torch / Nn
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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: composite and decorator classes like
Sequential
,Parallel
,Concat
andNaN
; -
Transfer functions: non-linear functions like
Tanh
andSigmoid
; -
Simple layers: like
Linear
,Mean
,Max
andReshape
; -
Table layers: layers for manipulating
table
s likeSplitTable
,ConcatTable
andJoinTable
; -
Convolution layers:
Temporal
,Spatial
andVolumetric
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;
-
Criterions: a list of all criterions, including
- 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.
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