gotch/example/mnist/linear.go

50 lines
1.2 KiB
Go

package main
import (
"fmt"
"github.com/sugarme/gotch"
ts "github.com/sugarme/gotch/tensor"
"github.com/sugarme/gotch/vision"
)
const (
ImageDim int64 = 784
Label int64 = 10
MnistDir string = "../../data/mnist"
epochs = 200
)
func runLinear() {
var ds vision.Dataset
ds = vision.LoadMNISTDir(MnistDir)
device := (gotch.CPU).CInt()
dtype := (gotch.Float).CInt()
ws := ts.MustZeros([]int64{ImageDim, Label}, dtype, device).MustSetRequiresGrad(true)
bs := ts.MustZeros([]int64{Label}, dtype, device).MustSetRequiresGrad(true)
for epoch := 0; epoch < epochs; epoch++ {
logits := ds.TrainImages.MustMm(ws).MustAdd(bs)
loss := logits.MustLogSoftmax(-1, dtype).MustNllLoss(ds.TrainLabels)
ws.ZeroGrad()
bs.ZeroGrad()
loss.MustBackward()
ts.NoGrad(func() {
ws.Add_(ws.MustGrad().MustMul1(ts.FloatScalar(-1.0)))
bs.Add_(bs.MustGrad().MustMul1(ts.FloatScalar(-1.0)))
})
testLogits := ds.TestImages.MustMm(ws).MustAdd(bs)
testAccuracy := testLogits.MustArgmax(-1, false).MustEq1(ds.TestLabels).MustTotype(gotch.Float).MustMean(gotch.Float.CInt()).MustView([]int64{-1}).MustFloat64Value([]int64{0})
fmt.Printf("Epoch: %v - Loss: %.3f - Test accuracy: %.2f%%\n", epoch, loss.Values()[0], testAccuracy*100)
}
}