How to choose the appropriate optimizer in PyTorch?

In PyTorch, choosing the right optimizer depends on your model and training task. Here are some commonly used optimizers and their suitable scenarios:

1. Stochastic Gradient Descent (SGD): SGD is the most fundamental optimizer and usually performs well when training simple models. However, for complex models or non-convex optimization problems, SGD may converge slowly.
2. Adam, an optimizer with adaptive learning rates, is commonly known for its quick convergence and suitability for most deep learning tasks by combining the benefits of momentum and adaptive learning rates.
3. RMSprop is also an optimizer with adaptive learning rates, suitable for non-stationary objectives.
4. Adagrad is an optimizer with adaptive learning rates, suitable for sparse data and non-convex optimization problems.
5. Adadelta is an optimizer with adaptive learning rates that do not require manual setting of the learning rate.

When choosing an optimizer, it is recommended to consider the characteristics of the model, the size of the dataset, and the complexity of the training task. It is typically advised to try out different optimizers during actual training and then select the most suitable one based on training performance and convergence speed.