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from theano import tensor
from blocks.bricks import application, MLP, Initializable, Tanh
from blocks.bricks.base import lazy
from blocks.bricks.recurrent import LSTM, recurrent
from blocks.utils import shared_floatx_zeros
from fuel.transformers import Batch, Padding
from fuel.streams import DataStream
from fuel.schemes import ConstantScheme, ShuffledExampleScheme
from model import ContextEmbedder
import data
from data import transformers
from data.hdf5 import TaxiDataset, TaxiStream
import error
from model.stream import StreamRec as Stream
class RNN(Initializable):
@lazy()
def __init__(self, config, rec_input_len=2, output_dim=2, **kwargs):
super(RNN, self).__init__(**kwargs)
self.config = config
self.pre_context_embedder = ContextEmbedder(config.pre_embedder, name='pre_context_embedder')
self.post_context_embedder = ContextEmbedder(config.post_embedder, name='post_context_embedder')
in1 = rec_input_len + sum(x[2] for x in config.pre_embedder.dim_embeddings)
self.input_to_rec = MLP(activations=[Tanh()], dims=[in1, config.hidden_state_dim], name='input_to_rec')
self.rec = LSTM(
dim = config.hidden_state_dim,
name = 'recurrent'
)
in2 = config.hidden_state_dim + sum(x[2] for x in config.post_embedder.dim_embeddings)
self.rec_to_output = MLP(activations=[Tanh()], dims=[in2, output_dim], name='rec_to_output')
self.sequences = ['latitude', 'latitude_mask', 'longitude']
self.context = self.pre_context_embedder.inputs + self.post_context_embedder.inputs
self.inputs = self.sequences + self.context
self.children = [ self.pre_context_embedder, self.post_context_embedder, self.input_to_rec, self.rec, self.rec_to_output ]
self.initial_state_ = shared_floatx_zeros((config.hidden_state_dim,),
name="initial_state")
self.initial_cells = shared_floatx_zeros((config.hidden_state_dim,),
name="initial_cells")
def _push_initialization_config(self):
for mlp in [self.input_to_rec, self.rec_to_output]:
mlp.weights_init = self.config.weights_init
mlp.biases_init = self.config.biases_init
self.rec.weights_init = self.config.weights_init
def get_dim(self, name):
return self.rec.get_dim(name)
def process_rto(self, rto):
return rto
def rec_input(self, latitude, longitude, **kwargs):
return (tensor.shape_padright(latitude), tensor.shape_padright(longitude))
@recurrent(states=['states', 'cells'], outputs=['destination', 'states', 'cells'])
def predict_all(self, **kwargs):
pre_emb = tuple(self.pre_context_embedder.apply(**kwargs))
itr_in = tensor.concatenate(pre_emb + self.rec_input(**kwargs), axis=1)
itr = self.input_to_rec.apply(itr_in)
itr = itr.repeat(4, axis=1)
(next_states, next_cells) = self.rec.apply(itr, kwargs['states'], kwargs['cells'], mask=kwargs['latitude_mask'], iterate=False)
post_emb = tuple(self.post_context_embedder.apply(**kwargs))
rto = self.rec_to_output.apply(tensor.concatenate(post_emb + (next_states,), axis=1))
rto = self.process_rto(rto)
return (rto, next_states, next_cells)
@predict_all.property('sequences')
def predict_all_sequences(self):
return self.sequences
@application(outputs=predict_all.states)
def initial_states(self, *args, **kwargs):
return self.rec.initial_states(*args, **kwargs)
@predict_all.property('contexts')
def predict_all_context(self):
return self.context
def before_predict_all(self, kwargs):
kwargs['latitude'] = (kwargs['latitude'].T - data.train_gps_mean[0]) / data.train_gps_std[0]
kwargs['longitude'] = (kwargs['longitude'].T - data.train_gps_mean[1]) / data.train_gps_std[1]
kwargs['latitude_mask'] = kwargs['latitude_mask'].T
@application(outputs=['destination'])
def predict(self, **kwargs):
self.before_predict_all(kwargs)
res = self.predict_all(**kwargs)[0]
last_id = tensor.cast(kwargs['latitude_mask'].sum(axis=0) - 1, dtype='int64')
return res[last_id, tensor.arange(kwargs['latitude_mask'].shape[1])]
@predict.property('inputs')
def predict_inputs(self):
return self.inputs
@application(outputs=['cost_matrix'])
def cost_matrix(self, **kwargs):
self.before_predict_all(kwargs)
res = self.predict_all(**kwargs)[0]
target = tensor.concatenate(
(kwargs['destination_latitude'].dimshuffle('x', 0, 'x'),
kwargs['destination_longitude'].dimshuffle('x', 0, 'x')),
axis=2)
target = target.repeat(kwargs['latitude'].shape[0], axis=0)
ce = error.erdist(target.reshape((-1, 2)), res.reshape((-1, 2)))
ce = ce.reshape(kwargs['latitude'].shape)
return ce * kwargs['latitude_mask']
@cost_matrix.property('inputs')
def cost_matrix_inputs(self):
return self.inputs + ['destination_latitude', 'destination_longitude']
@application(outputs=['cost'])
def cost(self, latitude_mask, **kwargs):
return self.cost_matrix(latitude_mask=latitude_mask, **kwargs).sum() / latitude_mask.sum()
@cost.property('inputs')
def cost_inputs(self):
return self.inputs + ['destination_latitude', 'destination_longitude']
@application(outputs=['cost'])
def valid_cost(self, **kwargs):
last_id = tensor.cast(kwargs['latitude_mask'].sum(axis=1) - 1, dtype='int64')
return self.cost_matrix(**kwargs)[last_id, tensor.arange(kwargs['latitude_mask'].shape[0])].mean()
@valid_cost.property('inputs')
def valid_cost_inputs(self):
return self.inputs + ['destination_latitude', 'destination_longitude']
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