Visualizing Filters of a CNN using TensorFlow

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在此免费指导项目中,您将:

Implement gradient ascent algorithm

Visualize image features that maximally activate filters of a CNN

在面试中展现此实践经验

1 hour
中级
无需下载
分屏视频
英语(English)
仅限桌面

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.

必备条件

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

您要培养的技能

  • Deep Learning

  • Artificial Neural Network

  • Convolutional Neural Network

  • Machine Learning

  • Tensorflow

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  1. Introduction

  2. Downloading the Model

  3. Get Submodels

  4. Image Visualization

  5. Training Loop

  6. Final Results

指导项目工作原理

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在分屏视频中,您的授课教师会为您提供分步指导

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