def test_log_nan_value(test_case):
arr = np.array([-0.7168, -0.5471, -0.8933, -1.4428, -0.1190])
input = flow.Tensor(arr, dtype=flow.float32)
np_out = np.full((5, ), np.nan)
of_out = flow.log(input)
test_case.assertTrue(
np.allclose(of_out.numpy(), np_out, 1e-5, 1e-5, equal_nan=True))
def _test_log(test_case, shape, device):
np_arr = np.abs(np.random.randn(*shape))
input = flow.Tensor(np_arr, dtype=flow.float32, device=flow.device(device))
of_out = flow.log(input)
np_out = np.log(np_arr)
test_case.assertTrue(
np.allclose(of_out.numpy(), np_out, 1e-5, 1e-5, equal_nan=True))
def test_log(test_case):
input = flow.Tensor(np.random.randn(2, 3, 4, 5), dtype=flow.float32)
of_out = flow.log(input)
np_out = np.log(input.numpy())
test_case.assertTrue(
np.allclose(of_out.numpy(), np_out, 1e-5, 1e-5, equal_nan=True))
test_case.assertTrue(
np.allclose(input.log().numpy(), np_out, equal_nan=True))
def _test_log_backward(test_case, shape, device):
x = flow.Tensor(
np.random.randn(*shape),
dtype=flow.float32,
device=flow.device(device),
requires_grad=True,
)
y = flow.log(x)
z = y.sum()
z.backward()
np_grad = 1 / x.numpy()
test_case.assertTrue(
np.allclose(x.grad.numpy(), np_grad, 1e-5, 1e-5, equal_nan=True))