def testAssertCloseEpsilon(self):
x = [0., 5, 10, 15, 20]
# x != y
y = [0.1, 5, 10, 15, 20]
# x = z
z = [1e-8, 5, 10, 15, 20]
with self.test_session():
with ops.control_dependencies([distribution_util.assert_close(x, z)]):
array_ops.identity(x).eval()
with self.assertRaisesOpError("Condition x ~= y"):
with ops.control_dependencies([distribution_util.assert_close(x, y)]):
array_ops.identity(x).eval()
with self.assertRaisesOpError("Condition x ~= y"):
with ops.control_dependencies([distribution_util.assert_close(y, z)]):
array_ops.identity(y).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_positive(x),
distribution_util.assert_close(
array_ops.ones((), dtype=self.dtype),
math_ops.reduce_sum(x, reduction_indices=[-1])),
], x)
def testAssertCloseEpsilon(self):
x = [0., 5, 10, 15, 20]
# x != y
y = [0.1, 5, 10, 15, 20]
# x = z
z = [1e-8, 5, 10, 15, 20]
with self.test_session():
with tf.control_dependencies([distribution_util.assert_close(x, z)]):
tf.identity(x).eval()
with self.assertRaisesOpError("Condition x ~= y"):
with tf.control_dependencies([distribution_util.assert_close(x, y)]):
tf.identity(x).eval()
with self.assertRaisesOpError("Condition x ~= y"):
with tf.control_dependencies([distribution_util.assert_close(y, z)]):
tf.identity(y).eval()
def testAssertCloseNonIntegerDtype(self):
x = np.array([1., 5, 10, 15, 20], dtype=np.float32)
y = x + 1e-8
z = [2., 5, 10, 15, 20]
with self.test_session():
with tf.control_dependencies([distribution_util.assert_close(x, y)]):
tf.identity(x).eval()
with tf.control_dependencies([distribution_util.assert_close(y, x)]):
tf.identity(x).eval()
with self.assertRaisesOpError("Condition x ~= y"):
with tf.control_dependencies([distribution_util.assert_close(x, z)]):
tf.identity(x).eval()
with self.assertRaisesOpError("Condition x ~= y"):
with tf.control_dependencies([distribution_util.assert_close(y, z)]):
tf.identity(y).eval()
def testAssertCloseIntegerDtype(self):
x = [1, 5, 10, 15, 20]
y = x
z = [2, 5, 10, 15, 20]
with self.test_session():
with tf.control_dependencies([distribution_util.assert_close(x, y)]):
tf.identity(x).eval()
with tf.control_dependencies([distribution_util.assert_close(y, x)]):
tf.identity(x).eval()
with self.assertRaisesOpError("Condition x ~= y"):
with tf.control_dependencies([distribution_util.assert_close(x, z)]):
tf.identity(x).eval()
with self.assertRaisesOpError("Condition x ~= y"):
with tf.control_dependencies([distribution_util.assert_close(y, z)]):
tf.identity(y).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, axis=[-1])),
], x)
def testAssertCloseNonIntegerDtype(self):
x = np.array([1., 5, 10, 15, 20], dtype=np.float32)
y = x + 1e-8
z = [2., 5, 10, 15, 20]
with self.test_session():
with tf.control_dependencies([distribution_util.assert_close(x, y)]):
tf.identity(x).eval()
with tf.control_dependencies([distribution_util.assert_close(y, x)]):
tf.identity(x).eval()
with self.assertRaisesOpError("Condition x ~= y"):
with tf.control_dependencies([distribution_util.assert_close(x, z)]):
tf.identity(x).eval()
with self.assertRaisesOpError("Condition x ~= y"):
with tf.control_dependencies([distribution_util.assert_close(y, z)]):
tf.identity(y).eval()
def testAssertCloseIntegerDtype(self):
x = [1, 5, 10, 15, 20]
y = x
z = [2, 5, 10, 15, 20]
with self.test_session():
with tf.control_dependencies([distribution_util.assert_close(x, y)]):
tf.identity(x).eval()
with tf.control_dependencies([distribution_util.assert_close(y, x)]):
tf.identity(x).eval()
with self.assertRaisesOpError("Condition x ~= y"):
with tf.control_dependencies([distribution_util.assert_close(x, z)]):
tf.identity(x).eval()
with self.assertRaisesOpError("Condition x ~= y"):
with tf.control_dependencies([distribution_util.assert_close(y, z)]):
tf.identity(y).eval()
def _check_x(self, x):
"""Check x for proper shape, values, then return tensor version."""
x = ops.convert_to_tensor(x, name="x_before_deps")
candidate_one = math_ops.reduce_sum(x, reduction_indices=[-1])
one = constant_op.constant(1., self.dtype)
dependencies = [check_ops.assert_positive(x), check_ops.assert_less(
x, one, message="x has components greater than or equal to 1"),
distribution_util.assert_close(one, candidate_one)
] if self.validate_args else []
return control_flow_ops.with_dependencies(dependencies, x)
def _check_x(self, x):
"""Check x for proper shape, values, then return tensor version."""
x = ops.convert_to_tensor(x, name="x_before_deps")
candidate_one = math_ops.reduce_sum(x, reduction_indices=[-1])
one = constant_op.constant(1., self.dtype)
dependencies = [check_ops.assert_positive(x), check_ops.assert_less(
x, one, message="x has components greater than or equal to 1"),
distribution_util.assert_close(one, candidate_one)
] if self.validate_args else []
return control_flow_ops.with_dependencies(dependencies, x)
def testAssertCloseNonIntegerDtype(self):
x = array_ops.placeholder(dtypes.float32)
y = x + 1e-8
z = array_ops.placeholder(dtypes.float32)
feed_dict = {x: [1., 5, 10, 15, 20], z: [2., 5, 10, 15, 20]}
with self.test_session():
with ops.control_dependencies([distribution_util.assert_close(x, y)]):
array_ops.identity(x).eval(feed_dict=feed_dict)
with ops.control_dependencies([distribution_util.assert_close(y, x)]):
array_ops.identity(x).eval(feed_dict=feed_dict)
with self.assertRaisesOpError("Condition x ~= y"):
with ops.control_dependencies([distribution_util.assert_close(x, z)]):
array_ops.identity(x).eval(feed_dict=feed_dict)
with self.assertRaisesOpError("Condition x ~= y"):
with ops.control_dependencies([distribution_util.assert_close(y, z)]):
array_ops.identity(y).eval(feed_dict=feed_dict)
def _maybe_assert_valid_sample(self, x):
"""Checks the validity of a sample."""
if not self.validate_args:
return x
return control_flow_ops.with_dependencies([
check_ops.assert_positive(x, message="samples must be positive"),
distribution_util.assert_close(
array_ops.ones([], dtype=self.dtype),
math_ops.reduce_sum(x, -1),
message="sample last-dimension must sum to `1`"),
], x)
def _maybe_assert_valid_sample(self, x):
"""Checks the validity of a sample."""
if not self.validate_args:
return x
return control_flow_ops.with_dependencies([
check_ops.assert_positive(
x,
message="samples must be positive"),
distribution_util.assert_close(
array_ops.ones((), dtype=self.dtype),
math_ops.reduce_sum(x, -1),
message="sample last-dimension must sum to `1`"),
], x)
def testAssertCloseNonIntegerDtype(self):
x = array_ops.placeholder(dtypes.float32)
y = x + 1e-8
z = array_ops.placeholder(dtypes.float32)
feed_dict = {x: [1., 5, 10, 15, 20], z: [2., 5, 10, 15, 20]}
with self.test_session():
with ops.control_dependencies(
[distribution_util.assert_close(x, y)]):
array_ops.identity(x).eval(feed_dict=feed_dict)
with ops.control_dependencies(
[distribution_util.assert_close(y, x)]):
array_ops.identity(x).eval(feed_dict=feed_dict)
with self.assertRaisesOpError("Condition x ~= y"):
with ops.control_dependencies(
[distribution_util.assert_close(x, z)]):
array_ops.identity(x).eval(feed_dict=feed_dict)
with self.assertRaisesOpError("Condition x ~= y"):
with ops.control_dependencies(
[distribution_util.assert_close(y, z)]):
array_ops.identity(y).eval(feed_dict=feed_dict)
