def __init__(self, config, output_dim, activation, **kwargs):
super(RecurrentEncoder, self).__init__(**kwargs)
self.config = config
self.context_embedder = ContextEmbedder(config)
self.rec = SegregatedBidirectional(LSTM(dim=config.rec_state_dim, name='encoder_recurrent'))
self.fwd_fork = Fork([name for name in self.rec.prototype.apply.sequences if name!='mask'],
prototype=Linear(), name='fwd_fork')
self.bkwd_fork = Fork([name for name in self.rec.prototype.apply.sequences if name!='mask'],
prototype=Linear(), name='bkwd_fork')
rto_in = config.rec_state_dim * 2 + sum(x[2] for x in config.dim_embeddings)
self.rec_to_output = MLP(
activations=[Rectifier() for _ in config.dim_hidden] + [activation],
dims=[rto_in] + config.dim_hidden + [output_dim],
name='encoder_rto')
self.children = [self.context_embedder, self.rec, self.fwd_fork, self.bkwd_fork, self.rec_to_output]
self.rec_inputs = ['latitude', 'longitude', 'latitude_mask']
self.inputs = self.context_embedder.inputs + self.rec_inputs
class RecurrentEncoder(Initializable):
def __init__(self, config, output_dim, activation, **kwargs):
super(RecurrentEncoder, self).__init__(**kwargs)
self.config = config
self.context_embedder = ContextEmbedder(config)
self.rec = SegregatedBidirectional(LSTM(dim=config.rec_state_dim, name='encoder_recurrent'))
self.fwd_fork = Fork([name for name in self.rec.prototype.apply.sequences if name!='mask'],
prototype=Linear(), name='fwd_fork')
self.bkwd_fork = Fork([name for name in self.rec.prototype.apply.sequences if name!='mask'],
prototype=Linear(), name='bkwd_fork')
rto_in = config.rec_state_dim * 2 + sum(x[2] for x in config.dim_embeddings)
self.rec_to_output = MLP(
activations=[Rectifier() for _ in config.dim_hidden] + [activation],
dims=[rto_in] + config.dim_hidden + [output_dim],
name='encoder_rto')
self.children = [self.context_embedder, self.rec, self.fwd_fork, self.bkwd_fork, self.rec_to_output]
self.rec_inputs = ['latitude', 'longitude', 'latitude_mask']
self.inputs = self.context_embedder.inputs + self.rec_inputs
def _push_allocation_config(self):
for i, fork in enumerate([self.fwd_fork, self.bkwd_fork]):
fork.input_dim = 2
fork.output_dims = [ self.rec.children[i].get_dim(name)
for name in fork.output_names ]
def _push_initialization_config(self):
for brick in self.children:
brick.weights_init = self.config.weights_init
brick.biases_init = self.config.biases_init
@application
def apply(self, latitude, longitude, latitude_mask, **kwargs):
latitude = (latitude.T - data.train_gps_mean[0]) / data.train_gps_std[0]
longitude = (longitude.T - data.train_gps_mean[1]) / data.train_gps_std[1]
latitude_mask = latitude_mask.T
rec_in = tensor.concatenate((latitude[:, :, None], longitude[:, :, None]),
axis=2)
path = self.rec.apply(merge(self.fwd_fork.apply(rec_in, as_dict=True),
{'mask': latitude_mask}),
merge(self.bkwd_fork.apply(rec_in, as_dict=True),
{'mask': latitude_mask}))[0]
last_id = tensor.cast(latitude_mask.sum(axis=0) - 1, dtype='int64')
path_representation = (path[0][:, -self.config.rec_state_dim:],
path[last_id - 1, tensor.arange(last_id.shape[0])]
[:, :self.config.rec_state_dim])
embeddings = tuple(self.context_embedder.apply(
**{k: kwargs[k] for k in self.context_embedder.inputs }))
inputs = tensor.concatenate(path_representation + embeddings, axis=1)
outputs = self.rec_to_output.apply(inputs)
return outputs
@apply.property('inputs')
def apply_inputs(self):
return self.inputs