tensorflow::ops::SparseApplyProximalAdagrad

#include <training_ops.h>

Sparse update entries in '*var' and '*accum' according to FOBOS algorithm.

Summary

That is for rows we have grad for, we update var and accum as follows: accum+=gradgrad proxv=var proxv=lrgrad(1/sqrt(accum)) var=sign(proxv)/(1+lrl2)max|proxv|lrl1,0

Args:

  • scope: A Scope object
  • var: Should be from a Variable().
  • accum: Should be from a Variable().
  • lr: Learning rate. Must be a scalar.
  • l1: L1 regularization. Must be a scalar.
  • l2: L2 regularization. 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:

Public functions

node() const
::tensorflow::Node *
operator::tensorflow::Input() const
operator::tensorflow::Output() const

Public static functions

UseLocking(bool x)

Public attributes

operation

Operation operation

out

::tensorflow::Output out

Public functions

SparseApplyProximalAdagrad

 SparseApplyProximalAdagrad(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input var,
  ::tensorflow::Input accum,
  ::tensorflow::Input lr,
  ::tensorflow::Input l1,
  ::tensorflow::Input l2,
  ::tensorflow::Input grad,
  ::tensorflow::Input indices
)

SparseApplyProximalAdagrad

 SparseApplyProximalAdagrad(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input var,
  ::tensorflow::Input accum,
  ::tensorflow::Input lr,
  ::tensorflow::Input l1,
  ::tensorflow::Input l2,
  ::tensorflow::Input grad,
  ::tensorflow::Input indices,
  const SparseApplyProximalAdagrad::Attrs & attrs
)

node

::tensorflow::Node * node() const 

operator::tensorflow::Input

 operator::tensorflow::Input() const 

operator::tensorflow::Output

 operator::tensorflow::Output() const 

Public static functions

UseLocking

Attrs UseLocking(
  bool x
)