CNN RNN Autoencoder - UNISTisystems.unist.ac.kr/wp-content/uploads/sites/209/2017/... · 2018. 12....
Transcript of CNN RNN Autoencoder - UNISTisystems.unist.ac.kr/wp-content/uploads/sites/209/2017/... · 2018. 12....
Today
1. Machine Learning
2. Deep Learning– CNN– RNN– Autoencoder
3. Deep Learning of Things (DoT)
4. Epilog
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Machine Learning
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• Learns from data
Machine Learning
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• Learns from data• predicts on data
Framework of Machine Learning
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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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이사?
간다
온다
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이사?
간다
온다
“의외의 정보가 문제를 해결하는
좋은 Feature����������� ������������������ (특성인자)����������� ������������������ 가 될 수 있다.”����������� ������������������
Weather Station
Temperature
Humidity
Brightness
Temperature
Humidity
Brightness
(Unexpected) Hidden Information
Brightness
Jul 31 05:27일출
Jul 30 23:26 학생퇴근
Jul 31 10:02학생출근
Temperature
Humidity
Brightness
(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 ?”����������� ������������������
Today
1. Machine Learning
2. Deep Learning– CNN– RNN– Autoencoder
3. Deep Learning of Things (DoT)
4. Epilog
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Machine Learning and Deep Learning
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Data Acquisition Feature Extraction Classification
- Time domain- Frequency domain
[ Machine Learning ]
대부분지도학습 현장전문가지식
Machine Learning and Deep Learning
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대부분지도학습 현장전문가지식
Data Acquisition Feature Extraction Classification
- Time domain- Frequency domain
[ Machine Learning ]
“����������� ������������������ 딥러닝은기계학습보다는
Domain Knowledge의존도가 낮다.����������� ������������������ ”����������� ������������������
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
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
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
Today
1. Machine Learning
2. Deep Learning– CNN– RNN– Autoencoder
3. Deep Learning of Things (DoT)
4. Epilog
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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
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
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
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
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Feature Extraction Classification
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
Convolutional Neural Networks (심층인공신경망)
• Structure– Weight sharing– Local connectivity
• Optimization– Smaller searching space
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Class 2Class 1
Today
1. Machine Learning
2. Deep Learning– CNN– RNN– Autoencoder
3. Deep Learning of Things (DoT)
4. Epilog
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Robocup 2011 Final: Team DARwIn -‐ CIT Brains
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Recurrent NN (RNN)
• Hidden state extraction and transformation
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Yn-‐1 Yn Yn+1
On+1 Classification
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
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
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
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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)
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1( ) ˆ( , , ) ( , )
N
i ii
iL x y l y yywq=
= ×åOK
NG
Labe
led data
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
Today
1. Machine Learning
2. Deep Learning– CNN– RNN– Autoencoder
3. Deep Learning of Things (DoT)
4. Epilog
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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
Deep Learning of Things (DoT)
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Handwritten Digits Recognition
Today
1. Machine Learning
2. Deep Learning– CNN– RNN– Autoencoder
3. Deep Learning of Things (DoT)
4. Epilog
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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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