matconvnet源代码backPropLim

 case 'conv'        [res(i).dzdx, dzdw{1}, dzdw{2}] = ...          vl_nnconv(res(i).x, l.weights{1}, l.weights{2}, res(i+1).dzdx, ...          'pad', l.pad, ...          'stride', l.stride, ...          'dilate', l.dilate, ...          l.opts{:}, ...          cudnn{:}) ;      case 'convt'        [res(i).dzdx, dzdw{1}, dzdw{2}] = ...          vl_nnconvt(res(i).x, l.weights{1}, l.weights{2}, res(i+1).dzdx, ...          'crop', l.crop, ...          'upsample', l.upsample, ...          'numGroups', l.numGroups, ...          l.opts{:}, ...          cudnn{:}) ;      case 'pool'        res(i).dzdx = vl_nnpool(res(i).x, l.pool, res(i+1).dzdx, ...                                'pad', l.pad, 'stride', l.stride, ...                                'method', l.method, ...                                l.opts{:}, ...                                cudnn{:}) ;      case {'normalize', 'lrn'}        res(i).dzdx = vl_nnnormalize(res(i).x, l.param, res(i+1).dzdx) ;      case 'softmax'        res(i).dzdx = vl_nnsoftmax(res(i).x, res(i+1).dzdx) ;      case 'loss'        res(i).dzdx = vl_nnloss(res(i).x, l.class, res(i+1).dzdx) ;      case 'softmaxloss'        res(i).dzdx = vl_nnsoftmaxloss(res(i).x, l.class, res(i+1).dzdx) ;      case 'relu'        if l.leak > 0, leak = {'leak', l.leak} ; else leak = {} ; end        if ~isempty(res(i).x)          res(i).dzdx = vl_nnrelu(res(i).x, res(i+1).dzdx, leak{:}) ;        else          % if res(i).x is empty, it has been optimized away, so we use this          % hack (which works only for ReLU):          res(i).dzdx = vl_nnrelu(res(i+1).x, res(i+1).dzdx, leak{:}) ;        end      case 'sigmoid'        res(i).dzdx = vl_nnsigmoid(res(i).x, res(i+1).dzdx) ;      case 'noffset'        res(i).dzdx = vl_nnnoffset(res(i).x, l.param, res(i+1).dzdx) ;      case 'spnorm'        res(i).dzdx = vl_nnspnorm(res(i).x, l.param, res(i+1).dzdx) ;      case 'dropout'        if testMode          res(i).dzdx = res(i+1).dzdx ;        else          res(i).dzdx = vl_nndropout(res(i).x, res(i+1).dzdx, ...                                     'mask', res(i+1).aux) ;        end      case 'bnorm'        [res(i).dzdx, dzdw{1}, dzdw{2}, dzdw{3}] = ...          vl_nnbnorm(res(i).x, l.weights{1}, l.weights{2}, res(i+1).dzdx, ...                     'epsilon', l.epsilon, ...                     bnormCudnn{:}) ;        % multiply the moments update by the number of images in the batch        % this is required to make the update additive for subbatches        % and will eventually be normalized away        dzdw{3} = dzdw{3} * size(res(i).x,4) ;      case 'pdist'        res(i).dzdx = vl_nnpdist(res(i).x, l.class, ...          l.p, res(i+1).dzdx, ...          'noRoot', l.noRoot, ...          'epsilon', l.epsilon, ...          'aggregate', l.aggregate, ...          'instanceWeights', l.instanceWeights) ;      case 'custom'        res(i) = l.backward(l, res(i), res(i+1)) ;