Debug MNIST denoiser training

Q: Debug MNIST denoiser training

This question evaluates a candidate's competency in debugging neural network training pipelines, specifically assessing understanding of data preprocessing and distribution mismatch, normalization consistency, model output-range behavior, and optimization mechanics.

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Question

Debugging a Colab Denoising Network on MNIST

Goal: Make a Colab notebook that trains a denoising neural network on MNIST such that:

(a) the training loss steadily decreases, and
(b) the evaluation loss is close to the training loss.

You should identify and fix issues in three areas and describe how you validate each fix:

1) Data

Detect and correct a train/test distribution mismatch so the test set covers all digits 0–9.
Ensure the same normalization statistics (mean and std) computed from the training set are applied to both training and test data.

2) Model

Remove or replace an inappropriate final ReLU so the output range supports negative values typical of the denoised signal.

3) Optimization

Add the missing optimizer.zero_grad() and put backward() and optimizer.step() in the correct order.

For each of the three sections, describe:

Sanity checks and assertions you would add.
Minimal code changes and key code snippets.
Metrics you would track to validate the fix.

Debug MNIST denoiser training

Debugging a Colab Denoising Network on MNIST

1) Data

2) Model

3) Optimization

Solution

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Debug MNIST denoiser training

Overview

Debugging a Colab Denoising Network on MNIST

1) Data

2) Model

3) Optimization

Solution

Comments (0)