# tf.feature_column.indicator_column

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/feature_column/feature_column_v2.py#L1722-L1764) |

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

> [!WARNING]
> **Warning:** tf.feature_column is not recommended for new code. Instead, feature preprocessing can be done directly using either [Keras preprocessing
> layers](https://www.tensorflow.org/guide/migrate/migrating_feature_columns) or through the one-stop utility [`tf.keras.utils.FeatureSpace`](https://www.tensorflow.org/api_docs/python/tf/keras/utils/FeatureSpace) built on top of them. See the [migration guide](https://tensorflow.org/guide/migrate) for details.

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.feature_column.indicator_column`](https://www.tensorflow.org/api_docs/python/tf/feature_column/indicator_column)

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    tf.feature_column.indicator_column(
        categorical_column
    )

> [!WARNING]
> **Deprecated:** THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use Keras preprocessing layers instead, either directly or via the [`tf.keras.utils.FeatureSpace`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/utils/FeatureSpace) utility. Each of `tf.feature_column.*` has a functional equivalent in `tf.keras.layers` for feature preprocessing when training a Keras model.

- 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"]

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| ## Args ||
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| `categorical_column` | A `CategoricalColumn` which is created by `categorical_column_with_*` or `crossed_column` functions. |

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| ## Returns ||
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| An `IndicatorColumn`. ||

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| ## Raises ||
|---|---|
| `ValueError` | If `categorical_column` is not CategoricalColumn type. |

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