|View source on GitHub|
"Builds input layer for sequence input.
tf.contrib.feature_column.sequence_input_layer( features, feature_columns, weight_collections=None, trainable=True )
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.
feature_columns are given with
num_elements each, their
outputs are concatenated. So, the final
Tensor has shape
[batch_size, T, D0 + D1 + ... + Dn].
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] features = tf.io.parse_example(..., features=make_parse_example_spec(columns)) input_layer, sequence_length = sequence_input_layer(features, columns) rnn_cell = tf.compat.v1.nn.rnn_cell.BasicRNNCell(hidden_size) outputs, state = tf.compat.v1.nn.dynamic_rnn( rnn_cell, inputs=input_layer, sequence_length=sequence_length)
||A dict mapping keys to tensors.|
An iterable of dense sequence columns. Valid columns are
A list of collection names to which the Variable will be
added. Note that variables will also be added to collections
If any of the