pytorch Vgg网络模型

Vgg网络

网络亮点

通过堆叠多个3x3的卷积核来代替大尺寸卷积核（减少参数）

堆叠两个3x3的卷积核代替一个5x5的卷积核，堆叠三个3x3的卷积核代替7X7卷积核

import torch.nn as nn
import torch# official pretrain weights
model_urls = {'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth','vgg13': 'https://download.pytorch.org/models/vgg13-c768596a.pth','vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth','vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth'
}class VGG(nn.Module):def __init__(self, features, num_classes=1000, init_weights=False):super(VGG, self).__init__()self.features = featuresself.classifier = nn.Sequential(nn.Linear(512*7*7, 4096), #512 channel  7x7  图像大小nn.ReLU(True),nn.Dropout(p=0.5),nn.Linear(4096, 4096),nn.ReLU(True),nn.Dropout(p=0.5),nn.Linear(4096, num_classes))if init_weights:self._initialize_weights()def forward(self, x):# N x 3 x 224 x 224 输入图像x = self.features(x)# N x 512 x 7 x 7 展平前x = torch.flatten(x, start_dim=1)# N x 512*7*7  送入分类x = self.classifier(x)return xdef _initialize_weights(self):for m in self.modules():if isinstance(m, nn.Conv2d):# nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')nn.init.xavier_uniform_(m.weight)if m.bias is not None:nn.init.constant_(m.bias, 0)elif isinstance(m, nn.Linear):nn.init.xavier_uniform_(m.weight)# nn.init.normal_(m.weight, 0, 0.01)nn.init.constant_(m.bias, 0)def make_features(cfg: list):layers = []in_channels = 3#RGB 三通道for v in cfg:if v == "M":layers += [nn.MaxPool2d(kernel_size=2, stride=2)]else:conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)layers += [conv2d, nn.ReLU(True)]in_channels = vreturn nn.Sequential(*layers)#非关键字参数#模型深度选择
cfgs = {'vgg11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],'vgg13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],'vgg16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],'vgg19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
}def vgg(model_name="vgg16", **kwargs):assert model_name in cfgs, "Warning: model number {} not in cfgs dict!".format(model_name)cfg = cfgs[model_name]model = VGG(make_features(cfg), **kwargs)return model#网络模型打印
vgg_module = vgg("vgg13")
print(vgg_module)

网络模型打印输出

VGG((features): Sequential((0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(1): ReLU(inplace=True)(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(3): ReLU(inplace=True)(4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)(5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(6): ReLU(inplace=True)(7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(8): ReLU(inplace=True)(9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)(10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(11): ReLU(inplace=True)(12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(13): ReLU(inplace=True)(14): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)(15): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(16): ReLU(inplace=True)(17): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(18): ReLU(inplace=True)(19): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)(20): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(21): ReLU(inplace=True)(22): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(23): ReLU(inplace=True)(24): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False))(classifier): Sequential((0): Linear(in_features=25088, out_features=4096, bias=True)(1): ReLU(inplace=True)(2): Dropout(p=0.5, inplace=False)(3): Linear(in_features=4096, out_features=4096, bias=True)(4): ReLU(inplace=True)(5): Dropout(p=0.5, inplace=False)(6): Linear(in_features=4096, out_features=1000, bias=True))
)

pytorch Vgg网络模型相关推荐

深度学习修炼（五）——基于pytorch神经网络模型进行气温预测
文章目录 5 基于pytorch神经网络模型进行气温预测 5.1 实现前的知识补充 5.1.1 神经网络的表示 5.1.2 隐藏层 5.1.3 线性模型出错 5.1.4 在网络中加入隐藏层 5.1.5 ...
pytorch vgg 网络
论文全名:<Very Deep Convolutional Networks for Large-Scale Image Recognition> 2014年,牛津大学计算机视觉组(Vis ...
基于PyTorch的卷积神经网络图像分类——猫狗大战（二）：使用Pytorch定义网络模型
文章目录 1. 需要用到的库 2. 模型定义 3. 测试基于上一篇文章 https://blog.csdn.net/linghu8812/article/details/100044971,这次介绍 ...
深度学习图像处理之VGG网络模型 (超级详细)
VGG介绍: 由牛津大学著名研究所VGG提出,斩获该年ImageNet竞赛中Localization Task(定位任务)第一名和Classification Task(分类任务)第二名. VGG网络 ...
图片分类猫狗大战 pytorch VGG
使用pytorch实现猫狗大战一.简介二.理论三.实现 1️⃣.实现准备 2️⃣.创建VGG16模型 3️⃣.训练模型 4️⃣.在验证集上测试训练的模型 5️⃣.在测试集上运行四.总结五.我 ...
pytorch VGG
相对alexn 增加了层数,filter变成3*3
pytorch——VGG网络搭建
目录
神经网络模型u-net 、VGG
[理论] 神经网络模型u-net .VGG 文章目录 [理论] 神经网络模型u-net .VGG 一.人脑视觉处理机理人脑视觉机理二.U-net网络 1.定义编码解码结构上采样.下采样.长连接 ...
【Pytorch】基于卷积神经网络实现的面部表情识别
作者:何翔学院:计算机学院学号:04191315 班级:软件1903 转载请标注本文链接: https://blog.csdn.net/HXBest/article/details/1219812 ...

pytorch Vgg网络模型

Vgg网络

pytorch Vgg网络模型相关推荐

最新文章

热门文章