What is the purpose of nn.parameter in PyTorch?

In PyTorch, nn.Parameter is a special type of Tensor that represents learnable parameters in a neural network model. It is used to define model parameters like weights and biases.

nn.Parameter, a subclass of Tensor, is automatically registered as a learnable parameter of a model. When used in a model, nn.Parameter is included in the model’s parameter list and can be accessed and updated through the model’s parameters() method.

One key feature of nn.Parameter is that it allows for automatic differentiation. When nn.Parameter is used during the forward pass of a model, PyTorch will automatically track its gradients and update it during the backward pass.

The benefit of using nn.Parameter is that it offers a convenient way to define and manage the learnable parameters of a model. It simplifies the training process and makes it easier to understand, allowing for optimization and training of the model alongside other PyTorch optimizers and loss functions.