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私は、43000種類の道路交通標識に分類された51000枚の画像を使用して自分のIMDBを作成しました。しかし、私自身のIMDBを使ってアレックスネットネットワークを訓練したいとき、インデックスが行列の次元を超えているというエラーが出ます。MATCONVVNET nnlossエラー 'インデックスエクサイズマトリックスディメンション'
Error in vl_nnloss (line 230)
t = - log(x(ci)) ;
あなたは私が間違っていることを知っていますか？私は自分のIMDBをチェックして、画像、ラベル、セットが自分のコードで指定されているように適切に作成されている。また、イメージ配列はuint8ではなくsingle型として宣言されます。
はここであなたのネットワークが真実ではありません
function [net, info] = alexnet_train(imdb, expDir)
run(fullfile(fileparts(mfilename('fullpath')), '../../', 'matlab', 'vl_setupnn.m')) ;
% some common options
opts.train.batchSize = 100;
opts.train.numEpochs = 20 ;
opts.train.continue = true ;
opts.train.gpus = [1] ;
opts.train.learningRate = [1e-1*ones(1, 10), 1e-2*ones(1, 5)];
opts.train.weightDecay = 3e-4;
opts.train.momentum = 0.;
opts.train.expDir = expDir;
opts.train.numSubBatches = 1;
% getBatch options
bopts.useGpu = numel(opts.train.gpus) > 0 ;
% network definition!
% MATLAB handle, passed by reference
net = dagnn.DagNN() ;
net.addLayer('conv1', dagnn.Conv('size', [11 11 3 96], 'hasBias', true, 'stride', [4, 4], 'pad', [0 0 0 0]), {'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('conv3', dagnn.Conv('size', [3 3 256 384], 'hasBias', true, 'stride', [1, 1], 'pad', [1 1 1 1]), {'pool2'}, {'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('fc6', dagnn.Conv('size', [6 6 256 4096], 'hasBias', true, 'stride', [1, 1], 'pad', [0 0 0 0]), {'pool5'}, {'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'}, {});
net.addLayer('classifier', dagnn.Conv('size', [1 1 4096 10], 'hasBias', true, 'stride', [1, 1], 'pad', [0 0 0 0]), {'relu7'}, {'classifier'}, {'conv8f' 'conv8b'});
net.addLayer('prob', dagnn.SoftMax(), {'classifier'}, {'prob'}, {});
net.addLayer('objective', dagnn.Loss('loss', 'log'), {'prob', 'label'}, {'objective'}, {});
net.addLayer('error', dagnn.Loss('loss', 'classerror'), {'prob','label'}, 'error') ;
% -- end of the network
% initialization of the weights (CRITICAL!!!!)
initNet(net, 1/100);
% do the training!
info = cnn_train_dag(net, imdb, @(i,b) getBatch(bopts,i,b), opts.train, 'val', find(imdb.images.set == 3)) ;
end
function initNet(net, f)
net.initParams();
f_ind = net.layers(1).paramIndexes(1);
b_ind = net.layers(1).paramIndexes(2);
net.params(f_ind).value = 10*f*randn(size(net.params(f_ind).value), 'single');
net.params(f_ind).learningRate = 1;
net.params(f_ind).weightDecay = 1;
for l=2:length(net.layers)
% is a conenter code herevolution layer?
if(strcmp(class(net.layers(l).block), 'dagnn.Conv'))
f_ind = net.layers(l).paramIndexes(1);
b_ind = net.layers(l).paramIndexes(2);
[h,w,in,out] = size(net.params(f_ind).value);
net.params(f_ind).value = f*randn(size(net.params(f_ind).value), 'single');
net.params(f_ind).learningRate = 1;
net.params(f_ind).weightDecay = 1;
net.params(b_ind).value = f*randn(size(net.params(b_ind).value), 'single');
net.params(b_ind).learningRate = 0.5;
net.params(b_ind).weightDecay = 1;
end
end
end
% function on charge of creating a batch of images + labels
function inputs = getBatch(opts, imdb, batch)
%[227 by 227 by 3] image
images = imdb.images.data(:,:,:,batch) ;
labels = imdb.images.labels(1,batch) ;
if opts.useGpu > 0
images = gpuArray(images) ;
end
inputs = {'input', images, 'label', labels} ;
end