run-mlp-autoresearch

name	run-mlp-autoresearch
description	Autonomously iterate and improve the MLP model in MLP-AutoResearch.
argument-hint	Run a series of ML experiments
user-invocable	true

MLP-AutoResearch Automation

When to Use

• When asked to autonomously run experiments and improve the MLP model in MLP-AutoResearch.

Context

This is an AutoResearch framework scaled down to an MLP on MNIST. The goal is to maximize test_accuracy while staying within the constraints (fixed epochs, dataset).

Procedure

For each iteration (repeat as many times as requested by the user):

1. Analyze Data: Read program.md, runner.py, and train.py to understand the current architecture and allowed changes. View results.tsv if it exists.
2. Propose Modification: Think of an improvement (e.g. optimizer, activation function, dropout, hidden size, learning rate schedule, layer normalization).
3. Apply Code Change: Modify train.py using file editing tools to implement the proposed idea. Ensure no infrastructure (like prepare.py) is altered.
4. Run Experiment: Execute python train.py in the terminal and wait for it to complete.
5. Evaluate Results: Read the output. Look for best_test_accuracy and compare it to previous baselines.
6. Log Result: Write a summary of the change, test accuracy, parameters, and whether the change was kept or reverted to results.tsv. If the result is worse, revert the change in train.py.