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

class ResourceSparseApplyKerasMomentum.Options Optional attributes for ResourceSparseApplyKerasMomentum

static <T, U extends Number> ResourceSparseApplyKerasMomentum	create ( Scope scope, Operand <?> var, Operand <?> accum, Operand <T> lr, Operand <T> grad, Operand <U> 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)

From class org.tensorflow.op.PrimitiveOp

final boolean	equals (Object obj)
final int	hashCode ()
Operation	op () Returns the underlying `Operation`
final String	toString ()

From class java.lang.Object

Public Methods

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

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

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.

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.