如何在python中测试经过训练的神经网络？

import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical

# 加载预训练模型
model = load_model('path_to_your_model.h5')

# 加载MNIST数据集（假设已经分为训练集和测试集）
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# 数据预处理
x_test = x_test.reshape((x_test.shape[0], 28, 28, 1)).astype('float32') / 255
y_test = to_categorical(y_test)

# 进行预测
predictions = model.predict(x_test)

# 将预测结果转换为类别标签
predicted_labels = predictions.argmax(axis=1)
true_labels = y_test.argmax(axis=1)

# 计算准确率
accuracy = (predicted_labels == true_labels).mean()
print(f'Test Accuracy: {accuracy * 100:.2f}%')