From 98139f573eb179c8f5a06ba6c8d8883376814ccf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=89tienne=20Simon?= Date: Thu, 2 Jul 2015 12:59:15 -0400 Subject: Remove _simple --- model/joint_simple_mlp_tgtcls.py | 71 ---------------------------------------- 1 file changed, 71 deletions(-) delete mode 100644 model/joint_simple_mlp_tgtcls.py (limited to 'model/joint_simple_mlp_tgtcls.py') diff --git a/model/joint_simple_mlp_tgtcls.py b/model/joint_simple_mlp_tgtcls.py deleted file mode 100644 index d6d4e49..0000000 --- a/model/joint_simple_mlp_tgtcls.py +++ /dev/null @@ -1,71 +0,0 @@ -import numpy -import theano -from theano import tensor -from blocks import roles -from blocks.bricks import application, MLP, Rectifier, Softmax - -import error -from model.mlp import FFMLP, Stream - - -class Model(FFMLP): - def __init__(self, config, **kwargs): - super(Model, self).__init__(config, **kwargs) - - self.dest_mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden_dest] + [Softmax()], - dims=[config.dim_hidden[-1]] + config.dim_hidden_dest + [config.dim_output_dest], - name='dest_mlp') - self.time_mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden_time] + [Softmax()], - dims=[config.dim_hidden[-1]] + config.dim_hidden_time + [config.dim_output_time], - name='time_mlp') - - self.dest_classes = theano.shared(numpy.array(config.dest_tgtcls, dtype=theano.config.floatX), name='dest_classes') - self.time_classes = theano.shared(numpy.array(config.time_tgtcls, dtype=theano.config.floatX), name='time_classes') - - self.inputs.append('input_time') - self.children.extend([self.dest_mlp, self.time_mlp]) - - def _push_initialization_config(self): - super(Model, self)._push_initialization_config() - for mlp in [self.dest_mlp, self.time_mlp]: - mlp.weights_init = self.config.mlp_weights_init - mlp.biases_init = self.config.mlp_biases_init - - @application(outputs=['destination', 'duration']) - def predict(self, **kwargs): - hidden = super(Model, self).predict(**kwargs) - - dest_cls_probas = self.dest_mlp.apply(hidden) - dest_outputs = tensor.dot(dest_cls_probas, self.dest_classes) - - time_cls_probas = self.time_mlp.apply(hidden) - time_outputs = kwargs['input_time'] + tensor.dot(time_cls_probas, self.time_classes) - - self.add_auxiliary_variable(dest_cls_probas, name='destination classes ponderations') - self.add_auxiliary_variable(time_cls_probas, name='time classes ponderations') - - return (dest_outputs, time_outputs) - - @predict.property('inputs') - def predict_inputs(self): - return self.inputs - - @application(outputs=['cost']) - def cost(self, **kwargs): - (destination_hat, time_hat) = self.predict(**kwargs) - - destination = tensor.concatenate((kwargs['destination_latitude'][:, None], - kwargs['destination_longitude'][:, None]), axis=1) - time = kwargs['travel_time'] - - destination_cost = error.erdist(destination_hat, destination).mean() - time_cost = error.rmsle(time_hat.flatten(), time.flatten()) - - self.add_auxiliary_variable(destination_cost, [roles.COST], 'destination_cost') - self.add_auxiliary_variable(time_cost, [roles.COST], 'time_cost') - - return destination_cost + self.config.time_cost_factor * time_cost - - @cost.property('inputs') - def cost_inputs(self): - return self.inputs + ['destination_latitude', 'destination_longitude', 'travel_time'] -- cgit v1.2.3