def testLog1p(self):
# Check that we can tell that log1p is a good idea
def bad_log1p(x):
# Extra indirection to fool Grappler in TF1
return tf.math.log(((1. + x) - 1.) + 1.)
small = tf.constant(1e-7, dtype=tf.float32)
self.assertGreater(
self.evaluate(nt.excess_wrong_bits(bad_log1p, small)), 21)
self.assertLessEqual(
self.evaluate(nt.excess_wrong_bits(tf.math.log1p, small)), 0)
def testLogProbAccuracy(self, dist_name, data):
self.skip_if_tf1()
dist = tfe.as_composite(
data.draw(
dhps.distributions(
dist_name=dist_name,
# Accuracy tools can't handle batches (yet?)
batch_shape=(),
# Variables presumably do not affect the numerics
enable_vars=False,
# Checking that samples pass validations (including in 64-bit
# arithmetic) is left for another test
validate_args=False)))
seed = test_util.test_seed(sampler_type='stateless')
with tfp_hps.no_tf_rank_errors():
sample = dist.sample(seed=seed)
if sample.dtype.is_floating:
hp.assume(self.evaluate(tf.reduce_all(~tf.math.is_nan(sample))))
hp.note('Testing on sample {}'.format(sample))
as_tensors = tf.nest.flatten(dist, expand_composites=True)
def log_prob_function(tensors, x):
dist_ = tf.nest.pack_sequence_as(dist,
tensors,
expand_composites=True)
return dist_.log_prob(x)
with tfp_hps.finite_ground_truth_only():
badness = nt.excess_wrong_bits(log_prob_function, as_tensors,
sample)
# TODO(axch): Lower the acceptable badness to 4, which corresponds
# to slightly better accuracy than 1e-6 relative error for
# well-conditioned functions.
self.assertAllLess(badness, 20)
def testCatastrophicCancellation(self):
# Check that we can detect catastrophic cancellations
def bad_identity(x):
return (1. + x) - 1.
# The relative error in this implementation of the identity
# function is terrible.
small = tf.constant(1e-6, dtype=tf.float32)
self.assertGreater(
self.evaluate(nt.relative_error_at(bad_identity, small)), 0.04)
# But the function itself is well-conditioned
self.assertAllEqual(
self.evaluate(nt.inputwise_condition_numbers(bad_identity, small)),
[1.])
# So the error due to poor conditioning is small
self.assertLess(
self.evaluate(nt.error_due_to_ill_conditioning(
bad_identity, small)), 1e-13)
# And we have a lot of excess wrong bits
self.assertGreater(
self.evaluate(nt.excess_wrong_bits(bad_identity, small)), 19)