int trainNet(NeuralNetwork* net, Sample* samples, unsigned int numberOfSamples,
unsigned int epochs, unsigned int batchSize, double learningRate) {
unsigned int epoch = 0, startIndex, i, currentBatchSize;
if(!samples || epochs < 1 || batchSize < 1 || batchSize > numberOfSamples
|| learningRate <= 0.0 || !isValidNet(net)) {
return -2;
}
while(epoch < epochs) {
shuffleSamples(samples, numberOfSamples);
startIndex = 0;
//while there are still mini batches
while(startIndex < numberOfSamples) {
currentBatchSize = fmin(numberOfSamples - startIndex, batchSize);
printf("epoch: %d, currentBatchSize: %d\n", epoch, currentBatchSize);
initializeDeltas(net);
for(i = 0; i < currentBatchSize; i++) {
//feedForward
feedForward(net, samples[startIndex + i].inputs);
//update deltas
updateDeltas(net, samples[startIndex + i].outputs);
}
updateWeights(net, learningRate/(double)currentBatchSize);
startIndex += currentBatchSize;
}
epoch++;
}
return 0;
}
float FCNetwork::trainEpochs(const Samples &samples, unsigned int epochs) {
for (int i = 0; i < epochs - 1; ++i) {
// Without calculating error
for (const Sample& sample : samples) {
activate(sample.getInputs());
updateDeltas(sample);
updateWeights();
}
}
// Calculating error
float sum = 0;
for (const Sample& sample : samples) {
activate(sample.getInputs());
updateDeltas(sample);
updateWeights();
sum = squaredError(sample.getOutputs());
}
return sum / samples.size();
}
