tensorflow::ops::SparseApplyAdagradDA

#include <training_ops.h>

Update entries in '*var' and '*accum' according to the proximal adagrad scheme.

Summary

Arguments:

  • scope: A Scope object
  • var: Should be from a Variable().
  • gradient_accumulator: Should be from a Variable().
  • gradient_squared_accumulator: Should be from a Variable().
  • grad: The gradient.
  • indices: A vector of indices into the first dimension of var and accum.
  • lr: Learning rate. Must be a scalar.
  • l1: L1 regularization. Must be a scalar.
  • l2: L2 regularization. Must be a scalar.
  • global_step: Training step number. Must be a scalar.

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

SparseApplyAdagradDA(const ::tensorflow::Scope & scope, ::tensorflow::Input var, ::tensorflow::Input gradient_accumulator, ::tensorflow::Input gradient_squared_accumulator, ::tensorflow::Input grad, ::tensorflow::Input indices, ::tensorflow::Input lr, ::tensorflow::Input l1, ::tensorflow::Input l2, ::tensorflow::Input global_step)
SparseApplyAdagradDA(const ::tensorflow::Scope & scope, ::tensorflow::Input var, ::tensorflow::Input gradient_accumulator, ::tensorflow::Input gradient_squared_accumulator, ::tensorflow::Input grad, ::tensorflow::Input indices, ::tensorflow::Input lr, ::tensorflow::Input l1, ::tensorflow::Input l2, ::tensorflow::Input global_step, const SparseApplyAdagradDA::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::SparseApplyAdagradDA::Attrs

Optional attribute setters for SparseApplyAdagradDA.

Public attributes

out

::tensorflow::Output out

Public functions

SparseApplyAdagradDA

 SparseApplyAdagradDA(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input var,
  ::tensorflow::Input gradient_accumulator,
  ::tensorflow::Input gradient_squared_accumulator,
  ::tensorflow::Input grad,
  ::tensorflow::Input indices,
  ::tensorflow::Input lr,
  ::tensorflow::Input l1,
  ::tensorflow::Input l2,
  ::tensorflow::Input global_step
)

SparseApplyAdagradDA

 SparseApplyAdagradDA(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input var,
  ::tensorflow::Input gradient_accumulator,
  ::tensorflow::Input gradient_squared_accumulator,
  ::tensorflow::Input grad,
  ::tensorflow::Input indices,
  ::tensorflow::Input lr,
  ::tensorflow::Input l1,
  ::tensorflow::Input l2,
  ::tensorflow::Input global_step,
  const SparseApplyAdagradDA::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
)