tf.estimator.classifier_parse_example_spec

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Generates parsing spec for tf.parse_example to be used with classifiers.

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tf.compat.v1.estimator.classifier_parse_example_spec

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

If users keep data in tf.Example format, they need to call tf.parse_example with a proper feature spec. There are two main things that this utility helps:

• Users need to combine parsing spec of features with labels and weights (if any) since they are all parsed from same tf.Example instance. This utility combines these specs.
• It is difficult to map expected label by a classifier such as DNNClassifier to corresponding tf.parse_example spec. This utility encodes it by getting related information from users (key, dtype).

Example output of parsing spec:

# 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)
}

Example usage with a classifier:

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)

Args
feature_columns	An iterable containing all feature columns. All items should be instances of classes derived from FeatureColumn.
label_key	A string identifying the label. It means tf.Example stores labels with this key.
label_dtype	A tf.dtype identifies the type of labels. By default it is tf.int64. If user defines a label_vocabulary, this should be set as tf.string. tf.float32 labels are only supported for binary classification.
label_default	used as label if label_key does not exist in given tf.Example. An example usage: let's say label_key is 'clicked' and tf.Example contains clicked data only for positive examples in following format key:clicked, value:1. This means that if there is no data with key 'clicked' it should count as negative example by setting label_deafault=0. Type of this value should be compatible with label_dtype.
weight_column	A string or a NumericColumn created by tf.feature_column.numeric_column defining feature column representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example. If it is a string, it is used as a key to fetch weight tensor from the features. If it is a NumericColumn, raw tensor is fetched by key weight_column.key, then weight_column.normalizer_fn is applied on it to get weight tensor.

Returns
A dict mapping each feature key to a FixedLenFeature or VarLenFeature value.

Raises
ValueError	If label is used in feature_columns.
ValueError	If weight_column is used in feature_columns.
ValueError	If any of the given feature_columns is not a _FeatureColumn instance.
ValueError	If weight_column is not a NumericColumn instance.
ValueError	if label_key is None.