class tf.contrib.rnn.AttentionCellWrapper

See the guide: RNN and Cells (contrib) > Core RNN Cell wrappers (RNNCells that wrap other RNNCells)

Basic attention cell wrapper.

Implementation based on





__init__(cell, attn_length, attn_size=None, attn_vec_size=None, input_size=None, state_is_tuple=False)

Create a cell with attention.


  • cell: an RNNCell, an attention is added to it.
  • attn_length: integer, the size of an attention window.
  • attn_size: integer, the size of an attention vector. Equal to cell.output_size by default.
  • attn_vec_size: integer, the number of convolutional features calculated on attention state and a size of the hidden layer built from base cell state. Equal attn_size to by default.
  • input_size: integer, the size of a hidden linear layer, built from inputs and attention. Derived from the input tensor by default.
  • state_is_tuple: If True, accepted and returned states are n-tuples, where n = len(cells). By default (False), the states are all concatenated along the column axis.


  • TypeError: if cell is not an RNNCell.
  • ValueError: if cell returns a state tuple but the flag state_is_tuple is False or if attn_length is zero or less.

zero_state(batch_size, dtype)

Return zero-filled state tensor(s).


  • batch_size: int, float, or unit Tensor representing the batch size.
  • dtype: the data type to use for the state.


If state_size is an int or TensorShape, then the return value is a N-D tensor of shape [batch_size x state_size] filled with zeros.

If state_size is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of 2-D tensors with the shapes [batch_size x s] for each s in state_size.

Defined in tensorflow/contrib/rnn/python/ops/