def __call__(self, inputs, initial_state=None, initial_readout=None, ground_truth=None, **kwargs):
req_num_inputs = 1 + self.num_states
inputs = _to_list(inputs)
inputs = inputs[:]
if len(inputs) == 1:
if initial_state is not None:
if type(initial_state) is list:
inputs += initial_state
else:
inputs.append(initial_state)
else:
if self.readout:
initial_state = self._get_optional_input_placeholder('initial_state', self.num_states - 1)
else:
initial_state = self._get_optional_input_placeholder('initial_state', self.num_states)
inputs += _to_list(initial_state)
if self.readout:
if initial_readout is None:
initial_readout = self._get_optional_input_placeholder('initial_readout')
inputs.append(initial_readout)
if self.teacher_force:
req_num_inputs += 1
if ground_truth is None:
ground_truth = self._get_optional_input_placeholder('ground_truth')
inputs.append(ground_truth)
assert len(inputs) == req_num_inputs, "Required " + str(req_num_inputs) + " inputs, received " + str(len(inputs)) + "."
with K.name_scope(self.name):
if not self.built:
self.build(K.int_shape(inputs[0]))
if self._initial_weights is not None:
self.set_weights(self._initial_weights)
del self._initial_weights
self._initial_weights = None
previous_mask = _collect_previous_mask(inputs[:1])
user_kwargs = kwargs.copy()
if not _is_all_none(previous_mask):
if 'mask' in inspect.getargspec(self.call).args:
if 'mask' not in kwargs:
kwargs['mask'] = previous_mask
input_shape = _collect_input_shape(inputs)
output = self.call(inputs, **kwargs)
output_mask = self.compute_mask(inputs[0], previous_mask)
output_shape = self.compute_output_shape(input_shape[0])
self._add_inbound_node(input_tensors=inputs, output_tensors=output,
input_masks=previous_mask, output_masks=output_mask,
input_shapes=input_shape, output_shapes=output_shape,
arguments=user_kwargs)
if hasattr(self, 'activity_regularizer') and self.activity_regularizer is not None:
regularization_losses = [self.activity_regularizer(x) for x in _to_list(output)]
self.add_loss(regularization_losses, _to_list(inputs))
return output
def __call__(self, inputs, **kwargs):
if isinstance(inputs, list):
inputs = inputs[:]
with K.name_scope(self.name):
# Raise exceptions in case the input is not compatible
# with the input_spec specified in the layer constructor.
self.assert_input_compatibility(inputs)
# Handle laying building (weight creating, input spec locking).
if not self.built:
self.build(inputs)
self.built = True
# Handle mask propagation.
previous_mask = _collect_previous_mask(inputs)
user_kwargs = copy.copy(kwargs)
if not _is_all_none(previous_mask):
# The previous layer generated a mask.
if has_arg(self.call, 'mask'):
if 'mask' not in kwargs:
# If mask is explicitly passed to __call__,
# we should override the default mask.
kwargs['mask'] = previous_mask
# Handle automatic shape inference (only useful for Theano).
input_shape = _collect_input_shape(inputs)
# Actually call the layer, collecting output(s), mask(s), and shape(s).
output = self.call(inputs, **kwargs)
output_mask = self.compute_mask(inputs, previous_mask)
# If the layer returns tensors from its inputs, unmodified,
# we copy them to avoid loss of tensor metadata.
output_ls = _to_list(output)
inputs_ls = _to_list(inputs)
output_ls_copy = []
for x in output_ls:
if x in inputs_ls:
x = K.identity(x)
output_ls_copy.append(x)
if len(output_ls_copy) == 1:
output = output_ls_copy[0]
else:
output = output_ls_copy
# Inferring the output shape is only relevant for Theano.
if all([s is not None for s in _to_list(input_shape)]):
output_shape = self.compute_output_shape(input_shape)
else:
if isinstance(input_shape, list):
output_shape = [None for _ in input_shape]
else:
output_shape = None
if not isinstance(output_mask,
(list, tuple)) and len(output_ls) > 1:
# Augment the mask to match the length of the output.
output_mask = [output_mask] * len(output_ls)
# Add an inbound node to the layer, so that it keeps track
# of the call and of all new variables created during the call.
# This also updates the layer history of the output tensor(s).
# If the input tensor(s) had not previous Keras history,
# this does nothing.
self._add_inbound_node(input_tensors=inputs,
output_tensors=output,
input_masks=previous_mask,
output_masks=output_mask,
input_shapes=input_shape,
output_shapes=output_shape,
arguments=user_kwargs)
# Apply activity regularizer if any:
if hasattr(self, 'activity_regularizer'
) and self.activity_regularizer is not None:
regularization_losses = [
self.activity_regularizer(x) for x in _to_list(output)
]
self.add_loss(regularization_losses, _to_list(inputs))
return output