Mercurial > hg > camir-aes2014
diff core/magnatagatune/tests_evals/rbm_subspace/training_rbm_.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/tests_evals/rbm_subspace/training_rbm_.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,97 @@ +function [W visB hidB] = training_rbm_(conf,W,data) +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Training RBM % +% conf: training setting % +% W: weights of connections % +% -*-sontran2012-*- % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +assert(~isempty(data),'[KBRBM] Data is empty'); +%% initialization +visNum = size(data,2); +hidNum = conf.hidNum; +sNum = conf.sNum; +lr = conf.params(1); +N = conf.N; % Number of epoch training with lr_1 +W = 0.1*randn(visNum - size(W,1),size(W,2)); +W = 0.1*randn(size(W,1),hidNum-size(W,2)); + +DW = zeros(size(W)); +visB = zeros(1,visNum); +DVB = zeros(1,visNum); +hidB = zeros(1,hidNum); +DHB = zeros(1,hidNum); +visP = zeros(sNum,visNum); +visN = zeros(sNum,visNum); +visNs = zeros(sNum,visNum); +hidP = zeros(sNum,hidNum); +hidPs = zeros(sNum,hidNum); +hidN = zeros(sNum,hidNum); +hidNs = zeros(sNum,hidNum); + +plot_ = 0; +%% Reconstruction error & evaluation error & early stopping +mse = 0; +omse = 0; +inc_count = 0; +MAX_INC = conf.MAX_INC; % If the error increase MAX_INC times continuously, then stop training +%% Average best settings +n_best = 1; +aW = size(W); +aVB = size(visB); +aHB = size(hidB); +%% Plotting +if plot_, h = plot(nan); end +%% ==================== Start training =========================== %% +for i=1:conf.eNum + if i== N+1 + lr = conf.params(2); + end + omse = mse; + mse = 0; + for j=1:conf.bNum + visP = data((j-1)*conf.sNum+1:j*conf.sNum,:); + %up + hidP = logistic(visP*W + repmat(hidB,sNum,1)); + hidPs = 1*(hidP >rand(sNum,hidNum)); + hidNs = hidPs; + for k=1:conf.gNum + % down + visN = logistic(hidNs*W' + repmat(visB,sNum,1)); + visNs = 1*(visN>rand(sNum,visNum)); + % up + hidN = logistic(visNs*W + repmat(hidB,sNum,1)); + hidNs = 1*(hidN>rand(sNum,hidNum)); + end + % Compute MSE for reconstruction +% rdiff = (visP - visN); +% mse = mse + sum(sum(rdiff.*rdiff))/(sNum*visNum); + % Update W,visB,hidB + diff = (visP'*hidP - visNs'*hidN)/sNum; + DW = lr*(diff - conf.params(4)*W) + conf.params(3)*DW; + W = W + DW; + DVB = lr*sum(visP - visN,1)/sNum + conf.params(3)*DVB; + visB = visB + DVB; + DHB = lr*sum(hidP - hidN,1)/sNum + conf.params(3)*DHB; + hidB = hidB + DHB; + end + %% + if plot_ + mse_plot(i) = mse; + axis([0 (conf.eNum+1) 0 5]); + set(h,'YData',mse_plot); + drawnow; + end +% % save(strcat('C:\Pros\Data\XOR\plot_',num2str(conf.params(2),3),'.mat'),'mse_plot'); +% % plot(mse_plot,'XDataSource','real(mse_plot)','YDataSource','imag(mse_plot)') +% % linkdata on; + + if mse > omse + inc_count = inc_count + 1; + else + inc_count = 0; + end + if inc_count> MAX_INC, break; end; +% fprintf('Epoch %d : MSE = %f\n',i,mse); +end +end \ No newline at end of file