Refactor architecture so that embedding sizes can be easily changed.

2 files changed, 33 insertions, 28 deletions

diff --git a/model/simple_mlp.py b/model/simple_mlp.py
index 896ccd3..fc065f7 100644
--- a/model/simple_mlp.py
+++ b/model/simple_mlp.py
@@ -17,25 +17,27 @@ class Model(object):
         x_lastk_latitude = (tensor.matrix('last_k_latitude') - data.porto_center[0]) / data.data_std[0]
         x_lastk_longitude = (tensor.matrix('last_k_longitude') - data.porto_center[1]) / data.data_std[1]
 
-        x_client = tensor.lvector('origin_call')
-        x_stand = tensor.lvector('origin_stand')
+        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']
+
+        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 = tensor.concatenate((tensor.vector('destination_latitude')[:, None],
                                 tensor.vector('destination_longitude')[:, None]), axis=1)
 
         # Define the model
-        client_embed_table = LookupTable(length=data.n_train_clients+1, dim=config.dim_embed, name='client_lookup')
-        stand_embed_table = LookupTable(length=data.n_stands+1, dim=config.dim_embed, name='stand_lookup')
         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)
-        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],
-                                    axis=1)
+        inputs = tensor.concatenate(input_list, axis=1)
         # inputs = theano.printing.Print("inputs")(inputs)
         outputs = mlp.apply(inputs)
 
@@ -55,13 +57,13 @@ class Model(object):
         hcost.name = 'hcost'
 
         # Initialization
-        client_embed_table.weights_init = IsotropicGaussian(0.001)
-        stand_embed_table.weights_init = IsotropicGaussian(0.001)
+        for tbl in embed_tables:
+            tbl.weights_init = IsotropicGaussian(0.001)
         mlp.weights_init = IsotropicGaussian(0.01)
         mlp.biases_init = Constant(0.001)
 
-        client_embed_table.initialize()
-        stand_embed_table.initialize()
+        for tbl in embed_tables:
+            tbl.initialize()
         mlp.initialize()
 
         self.cost = cost
diff --git a/model/simple_mlp_tgtcls.py b/model/simple_mlp_tgtcls.py
index fecd900..b2a1a6e 100644
--- a/model/simple_mlp_tgtcls.py
+++ b/model/simple_mlp_tgtcls.py
@@ -20,26 +20,29 @@ class Model(object):
         x_lastk_latitude = (tensor.matrix('last_k_latitude') - data.porto_center[0]) / data.data_std[0]
         x_lastk_longitude = (tensor.matrix('last_k_longitude') - data.porto_center[1]) / data.data_std[1]
 
-        x_client = tensor.lvector('origin_call')
-        x_stand = tensor.lvector('origin_stand')
+        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']
+
+        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 = tensor.concatenate((tensor.vector('destination_latitude')[:, None],
                                 tensor.vector('destination_longitude')[:, None]), axis=1)
 
         # Define the model
-        client_embed_table = LookupTable(length=data.n_train_clients+1, dim=config.dim_embed, name='client_lookup')
-        stand_embed_table = LookupTable(length=data.n_stands+1, dim=config.dim_embed, name='stand_lookup')
         mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden] + [Softmax()],
                            dims=[config.dim_input] + config.dim_hidden + [config.dim_output])
         classes = theano.shared(numpy.array(config.tgtcls, dtype=theano.config.floatX), name='classes')
 
         # Create the Theano variables
-        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],
-                                    axis=1)
+        inputs = tensor.concatenate(input_list, axis=1)
+
         # inputs = theano.printing.Print("inputs")(inputs)
         cls_probas = mlp.apply(inputs)
         outputs = tensor.dot(cls_probas, classes)
@@ -56,13 +59,13 @@ class Model(object):
         hcost.name = 'hcost'
 
         # Initialization
-        client_embed_table.weights_init = IsotropicGaussian(0.001)
-        stand_embed_table.weights_init = IsotropicGaussian(0.001)
+        for tbl in embed_tables:
+            tbl.weights_init = IsotropicGaussian(0.001)
         mlp.weights_init = IsotropicGaussian(0.01)
         mlp.biases_init = Constant(0.001)
 
-        client_embed_table.initialize()
-        stand_embed_table.initialize()
+        for tbl in embed_tables:
+            tbl.initialize()
         mlp.initialize()
 
         self.cost = cost