cnn rnn autoencoder - unistisystems.unist.ac.kr/wp-content/uploads/sites/209/2017/... · 2018. 12....
TRANSCRIPT
![Page 1: CNN RNN Autoencoder - UNISTisystems.unist.ac.kr/wp-content/uploads/sites/209/2017/... · 2018. 12. 28. · CNN RNN Autoencoder. Today 1. Machine*Learning 2. Deep*Learning –CNN –RNN](https://reader036.vdocuments.mx/reader036/viewer/2022071108/5fe281c0ab71247e4e19fb43/html5/thumbnails/1.jpg)
Today
1. Machine Learning
2. Deep Learning– CNN– RNN– Autoencoder
3. Deep Learning of Things (DoT)
4. Epilog
1
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Machine Learning
2
• Learns from data
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Machine Learning
3
• Learns from data• predicts on data
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Framework of Machine Learning
4
Sensor Data
Data Window
Features
Decision
Data acquisition and pre-‐processing
Windowing
Feature extraction
Model building and Classification (Inference)
Classification AlgorithmsSupport Vector Machine Logistic Regression
K-NN Algorithm Artificial Neural Networks
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5
이사?
간다
온다
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6
이사?
간다
온다
“의외의 정보가 문제를 해결하는
좋은 Feature����������� ������������������ (특성인자)����������� ������������������ 가 될 수 있다.”����������� ������������������
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Weather Station
Temperature
Humidity
Brightness
Temperature
Humidity
Brightness
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(Unexpected) Hidden Information
Brightness
Jul 31 05:27일출
Jul 30 23:26 학생퇴근
Jul 31 10:02학생출근
Temperature
Humidity
Brightness
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(Unexpected) Hidden Information
Brightness
Jul 31 05:27일출
Jul 30 23:26 학생퇴근
Jul 31 10:02학생출근
Temperature
Humidity
Brightness
“학생들의 출석 (정보) 를 알기 위해서 조도 데이터
과연 생각해 낼 수 있었을까 ?”����������� ������������������
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Today
1. Machine Learning
2. Deep Learning– CNN– RNN– Autoencoder
3. Deep Learning of Things (DoT)
4. Epilog
10
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Machine Learning and Deep Learning
11
Data Acquisition Feature Extraction Classification
- Time domain- Frequency domain
[ Machine Learning ]
대부분지도학습 현장전문가지식
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Machine Learning and Deep Learning
12
대부분지도학습 현장전문가지식
Data Acquisition Feature Extraction Classification
- Time domain- Frequency domain
[ Machine Learning ]
“����������� ������������������ 딥러닝은기계학습보다는
Domain Knowledge의존도가 낮다.����������� ������������������ ”����������� ������������������
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Deep Artificial Neural Networks (심층인공신경망)• Complex/Nonlinear function approximator– Linearly connected networks – Simple nonlinear neurons
• Hidden layers– Autonomous feature learning
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Classification
Class 2Class 1
Feature learning
nonlinear
linear
Input
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Deep Artificial Neural Networks (심층인공신경망)• Complex/Nonlinear function approximator– Linearly connected networks – Simple nonlinear neurons
• Hidden layers– Autonomous feature learning
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Class 2Class 1
nonlinear
linear
Feature learningClassification
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Deep Artificial Neural Networks (심층인공신경망)• Complex/Nonlinear function approximator– Linearly connected networks – Simple nonlinear neurons
• Hidden layers– Autonomous feature learning
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Class 2Class 1
nonlinear
linear
Feature learningClassification
“����������� ������������������ 은닉층의 개수만 늘어난 것이 아닌
독특한 구조의 딥러닝 모델 개발.����������� ������������������ ”����������� ������������������
CNN RNN Autoencoder
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Today
1. Machine Learning
2. Deep Learning– CNN– RNN– Autoencoder
3. Deep Learning of Things (DoT)
4. Epilog
16
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Convolutional Neural Networks (CNN)• Image pattern recognition problems (spatial)– Individual cortical neurons respond to restricted region of space– Perception like humans – Convolutional Neural Networks (CNN)
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1 Pixel cannot explainany information
Small area can explain context of image
Image Kernel Output
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Convolutional Neural Networks (CNN)• Image pattern recognition problems (spatial)– Individual cortical neurons respond to restricted region of space– Perception like humans – Convolutional Neural Networks (CNN)
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Image Kernel Output
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Convolutional Neural Networks (CNN)• Image pattern recognition problems (spatial)– Individual cortical neurons respond to restricted region of space– Perception like humans – Convolutional Neural Networks (CNN)
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Image Kernel Output
1 1 1 0 0 0
1 1 1
é ùê úê ú- - -ê úë û
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Convolutional Neural Networks (CNN)• Image pattern recognition problems (spatial)– Individual cortical neurons respond to restricted region of space– Perception like humans – Convolutional Neural Networks (CNN)
• NN: feature extraction and transformation
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Image
Convolution and pooling layers
Convolution and nonlinearity Max pooling
0
1
Fully connected layers Label
Convolutional Neural Networks
9
Feature Extraction Classification
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Deep Artificial Neural Networks (심층인공신경망)
• Complex/Nonlinear function approximator– Linearly connected networks – Simple nonlinear neurons
• Hidden layers– Autonomous feature learning
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Class 2Class 1
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Convolutional Neural Networks (심층인공신경망)
• Structure– Weight sharing– Local connectivity
• Optimization– Smaller searching space
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Class 2Class 1
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Today
1. Machine Learning
2. Deep Learning– CNN– RNN– Autoencoder
3. Deep Learning of Things (DoT)
4. Epilog
23
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Robocup 2011 Final: Team DARwIn -‐ CIT Brains
24
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Recurrent NN (RNN)
• Hidden state extraction and transformation
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Yn-‐1 Yn Yn+1
On+1 Classification
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Recurrent NN (RNN)
• Hidden state extraction and transformation
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Yn-‐1 Yn Yn+1
Xn+1XnXn-1
On+1
Learned latent state
Classification based on states
U U U
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Recurrent NN (RNN)
• Hidden state extraction and transformation • Good for sequential data (dynamic behavior)
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Yn-‐1 Yn Yn+1
Xn+1XnXn-1
On+1
… Learned latent state and its dynamics
Classification based on states
W
U
W W
U U
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Time Series Data and RNN
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Today
1. Machine Learning
2. Deep Learning– CNN– RNN– Autoencoder
3. Deep Learning of Things (DoT)
4. Epilog
30
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Dimension Reduction
Principal Component Analysis (PCA) in time signals– not easily seen
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Autoencoder
• Recover the input data
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Autoencoder
• Recover the input data • Data compression to lower dimension → Latent variable• Latent variables ≈ features• Realistic ← unsupervised learning• Nonlinear
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Artistic Style Transfer
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Imbalanced Data
• Not enough data from faulty status
• Data Imbalance – Under sampling– Over sampling– Re-‐weighting– (Ada)Boosting
• Crazy idea: – Can we generate phantom (fake) data?– Then use them for further data analysis (ML or DL)
35
1( ) ˆ( , , ) ( , )
N
i ii
iL x y l y yywq=
= ×åOK
NG
Labe
led data
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Data Generation
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Latent Space
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Generative Adversarial Networks (GAN)Analogous to Turing Test
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Generative Adversarial Networks (GAN)Analogous to Turing Test
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Generated
Real
RealFake
Generator Discriminator
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Today
1. Machine Learning
2. Deep Learning– CNN– RNN– Autoencoder
3. Deep Learning of Things (DoT)
4. Epilog
40
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Computation Environment for Model Learning
• Development environment (open source)– Ubuntu 14.04– Python3– TensorFlow
• Machine (약 1,500만원)– GPU: GeForce GTX TITAN X (PASCAL)– CPU: Intel i7-‐5930k 6 Core 3.5GHz processor
• Parallel computing– Multi GPUs
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Implementation of Deep Learning Model
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Server
Model Training at Server
학습
Module Internet of Things
Embedded Systems or Internet of Things
Load Model
실행
Save Model
Trained Model
w학습된모델
학습과실행은다르다- 학습은비싸고오래걸릴수있지만- 실행은대부분싸고빠르다
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Deep Learning of Things (DoT)
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Handwritten Digits Recognition
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Today
1. Machine Learning
2. Deep Learning– CNN– RNN– Autoencoder
3. Deep Learning of Things (DoT)
4. Epilog
44
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인공지능으로이런문제도해결할수있나요?
(최소한)인간이구별할수있는문제면딥러닝으로도해결할수있다.
(이론적으로)인간이구별할수없는문제도딥러닝으로해결할수있다.– 커제:알파고 2.0 “바둑의신에가까워지고있다.– 알파고의수를인간이배우려고하고있다.
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인공지능을성공적으로적용하기위한필요조건?
• 기본적으로데이터가많아야한다.– 특히불량또는비정상데이터 (현실적으로어렵다)– Data-‐driven 방식에대한단점이해필요
• 필요한데이터를가지고올수있는자동화팀역량필요– 하드웨어프로그래밍
• Data Analytics 역량필요– 소프트웨어프로그래밍– 컴퓨터공학,산업공학,통계– 제조분야에서해당인력을구하기가쉽지않다 (인공지능인재영입전쟁)
• 시작은조각모음방식 (작은성공사례부터만들자)
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딥러닝장·∙단점
• 기존의모든 function approximator 를대체하는분위기
• 기계학습보다는 domain knowledge 에대한의존도가낮다→범용성
• 개발속도가빨라진다.→ Fast deploy
• Lack of interpretability and explainability– Still acting as a black box
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http://isystems.unist.ac.kr/
All materials (codes + hardware design)are available
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