编写一段pytorch程序，class一个2层的LSTM- Attention网络，在这个网络中定义一个添加了萤火虫随机扰动算法的混沌粒子群算法FA_CPSO去优化这个网络参数，定义一个checkpoint，保存权重文件

好的，下面是代码实现：

import torch
import torch.nn as nn
import numpy as np
import random

# 定义2层LSTM-Attention网络
class LSTM_Attention(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(LSTM_Attention, self).__init__()
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers=2, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)
        self.softmax = nn.Softmax(dim=1)
        
    def forward(self, x):
        output, (h_n, c_n) = self.lstm(x)
        attention_weight = self.softmax(output)
        attention_output = torch.sum(attention_weight * output, dim=1)
        output = self.fc(attention_output)
        return output

# 定义萤火虫随机扰动算法
class FA_CPSO():
    def __init__(self, swarm_size, dim, max_iter, c1, c2, w_min, w_max, alpha_min, alpha_max, beta0):
        self.swarm_size = swarm_size
        self.dim = dim
        self.max_iter = max_iter
        self.c1 = c1
        self.c2 = c2
        self.w_min = w_min
        self.w_max = w_max
        self.alpha_min = alpha_min
        self.alpha_max = alpha_max
        self.beta0 = beta0
        self.fitness_func = None
        self.pos = torch.zeros((swarm_size, dim), dtype=torch.float32)
        self.vel = torch.zeros((swarm_size, dim), dtype=torch.float32)
        self.best_pos = torch.zeros((swarm_size, dim), dtype=torch.float32)
        self.best_fitness = torch.zeros(swarm_size, dtype=torch.float32)
        self.global_best_pos = torch.zeros(dim, dtype=torch.float32)
        self.global_best_fitness = torch.inf

    def set_fitness_func(self, fitness_func):
        self.fitness_func = fitness_func

    def init_pos(self, pos_min, pos_max):
        for i in range(self.swarm_size):
            self.pos[i] = torch.FloatTensor([random.uniform(pos_min[j], pos_max[j]) for j in range(self.dim)])
            self.best_pos[i] = self.pos[i].clone()
            self.best_fitness[i] = self.fitness_func(self.pos[i])

    def update_vel_pos(self, iter, max_iter):
        w = self.w_max - (self.w_max - self.w_min) * iter / max_iter
        alpha = self.alpha_max - (self.alpha_max - self.alpha_min) * iter / max_iter
        beta = self.beta0 * np.exp(-alpha * iter)

        for i in range(self.swarm_size):
            r1 = torch.FloatTensor(self.dim).uniform_()
            r2 = torch.FloatTensor(self.dim).uniform_()
            vel_cognitive = self.c1 * r1 * (self.best_pos[i] - self.pos[i])
            vel_social = self.c2 * r2 * (self.global_best_pos - self.pos[i])
            self.vel[i] = w * self.vel[i] + vel_cognitive + vel_social
            self.pos[i] += self.vel[i] + beta * self.levy_flight()

            # 限制粒子位置在合理范围内
            for j in range(self.dim):
                if self.pos[i][j] < 0:
                    self.pos[i][j] = 0
                elif self.pos[i][j] > 1:
                    self.pos[i][j] = 1

            fitness = self.fitness_func(self.pos[i])
            if fitness < self.best_fitness[i]:
                self.best_pos[i] = self.pos[i].clone()
                self.best_fitness[i] = fitness

            if fitness < self.global_best_fitness:
                self.global_best_pos = self.pos[i].clone()
                self.global_best_fitness = fitness

    def levy_flight(self):
        beta = 1.5
        sigma = (gamma(1 + beta) * np.sin(np.pi * beta / 2) / (gamma((1 + beta) / 2) * beta * (2 ** ((beta - 1) / 2)))) ** (1 / beta)
        u = torch.FloatTensor(self.dim).normal_(0, 1)
        v = torch.FloatTensor(self.dim).normal_(0, 1)
        step = u / (torch.abs(v) ** (1 / beta))
        step_size = 0.01 * step * sigma
        return step_size

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(lstm_att.parameters(), lr=0.001)

# 初始化FA-CPSO
swarm_size = 20
dim = 100
max_iter = 100
c1 = 1.0
c2 = 1.0
w_min = 0.1
w_max = 0.9
alpha_min = 0.1
alpha_max = 0.9
beta0 = 1.5
fa_cpso = FA_CPSO(swarm_size, dim, max_iter, c1, c2, w_min, w_max, alpha_min, alpha_max, beta0)

# 设置优化目标函数
def fitness_func(params):
    lstm_att.load_state_dict(params)
    lstm_att.eval()
    outputs = lstm_att(inputs)
    loss = criterion(outputs, targets)
    return loss.item()

fa_cpso.set_fitness_func(fitness_func)

# 初始化粒子群位置
fa_cpso.init_pos(np.zeros(dim), np.ones(dim))

# 训练模型
for epoch in range(num_epochs):
    fa_cpso.update_vel_pos(epoch, num_epochs)
    best_params = fa_cpso.global_best_pos
    lstm_att.load_state_dict(best_params)
    lstm_att.train()
    outputs = lstm_att(inputs)
    loss = criterion(outputs, targets)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    # 保存checkpoint
    if (epoch+1) % 10 == 0:
        checkpoint = {
            'epoch': epoch+1,
            'state_dict': lstm_att.state_dict(),
            'optimizer': optimizer.state_dict()
        }
        torch.save(checkpoint, 'lstm_att_checkpoint.pth')
        print('Checkpoint saved at epoch', epoch+1)

上面的代码实现了一个2层LSTM-Attention网络，并使用萤火虫随机扰动算法FA_CPSO去优化网络的参数。在训练过程中，我们使用PyTorch内置的交叉熵损失函数和Adam优化器来更新网络参数。在每个epoch的结束时，我们保存一个checkpoint，以便后续可以从这个checkpoint继续训练模型。