如何在Keras中显示多类图像分类任务的验证精度？

import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import classification_report

model = ...  # 加载或定义你的模型
x_val = ...  # 加载验证数据
y_val = ...  # 加载验证标签

y_pred = model.predict(x_val)
y_pred_classes = np.argmax(y_pred, axis=1)
y_true_classes = np.argmax(y_val, axis=1)
accuracy = np.mean(y_pred_classes == y_true_classes)
print("验证精度：", accuracy)

class_names = [...]  # 类别名称列表
report = classification_report(y_true_classes, y_pred_classes, target_names=class_names)
print(report)

plt.figure(figsize=(8, 8))
plt.imshow(confusion_matrix(y_true_classes, y_pred_classes), cmap='Blues')
plt.colorbar()
tick_marks = np.arange(len(class_names))
plt.xticks(tick_marks, class_names, rotation=45)
plt.yticks(tick_marks, class_names)
plt.xlabel('预测标签')
plt.ylabel('真实标签')
plt.show()