Metadata-Version: 1.0
Name: PreTrainingChain
Version: 0.0.0
Summary: Scalable, configurable and Pre-training DNN using chainer
Home-page: https://github.com/fukatani/PreTrainingChain
Author: Ryosuke Fukatani
Author-email: nannyakannya@gmail.com
License: Apache License 2.0
Description: Introduction

        ============

        

        Extension of chainer.ChainList for the purpose of network scalability

        for deep leaning.

        

        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]), 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 is implemented. You can execute pre-training only by

           calling AbstractChain.pre\_training(train\_data, test\_data).

           test\_data is optional. If you input any test\_data, result of test

           as autoencoder at each hidden layer will be printed. If length of

           train\_Data is zero, Pre-training is skipped.

        

        Software Requirements

        =====================

        

        -  Python (2.7)

        

        Installation

        ============

        

        ::

        

            $ git clone https://github.com/fukatani/PreTrainingChain.git

        

        Example

        =======

        

        Example.py is implement for mnist classification.

        

        ::

        

            $ python Example.py

        

            fetch MNIST dataset

            Successed data fetching

            Pre-training test loss: 0.0895392745733

            Pre-training test loss: 0.000182752759429

            Pre-training test loss: 5.92054857407e-05

            Pre-training test loss: 1.82532239705e-05

            test_loss: 2.30244994164

            test_accuracy: 0.0799999982119

            test_loss: 2.30086517334

            test_accuracy: 0.189999997616

            test_loss: 2.28533029556

            test_accuracy: 0.27500000596

            test_loss: 2.25788879395

            test_accuracy: 0.294999986887

            test_loss: 2.21044063568

            test_accuracy: 0.284999996424

            test_loss: 2.13255786896

            test_accuracy: 0.280000001192

            test_loss: 2.09592270851

            test_accuracy: 0.305000007153

            test_loss: 2.05419230461

            test_accuracy: 0.294999986887

            test_loss: 2.04007315636

            test_accuracy: 0.294999986887

            test_loss: 2.01762104034

            test_accuracy: 0.289999991655

        

        License

        =======

        

        Apache License 2.0 (http://www.apache.org/licenses/LICENSE-2.0)

        

        Copyright

        =========

        

        Copyright (C) 2015, Ryosuke Fukatani

        

        Related Project and Site

        ========================

        

        chainer http://docs.chainer.org/en/stable/index.html

        
Keywords: chainer,newral network,machine leaning
Platform: UNKNOWN
