c# – 读取MNIST数据库

我目前正在探索神经网络和机器学习,我在c#中实现了一个基本的神经网络.现在我想用MNIST数据库测试我的反向传播训练算法.虽然我在正确阅读文件时遇到了严重问题.

Spoiler代码目前针对性能进行了非常优化.我的目标是掌握主题,并在开始抛弃我的数据结构以获得更快的数据结构之前获得结构化的视图.

为了训练网络,我想为它提供一个自定义的TrainingSet数据结构：

[Serializable]
public class TrainingSet
{
    public Dictionary<List<double>, List<double>> data = new Dictionary<List<double>, List<double>>();
}

键将是我的输入数据(每个条目784像素(图像),它将表示0到1范围内的灰度值).值将是我的输出数据(10个条目表示0-9的数字,所有条目都在0,除了1中的exspected一个)

现在我想根据这份合同阅读MNIST数据库.我现在正在进行第二次尝试,受到这篇博文的启发：https://jamesmccaffrey.wordpress.com/2013/11/23/reading-the-mnist-data-set-with-c/.可悲的是,它仍然产生与我第一次尝试以奇怪的模式散射像素相同的废话：

我目前的阅读算法：

    public static TrainingSet GenerateTrainingSet(FileInfo imagesFile, FileInfo labelsFile)
    {
        MnistImageView imageView = new MnistImageView();
        imageView.Show();

        TrainingSet trainingSet = new TrainingSet();

        List<List<double>> labels = new List<List<double>>();
        List<List<double>> images = new List<List<double>>();

        using (BinaryReader brLabels = new BinaryReader(new FileStream(labelsFile.FullName, FileMode.Open)))
        {
            using (BinaryReader brImages = new BinaryReader(new FileStream(imagesFile.FullName, FileMode.Open)))
            {
                int magic1 = brImages.ReadBigInt32(); //Reading as BigEndian
                int numImages = brImages.ReadBigInt32();
                int numRows = brImages.ReadBigInt32();
                int numCols = brImages.ReadBigInt32();

                int magic2 = brLabels.ReadBigInt32();
                int numLabels = brLabels.ReadBigInt32();

                byte[] pixels = new byte[numRows * numCols];

                // each image
                for (int imageCounter = 0; imageCounter < numImages; imageCounter++)
                {
                    List<double> imageInput = new List<double>();
                    List<double> exspectedOutput = new List<double>();

                    for (int i = 0; i < 10; i++) //generate empty exspected output
                        exspectedOutput.Add(0);

                    //read image
                    for (int p = 0; p < pixels.Length; p++)
                    {
                        byte b = brImages.ReadByte();
                        pixels[p] = b;

                        imageInput.Add(b / 255.0f); //scale in 0 to 1 range
                    }

                    //read label
                    byte lbl = brLabels.ReadByte();
                    exspectedOutput[lbl] = 1; //modify exspected output

                    labels.Add(exspectedOutput);
                    images.Add(imageInput);

                    //Debug view showing parsed image.......................
                    Bitmap image = new Bitmap(numCols, numRows);

                    for (int y = 0; y < numRows; y++)
                    {
                        for (int x = 0; x < numCols; x++)
                        {
                            image.SetPixel(x, y, Color.FromArgb(255 - pixels[x * y], 255 - pixels[x * y], 255 - pixels[x * y])); //invert colors to have 0,0,0 be white as specified by mnist
                        }
                    }

                    imageView.SetImage(image);
                    imageView.Refresh();
                    //.......................................................
                }

                brImages.Close();
                brLabels.Close();
            }
        }

        for (int i = 0; i < images.Count; i++)
        {
            trainingSet.data.Add(images[i], labels[i]);
        }

        return trainingSet;
    }

所有图像都产生如上所示的图案.它永远不会是完全相同的模式,但总是似乎将像素“拉”到右下角.

解决方法:

这就是我做的方式：

public static class MnistReader
{
    private const string TrainImages = "mnist/train-images.idx3-ubyte";
    private const string TrainLabels = "mnist/train-labels.idx1-ubyte";
    private const string TestImages = "mnist/t10k-images.idx3-ubyte";
    private const string TestLabels = "mnist/t10k-labels.idx1-ubyte";

    public static IEnumerable<Image> ReadTrainingData()
    {
        foreach (var item in Read(TrainImages, TrainLabels))
        {
            yield return item;
        }
    }

    public static IEnumerable<Image> ReadTestData()
    {
        foreach (var item in Read(TestImages, TestLabels))
        {
            yield return item;
        }
    }

    private static IEnumerable<Image> Read(string imagesPath, string labelsPath)
    {
        BinaryReader labels = new BinaryReader(new FileStream(labelsPath, FileMode.Open));
        BinaryReader images = new BinaryReader(new FileStream(imagesPath, FileMode.Open));

        int magicNumber = images.ReadBigInt32();
        int numberOfImages = images.ReadBigInt32();
        int width = images.ReadBigInt32();
        int height = images.ReadBigInt32();

        int magicLabel = labels.ReadBigInt32();
        int numberOfLabels = labels.ReadBigInt32();

        for (int i = 0; i < numberOfImages; i++)
        {
            var bytes = images.ReadBytes(width * height);
            var arr = new byte[height, width];

            arr.ForEach((j,k) => arr[j, k] = bytes[j * height + k]);

            yield return new Image()
            {
                Data = arr,
                Label = labels.ReadByte()
            };
        }
    }
}

图像类：

public class Image
{
    public byte Label { get; set; }
    public byte[,] Data { get; set; }
}

一些扩展方法：

public static class Extensions
{
    public static int ReadBigInt32(this BinaryReader br)
    {
        var bytes = br.ReadBytes(sizeof(Int32));
        if (BitConverter.IsLittleEndian) Array.Reverse(bytes);
        return BitConverter.ToInt32(bytes, 0);
    }

    public static void ForEach<T>(this T[,] source, Action<int, int> action)
    {
        for (int w = 0; w < source.GetLength(0); w++)
        {
            for (int h = 0; h < source.GetLength(1); h++)
            {
                action(w, h);
            }
        }
    }
}

用法：

foreach (var image in MnistReader.ReadTrainingData())
{
    //use image here     
}

要么

foreach (var image in MnistReader.ReadTestData())
{
    //use image here     
}

标签：c,file-format,mnist
来源： https://codeday.me/bug/20190722/1500429.html