Understanding the exploding gradient problem

Source: Dev.to

Why Neural Networks Explode — A Simple Fix That Helps

Training some neural networks, especially RNNs, can feel like steering a boat in a storm: small changes sometimes grow out of control and cause learning to fail.
This runaway behavior is known as exploding gradients. When it occurs, the model makes huge jumps, effectively forgetting what it has learned.

Exploding gradients

• Occur when gradients become excessively large during back‑propagation.
• Lead to unstable updates and can cause training to diverge.

Gradient clipping

A straightforward and practical trick to tame exploding gradients is gradient clipping.
The idea is simple: limit the magnitude of the gradients before applying the update. This prevents huge parameter changes, keeping training stable.

• Acts like a safety rope that caps how far a step can go.
• Does not solve every problem, but it restores stability and lets the network continue learning.
• Often sufficient for tasks such as text or music prediction.

When to use it

• If training feels unstable or loss spikes dramatically, try gradient clipping.
• Many teams adopt this rule as a first line of defense, and it frequently yields noticeably better results.

Further reading

Understanding the exploding gradient problem

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