If TensorFlow is your primary framework, and you are looking for a simple & high-level model definition interface to make your life easier, this tutorial is for you. Keras layers and models are fully compatible with pure-TensorFlow tensors, and as a result, Keras makes a great model definition add-on for TensorFlow, and can even be used alongside other TensorFlow libraries. Note that this tutorial assumes that you have configured Keras to use the TensorFlow backend (instead of Theano). Here are instructions on how to do this. Let's start with a simple example: MNIST digits classification.
Just when I thought TensorFlow's market share would be eaten by the emergence (and rapid adoption) of PyTorch, Google has come roaring back. TensorFlow 2.0, recently released and open-sourced to the community, is a flexible and adaptable deep learning framework that has won back a lot of detractors. I love the ease with which even beginners can pick up TensorFlow 2.0 and start executing deep learning tasks. There are a plethora of offshoots that come with TensorFlow 2.0. You can read about them in this article that summarizes all the developments at the TensorFlow Dev Summit 2020.
In this tutorial you'll discover the difference between Keras and tf.keras, including what's new in TensorFlow 2.0. Today's tutorial is inspired from an email I received last Tuesday from PyImageSearch reader, Jeremiah. Hi Adrian, I saw that TensorFlow 2.0 was released a few days ago. TensorFlow developers seem to be promoting Keras, or rather, something called tf.keras, as the recommended high-level API for TensorFlow 2.0. But I thought Keras was its own separate package?
You already know what is Keras and to build a deep learning model using it. Instead of using TensorFlow directly you use Keras to build the model. But wait do you know you can also use the tools that are included in TensorFlow using Keras. There is a tool in the TensorFlow that is Tensorboard that lets you visualize your model's structure and monitor its training. In this entire intuition, you will learn how to view Tensorboard callbacks through Keras and do some analytics to improve your deep learning model.
Editor's note: Please note that, while this chart and post was up to date when it was first published, the landscape has changed in such a way that the table below is not depict a fully accurate picture at this point (e.g. Keras now supports a greater number of frameworks). At SVDS, our R&D team has been investigating different deep learning technologies, from recognizing images of trains to speech recognition. We needed to build a pipeline for ingesting data, creating a model, and evaluating the model performance. However, when we researched what technologies were available, we could not find a concise summary document to reference for starting a new deep learning project.