3강 - CNN 및 이미지 모델.pptx

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Network Science Lab, The Catholic University of Korea

Technology

Nam Gyu Jung
Intelligent Information Processing Lab
Dept. of Computer Engineering
Gachon University
E-mail: jng6017@gachon.ac.kr
2023.07.17

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 CNN 모델 개념 및 구현
 간단한 예제 실습
 CNN 커스텀

2
1. CNN 모델 개념 및 구현
CNN 흐름 및 구성요소
0.1 0.5 0.7 0.8 0.4 0.5 0.7
0.7 0.4 0.9 0.2 0.1 0.6 0.4
0.8 0.7 0.1 0.2 0.5 0.7 0.5
0.3 0.4 0.2 0.5 0.6 0.1 0.3
0.1 0.5 0.6 0.9 0.7 1.0 0.2
0.5 0.7 0.8 0.9 0.1 0.6 0.1
0.4 0.5 0.7 0.8 0.9 0.5 0.6
Convolution Pooling FC Layer
• Convolution : 이미지의 특성 추출
• Pooling : 이미지의 특성 축약
• FC Layer : 추출 및 축약된 특징을 입력에 사용하여 Downstream task 수행
Downstream
산
바다
하늘

3
1. CNN 모델 개념 및 구현
CNN 흐름 및 구성요소
0.1 0.5 0.7 0.8 0.4 0.5 0.7
0.7 0.4 0.9 0.2 0.1 0.6 0.4
0.8 0.7 0.1 0.2 0.5 0.7 0.5
0.3 0.4 0.2 0.5 0.6 0.1 0.3
0.1 0.5 0.6 0.9 0.7 1.0 0.2
0.5 0.7 0.8 0.9 0.1 0.6 0.1
0.4 0.5 0.7 0.8 0.9 0.5 0.6
• Stride : 필터를 움직이는 보폭
• Padding : 이미지 주변에 특정 값을 채우는 것
• Kernel : 필터의 사이즈
0.1 0.5 0.7 0.8 0.4 0.5 0.7
0.7 0.4 0.9 0.2 0.1 0.6 0.4
0.8 0.7 0.1 0.2 0.5 0.7 0.5
0.3 0.4 0.2 0.5 0.6 0.1 0.3
0.1 0.5 0.6 0.9 0.7 1.0 0.2
0.5 0.7 0.8 0.9 0.1 0.6 0.1
0.4 0.5 0.7 0.8 0.9 0.5 0.6

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1. CNN 모델 개념 및 구현
CNN 모델
convolution
Pooling
FC Layer
Downstream

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1. CNN 모델 개념 및 구현
CNN 모델
• reset_parameters : 레이어의 파라미터 초기화
• forward : 자체제작한 모듈 실행

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1. CNN 모델 개념 및 구현
CNN 모델
• 데이터셋 정의 -> 데이터로더 설정 -> 손실함수 및 Optimizer 설정

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1. CNN 모델 개념 및 구현
CNN 모델
• Train 함수 및 Evaluate 함수 정의

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1. CNN 모델 개념 및 구현
실행
• Train 함수 및 Evaluate 함수 정의

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1. CNN 모델 개념 및 구현
실행
• Train 함수 및 Evaluate 함수 정의

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1. CNN 모델 개념 및 구현
CNN Custom
• Residual Network

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1. CNN 모델 개념 및 구현
CNN Custom
• Inception module

모두의연구소에서 발표했던 “Neural Architecture Search with Reinforcement Learning”이라는 논문발표 자료를 공유합니다. 머신러닝 개발 업무중 일부를 자동화하는 구글의 AutoML이 뭘하려는지 이 논문을 통해 잘 보여줍니다. 이 논문에서는 딥러닝 구조를 만드는 딥러닝 구조에 대해서 설명합니다. 800개의 GPU를 혹은 400개의 CPU를 썼고 State of Art 혹은 State of Art 바로 아래이지만 더 빠르고 더 작은 네트워크를 이것을 통해 만들었습니다. 이제 Feature Engineering에서 Neural Network Engineering으로 페러다임이 변했는데 이것의 첫 시도 한 논문입니다.

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