使用二层神经网络超参数调优pytorch代码

对于超参数调优的 pytorch 代码，我可以给您一些参考资料和建议，例如使用 Grid Search 或 Random Search 等方法来搜索超参数空间，以及使用交叉验证等技巧来评估模型性能等。具体实现细节还需要根据您的具体需求和数据集情况进行调整。 我们也可以继续探讨其他相关问题，比如哪些超参数需要优化、如何选择合适的损失函数和优化器等。请问您还有什么其他问题吗？

相关问题

生成二层神经网络超参数调优pytorch代码

可以直接回答，以下是示例代码：

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


# Define neural network architecture
class Net(nn.Module):
    def __init__(self, num_hidden=32):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(784, num_hidden)
        self.fc2 = nn.Linear(num_hidden, 10)

    def forward(self, x):
        x = torch.flatten(x, 1)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x


# Set hyperparameters
num_epochs = 20
batch_size = 64
learning_rate = 0.1
num_hidden_choices = [8, 16, 32, 64, 128]

# Load the data
train_data = MNIST(root='./data', train=True, transform=ToTensor(), download=True)
test_data = MNIST(root='./data', train=False, transform=ToTensor(), download=True)
train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=False)

# Grid search for optimal hyperparameters
best_acc = 0
for num_hidden in num_hidden_choices:
    # Initialize neural network
    net = Net(num_hidden)

    # Define loss function and optimizer
    criterion = nn.CrossEntropyLoss()
    optimizer = torch.optim.SGD(net.parameters(), lr=learning_rate)

    # Train the neural network
    for epoch in range(num_epochs):
        net.train()
        for images, labels in train_loader:
            optimizer.zero_grad()
            outputs = net(images)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

        # Test the neural network
        net.eval()
        total = 0
        correct = 0
        with torch.no_grad():
            for images, labels in test_loader:
                outputs = net(images)
                _, predicted = torch.max(outputs.data, 1)
                total += labels.size(0)
                correct += (predicted == labels).sum().item()
        acc = correct / total

        # Adjust learning rate
        scheduler = StepLR(optimizer, step_size=1, gamma=0.1)
        scheduler.step()

        # Update best accuracy
        if acc > best_acc:
            best_acc = acc
            best_num_hidden = num_hidden

print("Best accuracy:", best_acc)
print("Best number of hidden neurons:", best_num_hidden)