如何在训练阶段获得PyCaffe上的Top-k准确率？

import caffe
import numpy as np

caffe.set_mode_gpu()  # 如果有GPU，使用GPU模式
net = caffe.Net('path_to_deploy.prototxt', 'path_to_model.caffemodel', caffe.TEST)
labels = np.loadtxt('path_to_labels.txt', str, delimiter='\n')

# 假设你已经准备好了测试数据
test_data = ... 

net.blobs['data'].data[...] = test_data
output = net.forward()

# 提取预测结果
predictions = output['prob']

# 计算Top-k准确率
top_k = 5  # 你可以根据需求自定义Top-k值
top_predictions = predictions.argsort()[:, -top_k:][:, ::-1]

accuracies = []
for i in range(len(top_predictions)):
    true_label = test_labels[i]
    top_k_labels = [labels[prediction] for prediction in top_predictions[i]]
    
    if true_label in top_k_labels:
        accuracies.append(1)
    else:
        accuracies.append(0)

top_k_accuracy = np.mean(accuracies)

print('Top-{} Accuracy: {:.2f}%'.format(top_k, top_k_accuracy * 100))