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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164
7
章 オートエンコーダ
TensorFlow
が最も広く使われている。
7.1.1.1
TensorFlow
の実行例
ここで、
TensorFlow
のグラフを作って計算を実行してみよう。まず
TensorFlow
をインポートし、
TensorFlow
の
API
を利用して変数を定義する（
TensorFlow
の
API
は、
6
章で使った
scikit-learn
を模し
て作られている）。変数の値を計算してみよう。
import tensorflow as tf
b = tf.constant(50)
x = b * 10
y = x + b
with tf.Session() as sess:
result = y.eval()
print(result)
このコードが
2
つのフェイズに分かれていることを理解することが重要だ。第
1
フェイズでは、
b
、
x
、
y
を定義して計算グラフを構築している。次に、
tf.Session()
を呼び出して、そのグラフを実行して
いる。この関数を呼び出すまでは、
CPU
でも
GPU
でも何も計算は行われない。計算命令は格納され
ているだけなのだ。このコードブロックを実行すると、
550
という予想通りの結果が得られる。
次章以降で、実際のニューラルネットワークを
TensorFlow
を用いて構築する。
7.1.2
Keras
Keras
は、
TensorFlow
上で動作する高レベル
API
を提供するオープンソースライブラリだ。
Keras
の
API
は
TensorFlow
の
API
よりもはるかにユーザにとってわかりや
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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.