def maybe_create_label_priors(label_priors,
labels,
weights,
variables_collections):
"""Creates moving average ops to track label priors, if necessary.
Args:
label_priors: As required in e.g. precision_recall_auc_loss.
labels: A `Tensor` of shape [batch_size] or [batch_size, num_labels].
weights: As required in e.g. precision_recall_auc_loss.
variables_collections: Optional list of collections for the variables, if
any must be created.
Returns:
label_priors: A Tensor of shape [num_labels] consisting of the
weighted label priors, after updating with moving average ops if created.
"""
if label_priors is not None:
label_priors = util.convert_and_cast(
label_priors, name='label_priors', dtype=labels.dtype.base_dtype)
return tf.squeeze(label_priors)
label_priors = util.build_label_priors(
labels,
weights,
variables_collections=variables_collections)
return label_priors
def maybe_create_label_priors(label_priors, labels, weights,
variables_collections):
if label_priors is not None:
label_priors = util.convert_and_cast(label_priors,
name='label_priors',
dtype=labels.dtype.base_dtype)
return tf.squeeze(label_priors)
label_priors = util.build_label_priors(
labels, weights, variables_collections=variables_collections)
return label_priors
def testLabelPriorConsistency(self):
# Checks that, with zero pseudocounts, the returned label priors reproduce
# label frequencies in the batch.
batch_shape = [4, 10]
labels = tf.Variable(
tf.to_float(tf.greater(tf.random_uniform(batch_shape), 0.678)))
label_priors_update = util.build_label_priors(
labels=labels, positive_pseudocount=0, negative_pseudocount=0)
expected_priors = tf.reduce_mean(labels, 0)
with self.test_session():
tf.global_variables_initializer().run()
self.assertAllClose(label_priors_update.eval(), expected_priors.eval())
def testLabelPriorConsistency(self):
# Checks that, with zero pseudocounts, the returned label priors reproduce
# label frequencies in the batch.
batch_shape = [4, 10]
labels = tf.Variable(
tf.to_float(tf.greater(tf.random_uniform(batch_shape), 0.678)))
label_priors_update = util.build_label_priors(
labels=labels, positive_pseudocount=0, negative_pseudocount=0)
expected_priors = tf.reduce_mean(labels, 0)
with self.test_session():
tf.global_variables_initializer().run()
self.assertAllClose(label_priors_update.eval(), expected_priors.eval())
def testLabelPriorsUpdateWithWeights(self):
# Checks the update of label priors with per-example weights.
batch_size = 6
num_labels = 5
batch_shape = [batch_size, num_labels]
labels = tf.Variable(
tf.to_float(tf.greater(tf.random_uniform(batch_shape), 0.6)))
weights = tf.Variable(tf.random_uniform(batch_shape) * 6.2)
update_op = util.build_label_priors(labels, weights=weights)
expected_weighted_label_counts = 1.0 + tf.reduce_sum(weights * labels, 0)
expected_weight_sum = 2.0 + tf.reduce_sum(weights, 0)
expected_label_posteriors = tf.divide(expected_weighted_label_counts,
expected_weight_sum)
with self.test_session() as session:
tf.global_variables_initializer().run()
updated_priors, expected_posteriors = session.run(
[update_op, expected_label_posteriors])
self.assertAllClose(updated_priors, expected_posteriors)
def testLabelPriorsUpdate(self):
# Checks that the update of label priors behaves as expected.
batch_shape = [1, 5]
labels = tf.Variable(
tf.to_float(tf.greater(tf.random_uniform(batch_shape), 0.4)))
label_priors_update = util.build_label_priors(labels)
label_sum = np.ones(shape=batch_shape)
weight_sum = 2.0 * np.ones(shape=batch_shape)
with self.test_session() as session:
tf.global_variables_initializer().run()
for _ in range(3):
label_sum += labels.eval()
weight_sum += np.ones(shape=batch_shape)
expected_posteriors = label_sum / weight_sum
label_priors = label_priors_update.eval().reshape(batch_shape)
self.assertAllClose(label_priors, expected_posteriors)
# Re-initialize labels to get a new random sample.
session.run(labels.initializer)
def testLabelPriorsUpdateWithWeights(self):
# Checks the update of label priors with per-example weights.
batch_size = 6
num_labels = 5
batch_shape = [batch_size, num_labels]
labels = tf.Variable(
tf.to_float(tf.greater(tf.random_uniform(batch_shape), 0.6)))
weights = tf.Variable(tf.random_uniform(batch_shape) * 6.2)
update_op = util.build_label_priors(labels, weights=weights)
expected_weighted_label_counts = 1.0 + tf.reduce_sum(weights * labels, 0)
expected_weight_sum = 2.0 + tf.reduce_sum(weights, 0)
expected_label_posteriors = tf.divide(expected_weighted_label_counts,
expected_weight_sum)
with self.test_session() as session:
tf.global_variables_initializer().run()
updated_priors, expected_posteriors = session.run(
[update_op, expected_label_posteriors])
self.assertAllClose(updated_priors, expected_posteriors)
def testLabelPriorsUpdate(self):
# Checks that the update of label priors behaves as expected.
batch_shape = [1, 5]
labels = tf.Variable(
tf.to_float(tf.greater(tf.random_uniform(batch_shape), 0.4)))
label_priors_update = util.build_label_priors(labels)
label_sum = np.ones(shape=batch_shape)
weight_sum = 2.0 * np.ones(shape=batch_shape)
with self.test_session() as session:
tf.global_variables_initializer().run()
for _ in range(3):
label_sum += labels.eval()
weight_sum += np.ones(shape=batch_shape)
expected_posteriors = label_sum / weight_sum
label_priors = label_priors_update.eval().reshape(batch_shape)
self.assertAllClose(label_priors, expected_posteriors)
# Re-initialize labels to get a new random sample.
session.run(labels.initializer)
def maybe_create_label_priors(label_priors, labels, weights,
variables_collections):
"""Creates moving average ops to track label priors, if necessary.
Args:
label_priors: As required in e.g. precision_recall_auc_loss.
labels: A `Tensor` of shape [batch_size] or [batch_size, num_labels].
weights: As required in e.g. precision_recall_auc_loss.
variables_collections: Optional list of collections for the variables, if
any must be created.
Returns:
label_priors: A Tensor of shape [num_labels] consisting of the
weighted label priors, after updating with moving average ops if created.
"""
if label_priors is not None:
label_priors = util.convert_and_cast(label_priors,
name='label_priors',
dtype=labels.dtype.base_dtype)
return tf.squeeze(label_priors)
label_priors = util.build_label_priors(
labels, weights, variables_collections=variables_collections)
return label_priors
