Yet another neural network toolkit.
Yet Another toolkit for Neural Network slightly flavoured by Ultra-High Energy Cosmic Rays.
CrayNN is highly influenced by Lasange:
Simplicity: Be easy to use, easy to understand and easy to extend, to facilitate use in research Transparency: Do not hide Theano behind abstractions, directly process and return Theano expressions or Python / numpy data types Modularity: Allow all parts (layers, regularizers, optimizers, ...) to be used independently of Lasagne Pragmatism: Make common use cases easy, do not overrate uncommon cases Restraint: Do not obstruct users with features they decide not to use Focus: "Do one thing and do it well"
pip install craynn
craynn can be installed directly from
pip install git+https://gitlab.com/craynn/craynn.git
however, as repository updates frequently, it is recommend to clone the repository
and install the package in development mode:
git clone email@example.com:craynn/craynn.git cd craynn/ pip install -e .
NB: don't forget to install proper version of
craygraph in a similar manner:
git clone firstname.lastname@example.org:craynn/craygraph.git cd craygraph/ pip install -e .
Check out jupyter notebooks in
craynn is designed for rapidly defining networks of all sorts:
from craynn import network, conv, max_pool net = network((None, 1, 28, 28))( conv(16), conv(24), max_pool(), conv(16), conv(24), max_pool(), conv(16), conv(24), max_pool(), )
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