CIFAR-10 model structure

通过已知参数（高、宽、dilation=1、kernel_size）推断stride和padding的大小

网络

import torch
from torch import nnclass Tudui(nn.Module):def __init__(self):super(Tudui, self).__init__()self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=5, stride=1, padding=2)self.maxpool1 = nn.MaxPool2d(kernel_size=2)self.conv2 = nn.Conv2d(in_channels=32, out_channels=32, kernel_size=5, stride=1, padding=2)self.maxpool2 = nn.MaxPool2d(2)self.conv3 = nn.Conv2d(32, 64, 5,padding=2)self.maxpool3 = nn.MaxPool2d(2)self.flatten = nn.Flatten()self.linear1 = nn.Linear(1024, 64)self.linear2 = nn.Linear(64, 10)def forward(self, x):x = self.conv1(x)x = self.maxpool1(x)x = self.conv2(x)x = self.maxpool2(x)x = self.conv3(x)x = self.maxpool3(x)x = self.flatten(x)x = self.linear1(x)x = self.linear2(x)return x
tudui = Tudui()
print(tudui)

对网络进行检验

input = torch.ones((64, 3, 32, 32))
output = tudui(input)
print(output.shape) # torch.Size([64, 10])

线性层如果不知道输入特征是多少，注释掉线性层，查看输入特征（这里是1024）

# x = self.linear1(x)
# x = self.linear2(x)

使用nn.Sequential

import torch
from torch import nnclass Tudui(nn.Module):def __init__(self):super(Tudui, self).__init__()self.model1 = nn.Sequential(nn.Conv2d(in_channels=3, out_channels=32, kernel_size=5, stride=1, padding=2),nn.MaxPool2d(kernel_size=2),nn.Conv2d(in_channels=32, out_channels=32, kernel_size=5, stride=1, padding=2),nn.MaxPool2d(2),nn.Conv2d(32, 64, 5, padding=2),nn.MaxPool2d(2),nn.Flatten(),nn.Linear(1024, 64),nn.Linear(64, 10))def forward(self, x):x = self.model1(x)return x
tudui = Tudui()
print(tudui)
input = torch.ones((64, 3, 32, 32))
output = tudui(input)
print(output.shape) # torch.Size([64, 10])

可视化模型结构

writer = SummaryWriter('logs_seq')
writer.add_graph(tudui, input)  # 将模型的计算图添加到TensorBoard中, 这可以帮助你可视化整个模型的结构, 包括各个层之间的连接关系
writer.close()