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}

Nested Classes

class

ResourceSparseApplyProximalGradientDescent.Options

Optional attributes for ResourceSparseApplyProximalGradientDescent

Constants

String

OP_NAME

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

Public Methods

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)

Inherited Methods

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

boolean	equals(Object arg0)
final Class<?>	getClass()
int	hashCode()
final void	notify()
final void	notifyAll()
String	toString()
final void	wait(long arg0, int arg1)
final void	wait(long arg0)
final void	wait()

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

public static final String OP_NAME

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

Constant Value: "ResourceSparseApplyProximalGradientDescent"

Public Methods

public static 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.

Parameters

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

Returns

• a new instance of ResourceSparseApplyProximalGradientDescent

public static ResourceSparseApplyProximalGradientDescent.Options useLocking (Boolean useLocking)

Parameters

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