帮我写一份书法分类的python代码

好的，以下是一份简单的书法分类 Python 代码，基于 TensorFlow 框架实现：

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# 数据集路径
dataset_path = "path/to/dataset"

# 训练集和验证集路径
train_dir = dataset_path + "/train"
val_dir = dataset_path + "/val"

# 超参数
batch_size = 32
img_height = 224
img_width = 224
num_classes = 5
epochs = 10

# 数据增强
train_datagen = ImageDataGenerator(
    rescale=1.0/255,
    rotation_range=20,
    width_shift_range=0.2,
    height_shift_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    fill_mode="nearest"
)

val_datagen = ImageDataGenerator(
    rescale=1.0/255
)

# 加载数据集
train_ds = train_datagen.flow_from_directory(
    directory=train_dir,
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode="categorical"
)

val_ds = val_datagen.flow_from_directory(
    directory=val_dir,
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode="categorical"
)

# 定义模型
model = keras.Sequential([
    layers.Conv2D(32, (3, 3), activation="relu", input_shape=(img_height, img_width, 3)),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation="relu"),
    layers.MaxPooling2D((2, 2)),
    layers.Flatten(),
    layers.Dense(128, activation="relu"),
    layers.Dense(num_classes, activation="softmax")
])

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

# 训练模型
model.fit(train_ds, epochs=epochs, validation_data=val_ds)

这份代码中，我们使用了卷积神经网络来对书法图像进行分类，训练集和验证集均使用了数据增强来增加数据集的多样性，提高模型的泛化能力。