# tf.feature_column.sequence_categorical_column_with_identity

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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/sequence_feature_column.py#L101-L148) |

Returns a feature column that represents sequences of integers. (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.sequence_categorical_column_with_identity`](https://www.tensorflow.org/api_docs/python/tf/feature_column/sequence_categorical_column_with_identity)

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    tf.feature_column.sequence_categorical_column_with_identity(
        key, num_buckets, default_value=None
    )

> [!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.

Pass this to `embedding_column` or `indicator_column` to convert sequence
categorical data into dense representation for input to sequence NN, such as
RNN.

#### Example:

    watches = sequence_categorical_column_with_identity(
        'watches', num_buckets=1000)
    watches_embedding = embedding_column(watches, dimension=10)
    columns = [watches_embedding]

    features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
    sequence_feature_layer = SequenceFeatures(columns)
    sequence_input, sequence_length = sequence_feature_layer(features)
    sequence_length_mask = tf.sequence_mask(sequence_length)

    rnn_cell = tf.keras.layers.SimpleRNNCell(hidden_size)
    rnn_layer = tf.keras.layers.RNN(rnn_cell)
    outputs, state = rnn_layer(sequence_input, mask=sequence_length_mask)

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| ## Args ||
|---|---|
| `key` | A unique string identifying the input feature. |
| `num_buckets` | Range of inputs. Namely, inputs are expected to be in the range `[0, num_buckets)`. |
| `default_value` | If `None`, this column's graph operations will fail for out-of-range inputs. Otherwise, this value must be in the range `[0, num_buckets)`, and will replace out-of-range inputs. |

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| ## Returns ||
|---|---|
| A `SequenceCategoricalColumn`. ||

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| ## Raises ||
|---|---|
| `ValueError` | if `num_buckets` is less than one. |
| `ValueError` | if `default_value` is not in range `[0, num_buckets)`. |

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