ConvNetSharp图像学习

Question

我正在尝试找出如何正确使用ConvNetShar来学习某些图像中的某些形状的方法。我有由白色背景上的黑色形状组成的测试数据，所有这些形状都属于1类（道路），并且有和没有图像。我尝试改编2d演示，它确实可以学习，但是当它测试失败时...

下面是我的代码...只是想知道是否有人在使用图像工作示例？

谢谢 （刚刚更新的代码）似乎正在学习..虽然仍然无法通过测试...

using System;
using System.Collections.Generic;
using ConvNetSharp.Core;
using ConvNetSharp.Core.Layers.Double;
using ConvNetSharp.Core.Training;
using ConvNetSharp.Volume;
using ConvNetSharp.Volume.Double;
using System.IO;
using System.Windows.Forms;
using System.Drawing;
using NDNBackpropNnTrainer;

namespace ClassifyImageDemo
{
internal class Program
{
    private static void ClassifyImage()
    {
        string inputFolder = GetInputFolder();
        var filelist = Directory.EnumerateFiles(inputFolder, "*.png");

        List<List<double>> matrix = new List<List<double>>();
        List<int>  expetedList = new List<int>();
        int looper = 0;
        int width = 1;
        int height = 1;
        foreach (var fileInImage in filelist)
        {
            matrix.Add(new List<double>());
            Bitmap source = (Bitmap) Image.FromFile(fileInImage);
            List<double> innerList = new List<double>();
            using (var bmp = new LockBitmap(source))
            {
                width = bmp.Width;
                height = bmp.Height;

                for (var y = 0; y < bmp.Height; y++)
                {
                    for (var x = 0; x < bmp.Width; x++)
                    {
                        var color = bmp.GetPixel(x, y);
                        int myR = color.R;
                        int myG = color.G;
                        int myB = color.B;
                        int total = myR + myG + myB;
                        //Adds new sub List
                        innerList.Add(total);
                    }
                }

            }
            matrix[looper]=innerList; //Add values to the sub List at index 0
            looper = looper + 1 ;
            byte[] myImageRaw = File.ReadAllBytes(fileInImage);
            int len = myImageRaw.Length;
            var lastOneByte = (byte) myImageRaw[len - 1];
            expetedList.Add(lastOneByte);
        }

        var net = new Net<double>();
        net.AddLayer(new InputLayer(1, 1, (width * height)));
        net.AddLayer(new FullyConnLayer(6));
        net.AddLayer(new TanhLayer());
        net.AddLayer(new FullyConnLayer(2));
        net.AddLayer(new TanhLayer());
        net.AddLayer(new FullyConnLayer(2));
        net.AddLayer(new SoftmaxLayer(2));

        // Data
        var data = new List<double[]>();
        var labels = new List<int>();

        foreach (var lstInputs in matrix)
        {
            double[] arrayIn = lstInputs.ToArray();
            data.Add(arrayIn);
        }
        foreach (var lbl in expetedList)
        {
            labels.Add(lbl);
        }
        var n = labels.Count;

        var trainer = new SgdTrainer<double>(net) { LearningRate = 0.01, L2Decay = 0.001, BatchSize = n };
        double loss = 1.0;
        // Training
        while (loss > 0.00001)
        {
            loss = ClassifyImageUpdate(width, height, n, data, trainer, labels);
        }


        // Testing
        var netx = BuilderInstance.Volume.From(new double[(width * height) * n], new Shape(1, 1, (width*height), n));
        for (var ix = 0; ix < n; ix++)
        {
            int subLooper2 = 0;
            foreach (var dtA2 in data[ix])
            {
                netx.Set(0, 0, subLooper2, ix, dtA2);
                subLooper2 = subLooper2 + 1;
            }
        }

        var result = net.Forward(netx);
        var c = net.GetPrediction();
        var accurate = c[0] == labels[0];
        Console.ReadLine();
    }


    private static string GetInputFolder()
    {

        return @"D:\temp\filtered\blackroads\subset_500";
    }

    private static double ClassifyImageUpdate(int width, int height, int n, List<double[]> data, TrainerBase<double> trainer, List<int> labels)
    {
        var avloss = 0.0;
        int dimensions = width * height;
        var netx = BuilderInstance.Volume.SameAs(new Shape(1, 1, dimensions, n));
        var hotLabels = BuilderInstance.Volume.SameAs(new Shape(1, 1, 1, n));

        for (var ix = 0; ix < n; ix++)
        {
            hotLabels.Set(0, 0, 0, ix, labels[ix]);
            int subLooper = 0;
            foreach (var dtA in data[ix])
            {
                netx.Set(0, 0, subLooper, ix, dtA);
                subLooper = subLooper + 1;
            }

           // netx.Set(0, 0, 1, ix, data[ix][1]);
        }

        for (var iters = 0; iters < 50; iters++)
        {
            trainer.Train(netx, hotLabels);
            avloss += trainer.Loss;
        }

        avloss /= 50.0;
        Console.WriteLine(" Loss:" + avloss);
        return avloss;
    }

    private static void Main(string[] args)
    {
        ClassifyImage();
    }
}

}