matconvnet源代码:skipForward

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 case 'conv'      res(i+1).x = vl_nnconv(res(i).x, l.weights{1}, l.weights{2}, ...        'pad', l.pad, ...        'stride', l.stride, ...        'dilate', l.dilate, ...        l.opts{:}, ...        cudnn{:}) ;    case 'convt'      res(i+1).x = vl_nnconvt(res(i).x, l.weights{1}, l.weights{2}, ...        'crop', l.crop, ...        'upsample', l.upsample, ...        'numGroups', l.numGroups, ...        l.opts{:}, ...        cudnn{:}) ;    case 'pool'      res(i+1).x = vl_nnpool(res(i).x, l.pool, ...        'pad', l.pad, 'stride', l.stride, ...        'method', l.method, ...        l.opts{:}, ...        cudnn{:}) ;    case {'normalize', 'lrn'}      res(i+1).x = vl_nnnormalize(res(i).x, l.param) ;    case 'softmax'      res(i+1).x = vl_nnsoftmax(res(i).x) ;    case 'loss'      res(i+1).x = vl_nnloss(res(i).x, l.class) ;    case 'softmaxloss'      res(i+1).x = vl_nnsoftmaxloss(res(i).x, l.class) ;    case 'relu'      if l.leak > 0, leak = {'leak', l.leak} ; else leak = {} ; end      res(i+1).x = vl_nnrelu(res(i).x,[],leak{:}) ;    case 'sigmoid'      res(i+1).x = vl_nnsigmoid(res(i).x) ;    case 'noffset'      res(i+1).x = vl_nnnoffset(res(i).x, l.param) ;    case 'spnorm'      res(i+1).x = vl_nnspnorm(res(i).x, l.param) ;    case 'dropout'      if testMode        res(i+1).x = res(i).x ;      else        [res(i+1).x, res(i+1).aux] = vl_nndropout(res(i).x, 'rate', l.rate) ;      end    case 'bnorm'      if testMode        res(i+1).x = vl_nnbnorm(res(i).x, l.weights{1}, l.weights{2}, ...                                'moments', l.weights{3}, ...                                'epsilon', l.epsilon, ...                                bnormCudnn{:}) ;      else        res(i+1).x = vl_nnbnorm(res(i).x, l.weights{1}, l.weights{2}, ...                                'epsilon', l.epsilon, ...                                bnormCudnn{:}) ;      end    case 'pdist'      res(i+1).x = vl_nnpdist(res(i).x, l.class, l.p, ...        'noRoot', l.noRoot, ...        'epsilon', l.epsilon, ...        'aggregate', l.aggregate, ...        'instanceWeights', l.instanceWeights) ;    case 'custom'      res(i+1) = l.forward(l, res(i), res(i+1)) ;
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