def testPairedJoint(self):
n = 7
np.random.seed(n)
positives = np.random.randn(n)
negatives = np.random.randn(n)
logits = np.array([positives, negatives]).T.ravel()
labels = np.array([True, False] * n)
with self.test_session() as sess:
joint_logits, joint_labels = sess.run(
model_utils.paired_joint_logits_and_labels(logits, labels))
self.assertEqual(joint_logits.shape, joint_labels.shape)
self.assertAllClose(joint_logits, positives - negatives)
self.assertAllEqual(joint_labels, [True] * n)
def make_metrics():
"""Builds a model and computes metrics."""
# Generate model
logits, labels = make_model()
if joint_safe:
joint_logits, joint_labels = model_utils.paired_joint_logits_and_labels(
logits, labels)
# Measure accuracy
metric_ops = [metrics.streaming_accuracy(logits > 0, labels)]
tf.contrib.deprecated.scalar_summary('eval accuracy', metric_ops[-1][0])
# Measure joint accuracy
if joint_safe:
metric_ops.append(metrics.streaming_accuracy(joint_logits > 0,
joint_labels))
tf.contrib.deprecated.scalar_summary('eval joint accuracy',
metric_ops[-1][0])
return metric_ops
def make_loss():
"""Builds a model and computes losses."""
# Generate model
logits, labels = make_model()
if joint_safe:
joint_logits, joint_labels = model_utils.paired_joint_logits_and_labels(
logits, labels)
# Compute loss
if hparams.loss == 'xent':
slim.losses.sigmoid_cross_entropy(
logits[:, None],
labels[:, None],
label_smoothing=hparams.label_smoothing)
elif hparams.loss == 'joint':
if not joint_safe:
raise ValueError('joint loss needs joint_safe=True')
slim.losses.sigmoid_cross_entropy(
joint_logits[:, None],
joint_labels[:, None],
label_smoothing=hparams.label_smoothing)
else:
raise ValueError('Unknown loss %r' % hparams.loss)
total_loss = slim.losses.get_total_loss()
tf.contrib.deprecated.scalar_summary('total loss', total_loss)
# Measure discrete accuracy
predictions = logits > 0
accuracy = metrics.accuracy(predictions, labels)
tf.contrib.deprecated.scalar_summary('train accuracy', accuracy)
# Measure joint accuracy
if joint_safe:
joint_predictions = joint_logits > 0
joint_accuracy = metrics.accuracy(joint_predictions, joint_labels)
tf.contrib.deprecated.scalar_summary('train joint accuracy',
joint_accuracy)
# All done!
return total_loss
