Exemple #1
0
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)
	}
}
Exemple #2
0
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)
		}
	}
}