'''
class LeNet(nn.Module):
def __init__(self):
super(LeNet, self).__init__() #调用基类的__init__()函数
torch.nn.Conv2d(in_channels, out_channels, kernel_size,
stride=1, padding=0, dilation=1, groups=1, bias=True)
torch.nn.MaxPool2d(kernel_size, stride=None,
padding=0, dilation=1, return_indices=False, ceil_mode=False)
stride – the stride of the window. Default value is kernel_size
self.conv=nn.Sequential( #顺序网络结构
nn.Conv2d(1,6,5,stride=1,padding=2), #卷积层 输入1通道，输出6通道，kernel_size=5*5
nn.ReLU(), #激活函数
nn.MaxPool2d(2,2), #最大池化，kernel_size=2*2，stride=2*2
#输出大小为14*14
nn.Conv2d(6,16,5,stride=1,padding=2), #卷积层 输入6通道，输出16通道，kernel_size=5*5
nn.ReLU(),
nn.MaxPool2d(2,2),
# 输出大小为7*7
nn.Conv2d(16,120,5,stride=1,padding=2), #卷积层 输入16通道，输出120通道，kernel_size=5*5
nn.ReLU(),
)
self.fc=nn.Sequential( #全连接层
nn.Linear(7*7*120,120),
nn.ReLU(),
nn.Linear(120, 84),
nn.ReLU(),
nn.Linear(84,10),
nn.Sigmoid(),
)
'''
import torch
import torch.nn as nn
import pdb
import torch.nn.functional as F
inputs=torch.randn(1,3,28,28)
class LeNet(nn.Module):
def __init__(self):#创建构造函数
super(LeNet,self).__init__()#调用基类构造函数
self.conv=nn.Sequential(
nn.Conv2d(3,6,5,stride=1,padding=2),
nn.MaxPool2d(2,2),
nn.Conv2d(6,16,5,stride=1,padding=2),
nn.MaxPool2d(2,2),
nn.Conv2d(16,120,5,stride=1,padding=2)
)
self.fc=nn.Sequential(
nn.Linear(120*7*7,120),
nn.Linear(120,84),
nn.Linear(84,10),
#nn.ReLU()
)
def forward(self,x):
x=self.conv(x)
print(x.shape)
x=x.view(x.size(0),-1)
out=self.fc(x)
out=F.relu(out)
#out=F.softmax(out,1)
#print(out.shape)
#pdb.set_trace()
return out
leNet=LeNet()
oupt=leNet(inputs)
print(oupt)
