自己搭建的回归神经网络收敛时损失很大怎么办，需要进行超参数调优吗，如何用pytorch做超参数调优，请写出代码

当神经网络的损失很大时，可能是因为模型过于复杂或者数据集不够充分，此时需要进行超参数调优以获得更好的性能。以下是使用PyTorch进行超参数调优的示例代码：

import torch
from torch.utils.data import DataLoader
from torchvision.datasets import MNIST
from torchvision.transforms import ToTensor
import torch.nn as nn
import torch.optim as optim
from torch.optim.lr_scheduler import StepLR
from sklearn.model_selection import ParameterGrid

# 加载MNIST数据集
train_dataset = MNIST(root='./data', train=True, transform=ToTensor(), download=True)
test_dataset = MNIST(root='./data', train=False, transform=ToTensor(), download=True)

# 定义超参数
params = {'lr': [0.1, 0.01, 0.001], 'batch_size': [32, 64, 128], 'num_epochs': [10, 20, 30]}

# 定义网格搜索器
param_grid = ParameterGrid(params)

# 定义模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.dropout1 = nn.Dropout2d(0.25)
        self.dropout2 = nn.Dropout2d(0.5)
        self.fc1 = nn.Linear(9216, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = nn.functional.relu(x)
        x = self.conv2(x)
        x = nn.functional.relu(x)
        x = nn.functional.max_pool2d(x, 2)
        x = self.dropout1(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = nn.functional.relu(x)
        x = self.dropout2(x)
        x = self.fc2(x)
        output = nn.functional.log_softmax(x, dim=1)
        return output

# 定义训练函数
def train(model, train_loader, optimizer, criterion, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % 100 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))

# 定义测试函数
def test(model, test_loader, criterion):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            output = model(data)
            test_loss += criterion(output, target).item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)
    print('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))

# 进行超参数搜索
for params in param_grid:
    print(params)
    # 定义超参数
    lr = params['lr']
    batch_size = params['batch_size']
    num_epochs = params['num_epochs']

    # 定义数据加载器
    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
    test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True)

    # 定义模型、损失函数和优化器
    model = Net()
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9)
    scheduler = StepLR(optimizer, step_size=1, gamma=0.7)

    # 训练模型
    for epoch in range(num_epochs):
        train(model, train_loader, optimizer, criterion, epoch)
        scheduler.step()

    # 测试模型
    test(model, test_loader, criterion)

上述代码中，我们首先定义了一个超参数字典 `params`，其中包含学习率 `lr`、批次大小 `batch_size` 和训练轮数 `num_epochs`。然后，使用 `ParameterGrid` 对象生成所有可能的超参数组合，并依次使用不同的超参数组合训练和测试模型。在训练过程中，我们使用 `StepLR` 调度器来动态调整学习率。在每个超参数组合的训练结束后，我们输出测试集的准确度。通过比较不同超参数组合的测试准确度，我们可以选择最优的超参数组合。