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import numpy
import theano
from theano import tensor
from blocks.bricks import application, Softmax
import error
from model.mlp import FFMLP, Stream
class Model(FFMLP):
def __init__(self, config, **kwargs):
super(Model, self, output_layer=Softmax).__init__(config, **kwargs)
self.classes = theano.shared(numpy.array(config.tgtcls, dtype=theano.config.floatX), name='classes')
self.inputs.append('input_time')
@application(outputs=['duration'])
def predict(self, **kwargs):
cls_probas = super(Model, self).predict(**kwargs)
return kwargs['input_time'] + tensor.dot(cls_probas, self.classes)
@predict.property('inputs')
def predict_inputs(self):
return self.inputs
@application(outputs=['cost'])
def cost(self, **kwargs):
y_hat = self.predict(**kwargs)
y = kwargs['travel_time']
return error.rmsle(y_hat.flatten(), y.flatten())
@cost.property('inputs')
def cost_inputs(self):
return self.inputs + ['travel_time']
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