ResourceApplyRmsProp

public final class ResourceApplyRmsProp

Update '*var' according to the RMSProp algorithm.

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

mean_square = decay * mean_square + (1-decay) * gradient ** 2 Delta = learning_rate * gradient / sqrt(mean_square + epsilon)

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

Nested Classes

class ResourceApplyRmsProp.Options Optional attributes for ResourceApplyRmsProp

Constants

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

Public Methods

static <T extends TType > ResourceApplyRmsProp
create ( Scope scope, Operand <?> var, 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 ResourceApplyRmsProp operation.
static ResourceApplyRmsProp.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: "ResourceApplyRMSProp"

Public Methods

public static ResourceApplyRmsProp create ( Scope scope, Operand <?> var, 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 ResourceApplyRmsProp operation.

Parameters
scope current scope
var 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.
epsilon Ridge term. Must be a scalar.
grad The gradient.
options carries optional attributes values
Returns
  • a new instance of ResourceApplyRmsProp

public static ResourceApplyRmsProp.Options useLocking (Boolean useLocking)

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