facenet怎么使用squeezenet进行轻量化改进，请写出代码

FaceNet是一个基于深度神经网络的人脸识别模型，其计算复杂度较高，因此可以通过将其与轻量级模型结合，实现模型的轻量化改进。SqueezeNet是一种轻量级卷积神经网络，可以用于减少模型的计算复杂度。

以下是使用SqueezeNet对FaceNet进行轻量化改进的示例代码：

import tensorflow as tf
import tensorflow.keras as keras
from keras.applications.imagenet_utils import preprocess_input
from keras.layers import Input, Conv2D, MaxPooling2D, Activation, concatenate, Dropout, GlobalAveragePooling2D, Dense
from keras.models import Model
from keras.optimizers import Adam

# 定义SqueezeNet模型
def SqueezeNet(input_shape=(160,160,3), n_classes=128):
    input_img = Input(shape=input_shape)
    
    x = Conv2D(64, (3,3), strides=(2,2), padding='valid', name='conv1')(input_img)
    x = Activation('relu', name='relu_conv1')(x)
    x = MaxPooling2D(pool_size=(3,3), strides=(2,2), name='pool1')(x)
    
    x = fire_module(x, squeeze=16, expand=64)
    x = fire_module(x, squeeze=16, expand=64)
    x = MaxPooling2D(pool_size=(3,3), strides=(2,2), name='pool2')(x)
    
    x = fire_module(x, squeeze=32, expand=128)
    x = fire_module(x, squeeze=32, expand=128)
    x = MaxPooling2D(pool_size=(3,3), strides=(2,2), name='pool3')(x)
    
    x = fire_module(x, squeeze=48, expand=192)
    x = fire_module(x, squeeze=48, expand=192)
    x = fire_module(x, squeeze=64, expand=256)
    x = fire_module(x, squeeze=64, expand=256)
    
    # 添加一层128维的输出层
    x = GlobalAveragePooling2D(name='avg_pool')(x)
    x = Dropout(rate=0.5, name='dropout')(x)
    x = Dense(n_classes, name='fc128')(x)
    x = Activation('softmax', name='softmax')(x)
    
    model = Model(input_img, x, name='squeezenet')
    return model

# 定义fire module
def fire_module(x, squeeze, expand):
    y = Conv2D(squeeze, (1,1), padding='same', name='squeeze')(x)
    y = Activation('relu', name='relu_squeeze')(y)
    
    left = Conv2D(expand, (1,1), padding='same', name='expand1x1')(y)
    left = Activation('relu', name='relu_expand1x1')(left)
    
    right = Conv2D(expand, (3,3), padding='same', name='expand3x3')(y)
    right = Activation('relu', name='relu_expand3x3')(right)
    
    y = concatenate([left, right], axis=3, name='concat')
    return y

# 加载FaceNet模型
facenet_model = keras.models.load_model('facenet.h5')

# 将FaceNet的权重拷贝到SqueezeNet中
for i, layer in enumerate(facenet_model.layers):
    if i > 0:
        squeezenet_model.layers[i+1].set_weights(layer.get_weights())

# 冻结SqueezeNet的前几层
for layer in squeezenet_model.layers[:5]:
    layer.trainable = False

# 编译SqueezeNet模型
optimizer = Adam(lr=0.001)
squeezenet_model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])

# 使用SqueezeNet模型进行训练
squeezenet_model.fit(X_train, Y_train, epochs=10, batch_size=32, validation_data=(X_test, Y_test))

在上面的示例代码中，我们首先定义了一个SqueezeNet模型，并在其中添加了一个128维的输出层。然后，我们将FaceNet模型的权重加载到SqueezeNet中，并冻结了SqueezeNet的前几层，以防止它们被重复训练。最后，我们编译并使用SqueezeNet模型进行训练。