You are mixing things up:
- Keras (https://keras.io/) is a library independent from TensorFlow, which specifies a high-level API for building and training neural networks and is capable of using one of multiple backends (among which, TensorFlow) for low-level tensor computation.
tf.keras
(https://www.tensorflow.org/guide/keras) implements the Keras API specification within TensorFlow. In addition, the tf.keras
API is optimized to work well with other TensorFlow modules: you can pass a tf.data
Dataset to the .fit()
method of a tf.keras
model, for instance, or convert a tf.keras
model to a TensorFlow estimator with tf.keras.estimator.model_to_estimator
. Currently, the tf.keras
API is the high-level API to look for when building models within TensorFlow, and the integration with other TensorFlow features will continue in the future.
So to answer your question: no, you don't need to convert Keras code to tf.keras code. Keras code uses the Keras library, potentially even runs on top of a different backend than TensorFlow, and will continue to work just fine in the future. Even more, it's important to not just mix up Keras and tf.keras
objects within the same script, since this might produce incompatabilities, as you can see for example in this question.
Update: Keras will be abandoned in favor of tf.keras: https://twitter.com/fchollet/status/1174019423541157888
与恶龙缠斗过久,自身亦成为恶龙;凝视深渊过久,深渊将回以凝视…