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J'ai créé ma propre IMDB en utilisant un ensemble de 51000 images classées dans 43 catégories différentes de panneaux de signalisation routière. Cependant, quand je veux utiliser mon propre IMDB pour former le réseau alexnet, j'obtiens une erreur qui dit: Index dépasse les dimensions de la matrice.MATCONVVNET Erreur nnloss 'Index Exceed Matrix Dimension'
Error in vl_nnloss (line 230)
t = - log(x(ci)) ;
Avez-vous une idée de ce que je fais mal? J'ai vérifié à travers mon IMDB, et les images, les étiquettes et les ensembles ont été correctement créés comme indiqué dans mon code. En outre, le tableau d'images est déclaré comme type single et non uint8.
Voici mon code de formation ci-dessous
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