tf.compat.v1.estimator.classifier_parse_example_spec Generates parsing spec for tf.parse_example to be used with classifiers. 
tf.compat.v1.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.