关于xecption模型训练的记录——自己搭的翻车了
自己按照图纸搭建的网络，十分感人，5个epoch后正确率25%左右，每个epoch训练时长15-20分钟左右，训练目的为5种花的分类
不如之前的inceptionv3 ，10个epoch后达到50%正确率，并且一共训练10分钟左右
model.py
import torch
import torch.nn as nn
class Basic_conv(nn.Module):
def __init__(self,in_channel,out_channel):
super().__init__()
self.conv=nn.Conv2d(in_channels=in_channel,out_channels=out_channel,kernel_size=3)
self.relu=nn.ReLU(inplace=False)
def forward(self,x):
x=self.conv(x)
x=self.relu(x)
return x
class Entry_flow(nn.Module):
def __init__(self,in_channel,out_channel):
super().__init__()
self.SeparableConv=nn.Sequential(nn.Conv2d(in_channels=in_channel,out_channels=in_channel,kernel_size=3,groups=in_channel,padding=1),
nn.Conv2d(in_channels=in_channel,out_channels=out_channel,kernel_size=1))
self.SeparableConv2=nn.Sequential(nn.Conv2d(in_channels=out_channel,out_channels=out_channel,kernel_size=3,groups=out_channel,padding=1),
nn.Conv2d(in_channels=out_channel,out_channels=out_channel,kernel_size=1))
self.relu=nn.ReLU(inplace=False)
self.maxpool=nn.MaxPool2d(kernel_size=3,stride=2,padding=1)
self.res=nn.Conv2d(in_channels=in_channel,out_channels=out_channel,kernel_size=1,stride=2)
def forward(self,x):
x1=x
x=self.SeparableConv(x)
x=self.relu(x)
x=self.SeparableConv2(x)
x=self.maxpool(x)
x1=self.res(x1)
result=x1+x
return result
class middle_flow(nn.Module):
def __init__(self,channel):
super().__init__()
self.relu=nn.ReLU(inplace=False)
self.SeparableConv_middle=nn.Conv2d(in_channels=channel,out_channels=channel,kernel_size=3,padding=1)
self.SeparableConv_middle_1x1=nn.Conv2d(in_channels=channel,out_channels=channel,kernel_size=1)
def forward(self,x):
x1=x
x=self.relu(x)
x=self.SeparableConv_middle(x)
x=self.relu(x)
x=self.SeparableConv_middle(x)
x=self.relu(x)
x=self.SeparableConv_middle(x)
result=x1+x
return result
class Separableconv(nn.Module):
def __init__(self,in_channel,out_channel):
super().__init__()
self.conv1=nn.Conv2d(in_channels=in_channel,out_channels=in_channel,kernel_size=3,padding=1)
self.conv2=nn.Conv2d(in_channels=in_channel,out_channels=out_channel,kernel_size=1)
def forward(self,x):
x=self.conv1(x)
x=self.conv2(x)
return x
class exit_flow(nn.Module):
def __init__(self):
super().__init__()
self.relu=nn.ReLU(inplace=False)
self.Separable_conv1=Separableconv(728,728)
self.Separable_conv2=Separableconv(728,1024)
self.maxpool=nn.MaxPool2d(kernel_size=3,padding=1,stride=2)
self.conv=nn.Conv2d(728,1024,kernel_size=1,stride=2)
self.Separable_conv3=Separableconv(1024,1536)
self.Separable_conv4=Separableconv(1536,2048)
self.avgpool=nn.AdaptiveAvgPool2d((1,1))
self.dropout=nn.Dropout(0.4)
def forward(self,x):
x1=x
x1=self.conv(x)
x=self.relu(x)
x=self.Separable_conv1(x)
x=self.relu(x)
x=self.Separable_conv2(x)
x=self.maxpool(x)
result=x1+x
result=self.Separable_conv3(result)
result=self.relu(result)
result=self.Separable_conv4(result)
result=self.relu(result)
result=self.avgpool(result)
result=torch.flatten(result,1)
#result=self.dropout(result)
return result
class Xception(nn.Module):
def __init__(self,num_class):
super().__init__()
self.conv1=Basic_conv(3,32)
self.conv2=Basic_conv(32,64)
self.entry_flow1=Entry_flow(in_channel=64,out_channel=128,)
self.entry_flow2=Entry_flow(in_channel=128,out_channel=256)
self.entry_flow3=Entry_flow(in_channel=256,out_channel=728)
self.middle_flow=middle_flow(728)
self.exit_flow=exit_flow()
self.fc=nn.Linear(2048,num_class)
def forward(self,x):
x=self.conv1(x)
x=self.conv2(x)
x=self.entry_flow1(x)
x=self.entry_flow2(x)
x=self.entry_flow3(x)
x=self.middle_flow(x)
x=self.middle_flow(x)
x=self.middle_flow(x)
x=self.middle_flow(x)
x=self.middle_flow(x)
x=self.middle_flow(x)
x=self.middle_flow(x)
x=self.middle_flow(x)
x=self.exit_flow(x)
x=self.fc(x)
return x
还不没明白什么原因，这篇博客只是记录而已
