tf.contrib.feature_column.sequence_categorical_column_with_hash_bucket(
key,
hash_bucket_size,
dtype=tf.dtypes.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_sizeis not greater than 1.ValueError:dtypeis neither string nor integer.