GoogLeNet

創(chuàng)新點(diǎn)：

通過多種卷積核疊加網(wǎng)絡(luò)復(fù)雜性

學(xué)習(xí)多種卷積

提升計(jì)算效率

GoogLeNet網(wǎng)絡(luò)深度達(dá)到22層

1x1卷積降低維度

• 降低計(jì)算量，提升計(jì)算效率

網(wǎng)絡(luò)結(jié)構(gòu)

代碼

import torch from torch import nnNUM_CLASSES = 10class BasicConv2d(nn.Module):def __init__(self, in_channels, out_channels, **kwargs):super(BasicConv2d, self).__init__()self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)self.bn = nn.BatchNorm2d(out_channels, eps=0.001)self.relu = nn.ReLU(inplace=True)def forward(self, x):x = self.conv(x)x = self.bn(x)x = self.relu(x)return xclass Inception(nn.Module):def __init__(self, in_channel, n1_1, n3x3red, n3x3, n5x5red, n5x5, pool_plane):super(Inception, self).__init__()# first lineself.branch1x1 = BasicConv2d(in_channel, n1_1, kernel_size=1)# second lineself.branch3x3 = nn.Sequential(BasicConv2d(in_channel, n3x3red, kernel_size=1),BasicConv2d(n3x3red, n3x3, kernel_size=3, padding=1))# third lineself.branch5x5 = nn.Sequential(BasicConv2d(in_channel, n5x5red, kernel_size=1),BasicConv2d(n5x5red, n5x5, kernel_size=5, padding=2))# fourth lineself.branch_pool = nn.Sequential(nn.MaxPool2d(3, stride=1, padding=1),BasicConv2d(in_channel, pool_plane, kernel_size=1))def forward(self, x):y1 = self.branch1x1(x)y2 = self.branch3x3(x)y3 = self.branch5x5(x)y4 = self.branch_pool(x)output = torch.cat([y1, y2, y3, y4], 1)return outputclass GoogLeNet(nn.Module):def __init__(self, num_classes=NUM_CLASSES):super(GoogLeNet, self).__init__()self.conv1 = BasicConv2d(3, 64, kernel_size=7, stride=2, padding=3)self.max_pool1 = nn.MaxPool2d(3, stride=2)self.conv2 = BasicConv2d(64, 192, kernel_size=3, stride=1, padding=1)self.max_pool2 = nn.MaxPool2d(3, stride=2)self.a3 = Inception(192, 64, 96, 128, 16, 32, 32)self.b3 = Inception(256, 128, 128, 192, 32, 96, 64)self.max_pool3 = nn.MaxPool2d(3, stride=2)self.a4 = Inception(480, 192, 96, 208, 16, 48, 64)self.b4 = Inception(512, 160, 112, 224, 24, 64, 64)self.c4 = Inception(512, 128, 128, 256, 24, 64, 64)self.d4 = Inception(512, 112, 144, 288, 32, 64, 64)self.e4 = Inception(528, 256, 160, 320, 32, 128, 128)self.max_pool4 = nn.MaxPool2d(3, stride=2)self.a5 = Inception(832, 256, 160, 320, 32, 128, 128)self.b5 = Inception(832, 384, 192, 384, 48, 128, 128)self.avg_pool = nn.AvgPool2d(7)self.dropout = nn.Dropout(0.4)self.classifier = nn.Linear(1024, num_classes)def forward(self, x):x = self.conv1(x)x = self.max_pool1(x)x = self.conv2(x)x = self.max_pool2(x)x = self.a3(x)x = self.b3(x)x = self.max_pool3(x)x = self.a4(x)x = self.b4(x)x = self.c4(x)x = self.d4(x)x = self.e4(x)x = self.max_pool4(x)x = self.a5(x)x = self.b5(x)x = self.avg_pool(x)x = self.dropout(x)x = x.view(x.size(0), -1)x = self.classifier(x)return x

Ref

Pytorch實(shí)現(xiàn)GoogLeNet解決各類數(shù)據(jù)集(cifar10/mnist/imagenet)分類

Pytorch實(shí)現(xiàn)GoogLeNet

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