RNN decoder for the sequence-to-sequence model.
tf.contrib.legacy_seq2seq.rnn_decoder(
decoder_inputs, initial_state, cell, loop_function=None, scope=None
)
Args |
|---|
decoder_inputs
|
A list of 2D Tensors [batch_size x input_size].
|
initial_state
|
2D Tensor with shape [batch_size x cell.state_size].
|
cell
|
rnn_cell.RNNCell defining the cell function and size.
|
loop_function
|
If not None, this function will be applied to the i-th output
in order to generate the i+1-st input, and decoder_inputs will be ignored,
except for the first element ("GO" symbol). This can be used for decoding,
but also for training to emulate http://arxiv.org/abs/1506.03099
Signature -- loop_function(prev, i) = next * prev is a 2D Tensor of
shape [batch_size x output_size], * i is an integer, the step number
(when advanced control is needed), * next is a 2D Tensor of shape
[batch_size x input_size].
|
scope
|
VariableScope for the created subgraph; defaults to "rnn_decoder".
|
Returns |
|---|
|
A tuple of the form (outputs, state), where:
outputs: A list of the same length as decoder_inputs of 2D Tensors with
shape [batch_size x output_size] containing generated outputs.
state: The state of each cell at the final time-step.
It is a 2D Tensor of shape [batch_size x cell.state_size].
(Note that in some cases, like basic RNN cell or GRU cell, outputs and
states can be the same. They are different for LSTM cells though.)
|
View source on GitHub