{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"这是Tensorflow对于回归问题(regression problem)的官方教程,个人感觉代码写的干净漂亮,不仅仅可以学到回归问题的解决方法,也可以学到诸如数据准备、结果展示等技术,因此详细的剖析了这个教程,加上了我个人认为需要补充的资料背景。\n",
"\n",
"本教程通过[Auto MPG](https://archive.ics.uci.edu/ml/datasets/auto+mpg)这个数据集构建了一个模型来预测70年代晚期和80年代早期的汽车燃油经济性,即通常所说的百公里油耗(MPG的本意是Miles Per Gallon)。关于这个数据集的内容,在下面的分析中会给出其结构。\n",
"\n",
"这个教程既可以运行于Tensorflow 1.x版本,也可以运行于Tensorflow 2.x版本,本人亲测。"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "1rRo8oNqZ-Rj"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.0.0-beta1\n"
]
}
],
"source": [
"from __future__ import absolute_import, division, print_function, unicode_literals\n",
"\n",
"import pathlib\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"from tensorflow.keras import layers\n",
"\n",
"print(tf.__version__)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "F_72b0LCNbjx"
},
"source": [
"# Auto MPG数据集\n",
"\n",
"Auto MPG数据集是[UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/)的476个数据集之一,包含了398条记录,结构如下:\n",
"\n",
"1. mpg: 百公里油耗\n",
"2. cylinders: 气缸数\n",
"3. displacement: 排气量\n",
"4. horsepower: 马力\n",
"5. weight: 车重 \n",
"6. acceleration: 加速能力\n",
"7. model year: 年份\n",
"8. origin: 原产地\n",
"9. car name: 车型名称\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "gFh9ne3FZ-On"
},
"source": [
"## 下载数据集\n",
"keras提供了几种下载数据的方式,一种是直接使用keras中已经包含了的数据集,比如mnist数据集。一种是如下的方式下载数据:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "p9kxxgzvzlyz"
},
"outputs": [
{
"data": {
"text/plain": [
"'/home/subaochen/.keras/datasets/auto-mpg.data'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset_path = keras.utils.get_file(\"auto-mpg.data\", \"http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data\")\n",
"dataset_path"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "nslsRLh7Zss4"
},
"source": [
"使用pandas处理Auto MPG数据集。Auto MPG本质上是csv格式的,因此pandas可以直接打开。由于\"car name\"这一列和MPG并没有关系,因此在实际读取数据集的时候通过column_names限定了用到的数据列。"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "CiX2FI4gZtTt"
},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" MPG | \n",
" Cylinders | \n",
" Displacement | \n",
" Horsepower | \n",
" Weight | \n",
" Acceleration | \n",
" Model Year | \n",
" Origin | \n",
"
\n",
" \n",
" \n",
" \n",
" 393 | \n",
" 27.0 | \n",
" 4 | \n",
" 140.0 | \n",
" 86.0 | \n",
" 2790.0 | \n",
" 15.6 | \n",
" 82 | \n",
" 1 | \n",
"
\n",
" \n",
" 394 | \n",
" 44.0 | \n",
" 4 | \n",
" 97.0 | \n",
" 52.0 | \n",
" 2130.0 | \n",
" 24.6 | \n",
" 82 | \n",
" 2 | \n",
"
\n",
" \n",
" 395 | \n",
" 32.0 | \n",
" 4 | \n",
" 135.0 | \n",
" 84.0 | \n",
" 2295.0 | \n",
" 11.6 | \n",
" 82 | \n",
" 1 | \n",
"
\n",
" \n",
" 396 | \n",
" 28.0 | \n",
" 4 | \n",
" 120.0 | \n",
" 79.0 | \n",
" 2625.0 | \n",
" 18.6 | \n",
" 82 | \n",
" 1 | \n",
"
\n",
" \n",
" 397 | \n",
" 31.0 | \n",
" 4 | \n",
" 119.0 | \n",
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" 2720.0 | \n",
" 19.4 | \n",
" 82 | \n",
" 1 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"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": 3,
"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",
"\n",
"dataset = raw_dataset.copy()\n",
"dataset.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "3MWuJTKEDM-f"
},
"source": [
"## 清理数据(缺失值)\n",
"\n",
"这个数据集中有几条没有数据的记录,需要清理掉。通常,新拿到一个数据集,都需要检测数据集是否存在无效记录。"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "JEJHhN65a2VV"
},
"outputs": [
{
"data": {
"text/plain": [
"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"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset.isna().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "9UPN0KBHa_WI"
},
"source": [
"使用dropna从数据集中删除无效记录。"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "4ZUDosChC1UN"
},
"outputs": [],
"source": [
"dataset = dataset.dropna()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "8XKitwaH4v8h"
},
"source": [
"`\"Origin\"`这个字段需要转换为one-hot编码。这是一种常见的技巧,将类别数字转化为one-hot编码,以便机器学习到原产地对于MPG的影响。如果不进行one-hot编码,`Origin`是用1,2,3等数字来表示的,直接这样使用的话机器学习会把`Origin`当做一个数量而不是**类别**来对待,这显然不是我们的本意。\n",
"\n",
"这里处理`Origin`字段的方法值得注意:`pop`弹出这一列,然后根据`pop`字段的值重新增加三个列。"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "gWNTD2QjBWFJ"
},
"outputs": [],
"source": [
"origin = dataset.pop('Origin')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "ulXz4J7PAUzk"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" MPG | \n",
" Cylinders | \n",
" Displacement | \n",
" Horsepower | \n",
" Weight | \n",
" Acceleration | \n",
" Model Year | \n",
" USA | \n",
" Europe | \n",
" Japan | \n",
"
\n",
" \n",
" \n",
" \n",
" 393 | \n",
" 27.0 | \n",
" 4 | \n",
" 140.0 | \n",
" 86.0 | \n",
" 2790.0 | \n",
" 15.6 | \n",
" 82 | \n",
" 1.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" 394 | \n",
" 44.0 | \n",
" 4 | \n",
" 97.0 | \n",
" 52.0 | \n",
" 2130.0 | \n",
" 24.6 | \n",
" 82 | \n",
" 0.0 | \n",
" 1.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" 395 | \n",
" 32.0 | \n",
" 4 | \n",
" 135.0 | \n",
" 84.0 | \n",
" 2295.0 | \n",
" 11.6 | \n",
" 82 | \n",
" 1.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" 396 | \n",
" 28.0 | \n",
" 4 | \n",
" 120.0 | \n",
" 79.0 | \n",
" 2625.0 | \n",
" 18.6 | \n",
" 82 | \n",
" 1.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" 397 | \n",
" 31.0 | \n",
" 4 | \n",
" 119.0 | \n",
" 82.0 | \n",
" 2720.0 | \n",
" 19.4 | \n",
" 82 | \n",
" 1.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"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": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"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": {
"colab_type": "text",
"id": "Cuym4yvk76vU"
},
"source": [
"## 切分训练数据集和测试数据集\n",
"\n",
"这里切分数据集的方法很巧妙,使用了`DataFrame`的`sample`方法,一次性完成了随机选取,也避免了`shuffle`操作,至少是简洁的做法,是否高效未可知。\n",
"\n",
"测试数据集的获得也很巧妙,直接从原始数据集中`drop`掉训练数据集即可。\n",
"\n",
"另外常见的切分数据集的方法是sklearn的train_test_split函数,比如:\n",
"\n",
"```\n",
"from sklearn.model_selection import train_test_split\n",
"train_dataset, test_dataset = train_test_split(dataset, train_size=0.8, test_size=0.2, shuffle=True)\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "qn-IGhUE7_1H"
},
"outputs": [],
"source": [
"train_dataset = dataset.sample(frac=0.8,random_state=0)\n",
"test_dataset = dataset.drop(train_dataset.index)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "J4ubs136WLNp"
},
"source": [
"## 查看训练数据集\n",
"\n",
"seaborn的pairplot提供了图形化的方式快速查看数据集,更高效直观,这里使用pairplot来表现两两的特征对比图。比如:\n",
"\n",
"* 从右上角的图可以看出,车子越重,燃油的经济性越低(百公里油耗越高,即每加仑行驶的里程越少)\n",
"* 从第一行第二列可以看出,气缸数越多,燃油经济型越低。\n",
"* 同样的,排气量大的车子,燃油经济性也越差。"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "oRKO_x8gWKv-"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.pairplot(train_dataset[[\"MPG\", \"Cylinders\", \"Displacement\", \"Weight\"]], diag_kind=\"kde\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "gavKO_6DWRMP"
},
"source": [
"看一下训练集的统计数据,这个统计数据在后面进行数据的标准化的时候会用到。"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "yi2FzC3T21jR"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" count | \n",
" mean | \n",
" std | \n",
" min | \n",
" 25% | \n",
" 50% | \n",
" 75% | \n",
" max | \n",
"
\n",
" \n",
" \n",
" \n",
" Cylinders | \n",
" 314.0 | \n",
" 5.477707 | \n",
" 1.699788 | \n",
" 3.0 | \n",
" 4.00 | \n",
" 4.0 | \n",
" 8.00 | \n",
" 8.0 | \n",
"
\n",
" \n",
" Displacement | \n",
" 314.0 | \n",
" 195.318471 | \n",
" 104.331589 | \n",
" 68.0 | \n",
" 105.50 | \n",
" 151.0 | \n",
" 265.75 | \n",
" 455.0 | \n",
"
\n",
" \n",
" Horsepower | \n",
" 314.0 | \n",
" 104.869427 | \n",
" 38.096214 | \n",
" 46.0 | \n",
" 76.25 | \n",
" 94.5 | \n",
" 128.00 | \n",
" 225.0 | \n",
"
\n",
" \n",
" Weight | \n",
" 314.0 | \n",
" 2990.251592 | \n",
" 843.898596 | \n",
" 1649.0 | \n",
" 2256.50 | \n",
" 2822.5 | \n",
" 3608.00 | \n",
" 5140.0 | \n",
"
\n",
" \n",
" Acceleration | \n",
" 314.0 | \n",
" 15.559236 | \n",
" 2.789230 | \n",
" 8.0 | \n",
" 13.80 | \n",
" 15.5 | \n",
" 17.20 | \n",
" 24.8 | \n",
"
\n",
" \n",
" Model Year | \n",
" 314.0 | \n",
" 75.898089 | \n",
" 3.675642 | \n",
" 70.0 | \n",
" 73.00 | \n",
" 76.0 | \n",
" 79.00 | \n",
" 82.0 | \n",
"
\n",
" \n",
" USA | \n",
" 314.0 | \n",
" 0.624204 | \n",
" 0.485101 | \n",
" 0.0 | \n",
" 0.00 | \n",
" 1.0 | \n",
" 1.00 | \n",
" 1.0 | \n",
"
\n",
" \n",
" Europe | \n",
" 314.0 | \n",
" 0.178344 | \n",
" 0.383413 | \n",
" 0.0 | \n",
" 0.00 | \n",
" 0.0 | \n",
" 0.00 | \n",
" 1.0 | \n",
"
\n",
" \n",
" Japan | \n",
" 314.0 | \n",
" 0.197452 | \n",
" 0.398712 | \n",
" 0.0 | \n",
" 0.00 | \n",
" 0.0 | \n",
" 0.00 | \n",
" 1.0 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"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": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_stats = train_dataset.describe()\n",
"# 不剔除MPG这一列也没有关系,只不过没有使用而已\n",
"train_stats.pop(\"MPG\")\n",
"train_stats = train_stats.transpose()\n",
"train_stats"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "Db7Auq1yXUvh"
},
"source": [
"## 获得标签数据集\n",
"\n",
"标签(label)是训练模型的“指导”或者“准星”,根据label训练模型的目的就是在预测时,能够尽量逼近label。这里直接将train_dataset/test_dataset中的MPG列pop出来即可。"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "t2sluJdCW7jN"
},
"outputs": [],
"source": [
"train_labels = train_dataset.pop('MPG')\n",
"test_labels = test_dataset.pop('MPG')"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "mRklxK5s388r"
},
"source": [
"## 数据的标准化处理\n",
"\n",
"关于为什么要对数据进行标准化处理,数据标准化处理有哪几种方式,感觉这几篇讲的还不错,值得参考:\n",
"* [ML入门:归一化、标准化、正则化](https://zhuanlan.zhihu.com/p/29957294)\n",
"* [归一化和标准化大全](https://www.jianshu.com/p/c1e112f3f57f)\n",
"\n",
"什么时候使用标准化,什么时候使用归一化?由于归一化是一维伸缩,标准化二维伸缩,所以基本上如果数据没有太大的噪声可以考虑归一化,处理起来比较简单,如果噪声多可以考虑标准化。下面这张图可以形象的看出标准化的过程(出处见上面第一个链接),先减去均值,然后再除以标准差:\n",
"\n",
"![数据的标准化过程](https://raw.githubusercontent.com/subaochen/subaochen.github.io/master/images/tensorflow/data-normalization.jpg)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"除了这里的数据标准化方法之外,sklean中已经提供了成熟的数据标准化、归一化的函数,比如:\n",
"```\n",
"from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
"scaler = StandardScaler()\n",
"# 标准化\n",
"normed_train_dataset = scaler.fit_transform(train_dataset)\n",
"# 归一化\n",
"mm_train_dataset = MinMaxScaler().fit_transform(train_dataset) \n",
"```\n",
"\n",
"这里自己写了一个norm方法,目的是为了能够演示标准化的步骤,也能够直接使用已经获得统计数据。"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "JlC5ooJrgjQF"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Cylinders Displacement Horsepower Weight Acceleration Model Year \\\n",
"146 -0.869348 -1.009459 -0.784052 -1.025303 -0.379759 -0.516397 \n",
"282 -0.869348 -0.530218 -0.442811 -0.118796 0.624102 0.843910 \n",
"69 1.483887 1.482595 1.447140 1.736877 -0.738281 -1.060519 \n",
"378 -0.869348 -0.865687 -1.099044 -1.025303 -0.308055 1.660094 \n",
"331 -0.869348 -0.942365 -0.994047 -1.001603 0.875068 1.115971 \n",
"\n",
" USA Europe Japan \n",
"146 0.774676 -0.465148 -0.495225 \n",
"282 0.774676 -0.465148 -0.495225 \n",
"69 0.774676 -0.465148 -0.495225 \n",
"378 0.774676 -0.465148 -0.495225 \n",
"331 -1.286751 -0.465148 2.012852 \n"
]
}
],
"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)\n",
"print(normed_train_data[:5])"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "BuiClDk45eS4"
},
"source": [
"需要特别两点:\n",
"\n",
"* 当对数据进行了标准操作的时候,所有喂入网络的数据必须根据同一个scaler对象进行标准化操作,即便是one-hot编码的数据也要做标准化处理。这很容易理解:只有数据同步缩放才能消除比较”突出“的数据。\n",
"* 测试数据集以及将来进行预测的其他数据也要进行同样的标准化处理。"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "SmjdzxKzEu1-"
},
"source": [
"# 模型"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "6SWtkIjhrZwa"
},
"source": [
"## 构建模型\n",
"\n",
"这里使用了高阶的keras API构建模型,这是目前主流的模型构建方法。在这个简单的模型中只包含了两个全连接隐藏层和一个全连接输出层,其中我们的输入层的shape为(len(train_dataset.keys()),),第一个全连接层的输出shape为(None, 64),第二个全连接层的输出为(None, 64)。其中,None的大小由BATCH_SIZE决定。"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "c26juK7ZG8j-"
},
"outputs": [],
"source": [
"def build_model():\n",
" model = keras.Sequential([\n",
" layers.Dense(64, activation=tf.nn.relu, input_shape=[len(train_dataset.keys())]),\n",
" layers.Dense(64, activation=tf.nn.relu),\n",
" layers.Dense(1)\n",
" ])\n",
"\n",
" optimizer = tf.keras.optimizers.RMSprop(0.001)\n",
"\n",
" model.compile(loss='mean_squared_error',\n",
" optimizer=optimizer,\n",
" metrics=['mean_absolute_error', 'mean_squared_error'])\n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "cGbPb-PHGbhs"
},
"outputs": [],
"source": [
"model = build_model()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "Sj49Og4YGULr"
},
"source": [
"## 查看模型的结构\n",
"\n",
"`.summary`函数可以打印出模型的简单描述:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "ReAD0n6MsFK-"
},
"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.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "Vt6W50qGsJAL"
},
"source": [
"先尝试运行一下模型。这里取批次(batch)大小为10,先喂入10组数据看看结果如何:`model.predict`给出了预测的结果,shape为(10,1),符合模型的定义。"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "-d-gBaVtGTSC"
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.33291826],\n",
" [ 0.20068091],\n",
" [-0.2432324 ],\n",
" [ 0.2772663 ],\n",
" [ 0.25557014],\n",
" [ 0.05211536],\n",
" [ 0.28773528],\n",
" [ 0.38458073],\n",
" [ 0.04117467],\n",
" [ 0.15005343]], 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": {
"colab_type": "text",
"id": "0-qWCsh6DlyH"
},
"source": [
"## 训练模型\n",
"\n",
"这里定义的训练轮次(epochs)为1000。训练和验证的准确率记录在`history`中了,方便通过图形化显示准确率的变动情况。\n",
"\n",
"注意fit函数的参数validation_split,表示将训练数据自动拿出指定比例的数据作为验证数据集,验证数据集是不参与训练的。因此,使用了validation_split还是很方便的一种手段,不需要手工准备验证数据集。"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "sD7qHCmNIOY0"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"....................................................................................................\n",
"....................................................................................................\n",
"....................................................................................................\n",
"....................................................................................................\n",
"....................................................................................................\n",
"....................................................................................................\n",
"....................................................................................................\n",
"....................................................................................................\n",
"....................................................................................................\n",
"...................................................................................................."
]
}
],
"source": [
"# Display training progress by printing a single dot for each completed epoch\n",
"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": {
"colab_type": "text",
"id": "tQm3pc0FYPQB"
},
"source": [
"图形化展示`history`对象中存储的训练过程。"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "4Xj91b-dymEy"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
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" \n",
" \n",
" | \n",
" loss | \n",
" mean_absolute_error | \n",
" mean_squared_error | \n",
" val_loss | \n",
" val_mean_absolute_error | \n",
" val_mean_squared_error | \n",
" epoch | \n",
"
\n",
" \n",
" \n",
" \n",
" 995 | \n",
" 2.856708 | \n",
" 1.074385 | \n",
" 2.856708 | \n",
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" 2.212823 | \n",
" 8.499714 | \n",
" 995 | \n",
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" \n",
" 996 | \n",
" 2.816071 | \n",
" 1.094623 | \n",
" 2.816071 | \n",
" 8.883725 | \n",
" 2.365754 | \n",
" 8.883725 | \n",
" 996 | \n",
"
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" \n",
" 997 | \n",
" 2.852280 | \n",
" 1.078358 | \n",
" 2.852280 | \n",
" 8.369126 | \n",
" 2.132020 | \n",
" 8.369126 | \n",
" 997 | \n",
"
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" 2.754595 | \n",
" 1.063068 | \n",
" 2.754595 | \n",
" 8.362683 | \n",
" 2.193305 | \n",
" 8.362682 | \n",
" 998 | \n",
"
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" \n",
" 999 | \n",
" 2.606283 | \n",
" 1.027212 | \n",
" 2.606283 | \n",
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" 8.404507 | \n",
" 999 | \n",
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\n",
" \n",
"
\n",
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"
],
"text/plain": [
" loss mean_absolute_error mean_squared_error val_loss \\\n",
"995 2.856708 1.074385 2.856708 8.499714 \n",
"996 2.816071 1.094623 2.816071 8.883725 \n",
"997 2.852280 1.078358 2.852280 8.369126 \n",
"998 2.754595 1.063068 2.754595 8.362683 \n",
"999 2.606283 1.027212 2.606283 8.404507 \n",
"\n",
" val_mean_absolute_error val_mean_squared_error epoch \n",
"995 2.212823 8.499714 995 \n",
"996 2.365754 8.883725 996 \n",
"997 2.132020 8.369126 997 \n",
"998 2.193305 8.362682 998 \n",
"999 2.265347 8.404507 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": 24,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "B6XriGbVPh2t"
},
"outputs": [
{
"data": {
"image/png": 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\n",
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