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精通機器學習
book

精通機器學習

by Aurélien Géron
April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
GoTop Information, Inc.
Content preview from 精通機器學習
生成對抗網路
|
579
例如
當我們建構生成網路的輸出
讓它從
4 × 4
變成
8 × 8
見圖
17-19),
我們要將
一個升抽樣層
使用最近鄰點過濾
加到既有的摺積層
讓它輸出
8 × 8
的特徵圖
接著
將它傳給新的摺積層
它使用
"same"
填補
步幅
1
所以它的輸出也是
8 × 8)。
在這一個
新階層後面
再加上一個新的輸出摺積層
這是一般的摺積層
它的核大小是
1
可將輸
出投射至期望的顏色通道數量
例如
3)。
為了避免加入新摺積層時破壞第一個摺積層訓
練好的權重
最後的輸出是原始輸出層
現在輸出
8 × 8
的特徵圖
與新輸出層的加權總
新輸出的權重是
α
原始輸出的是
1 – α
α
會慢慢從
0
遞增至
1
換句話說
新的
摺積層
在圖
17-19
的虛線部分
會慢慢加入
而原始的輸出層會慢慢淡出
將新摺積層
加入鑑別網路時
接著加入降抽樣的平均池化層
也會使用類似的加入
/
淡出技術
+
8 × 8 × 3
×a
×(1 – a)
8 × 8 × 25
6
8 × 8 × 512
4 × 4 × 512
摺積層 1
升抽樣
輸出摺積層
稠密
雜訊
4
× 4 × 512
摺積層 1
輸出摺積層
稠密
雜訊
4
× 4 × 512
4 × 4 × 3
輸出摺積層
摺積層 2
8 × 8 × 38 × 8 × 3
17-19 
逐漸建構
GANGAN
生成網路輸出
4 × 4
彩色圖像
);
我們將它擴展成輸出
8 × 8
圖像
這篇論文也介紹了一些旨在提升輸出多樣性
來避免模式崩潰
),
以及讓訓練更穩定的
技術
580
|
第十七章:使用自動編碼網路與 GAN ...
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Publisher Resources

ISBN: 9789865024345