def test_doesnt_raise_when_zero_and_negative(self):
with self.test_session():
tom = constant_op.constant([0, -2], name="tom")
with ops.control_dependencies([check_ops.assert_non_positive(tom)
]):
out = array_ops.identity(tom)
out.eval()
def test_raises_when_positive(self):
with self.test_session():
rachel = constant_op.constant([0, 2], name="rachel")
with ops.control_dependencies([check_ops.assert_non_positive(rachel)]):
out = array_ops.identity(rachel)
with self.assertRaisesOpError("rachel"):
out.eval()
def _assert_valid_sample(self, x):
if not self.validate_args: return x
return control_flow_ops.with_dependencies([
check_ops.assert_non_positive(x),
distribution_util.assert_close(
array_ops.zeros((), dtype=self.dtype),
math_ops.reduce_logsumexp(x, reduction_indices=[-1])),
], x)
def _assert_valid_sample(self, x):
if not self.validate_args: return x
return control_flow_ops.with_dependencies([
check_ops.assert_non_positive(x),
distribution_util.assert_close(
array_ops.zeros((), dtype=self.dtype),
math_ops.reduce_logsumexp(x, reduction_indices=[-1])),
], x)
def _assert_valid_sample(self, x):
if not self.validate_args:
return x
return control_flow_ops.with_dependencies([
check_ops.assert_non_positive(x),
check_ops.assert_near(array_ops.zeros([], dtype=self.dtype),
math_ops.reduce_logsumexp(x, axis=[-1])),
], x)
def _assert_valid_sample(self, x):
if not self.validate_args:
return x
return control_flow_ops.with_dependencies([
check_ops.assert_non_positive(x),
check_ops.assert_near(
array_ops.zeros([], dtype=self.dtype),
math_ops.reduce_logsumexp(x, axis=[-1])),
], x)
def test_empty_tensor_doesnt_raise(self):
# A tensor is non-positive when it satisfies:
# For every element x_i in x, x_i <= 0
# and an empty tensor has no elements, so this is trivially satisfied.
# This is standard set theory.
empty = constant_op.constant([], name="empty")
with ops.control_dependencies([check_ops.assert_non_positive(empty)]):
out = array_ops.identity(empty)
self.evaluate(out)
def test_empty_tensor_doesnt_raise(self):
# A tensor is non-positive when it satisfies:
# For every element x_i in x, x_i <= 0
# and an empty tensor has no elements, so this is trivially satisfied.
# This is standard set theory.
with self.test_session():
empty = constant_op.constant([], name="empty")
with ops.control_dependencies([check_ops.assert_non_positive(empty)]):
out = array_ops.identity(empty)
out.eval()
def test_doesnt_raise_when_zero_and_negative(self):
with self.test_session():
tom = constant_op.constant([0, -2], name="tom")
with ops.control_dependencies([check_ops.assert_non_positive(tom)]):
out = array_ops.identity(tom)
out.eval()
def test_raises_when_positive(self):
rachel = constant_op.constant([0, 2], name="rachel")
with self.assertRaisesOpError("x <= 0 did not hold"):
with ops.control_dependencies([check_ops.assert_non_positive(rachel)]):
out = array_ops.identity(rachel)
self.evaluate(out)