VGG16-4Piece-PCA-Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fd1efdf1",
   "metadata": {},
   "source": [
    "### Importing Necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0938de48",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pickle\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.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": 2,
   "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": 3,
   "id": "fd2fe082",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>0</th>\n",
       "      <td>0</td>\n",
       "      <td>./Data/train/train/0.tif</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>./Data/train/train/1.tif</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>./Data/train/train/2.tif</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>./Data/train/train/3.tif</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>./Data/train/train/4.tif</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15995</th>\n",
       "      <td>15995</td>\n",
       "      <td>./Data/train/train/15995.tif</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15996</th>\n",
       "      <td>15996</td>\n",
       "      <td>./Data/train/train/15996.tif</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15997</th>\n",
       "      <td>15997</td>\n",
       "      <td>./Data/train/train/15997.tif</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15998</th>\n",
       "      <td>15998</td>\n",
       "      <td>./Data/train/train/15998.tif</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15999</th>\n",
       "      <td>15999</td>\n",
       "      <td>./Data/train/train/15999.tif</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id                        images  label\n",
       "0          0      ./Data/train/train/0.tif      1\n",
       "1          1      ./Data/train/train/1.tif     13\n",
       "2          2      ./Data/train/train/2.tif     13\n",
       "3          3      ./Data/train/train/3.tif     14\n",
       "4          4      ./Data/train/train/4.tif      6\n",
       "...      ...                           ...    ...\n",
       "15995  15995  ./Data/train/train/15995.tif      2\n",
       "15996  15996  ./Data/train/train/15996.tif     15\n",
       "15997  15997  ./Data/train/train/15997.tif      3\n",
       "15998  15998  ./Data/train/train/15998.tif      9\n",
       "15999  15999  ./Data/train/train/15999.tif      9\n",
       "\n",
       "[16000 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "276e2059",
   "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": 6,
   "id": "2aea9ea9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>0</th>\n",
       "      <td>0</td>\n",
       "      <td>./Data/train/train/0.tif</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>./Data/train/train/1.tif</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>./Data/train/train/2.tif</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>./Data/train/train/3.tif</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>./Data/train/train/4.tif</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15195</th>\n",
       "      <td>15995</td>\n",
       "      <td>./Data/train/train/15995.tif</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15196</th>\n",
       "      <td>15996</td>\n",
       "      <td>./Data/train/train/15996.tif</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15197</th>\n",
       "      <td>15997</td>\n",
       "      <td>./Data/train/train/15997.tif</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15198</th>\n",
       "      <td>15998</td>\n",
       "      <td>./Data/train/train/15998.tif</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15199</th>\n",
       "      <td>15999</td>\n",
       "      <td>./Data/train/train/15999.tif</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15200 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id                        images  label\n",
       "0          0      ./Data/train/train/0.tif      1\n",
       "1          1      ./Data/train/train/1.tif     13\n",
       "2          2      ./Data/train/train/2.tif     13\n",
       "3          3      ./Data/train/train/3.tif     14\n",
       "4          4      ./Data/train/train/4.tif      6\n",
       "...      ...                           ...    ...\n",
       "15195  15995  ./Data/train/train/15995.tif      2\n",
       "15196  15996  ./Data/train/train/15996.tif     15\n",
       "15197  15997  ./Data/train/train/15997.tif      3\n",
       "15198  15998  ./Data/train/train/15998.tif      9\n",
       "15199  15999  ./Data/train/train/15999.tif      9\n",
       "\n",
       "[15200 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b9ea19c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</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>0</th>\n",
       "      <td>18</td>\n",
       "      <td>./Data/train/train/18.tif</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>./Data/train/train/25.tif</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>./Data/train/train/33.tif</td>\n",
       "      <td>6</td>\n",
       "    </tr>\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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795</th>\n",
       "      <td>15816</td>\n",
       "      <td>./Data/train/train/15816.tif</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>796</th>\n",
       "      <td>15824</td>\n",
       "      <td>./Data/train/train/15824.tif</td>\n",
       "      <td>2</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",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>15863</td>\n",
       "      <td>./Data/train/train/15863.tif</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>15994</td>\n",
       "      <td>./Data/train/train/15994.tif</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>800 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                        images  label\n",
       "0       18     ./Data/train/train/18.tif     11\n",
       "1       25     ./Data/train/train/25.tif      8\n",
       "2       33     ./Data/train/train/33.tif      6\n",
       "3       41     ./Data/train/train/41.tif      1\n",
       "4       65     ./Data/train/train/65.tif     14\n",
       "..     ...                           ...    ...\n",
       "795  15816  ./Data/train/train/15816.tif      2\n",
       "796  15824  ./Data/train/train/15824.tif      2\n",
       "797  15832  ./Data/train/train/15832.tif     11\n",
       "798  15863  ./Data/train/train/15863.tif     15\n",
       "799  15994  ./Data/train/train/15994.tif      6\n",
       "\n",
       "[800 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": "markdown",
   "id": "c2e251c1",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "37a36e2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16000, 5, 512)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fine_tune_data = np.load('./Data/VGG16-4P-train-precompute/whole_train_data_precomp.npy')\n",
    "fine_tune_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "568ffcb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16000,)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fine_tune_labels = labels['label'].to_numpy()\n",
    "fine_tune_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "937e1e86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15200, 5, 512)"
      ]
     },
     "execution_count": 11,
     "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": 12,
   "id": "af08c830",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15200,)"
      ]
     },
     "execution_count": 12,
     "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": 13,
   "id": "c00f0b32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(800, 5, 512)"
      ]
     },
     "execution_count": 13,
     "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": 14,
   "id": "16f4bb93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(800,)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fine_tune_labels_test = test_labels['label'].to_numpy()\n",
    "fine_tune_labels_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "772c2480",
   "metadata": {},
   "outputs": [],
   "source": [
    "N_COMPS = 256\n",
    "pcas = [PCA(N_COMPS) for _ in range(5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f0f2b450",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(5):\n",
    "    pcas[i].fit(fine_tune_data_train[:,i,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "210b64f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "fine_tune_data_train_red = np.zeros((fine_tune_data_train.shape[0], 5, N_COMPS))\n",
    "for i in range(5):\n",
    "    fine_tune_data_train_red[:,i,:] = pcas[i].transform(fine_tune_data_train[:,i,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fb7bb046",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15200, 5, 256)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fine_tune_data_train_red.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2c0e4617",
   "metadata": {},
   "outputs": [],
   "source": [
    "fine_tune_data_test_red = np.zeros((fine_tune_data_test.shape[0], 5, N_COMPS))\n",
    "for i in range(5):\n",
    "    fine_tune_data_test_red[:,i,:] = pcas[i].transform(fine_tune_data_test[:,i,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b9e1dec6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(800, 5, 256)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fine_tune_data_test_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": 23,
   "id": "96a2bdfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ftmodel():\n",
    "    inputs = Input(shape=(5, N_COMPS))\n",
    "    x = Flatten()(inputs)\n",
    "    x = BatchNormalization()(x)\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='VGG16-FineTune')\n",
    "    return model1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c6574006",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"VGG16-FineTune\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_2 (InputLayer)        [(None, 5, 256)]          0         \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 1280)              0         \n",
      "                                                                 \n",
      " batch_normalization_2 (Batc  (None, 1280)             5120      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " top_dropout_2 (Dropout)     (None, 1280)              0         \n",
      "                                                                 \n",
      " top_dense_2 (Dense)         (None, 512)               655872    \n",
      "                                                                 \n",
      " batch_normalization_3 (Batc  (None, 512)              2048      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " top_dropout_3 (Dropout)     (None, 512)               0         \n",
      "                                                                 \n",
      " pred (Dense)                (None, 16)                8208      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 671,248\n",
      "Trainable params: 667,664\n",
      "Non-trainable params: 3,584\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 ResNet's $2048D$ representation vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "96524578",
   "metadata": {},
   "outputs": [],
   "source": [
    "model1.compile(loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'], optimizer = Adam(learning_rate = 0.0003))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "20df8897",
   "metadata": {},
   "outputs": [],
   "source": [
    "EPOCHS = 1000\n",
    "checkpoint_filepaths = ['./Models/VGG16-4P-PCA/auto-VGG16-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": 27,
   "id": "03354278",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-08 20:27:18.707210: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
      "2022-10-08 20:27:19.031419: 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 22ms/step - loss: 3.2751 - accuracy: 0.1465 - val_loss: 2.2599 - val_accuracy: 0.3837\n",
      "Epoch 2/1000\n",
      " 8/19 [===========>..................] - ETA: 0s - loss: 2.5440 - accuracy: 0.3105"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-08 20:27:19.545172: 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 12ms/step - loss: 2.3870 - accuracy: 0.3539 - val_loss: 1.8141 - val_accuracy: 0.5250\n",
      "Epoch 3/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 1.9449 - accuracy: 0.4658 - val_loss: 1.5873 - val_accuracy: 0.5875\n",
      "Epoch 4/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 1.6927 - accuracy: 0.5370 - val_loss: 1.4516 - val_accuracy: 0.6250\n",
      "Epoch 5/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 1.5316 - accuracy: 0.5791 - val_loss: 1.3572 - val_accuracy: 0.6413\n",
      "Epoch 6/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 1.3979 - accuracy: 0.6158 - val_loss: 1.2891 - val_accuracy: 0.6500\n",
      "Epoch 7/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 1.3123 - accuracy: 0.6403 - val_loss: 1.2408 - val_accuracy: 0.6600\n",
      "Epoch 8/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 1.2395 - accuracy: 0.6589 - val_loss: 1.2016 - val_accuracy: 0.6737\n",
      "Epoch 9/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 1.1834 - accuracy: 0.6688 - val_loss: 1.1678 - val_accuracy: 0.6800\n",
      "Epoch 10/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 1.1237 - accuracy: 0.6863 - val_loss: 1.1414 - val_accuracy: 0.6887\n",
      "Epoch 11/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 1.0759 - accuracy: 0.6963 - val_loss: 1.1218 - val_accuracy: 0.6937\n",
      "Epoch 12/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 1.0497 - accuracy: 0.7063 - val_loss: 1.1076 - val_accuracy: 0.7000\n",
      "Epoch 13/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.9992 - accuracy: 0.7191 - val_loss: 1.0945 - val_accuracy: 0.7012\n",
      "Epoch 14/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.9676 - accuracy: 0.7288 - val_loss: 1.0835 - val_accuracy: 0.7088\n",
      "Epoch 15/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.9353 - accuracy: 0.7348 - val_loss: 1.0770 - val_accuracy: 0.7037\n",
      "Epoch 16/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.9216 - accuracy: 0.7400 - val_loss: 1.0738 - val_accuracy: 0.7037\n",
      "Epoch 17/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.8913 - accuracy: 0.7466 - val_loss: 1.0639 - val_accuracy: 0.7050\n",
      "Epoch 18/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.8679 - accuracy: 0.7540 - val_loss: 1.0521 - val_accuracy: 0.7100\n",
      "Epoch 19/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.8436 - accuracy: 0.7594 - val_loss: 1.0463 - val_accuracy: 0.7163\n",
      "Epoch 20/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.8276 - accuracy: 0.7666 - val_loss: 1.0485 - val_accuracy: 0.7150\n",
      "Epoch 21/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.8073 - accuracy: 0.7738 - val_loss: 1.0478 - val_accuracy: 0.7200\n",
      "Epoch 22/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.7888 - accuracy: 0.7758 - val_loss: 1.0455 - val_accuracy: 0.7212\n",
      "Epoch 23/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.7706 - accuracy: 0.7845 - val_loss: 1.0391 - val_accuracy: 0.7175\n",
      "Epoch 24/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.7660 - accuracy: 0.7800 - val_loss: 1.0389 - val_accuracy: 0.7225\n",
      "Epoch 25/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.7504 - accuracy: 0.7868 - val_loss: 1.0387 - val_accuracy: 0.7200\n",
      "Epoch 26/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.7251 - accuracy: 0.7943 - val_loss: 1.0375 - val_accuracy: 0.7250\n",
      "Epoch 27/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.7239 - accuracy: 0.7939 - val_loss: 1.0383 - val_accuracy: 0.7262\n",
      "Epoch 28/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.7040 - accuracy: 0.7974 - val_loss: 1.0377 - val_accuracy: 0.7237\n",
      "Epoch 29/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.6881 - accuracy: 0.8051 - val_loss: 1.0368 - val_accuracy: 0.7237\n",
      "Epoch 30/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.6717 - accuracy: 0.8134 - val_loss: 1.0367 - val_accuracy: 0.7375\n",
      "Epoch 31/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.6598 - accuracy: 0.8159 - val_loss: 1.0361 - val_accuracy: 0.7400\n",
      "Epoch 32/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.6538 - accuracy: 0.8164 - val_loss: 1.0357 - val_accuracy: 0.7337\n",
      "Epoch 33/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.6451 - accuracy: 0.8204 - val_loss: 1.0373 - val_accuracy: 0.7337\n",
      "Epoch 34/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.6333 - accuracy: 0.8201 - val_loss: 1.0382 - val_accuracy: 0.7287\n",
      "Epoch 35/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.6271 - accuracy: 0.8256 - val_loss: 1.0422 - val_accuracy: 0.7375\n",
      "Epoch 36/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.6104 - accuracy: 0.8291 - val_loss: 1.0409 - val_accuracy: 0.7387\n",
      "Epoch 37/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.5991 - accuracy: 0.8303 - val_loss: 1.0424 - val_accuracy: 0.7350\n",
      "Epoch 38/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.6018 - accuracy: 0.8280 - val_loss: 1.0388 - val_accuracy: 0.7350\n",
      "Epoch 39/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.5963 - accuracy: 0.8313 - val_loss: 1.0405 - val_accuracy: 0.7300\n",
      "Epoch 40/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.5714 - accuracy: 0.8424 - val_loss: 1.0415 - val_accuracy: 0.7362\n",
      "Epoch 41/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.5592 - accuracy: 0.8457 - val_loss: 1.0440 - val_accuracy: 0.7387\n",
      "Epoch 42/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.5543 - accuracy: 0.8479 - val_loss: 1.0455 - val_accuracy: 0.7387\n",
      "Epoch 43/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.5490 - accuracy: 0.8491 - val_loss: 1.0455 - val_accuracy: 0.7387\n",
      "Epoch 44/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.5371 - accuracy: 0.8532 - val_loss: 1.0425 - val_accuracy: 0.7362\n",
      "Epoch 45/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.5298 - accuracy: 0.8538 - val_loss: 1.0440 - val_accuracy: 0.7387\n",
      "Epoch 46/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.5182 - accuracy: 0.8593 - val_loss: 1.0493 - val_accuracy: 0.7300\n",
      "Epoch 47/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.5191 - accuracy: 0.8570 - val_loss: 1.0505 - val_accuracy: 0.7362\n",
      "Epoch 48/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.5105 - accuracy: 0.8604 - val_loss: 1.0507 - val_accuracy: 0.7400\n",
      "Epoch 49/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.4957 - accuracy: 0.8624 - val_loss: 1.0514 - val_accuracy: 0.7350\n",
      "Epoch 50/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.4952 - accuracy: 0.8674 - val_loss: 1.0541 - val_accuracy: 0.7375\n",
      "Epoch 51/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.4892 - accuracy: 0.8686 - val_loss: 1.0540 - val_accuracy: 0.7387\n",
      "Epoch 52/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.4787 - accuracy: 0.8704 - val_loss: 1.0524 - val_accuracy: 0.7362\n",
      "Epoch 53/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.4708 - accuracy: 0.8722 - val_loss: 1.0535 - val_accuracy: 0.7412\n",
      "Epoch 54/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.8732 - val_loss: 1.0522 - val_accuracy: 0.7412\n",
      "Epoch 55/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.4634 - accuracy: 0.8740 - val_loss: 1.0560 - val_accuracy: 0.7462\n",
      "Epoch 56/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.4483 - accuracy: 0.8794 - val_loss: 1.0624 - val_accuracy: 0.7400\n",
      "Epoch 57/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.4470 - accuracy: 0.8799 - val_loss: 1.0622 - val_accuracy: 0.7375\n",
      "Epoch 58/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.4348 - accuracy: 0.8861 - val_loss: 1.0653 - val_accuracy: 0.7400\n",
      "Epoch 59/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 9ms/step - loss: 0.4384 - accuracy: 0.8815 - val_loss: 1.0633 - val_accuracy: 0.7362\n",
      "Epoch 60/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.4259 - accuracy: 0.8880 - val_loss: 1.0636 - val_accuracy: 0.7387\n",
      "Epoch 61/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.4173 - accuracy: 0.8905 - val_loss: 1.0612 - val_accuracy: 0.7387\n",
      "Epoch 62/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.4180 - accuracy: 0.8908 - val_loss: 1.0602 - val_accuracy: 0.7425\n",
      "Epoch 63/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.4104 - accuracy: 0.8949 - val_loss: 1.0656 - val_accuracy: 0.7400\n",
      "Epoch 64/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.4010 - accuracy: 0.8947 - val_loss: 1.0661 - val_accuracy: 0.7450\n",
      "Epoch 65/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.3984 - accuracy: 0.8975 - val_loss: 1.0692 - val_accuracy: 0.7462\n",
      "Epoch 66/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.3907 - accuracy: 0.8995 - val_loss: 1.0808 - val_accuracy: 0.7387\n",
      "Epoch 67/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.3872 - accuracy: 0.9014 - val_loss: 1.0859 - val_accuracy: 0.7400\n",
      "Epoch 68/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.3796 - accuracy: 0.8994 - val_loss: 1.0886 - val_accuracy: 0.7412\n",
      "Epoch 69/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.3777 - accuracy: 0.9014 - val_loss: 1.0833 - val_accuracy: 0.7400\n",
      "Epoch 70/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.3707 - accuracy: 0.9045 - val_loss: 1.0792 - val_accuracy: 0.7487\n",
      "Epoch 71/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.3657 - accuracy: 0.9064 - val_loss: 1.0776 - val_accuracy: 0.7450\n",
      "Epoch 72/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.3529 - accuracy: 0.9120 - val_loss: 1.0782 - val_accuracy: 0.7475\n",
      "Epoch 73/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.3536 - accuracy: 0.9108 - val_loss: 1.0773 - val_accuracy: 0.7400\n",
      "Epoch 74/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.3495 - accuracy: 0.9105 - val_loss: 1.0760 - val_accuracy: 0.7412\n",
      "Epoch 75/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.3420 - accuracy: 0.9143 - val_loss: 1.0726 - val_accuracy: 0.7387\n",
      "Epoch 76/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.3357 - accuracy: 0.9164 - val_loss: 1.0734 - val_accuracy: 0.7362\n",
      "Epoch 77/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.3321 - accuracy: 0.9178 - val_loss: 1.0807 - val_accuracy: 0.7412\n",
      "Epoch 78/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.3218 - accuracy: 0.9207 - val_loss: 1.0796 - val_accuracy: 0.7412\n",
      "Epoch 79/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.3266 - accuracy: 0.9195 - val_loss: 1.0809 - val_accuracy: 0.7400\n",
      "Epoch 80/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.3157 - accuracy: 0.9232 - val_loss: 1.0793 - val_accuracy: 0.7412\n",
      "Epoch 81/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.3155 - accuracy: 0.9246 - val_loss: 1.0784 - val_accuracy: 0.7375\n",
      "Epoch 82/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.3055 - accuracy: 0.9282 - val_loss: 1.0852 - val_accuracy: 0.7387\n",
      "Epoch 83/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.3021 - accuracy: 0.9293 - val_loss: 1.0889 - val_accuracy: 0.7387\n",
      "Epoch 84/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.3038 - accuracy: 0.9267 - val_loss: 1.0931 - val_accuracy: 0.7387\n",
      "Epoch 85/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.2944 - accuracy: 0.9297 - val_loss: 1.0931 - val_accuracy: 0.7400\n",
      "Epoch 86/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.2920 - accuracy: 0.9316 - val_loss: 1.0966 - val_accuracy: 0.7450\n",
      "Epoch 87/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.2889 - accuracy: 0.9322 - val_loss: 1.0980 - val_accuracy: 0.7412\n",
      "Epoch 88/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.2845 - accuracy: 0.9316 - val_loss: 1.0972 - val_accuracy: 0.7387\n",
      "Epoch 89/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.2715 - accuracy: 0.9374 - val_loss: 1.0998 - val_accuracy: 0.7400\n",
      "Epoch 90/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.2776 - accuracy: 0.9363 - val_loss: 1.0956 - val_accuracy: 0.7462\n",
      "Epoch 91/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.2675 - accuracy: 0.9389 - val_loss: 1.0991 - val_accuracy: 0.7450\n",
      "Epoch 92/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.2700 - accuracy: 0.9374 - val_loss: 1.1030 - val_accuracy: 0.7475\n",
      "Epoch 93/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.2637 - accuracy: 0.9420 - val_loss: 1.1020 - val_accuracy: 0.7437\n",
      "Epoch 94/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.2619 - accuracy: 0.9384 - val_loss: 1.0999 - val_accuracy: 0.7475\n",
      "Epoch 95/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.2551 - accuracy: 0.9421 - val_loss: 1.0991 - val_accuracy: 0.7475\n",
      "Epoch 96/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.2548 - accuracy: 0.9416 - val_loss: 1.0998 - val_accuracy: 0.7512\n",
      "Epoch 97/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.2536 - accuracy: 0.9416 - val_loss: 1.1019 - val_accuracy: 0.7500\n",
      "Epoch 98/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.2533 - accuracy: 0.9417 - val_loss: 1.1000 - val_accuracy: 0.7500\n",
      "Epoch 99/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.2462 - accuracy: 0.9449 - val_loss: 1.1037 - val_accuracy: 0.7475\n",
      "Epoch 100/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.2445 - accuracy: 0.9485 - val_loss: 1.1110 - val_accuracy: 0.7500\n",
      "Epoch 101/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.2358 - accuracy: 0.9469 - val_loss: 1.1235 - val_accuracy: 0.7462\n",
      "Epoch 102/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.2376 - accuracy: 0.9486 - val_loss: 1.1273 - val_accuracy: 0.7487\n",
      "Epoch 103/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.2315 - accuracy: 0.9511 - val_loss: 1.1276 - val_accuracy: 0.7450\n",
      "Epoch 104/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.2306 - accuracy: 0.9488 - val_loss: 1.1266 - val_accuracy: 0.7450\n",
      "Epoch 105/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.2277 - accuracy: 0.9502 - val_loss: 1.1235 - val_accuracy: 0.7500\n",
      "Epoch 106/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.2190 - accuracy: 0.9555 - val_loss: 1.1270 - val_accuracy: 0.7487\n",
      "Epoch 107/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.2175 - accuracy: 0.9551 - val_loss: 1.1301 - val_accuracy: 0.7475\n",
      "Epoch 108/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.2198 - accuracy: 0.9526 - val_loss: 1.1273 - val_accuracy: 0.7475\n",
      "Epoch 109/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.2194 - accuracy: 0.9534 - val_loss: 1.1290 - val_accuracy: 0.7462\n",
      "Epoch 110/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.2153 - accuracy: 0.9552 - val_loss: 1.1374 - val_accuracy: 0.7450\n",
      "Epoch 111/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.2109 - accuracy: 0.9573 - val_loss: 1.1402 - val_accuracy: 0.7475\n",
      "Epoch 112/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.2084 - accuracy: 0.9579 - val_loss: 1.1426 - val_accuracy: 0.7425\n",
      "Epoch 113/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.2098 - accuracy: 0.9555 - val_loss: 1.1522 - val_accuracy: 0.7412\n",
      "Epoch 114/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.2052 - accuracy: 0.9585 - val_loss: 1.1529 - val_accuracy: 0.7387\n",
      "Epoch 115/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1999 - accuracy: 0.9595 - val_loss: 1.1488 - val_accuracy: 0.7387\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 116/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1970 - accuracy: 0.9620 - val_loss: 1.1510 - val_accuracy: 0.7387\n",
      "Epoch 117/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1947 - accuracy: 0.9618 - val_loss: 1.1536 - val_accuracy: 0.7450\n",
      "Epoch 118/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1938 - accuracy: 0.9618 - val_loss: 1.1567 - val_accuracy: 0.7450\n",
      "Epoch 119/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1907 - accuracy: 0.9642 - val_loss: 1.1575 - val_accuracy: 0.7425\n",
      "Epoch 120/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1896 - accuracy: 0.9640 - val_loss: 1.1577 - val_accuracy: 0.7425\n",
      "Epoch 121/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1850 - accuracy: 0.9636 - val_loss: 1.1533 - val_accuracy: 0.7437\n",
      "Epoch 122/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1891 - accuracy: 0.9611 - val_loss: 1.1470 - val_accuracy: 0.7425\n",
      "Epoch 123/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1834 - accuracy: 0.9643 - val_loss: 1.1471 - val_accuracy: 0.7462\n",
      "Epoch 124/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1821 - accuracy: 0.9651 - val_loss: 1.1458 - val_accuracy: 0.7462\n",
      "Epoch 125/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1782 - accuracy: 0.9670 - val_loss: 1.1560 - val_accuracy: 0.7450\n",
      "Epoch 126/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1837 - accuracy: 0.9634 - val_loss: 1.1656 - val_accuracy: 0.7400\n",
      "Epoch 127/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1750 - accuracy: 0.9659 - val_loss: 1.1678 - val_accuracy: 0.7412\n",
      "Epoch 128/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1740 - accuracy: 0.9667 - val_loss: 1.1697 - val_accuracy: 0.7387\n",
      "Epoch 129/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1692 - accuracy: 0.9690 - val_loss: 1.1771 - val_accuracy: 0.7375\n",
      "Epoch 130/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1706 - accuracy: 0.9699 - val_loss: 1.1762 - val_accuracy: 0.7412\n",
      "Epoch 131/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.1725 - accuracy: 0.9680 - val_loss: 1.1753 - val_accuracy: 0.7412\n",
      "Epoch 132/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1639 - accuracy: 0.9701 - val_loss: 1.1749 - val_accuracy: 0.7500\n",
      "Epoch 133/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1675 - accuracy: 0.9701 - val_loss: 1.1736 - val_accuracy: 0.7475\n",
      "Epoch 134/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1639 - accuracy: 0.9703 - val_loss: 1.1662 - val_accuracy: 0.7512\n",
      "Epoch 135/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1615 - accuracy: 0.9711 - val_loss: 1.1630 - val_accuracy: 0.7487\n",
      "Epoch 136/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1630 - accuracy: 0.9695 - val_loss: 1.1703 - val_accuracy: 0.7487\n",
      "Epoch 137/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1585 - accuracy: 0.9725 - val_loss: 1.1651 - val_accuracy: 0.7487\n",
      "Epoch 138/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1600 - accuracy: 0.9726 - val_loss: 1.1640 - val_accuracy: 0.7512\n",
      "Epoch 139/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1618 - accuracy: 0.9718 - val_loss: 1.1606 - val_accuracy: 0.7487\n",
      "Epoch 140/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1572 - accuracy: 0.9737 - val_loss: 1.1667 - val_accuracy: 0.7475\n",
      "Epoch 141/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1552 - accuracy: 0.9731 - val_loss: 1.1653 - val_accuracy: 0.7462\n",
      "Epoch 142/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1581 - accuracy: 0.9724 - val_loss: 1.1650 - val_accuracy: 0.7487\n",
      "Epoch 143/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1541 - accuracy: 0.9734 - val_loss: 1.1748 - val_accuracy: 0.7437\n",
      "Epoch 144/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1479 - accuracy: 0.9764 - val_loss: 1.1760 - val_accuracy: 0.7450\n",
      "Epoch 145/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1501 - accuracy: 0.9734 - val_loss: 1.1678 - val_accuracy: 0.7537\n",
      "Epoch 146/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1463 - accuracy: 0.9759 - val_loss: 1.1749 - val_accuracy: 0.7525\n",
      "Epoch 147/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1476 - accuracy: 0.9747 - val_loss: 1.1792 - val_accuracy: 0.7550\n",
      "Epoch 148/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1446 - accuracy: 0.9765 - val_loss: 1.1819 - val_accuracy: 0.7512\n",
      "Epoch 149/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1451 - accuracy: 0.9766 - val_loss: 1.1844 - val_accuracy: 0.7512\n",
      "Epoch 150/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1433 - accuracy: 0.9768 - val_loss: 1.1781 - val_accuracy: 0.7487\n",
      "Epoch 151/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1457 - accuracy: 0.9751 - val_loss: 1.1815 - val_accuracy: 0.7412\n",
      "Epoch 152/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1433 - accuracy: 0.9747 - val_loss: 1.1834 - val_accuracy: 0.7475\n",
      "Epoch 153/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1407 - accuracy: 0.9763 - val_loss: 1.1870 - val_accuracy: 0.7475\n",
      "Epoch 154/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1364 - accuracy: 0.9784 - val_loss: 1.1880 - val_accuracy: 0.7462\n",
      "Epoch 155/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1406 - accuracy: 0.9770 - val_loss: 1.1945 - val_accuracy: 0.7475\n",
      "Epoch 156/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1395 - accuracy: 0.9770 - val_loss: 1.1955 - val_accuracy: 0.7450\n",
      "Epoch 157/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1369 - accuracy: 0.9782 - val_loss: 1.2006 - val_accuracy: 0.7475\n",
      "Epoch 158/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.1378 - accuracy: 0.9780 - val_loss: 1.2015 - val_accuracy: 0.7475\n",
      "Epoch 159/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1343 - accuracy: 0.9793 - val_loss: 1.1972 - val_accuracy: 0.7487\n",
      "Epoch 160/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1320 - accuracy: 0.9795 - val_loss: 1.1915 - val_accuracy: 0.7462\n",
      "Epoch 161/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1361 - accuracy: 0.9785 - val_loss: 1.2003 - val_accuracy: 0.7475\n",
      "Epoch 162/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1280 - accuracy: 0.9801 - val_loss: 1.2050 - val_accuracy: 0.7487\n",
      "Epoch 163/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1332 - accuracy: 0.9792 - val_loss: 1.2138 - val_accuracy: 0.7475\n",
      "Epoch 164/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.1308 - accuracy: 0.9805 - val_loss: 1.2097 - val_accuracy: 0.7487\n",
      "Epoch 165/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.1291 - accuracy: 0.9801 - val_loss: 1.2145 - val_accuracy: 0.7450\n",
      "Epoch 166/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.1289 - accuracy: 0.9796 - val_loss: 1.2158 - val_accuracy: 0.7500\n",
      "Epoch 167/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1299 - accuracy: 0.9780 - val_loss: 1.2135 - val_accuracy: 0.7475\n",
      "Epoch 168/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1263 - accuracy: 0.9812 - val_loss: 1.2078 - val_accuracy: 0.7500\n",
      "Epoch 169/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1244 - accuracy: 0.9822 - val_loss: 1.2072 - val_accuracy: 0.7525\n",
      "Epoch 170/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1237 - accuracy: 0.9829 - val_loss: 1.2093 - val_accuracy: 0.7512\n",
      "Epoch 171/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1232 - accuracy: 0.9820 - val_loss: 1.2132 - val_accuracy: 0.7487\n",
      "Epoch 172/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1202 - accuracy: 0.9836 - val_loss: 1.2175 - val_accuracy: 0.7500\n",
      "Epoch 173/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1205 - accuracy: 0.9828 - val_loss: 1.2250 - val_accuracy: 0.7537\n",
      "Epoch 174/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1198 - accuracy: 0.9846 - val_loss: 1.2230 - val_accuracy: 0.7525\n",
      "Epoch 175/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1212 - accuracy: 0.9821 - val_loss: 1.2272 - val_accuracy: 0.7500\n",
      "Epoch 176/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1200 - accuracy: 0.9822 - val_loss: 1.2308 - val_accuracy: 0.7537\n",
      "Epoch 177/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1237 - accuracy: 0.9811 - val_loss: 1.2353 - val_accuracy: 0.7525\n",
      "Epoch 178/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1168 - accuracy: 0.9828 - val_loss: 1.2338 - val_accuracy: 0.7562\n",
      "Epoch 179/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1191 - accuracy: 0.9834 - val_loss: 1.2324 - val_accuracy: 0.7537\n",
      "Epoch 180/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1188 - accuracy: 0.9831 - val_loss: 1.2293 - val_accuracy: 0.7475\n",
      "Epoch 181/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1142 - accuracy: 0.9837 - val_loss: 1.2261 - val_accuracy: 0.7500\n",
      "Epoch 182/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1158 - accuracy: 0.9835 - val_loss: 1.2356 - val_accuracy: 0.7525\n",
      "Epoch 183/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1205 - accuracy: 0.9817 - val_loss: 1.2353 - val_accuracy: 0.7462\n",
      "Epoch 184/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1156 - accuracy: 0.9837 - val_loss: 1.2350 - val_accuracy: 0.7500\n",
      "Epoch 185/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1141 - accuracy: 0.9851 - val_loss: 1.2420 - val_accuracy: 0.7500\n",
      "Epoch 186/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1158 - accuracy: 0.9830 - val_loss: 1.2431 - val_accuracy: 0.7487\n",
      "Epoch 187/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1117 - accuracy: 0.9847 - val_loss: 1.2434 - val_accuracy: 0.7450\n",
      "Epoch 188/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1121 - accuracy: 0.9855 - val_loss: 1.2394 - val_accuracy: 0.7475\n",
      "Epoch 189/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1102 - accuracy: 0.9845 - val_loss: 1.2425 - val_accuracy: 0.7462\n",
      "Epoch 190/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1122 - accuracy: 0.9842 - val_loss: 1.2466 - val_accuracy: 0.7437\n",
      "Epoch 191/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1103 - accuracy: 0.9852 - val_loss: 1.2374 - val_accuracy: 0.7462\n",
      "Epoch 192/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.1110 - accuracy: 0.9850 - val_loss: 1.2412 - val_accuracy: 0.7400\n",
      "Epoch 193/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1153 - accuracy: 0.9843 - val_loss: 1.2343 - val_accuracy: 0.7412\n",
      "Epoch 194/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1133 - accuracy: 0.9847 - val_loss: 1.2330 - val_accuracy: 0.7450\n",
      "Epoch 195/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1131 - accuracy: 0.9832 - val_loss: 1.2370 - val_accuracy: 0.7475\n",
      "Epoch 196/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1066 - accuracy: 0.9860 - val_loss: 1.2346 - val_accuracy: 0.7475\n",
      "Epoch 197/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1061 - accuracy: 0.9861 - val_loss: 1.2343 - val_accuracy: 0.7437\n",
      "Epoch 198/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1064 - accuracy: 0.9877 - val_loss: 1.2330 - val_accuracy: 0.7425\n",
      "Epoch 199/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1071 - accuracy: 0.9855 - val_loss: 1.2341 - val_accuracy: 0.7412\n",
      "Epoch 200/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1086 - accuracy: 0.9857 - val_loss: 1.2352 - val_accuracy: 0.7450\n",
      "Epoch 201/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1075 - accuracy: 0.9851 - val_loss: 1.2387 - val_accuracy: 0.7450\n",
      "Epoch 202/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.1069 - accuracy: 0.9852 - val_loss: 1.2363 - val_accuracy: 0.7425\n",
      "Epoch 203/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1105 - accuracy: 0.9844 - val_loss: 1.2319 - val_accuracy: 0.7487\n",
      "Epoch 204/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1058 - accuracy: 0.9870 - val_loss: 1.2422 - val_accuracy: 0.7475\n",
      "Epoch 205/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1065 - accuracy: 0.9868 - val_loss: 1.2462 - val_accuracy: 0.7475\n",
      "Epoch 206/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1023 - accuracy: 0.9875 - val_loss: 1.2480 - val_accuracy: 0.7512\n",
      "Epoch 207/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1026 - accuracy: 0.9869 - val_loss: 1.2435 - val_accuracy: 0.7525\n",
      "Epoch 208/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1052 - accuracy: 0.9853 - val_loss: 1.2377 - val_accuracy: 0.7475\n",
      "Epoch 209/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1046 - accuracy: 0.9857 - val_loss: 1.2384 - val_accuracy: 0.7487\n",
      "Epoch 210/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1035 - accuracy: 0.9870 - val_loss: 1.2357 - val_accuracy: 0.7512\n",
      "Epoch 211/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0996 - accuracy: 0.9876 - val_loss: 1.2307 - val_accuracy: 0.7462\n",
      "Epoch 212/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1063 - accuracy: 0.9861 - val_loss: 1.2333 - val_accuracy: 0.7500\n",
      "Epoch 213/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.1022 - accuracy: 0.9869 - val_loss: 1.2427 - val_accuracy: 0.7462\n",
      "Epoch 214/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1036 - accuracy: 0.9867 - val_loss: 1.2442 - val_accuracy: 0.7462\n",
      "Epoch 215/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1033 - accuracy: 0.9870 - val_loss: 1.2474 - val_accuracy: 0.7512\n",
      "Epoch 216/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0982 - accuracy: 0.9886 - val_loss: 1.2497 - val_accuracy: 0.7500\n",
      "Epoch 217/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1021 - accuracy: 0.9875 - val_loss: 1.2479 - val_accuracy: 0.7475\n",
      "Epoch 218/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.1046 - accuracy: 0.9869 - val_loss: 1.2463 - val_accuracy: 0.7487\n",
      "Epoch 219/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1018 - accuracy: 0.9870 - val_loss: 1.2444 - val_accuracy: 0.7475\n",
      "Epoch 220/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0986 - accuracy: 0.9881 - val_loss: 1.2403 - val_accuracy: 0.7462\n",
      "Epoch 221/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0974 - accuracy: 0.9883 - val_loss: 1.2452 - val_accuracy: 0.7500\n",
      "Epoch 222/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0997 - accuracy: 0.9882 - val_loss: 1.2502 - val_accuracy: 0.7400\n",
      "Epoch 223/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1007 - accuracy: 0.9884 - val_loss: 1.2449 - val_accuracy: 0.7375\n",
      "Epoch 224/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.1008 - accuracy: 0.9875 - val_loss: 1.2390 - val_accuracy: 0.7412\n",
      "Epoch 225/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0981 - accuracy: 0.9884 - val_loss: 1.2411 - val_accuracy: 0.7425\n",
      "Epoch 226/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.1002 - accuracy: 0.9871 - val_loss: 1.2401 - val_accuracy: 0.7525\n",
      "Epoch 227/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0976 - accuracy: 0.9882 - val_loss: 1.2323 - val_accuracy: 0.7500\n",
      "Epoch 228/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0964 - accuracy: 0.9881 - val_loss: 1.2302 - val_accuracy: 0.7450\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 229/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0953 - accuracy: 0.9886 - val_loss: 1.2295 - val_accuracy: 0.7450\n",
      "Epoch 230/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0936 - accuracy: 0.9891 - val_loss: 1.2267 - val_accuracy: 0.7475\n",
      "Epoch 231/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0949 - accuracy: 0.9891 - val_loss: 1.2315 - val_accuracy: 0.7537\n",
      "Epoch 232/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0954 - accuracy: 0.9895 - val_loss: 1.2295 - val_accuracy: 0.7487\n",
      "Epoch 233/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0988 - accuracy: 0.9876 - val_loss: 1.2324 - val_accuracy: 0.7500\n",
      "Epoch 234/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0945 - accuracy: 0.9899 - val_loss: 1.2379 - val_accuracy: 0.7537\n",
      "Epoch 235/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0970 - accuracy: 0.9894 - val_loss: 1.2448 - val_accuracy: 0.7512\n",
      "Epoch 236/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0962 - accuracy: 0.9888 - val_loss: 1.2464 - val_accuracy: 0.7550\n",
      "Epoch 237/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0932 - accuracy: 0.9895 - val_loss: 1.2587 - val_accuracy: 0.7575\n",
      "Epoch 238/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0942 - accuracy: 0.9891 - val_loss: 1.2622 - val_accuracy: 0.7537\n",
      "Epoch 239/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0963 - accuracy: 0.9876 - val_loss: 1.2668 - val_accuracy: 0.7475\n",
      "Epoch 240/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0953 - accuracy: 0.9887 - val_loss: 1.2620 - val_accuracy: 0.7525\n",
      "Epoch 241/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0910 - accuracy: 0.9901 - val_loss: 1.2571 - val_accuracy: 0.7587\n",
      "Epoch 242/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0959 - accuracy: 0.9884 - val_loss: 1.2597 - val_accuracy: 0.7512\n",
      "Epoch 243/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0961 - accuracy: 0.9876 - val_loss: 1.2617 - val_accuracy: 0.7550\n",
      "Epoch 244/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0983 - accuracy: 0.9886 - val_loss: 1.2630 - val_accuracy: 0.7537\n",
      "Epoch 245/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0926 - accuracy: 0.9888 - val_loss: 1.2733 - val_accuracy: 0.7500\n",
      "Epoch 246/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0917 - accuracy: 0.9901 - val_loss: 1.2735 - val_accuracy: 0.7537\n",
      "Epoch 247/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0929 - accuracy: 0.9898 - val_loss: 1.2709 - val_accuracy: 0.7537\n",
      "Epoch 248/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0925 - accuracy: 0.9895 - val_loss: 1.2730 - val_accuracy: 0.7537\n",
      "Epoch 249/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0927 - accuracy: 0.9897 - val_loss: 1.2766 - val_accuracy: 0.7525\n",
      "Epoch 250/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0935 - accuracy: 0.9891 - val_loss: 1.2844 - val_accuracy: 0.7500\n",
      "Epoch 251/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0913 - accuracy: 0.9904 - val_loss: 1.2793 - val_accuracy: 0.7525\n",
      "Epoch 252/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0887 - accuracy: 0.9905 - val_loss: 1.2770 - val_accuracy: 0.7475\n",
      "Epoch 253/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0900 - accuracy: 0.9898 - val_loss: 1.2741 - val_accuracy: 0.7500\n",
      "Epoch 254/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0908 - accuracy: 0.9897 - val_loss: 1.2803 - val_accuracy: 0.7525\n",
      "Epoch 255/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0901 - accuracy: 0.9911 - val_loss: 1.2842 - val_accuracy: 0.7462\n",
      "Epoch 256/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0866 - accuracy: 0.9912 - val_loss: 1.2942 - val_accuracy: 0.7462\n",
      "Epoch 257/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0949 - accuracy: 0.9891 - val_loss: 1.2909 - val_accuracy: 0.7537\n",
      "Epoch 258/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0853 - accuracy: 0.9918 - val_loss: 1.2972 - val_accuracy: 0.7537\n",
      "Epoch 259/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0867 - accuracy: 0.9905 - val_loss: 1.2950 - val_accuracy: 0.7525\n",
      "Epoch 260/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0906 - accuracy: 0.9895 - val_loss: 1.2858 - val_accuracy: 0.7512\n",
      "Epoch 261/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0860 - accuracy: 0.9913 - val_loss: 1.2820 - val_accuracy: 0.7550\n",
      "Epoch 262/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0868 - accuracy: 0.9909 - val_loss: 1.2863 - val_accuracy: 0.7550\n",
      "Epoch 263/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0873 - accuracy: 0.9907 - val_loss: 1.2819 - val_accuracy: 0.7537\n",
      "Epoch 264/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0896 - accuracy: 0.9899 - val_loss: 1.2891 - val_accuracy: 0.7537\n",
      "Epoch 265/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0863 - accuracy: 0.9916 - val_loss: 1.2895 - val_accuracy: 0.7575\n",
      "Epoch 266/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0874 - accuracy: 0.9902 - val_loss: 1.2838 - val_accuracy: 0.7550\n",
      "Epoch 267/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0905 - accuracy: 0.9899 - val_loss: 1.2772 - val_accuracy: 0.7575\n",
      "Epoch 268/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0883 - accuracy: 0.9899 - val_loss: 1.2812 - val_accuracy: 0.7537\n",
      "Epoch 269/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0880 - accuracy: 0.9902 - val_loss: 1.2918 - val_accuracy: 0.7525\n",
      "Epoch 270/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0833 - accuracy: 0.9928 - val_loss: 1.2981 - val_accuracy: 0.7512\n",
      "Epoch 271/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0870 - accuracy: 0.9913 - val_loss: 1.3013 - val_accuracy: 0.7512\n",
      "Epoch 272/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0865 - accuracy: 0.9904 - val_loss: 1.3022 - val_accuracy: 0.7512\n",
      "Epoch 273/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0845 - accuracy: 0.9920 - val_loss: 1.2938 - val_accuracy: 0.7537\n",
      "Epoch 274/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0851 - accuracy: 0.9914 - val_loss: 1.2978 - val_accuracy: 0.7587\n",
      "Epoch 275/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0846 - accuracy: 0.9918 - val_loss: 1.3046 - val_accuracy: 0.7587\n",
      "Epoch 276/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0885 - accuracy: 0.9907 - val_loss: 1.3068 - val_accuracy: 0.7575\n",
      "Epoch 277/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0849 - accuracy: 0.9913 - val_loss: 1.3085 - val_accuracy: 0.7562\n",
      "Epoch 278/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0872 - accuracy: 0.9907 - val_loss: 1.3149 - val_accuracy: 0.7537\n",
      "Epoch 279/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0823 - accuracy: 0.9926 - val_loss: 1.3143 - val_accuracy: 0.7537\n",
      "Epoch 280/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0873 - accuracy: 0.9909 - val_loss: 1.3047 - val_accuracy: 0.7550\n",
      "Epoch 281/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0870 - accuracy: 0.9903 - val_loss: 1.2952 - val_accuracy: 0.7575\n",
      "Epoch 282/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0840 - accuracy: 0.9911 - val_loss: 1.3011 - val_accuracy: 0.7575\n",
      "Epoch 283/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0852 - accuracy: 0.9912 - val_loss: 1.3121 - val_accuracy: 0.7550\n",
      "Epoch 284/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0846 - accuracy: 0.9909 - val_loss: 1.3048 - val_accuracy: 0.7537\n",
      "Epoch 285/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0850 - accuracy: 0.9911 - val_loss: 1.3043 - val_accuracy: 0.7487\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 286/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0823 - accuracy: 0.9916 - val_loss: 1.3084 - val_accuracy: 0.7550\n",
      "Epoch 287/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0826 - accuracy: 0.9916 - val_loss: 1.3026 - val_accuracy: 0.7525\n",
      "Epoch 288/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0823 - accuracy: 0.9916 - val_loss: 1.2969 - val_accuracy: 0.7562\n",
      "Epoch 289/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0815 - accuracy: 0.9921 - val_loss: 1.2970 - val_accuracy: 0.7600\n",
      "Epoch 290/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0828 - accuracy: 0.9918 - val_loss: 1.2852 - val_accuracy: 0.7612\n",
      "Epoch 291/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0815 - accuracy: 0.9926 - val_loss: 1.2921 - val_accuracy: 0.7587\n",
      "Epoch 292/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0863 - accuracy: 0.9901 - val_loss: 1.2910 - val_accuracy: 0.7575\n",
      "Epoch 293/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0821 - accuracy: 0.9926 - val_loss: 1.2954 - val_accuracy: 0.7575\n",
      "Epoch 294/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0809 - accuracy: 0.9924 - val_loss: 1.3033 - val_accuracy: 0.7525\n",
      "Epoch 295/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0842 - accuracy: 0.9917 - val_loss: 1.3065 - val_accuracy: 0.7512\n",
      "Epoch 296/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0850 - accuracy: 0.9911 - val_loss: 1.2990 - val_accuracy: 0.7525\n",
      "Epoch 297/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0841 - accuracy: 0.9912 - val_loss: 1.3057 - val_accuracy: 0.7462\n",
      "Epoch 298/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0804 - accuracy: 0.9924 - val_loss: 1.3073 - val_accuracy: 0.7475\n",
      "Epoch 299/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0780 - accuracy: 0.9933 - val_loss: 1.3170 - val_accuracy: 0.7475\n",
      "Epoch 300/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0821 - accuracy: 0.9917 - val_loss: 1.3109 - val_accuracy: 0.7525\n",
      "Epoch 301/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0830 - accuracy: 0.9918 - val_loss: 1.3157 - val_accuracy: 0.7512\n",
      "Epoch 302/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0825 - accuracy: 0.9924 - val_loss: 1.3136 - val_accuracy: 0.7500\n",
      "Epoch 303/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0823 - accuracy: 0.9918 - val_loss: 1.3081 - val_accuracy: 0.7500\n",
      "Epoch 304/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0834 - accuracy: 0.9915 - val_loss: 1.3102 - val_accuracy: 0.7450\n",
      "Epoch 305/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0801 - accuracy: 0.9924 - val_loss: 1.3221 - val_accuracy: 0.7475\n",
      "Epoch 306/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0786 - accuracy: 0.9923 - val_loss: 1.3241 - val_accuracy: 0.7525\n",
      "Epoch 307/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0817 - accuracy: 0.9915 - val_loss: 1.3149 - val_accuracy: 0.7512\n",
      "Epoch 308/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0786 - accuracy: 0.9924 - val_loss: 1.3147 - val_accuracy: 0.7500\n",
      "Epoch 309/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0801 - accuracy: 0.9918 - val_loss: 1.3145 - val_accuracy: 0.7525\n",
      "Epoch 310/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0805 - accuracy: 0.9923 - val_loss: 1.3188 - val_accuracy: 0.7537\n",
      "Epoch 311/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0802 - accuracy: 0.9921 - val_loss: 1.3180 - val_accuracy: 0.7562\n",
      "Epoch 312/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0800 - accuracy: 0.9923 - val_loss: 1.3196 - val_accuracy: 0.7587\n",
      "Epoch 313/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0806 - accuracy: 0.9916 - val_loss: 1.3186 - val_accuracy: 0.7612\n",
      "Epoch 314/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0771 - accuracy: 0.9928 - val_loss: 1.3144 - val_accuracy: 0.7600\n",
      "Epoch 315/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0813 - accuracy: 0.9926 - val_loss: 1.3094 - val_accuracy: 0.7550\n",
      "Epoch 316/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0809 - accuracy: 0.9917 - val_loss: 1.3049 - val_accuracy: 0.7550\n",
      "Epoch 317/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0794 - accuracy: 0.9928 - val_loss: 1.3063 - val_accuracy: 0.7525\n",
      "Epoch 318/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0781 - accuracy: 0.9928 - val_loss: 1.3064 - val_accuracy: 0.7412\n",
      "Epoch 319/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0810 - accuracy: 0.9917 - val_loss: 1.3121 - val_accuracy: 0.7512\n",
      "Epoch 320/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0827 - accuracy: 0.9913 - val_loss: 1.3113 - val_accuracy: 0.7537\n",
      "Epoch 321/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0792 - accuracy: 0.9924 - val_loss: 1.3080 - val_accuracy: 0.7525\n",
      "Epoch 322/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0784 - accuracy: 0.9926 - val_loss: 1.3080 - val_accuracy: 0.7512\n",
      "Epoch 323/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0794 - accuracy: 0.9929 - val_loss: 1.2997 - val_accuracy: 0.7537\n",
      "Epoch 324/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0777 - accuracy: 0.9923 - val_loss: 1.3012 - val_accuracy: 0.7450\n",
      "Epoch 325/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0792 - accuracy: 0.9927 - val_loss: 1.3121 - val_accuracy: 0.7475\n",
      "Epoch 326/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0795 - accuracy: 0.9915 - val_loss: 1.3165 - val_accuracy: 0.7437\n",
      "Epoch 327/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0774 - accuracy: 0.9933 - val_loss: 1.3181 - val_accuracy: 0.7425\n",
      "Epoch 328/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0791 - accuracy: 0.9916 - val_loss: 1.3134 - val_accuracy: 0.7462\n",
      "Epoch 329/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0772 - accuracy: 0.9932 - val_loss: 1.3104 - val_accuracy: 0.7437\n",
      "Epoch 330/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0763 - accuracy: 0.9944 - val_loss: 1.3106 - val_accuracy: 0.7425\n",
      "Epoch 331/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0778 - accuracy: 0.9937 - val_loss: 1.3098 - val_accuracy: 0.7425\n",
      "Epoch 332/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0785 - accuracy: 0.9929 - val_loss: 1.3182 - val_accuracy: 0.7450\n",
      "Epoch 333/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0776 - accuracy: 0.9922 - val_loss: 1.3115 - val_accuracy: 0.7412\n",
      "Epoch 334/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0773 - accuracy: 0.9928 - val_loss: 1.3108 - val_accuracy: 0.7462\n",
      "Epoch 335/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0784 - accuracy: 0.9926 - val_loss: 1.3092 - val_accuracy: 0.7462\n",
      "Epoch 336/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0759 - accuracy: 0.9937 - val_loss: 1.3169 - val_accuracy: 0.7487\n",
      "Epoch 337/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0786 - accuracy: 0.9922 - val_loss: 1.3209 - val_accuracy: 0.7537\n",
      "Epoch 338/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0780 - accuracy: 0.9928 - val_loss: 1.3217 - val_accuracy: 0.7587\n",
      "Epoch 339/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0802 - accuracy: 0.9927 - val_loss: 1.3247 - val_accuracy: 0.7612\n",
      "Epoch 340/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0789 - accuracy: 0.9928 - val_loss: 1.3335 - val_accuracy: 0.7600\n",
      "Epoch 341/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0751 - accuracy: 0.9942 - val_loss: 1.3345 - val_accuracy: 0.7600\n",
      "Epoch 342/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0777 - accuracy: 0.9936 - val_loss: 1.3410 - val_accuracy: 0.7587\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 343/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0766 - accuracy: 0.9926 - val_loss: 1.3442 - val_accuracy: 0.7587\n",
      "Epoch 344/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.0786 - accuracy: 0.9924 - val_loss: 1.3578 - val_accuracy: 0.7550\n",
      "Epoch 345/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0780 - accuracy: 0.9922 - val_loss: 1.3568 - val_accuracy: 0.7587\n",
      "Epoch 346/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0768 - accuracy: 0.9932 - val_loss: 1.3521 - val_accuracy: 0.7575\n",
      "Epoch 347/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0756 - accuracy: 0.9927 - val_loss: 1.3437 - val_accuracy: 0.7525\n",
      "Epoch 348/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0785 - accuracy: 0.9921 - val_loss: 1.3448 - val_accuracy: 0.7550\n",
      "Epoch 349/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0755 - accuracy: 0.9932 - val_loss: 1.3413 - val_accuracy: 0.7512\n",
      "Epoch 350/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0741 - accuracy: 0.9936 - val_loss: 1.3473 - val_accuracy: 0.7525\n",
      "Epoch 351/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0780 - accuracy: 0.9928 - val_loss: 1.3466 - val_accuracy: 0.7500\n",
      "Epoch 352/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0774 - accuracy: 0.9918 - val_loss: 1.3428 - val_accuracy: 0.7537\n",
      "Epoch 353/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0749 - accuracy: 0.9936 - val_loss: 1.3425 - val_accuracy: 0.7512\n",
      "Epoch 354/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0753 - accuracy: 0.9932 - val_loss: 1.3476 - val_accuracy: 0.7550\n",
      "Epoch 355/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0769 - accuracy: 0.9927 - val_loss: 1.3563 - val_accuracy: 0.7537\n",
      "Epoch 356/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0765 - accuracy: 0.9928 - val_loss: 1.3523 - val_accuracy: 0.7537\n",
      "Epoch 357/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0760 - accuracy: 0.9933 - val_loss: 1.3581 - val_accuracy: 0.7550\n",
      "Epoch 358/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0752 - accuracy: 0.9931 - val_loss: 1.3637 - val_accuracy: 0.7550\n",
      "Epoch 359/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0752 - accuracy: 0.9922 - val_loss: 1.3715 - val_accuracy: 0.7500\n",
      "Epoch 360/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0746 - accuracy: 0.9934 - val_loss: 1.3682 - val_accuracy: 0.7475\n",
      "Epoch 361/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.0740 - accuracy: 0.9937 - val_loss: 1.3777 - val_accuracy: 0.7550\n",
      "Epoch 362/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0747 - accuracy: 0.9931 - val_loss: 1.3827 - val_accuracy: 0.7537\n",
      "Epoch 363/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0760 - accuracy: 0.9921 - val_loss: 1.3854 - val_accuracy: 0.7537\n",
      "Epoch 364/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0766 - accuracy: 0.9927 - val_loss: 1.3803 - val_accuracy: 0.7550\n",
      "Epoch 365/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0737 - accuracy: 0.9938 - val_loss: 1.3743 - val_accuracy: 0.7500\n",
      "Epoch 366/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0726 - accuracy: 0.9943 - val_loss: 1.3728 - val_accuracy: 0.7487\n",
      "Epoch 367/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0735 - accuracy: 0.9934 - val_loss: 1.3670 - val_accuracy: 0.7550\n",
      "Epoch 368/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0765 - accuracy: 0.9933 - val_loss: 1.3799 - val_accuracy: 0.7537\n",
      "Epoch 369/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0737 - accuracy: 0.9942 - val_loss: 1.3817 - val_accuracy: 0.7562\n",
      "Epoch 370/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.0752 - accuracy: 0.9934 - val_loss: 1.3754 - val_accuracy: 0.7487\n",
      "Epoch 371/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0761 - accuracy: 0.9933 - val_loss: 1.3704 - val_accuracy: 0.7500\n",
      "Epoch 372/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0725 - accuracy: 0.9941 - val_loss: 1.3693 - val_accuracy: 0.7487\n",
      "Epoch 373/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0741 - accuracy: 0.9934 - val_loss: 1.3684 - val_accuracy: 0.7475\n",
      "Epoch 374/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0764 - accuracy: 0.9934 - val_loss: 1.3608 - val_accuracy: 0.7512\n",
      "Epoch 375/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0720 - accuracy: 0.9936 - val_loss: 1.3699 - val_accuracy: 0.7475\n",
      "Epoch 376/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0726 - accuracy: 0.9940 - val_loss: 1.3664 - val_accuracy: 0.7487\n",
      "Epoch 377/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0718 - accuracy: 0.9941 - val_loss: 1.3666 - val_accuracy: 0.7487\n",
      "Epoch 378/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0732 - accuracy: 0.9936 - val_loss: 1.3655 - val_accuracy: 0.7550\n",
      "Epoch 379/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0754 - accuracy: 0.9936 - val_loss: 1.3705 - val_accuracy: 0.7475\n",
      "Epoch 380/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0743 - accuracy: 0.9940 - val_loss: 1.3791 - val_accuracy: 0.7425\n",
      "Epoch 381/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0724 - accuracy: 0.9945 - val_loss: 1.3815 - val_accuracy: 0.7475\n",
      "Epoch 382/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0750 - accuracy: 0.9928 - val_loss: 1.3803 - val_accuracy: 0.7500\n",
      "Epoch 383/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0715 - accuracy: 0.9936 - val_loss: 1.3940 - val_accuracy: 0.7475\n",
      "Epoch 384/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0755 - accuracy: 0.9933 - val_loss: 1.3871 - val_accuracy: 0.7512\n",
      "Epoch 385/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0763 - accuracy: 0.9932 - val_loss: 1.3917 - val_accuracy: 0.7512\n",
      "Epoch 386/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0730 - accuracy: 0.9935 - val_loss: 1.3937 - val_accuracy: 0.7525\n",
      "Epoch 387/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0714 - accuracy: 0.9947 - val_loss: 1.3864 - val_accuracy: 0.7562\n",
      "Epoch 388/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0712 - accuracy: 0.9941 - val_loss: 1.3840 - val_accuracy: 0.7575\n",
      "Epoch 389/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0748 - accuracy: 0.9928 - val_loss: 1.3853 - val_accuracy: 0.7587\n",
      "Epoch 390/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0734 - accuracy: 0.9939 - val_loss: 1.3833 - val_accuracy: 0.7525\n",
      "Epoch 391/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0722 - accuracy: 0.9945 - val_loss: 1.3801 - val_accuracy: 0.7512\n",
      "Epoch 392/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0726 - accuracy: 0.9938 - val_loss: 1.3875 - val_accuracy: 0.7500\n",
      "Epoch 393/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0716 - accuracy: 0.9945 - val_loss: 1.3942 - val_accuracy: 0.7512\n",
      "Epoch 394/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0728 - accuracy: 0.9934 - val_loss: 1.3873 - val_accuracy: 0.7525\n",
      "Epoch 395/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0712 - accuracy: 0.9939 - val_loss: 1.3744 - val_accuracy: 0.7500\n",
      "Epoch 396/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0721 - accuracy: 0.9936 - val_loss: 1.3831 - val_accuracy: 0.7500\n",
      "Epoch 397/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0709 - accuracy: 0.9942 - val_loss: 1.3887 - val_accuracy: 0.7475\n",
      "Epoch 398/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0725 - accuracy: 0.9941 - val_loss: 1.3954 - val_accuracy: 0.7487\n",
      "Epoch 399/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0748 - accuracy: 0.9929 - val_loss: 1.3991 - val_accuracy: 0.7462\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 400/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0718 - accuracy: 0.9941 - val_loss: 1.3968 - val_accuracy: 0.7500\n",
      "Epoch 401/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0725 - accuracy: 0.9940 - val_loss: 1.4025 - val_accuracy: 0.7562\n",
      "Epoch 402/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0700 - accuracy: 0.9946 - val_loss: 1.4072 - val_accuracy: 0.7550\n",
      "Epoch 403/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0711 - accuracy: 0.9939 - val_loss: 1.4024 - val_accuracy: 0.7537\n",
      "Epoch 404/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0706 - accuracy: 0.9939 - val_loss: 1.3975 - val_accuracy: 0.7550\n",
      "Epoch 405/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0710 - accuracy: 0.9943 - val_loss: 1.3986 - val_accuracy: 0.7525\n",
      "Epoch 406/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0744 - accuracy: 0.9933 - val_loss: 1.3949 - val_accuracy: 0.7612\n",
      "Epoch 407/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0701 - accuracy: 0.9939 - val_loss: 1.3879 - val_accuracy: 0.7625\n",
      "Epoch 408/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0714 - accuracy: 0.9939 - val_loss: 1.3886 - val_accuracy: 0.7625\n",
      "Epoch 409/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0709 - accuracy: 0.9945 - val_loss: 1.3890 - val_accuracy: 0.7575\n",
      "Epoch 410/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0701 - accuracy: 0.9943 - val_loss: 1.3953 - val_accuracy: 0.7550\n",
      "Epoch 411/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0693 - accuracy: 0.9942 - val_loss: 1.3945 - val_accuracy: 0.7587\n",
      "Epoch 412/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0709 - accuracy: 0.9941 - val_loss: 1.4008 - val_accuracy: 0.7512\n",
      "Epoch 413/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0680 - accuracy: 0.9952 - val_loss: 1.4064 - val_accuracy: 0.7512\n",
      "Epoch 414/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0697 - accuracy: 0.9943 - val_loss: 1.4083 - val_accuracy: 0.7537\n",
      "Epoch 415/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0716 - accuracy: 0.9939 - val_loss: 1.3988 - val_accuracy: 0.7512\n",
      "Epoch 416/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0726 - accuracy: 0.9933 - val_loss: 1.3899 - val_accuracy: 0.7537\n",
      "Epoch 417/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0686 - accuracy: 0.9947 - val_loss: 1.3864 - val_accuracy: 0.7562\n",
      "Epoch 418/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0709 - accuracy: 0.9937 - val_loss: 1.3871 - val_accuracy: 0.7537\n",
      "Epoch 419/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0699 - accuracy: 0.9946 - val_loss: 1.3935 - val_accuracy: 0.7562\n",
      "Epoch 420/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0719 - accuracy: 0.9941 - val_loss: 1.3901 - val_accuracy: 0.7512\n",
      "Epoch 421/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0696 - accuracy: 0.9951 - val_loss: 1.3881 - val_accuracy: 0.7487\n",
      "Epoch 422/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0708 - accuracy: 0.9939 - val_loss: 1.4081 - val_accuracy: 0.7475\n",
      "Epoch 423/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0697 - accuracy: 0.9943 - val_loss: 1.4181 - val_accuracy: 0.7550\n",
      "Epoch 424/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0694 - accuracy: 0.9937 - val_loss: 1.4065 - val_accuracy: 0.7587\n",
      "Epoch 425/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0719 - accuracy: 0.9933 - val_loss: 1.4058 - val_accuracy: 0.7600\n",
      "Epoch 426/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0720 - accuracy: 0.9936 - val_loss: 1.4018 - val_accuracy: 0.7600\n",
      "Epoch 427/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0703 - accuracy: 0.9939 - val_loss: 1.3981 - val_accuracy: 0.7600\n",
      "Epoch 428/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0693 - accuracy: 0.9942 - val_loss: 1.3869 - val_accuracy: 0.7612\n",
      "Epoch 429/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0680 - accuracy: 0.9947 - val_loss: 1.3876 - val_accuracy: 0.7600\n",
      "Epoch 430/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0724 - accuracy: 0.9931 - val_loss: 1.3924 - val_accuracy: 0.7625\n",
      "Epoch 431/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0718 - accuracy: 0.9938 - val_loss: 1.3952 - val_accuracy: 0.7587\n",
      "Epoch 432/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0716 - accuracy: 0.9947 - val_loss: 1.3896 - val_accuracy: 0.7612\n",
      "Epoch 433/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0690 - accuracy: 0.9945 - val_loss: 1.3946 - val_accuracy: 0.7637\n",
      "Epoch 434/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0723 - accuracy: 0.9941 - val_loss: 1.3923 - val_accuracy: 0.7637\n",
      "Epoch 435/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0669 - accuracy: 0.9954 - val_loss: 1.3890 - val_accuracy: 0.7662\n",
      "Epoch 436/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0692 - accuracy: 0.9944 - val_loss: 1.3875 - val_accuracy: 0.7688\n",
      "Epoch 437/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0712 - accuracy: 0.9934 - val_loss: 1.3876 - val_accuracy: 0.7650\n",
      "Epoch 438/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0681 - accuracy: 0.9948 - val_loss: 1.3895 - val_accuracy: 0.7662\n",
      "Epoch 439/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0687 - accuracy: 0.9949 - val_loss: 1.3890 - val_accuracy: 0.7662\n",
      "Epoch 440/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0699 - accuracy: 0.9939 - val_loss: 1.3865 - val_accuracy: 0.7637\n",
      "Epoch 441/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0722 - accuracy: 0.9939 - val_loss: 1.3844 - val_accuracy: 0.7662\n",
      "Epoch 442/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0695 - accuracy: 0.9942 - val_loss: 1.3846 - val_accuracy: 0.7637\n",
      "Epoch 443/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0703 - accuracy: 0.9941 - val_loss: 1.3919 - val_accuracy: 0.7625\n",
      "Epoch 444/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0679 - accuracy: 0.9947 - val_loss: 1.4044 - val_accuracy: 0.7575\n",
      "Epoch 445/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0695 - accuracy: 0.9945 - val_loss: 1.4141 - val_accuracy: 0.7550\n",
      "Epoch 446/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0728 - accuracy: 0.9931 - val_loss: 1.4176 - val_accuracy: 0.7525\n",
      "Epoch 447/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0709 - accuracy: 0.9945 - val_loss: 1.4141 - val_accuracy: 0.7600\n",
      "Epoch 448/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0682 - accuracy: 0.9942 - val_loss: 1.4107 - val_accuracy: 0.7600\n",
      "Epoch 449/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0699 - accuracy: 0.9935 - val_loss: 1.4085 - val_accuracy: 0.7612\n",
      "Epoch 450/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0661 - accuracy: 0.9953 - val_loss: 1.4006 - val_accuracy: 0.7587\n",
      "Epoch 451/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0705 - accuracy: 0.9939 - val_loss: 1.4025 - val_accuracy: 0.7575\n",
      "Epoch 452/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0671 - accuracy: 0.9950 - val_loss: 1.4188 - val_accuracy: 0.7587\n",
      "Epoch 453/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0682 - accuracy: 0.9947 - val_loss: 1.4251 - val_accuracy: 0.7587\n",
      "Epoch 454/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0676 - accuracy: 0.9947 - val_loss: 1.4323 - val_accuracy: 0.7600\n",
      "Epoch 455/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0681 - accuracy: 0.9946 - val_loss: 1.4258 - val_accuracy: 0.7562\n",
      "Epoch 456/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0675 - accuracy: 0.9948 - val_loss: 1.4193 - val_accuracy: 0.7575\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 457/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0718 - accuracy: 0.9942 - val_loss: 1.4155 - val_accuracy: 0.7587\n",
      "Epoch 458/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0688 - accuracy: 0.9948 - val_loss: 1.4147 - val_accuracy: 0.7612\n",
      "Epoch 459/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0708 - accuracy: 0.9938 - val_loss: 1.4086 - val_accuracy: 0.7662\n",
      "Epoch 460/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0677 - accuracy: 0.9950 - val_loss: 1.4188 - val_accuracy: 0.7625\n",
      "Epoch 461/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0698 - accuracy: 0.9935 - val_loss: 1.4219 - val_accuracy: 0.7587\n",
      "Epoch 462/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0670 - accuracy: 0.9953 - val_loss: 1.4175 - val_accuracy: 0.7562\n",
      "Epoch 463/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0655 - accuracy: 0.9959 - val_loss: 1.4224 - val_accuracy: 0.7575\n",
      "Epoch 464/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0678 - accuracy: 0.9942 - val_loss: 1.4251 - val_accuracy: 0.7550\n",
      "Epoch 465/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0673 - accuracy: 0.9948 - val_loss: 1.4246 - val_accuracy: 0.7562\n",
      "Epoch 466/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0680 - accuracy: 0.9950 - val_loss: 1.4268 - val_accuracy: 0.7637\n",
      "Epoch 467/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0675 - accuracy: 0.9938 - val_loss: 1.4201 - val_accuracy: 0.7600\n",
      "Epoch 468/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0688 - accuracy: 0.9947 - val_loss: 1.4160 - val_accuracy: 0.7625\n",
      "Epoch 469/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0655 - accuracy: 0.9950 - val_loss: 1.4096 - val_accuracy: 0.7625\n",
      "Epoch 470/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0663 - accuracy: 0.9946 - val_loss: 1.4183 - val_accuracy: 0.7637\n",
      "Epoch 471/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0671 - accuracy: 0.9947 - val_loss: 1.4247 - val_accuracy: 0.7612\n",
      "Epoch 472/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0685 - accuracy: 0.9944 - val_loss: 1.4284 - val_accuracy: 0.7612\n",
      "Epoch 473/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0673 - accuracy: 0.9949 - val_loss: 1.4324 - val_accuracy: 0.7575\n",
      "Epoch 474/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0689 - accuracy: 0.9945 - val_loss: 1.4388 - val_accuracy: 0.7550\n",
      "Epoch 475/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0671 - accuracy: 0.9943 - val_loss: 1.4323 - val_accuracy: 0.7550\n",
      "Epoch 476/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0673 - accuracy: 0.9939 - val_loss: 1.4306 - val_accuracy: 0.7612\n",
      "Epoch 477/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0673 - accuracy: 0.9947 - val_loss: 1.4352 - val_accuracy: 0.7600\n",
      "Epoch 478/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0676 - accuracy: 0.9947 - val_loss: 1.4366 - val_accuracy: 0.7612\n",
      "Epoch 479/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0681 - accuracy: 0.9947 - val_loss: 1.4307 - val_accuracy: 0.7587\n",
      "Epoch 480/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0669 - accuracy: 0.9943 - val_loss: 1.4213 - val_accuracy: 0.7637\n",
      "Epoch 481/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0680 - accuracy: 0.9942 - val_loss: 1.4080 - val_accuracy: 0.7612\n",
      "Epoch 482/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0674 - accuracy: 0.9944 - val_loss: 1.4221 - val_accuracy: 0.7550\n",
      "Epoch 483/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0658 - accuracy: 0.9948 - val_loss: 1.4324 - val_accuracy: 0.7587\n",
      "Epoch 484/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0678 - accuracy: 0.9945 - val_loss: 1.4270 - val_accuracy: 0.7587\n",
      "Epoch 485/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0647 - accuracy: 0.9952 - val_loss: 1.4360 - val_accuracy: 0.7550\n",
      "Epoch 486/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0675 - accuracy: 0.9948 - val_loss: 1.4421 - val_accuracy: 0.7562\n",
      "Epoch 487/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0677 - accuracy: 0.9939 - val_loss: 1.4461 - val_accuracy: 0.7562\n",
      "Epoch 488/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0677 - accuracy: 0.9947 - val_loss: 1.4534 - val_accuracy: 0.7537\n",
      "Epoch 489/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0683 - accuracy: 0.9941 - val_loss: 1.4551 - val_accuracy: 0.7512\n",
      "Epoch 490/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0681 - accuracy: 0.9950 - val_loss: 1.4606 - val_accuracy: 0.7525\n",
      "Epoch 491/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0677 - accuracy: 0.9942 - val_loss: 1.4554 - val_accuracy: 0.7487\n",
      "Epoch 492/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0655 - accuracy: 0.9959 - val_loss: 1.4467 - val_accuracy: 0.7512\n",
      "Epoch 493/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0639 - accuracy: 0.9958 - val_loss: 1.4479 - val_accuracy: 0.7500\n",
      "Epoch 494/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.0692 - accuracy: 0.9939 - val_loss: 1.4430 - val_accuracy: 0.7550\n",
      "Epoch 495/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0717 - accuracy: 0.9938 - val_loss: 1.4488 - val_accuracy: 0.7587\n",
      "Epoch 496/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0675 - accuracy: 0.9949 - val_loss: 1.4455 - val_accuracy: 0.7575\n",
      "Epoch 497/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0684 - accuracy: 0.9939 - val_loss: 1.4340 - val_accuracy: 0.7562\n",
      "Epoch 498/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0667 - accuracy: 0.9957 - val_loss: 1.4412 - val_accuracy: 0.7550\n",
      "Epoch 499/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0679 - accuracy: 0.9939 - val_loss: 1.4457 - val_accuracy: 0.7525\n",
      "Epoch 500/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0655 - accuracy: 0.9949 - val_loss: 1.4423 - val_accuracy: 0.7550\n",
      "Epoch 501/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0685 - accuracy: 0.9949 - val_loss: 1.4471 - val_accuracy: 0.7600\n",
      "Epoch 502/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0680 - accuracy: 0.9945 - val_loss: 1.4452 - val_accuracy: 0.7612\n",
      "Epoch 503/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0652 - accuracy: 0.9955 - val_loss: 1.4343 - val_accuracy: 0.7675\n",
      "Epoch 504/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0671 - accuracy: 0.9947 - val_loss: 1.4319 - val_accuracy: 0.7625\n",
      "Epoch 505/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0675 - accuracy: 0.9949 - val_loss: 1.4420 - val_accuracy: 0.7625\n",
      "Epoch 506/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0667 - accuracy: 0.9941 - val_loss: 1.4540 - val_accuracy: 0.7537\n",
      "Epoch 507/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0639 - accuracy: 0.9955 - val_loss: 1.4570 - val_accuracy: 0.7550\n",
      "Epoch 508/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0667 - accuracy: 0.9953 - val_loss: 1.4642 - val_accuracy: 0.7600\n",
      "Epoch 509/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0683 - accuracy: 0.9943 - val_loss: 1.4597 - val_accuracy: 0.7600\n",
      "Epoch 510/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0673 - accuracy: 0.9943 - val_loss: 1.4548 - val_accuracy: 0.7612\n",
      "Epoch 511/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0672 - accuracy: 0.9942 - val_loss: 1.4489 - val_accuracy: 0.7587\n",
      "Epoch 512/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0663 - accuracy: 0.9939 - val_loss: 1.4419 - val_accuracy: 0.7612\n",
      "Epoch 513/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0643 - accuracy: 0.9955 - val_loss: 1.4326 - val_accuracy: 0.7600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 514/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0679 - accuracy: 0.9938 - val_loss: 1.4311 - val_accuracy: 0.7537\n",
      "Epoch 515/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0640 - accuracy: 0.9955 - val_loss: 1.4418 - val_accuracy: 0.7550\n",
      "Epoch 516/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0653 - accuracy: 0.9951 - val_loss: 1.4403 - val_accuracy: 0.7562\n",
      "Epoch 517/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0648 - accuracy: 0.9950 - val_loss: 1.4330 - val_accuracy: 0.7562\n",
      "Epoch 518/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0672 - accuracy: 0.9945 - val_loss: 1.4225 - val_accuracy: 0.7550\n",
      "Epoch 519/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0632 - accuracy: 0.9960 - val_loss: 1.4217 - val_accuracy: 0.7537\n",
      "Epoch 520/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0705 - accuracy: 0.9942 - val_loss: 1.4273 - val_accuracy: 0.7537\n",
      "Epoch 521/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0675 - accuracy: 0.9936 - val_loss: 1.4171 - val_accuracy: 0.7525\n",
      "Epoch 522/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0663 - accuracy: 0.9946 - val_loss: 1.4101 - val_accuracy: 0.7500\n",
      "Epoch 523/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0645 - accuracy: 0.9956 - val_loss: 1.4116 - val_accuracy: 0.7525\n",
      "Epoch 524/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0670 - accuracy: 0.9944 - val_loss: 1.4246 - val_accuracy: 0.7550\n",
      "Epoch 525/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0678 - accuracy: 0.9944 - val_loss: 1.4057 - val_accuracy: 0.7562\n",
      "Epoch 526/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0663 - accuracy: 0.9958 - val_loss: 1.4084 - val_accuracy: 0.7600\n",
      "Epoch 527/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0667 - accuracy: 0.9947 - val_loss: 1.4127 - val_accuracy: 0.7562\n",
      "Epoch 528/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0662 - accuracy: 0.9952 - val_loss: 1.4168 - val_accuracy: 0.7525\n",
      "Epoch 529/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0643 - accuracy: 0.9953 - val_loss: 1.4172 - val_accuracy: 0.7525\n",
      "Epoch 530/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0681 - accuracy: 0.9948 - val_loss: 1.4167 - val_accuracy: 0.7525\n",
      "Epoch 531/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0681 - accuracy: 0.9945 - val_loss: 1.4123 - val_accuracy: 0.7562\n",
      "Epoch 532/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0647 - accuracy: 0.9949 - val_loss: 1.4161 - val_accuracy: 0.7525\n",
      "Epoch 533/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0651 - accuracy: 0.9954 - val_loss: 1.4279 - val_accuracy: 0.7550\n",
      "Epoch 534/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0646 - accuracy: 0.9956 - val_loss: 1.4197 - val_accuracy: 0.7587\n",
      "Epoch 535/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0660 - accuracy: 0.9948 - val_loss: 1.4168 - val_accuracy: 0.7575\n",
      "Epoch 536/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0647 - accuracy: 0.9951 - val_loss: 1.4147 - val_accuracy: 0.7550\n",
      "Epoch 537/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0652 - accuracy: 0.9957 - val_loss: 1.4085 - val_accuracy: 0.7562\n",
      "Epoch 538/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0672 - accuracy: 0.9938 - val_loss: 1.4046 - val_accuracy: 0.7600\n",
      "Epoch 539/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0638 - accuracy: 0.9954 - val_loss: 1.4084 - val_accuracy: 0.7587\n",
      "Epoch 540/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0659 - accuracy: 0.9943 - val_loss: 1.4040 - val_accuracy: 0.7562\n",
      "Epoch 541/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0664 - accuracy: 0.9949 - val_loss: 1.4012 - val_accuracy: 0.7512\n",
      "Epoch 542/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0677 - accuracy: 0.9943 - val_loss: 1.3968 - val_accuracy: 0.7537\n",
      "Epoch 543/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0656 - accuracy: 0.9952 - val_loss: 1.3947 - val_accuracy: 0.7562\n",
      "Epoch 544/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0663 - accuracy: 0.9948 - val_loss: 1.4043 - val_accuracy: 0.7600\n",
      "Epoch 545/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0663 - accuracy: 0.9947 - val_loss: 1.4081 - val_accuracy: 0.7612\n",
      "Epoch 546/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0650 - accuracy: 0.9942 - val_loss: 1.4070 - val_accuracy: 0.7600\n",
      "Epoch 547/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0653 - accuracy: 0.9951 - val_loss: 1.4020 - val_accuracy: 0.7612\n",
      "Epoch 548/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0639 - accuracy: 0.9960 - val_loss: 1.4035 - val_accuracy: 0.7575\n",
      "Epoch 549/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0643 - accuracy: 0.9959 - val_loss: 1.4123 - val_accuracy: 0.7637\n",
      "Epoch 550/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0652 - accuracy: 0.9947 - val_loss: 1.4194 - val_accuracy: 0.7600\n",
      "Epoch 551/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0654 - accuracy: 0.9948 - val_loss: 1.4148 - val_accuracy: 0.7575\n",
      "Epoch 552/1000\n",
      "19/19 [==============================] - 0s 8ms/step - loss: 0.0645 - accuracy: 0.9948 - val_loss: 1.4078 - val_accuracy: 0.7612\n",
      "Epoch 553/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0628 - accuracy: 0.9957 - val_loss: 1.4133 - val_accuracy: 0.7625\n",
      "Epoch 554/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0654 - accuracy: 0.9951 - val_loss: 1.4163 - val_accuracy: 0.7650\n",
      "Epoch 555/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0645 - accuracy: 0.9953 - val_loss: 1.4175 - val_accuracy: 0.7587\n",
      "Epoch 556/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0664 - accuracy: 0.9947 - val_loss: 1.4186 - val_accuracy: 0.7612\n",
      "Epoch 557/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0678 - accuracy: 0.9944 - val_loss: 1.4126 - val_accuracy: 0.7612\n",
      "Epoch 558/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0642 - accuracy: 0.9948 - val_loss: 1.4102 - val_accuracy: 0.7650\n",
      "Epoch 559/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0638 - accuracy: 0.9957 - val_loss: 1.4262 - val_accuracy: 0.7575\n",
      "Epoch 560/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0661 - accuracy: 0.9944 - val_loss: 1.4350 - val_accuracy: 0.7525\n",
      "Epoch 561/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0633 - accuracy: 0.9961 - val_loss: 1.4520 - val_accuracy: 0.7512\n",
      "Epoch 562/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0633 - accuracy: 0.9960 - val_loss: 1.4567 - val_accuracy: 0.7462\n",
      "Epoch 563/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0642 - accuracy: 0.9951 - val_loss: 1.4539 - val_accuracy: 0.7550\n",
      "Epoch 564/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0665 - accuracy: 0.9947 - val_loss: 1.4465 - val_accuracy: 0.7587\n",
      "Epoch 565/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0652 - accuracy: 0.9949 - val_loss: 1.4503 - val_accuracy: 0.7600\n",
      "Epoch 566/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0637 - accuracy: 0.9952 - val_loss: 1.4455 - val_accuracy: 0.7600\n",
      "Epoch 567/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0634 - accuracy: 0.9958 - val_loss: 1.4399 - val_accuracy: 0.7587\n",
      "Epoch 568/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0642 - accuracy: 0.9955 - val_loss: 1.4304 - val_accuracy: 0.7650\n",
      "Epoch 569/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0640 - accuracy: 0.9951 - val_loss: 1.4325 - val_accuracy: 0.7612\n",
      "Epoch 570/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0641 - accuracy: 0.9963 - val_loss: 1.4151 - val_accuracy: 0.7612\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 571/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0659 - accuracy: 0.9945 - val_loss: 1.4055 - val_accuracy: 0.7650\n",
      "Epoch 572/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0682 - accuracy: 0.9944 - val_loss: 1.4172 - val_accuracy: 0.7625\n",
      "Epoch 573/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0638 - accuracy: 0.9954 - val_loss: 1.4214 - val_accuracy: 0.7637\n",
      "Epoch 574/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0641 - accuracy: 0.9951 - val_loss: 1.4244 - val_accuracy: 0.7612\n",
      "Epoch 575/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0630 - accuracy: 0.9952 - val_loss: 1.4296 - val_accuracy: 0.7625\n",
      "Epoch 576/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0617 - accuracy: 0.9959 - val_loss: 1.4319 - val_accuracy: 0.7625\n",
      "Epoch 577/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0657 - accuracy: 0.9943 - val_loss: 1.4329 - val_accuracy: 0.7550\n",
      "Epoch 578/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0648 - accuracy: 0.9943 - val_loss: 1.4262 - val_accuracy: 0.7562\n",
      "Epoch 579/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0657 - accuracy: 0.9952 - val_loss: 1.4162 - val_accuracy: 0.7600\n",
      "Epoch 580/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0660 - accuracy: 0.9945 - val_loss: 1.4117 - val_accuracy: 0.7600\n",
      "Epoch 581/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0621 - accuracy: 0.9957 - val_loss: 1.4164 - val_accuracy: 0.7600\n",
      "Epoch 582/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0634 - accuracy: 0.9957 - val_loss: 1.4147 - val_accuracy: 0.7650\n",
      "Epoch 583/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0648 - accuracy: 0.9949 - val_loss: 1.4215 - val_accuracy: 0.7637\n",
      "Epoch 584/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0663 - accuracy: 0.9942 - val_loss: 1.4167 - val_accuracy: 0.7637\n",
      "Epoch 585/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0646 - accuracy: 0.9954 - val_loss: 1.4188 - val_accuracy: 0.7675\n",
      "Epoch 586/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0666 - accuracy: 0.9945 - val_loss: 1.4172 - val_accuracy: 0.7700\n",
      "Epoch 587/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0646 - accuracy: 0.9950 - val_loss: 1.4044 - val_accuracy: 0.7700\n",
      "Epoch 588/1000\n",
      "19/19 [==============================] - 0s 8ms/step - loss: 0.0663 - accuracy: 0.9945 - val_loss: 1.4025 - val_accuracy: 0.7625\n",
      "Epoch 589/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0624 - accuracy: 0.9956 - val_loss: 1.4067 - val_accuracy: 0.7600\n",
      "Epoch 590/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0646 - accuracy: 0.9955 - val_loss: 1.4012 - val_accuracy: 0.7637\n",
      "Epoch 591/1000\n",
      "19/19 [==============================] - 0s 8ms/step - loss: 0.0623 - accuracy: 0.9956 - val_loss: 1.3929 - val_accuracy: 0.7637\n",
      "Epoch 592/1000\n",
      "19/19 [==============================] - 0s 8ms/step - loss: 0.0657 - accuracy: 0.9945 - val_loss: 1.3809 - val_accuracy: 0.7713\n",
      "Epoch 593/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0652 - accuracy: 0.9949 - val_loss: 1.3942 - val_accuracy: 0.7675\n",
      "Epoch 594/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0629 - accuracy: 0.9959 - val_loss: 1.4033 - val_accuracy: 0.7713\n",
      "Epoch 595/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0614 - accuracy: 0.9957 - val_loss: 1.4054 - val_accuracy: 0.7650\n",
      "Epoch 596/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0625 - accuracy: 0.9953 - val_loss: 1.4141 - val_accuracy: 0.7650\n",
      "Epoch 597/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0639 - accuracy: 0.9948 - val_loss: 1.4246 - val_accuracy: 0.7700\n",
      "Epoch 598/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0650 - accuracy: 0.9950 - val_loss: 1.4319 - val_accuracy: 0.7675\n",
      "Epoch 599/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0618 - accuracy: 0.9961 - val_loss: 1.4350 - val_accuracy: 0.7675\n",
      "Epoch 600/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0636 - accuracy: 0.9951 - val_loss: 1.4282 - val_accuracy: 0.7637\n",
      "Epoch 601/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0636 - accuracy: 0.9953 - val_loss: 1.4251 - val_accuracy: 0.7650\n",
      "Epoch 602/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0658 - accuracy: 0.9940 - val_loss: 1.4332 - val_accuracy: 0.7650\n",
      "Epoch 603/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0633 - accuracy: 0.9955 - val_loss: 1.4238 - val_accuracy: 0.7650\n",
      "Epoch 604/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0632 - accuracy: 0.9957 - val_loss: 1.4244 - val_accuracy: 0.7700\n",
      "Epoch 605/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0642 - accuracy: 0.9951 - val_loss: 1.4222 - val_accuracy: 0.7675\n",
      "Epoch 606/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0652 - accuracy: 0.9956 - val_loss: 1.4186 - val_accuracy: 0.7750\n",
      "Epoch 607/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0624 - accuracy: 0.9960 - val_loss: 1.4149 - val_accuracy: 0.7725\n",
      "Epoch 608/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0639 - accuracy: 0.9950 - val_loss: 1.4196 - val_accuracy: 0.7700\n",
      "Epoch 609/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0643 - accuracy: 0.9955 - val_loss: 1.4150 - val_accuracy: 0.7688\n",
      "Epoch 610/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0633 - accuracy: 0.9951 - val_loss: 1.4149 - val_accuracy: 0.7662\n",
      "Epoch 611/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0649 - accuracy: 0.9941 - val_loss: 1.4254 - val_accuracy: 0.7662\n",
      "Epoch 612/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0635 - accuracy: 0.9951 - val_loss: 1.4285 - val_accuracy: 0.7650\n",
      "Epoch 613/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0617 - accuracy: 0.9953 - val_loss: 1.4229 - val_accuracy: 0.7725\n",
      "Epoch 614/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0631 - accuracy: 0.9954 - val_loss: 1.4180 - val_accuracy: 0.7750\n",
      "Epoch 615/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0629 - accuracy: 0.9959 - val_loss: 1.4146 - val_accuracy: 0.7688\n",
      "Epoch 616/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0619 - accuracy: 0.9959 - val_loss: 1.4180 - val_accuracy: 0.7688\n",
      "Epoch 617/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0616 - accuracy: 0.9958 - val_loss: 1.4208 - val_accuracy: 0.7625\n",
      "Epoch 618/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0621 - accuracy: 0.9957 - val_loss: 1.4230 - val_accuracy: 0.7637\n",
      "Epoch 619/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0630 - accuracy: 0.9947 - val_loss: 1.4306 - val_accuracy: 0.7662\n",
      "Epoch 620/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0634 - accuracy: 0.9955 - val_loss: 1.4435 - val_accuracy: 0.7675\n",
      "Epoch 621/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0633 - accuracy: 0.9951 - val_loss: 1.4398 - val_accuracy: 0.7688\n",
      "Epoch 622/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0633 - accuracy: 0.9949 - val_loss: 1.4324 - val_accuracy: 0.7713\n",
      "Epoch 623/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0634 - accuracy: 0.9952 - val_loss: 1.4302 - val_accuracy: 0.7662\n",
      "Epoch 624/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.0623 - accuracy: 0.9953 - val_loss: 1.4362 - val_accuracy: 0.7637\n",
      "Epoch 625/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0638 - accuracy: 0.9950 - val_loss: 1.4436 - val_accuracy: 0.7688\n",
      "Epoch 626/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0650 - accuracy: 0.9949 - val_loss: 1.4466 - val_accuracy: 0.7675\n",
      "Epoch 627/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0645 - accuracy: 0.9951 - val_loss: 1.4325 - val_accuracy: 0.7650\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 628/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0648 - accuracy: 0.9947 - val_loss: 1.4372 - val_accuracy: 0.7650\n",
      "Epoch 629/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0635 - accuracy: 0.9955 - val_loss: 1.4234 - val_accuracy: 0.7662\n",
      "Epoch 630/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0655 - accuracy: 0.9945 - val_loss: 1.4103 - val_accuracy: 0.7700\n",
      "Epoch 631/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0659 - accuracy: 0.9943 - val_loss: 1.4169 - val_accuracy: 0.7662\n",
      "Epoch 632/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0651 - accuracy: 0.9952 - val_loss: 1.4133 - val_accuracy: 0.7675\n",
      "Epoch 633/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0629 - accuracy: 0.9945 - val_loss: 1.4055 - val_accuracy: 0.7713\n",
      "Epoch 634/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0623 - accuracy: 0.9948 - val_loss: 1.4052 - val_accuracy: 0.7725\n",
      "Epoch 635/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0643 - accuracy: 0.9947 - val_loss: 1.4106 - val_accuracy: 0.7650\n",
      "Epoch 636/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0633 - accuracy: 0.9953 - val_loss: 1.4193 - val_accuracy: 0.7688\n",
      "Epoch 637/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0628 - accuracy: 0.9961 - val_loss: 1.4288 - val_accuracy: 0.7650\n",
      "Epoch 638/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0653 - accuracy: 0.9938 - val_loss: 1.4279 - val_accuracy: 0.7662\n",
      "Epoch 639/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0625 - accuracy: 0.9957 - val_loss: 1.4312 - val_accuracy: 0.7650\n",
      "Epoch 640/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0632 - accuracy: 0.9954 - val_loss: 1.4380 - val_accuracy: 0.7612\n",
      "Epoch 641/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0628 - accuracy: 0.9955 - val_loss: 1.4478 - val_accuracy: 0.7562\n",
      "Epoch 642/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0623 - accuracy: 0.9953 - val_loss: 1.4521 - val_accuracy: 0.7637\n",
      "Epoch 643/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0627 - accuracy: 0.9954 - val_loss: 1.4466 - val_accuracy: 0.7562\n",
      "Epoch 644/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0631 - accuracy: 0.9952 - val_loss: 1.4388 - val_accuracy: 0.7600\n",
      "Epoch 645/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0616 - accuracy: 0.9963 - val_loss: 1.4394 - val_accuracy: 0.7550\n",
      "Epoch 646/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0634 - accuracy: 0.9953 - val_loss: 1.4417 - val_accuracy: 0.7587\n",
      "Epoch 647/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0636 - accuracy: 0.9951 - val_loss: 1.4339 - val_accuracy: 0.7587\n",
      "Epoch 648/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0611 - accuracy: 0.9967 - val_loss: 1.4330 - val_accuracy: 0.7662\n",
      "Epoch 649/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0639 - accuracy: 0.9951 - val_loss: 1.4416 - val_accuracy: 0.7625\n",
      "Epoch 650/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0628 - accuracy: 0.9955 - val_loss: 1.4599 - val_accuracy: 0.7650\n",
      "Epoch 651/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0635 - accuracy: 0.9951 - val_loss: 1.4605 - val_accuracy: 0.7675\n",
      "Epoch 652/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0627 - accuracy: 0.9957 - val_loss: 1.4661 - val_accuracy: 0.7600\n",
      "Epoch 653/1000\n",
      "19/19 [==============================] - 0s 8ms/step - loss: 0.0632 - accuracy: 0.9953 - val_loss: 1.4628 - val_accuracy: 0.7600\n",
      "Epoch 654/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0622 - accuracy: 0.9953 - val_loss: 1.4682 - val_accuracy: 0.7637\n",
      "Epoch 655/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0622 - accuracy: 0.9959 - val_loss: 1.4721 - val_accuracy: 0.7650\n",
      "Epoch 656/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0635 - accuracy: 0.9956 - val_loss: 1.4642 - val_accuracy: 0.7725\n",
      "Epoch 657/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0628 - accuracy: 0.9957 - val_loss: 1.4668 - val_accuracy: 0.7688\n",
      "Epoch 658/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0626 - accuracy: 0.9949 - val_loss: 1.4761 - val_accuracy: 0.7662\n",
      "Epoch 659/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0614 - accuracy: 0.9959 - val_loss: 1.4742 - val_accuracy: 0.7688\n",
      "Epoch 660/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0604 - accuracy: 0.9964 - val_loss: 1.4714 - val_accuracy: 0.7700\n",
      "Epoch 661/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0606 - accuracy: 0.9960 - val_loss: 1.4606 - val_accuracy: 0.7675\n",
      "Epoch 662/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0611 - accuracy: 0.9959 - val_loss: 1.4509 - val_accuracy: 0.7637\n",
      "Epoch 663/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0649 - accuracy: 0.9944 - val_loss: 1.4443 - val_accuracy: 0.7650\n",
      "Epoch 664/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0608 - accuracy: 0.9956 - val_loss: 1.4565 - val_accuracy: 0.7662\n",
      "Epoch 665/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0653 - accuracy: 0.9952 - val_loss: 1.4537 - val_accuracy: 0.7675\n",
      "Epoch 666/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0624 - accuracy: 0.9953 - val_loss: 1.4588 - val_accuracy: 0.7675\n",
      "Epoch 667/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0613 - accuracy: 0.9956 - val_loss: 1.4618 - val_accuracy: 0.7713\n",
      "Epoch 668/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0630 - accuracy: 0.9948 - val_loss: 1.4655 - val_accuracy: 0.7688\n",
      "Epoch 669/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0624 - accuracy: 0.9957 - val_loss: 1.4576 - val_accuracy: 0.7700\n",
      "Epoch 670/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0618 - accuracy: 0.9951 - val_loss: 1.4515 - val_accuracy: 0.7763\n",
      "Epoch 671/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0622 - accuracy: 0.9953 - val_loss: 1.4560 - val_accuracy: 0.7725\n",
      "Epoch 672/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0594 - accuracy: 0.9967 - val_loss: 1.4580 - val_accuracy: 0.7713\n",
      "Epoch 673/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0637 - accuracy: 0.9950 - val_loss: 1.4545 - val_accuracy: 0.7675\n",
      "Epoch 674/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0645 - accuracy: 0.9943 - val_loss: 1.4491 - val_accuracy: 0.7637\n",
      "Epoch 675/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0640 - accuracy: 0.9955 - val_loss: 1.4451 - val_accuracy: 0.7637\n",
      "Epoch 676/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0609 - accuracy: 0.9960 - val_loss: 1.4365 - val_accuracy: 0.7612\n",
      "Epoch 677/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0605 - accuracy: 0.9957 - val_loss: 1.4349 - val_accuracy: 0.7600\n",
      "Epoch 678/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0608 - accuracy: 0.9957 - val_loss: 1.4312 - val_accuracy: 0.7650\n",
      "Epoch 679/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0601 - accuracy: 0.9964 - val_loss: 1.4296 - val_accuracy: 0.7675\n",
      "Epoch 680/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0639 - accuracy: 0.9955 - val_loss: 1.4256 - val_accuracy: 0.7700\n",
      "Epoch 681/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0620 - accuracy: 0.9957 - val_loss: 1.4347 - val_accuracy: 0.7688\n",
      "Epoch 682/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0602 - accuracy: 0.9957 - val_loss: 1.4372 - val_accuracy: 0.7662\n",
      "Epoch 683/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0608 - accuracy: 0.9963 - val_loss: 1.4413 - val_accuracy: 0.7688\n",
      "Epoch 684/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0611 - accuracy: 0.9957 - val_loss: 1.4405 - val_accuracy: 0.7713\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 685/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0622 - accuracy: 0.9952 - val_loss: 1.4460 - val_accuracy: 0.7675\n",
      "Epoch 686/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0598 - accuracy: 0.9962 - val_loss: 1.4422 - val_accuracy: 0.7688\n",
      "Epoch 687/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0589 - accuracy: 0.9967 - val_loss: 1.4493 - val_accuracy: 0.7688\n",
      "Epoch 688/1000\n",
      "19/19 [==============================] - 0s 8ms/step - loss: 0.0615 - accuracy: 0.9953 - val_loss: 1.4364 - val_accuracy: 0.7675\n",
      "Epoch 689/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0610 - accuracy: 0.9959 - val_loss: 1.4336 - val_accuracy: 0.7662\n",
      "Epoch 690/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0606 - accuracy: 0.9961 - val_loss: 1.4393 - val_accuracy: 0.7675\n",
      "Epoch 691/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0608 - accuracy: 0.9952 - val_loss: 1.4310 - val_accuracy: 0.7675\n",
      "Epoch 692/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0630 - accuracy: 0.9950 - val_loss: 1.4174 - val_accuracy: 0.7700\n",
      "Epoch 693/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0634 - accuracy: 0.9950 - val_loss: 1.4210 - val_accuracy: 0.7713\n",
      "Epoch 694/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0630 - accuracy: 0.9947 - val_loss: 1.4118 - val_accuracy: 0.7650\n",
      "Epoch 695/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0631 - accuracy: 0.9949 - val_loss: 1.4017 - val_accuracy: 0.7662\n",
      "Epoch 696/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0636 - accuracy: 0.9954 - val_loss: 1.4103 - val_accuracy: 0.7662\n",
      "Epoch 697/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0592 - accuracy: 0.9960 - val_loss: 1.4236 - val_accuracy: 0.7612\n",
      "Epoch 698/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0615 - accuracy: 0.9954 - val_loss: 1.4325 - val_accuracy: 0.7662\n",
      "Epoch 699/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0627 - accuracy: 0.9959 - val_loss: 1.4198 - val_accuracy: 0.7637\n",
      "Epoch 700/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0611 - accuracy: 0.9955 - val_loss: 1.4195 - val_accuracy: 0.7625\n",
      "Epoch 701/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0627 - accuracy: 0.9951 - val_loss: 1.4163 - val_accuracy: 0.7688\n",
      "Epoch 702/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0614 - accuracy: 0.9961 - val_loss: 1.4168 - val_accuracy: 0.7700\n",
      "Epoch 703/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0612 - accuracy: 0.9955 - val_loss: 1.4169 - val_accuracy: 0.7675\n",
      "Epoch 704/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0600 - accuracy: 0.9964 - val_loss: 1.4279 - val_accuracy: 0.7637\n",
      "Epoch 705/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0590 - accuracy: 0.9967 - val_loss: 1.4311 - val_accuracy: 0.7688\n",
      "Epoch 706/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0645 - accuracy: 0.9945 - val_loss: 1.4394 - val_accuracy: 0.7675\n",
      "Epoch 707/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0624 - accuracy: 0.9951 - val_loss: 1.4357 - val_accuracy: 0.7662\n",
      "Epoch 708/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0597 - accuracy: 0.9963 - val_loss: 1.4317 - val_accuracy: 0.7688\n",
      "Epoch 709/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0617 - accuracy: 0.9962 - val_loss: 1.4263 - val_accuracy: 0.7688\n",
      "Epoch 710/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.0589 - accuracy: 0.9963 - val_loss: 1.4267 - val_accuracy: 0.7725\n",
      "Epoch 711/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0628 - accuracy: 0.9956 - val_loss: 1.4283 - val_accuracy: 0.7675\n",
      "Epoch 712/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0620 - accuracy: 0.9953 - val_loss: 1.4316 - val_accuracy: 0.7637\n",
      "Epoch 713/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0608 - accuracy: 0.9961 - val_loss: 1.4241 - val_accuracy: 0.7675\n",
      "Epoch 714/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0613 - accuracy: 0.9959 - val_loss: 1.4081 - val_accuracy: 0.7650\n",
      "Epoch 715/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0603 - accuracy: 0.9961 - val_loss: 1.4068 - val_accuracy: 0.7700\n",
      "Epoch 716/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0615 - accuracy: 0.9952 - val_loss: 1.4179 - val_accuracy: 0.7713\n",
      "Epoch 717/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0594 - accuracy: 0.9962 - val_loss: 1.4327 - val_accuracy: 0.7675\n",
      "Epoch 718/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0595 - accuracy: 0.9961 - val_loss: 1.4355 - val_accuracy: 0.7637\n",
      "Epoch 719/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0607 - accuracy: 0.9955 - val_loss: 1.4214 - val_accuracy: 0.7625\n",
      "Epoch 720/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0601 - accuracy: 0.9957 - val_loss: 1.4203 - val_accuracy: 0.7662\n",
      "Epoch 721/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0606 - accuracy: 0.9956 - val_loss: 1.4278 - val_accuracy: 0.7688\n",
      "Epoch 722/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0605 - accuracy: 0.9959 - val_loss: 1.4438 - val_accuracy: 0.7650\n",
      "Epoch 723/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0627 - accuracy: 0.9961 - val_loss: 1.4546 - val_accuracy: 0.7650\n",
      "Epoch 724/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0618 - accuracy: 0.9960 - val_loss: 1.4521 - val_accuracy: 0.7700\n",
      "Epoch 725/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0616 - accuracy: 0.9955 - val_loss: 1.4547 - val_accuracy: 0.7713\n",
      "Epoch 726/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0600 - accuracy: 0.9952 - val_loss: 1.4607 - val_accuracy: 0.7738\n",
      "Epoch 727/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0584 - accuracy: 0.9965 - val_loss: 1.4554 - val_accuracy: 0.7763\n",
      "Epoch 728/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0596 - accuracy: 0.9959 - val_loss: 1.4565 - val_accuracy: 0.7713\n",
      "Epoch 729/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0600 - accuracy: 0.9960 - val_loss: 1.4639 - val_accuracy: 0.7662\n",
      "Epoch 730/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0599 - accuracy: 0.9952 - val_loss: 1.4625 - val_accuracy: 0.7675\n",
      "Epoch 731/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0587 - accuracy: 0.9963 - val_loss: 1.4592 - val_accuracy: 0.7675\n",
      "Epoch 732/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0589 - accuracy: 0.9963 - val_loss: 1.4558 - val_accuracy: 0.7675\n",
      "Epoch 733/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0611 - accuracy: 0.9956 - val_loss: 1.4454 - val_accuracy: 0.7675\n",
      "Epoch 734/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0601 - accuracy: 0.9959 - val_loss: 1.4503 - val_accuracy: 0.7650\n",
      "Epoch 735/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0590 - accuracy: 0.9959 - val_loss: 1.4511 - val_accuracy: 0.7675\n",
      "Epoch 736/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0609 - accuracy: 0.9955 - val_loss: 1.4562 - val_accuracy: 0.7688\n",
      "Epoch 737/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0613 - accuracy: 0.9954 - val_loss: 1.4527 - val_accuracy: 0.7700\n",
      "Epoch 738/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0587 - accuracy: 0.9961 - val_loss: 1.4452 - val_accuracy: 0.7725\n",
      "Epoch 739/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0612 - accuracy: 0.9953 - val_loss: 1.4357 - val_accuracy: 0.7750\n",
      "Epoch 740/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0622 - accuracy: 0.9950 - val_loss: 1.4320 - val_accuracy: 0.7750\n",
      "Epoch 741/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0619 - accuracy: 0.9953 - val_loss: 1.4363 - val_accuracy: 0.7725\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 742/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0603 - accuracy: 0.9955 - val_loss: 1.4460 - val_accuracy: 0.7700\n",
      "Epoch 743/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0605 - accuracy: 0.9959 - val_loss: 1.4569 - val_accuracy: 0.7725\n",
      "Epoch 744/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0610 - accuracy: 0.9955 - val_loss: 1.4459 - val_accuracy: 0.7750\n",
      "Epoch 745/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0610 - accuracy: 0.9958 - val_loss: 1.4436 - val_accuracy: 0.7750\n",
      "Epoch 746/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0590 - accuracy: 0.9962 - val_loss: 1.4330 - val_accuracy: 0.7738\n",
      "Epoch 747/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0650 - accuracy: 0.9945 - val_loss: 1.4276 - val_accuracy: 0.7688\n",
      "Epoch 748/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0581 - accuracy: 0.9965 - val_loss: 1.4292 - val_accuracy: 0.7662\n",
      "Epoch 749/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0598 - accuracy: 0.9959 - val_loss: 1.4419 - val_accuracy: 0.7713\n",
      "Epoch 750/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0603 - accuracy: 0.9961 - val_loss: 1.4542 - val_accuracy: 0.7700\n",
      "Epoch 751/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0588 - accuracy: 0.9956 - val_loss: 1.4526 - val_accuracy: 0.7700\n",
      "Epoch 752/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0592 - accuracy: 0.9963 - val_loss: 1.4488 - val_accuracy: 0.7700\n",
      "Epoch 753/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0609 - accuracy: 0.9957 - val_loss: 1.4428 - val_accuracy: 0.7675\n",
      "Epoch 754/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0611 - accuracy: 0.9957 - val_loss: 1.4463 - val_accuracy: 0.7637\n",
      "Epoch 755/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0599 - accuracy: 0.9960 - val_loss: 1.4352 - val_accuracy: 0.7662\n",
      "Epoch 756/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0628 - accuracy: 0.9948 - val_loss: 1.4279 - val_accuracy: 0.7700\n",
      "Epoch 757/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0620 - accuracy: 0.9950 - val_loss: 1.4347 - val_accuracy: 0.7675\n",
      "Epoch 758/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0586 - accuracy: 0.9963 - val_loss: 1.4534 - val_accuracy: 0.7675\n",
      "Epoch 759/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0592 - accuracy: 0.9961 - val_loss: 1.4610 - val_accuracy: 0.7700\n",
      "Epoch 760/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0582 - accuracy: 0.9958 - val_loss: 1.4526 - val_accuracy: 0.7688\n",
      "Epoch 761/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0599 - accuracy: 0.9958 - val_loss: 1.4483 - val_accuracy: 0.7662\n",
      "Epoch 762/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0591 - accuracy: 0.9959 - val_loss: 1.4464 - val_accuracy: 0.7600\n",
      "Epoch 763/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0585 - accuracy: 0.9963 - val_loss: 1.4458 - val_accuracy: 0.7612\n",
      "Epoch 764/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0581 - accuracy: 0.9965 - val_loss: 1.4582 - val_accuracy: 0.7713\n",
      "Epoch 765/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0622 - accuracy: 0.9947 - val_loss: 1.4562 - val_accuracy: 0.7788\n",
      "Epoch 766/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0588 - accuracy: 0.9957 - val_loss: 1.4581 - val_accuracy: 0.7763\n",
      "Epoch 767/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0605 - accuracy: 0.9955 - val_loss: 1.4501 - val_accuracy: 0.7700\n",
      "Epoch 768/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0603 - accuracy: 0.9959 - val_loss: 1.4612 - val_accuracy: 0.7700\n",
      "Epoch 769/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0599 - accuracy: 0.9953 - val_loss: 1.4656 - val_accuracy: 0.7725\n",
      "Epoch 770/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0637 - accuracy: 0.9950 - val_loss: 1.4764 - val_accuracy: 0.7688\n",
      "Epoch 771/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0618 - accuracy: 0.9955 - val_loss: 1.4818 - val_accuracy: 0.7675\n",
      "Epoch 772/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0608 - accuracy: 0.9955 - val_loss: 1.4641 - val_accuracy: 0.7650\n",
      "Epoch 773/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0583 - accuracy: 0.9965 - val_loss: 1.4625 - val_accuracy: 0.7688\n",
      "Epoch 774/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0589 - accuracy: 0.9955 - val_loss: 1.4618 - val_accuracy: 0.7662\n",
      "Epoch 775/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0586 - accuracy: 0.9961 - val_loss: 1.4694 - val_accuracy: 0.7675\n",
      "Epoch 776/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0606 - accuracy: 0.9955 - val_loss: 1.4824 - val_accuracy: 0.7637\n",
      "Epoch 777/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0580 - accuracy: 0.9962 - val_loss: 1.4815 - val_accuracy: 0.7675\n",
      "Epoch 778/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0617 - accuracy: 0.9952 - val_loss: 1.4815 - val_accuracy: 0.7612\n",
      "Epoch 779/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0594 - accuracy: 0.9954 - val_loss: 1.4766 - val_accuracy: 0.7650\n",
      "Epoch 780/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0590 - accuracy: 0.9960 - val_loss: 1.4726 - val_accuracy: 0.7650\n",
      "Epoch 781/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0589 - accuracy: 0.9955 - val_loss: 1.4685 - val_accuracy: 0.7650\n",
      "Epoch 782/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0604 - accuracy: 0.9956 - val_loss: 1.4760 - val_accuracy: 0.7675\n",
      "Epoch 783/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0617 - accuracy: 0.9953 - val_loss: 1.4746 - val_accuracy: 0.7675\n",
      "Epoch 784/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0615 - accuracy: 0.9956 - val_loss: 1.4640 - val_accuracy: 0.7688\n",
      "Epoch 785/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0600 - accuracy: 0.9957 - val_loss: 1.4614 - val_accuracy: 0.7662\n",
      "Epoch 786/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0637 - accuracy: 0.9949 - val_loss: 1.4498 - val_accuracy: 0.7662\n",
      "Epoch 787/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0596 - accuracy: 0.9959 - val_loss: 1.4432 - val_accuracy: 0.7688\n",
      "Epoch 788/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0590 - accuracy: 0.9955 - val_loss: 1.4434 - val_accuracy: 0.7725\n",
      "Epoch 789/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0619 - accuracy: 0.9952 - val_loss: 1.4462 - val_accuracy: 0.7738\n",
      "Epoch 790/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0596 - accuracy: 0.9957 - val_loss: 1.4473 - val_accuracy: 0.7688\n",
      "Epoch 791/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0578 - accuracy: 0.9966 - val_loss: 1.4433 - val_accuracy: 0.7650\n",
      "Epoch 792/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0597 - accuracy: 0.9960 - val_loss: 1.4446 - val_accuracy: 0.7675\n",
      "Epoch 793/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0589 - accuracy: 0.9957 - val_loss: 1.4560 - val_accuracy: 0.7650\n",
      "Epoch 794/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0621 - accuracy: 0.9948 - val_loss: 1.4625 - val_accuracy: 0.7637\n",
      "Epoch 795/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0613 - accuracy: 0.9945 - val_loss: 1.4712 - val_accuracy: 0.7637\n",
      "Epoch 796/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0603 - accuracy: 0.9955 - val_loss: 1.4719 - val_accuracy: 0.7650\n",
      "Epoch 797/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0601 - accuracy: 0.9951 - val_loss: 1.4680 - val_accuracy: 0.7637\n",
      "Epoch 798/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0579 - accuracy: 0.9961 - val_loss: 1.4594 - val_accuracy: 0.7662\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 799/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0588 - accuracy: 0.9963 - val_loss: 1.4638 - val_accuracy: 0.7650\n",
      "Epoch 800/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0592 - accuracy: 0.9959 - val_loss: 1.4626 - val_accuracy: 0.7688\n",
      "Epoch 801/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0602 - accuracy: 0.9954 - val_loss: 1.4662 - val_accuracy: 0.7725\n",
      "Epoch 802/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0597 - accuracy: 0.9963 - val_loss: 1.4646 - val_accuracy: 0.7750\n",
      "Epoch 803/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0600 - accuracy: 0.9956 - val_loss: 1.4529 - val_accuracy: 0.7713\n",
      "Epoch 804/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0607 - accuracy: 0.9950 - val_loss: 1.4391 - val_accuracy: 0.7750\n",
      "Epoch 805/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0625 - accuracy: 0.9956 - val_loss: 1.4326 - val_accuracy: 0.7725\n",
      "Epoch 806/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0603 - accuracy: 0.9954 - val_loss: 1.4404 - val_accuracy: 0.7662\n",
      "Epoch 807/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0597 - accuracy: 0.9955 - val_loss: 1.4345 - val_accuracy: 0.7662\n",
      "Epoch 808/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0592 - accuracy: 0.9957 - val_loss: 1.4395 - val_accuracy: 0.7675\n",
      "Epoch 809/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0614 - accuracy: 0.9953 - val_loss: 1.4305 - val_accuracy: 0.7650\n",
      "Epoch 810/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0595 - accuracy: 0.9959 - val_loss: 1.4343 - val_accuracy: 0.7650\n",
      "Epoch 811/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0629 - accuracy: 0.9950 - val_loss: 1.4234 - val_accuracy: 0.7662\n",
      "Epoch 812/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0612 - accuracy: 0.9949 - val_loss: 1.4030 - val_accuracy: 0.7725\n",
      "Epoch 813/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0577 - accuracy: 0.9967 - val_loss: 1.4080 - val_accuracy: 0.7725\n",
      "Epoch 814/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0575 - accuracy: 0.9961 - val_loss: 1.4207 - val_accuracy: 0.7725\n",
      "Epoch 815/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0619 - accuracy: 0.9947 - val_loss: 1.4202 - val_accuracy: 0.7738\n",
      "Epoch 816/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0576 - accuracy: 0.9963 - val_loss: 1.4109 - val_accuracy: 0.7738\n",
      "Epoch 817/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0587 - accuracy: 0.9972 - val_loss: 1.4138 - val_accuracy: 0.7750\n",
      "Epoch 818/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0591 - accuracy: 0.9961 - val_loss: 1.4079 - val_accuracy: 0.7825\n",
      "Epoch 819/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0604 - accuracy: 0.9957 - val_loss: 1.4131 - val_accuracy: 0.7812\n",
      "Epoch 820/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0599 - accuracy: 0.9959 - val_loss: 1.4149 - val_accuracy: 0.7850\n",
      "Epoch 821/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0590 - accuracy: 0.9959 - val_loss: 1.4197 - val_accuracy: 0.7825\n",
      "Epoch 822/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0598 - accuracy: 0.9959 - val_loss: 1.4269 - val_accuracy: 0.7800\n",
      "Epoch 823/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0603 - accuracy: 0.9954 - val_loss: 1.4327 - val_accuracy: 0.7725\n",
      "Epoch 824/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0568 - accuracy: 0.9964 - val_loss: 1.4286 - val_accuracy: 0.7763\n",
      "Epoch 825/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0603 - accuracy: 0.9961 - val_loss: 1.4416 - val_accuracy: 0.7775\n",
      "Epoch 826/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0604 - accuracy: 0.9955 - val_loss: 1.4440 - val_accuracy: 0.7725\n",
      "Epoch 827/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0575 - accuracy: 0.9960 - val_loss: 1.4436 - val_accuracy: 0.7750\n",
      "Epoch 828/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0571 - accuracy: 0.9962 - val_loss: 1.4418 - val_accuracy: 0.7688\n",
      "Epoch 829/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0593 - accuracy: 0.9955 - val_loss: 1.4517 - val_accuracy: 0.7675\n",
      "Epoch 830/1000\n",
      "19/19 [==============================] - 0s 8ms/step - loss: 0.0612 - accuracy: 0.9951 - val_loss: 1.4517 - val_accuracy: 0.7650\n",
      "Epoch 831/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0566 - accuracy: 0.9965 - val_loss: 1.4647 - val_accuracy: 0.7650\n",
      "Epoch 832/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0595 - accuracy: 0.9960 - val_loss: 1.4543 - val_accuracy: 0.7725\n",
      "Epoch 833/1000\n",
      "19/19 [==============================] - 0s 8ms/step - loss: 0.0583 - accuracy: 0.9959 - val_loss: 1.4531 - val_accuracy: 0.7700\n",
      "Epoch 834/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0592 - accuracy: 0.9958 - val_loss: 1.4536 - val_accuracy: 0.7763\n",
      "Epoch 835/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0589 - accuracy: 0.9964 - val_loss: 1.4530 - val_accuracy: 0.7750\n",
      "Epoch 836/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0618 - accuracy: 0.9954 - val_loss: 1.4564 - val_accuracy: 0.7700\n",
      "Epoch 837/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0580 - accuracy: 0.9957 - val_loss: 1.4569 - val_accuracy: 0.7688\n",
      "Epoch 838/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0592 - accuracy: 0.9960 - val_loss: 1.4497 - val_accuracy: 0.7700\n",
      "Epoch 839/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0576 - accuracy: 0.9956 - val_loss: 1.4512 - val_accuracy: 0.7713\n",
      "Epoch 840/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0589 - accuracy: 0.9961 - val_loss: 1.4532 - val_accuracy: 0.7700\n",
      "Epoch 841/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0621 - accuracy: 0.9946 - val_loss: 1.4502 - val_accuracy: 0.7700\n",
      "Epoch 842/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0578 - accuracy: 0.9963 - val_loss: 1.4463 - val_accuracy: 0.7662\n",
      "Epoch 843/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0589 - accuracy: 0.9963 - val_loss: 1.4563 - val_accuracy: 0.7637\n",
      "Epoch 844/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0568 - accuracy: 0.9962 - val_loss: 1.4581 - val_accuracy: 0.7612\n",
      "Epoch 845/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0587 - accuracy: 0.9963 - val_loss: 1.4626 - val_accuracy: 0.7675\n",
      "Epoch 846/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0586 - accuracy: 0.9959 - val_loss: 1.4585 - val_accuracy: 0.7750\n",
      "Epoch 847/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0591 - accuracy: 0.9953 - val_loss: 1.4598 - val_accuracy: 0.7750\n",
      "Epoch 848/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0585 - accuracy: 0.9961 - val_loss: 1.4683 - val_accuracy: 0.7688\n",
      "Epoch 849/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0564 - accuracy: 0.9968 - val_loss: 1.4723 - val_accuracy: 0.7738\n",
      "Epoch 850/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0588 - accuracy: 0.9963 - val_loss: 1.4668 - val_accuracy: 0.7750\n",
      "Epoch 851/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0577 - accuracy: 0.9964 - val_loss: 1.4560 - val_accuracy: 0.7738\n",
      "Epoch 852/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0571 - accuracy: 0.9966 - val_loss: 1.4591 - val_accuracy: 0.7738\n",
      "Epoch 853/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0607 - accuracy: 0.9953 - val_loss: 1.4506 - val_accuracy: 0.7713\n",
      "Epoch 854/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.0552 - accuracy: 0.9966 - val_loss: 1.4542 - val_accuracy: 0.7738\n",
      "Epoch 855/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0593 - accuracy: 0.9963 - val_loss: 1.4413 - val_accuracy: 0.7700\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 856/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0596 - accuracy: 0.9957 - val_loss: 1.4455 - val_accuracy: 0.7675\n",
      "Epoch 857/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0587 - accuracy: 0.9961 - val_loss: 1.4502 - val_accuracy: 0.7650\n",
      "Epoch 858/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0575 - accuracy: 0.9966 - val_loss: 1.4508 - val_accuracy: 0.7650\n",
      "Epoch 859/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0588 - accuracy: 0.9957 - val_loss: 1.4510 - val_accuracy: 0.7637\n",
      "Epoch 860/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0572 - accuracy: 0.9964 - val_loss: 1.4678 - val_accuracy: 0.7675\n",
      "Epoch 861/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0592 - accuracy: 0.9962 - val_loss: 1.4651 - val_accuracy: 0.7738\n",
      "Epoch 862/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0572 - accuracy: 0.9957 - val_loss: 1.4662 - val_accuracy: 0.7700\n",
      "Epoch 863/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0582 - accuracy: 0.9961 - val_loss: 1.4705 - val_accuracy: 0.7713\n",
      "Epoch 864/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0591 - accuracy: 0.9959 - val_loss: 1.4737 - val_accuracy: 0.7713\n",
      "Epoch 865/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0586 - accuracy: 0.9958 - val_loss: 1.4867 - val_accuracy: 0.7675\n",
      "Epoch 866/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0601 - accuracy: 0.9957 - val_loss: 1.4868 - val_accuracy: 0.7612\n",
      "Epoch 867/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0583 - accuracy: 0.9952 - val_loss: 1.4944 - val_accuracy: 0.7587\n",
      "Epoch 868/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0597 - accuracy: 0.9955 - val_loss: 1.5044 - val_accuracy: 0.7612\n",
      "Epoch 869/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0558 - accuracy: 0.9964 - val_loss: 1.5020 - val_accuracy: 0.7600\n",
      "Epoch 870/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0574 - accuracy: 0.9960 - val_loss: 1.5019 - val_accuracy: 0.7625\n",
      "Epoch 871/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0585 - accuracy: 0.9955 - val_loss: 1.5034 - val_accuracy: 0.7625\n",
      "Epoch 872/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0574 - accuracy: 0.9959 - val_loss: 1.5191 - val_accuracy: 0.7637\n",
      "Epoch 873/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0558 - accuracy: 0.9965 - val_loss: 1.5111 - val_accuracy: 0.7637\n",
      "Epoch 874/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0600 - accuracy: 0.9959 - val_loss: 1.5170 - val_accuracy: 0.7600\n",
      "Epoch 875/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0595 - accuracy: 0.9957 - val_loss: 1.5271 - val_accuracy: 0.7575\n",
      "Epoch 876/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0601 - accuracy: 0.9950 - val_loss: 1.5298 - val_accuracy: 0.7537\n",
      "Epoch 877/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0568 - accuracy: 0.9962 - val_loss: 1.5350 - val_accuracy: 0.7512\n",
      "Epoch 878/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0586 - accuracy: 0.9963 - val_loss: 1.5372 - val_accuracy: 0.7500\n",
      "Epoch 879/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0597 - accuracy: 0.9961 - val_loss: 1.5223 - val_accuracy: 0.7562\n",
      "Epoch 880/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0598 - accuracy: 0.9951 - val_loss: 1.5199 - val_accuracy: 0.7512\n",
      "Epoch 881/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0581 - accuracy: 0.9959 - val_loss: 1.5230 - val_accuracy: 0.7537\n",
      "Epoch 882/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0580 - accuracy: 0.9961 - val_loss: 1.5257 - val_accuracy: 0.7550\n",
      "Epoch 883/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0576 - accuracy: 0.9961 - val_loss: 1.5267 - val_accuracy: 0.7562\n",
      "Epoch 884/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0573 - accuracy: 0.9961 - val_loss: 1.5259 - val_accuracy: 0.7537\n",
      "Epoch 885/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0584 - accuracy: 0.9964 - val_loss: 1.5313 - val_accuracy: 0.7550\n",
      "Epoch 886/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0566 - accuracy: 0.9959 - val_loss: 1.5372 - val_accuracy: 0.7575\n",
      "Epoch 887/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0571 - accuracy: 0.9968 - val_loss: 1.5435 - val_accuracy: 0.7562\n",
      "Epoch 888/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0568 - accuracy: 0.9960 - val_loss: 1.5438 - val_accuracy: 0.7587\n",
      "Epoch 889/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0569 - accuracy: 0.9964 - val_loss: 1.5427 - val_accuracy: 0.7550\n",
      "Epoch 890/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0549 - accuracy: 0.9968 - val_loss: 1.5372 - val_accuracy: 0.7562\n",
      "Epoch 891/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0575 - accuracy: 0.9963 - val_loss: 1.5390 - val_accuracy: 0.7562\n",
      "Epoch 892/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0587 - accuracy: 0.9957 - val_loss: 1.5360 - val_accuracy: 0.7575\n",
      "Epoch 893/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0571 - accuracy: 0.9963 - val_loss: 1.5306 - val_accuracy: 0.7675\n",
      "Epoch 894/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0582 - accuracy: 0.9959 - val_loss: 1.5304 - val_accuracy: 0.7675\n",
      "Epoch 895/1000\n",
      "19/19 [==============================] - 0s 11ms/step - loss: 0.0585 - accuracy: 0.9957 - val_loss: 1.5163 - val_accuracy: 0.7625\n",
      "Epoch 896/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0597 - accuracy: 0.9957 - val_loss: 1.5149 - val_accuracy: 0.7625\n",
      "Epoch 897/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0583 - accuracy: 0.9955 - val_loss: 1.5206 - val_accuracy: 0.7562\n",
      "Epoch 898/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0587 - accuracy: 0.9961 - val_loss: 1.5268 - val_accuracy: 0.7575\n",
      "Epoch 899/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0575 - accuracy: 0.9961 - val_loss: 1.5182 - val_accuracy: 0.7637\n",
      "Epoch 900/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0582 - accuracy: 0.9953 - val_loss: 1.4926 - val_accuracy: 0.7587\n",
      "Epoch 901/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0579 - accuracy: 0.9952 - val_loss: 1.4876 - val_accuracy: 0.7625\n",
      "Epoch 902/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0585 - accuracy: 0.9961 - val_loss: 1.4875 - val_accuracy: 0.7587\n",
      "Epoch 903/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0551 - accuracy: 0.9964 - val_loss: 1.4943 - val_accuracy: 0.7600\n",
      "Epoch 904/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0581 - accuracy: 0.9962 - val_loss: 1.4967 - val_accuracy: 0.7562\n",
      "Epoch 905/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0584 - accuracy: 0.9957 - val_loss: 1.5044 - val_accuracy: 0.7600\n",
      "Epoch 906/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0585 - accuracy: 0.9960 - val_loss: 1.5102 - val_accuracy: 0.7587\n",
      "Epoch 907/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0597 - accuracy: 0.9951 - val_loss: 1.4940 - val_accuracy: 0.7600\n",
      "Epoch 908/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0581 - accuracy: 0.9959 - val_loss: 1.4963 - val_accuracy: 0.7650\n",
      "Epoch 909/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0584 - accuracy: 0.9961 - val_loss: 1.4937 - val_accuracy: 0.7612\n",
      "Epoch 910/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0571 - accuracy: 0.9969 - val_loss: 1.5002 - val_accuracy: 0.7637\n",
      "Epoch 911/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0584 - accuracy: 0.9952 - val_loss: 1.4942 - val_accuracy: 0.7637\n",
      "Epoch 912/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0579 - accuracy: 0.9959 - val_loss: 1.4857 - val_accuracy: 0.7625\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 913/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0598 - accuracy: 0.9947 - val_loss: 1.4820 - val_accuracy: 0.7575\n",
      "Epoch 914/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0585 - accuracy: 0.9959 - val_loss: 1.4855 - val_accuracy: 0.7600\n",
      "Epoch 915/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0585 - accuracy: 0.9961 - val_loss: 1.4832 - val_accuracy: 0.7662\n",
      "Epoch 916/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0575 - accuracy: 0.9958 - val_loss: 1.4835 - val_accuracy: 0.7688\n",
      "Epoch 917/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0596 - accuracy: 0.9947 - val_loss: 1.4773 - val_accuracy: 0.7650\n",
      "Epoch 918/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0570 - accuracy: 0.9960 - val_loss: 1.4765 - val_accuracy: 0.7600\n",
      "Epoch 919/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0572 - accuracy: 0.9964 - val_loss: 1.4761 - val_accuracy: 0.7600\n",
      "Epoch 920/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0582 - accuracy: 0.9964 - val_loss: 1.4727 - val_accuracy: 0.7600\n",
      "Epoch 921/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0583 - accuracy: 0.9958 - val_loss: 1.4681 - val_accuracy: 0.7600\n",
      "Epoch 922/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0584 - accuracy: 0.9956 - val_loss: 1.4736 - val_accuracy: 0.7587\n",
      "Epoch 923/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0585 - accuracy: 0.9952 - val_loss: 1.4834 - val_accuracy: 0.7637\n",
      "Epoch 924/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0559 - accuracy: 0.9966 - val_loss: 1.4852 - val_accuracy: 0.7637\n",
      "Epoch 925/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0589 - accuracy: 0.9956 - val_loss: 1.4923 - val_accuracy: 0.7637\n",
      "Epoch 926/1000\n",
      "19/19 [==============================] - 0s 10ms/step - loss: 0.0581 - accuracy: 0.9958 - val_loss: 1.4936 - val_accuracy: 0.7675\n",
      "Epoch 927/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0585 - accuracy: 0.9964 - val_loss: 1.4964 - val_accuracy: 0.7625\n",
      "Epoch 928/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0574 - accuracy: 0.9961 - val_loss: 1.4910 - val_accuracy: 0.7650\n",
      "Epoch 929/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0568 - accuracy: 0.9967 - val_loss: 1.4871 - val_accuracy: 0.7650\n",
      "Epoch 930/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0572 - accuracy: 0.9960 - val_loss: 1.4763 - val_accuracy: 0.7587\n",
      "Epoch 931/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0579 - accuracy: 0.9961 - val_loss: 1.4731 - val_accuracy: 0.7625\n",
      "Epoch 932/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0574 - accuracy: 0.9963 - val_loss: 1.4722 - val_accuracy: 0.7612\n",
      "Epoch 933/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0583 - accuracy: 0.9959 - val_loss: 1.4705 - val_accuracy: 0.7612\n",
      "Epoch 934/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0575 - accuracy: 0.9963 - val_loss: 1.4812 - val_accuracy: 0.7587\n",
      "Epoch 935/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0570 - accuracy: 0.9963 - val_loss: 1.4924 - val_accuracy: 0.7550\n",
      "Epoch 936/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0592 - accuracy: 0.9957 - val_loss: 1.4922 - val_accuracy: 0.7550\n",
      "Epoch 937/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0574 - accuracy: 0.9964 - val_loss: 1.4939 - val_accuracy: 0.7562\n",
      "Epoch 938/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0570 - accuracy: 0.9961 - val_loss: 1.4951 - val_accuracy: 0.7587\n",
      "Epoch 939/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0577 - accuracy: 0.9953 - val_loss: 1.4980 - val_accuracy: 0.7600\n",
      "Epoch 940/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0575 - accuracy: 0.9965 - val_loss: 1.5058 - val_accuracy: 0.7625\n",
      "Epoch 941/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0577 - accuracy: 0.9957 - val_loss: 1.5160 - val_accuracy: 0.7637\n",
      "Epoch 942/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0587 - accuracy: 0.9951 - val_loss: 1.5097 - val_accuracy: 0.7675\n",
      "Epoch 943/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0552 - accuracy: 0.9966 - val_loss: 1.5034 - val_accuracy: 0.7600\n",
      "Epoch 944/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0574 - accuracy: 0.9963 - val_loss: 1.4792 - val_accuracy: 0.7637\n",
      "Epoch 945/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0575 - accuracy: 0.9963 - val_loss: 1.4727 - val_accuracy: 0.7662\n",
      "Epoch 946/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0554 - accuracy: 0.9966 - val_loss: 1.4699 - val_accuracy: 0.7662\n",
      "Epoch 947/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0564 - accuracy: 0.9964 - val_loss: 1.4739 - val_accuracy: 0.7700\n",
      "Epoch 948/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0601 - accuracy: 0.9955 - val_loss: 1.4608 - val_accuracy: 0.7675\n",
      "Epoch 949/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0592 - accuracy: 0.9957 - val_loss: 1.4510 - val_accuracy: 0.7650\n",
      "Epoch 950/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0607 - accuracy: 0.9954 - val_loss: 1.4682 - val_accuracy: 0.7612\n",
      "Epoch 951/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0575 - accuracy: 0.9964 - val_loss: 1.4668 - val_accuracy: 0.7600\n",
      "Epoch 952/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0584 - accuracy: 0.9955 - val_loss: 1.4664 - val_accuracy: 0.7662\n",
      "Epoch 953/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0572 - accuracy: 0.9956 - val_loss: 1.4765 - val_accuracy: 0.7625\n",
      "Epoch 954/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0588 - accuracy: 0.9957 - val_loss: 1.4704 - val_accuracy: 0.7625\n",
      "Epoch 955/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0567 - accuracy: 0.9957 - val_loss: 1.4797 - val_accuracy: 0.7625\n",
      "Epoch 956/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0573 - accuracy: 0.9958 - val_loss: 1.4846 - val_accuracy: 0.7650\n",
      "Epoch 957/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0573 - accuracy: 0.9965 - val_loss: 1.4723 - val_accuracy: 0.7675\n",
      "Epoch 958/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0565 - accuracy: 0.9965 - val_loss: 1.4633 - val_accuracy: 0.7675\n",
      "Epoch 959/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0570 - accuracy: 0.9964 - val_loss: 1.4650 - val_accuracy: 0.7675\n",
      "Epoch 960/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0585 - accuracy: 0.9957 - val_loss: 1.4644 - val_accuracy: 0.7688\n",
      "Epoch 961/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0589 - accuracy: 0.9954 - val_loss: 1.4565 - val_accuracy: 0.7688\n",
      "Epoch 962/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0572 - accuracy: 0.9961 - val_loss: 1.4524 - val_accuracy: 0.7688\n",
      "Epoch 963/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0607 - accuracy: 0.9949 - val_loss: 1.4546 - val_accuracy: 0.7637\n",
      "Epoch 964/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0579 - accuracy: 0.9953 - val_loss: 1.4566 - val_accuracy: 0.7637\n",
      "Epoch 965/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0563 - accuracy: 0.9959 - val_loss: 1.4488 - val_accuracy: 0.7662\n",
      "Epoch 966/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0571 - accuracy: 0.9963 - val_loss: 1.4412 - val_accuracy: 0.7675\n",
      "Epoch 967/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0556 - accuracy: 0.9965 - val_loss: 1.4383 - val_accuracy: 0.7675\n",
      "Epoch 968/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0562 - accuracy: 0.9963 - val_loss: 1.4405 - val_accuracy: 0.7650\n",
      "Epoch 969/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0577 - accuracy: 0.9957 - val_loss: 1.4576 - val_accuracy: 0.7637\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 970/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0554 - accuracy: 0.9964 - val_loss: 1.4656 - val_accuracy: 0.7612\n",
      "Epoch 971/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0579 - accuracy: 0.9959 - val_loss: 1.4665 - val_accuracy: 0.7675\n",
      "Epoch 972/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0581 - accuracy: 0.9960 - val_loss: 1.4749 - val_accuracy: 0.7688\n",
      "Epoch 973/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0561 - accuracy: 0.9966 - val_loss: 1.4784 - val_accuracy: 0.7675\n",
      "Epoch 974/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0565 - accuracy: 0.9961 - val_loss: 1.4647 - val_accuracy: 0.7675\n",
      "Epoch 975/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0579 - accuracy: 0.9951 - val_loss: 1.4627 - val_accuracy: 0.7713\n",
      "Epoch 976/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0586 - accuracy: 0.9957 - val_loss: 1.4725 - val_accuracy: 0.7763\n",
      "Epoch 977/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0575 - accuracy: 0.9963 - val_loss: 1.4668 - val_accuracy: 0.7788\n",
      "Epoch 978/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0560 - accuracy: 0.9964 - val_loss: 1.4677 - val_accuracy: 0.7725\n",
      "Epoch 979/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0559 - accuracy: 0.9958 - val_loss: 1.4779 - val_accuracy: 0.7713\n",
      "Epoch 980/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0581 - accuracy: 0.9953 - val_loss: 1.4953 - val_accuracy: 0.7688\n",
      "Epoch 981/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0592 - accuracy: 0.9955 - val_loss: 1.4882 - val_accuracy: 0.7738\n",
      "Epoch 982/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0592 - accuracy: 0.9955 - val_loss: 1.4702 - val_accuracy: 0.7775\n",
      "Epoch 983/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0580 - accuracy: 0.9959 - val_loss: 1.4569 - val_accuracy: 0.7750\n",
      "Epoch 984/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0559 - accuracy: 0.9962 - val_loss: 1.4651 - val_accuracy: 0.7738\n",
      "Epoch 985/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0576 - accuracy: 0.9961 - val_loss: 1.4642 - val_accuracy: 0.7825\n",
      "Epoch 986/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0570 - accuracy: 0.9963 - val_loss: 1.4742 - val_accuracy: 0.7788\n",
      "Epoch 987/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0567 - accuracy: 0.9955 - val_loss: 1.4770 - val_accuracy: 0.7750\n",
      "Epoch 988/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0606 - accuracy: 0.9953 - val_loss: 1.4737 - val_accuracy: 0.7775\n",
      "Epoch 989/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0574 - accuracy: 0.9957 - val_loss: 1.4650 - val_accuracy: 0.7713\n",
      "Epoch 990/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0562 - accuracy: 0.9961 - val_loss: 1.4550 - val_accuracy: 0.7662\n",
      "Epoch 991/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0557 - accuracy: 0.9963 - val_loss: 1.4543 - val_accuracy: 0.7725\n",
      "Epoch 992/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0568 - accuracy: 0.9960 - val_loss: 1.4512 - val_accuracy: 0.7713\n",
      "Epoch 993/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0571 - accuracy: 0.9962 - val_loss: 1.4552 - val_accuracy: 0.7713\n",
      "Epoch 994/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0594 - accuracy: 0.9947 - val_loss: 1.4606 - val_accuracy: 0.7713\n",
      "Epoch 995/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0592 - accuracy: 0.9944 - val_loss: 1.4672 - val_accuracy: 0.7650\n",
      "Epoch 996/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0573 - accuracy: 0.9962 - val_loss: 1.4590 - val_accuracy: 0.7662\n",
      "Epoch 997/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0580 - accuracy: 0.9958 - val_loss: 1.4588 - val_accuracy: 0.7700\n",
      "Epoch 998/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0576 - accuracy: 0.9958 - val_loss: 1.4491 - val_accuracy: 0.7713\n",
      "Epoch 999/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0561 - accuracy: 0.9960 - val_loss: 1.4495 - val_accuracy: 0.7700\n",
      "Epoch 1000/1000\n",
      "19/19 [==============================] - 0s 9ms/step - loss: 0.0553 - accuracy: 0.9967 - val_loss: 1.4581 - val_accuracy: 0.7650\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x3029cdf40>"
      ]
     },
     "execution_count": 27,
     "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",
    "model1.save_weights(\"./Models/VGG16-4P-PCA/vgg16-4p-model-weights\")\n",
    "model1.load_weights('./Models/VGG16-4P-PCA/vgg16-4p-model-weights')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": 29,
   "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 VGG16-4Piece 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": 30,
   "id": "cf7ded38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 85/475 [====>.........................] - ETA: 0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-08 20:30:25.907675: 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": 31,
   "id": "f2a28269",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_pred_labels = np.argmax(train_preds, axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "768775b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1, 13, 13, ...,  3,  9,  9])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_pred_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "eff52a3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15197"
      ]
     },
     "execution_count": 33,
     "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": 34,
   "id": "8f594a33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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",
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      ],
      "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": 34,
     "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": 35,
   "id": "2ea8f949",
   "metadata": {},
   "outputs": [
    {
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       "  <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": 35,
     "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": "659ae313",
   "metadata": {},
   "source": [
    "#### On Train-Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0d1e8f85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25/25 [==============================] - 0s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "test_preds = model1.predict(fine_tune_data_test_red)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "49233509",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_pred_labels = np.argmax(test_preds, axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8f5cba39",
   "metadata": {},
   "outputs": [
    {
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       "        3,  5,  8, 12,  4, 11, 15,  0,  9, 12,  5, 13, 10,  2,  2, 11, 15,\n",
       "        6])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pred_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ee078822",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "612"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(test_pred_labels == test_labels['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c3db9b5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>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>12</th>\n",
       "      <td>179</td>\n",
       "      <td>./Data/train/train/179.tif</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>203</td>\n",
       "      <td>./Data/train/train/203.tif</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>239</td>\n",
       "      <td>./Data/train/train/239.tif</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>15528</td>\n",
       "      <td>./Data/train/train/15528.tif</td>\n",
       "      <td>5</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>791</th>\n",
       "      <td>15719</td>\n",
       "      <td>./Data/train/train/15719.tif</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>188 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                        images  label\n",
       "4       65     ./Data/train/train/65.tif     14\n",
       "7      100    ./Data/train/train/100.tif     10\n",
       "12     179    ./Data/train/train/179.tif      7\n",
       "14     203    ./Data/train/train/203.tif      1\n",
       "15     239    ./Data/train/train/239.tif     11\n",
       "..     ...                           ...    ...\n",
       "776  15528  ./Data/train/train/15528.tif      5\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",
       "791  15719  ./Data/train/train/15719.tif      9\n",
       "\n",
       "[188 rows x 3 columns]"
      ]
     },
     "execution_count": 40,
     "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": 41,
   "id": "b538b721",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>0.750000</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.763636</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.565217</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.577778</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.930233</td>\n",
       "      <td>0.919540</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.869565</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.825000</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.868421</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.694444</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.581395</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.869565</td>\n",
       "      <td>0.851064</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.763636</td>\n",
       "      <td>0.807692</td>\n",
       "      <td>0.785047</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.918919</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.680851</td>\n",
       "      <td>0.735632</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.725490</td>\n",
       "      <td>0.637931</td>\n",
       "      <td>0.678899</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.696429</td>\n",
       "      <td>0.728972</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.561404</td>\n",
       "      <td>0.744186</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.698630</td>\n",
       "      <td>0.796875</td>\n",
       "      <td>0.744526</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.843750</td>\n",
       "      <td>0.870968</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.651163</td>\n",
       "      <td>0.643678</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    precision    recall    fscore  support\n",
       "0    0.750000  0.777778  0.763636       54\n",
       "1    0.565217  0.590909  0.577778       44\n",
       "2    0.909091  0.930233  0.919540       43\n",
       "3    0.952381  0.869565  0.909091       46\n",
       "4    0.825000  0.916667  0.868421       36\n",
       "5    0.694444  0.500000  0.581395       50\n",
       "6    0.833333  0.869565  0.851064       46\n",
       "7    0.763636  0.807692  0.785047       52\n",
       "8    0.894737  0.944444  0.918919       54\n",
       "9    0.800000  0.680851  0.735632       47\n",
       "10   0.725490  0.637931  0.678899       58\n",
       "11   0.764706  0.696429  0.728972       56\n",
       "12   0.561404  0.744186  0.640000       43\n",
       "13   0.698630  0.796875  0.744526       64\n",
       "14   0.900000  0.843750  0.870968       64\n",
       "15   0.636364  0.651163  0.643678       43"
      ]
     },
     "execution_count": 41,
     "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": 42,
   "id": "ed4db76c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7635978649331854"
      ]
     },
     "execution_count": 42,
     "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": 43,
   "id": "4b085c0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        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": 43,
     "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": 44,
   "id": "095dff17",
   "metadata": {},
   "outputs": [],
   "source": [
    "fine_tune_data_validation = np.load('./Data/VGG16-4P-validation-precompute/whole_validation_data_precomp.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8685b228",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(900, 5, 512)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fine_tune_data_validation.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "69bbf5fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "fine_tune_data_validation_red = np.zeros((fine_tune_data_validation.shape[0], 5, N_COMPS))\n",
    "for i in range(5):\n",
    "    fine_tune_data_validation_red[:,i,:] = pcas[i].transform(fine_tune_data_validation[:,i,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "db7d1256",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29/29 [==============================] - 0s 2ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-08 20:30:27.060623: 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": 48,
   "id": "18b17d2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_pred_labels = np.argmax(valid_preds, axis=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bece594",
   "metadata": {},
   "source": [
    "### Submission"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "304c47de",
   "metadata": {},
   "source": [
    "For Kaggle Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e7ca9d19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<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>6</td>\n",
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       "    <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>1</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>4</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      6\n",
       "1    17802      6\n",
       "2    17803      4\n",
       "3    17804      8\n",
       "4    17805      3\n",
       "..     ...    ...\n",
       "895  18696      1\n",
       "896  18697     12\n",
       "897  18698      8\n",
       "898  18699      4\n",
       "899  18700      4\n",
       "\n",
       "[900 rows x 2 columns]"
      ]
     },
     "execution_count": 49,
     "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": 50,
   "id": "6eea7d9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_data.to_csv('./Submissions/submission-VGG16-4p-PCA.csv', index=False)"
   ]
  }
 ],
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