// Train performs mini-batch SGD-based backpropagation to optimize network.
func (d *MLP) Train(input [][]float64, target [][]float64, option TrainingOption) error {
d.Option = option
opt := nnet.BaseTrainingOption{
Epoches: d.Option.Epoches,
MiniBatchSize: d.Option.MiniBatchSize,
Monitoring: d.Option.Monitoring,
}
s := nnet.NewTrainer(opt)
return s.SupervisedMiniBatchTrain(d, input, target)
}
// Train performs Contrastive divergense learning algorithm to train GBRBM.
// The alrogithm is based on (mini-batch) Stochastic Gradient Ascent.
func (rbm *GBRBM) Train(data [][]float64, option TrainingOption) error {
rbm.Option = option
opt := nnet.BaseTrainingOption{
Epoches: rbm.Option.Epoches,
MiniBatchSize: rbm.Option.MiniBatchSize,
Monitoring: rbm.Option.Monitoring,
}
// Peistent Contrastive learning
if rbm.Option.UsePersistent {
rbm.PersistentVisibleUnits = nnet.MakeMatrix(len(data), len(data[0]))
copy(rbm.PersistentVisibleUnits, data)
}
s := nnet.NewTrainer(opt)
return s.UnSupervisedMiniBatchTrain(rbm, data)
}