When it comes to working with deep learning and Python I highly recommend that you use a Unix-based environment.ĭeep learning tools can be more easily configured and installed on Linux, allowing you to develop and run neural networks quickly. Pre-configured deep learning environments Figure 1: My deep learning Virtual Machine with TensorFlow, Keras, OpenCV, and all other Deep Learning and Computer Vision libraries you need, pre-configured and pre-installed. We’ll then configure and install TensorFlow 2.0 on our Ubuntu system. In the first part of this tutorial we’ll discuss the pre-configured deep learning development environments that are a part of my book, Deep Learning for Computer Vision with Python.įrom there, you’ll learn why you should use TensorFlow 2.0, including the Keras implementation inside of TensorFlow 2.0. To learn how to install TensorFlow 2.0 on Ubuntu, just keep reading. Inside this tutorial, you’ll learn how to install TensorFlow 2.0 on Ubuntu.Īlternatively, click here for my macOS + TensorFlow 2.0 installation instructions.
īoth Francois Chollet (the creator of Keras) as well as the TensorFlow developers and maintainers recommend you use tf.keras moving forward.įurthermore, if you own a copy of my book, Deep Learning for Computer Vision with Python, you should use this guide to install TensorFlow 2.0 on your Ubuntu system. The official Keras package will still receive bug fixes, but all new features and implementations will be inside tf.keras. In short - you should be using the Keras implementation inside TensorFlow 2.0 (i.e., tf.keras ) when training your own deep neural networks.
There are a number of important updates in TensorFlow 2.0, including eager execution, automatic differentiation, and better multi-GPU/distributed training support, but the most important update is that Keras is now the official high-level deep learning API for TensorFlow. In this tutorial, you will learn to install TensorFlow 2.0 on your Ubuntu system either with or without a GPU. Click here to download the source code to this post
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