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GPU 学習時間 精度(Map) 1 GPU (NC6) 7分22秒 0.9479 2 GPU (NC12) 3分43秒 0.9479 本番稼働に向けて Confusion Matrix for Karugamo

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GPU 学習時間 精度(Map) 1 GPU (NC6) 7分22秒 0.9479 2 GPU (NC12) 3分43秒 0.9479 本番稼働に向けて Confusion Matrix for Karugamo CNTK deep dive - DeepLearning関連 PJ の進め方から本番展開まで AI07 Agenda Profile 岩崎 喬一(Kyoichi Iwasaki) Today’s data データサイエンティスト、やっぱり要る？ データサイエンティストのスキルセット 課題背景を理解 →ビジネス課題を整理 ビジネス力 解決 情報処理、人工知能、 統計学などの知恵を理 解し、適用 データサイエ データエンジ データサイエンスを意味 ある形に使えるようにし、 ンス力 ニアリング力 実装、運用 Ref. データサイエンティスト協会：http://www.datascientist.or.jp/news/2014/pdf/1210.pdf データ分析プロジェクトの進め方 ビジネス 要件定義 ビジネス力 データ収 展開 集・確認 データサイエ データエンジ ンス力 ニアリング力 データ分 評価 析 機械学習と深層学習 深層学習？ 深層学習による主な画像解析(as of May2018) What are specified? Algorithms MSでの実装 単純 画像分類 What? CNN Custom Vision, CNTK Fast(er) Where? Custom Vision, CNTK 物体検知 What? R-CNN Mask Where? Shape? ..(In near future?) セグメンテーション What? 複雑 R-CNN 深層学習による主な画像解析 深層学習による主な画像解析(as of May2018) 2015 2015-16 2015-16 2015-16 2017 • Fast R-CNN • Faster R-CNN • YOLO • SSD • Mask R-CNN 物体検知 セグメンテーション 深層学習の「学習」？ 深層学習（機械学習の観点から） CNTKとは？ CNTKとは？ ▪ GPU / マルチGPU(1-bit SGD) https://www.microsoft.com/en-us/cognitive-toolkit/ CNTKの実行速度 小さいほど高速 DL F/W FCN-S AlexNet ResNet-50 LSTM-64 CNTK 0.017 0.031 0.168 0.017 Caffe 0.017 0.027 0.254 -- TensorFlow 0.020 0.317 0.227 0.065 Torch 0.016 0.043 0.144 0.324 https://arxiv.org/pdf/1608.07249.pdf Codes in CNTK https://github.com/Microsoft/CNTK CNTKで2値分類をやってみる 赤 青 CNTKで2値分類をやってみる パラメータw、b w、b bias 푏 1 疾患 年齢 푤 11 z1 あり 푤21 푏2 푤12 疾患 腫瘍 푤 z2 22 なし CNTKの処理フロー 入力・出力変数の定義 ネットワークの定義 損失関数、最適化方法の定義 モデル学習 モデル評価 CNTKの処理フロー – 1/5 入力・出力変数の定義 import cntk as C ## 入力変数(年齢, 腫瘍の大きさ)の2種類あり input_dim = 2 ## 分類数(疾患の有無なので2値) num_output_classes = 2 ## 入力変数 feature = C.input_variable(input_dim, np.float32) ## 出力変数 label = C.input_variable(num_output_classes, np.float32) CNTKの処理フロー – 2/5 ネットワークの定義 def linear_layer(input_var, output_dim): input_dim = input_var.shape[0] ## Define weight W weight_param = C.parameter(shape=(input_dim, output_dim)) ## Define bias b bias_param = C.parameter(shape=(output_dim)) ## Wx + b. Pay attention to the order of variables!! return bias_param + C.times(input_var, weight_param) z = linear_layer(input, num_outputs) CNTKの処理フロー – 3/5 損失関数、最適化方法の定義 ## 損失関数 loss = C.cross_entropy_with_softmax(z, label) ## 分類エラー("分類として"当たっているか否か) eval_error = C.classification_error(z, label) ## 最適化 learner = C.sgd(z.parameters, lr_schedule) trainer = C.Trainer(z, (loss, eval_error), [learner]) CNTKの処理フロー – 4/5 モデル学習 for i in range(0, num_minibatches_to_train): ## Extract training data features, labels = generate_random_data_sample(minibatch_size, input_dim, num_output_classes) ## Train trainer.train_minibatch({feature : features, label : labels}) CNTKの処理フロー – 5/5 モデル評価 out = C.softmax(z) result = out.eval({feature : features}) Demo: CNTK basic process Tips :: Jupyter Notebookの起動 > activate py35 (py35) > jupyter notebook https://notebooks.azure.com/ Demo :: training - evaluate https://youtu.be.com/70FMOdVUNPI Tips 混同行列(Confusion Matrix) 疾患[実際] 非疾患[実際] 疾患[予測] 14 0 非疾患[予測] 2 9 非疾患と予測（緑線の下） 実際は疾患 （緑線の下の赤2つ） ▶モデルの見逃し 物体検知からの分散学習 物体検知って？(recap) What are specified? Algorithms MSでの実装 単純 画像分類 What? CNN Custom Vision, CNTK Fast(er) Where? Custom Vision, CNTK 物体検知 What? R-CNN Mask Where? Shape? ..(In near future?) セグメンテーション What? 複雑 R-CNN 深層学習による主な画像解析 VoTT https://github.com/Microsoft/VoTT 静止画 動画 物体検知における“座標” x., y.ともに、左上の●から計った両端点● xmin xmax ymin ymax Quick demo on VoTT(静止画) Quick demo for VoTT https://youtu.be/j7OjxFY2Go8 Quick demo on VoTT(動画) Quick demo for VoTT https://youtu.be/RCuGLRtggrI Karugamo detection! Movie inferred with Faster R-CNN https://youtu.be/ziuEXyJ9SNs CNTKでの分散学習 – 1/6 深層学習での分散学習とは？ →主に学習時間短縮のためにマルチGPUを利用すること 分散学習実行準備 マルチGPU搭載 マルチホスト 1bit-SGD CNTKでの分散学習 – 2/6 入力・出力変数の定義 ネットワークの定義 損失関数、最適化方法の定義 モデル学習 モデル評価 CNTKでの分散学習 – 3/6 損失関数の定義 from cntk import distributed ... learner = cntk.learner.momentum_sgd(...) # create local learner distributed_after = epoch_size # number of samples to warm start with distributed_learner = distributed.data_parallel_distributed_learner( learner = learner, num_quantization_bits = 32, # non-quantized gradient accumulation distributed_after = 0) # no warm start CNTKでの分散学習 – 4/6 最適化方法の定義 minibatch_source = MinibatchSource(...) ... trainer = Trainer(z, ce, pe, distributed_learner) ... session = training_session(trainer=trainer, mb_source=minibatch_source, ...) session.train() ... distributed.Communicator.finalize() # must be called to finalize MPI in case of successful distributed training https://docs.microsoft.com/en-us/cognitive-toolkit/multiple-gpus-and- machines#2-configuring-parallel-training-in-cntk-in-python CNTKでの分散学習 – 5/6 分散学習の実行方法 # GPUを2つ利用し、学習用スクリプトがtraining.py > mpiexec –n 2 python training.py CNTKでの分散学習 – 6/6 CPU/GPU利用設定 import cntk ## CPU利用時 cntk.device.try_set_default_device(cntk.device.cpu()) ## GPU利用時 cntk.device.try_set_default_device(cntk.device.gpu()) Some preparation for Deep Learning! CNTKから2つのGPUが見えている GPU利用可能か確認 https://github.com/kyoro1/decode2018/blob/master/2.%20distributed%20learning%2 0with%20Faster%20R-CNN.ipynb Faster R-CNN with single GPU モデルパラメータ数は5700万超！ Single GPU vs Multi GPU 2つのGPU利用 Single GPU vs Multi GPU GPU 学習時間 精度(mAP) 1 GPU (NC6) 7分22秒 0.9479 2 GPU (NC12) 3分43秒 0.9479 本番稼働に向けて Confusion Matrix for karugamo karugamoが写っているのに、 モデルは推定できなかった ▶モデルの見逃し あり[実際] なし[実際] あり[予測] XX XX なし[予測] XX XX Confusion Matrix for karugamo あり[実際] なし[実際] あり[予測] XX XX なし[予測] XX XX karugamoでないものに、 karugamoと推定 ▶モデルの過検知？ Original movie vs inferred movie https://youtu.be/SYTw5OLBnzc データサイエンス活用推進体制(案) 【事業活動に結びつけるための 【分析結果を活用し、ビジネス企 戦略づくり・設計を行う人材】 画や改善を活かす人材】 ビジネス力 【実際に手を動かして #AI02 データサイエ データエンジ ンス力 ニアリング力 データ分析をする人材】 http://www.kantei.go.jp/jp/singi/keizaisaisei/miraitoshikaigi/jinzaiikusei_dai3/siryou4.pdf 本番展開あるある (技術寄り編) 本番展開あるある (システム運用編) Key takeaway • 機械学習 ほぼ同等 本来の意味 ビジネスサイドとの協業 • どんなKPI Key takeaway 平易にスクリプトが書け scrap & build • 出来ること 出来ないこと たち Appendix Reference https://github.com/Microsoft/CNTK https://docs.microsoft.com/en-us/cognitive-toolkit/setup-cntk-on-your- machine https://github.com/Microsoft/VoTT https://github.com/onnx/onnx https://www.edx.org/course/deep-learning-explained https://www.edx.org/course/computer-vision-and-image-analysis Reference https://github.com/kyoro1/decode2018 https://youtu.be/s5W4c9q_SAw https://youtu.be/ziuEXyJ9SNs https://youtu.be/SYTw5OLBnzc https://youtu.be.com/70FMOdVUNPI https://youtu.be/j7OjxFY2Go8 https://youtu.be/RCuGLRtggrI © 2018 Microsoft Corporation. All rights reserved. 本コンテンツの著作権、および本コンテンツ中に出てくる商標権、団体名、ロゴ、製品、サービスなどはそれぞれ、各権利保有者に帰属します。.
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