PyTorch 是一个流行的开源机器学习库，用于深度学习研究和开发。它由 Facebook 的 AI 研究团队开发，旨在提供灵活和高效的深度学习工具。

快速开始

  1. 安装 PyTorch

  2. 创建一个简单的神经网络

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

# 定义一个简单的神经网络
class SimpleNet(nn.Module):
    def __init__(self):
        super(SimpleNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(20, 50, 5)
        self.fc1 = nn.Linear(4*4*50, 500)
        self.fc2 = nn.Linear(500, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 4*4*50)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 实例化网络
net = SimpleNet()

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

# 训练网络
for epoch in range(2):  # loop over the dataset multiple times
    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.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 2000 == 1999:    # print every 2000 mini-batches
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print('Finished Training')

  3. 使用 PyTorch 进行图像识别

    PyTorch 提供了强大的图像处理工具，例如 torchvision 库。

    import torchvision.transforms as transforms
import torchvision.datasets as datasets
import matplotlib.pyplot as plt

# 加载数据集
transform = transforms.Compose([
    transforms.Resize((32, 32)),
    transforms.ToTensor(),
])

trainset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2)

# 显示图像
dataiter = iter(trainloader)
images, labels = dataiter.next()

fig = plt.figure()
for idx in range(4):
    ax = fig.add_subplot(2, 2, idx+1)
    plt.imshow(images[idx])
    ax.set_title('label: %d' % labels[idx])
plt.show()

扩展阅读

希望这些信息能帮助您开始使用 PyTorch！🎉