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367 lines
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367 lines
18 KiB
Markdown
# 用 tf.data 加载 CSV 数据
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> 原文：[https://tensorflow.google.cn/tutorials/load_data/csv](https://tensorflow.google.cn/tutorials/load_data/csv)
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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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这篇教程通过一个示例展示了怎样将 CSV 格式的数据加载进 [`tf.data.Dataset`](https://tensorflow.google.cn/api_docs/python/tf/data/Dataset)。
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这篇教程使用的是泰坦尼克号乘客的数据。模型会根据乘客的年龄、性别、票务舱和是否独自旅行等特征来预测乘客生还的可能性。
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## 设置
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```py
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import functools
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import numpy as np
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import tensorflow as tf
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import tensorflow_datasets as tfds
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```
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```py
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TRAIN_DATA_URL = "https://storage.googleapis.com/tf-datasets/titanic/train.csv"
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TEST_DATA_URL = "https://storage.googleapis.com/tf-datasets/titanic/eval.csv"
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train_file_path = tf.keras.utils.get_file("train.csv", TRAIN_DATA_URL)
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test_file_path = tf.keras.utils.get_file("eval.csv", TEST_DATA_URL)
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```
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```py
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Downloading data from https://storage.googleapis.com/tf-datasets/titanic/train.csv
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32768/30874 [===============================] - 0s 0us/step
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Downloading data from https://storage.googleapis.com/tf-datasets/titanic/eval.csv
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16384/13049 [=====================================] - 0s 0us/step
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```
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```py
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# 让 numpy 数据更易读。
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np.set_printoptions(precision=3, suppress=True)
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```
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## 加载数据
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开始的时候，我们通过打印 CSV 文件的前几行来了解文件的格式。
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```py
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head {train_file_path}
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```
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```py
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survived,sex,age,n_siblings_spouses,parch,fare,class,deck,embark_town,alone
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0,male,22.0,1,0,7.25,Third,unknown,Southampton,n
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1,female,38.0,1,0,71.2833,First,C,Cherbourg,n
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1,female,26.0,0,0,7.925,Third,unknown,Southampton,y
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1,female,35.0,1,0,53.1,First,C,Southampton,n
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0,male,28.0,0,0,8.4583,Third,unknown,Queenstown,y
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0,male,2.0,3,1,21.075,Third,unknown,Southampton,n
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1,female,27.0,0,2,11.1333,Third,unknown,Southampton,n
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1,female,14.0,1,0,30.0708,Second,unknown,Cherbourg,n
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1,female,4.0,1,1,16.7,Third,G,Southampton,n
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```
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正如你看到的那样，CSV 文件的每列都会有一个列名。dataset 的构造函数会自动识别这些列名。如果你使用的文件的第一行不包含列名，那么需要将列名通过字符串列表传给 `make_csv_dataset` 函数的 `column_names` 参数。
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```py
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CSV_COLUMNS = ['survived', 'sex', 'age', 'n_siblings_spouses', 'parch', 'fare', 'class', 'deck', 'embark_town', 'alone']
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dataset = tf.data.experimental.make_csv_dataset(
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...,
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column_names=CSV_COLUMNS,
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...)
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```
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这个示例使用了所有的列。如果你需要忽略数据集中的某些列，创建一个包含你需要使用的列的列表，然后传给构造器的（可选）参数 `select_columns`。
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```py
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dataset = tf.data.experimental.make_csv_dataset(
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...,
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select_columns = columns_to_use,
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...)
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```
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对于包含模型需要预测的值的列是你需要显式指定的。
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```py
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LABEL_COLUMN = 'survived'
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LABELS = [0, 1]
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```
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现在从文件中读取 CSV 数据并且创建 dataset。
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(完整的文档，参考 [`tf.data.experimental.make_csv_dataset`](https://tensorflow.google.cn/api_docs/python/tf/data/experimental/make_csv_dataset))
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```py
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def get_dataset(file_path):
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dataset = tf.data.experimental.make_csv_dataset(
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file_path,
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batch_size=12, # 为了示例更容易展示，手动设置较小的值
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label_name=LABEL_COLUMN,
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na_value="?",
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num_epochs=1,
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ignore_errors=True)
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return dataset
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raw_train_data = get_dataset(train_file_path)
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raw_test_data = get_dataset(test_file_path)
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```
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dataset 中的每个条目都是一个批次，用一个元组（*多个样本*，*多个标签*）表示。样本中的数据组织形式是以列为主的张量（而不是以行为主的张量），每条数据中包含的元素个数就是批次大小（这个示例中是 12）。
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阅读下面的示例有助于你的理解。
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```py
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examples, labels = next(iter(raw_train_data)) # 第一个批次
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print("EXAMPLES: \n", examples, "\n")
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print("LABELS: \n", labels)
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```
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```py
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EXAMPLES:
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OrderedDict([('sex', <tf.Tensor: shape=(12,), dtype=string, numpy=
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array([b'male', b'male', b'male', b'male', b'male', b'female', b'male',
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b'female', b'male', b'male', b'male', b'female'], dtype=object)>), ('age', <tf.Tensor: shape=(12,), dtype=float32, numpy=
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array([35., 30., 28., 40., 17., 19., 21., 7., 58., 26., 19., 29.],
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dtype=float32)>), ('n_siblings_spouses', <tf.Tensor: shape=(12,), dtype=int32, numpy=array([0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1], dtype=int32)>), ('parch', <tf.Tensor: shape=(12,), dtype=int32, numpy=array([0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0], dtype=int32)>), ('fare', <tf.Tensor: shape=(12,), dtype=float32, numpy=
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array([ 8.05 , 13\. , 7.225, 7.896, 8.663, 26.283, 7.925, 26.25 ,
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29.7 , 8.663, 0\. , 26\. ], dtype=float32)>), ('class', <tf.Tensor: shape=(12,), dtype=string, numpy=
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array([b'Third', b'Second', b'Third', b'Third', b'Third', b'First',
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b'Third', b'Second', b'First', b'Third', b'Third', b'Second'],
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dtype=object)>), ('deck', <tf.Tensor: shape=(12,), dtype=string, numpy=
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array([b'unknown', b'unknown', b'unknown', b'unknown', b'unknown', b'D',
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b'unknown', b'unknown', b'B', b'unknown', b'unknown', b'unknown'],
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dtype=object)>), ('embark_town', <tf.Tensor: shape=(12,), dtype=string, numpy=
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array([b'Southampton', b'Southampton', b'Cherbourg', b'Southampton',
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b'Southampton', b'Southampton', b'Southampton', b'Southampton',
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b'Cherbourg', b'Southampton', b'Southampton', b'Southampton'],
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dtype=object)>), ('alone', <tf.Tensor: shape=(12,), dtype=string, numpy=
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array([b'y', b'y', b'y', b'y', b'y', b'n', b'y', b'n', b'y', b'n', b'y',
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b'n'], dtype=object)>)])
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LABELS:
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tf.Tensor([0 0 0 0 0 1 0 1 0 0 0 1], shape=(12,), dtype=int32)
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```
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## 数据预处理
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### 分类数据
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CSV 数据中的有些列是分类的列。也就是说，这些列只能在有限的集合中取值。
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使用 [`tf.feature_column`](https://tensorflow.google.cn/api_docs/python/tf/feature_column) API 创建一个 [`tf.feature_column.indicator_column`](https://tensorflow.google.cn/api_docs/python/tf/feature_column/indicator_column) 集合，每个 [`tf.feature_column.indicator_column`](https://tensorflow.google.cn/api_docs/python/tf/feature_column/indicator_column) 对应一个分类的列。
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```py
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CATEGORIES = {
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'sex': ['male', 'female'],
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'class' : ['First', 'Second', 'Third'],
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'deck' : ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J'],
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'embark_town' : ['Cherbourg', 'Southhampton', 'Queenstown'],
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'alone' : ['y', 'n']
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}
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```
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```py
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categorical_columns = []
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for feature, vocab in CATEGORIES.items():
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cat_col = tf.feature_column.categorical_column_with_vocabulary_list(
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key=feature, vocabulary_list=vocab)
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categorical_columns.append(tf.feature_column.indicator_column(cat_col))
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```
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```py
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# 你刚才创建的内容
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categorical_columns
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```
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```py
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[IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='sex', vocabulary_list=('male', 'female'), dtype=tf.string, default_value=-1, num_oov_buckets=0)),
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IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='class', vocabulary_list=('First', 'Second', 'Third'), dtype=tf.string, default_value=-1, num_oov_buckets=0)),
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IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='deck', vocabulary_list=('A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J'), dtype=tf.string, default_value=-1, num_oov_buckets=0)),
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IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='embark_town', vocabulary_list=('Cherbourg', 'Southhampton', 'Queenstown'), dtype=tf.string, default_value=-1, num_oov_buckets=0)),
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IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='alone', vocabulary_list=('y', 'n'), dtype=tf.string, default_value=-1, num_oov_buckets=0))]
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```
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这将是后续构建模型时处理输入数据的一部分。
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### 连续数据
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连续数据需要标准化。
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写一个函数标准化这些值，然后将这些值改造成 2 维的张量。
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```py
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def process_continuous_data(mean, data):
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# 标准化数据
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data = tf.cast(data, tf.float32) * 1/(2*mean)
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return tf.reshape(data, [-1, 1])
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```
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现在创建一个数值列的集合。`tf.feature_columns.numeric_column` API 会使用 `normalizer_fn` 参数。在传参的时候使用 [`functools.partial`](https://docs.python.org/3/library/functools.html#functools.partial)，`functools.partial` 由使用每个列的均值进行标准化的函数构成。
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```py
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MEANS = {
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'age' : 29.631308,
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'n_siblings_spouses' : 0.545455,
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'parch' : 0.379585,
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'fare' : 34.385399
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}
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numerical_columns = []
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for feature in MEANS.keys():
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num_col = tf.feature_column.numeric_column(feature, normalizer_fn=functools.partial(process_continuous_data, MEANS[feature]))
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numerical_columns.append(num_col)
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```
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```py
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# 你刚才创建的内容。
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numerical_columns
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```
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```py
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[NumericColumn(key='age', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=functools.partial(<function process_continuous_data at 0x7f3f083021e0>, 29.631308)),
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NumericColumn(key='n_siblings_spouses', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=functools.partial(<function process_continuous_data at 0x7f3f083021e0>, 0.545455)),
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NumericColumn(key='parch', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=functools.partial(<function process_continuous_data at 0x7f3f083021e0>, 0.379585)),
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NumericColumn(key='fare', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=functools.partial(<function process_continuous_data at 0x7f3f083021e0>, 34.385399))]
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```
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这里使用标准化的方法需要提前知道每列的均值。如果需要计算连续的数据流的标准化的值可以使用 [TensorFlow Transform](https://tensorflow.google.cn/tfx/transform/get_started)。
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### 创建预处理层
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将这两个特征列的集合相加，并且传给 [`tf.keras.layers.DenseFeatures`](https://tensorflow.google.cn/api_docs/python/tf/keras/layers/DenseFeatures) 从而创建一个进行预处理的输入层。
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```py
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preprocessing_layer = tf.keras.layers.DenseFeatures(categorical_columns+numerical_columns)
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```
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## 构建模型
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从 `preprocessing_layer` 开始构建 [`tf.keras.Sequential`](https://tensorflow.google.cn/api_docs/python/tf/keras/Sequential)。
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```py
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model = tf.keras.Sequential([
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preprocessing_layer,
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tf.keras.layers.Dense(128, activation='relu'),
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tf.keras.layers.Dense(128, activation='relu'),
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tf.keras.layers.Dense(1, activation='sigmoid'),
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])
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model.compile(
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loss='binary_crossentropy',
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optimizer='adam',
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metrics=['accuracy'])
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```
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## 训练、评估和预测
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现在可以实例化和训练模型。
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```py
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train_data = raw_train_data.shuffle(500)
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test_data = raw_test_data
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```
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```py
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model.fit(train_data, epochs=20)
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```
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```py
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Epoch 1/20
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WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'collections.OrderedDict'> input: OrderedDict([('sex', <tf.Tensor 'ExpandDims_8:0' shape=(None, 1) dtype=string>), ('age', <tf.Tensor 'ExpandDims:0' shape=(None, 1) dtype=float32>), ('n_siblings_spouses', <tf.Tensor 'ExpandDims_6:0' shape=(None, 1) dtype=int32>), ('parch', <tf.Tensor 'ExpandDims_7:0' shape=(None, 1) dtype=int32>), ('fare', <tf.Tensor 'ExpandDims_5:0' shape=(None, 1) dtype=float32>), ('class', <tf.Tensor 'ExpandDims_2:0' shape=(None, 1) dtype=string>), ('deck', <tf.Tensor 'ExpandDims_3:0' shape=(None, 1) dtype=string>), ('embark_town', <tf.Tensor 'ExpandDims_4:0' shape=(None, 1) dtype=string>), ('alone', <tf.Tensor 'ExpandDims_1:0' shape=(None, 1) dtype=string>)])
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Consider rewriting this model with the Functional API.
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WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'collections.OrderedDict'> input: OrderedDict([('sex', <tf.Tensor 'ExpandDims_8:0' shape=(None, 1) dtype=string>), ('age', <tf.Tensor 'ExpandDims:0' shape=(None, 1) dtype=float32>), ('n_siblings_spouses', <tf.Tensor 'ExpandDims_6:0' shape=(None, 1) dtype=int32>), ('parch', <tf.Tensor 'ExpandDims_7:0' shape=(None, 1) dtype=int32>), ('fare', <tf.Tensor 'ExpandDims_5:0' shape=(None, 1) dtype=float32>), ('class', <tf.Tensor 'ExpandDims_2:0' shape=(None, 1) dtype=string>), ('deck', <tf.Tensor 'ExpandDims_3:0' shape=(None, 1) dtype=string>), ('embark_town', <tf.Tensor 'ExpandDims_4:0' shape=(None, 1) dtype=string>), ('alone', <tf.Tensor 'ExpandDims_1:0' shape=(None, 1) dtype=string>)])
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Consider rewriting this model with the Functional API.
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53/53 [==============================] - 0s 4ms/step - loss: 0.5501 - accuracy: 0.7225
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Epoch 2/20
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53/53 [==============================] - 0s 3ms/step - loss: 0.4399 - accuracy: 0.8102
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Epoch 3/20
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53/53 [==============================] - 0s 3ms/step - loss: 0.4158 - accuracy: 0.8150
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Epoch 4/20
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53/53 [==============================] - 0s 3ms/step - loss: 0.4137 - accuracy: 0.8118
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Epoch 5/20
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53/53 [==============================] - 0s 3ms/step - loss: 0.4011 - accuracy: 0.8278
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Epoch 6/20
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53/53 [==============================] - 0s 3ms/step - loss: 0.3953 - accuracy: 0.8198
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Epoch 7/20
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53/53 [==============================] - 0s 3ms/step - loss: 0.3834 - accuracy: 0.8325
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Epoch 8/20
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53/53 [==============================] - 0s 3ms/step - loss: 0.3831 - accuracy: 0.8309
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Epoch 9/20
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53/53 [==============================] - 0s 3ms/step - loss: 0.3768 - accuracy: 0.8453
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Epoch 10/20
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53/53 [==============================] - 0s 3ms/step - loss: 0.3710 - accuracy: 0.8437
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Epoch 11/20
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53/53 [==============================] - 0s 3ms/step - loss: 0.3704 - accuracy: 0.8389
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Epoch 12/20
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53/53 [==============================] - 0s 3ms/step - loss: 0.3670 - accuracy: 0.8325
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Epoch 13/20
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53/53 [==============================] - 0s 3ms/step - loss: 0.3603 - accuracy: 0.8517
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Epoch 14/20
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53/53 [==============================] - 0s 3ms/step - loss: 0.3548 - accuracy: 0.8501
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Epoch 15/20
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53/53 [==============================] - 0s 3ms/step - loss: 0.3554 - accuracy: 0.8469
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Epoch 16/20
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53/53 [==============================] - 0s 3ms/step - loss: 0.3519 - accuracy: 0.8453
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Epoch 17/20
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53/53 [==============================] - 0s 3ms/step - loss: 0.3472 - accuracy: 0.8596
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Epoch 18/20
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53/53 [==============================] - 0s 3ms/step - loss: 0.3513 - accuracy: 0.8581
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Epoch 19/20
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53/53 [==============================] - 0s 3ms/step - loss: 0.3448 - accuracy: 0.8469
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Epoch 20/20
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53/53 [==============================] - 0s 3ms/step - loss: 0.3390 - accuracy: 0.8581
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<tensorflow.python.keras.callbacks.History at 0x7f3f082606a0>
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```
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当模型训练完成的时候，你可以在测试集 `test_data` 上检查准确性。
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```py
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test_loss, test_accuracy = model.evaluate(test_data)
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print('\n\nTest Loss {}, Test Accuracy {}'.format(test_loss, test_accuracy))
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```
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```py
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WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'collections.OrderedDict'> input: OrderedDict([('sex', <tf.Tensor 'ExpandDims_8:0' shape=(None, 1) dtype=string>), ('age', <tf.Tensor 'ExpandDims:0' shape=(None, 1) dtype=float32>), ('n_siblings_spouses', <tf.Tensor 'ExpandDims_6:0' shape=(None, 1) dtype=int32>), ('parch', <tf.Tensor 'ExpandDims_7:0' shape=(None, 1) dtype=int32>), ('fare', <tf.Tensor 'ExpandDims_5:0' shape=(None, 1) dtype=float32>), ('class', <tf.Tensor 'ExpandDims_2:0' shape=(None, 1) dtype=string>), ('deck', <tf.Tensor 'ExpandDims_3:0' shape=(None, 1) dtype=string>), ('embark_town', <tf.Tensor 'ExpandDims_4:0' shape=(None, 1) dtype=string>), ('alone', <tf.Tensor 'ExpandDims_1:0' shape=(None, 1) dtype=string>)])
|
||
Consider rewriting this model with the Functional API.
|
||
22/22 [==============================] - 0s 3ms/step - loss: 0.4596 - accuracy: 0.7992
|
||
|
||
Test Loss 0.45956382155418396, Test Accuracy 0.7992424368858337
|
||
|
||
```
|
||
|
||
使用 [`tf.keras.Model.predict`](https://tensorflow.google.cn/api_docs/python/tf/keras/Model#predict) 推断一个批次或多个批次的标签。
|
||
|
||
```py
|
||
predictions = model.predict(test_data)
|
||
|
||
# 显示部分结果
|
||
for prediction, survived in zip(predictions[:10], list(test_data)[0][1][:10]):
|
||
print("Predicted survival: {:.2%}".format(prediction[0]),
|
||
" | Actual outcome: ",
|
||
("SURVIVED" if bool(survived) else "DIED"))
|
||
```
|
||
|
||
```py
|
||
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'collections.OrderedDict'> input: OrderedDict([('sex', <tf.Tensor 'ExpandDims_8:0' shape=(None, 1) dtype=string>), ('age', <tf.Tensor 'ExpandDims:0' shape=(None, 1) dtype=float32>), ('n_siblings_spouses', <tf.Tensor 'ExpandDims_6:0' shape=(None, 1) dtype=int32>), ('parch', <tf.Tensor 'ExpandDims_7:0' shape=(None, 1) dtype=int32>), ('fare', <tf.Tensor 'ExpandDims_5:0' shape=(None, 1) dtype=float32>), ('class', <tf.Tensor 'ExpandDims_2:0' shape=(None, 1) dtype=string>), ('deck', <tf.Tensor 'ExpandDims_3:0' shape=(None, 1) dtype=string>), ('embark_town', <tf.Tensor 'ExpandDims_4:0' shape=(None, 1) dtype=string>), ('alone', <tf.Tensor 'ExpandDims_1:0' shape=(None, 1) dtype=string>)])
|
||
Consider rewriting this model with the Functional API.
|
||
Predicted survival: 99.81% | Actual outcome: DIED
|
||
Predicted survival: 14.77% | Actual outcome: SURVIVED
|
||
Predicted survival: 11.87% | Actual outcome: DIED
|
||
Predicted survival: 6.05% | Actual outcome: DIED
|
||
Predicted survival: 10.83% | Actual outcome: DIED
|
||
Predicted survival: 29.45% | Actual outcome: SURVIVED
|
||
Predicted survival: 92.37% | Actual outcome: SURVIVED
|
||
Predicted survival: 4.18% | Actual outcome: SURVIVED
|
||
Predicted survival: 14.32% | Actual outcome: DIED
|
||
Predicted survival: 4.36% | Actual outcome: SURVIVED
|
||
|
||
``` |