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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248
11
章 深層信念ネットワークを用いた特徴量検出
それぞれ数字の「
0
」「
4
」「
1
」「
9
」だ。いずれの場合も生成された画像は元画像によく似ている。
11.5
完全な深層信念ネットワーク
さて、
DBN
クラスを定義しよう。このクラスは、これまでに学習した
3
つの
RBM
に
4
層目の
RBM
を加えたものだ。この
4
層目の
RBM
は、順方向パスと逆方向パスを行い生成モデル全体を改善する。
まずクラスのインスタンス変数を定義しよう。コンストラクタには、元の入力サイズ、学習済みの
3
層目の
RBM
の 出 力（
4
層目への入力）サイズ、この
DB
からの最終 的な出力サイズ、学 習 率 、学 習エポッ
ク数、学習バッチサイズ、それにこれまでに学習した
3
つの
RBM
を指定する。以前と同じように、重み、
隠れ層のバイアス、可視層のバイアスはゼロ行列で初期化する。
class DBN(object):
def __init__(self, original_input_size, input_size, output_size,
learning_rate, epochs, batchsize, rbmOne, rbmTwo, rbmThree):
# Define hyperparameters
self._original_input_size = original_input_size
self._input_size = input_size
self._output_size = output_size
self.learning_rate ...
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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.
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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.