Encog Propogation Training Algorithms


Hello everybody,

today I want to describe in simple words some training algos of Encog.

Before I'll continue, I want to show general block schema of training algorithms:

Init NN can look like this:

public BasicNetwork CreateNetwork()
   var network = new BasicNetwork();
   network.AddLayer(new BasicLayer(WindowSize));
   network.AddLayer(new BasicLayer(10));
   network.AddLayer(new BasicLayer(1));
   return network;

Steps "NN error < acceptable error" -> "Update weights according to learning algorithim" can look like this:

public void Train(BasicNetwork network, IMLDataSet training)
    ITrain train = new ResilientPropagation(network, training);
    int epoch = 1;
            Console.WriteLine(@"Epoch #" + epoch + @" Error:" + train.Error);
         } while (train.Error > acceptableError);

The first in my decription goes Backpropogation algorithm. Few characteristics of it: 

1. probably the oldest training algorithm

2. As name says error are propogated back

3.  usage: 

var train = new Backpropogation(network, trainingSet, learningRate, momentum);

The next learning algorithm goes Manhattan update rule. Few characteristics of it:

1. use only sign of the gradient

2. Updates weiht by constant value ( depending from the sign it will be added or subtracted from the weithg )

3.  usage: 

var train = new ManhattanPropogation(network, trainingSet, constantValue);

as usually constantValue is very low ( around 0.00001 )

Other one is Quck propogation algorithm. Features of this method:

1. Newton's method of error minimization

2. Use learning trate which is higher value ( I used values starting from 2 )

3. Usage: 

var train = new QuickPropogation(network, trainingSet, learningRate);

Yet one more training algorithm is Resilent propogation algorithm. Some details of this method:

1. One of the fastest algorithm in Encog.

2. Easiest usage ( you don't care about rate, momentum or constant value )

3. It uses sign of gradient value.

4. It tries to compute delta value for each weight and use it for calculated weight.

5.  Example of usage:

var train = new ResilentPropogation(network, trainingSet);

6. It has four variants of resilent propogation: RPROP+, RPROP-, iRPROP+, iRPROP- . The best is considered iRPROP+

One more detail goes Scaled conjugate gradient. Features of it:

1. Use conjugate gradient method

2. Not applicable to all dataset

3. Doesn't require any learning paramethers

4. Example of usage:

var train = new ScaledConjugateGradient(network, trainingSet);

And the last one propogation algorithm is Levenberg Marquardt Algorithm ( aka LMA ). This is my favorite method. Features of it:

1. Something in between Gauss-Newton Algorithm ( GNA ) & Gradient Method

2. Easy to use ( no learning parameters )

3. Example of creating:

var train = new LevenbergMarquardtTraining(network, trainingSet);

4. Sometime it can be very efficient.

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