func main() { flag.Parse() gen, err := poem.NewGenerator("data/quantangshi3000.int") if err != nil { log.Fatalf("%v", err) } h1Size := 512 numHeads := 8 n := 128 m := 32 c := ntm.NewEmptyController1(gen.InputSize(), gen.OutputSize(), h1Size, numHeads, n, m) assignWeights(c) p := [][]string{ {"红", "", "", "", ""}, {"春", "", "", "", ""}, {"愿", "", "", "", ""}, {"此", "", "", "", ""}, } //p := [][]string{ // {"阿", "", "", "", ""}, // {"扁", "", "", "", ""}, // {"无", "", "", "", ""}, // {"罪", "", "", "", ""}, //} //p := [][]string{ // {"九", "", "", "", "", "", ""}, // {"二", "", "", "", "", "", ""}, // {"共", "", "", "", "", "", ""}, // {"识", "", "", "", "", "", ""}, //} //p := [][]string{ // {"十", "", "", "", ""}, // {"四", "", "", "", ""}, // {"日", "", "", "", ""}, // {"罢", "", "", "", ""}, // {"免", "", "", "", ""}, // {"蔡", "", "", "", ""}, // {"正", "", "", "", ""}, // {"元", "", "", "", ""}, //} //p := [][]string{ // {"全", "", "", "", "", "", ""}, // {"力", "", "", "", "", "", ""}, // {"支", "", "", "", "", "", ""}, // {"持", "", "", "", "", "", ""}, // {"柯", "", "", "", "", "", ""}, // {"匹", "", "", "", "", "", ""}, // {"文", "", "", "", "", "", ""}, // {"哲", "", "", "", "", "", ""}, //} rand.Seed(15) pred := predict(c, p, gen) showPrediction(pred, gen, p) }
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 := 8096 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 = 16 rand.Seed(seed) genFunc := "bt" x, y := repeatcopy.G[genFunc](1, 1) h1Size := 100 numHeads := 2 n := 128 m := 20 c := ntm.NewEmptyController1(len(x[0]), len(y[0]), 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("genFunc: %s, seed: %d, numweights: %d, numHeads: %d", genFunc, seed, len(c.WeightsVal()), c.NumHeads()) for i := 1; ; i++ { x, y := repeatcopy.G[genFunc](rand.Intn(10)+1, rand.Intn(10)+1) model := &ntm.LogisticModel{Y: y} machines := rmsp.Train(x, model, 0.95, 0.5, 1e-3, 1e-3) l := model.Loss(ntm.Predictions(machines)) if i%1000 == 0 { bpc := l / float64(len(y)*len(y[0])) losses = append(losses, bpc) log.Printf("%d, bpc: %f, seq length: %d", i, bpc, len(y)) } handleHTTP(c, losses, &doPrint) if i%1000 == 0 && doPrint { printDebug(y, machines) } } }
func main() { flag.Parse() blas64.Use(cgo.Implementation{}) 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 := 8085 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 = 5 rand.Seed(seed) log.Printf("seed: %d", seed) gen, err := poem.NewGenerator("data/quantangshi3000.int") if err != nil { log.Fatalf("%v", err) } h1Size := 512 numHeads := 8 n := 128 m := 32 c := ntm.NewEmptyController1(gen.InputSize(), gen.OutputSize(), 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("numweights: %d", len(c.WeightsVal())) var bpcSum float64 = 0 for i := 1; ; i++ { x, y := gen.GenSeq() machines := rmsp.Train(x, &ntm.MultinomialModel{Y: y}, 0.95, 0.5, 1e-3, 1e-3) numChar := len(y) / 2 l := (&ntm.MultinomialModel{Y: y[numChar+1:]}).Loss(ntm.Predictions(machines[numChar+1:])) bpc := l / float64(numChar) bpcSum += bpc acc := 100 if i%acc == 0 { bpc := bpcSum / float64(acc) bpcSum = 0 losses = append(losses, bpc) log.Printf("%d, bpc: %f, seq length: %d", i, bpc, len(y)) } handleHTTP(c, losses, &doPrint) if i%10 == 0 && doPrint { printDebug(y, machines) } } }
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) } } }