protected override void Train(IDataset ds)
{
if (!(ds.nSamples() > 0))
{
throw new Exception("nSamples of IDataset must be > 0");
}
if (!(ds.nFeatures() > 0))
{
throw new Exception("nFeatures of IDataset must be > 0");
}
if (c2i.Length() < 1)
{
Intarray raw_classes = new Intarray();
raw_classes.ReserveTo(ds.nSamples());
for (int i = 0; i < ds.nSamples(); i++)
{
raw_classes.Push(ds.Cls(i));
}
ClassMap(c2i, i2c, raw_classes);
/*Intarray classes = new Intarray();
* ctranslate(classes, raw_classes, c2i);*/
//debugf("info","[mapped %d to %d classes]\n",c2i.length(),i2c.length());
}
TranslatedDataset mds = new TranslatedDataset(ds, c2i);
TrainDense(mds);
}
private int[] CreateClassesFromDataset(IDataset ds)
{
// create class list from dataset
SortedDictionary <int, int> keymap = new SortedDictionary <int, int>();
for (int i = 0; i < ds.nSamples(); i++)
{
if (!keymap.ContainsKey(ds.Cls(i)))
{
keymap.Add(ds.Cls(i), 1);
}
else
{
keymap[ds.Cls(i)]++;
}
}
int[] classes = new int[keymap.Count];
keymap.Keys.CopyTo(classes, 0);
// show class counts
Console.WriteLine("Classes counts:");
string showline = "";
int ishow = 0;
for (int i = 0; i < keymap.Count; i++)
{
ishow++;
if (classes[i] >= 32)
{
showline += String.Format("{0,-9}", String.Format("{0}[{1}]", (char)classes[i], keymap[classes[i]]));
}
else
{
showline += String.Format("{0,-9}", String.Format("{0}[{1}]", classes[i], keymap[classes[i]]));
}
if (ishow % 8 == 0)
{
Console.WriteLine(showline);
showline = "";
}
}
Console.WriteLine(showline);
return(classes);
}
protected virtual void Train(IDataset ds)
{
Floatarray v = new Floatarray();
for (int i = 0; i < ds.nSamples(); i++)
{
ds.Input(v, i);
Add(v, ds.Cls(i));
}
}
public TrainInfo TestDense(IDataset ts)
{
TrainInfo trInfo = new TrainInfo();
bool first = true;
Floatarray v = new Floatarray();
// Send Test Dataset to Lenet
for (int i = 0; i < ts.nSamples(); i++)
{
ts.Input(v, i);
if (v.Rank() == 1)
{
v.Reshape(csize, csize, 0, 0);
}
StdInput linput = new StdInput(v);
if (first)
{
first = false;
BeginTestEpoch(HrLenet, linput.Height, linput.Width, ts.nSamples()); // init test
}
try
{
if (C2i.ContainsKey(ts.Cls(i)))
{
AddSampleToTestOfEpoch(HrLenet, linput.GetDataBuffer(), linput.Length, C2i[ts.Cls(i)]);
}
}
catch (Exception e)
{
GetStdout(sbout); // get train messages
if (sbout.Length > 0)
{
Global.Debugf("error", sbout.ToString() + "\r\nException in AddSampleToTestOfEpoch");
}
throw new Exception("Exception in AddSampleToTestOfEpoch\r\n" + e.Message);
}
}
// do test one epoch
try
{
EndAndRunTestEpoch(HrLenet, ref trInfo);
}
catch (Exception e)
{
GetStdout(sbout); // get test messages
if (sbout.Length > 0)
{
Global.Debugf("error", sbout.ToString() + "\r\nException in EndAndRunTestEpoch");
}
throw new Exception("Exception in EndAndRunTestEpoch\r\n" + e.Message);
}
return(trInfo);
}
public static float estimate_errors(IModel classifier, IDataset ds, int n = 1000000)
{
Floatarray v = new Floatarray();
int errors = 0;
int count = 0;
for (int i = 0; i < ds.nSamples(); i++)
{
int cls = ds.Cls(i);
if (cls == -1)
{
continue;
}
ds.Input1d(v, i);
int pred = classifier.Classify(v);
count++;
if (pred != cls)
{
errors++;
}
}
return(errors / (float)count);
}
public override int Cls(int i)
{
return(_ds.Cls(i));
}
public virtual TrainInfo TrainBatch(IDataset ds, IDataset ts, int epochs)
{
TrainInfo trInfo = new TrainInfo();
bool first = true;
Floatarray v = new Floatarray();
// Send Train Dataset to Lenet
for (int i = 0; i < ds.nSamples(); i++)
{
ds.Input(v, i);
if (v.Rank() == 1)
{
v.Reshape(csize, csize, 0, 0);
}
StdInput linput = new StdInput(v);
try
{
if (first)
{
first = false;
//StartRedirectStdout(); // start redirect cout to string buffer
BeginTrainEpoch(HrLenet, linput.Height, linput.Width, ds.nSamples(), ts.nSamples()); // init train
}
if (C2i.ContainsKey(ds.Cls(i)))
{
AddSampleToTrainOfEpoch(HrLenet, linput.GetDataBuffer(), linput.Length, C2i[ds.Cls(i)]);
}
else
{
Global.Debugf("error", "class '{0}' is not contained in the char list", (char)ds.Cls(i));
}
}
catch (Exception e)
{
GetStdout(sbout); // get train messages
if (sbout.Length > 0)
{
Global.Debugf("error", sbout.ToString() + "\r\nException in AddSampleToTrainOfEpoch");
}
throw new Exception("Exception in AddSampleToTrainOfEpoch\r\n" + e.Message);
}
}
// Send Test Dataset to Lenet
for (int i = 0; i < ts.nSamples(); i++)
{
ts.Input(v, i);
if (v.Rank() == 1)
{
v.Reshape(csize, csize, 0, 0);
}
StdInput linput = new StdInput(v);
try
{
if (C2i.ContainsKey(ts.Cls(i)))
{
AddSampleToTestOfEpoch(HrLenet, linput.GetDataBuffer(), linput.Length, C2i[ts.Cls(i)]);
}
}
catch (Exception e)
{
GetStdout(sbout); // get train messages
if (sbout.Length > 0)
{
Global.Debugf("error", sbout.ToString() + "\r\nException in AddSampleToTestOfEpoch");
}
throw new Exception("Exception in AddSampleToTestOfEpoch\r\n" + e.Message);
}
}
// debug save mnist
//SaveTrainMnist(HrLenet, "debug-images-idx3-ubyte", "debug-labels-idx1-ubyte");
// do train epochs
for (int epoch = 0; epoch < epochs; epoch++)
{
trInfo = new TrainInfo();
try
{
DateTime startDate = DateTime.Now;
EndAndRunTrainEpoch(HrLenet, ref trInfo); // do train one epoch
// show train info
Global.Debugf("info",
String.Format("|{0,7}| Energy:{1:0.#####} Correct:{2:0.00#%} Errors:{3:0.00#%} Count:{4} ",
trInfo.age, trInfo.energy, (trInfo.correct / (float)trInfo.size),
(trInfo.error / (float)trInfo.size), trInfo.size));
Global.Debugf("info",
String.Format(" TEST Energy={0:0.#####} Correct={1:0.00#%} Errors={2:0.00#%} Count={3} ",
trInfo.tenergy, (trInfo.tcorrect / (float)trInfo.tsize),
(trInfo.terror / (float)trInfo.tsize), trInfo.tsize));
TimeSpan spanTrain = DateTime.Now - startDate;
Global.Debugf("info", String.Format(" training time: {0} minutes, {1} seconds",
(int)spanTrain.TotalMinutes, spanTrain.Seconds));
// get dll stdout messages
GetStdout(sbout);
if (sbout.Length > 0)
{
Console.Write(sbout.ToString());
}
}
catch (Exception e)
{
GetStdout(sbout); // get train messages
if (sbout.Length > 0)
{
Global.Debugf("error", sbout.ToString() + "\r\nException in EndAndRunTrainEpoch");
}
throw new Exception("Exception in EndAndRunTrainEpoch\r\n" + e.Message);
}
}
return(trInfo);
}
protected override void Train(IDataset ds)
{
bool use_junk = PGetb("junk") && !DisableJunk;
int nsamples = ds.nSamples();
if (PExists("%nsamples"))
{
nsamples += PGeti("%nsamples");
}
Global.Debugf("info", "Training content classifier");
if (CharClass.IsEmpty)
{
Initialize(CreateClassesFromDataset(ds));
}
if (use_junk /*&& !JunkClass.IsEmpty*/)
{
Intarray nonjunk = new Intarray();
for (int i = 0; i < ds.nSamples(); i++)
{
if (ds.Cls(i) != jc())
{
nonjunk.Push(i);
}
}
Datasubset nonjunkds = new Datasubset(ds, nonjunk);
CharClass.TrainDense(nonjunkds, PGeti("epochs"));
}
else
{
CharClass.TrainDense(ds, PGeti("epochs"));
}
if (use_junk /*&& !JunkClass.IsEmpty*/)
{
Global.Debugf("info", "Training junk classifier");
Intarray isjunk = new Intarray();
int njunk = 0;
for (int i = 0; i < ds.nSamples(); i++)
{
bool j = (ds.Cls(i) == jc());
isjunk.Push(JunkClass.Classes[Convert.ToInt32(j)]);
if (j)
{
njunk++;
}
}
if (njunk > 0)
{
MappedDataset junkds = new MappedDataset(ds, isjunk);
JunkClass.TrainDense(junkds, PGeti("epochs"));
}
else
{
Global.Debugf("warn", "you are training a junk class but there are no samples to train on");
JunkClass.DeleteLenet();
}
}
PSet("%nsamples", nsamples);
}
protected override void Train(IDataset ds)
{
bool use_junk = PGetb("junk") && !DisableJunk;
if (charclass.IsEmpty)
{
charclass.SetComponent(ComponentCreator.MakeComponent(PGet("charclass")));
TryAttachCharClassifierEvent(charclass.Object);
}
if (junkclass.IsEmpty)
{
junkclass.SetComponent(ComponentCreator.MakeComponent(PGet("junkclass")));
TryAttachJunkClassifierEvent(junkclass.Object);
}
if (ulclass.IsEmpty)
{
ulclass.SetComponent(ComponentCreator.MakeComponent(PGet("ulclass")));
}
Global.Debugf("info", "Training content classifier");
if (use_junk && !junkclass.IsEmpty)
{
Intarray nonjunk = new Intarray();
for (int i = 0; i < ds.nSamples(); i++)
{
if (ds.Cls(i) != jc())
{
nonjunk.Push(i);
}
}
Datasubset nonjunkds = new Datasubset(ds, nonjunk);
charclass.Object.XTrain(nonjunkds);
}
else
{
charclass.Object.XTrain(ds);
}
if (use_junk && !junkclass.IsEmpty)
{
Global.Debugf("info", "Training junk classifier");
Intarray isjunk = new Intarray();
int njunk = 0;
for (int i = 0; i < ds.nSamples(); i++)
{
bool j = (ds.Cls(i) == jc());
isjunk.Push(Convert.ToInt32(j));
if (j)
{
njunk++;
}
}
if (njunk > 0)
{
MappedDataset junkds = new MappedDataset(ds, isjunk);
junkclass.Object.XTrain(junkds);
}
else
{
Global.Debugf("warn", "you are training a junk class but there are no samples to train on");
junkclass.SetComponent(null);
}
if (PGeti("ul") > 0 && !ulclass.IsEmpty)
{
throw new Exception("ulclass not implemented");
}
}
}