Have a question? Connect with the community at the TensorFlow Forum Visit Forum

ResourceSparseApplyKerasMomentum

public final class ResourceSparseApplyKerasMomentum

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

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 - lr * grad var += accum

Nested Classes

class ResourceSparseApplyKerasMomentum.Options Optional attributes for ResourceSparseApplyKerasMomentum

Constants

String OP_NAME The name of this op, as known by TensorFlow core engine

Public Methods

static <T extends TType > ResourceSparseApplyKerasMomentum
create ( Scope scope, Operand <?> var, Operand <?> accum, Operand <T> lr, Operand <T> grad, Operand <? extends TNumber > indices, Operand <T> momentum, Options... options)
Factory method to create a class wrapping a new ResourceSparseApplyKerasMomentum operation.
static ResourceSparseApplyKerasMomentum.Options
useLocking (Boolean useLocking)
static ResourceSparseApplyKerasMomentum.Options
useNesterov (Boolean useNesterov)

Inherited Methods

Constants

public static final String OP_NAME

The name of this op, as known by TensorFlow core engine

Constant Value: "ResourceSparseApplyKerasMomentum"

Public Methods

public static ResourceSparseApplyKerasMomentum create ( Scope scope, Operand <?> var, Operand <?> accum, Operand <T> lr, Operand <T> grad, Operand <? extends TNumber > indices, Operand <T> momentum, Options... options)

Factory method to create a class wrapping a new ResourceSparseApplyKerasMomentum operation.

Parameters
scope current scope
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.
options carries optional attributes values
Returns
  • a new instance of ResourceSparseApplyKerasMomentum

public static ResourceSparseApplyKerasMomentum.Options useLocking (Boolean useLocking)

Parameters
useLocking 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.

public static ResourceSparseApplyKerasMomentum.Options useNesterov (Boolean useNesterov)

Parameters
useNesterov If `True`, the tensor passed to compute grad will be var + momentum * accum, so in the end, the var you get is actually var + momentum * accum.