1jz5jhmQdte7jW43XA6d7NWuOZa036cTguT805uWtEdTBOC/wGrpgBbYaQqI7cySogxEpAiLVR+bL4meh/TttGpRVCCHHq8okbnzUzcB2+Mu7yzOt8yP3e5NOhEYFYCCGEaAo+EYwb20zt9gRrwwejajYOvReMPjErqBBCiJOUT0ShRjdTe2boMtoLOD/8fsxmAwz9o8nzJ4QQQhyOjwTjAzXjwzdTuz3B2qCcxFnWw/jXGlw/WAghhDhRfCIYK6UwqkbUjMtLATDihDvWQUj9axYLIYQQJ5LPVAuNhsMH4zU/Z7D467V62iG3SyAWQgjRYvhMMDapwzdT71y1Gw0DacmrsI68/QTmTAghhDg8n2imhsPUjN1u2J6OvaiAcFM+A+64Ru4TCyGEaFF8JiqZlKo/GM+8Cz4+H0dJIWazG2zBh6YRQgghmpHP1IxNhvrnps5YkM2C4m8xqQqSE4683rEQQghxovlMzdio6l+1aUHxPwAIC6qk/ci+JzpbQgghxBH5VM34cL2pR905ltBo/xOYIyGEEKJxfCYYGw2qbjN1RQHkbal+arYZmyFXQgghxJH5bjP1V1fDu2dUPzVbJRgLIYRomXwmGJsNUOWsFYz3b6u73yLBWAghRMvkM83UFqOiyuGq2aD04BsR4SYkMRplkCUShRBCtEw+UzO2GGvVjMvyYP9WOPMxXKZADCYJxEIIIVounwnGZgNUHqgZl+aQY+/A7EUpFOaUo5QEYyGEEC2XzzRTmw2KSoenZlxZzPcFj+LYbwFg/56yZsyZEEIIcXg+UzPWm6k9NePKIhxarTHF2uHXORZCCCGak88EY72Z2lMzztvYvJkRQgghjoLvBGOjotLpQtM0KN7T3NkRQgghGs1ngrHFoLdGO1wa2Evr7Os8MK6ZciWEEEIcmU914AKodLqw2MsIteQS3KEzZ92UKmOMhRBCtGg+E4wPTLBV5XCDvRQXZvyCzBiMPlP5F0II4aN8JlKZFcQ7DZSW2XGX5lFiD8NolBqxEEKIls9nasYBBYrJpVY2fr0QQ5Zno0z2IYQQ4iTgMzVji0sPvM6cLKrcAQCkDotvziwJIYQQjeIzNWOzG5yAy2jCpZkBsNh8pnhCCNEgh8NBVlYWlZWVzZ0VAEJCQli/fn1zZ+O4HWs5bDYbCQkJmM3mRh/jM9HKpCmcgHJU4rJFQjEYTT5T8RdCiAZlZWURFBRE69atW8Rc/CUlJQQFBTV3No7bsZRD0zTy8/PJysqiTZs2jT7OZ6KV0TP5ll9pNm5LqL5NgrEQ4hRQWVlJREREiwjEpzqlFBEREUfdSuEz0Up5pqV2Y8RlDQeQpROFEKcMCcQtx7G8Fz4XjF2aCVd4ZwCMMsZYCCFOiMDAwObOwknNJ6JV1bZtqNwCAFyaBUdQO0BqxkIIIU4OPhGMK/7+m2J7GAA77b1ZMW8fIM02QghxommaxoMPPki3bt1ITU3l888/B2DPnj0MGTKEnj170q1bN37//XdcLhdXXnllddoXXnihmXPffHyiN7XBzw8AoyqhtW0lWysGg8RhIYQ44b755hsyMjJYtWoVeXl59O3blyFDhjBt2jRGjx7NAw88gMvlory8nJUrV5Kdnc2aNWsAKCwsbN7MNyOfCMZYbQBEmlcT77+CrRWDSewU1syZEkKIE+//fljLut3FXj1nl7hgHjmna6PS/vHHH0yYMAGj0Uh0dDRDhw5l6dKl9O3bl6uvvhqHw8F5551Hz549adu2Ldu2bePWW2/lrLPOYtSoUV7N98nEJ5qpDwRji+bArjV+kLUQQogTY8iQISxYsID4+HiuvPJKPvroI8LCwli1ahXDhg3jzTff5Nprr23ubDYbH6kZ+wGVWNx2Kj3t07YACcpCiFNPY2uwTWXw4MG89tpr/OMf/2D//v0sWLCAZ599lszMTBISErjuuuuoqqpixYoVjBs3DovFwoUXXkhKSgqXXXZZs+a9OflIMNZrxmbNgcG4i4EXtqPzwLhmzpQQQpx6zj//fH777Td69OiBUopnnnmGmJgYPvzwQ5599lnMZjOBgYF89NFHZGdnc9VVV+F267M2Pf30082c++bjG8HYbNV/uR1UYCVlYKzUjIUQ4gQqLS0F9FEsTzzxBC+99FKd/VOmTGHKlCmHHLdixYoTkr+WzifuGWuemrHRZaccK3mlVc2cIyGEEKLxfCIYY9FrxgangwrNSl6JBGMhhBAnD98IxiYLAAZPzTinpGUsIyaEEEI0hk/cM9YMRgCU00kFNor3VzRzjoQQQojGa1TNWCk1Rim1USm1RSl1bwNpLlZKrVNKrVVKTfNuNg9Pc2v6A7sTZfFnV0H5iby8EEIIcVyOWDNWShmB14AzgSxgqVJqhqZp62ql6QDcB5yuaVqBUiqqqTJcH7fLE4wdbqz+QeySmrEQQoiTSGNqxv2ALZqmbdM0zQ5MB8YflOY64DVN0woANE3L9W42D8/p0Meo4XDjHxAkNWMhhBAnlcYE43hgV63nWZ5ttXUEOiql/lRKLVJKjfFWBhvDUaUvZqzsVQQGhbCnqBKHy30isyCEEOIEcDqdzZ2FJuGtDlwmoAMwDEgAFiilUjVNK6ydSCl1PXA9QHR0NOnp6V65eHG23kxtqLBTWlqKy63x7S/pRPmfnJ3FS0tLvfbaNDcpS8vjK+UAKcsBISEhlJSUeDdDx2DSpElkZ2dTWVnJjTfeyFVXXcWcOXN47LHHcLlcRERE8MMPP1BaWsrdd9/N33//jVKKe++9l/HjxxMbG8uePXsA+O677/j555958803ueGGG7DZbKxatYoBAwZw4YUX8u9//5uqqipsNhtvvPEGHTp0wOVy8fDDDzN37lwMBgNTpkyhc+fOvPnmm3z22WcAzJs3j//9739Mm3bkrk0ul+uYX9fKysqjez81TTvsD3Aa8Eut5/cB9x2U5k3gqlrPfwX6Hu68aWlpmrdsWLRHe/Ufv2pLew3WNqV/oSX/+0ft5zV7vHb+E23+/PnNnQWvkbK0PL5SDk2Tshywbt0672XkOOTn52uapmk5OTla165dtb1792oJCQnatm3b6uy/5557tNtvv736uP3792uapmkBAQHV27788kttypQpmqZp2pQpU7SzzjpLczqdmqZpWlFRkeZwODRN07Q5c+ZoF1xwgaZpmvb6669rF154YfW+/Px8ze12aykpKVpubq6maZo2adIkbcaMGY0qT3Fx8VG/BgfU954Ay7QGYmJjasZLgQ5KqTZANnAJMPmgNN8Bk4D3lVKR6M3W2xr/leD4aJqGUTkwOe1EFZfRe18WWzOCcSUHYQwMOFHZEEKI5jfrXtib4d1zxqTC2P8cMdnLL7/Mt99+i9vtZteuXbz99tsMGTKENm3aABAeHg7A3LlzmT59evVxYWFHXvL2oosuwmjUh7EWFRUxZcoUNm/ejFIKh8NRfd4bbrgBk8lU53qXX345n3zyCVdddRULFy7ko48+OorCnxhHDMaapjmVUrcAvwBG4D1N09YqpR5Dj/IzPPtGKaXWAS7gbk3T8psy47V1GhCLc9UrGOcXsfvu//IkwJ9vs/3dRNr+MAODzXaisiKEEKek9PR05s6dy8KFC3G5XJxzzjn07NmTDRs2NPocSqnqx5WVdSdvCgioqVg99NBDDB8+nG+//ZYdO3YwbNiww573qquu4pxzzsFms3HRRRdVB+uWpFE50jRtJjDzoG0P13qsAf/y/DQLQ6iF6LRCKqMuYEVmKbbsncTv2sXOK68i/rmpmOLi6rzRQgjhkxpRg20KRUVFhIWF4e/vz/Lly1m0aBGVlZUsWLCA7du306ZNG/bv3094eDhnnnkmr732Gi+++CIABQUFhIWFER0dzfr160lJSeHbb78lKCiowWvFx+v9iD/44IPq7WeeeSZvvfUWw4cPx2QyVV8vLi6OuLg4nnjiCebOndvUL8UxOTl7ONXD6KoivEM5cf/3AHv++QjXDr8Lv0mXUrFyJVtGnsHWkWew7+WXcRYUNHdWhRDC54wZMwan00nnzp155JFHGDBgAK1ateLtt9/mggsuoEePHkycOBGABx98kIKCArp160aPHj2YP38+AP/5z384++yzGThwILGxsQ1e65577uG+++6jV69edXpXX3vttSQlJdG9e3d69OhRp5PWpZdeSmJiIp07d26iV+D4tLy6+jEyujxNGmZ/uifoC0dsm3gdffqlsf/TT6lcnUHe62+Q9/obRN50I61uu60ZcyuEEL7FarUya9YsAEpKSurUaseOHVsnbWBgIB9++OEh55gwYQITJkw4ZHvt2i/AaaedxqZNm6qfP/HEEwCYTCaef/55nn/++UPO8ccff3Ddddc1vkAnmA8FY89KTZYAuicYMBkUS3cUMGLsWILHjkVzuSiZM5eCTz8l7/U3qFizhvipUzEGBzdvxoUQQjSptLQ0AgICeO6555o7Kw3ymWZqg7sSDGYwmvGzGElLDmPe+pqJwJTRSPCY0SR98D6BI0dStuB3NvXrj7tCps4UQghftnz5chYsWIDVam3urDTIZ4Kx0VUJFv/q52O6xbAxp4Rt+0rrpFNGIwmvvIzF09W+8Isv0NwyW5cQQojm40PBuArMNV3fR3eNAeCXtTmHpFUGA+1mzcS/b19ynv4PmZdehnP//hOWVyGEEKI2HwrGdWvGcaF+9EgI4ee1exs8JvbJJ/Dr2ZOKv/9m65mjKF++/ERkVQghhKjDZ4KxwV0FZv8620Z3i2HVrkKyC+u/L2xJSqL19M9o/eUXmCIj2XXd9VSuW1dvWiGEEKKp+Eww1mvGdae+PKd7HErB50t2HvZYv9RUEt9+C03T2H7Bhdh3Hj69EEKIpvfBBx9wyy23APDoo48yderUZs5R0/GtYHxQzTgx3J+RnaKYtmQXdufhO2lZkpOJe+a/oBSZl16GIzu7KbMrhBA+S9M03NIx9qj4UDCuAGvgIdsvP601eaVV/Lh69xHPEXzmmSRP+xR3eTlbxowl84oplC5Y0BTZFUIIn7Jjxw5SUlK44oor6N+/P48//jh9+/ale/fuPPLII9XpPvroo+oZsi6//HIAfvjhB/r370+vXr0444wzyMk5tOOtr/OxYHzoPKaD20fSOTaYl3/djMN15G9q/r160eabrwm7aAIVK1ey6/p/UPTTT02RZSGE8CmbN2/mpptu4umnnyY7O5slS5awcuXK6nG+a9eu5YknnmDevHmsWrWKl156CYBBgwaxaNEi/v77by655BKeeeaZZi7JieczM3CZnBVgOTQYGwyKu0d35OoPlvHlsiwm90864rksycnEPPwwYZdeyrazzmb3nXehORyEnndeE+RcCCG8579L/suG/Y1fKakxOoV34t/9/n3EdMnJyQwYMIDbbruN2bNn06tXLwBKS0vZvHkzq1at4qKLLiIyMhKoWeIwKyuLiRMnsmfPHux2e/WSi6cS36gZa1qDNWOA4SlRpCWH8fKvm6l0uBp9Wmu7drSbOxdjRAR77r2PXTfcyP5p03CXlXkr50II4TMOLHOoaRr33XcfK1euZOXKlWzZsoVrrrmmweNuvfVWbrnlFjIyMnjrrbcOWT7xVOAbNWN7GQqt3nvGoK+ReffoFC55exGfLMrk2sFtG31qS0I8bX+YQdYtt1Kank5pejplf/xJwmuvypKMQogWpzE12KY2cuRInn76aS699FICAwPJzs7GbDYzYsQIzj//fP71r38RERFRvcRh7SUR61tA4lTgGzVju2fKS0v9wRhgQNsIBneI5LX5WyipdBzV6U3h4SR//BHxzz+HrVs3SufNI+/V12Q5RiGEqMfIkSOZPHkyp512GqmpqUyYMIGSkhK6du3KAw88wNChQ+nRowf/+te/AH3Y0kUXXURaWlp1E/YpR9O0ZvlJS0vTvGbfZk17JFjTVn1x2GSrdxVqbe79UfvX5yuP+VJuh0PbNGiwti6lk7ahd5rmKi8/5nM1ZP78+V4/Z3ORsrQ8vlIOTZOyHLBu3TrvZcQLiouLmzsLXnE85ajvPQGWaQ3ERN+oGVcV678baKY+IDUhhFuGt+frFVn8vKbhaTIPR5lMJH34IX590nCXlVE044djOo8QQghxgG8E40Y0Ux9w68gOdI0L5v5vM8gtPrZOAta2bUj++GNs3bqR98YblKSn4yopOaZzCSGEEL4RjKs8wbiB3tS1mY0GXrqkJ2VVTq54bwlVzsb3rq5NKUXMIw/jLi0l64Yb2XXd9RKQhRBCHBMfCcaeINiIYAzQPiqIZyZ0Z8PeEp79eeMxX9YvNZW2M38icMQIKlauZPPgITj37UO/NSCEEEI0jm8EY7MfpQHJYAtp9CHje8ZzxWnJ/O+P7XyxdNexXzoqioRXXib4rLPQKivZPHgI+zyzygghhBCN4RvBuMu5LOv7MgQcXZf4h8/uwqD2kTz4/Rr+3nnsw5SU0Uj8c1NJeO1VLK1bk//mW2weMYKq7duP+ZxCCCFOHb4RjI+RyWjg5Um9iA62cuX7S1mTXXRc5wsaOZK2P/1I6MUX49y9h21jx7Fl1GjsWVleyrEQQghfdEoHY4DwAAvTrh1AgMXI5e8uZsPe4uM6nzIaiX3s/4h/WW+qduzcSfadd6I5nd7IrhBC+ITAwIZHv+zYsYNu3bqdwNw0v1M+GIO+7vFn1w/AYjJw6TuL2Zxz/L2ig0eNouPSJcQ//xyVq1az98kncebleSG3QgghfI0EY4/kiACmXTcAg0Ex6Z3FbN1XetznNAYFETxuHCHnn0/hZ9PZPGgwW844kz3/93+4SmWxCSGE77j33nt57bXXqp8/+uijPPHEE4wcOZLevXuTmprK999/f9Tnrays5KqrriI1NZVevXoxf/58ANauXUu/fv3o2bMn3bt3Z/PmzZSVlXHWWWfRo0cPunXrxueff+618jU131gowkvatQpk2rX9ueTtRUx+ZxGfX38arSMDjvu8sU8+Qcj551Hy8y+ULlhA4WfTKfxsOsmffIx/nz5eyLkQQuj2PvUUVeu9u4SitXMnYu6//7BpJk6cyD//+U9uvvlmAL744gt++eUXbrvtNoKDg8nLy2PAgAGce+65R7XIzmuvvYZSioyMDDZs2MCoUaPYtGkTb775JrfffjuXXnopdrsdl8vFzJkziYuL4yfPGvRFRcfXD+hEkprxQTpEB/Hpdf2xO91c+MZfbNx7/E3WymAgoF8/Yh5+iHY/z6LV7behLBYyL7ucgi+/9EKuhRCiefXq1Yvc3Fx2795NRkYGYWFhxMTEcP/999O9e3fOOOMMsrOzycnJOarz/vHHH1x22WUAdOrUieTkZDZt2sRpp53GU089xX//+18yMzPx8/MjNTWVOXPm8O9//5vff/+dkJDGD3dtblIzrkenmGC+vOE0Jr+zmLNe/p23r0hjRKdor5xbmUxE3ngjweecy67rrmPvQw9T8vMvRN54g9SShRDH7Ug12KZ00UUX8dVXX7Fz504mTpzIp59+yr59+1i+fDlms5nWrVt7ba3iyZMn079/f3766SfGjRvHW2+9xYgRI1ixYgUzZ87kwQcfZOTIkTz88MNeuV5Tk5pxA9pHBfHFP04jKcKfGz5ewRfLjn1ikPpYEuJp89WXhJx/PmV//knmZZeT/977Xr2GEEKcSBMnTmT69Ol89913XHTRRRQVFREVFYXZbGb+/PlkZmYe9TkHDx7Mp59+CsCmTZvYuXMnKSkpbNu2jbZt23Lbbbcxfvx4Vq9eze7du/H39+eyyy7j7rvvZsWKFd4uYpORmvFhtI4M4JsbB3LLtL+556vVbNtXxp2jOmI2euc7jCEggNgnHifkvPPIf+stcp95BmW1gGeRbSGEOJl07dqVkpIS4uLiiI2N5dJLL+Wcc84hNTWVPn360KlTp6M+50033cSNN95IamoqJpOJDz74AKvVyhdffMHHH3+M2Wyubg5funQpd999NwaDAbPZzBtvvNEEpWwaEoyPINTfwvtX9eXh79fy5m9bWbZjPy9P6kVcqJ9Xzq+MRgL698M/rTdZt95GzhNPEjB6NO4BAzDYbF65hhBCnCgZGRmUeBbNiYyMZOHChfWmKy1teMRK69atWbNmDQA2m4333z+01fDee+/l3nvvrbNt9OjRjB49+liz3qykmboRzEYDT1+QyrMTurMss4AL3/jruGfrOpgymYh//jn8evQg8Oef2X7+BRR+9x3lK/5Gcx3bylJCCCFODhKMj8JFfRL58dZBKGDCm3/x/cpsr57f4OdH0kcfUnrO2di3b2fPvfeROXkyOyZNpmzRYgnKQgifkpGRQc+ePev89O/fv7mz1SykmfoodYsPYcatg7jxk+XcPn0l6/eUcPfoFIyGxo+bOxyDxULZWWeRevXVlC1aTNXWLRR99TU7r7yS4HFjiXv2WZTR6JVrCSFEc0pNTWXlypXNnY0WQYLxMYgMtPLptQP4vx/0+8ibckp4/uIehPpbvHYNW+fO2Dp3RtM0bCmdyHnySYpnzqJi7VoCTx+EOTGR8CunHNXgeSGEEC2TNFMfI4vJwJPnp/L4+K7M35jL2Jd+Z+66oxvM3hhKKcIvv4zOG9YTddedOHNyKZg2jdz//peto0az98mn0Ox2r19XCCHEiSPB+DhdflprvrphICaj4tqPljH1l42U25tmhaaIa68lZekS2s3+heiHH8JVXEzBxx9T+O13TXI9IYQQJ4YEYy9ISw5jzh1DuaBXPK/O38KFbyxk1/7yJrmWMpuxJCURPnkyHf/4HWtKCjlPPknu1KloDkeTXFMIIUTTkmDsJTazkecn9uSdK/qQtb+ccS/9zseLMnG7tSa7pjKbiX3qSTSHg/z/vUvmVVdh37Gjya4nhBDecrj1jE9FEoy97Mwu0fx42yBSE0J46Ls1XPzWQrbkHv9iEw3x69qVThmrCZ10CRXLlrNj0mRcJ9FKJUII0Zyczqa5rXi0pDd1E0iOCODTa/vz1fIsnvhpPeNe+oNbRrTn+iFtsZm9PyxJmUzEPvIIoRdcyI6LLmJT/wFYkpMJPusswiZPwhQZ6fVrCiFapt+/2ETeruNfj722yMRABl/c8bBp7r33XhITE6uXUHz00UcxmUzMnz+fgoICHA4HTzzxBOPHjz/i9UpLSxk/fny9x3300UdMnToVpRTdu3fn448/JicnhxtuuIFt27YB8MYbbxAXF8fZZ59dPZPX1KlTKS0t5dFHH2XYsGH07NmTP/74g0mTJtGxY0eeeOIJ7HY7ERERfPrpp0RHR1NaWsptt93GsmXLUErxyCOPUFRUxOrVq3nxxRcBeOedd1i3bh0vvPDCsb68gATjJqOU4qI+iQzvFMUjM9by/JxNfLl8Fw+M68zorjFNMiTJL7Ub0Q8/RP6bb2HPzCTv9dfJe/ttou74JxHXXOP16wkhxAHeXM/YZrPx7bffHnLcunXreOKJJ/jrr7+IjIxk//79ANx2220MHTqUb7/9FpfLRWlpKQUFBYe9ht1uZ9myZQAUFBSwaNEilFL873//45lnnuG5557jmWeeISQkhIyMjOp0ZrOZJ598kmeffRaz2cz777/PW2+9dbwvnwTjphYZaOW1yb2Z3C+Px35Yxw2frGBQ+0gePbcL7aOCvH698MmTCZ88GVdpKYWff0Hus8+S++xUyhYvJvL66/FLS5OxyUL4sCPVYJtK7fWMd+zYUb2e8R133MGCBQswGAzV6xnHxMQc9lyapnH//fcfcty8efO46KKLiPS09oWHhwMwb948PvroIwCMRiMhISFHDMYTJ06sfpyVlcXEiRPZs2cPdrudNm3aAJCens4XX3xRnS4sLAyAESNG8OOPP9K5c2ccDgepqalH+WodqlH3jJVSY5RSG5VSW5RS9x4m3YVKKU0pJQvzHuT09pH8dNsg/u/crqzOKmTMi7/z+I/ryC32ztqeBzMGBhJxzdW0+eZrAocNo3zJUjIvu5yNvXrjKixskmsKIU5tB9Yz/uabbw5Zz3jlypVER0c3aj3jYz2uNpPJhNvtrn5+8PEBAQHVj2+99VZuueUWMjIyeOutt454rWuvvZYPPviA999/n6uuuuqo8tWQIwZjpZQReA0YC3QBJimlutSTLgi4HVjslZz5IJPRwJSBrZl/1zAu6pPAe39uZ8iz83nzt61UOppm3mlbly4kvvkG7efPI2DgaWiVlWw751zcXlrgWwghDvDWesYNHTdixAi+/PJL8vPzAaqbqUeOHFm9XKLL5aKoqIjo6Ghyc3PJz8+nqqqKH3/88bDXi/csXfvhhx9Wbx8+fDivvfZa9fMDte3+/fuza9cupk2bxqRJkxr78hxWY2rG/YAtmqZt0zTNDkwH6rsD/zjwX0A+5Y8gItDK0xd0Z84dQ+nXJoL/zNrAiKnpzMrYg6Y1zVAoU1gYie++S+hFE3Du28fGnr3IvOxyXKVlTXI9IcSpp771jJctW0ZqaiofffRRo9czbui4rl278sADDzB06FB69OjBv/71LwBeeukl5s+fT2pqKmlpaaxbtw6z2czDDz9Mv379OPPMMw977UcffZSLLrqItLS06iZwgLvvvpuCggK6detGjx49mD9/fvW+iy++mNNPP7266fp4NeaecTywq9bzLKDOshpKqd5AoqZpPyml7vZKzk4B7aMC+ejqfvy+eR9PzdzAjZ+uoG/rMEZFuxjWBNdTShH7+OP49+1L/nvvU75sGVtHj8Y/LQ2UotUtN2Pt0KEJriyEOFV4Yz3jwx03ZcoUpkyZUmdbdHQ033///SFpb7vtNm677bZDtqenp9d5Pn78+Hp7eQcGBtapKdf2xx9/cMcddzRUhKOmjlQTU0pNAMZomnat5/nlQH9N027xPDcA84ArNU3boZRKB+7SNG1ZPee6HrgeIDo6Om369OleK0hpaelJPYjc5db4LcvJd1scFNs1ekUZmdDRQnxgEw0Fd7mwLV5C8CefoDz3VTSzmcq03pSeey5uT8eI43Wyvy+1+UpZfKUcIGU5ICQkhPbt23s5R8fO5XJh9IHV5eorR2FhIcOHD6+utTdky5YtFB0058Pw4cOXa5pWb5+qxgTj04BHNU0b7Xl+H4CmaU97nocAW4EDX3NigP3AufUF5AP69OmjHehW7g3p6ekMGzbMa+drLmVVTh76ZB6zd2qU251MSEvgjjM7Ehvi1yTXc1dWUvLrr9g6dybrppux79iBpV072s743itLNfrK+wK+UxZfKQdIWQ5Yv349nTt39m6GjkNJSQlBQUceLZKRkcHll19eZ5vVamXx4pbR9aix5ahPfe+JUqrBYNyYZuqlQAelVBsgG7gEmHxgp6ZpRUB1I/vhasbiyAKsJs5tZ+HBSwby6rwtfLIok+9X7ubKga25cVg7ry7TCGCw2Qg56ywAWn/xOXsefoSSn39m8+mDiLz5Zoyhofin9cYcF+fV6wohhKxnXOOIbaCapjmBW4BfgPXAF5qmrVVKPaaUOrepM3iqCg+w8PA5Xfj1zqGclRrL279vY8gzTdvz2hgcTMKLLxB1zz24CgvJefJJdt99Nzuvuhqt1hABIYQQ3tWoG5Kaps3UNK2jpmntNE170rPtYU3TZtSTdpjUir0nMdyf5yf2ZOZtg0lLDuM/szYw7Nl0Pl+6E6eraQJkxNVX0WHhX4Scdx62Ll2wZ2aysXca+f/7H5Xr1zfJNYUQ4lQmC0WcJDrHBvP+Vf2Yfv0AYkJs/PvrDMa89Dvf/p3VJEHZFBZG3H+epvVXXxL173+jORzkTn2O7edfQPnff3v9ekIIcSqTYHySGdA2gm9vGsibl6Xh1jTu+HwVY176nS+X7WqSoKwMBiKuupI233yDJTkZgMxJk1nfqTO7bryJfS+/gmPvXq9fVwghTiUSjE9CSinGdIthzh1DefOy3pgMiru/Ws25r/7JT6v3NMk9ZVtKR9r98jOJb79F0JlnAFA6fz55r7/OlmHDqdy4yevXFEL4ruYYkvboo48ydepUAK688kq++uqrE56HhkgwPokZDYox3WKZdftgXp3ci5IqBzdPW8GIqel8vTwLl9v7s3kFDhlCwiuvkLJiOW1/+pGYxx8DYPv48RT99JPXryeEOLW1lPWGm5qs2uQDlFKc3T2OMV1jmLs+h9fTt3Lnl6t487et3DS8Hed0j8Nk9O73LoO/P9Z27bC2a4cpIoK9jz3O7jvvYveddxF22WVEP3C/rA4lRDOY/8Hb5GZu8+o5o5LbMvzK6w+bxpvrGaenp/PQQw8RFhbGhg0bWL9+Pffeey/p6elUVVVx8803849//AOA//73v3zyyScYDAbGjh3Lf/7zH9555x3efvtt7HY77du35+OPP8bf3//4X4gmJDVjH2IyGhjTLZbvbjqdlyf1wqAUd3y+imFT0/liadPcUwYIGjGCtjO+xxQXC0DBJ5+weeDp7P/4E3A4muSaQoiWZeLEiXWWG/ziiy+YMmUK3377LStWrGD+/PnceeedjZ5/f8WKFbz00kts2rSJd999l5CQEJYuXcrSpUt555132L59O7NmzeL7779n8eLFrFq1invuuQeACy64gKVLl7Jq1So6d+7Mu+++2yRl9iapGfsgg0Fxbo84zk6N5dcNuTw/ZxP3fL2aN37byk3D2nFer3jMXq4pG4OD6TBvHprbzd7HH6fws+nkPPkk0cD2rl0JvegiQi+a4JVZvYQQDTtSDbapeHM9Y4B+/fpVrys8e/ZsVq9eXX2Pt6ioiM2bNzN37lyuuuqq6lrvgfWN16xZw4MPPkhhYSGlpaWMHj26iUrtPVIz9mEGg+LMLtH8dOsg3rwsDT+zkbu/Ws2I59L5dHEm5Xbv34tRBgOxjzxC+/T5xDz2f2hKUbl2LXsffZQNXbuRfc89uO12r19XCNH8vLWeMdRdb1jTNF555RVWrlzJypUr2b59O6NGjWrw2CuvvJJXX32VjIwMHnnkkaNeC7k5SDA+BRgMeu/rn24bxLtT+hAeYOWBb9eQ9vhcXpy7iX0lVV6/pjkmhrCLL2bfC88T8/hjRN9/PwDFM35g8+mDcOzZg3PfPtxlsoSjEL7CW+sZH2z06NG88cYbODy3vTZt2kRZWRlnnnkm77//PuXl5UDN+sYlJSXExsbicDj49NNPvVO4JibN1KcQpRQjO0czolMUC7fl886Cbbw4dzOvzd/CuNRYrhzYml5J3lmb8wDNZiNszBgAQs4bz97HHqf4xx/ZMnwEAMaQECJvuxXcGsFjx2CqtZaoEOLkUt96xueccw6pqan06dOn0esZH+zaa69lx44d9O7dG03TaNWqFd999x1jxoxh5cqV9OnTB4vFwrhx43jqqad4/PHH6d+/P61ataJ///7VSzq2ZEdctampyKpNDTuRZdm6r5SPF2by9fIsSqqc9EoK5cqBrTkrNdYrPbDrK0vp779T8fffKIuFwi+/wpGdXb0v+bNp+PfqddzXbQq+8jfmK+UAKcsBJ+uqTS1dS1u1Sfiwdq0CefTcrtw1OoXPl+7i00WZ3D59JVNnb+TyAcmc3yuBVkFWr14zcPBgAgcPBiB04kSKf5pJxcqVFP/4I5mTJuPXowfRDz6AX2qqV68rhBAtlQRjAUCg1cQ1g9pw1cDWzFmfw7u/b+epmRt45ueNDEuJ4v5xnWjbyvsz5pjCwgi/7FK47FKCzhhJ7jPPUrFqFTuvuZaIq67EVVhIyIUXYuvY0evXFkI0r5a+nvGJJMFY1GEwKEZ3jWF01xg255Tw5fIsPl2Uycjnf2NYx1ZMSEtkbLcYDAbvT+gRPGYMwWPGULVtG1m33Mq+l14GYP+HH9Hmu2+xHeP9JiFEyyTrGdeQYCwa1CE6iPvHdea6wW35eOEOPl28k/kb95EY7sel/ZOZkJZAZKB3m7ABrG3b0vb777BnZpLzzDOULfidXTfehH/fPrjLyrF17ULEtddisFi8fm0hTlaapsmsdy3EsfTFkqFN4ohaBVn516gUljxwBq9N7k1ciB//mbWBAU/9yi3TVrBoW77X58FWZjPW9u1Jevtt4p59BndZGcUzfqD011/Je/kVsm+9jaIZM9BOkXlrhTgcm81Gfn7+MQUB4V2appGfn4/NZjuq46RmLBrNaFCc1T2Ws7rHsjmnhM+W7OLL5bv4cfUeooOtnN8rgcn9kkiK8O4csCHnnEPwuHFUbd6Mc98+8l59jdLffqP0t9/I+e8ztJ72afXyjkKcihISEsjKymLfvn3NnRUAKisrjzoYtUTHWg6bzUZCQsJRHSPBWByTDtFBPHxOF+4c1ZGfMvbw3d/ZvPnbVv73+zbO6h7L+b3iOb2998YMK6NRv2fcqRMBAwZQ8uuvFM/6mZJff2XHpMnEPPQggUOGYKg1a48Qpwqz2Vw9dWRLkJ6eTq8WOkTxaJzIckgwFsclwGri4j6JXNwnkd2FFby9YBtfLtvF9yt3kxDmR79IJ9372gkP8N79XWU2V3f2qli9mp1XXU32Hf8CwP+0AQSNGImyWLB164ozJ4eAgQMx+MC3dCGE75JgLLwmLtSPR8/tyr1jOzFnXQ4fL8rk2837+f7JuQxsF8FlA5IZ2SnKq8s5+nXvTvt5v5L3+uu4ioop/eMPyhcuqpPGf8AAkt5/Tzq3CCFaLAnGwutsZiPn9IjjnB5xfPrjPLJNcXz3dzb/+Hg54QEWLkpLYHL/JJIjvNOkbAwJIfq++wBwFRZSkZGBwWaj9M8/cReXUDBtGpv6DyDi6quJ+Mf14HCgpCe2EKIFkWAsmlR8oIFLh3XiX2d2ZO76XKYv3cm7f2znrQXbSEsO47yecZzVPc5rzdjG0NDq2b38+/ZFc7mw79pF2e+/s+/FF9n34osAxD7xOKETJnjlmkIIcbwkGIsTwmQ0MKZbDGO6xZBTXMlnS3YyK2MvD32/lv/7YR1DO7ZifK94zuwcjZ/Fe2seK6ORxLffQrPbyf7nHZTOnw/AngcfwrF7N5G33irN10KIZifBWJxw0cE2/nlGR24f2YH1e0r4fmU236/cza8bcgmwGJnYN4nxPePonhDilUCplEJZrSS+8TrO/HyU2czOa68j7/U3qNq6jfApU1BmM/lvv0XEP27Ar1tXL5RSCCEaT4KxaDZKKbrEBdMlLph7xnRi8fZ8Pl6YyQd/bee9P7eTEh3EWd1jGd01hpQY76wAY4qIAKD19M/Iefo/FHz8MSW//FK9v2TOXFrdfhthl12GMzcXc2wsBn/vjpsWQoiDSTAWLYLRoBjYLpKB7SIprnQwY+VuvlqexfNzNvH8nE30SAzligHJjOkWQ4D1+P9slcFAzAP3E3r+eRR++x2myEisKR0pnvED+156uXpebICkjz7Ev2/f476mEEI0RIKxaHGCbWYuG5DMZQOS2VtUyceLdjArYy93frmKB77LYEzXGM7tGcfgDq0wH+cwKVuXLsR06VL9PHDoUEInXMje/3sMe2YmADuvmIK1S2dCbH5UxsTIghVCCK+TYCxatJgQG3eP7sRdo1JYvH0/M1bt5qfVe/hu5W7C/M2M6hLD8E5RDEtphc18/B2/lFIEDBxI25k/gcGAIzubws8/p/inmdh2r2f7eecTeMZI3GVl2Dp1Juqeu6UDmBDiuEkwFicFpRQD2kYwoG0Ej57TlQWb9jFj1W5mZuzh82W7sBgNXNw3gfN7JdArMfS4l3hURj2wWxISiLrzTqLuvJM/33uPqPc/oHzhItxlZZQvXETJnDlYU1KIvOlGLMmtMQbKdJxCiKMnwVicdCwmA2d0ieaMLtE4XW4Wbsvny2VZfLEsi08W7SQy0MqortGcnRpL/7YRGL209rKjbVs6/L4ATdPQKivJe/11ypcspTQ9ndJff8UcF0fi229hbd/eK9cTQpw6JBiLk5rJaGBwh1YM7tCKogoH6RtzmZWxl6+WZzFtsR6Yx6XqTdmnt4vEYjr+qTiVUig/P6LuvBOA8hV/U/jVVxT/8APbxp9H8OhRhF9xBdbOnTFYvb/esxDC90gwFj4jxM/M+J7xjO8ZT4XdxfyNufy4ejefL93FRwszsZgMnNk5mgt6xzOk4/F3/jrAv3cv/Hv3otXNN5H7wosU//gjxTNngclE4NChRN50I35dZeyyEKJhEoyFT/KzGBmXGsu41FjK7U7+2pLPzDV7mL8hl58y9hBsMzGyczSju0YzpGMr/C3H/69gjo8nfuqzBI8bS9nCRZQvX0bZggWU/vorfr17E3XXXVT8/TfWjh3wT0uT8ctCiGoSjIXP87eYqu8x251uftu0j5/X7OXXDTl8+3c2VpPe1D2qazRndI4+7nmyg0aMIGjECADsWdnkTp1Kyc8/kzl5cp10gSNGEH3P3Vhatz6u6wkhTn4SjMUpxWIycGaXaM70dP5asmM/s9fmMHvtXuauz8GgoF+bcEZ1iWFU12gSwo6v9mpJiCf+hecpPW88JT//gjUlBfvOTAo/m07pvHmUzptHwJDBRN93H9YWtDi8EOLEkmAsTlkmo6F61q9HzunCmuxiflm7l9nr9vLYj+t47Md1dIsPZlSXGEZ3jUHTtGO6jlKKoGHDCBo2rHpb7COPULlhA3mvv0HJ7NlsW/A7oRdfTMyjj6AM3lvvWQhxcpBgLAR6wExNCCE1IYS7RqewPa+M2Wv38svavbwwV5+SM8pfcV7FeoZ0aEWXuODjbs62depEwssvYc/KZt/zz1P4xRcUfvEF1g7tMQQEgtGIMpkIGjEcc3IylqQkLElJKJP82wrha+S/Woh6tIkM4B9D2/GPoe3ILa5kzvocpv++nvf/3M7bC7ZhMxsY2TmaoR1aMbhjJLEhfsd8LUtCPHHPTcW/Xz8Kv/ySyrVrMYSEYAwJwbFzJ+WLF1enNYSEYImPJ/KWm6vvSwshTn4SjIU4gqhgG5f2Tya+Yju9B5xO+sZ9/LZxHws27+On1XtQCnonhTE8pRXDUqLoEht81DOAKaUIu2QiYZdMxG23g8uFwc+PyvXr0RwOKjIyKJj2GY7sbCrXrSPrppsxhIRgbd2auP/+RzqBCXGSk2AsxFEItpk5t0cc5/aIQ9M0NuWU8vMavfPX1NmbmDp7EyF+Zs7sEs3wlCgGtY8kxN98VNcwWGqav22dOwPg17074ZdeiuZ24y6voPDLL8l/800qVq1i6znnEnzmGYScfwEGPxu2Ll1k2JQQJxkJxkIcI6UUKTFBpMQEcfsZHcgtrmTehlx+WbuXmRl7+Gp5FoYDteZOUQzt2IquccHHtbCEMhgwBgYQcdWVhF9+GZXr1rHngQcpnjlLn2gEUP7+hF86meCzz8HasYMsZCHESUCCsRBeEhVs45J+SVzSLwmHy83qrCJ+25jL/I37ePaXjTz7y0aigqwMah/JqK7R9G8TQdhxdAJTJhN+3bvT9ocZVK5fj2P3blCKfa+8Sv47/yP/nf9h8PfHXV6OX1oarW6/TWYCE6KFkmAsRBMwGw2kJYeRlhzGv0alkFtSyYJNecxZt5f5G3P55u9sALrFBzMiJYrT20eSmhByzDOB2Tp3rm7SDhw+nKqNGyn78y/2f/wx7vJyKpYvZ+cVU8BsJqRHd0pNZgIHne618gohjo8EYyFOgKggGxPSEpiQloDT5WbpjgIWb89n/sZ9vDp/Cy/P24LRoDi9fSSD20dyWruIY27SVkph69QJW6dOhF99FVp5OW67nZI5cyj4dBosW86ua68lcNgw/Pv3xxgaSuCwoRiDgmTYlBDNRP7zhDjBTEYDp7WL4LR2EfzzjI4UVThYun0/S3fsZ/a6HJ6cuR7Qh1cNah9JvzbhnN4+8pjGNSulUAEBGAICCLv4YkInTODPTz4h/sef9KUf09Or05qTkwjo15+Ia6/BkpzsreIKIRpBgrEQzSzEz1w9d/Z94zqT4+kINnvtXr5ekcXHizIB6BQTRL824aQlhzGofSQRgUe/PKMyGHAmJdF6+mdUbd6COSaakrlzKU1Pp2TOXAozd1I8ezax//comsNB8NixUlsW4gRo1H+ZUmoM8BJgBP6nadp/Dtr/L+BawAnsA67WNC3Ty3kV4pQQHWxjUr8kJvVLwuXWWJVVyMKt+Szals8nizL5aGEmSkH3+BB6JIZyVmosaclhmI5iSUhlMGBL6QhA6IUXEnrhhWguF8U//8zuf99L9j/vAGDPgw9hbdcOc3w8ti6dsXXvDoB/nz6yVrMQXnTEYKyUMgKvAWcCWcBSpdQMTdPW1Ur2N9BH07RypdSNwDPAxKbIsBCnEqNB0TspjN5JYdw8vD12p5ulO/Yzf0Muq7OL+HTxTj5amImf2Uh8mB/jusUwNCWK7gkhR71eszIaCTnrLGydu1C5fh2u/HwqVmdQ8uuvVK5bR8mcOdVprR3aE3bZ5Rj8/Qg++2wZPiXEcWpMzbgfsEXTtG0ASqnpwHigOhhrmja/VvpFwGXezKQQQmcxGTi9fSSnt48EoKTSwR+b81iyYz+Lt+3n5Xl6ZzA/s5E+rcMY0DaCHgmh9Gkdhs1sbNQ1rG3bYG1bs4KUpmlU/L0SZVBUrF5NydxfqVy7lr2PPAJA/nvvo0wmQi84n4BBgzBFRMikI0IcJXWklWiUUhOAMZqmXet5fjnQX9O0WxpI/yqwV9O0J+rZdz1wPUB0dHTa9OnTjzP7NUpLSwkMDPTa+ZqTlKVlOhnKUmzX2LTfxQbPT1Zpzf93crCBAbEmkmxVpEQHYDrKKTtrM23fjjVjDcpehTUjA1NObp39VandKLnwQlwxMcd8jcY4Gd6TxpKytDzeLsfw4cOXa5rWp759Xu2ZoZS6DOgDDK1vv6ZpbwNvA/Tp00cbVmtJueOVnp6ON8/XnKQsLdPJWJb9ZXaW7tjPL2v3Mn9DLp9vtAMKk6GCbvEhjOwURe/kMHokhhJoPYqPg4NeB1dJCWV//EHJ3F+p2rQJMtZgzViDsVUkBpsfAacPJOSss7B27IjmdmMMDfVK0/bJ+J40RMrS8pzIcjTmvy8bSKz1PMGzrQ6l1BnAA8BQTdOqvJM9IcTxCA+wMLqrvh4zQE5xJR/O/AN3SALpG3N5bs4mAAwKusaF0Ld1OL2SQo96KJUxKIjgsWMJHjsWAEduLiWzZlG1ZQuO3Xso/OJLCqd/XnOA2Yx/WhphkycRPGqU9wosxEmqMcF4KdBBKdUGPQhfAkyunUAp1Qt4C705O/fQUwghWoLoYBv9YkwMG9aJe8d2orhSH+O8alchS3bs59PFmbz353ZAnx2sT3I4vZPD6J0USnyoX6Nrs+aoKMKnTKl+7iospGzRIkpmz8a+I5OqLVsoX7SI8kWLyE1KwtqxA+GXXY5/3z4oY+PubQvhS44YjDVNcyqlbgF+QR/a9J6maWuVUo8ByzRNmwE8CwQCX3r+WXdqmnZuE+ZbCOEFwTYzIztHM7JzNAB2p5vVWYUs3r6f3zbt4/Olu/jgrx0ARAVZ9Z7dyaH0TgqjW3xIozuFGUNDCR4zhuAxY6q3aU4nuc89T8WKFZQvWkzp3F8BUH5+WNq0xtSqFZH/+Af+vXt7t9BCtECNukmkadpMYOZB2x6u9fgML+dLCNEMLCYDfVqH06d1ODcPb4/D5WbDnhJW7Cxgxc4C/t5ZyM9r9wJgNiq6xIXQOynUE6TDiAuxNbr2rEwmov99DwDO/fsp+ORTKlb+jWPPXqrWraeK9ZT9tgBjRAR+3bqhLGb8+/YlbNIklPnolqUUoqWTqXWEEA0yGw2kJoSQmhDClIGtAdhXUsXfOwtYsbOQFTsL+GzJTt7/cwcA0cHW6nHRvZND6RrXuNqzKTycVrfdWv1c0zSqNm8m/823KFu8GPuuXTjz8iiZM5fc518g6q67MPjZCP7xJwr25uDfry+mqGiMgQFN8TII0eQkGAshjkqrICujusYwytMp7ODa84qdBcxao9eeLUYDXeKC6zRvx4X6HfEaSilsHTsS//xz1ds0TaPom2/Y9/Ir5Dyhj5z0A/YuXAiAISAAa0oKxvAwIq+/Hj/PbGFCnAwkGAshjkt9tefckkr+9tSc/84srNMxLCbYRrf4YLrFh5DqmdIzshHzbCulCL3wQkLOPZfi2bPRquxkVFaSltKR4lk/UzB9OhUrVgBQ9tsCQidOJOD0gfj17IkpLKzJyi+EN0gwFkJ4XVSQrc6QKrvTzYa9xazILODvXYWs3V3MrxtyOTDnUEKYH90TQuidFEbPxFDaRAY0uBCGMpsJOessAFzp6finpeGflkbkTTeiTCac+flk33Y7BZ98QsEnn1QfZ+vaFXNiIoGDB2Np0wZjaGidmcaEaE4SjIUQTc5iMtA9IZTuCaFc6dlWVuVk3Z5ilu7YT0ZWEUt3FDAzY2/1MfGhfnSNC6Z/2wh6JobQOTYYf0vDH1mm8HAAjMHBtPnmawq//hrH7j24S0twFRbizMundN48Sn7+WT9AKUInXIi1Q0dM0dEEjRguHcNEs5FgLIRoFgFWE31bh9O3dXj1tt2FFazbXczGnBI27C3h750FzF6XA4BS0DYygK5xIXSN05u5S+31T+erzGbCLrnkkO2u0jJKfvkZd1UVxd/PoPDLr2p2GgwYAgIIHDwYU3Q0AQP6EzBkiCyCIU4ICcZCiBYjLtSPuFA/zugSXb1tb1Ela7KLWLu7mDW7i1ieWcCMVbur9z+9Yh5d4oJJDPOnb+sw+rUJb7CJ2xgYQOiFFwIQNnEizvx83MXFlMyfT8msn3Hm5VG2cCGuggL2v/8+/v37E33//WhVlSizGUtysiyCIZqEBGMhRIsWE2IjJsRWJ0DvL7OzbncxM35fQYVfGGuzi5izLqe6k1h8qB9d4oLpGhdMSnQQ3eJDSAirO4OYMhoxR0VBVBTW9u2JvO666n2u0lLy332X/R9+xPbx46u3G1tF4telK5UbNxI6YQIRV18lwVl4hQRjIcRJJzzAwqAOkTizLQwb1guACruLlbsKWZWldxBbu7uIuetzqjuJRQRY6JEYSoeoQLrF6/egW0f4Y6pn3WdjYCBRt99O+OTJFM/6Gc3hoHzJEkp/+42K1atxFRSQ9+qr5L36KqaYGGydOmFp0wZls+Lfs6c0b4ujJsFYCOET/CxGTmsXwWntIqq3lVU5WZNdxObcUlbuKmR1ViHzNtRMn28xGugQHciAthF0Twihe0IoyeH+GDzLS5patSL8issBiLjmatyVlSirFXdZGYWff0750mVgMlK5OoPS9HQA8gFbly7YunbB1rUbIRecj8FiwV1VhcF65CFc4tQkwVgI4bMCrCb6t42gf9sILhuQDOjDrDbuLWFTjv7z19Z8Pl6Yid3lBsDPbCQlJojOsUF0iQ2mfVQQvZJCsZmNGGw2QK85R1xzDRHXXFN9raotW1AWC2WLF7Nv6nNUrlsHX37F3kcfxZyYiGP3bvzT0nAVF+PXqyf+vXvj17075sRElOHQ2rk4tUgwFkKcUiymmklKDnC63GzMKWHt7mLW7ylmw54SZq3Zy2dLdlWnaR8VSM/EUNpHBdI9IYTWEQHE1pqL29q+vX7+pCRCzjoLV3ExlWvXUvbnn9gzd2Jp0xrnnj1gNFH0/QwKP5tefW5b1674d2hPcUWFPklJZCTKbEbTNGnuPkVIMBZCnPJMRoNnyFRNgNY0jezCCtZkF7N4ez7r9xTz26Z9fLU8qzpNkM1E55hgOscG0Sk2mIQwP7rEBhMR6I/B3x9zTAxBI0cecj3N6aRq61YqVq7Cvn07RTNmELR2LdnffQ+AstkwWK1oTieRN91IwKDBGAL8MbVqJU3dPkqCsRBC1EMpRUKYPwlh/ozpFlO9Pbekks05pWzLK2PDHr0m/eXyLMrtruo0sSE2UuNDaNMqgLSkMFJigkgMq7kXrUwmbCkp2FJSAIi+99/8NmsWfcLCqNq0GfuO7Tj37aNi1Wpyn50Kz07VT2w0EjZpEv5pvbF164YlMfHEvSCiSUkwFkKIoxAVZCMqyMbp7SOrt7ndGpn7y9m1v5zVWYWs3KWPi55Tqze3v8VI59hg2rUKoHVkAO1bBZISE0RSuD9KKTQ/PwIGDCBgwIDq82qahn37DsqXLMG+fRsVqzPqTPNp7diRkPHnYmndGoOfHxhNWNu3wxQRgTi5SDAWQojjZDAo2kQG0CYygCEdW1VvL6tyVncUW7+nhHV7ivl1fS75ZfbqNIFWE+1aBRDgrmKttoV2rQJpHxVAUngAFpMBa9s21XNo68F5O/bMTErTf6Nqwwa95nwQ5eeHITAAv+498OveHf++fbG21efj1jzfDuRedMsiwVgIIZpIgNVEr6QweiXVXTWqoMzOjvwyNuwtYePeErbuK2XtLhd//bKxOo3RoEgO96ddVCDtWgXSrlUA7aMCaRubSEjbtgQNHw6AY/duHHtzcObsxV1RiX37Nhx79uIuL6dqwwZKf/21+pzKbEZzODCGhBB6ySUEjToTa7t2uIqLMYaEyP3oZiTBWAghTrCwAAthAZY6QTo9PZ2+pw1i274ytu4rZUtuKVv36T/pG3NxuGrm4W4VZK0OznqgTqBd207E1erdfYBj924qN27EviMT+85MnHtzsGdmkv/WW+S/9VZ1OlOrVgSNGYN/Wm8CBgzAGBra5K+DqCHBWAghWogAq+mQYVegD73aVVDBVk+APhCoZ6zcTXGlszqdv8VI21YBelN3q0BPrTqI1oOHEDTcWJ1O0zSqNm6kct16/ffGjVRt3EjBxx9T8PHHGENDCRg0CGW1YG3XHnNiArbOnbEkJJyw1+JUI8FYCCFaOJPRUH1P+gxq5ujWNI38MntNLTq3jC37Slm2o4DvV9YspmFQkBTur9eiow40eUfRbkxboi84vzqdu6KC0vnzKfrhR8r++ANXYWGdfFi7dMbapi3WDh0IOH0gymLFkhCPISCgyV8DXyfBWAghTlJKKSIDrUQGWhnQtm4P6gq7i215B2rRZZ5gXcrvW/KwO93V6SICLLSJDCA21I+2kQG0jetB6/sH0ibSH//SIipWrQKTifIlS6navJmKlSsp/ukn9r34Yp3rGfz9CRgyBHN0NIHZ2eSuXIk5Ng5bt674de16Il6Ok5oEYyGE8EF+FuMhE5kAuNwa2QUVbNlXwtZcPUhvzytj5a4Cfly9u3ooFugLcsSF2mgbGUhK3/OJPcNGp5hg4kv2oq1bB4B9+3aqNm/GEBhI2Z9/4i4txd/hIH/u3OrzWDt2JGjUKAx+Npz78rBn7SJw6FBCJ0yQXt0eEoyFEOIUYjQokiL8SYrwZ0SnuvsqHS4y88vZnqfXpjPzy9i5v5wl2/fXWUMaoFVQIG0iA0hMaEP73ucSF2oj9qZ7SQ6zsnbpXwwbMgTH7t2Upv9GwfTPyHv11TrHl879lfy338EYGoo5NhZzbCyGAH9CJ0zAHBfX1C9DiyPBWAghBAA2zyIZKTFBh+yrdLjIKqhgw95iT8AuY3teGX9s2cfXK7LqpLUYoe3axbQKspIUnkab+wfRIVARhoPoqDAiwgIo+eEHin74EVwuypcvx5WfD0DeG2+izGbMCQm4Kyr02cq6dSXimmuxpXREs9tRfn4+t7iGBGMhhBBHZDMbaR8VSPuowEP2FVU42FdSyY68cnYXVfDHyo1UWW0Ultv5KWMPheWOOunDAywkhSfRZvzdJIb7kxjmR2KQmciy/QT+OQ/7ypUAuEpKqNq8mZJZP1My6+fq441hYVjatcWSkIg5IQFzbCzW9u2wdup00o6VlmAshBDiuIT4mQnxM9M+Sq9RJ1XtYNiwftX793t6fBeW29lVUMHmnBJ2FejN39+vzMat1T5bG4ITO9A6MoCkcH9iQ2x0UGUkbPibCFc5/pVlGEqKcGTupGTuXNylpTWHKoXB3x+/tN5Y27RBc7kxBAZg7dCB4FGjwGTyJGt596klGAshhGhS4QEW+rUJr3ef3elmd2EFuwrK2VNYSU5xJbklVWTuLycju4jZ63I8vb/jq48JCTATP9CPxLEWEoLMdMjPJNpRSvjmDPzycrBn7qRswe9gNIJLX8BjN/pqWMpgwBQdjSEgAEtSIqboGPx698LWqRPmhIRmC9QSjIUQQjQbi8lA60h98Yz6uNwauSWV5BRXsSOvjD1FlWQXlpNdUMG2/RX8tb2QkioLEA62oZAAhkSIGmymVbAfieE2eu/eQNs1C/ELDsDf34rf/n0YKsspX7YcZ24uvP8+oA/PMkVF6feqzWZCIiPRBg5EWSxN/jpIMBZCCNFiGQ2K2BA/YkP86JkYWm+akkoHe4oq2VtUye7CCrILK9hdWEluSSUb9pYxrySSyrizag7wLLgVkWKhtdVFR1cRKQU7iS3eR0j+HszxQfgZwJ2XA2Zz0xcSCcZCCCFOckE2M0E2Mx2jD+0FDvpMZZUOt94UXlRJTlEle4sr9cfFlawqCuAXZzj7DXYIrTnOGgUbTkwRJBgLIYTwbUop/CxGOkYHNRiwAaqcLooqHNU17KUr15ywe8gSjIUQQgjAajISFWQkKshG94RQbHkbj3yQl/jWqGkhhBDiJCTBWAghhGhmEoyFEEKIZibBWAghhGhmEoyFEEKIZibBWAghhGhmEoyFEEKIZibBWAghhGhmEoyFEEKIZibBWAghhGhmEoyFEEKIZibBWAghhGhmEoyFEEKIZtaoYKyUGqOU2qiU2qKUuree/Val1Oee/YuVUq29nlMhhBDCRx0xGCuljMBrwFigCzBJKdXloGTXAAWaprUHXgD+6+2MCiGEEL6qMTXjfsAWTdO2aZpmB6YD4w9KMx740PP4K2CkOlErMgshhBAnucYE43hgV63nWZ5t9abRNM0JFAER3sigEEII4etMJ/JiSqnrges9T0uVUhu9ePpIIM+L52tOUpaWyVfK4ivlAClLS+UrZfF2OZIb2tGYYJwNJNZ6nuDZVl+aLKWUCQgB8g8+kaZpbwNvN+KaR00ptUzTtD5Nce4TTcrSMvlKWXylHCBlaal8pSwnshyNaaZeCnRQSrVRSlmAS4AZB6WZAUzxPJ4AzNM0TfNeNoUQQgjfdcSasaZpTqXULcAvgBF4T9O0tUqpx4BlmqbNAN4FPlZKbQH2owdsIYQQQjRCo+4Za5o2E5h50LaHaz2uBC7ybtaOWpM0fzcTKUvL5Ctl8ZVygJSlpfKVspywcihpTRZCCCGal0yHKYQQQjQznwjGR5qusyVRSiUqpeYrpdYppdYqpW73bH9UKZWtlFrp+RlX65j7PGXbqJQa3Xy5P5RSaodSKsOT52WebeFKqTlKqc2e32Ge7Uop9bKnLKuVUr2bN/c1lFIptV77lUqpYqXUP0+W90Up9Z5SKlcptabWtqN+H5RSUzzpNyulptR3rWYqy7NKqQ2e/H6rlAr1bG+tlKqo9f68WeuYNM/f5hZPeU/oREQNlOOo/55awudbA2X5vFY5diilVnq2t9j3xJOHhj6Dm/f/RdO0k/oHvVPZVqAtYAFWAV2aO1+HyW8s0NvzOAjYhD7N6KPAXfWk7+IpkxVo4ymrsbnLUSt/O4DIg7Y9A9zreXwv8F/P43HALEABA4DFzZ3/w/xN7UUfE3hSvC/AEKA3sOZY3wcgHNjm+R3meRzWQsoyCjB5Hv+3Vlla10530HmWeMqnPOUd2wLKcVR/Ty3l862+shy0/zng4Zb+nnjy0NBncLP+v/hCzbgx03W2GJqm7dE0bYXncQmwnkNnNKttPDBd07QqTdO2A1vQy9yS1Z4e9UPgvFrbP9J0i4BQpVRsM+TvSEYCWzVNyzxMmhb1vmiatgB9JENtR/s+jAbmaJq2X9O0AmAOMKbJM3+Q+sqiadpsTZ/dD2AR+nwHDfKUJ1jTtEWa/sn5ETXlPyEaeE8a0tDfU4v4fDtcWTy124uBzw53jpbwnsBhP4Ob9f/FF4JxY6brbJGUvrpVL2CxZ9MtnmaQ9w40kdDyy6cBs5VSy5U+wxpAtKZpezyP9wLRnsctvSwHXELdD5aT8X2Bo38fToYyAVyNXlM5oI1S6m+l1G9KqcGebfHo+T+gJZXlaP6eTob3ZDCQo2na5lrbTor35KDP4Gb9f/GFYHxSUkoFAl8D/9Q0rRh4A2gH9AT2oDf7nAwGaZrWG31Vr5uVUkNq7/R8Az5puuwrfWKbc4EvPZtO1veljpPtfWiIUuoBwAl86tm0B0jSNK0X8C9gmlIquLny1wg+8fd0kEnU/fJ6Urwn9XwGV2uO/xdfCMaNma6zRVFKmdH/CD7VNO0bAE3TcjRNc2ma5gbeoabJs0WXT9O0bM/vXOBb9HznHGh+9vzO9SRv0WXxGAus0DQtB07e98XjaN+HFl0mpdSVwNnApZ4PSzzNuvmex8vR7692RM937absFlGWY/h7aunviQm4APj8wLaT4T2p7zOYZv5/8YVg3JjpOlsMz/2Vd4H1mqY9X2t77Xun5wMHei3OAC5RSlmVUm2ADuidIJqdUipAKRV04DF6J5s11J0edQrwvefxDOAKT+/EAUBRrWahlqLOt/yT8X2p5Wjfh1+AUUqpME/z6SjPtmanlBoD3AOcq2laea3trZS+5jpKqbbo78M2T3mKlVIDPP9zV1BT/mZzDH9PLf3z7Qxgg6Zp1c3PLf09aegzmOb+fznenmkt4Qe9t9sm9G9gDzR3fo6Q10HozR+rgZWen3HAx0CGZ/sMILbWMQ94yraRZuh9eJiytEXv3bkKWHvgtUdfPvNXYDMwFwj3bFfAa56yZAB9mrsMB5UnAH2Bk5Ba206K9wX9C8QewIF+7+qaY3kf0O/HbvH8XNWCyrIF/f7cgf+ZNz1pL/T87a0EVgDn1DpPH/RgtxV4Fc8kR81cjqP+e2oJn2/1lcWz/QPghoPSttj3xJOHhj6Dm/X/RWbgEkIIIZqZLzRTCyGEECc1CcZCCCFEM5NgLIQQQjQzCcZCCCFEM5NgLIQQQjQzCcZCCCFEM5NgLIQQQjQzCcZCCCFEM/t/N/Cb9QVoJ8AAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(history.history).plot(figsize=(8, 5))\n",
    "plt.grid(True)\n",
    "plt.gca().set_ylim(0, 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_orig = pd.read_csv(r\"DS_G3_HeartFailure_FULL_test.csv\",index_col=0)\n",
    "test_cleaned = test_orig.drop([\"time\",\"anaemia\",\"diabetes\",\"high_blood_pressure\",\"sex\",\"smoking\"],axis=1)\n",
    "feat_test, targ_test = test_cleaned.drop([\"DEATH_EVENT\"],axis=1),test_cleaned[\"DEATH_EVENT\"]\n",
    "feat_test = scaler.transform(feat_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7368421052631579"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = model.predict(feat_test)>0.5\n",
    "mask = 1*mask\n",
    "recall_score(mask,targ_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([135.,  18.,   5.,   3.,   0.,   2.,   5.,   4.,  16., 138.]),\n",
       " array([1.8865263e-07, 9.9999696e-02, 1.9999920e-01, 2.9999870e-01,\n",
       "        3.9999822e-01, 4.9999771e-01, 5.9999722e-01, 6.9999671e-01,\n",
       "        7.9999620e-01, 8.9999574e-01, 9.9999523e-01], dtype=float32),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(model.predict(feat_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([136.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0., 124.]),\n",
       " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(targ_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x27390e0ae20>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(targ_train, model.predict(feat_train)>0.5)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "disp.plot()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "d9579fd63862c719ce91d1dd1fb1c44b2ef43bdaa2f86d843d9fd8562746d35b"
  },
  "kernelspec": {
   "display_name": "Python 3.9.6 64-bit",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}