ゼロから始める深層強化学習(NLP2018講演資料)/ Introduction of Deep Reinforcement LearningPreferred Networks
Introduction of Deep Reinforcement Learning, which was presented at domestic NLP conference.
言語処理学会第24回年次大会(NLP2018) での講演資料です。
http://www.anlp.jp/nlp2018/#tutorial
ゼロから始める深層強化学習(NLP2018講演資料)/ Introduction of Deep Reinforcement LearningPreferred Networks
Introduction of Deep Reinforcement Learning, which was presented at domestic NLP conference.
言語処理学会第24回年次大会(NLP2018) での講演資料です。
http://www.anlp.jp/nlp2018/#tutorial
文献紹介:Rethinking Data Augmentation for Image Super-resolution: A Comprehensive...Toru Tamaki
Jaejun Yoo, Namhyuk Ahn, Kyung-Ah Sohn; Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 8375-8384
https://openaccess.thecvf.com/content_CVPR_2020/html/Yoo_Rethinking_Data_Augmentation_for_Image_Super-resolution_A_Comprehensive_Analysis_and_CVPR_2020_paper.html
文献紹介:Rethinking Data Augmentation for Image Super-resolution: A Comprehensive...Toru Tamaki
Jaejun Yoo, Namhyuk Ahn, Kyung-Ah Sohn; Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 8375-8384
https://openaccess.thecvf.com/content_CVPR_2020/html/Yoo_Rethinking_Data_Augmentation_for_Image_Super-resolution_A_Comprehensive_Analysis_and_CVPR_2020_paper.html
Apache Hivemall is a scalable machine learning library for Apache Hive, Apache Spark, and Apache Pig.
Hivemall provides a number of machine learning functionalities across classification, regression, ensemble learning, and feature engineering through UDFs/UDAFs/UDTFs of Hive.
We have released the first Apache release (v0.5.0-incubating) on Mar 5, 2018 and the project plans to release v0.5.2 in Q2, 2018.
We will first give a quick walk-through of features, usages, what's new in v0.5.0, and future roadmaps of Apache Hivemall. Next, we will introduce Hivemall on Apache Spark in depth such as DataFrame integration and Spark 2.3 supports in Hivemall.
12. UDTF (parameter-mix)
HadoopのInputSplitSizeの設定に応じたmapperが
select 立ち上がる(map-only)
feature,
CAST(avg(weight) as FLOAT) as weight
from
( select
TrainLogisticSgdUDTF(features,label,..) as (feature,weight)
from train
)t
group by feature;
どうやってiterative parameter mixさせよう???
古いmodelを渡さないといけない
毎行渡すのはあれだし…
12
13. UDTF(iterative parameter mix)
create table model1sgditor2 as
select
feature,
CAST(avg(weight) as FLOAT) as weight
from (
select
TrainLogisticIterUDTF(t.features, w.wlist, t.label, ..)
as (feature, weight)
from
training t join feature_weight w on (t.rowid = w.rowid)
)t
group by feature;
ここで必要なのは、各行の素性ごとに古いModel
Map<feature, weight>, label相当を渡せばよいので、
Array<feature>に対応するArray<weight>をテーブルを作って
inner joinで渡す
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14. Pig版のフローの一例
training_raw = load '$TARGET' as (clicks: int, impression: int, displayid: int, adid: int, advertiserid: int, depth: int, position: int, queryid: int, keywordid: int,
titleid: int, descriptionid: int, userid: int, gender: int, age: int);
training_bin = foreach training_raw generate flatten(predictor.ctr.BinSplit(clicks, impression)), displayid, adid, advertiserid, depth, position, queryid,
keywordid, titleid, descriptionid, userid, gender, age;
training_smp = sample training_bin 0.1;
training_rnd = foreach training_smp generate (int)(RANDOM() * 100) as dataid, TOTUPLE(*) as training;
training_dat = group training_rnd by dataid;
model = foreach training_dat generate predictor.ctr.TrainLinear(training_rnd.training.training_smp);
store model into '$MODEL';
model = load '$MODEL' as (mdl: map[]);
弱学習
model_lmt = limit model 10;
testing_raw = load '$TARGET' as (dataid: int, displayid: int, adid: int, advertiserid: int, depth: int, position: int, queryid: int, keywordid: int, titleid: int,
descriptionid: int, userid: int, gender: int, age: int);
testing_with_model = cross model_lmt, testing_raw;
result = foreach testing_with_model generate dataid, predictor.ctr.Pred(mdl, displayid, adid, advertiserid, depth, position, queryid, keywordid, titleid,
descriptionid, userid, gender, age) as ctr;
result_grp = group result by dataid;
result_ens = foreach result_grp generate group as dataid, predictor.ctr.Ensemble(result.ctr);
result_ens_ord = order result_ens by dataid;
result_fin = foreach result_ens_ord generate $1;
store result_fin into '$RESULT';
アンサンブル学習
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