def testScalarCongruency(self):
with self.test_session():
bijector = chain_lib.Chain(
(exp_lib.Exp(), softplus_lib.Softplus()))
bijector_test_util.assert_scalar_congruency(bijector,
lower_x=1e-3,
upper_x=1.5,
rtol=0.05)
def testBijectorLogDetJacobianEventDimsOne(self):
with self.test_session():
bijector = softplus_lib.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 testBijectorForwardInverseEventDimsOne(self):
with self.test_session():
bijector = softplus_lib.Softplus(event_ndims=1)
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 testBijectorLogDetJacobianEventDimsZero(self):
with self.test_session():
bijector = softplus_lib.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 testBijectiveAndFinite32bit(self):
with self.test_session():
bijector = softplus_lib.Softplus(event_ndims=0)
x = np.linspace(-20., 20., 100).astype(np.float32)
y = np.logspace(-10, 10, 100).astype(np.float32)
bijector_test_util.assert_bijective_and_finite(bijector,
x,
y,
rtol=1e-2,
atol=1e-2)
def testBijectorForwardInverseEventDimsZero(self):
with self.test_session():
bijector = softplus_lib.Softplus(event_ndims=0)
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())
self.assertAllClose(
x,
bijector.inverse_and_inverse_log_det_jacobian(y)[0].eval())
def testBijector(self):
with self.test_session():
chain = chain_lib.Chain((exp_lib.Exp(event_ndims=1),
softplus_lib.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 testBijectiveAndFinite16bit(self):
with self.test_session():
bijector = softplus_lib.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)
bijector_test_util.assert_bijective_and_finite(bijector,
x,
y,
rtol=1e-1,
atol=1e-3)
def __init__(self,
diag_bijector=None,
diag_shift=1e-5,
validate_args=False,
name="scale_tril"):
"""Instantiates the `ScaleTriL` bijector.
Args:
diag_bijector: `Bijector` instance, used to transform the output diagonal
to be positive.
Default value: `None` (i.e., `tfb.Softplus()`).
diag_shift: Float value broadcastable and added to all diagonal entries
after applying the `diag_bijector`. Setting a positive
value forces the output diagonal entries to be positive, but
prevents inverting the transformation for matrices with
diagonal entries less than this value.
Default value: `1e-5` (i.e., no shift is applied).
validate_args: Python `bool` indicating whether arguments should be
checked for correctness.
Default value: `False` (i.e., arguments are not validated).
name: Python `str` name given to ops managed by this object.
Default value: `scale_tril`.
"""
if diag_bijector is None:
diag_bijector = softplus.Softplus(validate_args=validate_args)
if diag_shift is not None:
diag_bijector = chain.Chain(
[affine_scalar.AffineScalar(shift=diag_shift), diag_bijector])
super(ScaleTriL, self).__init__([
transform_diagonal.TransformDiagonal(diag_bijector=diag_bijector),
fill_triangular.FillTriangular()
],
validate_args=validate_args,
name=name)
def testScalarCongruency(self):
with self.test_session():
bijector = softplus_lib.Softplus(event_ndims=0)
bijector_test_util.assert_scalar_congruency(bijector,
lower_x=-2.,
upper_x=2.)
