Tensorflow on ARC

Introduction to Tensorflow

Tensorflow is a tool for evaluating dataflow graphs that represent both the computations and model state in a machine learning algorithm. It enables distributed evaluation and explicit communication across a large number of computing devices (e.g. numerous CPUs or GPUs). The core tools of tensorflow consist of shared C libraries for constructing dataflow graphs and executing computations (typically linear algebra on tensors). This constitutes a fairly low level language for setting up data preprocessing, model training, and inference infrastructure for online machine learning applications. For an overview of tensorflow's design principles and its relationship with other popular machine learning technologies (like Caffe, Torch, and MXNet), Abadi's 2016 article is a good starting point.

However, in its most common usage, Tensorflow's implementation of common deep neural network operations are exposed through the Python API. Direct access to all of this functionality from a C++ API is under development but most of the core deep neural network functionality that users are familiar with are still only available through specific client languages with Python being the most developed. The high-level Tensorflow API is consistent with the modelling standards established by Keras (that support multiple alternate backend evaluation engines). The higher level abstractions do not necessarily offer the same flexibility of parallel computation as the core Tensorflow tool. However, they generally are more accessible to users that are primarily familiar with the modelling techniques that are common to artificial neural networks. Consequently, Keras has been integrated directly into the core utility libraries of the Tensorflow Python API.

Tensorflow documentation and tutorials

• https://www.tensorflow.org/api_docs/python