diff --git a/GPy/kern/_src/prod.py b/GPy/kern/_src/prod.py
index 241c2448..5e4c0d29 100644
--- a/GPy/kern/_src/prod.py
+++ b/GPy/kern/_src/prod.py
@@ -61,7 +61,6 @@ class Prod(CombinationKernel):
             for p in self.parts:
                 p.update_gradients_full(k/p.K(X,X2),X,X2)
         except FloatingPointError:
-            #print "WARNING: gradient calculation falling back to slow version due to zero-valued kernel"
             for combination in itertools.combinations(self.parts, len(self.parts) - 1):
                 prod = reduce(np.multiply, [p.K(X, X2) for p in combination])
                 to_update = list(set(self.parts) - set(combination))[0]
diff --git a/GPy/testing/kernel_tests.py b/GPy/testing/kernel_tests.py
index ac6d7ab4..f9d90607 100644
--- a/GPy/testing/kernel_tests.py
+++ b/GPy/testing/kernel_tests.py
@@ -408,11 +408,9 @@ class KernelTestsProductWithZeroValues(unittest.TestCase):
         lin = GPy.kern.Linear(2)
         bias = GPy.kern.Bias(2)
         k = lin * bias
-        m = GPy.models.GPRegression(X, Y, kernel=k)
-        try:
-            m.optimize()
-        except np.linalg.LinAlgError:
-            self.fail("Zero-valued kernel raised exception!")
+        k.update_gradients_full(1, X)
+        self.assertFalse(np.isnan(k['linear.variances'].gradient),
+                         "Gradient resulted in NaN")