public void Copy(MlpClassifier other)
{
w1.Copy(other.w1);
b1.Copy(other.b1);
w2.Copy(other.w2);
b2.Copy(other.b2);
if (c2i.Length() < 1)
{
c2i.Copy(other.c2i);
}
if (i2c.Length() < 1)
{
i2c.Copy(other.i2c);
}
}
public void ChangeHidden(int newn)
{
MlpClassifier temp = new MlpClassifier();
int ninput = w1.Dim(1);
int nhidden = w1.Dim(0);
int noutput = w2.Dim(0);
temp.InitRandom(ninput, newn, noutput);
for (int i = 0; i < newn; i++)
{
if (i >= nhidden)
{
for (int j = 0; j < ninput; j++)
{
temp.w1[i, j] = ClassifierUtil.rNormal(0.0f, 1.0f);
}
temp.b1[i] = ClassifierUtil.rNormal(0.0f, 1.0f);
}
else
{
for (int j = 0; j < ninput; j++)
{
temp.w1[i, j] = w1[i, j];
}
temp.b1[i] = b1[i];
}
}
for (int i = 0; i < noutput; i++)
{
for (int j = 0; j < newn; j++)
{
if (j >= nhidden)
{
temp.w2[i, j] = 1e-2f * ClassifierUtil.rNormal(0.0f, 1.0f);
}
else
{
temp.w2[i, j] = w2[i, j];
}
}
}
this.Copy(temp);
}
public void Copy(MlpClassifier other)
{
w1.Copy(other.w1);
b1.Copy(other.b1);
w2.Copy(other.w2);
b2.Copy(other.b2);
if (c2i.Length() < 1)
c2i.Copy(other.c2i);
if (i2c.Length() < 1)
i2c.Copy(other.i2c);
}
public void ChangeHidden(int newn)
{
MlpClassifier temp = new MlpClassifier();
int ninput = w1.Dim(1);
int nhidden = w1.Dim(0);
int noutput = w2.Dim(0);
temp.InitRandom(ninput, newn, noutput);
for (int i = 0; i < newn; i++)
{
if (i >= nhidden)
{
for (int j = 0; j < ninput; j++)
temp.w1[i, j] = ClassifierUtil.rNormal(0.0f, 1.0f);
temp.b1[i] = ClassifierUtil.rNormal(0.0f, 1.0f);
}
else
{
for (int j = 0; j < ninput; j++)
temp.w1[i, j] = w1[i, j];
temp.b1[i] = b1[i];
}
}
for (int i = 0; i < noutput; i++)
{
for (int j = 0; j < newn; j++)
{
if (j >= nhidden)
{
temp.w2[i, j] = 1e-2f * ClassifierUtil.rNormal(0.0f, 1.0f);
}
else
{
temp.w2[i, j] = w2[i, j];
}
}
}
this.Copy(temp);
}
public virtual void TrainBatch(IDataset ds, IDataset ts)
{
Stopwatch sw = Stopwatch.StartNew();
bool parallel = PGetb("parallel");
float eta_init = PGetf("eta_init"); // 0.5
float eta_varlog = PGetf("eta_varlog"); // 1.5
float hidden_varlog = PGetf("hidden_varlog"); // 1.2
int hidden_lo = PGeti("hidden_lo");
int hidden_hi = PGeti("hidden_hi");
int rounds = PGeti("rounds");
int mlp_noopt = PGeti("noopt");
int hidden_min = PGeti("hidden_min");
int hidden_max = PGeti("hidden_max");
CHECK_ARG(hidden_min > 1 && hidden_max < 1000000, "hidden_min > 1 && hidden_max < 1000000");
CHECK_ARG(hidden_hi >= hidden_lo, "hidden_hi >= hidden_lo");
CHECK_ARG(hidden_max >= hidden_min, "hidden_max >= hidden_min");
CHECK_ARG(hidden_lo >= hidden_min && hidden_hi <= hidden_max, "hidden_lo >= hidden_min && hidden_hi <= hidden_max");
int nn = PGeti("nensemble");
ObjList<MlpClassifier> nets = new ObjList<MlpClassifier>();
nets.Resize(nn);
for (int i = 0; i < nn; i++)
nets[i] = new MlpClassifier(i);
Floatarray errs = new Floatarray(nn);
Floatarray etas = new Floatarray(nn);
Intarray index = new Intarray();
float best = 1e30f;
if (PExists("%error"))
best = PGetf("%error");
int nclasses = ds.nClasses();
/*Floatarray v = new Floatarray();
for (int i = 0; i < ds.nSamples(); i++)
{
ds.Input1d(v, i);
CHECK_ARG(NarrayUtil.Min(v) > -100 && NarrayUtil.Max(v) < 100, "min(v)>-100 && max(v)<100");
}*/
CHECK_ARG(ds.nSamples() >= 10 && ds.nSamples() < 100000000, "ds.nSamples() >= 10 && ds.nSamples() < 100000000");
for (int i = 0; i < nn; i++)
{
// nets(i).init(data.dim(1),logspace(i,nn,hidden_lo,hidden_hi),nclasses);
if (w1.Length() > 0)
{
nets[i].Copy(this);
etas[i] = ClassifierUtil.rLogNormal(eta_init, eta_varlog);
}
else
{
nets[i].InitData(ds, (int)(logspace(i, nn, hidden_lo, hidden_hi)), c2i, i2c);
etas[i] = PGetf("eta");
}
}
etas[0] = PGetf("eta"); // zero position is identical to itself
Global.Debugf("info", "mlp training n {0} nc {1}", ds.nSamples(), nclasses);
for (int round = 0; round < rounds; round++)
{
Stopwatch swRound = Stopwatch.StartNew();
errs.Fill(-1);
if (parallel)
{
// For each network i
Parallel.For(0, nn, i =>
{
nets[i].PSet("eta", etas[i]);
nets[i].TrainDense(ds); // было XTrain
errs[i] = ClassifierUtil.estimate_errors(nets[i], ts);
});
}
else
{
for (int i = 0; i < nn; i++)
{
nets[i].PSet("eta", etas[i]);
nets[i].TrainDense(ds); // было XTrain
errs[i] = ClassifierUtil.estimate_errors(nets[i], ts);
//Global.Debugf("detail", "net({0}) {1} {2} {3}", i,
// errs[i], nets[i].Complexity(), etas[i]);
}
}
NarrayUtil.Quicksort(index, errs);
if (errs[index[0]] < best)
{
best = errs[index[0]];
cv_error = best;
this.Copy(nets[index[0]]);
this.PSet("eta", etas[index[0]]);
Global.Debugf("info", " best mlp[{0}] update errors={1} {2}", index[0], best, crossvalidate ? "cv" : "");
}
if (mlp_noopt == 0)
{
for (int i = 0; i < nn / 2; i++)
{
int j = i + nn / 2;
nets[index[j]].Copy(nets[index[i]]);
int n = nets[index[j]].nHidden();
int nm = Math.Min(Math.Max(hidden_min, (int)(ClassifierUtil.rLogNormal(n, hidden_varlog))), hidden_max);
nets[index[j]].ChangeHidden(nm);
etas[index[j]] = ClassifierUtil.rLogNormal(etas[index[i]], eta_varlog);
}
}
Global.Debugf("info", " end mlp round {0} err {1} nHidden {2}", round, best, nHidden());
swRound.Stop();
int totalTest= ts.nSamples();
int errCnt = Convert.ToInt32(best * totalTest);
OnTrainRound(this, new TrainEventArgs(
round, best, totalTest - errCnt, totalTest, best, swRound.Elapsed, TimeSpan.Zero
));
}
sw.Stop();
Global.Debugf("info", String.Format(" training time: {0} minutes, {1} seconds",
(int)sw.Elapsed.TotalMinutes, sw.Elapsed.Seconds));
PSet("%error", best);
int nsamples = ds.nSamples() * rounds;
if (PExists("%nsamples"))
nsamples += PGeti("%nsamples");
PSet("%nsamples", nsamples);
}
public virtual void TrainBatch(IDataset ds, IDataset ts)
{
Stopwatch sw = Stopwatch.StartNew();
bool parallel = PGetb("parallel");
float eta_init = PGetf("eta_init"); // 0.5
float eta_varlog = PGetf("eta_varlog"); // 1.5
float hidden_varlog = PGetf("hidden_varlog"); // 1.2
int hidden_lo = PGeti("hidden_lo");
int hidden_hi = PGeti("hidden_hi");
int rounds = PGeti("rounds");
int mlp_noopt = PGeti("noopt");
int hidden_min = PGeti("hidden_min");
int hidden_max = PGeti("hidden_max");
CHECK_ARG(hidden_min > 1 && hidden_max < 1000000, "hidden_min > 1 && hidden_max < 1000000");
CHECK_ARG(hidden_hi >= hidden_lo, "hidden_hi >= hidden_lo");
CHECK_ARG(hidden_max >= hidden_min, "hidden_max >= hidden_min");
CHECK_ARG(hidden_lo >= hidden_min && hidden_hi <= hidden_max, "hidden_lo >= hidden_min && hidden_hi <= hidden_max");
int nn = PGeti("nensemble");
ObjList <MlpClassifier> nets = new ObjList <MlpClassifier>();
nets.Resize(nn);
for (int i = 0; i < nn; i++)
{
nets[i] = new MlpClassifier(i);
}
Floatarray errs = new Floatarray(nn);
Floatarray etas = new Floatarray(nn);
Intarray index = new Intarray();
float best = 1e30f;
if (PExists("%error"))
{
best = PGetf("%error");
}
int nclasses = ds.nClasses();
/*Floatarray v = new Floatarray();
* for (int i = 0; i < ds.nSamples(); i++)
* {
* ds.Input1d(v, i);
* CHECK_ARG(NarrayUtil.Min(v) > -100 && NarrayUtil.Max(v) < 100, "min(v)>-100 && max(v)<100");
* }*/
CHECK_ARG(ds.nSamples() >= 10 && ds.nSamples() < 100000000, "ds.nSamples() >= 10 && ds.nSamples() < 100000000");
for (int i = 0; i < nn; i++)
{
// nets(i).init(data.dim(1),logspace(i,nn,hidden_lo,hidden_hi),nclasses);
if (w1.Length() > 0)
{
nets[i].Copy(this);
etas[i] = ClassifierUtil.rLogNormal(eta_init, eta_varlog);
}
else
{
nets[i].InitData(ds, (int)(logspace(i, nn, hidden_lo, hidden_hi)), c2i, i2c);
etas[i] = PGetf("eta");
}
}
etas[0] = PGetf("eta"); // zero position is identical to itself
Global.Debugf("info", "mlp training n {0} nc {1}", ds.nSamples(), nclasses);
for (int round = 0; round < rounds; round++)
{
Stopwatch swRound = Stopwatch.StartNew();
errs.Fill(-1);
if (parallel)
{
// For each network i
Parallel.For(0, nn, i =>
{
nets[i].PSet("eta", etas[i]);
nets[i].TrainDense(ds); // было XTrain
errs[i] = ClassifierUtil.estimate_errors(nets[i], ts);
});
}
else
{
for (int i = 0; i < nn; i++)
{
nets[i].PSet("eta", etas[i]);
nets[i].TrainDense(ds); // было XTrain
errs[i] = ClassifierUtil.estimate_errors(nets[i], ts);
//Global.Debugf("detail", "net({0}) {1} {2} {3}", i,
// errs[i], nets[i].Complexity(), etas[i]);
}
}
NarrayUtil.Quicksort(index, errs);
if (errs[index[0]] < best)
{
best = errs[index[0]];
cv_error = best;
this.Copy(nets[index[0]]);
this.PSet("eta", etas[index[0]]);
Global.Debugf("info", " best mlp[{0}] update errors={1} {2}", index[0], best, crossvalidate ? "cv" : "");
}
if (mlp_noopt == 0)
{
for (int i = 0; i < nn / 2; i++)
{
int j = i + nn / 2;
nets[index[j]].Copy(nets[index[i]]);
int n = nets[index[j]].nHidden();
int nm = Math.Min(Math.Max(hidden_min, (int)(ClassifierUtil.rLogNormal(n, hidden_varlog))), hidden_max);
nets[index[j]].ChangeHidden(nm);
etas[index[j]] = ClassifierUtil.rLogNormal(etas[index[i]], eta_varlog);
}
}
Global.Debugf("info", " end mlp round {0} err {1} nHidden {2}", round, best, nHidden());
swRound.Stop();
int totalTest = ts.nSamples();
int errCnt = Convert.ToInt32(best * totalTest);
OnTrainRound(this, new TrainEventArgs(
round, best, totalTest - errCnt, totalTest, best, swRound.Elapsed, TimeSpan.Zero
));
}
sw.Stop();
Global.Debugf("info", String.Format(" training time: {0} minutes, {1} seconds",
(int)sw.Elapsed.TotalMinutes, sw.Elapsed.Seconds));
PSet("%error", best);
int nsamples = ds.nSamples() * rounds;
if (PExists("%nsamples"))
{
nsamples += PGeti("%nsamples");
}
PSet("%nsamples", nsamples);
}