人工智能及大數(shù)據(jù)學(xué)習(xí)驗證和senet的特色結(jié)構(gòu)

1、分組分組參數(shù)個數(shù)GPU內(nèi)存分組分組參數(shù)個數(shù)GPU內(nèi)存defforward(self,x):#basicblock residual = xifresidual = self.downsout = self.layer1(x)out = self.relu(out) out2=self.layer2(out) out2 = self.relu(out2)out3=torch.cat(out,out2,1) out = self.layer3(out3)out4 = out5 = out= self.layer4(out5) # backto thespecified # 3x3 convolut

2、ion withdef conv3x3(in_channels, out_channels, stride=1, groups=1): returnnn.Conv2d(in_channels,out_channels,kernel_size=3,stride=stride,padding=1, 參數(shù)個數(shù)GPU內(nèi)存參數(shù)個數(shù)GPU內(nèi)存GPU內(nèi)存(M) defforward(self, x):# residual =defConv2d(in_channels,out_channels,kernel_size=1,padding=0,stride=1): return nn.Sequential(*n

3、n.Conv2d(in_channels,return參數(shù)個數(shù)內(nèi)存參數(shù)個數(shù)內(nèi)存out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out=self.conv2(out) out = self.bn2(out) if self.downsresidual = self.downs# attentionoriginal_out =out=F.avg_pool2d(out,out.size()2:) out = out.view(out.size(0), -1)out = self.fc1(out) out = self.relu

4、(out) out = self.fc2(out) out=self.sigmoid(out)out=out.view(out.size(0),out.size(1),1,1) out = out * original_outout += out=self.relu(out) return outChollet,Franois.Xception:DeepLearningwithSeparableConvolutionsJ.Chollet,Franois.Xception:DeepLearningwithSeparableConvolutionsJ.XieS,GirshickR,Dollr,Piotr,etal.AggregatedResidual Transformations for Deep Neural NetworksJ. 2016.HuangG,LiuZ,LaurensVDM,etal.DenselyConnected Convolutional NetworksJ. 2016.Howard A G , Zhu M , Chen B , et al. MobileNets: Efficient Convolutional Neural Networks fo