def testBetaWithSoftplusConcentration(self):
a, b = -4.2, -9.1
dist = beta_lib.BetaWithSoftplusConcentration(a, b)
self.assertAllClose(
self.evaluate(nn_ops.softplus(a)), self.evaluate(dist.concentration1))
self.assertAllClose(
self.evaluate(nn_ops.softplus(b)), self.evaluate(dist.concentration0))
def testBetaWithSoftplusConcentration(self):
with self.test_session():
a, b = -4.2, -9.1
dist = beta_lib.BetaWithSoftplusConcentration(a, b)
self.assertAllClose(
nn_ops.softplus(a).eval(), dist.concentration1.eval())
self.assertAllClose(
nn_ops.softplus(b).eval(), dist.concentration0.eval())
def testBetaBetaKL(self):
with self.test_session() as sess:
for shape in [(10, ), (4, 5)]:
a1 = 6.0 * np.random.random(size=shape) + 1e-4
b1 = 6.0 * np.random.random(size=shape) + 1e-4
a2 = 6.0 * np.random.random(size=shape) + 1e-4
b2 = 6.0 * np.random.random(size=shape) + 1e-4
# Take inverse softplus of values to test BetaWithSoftplusConcentration
a1_sp = np.log(np.exp(a1) - 1.0)
b1_sp = np.log(np.exp(b1) - 1.0)
a2_sp = np.log(np.exp(a2) - 1.0)
b2_sp = np.log(np.exp(b2) - 1.0)
d1 = beta_lib.Beta(concentration1=a1, concentration0=b1)
d2 = beta_lib.Beta(concentration1=a2, concentration0=b2)
d1_sp = beta_lib.BetaWithSoftplusConcentration(
concentration1=a1_sp, concentration0=b1_sp)
d2_sp = beta_lib.BetaWithSoftplusConcentration(
concentration1=a2_sp, concentration0=b2_sp)
if not special:
return
kl_expected = (special.betaln(a2, b2) -
special.betaln(a1, b1) +
(a1 - a2) * special.digamma(a1) +
(b1 - b2) * special.digamma(b1) +
(a2 - a1 + b2 - b1) * special.digamma(a1 + b1))
for dist1 in [d1, d1_sp]:
for dist2 in [d2, d2_sp]:
kl = kullback_leibler.kl_divergence(dist1, dist2)
kl_val = sess.run(kl)
self.assertEqual(kl.get_shape(), shape)
self.assertAllClose(kl_val, kl_expected)
# Make sure KL(d1||d1) is 0
kl_same = sess.run(kullback_leibler.kl_divergence(d1, d1))
self.assertAllClose(kl_same, np.zeros_like(kl_expected))