resnet18实现cifar10分类

实验步骤

搭建resnet18网络

数据集加载

模型训练和改进

分析评估

Kaggle提交

网络构建

实验初期拟采用torchvision中实现的resnet18作为网络结构，为了方便修改网络结构，于是重新实现了resnet18网络

resnet18由一个7x7的降采样卷积，一个max pooling层，8个basicblock，一个全局池化层，最后接一个全连接层组成，如下图

tensorboard网络结构可视化，左图为torchvision中的resnet实现，右图为自定义实现

代码如下

定义残差块

class IdentityBlock(nn.Module):
    def __init__(self, in_channels, out_channels, down_sampling=False):
        super().__init__()
        self.down_sampling = down_sampling
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.conv1 = nn.Sequential(OrderedDict([
            ('conv1', nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=3,
                                stride=(1 if in_channels == out_channels else 2), padding=1,
                                bias=False)),
            ('bn1', nn.BatchNorm2d(out_channels)),
            ('relu1', nn.ReLU())
        ]))
        self.shortcut = nn.Sequential(OrderedDict([
            (
                'conv',
                nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=2, bias=False)),
            ('bn', nn.BatchNorm2d(out_channels))
        ])) if in_channels != out_channels else nn.Sequential()
        self.conv2 = nn.Sequential(OrderedDict([
            ('conv2', nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1,
                                bias=False)),
            ('bn2', nn.BatchNorm2d(out_channels))
        ]))
        self.relu2 = nn.ReLU()

    def forward(self, x):
        fx = self.conv1(x)
        fx = self.conv2(fx)
        x = self.shortcut(x)
        hx = fx + x
        hx = self.relu2(hx)
        return hx

定义模型网络

class Resnet18(nn.Module):
    def __init__(self, num_classes):
        super(Resnet18, self).__init__()
        self.conv1 = nn.Sequential(OrderedDict([
            ('conv', nn.Conv2d(in_channels=3, out_channels=64, kernel_size=7, stride=2, padding=3, bias=False)),
            ('bn', nn.BatchNorm2d(64)),
            ('relu', nn.ReLU()),
        ]))
        self.max_pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self.make_layer(64