def testBijectorForwardInverseEventDimsOne(self):
bijector = Softsign(validate_args=True)
self.assertEqual("softsign", bijector.name)
x = 2. * self._rng.randn(2, 10)
y = self._softsign(x)
self.assertAllClose(y, self.evaluate(bijector.forward(x)))
self.assertAllClose(x, self.evaluate(bijector.inverse(y)))
def testBijectorLogDetJacobianEventDimsOne(self):
bijector = Softsign(validate_args=True)
y = self._rng.rand(2, 10)
ildj_before = self._softsign_ildj_before_reduction(y)
ildj = np.sum(ildj_before, axis=1)
self.assertAllClose(
ildj,
self.evaluate(bijector.inverse_log_det_jacobian(y, event_ndims=1)))
def testBijectorLogDetJacobianEventDimsZero(self):
bijector = Softsign(validate_args=True)
y = self._rng.rand(2, 10)
# No reduction needed if event_dims = 0.
ildj = self._softsign_ildj_before_reduction(y)
self.assertAllClose(
ildj,
self.evaluate(bijector.inverse_log_det_jacobian(y, event_ndims=0)))
def testBijectiveAndFinite(self):
with self.cached_session():
bijector = Softsign(validate_args=True)
x = np.linspace(-20., 20., 100).astype(np.float32)
y = np.linspace(-0.99, 0.99, 100).astype(np.float32)
assert_bijective_and_finite(bijector,
x,
y,
event_ndims=0,
rtol=1e-3,
atol=1e-3)
def testBijectorBounds(self):
bijector = Softsign(validate_args=True)
with self.assertRaisesOpError("greater than -1"):
self.evaluate(bijector.inverse(-3.))
with self.assertRaisesOpError("greater than -1"):
self.evaluate(bijector.inverse_log_det_jacobian(-3.,
event_ndims=0))
with self.assertRaisesOpError("less than 1"):
self.evaluate(bijector.inverse(3.))
with self.assertRaisesOpError("less than 1"):
self.evaluate(bijector.inverse_log_det_jacobian(3., event_ndims=0))
def testScalarCongruency(self):
with self.cached_session():
bijector = Softsign(validate_args=True)
assert_scalar_congruency(bijector, lower_x=-20., upper_x=20.)