org.tensorflow.op.core

Class SparseApplyFtrl<T>

java.lang.Object

org.tensorflow.op.PrimitiveOp

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

Type Parameters:

T - data type for out() output

All Implemented Interfaces:

Op, Operand<T>

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

Update relevant entries in '*var' according to the Ftrl-proximal scheme.

That is for rows we have grad for, we update var, accum and linear as follows: $$accum_new = accum + grad * grad$$ $$linear += grad + (accum_{new}^{-lr_{power}} - accum^{-lr_{power}} / lr * var$$ $$quadratic = 1.0 / (accum_{new}^{lr_{power}} * lr) + 2 * l2$$ $$var = (sign(linear) * l1 - linear) / quadratic\ if\ |linear| > l1\ else\ 0.0$$ $$accum = accum_{new}$$

Nested Class Summary

Nested Classes

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

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> SparseApplyFtrl<T>	create(Scope scope, Operand<T> var, Operand<T> accum, Operand<T> linear, Operand<T> grad, Operand<U> indices, Operand<T> lr, Operand<T> l1, Operand<T> l2, Operand<T> lrPower, SparseApplyFtrl.Options... options) Factory method to create a class to wrap a new SparseApplyFtrl operation to the graph.
Output<T>	out() Same as "var".
static SparseApplyFtrl.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> SparseApplyFtrl<T> create(Scope scope,
                                                             Operand<T> var,
                                                             Operand<T> accum,
                                                             Operand<T> linear,
                                                             Operand<T> grad,
                                                             Operand<U> indices,
                                                             Operand<T> lr,
                                                             Operand<T> l1,
                                                             Operand<T> l2,
                                                             Operand<T> lrPower,
                                                             SparseApplyFtrl.Options... options)

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

Parameters:

scope - current graph scope

var - Should be from a Variable().

accum - Should be from a Variable().

linear - Should be from a Variable().

grad - The gradient.

indices - A vector of indices into the first dimension of var and accum.

lr - Scaling factor. Must be a scalar.

l1 - L1 regularization. Must be a scalar.

l2 - L2 regularization. Must be a scalar.

lrPower - Scaling factor. Must be a scalar.

options - carries optional attributes values

Returns:

a new instance of SparseApplyFtrl

useLocking

public static SparseApplyFtrl.Options useLocking(Boolean useLocking)

Parameters:

useLocking - If `True`, updating of the var and accum tensors will be 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)