from theano import tensor
from fuel.transformers import Batch, MultiProcessing, Merge
from fuel.streams import DataStream
from fuel.schemes import ConstantScheme, ShuffledExampleScheme, SequentialExampleScheme
from blocks.bricks import application, MLP, Rectifier, Initializable, Softmax
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 StreamSimple as Stream
from memory_network import MemoryNetworkBase
class Model(MemoryNetworkBase):
def __init__(self, **kwargs):
super(Model, self).__init__(**kwargs)
self.prefix_encoder = MLP(activations=[Rectifier() for _ in config.prefix_encoder.dim_hidden]
+ [config.representation_activation()],
dims=[config.prefix_encoder.dim_input]
+ config.prefix_encoder.dim_hidden
+ [config.representation_size],
name='prefix_encoder')
self.candidate_encoder = MLP(
activations=[Rectifier() for _ in config.candidate_encoder.dim_hidden]
+ [config.representation_activation()],
dims=[config.candidate_encoder.dim_input]
+ config.candidate_encoder.dim_hidden
+ [config.representation_size],
name='candidate_encoder')
self.softmax = Softmax()
self.prefix_extremities = {'%s_k_%s' % (side, ['latitude', 'longitude'][axis]): axis
for side in ['first', 'last'] for axis in [0, 1]}
self.candidate_extremities = {'candidate_%s_k_%s' % (side, axname): axis
for side in ['first', 'last']
for axis, axname in enumerate(['latitude', 'longitude'])}
self.inputs = self.context_embedder.inputs \
+ ['candidate_%s'%k for k in self.context_embedder.inputs] \
+ self.prefix_extremities.keys() + self.candidate_extremities.keys()
self.children = [ self.context_embedder,
self.prefix_encoder,
self.candidate_encoder,
self.softmax ]
def _push_initialization_config(self):
for (mlp, config) in [[self.prefix_encoder, self.config.prefix_encoder],
[self.candidate_encoder, self.config.candidate_encoder]]:
mlp.weights_init = config.weights_init
mlp.biases_init = config.biases_init
@application(outputs=['destination'])
def predict(self, **kwargs):
prefix_embeddings = tuple(self.context_embedder.apply(
**{k: kwargs[k] for k in self.context_embedder.inputs }))
prefix_extremities = tuple((kwargs[k] - data.train_gps_mean[v]) / data.train_gps_std[v]
for k, v in self.prefix_extremities.items())
prefix_inputs = tensor.concatenate(prefix_extremities + prefix_embeddings, axis=1)
prefix_representation = self.prefix_encoder.apply(prefix_inputs)
if self.config.normalize_representation:
prefix_representation = prefix_representation \
/ tensor.sqrt((prefix_representation ** 2).sum(axis=1, keepdims=True))
candidate_embeddings = tuple(self.context_embedder.apply(**{k: kwargs['candidate_%s'%k]
for k in self.context_embedder.inputs }))
candidate_extremities = tuple((kwargs[k] - data.train_gps_mean[v]) / data.train_gps_std[v]
for k, v in self.candidate_extremities.items())
candidate_inputs = tensor.concatenate(candidate_extremities + candidate_embeddings, axis=1)
candidate_representation = self.candidate_encoder.apply(candidate_inputs)
if self.config.normalize_representation:
candidate_representation = candidate_representation \
/ tensor.sqrt((candidate_representation ** 2).sum(axis=1, keepdims=True))
similarity_score = tensor.dot(prefix_representation, candidate_representation.T)
similarity = self.softmax.apply(similarity_score)
candidate_destination = tensor.concatenate(
(tensor.shape_padright(kwargs['candidate_last_k_latitude'][:,-1]),
tensor.shape_padright(kwargs['candidate_last_k_longitude'][:,-1])),
axis=1)
return tensor.dot(similarity, candidate_destination)
@predict.property('inputs')
def predict_inputs(self):
return self.inputs
@application(outputs=['cost'])
def cost(self, **kwargs):
y_hat = self.predict(**kwargs)
y = tensor.concatenate((kwargs['destination_latitude'][:, None],
kwargs['destination_longitude'][:, None]), axis=1)
return error.erdist(y_hat, y).mean()
@cost.property('inputs')
def cost_inputs(self):
return self.inputs + ['destination_latitude', 'destination_longitude']
