November 2018
Intermediate to advanced
272 pages
6h 51m
Japanese
book
エレガントなSciPy ―Pythonによる科学技術計算
by Juan Nunez-Iglesias, Stéfan van der Walt, Harriet Dashnow, 山崎 邦子, 山崎 康宏
Content preview from エレガントなSciPy ―Pythonによる科学技術計算
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181
7.4 「何が最適か？」：適切な目的関数の選び方
7.3
ベイスン
-
ホッピング法で極小値を避ける
1997
年に
David Wales
と
Jonathan Doyle
によって考案された
†
ベイスン
-
ホッピング法は、極
小値を避ける手法です。まず、パラメータの何らかの初期値から最適化を始めて、見つかった極小
値から無作為の方向に遠ざかり、再び最適化を試みるというやり方です。この無作為の移動に適切
なステップサイズを選ぶことで、このアルゴリズムは同じ極小値に
2
度ひっかかることを避けられ
るため、勾配に基づく単純な最適化手法よりも、ずっと広いパラメータ空間を探索できるのです。
ベイスン
-
ホッピングの
SciPy
の実装を組み込むのは、読者のみなさんの演習に任せます。これ
は本章の後方で必要になるので、もしつまずいたら本書の終わりにある解答を覗いても構いませ
ん。
7.3.1
演習：
align
関数を修正する
極小値を避けるための明示的な戦略を持つ
scipy.optimize.basinhopping
を使えるように
align
関数を修正してみましょう。
ベイスン - ホッピング法の適用を、ガウシアンピラミッドの上位レベルに限定しましょう。この最
適化手法は遅いため、元画像の高い解像度で走らせるとかなり時間がかかるからです。
7.4
「何が最適か？」：適切な目的関数の選び方
現時点で、すばらしいことに、我々は実際に役立つレジストレーションの手法を手にしていま
す。しかし、実は、同じモダリティの画像の位置合わせをするという、最も簡単なレジストレー ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.