Google Git
Sign in
apache / mxnet-site / 82a1adfef83e5bbd9e128754f56a2a2e83809347 / . / versions / 1.9.1 / api / python / docs / _modules / mxnet / model.html
blob: 34755d48e311ebe65bdba1bb05dcf96e014e5162 [file] [log] [blame]
<!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.model &#8212; Apache MXNet documentation</title>
<link rel="stylesheet" href="../../_static/basic.css" type="text/css" />
<link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
<link rel="stylesheet" type="text/css" href="../../_static/mxnet.css" />
<link rel="stylesheet" href="../../_static/material-design-lite-1.3.0/material.blue-deep_orange.min.css" type="text/css" />
<link rel="stylesheet" href="../../_static/sphinx_materialdesign_theme.css" type="text/css" />
<link rel="stylesheet" href="../../_static/fontawesome/all.css" type="text/css" />
<link rel="stylesheet" href="../../_static/fonts.css" type="text/css" />
<link rel="stylesheet" href="../../_static/feedback.css" type="text/css" />
<script id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
<script src="../../_static/jquery.js"></script>
<script src="../../_static/underscore.js"></script>
<script src="../../_static/doctools.js"></script>
<script src="../../_static/language_data.js"></script>
<script src="../../_static/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.model</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.model</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"># &quot;License&quot;); 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"># &quot;AS IS&quot; 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"># pylint: disable=fixme, invalid-name, too-many-arguments, too-many-locals, too-many-lines</span>
<span class="c1"># pylint: disable=too-many-branches, too-many-statements</span>
<span class="sd">&quot;&quot;&quot;MXNet model module&quot;&quot;&quot;</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">time</span>
<span class="kn">import</span> <span class="nn">logging</span>
<span class="kn">import</span> <span class="nn">warnings</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">namedtuple</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">io</span>
<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">ndarray</span> <span class="k">as</span> <span class="n">nd</span>
<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">symbol</span> <span class="k">as</span> <span class="n">sym</span>
<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">optimizer</span> <span class="k">as</span> <span class="n">opt</span>
<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">metric</span>
<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">kvstore</span> <span class="k">as</span> <span class="n">kvs</span>
<span class="kn">from</span> <span class="nn">.context</span> <span class="kn">import</span> <span class="n">Context</span><span class="p">,</span> <span class="n">cpu</span>
<span class="kn">from</span> <span class="nn">.initializer</span> <span class="kn">import</span> <span class="n">Uniform</span>
<span class="kn">from</span> <span class="nn">.optimizer</span> <span class="kn">import</span> <span class="n">get_updater</span>
<span class="kn">from</span> <span class="nn">.executor_manager</span> <span class="kn">import</span> <span class="n">DataParallelExecutorManager</span><span class="p">,</span> <span class="n">_check_arguments</span><span class="p">,</span> <span class="n">_load_data</span>
<span class="kn">from</span> <span class="nn">.io</span> <span class="kn">import</span> <span class="n">DataDesc</span>
<span class="kn">from</span> <span class="nn">.base</span> <span class="kn">import</span> <span class="n">mx_real_t</span>
<span class="n">BASE_ESTIMATOR</span> <span class="o">=</span> <span class="nb">object</span>
<span class="k">try</span><span class="p">:</span>
<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span>
<span class="n">BASE_ESTIMATOR</span> <span class="o">=</span> <span class="n">BaseEstimator</span>
<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
<span class="n">SKLEARN_INSTALLED</span> <span class="o">=</span> <span class="kc">False</span>
<span class="c1"># Parameter to pass to batch_end_callback</span>
<span class="n">BatchEndParam</span> <span class="o">=</span> <span class="n">namedtuple</span><span class="p">(</span><span class="s1">&#39;BatchEndParams&#39;</span><span class="p">,</span>
<span class="p">[</span><span class="s1">&#39;epoch&#39;</span><span class="p">,</span>
<span class="s1">&#39;nbatch&#39;</span><span class="p">,</span>
<span class="s1">&#39;eval_metric&#39;</span><span class="p">,</span>
<span class="s1">&#39;locals&#39;</span><span class="p">])</span>
<span class="k">def</span> <span class="nf">_create_sparse_kvstore</span><span class="p">(</span><span class="n">kvstore</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Create kvstore assuming some parameters&#39; storage types are row_sparse.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> kvstore : KVStore or str</span>
<span class="sd"> The kvstore.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> kvstore : KVStore</span>
<span class="sd"> update_on_kvstore : bool. Always True.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="c1"># always update on kvstore</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">kvstore</span><span class="p">,</span> <span class="n">kvs</span><span class="o">.</span><span class="n">KVStore</span><span class="p">):</span>
<span class="n">kv</span> <span class="o">=</span> <span class="n">kvstore</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">kvstore</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
<span class="n">kv</span> <span class="o">=</span> <span class="n">kvs</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">kvstore</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;Cannot create &#39;</span><span class="si">%s</span><span class="s2">&#39; KVStore with row_sparse parameters. &quot;</span>
<span class="s2">&quot;The type must be KVStore or str.&quot;</span> <span class="o">%</span> <span class="n">kvstore</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">kv</span><span class="o">.</span><span class="n">is_capable</span><span class="p">(</span><span class="n">kvs</span><span class="o">.</span><span class="n">KVStoreBase</span><span class="o">.</span><span class="n">OPTIMIZER</span><span class="p">),</span> \
<span class="s2">&quot;KVStore with sparse weight requires optimizer support. &quot;</span> \
<span class="s2">&quot;However, type(kv) does not support optimizer. &quot;</span> \
<span class="s2">&quot;Please consider other kvstore backends (e.g. dist_device) instead.&quot;</span>
<span class="k">return</span> <span class="p">(</span><span class="n">kv</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_create_kvstore</span><span class="p">(</span><span class="n">kvstore</span><span class="p">,</span> <span class="n">num_device</span><span class="p">,</span> <span class="n">arg_params</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Create kvstore</span>
<span class="sd"> This function select and create a proper kvstore if given the kvstore type.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> kvstore : KVStore or str</span>
<span class="sd"> The kvstore.</span>
<span class="sd"> num_device : int</span>
<span class="sd"> The number of devices</span>
<span class="sd"> arg_params : dict of str to `NDArray`.</span>
<span class="sd"> Model parameter, dict of name to `NDArray` of net&#39;s weights.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">update_on_kvstore</span> <span class="o">=</span> <span class="nb">bool</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s1">&#39;MXNET_UPDATE_ON_KVSTORE&#39;</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">)))</span>
<span class="k">if</span> <span class="n">kvstore</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">kv</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">kvstore</span><span class="p">,</span> <span class="n">kvs</span><span class="o">.</span><span class="n">KVStoreBase</span><span class="p">):</span>
<span class="n">kv</span> <span class="o">=</span> <span class="n">kvstore</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">kvstore</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
<span class="c1"># create kvstore using the string type</span>
<span class="k">if</span> <span class="n">num_device</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="s1">&#39;dist&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">kvstore</span><span class="p">:</span>
<span class="c1"># no need to use kv for single device and single machine</span>
<span class="n">kv</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">kv</span> <span class="o">=</span> <span class="n">kvs</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">kvstore</span><span class="p">)</span>
<span class="k">if</span> <span class="n">kvstore</span> <span class="o">==</span> <span class="s1">&#39;local&#39;</span><span class="p">:</span>
<span class="c1"># automatically select a proper local</span>
<span class="n">max_size</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span><span class="n">param</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="k">for</span> <span class="n">param</span> <span class="ow">in</span>
<span class="n">arg_params</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
<span class="k">if</span> <span class="n">max_size</span> <span class="o">&gt;</span> <span class="mi">1024</span> <span class="o">*</span> <span class="mi">1024</span> <span class="o">*</span> <span class="mi">16</span><span class="p">:</span>
<span class="n">update_on_kvstore</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;kvstore must be KVStore, str or None&#39;</span><span class="p">)</span>
<span class="k">if</span> <span class="n">kv</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">update_on_kvstore</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">update_on_kvstore</span> <span class="o">&amp;=</span> <span class="n">kv</span><span class="o">.</span><span class="n">is_capable</span><span class="p">(</span><span class="n">kvs</span><span class="o">.</span><span class="n">KVStoreBase</span><span class="o">.</span><span class="n">OPTIMIZER</span><span class="p">)</span>
<span class="k">return</span> <span class="p">(</span><span class="n">kv</span><span class="p">,</span> <span class="n">update_on_kvstore</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_initialize_kvstore</span><span class="p">(</span><span class="n">kvstore</span><span class="p">,</span> <span class="n">param_arrays</span><span class="p">,</span> <span class="n">arg_params</span><span class="p">,</span> <span class="n">param_names</span><span class="p">,</span> <span class="n">update_on_kvstore</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Initialize kvstore&quot;&quot;&quot;</span>
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">param_on_devs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">param_arrays</span><span class="p">):</span>
<span class="n">name</span> <span class="o">=</span> <span class="n">param_names</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">update_on_kvstore</span> <span class="ow">or</span> <span class="n">arg_params</span><span class="p">[</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">stype</span> <span class="o">!=</span> <span class="s1">&#39;default&#39;</span><span class="p">:</span>
<span class="n">kvstore</span><span class="o">.</span><span class="n">init</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">arg_params</span><span class="p">[</span><span class="n">name</span><span class="p">])</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">kvstore</span><span class="o">.</span><span class="n">broadcast</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">arg_params</span><span class="p">[</span><span class="n">name</span><span class="p">],</span> <span class="n">out</span><span class="o">=</span><span class="n">param_on_devs</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_update_params_on_kvstore_nccl</span><span class="p">(</span><span class="n">param_arrays</span><span class="p">,</span> <span class="n">grad_arrays</span><span class="p">,</span> <span class="n">kvstore</span><span class="p">,</span> <span class="n">param_names</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Perform update of param_arrays from grad_arrays on NCCL kvstore.&quot;&quot;&quot;</span>
<span class="n">valid_indices</span> <span class="o">=</span> <span class="p">[</span><span class="n">index</span> <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">grad_list</span> <span class="ow">in</span>
<span class="nb">enumerate</span><span class="p">(</span><span class="n">grad_arrays</span><span class="p">)</span> <span class="k">if</span> <span class="n">grad_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">]</span>
<span class="n">valid_grad_arrays</span> <span class="o">=</span> <span class="p">[</span><span class="n">grad_arrays</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="n">valid_indices</span><span class="p">]</span>
<span class="n">valid_param_arrays</span> <span class="o">=</span> <span class="p">[</span><span class="n">param_arrays</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="n">valid_indices</span><span class="p">]</span>
<span class="n">valid_param_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">param_names</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="n">valid_indices</span><span class="p">]</span>
<span class="n">size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">valid_grad_arrays</span><span class="p">)</span>
<span class="n">start</span> <span class="o">=</span> <span class="mi">0</span>
<span class="c1"># Use aggregation by default only with NCCL</span>
<span class="n">default_batch</span> <span class="o">=</span> <span class="s1">&#39;16&#39;</span>
<span class="n">batch</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s1">&#39;MXNET_UPDATE_AGGREGATION_SIZE&#39;</span><span class="p">,</span> <span class="n">default_batch</span><span class="p">))</span>
<span class="k">while</span> <span class="n">start</span> <span class="o">&lt;</span> <span class="n">size</span><span class="p">:</span>
<span class="n">end</span> <span class="o">=</span> <span class="n">start</span> <span class="o">+</span> <span class="n">batch</span> <span class="k">if</span> <span class="n">start</span> <span class="o">+</span> <span class="n">batch</span> <span class="o">&lt;</span> <span class="n">size</span> <span class="k">else</span> <span class="n">size</span>
<span class="c1"># push gradient, priority is negative index</span>
<span class="c1"># pull back the weights</span>
<span class="n">kvstore</span><span class="o">.</span><span class="n">pushpull</span><span class="p">(</span><span class="n">valid_param_names</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="n">end</span><span class="p">],</span> <span class="n">valid_grad_arrays</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="n">end</span><span class="p">],</span>
<span class="n">out</span><span class="o">=</span><span class="n">valid_param_arrays</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="n">end</span><span class="p">],</span> <span class="n">priority</span><span class="o">=-</span><span class="n">start</span><span class="p">)</span>
<span class="n">start</span> <span class="o">=</span> <span class="n">end</span>
<span class="k">def</span> <span class="nf">_update_params_on_kvstore</span><span class="p">(</span><span class="n">param_arrays</span><span class="p">,</span> <span class="n">grad_arrays</span><span class="p">,</span> <span class="n">kvstore</span><span class="p">,</span> <span class="n">param_names</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Perform update of param_arrays from grad_arrays on kvstore.&quot;&quot;&quot;</span>
<span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">pair</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">param_arrays</span><span class="p">,</span> <span class="n">grad_arrays</span><span class="p">)):</span>
<span class="n">arg_list</span><span class="p">,</span> <span class="n">grad_list</span> <span class="o">=</span> <span class="n">pair</span>
<span class="k">if</span> <span class="n">grad_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">continue</span>
<span class="n">name</span> <span class="o">=</span> <span class="n">param_names</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
<span class="c1"># push gradient, priority is negative index</span>
<span class="c1"># pull back the weights</span>
<span class="k">if</span> <span class="n">grad_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">stype</span> <span class="o">==</span> <span class="s1">&#39;default&#39;</span> <span class="ow">and</span> <span class="n">arg_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">stype</span> <span class="o">==</span> <span class="s1">&#39;default&#39;</span><span class="p">:</span>
<span class="n">kvstore</span><span class="o">.</span><span class="n">pushpull</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">grad_list</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">arg_list</span><span class="p">,</span> <span class="n">priority</span><span class="o">=-</span><span class="n">index</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">kvstore</span><span class="o">.</span><span class="n">push</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">grad_list</span><span class="p">,</span> <span class="n">priority</span><span class="o">=-</span><span class="n">index</span><span class="p">)</span>
<span class="n">kvstore</span><span class="o">.</span><span class="n">pull</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">arg_list</span><span class="p">,</span> <span class="n">priority</span><span class="o">=-</span><span class="n">index</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_update_params</span><span class="p">(</span><span class="n">param_arrays</span><span class="p">,</span> <span class="n">grad_arrays</span><span class="p">,</span> <span class="n">updater</span><span class="p">,</span> <span class="n">num_device</span><span class="p">,</span>
<span class="n">kvstore</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_names</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Perform update of param_arrays from grad_arrays not on kvstore.&quot;&quot;&quot;</span>
<span class="n">updates</span> <span class="o">=</span> <span class="p">[[]</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_device</span><span class="p">)]</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">pair</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">param_arrays</span><span class="p">,</span> <span class="n">grad_arrays</span><span class="p">)):</span>
<span class="n">arg_list</span><span class="p">,</span> <span class="n">grad_list</span> <span class="o">=</span> <span class="n">pair</span>
<span class="k">if</span> <span class="n">grad_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">continue</span>
<span class="n">index</span> <span class="o">=</span> <span class="n">i</span>
<span class="k">if</span> <span class="n">kvstore</span><span class="p">:</span>
<span class="n">name</span> <span class="o">=</span> <span class="n">param_names</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
<span class="c1"># push gradient, priority is negative index</span>
<span class="k">if</span> <span class="n">grad_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">stype</span> <span class="o">==</span> <span class="s1">&#39;default&#39;</span> <span class="ow">and</span> <span class="n">arg_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">stype</span> <span class="o">==</span> <span class="s1">&#39;default&#39;</span><span class="p">:</span>
<span class="n">kvstore</span><span class="o">.</span><span class="n">pushpull</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">grad_list</span><span class="p">,</span> <span class="n">priority</span><span class="o">=-</span><span class="n">index</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">kvstore</span><span class="o">.</span><span class="n">push</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">grad_list</span><span class="p">,</span> <span class="n">priority</span><span class="o">=-</span><span class="n">index</span><span class="p">)</span>
<span class="n">kvstore</span><span class="o">.</span><span class="n">pull</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">grad_list</span><span class="p">,</span> <span class="n">priority</span><span class="o">=-</span><span class="n">index</span><span class="p">)</span>
<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">p</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">arg_list</span><span class="p">,</span> <span class="n">grad_list</span><span class="p">)):</span>
<span class="c1"># faked an index here, to make optimizer create diff</span>
<span class="c1"># state for the same index but on diff devs, TODO(mli)</span>
<span class="c1"># use a better solution later</span>
<span class="n">w</span><span class="p">,</span> <span class="n">g</span> <span class="o">=</span> <span class="n">p</span>
<span class="n">updates</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">index</span><span class="o">*</span><span class="n">num_device</span><span class="o">+</span><span class="n">k</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="n">w</span><span class="p">))</span>
<span class="k">for</span> <span class="n">dev_updates</span> <span class="ow">in</span> <span class="n">updates</span><span class="p">:</span>
<span class="c1"># update params if param_arrays and grad_arrays are not empty</span>
<span class="k">if</span> <span class="n">dev_updates</span><span class="p">:</span>
<span class="n">i</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">g</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">dev_updates</span><span class="p">)</span>
<span class="n">updater</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">g</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_multiple_callbacks</span><span class="p">(</span><span class="n">callbacks</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Sends args and kwargs to any configured callbacks.</span>
<span class="sd"> This handles the cases where the &#39;callbacks&#39; variable</span>
<span class="sd"> is ``None``, a single function, or a list.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">callbacks</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
<span class="k">for</span> <span class="n">cb</span> <span class="ow">in</span> <span class="n">callbacks</span><span class="p">:</span>
<span class="n">cb</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">return</span>
<span class="k">if</span> <span class="n">callbacks</span><span class="p">:</span>
<span class="n">callbacks</span><span class="p">(</span><span class="o">*</span><span class="n">args</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">_train_multi_device</span><span class="p">(</span><span class="n">symbol</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">arg_names</span><span class="p">,</span> <span class="n">param_names</span><span class="p">,</span> <span class="n">aux_names</span><span class="p">,</span>
<span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span><span class="p">,</span>
<span class="n">begin_epoch</span><span class="p">,</span> <span class="n">end_epoch</span><span class="p">,</span> <span class="n">epoch_size</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span>
<span class="n">kvstore</span><span class="p">,</span> <span class="n">update_on_kvstore</span><span class="p">,</span>
<span class="n">train_data</span><span class="p">,</span> <span class="n">eval_data</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">eval_metric</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">epoch_end_callback</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">batch_end_callback</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">logger</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">work_load_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">monitor</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">eval_end_callback</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">eval_batch_end_callback</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sym_gen</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Internal training function on multiple devices.</span>
<span class="sd"> This function will also work for single device as well.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> symbol : Symbol</span>
<span class="sd"> The network configuration.</span>
<span class="sd"> ctx : list of Context</span>
<span class="sd"> The training devices.</span>
<span class="sd"> arg_names: list of str</span>
<span class="sd"> Name of all arguments of the network.</span>
<span class="sd"> param_names: list of str</span>
<span class="sd"> Name of all trainable parameters of the network.</span>
<span class="sd"> aux_names: list of str</span>
<span class="sd"> Name of all auxiliary states of the network.</span>
<span class="sd"> arg_params : dict of str to NDArray</span>
<span class="sd"> Model parameter, dict of name to NDArray of net&#39;s weights.</span>
<span class="sd"> aux_params : dict of str to NDArray</span>
<span class="sd"> Model parameter, dict of name to NDArray of net&#39;s auxiliary states.</span>
<span class="sd"> begin_epoch : int</span>
<span class="sd"> The begining training epoch.</span>
<span class="sd"> end_epoch : int</span>
<span class="sd"> The end training epoch.</span>
<span class="sd"> epoch_size : int, optional</span>
<span class="sd"> Number of batches in a epoch. In default, it is set to</span>
<span class="sd"> ``ceil(num_train_examples / batch_size)``.</span>
<span class="sd"> optimizer : Optimizer</span>
<span class="sd"> The optimization algorithm</span>
<span class="sd"> train_data : DataIter</span>
<span class="sd"> Training data iterator.</span>
<span class="sd"> eval_data : DataIter</span>
<span class="sd"> Validation data iterator.</span>
<span class="sd"> eval_metric : EvalMetric</span>
<span class="sd"> An evaluation function or a list of evaluation functions.</span>
<span class="sd"> epoch_end_callback : callable(epoch, symbol, arg_params, aux_states)</span>
<span class="sd"> A callback that is invoked at end of each epoch.</span>
<span class="sd"> This can be used to checkpoint model each epoch.</span>
<span class="sd"> batch_end_callback : callable(BatchEndParams)</span>
<span class="sd"> A callback that is invoked at end of each batch.</span>
<span class="sd"> This can be used to measure speed, get result from evaluation metric. etc.</span>
<span class="sd"> kvstore : KVStore</span>
<span class="sd"> The KVStore.</span>
<span class="sd"> update_on_kvstore : bool</span>
<span class="sd"> Whether or not perform weight updating on kvstore.</span>
<span class="sd"> logger : logging logger</span>
<span class="sd"> When not specified, default logger will be used.</span>
<span class="sd"> work_load_list : list of float or int, optional</span>
<span class="sd"> The list of work load for different devices,</span>
<span class="sd"> in the same order as ``ctx``.</span>
<span class="sd"> monitor : Monitor, optional</span>
<span class="sd"> Monitor installed to executor,</span>
<span class="sd"> for monitoring outputs, weights, and gradients for debugging.</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> - This function will inplace update the NDArrays in `arg_params` and `aux_states`.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">logger</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span>
<span class="n">executor_manager</span> <span class="o">=</span> <span class="n">DataParallelExecutorManager</span><span class="p">(</span><span class="n">symbol</span><span class="o">=</span><span class="n">symbol</span><span class="p">,</span>
<span class="n">sym_gen</span><span class="o">=</span><span class="n">sym_gen</span><span class="p">,</span>
<span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span>
<span class="n">train_data</span><span class="o">=</span><span class="n">train_data</span><span class="p">,</span>
<span class="n">param_names</span><span class="o">=</span><span class="n">param_names</span><span class="p">,</span>
<span class="n">arg_names</span><span class="o">=</span><span class="n">arg_names</span><span class="p">,</span>
<span class="n">aux_names</span><span class="o">=</span><span class="n">aux_names</span><span class="p">,</span>
<span class="n">work_load_list</span><span class="o">=</span><span class="n">work_load_list</span><span class="p">,</span>
<span class="n">logger</span><span class="o">=</span><span class="n">logger</span><span class="p">)</span>
<span class="k">if</span> <span class="n">monitor</span><span class="p">:</span>
<span class="n">executor_manager</span><span class="o">.</span><span class="n">install_monitor</span><span class="p">(</span><span class="n">monitor</span><span class="p">)</span>
<span class="n">executor_manager</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">update_on_kvstore</span><span class="p">:</span>
<span class="n">updater</span> <span class="o">=</span> <span class="n">get_updater</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">kvstore</span><span class="o">.</span><span class="n">set_optimizer</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span>
<span class="k">if</span> <span class="n">kvstore</span><span class="p">:</span>
<span class="n">_initialize_kvstore</span><span class="p">(</span><span class="n">kvstore</span><span class="o">=</span><span class="n">kvstore</span><span class="p">,</span>
<span class="n">param_arrays</span><span class="o">=</span><span class="n">executor_manager</span><span class="o">.</span><span class="n">param_arrays</span><span class="p">,</span>
<span class="n">arg_params</span><span class="o">=</span><span class="n">arg_params</span><span class="p">,</span>
<span class="n">param_names</span><span class="o">=</span><span class="n">executor_manager</span><span class="o">.</span><span class="n">param_names</span><span class="p">,</span>
<span class="n">update_on_kvstore</span><span class="o">=</span><span class="n">update_on_kvstore</span><span class="p">)</span>
<span class="c1"># Now start training</span>
<span class="n">train_data</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">begin_epoch</span><span class="p">,</span> <span class="n">end_epoch</span><span class="p">):</span>
<span class="c1"># Training phase</span>
<span class="n">tic</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">eval_metric</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
<span class="n">nbatch</span> <span class="o">=</span> <span class="mi">0</span>
<span class="c1"># Iterate over training data.</span>
<span class="k">while</span> <span class="kc">True</span><span class="p">:</span>
<span class="n">do_reset</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">for</span> <span class="n">data_batch</span> <span class="ow">in</span> <span class="n">train_data</span><span class="p">:</span>
<span class="n">executor_manager</span><span class="o">.</span><span class="n">load_data_batch</span><span class="p">(</span><span class="n">data_batch</span><span class="p">)</span>
<span class="k">if</span> <span class="n">monitor</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">monitor</span><span class="o">.</span><span class="n">tic</span><span class="p">()</span>
<span class="n">executor_manager</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">is_train</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">executor_manager</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="k">if</span> <span class="n">update_on_kvstore</span><span class="p">:</span>
<span class="k">if</span> <span class="s1">&#39;nccl&#39;</span> <span class="ow">in</span> <span class="n">kvstore</span><span class="o">.</span><span class="n">type</span><span class="p">:</span>
<span class="n">_update_params_on_kvstore_nccl</span><span class="p">(</span><span class="n">executor_manager</span><span class="o">.</span><span class="n">param_arrays</span><span class="p">,</span>
<span class="n">executor_manager</span><span class="o">.</span><span class="n">grad_arrays</span><span class="p">,</span>
<span class="n">kvstore</span><span class="p">,</span> <span class="n">executor_manager</span><span class="o">.</span><span class="n">param_names</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">_update_params_on_kvstore</span><span class="p">(</span><span class="n">executor_manager</span><span class="o">.</span><span class="n">param_arrays</span><span class="p">,</span>
<span class="n">executor_manager</span><span class="o">.</span><span class="n">grad_arrays</span><span class="p">,</span>
<span class="n">kvstore</span><span class="p">,</span> <span class="n">executor_manager</span><span class="o">.</span><span class="n">param_names</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">_update_params</span><span class="p">(</span><span class="n">executor_manager</span><span class="o">.</span><span class="n">param_arrays</span><span class="p">,</span>
<span class="n">executor_manager</span><span class="o">.</span><span class="n">grad_arrays</span><span class="p">,</span>
<span class="n">updater</span><span class="o">=</span><span class="n">updater</span><span class="p">,</span>
<span class="n">num_device</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">ctx</span><span class="p">),</span>
<span class="n">kvstore</span><span class="o">=</span><span class="n">kvstore</span><span class="p">,</span>
<span class="n">param_names</span><span class="o">=</span><span class="n">executor_manager</span><span class="o">.</span><span class="n">param_names</span><span class="p">)</span>
<span class="k">if</span> <span class="n">monitor</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">monitor</span><span class="o">.</span><span class="n">toc_print</span><span class="p">()</span>
<span class="c1"># evaluate at end, so we can lazy copy</span>
<span class="n">executor_manager</span><span class="o">.</span><span class="n">update_metric</span><span class="p">(</span><span class="n">eval_metric</span><span class="p">,</span> <span class="n">data_batch</span><span class="o">.</span><span class="n">label</span><span class="p">)</span>
<span class="n">nbatch</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="c1"># batch callback (for print purpose)</span>
<span class="k">if</span> <span class="n">batch_end_callback</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">batch_end_params</span> <span class="o">=</span> <span class="n">BatchEndParam</span><span class="p">(</span><span class="n">epoch</span><span class="o">=</span><span class="n">epoch</span><span class="p">,</span>
<span class="n">nbatch</span><span class="o">=</span><span class="n">nbatch</span><span class="p">,</span>
<span class="n">eval_metric</span><span class="o">=</span><span class="n">eval_metric</span><span class="p">,</span>
<span class="nb">locals</span><span class="o">=</span><span class="nb">locals</span><span class="p">())</span>
<span class="n">_multiple_callbacks</span><span class="p">(</span><span class="n">batch_end_callback</span><span class="p">,</span> <span class="n">batch_end_params</span><span class="p">)</span>
<span class="c1"># this epoch is done possibly earlier</span>
<span class="k">if</span> <span class="n">epoch_size</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">nbatch</span> <span class="o">&gt;=</span> <span class="n">epoch_size</span><span class="p">:</span>
<span class="n">do_reset</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">break</span>
<span class="k">if</span> <span class="n">do_reset</span><span class="p">:</span>
<span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">&#39;Epoch[</span><span class="si">%d</span><span class="s1">] Resetting Data Iterator&#39;</span><span class="p">,</span> <span class="n">epoch</span><span class="p">)</span>
<span class="n">train_data</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
<span class="c1"># this epoch is done</span>
<span class="k">if</span> <span class="n">epoch_size</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">nbatch</span> <span class="o">&gt;=</span> <span class="n">epoch_size</span><span class="p">:</span>
<span class="k">break</span>
<span class="n">toc</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">&#39;Epoch[</span><span class="si">%d</span><span class="s1">] Time cost=</span><span class="si">%.3f</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">epoch</span><span class="p">,</span> <span class="p">(</span><span class="n">toc</span> <span class="o">-</span> <span class="n">tic</span><span class="p">))</span>
<span class="k">if</span> <span class="n">epoch_end_callback</span> <span class="ow">or</span> <span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">==</span> <span class="n">end_epoch</span><span class="p">:</span>
<span class="n">executor_manager</span><span class="o">.</span><span class="n">copy_to</span><span class="p">(</span><span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span><span class="p">)</span>
<span class="n">_multiple_callbacks</span><span class="p">(</span><span class="n">epoch_end_callback</span><span class="p">,</span> <span class="n">epoch</span><span class="p">,</span> <span class="n">symbol</span><span class="p">,</span> <span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span><span class="p">)</span>
<span class="c1"># evaluation</span>
<span class="k">if</span> <span class="n">eval_data</span><span class="p">:</span>
<span class="n">eval_metric</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
<span class="n">eval_data</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
<span class="n">total_num_batch</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">eval_batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">eval_data</span><span class="p">):</span>
<span class="n">executor_manager</span><span class="o">.</span><span class="n">load_data_batch</span><span class="p">(</span><span class="n">eval_batch</span><span class="p">)</span>
<span class="n">executor_manager</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">is_train</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">executor_manager</span><span class="o">.</span><span class="n">update_metric</span><span class="p">(</span><span class="n">eval_metric</span><span class="p">,</span> <span class="n">eval_batch</span><span class="o">.</span><span class="n">label</span><span class="p">)</span>
<span class="k">if</span> <span class="n">eval_batch_end_callback</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">batch_end_params</span> <span class="o">=</span> <span class="n">BatchEndParam</span><span class="p">(</span><span class="n">epoch</span><span class="o">=</span><span class="n">epoch</span><span class="p">,</span>
<span class="n">nbatch</span><span class="o">=</span><span class="n">i</span><span class="p">,</span>
<span class="n">eval_metric</span><span class="o">=</span><span class="n">eval_metric</span><span class="p">,</span>
<span class="nb">locals</span><span class="o">=</span><span class="nb">locals</span><span class="p">())</span>
<span class="n">_multiple_callbacks</span><span class="p">(</span><span class="n">eval_batch_end_callback</span><span class="p">,</span> <span class="n">batch_end_params</span><span class="p">)</span>
<span class="n">total_num_batch</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">if</span> <span class="n">eval_end_callback</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">eval_end_params</span> <span class="o">=</span> <span class="n">BatchEndParam</span><span class="p">(</span><span class="n">epoch</span><span class="o">=</span><span class="n">epoch</span><span class="p">,</span>
<span class="n">nbatch</span><span class="o">=</span><span class="n">total_num_batch</span><span class="p">,</span>
<span class="n">eval_metric</span><span class="o">=</span><span class="n">eval_metric</span><span class="p">,</span>
<span class="nb">locals</span><span class="o">=</span><span class="nb">locals</span><span class="p">())</span>
<span class="n">_multiple_callbacks</span><span class="p">(</span><span class="n">eval_end_callback</span><span class="p">,</span> <span class="n">eval_end_params</span><span class="p">)</span>
<span class="n">eval_data</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
<span class="c1"># end of all epochs</span>
<div class="viewcode-block" id="save_checkpoint"><a class="viewcode-back" href="../../api/mxnet/model/index.html#mxnet.model.save_checkpoint">[docs]</a><span class="k">def</span> <span class="nf">save_checkpoint</span><span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="n">epoch</span><span class="p">,</span> <span class="n">symbol</span><span class="p">,</span> <span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span><span class="p">,</span> <span class="n">remove_amp_cast</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Checkpoint the model data into file.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> prefix : str</span>
<span class="sd"> Prefix of model name.</span>
<span class="sd"> epoch : int</span>
<span class="sd"> The epoch number of the model.</span>
<span class="sd"> symbol : Symbol</span>
<span class="sd"> The input Symbol.</span>
<span class="sd"> arg_params : dict of str to NDArray</span>
<span class="sd"> Model parameter, dict of name to NDArray of net&#39;s weights.</span>
<span class="sd"> aux_params : dict of str to NDArray</span>
<span class="sd"> Model parameter, dict of name to NDArray of net&#39;s auxiliary states.</span>
<span class="sd"> remove_amp_cast : bool, optional</span>
<span class="sd"> Whether to remove the amp_cast and amp_multicast operators, before saving the model.</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> - ``prefix-symbol.json`` will be saved for symbol.</span>
<span class="sd"> - ``prefix-epoch.params`` will be saved for parameters.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">symbol</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">symbol</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">%s</span><span class="s1">-symbol.json&#39;</span> <span class="o">%</span> <span class="n">prefix</span><span class="p">,</span> <span class="n">remove_amp_cast</span><span class="o">=</span><span class="n">remove_amp_cast</span><span class="p">)</span>
<span class="n">save_dict</span> <span class="o">=</span> <span class="p">{(</span><span class="s1">&#39;arg:</span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">k</span><span class="p">)</span> <span class="p">:</span> <span class="n">v</span><span class="o">.</span><span class="n">as_in_context</span><span class="p">(</span><span class="n">cpu</span><span class="p">())</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">arg_params</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
<span class="n">save_dict</span><span class="o">.</span><span class="n">update</span><span class="p">({(</span><span class="s1">&#39;aux:</span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">k</span><span class="p">)</span> <span class="p">:</span> <span class="n">v</span><span class="o">.</span><span class="n">as_in_context</span><span class="p">(</span><span class="n">cpu</span><span class="p">())</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">aux_params</span><span class="o">.</span><span class="n">items</span><span class="p">()})</span>
<span class="n">param_name</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">%s</span><span class="s1">-</span><span class="si">%04d</span><span class="s1">.params&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="n">epoch</span><span class="p">)</span>
<span class="n">nd</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">param_name</span><span class="p">,</span> <span class="n">save_dict</span><span class="p">)</span>
<span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">&#39;Saved checkpoint to </span><span class="se">\&quot;</span><span class="si">%s</span><span class="se">\&quot;</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">param_name</span><span class="p">)</span></div>
<div class="viewcode-block" id="load_params"><a class="viewcode-back" href="../../api/mxnet/model/index.html#mxnet.model.load_params">[docs]</a><span class="k">def</span> <span class="nf">load_params</span><span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="n">epoch</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Load params from a file</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">save_dict</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2">-</span><span class="si">%04d</span><span class="s2">.params&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="n">epoch</span><span class="p">))</span>
<span class="n">arg_params</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">aux_params</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">save_dict</span><span class="p">:</span>
<span class="n">logging</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;Params file &#39;</span><span class="si">%s</span><span class="s2">&#39; is empty&quot;</span><span class="p">,</span> <span class="s1">&#39;</span><span class="si">%s</span><span class="s1">-</span><span class="si">%04d</span><span class="s1">.params&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="n">epoch</span><span class="p">))</span>
<span class="k">return</span> <span class="p">(</span><span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span><span class="p">)</span>
<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">save_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">tp</span><span class="p">,</span> <span class="n">name</span> <span class="o">=</span> <span class="n">k</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;:&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">if</span> <span class="n">tp</span> <span class="o">==</span> <span class="s2">&quot;arg&quot;</span><span class="p">:</span>
<span class="n">arg_params</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span>
<span class="k">if</span> <span class="n">tp</span> <span class="o">==</span> <span class="s2">&quot;aux&quot;</span><span class="p">:</span>
<span class="n">aux_params</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span>
<span class="k">return</span> <span class="p">(</span><span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span><span class="p">)</span></div>
<div class="viewcode-block" id="load_checkpoint"><a class="viewcode-back" href="../../api/mxnet/model/index.html#mxnet.model.load_checkpoint">[docs]</a><span class="k">def</span> <span class="nf">load_checkpoint</span><span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="n">epoch</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Load model checkpoint from file.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> prefix : str</span>
<span class="sd"> Prefix of model name.</span>
<span class="sd"> epoch : int</span>
<span class="sd"> Epoch number of model we would like to load.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> symbol : Symbol</span>
<span class="sd"> The symbol configuration of computation network.</span>
<span class="sd"> arg_params : dict of str to NDArray</span>
<span class="sd"> Model parameter, dict of name to NDArray of net&#39;s weights.</span>
<span class="sd"> aux_params : dict of str to NDArray</span>
<span class="sd"> Model parameter, dict of name to NDArray of net&#39;s auxiliary states.</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> - Symbol will be loaded from ``prefix-symbol.json``.</span>
<span class="sd"> - Parameters will be loaded from ``prefix-epoch.params``.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">symbol</span> <span class="o">=</span> <span class="n">sym</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">%s</span><span class="s1">-symbol.json&#39;</span> <span class="o">%</span> <span class="n">prefix</span><span class="p">)</span>
<span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span> <span class="o">=</span> <span class="n">load_params</span><span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="n">epoch</span><span class="p">)</span>
<span class="k">return</span> <span class="p">(</span><span class="n">symbol</span><span class="p">,</span> <span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span><span class="p">)</span></div>
<span class="kn">from</span> <span class="nn">.callback</span> <span class="kn">import</span> <span class="n">LogValidationMetricsCallback</span> <span class="c1"># pylint: disable=wrong-import-position</span>
<div class="viewcode-block" id="FeedForward"><a class="viewcode-back" href="../../api/mxnet/model/index.html#mxnet.model.FeedForward">[docs]</a><span class="k">class</span> <span class="nc">FeedForward</span><span class="p">(</span><span class="n">BASE_ESTIMATOR</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Model class of MXNet for training and predicting feedforward nets.</span>
<span class="sd"> This class is designed for a single-data single output supervised network.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> symbol : Symbol</span>
<span class="sd"> The symbol configuration of computation network.</span>
<span class="sd"> ctx : Context or list of Context, optional</span>
<span class="sd"> The device context of training and prediction.</span>
<span class="sd"> To use multi GPU training, pass in a list of gpu contexts.</span>
<span class="sd"> num_epoch : int, optional</span>
<span class="sd"> Training parameter, number of training epochs(epochs).</span>
<span class="sd"> epoch_size : int, optional</span>
<span class="sd"> Number of batches in a epoch. In default, it is set to</span>
<span class="sd"> ``ceil(num_train_examples / batch_size)``.</span>
<span class="sd"> optimizer : str or Optimizer, optional</span>
<span class="sd"> Training parameter, name or optimizer object for training.</span>
<span class="sd"> initializer : initializer function, optional</span>
<span class="sd"> Training parameter, the initialization scheme used.</span>
<span class="sd"> numpy_batch_size : int, optional</span>
<span class="sd"> The batch size of training data.</span>
<span class="sd"> Only needed when input array is numpy.</span>
<span class="sd"> arg_params : dict of str to NDArray, optional</span>
<span class="sd"> Model parameter, dict of name to NDArray of net&#39;s weights.</span>
<span class="sd"> aux_params : dict of str to NDArray, optional</span>
<span class="sd"> Model parameter, dict of name to NDArray of net&#39;s auxiliary states.</span>
<span class="sd"> allow_extra_params : boolean, optional</span>
<span class="sd"> Whether allow extra parameters that are not needed by symbol</span>
<span class="sd"> to be passed by aux_params and ``arg_params``.</span>
<span class="sd"> If this is True, no error will be thrown when ``aux_params`` and ``arg_params``</span>
<span class="sd"> contain more parameters than needed.</span>
<span class="sd"> begin_epoch : int, optional</span>
<span class="sd"> The begining training epoch.</span>
<span class="sd"> kwargs : dict</span>
<span class="sd"> The additional keyword arguments passed to optimizer.</span>
<span class="sd"> &quot;&quot;&quot;</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">symbol</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">num_epoch</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">epoch_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;sgd&#39;</span><span class="p">,</span>
<span class="n">initializer</span><span class="o">=</span><span class="n">Uniform</span><span class="p">(</span><span class="mf">0.01</span><span class="p">),</span>
<span class="n">numpy_batch_size</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span>
<span class="n">arg_params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">aux_params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">allow_extra_params</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">begin_epoch</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
<span class="s1">&#39;</span><span class="se">\033</span><span class="s1">[91mmxnet.model.FeedForward has been deprecated. &#39;</span> <span class="o">+</span> \
<span class="s1">&#39;Please use mxnet.mod.Module instead.</span><span class="se">\033</span><span class="s1">[0m&#39;</span><span class="p">,</span>
<span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">symbol</span><span class="p">,</span> <span class="n">sym</span><span class="o">.</span><span class="n">Symbol</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">symbol</span> <span class="o">=</span> <span class="n">symbol</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sym_gen</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">assert</span><span class="p">(</span><span class="n">callable</span><span class="p">(</span><span class="n">symbol</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">symbol</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sym_gen</span> <span class="o">=</span> <span class="n">symbol</span>
<span class="c1"># model parameters</span>
<span class="bp">self</span><span class="o">.</span><span class="n">arg_params</span> <span class="o">=</span> <span class="n">arg_params</span>
<span class="bp">self</span><span class="o">.</span><span class="n">aux_params</span> <span class="o">=</span> <span class="n">aux_params</span>
<span class="bp">self</span><span class="o">.</span><span class="n">allow_extra_params</span> <span class="o">=</span> <span class="n">allow_extra_params</span>
<span class="bp">self</span><span class="o">.</span><span class="n">argument_checked</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sym_gen</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_check_arguments</span><span class="p">()</span>
<span class="c1"># basic configuration</span>
<span class="k">if</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">ctx</span> <span class="o">=</span> <span class="p">[</span><span class="n">cpu</span><span class="p">()]</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">Context</span><span class="p">):</span>
<span class="n">ctx</span> <span class="o">=</span> <span class="p">[</span><span class="n">ctx</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">ctx</span> <span class="o">=</span> <span class="n">ctx</span>
<span class="c1"># training parameters</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_epoch</span> <span class="o">=</span> <span class="n">num_epoch</span>
<span class="bp">self</span><span class="o">.</span><span class="n">epoch_size</span> <span class="o">=</span> <span class="n">epoch_size</span>
<span class="bp">self</span><span class="o">.</span><span class="n">kwargs</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span> <span class="o">=</span> <span class="n">optimizer</span>
<span class="bp">self</span><span class="o">.</span><span class="n">initializer</span> <span class="o">=</span> <span class="n">initializer</span>
<span class="bp">self</span><span class="o">.</span><span class="n">numpy_batch_size</span> <span class="o">=</span> <span class="n">numpy_batch_size</span>
<span class="c1"># internal helper state</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_pred_exec</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">begin_epoch</span> <span class="o">=</span> <span class="n">begin_epoch</span>
<span class="k">def</span> <span class="nf">_check_arguments</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;verify the argument of the default symbol and user provided parameters&quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">argument_checked</span><span class="p">:</span>
<span class="k">return</span>
<span class="k">assert</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">symbol</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">argument_checked</span> <span class="o">=</span> <span class="kc">True</span>
<span class="c1"># check if symbol contain duplicated names.</span>
<span class="n">_check_arguments</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">symbol</span><span class="p">)</span>
<span class="c1"># rematch parameters to delete useless ones</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">allow_extra_params</span><span class="p">:</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_params</span><span class="p">:</span>
<span class="n">arg_names</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">list_arguments</span><span class="p">())</span>
<span class="bp">self</span><span class="o">.</span><span class="n">arg_params</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span> <span class="p">:</span> <span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_params</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
<span class="k">if</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">arg_names</span><span class="p">}</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">aux_params</span><span class="p">:</span>
<span class="n">aux_names</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">list_auxiliary_states</span><span class="p">())</span>
<span class="bp">self</span><span class="o">.</span><span class="n">aux_params</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span> <span class="p">:</span> <span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">aux_params</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
<span class="k">if</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">aux_names</span><span class="p">}</span>
<span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">_is_data_arg</span><span class="p">(</span><span class="n">name</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Check if name is a data argument.&quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">name</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">&#39;data&#39;</span><span class="p">)</span> <span class="ow">or</span> <span class="n">name</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">&#39;label&#39;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_init_params</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">overwrite</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Initialize weight parameters and auxiliary states.&quot;&quot;&quot;</span>
<span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">DataDesc</span><span class="p">)</span> <span class="k">else</span> <span class="n">DataDesc</span><span class="p">(</span><span class="o">*</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">]</span>
<span class="n">input_shapes</span> <span class="o">=</span> <span class="p">{</span><span class="n">item</span><span class="o">.</span><span class="n">name</span><span class="p">:</span> <span class="n">item</span><span class="o">.</span><span class="n">shape</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">}</span>
<span class="n">arg_shapes</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">aux_shapes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">infer_shape</span><span class="p">(</span><span class="o">**</span><span class="n">input_shapes</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">arg_shapes</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="n">input_dtypes</span> <span class="o">=</span> <span class="p">{</span><span class="n">item</span><span class="o">.</span><span class="n">name</span><span class="p">:</span> <span class="n">item</span><span class="o">.</span><span class="n">dtype</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">}</span>
<span class="n">arg_dtypes</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">aux_dtypes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">infer_type</span><span class="p">(</span><span class="o">**</span><span class="n">input_dtypes</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">arg_dtypes</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="n">arg_names</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">list_arguments</span><span class="p">()</span>
<span class="n">input_names</span> <span class="o">=</span> <span class="n">input_shapes</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
<span class="n">param_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">key</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">arg_names</span> <span class="k">if</span> <span class="n">key</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">input_names</span><span class="p">]</span>
<span class="n">aux_names</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">list_auxiliary_states</span><span class="p">()</span>
<span class="n">param_name_attrs</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">arg_names</span><span class="p">,</span> <span class="n">arg_shapes</span><span class="p">,</span> <span class="n">arg_dtypes</span><span class="p">)</span>
<span class="k">if</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">in</span> <span class="n">param_names</span><span class="p">]</span>
<span class="n">arg_params</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span> <span class="p">:</span> <span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">s</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">t</span><span class="p">)</span>
<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">param_name_attrs</span><span class="p">}</span>
<span class="n">aux_name_attrs</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">aux_names</span><span class="p">,</span> <span class="n">aux_shapes</span><span class="p">,</span> <span class="n">aux_dtypes</span><span class="p">)</span>
<span class="k">if</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">in</span> <span class="n">aux_names</span><span class="p">]</span>
<span class="n">aux_params</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span> <span class="p">:</span> <span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">s</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">t</span><span class="p">)</span>
<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">aux_name_attrs</span><span class="p">}</span>
<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">arg_params</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_params</span> <span class="ow">and</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_params</span> <span class="ow">and</span> <span class="p">(</span><span class="ow">not</span> <span class="n">overwrite</span><span class="p">):</span>
<span class="n">arg_params</span><span class="p">[</span><span class="n">k</span><span class="p">][:]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_params</span><span class="p">[</span><span class="n">k</span><span class="p">][:]</span>
<span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">initializer</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span>
<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">aux_params</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">aux_params</span> <span class="ow">and</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">aux_params</span> <span class="ow">and</span> <span class="p">(</span><span class="ow">not</span> <span class="n">overwrite</span><span class="p">):</span>
<span class="n">aux_params</span><span class="p">[</span><span class="n">k</span><span class="p">][:]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aux_params</span><span class="p">[</span><span class="n">k</span><span class="p">][:]</span>
<span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">initializer</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">arg_params</span> <span class="o">=</span> <span class="n">arg_params</span>
<span class="bp">self</span><span class="o">.</span><span class="n">aux_params</span> <span class="o">=</span> <span class="n">aux_params</span>
<span class="k">return</span> <span class="p">(</span><span class="n">arg_names</span><span class="p">,</span> <span class="nb">list</span><span class="p">(</span><span class="n">param_names</span><span class="p">),</span> <span class="n">aux_names</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__getstate__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">this</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">this</span><span class="p">[</span><span class="s1">&#39;_pred_exec&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">return</span> <span class="n">this</span>
<span class="k">def</span> <span class="nf">__setstate__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">state</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_init_predictor</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_shapes</span><span class="p">,</span> <span class="n">type_dict</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Initialize the predictor module for running prediction.&quot;&quot;&quot;</span>
<span class="n">shapes</span> <span class="o">=</span> <span class="p">{</span><span class="n">name</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_params</span><span class="p">[</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_params</span><span class="p">}</span>
<span class="n">shapes</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="nb">dict</span><span class="p">(</span><span class="n">input_shapes</span><span class="p">))</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pred_exec</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">arg_shapes</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">infer_shape</span><span class="p">(</span><span class="o">**</span><span class="n">shapes</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">arg_shapes</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">&quot;Incomplete input shapes&quot;</span>
<span class="n">pred_shapes</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pred_exec</span><span class="o">.</span><span class="n">arg_arrays</span><span class="p">]</span>
<span class="k">if</span> <span class="n">arg_shapes</span> <span class="o">==</span> <span class="n">pred_shapes</span><span class="p">:</span>
<span class="k">return</span>
<span class="c1"># for now only use the first device</span>
<span class="n">pred_exec</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">simple_bind</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">ctx</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">grad_req</span><span class="o">=</span><span class="s1">&#39;null&#39;</span><span class="p">,</span> <span class="n">type_dict</span><span class="o">=</span><span class="n">type_dict</span><span class="p">,</span> <span class="o">**</span><span class="n">shapes</span><span class="p">)</span>
<span class="n">pred_exec</span><span class="o">.</span><span class="n">copy_params_from</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">arg_params</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">aux_params</span><span class="p">)</span>
<span class="n">_check_arguments</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">symbol</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_pred_exec</span> <span class="o">=</span> <span class="n">pred_exec</span>
<span class="k">def</span> <span class="nf">_init_iter</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">is_train</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Initialize the iterator given input.&quot;&quot;&quot;</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">nd</span><span class="o">.</span><span class="n">NDArray</span><span class="p">)):</span>
<span class="k">if</span> <span class="n">y</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">if</span> <span class="n">is_train</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;y must be specified when X is numpy.ndarray&#39;</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">nd</span><span class="o">.</span><span class="n">NDArray</span><span class="p">)):</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;y must be ndarray when X is numpy.ndarray&#39;</span><span class="p">)</span>
<span class="k">if</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">y</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;The numbers of data points and labels not equal&quot;</span><span class="p">)</span>
<span class="k">if</span> <span class="n">y</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">2</span> <span class="ow">and</span> <span class="n">y</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
<span class="k">if</span> <span class="n">y</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Label must be 1D or 2D (with 2nd dimension being 1)&quot;</span><span class="p">)</span>
<span class="k">if</span> <span class="n">is_train</span><span class="p">:</span>
<span class="k">return</span> <span class="n">io</span><span class="o">.</span><span class="n">NDArrayIter</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="nb">min</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">numpy_batch_size</span><span class="p">),</span>
<span class="n">shuffle</span><span class="o">=</span><span class="n">is_train</span><span class="p">,</span> <span class="n">last_batch_handle</span><span class="o">=</span><span class="s1">&#39;roll_over&#39;</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">io</span><span class="o">.</span><span class="n">NDArrayIter</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="nb">min</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">numpy_batch_size</span><span class="p">),</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">io</span><span class="o">.</span><span class="n">DataIter</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;X must be DataIter, NDArray or numpy.ndarray&#39;</span><span class="p">)</span>
<span class="k">return</span> <span class="n">X</span>
<span class="k">def</span> <span class="nf">_init_eval_iter</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">eval_data</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Initialize the iterator given eval_data.&quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">eval_data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="n">eval_data</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">eval_data</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">))</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">eval_data</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
<span class="k">if</span> <span class="n">eval_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">if</span> <span class="n">eval_data</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">eval_data</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">io</span><span class="o">.</span><span class="n">DataIter</span><span class="p">):</span>
<span class="k">return</span> <span class="n">eval_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">input_data</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">eval_data</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">eval_data</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">list</span><span class="p">)</span>
<span class="k">else</span> <span class="n">eval_data</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">input_label</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">eval_data</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">eval_data</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="nb">list</span><span class="p">)</span>
<span class="k">else</span> <span class="n">eval_data</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_init_iter</span><span class="p">(</span><span class="n">input_data</span><span class="p">,</span> <span class="n">input_label</span><span class="p">,</span> <span class="n">is_train</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Eval data is NONE&quot;</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">eval_data</span><span class="p">,</span> <span class="n">io</span><span class="o">.</span><span class="n">DataIter</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;Eval data must be DataIter, or &#39;</span> \
<span class="s1">&#39;NDArray/numpy.ndarray/list pair (i.e. tuple/list of length 2)&#39;</span><span class="p">)</span>
<span class="k">return</span> <span class="n">eval_data</span>
<div class="viewcode-block" id="FeedForward.predict"><a class="viewcode-back" href="../../api/mxnet/model/index.html#mxnet.model.FeedForward.predict">[docs]</a> <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">num_batch</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">return_data</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">reset</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Run the prediction, always only use one device.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> X : mxnet.DataIter</span>
<span class="sd"> num_batch : int or None</span>
<span class="sd"> The number of batch to run. Go though all batches if ``None``.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> y : numpy.ndarray or a list of numpy.ndarray if the network has multiple outputs.</span>
<span class="sd"> The predicted value of the output.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_init_iter</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">is_train</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="k">if</span> <span class="n">reset</span><span class="p">:</span>
<span class="n">X</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
<span class="n">data_shapes</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">provide_data</span>
<span class="n">data_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">data_shapes</span><span class="p">]</span>
<span class="n">type_dict</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">((</span><span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_params</span><span class="o">.</span><span class="n">items</span><span class="p">())</span>
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">X</span><span class="o">.</span><span class="n">provide_data</span><span class="p">:</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">DataDesc</span><span class="p">):</span>
<span class="n">type_dict</span><span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">dtype</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">type_dict</span><span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">=</span> <span class="n">mx_real_t</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_init_predictor</span><span class="p">(</span><span class="n">data_shapes</span><span class="p">,</span> <span class="n">type_dict</span><span class="p">)</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">batch_size</span>
<span class="n">data_arrays</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_pred_exec</span><span class="o">.</span><span class="n">arg_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">data_names</span><span class="p">]</span>
<span class="n">output_list</span> <span class="o">=</span> <span class="p">[[]</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_pred_exec</span><span class="o">.</span><span class="n">outputs</span><span class="p">))]</span>
<span class="k">if</span> <span class="n">return_data</span><span class="p">:</span>
<span class="n">data_list</span> <span class="o">=</span> <span class="p">[[]</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">X</span><span class="o">.</span><span class="n">provide_data</span><span class="p">]</span>
<span class="n">label_list</span> <span class="o">=</span> <span class="p">[[]</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">X</span><span class="o">.</span><span class="n">provide_label</span><span class="p">]</span>
<span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">X</span><span class="p">:</span>
<span class="n">_load_data</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">data_arrays</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_pred_exec</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">is_train</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">padded</span> <span class="o">=</span> <span class="n">batch</span><span class="o">.</span><span class="n">pad</span>
<span class="n">real_size</span> <span class="o">=</span> <span class="n">batch_size</span> <span class="o">-</span> <span class="n">padded</span>
<span class="k">for</span> <span class="n">o_list</span><span class="p">,</span> <span class="n">o_nd</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">output_list</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pred_exec</span><span class="o">.</span><span class="n">outputs</span><span class="p">):</span>
<span class="n">o_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">o_nd</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">real_size</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
<span class="k">if</span> <span class="n">return_data</span><span class="p">:</span>
<span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">data</span><span class="p">):</span>
<span class="n">data_list</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">real_size</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
<span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">label</span><span class="p">):</span>
<span class="n">label_list</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">real_size</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
<span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">if</span> <span class="n">num_batch</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">i</span> <span class="o">==</span> <span class="n">num_batch</span><span class="p">:</span>
<span class="k">break</span>
<span class="n">outputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">output_list</span><span class="p">]</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">outputs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">outputs</span> <span class="o">=</span> <span class="n">outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">if</span> <span class="n">return_data</span><span class="p">:</span>
<span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">data_list</span><span class="p">]</span>
<span class="n">label</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">label_list</span><span class="p">]</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">label</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">return</span> <span class="n">outputs</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">label</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">outputs</span></div>
<div class="viewcode-block" id="FeedForward.score"><a class="viewcode-back" href="../../api/mxnet/model/index.html#mxnet.model.FeedForward.score">[docs]</a> <span class="k">def</span> <span class="nf">score</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">eval_metric</span><span class="o">=</span><span class="s1">&#39;acc&#39;</span><span class="p">,</span> <span class="n">num_batch</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">batch_end_callback</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">reset</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Run the model given an input and calculate the score</span>
<span class="sd"> as assessed by an evaluation metric.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> X : mxnet.DataIter</span>
<span class="sd"> eval_metric : metric.metric</span>
<span class="sd"> The metric for calculating score.</span>
<span class="sd"> num_batch : int or None</span>
<span class="sd"> The number of batches to run. Go though all batches if ``None``.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> s : float</span>
<span class="sd"> The final score.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="c1"># setup metric</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">eval_metric</span><span class="p">,</span> <span class="n">metric</span><span class="o">.</span><span class="n">EvalMetric</span><span class="p">):</span>
<span class="n">eval_metric</span> <span class="o">=</span> <span class="n">metric</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">eval_metric</span><span class="p">)</span>
<span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_init_iter</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">is_train</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="k">if</span> <span class="n">reset</span><span class="p">:</span>
<span class="n">X</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
<span class="n">data_shapes</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">provide_data</span>
<span class="n">data_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">data_shapes</span><span class="p">]</span>
<span class="n">type_dict</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">((</span><span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_params</span><span class="o">.</span><span class="n">items</span><span class="p">())</span>
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">X</span><span class="o">.</span><span class="n">provide_data</span><span class="p">:</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">DataDesc</span><span class="p">):</span>
<span class="n">type_dict</span><span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">dtype</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">type_dict</span><span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">=</span> <span class="n">mx_real_t</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_init_predictor</span><span class="p">(</span><span class="n">data_shapes</span><span class="p">,</span> <span class="n">type_dict</span><span class="p">)</span>
<span class="n">data_arrays</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_pred_exec</span><span class="o">.</span><span class="n">arg_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">data_names</span><span class="p">]</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">X</span><span class="p">):</span>
<span class="k">if</span> <span class="n">num_batch</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">i</span> <span class="o">==</span> <span class="n">num_batch</span><span class="p">:</span>
<span class="k">break</span>
<span class="n">_load_data</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">data_arrays</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_pred_exec</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">is_train</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">eval_metric</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">label</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pred_exec</span><span class="o">.</span><span class="n">outputs</span><span class="p">)</span>
<span class="k">if</span> <span class="n">batch_end_callback</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">batch_end_params</span> <span class="o">=</span> <span class="n">BatchEndParam</span><span class="p">(</span><span class="n">epoch</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">nbatch</span><span class="o">=</span><span class="n">i</span><span class="p">,</span>
<span class="n">eval_metric</span><span class="o">=</span><span class="n">eval_metric</span><span class="p">,</span>
<span class="nb">locals</span><span class="o">=</span><span class="nb">locals</span><span class="p">())</span>
<span class="n">_multiple_callbacks</span><span class="p">(</span><span class="n">batch_end_callback</span><span class="p">,</span> <span class="n">batch_end_params</span><span class="p">)</span>
<span class="k">return</span> <span class="n">eval_metric</span><span class="o">.</span><span class="n">get</span><span class="p">()[</span><span class="mi">1</span><span class="p">]</span></div>
<div class="viewcode-block" id="FeedForward.fit"><a class="viewcode-back" href="../../api/mxnet/model/index.html#mxnet.model.FeedForward.fit">[docs]</a> <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">eval_data</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">eval_metric</span><span class="o">=</span><span class="s1">&#39;acc&#39;</span><span class="p">,</span>
<span class="n">epoch_end_callback</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">batch_end_callback</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">kvstore</span><span class="o">=</span><span class="s1">&#39;local&#39;</span><span class="p">,</span> <span class="n">logger</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">work_load_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">monitor</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">eval_end_callback</span><span class="o">=</span><span class="n">LogValidationMetricsCallback</span><span class="p">(),</span>
<span class="n">eval_batch_end_callback</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Fit the model.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> X : DataIter, or numpy.ndarray/NDArray</span>
<span class="sd"> Training data. If `X` is a `DataIter`, the name or (if name not available)</span>
<span class="sd"> the position of its outputs should match the corresponding variable</span>
<span class="sd"> names defined in the symbolic graph.</span>
<span class="sd"> y : numpy.ndarray/NDArray, optional</span>
<span class="sd"> Training set label.</span>
<span class="sd"> If X is ``numpy.ndarray`` or `NDArray`, `y` is required to be set.</span>
<span class="sd"> While y can be 1D or 2D (with 2nd dimension as 1), its first dimension must be</span>
<span class="sd"> the same as `X`, i.e. the number of data points and labels should be equal.</span>
<span class="sd"> eval_data : DataIter or numpy.ndarray/list/NDArray pair</span>
<span class="sd"> If eval_data is numpy.ndarray/list/NDArray pair,</span>
<span class="sd"> it should be ``(valid_data, valid_label)``.</span>
<span class="sd"> eval_metric : metric.EvalMetric or str or callable</span>
<span class="sd"> The evaluation metric. This could be the name of evaluation metric</span>
<span class="sd"> or a custom evaluation function that returns statistics</span>
<span class="sd"> based on a minibatch.</span>
<span class="sd"> epoch_end_callback : callable(epoch, symbol, arg_params, aux_states)</span>
<span class="sd"> A callback that is invoked at end of each epoch.</span>
<span class="sd"> This can be used to checkpoint model each epoch.</span>
<span class="sd"> batch_end_callback: callable(epoch)</span>
<span class="sd"> A callback that is invoked at end of each batch for purposes of printing.</span>
<span class="sd"> kvstore: KVStore or str, optional</span>
<span class="sd"> The KVStore or a string kvstore type: &#39;local&#39;, &#39;dist_sync&#39;, &#39;dist_async&#39;</span>
<span class="sd"> In default uses &#39;local&#39;, often no need to change for single machiine.</span>
<span class="sd"> logger : logging logger, optional</span>
<span class="sd"> When not specified, default logger will be used.</span>
<span class="sd"> work_load_list : float or int, optional</span>
<span class="sd"> The list of work load for different devices,</span>
<span class="sd"> in the same order as `ctx`.</span>
<span class="sd"> Note</span>
<span class="sd"> ----</span>
<span class="sd"> KVStore behavior</span>
<span class="sd"> - &#39;local&#39;, multi-devices on a single machine, will automatically choose best type.</span>
<span class="sd"> - &#39;dist_sync&#39;, multiple machines communicating via BSP.</span>
<span class="sd"> - &#39;dist_async&#39;, multiple machines with asynchronous communication.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_init_iter</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">is_train</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">eval_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_init_eval_iter</span><span class="p">(</span><span class="n">eval_data</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sym_gen</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">symbol</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sym_gen</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">default_bucket_key</span><span class="p">)</span> <span class="c1"># pylint: disable=no-member</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_check_arguments</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;sym&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">symbol</span>
<span class="n">arg_names</span><span class="p">,</span> <span class="n">param_names</span><span class="p">,</span> <span class="n">aux_names</span> <span class="o">=</span> \
<span class="bp">self</span><span class="o">.</span><span class="n">_init_params</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">provide_data</span><span class="o">+</span><span class="n">data</span><span class="o">.</span><span class="n">provide_label</span><span class="p">)</span>
<span class="c1"># setup metric</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">eval_metric</span><span class="p">,</span> <span class="n">metric</span><span class="o">.</span><span class="n">EvalMetric</span><span class="p">):</span>
<span class="n">eval_metric</span> <span class="o">=</span> <span class="n">metric</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">eval_metric</span><span class="p">)</span>
<span class="c1"># create kvstore</span>
<span class="p">(</span><span class="n">kvstore</span><span class="p">,</span> <span class="n">update_on_kvstore</span><span class="p">)</span> <span class="o">=</span> <span class="n">_create_kvstore</span><span class="p">(</span>
<span class="n">kvstore</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ctx</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_params</span><span class="p">)</span>
<span class="n">param_idx2name</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">if</span> <span class="n">update_on_kvstore</span><span class="p">:</span>
<span class="n">param_idx2name</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="nb">enumerate</span><span class="p">(</span><span class="n">param_names</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">param_names</span><span class="p">):</span>
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ctx</span><span class="p">)):</span>
<span class="n">param_idx2name</span><span class="p">[</span><span class="n">i</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ctx</span><span class="p">)</span><span class="o">+</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">n</span>
<span class="bp">self</span><span class="o">.</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;param_idx2name&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">param_idx2name</span>
<span class="c1"># init optmizer</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">batch_size</span>
<span class="k">if</span> <span class="n">kvstore</span> <span class="ow">and</span> <span class="s1">&#39;dist&#39;</span> <span class="ow">in</span> <span class="n">kvstore</span><span class="o">.</span><span class="n">type</span> <span class="ow">and</span> <span class="s1">&#39;_async&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">kvstore</span><span class="o">.</span><span class="n">type</span><span class="p">:</span>
<span class="n">batch_size</span> <span class="o">*=</span> <span class="n">kvstore</span><span class="o">.</span><span class="n">num_workers</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">opt</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="p">,</span>
<span class="n">rescale_grad</span><span class="o">=</span><span class="p">(</span><span class="mf">1.0</span><span class="o">/</span><span class="n">batch_size</span><span class="p">),</span>
<span class="o">**</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">kwargs</span><span class="p">))</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">opt</span><span class="o">.</span><span class="n">Optimizer</span><span class="p">):</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">idx2name</span><span class="p">:</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">idx2name</span> <span class="o">=</span> <span class="n">param_idx2name</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="c1"># do training</span>
<span class="n">_train_multi_device</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">symbol</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ctx</span><span class="p">,</span> <span class="n">arg_names</span><span class="p">,</span> <span class="n">param_names</span><span class="p">,</span> <span class="n">aux_names</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">arg_params</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">aux_params</span><span class="p">,</span>
<span class="n">begin_epoch</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">begin_epoch</span><span class="p">,</span> <span class="n">end_epoch</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">num_epoch</span><span class="p">,</span>
<span class="n">epoch_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">epoch_size</span><span class="p">,</span>
<span class="n">optimizer</span><span class="o">=</span><span class="n">optimizer</span><span class="p">,</span>
<span class="n">train_data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">eval_data</span><span class="o">=</span><span class="n">eval_data</span><span class="p">,</span>
<span class="n">eval_metric</span><span class="o">=</span><span class="n">eval_metric</span><span class="p">,</span>
<span class="n">epoch_end_callback</span><span class="o">=</span><span class="n">epoch_end_callback</span><span class="p">,</span>
<span class="n">batch_end_callback</span><span class="o">=</span><span class="n">batch_end_callback</span><span class="p">,</span>
<span class="n">kvstore</span><span class="o">=</span><span class="n">kvstore</span><span class="p">,</span> <span class="n">update_on_kvstore</span><span class="o">=</span><span class="n">update_on_kvstore</span><span class="p">,</span>
<span class="n">logger</span><span class="o">=</span><span class="n">logger</span><span class="p">,</span> <span class="n">work_load_list</span><span class="o">=</span><span class="n">work_load_list</span><span class="p">,</span> <span class="n">monitor</span><span class="o">=</span><span class="n">monitor</span><span class="p">,</span>
<span class="n">eval_end_callback</span><span class="o">=</span><span class="n">eval_end_callback</span><span class="p">,</span>
<span class="n">eval_batch_end_callback</span><span class="o">=</span><span class="n">eval_batch_end_callback</span><span class="p">,</span>
<span class="n">sym_gen</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sym_gen</span><span class="p">)</span></div>
<div class="viewcode-block" id="FeedForward.save"><a class="viewcode-back" href="../../api/mxnet/model/index.html#mxnet.model.FeedForward.save">[docs]</a> <span class="k">def</span> <span class="nf">save</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">prefix</span><span class="p">,</span> <span class="n">epoch</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">remove_amp_cast</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Checkpoint the model checkpoint into file.</span>
<span class="sd"> You can also use `pickle` to do the job if you only work on Python.</span>
<span class="sd"> The advantage of `load` and `save` (as compared to `pickle`) is that</span>
<span class="sd"> the resulting file can be loaded from other MXNet language bindings.</span>
<span class="sd"> One can also directly `load`/`save` from/to cloud storage(S3, HDFS)</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> prefix : str</span>
<span class="sd"> Prefix of model name.</span>
<span class="sd"> remove_amp_cast : bool, optional</span>
<span class="sd"> Whether to remove the amp_cast and amp_multicast operators, before saving the model.</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> - ``prefix-symbol.json`` will be saved for symbol.</span>
<span class="sd"> - ``prefix-epoch.params`` will be saved for parameters.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">epoch</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">epoch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_epoch</span>
<span class="k">assert</span> <span class="n">epoch</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="n">save_checkpoint</span><span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="n">epoch</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">symbol</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_params</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">aux_params</span><span class="p">,</span> <span class="n">remove_amp_cast</span><span class="o">=</span><span class="n">remove_amp_cast</span><span class="p">)</span></div>
<div class="viewcode-block" id="FeedForward.load"><a class="viewcode-back" href="../../api/mxnet/model/index.html#mxnet.model.FeedForward.load">[docs]</a> <span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="n">epoch</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Load model checkpoint from file.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> prefix : str</span>
<span class="sd"> Prefix of model name.</span>
<span class="sd"> epoch : int</span>
<span class="sd"> epoch number of model we would like to load.</span>
<span class="sd"> ctx : Context or list of Context, optional</span>
<span class="sd"> The device context of training and prediction.</span>
<span class="sd"> kwargs : dict</span>
<span class="sd"> Other parameters for model, including `num_epoch`, optimizer and `numpy_batch_size`.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> model : FeedForward</span>
<span class="sd"> The loaded model that can be used for prediction.</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> - ``prefix-symbol.json`` will be saved for symbol.</span>
<span class="sd"> - ``prefix-epoch.params`` will be saved for parameters.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">symbol</span><span class="p">,</span> <span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span> <span class="o">=</span> <span class="n">load_checkpoint</span><span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="n">epoch</span><span class="p">)</span>
<span class="k">return</span> <span class="n">FeedForward</span><span class="p">(</span><span class="n">symbol</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span>
<span class="n">arg_params</span><span class="o">=</span><span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span><span class="o">=</span><span class="n">aux_params</span><span class="p">,</span>
<span class="n">begin_epoch</span><span class="o">=</span><span class="n">epoch</span><span class="p">,</span>
<span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
<div class="viewcode-block" id="FeedForward.create"><a class="viewcode-back" href="../../api/mxnet/model/index.html#mxnet.model.FeedForward.create">[docs]</a> <span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">create</span><span class="p">(</span><span class="n">symbol</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">num_epoch</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">epoch_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;sgd&#39;</span><span class="p">,</span> <span class="n">initializer</span><span class="o">=</span><span class="n">Uniform</span><span class="p">(</span><span class="mf">0.01</span><span class="p">),</span>
<span class="n">eval_data</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">eval_metric</span><span class="o">=</span><span class="s1">&#39;acc&#39;</span><span class="p">,</span>
<span class="n">epoch_end_callback</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">batch_end_callback</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">kvstore</span><span class="o">=</span><span class="s1">&#39;local&#39;</span><span class="p">,</span> <span class="n">logger</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">work_load_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">eval_end_callback</span><span class="o">=</span><span class="n">LogValidationMetricsCallback</span><span class="p">(),</span>
<span class="n">eval_batch_end_callback</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="sd">&quot;&quot;&quot;Functional style to create a model.</span>
<span class="sd"> This function is more consistent with functional</span>
<span class="sd"> languages such as R, where mutation is not allowed.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> symbol : Symbol</span>
<span class="sd"> The symbol configuration of a computation network.</span>
<span class="sd"> X : DataIter</span>
<span class="sd"> Training data.</span>
<span class="sd"> y : numpy.ndarray, optional</span>
<span class="sd"> If `X` is a ``numpy.ndarray``, `y` must be set.</span>
<span class="sd"> ctx : Context or list of Context, optional</span>
<span class="sd"> The device context of training and prediction.</span>
<span class="sd"> To use multi-GPU training, pass in a list of GPU contexts.</span>
<span class="sd"> num_epoch : int, optional</span>
<span class="sd"> The number of training epochs(epochs).</span>
<span class="sd"> epoch_size : int, optional</span>
<span class="sd"> Number of batches in a epoch. In default, it is set to</span>
<span class="sd"> ``ceil(num_train_examples / batch_size)``.</span>
<span class="sd"> optimizer : str or Optimizer, optional</span>
<span class="sd"> The name of the chosen optimizer, or an optimizer object, used for training.</span>
<span class="sd"> initializer : initializer function, optional</span>
<span class="sd"> The initialization scheme used.</span>
<span class="sd"> eval_data : DataIter or numpy.ndarray pair</span>
<span class="sd"> If `eval_set` is ``numpy.ndarray`` pair, it should</span>
<span class="sd"> be (`valid_data`, `valid_label`).</span>
<span class="sd"> eval_metric : metric.EvalMetric or str or callable</span>
<span class="sd"> The evaluation metric. Can be the name of an evaluation metric</span>
<span class="sd"> or a custom evaluation function that returns statistics</span>
<span class="sd"> based on a minibatch.</span>
<span class="sd"> epoch_end_callback : callable(epoch, symbol, arg_params, aux_states)</span>
<span class="sd"> A callback that is invoked at end of each epoch.</span>
<span class="sd"> This can be used to checkpoint model each epoch.</span>
<span class="sd"> batch_end_callback: callable(epoch)</span>
<span class="sd"> A callback that is invoked at end of each batch for print purposes.</span>
<span class="sd"> kvstore: KVStore or str, optional</span>
<span class="sd"> The KVStore or a string kvstore type: &#39;local&#39;, &#39;dist_sync&#39;, &#39;dis_async&#39;.</span>
<span class="sd"> Defaults to &#39;local&#39;, often no need to change for single machine.</span>
<span class="sd"> logger : logging logger, optional</span>
<span class="sd"> When not specified, default logger will be used.</span>
<span class="sd"> work_load_list : list of float or int, optional</span>
<span class="sd"> The list of work load for different devices,</span>
<span class="sd"> in the same order as `ctx`.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">FeedForward</span><span class="p">(</span><span class="n">symbol</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">num_epoch</span><span class="o">=</span><span class="n">num_epoch</span><span class="p">,</span>
<span class="n">epoch_size</span><span class="o">=</span><span class="n">epoch_size</span><span class="p">,</span>
<span class="n">optimizer</span><span class="o">=</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">initializer</span><span class="o">=</span><span class="n">initializer</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">eval_data</span><span class="o">=</span><span class="n">eval_data</span><span class="p">,</span> <span class="n">eval_metric</span><span class="o">=</span><span class="n">eval_metric</span><span class="p">,</span>
<span class="n">epoch_end_callback</span><span class="o">=</span><span class="n">epoch_end_callback</span><span class="p">,</span>
<span class="n">batch_end_callback</span><span class="o">=</span><span class="n">batch_end_callback</span><span class="p">,</span>
<span class="n">kvstore</span><span class="o">=</span><span class="n">kvstore</span><span class="p">,</span>
<span class="n">logger</span><span class="o">=</span><span class="n">logger</span><span class="p">,</span>
<span class="n">work_load_list</span><span class="o">=</span><span class="n">work_load_list</span><span class="p">,</span>
<span class="n">eval_end_callback</span><span class="o">=</span><span class="n">eval_end_callback</span><span class="p">,</span>
<span class="n">eval_batch_end_callback</span><span class="o">=</span><span class="n">eval_batch_end_callback</span><span class="p">)</span>
<span class="k">return</span> <span class="n">model</span></div></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>