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Voltar para Save, Load and Export Models with Keras

Comentários e feedback de alunos de Save, Load and Export Models with Keras da instituição Coursera Project Network

93 classificações
11 avaliações

Sobre o curso

In this 1 hour long project based course, you will learn to save, load and restore models with Keras. In Keras, we can save just the model weights, or we can save weights along with the entire model architecture. We can also export the models to TensorFlow's Saved Mode format which is very useful when serving a model in production, and we can load models from the Saved Model format back in Keras as well. In order to be successful in this project, you should be familiar with python programming, and basics of neural networks. 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....

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1 — 11 de 11 Avaliações para o Save, Load and Export Models with Keras

por Ramya G R

8 de jun de 2020

I really enjoyed working with this project. Thank you so much for the valuable teaching.

por Yaron K

16 de abr de 2021

Detailed explanations of various Keras save options, and their parameters. If there are problems with the Rhyme environment - you can download the completed notebook from the Resource section of the project and run it locally (or on a cloud platform like Google Colab)

por Karl J

15 de jun de 2020

Great course on saving and loading models.

por Gangone R

2 de jul de 2020

very useful course

por 17_055 M S

21 de set de 2020


por p s

22 de jun de 2020


por tale p

28 de jun de 2020


por Рюмин Д

9 de mai de 2020

Four, because the video viewing system and practice are slow. Waiting for downloads takes a long time.

por Pascal U E

27 de ago de 2020

I had a technical issue when creating the checkpoints


11 de jul de 2020


por Nahid I A

16 de mai de 2020

Rhyme texts are so tiny and blurry to follow, the virtual environment takes too much time to load. Otherwise it is a good course to understand basic keras.