Configuring the optimizer
Now that we have training logic, we need to specify how to optimize the model's parameters.
In this exercise, you'll complete the configure_optimizers method within a PyTorch Lightning module used for image classification tasks. Your goal is to set up an optimizer that will update the model's parameters during training. To complete this you'll use the Adam optimizer with a learning rate of 1e-3.
本练习是课程的一部分
Scalable AI Models with PyTorch Lightning
练习说明
- Create an Adam optimizer using the model's parameters, setting the learning rate to
1e-3.
交互式实操练习
通过完成这段示例代码来试试这个练习。
import torch
def configure_optimizers(self):
# Create an Adam optimizer for model parameters
optimizer = ____
return optimizer