ResourceApplyAdamWithAmsgrad

public final class ResourceApplyAdamWithAmsgrad

Update '*var' according to the Adam algorithm.

$$\text{lr}_t := \mathrm{learning_rate} * \sqrt{1 - \beta_2^t} / (1 - \beta_1^t)$$

$$m_t := \beta_1 * m_{t-1} + (1 - \beta_1) * g$$

$$v_t := \beta_2 * v_{t-1} + (1 - \beta_2) * g * g$$

$$\hat{v}_t := max{\hat{v}_{t-1}, v_t}$$

$$\text{variable} := \text{variable} - \text{lr}_t * m_t / (\sqrt{\hat{v}_t} + \epsilon)$$

class ResourceApplyAdamWithAmsgrad.Options Optional attributes for ResourceApplyAdamWithAmsgrad

static <T> ResourceApplyAdamWithAmsgrad	create ( Scope scope, Operand <?> var, Operand <?> m, Operand <?> v, Operand <?> vhat, Operand <T> beta1Power, Operand <T> beta2Power, 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 ResourceApplyAdamWithAmsgrad operation.
static ResourceApplyAdamWithAmsgrad.Options	useLocking (Boolean useLocking)

From class org.tensorflow.op.PrimitiveOp

final boolean	equals (Object obj)
final int	hashCode ()
Operation	op () Returns the underlying `Operation`
final String	toString ()

From class java.lang.Object

Public Methods

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

scope	current scope
var	Should be from a Variable().
m	Should be from a Variable().
v	Should be from a Variable().
vhat	Should be from a Variable().
beta1Power	Must be a scalar.
beta2Power	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

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.