April 2020
Intermediate to advanced
344 pages
8h 22m
Japanese
Content preview from Pythonではじめる教師なし学習 ―機械学習の可能性を広げるラベルなしデータの利用
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242
11
章 深層信念ネットワークを用いた特徴量検出
ax.set_axis_off()
fig.text(0.5,1, suptitle, fontsize=20, horizontalalignment='center')
fig.tight_layout()
plt.show()
return
この関数と他の関数を、
MNIST
データセットに適用してみよう。
11.4
3
つの
RBM
からなる
DBN
の学習
1
つの
RBM
の隠れ層が次の
RBM
の可視層になるように
3
つの
RBM
を積層し、
MNIST
のデータを
使って
1
つずつ学習させてみよう。この
3
つの
RBM
が画像クラス分類に用いる
DBN
となる。
まず訓練データをロードして
NumPy
の配列に格納する。次に複数の
RBM
を保持するためのリスト
rbm_list
を作る。次に
3
つの
RBM
のハイパーパラメータを定義する。入力サイズ、出力サイズ、学
習率、学習エポック数、学習時のバッチサイズだ。
これらは先程定義した
RBM
クラスで指定する。
ここで作る
DBN
では次の
3
つの
RBM
を用いる。
1
層目の
RBM
は、元の
784
次元行列を入力とし
700
次元行列を出力する。
2
層目の
RBM
は、
1
層目が作った
700
次元行列を入力とし
600
次元行列を出力す
る。
3
層目の
RBM
は、
2
層目が作った
600
次元行列を入力とし
500
次元行列を出力する。
これら
3
つの
RBM
を学習率
1.0
で、それぞれ
100
エポック学習させる。バッチサイズは
200
だ。
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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.