fixing the test that was failing before once the optimization was activated

theano/tests/test_ifelse.py

@@ -42,7 +42,7 @@ class test_ifelse(unittest.TestCase):
         x = tensor.vector('x')
         y = tensor.vector('y')
         c = tensor.iscalar('c')
-        f = theano.function([c, x, y], 2 * ifelse(c, x, y))
+        f = theano.function([c, x, y], ifelse(c, x, y))
         rng = numpy.random.RandomState(utt.fetch_seed())
 
         xlen = rng.randint(200)
@@ -50,10 +50,12 @@ class test_ifelse(unittest.TestCase):
 
         vx = numpy.asarray(rng.uniform(size=(xlen,)), theano.config.floatX)
         vy = numpy.asarray(rng.uniform(size=(ylen,)), theano.config.floatX)
-        assert numpy.all(x.op.inplace for x in f.maker.env.toposort() if
-                         x.op == IfElse)
-        assert numpy.allclose(vx * 2, f(1, vx, vy))
-        assert numpy.allclose(vy * 2, f(0, vx, vy))
+        assert numpy.all([x.op.as_view for x in f.maker.env.toposort() if
+                         isinstance(x.op, IfElse)])
+        assert len([x.op for x in f.maker.env.toposort()
+                   if isinstance(x.op, IfElse)]) > 0
+        assert numpy.allclose(vx, f(1, vx, vy))
+        assert numpy.allclose(vy, f(0, vx, vy))
 
     def test_lazy_if_on_generics(self):
         x = theano.generic()