tensorflow::ops::SparseApplyRMSProp

#include <training_ops.h>

Update '*var' according to the RMSProp algorithm.

Summary

Note that in dense implementation of this algorithm, ms and mom will update even if the grad is zero, but in this sparse implementation, ms and mom will not update in iterations during which the grad is zero.

mean_square = decay * mean_square + (1-decay) * gradient ** 2 Delta = learning_rate * gradient / sqrt(mean_square + epsilon)

$$ms <- rho * ms_{t-1} + (1-rho) * grad * grad$$ $$mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)$$ $$var <- var - mom$$

Args:

• scope: A Scope object
• var: Should be from a Variable().
• ms: Should be from a Variable().
• mom: Should be from a Variable().
• lr: Scaling factor. Must be a scalar.
• rho: Decay rate. Must be a scalar.
• epsilon: Ridge term. Must be a scalar.
• grad: The gradient.
• indices: A vector of indices into the first dimension of var, ms and mom.

Optional attributes (see Attrs):

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

Returns:

• Output: Same as "var".

Constructors and Destructors
SparseApplyRMSProp(const ::tensorflow::Scope & scope, ::tensorflow::Input var, ::tensorflow::Input ms, ::tensorflow::Input mom, ::tensorflow::Input lr, ::tensorflow::Input rho, ::tensorflow::Input momentum, ::tensorflow::Input epsilon, ::tensorflow::Input grad, ::tensorflow::Input indices)
SparseApplyRMSProp(const ::tensorflow::Scope & scope, ::tensorflow::Input var, ::tensorflow::Input ms, ::tensorflow::Input mom, ::tensorflow::Input lr, ::tensorflow::Input rho, ::tensorflow::Input momentum, ::tensorflow::Input epsilon, ::tensorflow::Input grad, ::tensorflow::Input indices, const SparseApplyRMSProp::Attrs & attrs)

Public attributes
operation	Operation
out	::tensorflow::Output

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

Public static functions
UseLocking(bool x)	Attrs

Structs
tensorflow::ops::SparseApplyRMSProp::Attrs	Optional attribute setters for SparseApplyRMSProp.

Public attributes

operation

Operation operation

out

::tensorflow::Output out

Public functions

SparseApplyRMSProp

 SparseApplyRMSProp(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input var,
  ::tensorflow::Input ms,
  ::tensorflow::Input mom,
  ::tensorflow::Input lr,
  ::tensorflow::Input rho,
  ::tensorflow::Input momentum,
  ::tensorflow::Input epsilon,
  ::tensorflow::Input grad,
  ::tensorflow::Input indices
)

SparseApplyRMSProp

 SparseApplyRMSProp(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input var,
  ::tensorflow::Input ms,
  ::tensorflow::Input mom,
  ::tensorflow::Input lr,
  ::tensorflow::Input rho,
  ::tensorflow::Input momentum,
  ::tensorflow::Input epsilon,
  ::tensorflow::Input grad,
  ::tensorflow::Input indices,
  const SparseApplyRMSProp::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
)