}
public static AparapiCDTrainer cdSigmoidTrainer(RBM rbm, TrainingInputProvider trainingSet, TrainingInputProvider testingSet, OutputError error, NNRandomInitializer rand, float learningRate, float momentum, float l1weightDecay, float l2weightDecay, int gibbsSampling, int trainingBatchSize, int epochs, boolean isPersistentCD) {
rbm.setLayerCalculator(NNFactory.lcSigmoid(rbm, null));
RBMLayerCalculator lc = NNFactory.rbmSigmoidSigmoid(rbm, trainingBatchSize);
return new AparapiCDTrainer(rbmProperties(rbm, lc, trainingSet, testingSet, error, rand, learningRate, momentum, l1weightDecay, l2weightDecay, gibbsSampling, trainingBatchSize, epochs, isPersistentCD));
}