tensorflow::ops::ApplyAdagrad

#include <training_ops.h>

Update '*var' according to the adagrad scheme.

Summary

accum += grad * grad var -= lr * grad * (1 / sqrt(accum))

Arguments:

  • scope: A Scope object
  • var: Should be from a Variable().
  • accum: Should be from a Variable().
  • lr: Scaling factor. Must be a scalar.
  • grad: The gradient.

Optional attributes (see Attrs):

  • use_locking: If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.

Returns:

Constructors and Destructors

ApplyAdagrad(const ::tensorflow::Scope & scope, ::tensorflow::Input var, ::tensorflow::Input accum, ::tensorflow::Input lr, ::tensorflow::Input grad)
ApplyAdagrad(const ::tensorflow::Scope & scope, ::tensorflow::Input var, ::tensorflow::Input accum, ::tensorflow::Input lr, ::tensorflow::Input grad, const ApplyAdagrad::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::ApplyAdagrad::Attrs

Optional attribute setters for ApplyAdagrad.

Public attributes

out

::tensorflow::Output out

Public functions

ApplyAdagrad

 ApplyAdagrad(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input var,
  ::tensorflow::Input accum,
  ::tensorflow::Input lr,
  ::tensorflow::Input grad
)

ApplyAdagrad

 ApplyAdagrad(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input var,
  ::tensorflow::Input accum,
  ::tensorflow::Input lr,
  ::tensorflow::Input grad,
  const ApplyAdagrad::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
)