| <!DOCTYPE html> |
| |
| <html xmlns="http://www.w3.org/1999/xhtml"> |
| <head> |
| <meta charset="utf-8" /> |
| <meta charset="utf-8"> |
| <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> |
| <meta http-equiv="x-ua-compatible" content="ie=edge"> |
| <style> |
| .dropdown { |
| position: relative; |
| display: inline-block; |
| } |
| |
| .dropdown-content { |
| display: none; |
| position: absolute; |
| background-color: #f9f9f9; |
| min-width: 160px; |
| box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2); |
| padding: 12px 16px; |
| z-index: 1; |
| text-align: left; |
| } |
| |
| .dropdown:hover .dropdown-content { |
| display: block; |
| } |
| |
| .dropdown-option:hover { |
| color: #FF4500; |
| } |
| |
| .dropdown-option-active { |
| color: #FF4500; |
| font-weight: lighter; |
| } |
| |
| .dropdown-option { |
| color: #000000; |
| font-weight: lighter; |
| } |
| |
| .dropdown-header { |
| color: #FFFFFF; |
| display: inline-flex; |
| } |
| |
| .dropdown-caret { |
| width: 18px; |
| } |
| |
| .dropdown-caret-path { |
| fill: #FFFFFF; |
| } |
| </style> |
| |
| <title>mxnet.gluon.rnn.rnn_layer — 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> |
| <link rel="shortcut icon" href="../../../../_static/mxnet-icon.png"/> |
| <link rel="index" title="Index" href="../../../../genindex.html" /> |
| <link rel="search" title="Search" href="../../../../search.html" /> |
| </head> |
| <body><header class="site-header" role="banner"> |
| <div class="wrapper"> |
| <a class="site-title" rel="author" href="/versions/1.9.1/"><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="/versions/1.9.1/get_started">Get Started</a> |
| <a class="page-link" href="/versions/1.9.1/features">Features</a> |
| <a class="page-link" href="/versions/1.9.1/ecosystem">Ecosystem</a> |
| <a class="page-link page-current" href="/versions/1.9.1/api">Docs & Tutorials</a> |
| <a class="page-link" href="/versions/1.9.1/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">1.9.1 |
| <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" href="/">master</a><br> |
| <a class="dropdown-option-active" href="/versions/1.9.1/">1.9.1</a><br> |
| <a class="dropdown-option" href="/versions/1.8.0/">1.8.0</a><br> |
| <a class="dropdown-option" href="/versions/1.7.0/">1.7.0</a><br> |
| <a class="dropdown-option" href="/versions/1.6.0/">1.6.0</a><br> |
| <a class="dropdown-option" href="/versions/1.5.0/">1.5.0</a><br> |
| <a class="dropdown-option" href="/versions/1.4.1/">1.4.1</a><br> |
| <a class="dropdown-option" href="/versions/1.3.1/">1.3.1</a><br> |
| <a class="dropdown-option" href="/versions/1.2.1/">1.2.1</a><br> |
| <a class="dropdown-option" href="/versions/1.1.0/">1.1.0</a><br> |
| <a class="dropdown-option" href="/versions/1.0.0/">1.0.0</a><br> |
| <a class="dropdown-option" href="/versions/0.12.1/">0.12.1</a><br> |
| <a class="dropdown-option" href="/versions/0.11.0/">0.11.0</a> |
| </div> |
| </div> |
| </div> |
| </nav> |
| </div> |
| </header> |
| <div class="mdl-layout mdl-js-layout mdl-layout--fixed-header mdl-layout--fixed-drawer"><header class="mdl-layout__header mdl-layout__header--waterfall "> |
| <div class="mdl-layout__header-row"> |
| |
| <nav class="mdl-navigation breadcrumb"> |
| <a class="mdl-navigation__link" href="../../../index.html">Module code</a><i class="material-icons">navigate_next</i> |
| <a class="mdl-navigation__link is-active">mxnet.gluon.rnn.rnn_layer</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> |
| |
| </nav> |
| </div> |
| <div class="mdl-layout__header-row header-links"> |
| <div class="mdl-layout-spacer"></div> |
| <nav class="mdl-navigation"> |
| </nav> |
| </div> |
| </header><header class="mdl-layout__drawer"> |
| |
| <div class="globaltoc"> |
| <span class="mdl-layout-title toc">Table Of Contents</span> |
| |
| |
| |
| <nav class="mdl-navigation"> |
| <ul> |
| <li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-nn.html">Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-train.html">Train the neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-predict.html">Predict with a pre-trained model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Use GPUs</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/image/image-augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/onnx/super_resolution.html">Importing an ONNX model into MXNet</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/optimizer/index.html">Optimizers</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/viz/index.html">Visualization</a><ul> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/performance/index.html">Performance</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/performance/compression/index.html">Compression</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/performance/compression/int8.html">Deploy with int-8</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/float16">Float16</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/performance/backend/index.html">Accelerated Backend Tools</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/performance/backend/mkldnn/index.html">Intel MKL-DNN</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/performance/backend/mkldnn/mkldnn_quantization.html">Quantize with MKL-DNN backend</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/performance/backend/mkldnn/mkldnn_quantization.html#Improving-accuracy-with-Intel®-Neural-Compressor">Improving accuracy with Intel® Neural Compressor</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/performance/backend/mkldnn/mkldnn_readme.html">Install MXNet with MKL-DNN</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/performance/backend/tensorrt/index.html">TensorRT</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/performance/backend/tensorrt/tensorrt.html">Optimizing Deep Learning Computation Graphs with TensorRT</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/performance/backend/tvm.html">Use TVM</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/performance/backend/profiler.html">Profiling MXNet Models</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/performance/backend/amp.html">Using AMP: Automatic Mixed Precision</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/deploy/index.html">Deployment</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/deploy/export/index.html">Export</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/deploy/export/onnx.html">Exporting to ONNX format</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/export_network.html">Export Gluon CV Models</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Save / Load Parameters</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/deploy/inference/index.html">Inference</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/deploy/inference/cpp.html">Deploy into C++</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/deploy/inference/image_classification_jetson.html">Image Classication using pretrained ResNet-50 model on Jetson module</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/deploy/inference/scala.html">Deploy into a Java or Scala Environment</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/deploy/inference/wine_detector.html">Real-time Object Detection with MXNet On The Raspberry Pi</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/deploy/run-on-aws/index.html">Run on AWS</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/deploy/run-on-aws/use_ec2.html">Run on an EC2 Instance</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/deploy/run-on-aws/use_sagemaker.html">Run on Amazon SageMaker</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/deploy/run-on-aws/cloud.html">MXNet on the Cloud</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/extend/index.html">Extend</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/extend/custom_layer.html">Custom Layers</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/extend/customop.html">Custom Numpy Operators</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/new_op">New Operator Creation</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/add_op_in_backend">New Operator in MXNet Backend</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l1"><a class="reference internal" href="../../../../api/index.html">Python API</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../api/ndarray/index.html">mxnet.ndarray</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/ndarray.html">ndarray</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/contrib/index.html">ndarray.contrib</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/image/index.html">ndarray.image</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/linalg/index.html">ndarray.linalg</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/op/index.html">ndarray.op</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/random/index.html">ndarray.random</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/register/index.html">ndarray.register</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/sparse/index.html">ndarray.sparse</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/utils/index.html">ndarray.utils</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../api/gluon/index.html">mxnet.gluon</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/gluon/block.html">gluon.Block</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/gluon/hybrid_block.html">gluon.HybridBlock</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/gluon/symbol_block.html">gluon.SymbolBlock</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/gluon/constant.html">gluon.Constant</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/gluon/parameter.html">gluon.Parameter</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/gluon/parameter_dict.html">gluon.ParameterDict</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/gluon/trainer.html">gluon.Trainer</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/gluon/contrib/index.html">gluon.contrib</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/gluon/data/index.html">gluon.data</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../api/gluon/data/vision/index.html">data.vision</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/gluon/data/vision/datasets/index.html">vision.datasets</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/gluon/data/vision/transforms/index.html">vision.transforms</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/gluon/loss/index.html">gluon.loss</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/gluon/model_zoo/index.html">gluon.model_zoo.vision</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/gluon/nn/index.html">gluon.nn</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/gluon/rnn/index.html">gluon.rnn</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/gluon/utils/index.html">gluon.utils</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../api/autograd/index.html">mxnet.autograd</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../api/initializer/index.html">mxnet.initializer</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../api/optimizer/index.html">mxnet.optimizer</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../api/lr_scheduler/index.html">mxnet.lr_scheduler</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../api/metric/index.html">mxnet.metric</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../api/kvstore/index.html">mxnet.kvstore</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../api/symbol/index.html">mxnet.symbol</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/symbol.html">symbol</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/contrib/index.html">symbol.contrib</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/image/index.html">symbol.image</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/linalg/index.html">symbol.linalg</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/op/index.html">symbol.op</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/random/index.html">symbol.random</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/register/index.html">symbol.register</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/sparse/index.html">symbol.sparse</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../api/module/index.html">mxnet.module</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../api/contrib/index.html">mxnet.contrib</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/contrib/autograd/index.html">contrib.autograd</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/contrib/io/index.html">contrib.io</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/contrib/ndarray/index.html">contrib.ndarray</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/contrib/onnx/index.html">contrib.onnx</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/contrib/quantization/index.html">contrib.quantization</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/contrib/symbol/index.html">contrib.symbol</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/contrib/tensorboard/index.html">contrib.tensorboard</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/contrib/tensorrt/index.html">contrib.tensorrt</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/contrib/text/index.html">contrib.text</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../api/mxnet/index.html">mxnet</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/attribute/index.html">mxnet.attribute</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/base/index.html">mxnet.base</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/callback/index.html">mxnet.callback</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/context/index.html">mxnet.context</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/engine/index.html">mxnet.engine</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/executor/index.html">mxnet.executor</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/executor_manager/index.html">mxnet.executor_manager</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/image/index.html">mxnet.image</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/io/index.html">mxnet.io</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/kvstore_server/index.html">mxnet.kvstore_server</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/libinfo/index.html">mxnet.libinfo</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/log/index.html">mxnet.log</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/model/index.html">mxnet.model</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/monitor/index.html">mxnet.monitor</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/name/index.html">mxnet.name</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/notebook/index.html">mxnet.notebook</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/operator/index.html">mxnet.operator</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/profiler/index.html">mxnet.profiler</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/random/index.html">mxnet.random</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/recordio/index.html">mxnet.recordio</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/registry/index.html">mxnet.registry</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/rtc/index.html">mxnet.rtc</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/runtime/index.html">mxnet.runtime</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/test_utils/index.html">mxnet.test_utils</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/torch/index.html">mxnet.torch</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/util/index.html">mxnet.util</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/visualization/index.html">mxnet.visualization</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| |
| </nav> |
| |
| </div> |
| |
| </header> |
| <main class="mdl-layout__content" tabIndex="0"> |
| |
| <script type="text/javascript" src="../../../../_static/sphinx_materialdesign_theme.js "></script> |
| <script type="text/javascript" src="../../../../_static/feedback.js"></script> |
| <header class="mdl-layout__drawer"> |
| |
| <div class="globaltoc"> |
| <span class="mdl-layout-title toc">Table Of Contents</span> |
| |
| |
| |
| <nav class="mdl-navigation"> |
| <ul> |
| <li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-nn.html">Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-train.html">Train the neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-predict.html">Predict with a pre-trained model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Use GPUs</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/image/image-augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/onnx/super_resolution.html">Importing an ONNX model into MXNet</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/optimizer/index.html">Optimizers</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/viz/index.html">Visualization</a><ul> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/performance/index.html">Performance</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/performance/compression/index.html">Compression</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/performance/compression/int8.html">Deploy with int-8</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/float16">Float16</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/performance/backend/index.html">Accelerated Backend Tools</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/performance/backend/mkldnn/index.html">Intel MKL-DNN</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/performance/backend/mkldnn/mkldnn_quantization.html">Quantize with MKL-DNN backend</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/performance/backend/mkldnn/mkldnn_quantization.html#Improving-accuracy-with-Intel®-Neural-Compressor">Improving accuracy with Intel® Neural Compressor</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/performance/backend/mkldnn/mkldnn_readme.html">Install MXNet with MKL-DNN</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/performance/backend/tensorrt/index.html">TensorRT</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/performance/backend/tensorrt/tensorrt.html">Optimizing Deep Learning Computation Graphs with TensorRT</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/performance/backend/tvm.html">Use TVM</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/performance/backend/profiler.html">Profiling MXNet Models</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/performance/backend/amp.html">Using AMP: Automatic Mixed Precision</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/deploy/index.html">Deployment</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/deploy/export/index.html">Export</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/deploy/export/onnx.html">Exporting to ONNX format</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/export_network.html">Export Gluon CV Models</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Save / Load Parameters</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/deploy/inference/index.html">Inference</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/deploy/inference/cpp.html">Deploy into C++</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/deploy/inference/image_classification_jetson.html">Image Classication using pretrained ResNet-50 model on Jetson module</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/deploy/inference/scala.html">Deploy into a Java or Scala Environment</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/deploy/inference/wine_detector.html">Real-time Object Detection with MXNet On The Raspberry Pi</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/deploy/run-on-aws/index.html">Run on AWS</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/deploy/run-on-aws/use_ec2.html">Run on an EC2 Instance</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/deploy/run-on-aws/use_sagemaker.html">Run on Amazon SageMaker</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/deploy/run-on-aws/cloud.html">MXNet on the Cloud</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/extend/index.html">Extend</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/extend/custom_layer.html">Custom Layers</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/extend/customop.html">Custom Numpy Operators</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/new_op">New Operator Creation</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/add_op_in_backend">New Operator in MXNet Backend</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l1"><a class="reference internal" href="../../../../api/index.html">Python API</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../api/ndarray/index.html">mxnet.ndarray</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/ndarray.html">ndarray</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/contrib/index.html">ndarray.contrib</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/image/index.html">ndarray.image</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/linalg/index.html">ndarray.linalg</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/op/index.html">ndarray.op</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/random/index.html">ndarray.random</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/register/index.html">ndarray.register</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/sparse/index.html">ndarray.sparse</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/ndarray/utils/index.html">ndarray.utils</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../api/gluon/index.html">mxnet.gluon</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/gluon/block.html">gluon.Block</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/gluon/hybrid_block.html">gluon.HybridBlock</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/gluon/symbol_block.html">gluon.SymbolBlock</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/gluon/constant.html">gluon.Constant</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/gluon/parameter.html">gluon.Parameter</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/gluon/parameter_dict.html">gluon.ParameterDict</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/gluon/trainer.html">gluon.Trainer</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/gluon/contrib/index.html">gluon.contrib</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/gluon/data/index.html">gluon.data</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../api/gluon/data/vision/index.html">data.vision</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/gluon/data/vision/datasets/index.html">vision.datasets</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/gluon/data/vision/transforms/index.html">vision.transforms</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/gluon/loss/index.html">gluon.loss</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/gluon/model_zoo/index.html">gluon.model_zoo.vision</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/gluon/nn/index.html">gluon.nn</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/gluon/rnn/index.html">gluon.rnn</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/gluon/utils/index.html">gluon.utils</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../api/autograd/index.html">mxnet.autograd</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../api/initializer/index.html">mxnet.initializer</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../api/optimizer/index.html">mxnet.optimizer</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../api/lr_scheduler/index.html">mxnet.lr_scheduler</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../api/metric/index.html">mxnet.metric</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../api/kvstore/index.html">mxnet.kvstore</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../api/symbol/index.html">mxnet.symbol</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/symbol.html">symbol</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/contrib/index.html">symbol.contrib</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/image/index.html">symbol.image</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/linalg/index.html">symbol.linalg</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/op/index.html">symbol.op</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/random/index.html">symbol.random</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/register/index.html">symbol.register</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/symbol/sparse/index.html">symbol.sparse</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../api/module/index.html">mxnet.module</a></li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../api/contrib/index.html">mxnet.contrib</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/contrib/autograd/index.html">contrib.autograd</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/contrib/io/index.html">contrib.io</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/contrib/ndarray/index.html">contrib.ndarray</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/contrib/onnx/index.html">contrib.onnx</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/contrib/quantization/index.html">contrib.quantization</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/contrib/symbol/index.html">contrib.symbol</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/contrib/tensorboard/index.html">contrib.tensorboard</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/contrib/tensorrt/index.html">contrib.tensorrt</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/contrib/text/index.html">contrib.text</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../api/mxnet/index.html">mxnet</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/attribute/index.html">mxnet.attribute</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/base/index.html">mxnet.base</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/callback/index.html">mxnet.callback</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/context/index.html">mxnet.context</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/engine/index.html">mxnet.engine</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/executor/index.html">mxnet.executor</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/executor_manager/index.html">mxnet.executor_manager</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/image/index.html">mxnet.image</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/io/index.html">mxnet.io</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/kvstore_server/index.html">mxnet.kvstore_server</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/libinfo/index.html">mxnet.libinfo</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/log/index.html">mxnet.log</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/model/index.html">mxnet.model</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/monitor/index.html">mxnet.monitor</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/name/index.html">mxnet.name</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/notebook/index.html">mxnet.notebook</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/operator/index.html">mxnet.operator</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/profiler/index.html">mxnet.profiler</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/random/index.html">mxnet.random</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/recordio/index.html">mxnet.recordio</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/registry/index.html">mxnet.registry</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/rtc/index.html">mxnet.rtc</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/runtime/index.html">mxnet.runtime</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/test_utils/index.html">mxnet.test_utils</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/torch/index.html">mxnet.torch</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/util/index.html">mxnet.util</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/mxnet/visualization/index.html">mxnet.visualization</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| |
| </nav> |
| |
| </div> |
| |
| </header> |
| |
| <div class="document"> |
| <div class="page-content" role="main"> |
| |
| <h1>Source code for mxnet.gluon.rnn.rnn_layer</h1><div class="highlight"><pre> |
| <span></span><span class="c1"># Licensed to the Apache Software Foundation (ASF) under one</span> |
| <span class="c1"># or more contributor license agreements. See the NOTICE file</span> |
| <span class="c1"># distributed with this work for additional information</span> |
| <span class="c1"># regarding copyright ownership. The ASF licenses this file</span> |
| <span class="c1"># to you under the Apache License, Version 2.0 (the</span> |
| <span class="c1"># "License"); you may not use this file except in compliance</span> |
| <span class="c1"># with the License. You may obtain a copy of the License at</span> |
| <span class="c1">#</span> |
| <span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span> |
| <span class="c1">#</span> |
| <span class="c1"># Unless required by applicable law or agreed to in writing,</span> |
| <span class="c1"># software distributed under the License is distributed on an</span> |
| <span class="c1"># "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY</span> |
| <span class="c1"># KIND, either express or implied. See the License for the</span> |
| <span class="c1"># specific language governing permissions and limitations</span> |
| <span class="c1"># under the License.</span> |
| |
| <span class="c1"># coding: utf-8</span> |
| <span class="c1"># pylint: disable=no-member, invalid-name, protected-access, no-self-use</span> |
| <span class="c1"># pylint: disable=too-many-branches, too-many-arguments, no-self-use</span> |
| <span class="c1"># pylint: disable=too-many-lines, arguments-differ</span> |
| <span class="sd">"""Definition of various recurrent neural network layers."""</span> |
| <span class="kn">import</span> <span class="nn">re</span> |
| |
| <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'RNN'</span><span class="p">,</span> <span class="s1">'LSTM'</span><span class="p">,</span> <span class="s1">'GRU'</span><span class="p">]</span> |
| |
| <span class="kn">from</span> <span class="nn">...</span> <span class="kn">import</span> <span class="n">ndarray</span><span class="p">,</span> <span class="n">symbol</span> |
| <span class="kn">from</span> <span class="nn">..</span> <span class="kn">import</span> <span class="n">HybridBlock</span><span class="p">,</span> <span class="n">tensor_types</span> |
| <span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">rnn_cell</span> |
| <span class="kn">from</span> <span class="nn">...util</span> <span class="kn">import</span> <span class="n">is_np_array</span> |
| |
| |
| <span class="k">class</span> <span class="nc">_RNNLayer</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="sd">"""Implementation of recurrent layers."""</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="n">hidden_size</span><span class="p">,</span> <span class="n">num_layers</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> |
| <span class="n">dropout</span><span class="p">,</span> <span class="n">bidirectional</span><span class="p">,</span> <span class="n">input_size</span><span class="p">,</span> |
| <span class="n">i2h_weight_initializer</span><span class="p">,</span> <span class="n">h2h_weight_initializer</span><span class="p">,</span> |
| <span class="n">i2h_bias_initializer</span><span class="p">,</span> <span class="n">h2h_bias_initializer</span><span class="p">,</span> |
| <span class="n">mode</span><span class="p">,</span> <span class="n">projection_size</span><span class="p">,</span> <span class="n">h2r_weight_initializer</span><span class="p">,</span> |
| <span class="n">lstm_state_clip_min</span><span class="p">,</span> <span class="n">lstm_state_clip_max</span><span class="p">,</span> <span class="n">lstm_state_clip_nan</span><span class="p">,</span> |
| <span class="n">dtype</span><span class="p">,</span> <span class="n">use_sequence_length</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">_RNNLayer</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="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">assert</span> <span class="n">layout</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'TNC'</span><span class="p">,</span> <span class="s1">'NTC'</span><span class="p">),</span> \ |
| <span class="s2">"Invalid layout </span><span class="si">%s</span><span class="s2">; must be one of ['TNC' or 'NTC']"</span><span class="o">%</span><span class="n">layout</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_hidden_size</span> <span class="o">=</span> <span class="n">hidden_size</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_projection_size</span> <span class="o">=</span> <span class="n">projection_size</span> <span class="k">if</span> <span class="n">projection_size</span> <span class="k">else</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_num_layers</span> <span class="o">=</span> <span class="n">num_layers</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_mode</span> <span class="o">=</span> <span class="n">mode</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_layout</span> <span class="o">=</span> <span class="n">layout</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_dropout</span> <span class="o">=</span> <span class="n">dropout</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_dir</span> <span class="o">=</span> <span class="mi">2</span> <span class="k">if</span> <span class="n">bidirectional</span> <span class="k">else</span> <span class="mi">1</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_input_size</span> <span class="o">=</span> <span class="n">input_size</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_i2h_weight_initializer</span> <span class="o">=</span> <span class="n">i2h_weight_initializer</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_h2h_weight_initializer</span> <span class="o">=</span> <span class="n">h2h_weight_initializer</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_i2h_bias_initializer</span> <span class="o">=</span> <span class="n">i2h_bias_initializer</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_h2h_bias_initializer</span> <span class="o">=</span> <span class="n">h2h_bias_initializer</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_h2r_weight_initializer</span> <span class="o">=</span> <span class="n">h2r_weight_initializer</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_lstm_state_clip_min</span> <span class="o">=</span> <span class="n">lstm_state_clip_min</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_lstm_state_clip_max</span> <span class="o">=</span> <span class="n">lstm_state_clip_max</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_lstm_state_clip_nan</span> <span class="o">=</span> <span class="n">lstm_state_clip_nan</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_dtype</span> <span class="o">=</span> <span class="n">dtype</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_use_sequence_length</span> <span class="o">=</span> <span class="n">use_sequence_length</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">skip_states</span> <span class="o">=</span> <span class="kc">None</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">_gates</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'rnn_relu'</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s1">'rnn_tanh'</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s1">'lstm'</span><span class="p">:</span> <span class="mi">4</span><span class="p">,</span> <span class="s1">'gru'</span><span class="p">:</span> <span class="mi">3</span><span class="p">}[</span><span class="n">mode</span><span class="p">]</span> |
| |
| <span class="n">ng</span><span class="p">,</span> <span class="n">ni</span><span class="p">,</span> <span class="n">nh</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_gates</span><span class="p">,</span> <span class="n">input_size</span><span class="p">,</span> <span class="n">hidden_size</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">projection_size</span><span class="p">:</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_layers</span><span class="p">):</span> |
| <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'l'</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">][:</span><span class="bp">self</span><span class="o">.</span><span class="n">_dir</span><span class="p">]:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_register_param</span><span class="p">(</span><span class="s1">'</span><span class="si">{}{}</span><span class="s1">_i2h_weight'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">),</span> |
| <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">ng</span><span class="o">*</span><span class="n">nh</span><span class="p">,</span> <span class="n">ni</span><span class="p">),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">i2h_weight_initializer</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_register_param</span><span class="p">(</span><span class="s1">'</span><span class="si">{}{}</span><span class="s1">_h2h_weight'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">),</span> |
| <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">ng</span><span class="o">*</span><span class="n">nh</span><span class="p">,</span> <span class="n">nh</span><span class="p">),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">h2h_weight_initializer</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_register_param</span><span class="p">(</span><span class="s1">'</span><span class="si">{}{}</span><span class="s1">_i2h_bias'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">),</span> |
| <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">ng</span><span class="o">*</span><span class="n">nh</span><span class="p">,),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">i2h_bias_initializer</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_register_param</span><span class="p">(</span><span class="s1">'</span><span class="si">{}{}</span><span class="s1">_h2h_bias'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">),</span> |
| <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">ng</span><span class="o">*</span><span class="n">nh</span><span class="p">,),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">h2h_bias_initializer</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="n">ni</span> <span class="o">=</span> <span class="n">nh</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dir</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">np</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_projection_size</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_layers</span><span class="p">):</span> |
| <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'l'</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">][:</span><span class="bp">self</span><span class="o">.</span><span class="n">_dir</span><span class="p">]:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_register_param</span><span class="p">(</span><span class="s1">'</span><span class="si">{}{}</span><span class="s1">_i2h_weight'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">),</span> |
| <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">ng</span><span class="o">*</span><span class="n">nh</span><span class="p">,</span> <span class="n">ni</span><span class="p">),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">i2h_weight_initializer</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_register_param</span><span class="p">(</span><span class="s1">'</span><span class="si">{}{}</span><span class="s1">_h2h_weight'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">),</span> |
| <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">ng</span><span class="o">*</span><span class="n">nh</span><span class="p">,</span> <span class="n">np</span><span class="p">),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">h2h_weight_initializer</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_register_param</span><span class="p">(</span><span class="s1">'</span><span class="si">{}{}</span><span class="s1">_i2h_bias'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">),</span> |
| <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">ng</span><span class="o">*</span><span class="n">nh</span><span class="p">,),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">i2h_bias_initializer</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_register_param</span><span class="p">(</span><span class="s1">'</span><span class="si">{}{}</span><span class="s1">_h2h_bias'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">),</span> |
| <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">ng</span><span class="o">*</span><span class="n">nh</span><span class="p">,),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">h2h_bias_initializer</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_register_param</span><span class="p">(</span><span class="s1">'</span><span class="si">{}{}</span><span class="s1">_h2r_weight'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">),</span> |
| <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">np</span><span class="p">,</span> <span class="n">nh</span><span class="p">),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">h2r_weight_initializer</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="n">ni</span> <span class="o">=</span> <span class="n">np</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dir</span> |
| |
| <span class="k">def</span> <span class="nf">_register_param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">init</span><span class="p">,</span> <span class="n">dtype</span><span class="p">):</span> |
| <span class="n">p</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="n">shape</span><span class="p">,</span> <span class="n">init</span><span class="o">=</span><span class="n">init</span><span class="p">,</span> |
| <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="nb">setattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">p</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">p</span> |
| |
| <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="n">s</span> <span class="o">=</span> <span class="s1">'</span><span class="si">{name}</span><span class="s1">(</span><span class="si">{mapping}</span><span class="s1">, </span><span class="si">{_layout}</span><span class="s1">'</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_layers</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span> |
| <span class="n">s</span> <span class="o">+=</span> <span class="s1">', num_layers=</span><span class="si">{_num_layers}</span><span class="s1">'</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dropout</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">s</span> <span class="o">+=</span> <span class="s1">', dropout=</span><span class="si">{_dropout}</span><span class="s1">'</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dir</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span> |
| <span class="n">s</span> <span class="o">+=</span> <span class="s1">', bidirectional'</span> |
| <span class="n">s</span> <span class="o">+=</span> <span class="s1">')'</span> |
| <span class="n">shape</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">l0_i2h_weight</span><span class="o">.</span><span class="n">shape</span> |
| <span class="n">mapping</span> <span class="o">=</span> <span class="s1">'</span><span class="si">{0}</span><span class="s1"> -> </span><span class="si">{1}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">if</span> <span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span> <span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">_gates</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">s</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> |
| <span class="n">mapping</span><span class="o">=</span><span class="n">mapping</span><span class="p">,</span> |
| <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">_collect_params_with_prefix</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="s1">''</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">prefix</span><span class="p">:</span> |
| <span class="n">prefix</span> <span class="o">+=</span> <span class="s1">'.'</span> |
| <span class="n">pattern</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="sa">r</span><span class="s1">'(l|r)(\d)_(i2h|h2h|h2r)_(weight|bias)\Z'</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">convert_key</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">bidirectional</span><span class="p">):</span> <span class="c1"># for compatibility with old parameter format</span> |
| <span class="n">d</span><span class="p">,</span> <span class="n">l</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="n">t</span> <span class="o">=</span> <span class="p">[</span><span class="n">m</span><span class="o">.</span><span class="n">group</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">)]</span> |
| <span class="k">if</span> <span class="n">bidirectional</span><span class="p">:</span> |
| <span class="k">return</span> <span class="s1">'_unfused.</span><span class="si">{}</span><span class="s1">.</span><span class="si">{}</span><span class="s1">_cell.</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">l</span><span class="p">,</span> <span class="n">d</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="n">t</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="s1">'_unfused.</span><span class="si">{}</span><span class="s1">.</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">l</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="n">t</span><span class="p">)</span> |
| <span class="n">bidirectional</span> <span class="o">=</span> <span class="nb">any</span><span class="p">(</span><span class="n">pattern</span><span class="o">.</span><span class="n">match</span><span class="p">(</span><span class="n">k</span><span class="p">)</span><span class="o">.</span><span class="n">group</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="s1">'r'</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_reg_params</span><span class="p">)</span> |
| |
| <span class="n">ret</span> <span class="o">=</span> <span class="p">{</span><span class="n">prefix</span> <span class="o">+</span> <span class="n">convert_key</span><span class="p">(</span><span class="n">pattern</span><span class="o">.</span><span class="n">match</span><span class="p">(</span><span class="n">key</span><span class="p">),</span> <span class="n">bidirectional</span><span class="p">)</span> <span class="p">:</span> <span class="n">val</span> |
| <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_reg_params</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span> |
| <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">child</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="n">ret</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">child</span><span class="o">.</span><span class="n">_collect_params_with_prefix</span><span class="p">(</span><span class="n">prefix</span> <span class="o">+</span> <span class="n">name</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">ret</span> |
| |
| <span class="k">def</span> <span class="nf">state_info</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span> |
| |
| <span class="k">def</span> <span class="nf">_unfuse</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Unfuses the fused RNN in to a stack of rnn cells."""</span> |
| <span class="k">assert</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_projection_size</span><span class="p">,</span> <span class="s2">"_unfuse does not support projection layer yet!"</span> |
| <span class="k">assert</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_lstm_state_clip_min</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_lstm_state_clip_max</span><span class="p">,</span> \ |
| <span class="s2">"_unfuse does not support state clipping yet!"</span> |
| <span class="n">get_cell</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'rnn_relu'</span><span class="p">:</span> <span class="k">lambda</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">rnn_cell</span><span class="o">.</span><span class="n">RNNCell</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_hidden_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="o">**</span><span class="n">kwargs</span><span class="p">),</span> |
| <span class="s1">'rnn_tanh'</span><span class="p">:</span> <span class="k">lambda</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">rnn_cell</span><span class="o">.</span><span class="n">RNNCell</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_hidden_size</span><span class="p">,</span> |
| <span class="n">activation</span><span class="o">=</span><span class="s1">'tanh'</span><span class="p">,</span> |
| <span class="o">**</span><span class="n">kwargs</span><span class="p">),</span> |
| <span class="s1">'lstm'</span><span class="p">:</span> <span class="k">lambda</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">rnn_cell</span><span class="o">.</span><span class="n">LSTMCell</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_hidden_size</span><span class="p">,</span> |
| <span class="o">**</span><span class="n">kwargs</span><span class="p">),</span> |
| <span class="s1">'gru'</span><span class="p">:</span> <span class="k">lambda</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">rnn_cell</span><span class="o">.</span><span class="n">GRUCell</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_hidden_size</span><span class="p">,</span> |
| <span class="o">**</span><span class="n">kwargs</span><span class="p">)}[</span><span class="bp">self</span><span class="o">.</span><span class="n">_mode</span><span class="p">]</span> |
| |
| <span class="n">stack</span> <span class="o">=</span> <span class="n">rnn_cell</span><span class="o">.</span><span class="n">HybridSequentialRNNCell</span><span class="p">(</span><span class="n">prefix</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">prefix</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="p">)</span> |
| <span class="k">with</span> <span class="n">stack</span><span class="o">.</span><span class="n">name_scope</span><span class="p">():</span> |
| <span class="n">ni</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_input_size</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_num_layers</span><span class="p">):</span> |
| <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'input_size'</span><span class="p">:</span> <span class="n">ni</span><span class="p">,</span> |
| <span class="s1">'i2h_weight_initializer'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_i2h_weight_initializer</span><span class="p">,</span> |
| <span class="s1">'h2h_weight_initializer'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_h2h_weight_initializer</span><span class="p">,</span> |
| <span class="s1">'i2h_bias_initializer'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_i2h_bias_initializer</span><span class="p">,</span> |
| <span class="s1">'h2h_bias_initializer'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_h2h_bias_initializer</span><span class="p">}</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dir</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span> |
| <span class="n">stack</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">rnn_cell</span><span class="o">.</span><span class="n">BidirectionalCell</span><span class="p">(</span> |
| <span class="n">get_cell</span><span class="p">(</span><span class="n">prefix</span><span class="o">=</span><span class="s1">'l</span><span class="si">%d</span><span class="s1">_'</span><span class="o">%</span><span class="n">i</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">),</span> |
| <span class="n">get_cell</span><span class="p">(</span><span class="n">prefix</span><span class="o">=</span><span class="s1">'r</span><span class="si">%d</span><span class="s1">_'</span><span class="o">%</span><span class="n">i</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">stack</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">get_cell</span><span class="p">(</span><span class="n">prefix</span><span class="o">=</span><span class="s1">'l</span><span class="si">%d</span><span class="s1">_'</span><span class="o">%</span><span class="n">i</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">))</span> |
| |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dropout</span> <span class="o">></span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">i</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_layers</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span> |
| <span class="n">stack</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">rnn_cell</span><span class="o">.</span><span class="n">DropoutCell</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_dropout</span><span class="p">))</span> |
| |
| <span class="n">ni</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_hidden_size</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dir</span> |
| |
| <span class="k">return</span> <span class="n">stack</span> |
| |
| <span class="k">def</span> <span class="nf">cast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dtype</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">_RNNLayer</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_dtype</span> <span class="o">=</span> <span class="n">dtype</span> |
| |
| <span class="k">def</span> <span class="nf">begin_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">func</span><span class="o">=</span><span class="n">ndarray</span><span class="o">.</span><span class="n">zeros</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="sd">"""Initial state for this cell.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> batch_size: int</span> |
| <span class="sd"> Only required for `NDArray` API. Size of the batch ('N' in layout).</span> |
| <span class="sd"> Dimension of the input.</span> |
| <span class="sd"> func : callable, default `ndarray.zeros`</span> |
| <span class="sd"> Function for creating initial state.</span> |
| |
| <span class="sd"> For Symbol API, func can be `symbol.zeros`, `symbol.uniform`,</span> |
| <span class="sd"> `symbol.var` etc. Use `symbol.var` if you want to directly</span> |
| <span class="sd"> feed input as states.</span> |
| |
| <span class="sd"> For NDArray API, func can be `ndarray.zeros`, `ndarray.ones`, etc.</span> |
| |
| <span class="sd"> **kwargs :</span> |
| <span class="sd"> Additional keyword arguments passed to func. For example</span> |
| <span class="sd"> `mean`, `std`, `dtype`, etc.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> states : nested list of Symbol</span> |
| <span class="sd"> Starting states for the first RNN step.</span> |
| <span class="sd"> """</span> |
| <span class="n">states</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">info</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">state_info</span><span class="p">(</span><span class="n">batch_size</span><span class="p">)):</span> |
| <span class="k">if</span> <span class="n">info</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">info</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">info</span> <span class="o">=</span> <span class="n">kwargs</span> |
| <span class="n">state</span> <span class="o">=</span> <span class="n">func</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'</span><span class="si">%s</span><span class="s1">h0_</span><span class="si">%d</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">prefix</span><span class="p">,</span> <span class="n">i</span><span class="p">),</span> <span class="o">**</span><span class="n">info</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">is_np_array</span><span class="p">():</span> |
| <span class="n">state</span> <span class="o">=</span> <span class="n">state</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span> |
| <span class="n">states</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">state</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">states</span> |
| |
| <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">states</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sequence_length</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">skip_states</span> <span class="o">=</span> <span class="n">states</span> <span class="ow">is</span> <span class="kc">None</span> |
| <span class="k">if</span> <span class="n">states</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">NDArray</span><span class="p">):</span> |
| <span class="n">batch_size</span> <span class="o">=</span> <span class="n">inputs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'N'</span><span class="p">)]</span> |
| <span class="n">states</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">begin_state</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">inputs</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">inputs</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">states</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">begin_state</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">func</span><span class="o">=</span><span class="n">symbol</span><span class="o">.</span><span class="n">zeros</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">states</span><span class="p">,</span> <span class="n">tensor_types</span><span class="p">):</span> |
| <span class="n">states</span> <span class="o">=</span> <span class="p">[</span><span class="n">states</span><span class="p">]</span> |
| |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_use_sequence_length</span><span class="p">:</span> |
| <span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">_RNNLayer</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__call__</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">states</span><span class="p">,</span> <span class="n">sequence_length</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">_RNNLayer</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__call__</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">states</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">hybrid_forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">states</span><span class="p">,</span> <span class="n">sequence_length</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">F</span> <span class="ow">is</span> <span class="n">ndarray</span><span class="p">:</span> |
| <span class="n">batch_size</span> <span class="o">=</span> <span class="n">inputs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'N'</span><span class="p">)]</span> |
| |
| <span class="k">if</span> <span class="n">F</span> <span class="ow">is</span> <span class="n">ndarray</span><span class="p">:</span> |
| <span class="k">for</span> <span class="n">state</span><span class="p">,</span> <span class="n">info</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">states</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">state_info</span><span class="p">(</span><span class="n">batch_size</span><span class="p">)):</span> |
| <span class="k">if</span> <span class="n">state</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="n">info</span><span class="p">[</span><span class="s1">'shape'</span><span class="p">]:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> |
| <span class="s2">"Invalid recurrent state shape. Expecting </span><span class="si">%s</span><span class="s2">, got </span><span class="si">%s</span><span class="s2">."</span><span class="o">%</span><span class="p">(</span> |
| <span class="nb">str</span><span class="p">(</span><span class="n">info</span><span class="p">[</span><span class="s1">'shape'</span><span class="p">]),</span> <span class="nb">str</span><span class="p">(</span><span class="n">state</span><span class="o">.</span><span class="n">shape</span><span class="p">)))</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_kernel</span><span class="p">(</span><span class="n">F</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">states</span><span class="p">,</span> <span class="n">sequence_length</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| |
| <span class="c1"># out is (output, state)</span> |
| <span class="k">return</span> <span class="n">out</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">skip_states</span> <span class="k">else</span> <span class="n">out</span> |
| |
| <span class="k">def</span> <span class="nf">_forward_kernel</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">states</span><span class="p">,</span> <span class="n">sequence_length</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="sd">""" forward using CUDNN or CPU kenrel"""</span> |
| <span class="n">swapaxes</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">np</span><span class="o">.</span><span class="n">swapaxes</span> <span class="k">if</span> <span class="n">is_np_array</span><span class="p">()</span> <span class="k">else</span> <span class="n">F</span><span class="o">.</span><span class="n">swapaxes</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_layout</span> <span class="o">==</span> <span class="s1">'NTC'</span><span class="p">:</span> |
| <span class="n">inputs</span> <span class="o">=</span> <span class="n">swapaxes</span><span class="p">(</span><span class="n">inputs</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="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_projection_size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">params</span> <span class="o">=</span> <span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s1">'</span><span class="si">{}{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">l</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="n">t</span><span class="p">)]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'weight'</span><span class="p">,</span> <span class="s1">'bias'</span><span class="p">]</span> |
| <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_num_layers</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'l'</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">][:</span><span class="bp">self</span><span class="o">.</span><span class="n">_dir</span><span class="p">]</span> |
| <span class="k">for</span> <span class="n">g</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'i2h'</span><span class="p">,</span> <span class="s1">'h2h'</span><span class="p">])</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">params</span> <span class="o">=</span> <span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s1">'</span><span class="si">{}{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">l</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="n">t</span><span class="p">)]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'weight'</span><span class="p">,</span> <span class="s1">'bias'</span><span class="p">]</span> |
| <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_num_layers</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'l'</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">][:</span><span class="bp">self</span><span class="o">.</span><span class="n">_dir</span><span class="p">]</span> |
| <span class="k">for</span> <span class="n">g</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'i2h'</span><span class="p">,</span> <span class="s1">'h2h'</span><span class="p">,</span> <span class="s1">'h2r'</span><span class="p">]</span> |
| <span class="k">if</span> <span class="n">g</span> <span class="o">!=</span> <span class="s1">'h2r'</span> <span class="ow">or</span> <span class="n">t</span> <span class="o">!=</span> <span class="s1">'bias'</span><span class="p">)</span> |
| |
| <span class="n">rnn_param_concat</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">np</span><span class="o">.</span><span class="n">_internal</span><span class="o">.</span><span class="n">rnn_param_concat</span> <span class="k">if</span> <span class="n">is_np_array</span><span class="p">()</span>\ |
| <span class="k">else</span> <span class="n">F</span><span class="o">.</span><span class="n">_internal</span><span class="o">.</span><span class="n">_rnn_param_concat</span> |
| <span class="n">params</span> <span class="o">=</span> <span class="n">rnn_param_concat</span><span class="p">(</span><span class="o">*</span><span class="n">params</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_use_sequence_length</span><span class="p">:</span> |
| <span class="n">rnn_args</span> <span class="o">=</span> <span class="n">states</span> <span class="o">+</span> <span class="p">[</span><span class="n">sequence_length</span><span class="p">]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">rnn_args</span> <span class="o">=</span> <span class="n">states</span> |
| |
| <span class="n">rnn_fn</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">npx</span><span class="o">.</span><span class="n">rnn</span> <span class="k">if</span> <span class="n">is_np_array</span><span class="p">()</span> <span class="k">else</span> <span class="n">F</span><span class="o">.</span><span class="n">RNN</span> |
| <span class="n">rnn</span> <span class="o">=</span> <span class="n">rnn_fn</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="o">*</span><span class="n">rnn_args</span><span class="p">,</span> <span class="n">use_sequence_length</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_use_sequence_length</span><span class="p">,</span> |
| <span class="n">state_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_hidden_size</span><span class="p">,</span> <span class="n">projection_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_projection_size</span><span class="p">,</span> |
| <span class="n">num_layers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_num_layers</span><span class="p">,</span> <span class="n">bidirectional</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_dir</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> |
| <span class="n">p</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_dropout</span><span class="p">,</span> <span class="n">state_outputs</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_mode</span><span class="p">,</span> |
| <span class="n">lstm_state_clip_min</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_lstm_state_clip_min</span><span class="p">,</span> |
| <span class="n">lstm_state_clip_max</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_lstm_state_clip_max</span><span class="p">,</span> |
| <span class="n">lstm_state_clip_nan</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_lstm_state_clip_nan</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mode</span> <span class="o">==</span> <span class="s1">'lstm'</span><span class="p">:</span> |
| <span class="n">outputs</span><span class="p">,</span> <span class="n">states</span> <span class="o">=</span> <span class="n">rnn</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="n">rnn</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">rnn</span><span class="p">[</span><span class="mi">2</span><span class="p">]]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">outputs</span><span class="p">,</span> <span class="n">states</span> <span class="o">=</span> <span class="n">rnn</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="n">rnn</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span> |
| |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_layout</span> <span class="o">==</span> <span class="s1">'NTC'</span><span class="p">:</span> |
| <span class="n">outputs</span> <span class="o">=</span> <span class="n">swapaxes</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="n">outputs</span><span class="p">,</span> <span class="n">states</span> |
| |
| |
| <div class="viewcode-block" id="RNN"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.RNN">[docs]</a><span class="k">class</span> <span class="nc">RNN</span><span class="p">(</span><span class="n">_RNNLayer</span><span class="p">):</span> |
| <span class="sa">r</span><span class="sd">"""Applies a multi-layer Elman RNN with `tanh` or `ReLU` non-linearity to an input sequence.</span> |
| |
| <span class="sd"> For each element in the input sequence, each layer computes the following</span> |
| <span class="sd"> function:</span> |
| |
| <span class="sd"> .. math::</span> |
| <span class="sd"> h_t = \tanh(w_{ih} * x_t + b_{ih} + w_{hh} * h_{(t-1)} + b_{hh})</span> |
| |
| <span class="sd"> where :math:`h_t` is the hidden state at time `t`, and :math:`x_t` is the output</span> |
| <span class="sd"> of the previous layer at time `t` or :math:`input_t` for the first layer.</span> |
| <span class="sd"> If nonlinearity='relu', then `ReLU` is used instead of `tanh`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> hidden_size: int</span> |
| <span class="sd"> The number of features in the hidden state h.</span> |
| <span class="sd"> num_layers: int, default 1</span> |
| <span class="sd"> Number of recurrent layers.</span> |
| <span class="sd"> activation: {'relu' or 'tanh'}, default 'relu'</span> |
| <span class="sd"> The activation function to use.</span> |
| <span class="sd"> layout : str, default 'TNC'</span> |
| <span class="sd"> The format of input and output tensors. T, N and C stand for</span> |
| <span class="sd"> sequence length, batch size, and feature dimensions respectively.</span> |
| <span class="sd"> dropout: float, default 0</span> |
| <span class="sd"> If non-zero, introduces a dropout layer on the outputs of each</span> |
| <span class="sd"> RNN layer except the last layer.</span> |
| <span class="sd"> bidirectional: bool, default False</span> |
| <span class="sd"> If `True`, becomes a bidirectional RNN.</span> |
| <span class="sd"> i2h_weight_initializer : str or Initializer</span> |
| <span class="sd"> Initializer for the input weights matrix, used for the linear</span> |
| <span class="sd"> transformation of the inputs.</span> |
| <span class="sd"> h2h_weight_initializer : str or Initializer</span> |
| <span class="sd"> Initializer for the recurrent weights matrix, used for the linear</span> |
| <span class="sd"> transformation of the recurrent state.</span> |
| <span class="sd"> i2h_bias_initializer : str or Initializer</span> |
| <span class="sd"> Initializer for the bias vector.</span> |
| <span class="sd"> h2h_bias_initializer : str or Initializer</span> |
| <span class="sd"> Initializer for the bias vector.</span> |
| <span class="sd"> input_size: int, default 0</span> |
| <span class="sd"> The number of expected features in the input x.</span> |
| <span class="sd"> If not specified, it will be inferred from input.</span> |
| <span class="sd"> dtype : str, default 'float32'</span> |
| <span class="sd"> Type to initialize the parameters and default states to</span> |
| <span class="sd"> prefix : str or None</span> |
| <span class="sd"> Prefix of this `Block`.</span> |
| <span class="sd"> params : ParameterDict or None</span> |
| <span class="sd"> Shared Parameters for this `Block`.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: input tensor with shape `(sequence_length, batch_size, input_size)`</span> |
| <span class="sd"> when `layout` is "TNC". For other layouts, dimensions are permuted accordingly</span> |
| <span class="sd"> using transpose() operator which adds performance overhead. Consider creating</span> |
| <span class="sd"> batches in TNC layout during data batching step.</span> |
| |
| <span class="sd"> - **states**: initial recurrent state tensor with shape</span> |
| <span class="sd"> `(num_layers, batch_size, num_hidden)`. If `bidirectional` is True,</span> |
| <span class="sd"> shape will instead be `(2*num_layers, batch_size, num_hidden)`. If</span> |
| <span class="sd"> `states` is None, zeros will be used as default begin states.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: output tensor with shape `(sequence_length, batch_size, num_hidden)`</span> |
| <span class="sd"> when `layout` is "TNC". If `bidirectional` is True, output shape will instead</span> |
| <span class="sd"> be `(sequence_length, batch_size, 2*num_hidden)`</span> |
| <span class="sd"> - **out_states**: output recurrent state tensor with the same shape as `states`.</span> |
| <span class="sd"> If `states` is None `out_states` will not be returned.</span> |
| |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> layer = mx.gluon.rnn.RNN(100, 3)</span> |
| <span class="sd"> >>> layer.initialize()</span> |
| <span class="sd"> >>> input = mx.nd.random.uniform(shape=(5, 3, 10))</span> |
| <span class="sd"> >>> # by default zeros are used as begin state</span> |
| <span class="sd"> >>> output = layer(input)</span> |
| <span class="sd"> >>> # manually specify begin state.</span> |
| <span class="sd"> >>> h0 = mx.nd.random.uniform(shape=(3, 3, 100))</span> |
| <span class="sd"> >>> output, hn = layer(input, h0)</span> |
| <span class="sd"> """</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="n">hidden_size</span><span class="p">,</span> <span class="n">num_layers</span><span class="o">=</span><span class="mi">1</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">layout</span><span class="o">=</span><span class="s1">'TNC'</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">bidirectional</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">i2h_weight_initializer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">h2h_weight_initializer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">i2h_bias_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> <span class="n">h2h_bias_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> |
| <span class="n">input_size</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'float32'</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">RNN</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="n">hidden_size</span><span class="p">,</span> <span class="n">num_layers</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> |
| <span class="n">dropout</span><span class="p">,</span> <span class="n">bidirectional</span><span class="p">,</span> <span class="n">input_size</span><span class="p">,</span> |
| <span class="n">i2h_weight_initializer</span><span class="p">,</span> <span class="n">h2h_weight_initializer</span><span class="p">,</span> |
| <span class="n">i2h_bias_initializer</span><span class="p">,</span> <span class="n">h2h_bias_initializer</span><span class="p">,</span> |
| <span class="s1">'rnn_'</span><span class="o">+</span><span class="n">activation</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> |
| <span class="n">dtype</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">state_info</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> |
| <span class="k">return</span> <span class="p">[{</span><span class="s1">'shape'</span><span class="p">:</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_num_layers</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dir</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_hidden_size</span><span class="p">),</span> |
| <span class="s1">'__layout__'</span><span class="p">:</span> <span class="s1">'LNC'</span><span class="p">,</span> <span class="s1">'dtype'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dtype</span><span class="p">}]</span></div> |
| |
| |
| <div class="viewcode-block" id="LSTM"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.LSTM">[docs]</a><span class="k">class</span> <span class="nc">LSTM</span><span class="p">(</span><span class="n">_RNNLayer</span><span class="p">):</span> |
| <span class="sa">r</span><span class="sd">"""Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence.</span> |
| |
| <span class="sd"> For each element in the input sequence, each layer computes the following</span> |
| <span class="sd"> function:</span> |
| |
| <span class="sd"> .. math::</span> |
| <span class="sd"> \begin{array}{ll}</span> |
| <span class="sd"> i_t = sigmoid(W_{ii} x_t + b_{ii} + W_{hi} h_{(t-1)} + b_{hi}) \\</span> |
| <span class="sd"> f_t = sigmoid(W_{if} x_t + b_{if} + W_{hf} h_{(t-1)} + b_{hf}) \\</span> |
| <span class="sd"> g_t = \tanh(W_{ig} x_t + b_{ig} + W_{hc} h_{(t-1)} + b_{hg}) \\</span> |
| <span class="sd"> o_t = sigmoid(W_{io} x_t + b_{io} + W_{ho} h_{(t-1)} + b_{ho}) \\</span> |
| <span class="sd"> c_t = f_t * c_{(t-1)} + i_t * g_t \\</span> |
| <span class="sd"> h_t = o_t * \tanh(c_t)</span> |
| <span class="sd"> \end{array}</span> |
| |
| <span class="sd"> where :math:`h_t` is the hidden state at time `t`, :math:`c_t` is the</span> |
| <span class="sd"> cell state at time `t`, :math:`x_t` is the hidden state of the previous</span> |
| <span class="sd"> layer at time `t` or :math:`input_t` for the first layer, and :math:`i_t`,</span> |
| <span class="sd"> :math:`f_t`, :math:`g_t`, :math:`o_t` are the input, forget, cell, and</span> |
| <span class="sd"> out gates, respectively.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> hidden_size: int</span> |
| <span class="sd"> The number of features in the hidden state h.</span> |
| <span class="sd"> num_layers: int, default 1</span> |
| <span class="sd"> Number of recurrent layers.</span> |
| <span class="sd"> layout : str, default 'TNC'</span> |
| <span class="sd"> The format of input and output tensors. T, N and C stand for</span> |
| <span class="sd"> sequence length, batch size, and feature dimensions respectively.</span> |
| <span class="sd"> dropout: float, default 0</span> |
| <span class="sd"> If non-zero, introduces a dropout layer on the outputs of each</span> |
| <span class="sd"> RNN layer except the last layer.</span> |
| <span class="sd"> bidirectional: bool, default False</span> |
| <span class="sd"> If `True`, becomes a bidirectional RNN.</span> |
| <span class="sd"> i2h_weight_initializer : str or Initializer</span> |
| <span class="sd"> Initializer for the input weights matrix, used for the linear</span> |
| <span class="sd"> transformation of the inputs.</span> |
| <span class="sd"> h2h_weight_initializer : str or Initializer</span> |
| <span class="sd"> Initializer for the recurrent weights matrix, used for the linear</span> |
| <span class="sd"> transformation of the recurrent state.</span> |
| <span class="sd"> i2h_bias_initializer : str or Initializer, default 'lstmbias'</span> |
| <span class="sd"> Initializer for the bias vector. By default, bias for the forget</span> |
| <span class="sd"> gate is initialized to 1 while all other biases are initialized</span> |
| <span class="sd"> to zero.</span> |
| <span class="sd"> h2h_bias_initializer : str or Initializer</span> |
| <span class="sd"> Initializer for the bias vector.</span> |
| <span class="sd"> projection_size: int, default None</span> |
| <span class="sd"> The number of features after projection.</span> |
| <span class="sd"> h2r_weight_initializer : str or Initializer, default None</span> |
| <span class="sd"> Initializer for the projected recurrent weights matrix, used for the linear</span> |
| <span class="sd"> transformation of the recurrent state to the projected space.</span> |
| <span class="sd"> state_clip_min : float or None, default None</span> |
| <span class="sd"> Minimum clip value of LSTM states. This option must be used together with</span> |
| <span class="sd"> state_clip_max. If None, clipping is not applied.</span> |
| <span class="sd"> state_clip_max : float or None, default None</span> |
| <span class="sd"> Maximum clip value of LSTM states. This option must be used together with</span> |
| <span class="sd"> state_clip_min. If None, clipping is not applied.</span> |
| <span class="sd"> state_clip_nan : boolean, default False</span> |
| <span class="sd"> Whether to stop NaN from propagating in state by clipping it to min/max.</span> |
| <span class="sd"> If the clipping range is not specified, this option is ignored.</span> |
| <span class="sd"> dtype : str, default 'float32'</span> |
| <span class="sd"> Type to initialize the parameters and default states to</span> |
| <span class="sd"> input_size: int, default 0</span> |
| <span class="sd"> The number of expected features in the input x.</span> |
| <span class="sd"> If not specified, it will be inferred from input.</span> |
| <span class="sd"> prefix : str or None</span> |
| <span class="sd"> Prefix of this `Block`.</span> |
| <span class="sd"> params : `ParameterDict` or `None`</span> |
| <span class="sd"> Shared Parameters for this `Block`.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: input tensor with shape `(sequence_length, batch_size, input_size)`</span> |
| <span class="sd"> when `layout` is "TNC". For other layouts, dimensions are permuted accordingly</span> |
| <span class="sd"> using transpose() operator which adds performance overhead. Consider creating</span> |
| <span class="sd"> batches in TNC layout during data batching step.</span> |
| <span class="sd"> - **states**: a list of two initial recurrent state tensors. Each has shape</span> |
| <span class="sd"> `(num_layers, batch_size, num_hidden)`. If `bidirectional` is True,</span> |
| <span class="sd"> shape will instead be `(2*num_layers, batch_size, num_hidden)`. If</span> |
| <span class="sd"> `states` is None, zeros will be used as default begin states.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: output tensor with shape `(sequence_length, batch_size, num_hidden)`</span> |
| <span class="sd"> when `layout` is "TNC". If `bidirectional` is True, output shape will instead</span> |
| <span class="sd"> be `(sequence_length, batch_size, 2*num_hidden)`</span> |
| <span class="sd"> - **out_states**: a list of two output recurrent state tensors with the same</span> |
| <span class="sd"> shape as in `states`. If `states` is None `out_states` will not be returned.</span> |
| |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> layer = mx.gluon.rnn.LSTM(100, 3)</span> |
| <span class="sd"> >>> layer.initialize()</span> |
| <span class="sd"> >>> input = mx.nd.random.uniform(shape=(5, 3, 10))</span> |
| <span class="sd"> >>> # by default zeros are used as begin state</span> |
| <span class="sd"> >>> output = layer(input)</span> |
| <span class="sd"> >>> # manually specify begin state.</span> |
| <span class="sd"> >>> h0 = mx.nd.random.uniform(shape=(3, 3, 100))</span> |
| <span class="sd"> >>> c0 = mx.nd.random.uniform(shape=(3, 3, 100))</span> |
| <span class="sd"> >>> output, hn = layer(input, [h0, c0])</span> |
| <span class="sd"> """</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="n">hidden_size</span><span class="p">,</span> <span class="n">num_layers</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="s1">'TNC'</span><span class="p">,</span> |
| <span class="n">dropout</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">bidirectional</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">input_size</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> |
| <span class="n">i2h_weight_initializer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">h2h_weight_initializer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">i2h_bias_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> <span class="n">h2h_bias_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> |
| <span class="n">projection_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">h2r_weight_initializer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">state_clip_min</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">state_clip_max</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">state_clip_nan</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">dtype</span><span class="o">=</span><span class="s1">'float32'</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">LSTM</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="n">hidden_size</span><span class="p">,</span> <span class="n">num_layers</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> |
| <span class="n">dropout</span><span class="p">,</span> <span class="n">bidirectional</span><span class="p">,</span> <span class="n">input_size</span><span class="p">,</span> |
| <span class="n">i2h_weight_initializer</span><span class="p">,</span> <span class="n">h2h_weight_initializer</span><span class="p">,</span> |
| <span class="n">i2h_bias_initializer</span><span class="p">,</span> <span class="n">h2h_bias_initializer</span><span class="p">,</span> |
| <span class="s1">'lstm'</span><span class="p">,</span> <span class="n">projection_size</span><span class="p">,</span> <span class="n">h2r_weight_initializer</span><span class="p">,</span> |
| <span class="n">state_clip_min</span><span class="p">,</span> <span class="n">state_clip_max</span><span class="p">,</span> <span class="n">state_clip_nan</span><span class="p">,</span> |
| <span class="n">dtype</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">state_info</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_projection_size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">return</span> <span class="p">[{</span><span class="s1">'shape'</span><span class="p">:</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_num_layers</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dir</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_hidden_size</span><span class="p">),</span> |
| <span class="s1">'__layout__'</span><span class="p">:</span> <span class="s1">'LNC'</span><span class="p">,</span> <span class="s1">'dtype'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dtype</span><span class="p">},</span> |
| <span class="p">{</span><span class="s1">'shape'</span><span class="p">:</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_num_layers</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dir</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_hidden_size</span><span class="p">),</span> |
| <span class="s1">'__layout__'</span><span class="p">:</span> <span class="s1">'LNC'</span><span class="p">,</span> <span class="s1">'dtype'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dtype</span><span class="p">}]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="p">[{</span><span class="s1">'shape'</span><span class="p">:</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_num_layers</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dir</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_projection_size</span><span class="p">),</span> |
| <span class="s1">'__layout__'</span><span class="p">:</span> <span class="s1">'LNC'</span><span class="p">,</span> <span class="s1">'dtype'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dtype</span><span class="p">},</span> |
| <span class="p">{</span><span class="s1">'shape'</span><span class="p">:</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_num_layers</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dir</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_hidden_size</span><span class="p">),</span> |
| <span class="s1">'__layout__'</span><span class="p">:</span> <span class="s1">'LNC'</span><span class="p">,</span> <span class="s1">'dtype'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dtype</span><span class="p">}]</span></div> |
| |
| |
| <div class="viewcode-block" id="GRU"><a class="viewcode-back" href="../../../../api/gluon/rnn/index.html#mxnet.gluon.rnn.GRU">[docs]</a><span class="k">class</span> <span class="nc">GRU</span><span class="p">(</span><span class="n">_RNNLayer</span><span class="p">):</span> |
| <span class="sa">r</span><span class="sd">"""Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence.</span> |
| <span class="sd"> Note: this is an implementation of the cuDNN version of GRUs</span> |
| <span class="sd"> (slight modification compared to Cho et al. 2014; the reset gate :math:`r_t`</span> |
| <span class="sd"> is applied after matrix multiplication).</span> |
| |
| <span class="sd"> For each element in the input sequence, each layer computes the following</span> |
| <span class="sd"> function:</span> |
| |
| <span class="sd"> .. math::</span> |
| <span class="sd"> \begin{array}{ll}</span> |
| <span class="sd"> r_t = sigmoid(W_{ir} x_t + b_{ir} + W_{hr} h_{(t-1)} + b_{hr}) \\</span> |
| <span class="sd"> i_t = sigmoid(W_{ii} x_t + b_{ii} + W_{hi} h_{(t-1)} + b_{hi}) \\</span> |
| <span class="sd"> n_t = \tanh(W_{in} x_t + b_{in} + r_t * (W_{hn} h_{(t-1)} + b_{hn})) \\</span> |
| <span class="sd"> h_t = (1 - i_t) * n_t + i_t * h_{(t-1)} \\</span> |
| <span class="sd"> \end{array}</span> |
| |
| <span class="sd"> where :math:`h_t` is the hidden state at time `t`, :math:`x_t` is the hidden</span> |
| <span class="sd"> state of the previous layer at time `t` or :math:`input_t` for the first layer,</span> |
| <span class="sd"> and :math:`r_t`, :math:`i_t`, :math:`n_t` are the reset, input, and new gates, respectively.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> hidden_size: int</span> |
| <span class="sd"> The number of features in the hidden state h</span> |
| <span class="sd"> num_layers: int, default 1</span> |
| <span class="sd"> Number of recurrent layers.</span> |
| <span class="sd"> layout : str, default 'TNC'</span> |
| <span class="sd"> The format of input and output tensors. T, N and C stand for</span> |
| <span class="sd"> sequence length, batch size, and feature dimensions respectively.</span> |
| <span class="sd"> dropout: float, default 0</span> |
| <span class="sd"> If non-zero, introduces a dropout layer on the outputs of each</span> |
| <span class="sd"> RNN layer except the last layer</span> |
| <span class="sd"> bidirectional: bool, default False</span> |
| <span class="sd"> If True, becomes a bidirectional RNN.</span> |
| <span class="sd"> i2h_weight_initializer : str or Initializer</span> |
| <span class="sd"> Initializer for the input weights matrix, used for the linear</span> |
| <span class="sd"> transformation of the inputs.</span> |
| <span class="sd"> h2h_weight_initializer : str or Initializer</span> |
| <span class="sd"> Initializer for the recurrent weights matrix, used for the linear</span> |
| <span class="sd"> transformation of the recurrent state.</span> |
| <span class="sd"> i2h_bias_initializer : str or Initializer</span> |
| <span class="sd"> Initializer for the bias vector.</span> |
| <span class="sd"> h2h_bias_initializer : str or Initializer</span> |
| <span class="sd"> Initializer for the bias vector.</span> |
| <span class="sd"> dtype : str, default 'float32'</span> |
| <span class="sd"> Type to initialize the parameters and default states to</span> |
| <span class="sd"> input_size: int, default 0</span> |
| <span class="sd"> The number of expected features in the input x.</span> |
| <span class="sd"> If not specified, it will be inferred from input.</span> |
| <span class="sd"> prefix : str or None</span> |
| <span class="sd"> Prefix of this `Block`.</span> |
| <span class="sd"> params : ParameterDict or None</span> |
| <span class="sd"> Shared Parameters for this `Block`.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: input tensor with shape `(sequence_length, batch_size, input_size)`</span> |
| <span class="sd"> when `layout` is "TNC". For other layouts, dimensions are permuted accordingly</span> |
| <span class="sd"> using transpose() operator which adds performance overhead. Consider creating</span> |
| <span class="sd"> batches in TNC layout during data batching step.</span> |
| <span class="sd"> - **states**: initial recurrent state tensor with shape</span> |
| <span class="sd"> `(num_layers, batch_size, num_hidden)`. If `bidirectional` is True,</span> |
| <span class="sd"> shape will instead be `(2*num_layers, batch_size, num_hidden)`. If</span> |
| <span class="sd"> `states` is None, zeros will be used as default begin states.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: output tensor with shape `(sequence_length, batch_size, num_hidden)`</span> |
| <span class="sd"> when `layout` is "TNC". If `bidirectional` is True, output shape will instead</span> |
| <span class="sd"> be `(sequence_length, batch_size, 2*num_hidden)`</span> |
| <span class="sd"> - **out_states**: output recurrent state tensor with the same shape as `states`.</span> |
| <span class="sd"> If `states` is None `out_states` will not be returned.</span> |
| |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> layer = mx.gluon.rnn.GRU(100, 3)</span> |
| <span class="sd"> >>> layer.initialize()</span> |
| <span class="sd"> >>> input = mx.nd.random.uniform(shape=(5, 3, 10))</span> |
| <span class="sd"> >>> # by default zeros are used as begin state</span> |
| <span class="sd"> >>> output = layer(input)</span> |
| <span class="sd"> >>> # manually specify begin state.</span> |
| <span class="sd"> >>> h0 = mx.nd.random.uniform(shape=(3, 3, 100))</span> |
| <span class="sd"> >>> output, hn = layer(input, h0)</span> |
| <span class="sd"> """</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="n">hidden_size</span><span class="p">,</span> <span class="n">num_layers</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="s1">'TNC'</span><span class="p">,</span> |
| <span class="n">dropout</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">bidirectional</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">input_size</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> |
| <span class="n">i2h_weight_initializer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">h2h_weight_initializer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">i2h_bias_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> <span class="n">h2h_bias_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> |
| <span class="n">dtype</span><span class="o">=</span><span class="s1">'float32'</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">GRU</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="n">hidden_size</span><span class="p">,</span> <span class="n">num_layers</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> |
| <span class="n">dropout</span><span class="p">,</span> <span class="n">bidirectional</span><span class="p">,</span> <span class="n">input_size</span><span class="p">,</span> |
| <span class="n">i2h_weight_initializer</span><span class="p">,</span> <span class="n">h2h_weight_initializer</span><span class="p">,</span> |
| <span class="n">i2h_bias_initializer</span><span class="p">,</span> <span class="n">h2h_bias_initializer</span><span class="p">,</span> |
| <span class="s1">'gru'</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> |
| <span class="n">dtype</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">state_info</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> |
| <span class="k">return</span> <span class="p">[{</span><span class="s1">'shape'</span><span class="p">:</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_num_layers</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dir</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_hidden_size</span><span class="p">),</span> |
| <span class="s1">'__layout__'</span><span class="p">:</span> <span class="s1">'LNC'</span><span class="p">,</span> <span class="s1">'dtype'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dtype</span><span class="p">}]</span></div> |
| </pre></div> |
| |
| <hr class="feedback-hr-top" /> |
| <div class="feedback-container"> |
| <div class="feedback-question">Did this page help you?</div> |
| <div class="feedback-answer-container"> |
| <div class="feedback-answer yes-link" data-response="yes">Yes</div> |
| <div class="feedback-answer no-link" data-response="no">No</div> |
| </div> |
| <div class="feedback-thank-you">Thanks for your feedback!</div> |
| </div> |
| <hr class="feedback-hr-bottom" /> |
| </div> |
| <div class="side-doc-outline"> |
| <div class="side-doc-outline--content"> |
| </div> |
| </div> |
| |
| <div class="clearer"></div> |
| </div><div class="pagenation"> |
| </div> |
| <footer class="site-footer h-card"> |
| <div class="wrapper"> |
| <div class="row"> |
| <div class="col-4"> |
| <h4 class="footer-category-title">Resources</h4> |
| <ul class="contact-list"> |
| <li><a class="u-email" href="mailto:dev@mxnet.apache.org">Dev list</a></li> |
| <li><a class="u-email" href="mailto:user@mxnet.apache.org">User mailing list</a></li> |
| <li><a href="https://cwiki.apache.org/confluence/display/MXNET/Apache+MXNet+Home">Developer Wiki</a></li> |
| <li><a href="https://issues.apache.org/jira/projects/MXNET/issues">Jira Tracker</a></li> |
| <li><a href="https://github.com/apache/incubator-mxnet/labels/Roadmap">Github Roadmap</a></li> |
| <li><a href="https://medium.com/apache-mxnet">Blog</a></li> |
| <li><a href="https://discuss.mxnet.io">Forum</a></li> |
| <li><a href="/community/contribute">Contribute</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> |
