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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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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

by Ankur A. Patel, 中田 秀基
April 2020
Intermediate to advanced
344 pages
8h 22m
Japanese
O'Reilly Japan, Inc.
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
10.5
RBM
を用いた協調フィルタリング
233
prv_hb = cur_hb
prv_vb = cur_vb
error = sess.run(err, feed_dict={v0: X,
_w: cur_w, _vb: cur_vb, _hb: cur_hb})
print ('Epoch: %d' % epoch,'reconstruction error: %f' % error)
self.w = prv_w
self.hb = prv_hb
self.vb = prv_vb
10.5.3
RBM
を用いた推薦システムの訓練
RBM
を訓練するために、まず
ratings_train
から
inputX
を作り、それを
float32
に変換する。
次に
RBM
を定義する。この際に、入力と出力を
1,000
、学
0.3
、訓
500
、バッ
チサイズを
200
とする。これえらのパラメータ値は最初の予備評価のためのパラメータにすぎない。い
ろいろ試してもっといいパラメータを探してみてほしい。
# Begin the training cycle
# Convert inputX into float32
inputX = ratings_train
inputX = inputX.astype(np.float32)
# Define the parameters of the RBMs we will train
rbm=RBM(1000,1000,0.3,500,200) ...
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Publisher Resources

ISBN: 9784873119106Other