simple_regression_tf.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we'll use the data of Boston housing prices which comes by default in scikit-learn.\n",
    "\n",
    "The basic computation unit in TensorFlow is a graph. A TensorFlow project is typically structured into 2 parts:\n",
    "\n",
    "* a construction phase where you design the computational graph, and\n",
    "* an analysis phase where you run the graph and perform calculations on it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(506, 13)\n",
      "[[6.3200e-03 1.8000e+01 2.3100e+00 ... 1.5300e+01 3.9690e+02 4.9800e+00]\n",
      " [2.7310e-02 0.0000e+00 7.0700e+00 ... 1.7800e+01 3.9690e+02 9.1400e+00]\n",
      " [2.7290e-02 0.0000e+00 7.0700e+00 ... 1.7800e+01 3.9283e+02 4.0300e+00]\n",
      " ...\n",
      " [6.0760e-02 0.0000e+00 1.1930e+01 ... 2.1000e+01 3.9690e+02 5.6400e+00]\n",
      " [1.0959e-01 0.0000e+00 1.1930e+01 ... 2.1000e+01 3.9345e+02 6.4800e+00]\n",
      " [4.7410e-02 0.0000e+00 1.1930e+01 ... 2.1000e+01 3.9690e+02 7.8800e+00]]\n",
      "[6.320e-03 1.800e+01 2.310e+00 0.000e+00 5.380e-01 6.575e+00 6.520e+01\n",
      " 4.090e+00 1.000e+00 2.960e+02 1.530e+01 3.969e+02 4.980e+00]\n",
      "(506,)\n",
      "[24.  21.6 34.7 33.4 36.2 28.7 22.9 27.1 16.5 18.9 15.  18.9 21.7 20.4\n",
      " 18.2 19.9 23.1 17.5 20.2 18.2 13.6 19.6 15.2 14.5 15.6 13.9 16.6 14.8\n",
      " 18.4 21.  12.7 14.5 13.2 13.1 13.5 18.9 20.  21.  24.7 30.8 34.9 26.6\n",
      " 25.3 24.7 21.2 19.3 20.  16.6 14.4 19.4 19.7 20.5 25.  23.4 18.9 35.4\n",
      " 24.7 31.6 23.3 19.6 18.7 16.  22.2 25.  33.  23.5 19.4 22.  17.4 20.9\n",
      " 24.2 21.7 22.8 23.4 24.1 21.4 20.  20.8 21.2 20.3 28.  23.9 24.8 22.9\n",
      " 23.9 26.6 22.5 22.2 23.6 28.7 22.6 22.  22.9 25.  20.6 28.4 21.4 38.7\n",
      " 43.8 33.2 27.5 26.5 18.6 19.3 20.1 19.5 19.5 20.4 19.8 19.4 21.7 22.8\n",
      " 18.8 18.7 18.5 18.3 21.2 19.2 20.4 19.3 22.  20.3 20.5 17.3 18.8 21.4\n",
      " 15.7 16.2 18.  14.3 19.2 19.6 23.  18.4 15.6 18.1 17.4 17.1 13.3 17.8\n",
      " 14.  14.4 13.4 15.6 11.8 13.8 15.6 14.6 17.8 15.4 21.5 19.6 15.3 19.4\n",
      " 17.  15.6 13.1 41.3 24.3 23.3 27.  50.  50.  50.  22.7 25.  50.  23.8\n",
      " 23.8 22.3 17.4 19.1 23.1 23.6 22.6 29.4 23.2 24.6 29.9 37.2 39.8 36.2\n",
      " 37.9 32.5 26.4 29.6 50.  32.  29.8 34.9 37.  30.5 36.4 31.1 29.1 50.\n",
      " 33.3 30.3 34.6 34.9 32.9 24.1 42.3 48.5 50.  22.6 24.4 22.5 24.4 20.\n",
      " 21.7 19.3 22.4 28.1 23.7 25.  23.3 28.7 21.5 23.  26.7 21.7 27.5 30.1\n",
      " 44.8 50.  37.6 31.6 46.7 31.5 24.3 31.7 41.7 48.3 29.  24.  25.1 31.5\n",
      " 23.7 23.3 22.  20.1 22.2 23.7 17.6 18.5 24.3 20.5 24.5 26.2 24.4 24.8\n",
      " 29.6 42.8 21.9 20.9 44.  50.  36.  30.1 33.8 43.1 48.8 31.  36.5 22.8\n",
      " 30.7 50.  43.5 20.7 21.1 25.2 24.4 35.2 32.4 32.  33.2 33.1 29.1 35.1\n",
      " 45.4 35.4 46.  50.  32.2 22.  20.1 23.2 22.3 24.8 28.5 37.3 27.9 23.9\n",
      " 21.7 28.6 27.1 20.3 22.5 29.  24.8 22.  26.4 33.1 36.1 28.4 33.4 28.2\n",
      " 22.8 20.3 16.1 22.1 19.4 21.6 23.8 16.2 17.8 19.8 23.1 21.  23.8 23.1\n",
      " 20.4 18.5 25.  24.6 23.  22.2 19.3 22.6 19.8 17.1 19.4 22.2 20.7 21.1\n",
      " 19.5 18.5 20.6 19.  18.7 32.7 16.5 23.9 31.2 17.5 17.2 23.1 24.5 26.6\n",
      " 22.9 24.1 18.6 30.1 18.2 20.6 17.8 21.7 22.7 22.6 25.  19.9 20.8 16.8\n",
      " 21.9 27.5 21.9 23.1 50.  50.  50.  50.  50.  13.8 13.8 15.  13.9 13.3\n",
      " 13.1 10.2 10.4 10.9 11.3 12.3  8.8  7.2 10.5  7.4 10.2 11.5 15.1 23.2\n",
      "  9.7 13.8 12.7 13.1 12.5  8.5  5.   6.3  5.6  7.2 12.1  8.3  8.5  5.\n",
      " 11.9 27.9 17.2 27.5 15.  17.2 17.9 16.3  7.   7.2  7.5 10.4  8.8  8.4\n",
      " 16.7 14.2 20.8 13.4 11.7  8.3 10.2 10.9 11.   9.5 14.5 14.1 16.1 14.3\n",
      " 11.7 13.4  9.6  8.7  8.4 12.8 10.5 17.1 18.4 15.4 10.8 11.8 14.9 12.6\n",
      " 14.1 13.  13.4 15.2 16.1 17.8 14.9 14.1 12.7 13.5 14.9 20.  16.4 17.7\n",
      " 19.5 20.2 21.4 19.9 19.  19.1 19.1 20.1 19.9 19.6 23.2 29.8 13.8 13.3\n",
      " 16.7 12.  14.6 21.4 23.  23.7 25.  21.8 20.6 21.2 19.1 20.6 15.2  7.\n",
      "  8.1 13.6 20.1 21.8 24.5 23.1 19.7 18.3 21.2 17.5 16.8 22.4 20.6 23.9\n",
      " 22.  11.9]\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_boston\n",
    "from sklearn.preprocessing import scale\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# Get the data\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_boston.html#sklearn.datasets.load_boston\n",
    "total_features, total_prices = load_boston(True)     # return (data, target) : tuple if return_X_y is True\n",
    "print(total_features.data.shape)\n",
    "print(total_features)\n",
    "print(total_features[0,:])\n",
    "print(total_prices.data.shape)\n",
    "print(total_prices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D:\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\sklearn\\datasets\\data\\boston_house_prices.csv\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  PTRATIO  \\\n",
       "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
       "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
       "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
       "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
       "4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
       "\n",
       "        B  LSTAT  MEDV  \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  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file=load_boston(False)['filename']\n",
    "print(file)\n",
    "dataset = pd.read_csv(file,header=1)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rleO1C7iivfpmE7AvM18d1sknYukmD3JbhYj4PeCxzNwF3Er/z7vd9K/kL125ipevY89/BLwf+Iv2vPNLmXnhihW9TB17LqVjz98AfiYingW+D/xaZv7nylW9PB17vhb4s4j4VfpLGFdO8oVbRHyV/tLb2va8w+eBIwEy80v0n4e4ANgNvAVcNdTHn+DvnSSpg0lZupEkDcigl6TiDHpJKs6gl6TiDHpJKs6gl6TiDHpJKs6gl6Ti/g+daTNXpfiJowAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SVIg6t6D7SEQ8GxFfOeS5kyPinojYWX1+0c2hJUnDVWcP/WPAhYc9dwOwNTPXAVurZUnSCHUN9Mz8PPDfhz19KbC5erwZuKzhuiRJPer3GHorM3cDVJ9Pba4kSVI/IjO7D4qYBO7KzNdVy/syc/UhX9+bmYseR4+IGWAGoNVqrZ+dne2r0B1P7+86pnUC7Hmhr9WvePY+6iqGb1h9T61dNfiN9Gh+fp6JiYmBrb9O3vTiaL+H09PT2zOz3W1cv/Oh74mINZm5OyLWAM8eaWBmbgI2AbTb7ex0On1t8Joa8xNvnDrATTvGc4p3ex+/3ofV966rOwPfRq/m5uboN0vqqJM3vRjW97DfQy53AhuqxxuALc2UI0nqV53TFm8F7gPOioinIuJa4EbgzRGxE3hztSxJGqGuv69l5lVH+NIFDdciSToK43fgUVJPmr6/JgzvHpvjxkv/JakQBrokFcJAl6RCGOiSVAgDXZIKYaBLUiEMdEkqhIEuSYUw0CWpEAa6JBXCQJekQhjoklQIA12SCmGgS1IhDHRJKsRRBXpEXBgRj0XE4xFxQ1NFSZJ613egR8QxwIeAtwBnA1dFxNlNFSZJ6s3R7KGfCzyemU9k5veBWeDSZsqSJPXqaAJ9LfCNQ5afqp6TJI1AZGZ/L4y4AviVzHxHtfw24NzMvO6wcTPATLV4FvBY/+V2dQrwzQGufzmz9/Ezrn3D+PX+05n5qm6DjuYm0U8Bpx+yfBrwzOGDMnMTsOkotlNbRGzLzPYwtrXc2Pv49T6ufcN4976Uoznk8gVgXUScERHHAVcCdzZTliSpV33voWfmgYj4beAzwDHARzLz4cYqkyT15GgOuZCZdwN3N1RLE4ZyaGeZsvfxM659w3j3fkR9/1FUkrS8eOm/JBViRQZ63SkHIuLyiMiIKOav4d16j4hrIuK5iHio+njHKOpsWp33PCLeGhGPRMTDEfHxYdc4KDXe85sPeb//MyL2jaLOQajR+09FxL0R8WBEfDkiLhpFnctGZq6oDxb+APtV4NXAccCXgLMXGfdy4PPA/UB71HUPq3fgGuCvR13rCPpeBzwInFQtnzrquofV+2Hjr2PhBIWR1z6k930T8BvV47OBXaOue5QfK3EPve6UA38G/AXwP8MsbsDGdbqFOn2/E/hQZu4FyMxnh1zjoPT6nl8F3DqUygavTu8JvKJ6vIpFroUZJysx0LtOORARbwBOz8y7hlnYENSdbuHXql8/74iI0xf5+kpTp+/XAK+JiH+LiPsj4sKhVTdYtafYiIifBs4APjeEuoahTu/vA349Ip5i4Yy76xhjKzHQY5HnfnSqTkS8BLgZ2Di0ioZnyd4r/whMZubPAf8MbB54VYNXp+9jWTjs0mFhL/XDEbF6wHUNQ53eD7oSuCMzfzjAeoapTu9XAR/LzNOAi4C/rTJgLK3ExrtNOfBy4HXAXETsAs4D7izkD6Ndp1vIzOcz83vV4t8A64dU2yDVmWbiKWBLZv4gM7/GwpxB64ZU3yDVmmKjciXlHG6Ber1fC9wOkJn3AcezMM/LWFqJgb7klAOZuT8zT8nMycycZOGPopdk5rbRlNuortMtRMSaQxYvAR4dYn2DUmeaiX8ApgEi4hQWDsE8MdQqB6PWFBsRcRZwEnDfkOsbpDq9PwlcABARr2Uh0J8bapXLyIoL9Mw8AByccuBR4PbMfDgi/jQiLhltdYNVs/d3VaftfQl4FwtnvaxoNfv+DPB8RDwC3Av8fmY+P5qKm9PDv/ergNmsTvcoQc3eNwLvrP693wpcU9L3oFdeKSpJhVhxe+iSpMUZ6JJUCANdkgphoEtSIQx0SSqEgS5JhTDQJakQBrokFeL/AGcT1hSYqqb0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6vR7dbndZNQz6JYOj4KoNB7hu5rn9qthBvfSyyTVxNLIPlbb7MIy8afJcmetDRCw57Pt5NU4A1wM75oK+dguwsX68Ebi56TkkSYPRz+3jOcCbgJmIuK8eex+wGbgpIq4AHgMu7q9ESVK/God9Zn4NiMOsPrfpcSVJg+c7aCWpAIa9JBXAsJekAhj2klQAw16SCmDYS1IBDHtJKoBhL0kFMOwlqQCGvSQVwLCXpAIY9pJUAMNekgpg2EtSAQx7SSqAYS9JBTDsJakAhr0kFcCwl6QCDC3sI+K8iHg4InZGxKZhnUeStLihhH1EHAN8AngdcBpwaUScNoxzSZIWN6w7+7OAnZn5aGb+BJgGLhzSuSRJixhW2K8BHp+3vLsekyS1YNWQjhsLjOX/2yBiCpiqF2cj4mHgJOD7Q6rpOeMdR0Ef4oMDO9RzvhcDYh8qR10fGj5X5vrwq0vdYVhhvxtYN295LfDE/A0ycwuwZf5YRGzLzM6QanrOsA8H2YuKfajYh0qTPgxrGucbwPqIODUing9cAtwypHNJkhYxlDv7zDwQEW8DvgIcA9yQmQ8O41ySpMUNaxqHzLwNuG2Zu21ZfJMi2IeD7EXFPlTsQ2XZfYjMXHwrSdJzmh+XIEkFGJmw9+MVKhGxKyJmIuK+iNjWdj0rJSJuiIgnI+KBeWMnRsTtEfFI/fWENmtcCYfpwwciYk99TdwXEee3WeNKiIh1EXFnROyIiAcj4p31eFHXxBH6sOxrYiSmceqPV/g28Bqql21+A7g0Mx9qtbAWRMQuoJOZR9VriRcTEa8CZoHPZubp9djfAE9l5ub6BuCEzLy6zTqH7TB9+AAwm5kfarO2lRQRpwCnZOa9EfGLwHbgIuByCromjtCHP2GZ18So3Nn78QqFy8y7gKcOGb4Q2Fo/3kp1kR/VDtOH4mTm3sy8t378Q2AH1bvwi7omjtCHZRuVsPfjFQ5K4KsRsb1+l3HJxjNzL1QXPXByy/W06W0RcX89zXNUT10cKiImgFcC91DwNXFIH2CZ18SohP2iH69QkHMy8wyqTwy9sv62XmX7JPAy4LeBvcB17ZazciJiDPgC8JeZ+YO262nLAn1Y9jUxKmG/6McrlCIzn6i/Pgl8iWqKq1T76jnLubnLJ1uupxWZuS8zf5qZPwM+RSHXREQ8jyrgPpeZX6yHi7smFupDk2tiVMLej1cAIuK4+ocwRMRxwGuBB46811HtFmBj/XgjcHOLtbRmLtxqf0QB10REBHA9sCMzPzxvVVHXxOH60OSaGIlX4wDULx36KAc/XuHalktacRHxUqq7eaje3fyPpfQhIm4EulSf5rcPuAb4F+Am4FeAx4CLM/Oo/uHlYfrQpfp2PYFdwJ/PzVsfrSLi94D/BGaAn9XD76Oary7mmjhCHy5lmdfEyIS9JGl4RmUaR5I0RIa9JBXAsJekAhj2klQAw16SCmDYS1IBDHtJKoBhL0kF+D9MqO3H5vbrbwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAD5xJREFUeJzt3W+MXNV5x/HvU5sIx04w5s/WsmmXKFaaCDdUrBAtbbWGqIUYxX4BFRGlduRq36TUbVwFJ29QKyGZF4SgqqpkAcWVKIYSUqNC21gOU9oX0K4N1UKcCkpdY+zaiWKTLEWh2zx9MdfyrrPG9tw7M7tnvh/Jmrln7p579tHMb4/P3LkTmYkkqVw/0+8BSJK6y6CXpMIZ9JJUOINekgpn0EtS4Qx6SSqcQS9JhTPoJalwBr0kFW5hvwcAcOmll+bw8HC/h1HLu+++y+LFi/s9jDnDepxiLWayHjPVqcfevXu/n5mXnW2/ORH0w8PDjI+P93sYtbRaLUZHR/s9jDnDepxiLWayHjPVqUdE/Ne57OfSjSQVzqCXpMIZ9JJUOINekgpn0EtS4Qx6SSqcQS9JhTPoJalwBr0kFe6sn4yNiEeAW4BjmXlV1bYMeAIYBg4Av5WZxyMigAeBzwL/A2zMzH3dGbrmsom332Hj1mcb6+/AtrWN9SUNmnOZ0T8K3HRa21ZgT2auAvZU2wA3A6uqf2PAnzczTElSp84a9Jn5AvCD05rXATuq+zuA9dPa/zLbXgSWRsTypgYrSTp/na7RD2XmEYDq9vKqfQXw1rT9DlVtkqQ+afrqlTFLW866Y8QY7eUdhoaGaLVaDQ+ltyYnJ+f979CkoUWwZfVUY/3N59r63JjJeszUi3p0GvRHI2J5Zh6plmaOVe2HgCum7bcSODxbB5m5HdgOMDIykvP9sqVeenWmP31sF/dPNDePOHDHaGN99ZrPjZmsx0y9qEenSzfPABuq+xuAXdPafyfargPeObnEI0nqj3M5vfJxYBS4NCIOAfcA24AnI2ITcBC4rdr9OdqnVr5B+/TKL3RhzJKk83DWoM/Mz5/hoRtn2TeBL9YdlCSpOX4yVpIKZ9BLUuEMekkqnEEvSYUz6CWpcAa9JBXOoJekwhn0klQ4g16SCmfQS1LhDHpJKpxBL0mFM+glqXAGvSQVzqCXpMIZ9JJUOINekgpn0EtS4Qx6SSqcQS9JhTPoJalwBr0kFc6gl6TCGfSSVDiDXpIKZ9BLUuEMekkq3MJ+D0D9N7z12cb73LK68S4ldcgZvSQVzqCXpMIZ9JJUOINekgpXK+gj4g8j4rWIeDUiHo+ICyPiyoh4KSJej4gnIuJDTQ1WknT+Og76iFgB/D4wkplXAQuA24H7gAcycxVwHNjUxEAlSZ2pu3SzEFgUEQuBDwNHgBuAp6rHdwDrax5DklRDZGbnPxyxGbgXeA/4FrAZeDEzP149fgXwd9WM//SfHQPGAIaGhq7ZuXNnx+OYCyYnJ1myZEm/h9GRibffabzPoUVw9L3m+lu94qLmOuux+fzc6AbrMVOdeqxZs2ZvZo6cbb+OPzAVERcD64ArgRPAXwM3z7LrrH9JMnM7sB1gZGQkR0dHOx3KnNBqtZivv8PGrnxgaor7J5r7PN6BO0Yb66vX5vNzoxusx0y9qEedpZvPAP+Zmd/LzP8FngZ+BVhaLeUArAQO1xyjJKmGOkF/ELguIj4cEQHcCHwHeB64tdpnA7Cr3hAlSXV0HPSZ+RLtN133ARNVX9uBu4EvRcQbwCXAww2MU5LUoVqLqJl5D3DPac1vAtfW6VeS1Bw/GStJhTPoJalwBr0kFc6gl6TCGfSSVDiDXpIKZ9BLUuEMekkqnEEvSYVr7vKCUhcNN3yFzQPb1jbanzSXOaOXpMIZ9JJUOINekgpn0EtS4Qx6SSqcQS9JhTPoJalwBr0kFc6gl6TCGfSSVDiDXpIKZ9BLUuEMekkqnEEvSYUz6CWpcAa9JBXOoJekwhn0klQ4g16SCmfQS1LhDHpJKlytoI+IpRHxVER8NyL2R8QvR8SyiNgdEa9Xtxc3NVhJ0vmrO6N/EPj7zPwF4NPAfmArsCczVwF7qm1JUp90HPQR8VHg14GHATLz/cw8AawDdlS77QDW1x2kJKlzdWb0HwO+B/xFRLwcEQ9FxGJgKDOPAFS3lzcwTklShyIzO/vBiBHgReD6zHwpIh4EfgjclZlLp+13PDN/ap0+IsaAMYChoaFrdu7c2dE45orJyUmWLFnS72F0ZOLtdxrvc2gRHH2v8W4bs3rFRT071nx+bnSD9ZipTj3WrFmzNzNHzrZfnaD/WeDFzByutn+N9nr8x4HRzDwSEcuBVmZ+4oP6GhkZyfHx8Y7GMVe0Wi1GR0f7PYyODG99tvE+t6ye4v6JhY3325QD29b27Fjz+bnRDdZjpjr1iIhzCvqOl24y87+BtyLiZIjfCHwHeAbYULVtAHZ1egxJUn11p1x3AY9FxIeAN4Ev0P7j8WREbAIOArfVPIYkqYZaQZ+ZrwCz/bfhxjr9SpKa4ydjJalwBr0kFc6gl6TCGfSSVDiDXpIKZ9BLUuEMekkqnEEvSYUz6CWpcAa9JBXOoJekwhn0klQ4g16SCmfQS1LhDHpJKtzc/a43nVE3vvpPUrmc0UtS4Qx6SSqcQS9JhTPoJalwBr0kFc6gl6TCGfSSVDiDXpIKZ9BLUuEMekkqnEEvSYUz6CWpcAa9JBXOoJekwhn0klQ4g16SClf7i0ciYgEwDrydmbdExJXATmAZsA+4MzPfr3uc+cwvCpHUT03M6DcD+6dt3wc8kJmrgOPApgaOIUnqUK2gj4iVwFrgoWo7gBuAp6pddgDr6xxDklRP3Rn914EvAz+pti8BTmTmVLV9CFhR8xiSpBo6XqOPiFuAY5m5NyJGTzbPsmue4efHgDGAoaEhWq1Wp0OZEyYnJ8/4O2xZPTVre8mGFs3t37uXz7cPem4MIusxUy/qUefN2OuBz0XEZ4ELgY/SnuEvjYiF1ax+JXB4th/OzO3AdoCRkZEcHR2tMZT+a7VanOl32DiAb8ZuWT3F/RO13+vvmgN3jPbsWB/03BhE1mOmXtSj46WbzPxKZq7MzGHgduDbmXkH8Dxwa7XbBmBX7VFKkjrWjfPo7wa+FBFv0F6zf7gLx5AknaNG/m+dmS2gVd1/E7i2iX4lSfX5yVhJKpxBL0mFM+glqXAGvSQVzqCXpMIZ9JJUOINekgpn0EtS4Qx6SSqcQS9JhTPoJalwBr0kFc6gl6TCGfSSVDiDXpIKZ9BLUuEMekkqnEEvSYUz6CWpcAa9JBXOoJekwi3s9wCkfhje+mzjfR7YtrbxPqUmOKOXpMIZ9JJUOINekgrnGr3UkDOt+29ZPcXGDt4TcM1fTXFGL0mFM+glqXAGvSQVzjX6WXRyjnWn67CS1G3O6CWpcAa9JBWu46CPiCsi4vmI2B8Rr0XE5qp9WUTsjojXq9uLmxuuJOl81VmjnwK2ZOa+iPgIsDcidgMbgT2ZuS0itgJbgbvrD1UaLE1fj8fz8gdXxzP6zDySmfuq+z8C9gMrgHXAjmq3HcD6uoOUJHUuMrN+JxHDwAvAVcDBzFw67bHjmflTyzcRMQaMAQwNDV2zc+fO2uNoysTb75z3zwwtgqPvdWEw85T1OGWu1GL1iov6PQQAJicnWbJkSb+HMWfUqceaNWv2ZubI2farHfQRsQT4R+DezHw6Ik6cS9BPNzIykuPj47XG0aROT6+8f8KzVU+yHqfMlVrMlaWbVqvF6Ohov4cxZ9SpR0ScU9DXevZFxAXAN4DHMvPpqvloRCzPzCMRsRw4VucYkjRXdON7DB69aXHjfZ6uzlk3ATwM7M/Mr0176BlgQ3V/A7Cr8+FJkuqqM6O/HrgTmIiIV6q2rwLbgCcjYhNwELit3hAlSXV0HPSZ+c9AnOHhGzvtV5LULD8ZK0mFM+glqXAGvSQVzqCXpML1/1McknrCa+cMLmf0klQ4g16SCmfQS1LhDHpJKpxBL0mFM+glqXAGvSQVbt6fR9+N60NLUkmc0UtS4Qx6SSqcQS9JhTPoJalwBr0kFc6gl6TCGfSSVDiDXpIKN+8/MCWpHH45Snc4o5ekwhn0klQ4g16SCucavaRiedHDNmf0klQ4g16SCmfQS1LhDHpJKpxBL0mF68pZNxFxE/AgsAB4KDO3deM4kvqn0zNatqyeYqNnw/RU4zP6iFgA/BlwM/Ap4PMR8ammjyNJOjfdWLq5FngjM9/MzPeBncC6LhxHknQOuhH0K4C3pm0fqtokSX0QmdlshxG3Ab+Zmb9bbd8JXJuZd5223xgwVm1+Avj3RgfSe5cC3+/3IOYQ63GKtZjJesxUpx4/n5mXnW2nbrwZewi4Ytr2SuDw6Ttl5nZgexeO3xcRMZ6ZI/0ex1xhPU6xFjNZj5l6UY9uLN38K7AqIq6MiA8BtwPPdOE4kqRz0PiMPjOnIuL3gH+gfXrlI5n5WtPHkSSdm66cR5+ZzwHPdaPvOayYZaiGWI9TrMVM1mOmrtej8TdjJUlzi5dAkKTCGfQdiIhHIuJYRLw6rW1ZROyOiNer24v7OcZeiYgrIuL5iNgfEa9FxOaqfVDrcWFE/EtE/FtVjz+u2q+MiJeqejxRnagwECJiQUS8HBF/W20Pci0ORMRERLwSEeNVW9dfKwZ9Zx4FbjqtbSuwJzNXAXuq7UEwBWzJzE8C1wFfrC55Maj1+DFwQ2Z+GrgauCkirgPuAx6o6nEc2NTHMfbaZmD/tO1BrgXAmsy8etoplV1/rRj0HcjMF4AfnNa8DthR3d8BrO/poPokM49k5r7q/o9ov6BXMLj1yMycrDYvqP4lcAPwVNU+MPWIiJXAWuChajsY0Fp8gK6/Vgz65gxl5hFohx9weZ/H03MRMQz8EvASA1yPaqniFeAYsBv4D+BEZk5VuwzSZUG+DnwZ+Em1fQmDWwto/9H/VkTsra4OAD14rfjl4GpERCwBvgH8QWb+sD1xG0yZ+X/A1RGxFPgm8MnZduvtqHovIm4BjmXm3ogYPdk8y67F12Ka6zPzcERcDuyOiO/24qDO6JtzNCKWA1S3x/o8np6JiAtoh/xjmfl01Tyw9TgpM08ALdrvXSyNiJMTq1kvC1Kg64HPRcQB2lexvYH2DH8QawFAZh6ubo/RngRcSw9eKwZ9c54BNlT3NwC7+jiWnqnWXB8G9mfm16Y9NKj1uKyayRMRi4DP0H7f4nng1mq3gahHZn4lM1dm5jDtS6F8OzPvYABrARARiyPiIyfvA78BvEoPXit+YKoDEfE4MEr7qnNHgXuAvwGeBH4OOAjclpmnv2FbnIj4VeCfgAlOrcN+lfY6/SDW4xdpv6G2gPZE6snM/JOI+BjtWe0y4GXgtzPzx/0baW9VSzd/lJm3DGotqt/7m9XmQuCvMvPeiLiELr9WDHpJKpxLN5JUOINekgpn0EtS4Qx6SSqcQS9JhTPoJalwBr0kFc6gl6TC/T9XxGZvcQbK4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for item in dataset.columns:\n",
    "    #print(item,type(item))\n",
    "    dataset[item].hist(bins = 15)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standardization of datasets is a common requirement for many machine learning estimators.\n",
    "\n",
    "In practice we often ignore the shape of the distribution and just transform the data to center it by removing the mean value of each feature, then scale it by dividing non-constant features by their standard deviation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.64113113  0.10080399 -1.03067021 ... -1.00268807  0.42054571\n",
      "  -0.92348369]\n",
      " [-0.60977124 -0.59350918 -0.28321604 ...  0.09274682  0.42054571\n",
      "  -0.25234807]\n",
      " [-0.60980112 -0.59350918 -0.28321604 ...  0.09274682  0.32645614\n",
      "  -1.07674783]\n",
      " ...\n",
      " [-0.43986906 -0.59350918  0.79242704 ... -0.6959663   0.42054571\n",
      "   0.82856747]\n",
      " [-0.55396886  2.10659757 -1.04166218 ... -1.22177505 -0.24201132\n",
      "  -0.925097  ]\n",
      " [-0.56748992  2.10659757 -1.04166218 ... -1.22177505 -0.16479777\n",
      "  -0.96220305]]\n"
     ]
    }
   ],
   "source": [
    "#https://scikit-learn.org/stable/modules/preprocessing.html#standardization-or-mean-removal-and-variance-scaling\n",
    "\n",
    "# Keep 300 samples for training\n",
    "train_features = scale(total_features[:300])\n",
    "train_prices = total_prices[:300]\n",
    "\n",
    "print(train_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keep 100 samples for validation\n",
    "valid_features = scale(total_features[300:400])\n",
    "valid_prices = total_prices[300:400]\n",
    "\n",
    "# Keep remaining samples as test set\n",
    "test_features = scale(total_features[400:])\n",
    "test_prices = total_prices[400:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$y*=f(x)=wx+b$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From D:\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    }
   ],
   "source": [
    "# https://www.tensorflow.org/api_docs/python/tf/random/truncated_normal\n",
    "\n",
    "w = tf.Variable(tf.truncated_normal([13, 1], mean=0.0, stddev=1.0, dtype=tf.float64))\n",
    "\n",
    "# https://www.tensorflow.org/api_docs/python/tf/zeros\n",
    "\n",
    "b = tf.Variable(tf.zeros([1], dtype = tf.float64))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Add_11:0\", shape=(300, 1), dtype=float64) Tensor(\"Mean_11:0\", shape=(), dtype=float64)\n"
     ]
    }
   ],
   "source": [
    "# https://www.tensorflow.org/api_docs/python/tf/math/add\n",
    "# https://www.tensorflow.org/api_docs/python/tf/linalg/matmul\n",
    "# https://www.tensorflow.org/api_docs/python/tf/math/square\n",
    "# https://www.tensorflow.org/api_docs/python/tf/math/reduce_mean\n",
    "\n",
    "def calc(x, y):\n",
    "    # Returns predictions and error\n",
    "    predictions = tf.add(b, tf.matmul(x, w))\n",
    "    error = tf.reduce_mean(tf.square(y - predictions))\n",
    "    return [ predictions, error ]\n",
    "\n",
    "y, lost = calc(train_features, train_prices)\n",
    "print(y, lost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feel free to tweak these 2 values:\n",
    "\n",
    "learning_rate = 0.005\n",
    "\n",
    "epochs = 3000\n",
    "\n",
    "points = [[], []] # You'll see later why I need this\n",
    "\n",
    "# https://www.tensorflow.org/api_docs/python/tf/initializers/global_variables\n",
    "# https://www.tensorflow.org/api_docs/python/tf/train/GradientDescentOptimizer\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(lost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python 語言提供了 with 這個獨特的語法，來幫助使用者管理使用的資源，像是開啟的檔案、網路 socket，包含tensorflow的session。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "728.5579441113848\n",
      "165.15813158785116\n",
      "90.79709733073146\n",
      "80.66638330081234\n",
      "79.20164079276772\n",
      "78.93607389826619\n",
      "78.85473700546525\n",
      "78.81312921934708\n",
      "78.7866669596242\n",
      "78.76875049694138\n",
      "78.75635926417917\n",
      "78.74768660111515\n",
      "78.74155852903593\n",
      "78.73719117845216\n",
      "78.73405380558401\n",
      "78.73178320763904\n",
      "78.73012846093145\n",
      "78.7289146689124\n",
      "78.72801890505464\n",
      "78.72735408567412\n",
      "78.72685805631676\n",
      "78.72648614022832\n",
      "78.7262060074985\n",
      "78.72599411366403\n",
      "78.72583320872869\n",
      "78.72571058212648\n",
      "78.72561681755658\n",
      "78.72554490365476\n",
      "78.72548959474459\n",
      "78.72544694852388\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation error = 104.34390164142957 \n",
      "\n",
      "Test error = 123.33430463616476 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "with tf.Session() as sess:\n",
    "\n",
    "    sess.run(init)\n",
    "\n",
    "    for i in list(range(epochs)):\n",
    "\n",
    "        sess.run(optimizer)\n",
    "\n",
    "        if i % 10 == 0:\n",
    "            points[0].append(i+1)\n",
    "            points[1].append(sess.run(lost))\n",
    "\n",
    "        if i % 100 == 0:\n",
    "            print(sess.run(lost))\n",
    "    \n",
    "    ax1=plt.subplot(221)\n",
    "    ax2=plt.subplot(222)\n",
    "    ax3=plt.subplot(212)\n",
    "    plt.sca(ax1)\n",
    "    pred = calc(valid_features, valid_prices)[0]\n",
    "    plt.plot(sess.run(pred), valid_prices, 'r.')\n",
    "    plt.sca(ax2)\n",
    "    pred = calc(test_features, test_prices)[0]\n",
    "    plt.plot(sess.run(pred), test_prices, 'b.')\n",
    "    plt.sca(ax3)\n",
    "    plt.plot(points[0], points[1], 'r.')\n",
    "    plt.axis([0, epochs, 50, 1000])\n",
    "    plt.show()\n",
    "    \n",
    "    valid_lost = calc(valid_features, valid_prices)[1]\n",
    "\n",
    "    print('Validation error =', sess.run(valid_lost), '\\n')\n",
    "\n",
    "    test_lost = calc(test_features, test_prices)[1]\n",
    "\n",
    "    print('Test error =', sess.run(test_lost), '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}