GRU_hpg.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\minht\\AppData\\Local\\Temp\\ipykernel_10320\\2532534021.py:17: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "  data['Price'].fillna(method='ffill', inplace=True)\n"
     ]
    }
   ],
   "source": [
    "# Load the data\n",
    "data = pd.read_csv('HPG.csv', parse_dates=['Date'])\n",
    "\n",
    "# Convert 'Price' column to numeric\n",
    "data['Price'] = pd.to_numeric(data['Price'].str.replace(',', ''), errors='coerce')\n",
    "\n",
    "# Set the 'Date' column as the index\n",
    "data.set_index('Date', inplace=True)\n",
    "\n",
    "# Sort the index\n",
    "data.sort_index(inplace=True)\n",
    "\n",
    "# Set the frequency of the index\n",
    "data = data.asfreq('B')\n",
    "\n",
    "# Fill missing values if any (forward fill)\n",
    "data['Price'].fillna(method='ffill', inplace=True)\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "train_data = data[:'2022-02-28']\n",
    "test_data = data['2022-03-01':'2023-03-31']\n",
    "\n",
    "train = train_data['Price'].values\n",
    "test = test_data['Price'].values\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "train_scaled = scaler.fit_transform(train.reshape(-1, 1))\n",
    "test_scaled = scaler.transform(test.reshape(-1, 1))\n",
    "model_path = \"GRU_hpg.h5\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Price</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Vol.</th>\n",
       "      <th>Change %</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>11991.0</td>\n",
       "      <td>11,789.9</td>\n",
       "      <td>12,003.6</td>\n",
       "      <td>11,676.8</td>\n",
       "      <td>15.73M</td>\n",
       "      <td>1.81%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>11965.9</td>\n",
       "      <td>12,066.4</td>\n",
       "      <td>12,192.1</td>\n",
       "      <td>11,890.5</td>\n",
       "      <td>18.51M</td>\n",
       "      <td>-0.21%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>12116.7</td>\n",
       "      <td>12,016.2</td>\n",
       "      <td>12,167.0</td>\n",
       "      <td>11,965.9</td>\n",
       "      <td>17.79M</td>\n",
       "      <td>1.26%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>12066.4</td>\n",
       "      <td>12,091.6</td>\n",
       "      <td>12,179.6</td>\n",
       "      <td>11,978.4</td>\n",
       "      <td>14.03M</td>\n",
       "      <td>-0.42%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>12443.5</td>\n",
       "      <td>11,991.0</td>\n",
       "      <td>12,443.5</td>\n",
       "      <td>11,940.7</td>\n",
       "      <td>14.19M</td>\n",
       "      <td>3.13%</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-22</th>\n",
       "      <td>35116.6</td>\n",
       "      <td>35,041.0</td>\n",
       "      <td>35,268.0</td>\n",
       "      <td>34,586.9</td>\n",
       "      <td>22.97M</td>\n",
       "      <td>-0.54%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-23</th>\n",
       "      <td>35343.7</td>\n",
       "      <td>35,305.8</td>\n",
       "      <td>35,722.1</td>\n",
       "      <td>35,003.1</td>\n",
       "      <td>16.88M</td>\n",
       "      <td>0.65%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-24</th>\n",
       "      <td>34813.9</td>\n",
       "      <td>35,381.5</td>\n",
       "      <td>35,457.2</td>\n",
       "      <td>34,132.8</td>\n",
       "      <td>29.18M</td>\n",
       "      <td>-1.50%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-25</th>\n",
       "      <td>34738.2</td>\n",
       "      <td>34,889.6</td>\n",
       "      <td>35,078.8</td>\n",
       "      <td>34,738.2</td>\n",
       "      <td>16.61M</td>\n",
       "      <td>-0.22%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-28</th>\n",
       "      <td>35722.1</td>\n",
       "      <td>34,851.7</td>\n",
       "      <td>36,176.2</td>\n",
       "      <td>34,851.7</td>\n",
       "      <td>45.76M</td>\n",
       "      <td>2.83%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1085 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Price      Open      High       Low    Vol. Change %\n",
       "Date                                                              \n",
       "2018-01-02  11991.0  11,789.9  12,003.6  11,676.8  15.73M    1.81%\n",
       "2018-01-03  11965.9  12,066.4  12,192.1  11,890.5  18.51M   -0.21%\n",
       "2018-01-04  12116.7  12,016.2  12,167.0  11,965.9  17.79M    1.26%\n",
       "2018-01-05  12066.4  12,091.6  12,179.6  11,978.4  14.03M   -0.42%\n",
       "2018-01-08  12443.5  11,991.0  12,443.5  11,940.7  14.19M    3.13%\n",
       "...             ...       ...       ...       ...     ...      ...\n",
       "2022-02-22  35116.6  35,041.0  35,268.0  34,586.9  22.97M   -0.54%\n",
       "2022-02-23  35343.7  35,305.8  35,722.1  35,003.1  16.88M    0.65%\n",
       "2022-02-24  34813.9  35,381.5  35,457.2  34,132.8  29.18M   -1.50%\n",
       "2022-02-25  34738.2  34,889.6  35,078.8  34,738.2  16.61M   -0.22%\n",
       "2022-02-28  35722.1  34,851.7  36,176.2  34,851.7  45.76M    2.83%\n",
       "\n",
       "[1085 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.12551255])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_scaled[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1085, 1)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_scaled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(868, 1)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train, x_val = train_test_split(train_scaled, test_size=0.2, shuffle=False)\n",
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.12551255])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[[0.12551255],\n",
       "         [0.12482458],\n",
       "         [0.1289579 ],\n",
       "         [0.12757921],\n",
       "         [0.13791525],\n",
       "         [0.16134196],\n",
       "         [0.15307258],\n",
       "         [0.15307258],\n",
       "         [0.15582995],\n",
       "         [0.17305668],\n",
       "         [0.18270201],\n",
       "         [0.16616599],\n",
       "         [0.18270201],\n",
       "         [0.20819537],\n",
       "         [0.23506743],\n",
       "         [0.23506743],\n",
       "         [0.23506743],\n",
       "         [0.22059807],\n",
       "         [0.23713409],\n",
       "         [0.22059807]]]),\n",
       " array([[0.21921938]]))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.preprocessing.sequence import TimeseriesGenerator\n",
    "n_input = 20\n",
    "train_generator = TimeseriesGenerator(x_train, x_train, length=n_input, batch_size=1)\n",
    "val_generator = TimeseriesGenerator(x_val, x_val, length=n_input, batch_size=1)\n",
    "train_generator[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " gru (GRU)                   (None, 64)                12864     \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 12929 (50.50 KB)\n",
      "Trainable params: 12929 (50.50 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import GRU, Dense\n",
    "from keras.optimizers import Adam\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "from keras.callbacks import ReduceLROnPlateau\n",
    "\n",
    "model = Sequential()\n",
    "model.add(GRU(64, activation='relu', input_shape=(n_input, 1)))\n",
    "model.add(Dense(1))  \n",
    "model.compile(optimizer=Adam(learning_rate=0.01), loss='mse')\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.001)\n",
    "checkpoint_callback = ModelCheckpoint(\n",
    "    filepath=model_path,\n",
    "    monitor='val_loss',\n",
    "    save_weights_only=False,\n",
    "    save_best_only=True,\n",
    "    verbose=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "847/848 [============================>.] - ETA: 0s - loss: 0.0012\n",
      "Epoch 1: val_loss improved from inf to 0.00351, saving model to GRU_hpg.h5\n",
      "848/848 [==============================] - 7s 6ms/step - loss: 0.0012 - val_loss: 0.0035 - lr: 0.0100\n",
      "Epoch 2/30\n",
      "848/848 [==============================] - ETA: 0s - loss: 3.3929e-04\n",
      "Epoch 2: val_loss improved from 0.00351 to 0.00123, saving model to GRU_hpg.h5\n",
      "848/848 [==============================] - 6s 7ms/step - loss: 3.3929e-04 - val_loss: 0.0012 - lr: 0.0100\n",
      "Epoch 3/30\n",
      "844/848 [============================>.] - ETA: 0s - loss: 3.1891e-04\n",
      "Epoch 3: val_loss improved from 0.00123 to 0.00117, saving model to GRU_hpg.h5\n",
      "848/848 [==============================] - 5s 6ms/step - loss: 3.1861e-04 - val_loss: 0.0012 - lr: 0.0100\n",
      "Epoch 4/30\n",
      "841/848 [============================>.] - ETA: 0s - loss: 3.2249e-04\n",
      "Epoch 4: val_loss improved from 0.00117 to 0.00081, saving model to GRU_hpg.h5\n",
      "848/848 [==============================] - 5s 6ms/step - loss: 3.2183e-04 - val_loss: 8.1404e-04 - lr: 0.0100\n",
      "Epoch 5/30\n",
      "842/848 [============================>.] - ETA: 0s - loss: 3.1053e-04\n",
      "Epoch 5: val_loss did not improve from 0.00081\n",
      "848/848 [==============================] - 5s 6ms/step - loss: 3.0914e-04 - val_loss: 0.0014 - lr: 0.0100\n",
      "Epoch 6/30\n",
      "845/848 [============================>.] - ETA: 0s - loss: 3.2823e-04\n",
      "Epoch 6: val_loss did not improve from 0.00081\n",
      "848/848 [==============================] - 5s 6ms/step - loss: 3.2720e-04 - val_loss: 0.0067 - lr: 0.0100\n",
      "Epoch 7/30\n",
      "848/848 [==============================] - ETA: 0s - loss: 3.0839e-04\n",
      "Epoch 7: val_loss improved from 0.00081 to 0.00061, saving model to GRU_hpg.h5\n",
      "848/848 [==============================] - 5s 6ms/step - loss: 3.0839e-04 - val_loss: 6.0676e-04 - lr: 0.0100\n",
      "Epoch 8/30\n",
      "845/848 [============================>.] - ETA: 0s - loss: 3.4597e-04\n",
      "Epoch 8: val_loss did not improve from 0.00061\n",
      "848/848 [==============================] - 5s 6ms/step - loss: 3.4612e-04 - val_loss: 0.0111 - lr: 0.0100\n",
      "Epoch 9/30\n",
      "846/848 [============================>.] - ETA: 0s - loss: 2.6829e-04\n",
      "Epoch 9: val_loss did not improve from 0.00061\n",
      "848/848 [==============================] - 5s 6ms/step - loss: 2.7224e-04 - val_loss: 7.4098e-04 - lr: 0.0100\n",
      "Epoch 10/30\n",
      "843/848 [============================>.] - ETA: 0s - loss: 1.9383e-04\n",
      "Epoch 10: val_loss improved from 0.00061 to 0.00049, saving model to GRU_hpg.h5\n",
      "848/848 [==============================] - 7s 8ms/step - loss: 2.1643e-04 - val_loss: 4.8861e-04 - lr: 0.0100\n",
      "Epoch 11/30\n",
      "842/848 [============================>.] - ETA: 0s - loss: 2.3723e-04\n",
      "Epoch 11: val_loss did not improve from 0.00049\n",
      "848/848 [==============================] - 6s 7ms/step - loss: 2.3600e-04 - val_loss: 6.3468e-04 - lr: 0.0100\n",
      "Epoch 12/30\n",
      "844/848 [============================>.] - ETA: 0s - loss: 2.4784e-04\n",
      "Epoch 12: val_loss did not improve from 0.00049\n",
      "848/848 [==============================] - 6s 8ms/step - loss: 2.4701e-04 - val_loss: 0.0066 - lr: 0.0100\n",
      "Epoch 13/30\n",
      "845/848 [============================>.] - ETA: 0s - loss: 2.2187e-04\n",
      "Epoch 13: val_loss did not improve from 0.00049\n",
      "848/848 [==============================] - 6s 7ms/step - loss: 2.2158e-04 - val_loss: 7.6089e-04 - lr: 0.0100\n",
      "Epoch 14/30\n",
      "846/848 [============================>.] - ETA: 0s - loss: 1.9435e-04\n",
      "Epoch 14: val_loss did not improve from 0.00049\n",
      "848/848 [==============================] - 6s 7ms/step - loss: 1.9404e-04 - val_loss: 8.4191e-04 - lr: 0.0100\n",
      "Epoch 15/30\n",
      "847/848 [============================>.] - ETA: 0s - loss: 2.1304e-04\n",
      "Epoch 15: val_loss did not improve from 0.00049\n",
      "848/848 [==============================] - 6s 7ms/step - loss: 2.1282e-04 - val_loss: 0.0034 - lr: 0.0100\n",
      "Epoch 16/30\n",
      "839/848 [============================>.] - ETA: 0s - loss: 9.7301e-05\n",
      "Epoch 16: val_loss did not improve from 0.00049\n",
      "848/848 [==============================] - 5s 6ms/step - loss: 9.6917e-05 - val_loss: 5.8946e-04 - lr: 0.0020\n",
      "Epoch 17/30\n",
      "847/848 [============================>.] - ETA: 0s - loss: 1.1030e-04\n",
      "Epoch 17: val_loss did not improve from 0.00049\n",
      "848/848 [==============================] - 5s 5ms/step - loss: 1.1035e-04 - val_loss: 9.7090e-04 - lr: 0.0020\n",
      "Epoch 18/30\n",
      "841/848 [============================>.] - ETA: 0s - loss: 9.9666e-05\n",
      "Epoch 18: val_loss did not improve from 0.00049\n",
      "848/848 [==============================] - 5s 5ms/step - loss: 9.8980e-05 - val_loss: 4.9587e-04 - lr: 0.0020\n",
      "Epoch 19/30\n",
      "848/848 [==============================] - ETA: 0s - loss: 1.0162e-04\n",
      "Epoch 19: val_loss did not improve from 0.00049\n",
      "848/848 [==============================] - 5s 6ms/step - loss: 1.0162e-04 - val_loss: 6.5725e-04 - lr: 0.0020\n",
      "Epoch 20/30\n",
      "843/848 [============================>.] - ETA: 0s - loss: 9.2544e-05\n",
      "Epoch 20: val_loss did not improve from 0.00049\n",
      "848/848 [==============================] - 5s 6ms/step - loss: 9.2655e-05 - val_loss: 5.1283e-04 - lr: 0.0020\n",
      "Epoch 21/30\n",
      "843/848 [============================>.] - ETA: 0s - loss: 8.6686e-05\n",
      "Epoch 21: val_loss did not improve from 0.00049\n",
      "848/848 [==============================] - 6s 7ms/step - loss: 8.7438e-05 - val_loss: 7.6456e-04 - lr: 0.0010\n",
      "Epoch 22/30\n",
      "844/848 [============================>.] - ETA: 0s - loss: 8.5977e-05\n",
      "Epoch 22: val_loss did not improve from 0.00049\n",
      "848/848 [==============================] - 6s 8ms/step - loss: 8.5773e-05 - val_loss: 9.9259e-04 - lr: 0.0010\n",
      "Epoch 23/30\n",
      "845/848 [============================>.] - ETA: 0s - loss: 8.6329e-05\n",
      "Epoch 23: val_loss did not improve from 0.00049\n",
      "848/848 [==============================] - 7s 8ms/step - loss: 8.6127e-05 - val_loss: 8.0008e-04 - lr: 0.0010\n",
      "Epoch 24/30\n",
      "847/848 [============================>.] - ETA: 0s - loss: 8.7247e-05\n",
      "Epoch 24: val_loss improved from 0.00049 to 0.00046, saving model to GRU_hpg.h5\n",
      "848/848 [==============================] - 7s 8ms/step - loss: 8.7690e-05 - val_loss: 4.6378e-04 - lr: 0.0010\n",
      "Epoch 25/30\n",
      "846/848 [============================>.] - ETA: 0s - loss: 8.6148e-05\n",
      "Epoch 25: val_loss did not improve from 0.00046\n",
      "848/848 [==============================] - 6s 7ms/step - loss: 8.5965e-05 - val_loss: 0.0011 - lr: 0.0010\n",
      "Epoch 26/30\n",
      "846/848 [============================>.] - ETA: 0s - loss: 8.8683e-05\n",
      "Epoch 26: val_loss did not improve from 0.00046\n",
      "848/848 [==============================] - 5s 6ms/step - loss: 8.8681e-05 - val_loss: 4.6757e-04 - lr: 0.0010\n",
      "Epoch 27/30\n",
      "845/848 [============================>.] - ETA: 0s - loss: 8.7195e-05\n",
      "Epoch 27: val_loss did not improve from 0.00046\n",
      "848/848 [==============================] - 5s 6ms/step - loss: 8.7253e-05 - val_loss: 5.6118e-04 - lr: 0.0010\n",
      "Epoch 28/30\n",
      "846/848 [============================>.] - ETA: 0s - loss: 8.5470e-05\n",
      "Epoch 28: val_loss did not improve from 0.00046\n",
      "848/848 [==============================] - 6s 8ms/step - loss: 8.5412e-05 - val_loss: 8.1619e-04 - lr: 0.0010\n",
      "Epoch 29/30\n",
      "846/848 [============================>.] - ETA: 0s - loss: 8.7987e-05\n",
      "Epoch 29: val_loss did not improve from 0.00046\n",
      "848/848 [==============================] - 7s 8ms/step - loss: 8.8010e-05 - val_loss: 4.8414e-04 - lr: 0.0010\n",
      "Epoch 30/30\n",
      "845/848 [============================>.] - ETA: 0s - loss: 8.8407e-05\n",
      "Epoch 30: val_loss did not improve from 0.00046\n",
      "848/848 [==============================] - 7s 8ms/step - loss: 8.8449e-05 - val_loss: 4.6538e-04 - lr: 0.0010\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_generator, validation_data=val_generator, batch_size=16, epochs=30, callbacks=[checkpoint_callback, reduce_lr])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2073bbab6d0>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = model.history.history['loss']\n",
    "plt.plot(range(len(loss)), loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2073bb4a810>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VV+Hyyy/Hb3/7W8TGxuIvf/mLz69rNBqRlJTk9UbRR1yTEBujg06r6fG6eCljw8CGiCiS+RXYGAwGFBQUoKysTHqfw+FAWVkZioqKfH5OUVGR1/UAsG3bNun63NxcZGVleV3T0tKCiooK6Zr29nbnzWq9b1er1cLh4EA16plUX9PDDBtRvJHTh4mI1KDnooMeLFu2DPPnz8fUqVMxbdo0rF69GmazGQsWLAAA3H777Rg6dChWrVoFAFiyZAmuvfZaPPPMM7jpppuwYcMG7N69Gy+++CIAZ/3M0qVL8fjjjyMvLw+5ubl45JFHkJ2djdLSUgDO4Cg1NRXz58/HihUrEBsbiz/84Q84efIkbrrpJpmeClIjcbN3TwswRWIrODM2RESRze/AZu7cuTh//jxWrFiBmpoaTJkyBVu3bpWKf6uqqrwyK9OnT8f69euxfPlyPPzww8jLy8PmzZsxYcIE6ZoHHngAZrMZixYtQlNTE2bMmIGtW7fCZDIBcB6Bbd26Fb/4xS9w/fXXw2q14tJLL8Ubb7yByZMnD/Q5IBVr78cMG8AjY8PiYSKiiOb3HJtIxTk20WnrgRrc9dc9KBiRin/9ZHqP161+9wusfvcYvl84HL/+9sQg3iEREfVG0Tk2RJGm3xkbsXiYXVFERBGNgQ2pWl+bvUXiURTn2BARRTYGNqRq4iThnjZ7i6TJwyweJiKKaAxsSNX6m7GRJg+z3ZuIKKIxsCFV63fGxsCMDRGRGjCwIVXrd8bGyIwNEZEaMLAhVet/VxQH9BERqQEDG1I1MQMT38tmb8+Pc6UCEVFkY2BDqubvHBuL3QGLjfvHiIgiFQMbUrX+1tjEegQ+HZxlQ0QUsRjYkKr1tyvKoNfCoHP+78A6GyKiyMXAhlStvZ8ZG8Ad/LDlm4gocjGwIVUTsy/xfWRsAHfw08YCYiKiiMXAhlRN7HKK60fGRlqrwEWYREQRi4ENqZbF5oDF7uxw6tdRlIGLMImIIh0DG1Itz+6m2D7avQEuwiQiUgMGNqRaYn2NQaeFQd/3jzoXYRIRRT4GNqRa0nC+fhQOA1yESUSkBgxsSLWkdQr9qK8B3Isw21g8TEQUsRjYkGqZ+7lOQZQg7oti8TARUcRiYEOqJbV697EAUyQGQGZmbIiIIhYDG1ItaThfPzM24pEVMzZERJGLgQ2plhig9Gc4H+AuMmbGhogocjGwIdUSA5T+rFMAmLEhIlIDBjakWn5nbFxHVuyKIiKKXAxsSLX8rbFxd0UxsCEiilQMbEi1/O6KMnLyMBFRpGNgQ6rlf1cUJw8TEUU6BjakWgFnbFg8TEQUsRjYkGoFmrGx2Byw2h2K3RcRESmHgQ2plv9dUe7r2llnQ0QUkRjYkGr5O8fGoNfCoHP+L2FmnQ0RUURiYEOq5W/GBnBPH2YBMRFRZGJgQ6olBif9zdgA7unDbPkmIopMDGwoJM41d2DV24dxtqlDsa8hBifx/mRsxA3fzNgQEUUkBjYUEq+Uf4Xfv/8l/rLzlCKPb3cI6LCKR1H9z9iILd8sHiYiikwMbCgkzrkyNbUtnYo8vhjUAEB8P+fYAO6Wb2ZsiIgiEwMbConzbV0AgEazRZHHb3d1RGk1gFHf/x/zeJWvVXjr83M4Xtca6tsgIlIMAxsKifOtzsCmoU2ZwEacHhxv0EOj0fT789S8VuFgdTN++re9WPaPz0J9K0REimFgQyFR7wpolMrYiDNs4vzoiHJer96MzenGdgBAletXIiI1YmBDQWe1O6SAprHdAkEQZP8a7Rb/O6Kc16s3YyMGk03tVti4MoKIVIqBDQWd5/GTxeZQZOmkWPzrd8ZGnGOjwsDGMzvW2K5MpoyIKNQY2FDQifU1okYF6mykzd7+ZmxcgZAaj6I8AxulapuIiEKNgQ0F3fk27xbvBnNXD1cGzt/N3iJ3V5T6MjYNnhkbhWqbiIhCjYENBV33jM0FBY5F2qXiYX9rbFwD+hQ4Hgu1hjb3817fJn8wSUQUDhjYUNB1D2yUOBZxt3v7W2Oj3gF9PIoiomjAwIaC7qIaGwWORcSuJv9rbNS7UoFHUUQUDRjYUNCJU4cNronASrzISgsw/e6KUmfGRhAEXPDM2ChQ10REFA4Y2FDQ1bc6X2DHDEoA4J1JkMuAMzYqq7Fp6bDB5nDPC6rnURQRqRQDGwo6MWOTn5UIAF6ZBLkEWmMjBjZtKuuKqjd3r2tixoaI1ImBDQWdWGMzzhXYKJKxCbgryhkIWWwOWFU0nbf7cR9rbIhIrRjYUFC1W2xSNmSsK7BRtMbGz6Moz6MrNR1HiV1QKXExXn8mIlKbgAKbtWvXYuTIkTCZTCgsLMSuXbt6vX7Tpk3Iz8+HyWTCxIkT8dZbb3l9XBAErFixAkOGDEFsbCyKi4tx7Nixix7nP//5DwoLCxEbG4vU1FSUlpYGcvsUQmJ9jSlGi+FpcQAUCmwCXKlg0GsRo3NuA1fTvijxOR472BlMtnbZ0GVTT+BGRCTyO7DZuHEjli1bhpUrV2Lv3r2YPHkySkpKUFdX5/P6nTt3Yt68eVi4cCEqKytRWlqK0tJSHDhwQLrmySefxJo1a7Bu3TpUVFQgPj4eJSUl6Ox0T6j917/+hR/+8IdYsGABPvvsM3z88cf43ve+F8C3TKEkTh0elGhEerwBgLOeRe4X2UCXYAIe+6JU1PIt1tSMzIiDXusM3HgcRURq5Hdg8+yzz+LOO+/EggULMH78eKxbtw5xcXH485//7PP65557DrNmzcL999+PSy65BI899hguv/xyvPDCCwCc2ZrVq1dj+fLlmD17NiZNmoRXXnkF1dXV2Lx5MwDAZrNhyZIleOqpp3DXXXdh7NixGD9+PL773e8G/p1TSIj1NYMSjEgyxUDnepG9YLbK+nXElQhxfhYPA+rc8C3WMaUnGJHmCih5HEVEauRXYGOxWLBnzx4UFxe7H0CrRXFxMcrLy31+Tnl5udf1AFBSUiJdf/LkSdTU1Hhdk5ycjMLCQumavXv34uzZs9BqtbjsssswZMgQ3HjjjV5Zn+66urrQ0tLi9UahJwU2iUZotRqkijUfMs9VkTI2fhYPe36OmjqjxOxMerwB6QlGAFyrQETq5FdgU19fD7vdjszMTK/3Z2Zmoqamxufn1NTU9Hq9+Gtv13z55ZcAgF/+8pdYvnw5tmzZgtTUVFx33XVobGz0+XVXrVqF5ORk6S0nJ8efb5UU4hnYAJCyB3IeiwiCEPASTMDdSaWm6cNSYJNgQEaC/M85EVG4iIiuKIfD2Xb7i1/8ArfeeisKCgrw0ksvQaPRYNOmTT4/56GHHkJzc7P0dvr06WDeMvXgvOv4Y1CCCYAygU2n1QHBNYvO33ZvwB0MqWn6sJidSYvnURQRqZtfgU1GRgZ0Oh1qa2u93l9bW4usrCyfn5OVldXr9eKvvV0zZMgQAMD48eOljxuNRowaNQpVVVU+v67RaERSUpLXG4Ve94xNerzzVzkDG8+AJC4mgIyNCjd8ex1FuZ7z7kP7iIjUwK/AxmAwoKCgAGVlZdL7HA4HysrKUFRU5PNzioqKvK4HgG3btknX5+bmIisry+ualpYWVFRUSNcUFBTAaDTi6NGj0jVWqxWnTp3CiBEj/PkWKMTEqcNKHkWJR0hxBh20ruJkf4j7pcwqqbERBAEX2t1HUeniURQzNkSkQn7n6ZctW4b58+dj6tSpmDZtGlavXg2z2YwFCxYAAG6//XYMHToUq1atAgAsWbIE1157LZ555hncdNNN2LBhA3bv3o0XX3wRAKDRaLB06VI8/vjjyMvLQ25uLh555BFkZ2dLc2qSkpJw1113YeXKlcjJycGIESPw1FNPAQDmzJkjx/NAQVLvytiIdR6p4rGIAhkbf/dEidSWsWnptMFqd57NpcUbpDZ7JSY+ExGFmt9/88+dOxfnz5/HihUrUFNTgylTpmDr1q1S8W9VVRW0WnciaPr06Vi/fj2WL1+Ohx9+GHl5edi8eTMmTJggXfPAAw/AbDZj0aJFaGpqwowZM7B161aYTCbpmqeeegp6vR4//OEP0dHRgcLCQmzfvh2pqakD+f4piARB8HEUJX/2QGzT9neztyhBZRkbMRuWYNTDqNdJXVHcF0VEahTQP2kXL16MxYsX+/zYjh07LnrfnDlzes2saDQaPProo3j00Ud7vCYmJgZPP/00nn76ab/vl8JDS4cNFtf+pYyEbkdR7TJmbKSjqIFlbNRSPNwgFQ47n2vxKIobvolIjSKiK4rUQZw6nGTSw+Qq6k1XosZmAK3egDvTo5Z2b/HISQpsFHjOiYjCBQMbCpq6bsdQgLvGRtauKDFjE0CrN6C+jI343Ip1TeJRVIfVrqrpykREAAMbCqLu9TWAO3twod0Cu0OQ5evIlrFRSfFwY7eMTbxBB6Pe+b8+Z9kQkdowsKGgEWs6BiW6i8LFjI0gAE0y1dmYLTLV2KikeNhzOB/grGkTa5zYGUVEasPAhoLGcwGmKEanRZLJGUhckCmwae8aaFeUurZ7ew7nE7mnD7MziojUhYENBY2voygAHu3H4ZKxUddKBc89USLx9zyKIiK1YWBDQSNOHc7weIEFIG34lquAeOA1Nuoa0CcGL2keGRtxrQKPoohIbRjYUND0lLFJk/lFduBdUeoa0Nfg2gklBjOAZ8aGR1FEpC4MbChoejyKEjujwiVj4zrC6rI5YHMNFIxUgiC4u6I8j6K4VoGIVIqBDQWF3SGg0dxDxiZB3hfZAWdsPIqO262RfRzV2uXeE+VZPJzOrigiUikGNhQUDeYuOARAq/E+EgHkn4Q70IyNUa9DjM65FTzSj6PEHVzxBp007RnwyNjwKIqIVIaBDQWFeAyVFm+ETqvx+lhqnLyBzUC7ojw/N9JbvsX6mrRuBdvsiiIitWJgQ0HhHs5nvOhjch9FDXSODeDO9kT6ygF3R5TvFvtGswWCIM/EZyKicMDAhoKip8JhQP7iYVkyNioZ0udrOJ/nny12B1oj/LiNiMgTAxsKCl9Th0VpHjU2cmQPpBobZmykLFj3wMYUo5O+Rx5HEZGaMLChoBADm4xEw0UfS/PIHrQNMHtgsTmkLiBZamwifEifr1ZvkXviMwuIiUg9GNhQUIhTh31lbOIMephinD+KAy0g9sywxAXYFQW4pw9HeleUGLR0z9gAHgXEbPkmIhVhYENBcb61E4DvGhvA3QI+0MBGzLAY9FrE6AL/8RaPsSI+sDH7Lh4GPFu+GdgQkXowsKGg6K14GPCusxkIqSNqANkawH0UFen7onwtwBRJ+6J4FEVEKsLAhoJCDGwG9xHYDPRYRI6OKMAdGEX6hu+euqIAHkURkToxsCHFdVrtaOl0BgiDEkw+r5E9YzOAjijA3e7dHsHt3oIg+NzsLZIrmCQiCicMbEhx4gunQadFUqzvTIpcgQ0zNm5tXTZYXEs8u6+xAIAMdkURkQoxsCHFedbXaDQan9fIlrGRYYaN8/MjvytKfC5jY3SI9VFzJB5FybXKgogoHDCwIcW5Z9j4rq8B5FuEKW32HmjGxigO6Ivco6iGXgqHAXcwWc+uKCJSEQY2pDj31GHfL7AAkCpTvcdAN3uL3EswIzdjI9bX+CocBtxHURfaLXA4uC+KiNSBgQ0prq9Wb8AzYzOweg8pY2McaI1N5Ld7i8+lr8JhwL1V3e4Q0NxhDdp9EREpiYENKe58m2s4n4+pwyKpxmaAxyKyZWyMkV883NtwPsA5xDDJpHddywJiIlIHBjakuP5lbJwfM1vs6LQGniURA5GBd0VFfru3GCRm9HIEKB5Hsc6GiNSCgQ0prj+BTVKsHjqts2PqQnvgL7JiIDLwrijn5w90KWcoSQsweziKAjyG9DGwISKVYGBDipMWYPYS2Gg0GqnmYyAvsnJnbLpsDthcs2AiTX1/AhtpRxePoohIHRjYkKIEQUB9q/MFtqepwyI5Wr7FYt+BTx52f377AI7GQkkMVnpq9waAtAS2fBORujCwIUWZLXZ0uAKDjMSeX2ABd2ZhIEdRYnv2QDM2Bp0WetfRWKTW2TS29V48DAAZUps9MzZEpA4MbEhRYn1NglHfZ7CRJkO9h5SxGWBgo9FoEBfBaxUEQXAP6Ou1xkY8imLGhojUgYENKUqaOtzLcYgoLW7gR1FSjc0Aj6IA91qFSMzYmC12dNlce6J6O4ri9GEiUhkGNqSo/nREieTYNi11RQ0wYwO4A5tI7IwSj6FMMdpeM2XurigeRRGROjCwIUWdb3UN5+tHYONeyhj4i6y7K0qGjI1B3BcVeYGNWDPja6u3pwweRRGRyjCwIUVJrd69TB0WScXD5sDG+9sdAjqtzuOX+AGuVAA89kVF4FqFxj4WYIrcBdvWiG1rJyLyxMCGFBXYUVRgGRvPzIosGRtxw3cEHkU1tPU9wwZw7ovSOJu/0DiAbjQionDBwIYUFUhgE+ixiNgRpdNqYNQP/Ec7kjM2Df0Yzgc4nys5iraJiMIFAxtSlNht409g09Rhhd0h+P213DNsdNCIaYgBiOSMjTScr4/ABuBaBSJSFwY2pCgpY9PH1GEA0koFQQCaAjgWkWuGjUh8nLaILB4Wa2z6H1DWszOKiFSAgQ0pxuEQpBfL/mRsYnRaJMfGAAjsWETK2Mgww8b5OJE7x6a/NTaAO/hhxoaI1ICBDSmmqcMKm+tIqa/uHNFAZtnIn7GJ3MnDjf2YOizKkGFHFxFRuGBgQ4oRj6FS42IQo+vfj9pACojlnGEDRHbGprGfxcPOa1wZG+6LIiIVYGBDivGnI0o0oIyNOHVYhhk2QGRnbPo7oA9wZ9O4VoGI1ICBDSnmfFv/pw6L0qUhfWGQsXEdabVHWLt3u8UmDSrszxFgRgKPoohIPRjYkGLcHVH+Z2wCeZGVu8YmwZX5MUdYu7dYBGzUa/sV5ElHUeyKIiIVYGBDign2UZT8XVGReRTV4FE43J95PpxjQ0RqwsCGFOPPcD6RO2Pjf/ZAqTk2kVY8LD53af3sRMtwZWxau2zoskXW90pE1B0DG1LMQDI2jQEswpQ9YxOhxcNi5qU/hcMAkBSrh17rzOywzoaIIh0DG1KMP1OHReKLcVhkbFw1Np1WR0ArHkKlwY8ZNgCg0WjcR4A8jiKiCMfAhhRz3lWMmpHYvxdYwH180mi2QBD8Cybk74pyP057BGVt/JlhI5KmDzNjQ0QRLqDAZu3atRg5ciRMJhMKCwuxa9euXq/ftGkT8vPzYTKZMHHiRLz11lteHxcEAStWrMCQIUMQGxuL4uJiHDt2zOdjdXV1YcqUKdBoNNi3b18gt09BYLU7pBdYv7qiXPuirHYBrX52I8k9x8ao10pHNJHU8i2tU+hnjQ3gbvlmZxQRRTq/A5uNGzdi2bJlWLlyJfbu3YvJkyejpKQEdXV1Pq/fuXMn5s2bh4ULF6KyshKlpaUoLS3FgQMHpGuefPJJrFmzBuvWrUNFRQXi4+NRUlKCzs7Oix7vgQceQHZ2tr+3TUEmvrjqtBppuWV/xBp0iI1xZkoa/TwWkTtjo9FopMdqi6CWb382e4t4FEVEauF3YPPss8/izjvvxIIFCzB+/HisW7cOcXFx+POf/+zz+ueeew6zZs3C/fffj0suuQSPPfYYLr/8crzwwgsAnNma1atXY/ny5Zg9ezYmTZqEV155BdXV1di8ebPXY7399tt455138PTTT/v/nVJQifU1GQkGaLV9txx7kgqI/dzwLdXYyJSx8XysSOqMctfY+DMY0XltPdcqEFGE8yuwsVgs2LNnD4qLi90PoNWiuLgY5eXlPj+nvLzc63oAKCkpka4/efIkampqvK5JTk5GYWGh12PW1tbizjvvxKuvvoq4uDh/bptCIJCpwyJxrorfGZsueTM2no8VSZ1RgRxFBfqcExGFG78Cm/r6etjtdmRmZnq9PzMzEzU1NT4/p6amptfrxV97u0YQBNxxxx246667MHXq1H7da1dXF1paWrzeKHgCmTosEo+u/G09lrsrCvDI2ERQYOPPZm9R+gAGIxIRhZOI6Ip6/vnn0draioceeqjfn7Nq1SokJydLbzk5OQreIXUXyHA+USAvsoIguGtsZJpjA3hkbCLkKKrDYkeH1XmvAXVFsXiYiCKcX4FNRkYGdDodamtrvd5fW1uLrKwsn5+TlZXV6/Xir71ds337dpSXl8NoNEKv12PMmDEAgKlTp2L+/Pk+v+5DDz2E5uZm6e306dP+fKs0QIEM5xOJL8gX/Kix6bQ6IHaHy5mxSYiwjI241dug10r33h/SWgVmbIgowvkV2BgMBhQUFKCsrEx6n8PhQFlZGYqKinx+TlFRkdf1ALBt2zbp+tzcXGRlZXld09LSgoqKCumaNWvW4LPPPsO+ffuwb98+qV1848aN+PWvf+3z6xqNRiQlJXm9UfAM5CgqLYDdRZ41MGJXlRzEDd9tEZKxcU8d7t+eKFE6u6KISCX8/qftsmXLMH/+fEydOhXTpk3D6tWrYTabsWDBAgDA7bffjqFDh2LVqlUAgCVLluDaa6/FM888g5tuugkbNmzA7t278eKLLwJwttQuXboUjz/+OPLy8pCbm4tHHnkE2dnZKC0tBQAMHz7c6x4SEhIAAKNHj8awYcMC/uZJOVJX1ACOovyZPix2LcUZdH53YfUm3nWs1R4h7d6BDOcD3EdRHVY72i02KaAjIoo0fv/tNXfuXJw/fx4rVqxATU0NpkyZgq1bt0rFv1VVVdBq3Ymg6dOnY/369Vi+fDkefvhh5OXlYfPmzZgwYYJ0zQMPPACz2YxFixahqakJM2bMwNatW2Ey9X8UP4UXcepwsIqH3TNs5H1BFh/PHCED+hoCDGziDToY9Vp02RxoaLMgLo2BDRFFpoD+9lq8eDEWL17s82M7duy46H1z5szBnDlzenw8jUaDRx99FI8++mi/vv7IkSP9HrdPwTWQGptA6j3EGph4GQuHAecLvufjhzsxy5XhZ0Cp0WiQHm9AdXMnGswW5KRxpAIRRaaI6IqiyNJusUmTegMrHnZ+zgV/MjbSUZTMGRtXAW6kdEVJM2z8zNgA7IwiInVgYEOyq291vriaYvzrzBGJL8pmix2d1v4FFFLGRsbhfEDkzbEJ9CgKYGcUEakDAxuSnefUYX86c0RJJr20fLK/dTZSxkbGdQqAO1CKlF1RgQznE4lrFdgZRUSRjIENye58q/9bvT1pNBqkxvtXQKxUxkY82oqU7d6yZGx4FEVEEYyBDclO6ogKoL5GlO5nYCN2LcldYyMWI5sjJGMjBiXpAQSVXKtARGrAwIZkN5COKFGavxmbLmW6oiItYzOgoyixeJiBDRFFMAY2JDtpOF+AR1GAO7Dp74us0hmbSCge7rTapQDMn83eIvf0YR5FEVHkYmAjE87VcZM3Y9O/F1nFuqIMkdPuLQaBMToNEgMook4PYJUFEVG4YWAzQB8dq8fNz3+IxX+vDPWthI2BTB0W+XsUpVhXlOvxOqx22B3hHbw2SnuiAutGE4+iGs0WBupEFLE4N32ADHotDpxtQV1LFwRBCOgFRW3qZcjY+Fs8rFxXlPvx2i02JJpiZH18OdW7sluBdEQB7ufcYnegtcuGpDD+XomIesKMzQBNHJoMnVaDutYunGvuDPXthJwgCDIdRbmzB/2hVMbGqNdC55qpE+4FxFLGJoD6GgAwxeikwJDHUUQUqRjYDFCsQYf8rEQAwL7TTaG9mTDQ0mmDxe4AMLDi4dR4Z7agv8XDSmVsNBqNlLUJ95bvQDd7e3IfR7GAmIgiEwMbGUzJSQHAwAZwFw4nmfQwxQQeZKT7m7FRqCsKcBcQh3vGZiDD+UTi59YzY0NEEYqBjQykwKaqKaT3EQ7kOIYC3C+wzR1W2FwZoN4oNcfG8zHDPWMjtmkPJFOWEaadUa9XnsHY5W/j4+P1ob4VIgpzDGxkcNnwFADA/rNNsPbjRVjN5Jg6DACpcc6jKEEAmjqsfV6vaMZG3PAd5rNsZDmKig/Po6jXK6thsTmwZX91qG+FiMIcAxsZjMpIQKJJj06rA0drWkN9OyElx3A+ANDrtEhxBTf9OY6SamwUyNi4a2zUfxQlFh6H01GUIAg4eLYZAHAkyv//IqK+MbCRgVarYZ2Ni1xHUQCQFte/YxGLzQGr3Tl3Rdkam8jI2ASyTkHk78TnYDjX3Cndz9GaVjjCfJ4QEYUWAxuZMLBxkjWw6ecsG8+AI07mrijA3UIe9hmbASzAFImZtnBaq3DAla0BnAXcZy50hPBuiCjcMbCRiRjYVFZdCO2NhJgcU4dFUmDT3ntgI9bXGPRaxOjk/5EWW8jDOWPTabVLz4McR1H97UYLhgPVLV5/PlLT0sOVREQMbGQjBjYnzpvR3I9iV7WSM2Mjvcj2cRQldUQpkK0BPIuHwzdj0+ixJyrJFPhxXDi2e4v1NTE656BE1tkQUW8Y2MgkPcGI4WlxAID9Z5pCezMhVC9TVxQApMb1bxGmkh1RgDtgCud2bzGwSY0zDGith3gUdaHdEja1LAeqnYHN9fmDASDqC/SJqHcMbGQU7fNs7A5Bqs2Qs8amr0JWJWfYAJFRYyMGlAM5hgLcwaTdIYRF5rGutRO1LV3QaIBvXzYMAHCYR1FE1AsGNjKS6myitIC40WyBQwC0Gvc8lIEQj6Iu9LPGRumMTTjX2IgZm4G22Rv0WukoqyEMZtkcdNXXjB6UgMtd86JO1ZvRaQ3fIJOIQouBjYzEQX37TjdBEMIjjR9MYn1NWrxRWhw5EOIizL7avZWcYQO4A6ZIqLEZaMYGcAdH4VBnI9bXTMhOwqBEI9LiDXAIwLHathDfGRGFKwY2MhqfnQSDTotGswWnG6OvJfW8NNJ/4C+ugHseS18dOtJmb6UyNq6AqT2Ma2zkGM4nCqfOqANnnRmbCUOTodFoMC7TuXCWnVFE1BMGNjIy6nW4JDsJAFB5OvravuXsiAKA1Hj3UVRvGTClNnuLIqIrqm3gw/lEUm1TGMyyEQuHL81OBgDkDxEDGxYQE5FvDGxkdpk0z6YppPcRCnIHNuKLtNUuoKWz52yJlLExKpOxkY6iwjpjM/DhfKL0MDmKamq3SMP4xrv+wZCf5Qxs2BlFRD1hYCMzzzqbaCN3YGOK0UmThC/0ciyifMYm/IuH5TyKyujnEaDSxMLhEelxSI517g3Lz3IGODyKIqKeMLCRmdgZdai6BV228D26UIKcU4dF/Wn5FrduK9cVFf7t3tKeKBnqm8SMTai7og5IhcPJ0vvGZiZCo3Fmk8RAmojIEwMbmQ1Pi0NavAEWuwOHz0VXurxe5owN0L8C4nZXwKFcV5TzcTusdtjDZGhdd2KNjRwZm3CZPiyuUrh0aJL0vliDDiPT4wHwOIqIfGNgIzONRoPJw5z/woy2vVHnZRzOJ0qN73v6sOIZG4/anY4wnJ/SZbOj1VX/kyHj/KBQFw8f9JGxAcDOKCLqFQMbBUzJSQUQfXU24tHAYBkDG/eG756n4LZblM3YGPVaaS5POLZ8i9ksvVaDpNiBB3fiHJtQ1ti0dlrxZb0ZAHBpdpLXx9gZRUS9YWCjgGgsIO6y2aUR/IMSTLI9bnp/MjZdymZsNBqNdBzVFoaBjTjAMDV+YHuiRGlSm70VNrtjwI8XCPEYNzvZdFGnFzujiKg3DGwUMNlVQPxVQ3vIO0uCRazHMOi0smQNRNL04V67olwZG4UCG8/Hbg/DWTZS4bAM9TWAuEjT9dh9rLNQilg4fOnQ5Is+JnZGfVHbGrY1T0QUOgxsFJAcG4NRg5wFjvuiZFCfeAyVkSBP1kCUFu9s8+0tQJRqbBQ6ivJ87HCcZSN2L8lROAwAOq0GaXGhbfkWB/N1r68BnAX6sTE6dNkcONVgDvatEVGYY2CjkGjb9C33DBuRmLHpV1dUlGZsxKMoOYbzidzTh0MT2ByUVikkXfQxrVaDsZkJAHgcRUQXY2CjkMuGOwuIo2XTt3KBTd+ZA3dXlIIZG9djm8NwSJ/cR1GAuzOqPgSdUR0WO47VOQOWCT6OogCPQX3n2BlFRN4Y2ChEXK3w2ekmOKKgDkCpwKavOTZ2h4BOq7PANV6hlQoAkOB67PYwHNIn52ZvUXoIO6OO1LTAITi7s3rqsBuXxc4oIvKNgY1CxmUlwqjXoqXTJrWtqlm9AlOHASDNlTlot9jR6WOGjOeaA0UzNtIizPDL2Mi5TkGUHsKjKHEw34ShST3Wa7Hlm4h6wsBGITE6LSa60ujR0PatVMYm0ahHjM754uarM0qsedFpNTDqlftxFvdQhWXxcJu7cFsu6fGhW6vQ02A+T+JRVFVje1j+NyGi0GFgoyD3PBv1d0YpMXUYcM6QSXV16PhahOmeYaOTtRurO2nDdxgWD7uPomRcZZEQyoyNK7DxUTgsSos3SMdUR2uZtSEiNwY2CoqmCcRKZWyA3hdhBmOGDeCx4TsMswNKHEWJ2Z/e5gcpwWJzSJ1Ol/aSsQHcdTbsjCIiTwxsFDTFlbE5fK4VHWH4L325CILgMcdG/sBGzB74mj4sZWwUnGEDhG/GxmJzoLXT+RzI2RUlDUYMclfUF7WtsNoFJMfGYFhqbK/XXjKEnVFEdDEGNgrKTjZhUKIRdocgpdfVyGyxS8shlQhsxKMoX8ciwcrYJIgZmzArHhaPoXRaDZJjY2R73FAdRYkTh3srHBa5l2EyY0NEbgxsFKTRaKS2bzUP6hOzNfEGnSIt1721fAdjho3z8V0ZmzBr9xaLe1PjDNBq5asxEreEt3bZ0GUL3vfc28Th7jw7owRB/SMViKh/GNgobEoULMRUsr4GcB+LXPCxt0iaOqzgDBvn44dnV5QSw/kAIClWD70rUArmLJsDronDvnZEdTdmcAJ0Wg2aO6yobQl+9xYRhScGNgoTVytUVqm3M0rxwKaXY5GgZ2zCrMZGieF8gDPbGOy1Cja7A4dd9TITsnvuiBIZ9TqMynDuZDtcwzobInJiYKOwScNSoNEA1c2dqGvpDPXtKKJeoVZvUW9HUUHvigqzGhtxq3qajDNsROL04WB1Rp04b0aXzYF4gw4j0+P79TnsjCKi7hjYKCzBqMfYwc6/fNW6N0rK2ChQOAy4i4d91tgEuysqzGpsxE6xDJkzNoDn9OHgHPOIhcOXZif3u15I7IxiYENEIgY2QXCZyutslD6Kktq9fdXYBK0rStzuHV4ZGyWG84mC3RklFg5f2stgvu7EzqjDbPkmIhcGNkGg9jobpaYOi8Raj6Z2K2x2h9fHgpexEY+i7GG11LRByaOo+OAeRR08K9bX9F04LBI7o06cb4O1288GEUUnBjZBIHZGfX6mGfYwelGUi5LD+QAgJTYG4kiTC+1Wr48Fr8bG/fjtPpZxhkqDQl1RgGfGRvmjKIdDwEFXxmbisP4HNkNTYpFo1MNqF/DlefUvmyWivjGwCYK8wYmIN+hgtthxrE59tQBKH0XpdVpp+Fz3OptgdUUZ9VqIZR/htFZBqXZvz8cMRsbmVIMZZosdphit1OnUHxqNRiogPsLOKCJCgIHN2rVrMXLkSJhMJhQWFmLXrl29Xr9p0ybk5+fDZDJh4sSJeOutt7w+LggCVqxYgSFDhiA2NhbFxcU4duyY9PFTp05h4cKFyM3NRWxsLEaPHo2VK1fCYgn+gr5A6LQaTBqWAkB9g/ocDkHxrijAc1+Ud/YgWHNsNBqNlBUKp5ZvMZuSHuFdUQeqnUHJJUOSoNf599eSO7BR3z8aiMh/fgc2GzduxLJly7By5Urs3bsXkydPRklJCerq6nxev3PnTsybNw8LFy5EZWUlSktLUVpaigMHDkjXPPnkk1izZg3WrVuHiooKxMfHo6SkBJ2dzvboI0eOwOFw4Pe//z0OHjyI3/zmN1i3bh0efvjhAL/t4BOPoypVFtg0dVhhcx2vpStQwCoSswcXzN5HUcHK2ADuOp5wGdJntTvQ4toTpWzxsPJHUQfP9n/icHf57IwiIg9+BzbPPvss7rzzTixYsADjx4/HunXrEBcXhz//+c8+r3/uuecwa9Ys3H///bjkkkvw2GOP4fLLL8cLL7wAwJmtWb16NZYvX47Zs2dj0qRJeOWVV1BdXY3NmzcDAGbNmoWXXnoJN9xwA0aNGoVvfetbuO+++/Daa68F/p0HmVhArLbOKPEYKjUuBga9ciebafG+F2FKNTYKZ2w8v0Z7mGRsLrgyKVqNsw5JbulBHNAnrVLwoyNKlC9mbNgZRUTwM7CxWCzYs2cPiouL3Q+g1aK4uBjl5eU+P6e8vNzregAoKSmRrj958iRqamq8rklOTkZhYWGPjwkAzc3NSEtL6/HjXV1daGlp8XoLJXFn1Bd1rWgLk3/xyyEYx1CAx7bp7jU2XcHL2LiPosLjv584nE/uPVEi8Siqw2pXtM1dEAT3KoUAMjZjXS3f1c2daO6w9nE1EamdX4FNfX097HY7MjMzvd6fmZmJmpoan59TU1PT6/Xir/485vHjx/H888/jxz/+cY/3umrVKiQnJ0tvOTk5vX9zChucZMLQlFgIArD/TFNI70VOShcOi9LifRcPB6srCnAHT+FyFCUVDitQXwM4l5oaXVk4JbM2Zy50oLnDihidRgpS/JEcG4OhKbEAeBxFRBHYFXX27FnMmjULc+bMwZ133tnjdQ899BCam5ult9OnTwfxLn1zz7NpCul9yEnpqcMiMWPjGdgIguCusVF4jg3gcRQVJtOHxUJqufdEiTQaTVA6o8Q273FZiQEfZ7pXK/A4iija+fW3SEZGBnQ6HWpra73eX1tbi6ysLJ+fk5WV1ev14q/9eczq6mp87Wtfw/Tp0/Hiiy/2eq9GoxFJSUleb6GmxjobpYfziXzti+q0OiC4xgIFNWMTJkdR7lZvBYu2xc4oBQuIDwQwmK87sc7mMDM2RFHPr8DGYDCgoKAAZWVl0vscDgfKyspQVFTk83OKioq8rgeAbdu2Sdfn5uYiKyvL65qWlhZUVFR4PebZs2dx3XXXoaCgAC+99BK02ohLNkmdUftON0EQ1DGoT+nhfKI0H4GNZ4ARGxO8GptwKR6Wpg4rlLEBPDqjFMzYuFcpBB7YcBkmEYn8/mfusmXLMH/+fEydOhXTpk3D6tWrYTabsWDBAgDA7bffjqFDh2LVqlUAgCVLluDaa6/FM888g5tuugkbNmzA7t27pYyLRqPB0qVL8fjjjyMvLw+5ubl45JFHkJ2djdLSUgDuoGbEiBF4+umncf78eel+esoUhaMJ2cnQazU439qF6uZOqS4gkgWvxubiF1jPwmElime7E4+iwqXGpkHhGhvA43lXqMbGWTgstnoHnlX1XIYpCAI0GuV/HogoPPkd2MydOxfnz5/HihUrUFNTgylTpmDr1q1S8W9VVZVXNmX69OlYv349li9fjocffhh5eXnYvHkzJkyYIF3zwAMPwGw2Y9GiRWhqasKMGTOwdetWmEwmAM4Mz/Hjx3H8+HEMGzbM634iKfMRa9Ahf0giDpxtQWXVBQY2fkiT5thYpBcucdN2XBCOoQAg3ujeFxUOxNZ3JaYOizIUPoqqa+1CfZsFOq1GCk4CkZsRjxidBm1dNpy50IGctDgZ75KIIklArwiLFy/G4sWLfX5sx44dF71vzpw5mDNnTo+Pp9Fo8Oijj+LRRx/1+fE77rgDd9xxRyC3Gnam5KTgwNkW7Ktqws2TskN9OwMWrBobMbCxOQS0dNqQHBsjtSDHB6FwGHAHUOHSrq/kZm+Rr9omOYnZmjGDEmAawHFijE6LMYMTcfhcC47WtDKwIYpikVeoEuGm5KQCUEcBsdXukF7wlO6KMsXoEO8q3hW/prjaIPgZm/AIbIJRYyM+dr1igY1rfk0Ag/m6y+fOKCICA5ugEzujPj/bDKvdEdqbGSAxwNBpNUiNU+7FVZSW4D19WFxGGR+E4XyAO4Ayh027t/I1NkofRUkThwfQESXK584oIgIDm6AblRGPJJMeXTYHjpyL7L+A3R1Ryky+7S4tzruQVcrYBGGdAuAOoMIhY2O1O6Qpu0rW2KQnKHsUJe2IGkBHlIjLMIkIYGATdFqtBpOleTYXQnszAxSswmGRVEDc7nyRlWpsgpSxcXdFhT5jIz4HGg2QomC2zD3HxiJ7oX5Dm7M7EADGD6AjSiQWH5+sN6PTGvr/RkQUGgxsQkDcG1UZ4XU2wZo6LOq+Lyp0XVGhz9iIGZTUOAN0CmbLxGyQxe5Aq8xF0weqnbUwozLikSBD1m1wohEpcTGwOwQcr2sb8OMRUWRiYBMCnoP6IpnYEaX0cD6RdCzS1i1jE/SuqNBnA4JROAx0K9qWeZaN2BE1kMF8njQajVRnw0F9RNGLgU0IiJ1RX543o7k9crcRB/soSixQbgxVxkaaPBz6jI1UOKxwYAN4HEeZ5S0gPlg98MF83eVnOR+LnVFE0YuBTQikxRswIt05Z2NfBG/6DnZg030hY7BrbOI8BvQ5HKEdDNnoypYp2RElklq+Zc/YuHZEyZSxAdgZRUQMbEJGWogZwZu+Q108HPyuKPfX6Qhxcap7OJ/ygU1GgvxrFZrbrahqbAcAXCpjxoadUUTEwCZEpqigM0qaOhys4uFuL7DBnmNjitFCrNMN9Ybv+iBMHRaJ28MbZTyKOnjOeQw1LDVW1q6usZmJ0GicQbeSG8mJKHwxsAmRy4a7JxBH0r4rT/UhOopyTx52LcEMUsZGo9G462xCXEAsFvJmBOMoKkH+o6iD4jGUDIP5PMUb9RjuWqfAAmKi6MTAJkQuGZIIg06LC+1WfNXQHurb8VtdS6fU/hu04mFXYNNhtaPDYpeWUQYrYwO462xCvS8qmEdR3Wub5CBNHJZhlUJ3rLMhim4MbELEqNdJQ8kire2702rHolf3AHC+iMgxg6Q/Eo16xOicZ0GN7RaYXcFFsLqiAM/OqNBmbMQOpeDU2Mh/FCV3q7enceyMIopqDGxCyF1n0xTS+/CHwyHgvk2fYd/pJiTHxuB3PyiARqP8OgXAeRQkvpA3tlncGZsgzbEB3BmbUNfYuNu9lc+Wic+5XMXD5i4bvqw3A5D/KAoAZ9kQRTkGNiF0mWtQX2VV5BQQry47hi37zyFGp8G6HxQgNyM+qF/fPX24KyQZm7gwqLGx2R1ocs0/CspRlMw1NofPtUAQgMwkoyLHmGJg80VtG+whbssnouBjYBNCl7kG9R061xIRu202V57FmrJjAIBff3siikanB/0ePAuIQ5GxEY/dQpmxueAKajQaIDUuRvGvJx5FXWi3yDK/RzyGUiJbAwAj0uNhitGiw2qXWsqJKHoE75+6dJGctFikxRvQaLZg8fq9GDM4EUNTTBiaGouhKXEYmhobtPqVvuw+1YgH/rkfAPDja0fhu1NzQnIfYgHxueZO2FwvssHN2LiG9IWweFgsHE6JjYFep/y/TcSJz3aHgOYOq/TfIFDijigl6msAQKfVYGxmIvafacbRmpagZxWJKLTC41UzSmk0Glw1JgNvflaNdw/X4d3DdRddk2TSY2hqHIamxGJYaiyyU0xS0JOdYsKgBKPiNS5VDe1Y9OoeWOwOlFyaiZ+X5Cv69XojZmzOXOiQ3hcXxK4osXjYHMLiYXE+SzCOoQDAoNciyaRHS6cNDeaugQc2rozNRIUCGwAY5wpsDp9rxawJQxT7OkQUfhjYhNgTt0zENyZk4cyFDpxtcr25ft/cYUVLpw0t51pw+JzvDg+DXoviSwbjsdkTpJ0+cmrptGLhXz5Fo9mCCUOT8Ju5U6BVcJt0X9KkwMZ5xGDQaxEThKyFSCoeDmHGJpiFw6KMBKMzsGmzYMzgwB+n02rHMdfmbSVavUX5Q5yPzQJiCoR45BrKv+socAxsQizeqMeNE33/i7Kty4ZqV6BzpqlD+v1Z1+9rWzphsTnw1uc12H3qAn4zdwquGpMh273Z7A7c/be9OFbXhswkI/54+xVBPfbxRQxsTrtqJ4I5w8b59ULf7i0eRQVjT5QoPcGAL+vNA55lc7SmFXaHgPR4A7KSTDLd3cXcs2zY8k3+qWvpxLd/uxNp8QZs/PGVIf87j/zH/2JhLMGox9jMRIzNTPT5cavdgQNnm3H/P/fjeF0bfvCnCvz4mtG494axA85iCIKAX715CB8eq0dsjA5/mn8FspKVeyHqLzGwOdvkPIoK9l864ZSxCdZRlOfXGuiaAnEw36VDkxU9QhUDm68a29FusfHFifrF4RCw7B+fSdnzR988hCdunRTq2yI/sSsqgsXotLhseCreXDwD86YNhyAA694/ge+sK8dXDeYBPfbLO0/h1U++gkYDrL5tiqwbmAdCfIG12p2p4mB2RAHhkrFxbfYOYmAjHnMOtOVb2ugt4+JLX9ITjMhIMEIQnG3fRP3xhw+/xEfH62HUa6HRABs+PY0t+6tDfVvkJwY2KhBr0GHVLROx7geXI8mkx2enm3DTmo+wufJsQI/33pE6PLblEADgwVn5KLk0S87bHZDuL+bB/pd4fBi0e4uD8oKZscnotqcrUAelVQrKB8qXDBEH9fE4ivr22ekmPPXfowCAX33rUtx93RgAwEOvfS4dfVNkYGCjIrMmDMHbS6/BtJFpaOuyYenGfVj2j31+7TU6UtOCe/5eCYcAzJ2ag0XXjFLwjv3X/cU8+BmbMDqKCtJWdcDjKGoAaxWsdgeOnHMW8yo1w8bTONcR7uFzLCAOhQtmCz4/0wyb3RHqW+lTW5cN/7uhEjaHgG9MzMLcK3KwpDgPlw9PQWunDUs2VEbE90FODGxUZmhKLNbfWYilxXnQaoDX9p7FzWs+xP4zTX1+7vnWLix8eTfaumwoGpWOx0onBG1dQn+lxBngeUvBr7FxZWxCOHlYzJpkRNhR1LHaNljsDiSa9MhJi5Xr1nokV2fUVw1mPPXfIzhexyOt/jjd2I4VbxxA0RNl+OYLH2HG/72HZ945GtZZjxVvHMBXDe0YmhKLVd+eBI1GgxidFs/ddhkSjXrsrWrCc67hpBT+GNiokF6nxdLisdj44yJkJ5twqqEdt/5uJ37//okeJ8d2Wu2485XdONvUgVEZ8fjdDy6HQR9+Px46rQYpse5pu8HvinIN6AvhUZS02TvIXVGeXzsQ0kbvbGULh0WenVGCENjE5HcO1uDmNR9h7Xsn8J11O6UZPHSxg9XN+N+/V+K6p3fglfKv0Gl1wKjXoqalE89vP45rnnoPP/xTBbbsr0aXLXwmrb+x7yxe23sWWlc9YbLHNO+ctDj8v1smAgBeeO84yk80hOo2yQ/h98pFsrliZBreXnINbpyQBatdwKq3j2D+S7tQ19rpdZ3nYsuUuBj86Y4rkBIXvBdNf3keR8UFeTJzXIgH9NkdAi60B7/GRpyZM5CuqIPiKgUF59d4GjM4AVqNcwXF+Vb/7ttmd+D/th7Bolf3oLXLBlOMFk3tVsz7wyfYG0G73ZQmCAJ2Hq/HD/9UgZvWfIR/f1YNu0PANWMHYf3/FGL/L2/AC9+7DFfnZUAQgA+P1WPx+koUrdqOx7ccwvG60B4TVjW04xevHwAA3HN9Hq4YmXbRNd+cnI25U3MgCMDSjZUDrjMj5TGwUbnkuBj89vuXY9UtE2GK0eLDY/W4cfWHeO+oe8pxqBdb+stzMF3QMzbG0K5UuNBugZh8SA1i8ClmbC60WwOuNRBXKQSrw84Uo5N+lg/7cRxV39aF2/+8C7/bcQIA8KOrclH+4ExMHZGK1k4bfvjHClR8Gd3/crc7BPxn/znMXvsxvvfHCnx4rB5aDfCtydn4z//OwCs/mobpYzJg1Otw86RsvLqwEB8+8DXcc/0YZCYZ0Wi24I8fnUTxsx/gO7/biU27Twc9C2q1O/C/GyrR1mXD1BGpuOf6MT1eu/Jb4zF6UDxqW7rwwD8/CzgDSMHBwCYKaDQazJs2HG8unoH8rEQ0mC1Y8NKneGzLIfxj92mvxZZXjgr+Ykt/eWVsQtQV1W61y7IQ0l/Snqi4mKBOXE71qG0Sl3D2h83uwInzbdh6oAaHxB1RQSgcFrnrbPrXGbXnqwu4ec1H2HmiAXEGHZ6fdxlWfHM8UuMN+MuPpqFoVDrMFjvmv7QLHx2rV/LWw1Kn1Y6/VXyFmc/swN3r92L/mWaYYrSYXzQC79//NayZd1mP/31z0uJw7w3j8PHPr8ef75iKr4/PhE6rwe6vLuD+f+5H4a/L8IvXP8fnZ4Jz3Lf63S+w73QTEk16rL5tSq971+IMejw/73IYdFq8e7gOr5R/FZR7pMBwalUUyctMxOa7r8ITbx/ByztP4U8fnZQ+dte1o0O22NJfnruKQjXHRhCADqtdCnSCJRSt3oCztiktzoAGswUN5i4MSvTuyLLaHThVb8axujYcq23DsbpWHK9rw5fnzbB4ZHgSjfqgZgTzMxPxH5zDkT4yNoIg4JXyr/D4fw7BahcwelA81v2gAHkewzHjjXq8tOAK/PjVPXj/i/P40V8+xe9/UICv5Q9gx0SEaO6w4q+ffIWXPj6FetdxZEpcDG4vGon5RSP8Wuei12lxfX4mrs/PRF1LJ/659ww2fnoaXzW0428VVfhbRRUuzU7CbVfkoPSyoUg0yb/BfueJevzWlZF74pZJGJYa1+fnjM9OwsPfyMcv3zyEX791GFeMTMN4hecx+UMQBOw4eh6vV57FNydn4+vjM0N9SyHDwCbKmGJ0+OW3LsWMMRm4/5+f4UK7FSWXZuKBknGhvrV+Sw9hxsYU4xzcJQjOWTZBD2xCMJxPlBbvDGz2fHXBFby04XhdK47VtuFkvVnatt5dbIwOYwYnIG9wAmZfNhS6IO7fETM2R3pp+W632PDQa5/jjX3OQWw3TRyC//vOJCT4+G9ritHhxdsLsHh9JbYdqsWiV3fj+XmXY9aE8Jn1JKea5k786aMvsb6iSqorG5oSi4UzcjH3ipwB//wPTjLhp9eNwV3XjMYnJxuw8dPTePtADQ5Wt+CRNw5izfbjePLWSbIGj41mC362cR8E10iLmyb1f0nq/Okj8eGxepQdqcM9f9+LN++ZERZTrXeeqMcz73yBPV8567/+/Vk1brlsKFZ+81KvYuhoEfr/IhQSxeMz8d+fXYNdJxvx9fGZEbXsLS2EGRuNRoN4gx5tXTa0d9kB39suFNMYgnUKovQEA47VQSq27C7eoMOYzETkuYKYvMwE5A1OxNCU2JD9fImdUcfr2mCzOy46bvjyfBvu+usefFHbBp1Wg4duzMfCGbm9dm0Z9Tr89vuXY+nGffjP/nO4e/1e/GbuFHxrcrai30sw1bZ0Yu17x/H3XVXSlO/8rET8+NpRuHlStuzHoFqtBtNHZ2D66Az8qt2C1yvP4i87T+FUQzsWvPwpbrsiB8tvHu8z2PSHIAh44J/7UdvShVGD4rHyW+P9+nyNRoOn5kzGrNUf4MR5Mx7bcgirbgndyoXKqgt4+p2j+Pi4s+bLFKPFNXmD8O7hWrxWeRYfn6jHE7dOwtfGqT+r6ImBTRQbnGjCzZMi7y9jz+WPofjXUoLRGdj87B/7cO/Xx+GqMelBm/cjHkUpscm9L1eNzsAnXzYi0ajHmExn8DI2M9GZjclMRHayKezmHg1NiZX+e52sN3sdLW09cA73bdqPti4bBiUasfZ7l2Na7sVdMb7E6LR4bu4UGHVavFZ5Fks3VMJic+A7BcOU+laCor6tC7/bcQJ//eQrdNmcR4jTctPwk2tH47pxg4Ly3zclzoAFV+Vi3rThePq/R/Gnj09iw6en8dHxejw9Z/KA6gD/+slXePdwLQw6LdbcdllAf3+kxRuweu4UfP9PFfj7rtOYMWaQX1kfORyqbsGz247i3cPOJpAYnQbfmzYcd39tDAYnmbC36gLu+8dn+LLejAUvfYq5U3Ow/OZLFDnWC0cMbCjieGVsQhDY/OS60Vj19mFUVjXhB3+qQGFuGu69YVy/XxQHQtrsHYKMzT0z83DHVSORYNSHXQDTE61Wg7GZCdhb1YQjNa3Iy0yEze7Ak/89ihc/+BKA84X7he9dhsGJ/i151eu0eHrOZBj0Wmz49DTu2/QZLDYHvlc4XIlvRVEXzBb8/oMv8Zedp9BhdR45XTEyFcu+Pg5Fo0PTUGCK0WH5zeNRPD4T9236DGcudGDeHz7Bj67Kxf0l42CK8S9be7SmFY//5zAA4IFZ4wbUnTd9TAZ+et1orH3vBB58bT8m5yT3q05noE6cb8Nvtn2BLfvPAQC0GuA7BcPwvzPzvL7+5cNT8Z//vRpPv3MUf/74JDbudgaGT35nEq4ak6H4fYYaAxuKOJ5tznFBPooCnOfsN07Mwm/fO4H1FVWoONmI7/6+HFfnZeDeG8ZhSk6KYl87lEdRACLyX3zjspJcgU0LCkelYfH6Suw62QgAuPPqXDwwKz/goxWtVoP/9+2JMOq1+Ev5V3j49c/RZbNjwVW5A77v5g4r3j1Ui/IvGzAyPQ5fyx+M8UOSZA0qmzus+NOHX+LPH5+SVq9MzknBvV8fi6vzMsIigL1yVDq2Lr0Gj285hA2fnsafPjqJ9784j2e/OxmThqX06zE6rXbc8/e96LI5cO3YQfiRDP99lhaPxc4TDaisasKSDfuwcdGVvXZWDcTpxnasKTuGf+09A7GU7ZuTs/Gz4jyMGpTg83NiDTo8cvN43DA+E/f98zOcbuzA9/9YgduLRuDBG/PDojZIKRohShryW1pakJycjObmZiQlhU8lO/nvXHMHilZtBwD8d+k1GJcV5EIXD9VNHXjhveP4x6enpeLZ4ksG42dfHytbW7PDIeBAdTPKDtfhr598hQazBc/dNgWzpwyV5fHV7pXyU1jxxkGMy0zEhXYL6lq7EG/Q4ak5k/GNifIcIQiCgCfePoLfu7JAP5+Vj59cN9rvx7lgtmDboVq8deAcPj5eL9W3iLKSTPha/iBcn5+Jq8akB/zi1NZlw8sfn8SLH3yJlk5nQDN+SBLuvWEsrs8fHBYBjS/bj9Ti5//6HOdbu6DTarD4a2Ow+PoxfQamj2w+gFc/+QoZCUa8veTqi7r6AnW6sR3feO5DtHbZcM/1Y3DvDfI2YdS2dOKF7cex4VN3rVPxJZm494axuGRI/1/HzF02rHr7MP76SRUAYER6HJ6eM9nnQMJw5O/rNwMbijhdNjvGLd8KAPjwga8hJ035FHBfqhrasWb7Mbzm8S+qb0zMws+Kx3rVdfRXu8WGj47VY/uROmw/Uoc6j8m5phgt/vO/V2N0D/9SI28VXzZg7oufSH/OG5yAdT8skP35EwQBv3n3mDQXamlxHpbMzOszSKhv68I7B2vx9oFz2HmiAXaP7rJxmYm4Ln8QTtSZ8fHxeumYCAAMei2uHJWO68c5A53h6X3/f9BhseOV8lNY9/4JaR7R2MwE/Kx4LEouzYqIJoILZgseeeOAdBwzcWgynv3u5B7/P3vnYA0WvboHAPCXH03DtWMHyXo/b35WjXv+XgmNBlj/P1fKcnTXaLZg3fsn8Jedp6RapxljMnDvDWNx2fDUgB/3o2P1eOCfn6G6uRMaDbDwqlzcF8CxXrAxsOkBAxt1uevVPaht7cQ/75oe1Pbhvpw434bn3j2GN/dXQxAAjQaYPTkbS4rH9jm/5cyFdmw/Uoeyw3Uo/7IBFpt7/kucQYer8zIw85JMfG3cYNn+xRkNmjusKHhsG2wOAd+cnI0nbpmoaJv+2veO46n/HgXgrMd6oGTcRcFNXWsn/nuwFm/tP4eKkw3w7JQfPyQJ35iYhVkThmDMYHfw1Wm145MvG/DekTpsP1qH040dXo85ZnACrs8fjOvzB6NgRKpXFqPTasf6iir8dscJaQ7NqIx4LCnOw82TssPq/6H++vdn1Xhk8wE0d1hh0Gtx/w3j8KMZuV7fS01zJ2Y99wGa2q248+pc/OIm/7qg+uvn/9yPjbtPIzPJiLeXXOP3UbHF5sDxujYcrG7G/jPNeL3yrHQ0WDAiFffdIF+tU0unFY9vOYR/7D4DABg1KB7PzJk8oIBJaQxsesDAhoLpaE0rfrPtC2w9WAPAOeDu1suH4p7r86QMk90hoLLqAsqO1GH74TocrfWetZKTFouZ+Zm4Pn8wCkelwagP739VhbP/HqyBxebAzZOGBOWY5U8fncRjWw4BABZcNRIrbh6P2pYubD1wDm8dqMGnpxrh+TfvxKHJuHFiFr4xYQhG9mOAoSAIOF7XJmX0dn91wSvTk2jS45qxg3D9uMFot9qxdvtx1LQ4d8TlpMViycyxKJ2SrVhNSLDUtnTi5//ajx1HzwNwFoI/M2cyctLiYHcI+MEfK1D+ZQMmDE3Caz+5SrHFvu0WG25+/iN8ed6M4ksy8YfbC3r8OWvrsuHwuRYcqm7BwepmHKxuwbHaNq9BloBzp9q9N4zDdWOV6UbbfqQWD/7rc9S1dkGrAX587WgsLc4Ly79nGNj0gIENhcKBs814dtsX2H7E3Zb5nYJh6LI68N7ROq/1BFoNMHVEGq6/ZDBm5g/GmMEJYVvrQH179ZOv8Mhm58yf3Ix4nKw3e318Sk4KvjExCzdOGDLg49TmDis++OI83jtShx1fnPe5qDE72YTF1+dhztRhQV3HoTRBELDh09N4fMshmC12xBuc3VSNZgue+u9RxBl02HLPjB6LbOVysLoZ3167Exa7A4/OvhS3F43E+dYuKXg55ApmTjWY4etVN9Gkx6XZSbg0OxlFo9Ix8xLla52a2i341ZuH8HrlWQDOo89Hbh6PrGQjTDE6xMboEGvQwaTXhfSYkoFNDxjYUCjtrbqAZ9/5Ah8d994vlGTS47pxgzHzksG4duygsN6qTv77x+7T+Pm/9ksvZFNHpOLGiUMwa0IWhqbEKvI17Q4B+043OY+sjtSh02bH/KKRuG1aTlj+a1wuVQ3tuG/TZ9h1qtHr/U9+Z1LQ1sW89PFJ/OrNQzDotEiJi/GqjfOUlWRyBTFJGJ+djEuzkzAsNTZk/5DZeqAGyzd/jvq2njeXG/RaZ6DjCnaMei1iDTrpfSbX2yVDEvE/V4+S9f4Y2PSAgQ2Fg4ovnWPjMxKNmOmqhYj04wDq3c7j9TjZYMbM/ExkJfs3K4f8Y3cI+PNHJ/HUO0elo8fn510WtIBBEAT8z192o8yVodVonNm6S13By/ghzmAmFAM2+9LQ1oVfv3UYO483oNNmR4fFLhUu++PasYPwlx9Nk/XeGNj0gIENEVF0OF7XhvIvG3Dr5UODPq+l3WLDtkO1GJYai/yspKDvk5OTwyGg02ZHp9WBDqsz2Om02tFhdf1qcf9evGZoSiy+KfN6EQY2PWBgQ0REFHn8ff1mDpyIiIhUg4ENERERqQYDGyIiIlINBjZERESkGgxsiIiISDUY2BAREZFqMLAhIiIi1WBgQ0RERKoRUGCzdu1ajBw5EiaTCYWFhdi1a1ev12/atAn5+fkwmUyYOHEi3nrrLa+PC4KAFStWYMiQIYiNjUVxcTGOHTvmdU1jYyO+//3vIykpCSkpKVi4cCHa2toCuX0iIiJSKb8Dm40bN2LZsmVYuXIl9u7di8mTJ6OkpAR1dXU+r9+5cyfmzZuHhQsXorKyEqWlpSgtLcWBAweka5588kmsWbMG69atQ0VFBeLj41FSUoLOzk7pmu9///s4ePAgtm3bhi1btuCDDz7AokWLAviWiYiISK38XqlQWFiIK664Ai+88AIAwOFwICcnB/fccw8efPDBi66fO3cuzGYztmzZIr3vyiuvxJQpU7Bu3ToIgoDs7Gzce++9uO+++wAAzc3NyMzMxMsvv4zbbrsNhw8fxvjx4/Hpp59i6tSpAICtW7fiG9/4Bs6cOYPs7L73UnClAhERUeRRdKWCxWLBnj17UFxc7H4ArRbFxcUoLy/3+Tnl5eVe1wNASUmJdP3JkydRU1PjdU1ycjIKCwula8rLy5GSkiIFNQBQXFwMrVaLiooKn1+3q6sLLS0tXm9ERESkbn4FNvX19bDb7cjMzPR6f2ZmJmpqanx+Tk1NTa/Xi7/2dc3gwYO9Pq7X65GWltbj1121ahWSk5Olt5ycnH5+l0RERBSpInefeh8eeughLFu2TPpzc3Mzhg8fzswNERFRBBFft/tbOeNXYJORkQGdTofa2lqv99fW1iIrK8vn52RlZfV6vfhrbW0thgwZ4nXNlClTpGu6FyfbbDY0Njb2+HWNRiOMRqP0Z/GJYeaGiIgo8rS2tiI5ObnP6/wKbAwGAwoKClBWVobS0lIAzuLhsrIyLF682OfnFBUVoaysDEuXLpXet23bNhQVFQEAcnNzkZWVhbKyMimQaWlpQUVFBX7yk59Ij9HU1IQ9e/agoKAAALB9+3Y4HA4UFhb2696zs7Nx+vRpJCYmQqPR+PNt96mlpQU5OTk4ffo0C5P9wOfNf3zOAsPnLTB83vzH5ywwvT1vgiCgtbW1X41C4if4ZcOGDYLRaBRefvll4dChQ8KiRYuElJQUoaamRhAEQfjhD38oPPjgg9L1H3/8saDX64Wnn35aOHz4sLBy5UohJiZG+Pzzz6VrnnjiCSElJUV44403hP379wuzZ88WcnNzhY6ODumaWbNmCZdddplQUVEhfPTRR0JeXp4wb948f29fEc3NzQIAobm5OdS3ElH4vPmPz1lg+LwFhs+b//icBUbO583vGpu5c+fi/PnzWLFiBWpqajBlyhRs3bpVKv6tqqqCVuuuSZ4+fTrWr1+P5cuX4+GHH0ZeXh42b96MCRMmSNc88MADMJvNWLRoEZqamjBjxgxs3boVJpNJuuZvf/sbFi9ejJkzZ0Kr1eLWW2/FmjVr/L19IiIiUjG/59jQxTgjJzB83vzH5ywwfN4Cw+fNf3zOAiPn88ZdUTIwGo1YuXKlV7Ey9Y3Pm//4nAWGz1tg+Lz5j89ZYOR83pixISIiItVgxoaIiIhUg4ENERERqQYDGyIiIlINBjZERESkGgxsBmjt2rUYOXIkTCYTCgsLsWvXrlDfUlj75S9/CY1G4/WWn58f6tsKOx988AG++c1vIjs7GxqNBps3b/b6uCAIWLFiBYYMGYLY2FgUFxfj2LFjobnZMNLX83bHHXdc9PM3a9as0NxsmFi1ahWuuOIKJCYmYvDgwSgtLcXRo0e9runs7MTdd9+N9PR0JCQk4NZbb71oVU606c/zdt11113083bXXXeF6I7Dw+9+9ztMmjQJSUlJSEpKQlFREd5++23p43L8rDGwGYCNGzdi2bJlWLlyJfbu3YvJkyejpKTkor1W5O3SSy/FuXPnpLePPvoo1LcUdsxmMyZPnoy1a9f6/PiTTz6JNWvWYN26daioqEB8fDxKSkrQ2dkZ5DsNL309bwAwa9Ysr5+/v//970G8w/Dz/vvv4+6778Ynn3yCbdu2wWq14oYbboDZbJau+dnPfoY333wTmzZtwvvvv4/q6mrccsstIbzr0OvP8wYAd955p9fP25NPPhmiOw4Pw4YNwxNPPIE9e/Zg9+7duP766zF79mwcPHgQgEw/awOeXRzFpk2bJtx9993Sn+12u5CdnS2sWrUqhHcV3lauXClMnjw51LcRUQAIr7/+uvRnh8MhZGVlCU899ZT0vqamJsFoNAp///vfQ3CH4an78yYIgjB//nxh9uzZIbmfSFFXVycAEN5//31BEJw/WzExMcKmTZukaw4fPiwAEMrLy0N1m2Gn+/MmCIJw7bXXCkuWLAndTUWI1NRU4Y9//KNsP2vM2ATIYrFgz549KC4ult6n1WpRXFyM8vLyEN5Z+Dt27Biys7MxatQofP/730dVVVWobyminDx5EjU1NV4/e8nJySgsLOTPXj/s2LEDgwcPxrhx4/CTn/wEDQ0Nob6lsNLc3AwASEtLAwDs2bMHVqvV6+ctPz8fw4cP58+bh+7Pm+hvf/sbMjIyMGHCBDz00ENob28Pxe2FJbvdjg0bNsBsNqOoqEi2nzW/d0WRU319Pex2u7QjS5SZmYkjR46E6K7CX2FhIV5++WWMGzcO586dw69+9StcffXVOHDgABITE0N9exGhpqYGAHz+7IkfI99mzZqFW265Bbm5uThx4gQefvhh3HjjjSgvL4dOpwv17YWcw+HA0qVLcdVVV0n7/GpqamAwGJCSkuJ1LX/e3Hw9bwDwve99DyNGjEB2djb279+Pn//85zh69Chee+21EN5t6H3++ecoKipCZ2cnEhIS8Prrr2P8+PHYt2+fLD9rDGwoqG688Ubp95MmTUJhYSFGjBiBf/zjH1i4cGEI74yiwW233Sb9fuLEiZg0aRJGjx6NHTt2YObMmSG8s/Bw991348CBA6x781NPz9uiRYuk30+cOBFDhgzBzJkzceLECYwePTrYtxk2xo0bh3379qG5uRn//Oc/MX/+fLz//vuyPT6PogKUkZEBnU53UbV2bW0tsrKyQnRXkSclJQVjx47F8ePHQ30rEUP8+eLP3sCNGjUKGRkZ/PkDsHjxYmzZsgXvvfcehg0bJr0/KysLFosFTU1NXtfz582pp+fNl8LCQgCI+p83g8GAMWPGoKCgAKtWrcLkyZPx3HPPyfazxsAmQAaDAQUFBSgrK5Pe53A4UFZWhqKiohDeWWRpa2vDiRMnMGTIkFDfSsTIzc1FVlaW189eS0sLKioq+LPnpzNnzqChoSGqf/4EQcDixYvx+uuvY/v27cjNzfX6eEFBAWJiYrx+3o4ePYqqqqqo/nnr63nzZd++fQAQ1T9vvjgcDnR1dcn3syZ/fXP02LBhg2A0GoWXX35ZOHTokLBo0SIhJSVFqKmpCfWtha17771X2LFjh3Dy5Enh448/FoqLi4WMjAyhrq4u1LcWVlpbW4XKykqhsrJSACA8++yzQmVlpfDVV18JgiAITzzxhJCSkiK88cYbwv79+4XZs2cLubm5QkdHR4jvPLR6e95aW1uF++67TygvLxdOnjwpvPvuu8Lll18u5OXlCZ2dnaG+9ZD5yU9+IiQnJws7duwQzp07J721t7dL19x1113C8OHDhe3btwu7d+8WioqKhKKiohDedej19bwdP35cePTRR4Xdu3cLJ0+eFN544w1h1KhRwjXXXBPiOw+tBx98UHj//feFkydPCvv37xcefPBBQaPRCO+8844gCPL8rDGwGaDnn39eGD58uGAwGIRp06YJn3zySahvKazNnTtXGDJkiGAwGIShQ4cKc+fOFY4fPx7q2wo77733ngDgorf58+cLguBs+X7kkUeEzMxMwWg0CjNnzhSOHj0a2psOA709b+3t7cINN9wgDBo0SIiJiRFGjBgh3HnnnVH/DxFfzxcA4aWXXpKu6ejoEH76058KqampQlxcnPDtb39bOHfuXOhuOgz09bxVVVUJ11xzjZCWliYYjUZhzJgxwv333y80NzeH9sZD7Ec/+pEwYsQIwWAwCIMGDRJmzpwpBTWCIM/PmkYQBGEAGSQiIiKisMEaGyIiIlINBjZERESkGgxsiIiISDUY2BAREZFqMLAhIiIi1WBgQ0RERKrBwIaIiIhUg4ENERERqQYDGyIiIlINBjZERESkGgxsiIiISDUY2BAREZFq/H/V2zgfIjpJ1wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "val_loss = model.history.history['val_loss']\n",
    "plt.plot(range(len(val_loss)), val_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.load_model(model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34/34 [==============================] - 0s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(train_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2073d0d8190>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = scaler.inverse_transform(y_pred)\n",
    "x_train = scaler.inverse_transform(x_train)\n",
    "x_val = scaler.inverse_transform(x_val)\n",
    "plt.plot(range(len(x_train)-1), y_pred[:len(x_train)-1], color =\"orange\", label = \"Train Pred\")\n",
    "plt.plot(range(len(x_train)-1), x_train[1:], color =\"green\", label = \"Train Actual\")\n",
    "plt.plot(range(len(x_train)-1,len(train_scaled)-1), y_pred[len(x_train)-1:-1], color =\"red\", label = \"Val Pred\")\n",
    "plt.plot(range(len(x_train)-1,len(train_scaled)-1), x_val, color =\"blue\", label = \"Val Actual\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "43/43 [==============================] - 3s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "data_scaled = np.concatenate((train_scaled, test_scaled))\n",
    "pred = model.predict(data_scaled)\n",
    "pred = pred[-len(test_scaled)-1:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "284"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared: 0.9817207992953129\n",
      "MAPE: 3.2228727832979254\n",
      "RMSE: 858.2445969595135\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import math\n",
    "\n",
    "plot_pred = scaler.inverse_transform(pred)\n",
    "actual = scaler.inverse_transform(test_scaled)\n",
    "\n",
    "def mean_absolute_percentage_error(y_true, y_pred): \n",
    "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n",
    "\n",
    "\n",
    "r2 = r2_score(test_scaled, pred)\n",
    "print(\"R-squared:\", r2)\n",
    "mape = mean_absolute_percentage_error(actual, plot_pred)\n",
    "print(\"MAPE:\", mape)\n",
    "rmse = math.sqrt(mean_squared_error(actual, plot_pred))\n",
    "print(\"RMSE:\", rmse)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x207314d9350>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.plot(range(len(actual)), plot_pred, color =\"orange\", label = \"Test Pred\")\n",
    "plt.plot(range(len(actual)), actual, color =\"green\", label = \"Test Actual\")\n",
    "plt.legend()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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