def testBasic(self):
for dtype in [dtypes.float32, dtypes.float64]:
one = constant_op.constant([1], dtype=dtype)
two = constant_op.constant([2], dtype=dtype)
zero = constant_op.constant([0], dtype=dtype)
nan = constant_op.constant([np.nan], dtype=dtype)
eps = constant_op.constant([np.finfo(dtype.as_numpy_dtype).eps],
dtype=dtype)
self.assertAllEqual(math_ops.nextafter(one, two) - one, eps)
self.assertAllLess(math_ops.nextafter(one, zero) - one, 0)
self.assertAllEqual(math_ops.is_nan(math_ops.nextafter(nan, one)), [True])
self.assertAllEqual(math_ops.is_nan(math_ops.nextafter(one, nan)), [True])
self.assertAllEqual(math_ops.nextafter(one, one), one)
def testBroadcastingWithGradientChecker(self):
for dtype in [dtypes.float32, dtypes.float64]:
with self.cached_session():
x1 = np.array([-1, 0, 1, 2, 3], dtype=dtype.as_numpy_dtype)
x2 = np.array([2], dtype=dtype.as_numpy_dtype)
err = gradient_checker_v2.max_error(
*gradient_checker_v2.compute_gradient(
lambda x: math_ops.nextafter(x, x2), [x1])) # pylint: disable=cell-var-from-loop
self.assertLess(err, 1e-3)
def testBasic(self):
for dtype in [dtypes.float32, dtypes.float64]:
one = constant_op.constant([1], dtype=dtype)
two = constant_op.constant([2], dtype=dtype)
zero = constant_op.constant([0], dtype=dtype)
nan = constant_op.constant([np.nan], dtype=dtype)
eps = constant_op.constant([np.finfo(dtype.as_numpy_dtype).eps],
dtype=dtype)
self.assertAllEqual(math_ops.nextafter(one, two) - one, eps)
self.assertAllLess(math_ops.nextafter(one, zero) - one, 0)
self.assertAllEqual(
math_ops.is_nan(math_ops.nextafter(nan, one)), [True])
self.assertAllEqual(
math_ops.is_nan(math_ops.nextafter(one, nan)), [True])
self.assertAllEqual(math_ops.nextafter(one, one), one)
def testBroadcasting(self):
for dtype in [dtypes.float32, dtypes.float64]:
one = constant_op.constant([1, 1], dtype=dtype)
two = constant_op.constant([2], dtype=dtype)
eps = np.finfo(dtype.as_numpy_dtype).eps
eps_const = constant_op.constant([eps, eps], dtype=dtype)
self.assertAllEqual(math_ops.nextafter(one, two) - one, eps_const)
def testBroadcasting(self):
for dtype in [dtypes.float32, dtypes.float64]:
one = constant_op.constant([1, 1], dtype=dtype)
two = constant_op.constant([2], dtype=dtype)
eps = np.finfo(dtype.as_numpy_dtype).eps
eps_const = constant_op.constant([eps, eps], dtype=dtype)
self.assertAllEqual(math_ops.nextafter(one, two) - one, eps_const)
def _nextafter_gradient(self, x1, x2):
with backprop.GradientTape() as tape:
tape.watch(x1)
tape.watch(x2)
y = math_ops.nextafter(x1, x2)
return tape.gradient(y, [x1, x2])
