May 2020
Intermediate to advanced
456 pages
9h 13m
Japanese
Content preview from ゼロからはじめるデータサイエンス 第2版 ―Pythonで学ぶ基本と実践
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250
■
18
章 ニューラルネットワーク
18.2
フィードフォワードニューラルネットワーク
脳の幾何学的構造は非常に複雑であるため、ニューロンの層を順に接続した理想的
なフィードフォワードニューラルネットワークでの近似が一般的に使われています。通
常、（入力を受け取り、そのまま次の層に送る）入力層と、（前段層の出力を受け取り、
何らかの計算を行い、出力を次の層に送る）いくつかの隠れ層と、（最終出力を生成す
る ）出 力 層 で 構 成 さ れ ま す 。
パーセプトロンと同じように、各（非入力層の）ニューロンには、各入力に対する重
みとバイアスを持ちます。表現を簡単にするため、バイアスを重みベクトルの最後に
付加することとし、バイアスは常に
1
とします。
パーセプトロンと同様に、各ニューロンは入力の加重和を計算します。ただし、こ
こでは出力にステップ関数（
step_function
）を適用するのではなく、ステップ関数を
滑らかに近似した次のシグモイド（
sigmoid
）関数を使います（図 18-2）。
import math
def sigmoid(t: float) -> float:
return 1 / (1 + math.exp(-t))
ステップ関数とシグモイド関数
ステップ関数
シグモイド関数
図18-2 シグモイド関数
単純なステップ関数ではなくシグモイド関数を使う理由は何でしょうか。ニューラ
ルネットワークを学習させるために、微積分を使いますが、微積分を行うには滑らか
18.2
フィードフォワード ...
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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.