tf.feature_column.indicator_column

Represents multi-hot representation of given categorical column. (deprecated)

View aliases

Compat aliases for migration

tf.compat.v1.feature_column.indicator_column

tf.feature_column.indicator_column(
    categorical_column
)

Used in the notebooks

Used in the guide	Used in the tutorials
Migrate `tf.feature_column`s to Keras preprocessing layers	Classify structured data with feature columns Build a linear model with Estimators End to end example for BigQuery TensorFlow reader Preprocessing data with TensorFlow Transform

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.

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Licensed under the Creative Commons Attribution License 4.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/feature_column/indicator_column