Watch talks from the 2019 TensorFlow Dev Summit Watch now



Defined in tensorflow/python/feature_column/

Represents multi-hot representation of given categorical column.

  • For DNN model, indicator_column can be used to wrap any categorical_column_* (e.g., to feed to DNN). Consider to Use embedding_column if the number of buckets/unique(values) are large.

  • For Wide (aka linear) model, indicator_column is the internal representation for categorical column when passing categorical column directly (as any element in feature_columns) to linear_model. See linear_model for details.

name = indicator_column(categorical_column_with_vocabulary_list(
    'name', ['bob', 'george', 'wanda'])
columns = [name, ...]
features = tf.parse_example(..., features=make_parse_example_spec(columns))
dense_tensor = input_layer(features, columns)

dense_tensor == [[1, 0, 0]]  # If "name" bytes_list is ["bob"]
dense_tensor == [[1, 0, 1]]  # If "name" bytes_list is ["bob", "wanda"]
dense_tensor == [[2, 0, 0]]  # If "name" bytes_list is ["bob", "bob"]


  • categorical_column: A CategoricalColumn which is created by categorical_column_with_* or crossed_column functions.


An IndicatorColumn.