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 StochasticGradient;
- Testing : how to test your modules.
- Experimental Modules : a package containing experimental modules and criteria.