tensorflow::ops::SparseApplyAdadelta

#include <training_ops.h>

var: Should be from a Variable().

Summary

Arguments:

• scope: A Scope object
• accum: Should be from a Variable().
• accum_update: : Should be from a Variable().
• lr: Learning rate. Must be a scalar.
• rho: Decay factor. Must be a scalar.
• epsilon: Constant factor. Must be a scalar.
• grad: The gradient.
• indices: A vector of indices into the first dimension of var and accum.

Optional attributes (see Attrs):

• use_locking: If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.

Returns:

• Output: Same as "var".

Public attributes

::tensorflow::Output out

Public functions

 SparseApplyAdadelta(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input var,
  ::tensorflow::Input accum,
  ::tensorflow::Input accum_update,
  ::tensorflow::Input lr,
  ::tensorflow::Input rho,
  ::tensorflow::Input epsilon,
  ::tensorflow::Input grad,
  ::tensorflow::Input indices
)

 SparseApplyAdadelta(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input var,
  ::tensorflow::Input accum,
  ::tensorflow::Input accum_update,
  ::tensorflow::Input lr,
  ::tensorflow::Input rho,
  ::tensorflow::Input epsilon,
  ::tensorflow::Input grad,
  ::tensorflow::Input indices,
  const SparseApplyAdadelta::Attrs & attrs
)

::tensorflow::Node * node() const 

operator::tensorflow::Input() const 

operator::tensorflow::Output() const 

Public static functions

Attrs UseLocking(
  bool x
)