Deep Flare Net (DeFN) TensorFlow 2 version

Seiji Zenitani, Research Center for Urban Safety and Security, Kobe University, Japan

Originally developed by Komei Sugiura, National Institute of Information and Communications Technology, Japan https://github.com/komeisugiura/defn18.git

0. License

• BSD 3-Clause Clear License

1. Requirements

• Ubuntu 22.04 LTS
• Python 3.9
  • numpy, pandas, scikit-learn (sklearn)
  • tensorflow 2

2. Install

• In the following procedure, ~/work is assumed to be used as a working directory.

$ cd ~/work/
$ git clone https://github.com/zenitani/defn18.git
$ cd defn18
$ pip3 install tensorflow
[ $ pip3 install tensorflow numpy pandas sklearn ]

3. Download data

• Visit http://wdc.nict.go.jp/IONO/wdc/solarflare/index.html and download defn_feature_database_v1_pl.zip.

$ cd ~/work/
$ mv defn_feature_database_v1_pl.zip ./
$ unzip defn_feature_database_v1_pl.zip
(password is required)
$ cp defn_feature_database_v1/defn_input_database/charval2017X_*.csv.gz ./defn18/data/

4. Run

$ cd ~/work/defn18/src
$ ./deepflarenet.py

• The following result will be shown. This means that TSS=0.8024 is obtained by using a pretrained model.

[008000]Acc: Tra=0.8345, Val=0.8584, Tes=0.8584, MaxVal=0.8584(0.8584), TSS=0.8024

5. Training DeFN from scratch

Modify src/deepflarenet.py.

• Uncomment the following line to train the model # net1.train_model(update_interval=100)

• Uncomment the following line to save the trained model. Current model is overwritten. # net1.save_model(myflag.outfile_model)

• Comment the following two lines out, if you don't like to load the model

net1.load_model(myflag.infile_model)
net1.show_training_status(epoch=8000)

A. References

1. N. Nishizuka, K. Sugiura, Y. Kubo, M. Den, and M. Ishii, "Deep Flare Net (DeFN) Model for Solar Flare Prediction", The Astrophysical Journal, Vol. 858, Issue 2, 113 (8pp), 2018. DOI: 10.3847/1538-4357/aab9a7

Local Variables:

coding: utf-8

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