[SOLVED] MATCONVVNET nnloss error 'Index Exceed Matrix Dimension'

I have made my own IMDB using a set of 51000 images categorized into 43 different categories of road traffic signs. However, when I want to use my own IMDB to train the alexnet network, I get an error which says: Index exceeds matrix dimensions.

      Error in vl_nnloss (line 230)
      t = - log(x(ci)) ;

Do you have an idea what I am doing wrong? I have checked through my IMDB, and the images, labels and sets have been appropriately created as specified in my code. Also, the image array is declared as type single and not uint8.

Here is my training code below

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