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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:
import tensorflow as tf
# 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 = tf.feature_column.sequence_numeric_column('rating')
watches = tf.feature_column.sequence_categorical_column_with_identity(
'watches', num_buckets=1000)
watches_embedding = tf.feature_column.embedding_column(watches,
dimension=10)
columns = [rating, watches_embedding]
features = {
'rating': tf.sparse.from_dense([[1.0,1.1, 0, 0, 0],
[2.0,2.1,2.2, 2.3, 2.5]]),
'watches': tf.sparse.from_dense([[2, 85, 0, 0, 0],[33,78, 2, 73, 1]])
}
sequence_input_layer = tf.keras.experimental.SequenceFeatures(columns)
sequence_input, sequence_length = sequence_input_layer(
features, training=training)
sequence_length_mask = tf.sequence_mask(sequence_length)
hidden_size = 32
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)
Raises | |
---|---|
ValueError
|
If any of the feature_columns is not a
SequenceDenseColumn .
|