tf.contrib.rnn.EmbeddingWrapper

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.

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.

Properties

output_size

state_size

Methods

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

Create a cell with an added input embedding.

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.

Raises:

• 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).

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.

Defined in tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py.