深度置信网络DBN算法实现 matlab

function [nn, L] = nntrain(nn, train_x, train_y, opts, val_x, val_y) %NNTRAIN trains a neural net % [nn, L] = nnff(nn, x, y, opts) trains the neural network nn with input x and % output y for opts.numepochs epochs, with minibatches of size % opts.batchsize. Returns a neural network nn with updated activations, % errors, weights and biases, (nn.a, nn.e, nn.W, nn.b) and L, the sum % squared error for each training minibatch. assert(isfloat(train_x), 'train_x must be a float'); assert(nargin == 4 || nargin == 6,'number ofinput arguments must be 4 or 6') loss.train.e = []; loss.train.e_frac = []; loss.val.e = []; loss.val.e_frac = []; opts.validation = 0; if nargin == 6 opts.validation = 1; end fhandle = []; if isfield(opts,'plot') && opts.plot == 1 fhandle = figure(); end m = size(train_x, 1); batchsize = opts.batchsize; numepochs = opts.numepochs; numbatches = m / batchsize; assert(rem(numbatches, 1) == 0, 'numbatches must be a integer'); L = zeros(numepochs*numbatches,1); n = 1; for i = 1 : numepochs tic; kk = randperm(m); for l = 1 : numbatches batch_x = train_x(kk((l - 1) * batchsize + 1 : l * batchsize), :); %Add noise to input (for use in denoising autoencoder) if(nn.inputZeroMaskedFraction ~= 0) batch_x = batch_x.*(rand(size(batch_x))>nn.inputZeroMaskedFraction); end batch_y = train_y(kk((l - 1) * batchsize + 1 : l * batchsize), :); nn = nnff(nn, batch_x, batch_y); nn = nnbp(nn); nn = nnapplygrads(nn); L(n) = nn.L; n = n + 1; end t = toc; if opts.validation == 1 loss = nneval(nn, loss, train_x, train_y, val_x, val_y); str_perf = sprintf('; Full-batch train mse = %f, val mse = %f', loss.train.e(end), loss.val.e(end)); else loss = nneval(nn, loss, train_x, train_y); str_perf = sprintf('; Full-batch train err = %f', loss.train.e(end)); end if ishandle(fhandle) nnupdatefigures(nn, fhandle, loss, opts, i); end disp(['epoch ' num2str(i) '/' num2str(opts.numepochs) '. Took ' num2str(t) ' seconds' '. Mini-batch mean squared error on training set is ' num2str(mean(L((n-numbatches):(n-1)))) str_perf]); nn.learningRate = nn.learningRate * nn.scaling_learningRate; end end