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DCGANによる霊符の自動生成
平田朋義
霊符について1
● なんか道教とかが起源らしい。(要検証)
● 呪符とか護符とかともいう。
● デザインかっこいい。
● amazonで売ってた。
amazonで売ってる霊符
https://amzn.to/2LQcjLm
霊符について2
● 本を買ったらたくさん図が載っていた。
本で面白かった記述
● Q.霊符が効かないのですが....?
● A.努力が足りないのかもしれません。
ともかく大量の霊符画像データを手に入れた
● 画像は著作権があるかもしれない(自作?昔からある?)
ので割愛。
● amazonで売っているみたいなやつが大量に載っていま
す。
● 日本の著作権法では、「情報解析を行うために著作物
を複製すること」が、営利・非営利問わず認められてい
ます。
Deep learningで自動生成しよう
他の人の例
いらすとや風 
https://mickey24.hatenablog.com/entry/irasutoya_deep_learning
美少女イラスト風
https://make.girls.moe/#/
アイドル風
https://gigazine.net/news/20180621-ai-idol-portrait-generate/
● DCGANを使用する
DCGANはGAN(敵対的生成ネットワーク)の一種。
与えられたサンプルデータに対して、それに似た画像を作るニューラルネット
ワークと、作られた画像と本物の画像を区別するニューラルネットワークを競い
合わせて成長させ、本物に近い画像を作るシステム。
偽札作りと偽札判定士によくたとえられる。
「判定士の腕と偽札作りの腕がイタチごっこで上がっていき、より精密な偽札が
作られていく」
構成
● 元データは185枚。(他のと違う形のは除いた)(本当は1000枚ぐらい欲し
い)
● コードは公開されているのをそのまま使った。(ますとみけいさんに教えて
もらった)https://github.com/carpedm20/DCGAN-tensorflow
● 計算はGPU1枚刺したGoogle Cloud Datalab上で。(設定が最初から入っ
てる。jupyter notebookっぽいのが使える)
予備実験 数字の自動生成
  学習初めの方(100世代) 学習終わりの方(25000世代)
生成実験結果 初期
49世代
99世代
199世代
生成実験結果 中期
1199世代
1349世代
1499世代
生成実験結果 後期
ここまででだいたい五時間ぐらい。
寝てたら勝手にインスタンスが落ちてた
2099世代
2249世代
2349世代
考察
● それなりのものはできた
● 同じ世代で似た絵が並ぶ->直前のデータに引っ張られ
がち(学習率の調整?)
● DatalabでGPUを増やす方法が分からない。
● すっきりした線を描くにはどうしたらいいだろう?(グレー
スケール、白と黒の二値データでする?)

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DCGANによる霊符の自動生成