function [networkHistory trReport] = ann_training(learningRate, noHiddenNeurons, noEpochs)

[tvec tlab tstv tstl] = readSets();
tlab += 1;
tstl += 1;

rand();
%rndstate = rand("state");
%save rndstate.txt rndstate;
load rndstate.txt;
rand("state", rndstate);

network = crann([columns(tvec) noHiddenNeurons numel(unique(tlab))]);
trainCorrect = zeros(1, noEpochs);
testCorrect = zeros(1, noEpochs);
trReport = [];
for epoch=1:noEpochs
    tic();
    terr = 0;
    for i=1:rows(tvec)
        [network terrN] = backprop(tvec(i, :), tlab(i, :), network, learningRate);
        terr += terrN;
    end
    terr /= rows(tvec);
    clsRes = anncls(tvec, network);
    cfmx = confMx(tlab, clsRes);
    errcf = compErrors(cfmx);
    trainCorrect(epoch) = 1-errcf(2);

    clsRes = anncls(tstv, network);
    cfmx = confMx(tstl, clsRes);
    errcf2 = compErrors(cfmx);
    testCorrect(epoch) = 1-errcf2(2);
    epochTime = toc();
    disp([epoch epochTime terr trainCorrect(epoch) testCorrect(epoch)])
    trReport = [trReport; epoch epochTime terr trainCorrect(epoch) testCorrect(epoch)];
    fflush(stdout);
  networkHistory{epoch} = network;
    %learningRate = learningRate / epoch;
endfor