121 lines
2.4 KiB
Go
121 lines
2.4 KiB
Go
package main
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import (
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"git.andr3h3nriqu3s.com/andr3/gotch"
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dbtypes "git.andr3h3nriqu3s.com/andr3/fyp/logic/db_types"
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"git.andr3h3nriqu3s.com/andr3/fyp/logic/models/train/torch"
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//my_nn "git.andr3h3nriqu3s.com/andr3/fyp/logic/models/train/torch/nn"
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torch "git.andr3h3nriqu3s.com/andr3/gotch/ts"
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"github.com/charmbracelet/log"
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)
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func main() {
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log.Info("Hello world")
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m := train.BuildModel([]*dbtypes.Layer{
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&dbtypes.Layer{
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LayerType: dbtypes.LAYER_INPUT,
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Shape: "[ 3, 28, 28 ]",
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},
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&dbtypes.Layer{
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LayerType: dbtypes.LAYER_FLATTEN,
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},
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&dbtypes.Layer{
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LayerType: dbtypes.LAYER_DENSE,
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Shape: "[ 27 ]",
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},
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&dbtypes.Layer{
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LayerType: dbtypes.LAYER_DENSE,
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Shape: "[ 18 ]",
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},
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// &dbtypes.Layer{
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// LayerType: dbtypes.LAYER_DENSE,
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// Shape: "[ 9 ]",
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// },
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}, 0, true)
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//var err error
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d := gotch.CudaIfAvailable()
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log.Info("device", "d", d)
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m.To(d)
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var count = 0
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// vars1 := m.Vs.Variables()
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//
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// for k, v := range vars1 {
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// ones := torch.MustOnes(v.MustSize(), gotch.Float, d)
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// v := ones.MustSetRequiresGrad(true, false)
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// v.MustDrop()
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// ones.RetainGrad(false)
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//
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// m.Vs.UpdateVarTensor(k, ones, true)
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// m.Refresh()
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// }
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//
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// opt, err := my_nn.DefaultAdamConfig().Build(m.Vs, 0.001)
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// if err != nil {
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// return
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// }
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log.Info("start")
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for count < 100 {
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ones := torch.MustOnes([]int64{1, 3, 28, 28}, gotch.Float, d)
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// ones = ones.MustSetRequiresGrad(true, true)
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// ones.RetainGrad(false)
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res := m.ForwardT(ones, true)
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//res = res.MustSetRequiresGrad(true, true)
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//res.RetainGrad(false)
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outs := torch.MustZeros([]int64{1, 18}, gotch.Float, d)
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loss, err := res.BinaryCrossEntropyWithLogits(outs, &torch.Tensor{}, &torch.Tensor{}, 2, false)
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if err != nil {
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log.Fatal(err)
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}
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// loss = loss.MustSetRequiresGrad(true, true)
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//opt.ZeroGrad()
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log.Info("loss", "loss", loss.Float64Values())
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loss.MustBackward()
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//opt.Step()
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// log.Info(mean.MustGrad(false).Float64Values())
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//ones_grad = ones.MustGrad(true).MustMax(true).Float64Values()[0]
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// log.Info(res.MustGrad(true).MustMax(true).Float64Values())
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// log.Info(ones_grad)
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vars := m.Vs.Variables()
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for k, v := range vars {
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log.Info("[grad check]", "k", k, "grad", v.MustGrad(false).MustMax(true).Float64Values())
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}
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m.Debug()
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outs.MustDrop()
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count += 1
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log.Fatal("grad zero")
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}
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log.Warn("out")
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}
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