ResourceApplyAdamWithAmsgrad

public final class ResourceApplyAdamWithAmsgrad

Update '*var' according to the Adam algorithm.

$$\text{lr}_t := \mathrm{learning_rate} * \sqrt{1 - \beta_2^t} / (1 - \beta_1^t)$$ $$m_t := \beta_1 * m_{t-1} + (1 - \beta_1) * g$$ $$v_t := \beta_2 * v_{t-1} + (1 - \beta_2) * g * g$$ $$\hat{v}_t := max{\hat{v}_{t-1}, v_t}$$ $$\text{variable} := \text{variable} - \text{lr}_t * m_t / (\sqrt{\hat{v}_t} + \epsilon)$$

Nested Classes

class

ResourceApplyAdamWithAmsgrad.Options

Optional attributes for ResourceApplyAdamWithAmsgrad

Public Methods

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)

Inherited Methods

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

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 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.

Parameters

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

Returns

• a new instance of ResourceApplyAdamWithAmsgrad

public static ResourceApplyAdamWithAmsgrad.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.