def test_square(test_case):
input_arr = np.random.randn(9, 4, 5, 6)
np_out = np.square(input_arr)
x = flow.Tensor(input_arr)
of_out = flow.square(x)
test_case.assertTrue(
np.allclose(of_out.numpy(), np_out, 1e-5, 1e-5, equal_nan=True))
def _test_square(test_case, shape, device):
np_arr = np.random.randn(*shape)
np_out = np.square(np_arr)
x = flow.Tensor(np_arr, device=flow.device(device))
of_out = flow.square(x)
test_case.assertTrue(
np.allclose(of_out.numpy(), np_out, 1e-5, 1e-5, equal_nan=True))
def _test_square_backward(test_case, shape, device):
np_arr = np.random.randn(*shape)
np_out = np.square(np_arr)
x = flow.Tensor(np_arr, device=flow.device(device), requires_grad=True)
y = flow.square(x)
z = y.sum()
z.backward()
np_grad = 2 * np_arr
test_case.assertTrue(
np.allclose(x.grad.numpy(), np_grad, 1e-5, 1e-5, equal_nan=True))