tf.contrib.rnn.EmbeddingWrapper

Operator adding input embedding to the given cell.

Inherits From: RNNCell

tf.contrib.rnn.EmbeddingWrapper(
    cell, embedding_classes, embedding_size, initializer=None, reuse=None
)

Note: in many cases it may be more efficient to not use this wrapper, but instead concatenate the whole sequence of your inputs in time, do the embedding on this batch-concatenated sequence, then split it and feed into your RNN.

Args
cell	an RNNCell, an embedding will be put before its inputs.
embedding_classes	integer, how many symbols will be embedded.
embedding_size	integer, the size of the vectors we embed into.
initializer	an initializer to use when creating the embedding; if None, the initializer from variable scope or a default one is used.
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 embedding_classes is not positive.

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.