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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> |
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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> |
| </li> |
| <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-l5"><a class="reference internal" href="../../tutorials/performance/backend/dnnl/dnnl_readme.html">Install MXNet with oneDNN</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="gluon-parameter"> |
| <h1>gluon.Parameter<a class="headerlink" href="#gluon-parameter" title="Permalink to this headline">¶</a></h1> |
| <dl class="class"> |
| <dt id="mxnet.gluon.Parameter"> |
| <em class="property">class </em><code class="sig-name descname">Parameter</code><span class="sig-paren">(</span><em class="sig-param">name='weight'</em>, <em class="sig-param">grad_req='write'</em>, <em class="sig-param">shape=None</em>, <em class="sig-param">dtype=<class 'numpy.float32'></em>, <em class="sig-param">lr_mult=1.0</em>, <em class="sig-param">wd_mult=1.0</em>, <em class="sig-param">init=None</em>, <em class="sig-param">allow_deferred_init=False</em>, <em class="sig-param">differentiable=True</em>, <em class="sig-param">stype='default'</em>, <em class="sig-param">grad_stype='default'</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/gluon/parameter.html#Parameter"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.Parameter" 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>A Container holding parameters (weights) of Blocks.</p> |
| <p><a class="reference internal" href="#mxnet.gluon.Parameter" title="mxnet.gluon.Parameter"><code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code></a> holds a copy of the parameter on each <code class="xref py py-class docutils literal notranslate"><span class="pre">Device</span></code> after |
| it is initialized with <code class="docutils literal notranslate"><span class="pre">Parameter.initialize(...)</span></code>. If <a class="reference internal" href="#mxnet.gluon.Parameter.grad_req" title="mxnet.gluon.Parameter.grad_req"><code class="xref py py-attr docutils literal notranslate"><span class="pre">grad_req</span></code></a> is |
| not <code class="docutils literal notranslate"><span class="pre">'null'</span></code>, it will also hold a gradient array on each <code class="xref py py-class docutils literal notranslate"><span class="pre">Device</span></code>:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">device</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</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">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">16</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span> |
| <span class="n">w</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="s1">'fc_weight'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span> <span class="n">init</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">Xavier</span><span class="p">())</span> |
| <span class="n">b</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="s1">'fc_bias'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">64</span><span class="p">,),</span> <span class="n">init</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">Zero</span><span class="p">())</span> |
| <span class="n">w</span><span class="o">.</span><span class="n">initialize</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span> |
| <span class="n">b</span><span class="o">.</span><span class="n">initialize</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">npx</span><span class="o">.</span><span class="n">fully_connected</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">w</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="n">b</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="n">num_hidden</span><span class="o">=</span><span class="mi">64</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.gluon.Parameter.cast" title="mxnet.gluon.Parameter.cast"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cast</span></code></a>(dtype)</p></td> |
| <td><p>Cast data and gradient of this Parameter to a new data type.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.Parameter.data" title="mxnet.gluon.Parameter.data"><code class="xref py py-obj docutils literal notranslate"><span class="pre">data</span></code></a>([device])</p></td> |
| <td><p>Returns a copy of this parameter on one device.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.Parameter.grad" title="mxnet.gluon.Parameter.grad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">grad</span></code></a>([device])</p></td> |
| <td><p>Returns a gradient buffer for this parameter on one device.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.Parameter.initialize" title="mxnet.gluon.Parameter.initialize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">initialize</span></code></a>([init, device, default_init, …])</p></td> |
| <td><p>Initializes parameter and gradient arrays.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.Parameter.list_ctx" title="mxnet.gluon.Parameter.list_ctx"><code class="xref py py-obj docutils literal notranslate"><span class="pre">list_ctx</span></code></a>()</p></td> |
| <td><p>This function has been deprecated.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.Parameter.list_data" title="mxnet.gluon.Parameter.list_data"><code class="xref py py-obj docutils literal notranslate"><span class="pre">list_data</span></code></a>()</p></td> |
| <td><p>Returns copies of this parameter on all devices, in the same order as creation.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.Parameter.list_device" title="mxnet.gluon.Parameter.list_device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">list_device</span></code></a>()</p></td> |
| <td><p>Returns a list of devices this parameter is initialized on.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.Parameter.list_grad" title="mxnet.gluon.Parameter.list_grad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">list_grad</span></code></a>()</p></td> |
| <td><p>Returns gradient buffers on all devices, in the same order as <code class="xref py py-meth docutils literal notranslate"><span class="pre">values()</span></code>.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.Parameter.list_row_sparse_data" title="mxnet.gluon.Parameter.list_row_sparse_data"><code class="xref py py-obj docutils literal notranslate"><span class="pre">list_row_sparse_data</span></code></a>(row_id)</p></td> |
| <td><p>Returns copies of the ‘row_sparse’ parameter on all devices, in the same order as creation.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.Parameter.reset_ctx" title="mxnet.gluon.Parameter.reset_ctx"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_ctx</span></code></a>(ctx)</p></td> |
| <td><p>This function has been deprecated.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.Parameter.reset_device" title="mxnet.gluon.Parameter.reset_device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_device</span></code></a>(device)</p></td> |
| <td><p>Re-assign Parameter to other devices.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.Parameter.row_sparse_data" title="mxnet.gluon.Parameter.row_sparse_data"><code class="xref py py-obj docutils literal notranslate"><span class="pre">row_sparse_data</span></code></a>(row_id)</p></td> |
| <td><p>Returns a copy of the ‘row_sparse’ parameter on the same device as row_id’s.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.Parameter.set_data" title="mxnet.gluon.Parameter.set_data"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_data</span></code></a>(data)</p></td> |
| <td><p>Sets this parameter’s value on all devices.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.Parameter.var" title="mxnet.gluon.Parameter.var"><code class="xref py py-obj docutils literal notranslate"><span class="pre">var</span></code></a>()</p></td> |
| <td><p>Returns a symbol representing this parameter.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.Parameter.zero_grad" title="mxnet.gluon.Parameter.zero_grad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code></a>()</p></td> |
| <td><p>Sets gradient buffer on all devices to 0.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><strong>Attributes</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.gluon.Parameter.dtype" title="mxnet.gluon.Parameter.dtype"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dtype</span></code></a></p></td> |
| <td><p>The type of the parameter.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.Parameter.shape" title="mxnet.gluon.Parameter.shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">shape</span></code></a></p></td> |
| <td><p>The shape of the parameter.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>name</strong> (<em>str</em><em>, </em><em>default 'weight'</em>) – Name of this parameter. It decides the corresponding default initializer.</p></li> |
| <li><p><strong>grad_req</strong> (<em>{'write'</em><em>, </em><em>'add'</em><em>, </em><em>'null'}</em><em>, </em><em>default 'write'</em>) – <p>Specifies how to update gradient to grad arrays.</p> |
| <ul> |
| <li><p><code class="docutils literal notranslate"><span class="pre">'write'</span></code> means everytime gradient is written to grad <code class="xref py py-class docutils literal notranslate"><span class="pre">NDArray</span></code>.</p></li> |
| <li><p><code class="docutils literal notranslate"><span class="pre">'add'</span></code> means everytime gradient is added to the grad <code class="xref py py-class docutils literal notranslate"><span class="pre">NDArray</span></code>. You need |
| to manually call <code class="docutils literal notranslate"><span class="pre">zero_grad()</span></code> to clear the gradient buffer before each |
| iteration when using this option.</p></li> |
| <li><p>’null’ means gradient is not requested for this parameter. gradient arrays |
| will not be allocated.</p></li> |
| </ul> |
| </p></li> |
| <li><p><strong>shape</strong> (<em>int</em><em> or </em><em>tuple of int</em><em>, </em><em>default None</em>) – Shape of this parameter. By default shape is not specified. Parameter with |
| unknown shape can be used for <code class="xref py py-class docutils literal notranslate"><span class="pre">Symbol</span></code> API, but <code class="docutils literal notranslate"><span class="pre">init</span></code> will throw an error |
| when using <code class="xref py py-class docutils literal notranslate"><span class="pre">NDArray</span></code> API.</p></li> |
| <li><p><strong>dtype</strong> (<em>numpy.dtype</em><em> or </em><em>str</em><em>, </em><em>default 'float32'</em>) – Data type of this parameter. For example, <code class="docutils literal notranslate"><span class="pre">numpy.float32</span></code> or <code class="docutils literal notranslate"><span class="pre">'float32'</span></code>.</p></li> |
| <li><p><strong>lr_mult</strong> (<em>float</em><em>, </em><em>default 1.0</em>) – Learning rate multiplier. Learning rate will be multiplied by lr_mult |
| when updating this parameter with optimizer.</p></li> |
| <li><p><strong>wd_mult</strong> (<em>float</em><em>, </em><em>default 1.0</em>) – Weight decay multiplier (L2 regularizer coefficient). Works similar to lr_mult.</p></li> |
| <li><p><strong>init</strong> (<a class="reference internal" href="../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a><em>, </em><em>default None</em>) – Initializer of this parameter. Will use the global initializer by default.</p></li> |
| <li><p><strong>stype</strong> (<em>{'default'</em><em>, </em><em>'row_sparse'</em><em>, </em><em>'csr'}</em><em>, </em><em>defaults to 'default'.</em>) – The storage type of the parameter.</p></li> |
| <li><p><strong>grad_stype</strong> (<em>{'default'</em><em>, </em><em>'row_sparse'</em><em>, </em><em>'csr'}</em><em>, </em><em>defaults to 'default'.</em>) – The storage type of the parameter’s gradient.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="attribute"> |
| <dt id="mxnet.gluon.Parameter.grad_req"> |
| <code class="sig-name descname">grad_req</code><a class="headerlink" href="#mxnet.gluon.Parameter.grad_req" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This can be set before or after initialization. Setting <code class="docutils literal notranslate"><span class="pre">grad_req</span></code> to <code class="docutils literal notranslate"><span class="pre">'null'</span></code> |
| with <code class="docutils literal notranslate"><span class="pre">x.grad_req</span> <span class="pre">=</span> <span class="pre">'null'</span></code> saves memory and computation when you don’t |
| need gradient w.r.t x.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Type</dt> |
| <dd class="field-odd"><p>{‘write’, ‘add’, ‘null’}</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="mxnet.gluon.Parameter.lr_mult"> |
| <code class="sig-name descname">lr_mult</code><a class="headerlink" href="#mxnet.gluon.Parameter.lr_mult" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Local learning rate multiplier for this Parameter. The actual learning rate |
| is calculated with <code class="docutils literal notranslate"><span class="pre">learning_rate</span> <span class="pre">*</span> <span class="pre">lr_mult</span></code>. You can set it with |
| <code class="docutils literal notranslate"><span class="pre">param.lr_mult</span> <span class="pre">=</span> <span class="pre">2.0</span></code></p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Type</dt> |
| <dd class="field-odd"><p>float</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="attribute"> |
| <dt id="mxnet.gluon.Parameter.wd_mult"> |
| <code class="sig-name descname">wd_mult</code><a class="headerlink" href="#mxnet.gluon.Parameter.wd_mult" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Local weight decay multiplier for this Parameter.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Type</dt> |
| <dd class="field-odd"><p>float</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.Parameter.cast"> |
| <code class="sig-name descname">cast</code><span class="sig-paren">(</span><em class="sig-param">dtype</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/gluon/parameter.html#Parameter.cast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.Parameter.cast" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Cast data and gradient of this Parameter to a new data type.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>dtype</strong> (<em>str</em><em> or </em><em>numpy.dtype</em>) – The new data type.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.Parameter.data"> |
| <code class="sig-name descname">data</code><span class="sig-paren">(</span><em class="sig-param">device=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/gluon/parameter.html#Parameter.data"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.Parameter.data" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a copy of this parameter on one device. Must have been |
| initialized on this device before. For sparse parameters, use |
| <a class="reference internal" href="#mxnet.gluon.Parameter.row_sparse_data" title="mxnet.gluon.Parameter.row_sparse_data"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.row_sparse_data()</span></code></a> instead.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>device</strong> (<a class="reference internal" href="../device/index.html#mxnet.device.Device" title="mxnet.device.Device"><em>Device</em></a>) – Desired device.</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>NDArray on device</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.Parameter.dtype"> |
| <em class="property">property </em><code class="sig-name descname">dtype</code><a class="headerlink" href="#mxnet.gluon.Parameter.dtype" title="Permalink to this definition">¶</a></dt> |
| <dd><p>The type of the parameter.</p> |
| <p>Setting the dtype value is equivalent to casting the value of the parameter</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.Parameter.grad"> |
| <code class="sig-name descname">grad</code><span class="sig-paren">(</span><em class="sig-param">device=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/gluon/parameter.html#Parameter.grad"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.Parameter.grad" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a gradient buffer for this parameter on one device.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>device</strong> (<a class="reference internal" href="../device/index.html#mxnet.device.Device" title="mxnet.device.Device"><em>Device</em></a>) – Desired device.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.Parameter.initialize"> |
| <code class="sig-name descname">initialize</code><span class="sig-paren">(</span><em class="sig-param">init=None</em>, <em class="sig-param">device=None</em>, <em class="sig-param">default_init=<mxnet.initializer.Uniform object></em>, <em class="sig-param">force_reinit=False</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/gluon/parameter.html#Parameter.initialize"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.Parameter.initialize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Initializes parameter and gradient arrays. Only used for <code class="xref py py-class docutils literal notranslate"><span class="pre">NDArray</span></code> API.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>init</strong> (<a class="reference internal" href="../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – The initializer to use. Overrides <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> and default_init.</p></li> |
| <li><p><strong>device</strong> (Device or list of Device, default <code class="xref py py-meth docutils literal notranslate"><span class="pre">device.current_device()</span></code>.) – <p>Assign Parameter to given device. If device is a list of Device, a |
| copy will be made for each device.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>Copies are independent arrays. User is responsible for keeping |
| their values consistent when updating. |
| Normally <code class="xref py py-class docutils literal notranslate"><span class="pre">gluon.Trainer</span></code> does this for you.</p> |
| </div> |
| </p></li> |
| <li><p><strong>default_init</strong> (<a class="reference internal" href="../initializer/index.html#mxnet.initializer.Initializer" title="mxnet.initializer.Initializer"><em>Initializer</em></a>) – Default initializer is used when both <code class="xref py py-func docutils literal notranslate"><span class="pre">init()</span></code> |
| and <code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.init()</span></code> are <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li> |
| <li><p><strong>force_reinit</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to force re-initialization if parameter is already initialized.</p></li> |
| </ul> |
| </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">weight</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="s1">'weight'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span> |
| <span class="gp">>>> </span><span class="n">weight</span><span class="o">.</span><span class="n">initialize</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span> |
| <span class="gp">>>> </span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">()</span> |
| <span class="go">[[-0.01068833 0.01729892]</span> |
| <span class="go"> [ 0.02042518 -0.01618656]]</span> |
| <span class="go"><NDArray 2x2 @cpu(0)></span> |
| <span class="gp">>>> </span><span class="n">weight</span><span class="o">.</span><span class="n">grad</span><span class="p">()</span> |
| <span class="go">[[ 0. 0.]</span> |
| <span class="go"> [ 0. 0.]]</span> |
| <span class="go"><NDArray 2x2 @cpu(0)></span> |
| <span class="gp">>>> </span><span class="n">weight</span><span class="o">.</span><span class="n">initialize</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">1</span><span class="p">)])</span> |
| <span class="gp">>>> </span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span> |
| <span class="go">[[-0.00873779 -0.02834515]</span> |
| <span class="go"> [ 0.05484822 -0.06206018]]</span> |
| <span class="go"><NDArray 2x2 @gpu(0)></span> |
| <span class="gp">>>> </span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span> |
| <span class="go">[[-0.00873779 -0.02834515]</span> |
| <span class="go"> [ 0.05484822 -0.06206018]]</span> |
| <span class="go"><NDArray 2x2 @gpu(1)></span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.Parameter.list_ctx"> |
| <code class="sig-name descname">list_ctx</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/gluon/parameter.html#Parameter.list_ctx"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.Parameter.list_ctx" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This function has been deprecated. Please refer to <code class="docutils literal notranslate"><span class="pre">Parameter.list_device</span></code>.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.Parameter.list_data"> |
| <code class="sig-name descname">list_data</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/gluon/parameter.html#Parameter.list_data"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.Parameter.list_data" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns copies of this parameter on all devices, in the same order |
| as creation. For sparse parameters, use <a class="reference internal" href="#mxnet.gluon.Parameter.list_row_sparse_data" title="mxnet.gluon.Parameter.list_row_sparse_data"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Parameter.list_row_sparse_data()</span></code></a> |
| instead.</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>list of NDArrays</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.Parameter.list_device"> |
| <code class="sig-name descname">list_device</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/gluon/parameter.html#Parameter.list_device"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.Parameter.list_device" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a list of devices this parameter is initialized on.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.Parameter.list_grad"> |
| <code class="sig-name descname">list_grad</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/gluon/parameter.html#Parameter.list_grad"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.Parameter.list_grad" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns gradient buffers on all devices, in the same order |
| as <code class="xref py py-meth docutils literal notranslate"><span class="pre">values()</span></code>.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.Parameter.list_row_sparse_data"> |
| <code class="sig-name descname">list_row_sparse_data</code><span class="sig-paren">(</span><em class="sig-param">row_id</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/gluon/parameter.html#Parameter.list_row_sparse_data"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.Parameter.list_row_sparse_data" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns copies of the ‘row_sparse’ parameter on all devices, in the same order |
| as creation. The copy only retains rows whose ids occur in provided row ids. |
| The parameter must have been initialized before.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>row_id</strong> (<a class="reference internal" href="../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Row ids to retain for the ‘row_sparse’ parameter.</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>list of NDArrays</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.Parameter.reset_ctx"> |
| <code class="sig-name descname">reset_ctx</code><span class="sig-paren">(</span><em class="sig-param">ctx</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/gluon/parameter.html#Parameter.reset_ctx"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.Parameter.reset_ctx" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This function has been deprecated. Please refer to <code class="docutils literal notranslate"><span class="pre">Parameter.reset_device</span></code>.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.Parameter.reset_device"> |
| <code class="sig-name descname">reset_device</code><span class="sig-paren">(</span><em class="sig-param">device</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/gluon/parameter.html#Parameter.reset_device"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.Parameter.reset_device" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Re-assign Parameter to other devices.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>device</strong> (Device or list of Device, default <code class="docutils literal notranslate"><span class="pre">device.current_device()</span></code>.) – Assign Parameter to given device. If device is a list of Device, a |
| copy will be made for each device.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.Parameter.row_sparse_data"> |
| <code class="sig-name descname">row_sparse_data</code><span class="sig-paren">(</span><em class="sig-param">row_id</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/gluon/parameter.html#Parameter.row_sparse_data"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.Parameter.row_sparse_data" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a copy of the ‘row_sparse’ parameter on the same device as row_id’s. |
| The copy only retains rows whose ids occur in provided row ids. |
| The parameter must have been initialized on this device before.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>row_id</strong> (<a class="reference internal" href="../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Row ids to retain for the ‘row_sparse’ parameter.</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>NDArray on row_id’s device</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.Parameter.set_data"> |
| <code class="sig-name descname">set_data</code><span class="sig-paren">(</span><em class="sig-param">data</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/gluon/parameter.html#Parameter.set_data"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.Parameter.set_data" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets this parameter’s value on all devices.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.Parameter.shape"> |
| <em class="property">property </em><code class="sig-name descname">shape</code><a class="headerlink" href="#mxnet.gluon.Parameter.shape" title="Permalink to this definition">¶</a></dt> |
| <dd><p>The shape of the parameter.</p> |
| <p>By default, an unknown dimension size is 0. However, when the NumPy semantic |
| is turned on, unknown dimension size is -1.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.Parameter.var"> |
| <code class="sig-name descname">var</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/gluon/parameter.html#Parameter.var"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.Parameter.var" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a symbol representing this parameter.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.gluon.Parameter.zero_grad"> |
| <code class="sig-name descname">zero_grad</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/gluon/parameter.html#Parameter.zero_grad"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.Parameter.zero_grad" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Sets gradient buffer on all devices to 0. No action is taken if |
| parameter is uninitialized or doesn’t require gradient.</p> |
| </dd></dl> |
| |
| </dd></dl> |
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