ResourceSparseApplyAdagradV2

public final class ResourceSparseApplyAdagradV2

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))

Nested Classes

class ResourceSparseApplyAdagradV2.Options Optional attributes for ResourceSparseApplyAdagradV2  

Public Methods

static <T, U extends Number> ResourceSparseApplyAdagradV2
create(Scope scope, Operand<?> var, Operand<?> accum, Operand<T> lr, Operand<T> epsilon, Operand<T> grad, Operand<U> indices, Options... options)
Factory method to create a class wrapping a new ResourceSparseApplyAdagradV2 operation.
static ResourceSparseApplyAdagradV2.Options
updateSlots(Boolean updateSlots)
static ResourceSparseApplyAdagradV2.Options
useLocking(Boolean useLocking)

Inherited Methods

org.tensorflow.op.PrimitiveOp
final boolean
equals(Object obj)
final int
hashCode()
Operation
op()
Returns the underlying Operation
final String
toString()
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()

Public Methods

public static ResourceSparseApplyAdagradV2 create (Scope scope, Operand<?> var, Operand<?> accum, Operand<T> lr, Operand<T> epsilon, Operand<T> grad, Operand<U> indices, Options... options)

Factory method to create a class wrapping a new ResourceSparseApplyAdagradV2 operation.

Parameters
scope current scope
var Should be from a Variable().
accum Should be from a Variable().
lr Learning rate. Must be a scalar.
epsilon Constant factor. 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 ResourceSparseApplyAdagradV2

public static ResourceSparseApplyAdagradV2.Options updateSlots (Boolean updateSlots)

public static ResourceSparseApplyAdagradV2.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.