Difference between Keras and tf.Keras
I'm interested in what is the difference between Keras framework for neural networks creation and the tf.keras module of Tensorflow? What is better to use in a new machine learning project?
Answers
Hello @LubNot04 They are almost the same - Keras is an open-source framework which is developed by a large number of contributors. On the other hand, tf.keras is a Keras implementation in Tensorflow. The last is owned by Google, which means that probably this tool is better tested and maybe organized in a more efficient way. The APIs should be very similar between these instruments. So, the only difference is under the hood, probably they are implemented in different ways.
When you start a new project I think it is not very important what tool you will use. Probably tf.keras has better integration with core TensorFlow features, so if you are going to use Tensorflow somewhere else in the project, it makes sense to choose tf.keras instead of Keras. Also, if you have huge experience in Tensorflow it is also may be good to use their implementation of Keras.
Best Lu.
Are you shure you want to close this post?
Are you shure you want to pin this post?
Report comment
Thank you for reporting this comment, the Imaginghub Community Management will verify your request.
Log in with Google