Set model params nn torch4/18/2024 There are some predefined modules that act as a container for other modules. Note that we used squeeze and unsqueeze since `torch.nn.Linear` operates on batch of vectors as opposed to scalars.īy default calling parameters() on a module will return the parameters of all its submodules: Yhat = self.linear(x.unsqueeze(1)).squeeze(1) ![]() We can rewrite our module above using `torch.nn.Linear` like this: One such module is `torch.nn.Linear` which is a more general form of a linear function than what we defined above. PyTorch comes with a number of predefined modules. Print(net.a, net.b) # Should be close to 5 and 3 Similar to the previous example, you can define a loss function and optimize the parameters of your model as follows: You can start by sampling some points from your function: Now, say you have an unknown function `y = 5x + 3 + some noise`, and you want to optimize the parameters of your model to fit this function. It's convenient to use parameters because you can simply retrieve them all with module's `parameters()` method: Parameters are essentially tensors with `requires_grad` set to true. X = torch.arange(100, dtype=torch.float32) To use this model in practice you instantiate the module and simply call it like a function: Self.b = torch.nn.Parameter(torch.rand(1)) ![]() Self.a = torch.nn.Parameter(torch.rand(1)) This model can be represented with the following code: For example say you want to represent a linear model `y = ax + b`. A module is simply a container for your parameters and encapsulates model operations. To make your code slightly more organized it's recommended to use PyTorch's modules. In the previous example we used bare bone tensors and tensor operations to build our model.
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