function test(net, classes, testClassList, imagePaths, batchSize)

    net:evaluate()

    class_performance = {0, 0}

    correct = 0

    class_number = {0, 0}

    -- compute size of each batch

    batchSizes, numBatches, numSamples = getBatchSizes(classes, testClassList, batchSize)

    local sampleSum = {}

    for i=1,#classes do

        sampleSum[i] = 0

    end

    for i=1,numBatches do

        -- split testClassList into batches

        sampleList = {}

        for j=1,#classes do

            sampleList[j] = testClassList[j][{{sampleSum[j] + 1, sampleSum[j] + batchSizes[j][i]}}]

            sampleSum[j] = sampleSum[j] + batchSizes[j][i]

        end

        local testset = getSample(classes, sampleList, imagePaths)

        if cudaFlag then

            testset.data = testset.data:cuda()

            testset.label = testset.label:cuda()

        end

        for j=1,testset:size() do

            local groundtruth = testset.label[j]

            local prediction = net:forward(testset.data[j])

            local confidences, indices = torch.sort(prediction, true)  -- true means sort in descending order

            if groundtruth == indices[1] then

                class_performance[groundtruth] = class_performance[groundtruth] + 1

                correct = correct + 1

            end

            class_number[groundtruth] = class_number[groundtruth] + 1

        end

    end

    for i=1,#classes do

        print(classes[i], 100*class_performance[i]/class_number[i] .. ' %')

    end

    print(100*correct/numSamples .. ' %')

end