dopt 0.3.5

A numerical optimisation and deep learning framework


To use this package, run the following command in your project's root directory:

Manual usage
Put the following dependency into your project's dependences section:

dopt

DUB

A numerical optimisation and deep learning framework for D.

Current features include:

  • Ability to construct symbolic representations of tensor-valued functions
  • Basic arithmetic and mathematical operations (add, sub, mul, div, abs, log, exp, ...)
  • Basic matrix operations (multiplication, transpose)
  • Reverse-mode automatic differentiation
  • Neural network primitives
  • Neural network construction utilities
  • Framework to add third party operations and their derivatives, and the ability register implementations for both the CPU and CUDA backends
  • Online optimisation algorithms: SGD, ADAM, AMSGrad, and more to come!

The project is still in the early stages, and some things might not work properly yet. Some planned future features include:

  • The ability to add optimisation passes to the CPU and CUDA backends
  • More utilities for training deep networks (data loaders, standard training loops, etc)

Docs

Documentation can be found here. A brief outline of how to use this framework for deep learning is provided here.

Using

The easiest way to use dopt is by adding it as a dependency in your project's dub configuration file. See dub's getting started page for more information about how to do this.

Example

Examples for training networks on MNIST and CIFAR10 are given in the examples/ folder.

Authors:
  • Henry Gouk
Dependencies:
derelict-cuda, derelict-cudnn, cblas, derelict-nvrtc
Versions:
0.3.18 2018-Jul-31
0.3.17 2018-Jun-27
0.3.16 2018-Jun-07
0.3.15 2018-Jun-02
0.3.14 2018-Jun-02
Show all 23 versions
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Short URL:
dopt.dub.pm