VGG网络结构 网络
第一层: 3x3x3x64, 步长为1, padding=1 ide
第二层: 3x3x64x64, 步长为1, padding=1 spa
第三层: 3x3x64x128, 步长为1, padding=1code
第四层: 3x3x128x128, 步长为1, padding=1blog
第五层: 3x3x128x256, 步长为1, padding=1it
第六层: 3x3x256x256, 步长为1, padding=1class
第七层: 3x3x256x256, 步长为1, padding=1import
第八层: 3x3x256x512, 步长为1, padding=1 im
第九层: 3x3x512x512, 步长为1, padding=1 img
第十层:3x3x512x512, 步长为1, padding=1
第十一层: 3x3x512x512, 步长为1, padding=1
第十二层: 3x3x512x512, 步长为1, padding=1
第十三层:3x3x512x512, 步长为1, padding=1
第十四层: 512*7*7, 4096的全链接操做
第十五层: 4096, 4096的全链接操做
第十六层: 4096, num_classes 的 全链接操做
import torch from torch import nn class VGG(nn.Module): def __init__(self, num_classes): super(VGG, self).__init__() self.features = nn.Sequential( nn.Conv2d(3, 64, kernel_size=3, padding=1), nn.ReLU(True), nn.Conv2d(64, 64, kernel_size=3, padding=1), nn.ReLU(True), nn.MaxPool2d(kernel_size=2, stride=2), nn.Conv2d(64, 128, kernel_size=3, padding=1), nn.ReLU(True), nn.Conv2d(128, 128, kernel_size=3, padding=1), nn.ReLU(True), nn.MaxPool2d(kernel_size=2, stride=2), nn.Conv2d(128, 256, kernel_size=3, padding=1), nn.ReLU(True), nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.ReLU(True), nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.ReLU(True), nn.MaxPool2d(kernel_size=2, stride=2), nn.Conv2d(256, 512, kernel_size=3, padding=1), nn.ReLU(True), nn.Conv2d(512, 512, kernel_size=3, padding=1), nn.ReLU(True), nn.Conv2d(512, 512, kernel_size=3, padding=1), nn.ReLU(True), nn.MaxPool2d(kernel_size=2, stride=2), nn.Conv2d(512, 512, kernel_size=3, padding=1), nn.ReLU(True), nn.Conv2d(512, 512, kernel_size=3, padding=1), nn.ReLU(True), nn.Conv2d(512, 512, kernel_size=3, padding=1), nn.ReLU(True), nn.MaxPool2d(kernel_size=2, stride=2),) self.classifier = nn.Sequential( nn.Linear(512*7*7, 4096), nn.ReLU(True), nn.Dropout(), nn.Linear(4096, 4096), nn.ReLU(True), nn.Dropout(), nn.Linear(4096, num_classes) ) def forward(self, x): x = self.features(x) x = x.view(x.size(0), -1) x = self.classifier(x) return x