Google's TensorFlow machine learning system can now be distributed across multiple machines in an update, TensorFlow 0.8. The machine learning software is already distributed across hundreds of machines to speed up the learning process. Google Updates TensorFlow To Version 0.8, Makes It Distributed Google has launched a brand new version of its TensorFlow machine learning system with the aim of improving machine learning and reducing the time consumed while running programs. Last November, Google opened up its in-house machine learning software TensorFlow, making the program that powers its translation services and photo analytics (among many other things) open-source and free to download. Announcing TensorFlow 0.8 – now with distributed computing support!
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. The TensorFlow API is composed of a set of Python modules that enable constructing and executing TensorFlow graphs. NOTE: You should NOT install TensorFlow with Anaconda as there are issues with the way Anaconda builds the python shared library that prevent dynamic linking from R. If you install TensorFlow within a Virtualenv environment you'll need to be sure to use that same environment when installing the tensorflow R package (see below for details).
We start by importing TensorFlow as tf. Then we print the version of TensorFlow that we are using. We are using TensorFlow 1.5.0. In this video, we're going to use tf.reshape to change the shape of a TensorFlow tensor as long as the number of elements stay the same. We will do three examples to show how reshape works.