public class NAdaGrad extends Object implements GradientUpdater
ScaledVector, where
the base vector is the datum, then NAdaGrad will work. If not the case,
NAdaGrad will degenerate into something similar to normal AdaGrad.
| Constructor and Description |
|---|
NAdaGrad()
Creates a new NAdaGrad updater
|
NAdaGrad(NAdaGrad toCopy)
Copy constructor
|
| Modifier and Type | Method and Description |
|---|---|
NAdaGrad |
clone() |
void |
setup(int d)
Sets up this updater to update a weight vector of dimension
d
by a gradient of the same dimension |
void |
update(Vec w,
Vec grad,
double eta)
Updates the weight vector
x such that x = x-ηf(grad),
where f(grad) is some function on the gradient that effectively returns a
new vector. |
double |
update(Vec w,
Vec grad,
double eta,
double bias,
double biasGrad)
Updates the weight vector
x such that x = x-ηf(grad),
where f(grad) is some function on the gradient that effectively returns a
new vector. |
public NAdaGrad()
public NAdaGrad(NAdaGrad toCopy)
toCopy - the object to copypublic void update(Vec w, Vec grad, double eta)
GradientUpdaterx such that x = x-ηf(grad),
where f(grad) is some function on the gradient that effectively returns a
new vector. It is not necessary for the internal implementation to ever
explicitly form any of these objects, so long as x is mutated to
have the correct result.update in interface GradientUpdaterw - the vector to mutate such that is has been updated by the
gradientgrad - the gradient to update the weight vector x frometa - the learning rate to applypublic double update(Vec w, Vec grad, double eta, double bias, double biasGrad)
GradientUpdaterx such that x = x-ηf(grad),
where f(grad) is some function on the gradient that effectively returns a
new vector. It is not necessary for the internal implementation to ever
explicitly form any of these objects, so long as x is mutated to
have the correct result. update in interface GradientUpdaterw - the vector to mutate such that is has been updated by the
gradientgrad - the gradient to update the weight vector x frometa - the learning rate to applybias - the bias term of the vectorbiasGrad - the gradient for the bias termbias = bias - returnValuepublic NAdaGrad clone()
clone in interface GradientUpdaterclone in class Objectpublic void setup(int d)
GradientUpdaterd
by a gradient of the same dimensionsetup in interface GradientUpdaterd - the dimension of the weight vector that will be updatedCopyright © 2017. All rights reserved.