如何在Keras中实现L2池层？

from keras.layers import Layer
from keras import backend as K

class L2Pool(Layer):
    def __init__(self, pool_size=(2, 2), strides=None, padding='valid', **kwargs):
        super(L2Pool, self).__init__(**kwargs)
        self.pool_size = pool_size
        self.strides = strides
        self.padding = padding

    def call(self, inputs):
        output = K.pool2d(inputs, pool_size=self.pool_size, strides=self.strides, padding=self.padding, pool_mode='avg')
        output = K.sqrt(K.sum(K.square(output), axis=-1, keepdims=True))
        return output

    def get_config(self):
        config = super(L2Pool, self).get_config()
        config.update({'pool_size': self.pool_size, 'strides': self.strides, 'padding': self.padding})
        return config

from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense

model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)))
model.add(L2Pool(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

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