def test_broadcast_grad():
# rng = numpy.random.RandomState(utt.fetch_seed())
x1 = tensor4("x")
# x1_data = rng.randn(1, 1, 300, 300)
sigma = scalar("sigma")
# sigma_data = 20
window_radius = 3
filter_1d = aet.arange(-window_radius, window_radius + 1)
filter_1d = filter_1d.astype(aesara.config.floatX)
filter_1d = exp(-0.5 * filter_1d ** 2 / sigma ** 2)
filter_1d = filter_1d / filter_1d.sum()
filter_W = filter_1d.dimshuffle(["x", "x", 0, "x"])
y = conv2d(x1, filter_W, border_mode="full", filter_shape=[1, 1, None, None])
aesara.grad(y.sum(), sigma)
def test_broadcast_grad():
x1 = tensor4("x")
sigma = scalar("sigma")
window_radius = 3
filter_1d = at.arange(-window_radius, window_radius + 1)
filter_1d = filter_1d.astype(aesara.config.floatX)
filter_1d = exp(-0.5 * filter_1d**2 / sigma**2)
filter_1d = filter_1d / filter_1d.sum()
filter_W = filter_1d.dimshuffle(["x", "x", 0, "x"])
y = conv2d(x1,
filter_W,
border_mode="full",
filter_shape=[1, 1, None, None])
# TODO FIXME: Make this a real test and `assert` something
aesara.grad(y.sum(), sigma)