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..."); LayerFull *lastlayer = static_cast<LayerFull*>(layers_.back()); std::vector<size_t> labels_dim = mexGetDimensions(mx_labels); mexAssert(labels_dim.size() == 2, "The label array must have 2 dimensions"); mexAssert(labels_dim[0] == lastlayer->length_, "Labels and last layer must have equal number of classes"); size_t train_num = labels_dim[1]; Mat labels(labels_dim); mexGetMatrix(mx_labels, labels); classcoefs_.assign(labels_dim[0], 1); if (params_.balance_) { Mat labels_mean(labels_dim[0], 1); labels.Mean(2, labels_mean); for (size_t i = 0; i < labels_dim[0]; ++i) { mexAssert(labels_mean(i) > 0, "Balancing impossible: one of the classes is not presented"); (classcoefs_[i] /= labels_mean(i)) /= labels_dim[0]; } } if (lastlayer->function_ == "SVM") { (labels *= 2) -= 1; } size_t mapnum = 1; if (mexIsCell(mx_data)) { mapnum = mexGetNumel(mx_data); } mexAssert(mapnum == layers_.front()->outputmaps_, "Data must have the same number of cells as outputmaps on the first layer"); std::vector< std::vector<Mat> > data(mapnum); for (size_t map = 0; map < mapnum; ++map) { const mxArray *mx_cell; if (mexIsCell(mx_data)) { mx_cell = mxGetCell(mx_data, map); } else { mx_cell = mx_data; } std::vector<size_t> data_dim = mexGetDimensions(mx_cell); mexAssert(data_dim.size() == 3, "The data array must have 3 dimensions"); mexAssert(data_dim[0] == layers_.front()->mapsize_[0] && data_dim[1] == layers_.front()->mapsize_[1], "Data and the first layer must have equal sizes"); mexAssert(data_dim[2] == train_num, "All data maps and labels must have equal number of objects"); mexGetMatrix3D(mx_cell, data[map]); } size_t numbatches = ceil((double) train_num/params_.batchsize_); trainerror_.assign(params_.numepochs_ * numbatches, 0); 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; std::vector< std::vector<Mat> > data_batch(mapnum); for (size_t map = 0; map < mapnum; ++map) { data_batch[map].resize(batchsize); for (size_t i = 0; i < batchsize; ++i) { data_batch[map][i] = data[map][batch_ind[i]]; } } Mat labels_batch(labels_dim[0], batchsize); Mat pred_batch(labels_dim[0], batchsize); labels.SubMat(batch_ind, 2 ,labels_batch); UpdateWeights(false); Forward(data_batch, pred_batch, true); Backward(labels_batch, trainerror_[epoch * numbatches + batch]); UpdateWeights(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 mexFunction(int nLhs, mxArray* pLhs[], int nRhs, const mxArray* pRhs[]) { mexAssert(NARGIN_MIN <= nRhs && nRhs <= NARGIN_MAX, "Number of input arguments in wrong!"); mexAssert(nLhs == NARGOUT, "Number of output arguments is wrong!" ); mexAssert(mexIsCell(IN_L), "Layers must be the cell array"); mexAssert(mexGetNumel(IN_L) == 2, "Layers array must contain 2 cells"); mexAssert(mexIsCell(IN_W), "Weights must be the cell array"); mexAssert(mexGetNumel(IN_W) == 2, "Weights array must contain 2 cells"); Net net; mxArray *mx_weights; net.InitLayers(mexGetCell(IN_L, 1)); net.InitWeights(mexGetCell(IN_W, 1), mx_weights); net.InitParams(IN_P); net.ReadLabels(IN_Y); const mxArray *mx_imweights = mexGetCell(IN_W, 0); size_t train_num = net.labels_.size1(); mexAssert(train_num == mexGetNumel(mx_imweights), "Weights and labels number must coincide"); bool is_multicoords = false; if (mexIsCell(IN_X)) { mexAssert(train_num == mexGetNumel(IN_X), "Coordinates and labels number must coincide"); is_multicoords = true; } Params params_ = net.params_; size_t numbatches = (size_t) ceil((ftype) train_num/params_.batchsize_); Mat trainerror_(params_.numepochs_, numbatches); Mat trainerror2_(params_.numepochs_, numbatches); trainerror2_.assign(0); std::vector<Net> imnets; imnets.resize(params_.batchsize_); for (size_t i = 0; i < params_.batchsize_; ++i) { imnets[i].InitLayers(mexGetCell(IN_L, 0)); if (!is_multicoords) { imnets[i].ReadData(IN_X); } else { imnets[i].ReadData(mexGetCell(IN_X, i)); // just to get pixels_num } } size_t pixels_num = imnets[0].data_.size1(); Layer *firstlayer = net.layers_[0]; size_t dimens_num = firstlayer->outputmaps_; mexAssert(imnets[0].layers_.back()->length_ == dimens_num, "Final layer length must coincide with the number of outputmaps"); mexAssert(pixels_num == firstlayer->mapsize_[0] * firstlayer->mapsize_[1], "Pixels number must coincide with the first layer elements number"); std::vector<size_t> pred_size(2); pred_size[0] = 1; pred_size[1] = pixels_num * dimens_num; Mat images_mat, labels_batch, pred_batch, pred_pixels; std::vector< std::vector<Mat> > images, images_der; 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; labels_batch = SubMat(net.labels_, batch_ind, 1); net.UpdateWeights(epoch, false); images_mat.resize(batchsize, pred_size[1]); InitMaps(images_mat, pred_size, images); // first pass for (size_t m = 0; m < batchsize; ++m) { imnets[m].InitWeights(mexGetCell(mx_imweights, batch_ind[m])); if (is_multicoords) { imnets[m].ReadData(mexGetCell(IN_X, batch_ind[m])); } imnets[m].InitActiv(imnets[m].data_); imnets[m].Forward(pred_pixels, 1); images[m][0].copy(Trans(pred_pixels).reshape(pred_size[0], pred_size[1])); } net.InitActiv(images_mat); net.Forward(pred_batch, 1); /* for (int i = 0; i < 5; ++i) { mexPrintMsg("pred_batch1", pred_batch(0, i)); }*/ // second pass net.InitDeriv(labels_batch, trainerror_(epoch, batch)); net.Backward(); net.CalcWeights(); InitMaps(firstlayer->deriv_mat_, pred_size, images_der); for (size_t m = 0; m < batchsize; ++m) { imnets[m].layers_.back()->deriv_mat_ = Trans(images_der[m][0].reshape(dimens_num, pixels_num)); imnets[m].Backward(); } // third pass ftype loss2 = 0, curloss = 0, invind = 0; std::vector<size_t> invalid; for (size_t m = 0; m < batchsize; ++m) { imnets[m].InitDeriv2(curloss); if (curloss > 0) { imnets[m].Forward(pred_pixels, 3); images[m][0].copy(Trans(pred_pixels).reshape(pred_size[0], pred_size[1])); loss2 += curloss; } else { invalid.push_back(m); } } if (invalid.size() < batchsize) { loss2 /= (batchsize - invalid.size()); trainerror2_(epoch, batch) = loss2; net.InitActiv(images_mat); net.Forward(pred_batch, 3); } net.CalcWeights2(invalid); // weights update net.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"); //net.weights_.get().copy(net.weights_.der()); OUT_W = mexSetCellMat(1, 2); mexSetCell(OUT_W, 0, mexDuplicateArray(mx_imweights)); mexSetCell(OUT_W, 1, mx_weights); OUT_E = mexSetCellMat(1, 2); mexSetCell(OUT_E, 0, mexSetMatrix(trainerror_)); mexSetCell(OUT_E, 1, mexSetMatrix(trainerror2_)); }