tf.keras: What’s the difference in TensorFlow 2.0? To learn the difference between Keras, tf.keras, and TensorFlow 2.0, just keep reading! Keras vs. Inside the rest of this tutorial, I’ll be discussing the similarities between Keras, tf.keras, and the TensorFlow 2.0 release, including the features you should care about. The transition from TensorFlow 1.x to TensorFlow 2.0 is going to be a bit of a rocky one, at least to start, but with the right understanding, you’ll be able to navigate the migration with ease. Are there TensorFlow 2.0 features that I should care about as a Keras user?.Or should I be using the tf.keras submodule inside TensorFlow 2.0 instead?.

Am I supposed to use the keras package for training my own neural networks?.What does the TensorFlow 2.0 release mean for me as a Keras user?.Just in case you didn’t hear, the long-awaited TensorFlow 2.0 was officially released on September 30th.Īnd while it’s certainly a time for celebration, many deep learning practitioners such as Jeremiah are scratching their heads: I’ve seen a few tutorials in the deep learning blogosphere discussing TensorFlow 2.0 but with all the confusion regarding Keras, tf.keras, and TensorFlow 2.0, I’m at a loss for where to start. Secondly, is TensorFlow 2.0 worth upgrading to? I’m so confused on “which Keras package” I should be using when training my own networks. TensorFlow developers seem to be promoting Keras, or rather, something called tf.keras, as the recommended high-level API for TensorFlow 2.0.īut I thought Keras was its own separate package? Hi Adrian, I saw that TensorFlow 2.0 was released a few days ago. Today’s tutorial is inspired from an email I received last Tuesday from PyImageSearch reader, Jeremiah. In this tutorial you’ll discover the difference between Keras and tf.keras, including what’s new in TensorFlow 2.0. Click here to download the source code to this post

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