使用pytorch框架编写 一个寻优算法优化分类bp神经网络的代码

当然可以，以下是使用 PyTorch 框架编写的一个寻优算法优化分类 BP 神经网络的代码：

import torch
import torch.nn as nn
import torch.optim as optim

# 定义 BP 神经网络
class BPNet(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(BPNet, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, output_size)
        self.relu = nn.ReLU()

    def forward(self, x):
        out = self.fc1(x)
        out = self.relu(out)
        out = self.fc2(out)
        return out

# 定义数据集
x_train = torch.randn(100, 10)
y_train = torch.randint(0, 2, (100,))

# 定义 BP 神经网络的参数
input_size = 10
hidden_size = 20
output_size = 2
lr = 0.01
epochs = 100

# 初始化 BP 神经网络
net = BPNet(input_size, hidden_size, output_size)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=lr)

# 训练 BP 神经网络
for epoch in range(epochs):
    optimizer.zero_grad()
    outputs = net(x_train)
    loss = criterion(outputs, y_train)
    loss.backward()
    optimizer.step()

# 测试 BP 神经网络
x_test = torch.randn(10, 10)
y_test = torch.randint(0, 2, (10,))
outputs = net(x_test)
_, predicted = torch.max(outputs.data, 1)
accuracy = (predicted == y_test).sum().item() / y_test.size(0)
print('Accuracy: {:.2f}%'.format(accuracy * 100))

希望这个代码能够帮到你！