def create_sequence_pretty_tensor(sequence_input, shape=None, save_state=True):
"""Creates a PrettyTensor object for the given sequence.
The first dimension is treated as a time-dimension * batch and a default is
set for `unroll` and `state_saver`.
TODO(eiderman): Remove shape.
Args:
sequence_input: A SequenceInput or StateSavingSequenceInput
shape: The shape of each item in the sequence (including batch).
save_state: If true, use the sequence_input's state and save_state methods.
Returns:
2 Layers: inputs, targets
"""
inputs = prettytensor.wrap_sequence(sequence_input.inputs, tensor_shape=shape)
targets = prettytensor.wrap_sequence(sequence_input.targets)
if save_state:
bookkeeper.set_recurrent_state_saver(sequence_input)
return inputs, targets
def create_sequence_pretty_tensor(sequence_input, shape=None, save_state=True):
"""Creates a PrettyTensor object for the given sequence.
The first dimension is treated as a time-dimension * batch and a default is
set for `unroll` and `state_saver`.
TODO(eiderman): Remove shape.
Args:
sequence_input: A SequenceInput or StateSavingSequenceInput
shape: The shape of each item in the sequence (including batch).
save_state: If true, use the sequence_input's state and save_state methods.
Returns:
2 Layers: inputs, targets
"""
inputs = prettytensor.wrap_sequence(sequence_input.inputs, tensor_shape=shape)
targets = prettytensor.wrap_sequence(sequence_input.targets)
if save_state:
bookkeeper.set_recurrent_state_saver(sequence_input)
return inputs, targets