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"Builds input layer for sequence input.


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].


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)


  • features: A dict mapping keys to tensors.
  • feature_columns: An iterable of dense sequence columns. Valid columns are
    • embedding_column that wraps a sequence_categorical_column_with_*
    • sequence_numeric_column.
  • weight_collections: A list of collection names to which the Variable will be added. Note that variables will also be added to collections tf.GraphKeys.GLOBAL_VARIABLES and ops.GraphKeys.MODEL_VARIABLES.
  • trainable: If True also add the variable to the graph collection GraphKeys.TRAINABLE_VARIABLES.


An (input_layer, sequence_length) tuple where: - input_layer: A float Tensor of shape [batch_size, T, D]. T is the maximum sequence length for this batch, which could differ from batch to batch. D is the sum of num_elements for all feature_columns. - sequence_length: An int Tensor of shape [batch_size]. The sequence length for each example.


  • ValueError: If any of the feature_columns is the wrong type.