org.tensorflow.op.core

Class SparseApplyRMSProp<T>

java.lang.Object

org.tensorflow.op.PrimitiveOp

org.tensorflow.op.core.SparseApplyRMSProp<T>

Type Parameters:

T - data type for out() output

All Implemented Interfaces:

Op, Operand<T>

@Operator
public final class SparseApplyRMSProp<T>
extends PrimitiveOp
implements Operand<T>

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 Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	SparseApplyRMSProp.Options Optional attributes for SparseApplyRMSProp

Field Summary

Fields inherited from class org.tensorflow.op.PrimitiveOp

operation

Method Summary

Modifier and Type	Method and Description
Output<T>	asOutput() Returns the symbolic handle of a tensor.
static <T,U extends Number> SparseApplyRMSProp<T>	create(Scope scope, Operand<T> var, Operand<T> ms, Operand<T> mom, Operand<T> lr, Operand<T> rho, Operand<T> momentum, Operand<T> epsilon, Operand<T> grad, Operand<U> indices, SparseApplyRMSProp.Options... options) Factory method to create a class to wrap a new SparseApplyRMSProp operation to the graph.
Output<T>	out() Same as "var".
static SparseApplyRMSProp.Options	useLocking(Boolean useLocking)

Methods inherited from class org.tensorflow.op.PrimitiveOp

equals, hashCode, toString

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Method Detail

create

public static <T,U extends Number> SparseApplyRMSProp<T> create(Scope scope,
                                                                Operand<T> var,
                                                                Operand<T> ms,
                                                                Operand<T> mom,
                                                                Operand<T> lr,
                                                                Operand<T> rho,
                                                                Operand<T> momentum,
                                                                Operand<T> epsilon,
                                                                Operand<T> grad,
                                                                Operand<U> indices,
                                                                SparseApplyRMSProp.Options... options)

Factory method to create a class to wrap a new SparseApplyRMSProp operation to the graph.

Parameters:

scope - current graph scope

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.

momentum -

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 SparseApplyRMSProp

useLocking

public static SparseApplyRMSProp.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.

out

public Output<T> out()

Same as "var".

asOutput

public Output<T> asOutput()

Description copied from interface: Operand

Returns the symbolic handle of a 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.

Specified by:

asOutput in interface Operand<T>

See Also:

OperationBuilder.addInput(Output)