RNN decoder with embedding and a pure-decoding option.
tf.contrib.legacy_seq2seq.embedding_rnn_decoder(
decoder_inputs, initial_state, cell, num_symbols, embedding_size,
output_projection=None, feed_previous=False, update_embedding_for_previous=True,
scope=None
)
Args |
decoder_inputs
|
A list of 1D batch-sized int32 Tensors (decoder inputs).
|
initial_state
|
2D Tensor [batch_size x cell.state_size].
|
cell
|
tf.compat.v1.nn.rnn_cell.RNNCell defining the cell function.
|
num_symbols
|
Integer, how many symbols come into the embedding.
|
embedding_size
|
Integer, the length of the embedding vector for each symbol.
|
output_projection
|
None or a pair (W, B) of output projection weights and
biases; W has shape [output_size x num_symbols] and B has shape
[num_symbols]; if provided and feed_previous=True, each fed previous
output will first be multiplied by W and added B.
|
feed_previous
|
Boolean; if True, only the first of decoder_inputs will be
used (the "GO" symbol), and all other decoder inputs will be generated by:
next = embedding_lookup(embedding, argmax(previous_output)), In effect,
this implements a greedy decoder. It can also be used
during training to emulate http://arxiv.org/abs/1506.03099 If False,
decoder_inputs are used as given (the standard decoder case).
|
update_embedding_for_previous
|
Boolean; if False and feed_previous=True,
only the embedding for the first symbol of decoder_inputs (the "GO"
symbol) will be updated by back propagation. Embeddings for the symbols
generated from the decoder itself remain unchanged. This parameter has no
effect if feed_previous=False.
|
scope
|
VariableScope for the created subgraph; defaults to
"embedding_rnn_decoder".
|
Returns |
A tuple of the form (outputs, state), where:
outputs: A list of the same length as decoder_inputs of 2D Tensors. The
output is of shape [batch_size x cell.output_size] when
output_projection is not None (and represents the dense representation
of predicted tokens). It is of shape [batch_size x num_decoder_symbols]
when output_projection is None.
state: The state of each decoder cell in each time-step. This is a list
with length len(decoder_inputs) -- one item for each time-step.
It is a 2D Tensor of shape [batch_size x cell.state_size].
|
Raises |
ValueError
|
When output_projection has the wrong shape.
|