//обучение для мниста такое же как и раньше только у здесь есть метки
public void LearningMnist(ReadMnist readMnist, List <List <float> > answers)
{
int setCount = readMnist.TrainDigits.Count;
System.Windows.Forms.RichTextBox console = FormConsole.console;
console.AppendText("Learning Start\r\n");
Information.form1.Refresh();
float epsEpErr = 0.0000001f;
float[] lastErrors = new float[3];
for (int i = 0; i < lastErrors.Length; i++)
{
lastErrors[i] = i;
}
for (int ep = 0; ep < MAX_EP; ep++)
{
DateTime startEpTime = DateTime.Now;
bool isStopLearning = true;
float sumEpErr = 0;
for (int setIter = 0; setIter < setCount; setIter++)
{
List <float> input = readMnist.TrainDigits[setIter].floatPixels;
if (IsBiasNeuron)
{
input.Add(1);
}
/*string inStr = "";
* foreach (var el in input)
* inStr += el + " ";
* File.WriteAllLines("input"+setIter+"-"+ep, new string[] { inStr });*/
int label = readMnist.TrainDigits[setIter].label;
List <float> output = Run(input, IsBiasNeuron);
float err = ErrorSet.MSE(answers[label], output);
sumEpErr += err;
if (err > 0.03)
{
isStopLearning = false;
}
string outStr = "";
string answStr = "";
foreach (var el in output)
{
outStr += Math.Round(el, 4) + " ";
}
foreach (var el in answers[label])
{
answStr += Math.Round(el, 4) + " ";
}
ChangeWeights(answers[label], output);
if (ep % DIST_PRINT == 0 && setIter % 1000 == 0)
{
console.AppendText("ep = " + ep + " setIter = " + setIter
+ " err = " + Math.Round(err, 4) + "\r\n");
console.AppendText("out: " + outStr + "\r\n");
console.AppendText("answ: " + answStr + "\r\n");
console.AppendText("label = " + label + "\r\n");
console.AppendText(readMnist.TrainDigits[setIter].ToString());
TimeSpan epTime = DateTime.Now - startEpTime;
console.AppendText("timeEp = " + epTime.ToString("h'h 'm'm 's's'") + "\r\n");
console.SelectionStart = console.TextLength;
console.ScrollToCaret();
//Information.form1.Refresh();
}
}
lastErrors[0] = lastErrors[1];
lastErrors[1] = lastErrors[2];
lastErrors[2] = sumEpErr / setCount;
if (Math.Abs(lastErrors[0] - lastErrors[1]) < epsEpErr ||
Math.Abs(lastErrors[1] - lastErrors[2]) < epsEpErr)
{
console.AppendText("exit epsEpErr\r\n");
console.AppendText(lastErrors[0] + "\r\n");
console.AppendText(lastErrors[1] + "\r\n");
console.AppendText(lastErrors[2] + "\r\n");
Information.form1.Refresh();
return;
}
if (isStopLearning)
{
console.AppendText("exit isStopLearning\r\n");
Information.form1.Refresh();
return;
}
}
console.AppendText("exit MAX_EP\r\n");
Information.form1.Refresh();
}
//обучение с наилучгим форматом
public Task LearningFormat(PatternsType patterns, KeysType keys, AnswersType answers)
{
int setCount = patterns.Count;
System.Windows.Forms.RichTextBox console = FormConsole.console;
console.AppendText("Learning Start\r\n");
Information.form1.Refresh();
if (IsBiasNeuron)
{
for (int i = 0; i < patterns.Count; i++)
{
if (patterns[i].Count != this.inputLayer.SizeNeurons)
{
patterns[i].Add(1);
}
}
}
float epsEpErr = 0.0000001f;
float[] lastErrors = new float[3];
for (int i = 0; i < lastErrors.Length; i++)
{
lastErrors[i] = i;
}
for (int ep = 0; ep < MAX_EP; ep++)
{
DateTime startEpTime = DateTime.Now;
bool isStopLearning = true;
float sumEpErr = 0;
for (int setIter = 0; setIter < setCount; setIter++)
{
List <float> input = patterns[setIter];
string label = keys[setIter];
List <float> output = Run(input, IsBiasNeuron);
float err = ErrorSet.MSE(answers[label], output);
sumEpErr += err;
if (err > 0.03)
{
isStopLearning = false;
}
string outStr = "";
string answStr = "";
foreach (var el in output)
{
outStr += Math.Round(el, 4) + " ";
}
foreach (var el in answers[label])
{
answStr += Math.Round(el, 4) + " ";
}
ChangeWeights(answers[label], output);
if (ep % DIST_PRINT == 0 && setIter % 100 == 0)
{
console.AppendText("ep = " + ep + " setIter = " + setIter
+ " err = " + Math.Round(err, 4) + "\r\n");
console.AppendText("out: " + outStr + "\r\n");
console.AppendText("answ: " + answStr + "\r\n");
console.AppendText("label = " + label + "\r\n");
TimeSpan epTime = DateTime.Now - startEpTime;
console.AppendText("timeEp = " + epTime.ToString("h'h 'm'm 's's'") + "\r\n");
console.SelectionStart = console.TextLength;
console.ScrollToCaret();
Information.form1.Refresh();
}
}
lastErrors[0] = lastErrors[1];
lastErrors[1] = lastErrors[2];
lastErrors[2] = sumEpErr / setCount;
if (Math.Abs(lastErrors[0] - lastErrors[1]) < epsEpErr ||
Math.Abs(lastErrors[1] - lastErrors[2]) < epsEpErr)
{
console.AppendText("exit epsEpErr\r\n");
console.AppendText(lastErrors[0] + "\r\n");
console.AppendText(lastErrors[1] + "\r\n");
console.AppendText(lastErrors[2] + "\r\n");
Information.form1.Refresh();
return(Task.CompletedTask);
}
if (isStopLearning)
{
console.AppendText("exit isStopLearning\r\n");
Information.form1.Refresh();
return(Task.CompletedTask);
}
}
console.AppendText("exit MAX_EP\r\n");
Information.form1.Refresh();
return(Task.CompletedTask);
}
//обучение МОР, количество образцов равно количеству ответов для них
//(плохо что ответы повторяются, лучше использовать метки)
public void Learning(int setCount, List <List <float> > patterns, List <List <float> > answers)
{
System.Windows.Forms.RichTextBox console = FormConsole.console;
if (!(setCount == patterns.Count && setCount == answers.Count))
{
console.AppendText("learning sizes error\r\n");
return;
}
if (IsBiasNeuron)
{
for (int i = 0; i < patterns.Count; i++)
{
patterns[i].Add(1);
}
}
console.AppendText("Learning Start\r\n");
Information.form1.Refresh();
float epsEpErr = 0.0001f;
float[] lastErrors = new float[3];
for (int i = 0; i < lastErrors.Length; i++)
{
lastErrors[i] = i;
}
for (int ep = 0; ep < MAX_EP; ep++)
{
bool isStopLearning = true;
float sumEpErr = 0;
for (int setIter = 0; setIter < setCount; setIter++)
{
List <float> input = patterns[setIter];
List <float> output = Run(input, IsBiasNeuron);
float err = ErrorSet.MSE(answers[setIter], output);
sumEpErr += err;
if (err > 0.03)
{
isStopLearning = false;
}
if (ep % DIST_PRINT == 0)
{
console.AppendText("ep = " + ep + " setIter = " + setIter
+ " err = " + Math.Round(err, 4) + "\r\n");
console.SelectionStart = console.TextLength;
console.ScrollToCaret();
Information.form1.Refresh();
}
ChangeWeights(answers[setIter], output);
}
lastErrors[0] = lastErrors[1];
lastErrors[1] = lastErrors[2];
lastErrors[2] = sumEpErr / setCount;
if (Math.Abs(lastErrors[0] - lastErrors[1]) < epsEpErr ||
Math.Abs(lastErrors[1] - lastErrors[2]) < epsEpErr)
{
console.AppendText("exit epsEpErr\r\n");
console.AppendText(lastErrors[0] + "\r\n");
console.AppendText(lastErrors[1] + "\r\n");
console.AppendText(lastErrors[2] + "\r\n");
Information.form1.Refresh();
return;
}
if (isStopLearning)
{
console.AppendText("exit isStopLearning\r\n");
Information.form1.Refresh();
return;
}
}
console.AppendText("exit MAX_EP\r\n");
Information.form1.Refresh();
}