06_1027_StockpricePredict(CNN).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "61f1758e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from scipy.interpolate import interp1d\n",
    "from scipy.stats import mode\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "from sklearn.metrics import confusion_matrix,classification_report,accuracy_score\n",
    "import seaborn as sns\n",
    "\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "from tensorflow import stack\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, GlobalAveragePooling1D, BatchNormalization, MaxPool1D, Reshape, Activation, Flatten\n",
    "from keras.layers import Conv1D, LSTM, Dropout, Bidirectional,LayerNormalization,SimpleRNN\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0aad8cb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./data/stock.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d4773307",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['종목코드', '종목명', '거래량', '시가', '고가', '저가', '종가'], dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "38576552",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['종목명']=='삼성전자']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "47241c93",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['시가','고가','저가','종가']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a9e4f4f1",
   "metadata": {},
   "outputs": [
    {
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       "<p>494 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "             시가     고가     저가     종가\n",
       "일자                                  \n",
       "20210601  80500  81300  80100  80600\n",
       "20210602  80400  81400  80300  80800\n",
       "20210603  81300  83000  81100  82800\n",
       "20210604  82700  82700  81500  82200\n",
       "20210607  82700  82800  81600  81900\n",
       "...         ...    ...    ...    ...\n",
       "20230523  68500  68700  68100  68400\n",
       "20230524  68100  68700  68000  68500\n",
       "20230525  69900  70000  68700  68800\n",
       "20230526  69800  70400  69500  70300\n",
       "20230530  71300  72300  71200  72300\n",
       "\n",
       "[494 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2d73bfd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def MinMaxScaler(data):\n",
    "    numerator = data - np.min(data, 0)\n",
    "    denominator = np.max(data, 0) - np.min(data, 0)\n",
    "\n",
    "    return numerator / (denominator + 1e-7)\n",
    "\n",
    "df = MinMaxScaler(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "90235257",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfx = df[['시가','고가','저가']]\n",
    "dfy = df[['종가']]\n",
    "\n",
    "dfx.columns = ['Open','High','Low']\n",
    "dfy.columns = [['Close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8cfbfc25",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>일자</th>\n",
       "      <th></th>\n",
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       "</table>\n",
       "<p>494 rows × 1 columns</p>\n",
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      ],
      "text/plain": [
       "             Close\n",
       "일자                \n",
       "20210601  0.924092\n",
       "20210602  0.930693\n",
       "20210603  0.996700\n",
       "20210604  0.976898\n",
       "20210607  0.966997\n",
       "...            ...\n",
       "20230523  0.521452\n",
       "20230524  0.524752\n",
       "20230525  0.534653\n",
       "20230526  0.584158\n",
       "20230530  0.650165\n",
       "\n",
       "[494 rows x 1 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8c3c0d48",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_step = 5\n",
    "step = 1\n",
    "\n",
    "def create_dataset(X, y, time_step, step):\n",
    "    Xs, ys = [], []\n",
    "\n",
    "    for i in range(0, len(X) - time_step, step):\n",
    "        v = X.iloc[i : (i + time_step)].values\n",
    "        labels = y.iloc[i + time_step]\n",
    "\n",
    "        Xs.append(v)\n",
    "        ys.append(labels)\n",
    "\n",
    "    return np.array(Xs), np.array(ys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f95604ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(489, 5, 3)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, y = create_dataset(dfx, dfy, time_step, step)\n",
    "\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dc18a9d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((327, 5, 3), (162, 5, 3), (327, 1), (162, 1))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42, shuffle=False)\n",
    "\n",
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a2f6a836",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv1d (Conv1D)             (None, 3, 16)             160       \n",
      "                                                                 \n",
      " conv1d_1 (Conv1D)           (None, 1, 8)              392       \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 8)                 0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1)                 9         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 561 (2.19 KB)\n",
      "Trainable params: 561 (2.19 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv1D(16, 3,input_shape = (X.shape[1:]), activation='relu'))\n",
    "model.add(Conv1D(8, 3, activation='relu'))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(1))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fd7dcac5",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='mse', optimizer='adam', metrics=['mae'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9d822166",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "7/7 [==============================] - 3s 125ms/step - loss: 0.2297 - mae: 0.4469 - val_loss: 0.0066 - val_mae: 0.0649\n",
      "Epoch 2/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0222 - mae: 0.1133 - val_loss: 0.0092 - val_mae: 0.0882\n",
      "Epoch 3/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0206 - mae: 0.1346 - val_loss: 0.0153 - val_mae: 0.1167\n",
      "Epoch 4/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0171 - mae: 0.1187 - val_loss: 0.0062 - val_mae: 0.0696\n",
      "Epoch 5/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0025 - mae: 0.0376 - val_loss: 0.0024 - val_mae: 0.0413\n",
      "Epoch 6/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0052 - mae: 0.0603 - val_loss: 0.0024 - val_mae: 0.0410\n",
      "Epoch 7/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0036 - mae: 0.0470 - val_loss: 0.0037 - val_mae: 0.0524\n",
      "Epoch 8/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0024 - mae: 0.0389 - val_loss: 0.0048 - val_mae: 0.0606\n",
      "Epoch 9/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0024 - mae: 0.0388 - val_loss: 0.0041 - val_mae: 0.0549\n",
      "Epoch 10/500\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0020 - mae: 0.0333 - val_loss: 0.0033 - val_mae: 0.0493\n",
      "Epoch 11/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0021 - mae: 0.0350 - val_loss: 0.0034 - val_mae: 0.0497\n",
      "Epoch 12/500\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0021 - mae: 0.0353 - val_loss: 0.0037 - val_mae: 0.0524\n",
      "Epoch 13/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0020 - mae: 0.0348 - val_loss: 0.0037 - val_mae: 0.0524\n",
      "Epoch 14/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0020 - mae: 0.0340 - val_loss: 0.0035 - val_mae: 0.0507\n",
      "Epoch 15/500\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0020 - mae: 0.0340 - val_loss: 0.0034 - val_mae: 0.0502\n",
      "Epoch 16/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0020 - mae: 0.0343 - val_loss: 0.0035 - val_mae: 0.0507\n",
      "Epoch 17/500\n",
      "7/7 [==============================] - 0s 18ms/step - loss: 0.0020 - mae: 0.0342 - val_loss: 0.0035 - val_mae: 0.0506\n",
      "Epoch 18/500\n",
      "7/7 [==============================] - 0s 17ms/step - loss: 0.0020 - mae: 0.0340 - val_loss: 0.0034 - val_mae: 0.0500\n",
      "Epoch 19/500\n",
      "7/7 [==============================] - 0s 17ms/step - loss: 0.0020 - mae: 0.0340 - val_loss: 0.0034 - val_mae: 0.0497\n",
      "Epoch 20/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0020 - mae: 0.0341 - val_loss: 0.0034 - val_mae: 0.0497\n",
      "Epoch 21/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0020 - mae: 0.0340 - val_loss: 0.0033 - val_mae: 0.0494\n",
      "Epoch 22/500\n",
      "7/7 [==============================] - 0s 17ms/step - loss: 0.0020 - mae: 0.0339 - val_loss: 0.0033 - val_mae: 0.0491\n",
      "Epoch 23/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0020 - mae: 0.0339 - val_loss: 0.0033 - val_mae: 0.0489\n",
      "Epoch 24/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0020 - mae: 0.0339 - val_loss: 0.0032 - val_mae: 0.0487\n",
      "Epoch 25/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0020 - mae: 0.0338 - val_loss: 0.0032 - val_mae: 0.0484\n",
      "Epoch 26/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0020 - mae: 0.0338 - val_loss: 0.0032 - val_mae: 0.0481\n",
      "Epoch 27/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0020 - mae: 0.0338 - val_loss: 0.0031 - val_mae: 0.0479\n",
      "Epoch 28/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0020 - mae: 0.0337 - val_loss: 0.0031 - val_mae: 0.0476\n",
      "Epoch 29/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0020 - mae: 0.0337 - val_loss: 0.0031 - val_mae: 0.0474\n",
      "Epoch 30/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0020 - mae: 0.0337 - val_loss: 0.0030 - val_mae: 0.0471\n",
      "Epoch 31/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0020 - mae: 0.0336 - val_loss: 0.0030 - val_mae: 0.0469\n",
      "Epoch 32/500\n",
      "7/7 [==============================] - 0s 23ms/step - loss: 0.0020 - mae: 0.0336 - val_loss: 0.0030 - val_mae: 0.0466\n",
      "Epoch 33/500\n",
      "7/7 [==============================] - 0s 17ms/step - loss: 0.0020 - mae: 0.0335 - val_loss: 0.0029 - val_mae: 0.0463\n",
      "Epoch 34/500\n",
      "7/7 [==============================] - 0s 32ms/step - loss: 0.0020 - mae: 0.0335 - val_loss: 0.0029 - val_mae: 0.0461\n",
      "Epoch 35/500\n",
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      "Epoch 61/500\n",
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      "Epoch 62/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0019 - mae: 0.0325 - val_loss: 0.0022 - val_mae: 0.0403\n",
      "Epoch 63/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0019 - mae: 0.0325 - val_loss: 0.0022 - val_mae: 0.0402\n",
      "Epoch 64/500\n",
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      "Epoch 78/500\n",
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      "Epoch 79/500\n",
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      "7/7 [==============================] - 0s 23ms/step - loss: 0.0018 - mae: 0.0319 - val_loss: 0.0020 - val_mae: 0.0379\n",
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      "Epoch 84/500\n",
      "7/7 [==============================] - 0s 20ms/step - loss: 0.0018 - mae: 0.0319 - val_loss: 0.0019 - val_mae: 0.0377\n",
      "Epoch 85/500\n",
      "7/7 [==============================] - 0s 19ms/step - loss: 0.0018 - mae: 0.0319 - val_loss: 0.0019 - val_mae: 0.0376\n",
      "Epoch 86/500\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0018 - mae: 0.0318 - val_loss: 0.0019 - val_mae: 0.0375\n",
      "Epoch 87/500\n",
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      "7/7 [==============================] - 0s 38ms/step - loss: 0.0018 - mae: 0.0317 - val_loss: 0.0019 - val_mae: 0.0372\n",
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      "Epoch 124/500\n",
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      "Epoch 125/500\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0017 - mae: 0.0309 - val_loss: 0.0017 - val_mae: 0.0349\n",
      "Epoch 126/500\n",
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      "Epoch 127/500\n",
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      "Epoch 128/500\n",
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      "Epoch 131/500\n",
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      "Epoch 132/500\n",
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      "Epoch 139/500\n",
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      "Epoch 140/500\n",
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      "Epoch 144/500\n",
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      "Epoch 145/500\n",
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      "Epoch 146/500\n",
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      "Epoch 147/500\n",
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      "Epoch 148/500\n",
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      "Epoch 151/500\n",
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      "Epoch 154/500\n",
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      "Epoch 155/500\n",
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      "Epoch 156/500\n",
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      "Epoch 157/500\n",
      "7/7 [==============================] - 0s 23ms/step - loss: 0.0016 - mae: 0.0303 - val_loss: 0.0016 - val_mae: 0.0337\n",
      "Epoch 158/500\n",
      "7/7 [==============================] - 0s 19ms/step - loss: 0.0016 - mae: 0.0303 - val_loss: 0.0016 - val_mae: 0.0337\n",
      "Epoch 159/500\n",
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      "Epoch 160/500\n",
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      "Epoch 173/500\n",
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      "Epoch 174/500\n",
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      "Epoch 175/500\n",
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      "Epoch 176/500\n",
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      "Epoch 177/500\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0016 - mae: 0.0299 - val_loss: 0.0015 - val_mae: 0.0331\n",
      "Epoch 178/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0016 - mae: 0.0299 - val_loss: 0.0015 - val_mae: 0.0331\n",
      "Epoch 179/500\n",
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      "Epoch 180/500\n",
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      "Epoch 181/500\n",
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      "Epoch 182/500\n",
      "7/7 [==============================] - 0s 18ms/step - loss: 0.0016 - mae: 0.0298 - val_loss: 0.0015 - val_mae: 0.0329\n",
      "Epoch 183/500\n",
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      "Epoch 184/500\n",
      "7/7 [==============================] - 0s 19ms/step - loss: 0.0016 - mae: 0.0298 - val_loss: 0.0015 - val_mae: 0.0329\n",
      "Epoch 185/500\n",
      "7/7 [==============================] - 0s 24ms/step - loss: 0.0016 - mae: 0.0298 - val_loss: 0.0015 - val_mae: 0.0329\n",
      "Epoch 186/500\n",
      "7/7 [==============================] - 0s 17ms/step - loss: 0.0016 - mae: 0.0297 - val_loss: 0.0015 - val_mae: 0.0328\n",
      "Epoch 187/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0016 - mae: 0.0297 - val_loss: 0.0015 - val_mae: 0.0328\n",
      "Epoch 188/500\n",
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      "Epoch 189/500\n",
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      "Epoch 190/500\n",
      "7/7 [==============================] - 0s 17ms/step - loss: 0.0015 - mae: 0.0297 - val_loss: 0.0015 - val_mae: 0.0327\n",
      "Epoch 191/500\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0015 - mae: 0.0296 - val_loss: 0.0015 - val_mae: 0.0327\n",
      "Epoch 192/500\n",
      "7/7 [==============================] - 0s 19ms/step - loss: 0.0015 - mae: 0.0296 - val_loss: 0.0015 - val_mae: 0.0326\n",
      "Epoch 193/500\n",
      "7/7 [==============================] - 0s 45ms/step - loss: 0.0015 - mae: 0.0296 - val_loss: 0.0015 - val_mae: 0.0326\n",
      "Epoch 194/500\n",
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      "Epoch 195/500\n",
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      "Epoch 196/500\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0015 - mae: 0.0295 - val_loss: 0.0015 - val_mae: 0.0325\n",
      "Epoch 197/500\n",
      "7/7 [==============================] - 0s 18ms/step - loss: 0.0015 - mae: 0.0295 - val_loss: 0.0015 - val_mae: 0.0325\n",
      "Epoch 198/500\n",
      "7/7 [==============================] - 0s 21ms/step - loss: 0.0015 - mae: 0.0295 - val_loss: 0.0015 - val_mae: 0.0325\n",
      "Epoch 199/500\n",
      "7/7 [==============================] - 0s 20ms/step - loss: 0.0015 - mae: 0.0295 - val_loss: 0.0015 - val_mae: 0.0325\n",
      "Epoch 200/500\n",
      "7/7 [==============================] - 0s 19ms/step - loss: 0.0015 - mae: 0.0295 - val_loss: 0.0015 - val_mae: 0.0324\n",
      "Epoch 201/500\n",
      "7/7 [==============================] - 0s 21ms/step - loss: 0.0015 - mae: 0.0294 - val_loss: 0.0015 - val_mae: 0.0324\n",
      "Epoch 202/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0015 - mae: 0.0294 - val_loss: 0.0015 - val_mae: 0.0324\n",
      "Epoch 203/500\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0015 - mae: 0.0294 - val_loss: 0.0015 - val_mae: 0.0324\n",
      "Epoch 204/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0015 - mae: 0.0294 - val_loss: 0.0015 - val_mae: 0.0323\n",
      "Epoch 205/500\n",
      "7/7 [==============================] - 0s 18ms/step - loss: 0.0015 - mae: 0.0294 - val_loss: 0.0015 - val_mae: 0.0323\n",
      "Epoch 206/500\n",
      "7/7 [==============================] - 0s 17ms/step - loss: 0.0015 - mae: 0.0294 - val_loss: 0.0015 - val_mae: 0.0323\n",
      "Epoch 207/500\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0015 - mae: 0.0293 - val_loss: 0.0015 - val_mae: 0.0323\n",
      "Epoch 208/500\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0015 - mae: 0.0293 - val_loss: 0.0015 - val_mae: 0.0322\n",
      "Epoch 209/500\n",
      "7/7 [==============================] - 0s 18ms/step - loss: 0.0015 - mae: 0.0293 - val_loss: 0.0015 - val_mae: 0.0322\n",
      "Epoch 210/500\n",
      "7/7 [==============================] - 0s 18ms/step - loss: 0.0015 - mae: 0.0293 - val_loss: 0.0015 - val_mae: 0.0322\n",
      "Epoch 211/500\n",
      "7/7 [==============================] - 0s 17ms/step - loss: 0.0015 - mae: 0.0293 - val_loss: 0.0015 - val_mae: 0.0322\n",
      "Epoch 212/500\n",
      "7/7 [==============================] - 0s 25ms/step - loss: 0.0015 - mae: 0.0293 - val_loss: 0.0015 - val_mae: 0.0321\n",
      "Epoch 213/500\n",
      "7/7 [==============================] - 0s 28ms/step - loss: 0.0015 - mae: 0.0292 - val_loss: 0.0015 - val_mae: 0.0321\n",
      "Epoch 214/500\n",
      "7/7 [==============================] - 0s 23ms/step - loss: 0.0015 - mae: 0.0292 - val_loss: 0.0015 - val_mae: 0.0321\n",
      "Epoch 215/500\n",
      "7/7 [==============================] - 0s 21ms/step - loss: 0.0015 - mae: 0.0292 - val_loss: 0.0015 - val_mae: 0.0321\n",
      "Epoch 216/500\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0015 - mae: 0.0292 - val_loss: 0.0015 - val_mae: 0.0320\n",
      "Epoch 217/500\n",
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      "Epoch 218/500\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0015 - mae: 0.0292 - val_loss: 0.0014 - val_mae: 0.0320\n",
      "Epoch 219/500\n",
      "7/7 [==============================] - 0s 17ms/step - loss: 0.0015 - mae: 0.0291 - val_loss: 0.0014 - val_mae: 0.0320\n",
      "Epoch 220/500\n",
      "7/7 [==============================] - 0s 17ms/step - loss: 0.0015 - mae: 0.0291 - val_loss: 0.0014 - val_mae: 0.0319\n",
      "Epoch 221/500\n",
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      "Epoch 222/500\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0015 - mae: 0.0291 - val_loss: 0.0014 - val_mae: 0.0319\n",
      "Epoch 223/500\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0015 - mae: 0.0291 - val_loss: 0.0014 - val_mae: 0.0319\n",
      "Epoch 224/500\n",
      "7/7 [==============================] - 0s 21ms/step - loss: 0.0015 - mae: 0.0290 - val_loss: 0.0014 - val_mae: 0.0319\n",
      "Epoch 225/500\n",
      "7/7 [==============================] - 0s 18ms/step - loss: 0.0015 - mae: 0.0290 - val_loss: 0.0014 - val_mae: 0.0318\n",
      "Epoch 226/500\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0015 - mae: 0.0290 - val_loss: 0.0014 - val_mae: 0.0318\n",
      "Epoch 227/500\n",
      "7/7 [==============================] - 0s 20ms/step - loss: 0.0015 - mae: 0.0290 - val_loss: 0.0014 - val_mae: 0.0318\n",
      "Epoch 228/500\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0015 - mae: 0.0290 - val_loss: 0.0014 - val_mae: 0.0318\n",
      "Epoch 229/500\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0015 - mae: 0.0289 - val_loss: 0.0014 - val_mae: 0.0317\n",
      "Epoch 230/500\n",
      "7/7 [==============================] - 0s 17ms/step - loss: 0.0015 - mae: 0.0289 - val_loss: 0.0014 - val_mae: 0.0317\n",
      "Epoch 231/500\n",
      "7/7 [==============================] - 0s 17ms/step - loss: 0.0015 - mae: 0.0289 - val_loss: 0.0014 - val_mae: 0.0317\n",
      "Epoch 232/500\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0015 - mae: 0.0289 - val_loss: 0.0014 - val_mae: 0.0317\n",
      "Epoch 233/500\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0015 - mae: 0.0289 - val_loss: 0.0014 - val_mae: 0.0316\n",
      "Epoch 234/500\n",
      "7/7 [==============================] - 0s 18ms/step - loss: 0.0015 - mae: 0.0289 - val_loss: 0.0014 - val_mae: 0.0316\n",
      "Epoch 235/500\n",
      "7/7 [==============================] - 0s 19ms/step - loss: 0.0015 - mae: 0.0288 - val_loss: 0.0014 - val_mae: 0.0316\n",
      "Epoch 236/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0015 - mae: 0.0288 - val_loss: 0.0014 - val_mae: 0.0316\n",
      "Epoch 237/500\n",
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      "Epoch 238/500\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0014 - mae: 0.0288 - val_loss: 0.0014 - val_mae: 0.0315\n",
      "Epoch 239/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0014 - mae: 0.0288 - val_loss: 0.0014 - val_mae: 0.0315\n",
      "Epoch 240/500\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0014 - mae: 0.0287 - val_loss: 0.0014 - val_mae: 0.0315\n",
      "Epoch 241/500\n",
      "7/7 [==============================] - 0s 19ms/step - loss: 0.0014 - mae: 0.0287 - val_loss: 0.0014 - val_mae: 0.0315\n",
      "Epoch 242/500\n",
      "7/7 [==============================] - 0s 24ms/step - loss: 0.0014 - mae: 0.0287 - val_loss: 0.0014 - val_mae: 0.0315\n",
      "Epoch 243/500\n",
      "7/7 [==============================] - 0s 29ms/step - loss: 0.0014 - mae: 0.0287 - val_loss: 0.0014 - val_mae: 0.0314\n",
      "Epoch 244/500\n",
      "7/7 [==============================] - 0s 31ms/step - loss: 0.0014 - mae: 0.0287 - val_loss: 0.0014 - val_mae: 0.0314\n",
      "Epoch 245/500\n",
      "7/7 [==============================] - 0s 20ms/step - loss: 0.0014 - mae: 0.0286 - val_loss: 0.0014 - val_mae: 0.0314\n",
      "Epoch 246/500\n",
      "7/7 [==============================] - 0s 19ms/step - loss: 0.0014 - mae: 0.0286 - val_loss: 0.0014 - val_mae: 0.0314\n",
      "Epoch 247/500\n",
      "7/7 [==============================] - 0s 18ms/step - loss: 0.0014 - mae: 0.0286 - val_loss: 0.0014 - val_mae: 0.0313\n",
      "Epoch 248/500\n",
      "7/7 [==============================] - 0s 19ms/step - loss: 0.0014 - mae: 0.0286 - val_loss: 0.0014 - val_mae: 0.0313\n",
      "Epoch 249/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0014 - mae: 0.0286 - val_loss: 0.0014 - val_mae: 0.0313\n",
      "Epoch 250/500\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0014 - mae: 0.0286 - val_loss: 0.0014 - val_mae: 0.0313\n",
      "Epoch 251/500\n",
      "7/7 [==============================] - 0s 19ms/step - loss: 0.0014 - mae: 0.0285 - val_loss: 0.0014 - val_mae: 0.0313\n",
      "Epoch 252/500\n",
      "7/7 [==============================] - 0s 18ms/step - loss: 0.0014 - mae: 0.0285 - val_loss: 0.0014 - val_mae: 0.0312\n",
      "Epoch 253/500\n",
      "7/7 [==============================] - 0s 17ms/step - loss: 0.0014 - mae: 0.0285 - val_loss: 0.0014 - val_mae: 0.0312\n",
      "Epoch 254/500\n",
      "7/7 [==============================] - 0s 26ms/step - loss: 0.0014 - mae: 0.0285 - val_loss: 0.0014 - val_mae: 0.0312\n",
      "Epoch 255/500\n",
      "7/7 [==============================] - 0s 17ms/step - loss: 0.0014 - mae: 0.0285 - val_loss: 0.0014 - val_mae: 0.0312\n",
      "Epoch 256/500\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0014 - mae: 0.0284 - val_loss: 0.0014 - val_mae: 0.0312\n",
      "Epoch 257/500\n",
      "7/7 [==============================] - 0s 19ms/step - loss: 0.0014 - mae: 0.0284 - val_loss: 0.0014 - val_mae: 0.0311\n",
      "Epoch 258/500\n",
      "7/7 [==============================] - 0s 18ms/step - loss: 0.0014 - mae: 0.0284 - val_loss: 0.0014 - val_mae: 0.0311\n",
      "Epoch 259/500\n",
      "7/7 [==============================] - 0s 20ms/step - loss: 0.0014 - mae: 0.0284 - val_loss: 0.0014 - val_mae: 0.0311\n",
      "Epoch 260/500\n",
      "7/7 [==============================] - 0s 18ms/step - loss: 0.0014 - mae: 0.0284 - val_loss: 0.0014 - val_mae: 0.0311\n",
      "Epoch 261/500\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0014 - mae: 0.0284 - val_loss: 0.0014 - val_mae: 0.0311\n",
      "Epoch 262/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0014 - mae: 0.0283 - val_loss: 0.0014 - val_mae: 0.0310\n",
      "Epoch 263/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0014 - mae: 0.0283 - val_loss: 0.0014 - val_mae: 0.0310\n",
      "Epoch 264/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0014 - mae: 0.0283 - val_loss: 0.0014 - val_mae: 0.0310\n",
      "Epoch 265/500\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0014 - mae: 0.0283 - val_loss: 0.0014 - val_mae: 0.0310\n",
      "Epoch 266/500\n",
      "7/7 [==============================] - 0s 21ms/step - loss: 0.0014 - mae: 0.0283 - val_loss: 0.0014 - val_mae: 0.0310\n",
      "Epoch 267/500\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0014 - mae: 0.0283 - val_loss: 0.0014 - val_mae: 0.0309\n",
      "Epoch 268/500\n",
      "7/7 [==============================] - 0s 17ms/step - loss: 0.0014 - mae: 0.0282 - val_loss: 0.0014 - val_mae: 0.0309\n",
      "Epoch 269/500\n",
      "7/7 [==============================] - 0s 18ms/step - loss: 0.0014 - mae: 0.0282 - val_loss: 0.0014 - val_mae: 0.0309\n",
      "Epoch 270/500\n",
      "7/7 [==============================] - 0s 17ms/step - loss: 0.0014 - mae: 0.0282 - val_loss: 0.0014 - val_mae: 0.0309\n",
      "Epoch 271/500\n",
      "7/7 [==============================] - 0s 19ms/step - loss: 0.0014 - mae: 0.0282 - val_loss: 0.0014 - val_mae: 0.0308\n",
      "Epoch 272/500\n",
      "7/7 [==============================] - 0s 21ms/step - loss: 0.0014 - mae: 0.0282 - val_loss: 0.0014 - val_mae: 0.0308\n",
      "Epoch 273/500\n",
      "7/7 [==============================] - 0s 23ms/step - loss: 0.0014 - mae: 0.0282 - val_loss: 0.0014 - val_mae: 0.0308\n",
      "Epoch 274/500\n",
      "7/7 [==============================] - 0s 18ms/step - loss: 0.0014 - mae: 0.0281 - val_loss: 0.0013 - val_mae: 0.0308\n",
      "Epoch 275/500\n",
      "7/7 [==============================] - 0s 24ms/step - loss: 0.0014 - mae: 0.0281 - val_loss: 0.0013 - val_mae: 0.0308\n",
      "Epoch 276/500\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0014 - mae: 0.0281 - val_loss: 0.0013 - val_mae: 0.0308\n",
      "Epoch 277/500\n",
      "7/7 [==============================] - 0s 20ms/step - loss: 0.0014 - mae: 0.0281 - val_loss: 0.0013 - val_mae: 0.0307\n",
      "Epoch 278/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0014 - mae: 0.0281 - val_loss: 0.0013 - val_mae: 0.0307\n",
      "Epoch 279/500\n",
      "7/7 [==============================] - 0s 19ms/step - loss: 0.0014 - mae: 0.0281 - val_loss: 0.0013 - val_mae: 0.0307\n",
      "Epoch 280/500\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0014 - mae: 0.0280 - val_loss: 0.0013 - val_mae: 0.0307\n",
      "Epoch 281/500\n",
      "7/7 [==============================] - 0s 22ms/step - loss: 0.0014 - mae: 0.0280 - val_loss: 0.0013 - val_mae: 0.0307\n",
      "Epoch 282/500\n",
      "7/7 [==============================] - 0s 19ms/step - loss: 0.0014 - mae: 0.0280 - val_loss: 0.0013 - val_mae: 0.0306\n",
      "Epoch 283/500\n",
      "7/7 [==============================] - 0s 22ms/step - loss: 0.0014 - mae: 0.0280 - val_loss: 0.0013 - val_mae: 0.0306\n",
      "Epoch 284/500\n",
      "7/7 [==============================] - 0s 24ms/step - loss: 0.0014 - mae: 0.0280 - val_loss: 0.0013 - val_mae: 0.0306\n",
      "Epoch 285/500\n",
      "7/7 [==============================] - 0s 18ms/step - loss: 0.0014 - mae: 0.0280 - val_loss: 0.0013 - val_mae: 0.0306\n",
      "Epoch 286/500\n",
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      "Epoch 289/500\n",
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      "Epoch 290/500\n",
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      "Epoch 291/500\n",
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      "Epoch 292/500\n",
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      "Epoch 293/500\n",
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      "Epoch 295/500\n",
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      "Epoch 296/500\n",
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      "Epoch 297/500\n",
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      "Epoch 298/500\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0013 - mae: 0.0277 - val_loss: 0.0013 - val_mae: 0.0304\n",
      "Epoch 299/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0013 - mae: 0.0277 - val_loss: 0.0013 - val_mae: 0.0304\n",
      "Epoch 300/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0013 - mae: 0.0277 - val_loss: 0.0013 - val_mae: 0.0303\n",
      "Epoch 301/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0013 - mae: 0.0277 - val_loss: 0.0013 - val_mae: 0.0303\n",
      "Epoch 302/500\n",
      "7/7 [==============================] - 0s 23ms/step - loss: 0.0013 - mae: 0.0277 - val_loss: 0.0013 - val_mae: 0.0303\n",
      "Epoch 303/500\n",
      "7/7 [==============================] - 0s 24ms/step - loss: 0.0013 - mae: 0.0277 - val_loss: 0.0013 - val_mae: 0.0303\n",
      "Epoch 304/500\n",
      "7/7 [==============================] - 0s 21ms/step - loss: 0.0013 - mae: 0.0277 - val_loss: 0.0013 - val_mae: 0.0303\n",
      "Epoch 305/500\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0013 - mae: 0.0276 - val_loss: 0.0013 - val_mae: 0.0303\n",
      "Epoch 306/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0013 - mae: 0.0276 - val_loss: 0.0013 - val_mae: 0.0303\n",
      "Epoch 307/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0013 - mae: 0.0276 - val_loss: 0.0013 - val_mae: 0.0302\n",
      "Epoch 308/500\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0013 - mae: 0.0276 - val_loss: 0.0013 - val_mae: 0.0302\n",
      "Epoch 309/500\n",
      "7/7 [==============================] - 0s 17ms/step - loss: 0.0013 - mae: 0.0276 - val_loss: 0.0013 - val_mae: 0.0302\n",
      "Epoch 310/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0013 - mae: 0.0276 - val_loss: 0.0013 - val_mae: 0.0302\n",
      "Epoch 311/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0013 - mae: 0.0276 - val_loss: 0.0013 - val_mae: 0.0302\n",
      "Epoch 312/500\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0013 - mae: 0.0276 - val_loss: 0.0013 - val_mae: 0.0302\n",
      "Epoch 313/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0013 - mae: 0.0275 - val_loss: 0.0013 - val_mae: 0.0302\n",
      "Epoch 314/500\n",
      "7/7 [==============================] - 0s 18ms/step - loss: 0.0013 - mae: 0.0275 - val_loss: 0.0013 - val_mae: 0.0301\n",
      "Epoch 315/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0013 - mae: 0.0275 - val_loss: 0.0013 - val_mae: 0.0301\n",
      "Epoch 316/500\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0013 - mae: 0.0275 - val_loss: 0.0013 - val_mae: 0.0301\n",
      "Epoch 317/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0013 - mae: 0.0275 - val_loss: 0.0013 - val_mae: 0.0301\n",
      "Epoch 318/500\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0013 - mae: 0.0275 - val_loss: 0.0013 - val_mae: 0.0301\n",
      "Epoch 319/500\n",
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      "Epoch 320/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0013 - mae: 0.0275 - val_loss: 0.0013 - val_mae: 0.0301\n",
      "Epoch 321/500\n",
      "7/7 [==============================] - 0s 17ms/step - loss: 0.0013 - mae: 0.0274 - val_loss: 0.0013 - val_mae: 0.0301\n",
      "Epoch 322/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0013 - mae: 0.0274 - val_loss: 0.0013 - val_mae: 0.0300\n",
      "Epoch 323/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0013 - mae: 0.0274 - val_loss: 0.0013 - val_mae: 0.0300\n",
      "Epoch 324/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0013 - mae: 0.0274 - val_loss: 0.0013 - val_mae: 0.0300\n",
      "Epoch 325/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0013 - mae: 0.0274 - val_loss: 0.0013 - val_mae: 0.0300\n",
      "Epoch 326/500\n",
      "7/7 [==============================] - 0s 26ms/step - loss: 0.0013 - mae: 0.0274 - val_loss: 0.0013 - val_mae: 0.0300\n",
      "Epoch 327/500\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0013 - mae: 0.0274 - val_loss: 0.0013 - val_mae: 0.0300\n",
      "Epoch 328/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0013 - mae: 0.0274 - val_loss: 0.0013 - val_mae: 0.0300\n",
      "Epoch 329/500\n",
      "7/7 [==============================] - 0s 19ms/step - loss: 0.0013 - mae: 0.0274 - val_loss: 0.0013 - val_mae: 0.0300\n",
      "Epoch 330/500\n",
      "7/7 [==============================] - 0s 18ms/step - loss: 0.0013 - mae: 0.0273 - val_loss: 0.0013 - val_mae: 0.0299\n",
      "Epoch 331/500\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0013 - mae: 0.0273 - val_loss: 0.0013 - val_mae: 0.0299\n",
      "Epoch 332/500\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0013 - mae: 0.0273 - val_loss: 0.0013 - val_mae: 0.0299\n",
      "Epoch 333/500\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0013 - mae: 0.0273 - val_loss: 0.0013 - val_mae: 0.0299\n",
      "Epoch 334/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0013 - mae: 0.0273 - val_loss: 0.0013 - val_mae: 0.0299\n",
      "Epoch 335/500\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0013 - mae: 0.0273 - val_loss: 0.0013 - val_mae: 0.0299\n",
      "Epoch 336/500\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0013 - mae: 0.0273 - val_loss: 0.0013 - val_mae: 0.0299\n",
      "Epoch 337/500\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0013 - mae: 0.0273 - val_loss: 0.0013 - val_mae: 0.0299\n",
      "Epoch 338/500\n",
      "7/7 [==============================] - 0s 18ms/step - loss: 0.0013 - mae: 0.0273 - val_loss: 0.0013 - val_mae: 0.0299\n",
      "Epoch 339/500\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0013 - mae: 0.0272 - val_loss: 0.0013 - val_mae: 0.0298\n",
      "Epoch 340/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0013 - mae: 0.0272 - val_loss: 0.0013 - val_mae: 0.0298\n",
      "Epoch 341/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0013 - mae: 0.0272 - val_loss: 0.0013 - val_mae: 0.0298\n",
      "Epoch 342/500\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0013 - mae: 0.0272 - val_loss: 0.0013 - val_mae: 0.0298\n",
      "Epoch 343/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0013 - mae: 0.0272 - val_loss: 0.0013 - val_mae: 0.0298\n",
      "Epoch 344/500\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0013 - mae: 0.0272 - val_loss: 0.0013 - val_mae: 0.0298\n",
      "Epoch 345/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0013 - mae: 0.0272 - val_loss: 0.0013 - val_mae: 0.0298\n",
      "Epoch 346/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0013 - mae: 0.0272 - val_loss: 0.0013 - val_mae: 0.0298\n",
      "Epoch 347/500\n",
      "7/7 [==============================] - 0s 17ms/step - loss: 0.0013 - mae: 0.0272 - val_loss: 0.0013 - val_mae: 0.0298\n",
      "Epoch 348/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0013 - mae: 0.0272 - val_loss: 0.0013 - val_mae: 0.0298\n",
      "Epoch 349/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0013 - mae: 0.0271 - val_loss: 0.0013 - val_mae: 0.0298\n",
      "Epoch 350/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0013 - mae: 0.0271 - val_loss: 0.0013 - val_mae: 0.0297\n",
      "Epoch 351/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0013 - mae: 0.0271 - val_loss: 0.0013 - val_mae: 0.0297\n",
      "Epoch 352/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0013 - mae: 0.0271 - val_loss: 0.0013 - val_mae: 0.0297\n",
      "Epoch 353/500\n",
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      "Epoch 354/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0013 - mae: 0.0271 - val_loss: 0.0013 - val_mae: 0.0297\n",
      "Epoch 355/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0013 - mae: 0.0271 - val_loss: 0.0013 - val_mae: 0.0297\n",
      "Epoch 356/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0013 - mae: 0.0271 - val_loss: 0.0013 - val_mae: 0.0297\n",
      "Epoch 357/500\n",
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      "Epoch 358/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0013 - mae: 0.0271 - val_loss: 0.0013 - val_mae: 0.0297\n",
      "Epoch 359/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0013 - mae: 0.0271 - val_loss: 0.0013 - val_mae: 0.0297\n",
      "Epoch 360/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0013 - mae: 0.0270 - val_loss: 0.0013 - val_mae: 0.0297\n",
      "Epoch 361/500\n",
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      "Epoch 362/500\n",
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      "Epoch 363/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0013 - mae: 0.0270 - val_loss: 0.0013 - val_mae: 0.0297\n",
      "Epoch 364/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0013 - mae: 0.0270 - val_loss: 0.0013 - val_mae: 0.0297\n",
      "Epoch 365/500\n",
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      "Epoch 366/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0013 - mae: 0.0270 - val_loss: 0.0013 - val_mae: 0.0297\n",
      "Epoch 367/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0013 - mae: 0.0270 - val_loss: 0.0013 - val_mae: 0.0297\n",
      "Epoch 368/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0013 - mae: 0.0270 - val_loss: 0.0013 - val_mae: 0.0297\n",
      "Epoch 369/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0013 - mae: 0.0270 - val_loss: 0.0013 - val_mae: 0.0297\n",
      "Epoch 370/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0013 - mae: 0.0270 - val_loss: 0.0013 - val_mae: 0.0297\n",
      "Epoch 371/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0013 - mae: 0.0269 - val_loss: 0.0013 - val_mae: 0.0297\n",
      "Epoch 372/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0013 - mae: 0.0269 - val_loss: 0.0013 - val_mae: 0.0297\n",
      "Epoch 373/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 17ms/step - loss: 0.0013 - mae: 0.0269 - val_loss: 0.0013 - val_mae: 0.0297\n",
      "Epoch 374/500\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0013 - mae: 0.0269 - val_loss: 0.0013 - val_mae: 0.0297\n",
      "Epoch 375/500\n",
      "7/7 [==============================] - 0s 19ms/step - loss: 0.0013 - mae: 0.0269 - val_loss: 0.0013 - val_mae: 0.0297\n",
      "Epoch 376/500\n",
      "7/7 [==============================] - 0s 18ms/step - loss: 0.0013 - mae: 0.0269 - val_loss: 0.0013 - val_mae: 0.0297\n",
      "Epoch 377/500\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0013 - mae: 0.0269 - val_loss: 0.0013 - val_mae: 0.0297\n",
      "Epoch 378/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0013 - mae: 0.0269 - val_loss: 0.0013 - val_mae: 0.0297\n",
      "Epoch 379/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0013 - mae: 0.0269 - val_loss: 0.0013 - val_mae: 0.0297\n",
      "Epoch 380/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0013 - mae: 0.0269 - val_loss: 0.0013 - val_mae: 0.0297\n",
      "Epoch 381/500\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0013 - mae: 0.0269 - val_loss: 0.0013 - val_mae: 0.0297\n",
      "Epoch 382/500\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0013 - mae: 0.0269 - val_loss: 0.0013 - val_mae: 0.0297\n",
      "Epoch 383/500\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0013 - mae: 0.0269 - val_loss: 0.0013 - val_mae: 0.0297\n",
      "Epoch 384/500\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0013 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0297\n",
      "Epoch 385/500\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0013 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0297\n",
      "Epoch 386/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0298\n",
      "Epoch 387/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0298\n",
      "Epoch 388/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0298\n",
      "Epoch 389/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0298\n",
      "Epoch 390/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0298\n",
      "Epoch 391/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0298\n",
      "Epoch 392/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0298\n",
      "Epoch 393/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0299\n",
      "Epoch 394/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0299\n",
      "Epoch 395/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0299\n",
      "Epoch 396/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0299\n",
      "Epoch 397/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0299\n",
      "Epoch 398/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0300\n",
      "Epoch 399/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0300\n",
      "Epoch 400/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0300\n",
      "Epoch 401/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0301\n",
      "Epoch 402/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0301\n",
      "Epoch 403/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0301\n",
      "Epoch 404/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0302\n",
      "Epoch 405/500\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0302\n",
      "Epoch 406/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0302\n",
      "Epoch 407/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0303\n",
      "Epoch 408/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0303\n",
      "Epoch 409/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0303\n",
      "Epoch 410/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0304\n",
      "Epoch 411/500\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0304\n",
      "Epoch 412/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0305\n",
      "Epoch 413/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0305\n",
      "Epoch 414/500\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0306\n",
      "Epoch 415/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0306\n",
      "Epoch 416/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0013 - val_mae: 0.0307\n",
      "Epoch 417/500\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0013 - val_mae: 0.0307\n",
      "Epoch 418/500\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0308\n",
      "Epoch 419/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0308\n",
      "Epoch 420/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0309\n",
      "Epoch 421/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0309\n",
      "Epoch 422/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0310\n",
      "Epoch 423/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0310\n",
      "Epoch 424/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0311\n",
      "Epoch 425/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0311\n",
      "Epoch 426/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0311\n",
      "Epoch 427/500\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0312\n",
      "Epoch 428/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0312\n",
      "Epoch 429/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0270 - val_loss: 0.0014 - val_mae: 0.0312\n",
      "Epoch 430/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0270 - val_loss: 0.0014 - val_mae: 0.0313\n",
      "Epoch 431/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0270 - val_loss: 0.0014 - val_mae: 0.0313\n",
      "Epoch 432/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0270 - val_loss: 0.0014 - val_mae: 0.0313\n",
      "Epoch 433/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0270 - val_loss: 0.0014 - val_mae: 0.0313\n",
      "Epoch 434/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0270 - val_loss: 0.0014 - val_mae: 0.0313\n",
      "Epoch 435/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0270 - val_loss: 0.0014 - val_mae: 0.0313\n",
      "Epoch 436/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0270 - val_loss: 0.0014 - val_mae: 0.0313\n",
      "Epoch 437/500\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0012 - mae: 0.0270 - val_loss: 0.0014 - val_mae: 0.0313\n",
      "Epoch 438/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0270 - val_loss: 0.0014 - val_mae: 0.0313\n",
      "Epoch 439/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0270 - val_loss: 0.0014 - val_mae: 0.0313\n",
      "Epoch 440/500\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0313\n",
      "Epoch 441/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0313\n",
      "Epoch 442/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0313\n",
      "Epoch 443/500\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0313\n",
      "Epoch 444/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0313\n",
      "Epoch 445/500\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0314\n",
      "Epoch 446/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0314\n",
      "Epoch 447/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0314\n",
      "Epoch 448/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0314\n",
      "Epoch 449/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0314\n",
      "Epoch 450/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0314\n",
      "Epoch 451/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0270 - val_loss: 0.0014 - val_mae: 0.0315\n",
      "Epoch 452/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0012 - mae: 0.0270 - val_loss: 0.0014 - val_mae: 0.0315\n",
      "Epoch 453/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0012 - mae: 0.0270 - val_loss: 0.0014 - val_mae: 0.0315\n",
      "Epoch 454/500\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0012 - mae: 0.0270 - val_loss: 0.0014 - val_mae: 0.0315\n",
      "Epoch 455/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0012 - mae: 0.0270 - val_loss: 0.0014 - val_mae: 0.0316\n",
      "Epoch 456/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0012 - mae: 0.0270 - val_loss: 0.0014 - val_mae: 0.0316\n",
      "Epoch 457/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0270 - val_loss: 0.0014 - val_mae: 0.0316\n",
      "Epoch 458/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0270 - val_loss: 0.0014 - val_mae: 0.0316\n",
      "Epoch 459/500\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0012 - mae: 0.0270 - val_loss: 0.0014 - val_mae: 0.0316\n",
      "Epoch 460/500\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0012 - mae: 0.0270 - val_loss: 0.0014 - val_mae: 0.0316\n",
      "Epoch 461/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0315\n",
      "Epoch 462/500\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0315\n",
      "Epoch 463/500\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0315\n",
      "Epoch 464/500\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0314\n",
      "Epoch 465/500\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0314\n",
      "Epoch 466/500\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0313\n",
      "Epoch 467/500\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0312\n",
      "Epoch 468/500\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0014 - val_mae: 0.0312\n",
      "Epoch 469/500\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0014 - val_mae: 0.0311\n",
      "Epoch 470/500\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0014 - val_mae: 0.0311\n",
      "Epoch 471/500\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0014 - val_mae: 0.0310\n",
      "Epoch 472/500\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0014 - val_mae: 0.0310\n",
      "Epoch 473/500\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0014 - val_mae: 0.0310\n",
      "Epoch 474/500\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0014 - val_mae: 0.0310\n",
      "Epoch 475/500\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0014 - val_mae: 0.0310\n",
      "Epoch 476/500\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0014 - val_mae: 0.0310\n",
      "Epoch 477/500\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0014 - val_mae: 0.0311\n",
      "Epoch 478/500\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0014 - val_mae: 0.0311\n",
      "Epoch 479/500\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0014 - val_mae: 0.0312\n",
      "Epoch 480/500\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0014 - val_mae: 0.0313\n",
      "Epoch 481/500\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0314\n",
      "Epoch 482/500\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0315\n",
      "Epoch 483/500\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0316\n",
      "Epoch 484/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0317\n",
      "Epoch 485/500\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0012 - mae: 0.0270 - val_loss: 0.0014 - val_mae: 0.0318\n",
      "Epoch 486/500\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0012 - mae: 0.0270 - val_loss: 0.0014 - val_mae: 0.0318\n",
      "Epoch 487/500\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0012 - mae: 0.0270 - val_loss: 0.0015 - val_mae: 0.0319\n",
      "Epoch 488/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0012 - mae: 0.0270 - val_loss: 0.0015 - val_mae: 0.0319\n",
      "Epoch 489/500\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0012 - mae: 0.0270 - val_loss: 0.0014 - val_mae: 0.0318\n",
      "Epoch 490/500\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0317\n",
      "Epoch 491/500\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0012 - mae: 0.0269 - val_loss: 0.0014 - val_mae: 0.0316\n",
      "Epoch 492/500\n",
      "7/7 [==============================] - 0s 18ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0014 - val_mae: 0.0314\n",
      "Epoch 493/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0268 - val_loss: 0.0014 - val_mae: 0.0312\n",
      "Epoch 494/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0267 - val_loss: 0.0014 - val_mae: 0.0310\n",
      "Epoch 495/500\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0012 - mae: 0.0267 - val_loss: 0.0014 - val_mae: 0.0308\n",
      "Epoch 496/500\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0012 - mae: 0.0266 - val_loss: 0.0013 - val_mae: 0.0306\n",
      "Epoch 497/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0012 - mae: 0.0266 - val_loss: 0.0013 - val_mae: 0.0303\n",
      "Epoch 498/500\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.0012 - mae: 0.0265 - val_loss: 0.0013 - val_mae: 0.0301\n",
      "Epoch 499/500\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0012 - mae: 0.0265 - val_loss: 0.0013 - val_mae: 0.0299\n",
      "Epoch 500/500\n",
      "7/7 [==============================] - 0s 23ms/step - loss: 0.0012 - mae: 0.0264 - val_loss: 0.0013 - val_mae: 0.0298\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, epochs=500, batch_size=32, validation_split=0.33, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0a6d5228",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "x = np.arange(0, len(history.history['loss']))\n",
    "\n",
    "plt.plot(x, loss, 'y', label='loss')\n",
    "plt.plot(x, val_loss, 'r', label='val loss')\n",
    "plt.title('Losses')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8681dbee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 [==============================] - 0s 3ms/step\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e2a48322",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y_test, color='red', label='real stock price')\n",
    "plt.plot(y_pred, color='blue', label='predicted stock price')\n",
    "plt.title('stock price prediction')\n",
    "plt.xlabel('time')\n",
    "plt.ylabel('stock price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}