用于识别C#中手写数字的神经网络

这是项目的链接：

http://www.codeproject.com/Articles/143059/Neural-Network-for-Recognition-of-Handwritten-Digi

但是,有一个代码示例提供,我运行代码,但我有这个错误’格式异常未处理’.

在Preferences.cs文件中.

private void Get(string lpAppName,string lpKeyName,out double nDefault)
{

       nDefault = Convert.ToDouble(m_Inifile.IniReadValue(lpAppName,lpKeyName));
       return; 
}

上面的代码行产生了运行时异常.

System.FormatException was unhandled
  HResult=-2146233033
  Message=Input string was not in a correct format.
  Source=mscorlib
  StackTrace:
       at System.Number.ParseDouble(String value,NumberStyles options,NumberFormatInfo numfmt)
       at System.Convert.ToDouble(String value)
       at NeuralNetworkLibrary.Preferences.Get(String lpAppName,String lpKeyName,Double& nDefault) in c:UsersPC_USERDownloadsExampleCode ProjectsourceHandwrittenRecognitionNeuralNetworkLibraryArchiveSerializationPreferences.cs:line 178
       at NeuralNetworkLibrary.Preferences.ReadIniFile() in c:UsersPC_USERDownloadsExampleCode ProjectsourceHandwrittenRecognitionNeuralNetworkLibraryArchiveSerializationPreferences.cs:line 109
       at NeuralNetworkLibrary.Preferences..ctor() in c:UsersPC_USERDownloadsExampleCode ProjectsourceHandwrittenRecognitionNeuralNetworkLibraryArchiveSerializationPreferences.cs:line 97
       at HandwrittenRecogniration.Mainform..ctor() in c:UsersPC_USERDownloadsExampleCode ProjectsourceHandwrittenRecognitionHandwrittenRecognitionMainform.cs:line 66
       at HandwrittenRecogniration.Program.Main() in c:UsersPC_USERDownloadsExampleCode ProjectsourceHandwrittenRecognitionHandwrittenRecognitionProgram.cs:line 18
       at System.AppDomain._nExecuteAssembly(RuntimeAssembly assembly,String[] args)
       at System.AppDomain.ExecuteAssembly(String assemblyFile,Evidence assemblySecurity,String[] args)
       at Microsoft.VisualStudio.HostingProcess.HostProc.RunUsersAssembly()
       at System.Threading.ThreadHelper.ThreadStart_Context(Object state)
       at System.Threading.ExecutionContext.RunInternal(ExecutionContext executionContext,ContextCallback callback,Object state,Boolean preserveSyncCtx)
       at System.Threading.ExecutionContext.Run(ExecutionContext executionContext,Object state)
       at System.Threading.ThreadHelper.ThreadStart()
  InnerException:

没有为这个问题提供足够的答案.所以,我想知道当他们运行这个项目时是否有人遇到过这个问题？

完整的Preferences.cs如下.

using System;

namespace NeuralNetworkLibrary
{
    public class Preferences
    {
        public const int g_cImageSize = 28;
        public const int g_cVectorSize = 29;

        public int m_cNumBackpropThreads;

        public uint m_nMagicTrainingLabels;
        public uint m_nMagicTrainingImages;

        public uint m_nItemsTrainingLabels;
        public uint m_nItemsTrainingImages;

        public int m_cNumTestingThreads;

        public int m_nMagicTestingLabels;
        public int m_nMagicTestingImages;

        public uint m_nItemsTestingLabels;
        public uint m_nItemsTestingImages;

        public uint m_nRowsImages;
        public uint m_nColsImages;

        public int m_nMagWindowSize;
        public int m_nMagWindowMagnification;

        public double m_dInitialEtaLearningRate;
        public double m_dLearningRateDecay;
        public double m_dMinimumEtaLearningRate;
        public uint m_nAfterEveryNBackprops;

        // for limiting the step size in backpropagation,since we are using second order
        // "Stochastic Diagonal Levenberg-Marquardt" update algorithm.  See Yann LeCun 1998
        // "Gradianet-Based Learning Applied to Document Recognition" at page 41

        public double m_dMicronLimitParameter;
        public uint m_nNumHessianPatterns;

        // for distortions of the input image,in an attempt to improve generalization

        public double m_dMaxScaling;  // as a percentage,such as 20.0 for plus/minus 20%
        public double m_dMaxRotation;  // in degrees,such as 20.0 for plus/minus rotations of 20 degrees
        public double m_dElasticSigma;  // one sigma value for randomness in Simard's elastic distortions
        public double m_dElasticScaling;  // after-smoohting scale factor for Simard's elastic distortions
        private IniFile m_Inifile;
        ////////////
        public Preferences()
        {
            // set default values

            m_nMagicTrainingLabels = 0x00000801;
            m_nMagicTrainingImages = 0x00000803;

            m_nItemsTrainingLabels = 60000;
            m_nItemsTrainingImages = 60000;

            m_nMagicTestingLabels = 0x00000801;
            m_nMagicTestingImages = 0x00000803;

            m_nItemsTestingLabels = 10000;
            m_nItemsTestingImages = 10000;

            m_nRowsImages = g_cImageSize;
            m_nColsImages = g_cImageSize;

            m_nMagWindowSize = 5;
            m_nMagWindowMagnification = 8;

            m_dInitialEtaLearningRate = 0.001;
            m_dLearningRateDecay = 0.794328235;  // 0.794328235 = 0.001 down to 0.00001 in 20 epochs 
            m_dMinimumEtaLearningRate = 0.00001;
            m_nAfterEveryNBackprops = 60000;
            m_cNumBackpropThreads = 2;

            m_cNumTestingThreads = 1;

            // parameters for controlling distortions of input image

            m_dMaxScaling = 15.0;  // like 20.0 for 20%
            m_dMaxRotation = 15.0;  // like 20.0 for 20 degrees
            m_dElasticSigma = 8.0;  // higher numbers are more smooth and less distorted; Simard uses 4.0
            m_dElasticScaling = 0.5;  // higher numbers amplify the distortions; Simard uses 34 (sic,maybe 0.34 ??)

            // for limiting the step size in backpropagation,since we are using second order
            // "Stochastic Diagonal Levenberg-Marquardt" update algorithm.  See Yann LeCun 1998
            // "Gradient-Based Learning Applied to Document Recognition" at page 41

            m_dMicronLimitParameter = 0.10;  // since we divide by this,update can never be more than 10x current eta
            m_nNumHessianPatterns = 500;  // number of patterns used to calculate the diagonal Hessian
            String path = System.IO.Directory.GetCurrentDirectory() + "DataDefault-ini.ini";
            m_Inifile = new IniFile(path);
            ReadIniFile();
        }
        public void ReadIniFile()
        {
            // now read values from the ini file

            String tSection;

            // Neural Network parameters

            tSection = "Neural Network Parameters";

            Get(tSection,"Initial learning rate (eta)",out m_dInitialEtaLearningRate);
            Get(tSection,"Minimum learning rate (eta)",out m_dMinimumEtaLearningRate);
            Get(tSection,"Rate of decay for learning rate (eta)",out m_dLearningRateDecay);
            Get(tSection,"Decay rate is applied after this number of backprops",out m_nAfterEveryNBackprops);
            Get(tSection,"Number of backprop threads",out m_cNumBackpropThreads);
            Get(tSection,"Number of testing threads",out m_cNumTestingThreads);
            Get(tSection,"Number of patterns used to calculate Hessian",out m_nNumHessianPatterns);
            Get(tSection,"Limiting divisor (micron) for learning rate amplification (like 0.10 for 10x limit)",out m_dMicronLimitParameter);


            // Neural Network Viewer parameters

            tSection = "Neural Net Viewer Parameters";

            Get(tSection,"Size of magnification window",out m_nMagWindowSize);
            Get(tSection,"Magnification factor for magnification window",out m_nMagWindowMagnification);


            // MNIST data collection parameters

            tSection = "MNIST Database Parameters";

            Get(tSection,"Training images magic number",out m_nMagicTrainingImages);
            Get(tSection,"Training images item count",out m_nItemsTrainingImages);
            Get(tSection,"Training labels magic number",out m_nMagicTrainingLabels);
            Get(tSection,"Training labels item count",out m_nItemsTrainingLabels);

            Get(tSection,"Testing images magic number",out m_nMagicTestingImages);
            Get(tSection,"Testing images item count",out m_nItemsTestingImages);
            Get(tSection,"Testing labels magic number",out m_nMagicTestingLabels);
            Get(tSection,"Testing labels item count",out m_nItemsTestingLabels);

            // these two are basically ignored

            uint uiCount = g_cImageSize;
            Get(tSection,"Rows per image",out uiCount);
            m_nRowsImages = uiCount;

            uiCount = g_cImageSize;
            Get(tSection,"Columns per image",out uiCount);
            m_nColsImages = uiCount;


            // parameters for controlling pattern distortion during backpropagation

            tSection = "Parameters for Controlling Pattern Distortion During Backpropagation";

            Get(tSection,"Maximum scale factor change (percent,like 20.0 for 20%)",out m_dMaxScaling);
            Get(tSection,"Maximum rotational change (degrees,like 20.0 for 20 degrees)",out m_dMaxRotation);
            Get(tSection,"Sigma for elastic distortions (higher numbers are more smooth and less distorted; Simard uses 4.0)",out m_dElasticSigma);
            Get(tSection,"Scaling for elastic distortions (higher numbers amplify distortions; Simard uses 0.34)",out m_dElasticScaling);
        }
        private void Get(string lpAppName,out int nDefault)
        {
            nDefault = Convert.ToInt32(m_Inifile.IniReadValue(lpAppName,lpKeyName));
            return;

        }
        private void Get(string lpAppName,out uint nDefault)
        {
            nDefault = Convert.ToUInt32(m_Inifile.IniReadValue(lpAppName,lpKeyName));
            return;
        }

        private void Get(string lpAppName,out double nDefault)
        {
               nDefault = Convert.ToDouble(m_Inifile.IniReadValue(lpAppName,lpKeyName));
               return;
        }
        private void Get(string lpAppName,out byte nDefault)
        {

           nDefault = Convert.ToByte(m_Inifile.IniReadValue(lpAppName,lpKeyName));
           return ;

        }

        private void Get(string lpAppName,out string nDefault)
        {
            nDefault = m_Inifile.IniReadValue(lpAppName,lpKeyName);
            return;

        }
        private void Get(string lpAppName,out bool nDefault)
        {
            nDefault = Convert.ToBoolean(m_Inifile.IniReadValue(lpAppName,lpKeyName));
            return;
        }

    }
}