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.
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.
[null,null,["Last updated 2020-10-01 UTC."],[],[],null,["# tf.contrib.rnn.AttentionCellWrapper\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/rnn/python/ops/rnn_cell.py#L1115-L1256) |\n\nBasic attention cell wrapper.\n\nInherits From: [`RNNCell`](../../../tf/nn/rnn_cell/RNNCell) \n\n tf.contrib.rnn.AttentionCellWrapper(\n cell, attn_length, attn_size=None, attn_vec_size=None, input_size=None,\n state_is_tuple=True, reuse=None\n )\n\nImplementation based on \u003chttps://arxiv.org/abs/1601.06733\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `cell` | an RNNCell, an attention is added to it. |\n| `attn_length` | integer, the size of an attention window. |\n| `attn_size` | integer, the size of an attention vector. Equal to cell.output_size by default. |\n| `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. |\n| `input_size` | integer, the size of a hidden linear layer, built from inputs and attention. Derived from the input tensor by default. |\n| `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. |\n| `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. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ------ ||\n|--------------|-----------------------------------------------------------------------------------------------------------|\n| `TypeError` | if cell is not an RNNCell. |\n| `ValueError` | if cell returns a state tuple but the flag `state_is_tuple` is `False` or if attn_length is zero or less. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Attributes ---------- ||\n|---------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `graph` | DEPRECATED FUNCTION \u003cbr /\u003e | **Warning:** THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Stop using this property because tf.layers layers no longer track their graph. |\n| `output_size` | Integer or TensorShape: size of outputs produced by this cell. |\n| `scope_name` | \u003cbr /\u003e |\n| `state_size` | size(s) of state(s) used by this cell. \u003cbr /\u003e It can be represented by an Integer, a TensorShape or a tuple of Integers or TensorShapes. |\n\n\u003cbr /\u003e\n\nMethods\n-------\n\n### `get_initial_state`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/ops/rnn_cell_impl.py#L281-L309) \n\n get_initial_state(\n inputs=None, batch_size=None, dtype=None\n )\n\n### `zero_state`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/ops/rnn_cell_impl.py#L311-L340) \n\n zero_state(\n batch_size, dtype\n )\n\nReturn zero-filled state tensor(s).\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ||\n|--------------|---------------------------------------------------------|\n| `batch_size` | int, float, or unit Tensor representing the batch size. |\n| `dtype` | the data type to use for the state. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ||\n|---|---|\n| 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. \u003cbr /\u003e 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`. ||\n\n\u003cbr /\u003e"]]
View source on GitHub