ResourceSparseApplyProximalGradientDescent

public final class ResourceSparseApplyProximalGradientDescent

Sparse update '*var' as FOBOS algorithm with fixed learning rate.

That is for rows we have grad for, we update var as follows: prox_v = var - alpha grad var = sign(prox_v)/(1+alphal2) max{|prox_v|-alphal1,0}

class ResourceSparseApplyProximalGradientDescent.Options Optional attributes for ResourceSparseApplyProximalGradientDescent

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

static <T extends TType> ResourceSparseApplyProximalGradientDescent	create(Scope scope, Operand<?> var, Operand<T> alpha, Operand<T> l1, Operand<T> l2, Operand<T> grad, Operand<? extends TNumber> indices, Options... options) Factory method to create a class wrapping a new ResourceSparseApplyProximalGradientDescent operation.
static ResourceSparseApplyProximalGradientDescent.Options	useLocking(Boolean useLocking)

From class org.tensorflow.op.RawOp

final boolean	equals(Object obj)
final int	hashCode()
Operation	op() Return this unit of computation as a single `Operation`.
final String	toString()

From class java.lang.Object

From interface org.tensorflow.op.Op

abstract ExecutionEnvironment	env() Return the execution environment this op was created in.
abstract Operation	op() Return this unit of computation as a single `Operation`.

Constants

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

Constant Value: "ResourceSparseApplyProximalGradientDescent"

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

scope	current scope
var	Should be from a Variable().
alpha	Scaling factor. 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.
options	carries optional attributes values

useLocking	If True, the subtraction will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.