RNN sequence-to-sequence model with tied encoder and decoder parameters.
tf.contrib.legacy_seq2seq.tied_rnn_seq2seq(
encoder_inputs, decoder_inputs, cell, loop_function=None,
dtype=tf.dtypes.float32, scope=None
)
This model first runs an RNN to encode encoder_inputs into a state vector, and
then runs decoder, initialized with the last encoder state, on decoder_inputs.
Encoder and decoder use the same RNN cell and share parameters.
Args |
|---|
encoder_inputs
|
A list of 2D Tensors [batch_size x input_size].
|
decoder_inputs
|
A list of 2D Tensors [batch_size x input_size].
|
cell
|
tf.compat.v1.nn.rnn_cell.RNNCell defining the cell function and size.
|
loop_function
|
If not None, this function will be applied to i-th output in
order to generate i+1-th input, and decoder_inputs will be ignored, except
for the first element ("GO" symbol), see rnn_decoder for details.
|
dtype
|
The dtype of the initial state of the rnn cell (default: tf.float32).
|
scope
|
VariableScope for the created subgraph; default: "tied_rnn_seq2seq".
|
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 the generated outputs.
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].
|
View source on GitHub