What is the difference between keras and tf.keras?

Question

I\'m learning TensorFlow and Keras. I\'d like to try https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438/, and it seems to be written in Keras.

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Answer 1

The history of Keras Vs tf.keras is long and twisted.

Keras: Keras is a high-level (easy to use) API, built by Google AI Developer/Researcher, Francois Chollet. Written in Python and capable of running on top of backend engines like TensorFlow, CNTK, or Theano.

TensorFlow: A library, also developed by Google, for the Deep Learning developer Community, for making deep learning applications accessible and usable to public. Open Sourced and available on GitHub.

With the release of Keras v1.1.0, Tensorflow was made default backend engine. That meant: if you installed Keras on your system, you were also installing TensorFlow.

Later, with TensorFlow v1.10.0, for the first time tf.keras submodule was introduced in Tensorflow. The first step in integrating Keras within TensorFlow

With the release of Keras 2.3.0,

• first release of Keras in sync with tf.keras
• Last major release to support other multi-backend engines
• And most importantly, going forward, recommend switching the code from keras to Tensorflow2.0 and tf.keras packages.

Refer this tweet from François Chollet to use tf.keras.

That means, Change Everywhere

From

from keras.models import Sequential
from keras.models import load_model

To

from tensorflow.keras.models import Sequential
from tensorflow.keras.models import load_model

And In requirements.txt,

tensorflow==2.3.0

*Disclaimer: it might give conflicts if you were using an older version of Keras. Do pip uninstall keras in that case.