def _log_prob(self, event):
event = self._maybe_assert_valid_sample(event)
# TODO(jaana): The current sigmoid_cross_entropy_with_logits has
# inconsistent behavior for logits = inf/-inf.
event = math_ops.cast(event, self.logits.dtype)
logits = self.logits
# sigmoid_cross_entropy_with_logits doesn't broadcast shape,
# so we do this here.
def _broadcast(logits, event):
return (array_ops.ones_like(event) * logits,
array_ops.ones_like(logits) * event)
# First check static shape.
if (event.get_shape().is_fully_defined()
and logits.get_shape().is_fully_defined()):
if event.get_shape() != logits.get_shape():
logits, event = _broadcast(logits, event)
else:
logits, event = control_flow_ops.cond(
distribution_util.same_dynamic_shape(logits, event), lambda:
(logits, event), lambda: _broadcast(logits, event))
return -nn.sigmoid_cross_entropy_with_logits(labels=event,
logits=logits)
def _log_prob(self, event):
# TODO(jaana): The current sigmoid_cross_entropy_with_logits has
# inconsistent behavior for logits = inf/-inf.
event = ops.convert_to_tensor(event, name="event")
event = math_ops.cast(event, self.logits.dtype)
logits = self.logits
# sigmoid_cross_entropy_with_logits doesn't broadcast shape,
# so we do this here.
broadcast = lambda logits, event: (array_ops.ones_like(event) * logits,
array_ops.ones_like(logits) * event)
# First check static shape.
if (event.get_shape().is_fully_defined()
and logits.get_shape().is_fully_defined()):
if event.get_shape() != logits.get_shape():
logits, event = broadcast(logits, event)
else:
logits, event = control_flow_ops.cond(
distribution_util.same_dynamic_shape(logits, event), lambda:
(logits, event), lambda: broadcast(logits, event))
return -nn.sigmoid_cross_entropy_with_logits(logits, event)
def _log_prob(self, event):
# TODO(jaana): The current sigmoid_cross_entropy_with_logits has
# inconsistent behavior for logits = inf/-inf.
event = math_ops.cast(event, self.logits.dtype)
logits = self.logits
# sigmoid_cross_entropy_with_logits doesn't broadcast shape,
# so we do this here.
def _broadcast(logits, event):
return (array_ops.ones_like(event) * logits,
array_ops.ones_like(logits) * event)
# First check static shape.
if (event.get_shape().is_fully_defined() and
logits.get_shape().is_fully_defined()):
if event.get_shape() != logits.get_shape():
logits, event = _broadcast(logits, event)
else:
logits, event = control_flow_ops.cond(
distribution_util.same_dynamic_shape(logits, event),
lambda: (logits, event),
lambda: _broadcast(logits, event))
return -nn.sigmoid_cross_entropy_with_logits(labels=event, logits=logits)
def testSameDynamicShape(self):
with self.test_session():
scalar = constant_op.constant(2.0)
scalar1 = array_ops.placeholder(dtype=dtypes.float32)
vector = [0.3, 0.4, 0.5]
vector1 = array_ops.placeholder(dtype=dtypes.float32, shape=[None])
vector2 = array_ops.placeholder(dtype=dtypes.float32, shape=[None])
multidimensional = [[0.3, 0.4], [0.2, 0.6]]
multidimensional1 = array_ops.placeholder(
dtype=dtypes.float32, shape=[None, None])
multidimensional2 = array_ops.placeholder(
dtype=dtypes.float32, shape=[None, None])
# Scalar
self.assertTrue(
distribution_util.same_dynamic_shape(scalar, scalar1).eval({
scalar1: 2.0
}))
# Vector
self.assertTrue(
distribution_util.same_dynamic_shape(vector, vector1).eval({
vector1: [2.0, 3.0, 4.0]
}))
self.assertTrue(
distribution_util.same_dynamic_shape(vector1, vector2).eval({
vector1: [2.0, 3.0, 4.0],
vector2: [2.0, 3.5, 6.0]
}))
# Multidimensional
self.assertTrue(
distribution_util.same_dynamic_shape(
multidimensional, multidimensional1).eval({
multidimensional1: [[2.0, 3.0], [3.0, 4.0]]
}))
self.assertTrue(
distribution_util.same_dynamic_shape(
multidimensional1, multidimensional2).eval({
multidimensional1: [[2.0, 3.0], [3.0, 4.0]],
multidimensional2: [[1.0, 3.5], [6.3, 2.3]]
}))
# Scalar, X
self.assertFalse(
distribution_util.same_dynamic_shape(scalar, vector1).eval({
vector1: [2.0, 3.0, 4.0]
}))
self.assertFalse(
distribution_util.same_dynamic_shape(scalar1, vector1).eval({
scalar1: 2.0,
vector1: [2.0, 3.0, 4.0]
}))
self.assertFalse(
distribution_util.same_dynamic_shape(scalar, multidimensional1).eval({
multidimensional1: [[2.0, 3.0], [3.0, 4.0]]
}))
self.assertFalse(
distribution_util.same_dynamic_shape(scalar1, multidimensional1).eval(
{
scalar1: 2.0,
multidimensional1: [[2.0, 3.0], [3.0, 4.0]]
}))
# Vector, X
self.assertFalse(
distribution_util.same_dynamic_shape(vector, vector1).eval({
vector1: [2.0, 3.0]
}))
self.assertFalse(
distribution_util.same_dynamic_shape(vector1, vector2).eval({
vector1: [2.0, 3.0, 4.0],
vector2: [6.0]
}))
self.assertFalse(
distribution_util.same_dynamic_shape(vector, multidimensional1).eval({
multidimensional1: [[2.0, 3.0], [3.0, 4.0]]
}))
self.assertFalse(
distribution_util.same_dynamic_shape(vector1, multidimensional1).eval(
{
vector1: [2.0, 3.0, 4.0],
multidimensional1: [[2.0, 3.0], [3.0, 4.0]]
}))
# Multidimensional, X
self.assertFalse(
distribution_util.same_dynamic_shape(
multidimensional, multidimensional1).eval({
multidimensional1: [[1.0, 3.5, 5.0], [6.3, 2.3, 7.1]]
}))
self.assertFalse(
distribution_util.same_dynamic_shape(
multidimensional1, multidimensional2).eval({
multidimensional1: [[2.0, 3.0], [3.0, 4.0]],
multidimensional2: [[1.0, 3.5, 5.0], [6.3, 2.3, 7.1]]
}))
def testSameDynamicShape(self):
with self.test_session():
scalar = constant_op.constant(2.0)
scalar1 = array_ops.placeholder(dtype=dtypes.float32)
vector = [0.3, 0.4, 0.5]
vector1 = array_ops.placeholder(dtype=dtypes.float32, shape=[None])
vector2 = array_ops.placeholder(dtype=dtypes.float32, shape=[None])
multidimensional = [[0.3, 0.4], [0.2, 0.6]]
multidimensional1 = array_ops.placeholder(dtype=dtypes.float32,
shape=[None, None])
multidimensional2 = array_ops.placeholder(dtype=dtypes.float32,
shape=[None, None])
# Scalar
self.assertTrue(
distribution_util.same_dynamic_shape(scalar, scalar1).eval(
{scalar1: 2.0}))
# Vector
self.assertTrue(
distribution_util.same_dynamic_shape(vector, vector1).eval(
{vector1: [2.0, 3.0, 4.0]}))
self.assertTrue(
distribution_util.same_dynamic_shape(vector1, vector2).eval({
vector1: [2.0, 3.0, 4.0],
vector2: [2.0, 3.5, 6.0]
}))
# Multidimensional
self.assertTrue(
distribution_util.same_dynamic_shape(multidimensional,
multidimensional1).eval({
multidimensional1:
[[2.0, 3.0],
[3.0, 4.0]]
}))
self.assertTrue(
distribution_util.same_dynamic_shape(multidimensional1,
multidimensional2).eval({
multidimensional1:
[[2.0, 3.0],
[3.0, 4.0]],
multidimensional2:
[[1.0, 3.5],
[6.3, 2.3]]
}))
# Scalar, X
self.assertFalse(
distribution_util.same_dynamic_shape(scalar, vector1).eval(
{vector1: [2.0, 3.0, 4.0]}))
self.assertFalse(
distribution_util.same_dynamic_shape(scalar1, vector1).eval({
scalar1:
2.0,
vector1: [2.0, 3.0, 4.0]
}))
self.assertFalse(
distribution_util.same_dynamic_shape(scalar,
multidimensional1).eval({
multidimensional1:
[[2.0, 3.0],
[3.0, 4.0]]
}))
self.assertFalse(
distribution_util.same_dynamic_shape(scalar1,
multidimensional1).eval({
scalar1:
2.0,
multidimensional1:
[[2.0, 3.0],
[3.0, 4.0]]
}))
# Vector, X
self.assertFalse(
distribution_util.same_dynamic_shape(vector, vector1).eval(
{vector1: [2.0, 3.0]}))
self.assertFalse(
distribution_util.same_dynamic_shape(vector1, vector2).eval({
vector1: [2.0, 3.0, 4.0],
vector2: [6.0]
}))
self.assertFalse(
distribution_util.same_dynamic_shape(vector,
multidimensional1).eval({
multidimensional1:
[[2.0, 3.0],
[3.0, 4.0]]
}))
self.assertFalse(
distribution_util.same_dynamic_shape(vector1,
multidimensional1).eval({
vector1:
[2.0, 3.0, 4.0],
multidimensional1:
[[2.0, 3.0],
[3.0, 4.0]]
}))
# Multidimensional, X
self.assertFalse(
distribution_util.same_dynamic_shape(multidimensional,
multidimensional1).eval({
multidimensional1:
[[1.0, 3.5, 5.0],
[6.3, 2.3, 7.1]]
}))
self.assertFalse(
distribution_util.same_dynamic_shape(multidimensional1,
multidimensional2).eval({
multidimensional1:
[[2.0, 3.0],
[3.0, 4.0]],
multidimensional2:
[[1.0, 3.5, 5.0],
[6.3, 2.3, 7.1]]
}))