In Tensorflow 2.0 Keras will be the default high-level API for building and training machine learning models, hence complete compatibility between a model defined using the old tf.layers and the new tf.Keras.layers is expected. In version 2 of the popular machine learning framework the eager execution will be enabled by default although the static graph definition session execution will be still supported (but hidden a little bit). In this post, you'll see that the compatibility between a model defined using tf.layers and tf.keras.layers is not always guaranteed when using the graph definition session execution, but it works as expected if the eager execution is enabled (at least from my tests). The post is organized as follows: definition of the common data input pipeline, definition of the same model using both tf.layers and tf.keras.layers, The model we're going to use to highlight the differences between the 2 versions is a simple binary classifier.
Earlier this year, Google announced TensorFlow 2.0, it is a major leap from the existing TensorFlow 1.0. Ease of use: Many old libraries (example tf.contrib) were removed, and some consolidated. For example, in TensorFlow1.x the model could be made using Contrib, layers, Keras or estimators, so many options for the same task confused many new users. TensorFlow 2.0 promotes TensorFlow Keras for model experimentation and Estimators for scaled serving, and the two APIs are very convenient to use. The writing of code was divided into two parts: building the computational graph and later creating a session to execute it.
In this article, we'll see 10 important updates from TensorFlow 2.0. TensorFlow 2.0 will be simple and easy to use for all users on all platforms. In this article, we'll see 10 important updates from TensorFlow 2.0. TensorFlow 2.0 will be simple and easy to use for all users on all platforms. TensorFlow 2.0 alpha has now been released.