def get_loss_weights(target, num_classes=2, eps=1e-08):
    """ 
    Gets loss weights by counting target classes examples

    To help with skewed datasets we need to weight the gradient.
    Each class need to have the same influence in gradient.
    So, each example will have weight equal to 1/num_classes * 1/class_count

    Usage:
    >>> tensor = Variable(torch.FloatTensor([1, 0, 0, 0]))                                                                                            
    >>> get_loss_weights(tensor)
        0.5000
        0.1667
        0.1667
        0.1667
        [torch.FloatTensor of size 4]
    """

    weights = Variable(torch.zeros(target.data.size()))

    for cls in range(num_classes):
        cls_members = (target.data == cls).float()
        weights += Variable(1 / (torch.sum(cls_members) + eps) * cls_members)

    weights /= num_classes
    return weights.data