func (d *DBN) AddLayer(numVisibleUnits, numHiddenUnits int) {
if d.RBMs != nil {
// Get the number of visible units of new layer
r := d.RBMs[len(d.RBMs)-1] // last layer
numVisibleUnitsOfNewLayer := r.NumHiddenUnits
if numVisibleUnits != numVisibleUnitsOfNewLayer {
panic("unexpected!")
}
}
// Add new RBM layer
newRbm := rbm.New(numVisibleUnits, numHiddenUnits)
d.RBMs = append(d.RBMs, newRbm)
d.NumLayers++
}
func main() {
outFilename := flag.String("output", "nn.json", "Output filename (*.json)")
modelFilename := flag.String("model", "", "Model filename (*.json)")
learningRate := flag.Float64("learning_rate", 0.1, "Learning rate")
epoches := flag.Int("epoch", 5, "Epoches")
usePersistent := flag.Bool("persistent", false, "Persistent constrastive learning")
orderOfGibbsSampling := flag.Int("order", 1, "Order of Gibbs sampling")
orderOfDownSampling := flag.Int("down", 1, "Order of down sampling")
miniBatchSize := flag.Int("size", 20, "Mini-batch size")
l2 := flag.Bool("l2", false, "L2 regularization")
numHiddenUnits := flag.Int("hidden_units", 100, "Number of hidden units")
flag.Parse()
trainingPath := "../data/train-images-idx3-ubyte"
file, err := os.Open(trainingPath)
if err != nil {
log.Fatal(err)
}
images, w, h := mnist.ReadMNISTImages(file)
// Convert image to data matrix
data := mnist.PrepareX(images)
data = mnist.NormalizePixel(mnist.DownSample(data, w, h, *orderOfDownSampling))
// w and h with down sampled data
w, h = w/(*orderOfDownSampling), h/(*orderOfDownSampling)
// Create RBM
var r *rbm.RBM
if *modelFilename != "" {
r, err = rbm.Load(*modelFilename)
if err != nil {
log.Fatal(err)
}
fmt.Println("Load parameters from", *modelFilename)
} else {
numVisibleUnits := w * h
r = rbm.New(numVisibleUnits, *numHiddenUnits)
}
// Training
option := rbm.TrainingOption{
LearningRate: *learningRate,
Epoches: *epoches,
OrderOfGibbsSampling: *orderOfGibbsSampling,
UsePersistent: *usePersistent,
MiniBatchSize: *miniBatchSize,
L2Regularization: *l2,
RegularizationRate: 0.0001,
Monitoring: true,
}
fmt.Println("Start training")
start := time.Now()
terr := r.Train(data, option)
if terr != nil {
log.Fatal(terr)
}
fmt.Println("Elapsed:", time.Now().Sub(start))
oerr := r.Dump(*outFilename)
if oerr != nil {
log.Fatal(oerr)
}
fmt.Println("Parameters are dumped to", *outFilename)
fmt.Println("Training finished.")
}
