Add model for a network that predicts both time and destination.

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']
+