Train, Diagnose, and Improve a Small Model

Trace one batch end to end

Before training, print the input shape, target shape, data type, value range, and device. Pass one batch through the model and inspect the output shape. Confirm that the loss accepts those outputs and targets. This catches silent mistakes earlier than a long run.

A minimal loop has a stable order: load batch, clear old gradients, run the forward pass, calculate loss, backpropagate, update parameters, and record evidence. Validation uses the same preprocessing but no parameter updates. Keep training and validation logic visibly separate.

Read curves as evidence

Training loss falling while validation loss rises suggests overfitting, leakage, or a distribution mismatch. Both curves staying flat may indicate a learning-rate problem, broken labels, saturated activations, or a model that cannot represent the task. A noisy metric may simply reflect too little validation data.

Change one factor per experiment. Record the hypothesis, code and data versions, parameter change, result, and interpretation. More epochs are not a diagnosis.

Inspect examples, not just averages

Create slices by class, data source, length, time period, and relevant user group. Review confident errors, borderline cases, and disagreements with the baseline. Check mislabeled or ambiguous examples before increasing model capacity.

Practice: two-experiment review

Train a small model for a fixed number of epochs and save the best validation checkpoint. Produce loss curves, a confusion matrix, and ten inspected errors. Choose exactly one next change based on that evidence, rerun, and write whether the hypothesis was supported.

Stop condition

Stop when additional complexity does not improve the decision-relevant metric enough to justify its latency, maintenance, interpretability, or data cost.