编写一个卷积神经网络识别汉服领型的代码

我可以提供一个基于卷积神经网络的示例代码来识别汉服领型：import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.preprocessing.image import ImageDataGenerator

# 加载训练数据
train_data_dir = 'data/train'
validation_data_dir = 'data/validation'
train_datagen = ImageDataGenerator(rescale=1./255)
validation_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(150, 150),
batch_size=32,
class_mode='categorical')
validation_generator = validation_datagen.flow_from_directory(
validation_data_dir,
target_size=(150, 150),
batch_size=32,
class_mode='categorical')

# 设置模型
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(150, 150, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(6))
model.add(Activation('softmax'))

# 编译模型
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])

# 训练模型
model.fit_generator(
train_generator,
steps_per_epoch=2000,
epochs=50,
validation_data=validation_generator,
validation_steps=800)

# 保存模型
model.save_weights('weights.h5')