While Keras has simple networks that are easy to debug, TensorFlow is much more difficult to understand and debug. For beginners, Keras is much easier to learn. But for more complex applications, TensorFlow has many more capabilities.

Is TensorFlow necessary for Keras?

However, one size does not fit all when it comes to Machine Learning applications – the proper difference between Keras and TensorFlow is that Keras won't work if you need to make low-level changes to your model. For that, you need TensorFlow.

Should I use TensorFlow 2 or Keras?

With TensorFlow 2.0, you should be using tf. keras rather than the separate Keras package. ... However, with the explosion of deep learning popularity, many developers, programmers, and machine learning practitioners flocked to Keras due to its easy-to-use API.