SparseApplyFtrl

public final class SparseApplyFtrl

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: grad_with_shrinkage = grad + 2 * l2_shrinkage * var accum_new = accum + grad * grad linear += grad_with_shrinkage - (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 Classes

class

SparseApplyFtrl.Options

Optional attributes for SparseApplyFtrl

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> SparseApplyFtrl<T>	create(Scope scope, Operand<T> var, Operand<T> accum, Operand<T> linear, Operand<T> grad, Operand<? extends TNumber> indices, Operand<T> lr, Operand<T> l1, Operand<T> l2, Operand<T> l2Shrinkage, Operand<T> lrPower, Options... options) Factory method to create a class wrapping a new SparseApplyFtrl operation.
static SparseApplyFtrl.Options	multiplyLinearByLr(Boolean multiplyLinearByLr)
Output<T>	out() Same as "var".
static SparseApplyFtrl.Options	useLocking(Boolean useLocking)

Inherited Methods

From class org.tensorflow.op.RawOp

final boolean	equals(Object obj)
final int	hashCode()
Operation	op() Return this unit of computation as a single Operation.
final String	toString()

From class java.lang.Object

boolean	equals(Object arg0)
final Class<?>	getClass()
int	hashCode()
final void	notify()
final void	notifyAll()
String	toString()
final void	wait(long arg0, int arg1)
final void	wait(long arg0)
final void	wait()

From interface org.tensorflow.op.Op

abstract ExecutionEnvironment	env() Return the execution environment this op was created in.
abstract Operation	op() Return this unit of computation as a single Operation.

From interface org.tensorflow.Operand

abstract Output<T>	asOutput() Returns the symbolic handle of the tensor.
abstract T	asTensor() Returns the tensor at this operand.
abstract Shape	shape() Returns the (possibly partially known) shape of the tensor referred to by the Output of this operand.
abstract Class<T>	type() Returns the tensor type of this operand

From interface org.tensorflow.ndarray.Shaped

abstract int	rank()
abstract Shape	shape()
abstract long	size() Computes and returns the total size of this container, in number of values.

Constants

public static final String OP_NAME

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

Constant Value: "SparseApplyFtrlV2"

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 SparseApplyFtrl<T> create (Scope scope, Operand<T> var, Operand<T> accum, Operand<T> linear, Operand<T> grad, Operand<? extends TNumber> indices, Operand<T> lr, Operand<T> l1, Operand<T> l2, Operand<T> l2Shrinkage, Operand<T> lrPower, Options... options)

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

Parameters

scope	current 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 shrinkage regularization. Must be a scalar.
lrPower	Scaling factor. Must be a scalar.
options	carries optional attributes values

Returns

• a new instance of SparseApplyFtrl

public static SparseApplyFtrl.Options multiplyLinearByLr (Boolean multiplyLinearByLr)

public Output<T> out ()

Same as "var".

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.