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This article introduces the relevant knowledge of "how to use Python torch.nn.Module". In the operation of actual cases, many people will encounter such a dilemma, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

Containers container Module module class torch.nn.Module Base class for all neural network modules. This is the parent class (base class) of all neural network modules. Your models should also subclass this class. Your own implemented model should also inherit this class. Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes: we can make this type of module contain other modules of this type, thus nesting them into a tree structure. Users can assign submodules to normal class attributes: import torch.nn as nnimport torch.nn.functional as Fclass Model (nn.Module): def _ init__ (self): super (Model, self). _ init__ () self.conv1 = nn.Conv2d (1,20,5) self.conv2 = nn.Conv2d (20,20,5) def forward (self, x): X = F.relu (self.conv1 (x) return F.relu (self.conv2 (x)) Submodules assigned in this way will be registered And will have their parameters converted too when you call to (), etc. Submodules that assign values to properties in this way will be automatically registered, and when you call the to () method, the parameters of these submodules will be converted correctly. "how to use Python torch.nn.Module" is introduced here, thank you for reading. If you want to know more about the industry, you can follow the website, the editor will output more high-quality practical articles for you!

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