class InnerRecurrent(BaseRecurrent, Initializable):
def __init__(self, inner_input_dim, outer_input_dim, inner_dim, **kwargs):
self.inner_gru = GatedRecurrent(dim=inner_dim, name='inner_gru')
self.inner_input_fork = Fork(
output_names=[name for name in self.inner_gru.apply.sequences
if 'mask' not in name],
input_dim=inner_input_dim, name='inner_input_fork')
self.outer_input_fork = Fork(
output_names=[name for name in self.inner_gru.apply.sequences
if 'mask' not in name],
input_dim=outer_input_dim, name='inner_outer_fork')
super(InnerRecurrent, self).__init__(**kwargs)
self.children = [
self.inner_gru, self.inner_input_fork, self.outer_input_fork]
def _push_allocation_config(self):
self.inner_input_fork.output_dims = self.inner_gru.get_dims(
self.inner_input_fork.output_names)
self.outer_input_fork.output_dims = self.inner_gru.get_dims(
self.outer_input_fork.output_names)
@recurrent(sequences=['inner_inputs'], states=['states'],
contexts=['outer_inputs'], outputs=['states'])
def apply(self, inner_inputs, states, outer_inputs):
forked_inputs = self.inner_input_fork.apply(inner_inputs, as_dict=True)
forked_states = self.outer_input_fork.apply(outer_inputs, as_dict=True)
gru_inputs = {key: forked_inputs[key] + forked_states[key]
for key in forked_inputs.keys()}
new_states = self.inner_gru.apply(
iterate=False,
**dict_union(gru_inputs, {'states': states}))
return new_states # mean according to the time axis
def get_dim(self, name):
if name == 'states':
return self.inner_gru.get_dim(name)
else:
return AttributeError
