ResourceSparseApplyProximalAdagrad

public final class ResourceSparseApplyProximalAdagrad

Sparse update entries in '*var' and '*accum' according to FOBOS algorithm.

That is for rows we have grad for, we update var and accum as follows: accum += grad grad prox_v = var prox_v -= lr grad (1 / sqrt(accum)) var = sign(prox_v)/(1+lr l2) max{|prox_v|-lr l1,0}

Nested Classes

class ResourceSparseApplyProximalAdagrad.Options Optional attributes for ResourceSparseApplyProximalAdagrad

Constants

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

Public Methods

static <T extends TType > ResourceSparseApplyProximalAdagrad
create ( Scope scope, Operand <?> var, Operand <?> accum, Operand <T> lr, Operand <T> l1, Operand <T> l2, Operand <T> grad, Operand <? extends TNumber > indices, Options... options)
Factory method to create a class wrapping a new ResourceSparseApplyProximalAdagrad operation.
static ResourceSparseApplyProximalAdagrad.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: "ResourceSparseApplyProximalAdagrad"

Public Methods

public static ResourceSparseApplyProximalAdagrad create ( Scope scope, Operand <?> var, Operand <?> accum, Operand <T> lr, Operand <T> l1, Operand <T> l2, Operand <T> grad, Operand <? extends TNumber > indices, Options... options)

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

Parameters
scope current scope
var Should be from a Variable().
accum Should be from a Variable().
lr Learning rate. 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 ResourceSparseApplyProximalAdagrad

public static ResourceSparseApplyProximalAdagrad.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.