diff options
Diffstat (limited to 'model/joint_simple_mlp_tgtcls.py')
| -rw-r--r-- | model/joint_simple_mlp_tgtcls.py | 150 |
1 files changed, 55 insertions, 95 deletions
diff --git a/model/joint_simple_mlp_tgtcls.py b/model/joint_simple_mlp_tgtcls.py index 0aaf554..d6d4e49 100644 --- a/model/joint_simple_mlp_tgtcls.py +++ b/model/joint_simple_mlp_tgtcls.py @@ -1,111 +1,71 @@ -from blocks.bricks import MLP, Rectifier, Linear, Sigmoid, Identity, Softmax -from blocks.bricks.lookup import LookupTable - -from blocks.filter import VariableFilter -from blocks.graph import ComputationGraph, apply_dropout - import numpy import theano from theano import tensor +from blocks import roles +from blocks.bricks import application, MLP, Rectifier, Softmax -import data import error +from model.mlp import FFMLP, Stream -class Model(object): - def __init__(self, config): - # The input and the targets - x_firstk_latitude = (tensor.matrix('first_k_latitude') - data.train_gps_mean[0]) / data.train_gps_std[0] - x_firstk_longitude = (tensor.matrix('first_k_longitude') - data.train_gps_mean[1]) / data.train_gps_std[1] - x_lastk_latitude = (tensor.matrix('last_k_latitude') - data.train_gps_mean[0]) / data.train_gps_std[0] - x_lastk_longitude = (tensor.matrix('last_k_longitude') - data.train_gps_mean[1]) / data.train_gps_std[1] +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') - x_input_time = tensor.lvector('input_time') + 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') - input_list = [x_firstk_latitude, x_firstk_longitude, x_lastk_latitude, x_lastk_longitude] - embed_tables = [] + self.inputs.append('input_time') + self.children.extend([self.dest_mlp, self.time_mlp]) - self.require_inputs = ['first_k_latitude', 'first_k_longitude', 'last_k_latitude', 'last_k_longitude', 'input_time'] + 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 - for (varname, num, dim) in config.dim_embeddings: - self.require_inputs.append(varname) - vardata = tensor.lvector(varname) - tbl = LookupTable(length=num, dim=dim, name='%s_lookup'%varname) - embed_tables.append(tbl) - input_list.append(tbl.apply(vardata)) + @application(outputs=['destination', 'duration']) + def predict(self, **kwargs): + hidden = super(Model, self).predict(**kwargs) - y_dest = tensor.concatenate((tensor.vector('destination_latitude')[:, None], - tensor.vector('destination_longitude')[:, None]), axis=1) - y_time = tensor.lvector('travel_time') + dest_cls_probas = self.dest_mlp.apply(hidden) + dest_outputs = tensor.dot(dest_cls_probas, self.dest_classes) - # Define the model - common_mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden], - dims=[config.dim_input] + config.dim_hidden) + time_cls_probas = self.time_mlp.apply(hidden) + time_outputs = kwargs['input_time'] + tensor.dot(time_cls_probas, self.time_classes) - 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') - dest_classes = theano.shared(numpy.array(config.dest_tgtcls, dtype=theano.config.floatX), name='dest_classes') + self.add_auxiliary_variable(dest_cls_probas, name='destination classes ponderations') + self.add_auxiliary_variable(time_cls_probas, name='time classes ponderations') - 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') - time_classes = theano.shared(numpy.array(config.time_tgtcls, dtype=theano.config.floatX), name='time_classes') - - # Create the Theano variables - inputs = tensor.concatenate(input_list, axis=1) - # inputs = theano.printing.Print("inputs")(inputs) - hidden = common_mlp.apply(inputs) - - dest_cls_probas = dest_mlp.apply(hidden) - # dest_cls_probas = theano.printing.Print("dest_cls_probas")(dest_cls_probas) - dest_outputs = tensor.dot(dest_cls_probas, dest_classes) - dest_outputs.name = 'dest_outputs' - - time_cls_probas = time_mlp.apply(hidden) - # time_cls_probas = theano.printing.Print("time_cls_probas")(time_cls_probas) - time_outputs = tensor.dot(time_cls_probas, time_classes) + x_input_time - time_outputs.name = 'time_outputs' - - # Calculate the cost - dest_cost = error.erdist(dest_outputs, y_dest).mean() - dest_cost.name = 'dest_cost' - dest_hcost = error.hdist(dest_outputs, y_dest).mean() - dest_hcost.name = 'dest_hcost' - - time_cost = error.rmsle(time_outputs.flatten(), y_time.flatten()) - time_cost.name = 'time_cost' - time_scost = config.time_cost_factor * time_cost - time_scost.name = 'time_scost' - - cost = dest_cost + time_scost - - if hasattr(config, 'dropout_p'): - cg = ComputationGraph(cost) - dropout_inputs = VariableFilter( - bricks=[b for b in list(common_mlp.children) + - list(dest_mlp.children) + - list(time_mlp.children) - if isinstance(b, Rectifier)], - name='output')(cg) - cg = apply_dropout(cg, dropout_inputs, config.dropout_p) - cost = cg.outputs[0] - - cost.name = 'cost' - - # Initialization - for tbl in embed_tables: - tbl.weights_init = config.embed_weights_init - tbl.initialize() - - for mlp in [common_mlp, dest_mlp, time_mlp]: - mlp.weights_init = config.mlp_weights_init - mlp.biases_init = config.mlp_biases_init - mlp.initialize() - - self.cost = cost - self.monitor = [cost, dest_cost, dest_hcost, time_cost, time_scost] - self.outputs = tensor.concatenate([dest_outputs, time_outputs[:, None]], axis=1) - self.outputs.name = 'outputs' - self.pred_vars = ['destination_longitude', 'destination_latitude', 'travel_time'] + 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'] |