Adam

java.lang.Object
- me.yixqiao.jlearn.optimizers.Optimizer
- - me.yixqiao.jlearn.optimizers.Adam

```
public class Adam
extends Optimizer
```
Adam optimizer.

Constructor Summary

Constructors
Constructor Description

Adam(double learningRate)
Initialize the optimizer.

Adam(double learningRate, double beta1, double beta2)
Initialize the optimizer.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`Matrix`	`apply(Matrix g)`	Apply the optimizer.
`Optimizer`	`cloneSettings()`	Return a clone of this optimizer's settings.
`void`	`multiplyLR(double d)`	Multiply the learning rate.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - Adam
```
public Adam(double learningRate)
```
    Initialize the optimizer.
    
    Parameters:
    
    learningRate - learning rate
  - Adam
```
public Adam(double learningRate,
            double beta1,
            double beta2)
```
    Initialize the optimizer.
    
    Parameters:
    
    learningRate - learning rate
    
    beta1 - beta 1 (default is 0.9)
    
    beta2 - beta 2 (default is 0.999)
- Method Detail
  - cloneSettings
```
public Optimizer cloneSettings()
```
    Description copied from class: Optimizer
    
    Return a clone of this optimizer's settings.
    
    Specified by:
    
    cloneSettings in class Optimizer
    
    Returns:
    
    the clone
  - multiplyLR
```
public void multiplyLR(double d)
```
    Description copied from class: Optimizer
    
    Multiply the learning rate.
    Used to scale the learning rate in different batch sizes.
    
    Specified by:
    
    multiplyLR in class Optimizer
    
    Parameters:
    
    d - amount
  - apply
```
public Matrix apply(Matrix g)
```
    Description copied from class: Optimizer
    
    Apply the optimizer.
    
    Specified by:
    
    apply in class Optimizer
    
    Parameters:
    
    g - errors calculated from backpropagation.
    
    Returns:
    
    the gradients from the optimizer