导入必要的库

import numpy as np import matplotlib.pyplot as plt from sklearn.metrics import roc_curve, auc

预测测试集标签

model1.eval() model2.eval() with torch.no_grad(): inputs = model1(x_train) predicted = model2(inputs) fpr, tpr, thresholds = roc_curve(y_train, torch.sigmoid(predicted).numpy())

计算AUC值

roc_auc = auc(fpr, tpr)

绘制ROC曲线

plt.figure() plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc) plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--') plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('ROC Curve') plt.legend(loc="lower right") plt.show()