RoI-Embeddings-InceptionResNet-VGG-ResNet.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",
    "from tqdm import tqdm\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.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": "29190fd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_labels = list(set(labels['label']))\n",
    "class_names = [\n",
    "    'letter', 'form', 'email', 'handwritten', 'advertisement', 'scientific report', 'scientific publication',\n",
    "    'specification', 'file folder', 'news article', 'budget', 'invoice', 'presentation', 'questionnaire', 'resume',\n",
    "    'memo'\n",
    "]\n",
    "label_names = pd.DataFrame({\n",
    "    'labels': class_labels,\n",
    "    'names': class_names\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b49810a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels = pd.read_csv('./Data/train_labels_1.csv')\n",
    "test_labels = pd.read_csv('./Data/test_labels_1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ca081f2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "roi_info = np.load('./Data/ROI-Extractions/train/train_roi_viz.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a77be477",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16000, 100, 6)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roi_info.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2e251c1",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "bb31ae4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 50\n",
    "checkpnt_each = 100\n",
    "assert checkpnt_each % BATCH_SIZE == 0\n",
    "checkpnt_i = checkpnt_each // BATCH_SIZE\n",
    "FEATURE_DIM = 2048"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9fb8348d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# roi_count = [np.sum([roi[-1]==1 for roi in doc]) for doc in roi_info] # RoIs per document Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fbc5e332",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16000, 100, 2048)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fine_tune_data = list()\n",
    "# for i in range(16000 // (BATCH_SIZE*checkpnt_i)):\n",
    "#     k = 0\n",
    "#     batch_data = np.load(f'./Data/ResNet-kP-train-precompute/train_data_precomp-{i+1}.npy')\n",
    "#     batch_roi = roi_count[(i*(BATCH_SIZE*checkpnt_i)):((i+1)*(BATCH_SIZE*checkpnt_i))]\n",
    "#     for j in range(BATCH_SIZE*checkpnt_i):\n",
    "#         fine_tune_data.append(np.concatenate([batch_data[k:(k+batch_roi[j])], np.zeros((100-batch_roi[j], FEATURE_DIM))])[np.newaxis,:])\n",
    "#         k += batch_roi[j]\n",
    "# fine_tune_data = np.concatenate(fine_tune_data)\n",
    "# fine_tune_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9589ddb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.save(f'./Data/ResNet-kP-train-precompute/whole_train_data_precomp.npy', fine_tune_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2f0d6808",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16000, 100, 2048)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fine_tune_data = np.load(f'./Data/ResNet-kP-train-precompute/whole_train_data_precomp.npy')\n",
    "# fine_tune_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "63c7ecb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16000, 2048)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fine_tune_data = np.squeeze(np.mean(fine_tune_data, axis=1, keepdims=True))\n",
    "# fine_tune_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "12bdde76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16000, 10240)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fine_tune_data_4P = np.load('./Data/ResNet-4P-train-precompute/whole_train_data_precomp.npy')\n",
    "# fine_tune_data_4P = np.reshape(fine_tune_data_4P, (16000, -1))\n",
    "# fine_tune_data_4P.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6f8bfc7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16000, 12288)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fine_tune_data = np.concatenate([fine_tune_data, fine_tune_data_4P], axis=-1)\n",
    "# fine_tune_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "58c0f4b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.save(f'./Data/ResNet-kP-train-precompute/whole_train_data_precomp_roi_n_4p.npy', fine_tune_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e1b35850",
   "metadata": {},
   "outputs": [],
   "source": [
    "fine_tune_data = np.load(f'./Data/ResNet-kP-train-precompute/whole_train_data_precomp_roi_n_4p.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "945aad41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16000, 12288)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fine_tune_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "568ffcb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16000,)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fine_tune_labels = labels['label'].to_numpy()\n",
    "fine_tune_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6e228ab3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15200, 12288)"
      ]
     },
     "execution_count": 31,
     "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": 32,
   "id": "bcfdf149",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15200,)"
      ]
     },
     "execution_count": 32,
     "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": 33,
   "id": "ccfac9c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(800, 12288)"
      ]
     },
     "execution_count": 33,
     "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": 34,
   "id": "132bb5a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(800,)"
      ]
     },
     "execution_count": 34,
     "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": "48661622",
   "metadata": {},
   "source": [
    "### Validation Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bc88528c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>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",
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       "    <tr>\n",
       "      <th>852</th>\n",
       "      <td>./Data/validation/validation/17803.tif</td>\n",
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       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>./Data/validation/validation/17804.tif</td>\n",
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       "      <th>750</th>\n",
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       "      <th>...</th>\n",
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       "    </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": 35,
     "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": 36,
   "id": "bf4cf1be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(900, 100, 6)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roi_info_validation = np.load('./Data/ROI-Extractions/validation/validation_roi_viz.npy')\n",
    "roi_info_validation.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "91896253",
   "metadata": {},
   "outputs": [],
   "source": [
    "# roi_count_validation = [np.sum([roi[-1]==1 for roi in doc]) for doc in roi_info_validation] # RoIs per document Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cf514e19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(900, 100, 2048)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fine_tune_data_validation = list()\n",
    "# for i in range(900 // (BATCH_SIZE*checkpnt_i)):\n",
    "#     k = 0\n",
    "#     batch_data = np.load(f'./Data/ResNet-kP-validation-precompute/validation_data_precomp-{i+1}.npy')\n",
    "#     batch_roi = roi_count_validation[(i*(BATCH_SIZE*checkpnt_i)):((i+1)*(BATCH_SIZE*checkpnt_i))]\n",
    "#     for j in range(BATCH_SIZE*checkpnt_i):\n",
    "#         fine_tune_data_validation.append(np.concatenate([batch_data[k:(k+batch_roi[j])], np.zeros((100-batch_roi[j], 2048))])[np.newaxis,:])\n",
    "#         k += batch_roi[j]\n",
    "# fine_tune_data_validation = np.concatenate(fine_tune_data_validation)\n",
    "# fine_tune_data_validation.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f5be8342",
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.save(f'./Data/ResNet-kP-validation-precompute/whole_validation_data_precomp.npy', fine_tune_data_validation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "38cb6238",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(900, 100, 2048)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fine_tune_data_validation = np.load(f'./Data/ResNet-kP-validation-precompute/whole_validation_data_precomp.npy')\n",
    "# fine_tune_data_validation.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5ef3e336",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(900, 2048)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fine_tune_data_validation = np.squeeze(np.mean(fine_tune_data_validation, axis=1, keepdims=True))\n",
    "# fine_tune_data_validation.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e6d23ba7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(900, 10240)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fine_tune_data_4P_validation = np.load('./Data/ResNet-4P-validation-precompute/whole_validation_data_precomp.npy')\n",
    "# fine_tune_data_4P_validation = np.reshape(fine_tune_data_4P_validation, (900, -1))\n",
    "# fine_tune_data_4P_validation.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d4b9fc5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(900, 12288)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fine_tune_data_validation = np.concatenate([fine_tune_data_validation, fine_tune_data_4P_validation], axis=-1)\n",
    "# fine_tune_data_validation.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0fd55b80",
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.save(f'./Data/ResNet-kP-validation-precompute/whole_validation_data_precomp_roi_n_4p.npy', fine_tune_data_validation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "23851219",
   "metadata": {},
   "outputs": [],
   "source": [
    "fine_tune_data_validation = np.load(f'./Data/ResNet-kP-validation-precompute/whole_validation_data_precomp_roi_n_4p.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "53ed0146",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(900, 12288)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fine_tune_data_validation.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": 56,
   "id": "96a2bdfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ftmodel():\n",
    "    inputs = Input(shape=(6*FEATURE_DIM))\n",
    "    x = Flatten()(inputs)\n",
    "    x = BatchNormalization()(x)\n",
    "\n",
    "    top_dropout_rate = 0.3\n",
    "    # x = Dropout(top_dropout_rate, name='top_dropout_1')(x)\n",
    "    # x = Dense(1024, name='top_dense_1', kernel_initializer=\"he_normal\", kernel_regularizer=l2(5e-5), activation='relu')(x)\n",
    "    # x = BatchNormalization()(x)\n",
    "    x = Dropout(top_dropout_rate, name='top_dropout_2')(x)\n",
    "    x = Dense(512, name='top_dense_2', kernel_initializer=\"he_normal\", kernel_regularizer=l2(5e-5), activation='relu')(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    outputs = Dense(16, activation='softmax', name='pred', kernel_regularizer=l2(5e-5))(x)\n",
    "\n",
    "    model1 = tf.keras.Model(inputs, outputs, name='ResNet-FineTune')\n",
    "    return model1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c6574006",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"ResNet-FineTune\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_2 (InputLayer)        [(None, 12288)]           0         \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 12288)             0         \n",
      "                                                                 \n",
      " batch_normalization_1 (Batc  (None, 12288)            49152     \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " top_dropout_2 (Dropout)     (None, 12288)             0         \n",
      "                                                                 \n",
      " top_dense_2 (Dense)         (None, 512)               6291968   \n",
      "                                                                 \n",
      " batch_normalization_2 (Batc  (None, 512)              2048      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " pred (Dense)                (None, 16)                8208      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 6,351,376\n",
      "Trainable params: 6,325,776\n",
      "Non-trainable params: 25,600\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model1 = ftmodel()\n",
    "model1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e56505e2",
   "metadata": {},
   "source": [
    "### Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70b7e206",
   "metadata": {},
   "source": [
    "The fine-tuning will be done on the ResNet's $2048D$ representation vectors for $5$ pieces of the image and the averaged ROI representation vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "96524578",
   "metadata": {},
   "outputs": [],
   "source": [
    "model1.compile(loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'], optimizer = Adam(learning_rate = 0.0003))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "20df8897",
   "metadata": {},
   "outputs": [],
   "source": [
    "EPOCHS = 1000\n",
    "checkpoint_filepaths = ['./Models/ResNet-kP/auto-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": 60,
   "id": "03354278",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-08 15:51:10.182097: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
      "2022-10-08 15:51:10.435493: 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": [
      "17/19 [=========================>....] - ETA: 0s - loss: 1.8058 - accuracy: 0.5202"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-08 15:51:11.131918: 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 41ms/step - loss: 1.7605 - accuracy: 0.5316 - val_loss: 1.8321 - val_accuracy: 0.4737\n",
      "Epoch 2/1000\n",
      "19/19 [==============================] - 0s 26ms/step - loss: 0.9927 - accuracy: 0.7281 - val_loss: 1.6909 - val_accuracy: 0.5813\n",
      "Epoch 3/1000\n",
      "19/19 [==============================] - 1s 31ms/step - loss: 0.7502 - accuracy: 0.7974 - val_loss: 1.6302 - val_accuracy: 0.6187\n",
      "Epoch 4/1000\n",
      "19/19 [==============================] - 1s 30ms/step - loss: 0.6015 - accuracy: 0.8418 - val_loss: 1.5687 - val_accuracy: 0.6350\n",
      "Epoch 5/1000\n",
      "19/19 [==============================] - 1s 28ms/step - loss: 0.4836 - accuracy: 0.8775 - val_loss: 1.5396 - val_accuracy: 0.6350\n",
      "Epoch 6/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.3912 - accuracy: 0.9104 - val_loss: 1.4995 - val_accuracy: 0.6413\n",
      "Epoch 7/1000\n",
      "19/19 [==============================] - 1s 28ms/step - loss: 0.3237 - accuracy: 0.9305 - val_loss: 1.4629 - val_accuracy: 0.6250\n",
      "Epoch 8/1000\n",
      "19/19 [==============================] - 1s 28ms/step - loss: 0.2718 - accuracy: 0.9453 - val_loss: 1.4380 - val_accuracy: 0.6325\n",
      "Epoch 9/1000\n",
      "19/19 [==============================] - 1s 28ms/step - loss: 0.2336 - accuracy: 0.9591 - val_loss: 1.3883 - val_accuracy: 0.6488\n",
      "Epoch 10/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.2042 - accuracy: 0.9659 - val_loss: 1.3764 - val_accuracy: 0.6500\n",
      "Epoch 11/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.1795 - accuracy: 0.9741 - val_loss: 1.3653 - val_accuracy: 0.6375\n",
      "Epoch 12/1000\n",
      "19/19 [==============================] - 0s 27ms/step - loss: 0.1576 - accuracy: 0.9789 - val_loss: 1.3419 - val_accuracy: 0.6375\n",
      "Epoch 13/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.1402 - accuracy: 0.9848 - val_loss: 1.3485 - val_accuracy: 0.6425\n",
      "Epoch 14/1000\n",
      "19/19 [==============================] - 1s 28ms/step - loss: 0.1270 - accuracy: 0.9889 - val_loss: 1.3152 - val_accuracy: 0.6550\n",
      "Epoch 15/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.1185 - accuracy: 0.9894 - val_loss: 1.2783 - val_accuracy: 0.6587\n",
      "Epoch 16/1000\n",
      "19/19 [==============================] - 1s 28ms/step - loss: 0.1102 - accuracy: 0.9910 - val_loss: 1.2316 - val_accuracy: 0.6737\n",
      "Epoch 17/1000\n",
      "19/19 [==============================] - 1s 29ms/step - loss: 0.1014 - accuracy: 0.9939 - val_loss: 1.1912 - val_accuracy: 0.6775\n",
      "Epoch 18/1000\n",
      "19/19 [==============================] - 1s 29ms/step - loss: 0.0939 - accuracy: 0.9954 - val_loss: 1.1134 - val_accuracy: 0.7000\n",
      "Epoch 19/1000\n",
      "19/19 [==============================] - 1s 29ms/step - loss: 0.0911 - accuracy: 0.9955 - val_loss: 1.1082 - val_accuracy: 0.6962\n",
      "Epoch 20/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0876 - accuracy: 0.9963 - val_loss: 1.0609 - val_accuracy: 0.7050\n",
      "Epoch 21/1000\n",
      "19/19 [==============================] - 1s 28ms/step - loss: 0.0828 - accuracy: 0.9967 - val_loss: 1.0076 - val_accuracy: 0.7225\n",
      "Epoch 22/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0812 - accuracy: 0.9963 - val_loss: 0.9976 - val_accuracy: 0.7237\n",
      "Epoch 23/1000\n",
      "19/19 [==============================] - 0s 27ms/step - loss: 0.0785 - accuracy: 0.9968 - val_loss: 0.9693 - val_accuracy: 0.7350\n",
      "Epoch 24/1000\n",
      "19/19 [==============================] - 1s 28ms/step - loss: 0.0760 - accuracy: 0.9966 - val_loss: 0.9637 - val_accuracy: 0.7225\n",
      "Epoch 25/1000\n",
      "19/19 [==============================] - 1s 28ms/step - loss: 0.0733 - accuracy: 0.9973 - val_loss: 0.9291 - val_accuracy: 0.7400\n",
      "Epoch 26/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0714 - accuracy: 0.9980 - val_loss: 0.9193 - val_accuracy: 0.7500\n",
      "Epoch 27/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0690 - accuracy: 0.9983 - val_loss: 0.9269 - val_accuracy: 0.7537\n",
      "Epoch 28/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0677 - accuracy: 0.9986 - val_loss: 0.9005 - val_accuracy: 0.7525\n",
      "Epoch 29/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0673 - accuracy: 0.9979 - val_loss: 0.9094 - val_accuracy: 0.7587\n",
      "Epoch 30/1000\n",
      "19/19 [==============================] - 1s 28ms/step - loss: 0.0656 - accuracy: 0.9988 - val_loss: 0.9307 - val_accuracy: 0.7625\n",
      "Epoch 31/1000\n",
      "19/19 [==============================] - 1s 29ms/step - loss: 0.0637 - accuracy: 0.9989 - val_loss: 0.9123 - val_accuracy: 0.7625\n",
      "Epoch 32/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0623 - accuracy: 0.9991 - val_loss: 0.9269 - val_accuracy: 0.7587\n",
      "Epoch 33/1000\n",
      "19/19 [==============================] - 0s 26ms/step - loss: 0.0614 - accuracy: 0.9991 - val_loss: 0.9248 - val_accuracy: 0.7575\n",
      "Epoch 34/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0610 - accuracy: 0.9991 - val_loss: 0.9290 - val_accuracy: 0.7612\n",
      "Epoch 35/1000\n",
      "19/19 [==============================] - 0s 26ms/step - loss: 0.0603 - accuracy: 0.9987 - val_loss: 0.9353 - val_accuracy: 0.7688\n",
      "Epoch 36/1000\n",
      "19/19 [==============================] - 1s 29ms/step - loss: 0.0597 - accuracy: 0.9989 - val_loss: 0.9532 - val_accuracy: 0.7575\n",
      "Epoch 37/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0580 - accuracy: 0.9989 - val_loss: 0.9380 - val_accuracy: 0.7738\n",
      "Epoch 38/1000\n",
      "19/19 [==============================] - 0s 27ms/step - loss: 0.0577 - accuracy: 0.9990 - val_loss: 0.9728 - val_accuracy: 0.7525\n",
      "Epoch 39/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0574 - accuracy: 0.9993 - val_loss: 0.9746 - val_accuracy: 0.7625\n",
      "Epoch 40/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0552 - accuracy: 0.9997 - val_loss: 0.9958 - val_accuracy: 0.7625\n",
      "Epoch 41/1000\n",
      "19/19 [==============================] - 0s 26ms/step - loss: 0.0547 - accuracy: 0.9993 - val_loss: 0.9981 - val_accuracy: 0.7575\n",
      "Epoch 42/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0550 - accuracy: 0.9991 - val_loss: 0.9641 - val_accuracy: 0.7725\n",
      "Epoch 43/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0556 - accuracy: 0.9990 - val_loss: 0.9624 - val_accuracy: 0.7625\n",
      "Epoch 44/1000\n",
      "19/19 [==============================] - 0s 26ms/step - loss: 0.0540 - accuracy: 0.9994 - val_loss: 0.9930 - val_accuracy: 0.7700\n",
      "Epoch 45/1000\n",
      "19/19 [==============================] - 0s 27ms/step - loss: 0.0524 - accuracy: 0.9997 - val_loss: 0.9730 - val_accuracy: 0.7637\n",
      "Epoch 46/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0527 - accuracy: 0.9993 - val_loss: 0.9857 - val_accuracy: 0.7662\n",
      "Epoch 47/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0520 - accuracy: 0.9992 - val_loss: 0.9561 - val_accuracy: 0.7688\n",
      "Epoch 48/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0516 - accuracy: 0.9995 - val_loss: 0.9844 - val_accuracy: 0.7800\n",
      "Epoch 49/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0525 - accuracy: 0.9991 - val_loss: 0.9940 - val_accuracy: 0.7763\n",
      "Epoch 50/1000\n",
      "19/19 [==============================] - 1s 29ms/step - loss: 0.0516 - accuracy: 0.9991 - val_loss: 0.9728 - val_accuracy: 0.7800\n",
      "Epoch 51/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0510 - accuracy: 0.9992 - val_loss: 1.0020 - val_accuracy: 0.7662\n",
      "Epoch 52/1000\n",
      "19/19 [==============================] - 1s 28ms/step - loss: 0.0509 - accuracy: 0.9989 - val_loss: 0.9832 - val_accuracy: 0.7688\n",
      "Epoch 53/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0520 - accuracy: 0.9991 - val_loss: 1.0310 - val_accuracy: 0.7688\n",
      "Epoch 54/1000\n",
      "19/19 [==============================] - 1s 28ms/step - loss: 0.0508 - accuracy: 0.9993 - val_loss: 1.0221 - val_accuracy: 0.7688\n",
      "Epoch 55/1000\n",
      "19/19 [==============================] - 1s 28ms/step - loss: 0.0500 - accuracy: 0.9994 - val_loss: 1.0189 - val_accuracy: 0.7662\n",
      "Epoch 56/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0487 - accuracy: 0.9993 - val_loss: 1.0240 - val_accuracy: 0.7738\n",
      "Epoch 57/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0486 - accuracy: 0.9995 - val_loss: 1.0099 - val_accuracy: 0.7738\n",
      "Epoch 58/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 1s 28ms/step - loss: 0.0477 - accuracy: 0.9993 - val_loss: 1.0126 - val_accuracy: 0.7713\n",
      "Epoch 59/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0481 - accuracy: 0.9995 - val_loss: 0.9944 - val_accuracy: 0.7750\n",
      "Epoch 60/1000\n",
      "19/19 [==============================] - 1s 28ms/step - loss: 0.0474 - accuracy: 0.9993 - val_loss: 1.0144 - val_accuracy: 0.7688\n",
      "Epoch 61/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0473 - accuracy: 0.9993 - val_loss: 1.0097 - val_accuracy: 0.7700\n",
      "Epoch 62/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0461 - accuracy: 0.9995 - val_loss: 1.0306 - val_accuracy: 0.7612\n",
      "Epoch 63/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0468 - accuracy: 0.9996 - val_loss: 0.9986 - val_accuracy: 0.7713\n",
      "Epoch 64/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0460 - accuracy: 0.9993 - val_loss: 0.9876 - val_accuracy: 0.7637\n",
      "Epoch 65/1000\n",
      "19/19 [==============================] - 1s 28ms/step - loss: 0.0459 - accuracy: 0.9995 - val_loss: 0.9912 - val_accuracy: 0.7637\n",
      "Epoch 66/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0458 - accuracy: 0.9992 - val_loss: 1.0067 - val_accuracy: 0.7688\n",
      "Epoch 67/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0466 - accuracy: 0.9991 - val_loss: 1.0235 - val_accuracy: 0.7625\n",
      "Epoch 68/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0461 - accuracy: 0.9994 - val_loss: 1.0056 - val_accuracy: 0.7738\n",
      "Epoch 69/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0461 - accuracy: 0.9989 - val_loss: 1.0044 - val_accuracy: 0.7750\n",
      "Epoch 70/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0453 - accuracy: 0.9995 - val_loss: 1.0259 - val_accuracy: 0.7662\n",
      "Epoch 71/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0456 - accuracy: 0.9993 - val_loss: 0.9958 - val_accuracy: 0.7713\n",
      "Epoch 72/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0452 - accuracy: 0.9992 - val_loss: 1.0243 - val_accuracy: 0.7725\n",
      "Epoch 73/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0459 - accuracy: 0.9989 - val_loss: 1.0188 - val_accuracy: 0.7587\n",
      "Epoch 74/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0457 - accuracy: 0.9995 - val_loss: 1.0453 - val_accuracy: 0.7650\n",
      "Epoch 75/1000\n",
      "19/19 [==============================] - 1s 28ms/step - loss: 0.0444 - accuracy: 0.9993 - val_loss: 1.0400 - val_accuracy: 0.7688\n",
      "Epoch 76/1000\n",
      "19/19 [==============================] - 1s 28ms/step - loss: 0.0434 - accuracy: 0.9997 - val_loss: 1.0004 - val_accuracy: 0.7750\n",
      "Epoch 77/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0440 - accuracy: 0.9993 - val_loss: 1.0110 - val_accuracy: 0.7788\n",
      "Epoch 78/1000\n",
      "19/19 [==============================] - 0s 27ms/step - loss: 0.0432 - accuracy: 0.9994 - val_loss: 1.0150 - val_accuracy: 0.7688\n",
      "Epoch 79/1000\n",
      "19/19 [==============================] - 1s 28ms/step - loss: 0.0431 - accuracy: 0.9995 - val_loss: 1.0396 - val_accuracy: 0.7725\n",
      "Epoch 80/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0427 - accuracy: 0.9996 - val_loss: 1.0412 - val_accuracy: 0.7662\n",
      "Epoch 81/1000\n",
      "19/19 [==============================] - 1s 30ms/step - loss: 0.0416 - accuracy: 0.9995 - val_loss: 1.0376 - val_accuracy: 0.7725\n",
      "Epoch 82/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0429 - accuracy: 0.9994 - val_loss: 1.0121 - val_accuracy: 0.7750\n",
      "Epoch 83/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0423 - accuracy: 0.9990 - val_loss: 1.0382 - val_accuracy: 0.7750\n",
      "Epoch 84/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0433 - accuracy: 0.9991 - val_loss: 1.0457 - val_accuracy: 0.7750\n",
      "Epoch 85/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0445 - accuracy: 0.9989 - val_loss: 1.0360 - val_accuracy: 0.7763\n",
      "Epoch 86/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0426 - accuracy: 0.9991 - val_loss: 1.0272 - val_accuracy: 0.7800\n",
      "Epoch 87/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0421 - accuracy: 0.9989 - val_loss: 1.0194 - val_accuracy: 0.7763\n",
      "Epoch 88/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0425 - accuracy: 0.9990 - val_loss: 1.0369 - val_accuracy: 0.7825\n",
      "Epoch 89/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0455 - accuracy: 0.9988 - val_loss: 1.0529 - val_accuracy: 0.7725\n",
      "Epoch 90/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0456 - accuracy: 0.9988 - val_loss: 1.1398 - val_accuracy: 0.7475\n",
      "Epoch 91/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0446 - accuracy: 0.9987 - val_loss: 1.0901 - val_accuracy: 0.7575\n",
      "Epoch 92/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0451 - accuracy: 0.9984 - val_loss: 1.0683 - val_accuracy: 0.7612\n",
      "Epoch 93/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0432 - accuracy: 0.9993 - val_loss: 1.0602 - val_accuracy: 0.7662\n",
      "Epoch 94/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0425 - accuracy: 0.9991 - val_loss: 1.0318 - val_accuracy: 0.7662\n",
      "Epoch 95/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0417 - accuracy: 0.9989 - val_loss: 1.0474 - val_accuracy: 0.7750\n",
      "Epoch 96/1000\n",
      "19/19 [==============================] - 1s 28ms/step - loss: 0.0408 - accuracy: 0.9994 - val_loss: 1.0418 - val_accuracy: 0.7750\n",
      "Epoch 97/1000\n",
      "19/19 [==============================] - 1s 28ms/step - loss: 0.0404 - accuracy: 0.9993 - val_loss: 1.0812 - val_accuracy: 0.7775\n",
      "Epoch 98/1000\n",
      "19/19 [==============================] - 1s 28ms/step - loss: 0.0400 - accuracy: 0.9993 - val_loss: 1.0807 - val_accuracy: 0.7763\n",
      "Epoch 99/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0406 - accuracy: 0.9992 - val_loss: 1.0682 - val_accuracy: 0.7688\n",
      "Epoch 100/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0406 - accuracy: 0.9990 - val_loss: 1.0989 - val_accuracy: 0.7662\n",
      "Epoch 101/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0406 - accuracy: 0.9990 - val_loss: 1.0726 - val_accuracy: 0.7775\n",
      "Epoch 102/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0409 - accuracy: 0.9991 - val_loss: 1.0831 - val_accuracy: 0.7650\n",
      "Epoch 103/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0399 - accuracy: 0.9991 - val_loss: 1.0994 - val_accuracy: 0.7725\n",
      "Epoch 104/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0401 - accuracy: 0.9992 - val_loss: 1.0901 - val_accuracy: 0.7800\n",
      "Epoch 105/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0409 - accuracy: 0.9993 - val_loss: 1.1331 - val_accuracy: 0.7612\n",
      "Epoch 106/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0408 - accuracy: 0.9992 - val_loss: 1.0971 - val_accuracy: 0.7700\n",
      "Epoch 107/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0407 - accuracy: 0.9991 - val_loss: 1.0733 - val_accuracy: 0.7725\n",
      "Epoch 108/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0404 - accuracy: 0.9990 - val_loss: 1.0615 - val_accuracy: 0.7738\n",
      "Epoch 109/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0393 - accuracy: 0.9992 - val_loss: 1.0771 - val_accuracy: 0.7688\n",
      "Epoch 110/1000\n",
      "19/19 [==============================] - 1s 28ms/step - loss: 0.0384 - accuracy: 0.9995 - val_loss: 1.0234 - val_accuracy: 0.7700\n",
      "Epoch 111/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0394 - accuracy: 0.9989 - val_loss: 1.1035 - val_accuracy: 0.7600\n",
      "Epoch 112/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0395 - accuracy: 0.9991 - val_loss: 1.0870 - val_accuracy: 0.7662\n",
      "Epoch 113/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0393 - accuracy: 0.9991 - val_loss: 1.1140 - val_accuracy: 0.7675\n",
      "Epoch 114/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0407 - accuracy: 0.9984 - val_loss: 1.1156 - val_accuracy: 0.7637\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 115/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0421 - accuracy: 0.9982 - val_loss: 1.1161 - val_accuracy: 0.7625\n",
      "Epoch 116/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0442 - accuracy: 0.9978 - val_loss: 1.1267 - val_accuracy: 0.7550\n",
      "Epoch 117/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0441 - accuracy: 0.9980 - val_loss: 1.1559 - val_accuracy: 0.7512\n",
      "Epoch 118/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0480 - accuracy: 0.9966 - val_loss: 1.1764 - val_accuracy: 0.7600\n",
      "Epoch 119/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0506 - accuracy: 0.9958 - val_loss: 1.2288 - val_accuracy: 0.7300\n",
      "Epoch 120/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0546 - accuracy: 0.9949 - val_loss: 1.2288 - val_accuracy: 0.7450\n",
      "Epoch 121/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0573 - accuracy: 0.9942 - val_loss: 1.1576 - val_accuracy: 0.7575\n",
      "Epoch 122/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0601 - accuracy: 0.9947 - val_loss: 1.2512 - val_accuracy: 0.7400\n",
      "Epoch 123/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0619 - accuracy: 0.9944 - val_loss: 1.2656 - val_accuracy: 0.7550\n",
      "Epoch 124/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0585 - accuracy: 0.9947 - val_loss: 1.2343 - val_accuracy: 0.7600\n",
      "Epoch 125/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0540 - accuracy: 0.9964 - val_loss: 1.2230 - val_accuracy: 0.7575\n",
      "Epoch 126/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0530 - accuracy: 0.9968 - val_loss: 1.1465 - val_accuracy: 0.7650\n",
      "Epoch 127/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0545 - accuracy: 0.9972 - val_loss: 1.1355 - val_accuracy: 0.7600\n",
      "Epoch 128/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0491 - accuracy: 0.9982 - val_loss: 1.0866 - val_accuracy: 0.7625\n",
      "Epoch 129/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0466 - accuracy: 0.9988 - val_loss: 1.0654 - val_accuracy: 0.7637\n",
      "Epoch 130/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0450 - accuracy: 0.9989 - val_loss: 1.0940 - val_accuracy: 0.7713\n",
      "Epoch 131/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0446 - accuracy: 0.9993 - val_loss: 1.0493 - val_accuracy: 0.7812\n",
      "Epoch 132/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0433 - accuracy: 0.9990 - val_loss: 1.0471 - val_accuracy: 0.7713\n",
      "Epoch 133/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0431 - accuracy: 0.9993 - val_loss: 1.0608 - val_accuracy: 0.7750\n",
      "Epoch 134/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0416 - accuracy: 0.9995 - val_loss: 1.0720 - val_accuracy: 0.7750\n",
      "Epoch 135/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0408 - accuracy: 0.9995 - val_loss: 1.0289 - val_accuracy: 0.7800\n",
      "Epoch 136/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0397 - accuracy: 0.9996 - val_loss: 1.0223 - val_accuracy: 0.7900\n",
      "Epoch 137/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0405 - accuracy: 0.9992 - val_loss: 1.1047 - val_accuracy: 0.7688\n",
      "Epoch 138/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0400 - accuracy: 0.9994 - val_loss: 1.0690 - val_accuracy: 0.7812\n",
      "Epoch 139/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0391 - accuracy: 0.9995 - val_loss: 1.0965 - val_accuracy: 0.7875\n",
      "Epoch 140/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0386 - accuracy: 0.9997 - val_loss: 1.0499 - val_accuracy: 0.7925\n",
      "Epoch 141/1000\n",
      "19/19 [==============================] - 0s 26ms/step - loss: 0.0379 - accuracy: 0.9997 - val_loss: 1.0511 - val_accuracy: 0.7900\n",
      "Epoch 142/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0374 - accuracy: 0.9997 - val_loss: 1.0699 - val_accuracy: 0.7837\n",
      "Epoch 143/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0375 - accuracy: 0.9996 - val_loss: 1.0835 - val_accuracy: 0.7775\n",
      "Epoch 144/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0363 - accuracy: 0.9997 - val_loss: 1.0559 - val_accuracy: 0.7862\n",
      "Epoch 145/1000\n",
      "19/19 [==============================] - 1s 30ms/step - loss: 0.0357 - accuracy: 0.9999 - val_loss: 1.0728 - val_accuracy: 0.7812\n",
      "Epoch 146/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0359 - accuracy: 0.9997 - val_loss: 1.0529 - val_accuracy: 0.7837\n",
      "Epoch 147/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0354 - accuracy: 0.9997 - val_loss: 1.0700 - val_accuracy: 0.7862\n",
      "Epoch 148/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0356 - accuracy: 0.9994 - val_loss: 1.0777 - val_accuracy: 0.7837\n",
      "Epoch 149/1000\n",
      "19/19 [==============================] - 1s 28ms/step - loss: 0.0352 - accuracy: 0.9996 - val_loss: 1.0618 - val_accuracy: 0.7825\n",
      "Epoch 150/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0345 - accuracy: 0.9997 - val_loss: 1.0808 - val_accuracy: 0.7788\n",
      "Epoch 151/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0348 - accuracy: 0.9997 - val_loss: 1.0769 - val_accuracy: 0.7775\n",
      "Epoch 152/1000\n",
      "19/19 [==============================] - 0s 27ms/step - loss: 0.0341 - accuracy: 0.9993 - val_loss: 1.1091 - val_accuracy: 0.7788\n",
      "Epoch 153/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0342 - accuracy: 0.9996 - val_loss: 1.1073 - val_accuracy: 0.7850\n",
      "Epoch 154/1000\n",
      "19/19 [==============================] - 1s 28ms/step - loss: 0.0338 - accuracy: 0.9996 - val_loss: 1.0896 - val_accuracy: 0.7800\n",
      "Epoch 155/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0329 - accuracy: 0.9997 - val_loss: 1.0930 - val_accuracy: 0.7837\n",
      "Epoch 156/1000\n",
      "19/19 [==============================] - 1s 29ms/step - loss: 0.0326 - accuracy: 0.9996 - val_loss: 1.0727 - val_accuracy: 0.7800\n",
      "Epoch 157/1000\n",
      "19/19 [==============================] - 1s 28ms/step - loss: 0.0324 - accuracy: 0.9999 - val_loss: 1.0618 - val_accuracy: 0.7825\n",
      "Epoch 158/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0321 - accuracy: 0.9997 - val_loss: 1.0744 - val_accuracy: 0.7825\n",
      "Epoch 159/1000\n",
      "19/19 [==============================] - 0s 26ms/step - loss: 0.0320 - accuracy: 0.9996 - val_loss: 1.0646 - val_accuracy: 0.7875\n",
      "Epoch 160/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0327 - accuracy: 0.9995 - val_loss: 1.0909 - val_accuracy: 0.7763\n",
      "Epoch 161/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0320 - accuracy: 0.9995 - val_loss: 1.1520 - val_accuracy: 0.7812\n",
      "Epoch 162/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0328 - accuracy: 0.9994 - val_loss: 1.1314 - val_accuracy: 0.7800\n",
      "Epoch 163/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0322 - accuracy: 0.9993 - val_loss: 1.1282 - val_accuracy: 0.7775\n",
      "Epoch 164/1000\n",
      "19/19 [==============================] - 0s 27ms/step - loss: 0.0319 - accuracy: 0.9993 - val_loss: 1.1157 - val_accuracy: 0.7725\n",
      "Epoch 165/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0326 - accuracy: 0.9995 - val_loss: 1.1004 - val_accuracy: 0.7763\n",
      "Epoch 166/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0327 - accuracy: 0.9989 - val_loss: 1.1102 - val_accuracy: 0.7738\n",
      "Epoch 167/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0320 - accuracy: 0.9995 - val_loss: 1.1645 - val_accuracy: 0.7713\n",
      "Epoch 168/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0325 - accuracy: 0.9991 - val_loss: 1.1669 - val_accuracy: 0.7713\n",
      "Epoch 169/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0334 - accuracy: 0.9992 - val_loss: 1.1329 - val_accuracy: 0.7600\n",
      "Epoch 170/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0361 - accuracy: 0.9984 - val_loss: 1.2010 - val_accuracy: 0.7512\n",
      "Epoch 171/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0381 - accuracy: 0.9981 - val_loss: 1.1056 - val_accuracy: 0.7587\n",
      "Epoch 172/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0376 - accuracy: 0.9985 - val_loss: 1.1724 - val_accuracy: 0.7512\n",
      "Epoch 173/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0390 - accuracy: 0.9980 - val_loss: 1.2482 - val_accuracy: 0.7587\n",
      "Epoch 174/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0433 - accuracy: 0.9972 - val_loss: 1.2898 - val_accuracy: 0.7562\n",
      "Epoch 175/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0480 - accuracy: 0.9958 - val_loss: 1.3041 - val_accuracy: 0.7450\n",
      "Epoch 176/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0487 - accuracy: 0.9961 - val_loss: 1.2441 - val_accuracy: 0.7650\n",
      "Epoch 177/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0536 - accuracy: 0.9953 - val_loss: 1.2955 - val_accuracy: 0.7462\n",
      "Epoch 178/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0539 - accuracy: 0.9949 - val_loss: 1.3769 - val_accuracy: 0.7375\n",
      "Epoch 179/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0540 - accuracy: 0.9957 - val_loss: 1.2748 - val_accuracy: 0.7450\n",
      "Epoch 180/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0487 - accuracy: 0.9974 - val_loss: 1.2162 - val_accuracy: 0.7738\n",
      "Epoch 181/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0462 - accuracy: 0.9980 - val_loss: 1.1752 - val_accuracy: 0.7788\n",
      "Epoch 182/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0440 - accuracy: 0.9982 - val_loss: 1.1629 - val_accuracy: 0.7675\n",
      "Epoch 183/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0423 - accuracy: 0.9988 - val_loss: 1.1797 - val_accuracy: 0.7662\n",
      "Epoch 184/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0410 - accuracy: 0.9989 - val_loss: 1.1684 - val_accuracy: 0.7688\n",
      "Epoch 185/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0408 - accuracy: 0.9989 - val_loss: 1.1261 - val_accuracy: 0.7750\n",
      "Epoch 186/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0419 - accuracy: 0.9985 - val_loss: 1.1383 - val_accuracy: 0.7675\n",
      "Epoch 187/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0389 - accuracy: 0.9993 - val_loss: 1.2070 - val_accuracy: 0.7562\n",
      "Epoch 188/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0392 - accuracy: 0.9994 - val_loss: 1.1449 - val_accuracy: 0.7688\n",
      "Epoch 189/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0389 - accuracy: 0.9991 - val_loss: 1.1295 - val_accuracy: 0.7713\n",
      "Epoch 190/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0376 - accuracy: 0.9993 - val_loss: 1.1413 - val_accuracy: 0.7713\n",
      "Epoch 191/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0366 - accuracy: 0.9995 - val_loss: 1.1633 - val_accuracy: 0.7725\n",
      "Epoch 192/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0359 - accuracy: 0.9995 - val_loss: 1.1473 - val_accuracy: 0.7713\n",
      "Epoch 193/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0355 - accuracy: 0.9997 - val_loss: 1.1797 - val_accuracy: 0.7700\n",
      "Epoch 194/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0354 - accuracy: 0.9997 - val_loss: 1.1430 - val_accuracy: 0.7713\n",
      "Epoch 195/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0346 - accuracy: 0.9995 - val_loss: 1.1490 - val_accuracy: 0.7812\n",
      "Epoch 196/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0342 - accuracy: 0.9998 - val_loss: 1.1339 - val_accuracy: 0.7763\n",
      "Epoch 197/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0338 - accuracy: 0.9996 - val_loss: 1.1609 - val_accuracy: 0.7788\n",
      "Epoch 198/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0338 - accuracy: 0.9996 - val_loss: 1.1590 - val_accuracy: 0.7812\n",
      "Epoch 199/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0332 - accuracy: 0.9997 - val_loss: 1.1583 - val_accuracy: 0.7713\n",
      "Epoch 200/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0337 - accuracy: 0.9994 - val_loss: 1.1516 - val_accuracy: 0.7738\n",
      "Epoch 201/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0342 - accuracy: 0.9991 - val_loss: 1.1679 - val_accuracy: 0.7650\n",
      "Epoch 202/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0345 - accuracy: 0.9991 - val_loss: 1.1347 - val_accuracy: 0.7725\n",
      "Epoch 203/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0337 - accuracy: 0.9995 - val_loss: 1.1382 - val_accuracy: 0.7775\n",
      "Epoch 204/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0340 - accuracy: 0.9994 - val_loss: 1.2475 - val_accuracy: 0.7550\n",
      "Epoch 205/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0344 - accuracy: 0.9993 - val_loss: 1.2163 - val_accuracy: 0.7725\n",
      "Epoch 206/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0382 - accuracy: 0.9983 - val_loss: 1.2569 - val_accuracy: 0.7612\n",
      "Epoch 207/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0369 - accuracy: 0.9982 - val_loss: 1.2136 - val_accuracy: 0.7637\n",
      "Epoch 208/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0385 - accuracy: 0.9982 - val_loss: 1.1838 - val_accuracy: 0.7675\n",
      "Epoch 209/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0404 - accuracy: 0.9976 - val_loss: 1.2060 - val_accuracy: 0.7675\n",
      "Epoch 210/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0392 - accuracy: 0.9984 - val_loss: 1.2016 - val_accuracy: 0.7700\n",
      "Epoch 211/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0388 - accuracy: 0.9985 - val_loss: 1.1575 - val_accuracy: 0.7650\n",
      "Epoch 212/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0383 - accuracy: 0.9984 - val_loss: 1.1643 - val_accuracy: 0.7612\n",
      "Epoch 213/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0407 - accuracy: 0.9978 - val_loss: 1.2200 - val_accuracy: 0.7562\n",
      "Epoch 214/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0409 - accuracy: 0.9980 - val_loss: 1.2152 - val_accuracy: 0.7625\n",
      "Epoch 215/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0425 - accuracy: 0.9980 - val_loss: 1.1714 - val_accuracy: 0.7688\n",
      "Epoch 216/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0413 - accuracy: 0.9983 - val_loss: 1.2941 - val_accuracy: 0.7587\n",
      "Epoch 217/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0404 - accuracy: 0.9984 - val_loss: 1.1975 - val_accuracy: 0.7662\n",
      "Epoch 218/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0459 - accuracy: 0.9968 - val_loss: 1.2963 - val_accuracy: 0.7537\n",
      "Epoch 219/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0511 - accuracy: 0.9956 - val_loss: 1.3267 - val_accuracy: 0.7700\n",
      "Epoch 220/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0434 - accuracy: 0.9972 - val_loss: 1.1837 - val_accuracy: 0.7738\n",
      "Epoch 221/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0415 - accuracy: 0.9986 - val_loss: 1.1704 - val_accuracy: 0.7725\n",
      "Epoch 222/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0410 - accuracy: 0.9986 - val_loss: 1.1862 - val_accuracy: 0.7688\n",
      "Epoch 223/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0401 - accuracy: 0.9987 - val_loss: 1.1983 - val_accuracy: 0.7800\n",
      "Epoch 224/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0389 - accuracy: 0.9988 - val_loss: 1.2222 - val_accuracy: 0.7750\n",
      "Epoch 225/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0386 - accuracy: 0.9989 - val_loss: 1.2074 - val_accuracy: 0.7750\n",
      "Epoch 226/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0377 - accuracy: 0.9993 - val_loss: 1.1992 - val_accuracy: 0.7837\n",
      "Epoch 227/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0363 - accuracy: 0.9993 - val_loss: 1.1919 - val_accuracy: 0.7875\n",
      "Epoch 228/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0356 - accuracy: 0.9993 - val_loss: 1.2190 - val_accuracy: 0.7812\n",
      "Epoch 229/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0354 - accuracy: 0.9995 - val_loss: 1.2138 - val_accuracy: 0.7763\n",
      "Epoch 230/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0343 - accuracy: 0.9996 - val_loss: 1.1779 - val_accuracy: 0.7738\n",
      "Epoch 231/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0340 - accuracy: 0.9997 - val_loss: 1.1866 - val_accuracy: 0.7763\n",
      "Epoch 232/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0331 - accuracy: 0.9998 - val_loss: 1.1698 - val_accuracy: 0.7850\n",
      "Epoch 233/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0329 - accuracy: 0.9997 - val_loss: 1.1763 - val_accuracy: 0.7763\n",
      "Epoch 234/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0328 - accuracy: 0.9997 - val_loss: 1.1528 - val_accuracy: 0.7788\n",
      "Epoch 235/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0327 - accuracy: 0.9997 - val_loss: 1.1747 - val_accuracy: 0.7788\n",
      "Epoch 236/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0323 - accuracy: 0.9996 - val_loss: 1.1998 - val_accuracy: 0.7763\n",
      "Epoch 237/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0321 - accuracy: 0.9995 - val_loss: 1.1757 - val_accuracy: 0.7800\n",
      "Epoch 238/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0322 - accuracy: 0.9994 - val_loss: 1.1851 - val_accuracy: 0.7763\n",
      "Epoch 239/1000\n",
      "19/19 [==============================] - 0s 27ms/step - loss: 0.0314 - accuracy: 0.9996 - val_loss: 1.1818 - val_accuracy: 0.7825\n",
      "Epoch 240/1000\n",
      "19/19 [==============================] - 0s 26ms/step - loss: 0.0310 - accuracy: 0.9997 - val_loss: 1.1891 - val_accuracy: 0.7962\n",
      "Epoch 241/1000\n",
      "19/19 [==============================] - 0s 27ms/step - loss: 0.0304 - accuracy: 0.9997 - val_loss: 1.1791 - val_accuracy: 0.7887\n",
      "Epoch 242/1000\n",
      "19/19 [==============================] - 0s 26ms/step - loss: 0.0298 - accuracy: 0.9997 - val_loss: 1.1684 - val_accuracy: 0.7925\n",
      "Epoch 243/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0296 - accuracy: 0.9997 - val_loss: 1.1602 - val_accuracy: 0.7962\n",
      "Epoch 244/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0293 - accuracy: 0.9997 - val_loss: 1.1788 - val_accuracy: 0.7900\n",
      "Epoch 245/1000\n",
      "19/19 [==============================] - 0s 26ms/step - loss: 0.0287 - accuracy: 0.9997 - val_loss: 1.1802 - val_accuracy: 0.7862\n",
      "Epoch 246/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0287 - accuracy: 0.9998 - val_loss: 1.1664 - val_accuracy: 0.7850\n",
      "Epoch 247/1000\n",
      "19/19 [==============================] - 0s 27ms/step - loss: 0.0284 - accuracy: 0.9996 - val_loss: 1.1831 - val_accuracy: 0.7750\n",
      "Epoch 248/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0293 - accuracy: 0.9995 - val_loss: 1.2180 - val_accuracy: 0.7637\n",
      "Epoch 249/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0306 - accuracy: 0.9990 - val_loss: 1.2317 - val_accuracy: 0.7650\n",
      "Epoch 250/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0314 - accuracy: 0.9989 - val_loss: 1.2104 - val_accuracy: 0.7738\n",
      "Epoch 251/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0346 - accuracy: 0.9982 - val_loss: 1.3240 - val_accuracy: 0.7637\n",
      "Epoch 252/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0378 - accuracy: 0.9977 - val_loss: 1.2667 - val_accuracy: 0.7675\n",
      "Epoch 253/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0397 - accuracy: 0.9971 - val_loss: 1.3117 - val_accuracy: 0.7550\n",
      "Epoch 254/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0419 - accuracy: 0.9974 - val_loss: 1.3136 - val_accuracy: 0.7450\n",
      "Epoch 255/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0421 - accuracy: 0.9973 - val_loss: 1.3788 - val_accuracy: 0.7312\n",
      "Epoch 256/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0498 - accuracy: 0.9955 - val_loss: 1.3417 - val_accuracy: 0.7575\n",
      "Epoch 257/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0479 - accuracy: 0.9961 - val_loss: 1.2877 - val_accuracy: 0.7487\n",
      "Epoch 258/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0493 - accuracy: 0.9962 - val_loss: 1.2202 - val_accuracy: 0.7625\n",
      "Epoch 259/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0490 - accuracy: 0.9965 - val_loss: 1.1986 - val_accuracy: 0.7650\n",
      "Epoch 260/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0482 - accuracy: 0.9970 - val_loss: 1.1313 - val_accuracy: 0.7637\n",
      "Epoch 261/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0459 - accuracy: 0.9981 - val_loss: 1.2016 - val_accuracy: 0.7625\n",
      "Epoch 262/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0431 - accuracy: 0.9985 - val_loss: 1.1479 - val_accuracy: 0.7587\n",
      "Epoch 263/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0407 - accuracy: 0.9991 - val_loss: 1.1215 - val_accuracy: 0.7725\n",
      "Epoch 264/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0402 - accuracy: 0.9986 - val_loss: 1.1427 - val_accuracy: 0.7675\n",
      "Epoch 265/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0393 - accuracy: 0.9994 - val_loss: 1.1366 - val_accuracy: 0.7650\n",
      "Epoch 266/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0386 - accuracy: 0.9992 - val_loss: 1.1533 - val_accuracy: 0.7725\n",
      "Epoch 267/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0379 - accuracy: 0.9995 - val_loss: 1.1489 - val_accuracy: 0.7675\n",
      "Epoch 268/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0377 - accuracy: 0.9989 - val_loss: 1.1748 - val_accuracy: 0.7650\n",
      "Epoch 269/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0384 - accuracy: 0.9993 - val_loss: 1.1166 - val_accuracy: 0.7625\n",
      "Epoch 270/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0372 - accuracy: 0.9993 - val_loss: 1.1062 - val_accuracy: 0.7688\n",
      "Epoch 271/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0363 - accuracy: 0.9993 - val_loss: 1.1073 - val_accuracy: 0.7763\n",
      "Epoch 272/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0354 - accuracy: 0.9996 - val_loss: 1.0894 - val_accuracy: 0.7688\n",
      "Epoch 273/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0354 - accuracy: 0.9994 - val_loss: 1.0727 - val_accuracy: 0.7750\n",
      "Epoch 274/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0342 - accuracy: 0.9998 - val_loss: 1.0624 - val_accuracy: 0.7812\n",
      "Epoch 275/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0337 - accuracy: 0.9997 - val_loss: 1.0679 - val_accuracy: 0.7850\n",
      "Epoch 276/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0331 - accuracy: 0.9998 - val_loss: 1.0609 - val_accuracy: 0.7887\n",
      "Epoch 277/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0328 - accuracy: 0.9996 - val_loss: 1.0699 - val_accuracy: 0.7825\n",
      "Epoch 278/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0322 - accuracy: 0.9998 - val_loss: 1.0740 - val_accuracy: 0.7825\n",
      "Epoch 279/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0319 - accuracy: 0.9996 - val_loss: 1.0639 - val_accuracy: 0.7850\n",
      "Epoch 280/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0315 - accuracy: 0.9998 - val_loss: 1.0689 - val_accuracy: 0.7738\n",
      "Epoch 281/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0310 - accuracy: 0.9997 - val_loss: 1.0690 - val_accuracy: 0.7775\n",
      "Epoch 282/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0306 - accuracy: 0.9998 - val_loss: 1.0748 - val_accuracy: 0.7812\n",
      "Epoch 283/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0303 - accuracy: 0.9996 - val_loss: 1.0978 - val_accuracy: 0.7688\n",
      "Epoch 284/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0304 - accuracy: 0.9996 - val_loss: 1.0557 - val_accuracy: 0.7775\n",
      "Epoch 285/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0301 - accuracy: 0.9995 - val_loss: 1.0887 - val_accuracy: 0.7850\n",
      "Epoch 286/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0297 - accuracy: 0.9997 - val_loss: 1.0625 - val_accuracy: 0.7875\n",
      "Epoch 287/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0300 - accuracy: 0.9995 - val_loss: 1.0823 - val_accuracy: 0.7887\n",
      "Epoch 288/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0292 - accuracy: 0.9997 - val_loss: 1.1137 - val_accuracy: 0.7850\n",
      "Epoch 289/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0289 - accuracy: 0.9997 - val_loss: 1.0887 - val_accuracy: 0.7850\n",
      "Epoch 290/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0286 - accuracy: 0.9997 - val_loss: 1.1603 - val_accuracy: 0.7763\n",
      "Epoch 291/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0288 - accuracy: 0.9995 - val_loss: 1.2085 - val_accuracy: 0.7750\n",
      "Epoch 292/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0289 - accuracy: 0.9995 - val_loss: 1.1778 - val_accuracy: 0.7688\n",
      "Epoch 293/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0301 - accuracy: 0.9991 - val_loss: 1.1348 - val_accuracy: 0.7738\n",
      "Epoch 294/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0294 - accuracy: 0.9993 - val_loss: 1.1951 - val_accuracy: 0.7675\n",
      "Epoch 295/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0298 - accuracy: 0.9993 - val_loss: 1.1720 - val_accuracy: 0.7637\n",
      "Epoch 296/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0324 - accuracy: 0.9984 - val_loss: 1.1504 - val_accuracy: 0.7625\n",
      "Epoch 297/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0334 - accuracy: 0.9987 - val_loss: 1.2533 - val_accuracy: 0.7625\n",
      "Epoch 298/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0343 - accuracy: 0.9982 - val_loss: 1.1664 - val_accuracy: 0.7700\n",
      "Epoch 299/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0387 - accuracy: 0.9971 - val_loss: 1.2582 - val_accuracy: 0.7537\n",
      "Epoch 300/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0428 - accuracy: 0.9963 - val_loss: 1.2173 - val_accuracy: 0.7500\n",
      "Epoch 301/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0514 - accuracy: 0.9939 - val_loss: 1.2872 - val_accuracy: 0.7500\n",
      "Epoch 302/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0511 - accuracy: 0.9951 - val_loss: 1.1930 - val_accuracy: 0.7650\n",
      "Epoch 303/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0529 - accuracy: 0.9947 - val_loss: 1.1976 - val_accuracy: 0.7437\n",
      "Epoch 304/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0490 - accuracy: 0.9961 - val_loss: 1.1982 - val_accuracy: 0.7600\n",
      "Epoch 305/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0468 - accuracy: 0.9965 - val_loss: 1.1386 - val_accuracy: 0.7713\n",
      "Epoch 306/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0471 - accuracy: 0.9969 - val_loss: 1.1232 - val_accuracy: 0.7587\n",
      "Epoch 307/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0442 - accuracy: 0.9982 - val_loss: 1.1609 - val_accuracy: 0.7550\n",
      "Epoch 308/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0439 - accuracy: 0.9977 - val_loss: 1.0651 - val_accuracy: 0.7738\n",
      "Epoch 309/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0418 - accuracy: 0.9986 - val_loss: 1.1200 - val_accuracy: 0.7750\n",
      "Epoch 310/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0407 - accuracy: 0.9986 - val_loss: 1.1170 - val_accuracy: 0.7700\n",
      "Epoch 311/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0393 - accuracy: 0.9993 - val_loss: 1.1245 - val_accuracy: 0.7725\n",
      "Epoch 312/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0388 - accuracy: 0.9990 - val_loss: 1.1010 - val_accuracy: 0.7775\n",
      "Epoch 313/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0388 - accuracy: 0.9991 - val_loss: 1.1469 - val_accuracy: 0.7763\n",
      "Epoch 314/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0392 - accuracy: 0.9989 - val_loss: 1.1713 - val_accuracy: 0.7688\n",
      "Epoch 315/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0398 - accuracy: 0.9990 - val_loss: 1.1569 - val_accuracy: 0.7788\n",
      "Epoch 316/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0373 - accuracy: 0.9993 - val_loss: 1.1088 - val_accuracy: 0.7738\n",
      "Epoch 317/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0368 - accuracy: 0.9992 - val_loss: 1.1434 - val_accuracy: 0.7738\n",
      "Epoch 318/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0371 - accuracy: 0.9991 - val_loss: 1.1341 - val_accuracy: 0.7875\n",
      "Epoch 319/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0377 - accuracy: 0.9990 - val_loss: 1.1758 - val_accuracy: 0.7725\n",
      "Epoch 320/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0376 - accuracy: 0.9989 - val_loss: 1.1863 - val_accuracy: 0.7800\n",
      "Epoch 321/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0379 - accuracy: 0.9989 - val_loss: 1.1870 - val_accuracy: 0.7650\n",
      "Epoch 322/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0367 - accuracy: 0.9991 - val_loss: 1.2025 - val_accuracy: 0.7713\n",
      "Epoch 323/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0358 - accuracy: 0.9995 - val_loss: 1.1632 - val_accuracy: 0.7875\n",
      "Epoch 324/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0351 - accuracy: 0.9993 - val_loss: 1.1498 - val_accuracy: 0.7862\n",
      "Epoch 325/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0347 - accuracy: 0.9995 - val_loss: 1.1872 - val_accuracy: 0.7775\n",
      "Epoch 326/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0343 - accuracy: 0.9995 - val_loss: 1.1835 - val_accuracy: 0.7763\n",
      "Epoch 327/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0336 - accuracy: 0.9997 - val_loss: 1.1643 - val_accuracy: 0.7812\n",
      "Epoch 328/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0329 - accuracy: 0.9997 - val_loss: 1.1641 - val_accuracy: 0.7825\n",
      "Epoch 329/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0326 - accuracy: 0.9995 - val_loss: 1.1565 - val_accuracy: 0.7775\n",
      "Epoch 330/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0325 - accuracy: 0.9995 - val_loss: 1.1699 - val_accuracy: 0.7825\n",
      "Epoch 331/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0322 - accuracy: 0.9995 - val_loss: 1.1756 - val_accuracy: 0.7750\n",
      "Epoch 332/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0323 - accuracy: 0.9993 - val_loss: 1.1736 - val_accuracy: 0.7750\n",
      "Epoch 333/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0324 - accuracy: 0.9995 - val_loss: 1.1853 - val_accuracy: 0.7713\n",
      "Epoch 334/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0317 - accuracy: 0.9996 - val_loss: 1.2135 - val_accuracy: 0.7625\n",
      "Epoch 335/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0311 - accuracy: 0.9997 - val_loss: 1.1524 - val_accuracy: 0.7800\n",
      "Epoch 336/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0309 - accuracy: 0.9995 - val_loss: 1.1324 - val_accuracy: 0.7725\n",
      "Epoch 337/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0302 - accuracy: 0.9999 - val_loss: 1.1260 - val_accuracy: 0.7775\n",
      "Epoch 338/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0300 - accuracy: 0.9997 - val_loss: 1.1128 - val_accuracy: 0.7775\n",
      "Epoch 339/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0295 - accuracy: 0.9997 - val_loss: 1.1056 - val_accuracy: 0.7775\n",
      "Epoch 340/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0302 - accuracy: 0.9995 - val_loss: 1.1483 - val_accuracy: 0.7788\n",
      "Epoch 341/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0294 - accuracy: 0.9995 - val_loss: 1.2266 - val_accuracy: 0.7650\n",
      "Epoch 342/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0302 - accuracy: 0.9991 - val_loss: 1.1557 - val_accuracy: 0.7700\n",
      "Epoch 343/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0330 - accuracy: 0.9984 - val_loss: 1.1531 - val_accuracy: 0.7750\n",
      "Epoch 344/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0345 - accuracy: 0.9981 - val_loss: 1.1401 - val_accuracy: 0.7688\n",
      "Epoch 345/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0383 - accuracy: 0.9979 - val_loss: 1.2612 - val_accuracy: 0.7550\n",
      "Epoch 346/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0388 - accuracy: 0.9976 - val_loss: 1.3047 - val_accuracy: 0.7562\n",
      "Epoch 347/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0398 - accuracy: 0.9978 - val_loss: 1.2374 - val_accuracy: 0.7537\n",
      "Epoch 348/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0406 - accuracy: 0.9975 - val_loss: 1.2754 - val_accuracy: 0.7475\n",
      "Epoch 349/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0450 - accuracy: 0.9966 - val_loss: 1.3273 - val_accuracy: 0.7550\n",
      "Epoch 350/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0456 - accuracy: 0.9969 - val_loss: 1.3840 - val_accuracy: 0.7425\n",
      "Epoch 351/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0438 - accuracy: 0.9972 - val_loss: 1.2746 - val_accuracy: 0.7600\n",
      "Epoch 352/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0416 - accuracy: 0.9982 - val_loss: 1.3285 - val_accuracy: 0.7612\n",
      "Epoch 353/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0399 - accuracy: 0.9984 - val_loss: 1.2740 - val_accuracy: 0.7525\n",
      "Epoch 354/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0382 - accuracy: 0.9988 - val_loss: 1.2222 - val_accuracy: 0.7662\n",
      "Epoch 355/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0397 - accuracy: 0.9986 - val_loss: 1.2638 - val_accuracy: 0.7475\n",
      "Epoch 356/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0389 - accuracy: 0.9989 - val_loss: 1.2157 - val_accuracy: 0.7587\n",
      "Epoch 357/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0393 - accuracy: 0.9990 - val_loss: 1.2306 - val_accuracy: 0.7637\n",
      "Epoch 358/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0384 - accuracy: 0.9988 - val_loss: 1.2463 - val_accuracy: 0.7575\n",
      "Epoch 359/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0369 - accuracy: 0.9993 - val_loss: 1.2611 - val_accuracy: 0.7550\n",
      "Epoch 360/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0370 - accuracy: 0.9993 - val_loss: 1.2801 - val_accuracy: 0.7550\n",
      "Epoch 361/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0366 - accuracy: 0.9990 - val_loss: 1.2117 - val_accuracy: 0.7562\n",
      "Epoch 362/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0360 - accuracy: 0.9991 - val_loss: 1.2118 - val_accuracy: 0.7637\n",
      "Epoch 363/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0357 - accuracy: 0.9995 - val_loss: 1.2460 - val_accuracy: 0.7612\n",
      "Epoch 364/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0350 - accuracy: 0.9994 - val_loss: 1.1717 - val_accuracy: 0.7688\n",
      "Epoch 365/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0344 - accuracy: 0.9996 - val_loss: 1.1923 - val_accuracy: 0.7637\n",
      "Epoch 366/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0341 - accuracy: 0.9995 - val_loss: 1.2277 - val_accuracy: 0.7625\n",
      "Epoch 367/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0347 - accuracy: 0.9995 - val_loss: 1.2384 - val_accuracy: 0.7525\n",
      "Epoch 368/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0332 - accuracy: 0.9995 - val_loss: 1.2334 - val_accuracy: 0.7525\n",
      "Epoch 369/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0348 - accuracy: 0.9995 - val_loss: 1.2148 - val_accuracy: 0.7612\n",
      "Epoch 370/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0324 - accuracy: 0.9996 - val_loss: 1.1963 - val_accuracy: 0.7587\n",
      "Epoch 371/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0317 - accuracy: 0.9997 - val_loss: 1.2013 - val_accuracy: 0.7525\n",
      "Epoch 372/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0315 - accuracy: 0.9998 - val_loss: 1.1914 - val_accuracy: 0.7525\n",
      "Epoch 373/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0309 - accuracy: 0.9997 - val_loss: 1.1890 - val_accuracy: 0.7587\n",
      "Epoch 374/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0306 - accuracy: 0.9997 - val_loss: 1.1745 - val_accuracy: 0.7587\n",
      "Epoch 375/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0304 - accuracy: 0.9997 - val_loss: 1.1821 - val_accuracy: 0.7550\n",
      "Epoch 376/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0303 - accuracy: 0.9995 - val_loss: 1.1573 - val_accuracy: 0.7650\n",
      "Epoch 377/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0302 - accuracy: 0.9993 - val_loss: 1.1803 - val_accuracy: 0.7587\n",
      "Epoch 378/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0308 - accuracy: 0.9992 - val_loss: 1.2297 - val_accuracy: 0.7738\n",
      "Epoch 379/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0297 - accuracy: 0.9995 - val_loss: 1.2292 - val_accuracy: 0.7587\n",
      "Epoch 380/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0293 - accuracy: 0.9997 - val_loss: 1.2202 - val_accuracy: 0.7637\n",
      "Epoch 381/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0291 - accuracy: 0.9995 - val_loss: 1.2313 - val_accuracy: 0.7688\n",
      "Epoch 382/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0292 - accuracy: 0.9997 - val_loss: 1.2302 - val_accuracy: 0.7600\n",
      "Epoch 383/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0286 - accuracy: 0.9995 - val_loss: 1.2379 - val_accuracy: 0.7625\n",
      "Epoch 384/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0285 - accuracy: 0.9995 - val_loss: 1.1931 - val_accuracy: 0.7587\n",
      "Epoch 385/1000\n",
      "19/19 [==============================] - 1s 28ms/step - loss: 0.0283 - accuracy: 0.9993 - val_loss: 1.2182 - val_accuracy: 0.7625\n",
      "Epoch 386/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0284 - accuracy: 0.9993 - val_loss: 1.2264 - val_accuracy: 0.7612\n",
      "Epoch 387/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0284 - accuracy: 0.9996 - val_loss: 1.2056 - val_accuracy: 0.7662\n",
      "Epoch 388/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0288 - accuracy: 0.9993 - val_loss: 1.1688 - val_accuracy: 0.7662\n",
      "Epoch 389/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0284 - accuracy: 0.9991 - val_loss: 1.1936 - val_accuracy: 0.7587\n",
      "Epoch 390/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0289 - accuracy: 0.9993 - val_loss: 1.1793 - val_accuracy: 0.7675\n",
      "Epoch 391/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0297 - accuracy: 0.9989 - val_loss: 1.1855 - val_accuracy: 0.7587\n",
      "Epoch 392/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0312 - accuracy: 0.9985 - val_loss: 1.2550 - val_accuracy: 0.7650\n",
      "Epoch 393/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0321 - accuracy: 0.9986 - val_loss: 1.2977 - val_accuracy: 0.7375\n",
      "Epoch 394/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0331 - accuracy: 0.9986 - val_loss: 1.2830 - val_accuracy: 0.7537\n",
      "Epoch 395/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0362 - accuracy: 0.9976 - val_loss: 1.3678 - val_accuracy: 0.7487\n",
      "Epoch 396/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0403 - accuracy: 0.9970 - val_loss: 1.3308 - val_accuracy: 0.7337\n",
      "Epoch 397/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0425 - accuracy: 0.9970 - val_loss: 1.2890 - val_accuracy: 0.7462\n",
      "Epoch 398/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0401 - accuracy: 0.9978 - val_loss: 1.3598 - val_accuracy: 0.7487\n",
      "Epoch 399/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0420 - accuracy: 0.9971 - val_loss: 1.2695 - val_accuracy: 0.7512\n",
      "Epoch 400/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0425 - accuracy: 0.9968 - val_loss: 1.2999 - val_accuracy: 0.7612\n",
      "Epoch 401/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0462 - accuracy: 0.9968 - val_loss: 1.2476 - val_accuracy: 0.7612\n",
      "Epoch 402/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0452 - accuracy: 0.9969 - val_loss: 1.2437 - val_accuracy: 0.7688\n",
      "Epoch 403/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0478 - accuracy: 0.9960 - val_loss: 1.2749 - val_accuracy: 0.7650\n",
      "Epoch 404/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0462 - accuracy: 0.9972 - val_loss: 1.2378 - val_accuracy: 0.7487\n",
      "Epoch 405/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0452 - accuracy: 0.9968 - val_loss: 1.2504 - val_accuracy: 0.7500\n",
      "Epoch 406/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0442 - accuracy: 0.9978 - val_loss: 1.2151 - val_accuracy: 0.7625\n",
      "Epoch 407/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0420 - accuracy: 0.9985 - val_loss: 1.2885 - val_accuracy: 0.7600\n",
      "Epoch 408/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0406 - accuracy: 0.9986 - val_loss: 1.1871 - val_accuracy: 0.7675\n",
      "Epoch 409/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0384 - accuracy: 0.9992 - val_loss: 1.1506 - val_accuracy: 0.7625\n",
      "Epoch 410/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0371 - accuracy: 0.9997 - val_loss: 1.1473 - val_accuracy: 0.7688\n",
      "Epoch 411/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0363 - accuracy: 0.9995 - val_loss: 1.1560 - val_accuracy: 0.7725\n",
      "Epoch 412/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0355 - accuracy: 0.9998 - val_loss: 1.1367 - val_accuracy: 0.7763\n",
      "Epoch 413/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0348 - accuracy: 0.9997 - val_loss: 1.1208 - val_accuracy: 0.7788\n",
      "Epoch 414/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0343 - accuracy: 0.9997 - val_loss: 1.1103 - val_accuracy: 0.7763\n",
      "Epoch 415/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0337 - accuracy: 0.9997 - val_loss: 1.1128 - val_accuracy: 0.7775\n",
      "Epoch 416/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0332 - accuracy: 0.9997 - val_loss: 1.1369 - val_accuracy: 0.7700\n",
      "Epoch 417/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0331 - accuracy: 0.9997 - val_loss: 1.1144 - val_accuracy: 0.7725\n",
      "Epoch 418/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0333 - accuracy: 0.9995 - val_loss: 1.1001 - val_accuracy: 0.7788\n",
      "Epoch 419/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0333 - accuracy: 0.9995 - val_loss: 1.1293 - val_accuracy: 0.7812\n",
      "Epoch 420/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0327 - accuracy: 0.9997 - val_loss: 1.1564 - val_accuracy: 0.7788\n",
      "Epoch 421/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0324 - accuracy: 0.9996 - val_loss: 1.1685 - val_accuracy: 0.7688\n",
      "Epoch 422/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0316 - accuracy: 0.9995 - val_loss: 1.1517 - val_accuracy: 0.7725\n",
      "Epoch 423/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0318 - accuracy: 0.9994 - val_loss: 1.1749 - val_accuracy: 0.7750\n",
      "Epoch 424/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0314 - accuracy: 0.9996 - val_loss: 1.1740 - val_accuracy: 0.7637\n",
      "Epoch 425/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0309 - accuracy: 0.9996 - val_loss: 1.1457 - val_accuracy: 0.7725\n",
      "Epoch 426/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0301 - accuracy: 0.9997 - val_loss: 1.1171 - val_accuracy: 0.7750\n",
      "Epoch 427/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0303 - accuracy: 0.9996 - val_loss: 1.1554 - val_accuracy: 0.7637\n",
      "Epoch 428/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0298 - accuracy: 0.9997 - val_loss: 1.1622 - val_accuracy: 0.7775\n",
      "Epoch 429/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0291 - accuracy: 0.9998 - val_loss: 1.1503 - val_accuracy: 0.7775\n",
      "Epoch 430/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0291 - accuracy: 0.9996 - val_loss: 1.1512 - val_accuracy: 0.7700\n",
      "Epoch 431/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0283 - accuracy: 0.9997 - val_loss: 1.1690 - val_accuracy: 0.7637\n",
      "Epoch 432/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0282 - accuracy: 0.9998 - val_loss: 1.1639 - val_accuracy: 0.7688\n",
      "Epoch 433/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0278 - accuracy: 0.9997 - val_loss: 1.1547 - val_accuracy: 0.7662\n",
      "Epoch 434/1000\n",
      "19/19 [==============================] - 1s 28ms/step - loss: 0.0278 - accuracy: 0.9996 - val_loss: 1.2010 - val_accuracy: 0.7713\n",
      "Epoch 435/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0275 - accuracy: 0.9997 - val_loss: 1.1480 - val_accuracy: 0.7825\n",
      "Epoch 436/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0269 - accuracy: 0.9997 - val_loss: 1.1760 - val_accuracy: 0.7763\n",
      "Epoch 437/1000\n",
      "19/19 [==============================] - 1s 30ms/step - loss: 0.0269 - accuracy: 0.9997 - val_loss: 1.1594 - val_accuracy: 0.7713\n",
      "Epoch 438/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0273 - accuracy: 0.9995 - val_loss: 1.1816 - val_accuracy: 0.7662\n",
      "Epoch 439/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0270 - accuracy: 0.9996 - val_loss: 1.1602 - val_accuracy: 0.7763\n",
      "Epoch 440/1000\n",
      "19/19 [==============================] - 1s 28ms/step - loss: 0.0264 - accuracy: 0.9996 - val_loss: 1.1599 - val_accuracy: 0.7725\n",
      "Epoch 441/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0262 - accuracy: 0.9997 - val_loss: 1.1679 - val_accuracy: 0.7738\n",
      "Epoch 442/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0264 - accuracy: 0.9996 - val_loss: 1.1879 - val_accuracy: 0.7650\n",
      "Epoch 443/1000\n",
      "19/19 [==============================] - 1s 28ms/step - loss: 0.0261 - accuracy: 0.9994 - val_loss: 1.1624 - val_accuracy: 0.7650\n",
      "Epoch 444/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0262 - accuracy: 0.9995 - val_loss: 1.1701 - val_accuracy: 0.7812\n",
      "Epoch 445/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0292 - accuracy: 0.9984 - val_loss: 1.1768 - val_accuracy: 0.7750\n",
      "Epoch 446/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0301 - accuracy: 0.9984 - val_loss: 1.2788 - val_accuracy: 0.7662\n",
      "Epoch 447/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0341 - accuracy: 0.9980 - val_loss: 1.2659 - val_accuracy: 0.7550\n",
      "Epoch 448/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0399 - accuracy: 0.9968 - val_loss: 1.3286 - val_accuracy: 0.7437\n",
      "Epoch 449/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0451 - accuracy: 0.9950 - val_loss: 1.3587 - val_accuracy: 0.7375\n",
      "Epoch 450/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0533 - accuracy: 0.9935 - val_loss: 1.4183 - val_accuracy: 0.7312\n",
      "Epoch 451/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0537 - accuracy: 0.9938 - val_loss: 1.4371 - val_accuracy: 0.7475\n",
      "Epoch 452/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0530 - accuracy: 0.9945 - val_loss: 1.3564 - val_accuracy: 0.7475\n",
      "Epoch 453/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0526 - accuracy: 0.9945 - val_loss: 1.3516 - val_accuracy: 0.7575\n",
      "Epoch 454/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0505 - accuracy: 0.9961 - val_loss: 1.3313 - val_accuracy: 0.7612\n",
      "Epoch 455/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0466 - accuracy: 0.9978 - val_loss: 1.2541 - val_accuracy: 0.7763\n",
      "Epoch 456/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0433 - accuracy: 0.9986 - val_loss: 1.2331 - val_accuracy: 0.7688\n",
      "Epoch 457/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0426 - accuracy: 0.9986 - val_loss: 1.1963 - val_accuracy: 0.7837\n",
      "Epoch 458/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0420 - accuracy: 0.9986 - val_loss: 1.1930 - val_accuracy: 0.7837\n",
      "Epoch 459/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0407 - accuracy: 0.9989 - val_loss: 1.1955 - val_accuracy: 0.7763\n",
      "Epoch 460/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0392 - accuracy: 0.9993 - val_loss: 1.2249 - val_accuracy: 0.7812\n",
      "Epoch 461/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0386 - accuracy: 0.9994 - val_loss: 1.2224 - val_accuracy: 0.7800\n",
      "Epoch 462/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0386 - accuracy: 0.9991 - val_loss: 1.2122 - val_accuracy: 0.7738\n",
      "Epoch 463/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0376 - accuracy: 0.9993 - val_loss: 1.2109 - val_accuracy: 0.7713\n",
      "Epoch 464/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0364 - accuracy: 0.9995 - val_loss: 1.1780 - val_accuracy: 0.7738\n",
      "Epoch 465/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0358 - accuracy: 0.9997 - val_loss: 1.1544 - val_accuracy: 0.7775\n",
      "Epoch 466/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0352 - accuracy: 0.9997 - val_loss: 1.1586 - val_accuracy: 0.7788\n",
      "Epoch 467/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0348 - accuracy: 0.9998 - val_loss: 1.1805 - val_accuracy: 0.7738\n",
      "Epoch 468/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0342 - accuracy: 0.9997 - val_loss: 1.1832 - val_accuracy: 0.7738\n",
      "Epoch 469/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0337 - accuracy: 0.9997 - val_loss: 1.1611 - val_accuracy: 0.7775\n",
      "Epoch 470/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0332 - accuracy: 0.9997 - val_loss: 1.1647 - val_accuracy: 0.7763\n",
      "Epoch 471/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0328 - accuracy: 0.9997 - val_loss: 1.1865 - val_accuracy: 0.7900\n",
      "Epoch 472/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0337 - accuracy: 0.9995 - val_loss: 1.1658 - val_accuracy: 0.7850\n",
      "Epoch 473/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0328 - accuracy: 0.9995 - val_loss: 1.1748 - val_accuracy: 0.7850\n",
      "Epoch 474/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0321 - accuracy: 0.9995 - val_loss: 1.1778 - val_accuracy: 0.7862\n",
      "Epoch 475/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0317 - accuracy: 0.9996 - val_loss: 1.1867 - val_accuracy: 0.7775\n",
      "Epoch 476/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0312 - accuracy: 0.9997 - val_loss: 1.1732 - val_accuracy: 0.7763\n",
      "Epoch 477/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0314 - accuracy: 0.9996 - val_loss: 1.1614 - val_accuracy: 0.7775\n",
      "Epoch 478/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0318 - accuracy: 0.9991 - val_loss: 1.1414 - val_accuracy: 0.7788\n",
      "Epoch 479/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0320 - accuracy: 0.9995 - val_loss: 1.1857 - val_accuracy: 0.7738\n",
      "Epoch 480/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0329 - accuracy: 0.9989 - val_loss: 1.1328 - val_accuracy: 0.7788\n",
      "Epoch 481/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0331 - accuracy: 0.9989 - val_loss: 1.2383 - val_accuracy: 0.7600\n",
      "Epoch 482/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0322 - accuracy: 0.9991 - val_loss: 1.1973 - val_accuracy: 0.7562\n",
      "Epoch 483/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0330 - accuracy: 0.9990 - val_loss: 1.1938 - val_accuracy: 0.7650\n",
      "Epoch 484/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0315 - accuracy: 0.9995 - val_loss: 1.2227 - val_accuracy: 0.7575\n",
      "Epoch 485/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0309 - accuracy: 0.9995 - val_loss: 1.2037 - val_accuracy: 0.7612\n",
      "Epoch 486/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0312 - accuracy: 0.9992 - val_loss: 1.2207 - val_accuracy: 0.7650\n",
      "Epoch 487/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0308 - accuracy: 0.9993 - val_loss: 1.2043 - val_accuracy: 0.7725\n",
      "Epoch 488/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0308 - accuracy: 0.9993 - val_loss: 1.2210 - val_accuracy: 0.7600\n",
      "Epoch 489/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0301 - accuracy: 0.9995 - val_loss: 1.1764 - val_accuracy: 0.7713\n",
      "Epoch 490/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0302 - accuracy: 0.9993 - val_loss: 1.1792 - val_accuracy: 0.7700\n",
      "Epoch 491/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0302 - accuracy: 0.9992 - val_loss: 1.1669 - val_accuracy: 0.7688\n",
      "Epoch 492/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0314 - accuracy: 0.9987 - val_loss: 1.2217 - val_accuracy: 0.7625\n",
      "Epoch 493/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0305 - accuracy: 0.9994 - val_loss: 1.1702 - val_accuracy: 0.7763\n",
      "Epoch 494/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0301 - accuracy: 0.9992 - val_loss: 1.1647 - val_accuracy: 0.7675\n",
      "Epoch 495/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0311 - accuracy: 0.9989 - val_loss: 1.1999 - val_accuracy: 0.7688\n",
      "Epoch 496/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0326 - accuracy: 0.9989 - val_loss: 1.2217 - val_accuracy: 0.7713\n",
      "Epoch 497/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0319 - accuracy: 0.9992 - val_loss: 1.2110 - val_accuracy: 0.7675\n",
      "Epoch 498/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0342 - accuracy: 0.9986 - val_loss: 1.2361 - val_accuracy: 0.7688\n",
      "Epoch 499/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0342 - accuracy: 0.9982 - val_loss: 1.2218 - val_accuracy: 0.7650\n",
      "Epoch 500/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0357 - accuracy: 0.9976 - val_loss: 1.3057 - val_accuracy: 0.7662\n",
      "Epoch 501/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0346 - accuracy: 0.9987 - val_loss: 1.2681 - val_accuracy: 0.7587\n",
      "Epoch 502/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0357 - accuracy: 0.9982 - val_loss: 1.2688 - val_accuracy: 0.7650\n",
      "Epoch 503/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0342 - accuracy: 0.9986 - val_loss: 1.2657 - val_accuracy: 0.7550\n",
      "Epoch 504/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0343 - accuracy: 0.9991 - val_loss: 1.2849 - val_accuracy: 0.7500\n",
      "Epoch 505/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0342 - accuracy: 0.9990 - val_loss: 1.2131 - val_accuracy: 0.7637\n",
      "Epoch 506/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0357 - accuracy: 0.9989 - val_loss: 1.2889 - val_accuracy: 0.7450\n",
      "Epoch 507/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0362 - accuracy: 0.9986 - val_loss: 1.3301 - val_accuracy: 0.7400\n",
      "Epoch 508/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0421 - accuracy: 0.9964 - val_loss: 1.3284 - val_accuracy: 0.7487\n",
      "Epoch 509/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0423 - accuracy: 0.9971 - val_loss: 1.2843 - val_accuracy: 0.7625\n",
      "Epoch 510/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0397 - accuracy: 0.9978 - val_loss: 1.2904 - val_accuracy: 0.7738\n",
      "Epoch 511/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0379 - accuracy: 0.9984 - val_loss: 1.2789 - val_accuracy: 0.7612\n",
      "Epoch 512/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0385 - accuracy: 0.9982 - val_loss: 1.3193 - val_accuracy: 0.7550\n",
      "Epoch 513/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0380 - accuracy: 0.9984 - val_loss: 1.2819 - val_accuracy: 0.7575\n",
      "Epoch 514/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0368 - accuracy: 0.9989 - val_loss: 1.2026 - val_accuracy: 0.7688\n",
      "Epoch 515/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0354 - accuracy: 0.9992 - val_loss: 1.1653 - val_accuracy: 0.7775\n",
      "Epoch 516/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0343 - accuracy: 0.9993 - val_loss: 1.1908 - val_accuracy: 0.7763\n",
      "Epoch 517/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0335 - accuracy: 0.9997 - val_loss: 1.1592 - val_accuracy: 0.7713\n",
      "Epoch 518/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0331 - accuracy: 0.9993 - val_loss: 1.1527 - val_accuracy: 0.7763\n",
      "Epoch 519/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0326 - accuracy: 0.9994 - val_loss: 1.1550 - val_accuracy: 0.7800\n",
      "Epoch 520/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0329 - accuracy: 0.9993 - val_loss: 1.1614 - val_accuracy: 0.7775\n",
      "Epoch 521/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0330 - accuracy: 0.9991 - val_loss: 1.1815 - val_accuracy: 0.7750\n",
      "Epoch 522/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0321 - accuracy: 0.9994 - val_loss: 1.1444 - val_accuracy: 0.7738\n",
      "Epoch 523/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0315 - accuracy: 0.9997 - val_loss: 1.1468 - val_accuracy: 0.7738\n",
      "Epoch 524/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0307 - accuracy: 0.9997 - val_loss: 1.1299 - val_accuracy: 0.7750\n",
      "Epoch 525/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0302 - accuracy: 0.9997 - val_loss: 1.1298 - val_accuracy: 0.7887\n",
      "Epoch 526/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0302 - accuracy: 0.9995 - val_loss: 1.1383 - val_accuracy: 0.7800\n",
      "Epoch 527/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0293 - accuracy: 0.9998 - val_loss: 1.1337 - val_accuracy: 0.7825\n",
      "Epoch 528/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0293 - accuracy: 0.9997 - val_loss: 1.1601 - val_accuracy: 0.7763\n",
      "Epoch 529/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0287 - accuracy: 0.9997 - val_loss: 1.1549 - val_accuracy: 0.7788\n",
      "Epoch 530/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0284 - accuracy: 0.9996 - val_loss: 1.1765 - val_accuracy: 0.7763\n",
      "Epoch 531/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0280 - accuracy: 0.9997 - val_loss: 1.2024 - val_accuracy: 0.7738\n",
      "Epoch 532/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0276 - accuracy: 0.9998 - val_loss: 1.2096 - val_accuracy: 0.7750\n",
      "Epoch 533/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0274 - accuracy: 0.9998 - val_loss: 1.2087 - val_accuracy: 0.7775\n",
      "Epoch 534/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0271 - accuracy: 0.9997 - val_loss: 1.1979 - val_accuracy: 0.7713\n",
      "Epoch 535/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0266 - accuracy: 0.9998 - val_loss: 1.1908 - val_accuracy: 0.7700\n",
      "Epoch 536/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0263 - accuracy: 0.9997 - val_loss: 1.1756 - val_accuracy: 0.7775\n",
      "Epoch 537/1000\n",
      "19/19 [==============================] - 0s 27ms/step - loss: 0.0259 - accuracy: 0.9997 - val_loss: 1.1539 - val_accuracy: 0.7812\n",
      "Epoch 538/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0255 - accuracy: 0.9997 - val_loss: 1.1406 - val_accuracy: 0.7750\n",
      "Epoch 539/1000\n",
      "19/19 [==============================] - 1s 28ms/step - loss: 0.0251 - accuracy: 0.9997 - val_loss: 1.1409 - val_accuracy: 0.7812\n",
      "Epoch 540/1000\n",
      "19/19 [==============================] - 1s 30ms/step - loss: 0.0250 - accuracy: 0.9997 - val_loss: 1.1591 - val_accuracy: 0.7812\n",
      "Epoch 541/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0248 - accuracy: 0.9997 - val_loss: 1.1658 - val_accuracy: 0.7800\n",
      "Epoch 542/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0247 - accuracy: 0.9997 - val_loss: 1.1232 - val_accuracy: 0.7763\n",
      "Epoch 543/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0243 - accuracy: 0.9998 - val_loss: 1.1208 - val_accuracy: 0.7775\n",
      "Epoch 544/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0242 - accuracy: 0.9997 - val_loss: 1.1417 - val_accuracy: 0.7700\n",
      "Epoch 545/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0237 - accuracy: 0.9998 - val_loss: 1.1364 - val_accuracy: 0.7738\n",
      "Epoch 546/1000\n",
      "19/19 [==============================] - 1s 27ms/step - loss: 0.0236 - accuracy: 0.9998 - val_loss: 1.1437 - val_accuracy: 0.7725\n",
      "Epoch 547/1000\n",
      "19/19 [==============================] - 1s 28ms/step - loss: 0.0231 - accuracy: 0.9998 - val_loss: 1.1522 - val_accuracy: 0.7713\n",
      "Epoch 548/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0246 - accuracy: 0.9993 - val_loss: 1.2434 - val_accuracy: 0.7675\n",
      "Epoch 549/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0272 - accuracy: 0.9989 - val_loss: 1.2176 - val_accuracy: 0.7587\n",
      "Epoch 550/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0258 - accuracy: 0.9991 - val_loss: 1.2574 - val_accuracy: 0.7550\n",
      "Epoch 551/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0291 - accuracy: 0.9983 - val_loss: 1.1920 - val_accuracy: 0.7562\n",
      "Epoch 552/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0313 - accuracy: 0.9976 - val_loss: 1.2595 - val_accuracy: 0.7450\n",
      "Epoch 553/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0378 - accuracy: 0.9957 - val_loss: 1.3556 - val_accuracy: 0.7337\n",
      "Epoch 554/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0557 - accuracy: 0.9908 - val_loss: 1.3973 - val_accuracy: 0.7350\n",
      "Epoch 555/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0697 - accuracy: 0.9883 - val_loss: 1.4636 - val_accuracy: 0.7437\n",
      "Epoch 556/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0674 - accuracy: 0.9903 - val_loss: 1.3941 - val_accuracy: 0.7462\n",
      "Epoch 557/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0662 - accuracy: 0.9922 - val_loss: 1.3198 - val_accuracy: 0.7512\n",
      "Epoch 558/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0555 - accuracy: 0.9957 - val_loss: 1.2799 - val_accuracy: 0.7537\n",
      "Epoch 559/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0478 - accuracy: 0.9986 - val_loss: 1.2420 - val_accuracy: 0.7625\n",
      "Epoch 560/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0448 - accuracy: 0.9986 - val_loss: 1.2339 - val_accuracy: 0.7600\n",
      "Epoch 561/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0418 - accuracy: 0.9993 - val_loss: 1.2333 - val_accuracy: 0.7550\n",
      "Epoch 562/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0413 - accuracy: 0.9989 - val_loss: 1.1770 - val_accuracy: 0.7650\n",
      "Epoch 563/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0407 - accuracy: 0.9993 - val_loss: 1.1464 - val_accuracy: 0.7625\n",
      "Epoch 564/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0405 - accuracy: 0.9991 - val_loss: 1.1802 - val_accuracy: 0.7637\n",
      "Epoch 565/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0396 - accuracy: 0.9993 - val_loss: 1.1842 - val_accuracy: 0.7750\n",
      "Epoch 566/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0381 - accuracy: 0.9996 - val_loss: 1.1527 - val_accuracy: 0.7775\n",
      "Epoch 567/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0374 - accuracy: 0.9995 - val_loss: 1.1972 - val_accuracy: 0.7662\n",
      "Epoch 568/1000\n",
      "19/19 [==============================] - 0s 15ms/step - loss: 0.0367 - accuracy: 0.9997 - val_loss: 1.1532 - val_accuracy: 0.7675\n",
      "Epoch 569/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0360 - accuracy: 0.9997 - val_loss: 1.1511 - val_accuracy: 0.7812\n",
      "Epoch 570/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0358 - accuracy: 0.9995 - val_loss: 1.1530 - val_accuracy: 0.7775\n",
      "Epoch 571/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0355 - accuracy: 0.9995 - val_loss: 1.1555 - val_accuracy: 0.7675\n",
      "Epoch 572/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0355 - accuracy: 0.9994 - val_loss: 1.1423 - val_accuracy: 0.7825\n",
      "Epoch 573/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0346 - accuracy: 0.9997 - val_loss: 1.1394 - val_accuracy: 0.7800\n",
      "Epoch 574/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0341 - accuracy: 0.9995 - val_loss: 1.1368 - val_accuracy: 0.7788\n",
      "Epoch 575/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0333 - accuracy: 0.9998 - val_loss: 1.1251 - val_accuracy: 0.7850\n",
      "Epoch 576/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0330 - accuracy: 0.9998 - val_loss: 1.1218 - val_accuracy: 0.7862\n",
      "Epoch 577/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0327 - accuracy: 0.9996 - val_loss: 1.1150 - val_accuracy: 0.7837\n",
      "Epoch 578/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0324 - accuracy: 0.9996 - val_loss: 1.1386 - val_accuracy: 0.7775\n",
      "Epoch 579/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0324 - accuracy: 0.9995 - val_loss: 1.1316 - val_accuracy: 0.7662\n",
      "Epoch 580/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0335 - accuracy: 0.9996 - val_loss: 1.1568 - val_accuracy: 0.7688\n",
      "Epoch 581/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0315 - accuracy: 0.9996 - val_loss: 1.1800 - val_accuracy: 0.7738\n",
      "Epoch 582/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0310 - accuracy: 0.9997 - val_loss: 1.1546 - val_accuracy: 0.7700\n",
      "Epoch 583/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0306 - accuracy: 0.9997 - val_loss: 1.1686 - val_accuracy: 0.7662\n",
      "Epoch 584/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0302 - accuracy: 0.9998 - val_loss: 1.1565 - val_accuracy: 0.7750\n",
      "Epoch 585/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0298 - accuracy: 0.9998 - val_loss: 1.1639 - val_accuracy: 0.7750\n",
      "Epoch 586/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0293 - accuracy: 0.9998 - val_loss: 1.1594 - val_accuracy: 0.7725\n",
      "Epoch 587/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0298 - accuracy: 0.9995 - val_loss: 1.1944 - val_accuracy: 0.7700\n",
      "Epoch 588/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0319 - accuracy: 0.9989 - val_loss: 1.1654 - val_accuracy: 0.7738\n",
      "Epoch 589/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0328 - accuracy: 0.9989 - val_loss: 1.1957 - val_accuracy: 0.7587\n",
      "Epoch 590/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0354 - accuracy: 0.9984 - val_loss: 1.2463 - val_accuracy: 0.7612\n",
      "Epoch 591/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0345 - accuracy: 0.9986 - val_loss: 1.2606 - val_accuracy: 0.7550\n",
      "Epoch 592/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0349 - accuracy: 0.9979 - val_loss: 1.2362 - val_accuracy: 0.7713\n",
      "Epoch 593/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0355 - accuracy: 0.9982 - val_loss: 1.2114 - val_accuracy: 0.7612\n",
      "Epoch 594/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0354 - accuracy: 0.9988 - val_loss: 1.2084 - val_accuracy: 0.7550\n",
      "Epoch 595/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0379 - accuracy: 0.9988 - val_loss: 1.2312 - val_accuracy: 0.7450\n",
      "Epoch 596/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0335 - accuracy: 0.9989 - val_loss: 1.2291 - val_accuracy: 0.7437\n",
      "Epoch 597/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0326 - accuracy: 0.9993 - val_loss: 1.2127 - val_accuracy: 0.7587\n",
      "Epoch 598/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0317 - accuracy: 0.9994 - val_loss: 1.1413 - val_accuracy: 0.7612\n",
      "Epoch 599/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0316 - accuracy: 0.9993 - val_loss: 1.1809 - val_accuracy: 0.7713\n",
      "Epoch 600/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0317 - accuracy: 0.9996 - val_loss: 1.1796 - val_accuracy: 0.7713\n",
      "Epoch 601/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0306 - accuracy: 0.9995 - val_loss: 1.1111 - val_accuracy: 0.7775\n",
      "Epoch 602/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0298 - accuracy: 0.9995 - val_loss: 1.1148 - val_accuracy: 0.7738\n",
      "Epoch 603/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0293 - accuracy: 0.9998 - val_loss: 1.1374 - val_accuracy: 0.7688\n",
      "Epoch 604/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0286 - accuracy: 0.9997 - val_loss: 1.1329 - val_accuracy: 0.7700\n",
      "Epoch 605/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0283 - accuracy: 0.9998 - val_loss: 1.1184 - val_accuracy: 0.7725\n",
      "Epoch 606/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0277 - accuracy: 0.9997 - val_loss: 1.0980 - val_accuracy: 0.7750\n",
      "Epoch 607/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0274 - accuracy: 0.9997 - val_loss: 1.0959 - val_accuracy: 0.7800\n",
      "Epoch 608/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0270 - accuracy: 0.9997 - val_loss: 1.0994 - val_accuracy: 0.7788\n",
      "Epoch 609/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0267 - accuracy: 0.9997 - val_loss: 1.0915 - val_accuracy: 0.7775\n",
      "Epoch 610/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0263 - accuracy: 0.9997 - val_loss: 1.0861 - val_accuracy: 0.7750\n",
      "Epoch 611/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0259 - accuracy: 0.9997 - val_loss: 1.0889 - val_accuracy: 0.7713\n",
      "Epoch 612/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0256 - accuracy: 0.9998 - val_loss: 1.1096 - val_accuracy: 0.7750\n",
      "Epoch 613/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0253 - accuracy: 0.9997 - val_loss: 1.1045 - val_accuracy: 0.7713\n",
      "Epoch 614/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0250 - accuracy: 0.9997 - val_loss: 1.1000 - val_accuracy: 0.7713\n",
      "Epoch 615/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0246 - accuracy: 0.9998 - val_loss: 1.1028 - val_accuracy: 0.7763\n",
      "Epoch 616/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0245 - accuracy: 0.9997 - val_loss: 1.0986 - val_accuracy: 0.7738\n",
      "Epoch 617/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0239 - accuracy: 0.9998 - val_loss: 1.0997 - val_accuracy: 0.7763\n",
      "Epoch 618/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0238 - accuracy: 0.9997 - val_loss: 1.1102 - val_accuracy: 0.7788\n",
      "Epoch 619/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0253 - accuracy: 0.9996 - val_loss: 1.1466 - val_accuracy: 0.7763\n",
      "Epoch 620/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0253 - accuracy: 0.9995 - val_loss: 1.1441 - val_accuracy: 0.7775\n",
      "Epoch 621/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0246 - accuracy: 0.9993 - val_loss: 1.1679 - val_accuracy: 0.7837\n",
      "Epoch 622/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0269 - accuracy: 0.9985 - val_loss: 1.2005 - val_accuracy: 0.7700\n",
      "Epoch 623/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0323 - accuracy: 0.9974 - val_loss: 1.2682 - val_accuracy: 0.7612\n",
      "Epoch 624/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0408 - accuracy: 0.9961 - val_loss: 1.4408 - val_accuracy: 0.7462\n",
      "Epoch 625/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0424 - accuracy: 0.9961 - val_loss: 1.3153 - val_accuracy: 0.7437\n",
      "Epoch 626/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0430 - accuracy: 0.9962 - val_loss: 1.2450 - val_accuracy: 0.7512\n",
      "Epoch 627/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0434 - accuracy: 0.9966 - val_loss: 1.3481 - val_accuracy: 0.7637\n",
      "Epoch 628/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0452 - accuracy: 0.9959 - val_loss: 1.2543 - val_accuracy: 0.7525\n",
      "Epoch 629/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0430 - accuracy: 0.9964 - val_loss: 1.2745 - val_accuracy: 0.7512\n",
      "Epoch 630/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0416 - accuracy: 0.9973 - val_loss: 1.2839 - val_accuracy: 0.7475\n",
      "Epoch 631/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0422 - accuracy: 0.9975 - val_loss: 1.2015 - val_accuracy: 0.7512\n",
      "Epoch 632/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0395 - accuracy: 0.9980 - val_loss: 1.1942 - val_accuracy: 0.7550\n",
      "Epoch 633/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0406 - accuracy: 0.9978 - val_loss: 1.1801 - val_accuracy: 0.7600\n",
      "Epoch 634/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0412 - accuracy: 0.9982 - val_loss: 1.2333 - val_accuracy: 0.7475\n",
      "Epoch 635/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0401 - accuracy: 0.9984 - val_loss: 1.2224 - val_accuracy: 0.7475\n",
      "Epoch 636/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0388 - accuracy: 0.9987 - val_loss: 1.1868 - val_accuracy: 0.7600\n",
      "Epoch 637/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0377 - accuracy: 0.9988 - val_loss: 1.1994 - val_accuracy: 0.7600\n",
      "Epoch 638/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0381 - accuracy: 0.9990 - val_loss: 1.1887 - val_accuracy: 0.7637\n",
      "Epoch 639/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0371 - accuracy: 0.9986 - val_loss: 1.1443 - val_accuracy: 0.7688\n",
      "Epoch 640/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0362 - accuracy: 0.9993 - val_loss: 1.1531 - val_accuracy: 0.7713\n",
      "Epoch 641/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0350 - accuracy: 0.9995 - val_loss: 1.1639 - val_accuracy: 0.7788\n",
      "Epoch 642/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0348 - accuracy: 0.9997 - val_loss: 1.1348 - val_accuracy: 0.7650\n",
      "Epoch 643/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0336 - accuracy: 0.9995 - val_loss: 1.1454 - val_accuracy: 0.7675\n",
      "Epoch 644/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0329 - accuracy: 0.9996 - val_loss: 1.1357 - val_accuracy: 0.7775\n",
      "Epoch 645/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0328 - accuracy: 0.9995 - val_loss: 1.1200 - val_accuracy: 0.7725\n",
      "Epoch 646/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0321 - accuracy: 0.9997 - val_loss: 1.1335 - val_accuracy: 0.7788\n",
      "Epoch 647/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0314 - accuracy: 0.9999 - val_loss: 1.1406 - val_accuracy: 0.7763\n",
      "Epoch 648/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0310 - accuracy: 0.9997 - val_loss: 1.1464 - val_accuracy: 0.7775\n",
      "Epoch 649/1000\n",
      "19/19 [==============================] - 0s 16ms/step - loss: 0.0305 - accuracy: 0.9998 - val_loss: 1.1422 - val_accuracy: 0.7763\n",
      "Epoch 650/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0300 - accuracy: 0.9997 - val_loss: 1.1521 - val_accuracy: 0.7725\n",
      "Epoch 651/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0296 - accuracy: 0.9997 - val_loss: 1.1418 - val_accuracy: 0.7713\n",
      "Epoch 652/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0294 - accuracy: 0.9997 - val_loss: 1.1353 - val_accuracy: 0.7750\n",
      "Epoch 653/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0292 - accuracy: 0.9997 - val_loss: 1.1320 - val_accuracy: 0.7725\n",
      "Epoch 654/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0285 - accuracy: 0.9998 - val_loss: 1.1374 - val_accuracy: 0.7725\n",
      "Epoch 655/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0283 - accuracy: 0.9997 - val_loss: 1.1299 - val_accuracy: 0.7775\n",
      "Epoch 656/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0281 - accuracy: 0.9997 - val_loss: 1.1483 - val_accuracy: 0.7775\n",
      "Epoch 657/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0277 - accuracy: 0.9997 - val_loss: 1.1501 - val_accuracy: 0.7763\n",
      "Epoch 658/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0272 - accuracy: 0.9997 - val_loss: 1.1434 - val_accuracy: 0.7775\n",
      "Epoch 659/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0268 - accuracy: 0.9997 - val_loss: 1.1401 - val_accuracy: 0.7750\n",
      "Epoch 660/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0265 - accuracy: 0.9997 - val_loss: 1.1347 - val_accuracy: 0.7812\n",
      "Epoch 661/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0261 - accuracy: 0.9998 - val_loss: 1.1268 - val_accuracy: 0.7800\n",
      "Epoch 662/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0258 - accuracy: 0.9999 - val_loss: 1.1316 - val_accuracy: 0.7788\n",
      "Epoch 663/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0258 - accuracy: 0.9996 - val_loss: 1.1415 - val_accuracy: 0.7725\n",
      "Epoch 664/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0256 - accuracy: 0.9997 - val_loss: 1.1387 - val_accuracy: 0.7775\n",
      "Epoch 665/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0259 - accuracy: 0.9995 - val_loss: 1.1555 - val_accuracy: 0.7738\n",
      "Epoch 666/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0254 - accuracy: 0.9997 - val_loss: 1.1458 - val_accuracy: 0.7675\n",
      "Epoch 667/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0262 - accuracy: 0.9993 - val_loss: 1.2291 - val_accuracy: 0.7525\n",
      "Epoch 668/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0264 - accuracy: 0.9991 - val_loss: 1.1964 - val_accuracy: 0.7713\n",
      "Epoch 669/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0278 - accuracy: 0.9988 - val_loss: 1.1859 - val_accuracy: 0.7637\n",
      "Epoch 670/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0262 - accuracy: 0.9996 - val_loss: 1.2002 - val_accuracy: 0.7612\n",
      "Epoch 671/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0263 - accuracy: 0.9992 - val_loss: 1.2375 - val_accuracy: 0.7600\n",
      "Epoch 672/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0272 - accuracy: 0.9991 - val_loss: 1.2154 - val_accuracy: 0.7537\n",
      "Epoch 673/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0288 - accuracy: 0.9986 - val_loss: 1.2521 - val_accuracy: 0.7575\n",
      "Epoch 674/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0293 - accuracy: 0.9985 - val_loss: 1.2354 - val_accuracy: 0.7537\n",
      "Epoch 675/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0319 - accuracy: 0.9980 - val_loss: 1.2827 - val_accuracy: 0.7500\n",
      "Epoch 676/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0342 - accuracy: 0.9977 - val_loss: 1.2856 - val_accuracy: 0.7537\n",
      "Epoch 677/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0365 - accuracy: 0.9969 - val_loss: 1.3492 - val_accuracy: 0.7475\n",
      "Epoch 678/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0429 - accuracy: 0.9955 - val_loss: 1.3501 - val_accuracy: 0.7500\n",
      "Epoch 679/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0452 - accuracy: 0.9953 - val_loss: 1.4012 - val_accuracy: 0.7450\n",
      "Epoch 680/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0486 - accuracy: 0.9956 - val_loss: 1.4136 - val_accuracy: 0.7387\n",
      "Epoch 681/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0432 - accuracy: 0.9964 - val_loss: 1.2867 - val_accuracy: 0.7500\n",
      "Epoch 682/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0419 - accuracy: 0.9974 - val_loss: 1.3402 - val_accuracy: 0.7475\n",
      "Epoch 683/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0413 - accuracy: 0.9978 - val_loss: 1.2722 - val_accuracy: 0.7612\n",
      "Epoch 684/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0396 - accuracy: 0.9989 - val_loss: 1.3287 - val_accuracy: 0.7562\n",
      "Epoch 685/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0382 - accuracy: 0.9986 - val_loss: 1.2287 - val_accuracy: 0.7725\n",
      "Epoch 686/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0367 - accuracy: 0.9987 - val_loss: 1.2301 - val_accuracy: 0.7662\n",
      "Epoch 687/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0361 - accuracy: 0.9992 - val_loss: 1.2000 - val_accuracy: 0.7625\n",
      "Epoch 688/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0351 - accuracy: 0.9993 - val_loss: 1.1828 - val_accuracy: 0.7738\n",
      "Epoch 689/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0342 - accuracy: 0.9995 - val_loss: 1.1776 - val_accuracy: 0.7750\n",
      "Epoch 690/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0338 - accuracy: 0.9996 - val_loss: 1.1606 - val_accuracy: 0.7800\n",
      "Epoch 691/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0328 - accuracy: 0.9996 - val_loss: 1.1431 - val_accuracy: 0.7775\n",
      "Epoch 692/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0332 - accuracy: 0.9995 - val_loss: 1.1629 - val_accuracy: 0.7800\n",
      "Epoch 693/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0328 - accuracy: 0.9994 - val_loss: 1.1529 - val_accuracy: 0.7763\n",
      "Epoch 694/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0324 - accuracy: 0.9994 - val_loss: 1.1436 - val_accuracy: 0.7788\n",
      "Epoch 695/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0316 - accuracy: 0.9997 - val_loss: 1.1765 - val_accuracy: 0.7750\n",
      "Epoch 696/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0306 - accuracy: 0.9998 - val_loss: 1.1536 - val_accuracy: 0.7812\n",
      "Epoch 697/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0307 - accuracy: 0.9997 - val_loss: 1.1486 - val_accuracy: 0.7812\n",
      "Epoch 698/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0301 - accuracy: 0.9998 - val_loss: 1.1566 - val_accuracy: 0.7763\n",
      "Epoch 699/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0297 - accuracy: 0.9997 - val_loss: 1.1562 - val_accuracy: 0.7750\n",
      "Epoch 700/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0293 - accuracy: 0.9996 - val_loss: 1.1370 - val_accuracy: 0.7788\n",
      "Epoch 701/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0292 - accuracy: 0.9997 - val_loss: 1.1314 - val_accuracy: 0.7788\n",
      "Epoch 702/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0289 - accuracy: 0.9997 - val_loss: 1.1551 - val_accuracy: 0.7800\n",
      "Epoch 703/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0306 - accuracy: 0.9996 - val_loss: 1.1625 - val_accuracy: 0.7738\n",
      "Epoch 704/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0284 - accuracy: 0.9996 - val_loss: 1.1613 - val_accuracy: 0.7800\n",
      "Epoch 705/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0280 - accuracy: 0.9997 - val_loss: 1.1553 - val_accuracy: 0.7788\n",
      "Epoch 706/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0278 - accuracy: 0.9995 - val_loss: 1.1908 - val_accuracy: 0.7725\n",
      "Epoch 707/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0272 - accuracy: 0.9998 - val_loss: 1.1723 - val_accuracy: 0.7725\n",
      "Epoch 708/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0272 - accuracy: 0.9997 - val_loss: 1.1653 - val_accuracy: 0.7775\n",
      "Epoch 709/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0269 - accuracy: 0.9997 - val_loss: 1.2204 - val_accuracy: 0.7700\n",
      "Epoch 710/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0266 - accuracy: 0.9997 - val_loss: 1.2031 - val_accuracy: 0.7763\n",
      "Epoch 711/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0268 - accuracy: 0.9994 - val_loss: 1.2266 - val_accuracy: 0.7725\n",
      "Epoch 712/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0272 - accuracy: 0.9995 - val_loss: 1.1971 - val_accuracy: 0.7750\n",
      "Epoch 713/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0282 - accuracy: 0.9991 - val_loss: 1.2117 - val_accuracy: 0.7775\n",
      "Epoch 714/1000\n",
      "19/19 [==============================] - 0s 17ms/step - loss: 0.0285 - accuracy: 0.9991 - val_loss: 1.2558 - val_accuracy: 0.7688\n",
      "Epoch 715/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0289 - accuracy: 0.9990 - val_loss: 1.3117 - val_accuracy: 0.7500\n",
      "Epoch 716/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0298 - accuracy: 0.9989 - val_loss: 1.2555 - val_accuracy: 0.7550\n",
      "Epoch 717/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0301 - accuracy: 0.9988 - val_loss: 1.2484 - val_accuracy: 0.7600\n",
      "Epoch 718/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0306 - accuracy: 0.9988 - val_loss: 1.2791 - val_accuracy: 0.7537\n",
      "Epoch 719/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0327 - accuracy: 0.9981 - val_loss: 1.2772 - val_accuracy: 0.7525\n",
      "Epoch 720/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0323 - accuracy: 0.9984 - val_loss: 1.2732 - val_accuracy: 0.7537\n",
      "Epoch 721/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0326 - accuracy: 0.9981 - val_loss: 1.2292 - val_accuracy: 0.7688\n",
      "Epoch 722/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0328 - accuracy: 0.9989 - val_loss: 1.2862 - val_accuracy: 0.7587\n",
      "Epoch 723/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0320 - accuracy: 0.9988 - val_loss: 1.2704 - val_accuracy: 0.7575\n",
      "Epoch 724/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0328 - accuracy: 0.9986 - val_loss: 1.2626 - val_accuracy: 0.7537\n",
      "Epoch 725/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0348 - accuracy: 0.9978 - val_loss: 1.3010 - val_accuracy: 0.7612\n",
      "Epoch 726/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0319 - accuracy: 0.9991 - val_loss: 1.2756 - val_accuracy: 0.7650\n",
      "Epoch 727/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0326 - accuracy: 0.9989 - val_loss: 1.2475 - val_accuracy: 0.7650\n",
      "Epoch 728/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0317 - accuracy: 0.9991 - val_loss: 1.2211 - val_accuracy: 0.7662\n",
      "Epoch 729/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0314 - accuracy: 0.9993 - val_loss: 1.2087 - val_accuracy: 0.7713\n",
      "Epoch 730/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0321 - accuracy: 0.9995 - val_loss: 1.2117 - val_accuracy: 0.7650\n",
      "Epoch 731/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0297 - accuracy: 0.9996 - val_loss: 1.2169 - val_accuracy: 0.7575\n",
      "Epoch 732/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0296 - accuracy: 0.9995 - val_loss: 1.2148 - val_accuracy: 0.7600\n",
      "Epoch 733/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0298 - accuracy: 0.9990 - val_loss: 1.1754 - val_accuracy: 0.7637\n",
      "Epoch 734/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0306 - accuracy: 0.9993 - val_loss: 1.2093 - val_accuracy: 0.7562\n",
      "Epoch 735/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0307 - accuracy: 0.9991 - val_loss: 1.3039 - val_accuracy: 0.7625\n",
      "Epoch 736/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0302 - accuracy: 0.9991 - val_loss: 1.1926 - val_accuracy: 0.7725\n",
      "Epoch 737/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0297 - accuracy: 0.9995 - val_loss: 1.2184 - val_accuracy: 0.7625\n",
      "Epoch 738/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0299 - accuracy: 0.9993 - val_loss: 1.2157 - val_accuracy: 0.7462\n",
      "Epoch 739/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0300 - accuracy: 0.9993 - val_loss: 1.2382 - val_accuracy: 0.7450\n",
      "Epoch 740/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0292 - accuracy: 0.9993 - val_loss: 1.2086 - val_accuracy: 0.7537\n",
      "Epoch 741/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0290 - accuracy: 0.9994 - val_loss: 1.2143 - val_accuracy: 0.7650\n",
      "Epoch 742/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0289 - accuracy: 0.9995 - val_loss: 1.1374 - val_accuracy: 0.7675\n",
      "Epoch 743/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0293 - accuracy: 0.9990 - val_loss: 1.1468 - val_accuracy: 0.7600\n",
      "Epoch 744/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0294 - accuracy: 0.9990 - val_loss: 1.2186 - val_accuracy: 0.7637\n",
      "Epoch 745/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0294 - accuracy: 0.9989 - val_loss: 1.1948 - val_accuracy: 0.7525\n",
      "Epoch 746/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0325 - accuracy: 0.9981 - val_loss: 1.2308 - val_accuracy: 0.7625\n",
      "Epoch 747/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0317 - accuracy: 0.9984 - val_loss: 1.3562 - val_accuracy: 0.7462\n",
      "Epoch 748/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0325 - accuracy: 0.9984 - val_loss: 1.3014 - val_accuracy: 0.7512\n",
      "Epoch 749/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0343 - accuracy: 0.9977 - val_loss: 1.2896 - val_accuracy: 0.7562\n",
      "Epoch 750/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0342 - accuracy: 0.9979 - val_loss: 1.3445 - val_accuracy: 0.7437\n",
      "Epoch 751/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0339 - accuracy: 0.9982 - val_loss: 1.2840 - val_accuracy: 0.7525\n",
      "Epoch 752/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0337 - accuracy: 0.9984 - val_loss: 1.2151 - val_accuracy: 0.7650\n",
      "Epoch 753/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0335 - accuracy: 0.9986 - val_loss: 1.2482 - val_accuracy: 0.7612\n",
      "Epoch 754/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0349 - accuracy: 0.9979 - val_loss: 1.2842 - val_accuracy: 0.7537\n",
      "Epoch 755/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0358 - accuracy: 0.9977 - val_loss: 1.2424 - val_accuracy: 0.7600\n",
      "Epoch 756/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0369 - accuracy: 0.9977 - val_loss: 1.2324 - val_accuracy: 0.7575\n",
      "Epoch 757/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0360 - accuracy: 0.9982 - val_loss: 1.2248 - val_accuracy: 0.7575\n",
      "Epoch 758/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0360 - accuracy: 0.9983 - val_loss: 1.2148 - val_accuracy: 0.7562\n",
      "Epoch 759/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0345 - accuracy: 0.9986 - val_loss: 1.2018 - val_accuracy: 0.7650\n",
      "Epoch 760/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0344 - accuracy: 0.9989 - val_loss: 1.2074 - val_accuracy: 0.7662\n",
      "Epoch 761/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0337 - accuracy: 0.9990 - val_loss: 1.1925 - val_accuracy: 0.7637\n",
      "Epoch 762/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0328 - accuracy: 0.9989 - val_loss: 1.2153 - val_accuracy: 0.7562\n",
      "Epoch 763/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0339 - accuracy: 0.9988 - val_loss: 1.2129 - val_accuracy: 0.7637\n",
      "Epoch 764/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0334 - accuracy: 0.9991 - val_loss: 1.1619 - val_accuracy: 0.7700\n",
      "Epoch 765/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0336 - accuracy: 0.9989 - val_loss: 1.1999 - val_accuracy: 0.7637\n",
      "Epoch 766/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0329 - accuracy: 0.9993 - val_loss: 1.1449 - val_accuracy: 0.7675\n",
      "Epoch 767/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0320 - accuracy: 0.9994 - val_loss: 1.1479 - val_accuracy: 0.7738\n",
      "Epoch 768/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0316 - accuracy: 0.9993 - val_loss: 1.1360 - val_accuracy: 0.7725\n",
      "Epoch 769/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0322 - accuracy: 0.9994 - val_loss: 1.1211 - val_accuracy: 0.7800\n",
      "Epoch 770/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0301 - accuracy: 0.9998 - val_loss: 1.1084 - val_accuracy: 0.7750\n",
      "Epoch 771/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0297 - accuracy: 0.9997 - val_loss: 1.0965 - val_accuracy: 0.7713\n",
      "Epoch 772/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0293 - accuracy: 0.9997 - val_loss: 1.0978 - val_accuracy: 0.7725\n",
      "Epoch 773/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0290 - accuracy: 0.9997 - val_loss: 1.1003 - val_accuracy: 0.7763\n",
      "Epoch 774/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0283 - accuracy: 0.9997 - val_loss: 1.0970 - val_accuracy: 0.7738\n",
      "Epoch 775/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0283 - accuracy: 0.9996 - val_loss: 1.0736 - val_accuracy: 0.7812\n",
      "Epoch 776/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0276 - accuracy: 0.9998 - val_loss: 1.0938 - val_accuracy: 0.7775\n",
      "Epoch 777/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0274 - accuracy: 0.9997 - val_loss: 1.0914 - val_accuracy: 0.7788\n",
      "Epoch 778/1000\n",
      "19/19 [==============================] - 0s 21ms/step - loss: 0.0270 - accuracy: 0.9998 - val_loss: 1.0947 - val_accuracy: 0.7800\n",
      "Epoch 779/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0267 - accuracy: 0.9997 - val_loss: 1.1055 - val_accuracy: 0.7725\n",
      "Epoch 780/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0262 - accuracy: 0.9998 - val_loss: 1.1115 - val_accuracy: 0.7725\n",
      "Epoch 781/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0259 - accuracy: 0.9998 - val_loss: 1.0987 - val_accuracy: 0.7775\n",
      "Epoch 782/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0256 - accuracy: 0.9997 - val_loss: 1.0906 - val_accuracy: 0.7775\n",
      "Epoch 783/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0261 - accuracy: 0.9994 - val_loss: 1.1149 - val_accuracy: 0.7725\n",
      "Epoch 784/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0253 - accuracy: 0.9998 - val_loss: 1.0714 - val_accuracy: 0.7750\n",
      "Epoch 785/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0253 - accuracy: 0.9996 - val_loss: 1.0914 - val_accuracy: 0.7763\n",
      "Epoch 786/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0253 - accuracy: 0.9996 - val_loss: 1.0883 - val_accuracy: 0.7725\n",
      "Epoch 787/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0248 - accuracy: 0.9995 - val_loss: 1.1257 - val_accuracy: 0.7763\n",
      "Epoch 788/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0251 - accuracy: 0.9996 - val_loss: 1.1072 - val_accuracy: 0.7662\n",
      "Epoch 789/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0242 - accuracy: 0.9997 - val_loss: 1.1172 - val_accuracy: 0.7688\n",
      "Epoch 790/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0239 - accuracy: 0.9998 - val_loss: 1.0901 - val_accuracy: 0.7675\n",
      "Epoch 791/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0235 - accuracy: 0.9997 - val_loss: 1.0703 - val_accuracy: 0.7688\n",
      "Epoch 792/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0238 - accuracy: 0.9997 - val_loss: 1.1219 - val_accuracy: 0.7612\n",
      "Epoch 793/1000\n",
      "19/19 [==============================] - 0s 20ms/step - loss: 0.0246 - accuracy: 0.9992 - val_loss: 1.1405 - val_accuracy: 0.7688\n",
      "Epoch 794/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0262 - accuracy: 0.9987 - val_loss: 1.2214 - val_accuracy: 0.7688\n",
      "Epoch 795/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0287 - accuracy: 0.9983 - val_loss: 1.2752 - val_accuracy: 0.7562\n",
      "Epoch 796/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0330 - accuracy: 0.9978 - val_loss: 1.3133 - val_accuracy: 0.7475\n",
      "Epoch 797/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0399 - accuracy: 0.9965 - val_loss: 1.3977 - val_accuracy: 0.7400\n",
      "Epoch 798/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0419 - accuracy: 0.9957 - val_loss: 1.3291 - val_accuracy: 0.7412\n",
      "Epoch 799/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0453 - accuracy: 0.9955 - val_loss: 1.3616 - val_accuracy: 0.7387\n",
      "Epoch 800/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0456 - accuracy: 0.9964 - val_loss: 1.2556 - val_accuracy: 0.7537\n",
      "Epoch 801/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0464 - accuracy: 0.9957 - val_loss: 1.3270 - val_accuracy: 0.7475\n",
      "Epoch 802/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0455 - accuracy: 0.9966 - val_loss: 1.2749 - val_accuracy: 0.7425\n",
      "Epoch 803/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0418 - accuracy: 0.9974 - val_loss: 1.2422 - val_accuracy: 0.7525\n",
      "Epoch 804/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0391 - accuracy: 0.9983 - val_loss: 1.2731 - val_accuracy: 0.7537\n",
      "Epoch 805/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0404 - accuracy: 0.9980 - val_loss: 1.2536 - val_accuracy: 0.7700\n",
      "Epoch 806/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0391 - accuracy: 0.9986 - val_loss: 1.2160 - val_accuracy: 0.7662\n",
      "Epoch 807/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0372 - accuracy: 0.9991 - val_loss: 1.2003 - val_accuracy: 0.7550\n",
      "Epoch 808/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0352 - accuracy: 0.9995 - val_loss: 1.1948 - val_accuracy: 0.7575\n",
      "Epoch 809/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0354 - accuracy: 0.9992 - val_loss: 1.1895 - val_accuracy: 0.7650\n",
      "Epoch 810/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0338 - accuracy: 0.9995 - val_loss: 1.1957 - val_accuracy: 0.7625\n",
      "Epoch 811/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0336 - accuracy: 0.9995 - val_loss: 1.1647 - val_accuracy: 0.7700\n",
      "Epoch 812/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0327 - accuracy: 0.9997 - val_loss: 1.1231 - val_accuracy: 0.7688\n",
      "Epoch 813/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0321 - accuracy: 0.9995 - val_loss: 1.1138 - val_accuracy: 0.7750\n",
      "Epoch 814/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0314 - accuracy: 0.9997 - val_loss: 1.1101 - val_accuracy: 0.7800\n",
      "Epoch 815/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0310 - accuracy: 0.9998 - val_loss: 1.0984 - val_accuracy: 0.7788\n",
      "Epoch 816/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0304 - accuracy: 0.9997 - val_loss: 1.1095 - val_accuracy: 0.7738\n",
      "Epoch 817/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0312 - accuracy: 0.9995 - val_loss: 1.0986 - val_accuracy: 0.7738\n",
      "Epoch 818/1000\n",
      "19/19 [==============================] - 0s 20ms/step - loss: 0.0301 - accuracy: 0.9997 - val_loss: 1.0898 - val_accuracy: 0.7713\n",
      "Epoch 819/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0297 - accuracy: 0.9997 - val_loss: 1.1220 - val_accuracy: 0.7675\n",
      "Epoch 820/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0291 - accuracy: 0.9998 - val_loss: 1.1083 - val_accuracy: 0.7650\n",
      "Epoch 821/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0287 - accuracy: 0.9997 - val_loss: 1.1012 - val_accuracy: 0.7675\n",
      "Epoch 822/1000\n",
      "19/19 [==============================] - 0s 21ms/step - loss: 0.0283 - accuracy: 0.9997 - val_loss: 1.0974 - val_accuracy: 0.7738\n",
      "Epoch 823/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0279 - accuracy: 0.9997 - val_loss: 1.0956 - val_accuracy: 0.7713\n",
      "Epoch 824/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0276 - accuracy: 0.9998 - val_loss: 1.0962 - val_accuracy: 0.7738\n",
      "Epoch 825/1000\n",
      "19/19 [==============================] - 0s 20ms/step - loss: 0.0272 - accuracy: 0.9997 - val_loss: 1.1143 - val_accuracy: 0.7738\n",
      "Epoch 826/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0269 - accuracy: 0.9997 - val_loss: 1.1181 - val_accuracy: 0.7675\n",
      "Epoch 827/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0269 - accuracy: 0.9998 - val_loss: 1.1265 - val_accuracy: 0.7725\n",
      "Epoch 828/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0267 - accuracy: 0.9996 - val_loss: 1.1321 - val_accuracy: 0.7738\n",
      "Epoch 829/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0261 - accuracy: 0.9997 - val_loss: 1.1113 - val_accuracy: 0.7800\n",
      "Epoch 830/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0259 - accuracy: 0.9997 - val_loss: 1.1402 - val_accuracy: 0.7750\n",
      "Epoch 831/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0255 - accuracy: 0.9997 - val_loss: 1.1431 - val_accuracy: 0.7750\n",
      "Epoch 832/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0254 - accuracy: 0.9997 - val_loss: 1.1303 - val_accuracy: 0.7775\n",
      "Epoch 833/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0251 - accuracy: 0.9999 - val_loss: 1.1388 - val_accuracy: 0.7788\n",
      "Epoch 834/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0248 - accuracy: 0.9998 - val_loss: 1.1526 - val_accuracy: 0.7738\n",
      "Epoch 835/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0244 - accuracy: 0.9997 - val_loss: 1.1555 - val_accuracy: 0.7825\n",
      "Epoch 836/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0240 - accuracy: 0.9998 - val_loss: 1.1415 - val_accuracy: 0.7775\n",
      "Epoch 837/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0241 - accuracy: 0.9996 - val_loss: 1.1372 - val_accuracy: 0.7788\n",
      "Epoch 838/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0245 - accuracy: 0.9993 - val_loss: 1.1356 - val_accuracy: 0.7763\n",
      "Epoch 839/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0257 - accuracy: 0.9989 - val_loss: 1.2715 - val_accuracy: 0.7625\n",
      "Epoch 840/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0265 - accuracy: 0.9987 - val_loss: 1.2196 - val_accuracy: 0.7587\n",
      "Epoch 841/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0290 - accuracy: 0.9983 - val_loss: 1.1379 - val_accuracy: 0.7738\n",
      "Epoch 842/1000\n",
      "19/19 [==============================] - 0s 21ms/step - loss: 0.0298 - accuracy: 0.9978 - val_loss: 1.1957 - val_accuracy: 0.7637\n",
      "Epoch 843/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0319 - accuracy: 0.9976 - val_loss: 1.2281 - val_accuracy: 0.7662\n",
      "Epoch 844/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0345 - accuracy: 0.9969 - val_loss: 1.2825 - val_accuracy: 0.7462\n",
      "Epoch 845/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0354 - accuracy: 0.9974 - val_loss: 1.3474 - val_accuracy: 0.7262\n",
      "Epoch 846/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0363 - accuracy: 0.9978 - val_loss: 1.2597 - val_accuracy: 0.7562\n",
      "Epoch 847/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0358 - accuracy: 0.9976 - val_loss: 1.2942 - val_accuracy: 0.7525\n",
      "Epoch 848/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0363 - accuracy: 0.9977 - val_loss: 1.2154 - val_accuracy: 0.7600\n",
      "Epoch 849/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0361 - accuracy: 0.9980 - val_loss: 1.2843 - val_accuracy: 0.7637\n",
      "Epoch 850/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0350 - accuracy: 0.9984 - val_loss: 1.1904 - val_accuracy: 0.7688\n",
      "Epoch 851/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0345 - accuracy: 0.9983 - val_loss: 1.1806 - val_accuracy: 0.7738\n",
      "Epoch 852/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0337 - accuracy: 0.9986 - val_loss: 1.2023 - val_accuracy: 0.7763\n",
      "Epoch 853/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0318 - accuracy: 0.9993 - val_loss: 1.1414 - val_accuracy: 0.7675\n",
      "Epoch 854/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0318 - accuracy: 0.9993 - val_loss: 1.2147 - val_accuracy: 0.7675\n",
      "Epoch 855/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0317 - accuracy: 0.9990 - val_loss: 1.2509 - val_accuracy: 0.7587\n",
      "Epoch 856/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0316 - accuracy: 0.9991 - val_loss: 1.2931 - val_accuracy: 0.7562\n",
      "Epoch 857/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0312 - accuracy: 0.9990 - val_loss: 1.2497 - val_accuracy: 0.7500\n",
      "Epoch 858/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0334 - accuracy: 0.9986 - val_loss: 1.3134 - val_accuracy: 0.7512\n",
      "Epoch 859/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0324 - accuracy: 0.9988 - val_loss: 1.3114 - val_accuracy: 0.7537\n",
      "Epoch 860/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0328 - accuracy: 0.9989 - val_loss: 1.2612 - val_accuracy: 0.7525\n",
      "Epoch 861/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0323 - accuracy: 0.9989 - val_loss: 1.3244 - val_accuracy: 0.7562\n",
      "Epoch 862/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0315 - accuracy: 0.9993 - val_loss: 1.2738 - val_accuracy: 0.7625\n",
      "Epoch 863/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0302 - accuracy: 0.9996 - val_loss: 1.2531 - val_accuracy: 0.7662\n",
      "Epoch 864/1000\n",
      "19/19 [==============================] - 0s 21ms/step - loss: 0.0303 - accuracy: 0.9995 - val_loss: 1.2451 - val_accuracy: 0.7650\n",
      "Epoch 865/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0324 - accuracy: 0.9994 - val_loss: 1.2401 - val_accuracy: 0.7562\n",
      "Epoch 866/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0308 - accuracy: 0.9994 - val_loss: 1.2277 - val_accuracy: 0.7575\n",
      "Epoch 867/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0294 - accuracy: 0.9994 - val_loss: 1.2079 - val_accuracy: 0.7575\n",
      "Epoch 868/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0299 - accuracy: 0.9993 - val_loss: 1.1840 - val_accuracy: 0.7612\n",
      "Epoch 869/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0290 - accuracy: 0.9995 - val_loss: 1.1996 - val_accuracy: 0.7575\n",
      "Epoch 870/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0289 - accuracy: 0.9993 - val_loss: 1.1747 - val_accuracy: 0.7662\n",
      "Epoch 871/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0284 - accuracy: 0.9996 - val_loss: 1.1259 - val_accuracy: 0.7650\n",
      "Epoch 872/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0280 - accuracy: 0.9997 - val_loss: 1.1478 - val_accuracy: 0.7700\n",
      "Epoch 873/1000\n",
      "19/19 [==============================] - 0s 20ms/step - loss: 0.0282 - accuracy: 0.9996 - val_loss: 1.1374 - val_accuracy: 0.7662\n",
      "Epoch 874/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0281 - accuracy: 0.9993 - val_loss: 1.1388 - val_accuracy: 0.7700\n",
      "Epoch 875/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0281 - accuracy: 0.9993 - val_loss: 1.1122 - val_accuracy: 0.7750\n",
      "Epoch 876/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0285 - accuracy: 0.9993 - val_loss: 1.1604 - val_accuracy: 0.7650\n",
      "Epoch 877/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0282 - accuracy: 0.9995 - val_loss: 1.1518 - val_accuracy: 0.7625\n",
      "Epoch 878/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0277 - accuracy: 0.9996 - val_loss: 1.1402 - val_accuracy: 0.7688\n",
      "Epoch 879/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0271 - accuracy: 0.9995 - val_loss: 1.1422 - val_accuracy: 0.7725\n",
      "Epoch 880/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0265 - accuracy: 0.9995 - val_loss: 1.1434 - val_accuracy: 0.7713\n",
      "Epoch 881/1000\n",
      "19/19 [==============================] - 0s 21ms/step - loss: 0.0259 - accuracy: 0.9999 - val_loss: 1.1374 - val_accuracy: 0.7675\n",
      "Epoch 882/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0259 - accuracy: 0.9997 - val_loss: 1.1288 - val_accuracy: 0.7775\n",
      "Epoch 883/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0258 - accuracy: 0.9995 - val_loss: 1.1982 - val_accuracy: 0.7700\n",
      "Epoch 884/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0260 - accuracy: 0.9993 - val_loss: 1.1886 - val_accuracy: 0.7587\n",
      "Epoch 885/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0258 - accuracy: 0.9993 - val_loss: 1.1627 - val_accuracy: 0.7662\n",
      "Epoch 886/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0266 - accuracy: 0.9991 - val_loss: 1.2565 - val_accuracy: 0.7625\n",
      "Epoch 887/1000\n",
      "19/19 [==============================] - 0s 20ms/step - loss: 0.0278 - accuracy: 0.9989 - val_loss: 1.2075 - val_accuracy: 0.7700\n",
      "Epoch 888/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0286 - accuracy: 0.9992 - val_loss: 1.2342 - val_accuracy: 0.7625\n",
      "Epoch 889/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0285 - accuracy: 0.9990 - val_loss: 1.2085 - val_accuracy: 0.7612\n",
      "Epoch 890/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0291 - accuracy: 0.9987 - val_loss: 1.2877 - val_accuracy: 0.7612\n",
      "Epoch 891/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0296 - accuracy: 0.9989 - val_loss: 1.2507 - val_accuracy: 0.7537\n",
      "Epoch 892/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0298 - accuracy: 0.9989 - val_loss: 1.2276 - val_accuracy: 0.7675\n",
      "Epoch 893/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0304 - accuracy: 0.9986 - val_loss: 1.2918 - val_accuracy: 0.7575\n",
      "Epoch 894/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0286 - accuracy: 0.9993 - val_loss: 1.2140 - val_accuracy: 0.7700\n",
      "Epoch 895/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0285 - accuracy: 0.9993 - val_loss: 1.2054 - val_accuracy: 0.7713\n",
      "Epoch 896/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0293 - accuracy: 0.9991 - val_loss: 1.2026 - val_accuracy: 0.7738\n",
      "Epoch 897/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0277 - accuracy: 0.9994 - val_loss: 1.1653 - val_accuracy: 0.7775\n",
      "Epoch 898/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0273 - accuracy: 0.9993 - val_loss: 1.1801 - val_accuracy: 0.7887\n",
      "Epoch 899/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0271 - accuracy: 0.9995 - val_loss: 1.1642 - val_accuracy: 0.7850\n",
      "Epoch 900/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0290 - accuracy: 0.9988 - val_loss: 1.1854 - val_accuracy: 0.7750\n",
      "Epoch 901/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0300 - accuracy: 0.9988 - val_loss: 1.1855 - val_accuracy: 0.7725\n",
      "Epoch 902/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0303 - accuracy: 0.9986 - val_loss: 1.2117 - val_accuracy: 0.7650\n",
      "Epoch 903/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0305 - accuracy: 0.9985 - val_loss: 1.2543 - val_accuracy: 0.7650\n",
      "Epoch 904/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0302 - accuracy: 0.9988 - val_loss: 1.2463 - val_accuracy: 0.7688\n",
      "Epoch 905/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0311 - accuracy: 0.9986 - val_loss: 1.2216 - val_accuracy: 0.7662\n",
      "Epoch 906/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0314 - accuracy: 0.9982 - val_loss: 1.2403 - val_accuracy: 0.7487\n",
      "Epoch 907/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0319 - accuracy: 0.9984 - val_loss: 1.2180 - val_accuracy: 0.7612\n",
      "Epoch 908/1000\n",
      "19/19 [==============================] - 0s 21ms/step - loss: 0.0328 - accuracy: 0.9985 - val_loss: 1.2135 - val_accuracy: 0.7562\n",
      "Epoch 909/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0318 - accuracy: 0.9989 - val_loss: 1.3126 - val_accuracy: 0.7437\n",
      "Epoch 910/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0320 - accuracy: 0.9986 - val_loss: 1.2534 - val_accuracy: 0.7637\n",
      "Epoch 911/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0308 - accuracy: 0.9991 - val_loss: 1.2111 - val_accuracy: 0.7500\n",
      "Epoch 912/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0319 - accuracy: 0.9988 - val_loss: 1.1631 - val_accuracy: 0.7600\n",
      "Epoch 913/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0305 - accuracy: 0.9991 - val_loss: 1.1570 - val_accuracy: 0.7662\n",
      "Epoch 914/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0301 - accuracy: 0.9990 - val_loss: 1.1469 - val_accuracy: 0.7600\n",
      "Epoch 915/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0295 - accuracy: 0.9993 - val_loss: 1.1628 - val_accuracy: 0.7713\n",
      "Epoch 916/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0297 - accuracy: 0.9992 - val_loss: 1.2058 - val_accuracy: 0.7625\n",
      "Epoch 917/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0294 - accuracy: 0.9991 - val_loss: 1.2026 - val_accuracy: 0.7437\n",
      "Epoch 918/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0302 - accuracy: 0.9989 - val_loss: 1.2064 - val_accuracy: 0.7525\n",
      "Epoch 919/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0299 - accuracy: 0.9989 - val_loss: 1.2069 - val_accuracy: 0.7650\n",
      "Epoch 920/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0297 - accuracy: 0.9993 - val_loss: 1.2238 - val_accuracy: 0.7512\n",
      "Epoch 921/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0305 - accuracy: 0.9989 - val_loss: 1.2965 - val_accuracy: 0.7525\n",
      "Epoch 922/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0302 - accuracy: 0.9990 - val_loss: 1.2299 - val_accuracy: 0.7600\n",
      "Epoch 923/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0297 - accuracy: 0.9993 - val_loss: 1.3085 - val_accuracy: 0.7462\n",
      "Epoch 924/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0288 - accuracy: 0.9994 - val_loss: 1.2714 - val_accuracy: 0.7400\n",
      "Epoch 925/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0292 - accuracy: 0.9991 - val_loss: 1.2602 - val_accuracy: 0.7525\n",
      "Epoch 926/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0305 - accuracy: 0.9988 - val_loss: 1.2531 - val_accuracy: 0.7512\n",
      "Epoch 927/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0325 - accuracy: 0.9986 - val_loss: 1.3105 - val_accuracy: 0.7487\n",
      "Epoch 928/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0342 - accuracy: 0.9980 - val_loss: 1.2900 - val_accuracy: 0.7537\n",
      "Epoch 929/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0354 - accuracy: 0.9977 - val_loss: 1.2930 - val_accuracy: 0.7525\n",
      "Epoch 930/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0349 - accuracy: 0.9985 - val_loss: 1.3026 - val_accuracy: 0.7450\n",
      "Epoch 931/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0343 - accuracy: 0.9986 - val_loss: 1.2762 - val_accuracy: 0.7625\n",
      "Epoch 932/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0352 - accuracy: 0.9982 - val_loss: 1.2590 - val_accuracy: 0.7625\n",
      "Epoch 933/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0346 - accuracy: 0.9984 - val_loss: 1.1944 - val_accuracy: 0.7612\n",
      "Epoch 934/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0340 - accuracy: 0.9990 - val_loss: 1.2412 - val_accuracy: 0.7675\n",
      "Epoch 935/1000\n",
      "19/19 [==============================] - 0s 21ms/step - loss: 0.0328 - accuracy: 0.9993 - val_loss: 1.2214 - val_accuracy: 0.7650\n",
      "Epoch 936/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0319 - accuracy: 0.9993 - val_loss: 1.1895 - val_accuracy: 0.7700\n",
      "Epoch 937/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0317 - accuracy: 0.9992 - val_loss: 1.1989 - val_accuracy: 0.7587\n",
      "Epoch 938/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0314 - accuracy: 0.9993 - val_loss: 1.2158 - val_accuracy: 0.7688\n",
      "Epoch 939/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0315 - accuracy: 0.9990 - val_loss: 1.1400 - val_accuracy: 0.7763\n",
      "Epoch 940/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0306 - accuracy: 0.9995 - val_loss: 1.1572 - val_accuracy: 0.7788\n",
      "Epoch 941/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0302 - accuracy: 0.9994 - val_loss: 1.1433 - val_accuracy: 0.7725\n",
      "Epoch 942/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0293 - accuracy: 0.9997 - val_loss: 1.1341 - val_accuracy: 0.7788\n",
      "Epoch 943/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0292 - accuracy: 0.9996 - val_loss: 1.1396 - val_accuracy: 0.7738\n",
      "Epoch 944/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0290 - accuracy: 0.9997 - val_loss: 1.1293 - val_accuracy: 0.7738\n",
      "Epoch 945/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0282 - accuracy: 0.9997 - val_loss: 1.1406 - val_accuracy: 0.7675\n",
      "Epoch 946/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0277 - accuracy: 0.9997 - val_loss: 1.1313 - val_accuracy: 0.7662\n",
      "Epoch 947/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0274 - accuracy: 0.9997 - val_loss: 1.1116 - val_accuracy: 0.7750\n",
      "Epoch 948/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0269 - accuracy: 0.9997 - val_loss: 1.1175 - val_accuracy: 0.7675\n",
      "Epoch 949/1000\n",
      "19/19 [==============================] - 0s 20ms/step - loss: 0.0263 - accuracy: 0.9997 - val_loss: 1.1184 - val_accuracy: 0.7637\n",
      "Epoch 950/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0260 - accuracy: 0.9997 - val_loss: 1.1178 - val_accuracy: 0.7675\n",
      "Epoch 951/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0256 - accuracy: 0.9997 - val_loss: 1.1255 - val_accuracy: 0.7688\n",
      "Epoch 952/1000\n",
      "19/19 [==============================] - 0s 20ms/step - loss: 0.0254 - accuracy: 0.9997 - val_loss: 1.1149 - val_accuracy: 0.7700\n",
      "Epoch 953/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0254 - accuracy: 0.9997 - val_loss: 1.1228 - val_accuracy: 0.7688\n",
      "Epoch 954/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0250 - accuracy: 0.9997 - val_loss: 1.1347 - val_accuracy: 0.7725\n",
      "Epoch 955/1000\n",
      "19/19 [==============================] - 0s 20ms/step - loss: 0.0246 - accuracy: 0.9996 - val_loss: 1.1281 - val_accuracy: 0.7738\n",
      "Epoch 956/1000\n",
      "19/19 [==============================] - 0s 20ms/step - loss: 0.0245 - accuracy: 0.9995 - val_loss: 1.1160 - val_accuracy: 0.7725\n",
      "Epoch 957/1000\n",
      "19/19 [==============================] - 0s 20ms/step - loss: 0.0242 - accuracy: 0.9997 - val_loss: 1.1548 - val_accuracy: 0.7763\n",
      "Epoch 958/1000\n",
      "19/19 [==============================] - 0s 20ms/step - loss: 0.0245 - accuracy: 0.9995 - val_loss: 1.1219 - val_accuracy: 0.7725\n",
      "Epoch 959/1000\n",
      "19/19 [==============================] - 0s 22ms/step - loss: 0.0239 - accuracy: 0.9996 - val_loss: 1.1184 - val_accuracy: 0.7750\n",
      "Epoch 960/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0238 - accuracy: 0.9995 - val_loss: 1.1055 - val_accuracy: 0.7850\n",
      "Epoch 961/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0238 - accuracy: 0.9995 - val_loss: 1.1188 - val_accuracy: 0.7763\n",
      "Epoch 962/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0252 - accuracy: 0.9995 - val_loss: 1.1150 - val_accuracy: 0.7800\n",
      "Epoch 963/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0246 - accuracy: 0.9994 - val_loss: 1.1743 - val_accuracy: 0.7650\n",
      "Epoch 964/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0258 - accuracy: 0.9991 - val_loss: 1.2801 - val_accuracy: 0.7612\n",
      "Epoch 965/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0263 - accuracy: 0.9991 - val_loss: 1.2358 - val_accuracy: 0.7512\n",
      "Epoch 966/1000\n",
      "19/19 [==============================] - 0s 20ms/step - loss: 0.0268 - accuracy: 0.9991 - val_loss: 1.2762 - val_accuracy: 0.7587\n",
      "Epoch 967/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0268 - accuracy: 0.9990 - val_loss: 1.3410 - val_accuracy: 0.7500\n",
      "Epoch 968/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0270 - accuracy: 0.9991 - val_loss: 1.2070 - val_accuracy: 0.7725\n",
      "Epoch 969/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0272 - accuracy: 0.9992 - val_loss: 1.1542 - val_accuracy: 0.7688\n",
      "Epoch 970/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0271 - accuracy: 0.9994 - val_loss: 1.1221 - val_accuracy: 0.7750\n",
      "Epoch 971/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0271 - accuracy: 0.9993 - val_loss: 1.0933 - val_accuracy: 0.7738\n",
      "Epoch 972/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0289 - accuracy: 0.9989 - val_loss: 1.1340 - val_accuracy: 0.7650\n",
      "Epoch 973/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0298 - accuracy: 0.9984 - val_loss: 1.1889 - val_accuracy: 0.7637\n",
      "Epoch 974/1000\n",
      "19/19 [==============================] - 0s 20ms/step - loss: 0.0342 - accuracy: 0.9976 - val_loss: 1.2316 - val_accuracy: 0.7562\n",
      "Epoch 975/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0351 - accuracy: 0.9967 - val_loss: 1.2636 - val_accuracy: 0.7525\n",
      "Epoch 976/1000\n",
      "19/19 [==============================] - 0s 20ms/step - loss: 0.0379 - accuracy: 0.9969 - val_loss: 1.3345 - val_accuracy: 0.7487\n",
      "Epoch 977/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0413 - accuracy: 0.9971 - val_loss: 1.3541 - val_accuracy: 0.7537\n",
      "Epoch 978/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0426 - accuracy: 0.9965 - val_loss: 1.2074 - val_accuracy: 0.7625\n",
      "Epoch 979/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0394 - accuracy: 0.9972 - val_loss: 1.3243 - val_accuracy: 0.7450\n",
      "Epoch 980/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0370 - accuracy: 0.9980 - val_loss: 1.3157 - val_accuracy: 0.7512\n",
      "Epoch 981/1000\n",
      "19/19 [==============================] - 0s 20ms/step - loss: 0.0346 - accuracy: 0.9985 - val_loss: 1.2138 - val_accuracy: 0.7675\n",
      "Epoch 982/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0333 - accuracy: 0.9987 - val_loss: 1.2238 - val_accuracy: 0.7575\n",
      "Epoch 983/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0336 - accuracy: 0.9986 - val_loss: 1.2420 - val_accuracy: 0.7562\n",
      "Epoch 984/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0332 - accuracy: 0.9992 - val_loss: 1.2182 - val_accuracy: 0.7462\n",
      "Epoch 985/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0319 - accuracy: 0.9993 - val_loss: 1.1619 - val_accuracy: 0.7662\n",
      "Epoch 986/1000\n",
      "19/19 [==============================] - 0s 18ms/step - loss: 0.0310 - accuracy: 0.9995 - val_loss: 1.1855 - val_accuracy: 0.7637\n",
      "Epoch 987/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0304 - accuracy: 0.9995 - val_loss: 1.1936 - val_accuracy: 0.7625\n",
      "Epoch 988/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0297 - accuracy: 0.9996 - val_loss: 1.1514 - val_accuracy: 0.7738\n",
      "Epoch 989/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0291 - accuracy: 0.9997 - val_loss: 1.1559 - val_accuracy: 0.7738\n",
      "Epoch 990/1000\n",
      "19/19 [==============================] - 0s 20ms/step - loss: 0.0287 - accuracy: 0.9998 - val_loss: 1.1516 - val_accuracy: 0.7688\n",
      "Epoch 991/1000\n",
      "19/19 [==============================] - 0s 20ms/step - loss: 0.0281 - accuracy: 0.9997 - val_loss: 1.1515 - val_accuracy: 0.7738\n",
      "Epoch 992/1000\n",
      "19/19 [==============================] - 0s 20ms/step - loss: 0.0278 - accuracy: 0.9997 - val_loss: 1.1414 - val_accuracy: 0.7738\n",
      "Epoch 993/1000\n",
      "19/19 [==============================] - 0s 20ms/step - loss: 0.0273 - accuracy: 0.9997 - val_loss: 1.1246 - val_accuracy: 0.7825\n",
      "Epoch 994/1000\n",
      "19/19 [==============================] - 0s 20ms/step - loss: 0.0272 - accuracy: 0.9997 - val_loss: 1.1219 - val_accuracy: 0.7788\n",
      "Epoch 995/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0272 - accuracy: 0.9995 - val_loss: 1.1193 - val_accuracy: 0.7862\n",
      "Epoch 996/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0274 - accuracy: 0.9996 - val_loss: 1.1696 - val_accuracy: 0.7788\n",
      "Epoch 997/1000\n",
      "19/19 [==============================] - 0s 20ms/step - loss: 0.0265 - accuracy: 0.9997 - val_loss: 1.1583 - val_accuracy: 0.7750\n",
      "Epoch 998/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0262 - accuracy: 0.9996 - val_loss: 1.1469 - val_accuracy: 0.7788\n",
      "Epoch 999/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0261 - accuracy: 0.9996 - val_loss: 1.1753 - val_accuracy: 0.7662\n",
      "Epoch 1000/1000\n",
      "19/19 [==============================] - 0s 19ms/step - loss: 0.0266 - accuracy: 0.9995 - val_loss: 1.1592 - val_accuracy: 0.7862\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x1200c9310>"
      ]
     },
     "execution_count": 60,
     "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",
    "# history = model1.fit(fine_tune_data, fine_tune_labels, epochs=EPOCHS, batch_size=1600, callbacks=[model_checkpoint_callback])\n",
    "model1.save_weights(\"./Models/ResNet-kP/resnet-kp-model-weights\")\n",
    "model1.load_weights('./Models/ResNet-kP/resnet-kp-model-weights')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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": 62,
   "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-kPiece Model')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03390fa6",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48701107",
   "metadata": {},
   "source": [
    "#### On Train Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0594069",
   "metadata": {},
   "source": [
    "Now, after the model is being trained, we will look at the performance of the model on the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "cf7ded38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 56/475 [==>...........................] - ETA: 1s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-08 15:57:13.418176: 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 [==============================] - 2s 4ms/step\n"
     ]
    }
   ],
   "source": [
    "train_preds = model1.predict(fine_tune_data_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f2a28269",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_pred_labels = np.argmax(train_preds, axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "768775b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1, 13, 13, ...,  3,  9,  9])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_pred_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "eff52a3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15193"
      ]
     },
     "execution_count": 66,
     "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": 67,
   "id": "8f594a33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>images</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3597</th>\n",
       "      <td>3781</td>\n",
       "      <td>./Data/train/train/3781.tif</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4633</th>\n",
       "      <td>4876</td>\n",
       "      <td>./Data/train/train/4876.tif</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5298</th>\n",
       "      <td>5578</td>\n",
       "      <td>./Data/train/train/5578.tif</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7417</th>\n",
       "      <td>7807</td>\n",
       "      <td>./Data/train/train/7807.tif</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12255</th>\n",
       "      <td>12896</td>\n",
       "      <td>./Data/train/train/12896.tif</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12916</th>\n",
       "      <td>13599</td>\n",
       "      <td>./Data/train/train/13599.tif</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14569</th>\n",
       "      <td>15333</td>\n",
       "      <td>./Data/train/train/15333.tif</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id                        images  label\n",
       "3597    3781   ./Data/train/train/3781.tif      0\n",
       "4633    4876   ./Data/train/train/4876.tif     12\n",
       "5298    5578   ./Data/train/train/5578.tif     11\n",
       "7417    7807   ./Data/train/train/7807.tif     11\n",
       "12255  12896  ./Data/train/train/12896.tif      9\n",
       "12916  13599  ./Data/train/train/13599.tif      5\n",
       "14569  15333  ./Data/train/train/15333.tif      7"
      ]
     },
     "execution_count": 67,
     "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": 68,
   "id": "2ea8f949",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>fscore</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998978</td>\n",
       "      <td>0.999488</td>\n",
       "      <td>978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.995694</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.997843</td>\n",
       "      <td>925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.998926</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999463</td>\n",
       "      <td>930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998960</td>\n",
       "      <td>0.999480</td>\n",
       "      <td>962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998905</td>\n",
       "      <td>0.999452</td>\n",
       "      <td>913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998951</td>\n",
       "      <td>0.999475</td>\n",
       "      <td>953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.997906</td>\n",
       "      <td>0.998952</td>\n",
       "      <td>955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.997955</td>\n",
       "      <td>0.998976</td>\n",
       "      <td>0.998465</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    0.995694  1.000000  0.997843      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  0.998960  0.999480      962\n",
       "6    1.000000  1.000000  1.000000      998\n",
       "7    1.000000  0.998905  0.999452      913\n",
       "8    1.000000  1.000000  1.000000     1005\n",
       "9    1.000000  0.998951  0.999475      953\n",
       "10   1.000000  1.000000  1.000000      916\n",
       "11   1.000000  0.997906  0.998952      955\n",
       "12   0.997955  0.998976  0.998465      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": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision, recall, fscore, support = score(train_labels['label'], train_pred_labels)\n",
    "pd.DataFrame({\n",
    "    'precision': precision,\n",
    "    'recall': recall,\n",
    "    'fscore': fscore,\n",
    "    'support': support\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6e51475",
   "metadata": {},
   "source": [
    "#### On Train-Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "14a7854d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25/25 [==============================] - 0s 3ms/step\n"
     ]
    }
   ],
   "source": [
    "test_preds = model1.predict(fine_tune_data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "d585fa70",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_pred_labels = np.argmax(test_preds, axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "9a77dc3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        6])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pred_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "8bce361f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "629"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(test_pred_labels == test_labels['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "e07d40ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>images</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41</td>\n",
       "      <td>./Data/train/train/41.tif</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100</td>\n",
       "      <td>./Data/train/train/100.tif</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>179</td>\n",
       "      <td>./Data/train/train/179.tif</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>372</td>\n",
       "      <td>./Data/train/train/372.tif</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>378</td>\n",
       "      <td>./Data/train/train/378.tif</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>15528</td>\n",
       "      <td>./Data/train/train/15528.tif</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779</th>\n",
       "      <td>15568</td>\n",
       "      <td>./Data/train/train/15568.tif</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780</th>\n",
       "      <td>15571</td>\n",
       "      <td>./Data/train/train/15571.tif</td>\n",
       "      <td>14</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>788</th>\n",
       "      <td>15687</td>\n",
       "      <td>./Data/train/train/15687.tif</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>171 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                        images  label\n",
       "3       41     ./Data/train/train/41.tif      1\n",
       "7      100    ./Data/train/train/100.tif     10\n",
       "12     179    ./Data/train/train/179.tif      7\n",
       "19     372    ./Data/train/train/372.tif     13\n",
       "20     378    ./Data/train/train/378.tif     12\n",
       "..     ...                           ...    ...\n",
       "776  15528  ./Data/train/train/15528.tif      5\n",
       "779  15568  ./Data/train/train/15568.tif      9\n",
       "780  15571  ./Data/train/train/15571.tif     14\n",
       "782  15582  ./Data/train/train/15582.tif      3\n",
       "788  15687  ./Data/train/train/15687.tif     12\n",
       "\n",
       "[171 rows x 3 columns]"
      ]
     },
     "execution_count": 73,
     "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": 74,
   "id": "0f0b85fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>fscore</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.741379</td>\n",
       "      <td>0.796296</td>\n",
       "      <td>0.767857</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.551724</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.627451</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.897436</td>\n",
       "      <td>0.813953</td>\n",
       "      <td>0.853659</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.913043</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.704545</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>0.659574</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.851064</td>\n",
       "      <td>0.869565</td>\n",
       "      <td>0.860215</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.784314</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.841270</td>\n",
       "      <td>0.981481</td>\n",
       "      <td>0.905983</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.869565</td>\n",
       "      <td>0.851064</td>\n",
       "      <td>0.860215</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.775000</td>\n",
       "      <td>0.534483</td>\n",
       "      <td>0.632653</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.719298</td>\n",
       "      <td>0.732143</td>\n",
       "      <td>0.725664</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.755556</td>\n",
       "      <td>0.790698</td>\n",
       "      <td>0.772727</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.813559</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.780488</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.912281</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.859504</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.702128</td>\n",
       "      <td>0.767442</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    precision    recall    fscore  support\n",
       "0    0.741379  0.796296  0.767857       54\n",
       "1    0.551724  0.727273  0.627451       44\n",
       "2    0.897436  0.813953  0.853659       43\n",
       "3    0.840000  0.913043  0.875000       46\n",
       "4    0.850000  0.944444  0.894737       36\n",
       "5    0.704545  0.620000  0.659574       50\n",
       "6    0.851064  0.869565  0.860215       46\n",
       "7    0.800000  0.769231  0.784314       52\n",
       "8    0.841270  0.981481  0.905983       54\n",
       "9    0.869565  0.851064  0.860215       47\n",
       "10   0.775000  0.534483  0.632653       58\n",
       "11   0.719298  0.732143  0.725664       56\n",
       "12   0.755556  0.790698  0.772727       43\n",
       "13   0.813559  0.750000  0.780488       64\n",
       "14   0.912281  0.812500  0.859504       64\n",
       "15   0.702128  0.767442  0.733333       43"
      ]
     },
     "execution_count": 74,
     "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": 75,
   "id": "ffd70520",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.787085876889608"
      ]
     },
     "execution_count": 75,
     "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": 76,
   "id": "20d57a31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29/29 [==============================] - 0s 4ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-08 15:57:39.999356: 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": 77,
   "id": "db7d1256",
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_pred_labels = np.argmax(valid_preds, axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "315c93f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        6,  1,  3, 11,  2, 13,  8,  4,  9, 12, 12, 14,  6, 15, 12,  9,  9,\n",
       "        5, 13, 13,  2,  3, 11, 10,  7,  9, 15,  8,  2, 12,  8, 10, 15, 13,\n",
       "       14, 12,  8,  5,  8,  0,  2, 10, 12,  3,  8,  4,  9,  4,  0, 13, 15,\n",
       "        5, 15,  3, 12, 10,  6, 13,  3,  5, 14, 12, 13, 10,  9,  8,  9, 11,\n",
       "        4,  6,  8,  7,  6,  9,  0,  8,  8,  3,  4,  8,  1,  2,  3,  7,  0,\n",
       "        7,  0, 11,  3, 11, 14, 15,  6,  5,  4,  4,  7,  0,  3,  5,  0,  4,\n",
       "        5, 14,  8, 13, 11,  4,  8,  2, 10,  9,  7,  7,  5,  2, 13,  9, 12,\n",
       "        6,  6,  1,  0,  4,  8, 11, 11, 13,  2,  2,  4, 14,  8, 10,  3, 14,\n",
       "        4, 13, 11, 14,  6,  3, 11, 10,  8, 12,  4, 14, 15, 14,  0, 12,  3,\n",
       "       14,  6,  1,  7, 14,  7,  1,  2, 10,  4,  2, 12,  3,  4,  5, 14,  9,\n",
       "        9,  4, 12,  2,  5,  2, 15,  0, 11,  6,  3,  8,  6,  5,  3,  7,  3,\n",
       "        2, 13, 13, 12,  0, 15,  2, 11,  4, 15, 12, 11, 11, 13, 13,  6, 14,\n",
       "        1,  9,  0,  1, 10, 15,  8, 11,  4, 15,  6, 11, 10,  3, 13, 13,  1,\n",
       "       12,  1,  4, 10,  8,  2, 14,  4,  5, 11,  3, 12,  6,  7,  9,  4,  8,\n",
       "        3,  3,  7, 10,  1,  7,  6, 13, 14,  9, 14,  3,  5, 11,  0, 13, 11,\n",
       "        5,  7,  4,  9,  4,  3, 12, 14,  5,  8, 10, 15, 13, 15,  5,  2,  0,\n",
       "        0,  6,  0,  9,  4,  7,  6,  1,  6,  8,  5,  2,  2,  4,  0,  0,  5,\n",
       "        6,  0,  7,  5,  4, 13,  1, 10, 11,  0, 11,  2,  7, 12,  0, 11,  1,\n",
       "        0,  3,  4, 12, 14,  9, 14,  2, 12,  2,  1,  0, 14, 11,  2, 11,  2,\n",
       "       10,  6,  4, 15, 10,  7,  2,  2,  8,  6,  9, 10,  7,  1, 15,  3,  2,\n",
       "       13,  9,  9,  6,  9, 12,  5, 14,  5,  9,  2,  4, 15, 14,  5, 13,  1,\n",
       "        6,  1,  8,  0,  0, 15,  4,  8,  9,  3,  4,  8, 14, 13,  5, 13, 11,\n",
       "        2,  5,  5, 12, 12,  1,  6,  0,  5,  9, 10,  0,  5,  6, 14,  1,  0,\n",
       "        8, 11,  8,  8,  5,  9,  0, 11,  3,  3,  3, 15,  2, 10, 11,  6, 15,\n",
       "       13, 12,  1,  2, 11,  4,  0,  7,  6, 15,  8, 13, 12,  8,  5,  4])"
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     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_pred_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bece594",
   "metadata": {},
   "source": [
    "### Submission"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "304c47de",
   "metadata": {},
   "source": [
    "For Kaggle Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "e7ca9d19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <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",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17802</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17803</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17804</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>18696</td>\n",
       "      <td>13</td>\n",
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       "    <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>5</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     13\n",
       "896  18697     12\n",
       "897  18698      8\n",
       "898  18699      5\n",
       "899  18700      4\n",
       "\n",
       "[900 rows x 2 columns]"
      ]
     },
     "execution_count": 79,
     "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": 80,
   "id": "6eea7d9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_data.to_csv('./Submissions/submission-resnet-kPiece.csv', index=False)"
   ]
  }
 ],
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