A sequence of categorical terms where ids are set by hashing.
tf.contrib.feature_column.sequence_categorical_column_with_hash_bucket(
key, hash_bucket_size, dtype=tf.dtypes.string
)
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.io.parse_example(..., features=make_parse_example_spec(columns))
input_layer, sequence_length = sequence_input_layer(features, columns)
rnn_cell = tf.compat.v1.nn.rnn_cell.BasicRNNCell(hidden_size)
outputs, state = tf.compat.v1.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.
|