Scalable, configurable and Pre-training DNN using chainer
Project description
Introduction
============
Extension of chainer. ChainList for the purpose of network
scalability/congirablity/Pre-training executablity for deep leaning.
(You need to get deep learning framework "chainer" from
http://chainer.org/)
Detail of this project is here.
https://github.com/fukatani/PreTrainingChain
feature:
========
1) You can define network structure by list or tuple such as [784, 250, 200, 160, 10].
--------
This feature accelerate your deep network development. If you call this
class by AbstractChain([784, 250, 200, 160, 10]), you can generate
ChainList-> (F.Linear(784, 250), F.Linear(250, 200), F.Linear(200, 160),
F.Linear(160, 10)) You can change network structure without any hard
coding.
2) Pre-training executable.
--------
You can execute pre-training only by calling
AbstractChain.pre\_training(train\_data). Pretraining is executed by
using Bengio method. (http://arxiv.org/pdf/1206.5538.pdf) If length of
train\_Data is zero, Pre-training is skipped.
============
Extension of chainer. ChainList for the purpose of network
scalability/congirablity/Pre-training executablity for deep leaning.
(You need to get deep learning framework "chainer" from
http://chainer.org/)
Detail of this project is here.
https://github.com/fukatani/PreTrainingChain
feature:
========
1) You can define network structure by list or tuple such as [784, 250, 200, 160, 10].
--------
This feature accelerate your deep network development. If you call this
class by AbstractChain([784, 250, 200, 160, 10]), you can generate
ChainList-> (F.Linear(784, 250), F.Linear(250, 200), F.Linear(200, 160),
F.Linear(160, 10)) You can change network structure without any hard
coding.
2) Pre-training executable.
--------
You can execute pre-training only by calling
AbstractChain.pre\_training(train\_data). Pretraining is executed by
using Bengio method. (http://arxiv.org/pdf/1206.5538.pdf) If length of
train\_Data is zero, Pre-training is skipped.
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