Torch.nn.Module

As explained in 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 Natural Language Processing in Action, Second Edition covers it

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 it assumes

Assumes TF-IDF features and binary classification while demonstrating the module structure from the model definition onward.

What you can do after

Build it from scratch by subclassing Module, defining forward propagation, and training the resulting model.

How it connects

Connects neural-network modules to Linear layers, sigmoid activation, binary cross-entropy, SGD, and backpropagation.

liveBookmanning.com

Read Natural Language Processing in Action, Second Edition on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  Deep Learning with PyTorch, Second EditionThe PyTorch base class for defining neural-network models as Python modules with parameters, submodules, and a forward method.
• 

  liveBookmanning.com
  Deep Learning with Python, Third EditionThe base class for defining stateful PyTorch models with parameters and a forward method.
• 

  liveBookmanning.com
  Math and Architectures of Deep LearningThe PyTorch base class that neural network models subclass to define learnable modules and a forward pass.
• 

  liveBookmanning.com
  Deep Learning with R, Third Editiontorch.nn.Module is PyTorch's base class for building objects that represent neural-network modules and manage their parameters.

Compare how 5 books explain 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.

This explainer was generated by Manning's knowledge graph from the text of Natural Language Processing in Action, Second Edition. The section links point to the parts of the book it draws on.