麻雀算法优化lstmpython代码

下面是一个使用麻雀算法优化LSTM模型的Python代码示例：

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

# 定义LSTM模型
class LSTMModel(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(LSTMModel, self).__init__()
        self.lstm = nn.LSTM(input_size, hidden_size)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        lstm_out, _ = self.lstm(x)
        output = self.fc(lstm_out[-1])
        return output

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam

# 定义麻雀算法
def mahjong_algorithm(params, num_games, num_simulations):
    best_params = params
    best_score = -1
    for i in range(num_games):
        new_params = [random.uniform(0, 1) * param for param in params] # 对参数进行随机扰动
        score = 0
        for j in range(num_simulations):
            # 构造LSTM模型
            input_size = int(new_params[0] * 100) + 1
            hidden_size = int(new_params[1] * 100) + 1
            output_size = 2
            model = LSTMModel(input_size, hidden_size, output_size)

            # 训练模型
            optimizer = optimizer(model.parameters(), lr=new_params[2])
            for epoch in range(new_params[3]):
                for data, label in train_loader:
                    optimizer.zero_grad()
                    output = model(data)
                    loss = criterion(output, label)
                    loss.backward()
                    optimizer.step()

            # 在验证集上测试模型性能
            correct = 0
            total = 0
            with torch.no_grad():
                for data, label in val_loader:
                    output = model(data)
                    _, predicted = torch.max(output.data, 1)
                    total += label.size(0)
                    correct += (predicted == label).sum().item()
            accuracy = correct / total
            score += accuracy

        # 计算平均分数
        score /= num_simulations

        # 更新最佳参数和最高分数
        if score > best_score:
            best_params = new_params
            best_score = score

    return best_params

# 设置超参数的初始值
params = [0.5, 0.5, 0.001, 10]

# 使用麻雀算法优化超参数
best_params = mahjong_algorithm(params, 10, 10)

# 使用最佳参数训练LSTM模型
input_size = int(best_params[0] * 100) + 1
hidden_size = int(best_params[1] * 100) + 1
output_size = 2
model = LSTMModel(input_size, hidden_size, output_size)
optimizer = optimizer(model.parameters(), lr=best_params[2])
for epoch in range(best_params[3]):
    for data, label in train_loader:
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, label)
        loss.backward()
        optimizer.step()

# 在测试集上测试模型性能
correct = 0
total = 0
with torch.no_grad():
    for data, label in test_loader:
        output = model(data)
        _, predicted = torch.max(output.data, 1)
        total += label.size(0)
        correct += (predicted == label).sum().item()
accuracy = correct / total
print("Test accuracy: %.2f%%" % (accuracy * 100))

在上面的示例代码中，我们首先定义了一个LSTM模型，并使用麻雀算法优化其超参数。然后，使用最佳参数训练LSTM模型，并在测试集上测试其性能。通过使用麻雀算法优化LSTM模型的超参数，可以提高模型的性能，并获得更好的结果。