A Recipe for Training Neural Networks

[howardyclo]

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Metadata

• Author: Andrej Karpathy
• Blog post: http://karpathy.github.io/2019/04/25/recipe/
• TL;DR. Just a shorter notes (that are useful for me) of Karpathy's blog post ;-)

[howardyclo]

Spend Time to Understand Data

1. Understand the distribution and patterns.
2. Look for data imbalances and biases.
3. Examples:
   • Are very local features enough or do we need global context?
   • How much variation is there and what form does it take?
   • What variation is spurious and could be preprocessed out?
   • Does spatial position matter or do we want to average pool it out?
   • How much does detail matter and how far could we afford to downsample the images?
   • How noisy are the labels?
4. Visualize the statistics and the outliers along any axis.

Setup Training/Evaluation and Start from Simple Model

• Establish baselines and visualize the train/eval metrics.
• Fix random seed and run code twice to ensure to get the same results.
• Disable any unnecessary fanciness, e.g., data augmentation.
• Plot test losses of entire data instead of only batches.
• Ensure the loss started with right value, e.g., -log(1/n_classes).
• Visualize the fixed test batch to see the "dynamics" of how model learns (be aware of very low or very high learning rates).
• Be aware of view and transpose/permute.
• Write simple code that can work first and refactor to more generalizable version later.

Overfit

• Follow the most related paper and try their simplest architecture that achieves good performance. Do not start to customize things in early stage.
• Use Adam optimizer with 3e-4 learning rate is safe.
• If we have multiple signals, plug them into model one by one to ensure that you get a performance boost you'd expect.
• Be careful with learning rate decay (different dataset size/problem requires different learning rate decay schedule). Disable learning rate decay first and tune this later.

Regularize

• Don't spend a lot of engineering costs to squeeze juice out of a small dataset when you could instead be collecting more data.
• Data augmentation.
• Creative augmentation: domain randomization, use of simulation, clever hybrids such as inserting (potentially simulated) data into scenes, or even GANs.
• Pretraining.
• Stick with supervised learning.
• Smaller input dimensionality (try input small image).
• Decrease the batch size (small batch = stronger regularization).
• Add dropout (dropout2d for CNNs).
• Weight decay.
• Try larger model (early stopped performance may be better than the small ones).

Tuning Hyperparameters

Squeeze Out the Juice

• Ensembles or "Distilling the Knowledge in a Neural Network".
• Leaving it training even the validation loss seems to be leveling off.