Represents multi-hot representation of given categorical column.
tf.feature_column.indicator_column(
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.io.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 .
|
Raises |
ValueError
|
If categorical_column is not CategoricalColumn type.
|