Sequential
Sequential is a PyTorch container module that applies child modules in a specified order to form a composite computation.
Inside Deep Learning: Math, Algorithms, Models
A PyTorch container module that applies a sequence of submodules in order.
- How it is used
- Organizes ordered feed-forward submodules into reusable PyTorch models for training and inference.
- How deep it goes
- Defines ordered execution and module reuse, but stops short of architectures that require nonsequential computation.
- Examples
- Uses stacked Linear layers, MNIST classifiers, shared hidden layers, and convolutional residual blocks.
- What you can do after
- Build it from scratch: define ordered PyTorch models and insert reusable submodules into larger architectures.
Read how Inside Deep Learning: Math, Algorithms, Models explains Sequential →
Deep Learning with PyTorch, Second Edition
A container module that groups child modules in order, forming a sequential network structure.
- How it is used
- Builds ordered feed-forward and convolutional networks from reusable PyTorch modules.
- How deep it goes
- Explains ordering, naming, parameter inspection, and visibility limits, but defers flexible architectures to custom nn.Module.
- Examples
- Uses named linear networks, parameter listings, gradient inspection, and convolutional pipelines that expose a reshaping problem.
- What you can do after
- Builds ordered PyTorch models, trains them, and inspects their named parameters and gradients.
Read how Deep Learning with PyTorch, Second Edition explains Sequential →
Related concepts
- torch.nn.Moduleuses · torch.nn.Module is PyTorch’s base class for neural-network components that encapsulate computation, parameters, and submodules.
- residual connectionused by · A residual connection is a neural-network pathway that carries an earlier representation around one or more transformations to a later computation, where it can be combined with the transformed representation.
- recurrent neural networkuses · A recurrent neural network is a neural network that processes sequences by updating an internal state across successive inputs, allowing earlier inputs to influence later computations.
- linear layeruses · A linear layer is a neural-network layer that maps input features to outputs by a learned affine transformation, typically connecting each output to every input feature.
- feedforward networkimplements · A feedforward network is a neural network or subnetwork in which information flows from inputs toward outputs through successive computations without recurrent connections or cycles.
- language modelimplements · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
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