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chainer/python/nlp

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studylog/北の雲 | studylog.hateblo.jp Reviews
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chainer/python/nlp
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1 スマートフォン用の表示で見る
2 スターをつけました
3 引用をストックしました
4 ストック一覧を見る
5 引用するにはまずログインしてください
6 ログイン
7 引用をストックできませんでした
8 再度お試しください
9 限定公開記事のため引用できません
10 読者です
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スマートフォン用の表示で見る,スターをつけました,引用をストックしました,ストック一覧を見る,引用するにはまずログインしてください,ログイン,引用をストックできませんでした,再度お試しください,限定公開記事のため引用できません,読者です,読者をやめる,読者になる,studylog/北の雲,chainer/python/nlp,数学的バックグラウンドが無い人は理論を勉強しようと思っても厳しい,という事を痛切に悟りました,無理 無茶です,読んでも何とか理解できました,でもそのレベルでは特に 自然言語処理
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studylog/北の雲 | studylog.hateblo.jp Reviews

https://studylog.hateblo.jp

chainer/python/nlp

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1

nlp カテゴリーの記事一覧 - studylog/北の雲

http://studylog.hateblo.jp/archive/category/nlp

ビジネス分野 味の素 を亡くした金融市場 日経ビジネスオンライン やじうまPCWatch タカラトミー、おしゃべりで爆誕 ぱっと見ではよく意味がわからない。 Word-vector-web-api/README.ja.md at master overlast/. 以前python3系のgensimのword2vecで作ったデータを2系で読もうとしたらエラーraise ValueError, unsupported pickle protocol: %d % proto ValueError: unsupported pickle protocol: 3pickleのプロトコルが違うらしい ValueError: unsupported pickle pro. DeepLearning - Pylearn2を使って手書き文字認識を行う - Qiitaqiita.comここ参考にPylearn2のチュートリアルやってたんだけどはまったのでメモ。 学習終わって最後のtest result.pyのところのpickle.l. Powered by Hatena Blog.

2

ChainerのcuDNN-RNN(NStepLSTM)のとっかかり - studylog/北の雲

http://studylog.hateblo.jp/entry/2016/10/03/095406

Optimizing Recurrent Neural Networks in cuDNN 5 Parallel Forall. X53EF;変長データのミニバッチをchainerのwhereでやる - studylog/北の雲. データB 1 2 3. データA 1 2 0 0. データB 1 2 3 0. データB 1 2 3. データB 1 2 3 4. データC 1 2 3. CuDNN5.1,Ubuntu14.04です。 File "/usr/local/lib/python3.4/dist-packages/chainer/links/connection/n step lstm.py", line 96, in call. Train=train, use cudnn=self.use cudnn). File "/usr/local/lib/python3.4/dist-packages/chainer/functions/connection/n step lstm.py", line 468, in n step lstm. Powered by Hatena Blog.

3

2016-09-24から1日間の記事一覧 - studylog/北の雲

http://studylog.hateblo.jp/archive/2016/09/24

Def cleargrad(self): Clears the g. 麻生 英樹 安田 宗樹 前田 新一 岡野原 大輔 岡谷 貴之 久保 陽太郎 ボレガラ ダヌシカ. Powered by Hatena Blog.

4

可変長データのミニバッチをchainerのwhereでやる - studylog/北の雲

http://studylog.hateblo.jp/entry/2016/02/04/020547

X4EE5;前にもちょこっと書いたのだけど. データB 1 2 3. データA 1 2 0 0. データB 1 2 3 0. A = numpy.asarray( [ 1,2,0,0],[1,2,3,0] ) a #array([ 1, 2, 0, 0], [1, 2, 3, 0] ) a.T #array([ 1, 1], [2, 2], [0, 3], [0, 0] ). X6559;えて頂いたコードをそのまま引用すると. C, h = lstm(c prev, lstm in) #ここでlstm. C next = where(enable, c , c prev) #x! H next = where(enable, h , h prev) #hも同じ. C next , h next = where( conditon , lstm(c prev, lstm in) , ( c , h ) ). データA 1 2 0 -1 -1 -1. データB 1 2 3 4 0 -1. H0 = self.embed(x) #自分はこうやってx=-1のときはZEROベクトルを返すようにしてる.

5

chainerのサンプルコードを集めてみた(チュートリアルも追加) - studylog/北の雲

http://studylog.hateblo.jp/entry/2015/07/14/000635

Chainerのバージョンごとの違い(2016年1月19日現在) - studylog/北の雲. 10 11 1.2 黎明期バージョン. Chainerチュートリアル -v1.5向け- ViEW2015. Introduction to Chainer (LL Ring Recursive). Chainerの使い方と自然言語処理への応用. Chainerチュートリアル の和訳【バージョン1.6】 - 俺とプログラミング. Chainerチュートリアル(ver. 1.5.1)を和訳 - オモンパカリスト. 以下サンプルですが、大半が1.0 1.3頃に書かれたものなので、最新版の1.5では全く動かないと思います。 Https:/ github.com/pfnet/chainer/blob/master/examples/mnist/train mnist.py. Refactored code for a Convolutional Autoencoder implemented with Chainer. · GitHub. Tochikuji/chainer-libDNN · GitHub.

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作って文章出力して 知性 (実際は ワードサラダ. 最低限CNNの畳み込み フィルタとかDropoutとかそのレベルぐらいまでは理解しないと誰かが書いたコードすらも弄れないので ゼロから作るDeep Learning Pythonで学ぶディープラーニングの理論と実装. 世間的に見れば で っていうレベルの小さい話かもしれないけど、 プログラマ. 個人の人生からすれば おお っていうレベルを在野の名無しの プログラマ. でも今年は ゼロから作るDeep Learning Pythonで学ぶディープラーニングの理論と実装. 読みながら これはQiitaのあの記事だ これは自分が書いたあの記事じゃないの とモヤモヤします。 Chainer python, keras python - Google トレンド. Mdash; 便所糞虫@30日東R23a (@y benjo) 2016年12月24日. LSTMを超える期待の新星、QRNN - Qiita. Mdash; Ryobot りょぼっと (@ Ryobot) 2016年12月26日. Semi-Supervised Learning with Deep ...

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