def tied_rnn_seq2seq(encoder_inputs, decoder_inputs, cell,
loop_function=None, dtype=dtypes.float32, scope=None):
"""RNN sequence-to-sequence model with tied encoder and decoder parameters.
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: 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].
"""
with variable_scope.variable_scope("combined_tied_rnn_seq2seq"):
scope = scope or "tied_rnn_seq2seq"
_, enc_state = rnn.rnn(
cell, encoder_inputs, dtype=dtype, scope=scope)
variable_scope.get_variable_scope().reuse_variables()
return rnn_decoder(decoder_inputs, enc_state, cell,
loop_function=loop_function, scope=scope)
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