def testScalarCongruency(self):
with self.test_session():
bijector = bijectors.Invert(bijectors.Exp())
assert_scalar_congruency(bijector,
lower_x=1e-3,
upper_x=1.5,
rtol=0.05)
def testBijector(self):
with self.test_session():
for fwd in [
bijectors.Identity(),
bijectors.Exp(event_ndims=1),
bijectors.Affine(shift=[0., 1.],
scale_diag=[2., 3.],
event_ndims=1),
bijectors.Softplus(event_ndims=1),
bijectors.SoftmaxCentered(event_ndims=1),
bijectors.SigmoidCentered(),
]:
rev = bijectors.Invert(fwd)
self.assertEqual("_".join(["invert", fwd.name]), rev.name)
x = [[[1., 2.], [2., 3.]]]
self.assertAllClose(
fwd.inverse(x).eval(),
rev.forward(x).eval())
self.assertAllClose(
fwd.forward(x).eval(),
rev.inverse(x).eval())
self.assertAllClose(
fwd.forward_log_det_jacobian(x).eval(),
rev.inverse_log_det_jacobian(x).eval())
self.assertAllClose(
fwd.inverse_log_det_jacobian(x).eval(),
rev.forward_log_det_jacobian(x).eval())
def testDocstringExample(self):
with self.cached_session():
exp_gamma_distribution = (
transformed_distribution_lib.TransformedDistribution(
distribution=gamma_lib.Gamma(concentration=1., rate=2.),
bijector=bijectors.Invert(bijectors.Exp())))
self.assertAllEqual(
[], array_ops.shape(exp_gamma_distribution.sample()).eval())
def testShapeGetters(self):
with self.cached_session():
bijector = bijectors.Invert(bijectors.SoftmaxCentered(validate_args=True))
x = tensor_shape.TensorShape([2])
y = tensor_shape.TensorShape([1])
self.assertAllEqual(y, bijector.forward_event_shape(x))
self.assertAllEqual(
y.as_list(),
bijector.forward_event_shape_tensor(x.as_list()).eval())
self.assertAllEqual(x, bijector.inverse_event_shape(y))
self.assertAllEqual(
x.as_list(),
bijector.inverse_event_shape_tensor(y.as_list()).eval())
