def concat(input_layer, concat_dim, other_tensors=None):
"""Concatenates input PrettyTensor with other_tensors along the specified dim.
This adds the Pretty Tensor passed via input_layer to the front of the list of
tensors to concat.
Args:
input_layer: The input layer.
concat_dim: The dimension along which to concat.
other_tensors: The tensors to concatenate with as an iterable or None if
this is called on a sequence.
Returns:
A new PrettyTensor.
Raises:
ValueError: If other_tensors is None and this is not a sequence.
"""
if input_layer.is_sequence():
all_tensors = input_layer.sequence
all_tensors.extend(other_tensors or [])
else:
all_tensors = [input_layer]
if other_tensors is None:
raise ValueError('Other Tensors must be supplied.')
all_tensors.extend(other_tensors)
# Edge cases really only apply when this is a sequence with 0 or 1 element.
if not all_tensors:
return prettytensor.wrap_sequence([])
else:
return tf.concat(all_tensors, concat_dim)
def concat(input_layer, concat_dim, other_tensors=None):
"""Concatenates input PrettyTensor with other_tensors along the specified dim.
This adds the Pretty Tensor passed via input_layer to the front of the list of
tensors to concat.
Args:
input_layer: The input layer.
concat_dim: The dimension along which to concat.
other_tensors: The tensors to concatenate with as an iterable or None if
this is called on a sequence.
Returns:
A new PrettyTensor.
Raises:
ValueError: If other_tensors is None and this is not a sequence.
"""
if input_layer.is_sequence():
all_tensors = input_layer.sequence
all_tensors.extend(other_tensors or [])
else:
all_tensors = [input_layer]
if other_tensors is None:
raise ValueError("Other Tensors must be supplied.")
all_tensors.extend(other_tensors)
# Edge cases really only apply when this is a sequence with 0 or 1 element.
if not all_tensors:
return prettytensor.wrap_sequence([])
else:
return tf.concat_v2(all_tensors, concat_dim)
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
def map_(input_layer, fn):
"""Maps the given function across this sequence.
To map an entire template across the sequence, use the `as_fn` method on the
template.
Args:
input_layer: The input tensor.
fn: A function of 1 argument that is applied to each item in the sequence.
Returns:
A new sequence Pretty Tensor.
"""
return prettytensor.wrap_sequence([fn(x) for x in input_layer])
def map_(input_layer, fn):
"""Maps the given function across this sequence.
To map an entire template across the sequence, use the `as_fn` method on the
template.
Args:
input_layer: The input tensor.
fn: A function of 1 argument that is applied to each item in the sequence.
Returns:
A new sequence Pretty Tensor.
"""
return prettytensor.wrap_sequence([fn(x) for x in input_layer])
