tf.contrib.rnn.AttentionCellWrapper

Basic attention cell wrapper.

Inherits From: RNNCell

tf.contrib.rnn.AttentionCellWrapper(
    cell, attn_length, attn_size=None, attn_vec_size=None, input_size=None,
    state_is_tuple=True, reuse=None
)

Implementation based on https://arxiv.org/abs/1601.06733

Args
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.
reuse	(optional) Python boolean describing whether to reuse variables in an existing scope. If not True, and the existing scope already has the given variables, an error is raised.

Raises
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.

Attributes
graph	DEPRECATED FUNCTION
output_size	Integer or TensorShape: size of outputs produced by this cell.
scope_name
state_size	size(s) of state(s) used by this cell. It can be represented by an Integer, a TensorShape or a tuple of Integers or TensorShapes.

Methods

get_initial_state

View source

get_initial_state(
    inputs=None, batch_size=None, dtype=None
)

zero_state

View source

zero_state(
    batch_size, dtype
)

Return zero-filled state tensor(s).

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

Returns
If state_size is an int or TensorShape, then the return value is a N-D tensor of shape [batch_size, 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, s] for each s in state_size.