01.02.03.SequenceModeling_LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time Series Analysis with LSTM  \n",
    "\n",
    "Multi-Layer Perceptron으로 수행하였던 time series forcasting을 가장 대표적인 RNN인 LSTM을 이용해서 동일한 조건으로 수행해 보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.pylab import rcParams\n",
    "rcParams['figure.figsize'] = 15, 6    # matlab 차트의 기본 크기를 15,6으로 지정해 줍니다.\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# csv 포맷의 데이터 경로를 지정합니다.\n",
    "dataset_filepath = 'datasets/airline-passengers.csv'\n",
    "# csv 파일을 읽어들여 DataFrame 객체를 생성합니다.\n",
    "df = pd.read_csv(dataset_filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = [([df.iat[i+j,1] for j in range(5)], df.iat[i+5,1]) for i in range(115)]\n",
    "test_data = [([df.iat[i+j,1] for j in range(5)], df.iat[i+5,1]) for i in range(115, 139)]\n",
    "X_train = np.array([X for (X,y) in train_data])\n",
    "X_test = np.array([X for (X,y) in test_data])\n",
    "y_train = np.array([y for (X,y) in train_data])\n",
    "y_test = np.array([y for (X,y) in test_data])\n",
    "X_val = X_test\n",
    "y_val = y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# RNN의 경우 Dense와 입출력 tensor shape가 다르다.\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))\n",
    "X_val = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to reproduce same training result\n",
    "from numpy.random import seed\n",
    "seed(1)\n",
    "from tensorflow import set_random_seed\n",
    "set_random_seed(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vanilla RNN을 사용할 때와 여기까지는 동일합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_3 (LSTM)                (None, 50)                10400     \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 10,451\n",
      "Trainable params: 10,451\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# define model\n",
    "model = Sequential()\n",
    "#model.add(rnn_layer)  \n",
    "model.add(LSTM(50, activation='relu', input_shape=(5, 1)))  # RNN Layer 대신 LSTM layer를 사용\n",
    "model.add(Dense(1))\n",
    "model.summary()\n",
    "\n",
    "model.compile(optimizer='adam', loss='mse')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LSTM layer는 내부적으로 LSTMCell layer를 가지고 있는 RNN Network layer 입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\uhmpp\\Anaconda3\\envs\\tf13\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Train on 115 samples, validate on 24 samples\n",
      "Epoch 1/1000\n",
      "115/115 [==============================] - 3s 26ms/step - loss: 66333.7598 - val_loss: 173567.5000\n",
      "Epoch 2/1000\n",
      "115/115 [==============================] - 0s 451us/step - loss: 53781.8635 - val_loss: 137196.8438\n",
      "Epoch 3/1000\n",
      "115/115 [==============================] - 0s 486us/step - loss: 41656.4975 - val_loss: 103866.0938\n",
      "Epoch 4/1000\n",
      "115/115 [==============================] - 0s 477us/step - loss: 31453.1186 - val_loss: 75611.1875\n",
      "Epoch 5/1000\n",
      "115/115 [==============================] - 0s 434us/step - loss: 22689.1010 - val_loss: 54048.1367\n",
      "Epoch 6/1000\n",
      "115/115 [==============================] - 0s 425us/step - loss: 15382.7178 - val_loss: 33859.3398\n",
      "Epoch 7/1000\n",
      "115/115 [==============================] - 0s 425us/step - loss: 8982.0059 - val_loss: 17489.2520\n",
      "Epoch 8/1000\n",
      "115/115 [==============================] - 0s 434us/step - loss: 4746.0969 - val_loss: 10048.5684\n",
      "Epoch 9/1000\n",
      "115/115 [==============================] - 0s 434us/step - loss: 2674.6259 - val_loss: 8509.1748\n",
      "Epoch 10/1000\n",
      "115/115 [==============================] - 0s 460us/step - loss: 2836.3444 - val_loss: 9013.5967\n",
      "Epoch 11/1000\n",
      "115/115 [==============================] - 0s 477us/step - loss: 2356.6386 - val_loss: 6770.8960\n",
      "Epoch 12/1000\n",
      "115/115 [==============================] - 0s 434us/step - loss: 1598.3952 - val_loss: 6012.2290\n",
      "Epoch 13/1000\n",
      "115/115 [==============================] - 0s 442us/step - loss: 1443.3859 - val_loss: 4609.6719\n",
      "Epoch 14/1000\n",
      "115/115 [==============================] - 0s 416us/step - loss: 1235.8640 - val_loss: 3978.4421\n",
      "Epoch 15/1000\n",
      "115/115 [==============================] - 0s 468us/step - loss: 1162.6388 - val_loss: 3693.0081\n",
      "Epoch 16/1000\n",
      "115/115 [==============================] - 0s 416us/step - loss: 1117.4130 - val_loss: 3582.9912\n",
      "Epoch 17/1000\n",
      "115/115 [==============================] - 0s 416us/step - loss: 1061.7987 - val_loss: 3190.2087\n",
      "Epoch 18/1000\n",
      "115/115 [==============================] - 0s 460us/step - loss: 1028.7201 - val_loss: 3247.3254\n",
      "Epoch 19/1000\n",
      "115/115 [==============================] - 0s 442us/step - loss: 916.6722 - val_loss: 2650.0559\n",
      "Epoch 20/1000\n",
      "115/115 [==============================] - 0s 399us/step - loss: 842.2772 - val_loss: 2276.8242\n",
      "Epoch 21/1000\n",
      "115/115 [==============================] - 0s 399us/step - loss: 766.2456 - val_loss: 2345.8665\n",
      "Epoch 22/1000\n",
      "115/115 [==============================] - 0s 416us/step - loss: 733.7830 - val_loss: 2211.6953\n",
      "Epoch 23/1000\n",
      "115/115 [==============================] - 0s 408us/step - loss: 816.7633 - val_loss: 2194.6658\n",
      "Epoch 24/1000\n",
      "115/115 [==============================] - 0s 494us/step - loss: 691.4722 - val_loss: 2143.9131\n",
      "Epoch 25/1000\n",
      "115/115 [==============================] - 0s 425us/step - loss: 667.3280 - val_loss: 2054.9001\n",
      "Epoch 26/1000\n",
      "115/115 [==============================] - 0s 432us/step - loss: 658.1417 - val_loss: 2241.0122\n",
      "Epoch 27/1000\n",
      "115/115 [==============================] - 0s 425us/step - loss: 638.3252 - val_loss: 1966.3872\n",
      "Epoch 28/1000\n",
      "115/115 [==============================] - 0s 416us/step - loss: 672.9640 - val_loss: 1970.5439\n",
      "Epoch 29/1000\n",
      "115/115 [==============================] - 0s 425us/step - loss: 653.2340 - val_loss: 2160.9709\n",
      "Epoch 30/1000\n",
      "115/115 [==============================] - 0s 390us/step - loss: 625.4752 - val_loss: 1951.4658\n",
      "Epoch 31/1000\n",
      "115/115 [==============================] - 0s 408us/step - loss: 655.9912 - val_loss: 1955.7262\n",
      "Epoch 32/1000\n",
      "115/115 [==============================] - 0s 408us/step - loss: 687.4361 - val_loss: 2423.2722\n",
      "Epoch 33/1000\n",
      "115/115 [==============================] - 0s 416us/step - loss: 661.3868 - val_loss: 2098.2571\n",
      "Epoch 34/1000\n",
      "115/115 [==============================] - 0s 425us/step - loss: 699.9312 - val_loss: 2274.5103\n",
      "Epoch 35/1000\n",
      "115/115 [==============================] - 0s 442us/step - loss: 734.2465 - val_loss: 2069.9834\n",
      "Epoch 36/1000\n",
      "115/115 [==============================] - 0s 520us/step - loss: 687.3566 - val_loss: 2007.2301\n",
      "Epoch 37/1000\n",
      "115/115 [==============================] - 0s 425us/step - loss: 612.0277 - val_loss: 2174.4109\n",
      "Epoch 38/1000\n",
      "115/115 [==============================] - 0s 416us/step - loss: 695.5105 - val_loss: 1868.1494\n",
      "Epoch 39/1000\n",
      "115/115 [==============================] - 0s 416us/step - loss: 609.7767 - val_loss: 1804.2188\n",
      "Epoch 40/1000\n",
      "115/115 [==============================] - 0s 425us/step - loss: 631.7576 - val_loss: 1871.6740\n",
      "Epoch 41/1000\n",
      "115/115 [==============================] - 0s 408us/step - loss: 675.3610 - val_loss: 2200.9768\n",
      "Epoch 42/1000\n",
      "115/115 [==============================] - 0s 416us/step - loss: 646.2893 - val_loss: 1981.1044\n",
      "Epoch 43/1000\n",
      "115/115 [==============================] - 0s 416us/step - loss: 659.6745 - val_loss: 1984.8871\n",
      "Epoch 44/1000\n",
      "115/115 [==============================] - 0s 416us/step - loss: 635.7411 - val_loss: 2085.3179\n",
      "Epoch 45/1000\n",
      "115/115 [==============================] - 0s 382us/step - loss: 602.1320 - val_loss: 1699.4971\n",
      "Epoch 46/1000\n",
      "115/115 [==============================] - 0s 408us/step - loss: 590.1864 - val_loss: 1736.7285\n",
      "Epoch 47/1000\n",
      "115/115 [==============================] - 0s 408us/step - loss: 581.2541 - val_loss: 1815.4171\n",
      "Epoch 48/1000\n",
      "115/115 [==============================] - 0s 399us/step - loss: 601.5219 - val_loss: 1769.0653\n",
      "Epoch 49/1000\n",
      "115/115 [==============================] - 0s 416us/step - loss: 726.3134 - val_loss: 1909.6216\n",
      "Epoch 50/1000\n",
      "115/115 [==============================] - 0s 442us/step - loss: 673.8354 - val_loss: 2438.6833\n",
      "Epoch 51/1000\n",
      "115/115 [==============================] - 0s 439us/step - loss: 728.2425 - val_loss: 2137.7214\n",
      "Epoch 52/1000\n",
      "115/115 [==============================] - 0s 442us/step - loss: 592.5709 - val_loss: 1800.4200\n",
      "Epoch 53/1000\n",
      "115/115 [==============================] - 0s 439us/step - loss: 640.4519 - val_loss: 1787.8228\n",
      "Epoch 54/1000\n",
      "115/115 [==============================] - 0s 425us/step - loss: 584.0935 - val_loss: 1756.3223\n",
      "Epoch 55/1000\n",
      "115/115 [==============================] - 0s 412us/step - loss: 656.3546 - val_loss: 1787.4491\n",
      "Epoch 56/1000\n",
      "115/115 [==============================] - 0s 425us/step - loss: 561.7113 - val_loss: 1869.7330\n",
      "Epoch 57/1000\n",
      "115/115 [==============================] - 0s 451us/step - loss: 564.8596 - val_loss: 1687.8346\n",
      "Epoch 58/1000\n",
      "115/115 [==============================] - 0s 434us/step - loss: 592.1702 - val_loss: 1653.1797\n",
      "Epoch 59/1000\n",
      "115/115 [==============================] - 0s 442us/step - loss: 564.3906 - val_loss: 1893.9189\n",
      "Epoch 60/1000\n",
      "115/115 [==============================] - 0s 460us/step - loss: 578.0666 - val_loss: 1758.1592\n",
      "Epoch 61/1000\n",
      "115/115 [==============================] - 0s 460us/step - loss: 554.6935 - val_loss: 1791.0065\n",
      "Epoch 62/1000\n",
      "115/115 [==============================] - 0s 434us/step - loss: 561.4178 - val_loss: 1686.2656\n",
      "Epoch 63/1000\n",
      "115/115 [==============================] - 0s 408us/step - loss: 582.6781 - val_loss: 1865.6774\n",
      "Epoch 64/1000\n",
      "115/115 [==============================] - 0s 434us/step - loss: 567.1837 - val_loss: 1711.5914\n",
      "Epoch 65/1000\n",
      "115/115 [==============================] - 0s 442us/step - loss: 545.9651 - val_loss: 1619.3102\n",
      "Epoch 66/1000\n",
      "115/115 [==============================] - 0s 442us/step - loss: 556.2524 - val_loss: 1819.3770\n",
      "Epoch 67/1000\n",
      "115/115 [==============================] - 0s 451us/step - loss: 548.9523 - val_loss: 1643.9645\n",
      "Epoch 68/1000\n",
      "115/115 [==============================] - 0s 434us/step - loss: 571.8406 - val_loss: 1692.7660\n",
      "Epoch 69/1000\n",
      "115/115 [==============================] - 0s 399us/step - loss: 582.6595 - val_loss: 1865.3763\n",
      "Epoch 70/1000\n",
      "115/115 [==============================] - 0s 408us/step - loss: 555.2625 - val_loss: 1667.6234\n",
      "Epoch 71/1000\n",
      "115/115 [==============================] - 0s 425us/step - loss: 570.8210 - val_loss: 1876.9557\n",
      "Epoch 72/1000\n",
      "115/115 [==============================] - 0s 434us/step - loss: 582.3491 - val_loss: 1786.6133\n",
      "Epoch 73/1000\n",
      "115/115 [==============================] - 0s 416us/step - loss: 594.1559 - val_loss: 1569.7906\n",
      "Epoch 74/1000\n",
      "115/115 [==============================] - 0s 434us/step - loss: 638.0648 - val_loss: 2072.3630\n",
      "Epoch 75/1000\n",
      "115/115 [==============================] - 0s 408us/step - loss: 609.0269 - val_loss: 1848.0814\n",
      "Epoch 76/1000\n",
      "115/115 [==============================] - 0s 416us/step - loss: 652.6238 - val_loss: 2123.6848\n",
      "Epoch 77/1000\n",
      "115/115 [==============================] - 0s 442us/step - loss: 665.2831 - val_loss: 1908.8691\n",
      "Epoch 78/1000\n",
      "115/115 [==============================] - 0s 416us/step - loss: 594.8833 - val_loss: 1762.1786\n",
      "Epoch 79/1000\n",
      "115/115 [==============================] - 0s 399us/step - loss: 557.4914 - val_loss: 1805.3353\n",
      "Epoch 80/1000\n",
      "115/115 [==============================] - 0s 408us/step - loss: 578.1817 - val_loss: 1640.9375\n",
      "Epoch 81/1000\n",
      "115/115 [==============================] - 0s 460us/step - loss: 574.4075 - val_loss: 1703.6572\n",
      "Epoch 82/1000\n",
      "115/115 [==============================] - 0s 451us/step - loss: 578.9455 - val_loss: 1958.2744\n",
      "Epoch 83/1000\n",
      "115/115 [==============================] - 0s 432us/step - loss: 538.3135 - val_loss: 1563.4655\n",
      "Epoch 84/1000\n",
      "115/115 [==============================] - 0s 408us/step - loss: 642.9577 - val_loss: 1587.5670\n",
      "Epoch 85/1000\n",
      "115/115 [==============================] - 0s 447us/step - loss: 715.5115 - val_loss: 2503.0466\n",
      "Epoch 86/1000\n",
      "115/115 [==============================] - 0s 416us/step - loss: 711.0560 - val_loss: 1916.0748\n",
      "Epoch 87/1000\n",
      "115/115 [==============================] - 0s 416us/step - loss: 604.2650 - val_loss: 1866.7914\n",
      "Epoch 88/1000\n",
      "115/115 [==============================] - 0s 416us/step - loss: 583.6595 - val_loss: 1990.3076\n",
      "Epoch 89/1000\n",
      "115/115 [==============================] - 0s 460us/step - loss: 583.0143 - val_loss: 1841.0670\n",
      "Epoch 90/1000\n",
      "115/115 [==============================] - 0s 399us/step - loss: 546.8146 - val_loss: 1723.5592\n",
      "Epoch 91/1000\n",
      "115/115 [==============================] - 0s 408us/step - loss: 543.9227 - val_loss: 1834.7526\n",
      "Epoch 92/1000\n",
      "115/115 [==============================] - 0s 408us/step - loss: 552.6949 - val_loss: 1700.7269\n",
      "Epoch 93/1000\n",
      "115/115 [==============================] - 0s 425us/step - loss: 554.7770 - val_loss: 1629.7471\n",
      "Epoch 94/1000\n",
      "115/115 [==============================] - 0s 416us/step - loss: 537.9080 - val_loss: 1672.1152\n",
      "Epoch 95/1000\n",
      "115/115 [==============================] - 0s 416us/step - loss: 550.9325 - val_loss: 1733.0514\n",
      "Epoch 96/1000\n",
      "115/115 [==============================] - 0s 442us/step - loss: 532.1227 - val_loss: 1695.7184\n",
      "Epoch 97/1000\n",
      "115/115 [==============================] - 0s 520us/step - loss: 525.6125 - val_loss: 1720.0718\n",
      "Epoch 98/1000\n",
      "115/115 [==============================] - 0s 460us/step - loss: 525.3221 - val_loss: 1771.2563\n",
      "Epoch 99/1000\n",
      "115/115 [==============================] - 0s 408us/step - loss: 524.0421 - val_loss: 1631.8949\n",
      "Epoch 100/1000\n",
      "115/115 [==============================] - 0s 425us/step - loss: 531.5128 - val_loss: 1764.9082\n",
      "Epoch 101/1000\n",
      "115/115 [==============================] - 0s 460us/step - loss: 522.9642 - val_loss: 1601.4821\n",
      "Epoch 102/1000\n",
      "115/115 [==============================] - 0s 486us/step - loss: 512.5799 - val_loss: 1607.5026\n",
      "Epoch 103/1000\n",
      "115/115 [==============================] - 0s 451us/step - loss: 516.3861 - val_loss: 1674.4111\n",
      "Epoch 104/1000\n",
      "115/115 [==============================] - 0s 408us/step - loss: 511.5972 - val_loss: 1641.0953\n",
      "Epoch 105/1000\n",
      "115/115 [==============================] - 0s 477us/step - loss: 494.4987 - val_loss: 1821.0533\n",
      "Epoch 106/1000\n",
      "115/115 [==============================] - 0s 408us/step - loss: 508.5022 - val_loss: 1635.4521\n",
      "Epoch 107/1000\n",
      "115/115 [==============================] - 0s 413us/step - loss: 531.9121 - val_loss: 1640.4613\n",
      "Epoch 108/1000\n",
      "115/115 [==============================] - 0s 451us/step - loss: 528.9150 - val_loss: 1859.1566\n",
      "Epoch 109/1000\n",
      "115/115 [==============================] - 0s 400us/step - loss: 521.6234 - val_loss: 1579.2886\n",
      "Epoch 110/1000\n",
      "115/115 [==============================] - 0s 434us/step - loss: 567.4079 - val_loss: 1700.3119\n",
      "Epoch 111/1000\n",
      "115/115 [==============================] - 0s 416us/step - loss: 592.7892 - val_loss: 1918.7666\n",
      "Epoch 112/1000\n",
      "115/115 [==============================] - 0s 425us/step - loss: 509.9893 - val_loss: 1506.7188\n",
      "Epoch 113/1000\n",
      "115/115 [==============================] - 0s 421us/step - loss: 554.0702 - val_loss: 1750.0884\n",
      "Epoch 114/1000\n",
      "115/115 [==============================] - 0s 408us/step - loss: 533.8497 - val_loss: 1952.9877\n",
      "Epoch 115/1000\n",
      "115/115 [==============================] - 0s 434us/step - loss: 522.0404 - val_loss: 1545.4717\n",
      "Epoch 116/1000\n",
      "115/115 [==============================] - 0s 408us/step - loss: 540.1551 - val_loss: 1712.1901\n",
      "Epoch 117/1000\n",
      "115/115 [==============================] - 0s 442us/step - loss: 538.2951 - val_loss: 1752.5752\n",
      "Epoch 118/1000\n",
      "115/115 [==============================] - 0s 468us/step - loss: 530.0324 - val_loss: 1468.5592\n",
      "Epoch 119/1000\n",
      "115/115 [==============================] - 0s 416us/step - loss: 525.5875 - val_loss: 1823.8096\n",
      "Epoch 120/1000\n",
      "115/115 [==============================] - 0s 382us/step - loss: 534.8297 - val_loss: 1876.9518\n",
      "Epoch 121/1000\n",
      "115/115 [==============================] - 0s 425us/step - loss: 549.3950 - val_loss: 1532.4526\n",
      "Epoch 122/1000\n",
      "115/115 [==============================] - 0s 460us/step - loss: 513.1396 - val_loss: 1667.1923\n",
      "Epoch 123/1000\n",
      "115/115 [==============================] - 0s 442us/step - loss: 496.7248 - val_loss: 1497.8627\n",
      "Epoch 124/1000\n",
      "115/115 [==============================] - 0s 390us/step - loss: 499.8960 - val_loss: 1657.8531\n",
      "Epoch 125/1000\n",
      "115/115 [==============================] - 0s 416us/step - loss: 503.1711 - val_loss: 1544.7822\n",
      "Epoch 126/1000\n",
      "115/115 [==============================] - 0s 399us/step - loss: 478.3549 - val_loss: 1745.2697\n",
      "Epoch 127/1000\n",
      "115/115 [==============================] - 0s 397us/step - loss: 495.9033 - val_loss: 1599.5591\n",
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     ]
    },
    {
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     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
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     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 590/1000\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 664/1000\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 738/1000\n",
      "115/115 [==============================] - 0s 442us/step - loss: 425.1071 - val_loss: 1676.6840\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 812/1000\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 886/1000\n",
      "115/115 [==============================] - 0s 434us/step - loss: 359.2081 - val_loss: 1405.5775\n",
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      "115/115 [==============================] - 0s 399us/step - loss: 385.2062 - val_loss: 1554.0498\n",
      "Epoch 944/1000\n",
      "115/115 [==============================] - 0s 408us/step - loss: 364.8020 - val_loss: 1634.5717\n",
      "Epoch 945/1000\n",
      "115/115 [==============================] - 0s 451us/step - loss: 337.9629 - val_loss: 1451.8105\n",
      "Epoch 946/1000\n",
      "115/115 [==============================] - 0s 399us/step - loss: 337.6807 - val_loss: 1364.3013\n",
      "Epoch 947/1000\n",
      "115/115 [==============================] - 0s 425us/step - loss: 338.7833 - val_loss: 1485.9918\n",
      "Epoch 948/1000\n",
      "115/115 [==============================] - 0s 399us/step - loss: 368.6616 - val_loss: 1714.9349\n",
      "Epoch 949/1000\n",
      "115/115 [==============================] - 0s 442us/step - loss: 360.3987 - val_loss: 1407.4629\n",
      "Epoch 950/1000\n",
      "115/115 [==============================] - 0s 416us/step - loss: 360.2364 - val_loss: 1341.8022\n",
      "Epoch 951/1000\n",
      "115/115 [==============================] - 0s 425us/step - loss: 348.2889 - val_loss: 1547.1323\n",
      "Epoch 952/1000\n",
      "115/115 [==============================] - 0s 416us/step - loss: 333.5025 - val_loss: 1518.4928\n",
      "Epoch 953/1000\n",
      "115/115 [==============================] - 0s 416us/step - loss: 329.2833 - val_loss: 1609.3975\n",
      "Epoch 954/1000\n",
      "115/115 [==============================] - 0s 399us/step - loss: 341.2698 - val_loss: 1626.5508\n",
      "Epoch 955/1000\n",
      "115/115 [==============================] - 0s 460us/step - loss: 330.9080 - val_loss: 1404.6498\n",
      "Epoch 956/1000\n",
      "115/115 [==============================] - 0s 416us/step - loss: 336.3458 - val_loss: 1407.4850\n",
      "Epoch 957/1000\n",
      "115/115 [==============================] - 0s 425us/step - loss: 329.4011 - val_loss: 1604.7764\n",
      "Epoch 958/1000\n",
      "115/115 [==============================] - 0s 408us/step - loss: 326.5725 - val_loss: 1618.4507\n",
      "Epoch 959/1000\n",
      "115/115 [==============================] - 0s 460us/step - loss: 327.5020 - val_loss: 1457.9684\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 960/1000\n",
      "115/115 [==============================] - 0s 399us/step - loss: 318.8462 - val_loss: 1403.2344\n",
      "Epoch 961/1000\n",
      "115/115 [==============================] - 0s 408us/step - loss: 321.0166 - val_loss: 1495.1637\n",
      "Epoch 962/1000\n",
      "115/115 [==============================] - 0s 434us/step - loss: 318.9669 - val_loss: 1581.3395\n",
      "Epoch 963/1000\n",
      "115/115 [==============================] - 0s 434us/step - loss: 325.0827 - val_loss: 1469.6514\n",
      "Epoch 964/1000\n",
      "115/115 [==============================] - 0s 425us/step - loss: 312.7529 - val_loss: 1433.2998\n",
      "Epoch 965/1000\n",
      "115/115 [==============================] - 0s 434us/step - loss: 315.0900 - val_loss: 1360.6685\n",
      "Epoch 966/1000\n",
      "115/115 [==============================] - 0s 451us/step - loss: 345.8552 - val_loss: 1364.7896\n",
      "Epoch 967/1000\n",
      "115/115 [==============================] - 0s 468us/step - loss: 342.1341 - val_loss: 1395.2135\n",
      "Epoch 968/1000\n",
      "115/115 [==============================] - 0s 503us/step - loss: 328.9284 - val_loss: 1687.3368\n",
      "Epoch 969/1000\n",
      "115/115 [==============================] - 0s 477us/step - loss: 361.8150 - val_loss: 1605.5503\n",
      "Epoch 970/1000\n",
      "115/115 [==============================] - 0s 503us/step - loss: 334.8936 - val_loss: 1568.2177\n",
      "Epoch 971/1000\n",
      "115/115 [==============================] - 0s 486us/step - loss: 315.3399 - val_loss: 1364.0170\n",
      "Epoch 972/1000\n",
      "115/115 [==============================] - 0s 486us/step - loss: 358.6083 - val_loss: 1376.6149\n",
      "Epoch 973/1000\n",
      "115/115 [==============================] - 0s 434us/step - loss: 370.1622 - val_loss: 1489.5765\n",
      "Epoch 974/1000\n",
      "115/115 [==============================] - 0s 494us/step - loss: 361.8304 - val_loss: 1638.4696\n",
      "Epoch 975/1000\n",
      "115/115 [==============================] - 0s 451us/step - loss: 349.0922 - val_loss: 1457.3291\n",
      "Epoch 976/1000\n",
      "115/115 [==============================] - 0s 468us/step - loss: 362.0197 - val_loss: 1465.0922\n",
      "Epoch 977/1000\n",
      "115/115 [==============================] - 0s 486us/step - loss: 363.8966 - val_loss: 1802.3156\n",
      "Epoch 978/1000\n",
      "115/115 [==============================] - 0s 425us/step - loss: 346.6751 - val_loss: 1517.7192\n",
      "Epoch 979/1000\n",
      "115/115 [==============================] - 0s 434us/step - loss: 334.6984 - val_loss: 1253.8036\n",
      "Epoch 980/1000\n",
      "115/115 [==============================] - 0s 425us/step - loss: 347.4912 - val_loss: 1371.9503\n",
      "Epoch 981/1000\n",
      "115/115 [==============================] - 0s 434us/step - loss: 394.4042 - val_loss: 1488.3228\n",
      "Epoch 982/1000\n",
      "115/115 [==============================] - 0s 425us/step - loss: 362.4600 - val_loss: 1774.3502\n",
      "Epoch 983/1000\n",
      "115/115 [==============================] - 0s 390us/step - loss: 402.2262 - val_loss: 1858.3756\n",
      "Epoch 984/1000\n",
      "115/115 [==============================] - 0s 408us/step - loss: 428.1007 - val_loss: 1473.3724\n",
      "Epoch 985/1000\n",
      "115/115 [==============================] - 0s 451us/step - loss: 371.2551 - val_loss: 1473.0846\n",
      "Epoch 986/1000\n",
      "115/115 [==============================] - 0s 425us/step - loss: 332.9529 - val_loss: 1465.0117\n",
      "Epoch 987/1000\n",
      "115/115 [==============================] - 0s 416us/step - loss: 351.2427 - val_loss: 1451.8877\n",
      "Epoch 988/1000\n",
      "115/115 [==============================] - 0s 520us/step - loss: 315.1492 - val_loss: 1502.2407\n",
      "Epoch 989/1000\n",
      "115/115 [==============================] - 0s 451us/step - loss: 394.6720 - val_loss: 1364.5885\n",
      "Epoch 990/1000\n",
      "115/115 [==============================] - 0s 408us/step - loss: 448.4303 - val_loss: 1561.8677\n",
      "Epoch 991/1000\n",
      "115/115 [==============================] - 0s 425us/step - loss: 358.1707 - val_loss: 1438.4648\n",
      "Epoch 992/1000\n",
      "115/115 [==============================] - 0s 408us/step - loss: 335.2128 - val_loss: 1517.8680\n",
      "Epoch 993/1000\n",
      "115/115 [==============================] - 0s 425us/step - loss: 330.6289 - val_loss: 1684.6473\n",
      "Epoch 994/1000\n",
      "115/115 [==============================] - 0s 468us/step - loss: 339.5483 - val_loss: 1549.2803\n",
      "Epoch 995/1000\n",
      "115/115 [==============================] - 0s 434us/step - loss: 411.2026 - val_loss: 1494.2456\n",
      "Epoch 996/1000\n",
      "115/115 [==============================] - 0s 451us/step - loss: 486.1791 - val_loss: 1361.1058\n",
      "Epoch 997/1000\n",
      "115/115 [==============================] - 0s 408us/step - loss: 427.3777 - val_loss: 1474.6373\n",
      "Epoch 998/1000\n",
      "115/115 [==============================] - 0s 442us/step - loss: 393.8703 - val_loss: 1736.6304\n",
      "Epoch 999/1000\n",
      "115/115 [==============================] - 0s 434us/step - loss: 372.9823 - val_loss: 1483.9452\n",
      "Epoch 1000/1000\n",
      "115/115 [==============================] - 0s 416us/step - loss: 347.3337 - val_loss: 1414.4180\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2061887bb38>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit model\n",
    "model.fit(X_train, y_train, epochs=1000, verbose=1, validation_data=(X_val, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN DATA 0 : predicted=122.598328, ground_truth=135\n",
      "TRAIN DATA 1 : predicted=151.324387, ground_truth=148\n",
      "TRAIN DATA 2 : predicted=157.454819, ground_truth=148\n",
      "TRAIN DATA 3 : predicted=158.581665, ground_truth=136\n",
      "TRAIN DATA 4 : predicted=133.253922, ground_truth=119\n",
      "TRAIN DATA 5 : predicted=111.643173, ground_truth=104\n",
      "TRAIN DATA 6 : predicted=127.384758, ground_truth=118\n",
      "TRAIN DATA 7 : predicted=126.483467, ground_truth=115\n",
      "TRAIN DATA 8 : predicted=120.861313, ground_truth=126\n",
      "TRAIN DATA 9 : predicted=139.225845, ground_truth=141\n",
      "TRAIN DATA 10 : predicted=135.654953, ground_truth=135\n",
      "TRAIN DATA 11 : predicted=137.831833, ground_truth=125\n",
      "TRAIN DATA 12 : predicted=123.584358, ground_truth=149\n",
      "TRAIN DATA 13 : predicted=162.037491, ground_truth=170\n",
      "TRAIN DATA 14 : predicted=171.966217, ground_truth=170\n",
      "TRAIN DATA 15 : predicted=162.287766, ground_truth=158\n",
      "TRAIN DATA 16 : predicted=144.429306, ground_truth=133\n",
      "TRAIN DATA 17 : predicted=129.329773, ground_truth=114\n",
      "TRAIN DATA 18 : predicted=141.707001, ground_truth=140\n",
      "TRAIN DATA 19 : predicted=143.953827, ground_truth=145\n",
      "TRAIN DATA 20 : predicted=144.261856, ground_truth=150\n",
      "TRAIN DATA 21 : predicted=158.621521, ground_truth=178\n",
      "TRAIN DATA 22 : predicted=157.842331, ground_truth=163\n",
      "TRAIN DATA 23 : predicted=164.729385, ground_truth=172\n",
      "TRAIN DATA 24 : predicted=184.149155, ground_truth=178\n",
      "TRAIN DATA 25 : predicted=199.791580, ground_truth=199\n",
      "TRAIN DATA 26 : predicted=214.930161, ground_truth=199\n",
      "TRAIN DATA 27 : predicted=213.246353, ground_truth=184\n",
      "TRAIN DATA 28 : predicted=184.121094, ground_truth=162\n",
      "TRAIN DATA 29 : predicted=154.116547, ground_truth=146\n",
      "TRAIN DATA 30 : predicted=173.298630, ground_truth=166\n",
      "TRAIN DATA 31 : predicted=176.560150, ground_truth=171\n",
      "TRAIN DATA 32 : predicted=176.365616, ground_truth=180\n",
      "TRAIN DATA 33 : predicted=197.412781, ground_truth=193\n",
      "TRAIN DATA 34 : predicted=198.050751, ground_truth=181\n",
      "TRAIN DATA 35 : predicted=195.623886, ground_truth=183\n",
      "TRAIN DATA 36 : predicted=208.879471, ground_truth=218\n",
      "TRAIN DATA 37 : predicted=232.169083, ground_truth=230\n",
      "TRAIN DATA 38 : predicted=232.114502, ground_truth=242\n",
      "TRAIN DATA 39 : predicted=227.867462, ground_truth=209\n",
      "TRAIN DATA 40 : predicted=183.375046, ground_truth=191\n",
      "TRAIN DATA 41 : predicted=169.424759, ground_truth=172\n",
      "TRAIN DATA 42 : predicted=200.484329, ground_truth=194\n",
      "TRAIN DATA 43 : predicted=206.538528, ground_truth=196\n",
      "TRAIN DATA 44 : predicted=199.028992, ground_truth=196\n",
      "TRAIN DATA 45 : predicted=229.741745, ground_truth=236\n",
      "TRAIN DATA 46 : predicted=234.704407, ground_truth=235\n",
      "TRAIN DATA 47 : predicted=244.136612, ground_truth=229\n",
      "TRAIN DATA 48 : predicted=231.803543, ground_truth=243\n",
      "TRAIN DATA 49 : predicted=272.967346, ground_truth=264\n",
      "TRAIN DATA 50 : predicted=293.536194, ground_truth=272\n",
      "TRAIN DATA 51 : predicted=298.789276, ground_truth=237\n",
      "TRAIN DATA 52 : predicted=219.166519, ground_truth=211\n",
      "TRAIN DATA 53 : predicted=184.319473, ground_truth=180\n",
      "TRAIN DATA 54 : predicted=210.928482, ground_truth=201\n",
      "TRAIN DATA 55 : predicted=223.211304, ground_truth=204\n",
      "TRAIN DATA 56 : predicted=202.051804, ground_truth=188\n",
      "TRAIN DATA 57 : predicted=205.435867, ground_truth=235\n",
      "TRAIN DATA 58 : predicted=241.744797, ground_truth=227\n",
      "TRAIN DATA 59 : predicted=231.300095, ground_truth=234\n",
      "TRAIN DATA 60 : predicted=252.543808, ground_truth=264\n",
      "TRAIN DATA 61 : predicted=269.277466, ground_truth=302\n",
      "TRAIN DATA 62 : predicted=299.162170, ground_truth=293\n",
      "TRAIN DATA 63 : predicted=289.519958, ground_truth=259\n",
      "TRAIN DATA 64 : predicted=229.469070, ground_truth=229\n",
      "TRAIN DATA 65 : predicted=206.176071, ground_truth=203\n",
      "TRAIN DATA 66 : predicted=233.998337, ground_truth=229\n",
      "TRAIN DATA 67 : predicted=246.388412, ground_truth=242\n",
      "TRAIN DATA 68 : predicted=240.317154, ground_truth=233\n",
      "TRAIN DATA 69 : predicted=270.650909, ground_truth=267\n",
      "TRAIN DATA 70 : predicted=276.731354, ground_truth=269\n",
      "TRAIN DATA 71 : predicted=295.357788, ground_truth=270\n",
      "TRAIN DATA 72 : predicted=305.390900, ground_truth=315\n",
      "TRAIN DATA 73 : predicted=321.622070, ground_truth=364\n",
      "TRAIN DATA 74 : predicted=347.278412, ground_truth=347\n",
      "TRAIN DATA 75 : predicted=338.148102, ground_truth=312\n",
      "TRAIN DATA 76 : predicted=271.857605, ground_truth=274\n",
      "TRAIN DATA 77 : predicted=250.363205, ground_truth=237\n",
      "TRAIN DATA 78 : predicted=268.179657, ground_truth=278\n",
      "TRAIN DATA 79 : predicted=291.541138, ground_truth=284\n",
      "TRAIN DATA 80 : predicted=267.525055, ground_truth=277\n",
      "TRAIN DATA 81 : predicted=325.189270, ground_truth=317\n",
      "TRAIN DATA 82 : predicted=330.170685, ground_truth=313\n",
      "TRAIN DATA 83 : predicted=355.245056, ground_truth=318\n",
      "TRAIN DATA 84 : predicted=362.026917, ground_truth=374\n",
      "TRAIN DATA 85 : predicted=383.798218, ground_truth=413\n",
      "TRAIN DATA 86 : predicted=413.031952, ground_truth=405\n",
      "TRAIN DATA 87 : predicted=399.921356, ground_truth=355\n",
      "TRAIN DATA 88 : predicted=321.568848, ground_truth=306\n",
      "TRAIN DATA 89 : predicted=290.399292, ground_truth=271\n",
      "TRAIN DATA 90 : predicted=303.134155, ground_truth=306\n",
      "TRAIN DATA 91 : predicted=325.844177, ground_truth=315\n",
      "TRAIN DATA 92 : predicted=302.127411, ground_truth=301\n",
      "TRAIN DATA 93 : predicted=353.533508, ground_truth=356\n",
      "TRAIN DATA 94 : predicted=372.150238, ground_truth=348\n",
      "TRAIN DATA 95 : predicted=390.215088, ground_truth=355\n",
      "TRAIN DATA 96 : predicted=399.952240, ground_truth=422\n",
      "TRAIN DATA 97 : predicted=425.608704, ground_truth=465\n",
      "TRAIN DATA 98 : predicted=463.557251, ground_truth=467\n",
      "TRAIN DATA 99 : predicted=445.512695, ground_truth=404\n",
      "TRAIN DATA 100 : predicted=362.233643, ground_truth=347\n",
      "TRAIN DATA 101 : predicted=323.762695, ground_truth=305\n",
      "TRAIN DATA 102 : predicted=338.079590, ground_truth=336\n",
      "TRAIN DATA 103 : predicted=365.313141, ground_truth=340\n",
      "TRAIN DATA 104 : predicted=338.166443, ground_truth=318\n",
      "TRAIN DATA 105 : predicted=364.057251, ground_truth=362\n",
      "TRAIN DATA 106 : predicted=411.181366, ground_truth=348\n",
      "TRAIN DATA 107 : predicted=371.374634, ground_truth=363\n",
      "TRAIN DATA 108 : predicted=427.408813, ground_truth=435\n",
      "TRAIN DATA 109 : predicted=442.084534, ground_truth=491\n",
      "TRAIN DATA 110 : predicted=465.683899, ground_truth=505\n",
      "TRAIN DATA 111 : predicted=443.814941, ground_truth=404\n",
      "TRAIN DATA 112 : predicted=368.020569, ground_truth=359\n",
      "TRAIN DATA 113 : predicted=325.312744, ground_truth=310\n",
      "TRAIN DATA 114 : predicted=336.164703, ground_truth=337\n"
     ]
    }
   ],
   "source": [
    "# check how exactly fit train data\n",
    "\n",
    "y_hats = []\n",
    "cur_y_hat = 0\n",
    "for i in range(115):\n",
    "    x_input = X_train[i]\n",
    "    x_input_reshape = x_input.reshape(1,5,1)\n",
    "    cur_y_hat = model.predict(x_input_reshape, verbose=0)\n",
    "    cur_y_hat = np.squeeze(cur_y_hat)\n",
    "    print('TRAIN DATA %d : predicted=%f, ground_truth=%d' % (i, cur_y_hat, y_train[i]))\n",
    "    y_hats.append(cur_y_hat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'RMSE: 18.9834')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y_train)\n",
    "plt.plot(y_hats)\n",
    "plt.title('RMSE: %.4f'% np.sqrt(sum((y_hats-y_train)**2)/len(y_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TEST DATA 0 : predicted=375.198608, ground_truth=360\n",
      "TEST DATA 1 : predicted=380.938019, ground_truth=342\n",
      "TEST DATA 2 : predicted=438.671783, ground_truth=406\n",
      "TEST DATA 3 : predicted=416.153412, ground_truth=396\n",
      "TEST DATA 4 : predicted=458.921112, ground_truth=420\n",
      "TEST DATA 5 : predicted=494.929016, ground_truth=472\n",
      "TEST DATA 6 : predicted=531.970215, ground_truth=548\n",
      "TEST DATA 7 : predicted=562.958435, ground_truth=559\n",
      "TEST DATA 8 : predicted=565.466919, ground_truth=463\n",
      "TEST DATA 9 : predicted=620.049438, ground_truth=407\n",
      "TEST DATA 10 : predicted=669.297058, ground_truth=362\n",
      "TEST DATA 11 : predicted=714.997559, ground_truth=405\n",
      "TEST DATA 12 : predicted=739.459412, ground_truth=417\n",
      "TEST DATA 13 : predicted=768.104370, ground_truth=391\n",
      "TEST DATA 14 : predicted=838.559204, ground_truth=419\n",
      "TEST DATA 15 : predicted=902.639099, ground_truth=461\n",
      "TEST DATA 16 : predicted=952.288940, ground_truth=472\n",
      "TEST DATA 17 : predicted=983.162964, ground_truth=535\n",
      "TEST DATA 18 : predicted=1043.883911, ground_truth=622\n",
      "TEST DATA 19 : predicted=1133.341797, ground_truth=606\n",
      "TEST DATA 20 : predicted=1211.192749, ground_truth=508\n",
      "TEST DATA 21 : predicted=1268.121704, ground_truth=461\n",
      "TEST DATA 22 : predicted=1319.248169, ground_truth=390\n",
      "TEST DATA 23 : predicted=1417.432251, ground_truth=432\n"
     ]
    }
   ],
   "source": [
    "# predict with trained model\n",
    "\n",
    "y_hats = []\n",
    "cur_y_hat = 0\n",
    "for i in range(24):\n",
    "    if i == 0:  # test data 의 맨 첫번째 row에 대해서만 X 값을 그대로 활용\n",
    "        x_input = X_test[0]\n",
    "    else:       # 두번째 row부터는 이전 row의 prediction을 반영하여 X를 재구성\n",
    "        x_input = x_input.tolist()\n",
    "        del x_input[0]\n",
    "        x_input.append(cur_y_hat)\n",
    "        x_input = np.array(x_input)\n",
    "    x_input_reshape = x_input.reshape(1,5,1)\n",
    "    cur_y_hat = model.predict(x_input_reshape, verbose=0)\n",
    "    cur_y_hat = np.squeeze(cur_y_hat)\n",
    "    print('TEST DATA %d : predicted=%f, ground_truth=%d' % (i, cur_y_hat, y_test[i]))\n",
    "    y_hats.append(cur_y_hat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'RMSE: 444.2842')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y_test)\n",
    "plt.plot(y_hats)\n",
    "plt.title('RMSE: %.4f'% np.sqrt(sum((y_hats-y_test)**2)/len(y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tf13",
   "language": "python",
   "name": "tf13"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}