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
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ステップ56CNNのメカニズム（2）
前ステップでは、縦と横方向に並ぶ2次元データ（2階テンソル）に対しての畳み込み演算を説明しました。しかし画像の場合、縦と横方向に加えて、RGBのように「チャンネル方向」にもデータが並ぶため、そのような3次元データ（3階テンソル）を扱う必要があります。ここでは、前ステップと同じ手順で、3階テンソルに対しての畳み込み演算を見ていきます。その後に、プーリングについて説明します。
56.1 3階テンソル
早速、図56-1に畳み込み演算の例を示します。ここでは、チャンネル数が3のデータを例に、畳み込み演算を行う様子を示します。
図56-1 3階テンソルに対する畳み込み演算の例
図56-1のとおり、畳み込み演算の手順は、2階テンソルの場合と同じです。奥行き方向にデータが増えたことを除けば、フィルタの動き方や計算方法に変更はありません。ここで注意すべき点は、入力データとフィルタの「チャンネル数」を同じにするということです。図56-1では、入力データとフィルタのチャンネル数はどちらも3です。一方、フィルタの縦・横のサイズは好きな数に設定できます。上の例ではフィルタのサイズは(3, 3)ですが、それは(1, 2)や(2, 1)といったように、好きな値に設定できます。
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図56-1のフィルタは、前ステップと同じく横と縦方向の2次元上を動きます。そのため、（3階テンソルに対しての畳み込み演算ですが）、これは「2次元の畳み込み層」に分類されます。多くのディープラーニングのフレームワークでは、 ... |
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