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tensorflow:: ops:: SparseApplyCenteredRMSProp

 #include <training_ops.h> 

Update '*var' according to the centered RMSProp algorithm.

Summary

The centered RMSProp algorithm uses an estimate of the centered second moment (i.e., the variance) for normalization, as opposed to regular RMSProp, which uses the (uncentered) second moment. This often helps with training, but is slightly more expensive in terms of computation and memory.

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

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

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

Args:

• scope: A Scope object
• var: Should be from a Variable().
• mg: 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.
• 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, mg, 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

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

Optional attribute setters for SparseApplyCenteredRMSProp .

Public attributes

operation

Operation operation

out

::tensorflow::Output out

Public functions

SparseApplyCenteredRMSProp

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

SparseApplyCenteredRMSProp

 SparseApplyCenteredRMSProp(
const ::tensorflow::Scope & scope,
::tensorflow::Input var,
::tensorflow::Input mg,
::tensorflow::Input ms,
::tensorflow::Input mom,
::tensorflow::Input lr,
::tensorflow::Input rho,
::tensorflow::Input momentum,
::tensorflow::Input epsilon,
::tensorflow::Input indices,
const SparseApplyCenteredRMSProp::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
)
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[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]