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GDLC#10 Deep Learningによるダウンスケーリング
Deep Learningによる
降⽔量ダウンスケーリング
GPU Deep Learning Community
第10回:LT
2019年1⽉17⽇(⼩寒)
GDLC#10 Deep Learningによるダウンスケーリング
Who are you?
GDLC#10 Deep Learningによるダウンスケーリング
気象庁
予報部 数値予報課
アプリケーション班
⽥中 基裕
GDLC#10 Deep Learningによるダウンスケーリング
気象庁
予報部 数値予報課
アプリケーション班
⽥中 基裕
GDLC#10 Deep Learningによるダウンスケーリング
数値予報
ユーザー
応⽤処理 予報担当者
天気予報の流れ
観測データ
収集
GDLC#10 Deep Learningによるダウンスケーリング
数値予報
ユーザー
応⽤処理 予報担当者
天気予報の流れ
アプリケーション
GDLC#10 Deep Learningによるダウンスケーリング
Motivation
GDLC#10 Deep Learningによるダウンスケーリング
予報担当者数値予報 応⽤処理
天気予報の流れ(再掲)
GDLC#10 Deep Learningによるダウンスケーリング
予報担当者
様々な数値予報モデル
数値予報B
数値予報A
数値予報C
応⽤処理B
応⽤処理A
応⽤処理C
GDLC#10 Deep Learningによるダウンスケーリング
⽔平解像度が異なる数値予報
20km
5km
2km
数値予報B
数値予報A
数値予報C
GDLC#10 Deep Learningによるダウンスケーリング
数値予報B 応⽤処理B
統合
プロダクト
統合プロダクトの開発計画
数値予報A 応⽤処理A
数値予報C 応⽤処理C
予報担当者
アンサンブル
GDLC#10 Deep Learningによるダウンスケーリング
数値予報B 応⽤処理B
統合
プロダクト
数値予報A 応⽤処理A
数値予報C 応⽤処理C
同じ⽔平スケールに統⼀
GDLC#10 Deep Learningによるダウンスケーリング
Deep Learning による超解像⼿法を
⽤いて降⽔量をダウンスケーリング
https://github.com/openimages/dataset
GDLC#10 Deep Learningによるダウンスケーリング
防災利⽤では、出現頻度の
少ない⼤⾬を適切に表現したい
GDLC#10 Deep Learningによるダウンスケーリング
Setting
GDLC#10 Deep Learningによるダウンスケーリング
開発環境
n CentOS 6.6
n GPU: NVIDIA Tesla K40c
n CUDA9.0
n TensorFlow 1.9.0
n Keras 2.1.6
GDLC#10 Deep Learningによるダウンスケーリング
データ
n 3時間積算の観測値
n  20km格⼦の降⽔量を⼊⼒
n  5km格⼦の降⽔量を⽬的変数
n 学習データ: 2006年4⽉ 〜 2013年7⽉
        7年分×8つのサブパッチ
n 検証データ: 2013年8⽉ 〜 2015年7⽉
        2年分×8つのサブパッチ
n テストデータ:2015年8⽉ 〜 2018年7⽉
       3年分の全領域
20km→5kmに
ダウンスケーリング
https://www.jma.go.jp/jma/kishou/know/kurashi/kaiseki.html
GDLC#10 Deep Learningによるダウンスケーリング
FCN(Fully Convolutional Network)
全結合層のない
ネットワーク
⼊⼒の画像サイズ
に依存しない
パラメータの
数が少ない ※Dong et al(2016)
GDLC#10 Deep Learningによるダウンスケーリング
8つのサブパッチ
GDLC#10 Deep Learningによるダウンスケーリング
学習
モデル
GDLC#10 Deep Learningによるダウンスケーリング
推論
モデル
GDLC#10 Deep Learningによるダウンスケーリング
実験設定
n 学習データの平均・標準偏差から 
⼊⼒データを標準化
n バッチサイズは32
n オプティマイザーは ADAM
n 学習率はスケジューリング
n 損失・評価関数は MSE
n ネットワークはEDSR17
GDLC#10 Deep Learningによるダウンスケーリング
EDSR17
Lim et al(2017)
GDLC#10 Deep Learningによるダウンスケーリング
6つの
ResBlock
ResBlockの中
GDLC#10 Deep Learningによるダウンスケーリング
4倍にアップサ
ンプリング
2倍のアップサン
プリングを2回
GDLC#10 Deep Learningによるダウンスケーリング
Experiment
GDLC#10 Deep Learningによるダウンスケーリング
バイキュービック内挿
https://en.wikipedia.org/wiki/Bicubic_interpolation
既存の⼿法がないため
バイキュービックと⽐較を⾏う
GDLC#10 Deep Learningによるダウンスケーリング
2018/7/30 の事例
GDLC#10 Deep Learningによるダウンスケーリング
2018/7/30 の事例
⼊⼒ 観測
Test ⽐較
GDLC#10 Deep Learningによるダウンスケーリング
128
13495
20km
Test
5km
(単位:mm/3h)2018/7/30 の事例
Bicubic
102
GDLC#10 Deep Learningによるダウンスケーリング
20km
Test
5km
2018/7/30 の事例
Bicubic
地形性降⽔
ライン状の
降⽔域
GDLC#10 Deep Learningによるダウンスケーリング
2018/7/27 の事例
20km
Test
5km
Bicubic
GDLC#10 Deep Learningによるダウンスケーリング
⼆次元ヒストグラム
Bicubic	 Test	
ü 出現頻度の少ない⼤⾬も適切に表現
Observation	
Observation
GDLC#10 Deep Learningによるダウンスケーリング
Problem
GDLC#10 Deep Learningによるダウンスケーリング
GPUを利⽤した計算が突然停⽌
n Deep Learningの学習中に突然停⽌する
n エラーを出⼒しない
GDLC#10 Deep Learningによるダウンスケーリング
試したこと
✖︎ CentOS7.3(Dockerイメージ)で実装
✖︎ CUDA10.0に更新
✖︎ NIVIDIAドライバーの更新
✖︎ Tensor Flowの様々なバーションでの実装
✖︎ GPUの熱暴⾛でないか確認
△1エポック毎にGPUのメモリを開放
1エポック毎にGPUメモリを開放する
ことでフリーズ頻度は減ったが未解決
GDLC#10 Deep Learningによるダウンスケーリング
Help me
GDLC#10 Deep Learningによるダウンスケーリング
Reference
n  B. Lim, S. Son, H. Kim, S. Nah, and K. M. Lee.
Enhanced deep residual networks for single image
super-resolution. In The IEEE Conference on
Computer Vision and Pattern Recognition (CVPR)
Workshops, July 2017.
n  C. Dong, C. C. Loy, K. He, and X. Tang. Image
super-resolution using deep convolutional networks.
IEEE Transactions on Pattern Analysis and Machine
Intelligence, 38(2):295–307, 2016.
n  W. Shi, J. Caballero, F. Huszar, J. Totz, A. P. Aitken,
R. Bishop, D. Rueckert, and Z. Wang. Real-Time
Single Image and Video Super-Resolution Using an
Efficient Sub-Pixel Convolutional Neural Network. In
IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), pages 1874–1883, 2016.
GDLC#10 Deep Learningによるダウンスケーリング
Enjoy!

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