How to choose between torch.nn and torch.nn.functional?
Both torch.nn and functional have methods such as Conv2d, Max Pooling, ReLU, etc. But when it comes to the implementation, there is a slight difference between them. Let us now discuss when to choose the torch.nn module and when we should opt for the torch.nn.functional.
- You should use the ‘torch.nn’ when you want to train the layers with learnable parameters. But if you want to make operations simple, ‘torch.nn.functional’ is suitable as it has stateless operations without any parameters.
- The ‘torch.nn’ module is less flexible than the ‘torch.nn.functional’ module. This is because we can use the torch.nn.functional to define custom operations and use them in the neural network. So, if you want flexibility, the torch.nn.functional module is used.
- If you want to create and train the neural network using the pre-defined layers, torch.nn is suitable. But if you want to customize some parts of the neural network, you can use the torch.nn.functional within the custom modules.
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