TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments. TensorFlow Serving makes it easy to deploy new algorithms and experiments, while keeping the same server architecture and APIs. TensorFlow Serving provides out-of-the-box integration with TensorFlow models, but can be easily extended to serve other types of models and data.
Проверьте, насколько хорошо вы понимаете случайные предложения на иностранном языке (русский там тоже есть, любопытства ради можете сравнить). Предложения записаны с нормальной речевой скоростью, необязательно хорошего качества итд. Как в жизни. Предупреждаю, что проверка может иметь отрезвляющий результат.
translate a Keras model in to a standalone code that can run without Keras installed.
Deployment September 29, 2016 at 1:03 am #
Hi,
Your blog and books were great, and thanks much to you I finally got my project working in Keras.
I can’t seem to find how to translate a Keras model in to a standalone code that can run without Keras installed.
The best I could find was to learn TensorFlow, build an equivalent model in TF, then use TF to create standalone code.
Does Keras not have such functionality?
Thanks
Reply
Jason Brownlee September 29, 2016 at 8:37 am #
Hi, my understanding is that Keras is required to use the model in prediction.
You could try to save the network weights and use them in your own code, but you are creating a lot of work for yourself.
