# tf.feature_column.categorical_column_with_hash_bucket

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/feature_column/feature_column_v2.py#L1210-L1279) |

Represents sparse feature where ids are set by hashing. (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.categorical_column_with_hash_bucket`](https://www.tensorflow.org/api_docs/python/tf/feature_column/categorical_column_with_hash_bucket)

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    tf.feature_column.categorical_column_with_hash_bucket(
        key,
        hash_bucket_size,
        dtype=https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/dtypes#string
    )

> [!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/r2.14/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.

Use this when your sparse features are in string or integer format, and you
want to distribute your inputs into a finite number of buckets by hashing.
output_id = Hash(input_feature_string) % bucket_size for string type input.
For int type input, the value is converted to its string representation first
and then hashed by the same formula.

For input dictionary `features`, `features[key]` is either `Tensor` or
`SparseTensor`. If `Tensor`, missing values can be represented by `-1` for int
and `''` for string, which will be dropped by this feature column.

#### Example:

    import tensorflow as tf
    keywords = tf.feature_column.categorical_column_with_hash_bucket("keywords",
    10000)
    columns = [keywords]
    features = {'keywords': tf.constant([['Tensorflow', 'Keras', 'RNN', 'LSTM',
    'CNN'], ['LSTM', 'CNN', 'Tensorflow', 'Keras', 'RNN'], ['CNN', 'Tensorflow',
    'LSTM', 'Keras', 'RNN']])}
    linear_prediction, _, _ = tf.compat.v1.feature_column.linear_model(features,
    columns)

    # or
    import tensorflow as tf
    keywords = tf.feature_column.categorical_column_with_hash_bucket("keywords",
    10000)
    keywords_embedded = tf.feature_column.embedding_column(keywords, 16)
    columns = [keywords_embedded]
    features = {'keywords': tf.constant([['Tensorflow', 'Keras', 'RNN', 'LSTM',
    'CNN'], ['LSTM', 'CNN', 'Tensorflow', 'Keras', 'RNN'], ['CNN', 'Tensorflow',
    'LSTM', 'Keras', 'RNN']])}
    input_layer = tf.keras.layers.DenseFeatures(columns)
    dense_tensor = input_layer(features)

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| ## Args ||
|---|---|
| `key` | A unique string identifying the input feature. It is used as the column name and the dictionary key for feature parsing configs, feature `Tensor` objects, and feature columns. |
| `hash_bucket_size` | An int \> 1. The number of buckets. |
| `dtype` | The type of features. Only string and integer types are supported. |

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| ## Returns ||
|---|---|
| A `HashedCategoricalColumn`. ||

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| ## Raises ||
|---|---|
| `ValueError` | `hash_bucket_size` is not greater than 1. |
| `ValueError` | `dtype` is neither string nor integer. |

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