深度学习学习笔记-模型的修改和CRUD

目录 1.打印模型,理解模型结构2.模型保存与加载3.模型的模块CRUD和模块的层的CRUD

1.打印模型,理解模型结构 import torch class MyModel(torch.nn.Module): def __init__(self): super().__init__() self.layer1 = torch.nn.Sequential( torch.nn.Linear(3, 4), torch.nn.Linear(4, 3), ) self.layer2 = torch.nn.Linear(3, 6) self.layer3 = torch.nn.Sequential( torch.nn.Linear(6, 7), torch.nn.Linear(7, 5), ) def forward(self, x): x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) return x net = MyModel() print(net)

2.模型保存与加载

本节介绍如何保存模型,如何保存模型参数

import torchvision.models as models from torchsummary import summary import torch # pytorch.org/vision/stable/models.html # alexnet = models.alexnet(weights=None) # resnet50 = models.resnet50(weights=models.ResNet50_Weights.DEFAULT) # print(resnet50) # ----------------------------------------------------------- # 保存模型 / 保存模型+参数 # ----------------------------------------------------------- # resnet50 = models.resnet50(weights=models.ResNet50_Weights.DEFAULT) # 1、仅保存模型的参数 # state_dict是存储模型参数的 # torch.save(resnet50.state_dict(), 'resnet50_weight.pth') # 2、保存模型 + 参数 # torch.save(resnet50, 'resnet50.pth') # ----------------------------------------------------------- # 加载模型 / 加载模型+参数 # ----------------------------------------------------------- # 1、加载模型+参数 net = torch.load("resnet50.pth") print(net) # 2、已有模型，加载预训练参数 # resnet50 = models.resnet50(weights=None) # resnet50.load_state_dict(torch.load('resnet50_weight.pth')) 3.模型的模块CRUD和模块的层的CRUD

本节介绍模型的层layer的CRUD

import torch.nn as nn import torchvision.models as models alexnet = models.alexnet(weights=models.AlexNet_Weights.DEFAULT) print(alexnet) # 1、----- 删除网络的最后一层 ----- # 代码解释del alexnet.classifier是直接删除AlexNet中名称为classifier的模块 # 代码解释del alexnet.classifier[6]是删除classifier模块里面的第六层,也就是括号(6) # del alexnet.classifier # del alexnet.classifier[6] # print(alexnet) # 2、----- 删除网络的最后多层 ----- # 代码解释: 列表切片 # alexnet.classifier = alexnet.classifier[:-2] # print(alexnet) # 3、----- 修改网络的某一层 ----- # alexnet.classifier[6] = nn.Linear(in_features=4096, out_features=1024) # print(alexnet) # 4、----- 网络添加层, 每次添加一层 ----- # alexnet.classifier.add_module('7', nn.ReLU(inplace=True)) # alexnet.classifier.add_module('8', nn.Linear(in_features=1024, out_features=20)) # print(alexnet) # 4、----- 网络添加层，一次添加多层 ----- # block = nn.Sequential(nn.ReLU(inplace=True), # nn.Linear(in_features=1024, out_features=20)) # 模型中添加名称为block的模块 # alexnet.add_module('block', block) # print(alexnet)

结合代码注释和下图理解即可