PSO优化LSTM的python，pytorch代码

以下是使用PyTorch实现PSO优化LSTM的Python代码示例：

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

# 定义LSTM模型
class LSTM(nn.Module):
    def __init__(self, input_dim, hidden_dim, layer_dim, output_dim):
        super(LSTM, self).__init__()
        self.hidden_dim = hidden_dim
        self.layer_dim = layer_dim
        self.lstm = nn.LSTM(input_dim, hidden_dim, layer_dim, batch_first=True)
        self.fc = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        h0 = torch.zeros(self.layer_dim, x.size(0), self.hidden_dim).requires_grad_()
        c0 = torch.zeros(self.layer_dim, x.size(0), self.hidden_dim).requires_grad_()
        out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))
        out = self.fc(out[:, -1, :])
        return out

# 定义PSO算法
class PSO():
    def __init__(self, pop_size, dim, max_iter, lr, c1, c2, w):
        self.pop_size = pop_size
        self.dim = dim
        self.max_iter = max_iter
        self.lr = lr
        self.c1 = c1
        self.c2 = c2
        self.w = w
        self.v = torch.zeros((pop_size, dim))
        self.pos = torch.rand((pop_size, dim))
        self.fitness = torch.zeros(pop_size)
        self.pbest = self.pos.clone()
        self.gbest = self.pos[0].clone()

    def update(self, fitness):
        self.fitness = fitness
        self.pbest = torch.where(fitness > self.fitness, self.pos, self.pbest)
        self.gbest = self.pos[self.fitness.argmax()]
        r1 = torch.rand((self.pop_size, self.dim))
        r2 = torch.rand((self.pop_size, self.dim))
        self.v = self.w * self.v + self.c1 * r1 * (self.pbest - self.pos) + self.c2 * r2 * (self.gbest - self.pos)
        self.pos = self.pos + self.lr * self.v

# 定义损失函数
def loss_fn(y_pred, y_true):
    criterion = nn.MSELoss()
    loss = criterion(y_pred, y_true)
    return loss

# 定义训练函数
def train(model, pso, x_train, y_train):
    for i in range(pso.max_iter):
        fitness = torch.zeros(pso.pop_size)
        for j in range(pso.pop_size):
            # 使用pso算法更新模型参数
            model.load_state_dict(dict(zip(model.state_dict().keys(), pso.pos[j])))
            # 计算模型损失
            y_pred = model(x_train)
            loss = loss_fn(y_pred, y_train)
            fitness[j] = 1 / (1 + loss.item())
        # 更新pso算法参数
        pso.update(fitness)
    # 使用最优参数重新训练模型
    model.load_state_dict(dict(zip(model.state_dict().keys(), pso.gbest)))
    y_pred = model(x_train)
    loss = loss_fn(y_pred, y_train)
    return model, loss.item()

# 模型参数
input_dim = 1
hidden_dim = 32
layer_dim = 2
output_dim = 1

# 训练数据
x_train = np.array([[i] for i in range(100)])
y_train = np.sin(x_train)

# 定义模型和PSO算法
model = LSTM(input_dim, hidden_dim, layer_dim, output_dim)
pso = PSO(pop_size=10, dim=sum(p.numel() for p in model.parameters()), max_iter=100, lr=0.01, c1=2, c2=2, w=0.7)

# 训练模型
model, loss = train(model, pso, torch.tensor(x_train).float().unsqueeze(dim=0), torch.tensor(y_train).float().unsqueeze(dim=0))

# 打印损失值和预测结果
print('loss:', loss)
print('prediction:', model(torch.tensor(x_train).float().unsqueeze(dim=0)).detach().numpy())