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CNN_AL_MRF_init.m

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+function [net] = CNN_AL_MRF_init(data, cnn_net, Train)
+
+rng('default');
+rng(0);
+
+% initialize network structure with LeNet5
+f = 1/100 ;
+net.layers = {} ;
+net.layers{end+1} = struct('type', 'conv', ...       % C1: 4-d weights with zero bias
+                           'weights', {{f*randn(cnn_net.SizeKer1Conv, cnn_net.SizeKer1Conv, size(Train.Data_Train,3), cnn_net.NumKer1Conv, 'single'), zeros(1, cnn_net.NumKer1Conv, 'single')}}, ... 
+                           'stride', 1, ...
+                           'pad', 2);
+net.layers{end+1} = struct('type', 'pool', ...       % P1 
+                           'method', 'max', ...
+                           'pool', [cnn_net.SizeKer1Pool, cnn_net.SizeKer1Pool], ...        %池化核大小为2*2
+                           'stride', cnn_net.Stride1Pool, ...
+                           'pad', 0);
+net.layers{end+1} = struct('type', 'conv', ...       % C2
+                           'weights', {{f*randn(cnn_net.SizeKer2Conv, cnn_net.SizeKer2Conv, cnn_net.NumKer1Conv, cnn_net.NumKer2Conv, 'single'), zeros(1,cnn_net.NumKer2Conv, 'single')}}, ... 
+                           'stride', 1, ...
+                           'pad', 0);
+net.layers{end+1} = struct('type', 'pool', ...       % P2 
+                           'method', 'max', ...
+                           'pool', [cnn_net.SizeKer2Pool, cnn_net.SizeKer2Pool], ...        %池化核大小为2*2
+                           'stride', cnn_net.Stride2Pool, ...
+                           'pad', 0);
+net.layers{end+1} = struct('type', 'conv', ...       % FC1
+                           'weights', {{f*randn(cnn_net.SizeKer1FC, cnn_net.SizeKer1FC, cnn_net.NumKer2Conv, cnn_net.NumKer1FC, 'single'), zeros(1,cnn_net.NumKer1FC,'single')}}, ...
+                           'stride', 1, ...
+                           'pad', 0);
+net.layers{end+1} = struct('type', 'relu') ;         % ReLu 
+net.layers{end+1} = struct('type', 'conv', ...       % FC1
+                           'weights', {{f*randn(cnn_net.SizeKer2FC, cnn_net.SizeKer2FC, cnn_net.NumKer1FC, cnn_net.NumKer2FC, 'single'), zeros(1,cnn_net.NumKer2FC,'single')}}, ...
+                           'stride', 1, ...
+                           'pad', 0);
+net.layers{end+1} = struct('type', 'softmaxloss');  % softmax
+
+% optionally switch to batch normalization
+if cnn_net.flagBN
+  net = insertBnorm(net, 1);
+  net = insertBnorm(net, 4);
+  net = insertBnorm(net, 7);
+end
+
+% Meta parameters
+net.meta.inputSize = [data.SizeWin, data.SizeWin, size(Train.Data_Train,3)];
+net.meta.trainOpts.learningRate = cnn_net.LearningRate;
+net.meta.trainOpts.weightDecay = cnn_net.WeightDecay;
+net.meta.trainOpts.batchSize = cnn_net.BatchSize;
+
+% Fill in defaul values
+net = vl_simplenn_tidy(net);
+end
+
+function net = insertBnorm(net, l)
+% --------------------------------------------------------------  
+% insert Bnorm layer between layer l and layer l+1
+% --------------------------------------------------------------------
+assert(isfield(net.layers{l}, 'weights'));                             % assert layer l has weights
+ndim = size(net.layers{l}.weights{1}, 4);                              % neuron number in layer l  
+layer = struct('type', 'bnorm', ...                                    % initialize Bnorm parameters
+               'weights', {{ones(ndim, 1, 'single'), zeros(ndim, 1, 'single')}}, ...
+               'learningRate', [1 1 0.05], ...                         % weights of Bnorm layer = #neuron of layer l
+               'weightDecay', [0 0]) ;
+net.layers{l}.biases = [] ;
+net.layers = horzcat(net.layers(1:l), layer, net.layers(l+1:end)) ;    % add Bnorm layer
+end

Index1Dto2D.m

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+function [x y] = Index1Dto2D(Index1D, H, W)
+% ½«1D×ø±êת»»Îª2D×ø±ê
+x = mod(Index1D,H);
+y = (Index1D-x)/H + 1;
+if (x==0)
+    y = y - 1;
+    x = H;
+end