我正在使用 MATCONVNET DagNN。使用 AlexNet 架构。我的架构的最后几层是
[![net = dagnn.DagNN() ;
imdb_32 =load('imdb_all_32_pd_norm.mat');
imdb_32=imdb_32.imdb;
% some common options
opts.train.batchSize = 100;
opts.train.numEpochs = 100 ;
opts.train.continue = true ;
opts.train.gpus = \[\] ;
opts.train.learningRate = 0.2;%\[0.1 * ones(1,30), 0.01*ones(1,30), 0.001*ones(1,30)\] ;%0.002;%\[2e-1*ones(1, 10), 2e-2*ones(1, 5)\];
opts.train.momentum = 0.9;
opts.train.expDir = expDir;
opts.train.numSubBatches = 1;
bopts.useGpu =0;%numel(opts.train.gpus) > 0 ;
%% NET
net.addLayer('conv1', dagnn.Conv('size', \[11 11 3 96\], 'hasBias', true, 'stride', \[4, 4\], 'pad', \[20 20 20 20\]), {'input'}, {'conv1'}, {'conv1f' 'conv1b'});
net.addLayer('relu1', dagnn.ReLU(), {'conv1'}, {'relu1'}, {});
net.addLayer('lrn1', dagnn.LRN('param', \[5 1 2.0000e-05 0.7500\]), {'relu1'}, {'lrn1'}, {});
net.addLayer('pool1', dagnn.Pooling('method', 'max', 'poolSize', \[3, 3\], 'stride', \[2 2\], 'pad', \[0 0 0 0\]), {'lrn1'}, {'pool1'}, {});
net.addLayer('conv2', dagnn.Conv('size', \[5 5 48 256\], 'hasBias', true, 'stride', \[1, 1\], 'pad', \[2 2 2 2\]), {'pool1'}, {'conv2'}, {'conv2f' 'conv2b'});
net.addLayer('relu2', dagnn.ReLU(), {'conv2'}, {'relu2'}, {});
net.addLayer('lrn2', dagnn.LRN('param', \[5 1 2.0000e-05 0.7500\]), {'relu2'}, {'lrn2'}, {});
net.addLayer('pool2', dagnn.Pooling('method', 'max', 'poolSize', \[3, 3\], 'stride', \[2 2\], 'pad', \[0 0 0 0\]), {'lrn2'}, {'pool2'}, {});
net.addLayer('drop2',dagnn.DropOut('rate',0.7),{'pool2'},{'drop2'});
net.addLayer('conv3', dagnn.Conv('size', \[3 3 256 384\], 'hasBias', true, 'stride', \[1, 1\], 'pad', \[1 1 1 1\]), {'drop2'}, {'conv3'}, {'conv3f' 'conv3b'});
net.addLayer('relu3', dagnn.ReLU(), {'conv3'}, {'relu3'}, {});
net.addLayer('conv4', dagnn.Conv('size', \[3 3 192 384\], 'hasBias', true, 'stride', \[1, 1\], 'pad', \[1 1 1 1\]), {'relu3'}, {'conv4'}, {'conv4f' 'conv4b'});
net.addLayer('relu4', dagnn.ReLU(), {'conv4'}, {'relu4'}, {});
net.addLayer('conv5', dagnn.Conv('size', \[3 3 192 256\], 'hasBias', true, 'stride', \[1, 1\], 'pad', \[1 1 1 1\]), {'relu4'}, {'conv5'}, {'conv5f' 'conv5b'});
net.addLayer('relu5', dagnn.ReLU(), {'conv5'}, {'relu5'}, {});
net.addLayer('pool5', dagnn.Pooling('method', 'max', 'poolSize', \[3 3\], 'stride', \[2 2\], 'pad', \[0 0 0 0\]), {'relu5'}, {'pool5'}, {});
net.addLayer('drop5',dagnn.DropOut('rate',0.5),{'pool5'},{'drop5'});
net.addLayer('fc6', dagnn.Conv('size', \[1 1 256 4096\], 'hasBias', true, 'stride', \[1, 1\], 'pad', \[0 0 0 0\]), {'drop5'}, {'fc6'}, {'conv6f' 'conv6b'});
net.addLayer('relu6', dagnn.ReLU(), {'fc6'}, {'relu6'}, {});
net.addLayer('fc7', dagnn.Conv('size', \[1 1 4096 4096\], 'hasBias', true, 'stride', \[1, 1\], 'pad', \[0 0 0 0\]), {'relu6'}, {'fc7'}, {'conv7f' 'conv7b'});
net.addLayer('relu7', dagnn.ReLU(), {'fc7'}, {'relu7'}, {});
classLabels=max(unique(imdb_32.images.labels));
net.addLayer('classifier', dagnn.Conv('size', \[1 1 4096 1\], 'hasBias', true, 'stride', \[1, 1\], 'pad', \[0 0 0 0\]), {'relu7'}, {'prediction'}, {'conv8f' 'conv8b'});
net.addLayer('prob', dagnn.SoftMax(), {'prediction'}, {'prob'}, {});
net.addLayer('l2_loss', dagnn.L2Loss(), {'prob', 'label'}, {'objective'});
net.addLayer('error', dagnn.Loss('loss', 'classerror'), {'prob','label'}, 'error') ;
opts.colorDeviation = zeros(3) ;
net.meta.augmentation.jitterFlip = true ;
net.meta.augmentation.jitterLocation = true ;
net.meta.augmentation.jitterFlip = true ;
net.meta.augmentation.jitterBrightness = double(0.1 * opts.colorDeviation) ;
net.meta.augmentation.jitterAspect = \[3/4, 4/3\] ;
net.meta.augmentation.jitterScale = \[0.4, 1.1\] ;
net.meta.augmentation.jitterSaturation = 0.4 ;
net.meta.augmentation.jitterContrast = 0.4 ;
% net.meta.augmentation.jitterAspect = \[2/3, 3/2\] ;
net.meta.normalization.averageImage=imdb_32.images.data_mean;
initNet_He(net);
info = cnn_train_dag(net, imdb_32, @(i,b) getBatch(bopts,i,b), opts.train, 'val', find(imdb_32.images.set == 2)) ;][1]][1]
每个 epoch 的结果如附件所示。为什么错误和目标不收敛？回归损失是 MSE 损失。
