tf.contrib.feature_column.sequence_categorical_column_with_hash_bucket

tf.contrib.feature_column.sequence_categorical_column_with_hash_bucket(
    key,
    hash_bucket_size,
    dtype=tf.string
)

Defined in tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py.

A sequence of categorical terms where ids are set by hashing.

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:

tokens = sequence_categorical_column_with_hash_bucket(
    'tokens', hash_bucket_size=1000)
tokens_embedding = embedding_column(tokens, dimension=10)
columns = [tokens_embedding]

features = tf.parse_example(..., features=make_parse_example_spec(columns))
input_layer, sequence_length = sequence_input_layer(features, columns)

rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size)
outputs, state = tf.nn.dynamic_rnn(
    rnn_cell, inputs=input_layer, sequence_length=sequence_length)

Args:

• key: A unique string identifying the input feature.
• hash_bucket_size: An int > 1. The number of buckets.
• dtype: The type of features. Only string and integer types are supported.

Returns:

A _SequenceCategoricalColumn.

Raises:

• ValueError: hash_bucket_size is not greater than 1.
• ValueError: dtype is neither string nor integer.