| commit | 95f7166b8860f568f056a6c20ff626f6a7f069fc | [log] [tgz] |
|---|---|---|
| author | A. Unique TensorFlower <gardener@tensorflow.org> | Tue Nov 01 14:10:52 2016 -0800 |
| committer | TensorFlower Gardener <gardener@tensorflow.org> | Tue Nov 01 15:18:15 2016 -0700 |
| tree | 4c287e4b2e82927a87729b65edf9c8693f015a45 | |
| parent | 1dadfdd27650e21e0c679e615ddd377f380c574a [diff] |
Change quantize_graph in eightbit mode to remove FakeQuantWithMinMaxVars nodes and use the information provided by them to set the min/max values on quantization-related nodes. In eightbit mode, also changed how constant weights are quantized - instead of doing it as a step after the main recursion, do it during the main recursion. This allows the float inputs to FakeQuantWithMinMaxVars to be excluded from quantization. In eightbit mode, maintain more state in the stack during recursion. Also change quantize reshape registration to register always and not use TF_CALL_xyz; this matches other quantized ops. Change: 137877226
- tensorflow/core/kernels/quantized_reshape_op.cc[diff]
- tensorflow/tools/quantization/quantize_graph.py[diff]
- tensorflow/tools/quantization/quantize_graph_test.py[diff]
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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 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.
If you'd like to contribute to TensorFlow, be sure to review the contribution guidelines.
We use GitHub issues for tracking requests and bugs, but please see Community for general questions and discussion.
Installation
See Download and Setup 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:
- Linux CPU-only: Python 2 (build history) / Python 3.4 (build history) / Python 3.5 (build history)
- Linux GPU: Python 2 (build history) / Python 3.4 (build history) / Python 3.5 (build history)
- Mac CPU-only: Python 2 (build history) / Python 3 (build history)
- Mac GPU: Python 2 (build history) / Python 3 (build history)
- Android (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 >>>
##For more information
The TensorFlow community has created amazing things with TensorFlow, please see the resources section of tensorflow.org for an incomplete list.