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Documentation	Linux CPU	Linux GPU	Mac OS CPU	Windows CPU	Android

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 enables you to 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.

Keep up to date with release announcements and security updates by subscribing to announce@tensorflow.org.

Installation

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

• We are pleased to announce that TensorFlow now offers nightly pip packages under the tf-nightly and tf-nightly-gpu project on pypi. Simply run pip install tf-nightly or pip install tf-nightly-gpu in a clean environment to install the nightly TensorFlow build. We support CPU and GPU packages on Linux, Mac, and Windows.

Individual whl files

• Linux CPU-only: Python 2 (build history) / Python 3.4 (build history) / Python 3.5 (build history) / Python 3.6 (build history)
• Linux GPU: Python 2 (build history) / Python 3.4 (build history) / Python 3.5 (build history) / Python 3.6 (build history)
• Mac CPU-only: Python 2 (build history) / Python 3 (build history)
• Windows CPU-only: Python 3.5 64-bit (build history) / Python 3.6 64-bit (build history)
• Windows GPU: Python 3.5 64-bit (build history) / Python 3.6 64-bit (build history)
• Android: demo APK, native libs (build history)

Try 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
>>> sess.close()

Contribution guidelines

If you want to contribute to TensorFlow, be sure to review the contribution guidelines. This project adheres to TensorFlow's code of conduct. By participating, you are expected to uphold this code.

We use GitHub issues for tracking requests and bugs. So please see TensorFlow Discuss for general questions and discussion, and please direct specific questions to Stack Overflow.

The TensorFlow project strives to abide by generally accepted best practices in open-source software development:

For more information

• TensorFlow Website
• TensorFlow White Papers
• TensorFlow YouTube Channel
• TensorFlow Model Zoo
• TensorFlow MOOC on Udacity
• TensorFlow Course at Stanford

Learn more about the TensorFlow community at the community page of tensorflow.org for a few ways to participate.

License

Apache License 2.0

cs263_spring2018

class project for cs263, Spring 2018

47c3b70319a9d8a3585c4ca9b81334f05af33831