def _log_prob(self, event):
if self.validate_args:
event = distribution_util.embed_check_integer_casting_closed(
event, target_dtype=dtypes.bool)
# 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, k):
k = ops.convert_to_tensor(k, name="k")
if self.validate_args:
k = distribution_util.embed_check_integer_casting_closed(
k, target_dtype=dtypes.int32)
if self.logits.get_shape()[:-1] == k.get_shape():
logits = self.logits
else:
logits = self.logits * array_ops.ones_like(
array_ops.expand_dims(k, -1), dtype=self.logits.dtype)
logits_shape = array_ops.shape(logits)[:-1]
k *= array_ops.ones(logits_shape, dtype=k.dtype)
k.set_shape(tensor_shape.TensorShape(logits.get_shape()[:-1]))
if k.dtype.is_integer:
pass
elif k.dtype.is_floating:
# When `validate_args=True` we've already ensured int/float casting
# is closed.
return ops.cast(k, dtype=dtypes.int32)
else:
raise TypeError("`value` should have integer `dtype` or "
"`self.dtype` ({})".format(
self.dtype.base_dtype))
return -nn_ops.sparse_softmax_cross_entropy_with_logits(labels=k,
logits=logits)
def _log_prob(self, event):
if self.validate_args:
event = distribution_util.embed_check_integer_casting_closed(
event, target_dtype=dtypes.bool)
# 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, k):
k = ops.convert_to_tensor(k, name="k")
if self.validate_args:
k = distribution_util.embed_check_integer_casting_closed(
k, target_dtype=dtypes.int32)
if self.logits.get_shape()[:-1] == k.get_shape():
logits = self.logits
else:
logits = self.logits * array_ops.ones_like(
array_ops.expand_dims(k, -1), dtype=self.logits.dtype)
logits_shape = array_ops.shape(logits)[:-1]
k *= array_ops.ones(logits_shape, dtype=k.dtype)
k.set_shape(tensor_shape.TensorShape(logits.get_shape()[:-1]))
if k.dtype.is_integer:
pass
elif k.dtype.is_floating:
# When `validate_args=True` we've already ensured int/float casting
# is closed.
return ops.cast(k, dtype=dtypes.int32)
else:
raise TypeError("`value` should have integer `dtype` or "
"`self.dtype` ({})".format(self.dtype.base_dtype))
return -nn_ops.sparse_softmax_cross_entropy_with_logits(labels=k,
logits=logits)
def testCorrectlyAssertsLargestPossibleInteger(self):
with self.test_session():
with self.assertRaisesOpError("Elements cannot exceed 32767."):
x = array_ops.placeholder(dtype=dtypes.int32)
x_checked = distribution_util.embed_check_integer_casting_closed(
x, target_dtype=dtypes.int16)
x_checked.eval(
feed_dict={x: np.array([1, 2**15], dtype=np.int32)})
def testCorrectlyAssertsNonnegative(self):
with self.test_session():
with self.assertRaisesOpError("Elements must be non-negative"):
x = array_ops.placeholder(dtype=dtypes.float16)
x_checked = distribution_util.embed_check_integer_casting_closed(
x, target_dtype=dtypes.int16)
x_checked.eval(
feed_dict={x: np.array([1, -1], dtype=np.float16)})
def testCorrectlyAssertsSmallestPossibleInteger(self):
with self.test_session():
with self.assertRaisesOpError(
"Elements cannot be smaller than 0."):
x = array_ops.placeholder(dtype=dtypes.int32)
x_checked = distribution_util.embed_check_integer_casting_closed(
x, target_dtype=dtypes.uint16, assert_nonnegative=False)
x_checked.eval(
feed_dict={x: np.array([1, -1], dtype=np.int32)})
def _log_prob(self, k):
k = ops.convert_to_tensor(k, name="k")
if self.validate_args:
k = distribution_util.embed_check_integer_casting_closed(
k, target_dtype=dtypes.int32)
k, logits = _broadcast_cat_event_and_params(
k, self.logits, base_dtype=self.dtype.base_dtype)
return -nn_ops.sparse_softmax_cross_entropy_with_logits(labels=k,
logits=logits)
def _log_prob(self, k):
k = ops.convert_to_tensor(k, name="k")
if self.validate_args:
k = distribution_util.embed_check_integer_casting_closed(
k, target_dtype=dtypes.int32)
k, logits = _broadcast_cat_event_and_params(
k, self.logits, base_dtype=self.dtype.base_dtype)
return -nn_ops.sparse_softmax_cross_entropy_with_logits(labels=k,
logits=logits)
def _cdf(self, k):
k = ops.convert_to_tensor(k, name="k")
if self.validate_args:
k = distribution_util.embed_check_integer_casting_closed(
k, target_dtype=dtypes.int32)
# If there are multiple batch dimension, flatten them into one.
batch_flattened_probs = array_ops.reshape(self._probs,
[-1, self._event_size])
batch_flattened_k = array_ops.reshape(k, [-1])
# Form a tensor to sum over.
# We don't need to cast k to integer since `sequence_mask` does this for us.
mask_tensor = array_ops.sequence_mask(batch_flattened_k, self._event_size)
to_sum_over = array_ops.where(mask_tensor,
batch_flattened_probs,
array_ops.zeros_like(batch_flattened_probs))
batch_flat_cdf = math_ops.reduce_sum(to_sum_over, axis=-1)
return array_ops.reshape(batch_flat_cdf, self._batch_shape())
def _cdf(self, k):
k = ops.convert_to_tensor(k, name="k")
if self.validate_args:
k = distribution_util.embed_check_integer_casting_closed(
k, target_dtype=dtypes.int32)
k, probs = _broadcast_cat_event_and_params(
k, self.probs, base_dtype=self.dtype.base_dtype)
# batch-flatten everything in order to use `sequence_mask()`.
batch_flattened_probs = array_ops.reshape(probs,
(-1, self._event_size))
batch_flattened_k = array_ops.reshape(k, [-1])
to_sum_over = array_ops.where(
array_ops.sequence_mask(batch_flattened_k, self._event_size),
batch_flattened_probs, array_ops.zeros_like(batch_flattened_probs))
batch_flattened_cdf = math_ops.reduce_sum(to_sum_over, axis=-1)
# Reshape back to the shape of the argument.
return array_ops.reshape(batch_flattened_cdf, array_ops.shape(k))
def _cdf(self, k):
k = ops.convert_to_tensor(k, name="k")
if self.validate_args:
k = distribution_util.embed_check_integer_casting_closed(
k, target_dtype=dtypes.int32)
# If there are multiple batch dimension, flatten them into one.
batch_flattened_probs = array_ops.reshape(self._probs,
[-1, self._event_size])
batch_flattened_k = array_ops.reshape(k, [-1])
# Form a tensor to sum over.
# We don't need to cast k to integer since `sequence_mask` does this for us.
mask_tensor = array_ops.sequence_mask(batch_flattened_k,
self._event_size)
to_sum_over = array_ops.where(
mask_tensor, batch_flattened_probs,
array_ops.zeros_like(batch_flattened_probs))
batch_flat_cdf = math_ops.reduce_sum(to_sum_over, axis=-1)
return array_ops.reshape(batch_flat_cdf, self._batch_shape())
def _cdf(self, k):
k = ops.convert_to_tensor(k, name="k")
if self.validate_args:
k = distribution_util.embed_check_integer_casting_closed(
k, target_dtype=dtypes.int32)
k, probs = _broadcast_cat_event_and_params(
k, self.probs, base_dtype=self.dtype.base_dtype)
# batch-flatten everything in order to use `sequence_mask()`.
batch_flattened_probs = array_ops.reshape(probs,
(-1, self._event_size))
batch_flattened_k = array_ops.reshape(k, [-1])
to_sum_over = array_ops.where(
array_ops.sequence_mask(batch_flattened_k, self._event_size),
batch_flattened_probs,
array_ops.zeros_like(batch_flattened_probs))
batch_flattened_cdf = math_ops.reduce_sum(to_sum_over, axis=-1)
# Reshape back to the shape of the argument.
return array_ops.reshape(batch_flattened_cdf, array_ops.shape(k))
