PyTorch Sequential Explained: How & When to Use

The nn.Sequential in PyTorch is a container used to sequentially organize and execute a series of module operations. It allows users to combine multiple layers or modules in a specific order to build a neural network model.

Using nn.Sequential allows for easy definition and management of neural network layers, simplifying the process of building and debugging models. Each layer or module in nn.Sequential is sequentially connected, where the output of one layer serves as the input for the next. This sequential connection method results in a clear and easily understandable model structure.

With nn.Sequential, the process of constructing a neural network model can be simplified into a sequential operation, by adding layers in order to the Sequential container. During the model’s forward propagation, simply calling the Sequential container will automatically execute the computation of each layer in order, eliminating the need to manually write the forward propagation function.

In short, the purpose of nn.Sequential is to simplify the construction and training process of neural network models, providing a convenient way to connect multiple layers or modules together.