0

The total error for the network did not change on over 100,000 iterations. The input is 22 values and the output is a single value. the input array is [195][22] and the output array is [195][1]. 

BasicNetwork network = new BasicNetwork();
    network.addLayer(new BasicLayer(null,true,22));
    network.addLayer(new BasicLayer(new ActivationSigmoid(),true,10));
    network.addLayer(new BasicLayer(new ActivationSigmoid(),false,1));
    network.getStructure().finalizeStructure();
    network.reset();


    MLDataSet training_data = new BasicMLDataSet(input, target_output);
    final Backpropagation train = new Backpropagation(network, training_data);

    int epoch = 1;

    do {
        train.iteration();

        System.out.println("Epoch #" + epoch + " Error:" + train.getError());

        epoch++;
    } 

    while(train.getError() > 0.01);
    {
        train.finishTraining();
    }

What is wrong with this code?

AZstudentCS
  • 21
  • 1

1 Answers1

0

Depending on what the data you are trying to classify your network may be too small to transform the search space into a linearly separable problem. So try adding more neurons or layers - this will probably take longer to train. Unless it is already linearly separable and then a NN may be an inefficient way to solve this.

Also you don't have a training strategy, if the NN falls into local minima on the error surface it will be stuck there. See the encog user guide https://s3.amazonaws.com/heatonresearch-books/free/Encog3Java-User.pdf pg 166 has a list of training strategy's.

final int strategyCycles = 50;
final double strategyError = 0.25; 
train.addStrategy(new ResetStrategy(strategyError,strategyCycles));
Aaron Baker
  • 66
  • 3