tensorflow::ops::ResourceApplyAdamWithAmsgrad

#include <training_ops.h>

Update '*var' according to the Adam algorithm.

Summary

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

Arguments:

• 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.
• grad: The gradient.

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

Public attributes

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,
  ::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
)

operator::tensorflow::Operation() const 

Public static functions

Attrs UseLocking(
  bool x
)