api/python/docs/tutorials/packages/ndarray/deepnumpy/arrays.ndarray.html - mxnet-site - Git at Google

 <!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;
     }

     .dropdown-caret-path {
         fill: #FFFFFF;
     }
     </style>

     <title>The N-dimensional array (ndarray) &#8212; 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/google_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>
     <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="Indexing" href="arrays.indexing.html" />
     <link rel="prev" title="Array objects" href="arrays.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="/blog">Blog</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="https://github.com/apache/incubator-mxnet">GitHub</a>
         <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="/">master</a><br>
             <a class="dropdown-option" href="/versions/1.6/">1.6</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">Packages</a><i class="material-icons">navigate_next</i>
             <a class="mdl-navigation__link" href="../index.html">NDArray</a><i class="material-icons">navigate_next</i>
             <a class="mdl-navigation__link" href="index.html">NP on MXNet reference</a><i class="material-icons">navigate_next</i>
             <a class="mdl-navigation__link" href="arrays.html">Array objects</a><i class="material-icons">navigate_next</i>
             <a class="mdl-navigation__link is-active">The N-dimensional array (<code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code>)</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-source"
     class="mdl-button mdl-js-button mdl-button--icon"
     href="../../../../_sources/tutorials/packages/ndarray/deepnumpy/arrays.ndarray.rst" rel="nofollow">
   <i class="material-icons">code</i>
 </a>
 <div class="mdl-tooltip" data-mdl-for="button-show-source">
 Show Source
 </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"><a class="reference internal" href="../../../getting-started/index.html">Getting Started</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../../getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../../getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="../../../getting-started/deepnumpy/index.html">What is NP on MXNet</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../../getting-started/deepnumpy/cheat-sheet.html">The NP on MXNet cheat sheet</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../getting-started/deepnumpy/deepnumpy-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="../../../getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../../getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="../../../getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../getting-started/logistic_regression_explained.html">Logistic regression explained</a></li>
 <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li>
 </ul>
 </li>
 <li class="toctree-l2 current"><a class="reference internal" href="../../index.html">Packages</a><ul class="current">
 <li class="toctree-l3"><a class="reference internal" href="../../autograd/index.html">Automatic Differentiation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../gluon/index.html">Gluon</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../gluon/blocks/index.html">Blocks</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/blocks/custom-layer.html">Custom Layers</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/blocks/hybridize.html">Hybridize</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/blocks/init.html">Initialization</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/blocks/naming.html">Parameter and Block Naming</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/blocks/nn.html">Layers and Blocks</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/blocks/parameters.html">Parameter Management</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/blocks/activations/activations.html">Activation Blocks</a></li>
 </ul>
 </li>
 <li class="toctree-l4"><a class="reference internal" href="../../gluon/data/index.html">Data Tutorials</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/data/data_augmentation.html">Image Augmentation</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../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="../../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="../../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="../../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="../../gluon/image/index.html">Image Tutorials</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/image/image-augmentation.html">Image Augmentation</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/image/mnist.html">Handwritten Digit Recognition</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li>
 </ul>
 </li>
 <li class="toctree-l4"><a class="reference internal" href="../../gluon/loss/index.html">Losses</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/loss/custom-loss.html">Custom Loss Blocks</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/loss/loss.html">Loss functions</a></li>
 </ul>
 </li>
 <li class="toctree-l4"><a class="reference internal" href="../../gluon/text/index.html">Text Tutorials</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/text/gnmt.html">Google Neural Machine Translation</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/text/transformer.html">Machine Translation with Transformer</a></li>
 </ul>
 </li>
 <li class="toctree-l4"><a class="reference internal" href="../../gluon/training/index.html">Training</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/training/trainer.html">Trainer</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/training/learning_rates/index.html">Learning Rates</a><ul>
 <li class="toctree-l6"><a class="reference internal" href="../../gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li>
 <li class="toctree-l6"><a class="reference internal" href="../../gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li>
 <li class="toctree-l6"><a class="reference internal" href="../../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="../../gluon/training/normalization/index.html">Normalization Blocks</a></li>
 </ul>
 </li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="../../kvstore/index.html">KVStore</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../kvstore/kvstore.html">Distributed Key-Value Store</a></li>
 </ul>
 </li>
 <li class="toctree-l3 current"><a class="reference internal" href="../index.html">NDArray</a><ul class="current">
 <li class="toctree-l4"><a class="reference internal" href="../01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../02-ndarray-operations.html">NDArray Operations</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../03-ndarray-contexts.html">NDArray Contexts</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../sparse/index.html">Tutorials</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li>
 </ul>
 </li>
 <li class="toctree-l4 current"><a class="reference internal" href="index.html">NP on MXNet reference</a><ul class="current">
 <li class="toctree-l5 current"><a class="reference internal" href="arrays.html">Array objects</a><ul class="current">
 <li class="toctree-l6 current"><a class="current reference internal" href="#">The N-dimensional array (<code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code>)</a></li>
 <li class="toctree-l6"><a class="reference internal" href="arrays.indexing.html">Indexing</a></li>
 </ul>
 </li>
 <li class="toctree-l5"><a class="reference internal" href="routines.html">Routines</a><ul>
 <li class="toctree-l6"><a class="reference internal" href="routines.array-creation.html">Array creation routines</a></li>
 <li class="toctree-l6"><a class="reference internal" href="routines.array-manipulation.html">Array manipulation routines</a></li>
 <li class="toctree-l6"><a class="reference internal" href="routines.io.html">Input and output</a></li>
 <li class="toctree-l6"><a class="reference internal" href="routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a></li>
 <li class="toctree-l6"><a class="reference internal" href="routines.math.html">Mathematical functions</a></li>
 <li class="toctree-l6"><a class="reference internal" href="random/index.html">np.random</a></li>
 <li class="toctree-l6"><a class="reference internal" href="routines.sort.html">Sorting, searching, and counting</a></li>
 <li class="toctree-l6"><a class="reference internal" href="routines.statistics.html">Statistics</a></li>
 </ul>
 </li>
 <li class="toctree-l5"><a class="reference internal" href="npx.html">NP Extensions</a></li>
 </ul>
 </li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="../../onnx/index.html">ONNX</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../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="../../optimizer/index.html">Optimizers</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../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/mkldnn/index.html">Intel MKL-DNN</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../../performance/backend/mkldnn/mkldnn_readme.html">Install MXNet with MKL-DNN</a></li>
 </ul>
 </li>
 <li class="toctree-l4"><a class="reference internal" href="../../../performance/backend/tensorrt/index.html">TensorRT</a><ul class="simple">
 </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>
 <li class="toctree-l4"><a class="reference internal" href="../../../deploy/inference/scala.html">Deploy into a Java or Scala Environment</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/custom_layer.html">Custom Layers</a></li>
 <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>
 </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/ndarray/index.html">mxnet.ndarray</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/ndarray.html">ndarray</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/contrib/index.html">ndarray.contrib</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/image/index.html">ndarray.image</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/linalg/index.html">ndarray.linalg</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/op/index.html">ndarray.op</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/random/index.html">ndarray.random</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/register/index.html">ndarray.register</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/sparse/index.html">ndarray.sparse</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/utils/index.html">ndarray.utils</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/parameter_dict.html">gluon.ParameterDict</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/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">mxnet.kvstore</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../api/symbol/index.html">mxnet.symbol</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/symbol.html">symbol</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/contrib/index.html">symbol.contrib</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/image/index.html">symbol.image</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/linalg/index.html">symbol.linalg</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/op/index.html">symbol.op</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/random/index.html">symbol.random</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/register/index.html">symbol.register</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/sparse/index.html">symbol.sparse</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../api/module/index.html">mxnet.module</a></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/autograd/index.html">contrib.autograd</a></li>
 <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/mxnet/index.html">mxnet</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/attribute/index.html">mxnet.attribute</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/base/index.html">mxnet.base</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/callback/index.html">mxnet.callback</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/context/index.html">mxnet.context</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/engine/index.html">mxnet.engine</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/executor/index.html">mxnet.executor</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/executor_manager/index.html">mxnet.executor_manager</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/image/index.html">mxnet.image</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/io/index.html">mxnet.io</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/kvstore_server/index.html">mxnet.kvstore_server</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/libinfo/index.html">mxnet.libinfo</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/log/index.html">mxnet.log</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/model/index.html">mxnet.model</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/monitor/index.html">mxnet.monitor</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/name/index.html">mxnet.name</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/notebook/index.html">mxnet.notebook</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/operator/index.html">mxnet.operator</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/profiler/index.html">mxnet.profiler</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/random/index.html">mxnet.random</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/recordio/index.html">mxnet.recordio</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/registry/index.html">mxnet.registry</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/rtc/index.html">mxnet.rtc</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/runtime/index.html">mxnet.runtime</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/test_utils/index.html">mxnet.test_utils</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/torch/index.html">mxnet.torch</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/util/index.html">mxnet.util</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/visualization/index.html">mxnet.visualization</a></li>
 </ul>
 </li>
 </ul>
 </li>
 </ul>

             </nav>

         </div>

 </header>
         <main class="mdl-layout__content" tabIndex="0">

         <script type="text/javascript" src="../../../../_static/sphinx_materialdesign_theme.js "></script>
         <script type="text/javascript" src="../../../../_static/feedback.js"></script>
     <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"><a class="reference internal" href="../../../getting-started/index.html">Getting Started</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../../getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../../getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="../../../getting-started/deepnumpy/index.html">What is NP on MXNet</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../../getting-started/deepnumpy/cheat-sheet.html">The NP on MXNet cheat sheet</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../getting-started/deepnumpy/deepnumpy-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="../../../getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../../getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="../../../getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../getting-started/logistic_regression_explained.html">Logistic regression explained</a></li>
 <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li>
 </ul>
 </li>
 <li class="toctree-l2 current"><a class="reference internal" href="../../index.html">Packages</a><ul class="current">
 <li class="toctree-l3"><a class="reference internal" href="../../autograd/index.html">Automatic Differentiation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../gluon/index.html">Gluon</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../gluon/blocks/index.html">Blocks</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/blocks/custom-layer.html">Custom Layers</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/blocks/hybridize.html">Hybridize</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/blocks/init.html">Initialization</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/blocks/naming.html">Parameter and Block Naming</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/blocks/nn.html">Layers and Blocks</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/blocks/parameters.html">Parameter Management</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/blocks/activations/activations.html">Activation Blocks</a></li>
 </ul>
 </li>
 <li class="toctree-l4"><a class="reference internal" href="../../gluon/data/index.html">Data Tutorials</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/data/data_augmentation.html">Image Augmentation</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../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="../../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="../../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="../../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="../../gluon/image/index.html">Image Tutorials</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/image/image-augmentation.html">Image Augmentation</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/image/mnist.html">Handwritten Digit Recognition</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li>
 </ul>
 </li>
 <li class="toctree-l4"><a class="reference internal" href="../../gluon/loss/index.html">Losses</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/loss/custom-loss.html">Custom Loss Blocks</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/loss/loss.html">Loss functions</a></li>
 </ul>
 </li>
 <li class="toctree-l4"><a class="reference internal" href="../../gluon/text/index.html">Text Tutorials</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/text/gnmt.html">Google Neural Machine Translation</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/text/transformer.html">Machine Translation with Transformer</a></li>
 </ul>
 </li>
 <li class="toctree-l4"><a class="reference internal" href="../../gluon/training/index.html">Training</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/training/trainer.html">Trainer</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../gluon/training/learning_rates/index.html">Learning Rates</a><ul>
 <li class="toctree-l6"><a class="reference internal" href="../../gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li>
 <li class="toctree-l6"><a class="reference internal" href="../../gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li>
 <li class="toctree-l6"><a class="reference internal" href="../../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="../../gluon/training/normalization/index.html">Normalization Blocks</a></li>
 </ul>
 </li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="../../kvstore/index.html">KVStore</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../kvstore/kvstore.html">Distributed Key-Value Store</a></li>
 </ul>
 </li>
 <li class="toctree-l3 current"><a class="reference internal" href="../index.html">NDArray</a><ul class="current">
 <li class="toctree-l4"><a class="reference internal" href="../01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../02-ndarray-operations.html">NDArray Operations</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../03-ndarray-contexts.html">NDArray Contexts</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../sparse/index.html">Tutorials</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li>
 </ul>
 </li>
 <li class="toctree-l4 current"><a class="reference internal" href="index.html">NP on MXNet reference</a><ul class="current">
 <li class="toctree-l5 current"><a class="reference internal" href="arrays.html">Array objects</a><ul class="current">
 <li class="toctree-l6 current"><a class="current reference internal" href="#">The N-dimensional array (<code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code>)</a></li>
 <li class="toctree-l6"><a class="reference internal" href="arrays.indexing.html">Indexing</a></li>
 </ul>
 </li>
 <li class="toctree-l5"><a class="reference internal" href="routines.html">Routines</a><ul>
 <li class="toctree-l6"><a class="reference internal" href="routines.array-creation.html">Array creation routines</a></li>
 <li class="toctree-l6"><a class="reference internal" href="routines.array-manipulation.html">Array manipulation routines</a></li>
 <li class="toctree-l6"><a class="reference internal" href="routines.io.html">Input and output</a></li>
 <li class="toctree-l6"><a class="reference internal" href="routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a></li>
 <li class="toctree-l6"><a class="reference internal" href="routines.math.html">Mathematical functions</a></li>
 <li class="toctree-l6"><a class="reference internal" href="random/index.html">np.random</a></li>
 <li class="toctree-l6"><a class="reference internal" href="routines.sort.html">Sorting, searching, and counting</a></li>
 <li class="toctree-l6"><a class="reference internal" href="routines.statistics.html">Statistics</a></li>
 </ul>
 </li>
 <li class="toctree-l5"><a class="reference internal" href="npx.html">NP Extensions</a></li>
 </ul>
 </li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="../../onnx/index.html">ONNX</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../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="../../optimizer/index.html">Optimizers</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../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/mkldnn/index.html">Intel MKL-DNN</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../../performance/backend/mkldnn/mkldnn_readme.html">Install MXNet with MKL-DNN</a></li>
 </ul>
 </li>
 <li class="toctree-l4"><a class="reference internal" href="../../../performance/backend/tensorrt/index.html">TensorRT</a><ul class="simple">
 </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>
 <li class="toctree-l4"><a class="reference internal" href="../../../deploy/inference/scala.html">Deploy into a Java or Scala Environment</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/custom_layer.html">Custom Layers</a></li>
 <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>
 </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/ndarray/index.html">mxnet.ndarray</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/ndarray.html">ndarray</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/contrib/index.html">ndarray.contrib</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/image/index.html">ndarray.image</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/linalg/index.html">ndarray.linalg</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/op/index.html">ndarray.op</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/random/index.html">ndarray.random</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/register/index.html">ndarray.register</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/sparse/index.html">ndarray.sparse</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/utils/index.html">ndarray.utils</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/parameter_dict.html">gluon.ParameterDict</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/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">mxnet.kvstore</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../api/symbol/index.html">mxnet.symbol</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/symbol.html">symbol</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/contrib/index.html">symbol.contrib</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/image/index.html">symbol.image</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/linalg/index.html">symbol.linalg</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/op/index.html">symbol.op</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/random/index.html">symbol.random</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/register/index.html">symbol.register</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/sparse/index.html">symbol.sparse</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../api/module/index.html">mxnet.module</a></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/autograd/index.html">contrib.autograd</a></li>
 <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/mxnet/index.html">mxnet</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/attribute/index.html">mxnet.attribute</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/base/index.html">mxnet.base</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/callback/index.html">mxnet.callback</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/context/index.html">mxnet.context</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/engine/index.html">mxnet.engine</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/executor/index.html">mxnet.executor</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/executor_manager/index.html">mxnet.executor_manager</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/image/index.html">mxnet.image</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/io/index.html">mxnet.io</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/kvstore_server/index.html">mxnet.kvstore_server</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/libinfo/index.html">mxnet.libinfo</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/log/index.html">mxnet.log</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/model/index.html">mxnet.model</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/monitor/index.html">mxnet.monitor</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/name/index.html">mxnet.name</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/notebook/index.html">mxnet.notebook</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/operator/index.html">mxnet.operator</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/profiler/index.html">mxnet.profiler</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/random/index.html">mxnet.random</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/recordio/index.html">mxnet.recordio</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/registry/index.html">mxnet.registry</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/rtc/index.html">mxnet.rtc</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/runtime/index.html">mxnet.runtime</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/test_utils/index.html">mxnet.test_utils</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/torch/index.html">mxnet.torch</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/util/index.html">mxnet.util</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/visualization/index.html">mxnet.visualization</a></li>
 </ul>
 </li>
 </ul>
 </li>
 </ul>

             </nav>

         </div>

 </header>

     <div class="document">
         <div class="page-content" role="main">

   <div class="section" id="the-n-dimensional-array-ndarray">
 <span id="arrays-ndarray"></span><h1>The N-dimensional array (<code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code>)<a class="headerlink" href="#the-n-dimensional-array-ndarray" title="Permalink to this headline">¶</a></h1>
 <p>An <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code> is a (usually fixed-size) multidimensional
 container of items of the same type and size. The number of dimensions
 and items in an array is defined by its <code class="xref py py-attr docutils literal notranslate"><span class="pre">shape</span></code>,
 which is a <code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code> of <em>N</em> non-negative integers that specify the
 sizes of each dimension. The type of items in the array is specified by
 a separate <span class="xref std std-ref">data-type object (dtype)</span>, one of which
 is associated with each ndarray.</p>
 <p>As with other container objects in Python, the contents of an
 <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code> can be accessed and modified by <a class="reference internal" href="arrays.indexing.html#arrays-indexing"><span class="std std-ref">indexing or
 slicing</span></a> the array (using, for example, <em>N</em> integers),
 and via the methods and attributes of the <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code>.</p>
 <p id="index-0">Different <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarrays</span></code> can share the same data, so that
 changes made in one <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code> may be visible in another. That
 is, an ndarray can be a <em>“view”</em> to another ndarray, and the data it
 is referring to is taken care of by the <em>“base”</em> ndarray.</p>
 <div class="admonition-example admonition">
 <p class="admonition-title">Example</p>
 <p>A 2-dimensional array of size 2 x 3, composed of 4-byte integer
 elements:</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]],</span> <span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">type</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
 <span class="go">&lt;class &#39;mxnet.numpy.ndarray&#39;&gt;</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">x</span><span class="o">.</span><span class="n">shape</span>
 <span class="go">(2, 3)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">x</span><span class="o">.</span><span class="n">dtype</span>
 <span class="go">dtype(&#39;int32&#39;)</span>
 </pre></div>
 </div>
 <p>The array can be indexed using Python container-like syntax:</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="c1"># The element of x in the *second* row, *third* column, namely, 6.</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
 <span class="go">array(6, dtype=int32)  # this is different than the official NumPy which returns a np.int32 object</span>
 </pre></div>
 </div>
 <p>For example <a class="reference internal" href="arrays.indexing.html#arrays-indexing"><span class="std std-ref">slicing</span></a> can produce views of
 the array if the elements to be sliced is continguous in memory:</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">,:]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y</span>
 <span class="go">array([9, 5, 6], dtype=int32)  # this also changes the corresponding element in x</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">x</span>
 <span class="go">array([[1, 2, 3],</span>
 <span class="go">        [9, 5, 6]], dtype=int32)</span>
 </pre></div>
 </div>
 </div>
 <div class="section" id="constructing-arrays">
 <h2>Constructing arrays<a class="headerlink" href="#constructing-arrays" title="Permalink to this headline">¶</a></h2>
 <p>New arrays can be constructed using the routines detailed in
 <a class="reference internal" href="routines.array-creation.html#routines-array-creation"><span class="std std-ref">Array creation routines</span></a>, and also by using the low-level
 <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code> constructor:</p>
 <table class="longtable docutils align-default">
 <colgroup>
 <col style="width: 10%" />
 <col style="width: 90%" />
 </colgroup>
 <tbody>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray</span></code></p></td>
 <td><p></p></td>
 </tr>
 </tbody>
 </table>
 </div>
 <div class="section" id="indexing-arrays">
 <h2>Indexing arrays<a class="headerlink" href="#indexing-arrays" title="Permalink to this headline">¶</a></h2>
 <p>Arrays can be indexed using an extended Python slicing syntax,
 <code class="docutils literal notranslate"><span class="pre">array[selection]</span></code>.  Similar syntax is also used for accessing
 fields in a <span class="xref std std-term">structured data type</span>.</p>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
 <p><a class="reference internal" href="arrays.indexing.html#arrays-indexing"><span class="std std-ref">Array Indexing</span></a>.</p>
 </div>
 </div>
 <div class="section" id="internal-memory-layout-of-an-ndarray">
 <span id="memory-layout"></span><h2>Internal memory layout of an ndarray<a class="headerlink" href="#internal-memory-layout-of-an-ndarray" title="Permalink to this headline">¶</a></h2>
 <p>An instance of class <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code> consists of a contiguous
 one-dimensional segment of computer memory (owned by the array, or by
 some other object), combined with an indexing scheme that maps <em>N</em>
 integers into the location of an item in the block.  The ranges in
 which the indices can vary is specified by the <code class="xref py py-obj docutils literal notranslate"><span class="pre">shape</span></code> of the array. How many bytes each item takes and how
 the bytes are interpreted is defined by the <span class="xref std std-ref">data-type object</span> associated with the array.</p>
 <div class="admonition note" id="index-1">
 <p class="admonition-title">Note</p>
 <p><cite>mxnet.numpy.ndarray</cite> currently only supports storing elements in
 C-order/row-major and contiguous memory space. The following content
 on explaining a variety of memory layouts of an ndarray
 are copied from the official NumPy documentation as a comprehensive reference.</p>
 </div>
 <p>A segment of memory is inherently 1-dimensional, and there are many
 different schemes for arranging the items of an <em>N</em>-dimensional array
 in a 1-dimensional block. NumPy is flexible, and <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code>
 objects can accommodate any <em>strided indexing scheme</em>. In a strided
 scheme, the N-dimensional index <span class="math notranslate nohighlight">\((n_0, n_1, ..., n_{N-1})\)</span>
 corresponds to the offset (in bytes):</p>
 <div class="math notranslate nohighlight">
 \[n_{\mathrm{offset}} = \sum_{k=0}^{N-1} s_k n_k\]</div>
 <p>from the beginning of the memory block associated with the
 array. Here, <span class="math notranslate nohighlight">\(s_k\)</span> are integers which specify the <code class="xref py py-obj docutils literal notranslate"><span class="pre">strides</span></code> of the array. The <span class="xref std std-term">column-major</span> order (used,
 for example, in the Fortran language and in <em>Matlab</em>) and
 <span class="xref std std-term">row-major</span> order (used in C) schemes are just specific kinds of
 strided scheme, and correspond to memory that can be <em>addressed</em> by the strides:</p>
 <div class="math notranslate nohighlight">
 \[s_k^{\mathrm{column}} = \mathrm{itemsize} \prod_{j=0}^{k-1} d_j ,
 \quad  s_k^{\mathrm{row}} = \mathrm{itemsize} \prod_{j=k+1}^{N-1} d_j .\]</div>
 <p id="index-2">where <span class="math notranslate nohighlight">\(d_j\)</span> <cite>= self.shape[j]</cite>.</p>
 <p>Both the C and Fortran orders are <span class="xref std std-term">contiguous</span>, <em>i.e.,</em>
 single-segment, memory layouts, in which every part of the
 memory block can be accessed by some combination of the indices.</p>
 <p>While a C-style and Fortran-style contiguous array, which has the corresponding
 flags set, can be addressed with the above strides, the actual strides may be
 different. This can happen in two cases:</p>
 <blockquote>
 <div><ol class="arabic simple">
 <li><p>If <code class="docutils literal notranslate"><span class="pre">self.shape[k]</span> <span class="pre">==</span> <span class="pre">1</span></code> then for any legal index <code class="docutils literal notranslate"><span class="pre">index[k]</span> <span class="pre">==</span> <span class="pre">0</span></code>.
 This means that in the formula for the offset <span class="math notranslate nohighlight">\(n_k = 0\)</span> and thus
 <span class="math notranslate nohighlight">\(s_k n_k = 0\)</span> and the value of <span class="math notranslate nohighlight">\(s_k\)</span> <cite>= self.strides[k]</cite> is
 arbitrary.</p></li>
 <li><p>If an array has no elements (<code class="docutils literal notranslate"><span class="pre">self.size</span> <span class="pre">==</span> <span class="pre">0</span></code>) there is no legal
 index and the strides are never used. Any array with no elements may be
 considered C-style and Fortran-style contiguous.</p></li>
 </ol>
 </div></blockquote>
 <p>Point 1. means that <code class="docutils literal notranslate"><span class="pre">self</span></code> and <code class="docutils literal notranslate"><span class="pre">self.squeeze()</span></code> always have the same
 contiguity and <code class="docutils literal notranslate"><span class="pre">aligned</span></code> flags value. This also means
 that even a high dimensional array could be C-style and Fortran-style
 contiguous at the same time.</p>
 <p id="index-3">An array is considered aligned if the memory offsets for all elements and the
 base offset itself is a multiple of <cite>self.itemsize</cite>. Understanding
 <cite>memory-alignment</cite> leads to better performance on most hardware.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>Points (1) and (2) are not yet applied by default. Beginning with
 NumPy 1.8.0, they are applied consistently only if the environment
 variable <code class="docutils literal notranslate"><span class="pre">NPY_RELAXED_STRIDES_CHECKING=1</span></code> was defined when NumPy
 was built. Eventually this will become the default.</p>
 <p>You can check whether this option was enabled when your NumPy was
 built by looking at the value of <code class="docutils literal notranslate"><span class="pre">np.ones((10,1),</span>
 <span class="pre">order='C').flags.f_contiguous</span></code>. If this is <code class="docutils literal notranslate"><span class="pre">True</span></code>, then your
 NumPy has relaxed strides checking enabled.</p>
 </div>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>It does <em>not</em> generally hold that <code class="docutils literal notranslate"><span class="pre">self.strides[-1]</span> <span class="pre">==</span> <span class="pre">self.itemsize</span></code>
 for C-style contiguous arrays or <code class="docutils literal notranslate"><span class="pre">self.strides[0]</span> <span class="pre">==</span> <span class="pre">self.itemsize</span></code> for
 Fortran-style contiguous arrays is true.</p>
 </div>
 <p>Data in new <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarrays</span></code> is in the <span class="xref std std-term">row-major</span>
 (C) order, unless otherwise specified, but, for example, <a class="reference internal" href="arrays.indexing.html#arrays-indexing"><span class="std std-ref">basic
 array slicing</span></a> often produces <span class="xref std std-term">views</span>
 in a different scheme.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>Several algorithms in NumPy work on arbitrarily strided arrays.
 However, some algorithms require single-segment arrays. When an
 irregularly strided array is passed in to such algorithms, a copy
 is automatically made.</p>
 </div>
 </div>
 <div class="section" id="array-attributes">
 <span id="arrays-ndarray-attributes"></span><h2>Array attributes<a class="headerlink" href="#array-attributes" title="Permalink to this headline">¶</a></h2>
 <p>Array attributes reflect information that is intrinsic to the array
 itself. Generally, accessing an array through its attributes allows
 you to get and sometimes set intrinsic properties of the array without
 creating a new array. The exposed attributes are the core parts of an
 array and only some of them can be reset meaningfully without creating
 a new array. Information on each attribute is given below.</p>
 <div class="section" id="id1">
 <h3>Memory layout<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h3>
 <p>The following attributes contain information about the memory layout
 of the array:</p>
 <table class="longtable docutils align-default">
 <colgroup>
 <col style="width: 10%" />
 <col style="width: 90%" />
 </colgroup>
 <tbody>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.shape</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.ndim</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.size</span></code></p></td>
 <td><p></p></td>
 </tr>
 </tbody>
 </table>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ndarray</span><span class="o">.</span><span class="n">flags</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">strides</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">data</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">itemsize</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">nbytes</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">base</span>
 </pre></div>
 </div>
 </div>
 <div class="section" id="data-type">
 <h3>Data type<a class="headerlink" href="#data-type" title="Permalink to this headline">¶</a></h3>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
 <p><span class="xref std std-ref">Data type objects</span></p>
 </div>
 <p>The data type object associated with the array can be found in the
 <code class="xref py py-attr docutils literal notranslate"><span class="pre">dtype</span></code> attribute:</p>
 <table class="longtable docutils align-default">
 <colgroup>
 <col style="width: 10%" />
 <col style="width: 90%" />
 </colgroup>
 <tbody>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.dtype</span></code></p></td>
 <td><p></p></td>
 </tr>
 </tbody>
 </table>
 </div>
 <div class="section" id="other-attributes">
 <h3>Other attributes<a class="headerlink" href="#other-attributes" title="Permalink to this headline">¶</a></h3>
 <table class="longtable docutils align-default">
 <colgroup>
 <col style="width: 10%" />
 <col style="width: 90%" />
 </colgroup>
 <tbody>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.T</span></code></p></td>
 <td><p></p></td>
 </tr>
 </tbody>
 </table>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ndarray</span><span class="o">.</span><span class="n">real</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">imag</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">flat</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">ctypes</span>
 </pre></div>
 </div>
 </div>
 </div>
 <div class="section" id="array-methods">
 <span id="array-ndarray-methods"></span><h2>Array methods<a class="headerlink" href="#array-methods" title="Permalink to this headline">¶</a></h2>
 <p>An <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code> object has many methods which operate on or with
 the array in some fashion, typically returning an array result. These
 methods are briefly explained below. (Each method’s docstring has a
 more complete description.)</p>
 <p>For the following methods there are also corresponding functions in
 <code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy</span></code>: <code class="xref py py-func docutils literal notranslate"><span class="pre">all()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">any()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">argmax()</span></code>,
 <code class="xref py py-func docutils literal notranslate"><span class="pre">argmin()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">argpartition()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">argsort()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">choose()</span></code>,
 <code class="xref py py-func docutils literal notranslate"><span class="pre">clip()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">compress()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">copy()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">cumprod()</span></code>,
 <code class="xref py py-func docutils literal notranslate"><span class="pre">cumsum()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">diagonal()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">imag()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">max</span></code>,
 <code class="xref py py-func docutils literal notranslate"><span class="pre">mean()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">min</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">nonzero()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">partition()</span></code>,
 <code class="xref py py-func docutils literal notranslate"><span class="pre">prod()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">ptp()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">put()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">ravel()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">real()</span></code>,
 <code class="xref py py-func docutils literal notranslate"><span class="pre">repeat()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">reshape()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">round</span></code>,
 <code class="xref py py-func docutils literal notranslate"><span class="pre">searchsorted()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">sort()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">squeeze()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">std()</span></code>,
 <code class="xref py py-func docutils literal notranslate"><span class="pre">sum()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">swapaxes()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">take()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">trace()</span></code>,
 <code class="xref py py-func docutils literal notranslate"><span class="pre">transpose()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">var()</span></code>.</p>
 <div class="section" id="array-conversion">
 <h3>Array conversion<a class="headerlink" href="#array-conversion" title="Permalink to this headline">¶</a></h3>
 <table class="longtable docutils align-default">
 <colgroup>
 <col style="width: 10%" />
 <col style="width: 90%" />
 </colgroup>
 <tbody>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.item</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.copy</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.tolist</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.astype</span></code></p></td>
 <td><p></p></td>
 </tr>
 </tbody>
 </table>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ndarray</span><span class="o">.</span><span class="n">itemset</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">tostring</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">tobytes</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">tofile</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">dump</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">dumps</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">byteswap</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">view</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">getfield</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">setflags</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">fill</span>
 </pre></div>
 </div>
 </div>
 <div class="section" id="shape-manipulation">
 <h3>Shape manipulation<a class="headerlink" href="#shape-manipulation" title="Permalink to this headline">¶</a></h3>
 <p>For reshape, resize, and transpose, the single tuple argument may be
 replaced with <code class="docutils literal notranslate"><span class="pre">n</span></code> integers which will be interpreted as an n-tuple.</p>
 <table class="longtable docutils align-default">
 <colgroup>
 <col style="width: 10%" />
 <col style="width: 90%" />
 </colgroup>
 <tbody>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.reshape</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.transpose</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.swapaxes</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.flatten</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.squeeze</span></code></p></td>
 <td><p></p></td>
 </tr>
 </tbody>
 </table>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ndarray</span><span class="o">.</span><span class="n">resize</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">ravel</span>
 </pre></div>
 </div>
 </div>
 <div class="section" id="item-selection-and-manipulation">
 <h3>Item selection and manipulation<a class="headerlink" href="#item-selection-and-manipulation" title="Permalink to this headline">¶</a></h3>
 <p>For array methods that take an <em>axis</em> keyword, it defaults to
 <code class="xref py py-const docutils literal notranslate"><span class="pre">None</span></code>. If axis is <em>None</em>, then the array is treated as a 1-D
 array. Any other value for <em>axis</em> represents the dimension along which
 the operation should proceed.</p>
 <table class="longtable docutils align-default">
 <colgroup>
 <col style="width: 10%" />
 <col style="width: 90%" />
 </colgroup>
 <tbody>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.nonzero</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.take</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.repeat</span></code></p></td>
 <td><p></p></td>
 </tr>
 </tbody>
 </table>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ndarray</span><span class="o">.</span><span class="n">argsort</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">sort</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">put</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">choose</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">partition</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">argpartition</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">searchsorted</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">compress</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">diagonal</span>
 </pre></div>
 </div>
 </div>
 <div class="section" id="calculation">
 <h3>Calculation<a class="headerlink" href="#calculation" title="Permalink to this headline">¶</a></h3>
 <p id="index-4">Many of these methods take an argument named <em>axis</em>. In such cases,</p>
 <ul class="simple">
 <li><p>If <em>axis</em> is <em>None</em> (the default), the array is treated as a 1-D
 array and the operation is performed over the entire array. This
 behavior is also the default if self is a 0-dimensional array or
 array scalar. (An array scalar is an instance of the types/classes
 float32, float64, etc., whereas a 0-dimensional array is an ndarray
 instance containing precisely one array scalar.)</p></li>
 <li><p>If <em>axis</em> is an integer, then the operation is done over the given
 axis (for each 1-D subarray that can be created along the given axis).</p></li>
 </ul>
 <div class="admonition-example-of-the-axis-argument admonition">
 <p class="admonition-title">Example of the <em>axis</em> argument</p>
 <p>A 3-dimensional array of size 3 x 3 x 3, summed over each of its
 three axes</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">x</span>
 <span class="go">array([[[ 0,  1,  2],</span>
 <span class="go">        [ 3,  4,  5],</span>
 <span class="go">        [ 6,  7,  8]],</span>
 <span class="go">       [[ 9, 10, 11],</span>
 <span class="go">        [12, 13, 14],</span>
 <span class="go">        [15, 16, 17]],</span>
 <span class="go">       [[18, 19, 20],</span>
 <span class="go">        [21, 22, 23],</span>
 <span class="go">        [24, 25, 26]]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">x</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 <span class="go">array([[27, 30, 33],</span>
 <span class="go">       [36, 39, 42],</span>
 <span class="go">       [45, 48, 51]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="c1"># for sum, axis is the first keyword, so we may omit it,</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="c1"># specifying only its value</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">x</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">x</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">x</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
 <span class="go">(array([[27, 30, 33],</span>
 <span class="go">        [36, 39, 42],</span>
 <span class="go">        [45, 48, 51]]),</span>
 <span class="go"> array([[ 9, 12, 15],</span>
 <span class="go">        [36, 39, 42],</span>
 <span class="go">        [63, 66, 69]]),</span>
 <span class="go"> array([[ 3, 12, 21],</span>
 <span class="go">        [30, 39, 48],</span>
 <span class="go">        [57, 66, 75]]))</span>
 </pre></div>
 </div>
 </div>
 <p>The parameter <em>dtype</em> specifies the data type over which a reduction
 operation (like summing) should take place. The default reduce data
 type is the same as the data type of <em>self</em>. To avoid overflow, it can
 be useful to perform the reduction using a larger data type.</p>
 <p>For several methods, an optional <em>out</em> argument can also be provided
 and the result will be placed into the output array given. The <em>out</em>
 argument must be an <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code> and have the same number of
 elements. It can have a different data type in which case casting will
 be performed.</p>
 <table class="longtable docutils align-default">
 <colgroup>
 <col style="width: 10%" />
 <col style="width: 90%" />
 </colgroup>
 <tbody>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.max</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.argmax</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.min</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.argmin</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.clip</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.sum</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.mean</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.prod</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.cumsum</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.var</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.std</span></code></p></td>
 <td><p></p></td>
 </tr>
 </tbody>
 </table>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ndarray</span><span class="o">.</span><span class="n">round</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">ptp</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">conj</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">trace</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">cumprod</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">all</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">any</span>
 </pre></div>
 </div>
 </div>
 </div>
 <div class="section" id="arithmetic-matrix-multiplication-and-comparison-operations">
 <h2>Arithmetic, matrix multiplication, and comparison operations<a class="headerlink" href="#arithmetic-matrix-multiplication-and-comparison-operations" title="Permalink to this headline">¶</a></h2>
 <p id="index-5">Arithmetic and comparison operations on <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarrays</span></code>
 are defined as element-wise operations, and generally yield
 <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code> objects as results.</p>
 <p>Each of the arithmetic operations (<code class="docutils literal notranslate"><span class="pre">+</span></code>, <code class="docutils literal notranslate"><span class="pre">-</span></code>, <code class="docutils literal notranslate"><span class="pre">*</span></code>, <code class="docutils literal notranslate"><span class="pre">/</span></code>, <code class="docutils literal notranslate"><span class="pre">//</span></code>,
 <code class="docutils literal notranslate"><span class="pre">%</span></code>, <code class="docutils literal notranslate"><span class="pre">divmod()</span></code>, <code class="docutils literal notranslate"><span class="pre">**</span></code> or <code class="docutils literal notranslate"><span class="pre">pow()</span></code>, <code class="docutils literal notranslate"><span class="pre">&lt;&lt;</span></code>, <code class="docutils literal notranslate"><span class="pre">&gt;&gt;</span></code>, <code class="docutils literal notranslate"><span class="pre">&amp;</span></code>,
 <code class="docutils literal notranslate"><span class="pre">^</span></code>, <code class="docutils literal notranslate"><span class="pre">|</span></code>, <code class="docutils literal notranslate"><span class="pre">~</span></code>) and the comparisons (<code class="docutils literal notranslate"><span class="pre">==</span></code>, <code class="docutils literal notranslate"><span class="pre">&lt;</span></code>, <code class="docutils literal notranslate"><span class="pre">&gt;</span></code>,
 <code class="docutils literal notranslate"><span class="pre">&lt;=</span></code>, <code class="docutils literal notranslate"><span class="pre">&gt;=</span></code>, <code class="docutils literal notranslate"><span class="pre">!=</span></code>) is equivalent to the corresponding
 universal function (or <span class="xref std std-term">ufunc</span> for short) in NumPy.  For
 more information, see the section on <span class="xref std std-ref">Universal Functions</span>.</p>
 <p>Comparison operators:</p>
 <table class="longtable docutils align-default">
 <colgroup>
 <col style="width: 10%" />
 <col style="width: 90%" />
 </colgroup>
 <tbody>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__lt__</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__le__</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__gt__</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__ge__</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__eq__</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__ne__</span></code></p></td>
 <td><p></p></td>
 </tr>
 </tbody>
 </table>
 <p>Truth value of an array (<code class="xref py py-func docutils literal notranslate"><span class="pre">bool()</span></code>):</p>
 <table class="longtable docutils align-default">
 <colgroup>
 <col style="width: 10%" />
 <col style="width: 90%" />
 </colgroup>
 <tbody>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__bool__</span></code></p></td>
 <td><p></p></td>
 </tr>
 </tbody>
 </table>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>Truth-value testing of an array invokes
 <code class="xref py py-meth docutils literal notranslate"><span class="pre">ndarray.__bool__()</span></code>, which raises an error if the number of
 elements in the array is larger than 1, because the truth value
 of such arrays is ambiguous.</p>
 </div>
 <p>Unary operations:</p>
 <table class="longtable docutils align-default">
 <colgroup>
 <col style="width: 10%" />
 <col style="width: 90%" />
 </colgroup>
 <tbody>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__neg__</span></code></p></td>
 <td><p></p></td>
 </tr>
 </tbody>
 </table>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ndarray</span><span class="o">.</span><span class="fm">__pos__</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="fm">__abs__</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="fm">__invert__</span>
 </pre></div>
 </div>
 <p>Arithmetic:</p>
 <table class="longtable docutils align-default">
 <colgroup>
 <col style="width: 10%" />
 <col style="width: 90%" />
 </colgroup>
 <tbody>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__add__</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__sub__</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__mul__</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__truediv__</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__mod__</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__pow__</span></code></p></td>
 <td><p></p></td>
 </tr>
 </tbody>
 </table>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ndarray</span><span class="o">.</span><span class="fm">__floordiv__</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="fm">__divmod__</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="fm">__lshift__</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="fm">__rshift__</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="fm">__and__</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="fm">__or__</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="fm">__xor__</span>
 </pre></div>
 </div>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
 <ul class="simple">
 <li><p>Any third argument to <code class="xref py py-func docutils literal notranslate"><span class="pre">pow()</span></code> is silently ignored,
 as the underlying <code class="xref py py-func docutils literal notranslate"><span class="pre">ufunc</span></code> takes only two arguments.</p></li>
 <li><p>The three division operators are all defined; <code class="xref py py-obj docutils literal notranslate"><span class="pre">div</span></code> is active
 by default, <code class="xref py py-obj docutils literal notranslate"><span class="pre">truediv</span></code> is active when
 <code class="xref py py-obj docutils literal notranslate"><span class="pre">__future__</span></code> division is in effect.</p></li>
 <li><p>Because <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code> is a built-in type (written in C), the
 <code class="docutils literal notranslate"><span class="pre">__r{op}__</span></code> special methods are not directly defined.</p></li>
 <li><p>The functions called to implement many arithmetic special methods
 for arrays can be modified using <code class="xref py py-class docutils literal notranslate"><span class="pre">__array_ufunc__</span></code>.</p></li>
 </ul>
 </div>
 <p>Arithmetic, in-place:</p>
 <table class="longtable docutils align-default">
 <colgroup>
 <col style="width: 10%" />
 <col style="width: 90%" />
 </colgroup>
 <tbody>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__iadd__</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__isub__</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__imul__</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__itruediv__</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__imod__</span></code></p></td>
 <td><p></p></td>
 </tr>
 </tbody>
 </table>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ndarray</span><span class="o">.</span><span class="fm">__ifloordiv__</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="fm">__ipow__</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="fm">__ilshift__</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="fm">__irshift__</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="fm">__iand__</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="fm">__ior__</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="fm">__ixor__</span>
 </pre></div>
 </div>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>In place operations will perform the calculation using the
 precision decided by the data type of the two operands, but will
 silently downcast the result (if necessary) so it can fit back into
 the array.  Therefore, for mixed precision calculations, <code class="docutils literal notranslate"><span class="pre">A</span> <span class="pre">{op}=</span>
 <span class="pre">B</span></code> can be different than <code class="docutils literal notranslate"><span class="pre">A</span> <span class="pre">=</span> <span class="pre">A</span> <span class="pre">{op}</span> <span class="pre">B</span></code>. For example, suppose
 <code class="docutils literal notranslate"><span class="pre">a</span> <span class="pre">=</span> <span class="pre">ones((3,3))</span></code>. Then, <code class="docutils literal notranslate"><span class="pre">a</span> <span class="pre">+=</span> <span class="pre">3j</span></code> is different than <code class="docutils literal notranslate"><span class="pre">a</span> <span class="pre">=</span> <span class="pre">a</span> <span class="pre">+</span>
 <span class="pre">3j</span></code>: while they both perform the same computation, <code class="docutils literal notranslate"><span class="pre">a</span> <span class="pre">+=</span> <span class="pre">3</span></code>
 casts the result to fit back in <code class="docutils literal notranslate"><span class="pre">a</span></code>, whereas <code class="docutils literal notranslate"><span class="pre">a</span> <span class="pre">=</span> <span class="pre">a</span> <span class="pre">+</span> <span class="pre">3j</span></code>
 re-binds the name <code class="docutils literal notranslate"><span class="pre">a</span></code> to the result.</p>
 </div>
 <p>Matrix Multiplication:</p>
 <table class="longtable docutils align-default">
 <colgroup>
 <col style="width: 10%" />
 <col style="width: 90%" />
 </colgroup>
 <tbody>
 </tbody>
 </table>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ndarray</span><span class="o">.</span><span class="fm">__matmul__</span>
 </pre></div>
 </div>
 </div>
 <div class="section" id="special-methods">
 <h2>Special methods<a class="headerlink" href="#special-methods" title="Permalink to this headline">¶</a></h2>
 <p>For standard library functions:</p>
 <table class="longtable docutils align-default">
 <colgroup>
 <col style="width: 10%" />
 <col style="width: 90%" />
 </colgroup>
 <tbody>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__reduce__</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__setstate__</span></code></p></td>
 <td><p></p></td>
 </tr>
 </tbody>
 </table>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ndarray</span><span class="o">.</span><span class="n">__copy__</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">__deepcopy__</span>
 </pre></div>
 </div>
 <p>Basic customization:</p>
 <table class="longtable docutils align-default">
 <colgroup>
 <col style="width: 10%" />
 <col style="width: 90%" />
 </colgroup>
 <tbody>
 </tbody>
 </table>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ndarray</span><span class="o">.</span><span class="n">__array__</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="fm">__new__</span>
 <span class="n">ndarray</span><span class="o">.</span><span class="n">__array_wrap__</span>
 </pre></div>
 </div>
 <p>Container customization: (see <a class="reference internal" href="arrays.indexing.html#arrays-indexing"><span class="std std-ref">Indexing</span></a>)</p>
 <table class="longtable docutils align-default">
 <colgroup>
 <col style="width: 10%" />
 <col style="width: 90%" />
 </colgroup>
 <tbody>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__len__</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__getitem__</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__setitem__</span></code></p></td>
 <td><p></p></td>
 </tr>
 </tbody>
 </table>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ndarray</span><span class="o">.</span><span class="fm">__contains__</span>
 </pre></div>
 </div>
 <p>Conversion; the operations <code class="xref py py-func docutils literal notranslate"><span class="pre">int()</span></code> and <code class="xref py py-func docutils literal notranslate"><span class="pre">float()</span></code>.
 They work only on arrays that have one element in them
 and return the appropriate scalar.</p>
 <table class="longtable docutils align-default">
 <colgroup>
 <col style="width: 10%" />
 <col style="width: 90%" />
 </colgroup>
 <tbody>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__int__</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__float__</span></code></p></td>
 <td><p></p></td>
 </tr>
 </tbody>
 </table>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ndarray</span><span class="o">.</span><span class="fm">__complex__</span>
 </pre></div>
 </div>
 <p>String representations:</p>
 <table class="longtable docutils align-default">
 <colgroup>
 <col style="width: 10%" />
 <col style="width: 90%" />
 </colgroup>
 <tbody>
 <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__str__</span></code></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__repr__</span></code></p></td>
 <td><p></p></td>
 </tr>
 </tbody>
 </table>
 </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="#">The N-dimensional array (<code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code>)</a><ul>
 <li><a class="reference internal" href="#constructing-arrays">Constructing arrays</a></li>
 <li><a class="reference internal" href="#indexing-arrays">Indexing arrays</a></li>
 <li><a class="reference internal" href="#internal-memory-layout-of-an-ndarray">Internal memory layout of an ndarray</a></li>
 <li><a class="reference internal" href="#array-attributes">Array attributes</a><ul>
 <li><a class="reference internal" href="#id1">Memory layout</a></li>
 <li><a class="reference internal" href="#data-type">Data type</a></li>
 <li><a class="reference internal" href="#other-attributes">Other attributes</a></li>
 </ul>
 </li>
 <li><a class="reference internal" href="#array-methods">Array methods</a><ul>
 <li><a class="reference internal" href="#array-conversion">Array conversion</a></li>
 <li><a class="reference internal" href="#shape-manipulation">Shape manipulation</a></li>
 <li><a class="reference internal" href="#item-selection-and-manipulation">Item selection and manipulation</a></li>
 <li><a class="reference internal" href="#calculation">Calculation</a></li>
 </ul>
 </li>
 <li><a class="reference internal" href="#arithmetic-matrix-multiplication-and-comparison-operations">Arithmetic, matrix multiplication, and comparison operations</a></li>
 <li><a class="reference internal" href="#special-methods">Special methods</a></li>
 </ul>
 </li>
 </ul>

 </div>
             </div>
         </div>

       <div class="clearer"></div>
     </div><div class="pagenation">
      <a id="button-prev" href="arrays.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>Array objects</div>
          </div>
      </a>
      <a id="button-next" href="arrays.indexing.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>Indexing</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 class="u-email" href="mailto:dev@mxnet.apache.org">Dev list</a></li>
                     <li><a class="u-email" href="mailto:user@mxnet.apache.org">User mailing list</a></li>
                     <li><a href="https://cwiki.apache.org/confluence/display/MXNET/Apache+MXNet+Home">Developer Wiki</a></li>
                     <li><a href="https://issues.apache.org/jira/projects/MXNET/issues">Jira Tracker</a></li>
                     <li><a href="https://github.com/apache/incubator-mxnet/labels/Roadmap">Github Roadmap</a></li>
                     <li><a href="https://discuss.mxnet.io">MXNet Discuss forum</a></li>
                     <li><a href="/community/contribute">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 The Apache Software Foundation (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>