Punya pertanyaan? Terhubung dengan komunitas di Forum TensorFlow

#include <training_ops.h>

Update '*var' according to the Adam algorithm.

## Summary

$$lr_t := {learning_rate} * {1 - beta_2^t} / (1 - beta_1^t)$$
$$m_t := beta_1 * m_{t-1} + (1 - beta_1) * g$$
$$v_t := beta_2 * v_{t-1} + (1 - beta_2) * g * g$$
$$variable := variable - lr_t * m_t / ({v_t} + )$$

Args:

• scope: A Scope object
• var: Should be from a Variable().
• m: Should be from a Variable().
• v: Should be from a Variable().
• beta1_power: Must be a scalar.
• beta2_power: 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.

Optional attributes (see Attrs ):

• use_locking: 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.
• use_nesterov: If True , uses the nesterov update.

Returns:

### Constructors and Destructors

ApplyAdam (const :: tensorflow::Scope & scope, :: tensorflow::Input var, :: tensorflow::Input m, :: tensorflow::Input v, :: tensorflow::Input beta1_power, :: tensorflow::Input beta2_power, :: tensorflow::Input lr, :: tensorflow::Input beta1, :: tensorflow::Input beta2, :: tensorflow::Input epsilon, :: tensorflow::Input grad)
ApplyAdam (const :: tensorflow::Scope & scope, :: tensorflow::Input var, :: tensorflow::Input m, :: tensorflow::Input v, :: tensorflow::Input beta1_power, :: tensorflow::Input beta2_power, :: tensorflow::Input lr, :: tensorflow::Input beta1, :: tensorflow::Input beta2, :: tensorflow::Input epsilon, :: tensorflow::Input grad, const ApplyAdam::Attrs & attrs)

operation
out

### Public functions

node () const
::tensorflow::Node *
operator::tensorflow::Input () const
operator::tensorflow::Output () const

### Public static functions

UseLocking (bool x)
UseNesterov (bool x)

### Structs

Optional attribute setters for ApplyAdam .

## Public attributes

### operation

Operation operation

### out

::tensorflow::Output out

## Public functions

const ::tensorflow::Scope & scope,
::tensorflow::Input var,
::tensorflow::Input m,
::tensorflow::Input v,
::tensorflow::Input beta1_power,
::tensorflow::Input beta2_power,
::tensorflow::Input lr,
::tensorflow::Input beta1,
::tensorflow::Input beta2,
::tensorflow::Input epsilon,
)

const ::tensorflow::Scope & scope,
::tensorflow::Input var,
::tensorflow::Input m,
::tensorflow::Input v,
::tensorflow::Input beta1_power,
::tensorflow::Input beta2_power,
::tensorflow::Input lr,
::tensorflow::Input beta1,
::tensorflow::Input beta2,
::tensorflow::Input epsilon,
)

### node

::tensorflow::Node * node() const

### operator::tensorflow::Input

operator::tensorflow::Input() const

### operator::tensorflow::Output

operator::tensorflow::Output() const

## Public static functions

### UseLocking

Attrs UseLocking(
bool x
)

### UseNesterov

Attrs UseNesterov(
bool x
)
[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]