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450 lines
15 KiB
Markdown
450 lines
15 KiB
Markdown
# Basic regression: Predict fuel efficiency
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> 原文：[https://tensorflow.google.cn/tutorials/keras/regression](https://tensorflow.google.cn/tutorials/keras/regression)
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<devsite-mathjax config="TeX-AMS-MML_SVG"></devsite-mathjax>
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**Note:** 我们的 TensorFlow 社区翻译了这些文档。因为社区翻译是尽力而为， 所以无法保证它们是最准确的，并且反映了最新的 [官方英文文档](https://tensorflow.google.cn/?hl=en)。如果您有改进此翻译的建议， 请提交 pull request 到 [tensorflow/docs](https://github.com/tensorflow/docs) GitHub 仓库。要志愿地撰写或者审核译文，请加入 [docs-zh-cn@tensorflow.org Google Group](https://groups.google.com/a/tensorflow.org/forum/#!forum/docs-zh-cn)。
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在 *回归 (regression)* 问题中，我们的目的是预测出如价格或概率这样连续值的输出。相对于*分类(classification)* 问题，*分类(classification)* 的目的是从一系列的分类出选择出一个分类 （如，给出一张包含苹果或橘子的图片，识别出图片中是哪种水果）。
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本 notebook 使用经典的 [Auto MPG](https://archive.ics.uci.edu/ml/datasets/auto+mpg) 数据集，构建了一个用来预测 70 年代末到 80 年代初汽车燃油效率的模型。为了做到这一点，我们将为该模型提供许多那个时期的汽车描述。这个描述包含：气缸数，排量，马力以及重量。
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本示例使用 [`tf.keras`](https://tensorflow.google.cn/api_docs/python/tf/keras) API，相关细节请参阅 [本指南](https://tensorflow.google.cn/guide/keras)。
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```py
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# 使用 seaborn 绘制矩阵图 (pairplot)
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pip install -q seaborn
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```
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```py
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WARNING: You are using pip version 20.2.2; however, version 20.2.3 is available.
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You should consider upgrading via the '/tmpfs/src/tf_docs_env/bin/python -m pip install --upgrade pip' command.
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```
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```py
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import pathlib
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import matplotlib.pyplot as plt
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import pandas as pd
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import seaborn as sns
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import tensorflow as tf
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from tensorflow import keras
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from tensorflow.keras import layers
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print(tf.__version__)
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```
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```py
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2.3.0
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```
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## Auto MPG 数据集
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该数据集可以从 [UCI 机器学习库](https://archive.ics.uci.edu/ml/) 中获取.
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### 获取数据
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首先下载数据集。
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```py
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dataset_path = keras.utils.get_file("auto-mpg.data", "http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data")
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dataset_path
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```
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```py
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Downloading data from http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data
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32768/30286 [================================] - 0s 1us/step
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'/home/kbuilder/.keras/datasets/auto-mpg.data'
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```
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使用 pandas 导入数据集。
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```py
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column_names = ['MPG','Cylinders','Displacement','Horsepower','Weight',
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'Acceleration', 'Model Year', 'Origin']
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raw_dataset = pd.read_csv(dataset_path, names=column_names,
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na_values = "?", comment='\t',
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sep=" ", skipinitialspace=True)
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dataset = raw_dataset.copy()
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dataset.tail()
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```
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<devsite-iframe><iframe src="/tutorials/keras/regression_c2c7b23a4a5727b4a9181f216946c4e868a70ec07f79be23388ba93b29ea1a47.frame" class="framebox inherit-locale " allowfullscreen="" is-upgraded=""></iframe></devsite-iframe>
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### 数据清洗
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数据集中包括一些未知值。
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```py
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dataset.isna().sum()
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```
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```py
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MPG 0
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Cylinders 0
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Displacement 0
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Horsepower 6
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Weight 0
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Acceleration 0
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Model Year 0
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Origin 0
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dtype: int64
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```
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为了保证这个初始示例的简单性，删除这些行。
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```py
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dataset = dataset.dropna()
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```
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`"Origin"` 列实际上代表分类，而不仅仅是一个数字。所以把它转换为独热码 （one-hot）:
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```py
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origin = dataset.pop('Origin')
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```
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```py
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dataset['USA'] = (origin == 1)*1.0
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dataset['Europe'] = (origin == 2)*1.0
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dataset['Japan'] = (origin == 3)*1.0
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dataset.tail()
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```
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<devsite-iframe><iframe src="/tutorials/keras/regression_e0297b2db24ea564c306921a48f1bc40c18ddc626a86746db61cd82dbc3c3c20.frame" class="framebox inherit-locale " allowfullscreen="" is-upgraded=""></iframe></devsite-iframe>
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### 拆分训练数据集和测试数据集
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现在需要将数据集拆分为一个训练数据集和一个测试数据集。
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我们最后将使用测试数据集对模型进行评估。
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```py
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train_dataset = dataset.sample(frac=0.8,random_state=0)
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test_dataset = dataset.drop(train_dataset.index)
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```
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### 数据检查
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快速查看训练集中几对列的联合分布。
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```py
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sns.pairplot(train_dataset[["MPG", "Cylinders", "Displacement", "Weight"]], diag_kind="kde")
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```
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```py
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<seaborn.axisgrid.PairGrid at 0x7f708ca93e80>
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```
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也可以查看总体的数据统计:
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```py
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train_stats = train_dataset.describe()
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train_stats.pop("MPG")
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train_stats = train_stats.transpose()
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train_stats
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```
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<devsite-iframe><iframe src="/tutorials/keras/regression_ba0849c742fe92394897de4a92ca81859740311a71cec773244f86a46b761fea.frame" class="framebox inherit-locale " allowfullscreen="" is-upgraded=""></iframe></devsite-iframe>
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### 从标签中分离特征
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将特征值从目标值或者"标签"中分离。 这个标签是你使用训练模型进行预测的值。
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```py
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train_labels = train_dataset.pop('MPG')
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test_labels = test_dataset.pop('MPG')
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```
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### 数据规范化
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再次审视下上面的 `train_stats` 部分，并注意每个特征的范围有什么不同。
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使用不同的尺度和范围对特征归一化是好的实践。尽管模型*可能* 在没有特征归一化的情况下收敛，它会使得模型训练更加复杂，并会造成生成的模型依赖输入所使用的单位选择。
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注意：尽管我们仅仅从训练集中有意生成这些统计数据，但是这些统计信息也会用于归一化的测试数据集。我们需要这样做，将测试数据集放入到与已经训练过的模型相同的分布中。
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```py
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def norm(x):
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return (x - train_stats['mean']) / train_stats['std']
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normed_train_data = norm(train_dataset)
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normed_test_data = norm(test_dataset)
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```
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我们将会使用这个已经归一化的数据来训练模型。
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警告: 用于归一化输入的数据统计（均值和标准差）需要反馈给模型从而应用于任何其他数据，以及我们之前所获得独热码。这些数据包含测试数据集以及生产环境中所使用的实时数据。
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## 模型
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### 构建模型
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让我们来构建我们自己的模型。这里，我们将会使用一个“顺序”模型，其中包含两个紧密相连的隐藏层，以及返回单个、连续值得输出层。模型的构建步骤包含于一个名叫 'build_model' 的函数中，稍后我们将会创建第二个模型。 两个密集连接的隐藏层。
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```py
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def build_model():
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model = keras.Sequential([
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layers.Dense(64, activation='relu', input_shape=[len(train_dataset.keys())]),
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layers.Dense(64, activation='relu'),
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layers.Dense(1)
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])
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optimizer = tf.keras.optimizers.RMSprop(0.001)
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model.compile(loss='mse',
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optimizer=optimizer,
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metrics=['mae', 'mse'])
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return model
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```
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```py
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model = build_model()
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```
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### 检查模型
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使用 `.summary` 方法来打印该模型的简单描述。
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```py
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model.summary()
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```
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```py
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Model: "sequential"
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_________________________________________________________________
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Layer (type) Output Shape Param #
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=================================================================
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dense (Dense) (None, 64) 640
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_________________________________________________________________
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dense_1 (Dense) (None, 64) 4160
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_________________________________________________________________
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dense_2 (Dense) (None, 1) 65
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=================================================================
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Total params: 4,865
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Trainable params: 4,865
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Non-trainable params: 0
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_________________________________________________________________
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```
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现在试用下这个模型。从训练数据中批量获取‘10’条例子并对这些例子调用 `model.predict` 。
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```py
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example_batch = normed_train_data[:10]
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example_result = model.predict(example_batch)
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example_result
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```
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```py
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array([[0.15074062],
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[0.0973136 ],
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[0.17310914],
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[0.08873479],
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[0.52456 ],
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[0.05311462],
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[0.49406645],
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[0.04333409],
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[0.12005241],
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[0.6703117 ]], dtype=float32)
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```
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它似乎在工作，并产生了预期的形状和类型的结果
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### 训练模型
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对模型进行 1000 个周期的训练，并在 `history` 对象中记录训练和验证的准确性。
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```py
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# 通过为每个完成的时期打印一个点来显示训练进度
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class PrintDot(keras.callbacks.Callback):
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def on_epoch_end(self, epoch, logs):
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if epoch % 100 == 0: print('')
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print('.', end='')
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EPOCHS = 1000
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history = model.fit(
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normed_train_data, train_labels,
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epochs=EPOCHS, validation_split = 0.2, verbose=0,
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callbacks=[PrintDot()])
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```
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```py
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....................................................................................................
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....................................................................................................
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....................................................................................................
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....................................................................................................
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```
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使用 `history` 对象中存储的统计信息可视化模型的训练进度。
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```py
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hist = pd.DataFrame(history.history)
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hist['epoch'] = history.epoch
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hist.tail()
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```
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<devsite-iframe><iframe src="/tutorials/keras/regression_52ac85b91795872c54d7abf74ad7251d16b43867b9d311b29a56d2648222a3f7.frame" class="framebox inherit-locale " allowfullscreen="" is-upgraded=""></iframe></devsite-iframe>
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```py
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def plot_history(history):
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hist = pd.DataFrame(history.history)
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hist['epoch'] = history.epoch
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plt.figure()
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plt.xlabel('Epoch')
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plt.ylabel('Mean Abs Error [MPG]')
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plt.plot(hist['epoch'], hist['mae'],
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label='Train Error')
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plt.plot(hist['epoch'], hist['val_mae'],
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label = 'Val Error')
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plt.ylim([0,5])
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plt.legend()
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plt.figure()
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plt.xlabel('Epoch')
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plt.ylabel('Mean Square Error [$MPG^2$]')
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plt.plot(hist['epoch'], hist['mse'],
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label='Train Error')
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plt.plot(hist['epoch'], hist['val_mse'],
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label = 'Val Error')
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plt.ylim([0,20])
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plt.legend()
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plt.show()
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plot_history(history)
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```
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该图表显示在约 100 个 epochs 之后误差非但没有改进，反而出现恶化。 让我们更新 `model.fit` 调用，当验证值没有提高上是自动停止训练。 我们将使用一个 *EarlyStopping callback* 来测试每个 epoch 的训练条件。如果经过一定数量的 epochs 后没有改进，则自动停止训练。
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你可以从[这里](https://tensorflow.google.cn/versions/master/api_docs/python/tf/keras/callbacks/EarlyStopping)学习到更多的回调。
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```py
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model = build_model()
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# patience 值用来检查改进 epochs 的数量
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early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)
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history = model.fit(normed_train_data, train_labels, epochs=EPOCHS,
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validation_split = 0.2, verbose=0, callbacks=[early_stop, PrintDot()])
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plot_history(history)
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```
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```py
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....................................................................................................
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...........................
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```
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如图所示，验证集中的平均的误差通常在 +/- 2 MPG 左右。 这个结果好么？ 我们将决定权留给你。
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让我们看看通过使用 **测试集** 来泛化模型的效果如何，我们在训练模型时没有使用测试集。这告诉我们，当我们在现实世界中使用这个模型时，我们可以期望它预测得有多好。
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```py
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loss, mae, mse = model.evaluate(normed_test_data, test_labels, verbose=2)
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print("Testing set Mean Abs Error: {:5.2f} MPG".format(mae))
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```
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```py
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3/3 - 0s - loss: 5.9941 - mae: 1.8809 - mse: 5.9941
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Testing set Mean Abs Error: 1.88 MPG
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```
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### 做预测
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最后，使用测试集中的数据预测 MPG 值:
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```py
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test_predictions = model.predict(normed_test_data).flatten()
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plt.scatter(test_labels, test_predictions)
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plt.xlabel('True Values [MPG]')
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plt.ylabel('Predictions [MPG]')
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plt.axis('equal')
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plt.axis('square')
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plt.xlim([0,plt.xlim()[1]])
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plt.ylim([0,plt.ylim()[1]])
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_ = plt.plot([-100, 100], [-100, 100])
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```
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这看起来我们的模型预测得相当好。我们来看下误差分布。
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```py
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error = test_predictions - test_labels
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plt.hist(error, bins = 25)
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plt.xlabel("Prediction Error [MPG]")
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_ = plt.ylabel("Count")
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```
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它不是完全的高斯分布，但我们可以推断出，这是因为样本的数量很小所导致的。
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## 结论
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本笔记本 (notebook) 介绍了一些处理回归问题的技术。
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* 均方误差（MSE）是用于回归问题的常见损失函数（分类问题中使用不同的损失函数）。
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* 类似的，用于回归的评估指标与分类不同。 常见的回归指标是平均绝对误差（MAE）。
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* 当数字输入数据特征的值存在不同范围时，每个特征应独立缩放到相同范围。
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* 如果训练数据不多，一种方法是选择隐藏层较少的小网络，以避免过度拟合。
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* 早期停止是一种防止过度拟合的有效技术。
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```py
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# MIT License
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#
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# Copyright (c) 2017 François Chollet
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#
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# Permission is hereby granted, free of charge, to any person obtaining a
|
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# copy of this software and associated documentation files (the "Software"),
|
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# to deal in the Software without restriction, including without limitation
|
||
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
|
||
# and/or sell copies of the Software, and to permit persons to whom the
|
||
# Software is furnished to do so, subject to the following conditions:
|
||
#
|
||
# The above copyright notice and this permission notice shall be included in
|
||
# all copies or substantial portions of the Software.
|
||
#
|
||
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
|
||
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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||
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
|
||
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
|
||
# DEALINGS IN THE SOFTWARE.
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``` |