tf.keras.experimental.SequenceFeatures

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A layer for sequence input.

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tf.compat.v1.keras.experimental.SequenceFeatures

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 embedding_column that wraps a sequence_categorical_column_with_* sequence_numeric_column.
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.