void Net::Train(const mxArray *mx_data, const mxArray *mx_labels) {
//mexPrintMsg("Start training...");
ReadData(mx_data);
ReadLabels(mx_labels);
InitNorm();
std::srand(params_.seed_);
size_t train_num = labels_.size1();
size_t numbatches = (size_t) ceil((ftype) train_num/params_.batchsize_);
trainerror_.resize(params_.numepochs_, numbatches);
for (size_t epoch = 0; epoch < params_.numepochs_; ++epoch) {
std::vector<size_t> randind(train_num);
for (size_t i = 0; i < train_num; ++i) {
randind[i] = i;
}
if (params_.shuffle_) {
std::random_shuffle(randind.begin(), randind.end());
}
std::vector<size_t>::const_iterator iter = randind.begin();
for (size_t batch = 0; batch < numbatches; ++batch) {
size_t batchsize = std::min(params_.batchsize_, (size_t)(randind.end() - iter));
std::vector<size_t> batch_ind = std::vector<size_t>(iter, iter + batchsize);
iter = iter + batchsize;
Mat data_batch = SubMat(data_, batch_ind, 1);
Mat labels_batch = SubMat(labels_, batch_ind, 1);
UpdateWeights(epoch, false);
InitActiv(data_batch);
Mat pred_batch;
Forward(pred_batch, 1);
InitDeriv(labels_batch, trainerror_(epoch, batch));
Backward();
CalcWeights();
UpdateWeights(epoch, true);
if (params_.verbose_ == 2) {
std::string info = std::string("Epoch: ") + std::to_string(epoch+1) +
std::string(", batch: ") + std::to_string(batch+1);
mexPrintMsg(info);
}
} // batch
if (params_.verbose_ == 1) {
std::string info = std::string("Epoch: ") + std::to_string(epoch+1);
mexPrintMsg(info);
}
} // epoch
//mexPrintMsg("Training finished");
}
void Net::Train(const mxArray *mx_data, const mxArray *mx_labels) {
//mexPrintMsg("Start training...");
ReadData(mx_data);
ReadLabels(mx_labels);
InitNorm();
size_t train_num = data_.size1();
size_t numbatches = DIVUP(train_num, params_.batchsize_);
trainerrors_.resize(params_.epochs_, 2);
trainerrors_.assign(0);
for (size_t epoch = 0; epoch < params_.epochs_; ++epoch) {
if (params_.shuffle_) {
Shuffle(data_, labels_);
}
StartTimer();
size_t offset = 0;
Mat data_batch, labels_batch, pred_batch;
for (size_t batch = 0; batch < numbatches; ++batch) {
size_t batchsize = MIN(train_num - offset, params_.batchsize_);
UpdateWeights(epoch, false);
data_batch.resize(batchsize, data_.size2());
labels_batch.resize(batchsize, labels_.size2());
SubSet(data_, data_batch, offset, true);
SubSet(labels_, labels_batch, offset, true);
ftype error1;
InitActiv(data_batch);
Forward(pred_batch, 1);
InitDeriv(labels_batch, error1);
trainerrors_(epoch, 0) += error1;
Backward();
UpdateWeights(epoch, true);
offset += batchsize;
if (params_.verbose_ == 2) {
mexPrintInt("Epoch", (int) epoch + 1);
mexPrintInt("Batch", (int) batch + 1);
}
} // batch
MeasureTime("totaltime");
if (params_.verbose_ == 1) {
mexPrintInt("Epoch", (int) epoch + 1);
}
} // epoch
trainerrors_ /= (ftype) numbatches;
//mexPrintMsg("Training finished");
}
