Proper handling of output values in Masking layer

keras/layers/core.py

@@ -119,8 +119,8 @@ def get_output_mask(self, train=False):
 class Masking(MaskedLayer):
     """Mask an input sequence by using a mask value to identify padding.
 
-    This layer copies the input to the output layer,
-    while creating an output mask in the process.
+    This layer copies the input to the output layer with identified padding
+    replaced with 0s and creates an output mask in the process.
 
     At each timestep, if the values all equal `mask_value`,
     then the corresponding mask value for the timestep is 0 (skipped),
@@ -136,6 +136,10 @@ def get_output_mask(self, train=False):
         X = self.get_input(train)
         return T.any(T.ones_like(X) * (1. - T.eq(X, self.mask_value)), axis=-1)
 
+    def get_output(self, train=False):
+        X = self.get_input(train)
+        return X * T.shape_padright(T.any((1. - T.eq(X, self.mask_value)), axis=-1))
+
     def get_config(self):
         return {"name": self.__class__.__name__,
                 "mask_value": self.mask_value}

tests/auto/keras/layers/test_core.py

@@ -144,6 +144,19 @@ def test_non_zero(self):
             # This is the expected output mask, one dimension less
             np.array([[1, 1, 1, 0], [1, 1, 1, 1]])))
 
+    def test_non_zero_output(self):
+        """Test output of masking layer with non-zero mask value"""
+        layer = core.Masking(5)
+        func = theano.function([layer.input], layer.get_output())
+        self.assertTrue(np.all(
+            # get output for this input, replace padding with 0
+            func(np.array(
+            [[[1, 1], [2, 1], [3, 1], [5, 5]],
+             [[1, 5], [5, 0], [0, 0], [0, 0]]], dtype=np.int32)) ==
+            # This is the expected output
+            np.array([[[1, 1], [2, 1], [3, 1], [0, 0]],
+             [[1, 5], [5, 0], [0, 0], [0, 0]]])))
+
 
 if __name__ == '__main__':
     unittest.main()