tensorflow::ops::ApplyGradientDescent

#include <training_ops.h>

Update '*var' by subtracting 'alpha' * 'delta' from it.

Summary

Arguments:

  • scope: A Scope object
  • var: Should be from a Variable().
  • alpha: Scaling factor. Must be a scalar.
  • delta: The change.

Optional attributes (see Attrs):

  • use_locking: If True, the subtraction will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.

Returns:

Constructors and Destructors

ApplyGradientDescent(const ::tensorflow::Scope & scope, ::tensorflow::Input var, ::tensorflow::Input alpha, ::tensorflow::Input delta)
ApplyGradientDescent(const ::tensorflow::Scope & scope, ::tensorflow::Input var, ::tensorflow::Input alpha, ::tensorflow::Input delta, const ApplyGradientDescent::Attrs & attrs)

Public attributes

out

Public functions

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

Public static functions

UseLocking(bool x)

Structs

tensorflow::ops::ApplyGradientDescent::Attrs

Optional attribute setters for ApplyGradientDescent.

Public attributes

out

::tensorflow::Output out

Public functions

ApplyGradientDescent

 ApplyGradientDescent(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input var,
  ::tensorflow::Input alpha,
  ::tensorflow::Input delta
)

ApplyGradientDescent

 ApplyGradientDescent(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input var,
  ::tensorflow::Input alpha,
  ::tensorflow::Input delta,
  const ApplyGradientDescent::Attrs & attrs
)

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
)