tf.feature_column.sequence_categorical_column_with_identity
Returns a feature column that represents sequences of integers.
tf.feature_column.sequence_categorical_column_with_identity(
key, num_buckets, default_value=None
)
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)
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.
|
Returns |
A SequenceCategoricalColumn .
|
Raises |
ValueError
|
if num_buckets is less than one.
|
ValueError
|
if default_value is not in range [0, num_buckets) .
|
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2020-10-01 UTC.
[null,null,["Last updated 2020-10-01 UTC."],[],[]]