Inception-ResNet-4Piece-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",
    "import tensorflow as tf\n",
    "from tensorflow.keras.preprocessing.image import load_img, img_to_array\n",
    "\n",
    "from tensorflow.keras.layers import Input, Flatten, Dense, Dropout, GlobalAvgPool2D, BatchNormalization\n",
    "# from tensorflow.keras.applications import EfficientNetV2L\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": {
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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": 4,
   "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": 5,
   "id": "2aea9ea9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": "markdown",
   "id": "85b2ccd1",
   "metadata": {},
   "source": [
    "We have already generated the Extracted Feature Vectors from the ResNet Model now we train using that, so we load those feature vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "621b1741",
   "metadata": {},
   "outputs": [],
   "source": [
    "FEATURE_DIM = 1536"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fcd66022",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fine_tune_data = np.concatenate([np.transpose(np.load(f'./Data/Inception-ResNet-4P-train-precompute/train_data_precomp-{idx}.npy'), (1,0,2)) for idx in range(10)])\n",
    "# np.save(f'./Data/Inception-ResNet-4P-train-precompute/whole_train_data_precomp.npy', fine_tune_data)\n",
    "# fine_tune_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "37a36e2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16000, 5, 1536)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fine_tune_data = np.load('./Data/Inception-ResNet-4P-train-precompute/whole_train_data_precomp.npy')\n",
    "fine_tune_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "568ffcb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16000,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fine_tune_labels = labels['label'].to_numpy()\n",
    "fine_tune_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "937e1e86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15200, 5, 1536)"
      ]
     },
     "execution_count": 12,
     "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": 13,
   "id": "af08c830",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15200,)"
      ]
     },
     "execution_count": 13,
     "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": 14,
   "id": "c00f0b32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(800, 5, 1536)"
      ]
     },
     "execution_count": 14,
     "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": 15,
   "id": "16f4bb93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(800,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fine_tune_labels_test = test_labels['label'].to_numpy()\n",
    "fine_tune_labels_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "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": 16,
   "id": "96a2bdfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ftmodel():\n",
    "    inputs = Input(shape=(5, FEATURE_DIM))\n",
    "    x = Flatten()(inputs)\n",
    "    x = BatchNormalization()(x)\n",
    "\n",
    "    top_dropout_rate = 0.5\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='Inception-RestNet-FineTune')\n",
    "    return model1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c6574006",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal device set to: Apple M1 Max\n",
      "Model: \"Inception-RestNet-FineTune\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 5, 1536)]         0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 7680)              0         \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 7680)             30720     \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " top_dropout_2 (Dropout)     (None, 7680)              0         \n",
      "                                                                 \n",
      " top_dense_2 (Dense)         (None, 512)               3932672   \n",
      "                                                                 \n",
      " batch_normalization_1 (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: 3,973,648\n",
      "Trainable params: 3,957,264\n",
      "Non-trainable params: 16,384\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-09 17:06:16.495787: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2022-10-09 17:06:16.495930: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\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": 18,
   "id": "96524578",
   "metadata": {},
   "outputs": [],
   "source": [
    "model1.compile(loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'], optimizer = Adam(learning_rate = 0.0003))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "20df8897",
   "metadata": {},
   "outputs": [],
   "source": [
    "EPOCHS = 1000\n",
    "checkpoint_filepaths = ['./Models/Inception-ResNet-4P/auto-Inception-ResNet-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": 20,
   "id": "03354278",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-09 17:06:16.694459: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-09 17:06:16.992509: 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 [==============================] - ETA: 0s - loss: 2.3646 - accuracy: 0.4230"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-09 17:06:17.783614: 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 39ms/step - loss: 2.3646 - accuracy: 0.4230 - val_loss: 2.1632 - val_accuracy: 0.3000\n",
      "Epoch 2/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 1.6566 - accuracy: 0.5554 - val_loss: 1.9514 - val_accuracy: 0.4375\n",
      "Epoch 3/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 1.4503 - accuracy: 0.5941 - val_loss: 1.8666 - val_accuracy: 0.5000\n",
      "Epoch 4/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 1.3263 - accuracy: 0.6235 - val_loss: 1.7817 - val_accuracy: 0.5275\n",
      "Epoch 5/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 1.2350 - accuracy: 0.6515 - val_loss: 1.7167 - val_accuracy: 0.5625\n",
      "Epoch 6/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 1.1624 - accuracy: 0.6694 - val_loss: 1.6396 - val_accuracy: 0.5813\n",
      "Epoch 7/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 1.1117 - accuracy: 0.6797 - val_loss: 1.5484 - val_accuracy: 0.6187\n",
      "Epoch 8/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 1.0579 - accuracy: 0.6928 - val_loss: 1.4866 - val_accuracy: 0.6375\n",
      "Epoch 9/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 1.0169 - accuracy: 0.7032 - val_loss: 1.4140 - val_accuracy: 0.6650\n",
      "Epoch 10/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.9788 - accuracy: 0.7109 - val_loss: 1.3500 - val_accuracy: 0.6787\n",
      "Epoch 11/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.9339 - accuracy: 0.7292 - val_loss: 1.2870 - val_accuracy: 0.6750\n",
      "Epoch 12/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.9026 - accuracy: 0.7397 - val_loss: 1.2293 - val_accuracy: 0.6787\n",
      "Epoch 13/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.8883 - accuracy: 0.7388 - val_loss: 1.1849 - val_accuracy: 0.6712\n",
      "Epoch 14/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.8477 - accuracy: 0.7509 - val_loss: 1.1284 - val_accuracy: 0.7075\n",
      "Epoch 15/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.8241 - accuracy: 0.7591 - val_loss: 1.0948 - val_accuracy: 0.7012\n",
      "Epoch 16/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.7889 - accuracy: 0.7661 - val_loss: 1.0506 - val_accuracy: 0.7050\n",
      "Epoch 17/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.7707 - accuracy: 0.7732 - val_loss: 1.0160 - val_accuracy: 0.7088\n",
      "Epoch 18/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.7594 - accuracy: 0.7742 - val_loss: 0.9970 - val_accuracy: 0.7188\n",
      "Epoch 19/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.7375 - accuracy: 0.7857 - val_loss: 0.9652 - val_accuracy: 0.7188\n",
      "Epoch 20/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.7174 - accuracy: 0.7913 - val_loss: 0.9437 - val_accuracy: 0.7188\n",
      "Epoch 21/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.7066 - accuracy: 0.7921 - val_loss: 0.9321 - val_accuracy: 0.7325\n",
      "Epoch 22/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.6771 - accuracy: 0.8005 - val_loss: 0.9107 - val_accuracy: 0.7337\n",
      "Epoch 23/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.6688 - accuracy: 0.8043 - val_loss: 0.8939 - val_accuracy: 0.7437\n",
      "Epoch 24/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.6655 - accuracy: 0.8057 - val_loss: 0.8895 - val_accuracy: 0.7325\n",
      "Epoch 25/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.6465 - accuracy: 0.8089 - val_loss: 0.8807 - val_accuracy: 0.7412\n",
      "Epoch 26/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.6356 - accuracy: 0.8155 - val_loss: 0.8687 - val_accuracy: 0.7525\n",
      "Epoch 27/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.6085 - accuracy: 0.8224 - val_loss: 0.8635 - val_accuracy: 0.7425\n",
      "Epoch 28/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.5988 - accuracy: 0.8246 - val_loss: 0.8607 - val_accuracy: 0.7400\n",
      "Epoch 29/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.5999 - accuracy: 0.8254 - val_loss: 0.8644 - val_accuracy: 0.7487\n",
      "Epoch 30/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.5782 - accuracy: 0.8313 - val_loss: 0.8587 - val_accuracy: 0.7500\n",
      "Epoch 31/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.5770 - accuracy: 0.8317 - val_loss: 0.8424 - val_accuracy: 0.7537\n",
      "Epoch 32/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.5651 - accuracy: 0.8372 - val_loss: 0.8458 - val_accuracy: 0.7462\n",
      "Epoch 33/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.5539 - accuracy: 0.8396 - val_loss: 0.8620 - val_accuracy: 0.7375\n",
      "Epoch 34/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.5408 - accuracy: 0.8464 - val_loss: 0.8444 - val_accuracy: 0.7562\n",
      "Epoch 35/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.5335 - accuracy: 0.8459 - val_loss: 0.8605 - val_accuracy: 0.7612\n",
      "Epoch 36/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.5316 - accuracy: 0.8499 - val_loss: 0.8485 - val_accuracy: 0.7600\n",
      "Epoch 37/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.5138 - accuracy: 0.8531 - val_loss: 0.8584 - val_accuracy: 0.7500\n",
      "Epoch 38/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.5048 - accuracy: 0.8555 - val_loss: 0.8435 - val_accuracy: 0.7650\n",
      "Epoch 39/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.5033 - accuracy: 0.8566 - val_loss: 0.8462 - val_accuracy: 0.7487\n",
      "Epoch 40/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.4938 - accuracy: 0.8611 - val_loss: 0.8591 - val_accuracy: 0.7425\n",
      "Epoch 41/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.4782 - accuracy: 0.8688 - val_loss: 0.8473 - val_accuracy: 0.7600\n",
      "Epoch 42/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.4729 - accuracy: 0.8669 - val_loss: 0.8641 - val_accuracy: 0.7550\n",
      "Epoch 43/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.4674 - accuracy: 0.8681 - val_loss: 0.8554 - val_accuracy: 0.7625\n",
      "Epoch 44/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.4567 - accuracy: 0.8711 - val_loss: 0.8626 - val_accuracy: 0.7587\n",
      "Epoch 45/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.4571 - accuracy: 0.8718 - val_loss: 0.8646 - val_accuracy: 0.7662\n",
      "Epoch 46/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.4462 - accuracy: 0.8728 - val_loss: 0.8587 - val_accuracy: 0.7625\n",
      "Epoch 47/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.4317 - accuracy: 0.8814 - val_loss: 0.8619 - val_accuracy: 0.7550\n",
      "Epoch 48/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.4351 - accuracy: 0.8776 - val_loss: 0.8568 - val_accuracy: 0.7675\n",
      "Epoch 49/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.4377 - accuracy: 0.8773 - val_loss: 0.8671 - val_accuracy: 0.7600\n",
      "Epoch 50/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.4211 - accuracy: 0.8831 - val_loss: 0.8487 - val_accuracy: 0.7713\n",
      "Epoch 51/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.4044 - accuracy: 0.8933 - val_loss: 0.8696 - val_accuracy: 0.7650\n",
      "Epoch 52/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.4148 - accuracy: 0.8847 - val_loss: 0.8560 - val_accuracy: 0.7688\n",
      "Epoch 53/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.4097 - accuracy: 0.8847 - val_loss: 0.8624 - val_accuracy: 0.7650\n",
      "Epoch 54/1000\n",
      "19/19 [==============================] - 0s 24ms/step - loss: 0.3999 - accuracy: 0.8916 - val_loss: 0.8744 - val_accuracy: 0.7788\n",
      "Epoch 55/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.4018 - accuracy: 0.8907 - val_loss: 0.8674 - val_accuracy: 0.7625\n",
      "Epoch 56/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.3813 - accuracy: 0.8964 - val_loss: 0.8621 - val_accuracy: 0.7612\n",
      "Epoch 57/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.3819 - accuracy: 0.8986 - val_loss: 0.8690 - val_accuracy: 0.7637\n",
      "Epoch 58/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 14ms/step - loss: 0.3865 - accuracy: 0.8974 - val_loss: 0.8684 - val_accuracy: 0.7612\n",
      "Epoch 59/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.3687 - accuracy: 0.9005 - val_loss: 0.8703 - val_accuracy: 0.7688\n",
      "Epoch 60/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.3691 - accuracy: 0.9018 - val_loss: 0.8667 - val_accuracy: 0.7788\n",
      "Epoch 61/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.3676 - accuracy: 0.9022 - val_loss: 0.8912 - val_accuracy: 0.7637\n",
      "Epoch 62/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.3618 - accuracy: 0.9020 - val_loss: 0.8867 - val_accuracy: 0.7688\n",
      "Epoch 63/1000\n",
      "19/19 [==============================] - 0s 25ms/step - loss: 0.3552 - accuracy: 0.9076 - val_loss: 0.8844 - val_accuracy: 0.7675\n",
      "Epoch 64/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.3511 - accuracy: 0.9055 - val_loss: 0.8758 - val_accuracy: 0.7662\n",
      "Epoch 65/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.3497 - accuracy: 0.9061 - val_loss: 0.8811 - val_accuracy: 0.7788\n",
      "Epoch 66/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.3385 - accuracy: 0.9114 - val_loss: 0.8949 - val_accuracy: 0.7700\n",
      "Epoch 67/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.3375 - accuracy: 0.9116 - val_loss: 0.8936 - val_accuracy: 0.7725\n",
      "Epoch 68/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.3403 - accuracy: 0.9088 - val_loss: 0.8898 - val_accuracy: 0.7675\n",
      "Epoch 69/1000\n",
      "19/19 [==============================] - 0s 21ms/step - loss: 0.3275 - accuracy: 0.9149 - val_loss: 0.8955 - val_accuracy: 0.7700\n",
      "Epoch 70/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.3231 - accuracy: 0.9162 - val_loss: 0.8913 - val_accuracy: 0.7713\n",
      "Epoch 71/1000\n",
      "19/19 [==============================] - 0s 24ms/step - loss: 0.3185 - accuracy: 0.9172 - val_loss: 0.8769 - val_accuracy: 0.7750\n",
      "Epoch 72/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.3154 - accuracy: 0.9173 - val_loss: 0.8909 - val_accuracy: 0.7800\n",
      "Epoch 73/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.3235 - accuracy: 0.9141 - val_loss: 0.8923 - val_accuracy: 0.7637\n",
      "Epoch 74/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.3166 - accuracy: 0.9170 - val_loss: 0.9095 - val_accuracy: 0.7637\n",
      "Epoch 75/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.3100 - accuracy: 0.9218 - val_loss: 0.9045 - val_accuracy: 0.7662\n",
      "Epoch 76/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.3086 - accuracy: 0.9214 - val_loss: 0.8963 - val_accuracy: 0.7688\n",
      "Epoch 77/1000\n",
      "19/19 [==============================] - 0s 21ms/step - loss: 0.3014 - accuracy: 0.9225 - val_loss: 0.9018 - val_accuracy: 0.7625\n",
      "Epoch 78/1000\n",
      "19/19 [==============================] - 0s 25ms/step - loss: 0.2985 - accuracy: 0.9247 - val_loss: 0.9119 - val_accuracy: 0.7675\n",
      "Epoch 79/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.2991 - accuracy: 0.9243 - val_loss: 0.9124 - val_accuracy: 0.7600\n",
      "Epoch 80/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.2918 - accuracy: 0.9276 - val_loss: 0.9273 - val_accuracy: 0.7600\n",
      "Epoch 81/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.2929 - accuracy: 0.9266 - val_loss: 0.9279 - val_accuracy: 0.7662\n",
      "Epoch 82/1000\n",
      "19/19 [==============================] - 0s 24ms/step - loss: 0.2807 - accuracy: 0.9316 - val_loss: 0.9068 - val_accuracy: 0.7788\n",
      "Epoch 83/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.2854 - accuracy: 0.9291 - val_loss: 0.9158 - val_accuracy: 0.7637\n",
      "Epoch 84/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.2750 - accuracy: 0.9331 - val_loss: 0.9552 - val_accuracy: 0.7637\n",
      "Epoch 85/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.2710 - accuracy: 0.9344 - val_loss: 0.9228 - val_accuracy: 0.7675\n",
      "Epoch 86/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.2687 - accuracy: 0.9370 - val_loss: 0.9146 - val_accuracy: 0.7675\n",
      "Epoch 87/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.2725 - accuracy: 0.9326 - val_loss: 0.9328 - val_accuracy: 0.7625\n",
      "Epoch 88/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.2732 - accuracy: 0.9348 - val_loss: 0.9367 - val_accuracy: 0.7600\n",
      "Epoch 89/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.2729 - accuracy: 0.9349 - val_loss: 0.9133 - val_accuracy: 0.7812\n",
      "Epoch 90/1000\n",
      "19/19 [==============================] - 0s 21ms/step - loss: 0.2665 - accuracy: 0.9369 - val_loss: 0.9254 - val_accuracy: 0.7775\n",
      "Epoch 91/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.2546 - accuracy: 0.9403 - val_loss: 0.9374 - val_accuracy: 0.7875\n",
      "Epoch 92/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.2540 - accuracy: 0.9413 - val_loss: 0.9455 - val_accuracy: 0.7688\n",
      "Epoch 93/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.2547 - accuracy: 0.9392 - val_loss: 0.9333 - val_accuracy: 0.7725\n",
      "Epoch 94/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.2537 - accuracy: 0.9397 - val_loss: 0.9259 - val_accuracy: 0.7775\n",
      "Epoch 95/1000\n",
      "19/19 [==============================] - 0s 21ms/step - loss: 0.2531 - accuracy: 0.9391 - val_loss: 0.9212 - val_accuracy: 0.7738\n",
      "Epoch 96/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.2509 - accuracy: 0.9413 - val_loss: 0.9309 - val_accuracy: 0.7725\n",
      "Epoch 97/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.2463 - accuracy: 0.9432 - val_loss: 0.9260 - val_accuracy: 0.7725\n",
      "Epoch 98/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.2402 - accuracy: 0.9446 - val_loss: 0.9324 - val_accuracy: 0.7775\n",
      "Epoch 99/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.2354 - accuracy: 0.9478 - val_loss: 0.9498 - val_accuracy: 0.7725\n",
      "Epoch 100/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.2453 - accuracy: 0.9416 - val_loss: 0.9435 - val_accuracy: 0.7725\n",
      "Epoch 101/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.2347 - accuracy: 0.9451 - val_loss: 0.9685 - val_accuracy: 0.7662\n",
      "Epoch 102/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.2363 - accuracy: 0.9466 - val_loss: 0.9511 - val_accuracy: 0.7775\n",
      "Epoch 103/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.2324 - accuracy: 0.9491 - val_loss: 0.9484 - val_accuracy: 0.7763\n",
      "Epoch 104/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.2316 - accuracy: 0.9475 - val_loss: 0.9410 - val_accuracy: 0.7812\n",
      "Epoch 105/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.2295 - accuracy: 0.9481 - val_loss: 0.9563 - val_accuracy: 0.7775\n",
      "Epoch 106/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.2270 - accuracy: 0.9477 - val_loss: 0.9564 - val_accuracy: 0.7800\n",
      "Epoch 107/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.2293 - accuracy: 0.9477 - val_loss: 0.9426 - val_accuracy: 0.7850\n",
      "Epoch 108/1000\n",
      "19/19 [==============================] - 0s 27ms/step - loss: 0.2263 - accuracy: 0.9489 - val_loss: 0.9632 - val_accuracy: 0.7763\n",
      "Epoch 109/1000\n",
      "19/19 [==============================] - 0s 25ms/step - loss: 0.2194 - accuracy: 0.9522 - val_loss: 0.9415 - val_accuracy: 0.7812\n",
      "Epoch 110/1000\n",
      "19/19 [==============================] - 1s 31ms/step - loss: 0.2189 - accuracy: 0.9525 - val_loss: 0.9560 - val_accuracy: 0.7700\n",
      "Epoch 111/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.2194 - accuracy: 0.9526 - val_loss: 0.9588 - val_accuracy: 0.7763\n",
      "Epoch 112/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.2225 - accuracy: 0.9501 - val_loss: 0.9566 - val_accuracy: 0.7738\n",
      "Epoch 113/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.2180 - accuracy: 0.9525 - val_loss: 0.9635 - val_accuracy: 0.7713\n",
      "Epoch 114/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.2140 - accuracy: 0.9532 - val_loss: 0.9681 - val_accuracy: 0.7675\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 115/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.2123 - accuracy: 0.9561 - val_loss: 0.9701 - val_accuracy: 0.7688\n",
      "Epoch 116/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.2111 - accuracy: 0.9557 - val_loss: 0.9625 - val_accuracy: 0.7763\n",
      "Epoch 117/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.2127 - accuracy: 0.9536 - val_loss: 0.9687 - val_accuracy: 0.7763\n",
      "Epoch 118/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.2060 - accuracy: 0.9567 - val_loss: 0.9640 - val_accuracy: 0.7662\n",
      "Epoch 119/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.2049 - accuracy: 0.9574 - val_loss: 0.9791 - val_accuracy: 0.7688\n",
      "Epoch 120/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.2035 - accuracy: 0.9583 - val_loss: 0.9904 - val_accuracy: 0.7725\n",
      "Epoch 121/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.1996 - accuracy: 0.9608 - val_loss: 0.9790 - val_accuracy: 0.7788\n",
      "Epoch 122/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.2029 - accuracy: 0.9595 - val_loss: 0.9882 - val_accuracy: 0.7700\n",
      "Epoch 123/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.1981 - accuracy: 0.9616 - val_loss: 0.9799 - val_accuracy: 0.7725\n",
      "Epoch 124/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.2052 - accuracy: 0.9540 - val_loss: 0.9917 - val_accuracy: 0.7800\n",
      "Epoch 125/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.1969 - accuracy: 0.9609 - val_loss: 0.9858 - val_accuracy: 0.7725\n",
      "Epoch 126/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1992 - accuracy: 0.9601 - val_loss: 0.9904 - val_accuracy: 0.7612\n",
      "Epoch 127/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.1939 - accuracy: 0.9600 - val_loss: 0.9777 - val_accuracy: 0.7675\n",
      "Epoch 128/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1977 - accuracy: 0.9601 - val_loss: 0.9886 - val_accuracy: 0.7812\n",
      "Epoch 129/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1973 - accuracy: 0.9598 - val_loss: 0.9892 - val_accuracy: 0.7763\n",
      "Epoch 130/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1980 - accuracy: 0.9603 - val_loss: 0.9872 - val_accuracy: 0.7763\n",
      "Epoch 131/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1993 - accuracy: 0.9603 - val_loss: 0.9947 - val_accuracy: 0.7713\n",
      "Epoch 132/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.2004 - accuracy: 0.9584 - val_loss: 0.9955 - val_accuracy: 0.7788\n",
      "Epoch 133/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1947 - accuracy: 0.9612 - val_loss: 1.0028 - val_accuracy: 0.7788\n",
      "Epoch 134/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1940 - accuracy: 0.9614 - val_loss: 1.0031 - val_accuracy: 0.7713\n",
      "Epoch 135/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.1878 - accuracy: 0.9632 - val_loss: 1.0076 - val_accuracy: 0.7700\n",
      "Epoch 136/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1918 - accuracy: 0.9601 - val_loss: 1.0208 - val_accuracy: 0.7700\n",
      "Epoch 137/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.1833 - accuracy: 0.9643 - val_loss: 1.0326 - val_accuracy: 0.7562\n",
      "Epoch 138/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1896 - accuracy: 0.9619 - val_loss: 1.0170 - val_accuracy: 0.7662\n",
      "Epoch 139/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1854 - accuracy: 0.9629 - val_loss: 1.0103 - val_accuracy: 0.7650\n",
      "Epoch 140/1000\n",
      "19/19 [==============================] - 0s 24ms/step - loss: 0.1822 - accuracy: 0.9662 - val_loss: 1.0289 - val_accuracy: 0.7688\n",
      "Epoch 141/1000\n",
      "19/19 [==============================] - 0s 21ms/step - loss: 0.1768 - accuracy: 0.9670 - val_loss: 1.0351 - val_accuracy: 0.7675\n",
      "Epoch 142/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1773 - accuracy: 0.9663 - val_loss: 1.0306 - val_accuracy: 0.7738\n",
      "Epoch 143/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1826 - accuracy: 0.9646 - val_loss: 1.0279 - val_accuracy: 0.7800\n",
      "Epoch 144/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1825 - accuracy: 0.9662 - val_loss: 1.0213 - val_accuracy: 0.7675\n",
      "Epoch 145/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.1767 - accuracy: 0.9670 - val_loss: 0.9900 - val_accuracy: 0.7837\n",
      "Epoch 146/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1767 - accuracy: 0.9668 - val_loss: 1.0180 - val_accuracy: 0.7875\n",
      "Epoch 147/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1771 - accuracy: 0.9676 - val_loss: 1.0207 - val_accuracy: 0.7775\n",
      "Epoch 148/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.1714 - accuracy: 0.9685 - val_loss: 1.0106 - val_accuracy: 0.7800\n",
      "Epoch 149/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1806 - accuracy: 0.9654 - val_loss: 1.0116 - val_accuracy: 0.7812\n",
      "Epoch 150/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1780 - accuracy: 0.9676 - val_loss: 1.0230 - val_accuracy: 0.7750\n",
      "Epoch 151/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1773 - accuracy: 0.9684 - val_loss: 1.0174 - val_accuracy: 0.7738\n",
      "Epoch 152/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1729 - accuracy: 0.9679 - val_loss: 1.0327 - val_accuracy: 0.7763\n",
      "Epoch 153/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1759 - accuracy: 0.9671 - val_loss: 1.0416 - val_accuracy: 0.7925\n",
      "Epoch 154/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1776 - accuracy: 0.9676 - val_loss: 1.0569 - val_accuracy: 0.7725\n",
      "Epoch 155/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.1712 - accuracy: 0.9679 - val_loss: 1.0360 - val_accuracy: 0.7800\n",
      "Epoch 156/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1724 - accuracy: 0.9680 - val_loss: 1.0324 - val_accuracy: 0.7738\n",
      "Epoch 157/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.1697 - accuracy: 0.9706 - val_loss: 1.0346 - val_accuracy: 0.7650\n",
      "Epoch 158/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1723 - accuracy: 0.9683 - val_loss: 1.0490 - val_accuracy: 0.7675\n",
      "Epoch 159/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1708 - accuracy: 0.9691 - val_loss: 1.0364 - val_accuracy: 0.7862\n",
      "Epoch 160/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.1679 - accuracy: 0.9705 - val_loss: 1.0593 - val_accuracy: 0.7800\n",
      "Epoch 161/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1741 - accuracy: 0.9666 - val_loss: 1.0241 - val_accuracy: 0.7763\n",
      "Epoch 162/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1711 - accuracy: 0.9697 - val_loss: 1.0319 - val_accuracy: 0.7850\n",
      "Epoch 163/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1691 - accuracy: 0.9688 - val_loss: 1.0255 - val_accuracy: 0.7837\n",
      "Epoch 164/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.1679 - accuracy: 0.9705 - val_loss: 1.0595 - val_accuracy: 0.7837\n",
      "Epoch 165/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.1650 - accuracy: 0.9717 - val_loss: 1.0486 - val_accuracy: 0.7725\n",
      "Epoch 166/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1685 - accuracy: 0.9697 - val_loss: 1.0363 - val_accuracy: 0.7788\n",
      "Epoch 167/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.1622 - accuracy: 0.9720 - val_loss: 1.0322 - val_accuracy: 0.7812\n",
      "Epoch 168/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1691 - accuracy: 0.9692 - val_loss: 1.0456 - val_accuracy: 0.7812\n",
      "Epoch 169/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1679 - accuracy: 0.9724 - val_loss: 1.0203 - val_accuracy: 0.7825\n",
      "Epoch 170/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1671 - accuracy: 0.9709 - val_loss: 1.0614 - val_accuracy: 0.7738\n",
      "Epoch 171/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 22ms/step - loss: 0.1578 - accuracy: 0.9732 - val_loss: 1.0793 - val_accuracy: 0.7775\n",
      "Epoch 172/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1706 - accuracy: 0.9683 - val_loss: 1.0586 - val_accuracy: 0.7725\n",
      "Epoch 173/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1626 - accuracy: 0.9738 - val_loss: 1.0871 - val_accuracy: 0.7738\n",
      "Epoch 174/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1609 - accuracy: 0.9729 - val_loss: 1.0821 - val_accuracy: 0.7763\n",
      "Epoch 175/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.1647 - accuracy: 0.9713 - val_loss: 1.0450 - val_accuracy: 0.7725\n",
      "Epoch 176/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1607 - accuracy: 0.9721 - val_loss: 1.0546 - val_accuracy: 0.7650\n",
      "Epoch 177/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1587 - accuracy: 0.9728 - val_loss: 1.0424 - val_accuracy: 0.7862\n",
      "Epoch 178/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1620 - accuracy: 0.9735 - val_loss: 1.0698 - val_accuracy: 0.7662\n",
      "Epoch 179/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1622 - accuracy: 0.9728 - val_loss: 1.0962 - val_accuracy: 0.7700\n",
      "Epoch 180/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1614 - accuracy: 0.9721 - val_loss: 1.0876 - val_accuracy: 0.7725\n",
      "Epoch 181/1000\n",
      "19/19 [==============================] - 0s 21ms/step - loss: 0.1556 - accuracy: 0.9759 - val_loss: 1.0589 - val_accuracy: 0.7713\n",
      "Epoch 182/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1566 - accuracy: 0.9755 - val_loss: 1.0620 - val_accuracy: 0.7688\n",
      "Epoch 183/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1626 - accuracy: 0.9736 - val_loss: 1.0609 - val_accuracy: 0.7688\n",
      "Epoch 184/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1572 - accuracy: 0.9752 - val_loss: 1.0616 - val_accuracy: 0.7738\n",
      "Epoch 185/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1569 - accuracy: 0.9746 - val_loss: 1.0638 - val_accuracy: 0.7788\n",
      "Epoch 186/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.1535 - accuracy: 0.9757 - val_loss: 1.0713 - val_accuracy: 0.7675\n",
      "Epoch 187/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1618 - accuracy: 0.9745 - val_loss: 1.0638 - val_accuracy: 0.7725\n",
      "Epoch 188/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1603 - accuracy: 0.9721 - val_loss: 1.0552 - val_accuracy: 0.7725\n",
      "Epoch 189/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1625 - accuracy: 0.9718 - val_loss: 1.1053 - val_accuracy: 0.7738\n",
      "Epoch 190/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1624 - accuracy: 0.9724 - val_loss: 1.0557 - val_accuracy: 0.7725\n",
      "Epoch 191/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1592 - accuracy: 0.9741 - val_loss: 1.0685 - val_accuracy: 0.7763\n",
      "Epoch 192/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1551 - accuracy: 0.9749 - val_loss: 1.0863 - val_accuracy: 0.7700\n",
      "Epoch 193/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1559 - accuracy: 0.9745 - val_loss: 1.1011 - val_accuracy: 0.7675\n",
      "Epoch 194/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1565 - accuracy: 0.9754 - val_loss: 1.0634 - val_accuracy: 0.7700\n",
      "Epoch 195/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1563 - accuracy: 0.9751 - val_loss: 1.0597 - val_accuracy: 0.7725\n",
      "Epoch 196/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1555 - accuracy: 0.9756 - val_loss: 1.0571 - val_accuracy: 0.7750\n",
      "Epoch 197/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1594 - accuracy: 0.9743 - val_loss: 1.0824 - val_accuracy: 0.7688\n",
      "Epoch 198/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1557 - accuracy: 0.9755 - val_loss: 1.0720 - val_accuracy: 0.7750\n",
      "Epoch 199/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.1522 - accuracy: 0.9762 - val_loss: 1.0709 - val_accuracy: 0.7750\n",
      "Epoch 200/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1527 - accuracy: 0.9766 - val_loss: 1.0661 - val_accuracy: 0.7850\n",
      "Epoch 201/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1527 - accuracy: 0.9763 - val_loss: 1.0615 - val_accuracy: 0.7763\n",
      "Epoch 202/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1547 - accuracy: 0.9748 - val_loss: 1.0729 - val_accuracy: 0.7775\n",
      "Epoch 203/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.1500 - accuracy: 0.9766 - val_loss: 1.0792 - val_accuracy: 0.7775\n",
      "Epoch 204/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1516 - accuracy: 0.9769 - val_loss: 1.0814 - val_accuracy: 0.7788\n",
      "Epoch 205/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1512 - accuracy: 0.9767 - val_loss: 1.0687 - val_accuracy: 0.7825\n",
      "Epoch 206/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1501 - accuracy: 0.9760 - val_loss: 1.0704 - val_accuracy: 0.7738\n",
      "Epoch 207/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1511 - accuracy: 0.9767 - val_loss: 1.0984 - val_accuracy: 0.7700\n",
      "Epoch 208/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1506 - accuracy: 0.9778 - val_loss: 1.0827 - val_accuracy: 0.7713\n",
      "Epoch 209/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1547 - accuracy: 0.9750 - val_loss: 1.0753 - val_accuracy: 0.7825\n",
      "Epoch 210/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1515 - accuracy: 0.9776 - val_loss: 1.0758 - val_accuracy: 0.7788\n",
      "Epoch 211/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.1480 - accuracy: 0.9787 - val_loss: 1.0762 - val_accuracy: 0.7675\n",
      "Epoch 212/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1489 - accuracy: 0.9790 - val_loss: 1.0778 - val_accuracy: 0.7800\n",
      "Epoch 213/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.1466 - accuracy: 0.9785 - val_loss: 1.0759 - val_accuracy: 0.7788\n",
      "Epoch 214/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.1448 - accuracy: 0.9778 - val_loss: 1.0782 - val_accuracy: 0.7912\n",
      "Epoch 215/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1477 - accuracy: 0.9788 - val_loss: 1.1218 - val_accuracy: 0.7788\n",
      "Epoch 216/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1460 - accuracy: 0.9787 - val_loss: 1.1033 - val_accuracy: 0.7788\n",
      "Epoch 217/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.1426 - accuracy: 0.9807 - val_loss: 1.1380 - val_accuracy: 0.7750\n",
      "Epoch 218/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.1418 - accuracy: 0.9799 - val_loss: 1.1275 - val_accuracy: 0.7763\n",
      "Epoch 219/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1421 - accuracy: 0.9799 - val_loss: 1.1259 - val_accuracy: 0.7788\n",
      "Epoch 220/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1508 - accuracy: 0.9768 - val_loss: 1.1166 - val_accuracy: 0.7775\n",
      "Epoch 221/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1467 - accuracy: 0.9791 - val_loss: 1.1434 - val_accuracy: 0.7662\n",
      "Epoch 222/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1443 - accuracy: 0.9792 - val_loss: 1.0953 - val_accuracy: 0.7713\n",
      "Epoch 223/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1473 - accuracy: 0.9780 - val_loss: 1.1149 - val_accuracy: 0.7750\n",
      "Epoch 224/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1473 - accuracy: 0.9782 - val_loss: 1.0984 - val_accuracy: 0.7850\n",
      "Epoch 225/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1452 - accuracy: 0.9789 - val_loss: 1.1090 - val_accuracy: 0.7800\n",
      "Epoch 226/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1444 - accuracy: 0.9789 - val_loss: 1.1597 - val_accuracy: 0.7725\n",
      "Epoch 227/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 17ms/step - loss: 0.1484 - accuracy: 0.9782 - val_loss: 1.1611 - val_accuracy: 0.7625\n",
      "Epoch 228/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1476 - accuracy: 0.9787 - val_loss: 1.1459 - val_accuracy: 0.7725\n",
      "Epoch 229/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1456 - accuracy: 0.9793 - val_loss: 1.1351 - val_accuracy: 0.7713\n",
      "Epoch 230/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1421 - accuracy: 0.9809 - val_loss: 1.1378 - val_accuracy: 0.7775\n",
      "Epoch 231/1000\n",
      "19/19 [==============================] - 0s 21ms/step - loss: 0.1415 - accuracy: 0.9806 - val_loss: 1.1203 - val_accuracy: 0.7775\n",
      "Epoch 232/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1421 - accuracy: 0.9805 - val_loss: 1.1152 - val_accuracy: 0.7837\n",
      "Epoch 233/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1432 - accuracy: 0.9810 - val_loss: 1.1166 - val_accuracy: 0.7825\n",
      "Epoch 234/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1459 - accuracy: 0.9791 - val_loss: 1.1372 - val_accuracy: 0.7812\n",
      "Epoch 235/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1466 - accuracy: 0.9784 - val_loss: 1.1548 - val_accuracy: 0.7763\n",
      "Epoch 236/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1464 - accuracy: 0.9789 - val_loss: 1.1359 - val_accuracy: 0.7750\n",
      "Epoch 237/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1461 - accuracy: 0.9790 - val_loss: 1.1191 - val_accuracy: 0.7788\n",
      "Epoch 238/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1438 - accuracy: 0.9793 - val_loss: 1.1343 - val_accuracy: 0.7800\n",
      "Epoch 239/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.1409 - accuracy: 0.9820 - val_loss: 1.1470 - val_accuracy: 0.7725\n",
      "Epoch 240/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1410 - accuracy: 0.9820 - val_loss: 1.1328 - val_accuracy: 0.7788\n",
      "Epoch 241/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1449 - accuracy: 0.9792 - val_loss: 1.1210 - val_accuracy: 0.7825\n",
      "Epoch 242/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1417 - accuracy: 0.9807 - val_loss: 1.1462 - val_accuracy: 0.7763\n",
      "Epoch 243/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1417 - accuracy: 0.9809 - val_loss: 1.1317 - val_accuracy: 0.7862\n",
      "Epoch 244/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1425 - accuracy: 0.9797 - val_loss: 1.1495 - val_accuracy: 0.7837\n",
      "Epoch 245/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1433 - accuracy: 0.9787 - val_loss: 1.1415 - val_accuracy: 0.7763\n",
      "Epoch 246/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.1407 - accuracy: 0.9803 - val_loss: 1.1170 - val_accuracy: 0.7862\n",
      "Epoch 247/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1447 - accuracy: 0.9792 - val_loss: 1.1457 - val_accuracy: 0.7788\n",
      "Epoch 248/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1418 - accuracy: 0.9803 - val_loss: 1.1799 - val_accuracy: 0.7688\n",
      "Epoch 249/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1444 - accuracy: 0.9791 - val_loss: 1.1423 - val_accuracy: 0.7775\n",
      "Epoch 250/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1427 - accuracy: 0.9795 - val_loss: 1.1187 - val_accuracy: 0.7887\n",
      "Epoch 251/1000\n",
      "19/19 [==============================] - 0s 21ms/step - loss: 0.1395 - accuracy: 0.9814 - val_loss: 1.1081 - val_accuracy: 0.7725\n",
      "Epoch 252/1000\n",
      "19/19 [==============================] - 0s 21ms/step - loss: 0.1395 - accuracy: 0.9816 - val_loss: 1.0917 - val_accuracy: 0.7862\n",
      "Epoch 253/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1403 - accuracy: 0.9811 - val_loss: 1.1134 - val_accuracy: 0.7850\n",
      "Epoch 254/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1434 - accuracy: 0.9809 - val_loss: 1.1290 - val_accuracy: 0.7812\n",
      "Epoch 255/1000\n",
      "19/19 [==============================] - 0s 21ms/step - loss: 0.1371 - accuracy: 0.9826 - val_loss: 1.1452 - val_accuracy: 0.7775\n",
      "Epoch 256/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1415 - accuracy: 0.9800 - val_loss: 1.0921 - val_accuracy: 0.7937\n",
      "Epoch 257/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1423 - accuracy: 0.9806 - val_loss: 1.1252 - val_accuracy: 0.7825\n",
      "Epoch 258/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1429 - accuracy: 0.9808 - val_loss: 1.1240 - val_accuracy: 0.7812\n",
      "Epoch 259/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1381 - accuracy: 0.9833 - val_loss: 1.1199 - val_accuracy: 0.7825\n",
      "Epoch 260/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1405 - accuracy: 0.9819 - val_loss: 1.1393 - val_accuracy: 0.7825\n",
      "Epoch 261/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1388 - accuracy: 0.9827 - val_loss: 1.1441 - val_accuracy: 0.7750\n",
      "Epoch 262/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1405 - accuracy: 0.9809 - val_loss: 1.1613 - val_accuracy: 0.7700\n",
      "Epoch 263/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1417 - accuracy: 0.9802 - val_loss: 1.1418 - val_accuracy: 0.7887\n",
      "Epoch 264/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1425 - accuracy: 0.9788 - val_loss: 1.1431 - val_accuracy: 0.7812\n",
      "Epoch 265/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1375 - accuracy: 0.9820 - val_loss: 1.1429 - val_accuracy: 0.7825\n",
      "Epoch 266/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1445 - accuracy: 0.9791 - val_loss: 1.1587 - val_accuracy: 0.7825\n",
      "Epoch 267/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1374 - accuracy: 0.9820 - val_loss: 1.1560 - val_accuracy: 0.7750\n",
      "Epoch 268/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1411 - accuracy: 0.9824 - val_loss: 1.1649 - val_accuracy: 0.7862\n",
      "Epoch 269/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1451 - accuracy: 0.9803 - val_loss: 1.1932 - val_accuracy: 0.7763\n",
      "Epoch 270/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1415 - accuracy: 0.9804 - val_loss: 1.1757 - val_accuracy: 0.7725\n",
      "Epoch 271/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1443 - accuracy: 0.9792 - val_loss: 1.1592 - val_accuracy: 0.7825\n",
      "Epoch 272/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1459 - accuracy: 0.9790 - val_loss: 1.1599 - val_accuracy: 0.7837\n",
      "Epoch 273/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1429 - accuracy: 0.9807 - val_loss: 1.1563 - val_accuracy: 0.7800\n",
      "Epoch 274/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1372 - accuracy: 0.9816 - val_loss: 1.1880 - val_accuracy: 0.7775\n",
      "Epoch 275/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1424 - accuracy: 0.9809 - val_loss: 1.1764 - val_accuracy: 0.7775\n",
      "Epoch 276/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1413 - accuracy: 0.9806 - val_loss: 1.1617 - val_accuracy: 0.7812\n",
      "Epoch 277/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1417 - accuracy: 0.9813 - val_loss: 1.2013 - val_accuracy: 0.7800\n",
      "Epoch 278/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1406 - accuracy: 0.9811 - val_loss: 1.2232 - val_accuracy: 0.7700\n",
      "Epoch 279/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1404 - accuracy: 0.9803 - val_loss: 1.2115 - val_accuracy: 0.7775\n",
      "Epoch 280/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1417 - accuracy: 0.9811 - val_loss: 1.1814 - val_accuracy: 0.7725\n",
      "Epoch 281/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.1367 - accuracy: 0.9835 - val_loss: 1.2031 - val_accuracy: 0.7738\n",
      "Epoch 282/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1397 - accuracy: 0.9816 - val_loss: 1.1892 - val_accuracy: 0.7738\n",
      "Epoch 283/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1397 - accuracy: 0.9821 - val_loss: 1.2238 - val_accuracy: 0.7662\n",
      "Epoch 284/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1412 - accuracy: 0.9809 - val_loss: 1.2296 - val_accuracy: 0.7700\n",
      "Epoch 285/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.1365 - accuracy: 0.9822 - val_loss: 1.2068 - val_accuracy: 0.7688\n",
      "Epoch 286/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1385 - accuracy: 0.9820 - val_loss: 1.2028 - val_accuracy: 0.7738\n",
      "Epoch 287/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1399 - accuracy: 0.9798 - val_loss: 1.1967 - val_accuracy: 0.7763\n",
      "Epoch 288/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1392 - accuracy: 0.9815 - val_loss: 1.1817 - val_accuracy: 0.7763\n",
      "Epoch 289/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1373 - accuracy: 0.9824 - val_loss: 1.1881 - val_accuracy: 0.7600\n",
      "Epoch 290/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1377 - accuracy: 0.9828 - val_loss: 1.1896 - val_accuracy: 0.7763\n",
      "Epoch 291/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1381 - accuracy: 0.9824 - val_loss: 1.1678 - val_accuracy: 0.7750\n",
      "Epoch 292/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1406 - accuracy: 0.9820 - val_loss: 1.1821 - val_accuracy: 0.7837\n",
      "Epoch 293/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.1354 - accuracy: 0.9839 - val_loss: 1.1422 - val_accuracy: 0.7775\n",
      "Epoch 294/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1409 - accuracy: 0.9807 - val_loss: 1.1528 - val_accuracy: 0.7725\n",
      "Epoch 295/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1384 - accuracy: 0.9832 - val_loss: 1.1983 - val_accuracy: 0.7713\n",
      "Epoch 296/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1368 - accuracy: 0.9824 - val_loss: 1.2016 - val_accuracy: 0.7738\n",
      "Epoch 297/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1362 - accuracy: 0.9831 - val_loss: 1.1621 - val_accuracy: 0.7775\n",
      "Epoch 298/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1450 - accuracy: 0.9803 - val_loss: 1.1606 - val_accuracy: 0.7713\n",
      "Epoch 299/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1387 - accuracy: 0.9830 - val_loss: 1.1762 - val_accuracy: 0.7850\n",
      "Epoch 300/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1368 - accuracy: 0.9828 - val_loss: 1.1503 - val_accuracy: 0.7775\n",
      "Epoch 301/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.1342 - accuracy: 0.9847 - val_loss: 1.1408 - val_accuracy: 0.7800\n",
      "Epoch 302/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1402 - accuracy: 0.9819 - val_loss: 1.1925 - val_accuracy: 0.7775\n",
      "Epoch 303/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1375 - accuracy: 0.9836 - val_loss: 1.1995 - val_accuracy: 0.7713\n",
      "Epoch 304/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1360 - accuracy: 0.9836 - val_loss: 1.1805 - val_accuracy: 0.7763\n",
      "Epoch 305/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1400 - accuracy: 0.9818 - val_loss: 1.1738 - val_accuracy: 0.7800\n",
      "Epoch 306/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1391 - accuracy: 0.9820 - val_loss: 1.1406 - val_accuracy: 0.7900\n",
      "Epoch 307/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1374 - accuracy: 0.9840 - val_loss: 1.1607 - val_accuracy: 0.7800\n",
      "Epoch 308/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1388 - accuracy: 0.9821 - val_loss: 1.1638 - val_accuracy: 0.7887\n",
      "Epoch 309/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1385 - accuracy: 0.9834 - val_loss: 1.1891 - val_accuracy: 0.7812\n",
      "Epoch 310/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.1340 - accuracy: 0.9839 - val_loss: 1.1577 - val_accuracy: 0.7837\n",
      "Epoch 311/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1358 - accuracy: 0.9840 - val_loss: 1.1830 - val_accuracy: 0.7763\n",
      "Epoch 312/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1373 - accuracy: 0.9826 - val_loss: 1.1672 - val_accuracy: 0.7862\n",
      "Epoch 313/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1371 - accuracy: 0.9836 - val_loss: 1.1971 - val_accuracy: 0.7800\n",
      "Epoch 314/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1344 - accuracy: 0.9826 - val_loss: 1.1748 - val_accuracy: 0.7812\n",
      "Epoch 315/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1357 - accuracy: 0.9847 - val_loss: 1.1917 - val_accuracy: 0.7812\n",
      "Epoch 316/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.1335 - accuracy: 0.9837 - val_loss: 1.1914 - val_accuracy: 0.7788\n",
      "Epoch 317/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1388 - accuracy: 0.9820 - val_loss: 1.1710 - val_accuracy: 0.7912\n",
      "Epoch 318/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.1325 - accuracy: 0.9843 - val_loss: 1.1961 - val_accuracy: 0.7775\n",
      "Epoch 319/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.1373 - accuracy: 0.9832 - val_loss: 1.1835 - val_accuracy: 0.7775\n",
      "Epoch 320/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1400 - accuracy: 0.9822 - val_loss: 1.2340 - val_accuracy: 0.7763\n",
      "Epoch 321/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1376 - accuracy: 0.9834 - val_loss: 1.2235 - val_accuracy: 0.7850\n",
      "Epoch 322/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1350 - accuracy: 0.9840 - val_loss: 1.2156 - val_accuracy: 0.7825\n",
      "Epoch 323/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1384 - accuracy: 0.9826 - val_loss: 1.2042 - val_accuracy: 0.7837\n",
      "Epoch 324/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1357 - accuracy: 0.9838 - val_loss: 1.2281 - val_accuracy: 0.7788\n",
      "Epoch 325/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1367 - accuracy: 0.9828 - val_loss: 1.1909 - val_accuracy: 0.7738\n",
      "Epoch 326/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1370 - accuracy: 0.9830 - val_loss: 1.2020 - val_accuracy: 0.7788\n",
      "Epoch 327/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1335 - accuracy: 0.9846 - val_loss: 1.1992 - val_accuracy: 0.7700\n",
      "Epoch 328/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1351 - accuracy: 0.9834 - val_loss: 1.1856 - val_accuracy: 0.7713\n",
      "Epoch 329/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1354 - accuracy: 0.9836 - val_loss: 1.1800 - val_accuracy: 0.7763\n",
      "Epoch 330/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1377 - accuracy: 0.9828 - val_loss: 1.1771 - val_accuracy: 0.7750\n",
      "Epoch 331/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1393 - accuracy: 0.9833 - val_loss: 1.1522 - val_accuracy: 0.7788\n",
      "Epoch 332/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1367 - accuracy: 0.9840 - val_loss: 1.1668 - val_accuracy: 0.7775\n",
      "Epoch 333/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1441 - accuracy: 0.9801 - val_loss: 1.1613 - val_accuracy: 0.7725\n",
      "Epoch 334/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1424 - accuracy: 0.9809 - val_loss: 1.1811 - val_accuracy: 0.7750\n",
      "Epoch 335/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1381 - accuracy: 0.9826 - val_loss: 1.1933 - val_accuracy: 0.7725\n",
      "Epoch 336/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1368 - accuracy: 0.9836 - val_loss: 1.1862 - val_accuracy: 0.7837\n",
      "Epoch 337/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1345 - accuracy: 0.9851 - val_loss: 1.2114 - val_accuracy: 0.7675\n",
      "Epoch 338/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1401 - accuracy: 0.9829 - val_loss: 1.2005 - val_accuracy: 0.7738\n",
      "Epoch 339/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1402 - accuracy: 0.9826 - val_loss: 1.2122 - val_accuracy: 0.7688\n",
      "Epoch 340/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1387 - accuracy: 0.9830 - val_loss: 1.2032 - val_accuracy: 0.7775\n",
      "Epoch 341/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1395 - accuracy: 0.9840 - val_loss: 1.2114 - val_accuracy: 0.7763\n",
      "Epoch 342/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1340 - accuracy: 0.9840 - val_loss: 1.1665 - val_accuracy: 0.7763\n",
      "Epoch 343/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1357 - accuracy: 0.9834 - val_loss: 1.1990 - val_accuracy: 0.7700\n",
      "Epoch 344/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1366 - accuracy: 0.9842 - val_loss: 1.1902 - val_accuracy: 0.7700\n",
      "Epoch 345/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1355 - accuracy: 0.9848 - val_loss: 1.2342 - val_accuracy: 0.7637\n",
      "Epoch 346/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1377 - accuracy: 0.9839 - val_loss: 1.2148 - val_accuracy: 0.7688\n",
      "Epoch 347/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1388 - accuracy: 0.9834 - val_loss: 1.2105 - val_accuracy: 0.7725\n",
      "Epoch 348/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1393 - accuracy: 0.9832 - val_loss: 1.2288 - val_accuracy: 0.7713\n",
      "Epoch 349/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1355 - accuracy: 0.9845 - val_loss: 1.2110 - val_accuracy: 0.7700\n",
      "Epoch 350/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1344 - accuracy: 0.9842 - val_loss: 1.1795 - val_accuracy: 0.7713\n",
      "Epoch 351/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1350 - accuracy: 0.9847 - val_loss: 1.1773 - val_accuracy: 0.7688\n",
      "Epoch 352/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1385 - accuracy: 0.9821 - val_loss: 1.2290 - val_accuracy: 0.7625\n",
      "Epoch 353/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1376 - accuracy: 0.9834 - val_loss: 1.2440 - val_accuracy: 0.7637\n",
      "Epoch 354/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1375 - accuracy: 0.9823 - val_loss: 1.1963 - val_accuracy: 0.7763\n",
      "Epoch 355/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1346 - accuracy: 0.9851 - val_loss: 1.1896 - val_accuracy: 0.7688\n",
      "Epoch 356/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1348 - accuracy: 0.9854 - val_loss: 1.2141 - val_accuracy: 0.7738\n",
      "Epoch 357/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1378 - accuracy: 0.9839 - val_loss: 1.2251 - val_accuracy: 0.7738\n",
      "Epoch 358/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1384 - accuracy: 0.9838 - val_loss: 1.2037 - val_accuracy: 0.7825\n",
      "Epoch 359/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1367 - accuracy: 0.9843 - val_loss: 1.2185 - val_accuracy: 0.7700\n",
      "Epoch 360/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1407 - accuracy: 0.9838 - val_loss: 1.2709 - val_accuracy: 0.7662\n",
      "Epoch 361/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1339 - accuracy: 0.9844 - val_loss: 1.2243 - val_accuracy: 0.7738\n",
      "Epoch 362/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1363 - accuracy: 0.9838 - val_loss: 1.2135 - val_accuracy: 0.7775\n",
      "Epoch 363/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1368 - accuracy: 0.9841 - val_loss: 1.2046 - val_accuracy: 0.7713\n",
      "Epoch 364/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.1293 - accuracy: 0.9875 - val_loss: 1.1802 - val_accuracy: 0.7763\n",
      "Epoch 365/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1339 - accuracy: 0.9849 - val_loss: 1.1899 - val_accuracy: 0.7788\n",
      "Epoch 366/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1344 - accuracy: 0.9848 - val_loss: 1.2060 - val_accuracy: 0.7725\n",
      "Epoch 367/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1315 - accuracy: 0.9864 - val_loss: 1.1904 - val_accuracy: 0.7725\n",
      "Epoch 368/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1376 - accuracy: 0.9836 - val_loss: 1.1937 - val_accuracy: 0.7750\n",
      "Epoch 369/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1390 - accuracy: 0.9836 - val_loss: 1.2227 - val_accuracy: 0.7750\n",
      "Epoch 370/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1354 - accuracy: 0.9852 - val_loss: 1.2253 - val_accuracy: 0.7700\n",
      "Epoch 371/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1415 - accuracy: 0.9836 - val_loss: 1.2362 - val_accuracy: 0.7688\n",
      "Epoch 372/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1372 - accuracy: 0.9841 - val_loss: 1.2479 - val_accuracy: 0.7725\n",
      "Epoch 373/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1350 - accuracy: 0.9851 - val_loss: 1.1963 - val_accuracy: 0.7713\n",
      "Epoch 374/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1366 - accuracy: 0.9832 - val_loss: 1.2240 - val_accuracy: 0.7700\n",
      "Epoch 375/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1365 - accuracy: 0.9846 - val_loss: 1.2431 - val_accuracy: 0.7688\n",
      "Epoch 376/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1332 - accuracy: 0.9855 - val_loss: 1.2311 - val_accuracy: 0.7788\n",
      "Epoch 377/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1297 - accuracy: 0.9872 - val_loss: 1.2301 - val_accuracy: 0.7775\n",
      "Epoch 378/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1309 - accuracy: 0.9866 - val_loss: 1.2282 - val_accuracy: 0.7700\n",
      "Epoch 379/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1329 - accuracy: 0.9868 - val_loss: 1.2288 - val_accuracy: 0.7725\n",
      "Epoch 380/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1357 - accuracy: 0.9848 - val_loss: 1.2163 - val_accuracy: 0.7788\n",
      "Epoch 381/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1356 - accuracy: 0.9856 - val_loss: 1.2300 - val_accuracy: 0.7700\n",
      "Epoch 382/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1326 - accuracy: 0.9853 - val_loss: 1.2339 - val_accuracy: 0.7713\n",
      "Epoch 383/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1324 - accuracy: 0.9868 - val_loss: 1.2303 - val_accuracy: 0.7800\n",
      "Epoch 384/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1326 - accuracy: 0.9855 - val_loss: 1.2296 - val_accuracy: 0.7800\n",
      "Epoch 385/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1356 - accuracy: 0.9842 - val_loss: 1.2457 - val_accuracy: 0.7713\n",
      "Epoch 386/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1322 - accuracy: 0.9864 - val_loss: 1.2513 - val_accuracy: 0.7750\n",
      "Epoch 387/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1355 - accuracy: 0.9843 - val_loss: 1.2386 - val_accuracy: 0.7763\n",
      "Epoch 388/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1345 - accuracy: 0.9857 - val_loss: 1.2644 - val_accuracy: 0.7675\n",
      "Epoch 389/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1338 - accuracy: 0.9855 - val_loss: 1.2737 - val_accuracy: 0.7675\n",
      "Epoch 390/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1360 - accuracy: 0.9848 - val_loss: 1.2950 - val_accuracy: 0.7675\n",
      "Epoch 391/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1373 - accuracy: 0.9841 - val_loss: 1.2568 - val_accuracy: 0.7713\n",
      "Epoch 392/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1360 - accuracy: 0.9842 - val_loss: 1.2525 - val_accuracy: 0.7812\n",
      "Epoch 393/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1366 - accuracy: 0.9853 - val_loss: 1.2476 - val_accuracy: 0.7625\n",
      "Epoch 394/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1371 - accuracy: 0.9847 - val_loss: 1.2239 - val_accuracy: 0.7675\n",
      "Epoch 395/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1400 - accuracy: 0.9843 - val_loss: 1.2886 - val_accuracy: 0.7637\n",
      "Epoch 396/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1368 - accuracy: 0.9839 - val_loss: 1.2848 - val_accuracy: 0.7713\n",
      "Epoch 397/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1368 - accuracy: 0.9840 - val_loss: 1.2479 - val_accuracy: 0.7688\n",
      "Epoch 398/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1330 - accuracy: 0.9861 - val_loss: 1.2902 - val_accuracy: 0.7750\n",
      "Epoch 399/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1360 - accuracy: 0.9846 - val_loss: 1.2821 - val_accuracy: 0.7725\n",
      "Epoch 400/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1367 - accuracy: 0.9851 - val_loss: 1.2698 - val_accuracy: 0.7675\n",
      "Epoch 401/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1340 - accuracy: 0.9862 - val_loss: 1.2258 - val_accuracy: 0.7713\n",
      "Epoch 402/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1323 - accuracy: 0.9868 - val_loss: 1.2187 - val_accuracy: 0.7775\n",
      "Epoch 403/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1321 - accuracy: 0.9863 - val_loss: 1.2407 - val_accuracy: 0.7750\n",
      "Epoch 404/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1402 - accuracy: 0.9837 - val_loss: 1.2701 - val_accuracy: 0.7725\n",
      "Epoch 405/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1334 - accuracy: 0.9857 - val_loss: 1.2865 - val_accuracy: 0.7738\n",
      "Epoch 406/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1333 - accuracy: 0.9860 - val_loss: 1.2764 - val_accuracy: 0.7675\n",
      "Epoch 407/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1351 - accuracy: 0.9859 - val_loss: 1.3003 - val_accuracy: 0.7650\n",
      "Epoch 408/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1312 - accuracy: 0.9863 - val_loss: 1.3027 - val_accuracy: 0.7650\n",
      "Epoch 409/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1355 - accuracy: 0.9863 - val_loss: 1.2814 - val_accuracy: 0.7625\n",
      "Epoch 410/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1348 - accuracy: 0.9851 - val_loss: 1.2734 - val_accuracy: 0.7662\n",
      "Epoch 411/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1317 - accuracy: 0.9863 - val_loss: 1.3036 - val_accuracy: 0.7675\n",
      "Epoch 412/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1362 - accuracy: 0.9847 - val_loss: 1.2737 - val_accuracy: 0.7637\n",
      "Epoch 413/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1377 - accuracy: 0.9837 - val_loss: 1.2682 - val_accuracy: 0.7587\n",
      "Epoch 414/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1343 - accuracy: 0.9857 - val_loss: 1.3299 - val_accuracy: 0.7650\n",
      "Epoch 415/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1333 - accuracy: 0.9865 - val_loss: 1.2560 - val_accuracy: 0.7713\n",
      "Epoch 416/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1347 - accuracy: 0.9852 - val_loss: 1.2682 - val_accuracy: 0.7662\n",
      "Epoch 417/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1329 - accuracy: 0.9861 - val_loss: 1.2631 - val_accuracy: 0.7688\n",
      "Epoch 418/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1372 - accuracy: 0.9847 - val_loss: 1.2410 - val_accuracy: 0.7700\n",
      "Epoch 419/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1324 - accuracy: 0.9861 - val_loss: 1.2767 - val_accuracy: 0.7700\n",
      "Epoch 420/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1365 - accuracy: 0.9837 - val_loss: 1.2699 - val_accuracy: 0.7650\n",
      "Epoch 421/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1344 - accuracy: 0.9854 - val_loss: 1.2842 - val_accuracy: 0.7637\n",
      "Epoch 422/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1339 - accuracy: 0.9864 - val_loss: 1.2461 - val_accuracy: 0.7788\n",
      "Epoch 423/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1362 - accuracy: 0.9844 - val_loss: 1.2780 - val_accuracy: 0.7713\n",
      "Epoch 424/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1370 - accuracy: 0.9847 - val_loss: 1.2766 - val_accuracy: 0.7725\n",
      "Epoch 425/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1343 - accuracy: 0.9864 - val_loss: 1.2892 - val_accuracy: 0.7662\n",
      "Epoch 426/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1365 - accuracy: 0.9855 - val_loss: 1.2757 - val_accuracy: 0.7725\n",
      "Epoch 427/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1405 - accuracy: 0.9846 - val_loss: 1.2699 - val_accuracy: 0.7738\n",
      "Epoch 428/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1348 - accuracy: 0.9865 - val_loss: 1.2400 - val_accuracy: 0.7713\n",
      "Epoch 429/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1333 - accuracy: 0.9862 - val_loss: 1.2673 - val_accuracy: 0.7725\n",
      "Epoch 430/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1356 - accuracy: 0.9856 - val_loss: 1.3050 - val_accuracy: 0.7688\n",
      "Epoch 431/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1349 - accuracy: 0.9845 - val_loss: 1.2905 - val_accuracy: 0.7675\n",
      "Epoch 432/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1352 - accuracy: 0.9857 - val_loss: 1.3146 - val_accuracy: 0.7675\n",
      "Epoch 433/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1360 - accuracy: 0.9853 - val_loss: 1.3544 - val_accuracy: 0.7625\n",
      "Epoch 434/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1307 - accuracy: 0.9880 - val_loss: 1.2827 - val_accuracy: 0.7738\n",
      "Epoch 435/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1310 - accuracy: 0.9866 - val_loss: 1.2849 - val_accuracy: 0.7763\n",
      "Epoch 436/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1306 - accuracy: 0.9872 - val_loss: 1.2904 - val_accuracy: 0.7738\n",
      "Epoch 437/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1331 - accuracy: 0.9857 - val_loss: 1.3005 - val_accuracy: 0.7750\n",
      "Epoch 438/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1330 - accuracy: 0.9861 - val_loss: 1.2935 - val_accuracy: 0.7788\n",
      "Epoch 439/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1385 - accuracy: 0.9843 - val_loss: 1.2712 - val_accuracy: 0.7688\n",
      "Epoch 440/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1324 - accuracy: 0.9876 - val_loss: 1.2723 - val_accuracy: 0.7713\n",
      "Epoch 441/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1343 - accuracy: 0.9859 - val_loss: 1.2738 - val_accuracy: 0.7788\n",
      "Epoch 442/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1361 - accuracy: 0.9842 - val_loss: 1.2991 - val_accuracy: 0.7713\n",
      "Epoch 443/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1345 - accuracy: 0.9864 - val_loss: 1.2887 - val_accuracy: 0.7800\n",
      "Epoch 444/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1332 - accuracy: 0.9871 - val_loss: 1.2833 - val_accuracy: 0.7825\n",
      "Epoch 445/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1391 - accuracy: 0.9832 - val_loss: 1.2942 - val_accuracy: 0.7738\n",
      "Epoch 446/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1340 - accuracy: 0.9859 - val_loss: 1.2956 - val_accuracy: 0.7800\n",
      "Epoch 447/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1315 - accuracy: 0.9875 - val_loss: 1.2942 - val_accuracy: 0.7837\n",
      "Epoch 448/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1336 - accuracy: 0.9855 - val_loss: 1.3000 - val_accuracy: 0.7675\n",
      "Epoch 449/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1371 - accuracy: 0.9849 - val_loss: 1.3123 - val_accuracy: 0.7800\n",
      "Epoch 450/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1362 - accuracy: 0.9856 - val_loss: 1.3157 - val_accuracy: 0.7812\n",
      "Epoch 451/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1362 - accuracy: 0.9859 - val_loss: 1.3420 - val_accuracy: 0.7600\n",
      "Epoch 452/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1340 - accuracy: 0.9858 - val_loss: 1.3490 - val_accuracy: 0.7750\n",
      "Epoch 453/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1374 - accuracy: 0.9847 - val_loss: 1.2874 - val_accuracy: 0.7825\n",
      "Epoch 454/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1341 - accuracy: 0.9866 - val_loss: 1.2752 - val_accuracy: 0.7675\n",
      "Epoch 455/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1340 - accuracy: 0.9864 - val_loss: 1.3041 - val_accuracy: 0.7713\n",
      "Epoch 456/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1325 - accuracy: 0.9865 - val_loss: 1.2836 - val_accuracy: 0.7812\n",
      "Epoch 457/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1343 - accuracy: 0.9856 - val_loss: 1.2503 - val_accuracy: 0.7775\n",
      "Epoch 458/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1341 - accuracy: 0.9861 - val_loss: 1.2709 - val_accuracy: 0.7750\n",
      "Epoch 459/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1328 - accuracy: 0.9860 - val_loss: 1.2790 - val_accuracy: 0.7812\n",
      "Epoch 460/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1318 - accuracy: 0.9870 - val_loss: 1.2683 - val_accuracy: 0.7862\n",
      "Epoch 461/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1338 - accuracy: 0.9857 - val_loss: 1.3106 - val_accuracy: 0.7750\n",
      "Epoch 462/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1343 - accuracy: 0.9861 - val_loss: 1.2846 - val_accuracy: 0.7775\n",
      "Epoch 463/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1352 - accuracy: 0.9857 - val_loss: 1.3101 - val_accuracy: 0.7675\n",
      "Epoch 464/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1326 - accuracy: 0.9861 - val_loss: 1.2831 - val_accuracy: 0.7688\n",
      "Epoch 465/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1323 - accuracy: 0.9859 - val_loss: 1.3175 - val_accuracy: 0.7650\n",
      "Epoch 466/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.1291 - accuracy: 0.9874 - val_loss: 1.2610 - val_accuracy: 0.7700\n",
      "Epoch 467/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1309 - accuracy: 0.9874 - val_loss: 1.2843 - val_accuracy: 0.7725\n",
      "Epoch 468/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1344 - accuracy: 0.9861 - val_loss: 1.2852 - val_accuracy: 0.7775\n",
      "Epoch 469/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1327 - accuracy: 0.9876 - val_loss: 1.2769 - val_accuracy: 0.7738\n",
      "Epoch 470/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1350 - accuracy: 0.9861 - val_loss: 1.2723 - val_accuracy: 0.7775\n",
      "Epoch 471/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1309 - accuracy: 0.9872 - val_loss: 1.2893 - val_accuracy: 0.7837\n",
      "Epoch 472/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1406 - accuracy: 0.9845 - val_loss: 1.2749 - val_accuracy: 0.7800\n",
      "Epoch 473/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1368 - accuracy: 0.9849 - val_loss: 1.2789 - val_accuracy: 0.7662\n",
      "Epoch 474/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1361 - accuracy: 0.9861 - val_loss: 1.2747 - val_accuracy: 0.7800\n",
      "Epoch 475/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1377 - accuracy: 0.9851 - val_loss: 1.3131 - val_accuracy: 0.7738\n",
      "Epoch 476/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1375 - accuracy: 0.9857 - val_loss: 1.2974 - val_accuracy: 0.7812\n",
      "Epoch 477/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1346 - accuracy: 0.9854 - val_loss: 1.3229 - val_accuracy: 0.7800\n",
      "Epoch 478/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1367 - accuracy: 0.9858 - val_loss: 1.2711 - val_accuracy: 0.7825\n",
      "Epoch 479/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1353 - accuracy: 0.9862 - val_loss: 1.2870 - val_accuracy: 0.7800\n",
      "Epoch 480/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1358 - accuracy: 0.9863 - val_loss: 1.2702 - val_accuracy: 0.7763\n",
      "Epoch 481/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1394 - accuracy: 0.9839 - val_loss: 1.2598 - val_accuracy: 0.7788\n",
      "Epoch 482/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1338 - accuracy: 0.9860 - val_loss: 1.2655 - val_accuracy: 0.7800\n",
      "Epoch 483/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1340 - accuracy: 0.9863 - val_loss: 1.2392 - val_accuracy: 0.7825\n",
      "Epoch 484/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1342 - accuracy: 0.9866 - val_loss: 1.2725 - val_accuracy: 0.7725\n",
      "Epoch 485/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.1285 - accuracy: 0.9891 - val_loss: 1.2974 - val_accuracy: 0.7775\n",
      "Epoch 486/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1324 - accuracy: 0.9876 - val_loss: 1.3135 - val_accuracy: 0.7650\n",
      "Epoch 487/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1312 - accuracy: 0.9872 - val_loss: 1.2883 - val_accuracy: 0.7713\n",
      "Epoch 488/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1293 - accuracy: 0.9886 - val_loss: 1.3061 - val_accuracy: 0.7750\n",
      "Epoch 489/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1333 - accuracy: 0.9863 - val_loss: 1.3272 - val_accuracy: 0.7725\n",
      "Epoch 490/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1314 - accuracy: 0.9880 - val_loss: 1.3249 - val_accuracy: 0.7688\n",
      "Epoch 491/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1327 - accuracy: 0.9876 - val_loss: 1.2834 - val_accuracy: 0.7775\n",
      "Epoch 492/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1310 - accuracy: 0.9876 - val_loss: 1.3205 - val_accuracy: 0.7738\n",
      "Epoch 493/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1353 - accuracy: 0.9857 - val_loss: 1.2977 - val_accuracy: 0.7800\n",
      "Epoch 494/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1353 - accuracy: 0.9855 - val_loss: 1.3038 - val_accuracy: 0.7725\n",
      "Epoch 495/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1313 - accuracy: 0.9875 - val_loss: 1.2987 - val_accuracy: 0.7750\n",
      "Epoch 496/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1361 - accuracy: 0.9873 - val_loss: 1.2988 - val_accuracy: 0.7763\n",
      "Epoch 497/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1349 - accuracy: 0.9862 - val_loss: 1.2615 - val_accuracy: 0.7713\n",
      "Epoch 498/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1331 - accuracy: 0.9875 - val_loss: 1.2712 - val_accuracy: 0.7713\n",
      "Epoch 499/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1366 - accuracy: 0.9862 - val_loss: 1.2744 - val_accuracy: 0.7763\n",
      "Epoch 500/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1335 - accuracy: 0.9876 - val_loss: 1.2975 - val_accuracy: 0.7775\n",
      "Epoch 501/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1314 - accuracy: 0.9882 - val_loss: 1.3369 - val_accuracy: 0.7675\n",
      "Epoch 502/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1312 - accuracy: 0.9878 - val_loss: 1.3138 - val_accuracy: 0.7788\n",
      "Epoch 503/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1310 - accuracy: 0.9872 - val_loss: 1.2655 - val_accuracy: 0.7850\n",
      "Epoch 504/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1307 - accuracy: 0.9883 - val_loss: 1.2666 - val_accuracy: 0.7837\n",
      "Epoch 505/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.1273 - accuracy: 0.9883 - val_loss: 1.2789 - val_accuracy: 0.7788\n",
      "Epoch 506/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1319 - accuracy: 0.9862 - val_loss: 1.3105 - val_accuracy: 0.7725\n",
      "Epoch 507/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1318 - accuracy: 0.9855 - val_loss: 1.3323 - val_accuracy: 0.7713\n",
      "Epoch 508/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1307 - accuracy: 0.9882 - val_loss: 1.3391 - val_accuracy: 0.7750\n",
      "Epoch 509/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1318 - accuracy: 0.9878 - val_loss: 1.2987 - val_accuracy: 0.7800\n",
      "Epoch 510/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1287 - accuracy: 0.9886 - val_loss: 1.3053 - val_accuracy: 0.7688\n",
      "Epoch 511/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1339 - accuracy: 0.9862 - val_loss: 1.3159 - val_accuracy: 0.7775\n",
      "Epoch 512/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1366 - accuracy: 0.9863 - val_loss: 1.3014 - val_accuracy: 0.7763\n",
      "Epoch 513/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1344 - accuracy: 0.9861 - val_loss: 1.2956 - val_accuracy: 0.7837\n",
      "Epoch 514/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1332 - accuracy: 0.9871 - val_loss: 1.2849 - val_accuracy: 0.7688\n",
      "Epoch 515/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1327 - accuracy: 0.9872 - val_loss: 1.3542 - val_accuracy: 0.7650\n",
      "Epoch 516/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1326 - accuracy: 0.9870 - val_loss: 1.2945 - val_accuracy: 0.7725\n",
      "Epoch 517/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1316 - accuracy: 0.9880 - val_loss: 1.2983 - val_accuracy: 0.7750\n",
      "Epoch 518/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1314 - accuracy: 0.9873 - val_loss: 1.2947 - val_accuracy: 0.7775\n",
      "Epoch 519/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1298 - accuracy: 0.9889 - val_loss: 1.3194 - val_accuracy: 0.7750\n",
      "Epoch 520/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1337 - accuracy: 0.9872 - val_loss: 1.3018 - val_accuracy: 0.7763\n",
      "Epoch 521/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1342 - accuracy: 0.9866 - val_loss: 1.3167 - val_accuracy: 0.7812\n",
      "Epoch 522/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1342 - accuracy: 0.9868 - val_loss: 1.3370 - val_accuracy: 0.7738\n",
      "Epoch 523/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1383 - accuracy: 0.9855 - val_loss: 1.2848 - val_accuracy: 0.7837\n",
      "Epoch 524/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1319 - accuracy: 0.9880 - val_loss: 1.3069 - val_accuracy: 0.7850\n",
      "Epoch 525/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1338 - accuracy: 0.9873 - val_loss: 1.3021 - val_accuracy: 0.7725\n",
      "Epoch 526/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1311 - accuracy: 0.9874 - val_loss: 1.3039 - val_accuracy: 0.7738\n",
      "Epoch 527/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1309 - accuracy: 0.9876 - val_loss: 1.2984 - val_accuracy: 0.7800\n",
      "Epoch 528/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1356 - accuracy: 0.9864 - val_loss: 1.2957 - val_accuracy: 0.7800\n",
      "Epoch 529/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1382 - accuracy: 0.9847 - val_loss: 1.2784 - val_accuracy: 0.7875\n",
      "Epoch 530/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1341 - accuracy: 0.9864 - val_loss: 1.2624 - val_accuracy: 0.7862\n",
      "Epoch 531/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1312 - accuracy: 0.9880 - val_loss: 1.2822 - val_accuracy: 0.7800\n",
      "Epoch 532/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1332 - accuracy: 0.9874 - val_loss: 1.2515 - val_accuracy: 0.7825\n",
      "Epoch 533/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1295 - accuracy: 0.9891 - val_loss: 1.2943 - val_accuracy: 0.7812\n",
      "Epoch 534/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1322 - accuracy: 0.9878 - val_loss: 1.3076 - val_accuracy: 0.7800\n",
      "Epoch 535/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1322 - accuracy: 0.9874 - val_loss: 1.2869 - val_accuracy: 0.7862\n",
      "Epoch 536/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1325 - accuracy: 0.9874 - val_loss: 1.2750 - val_accuracy: 0.7763\n",
      "Epoch 537/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1340 - accuracy: 0.9872 - val_loss: 1.2853 - val_accuracy: 0.7788\n",
      "Epoch 538/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1305 - accuracy: 0.9882 - val_loss: 1.2625 - val_accuracy: 0.7825\n",
      "Epoch 539/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1338 - accuracy: 0.9867 - val_loss: 1.2855 - val_accuracy: 0.7812\n",
      "Epoch 540/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1332 - accuracy: 0.9863 - val_loss: 1.2931 - val_accuracy: 0.7763\n",
      "Epoch 541/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1340 - accuracy: 0.9870 - val_loss: 1.2765 - val_accuracy: 0.7763\n",
      "Epoch 542/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1319 - accuracy: 0.9872 - val_loss: 1.2686 - val_accuracy: 0.7800\n",
      "Epoch 543/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1350 - accuracy: 0.9869 - val_loss: 1.2605 - val_accuracy: 0.7862\n",
      "Epoch 544/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1319 - accuracy: 0.9876 - val_loss: 1.2920 - val_accuracy: 0.7812\n",
      "Epoch 545/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1349 - accuracy: 0.9871 - val_loss: 1.2801 - val_accuracy: 0.7825\n",
      "Epoch 546/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1318 - accuracy: 0.9874 - val_loss: 1.2764 - val_accuracy: 0.7875\n",
      "Epoch 547/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1335 - accuracy: 0.9871 - val_loss: 1.2629 - val_accuracy: 0.7788\n",
      "Epoch 548/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1398 - accuracy: 0.9845 - val_loss: 1.2819 - val_accuracy: 0.7875\n",
      "Epoch 549/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1338 - accuracy: 0.9864 - val_loss: 1.2698 - val_accuracy: 0.7800\n",
      "Epoch 550/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1347 - accuracy: 0.9863 - val_loss: 1.2531 - val_accuracy: 0.7788\n",
      "Epoch 551/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1338 - accuracy: 0.9875 - val_loss: 1.2799 - val_accuracy: 0.7775\n",
      "Epoch 552/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1362 - accuracy: 0.9863 - val_loss: 1.2997 - val_accuracy: 0.7812\n",
      "Epoch 553/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1334 - accuracy: 0.9866 - val_loss: 1.3216 - val_accuracy: 0.7738\n",
      "Epoch 554/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1296 - accuracy: 0.9892 - val_loss: 1.3058 - val_accuracy: 0.7763\n",
      "Epoch 555/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1319 - accuracy: 0.9876 - val_loss: 1.3178 - val_accuracy: 0.7750\n",
      "Epoch 556/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1308 - accuracy: 0.9888 - val_loss: 1.2980 - val_accuracy: 0.7825\n",
      "Epoch 557/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1336 - accuracy: 0.9858 - val_loss: 1.2963 - val_accuracy: 0.7750\n",
      "Epoch 558/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1335 - accuracy: 0.9882 - val_loss: 1.3253 - val_accuracy: 0.7662\n",
      "Epoch 559/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1361 - accuracy: 0.9848 - val_loss: 1.3202 - val_accuracy: 0.7738\n",
      "Epoch 560/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1330 - accuracy: 0.9877 - val_loss: 1.2969 - val_accuracy: 0.7775\n",
      "Epoch 561/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1331 - accuracy: 0.9870 - val_loss: 1.2978 - val_accuracy: 0.7825\n",
      "Epoch 562/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1319 - accuracy: 0.9880 - val_loss: 1.3631 - val_accuracy: 0.7713\n",
      "Epoch 563/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1344 - accuracy: 0.9878 - val_loss: 1.3042 - val_accuracy: 0.7837\n",
      "Epoch 564/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1301 - accuracy: 0.9896 - val_loss: 1.2785 - val_accuracy: 0.7800\n",
      "Epoch 565/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1324 - accuracy: 0.9876 - val_loss: 1.2804 - val_accuracy: 0.7900\n",
      "Epoch 566/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1288 - accuracy: 0.9889 - val_loss: 1.3236 - val_accuracy: 0.7688\n",
      "Epoch 567/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1350 - accuracy: 0.9866 - val_loss: 1.3414 - val_accuracy: 0.7763\n",
      "Epoch 568/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1327 - accuracy: 0.9874 - val_loss: 1.3000 - val_accuracy: 0.7775\n",
      "Epoch 569/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1286 - accuracy: 0.9889 - val_loss: 1.3408 - val_accuracy: 0.7825\n",
      "Epoch 570/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1322 - accuracy: 0.9873 - val_loss: 1.3323 - val_accuracy: 0.7812\n",
      "Epoch 571/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1341 - accuracy: 0.9875 - val_loss: 1.2933 - val_accuracy: 0.7825\n",
      "Epoch 572/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1335 - accuracy: 0.9878 - val_loss: 1.2802 - val_accuracy: 0.7850\n",
      "Epoch 573/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1342 - accuracy: 0.9877 - val_loss: 1.3329 - val_accuracy: 0.7788\n",
      "Epoch 574/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1331 - accuracy: 0.9878 - val_loss: 1.2938 - val_accuracy: 0.7788\n",
      "Epoch 575/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1273 - accuracy: 0.9891 - val_loss: 1.3170 - val_accuracy: 0.7825\n",
      "Epoch 576/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1353 - accuracy: 0.9872 - val_loss: 1.2487 - val_accuracy: 0.7887\n",
      "Epoch 577/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1330 - accuracy: 0.9876 - val_loss: 1.2971 - val_accuracy: 0.7850\n",
      "Epoch 578/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1356 - accuracy: 0.9866 - val_loss: 1.2865 - val_accuracy: 0.7837\n",
      "Epoch 579/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1316 - accuracy: 0.9874 - val_loss: 1.2834 - val_accuracy: 0.7713\n",
      "Epoch 580/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1286 - accuracy: 0.9886 - val_loss: 1.3176 - val_accuracy: 0.7750\n",
      "Epoch 581/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1329 - accuracy: 0.9884 - val_loss: 1.3056 - val_accuracy: 0.7837\n",
      "Epoch 582/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1311 - accuracy: 0.9882 - val_loss: 1.3118 - val_accuracy: 0.7775\n",
      "Epoch 583/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1327 - accuracy: 0.9881 - val_loss: 1.3517 - val_accuracy: 0.7800\n",
      "Epoch 584/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1340 - accuracy: 0.9871 - val_loss: 1.3119 - val_accuracy: 0.7812\n",
      "Epoch 585/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1332 - accuracy: 0.9870 - val_loss: 1.3008 - val_accuracy: 0.7788\n",
      "Epoch 586/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1357 - accuracy: 0.9857 - val_loss: 1.2969 - val_accuracy: 0.7812\n",
      "Epoch 587/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1340 - accuracy: 0.9869 - val_loss: 1.3352 - val_accuracy: 0.7837\n",
      "Epoch 588/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1317 - accuracy: 0.9874 - val_loss: 1.3136 - val_accuracy: 0.7800\n",
      "Epoch 589/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1346 - accuracy: 0.9867 - val_loss: 1.3536 - val_accuracy: 0.7837\n",
      "Epoch 590/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1308 - accuracy: 0.9879 - val_loss: 1.4072 - val_accuracy: 0.7775\n",
      "Epoch 591/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1355 - accuracy: 0.9866 - val_loss: 1.3688 - val_accuracy: 0.7763\n",
      "Epoch 592/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1330 - accuracy: 0.9882 - val_loss: 1.3153 - val_accuracy: 0.7788\n",
      "Epoch 593/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1364 - accuracy: 0.9865 - val_loss: 1.3561 - val_accuracy: 0.7800\n",
      "Epoch 594/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1345 - accuracy: 0.9858 - val_loss: 1.3426 - val_accuracy: 0.7850\n",
      "Epoch 595/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1330 - accuracy: 0.9878 - val_loss: 1.3525 - val_accuracy: 0.7837\n",
      "Epoch 596/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1333 - accuracy: 0.9877 - val_loss: 1.3122 - val_accuracy: 0.7875\n",
      "Epoch 597/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1321 - accuracy: 0.9876 - val_loss: 1.3083 - val_accuracy: 0.7875\n",
      "Epoch 598/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1305 - accuracy: 0.9891 - val_loss: 1.3789 - val_accuracy: 0.7875\n",
      "Epoch 599/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1356 - accuracy: 0.9866 - val_loss: 1.3593 - val_accuracy: 0.7825\n",
      "Epoch 600/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1334 - accuracy: 0.9880 - val_loss: 1.3378 - val_accuracy: 0.7775\n",
      "Epoch 601/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1389 - accuracy: 0.9855 - val_loss: 1.3252 - val_accuracy: 0.7837\n",
      "Epoch 602/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1314 - accuracy: 0.9880 - val_loss: 1.3377 - val_accuracy: 0.7788\n",
      "Epoch 603/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1331 - accuracy: 0.9876 - val_loss: 1.3292 - val_accuracy: 0.7713\n",
      "Epoch 604/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1314 - accuracy: 0.9878 - val_loss: 1.3571 - val_accuracy: 0.7800\n",
      "Epoch 605/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1314 - accuracy: 0.9881 - val_loss: 1.3653 - val_accuracy: 0.7725\n",
      "Epoch 606/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.1386 - accuracy: 0.9857 - val_loss: 1.3532 - val_accuracy: 0.7788\n",
      "Epoch 607/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1348 - accuracy: 0.9882 - val_loss: 1.3500 - val_accuracy: 0.7650\n",
      "Epoch 608/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1308 - accuracy: 0.9882 - val_loss: 1.3235 - val_accuracy: 0.7763\n",
      "Epoch 609/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1274 - accuracy: 0.9901 - val_loss: 1.3201 - val_accuracy: 0.7850\n",
      "Epoch 610/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1322 - accuracy: 0.9881 - val_loss: 1.2970 - val_accuracy: 0.7750\n",
      "Epoch 611/1000\n",
      "19/19 [==============================] - 0s 23ms/step - loss: 0.1271 - accuracy: 0.9897 - val_loss: 1.3017 - val_accuracy: 0.7837\n",
      "Epoch 612/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1329 - accuracy: 0.9883 - val_loss: 1.3175 - val_accuracy: 0.7788\n",
      "Epoch 613/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1305 - accuracy: 0.9891 - val_loss: 1.3444 - val_accuracy: 0.7763\n",
      "Epoch 614/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1312 - accuracy: 0.9889 - val_loss: 1.2958 - val_accuracy: 0.7837\n",
      "Epoch 615/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1303 - accuracy: 0.9884 - val_loss: 1.3281 - val_accuracy: 0.7775\n",
      "Epoch 616/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1331 - accuracy: 0.9882 - val_loss: 1.3155 - val_accuracy: 0.7850\n",
      "Epoch 617/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1286 - accuracy: 0.9887 - val_loss: 1.3244 - val_accuracy: 0.7825\n",
      "Epoch 618/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1287 - accuracy: 0.9895 - val_loss: 1.3029 - val_accuracy: 0.7875\n",
      "Epoch 619/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1282 - accuracy: 0.9889 - val_loss: 1.3266 - val_accuracy: 0.7862\n",
      "Epoch 620/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1303 - accuracy: 0.9889 - val_loss: 1.3398 - val_accuracy: 0.7887\n",
      "Epoch 621/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1332 - accuracy: 0.9874 - val_loss: 1.3853 - val_accuracy: 0.7788\n",
      "Epoch 622/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1308 - accuracy: 0.9876 - val_loss: 1.3216 - val_accuracy: 0.7837\n",
      "Epoch 623/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1311 - accuracy: 0.9881 - val_loss: 1.3290 - val_accuracy: 0.7825\n",
      "Epoch 624/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1273 - accuracy: 0.9899 - val_loss: 1.2827 - val_accuracy: 0.7800\n",
      "Epoch 625/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1311 - accuracy: 0.9876 - val_loss: 1.2752 - val_accuracy: 0.7825\n",
      "Epoch 626/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1280 - accuracy: 0.9897 - val_loss: 1.2876 - val_accuracy: 0.7788\n",
      "Epoch 627/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1283 - accuracy: 0.9897 - val_loss: 1.2681 - val_accuracy: 0.7775\n",
      "Epoch 628/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1295 - accuracy: 0.9879 - val_loss: 1.2748 - val_accuracy: 0.7812\n",
      "Epoch 629/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1334 - accuracy: 0.9873 - val_loss: 1.2925 - val_accuracy: 0.7800\n",
      "Epoch 630/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1328 - accuracy: 0.9878 - val_loss: 1.3144 - val_accuracy: 0.7763\n",
      "Epoch 631/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1358 - accuracy: 0.9867 - val_loss: 1.3306 - val_accuracy: 0.7800\n",
      "Epoch 632/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1295 - accuracy: 0.9882 - val_loss: 1.3137 - val_accuracy: 0.7738\n",
      "Epoch 633/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.1317 - accuracy: 0.9887 - val_loss: 1.3280 - val_accuracy: 0.7763\n",
      "Epoch 634/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.1296 - accuracy: 0.9891 - val_loss: 1.3436 - val_accuracy: 0.7725\n",
      "Epoch 635/1000\n",
      "19/19 [==============================] - 0s 24ms/step - loss: 0.1255 - accuracy: 0.9903 - val_loss: 1.3101 - val_accuracy: 0.7800\n",
      "Epoch 636/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1304 - accuracy: 0.9886 - val_loss: 1.3065 - val_accuracy: 0.7825\n",
      "Epoch 637/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1309 - accuracy: 0.9882 - val_loss: 1.2704 - val_accuracy: 0.7800\n",
      "Epoch 638/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1310 - accuracy: 0.9878 - val_loss: 1.2686 - val_accuracy: 0.7875\n",
      "Epoch 639/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1324 - accuracy: 0.9886 - val_loss: 1.3013 - val_accuracy: 0.7837\n",
      "Epoch 640/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1338 - accuracy: 0.9876 - val_loss: 1.3127 - val_accuracy: 0.7713\n",
      "Epoch 641/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1327 - accuracy: 0.9882 - val_loss: 1.3442 - val_accuracy: 0.7788\n",
      "Epoch 642/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1309 - accuracy: 0.9887 - val_loss: 1.3250 - val_accuracy: 0.7775\n",
      "Epoch 643/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1302 - accuracy: 0.9887 - val_loss: 1.2978 - val_accuracy: 0.7837\n",
      "Epoch 644/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1317 - accuracy: 0.9882 - val_loss: 1.2909 - val_accuracy: 0.7775\n",
      "Epoch 645/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1318 - accuracy: 0.9878 - val_loss: 1.3512 - val_accuracy: 0.7763\n",
      "Epoch 646/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1311 - accuracy: 0.9874 - val_loss: 1.3465 - val_accuracy: 0.7812\n",
      "Epoch 647/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1295 - accuracy: 0.9884 - val_loss: 1.3122 - val_accuracy: 0.7862\n",
      "Epoch 648/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1295 - accuracy: 0.9882 - val_loss: 1.3422 - val_accuracy: 0.7837\n",
      "Epoch 649/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1315 - accuracy: 0.9872 - val_loss: 1.3187 - val_accuracy: 0.7812\n",
      "Epoch 650/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1310 - accuracy: 0.9878 - val_loss: 1.3562 - val_accuracy: 0.7763\n",
      "Epoch 651/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1322 - accuracy: 0.9878 - val_loss: 1.3707 - val_accuracy: 0.7713\n",
      "Epoch 652/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1338 - accuracy: 0.9870 - val_loss: 1.3283 - val_accuracy: 0.7800\n",
      "Epoch 653/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1320 - accuracy: 0.9880 - val_loss: 1.2918 - val_accuracy: 0.7763\n",
      "Epoch 654/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1325 - accuracy: 0.9872 - val_loss: 1.2845 - val_accuracy: 0.7825\n",
      "Epoch 655/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1316 - accuracy: 0.9882 - val_loss: 1.3132 - val_accuracy: 0.7763\n",
      "Epoch 656/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1331 - accuracy: 0.9878 - val_loss: 1.3288 - val_accuracy: 0.7700\n",
      "Epoch 657/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1341 - accuracy: 0.9871 - val_loss: 1.3535 - val_accuracy: 0.7837\n",
      "Epoch 658/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1345 - accuracy: 0.9870 - val_loss: 1.3529 - val_accuracy: 0.7738\n",
      "Epoch 659/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1320 - accuracy: 0.9886 - val_loss: 1.3551 - val_accuracy: 0.7738\n",
      "Epoch 660/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1296 - accuracy: 0.9886 - val_loss: 1.3326 - val_accuracy: 0.7812\n",
      "Epoch 661/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1335 - accuracy: 0.9871 - val_loss: 1.3588 - val_accuracy: 0.7900\n",
      "Epoch 662/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1320 - accuracy: 0.9883 - val_loss: 1.3645 - val_accuracy: 0.7800\n",
      "Epoch 663/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1339 - accuracy: 0.9870 - val_loss: 1.3717 - val_accuracy: 0.7850\n",
      "Epoch 664/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1332 - accuracy: 0.9879 - val_loss: 1.3688 - val_accuracy: 0.7850\n",
      "Epoch 665/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1355 - accuracy: 0.9860 - val_loss: 1.3407 - val_accuracy: 0.7812\n",
      "Epoch 666/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1330 - accuracy: 0.9874 - val_loss: 1.3548 - val_accuracy: 0.7788\n",
      "Epoch 667/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1308 - accuracy: 0.9880 - val_loss: 1.3536 - val_accuracy: 0.7912\n",
      "Epoch 668/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1293 - accuracy: 0.9896 - val_loss: 1.3241 - val_accuracy: 0.7900\n",
      "Epoch 669/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1313 - accuracy: 0.9878 - val_loss: 1.2991 - val_accuracy: 0.7900\n",
      "Epoch 670/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1309 - accuracy: 0.9889 - val_loss: 1.3118 - val_accuracy: 0.7837\n",
      "Epoch 671/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1337 - accuracy: 0.9879 - val_loss: 1.2994 - val_accuracy: 0.7887\n",
      "Epoch 672/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1296 - accuracy: 0.9886 - val_loss: 1.3196 - val_accuracy: 0.7862\n",
      "Epoch 673/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1357 - accuracy: 0.9864 - val_loss: 1.3449 - val_accuracy: 0.7837\n",
      "Epoch 674/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1340 - accuracy: 0.9874 - val_loss: 1.3649 - val_accuracy: 0.7788\n",
      "Epoch 675/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1302 - accuracy: 0.9885 - val_loss: 1.3524 - val_accuracy: 0.7837\n",
      "Epoch 676/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1327 - accuracy: 0.9874 - val_loss: 1.3098 - val_accuracy: 0.7887\n",
      "Epoch 677/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1364 - accuracy: 0.9870 - val_loss: 1.3619 - val_accuracy: 0.7825\n",
      "Epoch 678/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1387 - accuracy: 0.9859 - val_loss: 1.3873 - val_accuracy: 0.7875\n",
      "Epoch 679/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1340 - accuracy: 0.9873 - val_loss: 1.3688 - val_accuracy: 0.7763\n",
      "Epoch 680/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1370 - accuracy: 0.9876 - val_loss: 1.3501 - val_accuracy: 0.7837\n",
      "Epoch 681/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1314 - accuracy: 0.9880 - val_loss: 1.3750 - val_accuracy: 0.7925\n",
      "Epoch 682/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1341 - accuracy: 0.9867 - val_loss: 1.4159 - val_accuracy: 0.7837\n",
      "Epoch 683/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1330 - accuracy: 0.9880 - val_loss: 1.3692 - val_accuracy: 0.7862\n",
      "Epoch 684/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1286 - accuracy: 0.9893 - val_loss: 1.3465 - val_accuracy: 0.8000\n",
      "Epoch 685/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1360 - accuracy: 0.9870 - val_loss: 1.3314 - val_accuracy: 0.7912\n",
      "Epoch 686/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1295 - accuracy: 0.9891 - val_loss: 1.3416 - val_accuracy: 0.7937\n",
      "Epoch 687/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1300 - accuracy: 0.9884 - val_loss: 1.3492 - val_accuracy: 0.7825\n",
      "Epoch 688/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1296 - accuracy: 0.9893 - val_loss: 1.3356 - val_accuracy: 0.7962\n",
      "Epoch 689/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1291 - accuracy: 0.9884 - val_loss: 1.3365 - val_accuracy: 0.7850\n",
      "Epoch 690/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1313 - accuracy: 0.9882 - val_loss: 1.3482 - val_accuracy: 0.7875\n",
      "Epoch 691/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1323 - accuracy: 0.9873 - val_loss: 1.3265 - val_accuracy: 0.7900\n",
      "Epoch 692/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1327 - accuracy: 0.9882 - val_loss: 1.3492 - val_accuracy: 0.7887\n",
      "Epoch 693/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1344 - accuracy: 0.9878 - val_loss: 1.3400 - val_accuracy: 0.7775\n",
      "Epoch 694/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1320 - accuracy: 0.9882 - val_loss: 1.3682 - val_accuracy: 0.7800\n",
      "Epoch 695/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1352 - accuracy: 0.9879 - val_loss: 1.3543 - val_accuracy: 0.7812\n",
      "Epoch 696/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1299 - accuracy: 0.9895 - val_loss: 1.3628 - val_accuracy: 0.7825\n",
      "Epoch 697/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1306 - accuracy: 0.9882 - val_loss: 1.3498 - val_accuracy: 0.7800\n",
      "Epoch 698/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1289 - accuracy: 0.9891 - val_loss: 1.3763 - val_accuracy: 0.7812\n",
      "Epoch 699/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1357 - accuracy: 0.9868 - val_loss: 1.3406 - val_accuracy: 0.7825\n",
      "Epoch 700/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1298 - accuracy: 0.9897 - val_loss: 1.3179 - val_accuracy: 0.7850\n",
      "Epoch 701/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1300 - accuracy: 0.9900 - val_loss: 1.3306 - val_accuracy: 0.7837\n",
      "Epoch 702/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1298 - accuracy: 0.9885 - val_loss: 1.3508 - val_accuracy: 0.7887\n",
      "Epoch 703/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1298 - accuracy: 0.9893 - val_loss: 1.3103 - val_accuracy: 0.7800\n",
      "Epoch 704/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1341 - accuracy: 0.9867 - val_loss: 1.3737 - val_accuracy: 0.7837\n",
      "Epoch 705/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1296 - accuracy: 0.9887 - val_loss: 1.3746 - val_accuracy: 0.7812\n",
      "Epoch 706/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1350 - accuracy: 0.9870 - val_loss: 1.3543 - val_accuracy: 0.7850\n",
      "Epoch 707/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1313 - accuracy: 0.9893 - val_loss: 1.3840 - val_accuracy: 0.7800\n",
      "Epoch 708/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1329 - accuracy: 0.9886 - val_loss: 1.3435 - val_accuracy: 0.7812\n",
      "Epoch 709/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1328 - accuracy: 0.9874 - val_loss: 1.3374 - val_accuracy: 0.7763\n",
      "Epoch 710/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1271 - accuracy: 0.9909 - val_loss: 1.3455 - val_accuracy: 0.7850\n",
      "Epoch 711/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1333 - accuracy: 0.9877 - val_loss: 1.3148 - val_accuracy: 0.7850\n",
      "Epoch 712/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1345 - accuracy: 0.9878 - val_loss: 1.3421 - val_accuracy: 0.7825\n",
      "Epoch 713/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1345 - accuracy: 0.9865 - val_loss: 1.3593 - val_accuracy: 0.7750\n",
      "Epoch 714/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1343 - accuracy: 0.9871 - val_loss: 1.3424 - val_accuracy: 0.7875\n",
      "Epoch 715/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1304 - accuracy: 0.9888 - val_loss: 1.3264 - val_accuracy: 0.7850\n",
      "Epoch 716/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1315 - accuracy: 0.9875 - val_loss: 1.3552 - val_accuracy: 0.7850\n",
      "Epoch 717/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1314 - accuracy: 0.9889 - val_loss: 1.3555 - val_accuracy: 0.7850\n",
      "Epoch 718/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1339 - accuracy: 0.9879 - val_loss: 1.3590 - val_accuracy: 0.7700\n",
      "Epoch 719/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1346 - accuracy: 0.9881 - val_loss: 1.3094 - val_accuracy: 0.7775\n",
      "Epoch 720/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1351 - accuracy: 0.9866 - val_loss: 1.3085 - val_accuracy: 0.7825\n",
      "Epoch 721/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1302 - accuracy: 0.9892 - val_loss: 1.3432 - val_accuracy: 0.7700\n",
      "Epoch 722/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1361 - accuracy: 0.9882 - val_loss: 1.3396 - val_accuracy: 0.7837\n",
      "Epoch 723/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1338 - accuracy: 0.9875 - val_loss: 1.3312 - val_accuracy: 0.7837\n",
      "Epoch 724/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1336 - accuracy: 0.9880 - val_loss: 1.3482 - val_accuracy: 0.7850\n",
      "Epoch 725/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1285 - accuracy: 0.9894 - val_loss: 1.3227 - val_accuracy: 0.7837\n",
      "Epoch 726/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1302 - accuracy: 0.9884 - val_loss: 1.3348 - val_accuracy: 0.7875\n",
      "Epoch 727/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1314 - accuracy: 0.9881 - val_loss: 1.3445 - val_accuracy: 0.7850\n",
      "Epoch 728/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1285 - accuracy: 0.9890 - val_loss: 1.3639 - val_accuracy: 0.7862\n",
      "Epoch 729/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1331 - accuracy: 0.9872 - val_loss: 1.3723 - val_accuracy: 0.7825\n",
      "Epoch 730/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1333 - accuracy: 0.9882 - val_loss: 1.3488 - val_accuracy: 0.7800\n",
      "Epoch 731/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1312 - accuracy: 0.9883 - val_loss: 1.3532 - val_accuracy: 0.7875\n",
      "Epoch 732/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1305 - accuracy: 0.9896 - val_loss: 1.3408 - val_accuracy: 0.7788\n",
      "Epoch 733/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1308 - accuracy: 0.9875 - val_loss: 1.3340 - val_accuracy: 0.7788\n",
      "Epoch 734/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1286 - accuracy: 0.9901 - val_loss: 1.3403 - val_accuracy: 0.7812\n",
      "Epoch 735/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1276 - accuracy: 0.9905 - val_loss: 1.3563 - val_accuracy: 0.7788\n",
      "Epoch 736/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1309 - accuracy: 0.9888 - val_loss: 1.3311 - val_accuracy: 0.7850\n",
      "Epoch 737/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1334 - accuracy: 0.9880 - val_loss: 1.3201 - val_accuracy: 0.7950\n",
      "Epoch 738/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1303 - accuracy: 0.9880 - val_loss: 1.3443 - val_accuracy: 0.7775\n",
      "Epoch 739/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1283 - accuracy: 0.9897 - val_loss: 1.3355 - val_accuracy: 0.7800\n",
      "Epoch 740/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1339 - accuracy: 0.9873 - val_loss: 1.3101 - val_accuracy: 0.7825\n",
      "Epoch 741/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1278 - accuracy: 0.9888 - val_loss: 1.3275 - val_accuracy: 0.7750\n",
      "Epoch 742/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1326 - accuracy: 0.9872 - val_loss: 1.3354 - val_accuracy: 0.7700\n",
      "Epoch 743/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1312 - accuracy: 0.9894 - val_loss: 1.3265 - val_accuracy: 0.7775\n",
      "Epoch 744/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1293 - accuracy: 0.9895 - val_loss: 1.3231 - val_accuracy: 0.7812\n",
      "Epoch 745/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1338 - accuracy: 0.9870 - val_loss: 1.3130 - val_accuracy: 0.7650\n",
      "Epoch 746/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1358 - accuracy: 0.9872 - val_loss: 1.3593 - val_accuracy: 0.7738\n",
      "Epoch 747/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1302 - accuracy: 0.9883 - val_loss: 1.3266 - val_accuracy: 0.7738\n",
      "Epoch 748/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1305 - accuracy: 0.9882 - val_loss: 1.3475 - val_accuracy: 0.7725\n",
      "Epoch 749/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1303 - accuracy: 0.9893 - val_loss: 1.3344 - val_accuracy: 0.7763\n",
      "Epoch 750/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1326 - accuracy: 0.9886 - val_loss: 1.3090 - val_accuracy: 0.7825\n",
      "Epoch 751/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1318 - accuracy: 0.9879 - val_loss: 1.3619 - val_accuracy: 0.7725\n",
      "Epoch 752/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1306 - accuracy: 0.9880 - val_loss: 1.3688 - val_accuracy: 0.7738\n",
      "Epoch 753/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1332 - accuracy: 0.9878 - val_loss: 1.3504 - val_accuracy: 0.7763\n",
      "Epoch 754/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1310 - accuracy: 0.9886 - val_loss: 1.3465 - val_accuracy: 0.7788\n",
      "Epoch 755/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1355 - accuracy: 0.9869 - val_loss: 1.3522 - val_accuracy: 0.7825\n",
      "Epoch 756/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1310 - accuracy: 0.9890 - val_loss: 1.3650 - val_accuracy: 0.7788\n",
      "Epoch 757/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1314 - accuracy: 0.9878 - val_loss: 1.3921 - val_accuracy: 0.7725\n",
      "Epoch 758/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1304 - accuracy: 0.9892 - val_loss: 1.3747 - val_accuracy: 0.7725\n",
      "Epoch 759/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1317 - accuracy: 0.9883 - val_loss: 1.3225 - val_accuracy: 0.7725\n",
      "Epoch 760/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1339 - accuracy: 0.9882 - val_loss: 1.3432 - val_accuracy: 0.7775\n",
      "Epoch 761/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1329 - accuracy: 0.9876 - val_loss: 1.3449 - val_accuracy: 0.7688\n",
      "Epoch 762/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1307 - accuracy: 0.9880 - val_loss: 1.3085 - val_accuracy: 0.7738\n",
      "Epoch 763/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1307 - accuracy: 0.9895 - val_loss: 1.3211 - val_accuracy: 0.7725\n",
      "Epoch 764/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1302 - accuracy: 0.9887 - val_loss: 1.3124 - val_accuracy: 0.7812\n",
      "Epoch 765/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1310 - accuracy: 0.9884 - val_loss: 1.3223 - val_accuracy: 0.7788\n",
      "Epoch 766/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1313 - accuracy: 0.9886 - val_loss: 1.3543 - val_accuracy: 0.7750\n",
      "Epoch 767/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1268 - accuracy: 0.9900 - val_loss: 1.3496 - val_accuracy: 0.7725\n",
      "Epoch 768/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1302 - accuracy: 0.9895 - val_loss: 1.3460 - val_accuracy: 0.7800\n",
      "Epoch 769/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1308 - accuracy: 0.9897 - val_loss: 1.3315 - val_accuracy: 0.7775\n",
      "Epoch 770/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1348 - accuracy: 0.9883 - val_loss: 1.3446 - val_accuracy: 0.7800\n",
      "Epoch 771/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1340 - accuracy: 0.9879 - val_loss: 1.3356 - val_accuracy: 0.7825\n",
      "Epoch 772/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1358 - accuracy: 0.9876 - val_loss: 1.3617 - val_accuracy: 0.7850\n",
      "Epoch 773/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1343 - accuracy: 0.9878 - val_loss: 1.3407 - val_accuracy: 0.7812\n",
      "Epoch 774/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1334 - accuracy: 0.9871 - val_loss: 1.3438 - val_accuracy: 0.7875\n",
      "Epoch 775/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1328 - accuracy: 0.9888 - val_loss: 1.3486 - val_accuracy: 0.7850\n",
      "Epoch 776/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1322 - accuracy: 0.9888 - val_loss: 1.3186 - val_accuracy: 0.7862\n",
      "Epoch 777/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1322 - accuracy: 0.9893 - val_loss: 1.3987 - val_accuracy: 0.7812\n",
      "Epoch 778/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1316 - accuracy: 0.9887 - val_loss: 1.3914 - val_accuracy: 0.7763\n",
      "Epoch 779/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1313 - accuracy: 0.9884 - val_loss: 1.3534 - val_accuracy: 0.7825\n",
      "Epoch 780/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1269 - accuracy: 0.9910 - val_loss: 1.3329 - val_accuracy: 0.7837\n",
      "Epoch 781/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1298 - accuracy: 0.9889 - val_loss: 1.3560 - val_accuracy: 0.7788\n",
      "Epoch 782/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1324 - accuracy: 0.9882 - val_loss: 1.3326 - val_accuracy: 0.7812\n",
      "Epoch 783/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1337 - accuracy: 0.9882 - val_loss: 1.3429 - val_accuracy: 0.7812\n",
      "Epoch 784/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1311 - accuracy: 0.9888 - val_loss: 1.3505 - val_accuracy: 0.7800\n",
      "Epoch 785/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1292 - accuracy: 0.9888 - val_loss: 1.3510 - val_accuracy: 0.7825\n",
      "Epoch 786/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1342 - accuracy: 0.9884 - val_loss: 1.3427 - val_accuracy: 0.7725\n",
      "Epoch 787/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1307 - accuracy: 0.9882 - val_loss: 1.3357 - val_accuracy: 0.7788\n",
      "Epoch 788/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1276 - accuracy: 0.9897 - val_loss: 1.3371 - val_accuracy: 0.7662\n",
      "Epoch 789/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1336 - accuracy: 0.9879 - val_loss: 1.3337 - val_accuracy: 0.7775\n",
      "Epoch 790/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1320 - accuracy: 0.9884 - val_loss: 1.3538 - val_accuracy: 0.7725\n",
      "Epoch 791/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1308 - accuracy: 0.9888 - val_loss: 1.3688 - val_accuracy: 0.7775\n",
      "Epoch 792/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1325 - accuracy: 0.9877 - val_loss: 1.3350 - val_accuracy: 0.7725\n",
      "Epoch 793/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1296 - accuracy: 0.9891 - val_loss: 1.3432 - val_accuracy: 0.7675\n",
      "Epoch 794/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1311 - accuracy: 0.9895 - val_loss: 1.3522 - val_accuracy: 0.7700\n",
      "Epoch 795/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1313 - accuracy: 0.9884 - val_loss: 1.3701 - val_accuracy: 0.7738\n",
      "Epoch 796/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1287 - accuracy: 0.9900 - val_loss: 1.3559 - val_accuracy: 0.7688\n",
      "Epoch 797/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1310 - accuracy: 0.9888 - val_loss: 1.3570 - val_accuracy: 0.7675\n",
      "Epoch 798/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1312 - accuracy: 0.9880 - val_loss: 1.3607 - val_accuracy: 0.7800\n",
      "Epoch 799/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1340 - accuracy: 0.9872 - val_loss: 1.3542 - val_accuracy: 0.7763\n",
      "Epoch 800/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1316 - accuracy: 0.9888 - val_loss: 1.3331 - val_accuracy: 0.7738\n",
      "Epoch 801/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1322 - accuracy: 0.9884 - val_loss: 1.3415 - val_accuracy: 0.7713\n",
      "Epoch 802/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1305 - accuracy: 0.9895 - val_loss: 1.3194 - val_accuracy: 0.7750\n",
      "Epoch 803/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1305 - accuracy: 0.9894 - val_loss: 1.3457 - val_accuracy: 0.7812\n",
      "Epoch 804/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1318 - accuracy: 0.9886 - val_loss: 1.3334 - val_accuracy: 0.7775\n",
      "Epoch 805/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1288 - accuracy: 0.9907 - val_loss: 1.3502 - val_accuracy: 0.7738\n",
      "Epoch 806/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1286 - accuracy: 0.9897 - val_loss: 1.3564 - val_accuracy: 0.7750\n",
      "Epoch 807/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1321 - accuracy: 0.9885 - val_loss: 1.3496 - val_accuracy: 0.7675\n",
      "Epoch 808/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1308 - accuracy: 0.9882 - val_loss: 1.3785 - val_accuracy: 0.7750\n",
      "Epoch 809/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.1342 - accuracy: 0.9876 - val_loss: 1.3360 - val_accuracy: 0.7763\n",
      "Epoch 810/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1338 - accuracy: 0.9878 - val_loss: 1.3738 - val_accuracy: 0.7725\n",
      "Epoch 811/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1319 - accuracy: 0.9884 - val_loss: 1.3862 - val_accuracy: 0.7788\n",
      "Epoch 812/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1346 - accuracy: 0.9879 - val_loss: 1.3707 - val_accuracy: 0.7725\n",
      "Epoch 813/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1304 - accuracy: 0.9885 - val_loss: 1.3462 - val_accuracy: 0.7800\n",
      "Epoch 814/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1331 - accuracy: 0.9883 - val_loss: 1.3367 - val_accuracy: 0.7788\n",
      "Epoch 815/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1301 - accuracy: 0.9893 - val_loss: 1.3727 - val_accuracy: 0.7700\n",
      "Epoch 816/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1338 - accuracy: 0.9886 - val_loss: 1.3674 - val_accuracy: 0.7775\n",
      "Epoch 817/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1329 - accuracy: 0.9874 - val_loss: 1.3472 - val_accuracy: 0.7750\n",
      "Epoch 818/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1320 - accuracy: 0.9888 - val_loss: 1.3304 - val_accuracy: 0.7825\n",
      "Epoch 819/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1312 - accuracy: 0.9886 - val_loss: 1.3557 - val_accuracy: 0.7812\n",
      "Epoch 820/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1294 - accuracy: 0.9897 - val_loss: 1.3295 - val_accuracy: 0.7825\n",
      "Epoch 821/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1327 - accuracy: 0.9886 - val_loss: 1.3290 - val_accuracy: 0.7763\n",
      "Epoch 822/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1324 - accuracy: 0.9885 - val_loss: 1.3579 - val_accuracy: 0.7862\n",
      "Epoch 823/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1302 - accuracy: 0.9897 - val_loss: 1.3676 - val_accuracy: 0.7750\n",
      "Epoch 824/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1299 - accuracy: 0.9891 - val_loss: 1.3650 - val_accuracy: 0.7700\n",
      "Epoch 825/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1342 - accuracy: 0.9853 - val_loss: 1.3763 - val_accuracy: 0.7738\n",
      "Epoch 826/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1315 - accuracy: 0.9884 - val_loss: 1.3526 - val_accuracy: 0.7763\n",
      "Epoch 827/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1283 - accuracy: 0.9898 - val_loss: 1.3771 - val_accuracy: 0.7775\n",
      "Epoch 828/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1295 - accuracy: 0.9888 - val_loss: 1.3717 - val_accuracy: 0.7738\n",
      "Epoch 829/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1366 - accuracy: 0.9868 - val_loss: 1.3539 - val_accuracy: 0.7812\n",
      "Epoch 830/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1330 - accuracy: 0.9888 - val_loss: 1.3441 - val_accuracy: 0.7800\n",
      "Epoch 831/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1338 - accuracy: 0.9878 - val_loss: 1.3480 - val_accuracy: 0.7800\n",
      "Epoch 832/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1318 - accuracy: 0.9882 - val_loss: 1.3569 - val_accuracy: 0.7675\n",
      "Epoch 833/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1280 - accuracy: 0.9901 - val_loss: 1.3268 - val_accuracy: 0.7725\n",
      "Epoch 834/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1346 - accuracy: 0.9868 - val_loss: 1.3185 - val_accuracy: 0.7725\n",
      "Epoch 835/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1305 - accuracy: 0.9899 - val_loss: 1.3271 - val_accuracy: 0.7812\n",
      "Epoch 836/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1330 - accuracy: 0.9883 - val_loss: 1.3461 - val_accuracy: 0.7750\n",
      "Epoch 837/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1277 - accuracy: 0.9903 - val_loss: 1.3414 - val_accuracy: 0.7750\n",
      "Epoch 838/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1339 - accuracy: 0.9877 - val_loss: 1.3478 - val_accuracy: 0.7775\n",
      "Epoch 839/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1326 - accuracy: 0.9880 - val_loss: 1.3335 - val_accuracy: 0.7800\n",
      "Epoch 840/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1301 - accuracy: 0.9891 - val_loss: 1.3952 - val_accuracy: 0.7688\n",
      "Epoch 841/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1297 - accuracy: 0.9888 - val_loss: 1.3810 - val_accuracy: 0.7738\n",
      "Epoch 842/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1312 - accuracy: 0.9894 - val_loss: 1.3550 - val_accuracy: 0.7750\n",
      "Epoch 843/1000\n"
     ]
    },
    {
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     "text": [
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1311 - accuracy: 0.9884 - val_loss: 1.3548 - val_accuracy: 0.7750\n",
      "Epoch 844/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1375 - accuracy: 0.9868 - val_loss: 1.3019 - val_accuracy: 0.7825\n",
      "Epoch 845/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1315 - accuracy: 0.9888 - val_loss: 1.3458 - val_accuracy: 0.7763\n",
      "Epoch 846/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1342 - accuracy: 0.9886 - val_loss: 1.3763 - val_accuracy: 0.7738\n",
      "Epoch 847/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1351 - accuracy: 0.9882 - val_loss: 1.3506 - val_accuracy: 0.7700\n",
      "Epoch 848/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1347 - accuracy: 0.9876 - val_loss: 1.3436 - val_accuracy: 0.7775\n",
      "Epoch 849/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1284 - accuracy: 0.9897 - val_loss: 1.3346 - val_accuracy: 0.7725\n",
      "Epoch 850/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1336 - accuracy: 0.9888 - val_loss: 1.3543 - val_accuracy: 0.7825\n",
      "Epoch 851/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1329 - accuracy: 0.9878 - val_loss: 1.3002 - val_accuracy: 0.7825\n",
      "Epoch 852/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1319 - accuracy: 0.9898 - val_loss: 1.3381 - val_accuracy: 0.7763\n",
      "Epoch 853/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1352 - accuracy: 0.9887 - val_loss: 1.3850 - val_accuracy: 0.7675\n",
      "Epoch 854/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1333 - accuracy: 0.9883 - val_loss: 1.3701 - val_accuracy: 0.7725\n",
      "Epoch 855/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1312 - accuracy: 0.9884 - val_loss: 1.3790 - val_accuracy: 0.7625\n",
      "Epoch 856/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1289 - accuracy: 0.9891 - val_loss: 1.3539 - val_accuracy: 0.7675\n",
      "Epoch 857/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1322 - accuracy: 0.9883 - val_loss: 1.3439 - val_accuracy: 0.7713\n",
      "Epoch 858/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1334 - accuracy: 0.9888 - val_loss: 1.3910 - val_accuracy: 0.7700\n",
      "Epoch 859/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1331 - accuracy: 0.9883 - val_loss: 1.4067 - val_accuracy: 0.7688\n",
      "Epoch 860/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1360 - accuracy: 0.9869 - val_loss: 1.4064 - val_accuracy: 0.7788\n",
      "Epoch 861/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1348 - accuracy: 0.9887 - val_loss: 1.3753 - val_accuracy: 0.7775\n",
      "Epoch 862/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1306 - accuracy: 0.9889 - val_loss: 1.3540 - val_accuracy: 0.7812\n",
      "Epoch 863/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1298 - accuracy: 0.9894 - val_loss: 1.3515 - val_accuracy: 0.7825\n",
      "Epoch 864/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1302 - accuracy: 0.9897 - val_loss: 1.3736 - val_accuracy: 0.7850\n",
      "Epoch 865/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1343 - accuracy: 0.9871 - val_loss: 1.3897 - val_accuracy: 0.7775\n",
      "Epoch 866/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1315 - accuracy: 0.9886 - val_loss: 1.3446 - val_accuracy: 0.7837\n",
      "Epoch 867/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1265 - accuracy: 0.9903 - val_loss: 1.3319 - val_accuracy: 0.7812\n",
      "Epoch 868/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1287 - accuracy: 0.9905 - val_loss: 1.3649 - val_accuracy: 0.7725\n",
      "Epoch 869/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1347 - accuracy: 0.9881 - val_loss: 1.3780 - val_accuracy: 0.7800\n",
      "Epoch 870/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1323 - accuracy: 0.9888 - val_loss: 1.3816 - val_accuracy: 0.7775\n",
      "Epoch 871/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1327 - accuracy: 0.9884 - val_loss: 1.3564 - val_accuracy: 0.7713\n",
      "Epoch 872/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1325 - accuracy: 0.9884 - val_loss: 1.3712 - val_accuracy: 0.7763\n",
      "Epoch 873/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1335 - accuracy: 0.9876 - val_loss: 1.3636 - val_accuracy: 0.7675\n",
      "Epoch 874/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1309 - accuracy: 0.9886 - val_loss: 1.3418 - val_accuracy: 0.7788\n",
      "Epoch 875/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1317 - accuracy: 0.9878 - val_loss: 1.3420 - val_accuracy: 0.7763\n",
      "Epoch 876/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1317 - accuracy: 0.9880 - val_loss: 1.4094 - val_accuracy: 0.7662\n",
      "Epoch 877/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1355 - accuracy: 0.9866 - val_loss: 1.3587 - val_accuracy: 0.7713\n",
      "Epoch 878/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1318 - accuracy: 0.9889 - val_loss: 1.3694 - val_accuracy: 0.7713\n",
      "Epoch 879/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1309 - accuracy: 0.9884 - val_loss: 1.3639 - val_accuracy: 0.7800\n",
      "Epoch 880/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1311 - accuracy: 0.9890 - val_loss: 1.3844 - val_accuracy: 0.7700\n",
      "Epoch 881/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1341 - accuracy: 0.9888 - val_loss: 1.3522 - val_accuracy: 0.7788\n",
      "Epoch 882/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1313 - accuracy: 0.9884 - val_loss: 1.3405 - val_accuracy: 0.7812\n",
      "Epoch 883/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1299 - accuracy: 0.9886 - val_loss: 1.3456 - val_accuracy: 0.7763\n",
      "Epoch 884/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1347 - accuracy: 0.9874 - val_loss: 1.3229 - val_accuracy: 0.7775\n",
      "Epoch 885/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1306 - accuracy: 0.9895 - val_loss: 1.3253 - val_accuracy: 0.7750\n",
      "Epoch 886/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1284 - accuracy: 0.9903 - val_loss: 1.3107 - val_accuracy: 0.7725\n",
      "Epoch 887/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1305 - accuracy: 0.9894 - val_loss: 1.3458 - val_accuracy: 0.7662\n",
      "Epoch 888/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1341 - accuracy: 0.9871 - val_loss: 1.3355 - val_accuracy: 0.7650\n",
      "Epoch 889/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1316 - accuracy: 0.9888 - val_loss: 1.3736 - val_accuracy: 0.7600\n",
      "Epoch 890/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1303 - accuracy: 0.9892 - val_loss: 1.4137 - val_accuracy: 0.7738\n",
      "Epoch 891/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1284 - accuracy: 0.9901 - val_loss: 1.3818 - val_accuracy: 0.7800\n",
      "Epoch 892/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1296 - accuracy: 0.9894 - val_loss: 1.3656 - val_accuracy: 0.7750\n",
      "Epoch 893/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1290 - accuracy: 0.9895 - val_loss: 1.3406 - val_accuracy: 0.7650\n",
      "Epoch 894/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1287 - accuracy: 0.9891 - val_loss: 1.3360 - val_accuracy: 0.7812\n",
      "Epoch 895/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1306 - accuracy: 0.9888 - val_loss: 1.4048 - val_accuracy: 0.7750\n",
      "Epoch 896/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1305 - accuracy: 0.9893 - val_loss: 1.3979 - val_accuracy: 0.7650\n",
      "Epoch 897/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1352 - accuracy: 0.9880 - val_loss: 1.4138 - val_accuracy: 0.7738\n",
      "Epoch 898/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.1253 - accuracy: 0.9905 - val_loss: 1.4080 - val_accuracy: 0.7837\n",
      "Epoch 899/1000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1339 - accuracy: 0.9880 - val_loss: 1.3648 - val_accuracy: 0.7825\n",
      "Epoch 900/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1298 - accuracy: 0.9890 - val_loss: 1.3909 - val_accuracy: 0.7788\n",
      "Epoch 901/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1303 - accuracy: 0.9887 - val_loss: 1.4471 - val_accuracy: 0.7775\n",
      "Epoch 902/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1337 - accuracy: 0.9872 - val_loss: 1.3857 - val_accuracy: 0.7788\n",
      "Epoch 903/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1285 - accuracy: 0.9901 - val_loss: 1.4240 - val_accuracy: 0.7700\n",
      "Epoch 904/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1320 - accuracy: 0.9885 - val_loss: 1.3462 - val_accuracy: 0.7763\n",
      "Epoch 905/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1313 - accuracy: 0.9891 - val_loss: 1.3632 - val_accuracy: 0.7700\n",
      "Epoch 906/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1286 - accuracy: 0.9897 - val_loss: 1.3621 - val_accuracy: 0.7725\n",
      "Epoch 907/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1271 - accuracy: 0.9913 - val_loss: 1.3752 - val_accuracy: 0.7825\n",
      "Epoch 908/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1299 - accuracy: 0.9893 - val_loss: 1.3515 - val_accuracy: 0.7713\n",
      "Epoch 909/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1279 - accuracy: 0.9899 - val_loss: 1.3497 - val_accuracy: 0.7675\n",
      "Epoch 910/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1308 - accuracy: 0.9886 - val_loss: 1.3882 - val_accuracy: 0.7750\n",
      "Epoch 911/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1279 - accuracy: 0.9901 - val_loss: 1.3590 - val_accuracy: 0.7875\n",
      "Epoch 912/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1292 - accuracy: 0.9899 - val_loss: 1.3703 - val_accuracy: 0.7775\n",
      "Epoch 913/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1326 - accuracy: 0.9884 - val_loss: 1.3285 - val_accuracy: 0.7700\n",
      "Epoch 914/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1299 - accuracy: 0.9897 - val_loss: 1.3423 - val_accuracy: 0.7887\n",
      "Epoch 915/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1255 - accuracy: 0.9914 - val_loss: 1.3427 - val_accuracy: 0.7800\n",
      "Epoch 916/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1332 - accuracy: 0.9881 - val_loss: 1.3356 - val_accuracy: 0.7800\n",
      "Epoch 917/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1330 - accuracy: 0.9872 - val_loss: 1.3361 - val_accuracy: 0.7763\n",
      "Epoch 918/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1316 - accuracy: 0.9882 - val_loss: 1.3601 - val_accuracy: 0.7800\n",
      "Epoch 919/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1300 - accuracy: 0.9905 - val_loss: 1.3269 - val_accuracy: 0.7800\n",
      "Epoch 920/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1320 - accuracy: 0.9882 - val_loss: 1.3578 - val_accuracy: 0.7763\n",
      "Epoch 921/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1320 - accuracy: 0.9885 - val_loss: 1.3628 - val_accuracy: 0.7738\n",
      "Epoch 922/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1277 - accuracy: 0.9897 - val_loss: 1.3395 - val_accuracy: 0.7812\n",
      "Epoch 923/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1317 - accuracy: 0.9886 - val_loss: 1.3624 - val_accuracy: 0.7800\n",
      "Epoch 924/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1313 - accuracy: 0.9889 - val_loss: 1.3528 - val_accuracy: 0.7750\n",
      "Epoch 925/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1324 - accuracy: 0.9891 - val_loss: 1.3823 - val_accuracy: 0.7850\n",
      "Epoch 926/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1316 - accuracy: 0.9884 - val_loss: 1.3634 - val_accuracy: 0.7862\n",
      "Epoch 927/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1284 - accuracy: 0.9904 - val_loss: 1.3689 - val_accuracy: 0.7887\n",
      "Epoch 928/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1323 - accuracy: 0.9884 - val_loss: 1.4100 - val_accuracy: 0.7837\n",
      "Epoch 929/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1323 - accuracy: 0.9885 - val_loss: 1.3890 - val_accuracy: 0.7725\n",
      "Epoch 930/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1279 - accuracy: 0.9905 - val_loss: 1.3606 - val_accuracy: 0.7800\n",
      "Epoch 931/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1326 - accuracy: 0.9883 - val_loss: 1.3761 - val_accuracy: 0.7800\n",
      "Epoch 932/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1276 - accuracy: 0.9897 - val_loss: 1.3228 - val_accuracy: 0.7812\n",
      "Epoch 933/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1306 - accuracy: 0.9891 - val_loss: 1.3622 - val_accuracy: 0.7837\n",
      "Epoch 934/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1290 - accuracy: 0.9899 - val_loss: 1.3790 - val_accuracy: 0.7775\n",
      "Epoch 935/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1315 - accuracy: 0.9888 - val_loss: 1.3986 - val_accuracy: 0.7850\n",
      "Epoch 936/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1321 - accuracy: 0.9886 - val_loss: 1.4040 - val_accuracy: 0.7750\n",
      "Epoch 937/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1290 - accuracy: 0.9899 - val_loss: 1.3653 - val_accuracy: 0.7825\n",
      "Epoch 938/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1309 - accuracy: 0.9886 - val_loss: 1.4221 - val_accuracy: 0.7775\n",
      "Epoch 939/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1300 - accuracy: 0.9889 - val_loss: 1.3884 - val_accuracy: 0.7812\n",
      "Epoch 940/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1305 - accuracy: 0.9898 - val_loss: 1.4011 - val_accuracy: 0.7812\n",
      "Epoch 941/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1300 - accuracy: 0.9892 - val_loss: 1.3803 - val_accuracy: 0.7800\n",
      "Epoch 942/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1304 - accuracy: 0.9897 - val_loss: 1.3799 - val_accuracy: 0.7788\n",
      "Epoch 943/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1281 - accuracy: 0.9891 - val_loss: 1.4253 - val_accuracy: 0.7750\n",
      "Epoch 944/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1324 - accuracy: 0.9888 - val_loss: 1.4296 - val_accuracy: 0.7850\n",
      "Epoch 945/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1293 - accuracy: 0.9892 - val_loss: 1.4174 - val_accuracy: 0.7825\n",
      "Epoch 946/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1293 - accuracy: 0.9897 - val_loss: 1.3659 - val_accuracy: 0.7850\n",
      "Epoch 947/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1325 - accuracy: 0.9891 - val_loss: 1.4005 - val_accuracy: 0.7700\n",
      "Epoch 948/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1282 - accuracy: 0.9895 - val_loss: 1.3800 - val_accuracy: 0.7763\n",
      "Epoch 949/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1272 - accuracy: 0.9902 - val_loss: 1.4209 - val_accuracy: 0.7713\n",
      "Epoch 950/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1289 - accuracy: 0.9899 - val_loss: 1.4138 - val_accuracy: 0.7800\n",
      "Epoch 951/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1288 - accuracy: 0.9898 - val_loss: 1.3846 - val_accuracy: 0.7825\n",
      "Epoch 952/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1279 - accuracy: 0.9905 - val_loss: 1.3804 - val_accuracy: 0.7812\n",
      "Epoch 953/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1294 - accuracy: 0.9891 - val_loss: 1.4238 - val_accuracy: 0.7750\n",
      "Epoch 954/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1313 - accuracy: 0.9888 - val_loss: 1.4401 - val_accuracy: 0.7763\n",
      "Epoch 955/1000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1323 - accuracy: 0.9880 - val_loss: 1.4108 - val_accuracy: 0.7812\n",
      "Epoch 956/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1325 - accuracy: 0.9881 - val_loss: 1.3980 - val_accuracy: 0.7763\n",
      "Epoch 957/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1334 - accuracy: 0.9881 - val_loss: 1.4122 - val_accuracy: 0.7750\n",
      "Epoch 958/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1346 - accuracy: 0.9878 - val_loss: 1.3873 - val_accuracy: 0.7713\n",
      "Epoch 959/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1306 - accuracy: 0.9884 - val_loss: 1.4217 - val_accuracy: 0.7738\n",
      "Epoch 960/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1300 - accuracy: 0.9899 - val_loss: 1.3996 - val_accuracy: 0.7788\n",
      "Epoch 961/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1331 - accuracy: 0.9874 - val_loss: 1.3959 - val_accuracy: 0.7688\n",
      "Epoch 962/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1299 - accuracy: 0.9897 - val_loss: 1.4080 - val_accuracy: 0.7900\n",
      "Epoch 963/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1291 - accuracy: 0.9901 - val_loss: 1.4132 - val_accuracy: 0.7763\n",
      "Epoch 964/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1280 - accuracy: 0.9897 - val_loss: 1.4119 - val_accuracy: 0.7763\n",
      "Epoch 965/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1289 - accuracy: 0.9901 - val_loss: 1.3808 - val_accuracy: 0.7800\n",
      "Epoch 966/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1273 - accuracy: 0.9902 - val_loss: 1.3584 - val_accuracy: 0.7738\n",
      "Epoch 967/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1278 - accuracy: 0.9903 - val_loss: 1.3658 - val_accuracy: 0.7750\n",
      "Epoch 968/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.1249 - accuracy: 0.9909 - val_loss: 1.3627 - val_accuracy: 0.7850\n",
      "Epoch 969/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1280 - accuracy: 0.9906 - val_loss: 1.3778 - val_accuracy: 0.7800\n",
      "Epoch 970/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1294 - accuracy: 0.9893 - val_loss: 1.4508 - val_accuracy: 0.7763\n",
      "Epoch 971/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1265 - accuracy: 0.9905 - val_loss: 1.4131 - val_accuracy: 0.7763\n",
      "Epoch 972/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1305 - accuracy: 0.9889 - val_loss: 1.4115 - val_accuracy: 0.7738\n",
      "Epoch 973/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1280 - accuracy: 0.9896 - val_loss: 1.3862 - val_accuracy: 0.7675\n",
      "Epoch 974/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1282 - accuracy: 0.9897 - val_loss: 1.3933 - val_accuracy: 0.7700\n",
      "Epoch 975/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1293 - accuracy: 0.9898 - val_loss: 1.3745 - val_accuracy: 0.7738\n",
      "Epoch 976/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1322 - accuracy: 0.9889 - val_loss: 1.3589 - val_accuracy: 0.7825\n",
      "Epoch 977/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1316 - accuracy: 0.9888 - val_loss: 1.3730 - val_accuracy: 0.7800\n",
      "Epoch 978/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1335 - accuracy: 0.9884 - val_loss: 1.3852 - val_accuracy: 0.7738\n",
      "Epoch 979/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1291 - accuracy: 0.9899 - val_loss: 1.3847 - val_accuracy: 0.7738\n",
      "Epoch 980/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1282 - accuracy: 0.9895 - val_loss: 1.3407 - val_accuracy: 0.7763\n",
      "Epoch 981/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1313 - accuracy: 0.9888 - val_loss: 1.3866 - val_accuracy: 0.7738\n",
      "Epoch 982/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1297 - accuracy: 0.9899 - val_loss: 1.3797 - val_accuracy: 0.7713\n",
      "Epoch 983/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1284 - accuracy: 0.9899 - val_loss: 1.3420 - val_accuracy: 0.7788\n",
      "Epoch 984/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1270 - accuracy: 0.9899 - val_loss: 1.3357 - val_accuracy: 0.7788\n",
      "Epoch 985/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1289 - accuracy: 0.9899 - val_loss: 1.3751 - val_accuracy: 0.7738\n",
      "Epoch 986/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1295 - accuracy: 0.9895 - val_loss: 1.3558 - val_accuracy: 0.7750\n",
      "Epoch 987/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1323 - accuracy: 0.9877 - val_loss: 1.3829 - val_accuracy: 0.7725\n",
      "Epoch 988/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1261 - accuracy: 0.9902 - val_loss: 1.3857 - val_accuracy: 0.7662\n",
      "Epoch 989/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1268 - accuracy: 0.9905 - val_loss: 1.3936 - val_accuracy: 0.7688\n",
      "Epoch 990/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1287 - accuracy: 0.9900 - val_loss: 1.4206 - val_accuracy: 0.7688\n",
      "Epoch 991/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1279 - accuracy: 0.9909 - val_loss: 1.4284 - val_accuracy: 0.7650\n",
      "Epoch 992/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1251 - accuracy: 0.9899 - val_loss: 1.4573 - val_accuracy: 0.7600\n",
      "Epoch 993/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1272 - accuracy: 0.9903 - val_loss: 1.4253 - val_accuracy: 0.7625\n",
      "Epoch 994/1000\n",
      "19/19 [==============================] - 0s 14ms/step - loss: 0.1322 - accuracy: 0.9882 - val_loss: 1.4197 - val_accuracy: 0.7675\n",
      "Epoch 995/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1292 - accuracy: 0.9894 - val_loss: 1.4267 - val_accuracy: 0.7662\n",
      "Epoch 996/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1310 - accuracy: 0.9882 - val_loss: 1.4115 - val_accuracy: 0.7688\n",
      "Epoch 997/1000\n",
      "19/19 [==============================] - 0s 13ms/step - loss: 0.1313 - accuracy: 0.9891 - val_loss: 1.4097 - val_accuracy: 0.7725\n",
      "Epoch 998/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1322 - accuracy: 0.9884 - val_loss: 1.3945 - val_accuracy: 0.7713\n",
      "Epoch 999/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1282 - accuracy: 0.9902 - val_loss: 1.4091 - val_accuracy: 0.7800\n",
      "Epoch 1000/1000\n",
      "19/19 [==============================] - 0s 12ms/step - loss: 0.1292 - accuracy: 0.9899 - val_loss: 1.3508 - val_accuracy: 0.7800\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x29697b4c0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history = model1.fit(fine_tune_data_train, fine_tune_labels_train, epochs=EPOCHS, batch_size=800, validation_data=(fine_tune_data_test, fine_tune_labels_test), validation_batch_size=800, callbacks=[model_checkpoint_callback])\n",
    "model1.save_weights(\"./Models/Inception-ResNet-4P/effnet-4p-model-weights\")\n",
    "model1.load_weights('./Models/Inception-ResNet-4P/effnet-4p-model-weights')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e7b10c51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x28f8abe50>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.load_weights('./Models/Inception-ResNet-4P/auto-Inception-ResNet-FT-model-weight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 23,
   "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 ResNet-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": 24,
   "id": "cf7ded38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 71/475 [===>..........................] - ETA: 0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-09 17:10:54.061183: 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 3ms/step\n"
     ]
    }
   ],
   "source": [
    "train_preds = model1.predict(fine_tune_data_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f2a28269",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_pred_labels = np.argmax(train_preds, axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "768775b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1, 13, 13, ...,  3,  9,  9])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_pred_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "eff52a3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15197"
      ]
     },
     "execution_count": 27,
     "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": 28,
   "id": "8f594a33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>images</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3597</th>\n",
       "      <td>3781</td>\n",
       "      <td>./Data/train/train/3781.tif</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9198</th>\n",
       "      <td>9682</td>\n",
       "      <td>./Data/train/train/9682.tif</td>\n",
       "      <td>12</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id                        images  label\n",
       "3597    3781   ./Data/train/train/3781.tif      0\n",
       "9198    9682   ./Data/train/train/9682.tif     12\n",
       "12255  12896  ./Data/train/train/12896.tif      9"
      ]
     },
     "execution_count": 28,
     "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": 29,
   "id": "2ea8f949",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>fscore</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998978</td>\n",
       "      <td>0.999488</td>\n",
       "      <td>978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000000</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>925</td>\n",
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       "    <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",
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       "    <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>0.997814</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998906</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>1.000000</td>\n",
       "      <td>0.998976</td>\n",
       "      <td>0.999488</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    0.997814  1.000000  0.998906      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   1.000000  0.998976  0.999488      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": 29,
     "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": 30,
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "49233509",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_pred_labels = np.argmax(test_preds, axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8f5cba39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11,  8,  6,  7,  1,  6, 13, 12, 14,  1,  6, 15,  7, 14, 13, 11,  0,\n",
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       "        7,  8,  4, 14, 11,  9,  6, 12, 14,  2,  5, 14,  5, 15, 13,  4,  0,\n",
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       "       15,  8,  6,  5,  2,  3,  6, 11,  8, 14,  6,  9,  1,  9, 11,  0,  0,\n",
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       "        9, 11,  6, 11,  5,  8, 10, 14, 14,  9,  5, 15,  8, 13,  8,  6, 12,\n",
       "        9,  5,  0, 15, 14,  5,  3,  2, 12,  7, 15, 11, 10,  3, 13,  1, 15,\n",
       "        1, 11,  0,  4,  4,  9, 11, 15, 14, 13,  4,  8,  5, 12, 13,  4, 15,\n",
       "        7,  0,  1, 14, 13,  7,  6, 12,  8,  7,  5, 15,  5,  9,  4,  1, 14,\n",
       "        1, 13,  6,  6,  3,  7,  7,  5,  5, 12,  6,  8,  0,  4,  7,  7,  0,\n",
       "       12,  6, 12,  0,  3, 11,  2,  0,  8,  4, 13,  1, 12,  7, 14, 15,  4,\n",
       "        1, 12, 10,  7,  2, 13,  8,  8, 11,  6,  4,  2, 10, 13,  7,  6,  3,\n",
       "       13,  3,  2, 14,  0,  8, 12, 15,  8, 13, 13, 14,  4, 10,  2,  5, 14,\n",
       "       12, 13, 13,  4, 12, 15, 13, 15,  8, 11,  1,  8,  4,  9,  2,  8,  0,\n",
       "        1,  2, 14,  0,  0,  8,  9, 11,  0,  8,  6,  7,  4, 11, 13, 13,  3,\n",
       "        7,  7, 13,  8, 12,  2, 12, 15,  7, 10, 14,  0, 12,  6, 14,  0, 10,\n",
       "        5,  3, 12, 13,  5,  7,  2,  8,  5, 13,  2,  9,  8, 14, 12, 14, 12,\n",
       "        0,  5,  8, 12,  4, 11,  0,  0,  9,  9,  5, 11, 10,  2,  2, 11, 15,\n",
       "        6])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pred_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ee078822",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "628"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(test_pred_labels == test_labels['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c3db9b5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>images</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41</td>\n",
       "      <td>./Data/train/train/41.tif</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65</td>\n",
       "      <td>./Data/train/train/65.tif</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>67</td>\n",
       "      <td>./Data/train/train/67.tif</td>\n",
       "      <td>9</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>14</th>\n",
       "      <td>203</td>\n",
       "      <td>./Data/train/train/203.tif</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>779</th>\n",
       "      <td>15568</td>\n",
       "      <td>./Data/train/train/15568.tif</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>782</th>\n",
       "      <td>15582</td>\n",
       "      <td>./Data/train/train/15582.tif</td>\n",
       "      <td>3</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>793</th>\n",
       "      <td>15762</td>\n",
       "      <td>./Data/train/train/15762.tif</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>172 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                        images  label\n",
       "3       41     ./Data/train/train/41.tif      1\n",
       "4       65     ./Data/train/train/65.tif     14\n",
       "5       67     ./Data/train/train/67.tif      9\n",
       "7      100    ./Data/train/train/100.tif     10\n",
       "14     203    ./Data/train/train/203.tif      1\n",
       "..     ...                           ...    ...\n",
       "779  15568  ./Data/train/train/15568.tif      9\n",
       "782  15582  ./Data/train/train/15582.tif      3\n",
       "785  15621  ./Data/train/train/15621.tif     15\n",
       "788  15687  ./Data/train/train/15687.tif     12\n",
       "793  15762  ./Data/train/train/15762.tif     13\n",
       "\n",
       "[172 rows x 3 columns]"
      ]
     },
     "execution_count": 34,
     "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": 35,
   "id": "b538b721",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0.660714</td>\n",
       "      <td>0.685185</td>\n",
       "      <td>0.672727</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.613636</td>\n",
       "      <td>0.627907</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.906977</td>\n",
       "      <td>0.917647</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.872340</td>\n",
       "      <td>0.891304</td>\n",
       "      <td>0.881720</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.829268</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.883117</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.653061</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.646465</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.860465</td>\n",
       "      <td>0.804348</td>\n",
       "      <td>0.831461</td>\n",
       "      <td>46</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.807692</td>\n",
       "      <td>0.831683</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.843750</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.915254</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.883721</td>\n",
       "      <td>0.808511</td>\n",
       "      <td>0.844444</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.770833</td>\n",
       "      <td>0.637931</td>\n",
       "      <td>0.698113</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.763636</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.756757</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.660714</td>\n",
       "      <td>0.860465</td>\n",
       "      <td>0.747475</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.790323</td>\n",
       "      <td>0.765625</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.943396</td>\n",
       "      <td>0.781250</td>\n",
       "      <td>0.854701</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.744186</td>\n",
       "      <td>0.688172</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    precision    recall    fscore  support\n",
       "0    0.660714  0.685185  0.672727       54\n",
       "1    0.642857  0.613636  0.627907       44\n",
       "2    0.928571  0.906977  0.917647       43\n",
       "3    0.872340  0.891304  0.881720       46\n",
       "4    0.829268  0.944444  0.883117       36\n",
       "5    0.653061  0.640000  0.646465       50\n",
       "6    0.860465  0.804348  0.831461       46\n",
       "7    0.857143  0.807692  0.831683       52\n",
       "8    0.843750  1.000000  0.915254       54\n",
       "9    0.883721  0.808511  0.844444       47\n",
       "10   0.770833  0.637931  0.698113       58\n",
       "11   0.763636  0.750000  0.756757       56\n",
       "12   0.660714  0.860465  0.747475       43\n",
       "13   0.790323  0.765625  0.777778       64\n",
       "14   0.943396  0.781250  0.854701       64\n",
       "15   0.640000  0.744186  0.688172       43"
      ]
     },
     "execution_count": 35,
     "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": 36,
   "id": "ed4db76c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7859638237161761"
      ]
     },
     "execution_count": 36,
     "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": 37,
   "id": "4b085c0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>images</th>\n",
       "      <th>id</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>850</th>\n",
       "      <td>./Data/validation/validation/17801.tif</td>\n",
       "      <td>17801</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>./Data/validation/validation/17802.tif</td>\n",
       "      <td>17802</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>852</th>\n",
       "      <td>./Data/validation/validation/17803.tif</td>\n",
       "      <td>17803</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>./Data/validation/validation/17804.tif</td>\n",
       "      <td>17804</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>./Data/validation/validation/17805.tif</td>\n",
       "      <td>17805</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>./Data/validation/validation/18696.tif</td>\n",
       "      <td>18696</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>./Data/validation/validation/18697.tif</td>\n",
       "      <td>18697</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>./Data/validation/validation/18698.tif</td>\n",
       "      <td>18698</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>./Data/validation/validation/18699.tif</td>\n",
       "      <td>18699</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891</th>\n",
       "      <td>./Data/validation/validation/18700.tif</td>\n",
       "      <td>18700</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>900 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     images     id  label\n",
       "850  ./Data/validation/validation/17801.tif  17801     -1\n",
       "898  ./Data/validation/validation/17802.tif  17802     -1\n",
       "852  ./Data/validation/validation/17803.tif  17803     -1\n",
       "798  ./Data/validation/validation/17804.tif  17804     -1\n",
       "750  ./Data/validation/validation/17805.tif  17805     -1\n",
       "..                                      ...    ...    ...\n",
       "246  ./Data/validation/validation/18696.tif  18696     -1\n",
       "211  ./Data/validation/validation/18697.tif  18697     -1\n",
       "760  ./Data/validation/validation/18698.tif  18698     -1\n",
       "787  ./Data/validation/validation/18699.tif  18699     -1\n",
       "891  ./Data/validation/validation/18700.tif  18700     -1\n",
       "\n",
       "[900 rows x 3 columns]"
      ]
     },
     "execution_count": 37,
     "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": 38,
   "id": "53635253",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(900, 5, 1536)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fine_tune_data_validation = np.load('./Data/Inception-ResNet-4P-validation-precompute/whole_validation_data_precomp.npy')\n",
    "fine_tune_data_validation.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "db7d1256",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29/29 [==============================] - 0s 3ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-09 17:10:55.669809: 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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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": 41,
   "id": "e7ca9d19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17801</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17802</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17803</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17804</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17805</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>18696</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>18697</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>18698</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>18699</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>18700</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>900 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  label\n",
       "0    17801      4\n",
       "1    17802      6\n",
       "2    17803      4\n",
       "3    17804      8\n",
       "4    17805      3\n",
       "..     ...    ...\n",
       "895  18696      7\n",
       "896  18697     12\n",
       "897  18698      8\n",
       "898  18699      3\n",
       "899  18700      4\n",
       "\n",
       "[900 rows x 2 columns]"
      ]
     },
     "execution_count": 41,
     "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": 42,
   "id": "6eea7d9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_data.to_csv('./Submissions/submission-inception-resnet-4p.csv', index=False)"
   ]
  }
 ],
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