def testComputeAverageLossInvalidSampleWeights(self):
with self.assertRaisesRegex((ValueError, errors_impl.InvalidArgumentError),
(r"Incompatible shapes: \[3\] vs. \[2\]|"
"Dimensions must be equal")):
nn_impl.compute_average_loss([2.5, 6.2, 5.],
sample_weight=[0.2, 0.8],
global_batch_size=10)
def testComputeAverageLossInCrossReplicaContext(self, distribution):
with distribution.scope():
with self.assertRaisesRegex(
RuntimeError,
"You are calling `compute_average_loss` in cross replica context"
):
nn_impl.compute_average_loss([2, 3])
def testComputeAverageLossGlobalBatchSize_BatchSizeNegative(self):
per_example_loss = [1, 2, 3, 4, 5]
with self.assertRaisesWithPredicateMatch(
errors_impl.InvalidArgumentError,
"global_batch_size must be positive"):
nn_impl.compute_average_loss(per_example_loss,
global_batch_size=-1)
def testComputeAverageLossInvalidRank(self):
per_example_loss = constant_op.constant(2)
# Static rank
with self.assertRaisesRegex(
ValueError, "Invalid value passed for `per_example_loss`. "
"Expected a tensor with at least rank 1."):
nn_impl.compute_average_loss(per_example_loss)
with context.graph_mode():
# Dynamic rank
per_example_loss = array_ops.placeholder(dtype=dtypes.float32)
loss = nn_impl.compute_average_loss(per_example_loss)
with self.cached_session() as sess:
with self.assertRaisesRegex(
errors.InvalidArgumentError,
"Invalid value passed for `per_example_loss`. "
"Expected a tensor with at least rank 1."):
sess.run(loss, {per_example_loss: 2})
def testComputeAverageLossDefaultGlobalBatchSize(self, distribution):
# Without strategy - num replicas = 1
per_example_loss = constant_op.constant([2.5, 6.2, 5.])
loss = nn_impl.compute_average_loss(per_example_loss)
self.assertAllClose(self.evaluate(loss), (2.5 + 6.2 + 5.) / 3)
# With strategy - num replicas = 2
with distribution.scope():
per_replica_losses = distribution.run(
nn_impl.compute_average_loss, args=(per_example_loss,))
loss = distribution.reduce("SUM", per_replica_losses, axis=None)
self.assertAllClose(self.evaluate(loss), (2.5 + 6.2 + 5.) / 3)
def testComputeAverageLossGlobalBatchSize_BatchSizeFloat(self):
per_example_loss = [1, 2, 3, 4, 5]
with self.assertRaisesWithPredicateMatch(
TypeError, "global_batch_size must be an int"):
nn_impl.compute_average_loss(per_example_loss, global_batch_size=10.0)
def testComputeAverageLossGlobalBatchSize_BatchSizeNonScalar(self):
per_example_loss = [1, 2, 3, 4, 5]
with self.assertRaisesWithPredicateMatch(
ValueError, "global_batch_size must be scalar"):
nn_impl.compute_average_loss(per_example_loss, global_batch_size=[10])
def testComputeAverageLossGlobalBatchSize(self): per_example_loss = [1, 2, 3, 4, 5] loss = nn_impl.compute_average_loss(per_example_loss, global_batch_size=10) self.assertEqual(self.evaluate(loss), 1.5)
def testComputeAverageLossInvalidSampleWeights(self):
with self.assertRaisesIncompatibleShapesError(
(ValueError, errors_impl.InvalidArgumentError)):
nn_impl.compute_average_loss([2.5, 6.2, 5.],
sample_weight=[0.2, 0.8],
global_batch_size=10)
def testComputeAverageLossInvalidSampleWeights(self):
with self.assertRaisesRegex(ValueError,
"weights can not be broadcast to values"):
nn_impl.compute_average_loss([2.5, 6.2, 5.],
sample_weight=[0.2, 0.8],
global_batch_size=10)
