public override string Next()
{
// 0. validate parameters
if ((learningRate == null) || (momentum == null))
{
throw new Exception();
}
// 3. configure randomizer...
int[] batchRandomizer = new int[batchImages.Count()];
int[] dataSetRandomizer = new int[nofSet];
log += "\nTraining data...";
imagLyrG = new GrayImage();
imagLyrG.Configure(28, 28, -1.0, 1.0);
imagLyrG.fData = batchImages[0].fData[0];
convLyr = new Convolution();
convLyr.Input = imagLyrG.Output;
convLyr.Configure("depth=8; activation=relu(void); kernel=convolution(size = 5, stride = 1, padding = 0, weightfieldsize = 3); outputfieldSize =2");
convLyr1 = new Convolution();
convLyr1.Input = convLyr.Output;
convLyr1.Configure("depth=8; activation=relu(void); kernel=convolution(size = 5, stride = 1, padding = 0, weightfieldsize = 3); outputfieldSize =2");
poolLyr = new Pooling();
poolLyr.Input = convLyr1.Output;
poolLyr.Configure("kernel=maxpool(size=2, stride=2); outputfieldsize=2");
concLyr = new Concatenation();
concLyr.Input = poolLyr.Output;
concLyr.Configure("outputfieldsize=2");
// 1. initialize connection layers
hiddLyr = new Connected();
hiddLyr.Input = concLyr.Output;
hiddLyr.Configure("neuron=hiddenperceptron; activation=relu(void); nodes=128; outputfieldsize=2(def:2)");;
// 2. initialize connection layers
outpLyr = new Connected();
outpLyr.Input = hiddLyr.Output;
outpLyr.Configure("neuron=outputperceptron; activation=linear(void); nodes=10; outputfieldsize=2(def:2)");
for (int i = 0; i < Epochs; i++)
{
//log += "\n\n////////// Epoch[" + i + "]... ///////////////////////////////////////////////////////";
Global.NextIntArray(0, nofSet, dataSetRandomizer);
Global.NextIntArray(0, batchImages.Count(), batchRandomizer);
// single epoch
for (int j = 0; j < 100; j++) //batch
{
//DisplayWeights();
batchLoss = 0;
for (int k = 0; k < 6; k++)
{
imagLyrG.fData = batchImages[batchRandomizer[j]].fData[dataSetRandomizer[k]];
convLyr.Next(Propagate.Signal);
convLyr1.Next(Propagate.Signal);
poolLyr.Next(Propagate.Signal);
concLyr.Next(Propagate.Signal);
hiddLyr.Next(Propagate.Signal);
outpLyr.Next(Propagate.Signal);
Node node;
probs = new double[outpLyr.Output[0].Rows];
for (int l = 0; l < batchImages[batchRandomizer[j]].fData[dataSetRandomizer[k]].Label.Length; l++)
{
node = outpLyr.Output[0].GetElement(l, 0);
probs[l] = (double)((double?[])node.Element)[Global.Sig];
((double?[])node.Element)[Global.Err] = batchImages[batchRandomizer[j]].fData[dataSetRandomizer[k]].Label[l];
}
//Set softmax output
double[] softmaxOutput = Softmax(probs);
for (int l = 0; l < probs.Length; l++)
{
node = outpLyr.Output[0].GetElement(l, 0);
((double?[])node.Element)[Global.Sig] = softmaxOutput[l];
}
singleLoss = lossfunc.GetLoss(softmaxOutput, batchImages[batchRandomizer[j]].fData[dataSetRandomizer[k]].Label);
// Calculate batch loss
batchLoss += singleLoss;
//4.4 propagate error and set new weights
outpLyr.Next(Propagate.Error);
hiddLyr.Next(Propagate.Error);
concLyr.Next(Propagate.Error);
poolLyr.Next(Propagate.Error);
convLyr1.Next(Propagate.Error);
convLyr.Next(Propagate.Error);
SaveError(outpLyr);
SaveError(hiddLyr);
SaveError(concLyr);
SaveError(convLyr1);
SaveError(convLyr);
//log += "\n" + Model.ToString();
}
// 4.5 adjust weights
AdjustWeights(outpLyr);
AdjustWeights(hiddLyr);
AdjustWeights(concLyr);
AdjustWeights(convLyr1);
AdjustWeights(convLyr);
//DisplayWeights();
ClearError(outpLyr);
ClearError(hiddLyr);
ClearError(concLyr);
ClearError(convLyr1);
ClearError(convLyr);
batchLoss = batchLoss / (double)batchSize;
//log += "\n" + Model.ToString();
Console.WriteLine("Epoch[" + i.ToString() + "]" + "Batch[" + j.ToString() + "]" + "Loss: " + batchLoss.ToString("e4"));
log += "\n\nEpoch[" + i.ToString() + "]" + "Batch[" + j.ToString() + "]" + "Loss: " + batchLoss.ToString("e4");
}
}
return(log);
}
