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tensorflow:: ops:: ResourceApplyAdagrad

#include <training_ops.h>

Update '*var' according to the adagrad scheme.

Summary

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

Args:

  • 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

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

Public attributes

operation

Public functions

operator::tensorflow::Operation () const

Public static functions

UpdateSlots (bool x)
UseLocking (bool x)

Structs

tensorflow:: ops:: ResourceApplyAdagrad:: Attrs

Optional attribute setters for ResourceApplyAdagrad .

Public attributes

operation

Operation operation

Public functions

ResourceApplyAdagrad

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

ResourceApplyAdagrad

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

operator::tensorflow::Operation

 operator::tensorflow::Operation() const 

Public static functions

UpdateSlots

Attrs UpdateSlots(
  bool x
)

UseLocking

Attrs UseLocking(
  bool x
)