How can I improve the performance of my PyTorch optimizer?

How can I improve the performance of my PyTorch optimizer?

To improve the performance of your PyTorch optimizer, consider using learning rate scheduling, experimenting with different optimizers like Adam or RMSprop, and adjusting hyperparameters.

What are some common learning rate schedules I can use?

Common learning rate schedules include StepLR, ExponentialLR, and ReduceLROnPlateau. Each has its own use case depending on your training dynamics.

How do I implement a learning rate scheduler in my code?

You can implement a learning rate scheduler by importing it from torch.optim.lr_scheduler and then calling its step method after each epoch.

Can you give me a code example?

Sure! Here's a simple example:

import torch
import torch.optim as optim
from torch.optim.lr_scheduler import StepLR

optimizer = optim.Adam(model.parameters(), lr=0.01)
scheduler = StepLR(optimizer, step_size=30, gamma=0.1)

for epoch in range(num_epochs):
    train(...)  # Your training function
    scheduler.step()