Revert changes to test after new update to upcasting mechanism of random func.

theano/tensor/tests/test_raw_random.py

@@ -185,7 +185,7 @@ class T_random_function(unittest.TestCase):
                 broadcastable=(False, True, True))()
         high = tensor.TensorType(dtype='float64',
                 broadcastable=(True, True, True, False))()
-        post_out2, out2 = uniform(rng_R, size=None, ndim=2, low=low, high=high, dtype='float64')
+        post_out2, out2 = uniform(rng_R, size=None, ndim=2, low=low, high=high)
         self.assertEqual(out2.ndim, 4)
         self.assertEqual(out2.broadcastable, (True,False,True,False))
 
@@ -585,7 +585,7 @@ class T_random_function(unittest.TestCase):
         rng_R = random_state_type()
         low = tensor.dvector()
         high = tensor.dcol()
-        post_r, out = uniform(rng_R, low=low, high=high, dtype='float64')
+        post_r, out = uniform(rng_R, low=low, high=high)
         assert out.ndim == 2
         f = compile.function([rng_R, low, high], [post_r, out], accept_inplace=True)
 

theano/tensor/tests/test_shared_randomstreams.py

@@ -358,7 +358,7 @@ class T_SharedRandomStreams(unittest.TestCase):
     def test_vector_arguments(self):
         random = RandomStreams(utt.fetch_seed())
         low = tensor.dvector()
-        out = random.uniform(low=low, high=1, dtype='float64')
+        out = random.uniform(low=low, high=1)
         assert out.ndim == 1
         f = function([low], out)
 
@@ -372,7 +372,7 @@ class T_SharedRandomStreams(unittest.TestCase):
         assert numpy.all(val1 == numpy_val1)
 
         high = tensor.vector()
-        outb = random.uniform(low=low, high=high, dtype='float64')
+        outb = random.uniform(low=low, high=high)
         assert outb.ndim == 1
         fb = function([low, high], outb)
 
@@ -388,7 +388,7 @@ class T_SharedRandomStreams(unittest.TestCase):
         #self.assertRaises(ValueError, fb, [-4., -2], [-1])
 
         size = tensor.lvector()
-        outc = random.uniform(low=low, high=high, size=size, ndim=1, dtype='float64')
+        outc = random.uniform(low=low, high=high, size=size, ndim=1)
         fc = function([low, high, size], outc)
 
         numpy_rng = numpy.random.RandomState(int(seed_gen.randint(2**30)))
@@ -409,7 +409,7 @@ class T_SharedRandomStreams(unittest.TestCase):
         random = RandomStreams(utt.fetch_seed())
         low = tensor.dvector()
         high = tensor.dcol()
-        out = random.uniform(low=low, high=high, dtype='float64')
+        out = random.uniform(low=low, high=high)
         assert out.ndim == 2
         f = function([low, high], out)
 
@@ -431,7 +431,7 @@ class T_SharedRandomStreams(unittest.TestCase):
         random = RandomStreams(utt.fetch_seed())
         low = tensor.dvector()
         high = tensor.dvector()
-        out = random.uniform(low=low, high=high, dtype='float64')
+        out = random.uniform(low=low, high=high)
         assert out.ndim == 1
         f = function([low, high], out)
 
@@ -455,7 +455,7 @@ class T_SharedRandomStreams(unittest.TestCase):
         assert numpy.all(val1 == numpy_val1)
 
         # Specifying the size explicitly
-        g = function([low, high], random.uniform(low=low, high=high, size=(3,), dtype='float64'))
+        g = function([low, high], random.uniform(low=low, high=high, size=(3,)))
         val2 = g(low_val, high_val)
         numpy_rng = numpy.random.RandomState(int(seed_gen.randint(2**30)))
         numpy_val2 = numpy_rng.uniform(low=low_val, high=high_val, size=(3,))