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 testBijectorIdentity(self):
with self.test_session():
chain = chain_lib.Chain()
self.assertEqual("identity", chain.name)
x = np.asarray([[[1., 2.], [2., 3.]]])
self.assertAllClose(x, chain.forward(x).eval())
self.assertAllClose(x, chain.inverse(x).eval())
self.assertAllClose(0., chain.inverse_log_det_jacobian(x).eval())
self.assertAllClose(0., chain.forward_log_det_jacobian(x).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 testShapeGetters(self):
with self.test_session():
bijector = chain_lib.Chain([
softmax_centered_lib.SoftmaxCentered(
event_ndims=1, validate_args=True),
softmax_centered_lib.SoftmaxCentered(
event_ndims=0, validate_args=True)])
x = tensor_shape.TensorShape([])
y = tensor_shape.TensorShape([2 + 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())
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)