使用PSO优化卷积神经网络的学习率和损失函数系数的例子，PSO使用python自带库pyswarm中的模型，使用pytorch实现

首先，需要安装pyswarm库和pytorch库：

pip install pyswarm
pip install torch

接下来，我们定义一个卷积神经网络模型：

import torch.nn as nn

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
        self.bn1 = nn.BatchNorm2d(32)
        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.bn2 = nn.BatchNorm2d(64)
        self.relu2 = nn.ReLU()
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(64*8*8, 512)
        self.relu3 = nn.ReLU()
        self.dropout = nn.Dropout(p=0.5)
        self.fc2 = nn.Linear(512, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu1(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu2(x)
        x = self.pool2(x)
        x = x.view(-1, 64*8*8)
        x = self.fc1(x)
        x = self.relu3(x)
        x = self.dropout(x)
        x = self.fc2(x)
        return x

然后，我们定义一个计算损失函数的函数：

import torch.optim as optim

def train(net, lr, loss_coef, epochs):
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(net.parameters(), lr=lr, momentum=0.9)
    for epoch in range(epochs):
        running_loss = 0.0
        for i, data in enumerate(trainloader, 0):
            inputs, labels = data
            optimizer.zero_grad()
            outputs = net(inputs)
            loss = criterion(outputs, labels) + loss_coef * torch.norm(net.fc1.weight)
            loss.backward()
            optimizer.step()
            running_loss += loss.item()
        print('[%d] loss: %.3f' % (epoch + 1, running_loss / len(trainloader)))

在函数中，我们使用交叉熵损失函数，并且加上一个正则化项，该项是网络第一个全连接层权重的L2范数，系数为loss_coef。

最后，我们使用PSO优化学习率和损失函数系数：

import pyswarm

def optimize(params):
    lr, loss_coef = params
    net = Net()
    train(net, lr, loss_coef, 10)
    test_acc = test(net)
    return 1.0 - test_acc

lb = [0.001, 0.00001]
ub = [0.1, 0.001]
xopt, fopt = pyswarm.pso(optimize, lb, ub)
print('Optimal parameters: lr=%.5f, loss_coef=%.5f, test acc=%.3f' % (xopt[0], xopt[1], 1.0-fopt))

在PSO中，我们定义一个优化函数optimize，该函数接收学习率和损失函数系数作为参数，并返回测试集准确率的负数，因为PSO最小化目标函数。然后，我们定义学习率和损失函数系数的上下界，并使用pyswarm.pso函数进行优化。最后，输出最优参数和测试集准确率。

完整代码如下：