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TensorFlow 1 version
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View source on GitHub
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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 | |
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feature_columns
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An iterable of dense sequence columns. Valid columns are
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trainable
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Boolean, whether the layer's variables will be updated via gradient descent during training. |
name
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Name to give to the SequenceFeatures. |
**kwargs
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Keyword arguments to construct a layer. |
Raises | |
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ValueError
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If any of the feature_columns is not a
SequenceDenseColumn.
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