What are Stateless and Stateful Models?
- Stateless models are models that do not retain information between calls. Each inference or prediction is made independently, without any memory of past inputs or outputs. These models are typically used for simple, independent calculations where the context of previous inputs is not important.
- Stateful models, on the other hand, retain information between calls. They maintain a state that can include weights, hidden states, or other parameters that are updated and used in subsequent calls. Stateful models are often used for sequential data, such as time series, where the context of past inputs is important for making predictions.
Differences between torch.nn and torch.nn.functional
A neural network is a subset of machine learning that uses the interconnected layers of nodes to process the data and find patterns. These patterns or meaningful insights help us in strategic decision-making for various use cases. PyTorch is a Deep-learning framework that allows us to do this.
It includes various modules for creating and training the neural networks. Among these, torch.nn and torch.nn.functional are popular. Let us discuss them in more detail in this article.
Table of Content
- What is PyTorch?
- What is torch.nn?
- What is torch.nn.functional?
- What are Stateless and Stateful Models?
- Differences Between torch.nn and torch.nn.functional
- How to choose between torch.nn and torch.nn.functional?
- Frequently Asked Questions