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from theano import tensor
from blocks.bricks import application, MLP, Rectifier, Initializable, Softmax, Linear
from blocks.bricks.parallel import Fork
from blocks.bricks.recurrent import Bidirectional, LSTM
import data
from data import transformers
from data.cut import TaxiTimeCutScheme
from data.hdf5 import TaxiDataset, TaxiStream
import error
from model import ContextEmbedder
from memory_network import StreamRecurrent as Stream
from memory_network import MemoryNetworkBase
from bidirectional import SegregatedBidirectional
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
class Model(MemoryNetworkBase):
def __init__(self, config, **kwargs):
# Build prefix encoder : recurrent then MLP
prefix_encoder = RecurrentEncoder(config.prefix_encoder,
config.representation_size,
config.representation_activation(),
name='prefix_encoder')
# Build candidate encoder
candidate_encoder = RecurrentEncoder(config.candidate_encoder,
config.representation_size,
config.representation_activation(),
name='candidate_encoder')
# And... that's it!
super(Model, self).__init__(config, prefix_encoder, candidate_encoder, **kwargs)
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