MieAI_training.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f7fbfe8e-cce0-4ab7-948f-1aa0f94fb992",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 139 µs (started: 2023-08-19 21:33:10 +02:00)\n"
     ]
    }
   ],
   "source": [
    "%load_ext autotime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ac794bb8-5583-4fc8-bcad-44094343c561",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 485 µs (started: 2023-08-19 21:33:10 +02:00)\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "540c7fdd-56fd-41e0-a60d-b2aa171d0a2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.5 s (started: 2023-08-19 21:33:10 +02:00)\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "# from mei import Size_Distribution_Optics\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7ba7f0b9-5d79-4805-a484-c08748a61bb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 763 ms (started: 2023-08-19 21:33:12 +02:00)\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "import dask.dataframe as dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "97fe6320-99ab-4404-8571-1b8e20688854",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 815 ms (started: 2023-08-19 21:33:12 +02:00)\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "from sklearn.preprocessing import MinMaxScaler, PowerTransformer, QuantileTransformer\n",
    "from sklearn.metrics import r2_score \n",
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ed03b079-553e-473d-ac15-28e38dcededf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-08-19 21:33:13.653274: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-08-19 21:33:13.980874: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2023-08-19 21:33:13.980900: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "2023-08-19 21:33:15.791888: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
      "2023-08-19 21:33:15.792331: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
      "2023-08-19 21:33:15.792340: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 4.81 s (started: 2023-08-19 21:33:13 +02:00)\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Flatten\n",
    "from tensorflow.keras.layers import Dense, Dropout\n",
    "from tensorflow.keras.layers import Activation\n",
    "\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, TerminateOnNaN, ReduceLROnPlateau, EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9d7721d5-aa98-421a-b1ed-1ddb9efef636",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 180 µs (started: 2023-08-19 21:33:18 +02:00)\n"
     ]
    }
   ],
   "source": [
    "# from kerastuner.tuners import RandomSearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "60a0bf9d-87d3-4a03-af7b-3430914aae06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 2.46 ms (started: 2023-08-19 21:33:18 +02:00)\n"
     ]
    }
   ],
   "source": [
    "def normalize(df):\n",
    "    return (df - df.mean()) / df.std()\n",
    "\n",
    "def normalize1(df):    \n",
    "    mx = df.max()\n",
    "    mn = df.min()\n",
    "    \n",
    "    mx['SSA'] = 1.0\n",
    "    mx['Asym'] = 1.0\n",
    "    \n",
    "    mn['SSA'] = 0.0\n",
    "    mn['Asym'] = 0.0\n",
    "    \n",
    "    new = (df - mn) / (mx - mn)    \n",
    "    return new\n",
    "\n",
    "\n",
    "def load_opt(filename):\n",
    "    with open(filename, \"r\") as fp:\n",
    "        data = json.load(fp)\n",
    "    return data\n",
    "\n",
    "\n",
    "def create_model(\n",
    "    input_size,\n",
    "    output_size,\n",
    "    hidden=[14, 16, 5, 10],\n",
    "    dropout=[0.1, 0.3, 0.3, 0],\n",
    "    activation=[\"relu\", \"tanh\", \"tanh\", \"tanh\"],\n",
    "    loss=\"mean_absolute_error\",\n",
    "    optimizer=\"adam\",\n",
    "    metrics=[\"mean_squared_error\"],\n",
    "):\n",
    "    input_shape = (input_size,)\n",
    "    print(f\"Input shape: {input_shape}\")\n",
    "\n",
    "    # Create the model\n",
    "    model = Sequential()\n",
    "    model.add(\n",
    "        Dense(\n",
    "            hidden[0],\n",
    "            input_shape=input_shape,\n",
    "            activation=activation[0],\n",
    "            name=\"hidden_layer_1\",\n",
    "            use_bias=True,\n",
    "        ),\n",
    "    )\n",
    "    model.add(Dropout(dropout[0]))\n",
    "\n",
    "    if len(hidden) > 1:\n",
    "        for i, (ly , act, drp) in enumerate(zip(hidden[1:], activation[1:], dropout[1:])):\n",
    "            model.add(\n",
    "                Dense(\n",
    "                    ly,\n",
    "                    activation=act,\n",
    "                    name=\"hidden_layer_%s\" % (i + 2),\n",
    "                    use_bias=True,\n",
    "                )\n",
    "            )\n",
    "            model.add(Dropout(drp))\n",
    "    # model.add(Dense(output_size, activation=\"sigmoid\", name=\"output_layer\"))\n",
    "    model.add(Dense(output_size, name=\"output_layer\"))\n",
    "\n",
    "    # Configure the model and start training\n",
    "    model.compile(loss=loss, optimizer=optimizer, metrics=metrics)\n",
    "    print(model.summary())\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "69998211-344c-4b57-bbe5-ca7e96cdb005",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Extinction</th>\n",
       "      <th>Scattering</th>\n",
       "      <th>SSA</th>\n",
       "      <th>Asym</th>\n",
       "      <th>coating</th>\n",
       "      <th>core_dia</th>\n",
       "      <th>n_core</th>\n",
       "      <th>k_core</th>\n",
       "      <th>n_shell</th>\n",
       "      <th>k_shell</th>\n",
       "      <th>density</th>\n",
       "      <th>lambda</th>\n",
       "      <th>x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.371424</td>\n",
       "      <td>1.497649</td>\n",
       "      <td>0.631540</td>\n",
       "      <td>0.898342</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>1.451990</td>\n",
       "      <td>0.022362</td>\n",
       "      <td>1.330928</td>\n",
       "      <td>4.128916e-08</td>\n",
       "      <td>2.6</td>\n",
       "      <td>700.0</td>\n",
       "      <td>17.951958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.203139</td>\n",
       "      <td>1.330449</td>\n",
       "      <td>0.603888</td>\n",
       "      <td>0.882517</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>1.453480</td>\n",
       "      <td>0.023396</td>\n",
       "      <td>1.327650</td>\n",
       "      <td>2.061950e-06</td>\n",
       "      <td>2.6</td>\n",
       "      <td>950.0</td>\n",
       "      <td>16.534698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.173950</td>\n",
       "      <td>1.202943</td>\n",
       "      <td>0.553344</td>\n",
       "      <td>0.947042</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>1.451020</td>\n",
       "      <td>0.018632</td>\n",
       "      <td>1.332398</td>\n",
       "      <td>9.574458e-09</td>\n",
       "      <td>2.6</td>\n",
       "      <td>600.0</td>\n",
       "      <td>47.123890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.068879</td>\n",
       "      <td>1.123843</td>\n",
       "      <td>0.543213</td>\n",
       "      <td>0.952623</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1.483700</td>\n",
       "      <td>0.099080</td>\n",
       "      <td>1.389200</td>\n",
       "      <td>9.470000e-08</td>\n",
       "      <td>2.6</td>\n",
       "      <td>210.0</td>\n",
       "      <td>149.599650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.150144</td>\n",
       "      <td>1.163233</td>\n",
       "      <td>0.541003</td>\n",
       "      <td>0.941646</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1.453000</td>\n",
       "      <td>0.022940</td>\n",
       "      <td>1.329120</td>\n",
       "      <td>3.110181e-07</td>\n",
       "      <td>2.6</td>\n",
       "      <td>850.0</td>\n",
       "      <td>36.959914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599995</th>\n",
       "      <td>0.184309</td>\n",
       "      <td>0.033168</td>\n",
       "      <td>0.179958</td>\n",
       "      <td>0.086705</td>\n",
       "      <td>0.4</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1.394680</td>\n",
       "      <td>0.006191</td>\n",
       "      <td>1.420600</td>\n",
       "      <td>1.426000e-01</td>\n",
       "      <td>1.7</td>\n",
       "      <td>4600.0</td>\n",
       "      <td>0.478068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599996</th>\n",
       "      <td>0.181849</td>\n",
       "      <td>0.010640</td>\n",
       "      <td>0.058508</td>\n",
       "      <td>0.054755</td>\n",
       "      <td>0.4</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1.364826</td>\n",
       "      <td>0.022669</td>\n",
       "      <td>1.276529</td>\n",
       "      <td>1.827067e-01</td>\n",
       "      <td>1.7</td>\n",
       "      <td>7100.0</td>\n",
       "      <td>0.398230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599997</th>\n",
       "      <td>0.265074</td>\n",
       "      <td>0.004225</td>\n",
       "      <td>0.015940</td>\n",
       "      <td>0.016758</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>1.613500</td>\n",
       "      <td>0.648098</td>\n",
       "      <td>2.010000</td>\n",
       "      <td>6.500000e-01</td>\n",
       "      <td>1.7</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>0.188496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599998</th>\n",
       "      <td>0.193847</td>\n",
       "      <td>0.001301</td>\n",
       "      <td>0.006709</td>\n",
       "      <td>0.009228</td>\n",
       "      <td>0.4</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>1.613500</td>\n",
       "      <td>0.648098</td>\n",
       "      <td>2.020000</td>\n",
       "      <td>6.850000e-01</td>\n",
       "      <td>1.7</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>0.139626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599999</th>\n",
       "      <td>0.819451</td>\n",
       "      <td>0.106342</td>\n",
       "      <td>0.129773</td>\n",
       "      <td>0.083984</td>\n",
       "      <td>0.4</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>1.613500</td>\n",
       "      <td>0.648098</td>\n",
       "      <td>2.020000</td>\n",
       "      <td>6.850000e-01</td>\n",
       "      <td>1.7</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>0.418879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>600000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Extinction  Scattering       SSA      Asym  coating  core_dia  \\\n",
       "0         2.371424    1.497649  0.631540  0.898342      0.0    4000.0   \n",
       "1         2.203139    1.330449  0.603888  0.882517      0.0    5000.0   \n",
       "2         2.173950    1.202943  0.553344  0.947042      0.0    9000.0   \n",
       "3         2.068879    1.123843  0.543213  0.952623      0.0   10000.0   \n",
       "4         2.150144    1.163233  0.541003  0.941646      0.0   10000.0   \n",
       "...            ...         ...       ...       ...      ...       ...   \n",
       "599995    0.184309    0.033168  0.179958  0.086705      0.4     700.0   \n",
       "599996    0.181849    0.010640  0.058508  0.054755      0.4     900.0   \n",
       "599997    0.265074    0.004225  0.015940  0.016758      0.4    3000.0   \n",
       "599998    0.193847    0.001301  0.006709  0.009228      0.4    4000.0   \n",
       "599999    0.819451    0.106342  0.129773  0.083984      0.4   12000.0   \n",
       "\n",
       "          n_core    k_core   n_shell       k_shell  density   lambda  \\\n",
       "0       1.451990  0.022362  1.330928  4.128916e-08      2.6    700.0   \n",
       "1       1.453480  0.023396  1.327650  2.061950e-06      2.6    950.0   \n",
       "2       1.451020  0.018632  1.332398  9.574458e-09      2.6    600.0   \n",
       "3       1.483700  0.099080  1.389200  9.470000e-08      2.6    210.0   \n",
       "4       1.453000  0.022940  1.329120  3.110181e-07      2.6    850.0   \n",
       "...          ...       ...       ...           ...      ...      ...   \n",
       "599995  1.394680  0.006191  1.420600  1.426000e-01      1.7   4600.0   \n",
       "599996  1.364826  0.022669  1.276529  1.827067e-01      1.7   7100.0   \n",
       "599997  1.613500  0.648098  2.010000  6.500000e-01      1.7  50000.0   \n",
       "599998  1.613500  0.648098  2.020000  6.850000e-01      1.7  90000.0   \n",
       "599999  1.613500  0.648098  2.020000  6.850000e-01      1.7  90000.0   \n",
       "\n",
       "                 x  \n",
       "0        17.951958  \n",
       "1        16.534698  \n",
       "2        47.123890  \n",
       "3       149.599650  \n",
       "4        36.959914  \n",
       "...            ...  \n",
       "599995    0.478068  \n",
       "599996    0.398230  \n",
       "599997    0.188496  \n",
       "599998    0.139626  \n",
       "599999    0.418879  \n",
       "\n",
       "[600000 rows x 13 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 4.13 s (started: 2023-08-19 21:33:18 +02:00)\n"
     ]
    }
   ],
   "source": [
    "columns = ['Extinction', 'Scattering', 'SSA', 'Asym', 'coating', 'core_dia', 'n_core', 'k_core', 'n_shell', 'k_shell', 'density', 'lambda']\n",
    "df1 = pd.read_csv('/work/bb1070/b382177/mie/icon/12-05-2023/mie_x1.csv', index_col=0).sort_index().sort_index().iloc[:-1224, :]\n",
    "df2 = pd.read_csv('/work/bb1070/b382177/mie/icon/12-05-2023/mie_x2.csv', index_col=0).sort_index().sort_index().iloc[:-1224, :]\n",
    "df = pd.concat([df1, df2], axis=0).reset_index()\n",
    "df = df[columns].dropna().astype('float')\n",
    "df['x'] = np.pi * df['core_dia'] / df['lambda']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fdde746d-fe63-41d2-a5f5-ac522459179a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 238 µs (started: 2023-08-19 21:33:22 +02:00)\n"
     ]
    }
   ],
   "source": [
    "# df[[\"Extinction\", \"SSA\", \"Asym\"]].to_csv('/work/bb1070/b382177/mie/icon/mie_out.txt', float_format='%.3f', sep=' ')\n",
    "# df[['coating', 'x', 'n_core', 'k_core', 'n_shell', 'k_shell', 'lambda']].to_csv('/work/bb1070/b382177/mie/icon/mie_input.txt', float_format='%.3f', sep=' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f7585681-f732-4002-a277-e5ae027197df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 66.7 ms (started: 2023-08-19 21:33:22 +02:00)\n"
     ]
    }
   ],
   "source": [
    "mx = df.max()\n",
    "mn = df.min()\n",
    "pd.concat([pd.DataFrame(mx), pd.DataFrame(mn)], axis=1).to_csv('/work/bb1070/b382177/mie/icon/05-07-2023/model/mlp_min_max.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "af78f502-bd3b-4e14-b514-db822920d5ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Extinction</th>\n",
       "      <th>Scattering</th>\n",
       "      <th>Asym</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.385656</td>\n",
       "      <td>-0.134130</td>\n",
       "      <td>-0.108732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.825163</td>\n",
       "      <td>-0.511151</td>\n",
       "      <td>-0.509331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.483993</td>\n",
       "      <td>1.338700</td>\n",
       "      <td>0.826924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.051317</td>\n",
       "      <td>-0.452069</td>\n",
       "      <td>-0.513292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.518457</td>\n",
       "      <td>0.429787</td>\n",
       "      <td>1.835915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539995</th>\n",
       "      <td>-0.990207</td>\n",
       "      <td>-0.841703</td>\n",
       "      <td>-0.810697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539996</th>\n",
       "      <td>0.220158</td>\n",
       "      <td>0.192439</td>\n",
       "      <td>0.161772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539997</th>\n",
       "      <td>-0.956842</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>-1.599475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539998</th>\n",
       "      <td>-0.213032</td>\n",
       "      <td>-0.563891</td>\n",
       "      <td>-0.612526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539999</th>\n",
       "      <td>-0.487695</td>\n",
       "      <td>-0.670379</td>\n",
       "      <td>-0.625399</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>540000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Extinction  Scattering      Asym\n",
       "0        -0.385656   -0.134130 -0.108732\n",
       "1        -0.825163   -0.511151 -0.509331\n",
       "2         1.483993    1.338700  0.826924\n",
       "3        -0.051317   -0.452069 -0.513292\n",
       "4         0.518457    0.429787  1.835915\n",
       "...            ...         ...       ...\n",
       "539995   -0.990207   -0.841703 -0.810697\n",
       "539996    0.220158    0.192439  0.161772\n",
       "539997   -0.956842   -5.199338 -1.599475\n",
       "539998   -0.213032   -0.563891 -0.612526\n",
       "539999   -0.487695   -0.670379 -0.625399\n",
       "\n",
       "[540000 rows x 3 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 615 ms (started: 2023-08-19 21:33:22 +02:00)\n"
     ]
    }
   ],
   "source": [
    "qt = QuantileTransformer(n_quantiles=1000, output_distribution=\"normal\", random_state=0)\n",
    "df_shuffled = df.sample(frac=1, random_state=40).reset_index(drop=True)\n",
    "df_shuffled = normalize1(df_shuffled)\n",
    "\n",
    "out_col = [\"Extinction\", \"Scattering\", \"Asym\"]\n",
    "in_col = ['coating', 'x', 'n_core', 'k_core', 'n_shell', 'k_shell', 'lambda']\n",
    "\n",
    "y = df_shuffled[out_col]\n",
    "y[out_col] = qt.fit_transform(y)\n",
    "x = df_shuffled[in_col]\n",
    "\n",
    "split = 0.9\n",
    "train_size = int(split * len(x))\n",
    "train_x = x.iloc[:train_size, :].reset_index(drop=True)\n",
    "train_y = y.iloc[:train_size, :].reset_index(drop=True)\n",
    "test_x = x.iloc[train_size:, :].reset_index(drop=True)\n",
    "test_y = y.iloc[train_size:, :].reset_index(drop=True)\n",
    "train_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ac07d99f-d716-49d4-80a2-f6d471d25965",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input shape: (7,)\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " hidden_layer_1 (Dense)      (None, 64)                512       \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 64)                0         \n",
      "                                                                 \n",
      " hidden_layer_2 (Dense)      (None, 64)                4160      \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 64)                0         \n",
      "                                                                 \n",
      " hidden_layer_3 (Dense)      (None, 64)                4160      \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 64)                0         \n",
      "                                                                 \n",
      " hidden_layer_4 (Dense)      (None, 64)                4160      \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 64)                0         \n",
      "                                                                 \n",
      " output_layer (Dense)        (None, 3)                 195       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 13,187\n",
      "Trainable params: 13,187\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-08-19 21:33:23.294553: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
      "2023-08-19 21:33:23.294589: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2023-08-19 21:33:23.294607: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (l40010.lvt.dkrz.de): /proc/driver/nvidia/version does not exist\n",
      "2023-08-19 21:33:23.295652: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 1: val_loss improved from inf to 0.69586, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2: val_loss improved from 0.69586 to 0.56277, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 3: val_loss improved from 0.56277 to 0.38999, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 4: val_loss improved from 0.38999 to 0.18990, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 5: val_loss did not improve from 0.18990\n",
      "\n",
      "Epoch 6: val_loss did not improve from 0.18990\n",
      "\n",
      "Epoch 7: val_loss did not improve from 0.18990\n",
      "\n",
      "Epoch 8: val_loss did not improve from 0.18990\n",
      "\n",
      "Epoch 9: val_loss did not improve from 0.18990\n",
      "\n",
      "Epoch 10: val_loss improved from 0.18990 to 0.16412, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 11: val_loss did not improve from 0.16412\n",
      "\n",
      "Epoch 12: val_loss improved from 0.16412 to 0.12962, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 13: val_loss improved from 0.12962 to 0.10860, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 14: val_loss improved from 0.10860 to 0.09212, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 15: val_loss did not improve from 0.09212\n",
      "\n",
      "Epoch 16: val_loss improved from 0.09212 to 0.08000, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 17: val_loss did not improve from 0.08000\n",
      "\n",
      "Epoch 18: val_loss did not improve from 0.08000\n",
      "\n",
      "Epoch 19: val_loss did not improve from 0.08000\n",
      "\n",
      "Epoch 20: val_loss improved from 0.08000 to 0.06184, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 21: val_loss improved from 0.06184 to 0.05757, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 22: val_loss did not improve from 0.05757\n",
      "\n",
      "Epoch 23: val_loss did not improve from 0.05757\n",
      "\n",
      "Epoch 24: val_loss did not improve from 0.05757\n",
      "\n",
      "Epoch 25: val_loss did not improve from 0.05757\n",
      "\n",
      "Epoch 26: val_loss improved from 0.05757 to 0.05280, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 27: val_loss did not improve from 0.05280\n",
      "\n",
      "Epoch 28: val_loss did not improve from 0.05280\n",
      "\n",
      "Epoch 29: val_loss did not improve from 0.05280\n",
      "\n",
      "Epoch 30: val_loss did not improve from 0.05280\n",
      "\n",
      "Epoch 31: val_loss improved from 0.05280 to 0.05040, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 32: val_loss did not improve from 0.05040\n",
      "\n",
      "Epoch 33: val_loss did not improve from 0.05040\n",
      "\n",
      "Epoch 34: val_loss did not improve from 0.05040\n",
      "\n",
      "Epoch 35: val_loss did not improve from 0.05040\n",
      "\n",
      "Epoch 36: val_loss did not improve from 0.05040\n",
      "\n",
      "Epoch 37: val_loss did not improve from 0.05040\n",
      "\n",
      "Epoch 38: val_loss did not improve from 0.05040\n",
      "\n",
      "Epoch 39: val_loss improved from 0.05040 to 0.03617, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 40: val_loss did not improve from 0.03617\n",
      "\n",
      "Epoch 41: val_loss did not improve from 0.03617\n",
      "\n",
      "Epoch 42: val_loss improved from 0.03617 to 0.03548, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 43: val_loss improved from 0.03548 to 0.03219, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 44: val_loss did not improve from 0.03219\n",
      "\n",
      "Epoch 45: val_loss did not improve from 0.03219\n",
      "\n",
      "Epoch 46: val_loss did not improve from 0.03219\n",
      "\n",
      "Epoch 47: val_loss improved from 0.03219 to 0.03044, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 48: val_loss did not improve from 0.03044\n",
      "\n",
      "Epoch 49: val_loss did not improve from 0.03044\n",
      "\n",
      "Epoch 50: val_loss did not improve from 0.03044\n",
      "\n",
      "Epoch 51: val_loss did not improve from 0.03044\n",
      "\n",
      "Epoch 52: val_loss did not improve from 0.03044\n",
      "\n",
      "Epoch 53: val_loss improved from 0.03044 to 0.02929, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 54: val_loss improved from 0.02929 to 0.02909, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 55: val_loss did not improve from 0.02909\n",
      "\n",
      "Epoch 56: val_loss improved from 0.02909 to 0.02867, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 57: val_loss improved from 0.02867 to 0.02796, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 58: val_loss did not improve from 0.02796\n",
      "\n",
      "Epoch 59: val_loss improved from 0.02796 to 0.02783, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 60: val_loss improved from 0.02783 to 0.02732, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 61: val_loss improved from 0.02732 to 0.02731, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 62: val_loss improved from 0.02731 to 0.02634, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 63: val_loss did not improve from 0.02634\n",
      "\n",
      "Epoch 64: val_loss did not improve from 0.02634\n",
      "\n",
      "Epoch 65: val_loss did not improve from 0.02634\n",
      "\n",
      "Epoch 66: val_loss improved from 0.02634 to 0.02604, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 67: val_loss did not improve from 0.02604\n",
      "\n",
      "Epoch 68: val_loss did not improve from 0.02604\n",
      "\n",
      "Epoch 69: val_loss did not improve from 0.02604\n",
      "\n",
      "Epoch 70: val_loss did not improve from 0.02604\n",
      "\n",
      "Epoch 71: val_loss improved from 0.02604 to 0.02478, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 72: val_loss did not improve from 0.02478\n",
      "\n",
      "Epoch 73: val_loss did not improve from 0.02478\n",
      "\n",
      "Epoch 74: val_loss did not improve from 0.02478\n",
      "\n",
      "Epoch 75: val_loss did not improve from 0.02478\n",
      "\n",
      "Epoch 76: val_loss did not improve from 0.02478\n",
      "\n",
      "Epoch 77: val_loss improved from 0.02478 to 0.02432, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 78: val_loss improved from 0.02432 to 0.02430, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 79: val_loss did not improve from 0.02430\n",
      "\n",
      "Epoch 80: val_loss did not improve from 0.02430\n",
      "\n",
      "Epoch 81: val_loss did not improve from 0.02430\n",
      "\n",
      "Epoch 82: val_loss did not improve from 0.02430\n",
      "\n",
      "Epoch 83: val_loss improved from 0.02430 to 0.02388, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 84: val_loss did not improve from 0.02388\n",
      "\n",
      "Epoch 85: val_loss did not improve from 0.02388\n",
      "\n",
      "Epoch 86: val_loss improved from 0.02388 to 0.02379, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 87: val_loss did not improve from 0.02379\n",
      "\n",
      "Epoch 88: val_loss improved from 0.02379 to 0.02378, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 89: val_loss did not improve from 0.02378\n",
      "\n",
      "Epoch 90: val_loss did not improve from 0.02378\n",
      "\n",
      "Epoch 91: val_loss did not improve from 0.02378\n",
      "\n",
      "Epoch 92: val_loss improved from 0.02378 to 0.02373, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 93: val_loss did not improve from 0.02373\n",
      "\n",
      "Epoch 94: val_loss did not improve from 0.02373\n",
      "\n",
      "Epoch 95: val_loss did not improve from 0.02373\n",
      "\n",
      "Epoch 96: val_loss did not improve from 0.02373\n",
      "\n",
      "Epoch 97: val_loss improved from 0.02373 to 0.02357, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 98: val_loss improved from 0.02357 to 0.02354, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 99: val_loss did not improve from 0.02354\n",
      "\n",
      "Epoch 100: val_loss did not improve from 0.02354\n",
      "\n",
      "Epoch 101: val_loss did not improve from 0.02354\n",
      "\n",
      "Epoch 102: val_loss did not improve from 0.02354\n",
      "\n",
      "Epoch 103: val_loss improved from 0.02354 to 0.02350, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 104: val_loss improved from 0.02350 to 0.02343, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 105: val_loss did not improve from 0.02343\n",
      "\n",
      "Epoch 106: val_loss did not improve from 0.02343\n",
      "\n",
      "Epoch 107: val_loss did not improve from 0.02343\n",
      "\n",
      "Epoch 108: val_loss did not improve from 0.02343\n",
      "\n",
      "Epoch 109: val_loss improved from 0.02343 to 0.02338, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 110: val_loss did not improve from 0.02338\n",
      "\n",
      "Epoch 111: val_loss improved from 0.02338 to 0.02335, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 112: val_loss did not improve from 0.02335\n",
      "\n",
      "Epoch 113: val_loss did not improve from 0.02335\n",
      "\n",
      "Epoch 114: val_loss improved from 0.02335 to 0.02330, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 115: val_loss did not improve from 0.02330\n",
      "\n",
      "Epoch 116: val_loss did not improve from 0.02330\n",
      "\n",
      "Epoch 117: val_loss improved from 0.02330 to 0.02325, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 118: val_loss did not improve from 0.02325\n",
      "\n",
      "Epoch 119: val_loss did not improve from 0.02325\n",
      "\n",
      "Epoch 120: val_loss improved from 0.02325 to 0.02317, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 121: val_loss did not improve from 0.02317\n",
      "\n",
      "Epoch 122: val_loss did not improve from 0.02317\n",
      "\n",
      "Epoch 123: val_loss improved from 0.02317 to 0.02313, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 124: val_loss did not improve from 0.02313\n",
      "\n",
      "Epoch 125: val_loss did not improve from 0.02313\n",
      "\n",
      "Epoch 126: val_loss did not improve from 0.02313\n",
      "\n",
      "Epoch 127: val_loss improved from 0.02313 to 0.02307, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 128: val_loss did not improve from 0.02307\n",
      "\n",
      "Epoch 129: val_loss improved from 0.02307 to 0.02297, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 130: val_loss did not improve from 0.02297\n",
      "\n",
      "Epoch 131: val_loss did not improve from 0.02297\n",
      "\n",
      "Epoch 132: val_loss improved from 0.02297 to 0.02295, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 133: val_loss did not improve from 0.02295\n",
      "\n",
      "Epoch 134: val_loss did not improve from 0.02295\n",
      "\n",
      "Epoch 135: val_loss did not improve from 0.02295\n",
      "\n",
      "Epoch 136: val_loss improved from 0.02295 to 0.02287, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 137: val_loss did not improve from 0.02287\n",
      "\n",
      "Epoch 138: val_loss improved from 0.02287 to 0.02283, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 139: val_loss did not improve from 0.02283\n",
      "\n",
      "Epoch 140: val_loss did not improve from 0.02283\n",
      "\n",
      "Epoch 141: val_loss did not improve from 0.02283\n",
      "\n",
      "Epoch 142: val_loss did not improve from 0.02283\n",
      "\n",
      "Epoch 143: val_loss did not improve from 0.02283\n",
      "\n",
      "Epoch 144: val_loss improved from 0.02283 to 0.02277, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 145: val_loss did not improve from 0.02277\n",
      "\n",
      "Epoch 146: val_loss did not improve from 0.02277\n",
      "\n",
      "Epoch 147: val_loss improved from 0.02277 to 0.02265, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 148: val_loss did not improve from 0.02265\n",
      "\n",
      "Epoch 149: val_loss did not improve from 0.02265\n",
      "\n",
      "Epoch 150: val_loss did not improve from 0.02265\n",
      "\n",
      "Epoch 151: val_loss did not improve from 0.02265\n",
      "\n",
      "Epoch 152: val_loss did not improve from 0.02265\n",
      "\n",
      "Epoch 153: val_loss did not improve from 0.02265\n",
      "\n",
      "Epoch 154: val_loss did not improve from 0.02265\n",
      "\n",
      "Epoch 155: val_loss did not improve from 0.02265\n",
      "\n",
      "Epoch 156: val_loss did not improve from 0.02265\n",
      "\n",
      "Epoch 157: val_loss did not improve from 0.02265\n",
      "\n",
      "Epoch 158: val_loss improved from 0.02265 to 0.02257, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 159: val_loss improved from 0.02257 to 0.02253, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 160: val_loss did not improve from 0.02253\n",
      "\n",
      "Epoch 161: val_loss did not improve from 0.02253\n",
      "\n",
      "Epoch 162: val_loss did not improve from 0.02253\n",
      "\n",
      "Epoch 163: val_loss did not improve from 0.02253\n",
      "\n",
      "Epoch 164: val_loss improved from 0.02253 to 0.02245, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 165: val_loss improved from 0.02245 to 0.02238, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 166: val_loss did not improve from 0.02238\n",
      "\n",
      "Epoch 167: val_loss did not improve from 0.02238\n",
      "\n",
      "Epoch 168: val_loss did not improve from 0.02238\n",
      "\n",
      "Epoch 169: val_loss improved from 0.02238 to 0.02234, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 170: val_loss improved from 0.02234 to 0.02229, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 171: val_loss did not improve from 0.02229\n",
      "\n",
      "Epoch 172: val_loss improved from 0.02229 to 0.02226, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 173: val_loss did not improve from 0.02226\n",
      "\n",
      "Epoch 174: val_loss did not improve from 0.02226\n",
      "\n",
      "Epoch 175: val_loss did not improve from 0.02226\n",
      "\n",
      "Epoch 176: val_loss did not improve from 0.02226\n",
      "\n",
      "Epoch 177: val_loss did not improve from 0.02226\n",
      "\n",
      "Epoch 178: val_loss did not improve from 0.02226\n",
      "\n",
      "Epoch 179: val_loss improved from 0.02226 to 0.02226, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 180: val_loss did not improve from 0.02226\n",
      "\n",
      "Epoch 181: val_loss did not improve from 0.02226\n",
      "\n",
      "Epoch 182: val_loss improved from 0.02226 to 0.02218, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 183: val_loss did not improve from 0.02218\n",
      "\n",
      "Epoch 184: val_loss improved from 0.02218 to 0.02214, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 185: val_loss improved from 0.02214 to 0.02212, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 186: val_loss improved from 0.02212 to 0.02210, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 187: val_loss did not improve from 0.02210\n",
      "\n",
      "Epoch 188: val_loss improved from 0.02210 to 0.02205, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 189: val_loss did not improve from 0.02205\n",
      "\n",
      "Epoch 190: val_loss did not improve from 0.02205\n",
      "\n",
      "Epoch 191: val_loss did not improve from 0.02205\n",
      "\n",
      "Epoch 192: val_loss improved from 0.02205 to 0.02199, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 193: val_loss did not improve from 0.02199\n",
      "\n",
      "Epoch 194: val_loss improved from 0.02199 to 0.02194, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 195: val_loss did not improve from 0.02194\n",
      "\n",
      "Epoch 196: val_loss did not improve from 0.02194\n",
      "\n",
      "Epoch 197: val_loss improved from 0.02194 to 0.02190, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 198: val_loss did not improve from 0.02190\n",
      "\n",
      "Epoch 199: val_loss did not improve from 0.02190\n",
      "\n",
      "Epoch 200: val_loss did not improve from 0.02190\n",
      "\n",
      "Epoch 201: val_loss did not improve from 0.02190\n",
      "\n",
      "Epoch 202: val_loss improved from 0.02190 to 0.02182, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 203: val_loss did not improve from 0.02182\n",
      "\n",
      "Epoch 204: val_loss did not improve from 0.02182\n",
      "\n",
      "Epoch 205: val_loss did not improve from 0.02182\n",
      "\n",
      "Epoch 206: val_loss did not improve from 0.02182\n",
      "\n",
      "Epoch 207: val_loss did not improve from 0.02182\n",
      "\n",
      "Epoch 208: val_loss improved from 0.02182 to 0.02173, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 209: val_loss did not improve from 0.02173\n",
      "\n",
      "Epoch 210: val_loss did not improve from 0.02173\n",
      "\n",
      "Epoch 211: val_loss did not improve from 0.02173\n",
      "\n",
      "Epoch 212: val_loss did not improve from 0.02173\n",
      "\n",
      "Epoch 213: val_loss did not improve from 0.02173\n",
      "\n",
      "Epoch 214: val_loss did not improve from 0.02173\n",
      "\n",
      "Epoch 215: val_loss did not improve from 0.02173\n",
      "\n",
      "Epoch 216: val_loss did not improve from 0.02173\n",
      "\n",
      "Epoch 217: val_loss did not improve from 0.02173\n",
      "\n",
      "Epoch 218: val_loss improved from 0.02173 to 0.02171, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 219: val_loss improved from 0.02171 to 0.02156, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 220: val_loss did not improve from 0.02156\n",
      "\n",
      "Epoch 221: val_loss did not improve from 0.02156\n",
      "\n",
      "Epoch 222: val_loss did not improve from 0.02156\n",
      "\n",
      "Epoch 223: val_loss did not improve from 0.02156\n",
      "\n",
      "Epoch 224: val_loss did not improve from 0.02156\n",
      "\n",
      "Epoch 225: val_loss did not improve from 0.02156\n",
      "\n",
      "Epoch 226: val_loss did not improve from 0.02156\n",
      "\n",
      "Epoch 227: val_loss did not improve from 0.02156\n",
      "\n",
      "Epoch 228: val_loss improved from 0.02156 to 0.02153, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 229: val_loss did not improve from 0.02153\n",
      "\n",
      "Epoch 230: val_loss improved from 0.02153 to 0.02149, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 231: val_loss did not improve from 0.02149\n",
      "\n",
      "Epoch 232: val_loss did not improve from 0.02149\n",
      "\n",
      "Epoch 233: val_loss did not improve from 0.02149\n",
      "\n",
      "Epoch 234: val_loss did not improve from 0.02149\n",
      "\n",
      "Epoch 235: val_loss improved from 0.02149 to 0.02132, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 236: val_loss improved from 0.02132 to 0.02129, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 237: val_loss did not improve from 0.02129\n",
      "\n",
      "Epoch 238: val_loss did not improve from 0.02129\n",
      "\n",
      "Epoch 239: val_loss did not improve from 0.02129\n",
      "\n",
      "Epoch 240: val_loss did not improve from 0.02129\n",
      "\n",
      "Epoch 241: val_loss did not improve from 0.02129\n",
      "\n",
      "Epoch 242: val_loss improved from 0.02129 to 0.02126, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 243: val_loss improved from 0.02126 to 0.02124, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 244: val_loss did not improve from 0.02124\n",
      "\n",
      "Epoch 245: val_loss did not improve from 0.02124\n",
      "\n",
      "Epoch 246: val_loss did not improve from 0.02124\n",
      "\n",
      "Epoch 247: val_loss did not improve from 0.02124\n",
      "\n",
      "Epoch 248: val_loss did not improve from 0.02124\n",
      "\n",
      "Epoch 249: val_loss did not improve from 0.02124\n",
      "\n",
      "Epoch 250: val_loss did not improve from 0.02124\n",
      "\n",
      "Epoch 251: val_loss improved from 0.02124 to 0.02121, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 252: val_loss improved from 0.02121 to 0.02109, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 253: val_loss did not improve from 0.02109\n",
      "\n",
      "Epoch 254: val_loss did not improve from 0.02109\n",
      "\n",
      "Epoch 255: val_loss did not improve from 0.02109\n",
      "\n",
      "Epoch 256: val_loss improved from 0.02109 to 0.02104, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 257: val_loss did not improve from 0.02104\n",
      "\n",
      "Epoch 258: val_loss did not improve from 0.02104\n",
      "\n",
      "Epoch 259: val_loss did not improve from 0.02104\n",
      "\n",
      "Epoch 260: val_loss did not improve from 0.02104\n",
      "\n",
      "Epoch 261: val_loss improved from 0.02104 to 0.02102, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 262: val_loss did not improve from 0.02102\n",
      "\n",
      "Epoch 263: val_loss improved from 0.02102 to 0.02094, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 264: val_loss did not improve from 0.02094\n",
      "\n",
      "Epoch 265: val_loss did not improve from 0.02094\n",
      "\n",
      "Epoch 266: val_loss did not improve from 0.02094\n",
      "\n",
      "Epoch 267: val_loss did not improve from 0.02094\n",
      "\n",
      "Epoch 268: val_loss did not improve from 0.02094\n",
      "\n",
      "Epoch 269: val_loss improved from 0.02094 to 0.02084, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 270: val_loss did not improve from 0.02084\n",
      "\n",
      "Epoch 271: val_loss did not improve from 0.02084\n",
      "\n",
      "Epoch 272: val_loss did not improve from 0.02084\n",
      "\n",
      "Epoch 273: val_loss improved from 0.02084 to 0.02080, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 274: val_loss did not improve from 0.02080\n",
      "\n",
      "Epoch 275: val_loss did not improve from 0.02080\n",
      "\n",
      "Epoch 276: val_loss did not improve from 0.02080\n",
      "\n",
      "Epoch 277: val_loss did not improve from 0.02080\n",
      "\n",
      "Epoch 278: val_loss improved from 0.02080 to 0.02071, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 279: val_loss did not improve from 0.02071\n",
      "\n",
      "Epoch 280: val_loss did not improve from 0.02071\n",
      "\n",
      "Epoch 281: val_loss did not improve from 0.02071\n",
      "\n",
      "Epoch 282: val_loss did not improve from 0.02071\n",
      "\n",
      "Epoch 283: val_loss did not improve from 0.02071\n",
      "\n",
      "Epoch 284: val_loss did not improve from 0.02071\n",
      "\n",
      "Epoch 285: val_loss improved from 0.02071 to 0.02068, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 286: val_loss improved from 0.02068 to 0.02062, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 287: val_loss did not improve from 0.02062\n",
      "\n",
      "Epoch 288: val_loss did not improve from 0.02062\n",
      "\n",
      "Epoch 289: val_loss did not improve from 0.02062\n",
      "\n",
      "Epoch 290: val_loss improved from 0.02062 to 0.02056, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 291: val_loss did not improve from 0.02056\n",
      "\n",
      "Epoch 292: val_loss did not improve from 0.02056\n",
      "\n",
      "Epoch 293: val_loss did not improve from 0.02056\n",
      "\n",
      "Epoch 294: val_loss did not improve from 0.02056\n",
      "\n",
      "Epoch 295: val_loss did not improve from 0.02056\n",
      "\n",
      "Epoch 296: val_loss did not improve from 0.02056\n",
      "\n",
      "Epoch 297: val_loss improved from 0.02056 to 0.02053, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 298: val_loss improved from 0.02053 to 0.02047, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 299: val_loss improved from 0.02047 to 0.02042, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 300: val_loss did not improve from 0.02042\n",
      "\n",
      "Epoch 301: val_loss did not improve from 0.02042\n",
      "\n",
      "Epoch 302: val_loss did not improve from 0.02042\n",
      "\n",
      "Epoch 303: val_loss improved from 0.02042 to 0.02036, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 304: val_loss did not improve from 0.02036\n",
      "\n",
      "Epoch 305: val_loss did not improve from 0.02036\n",
      "\n",
      "Epoch 306: val_loss did not improve from 0.02036\n",
      "\n",
      "Epoch 307: val_loss did not improve from 0.02036\n",
      "\n",
      "Epoch 308: val_loss did not improve from 0.02036\n",
      "\n",
      "Epoch 309: val_loss did not improve from 0.02036\n",
      "\n",
      "Epoch 310: val_loss improved from 0.02036 to 0.02033, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 311: val_loss improved from 0.02033 to 0.02030, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 312: val_loss did not improve from 0.02030\n",
      "\n",
      "Epoch 313: val_loss improved from 0.02030 to 0.02024, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 314: val_loss did not improve from 0.02024\n",
      "\n",
      "Epoch 315: val_loss did not improve from 0.02024\n",
      "\n",
      "Epoch 316: val_loss did not improve from 0.02024\n",
      "\n",
      "Epoch 317: val_loss did not improve from 0.02024\n",
      "\n",
      "Epoch 318: val_loss improved from 0.02024 to 0.02019, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 319: val_loss improved from 0.02019 to 0.02013, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 320: val_loss did not improve from 0.02013\n",
      "\n",
      "Epoch 321: val_loss did not improve from 0.02013\n",
      "\n",
      "Epoch 322: val_loss did not improve from 0.02013\n",
      "\n",
      "Epoch 323: val_loss did not improve from 0.02013\n",
      "\n",
      "Epoch 324: val_loss improved from 0.02013 to 0.02010, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 325: val_loss did not improve from 0.02010\n",
      "\n",
      "Epoch 326: val_loss improved from 0.02010 to 0.02008, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 327: val_loss improved from 0.02008 to 0.02003, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 328: val_loss did not improve from 0.02003\n",
      "\n",
      "Epoch 329: val_loss did not improve from 0.02003\n",
      "\n",
      "Epoch 330: val_loss improved from 0.02003 to 0.02003, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 331: val_loss did not improve from 0.02003\n",
      "\n",
      "Epoch 332: val_loss did not improve from 0.02003\n",
      "\n",
      "Epoch 333: val_loss did not improve from 0.02003\n",
      "\n",
      "Epoch 334: val_loss improved from 0.02003 to 0.02003, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 335: val_loss did not improve from 0.02003\n",
      "\n",
      "Epoch 336: val_loss improved from 0.02003 to 0.01994, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 337: val_loss improved from 0.01994 to 0.01989, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 338: val_loss did not improve from 0.01989\n",
      "\n",
      "Epoch 339: val_loss did not improve from 0.01989\n",
      "\n",
      "Epoch 340: val_loss did not improve from 0.01989\n",
      "\n",
      "Epoch 341: val_loss did not improve from 0.01989\n",
      "\n",
      "Epoch 342: val_loss did not improve from 0.01989\n",
      "\n",
      "Epoch 343: val_loss did not improve from 0.01989\n",
      "\n",
      "Epoch 344: val_loss did not improve from 0.01989\n",
      "\n",
      "Epoch 345: val_loss did not improve from 0.01989\n",
      "\n",
      "Epoch 346: val_loss did not improve from 0.01989\n",
      "\n",
      "Epoch 347: val_loss improved from 0.01989 to 0.01987, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 348: val_loss did not improve from 0.01987\n",
      "\n",
      "Epoch 349: val_loss improved from 0.01987 to 0.01987, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 350: val_loss improved from 0.01987 to 0.01982, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 351: val_loss improved from 0.01982 to 0.01980, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 352: val_loss improved from 0.01980 to 0.01973, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 353: val_loss did not improve from 0.01973\n",
      "\n",
      "Epoch 354: val_loss did not improve from 0.01973\n",
      "\n",
      "Epoch 355: val_loss did not improve from 0.01973\n",
      "\n",
      "Epoch 356: val_loss did not improve from 0.01973\n",
      "\n",
      "Epoch 357: val_loss did not improve from 0.01973\n",
      "\n",
      "Epoch 358: val_loss improved from 0.01973 to 0.01972, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 359: val_loss did not improve from 0.01972\n",
      "\n",
      "Epoch 360: val_loss did not improve from 0.01972\n",
      "\n",
      "Epoch 361: val_loss improved from 0.01972 to 0.01968, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 362: val_loss did not improve from 0.01968\n",
      "\n",
      "Epoch 363: val_loss improved from 0.01968 to 0.01965, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 364: val_loss did not improve from 0.01965\n",
      "\n",
      "Epoch 365: val_loss did not improve from 0.01965\n",
      "\n",
      "Epoch 366: val_loss improved from 0.01965 to 0.01961, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 367: val_loss did not improve from 0.01961\n",
      "\n",
      "Epoch 368: val_loss improved from 0.01961 to 0.01954, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 369: val_loss did not improve from 0.01954\n",
      "\n",
      "Epoch 370: val_loss did not improve from 0.01954\n",
      "\n",
      "Epoch 371: val_loss did not improve from 0.01954\n",
      "\n",
      "Epoch 372: val_loss did not improve from 0.01954\n",
      "\n",
      "Epoch 373: val_loss improved from 0.01954 to 0.01952, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 374: val_loss did not improve from 0.01952\n",
      "\n",
      "Epoch 375: val_loss did not improve from 0.01952\n",
      "\n",
      "Epoch 376: val_loss did not improve from 0.01952\n",
      "\n",
      "Epoch 377: val_loss improved from 0.01952 to 0.01945, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 378: val_loss did not improve from 0.01945\n",
      "\n",
      "Epoch 379: val_loss did not improve from 0.01945\n",
      "\n",
      "Epoch 380: val_loss improved from 0.01945 to 0.01943, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 381: val_loss did not improve from 0.01943\n",
      "\n",
      "Epoch 382: val_loss did not improve from 0.01943\n",
      "\n",
      "Epoch 383: val_loss did not improve from 0.01943\n",
      "\n",
      "Epoch 384: val_loss did not improve from 0.01943\n",
      "\n",
      "Epoch 385: val_loss improved from 0.01943 to 0.01942, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 386: val_loss did not improve from 0.01942\n",
      "\n",
      "Epoch 387: val_loss improved from 0.01942 to 0.01938, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 388: val_loss did not improve from 0.01938\n",
      "\n",
      "Epoch 389: val_loss did not improve from 0.01938\n",
      "\n",
      "Epoch 390: val_loss did not improve from 0.01938\n",
      "\n",
      "Epoch 391: val_loss did not improve from 0.01938\n",
      "\n",
      "Epoch 392: val_loss did not improve from 0.01938\n",
      "\n",
      "Epoch 393: val_loss did not improve from 0.01938\n",
      "\n",
      "Epoch 394: val_loss did not improve from 0.01938\n",
      "\n",
      "Epoch 395: val_loss improved from 0.01938 to 0.01936, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 396: val_loss did not improve from 0.01936\n",
      "\n",
      "Epoch 397: val_loss did not improve from 0.01936\n",
      "\n",
      "Epoch 398: val_loss did not improve from 0.01936\n",
      "\n",
      "Epoch 399: val_loss did not improve from 0.01936\n",
      "\n",
      "Epoch 400: val_loss improved from 0.01936 to 0.01927, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 401: val_loss improved from 0.01927 to 0.01923, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 402: val_loss did not improve from 0.01923\n",
      "\n",
      "Epoch 403: val_loss improved from 0.01923 to 0.01921, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 404: val_loss did not improve from 0.01921\n",
      "\n",
      "Epoch 405: val_loss did not improve from 0.01921\n",
      "\n",
      "Epoch 406: val_loss did not improve from 0.01921\n",
      "\n",
      "Epoch 407: val_loss improved from 0.01921 to 0.01908, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 408: val_loss did not improve from 0.01908\n",
      "\n",
      "Epoch 409: val_loss did not improve from 0.01908\n",
      "\n",
      "Epoch 410: val_loss did not improve from 0.01908\n",
      "\n",
      "Epoch 411: val_loss did not improve from 0.01908\n",
      "\n",
      "Epoch 412: val_loss did not improve from 0.01908\n",
      "\n",
      "Epoch 413: val_loss did not improve from 0.01908\n",
      "\n",
      "Epoch 414: val_loss did not improve from 0.01908\n",
      "\n",
      "Epoch 415: val_loss did not improve from 0.01908\n",
      "\n",
      "Epoch 416: val_loss did not improve from 0.01908\n",
      "\n",
      "Epoch 417: val_loss did not improve from 0.01908\n",
      "\n",
      "Epoch 418: val_loss did not improve from 0.01908\n",
      "\n",
      "Epoch 419: val_loss improved from 0.01908 to 0.01901, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 420: val_loss did not improve from 0.01901\n",
      "\n",
      "Epoch 421: val_loss did not improve from 0.01901\n",
      "\n",
      "Epoch 422: val_loss did not improve from 0.01901\n",
      "\n",
      "Epoch 423: val_loss did not improve from 0.01901\n",
      "\n",
      "Epoch 424: val_loss improved from 0.01901 to 0.01891, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 425: val_loss did not improve from 0.01891\n",
      "\n",
      "Epoch 426: val_loss did not improve from 0.01891\n",
      "\n",
      "Epoch 427: val_loss did not improve from 0.01891\n",
      "\n",
      "Epoch 428: val_loss did not improve from 0.01891\n",
      "\n",
      "Epoch 429: val_loss did not improve from 0.01891\n",
      "\n",
      "Epoch 430: val_loss did not improve from 0.01891\n",
      "\n",
      "Epoch 431: val_loss did not improve from 0.01891\n",
      "\n",
      "Epoch 432: val_loss improved from 0.01891 to 0.01883, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 433: val_loss did not improve from 0.01883\n",
      "\n",
      "Epoch 434: val_loss improved from 0.01883 to 0.01882, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 435: val_loss improved from 0.01882 to 0.01881, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 436: val_loss did not improve from 0.01881\n",
      "\n",
      "Epoch 437: val_loss did not improve from 0.01881\n",
      "\n",
      "Epoch 438: val_loss did not improve from 0.01881\n",
      "\n",
      "Epoch 439: val_loss did not improve from 0.01881\n",
      "\n",
      "Epoch 440: val_loss improved from 0.01881 to 0.01881, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 441: val_loss did not improve from 0.01881\n",
      "\n",
      "Epoch 442: val_loss did not improve from 0.01881\n",
      "\n",
      "Epoch 443: val_loss did not improve from 0.01881\n",
      "\n",
      "Epoch 444: val_loss did not improve from 0.01881\n",
      "\n",
      "Epoch 445: val_loss improved from 0.01881 to 0.01873, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 446: val_loss did not improve from 0.01873\n",
      "\n",
      "Epoch 447: val_loss did not improve from 0.01873\n",
      "\n",
      "Epoch 448: val_loss did not improve from 0.01873\n",
      "\n",
      "Epoch 449: val_loss did not improve from 0.01873\n",
      "\n",
      "Epoch 450: val_loss did not improve from 0.01873\n",
      "\n",
      "Epoch 451: val_loss improved from 0.01873 to 0.01868, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 452: val_loss did not improve from 0.01868\n",
      "\n",
      "Epoch 453: val_loss did not improve from 0.01868\n",
      "\n",
      "Epoch 454: val_loss did not improve from 0.01868\n",
      "\n",
      "Epoch 455: val_loss did not improve from 0.01868\n",
      "\n",
      "Epoch 456: val_loss did not improve from 0.01868\n",
      "\n",
      "Epoch 457: val_loss improved from 0.01868 to 0.01865, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 458: val_loss did not improve from 0.01865\n",
      "\n",
      "Epoch 459: val_loss did not improve from 0.01865\n",
      "\n",
      "Epoch 460: val_loss did not improve from 0.01865\n",
      "\n",
      "Epoch 461: val_loss did not improve from 0.01865\n",
      "\n",
      "Epoch 462: val_loss did not improve from 0.01865\n",
      "\n",
      "Epoch 463: val_loss improved from 0.01865 to 0.01856, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 464: val_loss did not improve from 0.01856\n",
      "\n",
      "Epoch 465: val_loss did not improve from 0.01856\n",
      "\n",
      "Epoch 466: val_loss did not improve from 0.01856\n",
      "\n",
      "Epoch 467: val_loss improved from 0.01856 to 0.01854, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 468: val_loss did not improve from 0.01854\n",
      "\n",
      "Epoch 469: val_loss did not improve from 0.01854\n",
      "\n",
      "Epoch 470: val_loss improved from 0.01854 to 0.01851, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 471: val_loss improved from 0.01851 to 0.01851, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 472: val_loss did not improve from 0.01851\n",
      "\n",
      "Epoch 473: val_loss improved from 0.01851 to 0.01844, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 474: val_loss improved from 0.01844 to 0.01844, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 475: val_loss improved from 0.01844 to 0.01843, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 476: val_loss did not improve from 0.01843\n",
      "\n",
      "Epoch 477: val_loss did not improve from 0.01843\n",
      "\n",
      "Epoch 478: val_loss did not improve from 0.01843\n",
      "\n",
      "Epoch 479: val_loss did not improve from 0.01843\n",
      "\n",
      "Epoch 480: val_loss did not improve from 0.01843\n",
      "\n",
      "Epoch 481: val_loss did not improve from 0.01843\n",
      "\n",
      "Epoch 482: val_loss improved from 0.01843 to 0.01840, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 483: val_loss did not improve from 0.01840\n",
      "\n",
      "Epoch 484: val_loss did not improve from 0.01840\n",
      "\n",
      "Epoch 485: val_loss improved from 0.01840 to 0.01836, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 486: val_loss improved from 0.01836 to 0.01830, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 487: val_loss did not improve from 0.01830\n",
      "\n",
      "Epoch 488: val_loss did not improve from 0.01830\n",
      "\n",
      "Epoch 489: val_loss did not improve from 0.01830\n",
      "\n",
      "Epoch 490: val_loss did not improve from 0.01830\n",
      "\n",
      "Epoch 491: val_loss did not improve from 0.01830\n",
      "\n",
      "Epoch 492: val_loss did not improve from 0.01830\n",
      "\n",
      "Epoch 493: val_loss did not improve from 0.01830\n",
      "\n",
      "Epoch 494: val_loss improved from 0.01830 to 0.01827, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 495: val_loss did not improve from 0.01827\n",
      "\n",
      "Epoch 496: val_loss did not improve from 0.01827\n",
      "\n",
      "Epoch 497: val_loss did not improve from 0.01827\n",
      "\n",
      "Epoch 498: val_loss did not improve from 0.01827\n",
      "\n",
      "Epoch 499: val_loss did not improve from 0.01827\n",
      "\n",
      "Epoch 500: val_loss did not improve from 0.01827\n",
      "\n",
      "Epoch 501: val_loss did not improve from 0.01827\n",
      "\n",
      "Epoch 502: val_loss improved from 0.01827 to 0.01816, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 503: val_loss improved from 0.01816 to 0.01814, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 504: val_loss did not improve from 0.01814\n",
      "\n",
      "Epoch 505: val_loss did not improve from 0.01814\n",
      "\n",
      "Epoch 506: val_loss did not improve from 0.01814\n",
      "\n",
      "Epoch 507: val_loss did not improve from 0.01814\n",
      "\n",
      "Epoch 508: val_loss did not improve from 0.01814\n",
      "\n",
      "Epoch 509: val_loss did not improve from 0.01814\n",
      "\n",
      "Epoch 510: val_loss did not improve from 0.01814\n",
      "\n",
      "Epoch 511: val_loss did not improve from 0.01814\n",
      "\n",
      "Epoch 512: val_loss improved from 0.01814 to 0.01812, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 513: val_loss did not improve from 0.01812\n",
      "\n",
      "Epoch 514: val_loss improved from 0.01812 to 0.01810, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 515: val_loss did not improve from 0.01810\n",
      "\n",
      "Epoch 516: val_loss improved from 0.01810 to 0.01806, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 517: val_loss did not improve from 0.01806\n",
      "\n",
      "Epoch 518: val_loss improved from 0.01806 to 0.01802, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 519: val_loss did not improve from 0.01802\n",
      "\n",
      "Epoch 520: val_loss did not improve from 0.01802\n",
      "\n",
      "Epoch 521: val_loss did not improve from 0.01802\n",
      "\n",
      "Epoch 522: val_loss did not improve from 0.01802\n",
      "\n",
      "Epoch 523: val_loss did not improve from 0.01802\n",
      "\n",
      "Epoch 524: val_loss did not improve from 0.01802\n",
      "\n",
      "Epoch 525: val_loss did not improve from 0.01802\n",
      "\n",
      "Epoch 526: val_loss did not improve from 0.01802\n",
      "\n",
      "Epoch 527: val_loss did not improve from 0.01802\n",
      "\n",
      "Epoch 528: val_loss did not improve from 0.01802\n",
      "\n",
      "Epoch 529: val_loss did not improve from 0.01802\n",
      "\n",
      "Epoch 530: val_loss did not improve from 0.01802\n",
      "\n",
      "Epoch 531: val_loss did not improve from 0.01802\n",
      "\n",
      "Epoch 532: val_loss did not improve from 0.01802\n",
      "\n",
      "Epoch 533: val_loss did not improve from 0.01802\n",
      "\n",
      "Epoch 534: val_loss did not improve from 0.01802\n",
      "\n",
      "Epoch 535: val_loss did not improve from 0.01802\n",
      "\n",
      "Epoch 536: val_loss did not improve from 0.01802\n",
      "\n",
      "Epoch 537: val_loss did not improve from 0.01802\n",
      "\n",
      "Epoch 538: val_loss did not improve from 0.01802\n",
      "\n",
      "Epoch 539: val_loss improved from 0.01802 to 0.01795, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 540: val_loss improved from 0.01795 to 0.01794, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 541: val_loss did not improve from 0.01794\n",
      "\n",
      "Epoch 542: val_loss did not improve from 0.01794\n",
      "\n",
      "Epoch 543: val_loss did not improve from 0.01794\n",
      "\n",
      "Epoch 544: val_loss did not improve from 0.01794\n",
      "\n",
      "Epoch 545: val_loss improved from 0.01794 to 0.01784, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 546: val_loss did not improve from 0.01784\n",
      "\n",
      "Epoch 547: val_loss improved from 0.01784 to 0.01776, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 548: val_loss did not improve from 0.01776\n",
      "\n",
      "Epoch 549: val_loss did not improve from 0.01776\n",
      "\n",
      "Epoch 550: val_loss did not improve from 0.01776\n",
      "\n",
      "Epoch 551: val_loss did not improve from 0.01776\n",
      "\n",
      "Epoch 552: val_loss did not improve from 0.01776\n",
      "\n",
      "Epoch 553: val_loss did not improve from 0.01776\n",
      "\n",
      "Epoch 554: val_loss did not improve from 0.01776\n",
      "\n",
      "Epoch 555: val_loss did not improve from 0.01776\n",
      "\n",
      "Epoch 556: val_loss improved from 0.01776 to 0.01770, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 557: val_loss did not improve from 0.01770\n",
      "\n",
      "Epoch 558: val_loss did not improve from 0.01770\n",
      "\n",
      "Epoch 559: val_loss did not improve from 0.01770\n",
      "\n",
      "Epoch 560: val_loss did not improve from 0.01770\n",
      "\n",
      "Epoch 561: val_loss improved from 0.01770 to 0.01766, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 562: val_loss did not improve from 0.01766\n",
      "\n",
      "Epoch 563: val_loss did not improve from 0.01766\n",
      "\n",
      "Epoch 564: val_loss did not improve from 0.01766\n",
      "\n",
      "Epoch 565: val_loss did not improve from 0.01766\n",
      "\n",
      "Epoch 566: val_loss did not improve from 0.01766\n",
      "\n",
      "Epoch 567: val_loss did not improve from 0.01766\n",
      "\n",
      "Epoch 568: val_loss did not improve from 0.01766\n",
      "\n",
      "Epoch 569: val_loss did not improve from 0.01766\n",
      "\n",
      "Epoch 570: val_loss did not improve from 0.01766\n",
      "\n",
      "Epoch 571: val_loss did not improve from 0.01766\n",
      "\n",
      "Epoch 572: val_loss did not improve from 0.01766\n",
      "\n",
      "Epoch 573: val_loss did not improve from 0.01766\n",
      "\n",
      "Epoch 574: val_loss did not improve from 0.01766\n",
      "\n",
      "Epoch 575: val_loss improved from 0.01766 to 0.01759, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 576: val_loss did not improve from 0.01759\n",
      "\n",
      "Epoch 577: val_loss did not improve from 0.01759\n",
      "\n",
      "Epoch 578: val_loss did not improve from 0.01759\n",
      "\n",
      "Epoch 579: val_loss did not improve from 0.01759\n",
      "\n",
      "Epoch 580: val_loss did not improve from 0.01759\n",
      "\n",
      "Epoch 581: val_loss improved from 0.01759 to 0.01754, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 582: val_loss did not improve from 0.01754\n",
      "\n",
      "Epoch 583: val_loss did not improve from 0.01754\n",
      "\n",
      "Epoch 584: val_loss did not improve from 0.01754\n",
      "\n",
      "Epoch 585: val_loss did not improve from 0.01754\n",
      "\n",
      "Epoch 586: val_loss did not improve from 0.01754\n",
      "\n",
      "Epoch 587: val_loss did not improve from 0.01754\n",
      "\n",
      "Epoch 588: val_loss did not improve from 0.01754\n",
      "\n",
      "Epoch 589: val_loss did not improve from 0.01754\n",
      "\n",
      "Epoch 590: val_loss did not improve from 0.01754\n",
      "\n",
      "Epoch 591: val_loss improved from 0.01754 to 0.01751, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 592: val_loss did not improve from 0.01751\n",
      "\n",
      "Epoch 593: val_loss did not improve from 0.01751\n",
      "\n",
      "Epoch 594: val_loss did not improve from 0.01751\n",
      "\n",
      "Epoch 595: val_loss did not improve from 0.01751\n",
      "\n",
      "Epoch 596: val_loss did not improve from 0.01751\n",
      "\n",
      "Epoch 597: val_loss did not improve from 0.01751\n",
      "\n",
      "Epoch 598: val_loss did not improve from 0.01751\n",
      "\n",
      "Epoch 599: val_loss did not improve from 0.01751\n",
      "\n",
      "Epoch 600: val_loss did not improve from 0.01751\n",
      "\n",
      "Epoch 601: val_loss did not improve from 0.01751\n",
      "\n",
      "Epoch 602: val_loss improved from 0.01751 to 0.01749, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 603: val_loss did not improve from 0.01749\n",
      "\n",
      "Epoch 604: val_loss did not improve from 0.01749\n",
      "\n",
      "Epoch 605: val_loss did not improve from 0.01749\n",
      "\n",
      "Epoch 606: val_loss improved from 0.01749 to 0.01743, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 607: val_loss did not improve from 0.01743\n",
      "\n",
      "Epoch 608: val_loss did not improve from 0.01743\n",
      "\n",
      "Epoch 609: val_loss did not improve from 0.01743\n",
      "\n",
      "Epoch 610: val_loss improved from 0.01743 to 0.01738, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 611: val_loss did not improve from 0.01738\n",
      "\n",
      "Epoch 612: val_loss did not improve from 0.01738\n",
      "\n",
      "Epoch 613: val_loss did not improve from 0.01738\n",
      "\n",
      "Epoch 614: val_loss did not improve from 0.01738\n",
      "\n",
      "Epoch 615: val_loss did not improve from 0.01738\n",
      "\n",
      "Epoch 616: val_loss did not improve from 0.01738\n",
      "\n",
      "Epoch 617: val_loss did not improve from 0.01738\n",
      "\n",
      "Epoch 618: val_loss did not improve from 0.01738\n",
      "\n",
      "Epoch 619: val_loss did not improve from 0.01738\n",
      "\n",
      "Epoch 620: val_loss did not improve from 0.01738\n",
      "\n",
      "Epoch 621: val_loss did not improve from 0.01738\n",
      "\n",
      "Epoch 622: val_loss did not improve from 0.01738\n",
      "\n",
      "Epoch 623: val_loss improved from 0.01738 to 0.01727, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 624: val_loss did not improve from 0.01727\n",
      "\n",
      "Epoch 625: val_loss did not improve from 0.01727\n",
      "\n",
      "Epoch 626: val_loss did not improve from 0.01727\n",
      "\n",
      "Epoch 627: val_loss did not improve from 0.01727\n",
      "\n",
      "Epoch 628: val_loss did not improve from 0.01727\n",
      "\n",
      "Epoch 629: val_loss improved from 0.01727 to 0.01723, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 630: val_loss did not improve from 0.01723\n",
      "\n",
      "Epoch 631: val_loss did not improve from 0.01723\n",
      "\n",
      "Epoch 632: val_loss did not improve from 0.01723\n",
      "\n",
      "Epoch 633: val_loss did not improve from 0.01723\n",
      "\n",
      "Epoch 634: val_loss did not improve from 0.01723\n",
      "\n",
      "Epoch 635: val_loss did not improve from 0.01723\n",
      "\n",
      "Epoch 636: val_loss did not improve from 0.01723\n",
      "\n",
      "Epoch 637: val_loss did not improve from 0.01723\n",
      "\n",
      "Epoch 638: val_loss did not improve from 0.01723\n",
      "\n",
      "Epoch 639: val_loss did not improve from 0.01723\n",
      "\n",
      "Epoch 640: val_loss did not improve from 0.01723\n",
      "\n",
      "Epoch 641: val_loss improved from 0.01723 to 0.01721, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 642: val_loss did not improve from 0.01721\n",
      "\n",
      "Epoch 643: val_loss did not improve from 0.01721\n",
      "\n",
      "Epoch 644: val_loss did not improve from 0.01721\n",
      "\n",
      "Epoch 645: val_loss did not improve from 0.01721\n",
      "\n",
      "Epoch 646: val_loss did not improve from 0.01721\n",
      "\n",
      "Epoch 647: val_loss improved from 0.01721 to 0.01708, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 648: val_loss improved from 0.01708 to 0.01705, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 649: val_loss did not improve from 0.01705\n",
      "\n",
      "Epoch 650: val_loss did not improve from 0.01705\n",
      "\n",
      "Epoch 651: val_loss did not improve from 0.01705\n",
      "\n",
      "Epoch 652: val_loss did not improve from 0.01705\n",
      "\n",
      "Epoch 653: val_loss did not improve from 0.01705\n",
      "\n",
      "Epoch 654: val_loss did not improve from 0.01705\n",
      "\n",
      "Epoch 655: val_loss did not improve from 0.01705\n",
      "\n",
      "Epoch 656: val_loss did not improve from 0.01705\n",
      "\n",
      "Epoch 657: val_loss did not improve from 0.01705\n",
      "\n",
      "Epoch 658: val_loss did not improve from 0.01705\n",
      "\n",
      "Epoch 659: val_loss improved from 0.01705 to 0.01704, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 660: val_loss did not improve from 0.01704\n",
      "\n",
      "Epoch 661: val_loss did not improve from 0.01704\n",
      "\n",
      "Epoch 662: val_loss improved from 0.01704 to 0.01700, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 663: val_loss did not improve from 0.01700\n",
      "\n",
      "Epoch 664: val_loss did not improve from 0.01700\n",
      "\n",
      "Epoch 665: val_loss did not improve from 0.01700\n",
      "\n",
      "Epoch 666: val_loss did not improve from 0.01700\n",
      "\n",
      "Epoch 667: val_loss did not improve from 0.01700\n",
      "\n",
      "Epoch 668: val_loss did not improve from 0.01700\n",
      "\n",
      "Epoch 669: val_loss did not improve from 0.01700\n",
      "\n",
      "Epoch 670: val_loss improved from 0.01700 to 0.01697, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 671: val_loss did not improve from 0.01697\n",
      "\n",
      "Epoch 672: val_loss did not improve from 0.01697\n",
      "\n",
      "Epoch 673: val_loss did not improve from 0.01697\n",
      "\n",
      "Epoch 674: val_loss did not improve from 0.01697\n",
      "\n",
      "Epoch 675: val_loss did not improve from 0.01697\n",
      "\n",
      "Epoch 676: val_loss improved from 0.01697 to 0.01689, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 677: val_loss did not improve from 0.01689\n",
      "\n",
      "Epoch 678: val_loss did not improve from 0.01689\n",
      "\n",
      "Epoch 679: val_loss did not improve from 0.01689\n",
      "\n",
      "Epoch 680: val_loss improved from 0.01689 to 0.01684, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 681: val_loss did not improve from 0.01684\n",
      "\n",
      "Epoch 682: val_loss did not improve from 0.01684\n",
      "\n",
      "Epoch 683: val_loss did not improve from 0.01684\n",
      "\n",
      "Epoch 684: val_loss did not improve from 0.01684\n",
      "\n",
      "Epoch 685: val_loss did not improve from 0.01684\n",
      "\n",
      "Epoch 686: val_loss did not improve from 0.01684\n",
      "\n",
      "Epoch 687: val_loss did not improve from 0.01684\n",
      "\n",
      "Epoch 688: val_loss did not improve from 0.01684\n",
      "\n",
      "Epoch 689: val_loss did not improve from 0.01684\n",
      "\n",
      "Epoch 690: val_loss did not improve from 0.01684\n",
      "\n",
      "Epoch 691: val_loss did not improve from 0.01684\n",
      "\n",
      "Epoch 692: val_loss did not improve from 0.01684\n",
      "\n",
      "Epoch 693: val_loss improved from 0.01684 to 0.01682, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 694: val_loss did not improve from 0.01682\n",
      "\n",
      "Epoch 695: val_loss did not improve from 0.01682\n",
      "\n",
      "Epoch 696: val_loss did not improve from 0.01682\n",
      "\n",
      "Epoch 697: val_loss did not improve from 0.01682\n",
      "\n",
      "Epoch 698: val_loss improved from 0.01682 to 0.01677, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 699: val_loss did not improve from 0.01677\n",
      "\n",
      "Epoch 700: val_loss did not improve from 0.01677\n",
      "\n",
      "Epoch 701: val_loss did not improve from 0.01677\n",
      "\n",
      "Epoch 702: val_loss did not improve from 0.01677\n",
      "\n",
      "Epoch 703: val_loss did not improve from 0.01677\n",
      "\n",
      "Epoch 704: val_loss did not improve from 0.01677\n",
      "\n",
      "Epoch 705: val_loss did not improve from 0.01677\n",
      "\n",
      "Epoch 706: val_loss did not improve from 0.01677\n",
      "\n",
      "Epoch 707: val_loss did not improve from 0.01677\n",
      "\n",
      "Epoch 708: val_loss did not improve from 0.01677\n",
      "\n",
      "Epoch 709: val_loss improved from 0.01677 to 0.01673, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 710: val_loss did not improve from 0.01673\n",
      "\n",
      "Epoch 711: val_loss did not improve from 0.01673\n",
      "\n",
      "Epoch 712: val_loss did not improve from 0.01673\n",
      "\n",
      "Epoch 713: val_loss did not improve from 0.01673\n",
      "\n",
      "Epoch 714: val_loss did not improve from 0.01673\n",
      "\n",
      "Epoch 715: val_loss did not improve from 0.01673\n",
      "\n",
      "Epoch 716: val_loss did not improve from 0.01673\n",
      "\n",
      "Epoch 717: val_loss did not improve from 0.01673\n",
      "\n",
      "Epoch 718: val_loss improved from 0.01673 to 0.01662, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 719: val_loss did not improve from 0.01662\n",
      "\n",
      "Epoch 720: val_loss did not improve from 0.01662\n",
      "\n",
      "Epoch 721: val_loss did not improve from 0.01662\n",
      "\n",
      "Epoch 722: val_loss did not improve from 0.01662\n",
      "\n",
      "Epoch 723: val_loss did not improve from 0.01662\n",
      "\n",
      "Epoch 724: val_loss did not improve from 0.01662\n",
      "\n",
      "Epoch 725: val_loss did not improve from 0.01662\n",
      "\n",
      "Epoch 726: val_loss improved from 0.01662 to 0.01661, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 727: val_loss did not improve from 0.01661\n",
      "\n",
      "Epoch 728: val_loss did not improve from 0.01661\n",
      "\n",
      "Epoch 729: val_loss did not improve from 0.01661\n",
      "\n",
      "Epoch 730: val_loss did not improve from 0.01661\n",
      "\n",
      "Epoch 731: val_loss did not improve from 0.01661\n",
      "\n",
      "Epoch 732: val_loss did not improve from 0.01661\n",
      "\n",
      "Epoch 733: val_loss did not improve from 0.01661\n",
      "\n",
      "Epoch 734: val_loss did not improve from 0.01661\n",
      "\n",
      "Epoch 735: val_loss did not improve from 0.01661\n",
      "\n",
      "Epoch 736: val_loss did not improve from 0.01661\n",
      "\n",
      "Epoch 737: val_loss improved from 0.01661 to 0.01657, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 738: val_loss improved from 0.01657 to 0.01655, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 739: val_loss did not improve from 0.01655\n",
      "\n",
      "Epoch 740: val_loss did not improve from 0.01655\n",
      "\n",
      "Epoch 741: val_loss did not improve from 0.01655\n",
      "\n",
      "Epoch 742: val_loss did not improve from 0.01655\n",
      "\n",
      "Epoch 743: val_loss did not improve from 0.01655\n",
      "\n",
      "Epoch 744: val_loss did not improve from 0.01655\n",
      "\n",
      "Epoch 745: val_loss did not improve from 0.01655\n",
      "\n",
      "Epoch 746: val_loss did not improve from 0.01655\n",
      "\n",
      "Epoch 747: val_loss did not improve from 0.01655\n",
      "\n",
      "Epoch 748: val_loss did not improve from 0.01655\n",
      "\n",
      "Epoch 749: val_loss did not improve from 0.01655\n",
      "\n",
      "Epoch 750: val_loss did not improve from 0.01655\n",
      "\n",
      "Epoch 751: val_loss did not improve from 0.01655\n",
      "\n",
      "Epoch 752: val_loss improved from 0.01655 to 0.01646, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 753: val_loss did not improve from 0.01646\n",
      "\n",
      "Epoch 754: val_loss did not improve from 0.01646\n",
      "\n",
      "Epoch 755: val_loss did not improve from 0.01646\n",
      "\n",
      "Epoch 756: val_loss did not improve from 0.01646\n",
      "\n",
      "Epoch 757: val_loss did not improve from 0.01646\n",
      "\n",
      "Epoch 758: val_loss did not improve from 0.01646\n",
      "\n",
      "Epoch 759: val_loss improved from 0.01646 to 0.01644, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 760: val_loss did not improve from 0.01644\n",
      "\n",
      "Epoch 761: val_loss did not improve from 0.01644\n",
      "\n",
      "Epoch 762: val_loss did not improve from 0.01644\n",
      "\n",
      "Epoch 763: val_loss did not improve from 0.01644\n",
      "\n",
      "Epoch 764: val_loss improved from 0.01644 to 0.01640, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 765: val_loss did not improve from 0.01640\n",
      "\n",
      "Epoch 766: val_loss did not improve from 0.01640\n",
      "\n",
      "Epoch 767: val_loss did not improve from 0.01640\n",
      "\n",
      "Epoch 768: val_loss did not improve from 0.01640\n",
      "\n",
      "Epoch 769: val_loss did not improve from 0.01640\n",
      "\n",
      "Epoch 770: val_loss did not improve from 0.01640\n",
      "\n",
      "Epoch 771: val_loss did not improve from 0.01640\n",
      "\n",
      "Epoch 772: val_loss did not improve from 0.01640\n",
      "\n",
      "Epoch 773: val_loss improved from 0.01640 to 0.01636, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 774: val_loss did not improve from 0.01636\n",
      "\n",
      "Epoch 775: val_loss did not improve from 0.01636\n",
      "\n",
      "Epoch 776: val_loss did not improve from 0.01636\n",
      "\n",
      "Epoch 777: val_loss did not improve from 0.01636\n",
      "\n",
      "Epoch 778: val_loss did not improve from 0.01636\n",
      "\n",
      "Epoch 779: val_loss improved from 0.01636 to 0.01632, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 780: val_loss did not improve from 0.01632\n",
      "\n",
      "Epoch 781: val_loss did not improve from 0.01632\n",
      "\n",
      "Epoch 782: val_loss improved from 0.01632 to 0.01628, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 783: val_loss did not improve from 0.01628\n",
      "\n",
      "Epoch 784: val_loss did not improve from 0.01628\n",
      "\n",
      "Epoch 785: val_loss did not improve from 0.01628\n",
      "\n",
      "Epoch 786: val_loss did not improve from 0.01628\n",
      "\n",
      "Epoch 787: val_loss did not improve from 0.01628\n",
      "\n",
      "Epoch 788: val_loss did not improve from 0.01628\n",
      "\n",
      "Epoch 789: val_loss did not improve from 0.01628\n",
      "\n",
      "Epoch 790: val_loss did not improve from 0.01628\n",
      "\n",
      "Epoch 791: val_loss did not improve from 0.01628\n",
      "\n",
      "Epoch 792: val_loss did not improve from 0.01628\n",
      "\n",
      "Epoch 793: val_loss did not improve from 0.01628\n",
      "\n",
      "Epoch 794: val_loss did not improve from 0.01628\n",
      "\n",
      "Epoch 795: val_loss did not improve from 0.01628\n",
      "\n",
      "Epoch 796: val_loss improved from 0.01628 to 0.01624, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 797: val_loss did not improve from 0.01624\n",
      "\n",
      "Epoch 798: val_loss did not improve from 0.01624\n",
      "\n",
      "Epoch 799: val_loss did not improve from 0.01624\n",
      "\n",
      "Epoch 800: val_loss did not improve from 0.01624\n",
      "\n",
      "Epoch 801: val_loss improved from 0.01624 to 0.01616, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 802: val_loss did not improve from 0.01616\n",
      "\n",
      "Epoch 803: val_loss did not improve from 0.01616\n",
      "\n",
      "Epoch 804: val_loss did not improve from 0.01616\n",
      "\n",
      "Epoch 805: val_loss did not improve from 0.01616\n",
      "\n",
      "Epoch 806: val_loss did not improve from 0.01616\n",
      "\n",
      "Epoch 807: val_loss did not improve from 0.01616\n",
      "\n",
      "Epoch 808: val_loss did not improve from 0.01616\n",
      "\n",
      "Epoch 809: val_loss did not improve from 0.01616\n",
      "\n",
      "Epoch 810: val_loss did not improve from 0.01616\n",
      "\n",
      "Epoch 811: val_loss did not improve from 0.01616\n",
      "\n",
      "Epoch 812: val_loss did not improve from 0.01616\n",
      "\n",
      "Epoch 813: val_loss did not improve from 0.01616\n",
      "\n",
      "Epoch 814: val_loss did not improve from 0.01616\n",
      "\n",
      "Epoch 815: val_loss improved from 0.01616 to 0.01613, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 816: val_loss did not improve from 0.01613\n",
      "\n",
      "Epoch 817: val_loss improved from 0.01613 to 0.01612, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 818: val_loss did not improve from 0.01612\n",
      "\n",
      "Epoch 819: val_loss did not improve from 0.01612\n",
      "\n",
      "Epoch 820: val_loss did not improve from 0.01612\n",
      "\n",
      "Epoch 821: val_loss did not improve from 0.01612\n",
      "\n",
      "Epoch 822: val_loss did not improve from 0.01612\n",
      "\n",
      "Epoch 823: val_loss did not improve from 0.01612\n",
      "\n",
      "Epoch 824: val_loss did not improve from 0.01612\n",
      "\n",
      "Epoch 825: val_loss did not improve from 0.01612\n",
      "\n",
      "Epoch 826: val_loss did not improve from 0.01612\n",
      "\n",
      "Epoch 827: val_loss did not improve from 0.01612\n",
      "\n",
      "Epoch 828: val_loss did not improve from 0.01612\n",
      "\n",
      "Epoch 829: val_loss did not improve from 0.01612\n",
      "\n",
      "Epoch 830: val_loss did not improve from 0.01612\n",
      "\n",
      "Epoch 831: val_loss did not improve from 0.01612\n",
      "\n",
      "Epoch 832: val_loss did not improve from 0.01612\n",
      "\n",
      "Epoch 833: val_loss did not improve from 0.01612\n",
      "\n",
      "Epoch 834: val_loss improved from 0.01612 to 0.01600, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 835: val_loss did not improve from 0.01600\n",
      "\n",
      "Epoch 836: val_loss did not improve from 0.01600\n",
      "\n",
      "Epoch 837: val_loss did not improve from 0.01600\n",
      "\n",
      "Epoch 838: val_loss did not improve from 0.01600\n",
      "\n",
      "Epoch 839: val_loss did not improve from 0.01600\n",
      "\n",
      "Epoch 840: val_loss did not improve from 0.01600\n",
      "\n",
      "Epoch 841: val_loss did not improve from 0.01600\n",
      "\n",
      "Epoch 842: val_loss did not improve from 0.01600\n",
      "\n",
      "Epoch 843: val_loss did not improve from 0.01600\n",
      "\n",
      "Epoch 844: val_loss did not improve from 0.01600\n",
      "\n",
      "Epoch 845: val_loss did not improve from 0.01600\n",
      "\n",
      "Epoch 846: val_loss did not improve from 0.01600\n",
      "\n",
      "Epoch 847: val_loss did not improve from 0.01600\n",
      "\n",
      "Epoch 848: val_loss did not improve from 0.01600\n",
      "\n",
      "Epoch 849: val_loss improved from 0.01600 to 0.01596, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 850: val_loss did not improve from 0.01596\n",
      "\n",
      "Epoch 851: val_loss improved from 0.01596 to 0.01594, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 852: val_loss did not improve from 0.01594\n",
      "\n",
      "Epoch 853: val_loss did not improve from 0.01594\n",
      "\n",
      "Epoch 854: val_loss did not improve from 0.01594\n",
      "\n",
      "Epoch 855: val_loss did not improve from 0.01594\n",
      "\n",
      "Epoch 856: val_loss did not improve from 0.01594\n",
      "\n",
      "Epoch 857: val_loss did not improve from 0.01594\n",
      "\n",
      "Epoch 858: val_loss did not improve from 0.01594\n",
      "\n",
      "Epoch 859: val_loss did not improve from 0.01594\n",
      "\n",
      "Epoch 860: val_loss did not improve from 0.01594\n",
      "\n",
      "Epoch 861: val_loss did not improve from 0.01594\n",
      "\n",
      "Epoch 862: val_loss did not improve from 0.01594\n",
      "\n",
      "Epoch 863: val_loss did not improve from 0.01594\n",
      "\n",
      "Epoch 864: val_loss did not improve from 0.01594\n",
      "\n",
      "Epoch 865: val_loss did not improve from 0.01594\n",
      "\n",
      "Epoch 866: val_loss did not improve from 0.01594\n",
      "\n",
      "Epoch 867: val_loss did not improve from 0.01594\n",
      "\n",
      "Epoch 868: val_loss did not improve from 0.01594\n",
      "\n",
      "Epoch 869: val_loss did not improve from 0.01594\n",
      "\n",
      "Epoch 870: val_loss did not improve from 0.01594\n",
      "\n",
      "Epoch 871: val_loss did not improve from 0.01594\n",
      "\n",
      "Epoch 872: val_loss did not improve from 0.01594\n",
      "\n",
      "Epoch 873: val_loss did not improve from 0.01594\n",
      "\n",
      "Epoch 874: val_loss did not improve from 0.01594\n",
      "\n",
      "Epoch 875: val_loss did not improve from 0.01594\n",
      "\n",
      "Epoch 876: val_loss did not improve from 0.01594\n",
      "\n",
      "Epoch 877: val_loss did not improve from 0.01594\n",
      "\n",
      "Epoch 878: val_loss did not improve from 0.01594\n",
      "\n",
      "Epoch 879: val_loss improved from 0.01594 to 0.01591, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 880: val_loss did not improve from 0.01591\n",
      "\n",
      "Epoch 881: val_loss did not improve from 0.01591\n",
      "\n",
      "Epoch 882: val_loss did not improve from 0.01591\n",
      "\n",
      "Epoch 883: val_loss improved from 0.01591 to 0.01582, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 884: val_loss did not improve from 0.01582\n",
      "\n",
      "Epoch 885: val_loss did not improve from 0.01582\n",
      "\n",
      "Epoch 886: val_loss did not improve from 0.01582\n",
      "\n",
      "Epoch 887: val_loss did not improve from 0.01582\n",
      "\n",
      "Epoch 888: val_loss did not improve from 0.01582\n",
      "\n",
      "Epoch 889: val_loss did not improve from 0.01582\n",
      "\n",
      "Epoch 890: val_loss improved from 0.01582 to 0.01578, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 891: val_loss did not improve from 0.01578\n",
      "\n",
      "Epoch 892: val_loss did not improve from 0.01578\n",
      "\n",
      "Epoch 893: val_loss improved from 0.01578 to 0.01576, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 894: val_loss did not improve from 0.01576\n",
      "\n",
      "Epoch 895: val_loss did not improve from 0.01576\n",
      "\n",
      "Epoch 896: val_loss did not improve from 0.01576\n",
      "\n",
      "Epoch 897: val_loss did not improve from 0.01576\n",
      "\n",
      "Epoch 898: val_loss did not improve from 0.01576\n",
      "\n",
      "Epoch 899: val_loss did not improve from 0.01576\n",
      "\n",
      "Epoch 900: val_loss did not improve from 0.01576\n",
      "\n",
      "Epoch 901: val_loss did not improve from 0.01576\n",
      "\n",
      "Epoch 902: val_loss did not improve from 0.01576\n",
      "\n",
      "Epoch 903: val_loss did not improve from 0.01576\n",
      "\n",
      "Epoch 904: val_loss did not improve from 0.01576\n",
      "\n",
      "Epoch 905: val_loss did not improve from 0.01576\n",
      "\n",
      "Epoch 906: val_loss did not improve from 0.01576\n",
      "\n",
      "Epoch 907: val_loss did not improve from 0.01576\n",
      "\n",
      "Epoch 908: val_loss did not improve from 0.01576\n",
      "\n",
      "Epoch 909: val_loss did not improve from 0.01576\n",
      "\n",
      "Epoch 910: val_loss improved from 0.01576 to 0.01572, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 911: val_loss did not improve from 0.01572\n",
      "\n",
      "Epoch 912: val_loss did not improve from 0.01572\n",
      "\n",
      "Epoch 913: val_loss improved from 0.01572 to 0.01568, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 914: val_loss did not improve from 0.01568\n",
      "\n",
      "Epoch 915: val_loss did not improve from 0.01568\n",
      "\n",
      "Epoch 916: val_loss did not improve from 0.01568\n",
      "\n",
      "Epoch 917: val_loss did not improve from 0.01568\n",
      "\n",
      "Epoch 918: val_loss did not improve from 0.01568\n",
      "\n",
      "Epoch 919: val_loss did not improve from 0.01568\n",
      "\n",
      "Epoch 920: val_loss did not improve from 0.01568\n",
      "\n",
      "Epoch 921: val_loss did not improve from 0.01568\n",
      "\n",
      "Epoch 922: val_loss did not improve from 0.01568\n",
      "\n",
      "Epoch 923: val_loss did not improve from 0.01568\n",
      "\n",
      "Epoch 924: val_loss did not improve from 0.01568\n",
      "\n",
      "Epoch 925: val_loss did not improve from 0.01568\n",
      "\n",
      "Epoch 926: val_loss did not improve from 0.01568\n",
      "\n",
      "Epoch 927: val_loss did not improve from 0.01568\n",
      "\n",
      "Epoch 928: val_loss did not improve from 0.01568\n",
      "\n",
      "Epoch 929: val_loss did not improve from 0.01568\n",
      "\n",
      "Epoch 930: val_loss did not improve from 0.01568\n",
      "\n",
      "Epoch 931: val_loss did not improve from 0.01568\n",
      "\n",
      "Epoch 932: val_loss did not improve from 0.01568\n",
      "\n",
      "Epoch 933: val_loss did not improve from 0.01568\n",
      "\n",
      "Epoch 934: val_loss did not improve from 0.01568\n",
      "\n",
      "Epoch 935: val_loss did not improve from 0.01568\n",
      "\n",
      "Epoch 936: val_loss improved from 0.01568 to 0.01559, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 937: val_loss did not improve from 0.01559\n",
      "\n",
      "Epoch 938: val_loss did not improve from 0.01559\n",
      "\n",
      "Epoch 939: val_loss did not improve from 0.01559\n",
      "\n",
      "Epoch 940: val_loss did not improve from 0.01559\n",
      "\n",
      "Epoch 941: val_loss did not improve from 0.01559\n",
      "\n",
      "Epoch 942: val_loss did not improve from 0.01559\n",
      "\n",
      "Epoch 943: val_loss did not improve from 0.01559\n",
      "\n",
      "Epoch 944: val_loss did not improve from 0.01559\n",
      "\n",
      "Epoch 945: val_loss improved from 0.01559 to 0.01552, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 946: val_loss did not improve from 0.01552\n",
      "\n",
      "Epoch 947: val_loss did not improve from 0.01552\n",
      "\n",
      "Epoch 948: val_loss did not improve from 0.01552\n",
      "\n",
      "Epoch 949: val_loss did not improve from 0.01552\n",
      "\n",
      "Epoch 950: val_loss did not improve from 0.01552\n",
      "\n",
      "Epoch 951: val_loss did not improve from 0.01552\n",
      "\n",
      "Epoch 952: val_loss improved from 0.01552 to 0.01552, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 953: val_loss did not improve from 0.01552\n",
      "\n",
      "Epoch 954: val_loss did not improve from 0.01552\n",
      "\n",
      "Epoch 955: val_loss improved from 0.01552 to 0.01551, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 956: val_loss did not improve from 0.01551\n",
      "\n",
      "Epoch 957: val_loss did not improve from 0.01551\n",
      "\n",
      "Epoch 958: val_loss did not improve from 0.01551\n",
      "\n",
      "Epoch 959: val_loss did not improve from 0.01551\n",
      "\n",
      "Epoch 960: val_loss did not improve from 0.01551\n",
      "\n",
      "Epoch 961: val_loss did not improve from 0.01551\n",
      "\n",
      "Epoch 962: val_loss did not improve from 0.01551\n",
      "\n",
      "Epoch 963: val_loss did not improve from 0.01551\n",
      "\n",
      "Epoch 964: val_loss improved from 0.01551 to 0.01546, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 965: val_loss did not improve from 0.01546\n",
      "\n",
      "Epoch 966: val_loss did not improve from 0.01546\n",
      "\n",
      "Epoch 967: val_loss did not improve from 0.01546\n",
      "\n",
      "Epoch 968: val_loss improved from 0.01546 to 0.01543, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 969: val_loss did not improve from 0.01543\n",
      "\n",
      "Epoch 970: val_loss did not improve from 0.01543\n",
      "\n",
      "Epoch 971: val_loss did not improve from 0.01543\n",
      "\n",
      "Epoch 972: val_loss did not improve from 0.01543\n",
      "\n",
      "Epoch 973: val_loss did not improve from 0.01543\n",
      "\n",
      "Epoch 974: val_loss did not improve from 0.01543\n",
      "\n",
      "Epoch 975: val_loss did not improve from 0.01543\n",
      "\n",
      "Epoch 976: val_loss did not improve from 0.01543\n",
      "\n",
      "Epoch 977: val_loss did not improve from 0.01543\n",
      "\n",
      "Epoch 978: val_loss did not improve from 0.01543\n",
      "\n",
      "Epoch 979: val_loss did not improve from 0.01543\n",
      "\n",
      "Epoch 980: val_loss improved from 0.01543 to 0.01537, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 981: val_loss did not improve from 0.01537\n",
      "\n",
      "Epoch 982: val_loss did not improve from 0.01537\n",
      "\n",
      "Epoch 983: val_loss did not improve from 0.01537\n",
      "\n",
      "Epoch 984: val_loss did not improve from 0.01537\n",
      "\n",
      "Epoch 985: val_loss improved from 0.01537 to 0.01536, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 986: val_loss did not improve from 0.01536\n",
      "\n",
      "Epoch 987: val_loss did not improve from 0.01536\n",
      "\n",
      "Epoch 988: val_loss did not improve from 0.01536\n",
      "\n",
      "Epoch 989: val_loss did not improve from 0.01536\n",
      "\n",
      "Epoch 990: val_loss did not improve from 0.01536\n",
      "\n",
      "Epoch 991: val_loss did not improve from 0.01536\n",
      "\n",
      "Epoch 992: val_loss did not improve from 0.01536\n",
      "\n",
      "Epoch 993: val_loss did not improve from 0.01536\n",
      "\n",
      "Epoch 994: val_loss did not improve from 0.01536\n",
      "\n",
      "Epoch 995: val_loss did not improve from 0.01536\n",
      "\n",
      "Epoch 996: val_loss did not improve from 0.01536\n",
      "\n",
      "Epoch 997: val_loss did not improve from 0.01536\n",
      "\n",
      "Epoch 998: val_loss did not improve from 0.01536\n",
      "\n",
      "Epoch 999: val_loss did not improve from 0.01536\n",
      "\n",
      "Epoch 1000: val_loss improved from 0.01536 to 0.01533, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1001: val_loss did not improve from 0.01533\n",
      "\n",
      "Epoch 1002: val_loss did not improve from 0.01533\n",
      "\n",
      "Epoch 1003: val_loss did not improve from 0.01533\n",
      "\n",
      "Epoch 1004: val_loss did not improve from 0.01533\n",
      "\n",
      "Epoch 1005: val_loss did not improve from 0.01533\n",
      "\n",
      "Epoch 1006: val_loss did not improve from 0.01533\n",
      "\n",
      "Epoch 1007: val_loss did not improve from 0.01533\n",
      "\n",
      "Epoch 1008: val_loss did not improve from 0.01533\n",
      "\n",
      "Epoch 1009: val_loss improved from 0.01533 to 0.01529, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1010: val_loss did not improve from 0.01529\n",
      "\n",
      "Epoch 1011: val_loss did not improve from 0.01529\n",
      "\n",
      "Epoch 1012: val_loss did not improve from 0.01529\n",
      "\n",
      "Epoch 1013: val_loss did not improve from 0.01529\n",
      "\n",
      "Epoch 1014: val_loss did not improve from 0.01529\n",
      "\n",
      "Epoch 1015: val_loss did not improve from 0.01529\n",
      "\n",
      "Epoch 1016: val_loss did not improve from 0.01529\n",
      "\n",
      "Epoch 1017: val_loss did not improve from 0.01529\n",
      "\n",
      "Epoch 1018: val_loss did not improve from 0.01529\n",
      "\n",
      "Epoch 1019: val_loss did not improve from 0.01529\n",
      "\n",
      "Epoch 1020: val_loss improved from 0.01529 to 0.01526, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1021: val_loss did not improve from 0.01526\n",
      "\n",
      "Epoch 1022: val_loss did not improve from 0.01526\n",
      "\n",
      "Epoch 1023: val_loss did not improve from 0.01526\n",
      "\n",
      "Epoch 1024: val_loss did not improve from 0.01526\n",
      "\n",
      "Epoch 1025: val_loss improved from 0.01526 to 0.01523, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1026: val_loss did not improve from 0.01523\n",
      "\n",
      "Epoch 1027: val_loss did not improve from 0.01523\n",
      "\n",
      "Epoch 1028: val_loss did not improve from 0.01523\n",
      "\n",
      "Epoch 1029: val_loss improved from 0.01523 to 0.01518, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1030: val_loss did not improve from 0.01518\n",
      "\n",
      "Epoch 1031: val_loss did not improve from 0.01518\n",
      "\n",
      "Epoch 1032: val_loss did not improve from 0.01518\n",
      "\n",
      "Epoch 1033: val_loss did not improve from 0.01518\n",
      "\n",
      "Epoch 1034: val_loss did not improve from 0.01518\n",
      "\n",
      "Epoch 1035: val_loss did not improve from 0.01518\n",
      "\n",
      "Epoch 1036: val_loss did not improve from 0.01518\n",
      "\n",
      "Epoch 1037: val_loss did not improve from 0.01518\n",
      "\n",
      "Epoch 1038: val_loss did not improve from 0.01518\n",
      "\n",
      "Epoch 1039: val_loss did not improve from 0.01518\n",
      "\n",
      "Epoch 1040: val_loss did not improve from 0.01518\n",
      "\n",
      "Epoch 1041: val_loss did not improve from 0.01518\n",
      "\n",
      "Epoch 1042: val_loss improved from 0.01518 to 0.01517, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1043: val_loss did not improve from 0.01517\n",
      "\n",
      "Epoch 1044: val_loss did not improve from 0.01517\n",
      "\n",
      "Epoch 1045: val_loss did not improve from 0.01517\n",
      "\n",
      "Epoch 1046: val_loss did not improve from 0.01517\n",
      "\n",
      "Epoch 1047: val_loss did not improve from 0.01517\n",
      "\n",
      "Epoch 1048: val_loss improved from 0.01517 to 0.01512, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1049: val_loss did not improve from 0.01512\n",
      "\n",
      "Epoch 1050: val_loss did not improve from 0.01512\n",
      "\n",
      "Epoch 1051: val_loss did not improve from 0.01512\n",
      "\n",
      "Epoch 1052: val_loss did not improve from 0.01512\n",
      "\n",
      "Epoch 1053: val_loss did not improve from 0.01512\n",
      "\n",
      "Epoch 1054: val_loss did not improve from 0.01512\n",
      "\n",
      "Epoch 1055: val_loss did not improve from 0.01512\n",
      "\n",
      "Epoch 1056: val_loss did not improve from 0.01512\n",
      "\n",
      "Epoch 1057: val_loss did not improve from 0.01512\n",
      "\n",
      "Epoch 1058: val_loss did not improve from 0.01512\n",
      "\n",
      "Epoch 1059: val_loss improved from 0.01512 to 0.01511, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1060: val_loss did not improve from 0.01511\n",
      "\n",
      "Epoch 1061: val_loss improved from 0.01511 to 0.01508, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1062: val_loss did not improve from 0.01508\n",
      "\n",
      "Epoch 1063: val_loss did not improve from 0.01508\n",
      "\n",
      "Epoch 1064: val_loss did not improve from 0.01508\n",
      "\n",
      "Epoch 1065: val_loss did not improve from 0.01508\n",
      "\n",
      "Epoch 1066: val_loss did not improve from 0.01508\n",
      "\n",
      "Epoch 1067: val_loss did not improve from 0.01508\n",
      "\n",
      "Epoch 1068: val_loss did not improve from 0.01508\n",
      "\n",
      "Epoch 1069: val_loss did not improve from 0.01508\n",
      "\n",
      "Epoch 1070: val_loss did not improve from 0.01508\n",
      "\n",
      "Epoch 1071: val_loss did not improve from 0.01508\n",
      "\n",
      "Epoch 1072: val_loss did not improve from 0.01508\n",
      "\n",
      "Epoch 1073: val_loss did not improve from 0.01508\n",
      "\n",
      "Epoch 1074: val_loss did not improve from 0.01508\n",
      "\n",
      "Epoch 1075: val_loss did not improve from 0.01508\n",
      "\n",
      "Epoch 1076: val_loss did not improve from 0.01508\n",
      "\n",
      "Epoch 1077: val_loss did not improve from 0.01508\n",
      "\n",
      "Epoch 1078: val_loss did not improve from 0.01508\n",
      "\n",
      "Epoch 1079: val_loss did not improve from 0.01508\n",
      "\n",
      "Epoch 1080: val_loss improved from 0.01508 to 0.01503, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1081: val_loss did not improve from 0.01503\n",
      "\n",
      "Epoch 1082: val_loss did not improve from 0.01503\n",
      "\n",
      "Epoch 1083: val_loss did not improve from 0.01503\n",
      "\n",
      "Epoch 1084: val_loss improved from 0.01503 to 0.01498, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1085: val_loss did not improve from 0.01498\n",
      "\n",
      "Epoch 1086: val_loss did not improve from 0.01498\n",
      "\n",
      "Epoch 1087: val_loss improved from 0.01498 to 0.01496, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1088: val_loss did not improve from 0.01496\n",
      "\n",
      "Epoch 1089: val_loss did not improve from 0.01496\n",
      "\n",
      "Epoch 1090: val_loss did not improve from 0.01496\n",
      "\n",
      "Epoch 1091: val_loss did not improve from 0.01496\n",
      "\n",
      "Epoch 1092: val_loss did not improve from 0.01496\n",
      "\n",
      "Epoch 1093: val_loss did not improve from 0.01496\n",
      "\n",
      "Epoch 1094: val_loss did not improve from 0.01496\n",
      "\n",
      "Epoch 1095: val_loss did not improve from 0.01496\n",
      "\n",
      "Epoch 1096: val_loss did not improve from 0.01496\n",
      "\n",
      "Epoch 1097: val_loss did not improve from 0.01496\n",
      "\n",
      "Epoch 1098: val_loss did not improve from 0.01496\n",
      "\n",
      "Epoch 1099: val_loss did not improve from 0.01496\n",
      "\n",
      "Epoch 1100: val_loss did not improve from 0.01496\n",
      "\n",
      "Epoch 1101: val_loss did not improve from 0.01496\n",
      "\n",
      "Epoch 1102: val_loss did not improve from 0.01496\n",
      "\n",
      "Epoch 1103: val_loss did not improve from 0.01496\n",
      "\n",
      "Epoch 1104: val_loss did not improve from 0.01496\n",
      "\n",
      "Epoch 1105: val_loss did not improve from 0.01496\n",
      "\n",
      "Epoch 1106: val_loss did not improve from 0.01496\n",
      "\n",
      "Epoch 1107: val_loss did not improve from 0.01496\n",
      "\n",
      "Epoch 1108: val_loss did not improve from 0.01496\n",
      "\n",
      "Epoch 1109: val_loss did not improve from 0.01496\n",
      "\n",
      "Epoch 1110: val_loss did not improve from 0.01496\n",
      "\n",
      "Epoch 1111: val_loss did not improve from 0.01496\n",
      "\n",
      "Epoch 1112: val_loss did not improve from 0.01496\n",
      "\n",
      "Epoch 1113: val_loss did not improve from 0.01496\n",
      "\n",
      "Epoch 1114: val_loss did not improve from 0.01496\n",
      "\n",
      "Epoch 1115: val_loss did not improve from 0.01496\n",
      "\n",
      "Epoch 1116: val_loss did not improve from 0.01496\n",
      "\n",
      "Epoch 1117: val_loss did not improve from 0.01496\n",
      "\n",
      "Epoch 1118: val_loss did not improve from 0.01496\n",
      "\n",
      "Epoch 1119: val_loss improved from 0.01496 to 0.01492, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1120: val_loss did not improve from 0.01492\n",
      "\n",
      "Epoch 1121: val_loss did not improve from 0.01492\n",
      "\n",
      "Epoch 1122: val_loss improved from 0.01492 to 0.01492, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1123: val_loss improved from 0.01492 to 0.01492, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1124: val_loss did not improve from 0.01492\n",
      "\n",
      "Epoch 1125: val_loss did not improve from 0.01492\n",
      "\n",
      "Epoch 1126: val_loss did not improve from 0.01492\n",
      "\n",
      "Epoch 1127: val_loss did not improve from 0.01492\n",
      "\n",
      "Epoch 1128: val_loss did not improve from 0.01492\n",
      "\n",
      "Epoch 1129: val_loss did not improve from 0.01492\n",
      "\n",
      "Epoch 1130: val_loss did not improve from 0.01492\n",
      "\n",
      "Epoch 1131: val_loss improved from 0.01492 to 0.01485, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1132: val_loss improved from 0.01485 to 0.01484, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1133: val_loss did not improve from 0.01484\n",
      "\n",
      "Epoch 1134: val_loss did not improve from 0.01484\n",
      "\n",
      "Epoch 1135: val_loss did not improve from 0.01484\n",
      "\n",
      "Epoch 1136: val_loss did not improve from 0.01484\n",
      "\n",
      "Epoch 1137: val_loss did not improve from 0.01484\n",
      "\n",
      "Epoch 1138: val_loss did not improve from 0.01484\n",
      "\n",
      "Epoch 1139: val_loss did not improve from 0.01484\n",
      "\n",
      "Epoch 1140: val_loss did not improve from 0.01484\n",
      "\n",
      "Epoch 1141: val_loss did not improve from 0.01484\n",
      "\n",
      "Epoch 1142: val_loss did not improve from 0.01484\n",
      "\n",
      "Epoch 1143: val_loss did not improve from 0.01484\n",
      "\n",
      "Epoch 1144: val_loss did not improve from 0.01484\n",
      "\n",
      "Epoch 1145: val_loss did not improve from 0.01484\n",
      "\n",
      "Epoch 1146: val_loss improved from 0.01484 to 0.01480, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1147: val_loss improved from 0.01480 to 0.01478, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1148: val_loss did not improve from 0.01478\n",
      "\n",
      "Epoch 1149: val_loss did not improve from 0.01478\n",
      "\n",
      "Epoch 1150: val_loss did not improve from 0.01478\n",
      "\n",
      "Epoch 1151: val_loss did not improve from 0.01478\n",
      "\n",
      "Epoch 1152: val_loss did not improve from 0.01478\n",
      "\n",
      "Epoch 1153: val_loss did not improve from 0.01478\n",
      "\n",
      "Epoch 1154: val_loss did not improve from 0.01478\n",
      "\n",
      "Epoch 1155: val_loss did not improve from 0.01478\n",
      "\n",
      "Epoch 1156: val_loss did not improve from 0.01478\n",
      "\n",
      "Epoch 1157: val_loss did not improve from 0.01478\n",
      "\n",
      "Epoch 1158: val_loss did not improve from 0.01478\n",
      "\n",
      "Epoch 1159: val_loss did not improve from 0.01478\n",
      "\n",
      "Epoch 1160: val_loss did not improve from 0.01478\n",
      "\n",
      "Epoch 1161: val_loss did not improve from 0.01478\n",
      "\n",
      "Epoch 1162: val_loss did not improve from 0.01478\n",
      "\n",
      "Epoch 1163: val_loss improved from 0.01478 to 0.01474, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1164: val_loss did not improve from 0.01474\n",
      "\n",
      "Epoch 1165: val_loss did not improve from 0.01474\n",
      "\n",
      "Epoch 1166: val_loss did not improve from 0.01474\n",
      "\n",
      "Epoch 1167: val_loss did not improve from 0.01474\n",
      "\n",
      "Epoch 1168: val_loss did not improve from 0.01474\n",
      "\n",
      "Epoch 1169: val_loss did not improve from 0.01474\n",
      "\n",
      "Epoch 1170: val_loss did not improve from 0.01474\n",
      "\n",
      "Epoch 1171: val_loss did not improve from 0.01474\n",
      "\n",
      "Epoch 1172: val_loss improved from 0.01474 to 0.01469, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1173: val_loss did not improve from 0.01469\n",
      "\n",
      "Epoch 1174: val_loss did not improve from 0.01469\n",
      "\n",
      "Epoch 1175: val_loss did not improve from 0.01469\n",
      "\n",
      "Epoch 1176: val_loss did not improve from 0.01469\n",
      "\n",
      "Epoch 1177: val_loss did not improve from 0.01469\n",
      "\n",
      "Epoch 1178: val_loss did not improve from 0.01469\n",
      "\n",
      "Epoch 1179: val_loss did not improve from 0.01469\n",
      "\n",
      "Epoch 1180: val_loss did not improve from 0.01469\n",
      "\n",
      "Epoch 1181: val_loss did not improve from 0.01469\n",
      "\n",
      "Epoch 1182: val_loss did not improve from 0.01469\n",
      "\n",
      "Epoch 1183: val_loss did not improve from 0.01469\n",
      "\n",
      "Epoch 1184: val_loss did not improve from 0.01469\n",
      "\n",
      "Epoch 1185: val_loss improved from 0.01469 to 0.01466, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1186: val_loss did not improve from 0.01466\n",
      "\n",
      "Epoch 1187: val_loss improved from 0.01466 to 0.01464, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1188: val_loss did not improve from 0.01464\n",
      "\n",
      "Epoch 1189: val_loss did not improve from 0.01464\n",
      "\n",
      "Epoch 1190: val_loss did not improve from 0.01464\n",
      "\n",
      "Epoch 1191: val_loss did not improve from 0.01464\n",
      "\n",
      "Epoch 1192: val_loss did not improve from 0.01464\n",
      "\n",
      "Epoch 1193: val_loss did not improve from 0.01464\n",
      "\n",
      "Epoch 1194: val_loss did not improve from 0.01464\n",
      "\n",
      "Epoch 1195: val_loss did not improve from 0.01464\n",
      "\n",
      "Epoch 1196: val_loss did not improve from 0.01464\n",
      "\n",
      "Epoch 1197: val_loss did not improve from 0.01464\n",
      "\n",
      "Epoch 1198: val_loss did not improve from 0.01464\n",
      "\n",
      "Epoch 1199: val_loss did not improve from 0.01464\n",
      "\n",
      "Epoch 1200: val_loss did not improve from 0.01464\n",
      "\n",
      "Epoch 1201: val_loss improved from 0.01464 to 0.01463, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1202: val_loss did not improve from 0.01463\n",
      "\n",
      "Epoch 1203: val_loss did not improve from 0.01463\n",
      "\n",
      "Epoch 1204: val_loss did not improve from 0.01463\n",
      "\n",
      "Epoch 1205: val_loss did not improve from 0.01463\n",
      "\n",
      "Epoch 1206: val_loss did not improve from 0.01463\n",
      "\n",
      "Epoch 1207: val_loss did not improve from 0.01463\n",
      "\n",
      "Epoch 1208: val_loss did not improve from 0.01463\n",
      "\n",
      "Epoch 1209: val_loss did not improve from 0.01463\n",
      "\n",
      "Epoch 1210: val_loss did not improve from 0.01463\n",
      "\n",
      "Epoch 1211: val_loss did not improve from 0.01463\n",
      "\n",
      "Epoch 1212: val_loss improved from 0.01463 to 0.01457, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1213: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1214: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1215: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1216: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1217: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1218: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1219: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1220: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1221: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1222: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1223: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1224: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1225: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1226: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1227: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1228: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1229: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1230: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1231: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1232: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1233: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1234: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1235: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1236: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1237: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1238: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1239: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1240: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1241: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1242: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1243: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1244: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1245: val_loss did not improve from 0.01457\n",
      "\n",
      "Epoch 1246: val_loss improved from 0.01457 to 0.01445, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1247: val_loss did not improve from 0.01445\n",
      "\n",
      "Epoch 1248: val_loss did not improve from 0.01445\n",
      "\n",
      "Epoch 1249: val_loss did not improve from 0.01445\n",
      "\n",
      "Epoch 1250: val_loss did not improve from 0.01445\n",
      "\n",
      "Epoch 1251: val_loss did not improve from 0.01445\n",
      "\n",
      "Epoch 1252: val_loss did not improve from 0.01445\n",
      "\n",
      "Epoch 1253: val_loss did not improve from 0.01445\n",
      "\n",
      "Epoch 1254: val_loss did not improve from 0.01445\n",
      "\n",
      "Epoch 1255: val_loss did not improve from 0.01445\n",
      "\n",
      "Epoch 1256: val_loss did not improve from 0.01445\n",
      "\n",
      "Epoch 1257: val_loss did not improve from 0.01445\n",
      "\n",
      "Epoch 1258: val_loss did not improve from 0.01445\n",
      "\n",
      "Epoch 1259: val_loss did not improve from 0.01445\n",
      "\n",
      "Epoch 1260: val_loss did not improve from 0.01445\n",
      "\n",
      "Epoch 1261: val_loss did not improve from 0.01445\n",
      "\n",
      "Epoch 1262: val_loss did not improve from 0.01445\n",
      "\n",
      "Epoch 1263: val_loss did not improve from 0.01445\n",
      "\n",
      "Epoch 1264: val_loss did not improve from 0.01445\n",
      "\n",
      "Epoch 1265: val_loss did not improve from 0.01445\n",
      "\n",
      "Epoch 1266: val_loss did not improve from 0.01445\n",
      "\n",
      "Epoch 1267: val_loss did not improve from 0.01445\n",
      "\n",
      "Epoch 1268: val_loss did not improve from 0.01445\n",
      "\n",
      "Epoch 1269: val_loss did not improve from 0.01445\n",
      "\n",
      "Epoch 1270: val_loss did not improve from 0.01445\n",
      "\n",
      "Epoch 1271: val_loss improved from 0.01445 to 0.01444, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1272: val_loss did not improve from 0.01444\n",
      "\n",
      "Epoch 1273: val_loss did not improve from 0.01444\n",
      "\n",
      "Epoch 1274: val_loss did not improve from 0.01444\n",
      "\n",
      "Epoch 1275: val_loss did not improve from 0.01444\n",
      "\n",
      "Epoch 1276: val_loss did not improve from 0.01444\n",
      "\n",
      "Epoch 1277: val_loss did not improve from 0.01444\n",
      "\n",
      "Epoch 1278: val_loss did not improve from 0.01444\n",
      "\n",
      "Epoch 1279: val_loss improved from 0.01444 to 0.01436, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1280: val_loss did not improve from 0.01436\n",
      "\n",
      "Epoch 1281: val_loss did not improve from 0.01436\n",
      "\n",
      "Epoch 1282: val_loss did not improve from 0.01436\n",
      "\n",
      "Epoch 1283: val_loss did not improve from 0.01436\n",
      "\n",
      "Epoch 1284: val_loss did not improve from 0.01436\n",
      "\n",
      "Epoch 1285: val_loss did not improve from 0.01436\n",
      "\n",
      "Epoch 1286: val_loss did not improve from 0.01436\n",
      "\n",
      "Epoch 1287: val_loss did not improve from 0.01436\n",
      "\n",
      "Epoch 1288: val_loss did not improve from 0.01436\n",
      "\n",
      "Epoch 1289: val_loss did not improve from 0.01436\n",
      "\n",
      "Epoch 1290: val_loss did not improve from 0.01436\n",
      "\n",
      "Epoch 1291: val_loss did not improve from 0.01436\n",
      "\n",
      "Epoch 1292: val_loss did not improve from 0.01436\n",
      "\n",
      "Epoch 1293: val_loss did not improve from 0.01436\n",
      "\n",
      "Epoch 1294: val_loss did not improve from 0.01436\n",
      "\n",
      "Epoch 1295: val_loss did not improve from 0.01436\n",
      "\n",
      "Epoch 1296: val_loss did not improve from 0.01436\n",
      "\n",
      "Epoch 1297: val_loss did not improve from 0.01436\n",
      "\n",
      "Epoch 1298: val_loss did not improve from 0.01436\n",
      "\n",
      "Epoch 1299: val_loss did not improve from 0.01436\n",
      "\n",
      "Epoch 1300: val_loss did not improve from 0.01436\n",
      "\n",
      "Epoch 1301: val_loss did not improve from 0.01436\n",
      "\n",
      "Epoch 1302: val_loss did not improve from 0.01436\n",
      "\n",
      "Epoch 1303: val_loss did not improve from 0.01436\n",
      "\n",
      "Epoch 1304: val_loss did not improve from 0.01436\n",
      "\n",
      "Epoch 1305: val_loss improved from 0.01436 to 0.01426, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1306: val_loss did not improve from 0.01426\n",
      "\n",
      "Epoch 1307: val_loss did not improve from 0.01426\n",
      "\n",
      "Epoch 1308: val_loss did not improve from 0.01426\n",
      "\n",
      "Epoch 1309: val_loss did not improve from 0.01426\n",
      "\n",
      "Epoch 1310: val_loss did not improve from 0.01426\n",
      "\n",
      "Epoch 1311: val_loss did not improve from 0.01426\n",
      "\n",
      "Epoch 1312: val_loss did not improve from 0.01426\n",
      "\n",
      "Epoch 1313: val_loss did not improve from 0.01426\n",
      "\n",
      "Epoch 1314: val_loss did not improve from 0.01426\n",
      "\n",
      "Epoch 1315: val_loss did not improve from 0.01426\n",
      "\n",
      "Epoch 1316: val_loss did not improve from 0.01426\n",
      "\n",
      "Epoch 1317: val_loss did not improve from 0.01426\n",
      "\n",
      "Epoch 1318: val_loss did not improve from 0.01426\n",
      "\n",
      "Epoch 1319: val_loss did not improve from 0.01426\n",
      "\n",
      "Epoch 1320: val_loss did not improve from 0.01426\n",
      "\n",
      "Epoch 1321: val_loss did not improve from 0.01426\n",
      "\n",
      "Epoch 1322: val_loss improved from 0.01426 to 0.01421, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1323: val_loss did not improve from 0.01421\n",
      "\n",
      "Epoch 1324: val_loss did not improve from 0.01421\n",
      "\n",
      "Epoch 1325: val_loss did not improve from 0.01421\n",
      "\n",
      "Epoch 1326: val_loss did not improve from 0.01421\n",
      "\n",
      "Epoch 1327: val_loss did not improve from 0.01421\n",
      "\n",
      "Epoch 1328: val_loss did not improve from 0.01421\n",
      "\n",
      "Epoch 1329: val_loss did not improve from 0.01421\n",
      "\n",
      "Epoch 1330: val_loss did not improve from 0.01421\n",
      "\n",
      "Epoch 1331: val_loss improved from 0.01421 to 0.01419, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1332: val_loss did not improve from 0.01419\n",
      "\n",
      "Epoch 1333: val_loss did not improve from 0.01419\n",
      "\n",
      "Epoch 1334: val_loss did not improve from 0.01419\n",
      "\n",
      "Epoch 1335: val_loss improved from 0.01419 to 0.01415, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1336: val_loss did not improve from 0.01415\n",
      "\n",
      "Epoch 1337: val_loss did not improve from 0.01415\n",
      "\n",
      "Epoch 1338: val_loss did not improve from 0.01415\n",
      "\n",
      "Epoch 1339: val_loss did not improve from 0.01415\n",
      "\n",
      "Epoch 1340: val_loss did not improve from 0.01415\n",
      "\n",
      "Epoch 1341: val_loss did not improve from 0.01415\n",
      "\n",
      "Epoch 1342: val_loss did not improve from 0.01415\n",
      "\n",
      "Epoch 1343: val_loss did not improve from 0.01415\n",
      "\n",
      "Epoch 1344: val_loss did not improve from 0.01415\n",
      "\n",
      "Epoch 1345: val_loss did not improve from 0.01415\n",
      "\n",
      "Epoch 1346: val_loss did not improve from 0.01415\n",
      "\n",
      "Epoch 1347: val_loss improved from 0.01415 to 0.01412, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1348: val_loss did not improve from 0.01412\n",
      "\n",
      "Epoch 1349: val_loss did not improve from 0.01412\n",
      "\n",
      "Epoch 1350: val_loss did not improve from 0.01412\n",
      "\n",
      "Epoch 1351: val_loss did not improve from 0.01412\n",
      "\n",
      "Epoch 1352: val_loss did not improve from 0.01412\n",
      "\n",
      "Epoch 1353: val_loss did not improve from 0.01412\n",
      "\n",
      "Epoch 1354: val_loss did not improve from 0.01412\n",
      "\n",
      "Epoch 1355: val_loss did not improve from 0.01412\n",
      "\n",
      "Epoch 1356: val_loss did not improve from 0.01412\n",
      "\n",
      "Epoch 1357: val_loss did not improve from 0.01412\n",
      "\n",
      "Epoch 1358: val_loss did not improve from 0.01412\n",
      "\n",
      "Epoch 1359: val_loss did not improve from 0.01412\n",
      "\n",
      "Epoch 1360: val_loss did not improve from 0.01412\n",
      "\n",
      "Epoch 1361: val_loss did not improve from 0.01412\n",
      "\n",
      "Epoch 1362: val_loss did not improve from 0.01412\n",
      "\n",
      "Epoch 1363: val_loss did not improve from 0.01412\n",
      "\n",
      "Epoch 1364: val_loss did not improve from 0.01412\n",
      "\n",
      "Epoch 1365: val_loss did not improve from 0.01412\n",
      "\n",
      "Epoch 1366: val_loss did not improve from 0.01412\n",
      "\n",
      "Epoch 1367: val_loss did not improve from 0.01412\n",
      "\n",
      "Epoch 1368: val_loss did not improve from 0.01412\n",
      "\n",
      "Epoch 1369: val_loss did not improve from 0.01412\n",
      "\n",
      "Epoch 1370: val_loss did not improve from 0.01412\n",
      "\n",
      "Epoch 1371: val_loss improved from 0.01412 to 0.01409, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1372: val_loss did not improve from 0.01409\n",
      "\n",
      "Epoch 1373: val_loss did not improve from 0.01409\n",
      "\n",
      "Epoch 1374: val_loss did not improve from 0.01409\n",
      "\n",
      "Epoch 1375: val_loss did not improve from 0.01409\n",
      "\n",
      "Epoch 1376: val_loss did not improve from 0.01409\n",
      "\n",
      "Epoch 1377: val_loss did not improve from 0.01409\n",
      "\n",
      "Epoch 1378: val_loss did not improve from 0.01409\n",
      "\n",
      "Epoch 1379: val_loss did not improve from 0.01409\n",
      "\n",
      "Epoch 1380: val_loss did not improve from 0.01409\n",
      "\n",
      "Epoch 1381: val_loss did not improve from 0.01409\n",
      "\n",
      "Epoch 1382: val_loss did not improve from 0.01409\n",
      "\n",
      "Epoch 1383: val_loss did not improve from 0.01409\n",
      "\n",
      "Epoch 1384: val_loss did not improve from 0.01409\n",
      "\n",
      "Epoch 1385: val_loss did not improve from 0.01409\n",
      "\n",
      "Epoch 1386: val_loss did not improve from 0.01409\n",
      "\n",
      "Epoch 1387: val_loss did not improve from 0.01409\n",
      "\n",
      "Epoch 1388: val_loss did not improve from 0.01409\n",
      "\n",
      "Epoch 1389: val_loss did not improve from 0.01409\n",
      "\n",
      "Epoch 1390: val_loss did not improve from 0.01409\n",
      "\n",
      "Epoch 1391: val_loss did not improve from 0.01409\n",
      "\n",
      "Epoch 1392: val_loss improved from 0.01409 to 0.01404, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1393: val_loss did not improve from 0.01404\n",
      "\n",
      "Epoch 1394: val_loss did not improve from 0.01404\n",
      "\n",
      "Epoch 1395: val_loss improved from 0.01404 to 0.01404, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1396: val_loss did not improve from 0.01404\n",
      "\n",
      "Epoch 1397: val_loss did not improve from 0.01404\n",
      "\n",
      "Epoch 1398: val_loss did not improve from 0.01404\n",
      "\n",
      "Epoch 1399: val_loss did not improve from 0.01404\n",
      "\n",
      "Epoch 1400: val_loss improved from 0.01404 to 0.01402, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1401: val_loss did not improve from 0.01402\n",
      "\n",
      "Epoch 1402: val_loss did not improve from 0.01402\n",
      "\n",
      "Epoch 1403: val_loss did not improve from 0.01402\n",
      "\n",
      "Epoch 1404: val_loss did not improve from 0.01402\n",
      "\n",
      "Epoch 1405: val_loss did not improve from 0.01402\n",
      "\n",
      "Epoch 1406: val_loss did not improve from 0.01402\n",
      "\n",
      "Epoch 1407: val_loss did not improve from 0.01402\n",
      "\n",
      "Epoch 1408: val_loss did not improve from 0.01402\n",
      "\n",
      "Epoch 1409: val_loss did not improve from 0.01402\n",
      "\n",
      "Epoch 1410: val_loss did not improve from 0.01402\n",
      "\n",
      "Epoch 1411: val_loss did not improve from 0.01402\n",
      "\n",
      "Epoch 1412: val_loss did not improve from 0.01402\n",
      "\n",
      "Epoch 1413: val_loss did not improve from 0.01402\n",
      "\n",
      "Epoch 1414: val_loss improved from 0.01402 to 0.01399, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1415: val_loss did not improve from 0.01399\n",
      "\n",
      "Epoch 1416: val_loss did not improve from 0.01399\n",
      "\n",
      "Epoch 1417: val_loss did not improve from 0.01399\n",
      "\n",
      "Epoch 1418: val_loss did not improve from 0.01399\n",
      "\n",
      "Epoch 1419: val_loss did not improve from 0.01399\n",
      "\n",
      "Epoch 1420: val_loss improved from 0.01399 to 0.01396, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1421: val_loss did not improve from 0.01396\n",
      "\n",
      "Epoch 1422: val_loss did not improve from 0.01396\n",
      "\n",
      "Epoch 1423: val_loss did not improve from 0.01396\n",
      "\n",
      "Epoch 1424: val_loss did not improve from 0.01396\n",
      "\n",
      "Epoch 1425: val_loss did not improve from 0.01396\n",
      "\n",
      "Epoch 1426: val_loss did not improve from 0.01396\n",
      "\n",
      "Epoch 1427: val_loss did not improve from 0.01396\n",
      "\n",
      "Epoch 1428: val_loss did not improve from 0.01396\n",
      "\n",
      "Epoch 1429: val_loss did not improve from 0.01396\n",
      "\n",
      "Epoch 1430: val_loss did not improve from 0.01396\n",
      "\n",
      "Epoch 1431: val_loss did not improve from 0.01396\n",
      "\n",
      "Epoch 1432: val_loss did not improve from 0.01396\n",
      "\n",
      "Epoch 1433: val_loss improved from 0.01396 to 0.01395, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1434: val_loss did not improve from 0.01395\n",
      "\n",
      "Epoch 1435: val_loss did not improve from 0.01395\n",
      "\n",
      "Epoch 1436: val_loss did not improve from 0.01395\n",
      "\n",
      "Epoch 1437: val_loss did not improve from 0.01395\n",
      "\n",
      "Epoch 1438: val_loss did not improve from 0.01395\n",
      "\n",
      "Epoch 1439: val_loss improved from 0.01395 to 0.01394, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1440: val_loss did not improve from 0.01394\n",
      "\n",
      "Epoch 1441: val_loss did not improve from 0.01394\n",
      "\n",
      "Epoch 1442: val_loss improved from 0.01394 to 0.01391, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1443: val_loss did not improve from 0.01391\n",
      "\n",
      "Epoch 1444: val_loss did not improve from 0.01391\n",
      "\n",
      "Epoch 1445: val_loss did not improve from 0.01391\n",
      "\n",
      "Epoch 1446: val_loss did not improve from 0.01391\n",
      "\n",
      "Epoch 1447: val_loss did not improve from 0.01391\n",
      "\n",
      "Epoch 1448: val_loss did not improve from 0.01391\n",
      "\n",
      "Epoch 1449: val_loss did not improve from 0.01391\n",
      "\n",
      "Epoch 1450: val_loss did not improve from 0.01391\n",
      "\n",
      "Epoch 1451: val_loss did not improve from 0.01391\n",
      "\n",
      "Epoch 1452: val_loss did not improve from 0.01391\n",
      "\n",
      "Epoch 1453: val_loss improved from 0.01391 to 0.01384, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1454: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1455: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1456: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1457: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1458: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1459: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1460: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1461: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1462: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1463: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1464: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1465: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1466: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1467: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1468: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1469: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1470: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1471: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1472: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1473: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1474: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1475: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1476: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1477: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1478: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1479: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1480: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1481: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1482: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1483: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1484: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1485: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1486: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1487: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1488: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1489: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1490: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1491: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1492: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1493: val_loss did not improve from 0.01384\n",
      "\n",
      "Epoch 1494: val_loss improved from 0.01384 to 0.01377, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1495: val_loss improved from 0.01377 to 0.01375, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1496: val_loss did not improve from 0.01375\n",
      "\n",
      "Epoch 1497: val_loss did not improve from 0.01375\n",
      "\n",
      "Epoch 1498: val_loss did not improve from 0.01375\n",
      "\n",
      "Epoch 1499: val_loss did not improve from 0.01375\n",
      "\n",
      "Epoch 1500: val_loss did not improve from 0.01375\n",
      "\n",
      "Epoch 1501: val_loss improved from 0.01375 to 0.01373, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1502: val_loss did not improve from 0.01373\n",
      "\n",
      "Epoch 1503: val_loss did not improve from 0.01373\n",
      "\n",
      "Epoch 1504: val_loss did not improve from 0.01373\n",
      "\n",
      "Epoch 1505: val_loss did not improve from 0.01373\n",
      "\n",
      "Epoch 1506: val_loss did not improve from 0.01373\n",
      "\n",
      "Epoch 1507: val_loss did not improve from 0.01373\n",
      "\n",
      "Epoch 1508: val_loss improved from 0.01373 to 0.01372, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1509: val_loss did not improve from 0.01372\n",
      "\n",
      "Epoch 1510: val_loss did not improve from 0.01372\n",
      "\n",
      "Epoch 1511: val_loss did not improve from 0.01372\n",
      "\n",
      "Epoch 1512: val_loss did not improve from 0.01372\n",
      "\n",
      "Epoch 1513: val_loss did not improve from 0.01372\n",
      "\n",
      "Epoch 1514: val_loss did not improve from 0.01372\n",
      "\n",
      "Epoch 1515: val_loss did not improve from 0.01372\n",
      "\n",
      "Epoch 1516: val_loss did not improve from 0.01372\n",
      "\n",
      "Epoch 1517: val_loss did not improve from 0.01372\n",
      "\n",
      "Epoch 1518: val_loss did not improve from 0.01372\n",
      "\n",
      "Epoch 1519: val_loss did not improve from 0.01372\n",
      "\n",
      "Epoch 1520: val_loss did not improve from 0.01372\n",
      "\n",
      "Epoch 1521: val_loss improved from 0.01372 to 0.01367, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1522: val_loss did not improve from 0.01367\n",
      "\n",
      "Epoch 1523: val_loss did not improve from 0.01367\n",
      "\n",
      "Epoch 1524: val_loss did not improve from 0.01367\n",
      "\n",
      "Epoch 1525: val_loss did not improve from 0.01367\n",
      "\n",
      "Epoch 1526: val_loss did not improve from 0.01367\n",
      "\n",
      "Epoch 1527: val_loss did not improve from 0.01367\n",
      "\n",
      "Epoch 1528: val_loss did not improve from 0.01367\n",
      "\n",
      "Epoch 1529: val_loss did not improve from 0.01367\n",
      "\n",
      "Epoch 1530: val_loss did not improve from 0.01367\n",
      "\n",
      "Epoch 1531: val_loss did not improve from 0.01367\n",
      "\n",
      "Epoch 1532: val_loss did not improve from 0.01367\n",
      "\n",
      "Epoch 1533: val_loss did not improve from 0.01367\n",
      "\n",
      "Epoch 1534: val_loss did not improve from 0.01367\n",
      "\n",
      "Epoch 1535: val_loss did not improve from 0.01367\n",
      "\n",
      "Epoch 1536: val_loss improved from 0.01367 to 0.01360, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1537: val_loss did not improve from 0.01360\n",
      "\n",
      "Epoch 1538: val_loss did not improve from 0.01360\n",
      "\n",
      "Epoch 1539: val_loss did not improve from 0.01360\n",
      "\n",
      "Epoch 1540: val_loss did not improve from 0.01360\n",
      "\n",
      "Epoch 1541: val_loss did not improve from 0.01360\n",
      "\n",
      "Epoch 1542: val_loss did not improve from 0.01360\n",
      "\n",
      "Epoch 1543: val_loss did not improve from 0.01360\n",
      "\n",
      "Epoch 1544: val_loss did not improve from 0.01360\n",
      "\n",
      "Epoch 1545: val_loss did not improve from 0.01360\n",
      "\n",
      "Epoch 1546: val_loss did not improve from 0.01360\n",
      "\n",
      "Epoch 1547: val_loss did not improve from 0.01360\n",
      "\n",
      "Epoch 1548: val_loss did not improve from 0.01360\n",
      "\n",
      "Epoch 1549: val_loss did not improve from 0.01360\n",
      "\n",
      "Epoch 1550: val_loss did not improve from 0.01360\n",
      "\n",
      "Epoch 1551: val_loss did not improve from 0.01360\n",
      "\n",
      "Epoch 1552: val_loss did not improve from 0.01360\n",
      "\n",
      "Epoch 1553: val_loss did not improve from 0.01360\n",
      "\n",
      "Epoch 1554: val_loss did not improve from 0.01360\n",
      "\n",
      "Epoch 1555: val_loss did not improve from 0.01360\n",
      "\n",
      "Epoch 1556: val_loss did not improve from 0.01360\n",
      "\n",
      "Epoch 1557: val_loss did not improve from 0.01360\n",
      "\n",
      "Epoch 1558: val_loss did not improve from 0.01360\n",
      "\n",
      "Epoch 1559: val_loss did not improve from 0.01360\n",
      "\n",
      "Epoch 1560: val_loss improved from 0.01360 to 0.01354, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1561: val_loss did not improve from 0.01354\n",
      "\n",
      "Epoch 1562: val_loss did not improve from 0.01354\n",
      "\n",
      "Epoch 1563: val_loss improved from 0.01354 to 0.01352, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1564: val_loss did not improve from 0.01352\n",
      "\n",
      "Epoch 1565: val_loss did not improve from 0.01352\n",
      "\n",
      "Epoch 1566: val_loss did not improve from 0.01352\n",
      "\n",
      "Epoch 1567: val_loss did not improve from 0.01352\n",
      "\n",
      "Epoch 1568: val_loss did not improve from 0.01352\n",
      "\n",
      "Epoch 1569: val_loss did not improve from 0.01352\n",
      "\n",
      "Epoch 1570: val_loss did not improve from 0.01352\n",
      "\n",
      "Epoch 1571: val_loss did not improve from 0.01352\n",
      "\n",
      "Epoch 1572: val_loss did not improve from 0.01352\n",
      "\n",
      "Epoch 1573: val_loss did not improve from 0.01352\n",
      "\n",
      "Epoch 1574: val_loss did not improve from 0.01352\n",
      "\n",
      "Epoch 1575: val_loss did not improve from 0.01352\n",
      "\n",
      "Epoch 1576: val_loss did not improve from 0.01352\n",
      "\n",
      "Epoch 1577: val_loss did not improve from 0.01352\n",
      "\n",
      "Epoch 1578: val_loss did not improve from 0.01352\n",
      "\n",
      "Epoch 1579: val_loss did not improve from 0.01352\n",
      "\n",
      "Epoch 1580: val_loss did not improve from 0.01352\n",
      "\n",
      "Epoch 1581: val_loss did not improve from 0.01352\n",
      "\n",
      "Epoch 1582: val_loss did not improve from 0.01352\n",
      "\n",
      "Epoch 1583: val_loss did not improve from 0.01352\n",
      "\n",
      "Epoch 1584: val_loss did not improve from 0.01352\n",
      "\n",
      "Epoch 1585: val_loss did not improve from 0.01352\n",
      "\n",
      "Epoch 1586: val_loss did not improve from 0.01352\n",
      "\n",
      "Epoch 1587: val_loss did not improve from 0.01352\n",
      "\n",
      "Epoch 1588: val_loss improved from 0.01352 to 0.01350, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1589: val_loss did not improve from 0.01350\n",
      "\n",
      "Epoch 1590: val_loss did not improve from 0.01350\n",
      "\n",
      "Epoch 1591: val_loss did not improve from 0.01350\n",
      "\n",
      "Epoch 1592: val_loss did not improve from 0.01350\n",
      "\n",
      "Epoch 1593: val_loss did not improve from 0.01350\n",
      "\n",
      "Epoch 1594: val_loss did not improve from 0.01350\n",
      "\n",
      "Epoch 1595: val_loss did not improve from 0.01350\n",
      "\n",
      "Epoch 1596: val_loss did not improve from 0.01350\n",
      "\n",
      "Epoch 1597: val_loss did not improve from 0.01350\n",
      "\n",
      "Epoch 1598: val_loss did not improve from 0.01350\n",
      "\n",
      "Epoch 1599: val_loss did not improve from 0.01350\n",
      "\n",
      "Epoch 1600: val_loss did not improve from 0.01350\n",
      "\n",
      "Epoch 1601: val_loss did not improve from 0.01350\n",
      "\n",
      "Epoch 1602: val_loss improved from 0.01350 to 0.01350, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1603: val_loss did not improve from 0.01350\n",
      "\n",
      "Epoch 1604: val_loss did not improve from 0.01350\n",
      "\n",
      "Epoch 1605: val_loss did not improve from 0.01350\n",
      "\n",
      "Epoch 1606: val_loss did not improve from 0.01350\n",
      "\n",
      "Epoch 1607: val_loss did not improve from 0.01350\n",
      "\n",
      "Epoch 1608: val_loss improved from 0.01350 to 0.01346, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1609: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1610: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1611: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1612: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1613: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1614: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1615: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1616: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1617: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1618: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1619: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1620: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1621: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1622: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1623: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1624: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1625: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1626: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1627: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1628: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1629: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1630: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1631: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1632: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1633: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1634: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1635: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1636: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1637: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1638: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1639: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1640: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1641: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1642: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1643: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1644: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1645: val_loss did not improve from 0.01346\n",
      "\n",
      "Epoch 1646: val_loss improved from 0.01346 to 0.01339, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1647: val_loss did not improve from 0.01339\n",
      "\n",
      "Epoch 1648: val_loss improved from 0.01339 to 0.01339, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1649: val_loss did not improve from 0.01339\n",
      "\n",
      "Epoch 1650: val_loss did not improve from 0.01339\n",
      "\n",
      "Epoch 1651: val_loss did not improve from 0.01339\n",
      "\n",
      "Epoch 1652: val_loss did not improve from 0.01339\n",
      "\n",
      "Epoch 1653: val_loss did not improve from 0.01339\n",
      "\n",
      "Epoch 1654: val_loss did not improve from 0.01339\n",
      "\n",
      "Epoch 1655: val_loss did not improve from 0.01339\n",
      "\n",
      "Epoch 1656: val_loss did not improve from 0.01339\n",
      "\n",
      "Epoch 1657: val_loss did not improve from 0.01339\n",
      "\n",
      "Epoch 1658: val_loss improved from 0.01339 to 0.01339, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1659: val_loss did not improve from 0.01339\n",
      "\n",
      "Epoch 1660: val_loss did not improve from 0.01339\n",
      "\n",
      "Epoch 1661: val_loss did not improve from 0.01339\n",
      "\n",
      "Epoch 1662: val_loss did not improve from 0.01339\n",
      "\n",
      "Epoch 1663: val_loss did not improve from 0.01339\n",
      "\n",
      "Epoch 1664: val_loss did not improve from 0.01339\n",
      "\n",
      "Epoch 1665: val_loss improved from 0.01339 to 0.01338, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1666: val_loss did not improve from 0.01338\n",
      "\n",
      "Epoch 1667: val_loss did not improve from 0.01338\n",
      "\n",
      "Epoch 1668: val_loss improved from 0.01338 to 0.01330, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1669: val_loss did not improve from 0.01330\n",
      "\n",
      "Epoch 1670: val_loss did not improve from 0.01330\n",
      "\n",
      "Epoch 1671: val_loss did not improve from 0.01330\n",
      "\n",
      "Epoch 1672: val_loss did not improve from 0.01330\n",
      "\n",
      "Epoch 1673: val_loss did not improve from 0.01330\n",
      "\n",
      "Epoch 1674: val_loss did not improve from 0.01330\n",
      "\n",
      "Epoch 1675: val_loss did not improve from 0.01330\n",
      "\n",
      "Epoch 1676: val_loss did not improve from 0.01330\n",
      "\n",
      "Epoch 1677: val_loss did not improve from 0.01330\n",
      "\n",
      "Epoch 1678: val_loss did not improve from 0.01330\n",
      "\n",
      "Epoch 1679: val_loss did not improve from 0.01330\n",
      "\n",
      "Epoch 1680: val_loss did not improve from 0.01330\n",
      "\n",
      "Epoch 1681: val_loss did not improve from 0.01330\n",
      "\n",
      "Epoch 1682: val_loss did not improve from 0.01330\n",
      "\n",
      "Epoch 1683: val_loss improved from 0.01330 to 0.01329, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1684: val_loss did not improve from 0.01329\n",
      "\n",
      "Epoch 1685: val_loss did not improve from 0.01329\n",
      "\n",
      "Epoch 1686: val_loss did not improve from 0.01329\n",
      "\n",
      "Epoch 1687: val_loss did not improve from 0.01329\n",
      "\n",
      "Epoch 1688: val_loss did not improve from 0.01329\n",
      "\n",
      "Epoch 1689: val_loss improved from 0.01329 to 0.01329, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1690: val_loss did not improve from 0.01329\n",
      "\n",
      "Epoch 1691: val_loss did not improve from 0.01329\n",
      "\n",
      "Epoch 1692: val_loss did not improve from 0.01329\n",
      "\n",
      "Epoch 1693: val_loss did not improve from 0.01329\n",
      "\n",
      "Epoch 1694: val_loss did not improve from 0.01329\n",
      "\n",
      "Epoch 1695: val_loss improved from 0.01329 to 0.01327, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1696: val_loss did not improve from 0.01327\n",
      "\n",
      "Epoch 1697: val_loss did not improve from 0.01327\n",
      "\n",
      "Epoch 1698: val_loss improved from 0.01327 to 0.01327, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1699: val_loss did not improve from 0.01327\n",
      "\n",
      "Epoch 1700: val_loss did not improve from 0.01327\n",
      "\n",
      "Epoch 1701: val_loss did not improve from 0.01327\n",
      "\n",
      "Epoch 1702: val_loss did not improve from 0.01327\n",
      "\n",
      "Epoch 1703: val_loss did not improve from 0.01327\n",
      "\n",
      "Epoch 1704: val_loss did not improve from 0.01327\n",
      "\n",
      "Epoch 1705: val_loss did not improve from 0.01327\n",
      "\n",
      "Epoch 1706: val_loss did not improve from 0.01327\n",
      "\n",
      "Epoch 1707: val_loss did not improve from 0.01327\n",
      "\n",
      "Epoch 1708: val_loss did not improve from 0.01327\n",
      "\n",
      "Epoch 1709: val_loss improved from 0.01327 to 0.01323, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1710: val_loss did not improve from 0.01323\n",
      "\n",
      "Epoch 1711: val_loss did not improve from 0.01323\n",
      "\n",
      "Epoch 1712: val_loss did not improve from 0.01323\n",
      "\n",
      "Epoch 1713: val_loss did not improve from 0.01323\n",
      "\n",
      "Epoch 1714: val_loss improved from 0.01323 to 0.01322, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1715: val_loss did not improve from 0.01322\n",
      "\n",
      "Epoch 1716: val_loss did not improve from 0.01322\n",
      "\n",
      "Epoch 1717: val_loss did not improve from 0.01322\n",
      "\n",
      "Epoch 1718: val_loss did not improve from 0.01322\n",
      "\n",
      "Epoch 1719: val_loss did not improve from 0.01322\n",
      "\n",
      "Epoch 1720: val_loss did not improve from 0.01322\n",
      "\n",
      "Epoch 1721: val_loss did not improve from 0.01322\n",
      "\n",
      "Epoch 1722: val_loss did not improve from 0.01322\n",
      "\n",
      "Epoch 1723: val_loss did not improve from 0.01322\n",
      "\n",
      "Epoch 1724: val_loss did not improve from 0.01322\n",
      "\n",
      "Epoch 1725: val_loss did not improve from 0.01322\n",
      "\n",
      "Epoch 1726: val_loss did not improve from 0.01322\n",
      "\n",
      "Epoch 1727: val_loss improved from 0.01322 to 0.01317, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1728: val_loss did not improve from 0.01317\n",
      "\n",
      "Epoch 1729: val_loss did not improve from 0.01317\n",
      "\n",
      "Epoch 1730: val_loss did not improve from 0.01317\n",
      "\n",
      "Epoch 1731: val_loss did not improve from 0.01317\n",
      "\n",
      "Epoch 1732: val_loss did not improve from 0.01317\n",
      "\n",
      "Epoch 1733: val_loss did not improve from 0.01317\n",
      "\n",
      "Epoch 1734: val_loss did not improve from 0.01317\n",
      "\n",
      "Epoch 1735: val_loss did not improve from 0.01317\n",
      "\n",
      "Epoch 1736: val_loss did not improve from 0.01317\n",
      "\n",
      "Epoch 1737: val_loss did not improve from 0.01317\n",
      "\n",
      "Epoch 1738: val_loss improved from 0.01317 to 0.01314, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1739: val_loss did not improve from 0.01314\n",
      "\n",
      "Epoch 1740: val_loss did not improve from 0.01314\n",
      "\n",
      "Epoch 1741: val_loss did not improve from 0.01314\n",
      "\n",
      "Epoch 1742: val_loss did not improve from 0.01314\n",
      "\n",
      "Epoch 1743: val_loss did not improve from 0.01314\n",
      "\n",
      "Epoch 1744: val_loss did not improve from 0.01314\n",
      "\n",
      "Epoch 1745: val_loss did not improve from 0.01314\n",
      "\n",
      "Epoch 1746: val_loss did not improve from 0.01314\n",
      "\n",
      "Epoch 1747: val_loss did not improve from 0.01314\n",
      "\n",
      "Epoch 1748: val_loss did not improve from 0.01314\n",
      "\n",
      "Epoch 1749: val_loss did not improve from 0.01314\n",
      "\n",
      "Epoch 1750: val_loss did not improve from 0.01314\n",
      "\n",
      "Epoch 1751: val_loss did not improve from 0.01314\n",
      "\n",
      "Epoch 1752: val_loss did not improve from 0.01314\n",
      "\n",
      "Epoch 1753: val_loss did not improve from 0.01314\n",
      "\n",
      "Epoch 1754: val_loss did not improve from 0.01314\n",
      "\n",
      "Epoch 1755: val_loss did not improve from 0.01314\n",
      "\n",
      "Epoch 1756: val_loss did not improve from 0.01314\n",
      "\n",
      "Epoch 1757: val_loss did not improve from 0.01314\n",
      "\n",
      "Epoch 1758: val_loss did not improve from 0.01314\n",
      "\n",
      "Epoch 1759: val_loss improved from 0.01314 to 0.01312, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1760: val_loss did not improve from 0.01312\n",
      "\n",
      "Epoch 1761: val_loss did not improve from 0.01312\n",
      "\n",
      "Epoch 1762: val_loss did not improve from 0.01312\n",
      "\n",
      "Epoch 1763: val_loss did not improve from 0.01312\n",
      "\n",
      "Epoch 1764: val_loss did not improve from 0.01312\n",
      "\n",
      "Epoch 1765: val_loss did not improve from 0.01312\n",
      "\n",
      "Epoch 1766: val_loss did not improve from 0.01312\n",
      "\n",
      "Epoch 1767: val_loss did not improve from 0.01312\n",
      "\n",
      "Epoch 1768: val_loss did not improve from 0.01312\n",
      "\n",
      "Epoch 1769: val_loss did not improve from 0.01312\n",
      "\n",
      "Epoch 1770: val_loss did not improve from 0.01312\n",
      "\n",
      "Epoch 1771: val_loss improved from 0.01312 to 0.01307, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1772: val_loss did not improve from 0.01307\n",
      "\n",
      "Epoch 1773: val_loss did not improve from 0.01307\n",
      "\n",
      "Epoch 1774: val_loss did not improve from 0.01307\n",
      "\n",
      "Epoch 1775: val_loss did not improve from 0.01307\n",
      "\n",
      "Epoch 1776: val_loss did not improve from 0.01307\n",
      "\n",
      "Epoch 1777: val_loss did not improve from 0.01307\n",
      "\n",
      "Epoch 1778: val_loss did not improve from 0.01307\n",
      "\n",
      "Epoch 1779: val_loss did not improve from 0.01307\n",
      "\n",
      "Epoch 1780: val_loss did not improve from 0.01307\n",
      "\n",
      "Epoch 1781: val_loss did not improve from 0.01307\n",
      "\n",
      "Epoch 1782: val_loss did not improve from 0.01307\n",
      "\n",
      "Epoch 1783: val_loss did not improve from 0.01307\n",
      "\n",
      "Epoch 1784: val_loss did not improve from 0.01307\n",
      "\n",
      "Epoch 1785: val_loss did not improve from 0.01307\n",
      "\n",
      "Epoch 1786: val_loss improved from 0.01307 to 0.01303, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1787: val_loss did not improve from 0.01303\n",
      "\n",
      "Epoch 1788: val_loss did not improve from 0.01303\n",
      "\n",
      "Epoch 1789: val_loss did not improve from 0.01303\n",
      "\n",
      "Epoch 1790: val_loss did not improve from 0.01303\n",
      "\n",
      "Epoch 1791: val_loss improved from 0.01303 to 0.01300, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1792: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1793: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1794: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1795: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1796: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1797: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1798: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1799: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1800: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1801: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1802: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1803: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1804: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1805: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1806: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1807: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1808: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1809: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1810: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1811: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1812: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1813: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1814: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1815: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1816: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1817: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1818: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1819: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1820: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1821: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1822: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1823: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1824: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1825: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1826: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1827: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1828: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1829: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1830: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1831: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1832: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1833: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1834: val_loss did not improve from 0.01300\n",
      "\n",
      "Epoch 1835: val_loss improved from 0.01300 to 0.01292, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1836: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1837: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1838: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1839: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1840: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1841: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1842: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1843: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1844: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1845: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1846: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1847: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1848: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1849: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1850: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1851: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1852: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1853: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1854: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1855: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1856: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1857: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1858: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1859: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1860: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1861: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1862: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1863: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1864: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1865: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1866: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1867: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1868: val_loss did not improve from 0.01292\n",
      "\n",
      "Epoch 1869: val_loss improved from 0.01292 to 0.01291, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1870: val_loss did not improve from 0.01291\n",
      "\n",
      "Epoch 1871: val_loss did not improve from 0.01291\n",
      "\n",
      "Epoch 1872: val_loss did not improve from 0.01291\n",
      "\n",
      "Epoch 1873: val_loss did not improve from 0.01291\n",
      "\n",
      "Epoch 1874: val_loss did not improve from 0.01291\n",
      "\n",
      "Epoch 1875: val_loss did not improve from 0.01291\n",
      "\n",
      "Epoch 1876: val_loss did not improve from 0.01291\n",
      "\n",
      "Epoch 1877: val_loss did not improve from 0.01291\n",
      "\n",
      "Epoch 1878: val_loss did not improve from 0.01291\n",
      "\n",
      "Epoch 1879: val_loss improved from 0.01291 to 0.01289, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1880: val_loss did not improve from 0.01289\n",
      "\n",
      "Epoch 1881: val_loss did not improve from 0.01289\n",
      "\n",
      "Epoch 1882: val_loss did not improve from 0.01289\n",
      "\n",
      "Epoch 1883: val_loss did not improve from 0.01289\n",
      "\n",
      "Epoch 1884: val_loss did not improve from 0.01289\n",
      "\n",
      "Epoch 1885: val_loss did not improve from 0.01289\n",
      "\n",
      "Epoch 1886: val_loss did not improve from 0.01289\n",
      "\n",
      "Epoch 1887: val_loss did not improve from 0.01289\n",
      "\n",
      "Epoch 1888: val_loss did not improve from 0.01289\n",
      "\n",
      "Epoch 1889: val_loss did not improve from 0.01289\n",
      "\n",
      "Epoch 1890: val_loss did not improve from 0.01289\n",
      "\n",
      "Epoch 1891: val_loss did not improve from 0.01289\n",
      "\n",
      "Epoch 1892: val_loss improved from 0.01289 to 0.01288, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1893: val_loss improved from 0.01288 to 0.01287, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1894: val_loss did not improve from 0.01287\n",
      "\n",
      "Epoch 1895: val_loss improved from 0.01287 to 0.01283, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1896: val_loss did not improve from 0.01283\n",
      "\n",
      "Epoch 1897: val_loss did not improve from 0.01283\n",
      "\n",
      "Epoch 1898: val_loss did not improve from 0.01283\n",
      "\n",
      "Epoch 1899: val_loss did not improve from 0.01283\n",
      "\n",
      "Epoch 1900: val_loss did not improve from 0.01283\n",
      "\n",
      "Epoch 1901: val_loss did not improve from 0.01283\n",
      "\n",
      "Epoch 1902: val_loss did not improve from 0.01283\n",
      "\n",
      "Epoch 1903: val_loss did not improve from 0.01283\n",
      "\n",
      "Epoch 1904: val_loss did not improve from 0.01283\n",
      "\n",
      "Epoch 1905: val_loss did not improve from 0.01283\n",
      "\n",
      "Epoch 1906: val_loss did not improve from 0.01283\n",
      "\n",
      "Epoch 1907: val_loss did not improve from 0.01283\n",
      "\n",
      "Epoch 1908: val_loss improved from 0.01283 to 0.01282, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1909: val_loss did not improve from 0.01282\n",
      "\n",
      "Epoch 1910: val_loss did not improve from 0.01282\n",
      "\n",
      "Epoch 1911: val_loss did not improve from 0.01282\n",
      "\n",
      "Epoch 1912: val_loss did not improve from 0.01282\n",
      "\n",
      "Epoch 1913: val_loss did not improve from 0.01282\n",
      "\n",
      "Epoch 1914: val_loss improved from 0.01282 to 0.01281, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1915: val_loss did not improve from 0.01281\n",
      "\n",
      "Epoch 1916: val_loss did not improve from 0.01281\n",
      "\n",
      "Epoch 1917: val_loss did not improve from 0.01281\n",
      "\n",
      "Epoch 1918: val_loss did not improve from 0.01281\n",
      "\n",
      "Epoch 1919: val_loss did not improve from 0.01281\n",
      "\n",
      "Epoch 1920: val_loss did not improve from 0.01281\n",
      "\n",
      "Epoch 1921: val_loss did not improve from 0.01281\n",
      "\n",
      "Epoch 1922: val_loss did not improve from 0.01281\n",
      "\n",
      "Epoch 1923: val_loss did not improve from 0.01281\n",
      "\n",
      "Epoch 1924: val_loss did not improve from 0.01281\n",
      "\n",
      "Epoch 1925: val_loss did not improve from 0.01281\n",
      "\n",
      "Epoch 1926: val_loss did not improve from 0.01281\n",
      "\n",
      "Epoch 1927: val_loss did not improve from 0.01281\n",
      "\n",
      "Epoch 1928: val_loss did not improve from 0.01281\n",
      "\n",
      "Epoch 1929: val_loss did not improve from 0.01281\n",
      "\n",
      "Epoch 1930: val_loss did not improve from 0.01281\n",
      "\n",
      "Epoch 1931: val_loss did not improve from 0.01281\n",
      "\n",
      "Epoch 1932: val_loss did not improve from 0.01281\n",
      "\n",
      "Epoch 1933: val_loss did not improve from 0.01281\n",
      "\n",
      "Epoch 1934: val_loss did not improve from 0.01281\n",
      "\n",
      "Epoch 1935: val_loss improved from 0.01281 to 0.01281, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1936: val_loss did not improve from 0.01281\n",
      "\n",
      "Epoch 1937: val_loss did not improve from 0.01281\n",
      "\n",
      "Epoch 1938: val_loss did not improve from 0.01281\n",
      "\n",
      "Epoch 1939: val_loss did not improve from 0.01281\n",
      "\n",
      "Epoch 1940: val_loss did not improve from 0.01281\n",
      "\n",
      "Epoch 1941: val_loss did not improve from 0.01281\n",
      "\n",
      "Epoch 1942: val_loss did not improve from 0.01281\n",
      "\n",
      "Epoch 1943: val_loss did not improve from 0.01281\n",
      "\n",
      "Epoch 1944: val_loss did not improve from 0.01281\n",
      "\n",
      "Epoch 1945: val_loss improved from 0.01281 to 0.01271, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1946: val_loss did not improve from 0.01271\n",
      "\n",
      "Epoch 1947: val_loss did not improve from 0.01271\n",
      "\n",
      "Epoch 1948: val_loss did not improve from 0.01271\n",
      "\n",
      "Epoch 1949: val_loss did not improve from 0.01271\n",
      "\n",
      "Epoch 1950: val_loss did not improve from 0.01271\n",
      "\n",
      "Epoch 1951: val_loss did not improve from 0.01271\n",
      "\n",
      "Epoch 1952: val_loss did not improve from 0.01271\n",
      "\n",
      "Epoch 1953: val_loss did not improve from 0.01271\n",
      "\n",
      "Epoch 1954: val_loss did not improve from 0.01271\n",
      "\n",
      "Epoch 1955: val_loss did not improve from 0.01271\n",
      "\n",
      "Epoch 1956: val_loss did not improve from 0.01271\n",
      "\n",
      "Epoch 1957: val_loss did not improve from 0.01271\n",
      "\n",
      "Epoch 1958: val_loss improved from 0.01271 to 0.01271, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1959: val_loss did not improve from 0.01271\n",
      "\n",
      "Epoch 1960: val_loss did not improve from 0.01271\n",
      "\n",
      "Epoch 1961: val_loss did not improve from 0.01271\n",
      "\n",
      "Epoch 1962: val_loss did not improve from 0.01271\n",
      "\n",
      "Epoch 1963: val_loss did not improve from 0.01271\n",
      "\n",
      "Epoch 1964: val_loss did not improve from 0.01271\n",
      "\n",
      "Epoch 1965: val_loss did not improve from 0.01271\n",
      "\n",
      "Epoch 1966: val_loss did not improve from 0.01271\n",
      "\n",
      "Epoch 1967: val_loss did not improve from 0.01271\n",
      "\n",
      "Epoch 1968: val_loss did not improve from 0.01271\n",
      "\n",
      "Epoch 1969: val_loss did not improve from 0.01271\n",
      "\n",
      "Epoch 1970: val_loss did not improve from 0.01271\n",
      "\n",
      "Epoch 1971: val_loss did not improve from 0.01271\n",
      "\n",
      "Epoch 1972: val_loss improved from 0.01271 to 0.01271, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1973: val_loss did not improve from 0.01271\n",
      "\n",
      "Epoch 1974: val_loss did not improve from 0.01271\n",
      "\n",
      "Epoch 1975: val_loss improved from 0.01271 to 0.01266, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1976: val_loss did not improve from 0.01266\n",
      "\n",
      "Epoch 1977: val_loss did not improve from 0.01266\n",
      "\n",
      "Epoch 1978: val_loss did not improve from 0.01266\n",
      "\n",
      "Epoch 1979: val_loss did not improve from 0.01266\n",
      "\n",
      "Epoch 1980: val_loss did not improve from 0.01266\n",
      "\n",
      "Epoch 1981: val_loss did not improve from 0.01266\n",
      "\n",
      "Epoch 1982: val_loss did not improve from 0.01266\n",
      "\n",
      "Epoch 1983: val_loss did not improve from 0.01266\n",
      "\n",
      "Epoch 1984: val_loss did not improve from 0.01266\n",
      "\n",
      "Epoch 1985: val_loss did not improve from 0.01266\n",
      "\n",
      "Epoch 1986: val_loss improved from 0.01266 to 0.01266, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 1987: val_loss did not improve from 0.01266\n",
      "\n",
      "Epoch 1988: val_loss did not improve from 0.01266\n",
      "\n",
      "Epoch 1989: val_loss did not improve from 0.01266\n",
      "\n",
      "Epoch 1990: val_loss did not improve from 0.01266\n",
      "\n",
      "Epoch 1991: val_loss did not improve from 0.01266\n",
      "\n",
      "Epoch 1992: val_loss did not improve from 0.01266\n",
      "\n",
      "Epoch 1993: val_loss did not improve from 0.01266\n",
      "\n",
      "Epoch 1994: val_loss did not improve from 0.01266\n",
      "\n",
      "Epoch 1995: val_loss did not improve from 0.01266\n",
      "\n",
      "Epoch 1996: val_loss did not improve from 0.01266\n",
      "\n",
      "Epoch 1997: val_loss did not improve from 0.01266\n",
      "\n",
      "Epoch 1998: val_loss did not improve from 0.01266\n",
      "\n",
      "Epoch 1999: val_loss did not improve from 0.01266\n",
      "\n",
      "Epoch 2000: val_loss did not improve from 0.01266\n",
      "\n",
      "Epoch 2001: val_loss did not improve from 0.01266\n",
      "\n",
      "Epoch 2002: val_loss did not improve from 0.01266\n",
      "\n",
      "Epoch 2003: val_loss did not improve from 0.01266\n",
      "\n",
      "Epoch 2004: val_loss did not improve from 0.01266\n",
      "\n",
      "Epoch 2005: val_loss improved from 0.01266 to 0.01262, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2006: val_loss did not improve from 0.01262\n",
      "\n",
      "Epoch 2007: val_loss did not improve from 0.01262\n",
      "\n",
      "Epoch 2008: val_loss did not improve from 0.01262\n",
      "\n",
      "Epoch 2009: val_loss did not improve from 0.01262\n",
      "\n",
      "Epoch 2010: val_loss did not improve from 0.01262\n",
      "\n",
      "Epoch 2011: val_loss did not improve from 0.01262\n",
      "\n",
      "Epoch 2012: val_loss did not improve from 0.01262\n",
      "\n",
      "Epoch 2013: val_loss did not improve from 0.01262\n",
      "\n",
      "Epoch 2014: val_loss did not improve from 0.01262\n",
      "\n",
      "Epoch 2015: val_loss did not improve from 0.01262\n",
      "\n",
      "Epoch 2016: val_loss did not improve from 0.01262\n",
      "\n",
      "Epoch 2017: val_loss did not improve from 0.01262\n",
      "\n",
      "Epoch 2018: val_loss did not improve from 0.01262\n",
      "\n",
      "Epoch 2019: val_loss did not improve from 0.01262\n",
      "\n",
      "Epoch 2020: val_loss did not improve from 0.01262\n",
      "\n",
      "Epoch 2021: val_loss did not improve from 0.01262\n",
      "\n",
      "Epoch 2022: val_loss did not improve from 0.01262\n",
      "\n",
      "Epoch 2023: val_loss did not improve from 0.01262\n",
      "\n",
      "Epoch 2024: val_loss did not improve from 0.01262\n",
      "\n",
      "Epoch 2025: val_loss did not improve from 0.01262\n",
      "\n",
      "Epoch 2026: val_loss did not improve from 0.01262\n",
      "\n",
      "Epoch 2027: val_loss did not improve from 0.01262\n",
      "\n",
      "Epoch 2028: val_loss improved from 0.01262 to 0.01260, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2029: val_loss did not improve from 0.01260\n",
      "\n",
      "Epoch 2030: val_loss did not improve from 0.01260\n",
      "\n",
      "Epoch 2031: val_loss did not improve from 0.01260\n",
      "\n",
      "Epoch 2032: val_loss did not improve from 0.01260\n",
      "\n",
      "Epoch 2033: val_loss did not improve from 0.01260\n",
      "\n",
      "Epoch 2034: val_loss did not improve from 0.01260\n",
      "\n",
      "Epoch 2035: val_loss did not improve from 0.01260\n",
      "\n",
      "Epoch 2036: val_loss did not improve from 0.01260\n",
      "\n",
      "Epoch 2037: val_loss did not improve from 0.01260\n",
      "\n",
      "Epoch 2038: val_loss did not improve from 0.01260\n",
      "\n",
      "Epoch 2039: val_loss did not improve from 0.01260\n",
      "\n",
      "Epoch 2040: val_loss did not improve from 0.01260\n",
      "\n",
      "Epoch 2041: val_loss did not improve from 0.01260\n",
      "\n",
      "Epoch 2042: val_loss did not improve from 0.01260\n",
      "\n",
      "Epoch 2043: val_loss did not improve from 0.01260\n",
      "\n",
      "Epoch 2044: val_loss did not improve from 0.01260\n",
      "\n",
      "Epoch 2045: val_loss did not improve from 0.01260\n",
      "\n",
      "Epoch 2046: val_loss did not improve from 0.01260\n",
      "\n",
      "Epoch 2047: val_loss did not improve from 0.01260\n",
      "\n",
      "Epoch 2048: val_loss did not improve from 0.01260\n",
      "\n",
      "Epoch 2049: val_loss did not improve from 0.01260\n",
      "\n",
      "Epoch 2050: val_loss did not improve from 0.01260\n",
      "\n",
      "Epoch 2051: val_loss did not improve from 0.01260\n",
      "\n",
      "Epoch 2052: val_loss did not improve from 0.01260\n",
      "\n",
      "Epoch 2053: val_loss improved from 0.01260 to 0.01256, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2054: val_loss did not improve from 0.01256\n",
      "\n",
      "Epoch 2055: val_loss did not improve from 0.01256\n",
      "\n",
      "Epoch 2056: val_loss did not improve from 0.01256\n",
      "\n",
      "Epoch 2057: val_loss did not improve from 0.01256\n",
      "\n",
      "Epoch 2058: val_loss did not improve from 0.01256\n",
      "\n",
      "Epoch 2059: val_loss did not improve from 0.01256\n",
      "\n",
      "Epoch 2060: val_loss did not improve from 0.01256\n",
      "\n",
      "Epoch 2061: val_loss did not improve from 0.01256\n",
      "\n",
      "Epoch 2062: val_loss did not improve from 0.01256\n",
      "\n",
      "Epoch 2063: val_loss did not improve from 0.01256\n",
      "\n",
      "Epoch 2064: val_loss did not improve from 0.01256\n",
      "\n",
      "Epoch 2065: val_loss did not improve from 0.01256\n",
      "\n",
      "Epoch 2066: val_loss did not improve from 0.01256\n",
      "\n",
      "Epoch 2067: val_loss did not improve from 0.01256\n",
      "\n",
      "Epoch 2068: val_loss did not improve from 0.01256\n",
      "\n",
      "Epoch 2069: val_loss did not improve from 0.01256\n",
      "\n",
      "Epoch 2070: val_loss did not improve from 0.01256\n",
      "\n",
      "Epoch 2071: val_loss did not improve from 0.01256\n",
      "\n",
      "Epoch 2072: val_loss did not improve from 0.01256\n",
      "\n",
      "Epoch 2073: val_loss improved from 0.01256 to 0.01252, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2074: val_loss did not improve from 0.01252\n",
      "\n",
      "Epoch 2075: val_loss did not improve from 0.01252\n",
      "\n",
      "Epoch 2076: val_loss did not improve from 0.01252\n",
      "\n",
      "Epoch 2077: val_loss did not improve from 0.01252\n",
      "\n",
      "Epoch 2078: val_loss did not improve from 0.01252\n",
      "\n",
      "Epoch 2079: val_loss did not improve from 0.01252\n",
      "\n",
      "Epoch 2080: val_loss did not improve from 0.01252\n",
      "\n",
      "Epoch 2081: val_loss did not improve from 0.01252\n",
      "\n",
      "Epoch 2082: val_loss did not improve from 0.01252\n",
      "\n",
      "Epoch 2083: val_loss did not improve from 0.01252\n",
      "\n",
      "Epoch 2084: val_loss did not improve from 0.01252\n",
      "\n",
      "Epoch 2085: val_loss did not improve from 0.01252\n",
      "\n",
      "Epoch 2086: val_loss did not improve from 0.01252\n",
      "\n",
      "Epoch 2087: val_loss did not improve from 0.01252\n",
      "\n",
      "Epoch 2088: val_loss did not improve from 0.01252\n",
      "\n",
      "Epoch 2089: val_loss did not improve from 0.01252\n",
      "\n",
      "Epoch 2090: val_loss did not improve from 0.01252\n",
      "\n",
      "Epoch 2091: val_loss improved from 0.01252 to 0.01249, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2092: val_loss did not improve from 0.01249\n",
      "\n",
      "Epoch 2093: val_loss did not improve from 0.01249\n",
      "\n",
      "Epoch 2094: val_loss did not improve from 0.01249\n",
      "\n",
      "Epoch 2095: val_loss did not improve from 0.01249\n",
      "\n",
      "Epoch 2096: val_loss did not improve from 0.01249\n",
      "\n",
      "Epoch 2097: val_loss improved from 0.01249 to 0.01245, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2098: val_loss did not improve from 0.01245\n",
      "\n",
      "Epoch 2099: val_loss did not improve from 0.01245\n",
      "\n",
      "Epoch 2100: val_loss did not improve from 0.01245\n",
      "\n",
      "Epoch 2101: val_loss did not improve from 0.01245\n",
      "\n",
      "Epoch 2102: val_loss did not improve from 0.01245\n",
      "\n",
      "Epoch 2103: val_loss did not improve from 0.01245\n",
      "\n",
      "Epoch 2104: val_loss did not improve from 0.01245\n",
      "\n",
      "Epoch 2105: val_loss did not improve from 0.01245\n",
      "\n",
      "Epoch 2106: val_loss did not improve from 0.01245\n",
      "\n",
      "Epoch 2107: val_loss did not improve from 0.01245\n",
      "\n",
      "Epoch 2108: val_loss did not improve from 0.01245\n",
      "\n",
      "Epoch 2109: val_loss did not improve from 0.01245\n",
      "\n",
      "Epoch 2110: val_loss did not improve from 0.01245\n",
      "\n",
      "Epoch 2111: val_loss improved from 0.01245 to 0.01244, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2112: val_loss did not improve from 0.01244\n",
      "\n",
      "Epoch 2113: val_loss did not improve from 0.01244\n",
      "\n",
      "Epoch 2114: val_loss did not improve from 0.01244\n",
      "\n",
      "Epoch 2115: val_loss did not improve from 0.01244\n",
      "\n",
      "Epoch 2116: val_loss did not improve from 0.01244\n",
      "\n",
      "Epoch 2117: val_loss did not improve from 0.01244\n",
      "\n",
      "Epoch 2118: val_loss did not improve from 0.01244\n",
      "\n",
      "Epoch 2119: val_loss did not improve from 0.01244\n",
      "\n",
      "Epoch 2120: val_loss did not improve from 0.01244\n",
      "\n",
      "Epoch 2121: val_loss did not improve from 0.01244\n",
      "\n",
      "Epoch 2122: val_loss did not improve from 0.01244\n",
      "\n",
      "Epoch 2123: val_loss improved from 0.01244 to 0.01241, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2124: val_loss did not improve from 0.01241\n",
      "\n",
      "Epoch 2125: val_loss improved from 0.01241 to 0.01236, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2126: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2127: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2128: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2129: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2130: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2131: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2132: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2133: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2134: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2135: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2136: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2137: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2138: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2139: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2140: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2141: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2142: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2143: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2144: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2145: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2146: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2147: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2148: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2149: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2150: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2151: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2152: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2153: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2154: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2155: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2156: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2157: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2158: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2159: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2160: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2161: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2162: val_loss improved from 0.01236 to 0.01236, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2163: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2164: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2165: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2166: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2167: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2168: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2169: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2170: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2171: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2172: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2173: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2174: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2175: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2176: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2177: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2178: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2179: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2180: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2181: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2182: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2183: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2184: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2185: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2186: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2187: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2188: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2189: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2190: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2191: val_loss did not improve from 0.01236\n",
      "\n",
      "Epoch 2192: val_loss improved from 0.01236 to 0.01232, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2193: val_loss did not improve from 0.01232\n",
      "\n",
      "Epoch 2194: val_loss did not improve from 0.01232\n",
      "\n",
      "Epoch 2195: val_loss did not improve from 0.01232\n",
      "\n",
      "Epoch 2196: val_loss did not improve from 0.01232\n",
      "\n",
      "Epoch 2197: val_loss did not improve from 0.01232\n",
      "\n",
      "Epoch 2198: val_loss did not improve from 0.01232\n",
      "\n",
      "Epoch 2199: val_loss did not improve from 0.01232\n",
      "\n",
      "Epoch 2200: val_loss did not improve from 0.01232\n",
      "\n",
      "Epoch 2201: val_loss did not improve from 0.01232\n",
      "\n",
      "Epoch 2202: val_loss did not improve from 0.01232\n",
      "\n",
      "Epoch 2203: val_loss did not improve from 0.01232\n",
      "\n",
      "Epoch 2204: val_loss did not improve from 0.01232\n",
      "\n",
      "Epoch 2205: val_loss did not improve from 0.01232\n",
      "\n",
      "Epoch 2206: val_loss did not improve from 0.01232\n",
      "\n",
      "Epoch 2207: val_loss did not improve from 0.01232\n",
      "\n",
      "Epoch 2208: val_loss did not improve from 0.01232\n",
      "\n",
      "Epoch 2209: val_loss did not improve from 0.01232\n",
      "\n",
      "Epoch 2210: val_loss did not improve from 0.01232\n",
      "\n",
      "Epoch 2211: val_loss did not improve from 0.01232\n",
      "\n",
      "Epoch 2212: val_loss did not improve from 0.01232\n",
      "\n",
      "Epoch 2213: val_loss did not improve from 0.01232\n",
      "\n",
      "Epoch 2214: val_loss did not improve from 0.01232\n",
      "\n",
      "Epoch 2215: val_loss did not improve from 0.01232\n",
      "\n",
      "Epoch 2216: val_loss did not improve from 0.01232\n",
      "\n",
      "Epoch 2217: val_loss did not improve from 0.01232\n",
      "\n",
      "Epoch 2218: val_loss did not improve from 0.01232\n",
      "\n",
      "Epoch 2219: val_loss improved from 0.01232 to 0.01226, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2220: val_loss did not improve from 0.01226\n",
      "\n",
      "Epoch 2221: val_loss did not improve from 0.01226\n",
      "\n",
      "Epoch 2222: val_loss improved from 0.01226 to 0.01225, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2223: val_loss did not improve from 0.01225\n",
      "\n",
      "Epoch 2224: val_loss did not improve from 0.01225\n",
      "\n",
      "Epoch 2225: val_loss did not improve from 0.01225\n",
      "\n",
      "Epoch 2226: val_loss did not improve from 0.01225\n",
      "\n",
      "Epoch 2227: val_loss did not improve from 0.01225\n",
      "\n",
      "Epoch 2228: val_loss did not improve from 0.01225\n",
      "\n",
      "Epoch 2229: val_loss did not improve from 0.01225\n",
      "\n",
      "Epoch 2230: val_loss did not improve from 0.01225\n",
      "\n",
      "Epoch 2231: val_loss did not improve from 0.01225\n",
      "\n",
      "Epoch 2232: val_loss did not improve from 0.01225\n",
      "\n",
      "Epoch 2233: val_loss did not improve from 0.01225\n",
      "\n",
      "Epoch 2234: val_loss did not improve from 0.01225\n",
      "\n",
      "Epoch 2235: val_loss did not improve from 0.01225\n",
      "\n",
      "Epoch 2236: val_loss did not improve from 0.01225\n",
      "\n",
      "Epoch 2237: val_loss did not improve from 0.01225\n",
      "\n",
      "Epoch 2238: val_loss did not improve from 0.01225\n",
      "\n",
      "Epoch 2239: val_loss improved from 0.01225 to 0.01223, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2240: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2241: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2242: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2243: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2244: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2245: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2246: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2247: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2248: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2249: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2250: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2251: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2252: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2253: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2254: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2255: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2256: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2257: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2258: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2259: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2260: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2261: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2262: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2263: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2264: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2265: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2266: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2267: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2268: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2269: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2270: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2271: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2272: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2273: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2274: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2275: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2276: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2277: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2278: val_loss did not improve from 0.01223\n",
      "\n",
      "Epoch 2279: val_loss improved from 0.01223 to 0.01222, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2280: val_loss improved from 0.01222 to 0.01217, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2281: val_loss did not improve from 0.01217\n",
      "\n",
      "Epoch 2282: val_loss did not improve from 0.01217\n",
      "\n",
      "Epoch 2283: val_loss improved from 0.01217 to 0.01216, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2284: val_loss improved from 0.01216 to 0.01215, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2285: val_loss did not improve from 0.01215\n",
      "\n",
      "Epoch 2286: val_loss improved from 0.01215 to 0.01215, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2287: val_loss did not improve from 0.01215\n",
      "\n",
      "Epoch 2288: val_loss did not improve from 0.01215\n",
      "\n",
      "Epoch 2289: val_loss did not improve from 0.01215\n",
      "\n",
      "Epoch 2290: val_loss did not improve from 0.01215\n",
      "\n",
      "Epoch 2291: val_loss did not improve from 0.01215\n",
      "\n",
      "Epoch 2292: val_loss did not improve from 0.01215\n",
      "\n",
      "Epoch 2293: val_loss improved from 0.01215 to 0.01213, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2294: val_loss did not improve from 0.01213\n",
      "\n",
      "Epoch 2295: val_loss did not improve from 0.01213\n",
      "\n",
      "Epoch 2296: val_loss did not improve from 0.01213\n",
      "\n",
      "Epoch 2297: val_loss did not improve from 0.01213\n",
      "\n",
      "Epoch 2298: val_loss did not improve from 0.01213\n",
      "\n",
      "Epoch 2299: val_loss did not improve from 0.01213\n",
      "\n",
      "Epoch 2300: val_loss did not improve from 0.01213\n",
      "\n",
      "Epoch 2301: val_loss did not improve from 0.01213\n",
      "\n",
      "Epoch 2302: val_loss did not improve from 0.01213\n",
      "\n",
      "Epoch 2303: val_loss did not improve from 0.01213\n",
      "\n",
      "Epoch 2304: val_loss did not improve from 0.01213\n",
      "\n",
      "Epoch 2305: val_loss did not improve from 0.01213\n",
      "\n",
      "Epoch 2306: val_loss did not improve from 0.01213\n",
      "\n",
      "Epoch 2307: val_loss did not improve from 0.01213\n",
      "\n",
      "Epoch 2308: val_loss did not improve from 0.01213\n",
      "\n",
      "Epoch 2309: val_loss did not improve from 0.01213\n",
      "\n",
      "Epoch 2310: val_loss did not improve from 0.01213\n",
      "\n",
      "Epoch 2311: val_loss did not improve from 0.01213\n",
      "\n",
      "Epoch 2312: val_loss did not improve from 0.01213\n",
      "\n",
      "Epoch 2313: val_loss improved from 0.01213 to 0.01211, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2314: val_loss did not improve from 0.01211\n",
      "\n",
      "Epoch 2315: val_loss did not improve from 0.01211\n",
      "\n",
      "Epoch 2316: val_loss did not improve from 0.01211\n",
      "\n",
      "Epoch 2317: val_loss did not improve from 0.01211\n",
      "\n",
      "Epoch 2318: val_loss did not improve from 0.01211\n",
      "\n",
      "Epoch 2319: val_loss improved from 0.01211 to 0.01207, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2320: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2321: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2322: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2323: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2324: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2325: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2326: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2327: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2328: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2329: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2330: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2331: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2332: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2333: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2334: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2335: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2336: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2337: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2338: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2339: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2340: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2341: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2342: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2343: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2344: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2345: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2346: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2347: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2348: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2349: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2350: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2351: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2352: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2353: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2354: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2355: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2356: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2357: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2358: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2359: val_loss did not improve from 0.01207\n",
      "\n",
      "Epoch 2360: val_loss improved from 0.01207 to 0.01205, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2361: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2362: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2363: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2364: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2365: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2366: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2367: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2368: val_loss improved from 0.01205 to 0.01205, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2369: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2370: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2371: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2372: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2373: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2374: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2375: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2376: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2377: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2378: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2379: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2380: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2381: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2382: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2383: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2384: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2385: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2386: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2387: val_loss improved from 0.01205 to 0.01205, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2388: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2389: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2390: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2391: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2392: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2393: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2394: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2395: val_loss did not improve from 0.01205\n",
      "\n",
      "Epoch 2396: val_loss improved from 0.01205 to 0.01199, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2397: val_loss did not improve from 0.01199\n",
      "\n",
      "Epoch 2398: val_loss did not improve from 0.01199\n",
      "\n",
      "Epoch 2399: val_loss did not improve from 0.01199\n",
      "\n",
      "Epoch 2400: val_loss did not improve from 0.01199\n",
      "\n",
      "Epoch 2401: val_loss did not improve from 0.01199\n",
      "\n",
      "Epoch 2402: val_loss did not improve from 0.01199\n",
      "\n",
      "Epoch 2403: val_loss did not improve from 0.01199\n",
      "\n",
      "Epoch 2404: val_loss improved from 0.01199 to 0.01198, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2405: val_loss did not improve from 0.01198\n",
      "\n",
      "Epoch 2406: val_loss did not improve from 0.01198\n",
      "\n",
      "Epoch 2407: val_loss did not improve from 0.01198\n",
      "\n",
      "Epoch 2408: val_loss did not improve from 0.01198\n",
      "\n",
      "Epoch 2409: val_loss did not improve from 0.01198\n",
      "\n",
      "Epoch 2410: val_loss did not improve from 0.01198\n",
      "\n",
      "Epoch 2411: val_loss did not improve from 0.01198\n",
      "\n",
      "Epoch 2412: val_loss did not improve from 0.01198\n",
      "\n",
      "Epoch 2413: val_loss did not improve from 0.01198\n",
      "\n",
      "Epoch 2414: val_loss did not improve from 0.01198\n",
      "\n",
      "Epoch 2415: val_loss did not improve from 0.01198\n",
      "\n",
      "Epoch 2416: val_loss did not improve from 0.01198\n",
      "\n",
      "Epoch 2417: val_loss did not improve from 0.01198\n",
      "\n",
      "Epoch 2418: val_loss did not improve from 0.01198\n",
      "\n",
      "Epoch 2419: val_loss did not improve from 0.01198\n",
      "\n",
      "Epoch 2420: val_loss did not improve from 0.01198\n",
      "\n",
      "Epoch 2421: val_loss did not improve from 0.01198\n",
      "\n",
      "Epoch 2422: val_loss did not improve from 0.01198\n",
      "\n",
      "Epoch 2423: val_loss did not improve from 0.01198\n",
      "\n",
      "Epoch 2424: val_loss improved from 0.01198 to 0.01192, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2425: val_loss did not improve from 0.01192\n",
      "\n",
      "Epoch 2426: val_loss did not improve from 0.01192\n",
      "\n",
      "Epoch 2427: val_loss did not improve from 0.01192\n",
      "\n",
      "Epoch 2428: val_loss did not improve from 0.01192\n",
      "\n",
      "Epoch 2429: val_loss did not improve from 0.01192\n",
      "\n",
      "Epoch 2430: val_loss did not improve from 0.01192\n",
      "\n",
      "Epoch 2431: val_loss did not improve from 0.01192\n",
      "\n",
      "Epoch 2432: val_loss did not improve from 0.01192\n",
      "\n",
      "Epoch 2433: val_loss did not improve from 0.01192\n",
      "\n",
      "Epoch 2434: val_loss did not improve from 0.01192\n",
      "\n",
      "Epoch 2435: val_loss did not improve from 0.01192\n",
      "\n",
      "Epoch 2436: val_loss did not improve from 0.01192\n",
      "\n",
      "Epoch 2437: val_loss did not improve from 0.01192\n",
      "\n",
      "Epoch 2438: val_loss did not improve from 0.01192\n",
      "\n",
      "Epoch 2439: val_loss did not improve from 0.01192\n",
      "\n",
      "Epoch 2440: val_loss did not improve from 0.01192\n",
      "\n",
      "Epoch 2441: val_loss did not improve from 0.01192\n",
      "\n",
      "Epoch 2442: val_loss did not improve from 0.01192\n",
      "\n",
      "Epoch 2443: val_loss did not improve from 0.01192\n",
      "\n",
      "Epoch 2444: val_loss did not improve from 0.01192\n",
      "\n",
      "Epoch 2445: val_loss did not improve from 0.01192\n",
      "\n",
      "Epoch 2446: val_loss improved from 0.01192 to 0.01191, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2447: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2448: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2449: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2450: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2451: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2452: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2453: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2454: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2455: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2456: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2457: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2458: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2459: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2460: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2461: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2462: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2463: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2464: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2465: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2466: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2467: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2468: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2469: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2470: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2471: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2472: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2473: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2474: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2475: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2476: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2477: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2478: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2479: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2480: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2481: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2482: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2483: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2484: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2485: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2486: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2487: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2488: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2489: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2490: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2491: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2492: val_loss did not improve from 0.01191\n",
      "\n",
      "Epoch 2493: val_loss improved from 0.01191 to 0.01189, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2494: val_loss did not improve from 0.01189\n",
      "\n",
      "Epoch 2495: val_loss did not improve from 0.01189\n",
      "\n",
      "Epoch 2496: val_loss did not improve from 0.01189\n",
      "\n",
      "Epoch 2497: val_loss did not improve from 0.01189\n",
      "\n",
      "Epoch 2498: val_loss improved from 0.01189 to 0.01187, saving model to model/model_17-08-2023.h5\n",
      "\n",
      "Epoch 2499: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2500: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2501: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2502: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2503: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2504: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2505: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2506: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2507: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2508: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2509: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2510: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2511: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2512: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2513: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2514: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2515: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2516: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2517: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2518: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2519: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2520: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2521: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2522: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2523: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2524: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2525: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2526: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2527: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2528: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2529: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2530: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2531: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2532: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2533: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2534: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2535: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2536: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2537: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2538: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2539: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2540: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2541: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2542: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2543: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2544: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2545: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2546: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2547: val_loss did not improve from 0.01187\n",
      "\n",
      "Epoch 2548: val_loss did not improve from 0.01187\n",
      "Epoch 2548: early stopping\n",
      "1875/1875 [==============================] - 1s 570us/step\n",
      "time: 3h 19min 16s (started: 2023-08-19 21:33:23 +02:00)\n"
     ]
    }
   ],
   "source": [
    "opt = keras.optimizers.Adam(learning_rate=0.01)\n",
    "model = create_model(\n",
    "    7,\n",
    "    3,\n",
    "    # hidden=[15, 30, 60, 30, 15],\n",
    "    hidden=[64, 64, 64, 64],\n",
    "    # dropout=[0.05, 0.05, 0.05, 0.05],\n",
    "    dropout=[0, 0, 0, 0, 0],\n",
    "    # activation=[\"tanh\", \"tanh\", \"tanh\", \"tanh\"],\n",
    "    activation=[\"gelu\", \"gelu\", \"gelu\", 'gelu', 'gelu'],\n",
    "    # activation=[\"relu\", \"relu\", \"relu\", 'relu', 'relu'],\n",
    "    loss=\"mse\",\n",
    "    optimizer=opt,\n",
    "    metrics=['mse', 'mae', 'mape']\n",
    ")\n",
    "\n",
    "mc = ModelCheckpoint('model/model_17-08-2023.h5', monitor='val_loss', mode='min', verbose=1, save_best_only=True, save_weights_only=False)\n",
    "es = EarlyStopping(monitor=\"val_loss\", mode=\"min\", verbose=1, patience=50)\n",
    "rlr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.00001)\n",
    "tnan = TerminateOnNaN()\n",
    "callbacks = [es, mc, tnan, rlr]\n",
    "\n",
    "h = model.fit(\n",
    "    train_x,\n",
    "    train_y,\n",
    "    epochs=5000,\n",
    "    batch_size=128,\n",
    "    verbose=0,\n",
    "    validation_split=0.1,\n",
    "    callbacks=callbacks,\n",
    "    shuffle=True\n",
    ")\n",
    "\n",
    "pred_y = model.predict(test_x)\n",
    "pred_y = pd.DataFrame(pred_y, columns=out_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "70fcb6d5-5e00-4331-ab1e-328e7ad447b1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Xa9euXRo6dKg6d+6se++9t9DPk5tpmho8eLBCQkK0dOlSZWRk6IEHHtDQoUO1ZMkSSdLw4cN14YUXaurUqfL19dXGjRvl7+8vSYqKilJaWpr++OMPhYSEKCYmRlWrVi1yPYqCIO8gKipKUVFRSkxMdHlRlTemj3Xh+Mg7bjwAAAAAXiU9RXqlvmfO/cwRqUqIW0VHjRql119/XUuWLNEVV1whyepWP2TIEFWvXl3Vq1fXY489Zi//4IMPav78+ZozZ45bQX7RokXavn279u3bp4YNG0qSXnnllTzj2v/zn//Ynzdt2lSPPvqoZs+erSeeeEJBQUGqWrWq/Pz8FBERke+5vvjiC509e1affvqpQkKsz//uu+9q0KBBmjRpkurWrStJql69ut599135+vqqTZs2uvbaa/Xbb78VK8gvWrRImzdv1t69e9WoUSNJ0meffaZ27dppzZo16t69uw4cOKDHH39cbdq0kSS1bNnS/v4DBw7opptuUocOHSRJzZs3L3Idioqu9d7M1wryfmbxu5AAAAAA8G5t2rRRr169NG3aNEnS7t27tWzZMo0aNUqSlJmZqZdfflkdO3ZUzZo1VbVqVS1YsEAHDhxw6/jbt29X48aN7SFeknr27Jmn3Ndff61LL71UERERqlq1qsaNG+f2ORzP1alTJ3uIl6RLLrlENptNO3bssG9r166dfH197a/r1aun+Pj4Ip3L8ZyNGjWyh3hJioyMVLVq1bR9+3ZJ0iOPPKJ77rlHffv21cSJE7V792572YceekgvvfSSLrnkEo0fP16bN28uVj2KghZ5L5bdIu9rZni4JgAAAEAF5B9stYx76txFcPfdd2vMmDGKjo7W9OnT1aRJE1111VWSpMmTJ+utt97SlClT1KFDB4WEhGjs2LFKS0tz69imi+Wuc0+yvWrVKt166616/vnn1b9/f4WHh2vWrFmaPHlykT6HaZr5TuDtuD27W7vjPpvNVqRzFXZOx+0TJkzQbbfdprlz52revHkaP368Zs2apRtvvFH33HOP+vfvr7lz52rBggV69dVXNXnyZD344IPFqo87aJH3YoZvFUkEeQAAAKBUGIbVvd0TjyKuRnXLLbfI19dXX375pT755BPddddd9hC6bNky3XDDDbr99tvVqVMnNW/eXDt37nT72JGRkTpw4ICOHMm5qbFy5UqnMn/++aeaNGmiZ599Vt26dVPLli3zzKRfpUqVQucji4yM1MaNG3XmzBmnY/v4+KhVq1Zu17kosj/fwYMH7dtiYmKUkJCgtm3b2re1atVKDz/8sBYsWKAhQ4Zo+vTp9n2NGjXS6NGj9e233+rRRx/VRx99VCp1zUaQ92a0yAMAAACQVLVqVQ0dOlTPPPOMjhw5opEjR9r3tWjRQgsXLtSKFSu0fft2/d///Z/i4uLcPnbfvn3VunVr3XHHHdq0aZOWLVumZ5991qlMixYtdODAAc2aNUu7d+/W22+/re+++86pTNOmTbV3715t3LhRx48fV2pqap5zDR8+XIGBgbrzzju1detWLV68WA8++KBGjBhhHx9fXJmZmdq4caPTIyYmRn379lXHjh01fPhwrV+/XqtXr9Ydd9yhyy+/XN26ddPZs2c1ZswYLVmyRPv379eff/6pNWvW2EP+2LFj9euvv2rv3r1av369fv/9d6cbAKWBIO/N/LLHyBPkAQAAgMru7rvv1qlTp9S3b181btzYvn3cuHHq0qWL+vfvrz59+igiIkKDBw92+7g+Pj767rvvlJqaqosuukj33HOPXn75ZacyN9xwgx5++GGNGTNGnTt31ooVKzRu3DinMjfddJMGDBigK664QrVr13a5BF5wcLB+/fVXnTx5Ut27d9fNN9+sq666Su+++27RvgwXkpOTdeGFFzo9rrnmGvvyd9WrV1fv3r3Vt29fNW/eXLNnz5Yk+fr66sSJE7rjjjvUqlUr3XLLLRo4cKCef/55SdYNgqioKLVt21YDBgxQ69at9d577513fQtimK4GPFRy2bPWJyQkKCwszNPVydfO32ao5bJ/a4NvB9145mlJ0uP9WyvqihYerhkAAAAAoCiKkkNpkfdmvrTIAwAAAEBlQ5D3YkZWkPcVQR4AAAAAKguCvDejRR4AAAAAKh2CvIPo6GhFRkaqe/funq6KW7KXn/OjRR4AAAAAKg2CvIOoqCjFxMRozZo1nq6Ke1y0yBdxuUkAAAAAgJchyHsxwy+7RT7TwzUBAAAAAJQVgrwXM3yyWuQdutazmCAAAAAAVGwEeW+W1SLvzxh5AAAAAKg0CPJezMe+/FxO13rGyAMAAABAxUaQ92J+fr6SJB/T5uGaAAAAAADKCkHei/n5WS3yPiLIAwAAAEBlQZD3Yr5ZLfK+BHkAAAAAqDQI8l7MP2uyO8cWeUMMkgcAAACAiowg78X8/fysn0bOZHemWH8OAAAAACoygrwX880K8pJk0L0eAAAAACoFgrwX88+a7E7KGSdP13oAAAAAqNgI8l7Mx9fX/pwJ7wAAAACgciDIO4iOjlZkZKS6d+/u6aq4x8gJ8ixBBwAAAACVA0HeQVRUlGJiYrRmzRpPV8U9Pjlj5P0I8gAAAABQKRDkvZkPLfIAAAAAUNkQ5L2ZwRh5AAAAAKhsCPLezCfnn48gDwAAAACVA0Hey2Vm/RNmd603WH0OAAAAACo0gryXy5TVvZ4WeQAAAACoHAjyXs6W9U/oa2R6uCYAAAAAgLJAkPdyNiMryNMiDwAAAACVAkHey9noWg8AAAAAlQpB3suZRvZkd6aHawIAAAAAKAsEeS9nHyNPizwAAAAAVAoEeS9nM7K71luT3bH6HAAAAABUbAR5L2cajJEHAAAAgMqEIO/l6FoPAAAAAJULQd7LZbfI+xDkAQAAAKBSIMg7iI6OVmRkpLp37+7pqriPdeQBAAAAoFIhyDuIiopSTEyM1qxZ4+mquM0+2Z1BkAcAAACAyoAg7+2yWuQN1pEHAAAAgEqBIO/tDGvBuet8VukVv//Jx0z3cIUAAAAAAKXJz9MVwPkxsoL8bX6/S5L+iP1JUhsP1ggAAAAAUJpokfdydVJ2Ob0OSj/poZoAAAAAAMoCQb6CMfknBQAAAIAKjdRXwZgyPF0FAAAAAEApIshXNAZBHgAAAAAqMoJ8BUOLPAAAAABUbAT5CsY0+CcFAAAAgIqM1Ffh0CIPAAAAABUZQb6CsfFPCgAAAAAVGqnP2/n4Ob2kaz0AAAAAVGykPq/n3JXe9FAtAAAAAABlgyBfwZj8kwIAAABAhUbq83pmrldMdgcAAAAAFRlBvoIxDYI8AAAAAFRkBPkKhhZ5AAAAAKjYCPIVDEEeAAAAACo2gnyFwz8pAAAAAFRkpD4H0dHRioyMVPfu3T1dlWKz8U8KAAAAABUaqc9BVFSUYmJitGbNGk9XpdhYRx4AAAAAKjaCfAVjM/gnBQAAAICKjNTn7Uza4AEAAACgMiHIVzQEewAAAACo0AjyFQ1BHgAAAAAqNIJ8hUOQBwAAAICKjCBf0Zg2T9cAAAAAAFCKCPJejxZ4AAAAAKhMCPIVjGFmeroKAAAAAIBSRJD3ekYBrwAAAAAAFQ1B3uvl6lrPGHkAAAAAqNAI8hUNy88BAAAAQIVGkK9wCPIAAAAAUJER5CsYQ3StBwAAAICKjCBfwTj1rKebPQAAAABUOAR5b5crrBvZk93t/l16o6W0Y54HKgUAAAAAKC0E+QonK9h/dqN05pg081bPVgcAAAAAUKII8t7OyLVyPN3pAQAAAKBCI8h7uzzBncnuAAAAAKAiI8hXMAYt8gAAAABQoRHkKxiDdeQBAAAAoEIjyFc4BHkAAAAAqMgI8hUNXesBAAAAoEIjyHu9fNaRBwAAAABUSAT5CocWeQAAAACoyAjyXo915AEAAACgMiHIe73cwZ0gDwAAAAAVGUG+gmEdeQAAAACo2AjyDqKjoxUZGanu3bt7uipFkKtrvZjsDgAAAAAqMoK8g6ioKMXExGjNmjWerkoRmAW+BAAAAABULAT5CsagRR4AAAAAKjSCfAVj0CQPAAAAABUaQd7bDf9a8q2is35h1muTFnkAAAAAqMgI8t6u5dXSs3GKqX1N1gZa5AEAAACgIiPIVwQ+vjJ8fCVJ/hkpHq4MAAAAAKA0EeQriPD0Y5Kknie+9XBNAAAAAACliSBfQdQ/7U1L5gEAAAAAiosgX0H42tI9XQUAAAAAQBkgyFcQvrZUT1cBAAAAAFAGCPIVBC3yAAAAAFA5+LlTqEaNGkU6qGEYWr9+vZo0aVKsSgEAAAAAANfcCvKnT5/WlClTFB4eXmhZ0zT1wAMPKDMz87wrBwAAAAAAnLkV5CXp1ltvVZ06ddwq++CDDxa7Qiie1NAmCkjab70wTc9WBgAAAABQatwaI2+z2dwO8ZKUlJSk5s2bF7tSKLrY/h/mvLDRGwIAAAAAKiomu6sggupcYH9uMvEdAAAAAFRYbgf5Bx54QMnJyfbXn332mdPr06dP65prrinZ2sFt4VUD7c8TU1iKDgAAAAAqKreD/AcffKCUlBT766ioKMXHx9tfp6am6tdffy3Z2sFtgVWq2J8nnjnnwZoAAAAAAEqT20HezDWBWu7X8DCfnHkLE1JyBflD68q4MgAAAACA0sIY+YrCyPmnTMwd5A+sKOPKAAAAAABKC0G+ojAMZWb9c+YJ8gAAAACACsPtdeQl6bnnnlNwcLAkKS0tTS+//LLCw8MlyWn8PDzDZvjK17QpiSAPAAAAABWW20G+d+/e2rFjh/11r169tGfPnjxl4Dmm4SuZ6XmD/B+vSz3HSIbhmYoBAAAAAEqM20F+yZIlpVgNlAQza5x8cu7l584lSNt/kiKv90CtAAAAAAAl6bzHyGdkZDitJw/PMQ3rvsyZVBfryJ8+UMa1AQAAAACUBreD/C+//KLPPvvMadvLL7+sqlWrqlq1aurXr59OnTpV4hVEERi+kqRzqWl5963/tIwrAwAAAAAoDW4H+TfeeEOJiYn21ytWrNBzzz2ncePG6auvvtLBgwf14osvlkol4SYfK8ifPeeiRf74jrzbAAAAAABex+0gv3XrVvXq1cv++uuvv9bVV1+tZ599VkOGDNHkyZP1008/lUol4R4jO8i7apEHAAAAAFQIbgf5pKQk1axZ0/56+fLluvLKK+2v27VrpyNHjpRs7VA0vtYY+dQ0Fy3yAAAAAIAKwe0gX79+fW3fvl2SlJycrE2bNumSSy6x7z9x4oR9jXl4huFjBfmzqekergkAAAAAoLS4HeRvvvlmjR07Vp999pnuvfdeRURE6OKLL7bvX7t2rVq3bl0qlYR7fLJa5NPSCfIAAAAAUFG5vY78+PHjdeTIET300EOKiIjQ559/Ll9fX/v+mTNnatCgQaVSSbjHx6+KJMnXJMgDAAAAQEXldpAPDg7Os/yco8WLF5dIhVB8RlaQryKCPAAAAABUVG53rUf5Z/gGSJKqKMPDNQEAAAAAlBa3W+QdZ6gvyO+//17syuA8+RHkAQAAAKCiczvIL1myRE2aNNG1114rf3//0qwTisuXrvUAAAAAUNG5HeQnTpyoGTNmaM6cORo+fLhGjRql9u3bl2bdUFRZLfLBBuvIAwAAAEBF5fYY+SeeeEIxMTH6/vvvlZSUpEsuuUQXXXSR3n//fSUmJpZmHeGurBb5l/yne7giAAAAAIDSUuTJ7nr27KmPPvpIsbGxioqK0rRp01S/fn3CfHmQFeQBAAAAABVXsWetX79+vZYuXart27erffv2jJsvD/wKCfKmWTb1AAAAAACUmiIF+SNHjuiVV15Rq1atdPPNN6tGjRr666+/tGrVKgUFBZVWHeGurOXn8mXLLJt6AAAAAABKjduT3V1zzTVavHix+vXrp9dff13XXnut/PzcfjvKgl8hQd60lU09AAAAAAClxjBN9/pb+/j4qF69eqpTp44Mw8i33Pr160uscp6SmJio8PBwJSQkKCwszNPVcd/C8dKfU/Lf/8h2Kax+mVUHAAAAAOCeouRQt5vUx48ff94VQykrrEX+zbbS2C1StcZlUx8AAAAAQIkjyFckvm5MOLhjntTj/0q/LgAAAACAUlHsWetLynvvvadmzZopMDBQXbt21bJly/ItGxsbq9tuu02tW7eWj4+Pxo4d67LcN998o8jISAUEBCgyMlLfffddKdW+nClssjtJUv7DIgAAAAAA5Z9bQb5Lly46deqU2we99NJLdfjw4ULLzZ49W2PHjtWzzz6rDRs26LLLLtPAgQN14MABl+VTU1NVu3ZtPfvss+rUqZPLMitXrtTQoUM1YsQIbdq0SSNGjNAtt9yiv/76y+36ey13WuQBAAAAAF7NrcnufHx89Pvvv6tGjRpuHbRXr17avHmzmjdvXmC5Hj16qEuXLpo6dap9W9u2bTV48GC9+uqrBb63T58+6ty5s6ZMmeK0fejQoUpMTNS8efPs2wYMGKDq1atr5syZbtXfaye7++tDad7jBZcZ+LrU476yqQ8AAAAAwC2lMtndVVddJTcnuC9wVvtsaWlpWrdunZ566imn7f369dOKFSvcrVYeK1eu1MMPP+y0rX///nkCf4XkxvfuVhkAAAAAQLnlVpDfu3dvkQ/csGHDAvcfP35cmZmZqlu3rtP2unXrKi4ursjnyxYXF1fkY6ampio1NdX+OjExsdjn9yjD41MeAAAAAABKmVtBvkmTJqVWgdyt96ZputWiX5LHfPXVV/X888+f1znLBYI8AAAAAFR4Hkt+tWrVkq+vb56W8vj4+Dwt6kURERFR5GM+/fTTSkhIsD8OHjxY7PN7FEEeAAAAACo8jyW/KlWqqGvXrlq4cKHT9oULF6pXr17FPm7Pnj3zHHPBggUFHjMgIEBhYWFOD69EkAcAAACACs/tye5KwyOPPKIRI0aoW7du6tmzpz788EMdOHBAo0ePlmS1lB8+fFiffvqp/T0bN26UJCUnJ+vYsWPauHGjqlSposjISEnSv//9b/Xu3VuTJk3SDTfcoB9++EGLFi3S8uXLy/zzlTmCPAAAAABUeB4N8kOHDtWJEyf0wgsvKDY2Vu3bt9cvv/xiH5MfGxubZ035Cy+80P583bp1+vLLL9WkSRPt27dPkrX03axZs/Sf//xH48aN0wUXXKDZs2erR48eZfa5PIYgDwAAAAAVnlvryDs6ePCgDMOwz0q/evVqffnll4qMjNR991WM9cm9dh35XYukz28quMy1k6Xu95RNfQAAAAAAbilKDi1yE+5tt92mxYsXS7KWerv66qu1evVqPfPMM3rhhReKV2OUjAuu8nQNAAAAAAClrMhBfuvWrbroooskSV999ZXat2+vFStW6Msvv9SMGTNKun5lKjo6WpGRkerevbunq1I8hiH5eHS0BAAAAACglBU5yKenpysgIECStGjRIl1//fWSpDZt2ig2NrZka1fGoqKiFBMTozVr1ni6KsVn2gopYJRJNQAAAAAApaPIQb5du3Z6//33tWzZMi1cuFADBgyQJB05ckQ1a9Ys8QqiiIo25QEAAAAAwMsUOchPmjRJH3zwgfr06aNhw4apU6dOkqQff/zR3uUenkSQBwAAAICKrMgDqvv06aPjx48rMTFR1atXt2+/7777FBwcXKKVQykw6FoPAAAAAN6syC3yZ8+eVWpqqj3E79+/X1OmTNGOHTtUp06dEq8gSlZGJi32AAAAAODNihzkb7jhBn366aeSpNOnT6tHjx6aPHmyBg8erKlTp5Z4BVGy5m+L83QVAAAAAADnochBfv369brsssskSV9//bXq1q2r/fv369NPP9Xbb79d4hVEyfpz9wlPVwEAAAAAcB6KHORTUlIUGhoqSVqwYIGGDBkiHx8fXXzxxdq/f3+JVxBFZBT8T8oIeQAAAADwbkUO8i1atND333+vgwcP6tdff1W/fv0kSfHx8QoLCyvxCpal6OhoRUZGqnv37p6uSvHd8WOBuyf4zdC59MwyqgwAAAAAoKQVOcg/99xzeuyxx9S0aVNddNFF6tmzpySrdf7CCy8s8QqWpaioKMXExGjNmjWerkrxNbtMuuWzfHdXMTKVuu6LMqwQAAAAAKAkGaZpFnka87i4OMXGxqpTp07y8bHuBaxevVphYWFq06ZNiVeyrCUmJio8PFwJCQne2ctg+8/S7OEFl5mQUDZ1AQAAAAAUqig5tMjryEtSRESEIiIidOjQIRmGoQYNGuiiiy4qVmVRCgoZJw8AAAAA8F5FTnw2m00vvPCCwsPD1aRJEzVu3FjVqlXTiy++KJvNVhp1RFER5AEAAACgwipyi/yzzz6rjz/+WBMnTtQll1wi0zT1559/asKECTp37pxefvnl0qgnioIgDwAAAAAVVpGD/CeffKL//e9/uv766+3bOnXqpAYNGuiBBx4gyJcHBHkAAAAAqLCKnPhOnjzpckK7Nm3a6OTJkyVSKZwng9XiAQAAAKCiKnKQ79Spk9599908299991116tSpRCqF80SLPAAAAABUWEXuWv/aa6/p2muv1aJFi9SzZ08ZhqEVK1bo4MGD+uWXX0qjjigqgjwAAAAAVFhFTnyXX365/vnnH9144406ffq0Tp48qSFDhmjHjh267LLLSqOOZSY6OlqRkZHq3r27p6tyfgjyAAAAAFBhGaZpmiVxoIMHD2r8+PGaNm1aSRzOoxITExUeHq6EhASFhYV5ujpFt+9PacY1BZeZkFA2dQEAAAAAFKooObTEmm5PnjypTz75pKQOh/NBizwAAAAAVFgkvoqIIA8AAAAAFRaJryIiyAMAAABAhUXiq4gI8gAAAABQYbm9/NyQIUMK3H/69OnzrQtKimF4ugYAAAAAgFLidpAPDw8vdP8dd9xx3hVCCaBFHgAAAAAqLLeD/PTp00uzHihJBHkAAAAAqLBIfBVRQFVP1wAAAAAAUEoI8hVRYDVP1wAAAAAAUEoI8g6io6MVGRmp7t27e7oq5ycgzNM1AAAAAACUEoK8g6ioKMXExGjNmjWersr58XV76gMAAAAAgJchyFdWpunpGgAAAAAAioEgX1Fd/lTB+wnyAAAAAOCVCPIV1RVPSz3uL6AAQR4AAAAAvBFBviLzD8x/n2kru3oAAAAAAEoMQb4i8w/Ofx9d6wEAAADAKxHkKzI/WuQBAAAAoKIhyFdk/kEF7KRFHgAAAAC8EUG+ImvVP/99tMgDAAAAgFciyFdk1RpLj+5wvY8gDwAAAABeiSBf0YVGuN7OZHcAAAAA4JUI8pVB/S6SpGNVGuRsW/uxhyoDAAAAADgfBHkH0dHRioyMVPfu3T1dlZJ16xfSZY/qoyaTc7YtmuCx6gAAAAAAio8g7yAqKkoxMTFas2aNp6tSssLqS1c9p8QqdTxdEwAAAADAeSLIVyKG4evpKgAAAAAAzhNBvjIx+OcGAAAAAG9HsqtEfHwMT1cBAAAAAHCeCPKViC9BHgAAAAC8HkG+EvExCPIAAAAA4O0I8pUIOR4AAAAAvB9BvhKhRR4AAAAAvB9BvhJhiDwAAAAAeD+CfCXSuEaw84ZpA6XD6z1TGQAAAABAsRDkK5FOjao5bziwQvp8iEfqAgAAAAAoHoJ8JdKwenDejWdPSWkpZV8ZAAAAAECxEOQrkerB/q53vFJPOnOibCsDAAAAACgWgnwlYhQ0a/3OX8uuIgAAAACAYiPIAwAAAADgRQjyDqKjoxUZGanu3bt7uioAAAAAALhEkHcQFRWlmJgYrVmzxtNVAQAAAADAJYI8AAAAAABehCAPSVKmzdM1AAAAAAC4gyAPSdKafSw/BwAAAADegCAPSdKSHcc8XQUAAAAAgBsI8pAktakX6ukqAAAAAADcQJCHJMkwGSQPAAAAAN6AIF/Z3PCe6+2Z6WVbDwAAAABAsRDkK5sLh7vcbNgI8gAAAADgDQjykESQBwAAAABvQZCHJMmwZXi6CgAAAAAANxDkYaFFHgAAAAC8AkEekiQfWuQBAAAAwCsQ5CFJ8rWl5ryw2aRlb0r7V3iuQgAAAAAAl/w8XQGUD36OQX7r19Jvz1vPJyR4pkIAAAAAAJdoka+MLn8qzybfTIcgf/yfMqwMAAAAAKAoCPKVUZ+npGsnO286M0+K3269ME0PVAoAAAAA4A661ldGhiHVap13+5dDpQ43S8sm590HAAAAACgXaJGvrHxc3MM5vZ8QDwAAAADlHEG+sgpv6OkaAAAAAACKgSDvIDo6WpGRkerevbunq1L6qjWSolZrZ5dnCy63/SfpxO6yqRMAAAAAoFCGaTKzWW6JiYkKDw9XQkKCwsLCPF2dUrV52Y/q+NuIwguyDB0AAAAAlJqi5FBa5Cu5tPBmnq4CAAAAAKAICPKVnBHeQNEZ13u6GgAAAAAANxHkK7mODatpRkZ/T1cDAAAAAOAmgnwl5+/ro7lP3+TpagAAAAAA3ESQh+qEB2nFZZ8UXMhmK5vKAAAAAAAKRJCHJKnnlTcUXCDjXNlUBAAAAABQIII8JEmGYSi97ZD8CxDkAQAAAKBcIMjDzr9Bx/x3Ln9TOrzeep6WIplm2VQKAAAAAOCEII8cPUZLzS53vW/FO9JHV0in9kkTG0k/RJVp1QAAAAAAFoI8cvgHSXf+KA39XKnB9VyXWfW+ZMuQNn5RvHNsnCmtnVb8OgIAAABAJUeQR15tByngkgdc77Ol5zw/GlO042amS9+Pln5+WEo6Wvz6AQAAAEAlRpCHa+lnXW8/uSfn+Td3F3yMjFQpdlPOePq05Jx9js8BAAAAAG4jyMO1Nte43r7795zn8THS6QPS0W2uy357n/RBb2n9p9brtDM5+1JOSCknS6auAAAAAFCJEOThWkQH6cH1+r3jGwWXm9JBmtpL2vWbdOAv530x31s//5xi/UxLydn38dXSa82kdJa1AwAAAICiIMgjfzUvUELjq/RRxjVK8KlWcNnPh0jT+knL3pTit0vf3Z+z7+QeaedC6dzpvO9LOlK0Otkyi1a+OFJOSivfk5LjS/9cAAAAAFBEBHkUKCwkRC9n3K6+KS+794bfnpfeu1ja9KXz9i9ulr65J2/54zutn7sWSetmWLPaZ6bnLSdJi1+RJjWVjv0jnUuUlkySju9y96NIW76Wvh6V//j/bN/9n/Tr09Ks4e4fGwAAAADKiJ+nK4DyrXuzGqobFqCjidXV9NyXamkcUte6hjqGn9PVZ35S7eOr3T/Y6f15t315i/T4Hunzm3K2nUuQLh5trV3v4y+1HigdWCktnWTtX/yyFFTNCv7L3pCGfiEFhErH/pb2LJY6D5da9c97ruzJ+ep1ki75d/713LnA+nmoCJ8NAAAAAMqIYZrZU4ojW2JiosLDw5WQkKCwsDBPV8fjzqZl6tdtcRo7e6PL/YZsujLwHzWoV093JX6oeun7FZh2nhPZ1WieM0N+UA3pbK7jVWtsTbSXn/GnrdnyDcN6SNKEcOtnzzFS/wJ6GGSXk6QJCUWuepna+4e0/jNpwEQppKanawMAAACgmIqSQ2mRR6GCqvhq8IUNdEWbOjp86qz2HE/W4r+PaWd8kjYfSpApH/12ro20V/pUj9vfF6hUNTGO6sK6vno8ZYpqph12/6SOy9zlDvGSdLaQgJ1yQprWX6rWRBrxrfO+M8fdr0d598kg66eZKd08zbN1AQAAAFAmCPJwW3iQv8KD/BVZP0zXdawvSUrPtOnQqbM6cDJFe44l6/Cps9oel6g9x84oNkHaYTbWjjhpll63H6eBjilVVdSjWoLGpE9XUkA9VWnUWfVS96nuvu/dq0xqIUF+1yLpxC7r8dFVUo//y9m3eZZ06cNSnTZF/AbKsbitnq4BAAAAgDJC13oX6FpfMk6dSdP+kynaf+KMth1J1JZDCdpxNEknz6Tl+56aSlD9oHSNClysfmfnK0SFTExXXO1vymnBPrXf6gFwwRXW69xd6//8r7RvuXTLZ5JfQE5X/fIgu65hDaRHYjxbFwAAAADFVpQcSpB3gSBfukzT1N7jZ7TvxBntik/WoVNntSs+WTvjk3UsKdXVOyQZ8lWmhvovV9/A7Wpp26tGmQfPryIhdaQ+T0nzn5Yys877+B7p9eY5ZZ49Kr1c13p+7ZvS8ilS9SZS9abSZY9YY/k9KTvI+wdLN0RLTXpJoRGerRMAAACAIiPInyeCvOckp2Zod3yyftkaq7iEczqTmqnNh04r6VyGzqbnXUM+RGfV0DimajqjVj6H1LB6oG5P+VwhtqTiVaBxT2uGfHfU6yT93x/O2zLTreXtArOumy1fS2eOSV3vkvwDXR8n4bA0e7g12/5F9xatvo69ByQptL706PaiHQMAAACAxzHZHbxW1QA/dWpUTZ0aVcuz73RKmnbGJ2vb4QRl2Eyt239KS/85pqN+YdqRkq6/MttKx6VXdZn9PRcaO5WoYO02G2hM1SW6xf8PBRmZqp2y03UF3A3xkhS7yfqZkSb5VbGef3qDtP9P6YKrpPgYKSnW2n7muHTVOOt5Wop0ZL3U6GJrSb53uljbj2woepDPLenI+b2/ItgxX1o2WbrxfanmBZ6uDQAAAFDiaJF3gRZ573P49FltO5ygpHMZOnz6rGITzurw6XPadTRJRxLOOZUNVKq6+OzUMbOaLvPZojDfdEX4n9G15h8KtRVzubmIjlLra6SlE13vDwiTns4aCvDd/dKmL6W+E6ybAdu+yylX1OXucrfIu3MM05Rm3y6F1JYGTSna+bxB9nfSqId09wLP1gUAAABwEy3yqHQaVAtSg2pBLvftiEvSjqNJOpeWqfikc9p3IkU74uoo9vgZTUttKGVKSpOe0q0KULoaGscUrFT1812rDL+qusX8VQ2MYwVXIG6z9ciPr7+UclL6epS0Z7G1bdEEqcklectmpksxP0gNu1vj8SXp4Bpp/Qyp7/NSSC1rrH5muutz7Vok1e8iBddwvf/4Tunvn63nA1/L6U1QWmyZko9v6Z7DlZQTZX9OAAAAoAwQ5FHhtY4IVeuIUJf7Tqekae/xMzp0ymrF33I4UQdP1lFswllNTmwuZUj/1TWSpACl6SKfv7XZ1lxX+67Tc36fKsywZtVP9glV1YLG5aeckF5rlnf7/j+dX//2gnQuQVrzv5xtdy+SPu5rPTdNqeNQadH4/M/1+U3W+P17frNuIGS/b/+fUr3Oks3hBkBqouRXK/9jna9zCVJ0D6lZb2nIh6V3HlcMn7I9HwAAAFBG6FrvAl3rIUmJ59K19VCCthxO0LGkVJ1MSdOxpFRtPZygUynOreGhStHFPjFqYhxVkoJ1kc/fusl3mYdq7qBmSynqL+mv96Vfn5HqtrcCbnbvgZs+ljrcbD1f87G0aaY0bLYUUtO942ekWTcL8luSb+006eeHredFHTZQXNld62u3sT67J6UmSZtmSW0HsZqAtzpzQprWT+p4q3T5456uDQAAqMCYtb6YoqOjFR0drczMTP3zzz8EeeTLNE1tPZyo42dStffYGW0+dFqmpHX7Tyktw6Z4h2X02hgH1NFntzobu3W17zrVNsoo0LrruVOSj09OAO5yh5QcL4XWK3gMfWqS9E5Xq/V/+Jyc7elnpd2/S80ulzbPkuY+am13FeTTz1o9BZpeJvkF5H+upKPS9h+lTsOkgKoFfx57kG8rRa0quGxp+260dXOkPNxUQPEsfE7687/W87K6GQUAAColxsgXU1RUlKKiouxfIJAfwzDUoaF1jVzROu/+48mp+mHjEVUL8tex5DbacihBnx0/o1dPpehceqbSM03V0SlVMdJ12KylUKVoiO9y9fLZpppGogKNdLUz9pXNh9m7VKrTNuf1+k+d97UdJF39gvN7MtOlVxtaz3fmmlDul8elDZ9J7W+y1rXPZpp5W+7nPipt/EK66P+ka17Lv47f/Z81t8DBv6Sb/ifZbNLGz60J7Wq7+AeQXHetd1WH0rT9J+vnsb/L7pwoWfnNRQEAAOBBBHmgFNSqGqC7L3UxJl5S0rl0HU9Osybhi0vSqZQ0nUnN0Nq4+vrkSKIc+8jUUKK6++xQe5+9sslH//b7Ns/xDvo3V3JghNomrSheZT8bnP++k3us1sjej0sBDvMM7JjnXO615tJ1b0mRN1ghXpK2fmPN5p/t2/ukwVMlX4dfOxu/sH6u/sB1kE8/K/34YM4EgVvmWEF+82xru5R/K2nuIL/mY2nJRGnEt1JEh/w/c2VlmtLZU/lPkggAAIBygyAPlLHQQH+FBvqrWa0QDWifd9x0akamdsdb3fX3nUiRn08XbY1P0t7jZxR9/BYFVfFVwlmrlTBcyUo6Fyy/pEzVN25SF2On3qzyvtPx4o3aqmMWMut+YZZOko5uk07sli4cIe381Xl/ygnpqzukZ2KdtztOyrflK6n1AOnMcSvgR7R3fS5bpvTH61Zr/pENVnjP7aAbXebztP4/kvXzMenuX/OWLw1FHbmUFGdNdNh1pBTe0P332WzSyd1SzRbF73Gw4D/SyneloZ9bvTAKc3KPNbFirwelbqOKd04AAAAUC0EeKGcC/HwVWT9MkfXzjoux2Uz5+BiKOZKo/SfOKC7xnGKOJCokwE+bD9VWTFordU3qq+QzyWplHNIus75MGWpkHNPVPuvUz3eNOvvsKXqlVryT83zxS/mXe6VewcfZOFPatdD1vr3LpNMHrDHwS14teh0lK1xmy2/WelsBXaVLvOt9EYP8nJHSgZVWl3x3x9Rv/dbq2bBrkdR3gnTpwwWXP3ta2rdcanm187wEK9+1fv76rHtBfv7T1vf988MEeQAAgDJGkAe8iI+PFTLzC/rZUjMytelgghLPpis5NUMHTqZo6T8dNPPYzcrINJWclq7O2qVaRoKOmDUVrFR18tmtvr7r1c3YoWW2DqpjnFaSgtXF2KkqRkbJfIADBbSkf3Kd9bPVgKIdc/vP0tKJ0o0f5kxKJkmxG6VD66SGXZ3LG77SuUQpMNf3F7dVmnGtNYyg15ii1SE/RW2RP7DS+unumPpdv0lf35XzetGEgoN8Rqo0qYn1vNdDUr8X85Y5vV9a/pbU/R7n4RS5pae4V0cAAACUOII8UAEF+PnqombOY50fuqql/bnNZup4cqq2xyXpdEqa0jJs2n3sjF7dfVwyDKVl2LTv+BmdTc9UC+OQqitZHX12a5jvYoUaKaprnHY69mGzphoYJwqvWFpS4WX+mV/w/o+ulA6vy3k9e7j186d/K08L+P+ulKLWSLVb5Ww7tNoKswMmSg27Sw26WMF10QRr/4JnpfZDpKoR1mz+jq30pikd/0eqcYE11j81yeoK3+5GqXpTq0z8dqlKValaI+f6/PWh1OO+wj9/URxcnXdb3Fbr8/QdL1Vr7Lzvrw9ynm/43HWQl6zv4tT+glctYMGT0rHuE2tJx863ebomAACgHCPIA5WQj4+hOmGBqhMWmG8Z0zR1Ji1TcQnntPHgaaVn2vTb2Ue1+XCCQgP8dDolXat3x6p1Wow22i5QI+OYWhqHtcbWWnMDnnF7mb1M+cpXme5X3jHEOzrkItRKUnR3qc11uT6cTZr3hPV8+Dc5IT7bm22tFumuI6X3L5X8Q6THd0l//yx9e6908QPSgFel+U9ZgXjRBGnAJKs7/7ystcYnJDiH3XmPFy/IH14nLRhnhW7D1xoLf/qAVL+L62EC719i/fQLkAa/57zv9H73z7t3adHrWp6kn7O+g7JcpeB8pZyUfnrIeh45WKoS7NHqAACA8osgD8AlwzBUNcBPLepUVYs6rtduz7SZMs1rlJZpU8LZdG07nKg+KWn64vQinTyTpiOnz0o2m8xzp9XgyK/yV6Z+Se+qeFWTTYZM+UgydbfvPF3us0k9fWLkbxQh1Lvr75/z3/fFTa63r/mftDtrtvz0M9LyN6VVU63Xq96T2g2xQny2+U86vz8jLe8x/1kgternfr0l6bMh0rnT0oxBUnBNKeFAzr5eD+UqbMjeCyA5Pu+xHG8sFBZwTZu0/jMpLVm6+P6CyyYekcLqF1ymLKWclCa3kZpeaq1SkC1+u1S1bhFn5i/DGwEx3+c8zzjnOsiXxhKKh9ZJsRukbnd7142PwqQmSSujrR4z+S1TCQCAlyLIAyg2Xx9DkiE/Xx8FV/FTvfCgAkr3l81malTiOe09dkapGZk6npyq48lpSk1vqaVpmfr4UJyaHfxOK2zt1Cdwp260LdAhW22189mrQKUpRYFqaBwvq49ntX5n273YCrXZPu5b8HtXviNlpjpv+/JfUmh96aENkn/+vSEkWSsE1LzACvGSdTMh4YxzmYxcx3fsyh/g+uZLHvOfkRIO5t2emS79mDVXQJtrraEGJ/dYgSh32HuzrdWzoWUh30m25HgppHb+odFms1YsiGjvPCGfu2J+sL773b/lbIvfLr13seQfLD0bm/978yjDIQQ/O8xvkOffVlbvjM+GSFc9J3W/2/3jZqRZPSwa93R9XfzvSutnaH2pzTVFq3PyMesGRId/SUHVivbe0rbwOWntNGvyzPyWqUTJ2vqNFFJHanaZp2sClJ5lb1rz8Nw8XfLx9XRtUIkR5AGUGR8fQw2qBalBtfwCf6SkK+2vTNNUo7RMrdh1XIt3HFOLOlUVe/qs5q/7Rylnz6p3nRSdO5ui00Y1NU/drqaZ+/RJRj/FqYau9VmlW30Xq4PPXoUaZ8+/8ofXFq38by+43p50RHq5rnTh7VKDrlKN5lLzPnnLfXajNHZzwedwFcCzGbn+uDh9wAq4jjJSpVXRrt+f4jDnQXK89Osz1mz6/5phtXDmDuG/PV94kD+1T1o43gp+2cMTTh+0/iBqc13OMVe8bS1d2P4m6eZpBR/TXXuXWT+9ZZK+jFzX7F8f5gzbmPtI0YL8b89bqxK0GiDdNjv/csd3SCpikJ851LrBsHeptXRheXLAzZUfUDKO75S+zlrBwttvnPz9i/W7osPNnq4JyqPfnrd+/vNr0W9+AiWIIA+g3Mru3t+vXYT6tYuwb//PdZEuy59Ny9TA5FQdTTynrYc76FCV+7UjLUMnk1O170SK9h9P1IkUm9on/qFgnVM944QaGcf0g+0S7bVFaEaV19TW54DLY5e4DZ87d83PE7z3Sy/UKvgYO37Jf1/CIWucuH+gdGSj9NEVVnf5bCknpD9ez//9Gedyni951VreTrKGF7S7MW/5c2784f5OV8mWtQLCqvesIP/+JdZ7s9ev3/6zFeIlq3WvsCC/drr1ubqNsnoxnNjlupyvf+H1c8nhhoXNZk2AWFR7l0lrP7bmUQit6957crfIZ4f44lj9ofWzsIkk81uysSDZc1Zs/6nwsj/92/o3uuMHWrEqosTDnq5ByTiyQZo1zHrerLdUtY5n6yNZw5dO7LLqUxjTlDLTitebKbfi/s6rLLzlxjAqLII8gAojqIqvGtUIVqMawerWtKBx0FcqPdOmuIRzSs2wqUniOa0/cEobq87VVh9DB7et0MF/Nsqndis1yTyoU+dsuiX1a32deblizRp6r8rbJV9508XcAAWteV+Yg6ukTwZZx81vgsCCgryj7BAvWTPyz39G2vuHc5nT+6VFz0vbvpUiOkj/+jTnD8CV0VaLfm7Jx3JuAOxZYgX57FUI3HFit/TzWOt5WrLVlVqSOg7NKWPLtEKjb5WcbSkn8x8nb7NZwbdRd6vHhNO+DMmniuv35eY4lj17aUVbpjT0M/fen34evUiSjlqt69l/9DvewClIcYK8u0xTWjfDen5ordS4R+mdCzlSTkqH1kgXXGWttFFWSmMuh7JgmtKHfXJepyaVjyD/Zlvr5x0/uO7B5Wj27dbv7Ie3SSGF3AwuyMYvpV+ekIbNzH+oxK5F0vIp0vVvW73LAJQpgjyASsnf10eNaliTibWoU1W9Wjj8wdNtqKShud4xQc/aTKWkZ2rzsUdVNcBP+w8ekO3Ebh09naI9qdWUpEC1T/pTx06e1AW2fTpd40Ktt12gixLmq23aFq23tdQ9fvPK7DPmO5P/+dj9m/PYc0fL37R+ntpn3Txo1N167SrES9IbLXKe2zKscYeunD1lLZ3X5Q7nSfUcJ/Q7GpPz/MiGnOcZ56QqIc4twK81kwb911p+sHYbK8AahpSWYk2MmD1x4XVvOQ89sGVIyifI71kirXhHuvZNac9iawhB00udW2xO7Mq5sVAYxx4RRZFyUpqctdzibXOsyRVdBfmTe6T4v527hZ5vkN+/Qvr9Jema16W67ZzDnOONidK8YeCNzhy3rvFaLQsvW1QzrpXiY6R+L0m9Hiz54ztx7L2SWbY3DkpKZq6bp+XtZsTePwoP8tmTu275Wrp4dPHP9X3WJKezhktP59NT7fOsyWK/Gy3dvaD45/JW5e36QKXjhb9lAcAzfHysrv4dG1aTJDWvHSlrXL+jy51e3SlJulWpGZlqmpqpdfGJqhq3Wv8ERmrr1q3atf+gAuq2VNqZkxqdOkOnMvz16ZmeWmtrqVG+8/W4/1dFquO8zO4a6LummJ+wBH19l9T/Zat13h3ZrbW5/f6y9Mdr1vPlb0nPZE1Ut3SitHRSTjnHMf3H/8l5vu07az6C3MH4p3/nPG83xAo673R1HpvuOPmcJKUmWnMlNO6VN6R8eoP188cxOb0Vcq+WEB8jTWwidRkhtbxaanZ5/qHe3Rb5E7uld7pYzx/fk9NDQbK60ucX5N++0Pp5m8P1lV/AToqzhoF0ucNqnUxNkqYPtMbc57zZ2iZJX94qNegiHd0mjV5uDe9ITcopmv2ZM9OtR2VfZu/1C6yfY7dI1RqX7LHjs25wbfm6dIP86YPSNw7zNtjSvTPIZw/9sStnQc0swuSb7vbEKYw7X0FSXMmcC0CReOFvWQDwPgF+vgrw81WNZrWkZteotaRBFzbNVeoWSdLVkmw2U8eSr1Vylfe082iSTiSdVb3ANCUc3a/Zf6crM7CGBvssU7x/AzWMXaDeJ77SC+kjND2zvx4KXq6HU993OvLtaU/rPt+f1dt3S1l8XGsivq/uOP/jZId4yQrjb0VK5xKtWfwd7Vro+v0/RFmP3HMQONr2rVQ3Mu8Ec7l9c4+0b5k1a/xlj+Zstzn8wRy3teBjpCVZ8wOsek+6/h0rHEtSarJzOccbD2kuxmGe3CttmmXd0Mj21/vOQx7yhBIXHG82uPqO9q+UpmcF9l2/SaPmWaE+bov1sL/XyAkZCQdylkk8uMpqQUxNzCmbmbU047vdrLG/T+4nzEvS4fU5QT79rPW6UY+SCcRFmZMgNcnqXdJuiFSnjXvv+eYe6cyxnNeZaZJ/QauYlFO5hzOVVBguMR4I8u6oTC3TTsu40rsInkWQB4ByyMfHUN0wa4m6C5s4jOdu0Uy9Lsl+kT2G+1+SPtJzkqxR4oMkTVJCSrrCUvYp/li8JjfoqsSzUVp3+G/VXfqkDqVV1dwmTyjMN12Njy3WthOGXsicYj/NNrOZjthq6GrfvOPrU01/rbRFqo/vJklShukjP6OM/mhMKsrScQ5czUHg6PeXCj/GvqyZ7/98OyfIn9onTb00p8zZk+7XacscqfPt1lwG+5c77zu5J+f54pfzvvftznm35f6MW76Wrn7eRTmHP0TPOCzn6OqP8ekOre4HVlg/XXb7z+cPed8Aq7v/WodJCzPOWTc/Tu2zXh/dljMMIz+Z6dKmmda4/+pN8+4/tV8Kb5h/YD3wl3vj8tPOWDcXXHVzT022eni0vkYKqVn4sdzheBPIx+FPsu8fsG4w9X5CuvLZ8z9PUQLHgnHSuunS0tekCafde8+xv51fZ7pxE6msZWZYqzc06231GMmvjNPr85inpDQUpUW+LJfOzPbjg9Z/70M/r7jh3ub4e7aCfsb8pJ2xVnRoeXX5W260kiLIA0AFFR7sLwW3VN2sUFI3LFCq213q8rsaSrrYXvIK60fGM9Yfiv6Baicp0jS17e8YNU7erDSfQO1JNHTAt7EyfYOV6R+sJ3fu176tf+lIWCddFfSPupz4SV0CY7XDp4WuSl3kVJeotIfU3mev7vfLO7v5flsdNfGJz7O93Dp32gqGdSOl76OsVvbi2PuHNZ4/d4iXpAX/kXrcb7XGFrQ6gaPAcOfX6Wek2SPylnPstn/2VM7z7Jby4sjvRklmmjRzmNUyny0jzbn3gzt/8K/5nzT/KevGwLj4nEBjGNLfc6VZt1mrKfxrhuv3r3hbavxF4ef539VS/Dbp7kV5by7MfVTaPMsaWjGqhOa6cPweHG9CbPvW+rn8rcKDfPIxaeat1hCSbne5LlNYkE+Olw6ulloPlPb/mbXxPFp/z2eiTkdLX7duoA2fc36zsGekSus/zVkR46kD0jf3Sh1vcV5iLne93enVUqbKaYu8aVoBd/2n1uvjO6Xarcru/GXJk9fEinet37WX/LvwsqXh50es34HNekt3urFSCUodQR4AYMn1h7JhGGrXtp2kdpKkmpIco82wixpLyp7N+GpJUZKkhpLMs6dkpKUozT9UpzKqaJwpZZqm4s4cU+j2L3Vu5x/a59tEi9IilRbaRB0Slioms4H2pQTorfQXFaLzmLW9LEzrVzLH+d+V+e9LPGS1PrvbKmjLsG4yOMruReDIsQv0gZUOZf+0uuv3HCNVa+Q874CjHYUsY+fo77nOIV6yWuQdbybEbbZWCHAM9L+/bH2Wa7JWVtiz1PqZmWqFhk+vt4LDnT9LyyZb+7Z9lxPkT+23Ank207Rak9Z/Zk3wV62xFLvZCvj7V0h9n5c6/ivnPdu+yxvkN8+yfh5YIf0wRoroKPW4z/3vwhXHYROuwrYt3Vo+MjleWv+JNZdDjWbOZRa/bM3dcHit9Vn6vSiFRjiXKSzIf9jHWj7umjfyLn2YW0aa1XvAcVmy3KGxsGs2MyNnMs4G3SS/fCaRXJzVU2bL19KFRVjRwtGp/VlLXzrU6Y83pJ2/Wg/HIJ+73uUtyBdpjHwZt8g73ggs6k2EXb9ZPW6ueV0Kql6y9SpIarI1l0jLflJgmHvvcbwmyrLXQWqStCDrpl6XO8r2e8qW/Tsw96o18BiCPACgxBlB1aWg6qoiyWnl9GqNpQZPKaTvU6qpnMEB0vUabH8eJZmmMk1JR7fK9+hWmXXaKv6f1bIF1dQ/Vbuq+q7vVe/4CvnaMjQz8F+ql7xNETqhzsd+ULAtZ/z8nIAhOhAcqUdPWYHgifR79Zr/R5KkTNPQ6xlD9ZT/rFL7Hs6H7eMBSrnqFYWknHSvA+fyKYWXObJB+jL3igxZdsy1fn5XSDjNHcwLsvqDvNsyUq1Qne3nh61lCOu0ky640goE2XMj9IyybmY4jhNPOZnzh+SZeGu+gNwcJ16TrKEKk9tYY/X//K80cJL0lUNvhW/vsYJ8Nv/Agj/XhqxlBC+61xoacHSbFHm9tHux1Pxya6UEyQpTm2ZZY83rX5jz/pgfrRUTfP1ztmWmZ/VWyDV04UOHCTTPHMs7O7jjjZktX1krJdz6hTUEJJvha31vps31kmTZa8Bv/7HgnhkZadbcBiG1pHt+ywkyuUNjYb07Fr9k9TaQpK4jrVUkCrLxi+IH+RXv5G1pd/zOHFWkFvmy7lrv+G9e1ID7+RDrZ5Wq0qApzvs2fmltj7z+vKrn0s9jrSFOrQZIt8127z3FuSb+WWDdcBs81erJVRyON5kyzqP3FCoUgjwAoPwxDPkakup1kOp1kCGpbv3OkqR6ktRurKSxkrL7AWQx37NCRWaq5B8kezRLvV9mynE97FNXJ07fpXO7/9TBiL4aFBKitSsaKiikqkJjV6px7K+SpF1BHbTLv41and2okPQTqitr7PvXmb3VtcoBNcvcV6ofX5J8kmNV9Yd8ukq74jihXH4c18gujp9KoEtnZmreWfkXTbB+NrlEuuCKnO1nTkj//Gq1qmZz/Jyxm5znJfjtRWnZG3nPeWx7zvOkI84hPptjS3R2MF0y0Vr60LHV1lHKCen9rEkrfnvB6kXRsr908zQpoKrVXf37rCXAnjtltWLvXpxz/vtX5Bzr7ClrvfCU48pX9k2LmB+lxa9IN/0vbwt6dpmF4xw+j81adlGS/u8PqzdB9rAEx/kLbLaClz5MPCSd3m89Tu2zegeYZt6hFe90kW6eLrUf4vo42SFeslasuPQRqXqT/M9r7+5fynKPkS8PQT7paM7zwlrZHfeX9WR3JTGfQMLBXK8P5SyDN/50ybeAb5lj/fynCL2MChojn30jzj/YurGRPYnnl1n/J5pzpzSmmKvKOJ633E3CCE8hyAMAKg7DsB4+uWbMDqgqI6CqdRMgvJ3UpJ0aZO9rkh14cpaba5H1sEtNkpLjdXPNC6w/lnf9pozTB5TS9hads/kpLSNTfqd2KvnUMcWv/V4tTyxW7XSrlTMlsK6Czx3Vz4GD9GjycPWPOKMxyVPU6twWHTZr67OMq8ptr4A88lsmsCjSz+b8AZ3b/j+dQ5uroQeOE/R9eYvzPlch3l3rPsl5vvxNq/vqklet16s/dP2e7KX/JCvkSlZ37Wn9rRZrxyEYr9STHvtH+mxwzjbHrvU/jim8jj5+Vu+C7BsBX9+Vtxu9r5/zigKS8zwOH/S2hiC0u9GaW8DR/uWSf0j+51/qsIrE4XVWS/mKd12H/6/vsoYo3Ph+Tg8FSTq+K2/ZmbdKD6zMu71EuAi/uSd8zO6lkLtFvijhNCMtZ4hAyklrOENRJwRLPmbN9h9QNWfblPY5zwsK8mdO5PQUkc4v7LlaKaMwjjeUSuoGSLLDTYyM1MJ7ypQFp8+W698j+iLp1F4pvJG1JN+Te6WA0Jz9yQ5zwaSflRaOl9peZ405L4xjj4eSmoPC0aG10ld3Sv1fsn43FNfZ056ZCC8xVvriX1L3UVK3UWV/fg8hyAMAUJiA0Jw/yAxDatlXfpLCsh6SpBqdJUktul3t9NbshdWuy3pYrBbeBpKekqTkl2RLPqazwREKqlJFh+e+KtMvSEaj7jqwY4N8ardSh+2TdaZaGx1pebvSk4/rl702XZ22SJcc+0rH/CJUO8NL1nL+5bHze//HfUumHrnNe9z59ar3Cn/PuQTX249ulWbmGsKQcU6K+cF52xc3uV8/yepN8LZDF/3TB/KOlfXxl1ZGO2/LHcyWvZn/H+uOSzt+dafV7T2omnRitzWOOVvcFunPKQXXd/uPUq1W0lUOvQNiN+Ytl73evSNbPhMouuPMCavOPr6uw+9mhxtn399vTaYnuTdGftmbVkju7XAdL37VGrJx729S9WY5vR+ye2G44+xp6Y0WUpVQ6ZlDOdudhilkzfWw/SdryEbzPtKlY61dX98l7V3qUPQ8utYvecXhhRut4KZp3dTJdni9VLdd8c+fzak7+dnSDfLuBlDHayL39XEqqzdMds+Cg39JLRx+X5k2q8dMWH3reln9gfWYkM/vEafzOnwX2d/Lyb3WMq+9HnIeGlQcs2+3VoWZM9K6uVWtsdSqf9GOseZ/1qSgA18///lD3LHtO+u/vZs+kla9Lx3dYg3VIsgDAIAyU7W2fKrWVna7ZaObcpaca9Ql64+pvkNUVTlzDlwkSeYtkj5UbcOwWgGPbrVmdq/f2RoHvOh5ZfhXlWo0k29qgtS4p/b9vV4HfeorIu2QQo78qQbH/lBqUF3FhPVWu+O/6Fjtnvq22Xid3LFCNxz/UF9kXqWfMnvqcp/N+qCK1SV6nxmhpoaX3Dgorvxa4d21Z0nebbnnMcjvRoC7ck8cKEm+VfKG+9yBI+Oc9NPYwo8f870UUlu69g0r6Dg6utW9Oh7Ou4SlS4fWSQ1zZs3IM2TANAvuWn3mhNWDo2576e+frfkWRnxX+Hl3LrBuWHS4WQrONX/AqX1WQOh2lzUZ6LlE6besJR27jZKCa1j1WjrR2jbvSWmgQ6+FjHM53asd7Vkq7Zgn9Z2QE07js4Z/pCU5t+7n/g7mPiZt+jLrOItzgrxjiM8um233YmuuCf8gadciKfIG55bi3HYvyX+fK6bpvEzmj2OkjkOtmwtnjkuXP2HdxIq83prYrs9TeVfZcMVxLo30c1JQ/kXP26Qm0tit1kSfBXEM1LZCej3knmQyNdFaOrRBV9fLaBYk00WQn/uoNVlo9hwfcx+zrrerXyjasSVrbo1s2Tdbs28wbPna+ncrzNysZVnnPV4yQX7/Sqt31MBJUo3meff//LA1JOmD3lLbUphDwQsQ5AEA8FaOwSa4hnMXzfCG0k0f5fkffbOWfZVr3nMFSMpu520g6UFJ6t9R0mh1lvS6JJk3SqvqSEe3qmmfp60/eG2ZSk+Kl7nyPZntblLs4QMKOXtQ55oPVKPp1hE/7/KVuh2cpojEzUpSVQWeO6raxnkGWG91cnfJH/P4P86v/apYY3Qd5Z7c7cTuvO/Lz5qPrDHxnXN1w49zM8g7zmFQkG/vtQJOcE2rtT93kD28TmrYzfV7130i/fSQ9fzUPuvn7t/dO69k3bCI+T7v9p/HWj8Dw6zP73jTJPv51m8ctqXk1EPKP8h/mhU6QmpKvbN6gjh+3pTjVqttHmZOiC/MklekXg9aN1yyh3MEVrNWg0iOly57JP/3piU7n7Mwjr04si2dZN1QkXIms1ufNXwlI1W67k3p9EHr3z0/jvNhZJzNv5xkTWq4ebZ0x4/W78L8JB21btJ1vTPvvpgfpF4OQ1wO/GXNNTHgVevalJx7ijjeIMs9v4KU/2oRh9dZ3e+LwinIZ/XScFyl5NR+679VSbriP/mvBOFKYb03ck8e6q7UJGnaQGvd+b7ji/7+6QOsn0lx0mgXK7A4Lp9aSRHkAQBA4QxD6vmA8zYfX/mH15MGvChJatqoc86+rNac2yVJVq+Cao7vTTpq/eFc/0IraPr4Wi3JO36RMtJk6zFaScumKnz9e8r0C9KZm75UaPw6GdlLkmVZX32A2qasU1BqPjORZ9lr1lMzI7aon7r8yzU+fW+SoYjkUwqSZIbUkXEm3rm1Tco7OV1hdi6wHo6S3eyRYdqktdOtrrotrsq/3MndBd/o+N9V0sPbrBtUjtLPOYfnvBVwr54F+eMNK8g7TkSYdsYKQCveydl27B/n+Qj2LrVaEut1cn3cY/9YEw7+Pdd5YsADq6Sml0pV6+T6KPmM98+vp8L2n5xv4mQHvwMFrDyRkZbTRdxdaS6C/KYC5v3InsNh3hPOS2DmluRwjaUXMAmjJC34j/XzzykFt0j//LC1Qoc782lkz3Ex63bp0aweE47hPfu/I9N0fWNs6WvShs9dH7uocxg4DrFwNXeD482XzFT3g/xvL1h1dNU7yJZp/V4urg2fW93dj24pXpDPdnxn4WXKcinAcoQgDwAAyl5oXdfdLxtdJEnykRR+/avS9a/K1zQVZhhS2yutrsRnjtlbLLtIOd2w085YrWA75lnLPGUHKFummhk+0pH1Sl34kqrUaSEjO3y0GyLt/k1nqzZS5oaZqnpisyTJlKFjVRqqTlqumbSL6OG0+3WX33x19NmrPbYINfcp3SEJzY4tlo4tliS9nXCJ/u3nRvfy0hS3Jadlu05kwWG+MP/tLD0bZwXSkFrWxHkFhf+ko66XJyyqk7utXgxTe+VsS02SZlzrPObfMcRL1nhjSXrqoOt1ym0ZORMOOk7i+PVd1nU8PleLo6teA2lnnCfHc2QYrluFz56SNnxhdXV37GK/+iMXc1i4CEi2TOfjuprsMDM177Zsh1Zb/83m7imya5G0+Sup4y3W8IP5TxV8Dkn6+xfnoSSpSa7LSdY5s5fZLIqkI1YPjwuudD1GfuE45xs62QpabaHIQd6xRT5V+nqU87AVx+8nI83qZuWOZZPz35dxznmiyqLKPeynuByvpcKG2FQyBHkAAFC+Of7h5uuft9uxX4Dzz87DnPdntyo16KqAkS6Cbcu+1vDbS7N6HGSmy/DxUx3DyFpezZRk2lcvUHCNrMBgWkupndxrtTi3vsZaki7yeqUe3CSlntarra/T6bMTlBHsqxrppg6lpKjetG7yTckJMWuqX6vup/IPGMuqDtBlyUVYIiv7uLY2RX5PqYqPcT2xnbts6Ur94EoFxG/Suc6jFLhxWsHl32xb9N4H+Xm3u/PrHb+4vyze2mnWDSjTdG6Bduw6nruHg2nL20rqOIt7ttcvyH+W+KNbrUnVcju02nr88rh1E+Cm/0lNLyt8Isr9K6wJ3P6YLAVXL7hsYWudT2wiRbTPu/3nh60x/J/mGvOccc66meIXKIVnrTmSHC/NyvXfekGTJBa2zNy66VbPi+Aa1izujj67URo2y/l3jy3TqoOrEF+YogZ5x7H5J3Y7D+mQnHtFFHQTpSje6yk1v7z478/9396xf6TEw9YSo7sWWb83LypgaIX9OFnf1ewR1jCfe36z/j/gpHKGe4I8AACAI8c/ErOXNJSsWa2zZ7bOXrLsetd/xAe06mN/HlHF+nOrmr9ULbiKtQxdWrLV2nVyr7rXvECK3WTdDEhPscYy71tutR5ecJUua3u9Nd54zUfS3j+kPk9LNVtIPz8idR4mW7M+Sjy0XdWWP28/5+nQlrpv0N3auf60Wvz9gXY1uEFhqXGqe9wKkvEBTVQndX+xvp5ttiZqYBxXNcNFl+pSFhC/SZIKD/FSyYV4V8cqylKHi8ZL9TpaYdDRQYc1xbMnu3O04t3Cj51fS7XkOsQ7Sj9jPWbfLnW/x3WZc6et9dwTj0jTB+Zsz937oCj1kqwx7666c6clWzfHcnM8d9/nrcny6l+Yt9zpAq5pV0MAHJ3YZY3Zv/0bayhHbjNvle51mHshdqM0t4C5Bgri2HNgxTvWZKUFdT937Fqf4KKXkGP389wTRTrau8z6vdOgS/5lsp3eL63/tPBy+ck9GWB01s2wYbOs71KSqjWRWvVzLrftO2v+BEemaa2CIUlHNth7btm5O+dHBUOQBwAAKEs+PjldrWu1sH7W75y1M2uirtYDrEe2yOuth6MO1jKGPpKqtRkoXXy7FQgz01WtWW/19qsitXhFSntaLbNnCT/2j3Rgpep0Gma1Aps2Kzh1uMWaaX/HPKv1uHpTqUpVafZwmelndaTrE6rTc5hs275TYPWeCmnUSH8u+1WXLLH+IE9p1l9GwkEFnSx+i3vv1Lc0NfhDtct0EWq9Xe4QL0mpDkE200UL9h+v5d1WGs4lFNzF+q1iLCXnTqtwSj4TIcb8WPD7FhUQePcskT4ZZE0i2PQyadEEqVZL6cLb3Vvffteigid/c2zxP5+Qu89h8rbs8f0X3evc4n/2tDRruNVDwHGySVfDRRxvKDheSxlpVo8kH1+rJf+TrEVQJyScX9f376OsyRRPH5AOrbFuLubmePPL8TvNDvGS9OW/nJffM82cISmOHD+Tq94M59PTx4sR5AEAACqCqnXyTpDm4+O81FftVtZDsnoASNZa8ZLU9BLpymed3//gOhmyVjOQJF14qy7IenpJn4FS71OSYSg4u9dCZrrVoyEj1er+m3JCatLL+oM/rKG1bnbL/tbs8nXaSDvmS5tnydbvFf1x8ShpV2Pp85skWfMUZNw0TektBsrvk4GqErfhvL+iRDNYYUZK4QVR+hwnD3Tkk89s7+7a+4f16P24NfmdJHW6rfAW+WwH/8p/n6sbLiXl7GnnIP9DlLR/ufV83/Kc7YlHCj5Odov8uURpYiOpfhfpvsXOKzmsjJaWvVn8um783Hpki+iQt4zjTY/cQwEcLXtT6jpS+ufX/L9fxx4ehc2yX4kQ5AEAAFA8uUNX9rAEvwDnVsTs5bsa5Rpr3naQNDha9qO06GtvoTMk+Wc9dN9v0tFt1jrxZqY1FCH9rBUgDMOaVK9xL+v5P79KSbHWWuZVgqVfn7Vmzh81X2E1L5B8q1hdtQ/ldG03qzWR4aJbdvrVr0hbv5F/bM7EYntCOqv5mY1F/KLgtuxZ7c/XH6/nPJ9zh7TnD/feN61//vtyd/kuSY7Lye1bnrN8n+S8jGNhrc/ZYfitrDkIjqy3Zv1POJRT5tdnzquqeWxzMfeIY8t5QUvY/fa8NXt+QStMOK5aUFjPis9utCbWlKQO/7LmMQipZc2/cPH9UqdbC36/FzFMk9sauSUmJio8PFwJCQkKC3MxyygAAAC8l2laQSPhoFS1ruQf5Lwv98zY2dvSz1plD62VNs20VkZo2V8KqS2t/dgKYxdcaa1tH9FBan+T9PtLVji9+gWpUQ9rVvoDq6yl9Bp2l1klREbWmPuMkAgZPn7yTTrkdPqMVtfK58ha+WRNeHcoqI0anv27NL8ht3yScbWaGPFaZOuil/yne7o63m3YLKn1QOsaezmi+Mfp97I158HLdXO2tegrndxjPcrCuBPS/CelNf8rmeP9e5P036xVSIbNsoYQ/fZ8we9xpXoz6d8bS6ZOpaQoOZQg7wJBHgAAAGUmI9XqWh1a17ppkBxvTXxYpaqUcECqlzWxmy3D6vVgGNb459hN1qRvpk2aOdTqPn3jh9YwiXlPWq26XUdKYQ2s7snL38pqKTWkuu1lhkbI2LVQtqCaSr9miv6uepGO//OXem98RP5nc7q+b+s3U20X3Skfm3PX58fT79OGmtdpV3yyllYZqyY+8QV+zDTTV1WMEpyE8DzsstXXXFuPfJdofDF9uMb5f1Fm9fmtzQuKb36jmp5epZ4r3JjNvTxrNaDwVQKKwrdKTk+Dht2detMUmeOY/HKIIH+eCPIAAACokNLPSj7+kq+fddMgNdF5HoVsuxZJAWFWcMruoZBy0roRYMuQdi60JlzM7s2QdFTau9RaPi4jVVrwrFS7rZRwSOkdh8m/dgvJx0+afo10aI3MjrfI2Dxbpo+f1Ky3FLtZRq5x80cj+mh903s1cNXwEv8a5vn20RNnbte6gP9zeXPh/rR/a2qVQmb+L0ELM7voy8yrNL3K64UXRvER5Cs2gjwAAABQhmyZ1g2AzFTpyEapeZ+cGwj7lkv+wdayaSknraX1/pwi9Rgt9XpImveE1Z38wKqcteBTk6x154OqWzcvvrpDqtvOai3e8Ll0yUNS7daSpJS0DJ2a+4IabLKCe3KTq5UyZIYS5zyoszXaqNFlI+QzY6DCzuzLU+244NaKSNlR6Me7JfRzfZV0e8l8Vyg+gnzFRpAHAAAAKqG0M5JfUP6z59ts0rZvpf1/Sp1vlxp2lVKTpZ0LrPkWtn5j3YSwpUvhjaz5FOp1soY7nDkurYyWecEVSghuqmq1G1o9ImZcKx3dmrcqEV2UnpKgkMTdOl27mzKCaqnWgYK7rG9s9ZA6//O2+x9XfqqiDA1NHaeLfLbrUf+v3X7v+UgwgxVeCitIrLW1Ujcfa1359bYWClaq2vjkTFKY/p8T8vcrv/O9E+TPE0EeAAAAQJmwZQ1XOHvKGubg4yed2ivVbGHtP/a3VKO5tRpE3Farp8KRjdb68K0HWnMpnNpnLffY6CJrvoUTu6yfZ45Jba6xjpkUJy2dZN2sOJcg1Wop9XtJqhKSU5Vju+Qz71EprIHMJr1k/DBGZ5r2Vci+hZKk1D7P6Z+Q7mp4+BdV2zpdh9vfr4Yb38rzkbZfOE7rMi/Q7ZtHOm1PUxU9W+9DnfCP0L/Sf9Cik3VUN3m7hvkuVqKC1c4n7+oRBTlpVlUNI1mS9Gr6MH2QOUiS1M34WzvNhrrYZ7s+qJJTv20jNqvdBU2KdI6yRJA/TwR5AAAAAJVeRqp1AyEzw+o9EFwjb5n1n1o3HepfKJ3cK9VpmzMsYtcia+nIix+wbibkXhEit5N7rEkZDR+pQVeZB1fL8AuUjm5V8qVP62hmuPxP/K2Gv4/Rr20nKqLHTYrdtkyXJP6ihF7Pak9KgJrUDFHSuXSt2XdKbeuFqt7JNcrc9qPCOg1SnQ5XWZ+nnCLInyeCPAAAAACgLBUlh+Yz+AMAAAAAAJRHBHkAAAAAALwIQR4AAAAAAC9CkAcAAAAAwIsQ5AEAAAAA8CIEeQAAAAAAvIjHg/x7772nZs2aKTAwUF27dtWyZcsKLL906VJ17dpVgYGBat68ud5//32n/du2bdNNN92kpk2byjAMTZkypRRrDwAAAABA2fJokJ89e7bGjh2rZ599Vhs2bNBll12mgQMH6sCBAy7L7927V9dcc40uu+wybdiwQc8884weeughffPNN/YyKSkpat68uSZOnKiIiIiy+igAAAAAAJQJwzRN01Mn79Gjh7p06aKpU6fat7Vt21aDBw/Wq6++mqf8k08+qR9//FHbt2+3bxs9erQ2bdqklStX5inftGlTjR07VmPHji1SvRITExUeHq6EhASFhYUV6b0AAAAAABRVUXKox1rk09LStG7dOvXr189pe79+/bRixQqX71m5cmWe8v3799fatWuVnp5e7LqkpqYqMTHR6QEAAAAAQHnksSB//PhxZWZmqm7duk7b69atq7i4OJfviYuLc1k+IyNDx48fL3ZdXn31VYWHh9sfjRo1KvaxAAAAAAAoTR6f7M4wDKfXpmnm2VZYeVfbi+Lpp59WQkKC/XHw4MFiHwsAAAAAgNLk56kT16pVS76+vnla3+Pj4/O0umeLiIhwWd7Pz081a9Ysdl0CAgIUEBBQ7PcDAAAAAFBWPBbkq1Spoq5du2rhwoW68cYb7dsXLlyoG264weV7evbsqZ9++slp24IFC9StWzf5+/uXWN2yW/kZKw8AAAAAKAvZ+dOd+eg9FuQl6ZFHHtGIESPUrVs39ezZUx9++KEOHDig0aNHS7K6vB8+fFiffvqpJGuG+nfffVePPPKI7r33Xq1cuVIff/yxZs6caT9mWlqaYmJi7M8PHz6sjRs3qmrVqmrRooVb9UpKSpIkxsoDAAAAAMpUUlKSwsPDCyzj0eXnJOm9997Ta6+9ptjYWLVv315vvfWWevfuLUkaOXKk9u3bpyVLltjLL126VA8//LC2bdum+vXr68knn7QHf0nat2+fmjVrluc8l19+udNxCmKz2XTkyBGFhoYWOPa+e/fuWrNmjXsftJQkJiaqUaNGOnjwoEeXyisP3wV1KD914LqkDuWtDuXlmpQ8/11Qh/JTB65L6lAe68B1SR3KYx0qy3VpmqaSkpJUv359+fgUPJ2dR1vkJemBBx7QAw884HLfjBkz8my7/PLLtX79+nyP17RpU7e6IhTEx8dHDRs2LLScr6+vxy+kbGFhYR6tS3n4LqhD+alDNq5L6lCe6iB5/pqUysd3QR3KTx0krkvqUP7qIHFdUofyVwepclyXhbXEZ/P4rPXeLCoqytNVKDfKw3dBHcpPHcqL8vBdUIfyU4fyojx8F9Sh/NShvCgP3wV1KD91KC/Kw3dBHcpPHcqL8vJdeLxrPc5PYmKiwsPDlZCQ4PG7U0A2rkuUN1yTKI+4LlEecV2iPOK6zIsWeS8XEBCg8ePHs3weyhWuS5Q3XJMoj7guUR5xXaI84rrMixZ5AAAAAAC8CC3yAAAAAAB4EYI8AAAAAABehCAPAAAAAIAXIcgDAAAAAOBFCPJe7L333lOzZs0UGBiorl27atmyZZ6uEiqoCRMmyDAMp0dERIR9v2mamjBhgurXr6+goCD16dNH27ZtczpGamqqHnzwQdWqVUshISG6/vrrdejQobL+KPBif/zxhwYNGqT69evLMAx9//33TvtL6jo8deqURowYofDwcIWHh2vEiBE6ffp0KX86eKvCrsuRI0fm+f158cUXO5XhukRJe/XVV9W9e3eFhoaqTp06Gjx4sHbs2OFUht+ZKGvuXJf8znQfQd5LzZ49W2PHjtWzzz6rDRs26LLLLtPAgQN14MABT1cNFVS7du0UGxtrf2zZssW+77XXXtObb76pd999V2vWrFFERISuvvpqJSUl2cuMHTtW3333nWbNmqXly5crOTlZ1113nTIzMz3xceCFzpw5o06dOundd991ub+krsPbbrtNGzdu1Pz58zV//nxt3LhRI0aMKPXPB+9U2HUpSQMGDHD6/fnLL7847ee6RElbunSpoqKitGrVKi1cuFAZGRnq16+fzpw5Yy/D70yUNXeuS4nfmW4z4ZUuuugic/To0U7b2rRpYz711FMeqhEqsvHjx5udOnVyuc9ms5kRERHmxIkT7dvOnTtnhoeHm++//75pmqZ5+vRp09/f35w1a5a9zOHDh00fHx9z/vz5pVp3VEySzO+++87+uqSuw5iYGFOSuWrVKnuZlStXmpLMv//+u5Q/Fbxd7uvSNE3zzjvvNG+44YZ838N1ibIQHx9vSjKXLl1qmia/M1E+5L4uTZPfmUVBi7wXSktL07p169SvXz+n7f369dOKFSs8VCtUdDt37lT9+vXVrFkz3XrrrdqzZ48kae/evYqLi3O6HgMCAnT55Zfbr8d169YpPT3dqUz9+vXVvn17rlmUiJK6DleuXKnw8HD16NHDXubiiy9WeHg41yqKbcmSJapTp45atWqle++9V/Hx8fZ9XJcoCwkJCZKkGjVqSOJ3JsqH3NdlNn5nuocg74WOHz+uzMxM1a1b12l73bp1FRcX56FaoSLr0aOHPv30U/3666/66KOPFBcXp169eunEiRP2a66g6zEuLk5VqlRR9erV8y0DnI+Sug7j4uJUp06dPMevU6cO1yqKZeDAgfriiy/0+++/a/LkyVqzZo2uvPJKpaamSuK6ROkzTVOPPPKILr30UrVv314SvzPhea6uS4nfmUXh5+kKoPgMw3B6bZpmnm1ASRg4cKD9eYcOHdSzZ09dcMEF+uSTT+wTkBTneuSaRUkrievQVXmuVRTX0KFD7c/bt2+vbt26qUmTJpo7d66GDBmS7/u4LlFSxowZo82bN2v58uV59vE7E56S33XJ70z30SLvhWrVqiVfX988d5Ti4+Pz3FkFSkNISIg6dOignTt32mevL+h6jIiIUFpamk6dOpVvGeB8lNR1GBERoaNHj+Y5/rFjx7hWUSLq1aunJk2aaOfOnZK4LlG6HnzwQf34449avHixGjZsaN/O70x4Un7XpSv8zswfQd4LValSRV27dtXChQudti9cuFC9evXyUK1QmaSmpmr79u2qV6+emjVrpoiICKfrMS0tTUuXLrVfj127dpW/v79TmdjYWG3dupVrFiWipK7Dnj17KiEhQatXr7aX+euvv5SQkMC1ihJx4sQJHTx4UPXq1ZPEdYnSYZqmxowZo2+//Va///67mjVr5rSf35nwhMKuS1f4nVmAMp9eDyVi1qxZpr+/v/nxxx+bMTEx5tixY82QkBBz3759nq4aKqBHH33UXLJkiblnzx5z1apV5nXXXWeGhobar7eJEyea4eHh5rfffmtu2bLFHDZsmFmvXj0zMTHRfozRo0ebDRs2NBctWmSuX7/evPLKK81OnTqZGRkZnvpY8DJJSUnmhg0bzA0bNpiSzDfffNPcsGGDuX//ftM0S+46HDBggNmxY0dz5cqV5sqVK80OHTqY1113XZl/XniHgq7LpKQk89FHHzVXrFhh7t2711y8eLHZs2dPs0GDBlyXKFX333+/GR4ebi5ZssSMjY21P1JSUuxl+J2JslbYdcnvzKIhyHux6Ohos0mTJmaVKlXMLl26OC3dAJSkoUOHmvXq1TP9/f3N+vXrm0OGDDG3bdtm32+z2czx48ebERERZkBAgNm7d29zy5YtTsc4e/asOWbMGLNGjRpmUFCQed1115kHDhwo648CL7Z48WJTUp7HnXfeaZpmyV2HJ06cMIcPH26GhoaaoaGh5vDhw81Tp06V0aeEtynoukxJSTH79etn1q5d2/T39zcbN25s3nnnnXmuOa5LlDRX16Qkc/r06fYy/M5EWSvsuuR3ZtEYpmmaZdf+DwAAAAAAzgdj5AEAAAAA8CIEeQAAAAAAvAhBHgAAAAAAL0KQBwAAAADAixDkAQAAAADwIgR5AAAAAAC8CEEeAAAAAAAvQpAHAKAC2rdvnwzD0MaNGz1dFbu///5bF198sQIDA9W5c2dPV6dAhmHo+++/93Q1AABwiSAPAEApGDlypAzD0MSJE522f//99zIMw0O18qzx48crJCREO3bs0G+//eayTPb3lvsxYMCAMq4tAADlF0EeAIBSEhgYqEmTJunUqVOerkqJSUtLK/Z7d+/erUsvvVRNmjRRzZo18y03YMAAxcbGOj1mzpxZ7PMCAFDREOQBACglffv2VUREhF599dV8y0yYMCFPN/MpU6aoadOm9tcjR47U4MGD9corr6hu3bqqVq2ann/+eWVkZOjxxx9XjRo11LBhQ02bNi3P8f/++2/16tVLgYGBateunZYsWeK0PyYmRtdcc42qVq2qunXrasSIETp+/Lh9f58+fTRmzBg98sgjqlWrlq6++mqXn8Nms+mFF15Qw4YNFRAQoM6dO2v+/Pn2/YZhaN26dXrhhRdkGIYmTJiQ73cSEBCgiIgIp0f16tWdjjV16lQNHDhQQUFBatasmebMmeN0jC1btujKK69UUFCQatasqfvuu0/JyclOZaZNm6Z27dopICBA9erV05gxY5z2Hz9+XDfeeKOCg4PVsmVL/fjjj/Z9p06d0vDhw1W7dm0FBQWpZcuWmj59er6fCQCAkkSQBwCglPj6+uqVV17RO++8o0OHDp3XsX7//XcdOXJEf/zxh958801NmDBB1113napXr66//vpLo0eP1ujRo3Xw4EGn9z3++ON69NFHtWHDBvXq1UvXX3+9Tpw4IUmKjY3V5Zdfrs6dO2vt2rWaP3++jh49qltuucXpGJ988on8/Pz0559/6oMPPnBZv//+97+aPHmy3njjDW3evFn9+/fX9ddfr507d9rP1a5dOz366KOKjY3VY489dl7fx7hx43TTTTdp06ZNuv322zVs2DBt375dkpSSkqIBAwaoevXqWrNmjebMmaNFixY5BfWpU6cqKipK9913n7Zs2aIff/xRLVq0cDrH888/r1tuuUWbN2/WNddco+HDh+vkyZP288fExGjevHnavn27pk6dqlq1ap3XZwIAwG0mAAAocXfeead5ww03mKZpmhdffLE5atQo0zRN87vvvjMd//c7fvx4s1OnTk7vfeutt8wmTZo4HatJkyZmZmamfVvr1q3Nyy67zP46IyPDDAkJMWfOnGmapmnu3bvXlGROnDjRXiY9Pd1s2LChOWnSJNM0TXPcuHFmv379nM598OBBU5K5Y8cO0zRN8/LLLzc7d+5c6OetX7+++fLLLztt6969u/nAAw/YX3fq1MkcP358gce58847TV9fXzMkJMTp8cILL9jLSDJHjx7t9L4ePXqY999/v2mapvnhhx+a1atXN5OTk+37586da/r4+JhxcXH2+j777LP51kOS+Z///Mf+Ojk52TQMw5w3b55pmqY5aNAg86677irwswAAUFr8PHoXAQCASmDSpEm68sor9eijjxb7GO3atZOPT05Hurp166p9+/b2176+vqpZs6bi4+Od3tezZ0/7cz8/P3Xr1s3ecr1u3TotXrxYVatWzXO+3bt3q1WrVpKkbt26FVi3xMREHTlyRJdcconT9ksuuUSbNm1y8xPmuOKKKzR16lSnbTVq1HB67fi5sl9nz9C/fft2derUSSEhIU51sdls2rFjhwzD0JEjR3TVVVcVWI+OHTvan4eEhCg0NNT+/d5///266aabtH79evXr10+DBw9Wr169ivxZAQAoDoI8AAClrHfv3urfv7+eeeYZjRw50mmfj4+PTNN02paenp7nGP7+/k6vDcNwuc1msxVan+xZ8202mwYNGqRJkyblKVOvXj37c8dA7M5xs5mmWawZ+kNCQvJ0cy/K+Qs6r2EYCgoKcut4BX2/AwcO1P79+zV37lwtWrRIV111laKiovTGG28Uud4AABQVY+QBACgDEydO1E8//aQVK1Y4ba9du7bi4uKcwnxJrv2+atUq+/OMjAytW7dObdq0kSR16dJF27ZtU9OmTdWiRQunh7vhXZLCwsJUv359LV++3Gn7ihUr1LZt25L5ILk4fq7s19mfKzIyUhs3btSZM2fs+//880/5+PioVatWCg0NVdOmTfNdAs9dtWvX1siRI/X5559rypQp+vDDD8/reAAAuIsgDwBAGejQoYOGDx+ud955x2l7nz59dOzYMb322mvavXu3oqOjNW/evBI7b3R0tL777jv9/fffioqK0qlTpzRq1ChJUlRUlE6ePKlhw4Zp9erV2rNnjxYsWKBRo0YpMzOzSOd5/PHHNWnSJM2ePVs7duzQU089pY0bN+rf//53keucmpqquLg4p4fjTPqSNGfOHE2bNk3//POPxo8fr9WrV9snsxs+fLgCAwN15513auvWrVq8eLEefPBBjRgxQnXr1pVkrRYwefJkvf3229q5c6fWr1+f59+mIM8995x++OEH7dq1S9u2bdPPP/9cajctAADIjSAPAEAZefHFF/N0o2/btq3ee+89RUdHq1OnTlq9evV5z+juaOLEiZo0aZI6deqkZcuW6YcffrDPrl6/fn39+eefyszMVP/+/dW+fXv9+9//Vnh4uNN4fHc89NBDevTRR/Xoo4+qQ4cOmj9/vn788Ue1bNmyyHWeP3++6tWr5/S49NJLnco8//zzmjVrljp27KhPPvlEX3zxhSIjIyVJwcHB+vXXX3Xy5El1795dN998s6666iq9++679vffeeedmjJlit577z21a9dO1113nX2GfXdUqVJFTz/9tDp27KjevXvL19dXs2bNKvJnBQCgOAwz918UAAAA5ZhhGPruu+80ePBgT1cFAACPoEUeAAAAAAAvQpAHAAAAAMCLsPwcAADwKowKBABUdrTIAwAAAADgRQjyAAAAAAB4EYI8AAAAAABehCAPAAAAAIAXIcgDAAAAAOBFCPIAAAAAAHgRgjwAAAAAAF6EIA8AAAAAgBchyAMAAAAA4EUI8gAAAAAAeBGCPAAAAAAAXoQgDwAAAACAFyHIAwAAAADgRQjyAAAAAAB4EYI8AAAAAABehCAPAAAAAIAXIcgDAAAAAOBFCPIAAAAAAHgRgjwAAAAAAF6EIA8AAAAAgBchyAMAAAAA4EUI8gAAAAAAeBGCPAAAAAAAXoQgDwAAAACAFyHIAwAAAADgRfw8XYHyyGaz6ciRIwoNDZVhGJ6uDgAAAACggjNNU0lJSapfv758fApucyfIu3DkyBE1atTI09UAAAAAAFQyBw8eVMOGDQssQ5B3ITQ0VJL1BYaFhXm4NgAAAACAii4xMVGNGjWy59GCEORdyO5OHxYWRpAHAAAAAJQZd4Z3M9kdAAAAAABehCAPAAAAAIAXIcgDAAAAAOBFCPIAAAAAAHgRgryD6OhoRUZGqnv37p6uCgAAAAAALhmmaZqerkR5k5iYqPDwcCUkJDBrPQAAAACg1BUlh9IiDwAAAACAFyHIAwAAAADgRQjyAAAAAAB4EYI8AAAAAABehCAPAAAAAJVMnz59NHbsWLfL79u3T4ZhaOPGjaVWJ7iPIO/FNh86rc9W7tOqPSc8XRUAAAAApcAwjAIfI0eOLNZxv/32W7344otul2/UqJFiY2PVvn37Yp3PXdk3DPz8/HT48GGnfbGxsfLz85NhGNq3b1+e9/br10++vr5atWpVnn0jR460f2f+/v5q3ry5HnvsMZ05c8bpvK4ero7naQR5L/b73/Ea98M2/bjpiKerAgAAAKAUxMbG2h9TpkxRWFiY07b//ve/TuXT09PdOm6NGjUUGhrqdj18fX0VEREhPz+/ItW/uOrXr69PP/3Uadsnn3yiBg0auCx/4MABrVy5UmPGjNHHH3/sssyAAQMUGxurPXv26KWXXtJ7772nxx57zKnMokWLnL7f2NhYde3atWQ+VAkiyHsxQ4YkyTQ9XBEAAADAC5mmqZS0DI88TDf/iI+IiLA/wsPDZRiG/fW5c+dUrVo1ffXVV+rTp48CAwP1+eef68SJExo2bJgaNmyo4OBgdejQQTNnznQ6bu6u9U2bNtUrr7yiUaNGKTQ0VI0bN9aHH35o35+7a/2SJUtkGIZ+++03devWTcHBwerVq5d27NjhdJ6XXnpJderUUWhoqO655x499dRT6ty5c6Gf+84779T06dOdts2YMUN33nmny/LTp0/Xddddp/vvv1+zZ8+2t7Q7CggIUEREhBo1aqTbbrtNw4cP1/fff+9UpmbNmk7feUREhPz9/Qutb1krm9spKBWGkf2MJA8AAAAU1dn0TEU+96tHzh3zQn8FVymZOPbkk09q8uTJmj59ugICAnTu3Dl17dpVTz75pMLCwjR37lyNGDFCzZs3V48ePfI9zuTJk/Xiiy/qmWee0ddff637779fvXv3Vps2bfJ9z7PPPqvJkyerdu3aGj16tEaNGqU///xTkvTFF1/o5Zdf1nvvvadLLrlEs2bN0uTJk9WsWbNCP9P111+v999/X8uXL9ell16q5cuX6+TJkxo0aFCeIQGmaWr69OmKjo5WmzZt1KpVK3311Ve66667CjxHUFCQ2z0Yyhta5L1Ydo6nRR4AAACovMaOHashQ4aoWbNmql+/vho0aKDHHntMnTt3VvPmzfXggw+qf//+mjNnToHHueaaa/TAAw+oRYsWevLJJ1WrVi0tWbKkwPe8/PLLuvzyyxUZGamnnnpKK1as0Llz5yRJ77zzju6++27dddddatWqlZ577jl16NDBrc/k7++v22+/XdOmTZMkTZs2TbfffrvL1vFFixYpJSVF/fv3lyTdfvvt+Xavz7Z69Wp9+eWXuuqqq5y29+rVS1WrVnV6ZGZmulXnskSLvBfLaZEHAAAAUFRB/r6KeaG/x85dUrp16+b0OjMzUxMnTtTs2bN1+PBhpaamKjU1VSEhIQUep2PHjvbn2V344+Pj3X5PvXr1JEnx8fFq3LixduzYoQceeMCp/EUXXaTff//drc919913q2fPnnrllVc0Z84crVy5UhkZGXnKffzxxxo6dKh9/P6wYcP0+OOPa8eOHWrdurW93M8//6yqVasqIyND6enpuuGGG/TOO+84HWv27Nlq27at0zZf35L7tyopBPkKgBZ5AAAAoOgMwyix7u2elDugT548WW+99ZamTJmiDh06KCQkRGPHjlVaWlqBx8nd2m0Yhmw2m9vvMbJaGh3fY+RqfXR3bgBJat++vdq0aaNhw4apbdu2at++fZ7l706ePKnvv/9e6enpmjp1qn17Zmampk2bpkmTJtm3XXHFFZo6dar8/f1Vv359l637jRo1UosWLdyuo6fQtd5BdHS0IiMj1b17d09XxS3Z/1GYjJEHAAAAkGXZsmW64YYbdPvtt6tTp05q3ry5du7cWeb1aN26tVavXu20be3atUU6xqhRo7RkyRKNGjXK5f4vvvhCDRs21KZNm7Rx40b7Y8qUKfrkk0+cWvBDQkLUokULNWnSpFxOYFcU3n/7qQRFRUUpKipKiYmJCg8P93R13EaLPAAAAIBsLVq00DfffKMVK1aoevXqevPNNxUXF5eny3hpe/DBB3XvvfeqW7du6tWrl2bPnq3NmzerefPmbh/j3nvv1b/+9S9Vq1bN5f6PP/5YN998c5717Zs0aaInn3xSc+fO1Q033OD2+U6cOKG4uDinbdWqVVNgYKDbxygLtMh7sexeKuR4AAAAANnGjRunLl26qH///urTp48iIiI0ePDgMq/H8OHD9fTTT+uxxx5Tly5dtHfvXo0cObJIodjPz0+1atVyuX79unXrtGnTJt1000159oWGhqpfv36FTnqXW9++fVWvXj2nR+4l6soDwyzKIIVKIrtFPiEhQWFhYZ6uTr6mLtmtSfP/1k1dGmryLZ08XR0AAAAAKNDVV1+tiIgIffbZZ56uSrlTlBxK13ovltMiz70YAAAAAOVLSkqK3n//ffXv31++vr6aOXOmFi1apIULF3q6al6PIO/F7PM/kuMBAAAAlDOGYeiXX37RSy+9pNTUVLVu3VrffPON+vbt6+mqeT2CvBdjjDwAAACA8iooKEiLFi3ydDUqJCa782JGVps80xwAAAAAQOVBkPditMgDAAAAQOVDkK8AaJAHAAAAgMqDIO/FjKwmeXI8AAAAAFQeBHkvZhReBAAAAABQwRDkKwAmuwMAAABQkD59+mjs2LH2102bNtWUKVMKfI9hGPr+++/P+9wldRzkIMh7MSa7AwAAACq2QYMG5bvu+sqVK2UYhtavX1/k465Zs0b33Xff+VbPyYQJE9S5c+c822NjYzVw4MASPVduM2bMkGEYatu2bZ59X331lQzDUNOmTfPsO3v2rKpXr64aNWro7NmzefY3bdpUhmHIMAwFBwerffv2+uCDD/KcN/cjMDCwRD9fbgR5L2bvWk+SBwAAACqku+++W7///rv279+fZ9+0adPUuXNndenSpcjHrV27toKDg0uiioWKiIhQQEBAqZ8nJCRE8fHxWrlypdP2adOmqXHjxi7f880336h9+/aKjIzUt99+67LMCy+8oNjYWG3evFmDBw/W6NGjNXv2bPv+sLAwxcbGOj1c/XuVJIK8F8uZ7I4kDwAAAFRE1113nerUqaMZM2Y4bU9JSdHs2bN1991368SJExo2bJgaNmyo4OBgdejQQTNnzizwuLm71u/cuVO9e/dWYGCgIiMjtXDhwjzvefLJJ9WqVSsFBwerefPmGjdunNLT0yVZLdPPP/+8Nm3aZG+Vzq5z7q71W7Zs0ZVXXqmgoCDVrFlT9913n5KTk+37R44cqcGDB+uNN95QvXr1VLNmTUVFRdnPlR8/Pz/ddtttmjZtmn3boUOHtGTJEt12220u3/Pxxx/r9ttv1+23366PP/7YZZnQ0FBFRESoRYsWeumll9SyZUunz2MYhiIiIpwedevWLbCu58uvVI/uZaKjoxUdHa3MzExPV8Ut9q715HgAAACg6ExTSk/xzLn9g3P+oC+An5+f7rjjDs2YMUPPPfecvTFvzpw5SktL0/Dhw5WSkqKuXbvqySefVFhYmObOnasRI0aoefPm6tGjR6HnsNlsGjJkiGrVqqVVq1YpMTHRaTx9ttDQUM2YMUP169fXli1bdO+99yo0NFRPPPGEhg4dqq1bt2r+/PlatGiRJCk8PDzPMVJSUjRgwABdfPHFWrNmjeLj43XPPfdozJgxTjcrFi9erHr16mnx4sXatWuXhg4dqs6dO+vee+8t8LPcfffd6t27t/773/8qODhYM2bM0IABA1wG6927d2vlypX69ttvZZqmxo4dqz179qh58+YFniMwMLDQmwqljSDvICoqSlFRUUpMTHR50ZU32f/ZE+QBAACAYkhPkV6p75lzP3NEqhLiVtFRo0bp9ddf15IlS3TFFVdIsrqLDxkyRNWrV1f16tX12GOP2cs/+OCDmj9/vubMmeNWkF+0aJG2b9+uffv2qWHDhpKkV155Jc+49v/85z/2502bNtWjjz6q2bNn64knnlBQUJCqVq0qPz8/RURE5HuuL774Qmf/v737jm+qauMA/rvp3oNuOhhllT3Kkr23iIooIAjuggtRURRwgq8DkbpZgoALXCxB9p5lFcqebSndO22S+/5xSNo06SJpS9Lf9/NBk3tv7j1JL6HPOc95Tl4efvzxR7i4iPe/cOFCDB8+HPPmzdMF3F5eXli4cCFsbGzQtGlTDB06FP/991+5gXybNm3QsGFD/Pbbbxg/fjyWLl2Kzz77DJcuXTI4dvHixRg8eDC8vLwAAIMGDcLixYvx/vvvGz23SqXCihUrcPLkSTz33HO67RkZGXB1ddU7tmvXrvj333/LbKspGMhbMqbWExERERFZvaZNm6Jr165YvHgxevfujYsXL2LXrl26QFGtVmPu3Ln4+eefcfPmTSiVSiiVSl2gXJ4zZ84gNDRUF8QDQJcuXQyO++233zB//nxcuHAB2dnZUKlUcHd3r9R7OXPmDFq3bq3Xtvvuuw8ajQZxcXG6QL558+awsbHRHRMYGIiTJ09W6BqTJk3CkiVLEBoaiuzsbAwZMgQLFy7UO0atVmPZsmX44osvdNvGjRuHl19+GXPmzNG79uuvv46ZM2dCqVTC3t4e06dPxzPPPKPb7+bmZlBw0MnJqUJtvVsM5C0YR+SJiIiIiExg5yxGxmvq2pUwefJkTJkyBdHR0ViyZAnCwsLQt29fAMCnn36Kzz//HPPnz0fLli3h4uKCl156CQUFBRU6t7HlrKUSaf/79+/HmDFjMGfOHAwcOBAeHh5YvXo1Pv3000q9D1mWDc5t7Jp2dnYG+zQaTYWuMXbsWLz22muYPXs2Hn/8cdjaGoa9mzZtws2bN/HII4/obVer1fj333/1shGmT5+OiRMnwtnZGYGBgQbtVygUCA8Pr1DbzIWBvAXj8nNERERERCaQpAqnt9e00aNH48UXX8TKlSuxbNkyPPXUU7qActeuXbj//vsxbtw4AGLO+/nz540uxWZMREQErl27hvj4eAQFiakGJSu/79mzB2FhYXjrrbd020pWZre3ty+33lhERASWLVuGnJwc3aj8nj17oFAo0Lhx4wq1tzze3t4YMWIEfvnlF3zzzTdGj1m0aBHGjBmj934AYO7cuVi0aJFeIO/j41PtgXp5WLXegkl3xuQ5Ik9EREREZN1cXV3xyCOP4M0330R8fDwmTpyo2xceHo7Nmzdj7969OHPmDJ555hkkJiZW+Nz9+vVDkyZN8Pjjj+P48ePYtWuXQYAbHh6Oa9euYfXq1bh48SIWLFiAtWvX6h1Tr149XL58GTExMUhOToZSqTS41tixY+Ho6IgJEybg1KlT2LZtG6ZOnYrx48ebtdL70qVLkZycjKZNmxrsu337Nv7++29MmDABLVq00PszYcIE/PXXX7h9+3aFryXLMhITEw3+VDSD4G4wkLdgEheSJyIiIiKqNSZPnoy0tDT069dPb130t99+G+3atcPAgQPRq1cvBAQEYOTIkRU+r0KhwNq1a6FUKtGxY0c8+eST+OCDD/SOuf/++/Hyyy9jypQpaNOmDfbu3Yu3335b75gHH3wQgwYNQu/eveHr62t0CTxnZ2ds2rQJqampiIyMxEMPPYS+ffsazGE3lXZpO2O0hfa0UxOK6927N9zc3LB8+fIKXyszMxOBgYEGf5KSku66/eWRZGMTImo5bdX6jIyMShdvqE6rD17DG2tOom9TPyyaGFnTzSEiIiIiIqK7VJk4lCPyFqwCy04SERERERGRlWEgbwWYUkFERERERFR7MJC3YEXF7hjKExERERER1RYM5C0Zl58jIiIiIiKqdRjIWzDtFHkOyBMREREREdUeDOQtmHSn2h3jeCIiIiIiotqDgbwFKxqRZyhPRERERERUWzCQt2Bcfo6IiIiIiKj2YSBvwZqc+xbnHMZjfOrCmm4KERERERERVRMG8hZMAQ3sJTVsoKrpphAREREREVE1YSBv0bTryNdwM4iIiIiIiKjaMJAnIiIiIiIisiAM5IuJjo5GREQEIiMja7opFXOn2p3EBeiIiIiIiIhqDQbyxURFRSE2NhaHDh2q6aZUiMSy9URERERERLUOA3lrwEnyREREREREtQYDeYvGEXkiIiIiIqLahoG8VeCIPBERERERUW3BQN6CSSx2R0REREREVOswkLdkEteRJyIiIiIiqm0YyFsFRvJERERERES1BQN5i8bUeiIiIiIiotqGgbwl06bW13AziIiIiIiIqPowkLdg2sXnJEbyREREREREtQYDeUsmaX98jOSJiIiIiIhqCwbyFkwq/xAiIiIiIiKyMgzkLZmkDeU5Ik9ERERERFRbMJC3ZBySJyIiIiIiqnUYyFsBSeaIPBERERERUW3BQN6CSWCxOyIiIiIiotqGgbwlY2o9ERERERFRrcNA3qKx2B0REREREVFtw0Dekmmr1jOOJyIiIiIiqjUYyFuwosx6RvJERERERES1BQN5S3ZnRJ5T5YmIiIiIiGoPBvIWTYTwHI8nIiIiIiKqPRjIWzDtFHmJoTwREREREVGtwUDeommL3TGQJyIiIiIiqi0YyBcTHR2NiIgIREZG1nRTKoaT44mIiIiIiGodBvLFREVFITY2FocOHarpplSIpPvxcUSeiIiIiIiotmAgb8k4R56IiIiIiKjWYSBPREREREREZEEYyFs0FrsjIiIiIiKqbRjIWzBJ4o+PiIiIiIiotmEkaMl0Ves5Ik9ERERERFRbMJC3YNKdSJ6r0BEREREREdUeDOQtWbEI/sjVVPyw6xJkzpcnIiIiIiKyarY13QAyhTaSl/Hg1/sAAAEejhjWKqjmmkRERERERERViiPylkwyrFp/MSmnhhpDRERERERE1YGBvAUzNjdeZuE7IiIiIiIiq8ZA3pLdWX5OKha8c4o8ERERERGRdWMgb8EklqsnIiIiIiKqdRjIW7CiOF428oiIiIiIiIisEQN5S6ZLrS+GufVERERERERWjYG8NZCNPiQiIiIiIiIrxEDekklF68gTERERERFR7cBA3qIxkCciIiIiIqptGMhbMGNV6zlFnoiIiIiIyLoxkLdo0p3/Fq9az0ieiIiIiIjImjGQt2AckSciIiIiIqp9GMhbMm0kz+idiIiIiIio1mAgb8EkGK4jz5CeiIiIiIjIujGQt2TaAfnic+QZyRMREREREVk1BvIWTCrxf4DF7oiIiIiIiKwdA3lLJml/fMWCd8bxREREREREVo2BvAVTaIfiGccTERERERHVGgzkLZhCYWREnoiIiIiIiKwaA3kLZmRAHjKr3REREREREVk1BvIWTLozIi/JrFpPRERERERUWzCQt2AKydiYPBEREREREVkzBvIWTNKtI1+EIT0REREREZF1YyBvwYyl1hMREREREZF1YyBvwbSp9VKxbYzpiYiIiIiIrBsDeQum0AvhBZnJ9URERERERFaNgXwx0dHRiIiIQGRkZE03pWIUd0bkJVatJyIiIiIiqi0YyBcTFRWF2NhYHDp0qKabUiFFVeuJiIiIiIiotmAgb8Ek3Rx5DsMTERERERHVFgzkLZhCYWSOPHPriYiIiIiIrBoDeQtmvNgdERERERERWTMG8hZMUjC1noiIiIiIqLZhIG/BFEbmyDOznoiIiIiIyLoxkLdoXEeeiIiIiIiotmEgb8l0I/KAAwpQF7drtj1ERERERERU5RjIW4n/HF7FHscX4Zd7saabQkRERERERFWIgbxFK5ojHywlAwAiMnbWZIOIiIiIiIioijGQt2RGit1xAToiIiIiIiLrxkDe2rBsPRERERERkVVjIG/RpGL/1W5hIE9ERERERGTNGMhbAb115GuwHURERERERFT1bO/2hd7e3pU6XpIkHD16FGFhYXd7SSpJMlxHnqn1RERERERE1u2uA/n09HTMnz8fHh4e5R4ryzKef/55qNXqu70cGaUN5GWDLURERERERGSd7jqQB4AxY8bAz8+vQsdOnTrVlEtRGfSDd00NtYKIiIiIiIiqw10H8hpN5QLGrKysu70UlcZoan31N4OIiIiIiIiqD4vdWTSuI09ERERERFTbmBTIP//888jOztY9X758ud7z9PR0DBkyxJRLUKUxkCciIiIiIrJmJgXy3377LXJzc3XPo6KikJSUpHuuVCqxadMmUy5BZZGMjMgzjiciIiIiIrJqJgXycomlzko+p+qhP1OePwMiIiIiIiJrxjnyFs2w2J3EQJ6IiIiIiMiqMZC3ZJL2f8VT6xnIExERERERWTOT1pEHgHfeeQfOzs4AgIKCAnzwwQfw8PAAAL3581R1jCxCR0RERERERFbKpEC+R48eiIuL0z3v2rUrLl26ZHAMVRVjIbym2ltBRERERERE1cekQH779u1magaZgvPiiYiIiIiIag+TU+uzsrKwf/9+FBYWomPHjvDx8TFHu6giJCPF7hjTExERERERWTWTAvkTJ05g8ODBSEhIAAC4u7vjt99+Q79+/czSOCqPNpAvHr0zkiciIiIiIrJmJlWtf+ONNxAaGordu3fj8OHD6NmzJ6ZMmWKutlEFcR15IiIiIiKi2sOkEfnDhw9j/fr16NChAwBg8eLF8PPzQ3Z2NlxdXc3SQCqDkdR6Lj9HRERERERk3UwakU9OTkZoaKjueZ06deDs7Izbt2+b3DCqCOnOf4uCd5mBPBERERERkVUzaURekiRkZWXB0dERgAgitdsyMzN1x7m7u5vWSiqTfiDP5eeIiIiIiIismUmBvCzLaNy4scG2tm3b6h5LkgS1Wm3KZag0RlLr1YzjiYiIiIiIrJpJgfy2bdvM1Q4yQfFwniPyRERERERE1s2kQL5nz57lHsP58lXJcESec+SJiIiIiIism0nF7kojyzLWr1+PUaNGITg4uCouQYAutZ5z5ImIiIiIiGoPswbyly5dwsyZMxEaGoqxY8fC2dkZq1evNuclyAi9QF7DEXkiIiIiIiJrZlJqPQDk5+fjt99+ww8//ID9+/ejf//+SEhIQExMDFq0aGGONlKpDFPrNUytJyIiIiIismomjcg///zzCAoKQnR0NB5++GHcvHkTf//9NyRJgkJRJVn7VJyRqvVMrSciIiIiIrJuJo3If/fdd3j99dfxxhtvwM3NzVxtokoqnlrPzHoiIiIiIiLrZtKw+Y8//oiDBw8iMDAQjzzyCP755x+oVCpztY3KZWREXsMReSIiIiIiImtmUiD/2GOPYfPmzTh16hSaNm2KqKgoBAYGQqPRIDY21lxtpHLoV63nkDwREREREZE1M8tE9nr16mHOnDm4cuUKli9fjgcffBDjxo1DcHAwXnjhBXNcgowxOkeegTwREREREZE1M7lqfXGSJGHQoEEYNGgQUlNT8eOPP2LJkiXmvATpkYr9V9Cw2B0REREREZFVq7LS8t7e3njppZdw/PjxqroE3cF15ImIiIiIiGoPk0bk33333XKPkSQJb7/9timXodIYSa3XyCK9XjKyj4iIiIiIiCyfSYH87NmzERQUBD8/v1LnZjOQr3rFR+QlyChUy7C3ZSBPRERERERkjUwK5AcNGoRt27ahQ4cOmDRpEoYOHQobGxtztY3KZThHXoKMvEI17G2rbNYEERERERER1SCTor3169fj0qVL6NSpE6ZPn47g4GC8/vrriIuLM1f7qCxG0uclCcgvVNdAY4iIiIiIiKg6mDxsGxgYiBkzZiAuLg4///wzkpKSEBkZifvuuw95eXnmaCOVQ5L0U+sz8gprsDVERERERERUlcyafx0ZGYnevXujWbNmOHbsGAoLGVBWLWPz4GUMmr+z2ltCRERERERE1cMsgfy+ffvw1FNPISAgAF9++SUmTJiA+Ph4uLu7m+P0VBojcbwEUbmeiIiIiIiIrJNJxe4+/vhjLFmyBCkpKRg7dix2796Nli1bmqttVEHFq9YTERERERGRdTMpkH/jjTcQGhqK0aNHQ5IkLFmyxOhxn332mSmXoVIZKXbHoJ6IiIiIiMiqmRTI9+jRA5Ik4fTp06UeIxmprE7mVXIdeQCQZZmfPRERERERkRUyKZDfvn27mZoh7Ny5E//73/9w5MgRJCQkYO3atRg5cqRuvyzLmDNnDr777jukpaWhU6dOiI6ORvPmzXXHKJVKvPrqq1i1ahXy8vLQt29ffPXVVwgODjZrW+8JkuE68loqjQw7GwbyRERERERE1sasVetNlZOTg9atW2PhwoVG93/88cf47LPPsHDhQhw6dAgBAQHo378/srKydMe89NJLWLt2LVavXo3du3cjOzsbw4YNg1ptjWuri0DdwVZRYgtQqNbUQHuIiIiIiIioqt11IP/KK68gJyenwsfPmDEDqampZR4zePBgvP/++xg1apTBPlmWMX/+fLz11lsYNWoUWrRogWXLliE3NxcrV64EAGRkZGDRokX49NNP0a9fP7Rt2xYrVqzAyZMnsWXLlsq9QQtSPLVeARHAF6o4V56IiIiIiMga3XUg/8UXXyA3N7fCx0dHRyM9Pf1uL4fLly8jMTERAwYM0G1zcHBAz549sXfvXgDAkSNHUFhYqHdMUFAQWrRooTvGGKVSiczMTL0/FkGXWl8UtNtCZB4orTIDgYiIiIiIiO56jrwsy2jcuHGFC6pVZvTemMTERACAv7+/3nZ/f39cvXpVd4y9vT28vLwMjtG+3piPPvoIc+bMMal9NUN89p7qokwHJ0kFAChUc0SeiIiIiIjIGt11IF/aUnNlKRmE342SHQcVqc5e3jEzZszAK6+8onuemZmJkJAQ0xpaQxylQgBAoYpz5ImIiIiIiKzRXQfyEyZMMGc7yhUQEABAjLoHBgbqticlJek6CAICAlBQUIC0tDS9UfmkpCR07dq11HM7ODjAwcGhilpehYx0TjjoRuQZyBMREREREVmje6pqfVnq16+PgIAAbN68WbetoKAAO3bs0AXp7du3h52dnd4xCQkJOHXqVJmBvDVxkgoAAEqOyBMREREREVklk9aRN7fs7GxcuHBB9/zy5cuIiYmBt7c3QkND8dJLL+HDDz9Eo0aN0KhRI3z44YdwdnbGY489BgDw8PDA5MmTMW3aNNSpUwfe3t549dVX0bJlS/Tr16+m3lYVMjIijzup9RyRJyIiIiIiskr3VCB/+PBh9O7dW/dcO299woQJWLp0KV577TXk5eXh+eefR1paGjp16oR///0Xbm5uutd8/vnnsLW1xejRo5GXl4e+ffti6dKlsLGxqfb3U+WMpNbb3wnkCzgiT0REREREZJUkWZZZ3ryEzMxMeHh4ICMjA+7u7jXdnNIlngS+6aa3KVmqgw55X2LZpI7o2di3hhpGRERERERElVGZONTkOfIqlQq2trY4deqUqaeiylIpDTZpR+TzCriOPBERERERkTUyOZC3tbVFWFgY1GoGjtVOlW+wyR7aYnf8eRAREREREVkjs1StnzlzJmbMmIHU1FRznI4qysiIvK3MEXkiIiIiIiJrZpZidwsWLMCFCxcQFBSEsLAwuLi46O0/evSoOS5T5aKjoxEdHW052QXGAnmoUV9KQF5hRA00iIiIiIiIiKqaWQL5kSNHmuM0NS4qKgpRUVG6IgP3vKC2Rjdvc5iGX9JWA6hfve0hIiIiIiKiKmeWQH7WrFnmOA1Vlnsg4BkKpF8z2BV4exeAwdXfJiIiIiIiIqpSZl1H/siRIzhz5gwkSUJERATatjU+Ykxm5BlmNJBXqbmqIBERERERkTUySyCflJSEMWPGYPv27fD09IQsy8jIyEDv3r2xevVq+PpyPfPqVqhhIE9ERERERGSNzFK1furUqcjMzMTp06eRmpqKtLQ0nDp1CpmZmXjhhRfMcQkqTUG20c1qTTW3g4iIiIiIiKqFWUbkN27ciC1btqBZs2a6bREREYiOjsaAAQPMcQkqTfwxo5uZWU9ERERERGSdzDIir9FoYGdnZ7Ddzs4OGg2HhmuCmqn1REREREREVsksgXyfPn3w4osvIj4+Xrft5s2bePnll9G3b19zXIIqSS1LNd0EIiIiIiIiqgJmCeQXLlyIrKws1KtXDw0bNkR4eDjq16+PrKwsfPnll+a4BFWSiiPyREREREREVsksc+RDQkJw9OhRbN68GWfPnoUsy4iIiEC/fv3McfpqEx0djejoaKjV6ppuisk4R56IiIiIiMg6mRzIq1QqODo6IiYmBv3790f//v3N0a4aERUVhaioKGRmZsLDw6Omm1Mxkg0gG3Y8aGRG8kRERERERNbI5NR6W1tbhIWFWcUotkVSGO+LUbHGIBERERERkVUyyxz5mTNnYsaMGUhNTTXH6agySgnkmVpPRERERERkncwyR37BggW4cOECgoKCEBYWBhcXF739R48eNcdlyJjSAnmOyBMREREREVklswTyI0eONMdp6G4ojCdVaDSc6kBERERERGSNzFLsDgAmTZqEkJAQkxtElVRKUTtJo6rmhhAREREREVF1MEuxu08++YTF7u41DOSJiIiIiIisklmK3fXt2xfbt283x6mo0koZkTeyJB0RERERERFZPrPMkR88eDBmzJiBU6dOoX379gbF7kaMGGGOy5AxpVSnb60+Vb3tICIiIiIiomohyXIpk6wrQVFKwTUAkCTJ4tLuMzMz4eHhgYyMDLi7u9d0c8r2UQigzDS+b3ZG9baFiIiIiIiI7kpl4lCzjMhrNFzr7F6UX6iGo51NTTeDiIiIiIiIzMgsc+StRXR0NCIiIhAZGVnTTam4MhIqMvIKq7EhREREREREVB1MCuSHDBmCjIyi9O0PPvgA6enpuucpKSmIiIgw5RLVKioqCrGxsTh06FBNN6USSg/k03ILqrEdREREREREVB1MCuQ3bdoEpVKpez5v3jykpqbqnqtUKsTFxZlyCaqsB77VPTx2Lb3m2kFERERERERVwqRAvmSdPDPUzaPKKvmZB4tpAZmyE2asOYkCFesXEBERERERWRPOkbd4JQJ5SfxIFXe2rz12o7obRERERERERFXIpEBekiRIkmSwjapRyRH5EoF8Vr6qultEREREREREVcik5edkWcbEiRPh4OAAAMjPz8ezzz4LFxcXANCbP0/VRBfIi5R6BTtWiIiIiIiIrIpJgfyECRP0no8bN87gmMcff9yUS1C5jI/IS3e229owkCciIiIiIrImJgXyS5YsMVc76G6Vk1rPEXkiIiIiIiLrwmJ3Fq+0QF6k1heqWbWeiIiIiIjImjCQt3SljMjbSGJ7XqG6ultEREREREREVYiBvLWRiv9IZeQXckSeiIiIiIjImjCQt3glR+SL5sQrICOfI/JERERERERWhYG8tSk2Iq+ABjap54FfnwBuxdZgo4iIiIiIiMhcGMgXEx0djYiICERGRtZ0UyqulDnygBiRn3h+KnB6DbBseDU3jIiIiIiIiKoCA/lioqKiEBsbi0OHDtV0Uyqh9EBeggx/KV08yU2uviYRERERERFRlWEgb+nKHJFnoTsiIiIiIiJrw0Demgz9TC+Q93G2q8HGEBERERERUVVgIG/pGvQU//cIBSIn6wXymfkFNdQoIiIiIiIiqiq2Nd0AMtGoH4BDPwBtx4rnxdeRl4uWntNIduy1ISIiIiIisgIM5C2dqy/Qe0bR8+LF7orNn9coGMgTERERERFZA8Z21kaSdA8VxSra58k2NdEaIiIiIiIiMjMG8tZGkgCIYL6uVLTkXJ6agTwREREREZE1YCBvje6k1//tMFO3qQC2UKm5HB0REREREZGlYyBvjSTDH2uhbIPtcbeh1shGXkBERERERESWgoG8NTISyKthgyd/PIwP15+pgQYRERERERGRuTCQt0ZGAnn5zrz5RbsvV3driIiIiIiIyIwYyFsjo4E8ERERERERWQMG8tbISCCv4Y+aiIiIiIjIKjC6s0YFWQabtKn1REREREREZNkYyBcTHR2NiIgIREZG1nRTzK54IC/LTLQnIiIiIiKyVAzki4mKikJsbCwOHTpU000xu+Kh+54LKTXWDiIiIiIiIjINA/lawhEFCJZuAwASMvJquDVERERERER0txjI1xINFQnY7fAiGkjxcLa3renmEBERERER0V1iIF/LdFechJM9f+xERERERESWihFdLSMDcLS1qelmEBERERER0V1iIF/LcBk6IiIiIiIiy8ZAvpaRIUHN5eeIiIiIiIgsFgP5WkjDOJ6IiIiIiMhiMZCvZWRI0DCSJyIiIiIislgM5GshDVPriYiIiIiILBYDeWs04P1Sd8mQoOaIPBERERERkcViIG+NWo4udZcMzpEnIiIiIiKyZAzkrZGtfam7ZEhMrSciIiIiIrJgDOStkQ0DeSIiIiIiImvFQN4alRnIg3PkiYiIiIiILBgDeWuksC11lwwJHJAnIiIiIiKyXAzkrZEklbmbI/JERERERESWi4F8LcQ58kRERERERJaLgXwtw2J3RERERERElo2BfDHR0dGIiIhAZGRkTTelykiQIanygO/7AFs/qOnmEBERERERUSUxkC8mKioKsbGxOHToUE03pcrYQIN61/8Abh4Bdn5c080hIiIiIiKiSmIgb+16vq731AYaSOrCog3nNlVzg4iIiIiIiMgUpa9TRpbthRgg9aLBUnQSZGhQrKr9ytHAU1uBuu2rt31ERERERER0Vzgib6286wPh/QBJ/0dsAw00conl6W6drsaGERERERERkSkYyFu7EoF8T8VxsGY9ERERERGR5WIgb/X0R9/72MRAU0MtISIiIiIiItMxkLd2kuGP2CC1noiIiIiIiCwGA3lrZyyQr4FmEBERERERkXkwkLd2RgL5ayl5NdAQIiIiIiIiMgcG8tZOMkyjP3sruwYaQkRERERERObAQN7aGQnkS1atX3ngGi7dZnBPRERERERkCRjIWzsjqfVyiUr2MTfS8covx6urRURERERERGQCBvLWTmFrsKlkIA8AN9I4b56IiIiIiMgSMJC3dgo7w01G6tYHejhWR2uIiIiIiIjIRAzkrZ2NsUC+5Cx5wNGOtwIREREREZElYPRm7YwE8hM6BRtsUxgpikdERERERET3Hgby1s5Ian3j1G0G22wUDOSJiIiIiIgsAQN5a2dkRB5XdhkexkCeiIiIiIjIIjCQt3ZGqtYTERERERGR5WIgb+2MjcgbIRvWvyMiIiIiIqJ7EAN5a2djX6HDdl9IRlpOQRU3hoiIiIiIiEzFQN7aVSK1/o01JwAA7/x5CiOj90CpUldVq4iIiIiIiOguMZC3dpVYVm7T6VsoUGnw476riLmejoOXU6uwYURERERERHQ3GMiTnispObrHGhn450Q8On24BQcupdRgq4iIiIiIiEiLgXwx0dHRiIiIQGRkZE03pVoVH7OftPSQ7vEzyw9jyspjuJWpxHvrYqu/YURERERERGSAgXwxUVFRiI2NxaFDh8o/2Io0D3DRPb6Rlqd7nF+o0T0+dTMTMkvbExERERER1TgG8rWJo4fRzeM6hcLDqfxl6u6P3oOUbCUA4HJyDo5dS0NeQVFBvMz8Qvx9PB4qtQYnb2QgLacAN9JyoVSpoVSpEZeYBQDQaCrWISDLMvZcSEZWfmGFjiciIiIiIqoNKl7SnCyfVz0g4bjBZgU0iHmnP45eS4ckAb8duYGVB64ZHHfiRgbav7/F6Klb1HXHqZuZAICejX2x49xto8e1DvbAlZRcZOQVonsjH7w+qCnmbzmHenVc0KepHzadTkSLuh5o7O+G0/GZeHPtSfRr5ocfJkQiv1CNDacSEODuhDqu9nCys4Gboy0uJ+egbagX8grUWLH/Koa0CoSPqz0kSFBpNFi+7yoeaFsXuQVqONvbwM/dERqNjOwCFa4k56BJgBscbG302qlUqWFvo4BSpUG2UgUfV4dKfthERERERERVQ5KZL20gMzMTHh4eyMjIgLu7e003x3S/Pwmc/xfo/Raw4TXD/YM/Bjo9o3uanK3EY9/sQn1PG0Q2CcP7685UY2ON83CyQ0ae/si8QhIF+QCggY8LLiXnGHll+bTnGRDhj+kDm2DF/qtYtu8qHmwXjJvpuYi5no6fn+4COxsFUnMK8PxPR9AkwA3zx7RFRm4hGvi6wMFWgY2nEnE9LRdPdW+AG2l5WLT7MiZ3qw9XB1t4ONlBpZGxYv9VtA/zQsu6Hjh8NQ0+rvbIzFehdbAHpDsrDCRnKzF56SG0CvbE0z0aYOOpRAxsHoCpq45ibKcwjI4MMXgPKrUGSpUGMgBXB1tcvJ2NneduY1znMNjZlJ14cyEpG452CgR7OUOWZag1MmzLeQ0REREREZlXZeJQBvJGWF0gL8uARgWc+Rv47QnD/YPmAp2fAwrzAGU24OoLfNkeSLkAvHYZx5IlPPDV3upvdy0zukMwcpRqrDuZUOZxs4ZHwNZGgd+O3EAdF3vkFaix786qAu6OtniyewN8tvmc7vhvxrWHUqXGmYQsdKrvjZ8PXUeHel4I93PFot2Xset8MgDgh8c74MkfDwMAxncOw9vDImBvq0BegRrf77qEVQevoXODOvBzc8Cm04l4c0gztAnxhI+rA77ZeREFKg26NKiDyHreUCiKSigevpKK80nZeLRjqN77UGtk2CiKOi+y81Wo5yPqNaTlFCCnQIVgL2cTP1UiIiIiIsvAQN5EVhfIa8X+Bfwy3nD7gA+ArlOAT5oA2YnAK2eBz5qKfY+sAJoNR1Z+IQpUGvx9PB6z/47FzKHNEJ+ejz9jbiJbqYIkieJ4iyZ0wMmbGehUvw4y8gphZyPhmx0XcehKGno18cXoDiF4/qejuksHejjiwwdaom2oJ55YegjHrqXrNW1Q8wDsuZiMrHyV3nZnexvkFpufT/emN4c0xYfrz+qevzWkGWKup+s6Kzyd7SABSMsV2RbP9GiAdmFeeGb5Ed1rOoR5YckTkdgedxvNAt1w+EoaGvm7oX2YFwAgKTMfn/57DrEJmfj8kdYI93PDygPXkJVfiGd6NtSdZ/f5ZBRqNOjdxM+gnZn5hZAAuDnq14qQZVmXKQEAGXmFsFFIcHUoe1ZSgUqD5GwlgjydKvZBEREREVGtx0DeRFYbyMdtAFaNMdze/z3gvheA2XeK4Y36HljzlHj86M9Ak0G6Q2VZRl6hGs72IpBJyVZCqdKUG7DEJWahrpcTXB1skZlfiIT0fNT3cYG9rWEKd45ShbkbzmLFgav44/n74O1iDxuFhBylCjvPJ2Ni13qwUUg4dysLK/ZfhY+rAzqEeaF5kAe+2n4B+y6lYPmkTlDLMs4kZGLX+WS80DccZxIyEe7rBg9nO2g0Mgo1Gty/cA/OJmZh7qiWcLK3wdK9Vww6E7S+Gdceh6+k4u8T8biVqSz/8y5haKtAxCVm4UJSdqVfS3cnsp4XDl1J09u2ZGIknOxtYKOQ8PA3+/T2hXo7480hzRCfngcbhYTFey4jxMsZyyd3RHxGPkZ9tQfZ+Sp8Na49MvIKMbxVoC6zQBvwy7KMZu9sRH6hBv9M7YYWdY0Xmayskp0KRERERGRdGMibyGoD+fObgZ8eMtzebw7Q7aWiQP7+aODPKPF43O9AeL9qa6KWWiMjK78Qns721X5tjUbWSw3PK1Ajp6Co4F1+oRpbzyahbzM/XZG81JwCrDl6A452NriZnocnu9XH3ospuJaai+d6NtSdL69AjZd/joEkAXNHtYKTvQ1kyFCpZRSqNcgtUOs6Ra6n5mLMd/vxQNu6+GH3Jd1ygC/1a4RAD0cEejjhRloeCtUa9Ivwx1PLDiM2IRMt6rqjXzN/rDuRgPN3Og0iAt0xc2gz/H0iAQcupyA1pwCh3s5QFmrQPMgd55OycfJmBgLcHZGaU4ACddHSgxXVNtSz1E4Qa9W5gTf2X0oFAGx+uQcWbL2Av4/H6x3z7v3NMa5TGPZfSoGHsx18XB2Qo1TB1cEWSVlKHL+RjoHNA/BnTDyCvZwQcz0dbUI8EVnPG872NlBIEl777TiOXkvHn1H3wcneBo52Nvj3dCJe+/0EFj7aDr5uDvh250V0blAHdjYShrcKwrlb2Wga4KZ3LxMRERHRvYuBvImsNpC/uBVY/oDh9r7vAN2nFQXyg/8HbJguHj/+F9CgZ/W1kYxKyMjD2YQs+Lo5lDrCK8syjlxNQ/MgDzjZ2yC/UI1VB6+hb1N/hNYpf665UqWGjSQhNacA/9sUh8c6heL8rWwEejpCgoRsZSH+jInHc70aolWwJzQaGRl5hXj5lxg0CXDDjMHNsPXsLWQrRcX/gc39sf5kIkK8ndAiyAMJmfkYNH8nsvJVmDOiOfIL1Th0JRWezvaIuZ6OXKUKnz/SBmpZRsu6HnC2t0VqTgEe+XZfuYUM24R44sF2dfH2n6fv6vO1Vg18XfBIhxCE+7miZbAHDl9JE508J+ORnFWAJ7vXx8Xb2Qj0cIK9rQI2kqQX+GuLH770cwzScgswZ0QLhPu53nV7MvIK8dX2C3iwXTAa+7vptu+9kAxXR1u0CvY05e0SERERWTQG8iay2kD+8i5g2TDD7QEtgUmbgA+DxPN+c4Ats8TjieuAet2qr41ERsRcT0eOUoUcpQp9m/lDIQE30vKw+tA1dAv3RZeGdQAAt7OUiPxALJHYNMANF29no2djP8waHoH8QjX6f75T77xhdZwR1Ssc9zXywZn4TPx65Do2nb6Fh9sH49cjNwzaoZCApgHuiE3IrPo3XYMkSdTILM8XY9pg/6VUrDoolqsc3zkMYzuHYumeK/jnRAKylSqMaB2E3AI1LiVnQ62RcTUlF17Odjj6dn/M3XAWWUqVbrnL1wc1xdpjNzC5W314OttjQIS/3nSCd/48hY2nEvH7c10R4l1+51Tx6QgfrT+D/ZdSsPKpznApp8aBKYoXcSQiIiKqDAbyJrLaQP7qPmDJIOP7Wo4GTv4iHvd8HdgxTzx+/E+gQa9qaR6ROVxNyUF+oQZNAtwM9h26kopVB67hneER5U7byCtQY8X+q/Bzd0CQpxO8XezR0FeMRidk5GHpnivo09QP7687g5M3M+Dn5oBJ3erj6e4N8MovMfgjJr7M8wOiY+DNIc0qvMRjvTrOCPF21q00YMm+GttOr/Blafo180fPJr64cCsLy/Zd1W2f0jscHep54d2/Y3UZG7te641gLyd8vuU8Fvx3Hva2CvRt6ofkbKVerYRu4T5o6OuCRv5umPnHKQxs7o+nujdA21AvZOUXwsXBVm/ZxhylCvsupqB3Uz+k5xZgc+wtjGxbF+duZWHR7st4e1gEfFwdsHTPZczbGIflkzuiQz1vJGbko1CtQYi3M1RqDZd1JCIiojIxkDeR1QbyN48A3/cp/7guU4B9C8XjGpojT2QptLUNPJz0K96fv5UFRzsbnE/KQpcGPjh+Ix1Hr6WhaYAb+jT11zs2W6nC/Qt3QyMDG17sjtPxmQj1dsby/VcRWc8L4xcdRFgdZ+yY3huAGPX9aP0ZhNZxxsoD13A2MQvdG/ngmR4NceByCr7cegEA0MjPFR+NaomHShT1I+NsFRJUGvFP4uRu9bH1bBJeHdAEUStFh4NCAlrU9cCJGxl4vEsYlu+/qstc2PhSdwyav0t3rqEtA3WrM0zpHY7vd13C2E5h6FjfCyHezmge5IGM3EJcuJ2F49czMLBFAOp6OiFbqYKLvQ2O38hAoIcj/N0dDdoZG58JZ3sbeDnb41JyNloFe6JApcHtLCVCvJ0MiiImZytRx8UekiTpshRkWcbxGxnQyDI+WHcGrw9qio71vaviYyUiIqIKYiBvIqsN5FUFwNddgYzrgHMdIPOm8eM6TAIOLxaPS1StJ6Lqdzo+A/7ujrqCi+VRqTVQqjS6FPLkbCXWnUjAyLZ1cTYhE4383fD67ydQ19MJDnYKTO3TCBpZxoFLqejZ2BcXb2dj8e7LaB7kjrXHbuL4jQw807MBMvMKkZCRjx6NfLH7QjK2nk0CADTxd8OGF7vjt6M3cCMtDwv+O6/Xnt5NfOHj6mB0ukKghyMSMvJN/ISsw6T76mPxnssG26f2CYckSQjxcsL2c7ex7kSC3v56dZxxJSUXADCidRA6N6iD0/EZmNi1HjacSsRnm8/hjcFN4e1ijw/Xn8HjncPgaG+DjzfG6Z3n7WERmNytPq4k5yCnQIXmQaIeR7ZShSW7LyMxU6w28lD7YMRcT4edjQJdG9bBTweuoUVdD7QJ8dQ7nyzLUGlk2NkoDFZdUN0pqGlro8CtzHx4ONnB0c5Gtz+/UCwvWnwbERGRtWMgbyKrDeQBEcwXZIvq9TePGD/G3g0oyBKPH/kJaGZkXj0R1QqlLXunUmtwMz0PV1Jy0SbYEx7O+hkJBSoN7GwkyDL0Cuhl5RciMSMfL6yOQbifKxaMaYO5G87i252XAAD+7g7o2dgXs0c0h52NAsv3XYWnsx2OXkuDl7M91BoZgR6O+H7XZVxLFcHrrtd6o/vH24y2v7G/KzQy0D7UC78dvQG1puifPEc7Bab1b4IP1ldsakNtNLhFAHafT0aWUmV0f+sQTxy/nq63bffrvXHwcio+3hiHxEz9TprXBjVBXU8n/LDrMlJzCjCsdSC+3SF+9v9M7YYDl1PF9IjN55BboMa/L/dAXoEaM/84hQfb10XLup6IS8zCnL9Po7G/G17o2wheznbYcCoRCoWE8Z3DoNHIKFBr4GCrwObYW3C2t0W3Rj539f7/Oh4PHxd7dA2/u9cTERFVBgN5E1l1IK+17SNgx9zyjxu+AHB0BxoPAuzKXiueiOhu5BeqsWzvFTT0dUWPxr6wty1/LrlGI2P+f+fRwMcFI9vWxbFraRj7wwHkFqixfHJHBLg7wtPZHr5uRVkMt7OU6PvpdtwX7oOFj7WDQgIkSUJiRj6OXktDZl4hRrati/9tisOi3WJkvFmgO75/vD2CvZxx7lYWHvx6L5r4u+F6Wi7yCtQY1zkMV1NzIcsyTsdn4uqdkfGSHukQgi1nbiElp8A8HxoZtf6F7lh77Aa+36Wf2RDo4Yi+zfxw+EoaziZmwd/dAS3remBC13roWN8bS/Zcwe7zybiWmotO9b3x3sgWSMzIR69PtgMA/pvWE4EeYnnOeRvjcH/rIPRp6ofEzHxcSc5BtlIFHzcHNPF3w5WUHChVGrQL9TLaxqspOfhmx0W80LcRAj3Ev6tqjYybaXkVWmGEiIisFwN5E9WKQF6jBr7qDCSfq9jxXacCA96v2jYREVWx/EI1HGwVRrMMKqKiVelTspXYcCoRHk52GNoyEAqFhNtZSny386IuyNwxvRfC6rjolvlbdfAatsXdxsi2deHr6oBNpxOx9WwSxncOw64Lydh57jYA4Ojb/SHLMlYeuIZPN+t/hz/dowHahngit0CNU/EZyC9Uo46LAxZuE3UTIut54dCVNHSs542DV1Lv6jOgihncIgDNg9wxoHkAwn1dkZCZDwlA17lbAQBdG9bByqc6IyVbiV+P3MDcDWfRPMgd94X7YMbgpqXeo0qVGheSshER6K47RpZlaGRwxQQiIgvHQN5EtSKQB4CTvwG/T67Ysc4+wGsXq7Y9RERWTqXWoN9nO6DSyNj2ai+96vhlSc0pwHv/xGJ8lzDdSG9+oRp9P90BXzcH/O+hVnC0szG6LJ8sy7iQlI36Pi5lVs6v98Y6AMCs4RHwd3fE2YRMeDjbI9jLCf2b+SM5RwlZBk7cyED3Rj54evkRHLycgnfvb4Gvtl3AjbQ8jOkYgv2XUtGrsVgW8oP1ZzCxaz080LYuFvx3Xm+kPNjLCYVqDZ7u0RDzt5yDBCAz33gKf23zWKdQTOhSD75uDvgr5iZm/x2LOi72mD6wCd5YcxKAWO7x2V4NcSszHx+uO4Nzt7LgbG+L9mFe+N/DrbDz3G0s3nMFL/ZthIhAd3i5FK3UcTtLiVuZ+WhR10O37UJSFuISs/HPiXh8NKolPJzs8MOuy3B1tMWjHUPxzY6LcLa3weAWgbpMF1mWcSUlFyFeTrC1UUCl1mDLmVvo1sgXrmUs85iVXwg7GwVrIBARlcBA3kS1JpC/shtYOrRix/o0BqYcAvLSgBUPAS0fAjo/V7XtIyKyQkqVGrJsnkJuBSoNbBSSWUZizyZm4tytbIxoHVSh42VZRqFahr2tAhm5hcgrVCPAw7DKfnGx8ZkYsmAXejb2xbJJHUs9r1KlwfqTCXjll+MAgGWTOmLOX6cBQLfcYNMANwxpGYikrHxE1vPG7SylbinHyd3q66ZHaHm72MPPzQF+7o4I8XJC/wh/qNQynvzxcJltdnOwLbVGgLVydbCFBOje9+/PdcGDX+8zOKZDPS9sj7uNiV3r4ZUBjbF831X8b1McejXxxdInOuLotTScuJ6OkW3rIiEjH4evpiHIwxFPLz+CDmFeeHNIM5xJyMQjkSGQJAnrTybg2LU0TBvQBA62ClxIykZDX1ek5hbgzTUnMapdXQxqEVgDnwgRUfVgIG+iWhPIx8cA3/Ws2LF1OwBP/Qdsnwds/1Bsm51h/FhVAbBnPhDeF6jbHki/Dvw4Auj4tH7wryoAbMtey5uqiTILUNgBdmX/Em7RzvwjllV84FvAK6ymW0NUa11PzYWvm0OFOjISM/KhlmXU9XSCWiOmIJy8mYE/jt3EqwObGCz7mJlfCFd7W0gSsPvOdIRX+jeBk33p17qakoOsfBUiAt2x+0IyGvi6YNwPBxDu54bosW1xMSkHQxYULS2467XemP3Xafx3Z9WG4tMUHu8SBnsbBYa3DsLjiw8iI68QANA8yB2n4zMBiAA4uxZ0DJRckSLA3dGg+KFWmxBPtKjrjp8OXIMsi2wDT2c7fLn1AjrV98aBy0XTQL5/vANm/3Uag1oEoFWwBxr6usLRTgFfV0ecT8rC/C3n0dDXBV0a1oG3iwMa+LrAwVaBg5dT0b2RL2Kup8PeVoHNsYl4untDg0KdxsiyjG1xSdh7IQUPtg9G0wC3u56eU1kJGXmI3nYBE7vWQ7ifW5Vdp7TCpkRUvRjIm6jWBPK344Bo4yMiBkI6A5M3AV93A26JtD6DQD7pDHBsBWDrAOz6VGy7/yvg4lbg1G/i+fg/gIBWwNGlolPgifVAcAdzvBtDeWmAjQNgXyzV9PxmwMUXCGpTNdfUu3464OAGKO5y1E2jARTlpN2umwZoVMCw+UBl/gGOWSXa1mwYUJADzA0FXAOAV06LDp7fJwN93wEi7r+7tt+LZt9JIQ3vB4z7vWbbQkQW5fCVVLz0cwxmDo3AoBYByFaq8NP+q3iwfTB8XB2gUmtKnbagDZBSspX4bucldKjnje1xSfjpwDW4O9risU5hGNQiAIVqDR7+Zp/B6yVJLPF4NjFLt21Q8wBsPJ1YZe+3tujVxBdfPtoWJ29mwMHWBtvOJsHFwRZ/H4+Hp7Md3hraDD8duIaVB67pvS7czxV/T+kGRzsFvt5xEbYKCZ5O9vjp4DUMbRmAD9efxZTe4Xi2V0Psv5gCD2c7tAv1ws20PMzfcg4DmgfAViGhbzM/SJKEy8k5CPZyMphqk5ZTgMFf7EJiZj5cHWwREeiOcH9XfPhAS73jEjPysXTvFYzvEoa6nhUvTHwrMx+rDl5DlwZ18P2uy7h0OxvrXuheZscXEVUtBvImqjWBfPo1YH7L8o8DgNAuwKSNRcEQAEy/CDi4AxteE+e6fkAsbVeSV30grViKo28z4PaZoscT/gJkGXDzF/+/shsIaAk4ed71W0N+JvBZBOAeKKYEAEDyBWBhe/HYWDaBLAMHvgVcfADfJqINAHArFnD1E9tL0mjEb1klg+iUi8CX7YCIkcDoZZVr+/rXxGeZflVkMfR+0/hxeenAvDsjyy+fBjyC77RJDVzYAgRHAgpb0TaHYr34mQnAZ03F47duAYkngEX9xfPRPwL/vi2uDRR9Tho1kJ8BOHuX335VgWh/cGTVj/Bf3CY6jYZ/Ie49t0Bg7wLg4nbgoUXA6T+A/HRg0Ef6925p2SRaqZeB3yaJIo8tRlXhGyAiKpKWU4AtZ24BAD75Nw7fju+ANiGeAICpq47hWkoOVj3dGc72tridpcS+SynYeyEZgJj3nppbgCUTI/Hr4Rs4fDUVvZv4oVWwJxQKYOWBa3i6RwMEeThBqdLgq+0XkJiRj+tpuZh0X330bOKLJXuuYO6Gs0bb9kDbukjNKUCQpyNWHbxeLZ+HNanjYl/mqhXdwn1gZyNhW9ztcs81sWs9jOschhBvJ9jbKPDE0kPYHncbAe6OeLJ7fQDAI5EhyC1Qo0Clgb+7I2b+cRI30vJwX7gPNp1ORMu6HvipRAcFAPz6bBdE1hP/1suyjP2XUtHQ1wU+rg5Qy7Kus+Fmeh4WbDmPnw9fRxN/N3w9rh0a+LqW2e7cAhUcbW30liQ1F204w6wCsnQM5E1UawL57NvAJ+EVOzaglQi459XT3+7TBEiOM097hn8B2DoCa58BGvQGHv+jaJ+6ELCxAy7vBE6vBfrOAuxdxDYAyEkBtr0PBLUF2j0uArzlI8W+16+KToF1rwKHvhfbZtzQD24B4PohYFG/oud9ZwEeIcCaJwGPUODlk/rHx20E/p0p0tKnHAQc7wSK1/aLFO4zf4vns9KBU7+LjgHfJkDGDZEVYOsgOg+K/6OTdgX4orX+dUoLOnf8T7xnAHhyKxDYCjj5q1iJYPfn4nq3z4n3+ehqIKCFWELwxmHgh77idaO+F+9xySDj19Be+9cngNNrgInrgHrd9I9JuwqknAc8w0TgnBwn2tFhEjDsc+PnLY9GLeo3XNsHPLcX8G9eSvs8jG8HgKB2QPxR8XjKkaJOHAB44zqQfUtcx6cxcGI1UL8n4FFX7P9pNHB+051r3PkM8jPF+0y9DNg5A02HlP8+Ui+Le6Tby1WXeUJFlFnA2XViuUxTOgKJarGVB67hs83n4OfmgJlDmyEiyB2ZeSrd0niyLCPuVhYa+LgiOVuJ49fT0S/CH4VqDVbsv4pgL2c8/9NR3fmuzB2KPReSseFUAiLreeN0fCbGdw5DHVd7/HEsHm1DPbHpdCKOXE1Dz8a++HbnJaTnFuC+cB8Eejhi9aHr0P6m6uPqgORsZaXej4u9DXIK1Gb7fKydnY2E1wc1xZYzt7D/kpjS4GxvA3dHu1KnRmj1auKLApUGAyL8kZxdgMnd6kOSgGeWH9GbHuHpbIdlT3REkwA3LPjvPIa1CkJEkDsW/HcedjYKPNerIQDgmx0Xsf9SClwcbPHRqJZwdxS/8526mYHnfjqCbuE+mDk0AqduZuDR7/fjvZEtMLaT8alzl25nw8PJDnVcHaDRyDiTmIkm/m5lFgA15uSNDMzdeAZzRrRAuF/ZHRdl2XgqAZl5KoyODLnrc5B1YiBvoloTyCuzgY/q1nQrSjfwQ+DqXtGJoJ2XX9J9L4qA8eiPRen87R4Xz4vr/Raw7YOi54//BTToCRTmiSA1oAWgygf2fll6eyZtAja9CQz8CJDVwJLBRfsGzQP8mop55ktLBHg2DoBaCbj4AePXAN/cCYQdPADlnSCx7zuinsCPRlLZOz0rflYD3hMj4rIMrBglpixoNewDhPcHNs0ovf3alPLYP4FfHi/a3vwB0TlijLGOmuf3A6fWAJ4h4rN+zxdQlzLK8Ga8SN0/8A0ASfwcFArDDozcVGDxIKDpUKB+j6JOGADwbgC8cEw8vrwTqNNIdE7kpgBftCr9/do5A4V31vRW2AGawqJ9UYeA6EjxeNA8YOPrIrtkxp1RpgVtgdRL4rE2kF8+Crj4X9E5pl8CXOoYXjc7Cdj5CdB+IvD3i8CNg2L7rHRgw+uATyOg41Olt7s6ZSaI+7jtOKDHqzXdGtP98TwQ8xOnTxDVsCNXU/H8T0fx9rAIDGtVseKJWrIsQ5ahG7WVZRm5BWrYKCQ42tngyNU03EjLhYOtDXxc7VHfxwU5SjVcHW0xd8MZ1PNxwYnrGZgxpCnC6rgAALaevYVJS0VRwxBvJ1xPzQMAjOscin0XU3Dxdo5eG94Y3BSpOQVYfzIBHz7QEo8vPoiH2gdjQIQ/NDLgYCtGwQHAViFBpdH/Vfrh9sH49ciNyn9wJXQL98HuOxkXlsjeRoECtabU/U92q48fShSlLMu2V3shLbcAo77aW+oxL/QJx4KtYrlNd0dbzBjSDDPWFA3ENAt0x5kEUa+iRV13pOUU4oMHWqBXEz8AYhpNgIcjgr1Ex9W6Ewk4k5CJxgFu+PnQNey5kKI7V5CHI95/oAUiAj2QnleApgHuSMspwJWUHLQJ8dTLDoiNz8Tsv07D3ckWs0c0R7d52wAAe97og7qeTtBo5LvOVLiRlosDl1Jxf5ugSndMVES2UgU7GwkOtpxyUR0YyJuo1gTyGjXwbolU6bG/AWf/AY4srZEmWTwXPyAnqfT9wZHAjUOmXaNOOJBy4e5eO2YVsPpR065f3Lg1olOhNN4NgdRiyxa6BwOFOWJ0+5HlInBPvw7Mb1H2dV48LqY4mLPtWkFtgfg7HQUdnxadFLnFfnFS2AGtHgFiVui/LqQz8PBSkanQoDdwYbOYSlFa50LP14Ed88TjYfOBtuOB6/tFB45dKXMaL20XNQvue7H8GggateiYcjAyQpB2FXD1N5zqsOktkT0C6Gd+aDtatn4gplQM+Rg4vEScv8vzZbejJlVm+gQR1SrXU3ORW6CGj6s9nl1xBCPa1MX4zkWjt1NXHcPfx+PRsq4H/p7arYwzGcovVCM1pwBvrT2Jx7vUQ++mfjibmIlB80WRxIa+LkjLLcRbQ5qheyMfpOUWwt3JFu/8eRoPtgvGsr1XsO+SCBDfGRYBO1sFRrWtCxcHWyRl5WPIF7t1mQhlFQ0saWirQJy6mYGrKbmVej+10eAWAWgf5oX3152Bm6MtXuzbCHGJWXfdIdPA1wW9Gvth8Z6yOypWPtUJZxKy8N4/sZAk4Pz7g3H8Rgait12AWiPjwOUUjGoXjPfvbwGFQtJlvvi6OaBzgzrwcXVAh/e3IDlbiQ8eKMpI2HY2CasPXcNzvcJxOTkba4/F44tH2sDDyQ7nk7Lx5tqTiOrdED0b+6FApdHVRUjPLYC7ox3OJmYh3M8V2UoV+n+2A438XfH94x3w7Y5LWHnwGrqF++CjUS3hbG+Dc7eyEe7nanT1FFmW8c2OSwj3c0WbEE/d0pVl1RUpTfHOjkK1BokZ+UaXXLV0DORNVGsCeUD/F98BHwBdp4h535vfLvoFn4jMz9FDBMntJ4pOgWbDiqZ7aDSiw2fxAPG82ytA4kmg7ViRNn5tHxB2n8g4+OlhkZFxbb84Zvh8Me0kvB+Qkwz8T6QoIjgSeHJL0fXzM4F/Xi4qRPnQEtGpcGkb8N97wMNLgGXDxb4ph4GFd6YGTIsD3AKAxFOArBFTOgDg4PeApBAdFLF/AH7NAP8WlSvCWJaE4+Iz6TC59HN+FAIoxUhLuYF8TorosPFtYp72EZFFU2tkxKfnmTUw+GHXJazYfxUrnuykG+EtS4FKA3tb48HNPyfiEejhCH93R91obu8mvnhneHN4O9vDwU6Bl3+OQZsQT9goJBy7lo5PR7eGg60C9WesN3rOsDrO+G58B3y38xIa+Lrgj2M30aepH77dKTLSujfyQYFKo5cWrzUmMgQPtQ/G3A1ncfhqWrnvrTYu42huHet742CJn0XJqSNLJkaijqs9RizcY/D6gc39ceRqutHpKV+NbQeNLGPKymOlXv/BdsH4/WhRx4a3iz1S79R9iOrdEDaShIy8Qqw9dhP1fFzgYm+r66DStnXjSz3wwbozOHY9Dd+O74BziVkY0SYIydlKbI4V9UEC3B2hkUVGioezHTQaGZ/8G4dle69g8cRIdGpQB/M2nsXX2y9i7qiWGNMxVHeNc7ey4Opgi5jr6egf4Q+1RjbLUq/ViYG8iWptIP/aZf1iZsd+AjbOKEr/BsQv+ho1ENYV2P9V0fZur4g0a1tHUWBOloHfntBP2X5mlyis9mdU0baXTok0b+1cZlM9uEhUXCfzGvC+KHh49EfgaCWL91HFNB0mAmR7V7GixLkNxo/TTh2570XxfM8Xxo9rNgI485f+tr7viIKATYeJzIG88n/5MuB05zsi784vE09tE7UqtFNG7ntJLD8JiPoDkzYVfa/s/ER0YLR/Ajjxs+hsUNgC5zaK1RciRgBOXsavq/2uenAR0PIh8fjwYlFos8d0MT/+02JBeclAPjcVyLldFLh/FCq+26YeBeo0rPznUJ5jK4Cd/wPGrCy9xkNNOPmbKIwZ2rmmW0JEdymvQI3fj97A8FZBFVo+70JSNm5l5qNzgzr4eNNZdKznjVBvZ/i4OsDLRX8ZYFmW8euRG2gT4onG/qJzOT23AG6OdsjKL0R+oQaeznZwsFVAkiTIsoz/ziQhr1ANN0dbdG5QB3/G3MTrv4t09l+f7YJ6dVzg5WyHpCwlvtlxEQ+3D0HcrSzsvZCMG2l5uuUbXx/UFOtOxuPUzUyD97BicieMW3QALet6IKp3Q7y/7gxupOWZ+lHSPWBoq0AcupyKpCzDDoYvH22LBf+dx/mkomLao9rWxZpjN3XP173QDRtPJeKnA9d0HQtazvY2WPVUZ1xPy8XJGxloHeKJXk184WxvW3VvyEQM5E1UKwP5CX+LucklybIY6dMWQxuzUqRDa/eplGJueWmFpc6uF79s938X8I8ACnKB1Y+JUb9+s0URMABYOgy4UrROLx5aIjoCSvP8fjGn2c5JFBRb/SjQZ6YIcrbPFQHD438A/70rKqhXRnkF/LRz3gGRSh37h3hs6yg+C2O6vSLmTmvTs139RbE1cyle2M3Yc2OcvEUw1qCXSG8vngJffE75s3tEDQFA/MzneFa+fcU/p4p4cJGYD3/jMJB0uvLXM4d2E+79TgvPUBHI3us6TBYdDyWnHTTsI7ISbh4Rzx09gTeuFu0vzBcFKk//AdwU81vRdaroWLp5BPi+j9jW8mEgK1H/O2TGTZEhsPF1sbTikaVi2svzB0Q9C+1339BPgcgny26/MkvU62jQG7At9kvvjo9FYce+s0RGRXHa8zcbIaaRVERBDrAvWrzGr2nFXlMZCSeAb7vfaR+nHhBR1Tl1MwMFag3ahZbSOXtHRm4h/vfvWYxqF4x2oV5IyylAbEImujasA0mScD01F3Y2CgR4OCKvQA0HWwUUCrG9+8fbdOeZPrAJDl9JxX3hPvB2sYe/uyMKVBrM/+88pvYOR6CnI/45kYAV+69iZJu6qOvlhGd6NMBPB66hvo8L2od54Yv/zkOtkRFzLR0nbqYjv1DM73+sUyg2x97Cxw+2gkIhQaXWYPIy8W+Ss70NcouNiEc/1g6Xk7Pxyb/ndNtsFBLUmsqHW/XqOOMKp0WY3eGZ/eDj6lDTzSgVA3kT1apA/uo+UeW8/YTSj1EXAh/WFSNmr8SKUUNTlSx2duJXUR3e1hF4YoMocBcdCdjYi3nYN48A/80Rc8MfXioKtFXU708BJ38RwWS9bmLZNvdAEezePiPmbV/YIkblWo8RRc52fiKeX9ou5r1r0487Pg30fEN8FnlpYkm6PfNFYN5kiMhSaPGgKLSWdEa0t8/Movf622TxeU/eLF6/eADQZKjoCGnQGwjtJArbxawUAUNOMrD9I9HRolGLYl4Z18T67oM/BpzrFC0x9997wK5PgEYDxcjrL48XBeetxojK7E2HAf3mALdOAU0Gi4J5oZ3FKKhGAxz8Viy1V78noLAR20oWdLu6r0SVewkIbC2W2Vs/HTj/r9js5A1EHRCfhb2LqMj/37tiTrmWi68YJW0yFOj8rEjlHr6g6H5UFQDf9xbt1arbQczVbtBTdOZ0iRIjz+nXxc9ZW6SubgcR/I1eLgod5mcCWfFF5+n9lgjuMm+Kz7HHdODEL+Kz820qfib/Cxdtf3QVcOs08NfUotfbuYj5/lodnwYOflehW7JWGr8WWF6Bv7dNhgD3RwMJMaUf3+5xwN4N2B9d+XZ0e1nURdAWfOz5ujhf2lVxfw75X9F0gYybokPs53HieY/XgD5vicf5mcDcYtWGtVMOAECtAt678/dGYQc4ugMPLRadZoBYuWLlGKDTM6LQoLpQ/H3fOEN8h9g6AjMr2dEXHyOmZhTmAmufE8tWllxZ4eRvRRlLs9LNN+2BiKgG/BlzE94u9mgf5lUlI6wbTyWino8zmgYYxgJHrqbqpktk5asQ4CE6DryLZTjsPHcbSVlKPNQ+GBqNrPvK3Xo2Cem5hXigbV28sPoYrqTk4L6GPropDQDw2qAmeL6XWFnqWkoubmXl44st57H7QjL6NfOHUqXGrvOins8bg5tidIcQfPpvnNElBQFR2LF5kAcWbr2AZoHucLa3QVxiFuJuZekdNyYyBDfS8gyKLD7aMQQ7zyWja8M6enUD2oR4IuZ6eqmfYcw7/fHg13sNCkrWpCtzh9Z0E8rEQN5EtSqQr6iMGyLArIpRIkAE9jcOiXm12nnCKqUYUdMuMZebKgK6et0r9wuoLItRS48QUTH9bsz2BCADz+4uWl++uhTmlV4MzeDY/KKCZlm3RCDbePDdv+/SpF8ThdC6PC+CYAd3EazIMpB2GfCqX/rPSHUn7cnGruI/x+/7ivfSbgIwYkHpx2lHHJsMBR5dWeyaSlFZ/+B3ImNj4nog5E7V+vQ7leo9jSwBk5sqOjS0Swv+/SJwdLkISgNaik6ZcxuBXjPEcoLF36O2I2b352KNe696ohMpOBL4eSwQ0kmknUuSmP/9bbGMmLG/Az89WPS8fg8xJ12bzl6cZ6iovF8VhQCtkZNX+VMK7r/TQVB8GlBxQe1ER1Dxn9HTO0TH3Y6PRWfQz2MNXzczSXQOKYuljQa1BVIuApM2Ar9MEEscAsA7aWKVhOAOwJXdojPBM1TcM+4lqoBnJwGfNBKPQ7uIGgoA8HKsWFJRpRQdCid+Bv54Vux7K7H075XrB8UUDM8Q8Vll3wZ8G4slOtMuA61Gl/7ZAeJ7QNaIvzulUWYZLgFantxUkWHVaID+udOuiI6ZLlOBVg9X7pxERPcIWZb1Kt2XJb9QjUW7L2Ngc3+E+xV9l/535hb+iInHhw+0gL2tAj/uvYqu4XXQLMDdaFV8lVqDV389jrpeTpg+sKne9i1nkuDjao9Lt3PwcIdgXduylSr8czwehWoNxnUOw5y/Y/H7kRu6GghjO4WiVbAHRrULht2dgnayLEOp0uDNNSex5thN3BdeB90b+WLuhrMAAA8nOyye2AH7L6XC3kaB80lZuJycg0NXxL/XbwxuClkG1p9MwMmbGWga4IY5I5rjke/2V/pzZiBv5RjIk4H0a6IzI6xrTbekdsq6JVZTaDW6/F/+s5PuZCqUEkSolPpBd2WoVWL6hLHK8JWhzBYdGcXbEX8MuLhNjEj7NRXtXP+qKGrXeowIYr5sZxiEajMYTq0BPMOAfV+KTIv8DFFE79ZpkV2Ql3ZnfrhUlKYOAGHdRCfHjYMi+6TJ4KIpBU7eYgR63TTxvKzpI1S+0K7AtVKWTer/LrD/m6KsEa/6Imh2DwYyS1RNLp4WryoANr4BHF5keM5WY4Cre4CM66IDtOVDojMKAF49L7JvtC5uFZlPOSnAga+LrhPdCbh9Vn/VhUn/iuyh0qx6TBRejDoA2Jco8KVRA582FdMcxv4OhPcV20v75TXrlpjm4xEMfNNd1FkZNE9k8GiteFBkVQEi0yA/o/TpXsYkXxD3f6sx5u/0NBe1SnwOFe3UBYBfJ4rMkXG/my/7QpZFx6Sto1gy1KMuMPrH0o/PShQZa8z+ILJ6sixDrZErVY0+W6lCboEKfm6ORvenZCuhVGkQ5Gn8u+96ai7i0/OglmWE1XHB9rgkNPBxxaPf78fw1kF4fVATXLqdg2ylCj/suoT3RrZA8yAPo+e6VzCQNxEDeSK6px37qajY5BMbRDZEcQU5orZFg96GgUluKnB+sxhtjVkppn64+IkRVFkjMglSLopRXzsn8Yv78gfECOrkf8W+6DvZDCMWiikO841kqXSdKoraXd0L/DVFf59PExFoVbZ+BQntJohOxWbDgW97Fo3iV8bY3wGvMLEEYa83xBSWkqYeFZ1HJfWeKToX6vcUBQtTzgO/TBQZVY/9XFRHw8ZeZCGkXRG1UhJPiOlKWk7eooDqhc3AiydEewDg3L9iikCzEcC7d+bXDpsP/POSeBzYBnhmR9F5vuoCJMXqt/GpbUDdYm2XZWD/16LwYusx+sdq6xk88B3Q+pFSPjAzSDwparpEjBDZUwU5hlOXitNoRKdaSEexwkTqZeCFYxXrSCzMBz7wF4/NWdDxn1cMO43GrxXfNSWD9ZiVwB/PiWlLfWaa5/rWSttBUtksFSIyKiEjD76uDpVe4u5ewEDeRAzkiYiK0f4zof1FvWSNC21wFtoFWNAWgARMPVI0zePKbjGC599c1CVoNkJ0FOz69M7UDDcR2Ps2Ex0GkgSseEhMXxj9o6hloa2KP/JrERyU1GuGCBazb4kshqvFlt554ZgI9gD9jIKgtiIToioUr95/rwruKEaiTSHZALK69P1PbRNTBjIqUJRx6GciID/0g3j+6M/AqlIC6yGfAB2fEo+/aCOyF4qLuB9oPgqALFLxP2kCFNyZC/rsHlEHQ1KIji5tIG/jAEzaIDoXSlIViCKn2kArN1WsTNBqdMXqxtyKBb6+cw8++Z+YtnH7rKi5YmMrsmfqd9d/TfGaBlpjVgKXdgBNBolikaXJTQU+ri8eaws8GiPLIlun+Io1ZZldykjWoz+LNhX3fgCgulNVvCLFFVMuAv++DTQeIJblrKikM6JIZM/XxPQTS/TbZFGLJ+qQmMZCRLUWA3kTMZAnIrpLuXfm8Vc0MKgojVoEXpIkRhuXPyCCE2cfwLsB0OZRUYMgLw3wri9Gdf94Drh/oZgukJ8pChe+e6ddNvbA27fF8n2b3zG8XvHihY0HiVoIAPD4n8DW90VNj+JsnYChnxTNq58WJwKTpSUKztU2VdWh4dNYjAQDwHe9RNHM4rwbFBW+LEv3aaJDSUthC7x2qaguhkYNxPwEbPtQpOw/uUUUHU2IKXrN/dGi48DGvmi6TH6GqJlwbpNILV/3SvlteeBb0fmgXRlhw+vAgW/0jym+IklZBQtTLhZlU7QZB3R7CfBpZHjcpreAfQuBJzYCYV2KtuekiL/DJc9fWiAPAK0eEddoOVpkV3wQKDIrALHSjF+z0l97YYuYIqG7TjmBf146kJ8OZCYUFV+t2wF46r+yX1dZhfkiU6nkFBFz036uHSYBwz6v2mtRxWTGi5oqlZnOQmQGDORNxECeiMgKlMwcAICbR0XgPmiuWFZRVSCWOPyul9jf522xKkXP10Wqa26KqD2QfE50EmjXX89NBTa8JpafG/w/kS7t4itGaV39i0YntTUbCnKArzqLVRKK876T8px6URRETLti/L3MvA2872uGD6UMClvx3mu7BxeJzp+YlaLw38lfivZ5hIiaA6UZ+pnINlk1pvRjytKwj6hXUBEtR4sA/dIOIDcZiP0LGPOTqIXxdTfg1kn945/eLrJQitMGkEFtxf7934gsjVO/A52fBwZ9VHSsSgm874dy+TQBphwEPggqWtmj8SBR/6MwV9R0GPC+aG+/WWI1mZIdBDNu6k8hiFkp3pc2W+KTJkB2YokLS8DsdODyLlHPYcj/ROeBRi0+0+0fielA/hHlvwdAfH983lxkCU2/qL/0ZEUos0RnUkAr0emw+3NRh8HY9bXvv93jwLAvTKvVcHWv6MRMuwJ0jgIGfXj356opyReAy9vFNCJtwePqlHZVLJfq3UBkdBFVIwbyJmIgT0RUy6RdEdXZtasZVIQsi1Ebj7oVO15dKH653/mJCEIubRdLS3o3ECnqtvbifJJCjFDaOIilHW1sxTG7PwcO/iCKl13bJ4oWBrYRQUvObbH0ZZ1GYknGs/+IlPKUC2JE6Yf+IjUcKCqkB4iAqvWjosPBPUisoHBtnwh6ipNsRDX+LbMq/vmUZsgnopBjVYgYCcT+UTXnthTNhgNn/ja+L+J+8bPuMV0UGixrhB0QI8RNhoqlE7MSgPktKtaGNxOADwMrduzsDMN2FJ8OcHmnWJoUEB1a6VeBhR0MzyPZiKkOx1eJ5wEtgcinxLKo2nu/ZH2F0lzYIrIS1j4tnpdMeVff6fCyKWPJs8WDRXHL8WvF0qbHV4m/028nGR5b/P3X6w5M/Kf8NhbmifOVDPrneIksAt25y8hukGXxx1jHgbrQeBBdmZV0SpN+Hfhtkiha2eJBw/3az2PgR2J1nOp2aFFRFk1FpoXcy1TKouVJK9qJRTWKgfxdio6ORnR0NNRqNc6dO8dAnoiIqo6xjIG7cfucqDjfekzp58tNFWneYV3FPPRVY0QBuQ5PGD/+whZgzdNiVDPiAXFeSRIjZUeXAnu/FMeNWyM6EVx8gRWjil7feBDQ/AFg7TOiZsEjK4Cz60T6tY2dWC6veDq8s48YVfZrDoz9Ffi8xC+c3g1FfYSAFmKJv5Lp+jb2QHh/Uazul8cr8eHVYsVXIqiIgR8Cm940fzv6zgL+m2O4vdebQNx6UVsj5iexzdSpGgo7cV82GyY6NbTy0sTIvY2dCI61hQK1Hl4G/DtTZCl0egb4+j6RXeMXIYLRhn0ASPqFR7XBaIuHRGaHtk7E7AyRNXBuo3jv2YmGBUPfSTMMrpVZYrRdXSiyGBa0FUuShnUDHl4KuPrqX1erQW/RqWcsIP5lgpiq8ewe/bYfWQqsfw0Y8rGYshBwpwNn70Lg37cMR/pvxYrpFPYuhtcwZtVjQNy6os+jJO17aDoMGPW9yBYqmRGhUgJxG8TyrOaeyhWzqmipzpm3Da+t0dy7K1wUp1ICHwQUdeyUNR2H7hkM5E3EEXkiIrJqhflFxQgrS10I/PWCWLGgeAX4tCvAl+1FWvojK8S2m0dFATIXH/1z3D4n5pnvWwi0fgxoPBA48xfQYbJIqS7ILVotYdxvYmUF7einLIupCvYuYmRfUgCDPxa/oMbHAN/1FMcVr23Q83XgvhdFivb6V4Heb4lpBNpA1sVPjObuW6jfzoiRYgUGzzDgk3DDz6KsJQWrirElCauTjb1Ik7f0zIcRC0U2TdwG4MTPoq4BUH4RyEFzxfSA4twCRSHNp3eIoHP5A0BynPHXF+8Q6foCsHeB8eMCW4taDRoNcHW3/j5jWRe93hRTer7tYfx8ZQXMgW2AEQsAtyDx9++DEgUcXzkLuAfqdxK8fFp0+GmXtAQMV4sAxN9XjUp/dL/41I+y2hXeTxQztHMGog7qB8//vQfs+kQUipy8SWxLvw5sfU90uAS1Mf45lGXrByJTqt144K+p+u9d67fJ4v54bm/lVxkoLcuhqtw6DXxdbNnkt27d/fc+VRsG8iZiIE9ERHQX8jMAe7eaG63KSwfm3VnG7q1EUcxNUwgM+bRoVC0/U4w+atTAmqdEED94rth386gY8fdtIqYouBabE/7LBDFCPHmz+CU/9TIQdUBUTL+wRUyT2PwOcGK1SPN+eruYErFllphze2GzOM+gecDG18XjB74rSt82RjsNwsVXFIzTdogYnSNeCs8wkY5emoBWYmlAY7pOFaPHxYv7aVO/jS37R/euXjNEZot3fRFM1gkHPgzSP8bGXgTPcev1t49bI7IOtEtLAsCEf4Blw/SP8wgRBUFPrRH3zvYPRUFRhZ3IXLi8S2QBbfuo6P4tK5AvbvSPotijZ4h4XvzvgPYcP/QThUidvIHXLxueAxAdC1tmif/3naU/PUJ73bBuRZ0nz+wCAlsZHnN/NNB2nP65L24VnQE9XxftLF7gMfUy8E13oO1YYHAlMmEA0WmhLgAGvFe515UM5KdfFN8hR5aK75SmQ8XKLVd2i84PhU3lzl9R5so+qyUYyJuIgTwREZGFunZA/HJubBk5UxTmiwKILj7iF1OV0vjoVk6KKJJYfE61LIvR/sA2Ypm581vE2u7e9YuOSTgBfNtdTBGIuF+kgNs6iiC85Drw+RmiUGN+BnDsRzHn3cFNjN5+VFcUlQuOBDo/J+a4p10Rc86zk8QyjdoaCL1nAj2ni/bErBCjxZ/dCT66TxN1EQBRBX/Da0DyeTFy26AX8H1fUacBEJkUdduJkeb8DBHgHFshOjLqNALObzLtsx/wgci82DTDtPPQ3ek3W2RhRHes+GuaDC1Kny/LrHQxteLk7+Lv1uB5wKL+pR8/9Shw4T9gw/SibTNuiE6I4sUYXzkr7vO67cTUovteAi5tBQ58V3Q/PrFBdG5k3xKB97x6xq/55FYguL34O6ctOlp8/n5+hri+QSbDGVF7BBBTM7RTkmZniL9Trv76RR2NUWYBHwWLx9PiRM0HyGK6h7qg7HoFNw4DP/Qtev5CjGindurSW4lFbS5rqpUpzvwD/DVFTJFoVMbPlXQYyJuIgTwRERFZpNxUkcLr5l/6Mf/OFJ0LAz8w3Bf7p5iCcH+04ZSI4hKOA79OFNMUWj5UdG2Frch4SL4gpj84eYlpEznJYqpD9i0xWitritKXOz4DHPy2qHr+8lFiZPWZnSIrQjv3OjdVBC/HV4lpDwkxQKOBwO2z4rr7osWIsrO32Bf5lEjrPr1WtPfosrI/u8A24nVNh4mCkaYY+imwZ0HZ2RDaaRIeoUVz6GsbezegIMv087gGlJ2l0v89YPPbpe8f+FHpHUUNeoufp0al35kx9FPA0RP4fbLoaCpeZBAAxqwCmg4Rf9d2fCwyFABxj17YArR8WBQbXTFKTAVq8aCoY+IRXHSO/94tWiJz8MeiQw0oqivSbIToaFkyBFDlAY/+XLSU5MVtwPKRRed6do8YGdeO0j+xAVgyWDxuO078nS8uK1F0NpQ3mq6dbuXqK7IcFDbAyd9EhoK2vgVQfuHA2L/EtJWRX4upW4DIcEi7Aoz6zrAdhXli6kVQW6sa8WcgbyIG8kRERERVLOG4CBTcAkTBOXtXkfatUoo5547lVNWvjLw04Ny/QEhH4OoeYN00Ebx0fFpkKgS21i/4tvUDYOfH+ucYt0ZMRVg6RCxJWdxDS4Dfio1oPn9AZCSolSJoS7kIHFkmAvY+M4EuUwFbB/E+Af3RXEkhAqKre0UnRfHOgJdOiayKuPVin1adRkDKefF4xEIxCko1q2FfUT8h9g+RzWOMk7coWlhcj9eA5iNFocfyVpYoybuByGBZ/ajhvtAuQJuxRfdGyak9Y38HDv0gphYlnhCBeN93RHbOiV9EJsSIBWJUv3jgfP0QsKifePzUVpENZazdXaaIYpGeoUBmgqhN0e7xomKFX3YouodfOSOKSmo/m+JTHLSp+qvHig63hxaLTpDrB0Vwr+0EsFAM5E3EQJ6IiIjIihXmiakLpY3kqZTA7vlFo6ja0dXizm8Bji0Hhn4GuNQRdRdWPiICnTE/VW6UMD8DOLxEFGnzbVaUUaHRAD+OEEH7c3v16zYkXwB2zAW6vSKWFjv1u6irULedWCVizwKRqVCYC7R5TLzfhOOibsSRpYZtGPghcODbsrMITOXgDigzq+781sLRQ6T0LzTzFCFTBbYW95BHKNB+guigajIY+GW82D/2dxFIv1dKNk9oF2DSRuC7XmJ+vo29qEeScFx0HGiVzNRoOVpMU+gwSdRC0KiKgvzQLsD4P/RXm2j9GDDyK4scqWcgbyIG8kRERESE64dEOn+9+2q2HVVVMEx7XlWBKAgpy0B+upgSUZAr0tWXDhdL3vk1ExkMV3aLToMmg4HDi8SykQe+Ljrn1KMiy2LdNDENAhCp48M+F+ddMkQse6cV0BJoOryo0wQQGQZOXkUrCAS1A/q/K6rKp5wHhn8h0tozbwKfN9d/T8PmizaeXgM0HyWC4sOLyv4cvOqLkeLsW6LTxO3O3Pas+Mp/pg16iXaScd4NgdSLd/faet2BK7sqdmyfmeLnWK8bsOpRoNPTQPuJd3fdasRA3kQM5ImIiIiIKihmJbDtQ7EkZZ+ZYlt2klgZov1EUbxRS9t5oFaJlHMnz6J9apWoZdB4oOhAWfscEBIpKsGXttzbgW+L5o5r06xLyksX9Q9cfIErd+aKS5JoS+ST+p0kymwxxaN4CnnxVPHRy8X0h83vGAakT2wQSxh+3lx0gjh6io4RLW39heajREeDKTUC6nUXn8vmt8XoNhkXdp+YTgOIDpvJ/+pnttxjGMibiIE8EREREREBEBXgbxwC2k0A7J3FtotbgeUPiIB/yiGx1KM28E+9LNLFmw4DjiwBMm6IonSyLAoceoYWnXvvQuDft8Q0CI1KFNe7tK1ov70r8OZN8TgnGfjfnVUsxq0BwvuKzIm0K8C6V4Br+/Tb7dME6PgUsP5VMXc9pBOw/6uq+IQsx1uJZVf7r2EM5E3EQJ6IiIiIiMp0fjPg0xjwCjPtPOpCkQWgzALsXEQF+i/bA1kJwKgfgFYPFx1bmAekX9df4hIAbscBK0eL4nSxfwJX9wEvnRRTFC5tBUK7ijoJ5zYA618THQqAWMqu+Ugx3/3UGlHI0SMY2P3Z3b2X1o8Bx1eKx171RcG8ddNENkRynOHxXvVER0R1Ka96fg1jIG8iBvJERERERFRjCnLFfP2qWF7t8i7gp4eBAe+JEXtjfnkciNsgsg0UtmJqw9w7mQT2rmJahH8L4Lk9oqr9mqeAwf8T59syS1S5f2QF4F2/6JzzWwLpd5ZaHLcGCO4gahjkpQMnfwU8QoA988XUCu2ykQ16AW3GiVoK1/eL5fhG/wj4NgXO/C0q/yszgMs7i4o49poBbP/I+PtiIG/dGMgTEREREZHVUqsAG9vS92s0YoWB4jUMbscBt06L4Pr4KqDlw0XzzfPSxOh/Wa4fBLbMAQbPFYF5WbKTxDKLLR8G7F0q8o6A/EzRyaBQiJUnMuNFBoOjB/DfHLF6Q4NeFTtXDWEgbyIG8kRERERERFSdKhOHKqqpTURERERERERkBgzkiYiIiIiIiCwIA3kiIiIiIiIiC8JAnoiIiIiIiMiCMJAnIiIiIiIisiAM5ImIiIiIiIgsCAN5IiIiIiIiIgvCQJ6IiIiIiIjIgjCQJyIiIiIiIrIgDOSJiIiIiIiILAgDeSIiIiIiIiILwkCeiIiIiIiIyIIwkCciIiIiIiKyILY13YB7kSzLAIDMzMwabgkRERERERHVBtr4UxuPloWBvBFZWVkAgJCQkBpuCREREREREdUmWVlZ8PDwKPMYSa5IuF/LaDQaxMfHw83NDZIklXpcZGQkDh06VI0tM5SZmYmQkBBcv34d7u7uNdaOe+GzYBvunTbwvmQb7rU23Cv3JFDznwXbcO+0gfcl23AvtoH3JdtwL7ahttyXsiwjKysLQUFBUCjKngXPEXkjFAoFgoODyz3Oxsamxm8kLXd39xpty73wWbAN904btHhfsg33UhuAmr8ngXvjs2Ab7p02ALwv2YZ7rw0A70u24d5rA1A77svyRuK1WOzOBFFRUTXdhHvGvfBZsA33ThvuFffCZ8E23DttuFfcC58F23DvtOFecS98FmzDvdOGe8W98FmwDfdOG+4V98pnwdR6C5eZmQkPDw9kZGTUeO8UkRbvS7rX8J6kexHvS7oX8b6kexHvS0MckbdwDg4OmDVrFhwcHGq6KUQ6vC/pXsN7ku5FvC/pXsT7ku5FvC8NcUSeiIiIiIiIyIJwRJ6IiIiIiIjIgjCQJyIiIiIiIrIgDOSJiIiIiIiILAgDeSIiIiIiIiILwkDegn311VeoX78+HB0d0b59e+zataumm0RWavbs2ZAkSe9PQECAbr8sy5g9ezaCgoLg5OSEXr164fTp03rnUCqVmDp1Knx8fODi4oIRI0bgxo0b1f1WyILt3LkTw4cPR1BQECRJwh9//KG331z3YVpaGsaPHw8PDw94eHhg/PjxSE9Pr+J3R5aqvPty4sSJBt+fnTt31juG9yWZ20cffYTIyEi4ubnBz88PI0eORFxcnN4x/M6k6laR+5LfmRXHQN5C/fzzz3jppZfw1ltv4dixY+jevTsGDx6Ma9eu1XTTyEo1b94cCQkJuj8nT57U7fv444/x2WefYeHChTh06BACAgLQv39/ZGVl6Y556aWXsHbtWqxevRq7d+9GdnY2hg0bBrVaXRNvhyxQTk4OWrdujYULFxrdb6778LHHHkNMTAw2btyIjRs3IiYmBuPHj6/y90eWqbz7EgAGDRqk9/25fv16vf28L8ncduzYgaioKOzfvx+bN2+GSqXCgAEDkJOTozuG35lU3SpyXwL8zqwwmSxSx44d5WeffVZvW9OmTeU33nijhlpE1mzWrFly69atje7TaDRyQECAPHfuXN22/Px82cPDQ/7mm29kWZbl9PR02c7OTl69erXumJs3b8oKhULeuHFjlbadrBMAee3atbrn5roPY2NjZQDy/v37dcfs27dPBiCfPXu2it8VWbqS96Usy/KECRPk+++/v9TX8L6k6pCUlCQDkHfs2CHLMr8z6d5Q8r6UZX5nVgZH5C1QQUEBjhw5ggEDBuhtHzBgAPbu3VtDrSJrd/78eQQFBaF+/foYM2YMLl26BAC4fPkyEhMT9e5HBwcH9OzZU3c/HjlyBIWFhXrHBAUFoUWLFrxnySzMdR/u27cPHh4e6NSpk+6Yzp07w8PDg/cq3bXt27fDz88PjRs3xlNPPYWkpCTdPt6XVB0yMjIAAN7e3gD4nUn3hpL3pRa/MyuGgbwFSk5Ohlqthr+/v952f39/JCYm1lCryJp16tQJP/74IzZt2oTvv/8eiYmJ6Nq1K1JSUnT3XFn3Y2JiIuzt7eHl5VXqMUSmMNd9mJiYCD8/P4Pz+/n58V6luzJ48GD89NNP2Lp1Kz799FMcOnQIffr0gVKpBMD7kqqeLMt45ZVX0K1bN7Ro0QIAvzOp5hm7LwF+Z1aGbU03gO6eJEl6z2VZNthGZA6DBw/WPW7ZsiW6dOmChg0bYtmyZboCJHdzP/KeJXMzx31o7Hjeq3S3HnnkEd3jFi1aoEOHDggLC8O6deswatSoUl/H+5LMZcqUKThx4gR2795tsI/fmVRTSrsv+Z1ZcRyRt0A+Pj6wsbEx6FFKSkoy6FklqgouLi5o2bIlzp8/r6teX9b9GBAQgIKCAqSlpZV6DJEpzHUfBgQE4NatWwbnv337Nu9VMovAwECEhYXh/PnzAHhfUtWaOnUq/vrrL2zbtg3BwcG67fzOpJpU2n1pDL8zS8dA3gLZ29ujffv22Lx5s972zZs3o2vXrjXUKqpNlEolzpw5g8DAQNSvXx8BAQF692NBQQF27Nihux/bt28POzs7vWMSEhJw6tQp3rNkFua6D7t06YKMjAwcPHhQd8yBAweQkZHBe5XMIiUlBdevX0dgYCAA3pdUNWRZxpQpU7BmzRps3boV9evX19vP70yqCeXdl8bwO7MM1V5ej8xi9erVsp2dnbxo0SI5NjZWfumll2QXFxf5ypUrNd00skLTpk2Tt2/fLl+6dEnev3+/PGzYMNnNzU13v82dO1f28PCQ16xZI588eVJ+9NFH5cDAQDkzM1N3jmeffVYODg6Wt2zZIh89elTu06eP3Lp1a1mlUtXU2yILk5WVJR87dkw+duyYDED+7LPP5GPHjslXr16VZdl89+GgQYPkVq1ayfv27ZP37dsnt2zZUh42bFi1v1+yDGXdl1lZWfK0adPkvXv3ypcvX5a3bdsmd+nSRa5bty7vS6pSzz33nOzh4SFv375dTkhI0P3Jzc3VHcPvTKpu5d2X/M6sHAbyFiw6OloOCwuT7e3t5Xbt2ukt3UBkTo888ogcGBgo29nZyUFBQfKoUaPk06dP6/ZrNBp51qxZckBAgOzg4CD36NFDPnnypN458vLy5ClTpsje3t6yk5OTPGzYMPnatWvV/VbIgm3btk0GYPBnwoQJsiyb7z5MSUmRx44dK7u5uclubm7y2LFj5bS0tGp6l2Rpyrovc3Nz5QEDBsi+vr6ynZ2dHBoaKk+YMMHgnuN9SeZm7J4EIC9ZskR3DL8zqbqVd1/yO7NyJFmW5eob/yciIiIiIiIiU3COPBEREREREZEFYSBPREREREREZEEYyBMRERERERFZEAbyRERERERERBaEgTwRERERERGRBWEgT0RERERERGRBGMgTERERERERWRAG8kRERFboypUrkCQJMTExNd0UnbNnz6Jz585wdHREmzZtaro5ZZIkCX/88UdNN4OIiMgoBvJERERVYOLEiZAkCXPnztXb/scff0CSpBpqVc2aNWsWXFxcEBcXh//++8/oMdrPreSfQYMGVXNriYiI7l0M5ImIiKqIo6Mj5s2bh7S0tJpuitkUFBTc9WsvXryIbt26ISwsDHXq1Cn1uEGDBiEhIUHvz6pVq+76ukRERNaGgTwREVEV6devHwICAvDRRx+Veszs2bMN0sznz5+PevXq6Z5PnDgRI0eOxIcffgh/f394enpizpw5UKlUmD59Ory9vREcHIzFixcbnP/s2bPo2rUrHB0d0bx5c2zfvl1vf2xsLIYMGQJXV1f4+/tj/PjxSE5O1u3v1asXpkyZgldeeQU+Pj7o37+/0feh0Wjw7rvvIjg4GA4ODmjTpg02btyo2y9JEo4cOYJ3330XkiRh9uzZpX4mDg4OCAgI0Pvj5eWld66vv/4agwcPhpOTE+rXr49ff/1V7xwnT55Enz594OTkhDp16uDpp59Gdna23jGLFy9G8+bN4eDggMDAQEyZMkVvf3JyMh544AE4OzujUaNG+Ouvv3T70tLSMHbsWPj6+sLJyQmNGjXCkiVLSn1PRERE5sRAnoiIqIrY2Njgww8/xJdffokbN26YdK6tW7ciPj4eO3fuxGeffYbZs2dj2LBh8PLywoEDB/Dss8/i2WefxfXr1/VeN336dEybNg3Hjh1D165dMWLECKSkpAAAEhIS0LNnT7Rp0waHDx/Gxo0bcevWLYwePVrvHMuWLYOtrS327NmDb7/91mj7vvjiC3z66af45JNPcOLECQwcOBAjRozA+fPndddq3rw5pk2bhoSEBLz66qsmfR5vv/02HnzwQRw/fhzjxo3Do48+ijNnzgAAcnNzMWjQIHh5eeHQoUP49ddfsWXLFr1A/euvv0ZUVBSefvppnDx5En/99RfCw8P1rjFnzhyMHj0aJ06cwJAhQzB27Fikpqbqrh8bG4sNGzbgzJkz+Prrr+Hj42PSeyIiIqowmYiIiMxuwoQJ8v333y/Lsix37txZnjRpkizLsrx27Vq5+D+/s2bNklu3bq332s8//1wOCwvTO1dYWJisVqt125o0aSJ3795d91ylUskuLi7yqlWrZFmW5cuXL8sA5Llz5+qOKSwslIODg+V58+bJsizLb7/9tjxgwAC9a1+/fl0GIMfFxcmyLMs9e/aU27RpU+77DQoKkj/44AO9bZGRkfLzzz+ve966dWt51qxZZZ5nwoQJso2Njezi4qL3591339UdA0B+9tln9V7XqVMn+bnnnpNlWZa/++472cvLS87OztbtX7dunaxQKOTExERde996661S2wFAnjlzpu55dna2LEmSvGHDBlmWZXn48OHyE088UeZ7ISIiqiq2NdqLQEREVAvMmzcPffr0wbRp0+76HM2bN4dCUZRI5+/vjxYtWuie29jYoE6dOkhKStJ7XZcuXXSPbW1t0aFDB93I9ZEjR7Bt2za4uroaXO/ixYto3LgxAKBDhw5lti0zMxPx8fG477779Lbfd999OH78eAXfYZHevXvj66+/1tvm7e2t97z4+9I+11boP3PmDFq3bg0XFxe9tmg0GsTFxUGSJMTHx6Nv375ltqNVq1a6xy4uLnBzc9N9vs899xwefPBBHD16FAMGDMDIkSPRtWvXSr9XIiKiu8FAnoiIqIr16NEDAwcOxJtvvomJEyfq7VMoFJBlWW9bYWGhwTns7Oz0nkuSZHSbRqMptz3aqvkajQbDhw/HvHnzDI4JDAzUPS4eEFfkvFqyLN9VhX4XFxeDNPfKXL+s60qSBCcnpwqdr6zPd/Dgwbh69SrWrVuHLVu2oG/fvoiKisInn3xS6XYTERFVFufIExERVYO5c+fi77//xt69e/W2+/r6IjExUS+YN+fa7/v379c9VqlUOHLkCJo2bQoAaNeuHU6fPo169eohPDxc709Fg3cAcHd3R1BQEHbv3q23fe/evWjWrJl53kgJxd+X9rn2fUVERCAmJgY5OTm6/Xv27IFCoUDjxo3h5uaGevXqlboEXkX5+vpi4sSJWLFiBebPn4/vvvvOpPMRERFVFAN5IiKiatCyZUuMHTsWX375pd72Xr164fbt2/j4449x8eJFREdHY8OGDWa7bnR0NNauXYuzZ88iKioKaWlpmDRpEgAgKioKqampePTRR3Hw4EFcunQJ//77LyZNmgS1Wl2p60yfPh3z5s3Dzz//jLi4OLzxxhuIiYnBiy++WOk2K5VKJCYm6v0pXkkfAH799VcsXrwY586dw6xZs3Dw4EFdMbuxY8fC0dEREyZMwKlTp7Bt2zZMnToV48ePh7+/PwCxWsCnn36KBQsW4Pz58zh69KjBz6Ys77zzDv78809cuHABp0+fxj///FNlnRZEREQlMZAnIiKqJu+9955BGn2zZs3w1VdfITo6Gq1bt8bBgwdNruhe3Ny5czFv3jy0bt0au3btwp9//qmrrh4UFIQ9e/ZArVZj4MCBaNGiBV588UV4eHjozceviBdeeAHTpk3DtGnT0LJlS2zcuBF//fUXGjVqVOk2b9y4EYGBgXp/unXrpnfMnDlzsHr1arRq1QrLli3DTz/9hIiICACAs7MzNm3ahNTUVERGRuKhhx5C3759sXDhQt3rJ0yYgPnz5+Orr75C8+bNMWzYMF2F/Yqwt7fHjBkz0KpVK/To0QM2NjZYvXp1pd8rERHR3ZDkkr9REBEREd3DJEnC2rVrMXLkyJpuChERUY3giDwRERERERGRBWEgT0RERERERGRBuPwcERERWRTOCiQiotqOI/JEREREREREFoSBPBEREREREZEFYSBPREREREREZEEYyBMRERERERFZEAbyRERERERERBaEgTwRERERERGRBWEgT0RERERERGRBGMgTERERERERWRAG8kREREREREQW5P+UOdKAsS3DqQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 939 ms (started: 2023-08-20 00:52:39 +02:00)\n"
     ]
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 10))\n",
    "ax1.plot(h.history[\"loss\"], label=\"Training Loss\")\n",
    "ax1.plot(h.history[\"val_loss\"], label=\"Validation Loss\")\n",
    "ax1.semilogy()\n",
    "\n",
    "ax2.plot(h.history['mape'], label=\"Training MAPE\")\n",
    "ax2.plot(h.history[\"val_mape\"], label=\"Validation MAPE\")\n",
    "\n",
    "ax1.set_ylabel(\"Loss [MSE]\")\n",
    "ax2.set_ylabel(\"Error [MAPE]\")\n",
    "\n",
    "for ax in [ax1, ax2]:\n",
    "    ax.minorticks_on()\n",
    "    ax.semilogy()\n",
    "    ax.legend(frameon=False)\n",
    "    ax.set_xlabel(\"Number of Epochs\")\n",
    "    # ax.set_yticks([0.01, 0.05, 0.1, 0.5])\n",
    "    ax.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "plt.savefig(\"/work/bb1070/b382177/mie/icon/17-08-2023/figs/opt_epochs.png\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "97c48f5e-58ae-4ddd-bd7a-9052f1a4c8e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 66.4 ms (started: 2023-08-20 00:52:40 +02:00)\n"
     ]
    }
   ],
   "source": [
    "test_y[out_col] = qt.inverse_transform(test_y)\n",
    "pred_y[out_col] = qt.inverse_transform(pred_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3353962b-8b87-4308-b915-e7d10b5bbf46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2300x700 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 2.85 s (started: 2023-08-20 00:52:40 +02:00)\n"
     ]
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(23, 7))\n",
    "for col, ax in zip(y.columns, axes):\n",
    "    ntest = test_y[col]\n",
    "    npred = pred_y[col]\n",
    "        \n",
    "    corr = np.corrcoef(ntest, npred)[0,1]\n",
    "    r2 = r2_score(ntest, npred)\n",
    "    \n",
    "    x1 = test_y[col]\n",
    "    y1 = pred_y[col]    \n",
    "\n",
    "    x1 = (mx[col] - mn[col]) * x1 + mn[col]\n",
    "    y1 = (mx[col] - mn[col]) * y1 + mn[col]\n",
    "        \n",
    "    ax.scatter(x1, y1)\n",
    "    ax.scatter(x1, x1)\n",
    "    ax.set_title('%s [$R^2$=%0.3f]'%(col, r2), fontsize=20)\n",
    "    ax.set_xlabel('Mie', fontsize=20)\n",
    "    ax.minorticks_on()\n",
    "    ax.tick_params(labelsize=18)\n",
    "axes[0].set_ylabel('MieAI', fontsize=20)\n",
    "plt.savefig(\"/work/bb1070/b382177/mie/icon/17-08-2023/figs/mlp_accuracy_stats.png\", bbox_inches=\"tight\")\n",
    "plt.show()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "542bd12f-b6b6-4643-bf4b-7174dcd2d219",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1900x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 883 ms (started: 2023-08-20 00:52:43 +02:00)\n"
     ]
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(19, 6), sharey=True)\n",
    "axes = axes.flatten()\n",
    "for i, (col, ax) in enumerate(zip(y.columns, axes)):\n",
    "    ntest = test_y[col]\n",
    "    npred = pred_y[col]  \n",
    "    \n",
    "    x1 = test_y[col]\n",
    "    y1 = pred_y[col]  \n",
    "    \n",
    "    x1 = (mx[col] - mn[col]) * x1 + mn[col]\n",
    "    y1 = (mx[col] - mn[col]) * y1 + mn[col]\n",
    "    \n",
    "    corr = np.corrcoef(x1, y1)[0,1]    \n",
    "    error = 100 * (y1 - x1) / x1   \n",
    "    \n",
    "    ax.hist(error, bins=np.arange(-20, 21, 1), density=True, color='blue')\n",
    "    # ax.hist(error, bins=100, density=True)\n",
    "    \n",
    "    # ax.set_title('%s [R=%0.3f]'%(col, corr), fontsize=20)\n",
    "    ax.set_title('%s'%(col), fontsize=20, y=0.9)\n",
    "    ax.set_xlabel('Error [%]', fontsize=20)\n",
    "    ax.set_ylabel('Density', fontsize=20)\n",
    "    if i!=0:\n",
    "        ax.set_ylabel('', fontsize=20)\n",
    "        \n",
    "    if i!=1:\n",
    "        ax.set_xlabel('', fontsize=20)\n",
    "        \n",
    "    ax.minorticks_on()\n",
    "    ax.set_yticks(np.arange(0, 0.25, 0.05))\n",
    "    ax.tick_params(labelsize=16)\n",
    "    \n",
    "plt.subplots_adjust(hspace=0.01, wspace=0.05)\n",
    "plt.savefig('/work/bb1070/b382177/mie/icon/17-08-2023/figs/mlp_error_ana.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "11733fae-68f5-4521-b287-46fe547bd3b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 50.4 ms (started: 2023-08-20 00:52:44 +02:00)\n"
     ]
    }
   ],
   "source": [
    "model.save('/work/bb1070/b382177/mie/icon/17-08-2023/model/model_17-08-2023.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7ed18efc-8a9c-4d8a-b9bf-869e9b84390d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/work/bb1070/b382177/mie/icon/17-08-2023/model/transformer_17-08-2023.pkl']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 33.9 ms (started: 2023-08-20 00:52:44 +02:00)\n"
     ]
    }
   ],
   "source": [
    "joblib.dump(qt, '/work/bb1070/b382177/mie/icon/17-08-2023/model/transformer_17-08-2023.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d72c91a7-d5d8-4875-95f4-9350192a363d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 104 ms (started: 2023-08-20 00:52:44 +02:00)\n"
     ]
    }
   ],
   "source": [
    "lmodel = tf.keras.models.load_model('/work/bb1070/b382177/mie/icon/17-08-2023/model/model_17-08-2023.h5')"
   ]
  }
 ],
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