Python tf.compat.v1.estimator.classifier_parse_example_spec用法及代碼示例

為要與分類器一起使用的 tf.parse_example 生成解析規範。

用法

tf.compat.v1.estimator.classifier_parse_example_spec(
    feature_columns, label_key, label_dtype=tf.dtypes.int64, label_default=None,
    weight_column=None
)

如果用戶以 tf.Example 格式保存數據，他們需要使用適當的函數規範調用 tf.parse_example。此實用程序有兩個主要幫助：

• 用戶需要將特征的解析規範與標簽和權重(如果有的話)結合起來，因為它們都是從同一個 tf.Example 實例中解析出來的。該實用程序結合了這些規範。
• 很難將諸如DNNClassifier 之類的分類器的預期標簽映射到相應的 tf.parse_example 規範。該實用程序通過從用戶獲取相關信息(key、dtype)對其進行編碼。

解析規範的示例輸出：

# Define features and transformations
feature_b = tf.feature_column.numeric_column(...)
feature_c_bucketized = tf.feature_column.bucketized_column(
  tf.feature_column.numeric_column("feature_c"), ...)
feature_a_x_feature_c = tf.feature_column.crossed_column(
    columns=["feature_a", feature_c_bucketized], ...)

feature_columns = [feature_b, feature_c_bucketized, feature_a_x_feature_c]
parsing_spec = tf.estimator.classifier_parse_example_spec(
    feature_columns, label_key='my-label', label_dtype=tf.string)

# For the above example, classifier_parse_example_spec would return the dict:
assert parsing_spec == {
  "feature_a":parsing_ops.VarLenFeature(tf.string),
  "feature_b":parsing_ops.FixedLenFeature([1], dtype=tf.float32),
  "feature_c":parsing_ops.FixedLenFeature([1], dtype=tf.float32)
  "my-label":parsing_ops.FixedLenFeature([1], dtype=tf.string)
}

分類器的示例用法：

feature_columns = # define features via tf.feature_column
estimator = DNNClassifier(
    n_classes=1000,
    feature_columns=feature_columns,
    weight_column='example-weight',
    label_vocabulary=['photos', 'keep', ...],
    hidden_units=[256, 64, 16])
# This label configuration tells the classifier the following:
# * weights are retrieved with key 'example-weight'
# * label is string and can be one of the following ['photos', 'keep', ...]
# * integer id for label 'photos' is 0, 'keep' is 1, ...


# Input builders
def input_fn_train(): # Returns a tuple of features and labels.
  features = tf.contrib.learn.read_keyed_batch_features(
      file_pattern=train_files,
      batch_size=batch_size,
      # creates parsing configuration for tf.parse_example
      features=tf.estimator.classifier_parse_example_spec(
          feature_columns,
          label_key='my-label',
          label_dtype=tf.string,
          weight_column='example-weight'),
      reader=tf.RecordIOReader)
   labels = features.pop('my-label')
   return features, labels

estimator.train(input_fn=input_fn_train)