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 | |
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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 | |
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TypeError |
if cell is not an RNNCell. |
ValueError |
if embedding_classes is not positive. |
Attributes | |
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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
get_initial_state( inputs=None, batch_size=None, dtype=None )
zero_state
zero_state( batch_size, dtype )
Return zero-filled state tensor(s).
Args | |
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batch_size |
int, float, or unit Tensor representing the batch size. |
dtype |
the data type to use for the state. |
Returns | |
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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 |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/contrib/rnn/EmbeddingWrapper