Scalable, configurable and Pre-training DNN using chainer
Project 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.
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
Project details
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