kddcup 数据两层CNN入侵检测

function cnnTrain(cnn,kddcupdatas,labels,testkddcupdatas,testlabels) it = 0; C = []; epochs = cnn.numepochs; minibatch = cnn.minibatch; momIncrease = cnn.momIncrease; mom = cnn.mom; momentum = cnn.momentum; alpha = cnn.alpha; lambda = cnn.lambda; m =length(labels); numLayers = numel(cnn.layers); for e = 1:epochs % randomly permute indices of data for quick minibatch sampling rp = randperm(m); for s=1:minibatch:(m/10-minibatch+1) it = it + 1; % momentum enable if it == momIncrease mom = momentum; end; % mini-batch pick mb_kddcupdatas = kddcupdatas(:,:,:,rp(s:s+minibatch-1)); mb_labels = labels(rp(s:s+minibatch-1)); numkddcupdatas = size(mb_kddcupdatas,4); %feedforward %convolve and pooling activations = mb_kddcupdatas; for l = 1:numLayers layer = cnn.layers{l}; if(strcmp(layer.type,'c'))%convolutional layer activations = cnnConvolve4D(activations,layer.W,layer.b); else activations = cnnPool(layer.poolDim,activations); end layer.activations = activations; cnn.layers{l} = layer; end %softmax activations = reshape(activations,[],numkddcupdatas); probs = exp(bsxfun(@plus, cnn.Wd * activations, cnn.bd)); sumProbs = sum(probs, 1); probs = bsxfun(@times, probs, 1 ./ sumProbs); %% --------- Calculate Cost ---------- logp = log(probs); index = sub2ind(size(logp),mb_labels',1:size(probs,2)); ceCost = -sum(logp(index)); wCost = 0; for l = 1:numLayers layer = cnn.layers{l}; if(strcmp(layer.type,'c')) wCost = wCost + sum(layer.W(:) .^ 2); end end wCost = lambda/2*(wCost + sum(cnn.Wd(:) .^ 2)); finalcost = ceCost+wCost; %ignore weight cost for now %Backpropagation %errors %softmax layer output = zeros(size(probs)); output(index) = 1; DeltaSoftmax = (probs - output); t = -DeltaSoftmax; %last pooling layer numFilters2 = cnn.layers{numLayers-1}.numFilters; outputDim = size(cnn.layers{numLayers}.activations,1); cnn.layers{numLayers}.delta = reshape(cnn.Wd' * DeltaSoftmax,outputDim,outputDim,numFilters2,numkddcupdatas); %other layers for l = numLayers-1:-1:1 layer = cnn.layers{l}; if(strcmp(layer.type,'c'))% convolutional layer numFilters = cnn.layers{l}.numFilters; outputDim = size(cnn.layers{l+1}.activations,1); poolDim = cnn.layers{l+1}.poolDim; convDim = outputDim * poolDim; DeltaPool = cnn.layers{l+1}.delta; %unpool from last layer DeltaUnpool = zeros(convDim,convDim,numFilters,numkddcupdatas); for imNum = 1:numkddcupdatas for FilterNum = 1:numFilters unpool = DeltaPool(:,:,FilterNum,imNum); DeltaUnpool(:,:,FilterNum,imNum) = kron(unpool,ones(poolDim))./(poolDim ^ 2); end end activations = layer.activations; DeltaConv = DeltaUnpool .* activations .* (1 - activations); layer.delta = DeltaConv; cnn.layers{l} = layer; else numFilters1 = cnn.layers{l-1}.numFilters; numFilters2 = cnn.layers{l+1}.numFilters; outputDim1 = size(layer.activations,1); DeltaPooled = zeros(outputDim1,outputDim1,numFilters1,numkddcupdatas); DeltaConv = cnn.layers{l+1}.delta; Wc = cnn.layers{l+1}.W; for i = 1:numkddcupdatas for f1 = 1:numFilters1 for f2 = 1:numFilters2 DeltaPooled(:,:,f1,i) = DeltaPooled(:,:,f1,i) + convn(DeltaConv(:,:,f2,i),Wc(:,:,f1,f2),'full'); end end end layer.delta = DeltaPooled; cnn.layers{l} = layer; end end %gradients activationsPooled = cnn.layers{numLayers}.activations; activationsPooled = reshape(activationsPooled,[],numkddcupdatas); Wd_grad = DeltaSoftmax*(activationsPooled)'; bd_grad = sum(DeltaSoftmax,2); cnn.Wd_velocity = mom*cnn.Wd_velocity + alpha * (Wd_grad/minibatch+lambda*cnn.Wd); cnn.bd_velocity = mom*cnn.bd_velocity + alpha * (bd_grad/minibatch); cnn.Wd = cnn.Wd - cnn.Wd_velocity; cnn.bd = cnn.bd - cnn.bd_velocity; %other convolutions for l = numLayers:-1:1 layer = cnn.layers{l}; if(strcmp(layer.type,'c'))%if this is a convolutional layer numFilters2 = layer.numFilters; if(l == 1) numFilters1 = cnn.imageChannel; activationsPooled = mb_kddcupdatas; else numFilters1 = cnn.layers{l-2}.numFilters; activationsPooled = cnn.layers{l-1}.activations; end Wc_grad = zeros(size(layer.W)); bc_grad = zeros(size(layer.b)); DeltaConv = layer.delta; for fil2 = 1:numFilters2 for fil1 = 1:numFilters1 for im = 1:numkddcupdatas Wc_grad(:,:,fil1,fil2) = Wc_grad(:,:,fil1,fil2) + conv2(activationsPooled(:,:,fil1,im),rot90(DeltaConv(:,:,fil2,im),2),'valid'); end end temp = DeltaConv(:,:,fil2,:); bc_grad(fil2) = sum(temp(:)); end layer.W_velocity = mom*layer.W_velocity + alpha*(Wc_grad/numkddcupdatas+lambda*layer.W); layer.b_velocity = mom*layer.b_velocity + alpha*(bc_grad/numkddcupdatas); layer.W = layer.W - layer.W_velocity; layer.b = layer.b - layer.b_velocity; end cnn.layers{l} = layer; end fprintf('Epoch %d: Cost on iteration %d is %f\n',e,it,finalcost); C(length(C)+1) = finalcost; %break; end cnnTest(cnn,testkddcupdatas,testlabels); alpha = alpha/2.0; end plot(C); grid on; title('损失残差图'); xlabel('迭代次数 120'); ylabel('误差值'); legend('误差变化趋势'); end