def setUp(self):
extra1 = at.iscalar("extra1")
extra1_ = np.array(0, dtype=extra1.dtype)
extra1.dshape = tuple()
extra1.dsize = 1
val1 = at.vector("val1")
val1_ = np.zeros(3, dtype=val1.dtype)
val1.dshape = (3, )
val1.dsize = 3
val2 = at.matrix("val2")
val2_ = np.zeros((2, 3), dtype=val2.dtype)
val2.dshape = (2, 3)
val2.dsize = 6
self.val1, self.val1_ = val1, val1_
self.val2, self.val2_ = val2, val2_
self.extra1, self.extra1_ = extra1, extra1_
self.cost = extra1 * val1.sum() + val2.sum()
self.f_grad = ValueGradFunction([self.cost], [val1, val2],
{extra1: extra1_},
mode="FAST_COMPILE")
def test_invalid_type(self):
a = at.ivector("a")
a.tag.test_value = np.zeros(3, dtype=a.dtype)
a.dshape = (3, )
a.dsize = 3
with pytest.raises(TypeError) as err:
ValueGradFunction([a.sum()], [a], {}, mode="FAST_COMPILE")
err.match("Invalid dtype")
def test_no_extra(self):
a = at.vector("a")
a.tag.test_value = np.zeros(3, dtype=a.dtype)
f_grad = ValueGradFunction([a.sum()], [a], {}, mode="FAST_COMPILE")
assert f_grad._extra_vars == []
