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@@ -12,7 +12,7 @@ const (
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Hidden int = 64
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Output int = 7
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Sample int = 10000
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- Batch int = 5
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+ Batch int = 16
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Dropout float64 = 0.5
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Rate float64 = 0.01
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)
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@@ -122,9 +122,9 @@ func Train(G Graph) []Layer {
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p2 := Parameter{MakeRandomMatrix(Hidden, Output), MakeRandomMatrix(Hidden, Output)}
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l := []Layer{{d: Input}, {d: Hidden, f: ReLU, p: p1}, {d: Output, f: Softmax, p: p2}}
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for i := 0; i < Sample; i++ {
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- if i%1000 == 0 {
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+ if i%100 == 0 {
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Test(G, l, false)
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- fmt.Println("sampling", i)
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+ // fmt.Println("sampling", i)
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}
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var wg sync.WaitGroup
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l[0].D, l[1].D = MakeDropoutVector(Input), MakeDropoutVector(Hidden)
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@@ -148,7 +148,7 @@ func Train(G Graph) []Layer {
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StartCalc(&wg, DB1, Matrix{G.E[0][u]}, deltaB)
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deltaW := delta.Multiply(l[2].p.W.Transpose())
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for v := range G.A[u] {
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- delta = MakeMatrix(1, Hidden).Add(deltaW).Divide(float64(len(G.A[u])))
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+ delta = MakeMatrix(1, Hidden).Add(deltaW)
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for k := 0; k < Hidden; k++ {
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if G.E[1][v][k] == 0 {
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delta[0][k] = 0
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@@ -159,6 +159,7 @@ func Train(G Graph) []Layer {
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}
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wg.Wait()
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}
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+ Rate := 0.2 * math.Exp(-float64(i)/1000)
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StartRefine(&wg, l[2].p.W, DW2, 1/Rate)
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StartRefine(&wg, l[2].p.B, DB2, 1/Rate)
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StartRefine(&wg, l[1].p.W, DW1, 1/Rate)
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@@ -169,7 +170,7 @@ func Train(G Graph) []Layer {
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}
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func Test(G Graph, l []Layer, detail bool) {
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- cnt1, cnt2 := 0, 0
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+ cnt1, cnt2, loss := 0, 0, 0.
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for u := range G.X {
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GetEmbedding(G, u, 2, l, false)
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id, _ := G.E[2][u].Max()
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@@ -182,7 +183,16 @@ func Test(G Graph, l []Layer, detail bool) {
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if G.L[u] == id+Output {
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cnt2++
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}
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+ loss -= math.Log(G.E[2][u][G.L[u]%Output])
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+ }
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+ if detail {
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+ fmt.Println(cnt1, "/", len(nodeId), ",", cnt2, "/", Node-len(nodeId))
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+ fmt.Println(
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+ 100*float64(cnt1)/float64(len(nodeId)), ",",
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+ 100*float64(cnt2)/float64((Node-len(nodeId))), ",",
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+ loss/float64(len(G.X)),
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+ )
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+ } else {
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+ fmt.Println(100*float64(cnt2)/float64((Node-len(nodeId))), loss/float64(len(G.X)))
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}
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- fmt.Println(cnt1, "/", len(nodeId), ",", cnt2, "/", Node-len(nodeId))
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- fmt.Println(100.*cnt1/len(nodeId), ",", 100.*cnt2/(Node-len(nodeId)))
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}
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