def func(self, place):
prog = fluid.Program()
with fluid.program_guard(prog):
x = layers.create_parameter(dtype="float64", shape=[2, 8], name='x')
y = layers.create_parameter(dtype="float64", shape=[8, 4], name='y')
z = layers.mul(x=x, y=y)
gradient_checker.grad_check([x, y], z, place=place)
def func(self, place):
# use small size since Jacobian gradients is time consuming
root_data = self.root_data[..., :3, :3]
prog = fluid.Program()
with fluid.program_guard(prog):
root = layers.create_parameter(
dtype=root_data.dtype, shape=root_data.shape)
root_t = layers.transpose(root, self.trans_dims)
x = layers.matmul(x=root, y=root_t) + 1e-05
out = paddle.cholesky(x, upper=self.attrs["upper"])
grad_check(root, out, x_init=root_data, place=place)
