环球易购招聘网站建设,中山网上房地产,示范高职建设网站,怎么做网站讯息背景#xff1a; GoogLeNeta是2014年提出的一种全新的深度学习结构#xff0c;在这之前的AlexNet、VGG等结构都是通过增大网络的深度(层数)来获得更好的训练效果#xff0c;但层数的增加会带来很多负作用#xff0c;比如overfit、梯度消失、梯度爆炸等。GoogLeNet通过引入i…背景 GoogLeNeta是2014年提出的一种全新的深度学习结构在这之前的AlexNet、VGG等结构都是通过增大网络的深度(层数)来获得更好的训练效果但层数的增加会带来很多负作用比如overfit、梯度消失、梯度爆炸等。GoogLeNet通过引入inception从另一种角度来提升训练结果:能更高效的利用计算资源在相同的计算量下能提取到更多的特征从而提升训练结果。 因此GoogLeNet在专注于加深网络结构的同时引入了新的基本结构——Inception模块以增加网络的宽度。
网络结构图 1、Inception模块 Inception就是把多个卷积或池化操作放在一起组装成一个网络模块 实际中需要什么样的Inception 我们在上面提供了一种Inception的结构但是这个结构存在很多问题是不能够直接使用的。首要问题就是参数太多导致特征图厚度太大。为了解决这个问题作者在其中加入了1X1的卷积核改进后的Inception结构如下图 代码 定义BasicConv2d
class BasicConv2d(nn.Module):def __init__(self, in_channels, out_channels, **kwargs):super(BasicConv2d, self).__init__()self.conv nn.Conv2d(in_channels, out_channels, **kwargs)self.relu nn.ReLU(inplaceTrue)def forward(self, x):x self.conv(x)x self.relu(x)return x定义Inception
class Inception(nn.Module):def __init__(self, in_channels, ch1x1, ch3x3red, ch3x3, ch5x5red, ch5x5, pool_proj):super(Inception, self).__init__()self.branch1 BasicConv2d(in_channels, ch1x1, kernel_size1)self.branch2 nn.Sequential(BasicConv2d(in_channels, ch3x3red, kernel_size1),BasicConv2d(ch3x3red, ch3x3, kernel_size3, padding1) # 保证输出大小等于输入大小)self.branch3 nn.Sequential(BasicConv2d(in_channels, ch5x5red, kernel_size1),BasicConv2d(ch5x5red, ch5x5, kernel_size5, padding2) # 保证输出大小等于输入大小)self.branch4 nn.Sequential(nn.MaxPool2d(kernel_size3, stride1, padding1),BasicConv2d(in_channels, pool_proj, kernel_size1))定义分类器
class InceptionAux(nn.Module):def __init__(self, in_channels, num_classes):super(InceptionAux, self).__init__()self.averagePool nn.AvgPool2d(kernel_size5, stride3)self.conv BasicConv2d(in_channels, 128, kernel_size1) # output[batch, 128, 4, 4]self.fc1 nn.Linear(2048, 1024)self.fc2 nn.Linear(1024, num_classes)def forward(self, x):# aux1: N x 512 x 14 x 14, aux2: N x 528 x 14 x 14x self.averagePool(x)# aux1: N x 512 x 4 x 4, aux2: N x 528 x 4 x 4x self.conv(x)# N x 128 x 4 x 4x torch.flatten(x, 1)x F.dropout(x, 0.5, trainingself.training)# N x 2048x F.relu(self.fc1(x), inplaceTrue)x F.dropout(x, 0.5, trainingself.training)# N x 1024x self.fc2(x)# N x num_classesreturn x
model完整代码
import torch.nn as nn
import torch
import torch.nn.functional as Fclass GoogLeNet(nn.Module):def __init__(self, num_classes1000, aux_logitsTrue, init_weightsFalse):super(GoogLeNet, self).__init__()self.aux_logits aux_logitsself.conv1 BasicConv2d(3, 64, kernel_size7, stride2, padding3)self.maxpool1 nn.MaxPool2d(3, stride2, ceil_modeTrue)self.conv2 BasicConv2d(64, 64, kernel_size1)self.conv3 BasicConv2d(64, 192, kernel_size3, padding1)self.maxpool2 nn.MaxPool2d(3, stride2, ceil_modeTrue)self.inception3a Inception(192, 64, 96, 128, 16, 32, 32)self.inception3b Inception(256, 128, 128, 192, 32, 96, 64)self.maxpool3 nn.MaxPool2d(3, stride2, ceil_modeTrue)self.inception4a Inception(480, 192, 96, 208, 16, 48, 64)self.inception4b Inception(512, 160, 112, 224, 24, 64, 64)self.inception4c Inception(512, 128, 128, 256, 24, 64, 64)self.inception4d Inception(512, 112, 144, 288, 32, 64, 64)self.inception4e Inception(528, 256, 160, 320, 32, 128, 128)self.maxpool4 nn.MaxPool2d(3, stride2, ceil_modeTrue)self.inception5a Inception(832, 256, 160, 320, 32, 128, 128)self.inception5b Inception(832, 384, 192, 384, 48, 128, 128)if self.aux_logits:self.aux1 InceptionAux(512, num_classes)self.aux2 InceptionAux(528, num_classes)self.avgpool nn.AdaptiveAvgPool2d((1, 1))self.dropout nn.Dropout(0.4)self.fc nn.Linear(1024, num_classes)if init_weights:self._initialize_weights()def forward(self, x):# N x 3 x 224 x 224x self.conv1(x)# N x 64 x 112 x 112x self.maxpool1(x)# N x 64 x 56 x 56x self.conv2(x)# N x 64 x 56 x 56x self.conv3(x)# N x 192 x 56 x 56x self.maxpool2(x)# N x 192 x 28 x 28x self.inception3a(x)# N x 256 x 28 x 28x self.inception3b(x)# N x 480 x 28 x 28x self.maxpool3(x)# N x 480 x 14 x 14x self.inception4a(x)# N x 512 x 14 x 14if self.training and self.aux_logits: # eval model lose this layeraux1 self.aux1(x)x self.inception4b(x)# N x 512 x 14 x 14x self.inception4c(x)# N x 512 x 14 x 14x self.inception4d(x)# N x 528 x 14 x 14if self.training and self.aux_logits: # eval model lose this layeraux2 self.aux2(x)x self.inception4e(x)# N x 832 x 14 x 14x self.maxpool4(x)# N x 832 x 7 x 7x self.inception5a(x)# N x 832 x 7 x 7x self.inception5b(x)# N x 1024 x 7 x 7x self.avgpool(x)# N x 1024 x 1 x 1x torch.flatten(x, 1)# N x 1024x self.dropout(x)x self.fc(x)# N x 1000 (num_classes)if self.training and self.aux_logits: # eval model lose this layerreturn x, aux2, aux1return xdef _initialize_weights(self):for m in self.modules():if isinstance(m, nn.Conv2d):nn.init.kaiming_normal_(m.weight, modefan_out, nonlinearityrelu)if m.bias is not None:nn.init.constant_(m.bias, 0)elif isinstance(m, nn.Linear):nn.init.normal_(m.weight, 0, 0.01)nn.init.constant_(m.bias, 0)class Inception(nn.Module):def __init__(self, in_channels, ch1x1, ch3x3red, ch3x3, ch5x5red, ch5x5, pool_proj):super(Inception, self).__init__()self.branch1 BasicConv2d(in_channels, ch1x1, kernel_size1)self.branch2 nn.Sequential(BasicConv2d(in_channels, ch3x3red, kernel_size1),BasicConv2d(ch3x3red, ch3x3, kernel_size3, padding1) # 保证输出大小等于输入大小)self.branch3 nn.Sequential(BasicConv2d(in_channels, ch5x5red, kernel_size1),BasicConv2d(ch5x5red, ch5x5, kernel_size5, padding2) # 保证输出大小等于输入大小)self.branch4 nn.Sequential(nn.MaxPool2d(kernel_size3, stride1, padding1),BasicConv2d(in_channels, pool_proj, kernel_size1))def forward(self, x):branch1 self.branch1(x)branch2 self.branch2(x)branch3 self.branch3(x)branch4 self.branch4(x)outputs [branch1, branch2, branch3, branch4]return torch.cat(outputs, 1)class InceptionAux(nn.Module):def __init__(self, in_channels, num_classes):super(InceptionAux, self).__init__()self.averagePool nn.AvgPool2d(kernel_size5, stride3)self.conv BasicConv2d(in_channels, 128, kernel_size1) # output[batch, 128, 4, 4]self.fc1 nn.Linear(2048, 1024)self.fc2 nn.Linear(1024, num_classes)def forward(self, x):# aux1: N x 512 x 14 x 14, aux2: N x 528 x 14 x 14x self.averagePool(x)# aux1: N x 512 x 4 x 4, aux2: N x 528 x 4 x 4x self.conv(x)# N x 128 x 4 x 4x torch.flatten(x, 1)x F.dropout(x, 0.5, trainingself.training)# N x 2048x F.relu(self.fc1(x), inplaceTrue)x F.dropout(x, 0.5, trainingself.training)# N x 1024x self.fc2(x)# N x num_classesreturn xclass BasicConv2d(nn.Module):def __init__(self, in_channels, out_channels, **kwargs):super(BasicConv2d, self).__init__()self.conv nn.Conv2d(in_channels, out_channels, **kwargs)self.relu nn.ReLU(inplaceTrue)def forward(self, x):x self.conv(x)x self.relu(x)return x
