105-example_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TensorFlow2.0教程-回归"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "tensorflow2教程知乎专栏：https://zhuanlan.zhihu.com/c_1091021863043624960\n",
    "\n",
    "在回归问题中，我们的目标是预测连续值的输出，如价格或概率。 \n",
    "我们采用了经典的Auto MPG数据集，并建立了一个模型来预测20世纪70年代末和80年代初汽车的燃油效率。 为此，我们将为该模型提供该时段内许多汽车的描述。 此描述包括以下属性：气缸，排量，马力和重量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.0-alpha0\n"
     ]
    }
   ],
   "source": [
    "from __future__ import absolute_import, division, print_function\n",
    "\n",
    "import pathlib\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.Auto MPG数据集\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "获取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data\n",
      "32768/30286 [================================] - 1s 25us/step\n",
      "/home/czy/.keras/datasets/auto-mpg.data\n"
     ]
    }
   ],
   "source": [
    "dataset_path = keras.utils.get_file('auto-mpg.data',\n",
    "                                   'https://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data')\n",
    "\n",
    "print(dataset_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用pandas读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MPG</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Acceleration</th>\n",
       "      <th>Model Year</th>\n",
       "      <th>Origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2790.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2130.0</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2295.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2625.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  \\\n",
       "393  27.0          4         140.0        86.0  2790.0          15.6   \n",
       "394  44.0          4          97.0        52.0  2130.0          24.6   \n",
       "395  32.0          4         135.0        84.0  2295.0          11.6   \n",
       "396  28.0          4         120.0        79.0  2625.0          18.6   \n",
       "397  31.0          4         119.0        82.0  2720.0          19.4   \n",
       "\n",
       "     Model Year  Origin  \n",
       "393          82       1  \n",
       "394          82       2  \n",
       "395          82       1  \n",
       "396          82       1  \n",
       "397          82       1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names = ['MPG','Cylinders','Displacement','Horsepower','Weight',\n",
    "                'Acceleration', 'Model Year', 'Origin'] \n",
    "raw_dataset = pd.read_csv(dataset_path, names=column_names,\n",
    "                         na_values='?', comment='\\t',\n",
    "                         sep=' ', skipinitialspace=True)\n",
    "dataset = raw_dataset.copy()\n",
    "dataset.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.数据预处理\n",
    "### 清洗数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MPG             0\n",
      "Cylinders       0\n",
      "Displacement    0\n",
      "Horsepower      6\n",
      "Weight          0\n",
      "Acceleration    0\n",
      "Model Year      0\n",
      "Origin          0\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MPG</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Acceleration</th>\n",
       "      <th>Model Year</th>\n",
       "      <th>USA</th>\n",
       "      <th>Europe</th>\n",
       "      <th>Japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2790.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2130.0</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2295.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2625.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  \\\n",
       "393  27.0          4         140.0        86.0  2790.0          15.6   \n",
       "394  44.0          4          97.0        52.0  2130.0          24.6   \n",
       "395  32.0          4         135.0        84.0  2295.0          11.6   \n",
       "396  28.0          4         120.0        79.0  2625.0          18.6   \n",
       "397  31.0          4         119.0        82.0  2720.0          19.4   \n",
       "\n",
       "     Model Year  USA  Europe  Japan  \n",
       "393          82  1.0     0.0    0.0  \n",
       "394          82  0.0     1.0    0.0  \n",
       "395          82  1.0     0.0    0.0  \n",
       "396          82  1.0     0.0    0.0  \n",
       "397          82  1.0     0.0    0.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(dataset.isna().sum())\n",
    "dataset = dataset.dropna()\n",
    "origin = dataset.pop('Origin')\n",
    "dataset['USA'] = (origin == 1)*1.0\n",
    "dataset['Europe'] = (origin == 2)*1.0\n",
    "dataset['Japan'] = (origin == 3)*1.0\n",
    "dataset.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 划分训练集和测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = dataset.sample(frac=0.8,random_state=0)\n",
    "test_dataset = dataset.drop(train_dataset.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 检测数据\n",
    "\n",
    "观察训练集中几对列的联合分布。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f934072fe10>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(train_dataset[[\"MPG\", \"Cylinders\", \"Displacement\", \"Weight\"]], diag_kind=\"kde\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "整体统计数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cylinders</th>\n",
       "      <td>314.0</td>\n",
       "      <td>5.477707</td>\n",
       "      <td>1.699788</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Displacement</th>\n",
       "      <td>314.0</td>\n",
       "      <td>195.318471</td>\n",
       "      <td>104.331589</td>\n",
       "      <td>68.0</td>\n",
       "      <td>105.50</td>\n",
       "      <td>151.0</td>\n",
       "      <td>265.75</td>\n",
       "      <td>455.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horsepower</th>\n",
       "      <td>314.0</td>\n",
       "      <td>104.869427</td>\n",
       "      <td>38.096214</td>\n",
       "      <td>46.0</td>\n",
       "      <td>76.25</td>\n",
       "      <td>94.5</td>\n",
       "      <td>128.00</td>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight</th>\n",
       "      <td>314.0</td>\n",
       "      <td>2990.251592</td>\n",
       "      <td>843.898596</td>\n",
       "      <td>1649.0</td>\n",
       "      <td>2256.50</td>\n",
       "      <td>2822.5</td>\n",
       "      <td>3608.00</td>\n",
       "      <td>5140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acceleration</th>\n",
       "      <td>314.0</td>\n",
       "      <td>15.559236</td>\n",
       "      <td>2.789230</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.80</td>\n",
       "      <td>15.5</td>\n",
       "      <td>17.20</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model Year</th>\n",
       "      <td>314.0</td>\n",
       "      <td>75.898089</td>\n",
       "      <td>3.675642</td>\n",
       "      <td>70.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>76.0</td>\n",
       "      <td>79.00</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>314.0</td>\n",
       "      <td>0.624204</td>\n",
       "      <td>0.485101</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>314.0</td>\n",
       "      <td>0.178344</td>\n",
       "      <td>0.383413</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>314.0</td>\n",
       "      <td>0.197452</td>\n",
       "      <td>0.398712</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count         mean         std     min      25%     50%  \\\n",
       "Cylinders     314.0     5.477707    1.699788     3.0     4.00     4.0   \n",
       "Displacement  314.0   195.318471  104.331589    68.0   105.50   151.0   \n",
       "Horsepower    314.0   104.869427   38.096214    46.0    76.25    94.5   \n",
       "Weight        314.0  2990.251592  843.898596  1649.0  2256.50  2822.5   \n",
       "Acceleration  314.0    15.559236    2.789230     8.0    13.80    15.5   \n",
       "Model Year    314.0    75.898089    3.675642    70.0    73.00    76.0   \n",
       "USA           314.0     0.624204    0.485101     0.0     0.00     1.0   \n",
       "Europe        314.0     0.178344    0.383413     0.0     0.00     0.0   \n",
       "Japan         314.0     0.197452    0.398712     0.0     0.00     0.0   \n",
       "\n",
       "                  75%     max  \n",
       "Cylinders        8.00     8.0  \n",
       "Displacement   265.75   455.0  \n",
       "Horsepower     128.00   225.0  \n",
       "Weight        3608.00  5140.0  \n",
       "Acceleration    17.20    24.8  \n",
       "Model Year      79.00    82.0  \n",
       "USA              1.00     1.0  \n",
       "Europe           0.00     1.0  \n",
       "Japan            0.00     1.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_stats = train_dataset.describe()\n",
    "train_stats.pop(\"MPG\")\n",
    "train_stats = train_stats.transpose()\n",
    "train_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 取出标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels = train_dataset.pop('MPG')\n",
    "test_labels = test_dataset.pop('MPG')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 标准化数据\n",
    "最好使用不同比例和范围的特征进行标准化。 虽然模型可能在没有特征归一化的情况下收敛，但它使训练更加困难，并且它使得结果模型依赖于输入中使用的单位的选择。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def norm(x):\n",
    "    return (x - train_stats['mean']) / train_stats['std']\n",
    "normed_train_data = norm(train_dataset)\n",
    "normed_test_data = norm(test_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.构建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model():\n",
    "    model = keras.Sequential([\n",
    "        layers.Dense(64, activation='relu', input_shape=[len(train_dataset.keys())]),\n",
    "        layers.Dense(64, activation='relu'),\n",
    "        layers.Dense(1)\n",
    "    ])\n",
    "    \n",
    "    optimizer = tf.keras.optimizers.RMSprop(0.001)\n",
    "    model.compile(loss='mse',\n",
    "                 optimizer=optimizer,\n",
    "                 metrics=['mae', 'mse'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 64)                640       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 4,865\n",
      "Trainable params: 4,865\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = build_model()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.18062565],\n",
       "       [0.1714489 ],\n",
       "       [0.22555563],\n",
       "       [0.29366603],\n",
       "       [0.69764495],\n",
       "       [0.08851457],\n",
       "       [0.6851174 ],\n",
       "       [0.32245407],\n",
       "       [0.02959149],\n",
       "       [0.38945067]], dtype=float32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_batch = normed_train_data[:10]\n",
    "example_result = model.predict(example_batch)\n",
    "example_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "...................................................................................................."
     ]
    }
   ],
   "source": [
    "class PrintDot(keras.callbacks.Callback):\n",
    "    def on_epoch_end(self, epoch, logs):\n",
    "        if epoch % 100 == 0: print('')\n",
    "        print('.', end='')\n",
    "\n",
    "EPOCHS = 1000\n",
    "\n",
    "history = model.fit(\n",
    "  normed_train_data, train_labels,\n",
    "  epochs=EPOCHS, validation_split = 0.2, verbose=0,\n",
    "  callbacks=[PrintDot()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看训练记录"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loss</th>\n",
       "      <th>mae</th>\n",
       "      <th>mse</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_mae</th>\n",
       "      <th>val_mse</th>\n",
       "      <th>epoch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>2.191127</td>\n",
       "      <td>0.940755</td>\n",
       "      <td>2.191127</td>\n",
       "      <td>10.422818</td>\n",
       "      <td>2.594117</td>\n",
       "      <td>10.422818</td>\n",
       "      <td>995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2.113679</td>\n",
       "      <td>0.903680</td>\n",
       "      <td>2.113679</td>\n",
       "      <td>10.723925</td>\n",
       "      <td>2.631320</td>\n",
       "      <td>10.723926</td>\n",
       "      <td>996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>2.517261</td>\n",
       "      <td>0.989557</td>\n",
       "      <td>2.517261</td>\n",
       "      <td>9.497868</td>\n",
       "      <td>2.379198</td>\n",
       "      <td>9.497869</td>\n",
       "      <td>997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>2.250272</td>\n",
       "      <td>0.931618</td>\n",
       "      <td>2.250272</td>\n",
       "      <td>11.017041</td>\n",
       "      <td>2.658538</td>\n",
       "      <td>11.017041</td>\n",
       "      <td>998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1.976393</td>\n",
       "      <td>0.853547</td>\n",
       "      <td>1.976393</td>\n",
       "      <td>9.890977</td>\n",
       "      <td>2.491739</td>\n",
       "      <td>9.890977</td>\n",
       "      <td>999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         loss       mae       mse   val_loss   val_mae    val_mse  epoch\n",
       "995  2.191127  0.940755  2.191127  10.422818  2.594117  10.422818    995\n",
       "996  2.113679  0.903680  2.113679  10.723925  2.631320  10.723926    996\n",
       "997  2.517261  0.989557  2.517261   9.497868  2.379198   9.497869    997\n",
       "998  2.250272  0.931618  2.250272  11.017041  2.658538  11.017041    998\n",
       "999  1.976393  0.853547  1.976393   9.890977  2.491739   9.890977    999"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist = pd.DataFrame(history.history)\n",
    "hist['epoch'] = history.epoch\n",
    "hist.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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"
    }
   ],
   "source": [
    "def plot_history(history):\n",
    "    hist = pd.DataFrame(history.history)\n",
    "    hist['epoch'] = history.epoch\n",
    "\n",
    "    plt.figure()\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Mean Abs Error [MPG]')\n",
    "    plt.plot(hist['epoch'], hist['mae'],\n",
    "           label='Train Error')\n",
    "    plt.plot(hist['epoch'], hist['val_mae'],\n",
    "           label = 'Val Error')\n",
    "    plt.ylim([0,5])\n",
    "    plt.legend()\n",
    "\n",
    "    plt.figure()\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Mean Square Error [$MPG^2$]')\n",
    "    plt.plot(hist['epoch'], hist['mse'],\n",
    "           label='Train Error')\n",
    "    plt.plot(hist['epoch'], hist['val_mse'],\n",
    "           label = 'Val Error')\n",
    "    plt.ylim([0,20])\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用early stop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ".........................................................."
     ]
    },
    {
     "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"
    }
   ],
   "source": [
    "model = build_model()\n",
    "\n",
    "\n",
    "early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)\n",
    "\n",
    "history = model.fit(normed_train_data, train_labels, epochs=EPOCHS,\n",
    "                    validation_split = 0.2, verbose=0, callbacks=[early_stop, PrintDot()])\n",
    "\n",
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing set Mean Abs Error:  1.85 MPG\n"
     ]
    }
   ],
   "source": [
    "loss, mae, mse = model.evaluate(normed_test_data, test_labels, verbose=0)\n",
    "\n",
    "print(\"Testing set Mean Abs Error: {:5.2f} MPG\".format(mae))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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"
    }
   ],
   "source": [
    "test_predictions = model.predict(normed_test_data).flatten()\n",
    "plt.scatter(test_labels, test_predictions)\n",
    "plt.xlabel('True Values [MPG]')\n",
    "plt.ylabel('Predictions [MPG]')\n",
    "plt.axis('equal')\n",
    "plt.axis('square')\n",
    "plt.xlim([0,plt.xlim()[1]])\n",
    "plt.ylim([0,plt.ylim()[1]])\n",
    "_ = plt.plot([-100, 100], [-100, 100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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"
    }
   ],
   "source": [
    "error = test_predictions - test_labels\n",
    "plt.hist(error, bins = 25)\n",
    "plt.xlabel(\"Prediction Error [MPG]\")\n",
    "_ = plt.ylabel(\"Count\")"
   ]
  },
  {
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}