How do I use optimizers in PyTorch?

How do I use optimizers in PyTorch?

In PyTorch, you can use optimizers from the torch.optim module. First, you need to define your model and loss function.

Can you show me an example of using Adam optimizer?

Sure! Here's a simple example:

import torch
import torch.optim as optim

model = MyModel()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# In your training loop:
optimizer.zero_grad()  # Clear gradients
loss = loss_function(output, target)
loss.backward()  # Backpropagation
optimizer.step()  # Update weights

What are the different optimizers available in PyTorch?

PyTorch offers several optimizers like SGD, Adam, RMSprop, and Adagrad. Each has its own advantages depending on the task.

How do I adjust the learning rate for an optimizer?

You can adjust the learning rate by passing the lr parameter when you create the optimizer, or you can use a learning rate scheduler.