def testMeanVariance(self):
pln = tfd.PoissonLogNormalQuadratureCompound(
loc=tf1.placeholder_with_default(
0., shape=[] if self.static_shape else None),
scale=tf1.placeholder_with_default(
1., shape=[] if self.static_shape else None),
quadrature_size=10,
validate_args=True)
self.run_test_sample_consistent_mean_variance(self.evaluate, pln, rtol=0.02)
def testAssertValidSample(self):
pln = tfd.PoissonLogNormalQuadratureCompound(
loc=tf1.placeholder_with_default(
0., shape=[] if self.static_shape else None),
scale=tf1.placeholder_with_default(
1., shape=[] if self.static_shape else None),
quadrature_size=10,
validate_args=True)
with self.assertRaisesOpError('Sample must be non-negative.'):
self.evaluate(pln.log_prob([-1.2, 3., 4.2]))
def testMeanVarianceBroadcastBoth(self):
pln = tfd.PoissonLogNormalQuadratureCompound(
loc=tf1.placeholder_with_default(
[[0.], [-0.5]], shape=[2, 1] if self.static_shape else None),
scale=tf1.placeholder_with_default(
[[1., 0.9]], shape=[1, 2] if self.static_shape else None),
quadrature_size=10,
validate_args=True)
self.run_test_sample_consistent_mean_variance(
self.evaluate, pln, rtol=0.1, atol=0.01)
def testSampleProbConsistentBroadcastScalar(self):
pln = tfd.PoissonLogNormalQuadratureCompound(
loc=tf1.placeholder_with_default(
[0., -0.5], shape=[2] if self.static_shape else None),
scale=tf1.placeholder_with_default(
1., shape=[] if self.static_shape else None),
quadrature_size=10,
validate_args=True)
self.run_test_sample_consistent_log_prob(
self.evaluate, pln, batch_size=2, rtol=0.1, atol=0.01)
def testGradientThroughParams(self):
pln = tfd.PoissonLogNormalQuadratureCompound(
loc=tf.Variable([0., -0.5], shape=[2] if self.static_shape
else None),
scale=tf.Variable([1., 0.9], shape=[2] if self.static_shape
else None),
quadrature_size=10, validate_args=True)
with tf.GradientTape() as tape:
loss = -pln.log_prob([1., 2.])
grad = tape.gradient(loss, pln.trainable_variables)
self.assertLen(grad, 2)
self.assertFalse(any([g is None for g in grad]))
def testGradientThroughNonVariableParams(self):
pln = tfd.PoissonLogNormalQuadratureCompound(
loc=tf.convert_to_tensor([0., -0.5]),
scale=tf.convert_to_tensor([1., 0.9]),
quadrature_size=10, validate_args=True)
with tf.GradientTape() as tape:
tape.watch(pln.loc)
tape.watch(pln.scale)
loss = -pln.log_prob([1., 2.])
grad = tape.gradient(loss, [pln.loc, pln.scale])
self.assertLen(grad, 2)
self.assertFalse(any([g is None for g in grad]))
def testPdfBoundary(self):
pln = tfd.PoissonLogNormalQuadratureCompound(
loc=tf1.placeholder_with_default(
0., shape=[] if self.static_shape else None),
scale=tf1.placeholder_with_default(
1., shape=[] if self.static_shape else None),
quadrature_size=10,
validate_args=True)
pdf = self.evaluate(pln.prob(0.))
log_pdf = self.evaluate(pln.log_prob(0.))
self.assertAllFinite(pdf)
self.assertAllFinite(log_pdf)
