r_elias_2.py 文件源码

def build_objective(model, deterministic=False, epsilon=1e-12):
    predictions = nn.layers.get_output(model.l_out, deterministic=deterministic)
    targets = T.cast(nn.layers.get_output(model.l_target), 'int32')
    enable_targets = nn.layers.get_output(model.l_enable_target)
    predictions = T.clip(predictions, epsilon, 1.-epsilon)

    #is_nodule_ground_truth = T.cast(targets[:,0], 'float32')

    sum_of_objectives = 0
    unit_ptr = 0
    for obj_idx, obj_name in enumerate(order_objectives):
        n_classes = len(property_bin_borders[obj_name])
        v_obj = objective(obj_idx, (unit_ptr, unit_ptr+n_classes), predictions, targets)
        if deterministic:
            d_objectives_deterministic[obj_name] = T.mean(v_obj)
        else:
            d_objectives[obj_name] = T.mean(v_obj)
        #sum_of_objectives += T.mean(enable_targets[obj_idx] * v_obj)
        sum_of_objectives += T.mean(enable_targets[:,obj_idx] * v_obj)
        unit_ptr = unit_ptr+n_classes

    #print 'for debug purposes: unit_ptr', unit_ptr

    return sum_of_objectives