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学生对 Coursera Project Network 提供的 Creating Custom Callbacks in Keras 的评价和反馈

35 个评分
6 条评论


In this 1.5-hour long project-based course, you will learn to create a custom callback function in Keras and use the callback during a model training process. We will implement the callback function to perform three tasks: Write a log file during the training process, plot the training metrics in a graph during the training process, and reduce the learning rate during the training with each epoch. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your Internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with (e.g. Python, Jupyter, and Tensorflow) pre-installed. Prerequisites: In order to be successful in this project, you should be familiar with Python, Neural Networks, and the Keras framework. 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....

1 - Creating Custom Callbacks in Keras 的 6 个评论(共 6 个)

创建者 Atulya K

May 03, 2020

You learn how to update learning rate and plot validation and train accuracy dynamically in jupyter notebook. This is a very targeted course so if you don't know how to train ML models on keras or don't know ML at all this course is not for you.

The code needs to be slightly modified for newer version of keras and tensorflow but works perfectly in the Rhyme VM. Thanks for the code template, I can easily incorporate this bit in my future ML projects.

创建者 Doss D

Jun 14, 2020

Thank you very much

创建者 Kamlesh C

Jul 01, 2020


创建者 tale p

Jun 28, 2020


创建者 p s

Jun 24, 2020


创建者 gowthaman

Jul 05, 2020

Good to knowledge