Help protect the Great Barrier Reef with TensorFlow on Kaggle

 #include <training_ops.h> 

Update '*var' according to the Adam algorithm.

## Summary

$${lr}_t := {learning_rate} * {1 - ^t} / (1 - ^t)$$
$$m_t := * m_{t-1} + (1 - ) * g$$
$$v_t := * v_{t-1} + (1 - ) * g * g$$
$${v}_t := max{ {v}_{t-1}, v_t}$$
$${variable} := {variable} - {lr}_t * m_t / ({ {v}_t} + )$$

Args:

• scope: A Scope object
• var: Should be from a Variable().
• m: Should be from a Variable().
• v: Should be from a Variable().
• vhat: 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.

Returns:

• the created  Operation 

### Constructors and Destructors

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

### Public attributes

 operation 
 Operation 

### Public functions

 operator::tensorflow::Operation () const 
 

### Public static functions

 UseLocking (bool x) 
 Attrs 

## Public attributes

### operation

Operation operation

## Public functions

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

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

### operator::tensorflow::Operation

 operator::tensorflow::Operation() const

## Public static functions

### UseLocking

Attrs UseLocking(
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" }]