Google I/O is a wrap! Catch up on TensorFlow sessions

#include <training_ops.h>

Update '*var' according to the Adam algorithm.

## Summary

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

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" }]