tensorflow::ops::SparseApplyMomentum

#include <training_ops.h>

Update relevant entries in '*var' and '*accum' according to the momentum scheme.

Summary

Set use_nesterov = True if you want to use Nesterov momentum.

That is for rows we have grad for, we update var and accum as follows:

accum = accum * momentum + grad var -= lr * accum

Arguments:

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

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.
• use_nesterov: If True, the tensor passed to compute grad will be var - lr * momentum * accum, so in the end, the var you get is actually var - lr * momentum * accum.

Returns:

• Output: Same as "var".

Public attributes

::tensorflow::Output out

Public functions

 SparseApplyMomentum(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input var,
  ::tensorflow::Input accum,
  ::tensorflow::Input lr,
  ::tensorflow::Input grad,
  ::tensorflow::Input indices,
  ::tensorflow::Input momentum
)

 SparseApplyMomentum(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input var,
  ::tensorflow::Input accum,
  ::tensorflow::Input lr,
  ::tensorflow::Input grad,
  ::tensorflow::Input indices,
  ::tensorflow::Input momentum,
  const SparseApplyMomentum::Attrs & attrs
)

::tensorflow::Node * node() const 

operator::tensorflow::Input() const 

operator::tensorflow::Output() const 

Public static functions

Attrs UseLocking(
  bool x
)

Attrs UseNesterov(
  bool x
)