February 2025
Intermediate
376 pages
9h 25m
Japanese
Content preview from 仕組みからわかる大規模言語モデル 生成AI時代のソフトウェア開発入門
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図1.3.16
線形変換層
線形変換層は、デコーダスタックの出力（
n#D
model
の行列）を語彙サイズの次元（
n#| V |
の行列）に写像します。この変換は、次の数式で表すことができます。
Z
=
YW
out
+
B
out
ここで、
Y
はデコーダスタックの出力（
n#D
model
の行列）、
W
out
は重み行列（
D
model
#|V |
の行列）、
B
out
はバイアス項（
n#| V |
の行列）を表しています。線形変換の出力は、語彙サイ
ズの次元を持つ行列です。この行列の各行は、対応するトークンのスコアを表しています。た
だし、これらのスコアは確率分布ではありません。確率分布を得るために、線形変換の出力に
ソフトマックス関数が適用されます。
ソフトマックス関数（Softmax Function）
ソフトマックス関数は、実数値のベクトルを確率分布に変換する関数です。この関数は、ベ
クトルの各要素を0 から 1の範囲の値に変換し、それらの合計が 1になるように正規化します。
n#| V |
の行列
Z
が与えられたとき、ソフトマックス関数は次の数式で表すことができます。
e
e
P
ij
Z
k
V
Z
1
ik
ij
=
=
!
060
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