# tf.contrib.rnn.AttentionCellWrapper

### class tf.contrib.rnn.AttentionCellWrapper

Basic attention cell wrapper.

Implementation based on https://arxiv.org/abs/1409.0473.

## Methods

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

Create a cell with attention.

#### 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.

#### 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.

### 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 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.