EfficientNet-kPiece-PCA-Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fd1efdf1",
   "metadata": {},
   "source": [
    "### Importing Necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "0938de48",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pickle\n",
    "from tqdm import tqdm\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.preprocessing.image import load_img, img_to_array\n",
    "\n",
    "from tensorflow.keras.layers import Input, Flatten, Dense, Dropout, GlobalAvgPool2D, BatchNormalization\n",
    "from tensorflow.keras.applications import EfficientNetV2L, EfficientNetV2S, Xception\n",
    "from tensorflow.keras.regularizers import l2\n",
    "\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "from sklearn.metrics import precision_recall_fscore_support as score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c954b162",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68302673",
   "metadata": {},
   "source": [
    "A detailed discussion and visualization of the data can be seen in [here](Data-Overview.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "75b06a70",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = pd.read_csv(\"./Data/train_labels.csv\")\n",
    "images = ['./Data/train/train/'+str(name)+'.tif' for name in labels['id']]\n",
    "labels['images'] = images\n",
    "labels = labels[['id', 'images', 'label']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "29190fd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_labels = list(set(labels['label']))\n",
    "class_names = [\n",
    "    'letter', 'form', 'email', 'handwritten', 'advertisement', 'scientific report', 'scientific publication',\n",
    "    'specification', 'file folder', 'news article', 'budget', 'invoice', 'presentation', 'questionnaire', 'resume',\n",
    "    'memo'\n",
    "]\n",
    "label_names = pd.DataFrame({\n",
    "    'labels': class_labels,\n",
    "    'names': class_names\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "b49810a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels = pd.read_csv('./Data/train_labels_1.csv')\n",
    "test_labels = pd.read_csv('./Data/test_labels_1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "ca081f2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "roi_info = np.load('./Data/ROI-Extractions/train/train_roi_viz.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "a77be477",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16000, 100, 6)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roi_info.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2e251c1",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd1a53ca",
   "metadata": {},
   "source": [
    "Below we load the visual features for each document as generated in [EfficientNet kPiece Model](EfficientNet-kPiece-Model.ipynb). We will use this for fine-tuning."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc3707c1",
   "metadata": {},
   "source": [
    "### Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "e1b35850",
   "metadata": {},
   "outputs": [],
   "source": [
    "fine_tune_data = np.load(f'./Data/EffNet-kP-train-precompute/whole_train_data_precomp_roi_n_4p.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "945aad41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16000, 7680)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fine_tune_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "568ffcb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16000,)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fine_tune_labels = labels['label'].to_numpy()\n",
    "fine_tune_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "6e228ab3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15200, 7680)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fine_tune_data_train = fine_tune_data[list(train_labels['id'])]\n",
    "fine_tune_data_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "bcfdf149",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15200,)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fine_tune_labels_train = train_labels['label'].to_numpy()\n",
    "fine_tune_labels_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "ccfac9c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(800, 7680)"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fine_tune_data_test = fine_tune_data[list(test_labels['id'])]\n",
    "fine_tune_data_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "132bb5a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(800,)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fine_tune_labels_test = test_labels['label'].to_numpy()\n",
    "fine_tune_labels_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d052cb1",
   "metadata": {},
   "source": [
    "Now, we observe that the input vector for each image is $7680D$ which is pretty high dimensional. Since, it consistd of $6$ parts of $1280D$, on each part we use PCA to reduce the dimensions to a reasonable number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "0e282176",
   "metadata": {},
   "outputs": [],
   "source": [
    "N_COMPS = 512\n",
    "pcas = [PCA(N_COMPS) for _ in range(6)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "2ed8047c",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(6):\n",
    "    pcas[i].fit(fine_tune_data_train[:,(1280*i):(1280*(i+1))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "737f5590",
   "metadata": {},
   "outputs": [],
   "source": [
    "fine_tune_data_train_red = np.zeros((fine_tune_data_train.shape[0], 6 * N_COMPS))\n",
    "for i in range(6):\n",
    "    fine_tune_data_train_red[:,(N_COMPS*i):(N_COMPS*(i+1))] = pcas[i].transform(fine_tune_data_train[:,(1280*i):(1280*(i+1))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "ec6fbb3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15200, 3072)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fine_tune_data_train_red.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "bbd9ce98",
   "metadata": {},
   "outputs": [],
   "source": [
    "fine_tune_data_test_red = np.zeros((fine_tune_data_test.shape[0], 6 * N_COMPS))\n",
    "for i in range(6):\n",
    "    fine_tune_data_test_red[:,(N_COMPS*i):(N_COMPS*(i+1))] = pcas[i].transform(fine_tune_data_test[:,(1280*i):(1280*(i+1))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "fe643c0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(800, 3072)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fine_tune_data_test_red.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48661622",
   "metadata": {},
   "source": [
    "### Validation Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "bc88528c",
   "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>images</th>\n",
       "      <th>id</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>850</th>\n",
       "      <td>./Data/validation/validation/17801.tif</td>\n",
       "      <td>17801</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>./Data/validation/validation/17802.tif</td>\n",
       "      <td>17802</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>852</th>\n",
       "      <td>./Data/validation/validation/17803.tif</td>\n",
       "      <td>17803</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>./Data/validation/validation/17804.tif</td>\n",
       "      <td>17804</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>./Data/validation/validation/17805.tif</td>\n",
       "      <td>17805</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>./Data/validation/validation/18696.tif</td>\n",
       "      <td>18696</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>./Data/validation/validation/18697.tif</td>\n",
       "      <td>18697</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>./Data/validation/validation/18698.tif</td>\n",
       "      <td>18698</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>./Data/validation/validation/18699.tif</td>\n",
       "      <td>18699</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891</th>\n",
       "      <td>./Data/validation/validation/18700.tif</td>\n",
       "      <td>18700</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>900 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     images     id  label\n",
       "850  ./Data/validation/validation/17801.tif  17801     -1\n",
       "898  ./Data/validation/validation/17802.tif  17802     -1\n",
       "852  ./Data/validation/validation/17803.tif  17803     -1\n",
       "798  ./Data/validation/validation/17804.tif  17804     -1\n",
       "750  ./Data/validation/validation/17805.tif  17805     -1\n",
       "..                                      ...    ...    ...\n",
       "246  ./Data/validation/validation/18696.tif  18696     -1\n",
       "211  ./Data/validation/validation/18697.tif  18697     -1\n",
       "760  ./Data/validation/validation/18698.tif  18698     -1\n",
       "787  ./Data/validation/validation/18699.tif  18699     -1\n",
       "891  ./Data/validation/validation/18700.tif  18700     -1\n",
       "\n",
       "[900 rows x 3 columns]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_data = pd.DataFrame({'images':['./Data/validation/validation/'+name for name in os.listdir('./Data/validation/validation/')]})\n",
    "validation_data['id'] = [name.split('.')[0] for name in os.listdir('./Data/validation/validation/')]\n",
    "validation_data['label'] = -1 # Simply added to prevent re-writing code\n",
    "validation_data.sort_values(by=['id'], inplace=True)\n",
    "validation_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "bf4cf1be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(900, 100, 6)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roi_info_validation = np.load('./Data/ROI-Extractions/validation/validation_roi_viz.npy')\n",
    "roi_info_validation.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "23851219",
   "metadata": {},
   "outputs": [],
   "source": [
    "fine_tune_data_validation = np.load(f'./Data/EffNet-kP-validation-precompute/whole_validation_data_precomp_roi_n_4p.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "53ed0146",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(900, 7680)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fine_tune_data_validation.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "44350f4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "fine_tune_data_validation_red = np.zeros((fine_tune_data_validation.shape[0], 6 * N_COMPS))\n",
    "for i in range(6):\n",
    "    fine_tune_data_validation_red[:,(N_COMPS*i):(N_COMPS*(i+1))] = pcas[i].transform(fine_tune_data_validation[:,(1280*i):(1280*(i+1))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "cec17792",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(900, 3072)"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fine_tune_data_validation_red.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dff1f776",
   "metadata": {},
   "source": [
    "## Fine-Tuning Models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7380947f",
   "metadata": {},
   "source": [
    "The fine-tuning part of our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "96a2bdfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ftmodel():\n",
    "    inputs = Input(shape=(6*N_COMPS))\n",
    "    x = BatchNormalization()(inputs)\n",
    "\n",
    "    top_dropout_rate = 0.3\n",
    "    # x = Dropout(top_dropout_rate, name='top_dropout_1')(x)\n",
    "    # x = Dense(1024, name='top_dense_1', kernel_initializer=\"he_normal\", kernel_regularizer=l2(5e-5), activation='relu')(x)\n",
    "    # x = BatchNormalization()(x)\n",
    "    x = Dropout(top_dropout_rate, name='top_dropout_2')(x)\n",
    "    x = Dense(512, name='top_dense_2', kernel_initializer=\"he_normal\", kernel_regularizer=l2(5e-5), activation='relu')(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Dropout(top_dropout_rate, name='top_dropout_3')(x)\n",
    "    outputs = Dense(16, activation='softmax', name='pred', kernel_regularizer=l2(5e-5))(x)\n",
    "\n",
    "    model1 = tf.keras.Model(inputs, outputs, name='EfficientNet-FineTune')\n",
    "    return model1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "c6574006",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"EfficientNet-FineTune\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_9 (InputLayer)        [(None, 3072)]            0         \n",
      "                                                                 \n",
      " batch_normalization_15 (Bat  (None, 3072)             12288     \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " top_dropout_2 (Dropout)     (None, 3072)              0         \n",
      "                                                                 \n",
      " top_dense_2 (Dense)         (None, 512)               1573376   \n",
      "                                                                 \n",
      " batch_normalization_16 (Bat  (None, 512)              2048      \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " top_dropout_3 (Dropout)     (None, 512)               0         \n",
      "                                                                 \n",
      " pred (Dense)                (None, 16)                8208      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,595,920\n",
      "Trainable params: 1,588,752\n",
      "Non-trainable params: 7,168\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model1 = ftmodel()\n",
    "model1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e56505e2",
   "metadata": {},
   "source": [
    "### Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70b7e206",
   "metadata": {},
   "source": [
    "The fine-tuning will be done on the EfficientNet's $1280D$ representation vectors for $5$ pieces of the image and the averaged ROI representation vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "96524578",
   "metadata": {},
   "outputs": [],
   "source": [
    "model1.compile(loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'], optimizer = Adam(learning_rate = 0.0003))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "20df8897",
   "metadata": {},
   "outputs": [],
   "source": [
    "EPOCHS = 2000\n",
    "checkpoint_filepaths = ['./Models/EfficientNet-kP-PCA/auto-EffNet-FT-model-weight']\n",
    "model_checkpoint_callback = [tf.keras.callbacks.ModelCheckpoint(\n",
    "    filepath=checkpoint_filepath,\n",
    "    save_weights_only=True,\n",
    "    monitor = \"loss\",\n",
    "    mode='min',\n",
    "    save_best_only=True) for checkpoint_filepath in checkpoint_filepaths]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "03354278",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2000\n",
      " 1/19 [>.............................] - ETA: 7s - loss: 4.0174 - accuracy: 0.0550"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-07 23:21:46.003681: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 1s 25ms/step - loss: 3.0747 - accuracy: 0.1985 - val_loss: 2.7352 - val_accuracy: 0.3525\n",
      "Epoch 2/2000\n",
      " 6/19 [========>.....................] - ETA: 0s - loss: 2.0729 - accuracy: 0.4271"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-07 23:21:46.515805: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 14ms/step - loss: 1.8688 - accuracy: 0.4798 - val_loss: 2.6559 - val_accuracy: 0.4338\n",
      "Epoch 3/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 1.4401 - accuracy: 0.5895 - val_loss: 2.5968 - val_accuracy: 0.4387\n",
      "Epoch 4/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 1.2132 - accuracy: 0.6593 - val_loss: 2.5416 - val_accuracy: 0.4312\n",
      "Epoch 5/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 1.0595 - accuracy: 0.7007 - val_loss: 2.4856 - val_accuracy: 0.4175\n",
      "Epoch 6/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.9423 - accuracy: 0.7349 - val_loss: 2.4258 - val_accuracy: 0.4175\n",
      "Epoch 7/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.8746 - accuracy: 0.7557 - val_loss: 2.3637 - val_accuracy: 0.4212\n",
      "Epoch 8/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.7973 - accuracy: 0.7739 - val_loss: 2.2993 - val_accuracy: 0.4287\n",
      "Epoch 9/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.7146 - accuracy: 0.8028 - val_loss: 2.2324 - val_accuracy: 0.4425\n",
      "Epoch 10/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.6745 - accuracy: 0.8151 - val_loss: 2.1601 - val_accuracy: 0.4675\n",
      "Epoch 11/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.6220 - accuracy: 0.8311 - val_loss: 2.0840 - val_accuracy: 0.4887\n",
      "Epoch 12/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.5872 - accuracy: 0.8413 - val_loss: 2.0085 - val_accuracy: 0.5012\n",
      "Epoch 13/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.5416 - accuracy: 0.8522 - val_loss: 1.9338 - val_accuracy: 0.5250\n",
      "Epoch 14/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.5144 - accuracy: 0.8636 - val_loss: 1.8621 - val_accuracy: 0.5387\n",
      "Epoch 15/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.4814 - accuracy: 0.8741 - val_loss: 1.7803 - val_accuracy: 0.5637\n",
      "Epoch 16/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.4492 - accuracy: 0.8829 - val_loss: 1.7001 - val_accuracy: 0.5737\n",
      "Epoch 17/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.4302 - accuracy: 0.8902 - val_loss: 1.6298 - val_accuracy: 0.5888\n",
      "Epoch 18/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.4120 - accuracy: 0.8968 - val_loss: 1.5578 - val_accuracy: 0.6000\n",
      "Epoch 19/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.3940 - accuracy: 0.8994 - val_loss: 1.4819 - val_accuracy: 0.6250\n",
      "Epoch 20/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.3622 - accuracy: 0.9126 - val_loss: 1.4157 - val_accuracy: 0.6325\n",
      "Epoch 21/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.3461 - accuracy: 0.9180 - val_loss: 1.3522 - val_accuracy: 0.6475\n",
      "Epoch 22/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.3305 - accuracy: 0.9213 - val_loss: 1.2888 - val_accuracy: 0.6600\n",
      "Epoch 23/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.3195 - accuracy: 0.9232 - val_loss: 1.2316 - val_accuracy: 0.6787\n",
      "Epoch 24/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.3000 - accuracy: 0.9318 - val_loss: 1.1769 - val_accuracy: 0.6962\n",
      "Epoch 25/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.2900 - accuracy: 0.9352 - val_loss: 1.1295 - val_accuracy: 0.6925\n",
      "Epoch 26/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.2804 - accuracy: 0.9371 - val_loss: 1.0864 - val_accuracy: 0.7025\n",
      "Epoch 27/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.2671 - accuracy: 0.9407 - val_loss: 1.0465 - val_accuracy: 0.7200\n",
      "Epoch 28/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.2517 - accuracy: 0.9483 - val_loss: 1.0173 - val_accuracy: 0.7250\n",
      "Epoch 29/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.2436 - accuracy: 0.9501 - val_loss: 0.9911 - val_accuracy: 0.7300\n",
      "Epoch 30/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.2377 - accuracy: 0.9504 - val_loss: 0.9760 - val_accuracy: 0.7287\n",
      "Epoch 31/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.2257 - accuracy: 0.9561 - val_loss: 0.9581 - val_accuracy: 0.7262\n",
      "Epoch 32/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.2167 - accuracy: 0.9591 - val_loss: 0.9454 - val_accuracy: 0.7300\n",
      "Epoch 33/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.2055 - accuracy: 0.9620 - val_loss: 0.9379 - val_accuracy: 0.7325\n",
      "Epoch 34/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.2078 - accuracy: 0.9589 - val_loss: 0.9317 - val_accuracy: 0.7362\n",
      "Epoch 35/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1985 - accuracy: 0.9644 - val_loss: 0.9189 - val_accuracy: 0.7362\n",
      "Epoch 36/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1885 - accuracy: 0.9672 - val_loss: 0.9143 - val_accuracy: 0.7437\n",
      "Epoch 37/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1851 - accuracy: 0.9674 - val_loss: 0.9195 - val_accuracy: 0.7450\n",
      "Epoch 38/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1819 - accuracy: 0.9668 - val_loss: 0.9250 - val_accuracy: 0.7450\n",
      "Epoch 39/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1771 - accuracy: 0.9699 - val_loss: 0.9294 - val_accuracy: 0.7450\n",
      "Epoch 40/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1726 - accuracy: 0.9703 - val_loss: 0.9438 - val_accuracy: 0.7362\n",
      "Epoch 41/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1649 - accuracy: 0.9738 - val_loss: 0.9552 - val_accuracy: 0.7350\n",
      "Epoch 42/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1615 - accuracy: 0.9740 - val_loss: 0.9648 - val_accuracy: 0.7350\n",
      "Epoch 43/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1522 - accuracy: 0.9776 - val_loss: 0.9747 - val_accuracy: 0.7350\n",
      "Epoch 44/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1510 - accuracy: 0.9785 - val_loss: 0.9809 - val_accuracy: 0.7362\n",
      "Epoch 45/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1509 - accuracy: 0.9763 - val_loss: 0.9825 - val_accuracy: 0.7437\n",
      "Epoch 46/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1431 - accuracy: 0.9805 - val_loss: 0.9781 - val_accuracy: 0.7437\n",
      "Epoch 47/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.1447 - accuracy: 0.9799 - val_loss: 0.9827 - val_accuracy: 0.7412\n",
      "Epoch 48/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1391 - accuracy: 0.9823 - val_loss: 0.9884 - val_accuracy: 0.7337\n",
      "Epoch 49/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1379 - accuracy: 0.9809 - val_loss: 0.9904 - val_accuracy: 0.7400\n",
      "Epoch 50/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1328 - accuracy: 0.9832 - val_loss: 0.9954 - val_accuracy: 0.7387\n",
      "Epoch 51/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1297 - accuracy: 0.9832 - val_loss: 0.9980 - val_accuracy: 0.7387\n",
      "Epoch 52/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1324 - accuracy: 0.9824 - val_loss: 1.0072 - val_accuracy: 0.7425\n",
      "Epoch 53/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1252 - accuracy: 0.9854 - val_loss: 1.0202 - val_accuracy: 0.7375\n",
      "Epoch 54/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1244 - accuracy: 0.9855 - val_loss: 1.0259 - val_accuracy: 0.7400\n",
      "Epoch 55/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1195 - accuracy: 0.9871 - val_loss: 1.0279 - val_accuracy: 0.7425\n",
      "Epoch 56/2000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1193 - accuracy: 0.9864 - val_loss: 1.0265 - val_accuracy: 0.7412\n",
      "Epoch 57/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1136 - accuracy: 0.9879 - val_loss: 1.0228 - val_accuracy: 0.7450\n",
      "Epoch 58/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1134 - accuracy: 0.9874 - val_loss: 1.0219 - val_accuracy: 0.7387\n",
      "Epoch 59/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1130 - accuracy: 0.9880 - val_loss: 1.0289 - val_accuracy: 0.7337\n",
      "Epoch 60/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1103 - accuracy: 0.9887 - val_loss: 1.0369 - val_accuracy: 0.7325\n",
      "Epoch 61/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1092 - accuracy: 0.9891 - val_loss: 1.0345 - val_accuracy: 0.7375\n",
      "Epoch 62/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.1097 - accuracy: 0.9898 - val_loss: 1.0363 - val_accuracy: 0.7362\n",
      "Epoch 63/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1061 - accuracy: 0.9893 - val_loss: 1.0341 - val_accuracy: 0.7412\n",
      "Epoch 64/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1061 - accuracy: 0.9893 - val_loss: 1.0364 - val_accuracy: 0.7387\n",
      "Epoch 65/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1022 - accuracy: 0.9901 - val_loss: 1.0367 - val_accuracy: 0.7425\n",
      "Epoch 66/2000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1028 - accuracy: 0.9916 - val_loss: 1.0363 - val_accuracy: 0.7412\n",
      "Epoch 67/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1014 - accuracy: 0.9915 - val_loss: 1.0345 - val_accuracy: 0.7400\n",
      "Epoch 68/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.1026 - accuracy: 0.9900 - val_loss: 1.0381 - val_accuracy: 0.7412\n",
      "Epoch 69/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0990 - accuracy: 0.9911 - val_loss: 1.0423 - val_accuracy: 0.7412\n",
      "Epoch 70/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0962 - accuracy: 0.9918 - val_loss: 1.0424 - val_accuracy: 0.7437\n",
      "Epoch 71/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0962 - accuracy: 0.9919 - val_loss: 1.0400 - val_accuracy: 0.7450\n",
      "Epoch 72/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0966 - accuracy: 0.9916 - val_loss: 1.0453 - val_accuracy: 0.7400\n",
      "Epoch 73/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0941 - accuracy: 0.9920 - val_loss: 1.0427 - val_accuracy: 0.7437\n",
      "Epoch 74/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0916 - accuracy: 0.9938 - val_loss: 1.0437 - val_accuracy: 0.7487\n",
      "Epoch 75/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0940 - accuracy: 0.9912 - val_loss: 1.0416 - val_accuracy: 0.7425\n",
      "Epoch 76/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0925 - accuracy: 0.9925 - val_loss: 1.0433 - val_accuracy: 0.7462\n",
      "Epoch 77/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0912 - accuracy: 0.9926 - val_loss: 1.0495 - val_accuracy: 0.7400\n",
      "Epoch 78/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0912 - accuracy: 0.9921 - val_loss: 1.0554 - val_accuracy: 0.7400\n",
      "Epoch 79/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0904 - accuracy: 0.9932 - val_loss: 1.0579 - val_accuracy: 0.7412\n",
      "Epoch 80/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0894 - accuracy: 0.9938 - val_loss: 1.0560 - val_accuracy: 0.7437\n",
      "Epoch 81/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0883 - accuracy: 0.9938 - val_loss: 1.0536 - val_accuracy: 0.7450\n",
      "Epoch 82/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0874 - accuracy: 0.9941 - val_loss: 1.0515 - val_accuracy: 0.7425\n",
      "Epoch 83/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0858 - accuracy: 0.9941 - val_loss: 1.0473 - val_accuracy: 0.7425\n",
      "Epoch 84/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0874 - accuracy: 0.9932 - val_loss: 1.0490 - val_accuracy: 0.7462\n",
      "Epoch 85/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0820 - accuracy: 0.9953 - val_loss: 1.0505 - val_accuracy: 0.7425\n",
      "Epoch 86/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0837 - accuracy: 0.9939 - val_loss: 1.0540 - val_accuracy: 0.7450\n",
      "Epoch 87/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0832 - accuracy: 0.9942 - val_loss: 1.0581 - val_accuracy: 0.7462\n",
      "Epoch 88/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0846 - accuracy: 0.9938 - val_loss: 1.0572 - val_accuracy: 0.7475\n",
      "Epoch 89/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0827 - accuracy: 0.9945 - val_loss: 1.0559 - val_accuracy: 0.7412\n",
      "Epoch 90/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0822 - accuracy: 0.9943 - val_loss: 1.0482 - val_accuracy: 0.7462\n",
      "Epoch 91/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0827 - accuracy: 0.9940 - val_loss: 1.0511 - val_accuracy: 0.7475\n",
      "Epoch 92/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0815 - accuracy: 0.9941 - val_loss: 1.0575 - val_accuracy: 0.7512\n",
      "Epoch 93/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0801 - accuracy: 0.9949 - val_loss: 1.0610 - val_accuracy: 0.7525\n",
      "Epoch 94/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0801 - accuracy: 0.9939 - val_loss: 1.0671 - val_accuracy: 0.7437\n",
      "Epoch 95/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0781 - accuracy: 0.9953 - val_loss: 1.0652 - val_accuracy: 0.7475\n",
      "Epoch 96/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0786 - accuracy: 0.9954 - val_loss: 1.0675 - val_accuracy: 0.7450\n",
      "Epoch 97/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0782 - accuracy: 0.9951 - val_loss: 1.0726 - val_accuracy: 0.7450\n",
      "Epoch 98/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0762 - accuracy: 0.9955 - val_loss: 1.0736 - val_accuracy: 0.7450\n",
      "Epoch 99/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0758 - accuracy: 0.9957 - val_loss: 1.0686 - val_accuracy: 0.7412\n",
      "Epoch 100/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0774 - accuracy: 0.9953 - val_loss: 1.0600 - val_accuracy: 0.7400\n",
      "Epoch 101/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0762 - accuracy: 0.9956 - val_loss: 1.0648 - val_accuracy: 0.7375\n",
      "Epoch 102/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0781 - accuracy: 0.9949 - val_loss: 1.0631 - val_accuracy: 0.7400\n",
      "Epoch 103/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0760 - accuracy: 0.9955 - val_loss: 1.0668 - val_accuracy: 0.7412\n",
      "Epoch 104/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0749 - accuracy: 0.9954 - val_loss: 1.0676 - val_accuracy: 0.7387\n",
      "Epoch 105/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0734 - accuracy: 0.9962 - val_loss: 1.0675 - val_accuracy: 0.7425\n",
      "Epoch 106/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0756 - accuracy: 0.9955 - val_loss: 1.0692 - val_accuracy: 0.7412\n",
      "Epoch 107/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0731 - accuracy: 0.9957 - val_loss: 1.0688 - val_accuracy: 0.7412\n",
      "Epoch 108/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0724 - accuracy: 0.9961 - val_loss: 1.0711 - val_accuracy: 0.7450\n",
      "Epoch 109/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0739 - accuracy: 0.9962 - val_loss: 1.0765 - val_accuracy: 0.7412\n",
      "Epoch 110/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0733 - accuracy: 0.9960 - val_loss: 1.0832 - val_accuracy: 0.7362\n",
      "Epoch 111/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0728 - accuracy: 0.9963 - val_loss: 1.0815 - val_accuracy: 0.7437\n",
      "Epoch 112/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0725 - accuracy: 0.9956 - val_loss: 1.0764 - val_accuracy: 0.7412\n",
      "Epoch 113/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0712 - accuracy: 0.9959 - val_loss: 1.0737 - val_accuracy: 0.7412\n",
      "Epoch 114/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0698 - accuracy: 0.9968 - val_loss: 1.0760 - val_accuracy: 0.7450\n",
      "Epoch 115/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0701 - accuracy: 0.9963 - val_loss: 1.0837 - val_accuracy: 0.7425\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 116/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0709 - accuracy: 0.9963 - val_loss: 1.0873 - val_accuracy: 0.7425\n",
      "Epoch 117/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0692 - accuracy: 0.9965 - val_loss: 1.0865 - val_accuracy: 0.7425\n",
      "Epoch 118/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0689 - accuracy: 0.9966 - val_loss: 1.0807 - val_accuracy: 0.7462\n",
      "Epoch 119/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0676 - accuracy: 0.9972 - val_loss: 1.0835 - val_accuracy: 0.7550\n",
      "Epoch 120/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0674 - accuracy: 0.9972 - val_loss: 1.0844 - val_accuracy: 0.7500\n",
      "Epoch 121/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0678 - accuracy: 0.9967 - val_loss: 1.0888 - val_accuracy: 0.7487\n",
      "Epoch 122/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0681 - accuracy: 0.9965 - val_loss: 1.0840 - val_accuracy: 0.7437\n",
      "Epoch 123/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0676 - accuracy: 0.9964 - val_loss: 1.0815 - val_accuracy: 0.7400\n",
      "Epoch 124/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0669 - accuracy: 0.9966 - val_loss: 1.0760 - val_accuracy: 0.7375\n",
      "Epoch 125/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0675 - accuracy: 0.9970 - val_loss: 1.0723 - val_accuracy: 0.7437\n",
      "Epoch 126/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0659 - accuracy: 0.9974 - val_loss: 1.0747 - val_accuracy: 0.7462\n",
      "Epoch 127/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0684 - accuracy: 0.9963 - val_loss: 1.0704 - val_accuracy: 0.7437\n",
      "Epoch 128/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0672 - accuracy: 0.9966 - val_loss: 1.0734 - val_accuracy: 0.7425\n",
      "Epoch 129/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0664 - accuracy: 0.9970 - val_loss: 1.0742 - val_accuracy: 0.7500\n",
      "Epoch 130/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0669 - accuracy: 0.9974 - val_loss: 1.0735 - val_accuracy: 0.7512\n",
      "Epoch 131/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0649 - accuracy: 0.9973 - val_loss: 1.0783 - val_accuracy: 0.7537\n",
      "Epoch 132/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0669 - accuracy: 0.9966 - val_loss: 1.0829 - val_accuracy: 0.7437\n",
      "Epoch 133/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0649 - accuracy: 0.9978 - val_loss: 1.0800 - val_accuracy: 0.7425\n",
      "Epoch 134/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0649 - accuracy: 0.9974 - val_loss: 1.0807 - val_accuracy: 0.7462\n",
      "Epoch 135/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0634 - accuracy: 0.9975 - val_loss: 1.0761 - val_accuracy: 0.7450\n",
      "Epoch 136/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0664 - accuracy: 0.9959 - val_loss: 1.0754 - val_accuracy: 0.7450\n",
      "Epoch 137/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0632 - accuracy: 0.9969 - val_loss: 1.0743 - val_accuracy: 0.7437\n",
      "Epoch 138/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0646 - accuracy: 0.9970 - val_loss: 1.0734 - val_accuracy: 0.7437\n",
      "Epoch 139/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0632 - accuracy: 0.9978 - val_loss: 1.0731 - val_accuracy: 0.7450\n",
      "Epoch 140/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0641 - accuracy: 0.9965 - val_loss: 1.0735 - val_accuracy: 0.7500\n",
      "Epoch 141/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0617 - accuracy: 0.9976 - val_loss: 1.0614 - val_accuracy: 0.7500\n",
      "Epoch 142/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0627 - accuracy: 0.9974 - val_loss: 1.0629 - val_accuracy: 0.7487\n",
      "Epoch 143/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0627 - accuracy: 0.9972 - val_loss: 1.0710 - val_accuracy: 0.7500\n",
      "Epoch 144/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0603 - accuracy: 0.9983 - val_loss: 1.0753 - val_accuracy: 0.7450\n",
      "Epoch 145/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0618 - accuracy: 0.9974 - val_loss: 1.0660 - val_accuracy: 0.7475\n",
      "Epoch 146/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0600 - accuracy: 0.9982 - val_loss: 1.0715 - val_accuracy: 0.7475\n",
      "Epoch 147/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0618 - accuracy: 0.9974 - val_loss: 1.0694 - val_accuracy: 0.7450\n",
      "Epoch 148/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0619 - accuracy: 0.9966 - val_loss: 1.0749 - val_accuracy: 0.7437\n",
      "Epoch 149/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0614 - accuracy: 0.9971 - val_loss: 1.0702 - val_accuracy: 0.7462\n",
      "Epoch 150/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0608 - accuracy: 0.9974 - val_loss: 1.0738 - val_accuracy: 0.7512\n",
      "Epoch 151/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0598 - accuracy: 0.9978 - val_loss: 1.0805 - val_accuracy: 0.7450\n",
      "Epoch 152/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0610 - accuracy: 0.9973 - val_loss: 1.0825 - val_accuracy: 0.7462\n",
      "Epoch 153/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0597 - accuracy: 0.9982 - val_loss: 1.0786 - val_accuracy: 0.7487\n",
      "Epoch 154/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0594 - accuracy: 0.9982 - val_loss: 1.0802 - val_accuracy: 0.7500\n",
      "Epoch 155/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0596 - accuracy: 0.9974 - val_loss: 1.0794 - val_accuracy: 0.7500\n",
      "Epoch 156/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0593 - accuracy: 0.9974 - val_loss: 1.0791 - val_accuracy: 0.7512\n",
      "Epoch 157/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0592 - accuracy: 0.9978 - val_loss: 1.0853 - val_accuracy: 0.7525\n",
      "Epoch 158/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0596 - accuracy: 0.9972 - val_loss: 1.0909 - val_accuracy: 0.7487\n",
      "Epoch 159/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0576 - accuracy: 0.9984 - val_loss: 1.0941 - val_accuracy: 0.7462\n",
      "Epoch 160/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0596 - accuracy: 0.9970 - val_loss: 1.0885 - val_accuracy: 0.7437\n",
      "Epoch 161/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0587 - accuracy: 0.9971 - val_loss: 1.0897 - val_accuracy: 0.7437\n",
      "Epoch 162/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0596 - accuracy: 0.9970 - val_loss: 1.0915 - val_accuracy: 0.7412\n",
      "Epoch 163/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0581 - accuracy: 0.9975 - val_loss: 1.0842 - val_accuracy: 0.7412\n",
      "Epoch 164/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0586 - accuracy: 0.9972 - val_loss: 1.0827 - val_accuracy: 0.7387\n",
      "Epoch 165/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0584 - accuracy: 0.9973 - val_loss: 1.0974 - val_accuracy: 0.7325\n",
      "Epoch 166/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0576 - accuracy: 0.9976 - val_loss: 1.1032 - val_accuracy: 0.7312\n",
      "Epoch 167/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0571 - accuracy: 0.9981 - val_loss: 1.1072 - val_accuracy: 0.7362\n",
      "Epoch 168/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0570 - accuracy: 0.9972 - val_loss: 1.1070 - val_accuracy: 0.7412\n",
      "Epoch 169/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0571 - accuracy: 0.9978 - val_loss: 1.1133 - val_accuracy: 0.7412\n",
      "Epoch 170/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0567 - accuracy: 0.9978 - val_loss: 1.1172 - val_accuracy: 0.7412\n",
      "Epoch 171/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0567 - accuracy: 0.9980 - val_loss: 1.1198 - val_accuracy: 0.7475\n",
      "Epoch 172/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0574 - accuracy: 0.9969 - val_loss: 1.1122 - val_accuracy: 0.7437\n",
      "Epoch 173/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0564 - accuracy: 0.9976 - val_loss: 1.1065 - val_accuracy: 0.7437\n",
      "Epoch 174/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0558 - accuracy: 0.9976 - val_loss: 1.1053 - val_accuracy: 0.7462\n",
      "Epoch 175/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0556 - accuracy: 0.9981 - val_loss: 1.1066 - val_accuracy: 0.7437\n",
      "Epoch 176/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0565 - accuracy: 0.9972 - val_loss: 1.0971 - val_accuracy: 0.7562\n",
      "Epoch 177/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0562 - accuracy: 0.9974 - val_loss: 1.0905 - val_accuracy: 0.7525\n",
      "Epoch 178/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0563 - accuracy: 0.9977 - val_loss: 1.0879 - val_accuracy: 0.7500\n",
      "Epoch 179/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0566 - accuracy: 0.9975 - val_loss: 1.0860 - val_accuracy: 0.7450\n",
      "Epoch 180/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0548 - accuracy: 0.9980 - val_loss: 1.0974 - val_accuracy: 0.7425\n",
      "Epoch 181/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0569 - accuracy: 0.9972 - val_loss: 1.0932 - val_accuracy: 0.7500\n",
      "Epoch 182/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0557 - accuracy: 0.9974 - val_loss: 1.0883 - val_accuracy: 0.7487\n",
      "Epoch 183/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0551 - accuracy: 0.9977 - val_loss: 1.0886 - val_accuracy: 0.7500\n",
      "Epoch 184/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0555 - accuracy: 0.9972 - val_loss: 1.0967 - val_accuracy: 0.7462\n",
      "Epoch 185/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0556 - accuracy: 0.9975 - val_loss: 1.1050 - val_accuracy: 0.7512\n",
      "Epoch 186/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0534 - accuracy: 0.9982 - val_loss: 1.1030 - val_accuracy: 0.7562\n",
      "Epoch 187/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0532 - accuracy: 0.9984 - val_loss: 1.1038 - val_accuracy: 0.7512\n",
      "Epoch 188/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0541 - accuracy: 0.9979 - val_loss: 1.0998 - val_accuracy: 0.7537\n",
      "Epoch 189/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0544 - accuracy: 0.9977 - val_loss: 1.1088 - val_accuracy: 0.7525\n",
      "Epoch 190/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0531 - accuracy: 0.9980 - val_loss: 1.1147 - val_accuracy: 0.7537\n",
      "Epoch 191/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0536 - accuracy: 0.9982 - val_loss: 1.1140 - val_accuracy: 0.7575\n",
      "Epoch 192/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0519 - accuracy: 0.9984 - val_loss: 1.1155 - val_accuracy: 0.7562\n",
      "Epoch 193/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0528 - accuracy: 0.9982 - val_loss: 1.1231 - val_accuracy: 0.7562\n",
      "Epoch 194/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0534 - accuracy: 0.9978 - val_loss: 1.1219 - val_accuracy: 0.7537\n",
      "Epoch 195/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0532 - accuracy: 0.9982 - val_loss: 1.1284 - val_accuracy: 0.7537\n",
      "Epoch 196/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0533 - accuracy: 0.9977 - val_loss: 1.1319 - val_accuracy: 0.7537\n",
      "Epoch 197/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0518 - accuracy: 0.9986 - val_loss: 1.1266 - val_accuracy: 0.7475\n",
      "Epoch 198/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0539 - accuracy: 0.9972 - val_loss: 1.1302 - val_accuracy: 0.7537\n",
      "Epoch 199/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0519 - accuracy: 0.9980 - val_loss: 1.1286 - val_accuracy: 0.7475\n",
      "Epoch 200/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0539 - accuracy: 0.9972 - val_loss: 1.1221 - val_accuracy: 0.7500\n",
      "Epoch 201/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0531 - accuracy: 0.9976 - val_loss: 1.1193 - val_accuracy: 0.7500\n",
      "Epoch 202/2000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0505 - accuracy: 0.9988 - val_loss: 1.1150 - val_accuracy: 0.7512\n",
      "Epoch 203/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0519 - accuracy: 0.9980 - val_loss: 1.1224 - val_accuracy: 0.7475\n",
      "Epoch 204/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0520 - accuracy: 0.9982 - val_loss: 1.1196 - val_accuracy: 0.7462\n",
      "Epoch 205/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0518 - accuracy: 0.9978 - val_loss: 1.1150 - val_accuracy: 0.7425\n",
      "Epoch 206/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0507 - accuracy: 0.9987 - val_loss: 1.1225 - val_accuracy: 0.7450\n",
      "Epoch 207/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0512 - accuracy: 0.9982 - val_loss: 1.1214 - val_accuracy: 0.7525\n",
      "Epoch 208/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0522 - accuracy: 0.9976 - val_loss: 1.1282 - val_accuracy: 0.7512\n",
      "Epoch 209/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0510 - accuracy: 0.9980 - val_loss: 1.1296 - val_accuracy: 0.7550\n",
      "Epoch 210/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0508 - accuracy: 0.9978 - val_loss: 1.1318 - val_accuracy: 0.7625\n",
      "Epoch 211/2000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0507 - accuracy: 0.9979 - val_loss: 1.1261 - val_accuracy: 0.7600\n",
      "Epoch 212/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0508 - accuracy: 0.9981 - val_loss: 1.1223 - val_accuracy: 0.7562\n",
      "Epoch 213/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0518 - accuracy: 0.9976 - val_loss: 1.1291 - val_accuracy: 0.7537\n",
      "Epoch 214/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0501 - accuracy: 0.9983 - val_loss: 1.1373 - val_accuracy: 0.7575\n",
      "Epoch 215/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0499 - accuracy: 0.9982 - val_loss: 1.1310 - val_accuracy: 0.7562\n",
      "Epoch 216/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0497 - accuracy: 0.9980 - val_loss: 1.1314 - val_accuracy: 0.7550\n",
      "Epoch 217/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0508 - accuracy: 0.9979 - val_loss: 1.1373 - val_accuracy: 0.7537\n",
      "Epoch 218/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0518 - accuracy: 0.9974 - val_loss: 1.1374 - val_accuracy: 0.7587\n",
      "Epoch 219/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0509 - accuracy: 0.9974 - val_loss: 1.1319 - val_accuracy: 0.7587\n",
      "Epoch 220/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0505 - accuracy: 0.9977 - val_loss: 1.1350 - val_accuracy: 0.7537\n",
      "Epoch 221/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0496 - accuracy: 0.9979 - val_loss: 1.1298 - val_accuracy: 0.7537\n",
      "Epoch 222/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0504 - accuracy: 0.9976 - val_loss: 1.1318 - val_accuracy: 0.7500\n",
      "Epoch 223/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0514 - accuracy: 0.9977 - val_loss: 1.1145 - val_accuracy: 0.7550\n",
      "Epoch 224/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0502 - accuracy: 0.9978 - val_loss: 1.1258 - val_accuracy: 0.7550\n",
      "Epoch 225/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0501 - accuracy: 0.9977 - val_loss: 1.1416 - val_accuracy: 0.7500\n",
      "Epoch 226/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0503 - accuracy: 0.9977 - val_loss: 1.1384 - val_accuracy: 0.7525\n",
      "Epoch 227/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0499 - accuracy: 0.9980 - val_loss: 1.1299 - val_accuracy: 0.7537\n",
      "Epoch 228/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0494 - accuracy: 0.9979 - val_loss: 1.1324 - val_accuracy: 0.7550\n",
      "Epoch 229/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0492 - accuracy: 0.9980 - val_loss: 1.1281 - val_accuracy: 0.7537\n",
      "Epoch 230/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0488 - accuracy: 0.9984 - val_loss: 1.1407 - val_accuracy: 0.7512\n",
      "Epoch 231/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0493 - accuracy: 0.9986 - val_loss: 1.1585 - val_accuracy: 0.7500\n",
      "Epoch 232/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0491 - accuracy: 0.9980 - val_loss: 1.1538 - val_accuracy: 0.7525\n",
      "Epoch 233/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0495 - accuracy: 0.9976 - val_loss: 1.1464 - val_accuracy: 0.7500\n",
      "Epoch 234/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0492 - accuracy: 0.9978 - val_loss: 1.1580 - val_accuracy: 0.7512\n",
      "Epoch 235/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0487 - accuracy: 0.9978 - val_loss: 1.1584 - val_accuracy: 0.7525\n",
      "Epoch 236/2000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.0482 - accuracy: 0.9983 - val_loss: 1.1469 - val_accuracy: 0.7500\n",
      "Epoch 237/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0483 - accuracy: 0.9984 - val_loss: 1.1388 - val_accuracy: 0.7525\n",
      "Epoch 238/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0480 - accuracy: 0.9982 - val_loss: 1.1348 - val_accuracy: 0.7525\n",
      "Epoch 239/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0491 - accuracy: 0.9978 - val_loss: 1.1303 - val_accuracy: 0.7525\n",
      "Epoch 240/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0486 - accuracy: 0.9980 - val_loss: 1.1290 - val_accuracy: 0.7587\n",
      "Epoch 241/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0479 - accuracy: 0.9981 - val_loss: 1.1271 - val_accuracy: 0.7575\n",
      "Epoch 242/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0487 - accuracy: 0.9979 - val_loss: 1.1288 - val_accuracy: 0.7587\n",
      "Epoch 243/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0477 - accuracy: 0.9978 - val_loss: 1.1319 - val_accuracy: 0.7550\n",
      "Epoch 244/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0492 - accuracy: 0.9975 - val_loss: 1.1383 - val_accuracy: 0.7537\n",
      "Epoch 245/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0480 - accuracy: 0.9979 - val_loss: 1.1368 - val_accuracy: 0.7512\n",
      "Epoch 246/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0480 - accuracy: 0.9978 - val_loss: 1.1344 - val_accuracy: 0.7575\n",
      "Epoch 247/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0478 - accuracy: 0.9976 - val_loss: 1.1441 - val_accuracy: 0.7550\n",
      "Epoch 248/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0479 - accuracy: 0.9978 - val_loss: 1.1473 - val_accuracy: 0.7587\n",
      "Epoch 249/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0491 - accuracy: 0.9974 - val_loss: 1.1418 - val_accuracy: 0.7500\n",
      "Epoch 250/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0483 - accuracy: 0.9980 - val_loss: 1.1338 - val_accuracy: 0.7525\n",
      "Epoch 251/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0483 - accuracy: 0.9980 - val_loss: 1.1392 - val_accuracy: 0.7525\n",
      "Epoch 252/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0481 - accuracy: 0.9976 - val_loss: 1.1356 - val_accuracy: 0.7512\n",
      "Epoch 253/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0469 - accuracy: 0.9979 - val_loss: 1.1366 - val_accuracy: 0.7475\n",
      "Epoch 254/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0483 - accuracy: 0.9978 - val_loss: 1.1420 - val_accuracy: 0.7512\n",
      "Epoch 255/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0485 - accuracy: 0.9978 - val_loss: 1.1426 - val_accuracy: 0.7550\n",
      "Epoch 256/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0477 - accuracy: 0.9981 - val_loss: 1.1541 - val_accuracy: 0.7587\n",
      "Epoch 257/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0491 - accuracy: 0.9975 - val_loss: 1.1510 - val_accuracy: 0.7562\n",
      "Epoch 258/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0490 - accuracy: 0.9971 - val_loss: 1.1599 - val_accuracy: 0.7550\n",
      "Epoch 259/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0488 - accuracy: 0.9974 - val_loss: 1.1679 - val_accuracy: 0.7500\n",
      "Epoch 260/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0463 - accuracy: 0.9980 - val_loss: 1.1657 - val_accuracy: 0.7512\n",
      "Epoch 261/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0478 - accuracy: 0.9981 - val_loss: 1.1628 - val_accuracy: 0.7525\n",
      "Epoch 262/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0476 - accuracy: 0.9980 - val_loss: 1.1698 - val_accuracy: 0.7512\n",
      "Epoch 263/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0470 - accuracy: 0.9982 - val_loss: 1.1624 - val_accuracy: 0.7575\n",
      "Epoch 264/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0479 - accuracy: 0.9978 - val_loss: 1.1588 - val_accuracy: 0.7550\n",
      "Epoch 265/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0486 - accuracy: 0.9972 - val_loss: 1.1470 - val_accuracy: 0.7475\n",
      "Epoch 266/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0462 - accuracy: 0.9987 - val_loss: 1.1449 - val_accuracy: 0.7475\n",
      "Epoch 267/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0476 - accuracy: 0.9981 - val_loss: 1.1624 - val_accuracy: 0.7425\n",
      "Epoch 268/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0487 - accuracy: 0.9978 - val_loss: 1.1724 - val_accuracy: 0.7400\n",
      "Epoch 269/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0471 - accuracy: 0.9978 - val_loss: 1.1678 - val_accuracy: 0.7437\n",
      "Epoch 270/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0487 - accuracy: 0.9968 - val_loss: 1.1573 - val_accuracy: 0.7462\n",
      "Epoch 271/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0477 - accuracy: 0.9980 - val_loss: 1.1679 - val_accuracy: 0.7512\n",
      "Epoch 272/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0484 - accuracy: 0.9976 - val_loss: 1.1744 - val_accuracy: 0.7437\n",
      "Epoch 273/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0475 - accuracy: 0.9978 - val_loss: 1.1761 - val_accuracy: 0.7387\n",
      "Epoch 274/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0472 - accuracy: 0.9978 - val_loss: 1.1741 - val_accuracy: 0.7412\n",
      "Epoch 275/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0478 - accuracy: 0.9977 - val_loss: 1.1611 - val_accuracy: 0.7425\n",
      "Epoch 276/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0476 - accuracy: 0.9973 - val_loss: 1.1591 - val_accuracy: 0.7450\n",
      "Epoch 277/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0473 - accuracy: 0.9980 - val_loss: 1.1555 - val_accuracy: 0.7475\n",
      "Epoch 278/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0473 - accuracy: 0.9975 - val_loss: 1.1576 - val_accuracy: 0.7525\n",
      "Epoch 279/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0475 - accuracy: 0.9978 - val_loss: 1.1672 - val_accuracy: 0.7475\n",
      "Epoch 280/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0474 - accuracy: 0.9978 - val_loss: 1.1725 - val_accuracy: 0.7475\n",
      "Epoch 281/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0462 - accuracy: 0.9984 - val_loss: 1.1772 - val_accuracy: 0.7400\n",
      "Epoch 282/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0470 - accuracy: 0.9980 - val_loss: 1.1750 - val_accuracy: 0.7387\n",
      "Epoch 283/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0474 - accuracy: 0.9976 - val_loss: 1.1767 - val_accuracy: 0.7462\n",
      "Epoch 284/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0462 - accuracy: 0.9982 - val_loss: 1.1722 - val_accuracy: 0.7450\n",
      "Epoch 285/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0458 - accuracy: 0.9983 - val_loss: 1.1695 - val_accuracy: 0.7437\n",
      "Epoch 286/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0469 - accuracy: 0.9980 - val_loss: 1.1600 - val_accuracy: 0.7512\n",
      "Epoch 287/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0455 - accuracy: 0.9982 - val_loss: 1.1632 - val_accuracy: 0.7612\n",
      "Epoch 288/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0462 - accuracy: 0.9984 - val_loss: 1.1563 - val_accuracy: 0.7575\n",
      "Epoch 289/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0456 - accuracy: 0.9983 - val_loss: 1.1490 - val_accuracy: 0.7500\n",
      "Epoch 290/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0459 - accuracy: 0.9982 - val_loss: 1.1478 - val_accuracy: 0.7512\n",
      "Epoch 291/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0464 - accuracy: 0.9976 - val_loss: 1.1430 - val_accuracy: 0.7525\n",
      "Epoch 292/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0464 - accuracy: 0.9978 - val_loss: 1.1405 - val_accuracy: 0.7537\n",
      "Epoch 293/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0458 - accuracy: 0.9979 - val_loss: 1.1487 - val_accuracy: 0.7600\n",
      "Epoch 294/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0468 - accuracy: 0.9975 - val_loss: 1.1408 - val_accuracy: 0.7562\n",
      "Epoch 295/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0462 - accuracy: 0.9978 - val_loss: 1.1411 - val_accuracy: 0.7612\n",
      "Epoch 296/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0448 - accuracy: 0.9984 - val_loss: 1.1568 - val_accuracy: 0.7562\n",
      "Epoch 297/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0449 - accuracy: 0.9982 - val_loss: 1.1578 - val_accuracy: 0.7512\n",
      "Epoch 298/2000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.0459 - accuracy: 0.9976 - val_loss: 1.1623 - val_accuracy: 0.7525\n",
      "Epoch 299/2000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0463 - accuracy: 0.9980 - val_loss: 1.1565 - val_accuracy: 0.7575\n",
      "Epoch 300/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0469 - accuracy: 0.9978 - val_loss: 1.1566 - val_accuracy: 0.7550\n",
      "Epoch 301/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0452 - accuracy: 0.9982 - val_loss: 1.1539 - val_accuracy: 0.7525\n",
      "Epoch 302/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0467 - accuracy: 0.9978 - val_loss: 1.1501 - val_accuracy: 0.7562\n",
      "Epoch 303/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0448 - accuracy: 0.9986 - val_loss: 1.1481 - val_accuracy: 0.7525\n",
      "Epoch 304/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0456 - accuracy: 0.9978 - val_loss: 1.1484 - val_accuracy: 0.7525\n",
      "Epoch 305/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0460 - accuracy: 0.9983 - val_loss: 1.1573 - val_accuracy: 0.7550\n",
      "Epoch 306/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0457 - accuracy: 0.9976 - val_loss: 1.1717 - val_accuracy: 0.7500\n",
      "Epoch 307/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0451 - accuracy: 0.9983 - val_loss: 1.1842 - val_accuracy: 0.7562\n",
      "Epoch 308/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0464 - accuracy: 0.9977 - val_loss: 1.1926 - val_accuracy: 0.7562\n",
      "Epoch 309/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0450 - accuracy: 0.9980 - val_loss: 1.1930 - val_accuracy: 0.7575\n",
      "Epoch 310/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0455 - accuracy: 0.9980 - val_loss: 1.1931 - val_accuracy: 0.7512\n",
      "Epoch 311/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0456 - accuracy: 0.9978 - val_loss: 1.1933 - val_accuracy: 0.7500\n",
      "Epoch 312/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0458 - accuracy: 0.9975 - val_loss: 1.1903 - val_accuracy: 0.7462\n",
      "Epoch 313/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0455 - accuracy: 0.9976 - val_loss: 1.1984 - val_accuracy: 0.7462\n",
      "Epoch 314/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0447 - accuracy: 0.9984 - val_loss: 1.2097 - val_accuracy: 0.7450\n",
      "Epoch 315/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0455 - accuracy: 0.9980 - val_loss: 1.1967 - val_accuracy: 0.7437\n",
      "Epoch 316/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0456 - accuracy: 0.9982 - val_loss: 1.1925 - val_accuracy: 0.7475\n",
      "Epoch 317/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0461 - accuracy: 0.9977 - val_loss: 1.1837 - val_accuracy: 0.7375\n",
      "Epoch 318/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0466 - accuracy: 0.9978 - val_loss: 1.1878 - val_accuracy: 0.7362\n",
      "Epoch 319/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0468 - accuracy: 0.9975 - val_loss: 1.1871 - val_accuracy: 0.7425\n",
      "Epoch 320/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0464 - accuracy: 0.9972 - val_loss: 1.1851 - val_accuracy: 0.7425\n",
      "Epoch 321/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0461 - accuracy: 0.9979 - val_loss: 1.1914 - val_accuracy: 0.7387\n",
      "Epoch 322/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0462 - accuracy: 0.9975 - val_loss: 1.1847 - val_accuracy: 0.7450\n",
      "Epoch 323/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0449 - accuracy: 0.9980 - val_loss: 1.1872 - val_accuracy: 0.7525\n",
      "Epoch 324/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0458 - accuracy: 0.9977 - val_loss: 1.1849 - val_accuracy: 0.7487\n",
      "Epoch 325/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0453 - accuracy: 0.9976 - val_loss: 1.1937 - val_accuracy: 0.7450\n",
      "Epoch 326/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0464 - accuracy: 0.9978 - val_loss: 1.1963 - val_accuracy: 0.7475\n",
      "Epoch 327/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0454 - accuracy: 0.9977 - val_loss: 1.1777 - val_accuracy: 0.7512\n",
      "Epoch 328/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0458 - accuracy: 0.9977 - val_loss: 1.1687 - val_accuracy: 0.7500\n",
      "Epoch 329/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0444 - accuracy: 0.9981 - val_loss: 1.1557 - val_accuracy: 0.7450\n",
      "Epoch 330/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0451 - accuracy: 0.9980 - val_loss: 1.1521 - val_accuracy: 0.7487\n",
      "Epoch 331/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0450 - accuracy: 0.9977 - val_loss: 1.1566 - val_accuracy: 0.7437\n",
      "Epoch 332/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0450 - accuracy: 0.9980 - val_loss: 1.1623 - val_accuracy: 0.7425\n",
      "Epoch 333/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0450 - accuracy: 0.9980 - val_loss: 1.1705 - val_accuracy: 0.7450\n",
      "Epoch 334/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0445 - accuracy: 0.9984 - val_loss: 1.1699 - val_accuracy: 0.7487\n",
      "Epoch 335/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0462 - accuracy: 0.9975 - val_loss: 1.1648 - val_accuracy: 0.7525\n",
      "Epoch 336/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0458 - accuracy: 0.9979 - val_loss: 1.1515 - val_accuracy: 0.7525\n",
      "Epoch 337/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0445 - accuracy: 0.9983 - val_loss: 1.1534 - val_accuracy: 0.7487\n",
      "Epoch 338/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0444 - accuracy: 0.9980 - val_loss: 1.1628 - val_accuracy: 0.7450\n",
      "Epoch 339/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0445 - accuracy: 0.9984 - val_loss: 1.1574 - val_accuracy: 0.7437\n",
      "Epoch 340/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0442 - accuracy: 0.9986 - val_loss: 1.1556 - val_accuracy: 0.7400\n",
      "Epoch 341/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0444 - accuracy: 0.9982 - val_loss: 1.1506 - val_accuracy: 0.7462\n",
      "Epoch 342/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0457 - accuracy: 0.9980 - val_loss: 1.1455 - val_accuracy: 0.7437\n",
      "Epoch 343/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0435 - accuracy: 0.9986 - val_loss: 1.1500 - val_accuracy: 0.7437\n",
      "Epoch 344/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0428 - accuracy: 0.9989 - val_loss: 1.1594 - val_accuracy: 0.7450\n",
      "Epoch 345/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0444 - accuracy: 0.9984 - val_loss: 1.1547 - val_accuracy: 0.7450\n",
      "Epoch 346/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0432 - accuracy: 0.9982 - val_loss: 1.1486 - val_accuracy: 0.7487\n",
      "Epoch 347/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0441 - accuracy: 0.9981 - val_loss: 1.1534 - val_accuracy: 0.7512\n",
      "Epoch 348/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0437 - accuracy: 0.9982 - val_loss: 1.1534 - val_accuracy: 0.7550\n",
      "Epoch 349/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0446 - accuracy: 0.9980 - val_loss: 1.1636 - val_accuracy: 0.7550\n",
      "Epoch 350/2000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0449 - accuracy: 0.9980 - val_loss: 1.1719 - val_accuracy: 0.7562\n",
      "Epoch 351/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0447 - accuracy: 0.9977 - val_loss: 1.1639 - val_accuracy: 0.7562\n",
      "Epoch 352/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0431 - accuracy: 0.9984 - val_loss: 1.1553 - val_accuracy: 0.7562\n",
      "Epoch 353/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0454 - accuracy: 0.9981 - val_loss: 1.1554 - val_accuracy: 0.7487\n",
      "Epoch 354/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0450 - accuracy: 0.9986 - val_loss: 1.1639 - val_accuracy: 0.7562\n",
      "Epoch 355/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0446 - accuracy: 0.9979 - val_loss: 1.1644 - val_accuracy: 0.7575\n",
      "Epoch 356/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0445 - accuracy: 0.9978 - val_loss: 1.1544 - val_accuracy: 0.7537\n",
      "Epoch 357/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0446 - accuracy: 0.9980 - val_loss: 1.1541 - val_accuracy: 0.7612\n",
      "Epoch 358/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0450 - accuracy: 0.9980 - val_loss: 1.1556 - val_accuracy: 0.7587\n",
      "Epoch 359/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0434 - accuracy: 0.9982 - val_loss: 1.1579 - val_accuracy: 0.7512\n",
      "Epoch 360/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0439 - accuracy: 0.9984 - val_loss: 1.1610 - val_accuracy: 0.7525\n",
      "Epoch 361/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0429 - accuracy: 0.9988 - val_loss: 1.1562 - val_accuracy: 0.7537\n",
      "Epoch 362/2000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0430 - accuracy: 0.9986 - val_loss: 1.1594 - val_accuracy: 0.7575\n",
      "Epoch 363/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0445 - accuracy: 0.9982 - val_loss: 1.1591 - val_accuracy: 0.7612\n",
      "Epoch 364/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0428 - accuracy: 0.9986 - val_loss: 1.1597 - val_accuracy: 0.7600\n",
      "Epoch 365/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0430 - accuracy: 0.9982 - val_loss: 1.1487 - val_accuracy: 0.7600\n",
      "Epoch 366/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0422 - accuracy: 0.9989 - val_loss: 1.1461 - val_accuracy: 0.7550\n",
      "Epoch 367/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0428 - accuracy: 0.9983 - val_loss: 1.1521 - val_accuracy: 0.7600\n",
      "Epoch 368/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0428 - accuracy: 0.9985 - val_loss: 1.1534 - val_accuracy: 0.7587\n",
      "Epoch 369/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0431 - accuracy: 0.9987 - val_loss: 1.1708 - val_accuracy: 0.7550\n",
      "Epoch 370/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0428 - accuracy: 0.9982 - val_loss: 1.1812 - val_accuracy: 0.7487\n",
      "Epoch 371/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0426 - accuracy: 0.9986 - val_loss: 1.1811 - val_accuracy: 0.7512\n",
      "Epoch 372/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0429 - accuracy: 0.9984 - val_loss: 1.1749 - val_accuracy: 0.7537\n",
      "Epoch 373/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0419 - accuracy: 0.9988 - val_loss: 1.1621 - val_accuracy: 0.7575\n",
      "Epoch 374/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0426 - accuracy: 0.9983 - val_loss: 1.1465 - val_accuracy: 0.7650\n",
      "Epoch 375/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0416 - accuracy: 0.9988 - val_loss: 1.1402 - val_accuracy: 0.7650\n",
      "Epoch 376/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0424 - accuracy: 0.9984 - val_loss: 1.1458 - val_accuracy: 0.7625\n",
      "Epoch 377/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0439 - accuracy: 0.9977 - val_loss: 1.1498 - val_accuracy: 0.7587\n",
      "Epoch 378/2000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0417 - accuracy: 0.9984 - val_loss: 1.1558 - val_accuracy: 0.7575\n",
      "Epoch 379/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0432 - accuracy: 0.9980 - val_loss: 1.1659 - val_accuracy: 0.7562\n",
      "Epoch 380/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0425 - accuracy: 0.9981 - val_loss: 1.1468 - val_accuracy: 0.7625\n",
      "Epoch 381/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0422 - accuracy: 0.9984 - val_loss: 1.1441 - val_accuracy: 0.7650\n",
      "Epoch 382/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0420 - accuracy: 0.9985 - val_loss: 1.1564 - val_accuracy: 0.7562\n",
      "Epoch 383/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0423 - accuracy: 0.9988 - val_loss: 1.1597 - val_accuracy: 0.7500\n",
      "Epoch 384/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0425 - accuracy: 0.9984 - val_loss: 1.1543 - val_accuracy: 0.7500\n",
      "Epoch 385/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0421 - accuracy: 0.9988 - val_loss: 1.1610 - val_accuracy: 0.7487\n",
      "Epoch 386/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0418 - accuracy: 0.9988 - val_loss: 1.1651 - val_accuracy: 0.7512\n",
      "Epoch 387/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0424 - accuracy: 0.9985 - val_loss: 1.1586 - val_accuracy: 0.7525\n",
      "Epoch 388/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0434 - accuracy: 0.9976 - val_loss: 1.1588 - val_accuracy: 0.7537\n",
      "Epoch 389/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0422 - accuracy: 0.9981 - val_loss: 1.1656 - val_accuracy: 0.7537\n",
      "Epoch 390/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0415 - accuracy: 0.9988 - val_loss: 1.1666 - val_accuracy: 0.7550\n",
      "Epoch 391/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0432 - accuracy: 0.9979 - val_loss: 1.1675 - val_accuracy: 0.7525\n",
      "Epoch 392/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0412 - accuracy: 0.9986 - val_loss: 1.1820 - val_accuracy: 0.7450\n",
      "Epoch 393/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0433 - accuracy: 0.9980 - val_loss: 1.1804 - val_accuracy: 0.7425\n",
      "Epoch 394/2000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0422 - accuracy: 0.9983 - val_loss: 1.1549 - val_accuracy: 0.7450\n",
      "Epoch 395/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0418 - accuracy: 0.9982 - val_loss: 1.1558 - val_accuracy: 0.7450\n",
      "Epoch 396/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0413 - accuracy: 0.9984 - val_loss: 1.1527 - val_accuracy: 0.7512\n",
      "Epoch 397/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0432 - accuracy: 0.9978 - val_loss: 1.1551 - val_accuracy: 0.7525\n",
      "Epoch 398/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0425 - accuracy: 0.9984 - val_loss: 1.1442 - val_accuracy: 0.7512\n",
      "Epoch 399/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0434 - accuracy: 0.9980 - val_loss: 1.1501 - val_accuracy: 0.7487\n",
      "Epoch 400/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0420 - accuracy: 0.9985 - val_loss: 1.1502 - val_accuracy: 0.7525\n",
      "Epoch 401/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0425 - accuracy: 0.9982 - val_loss: 1.1459 - val_accuracy: 0.7512\n",
      "Epoch 402/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0411 - accuracy: 0.9986 - val_loss: 1.1469 - val_accuracy: 0.7500\n",
      "Epoch 403/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0424 - accuracy: 0.9979 - val_loss: 1.1540 - val_accuracy: 0.7487\n",
      "Epoch 404/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0432 - accuracy: 0.9977 - val_loss: 1.1633 - val_accuracy: 0.7450\n",
      "Epoch 405/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0426 - accuracy: 0.9982 - val_loss: 1.1622 - val_accuracy: 0.7487\n",
      "Epoch 406/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0416 - accuracy: 0.9988 - val_loss: 1.1664 - val_accuracy: 0.7437\n",
      "Epoch 407/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0426 - accuracy: 0.9983 - val_loss: 1.1716 - val_accuracy: 0.7412\n",
      "Epoch 408/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0412 - accuracy: 0.9986 - val_loss: 1.1730 - val_accuracy: 0.7400\n",
      "Epoch 409/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0429 - accuracy: 0.9981 - val_loss: 1.1828 - val_accuracy: 0.7375\n",
      "Epoch 410/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0417 - accuracy: 0.9980 - val_loss: 1.2020 - val_accuracy: 0.7475\n",
      "Epoch 411/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0425 - accuracy: 0.9982 - val_loss: 1.2062 - val_accuracy: 0.7500\n",
      "Epoch 412/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0427 - accuracy: 0.9978 - val_loss: 1.2115 - val_accuracy: 0.7425\n",
      "Epoch 413/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0425 - accuracy: 0.9979 - val_loss: 1.2134 - val_accuracy: 0.7400\n",
      "Epoch 414/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0427 - accuracy: 0.9978 - val_loss: 1.2224 - val_accuracy: 0.7462\n",
      "Epoch 415/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0427 - accuracy: 0.9979 - val_loss: 1.2262 - val_accuracy: 0.7425\n",
      "Epoch 416/2000\n",
      "19/19 [==============================] - 0s 24ms/step - loss: 0.0409 - accuracy: 0.9985 - val_loss: 1.2152 - val_accuracy: 0.7375\n",
      "Epoch 417/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0422 - accuracy: 0.9981 - val_loss: 1.1966 - val_accuracy: 0.7437\n",
      "Epoch 418/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0423 - accuracy: 0.9978 - val_loss: 1.1942 - val_accuracy: 0.7462\n",
      "Epoch 419/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0425 - accuracy: 0.9978 - val_loss: 1.1977 - val_accuracy: 0.7487\n",
      "Epoch 420/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0433 - accuracy: 0.9976 - val_loss: 1.1944 - val_accuracy: 0.7525\n",
      "Epoch 421/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0417 - accuracy: 0.9984 - val_loss: 1.1942 - val_accuracy: 0.7575\n",
      "Epoch 422/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0417 - accuracy: 0.9982 - val_loss: 1.2039 - val_accuracy: 0.7525\n",
      "Epoch 423/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0410 - accuracy: 0.9987 - val_loss: 1.2095 - val_accuracy: 0.7500\n",
      "Epoch 424/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0434 - accuracy: 0.9978 - val_loss: 1.2027 - val_accuracy: 0.7512\n",
      "Epoch 425/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0424 - accuracy: 0.9985 - val_loss: 1.1972 - val_accuracy: 0.7500\n",
      "Epoch 426/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0409 - accuracy: 0.9989 - val_loss: 1.2039 - val_accuracy: 0.7487\n",
      "Epoch 427/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0429 - accuracy: 0.9980 - val_loss: 1.1928 - val_accuracy: 0.7512\n",
      "Epoch 428/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0429 - accuracy: 0.9980 - val_loss: 1.1851 - val_accuracy: 0.7512\n",
      "Epoch 429/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0425 - accuracy: 0.9983 - val_loss: 1.1854 - val_accuracy: 0.7487\n",
      "Epoch 430/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0424 - accuracy: 0.9978 - val_loss: 1.1911 - val_accuracy: 0.7400\n",
      "Epoch 431/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0425 - accuracy: 0.9982 - val_loss: 1.1970 - val_accuracy: 0.7437\n",
      "Epoch 432/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0422 - accuracy: 0.9984 - val_loss: 1.1980 - val_accuracy: 0.7462\n",
      "Epoch 433/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0420 - accuracy: 0.9986 - val_loss: 1.2040 - val_accuracy: 0.7462\n",
      "Epoch 434/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0417 - accuracy: 0.9982 - val_loss: 1.2038 - val_accuracy: 0.7412\n",
      "Epoch 435/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0412 - accuracy: 0.9983 - val_loss: 1.2099 - val_accuracy: 0.7487\n",
      "Epoch 436/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0412 - accuracy: 0.9988 - val_loss: 1.2112 - val_accuracy: 0.7462\n",
      "Epoch 437/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0415 - accuracy: 0.9984 - val_loss: 1.2035 - val_accuracy: 0.7425\n",
      "Epoch 438/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0419 - accuracy: 0.9986 - val_loss: 1.2006 - val_accuracy: 0.7450\n",
      "Epoch 439/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0416 - accuracy: 0.9986 - val_loss: 1.2026 - val_accuracy: 0.7450\n",
      "Epoch 440/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0423 - accuracy: 0.9977 - val_loss: 1.2048 - val_accuracy: 0.7487\n",
      "Epoch 441/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0411 - accuracy: 0.9983 - val_loss: 1.1964 - val_accuracy: 0.7512\n",
      "Epoch 442/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0421 - accuracy: 0.9981 - val_loss: 1.1948 - val_accuracy: 0.7500\n",
      "Epoch 443/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0429 - accuracy: 0.9979 - val_loss: 1.2235 - val_accuracy: 0.7462\n",
      "Epoch 444/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0419 - accuracy: 0.9984 - val_loss: 1.2361 - val_accuracy: 0.7425\n",
      "Epoch 445/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0414 - accuracy: 0.9984 - val_loss: 1.2444 - val_accuracy: 0.7462\n",
      "Epoch 446/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0417 - accuracy: 0.9984 - val_loss: 1.2444 - val_accuracy: 0.7475\n",
      "Epoch 447/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0418 - accuracy: 0.9984 - val_loss: 1.2364 - val_accuracy: 0.7462\n",
      "Epoch 448/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0434 - accuracy: 0.9977 - val_loss: 1.2282 - val_accuracy: 0.7487\n",
      "Epoch 449/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0422 - accuracy: 0.9980 - val_loss: 1.2182 - val_accuracy: 0.7525\n",
      "Epoch 450/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0416 - accuracy: 0.9987 - val_loss: 1.2125 - val_accuracy: 0.7487\n",
      "Epoch 451/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0419 - accuracy: 0.9982 - val_loss: 1.2240 - val_accuracy: 0.7450\n",
      "Epoch 452/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0423 - accuracy: 0.9982 - val_loss: 1.2268 - val_accuracy: 0.7475\n",
      "Epoch 453/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0432 - accuracy: 0.9973 - val_loss: 1.2307 - val_accuracy: 0.7450\n",
      "Epoch 454/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0428 - accuracy: 0.9983 - val_loss: 1.2215 - val_accuracy: 0.7537\n",
      "Epoch 455/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0426 - accuracy: 0.9980 - val_loss: 1.2135 - val_accuracy: 0.7425\n",
      "Epoch 456/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0421 - accuracy: 0.9978 - val_loss: 1.2038 - val_accuracy: 0.7462\n",
      "Epoch 457/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0411 - accuracy: 0.9986 - val_loss: 1.2093 - val_accuracy: 0.7437\n",
      "Epoch 458/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0421 - accuracy: 0.9981 - val_loss: 1.2101 - val_accuracy: 0.7475\n",
      "Epoch 459/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0420 - accuracy: 0.9983 - val_loss: 1.1950 - val_accuracy: 0.7462\n",
      "Epoch 460/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0424 - accuracy: 0.9979 - val_loss: 1.2034 - val_accuracy: 0.7412\n",
      "Epoch 461/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0422 - accuracy: 0.9979 - val_loss: 1.2032 - val_accuracy: 0.7462\n",
      "Epoch 462/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0414 - accuracy: 0.9982 - val_loss: 1.2157 - val_accuracy: 0.7475\n",
      "Epoch 463/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0411 - accuracy: 0.9982 - val_loss: 1.2257 - val_accuracy: 0.7525\n",
      "Epoch 464/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0413 - accuracy: 0.9985 - val_loss: 1.2241 - val_accuracy: 0.7462\n",
      "Epoch 465/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0416 - accuracy: 0.9985 - val_loss: 1.2246 - val_accuracy: 0.7425\n",
      "Epoch 466/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0416 - accuracy: 0.9982 - val_loss: 1.2173 - val_accuracy: 0.7425\n",
      "Epoch 467/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0420 - accuracy: 0.9984 - val_loss: 1.2012 - val_accuracy: 0.7487\n",
      "Epoch 468/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0416 - accuracy: 0.9984 - val_loss: 1.2025 - val_accuracy: 0.7512\n",
      "Epoch 469/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0416 - accuracy: 0.9985 - val_loss: 1.1966 - val_accuracy: 0.7487\n",
      "Epoch 470/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0420 - accuracy: 0.9984 - val_loss: 1.2172 - val_accuracy: 0.7487\n",
      "Epoch 471/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0425 - accuracy: 0.9976 - val_loss: 1.2121 - val_accuracy: 0.7537\n",
      "Epoch 472/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0431 - accuracy: 0.9980 - val_loss: 1.1969 - val_accuracy: 0.7462\n",
      "Epoch 473/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0436 - accuracy: 0.9969 - val_loss: 1.1810 - val_accuracy: 0.7525\n",
      "Epoch 474/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0424 - accuracy: 0.9980 - val_loss: 1.1865 - val_accuracy: 0.7537\n",
      "Epoch 475/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0428 - accuracy: 0.9979 - val_loss: 1.1946 - val_accuracy: 0.7462\n",
      "Epoch 476/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0417 - accuracy: 0.9985 - val_loss: 1.2003 - val_accuracy: 0.7462\n",
      "Epoch 477/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0419 - accuracy: 0.9978 - val_loss: 1.1855 - val_accuracy: 0.7500\n",
      "Epoch 478/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0415 - accuracy: 0.9986 - val_loss: 1.1769 - val_accuracy: 0.7550\n",
      "Epoch 479/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0419 - accuracy: 0.9984 - val_loss: 1.1646 - val_accuracy: 0.7512\n",
      "Epoch 480/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0437 - accuracy: 0.9982 - val_loss: 1.1595 - val_accuracy: 0.7525\n",
      "Epoch 481/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0421 - accuracy: 0.9983 - val_loss: 1.1554 - val_accuracy: 0.7537\n",
      "Epoch 482/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0415 - accuracy: 0.9983 - val_loss: 1.1523 - val_accuracy: 0.7525\n",
      "Epoch 483/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0422 - accuracy: 0.9983 - val_loss: 1.1685 - val_accuracy: 0.7487\n",
      "Epoch 484/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0423 - accuracy: 0.9980 - val_loss: 1.1702 - val_accuracy: 0.7537\n",
      "Epoch 485/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0429 - accuracy: 0.9976 - val_loss: 1.1708 - val_accuracy: 0.7500\n",
      "Epoch 486/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0419 - accuracy: 0.9985 - val_loss: 1.1713 - val_accuracy: 0.7487\n",
      "Epoch 487/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0416 - accuracy: 0.9981 - val_loss: 1.1743 - val_accuracy: 0.7537\n",
      "Epoch 488/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0424 - accuracy: 0.9979 - val_loss: 1.1951 - val_accuracy: 0.7587\n",
      "Epoch 489/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0427 - accuracy: 0.9978 - val_loss: 1.1995 - val_accuracy: 0.7625\n",
      "Epoch 490/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0439 - accuracy: 0.9974 - val_loss: 1.1888 - val_accuracy: 0.7562\n",
      "Epoch 491/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0423 - accuracy: 0.9983 - val_loss: 1.1864 - val_accuracy: 0.7587\n",
      "Epoch 492/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0422 - accuracy: 0.9979 - val_loss: 1.1894 - val_accuracy: 0.7537\n",
      "Epoch 493/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0418 - accuracy: 0.9983 - val_loss: 1.1935 - val_accuracy: 0.7450\n",
      "Epoch 494/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0420 - accuracy: 0.9985 - val_loss: 1.1961 - val_accuracy: 0.7475\n",
      "Epoch 495/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0416 - accuracy: 0.9982 - val_loss: 1.1886 - val_accuracy: 0.7475\n",
      "Epoch 496/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0422 - accuracy: 0.9980 - val_loss: 1.1841 - val_accuracy: 0.7475\n",
      "Epoch 497/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0404 - accuracy: 0.9986 - val_loss: 1.1772 - val_accuracy: 0.7500\n",
      "Epoch 498/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0413 - accuracy: 0.9986 - val_loss: 1.1715 - val_accuracy: 0.7462\n",
      "Epoch 499/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0418 - accuracy: 0.9981 - val_loss: 1.1753 - val_accuracy: 0.7537\n",
      "Epoch 500/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0423 - accuracy: 0.9979 - val_loss: 1.1733 - val_accuracy: 0.7437\n",
      "Epoch 501/2000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0415 - accuracy: 0.9985 - val_loss: 1.1767 - val_accuracy: 0.7475\n",
      "Epoch 502/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0412 - accuracy: 0.9983 - val_loss: 1.1881 - val_accuracy: 0.7475\n",
      "Epoch 503/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0427 - accuracy: 0.9978 - val_loss: 1.1950 - val_accuracy: 0.7475\n",
      "Epoch 504/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0419 - accuracy: 0.9984 - val_loss: 1.2090 - val_accuracy: 0.7425\n",
      "Epoch 505/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0429 - accuracy: 0.9976 - val_loss: 1.2130 - val_accuracy: 0.7437\n",
      "Epoch 506/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0415 - accuracy: 0.9982 - val_loss: 1.2201 - val_accuracy: 0.7412\n",
      "Epoch 507/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0418 - accuracy: 0.9982 - val_loss: 1.2256 - val_accuracy: 0.7350\n",
      "Epoch 508/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0421 - accuracy: 0.9984 - val_loss: 1.2200 - val_accuracy: 0.7337\n",
      "Epoch 509/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0405 - accuracy: 0.9987 - val_loss: 1.2251 - val_accuracy: 0.7437\n",
      "Epoch 510/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0419 - accuracy: 0.9981 - val_loss: 1.2271 - val_accuracy: 0.7437\n",
      "Epoch 511/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0403 - accuracy: 0.9985 - val_loss: 1.2351 - val_accuracy: 0.7412\n",
      "Epoch 512/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0419 - accuracy: 0.9983 - val_loss: 1.2323 - val_accuracy: 0.7425\n",
      "Epoch 513/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0412 - accuracy: 0.9978 - val_loss: 1.2213 - val_accuracy: 0.7425\n",
      "Epoch 514/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0411 - accuracy: 0.9982 - val_loss: 1.2123 - val_accuracy: 0.7425\n",
      "Epoch 515/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0418 - accuracy: 0.9982 - val_loss: 1.2132 - val_accuracy: 0.7437\n",
      "Epoch 516/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0420 - accuracy: 0.9982 - val_loss: 1.2108 - val_accuracy: 0.7487\n",
      "Epoch 517/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0405 - accuracy: 0.9987 - val_loss: 1.2120 - val_accuracy: 0.7475\n",
      "Epoch 518/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0417 - accuracy: 0.9982 - val_loss: 1.2124 - val_accuracy: 0.7450\n",
      "Epoch 519/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0424 - accuracy: 0.9983 - val_loss: 1.2143 - val_accuracy: 0.7462\n",
      "Epoch 520/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0426 - accuracy: 0.9975 - val_loss: 1.1976 - val_accuracy: 0.7475\n",
      "Epoch 521/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0400 - accuracy: 0.9988 - val_loss: 1.1954 - val_accuracy: 0.7500\n",
      "Epoch 522/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0400 - accuracy: 0.9989 - val_loss: 1.2003 - val_accuracy: 0.7475\n",
      "Epoch 523/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0426 - accuracy: 0.9978 - val_loss: 1.2073 - val_accuracy: 0.7450\n",
      "Epoch 524/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0402 - accuracy: 0.9988 - val_loss: 1.2151 - val_accuracy: 0.7450\n",
      "Epoch 525/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0405 - accuracy: 0.9986 - val_loss: 1.2245 - val_accuracy: 0.7425\n",
      "Epoch 526/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0408 - accuracy: 0.9984 - val_loss: 1.2405 - val_accuracy: 0.7400\n",
      "Epoch 527/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0410 - accuracy: 0.9984 - val_loss: 1.2401 - val_accuracy: 0.7475\n",
      "Epoch 528/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0406 - accuracy: 0.9982 - val_loss: 1.2298 - val_accuracy: 0.7500\n",
      "Epoch 529/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0405 - accuracy: 0.9986 - val_loss: 1.2164 - val_accuracy: 0.7575\n",
      "Epoch 530/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0403 - accuracy: 0.9988 - val_loss: 1.2092 - val_accuracy: 0.7662\n",
      "Epoch 531/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0397 - accuracy: 0.9988 - val_loss: 1.2089 - val_accuracy: 0.7675\n",
      "Epoch 532/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0409 - accuracy: 0.9985 - val_loss: 1.1985 - val_accuracy: 0.7612\n",
      "Epoch 533/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0406 - accuracy: 0.9984 - val_loss: 1.1992 - val_accuracy: 0.7612\n",
      "Epoch 534/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0399 - accuracy: 0.9986 - val_loss: 1.2005 - val_accuracy: 0.7575\n",
      "Epoch 535/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0398 - accuracy: 0.9989 - val_loss: 1.1874 - val_accuracy: 0.7662\n",
      "Epoch 536/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0401 - accuracy: 0.9985 - val_loss: 1.1814 - val_accuracy: 0.7662\n",
      "Epoch 537/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0396 - accuracy: 0.9987 - val_loss: 1.1820 - val_accuracy: 0.7600\n",
      "Epoch 538/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0399 - accuracy: 0.9986 - val_loss: 1.1782 - val_accuracy: 0.7662\n",
      "Epoch 539/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0400 - accuracy: 0.9982 - val_loss: 1.1695 - val_accuracy: 0.7637\n",
      "Epoch 540/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0398 - accuracy: 0.9983 - val_loss: 1.1723 - val_accuracy: 0.7675\n",
      "Epoch 541/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0401 - accuracy: 0.9986 - val_loss: 1.1735 - val_accuracy: 0.7713\n",
      "Epoch 542/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0398 - accuracy: 0.9982 - val_loss: 1.1752 - val_accuracy: 0.7688\n",
      "Epoch 543/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0391 - accuracy: 0.9990 - val_loss: 1.1868 - val_accuracy: 0.7662\n",
      "Epoch 544/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0394 - accuracy: 0.9988 - val_loss: 1.1891 - val_accuracy: 0.7625\n",
      "Epoch 545/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0407 - accuracy: 0.9979 - val_loss: 1.1793 - val_accuracy: 0.7625\n",
      "Epoch 546/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0401 - accuracy: 0.9984 - val_loss: 1.1842 - val_accuracy: 0.7612\n",
      "Epoch 547/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0398 - accuracy: 0.9988 - val_loss: 1.1814 - val_accuracy: 0.7575\n",
      "Epoch 548/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0408 - accuracy: 0.9985 - val_loss: 1.1653 - val_accuracy: 0.7688\n",
      "Epoch 549/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0402 - accuracy: 0.9984 - val_loss: 1.1645 - val_accuracy: 0.7650\n",
      "Epoch 550/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0404 - accuracy: 0.9986 - val_loss: 1.1657 - val_accuracy: 0.7637\n",
      "Epoch 551/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0416 - accuracy: 0.9986 - val_loss: 1.1786 - val_accuracy: 0.7600\n",
      "Epoch 552/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0403 - accuracy: 0.9983 - val_loss: 1.1753 - val_accuracy: 0.7587\n",
      "Epoch 553/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0411 - accuracy: 0.9983 - val_loss: 1.1727 - val_accuracy: 0.7550\n",
      "Epoch 554/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0400 - accuracy: 0.9990 - val_loss: 1.1619 - val_accuracy: 0.7537\n",
      "Epoch 555/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0401 - accuracy: 0.9982 - val_loss: 1.1555 - val_accuracy: 0.7550\n",
      "Epoch 556/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0404 - accuracy: 0.9984 - val_loss: 1.1649 - val_accuracy: 0.7587\n",
      "Epoch 557/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0400 - accuracy: 0.9984 - val_loss: 1.1708 - val_accuracy: 0.7600\n",
      "Epoch 558/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0399 - accuracy: 0.9988 - val_loss: 1.1676 - val_accuracy: 0.7525\n",
      "Epoch 559/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0402 - accuracy: 0.9985 - val_loss: 1.1593 - val_accuracy: 0.7587\n",
      "Epoch 560/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0402 - accuracy: 0.9984 - val_loss: 1.1645 - val_accuracy: 0.7575\n",
      "Epoch 561/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0403 - accuracy: 0.9982 - val_loss: 1.1690 - val_accuracy: 0.7525\n",
      "Epoch 562/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0399 - accuracy: 0.9984 - val_loss: 1.1734 - val_accuracy: 0.7487\n",
      "Epoch 563/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0422 - accuracy: 0.9975 - val_loss: 1.1748 - val_accuracy: 0.7562\n",
      "Epoch 564/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0411 - accuracy: 0.9982 - val_loss: 1.1720 - val_accuracy: 0.7562\n",
      "Epoch 565/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0406 - accuracy: 0.9986 - val_loss: 1.1697 - val_accuracy: 0.7537\n",
      "Epoch 566/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0403 - accuracy: 0.9982 - val_loss: 1.1795 - val_accuracy: 0.7512\n",
      "Epoch 567/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0401 - accuracy: 0.9989 - val_loss: 1.1977 - val_accuracy: 0.7500\n",
      "Epoch 568/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0404 - accuracy: 0.9985 - val_loss: 1.2065 - val_accuracy: 0.7450\n",
      "Epoch 569/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0409 - accuracy: 0.9984 - val_loss: 1.2101 - val_accuracy: 0.7450\n",
      "Epoch 570/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0394 - accuracy: 0.9986 - val_loss: 1.2121 - val_accuracy: 0.7425\n",
      "Epoch 571/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0401 - accuracy: 0.9982 - val_loss: 1.2200 - val_accuracy: 0.7450\n",
      "Epoch 572/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0406 - accuracy: 0.9984 - val_loss: 1.2205 - val_accuracy: 0.7512\n",
      "Epoch 573/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0401 - accuracy: 0.9984 - val_loss: 1.2238 - val_accuracy: 0.7487\n",
      "Epoch 574/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0397 - accuracy: 0.9986 - val_loss: 1.1984 - val_accuracy: 0.7400\n",
      "Epoch 575/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0403 - accuracy: 0.9983 - val_loss: 1.1726 - val_accuracy: 0.7537\n",
      "Epoch 576/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0412 - accuracy: 0.9977 - val_loss: 1.1513 - val_accuracy: 0.7512\n",
      "Epoch 577/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0396 - accuracy: 0.9987 - val_loss: 1.1480 - val_accuracy: 0.7562\n",
      "Epoch 578/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0409 - accuracy: 0.9979 - val_loss: 1.1464 - val_accuracy: 0.7562\n",
      "Epoch 579/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0405 - accuracy: 0.9984 - val_loss: 1.1496 - val_accuracy: 0.7550\n",
      "Epoch 580/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0427 - accuracy: 0.9976 - val_loss: 1.1613 - val_accuracy: 0.7550\n",
      "Epoch 581/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0406 - accuracy: 0.9986 - val_loss: 1.1705 - val_accuracy: 0.7562\n",
      "Epoch 582/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0399 - accuracy: 0.9986 - val_loss: 1.1794 - val_accuracy: 0.7512\n",
      "Epoch 583/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0400 - accuracy: 0.9987 - val_loss: 1.1763 - val_accuracy: 0.7525\n",
      "Epoch 584/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0408 - accuracy: 0.9984 - val_loss: 1.1875 - val_accuracy: 0.7475\n",
      "Epoch 585/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0408 - accuracy: 0.9980 - val_loss: 1.1977 - val_accuracy: 0.7387\n",
      "Epoch 586/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0401 - accuracy: 0.9983 - val_loss: 1.2059 - val_accuracy: 0.7387\n",
      "Epoch 587/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0401 - accuracy: 0.9986 - val_loss: 1.2109 - val_accuracy: 0.7400\n",
      "Epoch 588/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0396 - accuracy: 0.9986 - val_loss: 1.2104 - val_accuracy: 0.7437\n",
      "Epoch 589/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0391 - accuracy: 0.9988 - val_loss: 1.2112 - val_accuracy: 0.7412\n",
      "Epoch 590/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0393 - accuracy: 0.9987 - val_loss: 1.2214 - val_accuracy: 0.7412\n",
      "Epoch 591/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0395 - accuracy: 0.9984 - val_loss: 1.2194 - val_accuracy: 0.7400\n",
      "Epoch 592/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0401 - accuracy: 0.9989 - val_loss: 1.2124 - val_accuracy: 0.7437\n",
      "Epoch 593/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0406 - accuracy: 0.9986 - val_loss: 1.2109 - val_accuracy: 0.7425\n",
      "Epoch 594/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0400 - accuracy: 0.9986 - val_loss: 1.2199 - val_accuracy: 0.7387\n",
      "Epoch 595/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0407 - accuracy: 0.9980 - val_loss: 1.2224 - val_accuracy: 0.7325\n",
      "Epoch 596/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0403 - accuracy: 0.9985 - val_loss: 1.2309 - val_accuracy: 0.7262\n",
      "Epoch 597/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0402 - accuracy: 0.9982 - val_loss: 1.2487 - val_accuracy: 0.7287\n",
      "Epoch 598/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0402 - accuracy: 0.9982 - val_loss: 1.2360 - val_accuracy: 0.7362\n",
      "Epoch 599/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0418 - accuracy: 0.9975 - val_loss: 1.2387 - val_accuracy: 0.7375\n",
      "Epoch 600/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0410 - accuracy: 0.9980 - val_loss: 1.2315 - val_accuracy: 0.7262\n",
      "Epoch 601/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0400 - accuracy: 0.9983 - val_loss: 1.2279 - val_accuracy: 0.7362\n",
      "Epoch 602/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0403 - accuracy: 0.9983 - val_loss: 1.2080 - val_accuracy: 0.7412\n",
      "Epoch 603/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0400 - accuracy: 0.9983 - val_loss: 1.1842 - val_accuracy: 0.7400\n",
      "Epoch 604/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0402 - accuracy: 0.9986 - val_loss: 1.1814 - val_accuracy: 0.7412\n",
      "Epoch 605/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0398 - accuracy: 0.9987 - val_loss: 1.1878 - val_accuracy: 0.7425\n",
      "Epoch 606/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0411 - accuracy: 0.9983 - val_loss: 1.1975 - val_accuracy: 0.7437\n",
      "Epoch 607/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0407 - accuracy: 0.9983 - val_loss: 1.1929 - val_accuracy: 0.7450\n",
      "Epoch 608/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0410 - accuracy: 0.9978 - val_loss: 1.2045 - val_accuracy: 0.7350\n",
      "Epoch 609/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0397 - accuracy: 0.9985 - val_loss: 1.2131 - val_accuracy: 0.7350\n",
      "Epoch 610/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0399 - accuracy: 0.9986 - val_loss: 1.2108 - val_accuracy: 0.7400\n",
      "Epoch 611/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0398 - accuracy: 0.9987 - val_loss: 1.2002 - val_accuracy: 0.7350\n",
      "Epoch 612/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0398 - accuracy: 0.9986 - val_loss: 1.2052 - val_accuracy: 0.7400\n",
      "Epoch 613/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0399 - accuracy: 0.9986 - val_loss: 1.2076 - val_accuracy: 0.7437\n",
      "Epoch 614/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0403 - accuracy: 0.9982 - val_loss: 1.2047 - val_accuracy: 0.7387\n",
      "Epoch 615/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0399 - accuracy: 0.9983 - val_loss: 1.2205 - val_accuracy: 0.7387\n",
      "Epoch 616/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0406 - accuracy: 0.9980 - val_loss: 1.2234 - val_accuracy: 0.7400\n",
      "Epoch 617/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0411 - accuracy: 0.9982 - val_loss: 1.2116 - val_accuracy: 0.7450\n",
      "Epoch 618/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0401 - accuracy: 0.9986 - val_loss: 1.2048 - val_accuracy: 0.7512\n",
      "Epoch 619/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0419 - accuracy: 0.9976 - val_loss: 1.2012 - val_accuracy: 0.7500\n",
      "Epoch 620/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0396 - accuracy: 0.9988 - val_loss: 1.1939 - val_accuracy: 0.7525\n",
      "Epoch 621/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0416 - accuracy: 0.9978 - val_loss: 1.1907 - val_accuracy: 0.7500\n",
      "Epoch 622/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0409 - accuracy: 0.9984 - val_loss: 1.1906 - val_accuracy: 0.7487\n",
      "Epoch 623/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0399 - accuracy: 0.9987 - val_loss: 1.1895 - val_accuracy: 0.7475\n",
      "Epoch 624/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0410 - accuracy: 0.9982 - val_loss: 1.1815 - val_accuracy: 0.7537\n",
      "Epoch 625/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0408 - accuracy: 0.9985 - val_loss: 1.1857 - val_accuracy: 0.7462\n",
      "Epoch 626/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0424 - accuracy: 0.9978 - val_loss: 1.1946 - val_accuracy: 0.7387\n",
      "Epoch 627/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0395 - accuracy: 0.9993 - val_loss: 1.2003 - val_accuracy: 0.7425\n",
      "Epoch 628/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0405 - accuracy: 0.9983 - val_loss: 1.1815 - val_accuracy: 0.7462\n",
      "Epoch 629/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0399 - accuracy: 0.9982 - val_loss: 1.1906 - val_accuracy: 0.7475\n",
      "Epoch 630/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0415 - accuracy: 0.9976 - val_loss: 1.1878 - val_accuracy: 0.7450\n",
      "Epoch 631/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0411 - accuracy: 0.9980 - val_loss: 1.1849 - val_accuracy: 0.7487\n",
      "Epoch 632/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0392 - accuracy: 0.9990 - val_loss: 1.1754 - val_accuracy: 0.7437\n",
      "Epoch 633/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0400 - accuracy: 0.9982 - val_loss: 1.1743 - val_accuracy: 0.7437\n",
      "Epoch 634/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0415 - accuracy: 0.9977 - val_loss: 1.1752 - val_accuracy: 0.7475\n",
      "Epoch 635/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0400 - accuracy: 0.9984 - val_loss: 1.1636 - val_accuracy: 0.7412\n",
      "Epoch 636/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0401 - accuracy: 0.9986 - val_loss: 1.1791 - val_accuracy: 0.7425\n",
      "Epoch 637/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0398 - accuracy: 0.9988 - val_loss: 1.1792 - val_accuracy: 0.7412\n",
      "Epoch 638/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0407 - accuracy: 0.9982 - val_loss: 1.1819 - val_accuracy: 0.7437\n",
      "Epoch 639/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0411 - accuracy: 0.9979 - val_loss: 1.1802 - val_accuracy: 0.7437\n",
      "Epoch 640/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0392 - accuracy: 0.9989 - val_loss: 1.1832 - val_accuracy: 0.7412\n",
      "Epoch 641/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0411 - accuracy: 0.9978 - val_loss: 1.1798 - val_accuracy: 0.7475\n",
      "Epoch 642/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0402 - accuracy: 0.9983 - val_loss: 1.1735 - val_accuracy: 0.7475\n",
      "Epoch 643/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0404 - accuracy: 0.9981 - val_loss: 1.1603 - val_accuracy: 0.7537\n",
      "Epoch 644/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0405 - accuracy: 0.9984 - val_loss: 1.1588 - val_accuracy: 0.7525\n",
      "Epoch 645/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0410 - accuracy: 0.9985 - val_loss: 1.1700 - val_accuracy: 0.7537\n",
      "Epoch 646/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0412 - accuracy: 0.9980 - val_loss: 1.1710 - val_accuracy: 0.7462\n",
      "Epoch 647/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0400 - accuracy: 0.9988 - val_loss: 1.1624 - val_accuracy: 0.7462\n",
      "Epoch 648/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0426 - accuracy: 0.9976 - val_loss: 1.1479 - val_accuracy: 0.7500\n",
      "Epoch 649/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0421 - accuracy: 0.9977 - val_loss: 1.1440 - val_accuracy: 0.7525\n",
      "Epoch 650/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0400 - accuracy: 0.9984 - val_loss: 1.1365 - val_accuracy: 0.7525\n",
      "Epoch 651/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0395 - accuracy: 0.9985 - val_loss: 1.1379 - val_accuracy: 0.7550\n",
      "Epoch 652/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0407 - accuracy: 0.9982 - val_loss: 1.1355 - val_accuracy: 0.7600\n",
      "Epoch 653/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0405 - accuracy: 0.9985 - val_loss: 1.1418 - val_accuracy: 0.7550\n",
      "Epoch 654/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0398 - accuracy: 0.9987 - val_loss: 1.1615 - val_accuracy: 0.7587\n",
      "Epoch 655/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0399 - accuracy: 0.9988 - val_loss: 1.1639 - val_accuracy: 0.7612\n",
      "Epoch 656/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0412 - accuracy: 0.9979 - val_loss: 1.1614 - val_accuracy: 0.7537\n",
      "Epoch 657/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0402 - accuracy: 0.9986 - val_loss: 1.1483 - val_accuracy: 0.7587\n",
      "Epoch 658/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0418 - accuracy: 0.9976 - val_loss: 1.1438 - val_accuracy: 0.7612\n",
      "Epoch 659/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0402 - accuracy: 0.9986 - val_loss: 1.1516 - val_accuracy: 0.7637\n",
      "Epoch 660/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0401 - accuracy: 0.9984 - val_loss: 1.1586 - val_accuracy: 0.7625\n",
      "Epoch 661/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0401 - accuracy: 0.9985 - val_loss: 1.1428 - val_accuracy: 0.7637\n",
      "Epoch 662/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0401 - accuracy: 0.9987 - val_loss: 1.1324 - val_accuracy: 0.7612\n",
      "Epoch 663/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0396 - accuracy: 0.9985 - val_loss: 1.1399 - val_accuracy: 0.7675\n",
      "Epoch 664/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0405 - accuracy: 0.9982 - val_loss: 1.1491 - val_accuracy: 0.7662\n",
      "Epoch 665/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0404 - accuracy: 0.9984 - val_loss: 1.1543 - val_accuracy: 0.7537\n",
      "Epoch 666/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0394 - accuracy: 0.9988 - val_loss: 1.1577 - val_accuracy: 0.7512\n",
      "Epoch 667/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0396 - accuracy: 0.9987 - val_loss: 1.1623 - val_accuracy: 0.7537\n",
      "Epoch 668/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0395 - accuracy: 0.9987 - val_loss: 1.1571 - val_accuracy: 0.7575\n",
      "Epoch 669/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0408 - accuracy: 0.9984 - val_loss: 1.1549 - val_accuracy: 0.7550\n",
      "Epoch 670/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0400 - accuracy: 0.9986 - val_loss: 1.1619 - val_accuracy: 0.7462\n",
      "Epoch 671/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0398 - accuracy: 0.9984 - val_loss: 1.1684 - val_accuracy: 0.7525\n",
      "Epoch 672/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0410 - accuracy: 0.9980 - val_loss: 1.1779 - val_accuracy: 0.7500\n",
      "Epoch 673/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0407 - accuracy: 0.9980 - val_loss: 1.1812 - val_accuracy: 0.7412\n",
      "Epoch 674/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0412 - accuracy: 0.9978 - val_loss: 1.1788 - val_accuracy: 0.7512\n",
      "Epoch 675/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0396 - accuracy: 0.9984 - val_loss: 1.1786 - val_accuracy: 0.7475\n",
      "Epoch 676/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0406 - accuracy: 0.9984 - val_loss: 1.1812 - val_accuracy: 0.7512\n",
      "Epoch 677/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0401 - accuracy: 0.9984 - val_loss: 1.1717 - val_accuracy: 0.7500\n",
      "Epoch 678/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0400 - accuracy: 0.9986 - val_loss: 1.1565 - val_accuracy: 0.7537\n",
      "Epoch 679/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0399 - accuracy: 0.9984 - val_loss: 1.1619 - val_accuracy: 0.7562\n",
      "Epoch 680/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0398 - accuracy: 0.9983 - val_loss: 1.1687 - val_accuracy: 0.7500\n",
      "Epoch 681/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0388 - accuracy: 0.9988 - val_loss: 1.1806 - val_accuracy: 0.7487\n",
      "Epoch 682/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0393 - accuracy: 0.9984 - val_loss: 1.1696 - val_accuracy: 0.7525\n",
      "Epoch 683/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0390 - accuracy: 0.9986 - val_loss: 1.1693 - val_accuracy: 0.7525\n",
      "Epoch 684/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0397 - accuracy: 0.9984 - val_loss: 1.1775 - val_accuracy: 0.7475\n",
      "Epoch 685/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0395 - accuracy: 0.9987 - val_loss: 1.1766 - val_accuracy: 0.7462\n",
      "Epoch 686/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0395 - accuracy: 0.9985 - val_loss: 1.1646 - val_accuracy: 0.7475\n",
      "Epoch 687/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0392 - accuracy: 0.9984 - val_loss: 1.1608 - val_accuracy: 0.7487\n",
      "Epoch 688/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0394 - accuracy: 0.9984 - val_loss: 1.1541 - val_accuracy: 0.7462\n",
      "Epoch 689/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0398 - accuracy: 0.9983 - val_loss: 1.1589 - val_accuracy: 0.7537\n",
      "Epoch 690/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0380 - accuracy: 0.9991 - val_loss: 1.1717 - val_accuracy: 0.7512\n",
      "Epoch 691/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0391 - accuracy: 0.9982 - val_loss: 1.1752 - val_accuracy: 0.7500\n",
      "Epoch 692/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0392 - accuracy: 0.9983 - val_loss: 1.1588 - val_accuracy: 0.7512\n",
      "Epoch 693/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0389 - accuracy: 0.9984 - val_loss: 1.1486 - val_accuracy: 0.7537\n",
      "Epoch 694/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0394 - accuracy: 0.9983 - val_loss: 1.1366 - val_accuracy: 0.7525\n",
      "Epoch 695/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0397 - accuracy: 0.9986 - val_loss: 1.1413 - val_accuracy: 0.7525\n",
      "Epoch 696/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0392 - accuracy: 0.9987 - val_loss: 1.1490 - val_accuracy: 0.7462\n",
      "Epoch 697/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0383 - accuracy: 0.9989 - val_loss: 1.1540 - val_accuracy: 0.7425\n",
      "Epoch 698/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0382 - accuracy: 0.9987 - val_loss: 1.1525 - val_accuracy: 0.7425\n",
      "Epoch 699/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0389 - accuracy: 0.9988 - val_loss: 1.1438 - val_accuracy: 0.7425\n",
      "Epoch 700/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0395 - accuracy: 0.9983 - val_loss: 1.1496 - val_accuracy: 0.7412\n",
      "Epoch 701/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0390 - accuracy: 0.9987 - val_loss: 1.1384 - val_accuracy: 0.7437\n",
      "Epoch 702/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0381 - accuracy: 0.9992 - val_loss: 1.1401 - val_accuracy: 0.7537\n",
      "Epoch 703/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0385 - accuracy: 0.9986 - val_loss: 1.1399 - val_accuracy: 0.7550\n",
      "Epoch 704/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0395 - accuracy: 0.9982 - val_loss: 1.1442 - val_accuracy: 0.7512\n",
      "Epoch 705/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0378 - accuracy: 0.9989 - val_loss: 1.1419 - val_accuracy: 0.7537\n",
      "Epoch 706/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0392 - accuracy: 0.9985 - val_loss: 1.1358 - val_accuracy: 0.7475\n",
      "Epoch 707/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0390 - accuracy: 0.9987 - val_loss: 1.1362 - val_accuracy: 0.7462\n",
      "Epoch 708/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0388 - accuracy: 0.9988 - val_loss: 1.1260 - val_accuracy: 0.7525\n",
      "Epoch 709/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0395 - accuracy: 0.9982 - val_loss: 1.1127 - val_accuracy: 0.7525\n",
      "Epoch 710/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0386 - accuracy: 0.9987 - val_loss: 1.1124 - val_accuracy: 0.7525\n",
      "Epoch 711/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0388 - accuracy: 0.9986 - val_loss: 1.1056 - val_accuracy: 0.7575\n",
      "Epoch 712/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0388 - accuracy: 0.9985 - val_loss: 1.0984 - val_accuracy: 0.7487\n",
      "Epoch 713/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0386 - accuracy: 0.9987 - val_loss: 1.0907 - val_accuracy: 0.7587\n",
      "Epoch 714/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0372 - accuracy: 0.9991 - val_loss: 1.0865 - val_accuracy: 0.7575\n",
      "Epoch 715/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0384 - accuracy: 0.9986 - val_loss: 1.0935 - val_accuracy: 0.7600\n",
      "Epoch 716/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0390 - accuracy: 0.9981 - val_loss: 1.1027 - val_accuracy: 0.7625\n",
      "Epoch 717/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0384 - accuracy: 0.9987 - val_loss: 1.0966 - val_accuracy: 0.7575\n",
      "Epoch 718/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0390 - accuracy: 0.9985 - val_loss: 1.0834 - val_accuracy: 0.7612\n",
      "Epoch 719/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0385 - accuracy: 0.9987 - val_loss: 1.0957 - val_accuracy: 0.7537\n",
      "Epoch 720/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0384 - accuracy: 0.9988 - val_loss: 1.1046 - val_accuracy: 0.7562\n",
      "Epoch 721/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0382 - accuracy: 0.9988 - val_loss: 1.1124 - val_accuracy: 0.7537\n",
      "Epoch 722/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0397 - accuracy: 0.9984 - val_loss: 1.1052 - val_accuracy: 0.7525\n",
      "Epoch 723/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0387 - accuracy: 0.9986 - val_loss: 1.0998 - val_accuracy: 0.7537\n",
      "Epoch 724/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0383 - accuracy: 0.9986 - val_loss: 1.1061 - val_accuracy: 0.7587\n",
      "Epoch 725/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0383 - accuracy: 0.9987 - val_loss: 1.1181 - val_accuracy: 0.7550\n",
      "Epoch 726/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0386 - accuracy: 0.9984 - val_loss: 1.1106 - val_accuracy: 0.7587\n",
      "Epoch 727/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0389 - accuracy: 0.9984 - val_loss: 1.1159 - val_accuracy: 0.7537\n",
      "Epoch 728/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0382 - accuracy: 0.9989 - val_loss: 1.1119 - val_accuracy: 0.7587\n",
      "Epoch 729/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0385 - accuracy: 0.9986 - val_loss: 1.1037 - val_accuracy: 0.7512\n",
      "Epoch 730/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0385 - accuracy: 0.9984 - val_loss: 1.0985 - val_accuracy: 0.7487\n",
      "Epoch 731/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0377 - accuracy: 0.9989 - val_loss: 1.1110 - val_accuracy: 0.7525\n",
      "Epoch 732/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0386 - accuracy: 0.9984 - val_loss: 1.1199 - val_accuracy: 0.7500\n",
      "Epoch 733/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0380 - accuracy: 0.9989 - val_loss: 1.1296 - val_accuracy: 0.7537\n",
      "Epoch 734/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0384 - accuracy: 0.9986 - val_loss: 1.1366 - val_accuracy: 0.7537\n",
      "Epoch 735/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0392 - accuracy: 0.9983 - val_loss: 1.1450 - val_accuracy: 0.7475\n",
      "Epoch 736/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0383 - accuracy: 0.9986 - val_loss: 1.1556 - val_accuracy: 0.7550\n",
      "Epoch 737/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0385 - accuracy: 0.9982 - val_loss: 1.1552 - val_accuracy: 0.7537\n",
      "Epoch 738/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0382 - accuracy: 0.9989 - val_loss: 1.1662 - val_accuracy: 0.7475\n",
      "Epoch 739/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0378 - accuracy: 0.9984 - val_loss: 1.1750 - val_accuracy: 0.7462\n",
      "Epoch 740/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0388 - accuracy: 0.9984 - val_loss: 1.1720 - val_accuracy: 0.7537\n",
      "Epoch 741/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0388 - accuracy: 0.9986 - val_loss: 1.1508 - val_accuracy: 0.7512\n",
      "Epoch 742/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0392 - accuracy: 0.9977 - val_loss: 1.1466 - val_accuracy: 0.7500\n",
      "Epoch 743/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0383 - accuracy: 0.9988 - val_loss: 1.1395 - val_accuracy: 0.7537\n",
      "Epoch 744/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0379 - accuracy: 0.9988 - val_loss: 1.1272 - val_accuracy: 0.7550\n",
      "Epoch 745/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0390 - accuracy: 0.9983 - val_loss: 1.1230 - val_accuracy: 0.7475\n",
      "Epoch 746/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0402 - accuracy: 0.9979 - val_loss: 1.1102 - val_accuracy: 0.7575\n",
      "Epoch 747/2000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0388 - accuracy: 0.9984 - val_loss: 1.1110 - val_accuracy: 0.7637\n",
      "Epoch 748/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0390 - accuracy: 0.9982 - val_loss: 1.1237 - val_accuracy: 0.7600\n",
      "Epoch 749/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0390 - accuracy: 0.9983 - val_loss: 1.1344 - val_accuracy: 0.7587\n",
      "Epoch 750/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0393 - accuracy: 0.9982 - val_loss: 1.1226 - val_accuracy: 0.7587\n",
      "Epoch 751/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0401 - accuracy: 0.9980 - val_loss: 1.1117 - val_accuracy: 0.7612\n",
      "Epoch 752/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0400 - accuracy: 0.9983 - val_loss: 1.1114 - val_accuracy: 0.7612\n",
      "Epoch 753/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0385 - accuracy: 0.9988 - val_loss: 1.1240 - val_accuracy: 0.7600\n",
      "Epoch 754/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0380 - accuracy: 0.9989 - val_loss: 1.1370 - val_accuracy: 0.7562\n",
      "Epoch 755/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0386 - accuracy: 0.9986 - val_loss: 1.1354 - val_accuracy: 0.7587\n",
      "Epoch 756/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0396 - accuracy: 0.9984 - val_loss: 1.1453 - val_accuracy: 0.7537\n",
      "Epoch 757/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0390 - accuracy: 0.9986 - val_loss: 1.1438 - val_accuracy: 0.7500\n",
      "Epoch 758/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0386 - accuracy: 0.9984 - val_loss: 1.1487 - val_accuracy: 0.7462\n",
      "Epoch 759/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0373 - accuracy: 0.9991 - val_loss: 1.1544 - val_accuracy: 0.7500\n",
      "Epoch 760/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0382 - accuracy: 0.9987 - val_loss: 1.1509 - val_accuracy: 0.7475\n",
      "Epoch 761/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0387 - accuracy: 0.9984 - val_loss: 1.1427 - val_accuracy: 0.7475\n",
      "Epoch 762/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0377 - accuracy: 0.9989 - val_loss: 1.1459 - val_accuracy: 0.7500\n",
      "Epoch 763/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0392 - accuracy: 0.9982 - val_loss: 1.1330 - val_accuracy: 0.7512\n",
      "Epoch 764/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0387 - accuracy: 0.9986 - val_loss: 1.1453 - val_accuracy: 0.7512\n",
      "Epoch 765/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0393 - accuracy: 0.9983 - val_loss: 1.1456 - val_accuracy: 0.7487\n",
      "Epoch 766/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9989 - val_loss: 1.1464 - val_accuracy: 0.7500\n",
      "Epoch 767/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0386 - accuracy: 0.9985 - val_loss: 1.1405 - val_accuracy: 0.7550\n",
      "Epoch 768/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0387 - accuracy: 0.9985 - val_loss: 1.1402 - val_accuracy: 0.7487\n",
      "Epoch 769/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0392 - accuracy: 0.9981 - val_loss: 1.1304 - val_accuracy: 0.7550\n",
      "Epoch 770/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0382 - accuracy: 0.9988 - val_loss: 1.1326 - val_accuracy: 0.7537\n",
      "Epoch 771/2000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0384 - accuracy: 0.9986 - val_loss: 1.1227 - val_accuracy: 0.7587\n",
      "Epoch 772/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0381 - accuracy: 0.9982 - val_loss: 1.1241 - val_accuracy: 0.7587\n",
      "Epoch 773/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0387 - accuracy: 0.9990 - val_loss: 1.1293 - val_accuracy: 0.7587\n",
      "Epoch 774/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0381 - accuracy: 0.9984 - val_loss: 1.1189 - val_accuracy: 0.7600\n",
      "Epoch 775/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0394 - accuracy: 0.9983 - val_loss: 1.1242 - val_accuracy: 0.7575\n",
      "Epoch 776/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0389 - accuracy: 0.9986 - val_loss: 1.1323 - val_accuracy: 0.7525\n",
      "Epoch 777/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0393 - accuracy: 0.9979 - val_loss: 1.1329 - val_accuracy: 0.7550\n",
      "Epoch 778/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0388 - accuracy: 0.9985 - val_loss: 1.1272 - val_accuracy: 0.7525\n",
      "Epoch 779/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0384 - accuracy: 0.9984 - val_loss: 1.1333 - val_accuracy: 0.7512\n",
      "Epoch 780/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0374 - accuracy: 0.9993 - val_loss: 1.1452 - val_accuracy: 0.7562\n",
      "Epoch 781/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0389 - accuracy: 0.9984 - val_loss: 1.1459 - val_accuracy: 0.7600\n",
      "Epoch 782/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0387 - accuracy: 0.9985 - val_loss: 1.1336 - val_accuracy: 0.7550\n",
      "Epoch 783/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0383 - accuracy: 0.9988 - val_loss: 1.1307 - val_accuracy: 0.7575\n",
      "Epoch 784/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0391 - accuracy: 0.9984 - val_loss: 1.1326 - val_accuracy: 0.7512\n",
      "Epoch 785/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0391 - accuracy: 0.9981 - val_loss: 1.1214 - val_accuracy: 0.7575\n",
      "Epoch 786/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0401 - accuracy: 0.9976 - val_loss: 1.1210 - val_accuracy: 0.7662\n",
      "Epoch 787/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0387 - accuracy: 0.9985 - val_loss: 1.1225 - val_accuracy: 0.7637\n",
      "Epoch 788/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0378 - accuracy: 0.9989 - val_loss: 1.1374 - val_accuracy: 0.7637\n",
      "Epoch 789/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0400 - accuracy: 0.9980 - val_loss: 1.1253 - val_accuracy: 0.7713\n",
      "Epoch 790/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0392 - accuracy: 0.9981 - val_loss: 1.1316 - val_accuracy: 0.7662\n",
      "Epoch 791/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0391 - accuracy: 0.9983 - val_loss: 1.1346 - val_accuracy: 0.7625\n",
      "Epoch 792/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0393 - accuracy: 0.9986 - val_loss: 1.1437 - val_accuracy: 0.7662\n",
      "Epoch 793/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0388 - accuracy: 0.9988 - val_loss: 1.1546 - val_accuracy: 0.7662\n",
      "Epoch 794/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0395 - accuracy: 0.9980 - val_loss: 1.1547 - val_accuracy: 0.7612\n",
      "Epoch 795/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0389 - accuracy: 0.9980 - val_loss: 1.1590 - val_accuracy: 0.7587\n",
      "Epoch 796/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0395 - accuracy: 0.9981 - val_loss: 1.1607 - val_accuracy: 0.7587\n",
      "Epoch 797/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0386 - accuracy: 0.9986 - val_loss: 1.1776 - val_accuracy: 0.7650\n",
      "Epoch 798/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0392 - accuracy: 0.9984 - val_loss: 1.1740 - val_accuracy: 0.7587\n",
      "Epoch 799/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0389 - accuracy: 0.9987 - val_loss: 1.1559 - val_accuracy: 0.7575\n",
      "Epoch 800/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0392 - accuracy: 0.9983 - val_loss: 1.1409 - val_accuracy: 0.7662\n",
      "Epoch 801/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0379 - accuracy: 0.9988 - val_loss: 1.1524 - val_accuracy: 0.7675\n",
      "Epoch 802/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0388 - accuracy: 0.9986 - val_loss: 1.1458 - val_accuracy: 0.7662\n",
      "Epoch 803/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0390 - accuracy: 0.9986 - val_loss: 1.1430 - val_accuracy: 0.7675\n",
      "Epoch 804/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0388 - accuracy: 0.9984 - val_loss: 1.1498 - val_accuracy: 0.7650\n",
      "Epoch 805/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0394 - accuracy: 0.9983 - val_loss: 1.1523 - val_accuracy: 0.7625\n",
      "Epoch 806/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0389 - accuracy: 0.9986 - val_loss: 1.1481 - val_accuracy: 0.7637\n",
      "Epoch 807/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0404 - accuracy: 0.9987 - val_loss: 1.1430 - val_accuracy: 0.7600\n",
      "Epoch 808/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0393 - accuracy: 0.9983 - val_loss: 1.1433 - val_accuracy: 0.7612\n",
      "Epoch 809/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0390 - accuracy: 0.9984 - val_loss: 1.1594 - val_accuracy: 0.7562\n",
      "Epoch 810/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0396 - accuracy: 0.9981 - val_loss: 1.1669 - val_accuracy: 0.7525\n",
      "Epoch 811/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0384 - accuracy: 0.9984 - val_loss: 1.1692 - val_accuracy: 0.7562\n",
      "Epoch 812/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0385 - accuracy: 0.9988 - val_loss: 1.1734 - val_accuracy: 0.7562\n",
      "Epoch 813/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0389 - accuracy: 0.9989 - val_loss: 1.1802 - val_accuracy: 0.7575\n",
      "Epoch 814/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0404 - accuracy: 0.9978 - val_loss: 1.1785 - val_accuracy: 0.7562\n",
      "Epoch 815/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0390 - accuracy: 0.9986 - val_loss: 1.1832 - val_accuracy: 0.7512\n",
      "Epoch 816/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0377 - accuracy: 0.9989 - val_loss: 1.1816 - val_accuracy: 0.7550\n",
      "Epoch 817/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0396 - accuracy: 0.9981 - val_loss: 1.1796 - val_accuracy: 0.7562\n",
      "Epoch 818/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0387 - accuracy: 0.9985 - val_loss: 1.1574 - val_accuracy: 0.7675\n",
      "Epoch 819/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0386 - accuracy: 0.9988 - val_loss: 1.1487 - val_accuracy: 0.7637\n",
      "Epoch 820/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0400 - accuracy: 0.9984 - val_loss: 1.1438 - val_accuracy: 0.7537\n",
      "Epoch 821/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0396 - accuracy: 0.9982 - val_loss: 1.1329 - val_accuracy: 0.7600\n",
      "Epoch 822/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0394 - accuracy: 0.9984 - val_loss: 1.1386 - val_accuracy: 0.7625\n",
      "Epoch 823/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0397 - accuracy: 0.9986 - val_loss: 1.1429 - val_accuracy: 0.7575\n",
      "Epoch 824/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0386 - accuracy: 0.9984 - val_loss: 1.1388 - val_accuracy: 0.7600\n",
      "Epoch 825/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0393 - accuracy: 0.9985 - val_loss: 1.1451 - val_accuracy: 0.7637\n",
      "Epoch 826/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0395 - accuracy: 0.9984 - val_loss: 1.1640 - val_accuracy: 0.7625\n",
      "Epoch 827/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0398 - accuracy: 0.9983 - val_loss: 1.1696 - val_accuracy: 0.7587\n",
      "Epoch 828/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0392 - accuracy: 0.9984 - val_loss: 1.1742 - val_accuracy: 0.7625\n",
      "Epoch 829/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0401 - accuracy: 0.9983 - val_loss: 1.1863 - val_accuracy: 0.7600\n",
      "Epoch 830/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0386 - accuracy: 0.9983 - val_loss: 1.1977 - val_accuracy: 0.7637\n",
      "Epoch 831/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0394 - accuracy: 0.9982 - val_loss: 1.2078 - val_accuracy: 0.7612\n",
      "Epoch 832/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0402 - accuracy: 0.9981 - val_loss: 1.2076 - val_accuracy: 0.7562\n",
      "Epoch 833/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0395 - accuracy: 0.9986 - val_loss: 1.1956 - val_accuracy: 0.7512\n",
      "Epoch 834/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0384 - accuracy: 0.9987 - val_loss: 1.2012 - val_accuracy: 0.7575\n",
      "Epoch 835/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0403 - accuracy: 0.9980 - val_loss: 1.1957 - val_accuracy: 0.7600\n",
      "Epoch 836/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0395 - accuracy: 0.9982 - val_loss: 1.2041 - val_accuracy: 0.7587\n",
      "Epoch 837/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0394 - accuracy: 0.9984 - val_loss: 1.2068 - val_accuracy: 0.7587\n",
      "Epoch 838/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0404 - accuracy: 0.9984 - val_loss: 1.2099 - val_accuracy: 0.7550\n",
      "Epoch 839/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0394 - accuracy: 0.9984 - val_loss: 1.2001 - val_accuracy: 0.7575\n",
      "Epoch 840/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0385 - accuracy: 0.9987 - val_loss: 1.2014 - val_accuracy: 0.7625\n",
      "Epoch 841/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0394 - accuracy: 0.9983 - val_loss: 1.2060 - val_accuracy: 0.7600\n",
      "Epoch 842/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0395 - accuracy: 0.9985 - val_loss: 1.1954 - val_accuracy: 0.7625\n",
      "Epoch 843/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0403 - accuracy: 0.9980 - val_loss: 1.1878 - val_accuracy: 0.7650\n",
      "Epoch 844/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0391 - accuracy: 0.9983 - val_loss: 1.2012 - val_accuracy: 0.7675\n",
      "Epoch 845/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0414 - accuracy: 0.9975 - val_loss: 1.2036 - val_accuracy: 0.7575\n",
      "Epoch 846/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0395 - accuracy: 0.9985 - val_loss: 1.1995 - val_accuracy: 0.7562\n",
      "Epoch 847/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0399 - accuracy: 0.9983 - val_loss: 1.1947 - val_accuracy: 0.7600\n",
      "Epoch 848/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0397 - accuracy: 0.9986 - val_loss: 1.1860 - val_accuracy: 0.7600\n",
      "Epoch 849/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0394 - accuracy: 0.9983 - val_loss: 1.1813 - val_accuracy: 0.7675\n",
      "Epoch 850/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0379 - accuracy: 0.9991 - val_loss: 1.1855 - val_accuracy: 0.7725\n",
      "Epoch 851/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0400 - accuracy: 0.9982 - val_loss: 1.1758 - val_accuracy: 0.7763\n",
      "Epoch 852/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0389 - accuracy: 0.9985 - val_loss: 1.1765 - val_accuracy: 0.7713\n",
      "Epoch 853/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0387 - accuracy: 0.9990 - val_loss: 1.1738 - val_accuracy: 0.7775\n",
      "Epoch 854/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0415 - accuracy: 0.9978 - val_loss: 1.1715 - val_accuracy: 0.7700\n",
      "Epoch 855/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0395 - accuracy: 0.9989 - val_loss: 1.1712 - val_accuracy: 0.7675\n",
      "Epoch 856/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0396 - accuracy: 0.9982 - val_loss: 1.1773 - val_accuracy: 0.7688\n",
      "Epoch 857/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0392 - accuracy: 0.9985 - val_loss: 1.1768 - val_accuracy: 0.7675\n",
      "Epoch 858/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0380 - accuracy: 0.9992 - val_loss: 1.1740 - val_accuracy: 0.7713\n",
      "Epoch 859/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0391 - accuracy: 0.9988 - val_loss: 1.1719 - val_accuracy: 0.7662\n",
      "Epoch 860/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0395 - accuracy: 0.9987 - val_loss: 1.1642 - val_accuracy: 0.7662\n",
      "Epoch 861/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0389 - accuracy: 0.9987 - val_loss: 1.1711 - val_accuracy: 0.7637\n",
      "Epoch 862/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0388 - accuracy: 0.9988 - val_loss: 1.1784 - val_accuracy: 0.7612\n",
      "Epoch 863/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0385 - accuracy: 0.9988 - val_loss: 1.1870 - val_accuracy: 0.7637\n",
      "Epoch 864/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0397 - accuracy: 0.9982 - val_loss: 1.1892 - val_accuracy: 0.7500\n",
      "Epoch 865/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0383 - accuracy: 0.9986 - val_loss: 1.1801 - val_accuracy: 0.7587\n",
      "Epoch 866/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0380 - accuracy: 0.9990 - val_loss: 1.1729 - val_accuracy: 0.7575\n",
      "Epoch 867/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0392 - accuracy: 0.9987 - val_loss: 1.1641 - val_accuracy: 0.7562\n",
      "Epoch 868/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0380 - accuracy: 0.9993 - val_loss: 1.1696 - val_accuracy: 0.7587\n",
      "Epoch 869/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0385 - accuracy: 0.9986 - val_loss: 1.1641 - val_accuracy: 0.7625\n",
      "Epoch 870/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0378 - accuracy: 0.9989 - val_loss: 1.1540 - val_accuracy: 0.7625\n",
      "Epoch 871/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0391 - accuracy: 0.9986 - val_loss: 1.1432 - val_accuracy: 0.7662\n",
      "Epoch 872/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0387 - accuracy: 0.9987 - val_loss: 1.1353 - val_accuracy: 0.7650\n",
      "Epoch 873/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0377 - accuracy: 0.9989 - val_loss: 1.1342 - val_accuracy: 0.7713\n",
      "Epoch 874/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0384 - accuracy: 0.9986 - val_loss: 1.1420 - val_accuracy: 0.7700\n",
      "Epoch 875/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0387 - accuracy: 0.9985 - val_loss: 1.1469 - val_accuracy: 0.7700\n",
      "Epoch 876/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0386 - accuracy: 0.9983 - val_loss: 1.1436 - val_accuracy: 0.7662\n",
      "Epoch 877/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0386 - accuracy: 0.9987 - val_loss: 1.1494 - val_accuracy: 0.7612\n",
      "Epoch 878/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0382 - accuracy: 0.9988 - val_loss: 1.1622 - val_accuracy: 0.7562\n",
      "Epoch 879/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0387 - accuracy: 0.9982 - val_loss: 1.1597 - val_accuracy: 0.7625\n",
      "Epoch 880/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9989 - val_loss: 1.1618 - val_accuracy: 0.7625\n",
      "Epoch 881/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0393 - accuracy: 0.9986 - val_loss: 1.1568 - val_accuracy: 0.7625\n",
      "Epoch 882/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0390 - accuracy: 0.9982 - val_loss: 1.1547 - val_accuracy: 0.7625\n",
      "Epoch 883/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0386 - accuracy: 0.9987 - val_loss: 1.1502 - val_accuracy: 0.7625\n",
      "Epoch 884/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0385 - accuracy: 0.9987 - val_loss: 1.1537 - val_accuracy: 0.7662\n",
      "Epoch 885/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0371 - accuracy: 0.9992 - val_loss: 1.1553 - val_accuracy: 0.7650\n",
      "Epoch 886/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0380 - accuracy: 0.9987 - val_loss: 1.1556 - val_accuracy: 0.7662\n",
      "Epoch 887/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0386 - accuracy: 0.9988 - val_loss: 1.1514 - val_accuracy: 0.7700\n",
      "Epoch 888/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0378 - accuracy: 0.9984 - val_loss: 1.1519 - val_accuracy: 0.7738\n",
      "Epoch 889/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0380 - accuracy: 0.9986 - val_loss: 1.1550 - val_accuracy: 0.7775\n",
      "Epoch 890/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0389 - accuracy: 0.9982 - val_loss: 1.1606 - val_accuracy: 0.7750\n",
      "Epoch 891/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0379 - accuracy: 0.9987 - val_loss: 1.1593 - val_accuracy: 0.7725\n",
      "Epoch 892/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0387 - accuracy: 0.9983 - val_loss: 1.1562 - val_accuracy: 0.7788\n",
      "Epoch 893/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0387 - accuracy: 0.9984 - val_loss: 1.1596 - val_accuracy: 0.7800\n",
      "Epoch 894/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0387 - accuracy: 0.9984 - val_loss: 1.1586 - val_accuracy: 0.7688\n",
      "Epoch 895/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0378 - accuracy: 0.9989 - val_loss: 1.1503 - val_accuracy: 0.7725\n",
      "Epoch 896/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0401 - accuracy: 0.9982 - val_loss: 1.1315 - val_accuracy: 0.7775\n",
      "Epoch 897/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0390 - accuracy: 0.9983 - val_loss: 1.1320 - val_accuracy: 0.7825\n",
      "Epoch 898/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0384 - accuracy: 0.9986 - val_loss: 1.1294 - val_accuracy: 0.7775\n",
      "Epoch 899/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0382 - accuracy: 0.9988 - val_loss: 1.1306 - val_accuracy: 0.7738\n",
      "Epoch 900/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0403 - accuracy: 0.9986 - val_loss: 1.1533 - val_accuracy: 0.7775\n",
      "Epoch 901/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0384 - accuracy: 0.9988 - val_loss: 1.1714 - val_accuracy: 0.7700\n",
      "Epoch 902/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0379 - accuracy: 0.9989 - val_loss: 1.1710 - val_accuracy: 0.7662\n",
      "Epoch 903/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0398 - accuracy: 0.9978 - val_loss: 1.1485 - val_accuracy: 0.7700\n",
      "Epoch 904/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0379 - accuracy: 0.9985 - val_loss: 1.1500 - val_accuracy: 0.7688\n",
      "Epoch 905/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0386 - accuracy: 0.9984 - val_loss: 1.1468 - val_accuracy: 0.7662\n",
      "Epoch 906/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0379 - accuracy: 0.9988 - val_loss: 1.1407 - val_accuracy: 0.7725\n",
      "Epoch 907/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0385 - accuracy: 0.9988 - val_loss: 1.1295 - val_accuracy: 0.7713\n",
      "Epoch 908/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0377 - accuracy: 0.9988 - val_loss: 1.1370 - val_accuracy: 0.7713\n",
      "Epoch 909/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0378 - accuracy: 0.9988 - val_loss: 1.1382 - val_accuracy: 0.7662\n",
      "Epoch 910/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0395 - accuracy: 0.9982 - val_loss: 1.1375 - val_accuracy: 0.7650\n",
      "Epoch 911/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0375 - accuracy: 0.9988 - val_loss: 1.1546 - val_accuracy: 0.7650\n",
      "Epoch 912/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0384 - accuracy: 0.9986 - val_loss: 1.1560 - val_accuracy: 0.7650\n",
      "Epoch 913/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9989 - val_loss: 1.1537 - val_accuracy: 0.7625\n",
      "Epoch 914/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0392 - accuracy: 0.9984 - val_loss: 1.1378 - val_accuracy: 0.7625\n",
      "Epoch 915/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9986 - val_loss: 1.1336 - val_accuracy: 0.7600\n",
      "Epoch 916/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0375 - accuracy: 0.9991 - val_loss: 1.1404 - val_accuracy: 0.7650\n",
      "Epoch 917/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9991 - val_loss: 1.1402 - val_accuracy: 0.7650\n",
      "Epoch 918/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0399 - accuracy: 0.9977 - val_loss: 1.1413 - val_accuracy: 0.7650\n",
      "Epoch 919/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0389 - accuracy: 0.9986 - val_loss: 1.1409 - val_accuracy: 0.7637\n",
      "Epoch 920/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9990 - val_loss: 1.1450 - val_accuracy: 0.7637\n",
      "Epoch 921/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0395 - accuracy: 0.9982 - val_loss: 1.1565 - val_accuracy: 0.7575\n",
      "Epoch 922/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0385 - accuracy: 0.9986 - val_loss: 1.1632 - val_accuracy: 0.7625\n",
      "Epoch 923/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0377 - accuracy: 0.9989 - val_loss: 1.1759 - val_accuracy: 0.7600\n",
      "Epoch 924/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0388 - accuracy: 0.9983 - val_loss: 1.1773 - val_accuracy: 0.7612\n",
      "Epoch 925/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0391 - accuracy: 0.9982 - val_loss: 1.1653 - val_accuracy: 0.7700\n",
      "Epoch 926/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0375 - accuracy: 0.9989 - val_loss: 1.1684 - val_accuracy: 0.7700\n",
      "Epoch 927/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0382 - accuracy: 0.9988 - val_loss: 1.1559 - val_accuracy: 0.7675\n",
      "Epoch 928/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0377 - accuracy: 0.9985 - val_loss: 1.1612 - val_accuracy: 0.7662\n",
      "Epoch 929/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0388 - accuracy: 0.9986 - val_loss: 1.1669 - val_accuracy: 0.7688\n",
      "Epoch 930/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0390 - accuracy: 0.9982 - val_loss: 1.1779 - val_accuracy: 0.7600\n",
      "Epoch 931/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0391 - accuracy: 0.9982 - val_loss: 1.1809 - val_accuracy: 0.7575\n",
      "Epoch 932/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0388 - accuracy: 0.9983 - val_loss: 1.1761 - val_accuracy: 0.7525\n",
      "Epoch 933/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0389 - accuracy: 0.9982 - val_loss: 1.1669 - val_accuracy: 0.7587\n",
      "Epoch 934/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0385 - accuracy: 0.9984 - val_loss: 1.1685 - val_accuracy: 0.7562\n",
      "Epoch 935/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0392 - accuracy: 0.9984 - val_loss: 1.1728 - val_accuracy: 0.7537\n",
      "Epoch 936/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0402 - accuracy: 0.9976 - val_loss: 1.1811 - val_accuracy: 0.7475\n",
      "Epoch 937/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0388 - accuracy: 0.9988 - val_loss: 1.1938 - val_accuracy: 0.7450\n",
      "Epoch 938/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0392 - accuracy: 0.9982 - val_loss: 1.1949 - val_accuracy: 0.7412\n",
      "Epoch 939/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0399 - accuracy: 0.9986 - val_loss: 1.2060 - val_accuracy: 0.7487\n",
      "Epoch 940/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0385 - accuracy: 0.9987 - val_loss: 1.2136 - val_accuracy: 0.7462\n",
      "Epoch 941/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0387 - accuracy: 0.9986 - val_loss: 1.2131 - val_accuracy: 0.7575\n",
      "Epoch 942/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0395 - accuracy: 0.9980 - val_loss: 1.2146 - val_accuracy: 0.7550\n",
      "Epoch 943/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0385 - accuracy: 0.9986 - val_loss: 1.2150 - val_accuracy: 0.7437\n",
      "Epoch 944/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0396 - accuracy: 0.9980 - val_loss: 1.2272 - val_accuracy: 0.7525\n",
      "Epoch 945/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0394 - accuracy: 0.9982 - val_loss: 1.2368 - val_accuracy: 0.7450\n",
      "Epoch 946/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0390 - accuracy: 0.9984 - val_loss: 1.2415 - val_accuracy: 0.7475\n",
      "Epoch 947/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0389 - accuracy: 0.9984 - val_loss: 1.2429 - val_accuracy: 0.7437\n",
      "Epoch 948/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0395 - accuracy: 0.9986 - val_loss: 1.2352 - val_accuracy: 0.7425\n",
      "Epoch 949/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0403 - accuracy: 0.9979 - val_loss: 1.2291 - val_accuracy: 0.7500\n",
      "Epoch 950/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0388 - accuracy: 0.9983 - val_loss: 1.2223 - val_accuracy: 0.7512\n",
      "Epoch 951/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0393 - accuracy: 0.9978 - val_loss: 1.2226 - val_accuracy: 0.7462\n",
      "Epoch 952/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0398 - accuracy: 0.9982 - val_loss: 1.2407 - val_accuracy: 0.7437\n",
      "Epoch 953/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0384 - accuracy: 0.9990 - val_loss: 1.2447 - val_accuracy: 0.7412\n",
      "Epoch 954/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0394 - accuracy: 0.9986 - val_loss: 1.2439 - val_accuracy: 0.7412\n",
      "Epoch 955/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0400 - accuracy: 0.9983 - val_loss: 1.2487 - val_accuracy: 0.7400\n",
      "Epoch 956/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0381 - accuracy: 0.9987 - val_loss: 1.2446 - val_accuracy: 0.7425\n",
      "Epoch 957/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0392 - accuracy: 0.9986 - val_loss: 1.2408 - val_accuracy: 0.7462\n",
      "Epoch 958/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0391 - accuracy: 0.9982 - val_loss: 1.2334 - val_accuracy: 0.7337\n",
      "Epoch 959/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0385 - accuracy: 0.9986 - val_loss: 1.2151 - val_accuracy: 0.7375\n",
      "Epoch 960/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0388 - accuracy: 0.9984 - val_loss: 1.2192 - val_accuracy: 0.7412\n",
      "Epoch 961/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0395 - accuracy: 0.9982 - val_loss: 1.2359 - val_accuracy: 0.7387\n",
      "Epoch 962/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0379 - accuracy: 0.9989 - val_loss: 1.2400 - val_accuracy: 0.7362\n",
      "Epoch 963/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0390 - accuracy: 0.9987 - val_loss: 1.2314 - val_accuracy: 0.7400\n",
      "Epoch 964/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9990 - val_loss: 1.2184 - val_accuracy: 0.7350\n",
      "Epoch 965/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0386 - accuracy: 0.9986 - val_loss: 1.2092 - val_accuracy: 0.7362\n",
      "Epoch 966/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0380 - accuracy: 0.9988 - val_loss: 1.2002 - val_accuracy: 0.7362\n",
      "Epoch 967/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0374 - accuracy: 0.9989 - val_loss: 1.1975 - val_accuracy: 0.7362\n",
      "Epoch 968/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0386 - accuracy: 0.9986 - val_loss: 1.1892 - val_accuracy: 0.7437\n",
      "Epoch 969/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0384 - accuracy: 0.9987 - val_loss: 1.1919 - val_accuracy: 0.7437\n",
      "Epoch 970/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0382 - accuracy: 0.9987 - val_loss: 1.1865 - val_accuracy: 0.7475\n",
      "Epoch 971/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9990 - val_loss: 1.1851 - val_accuracy: 0.7475\n",
      "Epoch 972/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0387 - accuracy: 0.9984 - val_loss: 1.1948 - val_accuracy: 0.7487\n",
      "Epoch 973/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0379 - accuracy: 0.9989 - val_loss: 1.2012 - val_accuracy: 0.7462\n",
      "Epoch 974/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0380 - accuracy: 0.9988 - val_loss: 1.2068 - val_accuracy: 0.7412\n",
      "Epoch 975/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0379 - accuracy: 0.9986 - val_loss: 1.2122 - val_accuracy: 0.7387\n",
      "Epoch 976/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0381 - accuracy: 0.9988 - val_loss: 1.2159 - val_accuracy: 0.7525\n",
      "Epoch 977/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0377 - accuracy: 0.9988 - val_loss: 1.2171 - val_accuracy: 0.7525\n",
      "Epoch 978/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0387 - accuracy: 0.9986 - val_loss: 1.2103 - val_accuracy: 0.7525\n",
      "Epoch 979/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0377 - accuracy: 0.9988 - val_loss: 1.1934 - val_accuracy: 0.7550\n",
      "Epoch 980/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0391 - accuracy: 0.9982 - val_loss: 1.1735 - val_accuracy: 0.7525\n",
      "Epoch 981/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0382 - accuracy: 0.9984 - val_loss: 1.1649 - val_accuracy: 0.7500\n",
      "Epoch 982/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0387 - accuracy: 0.9982 - val_loss: 1.1560 - val_accuracy: 0.7537\n",
      "Epoch 983/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0378 - accuracy: 0.9986 - val_loss: 1.1512 - val_accuracy: 0.7587\n",
      "Epoch 984/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0371 - accuracy: 0.9993 - val_loss: 1.1648 - val_accuracy: 0.7612\n",
      "Epoch 985/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9988 - val_loss: 1.1670 - val_accuracy: 0.7612\n",
      "Epoch 986/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0386 - accuracy: 0.9985 - val_loss: 1.1756 - val_accuracy: 0.7637\n",
      "Epoch 987/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0373 - accuracy: 0.9988 - val_loss: 1.1863 - val_accuracy: 0.7625\n",
      "Epoch 988/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0372 - accuracy: 0.9990 - val_loss: 1.1889 - val_accuracy: 0.7575\n",
      "Epoch 989/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0385 - accuracy: 0.9985 - val_loss: 1.1991 - val_accuracy: 0.7575\n",
      "Epoch 990/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0380 - accuracy: 0.9984 - val_loss: 1.2000 - val_accuracy: 0.7575\n",
      "Epoch 991/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9988 - val_loss: 1.2065 - val_accuracy: 0.7575\n",
      "Epoch 992/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0378 - accuracy: 0.9986 - val_loss: 1.2122 - val_accuracy: 0.7475\n",
      "Epoch 993/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0379 - accuracy: 0.9986 - val_loss: 1.2066 - val_accuracy: 0.7475\n",
      "Epoch 994/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0376 - accuracy: 0.9985 - val_loss: 1.2058 - val_accuracy: 0.7450\n",
      "Epoch 995/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0374 - accuracy: 0.9987 - val_loss: 1.2043 - val_accuracy: 0.7525\n",
      "Epoch 996/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0377 - accuracy: 0.9982 - val_loss: 1.2000 - val_accuracy: 0.7487\n",
      "Epoch 997/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0382 - accuracy: 0.9984 - val_loss: 1.2119 - val_accuracy: 0.7400\n",
      "Epoch 998/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0366 - accuracy: 0.9992 - val_loss: 1.2197 - val_accuracy: 0.7425\n",
      "Epoch 999/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0378 - accuracy: 0.9988 - val_loss: 1.2137 - val_accuracy: 0.7375\n",
      "Epoch 1000/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9988 - val_loss: 1.2083 - val_accuracy: 0.7450\n",
      "Epoch 1001/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0375 - accuracy: 0.9985 - val_loss: 1.2080 - val_accuracy: 0.7412\n",
      "Epoch 1002/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9988 - val_loss: 1.2269 - val_accuracy: 0.7375\n",
      "Epoch 1003/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0375 - accuracy: 0.9987 - val_loss: 1.2186 - val_accuracy: 0.7437\n",
      "Epoch 1004/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9988 - val_loss: 1.2166 - val_accuracy: 0.7475\n",
      "Epoch 1005/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0374 - accuracy: 0.9988 - val_loss: 1.2213 - val_accuracy: 0.7475\n",
      "Epoch 1006/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0386 - accuracy: 0.9983 - val_loss: 1.2216 - val_accuracy: 0.7525\n",
      "Epoch 1007/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0372 - accuracy: 0.9990 - val_loss: 1.2170 - val_accuracy: 0.7512\n",
      "Epoch 1008/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0376 - accuracy: 0.9988 - val_loss: 1.2070 - val_accuracy: 0.7550\n",
      "Epoch 1009/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9988 - val_loss: 1.2048 - val_accuracy: 0.7487\n",
      "Epoch 1010/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0376 - accuracy: 0.9988 - val_loss: 1.1952 - val_accuracy: 0.7525\n",
      "Epoch 1011/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0382 - accuracy: 0.9988 - val_loss: 1.1999 - val_accuracy: 0.7500\n",
      "Epoch 1012/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0377 - accuracy: 0.9986 - val_loss: 1.2171 - val_accuracy: 0.7575\n",
      "Epoch 1013/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0378 - accuracy: 0.9987 - val_loss: 1.2203 - val_accuracy: 0.7575\n",
      "Epoch 1014/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9993 - val_loss: 1.2232 - val_accuracy: 0.7500\n",
      "Epoch 1015/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0375 - accuracy: 0.9987 - val_loss: 1.2208 - val_accuracy: 0.7450\n",
      "Epoch 1016/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0385 - accuracy: 0.9981 - val_loss: 1.2231 - val_accuracy: 0.7475\n",
      "Epoch 1017/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0381 - accuracy: 0.9984 - val_loss: 1.2323 - val_accuracy: 0.7487\n",
      "Epoch 1018/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0367 - accuracy: 0.9987 - val_loss: 1.2393 - val_accuracy: 0.7487\n",
      "Epoch 1019/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0371 - accuracy: 0.9988 - val_loss: 1.2322 - val_accuracy: 0.7450\n",
      "Epoch 1020/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0378 - accuracy: 0.9986 - val_loss: 1.2384 - val_accuracy: 0.7475\n",
      "Epoch 1021/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0382 - accuracy: 0.9984 - val_loss: 1.2321 - val_accuracy: 0.7450\n",
      "Epoch 1022/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0391 - accuracy: 0.9980 - val_loss: 1.2245 - val_accuracy: 0.7387\n",
      "Epoch 1023/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0371 - accuracy: 0.9990 - val_loss: 1.2254 - val_accuracy: 0.7450\n",
      "Epoch 1024/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0385 - accuracy: 0.9982 - val_loss: 1.2323 - val_accuracy: 0.7450\n",
      "Epoch 1025/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9984 - val_loss: 1.2344 - val_accuracy: 0.7412\n",
      "Epoch 1026/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0368 - accuracy: 0.9989 - val_loss: 1.2379 - val_accuracy: 0.7437\n",
      "Epoch 1027/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0377 - accuracy: 0.9986 - val_loss: 1.2524 - val_accuracy: 0.7425\n",
      "Epoch 1028/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0381 - accuracy: 0.9986 - val_loss: 1.2500 - val_accuracy: 0.7425\n",
      "Epoch 1029/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0378 - accuracy: 0.9984 - val_loss: 1.2393 - val_accuracy: 0.7362\n",
      "Epoch 1030/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0379 - accuracy: 0.9989 - val_loss: 1.2360 - val_accuracy: 0.7362\n",
      "Epoch 1031/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0385 - accuracy: 0.9984 - val_loss: 1.2201 - val_accuracy: 0.7475\n",
      "Epoch 1032/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0381 - accuracy: 0.9986 - val_loss: 1.2141 - val_accuracy: 0.7450\n",
      "Epoch 1033/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0379 - accuracy: 0.9984 - val_loss: 1.2186 - val_accuracy: 0.7412\n",
      "Epoch 1034/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0387 - accuracy: 0.9984 - val_loss: 1.2224 - val_accuracy: 0.7437\n",
      "Epoch 1035/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0385 - accuracy: 0.9984 - val_loss: 1.2313 - val_accuracy: 0.7425\n",
      "Epoch 1036/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0384 - accuracy: 0.9986 - val_loss: 1.2269 - val_accuracy: 0.7475\n",
      "Epoch 1037/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0387 - accuracy: 0.9983 - val_loss: 1.2065 - val_accuracy: 0.7450\n",
      "Epoch 1038/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0387 - accuracy: 0.9984 - val_loss: 1.2029 - val_accuracy: 0.7437\n",
      "Epoch 1039/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0384 - accuracy: 0.9982 - val_loss: 1.2006 - val_accuracy: 0.7475\n",
      "Epoch 1040/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0385 - accuracy: 0.9983 - val_loss: 1.1986 - val_accuracy: 0.7525\n",
      "Epoch 1041/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9988 - val_loss: 1.1980 - val_accuracy: 0.7487\n",
      "Epoch 1042/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0378 - accuracy: 0.9988 - val_loss: 1.2003 - val_accuracy: 0.7512\n",
      "Epoch 1043/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0388 - accuracy: 0.9986 - val_loss: 1.2106 - val_accuracy: 0.7462\n",
      "Epoch 1044/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0385 - accuracy: 0.9982 - val_loss: 1.2134 - val_accuracy: 0.7425\n",
      "Epoch 1045/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0385 - accuracy: 0.9983 - val_loss: 1.2285 - val_accuracy: 0.7437\n",
      "Epoch 1046/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0385 - accuracy: 0.9988 - val_loss: 1.2415 - val_accuracy: 0.7375\n",
      "Epoch 1047/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0387 - accuracy: 0.9985 - val_loss: 1.2367 - val_accuracy: 0.7375\n",
      "Epoch 1048/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9987 - val_loss: 1.2401 - val_accuracy: 0.7387\n",
      "Epoch 1049/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0389 - accuracy: 0.9983 - val_loss: 1.2220 - val_accuracy: 0.7412\n",
      "Epoch 1050/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0393 - accuracy: 0.9981 - val_loss: 1.2184 - val_accuracy: 0.7425\n",
      "Epoch 1051/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0383 - accuracy: 0.9984 - val_loss: 1.2065 - val_accuracy: 0.7425\n",
      "Epoch 1052/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9989 - val_loss: 1.1958 - val_accuracy: 0.7525\n",
      "Epoch 1053/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0376 - accuracy: 0.9989 - val_loss: 1.1939 - val_accuracy: 0.7512\n",
      "Epoch 1054/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0380 - accuracy: 0.9990 - val_loss: 1.1987 - val_accuracy: 0.7550\n",
      "Epoch 1055/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0381 - accuracy: 0.9986 - val_loss: 1.2075 - val_accuracy: 0.7562\n",
      "Epoch 1056/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0390 - accuracy: 0.9983 - val_loss: 1.2050 - val_accuracy: 0.7537\n",
      "Epoch 1057/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0383 - accuracy: 0.9985 - val_loss: 1.2055 - val_accuracy: 0.7500\n",
      "Epoch 1058/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0380 - accuracy: 0.9982 - val_loss: 1.2160 - val_accuracy: 0.7512\n",
      "Epoch 1059/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0384 - accuracy: 0.9983 - val_loss: 1.2101 - val_accuracy: 0.7512\n",
      "Epoch 1060/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0375 - accuracy: 0.9992 - val_loss: 1.2177 - val_accuracy: 0.7512\n",
      "Epoch 1061/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0379 - accuracy: 0.9983 - val_loss: 1.2170 - val_accuracy: 0.7487\n",
      "Epoch 1062/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0380 - accuracy: 0.9986 - val_loss: 1.2137 - val_accuracy: 0.7550\n",
      "Epoch 1063/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0387 - accuracy: 0.9984 - val_loss: 1.2093 - val_accuracy: 0.7587\n",
      "Epoch 1064/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0385 - accuracy: 0.9983 - val_loss: 1.2083 - val_accuracy: 0.7625\n",
      "Epoch 1065/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0392 - accuracy: 0.9986 - val_loss: 1.2058 - val_accuracy: 0.7600\n",
      "Epoch 1066/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0396 - accuracy: 0.9984 - val_loss: 1.2125 - val_accuracy: 0.7637\n",
      "Epoch 1067/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0391 - accuracy: 0.9981 - val_loss: 1.2251 - val_accuracy: 0.7650\n",
      "Epoch 1068/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0384 - accuracy: 0.9983 - val_loss: 1.2241 - val_accuracy: 0.7587\n",
      "Epoch 1069/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0388 - accuracy: 0.9979 - val_loss: 1.2258 - val_accuracy: 0.7625\n",
      "Epoch 1070/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0379 - accuracy: 0.9988 - val_loss: 1.2365 - val_accuracy: 0.7612\n",
      "Epoch 1071/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0381 - accuracy: 0.9987 - val_loss: 1.2336 - val_accuracy: 0.7600\n",
      "Epoch 1072/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0398 - accuracy: 0.9986 - val_loss: 1.2335 - val_accuracy: 0.7587\n",
      "Epoch 1073/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0379 - accuracy: 0.9986 - val_loss: 1.2433 - val_accuracy: 0.7600\n",
      "Epoch 1074/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0393 - accuracy: 0.9985 - val_loss: 1.2377 - val_accuracy: 0.7612\n",
      "Epoch 1075/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0403 - accuracy: 0.9978 - val_loss: 1.2562 - val_accuracy: 0.7575\n",
      "Epoch 1076/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0385 - accuracy: 0.9987 - val_loss: 1.2480 - val_accuracy: 0.7562\n",
      "Epoch 1077/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0391 - accuracy: 0.9984 - val_loss: 1.2428 - val_accuracy: 0.7525\n",
      "Epoch 1078/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0378 - accuracy: 0.9988 - val_loss: 1.2479 - val_accuracy: 0.7575\n",
      "Epoch 1079/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0378 - accuracy: 0.9989 - val_loss: 1.2487 - val_accuracy: 0.7600\n",
      "Epoch 1080/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0384 - accuracy: 0.9984 - val_loss: 1.2420 - val_accuracy: 0.7587\n",
      "Epoch 1081/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0382 - accuracy: 0.9989 - val_loss: 1.2518 - val_accuracy: 0.7562\n",
      "Epoch 1082/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0386 - accuracy: 0.9984 - val_loss: 1.2636 - val_accuracy: 0.7450\n",
      "Epoch 1083/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0389 - accuracy: 0.9982 - val_loss: 1.2697 - val_accuracy: 0.7462\n",
      "Epoch 1084/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9990 - val_loss: 1.2637 - val_accuracy: 0.7462\n",
      "Epoch 1085/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0378 - accuracy: 0.9986 - val_loss: 1.2509 - val_accuracy: 0.7475\n",
      "Epoch 1086/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0369 - accuracy: 0.9991 - val_loss: 1.2542 - val_accuracy: 0.7450\n",
      "Epoch 1087/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0379 - accuracy: 0.9987 - val_loss: 1.2483 - val_accuracy: 0.7425\n",
      "Epoch 1088/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0376 - accuracy: 0.9986 - val_loss: 1.2374 - val_accuracy: 0.7425\n",
      "Epoch 1089/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0386 - accuracy: 0.9984 - val_loss: 1.2435 - val_accuracy: 0.7400\n",
      "Epoch 1090/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0382 - accuracy: 0.9983 - val_loss: 1.2527 - val_accuracy: 0.7475\n",
      "Epoch 1091/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0378 - accuracy: 0.9983 - val_loss: 1.2603 - val_accuracy: 0.7525\n",
      "Epoch 1092/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0372 - accuracy: 0.9989 - val_loss: 1.2554 - val_accuracy: 0.7462\n",
      "Epoch 1093/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0383 - accuracy: 0.9984 - val_loss: 1.2381 - val_accuracy: 0.7562\n",
      "Epoch 1094/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9989 - val_loss: 1.2450 - val_accuracy: 0.7550\n",
      "Epoch 1095/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0385 - accuracy: 0.9987 - val_loss: 1.2549 - val_accuracy: 0.7525\n",
      "Epoch 1096/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0382 - accuracy: 0.9985 - val_loss: 1.2658 - val_accuracy: 0.7525\n",
      "Epoch 1097/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0390 - accuracy: 0.9981 - val_loss: 1.2578 - val_accuracy: 0.7512\n",
      "Epoch 1098/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0381 - accuracy: 0.9984 - val_loss: 1.2541 - val_accuracy: 0.7525\n",
      "Epoch 1099/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0389 - accuracy: 0.9985 - val_loss: 1.2604 - val_accuracy: 0.7537\n",
      "Epoch 1100/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0387 - accuracy: 0.9987 - val_loss: 1.2649 - val_accuracy: 0.7487\n",
      "Epoch 1101/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0386 - accuracy: 0.9983 - val_loss: 1.2625 - val_accuracy: 0.7500\n",
      "Epoch 1102/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0387 - accuracy: 0.9984 - val_loss: 1.2537 - val_accuracy: 0.7487\n",
      "Epoch 1103/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0382 - accuracy: 0.9986 - val_loss: 1.2671 - val_accuracy: 0.7462\n",
      "Epoch 1104/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0374 - accuracy: 0.9987 - val_loss: 1.2684 - val_accuracy: 0.7462\n",
      "Epoch 1105/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0382 - accuracy: 0.9989 - val_loss: 1.2714 - val_accuracy: 0.7500\n",
      "Epoch 1106/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0381 - accuracy: 0.9986 - val_loss: 1.2668 - val_accuracy: 0.7450\n",
      "Epoch 1107/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0384 - accuracy: 0.9982 - val_loss: 1.2541 - val_accuracy: 0.7437\n",
      "Epoch 1108/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0375 - accuracy: 0.9987 - val_loss: 1.2552 - val_accuracy: 0.7475\n",
      "Epoch 1109/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0379 - accuracy: 0.9982 - val_loss: 1.2606 - val_accuracy: 0.7400\n",
      "Epoch 1110/2000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0383 - accuracy: 0.9984 - val_loss: 1.2548 - val_accuracy: 0.7487\n",
      "Epoch 1111/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0383 - accuracy: 0.9984 - val_loss: 1.2516 - val_accuracy: 0.7550\n",
      "Epoch 1112/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0379 - accuracy: 0.9988 - val_loss: 1.2534 - val_accuracy: 0.7512\n",
      "Epoch 1113/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0378 - accuracy: 0.9986 - val_loss: 1.2501 - val_accuracy: 0.7500\n",
      "Epoch 1114/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0387 - accuracy: 0.9981 - val_loss: 1.2347 - val_accuracy: 0.7512\n",
      "Epoch 1115/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0381 - accuracy: 0.9983 - val_loss: 1.2202 - val_accuracy: 0.7462\n",
      "Epoch 1116/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0374 - accuracy: 0.9989 - val_loss: 1.2061 - val_accuracy: 0.7475\n",
      "Epoch 1117/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0383 - accuracy: 0.9986 - val_loss: 1.2111 - val_accuracy: 0.7425\n",
      "Epoch 1118/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0387 - accuracy: 0.9984 - val_loss: 1.2182 - val_accuracy: 0.7412\n",
      "Epoch 1119/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0384 - accuracy: 0.9986 - val_loss: 1.2174 - val_accuracy: 0.7450\n",
      "Epoch 1120/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0375 - accuracy: 0.9988 - val_loss: 1.2197 - val_accuracy: 0.7475\n",
      "Epoch 1121/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0386 - accuracy: 0.9986 - val_loss: 1.2353 - val_accuracy: 0.7437\n",
      "Epoch 1122/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0384 - accuracy: 0.9984 - val_loss: 1.2375 - val_accuracy: 0.7512\n",
      "Epoch 1123/2000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.0377 - accuracy: 0.9988 - val_loss: 1.2324 - val_accuracy: 0.7550\n",
      "Epoch 1124/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0390 - accuracy: 0.9981 - val_loss: 1.2366 - val_accuracy: 0.7550\n",
      "Epoch 1125/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0379 - accuracy: 0.9989 - val_loss: 1.2521 - val_accuracy: 0.7550\n",
      "Epoch 1126/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0375 - accuracy: 0.9989 - val_loss: 1.2461 - val_accuracy: 0.7525\n",
      "Epoch 1127/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0383 - accuracy: 0.9988 - val_loss: 1.2312 - val_accuracy: 0.7562\n",
      "Epoch 1128/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0375 - accuracy: 0.9989 - val_loss: 1.2176 - val_accuracy: 0.7537\n",
      "Epoch 1129/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0378 - accuracy: 0.9989 - val_loss: 1.2098 - val_accuracy: 0.7587\n",
      "Epoch 1130/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0380 - accuracy: 0.9989 - val_loss: 1.2020 - val_accuracy: 0.7625\n",
      "Epoch 1131/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0378 - accuracy: 0.9988 - val_loss: 1.1944 - val_accuracy: 0.7612\n",
      "Epoch 1132/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0386 - accuracy: 0.9984 - val_loss: 1.1979 - val_accuracy: 0.7637\n",
      "Epoch 1133/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0386 - accuracy: 0.9983 - val_loss: 1.1879 - val_accuracy: 0.7637\n",
      "Epoch 1134/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0373 - accuracy: 0.9986 - val_loss: 1.1889 - val_accuracy: 0.7675\n",
      "Epoch 1135/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0380 - accuracy: 0.9986 - val_loss: 1.1865 - val_accuracy: 0.7662\n",
      "Epoch 1136/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0380 - accuracy: 0.9988 - val_loss: 1.1824 - val_accuracy: 0.7675\n",
      "Epoch 1137/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0377 - accuracy: 0.9985 - val_loss: 1.1880 - val_accuracy: 0.7650\n",
      "Epoch 1138/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0369 - accuracy: 0.9989 - val_loss: 1.1859 - val_accuracy: 0.7650\n",
      "Epoch 1139/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0372 - accuracy: 0.9989 - val_loss: 1.1775 - val_accuracy: 0.7625\n",
      "Epoch 1140/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0372 - accuracy: 0.9989 - val_loss: 1.1925 - val_accuracy: 0.7650\n",
      "Epoch 1141/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0374 - accuracy: 0.9989 - val_loss: 1.2070 - val_accuracy: 0.7600\n",
      "Epoch 1142/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0376 - accuracy: 0.9984 - val_loss: 1.2018 - val_accuracy: 0.7562\n",
      "Epoch 1143/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0390 - accuracy: 0.9983 - val_loss: 1.2078 - val_accuracy: 0.7562\n",
      "Epoch 1144/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0374 - accuracy: 0.9990 - val_loss: 1.2222 - val_accuracy: 0.7562\n",
      "Epoch 1145/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0362 - accuracy: 0.9992 - val_loss: 1.2113 - val_accuracy: 0.7537\n",
      "Epoch 1146/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0383 - accuracy: 0.9982 - val_loss: 1.2060 - val_accuracy: 0.7537\n",
      "Epoch 1147/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0380 - accuracy: 0.9985 - val_loss: 1.2091 - val_accuracy: 0.7500\n",
      "Epoch 1148/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0383 - accuracy: 0.9986 - val_loss: 1.2000 - val_accuracy: 0.7462\n",
      "Epoch 1149/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0372 - accuracy: 0.9988 - val_loss: 1.1936 - val_accuracy: 0.7525\n",
      "Epoch 1150/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0377 - accuracy: 0.9988 - val_loss: 1.1989 - val_accuracy: 0.7562\n",
      "Epoch 1151/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0393 - accuracy: 0.9980 - val_loss: 1.1834 - val_accuracy: 0.7537\n",
      "Epoch 1152/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0374 - accuracy: 0.9988 - val_loss: 1.1795 - val_accuracy: 0.7537\n",
      "Epoch 1153/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0380 - accuracy: 0.9986 - val_loss: 1.1977 - val_accuracy: 0.7550\n",
      "Epoch 1154/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0379 - accuracy: 0.9989 - val_loss: 1.2087 - val_accuracy: 0.7537\n",
      "Epoch 1155/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0375 - accuracy: 0.9989 - val_loss: 1.2193 - val_accuracy: 0.7500\n",
      "Epoch 1156/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0374 - accuracy: 0.9986 - val_loss: 1.2226 - val_accuracy: 0.7487\n",
      "Epoch 1157/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9991 - val_loss: 1.2221 - val_accuracy: 0.7487\n",
      "Epoch 1158/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0381 - accuracy: 0.9986 - val_loss: 1.2218 - val_accuracy: 0.7487\n",
      "Epoch 1159/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0381 - accuracy: 0.9985 - val_loss: 1.2196 - val_accuracy: 0.7487\n",
      "Epoch 1160/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0368 - accuracy: 0.9993 - val_loss: 1.2197 - val_accuracy: 0.7512\n",
      "Epoch 1161/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0380 - accuracy: 0.9982 - val_loss: 1.2266 - val_accuracy: 0.7537\n",
      "Epoch 1162/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0365 - accuracy: 0.9991 - val_loss: 1.2216 - val_accuracy: 0.7537\n",
      "Epoch 1163/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0378 - accuracy: 0.9989 - val_loss: 1.2258 - val_accuracy: 0.7600\n",
      "Epoch 1164/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0377 - accuracy: 0.9987 - val_loss: 1.2232 - val_accuracy: 0.7525\n",
      "Epoch 1165/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0367 - accuracy: 0.9984 - val_loss: 1.2098 - val_accuracy: 0.7512\n",
      "Epoch 1166/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0377 - accuracy: 0.9986 - val_loss: 1.1950 - val_accuracy: 0.7487\n",
      "Epoch 1167/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0370 - accuracy: 0.9989 - val_loss: 1.1873 - val_accuracy: 0.7575\n",
      "Epoch 1168/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0366 - accuracy: 0.9989 - val_loss: 1.1872 - val_accuracy: 0.7575\n",
      "Epoch 1169/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9986 - val_loss: 1.2027 - val_accuracy: 0.7575\n",
      "Epoch 1170/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0387 - accuracy: 0.9982 - val_loss: 1.2218 - val_accuracy: 0.7612\n",
      "Epoch 1171/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0371 - accuracy: 0.9987 - val_loss: 1.2294 - val_accuracy: 0.7662\n",
      "Epoch 1172/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9989 - val_loss: 1.2323 - val_accuracy: 0.7662\n",
      "Epoch 1173/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9988 - val_loss: 1.2210 - val_accuracy: 0.7587\n",
      "Epoch 1174/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0372 - accuracy: 0.9988 - val_loss: 1.2013 - val_accuracy: 0.7688\n",
      "Epoch 1175/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0374 - accuracy: 0.9985 - val_loss: 1.2061 - val_accuracy: 0.7600\n",
      "Epoch 1176/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0392 - accuracy: 0.9984 - val_loss: 1.2107 - val_accuracy: 0.7550\n",
      "Epoch 1177/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0374 - accuracy: 0.9986 - val_loss: 1.2187 - val_accuracy: 0.7512\n",
      "Epoch 1178/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0372 - accuracy: 0.9988 - val_loss: 1.2254 - val_accuracy: 0.7612\n",
      "Epoch 1179/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0363 - accuracy: 0.9991 - val_loss: 1.2282 - val_accuracy: 0.7550\n",
      "Epoch 1180/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0381 - accuracy: 0.9982 - val_loss: 1.2326 - val_accuracy: 0.7562\n",
      "Epoch 1181/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0365 - accuracy: 0.9989 - val_loss: 1.2269 - val_accuracy: 0.7625\n",
      "Epoch 1182/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0363 - accuracy: 0.9991 - val_loss: 1.2270 - val_accuracy: 0.7575\n",
      "Epoch 1183/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0364 - accuracy: 0.9991 - val_loss: 1.2367 - val_accuracy: 0.7575\n",
      "Epoch 1184/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0360 - accuracy: 0.9993 - val_loss: 1.2519 - val_accuracy: 0.7550\n",
      "Epoch 1185/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0379 - accuracy: 0.9984 - val_loss: 1.2486 - val_accuracy: 0.7575\n",
      "Epoch 1186/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0377 - accuracy: 0.9985 - val_loss: 1.2509 - val_accuracy: 0.7612\n",
      "Epoch 1187/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0366 - accuracy: 0.9985 - val_loss: 1.2471 - val_accuracy: 0.7662\n",
      "Epoch 1188/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0365 - accuracy: 0.9987 - val_loss: 1.2444 - val_accuracy: 0.7612\n",
      "Epoch 1189/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9989 - val_loss: 1.2504 - val_accuracy: 0.7575\n",
      "Epoch 1190/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0369 - accuracy: 0.9986 - val_loss: 1.2555 - val_accuracy: 0.7600\n",
      "Epoch 1191/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0368 - accuracy: 0.9984 - val_loss: 1.2570 - val_accuracy: 0.7612\n",
      "Epoch 1192/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0368 - accuracy: 0.9986 - val_loss: 1.2490 - val_accuracy: 0.7600\n",
      "Epoch 1193/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0369 - accuracy: 0.9988 - val_loss: 1.2480 - val_accuracy: 0.7612\n",
      "Epoch 1194/2000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0358 - accuracy: 0.9989 - val_loss: 1.2529 - val_accuracy: 0.7600\n",
      "Epoch 1195/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0372 - accuracy: 0.9986 - val_loss: 1.2478 - val_accuracy: 0.7625\n",
      "Epoch 1196/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0378 - accuracy: 0.9983 - val_loss: 1.2512 - val_accuracy: 0.7600\n",
      "Epoch 1197/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0376 - accuracy: 0.9984 - val_loss: 1.2585 - val_accuracy: 0.7575\n",
      "Epoch 1198/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9989 - val_loss: 1.2492 - val_accuracy: 0.7575\n",
      "Epoch 1199/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0365 - accuracy: 0.9989 - val_loss: 1.2648 - val_accuracy: 0.7537\n",
      "Epoch 1200/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0364 - accuracy: 0.9988 - val_loss: 1.2682 - val_accuracy: 0.7500\n",
      "Epoch 1201/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0377 - accuracy: 0.9985 - val_loss: 1.2602 - val_accuracy: 0.7525\n",
      "Epoch 1202/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0359 - accuracy: 0.9991 - val_loss: 1.2654 - val_accuracy: 0.7487\n",
      "Epoch 1203/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0374 - accuracy: 0.9985 - val_loss: 1.2574 - val_accuracy: 0.7487\n",
      "Epoch 1204/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9988 - val_loss: 1.2474 - val_accuracy: 0.7537\n",
      "Epoch 1205/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9988 - val_loss: 1.2425 - val_accuracy: 0.7487\n",
      "Epoch 1206/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0366 - accuracy: 0.9989 - val_loss: 1.2383 - val_accuracy: 0.7475\n",
      "Epoch 1207/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0365 - accuracy: 0.9990 - val_loss: 1.2261 - val_accuracy: 0.7525\n",
      "Epoch 1208/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0372 - accuracy: 0.9986 - val_loss: 1.2266 - val_accuracy: 0.7525\n",
      "Epoch 1209/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0360 - accuracy: 0.9989 - val_loss: 1.2289 - val_accuracy: 0.7550\n",
      "Epoch 1210/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0382 - accuracy: 0.9984 - val_loss: 1.2353 - val_accuracy: 0.7537\n",
      "Epoch 1211/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0365 - accuracy: 0.9986 - val_loss: 1.2523 - val_accuracy: 0.7537\n",
      "Epoch 1212/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0378 - accuracy: 0.9985 - val_loss: 1.2549 - val_accuracy: 0.7537\n",
      "Epoch 1213/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0369 - accuracy: 0.9986 - val_loss: 1.2536 - val_accuracy: 0.7500\n",
      "Epoch 1214/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0381 - accuracy: 0.9984 - val_loss: 1.2627 - val_accuracy: 0.7450\n",
      "Epoch 1215/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0367 - accuracy: 0.9992 - val_loss: 1.2677 - val_accuracy: 0.7437\n",
      "Epoch 1216/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0374 - accuracy: 0.9989 - val_loss: 1.2695 - val_accuracy: 0.7450\n",
      "Epoch 1217/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0368 - accuracy: 0.9988 - val_loss: 1.2623 - val_accuracy: 0.7487\n",
      "Epoch 1218/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0365 - accuracy: 0.9991 - val_loss: 1.2472 - val_accuracy: 0.7487\n",
      "Epoch 1219/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0362 - accuracy: 0.9989 - val_loss: 1.2457 - val_accuracy: 0.7487\n",
      "Epoch 1220/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0373 - accuracy: 0.9986 - val_loss: 1.2341 - val_accuracy: 0.7525\n",
      "Epoch 1221/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0367 - accuracy: 0.9988 - val_loss: 1.2353 - val_accuracy: 0.7487\n",
      "Epoch 1222/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0368 - accuracy: 0.9989 - val_loss: 1.2417 - val_accuracy: 0.7437\n",
      "Epoch 1223/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0361 - accuracy: 0.9993 - val_loss: 1.2323 - val_accuracy: 0.7487\n",
      "Epoch 1224/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0368 - accuracy: 0.9987 - val_loss: 1.2167 - val_accuracy: 0.7475\n",
      "Epoch 1225/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0356 - accuracy: 0.9995 - val_loss: 1.2121 - val_accuracy: 0.7462\n",
      "Epoch 1226/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0364 - accuracy: 0.9985 - val_loss: 1.2181 - val_accuracy: 0.7450\n",
      "Epoch 1227/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0375 - accuracy: 0.9986 - val_loss: 1.2245 - val_accuracy: 0.7550\n",
      "Epoch 1228/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0359 - accuracy: 0.9988 - val_loss: 1.2268 - val_accuracy: 0.7550\n",
      "Epoch 1229/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0365 - accuracy: 0.9988 - val_loss: 1.2204 - val_accuracy: 0.7550\n",
      "Epoch 1230/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0364 - accuracy: 0.9984 - val_loss: 1.2099 - val_accuracy: 0.7562\n",
      "Epoch 1231/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9986 - val_loss: 1.1901 - val_accuracy: 0.7550\n",
      "Epoch 1232/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0381 - accuracy: 0.9983 - val_loss: 1.1887 - val_accuracy: 0.7587\n",
      "Epoch 1233/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0371 - accuracy: 0.9985 - val_loss: 1.1905 - val_accuracy: 0.7550\n",
      "Epoch 1234/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0371 - accuracy: 0.9985 - val_loss: 1.1884 - val_accuracy: 0.7575\n",
      "Epoch 1235/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0365 - accuracy: 0.9989 - val_loss: 1.1915 - val_accuracy: 0.7525\n",
      "Epoch 1236/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0368 - accuracy: 0.9988 - val_loss: 1.1939 - val_accuracy: 0.7550\n",
      "Epoch 1237/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0377 - accuracy: 0.9987 - val_loss: 1.1975 - val_accuracy: 0.7575\n",
      "Epoch 1238/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0371 - accuracy: 0.9988 - val_loss: 1.2039 - val_accuracy: 0.7525\n",
      "Epoch 1239/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0381 - accuracy: 0.9982 - val_loss: 1.2104 - val_accuracy: 0.7512\n",
      "Epoch 1240/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0372 - accuracy: 0.9987 - val_loss: 1.2085 - val_accuracy: 0.7525\n",
      "Epoch 1241/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9986 - val_loss: 1.2206 - val_accuracy: 0.7525\n",
      "Epoch 1242/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0369 - accuracy: 0.9988 - val_loss: 1.2241 - val_accuracy: 0.7512\n",
      "Epoch 1243/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0372 - accuracy: 0.9985 - val_loss: 1.2250 - val_accuracy: 0.7475\n",
      "Epoch 1244/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0369 - accuracy: 0.9986 - val_loss: 1.2354 - val_accuracy: 0.7525\n",
      "Epoch 1245/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0378 - accuracy: 0.9986 - val_loss: 1.2377 - val_accuracy: 0.7562\n",
      "Epoch 1246/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0364 - accuracy: 0.9988 - val_loss: 1.2411 - val_accuracy: 0.7537\n",
      "Epoch 1247/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9988 - val_loss: 1.2389 - val_accuracy: 0.7475\n",
      "Epoch 1248/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0367 - accuracy: 0.9991 - val_loss: 1.2366 - val_accuracy: 0.7437\n",
      "Epoch 1249/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0384 - accuracy: 0.9980 - val_loss: 1.2427 - val_accuracy: 0.7450\n",
      "Epoch 1250/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0367 - accuracy: 0.9988 - val_loss: 1.2485 - val_accuracy: 0.7512\n",
      "Epoch 1251/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0380 - accuracy: 0.9984 - val_loss: 1.2373 - val_accuracy: 0.7537\n",
      "Epoch 1252/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0371 - accuracy: 0.9984 - val_loss: 1.2345 - val_accuracy: 0.7562\n",
      "Epoch 1253/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0385 - accuracy: 0.9983 - val_loss: 1.2231 - val_accuracy: 0.7537\n",
      "Epoch 1254/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0382 - accuracy: 0.9982 - val_loss: 1.2307 - val_accuracy: 0.7525\n",
      "Epoch 1255/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0379 - accuracy: 0.9984 - val_loss: 1.2475 - val_accuracy: 0.7525\n",
      "Epoch 1256/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0371 - accuracy: 0.9985 - val_loss: 1.2437 - val_accuracy: 0.7525\n",
      "Epoch 1257/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0375 - accuracy: 0.9983 - val_loss: 1.2488 - val_accuracy: 0.7562\n",
      "Epoch 1258/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0384 - accuracy: 0.9979 - val_loss: 1.2688 - val_accuracy: 0.7537\n",
      "Epoch 1259/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0388 - accuracy: 0.9984 - val_loss: 1.2739 - val_accuracy: 0.7537\n",
      "Epoch 1260/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0361 - accuracy: 0.9993 - val_loss: 1.2646 - val_accuracy: 0.7550\n",
      "Epoch 1261/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0380 - accuracy: 0.9986 - val_loss: 1.2458 - val_accuracy: 0.7487\n",
      "Epoch 1262/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0387 - accuracy: 0.9981 - val_loss: 1.2422 - val_accuracy: 0.7537\n",
      "Epoch 1263/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0375 - accuracy: 0.9985 - val_loss: 1.2401 - val_accuracy: 0.7525\n",
      "Epoch 1264/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0375 - accuracy: 0.9989 - val_loss: 1.2417 - val_accuracy: 0.7475\n",
      "Epoch 1265/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0375 - accuracy: 0.9986 - val_loss: 1.2558 - val_accuracy: 0.7437\n",
      "Epoch 1266/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0386 - accuracy: 0.9980 - val_loss: 1.2577 - val_accuracy: 0.7525\n",
      "Epoch 1267/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0383 - accuracy: 0.9978 - val_loss: 1.2468 - val_accuracy: 0.7500\n",
      "Epoch 1268/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0372 - accuracy: 0.9986 - val_loss: 1.2456 - val_accuracy: 0.7512\n",
      "Epoch 1269/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9987 - val_loss: 1.2534 - val_accuracy: 0.7487\n",
      "Epoch 1270/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0380 - accuracy: 0.9984 - val_loss: 1.2649 - val_accuracy: 0.7500\n",
      "Epoch 1271/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9987 - val_loss: 1.2643 - val_accuracy: 0.7500\n",
      "Epoch 1272/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0369 - accuracy: 0.9992 - val_loss: 1.2598 - val_accuracy: 0.7537\n",
      "Epoch 1273/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0377 - accuracy: 0.9984 - val_loss: 1.2429 - val_accuracy: 0.7575\n",
      "Epoch 1274/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0378 - accuracy: 0.9988 - val_loss: 1.2520 - val_accuracy: 0.7550\n",
      "Epoch 1275/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0364 - accuracy: 0.9993 - val_loss: 1.2598 - val_accuracy: 0.7550\n",
      "Epoch 1276/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0370 - accuracy: 0.9988 - val_loss: 1.2492 - val_accuracy: 0.7550\n",
      "Epoch 1277/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0379 - accuracy: 0.9983 - val_loss: 1.2342 - val_accuracy: 0.7550\n",
      "Epoch 1278/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0371 - accuracy: 0.9989 - val_loss: 1.2311 - val_accuracy: 0.7562\n",
      "Epoch 1279/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0369 - accuracy: 0.9987 - val_loss: 1.2257 - val_accuracy: 0.7612\n",
      "Epoch 1280/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9991 - val_loss: 1.2377 - val_accuracy: 0.7525\n",
      "Epoch 1281/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9986 - val_loss: 1.2433 - val_accuracy: 0.7550\n",
      "Epoch 1282/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0375 - accuracy: 0.9985 - val_loss: 1.2419 - val_accuracy: 0.7637\n",
      "Epoch 1283/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0373 - accuracy: 0.9986 - val_loss: 1.2492 - val_accuracy: 0.7587\n",
      "Epoch 1284/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0367 - accuracy: 0.9994 - val_loss: 1.2610 - val_accuracy: 0.7587\n",
      "Epoch 1285/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0369 - accuracy: 0.9989 - val_loss: 1.2652 - val_accuracy: 0.7550\n",
      "Epoch 1286/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0378 - accuracy: 0.9983 - val_loss: 1.2648 - val_accuracy: 0.7525\n",
      "Epoch 1287/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0360 - accuracy: 0.9990 - val_loss: 1.2601 - val_accuracy: 0.7550\n",
      "Epoch 1288/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0367 - accuracy: 0.9987 - val_loss: 1.2526 - val_accuracy: 0.7600\n",
      "Epoch 1289/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0372 - accuracy: 0.9986 - val_loss: 1.2513 - val_accuracy: 0.7650\n",
      "Epoch 1290/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0365 - accuracy: 0.9987 - val_loss: 1.2525 - val_accuracy: 0.7612\n",
      "Epoch 1291/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9991 - val_loss: 1.2523 - val_accuracy: 0.7512\n",
      "Epoch 1292/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0376 - accuracy: 0.9982 - val_loss: 1.2449 - val_accuracy: 0.7550\n",
      "Epoch 1293/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0371 - accuracy: 0.9985 - val_loss: 1.2367 - val_accuracy: 0.7587\n",
      "Epoch 1294/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0371 - accuracy: 0.9989 - val_loss: 1.2329 - val_accuracy: 0.7525\n",
      "Epoch 1295/2000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0371 - accuracy: 0.9986 - val_loss: 1.2404 - val_accuracy: 0.7525\n",
      "Epoch 1296/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0363 - accuracy: 0.9991 - val_loss: 1.2446 - val_accuracy: 0.7550\n",
      "Epoch 1297/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0381 - accuracy: 0.9982 - val_loss: 1.2541 - val_accuracy: 0.7550\n",
      "Epoch 1298/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0377 - accuracy: 0.9986 - val_loss: 1.2499 - val_accuracy: 0.7537\n",
      "Epoch 1299/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0374 - accuracy: 0.9988 - val_loss: 1.2496 - val_accuracy: 0.7587\n",
      "Epoch 1300/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0365 - accuracy: 0.9988 - val_loss: 1.2500 - val_accuracy: 0.7587\n",
      "Epoch 1301/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0370 - accuracy: 0.9985 - val_loss: 1.2443 - val_accuracy: 0.7650\n",
      "Epoch 1302/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0375 - accuracy: 0.9989 - val_loss: 1.2377 - val_accuracy: 0.7625\n",
      "Epoch 1303/2000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0378 - accuracy: 0.9985 - val_loss: 1.2381 - val_accuracy: 0.7612\n",
      "Epoch 1304/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0373 - accuracy: 0.9988 - val_loss: 1.2345 - val_accuracy: 0.7637\n",
      "Epoch 1305/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0380 - accuracy: 0.9981 - val_loss: 1.2327 - val_accuracy: 0.7625\n",
      "Epoch 1306/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0369 - accuracy: 0.9989 - val_loss: 1.2424 - val_accuracy: 0.7625\n",
      "Epoch 1307/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0368 - accuracy: 0.9989 - val_loss: 1.2532 - val_accuracy: 0.7650\n",
      "Epoch 1308/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0370 - accuracy: 0.9988 - val_loss: 1.2617 - val_accuracy: 0.7625\n",
      "Epoch 1309/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0363 - accuracy: 0.9988 - val_loss: 1.2684 - val_accuracy: 0.7625\n",
      "Epoch 1310/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0383 - accuracy: 0.9982 - val_loss: 1.2819 - val_accuracy: 0.7550\n",
      "Epoch 1311/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0382 - accuracy: 0.9984 - val_loss: 1.2789 - val_accuracy: 0.7525\n",
      "Epoch 1312/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0379 - accuracy: 0.9984 - val_loss: 1.2604 - val_accuracy: 0.7587\n",
      "Epoch 1313/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0375 - accuracy: 0.9988 - val_loss: 1.2558 - val_accuracy: 0.7612\n",
      "Epoch 1314/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9987 - val_loss: 1.2681 - val_accuracy: 0.7600\n",
      "Epoch 1315/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0375 - accuracy: 0.9988 - val_loss: 1.2815 - val_accuracy: 0.7500\n",
      "Epoch 1316/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0375 - accuracy: 0.9984 - val_loss: 1.2886 - val_accuracy: 0.7512\n",
      "Epoch 1317/2000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0377 - accuracy: 0.9986 - val_loss: 1.2950 - val_accuracy: 0.7500\n",
      "Epoch 1318/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0366 - accuracy: 0.9989 - val_loss: 1.2852 - val_accuracy: 0.7562\n",
      "Epoch 1319/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0362 - accuracy: 0.9989 - val_loss: 1.2778 - val_accuracy: 0.7525\n",
      "Epoch 1320/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0370 - accuracy: 0.9984 - val_loss: 1.2758 - val_accuracy: 0.7537\n",
      "Epoch 1321/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0358 - accuracy: 0.9992 - val_loss: 1.2795 - val_accuracy: 0.7550\n",
      "Epoch 1322/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0372 - accuracy: 0.9988 - val_loss: 1.2872 - val_accuracy: 0.7512\n",
      "Epoch 1323/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0370 - accuracy: 0.9986 - val_loss: 1.2798 - val_accuracy: 0.7537\n",
      "Epoch 1324/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9988 - val_loss: 1.2704 - val_accuracy: 0.7587\n",
      "Epoch 1325/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9983 - val_loss: 1.2900 - val_accuracy: 0.7562\n",
      "Epoch 1326/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0374 - accuracy: 0.9984 - val_loss: 1.2970 - val_accuracy: 0.7550\n",
      "Epoch 1327/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0390 - accuracy: 0.9982 - val_loss: 1.2947 - val_accuracy: 0.7612\n",
      "Epoch 1328/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0371 - accuracy: 0.9988 - val_loss: 1.2951 - val_accuracy: 0.7562\n",
      "Epoch 1329/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0379 - accuracy: 0.9980 - val_loss: 1.3029 - val_accuracy: 0.7487\n",
      "Epoch 1330/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0381 - accuracy: 0.9983 - val_loss: 1.2908 - val_accuracy: 0.7462\n",
      "Epoch 1331/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9986 - val_loss: 1.2922 - val_accuracy: 0.7525\n",
      "Epoch 1332/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0369 - accuracy: 0.9984 - val_loss: 1.2880 - val_accuracy: 0.7475\n",
      "Epoch 1333/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0366 - accuracy: 0.9988 - val_loss: 1.2891 - val_accuracy: 0.7525\n",
      "Epoch 1334/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0366 - accuracy: 0.9988 - val_loss: 1.2985 - val_accuracy: 0.7562\n",
      "Epoch 1335/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0367 - accuracy: 0.9986 - val_loss: 1.2992 - val_accuracy: 0.7525\n",
      "Epoch 1336/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9987 - val_loss: 1.2794 - val_accuracy: 0.7575\n",
      "Epoch 1337/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0369 - accuracy: 0.9987 - val_loss: 1.2745 - val_accuracy: 0.7587\n",
      "Epoch 1338/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9990 - val_loss: 1.2709 - val_accuracy: 0.7575\n",
      "Epoch 1339/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0386 - accuracy: 0.9983 - val_loss: 1.2738 - val_accuracy: 0.7525\n",
      "Epoch 1340/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0376 - accuracy: 0.9986 - val_loss: 1.2666 - val_accuracy: 0.7525\n",
      "Epoch 1341/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0377 - accuracy: 0.9984 - val_loss: 1.2644 - val_accuracy: 0.7537\n",
      "Epoch 1342/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0375 - accuracy: 0.9988 - val_loss: 1.2632 - val_accuracy: 0.7537\n",
      "Epoch 1343/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0384 - accuracy: 0.9980 - val_loss: 1.2579 - val_accuracy: 0.7462\n",
      "Epoch 1344/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0375 - accuracy: 0.9987 - val_loss: 1.2610 - val_accuracy: 0.7500\n",
      "Epoch 1345/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0381 - accuracy: 0.9984 - val_loss: 1.2586 - val_accuracy: 0.7475\n",
      "Epoch 1346/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9987 - val_loss: 1.2691 - val_accuracy: 0.7437\n",
      "Epoch 1347/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0388 - accuracy: 0.9984 - val_loss: 1.2816 - val_accuracy: 0.7537\n",
      "Epoch 1348/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0386 - accuracy: 0.9982 - val_loss: 1.2840 - val_accuracy: 0.7475\n",
      "Epoch 1349/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0380 - accuracy: 0.9982 - val_loss: 1.2751 - val_accuracy: 0.7500\n",
      "Epoch 1350/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0368 - accuracy: 0.9988 - val_loss: 1.2689 - val_accuracy: 0.7487\n",
      "Epoch 1351/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0368 - accuracy: 0.9988 - val_loss: 1.2694 - val_accuracy: 0.7550\n",
      "Epoch 1352/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0377 - accuracy: 0.9984 - val_loss: 1.2761 - val_accuracy: 0.7600\n",
      "Epoch 1353/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0380 - accuracy: 0.9988 - val_loss: 1.2697 - val_accuracy: 0.7600\n",
      "Epoch 1354/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0371 - accuracy: 0.9989 - val_loss: 1.2752 - val_accuracy: 0.7550\n",
      "Epoch 1355/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0374 - accuracy: 0.9986 - val_loss: 1.2705 - val_accuracy: 0.7500\n",
      "Epoch 1356/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0377 - accuracy: 0.9984 - val_loss: 1.2676 - val_accuracy: 0.7525\n",
      "Epoch 1357/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0386 - accuracy: 0.9982 - val_loss: 1.2659 - val_accuracy: 0.7537\n",
      "Epoch 1358/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0368 - accuracy: 0.9988 - val_loss: 1.2545 - val_accuracy: 0.7512\n",
      "Epoch 1359/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0382 - accuracy: 0.9984 - val_loss: 1.2583 - val_accuracy: 0.7487\n",
      "Epoch 1360/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0371 - accuracy: 0.9988 - val_loss: 1.2537 - val_accuracy: 0.7487\n",
      "Epoch 1361/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0374 - accuracy: 0.9988 - val_loss: 1.2465 - val_accuracy: 0.7525\n",
      "Epoch 1362/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0372 - accuracy: 0.9988 - val_loss: 1.2360 - val_accuracy: 0.7525\n",
      "Epoch 1363/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0375 - accuracy: 0.9987 - val_loss: 1.2407 - val_accuracy: 0.7550\n",
      "Epoch 1364/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0366 - accuracy: 0.9988 - val_loss: 1.2408 - val_accuracy: 0.7575\n",
      "Epoch 1365/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9986 - val_loss: 1.2472 - val_accuracy: 0.7537\n",
      "Epoch 1366/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0377 - accuracy: 0.9988 - val_loss: 1.2445 - val_accuracy: 0.7550\n",
      "Epoch 1367/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9988 - val_loss: 1.2404 - val_accuracy: 0.7600\n",
      "Epoch 1368/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0375 - accuracy: 0.9987 - val_loss: 1.2352 - val_accuracy: 0.7612\n",
      "Epoch 1369/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0384 - accuracy: 0.9979 - val_loss: 1.2370 - val_accuracy: 0.7650\n",
      "Epoch 1370/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0374 - accuracy: 0.9986 - val_loss: 1.2470 - val_accuracy: 0.7662\n",
      "Epoch 1371/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0383 - accuracy: 0.9984 - val_loss: 1.2560 - val_accuracy: 0.7575\n",
      "Epoch 1372/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0377 - accuracy: 0.9986 - val_loss: 1.2635 - val_accuracy: 0.7425\n",
      "Epoch 1373/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0369 - accuracy: 0.9989 - val_loss: 1.2595 - val_accuracy: 0.7437\n",
      "Epoch 1374/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0380 - accuracy: 0.9986 - val_loss: 1.2669 - val_accuracy: 0.7487\n",
      "Epoch 1375/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0368 - accuracy: 0.9986 - val_loss: 1.2666 - val_accuracy: 0.7562\n",
      "Epoch 1376/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9985 - val_loss: 1.2705 - val_accuracy: 0.7525\n",
      "Epoch 1377/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0371 - accuracy: 0.9988 - val_loss: 1.2782 - val_accuracy: 0.7525\n",
      "Epoch 1378/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0383 - accuracy: 0.9980 - val_loss: 1.2603 - val_accuracy: 0.7587\n",
      "Epoch 1379/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0372 - accuracy: 0.9986 - val_loss: 1.2493 - val_accuracy: 0.7562\n",
      "Epoch 1380/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0376 - accuracy: 0.9980 - val_loss: 1.2412 - val_accuracy: 0.7575\n",
      "Epoch 1381/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0366 - accuracy: 0.9988 - val_loss: 1.2442 - val_accuracy: 0.7612\n",
      "Epoch 1382/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0377 - accuracy: 0.9987 - val_loss: 1.2514 - val_accuracy: 0.7612\n",
      "Epoch 1383/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0371 - accuracy: 0.9988 - val_loss: 1.2515 - val_accuracy: 0.7600\n",
      "Epoch 1384/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0363 - accuracy: 0.9989 - val_loss: 1.2514 - val_accuracy: 0.7575\n",
      "Epoch 1385/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0377 - accuracy: 0.9983 - val_loss: 1.2496 - val_accuracy: 0.7587\n",
      "Epoch 1386/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0379 - accuracy: 0.9984 - val_loss: 1.2388 - val_accuracy: 0.7512\n",
      "Epoch 1387/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0369 - accuracy: 0.9989 - val_loss: 1.2359 - val_accuracy: 0.7587\n",
      "Epoch 1388/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0381 - accuracy: 0.9986 - val_loss: 1.2360 - val_accuracy: 0.7575\n",
      "Epoch 1389/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0369 - accuracy: 0.9986 - val_loss: 1.2282 - val_accuracy: 0.7550\n",
      "Epoch 1390/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0379 - accuracy: 0.9986 - val_loss: 1.2265 - val_accuracy: 0.7575\n",
      "Epoch 1391/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9986 - val_loss: 1.2288 - val_accuracy: 0.7537\n",
      "Epoch 1392/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0370 - accuracy: 0.9988 - val_loss: 1.2365 - val_accuracy: 0.7537\n",
      "Epoch 1393/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0367 - accuracy: 0.9989 - val_loss: 1.2377 - val_accuracy: 0.7512\n",
      "Epoch 1394/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0361 - accuracy: 0.9990 - val_loss: 1.2334 - val_accuracy: 0.7500\n",
      "Epoch 1395/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9988 - val_loss: 1.2284 - val_accuracy: 0.7562\n",
      "Epoch 1396/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0377 - accuracy: 0.9982 - val_loss: 1.2302 - val_accuracy: 0.7562\n",
      "Epoch 1397/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0384 - accuracy: 0.9986 - val_loss: 1.2350 - val_accuracy: 0.7600\n",
      "Epoch 1398/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9985 - val_loss: 1.2477 - val_accuracy: 0.7562\n",
      "Epoch 1399/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0372 - accuracy: 0.9989 - val_loss: 1.2525 - val_accuracy: 0.7550\n",
      "Epoch 1400/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0371 - accuracy: 0.9985 - val_loss: 1.2515 - val_accuracy: 0.7637\n",
      "Epoch 1401/2000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0384 - accuracy: 0.9983 - val_loss: 1.2536 - val_accuracy: 0.7562\n",
      "Epoch 1402/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0366 - accuracy: 0.9988 - val_loss: 1.2601 - val_accuracy: 0.7550\n",
      "Epoch 1403/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9983 - val_loss: 1.2637 - val_accuracy: 0.7512\n",
      "Epoch 1404/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9984 - val_loss: 1.2583 - val_accuracy: 0.7525\n",
      "Epoch 1405/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9985 - val_loss: 1.2278 - val_accuracy: 0.7625\n",
      "Epoch 1406/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0383 - accuracy: 0.9985 - val_loss: 1.2208 - val_accuracy: 0.7637\n",
      "Epoch 1407/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0379 - accuracy: 0.9984 - val_loss: 1.2147 - val_accuracy: 0.7575\n",
      "Epoch 1408/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0378 - accuracy: 0.9983 - val_loss: 1.2189 - val_accuracy: 0.7587\n",
      "Epoch 1409/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0369 - accuracy: 0.9990 - val_loss: 1.2099 - val_accuracy: 0.7612\n",
      "Epoch 1410/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0381 - accuracy: 0.9983 - val_loss: 1.2158 - val_accuracy: 0.7637\n",
      "Epoch 1411/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9986 - val_loss: 1.2137 - val_accuracy: 0.7662\n",
      "Epoch 1412/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0382 - accuracy: 0.9980 - val_loss: 1.2106 - val_accuracy: 0.7637\n",
      "Epoch 1413/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0379 - accuracy: 0.9987 - val_loss: 1.2232 - val_accuracy: 0.7650\n",
      "Epoch 1414/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0369 - accuracy: 0.9989 - val_loss: 1.2314 - val_accuracy: 0.7650\n",
      "Epoch 1415/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0372 - accuracy: 0.9985 - val_loss: 1.2305 - val_accuracy: 0.7650\n",
      "Epoch 1416/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0380 - accuracy: 0.9985 - val_loss: 1.2379 - val_accuracy: 0.7650\n",
      "Epoch 1417/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0371 - accuracy: 0.9989 - val_loss: 1.2479 - val_accuracy: 0.7600\n",
      "Epoch 1418/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0380 - accuracy: 0.9983 - val_loss: 1.2433 - val_accuracy: 0.7612\n",
      "Epoch 1419/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9986 - val_loss: 1.2466 - val_accuracy: 0.7600\n",
      "Epoch 1420/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0371 - accuracy: 0.9988 - val_loss: 1.2573 - val_accuracy: 0.7600\n",
      "Epoch 1421/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9985 - val_loss: 1.2638 - val_accuracy: 0.7575\n",
      "Epoch 1422/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0381 - accuracy: 0.9989 - val_loss: 1.2646 - val_accuracy: 0.7600\n",
      "Epoch 1423/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0364 - accuracy: 0.9987 - val_loss: 1.2621 - val_accuracy: 0.7575\n",
      "Epoch 1424/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0380 - accuracy: 0.9986 - val_loss: 1.2570 - val_accuracy: 0.7600\n",
      "Epoch 1425/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0364 - accuracy: 0.9988 - val_loss: 1.2496 - val_accuracy: 0.7587\n",
      "Epoch 1426/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0374 - accuracy: 0.9985 - val_loss: 1.2515 - val_accuracy: 0.7600\n",
      "Epoch 1427/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0374 - accuracy: 0.9989 - val_loss: 1.2413 - val_accuracy: 0.7587\n",
      "Epoch 1428/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9984 - val_loss: 1.2334 - val_accuracy: 0.7612\n",
      "Epoch 1429/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0383 - accuracy: 0.9985 - val_loss: 1.2449 - val_accuracy: 0.7600\n",
      "Epoch 1430/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0380 - accuracy: 0.9985 - val_loss: 1.2394 - val_accuracy: 0.7575\n",
      "Epoch 1431/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0378 - accuracy: 0.9986 - val_loss: 1.2323 - val_accuracy: 0.7637\n",
      "Epoch 1432/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0374 - accuracy: 0.9986 - val_loss: 1.2357 - val_accuracy: 0.7637\n",
      "Epoch 1433/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0380 - accuracy: 0.9986 - val_loss: 1.2381 - val_accuracy: 0.7625\n",
      "Epoch 1434/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9988 - val_loss: 1.2220 - val_accuracy: 0.7650\n",
      "Epoch 1435/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0384 - accuracy: 0.9982 - val_loss: 1.2128 - val_accuracy: 0.7675\n",
      "Epoch 1436/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0374 - accuracy: 0.9987 - val_loss: 1.2146 - val_accuracy: 0.7600\n",
      "Epoch 1437/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0383 - accuracy: 0.9980 - val_loss: 1.2121 - val_accuracy: 0.7625\n",
      "Epoch 1438/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0371 - accuracy: 0.9990 - val_loss: 1.2141 - val_accuracy: 0.7650\n",
      "Epoch 1439/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9989 - val_loss: 1.2193 - val_accuracy: 0.7675\n",
      "Epoch 1440/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0378 - accuracy: 0.9986 - val_loss: 1.2382 - val_accuracy: 0.7650\n",
      "Epoch 1441/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0368 - accuracy: 0.9988 - val_loss: 1.2403 - val_accuracy: 0.7600\n",
      "Epoch 1442/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0374 - accuracy: 0.9988 - val_loss: 1.2359 - val_accuracy: 0.7525\n",
      "Epoch 1443/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0375 - accuracy: 0.9985 - val_loss: 1.2260 - val_accuracy: 0.7525\n",
      "Epoch 1444/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0379 - accuracy: 0.9982 - val_loss: 1.2166 - val_accuracy: 0.7575\n",
      "Epoch 1445/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0372 - accuracy: 0.9988 - val_loss: 1.2216 - val_accuracy: 0.7562\n",
      "Epoch 1446/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0374 - accuracy: 0.9986 - val_loss: 1.2131 - val_accuracy: 0.7550\n",
      "Epoch 1447/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9989 - val_loss: 1.2020 - val_accuracy: 0.7612\n",
      "Epoch 1448/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0388 - accuracy: 0.9980 - val_loss: 1.1844 - val_accuracy: 0.7650\n",
      "Epoch 1449/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0373 - accuracy: 0.9986 - val_loss: 1.1832 - val_accuracy: 0.7650\n",
      "Epoch 1450/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9985 - val_loss: 1.1832 - val_accuracy: 0.7575\n",
      "Epoch 1451/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0383 - accuracy: 0.9986 - val_loss: 1.1756 - val_accuracy: 0.7575\n",
      "Epoch 1452/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0388 - accuracy: 0.9984 - val_loss: 1.1806 - val_accuracy: 0.7525\n",
      "Epoch 1453/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0389 - accuracy: 0.9981 - val_loss: 1.1980 - val_accuracy: 0.7500\n",
      "Epoch 1454/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0369 - accuracy: 0.9991 - val_loss: 1.1968 - val_accuracy: 0.7562\n",
      "Epoch 1455/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0375 - accuracy: 0.9985 - val_loss: 1.2024 - val_accuracy: 0.7637\n",
      "Epoch 1456/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0379 - accuracy: 0.9988 - val_loss: 1.2009 - val_accuracy: 0.7675\n",
      "Epoch 1457/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0375 - accuracy: 0.9988 - val_loss: 1.2107 - val_accuracy: 0.7612\n",
      "Epoch 1458/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0368 - accuracy: 0.9988 - val_loss: 1.2203 - val_accuracy: 0.7525\n",
      "Epoch 1459/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0384 - accuracy: 0.9983 - val_loss: 1.2180 - val_accuracy: 0.7537\n",
      "Epoch 1460/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0392 - accuracy: 0.9982 - val_loss: 1.2097 - val_accuracy: 0.7562\n",
      "Epoch 1461/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0384 - accuracy: 0.9986 - val_loss: 1.2140 - val_accuracy: 0.7525\n",
      "Epoch 1462/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0371 - accuracy: 0.9989 - val_loss: 1.2150 - val_accuracy: 0.7562\n",
      "Epoch 1463/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0385 - accuracy: 0.9983 - val_loss: 1.2229 - val_accuracy: 0.7475\n",
      "Epoch 1464/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0378 - accuracy: 0.9985 - val_loss: 1.2306 - val_accuracy: 0.7425\n",
      "Epoch 1465/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0378 - accuracy: 0.9983 - val_loss: 1.2403 - val_accuracy: 0.7525\n",
      "Epoch 1466/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9989 - val_loss: 1.2348 - val_accuracy: 0.7500\n",
      "Epoch 1467/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0374 - accuracy: 0.9991 - val_loss: 1.2357 - val_accuracy: 0.7550\n",
      "Epoch 1468/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9989 - val_loss: 1.2230 - val_accuracy: 0.7587\n",
      "Epoch 1469/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0388 - accuracy: 0.9980 - val_loss: 1.2041 - val_accuracy: 0.7525\n",
      "Epoch 1470/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0377 - accuracy: 0.9988 - val_loss: 1.1999 - val_accuracy: 0.7487\n",
      "Epoch 1471/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0384 - accuracy: 0.9985 - val_loss: 1.2033 - val_accuracy: 0.7462\n",
      "Epoch 1472/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0372 - accuracy: 0.9989 - val_loss: 1.2078 - val_accuracy: 0.7450\n",
      "Epoch 1473/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9988 - val_loss: 1.2083 - val_accuracy: 0.7475\n",
      "Epoch 1474/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0372 - accuracy: 0.9989 - val_loss: 1.2127 - val_accuracy: 0.7462\n",
      "Epoch 1475/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0371 - accuracy: 0.9989 - val_loss: 1.2235 - val_accuracy: 0.7512\n",
      "Epoch 1476/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0379 - accuracy: 0.9986 - val_loss: 1.2314 - val_accuracy: 0.7500\n",
      "Epoch 1477/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9989 - val_loss: 1.2376 - val_accuracy: 0.7525\n",
      "Epoch 1478/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0368 - accuracy: 0.9989 - val_loss: 1.2439 - val_accuracy: 0.7500\n",
      "Epoch 1479/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0378 - accuracy: 0.9984 - val_loss: 1.2479 - val_accuracy: 0.7487\n",
      "Epoch 1480/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0375 - accuracy: 0.9988 - val_loss: 1.2554 - val_accuracy: 0.7512\n",
      "Epoch 1481/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0383 - accuracy: 0.9983 - val_loss: 1.2547 - val_accuracy: 0.7512\n",
      "Epoch 1482/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9989 - val_loss: 1.2680 - val_accuracy: 0.7487\n",
      "Epoch 1483/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0359 - accuracy: 0.9993 - val_loss: 1.2578 - val_accuracy: 0.7537\n",
      "Epoch 1484/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9986 - val_loss: 1.2590 - val_accuracy: 0.7537\n",
      "Epoch 1485/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0369 - accuracy: 0.9989 - val_loss: 1.2645 - val_accuracy: 0.7562\n",
      "Epoch 1486/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0363 - accuracy: 0.9991 - val_loss: 1.2650 - val_accuracy: 0.7525\n",
      "Epoch 1487/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0360 - accuracy: 0.9992 - val_loss: 1.2709 - val_accuracy: 0.7525\n",
      "Epoch 1488/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0377 - accuracy: 0.9988 - val_loss: 1.2798 - val_accuracy: 0.7562\n",
      "Epoch 1489/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0361 - accuracy: 0.9992 - val_loss: 1.2779 - val_accuracy: 0.7562\n",
      "Epoch 1490/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9986 - val_loss: 1.2715 - val_accuracy: 0.7487\n",
      "Epoch 1491/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0369 - accuracy: 0.9989 - val_loss: 1.2773 - val_accuracy: 0.7500\n",
      "Epoch 1492/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0369 - accuracy: 0.9987 - val_loss: 1.2824 - val_accuracy: 0.7475\n",
      "Epoch 1493/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0367 - accuracy: 0.9988 - val_loss: 1.2889 - val_accuracy: 0.7450\n",
      "Epoch 1494/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0363 - accuracy: 0.9989 - val_loss: 1.2877 - val_accuracy: 0.7500\n",
      "Epoch 1495/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0371 - accuracy: 0.9984 - val_loss: 1.2851 - val_accuracy: 0.7500\n",
      "Epoch 1496/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0372 - accuracy: 0.9989 - val_loss: 1.2861 - val_accuracy: 0.7562\n",
      "Epoch 1497/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9988 - val_loss: 1.2878 - val_accuracy: 0.7512\n",
      "Epoch 1498/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0368 - accuracy: 0.9988 - val_loss: 1.2746 - val_accuracy: 0.7500\n",
      "Epoch 1499/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0372 - accuracy: 0.9984 - val_loss: 1.2667 - val_accuracy: 0.7500\n",
      "Epoch 1500/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0365 - accuracy: 0.9989 - val_loss: 1.2653 - val_accuracy: 0.7425\n",
      "Epoch 1501/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0377 - accuracy: 0.9986 - val_loss: 1.2715 - val_accuracy: 0.7462\n",
      "Epoch 1502/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0372 - accuracy: 0.9987 - val_loss: 1.2808 - val_accuracy: 0.7450\n",
      "Epoch 1503/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0373 - accuracy: 0.9988 - val_loss: 1.2849 - val_accuracy: 0.7462\n",
      "Epoch 1504/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0367 - accuracy: 0.9988 - val_loss: 1.2897 - val_accuracy: 0.7462\n",
      "Epoch 1505/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9987 - val_loss: 1.2803 - val_accuracy: 0.7550\n",
      "Epoch 1506/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0368 - accuracy: 0.9984 - val_loss: 1.2686 - val_accuracy: 0.7525\n",
      "Epoch 1507/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0365 - accuracy: 0.9988 - val_loss: 1.2653 - val_accuracy: 0.7487\n",
      "Epoch 1508/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0364 - accuracy: 0.9990 - val_loss: 1.2662 - val_accuracy: 0.7425\n",
      "Epoch 1509/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0365 - accuracy: 0.9988 - val_loss: 1.2483 - val_accuracy: 0.7450\n",
      "Epoch 1510/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0367 - accuracy: 0.9987 - val_loss: 1.2359 - val_accuracy: 0.7512\n",
      "Epoch 1511/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0363 - accuracy: 0.9989 - val_loss: 1.2322 - val_accuracy: 0.7537\n",
      "Epoch 1512/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0371 - accuracy: 0.9988 - val_loss: 1.2151 - val_accuracy: 0.7475\n",
      "Epoch 1513/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0374 - accuracy: 0.9983 - val_loss: 1.2199 - val_accuracy: 0.7500\n",
      "Epoch 1514/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0363 - accuracy: 0.9991 - val_loss: 1.2235 - val_accuracy: 0.7487\n",
      "Epoch 1515/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9986 - val_loss: 1.2268 - val_accuracy: 0.7425\n",
      "Epoch 1516/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9986 - val_loss: 1.2352 - val_accuracy: 0.7362\n",
      "Epoch 1517/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0368 - accuracy: 0.9989 - val_loss: 1.2377 - val_accuracy: 0.7387\n",
      "Epoch 1518/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0372 - accuracy: 0.9986 - val_loss: 1.2472 - val_accuracy: 0.7450\n",
      "Epoch 1519/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0373 - accuracy: 0.9986 - val_loss: 1.2682 - val_accuracy: 0.7450\n",
      "Epoch 1520/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0366 - accuracy: 0.9988 - val_loss: 1.2706 - val_accuracy: 0.7462\n",
      "Epoch 1521/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0358 - accuracy: 0.9994 - val_loss: 1.2742 - val_accuracy: 0.7487\n",
      "Epoch 1522/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9984 - val_loss: 1.2850 - val_accuracy: 0.7487\n",
      "Epoch 1523/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0364 - accuracy: 0.9987 - val_loss: 1.3001 - val_accuracy: 0.7437\n",
      "Epoch 1524/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0375 - accuracy: 0.9986 - val_loss: 1.2977 - val_accuracy: 0.7425\n",
      "Epoch 1525/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0381 - accuracy: 0.9984 - val_loss: 1.3079 - val_accuracy: 0.7475\n",
      "Epoch 1526/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0374 - accuracy: 0.9988 - val_loss: 1.3117 - val_accuracy: 0.7462\n",
      "Epoch 1527/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0385 - accuracy: 0.9982 - val_loss: 1.3061 - val_accuracy: 0.7462\n",
      "Epoch 1528/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0358 - accuracy: 0.9991 - val_loss: 1.3036 - val_accuracy: 0.7450\n",
      "Epoch 1529/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0374 - accuracy: 0.9985 - val_loss: 1.3007 - val_accuracy: 0.7525\n",
      "Epoch 1530/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0392 - accuracy: 0.9983 - val_loss: 1.2745 - val_accuracy: 0.7562\n",
      "Epoch 1531/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0383 - accuracy: 0.9983 - val_loss: 1.2624 - val_accuracy: 0.7587\n",
      "Epoch 1532/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0368 - accuracy: 0.9989 - val_loss: 1.2596 - val_accuracy: 0.7575\n",
      "Epoch 1533/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0388 - accuracy: 0.9984 - val_loss: 1.2615 - val_accuracy: 0.7512\n",
      "Epoch 1534/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0369 - accuracy: 0.9989 - val_loss: 1.2657 - val_accuracy: 0.7500\n",
      "Epoch 1535/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0374 - accuracy: 0.9986 - val_loss: 1.2604 - val_accuracy: 0.7525\n",
      "Epoch 1536/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0373 - accuracy: 0.9986 - val_loss: 1.2531 - val_accuracy: 0.7512\n",
      "Epoch 1537/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0377 - accuracy: 0.9985 - val_loss: 1.2559 - val_accuracy: 0.7500\n",
      "Epoch 1538/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0381 - accuracy: 0.9985 - val_loss: 1.2595 - val_accuracy: 0.7562\n",
      "Epoch 1539/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0366 - accuracy: 0.9989 - val_loss: 1.2658 - val_accuracy: 0.7512\n",
      "Epoch 1540/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0365 - accuracy: 0.9991 - val_loss: 1.2642 - val_accuracy: 0.7500\n",
      "Epoch 1541/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0380 - accuracy: 0.9986 - val_loss: 1.2628 - val_accuracy: 0.7512\n",
      "Epoch 1542/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9986 - val_loss: 1.2583 - val_accuracy: 0.7550\n",
      "Epoch 1543/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0375 - accuracy: 0.9986 - val_loss: 1.2436 - val_accuracy: 0.7562\n",
      "Epoch 1544/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0375 - accuracy: 0.9986 - val_loss: 1.2561 - val_accuracy: 0.7562\n",
      "Epoch 1545/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0372 - accuracy: 0.9984 - val_loss: 1.2655 - val_accuracy: 0.7525\n",
      "Epoch 1546/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0364 - accuracy: 0.9991 - val_loss: 1.2526 - val_accuracy: 0.7500\n",
      "Epoch 1547/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0380 - accuracy: 0.9981 - val_loss: 1.2322 - val_accuracy: 0.7525\n",
      "Epoch 1548/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0372 - accuracy: 0.9988 - val_loss: 1.2274 - val_accuracy: 0.7537\n",
      "Epoch 1549/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0369 - accuracy: 0.9988 - val_loss: 1.2245 - val_accuracy: 0.7487\n",
      "Epoch 1550/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0365 - accuracy: 0.9988 - val_loss: 1.2188 - val_accuracy: 0.7475\n",
      "Epoch 1551/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9988 - val_loss: 1.2159 - val_accuracy: 0.7500\n",
      "Epoch 1552/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0364 - accuracy: 0.9990 - val_loss: 1.2234 - val_accuracy: 0.7525\n",
      "Epoch 1553/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0377 - accuracy: 0.9986 - val_loss: 1.2373 - val_accuracy: 0.7500\n",
      "Epoch 1554/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9986 - val_loss: 1.2328 - val_accuracy: 0.7500\n",
      "Epoch 1555/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0367 - accuracy: 0.9988 - val_loss: 1.2278 - val_accuracy: 0.7512\n",
      "Epoch 1556/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0375 - accuracy: 0.9988 - val_loss: 1.2288 - val_accuracy: 0.7512\n",
      "Epoch 1557/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0365 - accuracy: 0.9991 - val_loss: 1.2242 - val_accuracy: 0.7500\n",
      "Epoch 1558/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0370 - accuracy: 0.9983 - val_loss: 1.2192 - val_accuracy: 0.7562\n",
      "Epoch 1559/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0381 - accuracy: 0.9981 - val_loss: 1.2134 - val_accuracy: 0.7587\n",
      "Epoch 1560/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0371 - accuracy: 0.9991 - val_loss: 1.2002 - val_accuracy: 0.7612\n",
      "Epoch 1561/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0382 - accuracy: 0.9983 - val_loss: 1.1882 - val_accuracy: 0.7637\n",
      "Epoch 1562/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0370 - accuracy: 0.9989 - val_loss: 1.2144 - val_accuracy: 0.7562\n",
      "Epoch 1563/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0379 - accuracy: 0.9985 - val_loss: 1.2125 - val_accuracy: 0.7587\n",
      "Epoch 1564/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0372 - accuracy: 0.9987 - val_loss: 1.2109 - val_accuracy: 0.7575\n",
      "Epoch 1565/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0370 - accuracy: 0.9984 - val_loss: 1.2162 - val_accuracy: 0.7625\n",
      "Epoch 1566/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0379 - accuracy: 0.9986 - val_loss: 1.2292 - val_accuracy: 0.7562\n",
      "Epoch 1567/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0375 - accuracy: 0.9985 - val_loss: 1.2420 - val_accuracy: 0.7575\n",
      "Epoch 1568/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0365 - accuracy: 0.9991 - val_loss: 1.2464 - val_accuracy: 0.7600\n",
      "Epoch 1569/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0376 - accuracy: 0.9985 - val_loss: 1.2448 - val_accuracy: 0.7625\n",
      "Epoch 1570/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0375 - accuracy: 0.9986 - val_loss: 1.2346 - val_accuracy: 0.7637\n",
      "Epoch 1571/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0374 - accuracy: 0.9986 - val_loss: 1.2261 - val_accuracy: 0.7575\n",
      "Epoch 1572/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0378 - accuracy: 0.9982 - val_loss: 1.2338 - val_accuracy: 0.7587\n",
      "Epoch 1573/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0367 - accuracy: 0.9989 - val_loss: 1.2425 - val_accuracy: 0.7587\n",
      "Epoch 1574/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0384 - accuracy: 0.9987 - val_loss: 1.2445 - val_accuracy: 0.7500\n",
      "Epoch 1575/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0380 - accuracy: 0.9984 - val_loss: 1.2351 - val_accuracy: 0.7537\n",
      "Epoch 1576/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0383 - accuracy: 0.9983 - val_loss: 1.2280 - val_accuracy: 0.7537\n",
      "Epoch 1577/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0381 - accuracy: 0.9986 - val_loss: 1.2162 - val_accuracy: 0.7487\n",
      "Epoch 1578/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0388 - accuracy: 0.9986 - val_loss: 1.2243 - val_accuracy: 0.7575\n",
      "Epoch 1579/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0367 - accuracy: 0.9990 - val_loss: 1.2333 - val_accuracy: 0.7637\n",
      "Epoch 1580/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0381 - accuracy: 0.9986 - val_loss: 1.2369 - val_accuracy: 0.7600\n",
      "Epoch 1581/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0370 - accuracy: 0.9990 - val_loss: 1.2412 - val_accuracy: 0.7562\n",
      "Epoch 1582/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0374 - accuracy: 0.9988 - val_loss: 1.2499 - val_accuracy: 0.7525\n",
      "Epoch 1583/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0369 - accuracy: 0.9988 - val_loss: 1.2540 - val_accuracy: 0.7562\n",
      "Epoch 1584/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0370 - accuracy: 0.9989 - val_loss: 1.2597 - val_accuracy: 0.7537\n",
      "Epoch 1585/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0375 - accuracy: 0.9987 - val_loss: 1.2573 - val_accuracy: 0.7550\n",
      "Epoch 1586/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0375 - accuracy: 0.9986 - val_loss: 1.2589 - val_accuracy: 0.7600\n",
      "Epoch 1587/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0359 - accuracy: 0.9993 - val_loss: 1.2568 - val_accuracy: 0.7612\n",
      "Epoch 1588/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0360 - accuracy: 0.9993 - val_loss: 1.2576 - val_accuracy: 0.7562\n",
      "Epoch 1589/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0366 - accuracy: 0.9987 - val_loss: 1.2577 - val_accuracy: 0.7537\n",
      "Epoch 1590/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0365 - accuracy: 0.9988 - val_loss: 1.2520 - val_accuracy: 0.7537\n",
      "Epoch 1591/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0360 - accuracy: 0.9993 - val_loss: 1.2433 - val_accuracy: 0.7587\n",
      "Epoch 1592/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0375 - accuracy: 0.9984 - val_loss: 1.2484 - val_accuracy: 0.7600\n",
      "Epoch 1593/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0369 - accuracy: 0.9986 - val_loss: 1.2666 - val_accuracy: 0.7562\n",
      "Epoch 1594/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0367 - accuracy: 0.9989 - val_loss: 1.2587 - val_accuracy: 0.7612\n",
      "Epoch 1595/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0366 - accuracy: 0.9989 - val_loss: 1.2597 - val_accuracy: 0.7550\n",
      "Epoch 1596/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0376 - accuracy: 0.9984 - val_loss: 1.2677 - val_accuracy: 0.7550\n",
      "Epoch 1597/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0369 - accuracy: 0.9989 - val_loss: 1.2627 - val_accuracy: 0.7537\n",
      "Epoch 1598/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0382 - accuracy: 0.9984 - val_loss: 1.2492 - val_accuracy: 0.7550\n",
      "Epoch 1599/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0366 - accuracy: 0.9988 - val_loss: 1.2497 - val_accuracy: 0.7625\n",
      "Epoch 1600/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0375 - accuracy: 0.9989 - val_loss: 1.2588 - val_accuracy: 0.7600\n",
      "Epoch 1601/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0371 - accuracy: 0.9988 - val_loss: 1.2505 - val_accuracy: 0.7575\n",
      "Epoch 1602/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0372 - accuracy: 0.9984 - val_loss: 1.2627 - val_accuracy: 0.7600\n",
      "Epoch 1603/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0363 - accuracy: 0.9990 - val_loss: 1.2738 - val_accuracy: 0.7587\n",
      "Epoch 1604/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0372 - accuracy: 0.9986 - val_loss: 1.2643 - val_accuracy: 0.7600\n",
      "Epoch 1605/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0385 - accuracy: 0.9983 - val_loss: 1.2765 - val_accuracy: 0.7612\n",
      "Epoch 1606/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0364 - accuracy: 0.9986 - val_loss: 1.2712 - val_accuracy: 0.7512\n",
      "Epoch 1607/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0378 - accuracy: 0.9982 - val_loss: 1.2723 - val_accuracy: 0.7525\n",
      "Epoch 1608/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0375 - accuracy: 0.9984 - val_loss: 1.2715 - val_accuracy: 0.7512\n",
      "Epoch 1609/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0371 - accuracy: 0.9988 - val_loss: 1.2991 - val_accuracy: 0.7550\n",
      "Epoch 1610/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9986 - val_loss: 1.2919 - val_accuracy: 0.7600\n",
      "Epoch 1611/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9987 - val_loss: 1.2789 - val_accuracy: 0.7575\n",
      "Epoch 1612/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0379 - accuracy: 0.9989 - val_loss: 1.2602 - val_accuracy: 0.7587\n",
      "Epoch 1613/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0372 - accuracy: 0.9985 - val_loss: 1.2531 - val_accuracy: 0.7625\n",
      "Epoch 1614/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0373 - accuracy: 0.9984 - val_loss: 1.2615 - val_accuracy: 0.7575\n",
      "Epoch 1615/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0366 - accuracy: 0.9989 - val_loss: 1.2620 - val_accuracy: 0.7550\n",
      "Epoch 1616/2000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.0377 - accuracy: 0.9982 - val_loss: 1.2425 - val_accuracy: 0.7525\n",
      "Epoch 1617/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0390 - accuracy: 0.9980 - val_loss: 1.2509 - val_accuracy: 0.7500\n",
      "Epoch 1618/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0371 - accuracy: 0.9988 - val_loss: 1.2579 - val_accuracy: 0.7487\n",
      "Epoch 1619/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0365 - accuracy: 0.9991 - val_loss: 1.2538 - val_accuracy: 0.7512\n",
      "Epoch 1620/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9988 - val_loss: 1.2411 - val_accuracy: 0.7450\n",
      "Epoch 1621/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0380 - accuracy: 0.9984 - val_loss: 1.2489 - val_accuracy: 0.7437\n",
      "Epoch 1622/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0374 - accuracy: 0.9984 - val_loss: 1.2622 - val_accuracy: 0.7475\n",
      "Epoch 1623/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0364 - accuracy: 0.9988 - val_loss: 1.2526 - val_accuracy: 0.7525\n",
      "Epoch 1624/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0387 - accuracy: 0.9986 - val_loss: 1.2507 - val_accuracy: 0.7425\n",
      "Epoch 1625/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0373 - accuracy: 0.9989 - val_loss: 1.2539 - val_accuracy: 0.7450\n",
      "Epoch 1626/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0371 - accuracy: 0.9988 - val_loss: 1.2418 - val_accuracy: 0.7487\n",
      "Epoch 1627/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0378 - accuracy: 0.9989 - val_loss: 1.2391 - val_accuracy: 0.7562\n",
      "Epoch 1628/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0375 - accuracy: 0.9986 - val_loss: 1.2463 - val_accuracy: 0.7500\n",
      "Epoch 1629/2000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0373 - accuracy: 0.9986 - val_loss: 1.2480 - val_accuracy: 0.7525\n",
      "Epoch 1630/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0386 - accuracy: 0.9981 - val_loss: 1.2380 - val_accuracy: 0.7550\n",
      "Epoch 1631/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0381 - accuracy: 0.9982 - val_loss: 1.2335 - val_accuracy: 0.7587\n",
      "Epoch 1632/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0377 - accuracy: 0.9986 - val_loss: 1.2460 - val_accuracy: 0.7512\n",
      "Epoch 1633/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0365 - accuracy: 0.9991 - val_loss: 1.2535 - val_accuracy: 0.7512\n",
      "Epoch 1634/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0378 - accuracy: 0.9985 - val_loss: 1.2403 - val_accuracy: 0.7537\n",
      "Epoch 1635/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0373 - accuracy: 0.9988 - val_loss: 1.2454 - val_accuracy: 0.7600\n",
      "Epoch 1636/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0380 - accuracy: 0.9985 - val_loss: 1.2456 - val_accuracy: 0.7525\n",
      "Epoch 1637/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0375 - accuracy: 0.9984 - val_loss: 1.2509 - val_accuracy: 0.7475\n",
      "Epoch 1638/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0370 - accuracy: 0.9986 - val_loss: 1.2486 - val_accuracy: 0.7475\n",
      "Epoch 1639/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0363 - accuracy: 0.9990 - val_loss: 1.2609 - val_accuracy: 0.7462\n",
      "Epoch 1640/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0371 - accuracy: 0.9984 - val_loss: 1.2552 - val_accuracy: 0.7462\n",
      "Epoch 1641/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0377 - accuracy: 0.9988 - val_loss: 1.2435 - val_accuracy: 0.7525\n",
      "Epoch 1642/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9986 - val_loss: 1.2363 - val_accuracy: 0.7550\n",
      "Epoch 1643/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0366 - accuracy: 0.9990 - val_loss: 1.2410 - val_accuracy: 0.7575\n",
      "Epoch 1644/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0378 - accuracy: 0.9988 - val_loss: 1.2423 - val_accuracy: 0.7512\n",
      "Epoch 1645/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0379 - accuracy: 0.9986 - val_loss: 1.2523 - val_accuracy: 0.7487\n",
      "Epoch 1646/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0369 - accuracy: 0.9989 - val_loss: 1.2409 - val_accuracy: 0.7562\n",
      "Epoch 1647/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0368 - accuracy: 0.9986 - val_loss: 1.2441 - val_accuracy: 0.7537\n",
      "Epoch 1648/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0387 - accuracy: 0.9984 - val_loss: 1.2501 - val_accuracy: 0.7512\n",
      "Epoch 1649/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0364 - accuracy: 0.9989 - val_loss: 1.2533 - val_accuracy: 0.7500\n",
      "Epoch 1650/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0374 - accuracy: 0.9984 - val_loss: 1.2544 - val_accuracy: 0.7487\n",
      "Epoch 1651/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0373 - accuracy: 0.9987 - val_loss: 1.2505 - val_accuracy: 0.7537\n",
      "Epoch 1652/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0367 - accuracy: 0.9991 - val_loss: 1.2472 - val_accuracy: 0.7512\n",
      "Epoch 1653/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0387 - accuracy: 0.9982 - val_loss: 1.2365 - val_accuracy: 0.7562\n",
      "Epoch 1654/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0362 - accuracy: 0.9991 - val_loss: 1.2305 - val_accuracy: 0.7625\n",
      "Epoch 1655/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0382 - accuracy: 0.9984 - val_loss: 1.2272 - val_accuracy: 0.7562\n",
      "Epoch 1656/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0385 - accuracy: 0.9982 - val_loss: 1.2298 - val_accuracy: 0.7575\n",
      "Epoch 1657/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0362 - accuracy: 0.9991 - val_loss: 1.2477 - val_accuracy: 0.7500\n",
      "Epoch 1658/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0366 - accuracy: 0.9988 - val_loss: 1.2491 - val_accuracy: 0.7525\n",
      "Epoch 1659/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0371 - accuracy: 0.9989 - val_loss: 1.2507 - val_accuracy: 0.7525\n",
      "Epoch 1660/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9988 - val_loss: 1.2447 - val_accuracy: 0.7525\n",
      "Epoch 1661/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0362 - accuracy: 0.9988 - val_loss: 1.2340 - val_accuracy: 0.7587\n",
      "Epoch 1662/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0381 - accuracy: 0.9982 - val_loss: 1.2410 - val_accuracy: 0.7550\n",
      "Epoch 1663/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0365 - accuracy: 0.9990 - val_loss: 1.2536 - val_accuracy: 0.7525\n",
      "Epoch 1664/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0368 - accuracy: 0.9989 - val_loss: 1.2662 - val_accuracy: 0.7437\n",
      "Epoch 1665/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0369 - accuracy: 0.9988 - val_loss: 1.2651 - val_accuracy: 0.7500\n",
      "Epoch 1666/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9990 - val_loss: 1.2642 - val_accuracy: 0.7500\n",
      "Epoch 1667/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0370 - accuracy: 0.9988 - val_loss: 1.2602 - val_accuracy: 0.7525\n",
      "Epoch 1668/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0372 - accuracy: 0.9987 - val_loss: 1.2470 - val_accuracy: 0.7562\n",
      "Epoch 1669/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0387 - accuracy: 0.9981 - val_loss: 1.2510 - val_accuracy: 0.7500\n",
      "Epoch 1670/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9989 - val_loss: 1.2581 - val_accuracy: 0.7450\n",
      "Epoch 1671/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0371 - accuracy: 0.9988 - val_loss: 1.2628 - val_accuracy: 0.7462\n",
      "Epoch 1672/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0386 - accuracy: 0.9986 - val_loss: 1.2648 - val_accuracy: 0.7487\n",
      "Epoch 1673/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0370 - accuracy: 0.9986 - val_loss: 1.2850 - val_accuracy: 0.7462\n",
      "Epoch 1674/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0373 - accuracy: 0.9987 - val_loss: 1.3030 - val_accuracy: 0.7450\n",
      "Epoch 1675/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0375 - accuracy: 0.9984 - val_loss: 1.3146 - val_accuracy: 0.7412\n",
      "Epoch 1676/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0379 - accuracy: 0.9985 - val_loss: 1.3078 - val_accuracy: 0.7387\n",
      "Epoch 1677/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0378 - accuracy: 0.9986 - val_loss: 1.2939 - val_accuracy: 0.7387\n",
      "Epoch 1678/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0360 - accuracy: 0.9988 - val_loss: 1.2958 - val_accuracy: 0.7387\n",
      "Epoch 1679/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0372 - accuracy: 0.9989 - val_loss: 1.2996 - val_accuracy: 0.7362\n",
      "Epoch 1680/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0357 - accuracy: 0.9993 - val_loss: 1.3023 - val_accuracy: 0.7412\n",
      "Epoch 1681/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0362 - accuracy: 0.9988 - val_loss: 1.2988 - val_accuracy: 0.7450\n",
      "Epoch 1682/2000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0362 - accuracy: 0.9991 - val_loss: 1.2980 - val_accuracy: 0.7425\n",
      "Epoch 1683/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0356 - accuracy: 0.9991 - val_loss: 1.2979 - val_accuracy: 0.7400\n",
      "Epoch 1684/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0364 - accuracy: 0.9988 - val_loss: 1.2950 - val_accuracy: 0.7412\n",
      "Epoch 1685/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0361 - accuracy: 0.9990 - val_loss: 1.2901 - val_accuracy: 0.7412\n",
      "Epoch 1686/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0374 - accuracy: 0.9986 - val_loss: 1.2702 - val_accuracy: 0.7425\n",
      "Epoch 1687/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0359 - accuracy: 0.9989 - val_loss: 1.2664 - val_accuracy: 0.7437\n",
      "Epoch 1688/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0367 - accuracy: 0.9988 - val_loss: 1.2771 - val_accuracy: 0.7412\n",
      "Epoch 1689/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0366 - accuracy: 0.9990 - val_loss: 1.2741 - val_accuracy: 0.7437\n",
      "Epoch 1690/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0376 - accuracy: 0.9982 - val_loss: 1.2671 - val_accuracy: 0.7475\n",
      "Epoch 1691/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0364 - accuracy: 0.9990 - val_loss: 1.2622 - val_accuracy: 0.7437\n",
      "Epoch 1692/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0360 - accuracy: 0.9990 - val_loss: 1.2641 - val_accuracy: 0.7412\n",
      "Epoch 1693/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0357 - accuracy: 0.9988 - val_loss: 1.2492 - val_accuracy: 0.7412\n",
      "Epoch 1694/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0358 - accuracy: 0.9989 - val_loss: 1.2568 - val_accuracy: 0.7450\n",
      "Epoch 1695/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0364 - accuracy: 0.9990 - val_loss: 1.2749 - val_accuracy: 0.7437\n",
      "Epoch 1696/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0358 - accuracy: 0.9989 - val_loss: 1.2903 - val_accuracy: 0.7462\n",
      "Epoch 1697/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0362 - accuracy: 0.9987 - val_loss: 1.3088 - val_accuracy: 0.7450\n",
      "Epoch 1698/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0361 - accuracy: 0.9989 - val_loss: 1.3027 - val_accuracy: 0.7462\n",
      "Epoch 1699/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0357 - accuracy: 0.9991 - val_loss: 1.2942 - val_accuracy: 0.7487\n",
      "Epoch 1700/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0362 - accuracy: 0.9990 - val_loss: 1.2959 - val_accuracy: 0.7475\n",
      "Epoch 1701/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0367 - accuracy: 0.9984 - val_loss: 1.2932 - val_accuracy: 0.7475\n",
      "Epoch 1702/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0369 - accuracy: 0.9986 - val_loss: 1.2930 - val_accuracy: 0.7487\n",
      "Epoch 1703/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0359 - accuracy: 0.9989 - val_loss: 1.2840 - val_accuracy: 0.7475\n",
      "Epoch 1704/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0363 - accuracy: 0.9991 - val_loss: 1.2734 - val_accuracy: 0.7512\n",
      "Epoch 1705/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0361 - accuracy: 0.9985 - val_loss: 1.2698 - val_accuracy: 0.7512\n",
      "Epoch 1706/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0368 - accuracy: 0.9985 - val_loss: 1.2590 - val_accuracy: 0.7500\n",
      "Epoch 1707/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0370 - accuracy: 0.9988 - val_loss: 1.2634 - val_accuracy: 0.7525\n",
      "Epoch 1708/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0368 - accuracy: 0.9984 - val_loss: 1.2643 - val_accuracy: 0.7475\n",
      "Epoch 1709/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0377 - accuracy: 0.9982 - val_loss: 1.2594 - val_accuracy: 0.7450\n",
      "Epoch 1710/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0377 - accuracy: 0.9984 - val_loss: 1.2637 - val_accuracy: 0.7462\n",
      "Epoch 1711/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0365 - accuracy: 0.9988 - val_loss: 1.2671 - val_accuracy: 0.7425\n",
      "Epoch 1712/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0373 - accuracy: 0.9984 - val_loss: 1.2680 - val_accuracy: 0.7387\n",
      "Epoch 1713/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0371 - accuracy: 0.9984 - val_loss: 1.2546 - val_accuracy: 0.7462\n",
      "Epoch 1714/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0371 - accuracy: 0.9988 - val_loss: 1.2374 - val_accuracy: 0.7525\n",
      "Epoch 1715/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0383 - accuracy: 0.9988 - val_loss: 1.2383 - val_accuracy: 0.7562\n",
      "Epoch 1716/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0377 - accuracy: 0.9984 - val_loss: 1.2459 - val_accuracy: 0.7562\n",
      "Epoch 1717/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0382 - accuracy: 0.9984 - val_loss: 1.2635 - val_accuracy: 0.7537\n",
      "Epoch 1718/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0366 - accuracy: 0.9991 - val_loss: 1.2747 - val_accuracy: 0.7412\n",
      "Epoch 1719/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0371 - accuracy: 0.9987 - val_loss: 1.2726 - val_accuracy: 0.7437\n",
      "Epoch 1720/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0367 - accuracy: 0.9986 - val_loss: 1.2676 - val_accuracy: 0.7462\n",
      "Epoch 1721/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0369 - accuracy: 0.9988 - val_loss: 1.2427 - val_accuracy: 0.7450\n",
      "Epoch 1722/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0376 - accuracy: 0.9987 - val_loss: 1.2276 - val_accuracy: 0.7425\n",
      "Epoch 1723/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0372 - accuracy: 0.9987 - val_loss: 1.2239 - val_accuracy: 0.7525\n",
      "Epoch 1724/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0375 - accuracy: 0.9986 - val_loss: 1.2114 - val_accuracy: 0.7500\n",
      "Epoch 1725/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0374 - accuracy: 0.9986 - val_loss: 1.2113 - val_accuracy: 0.7475\n",
      "Epoch 1726/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0375 - accuracy: 0.9986 - val_loss: 1.2123 - val_accuracy: 0.7575\n",
      "Epoch 1727/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0376 - accuracy: 0.9986 - val_loss: 1.2038 - val_accuracy: 0.7500\n",
      "Epoch 1728/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0374 - accuracy: 0.9987 - val_loss: 1.1979 - val_accuracy: 0.7525\n",
      "Epoch 1729/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0367 - accuracy: 0.9988 - val_loss: 1.1949 - val_accuracy: 0.7462\n",
      "Epoch 1730/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0383 - accuracy: 0.9982 - val_loss: 1.2148 - val_accuracy: 0.7487\n",
      "Epoch 1731/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0381 - accuracy: 0.9984 - val_loss: 1.2256 - val_accuracy: 0.7450\n",
      "Epoch 1732/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0376 - accuracy: 0.9986 - val_loss: 1.2099 - val_accuracy: 0.7450\n",
      "Epoch 1733/2000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.0378 - accuracy: 0.9986 - val_loss: 1.2122 - val_accuracy: 0.7500\n",
      "Epoch 1734/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0384 - accuracy: 0.9985 - val_loss: 1.2171 - val_accuracy: 0.7487\n",
      "Epoch 1735/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0376 - accuracy: 0.9984 - val_loss: 1.2323 - val_accuracy: 0.7462\n",
      "Epoch 1736/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0369 - accuracy: 0.9989 - val_loss: 1.2404 - val_accuracy: 0.7500\n",
      "Epoch 1737/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9989 - val_loss: 1.2366 - val_accuracy: 0.7487\n",
      "Epoch 1738/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9987 - val_loss: 1.2405 - val_accuracy: 0.7512\n",
      "Epoch 1739/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0375 - accuracy: 0.9988 - val_loss: 1.2427 - val_accuracy: 0.7487\n",
      "Epoch 1740/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0365 - accuracy: 0.9991 - val_loss: 1.2444 - val_accuracy: 0.7512\n",
      "Epoch 1741/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9988 - val_loss: 1.2431 - val_accuracy: 0.7500\n",
      "Epoch 1742/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0371 - accuracy: 0.9988 - val_loss: 1.2359 - val_accuracy: 0.7487\n",
      "Epoch 1743/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0365 - accuracy: 0.9990 - val_loss: 1.2345 - val_accuracy: 0.7500\n",
      "Epoch 1744/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0389 - accuracy: 0.9981 - val_loss: 1.2398 - val_accuracy: 0.7487\n",
      "Epoch 1745/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0366 - accuracy: 0.9991 - val_loss: 1.2392 - val_accuracy: 0.7512\n",
      "Epoch 1746/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0370 - accuracy: 0.9988 - val_loss: 1.2349 - val_accuracy: 0.7475\n",
      "Epoch 1747/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0374 - accuracy: 0.9986 - val_loss: 1.2336 - val_accuracy: 0.7462\n",
      "Epoch 1748/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0378 - accuracy: 0.9987 - val_loss: 1.2424 - val_accuracy: 0.7500\n",
      "Epoch 1749/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0376 - accuracy: 0.9982 - val_loss: 1.2368 - val_accuracy: 0.7537\n",
      "Epoch 1750/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0366 - accuracy: 0.9989 - val_loss: 1.2352 - val_accuracy: 0.7487\n",
      "Epoch 1751/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0369 - accuracy: 0.9988 - val_loss: 1.2283 - val_accuracy: 0.7475\n",
      "Epoch 1752/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0367 - accuracy: 0.9986 - val_loss: 1.2378 - val_accuracy: 0.7412\n",
      "Epoch 1753/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0359 - accuracy: 0.9989 - val_loss: 1.2481 - val_accuracy: 0.7375\n",
      "Epoch 1754/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0376 - accuracy: 0.9986 - val_loss: 1.2472 - val_accuracy: 0.7450\n",
      "Epoch 1755/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0386 - accuracy: 0.9982 - val_loss: 1.2391 - val_accuracy: 0.7475\n",
      "Epoch 1756/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0382 - accuracy: 0.9988 - val_loss: 1.2325 - val_accuracy: 0.7525\n",
      "Epoch 1757/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0384 - accuracy: 0.9980 - val_loss: 1.2271 - val_accuracy: 0.7462\n",
      "Epoch 1758/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0381 - accuracy: 0.9981 - val_loss: 1.2188 - val_accuracy: 0.7500\n",
      "Epoch 1759/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9987 - val_loss: 1.2187 - val_accuracy: 0.7425\n",
      "Epoch 1760/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9988 - val_loss: 1.2236 - val_accuracy: 0.7462\n",
      "Epoch 1761/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0373 - accuracy: 0.9987 - val_loss: 1.2154 - val_accuracy: 0.7500\n",
      "Epoch 1762/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0366 - accuracy: 0.9986 - val_loss: 1.2161 - val_accuracy: 0.7500\n",
      "Epoch 1763/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0380 - accuracy: 0.9982 - val_loss: 1.2229 - val_accuracy: 0.7512\n",
      "Epoch 1764/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0369 - accuracy: 0.9985 - val_loss: 1.2287 - val_accuracy: 0.7500\n",
      "Epoch 1765/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9988 - val_loss: 1.2324 - val_accuracy: 0.7525\n",
      "Epoch 1766/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0389 - accuracy: 0.9983 - val_loss: 1.2358 - val_accuracy: 0.7437\n",
      "Epoch 1767/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0381 - accuracy: 0.9986 - val_loss: 1.2313 - val_accuracy: 0.7487\n",
      "Epoch 1768/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0380 - accuracy: 0.9986 - val_loss: 1.2306 - val_accuracy: 0.7537\n",
      "Epoch 1769/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9987 - val_loss: 1.2316 - val_accuracy: 0.7462\n",
      "Epoch 1770/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0371 - accuracy: 0.9991 - val_loss: 1.2292 - val_accuracy: 0.7550\n",
      "Epoch 1771/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0386 - accuracy: 0.9980 - val_loss: 1.2206 - val_accuracy: 0.7500\n",
      "Epoch 1772/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0374 - accuracy: 0.9986 - val_loss: 1.2120 - val_accuracy: 0.7500\n",
      "Epoch 1773/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0370 - accuracy: 0.9989 - val_loss: 1.2156 - val_accuracy: 0.7462\n",
      "Epoch 1774/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0379 - accuracy: 0.9988 - val_loss: 1.2307 - val_accuracy: 0.7437\n",
      "Epoch 1775/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9988 - val_loss: 1.2476 - val_accuracy: 0.7462\n",
      "Epoch 1776/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0365 - accuracy: 0.9991 - val_loss: 1.2445 - val_accuracy: 0.7462\n",
      "Epoch 1777/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0377 - accuracy: 0.9986 - val_loss: 1.2367 - val_accuracy: 0.7462\n",
      "Epoch 1778/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0384 - accuracy: 0.9986 - val_loss: 1.2423 - val_accuracy: 0.7500\n",
      "Epoch 1779/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0369 - accuracy: 0.9990 - val_loss: 1.2478 - val_accuracy: 0.7500\n",
      "Epoch 1780/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0372 - accuracy: 0.9987 - val_loss: 1.2550 - val_accuracy: 0.7500\n",
      "Epoch 1781/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0365 - accuracy: 0.9989 - val_loss: 1.2587 - val_accuracy: 0.7537\n",
      "Epoch 1782/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0366 - accuracy: 0.9988 - val_loss: 1.2600 - val_accuracy: 0.7550\n",
      "Epoch 1783/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0366 - accuracy: 0.9986 - val_loss: 1.2598 - val_accuracy: 0.7500\n",
      "Epoch 1784/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0376 - accuracy: 0.9980 - val_loss: 1.2498 - val_accuracy: 0.7425\n",
      "Epoch 1785/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0376 - accuracy: 0.9986 - val_loss: 1.2579 - val_accuracy: 0.7500\n",
      "Epoch 1786/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0377 - accuracy: 0.9984 - val_loss: 1.2570 - val_accuracy: 0.7475\n",
      "Epoch 1787/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0385 - accuracy: 0.9983 - val_loss: 1.2619 - val_accuracy: 0.7587\n",
      "Epoch 1788/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0371 - accuracy: 0.9986 - val_loss: 1.2439 - val_accuracy: 0.7600\n",
      "Epoch 1789/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0374 - accuracy: 0.9987 - val_loss: 1.2368 - val_accuracy: 0.7612\n",
      "Epoch 1790/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0367 - accuracy: 0.9988 - val_loss: 1.2429 - val_accuracy: 0.7575\n",
      "Epoch 1791/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0371 - accuracy: 0.9986 - val_loss: 1.2496 - val_accuracy: 0.7537\n",
      "Epoch 1792/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0371 - accuracy: 0.9989 - val_loss: 1.2487 - val_accuracy: 0.7525\n",
      "Epoch 1793/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0367 - accuracy: 0.9987 - val_loss: 1.2467 - val_accuracy: 0.7512\n",
      "Epoch 1794/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0365 - accuracy: 0.9989 - val_loss: 1.2457 - val_accuracy: 0.7500\n",
      "Epoch 1795/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0367 - accuracy: 0.9986 - val_loss: 1.2427 - val_accuracy: 0.7525\n",
      "Epoch 1796/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9988 - val_loss: 1.2396 - val_accuracy: 0.7550\n",
      "Epoch 1797/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0361 - accuracy: 0.9993 - val_loss: 1.2434 - val_accuracy: 0.7575\n",
      "Epoch 1798/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0368 - accuracy: 0.9987 - val_loss: 1.2439 - val_accuracy: 0.7575\n",
      "Epoch 1799/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0370 - accuracy: 0.9991 - val_loss: 1.2443 - val_accuracy: 0.7550\n",
      "Epoch 1800/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0375 - accuracy: 0.9985 - val_loss: 1.2382 - val_accuracy: 0.7525\n",
      "Epoch 1801/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0367 - accuracy: 0.9986 - val_loss: 1.2428 - val_accuracy: 0.7537\n",
      "Epoch 1802/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0367 - accuracy: 0.9990 - val_loss: 1.2388 - val_accuracy: 0.7512\n",
      "Epoch 1803/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9988 - val_loss: 1.2299 - val_accuracy: 0.7575\n",
      "Epoch 1804/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9987 - val_loss: 1.2383 - val_accuracy: 0.7537\n",
      "Epoch 1805/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0362 - accuracy: 0.9986 - val_loss: 1.2443 - val_accuracy: 0.7575\n",
      "Epoch 1806/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0366 - accuracy: 0.9989 - val_loss: 1.2465 - val_accuracy: 0.7500\n",
      "Epoch 1807/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0367 - accuracy: 0.9988 - val_loss: 1.2433 - val_accuracy: 0.7537\n",
      "Epoch 1808/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0374 - accuracy: 0.9987 - val_loss: 1.2328 - val_accuracy: 0.7512\n",
      "Epoch 1809/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0368 - accuracy: 0.9989 - val_loss: 1.2388 - val_accuracy: 0.7500\n",
      "Epoch 1810/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0378 - accuracy: 0.9984 - val_loss: 1.2378 - val_accuracy: 0.7550\n",
      "Epoch 1811/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0365 - accuracy: 0.9988 - val_loss: 1.2532 - val_accuracy: 0.7525\n",
      "Epoch 1812/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0369 - accuracy: 0.9985 - val_loss: 1.2602 - val_accuracy: 0.7537\n",
      "Epoch 1813/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0372 - accuracy: 0.9986 - val_loss: 1.2644 - val_accuracy: 0.7437\n",
      "Epoch 1814/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0368 - accuracy: 0.9989 - val_loss: 1.2492 - val_accuracy: 0.7487\n",
      "Epoch 1815/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9986 - val_loss: 1.2426 - val_accuracy: 0.7475\n",
      "Epoch 1816/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0368 - accuracy: 0.9989 - val_loss: 1.2335 - val_accuracy: 0.7512\n",
      "Epoch 1817/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0369 - accuracy: 0.9989 - val_loss: 1.2299 - val_accuracy: 0.7500\n",
      "Epoch 1818/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0367 - accuracy: 0.9988 - val_loss: 1.2337 - val_accuracy: 0.7462\n",
      "Epoch 1819/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0369 - accuracy: 0.9984 - val_loss: 1.2290 - val_accuracy: 0.7437\n",
      "Epoch 1820/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0372 - accuracy: 0.9987 - val_loss: 1.2059 - val_accuracy: 0.7487\n",
      "Epoch 1821/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0375 - accuracy: 0.9989 - val_loss: 1.2021 - val_accuracy: 0.7462\n",
      "Epoch 1822/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0366 - accuracy: 0.9990 - val_loss: 1.2004 - val_accuracy: 0.7537\n",
      "Epoch 1823/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0375 - accuracy: 0.9982 - val_loss: 1.1894 - val_accuracy: 0.7562\n",
      "Epoch 1824/2000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0373 - accuracy: 0.9985 - val_loss: 1.1903 - val_accuracy: 0.7550\n",
      "Epoch 1825/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0365 - accuracy: 0.9986 - val_loss: 1.1981 - val_accuracy: 0.7625\n",
      "Epoch 1826/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0367 - accuracy: 0.9988 - val_loss: 1.2101 - val_accuracy: 0.7562\n",
      "Epoch 1827/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0367 - accuracy: 0.9986 - val_loss: 1.2134 - val_accuracy: 0.7562\n",
      "Epoch 1828/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0369 - accuracy: 0.9986 - val_loss: 1.2207 - val_accuracy: 0.7525\n",
      "Epoch 1829/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0370 - accuracy: 0.9989 - val_loss: 1.2205 - val_accuracy: 0.7600\n",
      "Epoch 1830/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0361 - accuracy: 0.9989 - val_loss: 1.2090 - val_accuracy: 0.7562\n",
      "Epoch 1831/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0370 - accuracy: 0.9985 - val_loss: 1.1963 - val_accuracy: 0.7587\n",
      "Epoch 1832/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0369 - accuracy: 0.9986 - val_loss: 1.1952 - val_accuracy: 0.7600\n",
      "Epoch 1833/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0376 - accuracy: 0.9987 - val_loss: 1.2079 - val_accuracy: 0.7625\n",
      "Epoch 1834/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9984 - val_loss: 1.2151 - val_accuracy: 0.7650\n",
      "Epoch 1835/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0365 - accuracy: 0.9989 - val_loss: 1.2222 - val_accuracy: 0.7637\n",
      "Epoch 1836/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0371 - accuracy: 0.9985 - val_loss: 1.2236 - val_accuracy: 0.7550\n",
      "Epoch 1837/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0372 - accuracy: 0.9988 - val_loss: 1.2181 - val_accuracy: 0.7600\n",
      "Epoch 1838/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0358 - accuracy: 0.9993 - val_loss: 1.2139 - val_accuracy: 0.7612\n",
      "Epoch 1839/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0368 - accuracy: 0.9988 - val_loss: 1.2102 - val_accuracy: 0.7575\n",
      "Epoch 1840/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0368 - accuracy: 0.9986 - val_loss: 1.2285 - val_accuracy: 0.7550\n",
      "Epoch 1841/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0364 - accuracy: 0.9988 - val_loss: 1.2341 - val_accuracy: 0.7575\n",
      "Epoch 1842/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0365 - accuracy: 0.9989 - val_loss: 1.2391 - val_accuracy: 0.7525\n",
      "Epoch 1843/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0369 - accuracy: 0.9988 - val_loss: 1.2276 - val_accuracy: 0.7500\n",
      "Epoch 1844/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0370 - accuracy: 0.9986 - val_loss: 1.2335 - val_accuracy: 0.7550\n",
      "Epoch 1845/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0369 - accuracy: 0.9986 - val_loss: 1.2292 - val_accuracy: 0.7550\n",
      "Epoch 1846/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0369 - accuracy: 0.9988 - val_loss: 1.2312 - val_accuracy: 0.7587\n",
      "Epoch 1847/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0365 - accuracy: 0.9988 - val_loss: 1.2287 - val_accuracy: 0.7637\n",
      "Epoch 1848/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0357 - accuracy: 0.9991 - val_loss: 1.2279 - val_accuracy: 0.7600\n",
      "Epoch 1849/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0369 - accuracy: 0.9986 - val_loss: 1.2238 - val_accuracy: 0.7500\n",
      "Epoch 1850/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0374 - accuracy: 0.9984 - val_loss: 1.2275 - val_accuracy: 0.7500\n",
      "Epoch 1851/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0370 - accuracy: 0.9988 - val_loss: 1.2286 - val_accuracy: 0.7487\n",
      "Epoch 1852/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0364 - accuracy: 0.9989 - val_loss: 1.2270 - val_accuracy: 0.7525\n",
      "Epoch 1853/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0364 - accuracy: 0.9986 - val_loss: 1.2272 - val_accuracy: 0.7525\n",
      "Epoch 1854/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0368 - accuracy: 0.9989 - val_loss: 1.2389 - val_accuracy: 0.7525\n",
      "Epoch 1855/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0361 - accuracy: 0.9989 - val_loss: 1.2413 - val_accuracy: 0.7487\n",
      "Epoch 1856/2000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.0355 - accuracy: 0.9992 - val_loss: 1.2392 - val_accuracy: 0.7475\n",
      "Epoch 1857/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0372 - accuracy: 0.9986 - val_loss: 1.2373 - val_accuracy: 0.7500\n",
      "Epoch 1858/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0371 - accuracy: 0.9987 - val_loss: 1.2310 - val_accuracy: 0.7500\n",
      "Epoch 1859/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0363 - accuracy: 0.9989 - val_loss: 1.2310 - val_accuracy: 0.7512\n",
      "Epoch 1860/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0358 - accuracy: 0.9992 - val_loss: 1.2374 - val_accuracy: 0.7462\n",
      "Epoch 1861/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0363 - accuracy: 0.9987 - val_loss: 1.2368 - val_accuracy: 0.7562\n",
      "Epoch 1862/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0357 - accuracy: 0.9993 - val_loss: 1.2327 - val_accuracy: 0.7512\n",
      "Epoch 1863/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0381 - accuracy: 0.9982 - val_loss: 1.2349 - val_accuracy: 0.7575\n",
      "Epoch 1864/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0372 - accuracy: 0.9986 - val_loss: 1.2293 - val_accuracy: 0.7562\n",
      "Epoch 1865/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0375 - accuracy: 0.9980 - val_loss: 1.2239 - val_accuracy: 0.7550\n",
      "Epoch 1866/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0358 - accuracy: 0.9991 - val_loss: 1.2265 - val_accuracy: 0.7500\n",
      "Epoch 1867/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0369 - accuracy: 0.9988 - val_loss: 1.2270 - val_accuracy: 0.7562\n",
      "Epoch 1868/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0374 - accuracy: 0.9985 - val_loss: 1.2288 - val_accuracy: 0.7587\n",
      "Epoch 1869/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0357 - accuracy: 0.9993 - val_loss: 1.2229 - val_accuracy: 0.7600\n",
      "Epoch 1870/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0374 - accuracy: 0.9986 - val_loss: 1.2180 - val_accuracy: 0.7587\n",
      "Epoch 1871/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0367 - accuracy: 0.9987 - val_loss: 1.2031 - val_accuracy: 0.7512\n",
      "Epoch 1872/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0363 - accuracy: 0.9989 - val_loss: 1.2058 - val_accuracy: 0.7512\n",
      "Epoch 1873/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0375 - accuracy: 0.9981 - val_loss: 1.2139 - val_accuracy: 0.7462\n",
      "Epoch 1874/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0374 - accuracy: 0.9984 - val_loss: 1.2295 - val_accuracy: 0.7512\n",
      "Epoch 1875/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0371 - accuracy: 0.9984 - val_loss: 1.2296 - val_accuracy: 0.7437\n",
      "Epoch 1876/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9988 - val_loss: 1.2356 - val_accuracy: 0.7412\n",
      "Epoch 1877/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0364 - accuracy: 0.9988 - val_loss: 1.2377 - val_accuracy: 0.7475\n",
      "Epoch 1878/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0371 - accuracy: 0.9984 - val_loss: 1.2228 - val_accuracy: 0.7525\n",
      "Epoch 1879/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0362 - accuracy: 0.9991 - val_loss: 1.2146 - val_accuracy: 0.7587\n",
      "Epoch 1880/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0361 - accuracy: 0.9989 - val_loss: 1.2196 - val_accuracy: 0.7550\n",
      "Epoch 1881/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0356 - accuracy: 0.9991 - val_loss: 1.2175 - val_accuracy: 0.7612\n",
      "Epoch 1882/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0365 - accuracy: 0.9989 - val_loss: 1.2266 - val_accuracy: 0.7612\n",
      "Epoch 1883/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0362 - accuracy: 0.9989 - val_loss: 1.2286 - val_accuracy: 0.7650\n",
      "Epoch 1884/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0366 - accuracy: 0.9986 - val_loss: 1.2321 - val_accuracy: 0.7612\n",
      "Epoch 1885/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0364 - accuracy: 0.9989 - val_loss: 1.2415 - val_accuracy: 0.7575\n",
      "Epoch 1886/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0365 - accuracy: 0.9988 - val_loss: 1.2509 - val_accuracy: 0.7587\n",
      "Epoch 1887/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0362 - accuracy: 0.9986 - val_loss: 1.2531 - val_accuracy: 0.7525\n",
      "Epoch 1888/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9983 - val_loss: 1.2598 - val_accuracy: 0.7500\n",
      "Epoch 1889/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0360 - accuracy: 0.9988 - val_loss: 1.2696 - val_accuracy: 0.7475\n",
      "Epoch 1890/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0364 - accuracy: 0.9989 - val_loss: 1.2787 - val_accuracy: 0.7412\n",
      "Epoch 1891/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0364 - accuracy: 0.9988 - val_loss: 1.2484 - val_accuracy: 0.7525\n",
      "Epoch 1892/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0375 - accuracy: 0.9982 - val_loss: 1.2340 - val_accuracy: 0.7512\n",
      "Epoch 1893/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0359 - accuracy: 0.9990 - val_loss: 1.2265 - val_accuracy: 0.7562\n",
      "Epoch 1894/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0375 - accuracy: 0.9982 - val_loss: 1.2266 - val_accuracy: 0.7562\n",
      "Epoch 1895/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9984 - val_loss: 1.2232 - val_accuracy: 0.7600\n",
      "Epoch 1896/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0362 - accuracy: 0.9987 - val_loss: 1.2224 - val_accuracy: 0.7612\n",
      "Epoch 1897/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0381 - accuracy: 0.9984 - val_loss: 1.2072 - val_accuracy: 0.7612\n",
      "Epoch 1898/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9989 - val_loss: 1.2042 - val_accuracy: 0.7600\n",
      "Epoch 1899/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0377 - accuracy: 0.9986 - val_loss: 1.2033 - val_accuracy: 0.7600\n",
      "Epoch 1900/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0370 - accuracy: 0.9988 - val_loss: 1.1907 - val_accuracy: 0.7675\n",
      "Epoch 1901/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0384 - accuracy: 0.9982 - val_loss: 1.1760 - val_accuracy: 0.7725\n",
      "Epoch 1902/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9989 - val_loss: 1.1736 - val_accuracy: 0.7612\n",
      "Epoch 1903/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0371 - accuracy: 0.9986 - val_loss: 1.1777 - val_accuracy: 0.7600\n",
      "Epoch 1904/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0361 - accuracy: 0.9989 - val_loss: 1.1748 - val_accuracy: 0.7587\n",
      "Epoch 1905/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9987 - val_loss: 1.1853 - val_accuracy: 0.7600\n",
      "Epoch 1906/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0369 - accuracy: 0.9988 - val_loss: 1.1805 - val_accuracy: 0.7575\n",
      "Epoch 1907/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0375 - accuracy: 0.9986 - val_loss: 1.1835 - val_accuracy: 0.7587\n",
      "Epoch 1908/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0374 - accuracy: 0.9984 - val_loss: 1.1920 - val_accuracy: 0.7562\n",
      "Epoch 1909/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0374 - accuracy: 0.9986 - val_loss: 1.1935 - val_accuracy: 0.7575\n",
      "Epoch 1910/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0363 - accuracy: 0.9987 - val_loss: 1.1956 - val_accuracy: 0.7600\n",
      "Epoch 1911/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0379 - accuracy: 0.9982 - val_loss: 1.1847 - val_accuracy: 0.7537\n",
      "Epoch 1912/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0365 - accuracy: 0.9990 - val_loss: 1.1795 - val_accuracy: 0.7525\n",
      "Epoch 1913/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0371 - accuracy: 0.9986 - val_loss: 1.1708 - val_accuracy: 0.7587\n",
      "Epoch 1914/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0357 - accuracy: 0.9992 - val_loss: 1.1719 - val_accuracy: 0.7612\n",
      "Epoch 1915/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0368 - accuracy: 0.9988 - val_loss: 1.1738 - val_accuracy: 0.7587\n",
      "Epoch 1916/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0377 - accuracy: 0.9986 - val_loss: 1.1837 - val_accuracy: 0.7600\n",
      "Epoch 1917/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0364 - accuracy: 0.9991 - val_loss: 1.1882 - val_accuracy: 0.7537\n",
      "Epoch 1918/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0362 - accuracy: 0.9990 - val_loss: 1.1903 - val_accuracy: 0.7587\n",
      "Epoch 1919/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0367 - accuracy: 0.9988 - val_loss: 1.1977 - val_accuracy: 0.7575\n",
      "Epoch 1920/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0370 - accuracy: 0.9988 - val_loss: 1.1976 - val_accuracy: 0.7537\n",
      "Epoch 1921/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0357 - accuracy: 0.9993 - val_loss: 1.1908 - val_accuracy: 0.7512\n",
      "Epoch 1922/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9986 - val_loss: 1.1733 - val_accuracy: 0.7600\n",
      "Epoch 1923/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0361 - accuracy: 0.9989 - val_loss: 1.1790 - val_accuracy: 0.7562\n",
      "Epoch 1924/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0371 - accuracy: 0.9989 - val_loss: 1.1909 - val_accuracy: 0.7662\n",
      "Epoch 1925/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0371 - accuracy: 0.9986 - val_loss: 1.1742 - val_accuracy: 0.7662\n",
      "Epoch 1926/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0371 - accuracy: 0.9986 - val_loss: 1.1756 - val_accuracy: 0.7662\n",
      "Epoch 1927/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0372 - accuracy: 0.9984 - val_loss: 1.1834 - val_accuracy: 0.7725\n",
      "Epoch 1928/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0368 - accuracy: 0.9988 - val_loss: 1.1859 - val_accuracy: 0.7700\n",
      "Epoch 1929/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0365 - accuracy: 0.9985 - val_loss: 1.2057 - val_accuracy: 0.7675\n",
      "Epoch 1930/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0381 - accuracy: 0.9984 - val_loss: 1.2042 - val_accuracy: 0.7625\n",
      "Epoch 1931/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0364 - accuracy: 0.9990 - val_loss: 1.1966 - val_accuracy: 0.7650\n",
      "Epoch 1932/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9988 - val_loss: 1.1887 - val_accuracy: 0.7650\n",
      "Epoch 1933/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0364 - accuracy: 0.9988 - val_loss: 1.1851 - val_accuracy: 0.7612\n",
      "Epoch 1934/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0361 - accuracy: 0.9990 - val_loss: 1.1880 - val_accuracy: 0.7675\n",
      "Epoch 1935/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0375 - accuracy: 0.9984 - val_loss: 1.1887 - val_accuracy: 0.7637\n",
      "Epoch 1936/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0359 - accuracy: 0.9992 - val_loss: 1.1891 - val_accuracy: 0.7625\n",
      "Epoch 1937/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0360 - accuracy: 0.9989 - val_loss: 1.1893 - val_accuracy: 0.7562\n",
      "Epoch 1938/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0363 - accuracy: 0.9989 - val_loss: 1.1890 - val_accuracy: 0.7587\n",
      "Epoch 1939/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9984 - val_loss: 1.2112 - val_accuracy: 0.7537\n",
      "Epoch 1940/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9984 - val_loss: 1.2168 - val_accuracy: 0.7487\n",
      "Epoch 1941/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0364 - accuracy: 0.9991 - val_loss: 1.2071 - val_accuracy: 0.7525\n",
      "Epoch 1942/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0368 - accuracy: 0.9986 - val_loss: 1.2261 - val_accuracy: 0.7487\n",
      "Epoch 1943/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0356 - accuracy: 0.9989 - val_loss: 1.2421 - val_accuracy: 0.7512\n",
      "Epoch 1944/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0364 - accuracy: 0.9984 - val_loss: 1.2374 - val_accuracy: 0.7487\n",
      "Epoch 1945/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0365 - accuracy: 0.9987 - val_loss: 1.2350 - val_accuracy: 0.7400\n",
      "Epoch 1946/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0373 - accuracy: 0.9988 - val_loss: 1.2401 - val_accuracy: 0.7450\n",
      "Epoch 1947/2000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.0363 - accuracy: 0.9988 - val_loss: 1.2333 - val_accuracy: 0.7425\n",
      "Epoch 1948/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0376 - accuracy: 0.9984 - val_loss: 1.2346 - val_accuracy: 0.7362\n",
      "Epoch 1949/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0384 - accuracy: 0.9981 - val_loss: 1.2374 - val_accuracy: 0.7425\n",
      "Epoch 1950/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0374 - accuracy: 0.9986 - val_loss: 1.2370 - val_accuracy: 0.7475\n",
      "Epoch 1951/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0370 - accuracy: 0.9984 - val_loss: 1.2268 - val_accuracy: 0.7437\n",
      "Epoch 1952/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0393 - accuracy: 0.9982 - val_loss: 1.1985 - val_accuracy: 0.7475\n",
      "Epoch 1953/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0390 - accuracy: 0.9984 - val_loss: 1.1827 - val_accuracy: 0.7612\n",
      "Epoch 1954/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0374 - accuracy: 0.9985 - val_loss: 1.1729 - val_accuracy: 0.7550\n",
      "Epoch 1955/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0378 - accuracy: 0.9986 - val_loss: 1.1756 - val_accuracy: 0.7575\n",
      "Epoch 1956/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0375 - accuracy: 0.9982 - val_loss: 1.1784 - val_accuracy: 0.7512\n",
      "Epoch 1957/2000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0379 - accuracy: 0.9986 - val_loss: 1.1810 - val_accuracy: 0.7550\n",
      "Epoch 1958/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0377 - accuracy: 0.9988 - val_loss: 1.1680 - val_accuracy: 0.7487\n",
      "Epoch 1959/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0378 - accuracy: 0.9983 - val_loss: 1.1670 - val_accuracy: 0.7525\n",
      "Epoch 1960/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0374 - accuracy: 0.9988 - val_loss: 1.1577 - val_accuracy: 0.7550\n",
      "Epoch 1961/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0370 - accuracy: 0.9988 - val_loss: 1.1566 - val_accuracy: 0.7500\n",
      "Epoch 1962/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0390 - accuracy: 0.9980 - val_loss: 1.1599 - val_accuracy: 0.7500\n",
      "Epoch 1963/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0373 - accuracy: 0.9986 - val_loss: 1.1570 - val_accuracy: 0.7525\n",
      "Epoch 1964/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0369 - accuracy: 0.9989 - val_loss: 1.1604 - val_accuracy: 0.7512\n",
      "Epoch 1965/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0369 - accuracy: 0.9988 - val_loss: 1.1629 - val_accuracy: 0.7512\n",
      "Epoch 1966/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0372 - accuracy: 0.9989 - val_loss: 1.1647 - val_accuracy: 0.7500\n",
      "Epoch 1967/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0374 - accuracy: 0.9986 - val_loss: 1.1810 - val_accuracy: 0.7575\n",
      "Epoch 1968/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0369 - accuracy: 0.9991 - val_loss: 1.1897 - val_accuracy: 0.7575\n",
      "Epoch 1969/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0372 - accuracy: 0.9987 - val_loss: 1.1917 - val_accuracy: 0.7587\n",
      "Epoch 1970/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0366 - accuracy: 0.9989 - val_loss: 1.1880 - val_accuracy: 0.7600\n",
      "Epoch 1971/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0363 - accuracy: 0.9991 - val_loss: 1.1920 - val_accuracy: 0.7587\n",
      "Epoch 1972/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0380 - accuracy: 0.9984 - val_loss: 1.1841 - val_accuracy: 0.7587\n",
      "Epoch 1973/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0372 - accuracy: 0.9988 - val_loss: 1.1831 - val_accuracy: 0.7550\n",
      "Epoch 1974/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0368 - accuracy: 0.9988 - val_loss: 1.2030 - val_accuracy: 0.7537\n",
      "Epoch 1975/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0376 - accuracy: 0.9986 - val_loss: 1.2120 - val_accuracy: 0.7525\n",
      "Epoch 1976/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0378 - accuracy: 0.9987 - val_loss: 1.2152 - val_accuracy: 0.7562\n",
      "Epoch 1977/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0372 - accuracy: 0.9984 - val_loss: 1.2135 - val_accuracy: 0.7525\n",
      "Epoch 1978/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0374 - accuracy: 0.9988 - val_loss: 1.2115 - val_accuracy: 0.7512\n",
      "Epoch 1979/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0386 - accuracy: 0.9984 - val_loss: 1.2172 - val_accuracy: 0.7487\n",
      "Epoch 1980/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0372 - accuracy: 0.9986 - val_loss: 1.2089 - val_accuracy: 0.7512\n",
      "Epoch 1981/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0377 - accuracy: 0.9984 - val_loss: 1.2068 - val_accuracy: 0.7487\n",
      "Epoch 1982/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0374 - accuracy: 0.9988 - val_loss: 1.2049 - val_accuracy: 0.7512\n",
      "Epoch 1983/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0374 - accuracy: 0.9988 - val_loss: 1.2055 - val_accuracy: 0.7512\n",
      "Epoch 1984/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0368 - accuracy: 0.9991 - val_loss: 1.2182 - val_accuracy: 0.7562\n",
      "Epoch 1985/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0368 - accuracy: 0.9991 - val_loss: 1.2241 - val_accuracy: 0.7575\n",
      "Epoch 1986/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0364 - accuracy: 0.9988 - val_loss: 1.2243 - val_accuracy: 0.7550\n",
      "Epoch 1987/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0365 - accuracy: 0.9988 - val_loss: 1.2205 - val_accuracy: 0.7575\n",
      "Epoch 1988/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9985 - val_loss: 1.2109 - val_accuracy: 0.7537\n",
      "Epoch 1989/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0370 - accuracy: 0.9989 - val_loss: 1.2014 - val_accuracy: 0.7537\n",
      "Epoch 1990/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0369 - accuracy: 0.9987 - val_loss: 1.1989 - val_accuracy: 0.7537\n",
      "Epoch 1991/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0381 - accuracy: 0.9987 - val_loss: 1.2088 - val_accuracy: 0.7537\n",
      "Epoch 1992/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0369 - accuracy: 0.9984 - val_loss: 1.2106 - val_accuracy: 0.7537\n",
      "Epoch 1993/2000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0363 - accuracy: 0.9992 - val_loss: 1.2078 - val_accuracy: 0.7512\n",
      "Epoch 1994/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0362 - accuracy: 0.9989 - val_loss: 1.1996 - val_accuracy: 0.7537\n",
      "Epoch 1995/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0367 - accuracy: 0.9991 - val_loss: 1.2090 - val_accuracy: 0.7550\n",
      "Epoch 1996/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0372 - accuracy: 0.9987 - val_loss: 1.2236 - val_accuracy: 0.7587\n",
      "Epoch 1997/2000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0362 - accuracy: 0.9989 - val_loss: 1.2262 - val_accuracy: 0.7537\n",
      "Epoch 1998/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0387 - accuracy: 0.9980 - val_loss: 1.2271 - val_accuracy: 0.7450\n",
      "Epoch 1999/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0370 - accuracy: 0.9986 - val_loss: 1.2241 - val_accuracy: 0.7500\n",
      "Epoch 2000/2000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0368 - accuracy: 0.9987 - val_loss: 1.2186 - val_accuracy: 0.7562\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x2970c2d30>"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history = model1.fit(fine_tune_data_train_red, fine_tune_labels_train, epochs=EPOCHS, batch_size=800, validation_data=(fine_tune_data_test_red, fine_tune_labels_test), validation_batch_size=800, callbacks=[model_checkpoint_callback])\n",
    "# history = model1.fit(fine_tune_data, fine_tune_labels, epochs=EPOCHS, batch_size=1600, callbacks=[model_checkpoint_callback])\n",
    "model1.save_weights(\"./Models/EfficientNet-kP-PCA/effnet-kp-model-weights\")\n",
    "model1.load_weights('./Models/EfficientNet-kP-PCA/effnet-kp-model-weights')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "9116a6a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_train_loss(history, model_name):  \n",
    "    plt.figure(figsize=(8,6))\n",
    "    plt.plot(history.history['loss'])\n",
    "    plt.plot(history.history['accuracy'])\n",
    "    plt.plot(history.history['val_loss'])\n",
    "    plt.plot(history.history['val_accuracy'])\n",
    "    plt.title(model_name+' loss')\n",
    "    plt.ylabel('loss')\n",
    "    plt.xlabel('epochs')\n",
    "    plt.legend(['train loss', 'train accuracy', 'val loss', 'val accuracy'], loc='upper left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "c55b5870",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_train_loss(history, 'Fine-Tuned EfficientNet-kPiece Model')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03390fa6",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48701107",
   "metadata": {},
   "source": [
    "#### On Train Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0594069",
   "metadata": {},
   "source": [
    "Now, after the model is being trained, we will look at the performance of the model on the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "cf7ded38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 69/475 [===>..........................] - ETA: 0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-07 23:28:44.029002: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "475/475 [==============================] - 1s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "train_preds = model1.predict(fine_tune_data_train_red)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "f2a28269",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_pred_labels = np.argmax(train_preds, axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "768775b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1, 13, 13, ...,  3,  9,  9])"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_pred_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "eff52a3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15197"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(train_pred_labels == train_labels['label'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fd403fa",
   "metadata": {},
   "source": [
    "Looking at the misclassified images to get a better idea about what features the model is unable to capture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "8f594a33",
   "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>id</th>\n",
       "      <th>images</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3597</th>\n",
       "      <td>3781</td>\n",
       "      <td>./Data/train/train/3781.tif</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12255</th>\n",
       "      <td>12896</td>\n",
       "      <td>./Data/train/train/12896.tif</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14569</th>\n",
       "      <td>15333</td>\n",
       "      <td>./Data/train/train/15333.tif</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id                        images  label\n",
       "3597    3781   ./Data/train/train/3781.tif      0\n",
       "12255  12896  ./Data/train/train/12896.tif      9\n",
       "14569  15333  ./Data/train/train/15333.tif      7"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mis_train_data = train_labels[train_pred_labels != train_labels['label']]\n",
    "mis_train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "2ea8f949",
   "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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>fscore</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998978</td>\n",
       "      <td>0.999488</td>\n",
       "      <td>978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.998926</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999463</td>\n",
       "      <td>930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998905</td>\n",
       "      <td>0.999452</td>\n",
       "      <td>913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998951</td>\n",
       "      <td>0.999475</td>\n",
       "      <td>953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.997957</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998978</td>\n",
       "      <td>977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>945</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    precision    recall    fscore  support\n",
       "0    1.000000  0.998978  0.999488      978\n",
       "1    1.000000  1.000000  1.000000      925\n",
       "2    1.000000  1.000000  1.000000      925\n",
       "3    1.000000  1.000000  1.000000      947\n",
       "4    0.998926  1.000000  0.999463      930\n",
       "5    1.000000  1.000000  1.000000      962\n",
       "6    1.000000  1.000000  1.000000      998\n",
       "7    1.000000  0.998905  0.999452      913\n",
       "8    1.000000  1.000000  1.000000     1005\n",
       "9    1.000000  0.998951  0.999475      953\n",
       "10   1.000000  1.000000  1.000000      916\n",
       "11   1.000000  1.000000  1.000000      955\n",
       "12   0.997957  1.000000  0.998978      977\n",
       "13   1.000000  1.000000  1.000000      943\n",
       "14   1.000000  1.000000  1.000000      928\n",
       "15   1.000000  1.000000  1.000000      945"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision, recall, fscore, support = score(train_labels['label'], train_pred_labels)\n",
    "pd.DataFrame({\n",
    "    'precision': precision,\n",
    "    'recall': recall,\n",
    "    'fscore': fscore,\n",
    "    'support': support\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6e51475",
   "metadata": {},
   "source": [
    "#### On Train-Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "14a7854d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25/25 [==============================] - 0s 3ms/step\n"
     ]
    }
   ],
   "source": [
    "test_preds = model1.predict(fine_tune_data_test_red)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "d585fa70",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_pred_labels = np.argmax(test_preds, axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "9a77dc3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11,  8,  6,  7,  1,  9, 13,  9, 14,  1,  6,  0,  7, 14,  1, 11,  0,\n",
       "        9,  6, 12, 12, 14,  3,  9,  6,  0,  3,  0,  3,  3, 11,  1,  9,  2,\n",
       "        1,  8,  6,  7,  1,  9,  6, 12, 14,  8,  5, 14,  3, 11, 13,  4,  0,\n",
       "       10, 12,  0, 11, 14, 15,  6,  9,  2, 15,  9,  2, 11,  4, 10,  8,  0,\n",
       "        6,  3,  4,  4, 12,  0,  8,  6,  7,  0, 12, 11, 11,  5,  4, 12,  9,\n",
       "        8,  5, 13, 13, 13, 10, 11,  4, 10,  1, 10, 11,  8, 13, 10,  9, 11,\n",
       "       12,  9,  1, 14,  8,  2,  5, 10,  7, 10,  9,  4, 12, 14, 12,  1, 10,\n",
       "        6,  8,  1,  0, 12,  1,  7, 10, 12, 15,  8,  3, 11,  6,  5,  6,  9,\n",
       "       14, 10,  7, 14, 10,  8, 11, 12,  8, 10,  8,  4,  2, 13,  0,  0,  4,\n",
       "       14,  4,  9,  4, 14,  7,  7,  8,  4,  4, 10, 13,  8,  2,  8,  8,  8,\n",
       "       13,  6,  3, 11,  7, 12, 10, 14, 14,  8,  6, 11,  0, 11,  6, 13,  4,\n",
       "       14,  1,  7, 15,  0,  3,  0, 10, 13, 13, 10,  1, 11,  7,  1,  9,  1,\n",
       "        9,  5, 13, 15, 12,  1,  9,  8,  7,  9, 13, 12,  4, 12,  8,  0,  9,\n",
       "       13, 15,  5,  0, 10,  2,  2,  9, 11,  7,  0,  0, 12,  3,  5,  2,  1,\n",
       "        4, 11, 12,  0, 12,  8, 13, 10,  0,  0, 11,  0, 11, 13, 15, 15,  8,\n",
       "        1, 10, 13, 12,  1,  9, 14,  6, 13, 12,  3, 11, 10, 11,  1, 14,  3,\n",
       "        0, 11,  4,  3,  0, 11,  6,  2,  7,  8, 10, 10,  8,  5,  7,  8, 13,\n",
       "        6,  8, 15,  0,  2,  3,  4, 10, 12,  8, 13,  2,  7, 15, 10, 10,  5,\n",
       "        3, 13, 11, 10,  9,  2, 13,  3,  0,  7, 12,  4, 14,  7, 13,  2, 15,\n",
       "        9, 15, 15,  1,  0, 15,  3, 11,  0, 15, 15, 13, 10,  4, 13,  1, 15,\n",
       "       13,  0,  3,  3,  0,  8,  2, 15, 11,  3,  3,  2,  5, 14,  4,  0,  3,\n",
       "        2, 12,  1,  3,  0, 12,  9,  0,  7, 15, 15,  3,  2, 12, 15,  3,  6,\n",
       "       13, 14, 14,  0,  5, 13, 12,  6, 12, 13,  6,  9,  8,  7, 10,  1,  2,\n",
       "       11, 14,  2, 14, 15, 13,  0,  1,  2, 10, 13,  0, 14,  6,  9,  5,  2,\n",
       "        1,  3, 14,  8, 13, 11,  4,  8, 13,  1,  4,  7, 13, 14,  5, 11,  8,\n",
       "        0,  1, 11,  0,  1,  3,  7,  2,  5, 14, 15,  9,  1, 13,  3, 11,  8,\n",
       "       12,  0,  3, 11,  5,  2,  6, 14,  7,  8,  3,  2, 13, 12, 11, 13, 11,\n",
       "        9,  2, 10,  5, 12,  0, 15,  1,  9,  9,  0, 12,  7, 14,  9,  9,  1,\n",
       "       15, 10,  4,  8,  9,  3,  7,  8,  6, 12, 13, 10,  7,  5,  1, 13, 10,\n",
       "        2,  3,  1, 10,  9,  7,  8,  1,  4, 11,  6, 11, 15,  0,  8,  7, 11,\n",
       "        0,  1,  0, 12, 11,  1, 10,  7,  9,  1, 14,  3, 15, 14,  6, 14,  7,\n",
       "        1, 14,  6, 14,  9,  7,  1, 14,  1, 14,  2, 13,  7,  2,  0,  8,  8,\n",
       "        0,  8,  6, 15, 14,  3,  6, 11,  8, 14,  6,  7,  1,  9, 11,  0,  0,\n",
       "        3, 10,  8, 10,  5,  2,  7, 12, 14,  9,  8, 12,  9, 13, 10,  5,  5,\n",
       "        9, 11,  6, 10, 12,  8, 10, 14, 14,  9,  6, 15,  8,  1,  8,  5,  5,\n",
       "        5,  5,  0, 15, 14,  5,  3,  2, 12,  1, 15, 11, 10,  8, 13,  1,  7,\n",
       "       12, 11,  4,  4,  4,  0, 11, 15, 14, 13, 12,  8,  5, 12, 13,  4, 13,\n",
       "       15, 15,  1,  5,  3,  7, 13, 12,  8,  7,  5, 15, 12,  9,  4,  2, 14,\n",
       "        1, 13,  6,  6,  3,  7,  7,  5,  5, 12,  6,  8,  0,  4,  7,  7,  0,\n",
       "       12,  6, 12,  0,  3, 11,  2,  3,  5,  4, 13,  1, 15,  7, 14,  0,  8,\n",
       "       12,  5, 12,  7,  2, 15,  8,  8,  4,  6, 12,  2,  5, 13,  7,  6,  3,\n",
       "       13,  3,  2, 14,  0,  8, 12, 12,  8, 13, 13, 14,  4,  6,  2,  5, 14,\n",
       "       12, 13, 13,  4, 12,  0,  1,  0,  8,  1,  0,  8,  4,  9,  2, 11,  0,\n",
       "        1,  2, 14, 15,  0,  8,  9, 11, 14,  8,  9,  7, 12, 11, 12, 13,  3,\n",
       "        7,  7, 13,  8, 12,  2,  5, 13,  7, 10,  7,  8, 12,  6, 14,  0, 10,\n",
       "       11,  3, 12, 13, 14,  7,  2,  8,  2, 13,  2,  5,  8, 14, 12, 14, 12,\n",
       "        3,  5,  8, 12,  4, 11, 15,  0,  9,  9,  5, 13, 10,  2,  2, 12, 15,\n",
       "        6])"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pred_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "8bce361f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "605"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(test_pred_labels == test_labels['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "e07d40ba",
   "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>id</th>\n",
       "      <th>images</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41</td>\n",
       "      <td>./Data/train/train/41.tif</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65</td>\n",
       "      <td>./Data/train/train/65.tif</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100</td>\n",
       "      <td>./Data/train/train/100.tif</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>164</td>\n",
       "      <td>./Data/train/train/164.tif</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>372</td>\n",
       "      <td>./Data/train/train/372.tif</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>15508</td>\n",
       "      <td>./Data/train/train/15508.tif</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779</th>\n",
       "      <td>15568</td>\n",
       "      <td>./Data/train/train/15568.tif</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785</th>\n",
       "      <td>15621</td>\n",
       "      <td>./Data/train/train/15621.tif</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>15687</td>\n",
       "      <td>./Data/train/train/15687.tif</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>797</th>\n",
       "      <td>15832</td>\n",
       "      <td>./Data/train/train/15832.tif</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>195 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                        images  label\n",
       "3       41     ./Data/train/train/41.tif      1\n",
       "4       65     ./Data/train/train/65.tif     14\n",
       "7      100    ./Data/train/train/100.tif     10\n",
       "11     164    ./Data/train/train/164.tif     15\n",
       "19     372    ./Data/train/train/372.tif     13\n",
       "..     ...                           ...    ...\n",
       "773  15508  ./Data/train/train/15508.tif     10\n",
       "779  15568  ./Data/train/train/15568.tif      9\n",
       "785  15621  ./Data/train/train/15621.tif     15\n",
       "788  15687  ./Data/train/train/15687.tif     12\n",
       "797  15832  ./Data/train/train/15832.tif     11\n",
       "\n",
       "[195 rows x 3 columns]"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mis_train_data = test_labels[test_pred_labels != test_labels['label']]\n",
    "mis_train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "0f0b85fa",
   "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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>fscore</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.650794</td>\n",
       "      <td>0.759259</td>\n",
       "      <td>0.700855</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.529412</td>\n",
       "      <td>0.613636</td>\n",
       "      <td>0.568421</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.911111</td>\n",
       "      <td>0.953488</td>\n",
       "      <td>0.931818</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.913043</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.810811</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.821918</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.641026</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.561798</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.878049</td>\n",
       "      <td>0.782609</td>\n",
       "      <td>0.827586</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.784314</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.776699</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.787879</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.787234</td>\n",
       "      <td>0.787234</td>\n",
       "      <td>0.787234</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.755556</td>\n",
       "      <td>0.586207</td>\n",
       "      <td>0.660194</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.837209</td>\n",
       "      <td>0.679245</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.786885</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.768000</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.796875</td>\n",
       "      <td>0.864407</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.725000</td>\n",
       "      <td>0.674419</td>\n",
       "      <td>0.698795</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    precision    recall    fscore  support\n",
       "0    0.650794  0.759259  0.700855       54\n",
       "1    0.529412  0.613636  0.568421       44\n",
       "2    0.911111  0.953488  0.931818       43\n",
       "3    0.933333  0.913043  0.923077       46\n",
       "4    0.810811  0.833333  0.821918       36\n",
       "5    0.641026  0.500000  0.561798       50\n",
       "6    0.878049  0.782609  0.827586       46\n",
       "7    0.784314  0.769231  0.776699       52\n",
       "8    0.787879  0.962963  0.866667       54\n",
       "9    0.787234  0.787234  0.787234       47\n",
       "10   0.755556  0.586207  0.660194       58\n",
       "11   0.692308  0.642857  0.666667       56\n",
       "12   0.571429  0.837209  0.679245       43\n",
       "13   0.786885  0.750000  0.768000       64\n",
       "14   0.944444  0.796875  0.864407       64\n",
       "15   0.725000  0.674419  0.698795       43"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision, recall, fscore, support = score(test_labels['label'], test_pred_labels)\n",
    "pd.DataFrame({\n",
    "    'precision': precision,\n",
    "    'recall': recall,\n",
    "    'fscore': fscore,\n",
    "    'support': support\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "ffd70520",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7564612780924312"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(fscore)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "067c5096",
   "metadata": {},
   "source": [
    "#### On Validation Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e665591",
   "metadata": {},
   "source": [
    "Generating Predictions for the Validation Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "20d57a31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29/29 [==============================] - 0s 3ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-07 23:28:45.355855: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    }
   ],
   "source": [
    "valid_preds = model1.predict(fine_tune_data_validation_red)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "db7d1256",
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_pred_labels = np.argmax(valid_preds, axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "315c93f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11,  6,  4,  8,  3,  8,  1,  1,  2,  4,  3,  0, 12, 12, 13,  9,  1,\n",
       "        1, 11, 12, 14,  9, 13,  6,  1,  2,  7,  3,  8,  1,  0, 14,  8, 10,\n",
       "       14,  6, 11,  8,  6, 10,  4, 14, 12,  3,  2, 10,  3,  2,  4,  2, 10,\n",
       "        0, 10,  0,  1,  9,  2,  8, 13,  1,  2, 11,  4,  5,  5,  8, 11,  7,\n",
       "        8,  4,  5,  5,  4,  8,  4,  6,  4, 15, 11,  0,  9, 15,  5, 11,  7,\n",
       "       15, 11, 10,  0, 10, 12,  5, 15, 15,  3,  4, 11, 10,  6,  5,  6, 11,\n",
       "        1, 12,  7,  1, 15,  9, 10,  1,  7,  7,  7, 14,  2,  4,  1, 14, 12,\n",
       "        1, 13,  1,  3,  6,  1, 10,  0,  6,  4, 12,  8,  1, 13,  8, 10,  3,\n",
       "       11, 11,  5,  5,  6,  6,  2,  3,  2,  9,  9,  0,  0, 14, 10, 10,  1,\n",
       "       11,  7,  3, 14,  1,  1, 14,  3, 11,  2, 13,  7, 14,  4,  5,  7,  9,\n",
       "       12, 10,  4,  7,  4,  0,  1,  6, 11,  8, 15, 11,  3,  2,  3,  3, 14,\n",
       "        3, 11,  2,  9,  5,  6,  2,  4,  9,  2,  9, 10,  7,  1, 13,  1,  0,\n",
       "       15,  1,  4,  1,  4,  4,  8,  6,  6,  7,  4,  4, 13,  3, 10, 12,  1,\n",
       "        6,  0,  8, 10,  2, 12,  4,  1,  1, 12, 15,  8, 11,  2,  6,  7, 12,\n",
       "       11, 12,  9,  3, 14, 13,  7, 10,  8,  0,  4,  4,  9,  8,  3,  7, 13,\n",
       "        8,  9, 15,  6,  0,  4, 11,  0,  2,  9,  5, 12,  5,  0,  4,  3,  2,\n",
       "        6,  5,  8, 14,  9,  2, 10,  2,  8,  1,  3,  3, 13, 11,  2,  1,  9,\n",
       "        9, 11, 11,  5,  0, 15, 11,  3, 11,  4,  0,  3,  2, 15,  5, 14,  8,\n",
       "       12,  2, 10,  9, 13,  0, 10,  0, 10,  7,  6,  8, 11,  4, 10,  6,  6,\n",
       "        3,  0,  5,  1,  0, 15, 12,  5, 10, 11, 13, 13,  2, 14,  1, 10,  4,\n",
       "       15, 11,  4,  9,  1,  4, 10,  1, 13,  0,  3, 12,  5, 14,  4,  0,  5,\n",
       "       15, 10,  8, 12, 15,  6,  0,  3,  1,  7, 10,  3, 11, 15,  6, 10,  2,\n",
       "        6, 11,  9, 10, 11,  7,  8, 10,  6,  3,  4, 10,  2,  0, 12, 11, 15,\n",
       "        1, 14,  4,  0, 15,  2,  1,  7, 15,  0,  2, 12,  5, 12, 10,  0,  1,\n",
       "        3, 14, 10,  6, 13,  1,  2,  1, 14,  4,  3,  0,  2,  0, 15, 14,  4,\n",
       "       11, 12,  9,  2,  6,  3,  2,  6,  9,  0,  2,  8,  6, 10, 12,  6,  5,\n",
       "       14, 12, 14,  7,  3,  7,  0, 15, 10, 11, 12,  1,  8,  0,  4, 11,  2,\n",
       "        5,  4,  3, 10, 14,  8, 11,  4,  4,  1,  9,  4,  6, 12,  6,  4,  9,\n",
       "        6,  1,  3, 11,  2, 15,  8,  4,  5,  4,  4, 14,  6,  5, 12, 13,  9,\n",
       "        7,  7, 13,  2,  3, 15,  1,  7,  9, 12,  8,  2,  0,  8, 10,  0, 13,\n",
       "       14, 15,  8,  5,  8,  0,  2,  7, 12,  3,  8, 12, 14,  9,  0, 13, 15,\n",
       "       13,  0,  3, 12, 10,  6, 13,  3, 12, 14,  5, 14, 10,  9,  8, 12, 11,\n",
       "        9,  9,  8,  7,  9,  1, 13,  8,  8, 12,  4, 11,  1,  2,  3,  0,  0,\n",
       "        1,  0, 11,  5, 11,  5, 14,  6, 10,  4,  4, 15, 13,  3, 15,  0,  4,\n",
       "        5,  0,  8, 13, 11,  3,  8,  2, 10,  9,  7,  0,  1,  2, 11,  9, 12,\n",
       "       13,  6, 12,  0,  4,  8,  1,  1,  1,  2,  0,  4,  7,  8, 10,  8, 14,\n",
       "        9, 13,  0, 14,  6,  3,  7, 11,  8, 12,  4, 14,  0, 14,  0, 11,  3,\n",
       "       14,  6,  1,  7, 14,  7, 11, 12, 10, 12,  2, 12,  3,  8,  1, 14,  9,\n",
       "        9,  4, 12,  2, 13,  2, 15,  0,  4,  6,  3,  8,  6,  5,  3,  7, 10,\n",
       "        2,  7, 13, 10, 15, 15,  2,  1,  4,  1, 12, 13, 15, 10, 13,  7, 14,\n",
       "       13,  9, 15,  7, 11,  0,  8,  5,  4, 15,  6,  8, 13,  3, 13, 14, 11,\n",
       "       12, 10,  4, 10,  8,  2, 15,  4,  5, 11,  3, 12, 13,  1,  5,  9,  8,\n",
       "        3,  3,  7, 10,  1,  7,  6, 13, 14,  6, 14,  3,  5,  1,  0,  5, 11,\n",
       "        5,  7,  4,  9,  4,  3, 12, 14,  5,  8, 10, 15, 13, 15, 15,  2,  0,\n",
       "        0,  6, 14,  9,  1,  7,  6,  1,  6,  8,  7,  2,  2,  4,  0,  7,  5,\n",
       "        6,  0, 13,  5,  4,  3, 14, 11, 15,  0,  1,  2,  7,  0,  0, 11,  6,\n",
       "        0,  3,  4,  8, 14,  6, 14,  2, 12,  0,  1, 13, 14,  1,  2,  6, 15,\n",
       "       10,  9,  4, 15, 10,  7,  2,  2,  8,  6,  4, 13,  7, 15, 15,  3,  2,\n",
       "       13,  4,  9, 11,  9, 12,  5, 14, 12,  9,  2,  4,  0,  5,  5, 15,  1,\n",
       "        6,  7,  8,  0, 15, 15,  4,  8,  9,  3,  4,  8, 14, 13,  5, 13, 11,\n",
       "        2, 15, 15,  7,  2, 12,  6, 15,  6,  9, 10,  0,  5,  6, 14,  1,  0,\n",
       "        8,  9,  8,  8, 15,  4,  0, 11,  3,  3,  3, 11,  2, 13,  5,  9, 15,\n",
       "        5,  1, 13,  2, 11,  4, 15,  7,  6, 15,  8,  7, 12,  8,  3,  4])"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_pred_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bece594",
   "metadata": {},
   "source": [
    "### Submission"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "304c47de",
   "metadata": {},
   "source": [
    "For Kaggle Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "0e59c09c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  6.31446934, 953.5032959 , 124.96561432, 983.04998779,\n",
       "          1.        ,   1.        ],\n",
       "       [  6.83573103, 967.67987061, 231.95384216, 982.98071289,\n",
       "          1.        ,   1.        ],\n",
       "       [ 21.61216927, 904.17651367, 725.11962891, 909.07617188,\n",
       "          5.        ,   1.        ],\n",
       "       [ 35.6613121 ,  15.60731411,  43.65130234,  22.02859116,\n",
       "          1.        ,   1.        ],\n",
       "       [ 78.91178131,  29.19843483, 692.08996582, 202.60029602,\n",
       "          6.        ,   1.        ],\n",
       "       [110.42622375, 127.7110672 , 637.99682617, 773.59735107,\n",
       "          2.        ,   1.        ],\n",
       "       [165.22764587, 847.66424561, 662.84649658, 854.0112915 ,\n",
       "          5.        ,   1.        ],\n",
       "       [532.09185791, 859.65454102, 639.74468994, 874.27435303,\n",
       "          1.        ,   1.        ],\n",
       "       [712.800354  ,   4.76252604, 715.38275146,  71.32498932,\n",
       "          1.        ,   1.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ]])"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roi_info_validation[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "121c612a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "e7ca9d19",
   "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>id</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17801</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17802</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17803</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17804</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17805</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>18696</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>18697</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>18698</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>18699</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>18700</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>900 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  label\n",
       "0    17801     11\n",
       "1    17802      6\n",
       "2    17803      4\n",
       "3    17804      8\n",
       "4    17805      3\n",
       "..     ...    ...\n",
       "895  18696      7\n",
       "896  18697     12\n",
       "897  18698      8\n",
       "898  18699      3\n",
       "899  18700      4\n",
       "\n",
       "[900 rows x 2 columns]"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission_data = pd.DataFrame({'id':list(range(17801, 18701))})\n",
    "submission_data['label'] = valid_pred_labels\n",
    "submission_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "6eea7d9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_data.to_csv('./Submissions/submission-effnet-kPiece-PCA.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}