ResourceApplyAdaMax

public final class ResourceApplyAdaMax

Update '*var' according to the AdaMax algorithm.

m_t <- beta1 * m_{t-1} + (1 - beta1) * g v_t <- max(beta2 * v_{t-1}, abs(g)) variable <- variable - learning_rate / (1 - beta1^t) * m_t / (v_t + epsilon)

Nested Classes

class ResourceApplyAdaMax.Options Optional attributes for ResourceApplyAdaMax

Constants

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

Public Methods

static <T extends TType > ResourceApplyAdaMax
create ( Scope scope, Operand <?> var, Operand <?> m, Operand <?> v, Operand <T> beta1Power, Operand <T> lr, Operand <T> beta1, Operand <T> beta2, Operand <T> epsilon, Operand <T> grad, Options... options)
Factory method to create a class wrapping a new ResourceApplyAdaMax operation.
static ResourceApplyAdaMax.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: "ResourceApplyAdaMax"

Public Methods

public static ResourceApplyAdaMax create ( Scope scope, Operand <?> var, Operand <?> m, Operand <?> v, Operand <T> beta1Power, Operand <T> lr, Operand <T> beta1, Operand <T> beta2, Operand <T> epsilon, Operand <T> grad, Options... options)

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

Parameters
scope current scope
var Should be from a Variable().
m Should be from a Variable().
v Should be from a Variable().
beta1Power Must be a scalar.
lr Scaling factor. Must be a scalar.
beta1 Momentum factor. Must be a scalar.
beta2 Momentum factor. Must be a scalar.
epsilon Ridge term. Must be a scalar.
grad The gradient.
options carries optional attributes values
Returns
  • a new instance of ResourceApplyAdaMax

public static ResourceApplyAdaMax.Options useLocking (Boolean useLocking)

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