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main.go
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main.go
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package main
import (
"fmt"
"log"
"math"
"math/rand"
"os"
"time"
"gorgonia.org/tensor"
"gorgonia.org/tensor/native"
)
func main() {
imgs, err := readImageFile(os.Open("train-images-idx3-ubyte"))
if err != nil {
log.Fatal(err)
}
labels, err := readLabelFile(os.Open("train-labels-idx1-ubyte"))
if err != nil {
log.Fatal(err)
}
log.Printf("len imgs %d", len(imgs))
data := prepareX(imgs)
lbl := prepareY(labels)
visualize(data, 10, 10, "image.png")
data1, _ := data.Slice(makeRS(0, 1))
log.Printf("%v", data1.Data())
data2, err := zca(data)
if err != nil {
log.Fatal(err)
}
visualize(data2, 10, 10, "image2.png")
data2x, _ := data2.Slice(makeRS(0, 1))
log.Printf("%v", data2x.Data())
_ = lbl
nat, err := native.MatrixF64(data2.(*tensor.Dense))
if err != nil {
log.Fatal(err)
}
log.Printf("Start Training")
nn := New(784, 784, 10)
costs := make([]float64, 0, data2.Shape()[0])
for e := 0; e < 5; e++ {
data2Shape := data2.Shape()
var oneimg, onelabel tensor.Tensor
for i := 0; i < data2Shape[0]; i++ {
if oneimg, err = data.Slice(makeRS(i, i+1)); err != nil {
log.Fatalf("Unable to slice one image %d", i)
}
if onelabel, err = lbl.Slice(makeRS(i, i+1)); err != nil {
log.Fatalf("Unable to slice one label %d", i)
}
var cost float64
if cost, err = nn.Train(oneimg, onelabel, 0.1); err != nil {
log.Fatalf("Training error: %+v", err)
}
costs = append(costs, cost)
}
log.Printf("%d\t%v", e, avg(costs))
shuffleX(nat)
costs = costs[:0]
}
log.Printf("End training")
log.Printf("Start testing")
testImgs, err := readImageFile(os.Open("t10k-images.idx3-ubyte"))
if err != nil {
log.Fatal(err)
}
testlabels, err := readLabelFile(os.Open("t10k-labels.idx1-ubyte"))
if err != nil {
log.Fatal(err)
}
testData := prepareX(testImgs)
testLbl := prepareY(testlabels)
shape := testData.Shape()
testData2, err := zca(testData)
if err != nil {
log.Fatal(err)
}
visualize(testData, 10, 10, "testData.png")
visualize(testData2, 10, 10, "testData2.png")
var correct, total float64
var oneimg, onelabel tensor.Tensor
var predicted, errcount int
for i := 0; i < shape[0]; i++ {
if oneimg, err = testData.Slice(makeRS(i, i+1)); err != nil {
log.Fatalf("Unable to slice one image %d", i)
}
if onelabel, err = testLbl.Slice(makeRS(i, i+1)); err != nil {
log.Fatalf("Unable to slice one label %d", i)
}
label := argmax(onelabel.Data().([]float64))
if predicted, err = nn.Predict(oneimg); err != nil {
log.Fatalf("Failed to predict %d", i)
}
if predicted == label {
correct++
} else if errcount < 5 {
visualize(oneimg, 1, 1, fmt.Sprintf("%d_%d_%d.png", i, label, predicted))
errcount++
}
total++
}
fmt.Printf("Correct/Totals: %v/%v = %1.3f\n", correct, total, correct/total)
log.Printf("Correct/Totals: %v/%v = %1.3f\n", correct, total, correct/total)
visualizeWeights(nn.hidden, 28, 28, "hiddenlayer.png")
visualizeWeights(nn.final, 2, 5, "finallayer.png")
}
func shuffleX(a [][]float64) {
r := rand.New(rand.NewSource(time.Now().UnixNano()))
tmp := make([]float64, len(a[0]))
for i := range a {
j := r.Intn(i + 1)
copy(tmp, a[i])
copy(a[i], a[j])
copy(a[j], tmp)
}
}
func argmax(a []float64) (retVal int) {
var max = math.Inf(-1)
for i := range a {
if a[i] > max {
retVal = i
max = a[i]
}
}
return
}
func avg(a []float64) (retVal float64) {
s := sum(a)
return s / float64(len(a))
}
func sum(a []float64) (retVal float64) {
for i := range a {
retVal += a[i]
}
return retVal
}