ResourceSparseApplyAdagradDa

public final class ResourceSparseApplyAdagradDa

Update entries in '*var' and '*accum' according to the proximal adagrad scheme.

Nested Classes

class ResourceSparseApplyAdagradDa.Options Optional attributes for ResourceSparseApplyAdagradDa

Constants

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

Public Methods

static <T extends TType > ResourceSparseApplyAdagradDa
create ( Scope scope, Operand <?> var, Operand <?> gradientAccumulator, Operand <?> gradientSquaredAccumulator, Operand <T> grad, Operand <? extends TNumber > indices, Operand <T> lr, Operand <T> l1, Operand <T> l2, Operand < TInt64 > globalStep, Options... options)
Factory method to create a class wrapping a new ResourceSparseApplyAdagradDa operation.
static ResourceSparseApplyAdagradDa.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: "ResourceSparseApplyAdagradDA"

Public Methods

public static ResourceSparseApplyAdagradDa create ( Scope scope, Operand <?> var, Operand <?> gradientAccumulator, Operand <?> gradientSquaredAccumulator, Operand <T> grad, Operand <? extends TNumber > indices, Operand <T> lr, Operand <T> l1, Operand <T> l2, Operand < TInt64 > globalStep, Options... options)

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

Parameters
scope current scope
var Should be from a Variable().
gradientAccumulator Should be from a Variable().
gradientSquaredAccumulator Should be from a Variable().
grad The gradient.
indices A vector of indices into the first dimension of var and accum.
lr Learning rate. Must be a scalar.
l1 L1 regularization. Must be a scalar.
l2 L2 regularization. Must be a scalar.
globalStep Training step number. Must be a scalar.
options carries optional attributes values
Returns
  • a new instance of ResourceSparseApplyAdagradDa

public static ResourceSparseApplyAdagradDa.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.