Have a question? Connect with the community at the TensorFlow Forum Visit Forum

SparseApplyCenteredRmsProp

public final class SparseApplyCenteredRmsProp

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

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)

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

Nested Classes

class SparseApplyCenteredRmsProp.Options Optional attributes for SparseApplyCenteredRmsProp

Constants

String OP_NAME The name of this op, as known by TensorFlow core engine

Public Methods

Output <T>
asOutput ()
Returns the symbolic handle of the tensor.
static <T extends TType > SparseApplyCenteredRmsProp <T>
create ( Scope scope, Operand <T> var, Operand <T> mg, Operand <T> ms, Operand <T> mom, Operand <T> lr, Operand <T> rho, Operand <T> momentum, Operand <T> epsilon, Operand <T> grad, Operand <? extends TNumber > indices, Options... options)
Factory method to create a class wrapping a new SparseApplyCenteredRmsProp operation.
Output <T>
out ()
Same as "var".
static SparseApplyCenteredRmsProp.Options
useLocking (Boolean useLocking)

Inherited Methods

Constants

public static final String OP_NAME

The name of this op, as known by TensorFlow core engine

Constant Value: "SparseApplyCenteredRMSProp"

Public Methods

public Output <T> asOutput ()

Returns the symbolic handle of the tensor.

Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.

public static SparseApplyCenteredRmsProp <T> create ( Scope scope, Operand <T> var, Operand <T> mg, Operand <T> ms, Operand <T> mom, Operand <T> lr, Operand <T> rho, Operand <T> momentum, Operand <T> epsilon, Operand <T> grad, Operand <? extends TNumber > indices, Options... options)

Factory method to create a class wrapping a new SparseApplyCenteredRmsProp operation.

Parameters
scope current scope
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.
grad The gradient.
indices A vector of indices into the first dimension of var, ms and mom.
options carries optional attributes values
Returns
  • a new instance of SparseApplyCenteredRmsProp

public Output <T> out ()

Same as "var".

public static SparseApplyCenteredRmsProp.Options useLocking (Boolean useLocking)

Parameters
useLocking 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.