tensorflow::ops::ApplyAdam

#include <training_ops.h>

Update '*var' according to the Adam algorithm.

Summary

lr_t

Arguments:

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

Returns:

• Output: Same as "var".

Public attributes

::tensorflow::Output out

Public functions

 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
)

::tensorflow::Node * node() const 

operator::tensorflow::Input() const 

operator::tensorflow::Output() const 

Public static functions

Attrs UseLocking(
  bool x
)

Attrs UseNesterov(
  bool x
)