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 <li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Python Tutorials</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="../../getting-started/index.html">Getting Started</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../getting-started/crash-course/index.html">Crash Course</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../getting-started/crash-course/0-introduction.html">Introduction</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../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="../../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="../../getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../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="../../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="../../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="../../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="../../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="../../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="../../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="../../getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="../../getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../getting-started/gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../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>
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 <li class="toctree-l2 current"><a class="reference internal" href="../index.html">Packages</a><ul class="current">
 <li class="toctree-l3"><a class="reference internal" href="../autograd/index.html">Automatic Differentiation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../gluon/index.html">Gluon</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../gluon/blocks/index.html">Blocks</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/custom-layer.html">Custom Layers</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/hybridize.html">Hybridize</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/init.html">Initialization</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/naming.html">Parameter and Block Naming</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/nn.html">Layers and Blocks</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/parameters.html">Parameter Management</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/activations/activations.html">Activation Blocks</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="../gluon/data/index.html">Data Tutorials</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/data/data_augmentation.html">Image Augmentation</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../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="../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="../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="../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>
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 <li class="toctree-l5"><a class="reference internal" href="../gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/image/mnist.html">Handwritten Digit Recognition</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="../gluon/loss/index.html">Losses</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/loss/custom-loss.html">Custom Loss Blocks</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/loss/loss.html">Loss functions</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="../gluon/text/index.html">Text Tutorials</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/text/gnmt.html">Google Neural Machine Translation</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/text/transformer.html">Machine Translation with Transformer</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="../gluon/training/index.html">Training</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/training/trainer.html">Trainer</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/training/learning_rates/index.html">Learning Rates</a><ul>
 <li class="toctree-l6"><a class="reference internal" href="../gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li>
 <li class="toctree-l6"><a class="reference internal" href="../gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li>
 <li class="toctree-l6"><a class="reference internal" href="../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="../gluon/training/normalization/index.html">Normalization Blocks</a></li>
 </ul>
 </li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="../kvstore/index.html">KVStore</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../kvstore/kvstore.html">Distributed Key-Value Store</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="../legacy/index.html">Legacy</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../legacy/ndarray/index.html">NDArray</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../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="../legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../legacy/ndarray/sparse/index.html">Tutorials</a><ul>
 <li class="toctree-l6"><a class="reference internal" href="../legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li>
 <li class="toctree-l6"><a class="reference internal" href="../legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li>
 </ul>
 </li>
 </ul>
 </li>
 </ul>
 </li>
 <li class="toctree-l3 current"><a class="reference internal" href="index.html">What is NP on MXNet</a><ul class="current">
 <li class="toctree-l4 current"><a class="current reference internal" href="#">The NP on MXNet cheat sheet</a></li>
 <li class="toctree-l4"><a class="reference internal" href="np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="../onnx/index.html">ONNX</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../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-l3"><a class="reference internal" href="../optimizer/index.html">Optimizers</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../viz/index.html">Visualization</a><ul>
 <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="../../performance/index.html">Performance</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../performance/compression/index.html">Compression</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../performance/compression/int8.html">Deploy with int-8</a></li>
 <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/float16">Float16</a></li>
 <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li>
 <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="../../performance/backend/dnnl/index.html">oneDNN</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../performance/backend/dnnl/dnnl_readme.html">Install MXNet with oneDNN</a></li>
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 <li class="toctree-l5"><a class="reference internal" href="../../performance/backend/dnnl/dnnl_quantization_inc.html">Improving accuracy with Intel® Neural Compressor</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="../../../api/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-l4"><a class="reference internal" href="../../../api/np/routines.sort.html">Sorting, searching, and counting</a><ul>
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 <li class="toctree-l2"><a class="reference internal" href="../../../api/npx/index.html">NPX: NumPy Neural Network Extension</a><ul>
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 <li class="toctree-l2"><a class="reference internal" href="../../../api/gluon/index.html">mxnet.gluon</a><ul>
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         <span class="mdl-layout-title toc">Table Of Contents</span>


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 <li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Python Tutorials</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="../../getting-started/index.html">Getting Started</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../getting-started/crash-course/index.html">Crash Course</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../getting-started/crash-course/0-introduction.html">Introduction</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../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="../../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="../../getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../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="../../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="../../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="../../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="../../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="../../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="../../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="../../getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="../../getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../getting-started/gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../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 current"><a class="reference internal" href="../index.html">Packages</a><ul class="current">
 <li class="toctree-l3"><a class="reference internal" href="../autograd/index.html">Automatic Differentiation</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../gluon/index.html">Gluon</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../gluon/blocks/index.html">Blocks</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/custom-layer.html">Custom Layers</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/hybridize.html">Hybridize</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/init.html">Initialization</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/naming.html">Parameter and Block Naming</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/nn.html">Layers and Blocks</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/parameters.html">Parameter Management</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/activations/activations.html">Activation Blocks</a></li>
 </ul>
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 <li class="toctree-l4"><a class="reference internal" href="../gluon/data/index.html">Data Tutorials</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/data/data_augmentation.html">Image Augmentation</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../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="../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="../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="../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>
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 <li class="toctree-l4"><a class="reference internal" href="../gluon/image/index.html">Image Tutorials</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/image/mnist.html">Handwritten Digit Recognition</a></li>
 </ul>
 </li>
 <li class="toctree-l4"><a class="reference internal" href="../gluon/loss/index.html">Losses</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/loss/custom-loss.html">Custom Loss Blocks</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/loss/loss.html">Loss functions</a></li>
 </ul>
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 <li class="toctree-l4"><a class="reference internal" href="../gluon/text/index.html">Text Tutorials</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/text/gnmt.html">Google Neural Machine Translation</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/text/transformer.html">Machine Translation with Transformer</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="../gluon/training/index.html">Training</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/training/trainer.html">Trainer</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../gluon/training/learning_rates/index.html">Learning Rates</a><ul>
 <li class="toctree-l6"><a class="reference internal" href="../gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li>
 <li class="toctree-l6"><a class="reference internal" href="../gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li>
 <li class="toctree-l6"><a class="reference internal" href="../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="../gluon/training/normalization/index.html">Normalization Blocks</a></li>
 </ul>
 </li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="../kvstore/index.html">KVStore</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../kvstore/kvstore.html">Distributed Key-Value Store</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="../legacy/index.html">Legacy</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../legacy/ndarray/index.html">NDArray</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../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="../legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../legacy/ndarray/sparse/index.html">Tutorials</a><ul>
 <li class="toctree-l6"><a class="reference internal" href="../legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li>
 <li class="toctree-l6"><a class="reference internal" href="../legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li>
 </ul>
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 </ul>
 </li>
 </ul>
 </li>
 <li class="toctree-l3 current"><a class="reference internal" href="index.html">What is NP on MXNet</a><ul class="current">
 <li class="toctree-l4 current"><a class="current reference internal" href="#">The NP on MXNet cheat sheet</a></li>
 <li class="toctree-l4"><a class="reference internal" href="np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="../onnx/index.html">ONNX</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li>
 <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="../optimizer/index.html">Optimizers</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../viz/index.html">Visualization</a><ul>
 <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li>
 </ul>
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 </ul>
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 <li class="toctree-l2"><a class="reference internal" href="../../performance/index.html">Performance</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../performance/compression/index.html">Compression</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../performance/compression/int8.html">Deploy with int-8</a></li>
 <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/float16">Float16</a></li>
 <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li>
 <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li>
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 <li class="toctree-l3"><a class="reference internal" href="../../performance/backend/index.html">Accelerated Backend Tools</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../performance/backend/dnnl/index.html">oneDNN</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../performance/backend/dnnl/dnnl_readme.html">Install MXNet with oneDNN</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../performance/backend/dnnl/dnnl_quantization.html">oneDNN Quantization</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../performance/backend/dnnl/dnnl_quantization_inc.html">Improving accuracy with Intel® Neural Compressor</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="../../performance/backend/tvm.html">Use TVM</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../performance/backend/profiler.html">Profiling MXNet Models</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../performance/backend/amp.html">Using AMP: Automatic Mixed Precision</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="../../deploy/index.html">Deployment</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../deploy/export/index.html">Export</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../deploy/export/onnx.html">Exporting to ONNX format</a></li>
 <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/export_network.html">Export Gluon CV Models</a></li>
 <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Save / Load Parameters</a></li>
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 <li class="toctree-l3"><a class="reference internal" href="../../deploy/inference/index.html">Inference</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../deploy/inference/cpp.html">Deploy into C++</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../deploy/inference/image_classification_jetson.html">Image Classication using pretrained ResNet-50 model on Jetson module</a></li>
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 <li class="toctree-l3"><a class="reference internal" href="../../deploy/run-on-aws/index.html">Run on AWS</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../deploy/run-on-aws/use_ec2.html">Run on an EC2 Instance</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../deploy/run-on-aws/use_sagemaker.html">Run on Amazon SageMaker</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../deploy/run-on-aws/cloud.html">MXNet on the Cloud</a></li>
 </ul>
 </li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../extend/index.html">Extend</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../extend/customop.html">Custom Numpy Operators</a></li>
 <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/new_op">New Operator Creation</a></li>
 <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/add_op_in_backend">New Operator in MXNet Backend</a></li>
 <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/using_rtc">Using RTC for CUDA kernels</a></li>
 </ul>
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 </ul>
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 <li class="toctree-l1"><a class="reference internal" href="../../../api/index.html">Python API</a><ul>
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 <li class="toctree-l3"><a class="reference internal" href="../../../api/np/arrays.html">Array objects</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../../api/np/arrays.ndarray.html">The N-dimensional array (<code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code>)</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../api/np/arrays.indexing.html">Indexing</a></li>
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 <li class="toctree-l3"><a class="reference internal" href="../../../api/np/routines.html">Routines</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.array-creation.html">Array creation routines</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.eye.html">mxnet.np.eye</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.empty.html">mxnet.np.empty</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.full.html">mxnet.np.full</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.identity.html">mxnet.np.identity</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ones.html">mxnet.np.ones</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ones_like.html">mxnet.np.ones_like</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.zeros.html">mxnet.np.zeros</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.zeros_like.html">mxnet.np.zeros_like</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.array.html">mxnet.np.array</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.copy.html">mxnet.np.copy</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arange.html">mxnet.np.arange</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linspace.html">mxnet.np.linspace</a></li>
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 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.meshgrid.html">mxnet.np.meshgrid</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tril.html">mxnet.np.tril</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.array-manipulation.html">Array manipulation routines</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.reshape.html">mxnet.np.reshape</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ravel.html">mxnet.np.ravel</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.flatten.html">mxnet.np.ndarray.flatten</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.swapaxes.html">mxnet.np.swapaxes</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.T.html">mxnet.np.ndarray.T</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.transpose.html">mxnet.np.transpose</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.moveaxis.html">mxnet.np.moveaxis</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rollaxis.html">mxnet.np.rollaxis</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.expand_dims.html">mxnet.np.expand_dims</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.squeeze.html">mxnet.np.squeeze</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.broadcast_to.html">mxnet.np.broadcast_to</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.broadcast_arrays.html">mxnet.np.broadcast_arrays</a></li>
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 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.concatenate.html">mxnet.np.concatenate</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.stack.html">mxnet.np.stack</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dstack.html">mxnet.np.dstack</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vstack.html">mxnet.np.vstack</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.column_stack.html">mxnet.np.column_stack</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hstack.html">mxnet.np.hstack</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.split.html">mxnet.np.split</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hsplit.html">mxnet.np.hsplit</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vsplit.html">mxnet.np.vsplit</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.array_split.html">mxnet.np.array_split</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dsplit.html">mxnet.np.dsplit</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tile.html">mxnet.np.tile</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.repeat.html">mxnet.np.repeat</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.unique.html">mxnet.np.unique</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.delete.html">mxnet.np.delete</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.insert.html">mxnet.np.insert</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.append.html">mxnet.np.append</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.resize.html">mxnet.np.resize</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trim_zeros.html">mxnet.np.trim_zeros</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.reshape.html">mxnet.np.reshape</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flip.html">mxnet.np.flip</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.roll.html">mxnet.np.roll</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rot90.html">mxnet.np.rot90</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fliplr.html">mxnet.np.fliplr</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flipud.html">mxnet.np.flipud</a></li>
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 </li>
 <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.io.html">Input and output</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.genfromtxt.html">mxnet.np.genfromtxt</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.tolist.html">mxnet.np.ndarray.tolist</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.set_printoptions.html">mxnet.np.set_printoptions</a></li>
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 </li>
 <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dot.html">mxnet.np.dot</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vdot.html">mxnet.np.vdot</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.inner.html">mxnet.np.inner</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.outer.html">mxnet.np.outer</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tensordot.html">mxnet.np.tensordot</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.einsum.html">mxnet.np.einsum</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.multi_dot.html">mxnet.np.linalg.multi_dot</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.matmul.html">mxnet.np.matmul</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_power.html">mxnet.np.linalg.matrix_power</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.kron.html">mxnet.np.kron</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.svd.html">mxnet.np.linalg.svd</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cholesky.html">mxnet.np.linalg.cholesky</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.qr.html">mxnet.np.linalg.qr</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eig.html">mxnet.np.linalg.eig</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigh.html">mxnet.np.linalg.eigh</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvals.html">mxnet.np.linalg.eigvals</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvalsh.html">mxnet.np.linalg.eigvalsh</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.norm.html">mxnet.np.linalg.norm</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trace.html">mxnet.np.trace</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cond.html">mxnet.np.linalg.cond</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.det.html">mxnet.np.linalg.det</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_rank.html">mxnet.np.linalg.matrix_rank</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.slogdet.html">mxnet.np.linalg.slogdet</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.solve.html">mxnet.np.linalg.solve</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorsolve.html">mxnet.np.linalg.tensorsolve</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.lstsq.html">mxnet.np.linalg.lstsq</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.inv.html">mxnet.np.linalg.inv</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.pinv.html">mxnet.np.linalg.pinv</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorinv.html">mxnet.np.linalg.tensorinv</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.math.html">Mathematical functions</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sin.html">mxnet.np.sin</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cos.html">mxnet.np.cos</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tan.html">mxnet.np.tan</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arcsin.html">mxnet.np.arcsin</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arccos.html">mxnet.np.arccos</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan.html">mxnet.np.arctan</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.degrees.html">mxnet.np.degrees</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.radians.html">mxnet.np.radians</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hypot.html">mxnet.np.hypot</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan2.html">mxnet.np.arctan2</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.deg2rad.html">mxnet.np.deg2rad</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rad2deg.html">mxnet.np.rad2deg</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.unwrap.html">mxnet.np.unwrap</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sinh.html">mxnet.np.sinh</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cosh.html">mxnet.np.cosh</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tanh.html">mxnet.np.tanh</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arcsinh.html">mxnet.np.arcsinh</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arccosh.html">mxnet.np.arccosh</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctanh.html">mxnet.np.arctanh</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rint.html">mxnet.np.rint</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fix.html">mxnet.np.fix</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.floor.html">mxnet.np.floor</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ceil.html">mxnet.np.ceil</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trunc.html">mxnet.np.trunc</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.around.html">mxnet.np.around</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.round_.html">mxnet.np.round_</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sum.html">mxnet.np.sum</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.prod.html">mxnet.np.prod</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cumsum.html">mxnet.np.cumsum</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanprod.html">mxnet.np.nanprod</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nansum.html">mxnet.np.nansum</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cumprod.html">mxnet.np.cumprod</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nancumprod.html">mxnet.np.nancumprod</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nancumsum.html">mxnet.np.nancumsum</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.diff.html">mxnet.np.diff</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ediff1d.html">mxnet.np.ediff1d</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cross.html">mxnet.np.cross</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trapz.html">mxnet.np.trapz</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.exp.html">mxnet.np.exp</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.expm1.html">mxnet.np.expm1</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log.html">mxnet.np.log</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log10.html">mxnet.np.log10</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log2.html">mxnet.np.log2</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log1p.html">mxnet.np.log1p</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.logaddexp.html">mxnet.np.logaddexp</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.i0.html">mxnet.np.i0</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ldexp.html">mxnet.np.ldexp</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.signbit.html">mxnet.np.signbit</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.copysign.html">mxnet.np.copysign</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.frexp.html">mxnet.np.frexp</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.spacing.html">mxnet.np.spacing</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.lcm.html">mxnet.np.lcm</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.gcd.html">mxnet.np.gcd</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.add.html">mxnet.np.add</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.reciprocal.html">mxnet.np.reciprocal</a></li>
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 <!--- Licensed to the Apache Software Foundation (ASF) under one --><!--- or more contributor license agreements.  See the NOTICE file --><!--- distributed with this work for additional information --><!--- regarding copyright ownership.  The ASF licenses this file --><!--- to you under the Apache License, Version 2.0 (the --><!--- "License"); you may not use this file except in compliance --><!--- with the License.  You may obtain a copy of the License at --><!---   http://www.apache.org/licenses/LICENSE-2.0 --><!--- Unless required by applicable law or agreed to in writing, --><!--- software distributed under the License is distributed on an --><!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY --><!--- KIND, either express or implied.  See the License for the --><!--- specific language governing permissions and limitations --><!--- under the License. --><div class="section" id="The-NP-on-MXNet-cheat-sheet">
 <h1>The NP on MXNet cheat sheet<a class="headerlink" href="#The-NP-on-MXNet-cheat-sheet" title="Permalink to this headline">¶</a></h1>
 <p>To begin, import the <code class="docutils literal notranslate"><span class="pre">np</span></code> and <code class="docutils literal notranslate"><span class="pre">npx</span></code> module and update MXNet to run in NumPy-like mode.</p>
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 <span></span><span class="kn">from</span> <span class="nn">mxnet</span> <span class="kn">import</span> <span class="n">np</span><span class="p">,</span> <span class="n">npx</span>
 <span class="n">npx</span><span class="o">.</span><span class="n">set_np</span><span class="p">()</span>  <span class="c1"># Change MXNet to the numpy-like mode.</span>
 </pre></div>
 </div>
 </div>
 <p>NDArray figure (TODO)</p>
 <div class="section" id="Creating-arrays">
 <h2>Creating arrays<a class="headerlink" href="#Creating-arrays" title="Permalink to this headline">¶</a></h2>
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 <span></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="c1"># default datatype is float32</span>
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 [04:51:27] /work/mxnet/src/storage/storage.cc:202: Using Pooled (Naive) StorageManager for CPU
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 <span></span>array([1., 2., 3.])
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 <span></span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([(</span><span class="mf">1.5</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">dtype</span><span class="o">=</span><span class="s1">&#39;float16&#39;</span><span class="p">)</span>
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 <span></span>array([[1.5, 2. , 3. ],
        [4. , 5. , 6. ]], dtype=float16)
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 <span></span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[(</span><span class="mi">15</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="p">[(</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</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">dtype</span><span class="o">=</span><span class="s1">&#39;int32&#39;</span><span class="p">)</span>
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 <span></span>array([[[15,  2,  3],
         [ 4,  5,  6]],

        [[ 3,  2,  1],
         [ 4,  5,  6]]], dtype=int32)
 </pre></div>
 </div>
 </div>
 <div class="section" id="Initial-placeholders">
 <h3>Initial placeholders<a class="headerlink" href="#Initial-placeholders" title="Permalink to this headline">¶</a></h3>
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 <span></span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>  <span class="c1"># Create an array of zeros</span>
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 <span></span>array([[0., 0., 0., 0.],
        [0., 0., 0., 0.],
        [0., 0., 0., 0.]])
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 <span></span><span class="n">np</span><span class="o">.</span><span class="n">ones</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="mi">4</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;int8&#39;</span><span class="p">)</span>  <span class="c1"># Create an array of ones</span>
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 <span></span>array([[[1, 1, 1, 1],
         [1, 1, 1, 1],
         [1, 1, 1, 1]],

        [[1, 1, 1, 1],
         [1, 1, 1, 1],
         [1, 1, 1, 1]]], dtype=int8)
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 <span></span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>  <span class="c1"># Create an array of evenly spaced values (step value)</span>
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 <span></span>array([10., 15., 20.])
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 <span></span><span class="c1"># Create an array of evenly spaced values (number of samples)</span>
 <span class="c1"># np.linspace(0, 2, 9)</span>
 </pre></div>
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 <span></span><span class="c1"># np.full((2, 2), 7)  # Create a constant array</span>
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 <span></span><span class="c1"># np.eye(2)  # Create a 2X2 identity matrix</span>
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 <span></span><span class="c1"># np.random.random((2, 2))  # Create an array with random values</span>
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 <span></span><span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>  <span class="c1"># Create an empty array</span>
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 <span></span>array([[3.3631163e-44,           nan],
        [1.3269378e+28, 4.5856091e-41],
        [1.4012985e-45, 0.0000000e+00]])
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 <div class="section" id="I/O">
 <h2>I/O<a class="headerlink" href="#I/O" title="Permalink to this headline">¶</a></h2>
 <div class="section" id="Saving-and-loading-on-disk">
 <h3>Saving and loading on disk<a class="headerlink" href="#Saving-and-loading-on-disk" title="Permalink to this headline">¶</a></h3>
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 <span></span><span class="c1"># Save one array</span>
 <span class="n">a</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="n">npx</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">&#39;my_array&#39;</span><span class="p">,</span> <span class="n">a</span><span class="p">)</span>
 <span class="n">npx</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;my_array&#39;</span><span class="p">)</span>
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 <span></span>array([1., 2., 3.])
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 <span></span><span class="c1"># Save a list of arrays</span>
 <span class="n">b</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">4</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">8</span><span class="p">])</span>
 <span class="n">npx</span><span class="o">.</span><span class="n">savez</span><span class="p">(</span><span class="s1">&#39;my_arrays&#39;</span><span class="p">,</span> <span class="o">*</span><span class="p">[</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">])</span>
 <span class="n">npx</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;my_arrays&#39;</span><span class="p">)</span>
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 <span></span>{&#39;arr_0&#39;: array([1., 2., 3.]), &#39;arr_1&#39;: array([4., 6., 8.])}
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 </div>
 <div class="section" id="Saving-and-loading-text-files">
 <h3>Saving and loading text files<a class="headerlink" href="#Saving-and-loading-text-files" title="Permalink to this headline">¶</a></h3>
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 <span></span><span class="c1"># np.loadtxt(&quot;myfile.txt&quot;)</span>
 <span class="c1"># np.genfromtxt(&quot;my_file.csv&quot;, delimiter=&#39;,&#39;)</span>
 <span class="c1"># np.savetxt(&quot;myarray.txt&quot;, a, delimiter=&quot; &quot;)</span>
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 </div>
 </div>
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 <div class="section" id="Data-types">
 <h2>Data types<a class="headerlink" href="#Data-types" title="Permalink to this headline">¶</a></h2>
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 <span></span><span class="c1"># np.int64    # Signed 64-bit integer types</span>
 <span class="c1"># np.float32  # Standard double-precision floating point</span>
 <span class="c1"># np.complex  # Complex numbers represented by 128 floats</span>
 <span class="c1"># np.bool     # Boolean type storing TRUE and FALSE values</span>
 <span class="c1"># np.object   # Python object type</span>
 <span class="c1"># np.string_  # Fixed-length string type</span>
 <span class="c1"># np.unicode_ # Fixed-length unicode type</span>
 </pre></div>
 </div>
 </div>
 </div>
 <div class="section" id="Inspecting-your-array">
 <h2>Inspecting your array<a class="headerlink" href="#Inspecting-your-array" title="Permalink to this headline">¶</a></h2>
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 <span></span><span class="n">a</span><span class="o">.</span><span class="n">shape</span> <span class="c1"># Array dimensions</span>
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 <span></span>(3,)
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 <span></span><span class="nb">len</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="c1"># Length of array</span>
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 <span></span>3
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 <span></span><span class="n">b</span><span class="o">.</span><span class="n">ndim</span> <span class="c1"># Number of array dimensions</span>
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 <span></span>1
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 <span></span><span class="n">b</span><span class="o">.</span><span class="n">size</span> <span class="c1"># Number of array elements</span>
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 <span></span>3
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 <span></span><span class="n">b</span><span class="o">.</span><span class="n">dtype</span> <span class="c1"># Data type of array elements</span>
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 <span></span>dtype(&#39;float32&#39;)
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 <span></span><span class="c1"># b.dtype.name # Name of data type</span>
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 <span></span><span class="n">b</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;int&#39;</span><span class="p">)</span> <span class="c1"># Convert an array to a different type</span>
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 <span></span>array([4, 6, 8], dtype=int64)
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 </div>
 <div class="section" id="Asking-For-Help">
 <h2>Asking For Help<a class="headerlink" href="#Asking-For-Help" title="Permalink to this headline">¶</a></h2>
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 <span></span><span class="c1"># np.info(np.ndarray.dtype)</span>
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 <div class="section" id="Array-mathematics">
 <h2>Array mathematics<a class="headerlink" href="#Array-mathematics" title="Permalink to this headline">¶</a></h2>
 <div class="section" id="Arithmetic-operations">
 <h3>Arithmetic operations<a class="headerlink" href="#Arithmetic-operations" title="Permalink to this headline">¶</a></h3>
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 <span></span><span class="n">a</span> <span class="o">-</span> <span class="n">b</span> <span class="c1"># Subtraction</span>
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 <span></span>array([-3., -4., -5.])
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 <span></span><span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span> <span class="c1"># Subtraction</span>
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 <span></span>array([-3., -4., -5.])
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 <span></span><span class="n">b</span> <span class="o">+</span> <span class="n">a</span> <span class="c1"># Addition</span>
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 <span></span>array([ 5.,  8., 11.])
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 <span></span><span class="n">np</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">a</span><span class="p">)</span> <span class="c1"># Addition</span>
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 <span></span>array([ 5.,  8., 11.])
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 <span></span><span class="n">a</span> <span class="o">/</span> <span class="n">b</span> <span class="c1"># Division</span>
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 <span></span>array([0.25      , 0.33333334, 0.375     ])
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 <span></span><span class="n">np</span><span class="o">.</span><span class="n">divide</span><span class="p">(</span><span class="n">a</span><span class="p">,</span><span class="n">b</span><span class="p">)</span> <span class="c1"># Division</span>
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 <span></span>array([0.25      , 0.33333334, 0.375     ])
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 <span></span><span class="n">a</span> <span class="o">*</span> <span class="n">b</span> <span class="c1"># Multiplication</span>
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 <span></span>array([ 4., 12., 24.])
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 <span></span><span class="n">np</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span> <span class="c1"># Multiplication</span>
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 <span></span>array([ 4., 12., 24.])
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 <span></span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">b</span><span class="p">)</span> <span class="c1"># Exponentiation</span>
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 <span></span>array([  54.59815,  403.4288 , 2980.958  ])
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 <span></span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">b</span><span class="p">)</span> <span class="c1"># Square root</span>
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 <span></span>array([2.       , 2.4494898, 2.828427 ])
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 <span></span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="c1"># Sines of an array</span>
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 <span></span>array([0.84147096, 0.9092974 , 0.14112   ])
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 <span></span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">b</span><span class="p">)</span> <span class="c1"># Element-wise cosine</span>
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 <span></span>array([-0.6536436 ,  0.96017027, -0.14550003])
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 <span></span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="c1"># Element-wise natural logarithm</span>
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 <span></span>array([0.       , 0.6931472, 1.0986123])
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 <span></span><span class="n">a</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">b</span><span class="p">)</span> <span class="c1"># Dot product</span>
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 <span></span>array(40.)
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 <div class="section" id="Comparison">
 <h3>Comparison<a class="headerlink" href="#Comparison" title="Permalink to this headline">¶</a></h3>
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 <div class="section" id="Aggregate-functions">
 <h3>Aggregate functions<a class="headerlink" href="#Aggregate-functions" title="Permalink to this headline">¶</a></h3>
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 <span></span><span class="n">a</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="c1"># Array-wise sum</span>
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 <span></span>array(6.)
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 <span></span><span class="c1"># a.min() # Array-wise minimum value</span>
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 <span></span><span class="n">c</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">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">]]))</span>
 <span class="c1"># c.max(axis=0) # Maximum value of an array row</span>
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 <span></span><span class="c1"># c.cumsum(axis=1) # Cumulative sum of the elements</span>
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 <span></span><span class="n">a</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="c1"># Mean</span>
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 <span></span>array(2.)
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 <span></span><span class="c1"># b.median() # Median</span>
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 <span></span><span class="c1"># a.corrcoef() # Correlation coefficient</span>
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 <span></span><span class="c1"># np.std(b) # Standard deviation</span>
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 <div class="section" id="Copying-arrays">
 <h2>Copying arrays<a class="headerlink" href="#Copying-arrays" title="Permalink to this headline">¶</a></h2>
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 <span></span><span class="c1"># a.view() # Create a view of the array with the same data</span>
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 <span></span><span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="c1"># Create a copy of the array</span>
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 <span></span>array([1., 2., 3.])
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 <span></span><span class="n">a</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> <span class="c1"># Create a deep copy of the array</span>
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 <span></span>array([1., 2., 3.])
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 <div class="section" id="Sorting-Arrays">
 <h2>Sorting Arrays<a class="headerlink" href="#Sorting-Arrays" title="Permalink to this headline">¶</a></h2>
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 <span></span><span class="c1"># a.sort() # Sort an array</span>
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 <span></span><span class="c1"># c.sort(axis=0) # Sort the elements of an array&#39;s axis</span>
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 <div class="section" id="Subsetting,-slicing,-indexing">
 <h2>Subsetting, slicing, indexing<a class="headerlink" href="#Subsetting,-slicing,-indexing" title="Permalink to this headline">¶</a></h2>
 <div class="section" id="Subsetting">
 <h3>Subsetting<a class="headerlink" href="#Subsetting" title="Permalink to this headline">¶</a></h3>
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 <span></span><span class="n">a</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="c1"># Select the element at the 2nd index 3</span>
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 <span></span>array(3.)
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 <span></span><span class="n">c</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span> <span class="c1"># Select the element at row 1 column 2</span>
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 <span></span>array(2.)
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 <h3>Slicing<a class="headerlink" href="#Slicing" title="Permalink to this headline">¶</a></h3>
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 <span></span><span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">2</span><span class="p">]</span> <span class="c1"># Select items at index 0 and 1</span>
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 <span></span>array([1., 2.])
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 <span></span><span class="n">c</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span> <span class="c1"># Select items at rows 0 and 1 in column 1</span>
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 <span></span>array([2., 3.])
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 <span></span><span class="n">c</span><span class="p">[:</span><span class="mi">1</span><span class="p">]</span> <span class="c1"># Select all items at row 0</span>
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 <span></span>array([[1., 2., 3.]])
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 <span></span><span class="c1"># c[1,...] # Same as [1,:,:]</span>
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 <span></span><span class="n">a</span><span class="p">[</span> <span class="p">:</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="c1">#Reversed array a array([3, 2, 1])</span>
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 <span></span>array([3., 2., 1.])
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 <div class="section" id="Boolean-Indexing">
 <h3>Boolean Indexing<a class="headerlink" href="#Boolean-Indexing" title="Permalink to this headline">¶</a></h3>
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 <span></span><span class="c1"># a[a&lt;2] # Select elements from a less than 2</span>
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 <div class="section" id="Fancy-indexing">
 <h3>Fancy indexing<a class="headerlink" href="#Fancy-indexing" title="Permalink to this headline">¶</a></h3>
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 <span></span><span class="n">c</span><span class="p">[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">]]</span> <span class="c1"># Select elements (1,0),(0,1),(1,2) and (0,0)</span>
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 <span></span>array([2., 2., 4., 1.])
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 <span></span><span class="n">c</span><span class="p">[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">]][:,[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">]]</span> <span class="c1"># Select a subset of the matrix’s rows</span>
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 <span></span>array([[2., 3., 4., 2.],
        [1., 2., 3., 1.],
        [2., 3., 4., 2.],
        [1., 2., 3., 1.]])
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 <div class="section" id="Array-manipulation">
 <h2>Array manipulation<a class="headerlink" href="#Array-manipulation" title="Permalink to this headline">¶</a></h2>
 <div class="section" id="Transposing-array">
 <h3>Transposing array<a class="headerlink" href="#Transposing-array" title="Permalink to this headline">¶</a></h3>
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 <span></span><span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">c</span><span class="p">)</span> <span class="c1"># Permute array dimensions</span>
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 <span></span>array([[1., 2.],
        [2., 3.],
        [3., 4.]])
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 <span></span><span class="n">c</span><span class="o">.</span><span class="n">T</span> <span class="c1"># Permute array dimensions</span>
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 <span></span>array([[1., 2.],
        [2., 3.],
        [3., 4.]])
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 <div class="section" id="Changing-array-shape">
 <h3>Changing array shape<a class="headerlink" href="#Changing-array-shape" title="Permalink to this headline">¶</a></h3>
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 <span></span><span class="c1"># b.ravel() # Flatten the array</span>
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 <span></span><span class="c1"># c.reshape(3,-2) # Reshape, but don’t change data</span>
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 <div class="section" id="Adding-and-removing-elements">
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 <span></span><span class="c1"># c.resize((6,2)) # Return a new array with shape (6, 2)</span>
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 <span></span><span class="c1"># np.append(h,g) # Append items to an array</span>
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 <span></span><span class="c1"># np.insert(a, 1, 5) # Insert items in an array</span>
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 <span></span><span class="c1"># np.delete(a, [1]) # Delete items from an array</span>
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 <div class="section" id="Combining-arrays">
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 <span></span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">a</span><span class="p">,</span><span class="n">b</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="c1"># Concatenate arrays</span>
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 <span></span>array([1., 2., 3., 4., 6., 8.])
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 <span></span><span class="c1"># np.vstack((a,b)) # Stack arrays vertically (row-wise)</span>
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 <span></span><span class="c1"># np.r_[e,f] # Stack arrays vertically (row-wise)</span>
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 <span></span><span class="c1"># np.hstack((e,f)) # Stack arrays horizontally (column-wise)</span>
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 <span></span><span class="c1"># np.column_stack((a,d)) # Create stacked column-wise arrays</span>
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 <span></span><span class="c1"># np.c_[a,d] # Create stacked column-wise arrays</span>
 </pre></div>
 </div>
 </div>
 </div>
 <div class="section" id="Splitting-arrays">
 <h3>Splitting arrays<a class="headerlink" href="#Splitting-arrays" title="Permalink to this headline">¶</a></h3>
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 <span></span><span class="c1"># np.hsplit(a,3) # Split the array horizontally at the 3rd index</span>
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 <span></span><span class="c1"># np.vsplit(c,2) # Split the array vertically at the 2nd index</span>
 </pre></div>
 </div>
 </div>
 </div>
 </div>
 <div class="section" id="Use-GPUs">
 <h2>Use GPUs<a class="headerlink" href="#Use-GPUs" title="Permalink to this headline">¶</a></h2>
 <p>Prerequisites: A GPU exists and GPU-enabled MXNet is installed.</p>
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 <span></span><span class="n">npx</span><span class="o">.</span><span class="n">num_gpus</span><span class="p">()</span>  <span class="c1"># Query number of GPUs</span>
 </pre></div>
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 <span></span>1
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 <span></span><span class="n">npx</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">npx</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="c1"># Context for the first and second GPUs</span>
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 <span></span>(gpu(0), gpu(1))
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 <span></span><span class="n">gpu_0</span> <span class="o">=</span> <span class="n">npx</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="k">if</span> <span class="n">npx</span><span class="o">.</span><span class="n">num_gpus</span><span class="p">()</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">npx</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
 <span class="n">g0</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">2</span><span class="p">,</span><span class="mi">3</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">gpu_0</span><span class="p">)</span>  <span class="c1"># Create array on GPU 0</span>
 <span class="n">g0</span>
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 <span></span>array([[0., 0., 0.],
        [0., 0., 0.]])
 </pre></div>
 </div>
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 <span></span><span class="n">gpu_1</span> <span class="o">=</span> <span class="n">npx</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="k">if</span> <span class="n">npx</span><span class="o">.</span><span class="n">num_gpus</span><span class="p">()</span> <span class="o">&gt;</span> <span class="mi">2</span> <span class="k">else</span> <span class="n">npx</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
 <span class="n">g1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">size</span><span class="o">=</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="n">device</span><span class="o">=</span><span class="n">gpu_1</span><span class="p">)</span>  <span class="c1"># Create array on GPU 1</span>
 <span class="n">g1</span>
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 <span></span>array([[0.6382473 , 0.795612  , 0.61245596],
        [0.07955429, 0.20390165, 0.67279226]])
 </pre></div>
 </div>
 </div>
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 <span></span><span class="c1"># Copy to another GPU</span>
 <span class="n">g1</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">gpu_0</span><span class="p">)</span>
 </pre></div>
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 <span></span>array([[0.6382473 , 0.795612  , 0.61245596],
        [0.07955429, 0.20390165, 0.67279226]])
 </pre></div>
 </div>
 </div>
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 <span></span><span class="c1"># Return itself if matching the device, otherwise copy</span>
 <span class="n">g1</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">gpu_0</span><span class="p">),</span> <span class="n">g1</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">gpu_0</span><span class="p">)</span>
 </pre></div>
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 <span></span>(array([[0.6382473 , 0.795612  , 0.61245596],
         [0.07955429, 0.20390165, 0.67279226]]),
  array([[0.6382473 , 0.795612  , 0.61245596],
         [0.07955429, 0.20390165, 0.67279226]]))
 </pre></div>
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 <span></span><span class="n">g1</span><span class="o">.</span><span class="n">device</span>  <span class="c1"># Query the device an array is on</span>
 </pre></div>
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 <span></span>cpu(0)
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 <span></span><span class="c1">## The computation is performed by the devices on which the input arrays are</span>
 <span class="n">g0</span> <span class="o">+</span> <span class="n">g1</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">gpu_0</span><span class="p">)</span>
 </pre></div>
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 <span></span>array([[0.6382473 , 0.795612  , 0.61245596],
        [0.07955429, 0.20390165, 0.67279226]])
 </pre></div>
 </div>
 </div>
 </div>
 <div class="section" id="Auto-differentiation">
 <h2>Auto differentiation<a class="headerlink" href="#Auto-differentiation" title="Permalink to this headline">¶</a></h2>
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 <span></span><span class="n">a</span><span class="o">.</span><span class="n">attach_grad</span><span class="p">()</span> <span class="c1"># Allocate gradient for a variable</span>
 <span class="n">a</span><span class="o">.</span><span class="n">grad</span> <span class="c1"># access the gradient</span>
 </pre></div>
 </div>
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 <span></span>array([0., 0., 0.])
 </pre></div>
 </div>
 </div>
 <p>Compute the <span class="math notranslate nohighlight">\(\nabla_a b=\exp(2a)^T a\)</span></p>
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 <span></span><span class="kn">from</span> <span class="nn">mxnet</span> <span class="kn">import</span> <span class="n">autograd</span>

 <span class="k">with</span> <span class="n">autograd</span><span class="o">.</span><span class="n">record</span><span class="p">():</span>
     <span class="n">b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">a</span><span class="p">)</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
 <span class="n">b</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
 <span class="n">a</span><span class="o">.</span><span class="n">grad</span>
 </pre></div>
 </div>
 </div>
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 <span></span>array([  22.167168,  272.99075 , 2824.0015  ])
 </pre></div>
 </div>
 </div>
 <p><strong>Acknowledgement</strong></p>
 <p>Adapted from www.datacamp.com.</p>
 </div>
 </div>


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       <span class="caption-text">Table Of Contents</span>
     </p>
     <ul>
 <li><a class="reference internal" href="#">The NP on MXNet cheat sheet</a><ul>
 <li><a class="reference internal" href="#Creating-arrays">Creating arrays</a><ul>
 <li><a class="reference internal" href="#Initial-placeholders">Initial placeholders</a></li>
 </ul>
 </li>
 <li><a class="reference internal" href="#I/O">I/O</a><ul>
 <li><a class="reference internal" href="#Saving-and-loading-on-disk">Saving and loading on disk</a></li>
 <li><a class="reference internal" href="#Saving-and-loading-text-files">Saving and loading text files</a></li>
 </ul>
 </li>
 <li><a class="reference internal" href="#Data-types">Data types</a></li>
 <li><a class="reference internal" href="#Inspecting-your-array">Inspecting your array</a></li>
 <li><a class="reference internal" href="#Asking-For-Help">Asking For Help</a></li>
 <li><a class="reference internal" href="#Array-mathematics">Array mathematics</a><ul>
 <li><a class="reference internal" href="#Arithmetic-operations">Arithmetic operations</a></li>
 <li><a class="reference internal" href="#Comparison">Comparison</a></li>
 <li><a class="reference internal" href="#Aggregate-functions">Aggregate functions</a></li>
 </ul>
 </li>
 <li><a class="reference internal" href="#Copying-arrays">Copying arrays</a></li>
 <li><a class="reference internal" href="#Sorting-Arrays">Sorting Arrays</a></li>
 <li><a class="reference internal" href="#Subsetting,-slicing,-indexing">Subsetting, slicing, indexing</a><ul>
 <li><a class="reference internal" href="#Subsetting">Subsetting</a></li>
 <li><a class="reference internal" href="#Slicing">Slicing</a></li>
 <li><a class="reference internal" href="#Boolean-Indexing">Boolean Indexing</a></li>
 <li><a class="reference internal" href="#Fancy-indexing">Fancy indexing</a></li>
 </ul>
 </li>
 <li><a class="reference internal" href="#Array-manipulation">Array manipulation</a><ul>
 <li><a class="reference internal" href="#Transposing-array">Transposing array</a></li>
 <li><a class="reference internal" href="#Changing-array-shape">Changing array shape</a></li>
 <li><a class="reference internal" href="#Adding-and-removing-elements">Adding and removing elements</a></li>
 <li><a class="reference internal" href="#Combining-arrays">Combining arrays</a></li>
 <li><a class="reference internal" href="#Splitting-arrays">Splitting arrays</a></li>
 </ul>
 </li>
 <li><a class="reference internal" href="#Use-GPUs">Use GPUs</a></li>
 <li><a class="reference internal" href="#Auto-differentiation">Auto differentiation</a></li>
 </ul>
 </li>
 </ul>

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