Writing your first training loop
Write a clear, correct training loop in any framework and know what each line is for
A taste of a lesson
My validation accuracy changes every time I run evaluation on the same model. Why?
If the weights have not changed, the outputs should not either, so something random is still active. The usual cause is that the model is still in training mode during evaluation, so dropout keeps switching units off at random, and batch normalisation uses the current batch's statistics instead of its stored averages. Switch to evaluation mode before the validation loop, and back to training mode afterwards. Also confirm no random augmentation is applied to validation data. Quick check: which two kinds of layers behave differently between training and evaluation mode?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Write a complete training and evaluation loop and explain every step
- Run sanity checks such as single batch overfitting before a full training run
- Save and restore checkpoints so training can resume after interruption
- Find and fix the most common training loop bugs
Lesson plan
- 1 Datasets, loaders and batches Feed data to a model in shuffled batches with consistent shapes. Start
- 2 One training step, line by line Write the forward, loss, reset, backward and step sequence and explain each part. Start
- 3 Evaluation done right Measure validation performance without gradients and with correct averaging. Start
- 4 Sanity checks before scaling up Catch bugs in minutes instead of after hours of wasted training. Start
- 5 Logging, checkpoints and reproducibility Make runs resumable, comparable and repeatable. Start
- 6 From hand written loop to trainer libraries Map your loop onto high level training tools and read their errors with confidence. Start
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About this tutor
For beginners who can follow a tutorial but feel lost when the training code does something unexpected. Working in general, framework neutral terms, you build a training loop step by step: datasets and batching, the forward pass, the loss, resetting gradients, the backward pass, the optimiser step, an evaluation pass, logging and checkpointing. Before scaling up you learn the sanity checks that experienced practitioners always run, such as overfitting a single batch and checking the starting loss. You can use whichever deep learning framework you like; the tutor explains concepts and asks you to map them to your own code.
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About the teacher
I teach how neural networks learn, one small worked example at a time
9 tutors 424 lessons taught Sample
I teach the core mechanics of deep learning: what a neuron computes, how a loss turns mistakes into numbers, and how gradients and optimisers change weights. My background is in building and training models for applied research teams, which mostly meant staring at loss curves that refused to go down. That shaped how I teach. I start every idea with...
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