tf.feature_column.indicator_column

tf.feature_column.indicator_column

tf.feature_column.indicator_column

indicator_column(categorical_column)

Defined in tensorflow/python/feature_column/feature_column.py.

Represents multi-hot representation of given categorical column.

Used to wrap any categorical_column_* (e.g., to feed to DNN). Use embedding_column if the inputs are sparse.

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"]

Args:

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

Returns:

An _IndicatorColumn.