What is gradient clipping in PyTorch?

What is gradient clipping in PyTorch?

Gradient clipping is a technique used to prevent exploding gradients by capping the gradients during backpropagation.

Why is gradient clipping important?

It's important because it helps stabilize training, especially in deep networks, by avoiding large updates that can lead to divergence.

How do I implement gradient clipping in my PyTorch model?

You can implement it using torch.nn.utils.clip_grad_norm_ or torch.nn.utils.clip_grad_value_ before the optimizer step.

Can you show me a code example?

Sure! Here's a simple example:

import torch

# Assuming model and optimizer are defined

# Forward pass
output = model(input)
loss = loss_function(output, target)

# Backward pass
loss.backward()

# Gradient clipping
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)

# Optimizer step
optimizer.step()