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Building Models with PyTorch — PyTorch Tutorials 2.0.0+cu117 documentation

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Introduction || Tensors || Autograd || Building Models || TensorBoard Support || Training Models || Model Understanding Follow along with the video below or on youtube. In this video, we’ll be discussing some of the tools PyTorch makes available for building deep learning networks. Except for Parameter, the classes we discuss in this video are all subclasses of torch.nn.Module. This is the PyTorch base class meant to encapsulate behaviors specific to PyTorch Models and their components. One important behavior of torch.nn.Module is registering parameters. If a particular Module subclass has learning weights, these weights are expressed as instances of torch.nn.Parameter. The Parameter class is a subclass of torch.Tensor, with the special behavior that when they are assigned as attributes of a Module, they are added to the list of that modules parameters. These parameters may be accessed through the parameters() method on the Module class. As a simple example, here’s a very simple model

Note Go to the end to download the full example code. Introduction || Tensors || Autograd || Building Models || TensorBoard Support || Training Models || Model Understanding Building Models with PyTorch # Created On: Nov 30, 2021 | Last Updated: May 13, 2026 | Last Verified: Nov 05, 2024 Follow along with the video below or on youtube . torch.nn.Module and torch.nn.Parameter # In this video, we’ll be discussing some of the tools PyTorch makes available for building deep learning networks. Except for Parameter , the classes we discuss in this video are all subclasses of torch.nn.Module . This i

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