A custom layer on the How to Make AI Models track. A custom layer is justified when the domain has a structure libraries do not express. It needs a parameter init, a forward, and a test that gradients exist.
This lesson assumes you already worked through The forward pass.
The idea in practice
Test the layer alone on a fixed tensor. Check a gradient with a finite difference if you write the math yourself.
A concrete check
goal = {
'track': 'How to Make AI Models',
'lesson': 'A custom layer',
}
checks = [
'input available at decision time',
'score matches the real decision',
'failure case written down',
]
print(goal['lesson'])
for item in checks:
print('-', item)
Run the sketch locally if you have Python. The printout is a reminder of the checks, not a trained model. Replace the strings with the real inputs from your own example before you treat it as a design.
What usually goes wrong
A custom layer with no test that poisons the whole net. When this happens, stop adding parameters or tools. Fix the check, the data, or the permission, then run the same example again.
What to write down
- The input you are allowed to use at decision time.
- The output and the score or pass rule.
- One failure you will test on purpose.
- What you will not claim the system can do.
Practice
Describe the test: fixed input, expected output shape, nonzero gradient.
Self-check
- Say A custom layer in one sentence that mentions an input and an output.
- Name the failure mode in this lesson and the check that would catch it.
Done when: you can explain this lesson without the page open, and you have a written failure case.