tf.keras.experimental.SequenceFeatures
View source on GitHub |
A layer for sequence input.
tf.keras.experimental.SequenceFeatures( feature_columns, trainable=True, name=None, **kwargs )
All feature_columns
must be sequence dense columns with the same sequence_length
. The output of this method can be fed into sequence networks, such as RNN.
The output of this method is a 3D Tensor
of shape [batch_size, T, D]
. T
is the maximum sequence length for this batch, which could differ from batch to batch.
If multiple feature_columns
are given with Di
num_elements
each, their outputs are concatenated. So, the final Tensor
has shape [batch_size, T, D0 + D1 + ... + Dn]
.
Example:
# Behavior of some cells or feature columns may depend on whether we are in # training or inference mode, e.g. applying dropout. training = True rating = sequence_numeric_column('rating') watches = sequence_categorical_column_with_identity( 'watches', num_buckets=1000) watches_embedding = embedding_column(watches, dimension=10) columns = [rating, watches_embedding] sequence_input_layer = SequenceFeatures(columns) features = tf.io.parse_example(..., features=make_parse_example_spec(columns)) sequence_input, sequence_length = sequence_input_layer( features, training=training) sequence_length_mask = tf.sequence_mask(sequence_length) rnn_cell = tf.keras.layers.SimpleRNNCell(hidden_size, training=training) rnn_layer = tf.keras.layers.RNN(rnn_cell, training=training) outputs, state = rnn_layer(sequence_input, mask=sequence_length_mask)
Args | |
---|---|
feature_columns | An iterable of dense sequence columns. Valid columns are
|
trainable | Boolean, whether the layer's variables will be updated via gradient descent during training. |
name | Name to give to the SequenceFeatures. |
**kwargs | Keyword arguments to construct a layer. |
Raises | |
---|---|
ValueError | If any of the feature_columns is not a SequenceDenseColumn . |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/keras/experimental/SequenceFeatures