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+alpha l2) max{|prox_v|-alpha l1,0}

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

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.