func main() {
test := flag.Bool("test", false, "whether tests neural network or not")
modelFilename := flag.String("m", "nn.json", "model filename (*.json)")
outFilename := flag.String("o", "nn.json", "outtput model filename (*.json)")
learningRate := flag.Float64("learning_rate", 0.1, "Learning rate")
epoches := flag.Int("epoch", 50000*10, "Epoches")
numHiddenUnits := flag.Int("hidden_units", 100, "Number of hidden units")
flag.Parse()
if *test == true {
Test(*modelFilename)
return
}
trainingPath := "../data/train-images-idx3-ubyte"
labelPath := "../data/train-labels-idx1-ubyte"
file, err := os.Open(trainingPath)
if err != nil {
log.Fatal(err)
}
images, w, h := mnist.ReadMNISTImages(file)
fmt.Println(len(images), w, h, w*h)
lfile, lerr := os.Open(labelPath)
if lerr != nil {
log.Fatal(lerr)
}
labels := mnist.ReadMNISTLabels(lfile)
// Convert image to data matrix
data := mnist.NormalizePixel(mnist.PrepareX(images))
target := mnist.PrepareY(labels)
// Setup Neural Network
net := mlp3.NewNeuralNetwork(w*h, *numHiddenUnits, 10)
option := mlp3.TrainingOption{
LearningRate: *learningRate,
Epoches: *epoches, // the number of iterations in SGD
Monitoring: true,
}
// Perform training
start := time.Now()
nerr := net.Train(data, target, option)
if nerr != nil {
log.Fatal(nerr)
}
elapsed := time.Now().Sub(start)
fmt.Println(elapsed)
oerr := net.Dump(*outFilename)
if oerr != nil {
log.Fatal(err)
}
fmt.Println("Parameters are dummped to", *outFilename)
fmt.Println("Training finished!")
}
// Classification test using MNIST dataset.
func Test(filename string) {
net, err := mlp3.Load(filename)
if err != nil {
log.Fatal(err)
}
testPath := "../data/t10k-images-idx3-ubyte"
targetPath := "../data/t10k-labels-idx1-ubyte"
file, err := os.Open(testPath)
if err != nil {
log.Fatal(err)
}
images, w, h := mnist.ReadMNISTImages(file)
fmt.Println(len(images), w, h, w*h)
lfile, lerr := os.Open(targetPath)
if lerr != nil {
log.Fatal(lerr)
}
labels := mnist.ReadMNISTLabels(lfile)
// Convert image to data matrix
data := mnist.NormalizePixel(mnist.PrepareX(images))
target := mnist.PrepareY(labels)
result := nnet.Test(net, data)
sum := 0.0
for i := range result {
if result[i] == nnet.Argmax(target[i]) {
sum += 1.0
}
}
fmt.Printf("Acc. %f (%d/%d)\n", sum/float64(len(result)),
int(sum), len(result))
}
