Remove optional loss masking (now automatic)

keras/models.py

@@ -345,8 +345,7 @@ class Sequential(Model, containers.Sequential):
             - set_weights
     '''
 
-    def compile(self, optimizer, loss, class_mode="categorical", theano_mode=None,
-                mask_cost=False):
+    def compile(self, optimizer, loss, class_mode="categorical", theano_mode=None):
         self.optimizer = optimizers.get(optimizer)
 
         self.loss = objectives.get(loss)
@@ -364,7 +363,7 @@ class Sequential(Model, containers.Sequential):
 
         self.weights = T.ones_like(self.y_train)
 
-        if mask_cost:
+        if hasattr(self.layers[-1], "get_output_mask"):
             mask = self.layers[-1].get_output_mask()
         else:
             mask = None
@@ -556,7 +555,7 @@ class Sequential(Model, containers.Sequential):
 
 
 class Graph(Model, containers.Graph):
-    def compile(self, optimizer, loss, theano_mode=None, mask_cost=False):
+    def compile(self, optimizer, loss, theano_mode=None):
         # loss is a dictionary mapping output name to loss functions
         ys = []
         ys_train = []
@@ -574,10 +573,10 @@ class Graph(Model, containers.Graph):
             ys_train.append(y_train)
             ys_test.append(y_test)
 
-            if mask_cost is None:
-                mask = None
+            if hasattr(self.layers[-1], "get_output_mask"):
+                mask = self.layers[-1].get_output_mask()
             else:
-                mask = output.get_output_mask()
+                mask = None
 
             weight = T.ones_like(y_test)
             weights.append(weight)

tests/auto/test_loss_masking.py

@@ -9,20 +9,11 @@ class TestLossMasking(unittest.TestCase):
         X = np.array(
             [[[1, 1], [2, 1], [3, 1], [5, 5]],
              [[1, 5], [5, 0], [0, 0], [0, 0]]], dtype=np.int32)
-
         model = Sequential()
         model.add(Masking(mask_value=0))
         model.add(TimeDistributedDense(2, 1, init='one'))
         model.compile(loss='mse', optimizer='sgd')
         y = model.predict(X)
-
-        loss = model.fit(X, 4*y, nb_epoch=1, batch_size=2, verbose=1).history['loss'][0]
-        assert loss == 213.75
-
-        model = Sequential()
-        model.add(Masking(mask_value=0))
-        model.add(TimeDistributedDense(2, 1, init='one'))
-        model.compile(loss='mse', optimizer='sgd', mask_cost=True)
         loss = model.fit(X, 4*y, nb_epoch=1, batch_size=2, verbose=1).history['loss'][0]
         assert loss == 282.375