class tf.contrib.rnn.EmbeddingWrapper

class tf.contrib.rnn.core_rnn_cell.EmbeddingWrapper

See the guide: RNN and Cells (contrib) > Core RNN Cell wrappers (RNNCells that wrap other RNNCells)

Operator adding input embedding to the given cell.





__init__(cell, embedding_classes, embedding_size, initializer=None)

Create a cell with an added input embedding.


  • 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.


  • TypeError: if cell is not an RNNCell.
  • ValueError: if embedding_classes is not positive.

zero_state(batch_size, dtype)

Return zero-filled state tensor(s).


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


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.

Defined in tensorflow/contrib/rnn/python/ops/