commit | 893d9a67ffb37c607ce6b551a53323cb2207f768 | [log] [tgz] |
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author | Anthony Liu <anthonyjliu@google.com> | Fri Dec 06 12:53:17 2019 -0800 |
committer | TensorFlower Gardener <gardener@tensorflow.org> | Fri Dec 06 13:08:47 2019 -0800 |
tree | c1c6ef2a745625739b3af6cd411a6fb89cc5e579 | |
parent | faf5ed46c0cf3bf2cf2c7f7834684ff86cf0835b [diff] |
[tfdbg] Add Shape mode to DebugNumericSummaryV2Op. - The TensorDebugMode added is SHAPE, a mode that computes a shape-[10] rank-1 tensor given any float-type tensor. The first element is the id of the tensor. The second element is the dtype of the tensor, represented by the enumerated type defined in tensorflow/core/framework/types.proto. The third and fourth elements are the rank and size of the tensor respectively, and finally the fourth to tenth elements represent the shape of the tensor. Shorter shapes are right-padded with zero and longer shapes have the head truncated. - The CPU and GPU kernels of the op are added. PiperOrigin-RevId: 284243269 Change-Id: I2adc2c68792ee284ac2401bedd816c0ea960f87b
Documentation |
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TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.
TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence Research organization to conduct machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.
TensorFlow provides stable Python and C++ APIs, as well as non-guaranteed backward compatible API for other languages.
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See the TensorFlow install guide for the pip package, to enable GPU support, use a Docker container, and build from source.
To install the current release for CPU-only:
$ pip install tensorflow
Use the GPU package for CUDA-enabled GPU cards (Ubuntu and Windows):
$ pip install tensorflow-gpu
Nightly binaries are available for testing using the tf-nightly and tf-nightly-gpu packages on PyPi.
$ python
>>> import tensorflow as tf >>> tf.add(1, 2).numpy() 3 >>> hello = tf.constant('Hello, TensorFlow!') >>> hello.numpy() 'Hello, TensorFlow!'
For more examples, see the TensorFlow tutorials.
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Build Type | Status | Artifacts |
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Linux CPU | PyPI | |
Linux GPU | PyPI | |
Linux XLA | TBA | |
macOS | PyPI | |
Windows CPU | PyPI | |
Windows GPU | PyPI | |
Android | ||
Raspberry Pi 0 and 1 | Py2 Py3 | |
Raspberry Pi 2 and 3 | Py2 Py3 |
Build Type | Status | Artifacts |
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Linux AMD ROCm GPU Nightly | Nightly | |
Linux AMD ROCm GPU Stable Release | Release 1.15 / 2.x | |
Linux s390x Nightly | Nightly | |
Linux s390x CPU Stable Release | Release | |
Linux ppc64le CPU Nightly | Nightly | |
Linux ppc64le CPU Stable Release | Release 1.15 / 2.x | |
Linux ppc64le GPU Nightly | Nightly | |
Linux ppc64le GPU Stable Release | Release 1.15 / 2.x | |
Linux CPU with Intel® MKL-DNN Nightly | Nightly | |
Linux CPU with Intel® MKL-DNN Stable Release | Release 1.15 / 2.x | |
Red Hat® Enterprise Linux® 7.6 CPU & GPU Python 2.7, 3.6 | 1.13.1 PyPI |
Learn more about the TensorFlow community and how to contribute.