def kl_loss(self):
"""Compute the sum of the Kullback–Leibler divergences between this
parameter's priors and its variational posteriors."""
if self.prior is None:
return O.zeros([])
else:
return O.sum(O.kl_divergence(self.posterior, self.prior),
axis=None)
def test_kl_divergence():
"""Tests kl_divergence"""
# Divergence between a distribution and itself should be 0
dist = tfp.distributions.Normal(0, 1)
assert ops.kl_divergence(dist, dist).numpy() == 0.0
# Divergence between two different distributions should be >0
d1 = tfp.distributions.Normal(0, 1)
d2 = tfp.distributions.Normal(1, 1)
assert ops.kl_divergence(d1, d2).numpy() > 0.0
# Divergence between more different distributions should be larger
d1 = tfp.distributions.Normal(0, 1)
d2 = tfp.distributions.Normal(1, 1)
d3 = tfp.distributions.Normal(2, 1)
assert (ops.kl_divergence(d1, d2).numpy() < ops.kl_divergence(d1,
d3).numpy())
def test_kl_divergence():
"""Tests kl_divergence"""
pf.set_backend('pytorch')
# Divergence between a distribution and itself should be 0
dist = torch.distributions.normal.Normal(0, 1)
assert ops.kl_divergence(dist, dist).numpy() == 0.0
# Divergence between two different distributions should be >0
d1 = torch.distributions.normal.Normal(0, 1)
d2 = torch.distributions.normal.Normal(1, 1)
assert ops.kl_divergence(d1, d2).numpy() > 0.0
# Divergence between more different distributions should be larger
d1 = torch.distributions.normal.Normal(0, 1)
d2 = torch.distributions.normal.Normal(1, 1)
d3 = torch.distributions.normal.Normal(2, 1)
assert (ops.kl_divergence(d1, d2).numpy() < ops.kl_divergence(d1,
d3).numpy())
# Should auto-convert probflow distibutions
dist = pf.Normal(0, 1)
assert ops.kl_divergence(dist, dist).numpy() == 0.0
def add_kl_loss(self, loss, d2=None):
"""Add additional loss due to KL divergences."""
if d2 is None:
self._kl_losses += [O.sum(loss, axis=None)]
else:
self._kl_losses += [O.sum(O.kl_divergence(loss, d2), axis=None)]
