| <!DOCTYPE html> |
| |
| <html xmlns="http://www.w3.org/1999/xhtml"> |
| <head> |
| <meta charset="utf-8" /> |
| <meta charset="utf-8"> |
| <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> |
| <meta http-equiv="x-ua-compatible" content="ie=edge"> |
| <style> |
| .dropdown { |
| position: relative; |
| display: inline-block; |
| } |
| |
| .dropdown-content { |
| display: none; |
| position: absolute; |
| background-color: #f9f9f9; |
| min-width: 160px; |
| box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2); |
| padding: 12px 16px; |
| z-index: 1; |
| text-align: left; |
| } |
| |
| .dropdown:hover .dropdown-content { |
| display: block; |
| } |
| |
| .dropdown-option:hover { |
| color: #FF4500; |
| } |
| |
| .dropdown-option-active { |
| color: #FF4500; |
| font-weight: lighter; |
| } |
| |
| .dropdown-option { |
| color: #000000; |
| font-weight: lighter; |
| } |
| |
| .dropdown-header { |
| color: #FFFFFF; |
| display: inline-flex; |
| } |
| |
| .dropdown-caret { |
| width: 18px; |
| height: 54px; |
| } |
| |
| .dropdown-caret-path { |
| fill: #FFFFFF; |
| } |
| </style> |
| |
| <title>Step 6: Train a Neural Network — Apache MXNet documentation</title> |
| |
| <link rel="stylesheet" href="../../../_static/basic.css" type="text/css" /> |
| <link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" /> |
| <link rel="stylesheet" type="text/css" href="../../../_static/mxnet.css" /> |
| <link rel="stylesheet" href="../../../_static/material-design-lite-1.3.0/material.blue-deep_orange.min.css" type="text/css" /> |
| <link rel="stylesheet" href="../../../_static/sphinx_materialdesign_theme.css" type="text/css" /> |
| <link rel="stylesheet" href="../../../_static/fontawesome/all.css" type="text/css" /> |
| <link rel="stylesheet" href="../../../_static/fonts.css" type="text/css" /> |
| <link rel="stylesheet" href="../../../_static/feedback.css" type="text/css" /> |
| <script id="documentation_options" data-url_root="../../../" src="../../../_static/documentation_options.js"></script> |
| <script src="../../../_static/jquery.js"></script> |
| <script src="../../../_static/underscore.js"></script> |
| <script src="../../../_static/doctools.js"></script> |
| <script src="../../../_static/language_data.js"></script> |
| <script src="../../../_static/matomo_analytics.js"></script> |
| <script src="../../../_static/autodoc.js"></script> |
| <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script> |
| <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script> |
| <script type="text/x-mathjax-config">MathJax.Hub.Config({"tex2jax": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true, "ignoreClass": "document", "processClass": "math|output_area"}})</script> |
| <script src="../../../_static/sphinx_materialdesign_theme.js"></script> |
| <link rel="shortcut icon" href="../../../_static/mxnet-icon.png"/> |
| <link rel="index" title="Index" href="../../../genindex.html" /> |
| <link rel="search" title="Search" href="../../../search.html" /> |
| <link rel="next" title="Step 7: Load and Run a NN using GPU" href="7-use-gpus.html" /> |
| <link rel="prev" title="Step 5: Datasets and DataLoader" href="5-datasets.html" /> |
| </head> |
| <body><header class="site-header" role="banner"> |
| <div class="wrapper"> |
| <a class="site-title" rel="author" href="/"><img |
| src="../../../_static/mxnet_logo.png" class="site-header-logo"></a> |
| <nav class="site-nav"> |
| <input type="checkbox" id="nav-trigger" class="nav-trigger"/> |
| <label for="nav-trigger"> |
| <span class="menu-icon"> |
| <svg viewBox="0 0 18 15" width="18px" height="15px"> |
| <path d="M18,1.484c0,0.82-0.665,1.484-1.484,1.484H1.484C0.665,2.969,0,2.304,0,1.484l0,0C0,0.665,0.665,0,1.484,0 h15.032C17.335,0,18,0.665,18,1.484L18,1.484z M18,7.516C18,8.335,17.335,9,16.516,9H1.484C0.665,9,0,8.335,0,7.516l0,0 c0-0.82,0.665-1.484,1.484-1.484h15.032C17.335,6.031,18,6.696,18,7.516L18,7.516z M18,13.516C18,14.335,17.335,15,16.516,15H1.484 C0.665,15,0,14.335,0,13.516l0,0c0-0.82,0.665-1.483,1.484-1.483h15.032C17.335,12.031,18,12.695,18,13.516L18,13.516z"/> |
| </svg> |
| </span> |
| </label> |
| |
| <div class="trigger"> |
| <a class="page-link" href="/get_started">Get Started</a> |
| <a class="page-link" href="/features">Features</a> |
| <a class="page-link" href="/ecosystem">Ecosystem</a> |
| <a class="page-link page-current" href="/api">Docs & Tutorials</a> |
| <a class="page-link" href="/trusted_by">Trusted By</a> |
| <a class="page-link" href="https://github.com/apache/incubator-mxnet">GitHub</a> |
| <div class="dropdown" style="min-width:100px"> |
| <span class="dropdown-header">Apache |
| <svg class="dropdown-caret" viewBox="0 0 32 32" class="icon icon-caret-bottom" aria-hidden="true"><path class="dropdown-caret-path" d="M24 11.305l-7.997 11.39L8 11.305z"></path></svg> |
| </span> |
| <div class="dropdown-content" style="min-width:250px"> |
| <a href="https://www.apache.org/foundation/">Apache Software Foundation</a> |
| <a href="https://incubator.apache.org/">Apache Incubator</a> |
| <a href="https://www.apache.org/licenses/">License</a> |
| <a href="/versions/1.9.1/api/faq/security.html">Security</a> |
| <a href="https://privacy.apache.org/policies/privacy-policy-public.html">Privacy</a> |
| <a href="https://www.apache.org/events/current-event">Events</a> |
| <a href="https://www.apache.org/foundation/sponsorship.html">Sponsorship</a> |
| <a href="https://www.apache.org/foundation/thanks.html">Thanks</a> |
| </div> |
| </div> |
| <div class="dropdown"> |
| <span class="dropdown-header">master |
| <svg class="dropdown-caret" viewBox="0 0 32 32" class="icon icon-caret-bottom" aria-hidden="true"><path class="dropdown-caret-path" d="M24 11.305l-7.997 11.39L8 11.305z"></path></svg> |
| </span> |
| <div class="dropdown-content"> |
| <a class="dropdown-option-active" href="/versions/master/">master</a><br> |
| <a class="dropdown-option" href="/versions/1.9.1/">1.9.1</a><br> |
| <a class="dropdown-option" href="/versions/1.8.0/">1.8.0</a><br> |
| <a class="dropdown-option" href="/versions/1.7.0/">1.7.0</a><br> |
| <a class="dropdown-option" href="/versions/1.6.0/">1.6.0</a><br> |
| <a class="dropdown-option" href="/versions/1.5.0/">1.5.0</a><br> |
| <a class="dropdown-option" href="/versions/1.4.1/">1.4.1</a><br> |
| <a class="dropdown-option" href="/versions/1.3.1/">1.3.1</a><br> |
| <a class="dropdown-option" href="/versions/1.2.1/">1.2.1</a><br> |
| <a class="dropdown-option" href="/versions/1.1.0/">1.1.0</a><br> |
| <a class="dropdown-option" href="/versions/1.0.0/">1.0.0</a><br> |
| <a class="dropdown-option" href="/versions/0.12.1/">0.12.1</a><br> |
| <a class="dropdown-option" href="/versions/0.11.0/">0.11.0</a> |
| </div> |
| </div> |
| </div> |
| </nav> |
| </div> |
| </header> |
| <div class="mdl-layout mdl-js-layout mdl-layout--fixed-header mdl-layout--fixed-drawer"><header class="mdl-layout__header mdl-layout__header--waterfall "> |
| <div class="mdl-layout__header-row"> |
| |
| <nav class="mdl-navigation breadcrumb"> |
| <a class="mdl-navigation__link" href="../../index.html">Python Tutorials</a><i class="material-icons">navigate_next</i> |
| <a class="mdl-navigation__link" href="../index.html">Getting Started</a><i class="material-icons">navigate_next</i> |
| <a class="mdl-navigation__link" href="index.html">Crash Course</a><i class="material-icons">navigate_next</i> |
| <a class="mdl-navigation__link is-active">Step 6: Train a Neural Network</a> |
| </nav> |
| <div class="mdl-layout-spacer"></div> |
| <nav class="mdl-navigation"> |
| |
| <form class="form-inline pull-sm-right" action="../../../search.html" method="get"> |
| <div class="mdl-textfield mdl-js-textfield mdl-textfield--expandable mdl-textfield--floating-label mdl-textfield--align-right"> |
| <label id="quick-search-icon" class="mdl-button mdl-js-button mdl-button--icon" for="waterfall-exp"> |
| <i class="material-icons">search</i> |
| </label> |
| <div class="mdl-textfield__expandable-holder"> |
| <input class="mdl-textfield__input" type="text" name="q" id="waterfall-exp" placeholder="Search" /> |
| <input type="hidden" name="check_keywords" value="yes" /> |
| <input type="hidden" name="area" value="default" /> |
| </div> |
| </div> |
| <div class="mdl-tooltip" data-mdl-for="quick-search-icon"> |
| Quick search |
| </div> |
| </form> |
| |
| <a id="button-show-github" |
| href="https://github.com/apache/mxnet/edit/master/docs/python_docs/python/tutorials/getting-started/crash-course/6-train-nn.md" class="mdl-button mdl-js-button mdl-button--icon"> |
| <i class="material-icons">edit</i> |
| </a> |
| <div class="mdl-tooltip" data-mdl-for="button-show-github"> |
| Edit on Github |
| </div> |
| </nav> |
| </div> |
| <div class="mdl-layout__header-row header-links"> |
| <div class="mdl-layout-spacer"></div> |
| <nav class="mdl-navigation"> |
| </nav> |
| </div> |
| </header><header class="mdl-layout__drawer"> |
| |
| <div class="globaltoc"> |
| <span class="mdl-layout-title toc">Table Of Contents</span> |
| |
| |
| |
| <nav class="mdl-navigation"> |
| <ul class="current"> |
| <li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Python Tutorials</a><ul class="current"> |
| <li class="toctree-l2 current"><a class="reference internal" href="../index.html">Getting Started</a><ul class="current"> |
| <li class="toctree-l3 current"><a class="reference internal" href="index.html">Crash Course</a><ul class="current"> |
| <li class="toctree-l4"><a class="reference internal" href="0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="5-datasets.html">Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="5-datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="5-datasets.html#Using-your-own-data-with-custom-Datasets">Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">New in MXNet 2.0: faster C++ backend dataloaders</a></li> |
| <li class="toctree-l4 current"><a class="current reference internal" href="#">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/np/np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/optimizer/index.html">Optimizers</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/viz/index.html">Visualization</a><ul> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../performance/index.html">Performance</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../performance/compression/index.html">Compression</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../performance/compression/int8.html">Deploy with int-8</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/float16">Float16</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../performance/backend/index.html">Accelerated Backend Tools</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../performance/backend/dnnl/index.html">oneDNN</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../performance/backend/dnnl/dnnl_readme.html">Install MXNet with oneDNN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../performance/backend/dnnl/dnnl_quantization.html">oneDNN Quantization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../performance/backend/dnnl/dnnl_quantization_inc.html">Improving accuracy with Intel® Neural Compressor</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../performance/backend/tvm.html">Use TVM</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../performance/backend/profiler.html">Profiling MXNet Models</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../performance/backend/amp.html">Using AMP: Automatic Mixed Precision</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../deploy/index.html">Deployment</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../deploy/export/index.html">Export</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../deploy/export/onnx.html">Exporting to ONNX format</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/export_network.html">Export Gluon CV Models</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Save / Load Parameters</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../deploy/inference/index.html">Inference</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../deploy/inference/cpp.html">Deploy into C++</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../deploy/inference/image_classification_jetson.html">Image Classication using pretrained ResNet-50 model on Jetson module</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../deploy/run-on-aws/index.html">Run on AWS</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../deploy/run-on-aws/use_ec2.html">Run on an EC2 Instance</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../deploy/run-on-aws/use_sagemaker.html">Run on Amazon SageMaker</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../deploy/run-on-aws/cloud.html">MXNet on the Cloud</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../extend/index.html">Extend</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../extend/customop.html">Custom Numpy Operators</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/new_op">New Operator Creation</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/add_op_in_backend">New Operator in MXNet Backend</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/using_rtc">Using RTC for CUDA kernels</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l1"><a class="reference internal" href="../../../api/index.html">Python API</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/np/index.html">mxnet.np</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/np/arrays.html">Array objects</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/arrays.ndarray.html">The N-dimensional array (<code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code>)</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/arrays.indexing.html">Indexing</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/np/routines.html">Routines</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.array-creation.html">Array creation routines</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.eye.html">mxnet.np.eye</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.empty.html">mxnet.np.empty</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.full.html">mxnet.np.full</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.identity.html">mxnet.np.identity</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ones.html">mxnet.np.ones</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ones_like.html">mxnet.np.ones_like</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.zeros.html">mxnet.np.zeros</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.zeros_like.html">mxnet.np.zeros_like</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.array.html">mxnet.np.array</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.copy.html">mxnet.np.copy</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arange.html">mxnet.np.arange</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linspace.html">mxnet.np.linspace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.logspace.html">mxnet.np.logspace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.meshgrid.html">mxnet.np.meshgrid</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tril.html">mxnet.np.tril</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.array-manipulation.html">Array manipulation routines</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.reshape.html">mxnet.np.reshape</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ravel.html">mxnet.np.ravel</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.flatten.html">mxnet.np.ndarray.flatten</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.swapaxes.html">mxnet.np.swapaxes</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.T.html">mxnet.np.ndarray.T</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.transpose.html">mxnet.np.transpose</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.moveaxis.html">mxnet.np.moveaxis</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rollaxis.html">mxnet.np.rollaxis</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.expand_dims.html">mxnet.np.expand_dims</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.squeeze.html">mxnet.np.squeeze</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.broadcast_to.html">mxnet.np.broadcast_to</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.broadcast_arrays.html">mxnet.np.broadcast_arrays</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.atleast_1d.html">mxnet.np.atleast_1d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.atleast_2d.html">mxnet.np.atleast_2d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.atleast_3d.html">mxnet.np.atleast_3d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.concatenate.html">mxnet.np.concatenate</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.stack.html">mxnet.np.stack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dstack.html">mxnet.np.dstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vstack.html">mxnet.np.vstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.column_stack.html">mxnet.np.column_stack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hstack.html">mxnet.np.hstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.split.html">mxnet.np.split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hsplit.html">mxnet.np.hsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vsplit.html">mxnet.np.vsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.array_split.html">mxnet.np.array_split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dsplit.html">mxnet.np.dsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tile.html">mxnet.np.tile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.repeat.html">mxnet.np.repeat</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.unique.html">mxnet.np.unique</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.delete.html">mxnet.np.delete</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.insert.html">mxnet.np.insert</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.append.html">mxnet.np.append</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.resize.html">mxnet.np.resize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trim_zeros.html">mxnet.np.trim_zeros</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.reshape.html">mxnet.np.reshape</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flip.html">mxnet.np.flip</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.roll.html">mxnet.np.roll</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rot90.html">mxnet.np.rot90</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fliplr.html">mxnet.np.fliplr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flipud.html">mxnet.np.flipud</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.io.html">Input and output</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.genfromtxt.html">mxnet.np.genfromtxt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.tolist.html">mxnet.np.ndarray.tolist</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.set_printoptions.html">mxnet.np.set_printoptions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dot.html">mxnet.np.dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vdot.html">mxnet.np.vdot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.inner.html">mxnet.np.inner</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.outer.html">mxnet.np.outer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tensordot.html">mxnet.np.tensordot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.einsum.html">mxnet.np.einsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.multi_dot.html">mxnet.np.linalg.multi_dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.matmul.html">mxnet.np.matmul</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_power.html">mxnet.np.linalg.matrix_power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.kron.html">mxnet.np.kron</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.svd.html">mxnet.np.linalg.svd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cholesky.html">mxnet.np.linalg.cholesky</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.qr.html">mxnet.np.linalg.qr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eig.html">mxnet.np.linalg.eig</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigh.html">mxnet.np.linalg.eigh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvals.html">mxnet.np.linalg.eigvals</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvalsh.html">mxnet.np.linalg.eigvalsh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.norm.html">mxnet.np.linalg.norm</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trace.html">mxnet.np.trace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cond.html">mxnet.np.linalg.cond</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.det.html">mxnet.np.linalg.det</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_rank.html">mxnet.np.linalg.matrix_rank</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.slogdet.html">mxnet.np.linalg.slogdet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.solve.html">mxnet.np.linalg.solve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorsolve.html">mxnet.np.linalg.tensorsolve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.lstsq.html">mxnet.np.linalg.lstsq</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.inv.html">mxnet.np.linalg.inv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.pinv.html">mxnet.np.linalg.pinv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorinv.html">mxnet.np.linalg.tensorinv</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.math.html">Mathematical functions</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sin.html">mxnet.np.sin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cos.html">mxnet.np.cos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tan.html">mxnet.np.tan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arcsin.html">mxnet.np.arcsin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arccos.html">mxnet.np.arccos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan.html">mxnet.np.arctan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.degrees.html">mxnet.np.degrees</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.radians.html">mxnet.np.radians</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hypot.html">mxnet.np.hypot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan2.html">mxnet.np.arctan2</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.deg2rad.html">mxnet.np.deg2rad</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rad2deg.html">mxnet.np.rad2deg</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.unwrap.html">mxnet.np.unwrap</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sinh.html">mxnet.np.sinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cosh.html">mxnet.np.cosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tanh.html">mxnet.np.tanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arcsinh.html">mxnet.np.arcsinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arccosh.html">mxnet.np.arccosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctanh.html">mxnet.np.arctanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rint.html">mxnet.np.rint</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fix.html">mxnet.np.fix</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.floor.html">mxnet.np.floor</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ceil.html">mxnet.np.ceil</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trunc.html">mxnet.np.trunc</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.around.html">mxnet.np.around</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.round_.html">mxnet.np.round_</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sum.html">mxnet.np.sum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.prod.html">mxnet.np.prod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cumsum.html">mxnet.np.cumsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanprod.html">mxnet.np.nanprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nansum.html">mxnet.np.nansum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cumprod.html">mxnet.np.cumprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nancumprod.html">mxnet.np.nancumprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nancumsum.html">mxnet.np.nancumsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.diff.html">mxnet.np.diff</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ediff1d.html">mxnet.np.ediff1d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cross.html">mxnet.np.cross</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trapz.html">mxnet.np.trapz</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.exp.html">mxnet.np.exp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.expm1.html">mxnet.np.expm1</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log.html">mxnet.np.log</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log10.html">mxnet.np.log10</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log2.html">mxnet.np.log2</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log1p.html">mxnet.np.log1p</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.logaddexp.html">mxnet.np.logaddexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.i0.html">mxnet.np.i0</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ldexp.html">mxnet.np.ldexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.signbit.html">mxnet.np.signbit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.copysign.html">mxnet.np.copysign</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.frexp.html">mxnet.np.frexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.spacing.html">mxnet.np.spacing</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.lcm.html">mxnet.np.lcm</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.gcd.html">mxnet.np.gcd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.add.html">mxnet.np.add</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.reciprocal.html">mxnet.np.reciprocal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.negative.html">mxnet.np.negative</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.divide.html">mxnet.np.divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.power.html">mxnet.np.power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.subtract.html">mxnet.np.subtract</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.mod.html">mxnet.np.mod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.multiply.html">mxnet.np.multiply</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.true_divide.html">mxnet.np.true_divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.remainder.html">mxnet.np.remainder</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.positive.html">mxnet.np.positive</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.float_power.html">mxnet.np.float_power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fmod.html">mxnet.np.fmod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.modf.html">mxnet.np.modf</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.divmod.html">mxnet.np.divmod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.floor_divide.html">mxnet.np.floor_divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.clip.html">mxnet.np.clip</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sqrt.html">mxnet.np.sqrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cbrt.html">mxnet.np.cbrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.square.html">mxnet.np.square</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.absolute.html">mxnet.np.absolute</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sign.html">mxnet.np.sign</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.maximum.html">mxnet.np.maximum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.minimum.html">mxnet.np.minimum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fabs.html">mxnet.np.fabs</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.heaviside.html">mxnet.np.heaviside</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fmax.html">mxnet.np.fmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fmin.html">mxnet.np.fmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nan_to_num.html">mxnet.np.nan_to_num</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.interp.html">mxnet.np.interp</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/random/index.html">np.random</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.choice.html">mxnet.np.random.choice</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.shuffle.html">mxnet.np.random.shuffle</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.normal.html">mxnet.np.random.normal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.uniform.html">mxnet.np.random.uniform</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.rand.html">mxnet.np.random.rand</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.randint.html">mxnet.np.random.randint</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.beta.html">mxnet.np.random.beta</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.chisquare.html">mxnet.np.random.chisquare</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.exponential.html">mxnet.np.random.exponential</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.f.html">mxnet.np.random.f</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.gamma.html">mxnet.np.random.gamma</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.gumbel.html">mxnet.np.random.gumbel</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.laplace.html">mxnet.np.random.laplace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.logistic.html">mxnet.np.random.logistic</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.lognormal.html">mxnet.np.random.lognormal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.multinomial.html">mxnet.np.random.multinomial</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.multivariate_normal.html">mxnet.np.random.multivariate_normal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.pareto.html">mxnet.np.random.pareto</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.power.html">mxnet.np.random.power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.rayleigh.html">mxnet.np.random.rayleigh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.weibull.html">mxnet.np.random.weibull</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.sort.html">Sorting, searching, and counting</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.sort.html">mxnet.np.ndarray.sort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sort.html">mxnet.np.sort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.lexsort.html">mxnet.np.lexsort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argsort.html">mxnet.np.argsort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.msort.html">mxnet.np.msort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.partition.html">mxnet.np.partition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argpartition.html">mxnet.np.argpartition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argmax.html">mxnet.np.argmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argmin.html">mxnet.np.argmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanargmax.html">mxnet.np.nanargmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanargmin.html">mxnet.np.nanargmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argwhere.html">mxnet.np.argwhere</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nonzero.html">mxnet.np.nonzero</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flatnonzero.html">mxnet.np.flatnonzero</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.where.html">mxnet.np.where</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.searchsorted.html">mxnet.np.searchsorted</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.extract.html">mxnet.np.extract</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.count_nonzero.html">mxnet.np.count_nonzero</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.statistics.html">Statistics</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.min.html">mxnet.np.min</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.max.html">mxnet.np.max</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.amin.html">mxnet.np.amin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.amax.html">mxnet.np.amax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanmin.html">mxnet.np.nanmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanmax.html">mxnet.np.nanmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ptp.html">mxnet.np.ptp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.percentile.html">mxnet.np.percentile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanpercentile.html">mxnet.np.nanpercentile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.quantile.html">mxnet.np.quantile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanquantile.html">mxnet.np.nanquantile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.mean.html">mxnet.np.mean</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.std.html">mxnet.np.std</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.var.html">mxnet.np.var</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.median.html">mxnet.np.median</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.average.html">mxnet.np.average</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanmedian.html">mxnet.np.nanmedian</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanstd.html">mxnet.np.nanstd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanvar.html">mxnet.np.nanvar</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.corrcoef.html">mxnet.np.corrcoef</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.correlate.html">mxnet.np.correlate</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cov.html">mxnet.np.cov</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.histogram.html">mxnet.np.histogram</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.histogram2d.html">mxnet.np.histogram2d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.histogramdd.html">mxnet.np.histogramdd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.bincount.html">mxnet.np.bincount</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.histogram_bin_edges.html">mxnet.np.histogram_bin_edges</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.digitize.html">mxnet.np.digitize</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/npx/index.html">NPX: NumPy Neural Network Extension</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.set_np.html">mxnet.npx.set_np</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.reset_np.html">mxnet.npx.reset_np</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.cpu.html">mxnet.npx.cpu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.cpu_pinned.html">mxnet.npx.cpu_pinned</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.gpu.html">mxnet.npx.gpu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.gpu_memory_info.html">mxnet.npx.gpu_memory_info</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.current_device.html">mxnet.npx.current_device</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.num_gpus.html">mxnet.npx.num_gpus</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.activation.html">mxnet.npx.activation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.batch_norm.html">mxnet.npx.batch_norm</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.convolution.html">mxnet.npx.convolution</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.dropout.html">mxnet.npx.dropout</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.embedding.html">mxnet.npx.embedding</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.fully_connected.html">mxnet.npx.fully_connected</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.layer_norm.html">mxnet.npx.layer_norm</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.pooling.html">mxnet.npx.pooling</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.rnn.html">mxnet.npx.rnn</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.leaky_relu.html">mxnet.npx.leaky_relu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.multibox_detection.html">mxnet.npx.multibox_detection</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.multibox_prior.html">mxnet.npx.multibox_prior</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.multibox_target.html">mxnet.npx.multibox_target</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.roi_pooling.html">mxnet.npx.roi_pooling</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.sigmoid.html">mxnet.npx.sigmoid</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.relu.html">mxnet.npx.relu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.smooth_l1.html">mxnet.npx.smooth_l1</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.softmax.html">mxnet.npx.softmax</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.log_softmax.html">mxnet.npx.log_softmax</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.topk.html">mxnet.npx.topk</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.waitall.html">mxnet.npx.waitall</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.load.html">mxnet.npx.load</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.save.html">mxnet.npx.save</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.one_hot.html">mxnet.npx.one_hot</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.pick.html">mxnet.npx.pick</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.reshape_like.html">mxnet.npx.reshape_like</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.batch_flatten.html">mxnet.npx.batch_flatten</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.batch_dot.html">mxnet.npx.batch_dot</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.gamma.html">mxnet.npx.gamma</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.sequence_mask.html">mxnet.npx.sequence_mask</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/gluon/index.html">mxnet.gluon</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/block.html">gluon.Block</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/hybrid_block.html">gluon.HybridBlock</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/symbol_block.html">gluon.SymbolBlock</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/constant.html">gluon.Constant</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/parameter.html">gluon.Parameter</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/trainer.html">gluon.Trainer</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/contrib/index.html">gluon.contrib</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/data/index.html">gluon.data</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/data/vision/index.html">data.vision</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/gluon/data/vision/datasets/index.html">vision.datasets</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/gluon/data/vision/transforms/index.html">vision.transforms</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/loss/index.html">gluon.loss</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/metric/index.html">gluon.metric</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/model_zoo/index.html">gluon.model_zoo.vision</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/nn/index.html">gluon.nn</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/rnn/index.html">gluon.rnn</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/utils/index.html">gluon.utils</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/autograd/index.html">mxnet.autograd</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/initializer/index.html">mxnet.initializer</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/optimizer/index.html">mxnet.optimizer</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/lr_scheduler/index.html">mxnet.lr_scheduler</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/kvstore/index.html">KVStore: Communication for Distributed Training</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/kvstore/index.html#horovod">Horovod</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/kvstore/generated/mxnet.kvstore.Horovod.html">mxnet.kvstore.Horovod</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/kvstore/index.html#byteps">BytePS</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/kvstore/generated/mxnet.kvstore.BytePS.html">mxnet.kvstore.BytePS</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/kvstore/index.html#kvstore-interface">KVStore Interface</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/kvstore/generated/mxnet.kvstore.KVStore.html">mxnet.kvstore.KVStore</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/kvstore/generated/mxnet.kvstore.KVStoreBase.html">mxnet.kvstore.KVStoreBase</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/kvstore/generated/mxnet.kvstore.KVStoreServer.html">mxnet.kvstore.KVStoreServer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/contrib/index.html">mxnet.contrib</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/io/index.html">contrib.io</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/ndarray/index.html">contrib.ndarray</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/onnx/index.html">contrib.onnx</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/quantization/index.html">contrib.quantization</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/symbol/index.html">contrib.symbol</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/tensorboard/index.html">contrib.tensorboard</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/tensorrt/index.html">contrib.tensorrt</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/text/index.html">contrib.text</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/legacy/callback/index.html">mxnet.callback</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/legacy/image/index.html">mxnet.image</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/legacy/io/index.html">mxnet.io</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/legacy/ndarray/index.html">mxnet.ndarray</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/ndarray.html">ndarray</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/contrib/index.html">ndarray.contrib</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/image/index.html">ndarray.image</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/linalg/index.html">ndarray.linalg</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/op/index.html">ndarray.op</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/random/index.html">ndarray.random</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/register/index.html">ndarray.register</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/sparse/index.html">ndarray.sparse</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/utils/index.html">ndarray.utils</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/legacy/recordio/index.html">mxnet.recordio</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/legacy/symbol/index.html">mxnet.symbol</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/symbol.html">symbol</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/contrib/index.html">symbol.contrib</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/image/index.html">symbol.image</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/linalg/index.html">symbol.linalg</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/op/index.html">symbol.op</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/random/index.html">symbol.random</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/register/index.html">symbol.register</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/sparse/index.html">symbol.sparse</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/legacy/visualization/index.html">mxnet.visualization</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/device/index.html">mxnet.device</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/engine/index.html">mxnet.engine</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/executor/index.html">mxnet.executor</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/kvstore_server/index.html">mxnet.kvstore_server</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/profiler/index.html">mxnet.profiler</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/rtc/index.html">mxnet.rtc</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/runtime/index.html">mxnet.runtime</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/runtime/generated/mxnet.runtime.Feature.html">mxnet.runtime.Feature</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/runtime/generated/mxnet.runtime.Features.html">mxnet.runtime.Features</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/runtime/generated/mxnet.runtime.feature_list.html">mxnet.runtime.feature_list</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/test_utils/index.html">mxnet.test_utils</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/util/index.html">mxnet.util</a></li> |
| </ul> |
| </li> |
| </ul> |
| |
| </nav> |
| |
| </div> |
| |
| </header> |
| <main class="mdl-layout__content" tabIndex="0"> |
| <header class="mdl-layout__drawer"> |
| |
| <div class="globaltoc"> |
| <span class="mdl-layout-title toc">Table Of Contents</span> |
| |
| |
| |
| <nav class="mdl-navigation"> |
| <ul class="current"> |
| <li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Python Tutorials</a><ul class="current"> |
| <li class="toctree-l2 current"><a class="reference internal" href="../index.html">Getting Started</a><ul class="current"> |
| <li class="toctree-l3 current"><a class="reference internal" href="index.html">Crash Course</a><ul class="current"> |
| <li class="toctree-l4"><a class="reference internal" href="0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="5-datasets.html">Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="5-datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="5-datasets.html#Using-your-own-data-with-custom-Datasets">Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">New in MXNet 2.0: faster C++ backend dataloaders</a></li> |
| <li class="toctree-l4 current"><a class="current reference internal" href="#">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/np/np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/optimizer/index.html">Optimizers</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/viz/index.html">Visualization</a><ul> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../performance/index.html">Performance</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../performance/compression/index.html">Compression</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../performance/compression/int8.html">Deploy with int-8</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/float16">Float16</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../performance/backend/index.html">Accelerated Backend Tools</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../performance/backend/dnnl/index.html">oneDNN</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../performance/backend/dnnl/dnnl_readme.html">Install MXNet with oneDNN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../performance/backend/dnnl/dnnl_quantization.html">oneDNN Quantization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../performance/backend/dnnl/dnnl_quantization_inc.html">Improving accuracy with Intel® Neural Compressor</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../performance/backend/tvm.html">Use TVM</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../performance/backend/profiler.html">Profiling MXNet Models</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../performance/backend/amp.html">Using AMP: Automatic Mixed Precision</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../deploy/index.html">Deployment</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../deploy/export/index.html">Export</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../deploy/export/onnx.html">Exporting to ONNX format</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/export_network.html">Export Gluon CV Models</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Save / Load Parameters</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../deploy/inference/index.html">Inference</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../deploy/inference/cpp.html">Deploy into C++</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../deploy/inference/image_classification_jetson.html">Image Classication using pretrained ResNet-50 model on Jetson module</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../deploy/run-on-aws/index.html">Run on AWS</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../deploy/run-on-aws/use_ec2.html">Run on an EC2 Instance</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../deploy/run-on-aws/use_sagemaker.html">Run on Amazon SageMaker</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../deploy/run-on-aws/cloud.html">MXNet on the Cloud</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../extend/index.html">Extend</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../extend/customop.html">Custom Numpy Operators</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/new_op">New Operator Creation</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/add_op_in_backend">New Operator in MXNet Backend</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/using_rtc">Using RTC for CUDA kernels</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l1"><a class="reference internal" href="../../../api/index.html">Python API</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/np/index.html">mxnet.np</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/np/arrays.html">Array objects</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/arrays.ndarray.html">The N-dimensional array (<code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code>)</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/arrays.indexing.html">Indexing</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/np/routines.html">Routines</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.array-creation.html">Array creation routines</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.eye.html">mxnet.np.eye</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.empty.html">mxnet.np.empty</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.full.html">mxnet.np.full</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.identity.html">mxnet.np.identity</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ones.html">mxnet.np.ones</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ones_like.html">mxnet.np.ones_like</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.zeros.html">mxnet.np.zeros</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.zeros_like.html">mxnet.np.zeros_like</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.array.html">mxnet.np.array</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.copy.html">mxnet.np.copy</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arange.html">mxnet.np.arange</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linspace.html">mxnet.np.linspace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.logspace.html">mxnet.np.logspace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.meshgrid.html">mxnet.np.meshgrid</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tril.html">mxnet.np.tril</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.array-manipulation.html">Array manipulation routines</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.reshape.html">mxnet.np.reshape</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ravel.html">mxnet.np.ravel</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.flatten.html">mxnet.np.ndarray.flatten</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.swapaxes.html">mxnet.np.swapaxes</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.T.html">mxnet.np.ndarray.T</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.transpose.html">mxnet.np.transpose</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.moveaxis.html">mxnet.np.moveaxis</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rollaxis.html">mxnet.np.rollaxis</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.expand_dims.html">mxnet.np.expand_dims</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.squeeze.html">mxnet.np.squeeze</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.broadcast_to.html">mxnet.np.broadcast_to</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.broadcast_arrays.html">mxnet.np.broadcast_arrays</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.atleast_1d.html">mxnet.np.atleast_1d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.atleast_2d.html">mxnet.np.atleast_2d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.atleast_3d.html">mxnet.np.atleast_3d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.concatenate.html">mxnet.np.concatenate</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.stack.html">mxnet.np.stack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dstack.html">mxnet.np.dstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vstack.html">mxnet.np.vstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.column_stack.html">mxnet.np.column_stack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hstack.html">mxnet.np.hstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.split.html">mxnet.np.split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hsplit.html">mxnet.np.hsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vsplit.html">mxnet.np.vsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.array_split.html">mxnet.np.array_split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dsplit.html">mxnet.np.dsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tile.html">mxnet.np.tile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.repeat.html">mxnet.np.repeat</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.unique.html">mxnet.np.unique</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.delete.html">mxnet.np.delete</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.insert.html">mxnet.np.insert</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.append.html">mxnet.np.append</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.resize.html">mxnet.np.resize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trim_zeros.html">mxnet.np.trim_zeros</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.reshape.html">mxnet.np.reshape</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flip.html">mxnet.np.flip</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.roll.html">mxnet.np.roll</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rot90.html">mxnet.np.rot90</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fliplr.html">mxnet.np.fliplr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flipud.html">mxnet.np.flipud</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.io.html">Input and output</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.genfromtxt.html">mxnet.np.genfromtxt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.tolist.html">mxnet.np.ndarray.tolist</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.set_printoptions.html">mxnet.np.set_printoptions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dot.html">mxnet.np.dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vdot.html">mxnet.np.vdot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.inner.html">mxnet.np.inner</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.outer.html">mxnet.np.outer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tensordot.html">mxnet.np.tensordot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.einsum.html">mxnet.np.einsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.multi_dot.html">mxnet.np.linalg.multi_dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.matmul.html">mxnet.np.matmul</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_power.html">mxnet.np.linalg.matrix_power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.kron.html">mxnet.np.kron</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.svd.html">mxnet.np.linalg.svd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cholesky.html">mxnet.np.linalg.cholesky</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.qr.html">mxnet.np.linalg.qr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eig.html">mxnet.np.linalg.eig</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigh.html">mxnet.np.linalg.eigh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvals.html">mxnet.np.linalg.eigvals</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvalsh.html">mxnet.np.linalg.eigvalsh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.norm.html">mxnet.np.linalg.norm</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trace.html">mxnet.np.trace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cond.html">mxnet.np.linalg.cond</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.det.html">mxnet.np.linalg.det</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_rank.html">mxnet.np.linalg.matrix_rank</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.slogdet.html">mxnet.np.linalg.slogdet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.solve.html">mxnet.np.linalg.solve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorsolve.html">mxnet.np.linalg.tensorsolve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.lstsq.html">mxnet.np.linalg.lstsq</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.inv.html">mxnet.np.linalg.inv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.pinv.html">mxnet.np.linalg.pinv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorinv.html">mxnet.np.linalg.tensorinv</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.math.html">Mathematical functions</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sin.html">mxnet.np.sin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cos.html">mxnet.np.cos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tan.html">mxnet.np.tan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arcsin.html">mxnet.np.arcsin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arccos.html">mxnet.np.arccos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan.html">mxnet.np.arctan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.degrees.html">mxnet.np.degrees</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.radians.html">mxnet.np.radians</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hypot.html">mxnet.np.hypot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan2.html">mxnet.np.arctan2</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.deg2rad.html">mxnet.np.deg2rad</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rad2deg.html">mxnet.np.rad2deg</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.unwrap.html">mxnet.np.unwrap</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sinh.html">mxnet.np.sinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cosh.html">mxnet.np.cosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tanh.html">mxnet.np.tanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arcsinh.html">mxnet.np.arcsinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arccosh.html">mxnet.np.arccosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctanh.html">mxnet.np.arctanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rint.html">mxnet.np.rint</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fix.html">mxnet.np.fix</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.floor.html">mxnet.np.floor</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ceil.html">mxnet.np.ceil</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trunc.html">mxnet.np.trunc</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.around.html">mxnet.np.around</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.round_.html">mxnet.np.round_</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sum.html">mxnet.np.sum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.prod.html">mxnet.np.prod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cumsum.html">mxnet.np.cumsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanprod.html">mxnet.np.nanprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nansum.html">mxnet.np.nansum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cumprod.html">mxnet.np.cumprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nancumprod.html">mxnet.np.nancumprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nancumsum.html">mxnet.np.nancumsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.diff.html">mxnet.np.diff</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ediff1d.html">mxnet.np.ediff1d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cross.html">mxnet.np.cross</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trapz.html">mxnet.np.trapz</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.exp.html">mxnet.np.exp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.expm1.html">mxnet.np.expm1</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log.html">mxnet.np.log</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log10.html">mxnet.np.log10</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log2.html">mxnet.np.log2</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log1p.html">mxnet.np.log1p</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.logaddexp.html">mxnet.np.logaddexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.i0.html">mxnet.np.i0</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ldexp.html">mxnet.np.ldexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.signbit.html">mxnet.np.signbit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.copysign.html">mxnet.np.copysign</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.frexp.html">mxnet.np.frexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.spacing.html">mxnet.np.spacing</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.lcm.html">mxnet.np.lcm</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.gcd.html">mxnet.np.gcd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.add.html">mxnet.np.add</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.reciprocal.html">mxnet.np.reciprocal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.negative.html">mxnet.np.negative</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.divide.html">mxnet.np.divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.power.html">mxnet.np.power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.subtract.html">mxnet.np.subtract</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.mod.html">mxnet.np.mod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.multiply.html">mxnet.np.multiply</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.true_divide.html">mxnet.np.true_divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.remainder.html">mxnet.np.remainder</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.positive.html">mxnet.np.positive</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.float_power.html">mxnet.np.float_power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fmod.html">mxnet.np.fmod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.modf.html">mxnet.np.modf</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.divmod.html">mxnet.np.divmod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.floor_divide.html">mxnet.np.floor_divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.clip.html">mxnet.np.clip</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sqrt.html">mxnet.np.sqrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cbrt.html">mxnet.np.cbrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.square.html">mxnet.np.square</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.absolute.html">mxnet.np.absolute</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sign.html">mxnet.np.sign</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.maximum.html">mxnet.np.maximum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.minimum.html">mxnet.np.minimum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fabs.html">mxnet.np.fabs</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.heaviside.html">mxnet.np.heaviside</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fmax.html">mxnet.np.fmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fmin.html">mxnet.np.fmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nan_to_num.html">mxnet.np.nan_to_num</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.interp.html">mxnet.np.interp</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/random/index.html">np.random</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.choice.html">mxnet.np.random.choice</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.shuffle.html">mxnet.np.random.shuffle</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.normal.html">mxnet.np.random.normal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.uniform.html">mxnet.np.random.uniform</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.rand.html">mxnet.np.random.rand</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.randint.html">mxnet.np.random.randint</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.beta.html">mxnet.np.random.beta</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.chisquare.html">mxnet.np.random.chisquare</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.exponential.html">mxnet.np.random.exponential</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.f.html">mxnet.np.random.f</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.gamma.html">mxnet.np.random.gamma</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.gumbel.html">mxnet.np.random.gumbel</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.laplace.html">mxnet.np.random.laplace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.logistic.html">mxnet.np.random.logistic</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.lognormal.html">mxnet.np.random.lognormal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.multinomial.html">mxnet.np.random.multinomial</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.multivariate_normal.html">mxnet.np.random.multivariate_normal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.pareto.html">mxnet.np.random.pareto</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.power.html">mxnet.np.random.power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.rayleigh.html">mxnet.np.random.rayleigh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.weibull.html">mxnet.np.random.weibull</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.sort.html">Sorting, searching, and counting</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.sort.html">mxnet.np.ndarray.sort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sort.html">mxnet.np.sort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.lexsort.html">mxnet.np.lexsort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argsort.html">mxnet.np.argsort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.msort.html">mxnet.np.msort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.partition.html">mxnet.np.partition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argpartition.html">mxnet.np.argpartition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argmax.html">mxnet.np.argmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argmin.html">mxnet.np.argmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanargmax.html">mxnet.np.nanargmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanargmin.html">mxnet.np.nanargmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argwhere.html">mxnet.np.argwhere</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nonzero.html">mxnet.np.nonzero</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flatnonzero.html">mxnet.np.flatnonzero</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.where.html">mxnet.np.where</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.searchsorted.html">mxnet.np.searchsorted</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.extract.html">mxnet.np.extract</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.count_nonzero.html">mxnet.np.count_nonzero</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.statistics.html">Statistics</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.min.html">mxnet.np.min</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.max.html">mxnet.np.max</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.amin.html">mxnet.np.amin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.amax.html">mxnet.np.amax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanmin.html">mxnet.np.nanmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanmax.html">mxnet.np.nanmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ptp.html">mxnet.np.ptp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.percentile.html">mxnet.np.percentile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanpercentile.html">mxnet.np.nanpercentile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.quantile.html">mxnet.np.quantile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanquantile.html">mxnet.np.nanquantile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.mean.html">mxnet.np.mean</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.std.html">mxnet.np.std</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.var.html">mxnet.np.var</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.median.html">mxnet.np.median</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.average.html">mxnet.np.average</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanmedian.html">mxnet.np.nanmedian</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanstd.html">mxnet.np.nanstd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanvar.html">mxnet.np.nanvar</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.corrcoef.html">mxnet.np.corrcoef</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.correlate.html">mxnet.np.correlate</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cov.html">mxnet.np.cov</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.histogram.html">mxnet.np.histogram</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.histogram2d.html">mxnet.np.histogram2d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.histogramdd.html">mxnet.np.histogramdd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.bincount.html">mxnet.np.bincount</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.histogram_bin_edges.html">mxnet.np.histogram_bin_edges</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.digitize.html">mxnet.np.digitize</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/npx/index.html">NPX: NumPy Neural Network Extension</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.set_np.html">mxnet.npx.set_np</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.reset_np.html">mxnet.npx.reset_np</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.cpu.html">mxnet.npx.cpu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.cpu_pinned.html">mxnet.npx.cpu_pinned</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.gpu.html">mxnet.npx.gpu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.gpu_memory_info.html">mxnet.npx.gpu_memory_info</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.current_device.html">mxnet.npx.current_device</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.num_gpus.html">mxnet.npx.num_gpus</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.activation.html">mxnet.npx.activation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.batch_norm.html">mxnet.npx.batch_norm</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.convolution.html">mxnet.npx.convolution</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.dropout.html">mxnet.npx.dropout</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.embedding.html">mxnet.npx.embedding</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.fully_connected.html">mxnet.npx.fully_connected</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.layer_norm.html">mxnet.npx.layer_norm</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.pooling.html">mxnet.npx.pooling</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.rnn.html">mxnet.npx.rnn</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.leaky_relu.html">mxnet.npx.leaky_relu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.multibox_detection.html">mxnet.npx.multibox_detection</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.multibox_prior.html">mxnet.npx.multibox_prior</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.multibox_target.html">mxnet.npx.multibox_target</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.roi_pooling.html">mxnet.npx.roi_pooling</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.sigmoid.html">mxnet.npx.sigmoid</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.relu.html">mxnet.npx.relu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.smooth_l1.html">mxnet.npx.smooth_l1</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.softmax.html">mxnet.npx.softmax</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.log_softmax.html">mxnet.npx.log_softmax</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.topk.html">mxnet.npx.topk</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.waitall.html">mxnet.npx.waitall</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.load.html">mxnet.npx.load</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.save.html">mxnet.npx.save</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.one_hot.html">mxnet.npx.one_hot</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.pick.html">mxnet.npx.pick</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.reshape_like.html">mxnet.npx.reshape_like</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.batch_flatten.html">mxnet.npx.batch_flatten</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.batch_dot.html">mxnet.npx.batch_dot</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.gamma.html">mxnet.npx.gamma</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.sequence_mask.html">mxnet.npx.sequence_mask</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/gluon/index.html">mxnet.gluon</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/block.html">gluon.Block</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/hybrid_block.html">gluon.HybridBlock</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/symbol_block.html">gluon.SymbolBlock</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/constant.html">gluon.Constant</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/parameter.html">gluon.Parameter</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/trainer.html">gluon.Trainer</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/contrib/index.html">gluon.contrib</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/data/index.html">gluon.data</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/gluon/data/vision/index.html">data.vision</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/gluon/data/vision/datasets/index.html">vision.datasets</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/gluon/data/vision/transforms/index.html">vision.transforms</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/loss/index.html">gluon.loss</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/metric/index.html">gluon.metric</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/model_zoo/index.html">gluon.model_zoo.vision</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/nn/index.html">gluon.nn</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/rnn/index.html">gluon.rnn</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/gluon/utils/index.html">gluon.utils</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/autograd/index.html">mxnet.autograd</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/initializer/index.html">mxnet.initializer</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/optimizer/index.html">mxnet.optimizer</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/lr_scheduler/index.html">mxnet.lr_scheduler</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/kvstore/index.html">KVStore: Communication for Distributed Training</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/kvstore/index.html#horovod">Horovod</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/kvstore/generated/mxnet.kvstore.Horovod.html">mxnet.kvstore.Horovod</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/kvstore/index.html#byteps">BytePS</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/kvstore/generated/mxnet.kvstore.BytePS.html">mxnet.kvstore.BytePS</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/kvstore/index.html#kvstore-interface">KVStore Interface</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/kvstore/generated/mxnet.kvstore.KVStore.html">mxnet.kvstore.KVStore</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/kvstore/generated/mxnet.kvstore.KVStoreBase.html">mxnet.kvstore.KVStoreBase</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/kvstore/generated/mxnet.kvstore.KVStoreServer.html">mxnet.kvstore.KVStoreServer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/contrib/index.html">mxnet.contrib</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/io/index.html">contrib.io</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/ndarray/index.html">contrib.ndarray</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/onnx/index.html">contrib.onnx</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/quantization/index.html">contrib.quantization</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/symbol/index.html">contrib.symbol</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/tensorboard/index.html">contrib.tensorboard</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/tensorrt/index.html">contrib.tensorrt</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/contrib/text/index.html">contrib.text</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/legacy/callback/index.html">mxnet.callback</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/legacy/image/index.html">mxnet.image</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/legacy/io/index.html">mxnet.io</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/legacy/ndarray/index.html">mxnet.ndarray</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/ndarray.html">ndarray</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/contrib/index.html">ndarray.contrib</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/image/index.html">ndarray.image</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/linalg/index.html">ndarray.linalg</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/op/index.html">ndarray.op</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/random/index.html">ndarray.random</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/register/index.html">ndarray.register</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/sparse/index.html">ndarray.sparse</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/ndarray/utils/index.html">ndarray.utils</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/legacy/recordio/index.html">mxnet.recordio</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/legacy/symbol/index.html">mxnet.symbol</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/symbol.html">symbol</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/contrib/index.html">symbol.contrib</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/image/index.html">symbol.image</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/linalg/index.html">symbol.linalg</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/op/index.html">symbol.op</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/random/index.html">symbol.random</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/register/index.html">symbol.register</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/legacy/symbol/sparse/index.html">symbol.sparse</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/legacy/visualization/index.html">mxnet.visualization</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/device/index.html">mxnet.device</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/engine/index.html">mxnet.engine</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/executor/index.html">mxnet.executor</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/kvstore_server/index.html">mxnet.kvstore_server</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/profiler/index.html">mxnet.profiler</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/rtc/index.html">mxnet.rtc</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/runtime/index.html">mxnet.runtime</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/runtime/generated/mxnet.runtime.Feature.html">mxnet.runtime.Feature</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/runtime/generated/mxnet.runtime.Features.html">mxnet.runtime.Features</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/runtime/generated/mxnet.runtime.feature_list.html">mxnet.runtime.feature_list</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/test_utils/index.html">mxnet.test_utils</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/util/index.html">mxnet.util</a></li> |
| </ul> |
| </li> |
| </ul> |
| |
| </nav> |
| |
| </div> |
| |
| </header> |
| |
| <div class="document"> |
| <div class="page-content" role="main"> |
| |
| |
| <style> |
| /* CSS for nbsphinx extension */ |
| |
| /* remove conflicting styling from Sphinx themes */ |
| div.nbinput, |
| div.nbinput div.prompt, |
| div.nbinput div.input_area, |
| div.nbinput div[class*=highlight], |
| div.nbinput div[class*=highlight] pre, |
| div.nboutput, |
| div.nbinput div.prompt, |
| div.nbinput div.output_area, |
| div.nboutput div[class*=highlight], |
| div.nboutput div[class*=highlight] pre { |
| background: none; |
| border: none; |
| padding: 0 0; |
| margin: 0; |
| box-shadow: none; |
| } |
| |
| /* avoid gaps between output lines */ |
| div.nboutput div[class*=highlight] pre { |
| line-height: normal; |
| } |
| |
| /* input/output containers */ |
| div.nbinput, |
| div.nboutput { |
| display: -webkit-flex; |
| display: flex; |
| align-items: flex-start; |
| margin: 0; |
| width: 100%; |
| } |
| @media (max-width: 540px) { |
| div.nbinput, |
| div.nboutput { |
| flex-direction: column; |
| } |
| } |
| |
| /* input container */ |
| div.nbinput { |
| padding-top: 5px; |
| } |
| |
| /* last container */ |
| div.nblast { |
| padding-bottom: 5px; |
| } |
| |
| /* input prompt */ |
| div.nbinput div.prompt pre { |
| color: #307FC1; |
| } |
| |
| /* output prompt */ |
| div.nboutput div.prompt pre { |
| color: #BF5B3D; |
| } |
| |
| /* all prompts */ |
| div.nbinput div.prompt, |
| div.nboutput div.prompt { |
| min-width: 7ex; |
| padding-top: 0.4em; |
| padding-right: 0.4em; |
| text-align: right; |
| flex: 0; |
| } |
| @media (max-width: 540px) { |
| div.nbinput div.prompt, |
| div.nboutput div.prompt { |
| text-align: left; |
| padding: 0.4em; |
| } |
| div.nboutput div.prompt.empty { |
| padding: 0; |
| } |
| } |
| |
| /* disable scrollbars on prompts */ |
| div.nbinput div.prompt pre, |
| div.nboutput div.prompt pre { |
| overflow: hidden; |
| } |
| |
| /* input/output area */ |
| div.nbinput div.input_area, |
| div.nboutput div.output_area { |
| padding: 0.4em; |
| -webkit-flex: 1; |
| flex: 1; |
| overflow: auto; |
| } |
| @media (max-width: 540px) { |
| div.nbinput div.input_area, |
| div.nboutput div.output_area { |
| width: 100%; |
| } |
| } |
| |
| /* input area */ |
| div.nbinput div.input_area { |
| border: 1px solid #e0e0e0; |
| border-radius: 2px; |
| background: #f5f5f5; |
| } |
| |
| /* override MathJax center alignment in output cells */ |
| div.nboutput div[class*=MathJax] { |
| text-align: left !important; |
| } |
| |
| /* override sphinx.ext.imgmath center alignment in output cells */ |
| div.nboutput div.math p { |
| text-align: left; |
| } |
| |
| /* standard error */ |
| div.nboutput div.output_area.stderr { |
| background: #fdd; |
| } |
| |
| /* ANSI colors */ |
| .ansi-black-fg { color: #3E424D; } |
| .ansi-black-bg { background-color: #3E424D; } |
| .ansi-black-intense-fg { color: #282C36; } |
| .ansi-black-intense-bg { background-color: #282C36; } |
| .ansi-red-fg { color: #E75C58; } |
| .ansi-red-bg { background-color: #E75C58; } |
| .ansi-red-intense-fg { color: #B22B31; } |
| .ansi-red-intense-bg { background-color: #B22B31; } |
| .ansi-green-fg { color: #00A250; } |
| .ansi-green-bg { background-color: #00A250; } |
| .ansi-green-intense-fg { color: #007427; } |
| .ansi-green-intense-bg { background-color: #007427; } |
| .ansi-yellow-fg { color: #DDB62B; } |
| .ansi-yellow-bg { background-color: #DDB62B; } |
| .ansi-yellow-intense-fg { color: #B27D12; } |
| .ansi-yellow-intense-bg { background-color: #B27D12; } |
| .ansi-blue-fg { color: #208FFB; } |
| .ansi-blue-bg { background-color: #208FFB; } |
| .ansi-blue-intense-fg { color: #0065CA; } |
| .ansi-blue-intense-bg { background-color: #0065CA; } |
| .ansi-magenta-fg { color: #D160C4; } |
| .ansi-magenta-bg { background-color: #D160C4; } |
| .ansi-magenta-intense-fg { color: #A03196; } |
| .ansi-magenta-intense-bg { background-color: #A03196; } |
| .ansi-cyan-fg { color: #60C6C8; } |
| .ansi-cyan-bg { background-color: #60C6C8; } |
| .ansi-cyan-intense-fg { color: #258F8F; } |
| .ansi-cyan-intense-bg { background-color: #258F8F; } |
| .ansi-white-fg { color: #C5C1B4; } |
| .ansi-white-bg { background-color: #C5C1B4; } |
| .ansi-white-intense-fg { color: #A1A6B2; } |
| .ansi-white-intense-bg { background-color: #A1A6B2; } |
| |
| .ansi-default-inverse-fg { color: #FFFFFF; } |
| .ansi-default-inverse-bg { background-color: #000000; } |
| |
| .ansi-bold { font-weight: bold; } |
| .ansi-underline { text-decoration: underline; } |
| |
| /* Some additional styling taken form the Jupyter notebook CSS */ |
| div.rendered_html table { |
| border: none; |
| border-collapse: collapse; |
| border-spacing: 0; |
| color: black; |
| font-size: 12px; |
| table-layout: fixed; |
| } |
| div.rendered_html thead { |
| border-bottom: 1px solid black; |
| vertical-align: bottom; |
| } |
| div.rendered_html tr, |
| div.rendered_html th, |
| div.rendered_html td { |
| text-align: right; |
| vertical-align: middle; |
| padding: 0.5em 0.5em; |
| line-height: normal; |
| white-space: normal; |
| max-width: none; |
| border: none; |
| } |
| div.rendered_html th { |
| font-weight: bold; |
| } |
| div.rendered_html tbody tr:nth-child(odd) { |
| background: #f5f5f5; |
| } |
| div.rendered_html tbody tr:hover { |
| background: rgba(66, 165, 245, 0.2); |
| } |
| </style> |
| <!--- Licensed to the Apache Software Foundation (ASF) under one --><!--- or more contributor license agreements. See the NOTICE file --><!--- distributed with this work for additional information --><!--- regarding copyright ownership. The ASF licenses this file --><!--- to you under the Apache License, Version 2.0 (the --><!--- "License"); you may not use this file except in compliance --><!--- with the License. You may obtain a copy of the License at --><!--- http://www.apache.org/licenses/LICENSE-2.0 --><!--- Unless required by applicable law or agreed to in writing, --><!--- software distributed under the License is distributed on an --><!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY --><!--- KIND, either express or implied. See the License for the --><!--- specific language governing permissions and limitations --><!--- under the License. --><div class="section" id="Step-6:-Train-a-Neural-Network"> |
| <h1>Step 6: Train a Neural Network<a class="headerlink" href="#Step-6:-Train-a-Neural-Network" title="Permalink to this headline">¶</a></h1> |
| <p>Now that you have seen all the necessary components for creating a neural network, you are now ready to put all the pieces together and train a model end to end.</p> |
| <div class="section" id="1.-Data-preparation"> |
| <h2>1. Data preparation<a class="headerlink" href="#1.-Data-preparation" title="Permalink to this headline">¶</a></h2> |
| <p>The typical process for creating and training a model starts with loading and preparing the datasets. For this Network you will use a <a class="reference external" href="https://data.mendeley.com/datasets/hb74ynkjcn/1">dataset of leaf images</a> that consists of healthy and diseased examples of leafs from twelve different plant species. To get this dataset you have to download and extract it with the following commands.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># Import all the necessary libraries to train</span> |
| <span class="kn">import</span> <span class="nn">time</span> |
| <span class="kn">import</span> <span class="nn">os</span> |
| <span class="kn">import</span> <span class="nn">zipfile</span> |
| |
| <span class="kn">import</span> <span class="nn">mxnet</span> <span class="k">as</span> <span class="nn">mx</span> |
| <span class="kn">from</span> <span class="nn">mxnet</span> <span class="kn">import</span> <span class="n">np</span><span class="p">,</span> <span class="n">npx</span><span class="p">,</span> <span class="n">gluon</span><span class="p">,</span> <span class="n">init</span><span class="p">,</span> <span class="n">autograd</span> |
| <span class="kn">from</span> <span class="nn">mxnet.gluon</span> <span class="kn">import</span> <span class="n">nn</span> |
| <span class="kn">from</span> <span class="nn">mxnet.gluon.data.vision</span> <span class="kn">import</span> <span class="n">transforms</span> |
| |
| <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span> |
| <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span> |
| <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> |
| |
| <span class="kn">from</span> <span class="nn">prepare_dataset</span> <span class="kn">import</span> <span class="n">process_dataset</span> <span class="c1">#utility code to rearrange the data</span> |
| |
| <span class="n">mx</span><span class="o">.</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># Download dataset</span> |
| <span class="n">url</span> <span class="o">=</span> <span class="s1">'https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/hb74ynkjcn-1.zip'</span> |
| <span class="n">zip_file_path</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">download</span><span class="p">(</span><span class="n">url</span><span class="p">)</span> |
| |
| <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="s1">'plants'</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| |
| <span class="k">with</span> <span class="n">zipfile</span><span class="o">.</span><span class="n">ZipFile</span><span class="p">(</span><span class="n">zip_file_path</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">)</span> <span class="k">as</span> <span class="n">zf</span><span class="p">:</span> |
| <span class="n">zf</span><span class="o">.</span><span class="n">extractall</span><span class="p">(</span><span class="s1">'plants'</span><span class="p">)</span> |
| |
| <span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">zip_file_path</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area docutils container"> |
| <div class="highlight"><pre> |
| Downloading hb74ynkjcn-1.zip from https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/hb74ynkjcn-1.zip... |
| </pre></div></div> |
| </div> |
| <div class="section" id="Data-inspection"> |
| <h3>Data inspection<a class="headerlink" href="#Data-inspection" title="Permalink to this headline">¶</a></h3> |
| <p>If you take a look at the dataset you find the following structure for the directories:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plants</span> |
| <span class="o">|--</span> <span class="n">Alstonia</span> <span class="n">Scholaris</span> <span class="p">(</span><span class="n">P2</span><span class="p">)</span> |
| <span class="o">|--</span> <span class="n">Arjun</span> <span class="p">(</span><span class="n">P1</span><span class="p">)</span> |
| <span class="o">|--</span> <span class="n">Bael</span> <span class="p">(</span><span class="n">P4</span><span class="p">)</span> |
| <span class="o">|--</span> <span class="n">diseased</span> |
| <span class="o">|--</span> <span class="mf">0016_0001.</span><span class="n">JPG</span> |
| <span class="o">|--</span> <span class="o">.</span> |
| <span class="o">|--</span> <span class="o">.</span> |
| <span class="o">|--</span> <span class="o">.</span> |
| <span class="o">|--</span> <span class="mf">0016_0118.</span><span class="n">JPG</span> |
| <span class="o">|--</span> <span class="o">.</span> |
| <span class="o">|--</span> <span class="o">.</span> |
| <span class="o">|--</span> <span class="o">.</span> |
| <span class="o">|--</span> <span class="n">Mango</span> <span class="p">(</span><span class="n">P0</span><span class="p">)</span> |
| <span class="o">|--</span> <span class="n">diseased</span> |
| <span class="o">|--</span> <span class="n">healthy</span> |
| </pre></div> |
| </div> |
| <p>Each plant species has its own directory, for each of those directories you might find subdirectories with examples of diseased leaves, healthy leaves, or both. With this dataset you can formulate different classification problems; for example, you can create a multi-class classifier that determines the species of a plant based on the leaves; you can instead create a binary classifier that tells you whether the plant is healthy or diseased. Additionally, you can create a multi-class, multi-label |
| classifier that tells you both: what species a plant is and whether the plant is diseased or healthy. In this example you will stick to the simplest classification question, which is whether a plant is healthy or not.</p> |
| <p>To do this, you need to manipulate the dataset in two ways. First, you need to combine all images with labels consisting of healthy and diseased, regardless of the species, and then you need to split the data into train, validation, and test sets. We prepared a small utility script that does this to get the dataset ready for you. Once you run this utility code on the data, the structure will be already organized in folders containing the right images in each of the classes, you can use the |
| <code class="docutils literal notranslate"><span class="pre">ImageFolderDataset</span></code> class to import the images from the file to MXNet.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># Call the utility function to rearrange the images</span> |
| <span class="n">process_dataset</span><span class="p">(</span><span class="s1">'plants'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>The dataset is located in the <code class="docutils literal notranslate"><span class="pre">datasets</span></code> folder and the new structure looks like this:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">datasets</span> |
| <span class="o">|--</span> <span class="n">test</span> |
| <span class="o">|--</span> <span class="n">diseased</span> |
| <span class="o">|--</span> <span class="n">healthy</span> |
| <span class="o">|--</span> <span class="n">train</span> |
| <span class="o">|--</span> <span class="n">validation</span> |
| <span class="o">|--</span> <span class="n">diseased</span> |
| <span class="o">|--</span> <span class="n">healthy</span> |
| <span class="o">|--</span> <span class="n">image1</span><span class="o">.</span><span class="n">JPG</span> |
| <span class="o">|--</span> <span class="n">image2</span><span class="o">.</span><span class="n">JPG</span> |
| <span class="o">|--</span> <span class="o">.</span> |
| <span class="o">|--</span> <span class="o">.</span> |
| <span class="o">|--</span> <span class="o">.</span> |
| <span class="o">|--</span> <span class="n">imagen</span><span class="o">.</span><span class="n">JPG</span> |
| </pre></div> |
| </div> |
| <p>Now, you need to create three different Dataset objects from the <code class="docutils literal notranslate"><span class="pre">train</span></code>, <code class="docutils literal notranslate"><span class="pre">validation</span></code>, and <code class="docutils literal notranslate"><span class="pre">test</span></code> folders, and the <code class="docutils literal notranslate"><span class="pre">ImageFolderDataset</span></code> class takes care of inferring the classes from the directory names. If you don’t remember how the <code class="docutils literal notranslate"><span class="pre">ImageFolderDataset</span></code> works, take a look at <a class="reference internal" href="5-datasets.html"><span class="doc">Step 5</span></a> of this course for a deeper description.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># Use ImageFolderDataset to create a Dataset object from directory structure</span> |
| <span class="n">train_dataset</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">ImageFolderDataset</span><span class="p">(</span><span class="s1">'./datasets/train'</span><span class="p">)</span> |
| <span class="n">val_dataset</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">ImageFolderDataset</span><span class="p">(</span><span class="s1">'./datasets/validation'</span><span class="p">)</span> |
| <span class="n">test_dataset</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">ImageFolderDataset</span><span class="p">(</span><span class="s1">'./datasets/test'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>The result from this operation is a different Dataset object for each folder. These objects hold a collection of images and labels and as such they can be indexed, to get the <span class="math notranslate nohighlight">\(i\)</span>-th element from the dataset. The <span class="math notranslate nohighlight">\(i\)</span>-th element is a tuple with two objects, the first object of the tuple is the image in array form and the second is the corresponding label for that image.</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">sample_idx</span> <span class="o">=</span> <span class="mi">888</span> <span class="c1"># choose a random sample</span> |
| <span class="n">sample</span> <span class="o">=</span> <span class="n">train_dataset</span><span class="p">[</span><span class="n">sample_idx</span><span class="p">]</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="n">sample</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">sample</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> |
| |
| <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span> |
| <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Data type: </span><span class="si">{</span><span class="n">data</span><span class="o">.</span><span class="n">dtype</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Label: </span><span class="si">{</span><span class="n">label</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Label description: </span><span class="si">{</span><span class="n">train_dataset</span><span class="o">.</span><span class="n">synsets</span><span class="p">[</span><span class="n">label</span><span class="p">]</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Image shape: </span><span class="si">{</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area stderr docutils container"> |
| <div class="highlight"><pre> |
| [01:56:51] /work/mxnet/src/storage/storage.cc:202: Using Pooled (Naive) StorageManager for CPU |
| </pre></div></div> |
| </div> |
| <div class="nboutput docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area docutils container"> |
| <div class="highlight"><pre> |
| Data type: uint8 |
| Label: 0 |
| Label description: diseased |
| Image shape: (4000, 6000, 3) |
| </pre></div></div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area docutils container"> |
| <img alt="../../../_images/tutorials_getting-started_crash-course_6-train-nn_12_2.png" src="../../../_images/tutorials_getting-started_crash-course_6-train-nn_12_2.png" /> |
| </div> |
| </div> |
| <p>As you can see from the plot, the image size is very large 4000 x 6000 pixels. Usually, you downsize images before passing them to a neural network to reduce the training time. It is also customary to make slight modifications to the images to improve generalization. That is why you add transformations to the data in a process called Data Augmentation.</p> |
| <p>You can augment data in MXNet using <code class="docutils literal notranslate"><span class="pre">transforms</span></code>. For a complete list of all the available transformations in MXNet check out <a class="reference internal" href="../../../api/gluon/data/vision/transforms/index.html"><span class="doc">available transforms</span></a>. It is very common to use more than one transform per image, and it is also common to process transforms sequentially. To this end, you can use the <code class="docutils literal notranslate"><span class="pre">transforms.Compose</span></code> class. This class is very useful to create a transformation pipeline for your images.</p> |
| <p>You have to compose two different transformation pipelines, one for training and the other one for validating and testing. This is because each pipeline serves different pursposes. You need to downsize, convert to tensor and normalize images across all the different datsets; however, you typically do not want to randomly flip or add color jitter to the validation or test images since you could reduce performance.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># Import transforms as compose a series of transformations to the images</span> |
| <span class="kn">from</span> <span class="nn">mxnet.gluon.data.vision</span> <span class="kn">import</span> <span class="n">transforms</span> |
| |
| <span class="n">jitter_param</span> <span class="o">=</span> <span class="mf">0.05</span> |
| |
| <span class="c1"># mean and std for normalizing image value in range (0,1)</span> |
| <span class="n">mean</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">]</span> |
| <span class="n">std</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">]</span> |
| |
| <span class="n">training_transformer</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">224</span><span class="p">,</span> <span class="n">keep_ratio</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">128</span><span class="p">),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">RandomFlipLeftRight</span><span class="p">(),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">RandomColorJitter</span><span class="p">(</span><span class="n">contrast</span><span class="o">=</span><span class="n">jitter_param</span><span class="p">),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">mean</span><span class="p">,</span> <span class="n">std</span><span class="p">)</span> |
| <span class="p">])</span> |
| |
| <span class="n">validation_transformer</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">224</span><span class="p">,</span> <span class="n">keep_ratio</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">128</span><span class="p">),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">mean</span><span class="p">,</span> <span class="n">std</span><span class="p">)</span> |
| <span class="p">])</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>With your augmentations ready, you can create the <code class="docutils literal notranslate"><span class="pre">DataLoaders</span></code> to use them. To do this the <code class="docutils literal notranslate"><span class="pre">gluon.data.DataLoader</span></code> class comes in handy. You have to pass the dataset with the applied transformations (notice the <code class="docutils literal notranslate"><span class="pre">.transform_first()</span></code> method on the datasets) to <code class="docutils literal notranslate"><span class="pre">gluon.data.DataLoader</span></code>. Additionally, you need to decide the batch size, which is how many images you will be passing to the network, and whether you want to shuffle the dataset.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># Create data loaders</span> |
| <span class="n">batch_size</span> <span class="o">=</span> <span class="mi">4</span> |
| <span class="n">train_loader</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">train_dataset</span><span class="o">.</span><span class="n">transform_first</span><span class="p">(</span><span class="n">training_transformer</span><span class="p">),</span> |
| <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> |
| <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">try_nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="n">validation_loader</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">val_dataset</span><span class="o">.</span><span class="n">transform_first</span><span class="p">(</span><span class="n">validation_transformer</span><span class="p">),</span> |
| <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> |
| <span class="n">try_nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="n">test_loader</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">test_dataset</span><span class="o">.</span><span class="n">transform_first</span><span class="p">(</span><span class="n">validation_transformer</span><span class="p">),</span> |
| <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> |
| <span class="n">try_nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>Now, you can inspect the transformations that you made to the images. A prepared utility function has been provided for this.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># Function to plot batch</span> |
| <span class="k">def</span> <span class="nf">show_batch</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">fig_size</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">pad</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span> |
| <span class="n">labels</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="n">batch</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="mi">2</span> <span class="o">+</span> <span class="mf">0.5</span> <span class="c1"># unnormalize</span> |
| <span class="n">batch</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">(),</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="c1"># clip values</span> |
| <span class="n">size</span> <span class="o">=</span> <span class="n">batch</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">rows</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">size</span> <span class="o">/</span> <span class="n">columns</span><span class="p">)</span> |
| <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">columns</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">fig_size</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">img</span><span class="p">,</span> <span class="n">label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">axes</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">batch</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span> |
| <span class="n">ax</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">img</span><span class="p">,</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">0</span><span class="p">)))</span> |
| <span class="n">ax</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="sa">f</span><span class="s2">"Label: </span><span class="si">{</span><span class="n">label</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> |
| <span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">(</span><span class="n">h_pad</span><span class="o">=</span><span class="n">pad</span><span class="p">,</span> <span class="n">w_pad</span><span class="o">=</span><span class="n">pad</span><span class="p">)</span> |
| <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">train_loader</span><span class="p">:</span> |
| <span class="n">a</span> <span class="o">=</span> <span class="n">batch</span> |
| <span class="k">break</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">show_batch</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area docutils container"> |
| <img alt="../../../_images/tutorials_getting-started_crash-course_6-train-nn_20_0.png" src="../../../_images/tutorials_getting-started_crash-course_6-train-nn_20_0.png" /> |
| </div> |
| </div> |
| <p>You can see that the original images changed to have different sizes and variations in color and lighting. These changes followed the specified transformations you stated in the pipeline. You are now ready to go to the next step: <strong>Create the architecture</strong>.</p> |
| </div> |
| </div> |
| <div class="section" id="2.-Create-Neural-Network"> |
| <h2>2. Create Neural Network<a class="headerlink" href="#2.-Create-Neural-Network" title="Permalink to this headline">¶</a></h2> |
| <p>Convolutional neural networks are a great tool to capture the spatial relationship of pixel values within images, for this reason they have become the gold standard for computer vision. In this example you will create a small convolutional neural network using what you learned from <a class="reference internal" href="2-create-nn.html"><span class="doc">Step 2</span></a> of this crash course series. First, you can set up two functions that will generate the two types of blocks you intend to use, the convolution block and the dense block. Then you can create |
| an entire network based on these two blocks using a custom class.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># The convolutional block has a convolution layer, a max pool layer and a batch normalization layer</span> |
| <span class="k">def</span> <span class="nf">conv_block</span><span class="p">(</span><span class="n">filters</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">batch_norm</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> |
| <span class="n">conv_block</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">HybridSequential</span><span class="p">()</span> |
| <span class="n">conv_block</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">channels</span><span class="o">=</span><span class="n">filters</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="n">kernel_size</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">),</span> |
| <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="n">stride</span><span class="p">))</span> |
| <span class="k">if</span> <span class="n">batch_norm</span><span class="p">:</span> |
| <span class="n">conv_block</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm</span><span class="p">())</span> |
| <span class="k">return</span> <span class="n">conv_block</span> |
| |
| <span class="c1"># The dense block consists of a dense layer and a dropout layer</span> |
| <span class="k">def</span> <span class="nf">dense_block</span><span class="p">(</span><span class="n">neurons</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mf">0.2</span><span class="p">):</span> |
| <span class="n">dense_block</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">HybridSequential</span><span class="p">()</span> |
| <span class="n">dense_block</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="n">neurons</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="n">activation</span><span class="p">))</span> |
| <span class="k">if</span> <span class="n">dropout</span><span class="p">:</span> |
| <span class="n">dense_block</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">dropout</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">dense_block</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># Create neural network blueprint using the blocks</span> |
| <span class="k">class</span> <span class="nc">LeafNetwork</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">LeafNetwork</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">conv_block</span><span class="p">(</span><span class="mi">32</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">conv_block</span><span class="p">(</span><span class="mi">64</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">conv3</span> <span class="o">=</span> <span class="n">conv_block</span><span class="p">(</span><span class="mi">128</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Flatten</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">dense1</span> <span class="o">=</span> <span class="n">dense_block</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">dense2</span> <span class="o">=</span> <span class="n">dense_block</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">dense3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">):</span> |
| <span class="n">batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> |
| <span class="n">batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> |
| <span class="n">batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv3</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> |
| <span class="n">batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> |
| <span class="n">batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense1</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> |
| <span class="n">batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense2</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> |
| <span class="n">batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense3</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="n">batch</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>You have concluded the architecting part of the network, so now you can actually build a model from that architecture for training. As you have seen previously on <a class="reference internal" href="4-components.html"><span class="doc">Step 4</span></a> of this crash course series, to use the network you need to initialize the parameters and hybridize the model.</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># Create the model based on the blueprint provided and initialize the parameters</span> |
| <span class="n">device</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">()</span> |
| |
| <span class="n">initializer</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">initializer</span><span class="o">.</span><span class="n">Xavier</span><span class="p">()</span> |
| |
| <span class="n">model</span> <span class="o">=</span> <span class="n">LeafNetwork</span><span class="p">()</span> |
| <span class="n">model</span><span class="o">.</span><span class="n">initialize</span><span class="p">(</span><span class="n">initializer</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span> |
| <span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">))</span> |
| <span class="n">model</span><span class="o">.</span><span class="n">hybridize</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area stderr docutils container"> |
| <div class="highlight"><pre> |
| [ |
| </pre></div></div> |
| </div> |
| <div class="nboutput docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area docutils container"> |
| <div class="highlight"><pre> |
| -------------------------------------------------------------------------------- |
| Layer (type) Output Shape Param # |
| ================================================================================ |
| Input (4, 3, 128, 128) 0 |
| Activation-1 (4, 32, 127, 127) 0 |
| Conv2D-2 (4, 32, 127, 127) 416 |
| MaxPool2D-3 (4, 32, 62, 62) 0 |
| BatchNorm-4 (4, 32, 62, 62) 128 |
| Activation-5 (4, 64, 61, 61) 0 |
| Conv2D-6 (4, 64, 61, 61) 8256 |
| MaxPool2D-7 (4, 64, 29, 29) 0 |
| BatchNorm-8 (4, 64, 29, 29) 256 |
| Activation-9 (4, 128, 28, 28) 0 |
| Conv2D-10 (4, 128, 28, 28) 32896 |
| MaxPool2D-11 (4, 128, 13, 13) 0 |
| BatchNorm-12 (4, 128, 13, 13) 512 |
| Flatten-13 (4, 21632) 0 |
| Activation-14 (4, 100) 0 |
| Dense-15 (4, 100) 2163300 |
| Dropout-16 (4, 100) 0 |
| Activation-17 (4, 10) 0 |
| Dense-18 (4, 10) 1010 |
| Dropout-19 (4, 10) 0 |
| Dense-20 (4, 2) 22 |
| LeafNetwork-21 (4, 2) 0 |
| ================================================================================ |
| Parameters in forward computation graph, duplicate included |
| Total params: 2206796 |
| Trainable params: 2206348 |
| Non-trainable params: 448 |
| Shared params in forward computation graph: 0 |
| Unique parameters in model: 2206796 |
| -------------------------------------------------------------------------------- |
| </pre></div></div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area stderr docutils container"> |
| <div class="highlight"><pre> |
| 01:56:59] /work/mxnet/src/storage/storage.cc:202: Using Pooled (Naive) StorageManager for GPU |
| </pre></div></div> |
| </div> |
| </div> |
| <div class="section" id="3.-Choose-Optimizer-and-Loss-function"> |
| <h2>3. Choose Optimizer and Loss function<a class="headerlink" href="#3.-Choose-Optimizer-and-Loss-function" title="Permalink to this headline">¶</a></h2> |
| <p>With the network created you can move on to choosing an optimizer and a loss function. The network you created uses these components to make an informed decision on how to tune the parameters to fit the final objective better. You can use the <code class="docutils literal notranslate"><span class="pre">gluon.Trainer</span></code> class to help with optimizing these parameters. The <code class="docutils literal notranslate"><span class="pre">gluon.Trainer</span></code> class needs two things to work properly: the parameters needing to be tuned and the optimizer with its corresponding hyperparameters. The trainer uses the error reported |
| by the loss function to optimize these parameters.</p> |
| <p>For this particular dataset you will use Stochastic Gradient Descent as the optimizer and Cross Entropy as the loss function.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># SGD optimizer</span> |
| <span class="n">optimizer</span> <span class="o">=</span> <span class="s1">'sgd'</span> |
| |
| <span class="c1"># Set parameters</span> |
| <span class="n">optimizer_params</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'learning_rate'</span><span class="p">:</span> <span class="mf">0.001</span><span class="p">}</span> |
| |
| <span class="c1"># Define the trainer for the model</span> |
| <span class="n">trainer</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">Trainer</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(),</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">optimizer_params</span><span class="p">)</span> |
| |
| <span class="c1"># Define the loss function</span> |
| <span class="n">loss_fn</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">loss</span><span class="o">.</span><span class="n">SoftmaxCrossEntropyLoss</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>Finally, you have to set up the training loop, and you need to create a function to evaluate the performance of the network on the validation dataset.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># Function to return the accuracy for the validation and test set</span> |
| <span class="k">def</span> <span class="nf">test</span><span class="p">(</span><span class="n">val_data</span><span class="p">):</span> |
| <span class="n">acc</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">Accuracy</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">val_data</span><span class="p">:</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">labels</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> |
| <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">device</span><span class="p">))</span> |
| <span class="n">acc</span><span class="o">.</span><span class="n">update</span><span class="p">([</span><span class="n">labels</span><span class="p">],</span> <span class="p">[</span><span class="n">outputs</span><span class="p">])</span> |
| |
| <span class="n">_</span><span class="p">,</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="n">acc</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| <span class="k">return</span> <span class="n">accuracy</span> |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="4.-Training-Loop"> |
| <h2>4. Training Loop<a class="headerlink" href="#4.-Training-Loop" title="Permalink to this headline">¶</a></h2> |
| <p>Now that you have everything set up, you can start training your network. This might take some time to train depending on the hardware, number of layers, batch size and images you use. For this particular case, you will only train for 2 epochs.</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># Start the training loop</span> |
| <span class="n">epochs</span> <span class="o">=</span> <span class="mi">2</span> |
| <span class="n">accuracy</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">Accuracy</span><span class="p">()</span> |
| <span class="n">log_interval</span> <span class="o">=</span> <span class="mi">5</span> |
| |
| <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span> |
| <span class="n">tic</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> |
| <span class="n">btic</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> |
| <span class="n">accuracy</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span> |
| |
| <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">train_loader</span><span class="p">):</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> |
| <span class="k">with</span> <span class="n">mx</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">record</span><span class="p">():</span> |
| <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">device</span><span class="p">))</span> |
| <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_fn</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">label</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">device</span><span class="p">))</span> |
| <span class="n">mx</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span> |
| <span class="n">trainer</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">batch_size</span><span class="p">)</span> |
| <span class="n">accuracy</span><span class="o">.</span><span class="n">update</span><span class="p">([</span><span class="n">label</span><span class="p">],</span> <span class="p">[</span><span class="n">outputs</span><span class="p">])</span> |
| <span class="k">if</span> <span class="n">log_interval</span> <span class="ow">and</span> <span class="p">(</span><span class="n">idx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">%</span> <span class="n">log_interval</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">_</span><span class="p">,</span> <span class="n">acc</span> <span class="o">=</span> <span class="n">accuracy</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| |
| <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"""Epoch[</span><span class="si">{</span><span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="si">}</span><span class="s2">] Batch[</span><span class="si">{</span><span class="n">idx</span> <span class="o">+</span> <span class="mi">1</span><span class="si">}</span><span class="s2">] Speed: </span><span class="si">{</span><span class="n">batch_size</span> <span class="o">/</span> <span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">btic</span><span class="p">)</span><span class="si">}</span><span class="s2"> samples/sec </span><span class="se">\</span> |
| <span class="s2"> batch loss = </span><span class="si">{</span><span class="n">loss</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">}</span><span class="s2"> | accuracy = </span><span class="si">{</span><span class="n">acc</span><span class="si">}</span><span class="s2">"""</span><span class="p">)</span> |
| <span class="n">btic</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> |
| |
| <span class="n">_</span><span class="p">,</span> <span class="n">acc</span> <span class="o">=</span> <span class="n">accuracy</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| |
| <span class="n">acc_val</span> <span class="o">=</span> <span class="n">test</span><span class="p">(</span><span class="n">validation_loader</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"[Epoch </span><span class="si">{</span><span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="si">}</span><span class="s2">] training: accuracy=</span><span class="si">{</span><span class="n">acc</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"[Epoch </span><span class="si">{</span><span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="si">}</span><span class="s2">] time cost: </span><span class="si">{</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">tic</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"[Epoch </span><span class="si">{</span><span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="si">}</span><span class="s2">] validation: validation accuracy=</span><span class="si">{</span><span class="n">acc_val</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area stderr docutils container"> |
| <div class="highlight"><pre> |
| [01:57:00] /work/mxnet/src/operator/cudnn_ops.cc:421: Auto-tuning cuDNN op, set MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable |
| </pre></div></div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area docutils container"> |
| <div class="highlight"><pre> |
| Epoch[1] Batch[5] Speed: 1.217683819115931 samples/sec batch loss = 0.6735858917236328 | accuracy = 0.45 |
| Epoch[1] Batch[10] Speed: 1.2656090464271201 samples/sec batch loss = 0.8982939124107361 | accuracy = 0.4 |
| Epoch[1] Batch[15] Speed: 1.2562159312815229 samples/sec batch loss = 0.7588587403297424 | accuracy = 0.45 |
| Epoch[1] Batch[20] Speed: 1.252267760764031 samples/sec batch loss = 0.4954342842102051 | accuracy = 0.475 |
| Epoch[1] Batch[25] Speed: 1.2545751171386574 samples/sec batch loss = 0.5244734287261963 | accuracy = 0.53 |
| Epoch[1] Batch[30] Speed: 1.259396068170606 samples/sec batch loss = 1.031864881515503 | accuracy = 0.525 |
| Epoch[1] Batch[35] Speed: 1.260041996853578 samples/sec batch loss = 0.7177473902702332 | accuracy = 0.5214285714285715 |
| Epoch[1] Batch[40] Speed: 1.2649163893265563 samples/sec batch loss = 0.2281455248594284 | accuracy = 0.5375 |
| Epoch[1] Batch[45] Speed: 1.2618425296153495 samples/sec batch loss = 0.24132265150547028 | accuracy = 0.5722222222222222 |
| Epoch[1] Batch[50] Speed: 1.257374699581475 samples/sec batch loss = 0.6740269660949707 | accuracy = 0.575 |
| Epoch[1] Batch[55] Speed: 1.252611826662616 samples/sec batch loss = 0.46210193634033203 | accuracy = 0.5818181818181818 |
| Epoch[1] Batch[60] Speed: 1.2607254401980705 samples/sec batch loss = 1.1121103763580322 | accuracy = 0.5833333333333334 |
| Epoch[1] Batch[65] Speed: 1.2578258673203848 samples/sec batch loss = 0.36141636967658997 | accuracy = 0.5807692307692308 |
| Epoch[1] Batch[70] Speed: 1.2580098775411348 samples/sec batch loss = 0.5868483781814575 | accuracy = 0.5821428571428572 |
| Epoch[1] Batch[75] Speed: 1.2618348423335966 samples/sec batch loss = 0.40905338525772095 | accuracy = 0.5866666666666667 |
| Epoch[1] Batch[80] Speed: 1.2583782483153272 samples/sec batch loss = 0.6796800494194031 | accuracy = 0.584375 |
| Epoch[1] Batch[85] Speed: 1.258685640612627 samples/sec batch loss = 1.0358772277832031 | accuracy = 0.5823529411764706 |
| Epoch[1] Batch[90] Speed: 1.2638413593294955 samples/sec batch loss = 0.22023503482341766 | accuracy = 0.6 |
| Epoch[1] Batch[95] Speed: 1.2665091344955446 samples/sec batch loss = 0.6975358128547668 | accuracy = 0.6026315789473684 |
| Epoch[1] Batch[100] Speed: 1.2631543442252673 samples/sec batch loss = 0.8707108497619629 | accuracy = 0.61 |
| Epoch[1] Batch[105] Speed: 1.2616285544744374 samples/sec batch loss = 0.7150247693061829 | accuracy = 0.6166666666666667 |
| Epoch[1] Batch[110] Speed: 1.2614829410633315 samples/sec batch loss = 1.9147394895553589 | accuracy = 0.6204545454545455 |
| Epoch[1] Batch[115] Speed: 1.2649282150982095 samples/sec batch loss = 0.4657955467700958 | accuracy = 0.6239130434782608 |
| Epoch[1] Batch[120] Speed: 1.2621433561838247 samples/sec batch loss = 0.5238597393035889 | accuracy = 0.6291666666666667 |
| Epoch[1] Batch[125] Speed: 1.2641694298275192 samples/sec batch loss = 0.4234311580657959 | accuracy = 0.632 |
| Epoch[1] Batch[130] Speed: 1.2604718793749126 samples/sec batch loss = 0.6855000257492065 | accuracy = 0.6326923076923077 |
| Epoch[1] Batch[135] Speed: 1.2648421971919162 samples/sec batch loss = 0.37160712480545044 | accuracy = 0.6388888888888888 |
| Epoch[1] Batch[140] Speed: 1.2625801848880647 samples/sec batch loss = 0.822640061378479 | accuracy = 0.6392857142857142 |
| Epoch[1] Batch[145] Speed: 1.262944582461308 samples/sec batch loss = 0.41148442029953003 | accuracy = 0.6379310344827587 |
| Epoch[1] Batch[150] Speed: 1.2627782298842476 samples/sec batch loss = 0.3013228178024292 | accuracy = 0.6416666666666667 |
| Epoch[1] Batch[155] Speed: 1.2593232783928068 samples/sec batch loss = 1.0028533935546875 | accuracy = 0.6419354838709678 |
| Epoch[1] Batch[160] Speed: 1.2565338431802742 samples/sec batch loss = 1.1875944137573242 | accuracy = 0.640625 |
| Epoch[1] Batch[165] Speed: 1.2630243521842015 samples/sec batch loss = 0.7235549092292786 | accuracy = 0.6348484848484849 |
| Epoch[1] Batch[170] Speed: 1.2647982391207606 samples/sec batch loss = 0.6829676032066345 | accuracy = 0.6367647058823529 |
| Epoch[1] Batch[175] Speed: 1.2681053206979456 samples/sec batch loss = 1.458935022354126 | accuracy = 0.64 |
| Epoch[1] Batch[180] Speed: 1.2646896443174955 samples/sec batch loss = 0.5408955812454224 | accuracy = 0.6416666666666667 |
| Epoch[1] Batch[185] Speed: 1.2546848905861105 samples/sec batch loss = 1.1030123233795166 | accuracy = 0.6391891891891892 |
| Epoch[1] Batch[190] Speed: 1.259587914268041 samples/sec batch loss = 0.6674180030822754 | accuracy = 0.6381578947368421 |
| Epoch[1] Batch[195] Speed: 1.2614111427852865 samples/sec batch loss = 0.5164763927459717 | accuracy = 0.6371794871794871 |
| Epoch[1] Batch[200] Speed: 1.2578497261923496 samples/sec batch loss = 0.6432218551635742 | accuracy = 0.64125 |
| Epoch[1] Batch[205] Speed: 1.2617551278964925 samples/sec batch loss = 0.3335733413696289 | accuracy = 0.6439024390243903 |
| Epoch[1] Batch[210] Speed: 1.2602247628536463 samples/sec batch loss = 0.6316834688186646 | accuracy = 0.6440476190476191 |
| Epoch[1] Batch[215] Speed: 1.2587717675915722 samples/sec batch loss = 0.7286267280578613 | accuracy = 0.6441860465116279 |
| Epoch[1] Batch[220] Speed: 1.2604416710460133 samples/sec batch loss = 0.4729064702987671 | accuracy = 0.6443181818181818 |
| Epoch[1] Batch[225] Speed: 1.2629018018983706 samples/sec batch loss = 0.3322046399116516 | accuracy = 0.6477777777777778 |
| Epoch[1] Batch[230] Speed: 1.2578666071267643 samples/sec batch loss = 0.4892130196094513 | accuracy = 0.65 |
| Epoch[1] Batch[235] Speed: 1.2597029175671446 samples/sec batch loss = 0.6078039407730103 | accuracy = 0.6531914893617021 |
| Epoch[1] Batch[240] Speed: 1.2616988593643141 samples/sec batch loss = 0.326188862323761 | accuracy = 0.6583333333333333 |
| Epoch[1] Batch[245] Speed: 1.2600750252094377 samples/sec batch loss = 0.6751788258552551 | accuracy = 0.6591836734693878 |
| Epoch[1] Batch[250] Speed: 1.2628945770227054 samples/sec batch loss = 0.4649132192134857 | accuracy = 0.66 |
| Epoch[1] Batch[255] Speed: 1.2591865130904702 samples/sec batch loss = 0.27224719524383545 | accuracy = 0.6598039215686274 |
| Epoch[1] Batch[260] Speed: 1.2641960067081324 samples/sec batch loss = 1.5420297384262085 | accuracy = 0.6596153846153846 |
| Epoch[1] Batch[265] Speed: 1.2664273944563698 samples/sec batch loss = 1.529706358909607 | accuracy = 0.6575471698113208 |
| Epoch[1] Batch[270] Speed: 1.2633038633995832 samples/sec batch loss = 0.4477691650390625 | accuracy = 0.6592592592592592 |
| Epoch[1] Batch[275] Speed: 1.2639092449962774 samples/sec batch loss = 0.33266931772232056 | accuracy = 0.66 |
| Epoch[1] Batch[280] Speed: 1.2586380491804028 samples/sec batch loss = 0.7104622721672058 | accuracy = 0.6607142857142857 |
| Epoch[1] Batch[285] Speed: 1.2660328962078438 samples/sec batch loss = 0.6546992659568787 | accuracy = 0.6622807017543859 |
| Epoch[1] Batch[290] Speed: 1.2644386795833653 samples/sec batch loss = 0.41161105036735535 | accuracy = 0.6586206896551724 |
| Epoch[1] Batch[295] Speed: 1.2603620378462834 samples/sec batch loss = 0.3379448652267456 | accuracy = 0.6593220338983051 |
| Epoch[1] Batch[300] Speed: 1.2646414071296908 samples/sec batch loss = 0.4988410770893097 | accuracy = 0.6591666666666667 |
| Epoch[1] Batch[305] Speed: 1.2622480003629377 samples/sec batch loss = 0.2974461615085602 | accuracy = 0.660655737704918 |
| Epoch[1] Batch[310] Speed: 1.2678144835794936 samples/sec batch loss = 0.9067622423171997 | accuracy = 0.6629032258064517 |
| Epoch[1] Batch[315] Speed: 1.2638370750650196 samples/sec batch loss = 0.6962606906890869 | accuracy = 0.6634920634920635 |
| Epoch[1] Batch[320] Speed: 1.2615783685972168 samples/sec batch loss = 0.446750670671463 | accuracy = 0.66484375 |
| Epoch[1] Batch[325] Speed: 1.2642987049413346 samples/sec batch loss = 0.4387769401073456 | accuracy = 0.6653846153846154 |
| Epoch[1] Batch[330] Speed: 1.263887059957964 samples/sec batch loss = 1.038114070892334 | accuracy = 0.6659090909090909 |
| Epoch[1] Batch[335] Speed: 1.2682681902521422 samples/sec batch loss = 0.6416564583778381 | accuracy = 0.6664179104477612 |
| Epoch[1] Batch[340] Speed: 1.263073892447447 samples/sec batch loss = 0.696865975856781 | accuracy = 0.6669117647058823 |
| Epoch[1] Batch[345] Speed: 1.2668105643860241 samples/sec batch loss = 0.23268650472164154 | accuracy = 0.6688405797101449 |
| Epoch[1] Batch[350] Speed: 1.2648509701011386 samples/sec batch loss = 0.4636657238006592 | accuracy = 0.67 |
| Epoch[1] Batch[355] Speed: 1.2652124816200785 samples/sec batch loss = 0.3166045546531677 | accuracy = 0.6690140845070423 |
| Epoch[1] Batch[360] Speed: 1.2632047506789534 samples/sec batch loss = 0.5997477173805237 | accuracy = 0.66875 |
| Epoch[1] Batch[365] Speed: 1.265266487619598 samples/sec batch loss = 0.42069604992866516 | accuracy = 0.6705479452054794 |
| Epoch[1] Batch[370] Speed: 1.2653354807884847 samples/sec batch loss = 0.5724571943283081 | accuracy = 0.6702702702702703 |
| Epoch[1] Batch[375] Speed: 1.2647044212674154 samples/sec batch loss = 0.4510500729084015 | accuracy = 0.672 |
| Epoch[1] Batch[380] Speed: 1.2715406536106226 samples/sec batch loss = 1.3714784383773804 | accuracy = 0.6703947368421053 |
| Epoch[1] Batch[385] Speed: 1.2620299954121583 samples/sec batch loss = 0.6534907817840576 | accuracy = 0.6707792207792208 |
| Epoch[1] Batch[390] Speed: 1.2683484421370819 samples/sec batch loss = 0.2851663827896118 | accuracy = 0.6717948717948717 |
| Epoch[1] Batch[395] Speed: 1.2641163746660988 samples/sec batch loss = 0.733774721622467 | accuracy = 0.670253164556962 |
| Epoch[1] Batch[400] Speed: 1.2637216012789358 samples/sec batch loss = 0.5183593034744263 | accuracy = 0.67125 |
| Epoch[1] Batch[405] Speed: 1.2647760228632254 samples/sec batch loss = 0.7961498498916626 | accuracy = 0.6703703703703704 |
| Epoch[1] Batch[410] Speed: 1.2679272569638367 samples/sec batch loss = 1.1904412508010864 | accuracy = 0.6707317073170732 |
| Epoch[1] Batch[415] Speed: 1.2660566852848631 samples/sec batch loss = 0.6808487176895142 | accuracy = 0.6704819277108434 |
| Epoch[1] Batch[420] Speed: 1.2652096192399278 samples/sec batch loss = 0.4387957453727722 | accuracy = 0.6726190476190477 |
| Epoch[1] Batch[425] Speed: 1.261818329230239 samples/sec batch loss = 1.3267109394073486 | accuracy = 0.6723529411764706 |
| Epoch[1] Batch[430] Speed: 1.265341302133095 samples/sec batch loss = 0.22103136777877808 | accuracy = 0.672093023255814 |
| Epoch[1] Batch[435] Speed: 1.2693330968764003 samples/sec batch loss = 0.17633040249347687 | accuracy = 0.6718390804597701 |
| Epoch[1] Batch[440] Speed: 1.2650934175421114 samples/sec batch loss = 0.6582935452461243 | accuracy = 0.6715909090909091 |
| Epoch[1] Batch[445] Speed: 1.2648022438510649 samples/sec batch loss = 0.7351402640342712 | accuracy = 0.6713483146067416 |
| Epoch[1] Batch[450] Speed: 1.2678994689657694 samples/sec batch loss = 0.5886645913124084 | accuracy = 0.6722222222222223 |
| Epoch[1] Batch[455] Speed: 1.2632218707670715 samples/sec batch loss = 0.562326967716217 | accuracy = 0.6736263736263737 |
| Epoch[1] Batch[460] Speed: 1.2616074929962773 samples/sec batch loss = 1.0697680711746216 | accuracy = 0.6733695652173913 |
| Epoch[1] Batch[465] Speed: 1.2661573927259187 samples/sec batch loss = 0.4504196047782898 | accuracy = 0.6731182795698925 |
| Epoch[1] Batch[470] Speed: 1.2621218027775198 samples/sec batch loss = 0.6507149338722229 | accuracy = 0.6728723404255319 |
| Epoch[1] Batch[475] Speed: 1.2629904084125807 samples/sec batch loss = 0.5363296866416931 | accuracy = 0.6721052631578948 |
| Epoch[1] Batch[480] Speed: 1.2697320572149322 samples/sec batch loss = 0.5722419023513794 | accuracy = 0.6729166666666667 |
| Epoch[1] Batch[485] Speed: 1.259825793799427 samples/sec batch loss = 0.7371057868003845 | accuracy = 0.6726804123711341 |
| Epoch[1] Batch[490] Speed: 1.2639997070770943 samples/sec batch loss = 0.4752271771430969 | accuracy = 0.6719387755102041 |
| Epoch[1] Batch[495] Speed: 1.2618320901196893 samples/sec batch loss = 0.23223747313022614 | accuracy = 0.6722222222222223 |
| Epoch[1] Batch[500] Speed: 1.2655902385829796 samples/sec batch loss = 0.5230560302734375 | accuracy = 0.674 |
| Epoch[1] Batch[505] Speed: 1.2596863656342583 samples/sec batch loss = 0.406080424785614 | accuracy = 0.6747524752475248 |
| Epoch[1] Batch[510] Speed: 1.2606840413446743 samples/sec batch loss = 0.518858790397644 | accuracy = 0.6754901960784314 |
| Epoch[1] Batch[515] Speed: 1.2586971613019489 samples/sec batch loss = 0.6960127353668213 | accuracy = 0.6766990291262136 |
| Epoch[1] Batch[520] Speed: 1.263347717656226 samples/sec batch loss = 0.25588661432266235 | accuracy = 0.6774038461538462 |
| Epoch[1] Batch[525] Speed: 1.2620290460779475 samples/sec batch loss = 0.4213707745075226 | accuracy = 0.6776190476190476 |
| Epoch[1] Batch[530] Speed: 1.2632400375902626 samples/sec batch loss = 0.4326092600822449 | accuracy = 0.6773584905660377 |
| Epoch[1] Batch[535] Speed: 1.2632567781621804 samples/sec batch loss = 0.35706716775894165 | accuracy = 0.6780373831775701 |
| Epoch[1] Batch[540] Speed: 1.2638174630251335 samples/sec batch loss = 0.6693456768989563 | accuracy = 0.6791666666666667 |
| Epoch[1] Batch[545] Speed: 1.259078785257148 samples/sec batch loss = 1.5915676355361938 | accuracy = 0.6788990825688074 |
| Epoch[1] Batch[550] Speed: 1.2636985661646185 samples/sec batch loss = 0.46044331789016724 | accuracy = 0.6804545454545454 |
| Epoch[1] Batch[555] Speed: 1.2576655742600844 samples/sec batch loss = 0.5710960030555725 | accuracy = 0.6801801801801802 |
| Epoch[1] Batch[560] Speed: 1.259586779471695 samples/sec batch loss = 0.13839317858219147 | accuracy = 0.6803571428571429 |
| Epoch[1] Batch[565] Speed: 1.2606911462177761 samples/sec batch loss = 0.5230515599250793 | accuracy = 0.6805309734513274 |
| Epoch[1] Batch[570] Speed: 1.2631015644203996 samples/sec batch loss = 0.2196439802646637 | accuracy = 0.6820175438596491 |
| Epoch[1] Batch[575] Speed: 1.2626849015175656 samples/sec batch loss = 0.3152848184108734 | accuracy = 0.6826086956521739 |
| Epoch[1] Batch[580] Speed: 1.2596905272221648 samples/sec batch loss = 0.34713417291641235 | accuracy = 0.680603448275862 |
| Epoch[1] Batch[585] Speed: 1.264194577815159 samples/sec batch loss = 1.0302459001541138 | accuracy = 0.6816239316239316 |
| Epoch[1] Batch[590] Speed: 1.2588572451602864 samples/sec batch loss = 0.8762767314910889 | accuracy = 0.6822033898305084 |
| Epoch[1] Batch[595] Speed: 1.2574419865543804 samples/sec batch loss = 0.49228760600090027 | accuracy = 0.6827731092436975 |
| Epoch[1] Batch[600] Speed: 1.2616241903266097 samples/sec batch loss = 0.37045031785964966 | accuracy = 0.6841666666666667 |
| Epoch[1] Batch[605] Speed: 1.2600104843058353 samples/sec batch loss = 0.4660239815711975 | accuracy = 0.6834710743801653 |
| Epoch[1] Batch[610] Speed: 1.2534809667899436 samples/sec batch loss = 0.3678503632545471 | accuracy = 0.684016393442623 |
| Epoch[1] Batch[615] Speed: 1.256952671100545 samples/sec batch loss = 0.4389304518699646 | accuracy = 0.6841463414634147 |
| Epoch[1] Batch[620] Speed: 1.2593424676083977 samples/sec batch loss = 0.32765743136405945 | accuracy = 0.6842741935483871 |
| Epoch[1] Batch[625] Speed: 1.2631545344307384 samples/sec batch loss = 0.37177154421806335 | accuracy = 0.6844 |
| Epoch[1] Batch[630] Speed: 1.2565092813626355 samples/sec batch loss = 0.7538040280342102 | accuracy = 0.6837301587301587 |
| Epoch[1] Batch[635] Speed: 1.264499004965075 samples/sec batch loss = 0.4994611144065857 | accuracy = 0.6846456692913386 |
| Epoch[1] Batch[640] Speed: 1.2550600479066643 samples/sec batch loss = 0.30505070090293884 | accuracy = 0.685546875 |
| Epoch[1] Batch[645] Speed: 1.2618246876728905 samples/sec batch loss = 0.684575080871582 | accuracy = 0.6856589147286821 |
| Epoch[1] Batch[650] Speed: 1.2528017048099065 samples/sec batch loss = 0.7096792459487915 | accuracy = 0.685 |
| Epoch[1] Batch[655] Speed: 1.2539993558546036 samples/sec batch loss = 0.4126964807510376 | accuracy = 0.6851145038167938 |
| Epoch[1] Batch[660] Speed: 1.2575443444138525 samples/sec batch loss = 0.6386780738830566 | accuracy = 0.6848484848484848 |
| Epoch[1] Batch[665] Speed: 1.2574123002374415 samples/sec batch loss = 1.0152369737625122 | accuracy = 0.6842105263157895 |
| Epoch[1] Batch[670] Speed: 1.260737187736357 samples/sec batch loss = 1.129343867301941 | accuracy = 0.6832089552238806 |
| Epoch[1] Batch[675] Speed: 1.253720011466193 samples/sec batch loss = 0.19109022617340088 | accuracy = 0.6822222222222222 |
| Epoch[1] Batch[680] Speed: 1.2515916800550015 samples/sec batch loss = 0.5513604283332825 | accuracy = 0.6816176470588236 |
| Epoch[1] Batch[685] Speed: 1.2555736364213297 samples/sec batch loss = 0.3983954191207886 | accuracy = 0.6817518248175183 |
| Epoch[1] Batch[690] Speed: 1.2510926319370448 samples/sec batch loss = 0.7198715209960938 | accuracy = 0.6815217391304348 |
| Epoch[1] Batch[695] Speed: 1.253253340888961 samples/sec batch loss = 0.48386457562446594 | accuracy = 0.6827338129496403 |
| Epoch[1] Batch[700] Speed: 1.2587784731469551 samples/sec batch loss = 0.5156959891319275 | accuracy = 0.6825 |
| Epoch[1] Batch[705] Speed: 1.25766293448108 samples/sec batch loss = 0.6890065670013428 | accuracy = 0.6826241134751773 |
| Epoch[1] Batch[710] Speed: 1.2552159209935658 samples/sec batch loss = 0.658888041973114 | accuracy = 0.6820422535211268 |
| Epoch[1] Batch[715] Speed: 1.258940278133899 samples/sec batch loss = 0.7276878952980042 | accuracy = 0.6821678321678322 |
| Epoch[1] Batch[720] Speed: 1.251724652473126 samples/sec batch loss = 0.5389629602432251 | accuracy = 0.6819444444444445 |
| Epoch[1] Batch[725] Speed: 1.2534553067537786 samples/sec batch loss = 0.3480612337589264 | accuracy = 0.6824137931034483 |
| Epoch[1] Batch[730] Speed: 1.2617613908091074 samples/sec batch loss = 0.377936452627182 | accuracy = 0.6825342465753425 |
| Epoch[1] Batch[735] Speed: 1.2552014588489704 samples/sec batch loss = 0.3268744647502899 | accuracy = 0.6833333333333333 |
| Epoch[1] Batch[740] Speed: 1.257635406017532 samples/sec batch loss = 0.43385910987854004 | accuracy = 0.683445945945946 |
| Epoch[1] Batch[745] Speed: 1.2549389443449186 samples/sec batch loss = 0.3178352117538452 | accuracy = 0.6838926174496645 |
| Epoch[1] Batch[750] Speed: 1.255490295312929 samples/sec batch loss = 0.6389017701148987 | accuracy = 0.6846666666666666 |
| Epoch[1] Batch[755] Speed: 1.2546891130254154 samples/sec batch loss = 0.3310072720050812 | accuracy = 0.6854304635761589 |
| Epoch[1] Batch[760] Speed: 1.2601673055803655 samples/sec batch loss = 0.6890795826911926 | accuracy = 0.6855263157894737 |
| Epoch[1] Batch[765] Speed: 1.2639347635141982 samples/sec batch loss = 0.5528118014335632 | accuracy = 0.6859477124183007 |
| Epoch[1] Batch[770] Speed: 1.2573448279815989 samples/sec batch loss = 0.501762866973877 | accuracy = 0.6853896103896104 |
| Epoch[1] Batch[775] Speed: 1.2555754217521358 samples/sec batch loss = 0.6985025405883789 | accuracy = 0.6851612903225807 |
| Epoch[1] Batch[780] Speed: 1.2506428312334503 samples/sec batch loss = 0.4378434419631958 | accuracy = 0.6858974358974359 |
| Epoch[1] Batch[785] Speed: 1.2547712216611544 samples/sec batch loss = 0.7262905836105347 | accuracy = 0.6869426751592357 |
| [Epoch 1] training: accuracy=0.6865482233502538 |
| [Epoch 1] time cost: 644.0103404521942 |
| [Epoch 1] validation: validation accuracy=0.7544444444444445 |
| Epoch[2] Batch[5] Speed: 1.2617113841345426 samples/sec batch loss = 0.7549525499343872 | accuracy = 0.65 |
| Epoch[2] Batch[10] Speed: 1.2544873122097349 samples/sec batch loss = 0.5018438696861267 | accuracy = 0.625 |
| Epoch[2] Batch[15] Speed: 1.2520347821933797 samples/sec batch loss = 0.15261180698871613 | accuracy = 0.7166666666666667 |
| Epoch[2] Batch[20] Speed: 1.2594578988245761 samples/sec batch loss = 0.3944702744483948 | accuracy = 0.725 |
| Epoch[2] Batch[25] Speed: 1.2598098062356817 samples/sec batch loss = 0.48125159740448 | accuracy = 0.75 |
| Epoch[2] Batch[30] Speed: 1.2673493258365516 samples/sec batch loss = 0.49969300627708435 | accuracy = 0.725 |
| Epoch[2] Batch[35] Speed: 1.2640464667655067 samples/sec batch loss = 0.7936878204345703 | accuracy = 0.7071428571428572 |
| Epoch[2] Batch[40] Speed: 1.257201897415984 samples/sec batch loss = 0.4994239807128906 | accuracy = 0.7125 |
| Epoch[2] Batch[45] Speed: 1.2603269115356852 samples/sec batch loss = 0.8216754794120789 | accuracy = 0.7 |
| Epoch[2] Batch[50] Speed: 1.2552707674792913 samples/sec batch loss = 0.9510053992271423 | accuracy = 0.7 |
| Epoch[2] Batch[55] Speed: 1.2555059855217185 samples/sec batch loss = 0.3547792136669159 | accuracy = 0.7045454545454546 |
| Epoch[2] Batch[60] Speed: 1.2539725498875012 samples/sec batch loss = 0.3899439871311188 | accuracy = 0.7125 |
| Epoch[2] Batch[65] Speed: 1.2563412327726136 samples/sec batch loss = 0.5768402218818665 | accuracy = 0.7115384615384616 |
| Epoch[2] Batch[70] Speed: 1.2608781752741645 samples/sec batch loss = 0.36024561524391174 | accuracy = 0.7214285714285714 |
| Epoch[2] Batch[75] Speed: 1.2565735581889725 samples/sec batch loss = 0.6500639915466309 | accuracy = 0.7233333333333334 |
| Epoch[2] Batch[80] Speed: 1.2580098775411348 samples/sec batch loss = 0.692675769329071 | accuracy = 0.71875 |
| Epoch[2] Batch[85] Speed: 1.2599489778676294 samples/sec batch loss = 0.4950321316719055 | accuracy = 0.7205882352941176 |
| Epoch[2] Batch[90] Speed: 1.2598337404317848 samples/sec batch loss = 0.47980797290802 | accuracy = 0.725 |
| Epoch[2] Batch[95] Speed: 1.2560932879647193 samples/sec batch loss = 0.43574878573417664 | accuracy = 0.7342105263157894 |
| Epoch[2] Batch[100] Speed: 1.2575278491573652 samples/sec batch loss = 0.28979504108428955 | accuracy = 0.745 |
| Epoch[2] Batch[105] Speed: 1.2581481800056378 samples/sec batch loss = 0.8071722388267517 | accuracy = 0.7476190476190476 |
| Epoch[2] Batch[110] Speed: 1.2589470799549796 samples/sec batch loss = 0.2784702777862549 | accuracy = 0.7477272727272727 |
| Epoch[2] Batch[115] Speed: 1.2564027638258315 samples/sec batch loss = 0.32850974798202515 | accuracy = 0.7369565217391304 |
| Epoch[2] Batch[120] Speed: 1.2588476106439046 samples/sec batch loss = 0.45890796184539795 | accuracy = 0.7395833333333334 |
| Epoch[2] Batch[125] Speed: 1.2524133112270834 samples/sec batch loss = 0.5404859185218811 | accuracy = 0.744 |
| Epoch[2] Batch[130] Speed: 1.25530364010174 samples/sec batch loss = 0.4293116629123688 | accuracy = 0.7423076923076923 |
| Epoch[2] Batch[135] Speed: 1.258069213687245 samples/sec batch loss = 0.4242939352989197 | accuracy = 0.7444444444444445 |
| Epoch[2] Batch[140] Speed: 1.257613346406287 samples/sec batch loss = 0.7123743891716003 | accuracy = 0.7428571428571429 |
| Epoch[2] Batch[145] Speed: 1.2575877997039306 samples/sec batch loss = 0.5462617874145508 | accuracy = 0.743103448275862 |
| Epoch[2] Batch[150] Speed: 1.259932135613423 samples/sec batch loss = 0.2399541139602661 | accuracy = 0.7483333333333333 |
| Epoch[2] Batch[155] Speed: 1.2564659946728385 samples/sec batch loss = 0.15769608318805695 | accuracy = 0.7483870967741936 |
| Epoch[2] Batch[160] Speed: 1.2580237441519933 samples/sec batch loss = 0.5245479941368103 | accuracy = 0.7484375 |
| Epoch[2] Batch[165] Speed: 1.2633291672194962 samples/sec batch loss = 0.37754347920417786 | accuracy = 0.7515151515151515 |
| Epoch[2] Batch[170] Speed: 1.2551022988342369 samples/sec batch loss = 0.4252931773662567 | accuracy = 0.75 |
| Epoch[2] Batch[175] Speed: 1.2563908146635103 samples/sec batch loss = 0.19753557443618774 | accuracy = 0.7528571428571429 |
| Epoch[2] Batch[180] Speed: 1.2559640869415105 samples/sec batch loss = 0.25816774368286133 | accuracy = 0.7486111111111111 |
| Epoch[2] Batch[185] Speed: 1.262342498898841 samples/sec batch loss = 0.4972204864025116 | accuracy = 0.745945945945946 |
| Epoch[2] Batch[190] Speed: 1.2679466134615094 samples/sec batch loss = 0.33740079402923584 | accuracy = 0.7473684210526316 |
| Epoch[2] Batch[195] Speed: 1.2584408286145223 samples/sec batch loss = 0.04797942191362381 | accuracy = 0.7474358974358974 |
| Epoch[2] Batch[200] Speed: 1.257480439486641 samples/sec batch loss = 0.6616382598876953 | accuracy = 0.74625 |
| Epoch[2] Batch[205] Speed: 1.2578360520245748 samples/sec batch loss = 0.39293068647384644 | accuracy = 0.7426829268292683 |
| Epoch[2] Batch[210] Speed: 1.2575340701746995 samples/sec batch loss = 0.6797146797180176 | accuracy = 0.7428571428571429 |
| Epoch[2] Batch[215] Speed: 1.2577284608434072 samples/sec batch loss = 0.6334095001220703 | accuracy = 0.7465116279069768 |
| Epoch[2] Batch[220] Speed: 1.2584413005861275 samples/sec batch loss = 0.839785099029541 | accuracy = 0.7454545454545455 |
| Epoch[2] Batch[225] Speed: 1.2561823524712301 samples/sec batch loss = 0.3120404779911041 | accuracy = 0.7455555555555555 |
| Epoch[2] Batch[230] Speed: 1.2571506500727214 samples/sec batch loss = 0.40063080191612244 | accuracy = 0.7478260869565218 |
| Epoch[2] Batch[235] Speed: 1.2548362593271944 samples/sec batch loss = 0.4312383234500885 | accuracy = 0.7489361702127659 |
| Epoch[2] Batch[240] Speed: 1.2601010516379414 samples/sec batch loss = 0.47553861141204834 | accuracy = 0.746875 |
| Epoch[2] Batch[245] Speed: 1.255401610727548 samples/sec batch loss = 0.15305250883102417 | accuracy = 0.7489795918367347 |
| Epoch[2] Batch[250] Speed: 1.2532634516793117 samples/sec batch loss = 0.3629148602485657 | accuracy = 0.745 |
| Epoch[2] Batch[255] Speed: 1.2587105708944577 samples/sec batch loss = 0.3944542706012726 | accuracy = 0.7421568627450981 |
| Epoch[2] Batch[260] Speed: 1.25426625957303 samples/sec batch loss = 0.5835664868354797 | accuracy = 0.7432692307692308 |
| Epoch[2] Batch[265] Speed: 1.2596630045034793 samples/sec batch loss = 0.42001938819885254 | accuracy = 0.7415094339622641 |
| Epoch[2] Batch[270] Speed: 1.2556413885866309 samples/sec batch loss = 0.30240824818611145 | accuracy = 0.7416666666666667 |
| Epoch[2] Batch[275] Speed: 1.2629119739029153 samples/sec batch loss = 0.4937003254890442 | accuracy = 0.7390909090909091 |
| Epoch[2] Batch[280] Speed: 1.2496060629804668 samples/sec batch loss = 0.5531719923019409 | accuracy = 0.7392857142857143 |
| Epoch[2] Batch[285] Speed: 1.2598179418683015 samples/sec batch loss = 1.7225431203842163 | accuracy = 0.7403508771929824 |
| Epoch[2] Batch[290] Speed: 1.2594840888676688 samples/sec batch loss = 0.2200007140636444 | accuracy = 0.7405172413793103 |
| Epoch[2] Batch[295] Speed: 1.2598256991972647 samples/sec batch loss = 0.8437265753746033 | accuracy = 0.7406779661016949 |
| Epoch[2] Batch[300] Speed: 1.2569671736276593 samples/sec batch loss = 0.327340304851532 | accuracy = 0.7425 |
| Epoch[2] Batch[305] Speed: 1.2582578247111995 samples/sec batch loss = 0.5016372799873352 | accuracy = 0.7418032786885246 |
| Epoch[2] Batch[310] Speed: 1.2551763856607538 samples/sec batch loss = 0.3968053460121155 | accuracy = 0.7451612903225806 |
| Epoch[2] Batch[315] Speed: 1.2591282999720288 samples/sec batch loss = 0.6359822154045105 | accuracy = 0.7428571428571429 |
| Epoch[2] Batch[320] Speed: 1.25529941352526 samples/sec batch loss = 0.41933053731918335 | accuracy = 0.74375 |
| Epoch[2] Batch[325] Speed: 1.2608809233284242 samples/sec batch loss = 0.546725332736969 | accuracy = 0.7423076923076923 |
| Epoch[2] Batch[330] Speed: 1.2592319722064353 samples/sec batch loss = 0.4064118266105652 | accuracy = 0.7416666666666667 |
| Epoch[2] Batch[335] Speed: 1.256769911441448 samples/sec batch loss = 0.2526073157787323 | accuracy = 0.7417910447761195 |
| Epoch[2] Batch[340] Speed: 1.2566895177923356 samples/sec batch loss = 0.23909629881381989 | accuracy = 0.7426470588235294 |
| Epoch[2] Batch[345] Speed: 1.2588620624737803 samples/sec batch loss = 0.3998275697231293 | accuracy = 0.7434782608695653 |
| Epoch[2] Batch[350] Speed: 1.257149237061784 samples/sec batch loss = 0.49278733134269714 | accuracy = 0.7435714285714285 |
| Epoch[2] Batch[355] Speed: 1.2582795294573643 samples/sec batch loss = 0.45485004782676697 | accuracy = 0.7443661971830986 |
| Epoch[2] Batch[360] Speed: 1.254152715515524 samples/sec batch loss = 0.2374734878540039 | accuracy = 0.7444444444444445 |
| Epoch[2] Batch[365] Speed: 1.257169678929427 samples/sec batch loss = 0.2514638900756836 | accuracy = 0.7445205479452055 |
| Epoch[2] Batch[370] Speed: 1.2578920708459207 samples/sec batch loss = 0.3932521641254425 | accuracy = 0.7439189189189189 |
| Epoch[2] Batch[375] Speed: 1.2587791342619183 samples/sec batch loss = 0.41232702136039734 | accuracy = 0.742 |
| Epoch[2] Batch[380] Speed: 1.2587959457043287 samples/sec batch loss = 0.2153298556804657 | accuracy = 0.7427631578947368 |
| Epoch[2] Batch[385] Speed: 1.2585160653579779 samples/sec batch loss = 0.22355233132839203 | accuracy = 0.7428571428571429 |
| Epoch[2] Batch[390] Speed: 1.257697158189758 samples/sec batch loss = 0.6913942694664001 | accuracy = 0.7423076923076923 |
| Epoch[2] Batch[395] Speed: 1.260200151189021 samples/sec batch loss = 0.21194294095039368 | accuracy = 0.7436708860759493 |
| Epoch[2] Batch[400] Speed: 1.2529121037876112 samples/sec batch loss = 0.582580029964447 | accuracy = 0.745 |
| Epoch[2] Batch[405] Speed: 1.2573196690916928 samples/sec batch loss = 0.43181464076042175 | accuracy = 0.7444444444444445 |
| Epoch[2] Batch[410] Speed: 1.2575837462635395 samples/sec batch loss = 0.22042907774448395 | accuracy = 0.7439024390243902 |
| Epoch[2] Batch[415] Speed: 1.2521520549933745 samples/sec batch loss = 0.2704675495624542 | accuracy = 0.7463855421686747 |
| Epoch[2] Batch[420] Speed: 1.2522446739794566 samples/sec batch loss = 0.3590560257434845 | accuracy = 0.7476190476190476 |
| Epoch[2] Batch[425] Speed: 1.2593217659671792 samples/sec batch loss = 0.6164566874504089 | accuracy = 0.7494117647058823 |
| Epoch[2] Batch[430] Speed: 1.2519636816556363 samples/sec batch loss = 0.2922435402870178 | accuracy = 0.7494186046511628 |
| Epoch[2] Batch[435] Speed: 1.2568870372241892 samples/sec batch loss = 0.5853227376937866 | accuracy = 0.7517241379310344 |
| Epoch[2] Batch[440] Speed: 1.257884903176274 samples/sec batch loss = 0.6815120577812195 | accuracy = 0.7511363636363636 |
| Epoch[2] Batch[445] Speed: 1.2544068351544317 samples/sec batch loss = 2.5780928134918213 | accuracy = 0.751123595505618 |
| Epoch[2] Batch[450] Speed: 1.2562407638252844 samples/sec batch loss = 0.6283458471298218 | accuracy = 0.7505555555555555 |
| Epoch[2] Batch[455] Speed: 1.2560165540206685 samples/sec batch loss = 0.3937249779701233 | accuracy = 0.7510989010989011 |
| Epoch[2] Batch[460] Speed: 1.2551289653283035 samples/sec batch loss = 0.2200493961572647 | accuracy = 0.7510869565217392 |
| Epoch[2] Batch[465] Speed: 1.2572095283520441 samples/sec batch loss = 0.7540868520736694 | accuracy = 0.7516129032258064 |
| Epoch[2] Batch[470] Speed: 1.2578300166244047 samples/sec batch loss = 0.43555617332458496 | accuracy = 0.7515957446808511 |
| Epoch[2] Batch[475] Speed: 1.2520602906620133 samples/sec batch loss = 1.0043278932571411 | accuracy = 0.7510526315789474 |
| Epoch[2] Batch[480] Speed: 1.2540557833487127 samples/sec batch loss = 0.28560200333595276 | accuracy = 0.7510416666666667 |
| Epoch[2] Batch[485] Speed: 1.2568324260239248 samples/sec batch loss = 1.0462672710418701 | accuracy = 0.7489690721649485 |
| Epoch[2] Batch[490] Speed: 1.2568375103146323 samples/sec batch loss = 0.16550016403198242 | accuracy = 0.7489795918367347 |
| Epoch[2] Batch[495] Speed: 1.257316182733195 samples/sec batch loss = 0.6804046630859375 | accuracy = 0.7484848484848485 |
| Epoch[2] Batch[500] Speed: 1.2595545331967963 samples/sec batch loss = 1.0331164598464966 | accuracy = 0.749 |
| Epoch[2] Batch[505] Speed: 1.2612743029175455 samples/sec batch loss = 0.985504150390625 | accuracy = 0.749009900990099 |
| Epoch[2] Batch[510] Speed: 1.2568574712610956 samples/sec batch loss = 0.539385974407196 | accuracy = 0.7490196078431373 |
| Epoch[2] Batch[515] Speed: 1.2603778500591116 samples/sec batch loss = 0.8818182945251465 | accuracy = 0.7475728155339806 |
| Epoch[2] Batch[520] Speed: 1.2526848714918817 samples/sec batch loss = 0.3355376720428467 | accuracy = 0.7490384615384615 |
| Epoch[2] Batch[525] Speed: 1.25570369696204 samples/sec batch loss = 0.45329707860946655 | accuracy = 0.7485714285714286 |
| Epoch[2] Batch[530] Speed: 1.2599628872597441 samples/sec batch loss = 0.3863265812397003 | accuracy = 0.7481132075471698 |
| Epoch[2] Batch[535] Speed: 1.2542560388282826 samples/sec batch loss = 0.4768379032611847 | accuracy = 0.7481308411214953 |
| Epoch[2] Batch[540] Speed: 1.2570395029149724 samples/sec batch loss = 0.4941532611846924 | accuracy = 0.7481481481481481 |
| Epoch[2] Batch[545] Speed: 1.2646089968569478 samples/sec batch loss = 0.6502300500869751 | accuracy = 0.7472477064220183 |
| Epoch[2] Batch[550] Speed: 1.259634820972661 samples/sec batch loss = 0.734358549118042 | accuracy = 0.7477272727272727 |
| Epoch[2] Batch[555] Speed: 1.263169465738974 samples/sec batch loss = 0.31757479906082153 | accuracy = 0.7486486486486487 |
| Epoch[2] Batch[560] Speed: 1.2605208407761859 samples/sec batch loss = 0.3366406261920929 | accuracy = 0.7482142857142857 |
| Epoch[2] Batch[565] Speed: 1.2609904762133823 samples/sec batch loss = 0.21407616138458252 | accuracy = 0.7504424778761062 |
| Epoch[2] Batch[570] Speed: 1.260785222287468 samples/sec batch loss = 0.4330037534236908 | accuracy = 0.7513157894736842 |
| Epoch[2] Batch[575] Speed: 1.2621477239241434 samples/sec batch loss = 0.878326952457428 | accuracy = 0.7508695652173913 |
| Epoch[2] Batch[580] Speed: 1.2645530453839584 samples/sec batch loss = 0.36087051033973694 | accuracy = 0.7512931034482758 |
| Epoch[2] Batch[585] Speed: 1.2638794429570346 samples/sec batch loss = 0.3411369025707245 | accuracy = 0.7508547008547009 |
| Epoch[2] Batch[590] Speed: 1.2626048896053366 samples/sec batch loss = 0.4880613088607788 | accuracy = 0.7516949152542373 |
| Epoch[2] Batch[595] Speed: 1.263586067879539 samples/sec batch loss = 0.14253920316696167 | accuracy = 0.7516806722689076 |
| Epoch[2] Batch[600] Speed: 1.2655916706326693 samples/sec batch loss = 0.6272110939025879 | accuracy = 0.75125 |
| Epoch[2] Batch[605] Speed: 1.2669423894118599 samples/sec batch loss = 0.7492455840110779 | accuracy = 0.7516528925619834 |
| Epoch[2] Batch[610] Speed: 1.26356665395861 samples/sec batch loss = 0.31361693143844604 | accuracy = 0.7520491803278688 |
| Epoch[2] Batch[615] Speed: 1.2609142799766353 samples/sec batch loss = 0.8916362524032593 | accuracy = 0.751219512195122 |
| Epoch[2] Batch[620] Speed: 1.2607898648739537 samples/sec batch loss = 0.7451463937759399 | accuracy = 0.7504032258064516 |
| Epoch[2] Batch[625] Speed: 1.2620911355431719 samples/sec batch loss = 0.2578258216381073 | accuracy = 0.7508 |
| Epoch[2] Batch[630] Speed: 1.2604176190546668 samples/sec batch loss = 0.1398373544216156 | accuracy = 0.7511904761904762 |
| Epoch[2] Batch[635] Speed: 1.2618472748977532 samples/sec batch loss = 0.8914506435394287 | accuracy = 0.7503937007874015 |
| Epoch[2] Batch[640] Speed: 1.2620690142839068 samples/sec batch loss = 0.7236940264701843 | accuracy = 0.751171875 |
| Epoch[2] Batch[645] Speed: 1.2593408606073162 samples/sec batch loss = 0.12208432704210281 | accuracy = 0.751937984496124 |
| Epoch[2] Batch[650] Speed: 1.2582100769052915 samples/sec batch loss = 0.9298136830329895 | accuracy = 0.7519230769230769 |
| Epoch[2] Batch[655] Speed: 1.2600737948957943 samples/sec batch loss = 0.8145483136177063 | accuracy = 0.7526717557251908 |
| Epoch[2] Batch[660] Speed: 1.2590356992848797 samples/sec batch loss = 0.41121986508369446 | accuracy = 0.7522727272727273 |
| Epoch[2] Batch[665] Speed: 1.260834397542423 samples/sec batch loss = 0.77345210313797 | accuracy = 0.7522556390977444 |
| Epoch[2] Batch[670] Speed: 1.265657930342108 samples/sec batch loss = 0.3243650794029236 | accuracy = 0.7533582089552239 |
| Epoch[2] Batch[675] Speed: 1.2622960551367308 samples/sec batch loss = 0.7287093997001648 | accuracy = 0.7533333333333333 |
| Epoch[2] Batch[680] Speed: 1.2644450644619767 samples/sec batch loss = 1.2532939910888672 | accuracy = 0.7525735294117647 |
| Epoch[2] Batch[685] Speed: 1.2600969819800265 samples/sec batch loss = 0.13647188246250153 | accuracy = 0.754014598540146 |
| Epoch[2] Batch[690] Speed: 1.2594127070155974 samples/sec batch loss = 0.7961306571960449 | accuracy = 0.7539855072463768 |
| Epoch[2] Batch[695] Speed: 1.2626984912463841 samples/sec batch loss = 0.6753146052360535 | accuracy = 0.7532374100719424 |
| Epoch[2] Batch[700] Speed: 1.2585872511861602 samples/sec batch loss = 0.4089619517326355 | accuracy = 0.7528571428571429 |
| Epoch[2] Batch[705] Speed: 1.2612674759263172 samples/sec batch loss = 0.11906025558710098 | accuracy = 0.7535460992907801 |
| Epoch[2] Batch[710] Speed: 1.2661687639274317 samples/sec batch loss = 0.2654539942741394 | accuracy = 0.7531690140845071 |
| Epoch[2] Batch[715] Speed: 1.263207223551897 samples/sec batch loss = 0.7863555550575256 | accuracy = 0.7513986013986014 |
| Epoch[2] Batch[720] Speed: 1.2609363607742232 samples/sec batch loss = 0.20007769763469696 | accuracy = 0.7527777777777778 |
| Epoch[2] Batch[725] Speed: 1.2568599193453476 samples/sec batch loss = 0.6439396142959595 | accuracy = 0.7531034482758621 |
| Epoch[2] Batch[730] Speed: 1.258898996435479 samples/sec batch loss = 0.3572985529899597 | accuracy = 0.7523972602739726 |
| Epoch[2] Batch[735] Speed: 1.2578461425885985 samples/sec batch loss = 0.5308008790016174 | accuracy = 0.753061224489796 |
| Epoch[2] Batch[740] Speed: 1.259931378668751 samples/sec batch loss = 0.3820754587650299 | accuracy = 0.7533783783783784 |
| Epoch[2] Batch[745] Speed: 1.2619374420450449 samples/sec batch loss = 0.6325419545173645 | accuracy = 0.7526845637583892 |
| Epoch[2] Batch[750] Speed: 1.2643521565416174 samples/sec batch loss = 0.5081655979156494 | accuracy = 0.7526666666666667 |
| Epoch[2] Batch[755] Speed: 1.2619741769874746 samples/sec batch loss = 0.1839897632598877 | accuracy = 0.7533112582781457 |
| Epoch[2] Batch[760] Speed: 1.2589177948594463 samples/sec batch loss = 0.018949266523122787 | accuracy = 0.7546052631578948 |
| Epoch[2] Batch[765] Speed: 1.2623622551547613 samples/sec batch loss = 0.4704032838344574 | accuracy = 0.75359477124183 |
| Epoch[2] Batch[770] Speed: 1.2652242175141066 samples/sec batch loss = 0.9202344417572021 | accuracy = 0.7532467532467533 |
| Epoch[2] Batch[775] Speed: 1.2643455820368625 samples/sec batch loss = 0.5954619646072388 | accuracy = 0.7541935483870967 |
| Epoch[2] Batch[780] Speed: 1.2581963005440315 samples/sec batch loss = 0.15097551047801971 | accuracy = 0.7548076923076923 |
| Epoch[2] Batch[785] Speed: 1.2626115410405556 samples/sec batch loss = 1.119104266166687 | accuracy = 0.7550955414012739 |
| [Epoch 2] training: accuracy=0.7550761421319797 |
| [Epoch 2] time cost: 643.7023549079895 |
| [Epoch 2] validation: validation accuracy=0.7788888888888889 |
| </pre></div></div> |
| </div> |
| </div> |
| <div class="section" id="5.-Test-on-the-test-set"> |
| <h2>5. Test on the test set<a class="headerlink" href="#5.-Test-on-the-test-set" title="Permalink to this headline">¶</a></h2> |
| <p>Now that your network is trained and has reached a decent accuracy, you can evaluate the performance on the test set. For that, you can use the <code class="docutils literal notranslate"><span class="pre">test_loader</span></code> data loader and the test function you created previously.</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">test</span><span class="p">(</span><span class="n">test_loader</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area stderr docutils container"> |
| <div class="highlight"><pre> |
| [02:19:40] /work/mxnet/src/operator/cudnn_ops.cc:421: Auto-tuning cuDNN op, set MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable |
| </pre></div></div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]: |
| </pre></div> |
| </div> |
| <div class="output_area highlight-none notranslate"><div class="highlight"><pre> |
| <span></span>0.7755555555555556 |
| </pre></div> |
| </div> |
| </div> |
| <p>You have a trained network that can confidently discriminate between plants that are healthy and the ones that are diseased. You can now start your garden and set cameras to automatically detect plants in distress! Or change your classification problem to create a model that classify the species of the plants! Either way you might be able to impress your botanist friends.</p> |
| </div> |
| <div class="section" id="6.-Save-the-parameters"> |
| <h2>6. Save the parameters<a class="headerlink" href="#6.-Save-the-parameters" title="Permalink to this headline">¶</a></h2> |
| <p>If you want to preserve the trained weights of the network you can save the parameters in a file. Later, when you want to use the network to make predictions you can load the parameters back!</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># Save parameters in the</span> |
| <span class="n">model</span><span class="o">.</span><span class="n">save_parameters</span><span class="p">(</span><span class="s1">'leaf_models.params'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>This is the end of this tutorial, to see how you can speed up the training by using GPU hardware continue to the <a class="reference internal" href="7-use-gpus.html"><span class="doc">next tutorial</span></a></p> |
| </div> |
| </div> |
| |
| |
| <hr class="feedback-hr-top" /> |
| <div class="feedback-container"> |
| <div class="feedback-question">Did this page help you?</div> |
| <div class="feedback-answer-container"> |
| <div class="feedback-answer yes-link" data-response="yes">Yes</div> |
| <div class="feedback-answer no-link" data-response="no">No</div> |
| </div> |
| <div class="feedback-thank-you">Thanks for your feedback!</div> |
| </div> |
| <hr class="feedback-hr-bottom" /> |
| </div> |
| <div class="side-doc-outline"> |
| <div class="side-doc-outline--content"> |
| <div class="localtoc"> |
| <p class="caption"> |
| <span class="caption-text">Table Of Contents</span> |
| </p> |
| <ul> |
| <li><a class="reference internal" href="#">Step 6: Train a Neural Network</a><ul> |
| <li><a class="reference internal" href="#1.-Data-preparation">1. Data preparation</a><ul> |
| <li><a class="reference internal" href="#Data-inspection">Data inspection</a></li> |
| </ul> |
| </li> |
| <li><a class="reference internal" href="#2.-Create-Neural-Network">2. Create Neural Network</a></li> |
| <li><a class="reference internal" href="#3.-Choose-Optimizer-and-Loss-function">3. Choose Optimizer and Loss function</a></li> |
| <li><a class="reference internal" href="#4.-Training-Loop">4. Training Loop</a></li> |
| <li><a class="reference internal" href="#5.-Test-on-the-test-set">5. Test on the test set</a></li> |
| <li><a class="reference internal" href="#6.-Save-the-parameters">6. Save the parameters</a></li> |
| </ul> |
| </li> |
| </ul> |
| |
| </div> |
| </div> |
| </div> |
| |
| <div class="clearer"></div> |
| </div><div class="pagenation"> |
| <a id="button-prev" href="5-datasets.html" class="mdl-button mdl-js-button mdl-js-ripple-effect mdl-button--colored" role="botton" accesskey="P"> |
| <i class="pagenation-arrow-L fas fa-arrow-left fa-lg"></i> |
| <div class="pagenation-text"> |
| <span class="pagenation-direction">Previous</span> |
| <div>Step 5: Datasets and DataLoader</div> |
| </div> |
| </a> |
| <a id="button-next" href="7-use-gpus.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>Step 7: Load and Run a NN using GPU</div> |
| </div> |
| </a> |
| </div> |
| <footer class="site-footer h-card"> |
| <div class="wrapper"> |
| <div class="row"> |
| <div class="col-4"> |
| <h4 class="footer-category-title">Resources</h4> |
| <ul class="contact-list"> |
| <li><a href="https://lists.apache.org/list.html?dev@mxnet.apache.org">Mailing list</a> <a class="u-email" href="mailto:dev-subscribe@mxnet.apache.org">(subscribe)</a></li> |
| <li><a href="https://discuss.mxnet.io">MXNet Discuss forum</a></li> |
| <li><a href="https://github.com/apache/incubator-mxnet/issues">Github Issues</a></li> |
| <li><a href="https://github.com/apache/incubator-mxnet/projects">Projects</a></li> |
| <li><a href="https://cwiki.apache.org/confluence/display/MXNET/Apache+MXNet+Home">Developer Wiki</a></li> |
| <li><a href="/community">Contribute To MXNet</a></li> |
| </ul> |
| </div> |
| |
| <div class="col-4"><ul class="social-media-list"><li><a href="https://github.com/apache/incubator-mxnet"><svg class="svg-icon"><use xlink:href="../../../_static/minima-social-icons.svg#github"></use></svg> <span class="username">apache/incubator-mxnet</span></a></li><li><a href="https://www.twitter.com/apachemxnet"><svg class="svg-icon"><use xlink:href="../../../_static/minima-social-icons.svg#twitter"></use></svg> <span class="username">apachemxnet</span></a></li><li><a href="https://youtube.com/apachemxnet"><svg class="svg-icon"><use xlink:href="../../../_static/minima-social-icons.svg#youtube"></use></svg> <span class="username">apachemxnet</span></a></li></ul> |
| </div> |
| |
| <div class="col-4 footer-text"> |
| <p>A flexible and efficient library for deep learning.</p> |
| </div> |
| </div> |
| </div> |
| </footer> |
| |
| <footer class="site-footer2"> |
| <div class="wrapper"> |
| <div class="row"> |
| <div class="col-3"> |
| <img src="../../../_static/apache_incubator_logo.png" class="footer-logo col-2"> |
| </div> |
| <div class="footer-bottom-warning col-9"> |
| <p>Apache MXNet is an effort undergoing incubation at <a href="http://www.apache.org/">The Apache Software Foundation</a> (ASF), <span style="font-weight:bold">sponsored by the <i>Apache Incubator</i></span>. Incubation is required |
| of all newly accepted projects until a further review indicates that the infrastructure, |
| communications, and decision making process have stabilized in a manner consistent with other |
| successful ASF projects. While incubation status is not necessarily a reflection of the completeness |
| or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF. |
| </p><p>"Copyright © 2017-2018, The Apache Software Foundation Apache MXNet, MXNet, Apache, the Apache |
| feather, and the Apache MXNet project logo are either registered trademarks or trademarks of the |
| Apache Software Foundation."</p> |
| </div> |
| </div> |
| </div> |
| </footer> |
| |
| </body> |
| </html> |
