|author||Miao Wang <firstname.lastname@example.org>||Fri Mar 23 14:24:44 2018 -0700|
|committer||Miao Wang <email@example.com>||Fri Mar 23 14:24:44 2018 -0700|
Port in minimal changes to support BroadcastSub and BroadcastDiv Bug: 73661777 Test: mm Test: NeuralNetworksTests Change-Id: I4edfa9e39ef0a28c03de9f725d8eeef716cf88cd
TensorFlow is an open source software library for numerical computation using data flow graphs. The graph nodes represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. TensorFlow also includes TensorBoard, a data visualization toolkit.
TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence Research organization for the purposes of conducting machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.
See Installing TensorFlow for instructions on how to install our release binaries or how to build from source.
People who are a little more adventurous can also try our nightly binaries:
Nightly pip packages
pip install tf-nightlyor
pip install tf-nightly-gpuin a clean environment to install the nightly TensorFlow build. We support CPU and GPU packages on Linux, Mac, and Windows.
Individual whl files
>>> import tensorflow as tf >>> hello = tf.constant('Hello, TensorFlow!') >>> sess = tf.Session() >>> sess.run(hello) 'Hello, TensorFlow!' >>> a = tf.constant(10) >>> b = tf.constant(32) >>> sess.run(a + b) 42 >>> sess.close()
The TensorFlow project strives to abide by generally accepted best practices in open-source software development:
Learn more about the TensorFlow community at the community page of tensorflow.org for a few ways to participate.