pos粒子群优化 卷积神经网络

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from pyswarm import pso

# 定义简单的 CNN 模型
class SimpleCNN(nn.Module):
    def __init__(self, num_classes=10):
        super(SimpleCNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
        self.relu1 = nn.ReLU()
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.relu2 = nn.ReLU()
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.flatten = nn.Flatten()
        self.fc1 = nn.Linear(64 * 8 * 8, 512)
        self.relu3 = nn.ReLU()
        self.fc2 = nn.Linear(512, num_classes)

    def forward(self, x):
        x = self.conv1(x)
        x = self.relu1(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.relu2(x)
        x = self.pool2(x)
        x = self.flatten(x)
        x = self.fc1(x)
        x = self.relu3(x)
        x = self.fc2(x)
        return x

# 定义目标函数
def objective_function(params):
    # 将 PSO 的参数映射到 CNN 模型的相关参数
    learning_rate = params[0]
    weight_decay = params[1]

    # 构建并初始化 CNN 模型
    cnn_model = SimpleCNN(num_classes=10)
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(cnn_model.parameters(), lr=learning_rate, weight_decay=weight_decay)

    # 训练 CNN 模型
    num_epochs = 5
    for epoch in range(num_epochs):
        # 训练过程略（根据实际情况填写）

    # 在验证集上评估性能
    validation_accuracy = evaluate_cnn_model(cnn_model, validation_data_loader)  # 自行定义验证集评估函数

    # 返回验证准确率，PSO 将尝试最小化这个值
    return -validation_accuracy

# 定义参数的搜索范围
lb = [1e-5, 1e-6]  # 学习率和权重衰减的下限
ub = [1e-2, 1e-3]  # 学习率和权重衰减的上限

# 使用 PSO 进行参数优化
best_params, _ = pso(objective_function, lb, ub, swarmsize=10, maxiter=10)

# 输出最优参数
print("最优参数:", best_params)