def testName(self):
# Default name.
loss_fn = loss_lib.AbsoluteCorrelationLoss()
self.assertEqual(loss_fn.name, 'absolute_correlation_loss')
# Custom name.
loss_fn = loss_lib.AbsoluteCorrelationLoss(name='custom_loss')
self.assertEqual(loss_fn.name, 'custom_loss')
def testNegativeCorrelationNoWeights(self):
loss_fn = loss_lib.AbsoluteCorrelationLoss()
membership = tf.constant([[1.0], [0.0], [1.0], [0.0], [1.0]])
predictions = tf.constant([[0.12], [0.7], [0.2], [0.86], [0.32]])
loss_value = loss_fn(membership, predictions)
self.assertAllClose(0.959671, loss_value)
def testNoWeights(self):
loss_fn = loss_lib.AbsoluteCorrelationLoss()
membership = tf.constant([[1.0], [0.0], [1.0], [0.0], [1.0]])
predictions = tf.constant([[0.3], [0.1], [0.86], [0.06], [0.75]])
loss_value = loss_fn(membership, predictions)
self.assertAllClose(0.823209, loss_value)
def testSerialization(self):
loss = loss_lib.AbsoluteCorrelationLoss(name='custom_name')
serialized_loss = tf.keras.utils.serialize_keras_object(loss)
deserialized_loss = tf.keras.utils.deserialize_keras_object(serialized_loss)
self.assertIsInstance(deserialized_loss, loss_lib.AbsoluteCorrelationLoss)
self.assertEqual(deserialized_loss.name, loss.name)
def testAllZeroWeights(self):
loss_fn = loss_lib.AbsoluteCorrelationLoss()
membership = tf.constant([[1.0], [0.0], [1.0], [0.0], [1.0]])
predictions = tf.constant([[0.12], [0.7], [0.2], [0.86], [0.32]])
sample_weights = tf.constant([[0.0], [0.0], [0.0], [0.0], [0.0]])
loss_value = loss_fn(membership, predictions, sample_weights)
self.assertAllClose(0, loss_value)
def testWithWeights(self):
loss_fn = loss_lib.AbsoluteCorrelationLoss()
membership = tf.constant([[1.0], [0.0], [1.0], [0.0], [1.0]])
predictions = tf.constant([[0.3], [0.1], [0.86], [0.06], [0.75]])
sample_weights = tf.constant([[1.0], [2.0], [2.5], [1.2], [0.9]])
loss_value = loss_fn(membership, predictions, sample_weights)
self.assertAllClose(0.872562, loss_value)
def testSomeNegativeWeights(self):
loss_fn = loss_lib.AbsoluteCorrelationLoss()
membership = tf.constant([[1.0], [0.0], [1.0], [0.0], [1.0]])
predictions = tf.constant([[0.12], [0.7], [0.2], [0.86], [0.32]])
sample_weights = tf.constant([[1.0], [2.0], [-2.5], [1.2], [0.9]])
with self.assertRaisesRegex(
tf.errors.InvalidArgumentError,
'sample_weight.*cannot contain any negative weights'):
loss_value = loss_fn(membership, predictions, sample_weights)
if not tf.executing_eagerly():
with self.cached_session() as sess:
sess.run(loss_value)
def testGradientsWithAllZeroWeights(self):
loss_fn = loss_lib.AbsoluteCorrelationLoss()
variables = tf.constant([[0.1], [0.3], [0.5], [0.7], [0.9]])
membership = tf.constant([[1.0], [0.0], [1.0], [0.0], [1.0]])
sample_weights = tf.constant([[0.0], [0.0], [0.0], [0.0], [0.0]])
with tf.GradientTape() as tape:
tape.watch(variables)
predictions = variables * 3 # arbitrary linear operation.
loss_value = loss_fn(membership, predictions, sample_weights)
gradients = tape.gradient(loss_value, variables)
# Gradients should all be 0 for weights that are all 0.
expected_gradients = [[0.0], [0.0], [0.0], [0.0], [0.0]]
self.assertAllClose(expected_gradients, gradients)
def testForAbsoluteCorrelationLoss(self):
loss = utils._get_loss('abs_corr')
self.assertIsInstance(loss, abscorrloss.AbsoluteCorrelationLoss)
loss = utils._get_loss('abS_coRr') # Strangely capitalized.
self.assertIsInstance(loss, abscorrloss.AbsoluteCorrelationLoss)
loss = utils._get_loss('abs_corr_loss') # Other accepted name.
self.assertIsInstance(loss, abscorrloss.AbsoluteCorrelationLoss)
loss = utils._get_loss('absolute_correlation') # Other accepted name.
self.assertIsInstance(loss, abscorrloss.AbsoluteCorrelationLoss)
loss = utils._get_loss(
'absolute_correlation_loss') # Other accepted name.
self.assertIsInstance(loss, abscorrloss.AbsoluteCorrelationLoss)
loss_name = 'custom_name'
loss = utils._get_loss(abscorrloss.AbsoluteCorrelationLoss(loss_name))
self.assertIsInstance(loss, abscorrloss.AbsoluteCorrelationLoss)
self.assertEqual(loss.name, loss_name)
def testGradients(self):
loss_fn = loss_lib.AbsoluteCorrelationLoss()
variables = tf.constant([[0.1], [0.3], [0.5], [0.7], [0.9]])
membership = tf.constant([[1.0], [0.0], [1.0], [0.0], [1.0]])
sample_weights = tf.constant([[1.0], [2.0], [2.5], [1.2], [0.9]])
with tf.GradientTape() as tape:
tape.watch(variables)
predictions = variables * 3 # arbitrary linear operation.
loss_value = loss_fn(membership, predictions, sample_weights)
gradients = tape.gradient(loss_value, variables)
# Assert that gradient computations are non trivial and do not change based
# on loss implementation.
expected_gradients = [[0.5481868], [-1.2328656], [1.1707227], [-0.83559656],
[0.34955233]]
self.assertAllClose(expected_gradients, gradients)
def testEnableSummaryHistogram(self):
loss = loss_lib.AbsoluteCorrelationLoss()
self.assertTrue(loss.enable_summary_histogram)
loss = loss_lib.AbsoluteCorrelationLoss(enable_summary_histogram=False)
self.assertFalse(loss.enable_summary_histogram)