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Neural Network configuration
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double regularize = 0.000001; | |
double dropOut = 0.0; | |
int numEpochs = 3000; | |
int batchSize = 2000; | |
int printEvery = Xarr.length/batchSize; | |
// Dimensions | |
int features = 13; | |
int lay1 = 100; | |
int lay2 = 6; | |
int outs = 6; | |
final DataSet allData = new DataSet(X,y); | |
final List<DataSet> list = allData.asList(); | |
Collections.shuffle(list, rng); | |
DataSetIterator iterator = new ListDataSetIterator(list, batchSize); | |
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() | |
.seed(rngSeed) | |
.optimizationAlgo(algo) | |
.iterations(iterations) | |
.activation(hiddenAct) | |
.weightInit(WeightInit.XAVIER_FAN_IN) | |
.updater(updater) | |
.regularization(true).l2(regularize).dropOut(dropOut) | |
.list() | |
.layer(0, new DenseLayer.Builder() | |
.learningRate(10) | |
.momentum(5) | |
.nIn(features) | |
.nOut(lay1) | |
.build()) | |
// .layer(1, new DenseLayer.Builder() | |
// .learningRate(3.0) | |
// .momentum(5.0) | |
// .nIn(lay1) | |
// .nOut(lay2) | |
// .build()) | |
.layer(1, new OutputLayer.Builder(LossFunction.SQUARED_LOSS) | |
.activation(outAct) | |
.learningRate(6) | |
.momentum(6) | |
.nIn(lay1) | |
.nOut(outs) | |
.build()) | |
.pretrain(false).backprop(true) | |
.build(); |
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