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Change 109695551 Update FAQ Change 109694725 Add a gradient for resize_bilinear op. Change 109694505 Don't mention variables module in docs variables.Variable should be tf.Variable. Change 109658848 Adding an option to create a new thread-pool for each session. Change 109640570 Take the snapshot of stream-executor. + Expose an interface for scratch space allocation in the interface. Change 109638559 Let image_summary accept uint8 input This allows users to do their own normalization / scaling if the default (very weird) behavior of image_summary is undesired. This required a slight tweak to fake_input.cc to make polymorphically typed fake inputs infer if their type attr is not set but has a default. Unfortunately, adding a second valid type to image_summary *disables* automatic implicit conversion from np.float64 to tf.float32, so this change is slightly backwards incompatible. Change 109636969 Add serialization operations for SparseTensor. Change 109636644 Update generated Op docs. Change 109634899 TensorFlow: add a markdown file for producing release notes for our releases. Seed with 0.5.0 with a boring but accurate description. Change 109634502 Let histogram_summary take any realnumbertype It used to take only floats, not it understands ints. Change 109634434 TensorFlow: update locations where we mention python 3 support, update them to current truth. Change 109632108 Move HSV <> RGB conversions, grayscale conversions, and adjust_* ops back to tensorflow - make GPU-capable version of RGBToHSV and HSVToRGB, allows only float input/output - change docs to reflect new size constraints - change HSV format to be [0,1] for all components - add automatic dtype conversion for all adjust_* and grayscale conversion ops - fix up docs Change 109631077 Improve optimizer exceptions 1. grads_and_vars is now a tuple, so must be wrapped when passed to format. 2. Use '%r' instead of '%s' for dtype formatting Base CL: 109697989
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#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.whl
Mac 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.whl
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
>>>
##For more information