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)
}
}