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loadtomem.go
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loadtomem.go
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package mnistgpu
/*
import (
"fmt"
"github.com/dereklstinson/gocunets/devices/gpu/nvidia/cudnn"
"github.com/dereklstinson/gocunets/layers"
"github.com/dereklstinson/gocunets/testing/mnist/dfuncs"
gocudnn "github.com/dereklstinson/gocudnn"
"github.com/dereklstinson/gocudnn/gocu"
)
//WithLabels return trainingimages,traininglabels, testimages,testlabels
func WithLabels(handle *cudnn.Handler, batchsize int, frmt cudnn.TensorFormat, dtype cudnn.DataType) ([]*layers.IO, []*layers.IO, []*layers.IO, []*layers.IO) {
filedirectory := "/home/derek/go/src/github.com/dereklstinson/gocunets/testing/mnist/files/"
trainingdata, err := dfuncs.LoadMNIST(filedirectory, "train-labels.idx1-ubyte", "train-images.idx3-ubyte")
cherror(err)
testingdata, err := dfuncs.LoadMNIST(filedirectory, "t10k-labels.idx1-ubyte", "t10k-images.idx3-ubyte")
cherror(err)
//Normalizing Data
averagetest := dfuncs.FindAverage(testingdata)
averagetrain := dfuncs.FindAverage(trainingdata)
fmt.Println("Finding Average Value")
averagetotal := ((6.0 * averagetrain) + averagetest) / float32(7)
fmt.Println("Normalizing Data")
trainingdata = dfuncs.NormalizeData(trainingdata, averagetotal)
testingdata = dfuncs.NormalizeData(testingdata, averagetotal)
fmt.Println("Length of Training Data", len(trainingdata))
fmt.Println("Length of Testing Data", len(testingdata))
//Since Data is so small we can load it all into the GPU
var gputrainingdata []*layers.IO
var gpuanswersdata []*layers.IO
var gputestingdata []*layers.IO
var gputestansdata []*layers.IO
for i := 0; i < len(trainingdata); { //Counting i inside the j loop, because I don't want to figure out the math
batchslice := make([]float32, 0)
batchlabelslice := make([]float32, 0)
for j := 0; j < batchsize; j++ {
batchslice = append(batchslice, trainingdata[i].Data...)
batchlabelslice = append(batchlabelslice, trainingdata[i].Label...)
i++
}
data, err := gocu.MakeGoMem(batchslice)
cherror(err)
label, err := gocudnn.MakeGoPointer(batchlabelslice)
cherror(err)
inpt, err := layers.BuildNetworkInputIO(handle, frmt, dtype, dims(batchsize, 1, 28, 28))
cherror(err)
err = inpt.LoadTValues(handle, data)
cherror(err)
ansr, err := layers.BuildIO(handle, frmt, dtype, dims(batchsize, 10, 1, 1))
cherror(err)
err = ansr.LoadDeltaTValues(handle, label)
cherror(err)
gputrainingdata = append(gputrainingdata, inpt)
gpuanswersdata = append(gpuanswersdata, ansr)
}
fmt.Println("Done Loading Training to GPU")
for i := 0; i < len(testingdata); {
batchslice := make([]float32, 0)
batchlabelslice := make([]float32, 0)
for j := 0; j < batchsize; j++ {
batchslice = append(batchslice, testingdata[i].Data...)
batchlabelslice = append(batchlabelslice, testingdata[i].Label...)
i++
}
data, err := gocudnn.MakeGoPointer(batchslice)
cherror(err)
label, err := gocudnn.MakeGoPointer(batchlabelslice)
cherror(err)
inpt, err := layers.BuildNetworkInputIO(handle, frmt, dtype, dims(batchsize, 1, 28, 28))
cherror(err)
err = inpt.LoadTValues(handle, data)
cherror(err)
gputestingdata = append(gputestingdata, inpt)
ansr, err := layers.BuildIO(handle, frmt, dtype, dims(batchsize, 10, 1, 1))
cherror(err)
err = ansr.LoadDeltaTValues(handle, label)
cherror(err)
gputestansdata = append(gputestansdata, ansr)
}
fmt.Println("Done Loading Testing To GPU")
return gputrainingdata, gpuanswersdata, gputestingdata, gputestansdata
}
//Labelbatch contains the labels
type Labelbatch struct {
labels [][]float32
}
//WithCPULabels return trainingimages,traininglabels, testimages,testlabels
func WithCPULabels(handle *cudnn.Handler, batchsize int, frmt cudnn.TensorFormat, dtype cudnn.DataType) ([]*layers.IO, []Labelbatch) {
filedirectory := "/home/derek/go/src/github.com/dereklstinson/gocunets/testing/mnist/files/"
trainingdata, err := dfuncs.LoadMNIST(filedirectory, "train-labels.idx1-ubyte", "train-images.idx3-ubyte")
cherror(err)
testingdata, err := dfuncs.LoadMNIST(filedirectory, "t10k-labels.idx1-ubyte", "t10k-images.idx3-ubyte")
cherror(err)
//Normalizing Data
averagetest := dfuncs.FindAverage(testingdata)
averagetrain := dfuncs.FindAverage(trainingdata)
fmt.Println("Finding Average Value")
averagetotal := ((6.0 * averagetrain) + averagetest) / float32(7)
fmt.Println("Normalizing Data")
trainingdata = dfuncs.NormalizeData(trainingdata, averagetotal)
testingdata = dfuncs.NormalizeData(testingdata, averagetotal)
fmt.Println("Length of Training Data", len(trainingdata))
fmt.Println("Length of Testing Data", len(testingdata))
//Since Data is so small we can load it all into the GPU
var gputrainingdata []*layers.IO
//var gpuanswersdata []*layers.IO
//var gputestingdata []*layers.IO
//var gputestansdata []*layers.IO
// var cputrainans [][]float32
// var cputestans [][]float32
batchlabels := make([]Labelbatch, 0)
sizeofdata := 28 * 28
for i := 0; i < len(trainingdata); { //Counting i inside the j loop, because I don't want to figure out the math
batchslice := make([]float32, sizeofdata*batchsize)
// batchlabelslice := make([]float32, 0)
var singlebatch Labelbatch
for j := 0; j < batchsize; j++ {
for k := 0; k < len(trainingdata[i].Data); k++ {
batchslice[j*sizeofdata+k] = trainingdata[i].Data[k]
}
singlebatch.labels = append(singlebatch.labels, trainingdata[i].Label)
// batchlabelslice = append(batchlabelslice, trainingdata[i].Label...)
i++
}
batchlabels = append(batchlabels, singlebatch)
data, err := gocudnn.MakeGoPointer(batchslice)
cherror(err)
//label, err := gocudnn.MakeGoPointer(batchlabelslice)
//cherror(err)
inpt, err := layers.BuildNetworkInputIO(handle, frmt, dtype, dims(batchsize, 1, 28, 28))
cherror(err)
err = inpt.LoadTValues(handle, data)
cherror(err)
// ansr, err := layers.BuildIO(frmt, dtype, dims(batchsize, 10, 1, 1), memmanaged)
// cherror(err)
// err = ansr.LoadDeltaTValues(label)
//cherror(err)
gputrainingdata = append(gputrainingdata, inpt)
// gpuanswersdata = append(gpuanswersdata, ansr)
}
fmt.Println("Done Loading Training to GPU")
fmt.Println("Done Loading Testing To GPU")
return gputrainingdata, batchlabels
}
func cherror(err error) {
if err != nil {
panic(err)
}
}
func dims(args ...int) []int32 {
length := len(args)
x := make([]int32, length)
for i := 0; i < length; i++ {
x[i] = int32(args[i])
}
return x
}
*/