Roman Kazantsev 55f1f9606f [MO] Implement transformation to avoid RandomUniform in dropout (#3678)
* [MO] Implement transformation to avoid RandomUniform in a particular drop-out block

Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com>

* Remove old RandomUniform transformation and correct const values

Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com>

* Fix atol value

Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com>

* Move DropoutWithRandomUniform transformation to the front

Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com>

* Change just RandomUniform to Broadcast in the transformation

Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com>

* Correct comment for the transformation

Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com>

* Remove redundant line

Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com>
2020-12-25 16:59:15 +03:00
2020-11-19 13:59:20 +03:00
2020-12-25 07:46:55 +03:00
2020-07-20 17:36:08 +03:00
2018-10-16 13:45:03 +03:00
2020-11-17 16:44:44 +03:00

OpenVINO™ Toolkit - Deep Learning Deployment Toolkit repository

Stable release Apache License Version 2.0 Azure DevOps builds (branch)

This toolkit allows developers to deploy pre-trained deep learning models through a high-level C++ Inference Engine API integrated with application logic.

This open source version includes several components: namely Model Optimizer, ngraph and Inference Engine, as well as CPU, GPU, MYRIAD, multi device and heterogeneous plugins to accelerate deep learning inferencing on Intel® CPUs and Intel® Processor Graphics. It supports pre-trained models from the Open Model Zoo, along with 100+ open source and public models in popular formats such as Caffe*, TensorFlow*, MXNet* and ONNX*.

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