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
class GatedRecurrentWithContext(Initializable):
def __init__(self, *args, **kwargs):
self.gated_recurrent = GatedRecurrent(*args, **kwargs)
self.children = [self.gated_recurrent]
@application(states=['states'],
outputs=['states'],
contexts=[
'readout_context', 'transition_context', 'update_context',
'reset_context'
])
def apply(self, transition_context, update_context, reset_context, *args,
**kwargs):
kwargs['inputs'] += transition_context
kwargs['update_inputs'] += update_context
kwargs['reset_inputs'] += reset_context
# readout_context was only added for the Readout brick, discard it
kwargs.pop('readout_context')
return self.gated_recurrent.apply(*args, **kwargs)
def get_dim(self, name):
if name in [
'readout_context', 'transition_context', 'update_context',
'reset_context'
]:
return self.dim
return self.gated_recurrent.get_dim(name)
def __getattr__(self, name):
if name == 'gated_recurrent':
raise AttributeError
return getattr(self.gated_recurrent, name)
@apply.property('sequences')
def apply_inputs(self):
sequences = ['mask', 'inputs']
if self.use_update_gate:
sequences.append('update_inputs')
if self.use_reset_gate:
sequences.append('reset_inputs')
return sequences
class Encoder(Initializable):
"""Encoder of RNNsearch model."""
def __init__(self, blockid, vocab_size, embedding_dim, state_dim, **kwargs):
super(Encoder, self).__init__(**kwargs)
self.vocab_size = vocab_size
self.embedding_dim = embedding_dim
self.state_dim = state_dim
self.blockid = blockid
self.lookup = LookupTable(name='embeddings' + '_' + self.blockid)
self.gru = GatedRecurrent(activation=Tanh(), dim=state_dim, name = "GatedRNN" + self.blockid)
self.fwd_fork = Fork(
[name for name in self.gru.apply.sequences
if name != 'mask'], prototype=Linear(), name='fwd_fork' + '_' + self.blockid)
self.children = [self.lookup, self.gru, self.fwd_fork]
def _push_allocation_config(self):
self.lookup.length = self.vocab_size
self.lookup.dim = self.embedding_dim
self.fwd_fork.input_dim = self.embedding_dim
self.fwd_fork.output_dims = [self.gru.get_dim(name)
for name in self.fwd_fork.output_names]
@application(inputs=['source_sentence', 'source_sentence_mask'],
outputs=['representation'])
def apply(self, source_sentence, source_sentence_mask):
# Time as first dimension
source_sentence = source_sentence.T
source_sentence_mask = source_sentence_mask.T
embeddings = self.lookup.apply(source_sentence)
grupara = merge( self.fwd_fork.apply(embeddings, as_dict=True) , {'mask': source_sentence_mask})
representation = self.gru.apply(**grupara)
return representation
class GatedRecurrentWithContext(Initializable):
def __init__(self, *args, **kwargs):
self.gated_recurrent = GatedRecurrent(*args, **kwargs)
self.children = [self.gated_recurrent]
@application(states=['states'], outputs=['states'],
contexts=['readout_context', 'transition_context',
'update_context', 'reset_context'])
def apply(self, transition_context, update_context, reset_context,
*args, **kwargs):
kwargs['inputs'] += transition_context
kwargs['update_inputs'] += update_context
kwargs['reset_inputs'] += reset_context
# readout_context was only added for the Readout brick, discard it
kwargs.pop('readout_context')
return self.gated_recurrent.apply(*args, **kwargs)
def get_dim(self, name):
if name in ['readout_context', 'transition_context',
'update_context', 'reset_context']:
return self.dim
return self.gated_recurrent.get_dim(name)
def __getattr__(self, name):
if name == 'gated_recurrent':
raise AttributeError
return getattr(self.gated_recurrent, name)
@apply.property('sequences')
def apply_inputs(self):
sequences = ['mask', 'inputs']
if self.use_update_gate:
sequences.append('update_inputs')
if self.use_reset_gate:
sequences.append('reset_inputs')
return sequences
