func main() {
flag.Parse()
h1Size := 100
numHeads := 1
n := 128
m := 20
c := ntm.NewEmptyController1(1, 1, h1Size, numHeads, n, m)
weightsFromFile(c)
runs := make([]Run, 0)
for i := 0; i < 1; i++ {
prob := ngram.GenProb()
var l float64 = 0
var x [][]float64
var y [][]float64
var machines []*ntm.NTM
sampletimes := 100
for j := 0; j < sampletimes; j++ {
x, y = ngram.GenSeq(prob)
model := &ntm.LogisticModel{Y: y}
machines = ntm.ForwardBackward(c, x, model)
l += model.Loss(ntm.Predictions(machines))
if (j+1)%10 == 0 {
log.Printf("%d %d %f", i, j+1, l/float64(j+1))
}
}
l = l / float64(sampletimes)
r := Run{
Conf: RunConf{Prob: prob},
BitsPerSeq: l,
X: x,
Y: y,
Predictions: ntm.Predictions(machines),
HeadWeights: ntm.HeadWeights(machines),
}
runs = append(runs, r)
//log.Printf("x: %v", x)
//log.Printf("y: %v", y)
//log.Printf("predictions: %s", ntm.Sprint2(ntm.Predictions(machines)))
}
http.HandleFunc("/", root(runs))
if err := http.ListenAndServe(":9000", nil); err != nil {
log.Printf("%v", err)
}
}
func main() {
flag.Parse()
vectorSize := 8
h1Size := 100
numHeads := 1
n := 128
m := 20
c := ntm.NewEmptyController1(vectorSize+2, vectorSize, h1Size, numHeads, n, m)
copy(c.WeightsVal(), weightsFromFile())
seqLens := []int{10, 20, 30, 50, 120}
runs := make([]Run, 0, len(seqLens))
for _, seql := range seqLens {
x, y := copytask.GenSeq(seql, vectorSize)
model := &ntm.LogisticModel{Y: y}
machines := ntm.ForwardBackward(c, x, model)
l := model.Loss(ntm.Predictions(machines))
bps := l / float64(len(y)*len(y[0]))
log.Printf("sequence length: %d, loss: %f", seql, bps)
r := Run{
SeqLen: seql,
BitsPerSeq: bps,
X: x,
Y: y,
Predictions: ntm.Predictions(machines),
HeadWeights: ntm.HeadWeights(machines),
}
runs = append(runs, r)
//log.Printf("x: %v", x)
//log.Printf("y: %v", y)
//log.Printf("predictions: %s", ntm.Sprint2(ntm.Predictions(machines)))
}
http.HandleFunc("/", root(runs))
if err := http.ListenAndServe(":9000", nil); err != nil {
log.Printf("%v", err)
}
}
func main() {
flag.Parse()
if *cpuprofile != "" {
f, err := os.Create(*cpuprofile)
if err != nil {
log.Fatal(err)
}
pprof.StartCPUProfile(f)
defer pprof.StopCPUProfile()
}
http.HandleFunc("/Weights", func(w http.ResponseWriter, r *http.Request) {
c := make(chan []byte)
weightsChan <- c
w.Write(<-c)
})
http.HandleFunc("/Loss", func(w http.ResponseWriter, r *http.Request) {
c := make(chan []float64)
lossChan <- c
json.NewEncoder(w).Encode(<-c)
})
http.HandleFunc("/PrintDebug", func(w http.ResponseWriter, r *http.Request) {
printDebugChan <- struct{}{}
})
port := 8087
go func() {
log.Printf("Listening on port %d", port)
if err := http.ListenAndServe(fmt.Sprintf(":%d", port), nil); err != nil {
log.Fatalf("%v", err)
}
}()
var seed int64 = 7
rand.Seed(seed)
h1Size := 100
numHeads := 1
n := 128
m := 20
c := ntm.NewEmptyController1(1, 1, h1Size, numHeads, n, m)
weights := c.WeightsVal()
for i := range weights {
weights[i] = 1 * (rand.Float64() - 0.5)
}
losses := make([]float64, 0)
doPrint := false
rmsp := ntm.NewRMSProp(c)
log.Printf("seed: %d, numweights: %d, numHeads: %d", seed, len(c.WeightsVal()), c.NumHeads())
for i := 1; ; i++ {
x, y := ngram.GenSeq(ngram.GenProb())
machines := rmsp.Train(x, &ntm.LogisticModel{Y: y}, 0.95, 0.5, 1e-3, 1e-3)
if i%1000 == 0 {
prob := ngram.GenProb()
var l float64 = 0
samn := 100
for j := 0; j < samn; j++ {
x, y = ngram.GenSeq(prob)
model := &ntm.LogisticModel{Y: y}
machines = ntm.ForwardBackward(c, x, model)
l += model.Loss(ntm.Predictions(machines))
}
l = l / float64(samn)
losses = append(losses, l)
log.Printf("%d, bits-per-seq: %f", i, l)
}
handleHTTP(c, losses, &doPrint)
if i%1000 == 0 && doPrint {
printDebug(x, y, machines)
}
}
}