LevenbergMarquardtTraining
org.encog.neural.networks.training.lma

Class LevenbergMarquardtTraining

• All Implemented Interfaces:

`public class LevenbergMarquardtTrainingextends BasicTrainingimplements MultiThreadable`
Trains a neural network using a Levenberg Marquardt algorithm (LMA). This training technique is based on the mathematical technique of the same name. The LMA interpolates between the Gauss-Newton algorithm (GNA) and the method of gradient descent (similar to what is used by backpropagation. The lambda parameter determines the degree to which GNA and Gradient Descent are used. A lower lambda results in heavier use of GNA, whereas a higher lambda results in a heavier use of gradient descent. Each iteration starts with a low lambda that builds if the improvement to the neural network is not desirable. At some point the lambda is high enough that the training method reverts totally to gradient descent. This allows the neural network to be trained effectively in cases where GNA provides the optimal training time, but has the ability to fall back to the more primitive gradient descent method LMA finds only a local minimum, not a global minimum. References: http://www.heatonresearch.com/wiki/LMA http://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm http://en.wikipedia.org/wiki/Finite_difference_method http://crsouza.blogspot.com/2009/11/neural-network-learning-by-levenberg_18.html http://mathworld.wolfram.com/FiniteDifference.html http://www-alg.ist.hokudai.ac.jp/~jan/alpha.pdf - http://www.inference.phy.cam.ac.uk/mackay/Bayes_FAQ.html
• Field Summary

Fields
Modifier and TypeField and Description
`static double``LAMBDA_MAX`
The max amount for the LAMBDA.
`static double``SCALE_LAMBDA`
The amount to scale the lambda by.
• Constructor Summary

Constructors
Constructor and Description
`LevenbergMarquardtTraining(BasicNetwork network, MLDataSet training)`
Construct the LMA object.
`LevenbergMarquardtTraining(BasicNetwork network, MLDataSet training, ComputeHessian h)`
Construct the LMA object.
• Method Summary

Methods
Modifier and TypeMethod and Description
`boolean``canContinue()`
`ComputeHessian``getHessian()`
`MLMethod``getMethod()`
Get the current best machine learning method from the training.
`int``getThreadCount()`
`void``iteration()`
Perform one iteration.
`TrainingContinuation``pause()`
Pause the training to continue later.
`void``resume(TrainingContinuation state)`
Resume training.
`void``setThreadCount(int numThreads)`
Set the number of threads to use.
`void``updateWeights()`
Update the weights in the neural network.
• Methods inherited from class org.encog.ml.train.BasicTraining

`addStrategy, finishTraining, getError, getImplementationType, getIteration, getStrategies, getTraining, isTrainingDone, iteration, postIteration, preIteration, setError, setIteration, setTraining`
• Methods inherited from class java.lang.Object

`equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`
• Field Detail

• SCALE_LAMBDA

`public static final double SCALE_LAMBDA`
The amount to scale the lambda by.
Constant Field Values
• LAMBDA_MAX

`public static final double LAMBDA_MAX`
The max amount for the LAMBDA.
Constant Field Values
• Constructor Detail

• LevenbergMarquardtTraining

`public LevenbergMarquardtTraining(BasicNetwork network,                          MLDataSet training)`
Construct the LMA object.
Parameters:
`network` - The network to train. Must have a single output neuron.
`training` - The training data to use. Must be indexable.
• LevenbergMarquardtTraining

`public LevenbergMarquardtTraining(BasicNetwork network,                          MLDataSet training,                          ComputeHessian h)`
Construct the LMA object.
Parameters:
`network` - The network to train. Must have a single output neuron.
`training` - The training data to use. Must be indexable.
• Method Detail

• canContinue

`public boolean canContinue()`
Specified by:
`canContinue` in interface `MLTrain`
Returns:
True if the training can be paused, and later continued.
• getMethod

`public MLMethod getMethod()`
Description copied from interface: `MLTrain`
Get the current best machine learning method from the training.
Specified by:
`getMethod` in interface `MLTrain`
Returns:
The trained network.
• iteration

`public void iteration()`
Perform one iteration.
Specified by:
`iteration` in interface `MLTrain`
• pause

`public TrainingContinuation pause()`
Pause the training to continue later.
Specified by:
`pause` in interface `MLTrain`
Returns:
A training continuation object.
• resume

`public void resume(TrainingContinuation state)`
Resume training.
Specified by:
`resume` in interface `MLTrain`
Parameters:
`state` - The training continuation object to use to continue.
• updateWeights

`public void updateWeights()`
Update the weights in the neural network.
• getHessian

`public ComputeHessian getHessian()`
Returns:
The Hessian calculation method used.

`public int getThreadCount()`
Specified by:
`getThreadCount` in interface `MultiThreadable`
Returns:
The number of threads to use, 0 to automatically determine based on core count.
`public void setThreadCount(int numThreads)`
Description copied from interface: `MultiThreadable`
`setThreadCount` in interface `MultiThreadable`
`numThreads` - The number of threads to use, or zero to automatically determine based on core count.