用pytorch训练一个简单的神经网络
时间: 2023-07-18 11:27:22 浏览: 52
好的,以下是一个简单的使用PyTorch训练神经网络的示例代码,该代码实现了一个简单的全连接神经网络,用于对MNIST手写数字数据集进行分类:
```
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
# 定义网络结构
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(784, 512)
self.fc2 = nn.Linear(512, 256)
self.fc3 = nn.Linear(256, 10)
def forward(self, x):
x = x.view(-1, 784)
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
x = self.fc3(x)
return x
# 加载数据集
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('../data', train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('../data', train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=64, shuffle=True)
# 定义损失函数和优化器
net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.5)
# 训练网络
epochs = 10
for epoch in range(epochs):
running_loss = 0.0
for i, data in enumerate(train_loader, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 100 == 99:
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 100))
running_loss = 0.0
# 在测试集上评估模型性能
correct = 0
total = 0
with torch.no_grad():
for data in test_loader:
images, labels = data
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: %d %%' % (
100 * correct / total))
```
在这个示例代码中,我们定义了一个三层全连接神经网络,使用交叉熵损失函数和随机梯度下降优化器进行训练。我们使用了PyTorch内置的MNIST数据集进行训练和测试。在训练过程中,我们迭代了10个epochs,并在每个epoch结束时输出了训练损失。最后,在测试集上评估了模型的性能。