matlab - ディープ ニューラル ネットワークのトレーニング、ネットワーク トレーニングが収束しないのはなぜですか?

Question

私は MATCONVNET DagNN を使用しています。AlexNet アーキテクチャの使用。私のアーキテクチャの最後の数層は

  [![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]

各エポックの結果を添付に示します。エラーと目的が収束しないのはなぜですか? 回帰損失は MSE 損失です。