122 lines
2.4 KiB
Go
122 lines
2.4 KiB
Go
package main
|
|
|
|
import (
|
|
"git.andr3h3nriqu3s.com/andr3/gotch"
|
|
|
|
dbtypes "git.andr3h3nriqu3s.com/andr3/fyp/logic/db_types"
|
|
"git.andr3h3nriqu3s.com/andr3/fyp/logic/models/train/torch"
|
|
|
|
my_nn "git.andr3h3nriqu3s.com/andr3/fyp/logic/models/train/torch/nn"
|
|
|
|
torch "git.andr3h3nriqu3s.com/andr3/gotch/ts"
|
|
"github.com/charmbracelet/log"
|
|
)
|
|
|
|
func main() {
|
|
|
|
log.Info("Hello world")
|
|
|
|
m := train.BuildModel([]*dbtypes.Layer{
|
|
&dbtypes.Layer{
|
|
LayerType: dbtypes.LAYER_INPUT,
|
|
Shape: "[ 3, 28, 28 ]",
|
|
},
|
|
&dbtypes.Layer{
|
|
LayerType: dbtypes.LAYER_FLATTEN,
|
|
},
|
|
&dbtypes.Layer{
|
|
LayerType: dbtypes.LAYER_DENSE,
|
|
Shape: "[ 27 ]",
|
|
},
|
|
&dbtypes.Layer{
|
|
LayerType: dbtypes.LAYER_DENSE,
|
|
Shape: "[ 18 ]",
|
|
},
|
|
// &dbtypes.Layer{
|
|
// LayerType: dbtypes.LAYER_DENSE,
|
|
// Shape: "[ 9 ]",
|
|
// },
|
|
}, 0, true)
|
|
|
|
//var err error
|
|
|
|
d := gotch.CudaIfAvailable()
|
|
|
|
log.Info("device", "d", d)
|
|
|
|
m.To(d)
|
|
|
|
var ones_grad float64 = 0
|
|
var count = 0
|
|
|
|
vars1 := m.Vs.Variables()
|
|
|
|
for k, v := range vars1 {
|
|
ones := torch.MustOnes(v.MustSize(), gotch.Float, d)
|
|
v := ones.MustSetRequiresGrad(true, false)
|
|
v.MustDrop()
|
|
ones.RetainGrad(false)
|
|
|
|
m.Vs.UpdateVarTensor(k, ones, true)
|
|
m.Refresh()
|
|
}
|
|
|
|
opt, err := my_nn.DefaultAdamConfig().Build(m.Vs, 0.001)
|
|
if err != nil {
|
|
return
|
|
}
|
|
|
|
log.Info("start")
|
|
|
|
for count < 100 {
|
|
|
|
ones := torch.MustOnes([]int64{1, 3, 28, 28}, gotch.Float, d)
|
|
// ones = ones.MustSetRequiresGrad(true, true)
|
|
// ones.RetainGrad(false)
|
|
|
|
res := m.ForwardT(ones, true)
|
|
res = res.MustSetRequiresGrad(true, true)
|
|
res.RetainGrad(false)
|
|
|
|
outs := torch.MustZeros([]int64{1, 18}, gotch.Float, d)
|
|
|
|
loss, err := res.BinaryCrossEntropyWithLogits(outs, &torch.Tensor{}, &torch.Tensor{}, 2, false)
|
|
if err != nil {
|
|
log.Fatal(err)
|
|
}
|
|
loss = loss.MustSetRequiresGrad(true, true)
|
|
|
|
opt.ZeroGrad()
|
|
|
|
log.Info("loss", "loss", loss.Float64Values())
|
|
|
|
loss.MustBackward()
|
|
|
|
opt.Step()
|
|
|
|
// log.Info(mean.MustGrad(false).Float64Values())
|
|
//ones_grad = ones.MustGrad(true).MustMax(true).Float64Values()[0]
|
|
log.Info(res.MustGrad(true).MustMax(true).Float64Values())
|
|
|
|
log.Info(ones_grad)
|
|
|
|
vars := m.Vs.Variables()
|
|
|
|
for k, v := range vars {
|
|
log.Info("[grad check]", "k", k, "grad", v.MustGrad(false).MustMax(true).Float64Values())
|
|
}
|
|
|
|
m.Debug()
|
|
|
|
outs.MustDrop()
|
|
|
|
count += 1
|
|
|
|
log.Fatal("grad zero")
|
|
|
|
}
|
|
|
|
log.Warn("out")
|
|
|
|
}
|