ResourceApplyCenteredRmsProp

public final class ResourceApplyCenteredRmsProp

Update '*var' according to the centered RMSProp algorithm.

The centered RMSProp algorithm uses an estimate of the centered second moment (i.e., the variance) for normalization, as opposed to regular RMSProp, which uses the (uncentered) second moment. This often helps with training, but is slightly more expensive in terms of computation and memory.

Note that in dense implementation of this algorithm, mg, ms, and mom will update even if the grad is zero, but in this sparse implementation, mg, ms, and mom will not update in iterations during which the grad is zero.

mean_square = decay * mean_square + (1-decay) * gradient ** 2 mean_grad = decay * mean_grad + (1-decay) * gradient

Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2)

mg <- rho * mg_{t-1} + (1-rho) * grad ms <- rho * ms_{t-1} + (1-rho) * grad * grad mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms - mg * mg + epsilon) var <- var - mom

Nested Classes

class

ResourceApplyCenteredRmsProp.Options

Optional attributes for ResourceApplyCenteredRmsProp

Constants

String

OP_NAME

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

Public Methods

static <T extends TType> ResourceApplyCenteredRmsProp	create(Scope scope, Operand<?> var, Operand<?> mg, Operand<?> ms, Operand<?> mom, Operand<T> lr, Operand<T> rho, Operand<T> momentum, Operand<T> epsilon, Operand<T> grad, Options... options) Factory method to create a class wrapping a new ResourceApplyCenteredRmsProp operation.
static ResourceApplyCenteredRmsProp.Options	useLocking(Boolean useLocking)

Inherited Methods

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

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()

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

public static final String OP_NAME

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

Constant Value: "ResourceApplyCenteredRMSProp"

Public Methods

public static ResourceApplyCenteredRmsProp create (Scope scope, Operand<?> var, Operand<?> mg, Operand<?> ms, Operand<?> mom, Operand<T> lr, Operand<T> rho, Operand<T> momentum, Operand<T> epsilon, Operand<T> grad, Options... options)

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

Parameters

scope	current scope
var	Should be from a Variable().
mg	Should be from a Variable().
ms	Should be from a Variable().
mom	Should be from a Variable().
lr	Scaling factor. Must be a scalar.
rho	Decay rate. Must be a scalar.
momentum	Momentum Scale. Must be a scalar.
epsilon	Ridge term. Must be a scalar.
grad	The gradient.
options	carries optional attributes values

Returns

• a new instance of ResourceApplyCenteredRmsProp

public static ResourceApplyCenteredRmsProp.Options useLocking (Boolean useLocking)

Parameters

useLocking	If `True`, updating of the var, mg, ms, and mom tensors is protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.