Welcome to Sknet (in construction)

Sknet is a lightweight library built mainly upon numpy and tensorflow. The aim is to provide a fully independent and self content toolbox. Similarly to matplotlib, the user can either run some state-of-the-art deep learning methods on the most comon dataset with just a few lines of codes and no prerequisites; but also accees any block and any subtleties of the provided methods to tweak, experiment, or improve upon them. As such, this library aims to fulfill two major goals:

  • provide an out-of-the-box solution for practicioner allowing to reproduce results and build upon previous work with a comon working environment without any prerequisites or additional ressources
  • Allow easy access and modification of any of the provided methods to allow anyone to experiment and improve upon existing methods

The above makes this toolbox oriented for any party interested in trying deep learning methods for some specific tasks to researchers in need of a qualitative and self content toolbox. By providing to the user enough transparency and flexibility to implement their own creation without requiring to redo all the other parts of the pipeline we hope to

  • allow fast and easy experimenting on any part of the deep learning pipeline
  • allow anyone to easily validate their idea without requiring time ressources in coding parts independent from the idea to test
  • ensure that provided and used methods follow the guidelines used by the developers and practicioners

This toolbox is oriented for research and education, and any projects do not requiring multi-GPU computing. We briefly describe here the fundamentals of the toolbox. The project is on _GitHub.

Sknet way of working

The library is built with the deep learning pipeline in mind. That is, it provides multiple blocks which are highly customizable. Those blocks are then combined into a pipeline to solve a task. Those blocks are:

  • Dataset: any collection of inputs or (input-output) pairs

  • Preprocess (optional): pre-processing that can be applied onto any dataset for increased performances s.a. zca whitening

  • Networks: a fully described network from input to output, in term of layers s.a. LeNet5. Any network is built by combining any Layer

  • Layer: low-level building blocks of any network. They are grouped per type as

    • dense
    • convolutional
    • data augmentation
    • shape
  • optimizer : any updating policy applied onto the learnable weights of a model s.a. adam or sgd

  • learning rate Schedules: combined with a loss function, a model and a dataset, the learning rate scheduler plays a crucial role to guarantee best performances s.a. stepwise or adaptive

  • Pipeline : a higher-level method assembling those independent blocks into a trainable pipeline, also containing the tensorflow session

User Guide

The Sknet user guide explains how to install Sknet, how to build and train neural networks, and how to contribute to the library as a developer.

API Reference

If you are looking for information on a specific function:


CIFAR100 with LeNet5

SVHN with Custom Model

About this documentation

Lots of documentation can be found online [1]