- Deep Recurrent Neural Networks (RNN)
- Long Short-Term Memory networks (LSTM)
- In fact, the library is more general because it has functionality to construct arbitrary expression graphs over which the library can perform automatic differentiation similar to what you may find in Theano for Python, or in Torch etc. Currently, the code uses this very general functionality to implement RNN/LSTM, but one can build arbitrary Neural Networks and do automatic backprop.
For further Information see the recurrentjs repository.
For Production Use
How to install as dependency
Install via command line:
npm install --save recurrent-js-gpu
How To use the Library in Production
Currently exposed Classes:
- R - Collection of Utility functions
- Mat - Sophisticated Matrix Structure for Weights in Networks.
- RandMat -
Matpopulated with random gaussian distributed values
- Graph - Graph holding the Operations
- NNModel - Genralized Class containing the Weights (and
- PreviousOutputs - Standardized Interface for parameter injection in forward-pass of
- Net - Simple Neural Network
- RNN - Recurrent Neural Network. Extends
- LSTM - Long Short Term Memory Network. Extends
These classes can be imported from this
npm module, e.g.:
require classes from this
npm module as follows:
const Graph = Graph;const Net = Net;
This project uses GPU-accelerated Matrix-Operations. The GPU-Kernel-functions are stored in a registry to optimize initialization timings and to ensure single-initialization.
Further Info for Production Usage
ES6, with a
CommonJS module format.
Clonethis project to a working directory.
npm installto setup the development dependencies.
- To compile the codebase:
tsc -p .
This project relies on Visual Studio Codes built-in Typescript linting facilities. It primarily follows the Google TypeScript Style-Guide through the provided tslint-google.json configuration file.
As of License-File: MIT