一、Sequential 的使用方法
在手撕代码中进一步体现
torch.nn.Sequential
二、手撕 CIFAR 10 model structure
手撕代码：
python
import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear
from torch.utils.tensorboard import SummaryWriter
class Mary(nn.Module):
def __init__(self):
super(Mary,self).__init__()
self.conv1 = Conv2d(3,32,5,padding=2)
self.maxpool1 = MaxPool2d(2)
self.conv2 = Conv2d(32,32,5,padding=2)
self.maxpool2 = MaxPool2d(2)
self.conv3 = Conv2d(32,64,5,padding=2)
self.maxpool3 = MaxPool2d(2)
self.flatten = Flatten()
self.linear1 = Linear(1024,64)
self.linear2 = 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
Yorelee = Mary()
print(Yorelee)
# 检测
input = torch.ones((64,3,32,32))
output = Yorelee(input)
print(output.shape) #如果是[64,10]即为正确
#用Tensorboard去检测
writer = SummaryWriter("logs")
writer.add_graph(Yorelee,input)
writer.close()Tensorboard 输出：
使用nn.Sequential的代码：
python
import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear
from torch.utils.tensorboard import SummaryWriter
class Mary(nn.Module):
def __init__(self):
super(Mary,self).__init__()
# self.conv1 = Conv2d(3,32,5,padding=2)
# self.maxpool1 = MaxPool2d(2)
# self.conv2 = Conv2d(32,32,5,padding=2)
# self.maxpool2 = MaxPool2d(2)
# self.conv3 = Conv2d(32,64,5,padding=2)
# self.maxpool3 = MaxPool2d(2)
# self.flatten = Flatten()
# self.linear1 = Linear(1024,64)
# self.linear2 = Linear(64,10)
self.model1 = nn.Sequential(
Conv2d(3, 32, 5, padding=2),
MaxPool2d(2),
Conv2d(32, 32, 5, padding=2),
MaxPool2d(2),
Conv2d(32, 64, 5, padding=2),
MaxPool2d(2),
Flatten(),
Linear(1024, 64),
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)
x = self.model1(x)
return x
Yorelee = Mary()
print(Yorelee)
# 检测
input = torch.ones((64,3,32,32))
output = Yorelee(input)
print(output.shape) #如果是[64,10]即为正确
#用Tensorboard去检测
writer = SummaryWriter("logs")
writer.add_graph(Yorelee,input)
writer.close()