April 2024
Intermediate to advanced
336 pages
3h 27m
Japanese
Content preview from ゼロから作るDeep Learning ❺ ―生成モデル編
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ステップ9拡散モデルの実装
前ステップでは、拡散モデルの「理論」について学びました。これから拡散モデルの「実装」へと進みます。拡散モデルではニューラルネットワークに「U-Net」と呼ばれるモデルがよく使われます。本ステップでは初めにU-Netについて説明し、その実装を行います。次に、時刻データ（整数データ）を効率良く処理するための「正弦波位置エンコーディング」について学びます。その後に、ガウスノイズを追加する「拡散過程」をひとつひとつ確かめながら実装し、最後にMNISTデータセットを使って拡散モデルの学習を行います。
9.1 U-Net
拡散モデルで使用されるニューラルネットワークは、数式で
と表されます。このニューラルネットワークは図9-1のようにノイズを予測します。
図9-1 拡散モデルで使われるニューラルネットワーク
への入力は、ノイズデータのと時刻です。入力が2つありますが、ここでは初めにを省略した、図9-2のニューラルネットワークを実装します。図9-2が完成した後に、時刻 ...
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I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.