It kind of works (at least it does something now)

1 files changed, 14 insertions, 8 deletions

diff --git a/model.py b/model.py
index 360237e..95b002f 100644
--- a/model.py
+++ b/model.py
@@ -29,7 +29,7 @@ n_dow = 7       # number of division for dayofweek/dayofmonth/hourofday
 n_dom = 31
 n_hour = 24
 
-n_clients = 57106
+n_clients = 57105
 n_stands = 63
 
 n_begin_end_pts = 5     # how many points we consider at the beginning and end of the known trajectory
@@ -45,9 +45,9 @@ def main():
     # The input and the targets
     x_firstk = tensor.matrix('first_k')
     x_lastk = tensor.matrix('last_k')
-    x_client = tensor.lmatrix('origin_call')
-    x_stand = tensor.lmatrix('origin_stand')
-    y = tensor.vector('destination')
+    x_client = tensor.lvector('origin_call')
+    x_stand = tensor.lvector('origin_stand')
+    y = tensor.matrix('destination')
 
     # Define the model
     client_embed_table = LookupTable(length=n_clients+1, dim=dim_embed, name='client_lookup')
@@ -60,12 +60,15 @@ def main():
 
     client_embed = client_embed_table.apply(x_client).flatten(ndim=2)
     stand_embed = stand_embed_table.apply(x_stand).flatten(ndim=2)
-    inputs = tensor.concatenate([x_firstk, x_lastk, client_embed, stand_embed], axis=1)
+    inputs = tensor.concatenate([x_firstk, x_lastk,
+                                 client_embed, stand_embed],
+                                axis=1)
     hidden = hidden_layer.apply(inputs)
     outputs = output_layer.apply(hidden)
 
     # Calculate the cost
     cost = (outputs - y).norm(2, axis=1).mean()
+    cost.name = 'cost'
 
     # Initialization
     client_embed_table.weights_init = IsotropicGaussian(0.001)
@@ -83,12 +86,14 @@ def main():
     train = DataStream(train)
     train = transformers.add_extremities(train, n_begin_end_pts)
     train = transformers.add_destination(train)
+    train = transformers.Select(train, ('origin_stand', 'origin_call', 'first_k', 'last_k', 'destination'))
     train_stream = Batch(train, iteration_scheme=ConstantScheme(batch_size))
 
     valid = data.valid_data
     valid = DataStream(valid)
     valid = transformers.add_extremities(valid, n_begin_end_pts)
     valid = transformers.add_destination(valid)
+    valid = transformers.Select(valid, ('origin_stand', 'origin_call', 'first_k', 'last_k', 'destination'))
     valid_stream = Batch(valid, iteration_scheme=ConstantScheme(batch_size))
 
 
@@ -103,9 +108,10 @@ def main():
     extensions=[DataStreamMonitoring([cost], valid_stream,
                                      prefix='valid',
                                      every_n_batches=100),
-                    Printing(every_n_batches=100),
-                    Dump('ngram_blocks_model', every_n_batches=100),
-                    LoadFromDump('ngram_blocks_model')]
+                Printing(every_n_batches=100),
+                # Dump('taxi_model', every_n_batches=100),
+                # LoadFromDump('taxi_model'),
+                ]
 
     main_loop = MainLoop(
         model=Model([cost]),