#TensorFlow
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) 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 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.
Note: Currently we do not accept pull requests on github -- see CONTRIBUTING.md for information on how to contribute code changes to TensorFlow through tensorflow.googlesource.com
We use github issues for tracking requests and bugs, but please see Community for general questions and discussion.
Download and Setup
To install the CPU version of TensorFlow using a binary package, see the instructions below. For more detailed installation instructions, including installing from source, GPU-enabled support, etc., see here.
Binary Installation
The TensorFlow Python API supports Python 2.7 and Python 3.3+.
The simplest way to install TensorFlow is using pip for both Linux and Mac.
For the GPU-enabled version, or if you encounter installation errors, or for more detailed installation instructions, see here.
Ubuntu/Linux 64-bit
# For CPU-only version
$ pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whlMac OS X
# Only CPU-version is available at the moment.
$ pip install https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whlTry your first TensorFlow program
$ python
>>> 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
>>>
##For more information