def testSample(self):
"""Ensure samples can be drawn."""
with ops.Graph().as_default(), self.test_session() as sess:
logits = np.asarray([
[0., 0., 0.], #
[1., -1., 0.]
]).astype(np.float32)
loss = loss_functions.OnehotCategoricalLogitsNegativeLogProbLoss(
array_ops.constant(logits))
sample = loss.sample(42)
sample = sess.run(sample)
self.assertEqual(sample.shape, (2, 3))
def testEvaluateOnSample(self):
"""Ensure log probability of a sample can be drawn."""
with ops.Graph().as_default(), self.test_session() as sess:
logits = np.asarray([
[0., 0., 0.], #
[1., -1., 0.]
]).astype(np.float32)
loss = loss_functions.OnehotCategoricalLogitsNegativeLogProbLoss(
array_ops.constant(logits))
neg_log_prob = loss.evaluate_on_sample(42)
# Simply ensure this doesn't crash. As the output is random, it's
# difficult to say if the output is correct or not...
neg_log_prob = sess.run(neg_log_prob)
def testMultiMinibatchRegistration(self):
"""Ensure this loss function supports registering multiple minibatches."""
with ops.Graph().as_default():
tower_logits = []
loss = None
num_towers = 5
for _ in range(num_towers):
logits = random_ops.random_uniform(shape=[2, 3])
tower_logits.append(logits)
if loss is None:
loss = loss_functions.OnehotCategoricalLogitsNegativeLogProbLoss(
logits)
else:
loss.register_additional_minibatch(logits)
self.assertListEqual(loss.input_minibatches, tower_logits)
self.assertEqual(loss.num_registered_minibatches, num_towers)
def testEvaluateOnTargets(self):
"""Ensure log probability can be evaluated correctly."""
with ops.Graph().as_default(), self.test_session() as sess:
logits = np.asarray([
[0., 0., 0.], #
[1., -1., 0.]
]).astype(np.float32)
targets = np.asarray([2, 1]).astype(np.int32)
loss = loss_functions.OnehotCategoricalLogitsNegativeLogProbLoss(
array_ops.constant(logits),
targets=array_ops.one_hot(targets, 3))
neg_log_prob = loss.evaluate()
neg_log_prob = sess.run(neg_log_prob)
# Calculate explicit log probability of targets.
probs = np.exp(logits) / np.sum(
np.exp(logits), axis=1, keepdims=True)
log_probs = np.log([
probs[0, targets[0]], #
probs[1, targets[1]]
])
expected_log_prob = np.sum(log_probs)
self.assertAllClose(neg_log_prob, -expected_log_prob)