```python import numpy as np from tensorflow.keras.datasets import mnist from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense from tensorflow.keras.utils import to_categorical import matplotlib.pyplot as plt # 加载MNIST数据集 (x_train, y_train), (x_test, y_test) = mnist.load_data() # 数据预处理 x_train = x_train.reshape(-1, 28, 28, 1) / 255.0 x_test = x_test.reshape(-1, 28, 28, 1) / 255.0 y_train = to_categorical(y_train, num_classes=10) y_test = to_categorical(y_test, num_classes=10) # 创建卷积神经网络模型 model = Sequential([ Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)), MaxPooling2D(pool_size=(2, 2)), Conv2D(64, (3, 3), activation='relu'), MaxPooling2D(pool_size=(2, 2)), Flatten(), Dense(128, activation='relu'), Dense(10, activation='softmax') ]) # 编译模型 model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) # 训练模型 history = model.fit(x_train, y_train, epochs=10, batch_size=128, validation_split=0.1) # 绘制训练过程中的损失和准确率变化 plt.figure(figsize=(10, 4)) plt.subplot(1, 2, 1) plt.plot(history.history['loss'], label='Training Loss') plt.plot(history.history['val_loss'], label='Validation Loss') plt.title('Loss') plt.xlabel('Epochs') plt.ylabel('Loss') plt.legend() plt.subplot(1, 2, 2) plt.plot(history.history['accuracy'], label='Training Accuracy') plt.plot(history.history['val_accuracy'], label='Validation Accuracy') plt.title('Accuracy') plt.xlabel('Epochs') plt.ylabel('Accuracy') plt.legend() plt.show() # 测试模型性能 test_loss, test_acc = model.evaluate(x_test, y_test) print(f"Test accuracy: {test_acc}") ``` 这个代码首先加载MNIST数据集并预处理，然后创建一个简单的两层卷积层加池化层的卷积神经网络模型。接着，模型进行编译，并通过训练数据对其进行训练。最后，展示了训练过程中损失函数和准确率的变化情况，以及测试阶段的模型精度。