June 2022
Intermediate to advanced
384 pages
6h 26m
Japanese
Content preview from 動かして学ぶAI・機械学習の基礎 ―TensorFlowによるコンピュータビジョン、自然言語処理、時系列データの予測とデプロイ
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11章畳み込みと回帰によるシーケンスモデルの構築
これまでの数章では、シーケンスデータについて紹介してきました。まず統計的手法を用いて予測し、次にディープニューラルネットワークを用いた基本的な機械学習手法を紹介しました。また、Keras Tunerを使ってモデルのハイパーパラメータを調整する方法も説明しました。本章では、畳み込みニューラルネットワークや回帰型ニューラルネットワークを使用して、シーケンスデータを予測する能力をさらに高めるため手法について説明します。
11.1 シーケンスデータの畳み込み
「3章 基礎からの発展: 画像の特徴量検出」では、画像に2次元のフィルタをかけて、特徴量を抽出しやすくする畳み込み処理を紹介しました。ニューラルネットワークは、ピクセルに加えられた変更をラベルと一致させるのに、どのフィルタ値が効果的かを時間をかけて学習し、画像から効果的に特徴量を抽出しました。同じ手法を数値の時系列データにも適用できますが、違いが1つあります。それは、畳み込みが2次元ではなく1次元であることです。
例えば、図11-1のような一連の数値について考えてみましょう。
図11-1 数値のシーケンス
1次元の畳み込みは、次のように操作します。まず、畳み込みを－0.5、1、－0.5の1×3フィルタであると考えます。この場合、図11-2に示すように、シーケンスの1番目の値は失われ、2番目の値は8から－1.5に変換されます。
図11-2 数値シーケンスの畳み込み
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