终于把卷积神经网络算法搞懂了！！！

import tensorflow as tf
from tensorflow.keras import layers, models
from tensorflow.keras.datasets import mnist
import matplotlib.pyplot as plt

# 加载MNIST数据集
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
train_images = train_images.reshape((60000, 28, 28, 1)).astype('float32') / 255
test_images = test_images.reshape((10000, 28, 28, 1)).astype('float32') / 255
train_labels = tf.keras.utils.to_categorical(train_labels)
test_labels = tf.keras.utils.to_categorical(test_labels)

# 构建CNN模型
model = models.Sequential()
# 第一个卷积层，32 个滤波器，3x3 滤波器尺寸，ReLU激活函数
model.add(layers.Conv2D(32, (3, 3), activatinotallow='relu', input_shape=(28, 28, 1)))
# 第二个卷积层，64 个滤波器
model.add(layers.Conv2D(64, (3, 3), activatinotallow='relu'))
# 池化层，2x2 池化
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Dropout(0.25))
# 将多维特征图展平
model.add(layers.Flatten())

# 全连接层，128个神经元
model.add(layers.Dense(128, activatinotallow='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(10, activatinotallow='softmax'))


model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

history = model.fit(train_images, train_labels, epochs=5, batch_size=64, validation_split=0.1)
test_loss, test_acc = model.evaluate(test_images, test_labels)
print(f"Test accuracy: {test_acc}")

# 绘制训练过程中的准确率和损失
plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.plot(history.history['accuracy'], label='Training Accuracy')
plt.plot(history.history['val_accuracy'], label='Validation Accuracy')
plt.legend()
plt.title('Training and Validation Accuracy')

plt.subplot(1, 2, 2)
plt.plot(history.history['loss'], label='Training Loss')
plt.plot(history.history['val_loss'], label='Validation Loss')
plt.legend()
plt.title('Training and Validation Loss')
plt.show()
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