ResourceSparseApplyAdagrad

public final class ResourceSparseApplyAdagrad

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

That is for rows we have grad for, we update var and accum as follows: accum += grad * grad var -= lr * grad * (1 / sqrt(accum))

class ResourceSparseApplyAdagrad.Options Optional attributes for ResourceSparseApplyAdagrad

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

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

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

scope	current scope
var	Should be from a Variable().
accum	Should be from a Variable().
lr	Learning rate. 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`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.