Torch.nn.Module
torch.nn.Module is PyTorch’s base class for neural-network components that encapsulate computation, parameters, and submodules.
Deep Learning with PyTorch, Second Edition
The PyTorch base class for defining neural-network models as Python modules with parameters, submodules, and a forward method.
- How it is used
- Organizes trainable layers and their computation into reusable components or complete PyTorch networks.
- How deep it goes
- Works through subclassing, calls, registration, and parameter collection; leaves backward computation to autograd rather than defining a backward method.
- Examples
- Uses a convolutional image classifier with an explicit reshape, plus parameter-count and named-parameter inspections.
- What you can do after
- Build it from scratch by subclassing nn.Module, defining forward, registering child layers, and supplying collected parameters to an optimizer.
Read how Deep Learning with PyTorch, Second Edition explains torch.nn.Module →
Deep Learning with Python, Third Edition
The base class for defining stateful PyTorch models with parameters and a forward method.
- How it is used
- Packages trainable state and forward computation into reusable PyTorch model objects for gradient-based training.
- How deep it goes
- Demonstrates subclass definition, Parameter tracking, invocation, optimizer integration, training steps, and gradient clearing.
- Examples
- Uses a linear classifier with weight and bias Parameters, mean squared error, SGD, and a compact training step.
- What you can do after
- Build it from scratch using torch.nn.Module, torch.nn.Parameter, and torch.optim.SGD.
Read how Deep Learning with Python, Third Edition explains torch.nn.Module →
Natural Language Processing in Action, Second Edition
A PyTorch module is a reusable neural-network component represented by the torch.nn.Module base class and its registered parameters and submodules.
- How it is used
- Defines a trainable PyTorch neuron for binary classification of names.
- How deep it goes
- Explains subclass construction and forward propagation, then demonstrates training without detailing Module internals.
- Examples
- Uses a LogisticRegressionNN module with 3,663 TF-IDF inputs and name predictions such as John, Ruby, and Sarah.
- What you can do after
- Build it from scratch by subclassing Module, defining forward propagation, and training the resulting model.
Read how Natural Language Processing in Action, Second Edition explains torch.nn.Module →
Math and Architectures of Deep Learning
The PyTorch base class that neural network models subclass to define learnable modules and a forward pass.
- How it is used
- Defines reusable neural-network models whose layers and forward computations participate in prediction and training.
- How deep it goes
- Descends through subclassing, initialization, Sequential chaining, forward execution, and optimizer integration, but not Module internals.
- Examples
- Uses a demand-prediction multilayer perceptron and a convolutional network with three Conv2d layers.
- What you can do after
- Build it from scratch by subclassing Module, composing layers, implementing forward(), and training through parameters and an optimizer.
Read how Math and Architectures of Deep Learning explains torch.nn.Module →
Deep Learning with R, Third Edition
torch.nn.Module is PyTorch's base class for building objects that represent neural-network modules and manage their parameters.
- How it is used
- Packages model state and forward computation for object-oriented PyTorch training.
- How deep it goes
- Defines subclass structure, invocation, optimizer integration, training steps, and gradient resetting, but not Module internals.
- Examples
- Demonstrates a linear classifier with weight and bias Parameters, matrix multiplication, loss backpropagation, SGD, and gradient clearing.
- What you can do after
- Build a trainable PyTorch model and operate its optimizer-driven training loop.
Read how Deep Learning with R, Third Edition explains torch.nn.Module →
Related concepts
- PyTorchused by · PyTorch is an open-source machine-learning framework for Python that provides tensor computation, automatic differentiation, and tools for building and training neural networks.
- Sequentialused by · Sequential is a PyTorch container module that applies child modules in a specified order to form a composite computation.
- forwardimplements · In PyTorch, forward is the module method that defines how inputs are transformed into outputs during a forward pass.
- super keyworduses · The super construct accesses or invokes inherited methods, constructors, or attributes according to a language’s inheritance rules.
- positional embeddingimplements · A positional embedding is a learned or fixed representation that encodes an element’s position or order in a sequence and is incorporated into its content representation.
- DataLoaderuses · PyTorch DataLoader is an iterable utility that retrieves dataset samples, optionally shuffles and transforms them, and groups them into batches.
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