def testScalarCongruency(self):
with self.test_session():
bijector = Chain((Exp(), Softplus()))
assert_scalar_congruency(bijector,
lower_x=1e-3,
upper_x=1.5,
rtol=0.05)
def testScalarCongruency(self):
with self.cached_session():
chain = Chain((Exp(), Softplus()))
assert_scalar_congruency(chain,
lower_x=1e-3,
upper_x=1.5,
rtol=0.05)
def testHingeSoftnessZeroRaises(self):
with self.test_session():
bijector = Softplus(event_ndims=0,
hinge_softness=0.,
validate_args=True)
with self.assertRaisesOpError("must be non-zero"):
bijector.forward([1., 1.]).eval()
def testBijectorForwardInverseWithHingeSoftnessEventDimsZero(self):
with self.test_session():
bijector = Softplus(hinge_softness=1.5)
x = 2 * rng.randn(2, 10)
y = 1.5 * self._softplus(x / 1.5)
self.assertAllClose(y, bijector.forward(x).eval())
self.assertAllClose(x, bijector.inverse(y).eval())
def testBijectorForwardInverseEventDimsOne(self):
with self.test_session():
bijector = Softplus()
self.assertEqual("softplus", bijector.name)
x = 2 * rng.randn(2, 10)
y = self._softplus(x)
self.assertAllClose(y, bijector.forward(x).eval())
self.assertAllClose(x, bijector.inverse(y).eval())
def testBijectorLogDetJacobianEventDimsOne(self):
with self.test_session():
bijector = Softplus(event_ndims=1)
y = 2 * rng.rand(2, 10)
ildj_before = self._softplus_ildj_before_reduction(y)
ildj = np.sum(ildj_before, axis=1)
self.assertAllClose(ildj,
bijector.inverse_log_det_jacobian(y).eval())
def testBijectorLogDetJacobianEventDimsZero(self):
with self.test_session():
bijector = Softplus(event_ndims=0)
y = 2 * rng.rand(2, 10)
# No reduction needed if event_dims = 0.
ildj = self._softplus_ildj_before_reduction(y)
self.assertAllClose(ildj,
bijector.inverse_log_det_jacobian(y).eval())
def testBijectiveAndFinite16bit(self):
with self.test_session():
bijector = Softplus(event_ndims=0)
# softplus(-20) is zero, so we can't use such a large range as in 32bit.
x = np.linspace(-10., 20., 100).astype(np.float16)
# Note that float16 is only in the open set (0, inf) for a smaller
# logspace range. The actual range was (-7, 4), so use something smaller
# for the test.
y = np.logspace(-6, 3, 100).astype(np.float16)
assert_bijective_and_finite(bijector, x, y, rtol=1e-1, atol=1e-3)
def testBijectiveAndFiniteWithNegativeHingeSoftness32Bit(self):
with self.test_session():
bijector = Softplus(hinge_softness=-0.7)
x = np.linspace(-20., 20., 100).astype(np.float32)
y = -np.logspace(-10, 10, 100).astype(np.float32)
assert_bijective_and_finite(bijector,
x,
y,
event_ndims=0,
rtol=1e-2,
atol=1e-2)
def testBijectiveAndFinite32bit(self):
with self.test_session():
bijector = Softplus()
x = np.linspace(-20., 20., 100).astype(np.float32)
y = np.logspace(-10, 10, 100).astype(np.float32)
assert_bijective_and_finite(bijector,
x,
y,
event_ndims=0,
rtol=1e-2,
atol=1e-2)
def testBijector(self):
with self.test_session():
chain = Chain((Exp(event_ndims=1), Softplus(event_ndims=1)))
self.assertEqual("chain_of_exp_of_softplus", chain.name)
x = np.asarray([[[1., 2.], [2., 3.]]])
self.assertAllClose(1. + np.exp(x), chain.forward(x).eval())
self.assertAllClose(np.log(x - 1.), chain.inverse(x).eval())
self.assertAllClose(-np.sum(np.log(x - 1.), axis=2),
chain.inverse_log_det_jacobian(x).eval())
self.assertAllClose(np.sum(x, axis=2),
chain.forward_log_det_jacobian(x).eval())
def testMinEventNdimsChain(self):
chain = Chain([Exp(), Exp(), Exp()])
self.assertEqual(0, chain.forward_min_event_ndims)
self.assertEqual(0, chain.inverse_min_event_ndims)
chain = Chain([Affine(), Affine(), Affine()])
self.assertEqual(1, chain.forward_min_event_ndims)
self.assertEqual(1, chain.inverse_min_event_ndims)
chain = Chain([Exp(), Affine()])
self.assertEqual(1, chain.forward_min_event_ndims)
self.assertEqual(1, chain.inverse_min_event_ndims)
chain = Chain([Affine(), Exp()])
self.assertEqual(1, chain.forward_min_event_ndims)
self.assertEqual(1, chain.inverse_min_event_ndims)
chain = Chain([Affine(), Exp(), Softplus(), Affine()])
self.assertEqual(1, chain.forward_min_event_ndims)
self.assertEqual(1, chain.inverse_min_event_ndims)
def testScalarCongruency(self):
with self.test_session():
bijector = Softplus()
assert_scalar_congruency(bijector, lower_x=-2., upper_x=2.)
def testScalarCongruencyWithNegativeHingeSoftness(self):
with self.test_session():
bijector = Softplus(hinge_softness=-1.3)
assert_scalar_congruency(bijector, lower_x=-2., upper_x=2.)
def testScalarCongruencyWithPositiveHingeSoftness(self):
with self.test_session():
bijector = Softplus(event_ndims=0, hinge_softness=1.3)
assert_scalar_congruency(bijector, lower_x=-2., upper_x=2.)
def testHingeSoftnessZeroRaises(self):
with self.cached_session():
with self.assertRaisesWithPredicateMatch(
errors.InvalidArgumentError, "must be non-zero"):
_ = Softplus(hinge_softness=0., validate_args=True)