public List <BPResponse> BPWatched(BPWDRequest bpr)
{
List <BPResponse> toret = new List <BPResponse>(bpr.MaxBPs);
bool trace = this.Inspector != null;
for (int i = 0; i < bpr.MaxBPs; i++)
{
if (trace)
{
Inspector.Trace("BP attempt {0}", i + 1);
}
BPResponse response = this.BP(bpr);
toret.Add(response);
if (response.Estimate != BPResponseEstimate.Failure)
{
if (trace)
{
Inspector.Trace("BPWatched process end, no failures found");
}
break;
}
if (trace)
{
Inspector.Trace("BP failed to learn network");
}
this.Reset(Lo, Hi);
}
return(toret);
}
//util
void StartLearning()
{
Stopped = false;
Thread.CurrentThread.Priority = ThreadPriority.Lowest;
Image[] bigimgs = Utility.Generate(i => Image.FromFile(@"mnist_train" + i.ToString() + ".jpg"), 10).ToArray();
int idx = 0, total = 0;
List <TrainingData> trainingList = new List <TrainingData>();
foreach (double[] input in GetAllImages(bigimgs))
{
if (Stopped)
{
return;
}
double[] output = Digitalizer.GetOutput(10, idx).ToArray();
Application.DoEvents();
trainingList.Add(new TrainingData(input, output));
GC.Collect();
GC.WaitForPendingFinalizers();
idx = (idx + 1) % 10;
if (idx == 0)
{
total++;
BPRequest request = new BPRequest(trainingList.ToArray(), 1)
{
ShuffleTrainingSet = true
};
BPResponse response = Network.BP(request);
double eps = response.Epochs[0].Epsilon;
double ms = response.BPTime.TotalMilliseconds;
trainingList.Clear();
}
}
StopLearning();
}
