tf.compat.v1.estimator.regressor_parse_example_spec Generates parsing spec for tf.parse_example to be used with regressors. 
tf.compat.v1.estimator.regressor_parse_example_spec(
    feature_columns, label_key, label_dtype=tf.dtypes.float32, label_default=None,
    label_dimension=1, 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 regressor such as DNNRegressor 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.regressor_parse_example_spec(
    feature_columns, label_key='my-label')

# For the above example, regressor_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.float32)
}
 Example usage with a regressor: feature_columns = # define features via tf.feature_column
estimator = DNNRegressor(
    hidden_units=[256, 64, 16],
    feature_columns=feature_columns,
    weight_column='example-weight',
    label_dimension=3)
# This label configuration tells the regressor the following:
# * weights are retrieved with key 'example-weight'
# * label is a 3 dimension tensor with float32 dtype.


# 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_dimension=3,
          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.float32.  
  label_default   used as label if label_key does not exist in given tf.Example. By default default_value is none, which means tf.parse_example will error out if there is any missing label.  
  label_dimension   Number of regression targets per example. This is the size of the last dimension of the labels and logits Tensor objects (typically, these have shape [batch_size, label_dimension]).  
  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.