书法风格识别python代码

下面是一个使用卷积神经网络（CNN）进行书法风格识别的Python代码示例：

import tensorflow as tf
from tensorflow.keras import layers, models
import numpy as np

# 准备数据集
train_data = ...
train_labels = ...
test_data = ...
test_labels = ...

# 构建模型
model = models.Sequential([
  layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 3)),
  layers.MaxPooling2D((2, 2)),
  layers.Conv2D(64, (3, 3), activation='relu'),
  layers.MaxPooling2D((2, 2)),
  layers.Conv2D(64, (3, 3), activation='relu'),
  layers.Flatten(),
  layers.Dense(64, activation='relu'),
  layers.Dense(10)
])

# 编译模型
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

# 训练模型
history = model.fit(train_data, train_labels, epochs=10, 
                    validation_data=(test_data, test_labels))

# 使用模型进行预测
predictions = model.predict(test_data)

需要注意的是，上面的代码仅是一个示例，实际应用中需要根据数据集和应用场景等因素进行相应的修改和调整。