ApplyAdam

public final class ApplyAdam

Update '*var' according to the Adam algorithm.

$$lr_t := \text{learning\_rate} * \sqrt{1 - beta_2^t} / (1 - beta_1^t)$$ $$m_t := beta_1 * m_{t-1} + (1 - beta_1) * g$$ $$v_t := beta_2 * v_{t-1} + (1 - beta_2) * g * g$$ $$variable := variable - lr_t * m_t / (\sqrt{v_t} + \epsilon)$$

Nested Classes

class ApplyAdam.Options Optional attributes for ApplyAdam  

Constants

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

Public Methods

Output<T>
asOutput()
Returns the symbolic handle of the tensor.
static <T extends TType> ApplyAdam<T>
create(Scope scope, Operand<T> var, Operand<T> m, Operand<T> v, 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 ApplyAdam operation.
Output<T>
out()
Same as "var".
static ApplyAdam.Options
useLocking(Boolean useLocking)
static ApplyAdam.Options
useNesterov(Boolean useNesterov)

Inherited Methods

Constants

public static final String OP_NAME

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

Constant Value: "ApplyAdam"

Public Methods

public Output<T> asOutput ()

Returns the symbolic handle of the tensor.

Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.

public static ApplyAdam<T> create (Scope scope, Operand<T> var, Operand<T> m, Operand<T> v, 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 ApplyAdam 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.
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 ApplyAdam

public Output<T> out ()

Same as "var".

public static ApplyAdam.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.

public static ApplyAdam.Options useNesterov (Boolean useNesterov)

Parameters
useNesterov If `True`, uses the nesterov update.