Win 7下MatConvNet使用DAG网络方法记录

来源：互联网发布：人工智能图书馆编辑：程序博客网时间：2024/06/07 05:46

1、修改ResNet网络

function net = resnet_single() net = load('imagenet-resnet-50-dag.mat') ; net = dagnn.DagNN.loadobj(net) ;net.removeLayer('fc1000') ; net.removeLayer('prob') ;dropoutBlock = dagnn.DropOut('rate',0.9); net.addLayer('dropout',dropoutBlock,{'pool5'},{'pool5d'},{});fc8Block = dagnn.Conv('size',[1 1 2048 8],'hasBias',true,'stride',[1,1],'pad',[0,0,0,0]); net.addLayer('fc8',fc8Block,{'pool5d'},{'prediction'},{'fc8f','fc8b'});lossBlock = dagnn.Loss('loss', 'softmaxlog'); net.addLayer('softmaxloss',lossBlock,{'prediction','label'},'objective');net.addLayer('top1err', dagnn.Loss('loss', 'classerror'), ... {'prediction','label'}, 'top1err') ;

2、构建Siamese网络

net1 = resnet_single();net1 = net1.saveobj() ;net2 = net1;for i = 1 : length(net2.layers);     net2.layers(i).name = strcat(net2.layers(i).name,'_2');     for j = 1 : length(net2.layers(i).inputs);        net2.layers(i).inputs{j} = strcat(net2.layers(i).inputs{j},'_2');     end;    for j = 1 : length(net2.layers(i).outputs);        net2.layers(i).outputs{j} = strcat(net2.layers(i).outputs{j},'_2');    end;end;for i = 1 : length(net2.vars);     net2.vars(i).name = strcat(net2.vars(i).name,'_2'); end;net1.vars = [net1.vars,net2.vars];net1.layers = [net1.layers,net2.layers];net = dagnn.DagNN.loadobj(net1) ;% Siamese Network Lossnet.addLayer('SiameseLoss',dagnn.SiameseLoss(), {'pool5','pool5_2'}, {'objective_s'});

3、构建TripletLoss网络

net1 = resnet_single();net1 = net1.saveobj() ;net2 = net1;for i = 1 : length(net2.layers);     net2.layers(i).name = strcat(net2.layers(i).name,'_2');     for j = 1 : length(net2.layers(i).inputs);        net2.layers(i).inputs{j} = strcat(net2.layers(i).inputs{j},'_2');     end;    for j = 1 : length(net2.layers(i).outputs);        net2.layers(i).outputs{j} = strcat(net2.layers(i).outputs{j},'_2');    end;end;for i = 1 : length(net2.vars);     net2.vars(i).name = strcat(net2.vars(i).name,'_2'); end;net3 = net1;for i = 1 : length(net3.layers);     net3.layers(i).name = strcat(net3.layers(i).name,'_3');     for j = 1 : length(net3.layers(i).inputs);        net3.layers(i).inputs{j} = strcat(net3.layers(i).inputs{j},'_3');     end;    for j = 1 : length(net3.layers(i).outputs);        net3.layers(i).outputs{j} = strcat(net3.layers(i).outputs{j},'_3');    end;end;for i = 1 : length(net3.vars);     net3.vars(i).name = strcat(net3.vars(i).name,'_3'); end;net1.vars = [net1.vars,net2.vars,net3.vars];net1.layers = [net1.layers,net2.layers,net3.layers];net = dagnn.DagNN.loadobj(net1) ;% Triplet Network Lossnet.addLayer('TripletLoss',dagnn.TripletLoss('margin',0.1),{'pool5','pool5_2','pool5_3'},{'objective_triplet'});

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