Fixes.

2 files changed, 21 insertions, 8 deletions

diff --git a/model.py b/model.py
index 5caad8b..c5c75d3 100644
--- a/model.py
+++ b/model.py
@@ -65,7 +65,7 @@ def setup_stream():
                         load_in_memory=True)
     valid = DataStream(valid, iteration_scheme=SequentialExampleScheme(config.n_valid))
     valid = transformers.add_first_k(config.n_begin_end_pts, valid)
-    valid = transformers.add_last_k(config.n_begin_end_pts, valid)
+    valid = transformers.add_random_k(config.n_begin_end_pts, valid)
     valid = transformers.add_destination(valid)
     valid = transformers.Select(valid, ('origin_stand', 'origin_call', 'first_k_latitude',
                                         'last_k_latitude', 'first_k_longitude', 'last_k_longitude',
@@ -88,23 +88,34 @@ def main():
     y = tensor.concatenate((tensor.vector('destination_latitude')[:, None],
                             tensor.vector('destination_longitude')[:, None]), axis=1)
 
+    # x_firstk_latitude = theano.printing.Print("x_firstk_latitude")(x_firstk_latitude)
+    # x_firstk_longitude = theano.printing.Print("x_firstk_longitude")(x_firstk_longitude)
+    # x_lastk_latitude = theano.printing.Print("x_lastk_latitude")(x_lastk_latitude)
+    # x_lastk_longitude = theano.printing.Print("x_lastk_longitude")(x_lastk_longitude)
+
     # Define the model
     client_embed_table = LookupTable(length=config.n_clients+1, dim=config.dim_embed, name='client_lookup')
     stand_embed_table = LookupTable(length=config.n_stands+1, dim=config.dim_embed, name='stand_lookup')
-    mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden] + [None],
+    mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden] + [Identity()],
                        dims=[config.dim_input] + config.dim_hidden + [config.dim_output])
 
     # Create the Theano variables
-    client_embed = client_embed_table.apply(x_client).flatten(ndim=2)
-    stand_embed = stand_embed_table.apply(x_stand).flatten(ndim=2)
+    client_embed = client_embed_table.apply(x_client)
+    stand_embed = stand_embed_table.apply(x_stand)
     inputs = tensor.concatenate([x_firstk_latitude, x_firstk_longitude,
-                                 x_lastk_latitude, x_lastk_longitude, client_embed, stand_embed],
+                                 x_lastk_latitude, x_lastk_longitude,
+                                 client_embed, stand_embed],
                                 axis=1)
     # inputs = theano.printing.Print("inputs")(inputs)
     outputs = mlp.apply(inputs)
 
     # Normalize & Center
+    # outputs = theano.printing.Print("normal_outputs")(outputs)
     outputs = data.data_std * outputs + data.porto_center
+
+    # outputs = theano.printing.Print("outputs")(outputs)
+    # y = theano.printing.Print("y")(y)
+
     outputs.name = 'outputs'
 
     # Calculate the cost
@@ -127,11 +138,12 @@ def main():
 
     # Training
     cg = ComputationGraph(cost)
+    params = cg.parameters # VariableFilter(bricks=[Linear])(cg.parameters) 
     algorithm = GradientDescent(
         cost=cost,
         # step_rule=AdaDelta(decay_rate=0.5),
         step_rule=Momentum(learning_rate=config.learning_rate, momentum=config.momentum),
-        params=cg.parameters)
+        params=params)
 
     extensions=[DataStreamMonitoring([cost, hcost], valid_stream,
                                      prefix='valid',
diff --git a/transformers.py b/transformers.py
index 3473fd5..c5b8d87 100644
--- a/transformers.py
+++ b/transformers.py
@@ -2,10 +2,11 @@ from fuel.transformers import Transformer, Filter, Mapping
 import numpy
 import theano
 import random
+import data
 
 def at_least_k(k, v, pad_at_begin, is_longitude):
     if len(v) == 0:
-        v = numpy.array([41.1573 if is_longitude else -8.61612], dtype=theano.config.floatX)
+        v = numpy.array([data.porto_center[1 if is_longitude else 0]], dtype=theano.config.floatX)
     if len(v) < k:
         if pad_at_begin:
             v = numpy.concatenate((numpy.full((k - len(v),), v[0]), v))
@@ -40,7 +41,7 @@ def add_random_k(k, stream):
     id_longitude = stream.sources.index('longitude')
     def random_k(x):
         lat = at_least_k(k, x[id_latitude], True, False)
-        lon = at_least_k(k, x[id_latitude], True, True)
+        lon = at_least_k(k, x[id_longitude], True, True)
         loc = random.randrange(len(lat)-k+1)
         return (numpy.array(lat[loc:loc+k], dtype=theano.config.floatX),
                 numpy.array(lon[loc:loc+k], dtype=theano.config.floatX))