| <!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>Step 7: Load and Run a NN using GPU — 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" /> |
| <link rel="next" title="Moving to MXNet from Other Frameworks" href="../to-mxnet/index.html" /> |
| <link rel="prev" title="Step 6: Train a Neural Network" href="6-train-nn.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/incubator-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">Python Tutorials</a><i class="material-icons">navigate_next</i> |
| <a class="mdl-navigation__link" href="../index.html">Getting Started</a><i class="material-icons">navigate_next</i> |
| <a class="mdl-navigation__link" href="index.html">Crash Course</a><i class="material-icons">navigate_next</i> |
| <a class="mdl-navigation__link is-active">Step 7: Load and Run a NN using GPU</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/tutorials/getting-started/crash-course/7-use-gpus.ipynb" 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 class="current"> |
| <li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Python Tutorials</a><ul class="current"> |
| <li class="toctree-l2 current"><a class="reference internal" href="../index.html">Getting Started</a><ul class="current"> |
| <li class="toctree-l3 current"><a class="reference internal" href="index.html">Crash Course</a><ul class="current"> |
| <li class="toctree-l4"><a class="reference internal" href="0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="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="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="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="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="6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4 current"><a class="current reference internal" href="#">Step 7: Load and Run a NN using GPU</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../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="../../packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../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="../../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="../../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="../../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="../../packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../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="../../packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../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="../../packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../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="../../packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../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="../../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="../../packages/np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../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="../../packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../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="../../packages/optimizer/index.html">Optimizers</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../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="../../performance/index.html">Performance</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../performance/compression/index.html">Compression</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../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="../../performance/backend/index.html">Accelerated Backend Tools</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../performance/backend/dnnl/index.html">oneDNN</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../performance/backend/dnnl/dnnl_readme.html">Install MXNet with oneDNN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../performance/backend/dnnl/dnnl_quantization.html">oneDNN Quantization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../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="../../performance/backend/tvm.html">Use TVM</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../performance/backend/profiler.html">Profiling MXNet Models</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../performance/backend/amp.html">Using AMP: Automatic Mixed Precision</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../deploy/index.html">Deployment</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../deploy/export/index.html">Export</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../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="../../deploy/inference/index.html">Inference</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../deploy/inference/cpp.html">Deploy into C++</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../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="../../deploy/run-on-aws/index.html">Run on AWS</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../deploy/run-on-aws/use_ec2.html">Run on an EC2 Instance</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../deploy/run-on-aws/use_sagemaker.html">Run on Amazon SageMaker</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../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="../../extend/index.html">Extend</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../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 class="current"> |
| <li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Python Tutorials</a><ul class="current"> |
| <li class="toctree-l2 current"><a class="reference internal" href="../index.html">Getting Started</a><ul class="current"> |
| <li class="toctree-l3 current"><a class="reference internal" href="index.html">Crash Course</a><ul class="current"> |
| <li class="toctree-l4"><a class="reference internal" href="0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="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="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="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="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="6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4 current"><a class="current reference internal" href="#">Step 7: Load and Run a NN using GPU</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../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="../../packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../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="../../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="../../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="../../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="../../packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../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="../../packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../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="../../packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../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="../../packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../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="../../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="../../packages/np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../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="../../packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../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="../../packages/optimizer/index.html">Optimizers</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../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="../../performance/index.html">Performance</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../performance/compression/index.html">Compression</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../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="../../performance/backend/index.html">Accelerated Backend Tools</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../performance/backend/dnnl/index.html">oneDNN</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../performance/backend/dnnl/dnnl_readme.html">Install MXNet with oneDNN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../performance/backend/dnnl/dnnl_quantization.html">oneDNN Quantization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../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="../../performance/backend/tvm.html">Use TVM</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../performance/backend/profiler.html">Profiling MXNet Models</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../performance/backend/amp.html">Using AMP: Automatic Mixed Precision</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../deploy/index.html">Deployment</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../deploy/export/index.html">Export</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../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="../../deploy/inference/index.html">Inference</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../deploy/inference/cpp.html">Deploy into C++</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../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="../../deploy/run-on-aws/index.html">Run on AWS</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../deploy/run-on-aws/use_ec2.html">Run on an EC2 Instance</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../deploy/run-on-aws/use_sagemaker.html">Run on Amazon SageMaker</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../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="../../extend/index.html">Extend</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../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"> |
| |
| |
| <style> |
| /* CSS for nbsphinx extension */ |
| |
| /* remove conflicting styling from Sphinx themes */ |
| div.nbinput, |
| div.nbinput div.prompt, |
| div.nbinput div.input_area, |
| div.nbinput div[class*=highlight], |
| div.nbinput div[class*=highlight] pre, |
| div.nboutput, |
| div.nbinput div.prompt, |
| div.nbinput div.output_area, |
| div.nboutput div[class*=highlight], |
| div.nboutput div[class*=highlight] pre { |
| background: none; |
| border: none; |
| padding: 0 0; |
| margin: 0; |
| box-shadow: none; |
| } |
| |
| /* avoid gaps between output lines */ |
| div.nboutput div[class*=highlight] pre { |
| line-height: normal; |
| } |
| |
| /* input/output containers */ |
| div.nbinput, |
| div.nboutput { |
| display: -webkit-flex; |
| display: flex; |
| align-items: flex-start; |
| margin: 0; |
| width: 100%; |
| } |
| @media (max-width: 540px) { |
| div.nbinput, |
| div.nboutput { |
| flex-direction: column; |
| } |
| } |
| |
| /* input container */ |
| div.nbinput { |
| padding-top: 5px; |
| } |
| |
| /* last container */ |
| div.nblast { |
| padding-bottom: 5px; |
| } |
| |
| /* input prompt */ |
| div.nbinput div.prompt pre { |
| color: #307FC1; |
| } |
| |
| /* output prompt */ |
| div.nboutput div.prompt pre { |
| color: #BF5B3D; |
| } |
| |
| /* all prompts */ |
| div.nbinput div.prompt, |
| div.nboutput div.prompt { |
| min-width: 7ex; |
| padding-top: 0.4em; |
| padding-right: 0.4em; |
| text-align: right; |
| flex: 0; |
| } |
| @media (max-width: 540px) { |
| div.nbinput div.prompt, |
| div.nboutput div.prompt { |
| text-align: left; |
| padding: 0.4em; |
| } |
| div.nboutput div.prompt.empty { |
| padding: 0; |
| } |
| } |
| |
| /* disable scrollbars on prompts */ |
| div.nbinput div.prompt pre, |
| div.nboutput div.prompt pre { |
| overflow: hidden; |
| } |
| |
| /* input/output area */ |
| div.nbinput div.input_area, |
| div.nboutput div.output_area { |
| padding: 0.4em; |
| -webkit-flex: 1; |
| flex: 1; |
| overflow: auto; |
| } |
| @media (max-width: 540px) { |
| div.nbinput div.input_area, |
| div.nboutput div.output_area { |
| width: 100%; |
| } |
| } |
| |
| /* input area */ |
| div.nbinput div.input_area { |
| border: 1px solid #e0e0e0; |
| border-radius: 2px; |
| background: #f5f5f5; |
| } |
| |
| /* override MathJax center alignment in output cells */ |
| div.nboutput div[class*=MathJax] { |
| text-align: left !important; |
| } |
| |
| /* override sphinx.ext.imgmath center alignment in output cells */ |
| div.nboutput div.math p { |
| text-align: left; |
| } |
| |
| /* standard error */ |
| div.nboutput div.output_area.stderr { |
| background: #fdd; |
| } |
| |
| /* ANSI colors */ |
| .ansi-black-fg { color: #3E424D; } |
| .ansi-black-bg { background-color: #3E424D; } |
| .ansi-black-intense-fg { color: #282C36; } |
| .ansi-black-intense-bg { background-color: #282C36; } |
| .ansi-red-fg { color: #E75C58; } |
| .ansi-red-bg { background-color: #E75C58; } |
| .ansi-red-intense-fg { color: #B22B31; } |
| .ansi-red-intense-bg { background-color: #B22B31; } |
| .ansi-green-fg { color: #00A250; } |
| .ansi-green-bg { background-color: #00A250; } |
| .ansi-green-intense-fg { color: #007427; } |
| .ansi-green-intense-bg { background-color: #007427; } |
| .ansi-yellow-fg { color: #DDB62B; } |
| .ansi-yellow-bg { background-color: #DDB62B; } |
| .ansi-yellow-intense-fg { color: #B27D12; } |
| .ansi-yellow-intense-bg { background-color: #B27D12; } |
| .ansi-blue-fg { color: #208FFB; } |
| .ansi-blue-bg { background-color: #208FFB; } |
| .ansi-blue-intense-fg { color: #0065CA; } |
| .ansi-blue-intense-bg { background-color: #0065CA; } |
| .ansi-magenta-fg { color: #D160C4; } |
| .ansi-magenta-bg { background-color: #D160C4; } |
| .ansi-magenta-intense-fg { color: #A03196; } |
| .ansi-magenta-intense-bg { background-color: #A03196; } |
| .ansi-cyan-fg { color: #60C6C8; } |
| .ansi-cyan-bg { background-color: #60C6C8; } |
| .ansi-cyan-intense-fg { color: #258F8F; } |
| .ansi-cyan-intense-bg { background-color: #258F8F; } |
| .ansi-white-fg { color: #C5C1B4; } |
| .ansi-white-bg { background-color: #C5C1B4; } |
| .ansi-white-intense-fg { color: #A1A6B2; } |
| .ansi-white-intense-bg { background-color: #A1A6B2; } |
| |
| .ansi-default-inverse-fg { color: #FFFFFF; } |
| .ansi-default-inverse-bg { background-color: #000000; } |
| |
| .ansi-bold { font-weight: bold; } |
| .ansi-underline { text-decoration: underline; } |
| |
| /* Some additional styling taken form the Jupyter notebook CSS */ |
| div.rendered_html table { |
| border: none; |
| border-collapse: collapse; |
| border-spacing: 0; |
| color: black; |
| font-size: 12px; |
| table-layout: fixed; |
| } |
| div.rendered_html thead { |
| border-bottom: 1px solid black; |
| vertical-align: bottom; |
| } |
| div.rendered_html tr, |
| div.rendered_html th, |
| div.rendered_html td { |
| text-align: right; |
| vertical-align: middle; |
| padding: 0.5em 0.5em; |
| line-height: normal; |
| white-space: normal; |
| max-width: none; |
| border: none; |
| } |
| div.rendered_html th { |
| font-weight: bold; |
| } |
| div.rendered_html tbody tr:nth-child(odd) { |
| background: #f5f5f5; |
| } |
| div.rendered_html tbody tr:hover { |
| background: rgba(66, 165, 245, 0.2); |
| } |
| </style> |
| <!--- Licensed to the Apache Software Foundation (ASF) under one --><!--- or more contributor license agreements. See the NOTICE file --><!--- distributed with this work for additional information --><!--- regarding copyright ownership. The ASF licenses this file --><!--- to you under the Apache License, Version 2.0 (the --><!--- "License"); you may not use this file except in compliance --><!--- with the License. You may obtain a copy of the License at --><!--- http://www.apache.org/licenses/LICENSE-2.0 --><!--- Unless required by applicable law or agreed to in writing, --><!--- software distributed under the License is distributed on an --><!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY --><!--- KIND, either express or implied. See the License for the --><!--- specific language governing permissions and limitations --><!--- under the License. --><div class="section" id="Step-7:-Load-and-Run-a-NN-using-GPU"> |
| <h1>Step 7: Load and Run a NN using GPU<a class="headerlink" href="#Step-7:-Load-and-Run-a-NN-using-GPU" title="Permalink to this headline">¶</a></h1> |
| <p>In this step, you will learn how to use graphics processing units (GPUs) with MXNet. If you use GPUs to train and deploy neural networks, you may be able to train or perform inference quicker than with central processing units (CPUs).</p> |
| <div class="section" id="Prerequisites"> |
| <h2>Prerequisites<a class="headerlink" href="#Prerequisites" title="Permalink to this headline">¶</a></h2> |
| <p>Before you start the steps, make sure you have at least one Nvidia GPU on your machine and make sure that you have CUDA properly installed. GPUs from AMD and Intel are not supported. Additionally, you will need to install the GPU-enabled version of MXNet. You can find information about how to install the GPU version of MXNet for your system <a class="reference external" href="https://mxnet.apache.org/versions/1.4.1/install/ubuntu_setup.html">here</a>.</p> |
| <p>You can use the following command to view the number GPUs that are available to MXNet.</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="kn">from</span> <span class="nn">mxnet</span> <span class="kn">import</span> <span class="n">np</span><span class="p">,</span> <span class="n">npx</span><span class="p">,</span> <span class="n">gluon</span><span class="p">,</span> <span class="n">autograd</span> |
| <span class="kn">from</span> <span class="nn">mxnet.gluon</span> <span class="kn">import</span> <span class="n">nn</span> |
| <span class="kn">import</span> <span class="nn">time</span> |
| <span class="n">npx</span><span class="o">.</span><span class="n">set_np</span><span class="p">()</span> |
| |
| <span class="n">npx</span><span class="o">.</span><span class="n">num_gpus</span><span class="p">()</span> <span class="c1">#This command provides the number of GPUs MXNet can access</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]: |
| </pre></div> |
| </div> |
| <div class="output_area highlight-none notranslate"><div class="highlight"><pre> |
| <span></span>1 |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="Allocate-data-to-a-GPU"> |
| <h2>Allocate data to a GPU<a class="headerlink" href="#Allocate-data-to-a-GPU" title="Permalink to this headline">¶</a></h2> |
| <p>MXNet’s ndarray is very similar to NumPy’s. One major difference is that MXNet’s ndarray has a <code class="docutils literal notranslate"><span class="pre">device</span></code> attribute specifying which device an array is on. By default, arrays are stored on <code class="docutils literal notranslate"><span class="pre">npx.cpu()</span></code>. To change it to the first GPU, you can use the following code, <code class="docutils literal notranslate"><span class="pre">npx.gpu()</span></code> or <code class="docutils literal notranslate"><span class="pre">npx.gpu(0)</span></code> to indicate the first GPU.</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">gpu</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">gpu</span><span class="p">()</span> <span class="k">if</span> <span class="n">npx</span><span class="o">.</span><span class="n">num_gpus</span><span class="p">()</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="n">npx</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">gpu</span><span class="p">)</span> |
| <span class="n">x</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area stderr docutils container"> |
| <div class="highlight"><pre> |
| [02:19:48] /work/mxnet/src/storage/storage.cc:202: Using Pooled (Naive) StorageManager for GPU |
| </pre></div></div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]: |
| </pre></div> |
| </div> |
| <div class="output_area highlight-none notranslate"><div class="highlight"><pre> |
| <span></span>array([[1., 1., 1., 1.], |
| [1., 1., 1., 1.], |
| [1., 1., 1., 1.]], device=gpu(0)) |
| </pre></div> |
| </div> |
| </div> |
| <p>If you’re using a CPU, MXNet allocates data on the main memory and tries to use as many CPU cores as possible. If there are multiple GPUs, MXNet will tell you which GPUs the ndarray is allocated on.</p> |
| <p>Assuming there is at least two GPUs. You can create another ndarray and assign it to a different GPU. If you only have one GPU, then you will get an error trying to run this code. In the example code here, you will copy <code class="docutils literal notranslate"><span class="pre">x</span></code> to the second GPU, <code class="docutils literal notranslate"><span class="pre">npx.gpu(1)</span></code>:</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">gpu_1</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="k">if</span> <span class="n">npx</span><span class="o">.</span><span class="n">num_gpus</span><span class="p">()</span> <span class="o">></span> <span class="mi">1</span> <span class="k">else</span> <span class="n">npx</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span> |
| <span class="n">x</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">gpu_1</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area stderr docutils container"> |
| <div class="highlight"><pre> |
| [02:19:48] /work/mxnet/src/storage/storage.cc:202: Using Pooled (Naive) StorageManager for CPU |
| </pre></div></div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]: |
| </pre></div> |
| </div> |
| <div class="output_area highlight-none notranslate"><div class="highlight"><pre> |
| <span></span>array([[1., 1., 1., 1.], |
| [1., 1., 1., 1.], |
| [1., 1., 1., 1.]]) |
| </pre></div> |
| </div> |
| </div> |
| <p>MXNet requries that users explicitly move data between devices. But several operators such as <code class="docutils literal notranslate"><span class="pre">print</span></code>, and <code class="docutils literal notranslate"><span class="pre">asnumpy</span></code>, will implicitly move data to main memory.</p> |
| </div> |
| <div class="section" id="Choosing-GPU-Ids"> |
| <h2>Choosing GPU Ids<a class="headerlink" href="#Choosing-GPU-Ids" title="Permalink to this headline">¶</a></h2> |
| <p>If you have multiple GPUs on your machine, MXNet can access each of them through 0-indexing with <code class="docutils literal notranslate"><span class="pre">npx</span></code>. As you saw before, the first GPU was accessed using <code class="docutils literal notranslate"><span class="pre">npx.gpu(0)</span></code>, and the second using <code class="docutils literal notranslate"><span class="pre">npx.gpu(1)</span></code>. This extends to however many GPUs your machine has. So if your machine has eight GPUs, the last GPU is accessed using <code class="docutils literal notranslate"><span class="pre">npx.gpu(7)</span></code>. This allows you to select which GPUs to use for operations and training. You might find it particularly useful when you want to leverage multiple GPUs |
| while training neural networks.</p> |
| </div> |
| <div class="section" id="Run-an-operation-on-a-GPU"> |
| <h2>Run an operation on a GPU<a class="headerlink" href="#Run-an-operation-on-a-GPU" title="Permalink to this headline">¶</a></h2> |
| <p>To perform an operation on a particular GPU, you only need to guarantee that the input of an operation is already on that GPU. The output is allocated on the same GPU as well. Almost all operators in the <code class="docutils literal notranslate"><span class="pre">np</span></code> and <code class="docutils literal notranslate"><span class="pre">npx</span></code> module support running on a GPU.</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">gpu</span><span class="p">)</span> |
| <span class="n">x</span> <span class="o">+</span> <span class="n">y</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]: |
| </pre></div> |
| </div> |
| <div class="output_area highlight-none notranslate"><div class="highlight"><pre> |
| <span></span>array([[1.0870043, 1.7762893, 1.4183636, 1.8169589], |
| [1.4344369, 1.8648567, 1.2758604, 1.668379 ], |
| [1.6557653, 1.4967787, 1.510894 , 1.3070612]], device=gpu(0)) |
| </pre></div> |
| </div> |
| </div> |
| <p>Remember that if the inputs are not on the same GPU, you will get an error.</p> |
| </div> |
| <div class="section" id="Run-a-neural-network-on-a-GPU"> |
| <h2>Run a neural network on a GPU<a class="headerlink" href="#Run-a-neural-network-on-a-GPU" title="Permalink to this headline">¶</a></h2> |
| <p>To run a neural network on a GPU, you only need to copy and move the input data and parameters to the GPU. To demonstrate this you can reuse the previously defined LeafNetwork in <a class="reference internal" href="6-train-nn.html"><span class="doc">Training Neural Networks</span></a>. The following code example shows this.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># The convolutional block has a convolution layer, a max pool layer and a batch normalization layer</span> |
| <span class="k">def</span> <span class="nf">conv_block</span><span class="p">(</span><span class="n">filters</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">batch_norm</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> |
| <span class="n">conv_block</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">HybridSequential</span><span class="p">()</span> |
| <span class="n">conv_block</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">channels</span><span class="o">=</span><span class="n">filters</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="n">kernel_size</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">),</span> |
| <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="n">stride</span><span class="p">))</span> |
| <span class="k">if</span> <span class="n">batch_norm</span><span class="p">:</span> |
| <span class="n">conv_block</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm</span><span class="p">())</span> |
| <span class="k">return</span> <span class="n">conv_block</span> |
| |
| <span class="c1"># The dense block consists of a dense layer and a dropout layer</span> |
| <span class="k">def</span> <span class="nf">dense_block</span><span class="p">(</span><span class="n">neurons</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mf">0.2</span><span class="p">):</span> |
| <span class="n">dense_block</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">HybridSequential</span><span class="p">()</span> |
| <span class="n">dense_block</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="n">neurons</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="n">activation</span><span class="p">))</span> |
| <span class="k">if</span> <span class="n">dropout</span><span class="p">:</span> |
| <span class="n">dense_block</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">dropout</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">dense_block</span> |
| |
| <span class="c1"># Create neural network blueprint using the blocks</span> |
| <span class="k">class</span> <span class="nc">LeafNetwork</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">LeafNetwork</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">conv_block</span><span class="p">(</span><span class="mi">32</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">conv_block</span><span class="p">(</span><span class="mi">64</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">conv3</span> <span class="o">=</span> <span class="n">conv_block</span><span class="p">(</span><span class="mi">128</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Flatten</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">dense1</span> <span class="o">=</span> <span class="n">dense_block</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">dense2</span> <span class="o">=</span> <span class="n">dense_block</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">dense3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">):</span> |
| <span class="n">batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> |
| <span class="n">batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> |
| <span class="n">batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv3</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> |
| <span class="n">batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> |
| <span class="n">batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense1</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> |
| <span class="n">batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense2</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> |
| <span class="n">batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense3</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="n">batch</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>Load the saved parameters onto GPU 0 directly as shown below; additionally, you could use <code class="docutils literal notranslate"><span class="pre">net.collect_params().reset_device(gpu)</span></code> to change the device.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">net</span> <span class="o">=</span> <span class="n">LeafNetwork</span><span class="p">()</span> |
| <span class="n">net</span><span class="o">.</span><span class="n">load_parameters</span><span class="p">(</span><span class="s1">'leaf_models.params'</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">gpu</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>Use the following command to create input data on GPU 0. The forward function will then run on GPU 0.</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">gpu</span><span class="p">)</span> |
| <span class="n">net</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area stderr docutils container"> |
| <div class="highlight"><pre> |
| [02:19:48] /work/mxnet/src/operator/cudnn_ops.cc:421: Auto-tuning cuDNN op, set MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable |
| </pre></div></div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]: |
| </pre></div> |
| </div> |
| <div class="output_area highlight-none notranslate"><div class="highlight"><pre> |
| <span></span>array([[ 8.609084 , -7.7890825]], device=gpu(0)) |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="Training-with-multiple-GPUs"> |
| <h2>Training with multiple GPUs<a class="headerlink" href="#Training-with-multiple-GPUs" title="Permalink to this headline">¶</a></h2> |
| <p>Finally, you will see how you can use multiple GPUs to jointly train a neural network through data parallelism. To elaborate on what data parallelism is, assume there are <em>n</em> GPUs, then you can split each data batch into <em>n</em> parts, and use a GPU on each of these parts to run the forward and backward passes on the seperate chunks of the data.</p> |
| <p>First copy the data definitions with the following commands, and the transform functions from the tutorial <a class="reference internal" href="6-train-nn.html"><span class="doc">Training Neural Networks</span></a>.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># Import transforms as compose a series of transformations to the images</span> |
| <span class="kn">from</span> <span class="nn">mxnet.gluon.data.vision</span> <span class="kn">import</span> <span class="n">transforms</span> |
| |
| <span class="n">jitter_param</span> <span class="o">=</span> <span class="mf">0.05</span> |
| |
| <span class="c1"># mean and std for normalizing image value in range (0,1)</span> |
| <span class="n">mean</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">]</span> |
| <span class="n">std</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">]</span> |
| |
| <span class="n">training_transformer</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">224</span><span class="p">,</span> <span class="n">keep_ratio</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">128</span><span class="p">),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">RandomFlipLeftRight</span><span class="p">(),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">RandomColorJitter</span><span class="p">(</span><span class="n">contrast</span><span class="o">=</span><span class="n">jitter_param</span><span class="p">),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">mean</span><span class="p">,</span> <span class="n">std</span><span class="p">)</span> |
| <span class="p">])</span> |
| |
| <span class="n">validation_transformer</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">224</span><span class="p">,</span> <span class="n">keep_ratio</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">128</span><span class="p">),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">mean</span><span class="p">,</span> <span class="n">std</span><span class="p">)</span> |
| <span class="p">])</span> |
| |
| <span class="c1"># Use ImageFolderDataset to create a Dataset object from directory structure</span> |
| <span class="n">train_dataset</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">ImageFolderDataset</span><span class="p">(</span><span class="s1">'./datasets/train'</span><span class="p">)</span> |
| <span class="n">val_dataset</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">ImageFolderDataset</span><span class="p">(</span><span class="s1">'./datasets/validation'</span><span class="p">)</span> |
| <span class="n">test_dataset</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">ImageFolderDataset</span><span class="p">(</span><span class="s1">'./datasets/test'</span><span class="p">)</span> |
| |
| <span class="c1"># Create data loaders</span> |
| <span class="n">batch_size</span> <span class="o">=</span> <span class="mi">4</span> |
| <span class="n">train_loader</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">train_dataset</span><span class="o">.</span><span class="n">transform_first</span><span class="p">(</span><span class="n">training_transformer</span><span class="p">),</span><span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">try_nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="n">validation_loader</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">val_dataset</span><span class="o">.</span><span class="n">transform_first</span><span class="p">(</span><span class="n">validation_transformer</span><span class="p">),</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">try_nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="n">test_loader</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">test_dataset</span><span class="o">.</span><span class="n">transform_first</span><span class="p">(</span><span class="n">validation_transformer</span><span class="p">),</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">try_nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="Define-a-helper-function"> |
| <h3>Define a helper function<a class="headerlink" href="#Define-a-helper-function" title="Permalink to this headline">¶</a></h3> |
| <p>This is the same test function defined previously in the <strong>Step 6</strong>.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># Function to return the accuracy for the validation and test set</span> |
| <span class="k">def</span> <span class="nf">test</span><span class="p">(</span><span class="n">val_data</span><span class="p">,</span> <span class="n">devices</span><span class="p">):</span> |
| <span class="n">acc</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">Accuracy</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">val_data</span><span class="p">:</span> |
| <span class="n">data</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">batch</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> |
| <span class="n">data_list</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">split_and_load</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">devices</span><span class="p">)</span> |
| <span class="n">label_list</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">split_and_load</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">devices</span><span class="p">)</span> |
| <span class="n">outputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">net</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="k">for</span> <span class="n">X</span> <span class="ow">in</span> <span class="n">data_list</span><span class="p">]</span> |
| <span class="n">acc</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">label_list</span><span class="p">,</span> <span class="n">outputs</span><span class="p">)</span> |
| |
| <span class="n">_</span><span class="p">,</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="n">acc</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| <span class="k">return</span> <span class="n">accuracy</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>The training loop is quite similar to that shown earlier. The major differences are highlighted in the following code.</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># Diff 1: Use two GPUs for training.</span> |
| <span class="n">available_gpus</span> <span class="o">=</span> <span class="p">[</span><span class="n">npx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="n">i</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">npx</span><span class="o">.</span><span class="n">num_gpus</span><span class="p">())]</span> |
| <span class="n">num_gpus</span> <span class="o">=</span> <span class="mi">2</span> |
| <span class="n">devices</span> <span class="o">=</span> <span class="n">available_gpus</span><span class="p">[:</span><span class="n">num_gpus</span><span class="p">]</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Using </span><span class="si">{}</span><span class="s1"> GPUs'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">devices</span><span class="p">)))</span> |
| |
| <span class="c1"># Diff 2: reinitialize the parameters and place them on multiple GPUs</span> |
| <span class="n">net</span><span class="o">.</span><span class="n">initialize</span><span class="p">(</span><span class="n">force_reinit</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">devices</span><span class="p">)</span> |
| |
| <span class="c1"># Loss and trainer are the same as before</span> |
| <span class="n">loss_fn</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">loss</span><span class="o">.</span><span class="n">SoftmaxCrossEntropyLoss</span><span class="p">()</span> |
| <span class="n">optimizer</span> <span class="o">=</span> <span class="s1">'sgd'</span> |
| <span class="n">optimizer_params</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'learning_rate'</span><span class="p">:</span> <span class="mf">0.001</span><span class="p">}</span> |
| <span class="n">trainer</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">Trainer</span><span class="p">(</span><span class="n">net</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(),</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">optimizer_params</span><span class="p">)</span> |
| |
| <span class="n">epochs</span> <span class="o">=</span> <span class="mi">2</span> |
| <span class="n">accuracy</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">Accuracy</span><span class="p">()</span> |
| <span class="n">log_interval</span> <span class="o">=</span> <span class="mi">5</span> |
| |
| <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span> |
| <span class="n">train_loss</span> <span class="o">=</span> <span class="mf">0.</span> |
| <span class="n">tic</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> |
| <span class="n">btic</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> |
| <span class="n">accuracy</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">train_loader</span><span class="p">):</span> |
| <span class="n">data</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">batch</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> |
| |
| <span class="c1"># Diff 3: split batch and load into corresponding devices</span> |
| <span class="n">data_list</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">split_and_load</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">devices</span><span class="p">)</span> |
| <span class="n">label_list</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">split_and_load</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">devices</span><span class="p">)</span> |
| |
| <span class="c1"># Diff 4: run forward and backward on each devices.</span> |
| <span class="c1"># MXNet will automatically run them in parallel</span> |
| <span class="k">with</span> <span class="n">autograd</span><span class="o">.</span><span class="n">record</span><span class="p">():</span> |
| <span class="n">outputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">net</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">X</span> <span class="ow">in</span> <span class="n">data_list</span><span class="p">]</span> |
| <span class="n">losses</span> <span class="o">=</span> <span class="p">[</span><span class="n">loss_fn</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">output</span><span class="p">,</span> <span class="n">label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">label_list</span><span class="p">)]</span> |
| <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">losses</span><span class="p">:</span> |
| <span class="n">l</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span> |
| <span class="n">trainer</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">batch_size</span><span class="p">)</span> |
| |
| <span class="c1"># Diff 5: sum losses over all devices. Here, the float</span> |
| <span class="c1"># function will copy data into CPU.</span> |
| <span class="n">train_loss</span> <span class="o">+=</span> <span class="nb">sum</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">l</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">losses</span><span class="p">])</span> |
| <span class="n">accuracy</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">label_list</span><span class="p">,</span> <span class="n">outputs</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">log_interval</span> <span class="ow">and</span> <span class="p">(</span><span class="n">idx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">%</span> <span class="n">log_interval</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">_</span><span class="p">,</span> <span class="n">acc</span> <span class="o">=</span> <span class="n">accuracy</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| |
| <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"""Epoch[</span><span class="si">{</span><span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="si">}</span><span class="s2">] Batch[</span><span class="si">{</span><span class="n">idx</span> <span class="o">+</span> <span class="mi">1</span><span class="si">}</span><span class="s2">] Speed: </span><span class="si">{</span><span class="n">batch_size</span> <span class="o">/</span> <span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">btic</span><span class="p">)</span><span class="si">}</span><span class="s2"> samples/sec </span><span class="se">\</span> |
| <span class="s2"> batch loss = </span><span class="si">{</span><span class="n">train_loss</span><span class="si">}</span><span class="s2"> | accuracy = </span><span class="si">{</span><span class="n">acc</span><span class="si">}</span><span class="s2">"""</span><span class="p">)</span> |
| <span class="n">btic</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> |
| |
| <span class="n">_</span><span class="p">,</span> <span class="n">acc</span> <span class="o">=</span> <span class="n">accuracy</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| |
| <span class="n">acc_val</span> <span class="o">=</span> <span class="n">test</span><span class="p">(</span><span class="n">validation_loader</span><span class="p">,</span> <span class="n">devices</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"[Epoch </span><span class="si">{</span><span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="si">}</span><span class="s2">] training: accuracy=</span><span class="si">{</span><span class="n">acc</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"[Epoch </span><span class="si">{</span><span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="si">}</span><span class="s2">] time cost: </span><span class="si">{</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">tic</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"[Epoch </span><span class="si">{</span><span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="si">}</span><span class="s2">] validation: validation accuracy=</span><span class="si">{</span><span class="n">acc_val</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area docutils container"> |
| <div class="highlight"><pre> |
| Using 1 GPUs |
| </pre></div></div> |
| </div> |
| <div class="nboutput docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area stderr docutils container"> |
| <div class="highlight"><pre> |
| [02:19:50] /work/mxnet/src/operator/cudnn_ops.cc:421: Auto-tuning cuDNN op, set MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable |
| </pre></div></div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area docutils container"> |
| <div class="highlight"><pre> |
| Epoch[1] Batch[5] Speed: 0.7177853733422043 samples/sec batch loss = 13.843648433685303 | accuracy = 0.55 |
| Epoch[1] Batch[10] Speed: 1.2559634287801846 samples/sec batch loss = 27.14639139175415 | accuracy = 0.575 |
| Epoch[1] Batch[15] Speed: 1.2568362863149618 samples/sec batch loss = 41.14137315750122 | accuracy = 0.5166666666666667 |
| Epoch[1] Batch[20] Speed: 1.2637007554162292 samples/sec batch loss = 54.54027533531189 | accuracy = 0.525 |
| Epoch[1] Batch[25] Speed: 1.2520211407282897 samples/sec batch loss = 67.5364465713501 | accuracy = 0.55 |
| Epoch[1] Batch[30] Speed: 1.2503268679167439 samples/sec batch loss = 81.33682060241699 | accuracy = 0.55 |
| Epoch[1] Batch[35] Speed: 1.2568414647913997 samples/sec batch loss = 95.43365550041199 | accuracy = 0.5285714285714286 |
| Epoch[1] Batch[40] Speed: 1.2502761795235464 samples/sec batch loss = 109.70234894752502 | accuracy = 0.50625 |
| Epoch[1] Batch[45] Speed: 1.2507797982689706 samples/sec batch loss = 123.46052312850952 | accuracy = 0.5166666666666667 |
| Epoch[1] Batch[50] Speed: 1.2521923346245942 samples/sec batch loss = 137.56995940208435 | accuracy = 0.5 |
| Epoch[1] Batch[55] Speed: 1.2525141039469552 samples/sec batch loss = 151.39230513572693 | accuracy = 0.4954545454545455 |
| Epoch[1] Batch[60] Speed: 1.251443240018457 samples/sec batch loss = 165.14783596992493 | accuracy = 0.5041666666666667 |
| Epoch[1] Batch[65] Speed: 1.25184214740134 samples/sec batch loss = 178.14174127578735 | accuracy = 0.5269230769230769 |
| Epoch[1] Batch[70] Speed: 1.253923720983628 samples/sec batch loss = 191.83379340171814 | accuracy = 0.5285714285714286 |
| Epoch[1] Batch[75] Speed: 1.25284632985047 samples/sec batch loss = 206.16407585144043 | accuracy = 0.52 |
| Epoch[1] Batch[80] Speed: 1.2491492388381014 samples/sec batch loss = 219.3322675228119 | accuracy = 0.528125 |
| Epoch[1] Batch[85] Speed: 1.2510152950440239 samples/sec batch loss = 232.61402010917664 | accuracy = 0.538235294117647 |
| Epoch[1] Batch[90] Speed: 1.2492232756257242 samples/sec batch loss = 246.95420742034912 | accuracy = 0.5361111111111111 |
| Epoch[1] Batch[95] Speed: 1.2480924747472268 samples/sec batch loss = 260.6686632633209 | accuracy = 0.5342105263157895 |
| Epoch[1] Batch[100] Speed: 1.250194658100344 samples/sec batch loss = 274.19995856285095 | accuracy = 0.5325 |
| Epoch[1] Batch[105] Speed: 1.2500179561571738 samples/sec batch loss = 287.53726410865784 | accuracy = 0.5404761904761904 |
| Epoch[1] Batch[110] Speed: 1.2504241564198844 samples/sec batch loss = 300.9876229763031 | accuracy = 0.5386363636363637 |
| Epoch[1] Batch[115] Speed: 1.2547286176676409 samples/sec batch loss = 315.1079041957855 | accuracy = 0.5347826086956522 |
| Epoch[1] Batch[120] Speed: 1.2563125391586387 samples/sec batch loss = 328.2441871166229 | accuracy = 0.5375 |
| Epoch[1] Batch[125] Speed: 1.2605615659223435 samples/sec batch loss = 341.8634924888611 | accuracy = 0.538 |
| Epoch[1] Batch[130] Speed: 1.2527423967833675 samples/sec batch loss = 355.60545563697815 | accuracy = 0.5346153846153846 |
| Epoch[1] Batch[135] Speed: 1.2535271387709226 samples/sec batch loss = 369.9278120994568 | accuracy = 0.5296296296296297 |
| Epoch[1] Batch[140] Speed: 1.2577477900673015 samples/sec batch loss = 383.7754728794098 | accuracy = 0.5267857142857143 |
| Epoch[1] Batch[145] Speed: 1.259765440506753 samples/sec batch loss = 396.8890235424042 | accuracy = 0.5293103448275862 |
| Epoch[1] Batch[150] Speed: 1.2559416158236576 samples/sec batch loss = 411.14211916923523 | accuracy = 0.525 |
| Epoch[1] Batch[155] Speed: 1.2558180860476942 samples/sec batch loss = 425.0277874469757 | accuracy = 0.5209677419354839 |
| Epoch[1] Batch[160] Speed: 1.2565550179317238 samples/sec batch loss = 438.277220249176 | accuracy = 0.5234375 |
| Epoch[1] Batch[165] Speed: 1.2576449277127717 samples/sec batch loss = 452.1134104728699 | accuracy = 0.5242424242424243 |
| Epoch[1] Batch[170] Speed: 1.2508244658920806 samples/sec batch loss = 465.7479600906372 | accuracy = 0.5279411764705882 |
| Epoch[1] Batch[175] Speed: 1.2549007405516959 samples/sec batch loss = 479.55320835113525 | accuracy = 0.5285714285714286 |
| Epoch[1] Batch[180] Speed: 1.254855499755831 samples/sec batch loss = 493.31907415390015 | accuracy = 0.5277777777777778 |
| Epoch[1] Batch[185] Speed: 1.2498438180944542 samples/sec batch loss = 506.64108967781067 | accuracy = 0.5297297297297298 |
| Epoch[1] Batch[190] Speed: 1.2502616446396309 samples/sec batch loss = 520.5267310142517 | accuracy = 0.5289473684210526 |
| Epoch[1] Batch[195] Speed: 1.2480579361611372 samples/sec batch loss = 534.096428155899 | accuracy = 0.5269230769230769 |
| Epoch[1] Batch[200] Speed: 1.2519611591830546 samples/sec batch loss = 548.2283039093018 | accuracy = 0.5275 |
| Epoch[1] Batch[205] Speed: 1.2541663097156608 samples/sec batch loss = 562.6295385360718 | accuracy = 0.526829268292683 |
| Epoch[1] Batch[210] Speed: 1.2527492253394819 samples/sec batch loss = 576.9309170246124 | accuracy = 0.525 |
| Epoch[1] Batch[215] Speed: 1.2512233520898823 samples/sec batch loss = 590.740716457367 | accuracy = 0.5267441860465116 |
| Epoch[1] Batch[220] Speed: 1.255738941993871 samples/sec batch loss = 604.7016122341156 | accuracy = 0.5272727272727272 |
| Epoch[1] Batch[225] Speed: 1.2574436829576865 samples/sec batch loss = 618.1548209190369 | accuracy = 0.5288888888888889 |
| Epoch[1] Batch[230] Speed: 1.2543648186039265 samples/sec batch loss = 631.6407222747803 | accuracy = 0.5315217391304348 |
| Epoch[1] Batch[235] Speed: 1.2536795398127987 samples/sec batch loss = 644.1229186058044 | accuracy = 0.5393617021276595 |
| Epoch[1] Batch[240] Speed: 1.2546553343067126 samples/sec batch loss = 657.8897314071655 | accuracy = 0.5416666666666666 |
| Epoch[1] Batch[245] Speed: 1.2552175174844977 samples/sec batch loss = 671.602787733078 | accuracy = 0.539795918367347 |
| Epoch[1] Batch[250] Speed: 1.2591585398812337 samples/sec batch loss = 684.8733282089233 | accuracy = 0.543 |
| Epoch[1] Batch[255] Speed: 1.2563415150113917 samples/sec batch loss = 698.1542699337006 | accuracy = 0.5441176470588235 |
| Epoch[1] Batch[260] Speed: 1.2496980264306574 samples/sec batch loss = 712.0803546905518 | accuracy = 0.5413461538461538 |
| Epoch[1] Batch[265] Speed: 1.2526756118166773 samples/sec batch loss = 725.936799287796 | accuracy = 0.5358490566037736 |
| Epoch[1] Batch[270] Speed: 1.2537779129578301 samples/sec batch loss = 739.746728181839 | accuracy = 0.5370370370370371 |
| Epoch[1] Batch[275] Speed: 1.254368757535966 samples/sec batch loss = 752.3876328468323 | accuracy = 0.54 |
| Epoch[1] Batch[280] Speed: 1.2532378941631552 samples/sec batch loss = 766.3519604206085 | accuracy = 0.5383928571428571 |
| Epoch[1] Batch[285] Speed: 1.2532947212670924 samples/sec batch loss = 779.8456904888153 | accuracy = 0.5394736842105263 |
| Epoch[1] Batch[290] Speed: 1.2538353512745244 samples/sec batch loss = 793.4142565727234 | accuracy = 0.5413793103448276 |
| Epoch[1] Batch[295] Speed: 1.2537878448189792 samples/sec batch loss = 805.6037783622742 | accuracy = 0.5457627118644067 |
| Epoch[1] Batch[300] Speed: 1.2530927135505179 samples/sec batch loss = 818.7810769081116 | accuracy = 0.5458333333333333 |
| Epoch[1] Batch[305] Speed: 1.2550211795655828 samples/sec batch loss = 832.5636684894562 | accuracy = 0.5483606557377049 |
| Epoch[1] Batch[310] Speed: 1.2557368742307056 samples/sec batch loss = 846.0395302772522 | accuracy = 0.5483870967741935 |
| Epoch[1] Batch[315] Speed: 1.2504314257089924 samples/sec batch loss = 859.6418216228485 | accuracy = 0.5476190476190477 |
| Epoch[1] Batch[320] Speed: 1.254514609220403 samples/sec batch loss = 873.6168415546417 | accuracy = 0.54765625 |
| Epoch[1] Batch[325] Speed: 1.258981090162882 samples/sec batch loss = 887.3363678455353 | accuracy = 0.546923076923077 |
| Epoch[1] Batch[330] Speed: 1.2557752229464463 samples/sec batch loss = 899.1359436511993 | accuracy = 0.55 |
| Epoch[1] Batch[335] Speed: 1.2578815079928467 samples/sec batch loss = 913.0066072940826 | accuracy = 0.5514925373134328 |
| Epoch[1] Batch[340] Speed: 1.2575014576470611 samples/sec batch loss = 925.8880631923676 | accuracy = 0.5529411764705883 |
| Epoch[1] Batch[345] Speed: 1.252473710111275 samples/sec batch loss = 940.6688208580017 | accuracy = 0.5507246376811594 |
| Epoch[1] Batch[350] Speed: 1.2561683383166617 samples/sec batch loss = 954.8609557151794 | accuracy = 0.5507142857142857 |
| Epoch[1] Batch[355] Speed: 1.2530433916237103 samples/sec batch loss = 967.4633071422577 | accuracy = 0.5542253521126761 |
| Epoch[1] Batch[360] Speed: 1.2525400995061127 samples/sec batch loss = 980.4945187568665 | accuracy = 0.5548611111111111 |
| Epoch[1] Batch[365] Speed: 1.2488599656469301 samples/sec batch loss = 993.2829101085663 | accuracy = 0.5561643835616439 |
| Epoch[1] Batch[370] Speed: 1.2555852881455176 samples/sec batch loss = 1007.3541176319122 | accuracy = 0.5547297297297298 |
| Epoch[1] Batch[375] Speed: 1.2517181152473076 samples/sec batch loss = 1020.9726240634918 | accuracy = 0.554 |
| Epoch[1] Batch[380] Speed: 1.256814819631379 samples/sec batch loss = 1033.9912648200989 | accuracy = 0.5565789473684211 |
| Epoch[1] Batch[385] Speed: 1.2516636721475705 samples/sec batch loss = 1046.8812930583954 | accuracy = 0.5590909090909091 |
| Epoch[1] Batch[390] Speed: 1.2520941166812507 samples/sec batch loss = 1061.0092597007751 | accuracy = 0.5576923076923077 |
| Epoch[1] Batch[395] Speed: 1.252534395352162 samples/sec batch loss = 1073.907539844513 | accuracy = 0.560126582278481 |
| Epoch[1] Batch[400] Speed: 1.2532786181707298 samples/sec batch loss = 1088.2042253017426 | accuracy = 0.559375 |
| Epoch[1] Batch[405] Speed: 1.2510975765991912 samples/sec batch loss = 1101.8907554149628 | accuracy = 0.5611111111111111 |
| Epoch[1] Batch[410] Speed: 1.2496675876363084 samples/sec batch loss = 1115.188992023468 | accuracy = 0.5628048780487804 |
| Epoch[1] Batch[415] Speed: 1.2554655863780184 samples/sec batch loss = 1128.7872099876404 | accuracy = 0.5632530120481928 |
| Epoch[1] Batch[420] Speed: 1.2543983003148031 samples/sec batch loss = 1141.3728995323181 | accuracy = 0.5642857142857143 |
| Epoch[1] Batch[425] Speed: 1.2512212058568362 samples/sec batch loss = 1155.6402504444122 | accuracy = 0.5629411764705883 |
| Epoch[1] Batch[430] Speed: 1.2499512751022628 samples/sec batch loss = 1169.9938955307007 | accuracy = 0.563953488372093 |
| Epoch[1] Batch[435] Speed: 1.2543506574097474 samples/sec batch loss = 1183.2387073040009 | accuracy = 0.5643678160919541 |
| Epoch[1] Batch[440] Speed: 1.2518762418706881 samples/sec batch loss = 1196.8701314926147 | accuracy = 0.5630681818181819 |
| Epoch[1] Batch[445] Speed: 1.2564235577919312 samples/sec batch loss = 1209.7240126132965 | accuracy = 0.5634831460674158 |
| Epoch[1] Batch[450] Speed: 1.2514673241217613 samples/sec batch loss = 1223.2256350517273 | accuracy = 0.5644444444444444 |
| Epoch[1] Batch[455] Speed: 1.2538467833455837 samples/sec batch loss = 1237.1581156253815 | accuracy = 0.5637362637362637 |
| Epoch[1] Batch[460] Speed: 1.2565948283965354 samples/sec batch loss = 1250.1992733478546 | accuracy = 0.5641304347826087 |
| Epoch[1] Batch[465] Speed: 1.2500902330441603 samples/sec batch loss = 1263.4323880672455 | accuracy = 0.5645161290322581 |
| Epoch[1] Batch[470] Speed: 1.256094980730941 samples/sec batch loss = 1276.9424521923065 | accuracy = 0.5648936170212766 |
| Epoch[1] Batch[475] Speed: 1.2553844201443767 samples/sec batch loss = 1289.4359819889069 | accuracy = 0.5647368421052632 |
| Epoch[1] Batch[480] Speed: 1.2553048611180218 samples/sec batch loss = 1302.6627161502838 | accuracy = 0.5645833333333333 |
| Epoch[1] Batch[485] Speed: 1.2560627249150238 samples/sec batch loss = 1315.8049447536469 | accuracy = 0.5654639175257732 |
| Epoch[1] Batch[490] Speed: 1.254397456216096 samples/sec batch loss = 1329.576895236969 | accuracy = 0.5658163265306122 |
| Epoch[1] Batch[495] Speed: 1.2560398741296102 samples/sec batch loss = 1344.3412885665894 | accuracy = 0.5641414141414142 |
| Epoch[1] Batch[500] Speed: 1.2531148956448153 samples/sec batch loss = 1357.5873770713806 | accuracy = 0.566 |
| Epoch[1] Batch[505] Speed: 1.2489455899396 samples/sec batch loss = 1370.839856147766 | accuracy = 0.5673267326732673 |
| Epoch[1] Batch[510] Speed: 1.2520152544344776 samples/sec batch loss = 1384.8081929683685 | accuracy = 0.567156862745098 |
| Epoch[1] Batch[515] Speed: 1.2563558152754635 samples/sec batch loss = 1398.1784181594849 | accuracy = 0.5684466019417476 |
| Epoch[1] Batch[520] Speed: 1.248956096226075 samples/sec batch loss = 1411.7963666915894 | accuracy = 0.5692307692307692 |
| Epoch[1] Batch[525] Speed: 1.2575630081401366 samples/sec batch loss = 1424.737160205841 | accuracy = 0.5695238095238095 |
| Epoch[1] Batch[530] Speed: 1.255093097278824 samples/sec batch loss = 1437.1862149238586 | accuracy = 0.5702830188679245 |
| Epoch[1] Batch[535] Speed: 1.2569474917076773 samples/sec batch loss = 1450.286569595337 | accuracy = 0.5710280373831775 |
| Epoch[1] Batch[540] Speed: 1.2588683911573457 samples/sec batch loss = 1463.449669122696 | accuracy = 0.5712962962962963 |
| Epoch[1] Batch[545] Speed: 1.2603772819487855 samples/sec batch loss = 1477.0765619277954 | accuracy = 0.5720183486238533 |
| Epoch[1] Batch[550] Speed: 1.2573038392950016 samples/sec batch loss = 1490.6500027179718 | accuracy = 0.5709090909090909 |
| Epoch[1] Batch[555] Speed: 1.247417183740468 samples/sec batch loss = 1503.893477678299 | accuracy = 0.5711711711711712 |
| Epoch[1] Batch[560] Speed: 1.2564545147902795 samples/sec batch loss = 1517.7484295368195 | accuracy = 0.5705357142857143 |
| Epoch[1] Batch[565] Speed: 1.2585710118001985 samples/sec batch loss = 1530.6849093437195 | accuracy = 0.5707964601769911 |
| Epoch[1] Batch[570] Speed: 1.2534749731108163 samples/sec batch loss = 1544.0472197532654 | accuracy = 0.5701754385964912 |
| Epoch[1] Batch[575] Speed: 1.2547769462067588 samples/sec batch loss = 1557.3838369846344 | accuracy = 0.5708695652173913 |
| Epoch[1] Batch[580] Speed: 1.2584786818616303 samples/sec batch loss = 1571.822722196579 | accuracy = 0.5702586206896552 |
| Epoch[1] Batch[585] Speed: 1.2536176194932078 samples/sec batch loss = 1586.8001997470856 | accuracy = 0.5700854700854701 |
| Epoch[1] Batch[590] Speed: 1.2597224021141018 samples/sec batch loss = 1601.2304260730743 | accuracy = 0.5690677966101695 |
| Epoch[1] Batch[595] Speed: 1.2548780258854435 samples/sec batch loss = 1613.8415384292603 | accuracy = 0.5701680672268907 |
| Epoch[1] Batch[600] Speed: 1.2547142605859216 samples/sec batch loss = 1627.3402824401855 | accuracy = 0.57 |
| Epoch[1] Batch[605] Speed: 1.254162372054958 samples/sec batch loss = 1641.05632686615 | accuracy = 0.5706611570247934 |
| Epoch[1] Batch[610] Speed: 1.2552245608753254 samples/sec batch loss = 1653.0199966430664 | accuracy = 0.5725409836065574 |
| Epoch[1] Batch[615] Speed: 1.258024498806253 samples/sec batch loss = 1664.464322090149 | accuracy = 0.5752032520325203 |
| Epoch[1] Batch[620] Speed: 1.2570344169898935 samples/sec batch loss = 1677.25244140625 | accuracy = 0.5758064516129032 |
| Epoch[1] Batch[625] Speed: 1.2597267531146807 samples/sec batch loss = 1690.4793710708618 | accuracy = 0.576 |
| Epoch[1] Batch[630] Speed: 1.2595881034009642 samples/sec batch loss = 1703.705129146576 | accuracy = 0.5769841269841269 |
| Epoch[1] Batch[635] Speed: 1.2545642346639778 samples/sec batch loss = 1717.4910068511963 | accuracy = 0.5771653543307087 |
| Epoch[1] Batch[640] Speed: 1.2579240434198269 samples/sec batch loss = 1730.489961385727 | accuracy = 0.57734375 |
| Epoch[1] Batch[645] Speed: 1.2573164654101907 samples/sec batch loss = 1743.1097581386566 | accuracy = 0.5782945736434109 |
| Epoch[1] Batch[650] Speed: 1.252743145114652 samples/sec batch loss = 1756.437433719635 | accuracy = 0.5788461538461539 |
| Epoch[1] Batch[655] Speed: 1.2581399715754167 samples/sec batch loss = 1769.8544661998749 | accuracy = 0.5793893129770993 |
| Epoch[1] Batch[660] Speed: 1.258259712050706 samples/sec batch loss = 1782.3674004077911 | accuracy = 0.5803030303030303 |
| Epoch[1] Batch[665] Speed: 1.2564034224476868 samples/sec batch loss = 1794.7534166574478 | accuracy = 0.5804511278195489 |
| Epoch[1] Batch[670] Speed: 1.2568967359163377 samples/sec batch loss = 1806.3188952207565 | accuracy = 0.5809701492537314 |
| Epoch[1] Batch[675] Speed: 1.2576489815475727 samples/sec batch loss = 1819.025753378868 | accuracy = 0.5811111111111111 |
| Epoch[1] Batch[680] Speed: 1.2577365696162135 samples/sec batch loss = 1832.3345657587051 | accuracy = 0.58125 |
| Epoch[1] Batch[685] Speed: 1.2577203521751565 samples/sec batch loss = 1845.097201704979 | accuracy = 0.5821167883211679 |
| Epoch[1] Batch[690] Speed: 1.2594878709133515 samples/sec batch loss = 1857.3521293401718 | accuracy = 0.5833333333333334 |
| Epoch[1] Batch[695] Speed: 1.251385460620204 samples/sec batch loss = 1870.4129148721695 | accuracy = 0.5834532374100719 |
| Epoch[1] Batch[700] Speed: 1.2510541022294233 samples/sec batch loss = 1881.8309689760208 | accuracy = 0.5842857142857143 |
| Epoch[1] Batch[705] Speed: 1.2598224827321989 samples/sec batch loss = 1896.8650976419449 | accuracy = 0.5843971631205673 |
| Epoch[1] Batch[710] Speed: 1.2559434962211014 samples/sec batch loss = 1911.0721300840378 | accuracy = 0.5852112676056338 |
| Epoch[1] Batch[715] Speed: 1.254250881634989 samples/sec batch loss = 1923.95953643322 | accuracy = 0.5853146853146853 |
| Epoch[1] Batch[720] Speed: 1.2531085310817256 samples/sec batch loss = 1937.198645234108 | accuracy = 0.5857638888888889 |
| Epoch[1] Batch[725] Speed: 1.2564386126765736 samples/sec batch loss = 1950.3175982236862 | accuracy = 0.5862068965517241 |
| Epoch[1] Batch[730] Speed: 1.2589064590592036 samples/sec batch loss = 1962.3570288419724 | accuracy = 0.586986301369863 |
| Epoch[1] Batch[735] Speed: 1.2572395819516766 samples/sec batch loss = 1976.541769862175 | accuracy = 0.5867346938775511 |
| Epoch[1] Batch[740] Speed: 1.257753258931155 samples/sec batch loss = 1989.7192677259445 | accuracy = 0.5871621621621622 |
| Epoch[1] Batch[745] Speed: 1.2508350038131448 samples/sec batch loss = 2001.4879661798477 | accuracy = 0.5875838926174497 |
| Epoch[1] Batch[750] Speed: 1.2469280383065156 samples/sec batch loss = 2015.212173819542 | accuracy = 0.587 |
| Epoch[1] Batch[755] Speed: 1.2488188775929854 samples/sec batch loss = 2028.3085576295853 | accuracy = 0.5867549668874172 |
| Epoch[1] Batch[760] Speed: 1.2535469010436202 samples/sec batch loss = 2042.6072140932083 | accuracy = 0.5861842105263158 |
| Epoch[1] Batch[765] Speed: 1.249778645202862 samples/sec batch loss = 2054.3626470565796 | accuracy = 0.5869281045751634 |
| Epoch[1] Batch[770] Speed: 1.2547898970108757 samples/sec batch loss = 2068.315253019333 | accuracy = 0.5863636363636363 |
| Epoch[1] Batch[775] Speed: 1.246637199139425 samples/sec batch loss = 2080.7661538124084 | accuracy = 0.5870967741935483 |
| Epoch[1] Batch[780] Speed: 1.2569492809476603 samples/sec batch loss = 2092.731680870056 | accuracy = 0.5881410256410257 |
| Epoch[1] Batch[785] Speed: 1.251923977247916 samples/sec batch loss = 2105.3103164434433 | accuracy = 0.589171974522293 |
| [Epoch 1] training: accuracy=0.5897842639593909 |
| [Epoch 1] time cost: 648.1717154979706 |
| [Epoch 1] validation: validation accuracy=0.7188888888888889 |
| Epoch[2] Batch[5] Speed: 1.235927918564913 samples/sec batch loss = 12.098094701766968 | accuracy = 0.7 |
| Epoch[2] Batch[10] Speed: 1.237564652829116 samples/sec batch loss = 24.429511070251465 | accuracy = 0.675 |
| Epoch[2] Batch[15] Speed: 1.235510335345016 samples/sec batch loss = 36.6859347820282 | accuracy = 0.6666666666666666 |
| Epoch[2] Batch[20] Speed: 1.2358075659441836 samples/sec batch loss = 50.16374707221985 | accuracy = 0.65 |
| Epoch[2] Batch[25] Speed: 1.2390471254284765 samples/sec batch loss = 64.08758544921875 | accuracy = 0.65 |
| Epoch[2] Batch[30] Speed: 1.2330715573588076 samples/sec batch loss = 76.74755859375 | accuracy = 0.6416666666666667 |
| Epoch[2] Batch[35] Speed: 1.2414104081640052 samples/sec batch loss = 88.16495084762573 | accuracy = 0.6571428571428571 |
| Epoch[2] Batch[40] Speed: 1.2343934053912948 samples/sec batch loss = 101.30535697937012 | accuracy = 0.65 |
| Epoch[2] Batch[45] Speed: 1.2365251121477796 samples/sec batch loss = 114.17909979820251 | accuracy = 0.6444444444444445 |
| Epoch[2] Batch[50] Speed: 1.2341006672458144 samples/sec batch loss = 125.54039347171783 | accuracy = 0.65 |
| Epoch[2] Batch[55] Speed: 1.2372870156227875 samples/sec batch loss = 139.20960402488708 | accuracy = 0.6454545454545455 |
| Epoch[2] Batch[60] Speed: 1.2338017164897386 samples/sec batch loss = 149.76260900497437 | accuracy = 0.6625 |
| Epoch[2] Batch[65] Speed: 1.232860614251647 samples/sec batch loss = 161.32416653633118 | accuracy = 0.6730769230769231 |
| Epoch[2] Batch[70] Speed: 1.239124636962279 samples/sec batch loss = 174.40132355690002 | accuracy = 0.6678571428571428 |
| Epoch[2] Batch[75] Speed: 1.2398064605522046 samples/sec batch loss = 187.60081219673157 | accuracy = 0.6566666666666666 |
| Epoch[2] Batch[80] Speed: 1.2394464089096557 samples/sec batch loss = 199.6213357448578 | accuracy = 0.6625 |
| Epoch[2] Batch[85] Speed: 1.2347977830303418 samples/sec batch loss = 211.5396727323532 | accuracy = 0.6705882352941176 |
| Epoch[2] Batch[90] Speed: 1.2403924608294767 samples/sec batch loss = 224.57935178279877 | accuracy = 0.6666666666666666 |
| Epoch[2] Batch[95] Speed: 1.2359158094116682 samples/sec batch loss = 237.30752885341644 | accuracy = 0.6631578947368421 |
| Epoch[2] Batch[100] Speed: 1.2365692231413024 samples/sec batch loss = 249.6424754858017 | accuracy = 0.665 |
| Epoch[2] Batch[105] Speed: 1.2387414749479815 samples/sec batch loss = 262.5543291568756 | accuracy = 0.669047619047619 |
| Epoch[2] Batch[110] Speed: 1.23809745901424 samples/sec batch loss = 273.36402666568756 | accuracy = 0.675 |
| Epoch[2] Batch[115] Speed: 1.2387106529664291 samples/sec batch loss = 284.7630659341812 | accuracy = 0.6804347826086956 |
| Epoch[2] Batch[120] Speed: 1.2437165213866523 samples/sec batch loss = 297.10825741291046 | accuracy = 0.6854166666666667 |
| Epoch[2] Batch[125] Speed: 1.2409851654734072 samples/sec batch loss = 310.7332853078842 | accuracy = 0.68 |
| Epoch[2] Batch[130] Speed: 1.2361936512911857 samples/sec batch loss = 324.06179106235504 | accuracy = 0.675 |
| Epoch[2] Batch[135] Speed: 1.240418689335642 samples/sec batch loss = 335.1174830198288 | accuracy = 0.6833333333333333 |
| Epoch[2] Batch[140] Speed: 1.245082185349734 samples/sec batch loss = 346.1454266309738 | accuracy = 0.6910714285714286 |
| Epoch[2] Batch[145] Speed: 1.2377630544621274 samples/sec batch loss = 357.4274808168411 | accuracy = 0.6931034482758621 |
| Epoch[2] Batch[150] Speed: 1.2394902707139859 samples/sec batch loss = 368.957638502121 | accuracy = 0.6916666666666667 |
| Epoch[2] Batch[155] Speed: 1.23856753834309 samples/sec batch loss = 381.85454535484314 | accuracy = 0.6903225806451613 |
| Epoch[2] Batch[160] Speed: 1.2367644794196768 samples/sec batch loss = 394.6979032754898 | accuracy = 0.6890625 |
| Epoch[2] Batch[165] Speed: 1.236084539520047 samples/sec batch loss = 406.2387070655823 | accuracy = 0.6878787878787879 |
| Epoch[2] Batch[170] Speed: 1.2389859100676335 samples/sec batch loss = 420.3978419303894 | accuracy = 0.6794117647058824 |
| Epoch[2] Batch[175] Speed: 1.2412501389054738 samples/sec batch loss = 434.89411902427673 | accuracy = 0.68 |
| Epoch[2] Batch[180] Speed: 1.2376268233957957 samples/sec batch loss = 447.0013165473938 | accuracy = 0.6819444444444445 |
| Epoch[2] Batch[185] Speed: 1.2356544734174204 samples/sec batch loss = 459.21682953834534 | accuracy = 0.6810810810810811 |
| Epoch[2] Batch[190] Speed: 1.2345135735510657 samples/sec batch loss = 470.90711975097656 | accuracy = 0.6815789473684211 |
| Epoch[2] Batch[195] Speed: 1.2335403667340277 samples/sec batch loss = 480.74191653728485 | accuracy = 0.6858974358974359 |
| Epoch[2] Batch[200] Speed: 1.2348160503264416 samples/sec batch loss = 496.3674374818802 | accuracy = 0.68 |
| Epoch[2] Batch[205] Speed: 1.23405037820579 samples/sec batch loss = 506.6456787586212 | accuracy = 0.6829268292682927 |
| Epoch[2] Batch[210] Speed: 1.2362287205164315 samples/sec batch loss = 516.1680475473404 | accuracy = 0.6857142857142857 |
| Epoch[2] Batch[215] Speed: 1.2353289366095255 samples/sec batch loss = 527.6636281013489 | accuracy = 0.6883720930232559 |
| Epoch[2] Batch[220] Speed: 1.2347368050748302 samples/sec batch loss = 537.0995403528214 | accuracy = 0.6920454545454545 |
| Epoch[2] Batch[225] Speed: 1.2343688841513696 samples/sec batch loss = 548.457486987114 | accuracy = 0.6911111111111111 |
| Epoch[2] Batch[230] Speed: 1.2308487802487447 samples/sec batch loss = 561.078108549118 | accuracy = 0.6902173913043478 |
| Epoch[2] Batch[235] Speed: 1.2360172422811617 samples/sec batch loss = 572.9004210233688 | accuracy = 0.6904255319148936 |
| Epoch[2] Batch[240] Speed: 1.2355819451964563 samples/sec batch loss = 583.0899051427841 | accuracy = 0.69375 |
| Epoch[2] Batch[245] Speed: 1.2353303919540877 samples/sec batch loss = 595.6568270921707 | accuracy = 0.6938775510204082 |
| Epoch[2] Batch[250] Speed: 1.2382430238926827 samples/sec batch loss = 607.7653371095657 | accuracy = 0.692 |
| Epoch[2] Batch[255] Speed: 1.2377123751938397 samples/sec batch loss = 621.5343587398529 | accuracy = 0.6911764705882353 |
| Epoch[2] Batch[260] Speed: 1.239567562950817 samples/sec batch loss = 636.51518034935 | accuracy = 0.6875 |
| Epoch[2] Batch[265] Speed: 1.2375350760738284 samples/sec batch loss = 646.5602411031723 | accuracy = 0.6886792452830188 |
| Epoch[2] Batch[270] Speed: 1.2334364381855007 samples/sec batch loss = 659.5934141874313 | accuracy = 0.6898148148148148 |
| Epoch[2] Batch[275] Speed: 1.2419688724871125 samples/sec batch loss = 671.5156997442245 | accuracy = 0.6890909090909091 |
| Epoch[2] Batch[280] Speed: 1.2432834324407502 samples/sec batch loss = 683.9289098978043 | accuracy = 0.6857142857142857 |
| Epoch[2] Batch[285] Speed: 1.2410101338856425 samples/sec batch loss = 695.305247426033 | accuracy = 0.6859649122807018 |
| Epoch[2] Batch[290] Speed: 1.2434298507666224 samples/sec batch loss = 707.2780250310898 | accuracy = 0.6844827586206896 |
| Epoch[2] Batch[295] Speed: 1.2403628404586555 samples/sec batch loss = 719.3348622322083 | accuracy = 0.6864406779661016 |
| Epoch[2] Batch[300] Speed: 1.229606148523253 samples/sec batch loss = 732.0174754858017 | accuracy = 0.6866666666666666 |
| Epoch[2] Batch[305] Speed: 1.2376903698128718 samples/sec batch loss = 744.4868746995926 | accuracy = 0.6868852459016394 |
| Epoch[2] Batch[310] Speed: 1.2343617096024335 samples/sec batch loss = 758.6581048965454 | accuracy = 0.6846774193548387 |
| Epoch[2] Batch[315] Speed: 1.2393073353240887 samples/sec batch loss = 769.1941927671432 | accuracy = 0.6873015873015873 |
| Epoch[2] Batch[320] Speed: 1.2352895526493701 samples/sec batch loss = 779.5193257331848 | accuracy = 0.68984375 |
| Epoch[2] Batch[325] Speed: 1.2380494017930344 samples/sec batch loss = 792.2831864356995 | accuracy = 0.6892307692307692 |
| Epoch[2] Batch[330] Speed: 1.2346552076360569 samples/sec batch loss = 804.0221924781799 | accuracy = 0.6878787878787879 |
| Epoch[2] Batch[335] Speed: 1.2355049672153917 samples/sec batch loss = 814.2888027429581 | accuracy = 0.6895522388059702 |
| Epoch[2] Batch[340] Speed: 1.238488725114186 samples/sec batch loss = 830.7507935762405 | accuracy = 0.6852941176470588 |
| Epoch[2] Batch[345] Speed: 1.2372579995955753 samples/sec batch loss = 841.0984942913055 | accuracy = 0.6869565217391305 |
| Epoch[2] Batch[350] Speed: 1.2364052810193562 samples/sec batch loss = 854.9326325654984 | accuracy = 0.6842857142857143 |
| Epoch[2] Batch[355] Speed: 1.233987658811255 samples/sec batch loss = 866.4674245119095 | accuracy = 0.6852112676056338 |
| Epoch[2] Batch[360] Speed: 1.2390252555692092 samples/sec batch loss = 878.1481471061707 | accuracy = 0.6861111111111111 |
| Epoch[2] Batch[365] Speed: 1.2365365952852425 samples/sec batch loss = 890.5753009319305 | accuracy = 0.6842465753424658 |
| Epoch[2] Batch[370] Speed: 1.2349089401633706 samples/sec batch loss = 903.164582490921 | accuracy = 0.6837837837837838 |
| Epoch[2] Batch[375] Speed: 1.240008697761509 samples/sec batch loss = 915.6524275541306 | accuracy = 0.6826666666666666 |
| Epoch[2] Batch[380] Speed: 1.2339238567651798 samples/sec batch loss = 926.7061468362808 | accuracy = 0.6815789473684211 |
| Epoch[2] Batch[385] Speed: 1.236940463431248 samples/sec batch loss = 940.0565336942673 | accuracy = 0.6798701298701298 |
| Epoch[2] Batch[390] Speed: 1.2448572298398906 samples/sec batch loss = 949.5780580043793 | accuracy = 0.6814102564102564 |
| Epoch[2] Batch[395] Speed: 1.2459431249176134 samples/sec batch loss = 964.6962628364563 | accuracy = 0.6816455696202531 |
| Epoch[2] Batch[400] Speed: 1.238179877610923 samples/sec batch loss = 973.9982264041901 | accuracy = 0.68375 |
| Epoch[2] Batch[405] Speed: 1.2307602922535725 samples/sec batch loss = 986.5976403951645 | accuracy = 0.6839506172839506 |
| Epoch[2] Batch[410] Speed: 1.2422421764258835 samples/sec batch loss = 997.471502661705 | accuracy = 0.6829268292682927 |
| Epoch[2] Batch[415] Speed: 1.238103854743219 samples/sec batch loss = 1009.50750041008 | accuracy = 0.6831325301204819 |
| Epoch[2] Batch[420] Speed: 1.2371328265507098 samples/sec batch loss = 1021.7103761434555 | accuracy = 0.6827380952380953 |
| Epoch[2] Batch[425] Speed: 1.2332137672133718 samples/sec batch loss = 1033.8837678432465 | accuracy = 0.6829411764705883 |
| Epoch[2] Batch[430] Speed: 1.2362971340043507 samples/sec batch loss = 1047.0343718528748 | accuracy = 0.6825581395348838 |
| Epoch[2] Batch[435] Speed: 1.2373453255285531 samples/sec batch loss = 1062.6568596363068 | accuracy = 0.6816091954022988 |
| Epoch[2] Batch[440] Speed: 1.2374793952781515 samples/sec batch loss = 1074.602697610855 | accuracy = 0.6806818181818182 |
| Epoch[2] Batch[445] Speed: 1.2350439370959445 samples/sec batch loss = 1086.6123806238174 | accuracy = 0.6820224719101123 |
| Epoch[2] Batch[450] Speed: 1.2347697015247447 samples/sec batch loss = 1098.1648248434067 | accuracy = 0.6822222222222222 |
| Epoch[2] Batch[455] Speed: 1.2301692841309106 samples/sec batch loss = 1110.8825577497482 | accuracy = 0.6818681318681319 |
| Epoch[2] Batch[460] Speed: 1.2373402152075466 samples/sec batch loss = 1122.0704996585846 | accuracy = 0.6826086956521739 |
| Epoch[2] Batch[465] Speed: 1.2393226236514405 samples/sec batch loss = 1134.8367938995361 | accuracy = 0.6811827956989247 |
| Epoch[2] Batch[470] Speed: 1.2324975226582462 samples/sec batch loss = 1146.116057395935 | accuracy = 0.6819148936170213 |
| Epoch[2] Batch[475] Speed: 1.236495220536184 samples/sec batch loss = 1156.5365760326385 | accuracy = 0.6831578947368421 |
| Epoch[2] Batch[480] Speed: 1.2310477441085546 samples/sec batch loss = 1168.0507190227509 | accuracy = 0.6828125 |
| Epoch[2] Batch[485] Speed: 1.233958343538347 samples/sec batch loss = 1179.5785083770752 | accuracy = 0.6824742268041237 |
| Epoch[2] Batch[490] Speed: 1.2394387173891523 samples/sec batch loss = 1190.3947613239288 | accuracy = 0.6836734693877551 |
| Epoch[2] Batch[495] Speed: 1.2348414073198206 samples/sec batch loss = 1202.3828763961792 | accuracy = 0.6828282828282828 |
| Epoch[2] Batch[500] Speed: 1.2329804841478762 samples/sec batch loss = 1215.0485572814941 | accuracy = 0.6825 |
| Epoch[2] Batch[505] Speed: 1.2350913066504434 samples/sec batch loss = 1227.3356637954712 | accuracy = 0.6816831683168317 |
| Epoch[2] Batch[510] Speed: 1.236425418315354 samples/sec batch loss = 1240.3300168514252 | accuracy = 0.6818627450980392 |
| Epoch[2] Batch[515] Speed: 1.2390181182926925 samples/sec batch loss = 1252.16508436203 | accuracy = 0.6820388349514563 |
| Epoch[2] Batch[520] Speed: 1.2341934494795666 samples/sec batch loss = 1263.264944434166 | accuracy = 0.6836538461538462 |
| Epoch[2] Batch[525] Speed: 1.2348582216710153 samples/sec batch loss = 1273.8235294818878 | accuracy = 0.6847619047619048 |
| Epoch[2] Batch[530] Speed: 1.237208821450065 samples/sec batch loss = 1284.4848382472992 | accuracy = 0.6858490566037736 |
| Epoch[2] Batch[535] Speed: 1.2402958101059631 samples/sec batch loss = 1299.4362967014313 | accuracy = 0.6836448598130841 |
| Epoch[2] Batch[540] Speed: 1.2380696840435483 samples/sec batch loss = 1313.2895181179047 | accuracy = 0.6824074074074075 |
| Epoch[2] Batch[545] Speed: 1.2406388324050661 samples/sec batch loss = 1323.6073710918427 | accuracy = 0.6825688073394496 |
| Epoch[2] Batch[550] Speed: 1.240464729465793 samples/sec batch loss = 1333.8011093139648 | accuracy = 0.6840909090909091 |
| Epoch[2] Batch[555] Speed: 1.2455233710613345 samples/sec batch loss = 1344.5472291707993 | accuracy = 0.6842342342342342 |
| Epoch[2] Batch[560] Speed: 1.2488247338698464 samples/sec batch loss = 1356.8668426275253 | accuracy = 0.6839285714285714 |
| Epoch[2] Batch[565] Speed: 1.2468521420505783 samples/sec batch loss = 1368.9232211112976 | accuracy = 0.6836283185840708 |
| Epoch[2] Batch[570] Speed: 1.2431500365782389 samples/sec batch loss = 1380.1921338438988 | accuracy = 0.6833333333333333 |
| Epoch[2] Batch[575] Speed: 1.2443011488926552 samples/sec batch loss = 1390.5724570155144 | accuracy = 0.6839130434782609 |
| Epoch[2] Batch[580] Speed: 1.244171594245545 samples/sec batch loss = 1401.4820081591606 | accuracy = 0.6836206896551724 |
| Epoch[2] Batch[585] Speed: 1.2444214078670126 samples/sec batch loss = 1411.3181390166283 | accuracy = 0.6846153846153846 |
| Epoch[2] Batch[590] Speed: 1.2415157767487817 samples/sec batch loss = 1423.601669728756 | accuracy = 0.6838983050847458 |
| Epoch[2] Batch[595] Speed: 1.24528901288754 samples/sec batch loss = 1439.0298333764076 | accuracy = 0.6840336134453782 |
| Epoch[2] Batch[600] Speed: 1.2434454252922156 samples/sec batch loss = 1451.7631059288979 | accuracy = 0.6833333333333333 |
| Epoch[2] Batch[605] Speed: 1.24347049280361 samples/sec batch loss = 1462.9870991110802 | accuracy = 0.6830578512396694 |
| Epoch[2] Batch[610] Speed: 1.2428576423685953 samples/sec batch loss = 1475.9910835623741 | accuracy = 0.6827868852459016 |
| Epoch[2] Batch[615] Speed: 1.243290895347631 samples/sec batch loss = 1488.1019017100334 | accuracy = 0.6813008130081301 |
| Epoch[2] Batch[620] Speed: 1.2446441744844479 samples/sec batch loss = 1495.819945037365 | accuracy = 0.6834677419354839 |
| Epoch[2] Batch[625] Speed: 1.2465142888135754 samples/sec batch loss = 1507.113108932972 | accuracy = 0.6844 |
| Epoch[2] Batch[630] Speed: 1.2427453258947845 samples/sec batch loss = 1518.9830525517464 | accuracy = 0.6845238095238095 |
| Epoch[2] Batch[635] Speed: 1.242345662364905 samples/sec batch loss = 1530.3129126429558 | accuracy = 0.684251968503937 |
| Epoch[2] Batch[640] Speed: 1.242609100370593 samples/sec batch loss = 1540.9317037463188 | accuracy = 0.684375 |
| Epoch[2] Batch[645] Speed: 1.2424730887363344 samples/sec batch loss = 1553.7256678938866 | accuracy = 0.6833333333333333 |
| Epoch[2] Batch[650] Speed: 1.2401427953155035 samples/sec batch loss = 1564.3865726590157 | accuracy = 0.6834615384615385 |
| Epoch[2] Batch[655] Speed: 1.239686175826128 samples/sec batch loss = 1577.229994237423 | accuracy = 0.682824427480916 |
| Epoch[2] Batch[660] Speed: 1.247026745060531 samples/sec batch loss = 1588.9549852013588 | accuracy = 0.6821969696969697 |
| Epoch[2] Batch[665] Speed: 1.2441360729335997 samples/sec batch loss = 1601.2336229681969 | accuracy = 0.6823308270676691 |
| Epoch[2] Batch[670] Speed: 1.2394723227110858 samples/sec batch loss = 1609.8839029669762 | accuracy = 0.6835820895522388 |
| Epoch[2] Batch[675] Speed: 1.2435849683296483 samples/sec batch loss = 1621.0886833071709 | accuracy = 0.6844444444444444 |
| Epoch[2] Batch[680] Speed: 1.2383548936089177 samples/sec batch loss = 1633.2947943806648 | accuracy = 0.6845588235294118 |
| Epoch[2] Batch[685] Speed: 1.241901024938224 samples/sec batch loss = 1646.6597489714622 | accuracy = 0.6843065693430657 |
| Epoch[2] Batch[690] Speed: 1.243196648176914 samples/sec batch loss = 1656.2861604094505 | accuracy = 0.6855072463768116 |
| Epoch[2] Batch[695] Speed: 1.2445146407173213 samples/sec batch loss = 1669.405971467495 | accuracy = 0.6848920863309352 |
| Epoch[2] Batch[700] Speed: 1.2429912517569055 samples/sec batch loss = 1684.2451193928719 | accuracy = 0.6842857142857143 |
| Epoch[2] Batch[705] Speed: 1.2395510780157952 samples/sec batch loss = 1698.285690009594 | accuracy = 0.6847517730496454 |
| Epoch[2] Batch[710] Speed: 1.2443650133891566 samples/sec batch loss = 1707.3399301171303 | accuracy = 0.6859154929577465 |
| Epoch[2] Batch[715] Speed: 1.2405296683672187 samples/sec batch loss = 1718.9710007309914 | accuracy = 0.686013986013986 |
| Epoch[2] Batch[720] Speed: 1.2455487073654508 samples/sec batch loss = 1730.3480442762375 | accuracy = 0.6864583333333333 |
| Epoch[2] Batch[725] Speed: 1.2474582723628014 samples/sec batch loss = 1742.2053760290146 | accuracy = 0.6868965517241379 |
| Epoch[2] Batch[730] Speed: 1.2426583406099176 samples/sec batch loss = 1752.1480971574783 | accuracy = 0.686986301369863 |
| Epoch[2] Batch[735] Speed: 1.240527925567804 samples/sec batch loss = 1762.912782907486 | accuracy = 0.6877551020408164 |
| Epoch[2] Batch[740] Speed: 1.2464698358851942 samples/sec batch loss = 1777.3255519866943 | accuracy = 0.6871621621621622 |
| Epoch[2] Batch[745] Speed: 1.2467142735911463 samples/sec batch loss = 1787.7800586223602 | accuracy = 0.6875838926174497 |
| Epoch[2] Batch[750] Speed: 1.2407186535658166 samples/sec batch loss = 1801.0167224407196 | accuracy = 0.688 |
| Epoch[2] Batch[755] Speed: 1.2393425814367678 samples/sec batch loss = 1811.7672287225723 | accuracy = 0.68841059602649 |
| Epoch[2] Batch[760] Speed: 1.2408604302565465 samples/sec batch loss = 1822.4459286928177 | accuracy = 0.6891447368421053 |
| Epoch[2] Batch[765] Speed: 1.2366370362606323 samples/sec batch loss = 1834.4717267751694 | accuracy = 0.6895424836601307 |
| Epoch[2] Batch[770] Speed: 1.237029021475969 samples/sec batch loss = 1845.7764229774475 | accuracy = 0.688961038961039 |
| Epoch[2] Batch[775] Speed: 1.2387745851354723 samples/sec batch loss = 1854.2711557149887 | accuracy = 0.6903225806451613 |
| Epoch[2] Batch[780] Speed: 1.2451565725081548 samples/sec batch loss = 1866.0589727163315 | accuracy = 0.6907051282051282 |
| Epoch[2] Batch[785] Speed: 1.2396198597297712 samples/sec batch loss = 1878.85012280941 | accuracy = 0.6901273885350319 |
| [Epoch 2] training: accuracy=0.6900380710659898 |
| [Epoch 2] time cost: 652.4668972492218 |
| [Epoch 2] validation: validation accuracy=0.7455555555555555 |
| </pre></div></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="Next-steps"> |
| <h2>Next steps<a class="headerlink" href="#Next-steps" title="Permalink to this headline">¶</a></h2> |
| <p>Now that you have completed training and predicting with a neural network on GPUs, you reached the conclusion of the crash course. Congratulations. If you are keen on studying more, checkout <a class="reference external" href="https://d2l.ai">D2L.ai</a>, <a class="reference external" href="https://cv.gluon.ai/tutorials/index.html">GluonCV</a>, <a class="reference external" href="https://nlp.gluon.ai">GluonNLP</a>, <a class="reference external" href="https://ts.gluon.ai/">GluonTS</a>, <a class="reference external" href="https://auto.gluon.ai">AutoGluon</a>.</p> |
| </div> |
| </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 class="localtoc"> |
| <p class="caption"> |
| <span class="caption-text">Table Of Contents</span> |
| </p> |
| <ul> |
| <li><a class="reference internal" href="#">Step 7: Load and Run a NN using GPU</a><ul> |
| <li><a class="reference internal" href="#Prerequisites">Prerequisites</a></li> |
| <li><a class="reference internal" href="#Allocate-data-to-a-GPU">Allocate data to a GPU</a></li> |
| <li><a class="reference internal" href="#Choosing-GPU-Ids">Choosing GPU Ids</a></li> |
| <li><a class="reference internal" href="#Run-an-operation-on-a-GPU">Run an operation on a GPU</a></li> |
| <li><a class="reference internal" href="#Run-a-neural-network-on-a-GPU">Run a neural network on a GPU</a></li> |
| <li><a class="reference internal" href="#Training-with-multiple-GPUs">Training with multiple GPUs</a><ul> |
| <li><a class="reference internal" href="#Define-a-helper-function">Define a helper function</a></li> |
| </ul> |
| </li> |
| <li><a class="reference internal" href="#Next-steps">Next steps</a></li> |
| </ul> |
| </li> |
| </ul> |
| |
| </div> |
| </div> |
| </div> |
| |
| <div class="clearer"></div> |
| </div><div class="pagenation"> |
| <a id="button-prev" href="6-train-nn.html" class="mdl-button mdl-js-button mdl-js-ripple-effect mdl-button--colored" role="botton" accesskey="P"> |
| <i class="pagenation-arrow-L fas fa-arrow-left fa-lg"></i> |
| <div class="pagenation-text"> |
| <span class="pagenation-direction">Previous</span> |
| <div>Step 6: Train a Neural Network</div> |
| </div> |
| </a> |
| <a id="button-next" href="../to-mxnet/index.html" class="mdl-button mdl-js-button mdl-js-ripple-effect mdl-button--colored" role="botton" accesskey="N"> |
| <i class="pagenation-arrow-R fas fa-arrow-right fa-lg"></i> |
| <div class="pagenation-text"> |
| <span class="pagenation-direction">Next</span> |
| <div>Moving to MXNet from Other Frameworks</div> |
| </div> |
| </a> |
| </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/incubator-mxnet/issues">Github Issues</a></li> |
| <li><a href="https://github.com/apache/incubator-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/incubator-mxnet"><svg class="svg-icon"><use xlink:href="../../../_static/minima-social-icons.svg#github"></use></svg> <span class="username">apache/incubator-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> |
