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/)
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 ChainClassfier([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.
3)Usage as scikit-learn library, and correpond to GridSearch parameter tuning.
You can use PreTraining_chain as scikit-learn library, So ChainClassfier.fit, ChainClassfier.predict, ChainClassfier.score is usable. Also you can use sklearn.gridsearchCV. Please see GridSearchExample.py.
Software Requirements
Python (2.7)
chainer >= 1.8.0
scikit-learn
Installation
$ pip install PreTrainingChain
or
$ git clone https://github.com/fukatani/PreTrainingChain.git
Example
Implement example is here https://github.com/fukatani/PreTrainingChain/blob/master/PreTrainingChain/Example.py You have to override add_last_layer method and loss_function method.
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
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