Update the subtensor test to have varying values in order to really check that…

theano/tensor/tests/test_subtensor.py

@@ -515,8 +515,8 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
             self.assertRaises(IndexError, g, shp)
 
     def test_adv_sub1_broadcast(self):
-        ones = numpy.ones((1, 3), dtype=self.dtype)
-        n = self.shared(ones * 5, broadcastable=(True, False))
+        v = numpy.arange(3, dtype=self.dtype).reshape((1, 3))
+        n = self.shared(v*5, broadcastable=(True, False))
         idx = tensor.lvector()
         t = n[idx]
         self.assertTrue(isinstance(t.owner.op, tensor.AdvancedSubtensor1))
@@ -529,10 +529,10 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
         self.assertTrue(isinstance(topo_[0].op, self.adv_sub1))
         f_0 = f([0])
         self.assertTrue(f_0.shape == (1, 3))
-        self.assertTrue(numpy.allclose(f_0, ones[0] * 5))
+        self.assertTrue(numpy.allclose(f_0, v*5))
         f_00 = f([0, 0])
         self.assertTrue(f_00.shape == (2, 3))
-        self.assertTrue(numpy.allclose(f_00, 5))
+        self.assertTrue(numpy.allclose(f_00, v*5))
         self.assertRaises(IndexError, f, [0, 1])
 
         # Test the gradient