tensorflow::ops::SparseApplyRMSProp

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)

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

Arguments:

• 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".

Public attributes

Operation operation

::tensorflow::Output out

 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
)

::tensorflow::Node * node() const 

 operator::tensorflow::Input() const 

 operator::tensorflow::Output() const 

Attrs UseLocking(
  bool x
)