| <!DOCTYPE html> |
| |
| <html xmlns="http://www.w3.org/1999/xhtml"> |
| <head> |
| <meta charset="utf-8" /> |
| <meta charset="utf-8"> |
| <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> |
| <meta http-equiv="x-ua-compatible" content="ie=edge"> |
| <style> |
| .dropdown { |
| position: relative; |
| display: inline-block; |
| } |
| |
| .dropdown-content { |
| display: none; |
| position: absolute; |
| background-color: #f9f9f9; |
| min-width: 160px; |
| box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2); |
| padding: 12px 16px; |
| z-index: 1; |
| text-align: left; |
| } |
| |
| .dropdown:hover .dropdown-content { |
| display: block; |
| } |
| |
| .dropdown-option:hover { |
| color: #FF4500; |
| } |
| |
| .dropdown-option-active { |
| color: #FF4500; |
| font-weight: lighter; |
| } |
| |
| .dropdown-option { |
| color: #000000; |
| font-weight: lighter; |
| } |
| |
| .dropdown-header { |
| color: #FFFFFF; |
| display: inline-flex; |
| } |
| |
| .dropdown-caret { |
| width: 18px; |
| height: 54px; |
| } |
| |
| .dropdown-caret-path { |
| fill: #FFFFFF; |
| } |
| </style> |
| |
| <title>mxnet.ndarray.contrib — Apache MXNet documentation</title> |
| |
| <link rel="stylesheet" href="../../../_static/basic.css" type="text/css" /> |
| <link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" /> |
| <link rel="stylesheet" type="text/css" href="../../../_static/mxnet.css" /> |
| <link rel="stylesheet" href="../../../_static/material-design-lite-1.3.0/material.blue-deep_orange.min.css" type="text/css" /> |
| <link rel="stylesheet" href="../../../_static/sphinx_materialdesign_theme.css" type="text/css" /> |
| <link rel="stylesheet" href="../../../_static/fontawesome/all.css" type="text/css" /> |
| <link rel="stylesheet" href="../../../_static/fonts.css" type="text/css" /> |
| <link rel="stylesheet" href="../../../_static/feedback.css" type="text/css" /> |
| <script id="documentation_options" data-url_root="../../../" src="../../../_static/documentation_options.js"></script> |
| <script src="../../../_static/jquery.js"></script> |
| <script src="../../../_static/underscore.js"></script> |
| <script src="../../../_static/doctools.js"></script> |
| <script src="../../../_static/language_data.js"></script> |
| <script src="../../../_static/matomo_analytics.js"></script> |
| <script src="../../../_static/autodoc.js"></script> |
| <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script> |
| <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script> |
| <script type="text/x-mathjax-config">MathJax.Hub.Config({"tex2jax": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true, "ignoreClass": "document", "processClass": "math|output_area"}})</script> |
| <script src="../../../_static/sphinx_materialdesign_theme.js"></script> |
| <link rel="shortcut icon" href="../../../_static/mxnet-icon.png"/> |
| <link rel="index" title="Index" href="../../../genindex.html" /> |
| <link rel="search" title="Search" href="../../../search.html" /> |
| </head> |
| <body><header class="site-header" role="banner"> |
| <div class="wrapper"> |
| <a class="site-title" rel="author" href="/"><img |
| src="../../../_static/mxnet_logo.png" class="site-header-logo"></a> |
| <nav class="site-nav"> |
| <input type="checkbox" id="nav-trigger" class="nav-trigger"/> |
| <label for="nav-trigger"> |
| <span class="menu-icon"> |
| <svg viewBox="0 0 18 15" width="18px" height="15px"> |
| <path d="M18,1.484c0,0.82-0.665,1.484-1.484,1.484H1.484C0.665,2.969,0,2.304,0,1.484l0,0C0,0.665,0.665,0,1.484,0 h15.032C17.335,0,18,0.665,18,1.484L18,1.484z M18,7.516C18,8.335,17.335,9,16.516,9H1.484C0.665,9,0,8.335,0,7.516l0,0 c0-0.82,0.665-1.484,1.484-1.484h15.032C17.335,6.031,18,6.696,18,7.516L18,7.516z M18,13.516C18,14.335,17.335,15,16.516,15H1.484 C0.665,15,0,14.335,0,13.516l0,0c0-0.82,0.665-1.483,1.484-1.483h15.032C17.335,12.031,18,12.695,18,13.516L18,13.516z"/> |
| </svg> |
| </span> |
| </label> |
| |
| <div class="trigger"> |
| <a class="page-link" href="/get_started">Get Started</a> |
| <a class="page-link" href="/features">Features</a> |
| <a class="page-link" href="/ecosystem">Ecosystem</a> |
| <a class="page-link page-current" href="/api">Docs & Tutorials</a> |
| <a class="page-link" href="/trusted_by">Trusted By</a> |
| <a class="page-link" href="https://github.com/apache/mxnet">GitHub</a> |
| <div class="dropdown" style="min-width:100px"> |
| <span class="dropdown-header">Apache |
| <svg class="dropdown-caret" viewBox="0 0 32 32" class="icon icon-caret-bottom" aria-hidden="true"><path class="dropdown-caret-path" d="M24 11.305l-7.997 11.39L8 11.305z"></path></svg> |
| </span> |
| <div class="dropdown-content" style="min-width:250px"> |
| <a href="https://www.apache.org/foundation/">Apache Software Foundation</a> |
| <a href="https://incubator.apache.org/">Apache Incubator</a> |
| <a href="https://www.apache.org/licenses/">License</a> |
| <a href="/versions/1.9.1/api/faq/security.html">Security</a> |
| <a href="https://privacy.apache.org/policies/privacy-policy-public.html">Privacy</a> |
| <a href="https://www.apache.org/events/current-event">Events</a> |
| <a href="https://www.apache.org/foundation/sponsorship.html">Sponsorship</a> |
| <a href="https://www.apache.org/foundation/thanks.html">Thanks</a> |
| </div> |
| </div> |
| <div class="dropdown"> |
| <span class="dropdown-header">master |
| <svg class="dropdown-caret" viewBox="0 0 32 32" class="icon icon-caret-bottom" aria-hidden="true"><path class="dropdown-caret-path" d="M24 11.305l-7.997 11.39L8 11.305z"></path></svg> |
| </span> |
| <div class="dropdown-content"> |
| <a class="dropdown-option-active" href="/versions/master/">master</a><br> |
| <a class="dropdown-option" href="/versions/1.9.1/">1.9.1</a><br> |
| <a class="dropdown-option" href="/versions/1.8.0/">1.8.0</a><br> |
| <a class="dropdown-option" href="/versions/1.7.0/">1.7.0</a><br> |
| <a class="dropdown-option" href="/versions/1.6.0/">1.6.0</a><br> |
| <a class="dropdown-option" href="/versions/1.5.0/">1.5.0</a><br> |
| <a class="dropdown-option" href="/versions/1.4.1/">1.4.1</a><br> |
| <a class="dropdown-option" href="/versions/1.3.1/">1.3.1</a><br> |
| <a class="dropdown-option" href="/versions/1.2.1/">1.2.1</a><br> |
| <a class="dropdown-option" href="/versions/1.1.0/">1.1.0</a><br> |
| <a class="dropdown-option" href="/versions/1.0.0/">1.0.0</a><br> |
| <a class="dropdown-option" href="/versions/0.12.1/">0.12.1</a><br> |
| <a class="dropdown-option" href="/versions/0.11.0/">0.11.0</a> |
| </div> |
| </div> |
| </div> |
| </nav> |
| </div> |
| </header> |
| <div class="mdl-layout mdl-js-layout mdl-layout--fixed-header mdl-layout--fixed-drawer"><header class="mdl-layout__header mdl-layout__header--waterfall "> |
| <div class="mdl-layout__header-row"> |
| |
| <nav class="mdl-navigation breadcrumb"> |
| <a class="mdl-navigation__link" href="../../index.html">Module code</a><i class="material-icons">navigate_next</i> |
| <a class="mdl-navigation__link is-active">mxnet.ndarray.contrib</a> |
| </nav> |
| <div class="mdl-layout-spacer"></div> |
| <nav class="mdl-navigation"> |
| |
| <form class="form-inline pull-sm-right" action="../../../search.html" method="get"> |
| <div class="mdl-textfield mdl-js-textfield mdl-textfield--expandable mdl-textfield--floating-label mdl-textfield--align-right"> |
| <label id="quick-search-icon" class="mdl-button mdl-js-button mdl-button--icon" for="waterfall-exp"> |
| <i class="material-icons">search</i> |
| </label> |
| <div class="mdl-textfield__expandable-holder"> |
| <input class="mdl-textfield__input" type="text" name="q" id="waterfall-exp" placeholder="Search" /> |
| <input type="hidden" name="check_keywords" value="yes" /> |
| <input type="hidden" name="area" value="default" /> |
| </div> |
| </div> |
| <div class="mdl-tooltip" data-mdl-for="quick-search-icon"> |
| Quick search |
| </div> |
| </form> |
| |
| <a id="button-show-github" |
| href="https://github.com/apache/mxnet/edit/master/docs/python_docs/python/_modules/mxnet/ndarray/contrib" class="mdl-button mdl-js-button mdl-button--icon"> |
| <i class="material-icons">edit</i> |
| </a> |
| <div class="mdl-tooltip" data-mdl-for="button-show-github"> |
| Edit on Github |
| </div> |
| </nav> |
| </div> |
| <div class="mdl-layout__header-row header-links"> |
| <div class="mdl-layout-spacer"></div> |
| <nav class="mdl-navigation"> |
| </nav> |
| </div> |
| </header><header class="mdl-layout__drawer"> |
| |
| <div class="globaltoc"> |
| <span class="mdl-layout-title toc">Table Of Contents</span> |
| |
| |
| |
| <nav class="mdl-navigation"> |
| <ul> |
| <li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html">Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html#Using-your-own-data-with-custom-Datasets">Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">New in MXNet 2.0: faster C++ backend dataloaders</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/np/np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/optimizer/index.html">Optimizers</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/viz/index.html">Visualization</a><ul> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/performance/index.html">Performance</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/compression/index.html">Compression</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/compression/int8.html">Deploy with int-8</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/float16">Float16</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/backend/index.html">Accelerated Backend Tools</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/index.html">oneDNN</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_readme.html">Install MXNet with oneDNN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_quantization.html">oneDNN Quantization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_quantization_inc.html">Improving accuracy with Intel® Neural Compressor</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/tvm.html">Use TVM</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/profiler.html">Profiling MXNet Models</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/amp.html">Using AMP: Automatic Mixed Precision</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/deploy/index.html">Deployment</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/export/index.html">Export</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/export/onnx.html">Exporting to ONNX format</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/export_network.html">Export Gluon CV Models</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Save / Load Parameters</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/inference/index.html">Inference</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/inference/cpp.html">Deploy into C++</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/inference/image_classification_jetson.html">Image Classication using pretrained ResNet-50 model on Jetson module</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/index.html">Run on AWS</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/use_ec2.html">Run on an EC2 Instance</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/use_sagemaker.html">Run on Amazon SageMaker</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/cloud.html">MXNet on the Cloud</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/extend/index.html">Extend</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/extend/customop.html">Custom Numpy Operators</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/new_op">New Operator Creation</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/add_op_in_backend">New Operator in MXNet Backend</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/using_rtc">Using RTC for CUDA kernels</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l1"><a class="reference internal" href="../../../api/index.html">Python API</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/np/index.html">mxnet.np</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/np/arrays.html">Array objects</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/arrays.ndarray.html">The N-dimensional array (<code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code>)</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/arrays.indexing.html">Indexing</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/np/routines.html">Routines</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.array-creation.html">Array creation routines</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.eye.html">mxnet.np.eye</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.empty.html">mxnet.np.empty</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.full.html">mxnet.np.full</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.identity.html">mxnet.np.identity</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ones.html">mxnet.np.ones</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ones_like.html">mxnet.np.ones_like</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.zeros.html">mxnet.np.zeros</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.zeros_like.html">mxnet.np.zeros_like</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.array.html">mxnet.np.array</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.copy.html">mxnet.np.copy</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arange.html">mxnet.np.arange</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linspace.html">mxnet.np.linspace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.logspace.html">mxnet.np.logspace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.meshgrid.html">mxnet.np.meshgrid</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tril.html">mxnet.np.tril</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.array-manipulation.html">Array manipulation routines</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.reshape.html">mxnet.np.reshape</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ravel.html">mxnet.np.ravel</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.flatten.html">mxnet.np.ndarray.flatten</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.swapaxes.html">mxnet.np.swapaxes</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.T.html">mxnet.np.ndarray.T</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.transpose.html">mxnet.np.transpose</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.moveaxis.html">mxnet.np.moveaxis</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rollaxis.html">mxnet.np.rollaxis</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.expand_dims.html">mxnet.np.expand_dims</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.squeeze.html">mxnet.np.squeeze</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.broadcast_to.html">mxnet.np.broadcast_to</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.broadcast_arrays.html">mxnet.np.broadcast_arrays</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.atleast_1d.html">mxnet.np.atleast_1d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.atleast_2d.html">mxnet.np.atleast_2d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.atleast_3d.html">mxnet.np.atleast_3d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.concatenate.html">mxnet.np.concatenate</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.stack.html">mxnet.np.stack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dstack.html">mxnet.np.dstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vstack.html">mxnet.np.vstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.column_stack.html">mxnet.np.column_stack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hstack.html">mxnet.np.hstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.split.html">mxnet.np.split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hsplit.html">mxnet.np.hsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vsplit.html">mxnet.np.vsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.array_split.html">mxnet.np.array_split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dsplit.html">mxnet.np.dsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tile.html">mxnet.np.tile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.repeat.html">mxnet.np.repeat</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.unique.html">mxnet.np.unique</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.delete.html">mxnet.np.delete</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.insert.html">mxnet.np.insert</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.append.html">mxnet.np.append</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.resize.html">mxnet.np.resize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trim_zeros.html">mxnet.np.trim_zeros</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.reshape.html">mxnet.np.reshape</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flip.html">mxnet.np.flip</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.roll.html">mxnet.np.roll</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rot90.html">mxnet.np.rot90</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fliplr.html">mxnet.np.fliplr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flipud.html">mxnet.np.flipud</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.io.html">Input and output</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.genfromtxt.html">mxnet.np.genfromtxt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.tolist.html">mxnet.np.ndarray.tolist</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.set_printoptions.html">mxnet.np.set_printoptions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dot.html">mxnet.np.dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vdot.html">mxnet.np.vdot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.inner.html">mxnet.np.inner</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.outer.html">mxnet.np.outer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tensordot.html">mxnet.np.tensordot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.einsum.html">mxnet.np.einsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.multi_dot.html">mxnet.np.linalg.multi_dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.matmul.html">mxnet.np.matmul</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_power.html">mxnet.np.linalg.matrix_power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.kron.html">mxnet.np.kron</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.svd.html">mxnet.np.linalg.svd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cholesky.html">mxnet.np.linalg.cholesky</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.qr.html">mxnet.np.linalg.qr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eig.html">mxnet.np.linalg.eig</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigh.html">mxnet.np.linalg.eigh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvals.html">mxnet.np.linalg.eigvals</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvalsh.html">mxnet.np.linalg.eigvalsh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.norm.html">mxnet.np.linalg.norm</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trace.html">mxnet.np.trace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cond.html">mxnet.np.linalg.cond</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.det.html">mxnet.np.linalg.det</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_rank.html">mxnet.np.linalg.matrix_rank</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.slogdet.html">mxnet.np.linalg.slogdet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.solve.html">mxnet.np.linalg.solve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorsolve.html">mxnet.np.linalg.tensorsolve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.lstsq.html">mxnet.np.linalg.lstsq</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.inv.html">mxnet.np.linalg.inv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.pinv.html">mxnet.np.linalg.pinv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorinv.html">mxnet.np.linalg.tensorinv</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.math.html">Mathematical functions</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sin.html">mxnet.np.sin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cos.html">mxnet.np.cos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tan.html">mxnet.np.tan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arcsin.html">mxnet.np.arcsin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arccos.html">mxnet.np.arccos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan.html">mxnet.np.arctan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.degrees.html">mxnet.np.degrees</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.radians.html">mxnet.np.radians</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hypot.html">mxnet.np.hypot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan2.html">mxnet.np.arctan2</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.deg2rad.html">mxnet.np.deg2rad</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rad2deg.html">mxnet.np.rad2deg</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.unwrap.html">mxnet.np.unwrap</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sinh.html">mxnet.np.sinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cosh.html">mxnet.np.cosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tanh.html">mxnet.np.tanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arcsinh.html">mxnet.np.arcsinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arccosh.html">mxnet.np.arccosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctanh.html">mxnet.np.arctanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rint.html">mxnet.np.rint</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fix.html">mxnet.np.fix</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.floor.html">mxnet.np.floor</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ceil.html">mxnet.np.ceil</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trunc.html">mxnet.np.trunc</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.around.html">mxnet.np.around</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.round_.html">mxnet.np.round_</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sum.html">mxnet.np.sum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.prod.html">mxnet.np.prod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cumsum.html">mxnet.np.cumsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanprod.html">mxnet.np.nanprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nansum.html">mxnet.np.nansum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cumprod.html">mxnet.np.cumprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nancumprod.html">mxnet.np.nancumprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nancumsum.html">mxnet.np.nancumsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.diff.html">mxnet.np.diff</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ediff1d.html">mxnet.np.ediff1d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cross.html">mxnet.np.cross</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trapz.html">mxnet.np.trapz</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.exp.html">mxnet.np.exp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.expm1.html">mxnet.np.expm1</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log.html">mxnet.np.log</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log10.html">mxnet.np.log10</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log2.html">mxnet.np.log2</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log1p.html">mxnet.np.log1p</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.logaddexp.html">mxnet.np.logaddexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.i0.html">mxnet.np.i0</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ldexp.html">mxnet.np.ldexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.signbit.html">mxnet.np.signbit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.copysign.html">mxnet.np.copysign</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.frexp.html">mxnet.np.frexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.spacing.html">mxnet.np.spacing</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.lcm.html">mxnet.np.lcm</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.gcd.html">mxnet.np.gcd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.add.html">mxnet.np.add</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.reciprocal.html">mxnet.np.reciprocal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.negative.html">mxnet.np.negative</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.divide.html">mxnet.np.divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.power.html">mxnet.np.power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.subtract.html">mxnet.np.subtract</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.mod.html">mxnet.np.mod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.multiply.html">mxnet.np.multiply</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.true_divide.html">mxnet.np.true_divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.remainder.html">mxnet.np.remainder</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.positive.html">mxnet.np.positive</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.float_power.html">mxnet.np.float_power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fmod.html">mxnet.np.fmod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.modf.html">mxnet.np.modf</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.divmod.html">mxnet.np.divmod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.floor_divide.html">mxnet.np.floor_divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.clip.html">mxnet.np.clip</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sqrt.html">mxnet.np.sqrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cbrt.html">mxnet.np.cbrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.square.html">mxnet.np.square</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.absolute.html">mxnet.np.absolute</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sign.html">mxnet.np.sign</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.maximum.html">mxnet.np.maximum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.minimum.html">mxnet.np.minimum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fabs.html">mxnet.np.fabs</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.heaviside.html">mxnet.np.heaviside</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fmax.html">mxnet.np.fmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fmin.html">mxnet.np.fmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nan_to_num.html">mxnet.np.nan_to_num</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.interp.html">mxnet.np.interp</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/random/index.html">np.random</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.choice.html">mxnet.np.random.choice</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.shuffle.html">mxnet.np.random.shuffle</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.normal.html">mxnet.np.random.normal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.uniform.html">mxnet.np.random.uniform</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.rand.html">mxnet.np.random.rand</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.randint.html">mxnet.np.random.randint</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.beta.html">mxnet.np.random.beta</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.chisquare.html">mxnet.np.random.chisquare</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.exponential.html">mxnet.np.random.exponential</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.f.html">mxnet.np.random.f</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.gamma.html">mxnet.np.random.gamma</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.gumbel.html">mxnet.np.random.gumbel</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.laplace.html">mxnet.np.random.laplace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.logistic.html">mxnet.np.random.logistic</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.lognormal.html">mxnet.np.random.lognormal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.multinomial.html">mxnet.np.random.multinomial</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.multivariate_normal.html">mxnet.np.random.multivariate_normal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.pareto.html">mxnet.np.random.pareto</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.power.html">mxnet.np.random.power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.rayleigh.html">mxnet.np.random.rayleigh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.weibull.html">mxnet.np.random.weibull</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.sort.html">Sorting, searching, and counting</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.sort.html">mxnet.np.ndarray.sort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sort.html">mxnet.np.sort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.lexsort.html">mxnet.np.lexsort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argsort.html">mxnet.np.argsort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.msort.html">mxnet.np.msort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.partition.html">mxnet.np.partition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argpartition.html">mxnet.np.argpartition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argmax.html">mxnet.np.argmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argmin.html">mxnet.np.argmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanargmax.html">mxnet.np.nanargmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanargmin.html">mxnet.np.nanargmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argwhere.html">mxnet.np.argwhere</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nonzero.html">mxnet.np.nonzero</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flatnonzero.html">mxnet.np.flatnonzero</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.where.html">mxnet.np.where</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.searchsorted.html">mxnet.np.searchsorted</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.extract.html">mxnet.np.extract</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.count_nonzero.html">mxnet.np.count_nonzero</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.statistics.html">Statistics</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.min.html">mxnet.np.min</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.max.html">mxnet.np.max</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.amin.html">mxnet.np.amin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.amax.html">mxnet.np.amax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanmin.html">mxnet.np.nanmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanmax.html">mxnet.np.nanmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ptp.html">mxnet.np.ptp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.percentile.html">mxnet.np.percentile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanpercentile.html">mxnet.np.nanpercentile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.quantile.html">mxnet.np.quantile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanquantile.html">mxnet.np.nanquantile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.mean.html">mxnet.np.mean</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.std.html">mxnet.np.std</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.var.html">mxnet.np.var</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.median.html">mxnet.np.median</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.average.html">mxnet.np.average</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanmedian.html">mxnet.np.nanmedian</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanstd.html">mxnet.np.nanstd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanvar.html">mxnet.np.nanvar</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.corrcoef.html">mxnet.np.corrcoef</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.correlate.html">mxnet.np.correlate</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cov.html">mxnet.np.cov</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.histogram.html">mxnet.np.histogram</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.histogram2d.html">mxnet.np.histogram2d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.histogramdd.html">mxnet.np.histogramdd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.bincount.html">mxnet.np.bincount</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.histogram_bin_edges.html">mxnet.np.histogram_bin_edges</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.digitize.html">mxnet.np.digitize</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/npx/index.html">NPX: NumPy Neural Network Extension</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.set_np.html">mxnet.npx.set_np</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.reset_np.html">mxnet.npx.reset_np</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.cpu.html">mxnet.npx.cpu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.cpu_pinned.html">mxnet.npx.cpu_pinned</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.gpu.html">mxnet.npx.gpu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.gpu_memory_info.html">mxnet.npx.gpu_memory_info</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.current_device.html">mxnet.npx.current_device</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.num_gpus.html">mxnet.npx.num_gpus</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.activation.html">mxnet.npx.activation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.batch_norm.html">mxnet.npx.batch_norm</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.convolution.html">mxnet.npx.convolution</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.dropout.html">mxnet.npx.dropout</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.embedding.html">mxnet.npx.embedding</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.fully_connected.html">mxnet.npx.fully_connected</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.layer_norm.html">mxnet.npx.layer_norm</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.pooling.html">mxnet.npx.pooling</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.rnn.html">mxnet.npx.rnn</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.leaky_relu.html">mxnet.npx.leaky_relu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.multibox_detection.html">mxnet.npx.multibox_detection</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.multibox_prior.html">mxnet.npx.multibox_prior</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.multibox_target.html">mxnet.npx.multibox_target</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.roi_pooling.html">mxnet.npx.roi_pooling</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.sigmoid.html">mxnet.npx.sigmoid</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.relu.html">mxnet.npx.relu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.smooth_l1.html">mxnet.npx.smooth_l1</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.softmax.html">mxnet.npx.softmax</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.log_softmax.html">mxnet.npx.log_softmax</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.topk.html">mxnet.npx.topk</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.waitall.html">mxnet.npx.waitall</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.load.html">mxnet.npx.load</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.save.html">mxnet.npx.save</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.one_hot.html">mxnet.npx.one_hot</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.pick.html">mxnet.npx.pick</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.reshape_like.html">mxnet.npx.reshape_like</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.batch_flatten.html">mxnet.npx.batch_flatten</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.batch_dot.html">mxnet.npx.batch_dot</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.gamma.html">mxnet.npx.gamma</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.sequence_mask.html">mxnet.npx.sequence_mask</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/gluon/index.html">mxnet.gluon</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/block.html">gluon.Block</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/hybrid_block.html">gluon.HybridBlock</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/symbol_block.html">gluon.SymbolBlock</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/constant.html">gluon.Constant</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/parameter.html">gluon.Parameter</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/trainer.html">gluon.Trainer</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/contrib/index.html">gluon.contrib</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/data/index.html">gluon.data</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/data/vision/index.html">data.vision</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/gluon/data/vision/datasets/index.html">vision.datasets</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/gluon/data/vision/transforms/index.html">vision.transforms</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/loss/index.html">gluon.loss</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/metric/index.html">gluon.metric</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/model_zoo/index.html">gluon.model_zoo.vision</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/nn/index.html">gluon.nn</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/rnn/index.html">gluon.rnn</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/utils/index.html">gluon.utils</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/autograd/index.html">mxnet.autograd</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/initializer/index.html">mxnet.initializer</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/optimizer/index.html">mxnet.optimizer</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/lr_scheduler/index.html">mxnet.lr_scheduler</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/kvstore/index.html">KVStore: Communication for Distributed Training</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/kvstore/index.html#horovod">Horovod</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/kvstore/generated/mxnet.kvstore.Horovod.html">mxnet.kvstore.Horovod</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/kvstore/index.html#byteps">BytePS</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/kvstore/generated/mxnet.kvstore.BytePS.html">mxnet.kvstore.BytePS</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/kvstore/index.html#kvstore-interface">KVStore Interface</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/kvstore/generated/mxnet.kvstore.KVStore.html">mxnet.kvstore.KVStore</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/kvstore/generated/mxnet.kvstore.KVStoreBase.html">mxnet.kvstore.KVStoreBase</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/kvstore/generated/mxnet.kvstore.KVStoreServer.html">mxnet.kvstore.KVStoreServer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/contrib/index.html">mxnet.contrib</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/io/index.html">contrib.io</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/ndarray/index.html">contrib.ndarray</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/onnx/index.html">contrib.onnx</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/quantization/index.html">contrib.quantization</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/symbol/index.html">contrib.symbol</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/tensorboard/index.html">contrib.tensorboard</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/tensorrt/index.html">contrib.tensorrt</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/text/index.html">contrib.text</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/legacy/callback/index.html">mxnet.callback</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/legacy/image/index.html">mxnet.image</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/legacy/io/index.html">mxnet.io</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/legacy/ndarray/index.html">mxnet.ndarray</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/ndarray.html">ndarray</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/contrib/index.html">ndarray.contrib</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/image/index.html">ndarray.image</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/linalg/index.html">ndarray.linalg</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/op/index.html">ndarray.op</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/random/index.html">ndarray.random</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/register/index.html">ndarray.register</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/sparse/index.html">ndarray.sparse</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/utils/index.html">ndarray.utils</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/legacy/recordio/index.html">mxnet.recordio</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/legacy/symbol/index.html">mxnet.symbol</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/symbol.html">symbol</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/contrib/index.html">symbol.contrib</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/image/index.html">symbol.image</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/linalg/index.html">symbol.linalg</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/op/index.html">symbol.op</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/random/index.html">symbol.random</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/register/index.html">symbol.register</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/sparse/index.html">symbol.sparse</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/legacy/visualization/index.html">mxnet.visualization</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/device/index.html">mxnet.device</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/engine/index.html">mxnet.engine</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/executor/index.html">mxnet.executor</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/kvstore_server/index.html">mxnet.kvstore_server</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/profiler/index.html">mxnet.profiler</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/rtc/index.html">mxnet.rtc</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/runtime/index.html">mxnet.runtime</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/runtime/generated/mxnet.runtime.Feature.html">mxnet.runtime.Feature</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/runtime/generated/mxnet.runtime.Features.html">mxnet.runtime.Features</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/runtime/generated/mxnet.runtime.feature_list.html">mxnet.runtime.feature_list</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/test_utils/index.html">mxnet.test_utils</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/util/index.html">mxnet.util</a></li> |
| </ul> |
| </li> |
| </ul> |
| |
| </nav> |
| |
| </div> |
| |
| </header> |
| <main class="mdl-layout__content" tabIndex="0"> |
| <header class="mdl-layout__drawer"> |
| |
| <div class="globaltoc"> |
| <span class="mdl-layout-title toc">Table Of Contents</span> |
| |
| |
| |
| <nav class="mdl-navigation"> |
| <ul> |
| <li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html">Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html#Using-your-own-data-with-custom-Datasets">Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">New in MXNet 2.0: faster C++ backend dataloaders</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/np/np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/optimizer/index.html">Optimizers</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/viz/index.html">Visualization</a><ul> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/performance/index.html">Performance</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/compression/index.html">Compression</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/compression/int8.html">Deploy with int-8</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/float16">Float16</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/backend/index.html">Accelerated Backend Tools</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/index.html">oneDNN</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_readme.html">Install MXNet with oneDNN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_quantization.html">oneDNN Quantization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_quantization_inc.html">Improving accuracy with Intel® Neural Compressor</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/tvm.html">Use TVM</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/profiler.html">Profiling MXNet Models</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/amp.html">Using AMP: Automatic Mixed Precision</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/deploy/index.html">Deployment</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/export/index.html">Export</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/export/onnx.html">Exporting to ONNX format</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/export_network.html">Export Gluon CV Models</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Save / Load Parameters</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/inference/index.html">Inference</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/inference/cpp.html">Deploy into C++</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/inference/image_classification_jetson.html">Image Classication using pretrained ResNet-50 model on Jetson module</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/index.html">Run on AWS</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/use_ec2.html">Run on an EC2 Instance</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/use_sagemaker.html">Run on Amazon SageMaker</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/cloud.html">MXNet on the Cloud</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/extend/index.html">Extend</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/extend/customop.html">Custom Numpy Operators</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/new_op">New Operator Creation</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/add_op_in_backend">New Operator in MXNet Backend</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/using_rtc">Using RTC for CUDA kernels</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l1"><a class="reference internal" href="../../../api/index.html">Python API</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/np/index.html">mxnet.np</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/np/arrays.html">Array objects</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/arrays.ndarray.html">The N-dimensional array (<code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code>)</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/arrays.indexing.html">Indexing</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/np/routines.html">Routines</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.array-creation.html">Array creation routines</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.eye.html">mxnet.np.eye</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.empty.html">mxnet.np.empty</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.full.html">mxnet.np.full</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.identity.html">mxnet.np.identity</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ones.html">mxnet.np.ones</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ones_like.html">mxnet.np.ones_like</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.zeros.html">mxnet.np.zeros</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.zeros_like.html">mxnet.np.zeros_like</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.array.html">mxnet.np.array</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.copy.html">mxnet.np.copy</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arange.html">mxnet.np.arange</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linspace.html">mxnet.np.linspace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.logspace.html">mxnet.np.logspace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.meshgrid.html">mxnet.np.meshgrid</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tril.html">mxnet.np.tril</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.array-manipulation.html">Array manipulation routines</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.reshape.html">mxnet.np.reshape</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ravel.html">mxnet.np.ravel</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.flatten.html">mxnet.np.ndarray.flatten</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.swapaxes.html">mxnet.np.swapaxes</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.T.html">mxnet.np.ndarray.T</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.transpose.html">mxnet.np.transpose</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.moveaxis.html">mxnet.np.moveaxis</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rollaxis.html">mxnet.np.rollaxis</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.expand_dims.html">mxnet.np.expand_dims</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.squeeze.html">mxnet.np.squeeze</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.broadcast_to.html">mxnet.np.broadcast_to</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.broadcast_arrays.html">mxnet.np.broadcast_arrays</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.atleast_1d.html">mxnet.np.atleast_1d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.atleast_2d.html">mxnet.np.atleast_2d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.atleast_3d.html">mxnet.np.atleast_3d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.concatenate.html">mxnet.np.concatenate</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.stack.html">mxnet.np.stack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dstack.html">mxnet.np.dstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vstack.html">mxnet.np.vstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.column_stack.html">mxnet.np.column_stack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hstack.html">mxnet.np.hstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.split.html">mxnet.np.split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hsplit.html">mxnet.np.hsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vsplit.html">mxnet.np.vsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.array_split.html">mxnet.np.array_split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dsplit.html">mxnet.np.dsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tile.html">mxnet.np.tile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.repeat.html">mxnet.np.repeat</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.unique.html">mxnet.np.unique</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.delete.html">mxnet.np.delete</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.insert.html">mxnet.np.insert</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.append.html">mxnet.np.append</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.resize.html">mxnet.np.resize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trim_zeros.html">mxnet.np.trim_zeros</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.reshape.html">mxnet.np.reshape</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flip.html">mxnet.np.flip</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.roll.html">mxnet.np.roll</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rot90.html">mxnet.np.rot90</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fliplr.html">mxnet.np.fliplr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flipud.html">mxnet.np.flipud</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.io.html">Input and output</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.genfromtxt.html">mxnet.np.genfromtxt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.tolist.html">mxnet.np.ndarray.tolist</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.set_printoptions.html">mxnet.np.set_printoptions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dot.html">mxnet.np.dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vdot.html">mxnet.np.vdot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.inner.html">mxnet.np.inner</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.outer.html">mxnet.np.outer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tensordot.html">mxnet.np.tensordot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.einsum.html">mxnet.np.einsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.multi_dot.html">mxnet.np.linalg.multi_dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.matmul.html">mxnet.np.matmul</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_power.html">mxnet.np.linalg.matrix_power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.kron.html">mxnet.np.kron</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.svd.html">mxnet.np.linalg.svd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cholesky.html">mxnet.np.linalg.cholesky</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.qr.html">mxnet.np.linalg.qr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eig.html">mxnet.np.linalg.eig</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigh.html">mxnet.np.linalg.eigh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvals.html">mxnet.np.linalg.eigvals</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvalsh.html">mxnet.np.linalg.eigvalsh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.norm.html">mxnet.np.linalg.norm</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trace.html">mxnet.np.trace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cond.html">mxnet.np.linalg.cond</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.det.html">mxnet.np.linalg.det</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_rank.html">mxnet.np.linalg.matrix_rank</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.slogdet.html">mxnet.np.linalg.slogdet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.solve.html">mxnet.np.linalg.solve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorsolve.html">mxnet.np.linalg.tensorsolve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.lstsq.html">mxnet.np.linalg.lstsq</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.inv.html">mxnet.np.linalg.inv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.pinv.html">mxnet.np.linalg.pinv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorinv.html">mxnet.np.linalg.tensorinv</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.math.html">Mathematical functions</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sin.html">mxnet.np.sin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cos.html">mxnet.np.cos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tan.html">mxnet.np.tan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arcsin.html">mxnet.np.arcsin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arccos.html">mxnet.np.arccos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan.html">mxnet.np.arctan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.degrees.html">mxnet.np.degrees</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.radians.html">mxnet.np.radians</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hypot.html">mxnet.np.hypot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan2.html">mxnet.np.arctan2</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.deg2rad.html">mxnet.np.deg2rad</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rad2deg.html">mxnet.np.rad2deg</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.unwrap.html">mxnet.np.unwrap</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sinh.html">mxnet.np.sinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cosh.html">mxnet.np.cosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tanh.html">mxnet.np.tanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arcsinh.html">mxnet.np.arcsinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arccosh.html">mxnet.np.arccosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctanh.html">mxnet.np.arctanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rint.html">mxnet.np.rint</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fix.html">mxnet.np.fix</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.floor.html">mxnet.np.floor</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ceil.html">mxnet.np.ceil</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trunc.html">mxnet.np.trunc</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.around.html">mxnet.np.around</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.round_.html">mxnet.np.round_</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sum.html">mxnet.np.sum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.prod.html">mxnet.np.prod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cumsum.html">mxnet.np.cumsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanprod.html">mxnet.np.nanprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nansum.html">mxnet.np.nansum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cumprod.html">mxnet.np.cumprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nancumprod.html">mxnet.np.nancumprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nancumsum.html">mxnet.np.nancumsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.diff.html">mxnet.np.diff</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ediff1d.html">mxnet.np.ediff1d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cross.html">mxnet.np.cross</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trapz.html">mxnet.np.trapz</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.exp.html">mxnet.np.exp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.expm1.html">mxnet.np.expm1</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log.html">mxnet.np.log</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log10.html">mxnet.np.log10</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log2.html">mxnet.np.log2</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log1p.html">mxnet.np.log1p</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.logaddexp.html">mxnet.np.logaddexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.i0.html">mxnet.np.i0</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ldexp.html">mxnet.np.ldexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.signbit.html">mxnet.np.signbit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.copysign.html">mxnet.np.copysign</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.frexp.html">mxnet.np.frexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.spacing.html">mxnet.np.spacing</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.lcm.html">mxnet.np.lcm</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.gcd.html">mxnet.np.gcd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.add.html">mxnet.np.add</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.reciprocal.html">mxnet.np.reciprocal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.negative.html">mxnet.np.negative</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.divide.html">mxnet.np.divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.power.html">mxnet.np.power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.subtract.html">mxnet.np.subtract</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.mod.html">mxnet.np.mod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.multiply.html">mxnet.np.multiply</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.true_divide.html">mxnet.np.true_divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.remainder.html">mxnet.np.remainder</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.positive.html">mxnet.np.positive</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.float_power.html">mxnet.np.float_power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fmod.html">mxnet.np.fmod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.modf.html">mxnet.np.modf</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.divmod.html">mxnet.np.divmod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.floor_divide.html">mxnet.np.floor_divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.clip.html">mxnet.np.clip</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sqrt.html">mxnet.np.sqrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cbrt.html">mxnet.np.cbrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.square.html">mxnet.np.square</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.absolute.html">mxnet.np.absolute</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sign.html">mxnet.np.sign</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.maximum.html">mxnet.np.maximum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.minimum.html">mxnet.np.minimum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fabs.html">mxnet.np.fabs</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.heaviside.html">mxnet.np.heaviside</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fmax.html">mxnet.np.fmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fmin.html">mxnet.np.fmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nan_to_num.html">mxnet.np.nan_to_num</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.interp.html">mxnet.np.interp</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/random/index.html">np.random</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.choice.html">mxnet.np.random.choice</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.shuffle.html">mxnet.np.random.shuffle</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.normal.html">mxnet.np.random.normal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.uniform.html">mxnet.np.random.uniform</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.rand.html">mxnet.np.random.rand</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.randint.html">mxnet.np.random.randint</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.beta.html">mxnet.np.random.beta</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.chisquare.html">mxnet.np.random.chisquare</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.exponential.html">mxnet.np.random.exponential</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.f.html">mxnet.np.random.f</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.gamma.html">mxnet.np.random.gamma</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.gumbel.html">mxnet.np.random.gumbel</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.laplace.html">mxnet.np.random.laplace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.logistic.html">mxnet.np.random.logistic</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.lognormal.html">mxnet.np.random.lognormal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.multinomial.html">mxnet.np.random.multinomial</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.multivariate_normal.html">mxnet.np.random.multivariate_normal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.pareto.html">mxnet.np.random.pareto</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.power.html">mxnet.np.random.power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.rayleigh.html">mxnet.np.random.rayleigh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.weibull.html">mxnet.np.random.weibull</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.sort.html">Sorting, searching, and counting</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.sort.html">mxnet.np.ndarray.sort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sort.html">mxnet.np.sort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.lexsort.html">mxnet.np.lexsort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argsort.html">mxnet.np.argsort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.msort.html">mxnet.np.msort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.partition.html">mxnet.np.partition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argpartition.html">mxnet.np.argpartition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argmax.html">mxnet.np.argmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argmin.html">mxnet.np.argmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanargmax.html">mxnet.np.nanargmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanargmin.html">mxnet.np.nanargmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argwhere.html">mxnet.np.argwhere</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nonzero.html">mxnet.np.nonzero</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flatnonzero.html">mxnet.np.flatnonzero</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.where.html">mxnet.np.where</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.searchsorted.html">mxnet.np.searchsorted</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.extract.html">mxnet.np.extract</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.count_nonzero.html">mxnet.np.count_nonzero</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.statistics.html">Statistics</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.min.html">mxnet.np.min</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.max.html">mxnet.np.max</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.amin.html">mxnet.np.amin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.amax.html">mxnet.np.amax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanmin.html">mxnet.np.nanmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanmax.html">mxnet.np.nanmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ptp.html">mxnet.np.ptp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.percentile.html">mxnet.np.percentile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanpercentile.html">mxnet.np.nanpercentile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.quantile.html">mxnet.np.quantile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanquantile.html">mxnet.np.nanquantile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.mean.html">mxnet.np.mean</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.std.html">mxnet.np.std</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.var.html">mxnet.np.var</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.median.html">mxnet.np.median</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.average.html">mxnet.np.average</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanmedian.html">mxnet.np.nanmedian</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanstd.html">mxnet.np.nanstd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanvar.html">mxnet.np.nanvar</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.corrcoef.html">mxnet.np.corrcoef</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.correlate.html">mxnet.np.correlate</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cov.html">mxnet.np.cov</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.histogram.html">mxnet.np.histogram</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.histogram2d.html">mxnet.np.histogram2d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.histogramdd.html">mxnet.np.histogramdd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.bincount.html">mxnet.np.bincount</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.histogram_bin_edges.html">mxnet.np.histogram_bin_edges</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.digitize.html">mxnet.np.digitize</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/npx/index.html">NPX: NumPy Neural Network Extension</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.set_np.html">mxnet.npx.set_np</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.reset_np.html">mxnet.npx.reset_np</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.cpu.html">mxnet.npx.cpu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.cpu_pinned.html">mxnet.npx.cpu_pinned</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.gpu.html">mxnet.npx.gpu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.gpu_memory_info.html">mxnet.npx.gpu_memory_info</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.current_device.html">mxnet.npx.current_device</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.num_gpus.html">mxnet.npx.num_gpus</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.activation.html">mxnet.npx.activation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.batch_norm.html">mxnet.npx.batch_norm</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.convolution.html">mxnet.npx.convolution</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.dropout.html">mxnet.npx.dropout</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.embedding.html">mxnet.npx.embedding</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.fully_connected.html">mxnet.npx.fully_connected</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.layer_norm.html">mxnet.npx.layer_norm</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.pooling.html">mxnet.npx.pooling</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.rnn.html">mxnet.npx.rnn</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.leaky_relu.html">mxnet.npx.leaky_relu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.multibox_detection.html">mxnet.npx.multibox_detection</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.multibox_prior.html">mxnet.npx.multibox_prior</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.multibox_target.html">mxnet.npx.multibox_target</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.roi_pooling.html">mxnet.npx.roi_pooling</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.sigmoid.html">mxnet.npx.sigmoid</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.relu.html">mxnet.npx.relu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.smooth_l1.html">mxnet.npx.smooth_l1</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.softmax.html">mxnet.npx.softmax</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.log_softmax.html">mxnet.npx.log_softmax</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.topk.html">mxnet.npx.topk</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.waitall.html">mxnet.npx.waitall</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.load.html">mxnet.npx.load</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.save.html">mxnet.npx.save</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.one_hot.html">mxnet.npx.one_hot</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.pick.html">mxnet.npx.pick</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.reshape_like.html">mxnet.npx.reshape_like</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.batch_flatten.html">mxnet.npx.batch_flatten</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.batch_dot.html">mxnet.npx.batch_dot</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.gamma.html">mxnet.npx.gamma</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.sequence_mask.html">mxnet.npx.sequence_mask</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/gluon/index.html">mxnet.gluon</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/block.html">gluon.Block</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/hybrid_block.html">gluon.HybridBlock</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/symbol_block.html">gluon.SymbolBlock</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/constant.html">gluon.Constant</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/parameter.html">gluon.Parameter</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/trainer.html">gluon.Trainer</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/contrib/index.html">gluon.contrib</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/data/index.html">gluon.data</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/data/vision/index.html">data.vision</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/gluon/data/vision/datasets/index.html">vision.datasets</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/gluon/data/vision/transforms/index.html">vision.transforms</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/loss/index.html">gluon.loss</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/metric/index.html">gluon.metric</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/model_zoo/index.html">gluon.model_zoo.vision</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/nn/index.html">gluon.nn</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/rnn/index.html">gluon.rnn</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/utils/index.html">gluon.utils</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/autograd/index.html">mxnet.autograd</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/initializer/index.html">mxnet.initializer</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/optimizer/index.html">mxnet.optimizer</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/lr_scheduler/index.html">mxnet.lr_scheduler</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/kvstore/index.html">KVStore: Communication for Distributed Training</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/kvstore/index.html#horovod">Horovod</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/kvstore/generated/mxnet.kvstore.Horovod.html">mxnet.kvstore.Horovod</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/kvstore/index.html#byteps">BytePS</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/kvstore/generated/mxnet.kvstore.BytePS.html">mxnet.kvstore.BytePS</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/kvstore/index.html#kvstore-interface">KVStore Interface</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/kvstore/generated/mxnet.kvstore.KVStore.html">mxnet.kvstore.KVStore</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/kvstore/generated/mxnet.kvstore.KVStoreBase.html">mxnet.kvstore.KVStoreBase</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/kvstore/generated/mxnet.kvstore.KVStoreServer.html">mxnet.kvstore.KVStoreServer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/contrib/index.html">mxnet.contrib</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/io/index.html">contrib.io</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/ndarray/index.html">contrib.ndarray</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/onnx/index.html">contrib.onnx</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/quantization/index.html">contrib.quantization</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/symbol/index.html">contrib.symbol</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/tensorboard/index.html">contrib.tensorboard</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/tensorrt/index.html">contrib.tensorrt</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/text/index.html">contrib.text</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/legacy/callback/index.html">mxnet.callback</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/legacy/image/index.html">mxnet.image</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/legacy/io/index.html">mxnet.io</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/legacy/ndarray/index.html">mxnet.ndarray</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/ndarray.html">ndarray</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/contrib/index.html">ndarray.contrib</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/image/index.html">ndarray.image</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/linalg/index.html">ndarray.linalg</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/op/index.html">ndarray.op</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/random/index.html">ndarray.random</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/register/index.html">ndarray.register</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/sparse/index.html">ndarray.sparse</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/utils/index.html">ndarray.utils</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/legacy/recordio/index.html">mxnet.recordio</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/legacy/symbol/index.html">mxnet.symbol</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/symbol.html">symbol</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/contrib/index.html">symbol.contrib</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/image/index.html">symbol.image</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/linalg/index.html">symbol.linalg</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/op/index.html">symbol.op</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/random/index.html">symbol.random</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/register/index.html">symbol.register</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/sparse/index.html">symbol.sparse</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/legacy/visualization/index.html">mxnet.visualization</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/device/index.html">mxnet.device</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/engine/index.html">mxnet.engine</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/executor/index.html">mxnet.executor</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/kvstore_server/index.html">mxnet.kvstore_server</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/profiler/index.html">mxnet.profiler</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/rtc/index.html">mxnet.rtc</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/runtime/index.html">mxnet.runtime</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/runtime/generated/mxnet.runtime.Feature.html">mxnet.runtime.Feature</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/runtime/generated/mxnet.runtime.Features.html">mxnet.runtime.Features</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/runtime/generated/mxnet.runtime.feature_list.html">mxnet.runtime.feature_list</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/test_utils/index.html">mxnet.test_utils</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/util/index.html">mxnet.util</a></li> |
| </ul> |
| </li> |
| </ul> |
| |
| </nav> |
| |
| </div> |
| |
| </header> |
| |
| <div class="document"> |
| <div class="page-content" role="main"> |
| |
| <h1>Source code for mxnet.ndarray.contrib</h1><div class="highlight"><pre> |
| <span></span><span class="c1"># Licensed to the Apache Software Foundation (ASF) under one</span> |
| <span class="c1"># or more contributor license agreements. See the NOTICE file</span> |
| <span class="c1"># distributed with this work for additional information</span> |
| <span class="c1"># regarding copyright ownership. The ASF licenses this file</span> |
| <span class="c1"># to you under the Apache License, Version 2.0 (the</span> |
| <span class="c1"># "License"); you may not use this file except in compliance</span> |
| <span class="c1"># with the License. You may obtain a copy of the License at</span> |
| <span class="c1">#</span> |
| <span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span> |
| <span class="c1">#</span> |
| <span class="c1"># Unless required by applicable law or agreed to in writing,</span> |
| <span class="c1"># software distributed under the License is distributed on an</span> |
| <span class="c1"># "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY</span> |
| <span class="c1"># KIND, either express or implied. See the License for the</span> |
| <span class="c1"># specific language governing permissions and limitations</span> |
| <span class="c1"># under the License.</span> |
| |
| <span class="c1"># coding: utf-8</span> |
| <span class="c1"># pylint: disable=wildcard-import, unused-wildcard-import,redefined-outer-name</span> |
| <span class="sd">"""Contrib NDArray API of MXNet."""</span> |
| <span class="kn">import</span> <span class="nn">math</span> |
| <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> |
| <span class="kn">import</span> <span class="nn">mxnet</span> <span class="k">as</span> <span class="nn">mx</span> |
| <span class="kn">from</span> <span class="nn">..device</span> <span class="kn">import</span> <span class="n">current_device</span> |
| <span class="kn">from</span> <span class="nn">..random</span> <span class="kn">import</span> <span class="n">uniform</span> |
| <span class="kn">from</span> <span class="nn">..base</span> <span class="kn">import</span> <span class="n">_as_list</span> |
| <span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">ndarray</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="kn">from</span> <span class="nn">.gen_contrib</span> <span class="kn">import</span> <span class="o">*</span> |
| <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span> |
| <span class="k">pass</span> |
| |
| <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"rand_zipfian"</span><span class="p">,</span> <span class="s2">"foreach"</span><span class="p">,</span> <span class="s2">"while_loop"</span><span class="p">,</span> <span class="s2">"cond"</span><span class="p">,</span> <span class="s2">"isinf"</span><span class="p">,</span> <span class="s2">"isfinite"</span><span class="p">,</span> <span class="s2">"isnan"</span><span class="p">]</span> |
| |
| <span class="k">def</span> <span class="nf">_flatten_list</span><span class="p">(</span><span class="n">nested_list</span><span class="p">):</span> |
| <span class="k">return</span> <span class="p">[</span><span class="n">item</span> <span class="k">for</span> <span class="n">sublist</span> <span class="ow">in</span> <span class="n">nested_list</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">sublist</span><span class="p">]</span> |
| |
| <span class="c1"># pylint: disable=line-too-long</span> |
| <div class="viewcode-block" id="rand_zipfian"><a class="viewcode-back" href="../../../api/legacy/ndarray/contrib/index.html#mxnet.ndarray.contrib.rand_zipfian">[docs]</a><span class="k">def</span> <span class="nf">rand_zipfian</span><span class="p">(</span><span class="n">true_classes</span><span class="p">,</span> <span class="n">num_sampled</span><span class="p">,</span> <span class="n">range_max</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Draw random samples from an approximately log-uniform or Zipfian distribution.</span> |
| |
| <span class="sd"> This operation randomly samples *num_sampled* candidates the range of integers [0, range_max).</span> |
| <span class="sd"> The elements of sampled_candidates are drawn with replacement from the base distribution.</span> |
| |
| <span class="sd"> The base distribution for this operator is an approximately log-uniform or Zipfian distribution:</span> |
| |
| <span class="sd"> P(class) = (log(class + 2) - log(class + 1)) / log(range_max + 1)</span> |
| |
| <span class="sd"> This sampler is useful when the true classes approximately follow such a distribution.</span> |
| <span class="sd"> For example, if the classes represent words in a lexicon sorted in decreasing order of \</span> |
| <span class="sd"> frequency. If your classes are not ordered by decreasing frequency, do not use this op.</span> |
| |
| <span class="sd"> Additionaly, it also returns the number of times each of the \</span> |
| <span class="sd"> true classes and the sampled classes is expected to occur.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> true_classes : NDArray</span> |
| <span class="sd"> A 1-D NDArray of the target classes.</span> |
| <span class="sd"> num_sampled: int</span> |
| <span class="sd"> The number of classes to randomly sample.</span> |
| <span class="sd"> range_max: int</span> |
| <span class="sd"> The number of possible classes.</span> |
| <span class="sd"> ctx : Context</span> |
| <span class="sd"> Device context of output. Default is current context.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> samples: NDArray</span> |
| <span class="sd"> The sampled candidate classes in 1-D `int64` dtype.</span> |
| <span class="sd"> expected_count_true: NDArray</span> |
| <span class="sd"> The expected count for true classes in 1-D `float64` dtype.</span> |
| <span class="sd"> expected_count_sample: NDArray</span> |
| <span class="sd"> The expected count for sampled candidates in 1-D `float64` dtype.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> true_cls = mx.nd.array([3])</span> |
| <span class="sd"> >>> samples, exp_count_true, exp_count_sample = mx.nd.contrib.rand_zipfian(true_cls, 4, 5)</span> |
| <span class="sd"> >>> samples</span> |
| <span class="sd"> [1 3 3 3]</span> |
| <span class="sd"> <NDArray 4 @cpu(0)></span> |
| <span class="sd"> >>> exp_count_true</span> |
| <span class="sd"> [ 0.12453879]</span> |
| <span class="sd"> <NDArray 1 @cpu(0)></span> |
| <span class="sd"> >>> exp_count_sample</span> |
| <span class="sd"> [ 0.22629439 0.12453879 0.12453879 0.12453879]</span> |
| <span class="sd"> <NDArray 4 @cpu(0)></span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="n">current_device</span><span class="p">()</span> |
| <span class="n">log_range</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">range_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> |
| <span class="n">rand</span> <span class="o">=</span> <span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">log_range</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">num_sampled</span><span class="p">,),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'float64'</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">)</span> |
| <span class="c1"># make sure sampled_classes are in the range of [0, range_max)</span> |
| <span class="n">sampled_classes</span> <span class="o">=</span> <span class="p">(</span><span class="n">rand</span><span class="o">.</span><span class="n">exp</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'int64'</span><span class="p">)</span> <span class="o">%</span> <span class="n">range_max</span> |
| |
| <span class="n">true_cls</span> <span class="o">=</span> <span class="n">true_classes</span><span class="o">.</span><span class="n">as_in_context</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'float64'</span><span class="p">)</span> |
| <span class="n">expected_count_true</span> <span class="o">=</span> <span class="p">((</span><span class="n">true_cls</span> <span class="o">+</span> <span class="mf">2.0</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">true_cls</span> <span class="o">+</span> <span class="mf">1.0</span><span class="p">))</span><span class="o">.</span><span class="n">log</span><span class="p">()</span> <span class="o">/</span> <span class="n">log_range</span> <span class="o">*</span> <span class="n">num_sampled</span> |
| <span class="c1"># cast sampled classes to fp64 to avoid interget division</span> |
| <span class="n">sampled_cls_fp64</span> <span class="o">=</span> <span class="n">sampled_classes</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'float64'</span><span class="p">)</span> |
| <span class="n">expected_prob_sampled</span> <span class="o">=</span> <span class="p">((</span><span class="n">sampled_cls_fp64</span> <span class="o">+</span> <span class="mf">2.0</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">sampled_cls_fp64</span> <span class="o">+</span> <span class="mf">1.0</span><span class="p">))</span><span class="o">.</span><span class="n">log</span><span class="p">()</span> <span class="o">/</span> <span class="n">log_range</span> |
| <span class="n">expected_count_sampled</span> <span class="o">=</span> <span class="n">expected_prob_sampled</span> <span class="o">*</span> <span class="n">num_sampled</span> |
| <span class="k">return</span> <span class="n">sampled_classes</span><span class="p">,</span> <span class="n">expected_count_true</span><span class="p">,</span> <span class="n">expected_count_sampled</span></div> |
| <span class="c1"># pylint: enable=line-too-long</span> |
| |
| |
| <span class="k">def</span> <span class="nf">_flatten</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">inout_str</span><span class="p">):</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">NDArray</span><span class="p">):</span> |
| <span class="k">return</span> <span class="p">[</span><span class="n">args</span><span class="p">],</span> <span class="nb">int</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> |
| |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)),</span> \ |
| <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">inout_str</span><span class="si">}</span><span class="s2"> must be (nested) list of NDArray, "</span> \ |
| <span class="sa">f</span><span class="s2">"but got </span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">args</span><span class="p">)</span><span class="si">}</span><span class="s2"> of type </span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">args</span><span class="p">))</span><span class="si">}</span><span class="s2">"</span> |
| <span class="n">flat</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">fmts</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">args</span><span class="p">:</span> |
| <span class="n">arg</span><span class="p">,</span> <span class="n">fmt</span> <span class="o">=</span> <span class="n">_flatten</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">inout_str</span><span class="p">)</span> |
| <span class="n">flat</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">arg</span><span class="p">)</span> |
| <span class="n">fmts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fmt</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">flat</span><span class="p">,</span> <span class="n">fmts</span> |
| |
| |
| <span class="k">def</span> <span class="nf">_regroup</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">fmt</span><span class="p">):</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">fmt</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">fmt</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">args</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> |
| <span class="k">return</span> <span class="n">args</span><span class="p">[:</span><span class="n">fmt</span><span class="p">],</span> <span class="n">args</span><span class="p">[</span><span class="n">fmt</span><span class="p">:]</span> |
| |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)),</span> \ |
| <span class="s2">"output must be (nested) list of NDArray, "</span> \ |
| <span class="sa">f</span><span class="s2">"but got </span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">args</span><span class="p">)</span><span class="si">}</span><span class="s2"> of type </span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">args</span><span class="p">))</span><span class="si">}</span><span class="s2">"</span> |
| <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">fmt</span><span class="p">:</span> |
| <span class="n">res</span><span class="p">,</span> <span class="n">args</span> <span class="o">=</span> <span class="n">_regroup</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span> |
| <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">res</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">ret</span><span class="p">,</span> <span class="n">args</span> |
| |
| |
| <div class="viewcode-block" id="foreach"><a class="viewcode-back" href="../../../api/legacy/ndarray/contrib/index.html#mxnet.ndarray.contrib.foreach">[docs]</a><span class="k">def</span> <span class="nf">foreach</span><span class="p">(</span><span class="n">body</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">init_states</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Run a for loop with user-defined computation over NDArrays on dimension 0.</span> |
| |
| <span class="sd"> This operator simulates a for loop and body has the computation for an iteration</span> |
| <span class="sd"> of the for loop. It runs the computation in body on each slice from the input</span> |
| <span class="sd"> NDArrays.</span> |
| |
| <span class="sd"> body takes two arguments as input and outputs a tuple of two elements,</span> |
| <span class="sd"> as illustrated below::</span> |
| |
| <span class="sd"> out, states = body(data1, states)</span> |
| |
| <span class="sd"> data1 can be either an NDArray or a list of NDArrays. If data is an NDArray,</span> |
| <span class="sd"> data1 is an NDArray. Otherwise, data1 is a list of NDArrays and has the same</span> |
| <span class="sd"> size as data. states is a list of NDArrays and have the same size as init_states.</span> |
| <span class="sd"> Similarly, out can be either an NDArray or a list of NDArrays, which are concatenated</span> |
| <span class="sd"> as the first output of foreach; states from the last execution of body</span> |
| <span class="sd"> are the second output of foreach.</span> |
| |
| <span class="sd"> The computation done by this operator is equivalent to the pseudo code below</span> |
| <span class="sd"> when the input data is NDArray::</span> |
| |
| <span class="sd"> states = init_states</span> |
| <span class="sd"> outs = []</span> |
| <span class="sd"> for i in data.shape[0]:</span> |
| <span class="sd"> s = data[i]</span> |
| <span class="sd"> out, states = body(s, states)</span> |
| <span class="sd"> outs.append(out)</span> |
| <span class="sd"> outs = stack(*outs)</span> |
| |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> body : a Python function.</span> |
| <span class="sd"> Define computation in an iteration.</span> |
| <span class="sd"> data: an NDArray or a list of NDArrays.</span> |
| <span class="sd"> The input data.</span> |
| <span class="sd"> init_states: an NDArray or nested lists of NDArrays.</span> |
| <span class="sd"> The initial values of the loop states.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> outputs: an NDArray or nested lists of NDArrays.</span> |
| <span class="sd"> The output data concatenated from the output of all iterations.</span> |
| <span class="sd"> states: an NDArray or nested lists of NDArrays.</span> |
| <span class="sd"> The loop states in the last iteration.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> step = lambda data, states: (data + states[0], [states[0] * 2])</span> |
| <span class="sd"> >>> data = mx.nd.random.uniform(shape=(2, 10))</span> |
| <span class="sd"> >>> states = [mx.nd.random.uniform(shape=(10))]</span> |
| <span class="sd"> >>> outs, states = mx.nd.contrib.foreach(step, data, states)</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">def</span> <span class="nf">check_input</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">in_type</span><span class="p">,</span> <span class="n">msg</span><span class="p">):</span> |
| <span class="n">is_NDArray_or_list</span> <span class="o">=</span> <span class="kc">True</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">:</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">in_type</span><span class="p">):</span> |
| <span class="n">is_NDArray_or_list</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="k">break</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">is_NDArray_or_list</span> <span class="o">=</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">in_type</span><span class="p">)</span> |
| <span class="k">assert</span> <span class="n">is_NDArray_or_list</span><span class="p">,</span> <span class="n">msg</span> |
| |
| <span class="n">flatten</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_flatten</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="s2">"foreach input"</span><span class="p">)</span> |
| <span class="n">check_input</span><span class="p">(</span><span class="n">flatten</span><span class="p">,</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">NDArray</span><span class="p">,</span> |
| <span class="s2">"data should be an NDArray or a nested list of NDArrays"</span><span class="p">)</span> |
| <span class="n">flatten</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_flatten</span><span class="p">(</span><span class="n">init_states</span><span class="p">,</span> <span class="s2">"foreach states"</span><span class="p">)</span> |
| <span class="n">check_input</span><span class="p">(</span><span class="n">flatten</span><span class="p">,</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">NDArray</span><span class="p">,</span> |
| <span class="s2">"init_states should be an NDArray or a nested list of NDArrays"</span><span class="p">)</span> |
| |
| <span class="n">not_data_list</span> <span class="o">=</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">NDArray</span><span class="p">)</span> |
| <span class="n">num_iters</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="n">not_data_list</span> <span class="k">else</span> <span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">states</span> <span class="o">=</span> <span class="n">init_states</span> |
| <span class="n">outputs</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_iters</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">not_data_list</span><span class="p">:</span> |
| <span class="n">eles</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">eles</span> <span class="o">=</span> <span class="p">[</span><span class="n">d</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span> |
| <span class="n">outs</span><span class="p">,</span> <span class="n">states</span> <span class="o">=</span> <span class="n">body</span><span class="p">(</span><span class="n">eles</span><span class="p">,</span> <span class="n">states</span><span class="p">)</span> |
| <span class="n">outs</span><span class="p">,</span> <span class="n">out_fmt</span> <span class="o">=</span> <span class="n">_flatten</span><span class="p">(</span><span class="n">outs</span><span class="p">,</span> <span class="s2">"foreach output"</span><span class="p">)</span> |
| <span class="n">outputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">outs</span><span class="p">)</span> |
| <span class="n">outputs</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">outputs</span><span class="p">)</span> |
| <span class="n">tmp_outputs</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">out</span> <span class="ow">in</span> <span class="n">outputs</span><span class="p">:</span> |
| <span class="n">tmp_outputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ndarray</span><span class="o">.</span><span class="n">op</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="o">*</span><span class="n">out</span><span class="p">))</span> |
| <span class="n">outputs</span> <span class="o">=</span> <span class="n">tmp_outputs</span> |
| <span class="n">outputs</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_regroup</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">out_fmt</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">states</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="while_loop"><a class="viewcode-back" href="../../../api/legacy/ndarray/contrib/index.html#mxnet.ndarray.contrib.while_loop">[docs]</a><span class="k">def</span> <span class="nf">while_loop</span><span class="p">(</span><span class="n">cond</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">loop_vars</span><span class="p">,</span> <span class="n">max_iterations</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Run a while loop with user-defined computation and loop condition.</span> |
| |
| <span class="sd"> This operator simulates a while loop which iterately does customized computation</span> |
| <span class="sd"> as long as the condition is satisfied.</span> |
| |
| <span class="sd"> `loop_vars` is a list of NDArrays on which the computation uses.</span> |
| |
| <span class="sd"> `cond` is a user-defined function, used as the loop condition.</span> |
| <span class="sd"> It consumes `loop_vars`, and produces a scalar MXNet NDArray,</span> |
| <span class="sd"> indicating the termination of the loop.</span> |
| <span class="sd"> The loop ends when `cond` returns false (zero).</span> |
| <span class="sd"> The `cond` is variadic, and its signature should be</span> |
| <span class="sd"> `cond(*loop_vars) => NDArray`.</span> |
| |
| <span class="sd"> `func` is a user-defined function, used as the loop body.</span> |
| <span class="sd"> It also consumes `loop_vars`, and produces `step_output` and `new_loop_vars` at each step.</span> |
| <span class="sd"> In each step, `step_output` should contain the same number elements.</span> |
| <span class="sd"> Through all steps, the i-th element of `step_output` should have the same shape and dtype.</span> |
| <span class="sd"> Also, `new_loop_vars` should contain the same number of elements as `loop_vars`,</span> |
| <span class="sd"> and the corresponding element should have the same shape and dtype.</span> |
| <span class="sd"> The `func` is variadic, and its signature should be</span> |
| <span class="sd"> `func(*loop_vars) =></span> |
| <span class="sd"> (NDArray or nested List[NDArray] step_output, NDArray or nested List[NDArray] new_loop_vars)`.</span> |
| |
| <span class="sd"> `max_iterations` is a scalar that defines the maximum number of iterations allowed.</span> |
| |
| <span class="sd"> This function returns two lists.</span> |
| <span class="sd"> The first list has the length of `|step_output|`,</span> |
| <span class="sd"> in which the i-th element are all i-th elements of</span> |
| <span class="sd"> `step_output` from all steps, stacked along axis 0.</span> |
| <span class="sd"> The second list has the length of `|loop_vars|`,</span> |
| <span class="sd"> which represents final states of loop variables.</span> |
| |
| <span class="sd"> .. warning::</span> |
| |
| <span class="sd"> For now, the axis 0 of all NDArrays in the first list are `max_iterations`,</span> |
| <span class="sd"> due to lack of dynamic shape inference.</span> |
| |
| <span class="sd"> .. warning::</span> |
| |
| <span class="sd"> When `cond` is never satisfied, we assume `step_output` is empty,</span> |
| <span class="sd"> because it cannot be inferred. This is different from the symbolic version.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> cond: a Python function.</span> |
| <span class="sd"> The loop condition.</span> |
| <span class="sd"> func: a Python function.</span> |
| <span class="sd"> The loop body.</span> |
| <span class="sd"> loop_vars: an NDArray or nested lists of NDArrays.</span> |
| <span class="sd"> The initial values of the loop variables.</span> |
| <span class="sd"> max_iterations: a python int.</span> |
| <span class="sd"> Maximum number of iterations.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> ------</span> |
| <span class="sd"> outputs: an NDArray or nested lists of NDArrays</span> |
| <span class="sd"> stacked output from each step</span> |
| <span class="sd"> states: an NDArray or nested lists of NDArrays</span> |
| <span class="sd"> final state</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> cond = lambda i, s: i <= 5</span> |
| <span class="sd"> >>> func = lambda i, s: ([i + s], [i + 1, s + i])</span> |
| <span class="sd"> >>> loop_vars = (mx.nd.array([0], dtype="int64"), mx.nd.array([1], dtype="int64"))</span> |
| <span class="sd"> >>> outputs, states = mx.nd.contrib.while_loop(cond, func, loop_vars, max_iterations=10)</span> |
| <span class="sd"> >>> outputs</span> |
| <span class="sd"> [</span> |
| <span class="sd"> [[ 1]</span> |
| <span class="sd"> [ 2]</span> |
| <span class="sd"> [ 4]</span> |
| <span class="sd"> [ 7]</span> |
| <span class="sd"> [11]</span> |
| <span class="sd"> [16]</span> |
| <span class="sd"> [...] # undefined value</span> |
| <span class="sd"> [...]</span> |
| <span class="sd"> [...]</span> |
| <span class="sd"> [...]]</span> |
| <span class="sd"> <NDArray 6x1 @cpu(0)>]</span> |
| <span class="sd"> >>> states</span> |
| <span class="sd"> [</span> |
| <span class="sd"> [6]</span> |
| <span class="sd"> <NDArray 1 @cpu(0)>,</span> |
| <span class="sd"> [16]</span> |
| <span class="sd"> <NDArray 1 @cpu(0)>]</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="nf">_to_python_scalar</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">type_</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Converts "inputs", possibly typed mxnet NDArray, a numpy ndarray, other python types,</span> |
| <span class="sd"> to the given type</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">NDArray</span><span class="p">):</span> |
| <span class="n">inputs</span> <span class="o">=</span> <span class="n">inputs</span><span class="o">.</span><span class="n">asscalar</span><span class="p">()</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="n">inputs</span> <span class="o">=</span> <span class="n">type_</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span> |
| <span class="k">except</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Cannot convert </span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2"> to python </span><span class="si">{</span><span class="n">type_</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">inputs</span> |
| |
| <span class="k">def</span> <span class="nf">_func_wrapper</span><span class="p">(</span><span class="n">loop_vars</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""This wrapper unifies</span> |
| <span class="sd"> "func: loop_vars -> new_loop_vars"</span> |
| <span class="sd"> and "func: loop_vars -> (step_output, new_loop_vars)"</span> |
| <span class="sd"> into "func: loop_vars -> (None or tuple of step_outputs, tuple of new_loop_vars)</span> |
| <span class="sd"> """</span> |
| <span class="n">step_output</span><span class="p">,</span> <span class="n">new_loop_vars</span> <span class="o">=</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">loop_vars</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">step_output</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">step_output</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">if</span> <span class="n">new_loop_vars</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">new_loop_vars</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">step_output</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span> |
| <span class="n">step_output</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">step_output</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">new_loop_vars</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span> |
| <span class="n">new_loop_vars</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">new_loop_vars</span><span class="p">)</span> |
| <span class="n">new_loop_vars</span> <span class="o">=</span> <span class="n">_as_list</span><span class="p">(</span><span class="n">new_loop_vars</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">loop_vars</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">new_loop_vars</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"The length of loop_vars should be consistent during the loop"</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">step_output</span><span class="p">,</span> <span class="n">new_loop_vars</span> |
| |
| <span class="k">if</span> <span class="n">max_iterations</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"max_iterations should be specified"</span><span class="p">)</span> |
| <span class="n">max_iterations</span> <span class="o">=</span> <span class="n">_to_python_scalar</span><span class="p">(</span><span class="n">max_iterations</span><span class="p">,</span> <span class="nb">int</span><span class="p">,</span> <span class="s2">"max_iteration"</span><span class="p">)</span> |
| <span class="c1"># It should be work as fine if loop_vars are empty I guess,</span> |
| <span class="c1"># but it is semantically unnecessary to include this case.</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">loop_vars</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"loop_vars should contain at least one element"</span><span class="p">)</span> |
| |
| <span class="n">steps</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="n">outputs</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="c1"># there might not be an iteration.</span> |
| <span class="n">out_fmt</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="n">not_loop_var_list</span> <span class="o">=</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">loop_vars</span><span class="p">,</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">NDArray</span><span class="p">)</span> |
| <span class="n">loop_vars</span> <span class="o">=</span> <span class="n">_as_list</span><span class="p">(</span><span class="n">loop_vars</span><span class="p">)</span> |
| <span class="k">while</span> <span class="n">steps</span> <span class="o"><</span> <span class="n">max_iterations</span> <span class="ow">and</span> \ |
| <span class="n">_to_python_scalar</span><span class="p">(</span><span class="n">cond</span><span class="p">(</span><span class="o">*</span><span class="n">loop_vars</span><span class="p">),</span> <span class="nb">bool</span><span class="p">,</span> <span class="s2">"Return value of cond"</span><span class="p">):</span> <span class="c1"># loop condition</span> |
| <span class="n">step_output</span><span class="p">,</span> <span class="n">loop_vars</span> <span class="o">=</span> <span class="n">_func_wrapper</span><span class="p">(</span><span class="n">loop_vars</span><span class="p">)</span> |
| <span class="n">step_output</span><span class="p">,</span> <span class="n">out_fmt</span> <span class="o">=</span> <span class="n">_flatten</span><span class="p">(</span><span class="n">step_output</span><span class="p">,</span> <span class="s2">"while output"</span><span class="p">)</span> |
| <span class="n">outputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">step_output</span><span class="p">)</span> |
| <span class="n">steps</span> <span class="o">+=</span> <span class="mi">1</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">outputs</span><span class="p">)</span> <span class="o">!=</span> <span class="n">steps</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">step_output</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Number of elements in step_output should be the same in each step"</span><span class="p">)</span> |
| <span class="n">stacked_outputs</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">i_th</span><span class="p">,</span> <span class="n">items</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">outputs</span><span class="p">),</span> <span class="mi">1</span><span class="p">):</span> |
| <span class="c1"># `mx.ndarray.pad` only support 4-D or 5-D inputs for now</span> |
| <span class="c1"># so we could not use it.</span> |
| <span class="n">items</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">items</span><span class="p">]</span> |
| <span class="k">if</span> <span class="n">steps</span> <span class="o">!=</span> <span class="n">max_iterations</span> <span class="ow">and</span> <span class="n">items</span><span class="p">:</span> |
| <span class="n">pad_shape</span> <span class="o">=</span> <span class="p">[</span><span class="n">max_iterations</span> <span class="o">-</span> <span class="n">steps</span><span class="p">]</span> <span class="o">+</span> <span class="nb">list</span><span class="p">(</span><span class="n">items</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span> <span class="p">])</span> |
| <span class="n">pad</span> <span class="o">=</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span> |
| <span class="n">shape</span><span class="o">=</span><span class="n">pad_shape</span><span class="p">,</span> |
| <span class="n">ctx</span><span class="o">=</span><span class="n">items</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> |
| <span class="n">dtype</span><span class="o">=</span><span class="n">items</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> |
| <span class="p">)</span> |
| <span class="n">items</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">items</span><span class="p">)</span> <span class="o">+</span> <span class="p">[</span><span class="n">pad</span><span class="p">]</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="n">stacked_outputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ndarray</span><span class="o">.</span><span class="n">op</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span><span class="o">*</span><span class="n">items</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span> |
| <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">join</span><span class="p">(</span> |
| <span class="p">[</span><span class="sa">f</span><span class="s2">"Shapes of </span><span class="si">{</span><span class="n">i_th</span><span class="si">}</span><span class="s2">-th elements in step_outputs are inconsistent, which are:"</span><span class="p">]</span> <span class="o">+</span> |
| <span class="p">[</span><span class="sa">f</span><span class="s2">" Step </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">, shape is </span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">items</span><span class="p">)]</span> |
| <span class="p">))</span> |
| <span class="k">if</span> <span class="n">out_fmt</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">stacked_outputs</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_regroup</span><span class="p">(</span><span class="n">stacked_outputs</span><span class="p">,</span> <span class="n">out_fmt</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">not_loop_var_list</span><span class="p">:</span> |
| <span class="n">loop_vars</span> <span class="o">=</span> <span class="n">loop_vars</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="k">return</span> <span class="n">stacked_outputs</span><span class="p">,</span> <span class="n">loop_vars</span></div> |
| |
| <div class="viewcode-block" id="cond"><a class="viewcode-back" href="../../../api/legacy/ndarray/contrib/index.html#mxnet.ndarray.contrib.cond">[docs]</a><span class="k">def</span> <span class="nf">cond</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">then_func</span><span class="p">,</span> <span class="n">else_func</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Run an if-then-else using user-defined condition and computation</span> |
| |
| <span class="sd"> This operator simulates a if-like branch which chooses to do one of</span> |
| <span class="sd"> the two customized computations according to the specified condition.</span> |
| |
| <span class="sd"> `pred` is a scalar MXNet NDArray,</span> |
| <span class="sd"> indicating which branch of computation should be used.</span> |
| |
| <span class="sd"> `then_func` is a user-defined function, used as computation of the then branch.</span> |
| <span class="sd"> It produces `outputs`, which is a list of NDArrays.</span> |
| <span class="sd"> The signature of `then_func` should be</span> |
| <span class="sd"> `then_func() => NDArray or nested List[NDArray]`.</span> |
| |
| <span class="sd"> `else_func` is a user-defined function, used as computation of the else branch.</span> |
| <span class="sd"> It produces `outputs`, which is a list of NDArrays.</span> |
| <span class="sd"> The signature of `else_func` should be</span> |
| <span class="sd"> `else_func() => NDArray or nested List[NDArray]`.</span> |
| |
| <span class="sd"> The `outputs` produces by `then_func` and `else_func` should have the same number</span> |
| <span class="sd"> of elements, all of which should be in the same shape, of the same dtype and stype.</span> |
| |
| <span class="sd"> This function returns a list of symbols, representing the computation result.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> pred: a MXNet NDArray representing a scalar.</span> |
| <span class="sd"> The branch condition.</span> |
| <span class="sd"> then_func: a Python function.</span> |
| <span class="sd"> The computation to be executed if `pred` is true.</span> |
| <span class="sd"> else_func: a Python function.</span> |
| <span class="sd"> The computation to be executed if `pred` is false.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> outputs: an NDArray or nested lists of NDArrays, representing the result of computation.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> a, b = mx.nd.array([1]), mx.nd.array([2])</span> |
| <span class="sd"> >>> pred = a * b < 5</span> |
| <span class="sd"> >>> then_func = lambda: (a + 5) * (b + 5)</span> |
| <span class="sd"> >>> else_func = lambda: (a - 5) * (b - 5)</span> |
| <span class="sd"> >>> outputs = mx.nd.contrib.cond(pred, then_func, else_func)</span> |
| <span class="sd"> >>> outputs[0]</span> |
| <span class="sd"> [42.]</span> |
| <span class="sd"> <NDArray 1 @cpu(0)></span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="nf">_to_python_scalar</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">type_</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Converts "inputs", possibly typed mxnet NDArray, a numpy ndarray, other python types,</span> |
| <span class="sd"> to the given type</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="s2">"asscalar"</span><span class="p">):</span> |
| <span class="n">inputs</span> <span class="o">=</span> <span class="n">inputs</span><span class="o">.</span><span class="n">asscalar</span><span class="p">()</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="n">inputs</span> <span class="o">=</span> <span class="n">type_</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span> |
| <span class="k">except</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Cannot convert </span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2"> to python </span><span class="si">{</span><span class="n">type_</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">inputs</span> |
| |
| <span class="n">branch</span> <span class="o">=</span> <span class="n">_to_python_scalar</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="nb">bool</span><span class="p">,</span> <span class="s2">"pred"</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">branch</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">then_func</span><span class="p">()</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">else_func</span><span class="p">()</span></div> |
| |
| <div class="viewcode-block" id="isinf"><a class="viewcode-back" href="../../../api/legacy/ndarray/contrib/index.html#mxnet.ndarray.contrib.isinf">[docs]</a><span class="k">def</span> <span class="nf">isinf</span><span class="p">(</span><span class="n">data</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Performs an element-wise check to determine if the NDArray contains an infinite element</span> |
| <span class="sd"> or not.</span> |
| |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> input : NDArray</span> |
| <span class="sd"> An N-D NDArray.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> output: NDArray</span> |
| <span class="sd"> The output NDarray, with same shape as input, where 1 indicates the array element is</span> |
| <span class="sd"> equal to positive or negative infinity and 0 otherwise.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> data = mx.nd.array([np.inf, -np.inf, np.NINF, -1])</span> |
| <span class="sd"> >>> output = mx.nd.contrib.isinf(data)</span> |
| <span class="sd"> >>> output</span> |
| <span class="sd"> [1. 1. 1. 0.]</span> |
| <span class="sd"> <NDArray 4 @cpu(0)></span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">data</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span></div> |
| |
| <div class="viewcode-block" id="isfinite"><a class="viewcode-back" href="../../../api/legacy/ndarray/contrib/index.html#mxnet.ndarray.contrib.isfinite">[docs]</a><span class="k">def</span> <span class="nf">isfinite</span><span class="p">(</span><span class="n">data</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Performs an element-wise check to determine if the NDArray contains an infinite element</span> |
| <span class="sd"> or not.</span> |
| |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> input : NDArray</span> |
| <span class="sd"> An N-D NDArray.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> output: NDArray</span> |
| <span class="sd"> The output NDarray, with same shape as input, where 1 indicates the array element is</span> |
| <span class="sd"> finite i.e. not equal to positive or negative infinity and 0 in places where it is</span> |
| <span class="sd"> positive or negative infinity.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> data = mx.nd.array([np.inf, -np.inf, np.NINF, -1])</span> |
| <span class="sd"> >>> output = mx.nd.contrib.isfinite(data)</span> |
| <span class="sd"> >>> output</span> |
| <span class="sd"> [0. 0. 0. 1.]</span> |
| <span class="sd"> <NDArray 4 @cpu(0)></span> |
| <span class="sd"> """</span> |
| <span class="n">is_data_not_nan</span> <span class="o">=</span> <span class="n">data</span> <span class="o">==</span> <span class="n">data</span> <span class="c1"># pylint: disable=comparison-with-itself</span> |
| <span class="n">is_data_not_infinite</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span> <span class="o">!=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span> |
| <span class="k">return</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">is_data_not_infinite</span><span class="p">,</span> <span class="n">is_data_not_nan</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="isnan"><a class="viewcode-back" href="../../../api/legacy/ndarray/contrib/index.html#mxnet.ndarray.contrib.isnan">[docs]</a><span class="k">def</span> <span class="nf">isnan</span><span class="p">(</span><span class="n">data</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Performs an element-wise check to determine if the NDArray contains a NaN element</span> |
| <span class="sd"> or not.</span> |
| |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> data : NDArray</span> |
| <span class="sd"> An N-D NDArray.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> output: NDArray</span> |
| <span class="sd"> The output NDarray, with same shape as input, where 1 indicates the array element is</span> |
| <span class="sd"> NaN i.e. Not a Number and 0 otherwise.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> data = mx.nd.array([np.nan, -1])</span> |
| <span class="sd"> >>> output = mx.nd.contrib.isnan(data)</span> |
| <span class="sd"> >>> output</span> |
| <span class="sd"> [1. 0.]</span> |
| <span class="sd"> <NDArray 2 @cpu(0)></span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">data</span> <span class="o">!=</span> <span class="n">data</span> <span class="c1"># pylint: disable=comparison-with-itself</span></div> |
| |
| <span class="k">def</span> <span class="nf">_get_rescale_grad</span><span class="p">(</span><span class="n">rescale_grad</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">()):</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">rescale_grad</span><span class="p">,</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">NDArray</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,),</span> <span class="n">val</span><span class="o">=</span><span class="n">rescale_grad</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">rescale_grad</span><span class="o">.</span><span class="n">as_in_context</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">adamw_update</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">var</span><span class="p">,</span> <span class="n">rescale_grad</span><span class="p">,</span> <span class="n">lr</span><span class="p">,</span> <span class="n">eta</span><span class="p">,</span> <span class="n">beta1</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">beta2</span><span class="o">=</span><span class="mf">0.999</span><span class="p">,</span> |
| <span class="n">epsilon</span><span class="o">=</span><span class="mf">1e-8</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">clip_gradient</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="n">rescale_grad</span> <span class="o">=</span> <span class="n">_get_rescale_grad</span><span class="p">(</span><span class="n">rescale_grad</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">_internal</span><span class="o">.</span><span class="n">_adamw_update</span><span class="p">(</span><span class="n">weight</span><span class="o">=</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="o">=</span><span class="n">grad</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="n">mean</span><span class="p">,</span> <span class="n">var</span><span class="o">=</span><span class="n">var</span><span class="p">,</span> |
| <span class="n">rescale_grad</span><span class="o">=</span><span class="n">rescale_grad</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">eta</span><span class="o">=</span><span class="n">eta</span><span class="p">,</span> |
| <span class="n">beta1</span><span class="o">=</span><span class="n">beta1</span><span class="p">,</span> <span class="n">beta2</span><span class="o">=</span><span class="n">beta2</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="n">epsilon</span><span class="p">,</span> |
| <span class="n">wd</span><span class="o">=</span><span class="n">wd</span><span class="p">,</span> <span class="n">clip_gradient</span><span class="o">=</span><span class="n">clip_gradient</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> |
| <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">mp_adamw_update</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">var</span><span class="p">,</span> <span class="n">weight32</span><span class="p">,</span> <span class="n">rescale_grad</span><span class="p">,</span> <span class="n">lr</span><span class="p">,</span> <span class="n">eta</span><span class="p">,</span> <span class="n">beta1</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> |
| <span class="n">beta2</span><span class="o">=</span><span class="mf">0.999</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="mf">1e-8</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">clip_gradient</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="n">rescale_grad</span> <span class="o">=</span> <span class="n">_get_rescale_grad</span><span class="p">(</span><span class="n">rescale_grad</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">_internal</span><span class="o">.</span><span class="n">_mp_adamw_update</span><span class="p">(</span><span class="n">weight</span><span class="o">=</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="o">=</span><span class="n">grad</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="n">mean</span><span class="p">,</span> <span class="n">var</span><span class="o">=</span><span class="n">var</span><span class="p">,</span> |
| <span class="n">weight32</span><span class="o">=</span><span class="n">weight32</span><span class="p">,</span> |
| <span class="n">rescale_grad</span><span class="o">=</span><span class="n">rescale_grad</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">eta</span><span class="o">=</span><span class="n">eta</span><span class="p">,</span> |
| <span class="n">beta1</span><span class="o">=</span><span class="n">beta1</span><span class="p">,</span> <span class="n">beta2</span><span class="o">=</span><span class="n">beta2</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="n">epsilon</span><span class="p">,</span> |
| <span class="n">wd</span><span class="o">=</span><span class="n">wd</span><span class="p">,</span> <span class="n">clip_gradient</span><span class="o">=</span><span class="n">clip_gradient</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> |
| <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">multi_adamw_update</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">var</span><span class="p">,</span> <span class="n">rescale_grad</span><span class="p">,</span> <span class="n">lrs</span><span class="p">,</span> <span class="n">wds</span><span class="p">,</span> <span class="n">etas</span><span class="p">,</span> |
| <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">size</span><span class="p">:</span> |
| <span class="n">size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span> |
| |
| <span class="n">rescale_grad</span> <span class="o">=</span> <span class="n">_get_rescale_grad</span><span class="p">(</span><span class="n">rescale_grad</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">context</span><span class="p">)</span> |
| <span class="n">temp_list</span> <span class="o">=</span> <span class="n">_flatten_list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">var</span><span class="p">))</span> <span class="o">+</span> <span class="p">[</span><span class="n">rescale_grad</span><span class="p">]</span> |
| <span class="k">return</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">_internal</span><span class="o">.</span><span class="n">_multi_adamw_update</span><span class="p">(</span><span class="o">*</span><span class="n">temp_list</span><span class="p">,</span> |
| <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> |
| <span class="n">num_weights</span><span class="o">=</span><span class="n">size</span><span class="p">,</span> |
| <span class="n">lrs</span><span class="o">=</span><span class="n">lrs</span><span class="p">,</span> |
| <span class="n">wds</span><span class="o">=</span><span class="n">wds</span><span class="p">,</span> |
| <span class="n">etas</span><span class="o">=</span><span class="n">etas</span><span class="p">,</span> |
| <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span> |
| <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">multi_mp_adamw_update</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">var</span><span class="p">,</span> <span class="n">weights32</span><span class="p">,</span> <span class="n">rescale_grad</span><span class="p">,</span> <span class="n">lrs</span><span class="p">,</span> <span class="n">wds</span><span class="p">,</span> <span class="n">etas</span><span class="p">,</span> |
| <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">size</span><span class="p">:</span> |
| <span class="n">size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span> |
| |
| <span class="n">rescale_grad</span> <span class="o">=</span> <span class="n">_get_rescale_grad</span><span class="p">(</span><span class="n">rescale_grad</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">context</span><span class="p">)</span> |
| <span class="n">temp_list</span> <span class="o">=</span> <span class="n">_flatten_list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">var</span><span class="p">,</span> <span class="n">weights32</span><span class="p">))</span> <span class="o">+</span> <span class="p">[</span><span class="n">rescale_grad</span><span class="p">]</span> |
| <span class="k">return</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">_internal</span><span class="o">.</span><span class="n">_multi_mp_adamw_update</span><span class="p">(</span><span class="o">*</span><span class="n">temp_list</span><span class="p">,</span> |
| <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> |
| <span class="n">num_weights</span><span class="o">=</span><span class="n">size</span><span class="p">,</span> |
| <span class="n">lrs</span><span class="o">=</span><span class="n">lrs</span><span class="p">,</span> |
| <span class="n">wds</span><span class="o">=</span><span class="n">wds</span><span class="p">,</span> |
| <span class="n">etas</span><span class="o">=</span><span class="n">etas</span><span class="p">,</span> |
| <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span> |
| <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">multi_lamb_update</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">var</span><span class="p">,</span> <span class="n">step_count</span><span class="p">,</span> |
| <span class="n">lrs</span><span class="p">,</span> <span class="n">wds</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">num_tensors</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Given a list of gradients, update weights, mean and variance of multiple tensors</span> |
| <span class="sd"> following LAMB Optimizer implementation.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> weights : List of NDArrays containing the input weights of multiple tensors</span> |
| |
| <span class="sd"> grads : List of NDArrays containing input gradients</span> |
| |
| <span class="sd"> mean : List of NDArrays containing mean of multiple tensors to be updated</span> |
| |
| <span class="sd"> var : List of NDArrays containing variance of multiple tensors to be updated</span> |
| |
| <span class="sd"> step_count : List of scalars with the number of update step for each tensor</span> |
| |
| <span class="sd"> lrs : List of learning rates (one for each tensor)</span> |
| |
| <span class="sd"> wds : List of weight decays (one for each tensor)</span> |
| |
| <span class="sd"> out: List of NDArrays where the updated weights will be stored</span> |
| |
| <span class="sd"> num_tensors : Number of NDArrays/tensors in the list</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">num_tensors</span><span class="p">:</span> |
| <span class="n">num_tensors</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span> |
| <span class="n">temp_list</span> <span class="o">=</span> <span class="n">_flatten_list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">var</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">_internal</span><span class="o">.</span><span class="n">_multi_lamb_update</span><span class="p">(</span><span class="o">*</span><span class="n">temp_list</span><span class="p">,</span> |
| <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> |
| <span class="n">num_tensors</span><span class="o">=</span><span class="n">num_tensors</span><span class="p">,</span> |
| <span class="n">step_count</span><span class="o">=</span><span class="n">step_count</span><span class="p">,</span> |
| <span class="n">learning_rates</span><span class="o">=</span><span class="n">lrs</span><span class="p">,</span> |
| <span class="n">wds</span><span class="o">=</span><span class="n">wds</span><span class="p">,</span> |
| <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">multi_mp_lamb_update</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">var</span><span class="p">,</span> <span class="n">weights32</span><span class="p">,</span> <span class="n">step_count</span><span class="p">,</span> |
| <span class="n">lrs</span><span class="p">,</span> <span class="n">wds</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">num_tensors</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Given a list of gradients, update weights, mean and variance of multiple tensors</span> |
| <span class="sd"> following LAMB Optimizer implementation, and using Mixed-Precision.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> weights : List of NDArrays containing the input weights of multiple tensors</span> |
| |
| <span class="sd"> grads : List of NDArrays containing input gradients</span> |
| |
| <span class="sd"> mean : List of NDArrays containing mean of multiple tensors to be updated</span> |
| |
| <span class="sd"> var : List of NDArrays containing variance of multiple tensors to be updated</span> |
| |
| <span class="sd"> weights32 : Master copy of weights in FP32</span> |
| |
| <span class="sd"> step_count : List of scalars with the number of update step for each tensor</span> |
| |
| <span class="sd"> lrs : List of learning rates (one for each tensor)</span> |
| |
| <span class="sd"> wds : List of weight decays (one for each tensor)</span> |
| |
| <span class="sd"> out: List of NDArrays where the updated weights will be stored</span> |
| |
| <span class="sd"> num_tensors : Number of NDArrays/tensors in the list</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">num_tensors</span><span class="p">:</span> |
| <span class="n">num_tensors</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span> |
| <span class="n">temp_list</span> <span class="o">=</span> <span class="n">_flatten_list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">var</span><span class="p">,</span> <span class="n">weights32</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">_internal</span><span class="o">.</span><span class="n">_multi_mp_lamb_update</span><span class="p">(</span><span class="o">*</span><span class="n">temp_list</span><span class="p">,</span> |
| <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> |
| <span class="n">num_tensors</span><span class="o">=</span><span class="n">num_tensors</span><span class="p">,</span> |
| <span class="n">step_count</span><span class="o">=</span><span class="n">step_count</span><span class="p">,</span> |
| <span class="n">learning_rates</span><span class="o">=</span><span class="n">lrs</span><span class="p">,</span> |
| <span class="n">wds</span><span class="o">=</span><span class="n">wds</span><span class="p">,</span> |
| <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">adabelief_update</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">var</span><span class="p">,</span> <span class="n">rescale_grad</span><span class="p">,</span> <span class="n">lr</span><span class="p">,</span> <span class="n">eta</span><span class="p">,</span> <span class="n">beta1</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">beta2</span><span class="o">=</span><span class="mf">0.999</span><span class="p">,</span> |
| <span class="n">epsilon</span><span class="o">=</span><span class="mf">1e-8</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">clip_gradient</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="n">rescale_grad</span> <span class="o">=</span> <span class="n">_get_rescale_grad</span><span class="p">(</span><span class="n">rescale_grad</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">_internal</span><span class="o">.</span><span class="n">_adabelief_update</span><span class="p">(</span><span class="n">weight</span><span class="o">=</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="o">=</span><span class="n">grad</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="n">mean</span><span class="p">,</span> <span class="n">var</span><span class="o">=</span><span class="n">var</span><span class="p">,</span> |
| <span class="n">rescale_grad</span><span class="o">=</span><span class="n">rescale_grad</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">eta</span><span class="o">=</span><span class="n">eta</span><span class="p">,</span> |
| <span class="n">beta1</span><span class="o">=</span><span class="n">beta1</span><span class="p">,</span> <span class="n">beta2</span><span class="o">=</span><span class="n">beta2</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="n">epsilon</span><span class="p">,</span> |
| <span class="n">wd</span><span class="o">=</span><span class="n">wd</span><span class="p">,</span> <span class="n">clip_gradient</span><span class="o">=</span><span class="n">clip_gradient</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> |
| <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">mp_adabelief_update</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">var</span><span class="p">,</span> <span class="n">weight32</span><span class="p">,</span> <span class="n">rescale_grad</span><span class="p">,</span> <span class="n">lr</span><span class="p">,</span> <span class="n">eta</span><span class="p">,</span> <span class="n">beta1</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> |
| <span class="n">beta2</span><span class="o">=</span><span class="mf">0.999</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="mf">1e-8</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">clip_gradient</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="n">rescale_grad</span> <span class="o">=</span> <span class="n">_get_rescale_grad</span><span class="p">(</span><span class="n">rescale_grad</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">weight</span><span class="o">.</span><span class="n">context</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">_internal</span><span class="o">.</span><span class="n">_mp_adabelief_update</span><span class="p">(</span><span class="n">weight</span><span class="o">=</span><span class="n">weight</span><span class="p">,</span> <span class="n">grad</span><span class="o">=</span><span class="n">grad</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="n">mean</span><span class="p">,</span> <span class="n">var</span><span class="o">=</span><span class="n">var</span><span class="p">,</span> |
| <span class="n">weight32</span><span class="o">=</span><span class="n">weight32</span><span class="p">,</span> |
| <span class="n">rescale_grad</span><span class="o">=</span><span class="n">rescale_grad</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">eta</span><span class="o">=</span><span class="n">eta</span><span class="p">,</span> |
| <span class="n">beta1</span><span class="o">=</span><span class="n">beta1</span><span class="p">,</span> <span class="n">beta2</span><span class="o">=</span><span class="n">beta2</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="n">epsilon</span><span class="p">,</span> |
| <span class="n">wd</span><span class="o">=</span><span class="n">wd</span><span class="p">,</span> <span class="n">clip_gradient</span><span class="o">=</span><span class="n">clip_gradient</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> |
| <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">multi_adabelief_update</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">var</span><span class="p">,</span> <span class="n">rescale_grad</span><span class="p">,</span> <span class="n">lrs</span><span class="p">,</span> <span class="n">wds</span><span class="p">,</span> <span class="n">etas</span><span class="p">,</span> |
| <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">size</span><span class="p">:</span> |
| <span class="n">size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span> |
| |
| <span class="n">rescale_grad</span> <span class="o">=</span> <span class="n">_get_rescale_grad</span><span class="p">(</span><span class="n">rescale_grad</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">context</span><span class="p">)</span> |
| <span class="n">temp_list</span> <span class="o">=</span> <span class="n">_flatten_list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">var</span><span class="p">))</span> <span class="o">+</span> <span class="p">[</span><span class="n">rescale_grad</span><span class="p">]</span> |
| <span class="k">return</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">_internal</span><span class="o">.</span><span class="n">_multi_adabelief_update</span><span class="p">(</span><span class="o">*</span><span class="n">temp_list</span><span class="p">,</span> |
| <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> |
| <span class="n">num_weights</span><span class="o">=</span><span class="n">size</span><span class="p">,</span> |
| <span class="n">lrs</span><span class="o">=</span><span class="n">lrs</span><span class="p">,</span> |
| <span class="n">wds</span><span class="o">=</span><span class="n">wds</span><span class="p">,</span> |
| <span class="n">etas</span><span class="o">=</span><span class="n">etas</span><span class="p">,</span> |
| <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span> |
| <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">multi_mp_adabelief_update</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">var</span><span class="p">,</span> <span class="n">weights32</span><span class="p">,</span> <span class="n">rescale_grad</span><span class="p">,</span> <span class="n">lrs</span><span class="p">,</span> <span class="n">wds</span><span class="p">,</span> <span class="n">etas</span><span class="p">,</span> |
| <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">size</span><span class="p">:</span> |
| <span class="n">size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span> |
| |
| <span class="n">rescale_grad</span> <span class="o">=</span> <span class="n">_get_rescale_grad</span><span class="p">(</span><span class="n">rescale_grad</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">context</span><span class="p">)</span> |
| <span class="n">temp_list</span> <span class="o">=</span> <span class="n">_flatten_list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">var</span><span class="p">,</span> <span class="n">weights32</span><span class="p">))</span> <span class="o">+</span> <span class="p">[</span><span class="n">rescale_grad</span><span class="p">]</span> |
| <span class="k">return</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">_internal</span><span class="o">.</span><span class="n">_multi_mp_adabelief_update</span><span class="p">(</span><span class="o">*</span><span class="n">temp_list</span><span class="p">,</span> |
| <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> |
| <span class="n">num_weights</span><span class="o">=</span><span class="n">size</span><span class="p">,</span> |
| <span class="n">lrs</span><span class="o">=</span><span class="n">lrs</span><span class="p">,</span> |
| <span class="n">wds</span><span class="o">=</span><span class="n">wds</span><span class="p">,</span> |
| <span class="n">etas</span><span class="o">=</span><span class="n">etas</span><span class="p">,</span> |
| <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span> |
| <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">multi_lans_update</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">var</span><span class="p">,</span> <span class="n">step_count</span><span class="p">,</span> |
| <span class="n">lrs</span><span class="p">,</span> <span class="n">wds</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">num_tensors</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Given a list of gradients, update weights, mean and variance of multiple tensors</span> |
| <span class="sd"> following LANS Optimizer implementation.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> weights : List of NDArrays containing the input weights of multiple tensors</span> |
| |
| <span class="sd"> grads : List of NDArrays containing input gradients</span> |
| |
| <span class="sd"> mean : List of NDArrays containing mean of multiple tensors to be updated</span> |
| |
| <span class="sd"> var : List of NDArrays containing variance of multiple tensors to be updated</span> |
| |
| <span class="sd"> step_count : List of scalars with the number of update step for each tensor</span> |
| |
| <span class="sd"> lrs : List of learning rates (one for each tensor)</span> |
| |
| <span class="sd"> wds : List of weight decays (one for each tensor)</span> |
| |
| <span class="sd"> out: List of NDArrays where the updated weights will be stored</span> |
| |
| <span class="sd"> num_tensors : Number of NDArrays/tensors in the list</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">num_tensors</span><span class="p">:</span> |
| <span class="n">num_tensors</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span> |
| <span class="n">temp_list</span> <span class="o">=</span> <span class="n">_flatten_list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">var</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">_internal</span><span class="o">.</span><span class="n">_multi_lans_update</span><span class="p">(</span><span class="o">*</span><span class="n">temp_list</span><span class="p">,</span> |
| <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> |
| <span class="n">num_tensors</span><span class="o">=</span><span class="n">num_tensors</span><span class="p">,</span> |
| <span class="n">step_count</span><span class="o">=</span><span class="n">step_count</span><span class="p">,</span> |
| <span class="n">learning_rates</span><span class="o">=</span><span class="n">lrs</span><span class="p">,</span> |
| <span class="n">wds</span><span class="o">=</span><span class="n">wds</span><span class="p">,</span> |
| <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| |
| |
| <span class="k">def</span> <span class="nf">multi_mp_lans_update</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">var</span><span class="p">,</span> <span class="n">weights32</span><span class="p">,</span> <span class="n">step_count</span><span class="p">,</span> |
| <span class="n">lrs</span><span class="p">,</span> <span class="n">wds</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">num_tensors</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Given a list of gradients, update weights, mean and variance of multiple tensors</span> |
| <span class="sd"> following LANS Optimizer implementation, and using Mixed-Precision.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> weights : List of NDArrays containing the input weights of multiple tensors</span> |
| |
| <span class="sd"> grads : List of NDArrays containing input gradients</span> |
| |
| <span class="sd"> mean : List of NDArrays containing mean of multiple tensors to be updated</span> |
| |
| <span class="sd"> var : List of NDArrays containing variance of multiple tensors to be updated</span> |
| |
| <span class="sd"> weights32 : Master copy of weights in FP32</span> |
| |
| <span class="sd"> step_count : List of scalars with the number of update step for each tensor</span> |
| |
| <span class="sd"> lrs : List of learning rates (one for each tensor)</span> |
| |
| <span class="sd"> wds : List of weight decays (one for each tensor)</span> |
| |
| <span class="sd"> out: List of NDArrays where the updated weights will be stored</span> |
| |
| <span class="sd"> num_tensors : Number of NDArrays/tensors in the list</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">num_tensors</span><span class="p">:</span> |
| <span class="n">num_tensors</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span> |
| <span class="n">temp_list</span> <span class="o">=</span> <span class="n">_flatten_list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">var</span><span class="p">,</span> <span class="n">weights32</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">_internal</span><span class="o">.</span><span class="n">_multi_mp_lans_update</span><span class="p">(</span><span class="o">*</span><span class="n">temp_list</span><span class="p">,</span> |
| <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> |
| <span class="n">num_tensors</span><span class="o">=</span><span class="n">num_tensors</span><span class="p">,</span> |
| <span class="n">step_count</span><span class="o">=</span><span class="n">step_count</span><span class="p">,</span> |
| <span class="n">learning_rates</span><span class="o">=</span><span class="n">lrs</span><span class="p">,</span> |
| <span class="n">wds</span><span class="o">=</span><span class="n">wds</span><span class="p">,</span> |
| <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| </pre></div> |
| |
| <hr class="feedback-hr-top" /> |
| <div class="feedback-container"> |
| <div class="feedback-question">Did this page help you?</div> |
| <div class="feedback-answer-container"> |
| <div class="feedback-answer yes-link" data-response="yes">Yes</div> |
| <div class="feedback-answer no-link" data-response="no">No</div> |
| </div> |
| <div class="feedback-thank-you">Thanks for your feedback!</div> |
| </div> |
| <hr class="feedback-hr-bottom" /> |
| </div> |
| <div class="side-doc-outline"> |
| <div class="side-doc-outline--content"> |
| </div> |
| </div> |
| |
| <div class="clearer"></div> |
| </div><div class="pagenation"> |
| </div> |
| <footer class="site-footer h-card"> |
| <div class="wrapper"> |
| <div class="row"> |
| <div class="col-4"> |
| <h4 class="footer-category-title">Resources</h4> |
| <ul class="contact-list"> |
| <li><a href="https://lists.apache.org/list.html?dev@mxnet.apache.org">Mailing list</a> <a class="u-email" href="mailto:dev-subscribe@mxnet.apache.org">(subscribe)</a></li> |
| <li><a href="https://discuss.mxnet.io">MXNet Discuss forum</a></li> |
| <li><a href="https://github.com/apache/mxnet/issues">Github Issues</a></li> |
| <li><a href="https://github.com/apache/mxnet/projects">Projects</a></li> |
| <li><a href="https://cwiki.apache.org/confluence/display/MXNET/Apache+MXNet+Home">Developer Wiki</a></li> |
| <li><a href="/community">Contribute To MXNet</a></li> |
| </ul> |
| </div> |
| |
| <div class="col-4"><ul class="social-media-list"><li><a href="https://github.com/apache/mxnet"><svg class="svg-icon"><use xlink:href="../../../_static/minima-social-icons.svg#github"></use></svg> <span class="username">apache/mxnet</span></a></li><li><a href="https://www.twitter.com/apachemxnet"><svg class="svg-icon"><use xlink:href="../../../_static/minima-social-icons.svg#twitter"></use></svg> <span class="username">apachemxnet</span></a></li><li><a href="https://youtube.com/apachemxnet"><svg class="svg-icon"><use xlink:href="../../../_static/minima-social-icons.svg#youtube"></use></svg> <span class="username">apachemxnet</span></a></li></ul> |
| </div> |
| |
| <div class="col-4 footer-text"> |
| <p>A flexible and efficient library for deep learning.</p> |
| </div> |
| </div> |
| </div> |
| </footer> |
| |
| <footer class="site-footer2"> |
| <div class="wrapper"> |
| <div class="row"> |
| <div class="col-3"> |
| <img src="../../../_static/apache_incubator_logo.png" class="footer-logo col-2"> |
| </div> |
| <div class="footer-bottom-warning col-9"> |
| <p>Apache MXNet is an effort undergoing incubation at <a href="http://www.apache.org/">The Apache Software Foundation</a> (ASF), <span style="font-weight:bold">sponsored by the <i>Apache Incubator</i></span>. Incubation is required |
| of all newly accepted projects until a further review indicates that the infrastructure, |
| communications, and decision making process have stabilized in a manner consistent with other |
| successful ASF projects. While incubation status is not necessarily a reflection of the completeness |
| or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF. |
| </p><p>"Copyright © 2017-2018, The Apache Software Foundation Apache MXNet, MXNet, Apache, the Apache |
| feather, and the Apache MXNet project logo are either registered trademarks or trademarks of the |
| Apache Software Foundation."</p> |
| </div> |
| </div> |
| </div> |
| </footer> |
| |
| </body> |
| </html> |
