September 2016
Intermediate to advanced
320 pages
4h 31m
Japanese
Content preview from ゼロから作るDeep Learning ―Pythonで学ぶディープラーニングの理論と実装
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付録ASoftmax-with-Lossレイヤの計算グラフ
ここでは、ソフトマックス関数と交差エントロピー誤差の計算グラフを示し、それらの逆伝播を求めます。ソフトマックス関数はSoftmaxレイヤ、交差エントロピー誤差はCross Entropy Errorレイヤと呼び、また、この2つを組み合わせたレイヤをSoftmax-with-Lossレイヤと呼ぶことにします。先に結果を示すと、Softmax-with-Lossレイヤは図A-1のような計算グラフで書くことができます。
図A-1 Softmax-with-Lossレイヤの計算グラフ
図A-1の計算グラフでは、3クラス分類を行うニューラルネットワークを想定しています。前レイヤからの入力は(a1, a2, a3)であり、Softmaxレイヤは(y1, y2, y3)を出力します。また、教師ラベルは(t1, t2, t3)であり、Cross Entropy Errorレイヤは損失Lを出力します。
本付録では、Softmax-with-Lossレイヤの逆伝播の結果が図A-1のように、(y1−t1, y2−t2, y3−t3)になることを示します。
A.1 順伝播
図A-1の計算グラフでは、SoftmaxレイヤとCross Entropy Errorレイヤの中身は省略して図示しました。ここでは、その2つのレイヤの中身を省略せずに描くことから始めたいと思います。
まずはSoftmaxレイヤですが、ソフトマックス関数は次の数式で表されます。 ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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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.