3. predict_boston_house.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\leesoojin\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.utils import np_utils\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "import keras.backend.tensorflow_backend as K\n",
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "import os\n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "session = tf.Session(config=config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      "0     506 non-null float64\n",
      "1     506 non-null float64\n",
      "2     506 non-null float64\n",
      "3     506 non-null int64\n",
      "4     506 non-null float64\n",
      "5     506 non-null float64\n",
      "6     506 non-null float64\n",
      "7     506 non-null float64\n",
      "8     506 non-null int64\n",
      "9     506 non-null float64\n",
      "10    506 non-null float64\n",
      "11    506 non-null float64\n",
      "12    506 non-null float64\n",
      "13    506 non-null float64\n",
      "dtypes: float64(12), int64(2)\n",
      "memory usage: 55.4 KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('./dataset/housing.csv', delim_whitespace=True, header=None)\n",
    "\n",
    "print(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        0     1     2   3      4      5     6       7   8      9     10  \\\n",
      "0  0.00632  18.0  2.31   0  0.538  6.575  65.2  4.0900   1  296.0  15.3   \n",
      "1  0.02731   0.0  7.07   0  0.469  6.421  78.9  4.9671   2  242.0  17.8   \n",
      "2  0.02729   0.0  7.07   0  0.469  7.185  61.1  4.9671   2  242.0  17.8   \n",
      "3  0.03237   0.0  2.18   0  0.458  6.998  45.8  6.0622   3  222.0  18.7   \n",
      "4  0.06905   0.0  2.18   0  0.458  7.147  54.2  6.0622   3  222.0  18.7   \n",
      "\n",
      "       11    12    13  \n",
      "0  396.90  4.98  24.0  \n",
      "1  396.90  9.14  21.6  \n",
      "2  392.83  4.03  34.7  \n",
      "3  394.63  2.94  33.4  \n",
      "4  396.90  5.33  36.2  \n"
     ]
    }
   ],
   "source": [
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:, 0:13].values\n",
    "y = df.iloc[:, 13].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 165/200\n",
      "404/404 [==============================] - 0s 232us/step - loss: 23.8572\n",
      "Epoch 166/200\n",
      "404/404 [==============================] - 0s 227us/step - loss: 26.8393\n",
      "Epoch 167/200\n",
      "404/404 [==============================] - 0s 239us/step - loss: 24.7674\n",
      "Epoch 168/200\n",
      "404/404 [==============================] - 0s 242us/step - loss: 29.7234\n",
      "Epoch 169/200\n",
      "404/404 [==============================] - 0s 227us/step - loss: 25.9919\n",
      "Epoch 170/200\n",
      "404/404 [==============================] - 0s 230us/step - loss: 23.7709\n",
      "Epoch 171/200\n",
      "404/404 [==============================] - ETA: 0s - loss: 22.95 - 0s 237us/step - loss: 24.0283\n",
      "Epoch 172/200\n",
      "404/404 [==============================] - 0s 237us/step - loss: 24.3686\n",
      "Epoch 173/200\n",
      "404/404 [==============================] - 0s 237us/step - loss: 25.7648\n",
      "Epoch 174/200\n",
      "404/404 [==============================] - 0s 235us/step - loss: 24.6780\n",
      "Epoch 175/200\n",
      "404/404 [==============================] - 0s 234us/step - loss: 24.5750\n",
      "Epoch 176/200\n",
      "404/404 [==============================] - 0s 230us/step - loss: 24.4545\n",
      "Epoch 177/200\n",
      "404/404 [==============================] - 0s 235us/step - loss: 23.2645\n",
      "Epoch 178/200\n",
      "404/404 [==============================] - 0s 239us/step - loss: 23.5010\n",
      "Epoch 179/200\n",
      "404/404 [==============================] - 0s 244us/step - loss: 25.2118\n",
      "Epoch 180/200\n",
      "404/404 [==============================] - 0s 239us/step - loss: 24.6237\n",
      "Epoch 181/200\n",
      "404/404 [==============================] - 0s 237us/step - loss: 25.8541\n",
      "Epoch 182/200\n",
      "404/404 [==============================] - 0s 237us/step - loss: 24.4790\n",
      "Epoch 183/200\n",
      "404/404 [==============================] - 0s 237us/step - loss: 24.2495\n",
      "Epoch 184/200\n",
      "404/404 [==============================] - 0s 230us/step - loss: 25.2081\n",
      "Epoch 185/200\n",
      "404/404 [==============================] - 0s 239us/step - loss: 22.2362\n",
      "Epoch 186/200\n",
      "404/404 [==============================] - 0s 242us/step - loss: 24.2375\n",
      "Epoch 187/200\n",
      "404/404 [==============================] - 0s 237us/step - loss: 22.1582\n",
      "Epoch 188/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "404/404 [==============================] - 0s 244us/step - loss: 23.7005\n",
      "Epoch 189/200\n",
      "404/404 [==============================] - 0s 242us/step - loss: 24.0025\n",
      "Epoch 190/200\n",
      "404/404 [==============================] - ETA: 0s - loss: 24.14 - 0s 232us/step - loss: 25.4144\n",
      "Epoch 191/200\n",
      "404/404 [==============================] - 0s 237us/step - loss: 30.1352\n",
      "Epoch 192/200\n",
      "404/404 [==============================] - 0s 235us/step - loss: 23.3425\n",
      "Epoch 193/200\n",
      "404/404 [==============================] - 0s 235us/step - loss: 22.6613\n",
      "Epoch 194/200\n",
      "404/404 [==============================] - 0s 247us/step - loss: 24.7335\n",
      "Epoch 195/200\n",
      "404/404 [==============================] - 0s 252us/step - loss: 22.1079\n",
      "Epoch 196/200\n",
      "404/404 [==============================] - 0s 235us/step - loss: 24.2412\n",
      "Epoch 197/200\n",
      "404/404 [==============================] - 0s 239us/step - loss: 23.6584\n",
      "Epoch 198/200\n",
      "404/404 [==============================] - 0s 239us/step - loss: 24.2968\n",
      "Epoch 199/200\n",
      "404/404 [==============================] - 0s 237us/step - loss: 26.1472\n",
      "Epoch 200/200\n",
      "404/404 [==============================] - 0s 279us/step - loss: 23.6032\n"
     ]
    }
   ],
   "source": [
    "with K.tf_ops.device('/device:GPU:0'):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(30, input_dim=X.shape[1], activation='relu'))\n",
    "    model.add(Dense(7, activation='relu'))\n",
    "    model.add(Dense(1))  #선형회귀에서는 마지막 출력에 활성화 함수가 있지 않다.\n",
    "    \n",
    "    model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "    \n",
    "    model.fit(X_train, y_train, epochs=200, batch_size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "실제 가격 :44.800, 예상 가격 : 29.597\n",
      "실제 가격 :17.100, 예상 가격 : 17.374\n",
      "실제 가격 :17.800, 예상 가격 : 17.432\n",
      "실제 가격 :33.100, 예상 가격 : 29.175\n",
      "실제 가격 :21.900, 예상 가격 : 22.060\n",
      "실제 가격 :21.000, 예상 가격 : 22.157\n",
      "실제 가격 :18.400, 예상 가격 : 16.429\n",
      "실제 가격 :10.400, 예상 가격 : 9.389\n",
      "실제 가격 :23.100, 예상 가격 : 22.239\n",
      "실제 가격 :20.000, 예상 가격 : 16.864\n"
     ]
    }
   ],
   "source": [
    "predict = model.predict(X_test).flatten()\n",
    "for i in range(10):\n",
    "    label = y_test[i]\n",
    "    prediction= predict[i]\n",
    "    print(\"실제 가격 :{:.3f}, 예상 가격 : {:.3f}\".format(label, prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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