PyTorch nn.Module Explained: Missing Parameters, state_dict, Buffers and Registration Bugs

Sat, 08 Aug 2026 13:09:00 +0100

PyTorch: Zero to Hero — Step 04

In the previous post we built a neural network using raw tensors and autograd.

Now we are going to add the abstraction PyTorch expects almost every real model to use:

class Model(torch.nn.Module):
    ...

But this is not going to be a tour of nn.Module methods.

The useful question for programmers is:

What exactly does nn.Module register, track, move, save and expose — and how do those mechanisms break?

Build a Neural Network From Scratch in PyTorch Without nn.Module

Sat, 08 Aug 2026 12:59:00 +0100

PyTorch: Zero to Hero — Step 03

Most PyTorch tutorials begin with something like this:

import torch.nn as nn

model = nn.Sequential(
    nn.Linear(2, 16),
    nn.ReLU(),
    nn.Linear(16, 2),
)

That is useful code.

It is also hiding almost everything interesting.

In this post we are going to build the same kind of neural network using ordinary PyTorch tensors.

No nn.Module.

No nn.Linear.

No torch.optim.Adam.

No optimizer.step().

We will manually create the parameters, write the forward pass, calculate the loss, call autograd, update the weights, zero the gradients, batch the data, evaluate the model and then compare the result with the idiomatic PyTorch version.

Nn.Module on Programmer.ie: Modern AI programming

PyTorch nn.Module Explained: Missing Parameters, state_dict, Buffers and Registration Bugs

PyTorch: Zero to Hero — Step 04

Build a Neural Network From Scratch in PyTorch Without nn.Module

PyTorch: Zero to Hero — Step 03