Visualizing Filters of a CNN using TensorFlow

4.6
별점
24개의 평가
제공자:
Coursera Project Network
학습자는 이 무료 안내 프로젝트에서 다음을 수행하게 됩니다.

Implement gradient ascent algorithm

Visualize image features that maximally activate filters of a CNN

인터뷰에서 이 안내형 체험 보여주기

Clock1 hour
Intermediate중급
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

In this short, 1 hour long guided project, we will use a Convolutional Neural Network - the popular VGG16 model, and we will visualize various filters from different layers of the CNN. We will do this by using gradient ascent to visualize images that maximally activate specific filters from different layers of the model. We will be using TensorFlow as our machine learning framework. The project uses the Google Colab environment which is a fantastic tool for creating and running Jupyter Notebooks in the cloud, and Colab even provides free GPUs for your notebooks. You will need prior programming experience in Python. This is a practical, hands on guided project for learners who already have theoretical understanding of Neural Networks, Convolutional Neural Networks, and optimization algorithms like gradient descent but want to understand how to use the TensorFlow to visualize various filters of a CNN. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

요구 사항

Prior experience in Python, theoretical understanding of Convolutional Neural Networks and optimization algorithms like gradient descent.

개발할 기술

  • Deep Learning
  • Artificial Neural Network
  • Convolutional Neural Network
  • Machine Learning
  • Tensorflow

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  1. Introduction

  2. Downloading the Model

  3. Get Submodels

  4. Image Visualization

  5. Training Loop

  6. Final Results

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

자주 묻는 질문

자주 묻는 질문

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