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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <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> |
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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> |
| <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> |
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| |
| <div class="section" id="the-n-dimensional-array-ndarray"> |
| <span id="arrays-ndarray"></span><h1>The N-dimensional array (<code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code>)<a class="headerlink" href="#the-n-dimensional-array-ndarray" title="Permalink to this headline">¶</a></h1> |
| <p>An <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code> is a (usually fixed-size) multidimensional |
| container of items of the same type and size. The number of dimensions |
| and items in an array is defined by its <code class="xref py py-attr docutils literal notranslate"><span class="pre">shape</span></code>, |
| which is a <code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code> of <em>N</em> non-negative integers that specify the |
| sizes of each dimension. The type of items in the array is specified by |
| a separate data-type object (dtype), one of which |
| is associated with each ndarray.</p> |
| <p>As with other container objects in Python, the contents of an |
| <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code> can be accessed and modified by <a class="reference internal" href="arrays.indexing.html#arrays-indexing"><span class="std std-ref">indexing or |
| slicing</span></a> the array (using, for example, <em>N</em> integers), |
| and via the methods and attributes of the <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code>.</p> |
| <p id="index-0">Different <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarrays</span></code> can share the same data, so that |
| changes made in one <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code> may be visible in another. That |
| is, an ndarray can be a <em>“view”</em> to another ndarray, and the data it |
| is referring to is taken care of by the <em>“base”</em> ndarray.</p> |
| <div class="admonition-example admonition"> |
| <p class="admonition-title">Example</p> |
| <p>A 2-dimensional array of size 2 x 3, composed of 4-byte integer |
| elements:</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">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]],</span> <span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="go"><class 'mxnet.numpy.ndarray'></span> |
| <span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">shape</span> |
| <span class="go">(2, 3)</span> |
| <span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">dtype</span> |
| <span class="go">dtype('int32')</span> |
| </pre></div> |
| </div> |
| <p>The array can be indexed using Python container-like syntax:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># The element of x in the *second* row, *third* column, namely, 6.</span> |
| <span class="gp">>>> </span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> |
| <span class="go">array(6, dtype=int32) # this is different than the official NumPy which returns a np.int32 object</span> |
| </pre></div> |
| </div> |
| <p>For example <a class="reference internal" href="arrays.indexing.html#arrays-indexing"><span class="std std-ref">slicing</span></a> can produce views of |
| the array if the elements to be sliced is continguous in memory:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">,:]</span> |
| <span class="gp">>>> </span><span class="n">y</span> |
| <span class="go">array([9, 5, 6], dtype=int32) # this also changes the corresponding element in x</span> |
| <span class="gp">>>> </span><span class="n">x</span> |
| <span class="go">array([[1, 2, 3],</span> |
| <span class="go"> [9, 5, 6]], dtype=int32)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="constructing-arrays"> |
| <h2>Constructing arrays<a class="headerlink" href="#constructing-arrays" title="Permalink to this headline">¶</a></h2> |
| <p>New arrays can be constructed using the routines detailed in |
| <a class="reference internal" href="routines.array-creation.html#routines-array-creation"><span class="std std-ref">Array creation routines</span></a>, and also by using the low-level |
| <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code> constructor:</p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray</span></code></p></td> |
| <td><p>ndarray(handle, writable=True):</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| <div class="section" id="indexing-arrays"> |
| <h2>Indexing arrays<a class="headerlink" href="#indexing-arrays" title="Permalink to this headline">¶</a></h2> |
| <p>Arrays can be indexed using an extended Python slicing syntax, |
| <code class="docutils literal notranslate"><span class="pre">array[selection]</span></code>.</p> |
| <div class="admonition seealso"> |
| <p class="admonition-title">See also</p> |
| <p><a class="reference internal" href="arrays.indexing.html#arrays-indexing"><span class="std std-ref">Array Indexing</span></a>.</p> |
| </div> |
| </div> |
| <div class="section" id="internal-memory-layout-of-an-ndarray"> |
| <span id="memory-layout"></span><h2>Internal memory layout of an ndarray<a class="headerlink" href="#internal-memory-layout-of-an-ndarray" title="Permalink to this headline">¶</a></h2> |
| <p>An instance of class <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code> consists of a contiguous |
| one-dimensional segment of computer memory (owned by the array, or by |
| some other object), combined with an indexing scheme that maps <em>N</em> |
| integers into the location of an item in the block. The ranges in |
| which the indices can vary is specified by the <code class="xref py py-obj docutils literal notranslate"><span class="pre">shape</span></code> of the array. How many bytes each item takes and how |
| the bytes are interpreted is defined by the data-type object |
| associated with the array.</p> |
| <div class="admonition note" id="index-1"> |
| <p class="admonition-title">Note</p> |
| <p><cite>mxnet.numpy.ndarray</cite> currently only supports storing elements in |
| C-order/row-major and contiguous memory space. The following content |
| on explaining a variety of memory layouts of an ndarray |
| are copied from the official NumPy documentation as a comprehensive reference.</p> |
| </div> |
| <p>A segment of memory is inherently 1-dimensional, and there are many |
| different schemes for arranging the items of an <em>N</em>-dimensional array |
| in a 1-dimensional block. NumPy is flexible, and <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code> |
| objects can accommodate any <em>strided indexing scheme</em>. In a strided |
| scheme, the N-dimensional index <span class="math notranslate nohighlight">\((n_0, n_1, ..., n_{N-1})\)</span> |
| corresponds to the offset (in bytes):</p> |
| <div class="math notranslate nohighlight"> |
| \[n_{\mathrm{offset}} = \sum_{k=0}^{N-1} s_k n_k\]</div> |
| <p>from the beginning of the memory block associated with the |
| array. Here, <span class="math notranslate nohighlight">\(s_k\)</span> are integers which specify the <code class="xref py py-obj docutils literal notranslate"><span class="pre">strides</span></code> of the array. The column-major order (used, |
| for example, in the Fortran language and in <em>Matlab</em>) and |
| row-major order (used in C) schemes are just specific kinds of |
| strided scheme, and correspond to memory that can be <em>addressed</em> by the strides:</p> |
| <div class="math notranslate nohighlight"> |
| \[s_k^{\mathrm{column}} = \mathrm{itemsize} \prod_{j=0}^{k-1} d_j , |
| \quad s_k^{\mathrm{row}} = \mathrm{itemsize} \prod_{j=k+1}^{N-1} d_j .\]</div> |
| <p id="index-2">where <span class="math notranslate nohighlight">\(d_j\)</span> <cite>= self.shape[j]</cite>.</p> |
| <p>Both the C and Fortran orders are contiguous, <em>i.e.,</em> |
| single-segment, memory layouts, in which every part of the |
| memory block can be accessed by some combination of the indices.</p> |
| <p>While a C-style and Fortran-style contiguous array, which has the corresponding |
| flags set, can be addressed with the above strides, the actual strides may be |
| different. This can happen in two cases:</p> |
| <blockquote> |
| <div><ol class="arabic simple"> |
| <li><p>If <code class="docutils literal notranslate"><span class="pre">self.shape[k]</span> <span class="pre">==</span> <span class="pre">1</span></code> then for any legal index <code class="docutils literal notranslate"><span class="pre">index[k]</span> <span class="pre">==</span> <span class="pre">0</span></code>. |
| This means that in the formula for the offset <span class="math notranslate nohighlight">\(n_k = 0\)</span> and thus |
| <span class="math notranslate nohighlight">\(s_k n_k = 0\)</span> and the value of <span class="math notranslate nohighlight">\(s_k\)</span> <cite>= self.strides[k]</cite> is |
| arbitrary.</p></li> |
| <li><p>If an array has no elements (<code class="docutils literal notranslate"><span class="pre">self.size</span> <span class="pre">==</span> <span class="pre">0</span></code>) there is no legal |
| index and the strides are never used. Any array with no elements may be |
| considered C-style and Fortran-style contiguous.</p></li> |
| </ol> |
| </div></blockquote> |
| <p>Point 1. means that <code class="docutils literal notranslate"><span class="pre">self</span></code> and <code class="docutils literal notranslate"><span class="pre">self.squeeze()</span></code> always have the same |
| contiguity and <code class="docutils literal notranslate"><span class="pre">aligned</span></code> flags value. This also means |
| that even a high dimensional array could be C-style and Fortran-style |
| contiguous at the same time.</p> |
| <p id="index-3">An array is considered aligned if the memory offsets for all elements and the |
| base offset itself is a multiple of <cite>self.itemsize</cite>. Understanding |
| <cite>memory-alignment</cite> leads to better performance on most hardware.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>Points (1) and (2) are not yet applied by default. Beginning with |
| NumPy 1.8.0, they are applied consistently only if the environment |
| variable <code class="docutils literal notranslate"><span class="pre">NPY_RELAXED_STRIDES_CHECKING=1</span></code> was defined when NumPy |
| was built. Eventually this will become the default.</p> |
| <p>You can check whether this option was enabled when your NumPy was |
| built by looking at the value of <code class="docutils literal notranslate"><span class="pre">np.ones((10,1),</span> |
| <span class="pre">order='C').flags.f_contiguous</span></code>. If this is <code class="docutils literal notranslate"><span class="pre">True</span></code>, then your |
| NumPy has relaxed strides checking enabled.</p> |
| </div> |
| <div class="admonition warning"> |
| <p class="admonition-title">Warning</p> |
| <p>It does <em>not</em> generally hold that <code class="docutils literal notranslate"><span class="pre">self.strides[-1]</span> <span class="pre">==</span> <span class="pre">self.itemsize</span></code> |
| for C-style contiguous arrays or <code class="docutils literal notranslate"><span class="pre">self.strides[0]</span> <span class="pre">==</span> <span class="pre">self.itemsize</span></code> for |
| Fortran-style contiguous arrays is true.</p> |
| </div> |
| <p>Data in new <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarrays</span></code> is in the row-major |
| (C) order, unless otherwise specified, but, for example, <a class="reference internal" href="arrays.indexing.html#arrays-indexing"><span class="std std-ref">basic |
| array slicing</span></a> often produces views |
| in a different scheme.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>Several algorithms in NumPy work on arbitrarily strided arrays. |
| However, some algorithms require single-segment arrays. When an |
| irregularly strided array is passed in to such algorithms, a copy |
| is automatically made.</p> |
| </div> |
| </div> |
| <div class="section" id="array-attributes"> |
| <span id="arrays-ndarray-attributes"></span><h2>Array attributes<a class="headerlink" href="#array-attributes" title="Permalink to this headline">¶</a></h2> |
| <p>Array attributes reflect information that is intrinsic to the array |
| itself. Generally, accessing an array through its attributes allows |
| you to get and sometimes set intrinsic properties of the array without |
| creating a new array. The exposed attributes are the core parts of an |
| array and only some of them can be reset meaningfully without creating |
| a new array. Information on each attribute is given below.</p> |
| <div class="section" id="id1"> |
| <h3>Memory layout<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h3> |
| <p>The following attributes contain information about the memory layout |
| of the array:</p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.shape</span></code></p></td> |
| <td><p>Tuple of array dimensions.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.ndim</span></code></p></td> |
| <td><p>Number of array dimensions.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.size</span></code></p></td> |
| <td><p>Number of elements in the array.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| <div class="section" id="data-type"> |
| <h3>Data type<a class="headerlink" href="#data-type" title="Permalink to this headline">¶</a></h3> |
| <p>The data type object associated with the array can be found in the |
| <code class="xref py py-attr docutils literal notranslate"><span class="pre">dtype</span></code> attribute:</p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.dtype</span></code></p></td> |
| <td><p>Data-type of the array’s elements.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| </div> |
| <div class="section" id="array-methods"> |
| <span id="array-ndarray-methods"></span><h2>Array methods<a class="headerlink" href="#array-methods" title="Permalink to this headline">¶</a></h2> |
| <p>An <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code> object has many methods which operate on or with |
| the array in some fashion, typically returning an array result. These |
| methods are briefly explained below. (Each method’s docstring has a |
| more complete description.)</p> |
| <p>For the following methods there are also corresponding functions in |
| <code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy</span></code>: <code class="xref py py-func docutils literal notranslate"><span class="pre">all()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">any()</span></code>, <a class="reference internal" href="generated/mxnet.np.argmax.html#mxnet.np.argmax" title="mxnet.np.argmax"><code class="xref py py-func docutils literal notranslate"><span class="pre">argmax()</span></code></a>, |
| <a class="reference internal" href="generated/mxnet.np.argmin.html#mxnet.np.argmin" title="mxnet.np.argmin"><code class="xref py py-func docutils literal notranslate"><span class="pre">argmin()</span></code></a>, <a class="reference internal" href="generated/mxnet.np.argpartition.html#mxnet.np.argpartition" title="mxnet.np.argpartition"><code class="xref py py-func docutils literal notranslate"><span class="pre">argpartition()</span></code></a>, <a class="reference internal" href="generated/mxnet.np.argsort.html#mxnet.np.argsort" title="mxnet.np.argsort"><code class="xref py py-func docutils literal notranslate"><span class="pre">argsort()</span></code></a>, <code class="xref py py-func docutils literal notranslate"><span class="pre">choose()</span></code>, |
| <a class="reference internal" href="generated/mxnet.np.clip.html#mxnet.np.clip" title="mxnet.np.clip"><code class="xref py py-func docutils literal notranslate"><span class="pre">clip()</span></code></a>, <code class="xref py py-func docutils literal notranslate"><span class="pre">compress()</span></code>, <a class="reference internal" href="generated/mxnet.np.copy.html#mxnet.np.copy" title="mxnet.np.copy"><code class="xref py py-func docutils literal notranslate"><span class="pre">copy()</span></code></a>, <a class="reference internal" href="generated/mxnet.np.cumprod.html#mxnet.np.cumprod" title="mxnet.np.cumprod"><code class="xref py py-func docutils literal notranslate"><span class="pre">cumprod()</span></code></a>, |
| <a class="reference internal" href="generated/mxnet.np.cumsum.html#mxnet.np.cumsum" title="mxnet.np.cumsum"><code class="xref py py-func docutils literal notranslate"><span class="pre">cumsum()</span></code></a>, <code class="xref py py-func docutils literal notranslate"><span class="pre">diagonal()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">imag()</span></code>, <a class="reference internal" href="generated/mxnet.np.amax.html#mxnet.np.amax" title="mxnet.np.amax"><code class="xref py py-func docutils literal notranslate"><span class="pre">max</span></code></a>, |
| <a class="reference internal" href="generated/mxnet.np.mean.html#mxnet.np.mean" title="mxnet.np.mean"><code class="xref py py-func docutils literal notranslate"><span class="pre">mean()</span></code></a>, <a class="reference internal" href="generated/mxnet.np.amin.html#mxnet.np.amin" title="mxnet.np.amin"><code class="xref py py-func docutils literal notranslate"><span class="pre">min</span></code></a>, <a class="reference internal" href="generated/mxnet.np.nonzero.html#mxnet.np.nonzero" title="mxnet.np.nonzero"><code class="xref py py-func docutils literal notranslate"><span class="pre">nonzero()</span></code></a>, <a class="reference internal" href="generated/mxnet.np.partition.html#mxnet.np.partition" title="mxnet.np.partition"><code class="xref py py-func docutils literal notranslate"><span class="pre">partition()</span></code></a>, |
| <a class="reference internal" href="generated/mxnet.np.prod.html#mxnet.np.prod" title="mxnet.np.prod"><code class="xref py py-func docutils literal notranslate"><span class="pre">prod()</span></code></a>, <a class="reference internal" href="generated/mxnet.np.ptp.html#mxnet.np.ptp" title="mxnet.np.ptp"><code class="xref py py-func docutils literal notranslate"><span class="pre">ptp()</span></code></a>, <code class="xref py py-func docutils literal notranslate"><span class="pre">put()</span></code>, <a class="reference internal" href="generated/mxnet.np.ravel.html#mxnet.np.ravel" title="mxnet.np.ravel"><code class="xref py py-func docutils literal notranslate"><span class="pre">ravel()</span></code></a>, <code class="xref py py-func docutils literal notranslate"><span class="pre">real()</span></code>, |
| <a class="reference internal" href="generated/mxnet.np.repeat.html#mxnet.np.repeat" title="mxnet.np.repeat"><code class="xref py py-func docutils literal notranslate"><span class="pre">repeat()</span></code></a>, <a class="reference internal" href="generated/mxnet.np.reshape.html#mxnet.np.reshape" title="mxnet.np.reshape"><code class="xref py py-func docutils literal notranslate"><span class="pre">reshape()</span></code></a>, <a class="reference internal" href="generated/mxnet.np.around.html#mxnet.np.around" title="mxnet.np.around"><code class="xref py py-func docutils literal notranslate"><span class="pre">round</span></code></a>, |
| <a class="reference internal" href="generated/mxnet.np.searchsorted.html#mxnet.np.searchsorted" title="mxnet.np.searchsorted"><code class="xref py py-func docutils literal notranslate"><span class="pre">searchsorted()</span></code></a>, <a class="reference internal" href="generated/mxnet.np.sort.html#mxnet.np.sort" title="mxnet.np.sort"><code class="xref py py-func docutils literal notranslate"><span class="pre">sort()</span></code></a>, <a class="reference internal" href="generated/mxnet.np.squeeze.html#mxnet.np.squeeze" title="mxnet.np.squeeze"><code class="xref py py-func docutils literal notranslate"><span class="pre">squeeze()</span></code></a>, <a class="reference internal" href="generated/mxnet.np.std.html#mxnet.np.std" title="mxnet.np.std"><code class="xref py py-func docutils literal notranslate"><span class="pre">std()</span></code></a>, |
| <a class="reference internal" href="generated/mxnet.np.sum.html#mxnet.np.sum" title="mxnet.np.sum"><code class="xref py py-func docutils literal notranslate"><span class="pre">sum()</span></code></a>, <a class="reference internal" href="generated/mxnet.np.swapaxes.html#mxnet.np.swapaxes" title="mxnet.np.swapaxes"><code class="xref py py-func docutils literal notranslate"><span class="pre">swapaxes()</span></code></a>, <code class="xref py py-func docutils literal notranslate"><span class="pre">take()</span></code>, <a class="reference internal" href="generated/mxnet.np.trace.html#mxnet.np.trace" title="mxnet.np.trace"><code class="xref py py-func docutils literal notranslate"><span class="pre">trace()</span></code></a>, |
| <a class="reference internal" href="generated/mxnet.np.transpose.html#mxnet.np.transpose" title="mxnet.np.transpose"><code class="xref py py-func docutils literal notranslate"><span class="pre">transpose()</span></code></a>, <a class="reference internal" href="generated/mxnet.np.var.html#mxnet.np.var" title="mxnet.np.var"><code class="xref py py-func docutils literal notranslate"><span class="pre">var()</span></code></a>.</p> |
| <div class="section" id="array-conversion"> |
| <h3>Array conversion<a class="headerlink" href="#array-conversion" title="Permalink to this headline">¶</a></h3> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.item</span></code>(*args)</p></td> |
| <td><p>Copy an element of an array to a standard Python scalar and return it.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.copy</span></code>([order])</p></td> |
| <td><p>Return a coyp of the array, keeping the same device.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="generated/mxnet.np.ndarray.tolist.html#mxnet.np.ndarray.tolist" title="mxnet.np.ndarray.tolist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.tolist</span></code></a>()</p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.astype</span></code>(dtype[, order, casting, …])</p></td> |
| <td><p>Copy of the array, cast to a specified type.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| <div class="section" id="shape-manipulation"> |
| <h3>Shape manipulation<a class="headerlink" href="#shape-manipulation" title="Permalink to this headline">¶</a></h3> |
| <p>For reshape, resize, and transpose, the single tuple argument may be |
| replaced with <code class="docutils literal notranslate"><span class="pre">n</span></code> integers which will be interpreted as an n-tuple.</p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.reshape</span></code>(*args, **kwargs)</p></td> |
| <td><p>Returns a copy of the array with a new shape.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.transpose</span></code>(*axes)</p></td> |
| <td><p>Permute the dimensions of an array.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.swapaxes</span></code>(axis1, axis2)</p></td> |
| <td><p>Return a copy of the array with axis1 and axis2 interchanged.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="generated/mxnet.np.ndarray.flatten.html#mxnet.np.ndarray.flatten" title="mxnet.np.ndarray.flatten"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.flatten</span></code></a>([order])</p></td> |
| <td><p>Return a copy of the array collapsed into one dimension.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.squeeze</span></code>([axis])</p></td> |
| <td><p>Remove single-dimensional entries from the shape of a.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| <div class="section" id="item-selection-and-manipulation"> |
| <h3>Item selection and manipulation<a class="headerlink" href="#item-selection-and-manipulation" title="Permalink to this headline">¶</a></h3> |
| <p>For array methods that take an <em>axis</em> keyword, it defaults to |
| <code class="xref py py-const docutils literal notranslate"><span class="pre">None</span></code>. If axis is <em>None</em>, then the array is treated as a 1-D |
| array. Any other value for <em>axis</em> represents the dimension along which |
| the operation should proceed.</p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.nonzero</span></code>()</p></td> |
| <td><p>Return the indices of the elements that are non-zero.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.take</span></code>(indices[, axis, mode])</p></td> |
| <td><p>Convenience fluent method for <code class="xref py py-func docutils literal notranslate"><span class="pre">take()</span></code>.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.repeat</span></code>(repeats[, axis])</p></td> |
| <td><p>Repeat elements of an array.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.argsort</span></code>([axis, descending, stable])</p></td> |
| <td><p>Convenience fluent method for <a class="reference internal" href="generated/mxnet.np.argsort.html#mxnet.np.argsort" title="mxnet.np.argsort"><code class="xref py py-func docutils literal notranslate"><span class="pre">argsort()</span></code></a>.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="generated/mxnet.np.ndarray.sort.html#mxnet.np.ndarray.sort" title="mxnet.np.ndarray.sort"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.sort</span></code></a>([axis, descending, stable])</p></td> |
| <td><p>Convenience fluent method for <a class="reference internal" href="generated/mxnet.np.sort.html#mxnet.np.sort" title="mxnet.np.sort"><code class="xref py py-func docutils literal notranslate"><span class="pre">sort()</span></code></a>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| <div class="section" id="calculation"> |
| <h3>Calculation<a class="headerlink" href="#calculation" title="Permalink to this headline">¶</a></h3> |
| <p id="index-4">Many of these methods take an argument named <em>axis</em>. In such cases,</p> |
| <ul class="simple"> |
| <li><p>If <em>axis</em> is <em>None</em> (the default), the array is treated as a 1-D |
| array and the operation is performed over the entire array. This |
| behavior is also the default if self is a 0-dimensional array or |
| array scalar. (An array scalar is an instance of the types/classes |
| float32, float64, etc., whereas a 0-dimensional array is an ndarray |
| instance containing precisely one array scalar.)</p></li> |
| <li><p>If <em>axis</em> is an integer, then the operation is done over the given |
| axis (for each 1-D subarray that can be created along the given axis).</p></li> |
| </ul> |
| <div class="admonition-example-of-the-axis-argument admonition"> |
| <p class="admonition-title">Example of the <em>axis</em> argument</p> |
| <p>A 3-dimensional array of size 3 x 3 x 3, summed over each of its |
| three axes</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> |
| <span class="go">array([[[ 0, 1, 2],</span> |
| <span class="go"> [ 3, 4, 5],</span> |
| <span class="go"> [ 6, 7, 8]],</span> |
| <span class="go"> [[ 9, 10, 11],</span> |
| <span class="go"> [12, 13, 14],</span> |
| <span class="go"> [15, 16, 17]],</span> |
| <span class="go"> [[18, 19, 20],</span> |
| <span class="go"> [21, 22, 23],</span> |
| <span class="go"> [24, 25, 26]]])</span> |
| <span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> |
| <span class="go">array([[27, 30, 33],</span> |
| <span class="go"> [36, 39, 42],</span> |
| <span class="go"> [45, 48, 51]])</span> |
| <span class="gp">>>> </span><span class="c1"># for sum, axis is the first keyword, so we may omit it,</span> |
| <span class="gp">>>> </span><span class="c1"># specifying only its value</span> |
| <span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">sum</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">sum</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">x</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> |
| <span class="go">(array([[27, 30, 33],</span> |
| <span class="go"> [36, 39, 42],</span> |
| <span class="go"> [45, 48, 51]]),</span> |
| <span class="go"> array([[ 9, 12, 15],</span> |
| <span class="go"> [36, 39, 42],</span> |
| <span class="go"> [63, 66, 69]]),</span> |
| <span class="go"> array([[ 3, 12, 21],</span> |
| <span class="go"> [30, 39, 48],</span> |
| <span class="go"> [57, 66, 75]]))</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>The parameter <em>dtype</em> specifies the data type over which a reduction |
| operation (like summing) should take place. The default reduce data |
| type is the same as the data type of <em>self</em>. To avoid overflow, it can |
| be useful to perform the reduction using a larger data type.</p> |
| <p>For several methods, an optional <em>out</em> argument can also be provided |
| and the result will be placed into the output array given. The <em>out</em> |
| argument must be an <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code> and have the same number of |
| elements. It can have a different data type in which case casting will |
| be performed.</p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.max</span></code>([axis, out, keepdims])</p></td> |
| <td><p>Return the maximum along a given axis.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.argmax</span></code>([axis, out, keepdims])</p></td> |
| <td><p>Return indices of the maximum values along the given axis.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.min</span></code>([axis, out, keepdims])</p></td> |
| <td><p>Convenience fluent method for <a class="reference internal" href="generated/mxnet.np.min.html#mxnet.np.min" title="mxnet.np.min"><code class="xref py py-func docutils literal notranslate"><span class="pre">min()</span></code></a>.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.argmin</span></code>([axis, out, keepdims])</p></td> |
| <td><p>Return indices of the minium values along the given axis.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.clip</span></code>([min, max, out])</p></td> |
| <td><p>Return an array whose values are limited to [min, max].</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.sum</span></code>([axis, dtype, out, keepdims])</p></td> |
| <td><p>Return the sum of the array elements over the given axis.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.mean</span></code>([axis, dtype, out, keepdims])</p></td> |
| <td><p>Returns the average of the array elements along given axis.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.prod</span></code>([axis, dtype, out, keepdims])</p></td> |
| <td><p>Return the product of the array elements over the given axis.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.cumsum</span></code>([axis, dtype, out])</p></td> |
| <td><p>Return the cumulative sum of the elements along the given axis.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.var</span></code>([axis, dtype, out, correction, …])</p></td> |
| <td><p>Returns the variance of the array elements, along given axis.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.std</span></code>([axis, dtype, out, correction, …])</p></td> |
| <td><p>Returns the standard deviation of the array elements along given axis.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.round</span></code>([decimals, out])</p></td> |
| <td><p>Convenience fluent method for <code class="xref py py-func docutils literal notranslate"><span class="pre">round()</span></code>.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.all</span></code>([axis, out, keepdims])</p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.any</span></code>([axis, out, keepdims])</p></td> |
| <td><p></p></td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| </div> |
| <div class="section" id="arithmetic-matrix-multiplication-and-comparison-operations"> |
| <h2>Arithmetic, matrix multiplication, and comparison operations<a class="headerlink" href="#arithmetic-matrix-multiplication-and-comparison-operations" title="Permalink to this headline">¶</a></h2> |
| <p id="index-5">Arithmetic and comparison operations on <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarrays</span></code> |
| are defined as element-wise operations, and generally yield |
| <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code> objects as results.</p> |
| <p>Each of the arithmetic operations (<code class="docutils literal notranslate"><span class="pre">+</span></code>, <code class="docutils literal notranslate"><span class="pre">-</span></code>, <code class="docutils literal notranslate"><span class="pre">*</span></code>, <code class="docutils literal notranslate"><span class="pre">/</span></code>, <code class="docutils literal notranslate"><span class="pre">//</span></code>, |
| <code class="docutils literal notranslate"><span class="pre">%</span></code>, <code class="docutils literal notranslate"><span class="pre">divmod()</span></code>, <code class="docutils literal notranslate"><span class="pre">**</span></code> or <code class="docutils literal notranslate"><span class="pre">pow()</span></code>, <code class="docutils literal notranslate"><span class="pre"><<</span></code>, <code class="docutils literal notranslate"><span class="pre">>></span></code>, <code class="docutils literal notranslate"><span class="pre">&</span></code>, |
| <code class="docutils literal notranslate"><span class="pre">^</span></code>, <code class="docutils literal notranslate"><span class="pre">|</span></code>, <code class="docutils literal notranslate"><span class="pre">~</span></code>) and the comparisons (<code class="docutils literal notranslate"><span class="pre">==</span></code>, <code class="docutils literal notranslate"><span class="pre"><</span></code>, <code class="docutils literal notranslate"><span class="pre">></span></code>, |
| <code class="docutils literal notranslate"><span class="pre"><=</span></code>, <code class="docutils literal notranslate"><span class="pre">>=</span></code>, <code class="docutils literal notranslate"><span class="pre">!=</span></code>) is equivalent to the corresponding |
| universal function (or ufunc for short) in NumPy.</p> |
| <p>Comparison operators:</p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__lt__</span></code>(other)</p></td> |
| <td><p>x.__lt__(y) <=> x < y</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__le__</span></code>(other)</p></td> |
| <td><p>x.__le__(y) <=> x <= y</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__gt__</span></code>(other)</p></td> |
| <td><p>x.__gt__(y) <=> x > y</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__ge__</span></code>(other)</p></td> |
| <td><p>x.__ge__(y) <=> x >= y</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__eq__</span></code>(other)</p></td> |
| <td><p>x.__eq__(y) <=> x == y</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__ne__</span></code>(other)</p></td> |
| <td><p>x.__ne__(y) <=> x != y</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>Truth value of an array (<code class="xref py py-func docutils literal notranslate"><span class="pre">bool()</span></code>):</p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__bool__</span></code>()</p></td> |
| <td><p></p></td> |
| </tr> |
| </tbody> |
| </table> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>Truth-value testing of an array invokes |
| <code class="xref py py-meth docutils literal notranslate"><span class="pre">ndarray.__bool__()</span></code>, which raises an error if the number of |
| elements in the array is larger than 1, because the truth value |
| of such arrays is ambiguous.</p> |
| </div> |
| <p>Unary operations:</p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__neg__</span></code>()</p></td> |
| <td><p>x.__neg__() <=> -x</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__abs__</span></code>()</p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__invert__</span></code>()</p></td> |
| <td><p>x.__invert__() <=> ~x</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>Arithmetic:</p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__add__</span></code>(other)</p></td> |
| <td><p>x.__add__(y) <=> x + y</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__sub__</span></code>(other)</p></td> |
| <td><p>x.__sub__(y) <=> x - y</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__mul__</span></code>(other)</p></td> |
| <td><p>x.__mul__(y) <=> x * y</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__truediv__</span></code>(other)</p></td> |
| <td><p>x.__truediv__(y) <=> x / y</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__mod__</span></code>(other)</p></td> |
| <td><p>x.__mod__(y) <=> x % y</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__pow__</span></code>(other)</p></td> |
| <td><p>x.__pow__(y) <=> x ** y</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__and__</span></code>(other)</p></td> |
| <td><p>x.__and__(y) <=> x & y</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__or__</span></code>(other)</p></td> |
| <td><p>x.__or__(y) <=> x | y</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__xor__</span></code>(other)</p></td> |
| <td><p>x.__xor__(y) <=> x ^ y</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <ul class="simple"> |
| <li><p>Any third argument to <code class="xref py py-func docutils literal notranslate"><span class="pre">pow()</span></code> is silently ignored, |
| as the underlying <a class="reference internal" href="generated/mxnet.np.power.html#mxnet.np.power" title="mxnet.np.power"><code class="xref py py-func docutils literal notranslate"><span class="pre">ufunc</span></code></a> takes only two arguments.</p></li> |
| <li><p>The three division operators are all defined; <code class="xref py py-obj docutils literal notranslate"><span class="pre">div</span></code> is active |
| by default, <code class="xref py py-obj docutils literal notranslate"><span class="pre">truediv</span></code> is active when |
| <code class="xref py py-obj docutils literal notranslate"><span class="pre">__future__</span></code> division is in effect.</p></li> |
| <li><p>Because <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code> is a built-in type (written in C), the |
| <code class="docutils literal notranslate"><span class="pre">__r{op}__</span></code> special methods are not directly defined.</p></li> |
| <li><p>The functions called to implement many arithmetic special methods |
| for arrays can be modified using <code class="xref py py-class docutils literal notranslate"><span class="pre">__array_ufunc__</span></code>.</p></li> |
| </ul> |
| </div> |
| <p>Arithmetic, in-place:</p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__iadd__</span></code>(other)</p></td> |
| <td><p>x.__iadd__(y) <=> x += y</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__isub__</span></code>(other)</p></td> |
| <td><p>x.__isub__(y) <=> x -= y</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__imul__</span></code>(other)</p></td> |
| <td><p>x.__imul__(y) <=> x *= y</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__itruediv__</span></code>(other)</p></td> |
| <td><p>x.__itruediv__(y) <=> x /= y</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__imod__</span></code>(other)</p></td> |
| <td><p>x.__imod__(y) <=> x %= y</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__iand__</span></code>(other)</p></td> |
| <td><p>x.__iand__(y) <=> x &= y</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__ior__</span></code>(other)</p></td> |
| <td><p>x.__ior__(y) <=> x |= y</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__ixor__</span></code>(other)</p></td> |
| <td><p>x.__ixor__(y) <=> x ^= y</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <div class="admonition warning"> |
| <p class="admonition-title">Warning</p> |
| <p>In place operations will perform the calculation using the |
| precision decided by the data type of the two operands, but will |
| silently downcast the result (if necessary) so it can fit back into |
| the array. Therefore, for mixed precision calculations, |
| <code class="docutils literal notranslate"><span class="pre">A</span> <span class="pre">{op}=</span> <span class="pre">B</span></code> can be different than <code class="docutils literal notranslate"><span class="pre">A</span> <span class="pre">=</span> <span class="pre">A</span> <span class="pre">{op}</span> <span class="pre">B</span></code>. For example, suppose |
| <code class="docutils literal notranslate"><span class="pre">a</span> <span class="pre">=</span> <span class="pre">ones((3,3))</span></code>. Then, <code class="docutils literal notranslate"><span class="pre">a</span> <span class="pre">+=</span> <span class="pre">3j</span></code> is different than <code class="docutils literal notranslate"><span class="pre">a</span> <span class="pre">=</span> <span class="pre">a</span> <span class="pre">+</span> <span class="pre">3j</span></code>: |
| while they both perform the same computation, <code class="docutils literal notranslate"><span class="pre">a</span> <span class="pre">+=</span> <span class="pre">3</span></code> |
| casts the result to fit back in <code class="docutils literal notranslate"><span class="pre">a</span></code>, whereas <code class="docutils literal notranslate"><span class="pre">a</span> <span class="pre">=</span> <span class="pre">a</span> <span class="pre">+</span> <span class="pre">3j</span></code> |
| re-binds the name <code class="docutils literal notranslate"><span class="pre">a</span></code> to the result.</p> |
| </div> |
| <p>Matrix Multiplication:</p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__matmul__</span></code>(other)</p></td> |
| <td><p>x.__matmul__(y) <=> x @ y</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| <div class="section" id="special-methods"> |
| <h2>Special methods<a class="headerlink" href="#special-methods" title="Permalink to this headline">¶</a></h2> |
| <p>For standard library functions:</p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__reduce__</span></code>()</p></td> |
| <td><p>Helper for pickle.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__setstate__</span></code>(state)</p></td> |
| <td><p></p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>Basic customization:</p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__new__</span></code></p></td> |
| <td><p>Create and return a new object.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>Container customization: (see <a class="reference internal" href="arrays.indexing.html#arrays-indexing"><span class="std std-ref">Indexing</span></a>)</p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__len__</span></code>()</p></td> |
| <td><p>Number of elements along the first axis.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__getitem__</span></code>(key)</p></td> |
| <td><p>Return self[key].</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__setitem__</span></code>(key, value)</p></td> |
| <td><p>Sets <code class="docutils literal notranslate"><span class="pre">self[key]</span></code> to <code class="docutils literal notranslate"><span class="pre">value</span></code>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>Conversion; the operations <code class="xref py py-func docutils literal notranslate"><span class="pre">index()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">int()</span></code> and <code class="xref py py-func docutils literal notranslate"><span class="pre">float()</span></code>. |
| They work only on arrays that have one element in them |
| and return the appropriate scalar.</p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__index__</span></code>()</p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__int__</span></code>()</p></td> |
| <td><p></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__float__</span></code>()</p></td> |
| <td><p></p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>String representations:</p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__str__</span></code>()</p></td> |
| <td><p>Returns a string representation of the array.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray.__repr__</span></code>()</p></td> |
| <td><p>Returns a string representation of the array.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| </div> |
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| <span class="caption-text">Table Of Contents</span> |
| </p> |
| <ul> |
| <li><a class="reference internal" href="#">The N-dimensional array (<code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code>)</a><ul> |
| <li><a class="reference internal" href="#constructing-arrays">Constructing arrays</a></li> |
| <li><a class="reference internal" href="#indexing-arrays">Indexing arrays</a></li> |
| <li><a class="reference internal" href="#internal-memory-layout-of-an-ndarray">Internal memory layout of an ndarray</a></li> |
| <li><a class="reference internal" href="#array-attributes">Array attributes</a><ul> |
| <li><a class="reference internal" href="#id1">Memory layout</a></li> |
| <li><a class="reference internal" href="#data-type">Data type</a></li> |
| </ul> |
| </li> |
| <li><a class="reference internal" href="#array-methods">Array methods</a><ul> |
| <li><a class="reference internal" href="#array-conversion">Array conversion</a></li> |
| <li><a class="reference internal" href="#shape-manipulation">Shape manipulation</a></li> |
| <li><a class="reference internal" href="#item-selection-and-manipulation">Item selection and manipulation</a></li> |
| <li><a class="reference internal" href="#calculation">Calculation</a></li> |
| </ul> |
| </li> |
| <li><a class="reference internal" href="#arithmetic-matrix-multiplication-and-comparison-operations">Arithmetic, matrix multiplication, and comparison operations</a></li> |
| <li><a class="reference internal" href="#special-methods">Special methods</a></li> |
| </ul> |
| </li> |
| </ul> |
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