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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <ul class="current"> |
| <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/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html">Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html#Using-your-own-data-with-custom-Datasets">Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">New in MXNet 2.0: faster C++ backend dataloaders</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../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/gluon_migration_guide.html">Gluon2.0: Migration Guide</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/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/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/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> |
| </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/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/np/np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../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 external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
| </ul> |
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| <li class="toctree-l2"><a class="reference internal" href="../npx/index.html">NPX: NumPy Neural Network Extension</a><ul> |
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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <nav class="mdl-navigation"> |
| <ul class="current"> |
| <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/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html">Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html#Using-your-own-data-with-custom-Datasets">Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">New in MXNet 2.0: faster C++ backend dataloaders</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../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/gluon_migration_guide.html">Gluon2.0: Migration Guide</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/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/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/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> |
| </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/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/np/index.html">What is NP on MXNet</a><ul> |
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| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
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| <li class="toctree-l2"><a class="reference internal" href="../npx/index.html">NPX: NumPy Neural Network Extension</a><ul> |
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| |
| <div class="section" id="module-mxnet.autograd"> |
| <span id="mxnet-autograd"></span><h1>mxnet.autograd<a class="headerlink" href="#module-mxnet.autograd" title="Permalink to this headline">¶</a></h1> |
| <p>Autograd for NDArray.</p> |
| <p><strong>Functions</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.autograd.backward" title="mxnet.autograd.backward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">backward</span></code></a>(heads[, head_grads, retain_graph, …])</p></td> |
| <td><p>Compute the gradients of heads w.r.t previously marked variables.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.autograd.get_symbol" title="mxnet.autograd.get_symbol"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_symbol</span></code></a>(x)</p></td> |
| <td><p>Retrieve recorded computation history as <cite>Symbol</cite>.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.autograd.grad" title="mxnet.autograd.grad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">grad</span></code></a>(heads, variables[, head_grads, …])</p></td> |
| <td><p>Compute the gradients of heads w.r.t variables.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.autograd.is_recording" title="mxnet.autograd.is_recording"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_recording</span></code></a>()</p></td> |
| <td><p>Get status on recording/not recording.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.autograd.is_training" title="mxnet.autograd.is_training"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_training</span></code></a>()</p></td> |
| <td><p>Get status on training/predicting.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.autograd.mark_variables" title="mxnet.autograd.mark_variables"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mark_variables</span></code></a>(variables, gradients[, grad_reqs])</p></td> |
| <td><p>Mark NDArrays as variables to compute gradient for autograd.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.autograd.pause" title="mxnet.autograd.pause"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pause</span></code></a>([train_mode])</p></td> |
| <td><p>Returns a scope context to be used in ‘with’ statement for codes that do not need gradients to be calculated.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.autograd.predict_mode" title="mxnet.autograd.predict_mode"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict_mode</span></code></a>()</p></td> |
| <td><p>Returns a scope context to be used in ‘with’ statement in which forward pass behavior is set to inference mode, without changing the recording states.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.autograd.record" title="mxnet.autograd.record"><code class="xref py py-obj docutils literal notranslate"><span class="pre">record</span></code></a>([train_mode])</p></td> |
| <td><p>Returns an autograd recording scope context to be used in ‘with’ statement and captures code that needs gradients to be calculated.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.autograd.set_recording" title="mxnet.autograd.set_recording"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_recording</span></code></a>(is_recording)</p></td> |
| <td><p>Set status to recording/not recording.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.autograd.set_training" title="mxnet.autograd.set_training"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_training</span></code></a>(train_mode)</p></td> |
| <td><p>Set status to training/predicting.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.autograd.train_mode" title="mxnet.autograd.train_mode"><code class="xref py py-obj docutils literal notranslate"><span class="pre">train_mode</span></code></a>()</p></td> |
| <td><p>Returns a scope context to be used in ‘with’ statement in which forward pass behavior is set to training mode, without changing the recording states.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><strong>Classes</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.autograd.Function" title="mxnet.autograd.Function"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Function</span></code></a>()</p></td> |
| <td><p>Customize differentiation in autograd.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="class"> |
| <dt id="mxnet.autograd.Function"> |
| <em class="property">class </em><code class="sig-name descname">Function</code><a class="reference internal" href="../../_modules/mxnet/autograd.html#Function"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.autograd.Function" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> |
| <p>Customize differentiation in autograd.</p> |
| <p>If you don’t want to use the gradients computed by the default |
| chain-rule, you can use Function to customize differentiation for |
| computation. You define your computation in |
| the forward method and provide the customized differentiation |
| in the backward method. During gradient computation, autograd will |
| use the user-defined backward function instead of the default chain-rule. |
| You can also cast to numpy array and back for some operations in |
| forward and backward.</p> |
| <p>For example, a stable sigmoid function can be defined as:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">sigmoid</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">Function</span><span class="p">):</span> |
| <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> |
| <span class="n">y</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">x</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">save_for_backward</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">y</span> |
| |
| <span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dy</span><span class="p">):</span> |
| <span class="c1"># backward takes as many inputs as forward's return value,</span> |
| <span class="c1"># and returns as many NDArrays as forward's arguments.</span> |
| <span class="n">y</span><span class="p">,</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">saved_tensors</span> |
| <span class="k">return</span> <span class="n">dy</span> <span class="o">*</span> <span class="n">y</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">y</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.autograd.Function.backward" title="mxnet.autograd.Function.backward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">backward</span></code></a>(*output_grads)</p></td> |
| <td><p>Backward computation.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.autograd.Function.forward" title="mxnet.autograd.Function.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(*inputs)</p></td> |
| <td><p>Forward computation.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>Then, the function can be used in the following way:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">func</span> <span class="o">=</span> <span class="n">sigmoid</span><span class="p">()</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,))</span> |
| <span class="n">x</span><span class="o">.</span><span class="n">attach_grad</span><span class="p">()</span> |
| |
| <span class="k">with</span> <span class="n">mx</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">record</span><span class="p">():</span> |
| <span class="n">m</span> <span class="o">=</span> <span class="n">func</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">m</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span> |
| <span class="n">dx</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| <dl class="method"> |
| <dt id="mxnet.autograd.Function.backward"> |
| <code class="sig-name descname">backward</code><span class="sig-paren">(</span><em class="sig-param">*output_grads</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/autograd.html#Function.backward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.autograd.Function.backward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Backward computation.</p> |
| <p>Takes as many inputs as forward’s outputs, |
| and returns as many NDArrays as forward’s inputs.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.autograd.Function.forward"> |
| <code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">*inputs</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/autograd.html#Function.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.autograd.Function.forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Forward computation.</p> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.autograd.backward"> |
| <code class="sig-name descname">backward</code><span class="sig-paren">(</span><em class="sig-param">heads</em>, <em class="sig-param">head_grads=None</em>, <em class="sig-param">retain_graph=False</em>, <em class="sig-param">train_mode=True</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/autograd.html#backward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.autograd.backward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Compute the gradients of heads w.r.t previously marked variables.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>heads</strong> (<a class="reference internal" href="../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>list of NDArray</em>) – Output NDArray(s)</p></li> |
| <li><p><strong>head_grads</strong> (<a class="reference internal" href="../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>list of NDArray</em><em> or </em><em>None</em>) – Gradients with respect to heads.</p></li> |
| <li><p><strong>train_mode</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to do backward for training or predicting.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.autograd.get_symbol"> |
| <code class="sig-name descname">get_symbol</code><span class="sig-paren">(</span><em class="sig-param">x</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/autograd.html#get_symbol"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.autograd.get_symbol" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Retrieve recorded computation history as <cite>Symbol</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>x</strong> (<a class="reference internal" href="../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Array representing the head of computation graph.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The retrieved Symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.autograd.grad"> |
| <code class="sig-name descname">grad</code><span class="sig-paren">(</span><em class="sig-param">heads</em>, <em class="sig-param">variables</em>, <em class="sig-param">head_grads=None</em>, <em class="sig-param">retain_graph=None</em>, <em class="sig-param">create_graph=False</em>, <em class="sig-param">train_mode=True</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/autograd.html#grad"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.autograd.grad" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Compute the gradients of heads w.r.t variables. Gradients will be |
| returned as new NDArrays instead of stored into <cite>variable.grad</cite>. |
| Supports recording gradient graph for computing higher order gradients.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>Currently only a very limited set of operators support higher order gradients.</p> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>heads</strong> (<a class="reference internal" href="../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>list of NDArray</em>) – Output NDArray(s)</p></li> |
| <li><p><strong>variables</strong> (<a class="reference internal" href="../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>list of NDArray</em>) – Input variables to compute gradients for.</p></li> |
| <li><p><strong>head_grads</strong> (<a class="reference internal" href="../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>list of NDArray</em><em> or </em><em>None</em>) – Gradients with respect to heads.</p></li> |
| <li><p><strong>retain_graph</strong> (<em>bool</em>) – Whether to keep computation graph to differentiate again, instead |
| of clearing history and release memory. Defaults to the same value |
| as create_graph.</p></li> |
| <li><p><strong>create_graph</strong> (<em>bool</em>) – Whether to record gradient graph for computing higher order</p></li> |
| <li><p><strong>train_mode</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to do backward for training or prediction.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>Gradients with respect to variables.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArray</p> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,))</span> |
| <span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">attach_grad</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="k">with</span> <span class="n">mx</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">record</span><span class="p">():</span> |
| <span class="gp">... </span> <span class="n">z</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">elemwise_add</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="n">x</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">dx</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">grad</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="p">[</span><span class="n">x</span><span class="p">],</span> <span class="n">create_graph</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">dx</span><span class="p">)</span> |
| <span class="go">[</span> |
| <span class="go">[ 3.71828175]</span> |
| <span class="go"><NDArray 1 @cpu(0)>]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.autograd.is_recording"> |
| <code class="sig-name descname">is_recording</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/autograd.html#is_recording"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.autograd.is_recording" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get status on recording/not recording.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p></p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>Current state of recording.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.autograd.is_training"> |
| <code class="sig-name descname">is_training</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/autograd.html#is_training"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.autograd.is_training" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get status on training/predicting.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p></p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>Current state of training/predicting.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.autograd.mark_variables"> |
| <code class="sig-name descname">mark_variables</code><span class="sig-paren">(</span><em class="sig-param">variables</em>, <em class="sig-param">gradients</em>, <em class="sig-param">grad_reqs='write'</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/autograd.html#mark_variables"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.autograd.mark_variables" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Mark NDArrays as variables to compute gradient for autograd.</p> |
| <p>This is equivalent to the function .attach_grad() in a variable, but with this |
| call we can set the gradient to any value.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>variables</strong> (<a class="reference internal" href="../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>list of NDArray</em>) – </p></li> |
| <li><p><strong>gradients</strong> (<a class="reference internal" href="../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>list of NDArray</em>) – </p></li> |
| <li><p><strong>grad_reqs</strong> (<em>str</em><em> or </em><em>list of str</em>) – </p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.autograd.pause"> |
| <code class="sig-name descname">pause</code><span class="sig-paren">(</span><em class="sig-param">train_mode=False</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/autograd.html#pause"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.autograd.pause" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a scope context to be used in ‘with’ statement for codes that do not need |
| gradients to be calculated.</p> |
| <p>Example:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">autograd</span><span class="o">.</span><span class="n">record</span><span class="p">():</span> |
| <span class="n">y</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">backward</span><span class="p">([</span><span class="n">y</span><span class="p">])</span> |
| <span class="k">with</span> <span class="n">autograd</span><span class="o">.</span><span class="n">pause</span><span class="p">():</span> |
| <span class="c1"># testing, IO, gradient updates...</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>train_mode</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to do forward for training or predicting.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.autograd.predict_mode"> |
| <code class="sig-name descname">predict_mode</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/autograd.html#predict_mode"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.autograd.predict_mode" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a scope context to be used in ‘with’ statement |
| in which forward pass behavior is set to inference mode, |
| without changing the recording states.</p> |
| <p>Example:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">autograd</span><span class="o">.</span><span class="n">record</span><span class="p">():</span> |
| <span class="n">y</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="k">with</span> <span class="n">autograd</span><span class="o">.</span><span class="n">predict_mode</span><span class="p">():</span> |
| <span class="n">y</span> <span class="o">=</span> <span class="n">sampling</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> |
| <span class="n">backward</span><span class="p">([</span><span class="n">y</span><span class="p">])</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.autograd.record"> |
| <code class="sig-name descname">record</code><span class="sig-paren">(</span><em class="sig-param">train_mode=True</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/autograd.html#record"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.autograd.record" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns an autograd recording scope context to be used in ‘with’ statement |
| and captures code that needs gradients to be calculated.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>When forwarding with train_mode=False, the corresponding backward |
| should also use train_mode=False, otherwise gradient is undefined.</p> |
| </div> |
| <p>Example:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">autograd</span><span class="o">.</span><span class="n">record</span><span class="p">():</span> |
| <span class="n">y</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">backward</span><span class="p">([</span><span class="n">y</span><span class="p">])</span> |
| <span class="n">metric</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="o">...</span><span class="p">)</span> |
| <span class="n">optim</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="o">...</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>train_mode</strong> (<em>bool</em><em>, </em><em>default True</em>) – Whether the forward pass is in training or predicting mode. This controls the behavior |
| of some layers such as Dropout, BatchNorm.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.autograd.set_recording"> |
| <code class="sig-name descname">set_recording</code><span class="sig-paren">(</span><em class="sig-param">is_recording</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/autograd.html#set_recording"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.autograd.set_recording" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Set status to recording/not recording. When recording, graph will be constructed |
| for gradient computation.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>is_recording</strong> (<em>bool</em>) – </p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>previous state before this set.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.autograd.set_training"> |
| <code class="sig-name descname">set_training</code><span class="sig-paren">(</span><em class="sig-param">train_mode</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/autograd.html#set_training"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.autograd.set_training" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Set status to training/predicting. This affects ctx.is_train in operator |
| running context. For example, Dropout will drop inputs randomly when |
| train_mode=True while simply passing through if train_mode=False.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>train_mode</strong> (<em>bool</em>) – </p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>previous state before this set.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.autograd.train_mode"> |
| <code class="sig-name descname">train_mode</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/autograd.html#train_mode"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.autograd.train_mode" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a scope context to be used in ‘with’ statement |
| in which forward pass behavior is set to training mode, |
| without changing the recording states.</p> |
| <p>Example:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">y</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="k">with</span> <span class="n">autograd</span><span class="o">.</span><span class="n">train_mode</span><span class="p">():</span> |
| <span class="n">y</span> <span class="o">=</span> <span class="n">dropout</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| </div> |
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