溫馨提示×

pytorch卷積神經網絡怎樣訓練

小樊
122
2024-12-26 03:06:39
欄目: 深度學習

PyTorch是一種基于Python的科學計算庫,用于深度學習研究。以下是使用PyTorch訓練卷積神經網絡的基本步驟:

  1. 導入所需庫:
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
  1. 定義卷積神經網絡模型:
class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(16),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.layer2 = nn.Sequential(
            nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.fc = nn.Linear(32 * 25 * 25, 10)

    def forward(self, x):
        x = self.layer1(x)
        x = self.layer2(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x
  1. 準備數據集:
transform = transforms.Compose([transforms.Resize((32, 32)),
                                transforms.ToTensor(),
                                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=100, shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=2)
  1. 初始化模型、損失函數和優化器:
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = ConvNet().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
  1. 訓練模型:
num_epochs = 10
for epoch in range(num_epochs):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)

        optimizer.zero_grad()

        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()

    print(f"Epoch {epoch + 1}, Loss: {running_loss / (i + 1)}")

print("Training finished.")
  1. 測試模型:
correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        images, labels = images.to(device), labels.to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print(f"Accuracy of the network on the test images: {100 * correct / total}%")

以上就是使用PyTorch訓練卷積神經網絡的基本步驟。你可以根據自己的需求對網絡結構、數據集和訓練參數進行調整。

0
亚洲午夜精品一区二区_中文无码日韩欧免_久久香蕉精品视频_欧美主播一区二区三区美女