如何将粒子群优化算法应用于keras中的神经网络模型

import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from pyswarm import pso

# 定义神经网络模型
def create_model(input_dim, output_dim):
    model = Sequential()
    model.add(Dense(10, input_dim=input_dim, activation='relu'))
    model.add(Dense(output_dim, activation='linear'))
    return model

# 定义适应度函数
def fitness_function(weights):
    model.set_weights(weights)
    loss = model.evaluate(X_train, y_train, verbose=0)
    return loss

# 初始化模型和数据
input_dim = X_train.shape[1]
output_dim = y_train.shape[1]
model = create_model(input_dim, output_dim)

# 获取初始权重
initial_weights = model.get_weights()

# 使用PSO优化权重
optimized_weights, _ = pso(fitness_function, lb=-1, ub=1, args=(initial_weights,), swarmsize=10, maxiter=50)

# 设置优化后的权重
model.set_weights(optimized_weights)