diff options
Diffstat (limited to 'model')
| -rw-r--r-- | model/joint_simple_mlp_tgtcls.py | 90 |
1 files changed, 90 insertions, 0 deletions
diff --git a/model/joint_simple_mlp_tgtcls.py b/model/joint_simple_mlp_tgtcls.py new file mode 100644 index 0000000..0a38e06 --- /dev/null +++ b/model/joint_simple_mlp_tgtcls.py @@ -0,0 +1,90 @@ +from blocks.bricks import MLP, Rectifier, Linear, Sigmoid, Identity, Softmax +from blocks.bricks.lookup import LookupTable + +import numpy +import theano +from theano import tensor + +import data +import error + +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] + + x_input_time = tensor.lvector('input_time') + + input_list = [x_firstk_latitude, x_firstk_longitude, x_lastk_latitude, x_lastk_longitude] + embed_tables = [] + + self.require_inputs = ['first_k_latitude', 'first_k_longitude', 'last_k_latitude', 'last_k_longitude', 'input_time'] + + 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)) + + y_dest = tensor.concatenate((tensor.vector('destination_latitude')[:, None], + tensor.vector('destination_longitude')[:, None]), axis=1) + y_time = tensor.lvector('travel_time') + + # Define the model + common_mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden], + dims=[config.dim_input] + config.dim_hidden) + + 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') + + 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_outputs = tensor.dot(dest_cls_probas, dest_classes) + dest_outputs.name = 'dest_outputs' + + time_cls_probas = time_mlp.apply(hidden) + 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' + cost = dest_cost + time_cost + 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] + self.outputs = tensor.concatenate([dest_outputs, time_outputs[:, None]], axis=1) + self.outputs.name = 'outputs' + self.pred_vars = ['destination_longitude', 'destination_latitude', 'travel_time'] + |