import MLBase

train("K1-9_original_set", 20, glob_pattern, true, 0.90, 64)

train("K1-9_original_set", 2, glob_pattern, true, 0.90, 64)

    images = glob(glob_pattern) #|> shuffle! |> x -> x[1:1000]

    images = glob(glob_pattern) |> shuffle! |> x -> x[1:10000]

        label_to_index,

        label_to_index

function evaluate(m, d, c=classes)

function evaluate(m, d, c)

    pred = []
    actual = []

    pred = Int64[]
    actual = Int64[]

    r=roc(actual, pred)

    r=MLBase.roc(actual, pred)

    @time eval, vcm, vroc = evaluate(model, test)

    @time eval, vcm, vroc = evaluate(model, test, classes)

        @time metric_train, train_confusion_matrix, train_roc = evaluate(model, train_sample)

        @time metric_train, train_confusion_matrix, train_roc = evaluate(model, train_sample, classes)

        @time metric_test, test_confusion_matrix, test_roc = evaluate(model, test)

        @time metric_test, test_confusion_matrix, test_roc = evaluate(model, test, classes)