Vietdungiiitb という名前のコード プロジェクトの寄稿者による、C# での手書き数字の認識のためのニューラル ネットワークに関する非常に優れたコード プロジェクトの記事を読みました。
プロジェクトのリンクは次のとおりです。
http://www.codeproject.com/Articles/143059/Neural-Network-for-Recognition-of-Handwritten-Digi
しかし、提供されたコード サンプルがあり、コードを実行しましたが、「Format Exception was unhandled」というエラーが発生しました。
Preferences.cs ファイル内。
private void Get(string lpAppName, string lpKeyName, out double nDefault)
{
nDefault = Convert.ToDouble(m_Inifile.IniReadValue(lpAppName, lpKeyName));
return;
}
上記のコード行で実行時例外が発生しました。
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:\Users\PC_USER\Downloads\Example\Code Project\source\HandwrittenRecognition\NeuralNetworkLibrary\ArchiveSerialization\Preferences.cs:line 178
at NeuralNetworkLibrary.Preferences.ReadIniFile() in c:\Users\PC_USER\Downloads\Example\Code Project\source\HandwrittenRecognition\NeuralNetworkLibrary\ArchiveSerialization\Preferences.cs:line 109
at NeuralNetworkLibrary.Preferences..ctor() in c:\Users\PC_USER\Downloads\Example\Code Project\source\HandwrittenRecognition\NeuralNetworkLibrary\ArchiveSerialization\Preferences.cs:line 97
at HandwrittenRecogniration.Mainform..ctor() in c:\Users\PC_USER\Downloads\Example\Code Project\source\HandwrittenRecognition\HandwrittenRecognition\Mainform.cs:line 66
at HandwrittenRecogniration.Program.Main() in c:\Users\PC_USER\Downloads\Example\Code Project\source\HandwrittenRecognition\HandwrittenRecognition\Program.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, ContextCallback callback, Object state, Boolean preserveSyncCtx)
at System.Threading.ExecutionContext.Run(ExecutionContext executionContext, ContextCallback callback, Object state)
at System.Threading.ThreadHelper.ThreadStart()
InnerException:
この問題に対して十分な答えが提供されていません。それで、このプロジェクトを実行したときに誰かがこの問題を抱えていたのではないかと思っていましたか?
完全な Preferences.cs は次のとおりです。
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() + "\\Data\\Default-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, string lpKeyName, out int nDefault)
{
nDefault = Convert.ToInt32(m_Inifile.IniReadValue(lpAppName, lpKeyName));
return;
}
private void Get(string lpAppName, string lpKeyName, out uint nDefault)
{
nDefault = Convert.ToUInt32(m_Inifile.IniReadValue(lpAppName, lpKeyName));
return;
}
private void Get(string lpAppName, string lpKeyName, out double nDefault)
{
nDefault = Convert.ToDouble(m_Inifile.IniReadValue(lpAppName, lpKeyName));
return;
}
private void Get(string lpAppName, string lpKeyName, out byte nDefault)
{
nDefault = Convert.ToByte(m_Inifile.IniReadValue(lpAppName, lpKeyName));
return ;
}
private void Get(string lpAppName, string lpKeyName, out string nDefault)
{
nDefault = m_Inifile.IniReadValue(lpAppName, lpKeyName);
return;
}
private void Get(string lpAppName, string lpKeyName, out bool nDefault)
{
nDefault = Convert.ToBoolean(m_Inifile.IniReadValue(lpAppName, lpKeyName));
return;
}
}
}