resnet16训练mnist

ResNet是一个深度神经网络结构，通常用于图像分类任务。而MNIST是一个手写数字图像数据集，通常也用于图像分类任务。因此，我们可以使用ResNet模型对MNIST数据集进行训练。

首先，我们需要对ResNet进行一些修改，以适应MNIST数据集。具体来说，我们需要修改网络的输入通道数和输出类别数。对于ResNet-16，我们可以将输入通道数设置为1（因为MNIST图像是灰度图像），输出类别数设置为10。

接下来，我们可以使用PyTorch框架来实现ResNet-16模型，并对MNIST数据集进行训练。具体的代码如下：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms

# 定义ResNet-16模型
class BasicBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super().__init__()

        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_channels != out_channels:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_channels)
            )

    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out += self.shortcut(x)
        out = self.relu(out)
        return out

class ResNet16(nn.Module):
    def __init__(self, num_classes=10):
        super().__init__()

        self.in_channels = 16

        self.conv1 = nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(16)
        self.relu = nn.ReLU(inplace=True)
        self.layer1 = self.make_layer(16, 2, stride=1)
        self.layer2 = self.make_layer(32, 2, stride=2)
        self.layer3 = self.make_layer(64, 2, stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(64, num_classes)

    def make_layer(self, out_channels, num_blocks, stride):
        layers = []
        layers.append(BasicBlock(self.in_channels, out_channels, stride))
        self.in_channels = out_channels
        for _ in range(1, num_blocks):
            layers.append(BasicBlock(self.in_channels, out_channels))
            self.in_channels = out_channels
        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.avgpool(out)
        out = out.view(out.size(0), -1)
        out = self.fc(out)
        return out

# 定义数据预处理流程
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307,), (0.3081,))
])

# 加载MNIST数据集
trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)

# 定义优化器和损失函数
net = ResNet16()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.1, momentum=0.9, weight_decay=1e-4)

# 训练网络
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net.to(device)
for epoch in range(10):
    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 = 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

print('Finished Training')

在上述代码中，我们首先定义了一个ResNet-16模型，然后使用PyTorch框架加载MNIST数据集，并定义了优化器和损失函数。接着，我们将网络模型移动到GPU上，并进行训练。在训练过程中，我们使用了SGD优化器，并设置学习率为0.1，动量为0.9，权重衰减为1e-4，训练10个epoch。每个epoch中，我们使用一个批次大小为128的批次进行训练。运行完毕后，我们可以得到一个在MNIST数据集上训练的ResNet-16模型。