hangzhaomit/nn

A personal modified version of Torch NN for deep learning research

Neural Network Package

This package provides an easy and modular way to build and train simple or complex neural networks using Torch:

• Modules are the bricks used to build neural networks. Each are themselves neural networks, but can be combined with other networks using containers to create complex neural networks:
  • Module: abstract class inherited by all modules;
  • Containers: container classes like Sequential, Parallel and Concat;
  • Transfer functions: non-linear functions like Tanh and Sigmoid;
  • Simple layers: like Linear, Mean, Max and Reshape;
  • Table layers: layers for manipulating tables like SplitTable, ConcatTable and JoinTable;
  • Convolution layers: Temporal, Spatial and Volumetric convolutions;
• Criterions compute a gradient according to a given loss function given an input and a target:
  • Criterions: a list of all criterions, including Criterion, the abstract class;
  • MSECriterion: the Mean Squared Error criterion used for regression;
  • ClassNLLCriterion: the Negative Log Likelihood criterion used for classification;
• Additional documentation:
  • Overview of the package essentials including modules, containers and training;
  • Training: how to train a neural network using optim;
  • Testing: how to test your modules.
  • Experimental Modules: a package containing experimental modules and criteria.