def _initialize(self):
if self._is_scalar:
return gen_stats_accumulator_ops.create_stats_accumulator_scalar(
self.resource_handle, self._stamp_token)
else:
return gen_stats_accumulator_ops.create_stats_accumulator_tensor(
self.resource_handle, self._stamp_token,
self._gradient_shape.as_list(), self._hessian_shape.as_list())
def initialize(self):
if self._is_scalar:
return gen_stats_accumulator_ops.create_stats_accumulator_scalar(
self.resource_handle, self._stamp_token)
else:
return gen_stats_accumulator_ops.create_stats_accumulator_tensor(
self.resource_handle, self._stamp_token,
self._gradient_shape.as_list(), self._hessian_shape.as_list())
def __init__(self,
stamp_token,
gradient_shape,
hessian_shape,
name=None,
container=None):
"""Creates a stats accumulator and returns a handle to it.
Args:
stamp_token: An int64, initial value to use for the stamp token.
gradient_shape: A TensorShape, containing shape of gradients.
hessian_shape: A TensorShape, containing shape of hessians.
name: A name for the stats accumulator variable.
container: An optional `string`. Defaults to `""`.
Returns:
A `Tensor` of type mutable `string`. The handle to the stats accumulator.
"""
with ops.name_scope(name, "StatsAccumulator") as name:
# Both values are scalars.
if (gradient_shape == tensor_shape.scalar() and
hessian_shape == tensor_shape.scalar()):
self._is_scalar = True
self._resource_handle = (gen_stats_accumulator_ops.
stats_accumulator_scalar_resource_handle_op(
container, name, name=name))
create_op = gen_stats_accumulator_ops.create_stats_accumulator_scalar(
self._resource_handle, stamp_token)
is_initialized_op = (
gen_stats_accumulator_ops.stats_accumulator_scalar_is_initialized(
self._resource_handle))
else:
self._is_scalar = False
self._resource_handle = (gen_stats_accumulator_ops.
stats_accumulator_tensor_resource_handle_op(
container, name, name=name))
create_op = gen_stats_accumulator_ops.create_stats_accumulator_tensor(
self._resource_handle, stamp_token, gradient_shape.as_list(),
hessian_shape.as_list())
is_initialized_op = (
gen_stats_accumulator_ops.stats_accumulator_scalar_is_initialized(
self._resource_handle))
self._create_op = create_op
slice_spec = ""
saver_name = self._resource_handle.name
(stamp_token, num_updates, partition_ids, feature_ids, gradients,
hessians) = self.serialize()
specs = [
saver.BaseSaverBuilder.SaveSpec(stamp_token, slice_spec,
saver_name + "_stamp"),
saver.BaseSaverBuilder.SaveSpec(num_updates, slice_spec,
saver_name + "_num_updates"),
saver.BaseSaverBuilder.SaveSpec(partition_ids, slice_spec,
saver_name + "_partition_ids"),
saver.BaseSaverBuilder.SaveSpec(feature_ids, slice_spec,
saver_name + "_feature_ids"),
saver.BaseSaverBuilder.SaveSpec(gradients, slice_spec,
saver_name + "_gradients"),
saver.BaseSaverBuilder.SaveSpec(hessians, slice_spec,
saver_name + "hessians"),
]
super(StatsAccumulator, self).__init__(self._resource_handle, specs, name)
resources.register_resource(self._resource_handle, create_op,
is_initialized_op)
ops.add_to_collection(ops.GraphKeys.SAVEABLE_OBJECTS, self)
def __init__(self,
stamp_token,
gradient_shape,
hessian_shape,
name=None,
container=None):
"""Creates a stats accumulator and returns a handle to it.
Args:
stamp_token: An int64, initial value to use for the stamp token.
gradient_shape: A TensorShape, containing shape of gradients.
hessian_shape: A TensorShape, containing shape of hessians.
name: A name for the stats accumulator variable.
container: An optional `string`. Defaults to `""`.
Returns:
A `Tensor` of type mutable `string`. The handle to the stats accumulator.
"""
if name is not None:
name = _PATTERN.sub("", name)
with ops.name_scope(name, "StatsAccumulator") as name:
# Both values are scalars.
if (gradient_shape == tensor_shape.scalar()
and hessian_shape == tensor_shape.scalar()):
self._is_scalar = True
self._resource_handle = (
gen_stats_accumulator_ops.
stats_accumulator_scalar_resource_handle_op(container,
name,
name=name))
create_op = gen_stats_accumulator_ops.create_stats_accumulator_scalar(
self._resource_handle, stamp_token)
is_initialized_op = (gen_stats_accumulator_ops.
stats_accumulator_scalar_is_initialized(
self._resource_handle))
else:
self._is_scalar = False
self._resource_handle = (
gen_stats_accumulator_ops.
stats_accumulator_tensor_resource_handle_op(container,
name,
name=name))
create_op = gen_stats_accumulator_ops.create_stats_accumulator_tensor(
self._resource_handle, stamp_token,
gradient_shape.as_list(), hessian_shape.as_list())
is_initialized_op = (gen_stats_accumulator_ops.
stats_accumulator_tensor_is_initialized(
self._resource_handle))
self._create_op = create_op
slice_spec = ""
saver_name = self._resource_handle.name
(stamp_token, num_updates, partition_ids, feature_ids, gradients,
hessians) = self.serialize()
specs = [
saver.BaseSaverBuilder.SaveSpec(stamp_token, slice_spec,
saver_name + "_stamp"),
saver.BaseSaverBuilder.SaveSpec(num_updates, slice_spec,
saver_name + "_num_updates"),
saver.BaseSaverBuilder.SaveSpec(partition_ids, slice_spec,
saver_name + "_partition_ids"),
saver.BaseSaverBuilder.SaveSpec(feature_ids, slice_spec,
saver_name + "_feature_ids"),
saver.BaseSaverBuilder.SaveSpec(gradients, slice_spec,
saver_name + "_gradients"),
saver.BaseSaverBuilder.SaveSpec(hessians, slice_spec,
saver_name + "hessians"),
]
super(StatsAccumulator, self).__init__(self._resource_handle, specs,
name)
resources.register_resource(self._resource_handle, create_op,
is_initialized_op)
ops.add_to_collection(ops.GraphKeys.SAVEABLE_OBJECTS, self)
