April 2020
Intermediate to advanced
552 pages
6h 13m
Japanese
Content preview from ゼロから作るDeep Learning ❸ ―フレームワーク編
![ローゼンブロック関数の形状（画像は文献<a href="bib.xhtml#bib-wiki2">[19]</a>より引用）](/api/v2/epubs/urn:orm:book:9784873119069/files/images/html/ch3/3-10.png)
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ステップ28関数の最適化
私たちのDeZeroは、今では微分を自動で求めることができます。この微分は、様々な用途に利用できます。その1つの用途に――そして最も重要な用途に――関数の最適化が挙げられます。ここでは、具体的な関数に対して最適化を行ってみます。
![[注記]](/api/v2/epubs/urn:orm:book:9784873119069/files/images/note.png) |
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最適化とは、ある関数が与えられたときに、その最小値（もしくは最大値）を取る「関数の引数（入力）」を見つけることです。ニューラルネットワークの学習も、最適化問題の1つです。その目標は、損失関数の出力を最小化するパラメータを見つけることです。そのため、本ステップで行う内容は、そのままニューラルネットワークの学習にも適用できます。 |
28.1 ローゼンブロック関数
本ステップでは、ローゼンブロック関数を扱います。この関数は、次の式(28.1)で表され、図28-1のような形状をしています。
| (28.1) |
図28-1 ローゼンブロック関数の形状（画像は文献[19]より引用）
図28-1を見ると、放物線状の谷間があり、その谷間は長く伸びていることが分かります。ちなみに ...
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