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Volver a Save, Load and Export Models with Keras

Opiniones y comentarios de aprendices correspondientes a Save, Load and Export Models with Keras por parte de Coursera Project Network

4.6
estrellas
93 calificaciones
11 reseña

Acerca del 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 revisiones para 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 P_17_055 M S

21 de sep. de 2020

170490107055

por p s

22 de jun. de 2020

Super

por tale p

28 de jun. de 2020

good

por Рюмин Д

9 de may. 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

por SARAVANAN.V

11 de jul. de 2020

super

por Nahid I A

16 de may. 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.