Update '*var' according to the RMSProp algorithm.
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
Nested Classes
class | ResourceApplyRmsProp.Options | Optional attributes for ResourceApplyRmsProp
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Constants
String | OP_NAME | The name of this op, as known by TensorFlow core engine |
Public Methods
static <T extends TType> ResourceApplyRmsProp | |
static ResourceApplyRmsProp.Options |
useLocking(Boolean useLocking)
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Inherited Methods
Constants
public static final String OP_NAME
The name of this op, as known by TensorFlow core engine
Public Methods
public static ResourceApplyRmsProp create (Scope scope, Operand<?> var, Operand<?> ms, Operand<?> mom, Operand<T> lr, Operand<T> rho, Operand<T> momentum, Operand<T> epsilon, Operand<T> grad, Options... options)
Factory method to create a class wrapping a new ResourceApplyRmsProp operation.
Parameters
scope | current scope |
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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. |
options | carries optional attributes values |
Returns
- a new instance of ResourceApplyRmsProp
public static ResourceApplyRmsProp.Options useLocking (Boolean useLocking)
Parameters
useLocking | 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. |
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